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==== Front
Clin Nutr ESPEN
Clin Nutr ESPEN
Clinical Nutrition Espen
2405-4577
Published by Elsevier Ltd on behalf of European Society for Clinical Nutrition and Metabolism.
S2405-4577(22)01411-5
10.1016/j.clnesp.2022.12.002
Letter to the Editor
Letter to the editor: "Clinical significance of micronutrient supplements in patients with coronavirus 2019 disease: A comprehensive systematic review and meta-analysis"
Córdova Claudio ab∗
Brito Ciro José c
Nóbrega Otávio Toledo d
a Private Academic Consultor, Brasília, Brazil
b Federal Institute of Education, Goias, Brazil
c Department of Physical Education. Federal University of Juiz de Fora. Governador Valadares, MG, Brazil
d Graduation Program in Medical Sciences, University of Brasilia, Brasília, Brazil
∗ Corresponding author. Private Academic Consultor, Brasília, Brazil.–
7 12 2022
7 12 2022
7 6 2022
2 12 2022
© 2022 Published by Elsevier Ltd on behalf of European Society for Clinical Nutrition and Metabolism.
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Keywords
Micronutrient
Supplementation
COVID-19
Clinical Significance
==== Body
pmc
| 0 | PMC9727965 | NO-CC CODE | 2022-12-13 23:17:25 | no | Clin Nutr ESPEN. 2022 Dec 7; doi: 10.1016/j.clnesp.2022.12.002 | utf-8 | Clin Nutr ESPEN | 2,022 | 10.1016/j.clnesp.2022.12.002 | oa_other |
==== Front
Sustainable Materials and Technologies
2214-9937
2214-9937
Elsevier B.V.
S2214-9937(22)00158-0
10.1016/j.susmat.2022.e00544
e00544
Article
Nanostructured coatings based on metallic nanoparticles as viral entry inhibitor to combat COVID-19
Singh Arun K.
Department of Chemistry, M. M. Engineering College, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, Haryana 133207, India
7 12 2022
4 2023
7 12 2022
35 e00544e00544
16 12 2021
6 9 2022
4 12 2022
© 2022 Elsevier B.V. All rights reserved.
2022
Elsevier B.V.
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
The rapid transmission of contagious viruses responsible for global pandemic and various extraordinary risk to precious human life including death. For instance, the current ongoing worldwide COVID-19 pandemic caused by novel coronavirus (SARS-CoV-2) is a communicable disease which is transmitted via touching the contaminated surfaces and then nosocomial route. In absence of effective vaccines and therapies, antiviral coatings are essential in order to prevent or slowdown rapid transmission of viruses. In this prospective, sustainable nanotechnology and material engineering have provided substantial contribution in development of engineered nanomaterial based antiviral coated surfaces to the humanity. In the recent past, nanomaterials based on silver (Ag), titanium oxide (TiO2), copper sulfide (CuS) and copper oxide (CuO) have been modified in the form of engineered nanomaterials with effective antiviral efficacy against SARS-CoV-2. In this review, various recent fundamental aspects for fabrication of metallic nanoparticles (Ag, Ti, Cu etc.) based coated surfaces on various substrates and their antiviral efficacy to inhibit viral transmission of SARS-CoV-2 are discussed along with their respective conceptual mechanisms. The antiviral mechanism based on chemistry of engineered nanomaterials is the key outcome of this review that would be useful for future research in designing and development of more advance antiviral materials and coated surfaces in order to control of future epidemics.
Graphical abstract
Unlabelled Image
Keywords
Engineered nanomaterials
Nanostructured coatings
COVID-19
Antiviral efficacy
==== Body
pmc1 Introduction
In general, a single or double strained (DNA or RNA) based genetic material and protein-based capsid with an outer lipid envelope are main constituent of the viruses [1,2]. Viruses are considered as submicroscopic entities because of their multiplication ability only inside the cell of micro and macro-organism. In addition, they are one of the air-transmitted pathogens and responsible for various human diseases from common cold to the severe respiratory illnesses in crowded and indoor places either via directly human contact (blood transfusion, sneezing, coughing etc.) or through infected vectors such as animals and insects [2,3]. For instance, the current ongoing pandemic of Coronavirus disease 2019 (COVID-19) caused by a virus of Coronaviridae family named as severe acute respiratory syndrome coronavirus 2 (SARSCoV-2), which is a contagious disease [4]. Initially, this disease was reported in Wuhan city of central China in December 2019. Within a short period of time spread to >200 countries including India and declared as pandemic by the World Health Organization on 11th March 2020 [4,5]. Because of the contagious nature and rapid spreadability, COVID-19 becomes a global health crisis and leads to millions of death throughout the glove [1]. The main reason of COVID-19 transmission is the persistent nature of the SARSCoV-2 on the various surfaces (cellulose, metallic and polymeric substances) from hours to days which quickly fall from virus-laden droplets of cough, sneeze and exhale from infected persons [[6], [7], [8]]. The contact of such virus contaminated surfaces through the respiratory system (by touching the mouth, nose and eyes) might be able to infect other healthy persons [9,10]. Thus, the lack of effective approach to prevent the viral transmission and their stability on various surfaces from hours to day remains a primary challenge in the fight against diseases spread by the contagious SARSCoV-2. Therefore, in order to control the ongoing and in future contagious viral disease, economic and effective antiviral agents are highly needful.
In the recent past years, nano-materials with particle size in the range of 1–100 nm are considered as effective alternative for environmental remediation application [[11], [12], [13], [14]] [15,16] as well as against various diseases caused by harmful viruses and bacteria because of their high surface area to volume ratio, small sizes, modifiable surfaces and excellent biological activities [17]. The excellent virucidal efficacy of metallic nanoparticles such as titanium (Ti), zinc (Zn), silver (Ag), copper (Cu) etc. make them highly useful as a coating materials to tackle the severe viruses infections [[17], [18], [19], [20], [21], [22]]. Among the various nano-materials, the use of Cu nanoparticles in order to inactivate or blocking the entry of viruses has been reported remarkably because of its easy modification ability in desired properties. Cu is one of the highly essential elements for a wide range of essential biological functions. According to the result of National Health and Nutrition Examination Survey (NHANES III, 2003) in the USA, the daily intake of Cu in human body was recommended as 1.54–1.7 mg/day for men and 1.13–1.18 mg/day for women (varied with the age) for the proper biological functions [19]. Moreover, Cu also needful for the human immune system and plays an important role in the maintenance of neutrophils, white blood cells, B cells, natural killer cells and T helper cells [23]. These cells are highly needful for the production of specific antibodies, to enhance cell-mediated immunity as well as also to killing the infectious microbes [19,24]. In addition, it has been also reported that Cu can kill variety of enveloped and non-enveloped viruses with single or double strained DNA and RNA genetic materials such as human immunodeficiency virus type 1(HIV-1), poliovirus, and bronchitis virus including SARSCoV-2. Because of this excellent ability, researchers have developed various antiviral solutions using this nanoparticle and examined their efficacy to prevent the transmission of contagious viral infections in humans since beginning of COVID-19.
In this review, the recent advances related to the application of Ti, Ag, Zn, Cu nanoparticles and its modifications for developing antiviral surfaces on different substrate using different approaches have been highlighted. Moreover, this review specially focused on the use of Cu nanoparticles (CuS, CuO, and Cu2O) with different methodologies to develop antiviral coatings. Also discussed the mechanism of action based on the platform of the Cu nanoparticles in order to control the emerging transmission of viral infections including COVID-19.
2 Metal and metal oxide nanoparticles based antiviral surfaces and coatings
The metal or metal-oxide based nanoparticles have been widely studied for antiviral surface coatings because of their unique physicochemical properties along with high specific surface area to volume ratio [25,26]. Metallic nanoparticles based coated surfaces could attack viruses through various pathways including (i) generation of reactive oxidative radicals and controlled release of disinfectant metal ions to inactivate viruses (via lipid envelope damage, protein disruption, oxidative stress etc.), (ii) high binding affinity with protein of virus surfaces and cleavage of disulfide bonds, (iii) photothermal effect to converge in a particular source of light [[25], [26], [27], [28], [29]]. Several methods have been reported for fabrication of metallic nanoparticle based coating on the surface of various substrates. Some of the commonly used methods to apply metallic nanoparticles based surface coatings are summarized in Table 1 along with the brief description about advantages and disadvantages of each approaches [22,[30], [31], [32]]. Among these methods, spray-coating and dip coating have been used extensively for nanoparticle coatings because of their simplicity in application and easy to handle without requirement of any special equipment [22]. Moreover, metallic nanoparticles can be functionalized with specific functional groups or antibodies before their utilization in coating solution in order to enhance viral binding ability and higher potency to inactivate viruses. Recently developed metallic nanoparticles (Ag, Ti, Zn, Fe, and Cu) based coated surfaces along with their antiviral properties are summarized in the subsequent sections.Table 1 Commonly applicable methods for fabrication of metallic nanoparticle based coating on the surface of various substrates.
Table 1Coatings methods Process Advantages Disadvantages References
Dip-coating Dip coating method involves the use of coating solution containing functionalized nanoparticles along with chemically active components. Selected substrates for coating are fully immersed in coating solution for a specific period of time, after that lifted out and subjected to dry either via thermally cured or in air at a particular temperature. • Easy to applicable
• Scalable
• Reduced waste generation
• Applicable for planar as well as 3D materials.
• Requirement of high smooth surfaces of selected substrates.
• Issues in controlling coating thickness at μm to nm scale.
[22,[33], [34], [35]]
Spray coating In this method, coating material along with nanoparticles is sprayed onto surface of the selected substrate at a particular rate of solvent evaporation.
• Easy operation
• Scalable process
• Respraying option offer repairing
• No any requirement of specific substrate
• Optimization of evaporation rate of solvents.
[22,30,33]
Spin-coating In this process, coating procedure generally completed in four successive stages: (i) applying coating solution on the surface of target substrate, (ii) spin up at a particular speed for a certain time, (iii) spin off, (iv) evaporation of residual solvents and drying. • Fine coating can be prepared with uniform and thin layer.
• Thickness of coating can be controlled according to desire application.
• Difficult to operate on substrate of larger size.
• Possibility of wastes of coating materials during high speed of spinning process.
[22,30,33]
Vapor deposition method This method involves multidirectional deposition of coating materials on the surface of heated substrate. • Facile, rapid and easy applicable
• Hierarchical structure can be prepared
• Commonly applicable to selective substrates such as metallic or ceramic composition
• Unsuitable for cellulose other soft materials
• Requirement of specific heat treatment at high temperature
[36]
2.1 Silver oxide nanoparticles
Ag nanoparticle (Ag NPs) based coatings have been explored extensively for their antiviral potential on the surface of various types of substrates [26,37,38]. Ag NPs show efficient antiviral potential owing to their higher surface area and continuous silver ions releasing ability from coated surfaces [39]. The interaction of silver ions with viruses resulting in inactivation of various cellular factors which are essential for viral replication [38,39] [Fig. 1 ].Fig. 1 Schematic representation of antiviral mechanism of silver nanoparticles. Reproduced with permission from ref. [38], Copyright 2021, Elsevier.
Fig. 1
Moreover, many recent works evaluated the influence of physiochemical properties of Ag NPs on their antiviral potential in order to prevent the contagious viral infections including SARS-CoV-2. For instance, Jeremiah et al. [37] evaluated antiviral properties of Ag NPs with different sizes (1 to 1000 nm) and concentrations (1 to 10 ppm) against SARS-CoV-2 through virus pre-treatment assay approach. In the present work, initially, virus was treated with Ag NPs and the resultant mixture of virus-Ag NPs was added to the cell lines VeroE6/TMPRSS2 (non-human origin) and Calu-3 (human lung epithelial cell). After 96 h, viral copies (viral load) in supernatant was examined using real time reverse transcriptase quantitative polymerase chain reaction (RT-qPCR). The authors observed steep fall in the viral load to negligible levels and excellent reduction in cell death by the use of Ag NPs around 10 nm diameters at concentration in the range of 1 to 10 ppm. The major reason behind the excellent antiviral efficacy of Ag NPs is the cleavage of disulfide bridges (according to following reactions) of virus spike proteins and cellular dysfunction which leads to inhibition in viral infectivity newly generated visrus [[40], [41], [42], [43], [44]]. Here disulfide bond and cysteine residues are represented as R − S − S − R and R − SH.R−S−S−R
R−S−S−R+Ag+→2R−S−Ag
R−SH+Ag+→R−S−Ag+H+
Balagna et al. [45] reported coating (>200 nm) of silver nanocluster/silica based composite on facial masks through co-sputtering process in pure argon atmosphere and evaluated its antiviral behavior towards Coronavirus SARS-CoV-2. In this study, authors isolated 100 μl of 50 TCID50/ml SARS-CoV-2 viral strain from a symptomatic patient and added to the pieces (1 cm2) coated and uncoated facial masks. Thereafter, both types of treated facial mask pieces were placed in petri dish for incubation at room temperature. The formation of cytopathic effect and staining of viable cells were used as parameter in order to assess the infectivity of the virus.
It was observed that coated facial masks completely reduced cytopathic effect, while higher infectivity was observed in case of uncoated mask. Additionally, it was also reported that this coating can be applied on the surface of various types of substrates such as glasses, ceramic, metals, polymers etc. From the obtained results, authors concluded that silver nanocluster/silica composite coated facial masks can be effective contribution for safety in crowded areas from viral infection.
Tremiliosi et al. [21] also reported Ag NPs (average size ∼23.51 ± 5.18 nm) coatings on polycotton (67% polyster and 33% cotton) fabrics using pad-dry-cure method. In this method, a piece of polycotton fabric (30 × 30 cm) was immersed in colloidal solution of Ag NPs along with organic polymers (acrylic based binder) for a specific period of time. Thereafter, treated polycotton fabric was dried (at 80 °C for 3 min), annealed (at 170 °C for 3 min), washing with deionized water and finally dried in an ventilated oven at 80 °C for 3 min. The antiviral activity of the Ag NPs coated polycotton fabric was examined via inoculation of SARS-CoV-2 into three separate liquid media containing coated fabric, uncoated fabric and without any fabric. After the incubation period (certain different period of time) the genetic material of virus (viral load) was examined using real-time quantitative PCR. Authors observed excellent inhibition rate (>80%) of Ag NPs coated polycotton fabric with respect to SARS-CoV-2. The authors postulated the major reasons behind the high anti-SARS-CoV-2 activity are the (i) generation of reactive oxygen species from Ag NPs and its interaction with DNA, (ii) binding ability of Ag NPs with the sulfur residues of glycoproteins on virus's surface and responsible for inhibition of viral replication.
2.2 Titanium dioxide nanoparticles
Titanium dioxide (TiO2) nanoparticle is known for its photocatalytic application with a wide band gap of 3.2 eV [[46], [47], [48]]. The excitation of electron takes place from electron band to conduction band when TiO2 exposed to UV light having energy equal to or higher than its band gap [48]. This phenomenon led to formation of holes and electron which are capable to produce reactive oxygen species (ROS) with unpaired electrons by the interaction with water (H2O) molecules or ambient oxygen (O2) or moisture [26]. These generated ROS on the surface of TiO2 are not only useful in degradation of organic matter during water treatment application but also for the disinfection of bacteria/microbes [46,49]. The potential of TiO2 nanoparticles has been extended to antiviral activity (disinfection of viruses including SARS-CoV-2) by the coatings on the surface of various substrates. For instance, Khaiboullina et al. [29] studied, virucidal efficacy of TiO2 nanoparticles induced by the UV radiation towards deactivation of SAR-CoV-2. In the present work, TiO2 nanoparticles were coated on glass coverslip. An aliquot (100 μL, 2.1 × 105 TCID50) SAR-CoV-2 was placed on TiO2 nanoparticles coated and uncoated coverslips (18 mm diameter, 1017.88 mm2) and exposed to a source of UV light (wavelength: 254 nm, 99 V, 30 W, 0.355 A) for various time points. Virus infectivity assays was determined by genomic RNA quantitation using RT-qPCR. Authors observed total inactivation of virus within 5 min of UV light exposure on the surface of TiO2 nanoparticles coated coverslips. On other hand significant copies of intracellular genomic RNA was observed on the without treated coverslips. Additionally, authors also reported that viral inactivation activity of TiO2 nanoparticles coated surfaces was maintained even on the virus droplet has been dried. Hence from the above observation, it can be concluded that TiO2 nanoparticles based coatings can be used for various substrates in order to inactivation of contagious SAR-CoV-2.
In another study, Hamza et al. [50] synthesized TiO2 nanotubes by the sol-gel method in basic medium, followed by the hydrothermal treatment at 150 °C for 12 h. The antiviral efficacy of the synthesized TiO2 nanotubes was examined respect to SAR-CoV-2 and determined Inhibitory Concentration 50% (IC50). Authors observed that synthesized TiO2 nanotubes exhibited excellent anti-SARSCoV-2 activity even at very low concentrations (IC50 = 568.6 ng mL−1) along with weak cytotoxic effect. Authors postulated that major reason behind this excellent antiviral activity is the ability of TiO2 nanotubes to release a large quantity of Ti+2 ions and variety of free radicals (reactive oxygen species). These free radical and metallic cations and can damage protein and lipids including nucleic acid strains. Thus authors concluded that TiO2 nanotubes based coated surface can be used for inactivation of SAR-CoV-2 on various substrates.
2.3 Zinc oxide nanoparticles
Zinc is essential metal in biological systems because of its utility as coenzyme, body's immunity booster and as signaling molecule in regulation of inflammatory responses [[51], [52], [53]]. In addition to this zinc oxide (ZnO) nanoparticles has been considered as potential antibacterial metallic nanoparticles including viruses [54]. ZnO can also act as photocatalyst in presence of artificial UV or sunlight and water because of its similarity in band gap to TiO2 nanoparticles [55,56]. During the exposure with artificial UV or sunlight reactive oxygen species are generated from the surface of ZnO nanoparticles [[55], [56], [57]]. These reactive oxygen species (hydrogen peroxide, superoxide, hydroxyl radicals etc.) can damage the biological membranes of bacteria or viruses [57,58]. It has been reported that nanoparticle form of ZnO is safe for human contact, as it is consumed as supplement in limited level and also utilized in sunscreens [59]. Because of these specific features, ZnO nanoparticles can be used as coating materials for the inactivation of contagious viruses.
2.4 Iron oxide nanoparticles
Iron oxide (IO) (Fe2O3 or Fe3O4) nanoparticles have drawn prominence in variety of applications including biomedicine, water treatment, electronics and agriculture because of their their high biocompatibility, electrical, magnetic and optical properties [[60], [61], [62]]. In addition to these applications, antiviral efficacy of the IO nanoparticles is also reported via various mechanisms including binding ability to virus surface proteins and damaging of viral envelope, lipid peroxidation and ROS generation [26]. In a theoretical study, Abo-zeid et al. [63] evaluated binding affinity of SARS-CoV-2 spike protein to Fe3O4 (magnetite) and Fe2O3 (hematite) nanoparticles. Authors reported that IO nanoparticles may inhibit the attachment of SARS-CoV-2 spike protein to the host cell. In addition authors also proposed that reactive oxygen species on the surface of IO nanoparticles can inactivate the virus by oxidative damage the viral lipid envelope.
3 Copper-based antiviral surfaces and coatings
In the recent past, Cu-based nanoparticles such as copper sulfide, cuprous oxide and cupric oxide have been explored extensively for antiviral coatings on various surfaces because of their various favorable characteristic features such highly non-cytotoxic, non-irritating to skin and safe for human contact [64]. Experimental studies provided evidence with respect to antiviral efficacy of Cu coated surfaces against various viruses including SARSCoV-2.
For instance, Hewawaduge et al. [65] fabricated self-sterilizing antiviral three layer mask design based on nylon fiber along with the incorporation (coating as well as impregnation) of copper sulfide and evaluated the antiviral efficacy of the designed mask against SARSCoV-2. In this study, copper sulphide was incorporated in only outer and middle layer and its percentage (w/w) was varied in the mask layers. Authors impregnated total of 17.6% CuS (w/w) in middle entrapment area and 4.4% CuS (w/w) (2.2% CuS coated & 2.2% CuS impregnated) in outer layer. Thus total load of copper sulfide in the three layer nylon mask was approximately 22 g per 100 g of total mask weight. The inner layer was designed in specific way to provide comfort and safety for users without incorporation of copper sulfide. The authors examined consistency of fiber thickness after coating and uniform distribution of particle by the use of scanning electron microscopy (SEM). The rough and smooth surface of CuS coated and CuS impregnated were clearly observed in the analyses of SEM images [Fig. 2a].Fig. 2 a. SEM images mask fibers: (a) coated CuS, (b) impregnated CuS. Fig. 2b. Schematic representation of copper sulfide incorporated nylon fiber based three layered mask and virus capture mechanism (a) The arrangement of mask layers and CuS incorporated fiber composition. (b) Entrapment mechanism of the three-layer mask. Reproduced with permission from ref.[Hewawaduge et al. [65], Copyright 2021, Elsevier.
Reproduced with permission from ref. [65], Copyright 2021, Elsevier.
Fig. 2
Authors evaluated the antiviral properties of solid state CuSO4, copper sulfide and copper sulfide incorporated masks of nylon fiber against SARS-CoV-2 by their interactions with a known viral titer (0.1 MOI) for 30 min, 1 h and 2 h of variable durations. Viral inactivation ability was examined by the viral copy number, cytopathy and fluorescence. They observed that the generated Cu2+ did not show any appreciable viral inactivation ability even after increment of their molar concentration as well as contact (incubation) time. However, in the copper sulfide coated mask, authors observed excellent antiviral efficacy (completely blockage the passing of virus containing droplet) within 30 min exposure and considered as ideal remedy to prevent the SARS-CoV-2 transmission. The schematic representation of three layered nylon mask architecture along with the incorporation of copper sulfide and virus capture mechanism are shown in Fig. 2b.
Authors reported that the major reason behind excellent antiviral efficacy coated mask is the potential involvement of sulfide ions (S− 2) along with the combination of generated reactive oxygen species due to increased stress of copper. Thus, on the basis of experimental observation authors concluded that the developed self-sterilizing antiviral masks could be advantageous in order to save precious human lives in this ongoing COVID-19 pandemic.
Hosseini et al. [66] fabricated cupric oxide based antiviral coatings with porous and hydrophilic in nature on the glass surface by the dispersion of cuprous oxide suspension in ethanol followed by thermal treatment for 2 h at 700 °C. Because of the thermal treatment, cuprous oxide was converted into cupric oxide and sintered the particles in the form of robust film of approximately 30 μm thick. In this study, the oxidation state of copper was analyzed before and after coating formation by the use X-ray photoelectron spectroscopy. It was observed that hydrophilicity of the developed coating was maintained for at least five months. The authors used Vero E6 cells to prepare virus stock and inactivation ability of the developed cupric oxide coated surface was evaluated against SARS-CoV-2 at 22–23 °C and 60–70% humidity. Excellent infectivity (99.8% in 30 min) from the CuO film was observed. The authors postulated that attractive charge-charge interaction was the main reason behind the SARS-CoV-2 inactivation efficacy. They explained that the virus spike proteins have net charge of about positive 3.5 at pH 7.4 (spike protein have 1 histidine, 7 anionic and 10 cationic amino acids). As well as virus envelope (E) protein also have net charge positive. However, in the culture medium, surface of cupric oxide have negative zeta potential (−17 mV). Therefore because of electrostatic force of attraction, SARS-CoV-2 attracted on the cupric oxide coated surface become inactivated. Thus authors concluded the charge-charge interaction mechanism behind the inactivation of SARS-CoV-2.
Although, both form of CuO nanoparticles either as Cupric oxide (CuO) OR cuprous oxide (Cu2O) are antiviral against both non-enveloped and enveloped, some studies are performed to compared the antiviral efficacy of the CuO nanoparticles in these two oxidation states. For instance, Mazurkow et al. [67] explained the antiviral efficacy of these two forms of CuO nanoparticles on the basis of determination of isoelectric point. They reported 11.0 isoelectric point value (higher positive charge) for Cu2O and 7.4 for CuO nanoparticles. Authors concluded that Cu2O with higher positive surface charge will be better in antiviral efficacy due to electrostatic interaction. Similar observations have been reported in other studies with respect different viruses [42,68].
In addition to glass, cotton and nylon fiber, copper nanoparticles based coated was also found effective on metallic surfaces in order to inactivate SARS-CoV-2. For instance, Hotasoit et al. [69] fabricated copper coated touch surfaces on steel parts using cold spray technique. The virus inactivation efficacy of the copper coated surface was examined in vitro by the exposure of 50 mL volume of SARS-CoV-2 containing 105.5 TCID50 mL−1 (TCID50 is a measurement of virus titer and represents the amount of virus that produces an infection in 50% of the cells exposed) to copper surface and left in contact at room temperature for various time interval 1, 10, 30, 120 or 300 min. The authors observed that synthesized coating of copper significantly reduces the life time of SARS-CoV-2 to below the 5-h. The authors concluded that very short manufacturing time of coatings and high efficiency against viral infection with viral killing property in very short time are highly useful in real life applications.
Furthermore, in order to reduce the virus inactivation time some researchers are tried to utilize the coating of copper nanoparticles along with the combination of some other nanoparticles also on various solid surfaces. For instance, Mosselhy et al. [70] fabricated antiviral coatings of copper‑silver (Cu—Ag) nanohybrids via powder coating/wet painting (spray coatings) with thickness of 40 μm. The authors evaluated the antiviral efficacy of the developed Cu—Ag nanohybrid against SARS-CoV-2 on public places, people's homes, and health care settings (as shown in Fig. 3 ). The average size of Cu and Ag particles utilized in coatings were ∼ 26 ± 2 nm and ∼ 212 ± 16 nm, respectively. The authors observed that developed coating effectively inhibited SARS-CoV-2 in <5 min. On the basis excellent performance of coated surfaces, they concluded that Cu—Ag nanohybrids based coatings could be employed to prevent the transmission of SARS-CoV-2 in this currently ongoing pandemic. However, the mechanism of virus inhibition efficacy was not discussed in details.Fig. 3 Schematic representation of antiviral efficacy of Cu—Ag nanohybrids based coatings against SARS-CoV-2 (after 1 and 5 min), breaking the SARS-CoV-2 transmission chains and containing the pandemic within the hospital and livestock settings, and in public reservoirs. Nanohybrids A and B represent samples 2 and 3, containing ∼65 and 78 wt% Cu and ∼ 7 and 9 wt% Ag, respectively Reproduced with permission from ref. [Mosselhy et al. [70], Copyright 2021, MDPI.
Fig. 3
El-Nahhal et al. [71] fabricated copper-coated cotton fabrics by the use of three different types of copper based coating materials such as copper oxide nanoparticles (CuO-NPs), functionalized CuO–Ag nanocomposites and Cu(II)-curcumin complex by the use of dip coating approach along with ultrasonication [Fig. 4 ]. The antimicrobial activity of the coated fabrics was examined according to the standard quantitative test (AATCC 100, 2004) method.Fig. 4 (a) Schematic representation of synthetic pathway CuO nanoparticle synthesis, coating on cotton fabrics and evaluation of antimicrobial efficacy, (b) medical facility that could be help to inhibit the spreading of COVID-19. Reproduced with permission from ref. [71], Copyright 2022, Elsevier.
Fig. 4
The particle size of the synthesized CuO-Ag nanocomposite was found 29 nm as examined by the TEM. In addition surface morphology of the coated and uncoated cotton fabrics was analyses by SEM [Fig. 5 ]. It was observed that CuO coating on have a different morphology as compared to the pristine fabric. Authors reported that the antimicrobial activity CuO–Ag/cotton material was better among all coated surfaces both E. coli and S. aureus. This behavior might be due to the generation of reactive oxygen species as hydrogen peroxide and electrostatic force of attraction between bacterial cell surface and CuO particles. Thus author concluded that such type of copper and silver nanoparticles based could be applied on medical facilities in order to inhibit the spreading of contagious viruses including SARS-CoV-2.Fig. 5 (a) Scanning electron microscopy (SEM) images for (a) uncoated cotton fabric, (b) CuO coated cotton fabric, (c) CuO/starched cooton fabric, (d) CuO-Ag coated cotton. Reproduced with permission from ref. [71], Copyright 2022, Elsevier.
Fig. 5
In addition to the single and bi-metallic nanoparticles, some studies have been reported with the use of tri-metallic nanoparticles for inactivation of contagious including SARS-CoV-2 [7,8]. For instance, Robinson et al. [7] reported the utilization of additive manufacturing and surrogate modeling for the development of microporous architecture based on combination copper‑tungsten‑silver (Cu-W-Ag). In this study, surrogate modeling was highly useful in order to obtain optimal parametric combination which led to obtain microporous system of Cu-W-Ag with average pore size of 80 μm. Interestingly, it was observed that The Cu-W-Ag architecture exhibited 100% viral inactivation with respect to SARS-CoV-2 (enveloped ribonucleic acid viral model). Thus on the basis of observed excellent antiviral behavior authors concluded that Cu-W-Ag architecture is suitable to reduce viral contamination of SARS-CoV-2 on various surfaces.
4 Mechanism behind the antiviral efficacy of cu nanoparticles as viral entry inhibitors
The SARS-CoV-2 genome consist of mainly four type of structural protein which are useful for different functional activity such as spike (S) protein (useful for the attachment of virus to host cell), envelope (E) protein (viroporins, phospholipids hydrophobic in nature), membrane (M) protein (shape of the cell can be determine), and nucleocapsid (N) protein (useful for replication cycle in host cell)) (Fig. 6 ) [4,72]. The presence of copper nanoparticles as a coating material on various surfaces significantly contributed to prevent the transmission of SARS-CoV-2 via direct/indirect disinfection and receptor inactivation pathways.Fig. 6 Schematic representation of the structure of coronavirus along with description of four main structural proteins. Reproduced with permission from ref. [54], Copyright 2022, Elsevier.
Fig. 6
The excitonic effects to generate free charges, light, heat, free radicals or carriers are the significant reason for the metal or inorganic nanoparticles to provide antiviral efficacy [73]. Under the visible light irradiation, Cu nanoparticles are able to exhibit surface plasmon resonance which are effective against viral infection via interface the replication or adhesion of the viruses on the surfaces [73,74]. In addition copper oxide (CuO) nanoparticles are semiconductor in nature. Thus, such nanoparticles have ability to produce reactive oxygen species such as OH•, O2·-, hydrogen peroxide or some other type free radicals via the interaction with moisture or light. It has been reported that such free radicals are highly efficient to inactivate the single or double-strained DNA or RNA including enveloped or non-enveloped viruses and also bacteria on the Cu nanoparticles coated surfaces and prevented the virus entry [19,23,75,76]. The surface redox reactions between Cu nanoparticle coated surfaces and viruses are another potential way in order to restrain the proliferation viruses [73,75]. The generated reactive oxygen species and free radicals on Cu nanoparticles coated surfaces are highly effective against the SARSCoV-2 (by the disintegration of viral surface spikes, genomes and degradation of viral proteins such as neuraminidase, hemagglutinin etc.) which is responsible for current ongoing pandemic of COVID-19 [23,75]. The “contact killing” phenomenon for SARSCoV-2 also has been reported by Cu nanoparticles coated surfaces. Because of exceptional sensitivity of SARSCoV-2 to Cu surfaces, the inactivation of such virus is reported by >99.99% within 1 min of contact on the surface of masks coated with copper oxide [75,77]. Behzadinasab et al. [75] also reported that coating of cuprous oxide particles along with polyurethane on the surfaces of glass slides and stainless steel exhibits by >99.99% inactivation of SARSCoV-2 within 1 h as compared to uncoated surfaces. The author also observed that the combination of polyurethane and cuprous oxide coating adheres well not only on stainless steel and glass surfaces, also suitable for everyday items such as doorknobs, keypad button, credit card and pen which people may fear to touch during the adverse time of COVID-19 pandemic.
It has been also reported that functionalized copper nanoparticles coated surfaces like cotton fabrics, face masks or other personal protective equipment exhibited antiviral efficacy because of their high affinity to capture the viruses. Such surfaces resist the entry the virus into human cell by passivation of receptors spikes. For instance, Archana et al. [78] reported about coating of copper iodide on cotton fabrics by ultrasonication method. In this study, copper iodide was synthesized by the use of aqueous extract of Hibiscus flower as a source of reducing, stabilizing and capping agents. The coating on the surface of cotton fabrics was performed by the dip coating approach in the solution of copper iodide (1 mg/mL) in acetonitrile under ultrasonication for 30 min. Authors performed molecular docking study in order to evaluate the interaction of coated surface and COVID-19. From the experimental findings, it was observed that the cyanidin-3-sophoroside capped copper iodide particles based coating exhibited better binding affinity against COVID-19 main protease protein with −80.34 kcal/mol minimum binding energy. Authors concluded that better binding energy of cyanidine-3-sophoroside bound copper iodide with the COVID-19 main protease protein will be useful in order to prevent the viral infection. Thus according to above literature discussion, the possible pathways by which copper nanoparticles act on different viruses to inhibit the transmission or viral entry are summarized in Fig. 7 [64,73].Fig. 7 Possible pathways to exhibit antiviral efficacy of the Cu nanoparticles coated surfaces as viral entry inhibitors.
Fig. 7
5 Concluding remarks and future perspectives
SARS-CoV-2 spread quickly with very high rate since at the end of 2019, and causes extraordinary risk to precious human life. Because of its contagious spreading nature, antiviral surfaces are urgently required from common public to medical healthcare persons in order to prevent or reduce the transmission of SARS-CoV-2. In this perspective, nanotechnology, particularly Ag, Ti and Cu nanoparticles (CuO, Cu2O, CuS etc.) based coating approach has provided innovative solution and made significant contribution in inactivation of SARS-CoV-2. Among these nanoparticles, copper nanoparticles based antiviral coatings against SARS-CoV-2 have been developed on various surfaces such as fiber, cotton, metals, glass and polymeric substrates including public places, people's homes, and health care settings for the safety of precious human life. Thus, results of the review study demonstrated that copper nanoparticles based coating could be effective and promising in the prevention contagious viral transmission. Charge-charge interaction generated of reactive oxygen species and excitonic effects are the significant mode of action of copper nanoparticles based coatings to inactivate the viruses within very short time of interaction. Besides having a number of advantages in the copper based coatings as viral entry inhibitor in lab scale, considering the long-term stability of coating materials on the surface of applied substrates in real environmental conditions and toxicity of nanomaterial at large scale application are a matter of serious concern. Metallic nanoparticles have higher toxicity as compared to their bulk materials, thus safety and cytotoxicity of the metallic nanoparticles are the significant limitation at large scale application as coating materials. Therefore, it is highly essential to perform further research about short and long-term toxicity assessment of coated nanoparticles on environment and human health during large scale application. Moreover, virus inactivation time of most of the nanoparticles-based coated surfaces is needed longer time. Thus further optimization of key factor is essential to reduce the inactivation time for contagious viruses. Additionally, only few studies are reported in fabrication of antiviral coatings surfaces on various substrates which are commonly used in daily life applications. However, infection/transmission of some other pathogens is possible in coming future similar to the current ongoing infection of COVID-19. Thus, further studies based on development of nanoparticles coated surfaces with multiple viral inactivation efficacies are also recommended.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Data availability
No data was used for the research described in the article.
Acknowledgements
The author acknowledges the support from the Department of Chemistry, and Research & Development Cell of Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, Haryana, India.
==== Refs
References
1 Li Y. Xiao Y. Chen Y. Huang K. Nano-based approaches in the development of antiviral agents and vaccines Life Sci. 265 Jan. 2021 118761 10.1016/J.LFS.2020.118761
2 Rai P.K. Usmani Z. Thakur V.K. Gupta V.K. Mishra Y.K. Tackling COVID-19 pandemic through nanocoatings: confront and exactitude Curr. Res. Green Sustain. Chem. 3 Jun. 2020 100011 10.1016/J.CRGSC.2020.100011
3 Balagna C. Francese R. Perero S. Lembo D. Ferraris M. Nanostructured composite coating endowed with antiviral activity against human respiratory viruses deposited on fibre-based air filters Surf. Coat. Technol. 409 Mar. 2021 126873 10.1016/J.SURFCOAT.2021.126873
4 Li H. Liu S.M. Yu X.H. Tang S.L. Tang C.K. Coronavirus disease 2019 (COVID-19): current status and future perspectives Int. J. Antimicrob. Agents 55 5 May 2020 105951 10.1016/J.IJANTIMICAG.2020.105951
5 Chitrakar B. Zhang M. Bhandari B. Improvement strategies of food supply chain through novel food processing technologies during COVID-19 pandemic Food Control 125 2021 108010 Elsevier Ltd. Jul. 01 10.1016/j.foodcont.2021.108010 33679006
6 Singh A.K. Surface engineering using PDMS and functionalized nanoparticles for superhydrophobic coatings: selective liquid repellence and tackling COVID-19 Prog. Org. Coat. 171 Oct. 2022 107061 10.1016/J.PORGCOAT.2022.107061
7 Robinson J. Additive manufacturing of anti-SARS-CoV-2 copper-tungsten-silver alloy Rapid Prototyp. J. 27 10 2021 1831 1849 10.1108/RPJ-06-2021-0131
8 Arjunan A. Robinson J. Baroutaji A. Tuñón-Molina A. Martí M. Serrano-Aroca Á. 3D printed cobalt-chromium-molybdenum porous superalloy with superior antiviral activity Int. J. Mol. Sci. 22 23 2021 10.3390/ijms222312721
9 Duda-Chodak A. Lukasiewicz M. Zięć G. Florkiewicz A. Filipiak-Florkiewicz A. Covid-19 pandemic and food: Present knowledge, risks, consumers fears and safety Trends Food Sci. Technol. 105 2020 145 160 Elsevier Ltd. Nov. 01 10.1016/j.tifs.2020.08.020 32921922
10 Sharma H.B. Challenges, opportunities, and innovations for effective solid waste management during and post COVID-19 pandemic Resour. Conserv. Recycl. 162 Nov. 2020 105052 10.1016/j.resconrec.2020.105052
11 Singh A.K. Singh K.P. Optimization of phosphate removal from aqueous solution using activated carbon supported zero-valent iron nanoparticles: application of RSM approach Appl Water Sci 8 8 2018 10.1007/s13201-018-0875-7
12 Singh A.K. Singh J.K. Fabrication of zirconia based durable superhydrophobic-superoleophilic fabrics using non fluorinated materials for oil-water separation and water purification RSC Adv. 6 105 2016 10.1039/c6ra24460b
13 Singh A.K. Singh J.K. Fabrication of durable superhydrophobic coatings on cotton fabrics with photocatalytic activity by fluorine-free chemical modification for dual-functional water purification New J. Chem. 41 11 May 2017 4618 4628 10.1039/c7nj01042g
14 Singh A.K. Singh K.P. Response surface optimization of nitrite removal from aqueous solution by Fe3O4 stabilized zero-valent iron nanoparticles using a three-factor, three-level Box-Behnken design Res. Chem. Intermed. 42 3 Mar. 2016 2247 2265 10.1007/s11164-015-2147-6
15 Singh A.K. A review on plant extract-based route for synthesis of cobalt nanoparticles: photocatalytic, electrochemical sensing and antibacterial applications Curr. Res. Green Sustain. Chem. 5 2022 10.1016/j.crgsc.2022.100270
16 Singh A.K. Flower extract-mediated green synthesis of bimetallic Cu[sbnd]Zn oxide nanoparticles and its antimicrobial efficacy in hydrocolloid films Bioresour. Technol. Rep. 18 2022 10.1016/j.biteb.2022.101034
17 Balasubramaniam B. Antibacterial and antiviral functional materials: chemistry and biological activity toward tackling COVID-19-like pandemics ACS Pharmacol. Transl. Sci. 4 1 2021 8 54 10.1021/acsptsci.0c00174 33615160
18 Kelleni M.T. Resveratrol-zinc nanoparticles or pterostilbene-zinc: potential COVID-19 mono and adjuvant therapy Biomed. Pharmacother. 139 Jul. 2021 111626 10.1016/J.BIOPHA.2021.111626
19 Raha S. Mallick R. Basak S. Duttaroy A.K. Is copper beneficial for COVID-19 patients? Med. Hypotheses 142 Sep. 2020 109814 10.1016/J.MEHY.2020.109814
20 Duan Y. Wang S. Zhang Q. Gao W. Zhang L. Nanoparticle approaches against SARS-CoV-2 infection Curr. Opin. Solid State Mater. Sci. 25 6 Dec. 2021 100964 10.1016/J.COSSMS.2021.100964
21 Tremiliosi G.C. Ag nanoparticles-based antimicrobial polycotton fabrics to prevent the transmission and spread of SARS-CoV-2 bioRxiv 2020 10.1101/2020.06.26.152520
22 Ramaiah G.B. Tegegne A. Melese B. Developments in Nano-materials and Analysing its role in fighting COVID-19 Mater. Today Proc. 47 2021 4357 4363 10.1016/j.matpr.2021.05.020 33996513
23 Rani I. Potential molecular mechanisms of zinc- and copper-mediated antiviral activity on COVID-19 Nutr. Res. 92 Aug. 2021 109 128 10.1016/J.NUTRES.2021.05.008 34284268
24 Besold A.N. Culbertson E.M. Culotta V.C. The yin and Yang of copper during infection J. Biol. Inorg. Chem. 21 2 2016 137 144 10.1007/s00775-016-1335-1 26790881
25 Alavi M. Kamarasu P. McClements D.J. Moore M.D. Metal and metal oxide-based antiviral nanoparticles: properties, mechanisms of action, and applications Adv. Colloid Interf. Sci. 306 Aug. 2022 102726 10.1016/J.CIS.2022.102726
26 Lin N. Antiviral nanoparticles for sanitizing surfaces: a roadmap to self-sterilizing against COVID-19 Nano Today 40 2021 10.1016/j.nantod.2021.101267
27 Cai X. A neutralizing antibody-conjugated photothermal nanoparticle captures and inactivates SARS-CoV-2 bioRxiv 2020 10.1101/2020.11.30.404624v1.abstract 2020.11.30.404624. [Online]. Available:
28 Duan Y. Wang S. Zhang Q. Gao W. Zhang L. Nanoparticle approaches against SARS-CoV-2 infection Curr. Opin. Solid State Mater. Sci. 25 6 2021 10.1016/j.cossms.2021.100964
29 Khaiboullina S. Uppal T. Dhabarde N. Subramanian V.R. Verma S.C. Inactivation of human coronavirus by titania nanoparticle coatings and uvc radiation: throwing light on sars-cov-2 Viruses 13 1 2021 10.3390/v13010019
30 Farooq S.A. Raina A. Mohan S. Singh R.A. Jayalakshmi S. Haq M.I.U. Nanostructured coatings: review on processing techniques, corrosion behaviour and tribological performance Nanomaterials 12 8 2022 10.3390/nano12081323
31 Singh A.K. Singh J.K. An efficient use of waste PE for hydrophobic surface coating and its application on cotton fibers for oil-water separator Prog. Org. Coat. 131 2019 301 310 10.1016/j.porgcoat.2019.02.025
32 Singh A.K. Singh J.K. Fabrication of durable super-repellent surfaces on cotton fabric with liquids of varying surface tension: low surface energy and high roughness Appl. Surf. Sci. 416 2017 639 648 10.1016/j.apsusc.2017.04.148
33 Yilmaz O. Yorgancioglu A. Nanocoatings: preparation, properties, and biomedical applications Polym. Nanomater. Nanotherapeut. 2018 299 331 10.1016/B978-0-12-813932-5.00008-X
34 Nyankson E. Agbe H. Takyi G.K.S. Bensah Y.D. Sarkar D.K. Recent advances in nanostructured superhydrophobic surfaces: fabrication and long-term durability challenges Curr. Opin. Chem. Eng. 36 2022 10.1016/j.coche.2021.100790
35 Wang Q. Sun G. Tong Q. Yang W. Hao W. Fluorine-free superhydrophobic coatings from polydimethylsiloxane for sustainable chemical engineering: preparation methods and applications Chem. Eng. J. 426 2021 10.1016/j.cej.2021.130829
36 Shishkovsky I.V. Lebedev P.N. Chemical and physical vapor deposition methods for nanocoatings Nanocoatings Ultra-Thin Film 2011 57 77 10.1533/9780857094902.1.57
37 Jeremiah S.S. Miyakawa K. Morita T. Yamaoka Y. Ryo A. Potent antiviral effect of silver nanoparticles on SARS-CoV-2 Biochem. Biophys. Res. Commun. 533 1 2020 195 200 10.1016/j.bbrc.2020.09.018 32958250
38 Allawadhi P. Silver nanoparticle based multifunctional approach for combating COVID-19 Sensors Int. 2 2021 10.1016/j.sintl.2021.100101
39 Sun Z. Ostrikov K. Ken Future antiviral surfaces: Lessons from COVID-19 pandemic Sustain. Mater. Technol. 25 2020 10.1016/j.susmat.2020.e00203
40 Elechiguerra J.L. Interaction of silver nanoparticles with HIV-1 J. Nanobiotechnol. 3 2005 10.1186/1477-3155-3-6
41 Hati S. Bhattacharyya S. Impact of thiol-disulfide balance on the binding of Covid-19 spike protein with angiotensin-converting enzyme 2 receptor ACS Omega 5 26 2020 16292 16298 10.1021/acsomega.0c02125 32656452
42 Minoshima M. Comparison of the antiviral effect of solid-state copper and silver compounds J. Hazard. Mater. 312 2016 1 7 10.1016/j.jhazmat.2016.03.023 27015373
43 Russell A.D. Hugo W.B. Antimicrobial activity and action of silver Prog. Med. Chem. 31 C 1994 351 370 10.1016/S0079-6468(08)70024-9 8029478
44 Castro-Mayorga J.L. Randazzo W. Fabra M.J. Lagaron J.M. Aznar R. Sánchez G. Antiviral properties of silver nanoparticles against norovirus surrogates and their efficacy in coated polyhydroxyalkanoates systems LWT Food Sci. Technol. 79 2017 503 510 10.1016/j.lwt.2017.01.065
45 Balagna C. Perero S. Percivalle E. Nepita E.V. Ferraris M. Virucidal effect against coronavirus SARS-CoV-2 of a silver nanocluster/silica composite sputtered coating Open Ceram. 1 2020 10.1016/j.oceram.2020.100006
46 Prakash J. Cho J. Mishra Y.K. Photocatalytic TiO2 nanomaterials as potential antimicrobial and antiviral agents: scope against blocking the SARS-COV-2 spread Micro Nano Eng. 14 2022 10.1016/j.mne.2021.100100
47 Gupta T. Samriti J. Cho Prakash J. Hydrothermal synthesis of TiO2 nanorods: formation chemistry, growth mechanism, and tailoring of surface properties for photocatalytic activities Mater. Today Chem. 20 2021 10.1016/j.mtchem.2021.100428
48 Prakash J. Novel rare earth metal–doped one-dimensional TiO2 nanostructures: fundamentals and multifunctional applications Mater. Today Sustain. 13 2021 10.1016/j.mtsust.2021.100066
49 Foster H.A. Ditta I.B. Varghese S. Steele A. Photocatalytic disinfection using titanium dioxide: Spectrum and mechanism of antimicrobial activity Appl. Microbiol. Biotechnol. 90 6 2011 1847 1868 10.1007/s00253-011-3213-7 21523480
50 Hamza R.Z. Gobouri A.A. Al-Yasi H.M. Al-Talhi T.A. El-Megharbel S.M. A new sterilization strategy using tio2 nanotubes for production of free radicals that eliminate viruses and application of a treatment strategy to combat infections caused by emerging sars-cov-2 during the covid-19 pandemic Coatings 11 6 2021 10.3390/coatings11060680
51 Prasad A.S. Discovery of zinc for human health and biomarkers of zinc deficiency Mol. Genet. Nutr. Asp. Major Trace Miner. 2017 241 260 10.1016/B978-0-12-802168-2.00020-8
52 Wessels I. Maywald M. Rink L. Zinc as a gatekeeper of immune function Nutrients 9 12 2017 10.3390/nu9121286
53 Maywald M. Wessels I. Rink L. Zinc signals and immunity Int. J. Mol. Sci. 18 10 2017 10.3390/ijms18102222
54 Sarkar J. Das S. Aich S. Bhattacharyya P. Acharya K. Antiviral potential of nanoparticles for the treatment of coronavirus infections J. Trace Elem. Med. Biol. 72 2022 10.1016/j.jtemb.2022.126977
55 Raghupathi K.R. Koodali R.T. Manna A.C. Size-dependent bacterial growth inhibition and mechanism of antibacterial activity of zinc oxide nanoparticles Langmuir 27 7 2011 4020 4028 10.1021/la104825u 21401066
56 Ong C.B. Ng L.Y. Mohammad A.W. A review of ZnO nanoparticles as solar photocatalysts: synthesis, mechanisms and applications Renew. Sust. Energ. Rev. 81 2018 536 551 10.1016/j.rser.2017.08.020
57 Premanathan M. Karthikeyan K. Jeyasubramanian K. Manivannan G. Selective toxicity of ZnO nanoparticles toward Gram-positive bacteria and cancer cells by apoptosis through lipid peroxidation Nanomed. Nanotechnol. Biol. Med 7 2 2011 184 192 10.1016/j.nano.2010.10.001
58 Rani I. Potential molecular mechanisms of zinc- and copper-mediated antiviral activity on COVID-19 Nutr. Res. 92 2021 109 128 10.1016/j.nutres.2021.05.008 34284268
59 Mohammed Y.H. Support for the safe use of zinc oxide nanoparticle sunscreens: lack of skin penetration or cellular toxicity after repeated application in volunteers J. Invest. Dermatol. 139 2 2019 308 315 10.1016/j.jid.2018.08.024 30448212
60 Attia N.F. Iron oxide nanoparticles and their pharmaceutical applications Appl. Surf. Sci. Adv. 11 Oct. 2022 100284 10.1016/J.APSADV.2022.100284
61 Yusefi M. Shameli K. Ali R.R. Pang S.W. Teow S.Y. Evaluating anticancer activity of plant-mediated synthesized Iron oxide nanoparticles using Punica Granatum fruit Peel extract J. Mol. Struct. 1204 2020 10.1016/j.molstruc.2019.127539
62 Hermosa G.C. Green synthesis of magnetic ferrites (Fe3O4, CoFe2O4, and NiFe2O4) stabilized by aloe Vera extract for Cancer hyperthermia activities IEEE Trans. Magn. 2022 10.1109/TMAG.2022.3158835
63 Abo-zeid Y. Ismail N.S. McLean G.R. Hamdy N.M. A molecular docking study repurposes FDA approved iron oxide nanoparticles to treat and control COVID-19 infection Eur. J. Pharm. Sci. 153 2020 10.1016/j.ejps.2020.105465
64 Lin N. Antiviral nanoparticles for sanitizing surfaces: a roadmap to self-sterilizing against COVID-19 Nano Today 40 Oct. 2021 101267 10.1016/J.NANTOD.2021.101267
65 Hewawaduge C. Senevirathne A. Jawalagatti V. Kim J.W. Lee J.H. Copper-impregnated three-layer mask efficiently inactivates SARS-CoV2 Environ. Res. 196 May 2021 110947 10.1016/J.ENVRES.2021.110947
66 Hosseini M. Chin A.W.H. Behzadinasab S. Poon L.L.M. Ducker W.A. Cupric oxide coating that rapidly reduces infection by SARS-CoV-2 via solids ACS Appl. Mater. Interfaces 13 5 2021 5919 5928 10.1021/acsami.0c19465 33480246
67 Mazurkow J.M. Yüzbasi N.S. Domagala K.W. Pfeiffer S. Kata D. Graule T. Nano-sized copper (oxide) on alumina granules for water filtration: effect of copper oxidation state on virus removal performance Environ. Sci. Technol. 54 2 2020 1214 1222 10.1021/acs.est.9b05211 31855599
68 Sunada K. Minoshima M. Hashimoto K. Highly efficient antiviral and antibacterial activities of solid-state cuprous compounds J. Hazard. Mater. 235–236 2012 265 270 10.1016/j.jhazmat.2012.07.052
69 Hutasoit N. Kennedy B. Hamilton S. Luttick A. Rahman Rashid R.A. Palanisamy S. Sars-CoV-2 (COVID-19) inactivation capability of copper-coated touch surface fabricated by cold-spray technology Manuf. Lett. 25 2020 93 97 10.1016/j.mfglet.2020.08.007 32904558
70 Mosselhy D.A. Copper-silver nanohybrids: Sars-cov-2 inhibitory surfaces Nanomaterials 11 7 2021 10.3390/nano11071820
71 El-Nahhal I.M. Salem J. Kodeh F.S. Elmanama A. Anbar R. CuO–NPs, CuO–Ag nanocomposite and Cu(II)-curcumin complex coated cotton/starched cotton antimicrobial materials Mater. Chem. Phys. 285 2022 10.1016/j.matchemphys.2022.126099
72 Ghaemi F. Amiri A. Bajuri M.Y. Yuhana N.Y. Ferrara M. Role of different types of nanomaterials against diagnosis, prevention and therapy of COVID-19 Sustain. Cities Soc. 72 Sep. 2021 103046 10.1016/J.SCS.2021.103046
73 Sun Z. Ostrikov K. Ken Future antiviral surfaces: lessons from COVID-19 pandemic Sustain. Mater. Technol. 25 Sep. 2020 e00203 10.1016/J.SUSMAT.2020.E00203
74 Qi M. Novel nanomaterial-based antibacterial photodynamic therapies to combat oral bacterial biofilms and infectious diseases Int. J. Nanomedicine 14 2019 6937 6956 10.2147/IJN.S212807 31695368
75 Behzadinasab S. Chin A. Hosseini M. Poon L. Ducker W.A. A surface coating that rapidly inactivates SARS-CoV-2 ACS Appl. Mater. Interfaces 12 31 2020 34723 34727 10.1021/acsami.0c11425 32657566
76 Ishida T. Antiviral activities of Cu2+ ions in viral prevention, replication, RNA degradation, and for antiviral efficacies of lytic virus, ROS-mediated virus, copper chelation World Sci. News 99 2018 148 168
77 Borkow G. Copper-oxide impregnated respiratory masks may significantly reduce the risk of SARS-CoV-2 cross-contamination Res. Sq. 9 2020 1 8 10.21203/rs.3.rs-60610/v1
78 Archana K.M. Rajagopal R. Krishnaswamy V.G. Aishwarya S. Application of green synthesised copper iodide particles on cotton fabric-protective face mask material against COVID-19 pandemic J. Mater. Res. Technol. 15 Nov. 2021 2102 2116 10.1016/J.JMRT.2021.09.020 35864980
| 0 | PMC9727968 | NO-CC CODE | 2022-12-14 23:45:38 | no | 2023 Apr 7; 35:e00544 | utf-8 | null | null | null | oa_other |
==== Front
Diabetes Res Clin Pract
Diabetes Res Clin Pract
Diabetes Research and Clinical Practice
0168-8227
1872-8227
Elsevier B.V.
S0168-8227(22)01016-6
10.1016/j.diabres.2022.110202
110202
Article
The Bidirectional Association Between Diabetes and Long-COVID-19 – A Systematic Review
Harding Jessica L abc⁎
Oviedo Sofia A ab
Ali Mohammed K de
Ofotokun Igho fgh
Gander Jennifer C i
Patel Shivani A d
Magliano Dianna J j
Patzer Rachel E abc
a Department of Surgery, Emory University School of Medicine, Atlanta, United States
b Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, United States
c Department of Medicine, Emory University School of Medicine, Atlanta, United States
d Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, United States
e Department of Family and Preventive Medicine, School of Medicine, Emory University, Atlanta, United States
f Division of Infectious Diseases, Department of Medicine, Emory University School of Medicine, Atlanta, United States
g Department of Behavioral Science and Education, Rollins School of Public Health, Emory University, Atlanta, United States
h Grady Healthcare System Infection Disease Program
i Center for Research and Evaluation, Kaiser Permanente Georgia, Atlanta, United States
j Diabetes and Population Health, Baker Heart and Diabetes Institute, Melbourne, Australia
⁎ Corresponding author.
7 12 2022
7 12 2022
11020227 9 2022
15 11 2022
1 12 2022
© 2022 Elsevier B.V. All rights reserved.
2022
Elsevier B.V.
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Some evidence suggests that diabetes may be a risk factor for the development of post-acute sequelae of COVID-19 (PASC). Recent data also indicate that new-onset diabetes may be a complication of COVID-19. Here, we review the existing evidence.
Following PRISMA guidelines, we conducted a systematic review through August 8, 2022. We included longitudinal studies reporting on the risk of PASC (i.e., sequelae that extend beyond four weeks after initial infection) in people with and without diabetes, and studies reporting on the risk of new-onset diabetes in people with vs. without COVID-19 with a minimum of 4-weeks of follow-up. All studies were published in English.
Among 5,532 studies screened, 39 were included in the final review. Among 25 studies reporting on diabetes and PASC, 44% (n=11) identified diabetes as a significant risk factor for PASC (increased relative risk ranging from 7% to 342%) while 56% (n=14) did not. Among 14 studies reporting on new-onset diabetes, 12 (86%) reported that COVID-19 (vs. no COVID) was significantly associated with new-onset diabetes with increased risks ranging from 11% to 276%.
COVID-19 survivors may be at increased risk for new-onset diabetes, but whether pre-existing diabetes is also a risk factor for PASC remains unclear.
Keywords
Epidemiology
type 2 diabetes
infectious disease
outcomes
==== Body
pmc1 Background
It is estimated that approximately 20-30% and 50-89% of non-hospitalized and hospitalized COVID-19 patients, respectively, will suffer from post-acute sequelae of COVID-19 (PASC), also known as long-COVID, four weeks beyond initial symptom onset of COVID-19.[1] PASC is currently defined by the National Institute of Health (NIH) and the Centers for Disease Control and Prevention (CDC) as “sequelae that extend beyond four weeks after initial infection”[2] and can include several symptoms (e.g., fatigue, shortness of breath, memory loss, anosmia, gastrointestinal distress)[3] and affect multiple organs and systems.[4] Although diabetes has been widely reported as a key risk factor for the development of severe COVID-19 (i.e., hospitalization, intensive care unit admission, and mortality)[5], [6], [7] in the acute phase, it is less clear whether it is a risk factor for PASC.
Further, emerging evidence indicates a potential bi-directional relationship between diabetes and COVID-19 such that diabetes may be both a risk factor for COVID-19-related complications and a complication of COVID-19. For example, several studies have suggested that the incidence of both type 1 and type 2 diabetes[8], [9], [10] increased in 2020-2021 as compared with pre-COVID years, and a 2021 meta-analysis reported a high proportion of new-onset diabetes in people with COVID-19.[11] However, these studies do not compare the incidence of new-onset diabetes in people with vs. without COVID-19 and thus cannot disentangle the causal impact of COVID-19 infection as compared with more broad pandemic factors (i.e., reduced physical activity, weight gain, job loss) on the risk of developing diabetes. One recent study from the US Department of Veterans Affairs reported that in the post-acute phase, people with COVID-19 were 40% more likely to develop new diabetes compared to matched people without COVID-19.[12]
A detailed assessment of the risk and burden of diabetes in the post-acute phase of COVID-19 is needed to inform post-acute COVID-19 care strategies. Therefore, we conducted a systematic review to examine whether diabetes is: 1) a risk factor for PASC; and 2) a manifestation of PASC.
2 Methods
This review adheres to the Preferring Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelinesp[13] (Supplementary Table 1 ) and has been registered with the PROSPERO International Prospective Register of Systematic Reviews (#CRD42022326929).Table 1 Summary of studies included in systematic review of diagnosed diabetes and the post-acute sequelae of COVID-19 (PASC) from Jan 1, 2020 until Nov 9, 2022
Study characteristics Sample characteristics PASC outcome Findings
First Author COVIDdiagnosis date Country Study population Sample sizeN Diabetes (%) Men(%) Age(years) PASC definition Follow-up time Analysis Risk estimate(diabetes vs. no diabetes)(95% CI)
Alkwai[45] Nov 2020 – Dec 2020 Saudi Arabia Social media users with self-reported COVID-19 213 3.8 23.9 90.1% 18-44 Persistent symptoms ≥3 months Unadjusted RR: 0.99 (0.30-1.69)1
Akter[20] Apr 1, 2020 – Jun 30, 2020 Bangladesh Confirmed COVID-19 diagnosis 734 19.9 76 Range:0 -≥60 Physical and mental health 4 weeks Unadjusted Mobility:RR: 2.00 (1.43 - 2.76)2
Self-care:RR: 1.10 (0.64 - 1.89)2
Pain/discomfort:RR: 1.53 (1.22 - 1.91)2
Anxiety/depression:RR: 1.22 (0.89 - 1.68)2
Sleep:RR: 1.31 (1.03 - 1.67)2
Panic attack:RR: 0.91 (0.56 - 1.46)2
Loss of concentration:RR: 1.15 (0.87 - 1.55)2
Memory loss:RR: 1.38 (0.99 - 1.93)2
Basic-Jukic[22] Mar 2020 – Jan 2021 Croatia Kidney transplant recipients with known prior SARS-CoV-2 infection; 77% hospitalized 104 20.2 66.3 Median [IQR]: 56 [45-65] Persistent symptoms/new-onset clinical problem Median: 64 days [IQR: 50-76] Adjusted OR: 4.42 (1.16-16.8)
Bellan[46] Mar 1 – Jun 29, 2020 Italy Hospitalized COVID-19 patients 238 15.1 59.7 Median [IQR]: 61 [50-71] Pulmonary function/ physical functioning 3-4 months Unadjusted Impaired Pulmonary Function:OR: 2.17 (0.68-6.92)
Functional Impairment:OR: 0.95 (0.35-2.60)
Blomberg[47] Feb 28, 2020 – Apr 4, 2020 Norway 79% hospitalized and 21% non-hospitalized COVID-19 patients 312 4 49 Median [IQR]: 46 [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58] Persistent symptoms 6 months Adjusted Total number of PASC symptoms: Unadjusted RR: 1.33 (0.67-2.87)
Fatigue:RR: 1.06 (0.91-1.23)
Budhiraja[25] Mar 2020 – Feb 2022 India Hospitalized COVID-19 patients 5,529 18.1 64.6 Mean (SD):54.4 (17.0) Persistent symptoms 1-5 and ≥ 6 months Adjusted OR: 1.96 (1.45-2.66)
Cervia[48] Apr 2020 – Aug 2021 Switzerland 66% mild cases, 34% severe cases 134 14 56 IQR: 27-74 Persistent symptoms Median 383 (IQR: 371-397) days Unadjusted OR: 2.34 (0.78-8.87)
Chai[18] Jan 1, 2020 – Mar 18, 2020 China Hospitalized COVID-19 patients Total: 2545 28.9[3], [4] No diabe-tes4: 43.6Diabe-tes5: 56.7 Range: 0 - 65 Fatigue, shortness of breath, chest tightness, cough 1 year Unadjusted Fatigue:RR: 1.1 (0.7 - 1.6)5
Chest tightness:RR: 0.96 (0.7 - 1.4)5
Cough:RR: 0.98 (0.6 - 1.6)5
Shortness of breath:RR: 1.2 (0.8 - 1.7)5
Crankson[23] Mar 2020 – Aug 2021 Ghana Hospitalized COVID-19 patients 2,334 5.4 60.1 Range: 30-59 Persistent symptoms 4 weeks Unadjusted OR: 4.18 (1.61-10.85)6
Fernández-de-Las-Peñas[49] Mar 1, 2020 – May 31, 2020 Spain Hospitalized COVID-19 patients 435 33 62.1 Mean (SD): 70.2 (13.2) Persistent symptoms Mean: 7.2 ± 0.6 months Matched Number of symptoms:RR: 1.06 (0.92-1.24)
Fatigue:OR: 1.45 (0.93-2.25)
Dyspnea:OR: 0.97 (0.64-1.47)
Musculoskeletal Pain: (OR: 0.95 (0.76-1.18)
Anxiety:OR: 1.30 (0.77-2.20)
Depressive Symptoms:OR: 1.31 (0.79-2.17)
Poor Sleep Quality:OR: 1.34 (0.89-2.03)
Limitations in Occupational Activities:OR: 0.73 (0.40-1.35)
Limitations in Leisure Activities:OR: 1.34 (0.87-2.06)
Limitations in Activities of Daily Living:OR: 1.05 (0.67-1.65)
Limitations in Basic Activities of Daily Living:OR: 1.04 (0.63-1.71)
Ioannou[29] Feb 1, 2020 – Apr 30, 2020 USA Veterans with diagnosed COVID-9=19 198,601 34.2 89.1 Mean (SD): 60.4 (17.7) ICD-10 diagnoses specific to COVID-19 ≥3 months Adjusted Unadjusted OR: 1.37 (1.33-1.40)Adjusted OR: 1.07 (1.04-1.11)
Jones[50] Aug 7, 2020 – Jan 22, 2021 UK Self-diagnosed, clinician-diagnosed, or test-confirmed COVID-19 3,151 21.1 35 Median (IQR: 52 (40-61) Persistent symptoms ≥4 weeks Adjusted OR: 1.07 (0.78-1.45)
Loosen[19] Mar 1, 2020 – Mar 31, 2021 Germany Confirmed COVID-19 diagnosis 50,402 People with T1D: 0.7People with T2D: 10.0 45.5 Mean (SD): 48.8 (19.3) ICD-10 diagnoses Range: 90 -183 days Adjusted People withT1D:OR: 1.00 (0.59–1.69)
Women with T1D:OR: 0.98 (0.45–2.11)
Men with T1D:OR: 0.99 (0.48-2.05)
People with T2D:OR: 0.93 (0.79–1.10)
Women with T2D:OR: 0.80 (0.64–1.02)
Men with T2D:OR: 1.10 (0.87–1.41)
Mechi[51] May 20, 2020 – Jun 1, 2021 Iraq Hospitalized COVID-19 patients 112 37.5 66.1 People with DM: mean (SD): 60 (10)People without DM: mean (SD): 45 (12) Persistent symptoms ≥9 months Unadjusted RR: 1.12 (0.96-1.29)7
Messin[52] Mar 2020 France Hospitalized COVID-19 patients 74 8.1 40.5 Mean (SD): 52.3 (18) Persistent symptoms ≥6 months Unadjusted RR: 1.18 (0.79-1.57)8
Nesan[21] Jun 1, 2020 – Nov 10, 2020 India Hospitalized COVID-19 patients 1354 9.7 73 Range: ≤10 -≥60 Cardio-respiratory, abdominal, psychological, neurological, renal ≥3 months Adjusted General symptoms:OR: 0.77 (CI: 0.53-1.11)
Cardio-respiratory symptoms:OR: 2.29 (CI: 1.97-5.40)
Abdominal symptoms:OR: 0.89 (CI: 0.46-1.72)
Psychological symptoms:OR: 1.02 (CI: 0.70-1.50)
Neurological symptoms:OR: 2.64 (CI: 1.46-4.77)
Renal symptoms:OR: 0.79 (CI: 0.38-1.61)
Nguyen[53] Early 2020 France PCR-confirmed COVID-19 patients who reported smell and/or taste disorders during the acute phase upon admission 605 5 36.2 Mean (SD): 40.0 (13.3) Persistent loss of smell and/or taste ≥6 months Unadjusted OR: 0.72 (0.29-1.79)
Peghin[54] Mar 2020 – May 2020 Italy Confirmed COVID-19 inpatient and outpatients 599 5.5 31.6 ≥18 years Persistent symptoms Mean (SD): 187 (22) days Unadjusted RR: 1.14 (0.75-1.53)9
Pfaff[27] Not available USA Confirmed COVID-19 diagnosis 846,981 9.9 40.8 ≥18 years ICD-10 diagnosis 45-365 days Unadjusted All patients:OR: 1.49 (1.13–1.96)
Hospitalized patients:OR: 1.16 (0.84–1.58)
Profili[17] < March 1, 2020 Italy Confirmed COVID-19 diagnosis 92,304 9.96 46.3 Range: 45 - 97 First hospitalization for myocardial infarction or stroke ≤6 months Adjusted Myocardial infarction:RR: 2.710
Stroke:RR: 2.510
Myocardial infarctionor stroke:IRR: 2.24 (2.18 - 4.22
Rinaldi[26] Mar 2021 – Jan 2022 Italy Confirmed COVID-19 and ≥1assessment of high sensitivity cardiac troponin I (hs-cTnI) 701 17.4 59.8 Mean (SD): 66.4 (14.4) MACE Median (IQR): 270 (165-380) days Adjusted Unadjusted HR: 4.04 (2.26-7.20)Adjusted HR: 2.35 (1.25-4.43)
Su[24] Not reported USA 71% hospitalized 209 22.4 50 Mean (SD): 56 (18) Respiratory viral, gastrointestinal, neurologic and anosmia/dysgeusia 2-3 months Adjusted Diabetes was significantly correlated with respiratory viral (defined as at least 2 respiratory symptoms)11
Sudre[55] Mar 25, 2020 - Jun 30, 2020 UK, USA, Sweden Incident COVID-19 cases; 13.9% hospitalized 4,182 2.9 28.5 Median [IQR]: 42 [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53] Persistent symptoms 4 weeks Propensity score 18-49 years12:OR: 1.5 (0.5-4.2)
50-69 years12:OR: 0.5 (0.3-1.1)
≥70 years12:OR: 1.8 (0.4->7.0)
Yaksi[56] Jan 1, 2021 – Feb 28, 2021 Turkey Hospitalized patients with confirmed COVID-19 133 38.3 51.9 Mean (SD): 65.7 (13.1) Persistent symptoms 4 weeks Unadjusted OR: 1.98 (0.80-4.86)
Yoo[28] Apr 2020 – Feb 2021 USA Adults with laboratory confirmed SARS-CoV-2 infection 1,038 37.9 50.4 Median [IQR]: 60 [37-83] Persistent symptoms ≥30 days Adjusted OR: 1.39 (1.02-1.88)
Acronyms: CI = confidence interval; DM = diabetes mellitus; FBG = fasting blood glucose; HR = hazard ratio; ICD= international classification of disease; IRR: incident rate ratio; MACE = major adverse cardiac events; OR = odds ratio; PASC: post-acute sequela of COVID-19; RR = rate ratio; TIA = transient ischemic attack; T1D = type 1 diabetes; T2D = type 2 diabetes; UK = United Kingdom; USA = United States of America
1RR and CI calculated using counts in Table 1 of original manuscript2RR and CI calculated using counts in Table 4 of original manuscript
3Denied history of diabetes and have a FBG of <7 mmol/L
4History of diabetes or FBG ≥7 mmol/L
5Sequelae RR and CI calculated using counts from Table 1 and Table 3 of original manuscript
6Odds Ratio compared people with hypertension and diabetes to people with no comorbidities7RR and CI calculated using counts in Table 1 of original manuscript8RR and CI calculated using counts in Table 1 of original manuscript9RR and CI calculated using counts in Table 1 of original manuscript
10Calculated using adjusted rates in Table 2a of original manuscript
11ln(odds ratio) reported though precise estimates not available from Figure 1D.
12OR values estimated visually from Extended Data Fig. 3 of original manuscript.
2.1 Search Strategy
A literature search was performed in PubMed and Embase on May 30, 2022 and updated on 9 November, 2022. We used medical evidence subject heading (MeSH) related to COVID-19 combined with the operator ‘AND’ with text word ‘diabetes’. The reference lists of included studies were also screened for additional articles.
2.2 Study Selection
We included all peer-reviewed full-text research articles published in English that included a longitudinal study design and reported on 1) the risk of PASC in people with vs. without diabetes with a minimum of 30-days follow-up after COVID-19 diagnosis, or 2) the risk of new-onset diabetes among people with vs. without COVID-19 with a minimum of 30-days follow-up after COVID-19 diagnosis. We included studies of type 1 or type 2 diabetes across all ages (pediatric and adult populations) for both research questions, and included populations with existing additional comorbidities (e.g., kidney disease, hypertension). For the risk of new-onset diabetes in people with vs. without COVID-19, a statement pertaining to ‘new-onset’ or ‘incident’ diabetes among people previously undiagnosed with diabetes was needed to meet our inclusion criteria. We excluded case reports, editorials and reviews, and clinical trials. All identified articles from the literature search were entered into Covidence for screening. Where two or more studies reported on the same dataset, we included all owing to different study methodologies and COVID-19 waves. Two investigators (JLH and SO) screened the titles, abstracts, and full-text articles for eligibility. Disagreements were discussed until a resolution was reached.
2.3 Quality Assessment
The methodological quality of each study was critically appraised by two authors (JLH & SO) using a modified version of the Newcastle-Ottawa tool,[14] and conflicts resolved until a consensus was reached. This modified tool, previously utilized in studies of type 2 diabetes incidence,[15] includes items to assess the representativeness of the study population, the sample size, completeness of the data, and the method of assessing diabetes status. For the current review, we further tailored the quality assessment to include PASC, Supplementary Table 2 . The maximum score was 11 and final scores were defined as low quality (score 0 – 4), medium (score 5 –7), or high (score 8 – 11) quality.Table 2 Summary of studies included in systematic review of COVID-19 and new-onset diabetes from Jan 1, 2020 until Nov 9, 2022
Study characteristics Sample characteristics Diabetes Outcome Analysis Findings
First Author Cohort Name COVID-19 diagnosis date Country Study population Sample sizeN COVID-19 (%) non-COVID comparator Men(%) Age (years) Diabetes Type DiabetesDefinition Follow-up time Adjusted Risk estimate (95%CI)
Al-Aly[33] Veterans Affairs Mar 1,2020 – Nov 30, 2020 USA Non-hospitalized VA members 5.1 million 1.5 Non-COVID; non-hospitalized 90.4 Median (IQR):COVID patients: 60.9 (47.6-71.6)Non-COVID patient: 66.7 (51.9-73.9) Type 1/Type 2 ICD code: not specified Median (IQR):COVID-19 patients: 126 (81-203) daysNon-COVID-19 patients: 130 (82-205) Propensity score Type 1 diabetesHR: 1.22 (0.91-1.64)Type 2 diabetes:HR: 1.44 (1.30-1.60)
Hospitalized VA members 27,651 49 Hospitalized with influenza 94.0 Median (IQR):COVID patients: 70.3 (60.7-75.7)Influenza patient: 70.1 (63.0-77.0) Median (IQR):COVID-19 patients: 126 (81-203) daysNon-COVID-19 patients: 130 (82-205) Type 1 diabetesHR: 0.70 (0.49-1.01)Type 2 diabetes:HR: 1.14 (0.96-1.34)
Ayoubkhani[57] N/A <31 Aug 2020 England Hospitalized COVID-19 patients and general population with at least one GP visit (1 Jan 2019-30 Sept 2020) 287,160 16.6 Non-COVID-19 46.7 Mean (SD): 64.5 (19.2) Type 1/Type 2 ICD code: not specified 30-253 days Matched Overall COVID vs. non COVID:RR: 3.5 (2.9 - 4.3)1
Barrett[38] IQVIA Mar 1,2020 – Feb 26, 2021 USA Closed payor system; 0.7% hospitalized with COVID-19 1.7 million 4.8 1) non- COVID-19; 2) ARI 49.9 Mean (SD): 12.3 (4.3) All ICD-10-CM: E08–E13 >30 days Matched 1) COVID vs. non-COVID-19:HR: 2.66 (1.98-3.56)
2) COVID-19 vs. ARI: HR 2.16 (1.64-2.86)
HealthVerity Mar 1, 2020 – Jun 28, 2021 Closed payor system; 0.9% hospitalized with COVID-19 878,878 50 Non-COVID-19 49.9 Mean (SD):12.7 (4.3) HR: 1.31 (1.20-1.44)
Daugherty[35] United Health <1 Apr 2020 USA Insured population; continuous enrolment 9.25 million 3 1) non-COVID 2020; 2) non-COVID 2019; 3) ARI 50.2 Mean (SD): 42.4 (13.6) Type 2 ICD-10-CM: E11 median (IQR): 95 (42-135) Propensity score COVID-19 vs. non-COVID 2020:HR: 1.83 (1.60-2.10)
COVID-19 vs. non COVID 2019:HR: 1.80 (1.57-2.06)
COVID-19 vs. ARI:HR: 1.39 (1.22-1.58)
Not hospitalized COVID-19 vs non-COVID 2020:RR: 1.702
Hospitalized COVID-19 vs. non-COVID 2020:RR: 3.662
Hernandez-Romieu[34] PCORnet Mar – Dec 2020 USA Adults (≥20 years) who had undergone a PCR test for COVID-19 1.79 million 12.2 Negative COVID-19 test 40 ≥20 Type 2 ICD-10-CM: E11 31-150 days Unadjusted Non-hospitalized positive COVID test vs. negative COVID test:PR: 0.9 (99%CI: 0.85-0.96)
Hospitalized positive COVID test vs. negative COVID test:PR: 2.03 (99%CI: 1.87-2.19)
Mechanically ventilated positive COVID test vs. negative COVID test:PR: 2.25 (99%CI: 1.82-2.77)
Children (<20 years) who had undergone a PCR test for COVID-19 338,024 10.6 50 <20 Non-hospitalized positive COVID test vs. negative COVID test:PR: 1.27 (99%CI: 1.75-2.14)
Hospitalized positive COVID test vs. negative COVID test:PR: 2.14 (99%CI: 1.13-4.06)
Horberg[58] KPMAS Jan 2020 – Dec 2020 USA Insured adults (>18 years); continuous enrolment 98,411 28.6 Non-COVID-19 42.7 ≥18 All CCS 49 and 50 30-120 days Matched RR: 1.20 (1.03-1.38)
Kendall[39] TriNetX Mar 2020 – Dec 2021 USA Insured population 1.09 million 28.9 ARI 50 0-18 Type 1 ICD-10 codeE10 3 and 6 months Matched 3 months0-18 years:HR: 2.10 (1.48-3.00)0-9 years:HR: 1.75 (0.92-3.32)10-18 years:HR: 2.40 (1.62-3.56)
6 months0-18 years:HR: 1.83 (1.36-2.44)0-9 years:HR: 1.73 (1.02-2.94)10-18 years:HR: 2.18 (1.57-3.03)
McKeigue[30] REACT-SCOT Mar 1 2020 – Nov 22, 2021 Scotland <35 years 1.85 million 19.7 Non-COVID-19 50 0-35 Type 1 ICD-10 (E10-E14), outpatient code, or medication ≥30 days Matched HR: 0.86 (0.62-1.210
Nayar[31] COVIDPAN Mar 1 2020 – Jul 23, 2020 Multi-country Patients hospitalized with acute pancreatitis 1,476 8.0 Non-COVID-19 52.3 Mean (SD): 54.5 (18.1) All Not reported 12 months Adjusted OR: 0.61 (0.13-2.96)
Rezel-Potts[36] CPRD Aurum Jan 2020 – Feb 2021 UK Family Practices 857,300 50 Non-COVID-19 44.0 Median (IQR): 35 (22-50) All ICD codes, diabetic medication, or HbA1c ≥48mmol/mol Range 5-52 weeks Matched and adjusted 5-12 weeks from index date:RR: 1.27 (1.11-1.46)
13-52 weeks from index date:RR: 1.07 (0.99-1.16)
Wander[32] VHA Mar 2020 –Mar 2021 USA US Veterans; 2% hospitalized with COVID 2.8 million 4.6 Non-COVID-19 86.0 Mean (SD):59.0 (17.1) All Abnormal laboratory values (e.g., HbA1c) or ICD-10 E08-E13 or antihyperglycemic medication COVID-19 group: mean 193 days [range 32–456]; non-COVID-19 group: mean 239 days [range32–457]) Adjusted Men 120 days:OR: 2.56 (2.32-2.83)
Men total time:OR: 1.95 (1.80-2.12)
Women 120 days: OR: 1.21 (0.88-1.68)
Women total time: OR: 1.04 (0.82-2.12)
Hospitalized men 120 days:OR: 1.42 (1.22-1.65)
Hospitalized men total time:OR: 1.32 (1.16-1.50)
Hospitalized women 120 days:OR: 0.72 (0.34-1.52)
Hospitalized women total time:OR: 0.80 (0.44-1.45)
Xie[12] VHA Mar 1, 2020, – Sept 30, 2021 USA US Veterans who survived 30 days; 8.3% of COVID-19 patients hospitalized and 2.3% admitted to ICU contemporary cohort: 4.5 million; historical cohort; 4.3 million 4.2 Contemporary control: Non-COVID-19 who used the VHA services in 2019; Historicalcohort: used the VHA services in 2017 COVID-19: 88.1; contemporary control: 88.8; historical control: 88.7 Mean (SD):COVID-19: 60.6 (17.0); contemporary control: 61.5 (17.1); historical control: 61.5 (17.1) All ICD-10 codes (E08.X to E13.X) or a HbA1c measurementof more than 6·4% Median (IQR):COVID-19: 352 (244-406); contemporary control: 352 (245-406); historical control: 352 (245-406) days Propensity Score COVID-19 vs. contemporary control:HR 1.40 (1.36–1.44)
COVID-19 vs. contemporary controlnon-hospitalized:HR 1.25 (1.21–1.29)
COVID-19 vs. contemporary control hospitalized:HR 2.73 (2.50-2.99)
COVID-19 vs. contemporary controlICU admission:HR 3.76 (3.24-4.37)
COVID-19 vs. historical control: HR 1.35 (1.31–1.39)
COVID-19 vs. historical control non-hospitalized:HR 1.21 (1.17–1.25)
COVID-19 vs. historical control hospitalized:HR 2.66 (2.43-2.91)
COVID-19 vs. COVID-19 vs. historical control ICU admission:HR 3.66 (3.15-4.25)
Xie[59] VHA Mar 1, 2020, – Mar 15, 2021 USA US Veterans who survived 30 days 4.4 million 4.1 General VHA users 90.5 Median (IQR)67.1 (53.1-74.5) All ICD-10 code E08-13 or HbA1c>6.5% or use of antihyperglycemics 6-months Unadjusted HR: 1.39 (1.33-1.44)
Zhang [37] n/a Jan 1, 2020, – Mar 30, 2021 Multi-country Hospitalized patients 580,287 13.0 Non-COVID-19 hospitalized patients With COVID-19: 74.0Without COVID-19: 76.0 ≥18 years Type 2 ICD codes ≥30 days Unadjusted No increased diabetes risk, RR not reported
Non-hospitalized patients 2.2 million 15.9 Non-COVID-19 hospitalized patients With COVID-19: 64.0Without COVID-19: 59.0 Mid-stage (30-89 days post infection):RR: 1.26 (1.16-1.36)Late stage (≥90 days):RR: 1.11 (1.02-1.21)
Abbreviations: ARI = Acute Respiratory Infection; AURI = Acute Upper Respiratory Infection; CCS = Certified Coding Specialist; CI = Confidence Interval; GP = General Practitioner; HR = Hazard Ratio; ICU = Intensive Care Unit; IRR: Incident Rate Ratio; KPMAS = Kaiser Permanente Mid Atlantic Sites; OR = Odds Ratio; PR = Prevalence Ratio; RR = Relative Risk; UK = United Kingdom; USA = United States of America; VHA: Veterans’ Health Administration1Risk estimate calculated using values from Supplementary Table 2 of original manuscript2Risk estimates calculated using values from Supplementary Table 4d of original manuscript
2.4 Data Synthesis and Analysis
We extracted the following data from included articles: publication characteristics (i.e., year of publication, author names, PMID, journal source); study characteristics (e.g., study design, country); sample characteristics (e.g., sample size, % diabetes); PASC and diabetes outcomes (e.g., definition, follow-up time); and findings (e.g., rate ratios, odds ratio). Where studies reported counts only, we estimated relative risks and 95%CI using standard methods[16] as provided in Supplementary Table 3. Data extraction was performed by two authors (JLH and SO). Due to the heterogeneity and relatively limited number of studies, we adopted a qualitative approach to analysis of results and narratively synthesized results of included studies. Estimates of PASC or new-onset diabetes are provided per study and overall patterns across studies described. For patterns of new-onset diabetes, we stratified the results by pediatric and adult populations.
2.5 IRB approval
As this is a review of existing studies, this study was exempt from Institutional Review Board approval.
3 Results
The search yielded 8,141 records. After excluding duplicates (n=2,609), 5,532 records were screened and 39 were included in full text review. An additional six studies were identified from screening of reference lists and included in the review. Of 17 full text articles excluded, eight (47%) did not include an appropriate comparison population, two (12%) reported outcomes which were not relevant, two (12%) were reviews, editorials, or trials, and five (29%) did not include a minimum of 30-days follow-up. In total, 39 articles met our inclusion criteria: 25 for the assessment of diabetes and PASC, and 14 for the assessment of COVID-19 and incident diabetes, Figure 1 .Figure 1 PRISMA 2020 flow diagram for new systematic reviews which included searches of databases, registers and other sources
3.1 Diabetes and PASC
Twenty-five studies examining diabetes as a risk factor for PASC are summarized in Table 1. PASC was defined with a range of definitions, but commonly included ongoing symptoms such as fatigue, cough, and dyspnea, and follow-up time spanned from 4 weeks to more than 15 months. Four studies included new diagnosis following a COVID-19 infection, including two studies that examined hospitalization for myocardial infarction or stroke.[17] Supplementary Table 4 includes a detailed summary of PASC definitions. Most studies were from high-income countries (Croatia, Italy, Norway, China, Spain, Germany, Sweden, Switzerland, France, the United Kingdom, and the United States), with seven studies from middle-income countries (India (2 studies), Ghana, Turkey, Iraq, Saudi Arabia, and Bangladesh). The sample sizes of the studies ranged from 74 to 846,987 participants. In total, nine (36%) studies reported PASC among hospitalized COVID-19 cohorts, nine (36%) among people with a COVID-19 diagnosis, four (16%) among a COVID-19 cohort of which a variable portion (13.1% to 71%) had been hospitalized, one (4%) study included a cohort of COVID-19 patients with a functioning kidney transplant, one (4%) included individuals who had confirmed COVID-19 and at least one assessment of high sensitivity for cardiac troponin I, and one (4%) included social media users with self-reported COVID-19 . Eleven studies (44%) were conducted among people diagnosed with COVID-19 during the first pandemic wave (prior to June 2020), two studies did not report on the COVID-19 diagnosis date, and 12 (48%) studies included data on COVID-19 cases spanning from 2020 to 2021. All studies ascertained diabetes status using electronic medical record data defined with International Classification of Disease coding, excluding the Chinese study by Chai et al.[18] which used a combination of previous diabetes diagnosis and fasting plasma glucose at admission to ascertain diabetes status.
Only one German study[19] reported risk of PASC in people with type 1 and type 2 diabetes, separately. Twenty-three studies reporting risk of PASC among adults. Two studies, in Bangladesh and India, included children. Specifically, in the Bangladesh study,[20] 6.8% of the study population was ≤19 years, and in India,[21] 4.1% of the study population was ≤20 years. In both studies, risk of PASC was not reported separately for children and adults.
Overall, 11 (44%) studies reported that diabetes was a significant risk factor for the development of PASC, while 14 (56%) studies indicated it was not a significant risk factor. Among the 11 studies indicating diabetes was associated with PASC, 3 (38%) reported ORs >4. One such study was among kidney transplant recipients (OR for diabetes vs. no diabetes: 4.42 (95%CI: 1.16-16.8))[22] and adjusted for several confounding factors; Another examined the association between diabetes, hypertension, and PASC in comparison to people with no comorbidities (unadjusted OR: 4.18 (1.61-10.85));[23] and a multi-omics study reported a strong and significant correlation between type 2 diabetes and at least two PASC-related respiratory symptoms, adjusted for age, gender and severity of COVID-19.[24] Among the remaining eight positive studies, one study from Bangladesh[20] among people with confirmed COVID-19 reported significant increased risks of impaired mobility, sleep, and pain/discomfort in people with vs. without diabetes (RR from 1.31 to 2.00), but did not demonstrate an association with impaired self-care, panic attacks, loss of concentration, or memory loss. In India,[21] people with (vs. without) diabetes had an increased risk for ongoing cardio-respiratory (OR: 2.29, 95%CI: 1.97-5.40) and neurological (OR: 2.64, 95%CIL 1.46-4.77) symptoms, but not abdominal or psychological symptoms. Among hospitalized patients in an India center,[25] diabetes was associated with a 96% (95%CI: 1.45-2.66) increased for persistent symptoms 6 months after initial diagnosis. In Italy,[17] people with diabetes had an increased risk of post-COVID hospitalization for first myocardial infarction or stroke (adjusted IRR: 2.24, 95%CI: 2.18-4.22), or MACE in people who had been assessed for high sensitivity cardiac troponin I (adjusted HR: 2.35 (1.25-4.43),[26] and three US studies[27], [28], [29] demonstrated a 7% to 49% increased risk of persistent symptoms in people with vs. without diabetes. The remaining 14 studies reported a non-significant association between diabetes and a range of PASC including impaired pulmonary function, functional impairment, fatigue, shortness of breath, cough, loss of smell/taste, limitations in activities of daily living, depressive symptoms, poor sleep, and musculoskeletal pain.
3.2 COVID-19 and New-Onset Diabetes
The 14 included studies examining new-onset diabetes in people with and without COVID-19 are summarized in Table 2. Overall, nine studies were from the USA, three from the UK (England and Scotland), and two multi-country studies.. Seven studies examined total diabetes incidence in adults, two examined type 2 diabetes in adults, one study examined type 2 diabetes in both children and adults, one study examined type 1 and type 2 diabetes separately, and three studies examined type 1 diabetes in children. All studies were conducted from early 2020 (< August 2020) with follow-up time ranging from 30 to 457 days and sample size ranging from 1,476 to 9.3 million.
Overall, 12/14 (86%) studies reported that COVID-19 was associated with an 11% to 276% increased risk for incident diabetes, with risk estimates varying depending on comparison population (i.e., non-COVID-19 vs. acute upper respiratory infection (AURI)), hospitalization status, and age (i.e., adults vs. children). Of the two studies that report no association, one examined any new diabetes diagnosis in 1.85 million people aged <35 years (i.e., Type 1 diabetes) in Scotland (Hazard Ratio: 0.86, 95%CI: 0.62-1.21)[30] and the other examined new-onset diabetes 12-months after hospitalization with acute pancreatitis (and with vs. without COVID-19 infection) (OR: 0.61 (0.13-2.96).[31]
Of the studies examining new-onset diabetes in adults, all demonstrated an increased risk for new-onset diabetes in people with COVID-19 with some sub-group exceptions. For example, a Veteran’s Affair’s study by Wander et al.[32] reported an increased risk for new-onset diabetes associated with COVID-19 in men (OR: 1.95 (1.80-2.12), but not women (1.04 (0.82-2.12); Al-Aly reported an increased risk for new-onset type 2 diabetes in non-hospitalized but not hospitalized veterans, and for new-onset type 2 but not new-onset type 1 diabetes[33]; Hernandez-Romieu etl a.[34] reported a 87% and 125% increased risk of new-onset diabetes in hospitalized and mechanically ventilated COVID-19 patients (vs. no COVID-19), but a decreased risk for new-onset diabetes in non-hospitalized COVID-19 patients (prevalence ratio: 0.90 (0.85-0.96)).
Among studies that stratified by COVID-19 severity (i.e., not hospitalized, hospitalized), greater severity was generally associated with higher diabetes risk. For example, in a study of US veterans by Xie et al.,[12] non-hospitalized COVID-19 patients had a 25% (95%CI: 21%-29%) increased risk of diabetes relative to people without non-COVID-19 patients, which increased to 173% and 276% in patients hospitalized, and admitted to the intensive care unit (ICU), respectively. However, in a similar study of US veterans by Al-Aly,[33] an increased risk for type 2 diabetes was seen in non-hospitalized COVID-19 patients, but not hospitalized patents. In another US study,[35] risk of new-onset diabetes increased from 70% to 266% in non-hospitalized and hospitalized COVID-19 patients, respectively, as compared with historical non-COVID-19 populations (i.e., data from pre-2020).
Risk of new-onset diabetes also appears to decrease with increasing time from COVID-19 infection. For example, one study from the United Kingdom[36] examined risk of new-onset diabetes at varying follow-up times. In the 5-12 weeks from index date, the increased risk of new-onset diabetes in people with vs. without COVID-19 was 81% (95%CI:51%-119%), which decreased to 7% (-1%-16%) and become non-significant at 13-52 weeks from index date.[36] In a multi-country study, risk of new-onset diabetes at 30-89 days following COVID-19 infection was 26% (16%-36%), which decreased to 11% (2%-21%) at ≥90 days following infection.[37] Finally, in a second study of US veterans, Wander et al. demonstrated a 156% increased risk for new-onset diabetes in men with vs. without COVID-19 within 120 days from infection, but this decreased to 95% when the study included the total follow-up time (i.e., 456 days).[32]
Among two studies, risk estimates also varied depending on the comparison population. For example, Daugherty et al.[35] examined the risk of new-onset diabetes in people with COVID-19 compared to people without COVID-19 in 2020, without COVID-19 in 2019 (arguably a true non-COVID population) and in people with AURI. Risk estimates were 83%, 80% and 39%, respectively.[35] In Xie et al. a veterans-based study, risk of new-onset diabetes was 40% (95%CI: 36-44) in people with COVID-19 vs. a 2020 contemporary cohort of people without COVID-19. This risk decreased slightly to 35% (31%-39%) when the comparison was a historical cohort of people without COVID-19 from 2018-2020.
In children, Barrett et al.[38] reported that COVID-19 increased the risk of new-onset diabetes in US people aged <18 years by 31% to 166% across two insured populations. Risk estimates were lower when the comparison population was AURI (Hazard Ratio: 2.66 (1.98-3.56) vs. non-COVID-19 (2.16 (1.64-2.86). In Scotland,[30] no increased risk was observed in people with vs. without COVID-19 aged <35 years, and in another US study, non-hospitalized and hospitalized children aged <20 years with COVID-19 had a 27% and 114% increased risk of diabetes as compared to people without COVID-19, respectively.[34] Finally, in another US study, new-onset diabetes was increased in children aged 10-18 years at 3 months (HR: 2.40 (1.62-3.56)) and 6 months (HR: 2.18 (1.57-3.03), but not in children aged 0-9 years. [39]
3.3 Quality Assessment
Using the modified Newcastle-Ottowa Scale, the quality of studies examining diabetes and PASC ranged from 4 to 11. Four studies were deemed low quality (score 1-4) owing to a small sample size, poor characterization of PASC, and crude analysis, ten were deemed moderate (score 5-7), and 11 were deemed high quality (score 8-11), Supplementary Table 5. Among the 14 studies examining COVID-19 and incident diabetes, 11 were scored as high-quality with scores ranging from 8-10, one study was moderate quality with a score of 7, and one was deemed low quality (score 5).
4 Discussion
In this systematic review of the bi-directional relationship between diabetes and PASC, our findings are two-fold. First, we report that COVID-19 survivors may be at increased risk for new-onset diabetes. This risk appears to increase in a graded fashion according to the severity of the initial infection (i.e., hospitalized vs. not hospitalized), is greater than what is observed for other acute respiratory infections, but declines with increasing time from infection. Diabetes, therefore, may arguably be considered as a component of the multifaceted PASC diagnosis and post-acute care strategies might consider the integration of diabetic screening and management. Second, whether pre-existing diabetes is a risk factor for the development of PASC remains unclear due, in part, to the heterogeneity of studies with regard to PASC definitions, populations at risk, small sample sizes, and short follow-up times. Regardless, careful monitoring of people with diabetes for development of PASC should be strongly considered.
The mechanisms underpinning the bi-directional association between diabetes and PASC are not entirely clear. The association between diabetes and severe COVID-19 (i.e., hospitalization, intensive care unit admission, and mortality) in the acute phase[5], [6], [7] is thought to be explained, in part, by the virus’ tropism for islet β-cells that express ACE2 receptors resulting in impaired production and secretion of insulin and subsequently worsening hyperglycemia, ketoacidosis, and hyperosmolarity.[6] It is possible that this same mechanism increases the risk both for PASC as well as new-onset diabetes. Other possible explanations include autonomic dysfunction, hyperactivated immune response or autoimmunity, and persistent low-grade inflammation leading to insulin resistance.[12] It is also possible that people with COVID-19 have been differentially exposed to social, economic, and environmental changes that occurred during the pandemic (i.e., lockdowns, job loss) that might have indirectly contributed to the increased risks of diabetes. Future research should consider the use of multi-level and multi-factorial models that consider the complex interplay between diabetes, COVID-19, comorbidities, and the social determinants of health.
In the case of new-onset diabetes, it is worth considering that the detection of new-onset diabetes in COVID-19 patients could be a case of undiagnosed prediabetes, diabetes, or pre-existing hyperglycemia. For instance, certain population groups who do not routinely access health-care services, including those living in remote, and rural regions, may be diagnosed with diabetes as they receive in-hospital COVID-19 testing and treatment. This notion is supported by studies that demonstrate declining risk of new-onset diabetes further out from COVID-19 infection. For example, Rezel-Potts et al. report an increased risk for post-acute new-onset diabetes at 13-52 weeks post COVID-19 infection in people with vs. without COVID-19, but this becomes non-significant at 13-52 weeks.[36] Whether detection bias is the whole story remains unknown. Studies with longer term follow-up, that stratify by time since infection, are needed to tease this out.
In our study, we did not perform a meta-analysis owing to the heterogeneity of included studies. However, a recent meta-analysis published in May 2022 reported a pooled risk estimate for incident diabetes in people with vs. without COVID-19, despite high heterogeneity estimates (I2 reported = 94%).[40] In this review, Banerjee et al.[40] report a 59% (95CI: 40%-81%) higher risk of developing incident diabetes in the post-acute COVID-19 phase versus healthy controls among 5,787,027 subjects from four observational studies, and a 22% (14%-31%) and 52% (36%-70%) increase in new-onset diabetes among mild and moderate-severe COVID-19 cases, respectively, as compared with non-COVID-19 ARI comparisons across three studies. In the current review, we include data from an additional eight studies. Though the conclusions between the current and earlier review are similar, we caution researchers against pooling of risk estimates when heterogeneity is high as it can lead to misleading interpretations of the available data. In particular, studies included in this review differed by methods of detecting new-onset diabetes (i.e., ICD codes vs HbA1C and use of hyperglycemic medication, and type 1 vs. type 2), comparison populations (i.e., non-COVID-19, general population, or ARI), follow-up time, and analytical methods (i.e., propensity score, matching, adjustment). This issue is highlighted when studies using the same study population, but differing methodologies produce different results. For example, using the Veterans Health Administration (VHA) data, Al-Aly[33] showed an increased risk for type 2 diabetes in non-hospitalized, but not hospitalized patients, while the study by Xie et al.[12] using the same data demonstrated a graded increased risk whereby patients hospitalized with COVID-19 and admitted to the ICU had a greater risk than non-hospitalized COVID-19 patients. These differences are likely to be explained primarily by the different comparison populations used in these two studies. In Al-Aly, people hospitalized with COVID-19 are compared to a historical cohort hospitalized with influenza, while in the Xie et al. study, they are compared to a non-hospitalized population.
Given the large and growing number of people infected with COVID-19 (562 million people globally as of July 20, 2022[41]), identifying people at high risk of COVID-19-related complications, including PASC and new-onset diabetes, to manage and prevent complications is of high importance. The findings from this review underscore the importance of COVID-19 prevention strategies, including vaccination, in addition to screening for and managing PASC. Prevention should include screening and monitoring for signs of diabetes following COVID-19 infection. In addition, it may be important to identify people with diabetes as high-risk for PASC to allow for additional screening, monitoring, and possible prevention and treatment. For example, poorly controlled diabetes increases the risk of severe COVID-19 and is associated with increased morbidity and mortality.[42] Regular monitoring of glucose levels, coupled with the use of glucose-lowering agents as appropriate,[43] may therefore help in reducing and managing PASC risk.
This review examines diabetes as a risk factor for PASC (long COVID) and incident diabetes as a key complication of COVID. The key strength of this review is in the systematic approach to the search strategy which includes two databases (Embase and Pubmed), and broad search terms to attempt to capture all published data on diabetes and COVID-19. Key limitations include the possible exclusion of relevant studies not published in English, the heterogeneity of studies, particularly with respect to PASC definitions, limiting our ability to pool risk estimates across studies, and possible misclassification of diabetes status due to undiagnosed cases. As the evidence base builds, researchers are encouraged to adopt rigorous approaches to assess and define PASC, and carefully document the association between diabetes and PASC to enable meaningful comparisons between studies. This includes the use of validated algorithms for identifying people with diabetes in administrative data,[44] refining PASC definitions as per NIH and CDC guidelines,[2] and the use of appropriate comparison groups (i.e., people without diabetes rather than people without comorbidities).
5 Conclusion
The conclusions of this systematic review are two-fold. First, among 14 studies, 86% report that COVID-19 survivors may be at increased risk for new-onset diabetes and thus careful monitoring of high-risk individuals (i.e., those with pre-diabetes or those hospitalized with COVID-19) for the development of diabetes may be advised. Second, among 25 studies, whether pre-existing diabetes is also a risk factor for PASC remains unclear with 44% indicating diabetes is a PASC risk factor, and 56% indicating it is not. More high-quality studies across multiple populations and settings are needed to determine if diabetes is indeed a risk factor for PASC. In the meantime, careful monitoring of people with diabetes for development of PASC may be advised.
6 Contribution statement
JLH conceptualized the paper, conducted the literature review, extracted data, and wrote the paper. SO screened studies for eligibility, extracted data, and reviewed the final manuscript. MKA, IO, DJM, and REP provided intellectual input, and reviewed the final manuscript. JLH is the guarantor of this work and takes responsibility for final responsibility for the decision to submit for publication.
Funding
Research reported in this publication was supported by the National Heart, Lung, And Blood Institute grant OT2HL161847 (Researching COVID to Enhance Recovery (RECOVER) study) and National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health under Award Number P30DK111024. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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References
1 National Institute for Health and Care Research. NIHR Themed Review: Living with Covid19 - Second review 2021 [Available from: https://evidence.nihr.ac.uk/wp-content/uploads/2021/03/NIHR_COVID_REPORT_FINAL-150321-1_1_.pdf accessed 10 August 2022.
2 Datta S.D. Talwar A. Lee J.T. A Proposed Framework and Timeline of the Spectrum of Disease Due to SARS-CoV-2 Infection: Illness Beyond Acute Infection and Public Health Implications JAMA 324 22 2020 2251 3222 10.1001/jama.2020.22717 [published Online First: 2020/11/19] 33206133
3 Crook H. Raza S. Nowell J. Long covid-mechanisms, risk factors, and management BMJ 374 2021 n1648 10.1136/bmj.n1648 [published Online First: 2021/07/28]
4 Raman B. Cassar M.P. Tunnicliffe E.M. Medium-term effects of SARS-CoV-2 infection on multiple vital organs, exercise capacity, cognition, quality of life and mental health, post-hospital discharge EClinicalMedicine 31 2021 100683 10.1016/j.eclinm.2020.100683 [published Online First: 2021/01/26]
5 Lima-Martinez M.M. Carrera Boada C. Madera-Silva M.D. COVID-19 and diabetes: A bidirectional relationship Clin Investig Arterioscler 33 3 2021 151 217 10.1016/j.arteri.2020.10.001 [published Online First: 2020/12/12]
6 Apicella M. Campopiano M.C. Mantuano M. COVID-19 in people with diabetes: understanding the reasons for worse outcomes Lancet Diabetes Endocrinol 8 9 2020 782 792 10.1016/S2213-8587(20)30238-2 [published Online First: 2020/07/21] 32687793
7 Roncon L. Zuin M. Rigatelli G. Diabetic patients with COVID-19 infection are at higher risk of ICU admission and poor short-term outcome J Clin Virol 127 2020 104354 10.1016/j.jcv.2020.104354 [published Online First: 2020/04/20]
8 Gottesman BL, Yu J, Tanaka C, et al. Incidence of New-Onset Type 1 Diabetes Among US Children During the COVID-19 Global Pandemic. JAMA Pediatrics 2022;176(4) 10.1001/jamapediatrics.2021.5801
9 Kamrath C. Rosenbauer J. Eckert A.J. Incidence of Type 1 Diabetes in Children and Adolescents During the COVID-19 Pandemic in Germany: Results From the DPV Registry Diabetes Care 45 8 2022 1762 1771 10.2337/dc21-0969 [published Online First: 2022/01/20] 35043145
10 Shulman R. Cohen E. Stukel T.A. Examination of Trends in Diabetes Incidence Among Children During the COVID-19 Pandemic in Ontario, Canada, From March 2020 to September 2021 JAMA Netw Open 5 7 2022 e2223394 35877126
11 Sathish T. Kapoor N. Cao Y. Proportion of newly diagnosed diabetes in COVID-19 patients: A systematic review and meta-analysis Diabetes Obes Metab 23 3 2021 870 884 10.1111/dom.14269 [published Online First: 2020/11/28] 33245182
12 Xie Y. Al-Aly Z. Risks and burdens of incident diabetes in long COVID: a cohort study The Lancet Diabetes & Endocrinology 10 5 2022 311 321 10.1016/s2213-8587(22)00044-4 35325624
13 Page M.J. McKenzie J.E. Bossuyt P.M. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews BMJ 372 2021 n71 10.1136/bmj.n71 [published Online First: 2021/03/31]
14 Norris J.M. Simpson B.S. Ball R. A Modified Newcastle-Ottawa Scale for Assessment of Study Quality in Genetic Urological Research Eur Urol 79 3 2021 325 336 10.1016/j.eururo.2020.12.017 [published Online First: 2020/12/31] 33375994
15 Magliano D.J. Islam R.M. Barr E.L.M. Trends in incidence of total or type 2 diabetes: systematic review BMJ 366 2019 l5003 10.1136/bmj.l5003 [published Online First: 2019/09/13]
16 Kirkwood B. Sterne J. Essentials of Medical Statistics 2nd edition: 2001 Wiley-Blackwell
17 Profili F. Seghieri G. Francesconi P. Effect of diabetes on short-term mortality and incidence of first hospitalizations for cardiovascular events after recovery from SARS-CoV-2 infection Diabetes Research and Clinical Practice 187 2022 109872 10.1016/j.diabres.2022.109872
18 Chai C. Chen K. Li S. Effect of elevated fasting blood glucose level on the 1-year mortality and sequelae in hospitalized COVID-19 patients: A bidirectional cohort study Journal of Medical Virology 94 7 2022 3240 3250 10.1002/jmv.27737 35357022
19 Loosen SH, Jensen B-EO, Tanislav C, et al. Obesity and lipid metabolism disorders determine the risk for development of long COVID syndrome: a cross-sectional study from 50,402 COVID-19 patients. Infection 2022 10.1007/s15010-022-01784-0
20 Akter F. Mannan A. Mehedi H.M.H. Clinical characteristics and short term outcomes after recovery from COVID-19 in patients with and without diabetes in Bangladesh Diabetes Metab Syndr 14 6 2020 2031 2208 10.1016/j.dsx.2020.10.016 [published Online First: 2020/10/29] 33113469
21 Nesan GSCQ, Keerthana D, Yamini R, et al. 3-Month Symptom-Based Ambidirectional Follow-up Study Among Recovered COVID-19 Patients from a Tertiary Care Hospital Using Telehealth in Chennai, India. INQUIRY: The Journal of Health Care Organization, Provision, and Financing 2021;58:004695802110601. 10.1177/00469580211060165
22 Basic-Jukic N. Juric I. Furic-Cunko V. Follow-up of renal transplant recipients after acute COVID-19—A prospective cohort single-center study Immunity, Inflammation and Disease 9 4 2021 1563 1572 10.1002/iid3.509 34414665
23 Crankson S, Pokhrel S, Anokye NK. Determinants of COVID-19-Related Length of Hospital Stays and Long COVID in Ghana: A Cross-Sectional Analysis. Int J Environ Res Public Health 2022;19(1) 10.3390/ijerph19010527 [published Online First: 2022/01/12]
24 Su Y, Yuan D, Chen DG, et al. Multiple early factors anticipate post-acute COVID-19 sequelae. Cell 2022;185(5):881-95.e20. 10.1016/j.cell.2022.01.014
25 Budhiraja S, Indrayan A, Mahajan M. Effect of COVID-19 vaccine on long-COVID: A 2-year follow-up observational study from hospitals in north India. medRxiv 2022:2022.07.18.22277740. 10.1101/2022.07.18.22277740
26 Rinaldi R, Basile M, Salzillo C, et al. Myocardial Injury Portends a Higher Risk of Mortality and Long-Term Cardiovascular Sequelae after Hospital Discharge in COVID-19 Survivors. J Clin Med 2022;11(19) 10.3390/jcm11195964 [published Online First: 20221010]
27 Pfaff E.R. Girvin A.T. Bennett T.D. Identifying who has long COVID in the USA: a machine learning approach using N3C data The Lancet Digital Health 4 7 2022 e532 e541 10.1016/s2589-7500(22)00048-6 35589549
28 Yoo S.M. Liu T.C. Motwani Y. Factors Associated with Post-Acute Sequelae of SARS-CoV-2 (PASC) After Diagnosis of Symptomatic COVID-19 in the Inpatient and Outpatient Setting in a Diverse Cohort Journal of General Internal Medicine 37 8 2022 1988 1995 10.1007/s11606-022-07523-3 35391623
29 Ioannou G.N. Baraff A. Fox A. Rates and Factors Associated With Documentation of Diagnostic Codes for Long COVID in the National Veterans Affairs Health Care System JAMA Netw Open 5 7 2022 e2224359 35904783
30 McKeigue P.M. McGurnaghan S. Blackbourn L. Relation of Incident Type 1 Diabetes to Recent COVID-19 Infection: Cohort Study Using e-Health Record Linkage in Scotland Diabetes Care 2022 10.2337/dc22-0385 [published Online First: 2022/07/27]
31 Nayar M. Varghese C. Kanwar A. SARS-CoV-2 infection is associated with an increased risk of idiopathic acute pancreatitis but not pancreatic exocrine insufficiency or diabetes: long-term results of the COVIDPAN study Gut 71 7 2022 1444 2147 10.1136/gutjnl-2021-326218 34764192
32 Wander P.L. Lowy E. Beste L.A. The Incidence of Diabetes Among 2,777,768 Veterans With and Without Recent SARS-CoV-2 Infection Diabetes Care 45 4 2022 782 878 10.2337/dc21-1686 35085391
33 Al-Aly Z. Xie Y. Bowe B. High-dimensional characterization of post-acute sequelae of COVID-19 Nature 594 7862 2021 259 264 10.1038/s41586-021-03553-9 [published Online First: 2021/04/23] 33887749
34 Hernandez-Romieu A.C. Carton T.W. Saydah S. Prevalence of Select New Symptoms and Conditions Among Persons Aged Younger Than 20 Years and 20 Years or Older at 31 to 150 Days After Testing Positive or Negative for SARS-CoV-2 JAMA Network Open 5 2 2022 e2147053 35119459
35 Daugherty S.E. Guo Y. Heath K. Risk of clinical sequelae after the acute phase of SARS-CoV-2 infection: retrospective cohort study BMJ 2021:n1098. 10.1136/bmj.n1098
36 Rezel-Potts E, Douiri A, Sun X, et al. Cardiometabolic outcomes up to 12 months after COVID-19 infection. A matched cohort study in the UK. PLoS Med 2022;19(7):e1004052. 10.1371/journal.pmed.1004052 [published Online First: 2022/07/20]
37 Zhang HG, Dagliati A, Shakeri Hossein Abad Z, et al. International electronic health record-derived post-acute sequelae profiles of COVID-19 patients. NPJ Digit Med 2022;5(1):81. 10.1038/s41746-022-00623-8 [published Online First: 2022/06/30]
38 Barrett CE, Koyama AK, Alvarez P, et al. Risk for Newly Diagnosed Diabetes >30 Days After SARS-CoV-2 Infection Among Persons Aged <18 Years — United States, March 1, 2020–June 28, 2021. MMWR Morbidity and Mortality Weekly Report 2022;71(2):59-65. 10.15585/mmwr.mm7102e2
39 Kendall E.K. Olaker V.R. Kaelber D.C. Association of SARS-CoV-2 Infection With New-Onset Type 1 Diabetes Among Pediatric Patients From 2020 to 2021 JAMA Netw Open 5 9 2022 e2233014 36149658
40 Banerjee M. Pal R. Dutta S. Risk of incident diabetes post-COVID-19: A systematic review and meta-analysis Prim Care Diabetes 16 4 2022 591 653 10.1016/j.pcd.2022.05.009 [published Online First: 2022/06/03] 35654679
41 WHO. WHO coronavirus (COVID-19) dashboard 2022 [Available from: https://covid19who.int/ accessed 20 July 2022.
42 Smith S.M. Boppana A. Traupman J.A. Impaired glucose metabolism in patients with diabetes, prediabetes, and obesity is associated with severe COVID-19 J Med Virol 93 1 2021 409 415 10.1002/jmv.26227 [published Online First: 2020/06/27] 32589756
43 Bellido V, Perez A. COVID-19 and Diabetes. J Clin Med 2021;10(22) 10.3390/jcm10225341 [published Online First: 2021/11/28]
44 Lipscombe L.L. Hwee J. Webster L. Identifying diabetes cases from administrative data: a population-based validation study BMC Health Serv Res 18 1 2018 316 10.1186/s12913-018-3148-0 [published Online First: 2018/05/04] 29720153
45 Alkwai HM, Khalifa AM, Ahmed AM, et al. Persistence of COVID-19 symptoms beyond 3 months and the delayed return to the usual state of health in Saudi Arabia: A cross-sectional study. SAGE Open Med 2022;10:20503121221129918. 10.1177/20503121221129918 [published Online First: 20221008]
46 Bellan M. Soddu D. Balbo P.E. Respiratory and Psychophysical Sequelae Among Patients With COVID-19 Four Months After Hospital Discharge JAMA Netw Open 4 1 2021 e2036142 33502487
47 Blomberg B. Mohn K.G. Brokstad K.A. Long COVID in a prospective cohort of home-isolated patients Nat Med 27 9 2021 1607 1613 10.1038/s41591-021-01433-3 [published Online First: 2021/06/25] 34163090
48 Cervia C. Zurbuchen Y. Taeschler P. Immunoglobulin signature predicts risk of post-acute COVID-19 syndrome Nat Commun 13 1 2022 446 10.1038/s41467-021-27797-1 [published Online First: 2022/01/27] 35078982
49 Fernández-de-las-Peñas C. Guijarro C. Torres-Macho J. Diabetes and the Risk of Long-term Post-COVID Symptoms Diabetes 70 12 2021 2917 2921 10.2337/db21-0329 34580087
50 Jones R. Davis A. Stanley B. Risk Predictors and Symptom Features of Long COVID Within a Broad Primary Care Patient Population Including Both Tested and Untested Patients Pragmat Obs Res 12 2021 93 104 10.2147/POR.S316186 [published Online First: 2021/08/20] 34408531
51 Mechi A. Al-Khalidi A. Al-Darraji R. Long-term persistent symptoms of COVID-19 infection in patients with diabetes mellitus Int J Diabetes Dev Ctries 42 1 2022 49 52 10.1007/s13410-021-00994-w [published Online First: 20210824] 34456528
52 Messin L, Puyraveau M, Benabdallah Y, et al. COVEVOL: Natural Evolution at 6 Months of COVID-19. Viruses 2021;13(11) 10.3390/v13112151 [published Online First: 20211025]
53 Nguyen NN, Hoang VT, Dao TL, et al. Long-Term Persistence of Olfactory and Gustatory Disorders in COVID-19 Patients. Frontiers in Medicine 2022;9 10.3389/fmed.2022.794550
54 Peghin M. Palese A. Venturini M. Post-COVID-19 symptoms 6 months after acute infection among hospitalized and non-hospitalized patients Clin Microbiol Infect 27 10 2021 1507 1513 10.1016/j.cmi.2021.05.033 [published Online First: 20210607] 34111579
55 Sudre C.H. Murray B. Varsavsky T. Attributes and predictors of long COVID Nat Med 27 4 2021 626 631 10.1038/s41591-021-01292-y [published Online First: 2021/03/12] 33692530
56 Yaksi N, Teker AG, Imre A. Long COVID in Hospitalized COVID-19 Patients: A Retrospective Cohort Study. Iranian Journal of Public Health 2022 10.18502/ijph.v51i1.8297
57 Ayoubkhani D. Khunti K. Nafilyan V. Post-covid syndrome in individuals admitted to hospital with covid-19: retrospective cohort study BMJ 372 2021 n693 10.1136/bmj.n693 [published Online First: 2021/04/02]
58 Horberg M.A. Watson E. Bhatia M. Post-acute sequelae of SARS-CoV-2 with clinical condition definitions and comparison in a matched cohort Nat Commun 13 1 2022 5822 10.1038/s41467-022-33573-6 [published Online First: 20221012] 36224218
59 Xie Y. Bowe B. Al-Aly Z. Burdens of post-acute sequelae of COVID-19 by severity of acute infection, demographics and health status Nat Commun 12 1 2021 6571 10.1038/s41467-021-26513-3 [published Online First: 20211112] 34772922
| 36496030 | PMC9727969 | NO-CC CODE | 2022-12-13 23:16:44 | no | Diabetes Res Clin Pract. 2023 Jan 7; 195:110202 | utf-8 | Diabetes Res Clin Pract | 2,022 | 10.1016/j.diabres.2022.110202 | oa_other |
==== Front
Clin Microbiol Infect
Clin Microbiol Infect
Clinical Microbiology and Infection
1198-743X
1469-0691
European Society of Clinical Microbiology and Infectious Diseases. Published by Elsevier Ltd.
S1198-743X(22)00601-2
10.1016/j.cmi.2022.11.028
Commentary
Who should receive oral anti-viral therapy for SARS-CoV2 infection in the omicron era? - Choose wisely!
Weiss Günter MD
Professor of Medicine, Department of Internal Medicine II (Infectious Disease, Immunology, Pneumology, Rheumatology), Medical University of Innsbruck, Austria
7 12 2022
7 12 2022
4 10 2022
7 11 2022
29 11 2022
© 2022 European Society of Clinical Microbiology and Infectious Diseases. Published by Elsevier Ltd. All rights reserved.
2022
European Society of Clinical Microbiology and Infectious Diseases
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Editor: Dr R Chemaly
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pmcThe COVID-19 pandemic, which emerged at the end of 2019, has caused millions of fatalities worldwide. The development and clinical introduction of anti-viral drugs directed against the SARS-CoV2 virus has to be considered as a milestone to combat this pandemic and to treat this infection. Antiviral therapy is most effective when given early in the course of COVID-19(1, 2). Accordingly, two orally applicable drugs, nirmatrelvir/ritonavir and molnupiravir have demonstrated in two double blinded randomized controlled trials that they significantly reduced the risk for hospitalization or death in unvaccinated ambulatory patients with risk factors for severe infection when given within the first days after onset of COVID-19 related symptoms(3, 4). The numbers needed to treat (NNT) to avoid one event (hospitalization or death) were between 1:19 for nirmatrelvir/ritonavir and 1:35 for molnupiravir. However, these studies were carried out in 2021 when alpha and delta variants of SARS-CoV2 were dominant. Since then, the clinical picture of COVID-19 has dramatically changed with the emergence of the different Omicron variants resulting in milder courses of the disease and reduced need for hospitalization, ICU admission or death (0,3%; 0,1%; 0.03%) than observed for the delta variant (2,8%; 1,0%; 0.5% respectively)(5). This general reduction in hospitalization because of COVID-19 can be also attributed to increased immune protection in the population on the basis of broad vaccination, previous infection or the combination of both. Of note, the presence of serum antibodies against SARS-CoV2 resulted in an almost complete disappearance of the protective effect of nirmatrelvir/ritonavir and molnupiravir in the studies mentioned above(3, 4).
Thus, the question emerges who might still benefit from such anti-viral treatment in the current situation. To this end data from the current Omicron wave have been urgently awaited. Arbel et al.(6) report on a retrospective analysis of data extracted from a health care data base in Israel on the effects of nirmatrelvir/ritonavir treatment toward the risk of hospitalization and death from COVID-19 caused by the Omicron variant. The authors found that early treatment with nirmatrelvir/ritonavir resulted in reduced hospitalization and death in patients at risk and aged >65 yrs as compared to those without therapy, whereas patients below the age of 65 had no benefit from that therapy. The latter raises the question on the causes of hospitalisation and death, because severe infection with lung failure and hyperinflammation syndromes became very infrequent during the Omicron wave due to the waning pathogenicity of the virus and immune protection by vaccination coverage and/or natural immunity (7). This is also reflected by the low hospitalisation rate reported in this study (6). Nowadays many patients are hospitalized “with” COVID-19 and another leading diagnose but not “because of” COVID-19 which makes an important distinction in regard to evaluation of anti-viral therapy efficacy. Thus, we need an estimate on the NNT to prevent one severe SARS-CoV2 infection resulting in hospitalization or death in the current pandemic situation, and we need to know who may specifically benefit from such therapy. This retrospective analysis also indicated that unvaccinated people – who were less frequent in the group receiving nirmatrelvir/ritonavir - were at a 5.79 fold high risk to be hospitalized with SARS-Cov2 positivity (6). This undermines the importance of immune protection by vaccination and/or previous infection for the prevention of severe disease.
Nirmatrelvir/ritonavir has several drug interactions and transient cessation of essential drugs including f.e. anti-arrhythmic or anti-coagulant medications to enable anti-viral therapy may eventually cause serious health problems. Thus, a risk benefit estimation on an individual basis taking into account immune status, vaccination coverage but also a combination of risk factors, which could put the patients at a higher risk for hospitalization or death, is needed.
Data on the real-life effects of nirmatrelvir/ritonavir and molnupriavir were reported recently from USA and Hongkong (8, 9). In a retrospective analysis from health care system data in Massachusetts and New Hampshire, USA, the effect of nirmatrelvir/ritonavir therapy was accessed in non-hospitalized adults aged over 50 years with COVID-19 and no contra-indication against the use of this anti-viral medication ((8). The outcome was hospitalization within 14 days after initial diagnosis of COVID-19. 6036 patients receiving anti-viral therapy were compared with 24286 matched patients not receiving specific COVID-19 therapy. While patients prescribed nirmatrelvir/ritonavir were significantly older, patients in the control group had a significantly poorer vaccination status and lower prevalence of vaccine boosters. Although the overall incidence of hospitalisation was lower than 1%, nirmatrelvir use was associated with a 45% reduction of hospitalization (8). According to these data, the NNT to avoid one hospitalization is above 200, and it is questionable whether or not hospitalisation was really due to COVID-19 or-according to the milder course of the infection- “with Covid-19” and another leading diagnosis. Finally, it is not clear to which extent the reduction in hospitalization was at least partly due to better vaccination status in the treatment group. Given the limited beneficial effects of the anti-viral drugs in SARS-CoV2 seropositive individuals(3, 4), this would suggest that in patients with effective vaccination and/or natural immunity the NNT to prevent SARS-CoV2 induced hospitalization is far higher than 200.
The importance of vaccination status in COVID-19 disease outcome was confirmed by a study summarizing real-world data on the use of nirmatrelvir/ritonviar or molnupiravir in Hongkong during the Omicron BA.2 wave. A retrospective analysis was performed using data extraction from health registers. This study investigated elderly hospitalized patients with mild to moderate symptoms who received anti-viral therapy with nirmatrelvir/ritonavir or molnupiravir within three days after symptom onset(8). A comparison was performed to untreated patients using propensity score matching. Treatment with nirmatrelvir reduced in-hospital mortality from 15,9% in matched controls to 8.1% in the treatment group. Surprisingly, only 0,1% of all patients were treated at an intensive care unit and 0,4% versus 0.9% underwent mechanical ventilation. Patients receiving nirmatrelvir/ritonavir had an all cause mortality of 3,6% in comparison to 10.3% of matched controls. Intensive care treatment was documented in 0 versus 0.1% of patients, and 0.7% of both groups received mechanical ventilation(3). While the reduction of in-hospital mortality appears to be impressive, several facts in that study are puzzling. First, the causes of death as well as the specific risk factors in patients who died are not clear, and it is questionable to which extent COVID-19 contributed to mortality given the low ICU admission and mechanical ventilation rates. Second, the mortality rates in the two controls are significantly different (15,9% versus 10,3%) raising questions on the validity of the propensity score matching process. Third, 90% or more patients were unvaccinated which would be in line with data that unvaccinated patients have the greatest benefit of anti-viral therapy(3, 4). This data on mortality rates are in contrast to evidence published from the USA or Canada were both ICU admission rates and mortality in patients with COVID-19 during the Omicron wave were low (5, 7). However, this study may suggest that patients hospitalized for COVID-19 and specifically those who are unvaccinated may benefit from initiation of anti-viral therapy after hospital admission with earlier administration likewise being more promising. Of note, a more detailed analysis of the causes of death and underlying co-morbidities in that study would be very important to generate knowledge, whether or not anti-viral therapy may have beneficial effects in patients with COVID-19 as a secondary diagnose, f.e. by reducing inflammation, hyper-coagulopathy and subsequent thrombo-embolic events(10).
It has to be emphasized that those studies have not been not adjusted by vaccine status and its efficacy and/or sero-positivity for SARS-CoV2 as immune protection has been shown to exert the best effects in regard to risk reduction for severe COVID-19. This is in line with reported but not yet published results on the effects of molnupriavir therapy in vaccinated people or the use of nirmatrelvir/ritonavir in vaccinated subjects with one risk factor for severe disease. In a study involving more than 25000 patients in the United Kingdom, the early administration of molnupiravir did not result in a reduction of hospitalization or death in patients at risk who had previously received one vaccine dose (0,8% in verum and placebo group) (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4237902).
In the EPIC-SR study nirmatrelvir/ritonavir therapy did neither result in a significant reduction of the risk of hospitalization or death in fully vaccinated adults with at least one risk factor for progression to severe COVID-19 nor in alleviation of symptoms on four consecutive days as compared to placebo. The manufacturer of nirmatrelvir/ritonavir then closed the recruitment of patients due to the low rate of hospitalization in the current pandemic situation with less aggressive virus and broad immune protection by vaccination and/or previous infection (supporting" title="https://www.pfizer.com/news/press-release/press-release-detail/pfizer-reports-additional-data-paxlovidtm-supporting">https://www.pfizer.com/news/press-release/press-release-detail/pfizer-reports-additional-data-paxlovidtm-supporting). Given the fortunately low incidence of severe infection in the current pandemic surge these data also question whether the rate of hospitalisaton or death are still appropriate endpoints for evaluating the clinical efficacy of a specific anti-viral medication.
Concern were also raised regarding the observation of rebound infection following anti-viral therapy. This appears to result from re-emerge of the virus which caused the primary infection and not being due to development of viral mutations (11)). This also indicates, that some anti-virals exert virustatic rather than virocidal effects, a phenomenon which could also promote the emergence of resistance to anti-viral therapies.
In summary, we are very pleased to have several anti-viral drugs at hand, which have been shown to significantly reduce the risk for a severe course of COVID-19 when caused by the aggressive alpha or delta variants and when given early after symptom onset to un-vaccinated patients at risk. However, due to the emergence of the milder omicron variants and the broader immune protection based on vaccination and/or pervious infection severe COVID-19 or need of ICU treatment for lung failure became a rare clinical event. Nowadays, we need to define those people who are still at risk for a severe course of COVID-19 or COVID-19 driven morbidity and mortality and who might benefit from anti-viral therapy in the current pandemic situation (Table 1 ). We thus need an individual risk-benefit estimation for patients in order to keep the NNT as low as possible and to avoid morbidity due to side effects of unnecessary therapy and to hinder the emerge of viral drug resistance. Moreover, data from well conducted randomized clinical or observational studies providing more specific information on patients who may benefit from therapy are urgently awaited. Specifically, data on the effects of that drugs on acute but also long-term risk reduction of most vulnerable but vaccinated/boostered people, specifically immuno-compromised patients and elderly people with multiple risk factors (12) would be important to guide and optimize therapy with those anti-viral drugs.Table 1 Indications for eventual use of oral anti-viral drugs directed against SARS-CoV2-Omicron
Table 1Outpatients without vaccination and higha risk of complicated infection
Immuno-compromised patients with insufficient immune response to vaccination or infection
Higha risk for severe course in vaccinated/previously infected patients due to presence of several risk factors
Hospitalized patients due to COVID-19 and symptom onset < 5-7 days
a Risk or combinatory risk by presence of multiple risk factors is not well defined in the Omicron era but may include combinations of advanced age(>65a), hypertension, diabetes, obesity, renal insufficiency, chronic cardiac or pulmonary diseases.
Conflict of interest statement
GW received honoraria for lectures or advisory board participations from Astra-Zeneca, Astro-Pharma, Insmed, Lilly, Menarini, MSD, Pfizer, Shionogi, Takeda, Vifor. This study received no external funding.
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References
1 Sendi P. Razonable R.R. Nelson S.B. Soriano A. Gandhi R.T. First-generation oral antivirals against SARS-CoV-2 Clin Microbiol Infect 28 2022 1230 1235 35545195
2 Burkert F.R. Lanser L. Bellmann-Weiler R. Weiss G. Coronavirus Disease 2019: Clinics, Treatment, and Prevention Front Microbiol 12 2021 761887
3 Hammond J. Leister-Tebbe H. Gardner A. Abreu P. Bao W. Wisemandle W. Baniecki M. Hendrick V.M. Damle B. Simon-Campos A. Pypstra R. Rusnak J.M. Investigators E.-H. Oral Nirmatrelvir for High-Risk, Nonhospitalized Adults with Covid-19 N Engl J Med 386 2022 1397 1408 35172054
4 Jayk Bernal A. Gomes da Silva M.M. Musungaie D.B. Kovalchuk E. Gonzalez A. Delos Reyes V. Martin-Quiros A. Caraco Y. Williams-Diaz A. Brown M.L. Du J. Pedley A. Assaid C. Strizki J. Grobler J.A. Shamsuddin H.H. Tipping R. Wan H. Paschke A. Butterton J.R. Johnson M.G. De Anda C. Group M.O.-O.S. Molnupiravir for Oral Treatment of Covid-19 in Nonhospitalized Patients N Engl J Med 386 2022 509 520 34914868
5 Ulloa A.C. Buchan S.A. Daneman N. Brown K.A. Estimates of SARS-CoV-2 Omicron Variant Severity in Ontario, Canada JAMA 327 2022 1286 1288 35175280
6 Arbel R. Wolff Sagy Y. Hoshen M. Battat E. Lavie G. Sergienko R. Friger M. Waxman J.G. Dagan N. Balicer R. Ben-Shlomo Y. Peretz A. Yaron S. Serby D. Hammerman A. Netzer D. Nirmatrelvir Use and Severe Covid-19 Outcomes during the Omicron Surge N Engl J Med 2022 10.1056/NEJMoa2204919
7 Lewnard J.A. Hong V.X. Patel M.M. Kahn R. Lipsitch M. Tartof S.Y. Clinical outcomes associated with SARS-CoV-2 Omicron (B.1.1.529) variant and BA.1/BA.1.1 or BA.2 subvariant infection in Southern California Nat Med 2022 10.1038/s41591-022-01887-z
8 Dryden-Peterson S. Kim A. Kim A.Y. Caniglia E.C. Lennes I. Patel R. Gainer L. Dutton L. Donahue E. Gandhi R.T. Baden L.R. Woolley A.E. Nirmatrelvir plus ritonavir for early COVID-19 and hospitalization in a large US health system medRxiv 2022 10.1101/2022.06.14.22276393
9 Wong C.K.H. Au I.C.H. Lau K.T.K. Lau E.H.Y. Cowling B.J. Leung G.M. Real-world effectiveness of early molnupiravir or nirmatrelvir-ritonavir in hospitalised patients with COVID-19 without supplemental oxygen requirement on admission during Hong Kong's omicron BA.2 wave: a retrospective cohort study Lancet Infect Dis 2022 10.1016/S1473-3099(22)00507-2
10 Musher D.M. Abers M.S. Corrales-Medina V.F. Acute Infection and Myocardial Infarction N Engl J Med 380 2019 171 176 30625066
11 Charness M.E. Gupta K. Stack G. Strymish J. Adams E. Lindy D.C. Mohri H. Ho D.D. Rebound of SARS-CoV-2 Infection after Nirmatrelvir-Ritonavir Treatment N Engl J Med 387 2022 1045 1047 36069968
12 Nafilyan V. Ward I.L. Robertson C. Sheikh A. National Core Studies-Immunology Breakthrough C. Evaluation of Risk Factors for Postbooster Omicron COVID-19 Deaths in England JAMA Netw Open 5 2022 e2233446
| 36496153 | PMC9728013 | NO-CC CODE | 2022-12-08 23:18:56 | no | Clin Microbiol Infect. 2022 Dec 7; doi: 10.1016/j.cmi.2022.11.028 | utf-8 | Clin Microbiol Infect | 2,022 | 10.1016/j.cmi.2022.11.028 | oa_other |
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International Journal of Transportation Science and Technology
2046-0430
2046-0430
Tongji University and Tongji University Press. Publishing Services by Elsevier B.V.
S2046-0430(22)00100-9
10.1016/j.ijtst.2022.11.004
Article
A Study on Airlines’ Responses and Customer Satisfaction During the COVID-19 Pandemic
Mojib Zahraee Seyed ⁎
Shiwakoti Nirajan
Jiang Hongwei
Qi Zhuoqun
He Yunfeng
Guo Tianan
Li Yifeng
School of Engineering, RMIT University, Carlton, VIC 3053, Melbourne, Australia
⁎ Corresponding author.
7 12 2022
7 12 2022
20 7 2022
27 10 2022
22 11 2022
© 2022 Tongji University and Tongji University Press. Publishing Services by Elsevier B.V.
2022
Tongji University and Tongji University Press
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
The Coronavirus (COVID-19) pandemic outbreak has significantly impacted the airline industry worldwide. However, limited studies have systematically investigated the airlines' responses and customer satisfaction in the aviation industry during the COVID-19 pandemic. The present study attempts to address this knowledge gap.
The first aim of this study is to determine customers' satisfaction with the aviation industry during the COVID-19 pandemic. A questionnaire survey was conducted in China to investigate the Chinese passengers' satisfaction with 22 constructs in four stages: Pre-Flight, In-Flight, After-Arrival, and Others (Face mask requirement, HEPA filters, etc.). Second, this work explored the measures that will benefit the airlines by investigating the measures taken by 49 major airlines worldwide, especially considering the operational cost and passengers’ safety.
It was found that cabin selection and passengers who travelled after the start of COVID-19 were the groups that affected passengers’ satisfaction levels on responses. The top 3 satisfied measures were “Provide hygiene products for passengers and staff”, “A thermal scanner to monitor body temperature during check-in”, and “Disinfect the cabin after each flight, even for a previous flight of the connecting flight”. In contrast, the bottom 3 measures were “Protective clothing is required to board the plane”, “Adopt a special boarding method such as boarding in the order from back to front”, and “No in-flight meals and drinks (only snacks and water)”. Airlines’ responses primarily focused on reducing the operation cost, ensuring the safety and interests of the passengers and improving the income and cash of the company.
Keywords
Coronavirus
COVID-19
Aviation industry, Passengers’ safety
Satisfaction
Global airlines
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pmc1 Introduction
Coronavirus (COVID-19) is a sheer devastating factor in the aviation and tourism industries. The airline industry has recorded constant and extraordinary growth in the past century. In this way, it has resisted some huge global catastrophes, for example, the 9/11 terrorist attacks in 2001 and the global financial crisis in 2008. Only a half-century after its commencement, the air travel market reached a milestone in 1987: one billion passengers in one year. Afterwards, it was exponentially developed for around two decades; it succeeded in surpassing 2 billion in 2005 and then 3 billion in 2013, and it reached even 4.5 billion passengers in 2019. Two factors have principally altered the international travel status: low airfares and a growing population of prosperous middle-class people. Air travel market share abruptly increased to 58% by 2019, which means 14% more than this number 20 years ago (UNWTO, 2020).
Suddenly in early 2020, the aviation industry encountered a critical point induced by the rapid outbreak and spread of COVID-19. After its primary identification in Wuhan, China, it started to spread to 218 countries in a short period (Lai et al., 2020). It infected over one million people worldwide by April 2020. Then, in November of the same year, 53 million people were identified as confirmed cases, with around 1.38 million deaths (World Health Organization, 2020a). Countries set up travel restrictions for their people, and, in addition to that, people were reluctant to travel, fearing COVID-19. This condition seriously and suddenly damaged the aviation and tourism sectors (Zahraee et al., 2022). Tourists were more likely to delay or cancel their travels to prevent them from being infected by COVID-19 (Reisinger and Mavondo, 2005). This stance has been internalized in the attitudes of today's tourists, and they are still trying to avoid high-risk destinations, which has an adverse impact on the tourism sector (Zhang et al., 2020). The existing research studies show that the global airline industry has been hit hard by the COVID-19 pandemic, and the demand for airline business has shrunk significantly. After the declaration of the pandemic by the World Health Organization (World Health Organization, 2020a) on 11 March 2020, many countries have closed their borders or issued travel bans in succession. Many international routes have been cancelled due to policy influences, and the aviation market has almost collapsed. Lange (2020) notes that at the worst point in April, two-thirds of the world’s fleet of aircraft was grounded, 90% of aviation business did not keep operation anymore, and even more so for international operations, where it was even more hovering around 98%. Airlines tried to restore routes in the shortest time, but the continuous outbreak of COVID-19 has made the disaster continue. Compared with September 2019, the number of flights of most airlines dropped significantly in September 2020, and the average reduction rate is 50%. The Revenue Passenger Kilometres (RPKs) of domestic routes initially decreased by 80% and then showed a clear upward trend (IATA, 2020b). The decline rate increased to 40% in September. The impact on cargo was even smaller, the lowest trough was only a 20% decline rate, and then it continued to rise. Finally, the cargo flight only dropped by about 10%.
Airlines still have to pay for aircraft storage and maintenance costs in a hostile environment and face cash flow interruption. The disparity between income and expenditure has deteriorated the economic situation of airlines. In March and April, international RPKs experienced a cliff-like decline, with a rate of decrease approaching 100%. Even in September, RPK was still at a low level, with a rate of change of around 89% (Iata, 2020a, IATA, 2020a).
This work tries to fill the knowledge gap by bridging the operational cost, passengers’ safety, and satisfaction with the airlines by collecting the measures taken by 49 major airlines worldwide and collecting the responses through a questionnaire survey of the airline passengers. This work would determine the measures airlines have taken to deal with COVID-19 and analyze passengers' satisfaction with 22 measures in four stages: Pre-Flight, In-Flight, After-Arrival, and Others (Face mask requirement, HEPA filters, etc.). Based on the previous studies, it can be seen that most of the existing studies usually list which airlines have taken which measures without integrating with the passengers' perceptions. This paper explores the possibility of combining airlines' measures with passengers' satisfaction and thus provides robust evidence to airlines in their future decisions.
The rest of this article is structured as follows. The literature review section discusses the impact of different viruses and COVID-19 on the global aviation industry and the impact of travel restrictions on the tourism/aviation industry. The airline response measure and passengers’ satisfaction criteria are discussed. Then, the methodology section describes the method used and the data collected for the present paper. Afterwards, the empirical findings are presented and analysed in the results and discussion section. Finally, conclusions from the paper are presented.
2 Literature Review
2.1 Effect of viruses and COVID-19 on the global aviation industry
According to scholarly findings, air transport has a considerable impact on spreading the pandemic worldwide (Wilder‐Smith et al., 2003). In addition, several studies have maintained that airline travel has the potential to affect the spread of many viruses, e.g., Severe Acute Respiratory Syndrome (SARS) (McLean et al., 2005), influenza (Grais et al., 2003), and Ebola (Bogoch et al., 2015). SARS infected 37 countries (8000 cases), whereas the Middle East respiratory syndrome infected as many as 27 countries (2494 cases) (Oztig and Askin, 2020). In these cases, the transmission deteriorated partly by those who took flights at the time of the case. Another investigation asserted that the avian flu (H5N1) outbreak spread to about 60 countries, killed nearly 191 people, and reduced around 12 million tourist arrivals within the Asia Pacific (Wilder-Smith, 2006).
SARS negatively influenced people’s inclination to travel (Wen et al., 2005). This finding was confirmed by research conducted by Kuo et al. (2008), which reported a considerable shrinkage in the number of visitors arriving in SARS-affected countries. With the help of the econometric method, Rosselló et al. (2017) attempted to quantify the influence of various pandemics on visitors’ arrivals. Their findings showed that pandemics meaningfully decrease the number of visitors. For instance, the number of visitor arrivals decreased by 47% after the spread of malaria. Another study reported that foot and mouth diseases had reduced the tourism receipts in the UK (Blake et al., 2003). The above evidence clearly shows that aviation and tourism are two sectors of high vulnerability to infectious disease outbreaks because of their contact-intensive and face-to-face nature and the high mobility rate of both people and goods.
Though COVID-19 exceeded all the former pandemics (it spread to over 200 countries), the scientific reports indicated that the aviation industry significantly contributed to this situation (Sun et al., 2020). This disease led to an extraordinary economic disaster for airline operators on a global scale. In March 2020, the global traffic level fell by 21% compared to the same month in 2019. Then, an unexpected escalation was observed, resulting in more contraction as the global traffic levels dropped by 66% by April 2020 and declined by 69% by May (UNWTO, 2020). This considerable drop occurred when people became aware of the fact that the disease could kill infected people in a short time after infection. Estimations indicated that international tourist arrivals would decrease by 70% in 2020, that is, the loss of 700$ million in the number of visitors and 730$ billion loss in the inbound tourism market (UNWTO, 2020). Statistics indicate that, in 2020, the COVID-19 induced loss was eight times more than the loss during the Global Financial Crisis in 2008-2009 (UNWTO, 2020). The aviation sector suffered from the same loss; in 2020, the airline passenger revenues dropped by about 69% that accounting for around US$421 billion loss in comparison with 2019 (IATA, 2020b). The aggregated loss was estimated to be about US$118 billion, which is more than four times higher than the losses in this sector due to the global financial crisis of 2009 (IATA, 2020b). Historically, COVID-19 is recognized as the most serious threat to companies working in the aviation sector (Amankwah-Amoah, 2020); this impact could even last until 2024 (IATA, 2020c). Recovery may occur optimistically in mid-2022 or delay up to 2026 most pessimistically (Gudmundsson et al., 2020). Several scholars have even stated that the pandemic could cause the collapse of the entire international tourism market (Thams et al., 2020).
Table 1 summarizes several researchers who have paid attention to the impacts of COVID-19 on the air travel industry; however, the literature lacks empirical research on the influence of COVID-19 on operating costs and passengers’ safety and satisfaction.Table 1 Summary of COVID-19 investigation in the Aviation industry, including the present study.
Author Objective Approach Time
Gallego and Font (2021) To implement an approach for the early detection of reactivation of tourist markets to help mitigate the effects of the COVID-19 crisis, using Skyscanner data on air passenger searches • Big Data
✓November 2018 - December 2020
Gössling et al. (2020) To compare the effects of COVID-19 to previous epidemic/pandemics and other types of global crises and explores how the pandemic may change society, the economy, and tourism • Review
✓March 2020
Graham et al. (2020) to assess the attitudes of ageing passengers by analysing air travel plans, examining the factors affecting future flying decisions, and evaluating the effect of the COVID-19 on perceived risks and experiences associated with flying • Online survey of UK residents aged 65+
✓10 June 2020 - 15 June 2020
Hall et al. (2020) To provide a comprehensive overview of pandemics and their effects • Review
✓April 2020
Iacus et al. (2020) To collect and prepare data on air passengers traffic worldwide with the scope of analyze the impact of the travel ban on the aviation sector • Historical Data
✓First Quarter of 2020
Suau-Sanchez et al. (2020) To estimate the medium- and long-term impacts of COVID-19 as seen within the aviation industry itself • Empirical study
✓January 2020 - April 2020
Current Study To investigate the effect of COVID-19 on operating costs and passengers’ safety and satisfaction. • Empirical study
✓14 April 2021 - 7 May 2021
2.2 The effect of travel restrictions on the aviation/tourism industry
Both aviation and tourism industries have a high vulnerability to the outbreak of infectious diseases. With the start of COVID-19 spread among people globally, countries restricted travel and closed their borders to minimize the spread of the virus by limiting its import and export via tourists (Vaidya et al., 2020). Recent decades have witnessed increased affordability of air travel for people; consumers have been given a chance to select from numerous airlines. On the other hand, disease transmission could only be confined by limiting people’s travels and mobility in situations such as disease outbreaks. This typically prompts governments to enact the necessary legislation quickly.
As revealed by the reports UNWTO (2020), as many as 90 destinations partially or completely suspended inbound tourism, and 44 destinations closed their borders to certain countries of origin in case of the COVID-19 outbreak. Many governments have imposed lockdowns, travel bans, shutdowns, and stay-at-home directives to control the virus spread (Luo et al., 2020). However, these measures were taken into action in a highly uncoordinated and almost chaotic manner (Sun et al., 2020). Moreover, the inconsistent travel restrictions caused a significant reduction in the number of visitors intending to travel by air during the COVID-19 outbreak (Salari et al., 2020). According to UNWTO (2020), it was the first time international travel was limited in such a manner. Numerous people were either discouraged from travelling or informed that they could enter their destination country only if they followed a quarantine procedure lasting for up to 14 days at their own expense. Such conditions had wide-ranging consequences. As reported by Adrienne et al. (2020), by mid-April 2020, the air travel market dropped by 64%; meanwhile, around 17,000 aircraft were consigned to their shelters.
The travel restrictions mentioned above have obliged airlines to lower their flight operations as much as possible and cut costs. When the vaccination programs were implemented unevenly on a global scale, the accessible tools were confined to measures such as control and containment, including travel restrictions, quarantining, and social distancing (Petersen et al., 2020). The existing literature has empirically confirmed that travel restrictions and control measures can effectively minimize the spread of infectious viruses. The isolation of large cities played a significant role in controlling the SARS epidemic (Hufnagel et al., 2004). Brownstein et al. (2006) investigated the case of influenza spread in the United States and emphasized the prominence of flight restrictions. A significantly-delayed timeframe was reported before influenza peaked in 2001-2002 due to the reduction of flights. On the other hand, the conditions deteriorated in France, where any flight restriction was not imposed. Another study proposed a two-city dispersal model of avian influenza spread via air travel and asserted that control measures such as quarantine and isolation depend on the air travel rate, which refers to the proportion of air passengers in the population of the departure city. As a result, it can be said that restricting people’s travels plays an important role in decreasing the pandemic prevalence (Tuncer and Le, 2014).
On the other hand, some other studies indicate the ineffectiveness of travel restrictions in controlling the spread of infectious diseases. For instance, Cooper et al. (2006) showed that travel restrictions could not significantly delay the worldwide influenza pandemic spread because, initially, many people were infected, and the confirmed cases grew quickly. Likewise, another research argued that travel restrictions could lower the speed of virus spread only for less than 2 to 3 weeks (Ferguson et al., 2006). A population transmission model was used to investigate the relationships between travel restrictions and the COVID-19 spread (Chinazzi et al., 2020). According to their findings, the lockdown of Wuhan was not as effective as the global travel restrictions, which delayed the spread of the virus to other countries until mid-February 2020. Moreover, according to Borkowski et al. (2021), the virus can be spread on a domestic scale because of the regular daily mobility of people, such as going to school/work, carrying out social activities, and visiting hospitals. Nevertheless, this in not covered by the scope of the present paper that is mainly focused on the virus transmission via the cross-border flights. As a result, daily mobility of people at the domestic level is not incorporated into the analyses conducted in this research.
2.3 Effect of COVID-19 on passenger load and safety
From the perspective of global aviation, the industry net loss announced by airlines in 2020 was 118.5 billion, and RPKs & APK dropped by 66.3% & 57.6%, respectively. The passenger load factor is around 65.5%. The total number of flights in 2020 was 16.4 million, substantially lower than 38.9 million in 2019 (IATA, 2020b). In addition to the airline business affected, airline stock prices have also been hit by COVID-19. When Thailand reported the first case of infection outside China (January 13, 2020), the market did not show a significant decrease in accumulated abnormal returns. But with the outbreak of the Italian epidemic (February 21, 2020) and the WHO statement regarding the global pandemic, and the announcement of the US ban, travelers from 26 European countries/regions (March 11, 2020), global airline stock prices, fell sharply (Maneenop and Kotcharin, 2020). For some time in the future, the stock price will remain depressed. This phenomenon can be attributed to various factors: continuous blockades and traffic restrictions, cash consumption and downgrades of global rating agencies, and poor business prospects (Dube et al., 2021). In October and November of 2020, the aviation safety layer adopted the COVID-19 safety protocol and the arrival test at many destinations, coupled with the news of the successful development of the COVID-19 vaccine, which improved the airline’s performance and revenue. People's renewed trust in the aviation industry also increased stock prices (Dube et al., 2021).
2.4 Effect of COVID-19 on aviation-related industries
The Air Transport Action Group (ATAG) announced that 65.5 million jobs worldwide are supported by the aviation industry, including direct hiring of crew members, airport operators, airlines, etc. (IATA, 2020b). Service providers and indirect employment, such as fuel suppliers, construction companies, suppliers of aircraft companies, etc. The article proves that aviation is vital to every country's international trade and economic development (Serrano and Kazda, 2020). The disappearance of flights caused by the outbreak of COVID-19 has also dealt a heavy blow to these aviation-related industries. Air transportation depends on the upstream sector: airports, aircraft manufacturing, aircraft maintenance, etc. Usually, airlines and airports have a mutually beneficial relationship, and some airports rely heavily on one, two, or several companies that use it as a hub (OECD, 2020a). Aviation manufacturing and maintenance orders depend on the daily operation and loss of aircraft, and few flights make many aircraft grounded. Therefore, airlines must cancel aircraft purchase plans and reduce maintenance expenditures.
The downstream air transportation sectors rely on the flow of people and goods to stimulate economic activities, such as duty-free shops at airports, tourism, hotels, etc. The promulgation of travel bans and flight restrictions has made the number of visitors low. In transportation, aviation and other modes of transportation are interchangeable. For instance, with a well-developed railway network in China, high-speed rail can allow passengers to maintain a safe social distance while transporting many passengers. Although wide-body jets can still meet the conditions, airlines have to undertake higher costs. Besides, the number of destinations is limited by whether the landing airport is qualified to land large passenger aircraft. The air transport industry also faces increasing market pressure brought about by the increased availability of Internet connections. The connectivity established by video conferencing does not pose health risks associated with passenger transportation (Peoples et al., 2020).
2.5 Airline's response measures
Albers and Rundshagen (2020) made statistics on European airlines' strategic responses to COVID-19 from January to May 2020. The types of responses include retrenchment, persevering, innovating, and exit. The classification of response measures is based on the research of Wenzel et al. (Wenzel et al., 2020) on the response plan of enterprises during the COVID-19 pandemic. Almost all airlines have made layoff decisions (Albers and Rundshagen, 2020), and some airlines have begun to retire their aircraft earlier than scheduled (Budd et al., 2020). Airlines have also made some countermeasures for passengers, such as requesting social distance between passengers and reducing physical contact between people. Some major airlines have introduced more flexible refund and change policies, enhancing their competitiveness in the aviation market (Chevtaeva and Guillet, 2021). Hygiene measures, mask use, and distancing have been proved effective in preventing coronavirus, while temperature screening was unreliable. Besides, in-airport rapid tests with telemedicine and facilities would be the appropriate future strategy at airports (Bielecki et al., 2020). Qatar Airways implements state-of-the-art safety and health measures, including personal protective equipment (PPE) for crew members, free protective kits, and disposable face masks for passengers. In addition, the airline is the first to deploy Honeywell International airlines with ultraviolet (UV) cabin systems that have further promoted sanitary measures on board (Athena Information Solutions Pvt. Ltd, 2020).
There are essential factors for passengers, such as safety and security, customer service, driver friendliness, and the quality of the passenger environment (Batarce et al., 2022). Airlines try new boarding methods to reduce or avoid interactions between passengers. By measuring the performance indicators related to the health of passengers and the boarding time indicators of single-door aircraft, they have evaluated the best use method at present, but due to the cost the time is too long, airlines are also placing greater emphasis on fast boarding times new methods (Milne et al., 2021). Many airlines have also begun to seek help from the government because of the COVID-19 pandemic. The government's assistance to airlines includes supporting loans, helping with capital restructuring, nationalization, and providing flight subsidies. In addition, the government's help may make the competition between airlines unfair, which is what the government needs to consider when assisting airlines (Abate et al., 2020). The main measures taken by the airlines against the business itself are mainly aimed at reducing expenses. As for the passengers, the airlines mainly aim to ensure their flight safety and avoid the spread of the pandemic on their flights. These previous studies are relevant to this report and will be contrasted with the findings of this report in anticipation of more accurate conclusions.
2.6 Passenger satisfaction with airlines
A search of Twitter keywords measured passenger satisfaction with individual airline flight cancellations, refunds, and other measures during the COVID-19 outbreak, with Southwest Airlines, for example, earning high scores (Monmousseau et al., 2020). By mid-October last year, most of the world's 20 major airlines had introduced mask and temperature testing requirements at the Pre-Flight stage, but less than half of them offered hygiene kits. Most airlines continue to require masks and Apply HEPA Filters during the In-Flight stage, and most airlines have set restrictions on the meals they serve (Bielecki et al., 2020). Passengers of different age groups expressed different levels of satisfaction with various aspects of airline service quality, with older passengers being more satisfied with international air travel services than younger passengers. In terms of gender, the results show that at the 10% significance level, male passengers are more satisfied with the safety and security of airlines than female passengers. The ANOVA test was used to determine whether there was a significant difference (Clemes et al., 2008).
3 Methodology
The critical design method of this research is an induction based on mixed data utilization, which includes identifying the research aim, questions, and scope; conducting a literature review and finding gaps; data collection and data analysis; getting results and discussing results. Besides, the theoretical framework is achieved by integrating literature sources. It consists of COVID-19 responses taken by the major airlines worldwide, an analysis of passengers’ differences in perception of measures adopted by airlines, and an evaluation of the responses. The ethics clearance has been acquired from the university. The questionnaires are all voluntarily completed by participants.
Data used in the report are both quantitative and qualitative. Quantitative data refers to passengers’ satisfaction among different responses, which is the primary data collected by the questionnaire. As it is not feasible to send the questionnaire to airlines and obtain their responses against COVID-19, the research collects 22 responses from passengers. The questionnaire used in this study is presented in the appendix. The 22 constructs were developed by considering the guidelines of regulators (e.g., Civil Aviation Safety Authority, Civil Aviation Administration of China, International Air Transport Association), and some other research in the aviation field, such as Bureau (2020), Dube et al., 2021, Bielecki et al., 2020, Czerny et al., 2021, and the international air transport rating organisation such as Skytrax (2021). These 22 responses were widely adopted by Chinese airlines and implemented in four stages: Pre-Flight, In-Flight, After-Arrival, and Others (Face mask requirement, HEPA filters, etc.). The questionnaire was sent to passengers and asked for their satisfaction with each response. The choices of each response are from 1 to 5, representing the most negative attitude to the most positive attitude. The questionnaire survey was conducted online from April 14, 2021, to May 7, 2021, and the survey was mainly distributed through Chinese social media platforms such as WeChat.
The questionnaire collects passengers’ basic information under 12 items to determine whether there are differences between different groups of passengers (Age, Occupation, etc.). Finally, 449 of 500 questionnaires were considered meaningful after excluding repetitive and blank questionnaires. Qualitative data include categories of airline responses, which are the secondary data collected from three sources: airlines' official websites, airlines' annual reports, and credible internet sources. Keywords searching is the main qualitative data collection method (COVID-19, bankruptcy, layoff, etc.). Also, the annual reports of some airlines have a specific COVID-19 section from which the data could also be collected. This resulted in the systematic collection of airlines' responses to the COVID-19 pandemic from 49 airlines.
As for data analysis, IBM SPSS was applied to analyze quantitative data on passengers’ satisfaction. The quantitative analysis focused on two parts: one was to analyze average passengers’ satisfaction with each response by comparing the means of each response, and the other was to figure out whether there are differences between different groups of passengers on the same responses by using the One-Way ANOVA Test. One-Way ANOVA test consists of a series of tests to ensure completeness and preciseness, including Variance Homogeneity Test, ANOVA Test, Welch & Brown-Forsythe Test, and Tamhane T2 Post Hoc Tests in sequential. Homogeneity of variance is assessed using Levene's Test for Equality of Variances. In order to meet the assumption of homogeneity of variance, the p-value for Levene's Test should be above 0.05. If Levene's Test yields a p-value below 0.05, then the assumption of homogeneity of variance has been violated. ANOVA test is a type of statistical test used to determine if there is a statistically significant difference between two or more categorical groups by testing for differences of means using a variance. Another Key part of ANOVA is that it splits the independent variable into 2 or more groups. Welch and Brown-Forsythe ANOVA compares three or more sets of unpaired measurements (data expressed using an interval or ratio scale), assumed to be sampled from a Gaussian distribution but without assuming that the groups have equal variances. The tests of between-subjects effects help in determining the importance of a factor; therefore, the Tamhane T2 Post hoc test is used. It reveals the differences in model-predicted means for each pair at factor levels (Ross and Willson, 2018). When it comes to qualitative analysis of responses taken by airlines, responses are firstly fit into a table to directly show what responses have been taken by each airline in different areas. Then, responses are classified into three groups (reduce the operation cost, ensure the safety and interests of passengers, and improve the company's income) to illustrate the common-adopted responses of airlines in different areas.
4 Results and discussion
4.1 Impact of pandemic policies on airlines and passengers
The whole aviation industry was affected by COVID-19. The change in the behaviour of passengers following the COVID-19 crisis and travel restrictions resulted in a dramatic drop in demand for airline services. The cost of health-related measures and operating costs are likely to increase in the short-run for both airlines and airports because of additional health and safety requirements (e.g. disinfection, PPE, temperature checks or viral tests) before they can be passed on to consumers. Moreover, social distancing measures (if implemented for air transport) could force a reduction in the passenger load factor (i.e. the number of seats that can be occupied during a flight) by up to 50% (OECD, 2020b).
Both the airlines and passengers are affected by the pandemic policies, which are obviously different among countries. Mainland China, for example, had almost full control of the pandemic and tried to contain the virus at a relatively early stage. In many other countries, the target is to “flatten the curve”, so that the outbreak is contained at a level that the healthcare system can handle while essential economic activities can be restored early. As a result, the aviation industry should focus on preventing infection at airports and on-board aircraft, with capacity and flight frequency “reactively” adjusted in response to travel demands (Czerny et al., 2021).
Following the COVID-19-induced crisis, British Airways (BA) decided to bring forward its decision to discontinue Boeing 747 fleets as part of its recovery strategy. The airliner was once dubbed the “Queen of the Skies”, the “most recognizable” among the public as well as the preferred choice of global airlines for long-haul routes (Specia, 2020). Likewise, Cathay Pacific reassured their customers of measures being taken by highlighting the “intensifying disinfection of aircraft after landing, making cabin crews don gloves and masks, removing blankets, magazines and pillows, and adding safeguards to the in-flight food and drink service”(Lee, 2020)
As the crisis unfolded, many airlines started moving towards introducing some elements of in-flight social distancing, compulsory temperature checks and demanding that passengers put on masks (Lee, 2020). In line with WHO’s Guide to Hygiene and Sanitation in Aviation, some of the operational responses emphasized enhanced cleaning and disinfection, which covers airports and service providers (World Health Organization, 2020b). In addition, it re-emphasized post-event cleaning procedures and disinfecting contaminated surfaces following notification of suspected cases (World Health Organization, 2020b).
In an attempt to avert a second outbreak in China, the government limited inter-China flights for both Chinese and foreign airlines by allowing just one flight a week, and each flight was not to exceed 75% capacity (BBC, 2020). The International Air Transport Association, in collaboration with the World Health Organization, have developed guidelines to guide cabin crew and airport workers, e.g. captains are required to inform air traffic control of suspected communicable disease (IATA, 2020c). The Chinese government attempt to micro-manage the airlines and airport services to achieve their policy objectives and to deal with the conflicting needs for improving international connectivity for economic/social reasons and tightly controlling the spread of COVID-19 virus cases (Czerny et al., 2021).
4.2 Airlines’ strategic responses to the COVID-19 pandemic
After an investigation of the response of 49 airlines to the outbreak of COVID-19, a conclusion can be reached that airlines’ responses to the outbreak of COVID-19 can be broadly classified into three categories according to their purpose: to reduce the operation cost, to ensure the safety and interests of the passengers and to improve the income and cash of the company. Table 2 summarizes the different responses of airlines to the COVD-19 pandemic by classifying airline responses into 9 categories. In the table, if a response has been taken to a particular category, it is referred to as “Y”, or else it is left blank. According to table 2, it is concluded that reducing flights is a measure taken by all airlines to reduce operating costs. This is arguably what airlines have been forced to do because of a sharp drop in passenger travel demand during the outbreak. Most of the airlines in the survey have reduced the salary of their employees or furloughed their workers to reduce labour costs. By contrast, fewer airlines lay off workers. In fact, most airlines reduced their staff numbers by taking many measures, including laying off. As a result, the number of staff of almost all airlines worldwide decreased. Many employees are retrained, which is possible because many airlines believe the outbreak of the COVID-19 pandemic will have a limited impact on the aviation market after the end of the epidemic and cutting too many jobs is not suitable for their future growth. Among the airlines surveyed, many in Europe and the Americas have opted to retire aircraft early to save operating costs. By contrast, few of the airlines in East Asia took the step of decommissioning their aircraft. Many airlines' annual reports show that many airlines did not stop buying planes during the COVID-19 outbreak.Table 2 Airlines' different responses to the COVID-19 pandemic (Source: authors’ compilation of 49 major airlines’ annual reports in 2020).
Methods:1. Lay off
2. Retire/Ground aircraft
3. Cut pay level
4. Government/institution support
5. Cut/Suspend flights
6. Add passenger’s safety measures
7. Change/improve the change fee system
8. Add cargo flights
9. Bankruptcy/Corporate Restructuring
1 2 3 4 5 6 7 8 9
Asia:
Aeroflot Y Y Y Y
AirAsia Y Y Y Y Y Y Y Y
Asiana Y Y Y Y
Air China Y Y Y Y
All Nippon Airways Y Y Y
China Eastern Airlines Y Y Y Y
Cathay Pacific Y Y Y Y
China southern Airlines Y Y Y Y
EVA Air Y Y Y
Emirates Y Y Y Y
Etihad Airways Y Y Y
Hainan Airlines Y Y Y Y
Indigo Y Y Y Y Y Y
Juneyao Airlines Y Y Y
Japan Airlines Y Y Y Y Y
Korean Air Y Y Y Y Y
Malaysia Airlines Y Y Y Y
Oman Air Y Y Y Y
Royal Jordanian Y Y
Thai airways Y Y Y Y
Spring airlines Y Y Y
Singapore Airlines Y Y Y Y Y
Vietnam airline Y Y Y
Vistara Y Y Y Y Y Y
Europe:
Air France Y Y Y Y Y
Air Portugal Y Y Y Y Y
Austrian Airlines Y Y Y Y Y Y
British Airways Y Y Y Y Y Y
EasyJet Y Y Y
Euro wings Y Y
KLM Y Y Y Y
Lufthansa Y Y Y Y Y
Turkish Airlines Y Y Y
Americas:
Aero Mexico Y Y
American Airlines Y Y Y Y Y Y
Air Canada Y Y Y Y Y
Air Transat Y Y Y Y Y Y Y
Allegiant Y Y Y Y
Delta Airlines Y Y Y Y Y Y
Hawaii Airline Y Y Y Y Y Y
JetBlue Y Y Y Y Y Y
Latam airlines Y Y Y
Southwest Airlines Y Y Y Y Y Y
United airlines Y Y Y Y Y
Africa:
Air Mauritius Y Y Y Y
Ethiopian airlines Y Y Y
Rwanda Air Y Y
Oceania
Air New Zealand Y Y Y Y Y Y Y
Qantas Y Y Y Y Y
According to the survey, Asian airlines are more likely than their United States and Europe counterparts to increase their revenue by adding more cargo planes or converting passenger flights to freighters. This is because the industry in the Asia region has recovered quickly, and demand for cargo has increased, giving airlines a chance to make temporary changes. Not many airlines surveyed successfully sought financial assistance from governments and institutions, accounting for about 15 percent of all the airlines surveyed. But more than half of the carriers surveyed in the Americas have successfully sought financial assistance from governments or agencies.
As for the safety of passengers during the epidemic, the vast majority of airlines have taken more or fewer measures. Almost all airlines have put in place comprehensive measures to ensure passenger safety. Due to the different levels of bans issued by various countries during the epidemic, the flight cancellation rate increased significantly. To safeguard the interests of passengers, the vast majority of investigated airlines have introduced more humane ticket cancellation and change services. In particular, airlines in Asia, where nearly 80 percent of the airlines have similar policies to meet passenger travel needs. Only a handful of the airlines in the survey, less than 10 percent, declared bankruptcy or restructured. The vast majority of airlines did not stop operating and continued to do their business.
In the following sub-sections, the details of responses are discussed.
4.2.1 Reduce the operation cost
Due to the impact of the outbreak of the COVID-19 pandemic, there has been a significant decrease in the number of passengers who travel by air, leading to a dramatic decline in all airlines worldwide. To reduce operating costs to tide over COVID-19 epidemic, airlines have taken different measures to reduce operating costs during the epidemic. The main ways to reduce operating costs include layoffs, wage cuts, early decommissioning of aircraft, postponing aircraft orders, and significantly decreasing flight schedules. Layoffs and wage cuts are the fastest way for airlines to reduce operating costs. The drastic reduction in flight schedules is something the airlines have been forced to do.
4.2.2 Ensure the safety and interests of the passengers
As the COVID-19 epidemic gets more and more serious, airlines have to take strict measures to ensure the safety and interests of passengers to maintain their satisfaction of passengers. To maintain the safety of passengers before check-in, on aircraft, and after landing, many airlines have strengthened the training of their staff and provided passengers with necessary medical supplies. Some airlines also cooperate with medical institutions to provide travellers with epidemic prevention services. As for protecting the interests of passengers, many airlines have introduced more flexible ticket cancellations and rebooking policies to ensure passengers' normal travel.
4.2.3 Increase the revenue and cash flow of the company
Many airlines are looking for more ways to increase revenue and cash flow to keep operating the business during the epidemic and lay the foundation for market recovery. Some airlines tried to turn their passenger flights into cargo flights. The revenue from flying thousands of cargo flights has greatly eased their financial constraints. Some airlines have increased cash flow by raising capital and seeking government assistance. These assistances can help airlines increase their cash flow temporally.
4.3 Average satisfaction score of responses
This section analyses the average satisfaction score of different airline responses, including an analysis of each stage and the overall analysis. The data was collected from 449 participants in the questionnaire. The average score from the participants to each response is shown in Table 3, Table 4 . Table 3 contains the average score for four stages, while Table 4 illustrates the satisfaction ranking score for all 22 responses.Table 3 Satisfaction Ranking (each stage).
Stage Response Score Rank Average
Pre-Flight Provide hygiene products for passengers and staff 4.09 1 3.90
A thermal scanner to monitor body temperature during check-in 4.07 2
Regularly check the health of employees 4.00 3
Keep a safe distance when boarding 3.97 4
The COVID-19 nucleic acid negative certificate is required to allow boarding 3.96 5
Each passenger needs to be seated one seat apart 3.77 6
Adopt a special boarding method, such as boarding in the order from back to front 3.68 7
Protective clothing is required to board the plane 3.66 8
In-Flight Temperature monitoring on the plane 3.99 1 3.85
Masks are required throughout the flight 3.93 2
Apply HEPA filters on the aircraft (remove over 99.97% of particles characterized by diameter of 0.3 μm or larger) 3.93 3
Social distancing is required on the plane 3.88 4
Each passenger needs to be seated one seat apart 3.77 5
It is not allowed to line up to go to the toilet, and the crew will disinfect the toilet after everyone has used the toilet 3.73 6
No in-flight meals and drinks (only snacks and water) 3.72 7
After-Arrival Disinfect the cabin after each flight, even for a previous flight of the connecting flight 4.06 1 3.99
Crew members take 14 days of isolation after working on flights passing through risk areas 4.01 2
Disembark in batches 3.88 3
Others Standard of COVID-19 travel information 4.04 1 4.00
Free refund and change policy 4.02 2
Flight frequency has dropped, while on-time rate has risen 4.00 3
The latest information on COVID-19 is visible on the airlines' official websites 3.99 4
Table 4 Satisfaction Ranking (All).
Response Score Rank
Provide hygiene products for passengers and staff 4.09 1
A thermal scanner to monitor body temperature during check-in 4.07 2
Disinfect the cabin after each flight, even for a previous flight of the connecting flight 4.06 3
Standard of COVID-19 travel information 4.04 4
Free refund and change policy 4.02 5
Crew members take 14 days of isolation after working on flights passing through risk areas 4.01 6
Regularly check the health of employees 4.00 7
Flight frequency has dropped, while on-time rate has risen 4.00 8
Temperature monitoring on the plane 3.99 9
The latest information on COVID-19 is visible on the airlines' official websites 3.99 10
Keep a safe distance when boarding 3.97 11
The COVID-19 nucleic acid negative certificate is required to allow boarding 3.96 12
Masks are required throughout the flight 3.93 13
Apply HEPA filters on the aircraft (remove over 99.97% of particles characterized by diameter of 0.3 μm or larger) 3.93 14
Social distancing is required on the plane 3.88 15
Disembark in batches 3.88 16
Quick health test before boarding 3.77 17
Each passenger needs to be seated one seat apart 3.77 18
It is not allowed to line up to go to the toilet, and the crew will disinfect the toilet after everyone has used the toilet 3.73 19
No in-flight meals and drinks (only snacks and water) 3.72 20
Adopt a special boarding method, such as boarding in the order from back to front 3.68 21
Protective clothing is required to board the plane 3.66 22
The satisfaction score of the after-arrival and additional preventive measures, which are rated at 3.99 and 4.00, is higher than the pre-flight and in-flight periods. The lowest score, rated as 3.85, is shown in the in-flight period.
Providing hygiene products for passengers and staff, with an average rating of 4.09, is most acceptable to participants at the pre-flight stage. However, the participants are most unsatisfied with wearing protective clothing and adopting the boarding order, with an average rating of 3.66 and 3.68, respectively. Based on the average satisfaction score of in-flight responses, the participants' satisfaction with temperature monitoring gets the highest rating of 3.99. But for suspending the in-flight services, such as meals and drinks, and the restriction of toilet use, the participants provide the lowest rating of 3.72 and 3.73, respectively.
With regard to the after-arrival response, the participants are most satisfied with the disinfection of the cabin, and the average score is 4.06. The batched disembarkation is rated only 3.88, the lowest average score in the after-arrival response. As for other stages, the standard of COVID-19 travel information is rated 4.04, the highest average score in the table. The latest COVID-19 details on the official website get the lowest score, a rating of 3.99.
Overall, in table 4, participants are most satisfied with the supply of hygiene products during the pre-flight period and a rate of 4.09. Wearing protective clothing is the most unsatisfactory measure among 22 responses, which only get a 3.66 score.
4.4 Difference between passengers on satisfaction
This section aims to determine whether there are differences between the satisfaction of different passengers’ groups on responses at four stages (Pre-Flight, In-Flight, After-Arrival, and Others). One-Way ANOVA Test analysis is applied in addressing this question. As the number of participants is 449, larger than 385, the test is regarded as meaningful at a 95% confidence level. The first step of One-Way ANOVA is to test the Homogeneity of Variances. Groups that meet the homogeneity of variance test (Sig. > 0.05) will be able to perform the subsequent ANOVA Test, while groups that violate the homogeneity of variance test (Sig. <= 0.05) will perform Welch & Brown-Forsythe Test instead of the ANOVA Test.
Table 5 shows the groups of indicators that meet the homogeneity of variance test (Sig. > 0.05). Gender, Nationality, Travel Frequency, and Education all meet the test of Homogeneity of Variances at all four stages (Pre-Flight, In-Flight, After-Arrival, and Others). In addition, Occupation, Income, and Travel Purpose all meet the Sig.>0.05 condition only at the After-Arrival stage. The Frequent Flyers Points (FFP) members group meets the condition at all three stages except Pre-Flight. Finally, the Cabin Selection group only had Sig.>0.05 at the other stage. Hence, the ANOVA test will be used to test these groups to see if there is a significant difference.Table 5 Test of Homogeneity of Variances (Sig.>0.05).
Test of Homogeneity of Variances Std. Deviation F Sig.
Pre-Flight 0.85 0.93 1.604 0.206
In-Flight 0.94 0.95 0.013 0.910
After Arrival 0.91 0.91 0.331 0.565
Others 0.86 0.92 1.410 0.236
Nationality Type Chinese (N=447) Non-Chinese (N=2)
Pre-Flight 0.90 0.71 0.072 0.788
In-Flight 0.94 1.11 0.042 0.837
After Arrival 0.91 0.24 1.149 0.284
Others 0.89 0.00 1.972 0.161
Occupation Type Student (N=63) Business (N=246) Owners (N=44) Government(N=34) Private(N=46) Others (N=7) Retired (N=9)
After Arrival 0.69 0.98 0.98 0.82 0.9 0.49 0.5 1.066 0.382
Income Type <=3000N=64) 3001-10K(N=252) 101000-20K (N=86) >20K (N=47)
After Arrival 0.7 0.97 0.79 0.98 1.343 0.26
Age Type <=18 (N=7) 18-30 (N=180) 31-40 (N=105) 41-50 (N=94) 51-80(N=61) >=61 (N=2)
In-Flight 0.92 0.95 0.93 0.88 1.06 1.21 1.639 0.328
After Arrival 1.36 0.87 0.86 0.95 0.98 0.24 0.805 0.425
Travel Frequency Type 0-1 (N=112) 2-4 (N=185) 5-10 (N=69) 51-60 (N=61) >= 61(N=2)
Pre-Flight 0.87 0.88 0.85 1.01 0.99 0.913 0.456
In-Flight 0.88 0.92 0.93 1.03 1.26 1.859 0.117
After Arrival 0.92 0.86 0.97 1.02 0.63 1.178 0.32
Others 0.93 0.83 1.01 0.94 0.49 1.991 0.095
Education Type Senior High School or Lower (N=47) College(N=160) Bachelor (N=211) Master and Above (N=31)
Pre-Flight 1.07 0.94 0.81 0.98 2.062 0.105
In-Flight 0.99 0.96 0.91 0.97 0.035 0.991
After Arrival 0.97 1.00 0.85 0.66 1.957 0.12
Others 0.76 0.98 0.85 0.95 2.531 0.057
Travel Purpose Type Business (N=47) Visiting family (N=107) Holiday (N=186) Study (N=62) Others(N=6)
After Arrival 0.95 1.01 0.78 1.04 0.69 1.942 0.102
FFP members Type Yes (N=170) No (N=279)
In-Flight 1.01 0.9 2.385 0.123
After Arrival 0.94 0.89 0.04 0.842
Others 0.93 0.88 0.503 0.479
Cabin Selection (class) Type Economy(N=219) Business (N=140) First(N=90)
Others 0.82 0.95 0.98 0.534 0.587
In contrast to Table 5, Table 6 shows which groups did not meet the test of Homogeneity of Variances (Sig. <0.05). Groups of Travelled or not after COVID-19 and Travel Frequency (After COVID-19) do not satisfy the test of Homogeneity of Variances at any of the four stages. The data from the other groups are also presented below, all of which will be subjected to the Welch & Brown-Forsythe Test to determine if there are significant differences.Table 6 Test of Homogeneity of Variance (Sig.<=0.05).
Test of Homogeneity of Variances Std. Deviation F Sig.
Occupation Type Student (N=63) Business (N=246) Owners (N=44) Government(N=34) Private(N=46) Others (N=7) Retired (N=9)
Pre-Flight 0.34 1.01 0.98 1.06 0.65 3.86 0.25 9.536 <0.001
In-Flight 0.45 0.96 1.12 0.12 1.07 0.37 0.33 7.238 <0.001
Others 0.5 0.93 0.84 1.19 1.01 0.49 0.45 4.463 <0.001
Income Type <=3000N=64) 3001-10K(N=252) 101000-20K (N=86) >20K (N=47)
Pre-Flight 0.36 0.95 0.84 1.16 11.739 <0.001
In-Flight 0.48 0.96 0.92 1.27 13.425 <0.001
Others 0.5 0.94 0.98 0.91 4.461 0.004
Age Type <=18 (N=7) 18-30 (N=180) 31-40 (N=105) 41-50 (N=94) 51-80(N=61) >=61 (N=2)
Pre-Flight 0.25 0.83 0.92 0.8 1.18 0.88 5.403 <0.001
Others 0.19 0.77 0.94 1.06 0.92 0.88 3.460 0.002
Travel Purpose Type Business (N=47) Visiting family (N=107) Holiday (N=186) Study (N=62) Others(N=6)
Pre-Flight 1.17 0.94 0.77 0.73 0.39 10.174 <0.001
In-Flight 1.07 1.05 0.82 0.94 0.35 4.044 0.003
Others 1.01 0.99 0.72 1.01 0.78 3.881 0.004
FFP members Type Yes (N=170) No (N=279)
Pre-Flight 0.01 0.82 9.913 0.002
Travelled or not after COVID-19 Type Yes (N=170) No (N=279)
Pre-Flight 0.96 0.3 26.579 <0.001
In-Flight 1 0.44 19.903 <0.001
After Arrival 0.95 0.57 6.231 0.013
Others 0.94 0.49 9.647 0.002
Travelled frequency after COVID-19 Type 0-1(N=123) 2-4(N=105) 5-10(N=135) >=11(N=86)
Pre-Flight 0.65 0.8 1.09 0.97 12.966 <0.001
In-Flight 0.76 0.88 1.06 1.06 5.26 0.001
After Arrival 0.8 0.93 0.82 1.13 4.450 0.004
Others 0.62 0.97 1.07 0.8 11.786 <0.001
Cabin Selection (class) Type Economy(N=219) Business (N=140) First(N=90)
Pre-Flight 0.74 1.00 1.07 8.776 <0.001
In-Flight 0.64 1.20 1.06 47.824 <0.001
After Arrival 0.93 0.81 0.99 4.355 0.013
After the Test of Homogeneity of Variances, all groups are distributed to the corresponding ANOVA Test or Welch & Brown-Forsythe Test. Same as the previous test, groups are regarded as having no significant difference if Sig. > 0.05, and regarded as having a significant difference if Sig. <= 0.05. Then, the Tamhane T2 Post Hoc Test is performed on groups having significant differences to figure out the exact group pairs that are different. In the ANOVA test, all significance is greater than 0.05, which means that there is no significant difference between each group in the data taken for the ANOVA test (Table 7 ).Table 7 ANOVA Test.
Test of Homogeneity of Variances Std. Deviation F Sig.
Pre-Flight 3.91±0.85 3.89±0.93 0.118 0.731
In-Flight 3.89±0.94 3.82±0.95 0.535 0.465
After Arrival 3.98±0.91 4.00±0.91 0.055 0.815
Others 4.05±0.86 3.98±0.92 0.665 0.415
Nationality Type Chinese (N=447) Non-Chinese (N=2)
Pre-Flight 3.90±0.90 3.38±0.71 0.686 0.408
In-Flight 3.86±0.94 3.21±1.11 0.923 0.337
After Arrival 3.99±0.91 3.50±0.24 0.574 0.449
Others 4.02±0.89 3.00±0.00 2.575 0.109
Occupation Type Student (N=63) Business (N=246) Owners (N=44) Government(N=34) Private(N=46) Others (N=7) Retired (N=9)
After Arrival 4.04±0.69 3.93±0.98 3.97±098 4.09±0.82 4.04±0.9 4.38±0.49 4.19±0.5 0.331 0.565
Income Type <=3000N=64) 3001-10K(N=252) 101000-20K(N=86) >20K (N=47)
After Arrival 4.06±0.7 3.96±0.97 4.13±0.79 3.78±0.98 1.739 0.158
Age Type <=18 (N=7) 18-30 (N=180) 31-40 (N=105) 41-50 (N=94) 51-80(N=61) >=61 (N=2)
In-Flight 3.84±0.92 3.8±0.95 3.93±0.93 3.91±0.88 3.81±1.06 3.29±1.21 0.515 0.765
After Arrival 3.57±1.36 3.93±0.87 4.03±0.86 4±0.95 4.11±0.98 3.5±0.24 0.823 0.534
Travel Frequency Type 0-1 (N=112) 2-4 (N=185) 5-10 (N=69) 51-60 (N=61) >= 61(N=2)
Pre-Flight 3.85±0.87 3.88±0.88 4.01±0.85 3.95±1.01 3.77±0.99 0.4860 0.746
In-Flight 3.82±0.88 3.84±0.92 3.94±0.93 3.96±1.03 3.38±1.26 1.313 0.264
After Arrival 3.96±0.92 4.03±0.86 3.96±0.97 3.98±1.02 3.76±0.63 0.356 0.84
Others 3.86±0.93 4.06±0.83 4±1.01 4.09±0.94 4.21±0.49 1.274 0.279
Education Type Senior High School or Lower (N=47) College(N=160) Bachelor (N=211) Master and Above (N=31)
Pre-Flight 3.82±1.07 3.94±0.94 3.89±0.81 3.84±0.98 0.276 0.843
In-Flight 3.95±0.99 3.91±0.96 3.78±0.91 3.93±0.97 0.827 0.479
After Arrival 4.01±0.97 3.92±1.00 4±0.85 4.17±0.66 0.356 0.54
Others 4.11±0.76 3.92±0.98 4.05±0.85 4.06±0.95 0.888 0.447
Travel Purpose Type Business (N=47) Visiting family (N=107) Holiday (N=186) Study (N=62) Others(N=6)
After Arrival 3.97±0.95 3.93±1.01 4.08±0.78 3.85±1.04 3.83±0.69 1.021 0.396
FFP members Type Yes (N=170) No (N=279)
In-Flight 3.84±1.01 3.86±0.9 0.037 0.847
After Arrival 4±0.94 3.98±0.89 0.043 0.836
Others 3.99±0.93 4.02±0.88 0.136 0.713
Cabin Selection (class) Type Economy(N=219) Business (N=140) First(N=90)
Others 4±0.82 4.02±0.95 4.04±0.98 0.103 0.902
Table 8 illustrates groups that take Welch & Brown-Forsythe Test. Table 7 and Table 8 illustrate groups with no significant difference (Sig.>0.05). Travel Purpose is the only indicator that does not have a significant difference in all four stages. Travel Frequency (After COVID-19) has no significant difference in Pre-Flight, In-Flight, and Others. Occupation, Income, and Travelled After COVID-19 are indicators that do not significantly differ in the same three stages (Pre-Flight, In-Flight, and After-Arrival). Besides, Age, FFP members, and Cabin Selection showed a similar trend in that they do not significantly differ in Pre-Flight.Table 8 Welch & Brown-Forsythe Test (Sig.>0.05).
Test of Homogeneity of Variances Std. Deviation F Sig.
Occupation Type Student (N=63) Business (N=246) Owners (N=44) Government(N=34) Private(N=46) Others (N=7) Retired (N=9)
Pre-Flight Welch 3.90±0.34 3.84±1.01 3.93±0.98 3.86±1.06 4.24±0.65 3.86±0.37 3.89±0.25 2.061 0.075
Brown-Forsythe 2.059 0.061
In-Flight Welch 3.92±0.45 3.89±0.96 3.69±1.12 3.76±1.23 3.78±0.92 3.84±0.37 3.92±0.33 0.417 0.864
Brown-Forsythe 0.539 0.778
Others Welch 4.16±0.92 4.02±0.93 4.06±0.84 3.6±1.19 3.99±1.01 4.18±0.49 3.94±0.45 1.45 0.216
Brown-Forsythe 1.928 0.079
Income Type <=3000N=64) 3001-10K(N=252) 101000-20K (N=86) >20K (N=47)
Pre-Flight Welch 3.87±0.36 3.89±0.95 4.02±0.84 3.78±1.16 0.842 0.473
Brown-Forsythe 0.858 0.465
In-Flight Welch 3.9±0.48 3.84±0.96 4.01±0.92 3.59±1.27 1.53 0.209
Brown-Forsythe 2.006 0.116
Others Welch 4.15±0.5 3.99±0.94 3.93±0.98 4.07±0.91 1.563 0.201
Brown-Forsythe 0.965 0.41
Age Type <=18 (N=7) 18-30 (N=180) 31-40 (N=105) 41-50 (N=94) 51-80(N=61) >=61 (N=2)
Pre-Flight Welch 4.01±0.25 3.9±0.83 3.96±0.92 3.97±0.80 3.68±1.18 3.5±0.88 0.756 0.6
Brown-Forsythe 1.202 0.343
Travel Purpose Type Business (N=47) Visiting family (N=107) Holiday (N=186) Study (N=62) Others(N=6)
Pre-Flight Welch 3.73±1.17 3.98±0.94 3.9±0.77 4.04±0.73 3.63±0.39 1.901 0.13
Brown-Forsythe 1.806 0.128
In-Flight Welch 3.81±1.07 3.81±1.05 3.88±0.82 3.91±0.94 3.69±0.35 0.497 0.738
Brown-Forsythe 0.304 0.875
Others Welch 3.99±1.01 3.94±0.99 4.11±0.72 3.92±1.01 3.71±0.78 1.194 0.33
Brown-Forsythe 1.040 0.39
FFP members Type Yes (N=170) No (N=279)
Pre-Flight Welch 3.84±1.01 3.93±0.82 1.037 0.309
Brown-Forsythe 1.037 0.309
Travelled or not after COVID-19 Type Yes (N=170) No (N=279)
Pre-Flight Welch 3.9±0.96 3.92±0.30 0.152 0.697
Brown-Forsythe 0.152 0.697
In-Flight Welch 3.84±1.00 3.96±0.44 2.546 0.112
Brown-Forsythe 2.546 0.112
Others Welch 3.99±0.94 4.12±0.49 2.466 0.119
Brown-Forsythe 2.466 0.119
Travelled frequency after COVID-19 Type 0-1(N=123) 2-4(N=105) 5-10(N=135) >=11(N=86)
Pre-Flight Welch 3.92±0.65 3.95±0.8 3.78±1.09 4±0.97 0.896 0.444
Brown-Forsythe 1.198 0.31
In-Flight Welch 3.87±0.76 3.89±0.88 3.82±1.06 3.83±1.06 0.145 0.933
Brown-Forsythe 0.148 0.931
After arrival Welch 4.06±0.8 4±0.93 4.03±0.82 3.81±1.13 1.061 0.367
Brown-Forsythe 1.338 0.262
Cabin Selection (class) Type Economy(N=219) Business (N=140) First(N=90)
Pre-Flight Welch 3.89±0.74 3.91±1.00 3.91±1.07 0.021 0.979
Brown-Forsythe 0.018 0.982
Table 9 illustrates the groups taking Welch & Brown-Forsythe Test with significant differences (Sig.<=0.05), which required Tamhane T2 Post Hoc Tests to analyse further where the significant difference was produced. As the indicator Travelled After COVID-19 consists of only two groups, the post hoc test is unnecessary. The two groups are regarded as having significant differences in the After-Arrival stage.Table 9 Welch & Brown-Forsythe Test (Sig.<=0.05).
Test of Homogeneity of Variances Std. Deviation F Sig.
Age Type <=18 (N=7) 18-30 (N=180) 31-40 (N=105) 41-50 (N=94) 51-80(N=61) >=61 (N=2)
Others Welch 4.57±0.19 4.07±0.77 3.97±0.94 3.91±1.06 4.03±0.92 3.63±0.88 7.563 0.003
Brown-Forsythe 1.221 0.337
Travelled or not after COVID-19 Type Yes (N=170) No (N=279)
After arrival Welch 3.95±0.95 4.22±0.57 0.152 0.697
Brown-Forsythe 0.152 0.697
Travelled frequency after COVID-19 Type 0-1(N=123) 2-4(N=105) 5-10(N=135) >=11(N=86)
Others Welch 4.12±0.62 3.92±0.97 3.88±1.07 4.16±0.80 2.811 0.04
Brown-Forsythe 2.825 0.038
Cabin Selection (class) Type Economy(N=219) Business (N=140) First(N=90)
In-Flight Welch 3.97±0.64 3.66±1.2 3.87±1.06 4.047 0.016
Brown-Forsythe 3.903 0.021
After arrival Welch 3.9±0.93 4.14±0.81 3.96±0.99 3.539 0.031
Brown-Forsythe 3.0821 0.047
Table 10 illustrates the result of the Tamhane T2 Post Hoc Test, group pairs whose Sig. is less than or equal to 0.05 are marked grey. Age <=18 significantly differs from age groups of 18-31, 31-40, 41-50, and 51-60 in the “Others stage”. As for Cabin selection, economy class significantly differs from business class in stages of In-Flight and After-Arrival. Thus, Table 11 concludes all groups with significant differences in specific stages.Table 10 Tamhane T2 Post Hoc Tests.
Tamhane T2 Post Hoc Tests
Group Stage Group (I) Group (J) Mean Difference (I-J) Sig.
Age Others <=18 18-30 0.51 <0.001
31-40 0.6 <0.001
41-50 0.66 <0.001
51-60 0.54 0.004
Travel Frequency (After COVID-19) Others 0-1 2-4 0.2 0.357
5-10 0.24 0.142
>=11 -0.04 1.00
2-4 2-4 -0.2 0.357
5-10 0.04 1.00
>=11 -0.24 0.339
5-10 2-4 -0.24 0.142
5-10 -0.04 1.00
>=11 -0.28 0.157
>=11 2-4 0.04 1.00
5-10 0.24 0.339
>=11 0.28 0.157
Cabin selection (Class) In-Flight Economy Business 0.31 0.016
First 0.11 0.763
After- Arrival Economy Business -0.24 0.027
First -0.06 0.956
Table 11 The Summery of Selected Groups.
Group Stage Group (I) Group (J)
Age Others <=18 18-30
31-40
41-50
51-60
Cabin selection (Class) In-Flight Economy Business
After- Arrival Economy Business
Travel (After COVID-19) After- Arrival Yes No
4.5 Discussion
After investigating the measures taken by 49 airlines, following insights could be obtained. First, airlines worldwide have adopted many measures to reduce expenditures, but few have been implemented to increase revenue. The conditions that can bring profits to airlines basically rely on government policies, such as economic assistance and open borders. Improving cargo services, an essential source of income that could practice by airlines, also requires cooperation with the government or other institutions. Such uncontrollable factors are unforeseeable compared to reducing expenditures, which is regarded as a controllable response.
The second point is that many words frequently appear in all the news released by 49 airlines, which seem ordinary but can subconsciously consolidate the company's image. In past research, it was found that different vocabulary represents what the company wants to express. For example, through keywords such as ‘volunteer’, ‘medical’, ‘food’, and ‘necessities’, the image of medical and charity responsible for society is enhanced, and through the collective pronoun ‘we’, a bond of unity between management, employees, and customers is established. It was found that airlines around the world are using public relations to bring confidence to stakeholders and passengers.
Measures such as travel restrictions, isolation, and social distance planning are detrimental to airline profitability, but airlines have to adopt some measures because of government regulations. These measures include reducing flight plans and arranging strict and complex check-in procedures. Many airlines seek to minimize the loss of market capacity, route networks, customer base, and customer trust built up over the years before the COVID-19 outbreak to prepare them for recovery.
Most airlines only have enough cash to make up for about two months of lost revenue (IATA, 2020b). Most governments placed a high priority on maintaining connectivity in air transport. As a result, almost all major airlines received government support. In addition, many airlines in developed countries have received financial assistance (Iata, 2020a, IATA, 2020a). Most airlines have tried to seek financial aid, but many failed. However, some people are concerned that some countries may abandon the policies of liberalization and deregulation, which could jeopardize important progress in levelling the playing field. In addition, the financial assistance received by these airlines is limited to a certain extent; these assistances help the airlines survive. Moreover, offering aid to maintain airline operations creates a promising future for the airlines and the aviation market. It is conducive to economic recovery and production recovery after the outbreak of the COVID-19 pandemic is over because these financial assistances prevent millions of employees of airlines around the world from being fired.
The rapid production recovery in Asia and the increase in cargo demand have led airlines to add cargo flights. According to their annual reports, some airlines have added thousands of extra cargo flights over six months. Many of these cargo flights carry medical materials, so these flights are profitable for the airlines and contribute to the fight against the epidemic.
According to the data above, different measures will benefit the airlines differently. Reducing flight plans, reducing the salary of the employees and managers, and retiring aircraft can help airlines reduce their operating costs. Some measures to guarantee passengers’ safety and interests can improve the passengers’ satisfaction. Adding cargo flights and seeking financial assistance can improve the company’s income and cash flow. Most airlines reduced flight plans and reduced the salary of the employees and managers. Some airlines changed passenger flights to cargo flights or retired aircraft. Not many airlines laid off workers or declared bankruptcy.
Findings showed that passengers were generally satisfied with the airlines' response measures, with even the least satisfactory measures scoring an average of over 3. The measure most satisfied by passengers is providing hygiene products for passengers and staff, while passengers are least satisfied with the measure of protective clothing required to board the plane. In addition, for each flight stage, passengers are most satisfied with the “Other stage” and least satisfied with the “In-Flight stage”.
From the questionnaire, it can be seen that for travellers who were surveyed, providing hygiene products for passengers and staff and a thermal scanner to monitor body temperature during check-in were the two measures they were most satisfied with at the Pre-flight stage. However, according to previous research (, only a small number of airlines worldwide provide hygiene products. A 2021 study (Bielecki et al., 2020) shows that of the 20 major airlines surveyed, three airlines from China - China Southern, China Eastern, and Air China - all provide hygiene products. The remaining 17 airlines do not provide hygiene products. Considering that China has the best COVID-19 outbreak control and the fastest recovery in the airline industry, providing hygiene products might be an idea that other airlines worldwide could learn from it. Also, as this measure has received a very high level of satisfaction, applying this measure will enhance the feeling of safety for passengers travelling by air after the outbreak. Sixteen of the 20 airlines test passengers' body temperatures before take-off, while only one airline, Southern Airways, tests passengers' temperatures during the flight. Our survey shows that passengers are very satisfied with this measure, which means that most airlines are doing a good job of checking their temperature.
Passengers were most satisfied with temperature monitoring during the In-Flight stage, but only China Southern offered this service in previous studies. Passengers are most unhappy with the fact that airlines do not provide regular meals, and as can be seen from previous studies, almost all airlines have placed restrictions on eating on board. Airlines could consider adding temperature monitoring services and offering a bit of high-calorie ready-to-eat food. Regarding passenger satisfaction with the airline's response strategies, airlines may need to provide onboard temperature monitoring and hygiene products to enhance passenger satisfaction.
The ANOVA test results illustrate that “Age”, “Cabin Selection”, and “Travelled after COVID-19” are the groups that affect passengers’ satisfaction levels on responses. An analysis of previous research (Clemes et al., 2008) assessed whether passengers with different socio-demographic characteristics have a different perception of airlines' service quality in many aspects. Age was also the group that was significant at a 5% level when assessing passengers’ perception of safety or security. Therefore, it could be concluded that people of different ages are significantly different when facing airline safety issues such as COVID-19 responses. It is evident that people who have not travelled after COVID-19 did not know COVID-19 responses; thus, the different groups of travelled after COVID-19 could be ignored. As there are significant differences in the perception of airline response strategies for different age groups and cabin selections, airlines need to consider passengers' age composition and other cabins for developing COVID-19 measures. This may lead to adopting response strategies that are more likely to satisfy passengers.
4.6 General recommendations and strategies
Airline firms are trying to cope with the COVID-19 crisis that has significantly affected their financial viability. As statistics predict, the aviation sector cannot be recovered in a few years. Airlines require to resume their operations with a low number of customers and under strict regulations. Generally, domestic flights have taken the first place; after that, the border gates have been opened. Then, short-haul international flights and long-haul flights started their operations. A demand shock was induced worldwide by COVID-19 for passenger flights because of the travel confinements and people’s unwillingness to travel in such risky conditions. On the contrary, there was a surge in demand for cargo flights to rapidly transport the required medical equipment, PCR tests, vaccines, etc. Moreover, there were some restrictions on the supply of “belly cargo” carried on passenger flights, which increased the demand for cargo flights. Because of the inadequate financial arrangements taken into action, quarantine measures, the reduction of passenger demand, etc., airline companies have not succeeded yet in reaching their pre-2019 conditions. Some measures taken into action during the COVID-19 crisis will have long-term influences. As the restrictions on international travel should still be obeyed, changes to networks and fleets would not be at the desired level, at least in the near future. Airlines will continue to be suffered from such issues. Nevertheless, these effects could be recovered sooner if countries' relevant authorities allow international travel to be opened to those individuals vaccinated for COVID-19. During the recovery course, airline companies need to avoid quickly increasing the seating capacity because passenger demand may not rapidly return. Many countries have some constraints still taken into action. Airlines must adjust their staff's salaries as the passenger numbers and capacities gradually approach the levels before the COVID-19 crisis. During the post-COVID-19 period, cargo transportation may continue to be a remarkable source of revenue for airlines. The airline operators of adequate size will be able to incorporate cargo units, and as such, post-COVID-19 aviation services may differ from those before the COVID-19 crisis.
5 Conclusion
This research explored airlines’ responses and customer satisfaction in the aviation industry during the COVID-19 pandemic by collecting and analyzing the organization-level responses adopted by airlines and analyzing passengers’ satisfaction with individual-level responses during flights. The study classified organization-level responses into three categories: reduce the operation cost, ensure the safety and interests of the passengers, and increase the revenue and cash flow. It was found that all airlines took “reduce the operation cost” responses, such as cutting flights and reducing employees’ salary, while few airlines adopted responses of increasing the revenue and cash flow of the company. Importantly, airlines in different areas adopted considerably different responses. Airlines in developed countries have usually received financial support, while many airlines failed to be assisted by governments. Besides, in Asia, where production is recovering rapidly, airlines adopt some responses to increase revenue and cash flow, such as adding cargo flights. However, airlines cannot adopt such responses in other areas where COVID-19 is still severe. As for responses to ensure the safety and interests of the passengers, all airlines have taken measures that substantially depend on local government policies. Thus, this research conducted a questionnaire survey to analyze the satisfaction of responses in the Chinese market.
With a sample of 449 questionnaires, which collected passengers’ basic information and their satisfaction with responses in four stages, the airlines’ individual-level responses for passengers were ranked based on passenger satisfaction. It was found that passengers’ satisfaction varied among different COVID-19 measures adopted by airlines. For example, among 22 measures considered in this study, the top 3 measures that passengers were satisfied with were “ Provide hygiene products for passengers and staff”, “A thermal scanner to monitor body temperature during check-in” and “Disinfect the cabin after each flight, even for the previous flight of the connecting flight”. In contrast, the bottom 3 measures were “Protective clothing is required to board the plane”, “Adopt a special boarding method such as boarding in the order from back to front” and “No in-flight meals and drinks (only snacks and water). As China is one of the best countries to overcome COVID-19, the questionnaire is a suitable reference for airlines struggling with COVID-19. Moreover, the “others stage” is the most satisfying of the four stages, while the “In-Flight stage” is the least. The ANOVA and Welch & Brown-Forsythe test found significant differences in travelers' satisfaction between different age groups with the same measure. Therefore, airlines will have to consider this difference in the future.
The results of this study provide airline operators with a better understanding of how customers assessed the quality of airline service during the COVID-19 pandemic and what operators can do in future to improve customer satisfaction post-COVID. Airline operators can develop strategies to focus on key COVID-19 measures that can improve passengers’ satisfaction without compromising passengers’ health and safety. As found in this study, there are opportunities to improve the in-flight stage COVID-19 measures, especially the measures like “each passenger needs to be seated one seat apart”, “It is not allowed to line up to go to the toilet, and the crew will disinfect the toilet after everyone has used the toilet” and “No in-flight meals and drinks (only snacks and water)”. Likewise, airlines should attempt to meet passengers' travel expectations and measures in different cabins (economy, first class and business) as cabin selection groups affected passengers’ satisfaction level in responses.
The data in the questionnaire is limited to Chinese travellers and may not be representative of the views of travellers from other countries around the world. In addition, the number of travellers under 18 and over 60 surveyed in our questionnaire was small, and these smaller samples may not be representative of this age group. Future studies could focus on how the world's mainstream airlines respond to COVID-19 and whether the responses positively impact the airlines themselves. When evaluating whether the strategy adopted by an airline is effective, please consider combining various performance indicators of the airline with building a model that can rate the airline and score the airline's overall performance during the COVID-19 period. This report may help small and medium-sized airlines learn from the responses of large airlines and help airlines around the world adopt more satisfactory responses to passengers. In addition, it may also enable them to consider the differences between different passengers.
Limited studies have systematically investigated passengers’ satisfaction in four stages of air travel: Pre-Flight, In-Flight, After-Arrival, and Others (Face mask requirement, HEPA filters, etc.). As found in this study, through a systematic understanding of passengers’ satisfaction with the COVID-19 measures adopted by airlines in those four stages, there is an opportunity for the airline operator to improve their market share of air travel by improving post-COVID-19 and service measures that can influence passengers' satisfaction. Further, it provides a benchmarking resource for airline operators in case of a similar pandemic in future. Complementing the present study, future research could focus on passengers’ perceptions and satisfaction with new technology that could enhance some of the COVID-19 measures explored in this study, especially for the in-flight stage (e.g., the use of cleaning robots or ultraviolet light and antimicrobial cabin cleaning for toilets and cabins, and the use of application controlled in-flight entertainment systems).
From the investigation of measures adopted by 49 airlines, it can be expected that several significant changes will occur post-COVID-19, for instance, security measures and new operational standards. Airlines may need to re-plan their networks, crew, fleet, and cash flow to adapt well to such changes and for a future pandemic. Another significant aspect of demand recovery is that airline operators need to explore the factors leading to the reduction of passengers’ confidence and ways to restore it.
Author contributions All authors: Conceptualization, Investigation, Data Collection and Compilation, Methodology, Software, Formal analysis, Seyed Mojib Zahraee: Writing – original draft, Hongwei Jiang: Supervision, Writing – review & editing, Nirajan Shiwakoti: Supervision, Writing – original draft, Writing – review & editing.
Uncited references
Amankwah-amoah, j. , 2020, ATHENA INFORMATION SOLUTIONS PVT. LTD, N. D., 2020, Bureau, a. t. , 2020, Lange, r. , 2020, LEE, D., 2020, Specia, m. , 2020, Wilder-smith, a. , 2006, Wilder-Smith et al., 2003.
Appendix: Customer satisfaction rating on airline response to COVID-19
1、Basic Information
Gender Male □ Female □
Nationality Chinese □ Non-Chinese □
Occupation Student □ Business □ Owner □ Government sector □ Private Sector □ Others □ Retired □
Income (Monthly RMB) ≤3000 □ 3001-10000 □ 10001-20000 □ ≥20000 □
Age ≤18 □ 18-30 □ 31-40 □ 41-50 □ 51-60 □ ≥60 □
Education Senior High or lower □ College□ Bachelor□ Master and above□
Annual travel times by air (before the COVID-19 pandemic) 0-1 □ 2-4 □ 5-10□ 11-20 □ ≥21□
Travel purpose Business □ Visiting friends and relatives □ Holiday □ Study □ other □
Are you Airlines FFP (Frequent Flyers Points) members? Yes □ No □
Have you ever travelled by air after the start of COVID-19 pandemic? Yes □ No □
IF YES, How many times have you travelled by air after the outbreak of the pandemic? 0-1 □ 2-4 □ 5-10□ ≥11 □
Which class do you prefer when you travel by air? Economy class □ Business class□ First class□
2、Airline safety response questions
Please rate your satisfaction with the different policies implemented by airlines in response to the COVID-19 pandemic based on your actual situation and feelings. 1: very dissatisfied. 2: dissatisfied 3 : neutral. 4 : satisfied. 5 : very satisfied.Questions Satisfaction
Measures taken by airlines for the pre-flight phase
1.1 The COVID-19 nucleic acid negative certificate is required to allow boarding 1 2 3 4 5
1.2 A thermal scanner to monitor body temperature during check-in 1 2 3 4 5
1.3 Provide hygiene products for passengers and staff 1 2 3 4 5
1.4 Regularly check the health of employees 1 2 3 4 5
1.5 Protective clothing is required to board the plane 1 2 3 4 5
1.6 Keep a safe distance when boarding 1 2 3 4 5
1.7 Adopt a special boarding method, such as boarding in the order from back to front 1 2 3 4 5
1.8 Quick health test before boarding 1 2 3 4 5
The measures taken by the airline for the in-flight phase
2.1 Masks are required throughout the flight 1 2 3 4 5
2.2 Social distancing is required on the plane 1 2 3 4 5
2.3 No in-flight meals and drinks 1 2 3 4 5
2.4 Apply HEPA filters on the aircraft (remove over 99.97% of particles characterized by diameter of 0.3 μm or larger) 1 2 3 4 5
2.5 Each passenger needs to be seated one seat apart 1 2 3 4 5
2.6 Temperature monitoring on the plane 1 2 3 4 5
2.7 It is not allowed to line up to go to the toilet, and the crew will disinfect the toilet after everyone has used the toilet 1 2 3 4 5
The measure taken by airline for the after-arrival phase
3.1 Crew members take 14 days of isolation after working in flights passing through risk areas 1 2 3 4 5
3.2 Disinfect the cabin after each flight, even for a previous flight of the connecting flight 1 2 3 4 5
3.3 Disembark in batches 1 2 3 4 5
Other measures taken by airlines
4.1 Visibility of COVID-19 information on home page 1 2 3 4 5
4.2 Standard of COVID-19 travel information 1 2 3 4 5
4.3 Free refund and change policy 1 2 3 4 5
4.4 Flight frequency has dropped, while on-time rate has risen 1 2 3 4 5
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References
Abate M. Christidis P. Purwanto A.J. Government support to airlines in the aftermath of the COVID-19 pandemic Journal of air transport management 89 2020 101931
Adrienne N. Budd L. Ison S. Grounded aircraft: An airfield operations perspective of the challenges of resuming flights post COVID Journal of Air Transport Management 89 2020 101921
Albers S. Rundshagen V. European airlines′ strategic responses to the COVID-19 pandemic (January-May, 2020) Journal of air transport management 87 2020 101863
Amankwah-amoah, j. Note: Mayday, Mayday, Mayday! Responding to environmental shocks: Insights on global airlines’ responses to COVID-19 Transportation Research Part E: Logistics and Transportation Review 143 2020 102098
ATHENA INFORMATION SOLUTIONS PVT. LTD, N. D. 2020. Qatar Airways Reflects on an Extraordinary Year in 2020 [Online].
Batarce M. Muñoz J.C. Torres I. Characterizing the public transport service level experienced by users: An application to six Latin American transit systems Journal of Public Transportation 24 2022 100006
BBC 2020. Coronavirus: China reports no Covid-19 deaths for first time. viewed 2 Sep 2021
Bielecki M. Patel D. Hinkelbein J. Komorowski M. Kester J. Ebrahim S. Rodriguez-Morales A.J. Memish Z.A. Schlagenhauf P. Reprint of: Air travel and COVID-19 prevention in the pandemic and peri-pandemic period: A narrative review Travel medicine and infectious disease 38 2020 101939
Blake A. Sinclair M.T. Sugiyarto G. Quantifying the impact of foot and mouth disease on tourism and the UK economy Tourism Economics 9 2003 449 465
Bogoch I.I. Creatore M.I. Cetron M.S. Brownstein J.S. Pesik N. Miniota J. Tam T. Hu W. Nicolucci A. Ahmed S. Assessment of the potential for international dissemination of Ebola virus via commercial air travel during the 2014 west African outbreak The Lancet 385 2015 29 35
Borkowski P. Jażdżewska-Gutta M. Szmelter-Jarosz A. Lockdowned: Everyday mobility changes in response to COVID-19 Journal of Transport Geography 90 2021 102906
Brownstein J.S. Wolfe C.J. Mandl K.D. Empirical evidence for the effect of airline travel on inter-regional influenza spread in the United States PLoS medicine 3 2006 e401 16968115
Budd L. Ison S. Adrienne N. European airline response to the COVID-19 pandemic–Contraction, consolidation and future considerations for airline business and management Research in Transportation Business & Management 37 2020 100578
BUREAU, A. T. 2020. Effects of novel coronavirus (COVID-19) on civil aviation: economic impact analysis. International Civil Aviation Organization (ICAO), Montréal, Canada.
Chevtaeva E. Guillet B.D. A review of communication trends due to the pandemic: perspective from airlines Anatolia 32 2021 168 171
CHINAZZI, M., DAVIS, J. T., AJELLI, M., GIOANNINI, C., LITVINOVA, M., MERLER, S., PASTORE Y PIONTTI, A., MU, K., ROSSI, L. & SUN, K. 2020. The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science, 368, 395-400.
Clemes M.D. Gan C. Kao T.-H. Choong M. An empirical analysis of customer satisfaction in international air travel Innovative marketing 4 2008
Cooper B.S. Pitman R.J. Edmunds W.J. Gay N.J. Delaying the international spread of pandemic influenza PLoS medicine 3 2006 e212 16640458
Czerny A.I. Fu X. Lei Z. Oum T.H. Post pandemic aviation market recovery: Experience and lessons from China Journal of Air Transport Management 90 2021 101971
Dube K. Nhamo G. Chikodzi D. COVID-19 pandemic and prospects for recovery of the global aviation industry Journal of Air Transport Management 92 2021 102022
Ferguson N.M. Cummings D.A. Fraser C. Cajka J.C. Cooley P.C. Burke D.S. Strategies for mitigating an influenza pandemic Nature 442 2006 448 452 16642006
Gallego I. Font X. Changes in air passenger demand as a result of the COVID-19 crisis: Using Big Data to inform tourism policy Journal of Sustainable Tourism 29 2021 1470 1489
Gössling S. Scott D. Hall C.M. Pandemics, tourism and global change: a rapid assessment of COVID-19 Journal of sustainable tourism 29 2020 1 20
Graham A. Kremarik F. Kruse W. Attitudes of ageing passengers to air travel since the coronavirus pandemic Journal of air transport management 87 2020 101865
Grais, r. f., hugh ellis, j. & glass, g. e. Assessing the impact of airline travel on the geographic spread of pandemic influenza European journal of epidemiology 18 2003 1065 1072 14620941
GUDMUNDSSON, S., CATTANEO, M. & REDONDI, R. 2020. Forecasting recovery time in air transport markets in the presence of large economic shocks: COVID-19. Available at: SSRN, 3623040.
Hall C.M. Scott D. Gössling S. Pandemics, transformations and tourism: be careful what you wish for Tourism geographies 22 2020 577 598
Hufnagel L. Brockmann D. Geisel T. Forecast and control of epidemics in a globalized world Proceedings of the national academy of sciences 101 2004 15124 15129
Iacus S.M. Natale F. Santamaria C. Spyratos S. Vespe M. Estimating and projecting air passenger traffic during the COVID-19 coronavirus outbreak and its socio-economic impact Safety Science 129 2020 104791
IATA. 2020a. Annual review 2020 [Online]. Available: https://www.iata.org/contentassets/c81222d96c9a4e0bb4ff6ced0126f0bb/iata-annual-review-2020.pdf.
Iata Deep Losses Continue Into 2021 [Online] Available 2020 https://www.iata.org/en/pressroom/pr/2020-11-24-01/
Iata Recovery Delayed as International Travel Remains Locked Down [Online] Available 2020 https://www.iata.org/en/pressroom/pr/2020-07-28-02/
Kuo H.-I. Chen C.-C. Tseng W.-C. Ju L.-F. Huang B.-W. Assessing impacts of SARS and Avian Flu on international tourism demand to Asia Tourism Management 29 2008 917 928 32287724
Lai C.-C. Shih T.-P. Ko W.-C. Tang H.-J. Hsueh P.-R. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): The epidemic and the challenges International journal of antimicrobial agents 55 2020 105924
Lange, r. Senior Vice President at Airbus. Keynote speaker AGIFORS 60th Annual Symposium. October 2020
LEE, D. 2020. Coronavirus: Social distancing takes flight as airlines in Asia-Pacific experiment with putting space between passengers, viewed 2 Sep 2021
Luo X.S. Imai N. Dorigatti I. Quantifying the risk of Zika virus spread in Asia during the 2015–16 epidemic in Latin America and the Caribbean: a modeling study Travel medicine and infectious disease 33 2020 101562
Maneenop S. Kotcharin S. The impacts of COVID-19 on the global airline industry: An event study approach Journal of air transport management 89 2020 101920
McLean A.R. May R.M. Pattison J. Weiss R.A. SARS: a case study in emerging infections 2005 Oxford University Press
Milne R.J. Delcea C. Cotfas L.-A. Airplane boarding methods that reduce risk from COVID-19 Safety Science 134 2021 105061
Monmousseau P. Marzuoli A. Feron E. Delahaye D. Impact of Covid-19 on passengers and airlines from passenger measurements: Managing customer satisfaction while putting the US Air Transportation System to sleep Transportation Research Interdisciplinary Perspectives 7 2020 100179
OECD. 2020a. COVID-19 and the aviation industry: Impact and policy responses, [Online]. Available: https://www.oecd.org/coronavirus/policy-responses/covid-19-and-the-aviation-industry-impact-and-policy-responses-26d521c1/.
OECD 2020b. COVID-19 and the aviation industry: Impact and policy responses, OECD, viewed 2 March 2021.
Oztig L.I. Askin O.E. Human mobility and coronavirus disease 2019 (COVID-19): a negative binomial regression analysis Public health 185 2020 364 367 32739776
Peoples J. Abdullah M.A. Satar N.M. COVID-19 and Airline Performance in the Asia Pacific region Emerald Open Research 2 2020 62
PETERSEN, E., WASSERMAN, S., LEE, S.-S., GO, U., HOLMES, A. H., AL-ABRI, S., MCLELLAN, S., BLUMBERG, L. & TAMBYAH, P. 2020. COVID-19–We urgently need to start developing an exit strategy. International Journal of Infectious Diseases, 96, 233-239.
Reisinger Y. Mavondo F. Travel anxiety and intentions to travel internationally: Implications of travel risk perception Journal of travel research 43 2005 212 225
Ross A. Willson V.L. Basic and advanced statistical tests: Writing results sections and creating tables and ‘ures 2018 Springer
Rosselló J. Santana-Gallego M. Awan W. Infectious disease risk and international tourism demand Health policy and planning 32 2017 538 548 28104695
Salari M. Milne R.J. Delcea C. Kattan L. Cotfas L.-A. Social distancing in airplane seat assignments Journal of Air Transport Management 89 2020 101915
Serrano F. Kazda A. The future of airports post COVID-19 Journal of Air Transport Management 89 2020 101900
Skytrax COID-19 Safety Policies [Online] Available 2021 https://skytraxratings.com/airlines
Specia, m. British Airways pulls 747s from fleet, ending an era 2020 New York Times
Suau-Sanchez P. Voltes-Dorta A. Cugueró-Escofet N. An early assessment of the impact of COVID-19 on air transport: Just another crisis or the end of aviation as we know it? Journal of Transport Geography 86 2020 102749
Sun X. Wandelt S. Zhang A. How did COVID-19 impact air transportation? A first peek through the lens of complex networks Journal of Air Transport Management 89 2020 101928
THAMS, A., ZECH, N., REMPEL, D. & AYIA-KOI, A. 2020. An initial assessment of economic impacts and operational challenges for the tourism & hospitality industry due to COVID-19. IUBH Discussion Papers-Tourismus & Hospitality.
Tuncer N. Le T. Effect of air travel on the spread of an avian influenza pandemic to the United States International journal of critical infrastructure protection 7 2014 27 47
Unwto Impact assessment of the COVID-19 outbreak on international tourism 2020 World Tourism Organization
Vaidya R. Herten-Crabb A. Spencer J. Moon S. Lillywhite L. Travel restrictions and infectious disease outbreaks Journal of travel medicine 27 2020 taaa050 32297935
Wen Z. Huimin G. Kavanaugh R.R. The impacts of SARS on the consumer behaviour of Chinese domestic tourists Current Issues in Tourism 8 2005 22 38
Wenzel M. Stanske S. Lieberman M.B. Strategic responses to crisis Strategic Management Journal 41 2020
Wilder-smith, a. The severe acute respiratory syndrome: impact on travel and tourism Travel medicine and infectious disease 4 2006 53 60 16887725
Wilder-Smith A. Paton N.I. Goh K.T. Low risk of transmission of severe acute respiratory syndrome on airplanes: the Singapore experience Tropical medicine & international health 8 2003 1035 1037 14629772
WORLD HEALTH ORGANIZATION. 2020a. Coronavirus disease News [Online]. Available: https://www.who.int/emergencies/diseases/novel-coronavirus-2019.
WORLD HEALTH ORGANIZATION 2020b. Operational considerations for managing COVID-19 cases or outbreak in aviation., viewed 2 Mar 2021
Zahraee S.M. Shiwakoti N. Stasinopoulos P. Agricultural biomass supply chain resilience: COVID-19 outbreak vs. sustainability compliance, technological change, uncertainties, and policies Cleaner Logistics and Supply Chain 4 2022 100049
Zhang K. Hou Y. Li G. Threat of infectious disease during an outbreak: Influence on tourists' emotional responses to disadvantaged price inequality Annals of Tourism Research 84 2020 102993
| 0 | PMC9728014 | NO-CC CODE | 2022-12-08 23:18:56 | no | 2022 Dec 7; doi: 10.1016/j.ijtst.2022.11.004 | utf-8 | null | null | null | oa_other |
==== Front
Trends Analyt Chem
Trends Analyt Chem
Trends in Analytical Chemistry
0165-9936
1879-3142
Elsevier B.V.
S0165-9936(22)00361-2
10.1016/j.trac.2022.116878
116878
Article
Achieving broad availability of SARS-CoV-2 detections via smartphone-based analysis
Li Dan a
Sun Cai c
Mei Xifan a∗
Yang Liqun b∗∗
a Jinzhou Medical University, Jinzhou, China
b NHC Key Laboratory of Reproductive Health and Medical Genetics (China Medical University), Liaoning Research Institute of Family Planning (The Affiliated Reproductive Hospital of China Medical University), Shenyang, China
c AECC Shenyang Liming Aero-Engine Co, Ltd., Shenyang, China
∗ Corresponding author.
∗∗ Corresponding author.
7 12 2022
1 2023
7 12 2022
158 116878116878
15 8 2022
1 12 2022
6 12 2022
© 2022 Elsevier B.V. All rights reserved.
2022
Elsevier B.V.
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
With the development of COVID-19, widely available tests are in great demand. Naked-eye SARS-CoV-2 test kits have recently been developed as home tests, but their sensitivity and accuracy are sometimes limited. Smartphones can convert various signals into digital information, potentially improving the sensitivity and accuracy of these home tests. Herein, we summarize smartphone-based detections for SARS-CoV-2. Optical detections of non-nucleic acids using various sensors and portable imaging systems, as well as nucleic acid analyses based on LAMP, CRISP, CATCH, and biosensors are discussed. Furthermore, different electrochemical detections were compared. We show results obtained using relatively complex equipment, complicated programming procedures, or custom smartphone apps, and describe methods for obtaining information with only simple setups and free software on smartphones. Then, the combined costs of typical smartphone-based detections are evaluated. Finally, the prospect of improving smartphone-based strategies to achieve broad availability of SARS-CoV-2 detection is proposed.
Keywords
SARS-CoV-2
Smartphone
Wide available
Sensors
Optical detections
Electrochemistry detections
Low cost
==== Body
pmcAbbreviations
SARS-CoV-2 Severe acute respiratory syndrome coronavirus 2
PCR polymerase chain reaction
RT-PCR real-time quantitative polymerase chain reaction
LFA lateral flow assay
ELISA enzyme-linked immunosorbent assay
RNA ribonucleic acid
HRP horseradish peroxidase
NPs nanoparticles
TMB 3,3′,5,5′- tetramethylbenzidine
NLICS nanozyme-linked immunochromatographic sensor
ICA immunochromatography assay
PPA positive percent agreement
T-line Test line
SP spike protein
CATCH Catalytic amplification by a transition-state molecular switch
Au NPs gold NPs
HFIS high-throughput fiber integrated immunosensing system
PTEM Polycarbonate track-etched membrane
SPR surface plasmon resonance
SERS surface-enhanced Raman scattering
SEIRA surface-enhanced infrared absorption spectroscopy
SEF surface-enhanced fluorescence
Sens sensitivity
Spec specificity
PPV positive predictive value
NPV negative predictive value
PS positive
NG negative (NG)
THz terahertz
vp virus particles
SARS Severe acute respiratory syndrome
MERS Middle East respiratory syndrome
VSV Vesicular stomatitis viruses
MATLAB MATrix LABoratory
LAMP loop-mediated isothermal amplification
RT-LAMP Reverse-transcription LAMP
RT-eLAMP RT-LAMP onto a PoC platform
CMOS complementary metal-oxide-semiconductor
ISFET ion-sensitive field-effect transistors
RT-qPCR RT-PCR using a real-time benchtop platform
RT-qLAMP RT-LAMP assay used a real-time benchtop instrument
RT-eLAMP The lab-on-chip (LoC) platform used a smartphone with a customized App to process the sensing data of RT-LAMP
AWS Cloud Server
Cap-iLAMP capture and improved LAMP
VTM viral transport medium
IoT internet of things
smaRT-LAMP smartphone-based RT-LAMP
CRISPR clustered regularly interspaced short palindromic repeat
crRNA CRISPR RNA
RNP nuclease-inactive ribonucleoprotein complex
HEPN higher eukaryotic and prokaryotic nucleotide-binding domain
PLA Black poly(lactic acid)
RT-RPA reverse transcription recombinase polymerase amplification
DM droplet magnetofluidics
TOPSE True Outcome Predicted via Strip Evaluation
FnCas9 Cas9 ortholog from Francisella novicida
iSCAN RT-LAMP-coupled CRISPR-Cas12 module for rapid sensitive detection of SARS-CoV-2
LCs Liquid Crystals
μPADs microfluidic paper-based analytical devices
CV cyclic voltammetry
EIS eLectrochemical impedance spectroscopy
RCT charge-transfer resistance
ePAD electrochemical paper-based analytical device
OECT Organic electrochemical transistors
MIP Molecularly imprinted polymers
LSG laser-scribed graphene
TB toluidine blue
EAB electrochemical aptamer
CNF carbon nanofiber
RCA rolling circle amplification
RBD receptor-binding domain
ssDNA single-strand DNA
LSG laser-scribed graphene
ACE2 Angiotensin-Converting Enzyme 2
SCX8 p-sulfocalix [8]arene
SCX8-RGO SCX8 functionalized graphene
TAMRA-FAM TAMRA dye works as an internal standard, and FAM dye serves as a sensitive sensing agent. The TAMRA and FAM are orange-red-emitting and green fluorescent dyes used to label peptides
MECS self-actuated molecular-electrochemical system
E-INAATs electrochemical isothermal nucleic acid amplification tests. Nab, neutralizing antibody
PtNP platinum nanoparticle
MSAA Microbubbling SARS-CoV-2 Antigen Assay
ML machine learning
QD-LFIA quantum dot lateral flow immunoassay strip
IPCF an optical sensor based on an imprinted photonic crystal film
Go Spectro a device that turns a smartphone into an ultracompact and powerful light hand spectrometer
F-IPCF ANTIBIDOY functionalized-IPCF
OPTIMA-dx a mobile phone application developed by the related authors
DAMPR DNAzyme reaction triggered by LAMP with clustered regularly interspaced short palindromic repeats (CRISPR)
ABTS 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)
1 Introduction
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a biological hazard responsible for COVID-19. With the continued transmission of this virus around the world, there has been increasing demand for more convenient, sensitive, accurate, and inexpensive tests [1,2]. Currently, the SARS-CoV-2 analysis mainly depends on recognizing the following targets: nucleic acids [[3], [4], [5]], non-nucleic acids such as antigens [[6], [7], [8], [9]], antibodies [[10], [11], [12]], associated biomarkers [13,14], symptom-related parameters [15], or multiplex factors [16,17]. Among these strategies, the real-time quantitative polymerase chain reaction (PCR) (RT-PCR) relying on nucleic acid amplification and detection is the most widely used and accurate [18]. However, the PCR-based methods last for several hours and are dependent on professional equipment and operators [[19], [20], [21]]. Once large-scale tests are required, this method may be labor-intensive and require high costs [22,23]. In addition, many people may gather together to take these PCR tests for sample collections, which increases the risk of uninfected cases in susceptible environments [[24], [25], [26]].
Recently, convenient home tests mainly based on the analysis of the protein of the virus were investigated. The sandwich lateral flow assay (LFA) and immunoassay including gold-labeled strips [[27], [28], [29], [30]], chemiluminescence assay [31], fluorescence-based LFA [[32], [33], [34]], and enzyme-linked immunosorbent assay (ELISA) [35], etc., have been used. Normally, the antibodies or other biorecetors are modified on test devices that can recognize the targets and then show signal changes. These home tests can quickly display results that are visible to the naked eye within 15–20 min. Both the professional staff and normal people can operate [36]. However, according to some references, these commercial home tests have an accuracy of about 70%–90% [37,38], and may also have more “false positive/negative” probability compared to PCR tests [39,40]. Especially, with relatively few mounts of the virus load (e.g. the sample from a just infected person), these home tests may be even less sensitive and accurate.
Recently, some techniques were summarized for the diagnosis of COVID-19. For instance, Bukkitgar et al. presented electrochemical methods for the analysis of various viruses including SARS-CoV-2 with a focus on nanotechnology-based detections [41]. Suleman et al. reviewed the field-effect transistor, optical biosensors, RT-LAMP-based detections, and miscellaneous biosensors for the diagnosis of COVID-19 [42] with a focus on nucleic acid detections. Shetti et al. summarized early detection methods for SARS-CoV-2 analysis based on the sensing of targets including ribonucleic acid (RNA), spike protein (SP), and antibodies such as IgG and IgM, and primarily mention nanosensors [43]. In these earlier reviews, different nanomaterials and sensing strategies for the construction of electrochemical or optical biosensors were shown, such as magnetic nanoparticle carbon nanomaterial, gold silica, novel metallic nanomaterial, and quantum dot functionalized biosensors; The label-free, aptamer modification methods, and immune sensing systems were discussed. Because the research at the early stages was limited, the reported reviews mainly focused on the primary detection strategies or propose possible family sensing methods, but a further discussion of widely available tests was still lacking. For instance, smartphone-based SARS-CoV-2 detection strategies were mainly proposed as prospects.
Smartphone-based tests can convert signals into precise digital data that may show more sensitive and accurate readings than visual observations; smartphones can also control the progress of detections through a well-designed program; at the same time, due to smartphones are widely used, so it doesn't take much of a burden [[44], [45], [46], [47], [48], [49]]. Some smartphone-based tests have already shown great promise for the accurate and sensitive detections of various analytes [[50], [51], [52], [53]]. Until now, a few smartphone-based detections have been investigated for nucleic acid analysis. The non-nucleic acid detections depending on the recognization of antigens, antibodies, or virus particles using smartphones also access the accuracy of PCR tests [[54], [55], [56], [57]]. These methods have broad application potential and deserve in-depth discussion. Among the smartphone-based detection techniques, it is worth comparing which strategy will be more widely adopted and reproduced. Therefore, we comprehensively compare these reported smartphone-based detection tests and discuss their cost, sensitivity, accuracy, and possible availability (Fig. 1 ). Typical examples are employed to represent the mechanisms of different tests. The required accessories, sample collections, and the way smartphones acquire signals, etc., are exhibited to evaluate the feasibility of the methods. Both optical and electrochemical tests are deeply discussed. The nucleic acid and non-nucleic acid detections are compared respectively. Then, the prospect of further improving smartphone-based SARS-CoV-2 detection is proposed. This review will facilitate the development of smartphone-based tests and enable the facile analysis of SARS-CoV-2 more broadly.Fig. 1 Smartphone-based analysis of SARS-CoV-2: the red and green color indicates the relatively expensive and low-cost component of the test respectively; typical samplers, biorecognition, and transducer setup are exhibited; Mini devices and Mini instrument indicate the relatively complicated-designed or expensive instruments.
Fig. 1
2 Smartphone-based optical analysis
Optical detections based on UV–visible absorption, phosphorescence, surface plasmon resonance, fluorescence, chemiluminescence, and surface-enhanced Raman scattering techniques are widely used to detect various analytes. Traditional optical detections require instrumental analysis, which needs a relatively high cost for the general public. For example, UV–Vis detections usually use UV–Vis spectrophotometers and microplate readers for a single sample and multiple sample detections respectively; Fluorescent detection of single analytes and multiple biological analytes can be performed with the aid of spectrofluorometers and flow cytometers; Other optical detections such as surface plasmon resonance (SPR)-, chemiluminescence-, and phosphorescence-based analysis also require relatively expensive instruments. On the other hand, signals such as absorbance, fluorescence, SPR change, etc., can be captured by smartphone cameras or portable smartphone attachments, whose information may be further analyzed by certain software. This process may replace traditional instrument detection and realize convenient home testing.
Nucleic acid detection is normally considered to be the most accurate and reliable method because other tests all tend to have more false results. For instance, the antigen and virus particle tests have relatively poor accuracy for low virus load samples. Meanwhile, the antibody levels of a person are uncertain resulting in a significant limitation of accurate diagnosis of SARS-CoV-2 infections. On the other hand, the analyses of antigens, antibodies, or viral particles have the advantages of rapidity, non-invasiveness, simplicity, and low cost. Both simple non-nucleic acid tests and nucleic acid analyses are important supplements to each other, which deserve further improvement.
2.1 Non-nucleic acid tests based on optical analysis
2.1.1 Indirect and direct data conversion by a smartphone
Some tests require simple optical signal capture equipment and then indirectly use a smartphone to calculate the test data. During the progress, an optical sensor is normally employed. Typically, some element is functionalized with certain receptors. It will induce color, fluorescence, or other optical signal change by recognizing the analytes. Through a smartphone, the information on the reaction sample can be obtained by analysis of image grayscale, absorbance, color RGB, intensity, etc. For instance, Nanozyme is an element that can catalyze the reaction of small molecules leading to a color change. Among the nanozymes, the horseradish peroxidase (HRP) mimicking nanoparticles (NPs) has been most widely used, which can catalyze the oxidation of H2O2 to ‧OH radical that further oxidize colorless 3,3′,5,5′- tetramethylbenzidine (TMB) to generate blue color ox-TMB. Liang et al. developed an HRP-like nanozyme-linked immunochromatographic sensor (NLICS) for the fast analysis (<1 h) of the antigen (nucleocapsid protein (NP)) of SARS-CoV-2 [58]. The system consists of a 3D printed U-shape immunochromatography assay (ICA) device ($1.50), an inexpensive photometer (<$10), and a smartphone with an app specially designed for this platform. During the test progress, NP interacted with the first specific monoclonal antibody (mAb1) which was coupled with the HPR-like nanozyme (Au@PtNPs) and sprayed on a conjugate pad of a testing strip. Then, Au@PtNPs-mAb1-NP migrated and conjugated to mAb2 immobilized on the T-line (Test line) of the test strip, forming Au@PtNPs-mAb1-NP-mAb2. This conjugate further catalyzed the TMB substrate solution to ox-TMB and exhibited blue color. A 450 nm laser (<$100) was used to generate blue light that could cross the reacted substrate. The light after filtration was then captured by the portable photometer. The absorbance value (OD450) and the corresponding concentration were demonstrated by the smartphone with the app. NLICS detected NP with a detection limit of 0.026 ng/mL and had a linear range between 0.05 and 1.6 ng/mL within 25 min. Of 21 COVID-19 cases, NLICS found 76.2% NP-positive clinical serum samples, while a commonly used enzyme-linked immunosorbent assay (ELISA) only found 47.6% NP-positive cases. Both NLICS and ELISA give 100% accuracy for the NP-negative cases of 80 healthy blood donor samples. Though the accuracy of this method for the detection of positive samples is expected to be improved, this work provides a promising strategy for designing reproducible optical sensing devices for the analysis of SARS-CoV-2 antigens.
Smartphone apps and sampling devices are difficult to prepare under conditions without enough facilities. Additional optical signal capture equipment may increase the complexity of the test. To solve this problem, some strategies directly use images taken by smartphone cameras to simulate corresponding data. For instance, Fabiani et al. fabricated a simply prepared paper-based immunoassay using 96-well wax-printed cardboard (<$1) for colorimetric sensing of SARS-CoV-2 spike protein (SP) (Fig. 2 ). Sandwich-like immune chains were supported with anti-mouse IgG-conjugated magnetic beads and SARS-CoV-2 spike antibody-HRP chimeric monoclonal antibody (MAb-HRP). This “sandwich” catalyze TMB substrates to show different shades of blue and color-to-intensity data were transformed using a smartphone in conjunction with a free app (Spotxel reader) [59]. Spotxel Reader enables the reading of multiple samples in array formats such as samples in commercial 96, 48, or other numbers of well plates or printed arrays. This method detected SP in saliva up to 10 μg/mL and a detection limit of 0.1 μg/mL within 30 min. By comparison with 12 nasopharyngeal swab samples from patients infected with the same Delta variant studied by RT-PCR, 100% accuracy was found. Compared to the analysis of nasopharyngeal swabs using RT-PCR, the cost of analyzing one patient's specimen is reduced from about $20 to $3. On the other hand, a paid premium version ($1195 for a Perpetual License) of the Spotex reader can directly plot standard concentration curves and calculate the concentration of SP in unknown samples. However, if the microwell contains standard samples, it may directly recognize SARS-CoV-2 as positive or negative based on the color comparison, which is completely free and has the potential to meet the needs of the general public. Some other free software such as the Color Picker has also been used to analyze SARS-CoV-2 sensing results [54,60]. The application of these simply fabricated devices and free programs on a smartphone will be of great importance to reduce the cost and facilitate broad availability.Fig. 2 Direct data conversion from a smartphone without the assistance of any instrument: A scheme of the setups of the smartphone-assisted optical-sensing devices for analysis of SARS-CoV-2 using a simple sampler and a free smartphone app (Spotxel Reader). Reprinted with permission from Ref. [59], Copyright 2022, Elsevier.
Fig. 2
Various smartphone-based optical sensors have been reported to detect SARS-CoV-2 antigens and antibodies (Table 1 ). Some of these methods are near 100% agreement with the PCR assays for analysis of clinical samples [59]. Since these tests have advantages including simple setup and operation, low cost, excellent accuracy, and satisfactory precision, it can be expected further modification of these methods may provide wider availability.Table 1 Detection of SARS-CoV-2 antigens and antibodies by smartphone-based optical analysis.
Table 1Sensor Samples Mechanisms Detection limit; time; sensitivity/accuracy Data analysis Ref.
NPs transfer biosensors Virus in face masks A polymer-modified filter paper stored antibody-decorated Au NPs for recognizing NP NP (3 ng mL−1); <10 min; 96.2% sensitivity and 100% specificity The smartphone camera with a commercial reader [61]
NLICS Clinical samples LFA for color reaction on the test strip for recognizing NP 0.026 ng/mL NP; 25 min; 76.2% sensitivity and 95.1% accuracy Smartphone with author-designed App and portable photometer [58]
Colorimetric immunosensor Saliva samples Antibody conjugated magnetic beads to recognize SP and a 96-well wax-printed paper plate for color visualization 100 fg/mL SP, 1.6 × 101 PFU/mL SARS-CoV-2; 45 min; 100% accurate for 6 negative and 6 positive saliva samples Smartphone with Spotxel free-charge app for image analysis [59]
HFIS Clinical serum samples PTEM-coated microplate for the immunoassay and a sandwich recognition method for analysis of NP NP (7.5 pg/mL); 45 min; 72% sensitivity and 95% accuracy Optical fibers for light transmission and collection; Designed App for data processing [62]
TEMFIS Patient samples, vaccinees and healthy blood TEM-microplate with optical fibers transmission immunosensing of Nab Nab; 45 min; positivity (sensitivity) in 92.68% and 76% vaccinees, negativity (specificity) in 100% Optical fibers for light transmission and collection; Designed App for data processing [63]
MSAA Clinical swab samples Sandwich complexes formed between magnetic bead/NP/PtNP and bright field images of oxygen microbubbles generated through catalysis of H2O2 decomposition NP (0.5 pg/mL); 30 min; PPA = 97%, 53%, 26%, 45 for symptom onset <7, 7–12, >12 days and Asymptomatic, 97% for negative cases Computer vision image recognition and ML-based algorithms on smartphones [64]
QD-LFIA Human serum or whole blood samples IgG or NAb could combine with RBD-His, reacting with QD@anti-His mAb, which migrates to T1 and T2 lines respectively IgG; Nab 98.8% (80/81) and 88.9% (72/81) were positive for IgG and Nab of recovered patients; 90% (63/70) and 82.9% (58/70) were positive for IgG and Nab for 64 vaccinated people Self-produced portable fluorescence real-time camera reader; Data transfer by WIFI to smartphone [65]
IPCF Atificial saliva Label-free detection of SP was realized based on an antigen-antibody reaction SP (429 fg/mL); <1 h; Not investigated Go Spectro on a smartphone [66]
Note: Au NPs, gold NPs; PFU, plaque-forming units; HFIS, high-throughput fiber integrated immunosensing system, which was constructed by a PTEM-based high-throughput immunoassay platform and handheld microplate reader connected with a bundle of optical fiber; PTEM, Polycarbonate track-etched membrane; TEMFIS, A track-etched membrane microplate and optical fibers transmitted immunosensing smartphone platform; Nab, neutralizing antibody; PtNP, platinum nanoparticle; MSAA, Microbubbling SARS-CoV-2 Antigen Assay; ML, machine learning; QD-LFIA, quantum dot lateral flow immunoassay strip; IPCF, an optical sensor based on an imprinted photonic crystal film; Go Spectro, a device that turns a smartphone into an ultracompact and powerful light hand spectrometer; F-IPCF, antibody functionalized-IPCF; PPA, positive percent agreement.
2.1.2 Portable smartphone imaging system
Microscopy provides an important strategy for observing microorganisms. Large microscopes are expensive and complex to operate. Imaging bioassays normally require expensive microscopes ($750 to over $89,000) for detection. This makes the broadly available detection of viruses difficult to achieve. On the other hand, some portable microscopes are developed for smartphones, which are cost-effective and easy to use. It is difficult to observe small-size viruses with ordinary portable microscopes. However, through a certain virus particle identification strategy, the real-time infection status of the virus can be observed. Many virus particles were carried by droplets and aerosols. Digital imaging systems can be developed to obtain information on droplet species [67]. In typical imaging progress, recognization elements with catalytic activity and optical properties are labeled and confined to tiny microreactors. When the target molecule appears in the droplet, the trapped droplet interacts with the recognition element and displays an optical signal change in the microreactor within a short time. Kim et al. combined a smartphone with a handheld microscope. A paper-based microfluidic chip was modified with antibody-conjugated submicron particles, which captured the airborne droplets of human saliva samples spiked with SARS-CoV-2 (Fig. 3 a) [68]. Based on antibody-antigen binding and subsequent particle aggregation, the capture-to-assay time was smaller than 30 min. Two sprays at a distance of 6 inches showed the most significant differences between virus samples and controls (Fig. 3b and c). A fan was set up since the virus capture required air circulation (Fig. 3d). The virus was observed by a smartphone-based fluorescence microscope by counting the immunoagglutinated particles on the paper chip and the data was simulated by MATrix LABoratory (MATLAB) (Fig. 3a). Besides the smartphone, this portable imaging system was simply produced using low-cost components including an LED, a 9-V battery, acrylic film, and a mini microscope attachment with a total cost of $46.60. The setup for fabricating this mini imaging system was simple and inexpensive, but the calculation software (MATLAB) is a computing platform that is normally used by professionals. A more complete integration program is expected to be developed for the users in the future. Breshears et al. employed a smartphone-based fluorescence microscope and paper microfluidic chips to construct particulometric SARS-CoV-2 assay for clinical saline gargle samples [69]. The unprocessed image indicated the virus infection conditions and free ImageJ was used to process the imaging data. The limit of detection was 10 ag/μL; for n = 27 clinical human samples, 13 of which were positive by RT-qPCR, the sensitivity, specificity, and accuracy were 100%, 86%, and 93%, respectively. The method is simple, sensitive, and accurate, and at a total cost of only $46.40 per device, These portable smartphone imaging systems have the potential for a wide range of applications.Fig. 3 Detection of SARS-CoV-2 from airborne droplets by a smartphone-based fluorescence microscope. (a) From left to right: The air collection and capture by spraying the spiked SARS-CoV-2 salvia samples; The antibody particles were modified on the chip; Smartphone-based fluorescence microscopic observation; The data analysis with a MATLAB script; (b) Two-times spraying results; (c) Five-times spraying results, and (d) comparison of two-times spraying with fans on and off. 4 times repeating pixel counts from 5 different images of a single channel for the control (0) and samples (600 pg/mL) were compared by column plots. Reprinted with permission from Ref. [68], Copyright 2022, Elsevier.
Fig. 3
2.1.3 Plasmonic sensors for signal magnifications
Detections of SARS-COV-2 without target amplification may have poor sensitivity for analysis. On the other hand, certain nanotechnologies have significant signal-enhancing capabilities that may overcome this problem. Plasmons are generated when electromagnetic lightwave interacts with the free surface electrons on nanosized metals [70]. Surface plasmonic enhancements have been employed for amplifying the signal in biosensors [71]. This includes SPR [72,73], surface-enhanced Raman scattering (SERS) [74], surface-enhanced fluorescence (SEF), etc [75]. These surface plasmonic enhancement strategies have also been employed for SARS-CoV-2 detections in combination with smartphones. Noble metal NPs possess remarkable plasmonic properties [76,77], which can be modified with bioreceptors and show plasmonic resonance change after interacting with the antigen of SARS-CoV-2 [78]. For instance, Olalla Calvo-Lozano et al. employed an electron beam-deposition system and fabricated 1 nm of titanium (Ti) and 49 nm of gold (Au) sensor chips by metal evaporation [79]. With surface cleaning, modification biofunctionalization, immobilization, etc., a serological biosensor assay was prepared for analysis of multiantigen (RBD peptide and NP). The sensor surface was excited by a collimated halogen light source ($1000 - $3000), and the reflected light was collected and coupled to a CCD spectrometer ($2000 - $3000) via an optical fiber. The resonance peak position (Δλ) that indicated the interactions of the antigens could be tracked in real-time using custom readout software (Fig. 4 a), which might be set up on a smartphone in the future. This plasmonic sensor rapidly (<15 min) analyzed SARS-CoV-2 in clinical samples (n = 120) with detection limits in ng mL−1 and showed sensitivity and specificity of 99% and 100% respectively (Fig. 4b). Ahmadiv et al. developed terahertz (THz) (Terahertz band range from 1 mm to 0.1 mm) plasmonic metasensors for the analysis of SARS-CoV-2 antigen [80]. A miniaturized plasmonic immunosensor was fabricated based on toroidal electrodynamics that could confine plasmonic modes in the THz frequencies. The designed metasurface with metallic unit cells was fabricated by photolithography technique and e-beam metallization. The toroidal dipole mode was excited by a quasi-infinite metasurface and an S1 protein antibody functionalized Au NPs. In the presence of S1 protein spiked samples, the resonance shifts could be induced and measured by a THz time-domain spectroscopy instrument (about $29,950). The detection limit was as low as ∼4.2 fM and the authors suggested that the smartphone-based operation of plasmonic metasensors may be published elsewhere for the diagnosis of SARS-CoV-2 infections. Huang et al. developed an SP-specific nanoplasmonic resonance sensor using a generic microplate reader with a specific nanostructure and proper antibody functionalization on the surface [81]. The nanoplasmonic assays were produced by replica molding progress. The tapered nanopillar arrays were fabricated by photolithography equipment ($8,730.99-$9,492.99) and plasma etching ($2000-$15000). After spraying with optical adhesive liquid, ultraviolet irradiation, nano Ti and gold Au deposition, etc., a sheet was prepared and glued to a 3D printed chip cartridge or an open-bottom 96-well plate ($3-$20). The plasmonic sensor was then fabricated and showed resonance change with the capture of the pseudovirus without the need for additional optics (Fig. 4c and d). A designed smartphone app directly calculated the SARS-CoV-2 pseudovirus in spiked samples in one step within 15 min from 0 to 6.0 × 106 virus particles (vp)/mL with a quantification limit of about 4000 vp/mL SARS-CoV-2. These nanoplasmonic biosensing platforms may enable amplification-free, accurate, selective, and sensitive detections [[82], [83], [84]]. Normally, low-cost plasmon-enhanced substrates can be constructed by noble metal nanomaterials such as Au or Ag NPs, nanostars, and nanorods with facile synthetic approaches [85,86]. However, the photolithography instrument, nano metal evaporation setup ($10000-$20000), and plasma etching progress mentioned for fabrication of the sensor chips are not widely available. Therefore, whether low-cost plasma detection products can be produced at low cost will be an important factor in their availability.Fig. 4 (a) A two-antigen co-immobilized SPR sensor biochips for COVID-19 serology tests. (b) Sensor signal distribution, sensitivity (Sens), specificity (Spec), positive predictive value (PPV), negative predictive value (NPV), and threshold of 100 COVID-19 positive (PS) and 20 negative (NG) clinical samples; the right axis is the total immunoglobulins (Ig) concentration calculated based on the WHO standard; Reprinted with permission from Ref. [79], Copyright 2022, American Chemical Society. Nanoplasmonic sensor chips for analysis of SARS-CoV-2 pseudovirus. (c) Scheme of nanoplasmonic sensor chip cartridge for analysis of SARS-CoV-2 pseudovirus. (d) The modification of the proper antibody on the sensor chip cartridge for specific SP recognition. Reprinted with permission from Ref. [81], Copyright 2021, Elsevier.
Fig. 4
2.2 Nucleic acid tests based on optical analysis
The samples for nucleic acid tests are frequently collected from a swab of a specimen from a patient's throat or nose and then the virus RNA was detected by RT-PCR after the pretreatment. One disadvantage of these tests is the requirement of a long time and relatively advanced laboratory conditions. However, several quick and simple optical detection strategies have recently emerged that are expected to be popularized in the field of nucleic acid tests.
2.2.1 Smartphone-based LAMP
The loop-mediated isothermal amplification (LAMP)-based method has been used for point of care (PoC) detection of various viruses in a short time. Normally, four to six different primers were designed for recognizing the corresponding segments on a target and the reaction was performed at a relatively low temperature (60–65 °C). The strand displacement activity of DNA polymerase facilitates the denaturation of DNA by heating unnecessarily. Some LAMP attachments tend to be simpler, less expensive, and smaller than the thermal cyclers required for PCR. Reverse-transcription LAMP (RT-LAMP) can take one-step RNA preparation in a thermos by noninvasive sample collection, and an optical signal change can be observed with the naked eye. The entire sample-to-result normally takes less than 60 min [[87], [88], [89], [90], [91]]. At present, LAMP commercial household SARS-CoV-2 test strips have been adopted and each test cost about $50. Meanwhile, convenient smartphone detection methods based on LAMP are being explored. For instance, Manzano et al. developed a rapid smartphone-based diagnostic test (<20 min) for the detection of RNA of SARS-CoV-2 in the extracted clinical samples based on RT-LAMP onto a PoC platform (RT-eLAMP) [92]. A kit consisting of a complementary metal-oxide-semiconductor (CMOS) ion-sensitive field-effect transistors (ISFET) microchip and a microfluidic module, which accommodates two wells, one for the sample and one for the control reaction was used for the sample test. After RNA was extracted, it was analyzed by different methods including RT-qPCR (RT-PCR using a real-time benchtop platform), RT-qLAMP (RT-LAMP assay used a real-time benchtop instrument), and RT-eLAMP (The lab-on-chip (LoC) platform used a smartphone with a customized App to process the sensing data. When the reaction was stopped, the fitted data was synchronized to Cloud Server (AWS) and the GPS location of the test sample could be shared on the data map. This RT-eLAMP method showed a sensitivity and specificity of 90.55% and 100% with a detection limit of 10 copies per reaction for screening 52 samples, including 34 positive and 18 negative isolates, which is comparable to RT-qPCR and RT-qLAMP.
Several smartphone-based LAMP methods have provided promising strategies for the detection of SARS-CoV-2 (Table 2 ). Most of these methods have advantages such as the short time, and extraction-free, which can be performed at lower temperatures compared to PCR. Some of these methods also achieve the sensitivity and accuracy of the PCR tests. However, the designed app, the sampling progress, and the primer design for LAMP have not been communalized. The high sensitivity and improper operation may lead to false positives, so strategies to further modify the smartphone-based LAMP analysis are still needed.Table 2 Detection of SARS-CoV-2 using smartphone-based LAMP.
Table 2LAMP Detection Limit Time Devices Target Compared to Traditional PCR Ref
PD-LAMP 35 × 104 vp/mL in saliva 35 min A microfluidic chip using a portable heating unit N gene and the ORF1ab gene No cold storage or extraction, low cost, fast [56]
RT-LAMP 50 RNA copies/μL in the VTM solution 30 min Manufactured 3D cartridge Orf 1 ab, S, and Orf 8 100% accurate, no sample/reagent mixing, amplification, or extraction, low cost, fast [90]
RT-LAMP 2 × 101 genome copies/μL for nasopharyngeal swab samples 13–51 min A portable IoT-based POC genetic analyzer Three target genes (As1e, N, and E genes) More specifically for SARS-CoV-2 from respiratory viruses, the pre-extraction is not needed but has lower sensitivity, low cost, fast [93]
RT-LAMP 1 × 103 copies/μL in saliva samples 30 min Simple tube RNA 98.8% accurate, low cost, fast [94]
RT-LAMP 5 copies/μL of the saliva sample <45 min Microfluidic Reagent Cartridge RNA Without commercial thermocyclers, faster and more sensitive than PCR; 100% agreement with PCR results for 2 clinical samples [95]
RT-LAMP 0.5 copy/μL for cold chain fruits 15 min Gel RT-LAMP system Virus No virus pre-lysisor, purification or RNA extraction is required, low cost [96]
smaRT-LAMP 103 copies/mL in spiked saliva samples 25 min A hot plate, cardboard box, and LED lights 2 nucleocapsid (N) and ORF1ab genes 100% accurate, low cost, fast [97]
Cap-iLAMP 5-25 viral genome copies per μL in gargle lavage <1 h PCR tube Orf1a and N gene 100% accurate for high viral load and 83% of all investigated positive samples, low cost, fast [60]
Note: Cap-iLAMP, capture and improved loop-mediated isothermal amplification; VTM, viral transport medium; Internet of things (IoT) [98]-based diagnostic devices; smaRT-LAMP, smartphone-based RT-LAMP.
2.2.2 Smartphone-based CRISP-Cas-biosensing technologies
The CRISPR systems were first identified in archaea and bacteria that had immune functions to degrade foreign viruses and plasmids with the CRISPR-associated (Cas) enzyme. CRISPR/Cas-system could mediate genome editing for various applications [98]. CRISPR-Cas-based strategies have been developed to target nucleic acid by using CRISPR RNA (crRNA), which guides Cas enzymes to cleave the targets by hybridizing to complementary sequences. CRISPR with Cas9, Cas10, Cas12, Cas13, and the enzyme combined system (such as Cas13 and Cas12) have been involved to analyze the SARS-CoV-2 virus and its variants [[99], [100], [101], [102], [103], [104], [105]]. For instance, Cas13 could be complexed with an editable CRISPR RNA (crRNA), generating a nuclease-inactive ribonucleoprotein complex (RNP). When the RNP hybridizes to the complementary target RNA, it activates the HEPN (higher eukaryotic and prokaryotic nucleotide-binding domain) motif of Cas13a and then cleaves the surrounding ssRNA, causing the initially quenched fluorophore to fluoresce. CRISPR-Cas-based detections tend to be very sensitive, so in many cases, the virus can be directly detected without extracting RNA or nucleic acid amplification. Fozouni et al. developed an amplification-free CRISPR-Cas13a-based assay for the analysis of SARS-CoV-2 RNA from a nasal swab [106]. The assay accurately analyzed pre-extracted RNA from positive clinical samples within 5 min and achieved a sensitivity of 100 copies/mL within 30 min. The further combinations of two or three crRNAs significantly increased the sensitivity of detections to ∼30 copies/μL by activating more Cas13a per target RNA (Fig. 5 a), which targeted multiple regions of the viral RNA and thus enhanced the sensitivity. Furthermore, a low-cost laser illuminator ($10 - $70) was used as the excitation light source, and the fluorescence was detected by a smartphone with an inexpensive optical collector (Fig. 5b and c). The imaging system consists of a compact triplet lens and interference filter. The optics and lighting components were packaged in a custom cassette into which the sample chip could be placed for loading images. Automatic time-lapse imaging was enabled by a custom Android app and Bluetooth receiver. The device was placed in a constant temperature incubator at 37 °C facilitating the Cas13a reaction. Finally, the response curve was obtained and analyzed by MATLAB, and the concentration of SARS-CoV-2 RNA could be calculated. This strategy detected SARS-CoV-2 RNA using the three crRNA Cas13a assay and a genomic RNA isolated from supernatants of virus-infected Vero CCL-81 cells (Fig. 5d) of different dilutions within 30 s. The accuracies for 500 copies/μL, 200 copies/μL, and 50 copies/μL were 100%, 100%, and 50% respectively (Fig. 5e). This method also correctly identified all five SARS-CoV-2 positive patient RNA samples within 5 min (Ct values 14.37 to 22.13).Fig. 5 (a) Combining crRNAs and Cas13a for the detection of SARS-CoV-2. (a) Scheme of two different RNPs binding to the same SARS-CoV-2 RNA at different locations, which cleave RNA reporter and generate fluorescence signal. (b) Scheme of smartphone-based microscope for fluorescence detection (left). Picture of the device and sample image after running a Cas13a assay captured by the smartphone camera (right). (c) Results of fluorescence signal as a function of time from the Cas13a assay obtained from the smartphone-based device using 3 combined crRNAs (crRNA 2, crRNA 4, and crRNA 21) for the analysis of 2 different dilutions of SARS-CoV-2 viral RNA, which was isolated from infected Vero CCL-81 cells (500 and 200 copies/μL) and RNP alone. (d) The slope of the curve from (c) with ±95% confidence interval. (e) Detection accuracy of the Cas13a assay using genomic SARS-CoV-2 viral RNA. Reprinted with permission from Ref. [106], Copyright 2021, Elsevier.
Fig. 5
Liang et al. found that CRISPR-Cas12 assay could detect the mutations such as K417 N/T, L452R/Q, N501Y, E484K/Q, D614G, and T478K) using mutation-specific crRNAs, to distinguish the variants of SARS-CoV-2. This method showed 100% concordance with the sequencing approach for the major SARS-CoV-2 variants with a detection limit of 10 copies/reaction [107], which achieved the same accuracy as PCR. The authors suggested a microfluidic chip and smartphone-based analysis would be designed to detect these mutations simultaneously. However, this method was adapted for emerging mutations that already have been conducted with SARS-CoV-2 nucleic acid amplification tests using extracting nucleic acid. Further development is still expected.
By combining LAMP technology with CRISPR technology and biosensing strategies, the sensitivity and accuracy of the test were further improved [108,109]. Song et al. reported a colorimetric sensor based on DNAzyme reaction triggered by LAMP with CRISPR-Cas9 (which is defined as DAMPR assay) for ultra-sensitively detecting SARS-CoV-2 and variant genes with a detection limit of 1.08 aM (10 copies/sample), 0.92 aM (9 copies/sample), and 1.37 aM (13 copies/sample) for ORF1, N, and S genes, respectively [110]. Black poly(lactic acid) (PLA) filament was used to print the housing of a dark room. A heating bed was designed to facilitate the RT-LAMP reaction evenly to the entire 96-well plate at 65 °C. The LED lights were attached to the dark room as the light source. A consistent focal distance of the smartphone to the system was controlled to maintain the nonuniformity of the lighting condition by a holder. This DAMPR fastly detected the SARS-CoV-2 within an hour and showed a clinical sensitivity and specificity of 100% of 136 clinical samples. It also successfully discriminated the D614G (variant-common), T478K (delta-specific), and A67V (omicron-specific) mutations of the SARS-CoV-2 S gene of 70 SARS-CoV-2 delta or omicron variant patients. CRISPR-Cas systems combined with different techniques and smartphones are promising for realizing accurate, sensitive, and general detection of SARS-CoV-2 (Table 3 ). Most of these detections read signals based on fluorescence or color change. Thus, cheap optical meters (less than $10) can be used to analyze the sensing results. On the other hand, the sampling process of CRISPR-Cas-biosensing technology is still relatively complicated for common people. Further development of easy-to-handle semi-finished sensors may show potential to facilitate public use.Table 3 Smartphone-based detection of SARS-CoV-2 RNA using CRISPR-Cas-biosensing strategies.
Table 3App Mechanism Accuracy Detection Limit, Time Target Ref.
Free Color Picker App The nucleic acids triggered CRISPR-Cas12a-based degradation of ssDNA that linked two AuNPs, generating the color of dispersed AuNPs 100% positive and 100% negative agreement with qPCR for 20 positive and 30 negative clinical swab samples 1 copy/μL, 90 min; Virus RNA of clinical samples [54]
Customized software for fluorescence measurement RNA facilitated the CRISPR-Cas12a cleaved a fluorophore quencher-labeled nucleotide reporter, generating fluorescence 90% accuracy of 115 nasopharyngeal swab samples from individuals showing COVID-19-like symptoms 6.25 copies/μL, <1 h; Viru RNA of Clinical samples [111]
Designed app for fluorescence measurement One-pot SHERLOCK reaction with an RNA paper-capture process 96% sensitivity and 95% specificity in clinical saliva samples 1000 copies/ml, 55 min RNA of B.1.1.7, B.1.351, or P.1 variants [112]
TOPSE smartphone app A paper-strip-based platform using FnCas9 to cleave the ssDNA probe and generate fluorescence upon target binding 87% sensitivity and 97% specificity for clinical samples Not mentioned, 75 min S gene mutation N501Y for clinical samples [113]
Customized App for fluorescence measurement CRISPR-Cas12a-assisted RT-RPA fluorescence assay via magnetic-based nucleic acid concentration and transport 27 out of 27 agreements with RT-qPCR 1 genome equivalent/μL, <30 min RNA from unprocessed clinical NP swab eluates [114]
OPTIMA-dx app RT-LAMP isothermal amplification was coupled with in vitro transcription and Cas13-based detection in one step 95% sensitivity and 100% specificity for clinical samples 10 copies/μL for a synthetic SARS-CoV-2 genome RNA from clinical samples [115]
Author-designed DAMPR app DNAzyme reaction triggered by LAMP with the recognization of the target and induce a color change of ABTS and H2O2 Both sensitivity and specificity are 100% for 137 clinical samples; Effective for delta or omicron variant patients ORF1 gene (1.08 aM), N gene (0.92 aM), S gene (1.37 aM), <1 h SARS-CoV-2 S gene, ORF1 gene, N gene, and the mutations [110]
Note: NP, nasopharyngeal; RT-RPA, reverse transcription recombinase polymerase amplification; DM, droplet magnetofluidics; TOPSE [116], True Outcome Predicted via Strip Evaluation; FnCas9, Cas9 ortholog from Francisella novicida; iSCAN, RT-LAMP-coupled CRISPR-Cas12 module for rapid, sensitive detection of SARS-CoV-2; gRNAs, guide RNAs; OPTIMA-dx, a smartphone application developed by the related authors; DAMPR, DNAzyme reaction triggered by LAMP with clustered regularly interspaced short palindromic repeats (CRISPR); ABTS, 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid).
2.2.3 CATCH detections
A considerable number of people have samples with low viral loads [117], requiring extremely sensitive detection methods. However, sensitive methods normally require either nucleic acid amplification, RNA extraction, or complicated primer-designed progress, and these procedures are temporarily difficult to apply to the public. To address these issues, Noah R. Sundaah et al. investigated new nanotechnology for catalytic amplification of transition-state molecular switches (CATCH), which enable simple, accurate, and sensitive detection of RNA targets in SARS-CoV-2. A DNA-enzyme hybrid complex was used to form a molecular switch (Fig. 6 A). By adjusting the ratio of its components, the multicomponent molecular switch was fabricated into a highly reactive transition state that is easily activated upon sparse RNA target binding, thereby significantly turning on enzymatic activities, resulting in very sensitive fluorescent detection (<1 h at room temperature) without the requirement of PCR amplification and heating steps (Fig. 6B). This method recognized 100% positive (n = 24) and 92% negative (n-25) swab extracted RNA samples compared to the clinical RT-qPCR results. The CATCH approach had a very low detection limit (∼8 RNA copies/μl) and sensitivity, which correctly identified 93.34% (n = 15) and 100% positive (n = 9) heat-treated swab samples without extraction of RNA. The sampling progress could be performed in both high-throughput 96-well or portable microfluidic assays (Fig. 6C-D). Meanwhile, smartphone detection devices can be simply constructed with a light-emitting diode (LED) source, an optical filter, and a magnifying glass placed in front of the smartphone camera to improve image quality. The chemifluorescence can be readily detected by the smartphone (Fig. 6D-F) enabling direct detection of SARS-CoV-2. Without nucleic acid amplification, RNA extraction, specialized instrument, complicated primer design, and dedicated fluorescence probes, this method significantly reduces the complexity of nucleic acid detection and has great potential for further improvement.Fig. 6 (A) Scheme of the CATCH assay. The CATCH assay employs the specific binding of SARS-CoV-2 RNA to activate molecular switches. Each molecular switch consists of an inhibitory DNA complex that binds and inactivates the polymerase and Taq DNA polymerase by inhibitory and enhancer strands. The viral RNA target destabilizes the inhibitory complex and releases the active polymerase while hybridizing it to the enhancer strand. The molecular switches in different states of target responsiveness including closed, transition, and open (right) were prepared. The switch is completely inactive and less susceptible to activation by sparse RNA targets in the closed state while the switch is activated and largely unresponsive to the target in the open state. On the other hand, different forms of switches exist in a delicate balance that can easily be altered by trace amounts of RNA targets in the transition state, which exhibits sensitive responsiveness. (B) The CATCH assay utilizes an additional enzymatic cascade to transduce and amplify target-induced polymerase activity as a fluorescent readout, greatly enhancing the signal response for sensitive analysis of low-load clinical viral samples. (C) A 96-well format for high-throughput applications (top) and a miniaturized microfluidic device (bottom). (D) The photograph of the smartphone-based fluorescence analyzer. (E) Unfiltered and filtered fluorescence emission spectra of the LED source. (F) CATCH assay performance in microfluidic and plate formats showed good agreement with each other. Reprinted with permission from Ref. [118], Copyright 2021, The Authors.
Fig. 6
2.2.4 Simple smartphone-based optical sensors
Recently, there have been facile optical sensors with very simple designs that change their optical signal when exposed to SARS-CoV-2 RNA. For instance, thermotropic liquid crystals (LCs) are very sensitive to phase transition. LCs-based sensors have been used to detect several analytes of the specific binding of molecules that cause transformations of LC and its color change. Compared to many other biosensors, LC-based sensing provides simpler, cost-effective, rapid, and selective detection of various targets [119]. Xu et al. reported the design of an LC-based smartphone analysis method for the detection of SARS-CoV-2 RNA (Fig. 7 A-B) [120]. A 2.5 × 2.5 cm optical cell-based kit as constructed in Fig. 7C was prepared by pairing a bare glass slide and a DMOAP-functionalized glass slide. An opening 2-mm-thick poly(dimethylsiloxane) (PDMS) spacer was used to space the two surfaces and allowed the analysis and injection of test samples. A partially self-assembled cationic surfactant monolayer was formed at the aqueous-LC interface, where LC reorientation enabled the adsorption of ssRNA and/or ssDNA at the interface. Then, a 15-mer ssDNA probe that contains a complementary sequence of the SARS-CoV-2 RNA was adsorbed onto the cationic surfactant-loaded aqueous-LC interface. The ordering transition in the LC surface has a close relationship with the targeted nucleotide sequence. A very low concentration (30 fM) of SARS-CoV-2 RNA selectively drove the ordering transition in the LC film and induced the color change, but a 3-base pair mismatch of SARS ssRNA insignificantly influenced the transition. This allowed the LC-based sensor to analyze SARS-CoV-2 sensitively and selectively, which can be viewed by a smartphone with an app (Fig. 7D) to enhance the accuracy of the color readout.Fig. 7 (A) Schematic illustration of the LC film to the adsorption of SARS-CoV-2 ssRNA (ssRNACoV). The inset shows the dynamic response of the optical micrographs (crossed polarizers) for the dodecyltrimethylammonium bromide (DTAB)/ssDNA probe-decorated E7 film before and after the adsorption of ssRNACoV. Scale bars, 100 μm. (B) The optical appearance of the test kit viewed under a lamp; (C) Photograph of the kit. (D) Test result readout by a smartphone App: The negative results for ssRNASARS <100 nM and positive results for ssRNACoV >30 fM indicate excellent selectivity of the test. Reprinted with permission from Ref. [120], Copyright 2020, Elsevier.
Fig. 7
Zhao et al. developed a 3D-printed smartphone platform for the detection of SARS-CoV-2 RNA [121]. This probe was functionalized with orange-red emitting TAMRA and green-emitting FA dyes used as internal standard and sensing agents. Under 365 nm UV excitation, the emission intensity shows a ratiometric change, switching on and off at 580 nm and 518 nm, respectively. The color change from orange-red to green and the signal can be analyzed by a smartphone with an RGB system within 25 min. The detection limit of SARS-CoV-2 nucleic acid is 0.23 nM. These facile sensors are very convenient, simple in design, and easily repeatable. However, the verification of the sensitivity and accuracy for real sample analysis is expected.
3 Mini electrochemistry test platforms
Electrochemical detections tend to have higher sensitivity and selectivity than optical analysis, which has been used for the accurate analysis of both nucleic acids of SARS-CoV-2 and non-nucleic acids associated with the virus infections [[122], [123], [124], [125]]. A normal system includes three electrodes, an electrochemical cell, and an electrochemical workstation for reading the signals [126,127]. Different analytes can be selectively analyzed by modifying the relevant probes with different recognition agents. Nanomaterials and bioreceptors could be employed to modify the electrode for improving the detection limit, selectivity, and sensitivity [128]. Both the size of the whole system can be minimized for realizing PoC detections. For instance, the electrodes and electrochemical cells have been fabricated on microfluidic paper-based analytical devices (μPADs) which are mini-analytical devices based on cellulose materials [129]. μPADs have several advantages including low cost and easy fabrication based on established patterning methods [130,131]. Some common electrochemical techniques such as cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), chronoamperometry, and electroluminescence were used to show the sensing signals using mini instruments [[132], [133], [134], [135]].
3.1 Smartphone-based electrochemical analysis of non-nucleic acids
Currently, a few electrochemical methods (Table 4 ) were combined with smartphone-based analysis for the detection of antigens, and antibodies. Among these electrochemical detection techniques, EIS depends on the characterization of the resistance of electrode surfaces and the transduction of biosensors [136,137], which have been used frequently for the analysis of viruses. For instance, Marcelo D.T.Torres et al. developed a real-time accurate portable impedimetric detection prototype 1.0 (RAPID 1.0) for analysis of SARS-CoV-2 antigen (Fig. 8 ). The screen-printing method and wax-printing were used to fabricate the electrodes and pattern the electrochemical cells on the phenolic paper circuit board ($40.00/m2) or filter paper ($0.50/m2). Only 10 μL of saliva samples were required and the result could be obtained within 4 min. The selective interaction between the bioreceptor on the electrode surface (such as Angiotensin-Converting Enzyme 2 (ACE2)) to the SARS-CoV-2 antigen (i.e., SP) causes a change in interfacial electron transfer and charge-transfer resistance (RCT). Thus, the SP was analyzed based on the increased resistance to charge transfer of the redox probe and measured by EIS using Sensit Smart (PalmSens) potentiostats. The sensitivity and specificity of RAPID 1.0 for nasopharyngeal/oropharyngeal swabs and saliva samples were 85.3% and 100%, 100% and 86.5%, respectively.Table 4 Smartphone-based electrochemical analysis of non-nucleic acids.
Table 4Sensor Samples Mechanisms Analyte (detection limit); time Ref.
Portable three-in-one biosensor Pseudovirus or spiked samples Selective binding of SARS-CoV-2 biomarkers to surface-linked capture probes produces current changes RNA S gene (100 pM in PBS), SP (100 pg mL−1 in serum), SP antibody (10 ng mL−1 in serum), about 2 h [139]
OECT test platform Spiked serum and saliva sample SARS-CoV-2 IgG bonded with SP through antibody-antigen reaction IgG (1 fM); ≤30 min [140]
MIP-based sensor Nasopharyngeal swab samples Sensor chip - TFE - interfaced with MIP for recognizing NP NP (15 fM); <1 h [141]
EAB-based sensor Serum and artificial saliva The binding of aptamer-modified electrode induced conformational electrochemical signal change SP (10 pM); <5 min [142]
Cotton-Tipped Electrochemical Immunosensor Spiked nasal samples NP antibody was immobilized on CNF-modified screen-printed carbon electrodes for recognizing N antigen NP (0.8 pg/mL); >20 min [143]
EIS based detector Human serological samples A 16-well plate containing sensing electrodes pre-coated with RBD of SP, which recognized anti-SARS-CoV-2 monoclonal antibody CR3022 Spike Antibody (CR3022) (0.1 μg/mL); not mentioned [144]
A label-free voltammetric-based immunosensor Clinical Samples of nasopharyngeal swabs Attachment of the anti-nucleocapsid antibody on Au NPs-modified electrodes for recognizing NP NP (0.4 pg.mL−1); about 3 h [145]
LSG-based electrochemical platform Clinical nasopharyngeal swabs LSG sensors are coupled with Au NPs as sensing platforms where ACE2 is chosen as a biorecognition unit to recognize SP. 5.14 ng/mL and 2.09 ng/mL for S1 and S2 protein; 1 min [146]
Capillary-flow immunoassay device Human blood samples Anti-N antibody is detected using chronoamperometry in a sandwich assay setup IgG (5 ng/mL); <20 min [147]
Aptamer based biosensor Spike RBD recombinant protein Aptamer-based sensing platform for impedimetric analysis of SP SP (66 pg/mL); <40 min [148]
Note: OECT, Organic electrochemical transistors; MIP, Molecularly imprinted polymers; LSG, laser-scribed graphene; TB, toluidine blue; EAB, Electrochemical aptamer; CNF, carbon nanofiber; RCA, rolling circle amplification that is an isothermal amplification method; RBD, receptor-binding domain; AuNPs, gold NPs; ssDNA, single-strand DNA; LSG, laser-scribed graphene; ACE2; Angiotensin-Converting Enzyme 2; SCX8, p-sulfocalix [8]arene; SCX8-RGO, SCX8 functionalized graphene; TFE, thin film electrode; BioFET, electrical double layer (EDL)-gated field-effect transistor-based biosensor.
Fig. 8 Smartphone-based detection of SARS-CoV-2 using RAPID 1.0: (a) Detection of SARS-CoV-2 antigen by RAPID 1.0 in neat saliva and nasopharyngeal/oropharyngeal (NP/OP) swab samples; three-electrode configuration cell and electrodes (CE, counter electrode; WE, working electrode; and RE, reference electrode) were screen-printed on a phenolic paper circuit board or filter paper with conductive carbon; The reference electrode was printed by Ag/AgCl inks. The WE was modified by glutaraldehyde and ACE2, and bovine serum albumin subsequently. A Nafion permeable membrane was used for chemical preconcentration of cation species and protecting the electrode's surface against biofouling with the biological sample matrix; (b) The comparison of cost and detection time comparison between RAPID 1.0 and typical FDA-approved tests. (c) Photo of smartphone-based detections; (d) Nyquist plots measuring by EIS were obtained for different concentrations from 1 pg mL−1 to 100 ng mL−1 of SARS-CoV-2 SP. The inset shows the calibration curve based on the normalized charge-transfer resistance (RCT) values as a function of SP recorded in triplicate. Reprinted with permission from Ref. [138], Copyright 2021, Elsevier.
Fig. 8
3.2 Smartphone-based electrochemical analysis of nucleic acids
Recently, various electrochemical probes modified by nanomaterials have been used for nucleic acid detections for the diagnosis of SARS-CoV-2 infections (Table 5 ). For typical electrochemical nucleic acid sensors, capture probes are used to modify the working electrode, and complementary target nucleotides are hybridized into the sensing interface. When a target nucleic acid is present, it hybridizes with the recognition nucleotide on the electrochemical probe, causing an electrochemical signal change. Compared to optical detections, the electrochemical analysis of the virus may have many advantages. For instance, some electrochemical sensors can detect non-nucleic acids such as antigens and antibodies while detecting nucleic acids [139]; Some electrochemical analyses can recognize unamplified SARS-CoV-2 RNAs so fast and sensitive that 4 copies of the virus in 80 μL saliva can be detected within 1 min [149]; Some reported electrochemical probes even have higher sensitivity than the PCR test [150], but most of these approaches require further validation with clinical samples. Although these methods have been considered low-cost strategies, the frequently used signal output instrument (Sensi-smart) in combination with a smartphone is relatively expensive (about 15,000 US dollars or more) [[151], [152], [153]]. On the other hand, some USB-disk electrochemical workstations have recently been developed and achieved lower-cost analysis [154]. In future research, simpler and cheaper electrochemical signal reading methods may be of great help in the popularization of the detections [155,156].Table 5 Smartphone-based electrochemical analysis of Nucleic acids.
Table 5Sensor Samples Mechanisms Analyte (detection limit); time Ref.
MECS Clinical samples The configuration of the tentacles nearby changes with the recognization of the nucleic acid, pushing the signal change. RNA (4 copies in 80 μL); 1 min [149]
Portable three-in-one biosensor Pseudovirus or spiked samples Selective binding of SARS-CoV-2 biomarkers to surface-linked capture probes produces current changes RNA (100 pM in PBS), SP (100 pg mL−1 in serum), SP antibody (10 ng mL−1 in serum); about 2 h [139]
Supersandwich-type electrochemical biosensor Artificial targets and clinical RNA samples TB enrichment by SCX8-RGO for supersandwich-type recognition of SARS-CoV-2 RNA RNA (200 copies/mL); about 2 h [157]
Multiplex RCA-based sensor Clinical samples Multiplex RCA for the detection of the N and S genes of SARS-CoV-2 N and S genes (1 copy/μL); <2 h [158]
Paper-based electrochemical sensor chip Clinical Samples of nasopharyngeal swabs Thiol-modified ssDNA-capped Au NPs on top of the gold electrode was designed to target two separate regions of the viral N-gene RNA (6.9 copies/mL); <5 min [159]
E-INAATs Artificial swab samples The sensing device is pH-sensitive and shows potentiometric change to the N gene of the virus N gene (2 × 102 copies/test); 10 min [160]
Note: OECT, Organic electrochemical transistors; MIP, Molecularly imprinted polymers; LSG, laser-scribed graphene; TB, toluidine blue; EAB, Electrochemical aptamer; CNF, carbon nanofiber; RCA, rolling circle amplification that is an isothermal amplification method; RBD, receptor-binding domain; AuNPs, gold NPs; ssDNA, single-strand DNA; LSG, laser-scribed graphene; ACE2; Angiotensin-Converting Enzyme 2; SCX8, p-sulfocalix [8]arene; SCX8-RGO, SCX8 functionalized graphene; TFE, thin film electrode; MECS, self-actuated molecular-electrochemical system; E-INAATs, Electrochemical isothermal nucleic acid amplification tests.
4 Comprehensive cost compare
Different strategies were used for the detection of SARS-CoV-2 with the aid of smartphones. From these methods, several typical strategies with detailed setup information were selected for price comparison (Table 6 ). Because these methods detect different targets, the sensitivity and accuracy are not comparable under the current conditions. As can be seen from Table 6, the optical detections based on NLICS and smartphone microscopes may be the most cost-effective and simplest to be designed and repeated by common people. The recognition of SARS-CoV-2 by both technologies is based on simple optical sensors. Compared with the acquisition of other signals, optical signals are currently more convenient to be obtained for personal use. These methods are not necessarily the most sensitive. If they can satisfy the accuracy for recognizing initially infected individuals, further development of these techniques will make an important contribution to the widespread availability.Table 6 Cost comparison of different smartphone-based detection techniques for analysis of SARS-CoV-2.
Table 6Devices Attachment Cost ($) Additional (Cost $) Strategy Test Ref.
NLICS 1.5 Hand Photometer (Sanfu) (about 30) Immunoreaction and enzyme-catalyzed substrate color reaction NP [58]
RAPID 1.0 4.67 Sensit Smart (PalmSens) (about 15 k) Electrochemical reaction-induced EIS change SP [138]
miSHERLOCK 15 Author made app (unkonw) CRISPR-based PoC diagnostic platform provides fluorescent visual output Viral RNA [161]
Smartphone microscope 46.4 Pocket Microscope （25） Isolated and counted the immunoagglutinated particles on the paper chip. Droplets/aerosols containing virus particle [68]
Harmony COVID-19 300 Author made software Wet RT-LAMP reactions and heater/reader operated by a cell phone Viral RNA [162]
Nanoplasmonic sensors Not mentioned Xlement
SPR100 (>20 k) The plasmon resonance wavelength and intensity change on the virus-capturing Virus particles [81]
Note: k, 1000 dollars.
5 Other simple sensors
A widely applicable method requires a simple way to read the detection signal. SARS-CoV-2 may cause different physical/chemical state changes for a sensor, and by constructing corresponding analytical devices, the virus can be analyzed. However, besides electrochemical detection and optical analysis of SARS-CoV-2, other smartphone-based sensing strategies are still limited. Common signal reading sources including sound, light, electricity, heat, force, chemistry, etc., have been developed for sensing various analytes. Accordingly, more types of simple sensors may be expected for effective SARS-CoV-2 detections.
Currently, in addition to electrochemical and optical analysis, several other assays have been investigated for the analysis of SARS-CoV-2. For instance, microdevices attached to a smartphone have been developed to detect viruses [17,18] using flow rate assays recently. The microfluidic chip flow sensor measures the flow rate of various liquids within microchannels in real time through contact with the analytes [163]. Akarapipad et al. fabricated a device combing a paper-based microfluidic chip, which was designed by SolidWorks 2020 and printed with a 3D and a wax printer (Fig. 9 ) [164]. 4 parallel channels were fabricated on a single chip for high throughput analysis. The outer green boxes and three red squares at the three corners of the chip allow for orientation identification and localization of the channel area with automated flow measurements. The surface tension and capillary flow velocity profile were changed by the particle-target immunoagglutination. The SARS-CoV-2 negative samples enable Ab-particles to take more time to reach constant velocity, while the positive samples facilitate Ab-particles to take less time to reach constant velocity due to immunoagglutination. An antibody-conjugated particle suspension and a smartphone were used to recognize and monitor the virus particles from saliva samples based on the flow rate change. The flow profile was videoed by the smartphone camera and extracted using an author-designed program (Python script) that automatically searched the channels and provided results. This method detected SARS-CoV-2 virus particles from 1% saliva samples and simulated saline gargle samples with a detection limit of 1 fg/μL and 10 fg/μL respectively in 16 min. The method does not require laboratory equipment, sample pretreatment, or complicated manipulations, making it easy to use widely. However, the accuracy was 89% for the analysis of relatively clean clinical saline gargle samples and showed some limitations in accurately analyzing turbid clinical samples. Some modifications such as the substrate functionalizations may enable more accurate detection of SARS-CoV-2 in the future at a low cost [165,166].Fig. 9 The paper-based microfluidic chip and flow profile assay for smartphone-based analysis of SARS-CoV-2 virus particles. (a) Paper microfluidic chip with green edges and three red squares for identification of chip regions in automated flow distance. (b) The chip lock and chip holder. (c) A chip holder was used to flatten the paper-based microfluidic chip. (d) 4 μL of the sample was loaded into the square inlet (top) of each channel and dried for 10 min. (e) 4 μL of antibody-conjugated (Ab) particles were loaded onto the chip. (f) A smartphone camera recorded liquid flow on a paper microfluidic chip and analyzed how particle immune agglutination affected velocity distribution and flow distance. (g) In the absence of the virus particle, singlet Ab-particles (green) diffused rapidly to the wetting front, reducing surface tension and flow rate. Nitrocellulose fibers are light orange and salivary proteins are dark orange. (i) In the presence of virus particles (blue), the occurrence of immune agglutination produced clusters of larger and heavier particles, leaving only a few singlet antibody Ab-particles to diffuse to the wetting front. Reprinted with permission from Ref. [164], Copyright 2022, Elsevier.
Fig. 9
A field-effect transistor (FET)-based biosensor is gated by changes in the surface potential induced by the binding of the target. The sensing elements are normally immobilized on the sensing channels, which are connected to the source (S) and drain (D) electrodes. The electrical signals can be measured by an electrical signal reading instrument such as a power supply (E3631A, Agilent) [167]. Ban et al. developed a label-free, rapid (≤20 min), DNA aptamer-derivatized graphene field-effect transistor (GFET) to analyze SARS-CoV-2 [168]. Even the unprocessed intact virus and its variants were detected at levels as low as 7 to 10 viruses. A smartphone provided real-time device location identification. This method is effective for both early-stage viral infections and diseases with accessible biofluids and the device provided handheld wireless readout. Chen et al. developed a saliva-based antigen test using the electrical double layer (EDL)-gated field-effect transistor-based biosensor (BioFET) system. Disposable testing sticks were used for sample collections. The biosensor facilitates the changes in EDL capacitance that can be detected by the author-designed BioFET system in real-time using a Bluetooth-embedded reader on an iPhone. The detection limits of SARS-CoV-2 NP are 0.34 ng/mL in 1 × PBS and 0.14 ng/mL in artificial saliva for short time (60 min). Excellent selectivity against MERS-CoV, Influenza A virus, and Influenza B virus was exhibited. Portable electric signal readers have been designed by the authors, but their availability to the public is still unknown.
6 Future prospect
There are some challenges to the ease of operation, cost, sensitivity, and accuracy of the detection of SARS-CoV-2 using smartphone-based tests. The price and complexity of some smartphone-based detection methods still need to be further optimized. Hence, modified approaches are needed to be proposed as a perspective covering the challenge. For the improvement of the optical detections by smartphone-based method, rather than the amplification of the target such as nucleic acid, the amplification of the detection signal without complicated strategies may be an effective strategy to improve the sensitivity and accuracy. For instance, the signal-amplified nanomaterials or polymers might facilitate the fabrication of new nanoplasmonic sensors for more specific and sensitive detection of SARS-CoV-2 antigens; The latest molecular nanotechnology utilizes CATCH to transduce a more sensitive signal output through an enzymatic cascade reaction, bringing new hope for the simultaneous realization of simplicity and sensitivity in nucleic acid detection.
Electrochemical signals still require relatively expensive instrumentation to detect, but optoelectronic signals can be acquired inexpensively. Thus, the combination of electrochemical sensors and optical detections may have the potential to improve sensitivity and availability. In future studies for analysis of SARS-CoV-2 using smartphone-based detections, the combination of different strategies, probes or physical/chemical signal changes may achieve higher sensitivity, accuracy, lower cost, as well as easier operations. Once the system more simply, accurately, and inexpensively recognized the virus, the availability will be broadened.
7 Conclusions
Smartphone-based tests offer promising strategies for diagnosing SARS-CoV-2 more broadly than conventional testing strategies. After an overall comparison of optical and electrochemical analysis, non-nucleic acid, and nucleic acid detections, we summarize relatively inexpensive, sensitive, and accurate methods. So far, optical sensor-based detections are convenient and inexpensive for wide availability. However, the sensitivities and accuracies of these strategies are expected to be further improved. Among the optical detection methods, CRISPR-Cas and LAMP are sensitive and accurate for nucleic acid analysis, but the preprocessing progress is relatively complicated. The electrochemical detection methods tend to be sensitive without pre-extraction or amplification of the targets, but the strategies still require expensive instruments. Some other infrequently reported smartphone-based detections such as flow rate assays and liquid crystals-based optical sensors are still at the primary stages for the analysis of pseudovirus or spiked samples. With various options for the development of nucleic acid and non-nucleic acid tests based on smartphones, the general population may benefit from wide-available detections in the coming years.
Funding
This study was funded by the 2021 Scientific Research Funding Project of Liaoning Provincial Department of Education (No. LJKZ0818) and the “Double First-Class” project of Liaoning Province.
Author's contributions
Dan Li: Conceptualization, Investigation, Writing - original draft. Cai Sun: Scheme drawing. Xifan Mei: Review, Supervision. Liqun Yang: Review, Supervision.
Availability of data and materials
Any data related to this review are available from the corresponding author on reasonable request.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Data availability
No data was used for the research described in the article.
Acknowledgments
We acknowledge contributions from the members of The Affiliated Reproductive Hospital of China Medical University and The Third affiliated Hospital of Jinzhou Medical University.
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References
1 Kevadiya B.D. Machhi J. Herskovitz J. Oleynikov M.D. Blomberg W.R. Bajwa N. Diagnostics for SARS-CoV-2 infections Nat. Mater. 20 2021 593 605 33589798
2 Talebian S. Wallace G.G. Schroeder A. Stellacci F. Conde J. Nanotechnology-based disinfectants and sensors for SARS-CoV-2 Nat. Nanotechnol. 15 2020 618 621 32728083
3 Cao S. Gan Y. Wang C. Bachmann M. Wei S. Gong J. Post-lockdown SARS-CoV-2 nucleic acid screening in nearly ten million residents of Wuhan, China Nat. Commun. 11 2020 5917 33219229
4 Li C. Soleyman R. Kohandel M. Cappellaro P. SARS-CoV-2 quantum sensor based on nitrogen-vacancy centers in diamond Nano Lett. 22 2022 43 49 34913700
5 Koksaldi I.C. Kose S. Ahan R.E. Haciosmanoglu N. Sahin Kehribar E. Gungen M.A. SARS-CoV-2 detection with de novo-designed synthetic riboregulators Anal. Chem. 93 2021 9719 9727 34192453
6 Mina M.J. Peto T.E. García-Fiñana M. Semple M.G. Buchan I.E. Clarifying the evidence on SARS-CoV-2 antigen rapid tests in public health responses to COVID-19 Lancet 397 2021 1425 1427 33609444
7 Payne T.D. Klawa S.J. Jian T. Kim S.H. Papanikolas M.J. Freeman R. Catching COVID: engineering peptide-modified surface-enhanced Raman spectroscopy sensors for SARS-CoV-2 ACS Sens. 6 2021 3436 3444 34491043
8 Agarwal D.K. Nandwana V. Henrich S.E. Josyula V. Thaxton C.S. Qi C. Highly sensitive and ultra-rapid antigen-based detection of SARS-CoV-2 using nanomechanical sensor platform Biosens. Bioelectron. 195 2022 113647
9 Mahari S. Roberts A. Shahdeo D. Gandhi S. eCovSens-Ultrasensitive Novel In-House Built Printed Circuit Board Based Electrochemical Device for Rapid Detection of nCovid-19 Antigen, a Spike Protein Domain 1 of SARS-CoV-2 2020 bioRxiv 10.1101/2020.1104.1124.059204
10 Petherick A. Developing antibody tests for SARS-CoV-2 Lancet 395 2020 1101 1102 32247384
11 Lin Q. Wu J. Liu L. Wu W. Fang X. Kong J. Sandwich/competitive immuno-sensors on micro-interface for SARS-CoV-2 neutralizing antibodies Anal. Chim. Acta 1187 2021 339144
12 Lew T.T.S. Aung K.M.M. Ow S.Y. Amrun S.N. Sutarlie L. Ng L.F.P. Epitope-functionalized gold nanoparticles for rapid and selective detection of SARS-CoV-2 IgG antibodies ACS Nano 15 2021 12286 12297 34133128
13 Patchett S. Lv Z. Rut W. Bekes M. Drag M. Olsen S.K. A molecular sensor determines the ubiquitin substrate specificity of SARS-CoV-2 papain-like protease Cell Rep. 36 2021 109754
14 Jin Z. Mantri Y. Retout M. Cheng Y. Zhou J. Jorns A. A charge-switchable zwitterionic peptide for rapid detection of SARS-CoV-2 main protease Angew. Chem., Int. Ed. Engl. 61 2022 e202112995
15 Mitratza M. Goodale B.M. Shagadatova A. Kovacevic V. van de Wijgert J. Brakenhoff T.B. The performance of wearable sensors in the detection of SARS-CoV-2 infection: a systematic review Lancet Digit. Health 4 2022 e370 e383 35461692
16 Torrente-Rodriguez R.M. Lukas H. Tu J. Min J. Yang Y. Xu C. SARS-CoV-2 RapidPlex: a graphene-based multiplexed telemedicine platform for rapid and low-cost COVID-19 diagnosis and monitoring Matter 3 2020 1981 1998 33043291
17 Orooji Y. Sohrabi H. Hemmat N. Oroojalian F. Baradaran B. Mokhtarzadeh A. An overview on SARS-CoV-2 (COVID-19) and other human coronaviruses and their detection capability via amplification assay, chemical sensing, biosensing, immunosensing, and clinical assays Nano-Micro Lett. 13 2021 18
18 Tsang N.N.Y. So H.C. Ng K.Y. Cowling B.J. Leung G.M. Ip D.K.M. Diagnostic performance of different sampling approaches for SARS-CoV-2 RT-PCR testing: a systematic review and meta-analysis Lancet Infect. Dis. 21 2021 1233 1245 33857405
19 Wang W. Xu Y. Gao R. Lu R. Han K. Wu G. Detection of SARS-CoV-2 in different types of clinical specimens JAMA 323 2020 1843 1844 32159775
20 Asrani P. Hussain A. Nasreen K. AlAjmi M.F. Amir S. Sohal S.S. Guidelines and safety considerations in the laboratory diagnosis of SARS-CoV-2 infection: a prerequisite study for health professionals Risk Manag. Healthc. Pol. 14 2021 379 389
21 Toropov N. Osborne E. Joshi L.T. Davidson J. Morgan C. Page J. SARS-CoV-2 tests: bridging the gap between laboratory sensors and clinical applications ACS Sens. 6 2021 2815 2837 34392681
22 Mutesa L. Ndishimye P. Butera Y. Souopgui J. Uwineza A. Rutayisire R. A pooled testing strategy for identifying SARS-CoV-2 at low prevalence Nature 589 2021 276 280 33086375
23 Mattioli I.A. Hassan A. Oliveira O.N. Jr. Crespilho F.N. On the challenges for the diagnosis of SARS-CoV-2 based on a review of current methodologies ACS Sens. 5 2020 3655 3677 33267587
24 Delaugerre C. Foissac F. Abdoul H. Masson G. Choupeaux L. Dufour E. Prevention of SARS-CoV-2 transmission during a large, live, indoor gathering (SPRING): a non-inferiority, randomised, controlled trial Lancet Infect. Dis. 22 2022 341 348 34843662
25 Walsh K.A. Tyner B. Broderick N. Harrington P. O'Neill M. Fawsitt C.G. Effectiveness of Public Health Measures to Prevent the Transmission of SARS-CoV-2 at Mass Gatherings: A Rapid Review 2021 Rev. Med. Virol. e2285
26 Revollo B. Blanco I. Soler P. Toro J. Izquierdo-Useros N. Puig J. Same-day SARS-CoV-2 antigen test screening in an indoor mass-gathering live music event: a randomised controlled trial Lancet Infect. Dis. 21 2021 1365 1372 34051886
27 Jia J. Ao L. Luo Y. Liao T. Huang L. Zhuo D. Quantum dots assembly enhanced and dual-antigen sandwich structured lateral flow immunoassay of SARS-CoV-2 antibody with simultaneously high sensitivity and specificity Biosens. Bioelectron. 198 2022 113810
28 Zhou Y. Chen Y. Liu W. Fang H. Li X. Hou L. Development of a rapid and sensitive quantum dot nanobead-based double-antigen sandwich lateral flow immunoassay and its clinical performance for the detection of SARS-CoV-2 total antibodies Sensor. Actuator. B Chem. 343 2021 130139
29 Ye L. Xu X. Song S. Xu L. Kuang H. Xu C. Rapid colloidal gold immunochromatographic assay for the detection of SARS-CoV-2 total antibodies after vaccination J. Mater. Chem. B 10 2022 1786 1794 35212337
30 Xu J. Suo W. Goulev Y. Sun L. Kerr L. Paulsson J. Handheld microfluidic filtration platform enables rapid, low-cost, and robust self-testing of SARS-CoV-2 virus Small 17 2021 e2104009
31 Zhang R. Wu J. Ao H. Fu J. Qiao B. Wu Q. A rolling circle-amplified G-quadruplex/hemin DNAzyme for chemiluminescence immunoassay of the SARS-CoV-2 protein Anal. Chem. 93 2021 9933 9938 34227801
32 Chen R. Ren C. Liu M. Ge X. Qu M. Zhou X. Early detection of SARS-CoV-2 seroconversion in humans with aggregation-induced near-infrared emission nanoparticle-labeled lateral flow immunoassay ACS Nano 15 2021 8996 9004 33928784
33 Dighe K. Moitra P. Alafeef M. Gunaseelan N. Pan D. A rapid RNA extraction-free lateral flow assay for molecular point-of-care detection of SARS-CoV-2 augmented by chemical probes Biosens. Bioelectron. 200 2022 113900
34 Wang C. Cheng X. Liu L. Zhang X. Yang X. Zheng S. Ultrasensitive and simultaneous detection of two specific SARS-CoV-2 antigens in human specimens using direct/enrichment dual-mode fluorescence lateral flow immunoassay ACS Appl. Mater. Interfaces 13 2021 40342 40353 34412466
35 Klumpp-Thomas C. Kalish H. Drew M. Hunsberger S. Snead K. Fay M.P. Standardization of ELISA protocols for serosurveys of the SARS-CoV-2 pandemic using clinical and at-home blood sampling Nat. Commun. 12 2021 113 33397956
36 Harmon A. Chang C. Salcedo N. Sena B. Herrera B.B. Bosch I. Validation of an at-home direct antigen rapid test for COVID-19 JAMA Netw. Open 4 2021 e2126931
37 Klein J.A.F. Kruger L.J. Tobian F. Gaeddert M. Lainati F. Schnitzler P. Head-to-head performance comparison of self-collected nasal versus professional-collected nasopharyngeal swab for a WHO-listed SARS-CoV-2 antigen-detecting rapid diagnostic test Med. Microbiol. Immunol. 210 2021 181 186 34028625
38 Basso D. Aita A. Padoan A. Cosma C. Navaglia F. Moz S. Salivary SARS-CoV-2 antigen rapid detection: a prospective cohort study Clin. Chim. Acta 517 2021 54 59 33626369
39 Ogawa T. Fukumori T. Nishihara Y. Sekine T. Okuda N. Nishimura T. Another false-positive problem for a SARS-CoV-2 antigen test in Japan J. Clin. Virol. 131 2020 104612
40 Fearon E. Buchan I.E. Das R. Davis E.L. Fyles M. Hall I. SARS-CoV-2 antigen testing: weighing the false positives against the costs of failing to control transmission Lancet Respir. Med. 9 2021 685 687 34139150
41 Bukkitgar S.D. Shetti N.P. Aminabhavi T.M. Electrochemical investigations for COVID-19 detection-A comparison with other viral detection methods Chem. Eng. J. 420 2021 127575
42 Suleman S. Shukla S.K. Malhotra N. Bukkitgar S.D. Shetti N.P. Pilloton R. Point of care detection of COVID-19: advancement in biosensing and diagnostic methods Chem. Eng. J. 414 2021 128759
43 Shetti N.P. Mishra A. Bukkitgar S.D. Basu S. Narang J. Raghava Reddy K. Conventional and nanotechnology-based sensing methods for SARS coronavirus (2019-nCoV) ACS Appl. Bio Mater. 4 2021 1178 1190
44 Huang J. Mo X. Fu H. Sun Y. Gao Q. Chen X. Tyndall-effect-enhanced supersensitive naked-eye determination of mercury (II) ions with silver nanoparticles Sensor. Actuator. B Chem. 2021 344
45 Wu Z. Lu J. Fu Q. Sheng L. Liu B. Wang C. A smartphone-based enzyme-linked immunochromatographic sensor for rapid quantitative detection of carcinoembryonic antigen Sensor. Actuator. B Chem. 329 2021
46 Chen J. Chen X. Zhao J. Liu S. Chi Z. Instrument-free and visual detection of organophosphorus pesticide using a smartphone by coupling aggregation-induced emission nanoparticle and two-dimension MnO2 nanoflake Biosens. Bioelectron. 170 2020 112668
47 Dong C. Ma X. Qiu N. Zhang Y. Wu A. An ultra-sensitive colorimetric sensor based on smartphone for pyrophosphate determination Sensor. Actuator. B Chem. 329 2021
48 Kaci K. Del Cano R. Luna M. Milan-Rois P. Castellanos M. Abreu M. Paving the way to point of care (POC) devices for SARS-CoV-2 detection Talanta 247 2022 123542
49 Song Q. Sun X. Dai Z. Gao Y. Gong X. Zhou B. Point-of-care testing detection methods for COVID-19 Lab Chip 21 2021 1634 1660 33705507
50 Aziz A. Asif M. Ashraf G. Farooq U. Yang Q. Wang S. Trends in biosensing platforms for SARS-CoV-2 detection: a critical appraisal against standard detection tools Curr. Opin. Colloid Interface Sci. 52 2021 101418
51 Li H. Wang X. Li X. Yu H.Z. Quantitative pH determination based on the dominant wavelength analysis of commercial test strips Anal. Chem. 93 2021 15452 15458 34762419
52 Kim D.H. Bae J. Lee J. Ahn J. Hwang W.T. Ko J. Porous nanofiber membrane: rational platform for highly sensitive thermochromic sensor Adv. Funct. Mater. 32 2022 2200463
53 Jiang R. Lin D. Zhang Q. Li L. Yang L. Multiplex chroma-response based fluorescent smartphone sensing platform for rapid and visual quantitative determination of antibiotic residues Sensor. Actuator. B Chem. 350 2022 Article 130902
54 Ma L. Yin L. Li X. Chen S. Peng L. Liu G. A smartphone-based visual biosensor for CRISPR-Cas powered SARS-CoV-2 diagnostics Biosens. Bioelectron. 195 2022 Article 113646
55 Chen Y. Shi Y. Chen Y. Yang Z. Wu H. Zhou Z. Contamination-free visual detection of SARS-CoV-2 with CRISPR/Cas12a: a promising method in the point-of-care detection Biosens. Bioelectron. 169 2020 Article 112642
56 Colbert A.J. Lee D.H. Clayton K.N. Wereley S.T. Linnes J.C. Kinzer-Ursem T.L. PD-LAMP smartphone detection of SARS-CoV-2 on chip Anal. Chim. Acta 1203 2022 Article 339702
57 Zhang Y. Ding D. Portable and visual assays for the detection of SARS-CoV-2 View 3 2022 Article 20200138
58 Liang C. Liu B. Li J. Lu J. Zhang E. Deng Q. A nanoenzyme linked immunochromatographic sensor for rapid and quantitative detection of SARS-CoV-2 nucleocapsid protein in human blood Sensor. Actuator. B Chem. 349 2021 Article 130718
59 Fabiani L. Mazzaracchio V. Moscone D. Fillo S. De Santis R. Monte A. Paper-based immunoassay based on 96-well wax-printed paper plate combined with magnetic beads and colorimetric smartphone-assisted measure for reliable detection of SARS-CoV-2 in saliva Biosens. Bioelectron. 200 2022 Article 113909
60 Bokelmann L. Nickel O. Maricic T. Paabo S. Meyer M. Borte S. Point-of-care bulk testing for SARS-CoV-2 by combining hybridization capture with improved colorimetric LAMP Nat. Commun. 12 2021 1467 33674580
61 Vaquer A. Alba-Patiño A. Adrover-Jaume C. Russell S.M. Aranda M. Borges M. Nanoparticle transfer biosensors for the non-invasive detection of SARS-CoV-2 antigens trapped in surgical face masks Sensor. Actuator. B Chem. 345 2021 Article 130347
62 Wu Z. Wang C. Liu B. Liang C. Lu J. Li J. Smartphone-based high-throughput fiber-integrated immunosensing system for point-of-care testing of the SARS-CoV-2 nucleocapsid protein ACS Sens. 2022 1985 1995 35766020
63 Wang C. Wu Z. Liu B. Zhang P. Lu J. Li J. Track-etched membrane microplate and smartphone immunosensing for SARS-CoV-2 neutralizing antibody Biosens. Bioelectron. 192 2021 Article 113550
64 Chen H. Li Z. Feng S. Richard-Greenblatt M. Hutson E. Andrianus S. Femtomolar SARS-CoV-2 antigen detection using the microbubbling digital assay with smartphone readout enables antigen burden quantitation and tracking Clin. Chem. 68 2021 230 239 34383886
65 Li J. Liu B. Tang X. Wu Z. Lu J. Liang C. Development of a smartphone-based quantum dot lateral flow immunoassay strip for ultrasensitive detection of anti-SARS-CoV-2 IgG and neutralizing antibodies Int. J. Infect. Dis. 121 2022 58 65 35483554
66 Kawasaki D. Yamada H. Sueyoshi K. Hisamoto H. Endo T. Imprinted photonic crystal-film-based smartphone-compatible label-free optical sensor for SARS-CoV-2 testing Biosensors 12 2022 200 35448260
67 Minagawa Y. Ueno H. Tabata K.V. Noji H. Mobile imaging platform for digital influenza virus counting Lab Chip 19 2019 2678 2687 31312832
68 Kim S. Akarapipad P. Nguyen B.T. Breshears L.E. Sosnowski K. Baker J. Direct capture and smartphone quantification of airborne SARS-CoV-2 on a paper microfluidic chip Biosens. Bioelectron. 200 2022 Article 113912
69 Breshears L.E. Nguyen B.T. Akarapipad P. Sosnowski K. Kaarj K. Quirk G. Sensitive, smartphone-based SARS-CoV-2 detection from clinical saline gargle samples PNAS Nexus 1 2022 Article pgac028
70 Das C.M. Guo Y. Yang G. Kang L. Xu G. Ho H.P. Gold nanorod assisted enhanced plasmonic detection scheme of COVID-19 SARS-CoV-2 spike protein Adv .Theory. Simul. 3 2020 Article 2000185
71 Li M. Cushing S.K. Wu N. Plasmon-enhanced optical sensors: a review Analyst 140 2015 386 406 25365823
72 Behrouzi K. Lin L. Gold nanoparticle based plasmonic sensing for the detection of SARS-CoV-2 nucleocapsid proteins Biosens. Bioelectron. 195 2022 Article 113669
73 Oh H.K. Kim K. Park J. Im H. Maher S. Kim M.G. Plasmon color-preserved gold nanoparticle clusters for high sensitivity detection of SARS-CoV-2 based on lateral flow immunoassay Biosens. Bioelectron. 205 2022 Article 114094
74 Peng Y. Lin C. Li Y. Gao Y. Wang J. He J. Identifying infectiousness of SARS-CoV-2 by ultra-sensitive SnS2 SERS biosensors with capillary effect Matter 5 2022 694 709 34957388
75 Bushira F.A. Wang P. Wang Y. Hou S. Diao X. Li H. Plasmon-boosted Fe, Co dual single-atom catalysts for ultrasensitive luminol-dissolved O2 electrochemiluminescence detection of prostate-specific antigen Anal. Chem. 94 2022 9758 9765 35749700
76 Georgiou P.G. Guy C.S. Hasan M. Ahmad A. Richards S.J. Baker A.N. Plasmonic detection of SARS-CoV-2 spike protein with polymer-stabilized glycosylated gold nanorods ACS Macro Lett. 11 2022 317 322 35575357
77 Ligiero C.B.P. Fernandes T.S. D'Amato D.L. Gaspar F.V. Duarte P.S. Strauch M.A. Influence of particle size on the SARS-CoV-2 spike protein detection using IgG-capped gold nanoparticles and dynamic light scattering Mater. Today Chem. 25 2022 100924
78 Pirzada M. Altintas Z. Nanomaterials for virus sensing and tracking Chem. Soc. Rev. 51 2022 5805 5841 35735186
79 Calvo-Lozano O. Sierra M. Soler M. Estevez M.C. Chiscano-Camon L. Ruiz-Sanmartin A. Label-free plasmonic biosensor for rapid, quantitative, and highly sensitive COVID-19 serology: implementation and clinical validation Anal. Chem. 94 2022 975 984 34971311
80 Ahmadivand A. Gerislioglu B. Ramezani Z. Kaushik A. Manickam P. Ghoreishi S.A. Functionalized terahertz plasmonic metasensors: femtomolar-level detection of SARS-CoV-2 spike proteins Biosens. Bioelectron. 177 2021 Article 112971
81 Huang L. Ding L. Zhou J. Chen S. Chen F. Zhao C. One-step rapid quantification of SARS-CoV-2 virus particles via low-cost nanoplasmonic sensors in generic microplate reader and point-of-care device Biosens. Bioelectron. 171 2021 Article 112685
82 Li J. Wuethrich A. Edwardraja S. Lobb R.J. Puttick S. Rose S. Amplification-free SARS-CoV-2 detection using nanoyeast-scFv and ultrasensitive plasmonic nanobox-integrated nanomixing microassay Anal. Chem. 93 2021 10251 10260 34264067
83 Masterson A.N. Sardar R. Selective detection and ultrasensitive quantification of SARS-CoV-2 IgG antibodies in clinical plasma samples using epitope-modified nanoplasmonic biosensing platforms ACS Appl. Mater. Interfaces 14 2022 26517 26527
84 Wang Z. Morrissey J.J. Liu L. Wang Y. Zhou Q. Naik R.R. Plasmonically enhanced ultrasensitive epitope-specific serologic assay for COVID-19 Anal. Chem. 94 2022 909 917 34935364
85 Lu X. Yao C. Sun L. Li Z. Plasmon-enhanced biosensors for microRNA analysis and cancer diagnosis Biosens. Bioelectron. 203 2022 Article 114041
86 Mitra S. Basak M. Diverse bio-sensing and therapeutic applications of plasmon enhanced nanostructures Mater. Today 2022 225 261
87 Du Z. Pandey A. Bai Y. Fitzpatrick M.C. Chinazzi M. Pastore y Piontti A. Comparative cost-effectiveness of SARS-CoV-2 testing strategies in the USA: a modelling study Lancet Public Health 6 2021 e184 e191 33549196
88 Stohr J. Zwart V.F. Goderski G. Meijer A. Nagel-Imming C.R.S. Kluytmans-van den Bergh M.F.Q. Self-testing for the detection of SARS-CoV-2 infection with rapid antigen tests for people with suspected COVID-19 in the community Clin. Microbiol. Infect. 28 2022 695 700 34363945
89 Corman V.M. Haage V.C. Bleicker T. Schmidt M.L. Mühlemann B. Zuchowski M. Comparison of seven commercial SARS-CoV-2 rapid point-of-care antigen tests: a single-centre laboratory evaluation study Lancet Microbe 2 2021 e311 e319 33846704
90 Ganguli A. Mostafa A. Berger J. Aydin M.Y. Sun F. Ramirez S.A.S. Rapid isothermal amplification and portable detection system for SARS-CoV-2 Proc. Natl. Acad. Sci. U. S. A 117 2020 22727 22735 32868442
91 Artik Y. Cosgun A.B. Cesur N.P. Hizel N. Uyar Y. Sur H. Comparison of COVID-19 laboratory diagnosis by commercial kits: effectivity of RT-PCR to the RT-LAMP J. Med. Virol. 94 2022 1998 2007 34997587
92 Rodriguez-Manzano J. Malpartida-Cardenas K. Moser N. Pennisi I. Cavuto M. Miglietta L. Handheld point-of-care system for rapid detection of SARS-CoV-2 extracted RNA in under 20 min ACS Cent. Sci. 7 2021 307 317 33649735
93 Nguyen H.Q. Bui H.K. Phan V.M. Seo T.S. An internet of things-based point-of-care device for direct reverse-transcription-loop mediated isothermal amplification to identify SARS-CoV-2 Biosens. Bioelectron. 195 2022 113655
94 Newman C.M. Ramuta M.D. McLaughlin M.T. Wiseman R.W. Karl J.A. Dudley D.M. Initial evaluation of a mobile SARS-CoV-2 RT-lamp testing strategy J. Biomol. Tech. 32 2021 137 147 35035293
95 Tang Z. Cui J. Kshirsagar A. Liu T. Yon M. Kuchipudi S.V. SLIDE: saliva-based SARS-CoV-2 self-testing with RT-LAMP in a mobile device ACS Sens. 7 2022 2370 2378 35920555
96 Yang T. Li D. Yan Y. Ettoumi F.E. Wu R.A. Luo Z. Ultrafast and absolute quantification of SARS-CoV-2 on food using hydrogel RT-LAMP without pre-lysis J. Hazard Mater. 442 2022 130050 Forth coming
97 Heithoff D.M. Barnes L.t. Mahan S.P. Fox G.N. Arn K.E. Ettinger S.J. Assessment of a smartphone-based loop-mediated isothermal amplification assay for detection of SARS-CoV-2 and influenza viruses JAMA Netw. Open 5 2022 Article e2145669
98 Chen J. Liu Y. Diep P. Mahadevan R. Genetic engineering of extremely acidophilic Acidithiobacillus species for biomining: progress and perspectives J. Hazard Mater. 438 2022 Article 129456
99 Safarchi A. Fatima S. Ayati Z. Vafaee F. An update on novel approaches for diagnosis and treatment of SARS-CoV-2 infection Cell Biosci. 11 2021 164 34420513
100 Mahas A. Wang Q. Marsic T. Mahfouz M.M. A novel miniature CRISPR-cas13 system for SARS-CoV-2 diagnostics ACS Synth. Biol. 10 2021 2541 2551 34546709
101 Li Z. Ding X. Yin K. Avery L. Ballesteros E. Liu C. Instrument-free, CRISPR-based diagnostics of SARS-CoV-2 using self-contained microfluidic system Biosens. Bioelectron. 199 2022 Article 113865
102 Cao G. Huo D. Chen X. Wang X. Zhou S. Zhao S. Automated, portable, and high-throughput fluorescence analyzer (APHF-analyzer) and lateral flow strip based on CRISPR/Cas13a for sensitive and visual detection of SARS-CoV-2 Talanta 248 2022 Article 123594
103 Santiago-Frangos A. Hall L.N. Nemudraia A. Nemudryi A. Krishna P. Wiegand T. Intrinsic signal amplification by type III CRISPR-Cas systems provides a sequence-specific SARS-CoV-2 diagnostic Cell Rep. Med. 2 2021 Article 100319
104 Ali Z. Sanchez E. Tehseen M. Mahas A. Marsic T. Aman R. Bio-SCAN: a CRISPR/dCas9-based lateral flow assay for rapid, specific, and sensitive detection of SARS-CoV-2 ACS Synth. Biol. 11 2022 406 419 34939798
105 Welch N.L. Zhu M. Hua C. Weller J. Mirhashemi M.E. Nguyen T.G. Multiplexed CRISPR-based microfluidic platform for clinical testing of respiratory viruses and identification of SARS-CoV-2 variants Nat. Med. 28 2022 1083 1094 35130561
106 Fozouni P. Son S. Diaz de Leon Derby M. Knott G.J. Gray C.N. D'Ambrosio M.V. Amplification-free detection of SARS-CoV-2 with CRISPR-Cas13a and mobile phone microscopy Cell 184 2021 323 333 e329 33306959
107 Liang Y. Lin H. Zou L. Zhao J. Li B. Wang H. CRISPR-Cas12a-Based detection for the major SARS-CoV-2 variants of concern Microbiol. Spectr. 9 2021 e0101721
108 Zhang Y. Chen M. Liu C. Chen J. Luo X. Xue Y. Sensitive and rapid on-site detection of SARS-CoV-2 using a gold nanoparticle-based high-throughput platform coupled with CRISPR/Cas12-assisted RT-LAMP Sensor. Actuator. B Chem. 345 2021 Article 130411
109 Ali Z. Aman R. Mahas A. Rao G.S. Tehseen M. Marsic T. iSCAN: an RT-LAMP-coupled CRISPR-Cas12 module for rapid, sensitive detection of SARS-CoV-2 Virus Res. 288 2020 198129
110 Song J. Cha B. Moon J. Jang H. Kim S. Jang J. Smartphone-based SARS-CoV-2 and variants detection system using colorimetric DNAzyme reaction triggered by loop-mediated isothermal amplification (LAMP) with clustered regularly interspaced short palindromic repeats (CRISPR) ACS Nano 2022 11310 11314
111 Samacoits A. Nimsamer P. Mayuramart O. Chantaravisoot N. Sitthi-Amorn P. Nakhakes C. Machine learning-driven and smartphone-based fluorescence detection for CRISPR diagnostic of SARS-CoV-2 ACS Omega 6 2021 2727 2733 33553890
112 Puig H.D. Najjar A.L. Soenksen T.R. Angenent-Mari M. Donghia M. Ory E.W. Minimally instrumented SHERLOCK (miSHERLOCK) for CRISPR-based point-of-care diagnosis of SARS-CoV-2 and emerging variants Sci. Adv. 7 2021 Article eabh2944
113 Kumar M. Gulati S. Ansari A.H. Phutela R. Acharya S. Azhar M. FnCas9-based CRISPR diagnostic for rapid and accurate detection of major SARS-CoV-2 variants on a paper strip Elife 10 2021 Article e67130
114 Chen F.E. Lee P.W. Trick A.Y. Park J.S. Chen L. Shah K. Point-of-care CRISPR-Cas-assisted SARS-CoV-2 detection in an automated and portable droplet magnetofluidic device Biosens. Bioelectron. 190 2021 Article 113390
115 Mahas A. Marsic T. Lopez-Portillo Masson M. Wang Q. Aman R. Zheng C. Characterization of a thermostable Cas13 enzyme for one-pot detection of SARS-CoV-2 Proc. Natl. Acad. Sci. U. S. A 119 2022 Article e2118260119
116 Azhar M. Phutela R. Kumar M. Ansari A.H. Rauthan R. Gulati S. Rapid and accurate nucleobase detection using FnCas9 and its application in COVID-19 diagnosis Biosens. Bioelectron. 183 2021 Article 113207
117 Pan Y. Zhang D. Yang P. Poon L.L.M. Wang Q. Viral load of SARS-CoV-2 in clinical samples Lancet Infect. Dis. 20 2020 411 412 32105638
118 Sundah N.R. Natalia A. Liu Y. Ho N.R.Y. Zhao H. Chen Y. Catalytic amplification by transition-state molecular switches for direct and sensitive detection of SARS-CoV-2 Sci. Adv. 7 2021 Article eabe5940
119 Duong T.D.S. Jang C.-H. Detection of arginase through the optical behaviour of liquid crystals due to the pH-dependent adsorption of stearic acid at the aqueous/liquid crystal interface Sensors Actuators Chem 339 2021 Article 129906
120 Xu Y. Rather A.M. Song S. Fang J.C. Dupont R.L. Kara U.I. Ultrasensitive and selective detection of SARS-CoV-2 using thermotropic liquid crystals and image-based machine learning Cell Rep. Phys. Sci. 1 2020 100276
121 Zhao T. Yan W. Dong F. Hu X. Xu Y. Wang Z. A smartphone-based platform for ratiometric visualization of SARS-CoV-2 via an oligonucleotide probe Michrochim. Acta 189 2022 268
122 Kumar N. Shetti N.P. Jagannath S. Aminabhavi T.M. Electrochemical sensors for the detection of SARS-CoV-2 virus Chem. Eng. J. 430 2022 Article 132966
123 de Lima L.F. Ferreira A.L. Torres M.D.T. de Araujo W.R. de la Fuente-Nunez C. Minute-scale detection of SARS-CoV-2 using a low-cost biosensor composed of pencil graphite electrodes Proc. Natl. Acad. Sci. U. S. A 118 2021 Article e2106724118
124 Yousefi H. Mahmud A. Chang D. Das J. Gomis S. Chen J.B. Detection of SARS-CoV-2 viral particles using direct, reagent-free electrochemical sensing J. Am. Chem. Soc. 143 2021 1722 1727 33481575
125 Peng Y. Pan Y. Sun Z. Li J. Yi Y. Yang J. An electrochemical biosensor for sensitive analysis of the SARS-CoV-2 RNA Biosens. Bioelectron. 186 2021 Article 113309
126 Kumar M.S. Nandeshwar R. Lad S.B. Megha K. Mangat M. Butterworth A. Electrochemical sensing of SARS-CoV-2 amplicons with PCB electrodes Sensor. Actuator. B Chem. 343 2021 Article 130169
127 Ratautaite V. Boguzaite R. Brazys E. Ramanaviciene A. Ciplys E. Juozapaitis M. Molecularly imprinted polypyrrole based sensor for the detection of SARS-CoV-2 spike glycoprotein Electrochim. Acta 403 2022 Article 139581
128 Najjar D. Rainbow J. Sharma Timilsina S. Jolly P. de Puig H. Yafia M. A lab-on-a-chip for the concurrent electrochemical detection of SARS-CoV-2 RNA and anti-SARS-CoV-2 antibodies in saliva and plasma Nat. Biomed. Eng. 2022 968 978 35941191
129 Noviana E. Ozer T. Carrell C.S. Link J.S. McMahon C. Jang I. Microfluidic paper-based analytical devices: from design to applications Chem. Rev. 121 2021 11835 11885 34125526
130 Hwang C. Park N. Kim E.S. Kim M. Kim S.D. Park S. Ultra-fast and recyclable DNA biosensor for point-of-care detection of SARS-CoV-2 (COVID-19) Biosens. Bioelectron. 185 2021 Article 113177
131 Qi H. Hu Z. Yang Z. Zhang J. Wu J.J. Cheng C. Capacitive aptasensor coupled with microfluidic enrichment for real-time detection of trace SARS-CoV-2 nucleocapsid protein Anal. Chem. 94 2022 2812 2819 34982528
132 Salimiyan rizi K. The smartphone biosensors for point-of-care detection of human infectious diseases: overview and perspectives-A systematic review Curr. Opin. Electrochem. 32 2022 Article 100925
133 Beduk T. Beduk D. de Oliveira Filho J.I. Zihnioglu F. Cicek C. Sertoz R. Rapid point-of-care COVID-19 diagnosis with a gold-nanoarchitecture-assisted laser-scribed graphene biosensor Anal. Chem. 93 2021 8585 8594 34081452
134 Antiochia R. Electrochemical biosensors for SARS-CoV-2 detection: voltametric or impedimetric transduction? Bioelectrochemistry 147 2022 Article 108190
135 Zhang K. Fan Z. Huang Y. Ding Y. Xie M. A strategy combining 3D-DNA Walker and CRISPR-Cas12a trans-cleavage activity applied to MXene based electrochemiluminescent sensor for SARS-CoV-2 RdRp gene detection Talanta 236 2022 122868
136 Lasserre P. Balansethupathy B. Vezza V.J. Butterworth A. Macdonald A. Blair E.O. SARS-CoV-2 aptasensors based on electrochemical impedance spectroscopy and low-cost gold electrode substrates Anal. Chem. 94 2022 2126 2133 35043638
137 Soto D. Orozco J. Peptide-based simple detection of SARS-CoV-2 with electrochemical readout Anal. Chim. Acta 1205 2022 Article 339739
138 Torres M.D.T. de Araujo W.R. de Lima L.F. Ferreira A.L. de la Fuente-Nunez C. Low-cost biosensor for rapid detection of SARS-CoV-2 at the point of care Matter 4 2021 2403 2416 33997767
139 Dou Y. Su J. Chen S. Li T. Wang L. Ding X. A smartphone-based three-in-one biosensor for co-detection of SARS-CoV-2 viral RNA, antigen and antibody Chem. Commun. (J. Chem. Soc. Sect. D) 58 2022 6108 6111
140 Liu H. Yang A. Song J. Wang N. Law L.L.K.-W. Yan F. Ultrafast, sensitive, and portable detection of COVID-19 IgG using flexible organic electrochemical transistors Sci. Adv. 7 2021 Article eabg8387
141 Raziq A. Kidakova A. Boroznjak R. Reut J. Opik A. Syritski V. Development of a portable MIP-based electrochemical sensor for detection of SARS-CoV-2 antigen Biosens. Bioelectron. 178 2021 Article 113029
142 Idili A. Parolo C. Alvarez-Diduk R. Merkoci A. Rapid and efficient detection of the SARS-CoV-2 spike protein using an electrochemical aptamer-based sensor ACS Sens. 6 2021 3093 3101 34375076
143 Eissa S. Zourob M. Development of a low-cost cotton-tipped electrochemical immunosensor for the detection of SARS-CoV-2 Anal. Chem. 93 2021 1826 1833 33370087
144 Rashed M.Z. Kopechek J.A. Priddy M.C. Hamorsky K.T. Palmer K.E. Mittal N. Rapid detection of SARS-CoV-2 antibodies using electrochemical impedance-based detector Biosens. Bioelectron. 171 2021 112709
145 Eissa S. Alhadrami H.A. Al-Mozaini M. Hassan A.M. Zourob M. Voltammetric-based immunosensor for the detection of SARS-CoV-2 nucleocapsid antigen Michrochim. Acta 188 2021 199
146 Beduk D. Ilton de Oliveira Filho J. Beduk T. Harmanci D. Zihnioglu F. Cicek C. All in One' SARS-CoV-2 variant recognition platform: machine learning-enabled point of care diagnostics Biosens. Bioelectron. X 10 2022 Article 100105
147 Samper I.C. Sanchez-Cano A. Khamcharoen W. Jang I. Siangproh W. Baldrich E. Electrochemical capillary-flow immunoassay for detecting anti-SARS-CoV-2 nucleocapsid protein antibodies at the point of care ACS Sens. 6 2021 4067 4075 34694794
148 Abrego-Martinez J.C. Jafari M. Chergui S. Pavel C. Che D. Siaj M. Aptamer-based electrochemical biosensor for rapid detection of SARS-CoV-2: nanoscale electrode-aptamer-SARS-CoV-2 imaging by photo-induced force microscopy Biosens. Bioelectron. 195 2022 Article 113595
149 Ji D. Guo M. Wu Y. Liu W. Luo S. Wang X. Electrochemical detection of a few copies of unamplified SARS-CoV-2 nucleic acids by a self-actuated molecular system J. Am. Chem. Soc. 144 2022 13526 13537 35858825
150 Wang L. Wang X. Wu Y. Guo M. Gu C. Dai C. Rapid and ultrasensitive electromechanical detection of ions, biomolecules and SARS-CoV-2 RNA in unamplified samples Nat. Biomed. Eng. 6 2022 276 285 35132229
151 Caratelli V. Fegatelli G. Moscone D. Arduini F. A paper-based electrochemical device for the detection of pesticides in aerosol phase inspired by nature: a flower-like origami biosensor for precision agriculture Biosens. Bioelectron. 205 2022 Article 114119
152 Prasertying P. Ninlapath T. Jantawong N. Wongpakdee T. Sonsa-Ard T. Uraisin K. Disposable microchamber with a microfluidic paper-based lid for generation and membrane separation of SO2 gas employing an in situ electrochemical gas sensor for quantifying sulfite in wine Anal. Chem. 94 2022 7892 7900 35609256
153 Hanpanich O. Lomae A. Maruyama A. Palaga T. Chailapakul O. Ngamrojanavanich N. Label-free Detection of HPV mRNA with an Artificial Chaperone-Enhanced MNAzyme (ACEzyme)-Based Electrochemical Sensor 2022 Biosens. Bioelectron. Article 114352
154 Brown E.W. Glasscott M.W. Conley K. Barr J. Ray J.D. Moores L.C. ACEstat: a diy guide to unlocking the potential of integrated circuit potentiostats for open-source electrochemical analysis Anal. Chem. 94 2022 4906 4912 35258920
155 Kaushik A.K. Dhau J.S. Gohel H. Mishra Y.K. Kateb B. Kim N.Y. Electrochemical SARS-CoV-2 sensing at point-of-care and artificial intelligence for intelligent COVID-19 management ACS Appl. Bio Mater. 3 2020 7306 7325
156 Cordova-Huaman A.V. Jauja-Ccana V.R. La Rosa-Toro A. Low-cost smartphone-controlled potentiostat based on Arduino for teaching electrochemistry fundamentals and applications Heliyon 7 2021 Article e06259
157 Zhao H. Liu F. Xie W. Zhou T.C. OuYang J. Jin L. Ultrasensitive supersandwich-type electrochemical sensor for SARS-CoV-2 from the infected COVID-19 patients using a smartphone Sensor. Actuator. B Chem. 327 2021 Article 128899
158 Chaibun T. Puenpa J. Ngamdee T. Boonapatcharoen N. Athamanolap P. O'Mullane A.P. Rapid electrochemical detection of coronavirus SARS-CoV-2 Nat. Commun. 12 2021 802 33547323
159 Alafeef M. Dighe K. Moitra P. Pan D. Rapid, ultrasensitive, and quantitative detection of SARS-CoV-2 using antisense oligonucleotides directed electrochemical biosensor chip ACS Nano 14 2020 17028 17045 33079516
160 Li Q. Li Y. Gao Q. Jiang C. Tian Q. Ma C. Real-time monitoring of isothermal nucleic acid amplification on a smartphone by using a portable electrochemical device for home-testing of SARS-CoV-2 Anal. Chim. Acta 1229 2022 Article 340343
161 Puig H.d. Lee R.A. Najjar D. Xiao Tan L. Soekensen R. Angenent-Mari N.M. Minimally instrumented SHERLOCK (miSHERLOCK) for CRISPR-based point-of-care diagnosis of SARS-CoV-2 and emerging variants Sci. Adv. 7 2021 Article eabh2944
162 Panpradist N. Kline C. Roller G.A. Hull W.T. Kotnik H. Oreskovic K. Harmony COVID-19: a ready-to-use kit, low-cost detector, and smartphone app for point-of-care SARS-CoV-2 RNA detection Sci. Adv. 7 2021 Article eabj1281
163 Xing G. Ai J. Wang N. Pu Q. Recent progress of smartphone-assisted microfluidic sensors for point of care testing TRAC-Trend. Anal. Chem. 157 2022 Article 116792
164 Akarapipad P. Kaarj K. Breshears L.E. Sosnowski K. Baker J. Nguyen B.T. Smartphone-based sensitive detection of SARS-CoV-2 from saline gargle samples via flow profile analysis on a paper microfluidic chip Biosens. Bioelectron. 207 2022 Article 114192
165 Kim J. Cho H. Kim J. Park J.S. Han K.H. A disposable smart microfluidic platform integrated with on-chip flow sensors Biosens. Bioelectron. 176 2021 Article 112897
166 Cavaniol C. Cesar W. Descroix S. Viovy J.L. Flowmetering for Microfluidics 2022 Lab Chip
167 Wang C. Li Y. Zhu Y. Zhou X. Lin Q. He M. High-κ solid-gate transistor configured graphene biosensor with fully integrated structure and enhanced sensitivity Adv. Funct. Mater. 26 2016 7668 7678
168 Ban D.K. Bodily T. Karkisaval A.G. Dong Y. Natani S. Ramanathan A. Rapid self-test of unprocessed viruses of SARS-CoV-2 and its variants in saliva by portable wireless graphene biosensor Proc. Natl. Acad. Sci. U. S. A 119 2022 Article e2206521119
| 36506266 | PMC9728015 | NO-CC CODE | 2022-12-14 23:52:23 | no | Trends Analyt Chem. 2023 Jan 7; 158:116878 | utf-8 | Trends Analyt Chem | 2,022 | 10.1016/j.trac.2022.116878 | oa_other |
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Util Policy
Util Policy
Utilities Policy
0957-1787
1878-4356
Elsevier Ltd.
S0957-1787(22)00118-7
10.1016/j.jup.2022.101454
101454
Full-Length Article
Determining factors affecting customer satisfaction of the national electric power company (MERALCO) during the COVID-19 pandemic in the Phillippines
Ong Ardvin Kester S. a
Prasetyo Yogi Tri abc∗
Kishimoto Ryuichi T. ade
Mariñas Klint Allen af
Robas Kirstien Paola E. a
Nadlifatin Reny g
Persada Satria Fadil h
Kusonwattana Poonyawat i
Yuduang Nattakit ad
a School of Industrial Engineering and Engineering Management, Mapúa University, Manila, Philippines. 658 Muralla St., Intramuros, Manila, 1002, Philippines
b International Program in Engineering for Bachelor, Yuan Ze University, 135 Yuan-Tung Road, Chung-Li, 32003, Taiwan
c Department of Industrial Engineering and Management, Yuan Ze University, 135 Yuan-Tung Road, Chung-Li, 32003, Taiwan
d School of Graduate Studies, Mapúa University, Manila, Philippines. 658 Muralla St., Intramuros, Manila, 1002, Philippines
e College of Engineering and Information Technology, Pamantasan Ng Lungsod Ng Valenzuela. MXV9+GJF, Maysan Rd, Valenzuela, Metro Manila, Philippines
f Department of Industrial and Systems Engineering, Chung Yuan Christian University, Taoyuan, 320, Taiwan
g Department of Information Systems, Institut Teknologi Sepuluh Nopember, Kampus ITS Sukolilo, Surabaya, 60111, Indonesia
h Entrepreneurship Department, BINUS Business School Undergraduate Program, Bina Nusantara University, Jakarta, 11480, Indonesia
i Department of Industrial Engineering, Faculty of Engineering, North-Chiang Mai University, Chiang Mai, 50230, Thailand
∗ Corresponding author. School of Industrial Engineering and Engineering Management, Mapúa University, Manila, Philippines. 658 Muralla St., Intramuros, Manila, 1002, Philippines.
7 12 2022
2 2023
7 12 2022
80 101454101454
16 1 2022
5 11 2022
5 11 2022
© 2022 Elsevier Ltd. All rights reserved.
2022
Elsevier Ltd
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
This study aimed to determine factors affecting customer satisfaction of national electric power companies during the COVID-19 pandemic by integrating SERVQUAL and Expectation-Confirmation Theory approaches. A total of 529 participants voluntarily participated and answered an online questionnaire of 49 questions. Structural equation modeling indicated that Tangibility, Empathy, and Responsiveness were positively related to Service Quality which subsequently led to Customer Expectation, Energy Consumption, and Perceived Performance (PE). In addition, a higher PE was positively related to Confirmation, which eventually led to Customer Satisfaction. It was evident that integrating SERVQUAL and ECT could holistically measure customer satisfaction among electricity service providers.
Graphical abstract
The Final Model of the Study: Integration of SERVQUAL and ECT.Image 1
Keywords
Electric power company
SERVQUAL
Expectation-confirmation theory
Satisfaction
==== Body
pmc1 Introduction
A national electric power company is a government-based company that mainly deals with the power system industry. This company ensures that the electrical supply is delivered to its customers successfully (Joskow et al., 1996). They are also responsible for the sales of electricity according to the price set by the Government under the law. It is also responsible for the maintenance of different electrical equipment such as the transformers and service entrances, the regulation of rules providing a proper installation of electrical supply in a particular establishment, and providing meter reading equipment wherein this is the basis of their electrical charges with their customers.
Internationally, electrical power utilities have been explored mainly in determining customer satisfaction. Mutua et al. (2012) investigated factors affecting customer satisfaction in the energy sector. It was concluded that the image of the service provider, perceived quality, perceived value, and customer expectation significantly affect customer satisfaction levels in Kenya. Resende and Cardoso (2019) assessed the quality of service in electrical distribution in Brazil. Their correlation analysis implied a weak association between service quality and overall customer satisfaction. Fiorio and Fiorio (2011) found that Europeans are satisfied with the prices they pay for their Utility Reform (Fiorio and Fiorio, 2011).
In the Philippines, Manila Electric Company (MERALCO) is one major electricity distributor, especially in National Capital Region (NCR), as presented in Fig. 1 (Metro Pacific Investments Corporation, 2019). It is an electrical distribution company serving almost 118 years, providing electricity to 25% of the country's cities and 75 municipalities. Its mission is to provide a world-class electric service highlighting the attributes that affect the company's growth and development, such as excellence in customer service, good performance, accountability, employees, investment, and integrity (MERALCO, 2018).Fig. 1 Geographic setting of the study.
Fig. 1
Prior to the COVID-19 pandemic, several complaints and dissatisfaction among consumers were evident with the MERALCO service. During the quarantine, the employees of MERALCO could not read the electric meter, resulting in overestimation, which caused ‘bill shock’ among consumers. MERALCO acknowledged its mistake and announced that the mode of payment for March 2020 to May 2020 was only estimated using their past three months of electricity bills. They considered providing an installment payment within this period since some of their customers lost their job due to the lockdown (MERALCO, 2020). With the consideration and empathy of this company towards its customers, the service quality of MERALCO has been challenged and underexplored. Since MERALCO is the largest electricity provider in the country, the need to assess and determine service quality and customer satisfaction should be deduced.
Service quality is one of the crucial aspects of every business wherein different companies compete in this area, which acts as a differentiator considering the same sector or business type (Afroj et al., 2021; Alam and Mondal, 2019). The SERVQUAL developed by Zeithaml et al. (1990) group provided five factors (reliability, assurance, tangibility, empathy, and responsiveness) that affect the service quality of a specific business. Previously, researchers used the SERVQUAL and Expectation-Confirmation Theory (ECT) in different applications. Moreover, recent studies explored the quality of service in electric distribution companies. Mirza et al. (2021) investigated the efficiency of electric distribution companies with the parameters used in service quality. The results showed that Pakistan's electric distribution companies do not operate optimally.
Despite available studies on satisfaction applying the SERVQUAL and the Expectation-Confirmation Theory (ECT), the results provided only apply to specific services. The two theories were used to describe the factors affecting the satisfaction of customers. Thus, the integration of the two theories was utilized for this study to cover the application described with the MERALCO customers holistically. In order to close the theoretical gaps, it is necessary to add some latent variables to the type of service that MERALCO provides to its customers. Integrating the SERVQUAL and the Expectation-Confirmation Theory (ECT) forms a framework applicable to an electric distribution company such as MERALCO and can be extended to other utilities (e.g. water supply and distribution) as service providers.
This study aimed to determine factors affecting customer satisfaction with electrical power utility companies during the COVID-19 pandemic. Supported by the integration of SERVQUAL and ECT, this study explored and investigated deeply customer satisfaction focusing on customers of MERALCO during the new environment set by the COVID-19 pandemic. This study may contribute additional insight and shed some light on exploring customer satisfaction in electrical power distribution providers (Hsiao et al., 2018; Lee et al., 2019). Finally, the findings can be applied and extended, particularly for enhancing the electrical power utilities worldwide. The paper is organized as follows: (1) introduction, (2) theoretical research framework, (3) methodology, (4) results, (5) discussion and contribution, (6) conclusion and implication.
2 Related studies and theoretical research framework
2.1 SERVQUAL dimensions
SERVQUAL dimensions have been widely utilized to assess customer satisfaction. Five dimensions identified by Parasuraman et al. (1991, Fig. 2 ) set a benchmark for assessing service quality among service providers. SERVQUAL was utilized to evaluate public utility vehicle service quality in the Philippines (Chuenyindee et al., 2022). Similarly, German et al. (2022) analyzed factors affecting consumer preference for package carriers in the Philippines. However, it was stated that SERVQUAL dimensions alone should be backed-up with other theories and extended latent variables to analyze service quality and customer satisfaction thoroughly. Therefore, SERVQUAL is utilized for analyzing service quality among service-providing companies like other theories, such as the Expectation-Confirmation Theory (Jumaan et al., 2020; Chuenyindee et al., 2022).Fig. 2 Servqual dimensions.
Fig. 2
2.2 Expectation-Confirmation Theory
Expectation-Confirmation Theory (ECT) can also describe the satisfaction of a consumer. According to Oliver (1977), this theory provides the idea that perceived performance relative to customer expectations may indicate whether the customer will be satisfied or not. Fig. 3 shows the theoretical framework of the ECT. It describes that satisfaction will be positively affected if the product or service perceived by the customer outperforms its expectations. In contrast, if the perceived performance underperformed with consumer satisfaction, a negative effect will indicate dissatisfaction. This theory was also applied by Jumaan et al. (2020), whose study utilized ECT and investigated mobile internet users, including factors affecting the user's intention to continue the usage of mobile devices.Fig. 3 Expectation-confirmation theory framework.
Fig. 3
2.3 Theoretical research framework
Fig. 4 represents the theoretical framework of this study that integrated the SERVQUAL and Expectation-Confirmation Theory. Since this study aimed to determine the energy consumption and the satisfaction of MERALCO's consumers during this pandemic, integrating both theories would holistically measure customer satisfaction (Jumaan et al., 2020; Thaicon et al., 2014).Fig. 4 Theoretical research framework.
Fig. 4
Service Quality (SERVQUAL) has five elements: Reliability, Assurance, Tangibility, Empathy, and Responsiveness (Chuenyindee et al., 2022; Zeithaml et al., 1990). These elements are defined in terms of some core capabilities of service providers:• Reliability – To provide service in a consistent and accurate performance.
• Assurance – To convey a feeling of trust toward its consumers.
• Tangibility – To maintain the quality of equipment, such as the electrical meter reading, wirings, and payment schemes that the service provider has.
• Empathy – to give consideration and attention to customer needs.
• Responsiveness – To respond and take action if the consumer contacts them.
Different studies have presented how reliability directly and positively affects service quality. Shahin and Pourhamidi (2011) presented the significant relationship between reliability affecting service quality and customer satisfaction among service providers. In addition, Lee et al. (2019) showed how reliability highlights service quality that indirectly reflects consumer behavioral intentions. Lastly, Zhao et al. (2015) discussed how the environment where the service provided affects the consumer's quality preference. Thus, it was hypothesized that:H1 Reliability has a positive effect on Service Quality.
Assurance is highly needed, especially in retail and customer relations (Tumsekcali et al., 2021). Chuah and Hilmi (2011) and Sam et al. (2018) showed the significant effect of assurance on service quality and customer satisfaction. It is indicated that consumers want the promise of service delivered to be effective and efficient. Thus, it was hypothesized that:
H2 Assurance has a positive effect on Service Quality.
Tangible such as how the equipment (e.g., electric meters) would influence the perception of consumers when it comes to service quality (Chuenyindee et al., 2022). The appearance, machinery, equipment, and utilities that a service provider considers would affect the tangible latent variable (Alam and Mondal, 2019). Thus, it was hypothesized that:
H3 Tangibility has a positive effect on Service Quality.
Chuah and Hilmi (2011) expressed that service providers' relations and feelings for consumers influence service quality. When high empathy is implied, consumers would feel their importance as clients, leading to a highly significant effect on service quality and satisfaction (Chuenyindee et al., 2022). Tumsekcali et al. (2021) expounded on the latent empathy variable as one of the most crucial aspects of customer relationships during the COVID-19 pandemic. Thus, it was hypothesized that:
H4 Empathy has a positive effect on Service Quality.
Responsiveness as one of the SERVQUAL dimensions has been seen to affect service quality directly in terms of promptness, acknowledgment of service providers, and effective customer relation (Lee et al., 2019). The more responsive the service providers are, the higher the effect on service quality and the more satisfied customers are. Similarly, Chou et al. (2011) showed a highly positive direct effect of responsiveness on service quality and customer satisfaction. Thus, it was hypothesized that:
H5 Responsiveness positively affects Service Quality.
Additionally, Service Cost was added to this model (Fiorio and Fiorio, 2011), which is the price or the amount of electricity a particular consumer pays monthly. Based on Fiorio and Fiorio (2011), the price of service may affect consumer behavior. Thus, it was hypothesized that:
H6 Service Cost positively affects Customer Expectation.
H7 Service Cost positively affects Energy Consumption.
Moreover, it can be reflected that the service quality correlates to ECT as customers are expecting to receive and consume or use a service (Fu et al., 2018). Fu et al. (2018) emphasized that there is a relationship between the perceived service quality to the expectations of customers and its perceived value. Jumaan et al. (2020) explained that when the service providers provide (under-provide) services affects customers' prior experience. It could be deduced that a company's mission would be the basis of a consumer's expectation, and it would depend on the providers to confirm this, which would lead to a level of satisfaction among consumers (Afroj et al., 2021; Alam and Mondal, 2019). Thus, it was hypothesized that:
H8 Service Quality positively affects Customer Expectation.
H9 Service Quality positively affects Perceived Performance.
H10 Service Quality positively affects Energy Consumption.
Based on the ECT, an individual has an initial expectation of the product or service. Customers will then compare the initial expectation to the product or service experience. These differences between perceived initial expectation and actual performance can determine the customer's level of satisfaction (Rezaei et al., 2018). In line with the objectives of this study, the additional latent variable included customer expectation as part of the theoretical framework. Energy consumption as a latent variable was also included, defined as the amount of energy in kilowatt-hour (kWh) consumed in which MERALCO set this basis for their billing statements, hypothesizing that:
H11 Customer Expectation positively affects Perceived Performance.
H12 Energy Consumption positively affects Perceived Performance.
Under ECT, Confirmation is a latent variable related to perceived performance as part of actual service usage. The user or a particular consumer will have a separate confirmation of comparing the initial expectation to actual perceived performance (Oliver, 1977; Jumaan et al., 2020). This study was related to the actual usage of electricity that a consumer uses monthly. The consumer will have their expectation on how much they will pay on that particular month based on their actual usage of electrical supply. Having this confirmation and perceived performance as part of the latent variables, the theory states that it may be a good determinant in conceptualizing and assessing customer satisfaction. Thus, the researchers hypothesized the following:
H13 Customer Expectation positively affects Confirmation.
H14 Perceived Performance positively affects Confirmation.
H15 Perceived Performance positively affects Customer Satisfaction.
H16 Energy Consumption positively affects Customer Satisfaction.
H17 Confirmation positively affects Customer Satisfaction.
3 Methodology
3.1 Participants
There was a total of 529 participants gathered in this study. The researchers utilized the online form of a survey due to the COVID-19 pandemic. The approach is similar to the study of Abrahim et al. (2019). The questions were distributed using social media platforms such as Facebook, Twitter, Instagram, and Viber. Purposive sampling was considered to gather the 529 participants who paid and benefited from the service provided by MERALCO. The online survey question consisted of 49 questions using the 5-point Likert scale. Table 1 shows the demographic profile of the respondents.Table 1 Demographic profile of participants (N=529).
Table 1Characteristics Description N %
Gender Male 241 45.6
Female 288 54.4
Age 20–29 236 44.6
30–39 38 7.18
40–49 137 26.0
50–59 101 19.1
60 and above 17 3.21
Occupation Factory worker 70 13.2
Sales 47 8.89
Engineer 74 14.0
Education Based 13 2.46
Free Lancers 325 61.4
Monthly income (PHP) Less than PHP 10,000 272 51.4
PHP 10,000 – PHP 20,000 147 27.8
PHP 20,000 – PHP 30,000 87 16.5
PHP 30,000 – PHP 40,000 12 2.27
PHP 40,000 – PHP 50,000 11 2.08
Monthly Electricity Consumption (PHP) Below PHP 1000 104 19.7
PHP 1000 – PHP 3000 327 61.8
PHP 3000 – PHP 5000 72 13.6
PHP 5000 – PHP 7000 19 3.59
PHP 7000 – PHP 9000 7 1.32
MERALCO Service (Years) Less than 1 year 44 8.32
1–2 years 28 5.29
2–3 years 10 1.89
3–4 years 6 1.13
4–5 years 12 2.27
5 years and above 429 81.1
3.2 Questionnaire
Following the theoretical framework provided in this study, we developed and adapted questionnaires administered online to determine the consumption and satisfaction of MERALCO customers during COVID-19. The questionnaires consisted of 13 sections: (1) The Demographic Profile Information (gender, age, occupation, monthly income, monthly electricity bill, years being with MERALCO), (2) Reliability, (3) Assurance, (4) Tangibility, (5) Empathy, (6) Responsiveness, (7) Service Quality, (8) Service Cost, (9) Customer Expectation, (10) Energy Consumption, (11) Perceived Performance, (12) Confirmation, and (13) Satisfaction. This study utilized a 5-point Likert scale to evaluate the questionnaires for the latent constructs or variables included in the Structural Equation Modeling (SEM) seen in Table 2 .Table 2 Questionnaire.
Table 2Construct Item Measures Source
Reliability RE1 The MERALCO gives a consistent power supply in our house during the pandemic. Jun and Cai (2001)
RE2 We receive our billing statement regularly during the pandemic. Rezaei et al. (2018)
RE3 They regularly respond to our concerns. Han and Baek (2004)
RE 4 On-time action in resolving electrical issues (long-time brownouts, damaged meters).
Assurance AS1 I trust MERALCO. Zhou (2013)
AS2 I think our electrical service is safe and free from illegal electrical connections. Yang et al. (2004)
AS3 The employees of MERALCO are polite with customers. Wheaton et al. (1977)
AS4 Since the pandemic, I feel the assurance of their service during this pandemic. Zhou (2013)
AS5 I think MERALCO is assuring us through good communication during this pandemic.
Tangibility TA1 Our electric meter reading equipment is updated. Han and Baek (2004)
TA2 We have good electrical wiring service (service cap, service drop, wiring arrangement, etc.). Zhou (2013)
TA3 The monthly billing paper is easy to read and understand. Wheaton et al. (1977)
TA4 Quick time of paying my bills (waiting line/process). Han and Baek (2004)
Empathy EM1 We feel that MERALCO cares about their customers.
EM2 During the pandemic, we see their consideration. Han and Baek (2004)
EM3 I think MERALCO has an interest in their customers' needs. Zhou (2013)
EM4 The installation payment during the pandemic has been well-communicated and executed Wheaton et al. (1977)
Responsiveness RS1 We are informed every time they will cut the power supply. Han and Baek (2004)
RS2 The communication with their customer service is good. Yang et al. (2004)
RS3 They immediately respond if there is a damaged transformer, sudden short circuit, questionable brownouts, etc. Zhou (2013)
RS4 The feedback for our payment and other inquiries and concerns is good. Zhou (2013)
Service Quality SQ1 The quality service of MERALCO is good. Kim and Oh (2011)
SQ2 Payment methods (e.g., over-the-counter, online banking, debit/credit card, etc.) are working properly. Rezaei et al. (2018)
SQ3 I think MERALCO is giving a good service during the pandemic. Jun and Cai (2001)
SQ4 During the pandemic, we experienced no brownouts or electricity cut-outs. Kim and Oh (2011)
Service Cost SC1 Our MERALCO bill charges our residential at a reasonable price. Han and Baek (2004)
SC2 During the pandemic, the cost of our electricity supply was fair. Zhou (2013)
SC3 We feel that we are paying at a reasonable charge with our electric bill. Kim and Oh (2011)
SC 4 I am satisfied with the online payment mode due to the additional charges they implemented. Mutua et al. (2012)
Customer Expectation CE1 My expectation for MERALCO exceeds having a good quality of service. Chiou (2004)
CE2 I am satisfied if my expected bill to actual electric bill meets.
CE3 I am expecting a good response from customer service during this quarantine. Wheaton et al. (1977)
CE4 I am confident that my bill has no charges and that we are not being taken advantage of by increasing our bill during quarantine.
Energy Consumption EC1 I feel confident that my energy consumption will reflect on my bill. Mutua et al. (2012)
EC2 I am not worried that using a different appliance could cause a sudden increase in my bill. Shokouhyar et al. (2020)
EC3 I am confident with my electricity consumption during this quarantine. Thaicon et al. (2014)
EC4 The longer I use electrical appliances (electric fan, air conditioning unit, TV, computer, etc.), the more I feel satisfied. Park (2019)
Perceived Performance PE1 With MERALCO, they are very efficient and effective. Fu et al. (2018)
PE2 We are using the electric supply very well. Park (2019)
PE3 I am satisfied with their billing statement during this pandemic. Chou et al. (2011)
PE4 I can say that the no-disconnection policy during the pandemic is being followed. Park (2019)
Confirmation CO1 I believe that our billing statement during the pandemic is accurate. Jumaan et al. (2020)
CO2 I believe that MERALCO is doing their best to give us the service we need.
CO3 I think there is a need to change and/or improve the payment process and regulation of their price. Chou et al. (2011)
CO4 I think the Government should break the monopoly of MERALCO as the only electricity provider, especially in Metro Manila. Mouton (2015)
Customer Satisfaction CS1 Overall, I am satisfied with the service of MERALCO (electric supply, payment, customer service).
CS2 I am satisfied with the MERALCO during this pandemic.
CS3 I feel satisfied with the monthly bill I am receiving with MERALCO. Chakraborty and Sengupta (2014)
CS4 Overall, I can confidently say that MERALCO guarantees a transparent charge/fee in our bill during this pandemic. Mouton (2015)
3.3 Structural equation modeling
AMOS 26 was utilized for Structural Equation Modeling (SEM). SEM is an advanced statistical approach wherein the causal relationships between latent constructs are simultaneously calculated (Hair et al., 2010; Li et al., 2020a, Li et al., 2020b; Ouyang et al., 2018). Moreover, confirmatory factor analysis (CFA) was used to determine different items and relationships considered in the integrated framework. In addition, Irfan et al. (2020) utilized SEM to determine factors affecting willingness to pay for renewable energy. Their results indicated that SEM could highly determine the factor influencing human behavior.
4 Results
4.1 The initial model
Fig. 5 represents the initial results in determining the factors affecting customers' consumption and satisfaction during COVID-19. The initial model shows the path coefficients together with their indicators. Based on the figure, the reliability has a low path coefficient, making it insignificant. Some paths are considered insignificant, such as Reliability to Service Quality, Customer Expectation to Confirmation, Perceived Performance to Customer Satisfaction, Assurance, and Energy Consumption to Customer Satisfaction. Hence, following the suggestion of Hair et al. (2010), these non-significant latent constructs may be removed to enhance the model fit. Validity and reliability tests were run using Cronbach's Alpha, Composite Reliability, Standardized Covariances, and modification of indices (Hair et al., 2010).Fig. 5 Initial model result.
Fig. 5
4.2 The final model
The final model of this study is represented in Fig. 6 . The model shows the path coefficients between its latent variables by integrating SERVQUAL and the Expectation-Confirmation Theory. From the model, tangibility, empathy, and responsiveness relate to SERVQUAL. Under ECT, Service Quality (SQ) and Service Cost (SC) are positively related to Customer Expectation and Perceived Performance (PE). Additionally, Customer Expectation (CE) and Energy Consumption (EC) were also positively related to Perceived Performance (PE), and Service Cost (SC) was positively related to Energy Consumption (EC). Moreover, Perceived Performance (PE) is positively related to Confirmation (CO), and Confirmation (CO) is positively related to Customer Satisfaction (CS).Fig. 6 Final model of the study: Integration of servqual and ECT
Fig. 6
4.3 Path analysis
Table 3 shows the summary of the path coefficients (β) between each latent variable, standard error (S.E.), Critical Ratio (C.R.), and the P-value. Based on the result, Tangibility (TA), Empathy (EM), and Responsiveness (RE) positively related to Service Quality (SQ) (β = 0.23, 0.47, 0.34 at p < 0.001). Moreover, Service Cost (SC) and Service Quality (SQ) were positively related to Customer Expectation (β = 0.45, 0.46 at p < 0.001). In addition, the Service Cost (SC) and Service Quality (SQ) were positively related to Energy Consumption (EC) (β = 0.47, 0.46 at p < 0.001). With Perceived Performance, the latent variables, which were the Service Quality (SQ), Energy Consumption (EC), and Customer Expectation, were seen to be positively related (β = 0.48, 0.48, 0.35 at p < 0.001). The Perceived Performance is positively related to confirmation (β = 0.98 at p < 0.001), which was the highest factor loading value in this model. Lastly, Confirmation is positively related to Customer Satisfaction (β = 0.95 at p < 0.001).Table 3 Path analysis for final model.
Table 3Hypothesis Estimate S.E. C.R. P
TA → SQ 0.23 0.046 4.524 ***
EM → SQ 0.47 0.054 7.144 ***
RS → SQ 0.34 0.068 5.249 ***
SC → CE 0.45 0.046 8.161 ***
SC → EC 0.43 0.049 7.737 ***
SQ → CE 0.46 0.064 8.067 ***
SQ → PE 0.48 0.063 8.354 ***
SQ → EC 0.46 0.068 8.072 ***
CE → PE 0.35 0049 7.078 ***
EC → PE 0.48 0.042 5.031 ***
PE → CO 0.98 1.055 16.328 ***
CO → CS 0.95 0.054 16.791 ***
Note: *** Indicates that the p-value is less than 0.001.
4.4 Statistical descriptive results
Table 4 shows the descriptive statistical results of the model. Each indicator for the latent variable was listed as the mean, standard deviation, and factor loadings. Additionally, Cronbach's alpha (∝), Average Variance Extracted (AVE), and Composite Reliability (CR) were also given to observe the internal consistency, reliability, and validity of the measured constructs. Values greater than 0.6 are desirable for Cronbach's alpha and Composite Reliability (Hair et al., 2010). From the result, all measured constructs surpassed the suggested value. The table shows that TA and RE had AVE values less than 0.5, which is the suggested value for AVE. However, these measured items can still be considered since the value of their reliability is higher than 0.6 (Fornell and Larcker, 1981).Table 4 Composite reliability.
Table 4Factor Item M SD ∝ AVE CR Factor Loading
Tangibility TA1 3.83 0.82 0.706 0.467 0.721 0.735
TA2 3.86 0.82 0.747
TA4 3.87 0.80 0.550
Empathy EM1 3.63 0.82 0.819 0.544 0.826 0.818
EM2 3.83 0.85 0.736
EM3 3.68 0.75 0.756
EM4 3.65 0.88 0.628
Responsiveness RS1 3.63 3.57 0.770 0.471 0.778 0.549
RS2 3.83 3.57 0.762
RS3 3.68 3.57 0.624
RS4 3.65 3.65 0.784
Service Cost SC1 3.61 0.89 0.815 0.567 0.836 0.809
SC2 3.49 0.95 0.805
SC3 3.56 0.92 0.827
SC4 3.61 0.91 0.530
Service Quality SQ1 3.91 0.71 0.719 0.554 0.610 0.737
SQ2 3.84 0.73 0.751
Customer Expectation CE1 3.64 0.83 0.518 0.811 0.732
CE2 3.79 0.86 0.736
CE3 3.94 0.78 0.691
CE4 3.64 0.93 0.719
Energy Consumption EC1 3.75 0.86 0.808 0.515 0.807 0.758
EC2 3.53 0.96 0.676
EC3 3.64 0.87 0.836
EC4 3.56 0.92 0.574
Perceived Performance PE1 3.71 0.77 0.732 0.526 0.768 0.779
PE2 4.03 0.63 0.698
PE3 3.57 0.89 0.695
Confirmation CO1 3.57 0.93 0.754 0.616 0.635 0.691
CO2 3.83 0.75 0.727
Customer Satisfaction CS1 3.79 0.74 0.890 0.646 0.880 0.834
CS2 3.72 0.82 0.827
CS3 3.64 0.85 0.786
CS4 3.56 0.89 0.766
Note: M denotes mean, SD denotes standard deviation, ∝ denotes Cronbach's Alpha, and AVE denotes Average Variance Extracted.
To test the validity of the constructs and the model, analyses such as the Fornell-Larcker Criterion (FLC) and Heterotrait-Monotrait (HTMT) Ratio were conducted as discriminant validity tests. Presented in Table 5 are the FLC results presenting how the diagonal values are more significant than the vertical and horizontal values. The number indicates a valid result for the constructs and model (Hair et al., 2010; Ong et al., 2021a, Ong et al., 2021b).Table 5 Fornell-larcker criterion.
Table 5Latent TA EM RS SC SQ CE EC PE CO CS
TA 0.683
EM 0.504 0.738
RS 0.465 0.564 0.686
SC 0.404 0.571 0.471 0.753
SQ 0.574 0.562 0.537 0.513 0.744
CE 0.432 0.548 0.515 0.603 0.511 0.720
EC 0.464 0.547 0.482 0.578 0.535 0.59 0.718
PE 0.559 0.654 0.599 0.659 0.66 0.667 0.662 0.725
CO 0.366 0.504 0.435 0.543 0.469 0.557 0.507 0.630 0.785
CS 0.507 0.670 0.585 0.640 0.572 0.663 0.612 0.709 0.576 0.804
4.5 Model of fit
Table 6 shows the model of fit of different indices with their recommended values (>0.80; Gefen et al., 2000; RMSEA< 0.70; Steiger, 2007). Based on the results, the data fit the model as the final model for assessing the customers of MERALCO during the pandemic. With the following data results: GFI = 0.869, AGFI = 0.844, CFI = 0.926, IFI = 0.926, TLI = 0.917, CMIN/DF = 2.469 and RMSEA = 0.053 were deemed acceptable (Gumasing et al., 2022).Table 6 Model of fit measurement.
Table 6Goodness of Fit Measurement Estimates Cut-off Reference
Goodness of Fit Index (GFI) 0.869 >0.80 Gefen et al. (2000)
Gefen et al. (2000)
Adjusted Goodness of Fit Index (AGFI) 0.844 >0.80
Comparative of Fit Index (CFI) 0.926 >0.80 Gefen et al. (2000)
Tucker Lewis Index (TLI) 0.917 >0.80 Gefen et al. (2000)
Incremental Fit Index (IFI) 0.926 >0.80 Gefen et al. (2000)
Minimum Discrepancy (CMIN/DF) 2.469 <5.00 Wheaton et al. (1977)
Root Mean Square Error of Approximation (RMSEA) 0.053 <0.07 Steiger (2007)
5 Discussion
This study investigated customer consumption and satisfaction with MERALCO during COVID-19 using the integrated theories, SERVQUAL dimensions, and the Expectation-Confirmation Theory (ECT).
5.1 Service quality (SERVQUAL)
From the result, Empathy had the highest path coefficient to service quality (β = 0.39; p = 0.001). Indicators revealed that caring, consideration, attention to inquiries, and informative guidelines with the payment were relevant factors in service quality. These findings can be compared to the study by Nadiri et al. (2008), wherein they found that empathy positively influences service quality with national airlines. From their discussion, customers on national flights were delighted to assist with their luggage, support, and interest in customer needs (Nadiri et al., 2008). These findings can also be reflected in the recent actions taken by MERALCO wherein they considered the ‘No Disconnection Policy’ until the end of January 2021 for households or consumers who are consuming below 200 kWh per month (CNN Staff, 2020). With the policy implemented by MERALCO, many people got a sense of relief for the time, thus, increasing customer satisfaction.
Additionally, responsiveness positively affected service quality (β = 0.34; p = 0.001). Service quality factors were indicators such as having an early announcement or giving information, good communication, and immediate response to reports. Similarly, Ocampo et al. (2017) indicated that responsiveness is one of the service industry's most important factors or dimensions. Recently, the workforce and operation capacity of industries like MERALCO during this period are slowly increasing. Based on the results, it could be seen that consumers look forward to having good responsiveness towards the service providers during the COVID-19 pandemic (e.g., answering inquiries, reporting some electrical fault issues, and electric bill clarification).
Under SERVQUAL, the result shows that tangibility positively relates to service quality (β = 0.228; p = 0.001). Relevant factors include meter reading, electrical wiring service, and quick payment time. These findings were supported by Nadiri et al. (2008), who mentioned that tangibility was one of the factors that the service provider should observe and consider. Reflecting the current COVID-19 pandemic, Filipinos are now using online payment applications that they can easily transact with MERALCO. With this, technology can lessen the difficulty in transactions due to the service providers' limited operation.
5.2 Expectation-Confirmation Theory (ECT)
Service quality is positively related to customer expectation (β = 0.46; p = 0.001). The indicators revealed that overall good electricity service influenced the service quality positively. This result was supported by Lierop et al. (2018), who mentioned that perceived service quality positively affects customers’ expectations of public transportation. With the continuance of paying for the service, it may be seen that customers would want to have better service and performance provided for them.
The perceived performance is positively related to confirmation (β = 0.98; p = 0.001). The announcement, such as having no disconnection policy followed, was a factor for Confirmation. According to Oliver (1977), customers tend to evaluate a given actual performance which gives a significant development with his confirmation judgment. With that, confirmation showed a positive effect related to customer satisfaction (β = 0.95; p = 0.001). An accurate billing statement, flexibility in payment schemes, and informing customers that the service provider is doing their best during this pandemic affected satisfaction. These findings are in line with Lierop et al. (2018), wherein they mentioned that when the user or consumer confirms based on the product experience, it will affect overall satisfaction due to its realizations.
In addition, the result shows that service quality is positively related to perceived performance (β = 0.48; p = 0.001). Having good overall service performance shows a significant impact on perceived performance. This result can be compared with the study of Jumaan et al. (2020), wherein they implied that having an actual service experience of the users on IT performance greatly affects the quality of service that the provider gives. Moreover, energy consumption positively affects perceived performance (β:0.48 = p = 0.001). It indicated that trust in the electricity provider and continuous electricity supply were important to perceived performance. Similarly, Fu et al. (2018) mentioned that the actual consumption of such customers was said to influence perceived performance. The service provider's overall performance may also be associated with customer needs.
Overall, it could be seen that the integration of SERVQUAL and ECT could measure customer satisfaction among electricity service providers. From the result, the highest factor was perceived performance to confirmation, leading to customer satisfaction. It is also seen that service quality and energy consumption relate to perceived performance. The key indicators for customer satisfaction include the availability of payment channels, the responsiveness of service providers, and reasonable billing.
6 Theoretical, practical, managerial implications and conclusion
6.1 Theoretical contributions
This study provided a framework that may describe and determine customer behaviors in energy consumption and satisfaction through the integrated theories, SERVQUAL Dimensions and the Expectation-Confirmation Theory (ECT). Other research focused on one theory or utilized the theories separately (Thaicon et al., 2014; Jumaan et al., 2020). However, this study provided additional insight as well as an original integration and application to measure customer satisfaction during the COVID-19 pandemic. Observing the comprehensive, integrated model can more deeply describe customers' behavior among different service providers, especially during the COVID-19 pandemic. The SERVQUAL-ECT framework could be deduced as a model that can holistically measure consumer service quality and satisfaction. It could be seen that the assessment of human behavior regarding utility service quality and satisfaction in a non-conventional scenario is positive, implying that the study contributes to knowledge and theories in assessing service value. The results of this study wanted to highlight that PE, CO, and CE are the primary reasons under ECT that significantly contribute to highly positive satisfaction.
6.2 Practical implications
Different practical implications were deduced in this study in line with the service quality and satisfaction of national electric power companies during the COVID-19 pandemic. It could be seen that Tangible, Responsiveness, and Empathy significantly contributed to positive service quality, leading to high customer satisfaction. Consumers projected satisfaction by highlighting the experience of care, communication, immediate response, electricity service, and quick time paying towards MERALCO. The insight reflects other utility service providers since the indicators portray a generalized action that other service providers could readily implement.
Subsequently, customer satisfaction was affected by the performance highlight, confirmation of expectation, consumption, and costs. In line with consumption and costs, it could be deduced that consumers were well aware of electricity usage, which resulted in billing costs. The reflection of being the largest electricity provider showed empathy by suspending the electricity disconnection during the COVID-19 pandemic. In this context, MERALCO was able to deliver, which confirmed consumer expectations and had a significant positive effect on their satisfaction.
6.3 Managerial & policy implications
The findings of this study suggested that the management should extend more empathy towards their customers as we are currently in the COVID-19 pandemic. In this situation, we suggest finding ways to have a program together with the movement and support of the Government to prolong and extend the due date of bills to customers. For instance, the Government will fund MERALCO since their customer may not be able to pay their bills on time/Additionally, people nowadays can use their mobile phones in different ways. It is suggested that the management should consider the application to be more comprehensible and has easy access so that people at any level of technology experience can easily utilize it. They may also consider increasing the number of customer service assistants to increase the response time. The Government may promote loans and support among people by creating programs and providing channels for individuals to utilize, especially the drawback on employment during the COVID-19 pandemic.
6.4 Limitations and future research
Along with the theoretical and practical contributions and observing its managerial implications, there are some limitations existing in this study. First, the location of this study took place solely in the Philippines. It could be suggested to measure satisfaction utilizing the integrated framework with other electricity service providers in other countries. Second, the study was conducted during the COVID-19 pandemic, which had a lot of new protocols due to the strict lockdown implementation. Conducting the study after the COVID-19 pandemic would be recommended to measure customer satisfaction and additional data analysis. Lastly, the study utilized SEM to measure satisfaction. The trend in machine learning algorithms would provide justification and verification in consumer behavior-related studies. Moreover, limitations of SEM may be uncovered with the utilization of higher computational power.
7 Conclusion
This study aimed to determine factors affecting customer satisfaction of national electric power companies during the COVID-19 pandemic by integrating SERVQUAL and Expectation-Confirmation Theory approaches. Structural equation modeling (SEM) indicated that Tangibility (TA), Empathy (EM), and Responsiveness (RE) were positively related to Service Quality (SQ) which subsequently led to Customer Expectation (CE), Energy Consumption (EC), and Perceived Performance (PE). In addition, Service Cost (SC) was also found to significantly affect CE. Finally, higher PE was positively related to Confirmation (CO) which eventually led to Customer Satisfaction (CS).
Based on the result, it could be seen that the integration of SERVQUAL and ECT could holistically measure customer satisfaction among electricity service providers. Moreover, the highest factor was PE to CO, leading to CS. The key indicators for customer satisfaction were the availability of payment channels, the responsiveness of service providers, and reasonable billing. The exploration could be extended to other service industries for further validation.
The present research is one of the first in-depth studies that analyzed a national electric power company. Due to the lockdown, the community has limited access outside their respective houses, so utilizing technology is considered the most efficient method to transact and communicate with customers. This model may shed some light to see the current satisfaction level of the customers of MERALCO and other service-providing companies during this pandemic. With this model, the management of the service providers may get some insights into areas that may improve to enhance its service capability. Moreover, the battle against the COVID-19 pandemic is still ongoing, and it continuously affects the service quality of every business sector worldwide/
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Data availability
Data will be made available on request.
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References
Abrahim S. Mir B.A. Suhara H. Mohamed F.A. Sato M. Structural equation modeling and confirmatory factor analysis of social media use and education Inter J Edu Technol Higher Edu. 16 32 2019 1 25 10.1186/s41239-019-0157-y
Afroj S. Hanif F. Hossain M.B. Fuad N. Islam I. Sharmin N. Siddiq F. Assessing the municipal service quality of residential neighborhoods based on SERVQUAL, AHP and Citizen's score card: a case study of dhaka north city corporation area, Bangladesh J Urban Manag. 10 3 2021 179 191 10.1016/j.jum.2021.03.001
Alam M.S. Mondal M. Assessment of Sanitation Service Quality in urban slums of Khulna City based on SERVQUAL and AHP model: a case study of railway slum, Khulna, Bangladesh J Urban Manag. 8 1 2019 20 27 10.1016/j.jum.2018.08.002
Chakraborty S. Sengupta K. Structural equation modelling of determinants of customer satisfaction of mobile network providers: case of Kolkata, India IIMB Management Review 26 4 2014 211 212 10.1016/j.iimb.2014.10.005
Chiou J.-S. The antecedents of consumers' loyalty toward internet service providers Inf. Manag. 41 6 2004 685 695 10.1016/j.im.2003.08.006
Chou C. Liu L. Huang S. Yih J. Han T. An evaluation of airline service quality using the fuzzy weighted SERVQUAL method Appl. Soft Comput. 11 2 2011 2117 2128 10.1016/j.asoc.2010.07.010
Chuah H.W. Hilmi M.F. Exploring Service Quality, Customer Satisfaction and Customer Loyalty in the Malaysian Mobile Telecommunication Industry 2011 10.1109/chuser.2011.6163832 IEEE Colloquium on Humanities, Science and Engineering
Chuenyindee T. Ong A.K. Ramos J.P. Prasetyo Y.T. Nadlifatin R. Kurata Y.B. Sittiwatethanasiri T. Public utility vehicle service quality and customer satisfaction in the Philippines during the COVID-19 pandemic Util. Pol. 75 2022 101336 10.1016/j.jup.2022.101336
Fiorio C. Fiorio M. Would you say that the price you pay for electricity is fair? Consumers' satisfaction and utility reforms in the EU15 Energy Econ. 33 2011 178 187 10.1016/j.eneco.2010.05.006
Fornell C. Larcker D.F. Evaluating structural equation models with unobservable variables and measurement error J. Market. Res. 18 1981 39 50 10.1177/002224378101800104
Fu X. Zhang J. Chan F. Determinants of loyalty to public transit: a model integrating Satisfaction-Loyalty Theory and Expectation-Confirmation Theory Transportation Part A 113 2018 476 490 10.1016/j.tra.2018.05.012
Gefen D. Straub D. Boudreau M. Structural equation modeling and regression: guidelines for research practice Commun. Assoc. Inf. Syst. 4 2000 10.17705/1CAIS.00407
German J.D. Redi A.A. Prasetyo Y.T. Persada S.F. Ong A.K. Young M.N. Nadlifatin R. Choosing a package carrier during COVID-19 pandemic: an integration of pro-environmental planned behavior (PEPB) theory and Service Quality (SERVQUAL) J. Clean. Prod. 346 2022 131123 10.1016/j.jclepro.2022.131123
Gumasing M.J. Prasetyo Y.T. Ong A.K. Nadlifatin R. Determination of factors affecting the response efficacy of Filipinos under Typhoon Conson 2021 (jolina): an extended protection motivation theory approach Int. J. Disaster Risk Reduc. 70 2022 102759 10.1016/j.ijdrr.2021.102759
Hair J.F. Black W.C. Babin B.J. Multivariate Data Analysis: A Global Perspective Global Edition 2010 Pearson Education
Han S. Baek S. Antecedents and consequences of service quality in online banking: an application of the SERVQUAL instrument Adv. Consum. Res. 31 2004 208 214
Hsiao C.-T. Liu C.-S. Chang D.-S. Chen C.-C. Dynamic modeling of the policy effect and development of electric power systems: a case in Taiwan Energy Pol. 122 2018 377 387 10.1016/j.enpol.2018.07.001
Irfan M. Zhao Z.-Y. Li H. Rehman A. The influence of consumers' intention factors on willingness to pay for renewable energy: a structural equation modeling approach Environ. Sci. Pollut. Control Ser. 27 17 2020 21747 21761 10.1007/s11356-020-08592-9
Joskow P. Rose N. Wolfram C. Political constraints on executive compensation: evidence from the electric utility industry Rand J. Econ. 27 1 1996 165 182 10.2307/2555797
Jumaan I. Hashim N. Al-Ghazali B. The role of cognitive absorption in predicting mobile internet users' continuance intention: an extension of the expectation-confirmation model Technol. Soc. 63 2 2020 10.1016/j.techsoc.2020.101355
Jun M. Cai C. The key determinants of Internet banking service quality: a content analysis Int. J. Bank Market. 19 7 2001 276 291 10.1108/02652320110409825
Kim B. Oh J. The difference of determinants of acceptance and continuance of mobile data services: a value perspective Expert Syst. Appl. 38 3 2011 1798 1804 10.1016/j.eswa.2010.07.107
Lee C.-H. Zhao X. Lee Y.-C. Service quality driven approach for innovative retail service system design and evaluation: a case study Comput. Ind. Eng. 135 2019 275 285 10.1016/j.cie.2019.06.001
Li W. Feng T. Timmermans H.J.P. Li Z. Zhang M. Li B. Analysis of citizens' motivation and participation intention in urban planning Cities 106 2020 102921 10.1016/j.cities.2020.102921
Li Z. Gallagher K.P. Mauzerall D.L. China's global POWER: estimating Chinese foreign direct investment in the electric power sector Energy Pol. 136 2020 111056 10.1016/j.enpol.2019.111056
Lierop D. Badami M. El-Geneidy A. What influences satisfaction and loyalty in public transport? A review of the literature Transport Rev. 38 1 2018 52 72 10.1080/01441647.2017.1298683
MERALCO Corporate Profile 2018 https://company.meralco.com.ph/corporate-profile
MERALCO FAQS during COVID-19 2020 https://corporatepartners.meralco.com.ph/meralco-faqs-during-covid-19
Metro Pacific Investments Corporation Meralco. Metro Pacific Investments Corporation 2019 Retrieved https://www.mpic.com.ph/investor-relations/investments/meralco/
Mirza F. Rizvi S. Bergland Olvar Service quality, technical efficiency, and total factor productivity growth in Pakistan's post-reform electricity distribution companies Util. Pol. 68 2021 10.1016/j.jup.2020.101156
Mouton M. The Philippine electricity sector reform and the urban question: how metro Manila's utility is tackling urban poverty Energy Pol. 78 2015 225 234 10.1016/j.enpol.2014.11.005
Mutua J. Ngui D. Osiolo H. Aligula E. Gachanja J. Consumers satisfaction in the energy sector in Kenya Energy Pol. 48 2012 702 710 10.1016/j.enpol.2012.06.004
Nadiri H. Hussain Ekiz E. Erdogan S. An investigation on the factors influencing passengers' loyalty in the north Cyprus national airline TQM Journal 23 2008 265 280 10.1108/17542730810867272
Ocampo L. Alinsub J. Casub R. Casul R. Enquig G. Luar M. Panuncillion N. Bongo M. Ocampo C. Public service quality evaluation with SERVQUAL and AHP-TOPSIS: a case of Philippine government agencies Soc. Econ. Plann. Sci. 68 2017 10.1016/j.seps.2017.12.002
Oliver R.L. Effect of expectation and disconfirmation on postexposure product evaluations: an alternative interpretation J. Appl. Psychol. 62 4 1977 480 486 10.1037/0021-9010.62.4.480
Ong A.K. Prasetyo Y.T. Salazar J.M. Erfe J.J. Abella A.A. Young M.N. Chuenyindee T. Nadlifatin R. Ngurah Perwira Redi A.A. Investigating the Acceptance of the Reopening Bataan Nuclear Power Plant: Integrating Protection Motivation Theory and Extended Theory of Planned Behavior. Nuclear Engineering And Technology 2021 10.1016/j.net.2021.08.032
Ong A.K. Prasetyo Y.T. Lagura F.C. Ramos R.N. Sigua K.M. Villas J.A. Young M.N. Diaz J.F. Persada S.F. Redi A.A. Factors affecting intention to prepare for mitigation of “The big one” earthquake in the Philippines: integrating protection motivation theory and extended theory of planned behavior Int. J. Disaster Risk Reduc. 63 2021 102467 10.1016/j.ijdrr.2021.102467
Ouyang W. Li J. Tian L. Jiang Y. Xiao Y. Hou D. Li S. Examining the impacts of land use on cancer incidence through structural equation modeling: a case of the Pan-Yangtze River Delta, China Cities 83 2018 11 23 10.1016/j.cities.2018.05.014
Parasuraman A. Berry L.L. Zeithaml V.A. Perceived service quality as a customer-based performance measure: an empirical examination of organizational barriers using an extended service quality model Hum. Resour. Manag. 30 3 1991 335 364 10.1002/hrm.3930300304
Park E. Social acceptance of Green Electricity: evidence from the structural equation modeling method J. Clean. Prod. 215 2019 796 805 10.1016/j.jclepro.2019.01.075
Resende M. Cardoso V. Mapping service quality in electricity distribution: an exploratory study of Brazil Util. Pol. 56 2019 41 52 10.1016/j.jup.2018.08.009
Rezaei J. Kothadiya O. Tavassy L. Kroessen M. Quality assessment of airline baggage handling systems using SERVQUAL and BWM Tourism Manag. 66 2018 85 93 10.1016/j.tourman.2017.11.009
Sam E.F. Hamidu O. Daniels S. SERVQUAL analysis of public bus transport services in Kumasi Metropolis, Ghana: core user perspectives Case Studies on Transport Policy 6 1 2018 25 31 10.1016/j.cstp.2017.12.004
Shahin A. Pourhamidi M. Service TRIZ: an approach for service quality design – with a case study in the hospitality industry Int. J. Bus. Innovat. Res. 5 4 2011 291 10.1504/ijbir.2011.041052
Shokouhyar S. Shokouhyar S. Safari S. Research on the influence of after-sales service quality factors on customer satisfaction J. Retailing Consum. Serv. 56 2020 10.1016/j.jretconser.2020.102139
Staff C.N.N. Meralco extends 'no disconnection' policy until end-January 2021 How to Dispute Your Meralco Bill: Here Are Tips from Customers Who've Successfully Done So. eCompareMo 2020 Retrieved https://www.cnn.ph/news/2020/12/20/Meralco-extends-no-disconnection-policy-until-end-January-2021.html. eCompareMo https://www.ecomparemo.com/info/how-to-dispute-your-meralco-bill
Steiger J. Understanding the limitations of global fit assessment in structural equation modeling Pers. Indiv. Differ. 45 2007 893 898 10.1016/j.paid.2006.09.017
Thaicon P. Lobo A. Prentice C. Quach T. The development of service quality dimensions for internet service providers: retaining customers of different usage patterns J. Retailing Consum. Serv. 21 6 2014 1047 1058 10.1016/j.jretconser.2014.06.006
Tumsekcali E. Ayyildiz E. Taskin A. Interval valued intuitionistic fuzzy AHP-WASPAS based public transportation service quality evaluation by a new extension of SERVQUAL model: P-SERVQUAL 4.0 Expert Syst. Appl. 186 2021 115757 10.1016/j.eswa.2021.115757
Wheaton B. Muthen B. Alwin D.F. Summers G.F. Assessing reliability and stability in panel models Socio. Methodol. 8 1977 84 136
Yang Z. Jun M. Peterson R.T. Measuring customer perceived online service quality Int. J. Oper. Prod. Manag. 24 11 2004 1149 1174 10.1108/01443570410563278
Zeithaml V. Parasuraman A. Berry L. Delivering Quality Service: Balancing Customer Perceptions and Expectations 1990 The Free Press
Zhao Y.-S. Liu Y.-P. Zeng Q.-A. A weight-based item recommendation approach for Electronic Commerce Systems Electron. Commer. Res. 17 2 2015 205 226 10.1007/s10660-015-9188-1
Zhou T. Understanding continuance usage intention of mobile internet sites Univers. Access Inf. Soc. 13 2013 329 337 10.1007/s10209-013-0313-4
| 36506908 | PMC9728044 | NO-CC CODE | 2022-12-08 23:18:56 | no | Util Policy. 2023 Feb 7; 80:101454 | utf-8 | Util Policy | 2,022 | 10.1016/j.jup.2022.101454 | oa_other |
==== Front
Int J Med Inform
Int J Med Inform
International Journal of Medical Informatics
1386-5056
1872-8243
Elsevier B.V.
S1386-5056(22)00254-4
10.1016/j.ijmedinf.2022.104940
104940
Article
Discrete-event simulation study of a COVID-19 mass vaccination centre
Sala Francesca
D'Urso Gianluca ⁎
Giardini Claudio
Department of Management, Information and Production Engineering – University of Bergamo, via Pasubio 7/b, 24044 Dalmine, BG, Italy
⁎ Corresponding author.
28 11 2022
2 2023
28 11 2022
170 104940104940
3 8 2022
2 11 2022
25 11 2022
© 2022 Elsevier B.V. All rights reserved.
2022
Elsevier B.V.
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
The global spread of COVID-19 and the declaration of the pandemic status made by the World Health Organization (WHO) led to the establishment of mass vaccination campaigns. The challenges posed by the request to immunise the entire population necessitated the set-up of new vaccination sites, named Mass Vaccination Centres (MVCs), capable of handling large numbers of patients rapidly and safely. The present study focused on the evolution of MVC performances, in terms of the maximum number of vaccinated patients and primary resource utilisation ratio, while involving statistics belonging to the patient dimension. The research involved the creation of a digital model of the MVC, using the Discrete-Event Simulation (DES) software (FlexSim Healthcare), and consequent what-if analyses. The results were derived from the study of an existing facility, located within a sports centre in the province of Bergamo (Italy) and operating with an advanced MVC organisational model, in compliance with the national anti-SARS-CoV-2 legislation. The research provided additional evidence on innovative MVC organisational models, identifying an optimal MVC configuration. Besides, the obtained results remain relevant for countries where a significant portion of the population has not yet addressed the emergency, either for upcoming vaccination treatments. Furthermore, the methodology adopted in the present article proved to be a valuable resource in the analysis of the healthcare processes.
Keywords
Mass vaccination
Mass vaccination centre
Vaccines
COVID-19
Discrete-event simulation
FlexSim Healthcare
==== Body
pmc1 Introduction
The global spread of COVID-19 led the World Health Organization (WHO) to declare pandemic status and urge the development of vaccines against the virus [1], [2], [3]. As a result of the authorization of the first anti-COVID-19 vaccines [4], a mass immunisation plan was initiated. In Italy, the provision of vaccines started from the health care personnel and other limited population categories [5] in conventional sites, already equipped for the execution of regular vaccination activities. The challenges posed by the request to immunise the entire population necessitated the set-up of new vaccination sites: non-healthcare structures were appropriately modified for conducting vaccination activities, treating large numbers of people rapidly and safely. These structures, involving parking lots, schools, auditoriums and sports centres, are defined as Mass Vaccination Centres (MVCs) [6], [7] and are organized to guarantee the performance of vaccination operations. Commonly, the flow of activities is structured in admission, administrative acceptance, medical evaluation, vaccination, monitoring and exit.
Drive-through MVCs consist of vaccination campaigns organised in parking lots, where the patients, seated in their vehicles, move through all the phases of the vaccination process. These settings were well explored in literature, both from the past epidemics’ perspective [8], [9], [10] and the COVID-19 perspective [11], [12], [13], [14], and demonstrated to deliver vaccines in a rapid and safe way, minimizing physical contacts. In terms of operations, the organization of drive-through MVCs is similar to the one of the non-healthcare facilities that were converted into immunisation sites: operations are distinguished in arrival, acceptance, medical evaluation, vaccination and depart. Still, in drive-through MVCs, it is unclear how the post-vaccination monitoring phase with the treatment of the side effects is tackled, thus, necessitating further studies. Great attention was paid to the vaccination processes that take place in schools and auditoriums [15], [16], [17], [18], [19]. These settings tend to be small and decentralized, allowing the population to reach the vaccination site easily. However, spacing and ventilation are not always prioritized. On the other side, anti-SARS-CoV-2 MVCs set-up in stadiums and sports centres are examples of high-volume and high-speed settings, that prioritize physical distancing. Nonetheless, these solutions were not emphasised nor deepened in literature.
Despite the popularity of mass vaccination campaigns, the organisation of MVCs is not uniquely structured in terms of operations and physical layout. Considering the Italian legislation, defined at national [20], [21] and regional level [22], the regular performance of the anti-COVID-19 immunisation process consists of five activities: reception and administrative acceptance, medical assessment, waiting, vaccination and monitoring. Nevertheless, the lack of specific indications in the guidelines led to a certain grade of variation at the local level, thus providing additional evidence on new MVC organisational models [23], [24], [25]. For instance, the vaccination-islands model [23] redesigned the conventional MVC layout and brought medical assessment workstations and inoculation workstations close to decrease patient travelling between the two separate activities, rationalising timings.
Hence, the present study aimed at extending the available scientific data on anti-SARS-CoV-2 MVCs, by studying a facility that operates with an advanced organisational structure: the phases of medical assessment and vaccination were joined together and performed in the same location, with the objective of improving the performance of the MVC by ensuring high-quality standards. The evolution of the MVC behaviour was analysed while changing its critical parameters, like the number of active vaccination workstations, the number of active registration and acceptance workstations and the number of people entering the system. The pursued methodology consisted of a Discrete-Event Simulation (DES), able to virtually replicate the processes of a real-world system as a discrete series of events over time. FlexSim Healthcare was the selected software-package and it is completely dedicated to medical environments. The software was successfully adopted in distinct healthcare challenges [26], [27], [28], demonstrating the capability to handle the anti-SARS-CoV-2 MVCs analysis too. Experiments were conducted on an existing MVC for the prevention of SARS-CoV-2 infections, established within a university sports centre in the province of Bergamo. At the time of the analyses, the facility was serving first, second and third doses, planning to be the reference point for the delivery of the fourth doses too.
2 Methodology
2.1 Vaccination centre process
The mass vaccination process under examination was conceptualized as a set of five macro-activities: entrance, reception and administrative acceptance, medical assessment and vaccination, monitoring and leaving.
The immunisation process begins when the patient arrives at the outdoor entrance, where a first checkpoint station is located. There, an operator is responsible for managing the incoming flow of patients and verifying the reservations. Then, the patient walks down a pathway leading to an outdoor area, enclosing two sanitization spots and the outdoor waiting area. The patient is subjected to rapid sanitization and waits his turn in a waiting area organised according to the dose of inoculated vaccine. An operator responsible for the temperature control determines the access to the facility and, in particular, the reception and administrative acceptance area. Each workstation is handled by a clerk whose role consists of recording the patient's administrative data, issuing an alphanumeric code and guiding the patient to the subsequent activities. The patient reaches the appropriate waiting area and waits until his alphanumeric code appears on the digital screens. Then, the patient goes to the proper inoculation workstation, where a doctor and a registered nurse are in charge to execute the medical assessment and vaccination, respectively. The two activities are performed simultaneously: the doctor collects the pre-vaccination medical history and informed consent, while the registered nurse prepares the equipment and executes the inoculation. An additional waiting period for surveillance of vaccinated patients occurs. The person stays in the monitoring area and, if no adverse reaction arises during the waiting period, he is allowed to exit the process. Otherwise, the waiting in the monitoring area is being extended.
The average mass vaccination process under investigation was conceptualized in Fig. 1 , using the BPMN graphical representation. Besides, variations from the ordinary activities were observed throughout the entire process. Although unusual, the patient might be unable to continue the vaccination pathway due to inadequate temperature (at the entrance) or inadequate clinical conditions (during the medical assessment); in rare instances, the patient may have to change vaccination stations because of his ineligibility for the type of vaccine delivered by that station. All the mentioned variations were included in the development of the virtual replica of the MVC to better capture the variability of the process.Fig. 1 Main patient flow of the mass vaccination process.
2.2 Vaccination centre modelling and simulation
Considering the physical constraints of the system (Fig. 2 ), a 3D virtual model was developed using FlexSim Healthcare DES software. The healthcare process was accurately replicated following the elements defining the macro-activities of the conceptualised process (Section 2.1). All structural components were represented through fixed resources. Sets of multiple location objects (chairs) represented the twelve waiting areas: two external and ten internal (eight assigned to the eight rows of vaccination stations and two linked to post-vaccination monitoring). The waiting areas were designed based on rules of distancing in closed environments and their capacity was sufficiently large to face great variations of the incoming demand. Two rows of five workstations stood for the registration and acceptance desks (R1-R10). The medical assessment and vaccination workstations, organised into eight rows of six stations, were distributed equally over the two halves of the facility and categorised with a letter, (A-H) indicating the row, and a number (1–48), identifying their position uniquely. For the virtual representation of the healthcare operators, task executors were involved (Table 1 ).Fig. 2 Simplified layout of the mass vaccination centre. Note: blue arrows represent the patient routes, while the grey rectangles are the stations where the patients might transit. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article)
Table 1 Roles and quantities of the task executors.
Role of the task executor Number of task executors Position of the task executor
Per patient Overall
Reservation controller 1 1 Outdoor entrance
Temperature controller 1 2 Facility entrance
Registration clerk 1 10 Registration station (R1-R10)
Registered nurse 1 48 Vaccination station (A1-H48)
Physician 1 48 Vaccination station (A1-H48)
2.2.1 Process parameters and distributions
The resulting model consisted of the 3D interface (Fig. 3 ) integrated with two process flows.Fig. 3 3D Simulation model of the mass vaccination centre.
The first process flow was responsible for creating flow objects (patients): the flow of patients arriving at the vaccination centre was scheduled with an equally time-spaced frequency, as access to the vaccination centre was programmed by appointment. According to the daily reports collected by the MVC structure at the time of the study, 60 % of the patients accessing the structure received the third dose.
The second process flow regulated the interactions between the 3D elements: logics and activities of the vaccination process observed in Section 2.1 were digitally translated with a sufficient level of accuracy to allow a dynamic representation of the healthcare activities. The main time parameters are displayed in Table 2 . The values of the registration and medical assessment and vaccination (dose 1 and dose 2/3) activities came from daily reports collected by the MVC structure under investigation: inconsistent records and outliers were discarded and the probability distributions that best represented the cleansed data set were automatically and precisely determined using FlexSim ExpertFit tool. The physical or geometric interpretation of the identified continuous time distributions is determined by three parameters: gamma (location point of the distribution’s range of value), beta (scale of measurement for the values in the distribution's range) and alpha (distribution shape within the general family of distributions of interest). In some distributions (e.g., normal and exponential), the gamma parameter is absent. Along with the characterisation of time distribution parameters, ExpertFit provided an indication of the sampling error (Table 2). Furthermore, the goodness-of-fit of the fitted distributions was confirmed through a Chi-Square test (Table 3 ).Table 2 Time distribution of the process activities.
Process activity Time distribution (parameters) [s] Sampling Error [s]
Reservation check Normal (7.00, 2.00) /
Sanitization Deterministic /
Temperature check Deterministic /
Administrative acceptance Lognormal2 (6.50, 20.00, 0.60) − 0.07 = 0.23 %
Medical assessment and vaccination (dose 1) Invertedweibull (39.50, 170.30, 2.50) − 9.52 = 3.38 %
Medical assessment and vaccination (dose 2/3) Loglogistic (41.30, 84.50, 2.60) − 2.51 = 1.68 %
Monitoring Exponential (900.00, 1.00) /
Monitoring (presence of adverse events) Exponential (1200.00, 1.00) /
Table 3 Results of the Chi-Square test.
Process activity Statistic test Critical value for level of significance
0.10 0.05 0.01
Administrative acceptance 11.23 21.06 23.69 29.14
Medical assessment and vaccination (dose 1) 8.34 13.36 15.51 20.09
Medical assessment and vaccination (dose 2/3) 14.76 18.55 21.03 26.22
The time information of the reservation check, sanitization, temperature check and monitoring activities came directly from semi-structured interviews with the MVC administrative experts. The collected data converged into the distribution’s values reported in Table 2 and their appropriateness was further verified by experienced medical personnel, responsible of these processes on a day-to-day basis. Note that the time values belonging to the sanitization and temperature check activities were assumed to be deterministic as these tasks, given their automation, were not subjected to randomness.
Along with the main activities reported in Table 3, the second process flow included additional activities generating alternative sub-processes to the primary vaccination pathway. The frequency of these tasks was determined in accordance with the workers of the MVC under investigation and was extremely low.
2.2.2 Process performance measures
The performances of the mass vaccination system, as well as patient statistics, were evaluated through process performance indicators (KPIs). The primary performance measure was the MVC productivity, measured as the maximum number of patients treated by the healthcare facility daily. Productivity was also measured in relation to the number of active vaccination stations, resulting in the productivity indicator of each vaccination site. Productivity involved the average utilisation rate of the vaccination workstation, defined as the time dedicated to medical assessment and vaccination activity over the workstation total available time. Conceptually, this indicator represented an approximate measure of the utilisation percentage of the operators working within the vaccination workstation. Additional performance measures regarded patient dimension.
2.2.3 Process validation
The adequacy of the simulation model with respect to the intended application was assessed during the validation phase. The virtual model representing the vaccination processes of a mass vaccination centre underwent an initial qualitative validation (face validity) [29]: health workers with distinct professional roles confirmed the correctness of the representation of the vaccination process, viewed in terms of conceptual and virtual translation. Also, performance results and patient statistics were considered reasonable, thus validating the model subjectively. A subsequent quantitative validation [29] was performed: a model, appropriately fed with information coming from a specific working day, was developed and compared to the observable system using statistical tests. The selection of the parameter for the validation analysis fell on an element, whose values were not provided as input to the modelling task: patient waiting between the registration phase and the medical assessment and vaccination phase. In particular, the patient waiting data obtained from the simulation was compared to the actual waiting data collected on that specific day. The p-value obtained from the analysis of variance (ANOVA) (Table 4 ) showed no statistically significant difference between the developed model and the real system, thus validating the model objectively.Table 4 Results of the ANOVA.
Validation parameter Real data [s] Simulation data [s] p-value
Average waiting time for vaccination workstation availability 247 213 0.165
3 Results and discussion
Multiple simulations of the mass vaccination process over a single working day (12 h, run time) were conducted respecting the operating conditions of the considered facility. The simulation of a single standard vaccination day was possible due to the fact that the MVC operated 7 days a week, without distinction on weekdays and workdays (the condition was tested by analysis on long-term data). Simulation runs were iterated four times, ensuring the possibility to capture consistent results and returning outputs described by average values (95 % confidence interval) and standard deviations.
The initial simulations (Fig. 4 ) examined the productivity of the system. Fig. 4a reports the maximum number of patients able to be processed for each level of active inoculation stations, ranging from a minimum of 10 sites to a maximum of 40. Specifically, the number of active vaccination stations was allocated following the operating conditions dictated by the MVC structure under investigation, namely, the allocation of stations in proportion to the scheduled type of vaccine dose (60 % of patients performed the third dose, while the remaining 40 % were divided into first and second dose). As a result, out of 10 active vaccine stations, 4 were allocated to the first and second dose, while 6 were allocated to the third dose. Considering the mentioned percentages of scheduled vaccination doses and the MVC working conditions, the increase in active inoculation stations occurred by multiples of 5: 1 station dedicated to the first dose, 1 to the second dose and 3 to the third dose. Depending on the number of active vaccination stations, the facility immunised between one thousand and three thousand people daily. Moreover, the rate of patients processed per vaccination site reported a performance reversal. Growing gains were observed by increasing the number of active inoculation sites up to 20, while decreasing gains were detected by overpassing it. Additionally, a reduction in productivity rate was detected expanding the accessibility of vaccination sites beyond 35 inoculation sites.Fig. 4 Productivity indicators. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article)
The average utilisation rates of vaccine sites dedicated to the third dose are plotted in Fig. 4b and confirmed the productivity trend (processed patients per vaccination site, Fig. 4a). In fact, considering the four efficient scenarios (coloured markers), the utilisation rate increased up to the point of performance reversal point, beyond which, it decreased. The utilisation rates of the vaccination station dedicated to the delivery of third doses differed widely for the four efficient scenarios: going from a minimum of 59 % to a maximum of 82 %. The non-utilisation of the inoculation station might be attributable to several events, such as the sanitisation activity that takes place between the vaccination of a patient and the following one and the patient travelling to reach the inoculation station. In addition, the inactivity was also caused by the absence of a patient when the workstation was actually available, occurring especially at the beginning (8.00 – 9.00 am) and ending (7.00 – 8.00 pm) phases of the process. Moreover, these events might also be dictated by system inefficiencies, such as the lack of logic in guiding patients to less saturated vaccine lines.
Patient statistics are reported in Fig. 5 . Patient stay time within the facility is represented in Fig. 5a, presenting a value of 25 min approximately for the efficient scenarios involving more vaccination resources. The configuration involving less vaccination stations presented a slightly longer patient stay time, exceeding 27 min. For the same level of active vaccination sites, almost no difference in terms of the average patient length of stay was recorded between the under-saturated scenarios (points fed by a fewer number of input patients and located prior to the efficient scenario) and the efficient scenario (coloured markers). Conversely, exceeding the efficient scenario, the patient stay time tended to increase with a linear trend. Therefore, the efficient MVC settings could be reached without negatively impacting on patient metrics: first and foremost, patient stay time. In contrast, using a number of vaccine resources that exceeded the one defined by the efficient scenario, a fraction of the patients accessing the process did not complete the pathway and, simultaneously, the patient statistics worsen in a considerable way, impacting the quality perception of the service provided. Fig. 5b reported the information on stay time dispersion for the four efficient scenarios (coloured data): as the number of active vaccine sites increased, there was a gradual growth in the dispersion of stay time data and in the likelihood of patients staying in the facility for a longer time. Actually, the progressive elongation of the right tail for the efficient configurations still corresponded to modest stay time values.Fig. 5 Patient indicators. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article)
Fig. 5c explores the composition of the patient stay time variable, partitioned into receiving direct care (e.g., medical assessment and vaccination), receiving indirect care (e.g., non-healthcare activities) and idleness. Depending on the number of active vaccine sites, the average idle time varied from 22 % to 35 % of the stay time value of the efficient scenarios. Idle was prompted by two main causes: waiting (e.g., waiting for the registration or medical assessment and vaccination tasks) and travelling to the different locations within the system (e.g., movement for reaching the outdoor waiting room from the reservation control station). Waiting represents pure waste: the inactivity related to the outdoor waiting area was negligible, while the inactivity related to the indoor waiting area was between the 13 % and 27 % of the stay time value for the efficient configurations. Travelling may be considered as an unavoidable non-value-added activity in the immunisation process and its value must be minimised by simplifying trajectories and preventing unnecessary motion. Given the physical design of the MVC, patient trajectories were already set up to perform all healthcare activities while minimising motions. Travelling time of the efficient configurations varied between 8 % and 10 % of the average patient stay time. Hence, the outcomes further validated the adoption of the efficient scenarios, showing that the portion of patient inactivity was adequate and sustainable when compared to its total length of stay.
The MVC optimal configuration, identifiable as the system design able to process the maximum number of patients given the same number of active vaccination sites while maintaining suitable patient metrics, was characterised by 20 active vaccination workstations. Statistics are summarised in Table 5 .Table 5 Simulation KPIs and patient metrics of the optimal configuration, 20 active vaccination workstations.
KPIs Optimal configuration:
20 active vaccination sites
Processed patients 2921 patients
Avg. processed patients per vaccination site 146 patients / site
Avg. patient stay time 24.73 min
Avg. patient idle time 5.61 min
Avg. patient moving time 2.35 min
Avg. patient waiting for vaccination site time 3.26 min
Avg. utilisation of the D3 vaccination site 9.84 h/day
As shown in Fig. 6 , additional simulations were conducted on the optimal configuration. Sensitivity analyses assessed the impact, in terms of processed patients and patient stay time, of varying the length of the patient arrival period (Fig. 6a) and the number of active acceptance and registration stations (Fig. 6b).Fig. 6 Additional simulations.
The MVC access time period that ensured to process within the scheduled time all patients entering the healthcare service (equally spaced in time, between 8.00 am and 7.00 pm), was computed and found to be equal to a single interval of 11 h. Extending the length of the access time by one hour (until 8.00 pm), 3 % of the input patients were not treated as these patients would end their vaccination process beyond the operating time of the MVC. Reducing the variable by one hour (until 6.00 pm) made possible to treat all 100 % of the input patients, however the stay time of the patients increased considerably. This latter variation impacted on the stay time of just a portion of patients, as evidenced by the dispersion increase. In fact, unlike patients entering at the beginning of the day, those who access the facility in the late afternoon experienced an extension of their stay. Besides, strategies that include splitting the length of the access time into multiple intervals did not lead to improved results.
The impact of changing the number of active registration stations was investigated: activating fewer than 6 registration stations avoided executing all the patients accessing the system and affected patient metrics negatively. The use of 6 or more registration stations allowed to treat 100 % of patients in input to the process. Moreover, moving from 6 to 10 acceptance stations, a detectable reduction in the patient length of stay was recorded.
Lastly, a system blockage that prevents the vaccination process from running properly was tested in Fig. 6c: the block was conceived as an interruption of the crucial healthcare activities (e.g., registration, medical assessment and vaccination), characterised by 4 different durations (15, 30, 45 and 60 min) and 4 different times of onset (8.00 am, 11.00 am, 2.00 pm and 5.00 pm). During this event, the patients kept accessing the system, positioning themselves in the external waiting area. The MVC system reacted positively to the 15-minute and 30-minutes block, by treating 100 % of patients in almost all the simulations. Although MVC systems with 45-minute and 60-minute blocks were able to vaccinate large numbers of patients entering the process (at least 99 % and 97 % of input patients, respectively), they exhibited the first difficulties.
4 Conclusion
The present article aimed at extending the available scientific evidence on anti-SARS-CoV-2 MVCs, leveraging on an existing facility located within an Italian university sports centre. The study was conducted by means of simulation experiments on a digital model of the MVC, using FlexSim Healthcare software, in combination with sensitivity analyses. The performance evolution of the MVC, measured in terms of the maximum number of vaccinated and utilisation rate of the vaccination workstation, was assessed while modifying the most important parameters. Special considerations were given to the patient dimension.
Furthermore, for MVCs comparable to the one described in the current document, an optimal configuration was defined. Activating 20 active inoculation sites, managed by 20 physicians and 20 registered nurses, the facility processed a total of 2921 patients daily with a ratio of processed patients per inoculation site which was maximum and amounted to 146. The considered configuration involved the activation of only 41 % of the available vaccination resources, underlining that the 48 vaccination sites were oversized compared to the efficient operating conditions of the facility. Nevertheless, the percentage also suggested the likelihood of achieving efficient results with a fair number of resources, bearing in mind that medical operators are scarce resources. The utilisation rate of the vaccination workstations dedicated to the inoculation of the third doses reached an elevated level, on average amounted to 82 %. The inactivity of the vaccination resource was attributable to sanitation activities, patient time to reach the vaccination site from the waiting area and, lastly, system inefficiencies. To further minimise this latter, it is critical to design real-time systems guiding patients to less saturated vaccination lines. On behalf, the utilisation rate of the vaccination resources should be further investigated to not overburden the healthcare operators. Observing patient statistics, the stay time within the facility averaged 24.73 min. Idle time, distinguishable in patient waiting times and travelling times, constituted 23 % of the overall stay time. The perceived level of idle time attested a less overcrowded system, thereby leading to higher service quality.
However, it should be noted that MVC configurations, widely divergent in terms of structural and functional conditions from the one considered, may experience different productivity and patient outcomes. For instance, different logics in terms of patient arrival rate during the day and week significantly affect patient metrics (e.g., waiting times). Future studies on this aspect would broaden the outcomes, justifying the presence of differences in comparable instances.
To conclude, the present study brought scientific evidence on the viability of vaccination processes characterized by organizational layouts which were distinct from those available in the literature and those specified by the anti-COVID-19 guidelines. The results derived from the research are particularly important for countries and institutions that, unlike Italy, are characterized by a low level of vaccine cycle completion; in fact, being able to leverage from the very beginning on an optimized organizational layout with a sufficient, but minimum number of healthcare resources, favours a rapid and effective mass immunisation program. Also, the findings are adequate in facing comparable and future epidemics, driving researches towards the development of innovative and improved mass vaccination models.
Lastly, the study and analysis of the mass vaccination processes demonstrated to be successfully performed through the methodology proposed in the current article. Indeed, the execution of a simulation study by means of a dedicated DES software allowed the analysis of a specific healthcare process: the mass vaccination campaign. Besides the possibility of gaining knowledge about the normal operation of a healthcare process, this methodology allowed for the investigation of alternative strategies, without committing resources for their implementation. Therefore, the result deriving from the execution of the current methodology broadened the scope of application of the DES techniques in the healthcare field.
CRediT authorship contribution statement
Francesca Sala: Software, Validation. Gianluca D'Urso: Conceptualization, Methodology, Supervision. Claudio Giardini: Conceptualization, Validation, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Supervision.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
This work was made possible thanks to the professional contribution of the healthcare operators and volunteers of the civil protection operating at the CUS Mass Vaccination Centre in Dalmine (Bergamo). The authors wish to acknowledge the support received by Asst Bg Ovest, especially Eng. Andrea Ghedi, and the president of CUS, Dr. Claudio Bertoletti.
Summary points • The global spread of COVID-19 and the establishment of mass vaccination campaigns required the set-up of specific vaccination sites, known as Mass Vaccination Centres (MVCs), capable of handling large numbers of patients rapidly and safely.
• Multiple and different MVCs physical layouts and organizational structures are present, leading to a certain grade of variation in the execution and performance of the vaccination process.
• The present study provided evidence on the efficiency of the vaccination campaign within a sports centre, operating with an organizational layout distinct from those available in the literature and those specified by the anti-COVID-19 guidelines.
• The work outlined a methodology, based on DES tools and techniques, for the study and analysis of healthcare processes.
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References
1 T.A. Ghebreyesus, WHO Director - General’s opening remarks at the media briefing on COVID-19, World Heal. Organ. (2020). https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020.
2 Cucinotta D. Vanelli M. WHO declares COVID-19 a pandemic Acta Biomed. 91 2020 157 160 10.23750/ABM.V91I1.9397 32191675
3 Bull S. Jamrozik E. Binik A. Parker M.J. SARS-CoV-2 challenge studies: ethics and risk minimisation J. Med. Ethics. 47 12 2021 10.1136/MEDETHICS-2020-106504
4 European Medicines Agency, EMA recommends first COVID-19 vaccine for authorisation in the EU, Eur. Med. Agency. (2020). https://www.ema.europa.eu/en/news/ema-recommends-first-covid-19-vaccine-authorisation-eu.
5 Ecdc, COVID-19 vaccination and prioritisation strategies in the EU/EEA Key facts, (2020).
6 Goralnick E. Kaufmann C. Gawande A.A. Mass-vaccination sites - an essential innovation to curb the covid-19 pandemic N. Engl. J. Med. 384 2021 10.1056/NEJMP2102535/SUPPL_FILE/NEJMP2102535_DISCLOSURES.PDF
7 Gianfredi V. Pennisi F. Lume A. Ricciardi G.E. Minerva M. Riccò M. Odone A. Signorelli C. Challenges and opportunities of mass vaccination centers in covid-19 times: A rapid review of literature Vaccines 9 6 2021 574 34205891
8 Wiggers J. Van De Kracht T. Gupta A. Heragu S.S. Design and analysis of a simulation model for drive-through mass vaccination Ind. Eng. Res. Conf. 2011 https://www.proquest.com/openview/f001117c67d4765c691234c66f146f38/1?pq-origsite=gscholar&cbl=51908
9 Weiss E.A. Ngo J. Gilbert G.H. Quinn J.V. Drive-through medicine: a novel proposal for rapid evaluation of patients during an influenza pandemic Ann. Emerg. Med. 55 2010 268 273 10.1016/J.ANNEMERGMED.2009.11.025 20079956
10 Gupta A. Evans G.W. Heragu S.S. Simulation and optimization modeling for drive-through mass vaccination-a generalized approach Simul. Model. Pract. Theory 37 2013 99 106
11 Swift M.D. Aliyu M.H. Byrne D.W. Qian K. McGown P. Kinman P.O. Hanson K.L. Culpepper D. Cooley T.J. Yarbrough M.I. Emergency preparedness in the workplace: the flulapalooza model for mass vaccination Am. J. Public Health. 107 2017 S168 S176 10.2105/AJPH.2017.303953/SUPPL_FILE/SWIFT_201719054_SUPPLEMENTAL_TABLE_A.XLSX 28892449
12 D. Resnick-Ault, S.K. Wendel, M.D. Skaggs, S. White, R.D. Zane, Drive-Through Efficiency: How to Prepare for and Execute a Mass-Vaccination Event | Catalyst non-issue content, (2021). https://catalyst.nejm.org/doi/full/10.1056/CAT.21.0058.
13 Asgary A. Valtchev S.Z. Chen M. Najafabadi M.M. Wu J. Artificial intelligence model of drive-through vaccination simulation Int. J. Environ. Res. Public Health. 18 2021 1 10 10.3390/IJERPH18010268
14 A. Asgary, M.M. Najafabadi, R. Karsseboom, J. Wu, A Drive-through Simulation Tool for Mass Vaccination during COVID-19 Pandemic, Healthc. (Basel, Switzerland). 8 (2020). https://doi.org/10.3390/HEALTHCARE8040469.
15 K. Aaby, J.W. Herrmann, C. Jordan, M. Treadwell, K. Wood, Improving Mass Vaccination Clinic Operations, in: Int. Conf. Heal. Sci. Simul., 2005. http://130.203.136.95/viewdoc/summary?doi=10.1.1.150.5518.
16 Ha C. Taylor C. Modi J.R. Mass Vaccinations at the United States Naval Academy Heal. Secur. 14 2016 382 388 10.1089/HS.2016.0030
17 J. Caum, S. Alles, Ready or Not: Analysis of a No-Notice Mass Vaccination Field Response in Philadelphia, Https://Home.Liebertpub.Com/Bsp. 11 (2013) 262–270. https://doi.org/10.1089/BSP.2013.0064.
18 Herl Jenlink C. Kuehnert P. Mazyck D. Key components of a school-located vaccination clinic: lessons learned from fall 2009 J. Sch. Nurs. 26 4_suppl 2010 14S 26S 20479010
19 Phillips F.B. Williamson J.P. Local Health Department Applies Incident Management System for Successful Mass Influenza Clinics J. Public Heal. Manag. Pract. 11 2005 269 273 10.1097/00124784-200507000-00002
20 Ministero della Salute, Adozione Piano strategico nazionale dei vaccini per la prevenzione delle infezioni da SARS-CoV2, (2021). https://www.trovanorme.salute.gov.it/norme/renderNormsanPdf?anno=2021&codLeg=78657&parte=1 &serie=null.
21 Ministero della Salute, Raccomandazioni per l’organizzazione della campagna vaccinale contro SARS-CoV-2/COVID-19 e procedure di vaccinazione, (2021). http://www.salute.gov.it/portale/documentazione/p6_2_2_1.jsp?lingua=italiano&id=2986.
22 La Giunta Regione Lombardia, Approvazione del piano regionale vaccini per la prevenzione delle infezioni da SARS-COV2, (2021). https://www.regione.lombardia.it/wps/wcm/connect/6031d158-b1a0-4ab4-91ec-657bb3231d1f/DGR+4353.pdf?MOD=AJPERES&CACHEID=ROOTWORKSPACE-6031d158-b1a0-4ab4-91ec-657bb3231d1f-nZeVlyV.
23 Signorelli C. Odone A. Gianfredi V. Capraro M. Kacerik E. Chiecca G. Scardoni A. Minerva M. Mantecca R. Musarò P. Brazzoli P. Basteri P. Bertini B. Esposti F. Ferri C. Alberti V.A. Gastaldi G. Application of the “immunization islands” model to improve quality, efficiency and safety of a COVID-19 mass vaccination site Ann. Ig. 33 2021 499 512 10.7416/AI.2021.2456 34113956
24 Brambilla A. Mangili S. Macchi M. Trucco P. Perego A. Capolongo S. Covid-19 Massive Vaccination Center Layouts Acta Biomed. Atenei Parm. 92 2021 e2021446 e 10.23750/ABM.V92IS6.12229
25 Pilati F. Tronconi R. Nollo G. Heragu S.S. Zerzer F. Digital twin of covid-19 mass vaccination centers Sustain. 13 13 2021 7396
26 Pan C. Zhang D. Kon A.W.M. Wai C.S.L. Ang W.B. Patient flow improvement for an ophthalmic specialist outpatient clinic with aid of discrete event simulation and design of experiment, Health Care Manag. Sci. 18 2 2015 137 155
27 Pongjetanapong K. O’Sullivan M. Walker C. Furian N. Implementing complex task allocation in a cytology lab via HCCM using Flexsim HC Simul. Model. Pract. Theory. 86 2018 139 154 10.1016/J.SIMPAT.2018.05.007
28 F. Sala, M. Quarto, G. D’urso, Simulation Study of the Impact of COVID-19 Policies on the Efficiency of a Smart Clinic MRI Service, Healthc. 2022, Vol. 10, Page 619. 10 (2022) 619. https://doi.org/10.3390/HEALTHCARE10040619.
29 R.G. Sargent, Verification and validation of simulation models, Proc. - Winter Simul. Conf. (2010) 166–183. https://doi.org/10.1109/WSC.2010.5679166.
| 36495700 | PMC9728082 | NO-CC CODE | 2022-12-08 23:18:56 | no | Int J Med Inform. 2023 Feb 28; 170:104940 | utf-8 | Int J Med Inform | 2,022 | 10.1016/j.ijmedinf.2022.104940 | oa_other |
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Micros Today
Micros Today
mt
Microscopy Today
1551-9295
2150-3583
Oxford University Press
10.1017/S1551929521001322
mt-29-6-0042
Microscopy Education
AcademicSubjects/SCI00960
Remote Learning Facilitated by MyScope Explore
Holmes Natalie P Australian Centre for Microscopy and Microanalysis, The University of Sydney, Sydney, NSW 2006, Australia
Griffith Matthew J School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, NSW 2006, Australia
Barr Matthew G Centre for Organic Electronics (COE), University of Newcastle, Callaghan, NSW 2308, Australia
Nicolaidis Nicolas C Centre for Organic Electronics (COE), University of Newcastle, Callaghan, NSW 2308, Australia
Bhatia Vijay Australian Centre for Microscopy and Microanalysis, The University of Sydney, Sydney, NSW 2006, Australia
Duncan Michael Hawkesbury Institute for the Environment, Western Sydney University, Penrith, NSW 2751, Australia
McCarroll Ingrid Australian Centre for Microscopy and Microanalysis, The University of Sydney, Sydney, NSW 2006, Australia
Whiting Jenny Microscopy Australia Headquarters, The University of Sydney, Sydney, NSW 2006, Australia
Dastoor Paul C Centre for Organic Electronics (COE), University of Newcastle, Callaghan, NSW 2308, Australia
Cairney Julie M Australian Centre for Microscopy and Microanalysis, The University of Sydney, Sydney, NSW 2006, Australia
School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, NSW 2006, Australia
Microscopy Australia Headquarters, The University of Sydney, Sydney, NSW 2006, Australia
† These authors contributed equally.
[email protected]
01 11 2021
01 11 2021
01 11 2021
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© The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America
2021
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Abstract:
In response to the requirements imposed by the COVID-19 pandemic in 2020, we developed a remote learning undergraduate workshop for 44 students at the University of Newcastle by embedding scanning electron microscope (SEM) images of Maratus (Peacock) spiders into the MyScope Explore environment. The workshop session had two main components: 1) to use the online MyScope Explore tool to virtually image scales with structural color and pigmented color on Maratus spiders; 2) to join a live SEM session via Zoom to image an actual Maratus spider. In previous years, the undergraduate university students attending this annual workshop would enter the Microscopy Facility at the University of Newcastle to image specimens with SEM; however, in 2020 the Microscopy Facility was closed to student visitors, and this virtual activity was developed in order to proceed with the educational event. The program was highly successful and constitutes a platform that can be used in the future by universities for teaching microscopy remotely.
microscopy
remote learning
virtual tools
outreach
structural color
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pmcIntroduction
In this article we detail the virtual lab that was developed, including both the MyScope Explore simulation and the remote interactive live SEM demonstration, showcasing the journey to expanding remote learning tools during the COVID-19 pandemic. In 2011 Microscopy Australia (formerly AMMRF) created—and now maintains—the online MyScope microscopy simulation training platform, which is a highly accessed tool. In the period from June 25, 2020 to June 24, 2021, MyScope was visited by 141,699 users and received 1,182,252 page views. The user visits were from a total of 184 countries, with the top 7 countries accessing the site comprising 57% of all users, including the USA, India, UK, Australia, Pakistan, Germany, and China.
The MyScope (myscope.training) online learning modules, including MyScope Explore (myscope-explore.org.au), were developed to provide an online learning environment for those who want to learn about microscopy [1–3]. The platform provides insights into the fundamental science behind different types of microscopies, explores what can and cannot be measured by different material systems, and provides a realistic simulated operating experience for state-of-the-art microscopes (Figure 1).
Figure 1: Images of the MyScope Explore SEM simulation environment, which can be accessed at myscope-explore.org.au/virtualSEM_explore.html.
The Workshop Platform – Pre-COVID-19 versus Now
The University of Newcastle Centre for Organic Electronics (COE) Spring and Winter Schools were developed as a platform for teaching undergraduate students from a range of disciplines and for developing and testing laboratory exercises that can be used beyond this event at other teaching institutions [4]. The electron microscopy component of the 3-day workshop is usually comprised of 45-minute excursions to the electron microscopy and X-ray (EMX) facility, whereby the undergraduate students participate in an electron microscopy demonstration of all stages of data collection, including sample loading, alignment, and detector selection—to careful selection of aperture size, working distance, brightness, contrast, focus, and magnification settings for image optimization. For the 2020 workshop, held October 7–9, undergraduate students were not permitted to enter the EMX facility due to capacity limits associated with social distancing rules imposed during the COVID-19 pandemic; hence, a remote learning program was developed. The remote learning program comprised two main components:
1. MyScope Explore Online Simulation: A remote learning simulation environment where the students simulated measuring Maratus (Peacock) spider specimens.
2. Interactive Live SEM Session: A practical session where the students remotely measured the real Peacock spider specimens on a Zeiss Sigma VP field emission SEM at the University of Newcastle EMX facility by interacting over Zoom with a scientist operating the microscope (Figure 2).
Figure 2: (a) Teaching SEM at the University of Newcastle workshop in 2019, an in-person event. (b) Teaching SEM for the same workshop in 2020 via a remote learning program during the COVID-19 pandemic, comprising both an online MyScope Explore simulation component and an interactive live SEM session via Zoom. In 2020, social distancing rules were in place, a major driver for the development of the remote learning program. Note that in the future, students will also be able to join from home, rather than a university computer lab space as pictured.
The Undergraduate Student Cohort
The undergraduate student cohort in 2020 comprised 44 students from a range of degrees, including Bachelors of Science, Engineering, Mathematics, Education, Computer Science, Biomedical Science, and Technology. Students ranged from first year to fourth (final) year undergraduate levels, and all were enrolled at the University of Newcastle.
A Unique Specimen for Teaching – Structural Color in Maratus (Peacock) Spiders
Maratus occasus is a recently discovered Peacock spider species in Queensland, Australia, belonging to the Maratus tasmanicus group (Figures 3a and 3b) [5]. This particular specimen was chosen for the workshop to complement the samples examined in previous years when students had studied organic semiconductor nanoparticle films (at Spring and Winter Schools) and self-assembled colloidal nanoparticle photonic crystals (in the PHYS3390 course). The choice of naturally occurring structural-colored specimens in 2020 enabled us to build upon previous years’ work imaging nanostructured materials that interact with light.
Figure 3: Maratus spiders containing structurally colored blue scales located on the abdomen (dorsal opisthomal plate). (a) Photograph of species Maratus occasus (male) collected from Lake Broadwater, Queensland. (b) Photograph of species Maratus tasmanicus (male) collected from Point Cook, Victoria. The typical species size for Maratus occasus and Maratus tasmanicus is 4 mm. In (a) the blue plate-like scales (PLS) (Type I) and orange brush like-scales (BLS) (Type II) are annotated. Photographs courtesy of Joseph Schubert.
Structural color occurs in nature in many organisms, including butterflies [6], peacocks [7], spiders [8,9], and rainforest beetles [10]. In addition, structural color occurs in naturally occurring minerals such as opal gemstones and can also be produced synthetically, for example, in self-assembled colloidal nanoparticle arrays [11] and 3D printed microscale objects [12]. Structural color results from the interaction of light waves with a structural feature that exhibits the same order of size as the wavelength of light, noting the wavelength of visible light ranges from 380 to 750 nm. Spiders employ a variety of structural coloration mechanisms, including multilayer reflectors, three-dimensional photonic crystals, and diffraction gratings.
Structural color in Australian Peacock spiders is varied, from blue in the scales of Maratus occasus and Maratus splendens [5,13] to super-black in Maratus speciosus and Maratus karrie [14] to full-spectrum rainbow iridescence in Maratus robinsoni and Maratus chrysomelas [9]. The super-black regions in the species Maratus speciosus and Maratus karrie reflect as little as 0.44% and 0.35% of visible light, respectively, owing to their nanoscale structures. Both species evolved microlens arrays, comprising tall and tightly packed cuticular bumps. The super-black is a combination of pigment and structural effects. The microlens arrays achieve structurally assisted enhanced absorption of light by melanin pigment. The species Maratus robinsoni displays angle-dependent rainbow iridescence attributed to their scales that are comprised of 2D nanogratings on microscale 3D convex surfaces.
Maratus occasus is a newly discovered species, hence, a detailed investigation of the structural and pigmented color origins of its scales does not exist in the literature. Therefore, we draw on the literature from similar species in the spider genus Maratus, such as Maratus splendens, with scales of similar color that have been studied in detail [13]. Maratus occasus has two types of scales, which the students investigated during the workshop. Type I: plate-like scales, which are blue in color, and Type II: brush-like scales, which are orange in color (Figure 3a, Figure 4). The brush-like scales, being similar in structure to the red brush-like scales of Maratus splendens, are likely to have pigments that give rise to their color. The plate-like scales, being similar in structure to the blue plate-like scales of Maratus splendens, are likely to have structural color origins. The dual thin film structure of the chitin plate-like scales, with an internal filament array (Figure 4b), likely leads to the blue structural color in Maratus occasus, as it does in Maratus splendens. The interior and exterior structure is evident in the micrograph of a broken plate-like scale in Figures 4a and 4b. For the MyScope Explore demonstration, an intact plate-like scale is zoomed in upon (Figure 5a). While the interior structure of the plate-like scale is not visible, the exterior parallel ridges with a periodicity of 120 nm are visible; students were directed to observe these during the workshop.
Figure 4: Scanning electron micrographs depicting (a) blue plate-like scales (PLS) (Type I) and orange brush like-scales (BLS) (Type II) of a Maratus occasus (male) specimen (scale bar = 10 μm). (b) The internal structure of the plate-like scales of Maratus occasus that leads to the blue structural color (scale bar = 2 μm).
Figure 5: A subset of the scanning electron micrographs of Maratus occasus embedded into MyScope Explore under the “Arthropods” category. (a) Zoom series for color 1 (blue plate-like scales, Type I). (b) Zoom series for color 2 (orange brush-like scales, Type II).
Students were provided with a background tutorial introducing the origin of both structural color and pigmented color, which is attributed to chemical molecules rather than nanoscale structures. Ommochromes such as xanthommatin [13] have been demonstrated to be the main pigments in spiders, in general [15,16]. In addition, structurally assisted absorption mechanisms have also been proposed for the brush-like scales of some Maratus species [14]. Multiple scattering between the spikes and iterative absorption can occur as light propagates through the cuticle into an absorbing layer of melanin pigment in the abdomen [14].
MyScope Explore Background
This remote learning solution made use of the existing online learning infrastructure provided by Microscopy Australia. Microscopy Australia (supported by NCRIS, the Australian Government's National Collaborative Research Infrastructure Strategy) is a national grid of equipment, instrumentation, and expertise in microscopy and microanalysis. This national grid provides open-access nanostructural characterization capability and services, from pulsed-laser local electrode atom probe tomography and high-precision focused ion microprobes to high-resolution SEM and high-throughput cryo-transmission electron microscopy. The collaborative facility comprises a distributed network of microscopy and microanalysis core facilities across nine institutions (University of Sydney, University of Queensland, University of New South Wales, Australian National University, University of Western Australia, Flinders University, University of Adelaide, University of South Australia, Monash University). MyScope was developed by the participating universities to support the face-to-face training offered within their facilities but has evolved into one of the best-known and heavily used training tools for microscopy worldwide.
The MyScope SEM simulation is available in two differing versions:
1. A simplified simulation of an SEM instrument that explains each of the different instrument controls and what their effect on an SEM image will be (for example, evacuate the chamber to remove air molecules that will interfere with the electron beam). Students are prompted to learn the influence of various controls through this environment, by loading simulated samples from the specimen library and choosing imaging conditions. This is “MyScope Explore,” which is aimed at a general and younger audience where they can both learn to use the SEM and also make discoveries by exploring over 70 stored samples.
2. A realistic SEM simulation environment that mimics an actual state-of-the-art instrument. The students can develop advanced skills through active learning activities designed to teach the impact of realistic setting changes on a sample under measurement, with a full range of controls including detector type and astigmatism correction. This is “MyScope,” a professional training environment.
Here we briefly detail the programming behind the MyScope Explore graphical user interface (GUI) that makes the online interface possible. For each specimen type in the MyScope Explore library, the simulation is built using the HTML5 canvas element. First, a series of scanning electron micrographs of the specimen are embedded, each at double the magnification of the previous item in the image string (Figure 5). The canvas element is used to write three image filters: blur, contrast, and brightness. The micrographs are then drawn onto the canvas, the canvas pixels are read back, and the three image filters run on them using JavaScript. The simulation includes Accelerating Voltage, Spot Size, Z Height Distance, Brightness, Contrast, Focus, and Magnification functions. The Magnification function is established by replacing the image with the next image in the sequence, the Focus function operates by applying a blur, to varying degrees, both above and below the focus point. The Accelerating Voltage, Spot Size, and Z Height Distance functions operate based on a combination, to varying degrees, of the brightness, contrast, and blur filters. Hence, as new teaching and outreach activities arise, new specimens can be programmed into the MyScope Explore platform to benefit the microscopy community, as was done for Maratus occacus specifically for the University of Newcastle 2020 workshop.
During the workshop, the students were instructed to open MyScope Explore and load the SEM simulation for both Type I and Type II scales of the Maratus occasus specimen. They were then directed to optimize the SEM image quality, firstly without prompting, then with an increasing degree of guidance as the session progressed. Students were directed to show the activity coordinator an optimized in-focus micrograph of each scale type once achieved. The students performed the following steps in order to complete the MyScope Explore simulation for each scale type:
1. Choose the sample.
2. Load the sample, and press the evacuate button.
3. Select accelerating voltage (options include 5, 10, 15, 20, and 30 kV).
4. Select a spot size (options include 5, 10, 15, and 20 nm).
5. Select a Z height distance (options include 8, 10, and 20 mm).
6. Select HV ON.
7. Adjust brightness using the scroll bar.
8. Adjust contrast using the scroll bar.
9. Adjust focus using the scroll bar.
10. Adjust magnification using the scroll bar.
11. Save and/or print the resultant micrograph.
Note that upon loading a sample, the starting point for the focus, contrast, and brightness is randomized in the MyScope Explore software such that it differs each time the same sample is loaded. This ensures that each student must optimize these settings for each increase in magnification rather than being able to copy another student's settings.
Teaching Scanning Electron Microscopy via Zoom
The interactive live SEM session component of the program was performed at the EMX facility at the University of Newcastle, with students joining the session remotely via Zoom from a computer lab on campus with a large seating capacity. The usual 45-minute sessions were expanded to 90-minute sessions and were scheduled four times across the 3-day workshop program (Figure 6). This change ensured that all students could perform the activity in manageable class sizes of 10–12 whilst maintaining the social distancing requirements for the computer laboratories. There were two Zoom channels coming through to the students from the SEM lab. The first was a screen-share to the SEM computer, showing the SmartSEM user interface live during imaging, whilst the second was a video link of the scientist performing the measurements so that the students could see the sample loading and operation of the keyboard controls for functions including focus, magnification, etc. A standard optical microscope was also assembled in the computer laboratory to enable students to physically load a Maratus occasus specimen and view the vibrant colors, and then match the colors to the grayscale SEM images of the nanostructured scales of a duplicate specimen loaded into the SEM instrument at the EMX facility.
Figure 6: The afternoon activity daily schedule for the University of Newcastle COE Spring School in 2020 with the Electron Microscopy sessions integrated into 90-minute blocks on a repeating basis.
The scientist operating the SEM recorded micrographs of the Peacock spider specimen by asking a series of prompt questions of the student cohort watching via Zoom and recording the accuracy, timeliness, and level of cohort agreement for the answers. This practical SEM session aimed to estimate how successful the students would work as “independent” operators of the microscope.
Student Engagement
Following the workshop, students were asked to comment on their experience, with the feedback from the students being very positive—an indication that student engagement was high. Comments from students included, “Experimenting with multiple imaging settings for the SEM simulation improved my understanding of the equipment's utility in real lab settings,” and “Two-way communication (Zoom SEM session) was way better than a PowerPoint presentation, and the online simulation tool allowed us to have valuable personal experiences with the SEM.” In addition, it was noted that the choice of specimen was a critical feature in promoting student engagement during the remote learning activity, as Peacock (Maratus) spiders are a species of great interest due to their well-known “spider dance” and their vibrant structural colors.
Conclusion
In summary, we have developed a remote learning electron microscopy program for undergraduate university students during the COVID-19 pandemic at the University of Newcastle. In addition to learning electron microscopy via both an online simulation and an interactive live SEM session, the students learned about structural color, that is, color that originates from the precise and periodic nanostructure of a specimen. Structurally colored features require high-resolution microscopy tools to investigate and are, hence, an ideal choice for an electron microscopy workshop. The MyScope Explore online platform enabled the students to learn independently and to experience all of the steps in imaging a nanostructured sample with SEM, providing an enriching learning experience for students during a year with strict access limitations to existing scientific infrastructure.
Acknowledgements
Biological photographer Michael Doe from Project Maratus is thanked for contribution of Maratus spider specimens; Project Maratus promotes research into the iconic Australian Peacock (Maratus) spiders. We acknowledge University of Newcastle undergraduate students Isaac A. Gill and Benjamin Stanwell for their assistance in coordinating the Spring School microscopy session; Microscopy Australia staff member Susan Warner for facilitating the new content entry into MyScope Explore; and Andres Vasquez for programming in MyScope Explore. Graphic designer Nicole Weaver is thanked for contributing Figure 6 – creative design of schedule. The authors acknowledge the research facilities and expertise supported by Microscopy Australia and the University of Sydney. We further acknowledge the technical assistance provided by Sydney Microscopy and Microanalysis, a Core Research Facility of the University of Sydney. This work was performed in part at the Materials Node (Newcastle) of the Australian National Fabrication Facility (ANFF), which is a company established under the National Collaborative Research Infrastructure Strategy to provide nano- and micro-fabrication facilities for Australia's researchers. The University of Newcastle Electron Microscopy and X-ray (EMX) Unit provided access to electron microscopes.
==== Refs
References
[1] B Cribb et al , Microsc Microanal 17 (2011) 10.1017/s1431927611005228.
[2] M Apperley et al , Microsc Microanal 21 (2015) 10.1017/s1431927615003438.
[3] C Fuery et al , Microsc Microanal 23 (2017) 10.1017/s1431927617012168.
[4] NC Nicolaidis et al , J Chem Educ 97 (2020) 10.1021/acs.jchemed.9b00941.
[5] J Schubert and RWhyte, Peckhamia 187 .1 (2019) 1–10. ISSN 1944-8120.
[6] VJ Lloyd and NJNadeau, Curr Opin Genet Dev 69 (2021) 10.1016/j.gde.2021.01.004.
[7] J Zi et al , PNAS 100 (2003) 10.1073/pnas.2133313100.
[8] S Kariko et al , J Royal Soc Interface 15 (2018) 10.1098/rsif.2017.0930.
[9] BK Hsiung et al , Nat Commun 8 (2017) 10.1038/s41467-017-02451-x.
[10] AE Seago et al , J Royal Soc Interface 6 (2009) 10.1098/rsif.2008.0354.focus.
[11] F Kopnov et al , Synthetic Metals 137 (2003) 10.1016/S0379-6779(02)00839-1.
[12] Y Liu et al , Nat Commun 10 (2019) 10.1038/s41467-019-12360-w.
[13] DG Stavenga et al , J Royal Soc Interface 13 (2016) 10.1098/rsif.2016.0437.
[14] DE McCoy et al , Proc Royal Soc B Biol Sci 286 (2019) 10.1098/rspb.2019.0589.
[15] TC Insausti and JCasas, J Exper Biol 211 (2008) 10.1242/jeb.014043.
[16] VL Seligy , Comp Biochem Physiol Part A Physiol 42 (1972) 10.1016/0300-9629(72)90448-3.
| 36511770 | PMC9728105 | NO-CC CODE | 2022-12-09 23:26:00 | no | Micros Today. 2021 Nov 1; 29(6):42-48 | utf-8 | Micros Today | 2,021 | 10.1017/S1551929521001322 | oa_other |
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J Appl Microbiol
J Appl Microbiol
jambio
Journal of Applied Microbiology
1364-5072
1365-2672
Blackwell Science Ltd Oxford, UK
21054699
10.1111/j.1365-2672.2010.04881.x
jambio-110-1-0287
Original Articles
A method to determine the available UV‐C dose for the decontamination of filtering facepiece respirators
Fisher E.M. National Institute for Occupational Safety and Health, National Personal Protective, Technology Laboratory, Pittsburgh, PA, USA
Shaffer R.E. National Institute for Occupational Safety and Health, National Personal Protective, Technology Laboratory, Pittsburgh, PA, USA
Ronald E. Shaffer, National Institute for Occupational Safety and Health, National, Personal Protective Technology Laboratory, 626 Cochrans Mill Rd., PO Box 18070, Pittsburgh, PA 15236, USA. E‐mail: [email protected]
01 1 2011
01 1 2011
01 1 2011
110 1 287295
06 10 2009
25 6 2010
30 9 2010
© 2011 The Society for Applied Microbiology
2011
https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)
This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.
Abstract
Aims: To develop a method to assess model‐specific parameters for ultraviolet‐C (UV‐C, 254 nm) decontamination of filtering facepiece respirators (FFRs).
Methods and Results: UV‐C transmittance was quantified for the distinct composite layers of six N95 FFR models and used to calculate model‐specific α‐values, the percentage of the surface UV‐C irradiance available for the internal filtering medium (IFM). Circular coupons, excised from the FFRs, were exposed to aerosolized particles containing MS2 coliphage and treated with IFM‐specific UV‐C doses ranging from 38 to 4707 J m−2. Models exposed to a minimum IFM dose of 1000 J m−2 demonstrated at least a 3 log reduction (LR) in viable MS2. Model‐specific exposure times to achieve this IFM dose ranged from 2 to 266 min.
Conclusions: UV‐C transmits into and through FFR materials. LR of MS2 was a function of model‐specific IFM UV‐C doses.
Significance and Impact of the Study: Filtering facepiece respirators are in high demand during infectious disease outbreaks, potentially leading to supply shortages. Reuse of disposable FFRs after decontamination has been discussed as a possible remediation strategy, but to date lacks supporting scientific evidence. The methods described here can be used to assess the likelihood that UV‐C decontamination will be successful for specific FFR models.
decontamination
filtering facepiece respirator
influenza
pandemic
UV‐C
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pmcIntroduction
The supply of National Institute for Occupational Safety and Health (NIOSH)‐certified N95 filtering facepiece respirators (FFRs) may become limited during an influenza pandemic [Institute of Medicine (U.S.) Committee on the Development of Reusable Facemasks for Use During an Influenza Pandemic et al. (2006)]. Current guidance states that these disposable devices should be discarded after each use (donning and doffing) to prevent self‐inoculation with infectious material on the surface of the respirator. Extending the lifetime of FFRs for multiple uses (e.g. multiple donnings) may help to alleviate the supply demand (Viscusi et al. 2007, 2009a,b; Roberge 2008). One option that may permit FFR reuse is the decontamination or removal of the infectious material from the FFR through one or more physical or chemical treatments. For this option to be practical, the decontamination treatment must maintain FFR fit and filtration performance and not leave hazardous residues. Other desired attributes for a decontamination method for FFR reuse would be low cost, high throughput and ease of use (Viscusi et al. 2009b). Today, no validated decontamination methods for FFRs exist (OSHA 2008; HPA 2009), but research is underway to determine whether this is feasible (Viscusi et al. 2007, 2009b; Fisher et al. 2009; Vo et al. 2009).
Ultraviolet germicidal irradiation (UVGI) using ultraviolet‐C (UV‐C, 254 nm) has been suggested as a viable option for this application (Viscusi et al. 2007). Most recently, nine FFR models were evaluated for changes in physical appearance, odour and laboratory performance (filter aerosol penetration and filter airflow resistance) following simulated decontamination using five different methods, including UV‐C (Viscusi et al. 2009b). In that study, UV‐C treatment did not affect the filter aerosol penetration, filter airflow resistance or physical appearance of the FFRs. In another study, (Vo et al. 2009) demonstrated that a 4·32 J cm−2 (43 200 J m−2) dose of UV‐C achieved approximately 3 log reduction (LR) of MS2 virus applied as droplets to an FFR, while higher UV‐C doses (>7·20 or 72 000 J m−2) yielded no detectable MS2 virus. However, concerns remain about the ability of UV‐C to inactivate infectious particles within the fibrous web of layers of an FFR.
The effectiveness of UV‐C as a decontamination method is affected by several parameters, including the topography of the contaminated surface and the location of the micro‐organisms within the substrate. The use of UV‐C for surfaces is mainly for hard, nonporous substrates (Andersen et al. 2006; Rastogi et al. 2007). For these substrates, the microbial contamination is deposited on the surface exterior, thus UV‐C decontamination of viruses (Tseng and Li 2007), spores (Gardner and Shama 1998) and other microbes (Gorsuch et al. 1998) has been shown to be effective. Irregular and porous surfaces are considered problematic because of the lack of UV‐C penetration. UV‐C does not readily penetrate into solid surfaces as the light is absorbed or reflected by the substrate material. Decontamination efficacy decreases if UV‐C cannot effectively penetrate shielded areas (Gardner and Shama 2000) (Tseng and Li 2007).
UV‐C transmittance is possible through the small gaps and fibres of porous surfaces such as textiles. In fact, UV‐C transmission through clothing and textiles (both woven and nonwoven) has been previously described (Kerr et al. 2000; Hoffmann et al. 2001). Furthermore, a NIOSH health hazard evaluation report of surgical suite personnel found that UV‐C penetrated some surgical masks and gowns, suggesting that UV‐C should also penetrate FFRs (Sylvain and Tapp 2009).
Research to evaluate the penetration and decontamination efficacy of UV‐C applied to FFRs is lacking. NIOSH‐certified N95 FFRs are manufactured using a variety of materials in assorted shapes and colours, although they all share some common attributes. For example, the nine FFR models studied previously (Viscusi et al. 2009b) were comprised of multiple layers of woven and nonwoven materials. Generally, the inner and outer layers provide form, stability, comfort, water resistance and filtration of large particles. The internal filtering medium (IFM) is usually comprised of spun‐bonded polypropylene and provides the bulk of the filtration efficiency (Barrett and Rousseau 1998). The aim of this study was to develop a method to assess model‐specific parameters for UV‐C decontamination of FFRs using measured UV‐C transmittance values. UV‐C transmittance through the layers of several FFR models was quantified and used to calculate FFR model‐specific UV‐C doses. The methodology used to generate this data will be useful to respirator manufacturers, infection control experts and other researchers to optimize FFR design and UV‐C parameters (irradiance, time, etc.) for possible application to FFR reuse if authorized during an FFR supply shortages for emergency situations.
Materials and methods
FFR spatial relation and layer terminology
Figures 1a,b demonstrate the interfaces and the layers of the respirator as defined by this research. For the purposes of this study, the term outer layer corresponds to the layer most distant from the wearer (environment interface). The term inner layer corresponds to the layer closest to the wearer (user interface). Both the outer and the inner layers are the exterior layers of the respirator. Interior layers are situated between the outer and inner layers and are normally comprised of filtering medium. The filtering medium, whether single or multilayered, is designated as the IFM.
Figure 1 FFR interfaces and layers, (a) environment interface, (b) user interface, (c) outer layer, (d) internal layer, and (e) inner layer.
FFR selection and layer determination
The six FFR models, used in this study, are NIOSH‐approved N95 FFRs. Two models, the Cardinal N95‐ML (Model A) and the Wilson SAF‐T‐FIT® Plus (Model B), were selected from laboratory stock. Four models, namely, the 8210 (Model C), 1860 (Model D), and 1870 (Model F) from 3M™ and the Kimberly‐Clark PFR95‐174 (Model E), were selected from the list of models available in the Center for Disease Control and Prevention’s Strategic National Stockpile and used in previous research (Viscusi et al. 2009b). The FFRs were cut into 5‐cm2 circular coupons and then pulled apart to generate multiple, distinct layers. A layer was determined to be distinct if it separated from adjoining layers without damage to any portion of the respirator media.
Aerosol penetration and airflow resistance by composite layers
A Model 8130 Automated Filter Tester (AFT) (TSI, Inc., St. Paul, MN, USA) was used to measure initial sodium chloride penetration and airflow resistance (R) for all layers of each FFR model. The percentages of aerosol penetrating each layer (%P) were determined by placing an approximately 3″ diameter swatch of each FFR layer in between Plexiglas test plates. The Plexiglas plates containing a filter layer were placed between the two chucks of the Model 8130 AFT filter holder under conditions previously described (Viscusi et al. 2007). The filter penetration results were converted to filtration efficiencies (FE = 100–%P).
UV‐C penetration (transmittance) measurement
UV‐C transmittance through the respirator layers was determined for the six N95 FFR models, using a biological safety cabinet (SterilGARD® III Advance; The Baker Company, Sanford, ME, USA) equipped with a low pressure mercury arc lamp (TUV 36T5 40 W, Philips, Somerset, NJ) to generate UV‐C light. The light irradiance was measured with a UV‐X‐25 sensor connected to a UV‐X radiometer (UV‐CP Inc., Upland, CA, USA). The operating manual for the radiometer defines the measurement as UV intensity, but will be termed irradiance in this manuscript.
Three 5‐cm2 circular coupons were excised from the studied respirators (1 coupon/respirator) and separated into layers as described previously. Bidirectional UV‐C irradiance was measured both from the outer layer to the inner layer (towards the user interface) and from the inner layer to the outer layer (away from the user interface). UV‐C transmittance was determined after the successive addition of each coupon layer starting with the outer or inner layer and adding the layers in the proper order and orientation. FFR coupons were placed over the UV‐X sensor and exposed to UV‐C light. UV‐C transmittance was measured in triplicate from coupons excised from three FFRs for each model.
Calculation of α, the available UV‐C for decontamination
The measured transmittance data were used to determine the effective UV‐C irradiance for each layer of each model. Simply, the measured transmitted irradiance (i.e. the UV‐C that gets through a given layer (i), or , becomes the incoming irradiance for the next layer (). The surface irradiance (IS) is the () for the most exterior layers. The factor α, which is the intercepted or nontransmitted UV‐C fraction, can be calculated for each layer (relative to its orientation in the FFR) in either direction using the following equation:
1
Summing the α‐values of the filtering layers (αi) of the IFM made from both directions (n = number of filtering layers × 2) results in αIFM, which is the available UV‐C reaching the IFM for each model as a percentage of the IS.
2
This factor represents the percentage of UV‐C available for decontamination and is similar to the exposure factor calculated by Gardner and Shama 2000. The αIFM values are related to the UV‐C transmittance of the exterior layers. Higher transmittance values of exterior layers can lead to higher αIFM values, while exterior layers that shield the UV‐C from reaching the internal layers (i.e. lower IT) will lead to lower αIFM values.
Media, virus, and host cells
The media, virus and host cells, used in this research, have been described previously (Fisher et al. 2009). Briefly, American Type Culture Collection (ATCC) medium 271 (http://www.atcc.org/Attachments/3600.pdf) was used for growth of Escherichia coli and preparation, storage, recovery and assay of MS2 bacteriophage (ATCC 15597‐B1). The aerosol‐generating medium was comprised of 1% ATCC medium 271 (deionized water was used as the diluent for 1% ATCC medium 271).
UV‐C decontamination experiment
The αIFM was used to categorize FFRs as most shielded (≤5%), moderately shielded (>5% but ≤30%) and least shielded (>30%). UV‐C decontamination of MS2 was determined for FFR coupons excised from two FFR models from each shielded category. Experimental coupons (n = 4) and control coupons (n = 2) were loaded with approximately 107 plaque‐forming units of MS2 as previously described (Fisher et al. 2009). For models C and F, the MS2‐contaminated coupons were exposed to UV‐C (25 ± 1·0 W m−2) for combined bidirectional treatment times of 1, 2, 4 and 10 min (0·5, 1, 2, 5 min per side). Only the 10‐min exposure was performed on model A because of a low αIFM. A supplemental experiment for Model A was performed using a bidirectional treatment time of 5 h.
Specific UV‐C doses (J m−2) to the IFM (DIFM) for each model can be calculated as follows:
3
where αIFM is given by eqn (2), t is the exposure time (s), and Is is the surface irradiance (W m−2) from eqn (1). FFR models B, D and E were exposed to UV‐C (25 ± 1·0 W m−2) in a bidirectional manner for the model‐dependent treatment times corresponding to DIFMs of 300, 1000 and 3000 J m−2. To account for any temperature effects from long exposure times, contaminated control coupons were covered with a plastic cap and exposed alongside experimental coupons under the UV‐C source.
A dose–response curve was constructed for the MS2 decontamination by plotting the average LR per coupon as a function of the DIFM for each model for each time‐point.
Virus recovery and enumeration
The control and experimental coupons were placed in 50‐ml conical tubes containing 10 ml of 271 B medium. Virus was recovered from the coupons by agitation for 1 min using a Vortex‐Genie® 2 G‐560 (Scientific Industries, Bohemia, NY, USA). The coupons were discarded, and the virus was enumerated using a single agar layer method as previously described (Fisher et al. 2009).
Data analysis
The antiviral activity of the UV‐C treatment methods of the FFR coupons was determined by calculating the log10 N N0−1; where N0 is the titre of viable MS2 recovered from the covered control coupons and N is the titre of the viable virus recovered from the treated coupons. A two‐way analysis of variance with replication (95% confidence level) was performed, using Microsoft EXCEL (Microsoft Office 2007), to determine statistical significance of the data among FFR models and doses. Regression analysis of MS2 inactivation curves was performed using the Gearaerd and Van Impe Inactivation Model Fitting Tool (GInaFit) freeware tool for Microsoft EXCEL (Cerf 1977; Geeraerd et al. 2005). The biphasic model was used to model MS2 inactivation for supplied (exposed) dose and DIFM and the coefficient of determination (R2) and the root mean sum of squared errors (RMSE) were compared (Cerf 1977; Kowalski 2000).
Results
Table 1 identifies the filtration layers (bold values) of each FFR model and lists the bidirectional available UV‐C (α‐values) for all layers. The exterior layers received the majority of the available UV‐C with α‐values measuring from 23 to 50%. The α‐values for the individual filtering layers ranged from 0·05 to 22%. Four models, A, B, C and D, demonstrated αIFM values of <10%, while the models E and F had values of 30 and 31%, respectively. The αIFM values permitted the division of FFR models into three UV‐C shielding categories as shown in the bottom row of Table 1.
Table 1 Bidirectional α‐values of the composite layers of filtering facepiece respirators (FFRs) and categorization by αIFM
FFR A B C D E F
Layer 1 (outer) 49·98 48·9 43·6 36·21 33·63 36·86
Layer 2 0·05 1·09 5·25 4·61 16·17 7·89
Layer 3 0·05 1·09 0·86 8·50 13·84 9·42
Layer 4 (inner B, C, D) 0·16 49·0 50·3 50·67 2·88 21·96
Layer 5 (inner A, E, F) 49·76 NA NA NA 33·33 23·44
Shielded category Most Moderately Least
αIFM 0·25 2·18 6·11 8·5 30·01 31·38
All values in percentage. Bold values indicate filtering layers.
The FEs of the individual layers of each FFR model using the TSI 8130 AFT confirmed the visual determination of the layers which comprise the IFM. The filtration layers, marked as bold values in Table 2, had efficiencies >87% with the majority performing at 94% or better. The filtration performance for layers not determined to be filtering in function were <31%. Likewise, Table 2 shows the differential airflow resistances of the layers of the IFM (ranging from 2·5 to 7·6 mm H2O) exceeded the values for all other layers for all FFRs (ranging from 0·1 to 0·7 mm H2O).
Table 2 Filtration efficiency (FE) and airflow resistance (R) of the composite layers of filtering facepiece respirators (FFRs)
FFR A B C D E F
FE (%) R (mm H2O) FE (%) R (mm H2O) FE (%) R (mm H2O) FE (%) R (mm H2O) FE (%) R (mm H2O) FE (%) R (mm H2O)
Layer 1 15·1 0·3 25·6 0·3 30·3 0·3 20·2 0·3 15·6 0·4 13·0 0·7
Layer 2 94·3 4·2 94·5 4·7 90·5 2·5 12·6 0·1 87·2 7·6 11·8 0·5
Layer 3 94·3 4·9 92·3 4·6 96·7 3·7 99·5 7·1 87·2 6·8 97·2 4·2
Layer 4 NT NT 13·4 0·3 16·9 0·5 16·8 0·5 12·4 0·2 97·2 4·5
Layer 5 8·5 0·2 11·0 11·1 0·7
Bold values indicate filtering layers.
NT, not tested.
Table 3 lists the DIFMs and measured LRs of Models A, C and F for the bidirectional treatment times of 1, 2, 4 and 10 min. The supplied dose at each treatment time was the same for each FFR model, but the DIFM varied because of the model‐specific αIFM values. LRs within a given treatment time differed among the FFR models. A 10‐min treatment (the only treatment condition with data for all three models) produced LRs of 0·1 (model A), 2·9 (model C), and >4·8 (model F). Across models A, C and F and within models C and F, LR increased with increasing DIFMs.
Table 3 Calculated DIFM and measured log reductions (LR) of MS2 for targeted UV‐C exposure times. The supplied dose for each treatment time was equivalent for each model
Time (min) 1 2 4 10
Model J m−2 LR J m−2 LR J m−2 LR J m−2 LR
A 4 NT 8 NT 15 NT 38 0·1 ± 0·2
C 92 1·7 ± 0·1 183 2·4 ± 0·1 367 2·6 ± 0·4 917 2·9 ± 0·2
F 471 2·5 ± 0·3 941 3·1 ± 0·5 1883 4·1 ± 0·3 4707 >4·8*
NT, not tested.
*Reached detection limits.
The calculated treatment times and LRs for models B, D and E are shown in Table 4. The treatment times required to reach the targeted DIFMs of 300, 1000 and 3000 J m−2 differed for each model. LRs were statistically different among the models for DIFMs of 300 J m−2 (P = 0·006) and 3000 J m−2 (P < 0·001). The results for the 1000 J m−2 treatment among the models were statistically similar (P = 0·79). LRs increased with increased DIFM for models B (P < 0·001) and D (P < 0·001). The LRs reported for model E did not differ significantly among all three doses (P = 0·18).
Table 4 Calculated exposure times (min) and log reductions (LR) of MS2 for targeted DIFM
DIFM (J m−2) 300 1000 3000
Model Min LR Min LR Min LR
B 9·6 2·3 ± 0·2 31·9 3·3 ± 0·1 95·6 4·0 ± 0·1
D 2·5 3·0 ± 0·4 8·2 3·5 ± 0·5 24·5 >5·1*
E 0·7 3·2 ± 0·3 2·3 3·6 ± 0·4 6·9 3·4 ± 0·2
The measured irradiance was approximately 24 W m−2.
*Reached detection limits.
Table 5 contains the calculated exposure times required to achieve a DIFM of 1000 J m−2 for each model‐specific αIFM with a UV‐C irradiance of 25 W m−2, which range from 266 to 2 min. Table 5 also depicts the LRs associated with tested DIFMs of 1000 ± 125 J m−2. LRs for the approximate 1000 J m−2 DIFM ranged from 2·86 to 3·59, with the lowest LR value (2·86) corresponding to the lowest DIFM (917 J m−2).
Table 5 Calculated αIFM exposure times and log reductions (LRs) for DIFM of 1000
Model A B C D E F
α internal filtering medium 0·25 2·18 6·11 8·5 30·01 31·38
Time (min) 266 32 10 7 2 2
LR 3·0* 3·3† 2·9‡ 3·5* 3·6* 3·1§
Reported LRs are for measured doses as indicated.
*Dose, 1125 J m−2.
†Dose, 1000 J m−2.
‡Dose, 917 J m−2.
§Dose, 941 J m−2.
Discussion
The information presented in this study provides an effective method to calculate UV‐C doses for the decontamination of FFRs. UV‐C decontamination is based on supplying an adequate dose to the contaminated area. UV‐C dose required for decontamination, which is microbe specific, is a function of irradiance and time. The UV‐C irradiance decreases with distance, especially when travelling through a substrate (e.g. FFR), where UV absorption and reflection are factors. Irradiance is also affected by microbial concentrations and the level of protective residues; however, consideration of these factors is the subject of another manuscript. Determining the UV‐C transmittance through the FFR material provides for a more accurate estimate of the irradiance supplied to the microbial contaminant. With a more accurate assessment of the irradiance, treatment times can be adjusted to achieve the targeted dose for the specific micro‐organism.
UV‐C radiation applied to FFRs will be transmitted, reflected or absorbed by the fibres of the multilayered substrate similar to other textiles (Hoffmann et al. 2001; Duleba‐Majek 2009). The transmittance of UV‐C through the layers of the FFR will largely occur through the gaps and pores between the fibres of the material. The materials in the inner and outer layers are more porous than the IFM as demonstrated by a comparative decrease in airflow resistance during the aerosol penetration tests (Table 2). The porosity of the inner and outer layers allows UV‐C to reach the IFM. The reflection and absorption of UV‐C will also be dependent upon other textile properties (e.g. weight and thickness) and the chemical composition of the fibres and/or other materials present such as dyes and delustrants. For a given layer, the reflected and absorbed UV‐C, which is the collective radiation that has impacted the surface, is assumed to be available for decontamination. The UV‐C that is transmitted through the pores or gaps of a layer becomes the potential available UV‐C for the next stratum.
The irradiance values used in this study for calculating UV‐C doses for FFR decontamination are based on the effective irradiation for the IFM. Being in the interior of the FFR, the IFM is the most challenging layer(s) to expose to UV‐C irradiation. The IFM, by design, provides the majority of the filtration performance to FFRs and should be the target of the UV‐C dose. In some instances (e.g. cough or sneeze that contain large wet droplets), the layers exterior to the IFM may capture the majority of the particles. However, even a fraction of the microbial contamination deposited on the IFM would be challenging to decontaminate. Thus, a specific dose calculated to decontaminate the IFM will provide an excess amount of UV‐C to the inner or outer layers. The excess UV‐C to the inner and outer layers provides a ‘safety factor’ to ensure a higher level of decontamination for the FFR exterior, which may reduce the hazard caused by contact transmission of infectious organisms.
The effectiveness of calculating model‐specific DIFMs is supported by the goodness of fit (R2 = 0·87, RMSE = 0·39) of the biphasic curve for all the decontamination experiments performed (Fig. 2). The biphasic analysis of the LRs (not shown) vs the supplied dose is more scattered because of the exclusion of model‐specific αIFM values (R2 = 0·36, RMSE = 0·88). Biphasic curves have been used to model thermal, nonthermal, pulsed field and UV‐C inactivation and occur as a result of a resistant subpopulation of microbes or other factors, such as, microbial clumping, microbial protection via particulate matter or localization on the substrate (Cerf 1977; Kowalski 2000; Geeraerd et al. 2005).
Figure 2 Dose–response relationship of MS2 inactivation on FFR coupons exposed to UV‐C. The data points represent the average LR of MS2 on FFR coupons exposed to UV‐C doses determined for the internal filtering medium using αIFM. The error bars represent ±1 standard deviation range for quadruplicate coupons (n = 4). The trend line is the biphasic mdel generated using the GInaFit freeware tool.
An examination of the data for the 10‐min UV‐C exposure of FFR representatives of the most shielded, moderately shielded and least shielded categories reveals the importance of calculating the DIFM (Table 3). A 10‐min exposure to a UV‐C surface irradiance of 25 W m−2 equates to a supplied dose of 15 000 J m−2 for each tested model. The LRs of 0·1, 2·9 and >4·8 for models A, C and F, respectively, would have been inconsistent if an equal dose was assumed. However, DIFMs of 38, 917 and 4707 J m−2 for models A, C and F, respectively, are consistent with the LR values. Variation in the decontamination efficiency among the FFR models is expected considering the differences in DIFMs.
The results of the targeted dose treatments (Table 4) further support the relationship of DIFM and MS2 decontamination, although unanticipated variation in decontamination efficacy across FFR models was evident for the 300 and 3000 J m−2 DIFMs. This variation may provide insight into factors important to UV‐C decontamination of FFRs. For the DIFM of 300 J m−2, some variability would be expected because of the steep slope of the initial phase of the biphasic curve (Fig. 2). In this initial phase, the shielding effect, or protection of the virus from UV‐C, seems to be limited, which is in contrast to the results of the 3000 J m−2 treatment. The statistical variation for this treatment may hint at an FFR model‐specific decontamination limit, which results from shielding or blocking of UV‐C by the composite layers of the FFR. This is similar to the shielding effect described in Gardner and Shama (2000). Across the models, this effect can be observed as the tailing of the biphasic curve (Fig. 2), although other factors such as virus clumping and UV‐C resistance of a subpopulation of virus may produce similar results (Kowalski 2000). The results for the UV‐C decontamination of model E provide further support for a model‐specific decontamination limit. For this FFR model, UV‐C efficacy approaching the decontamination limit was achieved with the lowest tested DIFM of 300 J m−2 and remained statistically the same for all three DIFMs tested (Table 4).
UV‐C treatments of an approximate DIFM of 1000 J m−2 produced similar LRs for all models tested (Table 5). The consistent MS2 LR values demonstrate the importance of αIFM values in determining the duration of UV‐C exposure times. Calculated exposure times to achieve a model‐specific DIFM of 1000 J m−2 ranged from 266 min for Model A to 2 min for Model F, representing the most shielded to least shielded categories. Exposure times of <8 min for the targeted DIFM, given an irradiance of 25 W m−2, are possible with αIFM values of 8·5 and greater (Models D–F). Model B, of the most shielded category (α = 2·18%), demonstrated a 3·2 LR with a treatment time of 32 min. Even with low αIFM values, significant LRs are possible with longer exposure times as demonstrated in this study or increased UV‐C irradiance, which would also provide an increase in dose. The demonstrated decontamination of FFRs with low αIFM values is promising for real world application.
The determination of the UV‐C transmittance and α‐values for the layers of an FFR is simple when using a radiometer. This process can be further simplified because of the ease by which filtering media is visually distinguishable from the other layers of an FFR (although filtration data would be preferable). The material used in the IFM appears to be less porous than the surrounding layers and of similar composition across model types. For all models examined, the filtering medium was located in the interior of the mask. By combining all the media that comprise the filtering layers into one stratum (the IFM) and compiling separate strata for the media to the exterior of the IFM in both directions, the number of layers can be minimized along with the number of measurements required to ascertain the transmittance data. Simple calculation [eqns (1) and (2)] using the transmittance data produces αIFM values, which is necessary to generate FFR model‐specific DIFMs. Reducing the number of layers by separating the IFM from the other composite strata of the FFR also minimizes the complexity of deciphering what is or is not a separate and distinctive layer.
Model‐specific αIFM values will determine the necessary UV‐C exposure time, given a targeted dose and the UV‐C source. The potential to decontaminate specific FFR models to a targeted LR value can be determined by evaluating the exposure times required to achieve desired doses. A minimum αIFM could be adopted during a reuse emergency, thereby defining FFR models that are more likely to lead to successful UV‐C decontamination. The αIFM values may also help in the selection of an adequate UV‐C source as an increase in irradiance would decrease treatment times given the targeted DIFM.
Producing an IFM model‐specific bidirectional (from both directions) DIFM provides the highest level of decontamination potential. Microbial contamination may emanate from the environment and/or from the user and deposit on all the layers of an FFR. A bidirectional UV‐C exposure targeted for the IFM will provide a dose adequate for microbial decontamination on the IFM, while providing an excessive dose for the exterior layers. However, a bidirectional target of the IFM may not be essential. Limiting the exposure to one direction, from outer to inner, may provide an adequate dose for decontamination while limiting the complexities of a two‐sided bidirectional treatment. The method to determine αIFM values and DIFMs described previously for bidirectional treatments is applicable to single direction exposures as well. Depending on the model‐specific composition of the exterior layers, the αIFM may be greater for a single direction exposure compared to a bidirectional treatment. The outer layer and IFM are more likely to contain the microbial threat, assuming that the wearer is healthy.
Limitations
Examining the effect of the FFR materials on UV‐C decontamination would provide valuable insight and assist in discerning the model‐specific‐decontamination efficacies, but is beyond the scope of this research. Likewise, material constituents are proprietary information of the manufacturers and are not readily known.
Further research is necessary for the development of protocols to generalize decontamination of FFRs with UV‐C. These studies were performed on a single plane, flat surface (coupons) with a single UV‐C source. Furthermore, the model‐specific αIFM values were determined by disassembling and then reassembling the FFR layers, which might have resulted in additional variation because of the incorrect orientation. The testing of complete, intact FFRs would provide a perspective on the effects of the multiple planes of the three‐dimensional form and other components such as straps and face‐seal interfaces. An examination of the use of multiple UV‐C sources may also be important to develop an FFR decontamination method. This issue has previously been explored for dental equipment, which presents similar surface irregularity challenges as FFRs in terms of irregular shapes and protein challenges (von Woedtke et al. 2003). The use of multiple sources may also lessen the effects of shielding from the material composition of the distinct layers and therefore increase the model‐specific limit of decontamination.
Studies to examine the effect of UV‐C on other micro‐organisms specific to respiratory illnesses will provide a better assessment of the influence of multilayered air‐permeable materials on microbial decontamination kinetics. The physical and chemical protection of micro‐organisms by residues, such as, sputum, blood, soil, etc., is also a concern for all methods of decontamination. Although well studied in water and air decontamination, protection residues were not examined in this study. Therefore, the effects of residues on UV‐C FFR decontamination are unknown but may be similar to those described in air and water research. We are currently exploring the efficacy of multiple decontaminations of FFRs repeatedly challenged with virus‐containing droplet nuclei and exposed to UV‐C. This research, which is being conducted using varying levels of organic challenge as a protection factor, will provide insight into the effects of protein deposition on the penetration/decontamination efficacy of UV‐C.
Acknowledgements
The authors express their sincere gratitude to Dr Debra Novak, Dr Samy Rengasamy, Dr Evanly Vo, Dr Benjamin C. Eimer, Mr Dennis Viscusi, Mr Mike Bergman and the manuscript reviewers for their suggestions and contributions.
Disclaimer
The findings and conclusions in this manuscript are those of the authors and do not necessarily represent the views of the National Institute for Occupational Safety and Health (NIOSH). Mention of company names or products does not constitute endorsement by NIOSH.
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References
Andersen,B.M., Banrud,H., Boe,E., Bjordal,O. and Drangsholt,F. (2006) Comparison of UV C light and chemicals for disinfection of surfaces in hospital isolation units. Infect Control Hosp Epidemiol 27 , 729–734.16807849
Barrett,L.W. and Rousseau,A.D. (1998) Aerosol loading performance of electret filter media. Am Ind Hyg Assoc J 59 , 532–539.
Cerf,O. (1977) Tailing of survival curves of bacterial spores. J Appl Bacteriol 42 , 1–19.323208
Duleba‐Majek,M. (2009) Transmission of UV radiation through woven fabrics in dependence on the inter‐thread spaces. Fibres Text East Eur 17 , 34–38.
Fisher,E.M., Rengasamy,S., Viscusi,D.J., Vo,E. and Shaffer,R.E. (2009) Development of a test system to apply virus‐containing particles to filtering facepiece respirators for the evaluation of decontamination procedures. Appl Environ Microbiol 75 , 1500–1507.19139225
Gardner,D.W.M. and Shama,G. (1998) The kinetics of Bacillus subtilis spore inactivation on filter paper by uv light and uv light in combination with hydrogen peroxide. J Appl Microbiol 84 , 633–641.
Gardner,D.W.M. and Shama,G. (2000) Modeling UV‐induced inactivation of microorganisms on surfaces. J Food Prot 63 , 63–70.10643771
Geeraerd,A.H., Valdramidis,V.P. and Van Impe,J.F. (2005) GInaFiT, a freeware tool to assess non‐log‐linear microbial survivor curves. Int J Food Microbiol 102 , 95–105.15893399
Gorsuch,E.L., Grinshpun,S.A., Willeke,K., Reponen,T., Moss,C.E. and Jensen,P.A. (1998) Method for evaluating germicidal ultraviolet inactivation of biocontaminated surfaces. Int J Occup Saf Ergon 4 , 287–297.10602623
Hoffmann,K., Laperre,J., Avermaete,A., Altmeyer,P. and Gambichler,T. (2001) Defined UV protection by apparel textiles. Arch Dermatol 137 , 1089–1094.11493104
HPA (2009) HPA Interim Advice on Extending the Lifespan of Facemasks and Respirators. UK: Health Protection Agency.
Institute of Medicine (U.S.) Committee on the Development of Reusable Facemasks for Use During an Influenza Pandemic, Institute of Medicine (U.S.) and Board on Health Sciences Policy and National Academies Press (U.S.) (2006) Reusability of Facemasks During an Influenza Pandemic: Facing the Flu. Washington, D.C.: National Academies Press.
Kerr,N., Capjack,L. and Fedosejevs,R. (2000) Ability of textile covers to protect artifacts from ultraviolet radiation. JAIC 39 , 345–353.
Kowalski,W.J. (2000) Mathematical modeling of ultraviolet germicidal irradiation for air disinfection. Quant Microbiol 2 , 249.
OSHA (2008) Proposed Guidance on Workplace Stockpiling of Respirators and Facemasks for Pandemic Influenza ed. Washington, DC: DOL.
Rastogi,V.K., Wallace,L. and Smith,L.S. (2007) Disinfection of Acinetobacter baumannii‐contaminated surfaces relevant to medical treatment facilities with ultraviolet C light. Mil Med 172 , 1166–1169.18062390
Roberge,R.J. (2008) Effect of surgical masks worn concurrently over N95 filtering facepiece respirators: extended service life versus increased user burden. J Public Health Manag Pract 14 , E19–E26.18287908
Sylvain,D. and Tapp,L. (2009) Health hazard evaluation report: UV‐C exposure and health effects in surgical suite personnel. Department of Health and Human Services, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health.
Tseng,C.C. and Li,C.S. (2007) Inactivation of viruses on surfaces by ultraviolet germicidal irradiation. J Occup Environ Hyg 4 , 400–405.17474029
Viscusi,D.J., King,W.P. and Shaffer,R.E. (2007) Effect of decontamination on the filtration efficiency of two filtering facepiece respirator models. J Int Soc Respir Prot 24 , 93–107.
Viscusi,D.J., Bergman,M.S., Sinkule,E. and Shaffer,R.E. (2009a) Evaluation of the filtration performance of 21 N95 filtering face piece respirators after prolonged storage. Am J Infect Control 37 , 381–386.19188003
Viscusi,D.J., Bergman,M.S., Eimer,B.C. and Shaffer,R.E. (2009b) Evaluation of five decontamination methods for filtering facepiece respirators. Ann of Occup Hyg 53 , 815–827.19805391
Vo,E., Rengasamy,S. and Shaffer,R.E. (2009) Development of a test system to evaluate decontamination procedures for viral droplets on respirators. Appl Environ Microbiol 75 , 7303–7309.19801477
Von Woedtke,T., Julich,W.D., Thal,S., Diederich,M., Stieber,M. and Kindel,E. (2003) Antimicrobial efficacy and potential application of a newly developed plasma‐based ultraviolet irradiation facility. J Hosp Infect 55 , 204–211.14572488
| 21054699 | PMC9728109 | NO-CC CODE | 2022-12-09 23:26:00 | no | J Appl Microbiol. 2011 Jan 1; 110(1):287-295 | utf-8 | J Appl Microbiol | 2,011 | 10.1111/j.1365-2672.2010.04881.x | oa_other |
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J Appl Microbiol
J Appl Microbiol
jambio
Journal of Applied Microbiology
1364-5072
1365-2672
Blackwell Science Ltd Oxford, UK
21054699
10.1111/j.1365-2672.2010.04881.x
jambio-110-1-0287
Original Articles
A method to determine the available UV‐C dose for the decontamination of filtering facepiece respirators
Fisher E.M. National Institute for Occupational Safety and Health, National Personal Protective, Technology Laboratory, Pittsburgh, PA, USA
Shaffer R.E. National Institute for Occupational Safety and Health, National Personal Protective, Technology Laboratory, Pittsburgh, PA, USA
Ronald E. Shaffer, National Institute for Occupational Safety and Health, National, Personal Protective Technology Laboratory, 626 Cochrans Mill Rd., PO Box 18070, Pittsburgh, PA 15236, USA. E‐mail: [email protected]
01 1 2011
01 1 2011
01 1 2011
110 1 287295
06 10 2009
25 6 2010
30 9 2010
© 2011 The Society for Applied Microbiology
2011
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Abstract
Aims: To develop a method to assess model‐specific parameters for ultraviolet‐C (UV‐C, 254 nm) decontamination of filtering facepiece respirators (FFRs).
Methods and Results: UV‐C transmittance was quantified for the distinct composite layers of six N95 FFR models and used to calculate model‐specific α‐values, the percentage of the surface UV‐C irradiance available for the internal filtering medium (IFM). Circular coupons, excised from the FFRs, were exposed to aerosolized particles containing MS2 coliphage and treated with IFM‐specific UV‐C doses ranging from 38 to 4707 J m−2. Models exposed to a minimum IFM dose of 1000 J m−2 demonstrated at least a 3 log reduction (LR) in viable MS2. Model‐specific exposure times to achieve this IFM dose ranged from 2 to 266 min.
Conclusions: UV‐C transmits into and through FFR materials. LR of MS2 was a function of model‐specific IFM UV‐C doses.
Significance and Impact of the Study: Filtering facepiece respirators are in high demand during infectious disease outbreaks, potentially leading to supply shortages. Reuse of disposable FFRs after decontamination has been discussed as a possible remediation strategy, but to date lacks supporting scientific evidence. The methods described here can be used to assess the likelihood that UV‐C decontamination will be successful for specific FFR models.
decontamination
filtering facepiece respirator
influenza
pandemic
UV‐C
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pmcIntroduction
The supply of National Institute for Occupational Safety and Health (NIOSH)‐certified N95 filtering facepiece respirators (FFRs) may become limited during an influenza pandemic [Institute of Medicine (U.S.) Committee on the Development of Reusable Facemasks for Use During an Influenza Pandemic et al. (2006)]. Current guidance states that these disposable devices should be discarded after each use (donning and doffing) to prevent self‐inoculation with infectious material on the surface of the respirator. Extending the lifetime of FFRs for multiple uses (e.g. multiple donnings) may help to alleviate the supply demand (Viscusi et al. 2007, 2009a,b; Roberge 2008). One option that may permit FFR reuse is the decontamination or removal of the infectious material from the FFR through one or more physical or chemical treatments. For this option to be practical, the decontamination treatment must maintain FFR fit and filtration performance and not leave hazardous residues. Other desired attributes for a decontamination method for FFR reuse would be low cost, high throughput and ease of use (Viscusi et al. 2009b). Today, no validated decontamination methods for FFRs exist (OSHA 2008; HPA 2009), but research is underway to determine whether this is feasible (Viscusi et al. 2007, 2009b; Fisher et al. 2009; Vo et al. 2009).
Ultraviolet germicidal irradiation (UVGI) using ultraviolet‐C (UV‐C, 254 nm) has been suggested as a viable option for this application (Viscusi et al. 2007). Most recently, nine FFR models were evaluated for changes in physical appearance, odour and laboratory performance (filter aerosol penetration and filter airflow resistance) following simulated decontamination using five different methods, including UV‐C (Viscusi et al. 2009b). In that study, UV‐C treatment did not affect the filter aerosol penetration, filter airflow resistance or physical appearance of the FFRs. In another study, (Vo et al. 2009) demonstrated that a 4·32 J cm−2 (43 200 J m−2) dose of UV‐C achieved approximately 3 log reduction (LR) of MS2 virus applied as droplets to an FFR, while higher UV‐C doses (>7·20 or 72 000 J m−2) yielded no detectable MS2 virus. However, concerns remain about the ability of UV‐C to inactivate infectious particles within the fibrous web of layers of an FFR.
The effectiveness of UV‐C as a decontamination method is affected by several parameters, including the topography of the contaminated surface and the location of the micro‐organisms within the substrate. The use of UV‐C for surfaces is mainly for hard, nonporous substrates (Andersen et al. 2006; Rastogi et al. 2007). For these substrates, the microbial contamination is deposited on the surface exterior, thus UV‐C decontamination of viruses (Tseng and Li 2007), spores (Gardner and Shama 1998) and other microbes (Gorsuch et al. 1998) has been shown to be effective. Irregular and porous surfaces are considered problematic because of the lack of UV‐C penetration. UV‐C does not readily penetrate into solid surfaces as the light is absorbed or reflected by the substrate material. Decontamination efficacy decreases if UV‐C cannot effectively penetrate shielded areas (Gardner and Shama 2000) (Tseng and Li 2007).
UV‐C transmittance is possible through the small gaps and fibres of porous surfaces such as textiles. In fact, UV‐C transmission through clothing and textiles (both woven and nonwoven) has been previously described (Kerr et al. 2000; Hoffmann et al. 2001). Furthermore, a NIOSH health hazard evaluation report of surgical suite personnel found that UV‐C penetrated some surgical masks and gowns, suggesting that UV‐C should also penetrate FFRs (Sylvain and Tapp 2009).
Research to evaluate the penetration and decontamination efficacy of UV‐C applied to FFRs is lacking. NIOSH‐certified N95 FFRs are manufactured using a variety of materials in assorted shapes and colours, although they all share some common attributes. For example, the nine FFR models studied previously (Viscusi et al. 2009b) were comprised of multiple layers of woven and nonwoven materials. Generally, the inner and outer layers provide form, stability, comfort, water resistance and filtration of large particles. The internal filtering medium (IFM) is usually comprised of spun‐bonded polypropylene and provides the bulk of the filtration efficiency (Barrett and Rousseau 1998). The aim of this study was to develop a method to assess model‐specific parameters for UV‐C decontamination of FFRs using measured UV‐C transmittance values. UV‐C transmittance through the layers of several FFR models was quantified and used to calculate FFR model‐specific UV‐C doses. The methodology used to generate this data will be useful to respirator manufacturers, infection control experts and other researchers to optimize FFR design and UV‐C parameters (irradiance, time, etc.) for possible application to FFR reuse if authorized during an FFR supply shortages for emergency situations.
Materials and methods
FFR spatial relation and layer terminology
Figures 1a,b demonstrate the interfaces and the layers of the respirator as defined by this research. For the purposes of this study, the term outer layer corresponds to the layer most distant from the wearer (environment interface). The term inner layer corresponds to the layer closest to the wearer (user interface). Both the outer and the inner layers are the exterior layers of the respirator. Interior layers are situated between the outer and inner layers and are normally comprised of filtering medium. The filtering medium, whether single or multilayered, is designated as the IFM.
Figure 1 FFR interfaces and layers, (a) environment interface, (b) user interface, (c) outer layer, (d) internal layer, and (e) inner layer.
FFR selection and layer determination
The six FFR models, used in this study, are NIOSH‐approved N95 FFRs. Two models, the Cardinal N95‐ML (Model A) and the Wilson SAF‐T‐FIT® Plus (Model B), were selected from laboratory stock. Four models, namely, the 8210 (Model C), 1860 (Model D), and 1870 (Model F) from 3M™ and the Kimberly‐Clark PFR95‐174 (Model E), were selected from the list of models available in the Center for Disease Control and Prevention’s Strategic National Stockpile and used in previous research (Viscusi et al. 2009b). The FFRs were cut into 5‐cm2 circular coupons and then pulled apart to generate multiple, distinct layers. A layer was determined to be distinct if it separated from adjoining layers without damage to any portion of the respirator media.
Aerosol penetration and airflow resistance by composite layers
A Model 8130 Automated Filter Tester (AFT) (TSI, Inc., St. Paul, MN, USA) was used to measure initial sodium chloride penetration and airflow resistance (R) for all layers of each FFR model. The percentages of aerosol penetrating each layer (%P) were determined by placing an approximately 3″ diameter swatch of each FFR layer in between Plexiglas test plates. The Plexiglas plates containing a filter layer were placed between the two chucks of the Model 8130 AFT filter holder under conditions previously described (Viscusi et al. 2007). The filter penetration results were converted to filtration efficiencies (FE = 100–%P).
UV‐C penetration (transmittance) measurement
UV‐C transmittance through the respirator layers was determined for the six N95 FFR models, using a biological safety cabinet (SterilGARD® III Advance; The Baker Company, Sanford, ME, USA) equipped with a low pressure mercury arc lamp (TUV 36T5 40 W, Philips, Somerset, NJ) to generate UV‐C light. The light irradiance was measured with a UV‐X‐25 sensor connected to a UV‐X radiometer (UV‐CP Inc., Upland, CA, USA). The operating manual for the radiometer defines the measurement as UV intensity, but will be termed irradiance in this manuscript.
Three 5‐cm2 circular coupons were excised from the studied respirators (1 coupon/respirator) and separated into layers as described previously. Bidirectional UV‐C irradiance was measured both from the outer layer to the inner layer (towards the user interface) and from the inner layer to the outer layer (away from the user interface). UV‐C transmittance was determined after the successive addition of each coupon layer starting with the outer or inner layer and adding the layers in the proper order and orientation. FFR coupons were placed over the UV‐X sensor and exposed to UV‐C light. UV‐C transmittance was measured in triplicate from coupons excised from three FFRs for each model.
Calculation of α, the available UV‐C for decontamination
The measured transmittance data were used to determine the effective UV‐C irradiance for each layer of each model. Simply, the measured transmitted irradiance (i.e. the UV‐C that gets through a given layer (i), or , becomes the incoming irradiance for the next layer (). The surface irradiance (IS) is the () for the most exterior layers. The factor α, which is the intercepted or nontransmitted UV‐C fraction, can be calculated for each layer (relative to its orientation in the FFR) in either direction using the following equation:
1
Summing the α‐values of the filtering layers (αi) of the IFM made from both directions (n = number of filtering layers × 2) results in αIFM, which is the available UV‐C reaching the IFM for each model as a percentage of the IS.
2
This factor represents the percentage of UV‐C available for decontamination and is similar to the exposure factor calculated by Gardner and Shama 2000. The αIFM values are related to the UV‐C transmittance of the exterior layers. Higher transmittance values of exterior layers can lead to higher αIFM values, while exterior layers that shield the UV‐C from reaching the internal layers (i.e. lower IT) will lead to lower αIFM values.
Media, virus, and host cells
The media, virus and host cells, used in this research, have been described previously (Fisher et al. 2009). Briefly, American Type Culture Collection (ATCC) medium 271 (http://www.atcc.org/Attachments/3600.pdf) was used for growth of Escherichia coli and preparation, storage, recovery and assay of MS2 bacteriophage (ATCC 15597‐B1). The aerosol‐generating medium was comprised of 1% ATCC medium 271 (deionized water was used as the diluent for 1% ATCC medium 271).
UV‐C decontamination experiment
The αIFM was used to categorize FFRs as most shielded (≤5%), moderately shielded (>5% but ≤30%) and least shielded (>30%). UV‐C decontamination of MS2 was determined for FFR coupons excised from two FFR models from each shielded category. Experimental coupons (n = 4) and control coupons (n = 2) were loaded with approximately 107 plaque‐forming units of MS2 as previously described (Fisher et al. 2009). For models C and F, the MS2‐contaminated coupons were exposed to UV‐C (25 ± 1·0 W m−2) for combined bidirectional treatment times of 1, 2, 4 and 10 min (0·5, 1, 2, 5 min per side). Only the 10‐min exposure was performed on model A because of a low αIFM. A supplemental experiment for Model A was performed using a bidirectional treatment time of 5 h.
Specific UV‐C doses (J m−2) to the IFM (DIFM) for each model can be calculated as follows:
3
where αIFM is given by eqn (2), t is the exposure time (s), and Is is the surface irradiance (W m−2) from eqn (1). FFR models B, D and E were exposed to UV‐C (25 ± 1·0 W m−2) in a bidirectional manner for the model‐dependent treatment times corresponding to DIFMs of 300, 1000 and 3000 J m−2. To account for any temperature effects from long exposure times, contaminated control coupons were covered with a plastic cap and exposed alongside experimental coupons under the UV‐C source.
A dose–response curve was constructed for the MS2 decontamination by plotting the average LR per coupon as a function of the DIFM for each model for each time‐point.
Virus recovery and enumeration
The control and experimental coupons were placed in 50‐ml conical tubes containing 10 ml of 271 B medium. Virus was recovered from the coupons by agitation for 1 min using a Vortex‐Genie® 2 G‐560 (Scientific Industries, Bohemia, NY, USA). The coupons were discarded, and the virus was enumerated using a single agar layer method as previously described (Fisher et al. 2009).
Data analysis
The antiviral activity of the UV‐C treatment methods of the FFR coupons was determined by calculating the log10 N N0−1; where N0 is the titre of viable MS2 recovered from the covered control coupons and N is the titre of the viable virus recovered from the treated coupons. A two‐way analysis of variance with replication (95% confidence level) was performed, using Microsoft EXCEL (Microsoft Office 2007), to determine statistical significance of the data among FFR models and doses. Regression analysis of MS2 inactivation curves was performed using the Gearaerd and Van Impe Inactivation Model Fitting Tool (GInaFit) freeware tool for Microsoft EXCEL (Cerf 1977; Geeraerd et al. 2005). The biphasic model was used to model MS2 inactivation for supplied (exposed) dose and DIFM and the coefficient of determination (R2) and the root mean sum of squared errors (RMSE) were compared (Cerf 1977; Kowalski 2000).
Results
Table 1 identifies the filtration layers (bold values) of each FFR model and lists the bidirectional available UV‐C (α‐values) for all layers. The exterior layers received the majority of the available UV‐C with α‐values measuring from 23 to 50%. The α‐values for the individual filtering layers ranged from 0·05 to 22%. Four models, A, B, C and D, demonstrated αIFM values of <10%, while the models E and F had values of 30 and 31%, respectively. The αIFM values permitted the division of FFR models into three UV‐C shielding categories as shown in the bottom row of Table 1.
Table 1 Bidirectional α‐values of the composite layers of filtering facepiece respirators (FFRs) and categorization by αIFM
FFR A B C D E F
Layer 1 (outer) 49·98 48·9 43·6 36·21 33·63 36·86
Layer 2 0·05 1·09 5·25 4·61 16·17 7·89
Layer 3 0·05 1·09 0·86 8·50 13·84 9·42
Layer 4 (inner B, C, D) 0·16 49·0 50·3 50·67 2·88 21·96
Layer 5 (inner A, E, F) 49·76 NA NA NA 33·33 23·44
Shielded category Most Moderately Least
αIFM 0·25 2·18 6·11 8·5 30·01 31·38
All values in percentage. Bold values indicate filtering layers.
The FEs of the individual layers of each FFR model using the TSI 8130 AFT confirmed the visual determination of the layers which comprise the IFM. The filtration layers, marked as bold values in Table 2, had efficiencies >87% with the majority performing at 94% or better. The filtration performance for layers not determined to be filtering in function were <31%. Likewise, Table 2 shows the differential airflow resistances of the layers of the IFM (ranging from 2·5 to 7·6 mm H2O) exceeded the values for all other layers for all FFRs (ranging from 0·1 to 0·7 mm H2O).
Table 2 Filtration efficiency (FE) and airflow resistance (R) of the composite layers of filtering facepiece respirators (FFRs)
FFR A B C D E F
FE (%) R (mm H2O) FE (%) R (mm H2O) FE (%) R (mm H2O) FE (%) R (mm H2O) FE (%) R (mm H2O) FE (%) R (mm H2O)
Layer 1 15·1 0·3 25·6 0·3 30·3 0·3 20·2 0·3 15·6 0·4 13·0 0·7
Layer 2 94·3 4·2 94·5 4·7 90·5 2·5 12·6 0·1 87·2 7·6 11·8 0·5
Layer 3 94·3 4·9 92·3 4·6 96·7 3·7 99·5 7·1 87·2 6·8 97·2 4·2
Layer 4 NT NT 13·4 0·3 16·9 0·5 16·8 0·5 12·4 0·2 97·2 4·5
Layer 5 8·5 0·2 11·0 11·1 0·7
Bold values indicate filtering layers.
NT, not tested.
Table 3 lists the DIFMs and measured LRs of Models A, C and F for the bidirectional treatment times of 1, 2, 4 and 10 min. The supplied dose at each treatment time was the same for each FFR model, but the DIFM varied because of the model‐specific αIFM values. LRs within a given treatment time differed among the FFR models. A 10‐min treatment (the only treatment condition with data for all three models) produced LRs of 0·1 (model A), 2·9 (model C), and >4·8 (model F). Across models A, C and F and within models C and F, LR increased with increasing DIFMs.
Table 3 Calculated DIFM and measured log reductions (LR) of MS2 for targeted UV‐C exposure times. The supplied dose for each treatment time was equivalent for each model
Time (min) 1 2 4 10
Model J m−2 LR J m−2 LR J m−2 LR J m−2 LR
A 4 NT 8 NT 15 NT 38 0·1 ± 0·2
C 92 1·7 ± 0·1 183 2·4 ± 0·1 367 2·6 ± 0·4 917 2·9 ± 0·2
F 471 2·5 ± 0·3 941 3·1 ± 0·5 1883 4·1 ± 0·3 4707 >4·8*
NT, not tested.
*Reached detection limits.
The calculated treatment times and LRs for models B, D and E are shown in Table 4. The treatment times required to reach the targeted DIFMs of 300, 1000 and 3000 J m−2 differed for each model. LRs were statistically different among the models for DIFMs of 300 J m−2 (P = 0·006) and 3000 J m−2 (P < 0·001). The results for the 1000 J m−2 treatment among the models were statistically similar (P = 0·79). LRs increased with increased DIFM for models B (P < 0·001) and D (P < 0·001). The LRs reported for model E did not differ significantly among all three doses (P = 0·18).
Table 4 Calculated exposure times (min) and log reductions (LR) of MS2 for targeted DIFM
DIFM (J m−2) 300 1000 3000
Model Min LR Min LR Min LR
B 9·6 2·3 ± 0·2 31·9 3·3 ± 0·1 95·6 4·0 ± 0·1
D 2·5 3·0 ± 0·4 8·2 3·5 ± 0·5 24·5 >5·1*
E 0·7 3·2 ± 0·3 2·3 3·6 ± 0·4 6·9 3·4 ± 0·2
The measured irradiance was approximately 24 W m−2.
*Reached detection limits.
Table 5 contains the calculated exposure times required to achieve a DIFM of 1000 J m−2 for each model‐specific αIFM with a UV‐C irradiance of 25 W m−2, which range from 266 to 2 min. Table 5 also depicts the LRs associated with tested DIFMs of 1000 ± 125 J m−2. LRs for the approximate 1000 J m−2 DIFM ranged from 2·86 to 3·59, with the lowest LR value (2·86) corresponding to the lowest DIFM (917 J m−2).
Table 5 Calculated αIFM exposure times and log reductions (LRs) for DIFM of 1000
Model A B C D E F
α internal filtering medium 0·25 2·18 6·11 8·5 30·01 31·38
Time (min) 266 32 10 7 2 2
LR 3·0* 3·3† 2·9‡ 3·5* 3·6* 3·1§
Reported LRs are for measured doses as indicated.
*Dose, 1125 J m−2.
†Dose, 1000 J m−2.
‡Dose, 917 J m−2.
§Dose, 941 J m−2.
Discussion
The information presented in this study provides an effective method to calculate UV‐C doses for the decontamination of FFRs. UV‐C decontamination is based on supplying an adequate dose to the contaminated area. UV‐C dose required for decontamination, which is microbe specific, is a function of irradiance and time. The UV‐C irradiance decreases with distance, especially when travelling through a substrate (e.g. FFR), where UV absorption and reflection are factors. Irradiance is also affected by microbial concentrations and the level of protective residues; however, consideration of these factors is the subject of another manuscript. Determining the UV‐C transmittance through the FFR material provides for a more accurate estimate of the irradiance supplied to the microbial contaminant. With a more accurate assessment of the irradiance, treatment times can be adjusted to achieve the targeted dose for the specific micro‐organism.
UV‐C radiation applied to FFRs will be transmitted, reflected or absorbed by the fibres of the multilayered substrate similar to other textiles (Hoffmann et al. 2001; Duleba‐Majek 2009). The transmittance of UV‐C through the layers of the FFR will largely occur through the gaps and pores between the fibres of the material. The materials in the inner and outer layers are more porous than the IFM as demonstrated by a comparative decrease in airflow resistance during the aerosol penetration tests (Table 2). The porosity of the inner and outer layers allows UV‐C to reach the IFM. The reflection and absorption of UV‐C will also be dependent upon other textile properties (e.g. weight and thickness) and the chemical composition of the fibres and/or other materials present such as dyes and delustrants. For a given layer, the reflected and absorbed UV‐C, which is the collective radiation that has impacted the surface, is assumed to be available for decontamination. The UV‐C that is transmitted through the pores or gaps of a layer becomes the potential available UV‐C for the next stratum.
The irradiance values used in this study for calculating UV‐C doses for FFR decontamination are based on the effective irradiation for the IFM. Being in the interior of the FFR, the IFM is the most challenging layer(s) to expose to UV‐C irradiation. The IFM, by design, provides the majority of the filtration performance to FFRs and should be the target of the UV‐C dose. In some instances (e.g. cough or sneeze that contain large wet droplets), the layers exterior to the IFM may capture the majority of the particles. However, even a fraction of the microbial contamination deposited on the IFM would be challenging to decontaminate. Thus, a specific dose calculated to decontaminate the IFM will provide an excess amount of UV‐C to the inner or outer layers. The excess UV‐C to the inner and outer layers provides a ‘safety factor’ to ensure a higher level of decontamination for the FFR exterior, which may reduce the hazard caused by contact transmission of infectious organisms.
The effectiveness of calculating model‐specific DIFMs is supported by the goodness of fit (R2 = 0·87, RMSE = 0·39) of the biphasic curve for all the decontamination experiments performed (Fig. 2). The biphasic analysis of the LRs (not shown) vs the supplied dose is more scattered because of the exclusion of model‐specific αIFM values (R2 = 0·36, RMSE = 0·88). Biphasic curves have been used to model thermal, nonthermal, pulsed field and UV‐C inactivation and occur as a result of a resistant subpopulation of microbes or other factors, such as, microbial clumping, microbial protection via particulate matter or localization on the substrate (Cerf 1977; Kowalski 2000; Geeraerd et al. 2005).
Figure 2 Dose–response relationship of MS2 inactivation on FFR coupons exposed to UV‐C. The data points represent the average LR of MS2 on FFR coupons exposed to UV‐C doses determined for the internal filtering medium using αIFM. The error bars represent ±1 standard deviation range for quadruplicate coupons (n = 4). The trend line is the biphasic mdel generated using the GInaFit freeware tool.
An examination of the data for the 10‐min UV‐C exposure of FFR representatives of the most shielded, moderately shielded and least shielded categories reveals the importance of calculating the DIFM (Table 3). A 10‐min exposure to a UV‐C surface irradiance of 25 W m−2 equates to a supplied dose of 15 000 J m−2 for each tested model. The LRs of 0·1, 2·9 and >4·8 for models A, C and F, respectively, would have been inconsistent if an equal dose was assumed. However, DIFMs of 38, 917 and 4707 J m−2 for models A, C and F, respectively, are consistent with the LR values. Variation in the decontamination efficiency among the FFR models is expected considering the differences in DIFMs.
The results of the targeted dose treatments (Table 4) further support the relationship of DIFM and MS2 decontamination, although unanticipated variation in decontamination efficacy across FFR models was evident for the 300 and 3000 J m−2 DIFMs. This variation may provide insight into factors important to UV‐C decontamination of FFRs. For the DIFM of 300 J m−2, some variability would be expected because of the steep slope of the initial phase of the biphasic curve (Fig. 2). In this initial phase, the shielding effect, or protection of the virus from UV‐C, seems to be limited, which is in contrast to the results of the 3000 J m−2 treatment. The statistical variation for this treatment may hint at an FFR model‐specific decontamination limit, which results from shielding or blocking of UV‐C by the composite layers of the FFR. This is similar to the shielding effect described in Gardner and Shama (2000). Across the models, this effect can be observed as the tailing of the biphasic curve (Fig. 2), although other factors such as virus clumping and UV‐C resistance of a subpopulation of virus may produce similar results (Kowalski 2000). The results for the UV‐C decontamination of model E provide further support for a model‐specific decontamination limit. For this FFR model, UV‐C efficacy approaching the decontamination limit was achieved with the lowest tested DIFM of 300 J m−2 and remained statistically the same for all three DIFMs tested (Table 4).
UV‐C treatments of an approximate DIFM of 1000 J m−2 produced similar LRs for all models tested (Table 5). The consistent MS2 LR values demonstrate the importance of αIFM values in determining the duration of UV‐C exposure times. Calculated exposure times to achieve a model‐specific DIFM of 1000 J m−2 ranged from 266 min for Model A to 2 min for Model F, representing the most shielded to least shielded categories. Exposure times of <8 min for the targeted DIFM, given an irradiance of 25 W m−2, are possible with αIFM values of 8·5 and greater (Models D–F). Model B, of the most shielded category (α = 2·18%), demonstrated a 3·2 LR with a treatment time of 32 min. Even with low αIFM values, significant LRs are possible with longer exposure times as demonstrated in this study or increased UV‐C irradiance, which would also provide an increase in dose. The demonstrated decontamination of FFRs with low αIFM values is promising for real world application.
The determination of the UV‐C transmittance and α‐values for the layers of an FFR is simple when using a radiometer. This process can be further simplified because of the ease by which filtering media is visually distinguishable from the other layers of an FFR (although filtration data would be preferable). The material used in the IFM appears to be less porous than the surrounding layers and of similar composition across model types. For all models examined, the filtering medium was located in the interior of the mask. By combining all the media that comprise the filtering layers into one stratum (the IFM) and compiling separate strata for the media to the exterior of the IFM in both directions, the number of layers can be minimized along with the number of measurements required to ascertain the transmittance data. Simple calculation [eqns (1) and (2)] using the transmittance data produces αIFM values, which is necessary to generate FFR model‐specific DIFMs. Reducing the number of layers by separating the IFM from the other composite strata of the FFR also minimizes the complexity of deciphering what is or is not a separate and distinctive layer.
Model‐specific αIFM values will determine the necessary UV‐C exposure time, given a targeted dose and the UV‐C source. The potential to decontaminate specific FFR models to a targeted LR value can be determined by evaluating the exposure times required to achieve desired doses. A minimum αIFM could be adopted during a reuse emergency, thereby defining FFR models that are more likely to lead to successful UV‐C decontamination. The αIFM values may also help in the selection of an adequate UV‐C source as an increase in irradiance would decrease treatment times given the targeted DIFM.
Producing an IFM model‐specific bidirectional (from both directions) DIFM provides the highest level of decontamination potential. Microbial contamination may emanate from the environment and/or from the user and deposit on all the layers of an FFR. A bidirectional UV‐C exposure targeted for the IFM will provide a dose adequate for microbial decontamination on the IFM, while providing an excessive dose for the exterior layers. However, a bidirectional target of the IFM may not be essential. Limiting the exposure to one direction, from outer to inner, may provide an adequate dose for decontamination while limiting the complexities of a two‐sided bidirectional treatment. The method to determine αIFM values and DIFMs described previously for bidirectional treatments is applicable to single direction exposures as well. Depending on the model‐specific composition of the exterior layers, the αIFM may be greater for a single direction exposure compared to a bidirectional treatment. The outer layer and IFM are more likely to contain the microbial threat, assuming that the wearer is healthy.
Limitations
Examining the effect of the FFR materials on UV‐C decontamination would provide valuable insight and assist in discerning the model‐specific‐decontamination efficacies, but is beyond the scope of this research. Likewise, material constituents are proprietary information of the manufacturers and are not readily known.
Further research is necessary for the development of protocols to generalize decontamination of FFRs with UV‐C. These studies were performed on a single plane, flat surface (coupons) with a single UV‐C source. Furthermore, the model‐specific αIFM values were determined by disassembling and then reassembling the FFR layers, which might have resulted in additional variation because of the incorrect orientation. The testing of complete, intact FFRs would provide a perspective on the effects of the multiple planes of the three‐dimensional form and other components such as straps and face‐seal interfaces. An examination of the use of multiple UV‐C sources may also be important to develop an FFR decontamination method. This issue has previously been explored for dental equipment, which presents similar surface irregularity challenges as FFRs in terms of irregular shapes and protein challenges (von Woedtke et al. 2003). The use of multiple sources may also lessen the effects of shielding from the material composition of the distinct layers and therefore increase the model‐specific limit of decontamination.
Studies to examine the effect of UV‐C on other micro‐organisms specific to respiratory illnesses will provide a better assessment of the influence of multilayered air‐permeable materials on microbial decontamination kinetics. The physical and chemical protection of micro‐organisms by residues, such as, sputum, blood, soil, etc., is also a concern for all methods of decontamination. Although well studied in water and air decontamination, protection residues were not examined in this study. Therefore, the effects of residues on UV‐C FFR decontamination are unknown but may be similar to those described in air and water research. We are currently exploring the efficacy of multiple decontaminations of FFRs repeatedly challenged with virus‐containing droplet nuclei and exposed to UV‐C. This research, which is being conducted using varying levels of organic challenge as a protection factor, will provide insight into the effects of protein deposition on the penetration/decontamination efficacy of UV‐C.
Acknowledgements
The authors express their sincere gratitude to Dr Debra Novak, Dr Samy Rengasamy, Dr Evanly Vo, Dr Benjamin C. Eimer, Mr Dennis Viscusi, Mr Mike Bergman and the manuscript reviewers for their suggestions and contributions.
Disclaimer
The findings and conclusions in this manuscript are those of the authors and do not necessarily represent the views of the National Institute for Occupational Safety and Health (NIOSH). Mention of company names or products does not constitute endorsement by NIOSH.
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References
Andersen,B.M., Banrud,H., Boe,E., Bjordal,O. and Drangsholt,F. (2006) Comparison of UV C light and chemicals for disinfection of surfaces in hospital isolation units. Infect Control Hosp Epidemiol 27 , 729–734.16807849
Barrett,L.W. and Rousseau,A.D. (1998) Aerosol loading performance of electret filter media. Am Ind Hyg Assoc J 59 , 532–539.
Cerf,O. (1977) Tailing of survival curves of bacterial spores. J Appl Bacteriol 42 , 1–19.323208
Duleba‐Majek,M. (2009) Transmission of UV radiation through woven fabrics in dependence on the inter‐thread spaces. Fibres Text East Eur 17 , 34–38.
Fisher,E.M., Rengasamy,S., Viscusi,D.J., Vo,E. and Shaffer,R.E. (2009) Development of a test system to apply virus‐containing particles to filtering facepiece respirators for the evaluation of decontamination procedures. Appl Environ Microbiol 75 , 1500–1507.19139225
Gardner,D.W.M. and Shama,G. (1998) The kinetics of Bacillus subtilis spore inactivation on filter paper by uv light and uv light in combination with hydrogen peroxide. J Appl Microbiol 84 , 633–641.
Gardner,D.W.M. and Shama,G. (2000) Modeling UV‐induced inactivation of microorganisms on surfaces. J Food Prot 63 , 63–70.10643771
Geeraerd,A.H., Valdramidis,V.P. and Van Impe,J.F. (2005) GInaFiT, a freeware tool to assess non‐log‐linear microbial survivor curves. Int J Food Microbiol 102 , 95–105.15893399
Gorsuch,E.L., Grinshpun,S.A., Willeke,K., Reponen,T., Moss,C.E. and Jensen,P.A. (1998) Method for evaluating germicidal ultraviolet inactivation of biocontaminated surfaces. Int J Occup Saf Ergon 4 , 287–297.10602623
Hoffmann,K., Laperre,J., Avermaete,A., Altmeyer,P. and Gambichler,T. (2001) Defined UV protection by apparel textiles. Arch Dermatol 137 , 1089–1094.11493104
HPA (2009) HPA Interim Advice on Extending the Lifespan of Facemasks and Respirators. UK: Health Protection Agency.
Institute of Medicine (U.S.) Committee on the Development of Reusable Facemasks for Use During an Influenza Pandemic, Institute of Medicine (U.S.) and Board on Health Sciences Policy and National Academies Press (U.S.) (2006) Reusability of Facemasks During an Influenza Pandemic: Facing the Flu. Washington, D.C.: National Academies Press.
Kerr,N., Capjack,L. and Fedosejevs,R. (2000) Ability of textile covers to protect artifacts from ultraviolet radiation. JAIC 39 , 345–353.
Kowalski,W.J. (2000) Mathematical modeling of ultraviolet germicidal irradiation for air disinfection. Quant Microbiol 2 , 249.
OSHA (2008) Proposed Guidance on Workplace Stockpiling of Respirators and Facemasks for Pandemic Influenza ed. Washington, DC: DOL.
Rastogi,V.K., Wallace,L. and Smith,L.S. (2007) Disinfection of Acinetobacter baumannii‐contaminated surfaces relevant to medical treatment facilities with ultraviolet C light. Mil Med 172 , 1166–1169.18062390
Roberge,R.J. (2008) Effect of surgical masks worn concurrently over N95 filtering facepiece respirators: extended service life versus increased user burden. J Public Health Manag Pract 14 , E19–E26.18287908
Sylvain,D. and Tapp,L. (2009) Health hazard evaluation report: UV‐C exposure and health effects in surgical suite personnel. Department of Health and Human Services, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health.
Tseng,C.C. and Li,C.S. (2007) Inactivation of viruses on surfaces by ultraviolet germicidal irradiation. J Occup Environ Hyg 4 , 400–405.17474029
Viscusi,D.J., King,W.P. and Shaffer,R.E. (2007) Effect of decontamination on the filtration efficiency of two filtering facepiece respirator models. J Int Soc Respir Prot 24 , 93–107.
Viscusi,D.J., Bergman,M.S., Sinkule,E. and Shaffer,R.E. (2009a) Evaluation of the filtration performance of 21 N95 filtering face piece respirators after prolonged storage. Am J Infect Control 37 , 381–386.19188003
Viscusi,D.J., Bergman,M.S., Eimer,B.C. and Shaffer,R.E. (2009b) Evaluation of five decontamination methods for filtering facepiece respirators. Ann of Occup Hyg 53 , 815–827.19805391
Vo,E., Rengasamy,S. and Shaffer,R.E. (2009) Development of a test system to evaluate decontamination procedures for viral droplets on respirators. Appl Environ Microbiol 75 , 7303–7309.19801477
Von Woedtke,T., Julich,W.D., Thal,S., Diederich,M., Stieber,M. and Kindel,E. (2003) Antimicrobial efficacy and potential application of a newly developed plasma‐based ultraviolet irradiation facility. J Hosp Infect 55 , 204–211.14572488
| 36511771 | PMC9728111 | NO-CC CODE | 2022-12-09 23:26:00 | no | Micros Today. 2020 May 1; 28(3):7 | latin-1 | Micros Today | 2,020 | 10.1017/S1551929520000784 | oa_other |
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pmcAs many of my colleagues know, I recently experienced a “vaccination breakthrough” case and paid my COVID-19 dues with a stay in an intensive care unit in August. While too numerous to name here, I would like to thank all of those from the microscopy and microanalysis communities who provided support through their thoughts and prayers during that difficult time. While in the ICU and in recovery, I realized how important the MSA and MAS friendships I have established over the years are in my life. The support from all of you certainly helped in my recovery.
My stay in the ICU provided me with some unexpected free time, and, not able to do much more than stare at the ceiling, I began to think about the important role that microscopy and microanalysis have played in combating the COVID-19 pandemic. From the early cryo-EM 3D reconstructions of the coronavirus presented on the news media to raise public awareness, to the development of vaccines as detailed in the M&M 2021 Plenary presentation by Jason McLellan, imaging has been at the forefront of understanding COVID-19 structure and function. Many sessions at the meeting including Imaging, Microscopy, and Micro/Nano-Analysis of Pharmaceutical, Biopharmaceutical, and Medical Health Products - Research, Development, Analysis, Regulation, and Commercialization; Cryo-EM in Drug Discovery; and Challenges and Advances in Electron Microscopy Research and Diagnosis of Diseases in Humans, Plants and Animals also addressed topics relevant to studying COVID-19 and other diseases.
Many MSA Focused Interest Groups (FIG), including 3D EM in the Biological Sciences; Cryopreparation; Diagnostic and Biomedical Microscopy; and the Pharmaceuticals group, also use a range of imaging techniques to study viruses as well as other microbes and diseases. If your research and interests align with any of these or other MSA FIGs, please consider joining and contributing to the important research being performed in these groups. A full list of FIGs can be found on the MSA website at Communities | Focused Interest Groups (https://www.microscopy.org/communities/fig.cfm). I can personally vouch for the personal and professional benefits that being a member of these communities provides, and I encourage all to join the group(s) that fit your interests and research.
| 36511766 | PMC9728113 | NO-CC CODE | 2022-12-09 23:26:00 | no | Micros Today. 2021 Nov 1; 29(6):7 | utf-8 | Micros Today | 2,021 | 10.1017/S1551929521001218 | oa_other |
==== Front
J Appl Microbiol
J Appl Microbiol
jambio
Journal of Applied Microbiology
1364-5072
1365-2672
Blackwell Science Ltd Oxford, UK
10.1046/j.1365-2672.94.s1.17.x
jambio-94-s1-0131
Subject Index
Subject index
01 5 2003
01 5 2003
01 5 2003
94 Suppl 1 131138
© 2003 The Society for Applied Microbiology
2003
https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)
This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.
Acanthamoeba keratitis
Adelaide River virus
Aedes spp.
A. aegypti
A. albopictus
A. funereus
airborne infections
airplanes in infection transmission
Albufeira gastroenteritis outbreak
algal blooms
American crows (Corvus brachyrhynchos)
residency status
amoebiasis
amoebic meningoencephalitis
amplification in Campylobacter
anaerobic digestion
animal faecal wastes
aquaculture, infection transmission in
Asian tiger mosquito see Aedes spp., albopictus
Australian bat lyssavirus (ABLV)
management strategies
in South–East Asia
vaccination
badgers (Meles meles)
population numbers
and tuberculosis
immunological response to BCG
as maintenance host
mortality rates
vaccination
Barmah Forest virus
bartonellosis
bats see fruit bats
biomarkers
vaccination
rabies‐like viruses
tuberculosis
biopsies for rabies virus detection
bio‐terrorism
birds
andCampylobacter
Salmonella enteritidis in
and VTEC
and West Nile virus
see also poultry
blue jays (Cyanocitta cristata)
bone meal
Borrelia
boutoneuse fever
bovine spongiform encephalopathy (BSE)
confirmations worldwide
epidemics
Britain
Europe
scale
Switzerland
future aspects
geographical risk
likelihood by country
presentation
prevalence
rate of reporting
risks of livestock/product movement
routes of infection
surveillance
transmission, maternal
branching process model
breakbone fever
British Egg Industry Council Lion code of practice
Brock test
Campylobacter
in animal faecal wastes
C. jejuni
in cattle
geographical variations
seasonal effect
transmission
in cattle/sheep
colonization of young animals
infection sources in adults
isolation rates at slaughter
seasonal variation
environmental/non‐poultry strains
historical aspects
in humans
epidemiology
returning travellers
role of ruminants
seasonal variation
outbreaks
shedding
factors affecting
patterns
and temperature change
thermophilic
transmission mechanisms
Catharus swainsonii
cattle
BSE
Campylobacter in
faecal waste production
and tuberculosis
problem herds
VTEC
survival in faecal wastes
Chagas disease
Chinese Hippocratic Corpus
cholera
Ciconia ciconia
climate change
flooding
heavy rainfall events
temperature effects
and vector‐borne infections
and waterborne infections
Code of Good Agricultural Practice for the Protection of Water
codes of practice for Salmonella
common grackle (Quiscalus quiscula)
Communicable Disease Report
communicable diseases
surveillance
surveillance programmes
composting
Corvus brachyrhynchos
couriers, travellers as
Crimean‐Congo haemorrhagic fever
cruise ships in infection transmission
Cryptosporidium/cryptosporidiosis
in animal faecal wastes
C. parvum
outbreaks in UK
on package holidays
in water supplies
Culex spp.
C. nigripalpus
C. pipiens
cutaneous larva migrans
cyanobacteria
blooms
Cyanocitta cristata
Cyclospora cayetanensis
dengue fever
dengue haemorrhagic fever
dengue shock syndrome
diagnosis
foot‐and‐mouth disease
rabies
tuberculosis
diatoms
digestion, anaerobic
dinoflagellates
DNA fingerprinting
domestic pets
Dominican Republic gastroenteritis outbreaks
Dracunculus medinensis
drinking water contamination
eastern equine encephalitis
economic consequences
of flooding
of travel‐related illness
eggs
ELISA
rabies virus detection
tuberculosis testing
emerging infections
fruit bats, borne by
in Australia
future emergence
in healthy‐seeming bats
management strategies
current
future
in South‐East Asia
infectious dose
travel‐associated
en route transmission
environment factors
on package holidays
pathogen diversity
population factors
silent introduction
socioeconomic/political factors
time of consequences
transmission mechanisms
travellers
as sentinels/couriers
as transmitters/disseminators
encephalitis/encephalitic illness
see also specific diseases
Entamoeba histolytica
Enternet surveillance network
enteroviruses
environmental pollution from farms
eosinophilia
epidemics
BSE
foot‐and‐mouth disease
Salmonella enteritidis
control
and travel
equine cases
Hendra virus
West Nile virus
Escherichia coli
Enternet surveillance network
enterotoxogenic (ETEC)
O157
O157:H7
in cattle faeces
direct contact with faecal wastes
outbreaks
seasonal effect
transmission
resistance spread
toxins as biomarkers
transport in surface runoff
travellers’ diarrhoea
verocytoxigenic (VTEC)
in animal faecal wastes
persistence
vegetable contamination
control measures
prevalence
survival times
vulnerable targets
estuary‐associated syndrome
European Commission
Scientific Steering Committee
surveillance outcome
faecal organisms
catchment sources
coliform number prediction
overland flow transport
deposition
entrainment
partitioning between water and particles
sources/survival in soil
transport process
faecal wastes
application
in organic production
processing
risks of infection
see also composting; manures; slurries
farm visits
fimbriae
fish crow (Corvus ossifragus)
flooding
see also rainfall events
fluorescent antibody test
rabies virus detection
flying foxes
distribution
mosquito‐transmitted viruses
novel viruses in
Australian bat lyssavirus
Hendra virus
Menangle virus
Nipah virus
Tioman virus
vaccination strategies
see also fruit bats
food hygiene on package holidays
food‐borne infections
on cruise ships
on package holidays
surveillance
in travel
food/waterborne infections
investigations
outbreaks
prevention
surveillance
consumer groups
consumer satisfaction questionnaire
formal systems
resort staff records
see also waterborne infections
foot‐and‐mouth disease
biology
case reproduction ratio
clinical signs
epidemic, UK 2001
incubation period
infectious period
mathematical models
modelling vaccination
output
number/distribution of cases
transmission data
transmission routes
virus
Francisella tularensis
fruit bats in virus spread
bat species
emerging diseases
established diseases
experimental infections
Nipah virus
see also flying foxes
garbage disposal and West Nile virus
gastroenteritis outbreaks
in Albufeira
in Dominican Republic
in Portinatx
Salmonella in‐flight meals
in Salou
gastrointestinal infections
and holiday destination
management
returning travellers
Giardia spp.
in animal faecal wastes
G. duodenalis
G. lamblia
in water supplies
global warming
Guanarito virus
haemolytic uretic syndrome
‘hamburger bug’
hantaviruses
Health Protection Agency
Helicobacter pylori
hemi‐nested reverse transcriptase polymerase chain reaction (hn‐RT‐PCR)
Hemipaviruses
Hendra virus
biological properties
clinical properties
genome sequence
management strategies
transmission mechanisms
hepatitis A
hepatitis E
HIV virus
horses see equine cases
host competence
house sparrows (Passer domesticus)
dispersal patterns
human immunoglobulin (HRIG )
hygiene
on farms
food
and Salmonella
in zoos
immunization see vaccination
infections
role of travel in microbial spread
see also emerging infections; specific diseases
infectious intestinal disease
influenza
ecology
H3N2
H5N1
incident of 2001
incident of 2002
lessons learned from incidents
as pandemic virus
candidate
H5N1/97
reassortment
transmissibility
H5N1‐like viruses
reassortment
H6N1
as pandemic virus candidate
H9N2
as pandemic virus candidate
transmissibility
pandemic
adjunct factors
baseline preparedness
epicentre
immediate factors
incident of 1997
‘mixing vessel’, avian
‘mixing vessel’, porcine
sentinel posts
as zoonosis
in poultry
land poultry
water poultry
water poultry adapting to land poultry
reservoir vs source
subtypes
influenza A
avian
insect vectors
and West Nile virus
see also mosquitoes
International Food Safety Standard for the Tourism Sector
Japanese encephalitis
Katayama syndrome
Kyassanur Forest virus
legionnaires’ disease
Leishmania/leishmaniasis
leptospirosis
lipopolysaccharide
Listeria
L. monocytogenes
livestock feeds and BSE
loaiasis
Lyme disease
lymphocyte transformation assay (LTA)
Lyssavirus
malaria
and climate change
future spread
returning travellers
manures
erosion calculation
faecal organisms in runoff
infection of vegetables
in organic production
treatment
for Campylobacter
for VTEC
VTEC survival in
Mapuera virus
mass action model
mathematical models
meat carcasses/products
BSE
Campylobacter
media in pandemic control
Meles meles
Menangle virus
management strategies
Meyer and Wischmeier's model
microsimulation model
milk
Modified Universal Soil Loss Equation
Monte Carlo simulation model
mosquitoes
and dengue fever
and diseases of clinical importance
and malaria
and West Nile virus
future movement
mode of entry
murine typhus
Murray River encephalitis
Mycobacteria
Mycobacterium bovis infection see tuberculosis
national bovine tuberculosis eradication programme
national control programme in cattle
Neisseria meningitidis
epidemics and travel
Nipah virus
clinical properties
management strategies
transmission mechanisms
Norwalk Like Virus
investigation
on package holidays
onchocerciasis
organic produce, animal faecal wastes in
ovenbird (Seiurus aurocapillus)
Over Thirty Months scheme
overland flow mitigation methods
overland flow transport
contaminated runoff
deposition
empirical transport models
initiation
process
package holidays
food/waterborne infections
statistics
parainfluenza virus
paramyxoviruses
Passer domesticus see house sparrows
pathogen transport processes
models
overland flow transport
see also specific pathogens/diseases
pathogens
in animal faecal wastes
transport process
in sewage sludge
survival
in soil
and temperature
zoonotic, in animal faeces
Pfiesteria piscicida
phylogenetic analysis
pigeons
VTEC in
VTEC survival in faecal wastes
pigs
influenza in
Menangle virus in
Nipah virus in
VTEC in
pilgrimage and epidemic
plague
Plasmodium
polymerase chain reaction
ELISA
hemi‐nested reverse transcriptase (hn‐RT‐PCR)
for rapid rabies diagnosis
reverse transcriptase (RT‐PCR)
population factors in emerging infections
movement
size/vulnerability
population reduction in bats
Portinatx gastroenteritis outbreak
poultry
Campylobacter in
role of hygiene
transmission route
influenza in
as reservoir
marketing system modification
Salmonella enteritidis in
VTEC in
Pteropus
public health infrastructure
problems
and West Nile virus
quarantine
Quiscalus quiscula see common grackle
rabies
in Australia
bat population reduction in
diagnosis
example cases
in fruit bats
risk factors
vaccination
in Australian bat lyssavirus
post–exposure treatment (PET)
in related diseases
virus detection
in brain samples
in skin/saliva
rabies virus RNA sequence
rainfall events
climate change
overland flow transport
sediment yield calculation
and water pollution
see also flooding
rapid molecular detection
rats
reactive vaccination
red‐tailed hawk
refuse disposal
Regional Infectious Disease units
relapsing fever
resistance and spread of genetic material
restriction fragment length polymorphism analysis
reverse transcriptase polymerase chain reaction (RT‐PCR)
rhabdoviruses
Rickettsia
Rift Valley fever
ring vaccination
Rocky Mountain spotted fever
Ross River virus
rotavirus
Rubulavirus
ruminants
see also cattle; sheep
runoff
Safe Sludge Matrix
Salmonella spp.
in animal faecal wastes
codes of practice for
Enternet surveillance network
host adapted
in‐flight meals
returning travellers
in rodents
travellers’ diarrhoea
S. agona
S. diarizonae
S. dublin
S. enteritidis
epidemic
epidemic, control
in humans
on package holidays
in poultry
PT4
in sheep
survival
in UK
vehicles of infection
virulence factors
S. gallinarum
S. paratyphi
S. pullorum
S. senftenberg
S. typhi
on package holidays
resistance patterns
S. typhimurium
Salou gastroenteritis outbreak
scenario modelling
Schistosoma
Schistosoma/schistosomiasis
S. mansoni
Seiurus aurocapillus see ovenbird
sentinels, travellers as
sewage sludge
sheep
Campylobacter in
Salmonella enteritidis in
VTEC in
VTEC survival in faecal wastes
Shigella spp.
S. sonnei
Sludge (Use in Agriculture) Regulations
slurries
Campylobacter in
faecal organisms in runoff
treatment for VTEC
VTEC survival in
smouldering viruses
soil erosion processes
soil‐slurry mixture
microorganism state
pathogen transport
St Louis encephalitis
Staphylococcus aureus resistance
Stimulation Index analysis
Strategy for Control of Infectious Diseases
stress and Campylobacter shedding
Strongyloides stercoralis
subjective travellers’ diarrhoea (STD)
geographical variations
outbreaks
surveillance, disease
BSE
communicable diseases
Enternet
food‐borne infections
food/waterborne infections
influenza virus
travellers’ diarrhoea
Swainson's thrush (Catharus swainsonii)
swine transmissible gastroenteritis virus vaccination
temperature increase and infectious disease
tick‐borne encephalitis
Tioman virus
Toxoplasma gondii
transmissible spongiform encephalopathies (TSEs)
legal basis of controls
trap vaccination release (TVR) programmes
travel
advice
and emerging infections
pandemic virus seeding
to rabies endemic countries
travellers
returning, infectious diseases in
as sentinels/couriers
as transmitters/disseminators
travellers’ diarrhoea
classic
prevention
reviews on
subjective (STD)
Albufeira
Dominican Republic
geographical variations
Portinatx
Salou
surveillance
consumer groups
consumer satisfaction questionnaire
formal systems
resort staff records
travel‐related illness
bacterial infections
non‐infectious
respiratory tract infections
skin infections
viral infections
Trypanosoma spp.
T. cruzi
tuberculin
reactor cattle
testing
tuberculosis
in badgers
diagnosis
immunodiagnosis
tests vs immunological analysis
route of infection
vaccination
in cattle
identification/distribution
epidemiological analysis
prevalence
strain typing
transmission
interspecies
tularaemia
typhoid fever
vaccination
biomarkers
foot‐and‐mouth disease
influenza
Neisseria meningitidis
rabies
rabies‐like viruses
Australian bat lyssavirus
oral live vaccine
oral subunit vaccine
parenteral TVR
Salmonella enteritidis
transgenic‐plant produced antigens
tuberculosis
VTEC
vaccines, properties of
vector‐borne infections
climate change and epidemiology
global warming, future impact of
vectors
competence
insect
vegetative filter strips
Venezuelan haemorrhagic fever
Vibrio cholerae
survival in sea water
virus surveillance
influenza
see also surveillance, disease
virus typing
viruses borne by fruit bats
VT encoding phages
vt genes
wastewater treatment
water pollution/contamination
Campylobacter
livestock drinking water
see also overland flow transport
waterborne infections
Albufeira
climate change and epidemiology
on cruise ships
global warming, future impact of
prevention
Salou
and shipping
see also food/waterborne infections
West Nile virus
in fruit bats
future movement
hypotheses for movement
dispersing sedentary bird as host
insect vectors
migratory birds as host
sick migratory birds as host
immunity
and migratory birds
identification
as introductory host
rate of movement
residency status of bird hosts
in New World
arrival/movement
barriers to dispersal
first human case
mode of entry
pattern of recurrence
in Old World
oral transmission
range expansion, rate of
and resident bird hosts
white storks (Ciconia ciconia)
WHO Global Agenda on Influenza
WHO Influenza Collaborating Center
yellow fever
Yersinia
zoos, hygiene in
==== Body
pmc
| 0 | PMC9728115 | NO-CC CODE | 2022-12-09 23:26:00 | no | J Appl Microbiol. 2003 May 1; 94(Suppl 1):131-138 | utf-8 | J Appl Microbiol | 2,003 | 10.1046/j.1365-2672.94.s1.17.x | oa_other |
==== Front
Lett Appl Microbiol
Lett Appl Microbiol
lambio
Letters in Applied Microbiology
0266-8254
1472-765X
Blackwell Science Ltd Oxford, UK
32734625
10.1111/lam.13365
lambio-71-5-0498
Original Articles
BCG vaccination early in life does not improve COVID‐19 outcome of elderly populations, based on nationally reported data
https://orcid.org/0000-0002-7024-1139
Wassenaar T.M. Molecular Microbiology and Genomics Consultants Zotzenheim Germany
European Molecular Biology Laboratory (EMBL)
Buzard G.S. Independent scolar Middletown MD USA
https://orcid.org/0000-0002-4959-2428
Newman D.J. Newman Consulting LLC Wayne PA USA
Correspondence Trudy M. Wassenaar, Molecular Microbiology and Genomics Consultants, Tannenstrasse 7, 55576 Zotzenheim, Germany. E‐mail: [email protected]
[Correction added on 27 January 2021, after first online publication: Affiliation ‘Molecular Biology Laboratory (EMBL)’ was added for T.M. Wassenaar.]
01 11 2020
01 11 2020
01 11 2020
71 5 498505
12 5 2020
21 7 2020
21 7 2020
© 2020 The Society for Applied Microbiology
2020
https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)
This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.
Abstract
The reported numbers of Covid‐19 cases and deaths were compared for 18 countries (14 in Western Europe, plus Australia, Brazil, Israel and the USA) to assess the effect of historic and current national BCG immunizations. In view of the high death rate for Covid‐19 patients over 70 years of age, and given the fact that BCG vaccination is typically given early in life, we compared countries that had introduced BCG in the 1950s with those that had not. No effect on Covid‐19 case fatality rate (CFR) or number of deaths per population could be demonstrated. Since some countries test for Covid‐19 more than others, the effect of tests performed per million population on reported deaths per million was also assessed, but again did not demonstrate an effect of BCG vaccination in the 1950s. Whether countries had never used the vaccine, had historically used it but since ceased to do so, or were presently vaccinating with BCG did not correlate with national total number of deaths or CFR. We conclude that there is currently no evidence for a beneficial effect of BCG vaccination on Covid‐19 reported cases or fatalities.
BCG
Covid‐19
Mycobacterium tuberculosis vaccine
Sars‐CoV‐2
TBC
trained immunity
tuberculosis
==== Body
pmcIntroduction
Bacillus Calmette‐Guérin (BCG) is a vaccine containing an attenuated strain of Mycobacterium bovis that has been in use for nearly a century, with some genetic variations accruing in the strains being used (Crispen 1989; Luca and Mihaescu 2013). BCG was designed to protect against tuberculosis (Mycobacterium tuberculosis), but the vaccine can result in immune protection well beyond its target organism (Netea and Crevel 2014). This can be explained by a third leg of the immune system that is activated upon microbial exposure besides the classical native and adapted immunity and for which the name ‘trained immunity’ was coined. Trained immunity does not involve permanent genetic changes but depends on epigenetic and transcriptional changes that are generally relatively short‐lived ((Netea et al. 2016, Netea et al. 2020). Neonatal vaccination with BCG can reduce infant mortality rates from other bacterial infections, as was demonstrated in Guinea‐Bissau (Kristensen et al. 2000). More importantly, the BCG anti‐bacterial vaccine was shown to reduce viremic loads of attenuated yellow fever virus in experimentally infected individuals, presumably due to epigenetic reprogramming of parts of the immune system (Arts et al. 2018). This broad immunity enhancement has recently led researchers to propose that BCG vaccination might be beneficial for combating Covid‐19 (Curtis et al. 2020).
The mounting evidence for the relationship between BCG vaccination and Covid‐19 is based on an inferred association among two somewhat ambiguous data sets; reports of national BCG vaccine coverage and current Covid‐19 reporting (Ozdemir et al. 2020; Gursel and Gursel 2020), although none of the submitted papers to date have acknowledged the numerous confounders that make these data so ambiguous. These variables, such as the differences in testing strategies, reporting biases, a nation's ability to respond to the pandemic, prevalence of co‐morbidities and different stages of the pandemic across various countries, will all have significant impacts on suspected correlations between BCG vaccination and Covid‐19 severity, and thus all must be looked at critically to avoid confirmational bias in interpretations. Demography is a further factor affecting the Covid‐19 case fatality rate (CFR), especially when comparing countries with an ageing population versus countries with a lower average age. Lastly, in some countries the epidemic is further advanced than in others, which further affects the CFR: early in the epidemic the deaths, which can take weeks to occur, lag behind reported infections and are typically low, then CFR increases as patients begin succumbing. Therefore, at this still early stage of the pandemic, the association between Covid‐19 manifestations and BCG vaccination should be considered as a hypothesis only, and should be tested through appropriate carefully designed studies (Kumar and Meena 2020).
Covid‐19 is currently manifested in the human population in four distinct ways. Approximately 15–20% (this number is in flux) of the population that becomes infected will be asymptomatic or paucisymptomatic (Keeley et al. 2020). Upon admission to hospital, 92% of patients have symptoms, and 8% are asymptomatic (Mei et al. 2020). Another 60% (range 40–80%) will have a range of mild symptoms (Chen et al. 2020a), but even so might be incurring serious organ damage that will haunt them later in life (Mitrani et al. 2020). Another roughtly 10‐25% of cases will have severe to critical symptoms resulting in pneumonia leading to acute respiratory distress (ARDS), coagulopathies, and a form of hyperactive immune response termed a cytokine storm (Ragab et al. 2020; Mangalmurti and Hunter 2020; Guan et al. 2020; Guo et al. 2020). Finally, roughly 3–5% of confirmed symptomatic cases of Covid‐19 will die from multiple organ failures or other sequelae. The CFR for confirmed Covid‐19 cases reported to WHO stands at 4·33% (13 405 694 confirmed cases and 580 552 deaths) (15 July 2020; https://coronavirus.jhu.edu/map.html). A report from the United States Centers for Disease Control and Prevention COVID‐19 Response Team showed that 80% of deaths associated with COVID‐19 were among adults aged ≥65 years; a major European report found the number to be even higher, 91%; and if the 8% of deaths among 45–64 is included, 99% of all Covid‐19 deaths are accounted for, and the majority of had specific comorbidities that further predisposed them to the cytokine storm (Vestergaard et al. 2020).
Preventing, slowing, stopping and reversing the Covid‐19 cytokine storm is now the number one goal for scientists and physicians around the world, and there have been dozens, if not hundreds, of suggestions as how to do it. One of these has been the suggestion to use the BCG vaccine to prophylactically limit the infection and prevent the storm. Clinical trials have now been initiated to assess if BCG vaccination can achieve those goals. Until more data are available, the WHO currently recommends using BCG vaccination only in carefully monitored randomized clinical trials (WHO 2020).
If the hypothesis that BCG vaccination affects severity and outcome of Covid‐19 is correct, then this should be best reflected by lower infection fatality rates reported by various countries that included this vaccine in their national vaccination programs, currently or in the past. One publication described such an association (Ozdemir et al. 2020), where countries that currently include BCG in their national vaccination programs were compared to countries that excluded it. However, that comparison was skewed, as most of the included countries currently practicing BCG vaccination are in Africa where the epidemic arrived later and cases so far have more often remained unreported, while all the countries included in the analysis without current nation‐wide BCG vaccination programs were in Europe, where the epidemic is significantly more advanced and reporting should have been more inclusive (though by no means complete). A similar approach was followed by Gursel and Gursel (2020), who compared (mostly European) countries that have never routinely used BCG, or had ceased to do so, with countries that continued its use in immunization programs. They compared the number of cases and deaths per million, which was lower for countries with current BCG vaccination (P < 0·0001). Countries that had ceased using the vaccine in the last two decades had significantly (P = 0·109) fewer Covid‐19 confirmed deaths per million cases compared to countries that stopped three to four decades ago (Gursel and Gursel 2020).
It is now well‐established that Covid‐19 is the most serious for individuals who are at the higher age spectrum, have underlying conditions, such as cardiovascular disease, obesity, diabetes or a combination of these, are male, and are regular smokers (Chen et al. 2020). This does not mean that young, healthy individuals cannot be infected, only they do not appear to suffer from severe symptoms as frequently as the aforementioned patient groups, and age cohorts younger than 60 show much lower fatality rates (The Novel Coronavirus Pneumonia Emergency Response Epidemiology Team 2019; CDC COVID‐19 Response Team 2020, Aggarwal et al. 2020). For these reasons, it is not informative to compare fatality rates between countries that currently use BCG vaccination on a national scale with countries that do not, as vaccines are typically administered at a young age, while the Covid‐19 fatalities are restricted mostly to elderly patients. For a correlation to be assessed between the severity of Covid‐19 and the vaccination state, we suggest that the analysis should concentrate on people who are at the higher end of the age spectrum, who would or would not have been vaccinated in their youth.
With this in mind, and being aware of all the caveats listed above, we compared some epidemiological parameters of Covid‐19 between countries and took the historic BCG vaccination data into account. We assessed whether nationally reported Covid‐19 cases and fatality rates correlated with BCG vaccination for individuals who were born from 1950 and onwards and who are therefore younger than 70. Before that year, few countries employed the vaccine, and Covid‐19 patients born prior to 1945 will have a substantial risk of dying, regardless of any protective effect of previous vaccinations. Hence, we compared the reported incidence and outcome of Covid‐19 infections for a number of countries that are well into the developing pandemic and compared the BCG vaccination programs in place since the 1950s to assess if a protective effect of BCG vaccination could be demonstrated.
Results and discussion
The 18 countries we compared included those reporting >100 000 total cases of Covid‐19 on 7 May 2020 (USA, Spain, Italy, UK, Russia, France, Germany, Brazil, Turkey and Iran). Of these, the UK and France had BCG vaccination programs in place from the 1950s onwards. In order to include countries that had relatively low reported numbers of cases while the epidemic was nationally far enough advanced in time, Austria, Ireland and Israel were included, all three of which had BCG vaccination programs introduced in the 1950s. To include countries not using BCG in the 1950s, which could be compared to these in terms of population size, Portugal, the Netherlands, Belgium and Switzerland were added. Furthermore, Australia was included because they had a vaccination regime in place since the 1950s that was similar to that of the UK. The dates countries introduced BCG vaccination (if at all) is summarized in Table 1.
Table 1 Data used in this study
Covid‐19 cases* Covid‐19 deaths* Deaths/mil pop* Nationwide BCG vaccination† Strain† Current vaccination
USA 1 292 623 76 928 232 Never Not applicable No
Spain 256 855 26 070 558 From 1965, at birth Danish No
Italy 215 858 29 958 495 Never Not applicable No
UK 206 715 30 615 451 From 1953, at age 12–14 years Evans Medical/Medeva No
Russia 177 160 1625 11 Start date unknown, at birth Not specified Yes
France 174 791 25 987 398 From 1950, at birth Danish SSI 1331 No
Germany 169 430 7392 88 From 1960, at birth Danish SSI 1331 No
Brazil 135 693 9188 43 Start date unknown, at birth to 1 year BCG Moreau Rio Yes
Turkey 133 721 3641 43 From 1952, at birth to 1 year Serum Inst. of India Yes
Iran 103 135 6486 77 From 1984, at birth to 1 year Pasteur Inst. 1173‐P2 Yes
Belgium 51 420 8415 726 Never Not applicable No
Netherlands 41 774 5288 309 Never Not applicable No
Switzerland 30 126 1810 209 From 1960, at birth to 1 year Merieux No
Portugal 26 715 1,105 108 From 1964, at birth to 1 year Not specified Yes
Ireland 22 385 1,403 284 From 1950, at birth Danish SSI 1331 Yes
Israel 16 381 240 28 From 1955, at birth Not specified No
Austria 15 752 609 68 From 1952, at birth to 1 year Not specified No
Australia 6896 97 4 From 1950, at age 12–14 years Not specified No
mil pop, million population.
* As of 7 May 2020. Source: Johns Hopkins University. See Table S1 for data extracted on 18 July 2020.
† Source: http://www.bcgatlas.org (Zwerling et al. 2011).
Figure 1a shows the correlation between the number of reported cases and the number of reported deaths. The arithmetic average CFR of 5·2% for these 18 countries is indicated as a dotted line. Two of the six countries that employed BCG in the 1950s have CFRs that lay well below this average (Australia and Israel), while France is above it. To date, Russia has reported relatively low numbers of deaths compared to its number of cases, which could be due to the fact that at the time of analysis the epidemic may be at an earlier stage of progression compared to most other countries included here. The true number of cases is obviously much higher than what each of these countries has reported, with a factor that will vary per country. Fatality rates are also not equally assessed, as some countries do not count deaths occurring outside of the hospital, be it in elderly care facilities or in private homes, while countries also use different definitions of Covid‐19‐related deaths (positive by tests or positive based on symptoms).
Figure 1 Analysis of 18 countries: Australia, Austria, Belgium, Sweden, Switzerland, Canada, Brazil, Turkey, Germany, France, Russia, United Kingdom, Italy, Spain and USA (ordered for increasing number of total reported cases on the day of data download, 7 May 2020). Panel a: Reported total number of deaths per reported total number of cases. The dotted line shows the average case fatality rates of these countries. The arrow indicates Russia. Panel b: Reported total number of deaths per national population size. Panel c: Number of deaths and number of cases, both expressed per million population. The hatched line represents 100 deaths per million for 1000 cases per million. Panel d: Number of deaths per number of tests performed, both per million population. In all panels, blue indicates countries not employing BCG in the 1950s, regardless of whether they started to use this vaccine later. See Fig. S1 for data extracted on 18 July 2020 ( BCG in 1950s; no BCG in 1950s). [Colour figure can be viewed at wileyonlinelibrary.com]
Population size was also taken into account. There was only a weak correlation between the reported number of deaths and the population size of the analysed countries (Fig. 1b). Four of the countries that used BCG in the 1950s have relatively small population sizes, while Turkey and the UK have larger populations. Expressing the number of cases per million population provides a rather poor correction for the vast differences in local population densities, as the epidemic is highly unevenly distributed within countries. For instance, one‐third of all cases in Spain occurred in the Madrid area that harbours only 14% of the total national population. Fatality rates are also deeply affected by the state of local healthcare services. The quality of healthcare not only varies per country, but even countries with excellent healthcare may not be able to locally offer this while facilities are being overloaded with cases at the peak of the outbreak, as was evident in Lombardy (Italy), Madrid (Spain) or New York City (NY, USA). When the number of cases and the number of deaths were both expressed per million population (Fig. 1c), four countries were found above the line of 100 deaths/M for 1000 cases/M: the Netherlands, France, UK and Italy. Two of these had vaccinated in the 1950s and two had not.
The degree of testing varies per country, for instance due to limited testing capacity, which affects the total number of detected cases. If fewer tests are performed, total cases can be artificially low, pushing the CFR up. Testing may be mostly restricted to symptomatic, hospitalized individuals in some countries, or be more widespread to include asymptomatic communities in others. This will result in a difference of detected cases, skewing the CFR towards low or high rates, respectively. We could find no clear correlation between the number of tests performed per million population and the number of reported deaths per million (Fig. 1d). Countries with BCG vaccination in the 1950s reported either low (Australia, Israel, Austria, Turkey) or high (Ireland, UK, France) numbers of deaths per million population.
When a country introduced BCG vaccination in the 1950s, it may have taken years to reach nation‐wide coverage, if that level was reached at all. When BCG coverage was compared between EU countries in 2000–2004, four countries still vaccinated all children, with reported coverages of >90% in Ireland and France, 83% in Portugal and 75% in UK (Infuso et al. 2006). Of the countries that did not include BCG in their vaccination program in the 1950s, most introduced it later, but very few have continued the practice to the present (Table 1). We used these data to determine if differences could be seen when comparing figures from countries that had never applied any vaccination with BCG on a national scale. These included Italy, the Netherlands, the USA and Belgium (although in these countries small specific target groups may have been recommended for vaccination). Ireland, Portugal Turkey, Russia, Brazil and Iran currently vaccinate, in contrast with the other countries. No trend could be detected between countries that had never vaccinated and those that currently vaccinate, in terms of total number of cases per population (Fig. 2a). The total number of deaths was generally higher in countries that had never vaccinated, and the three countries currently using BCG vaccination had medium (Portugal, Ireland) to high (Brazil) death rates (Fig. 2b).
Figure 2 Effect of current BCG vaccination and age at which the vaccination was administered. Panel a: Reported total number of cases, and panel b, total number of deaths, plotted against the population sizes (both on a logarithmic scale). Countries are coloured for those currently using BCG vaccination on a national scale, those who have ceased to do so, and those that never used large‐scale BCG vaccination. Panel c: Total number of deaths against the total number of cases (as in Fig. 1a), now coloured for the time the vaccine was administered. See Fig. S2 for data extracted on 18 July 2020 (a: BCG at present; BCG (historic); no BCG; b: BCG at present; BCG (historic); no BCG; c: BCG at birth; BCG at birth–1y; BCG >10j; no BCG). [Colour figure can be viewed at wileyonlinelibrary.com]
If the effect of BCG on ‘immune training’ truly affects Covid‐19 outcome, the strongest effect should be expected when the vaccine was administered soon after birth, when epigenetically controlled patterns of gene expression are still most malleable. The age at which BCG was administrated varied between countries. Only four countries started vaccination early (from or prior to 1960) and vaccinated at birth: France (CFR: 14·8%), Germany (CFR: 4·3%), Ireland (CFR: 6·2%) and Israel (CFR: 0·6%); Spain introduced vaccination at birth in 1965 (CFR: 10·1%) (Table 1). In other countries, the vaccine was or is being delivered during a time span from birth up to one year of age. Both Australia and the UK typically vaccinated during childhood, but their Covid‐19 CFR rates differ by a factor of 10 (1·4 and 14·8%, respectively). Irrespective of whether the vaccine was used in the past or present, Fig. 2c shows that there is no grouping of countries with a particular age at which the vaccination was administered, with respect to reported CFR.
At revision stage, on July18, we downloaded the numbers for the selected countries again (Table S1) and the figures were reproduced with these updated numbers. This did not change the overall conclusions (Figs S1 and S2).
A number of publications were published on the subject of BCG vaccination and Covid‐19 as this manuscript was under review. These either supported the view that BCG could be protective (Ebina‐Shibuya et al. 2020; Escobar et al. 2020; Hauer et al. 2020; Madan et al. 2020; Macedo and Febra 2020; Sharma et al. 2020), or reported the lack of such an effect (Meena et al. 2020; Hamiel et al. 2020). However, all studies used datasets from spring. Sharma et al. used data from 29 May, Madan and colleagues downloaded data from April 1, Macedo and Febra assessed the situation on 4 April 2020, Shibuya and coworkers downloaded their data on April 10 and Escobar and colleagues assessed the BCG index (taking into account the age of vaccinated individuals and the number of years vaccination programmes were in place), using data from 21 April 2020. However, it took time for the pandemic to reach different continents: in April it had hardly touched South America or Africa. Now it is clear that countries such Brazil, India and South Africa, which all have current BCG vaccination programs, suffer from high attack rates, thus countering a presumed protective effect against infection (death rates are lagging behind even further and may not always be accurately assessed or reported in these countries). When data from 13 May 2020 were analysed, a protective effect of BCG on Covid‐19 was not detected (Meena et al. 2020). Methodological flaws include ignoring confounding factors that are likely to affect the outcome of Covid‐19 (which we did not correct for, either) as has been pointed out (Riccò et al. 2020), and the proposed duration of immunological protection lasting at most 15 years (Kantor 2020) also weakens conclusions (including those based on our Fig. 1). Nevertheless, the latest developments of the pandemic in a number of countries suggest that protection by BCG against Covid‐19 does not occur.
From our analyses, and subject to the caveats expressed as to the data sources, at this moment in time the involvement of BCG vaccination as having an ameliorative effecter as prophylactic treatment against Covid‐19 is not proven.
Materials and methods
Data on Covid‐19 cases were extracted from the Johns Hopkins University database (https://www.worldometers.info/coronavirus) on 7 May 2020. Historic BCG vaccination programs for individual countries were extracted from http://www.bcgatlas.org (Zwerling et al. 2011) (last accessed on 7 May 2020). The data we used are summarized in Table 1. The type of strain used for vaccination is added for completeness, but given the variation in strain usage, the effect of strain was not analysed. Figures were produced with Excel.
During revision of the manuscript, the data were again extracted for the same selection of countries on 18 July 2020. These data are summarized in the Supplementary file.
Conflict of interest
The authors have no conflict of interest to declare.
Supplementary Material
lam13365-sup-0001-SupInfo Figure S1. Analysis of the same 18 countries used in Fig. 1 of original submission, now with data downloaded on 18 July 2020.
Figure S2. Effect of current BCG vaccination and age at which the vaccination was administered, as Fig. 2, now with data downloaded on 18 July 2020.
Table S1. Data extracted at revision stage, as of 18 July, 2020.
Click here for additional data file.
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References
Aggarwal,S., Garcia‐Telles,N., Aggarwal,G., Lavie,C., Lippi,G. and Henry,B.M. (2020) Clinical features, laboratory characteristics, and outcomes of patients hospitalized with coronavirus disease 2019 (COVID‐19): early report from the United States. Diagnosis (Berl) 7 , 91–96.32352401
Arts,R.J.W., Moorlag,S.J.C.F.M., Novakovic,B., Li,Y., Wang,S.Y., Oosting,M., Kumar,V., Xavier,R.J. et al. (2018) BCG vaccination protects against experimental viral infection in humans through the induction of cytokines associated with trained immunity. Cell Host Microbe 23 , 89–100.e5.29324233
CDC COVID‐19 Response Team (2020) Severe outcomes among patients with coronavirus disease 2019 (COVID‐19) — United States, February 12–March 16, 2020. MMWR Morb Mortal Wkly Rep 69 , 343–346.32214079
Chen,J., Lu,H., Melino,G., Boccia,S., Piacentini,M., Ricciardi,W., Wang,Y., Shi,Y. et al. (2020a) COVID‐19 infection: the China and Italy perspectives. Cell Death Dis 11 , 438.32513951
Chen,N., Zhou,M., Dong,X., Qu,J., Gong,F., Han,Y., Qiu,Y., Wang,J. et al. (2020b) Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 395 , 507–513.32007143
Crispen,R. (1989) History of BCG and its substrains. Prog Clin Biol Res 310 , 35–50.2672021
Kristensen,I., Aaby,P. and Jensen,H. (2000) Routine vaccinations and child survival: follow up study in Guinea‐Bissau, West Africa. BMJ 321 , 1435–1438.11110734
Curtis,N., Sparrow,A., Ghebreyesus,T.A. and Netea,M.G. (2020) Considering BCG vaccination to reduce the impact of COVID‐19. Lancet 395 , 1545–1546.32359402
Ebina‐Shibuya,R., Horita,N., Namkoong,H. and Kaneko,T. (2020) National policies for paediatric universal BCG vaccination were associated with decreased mortality due to COVID‐19. Respirology, 10.1111/resp.13885. Advance online publication
Escobar,L.E., Molina‐Cruz,A. and Barillas‐Mury,C. (2020) BCG vaccine protection from severe coronavirus disease 2019 (COVID‐19). Proc Natl Acad Sci 117 , 17720–17726. 10.1073/pnas.2008410117.32647056
Guan,W.‐J., Ni,Z.‐Y., Hu,Y., Liang,W.‐H., Ou,C.‐Q., He,J.‐X., Liu,L., Shan,H. et al. (2020) Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med 382 , 1708–1720.32109013
Guo,G., Ye,L., Pan,K., Chen,Y., Xing,D., Yan,K., Chen,Z., Ding,N. et al. (2020) New insights of emerging SARS‐CoV‐2: epidemiology, etiology, clinical features, clinical treatment, and prevention. Front Cell Dev Biol 8 , 410.32574318
Gursel,M. and Gursel,I. (2020) Is global BCG vaccination‐induced trained immunity relevant to the progression of SARS‐CoV‐2 pandemic? Allergy 75 , 1815–1819.32339299
Hamiel,U., Kozer,E. & Youngster,I. (2020) SARS‐CoV‐2 rates in BCG‐vaccinated and unvaccinated young adults. JAMA, 323 2340–2341.32401274
Hauer,J., Fischer,U., Auer,F. and Borkhardt,A. (2020) Regional BCG vaccination policy in former East‐ and West Germany may impact on both severity of SARS‐CoV‐2 and incidence of childhood leukemia. Leukemia 34 , 2217–2219. 10.1038/s41375-020-0871-4.32555367
Kantor,I.N. (2020) BCG versus COVID‐19? Medicina (B Aires) 80 , 292–294.32442946
Keeley,A.J., Evans,C.M. and de Silva,T.I. (2020) Asymptomatic SARS‐CoV‐2 infection: the tip or the iceberg? Thorax 75 , 621–622.32580993
Kumar,J. and Meena,J. (2020) Demystifying BCG vaccine and COVID‐19 relationship. Indian Pediatr 57 , 588–589.32358227
Infuso,A., Falzon,D. and Network,Euro T.B. (2006) European survey of BCG vaccination policies and surveillance in children, 2005. Euro Surveill 11 , 6–11.16567882
Luca,S. and Mihaescu,T. (2013) History of BCG vaccine. Maedica (Buchar) 8 , 53–58.
Macedo,A. and Febra,C. (2020) Relation between BCG coverage rate and COVID‐19 infection worldwide. Med Hypotheses 142 , 109816.32408071
Madan,M., Pahuja,S., Mohan,A., Pandey,R.M., Madan,K., Hadda,V., Tiwari,P., Guleria,R. et al. (2020) TB infection and BCG vaccination: are we protected from COVID‐19? Public Health 185 , 91–92.32590235
Mangalmurti,N. and Hunter,C.A. (2020) Cytokine storms: understanding COVID‐19. Immunity. 53 , 19–25.32610079
Meena,J., Yadav,A. and Kumar,J. (2020) BCG vaccination policy and protection against COVID‐19. Indian J Pediatr 10.1007/s12098-020-03371-3.
Mei,X., Zhang,Y., Zhu,H., Ling,Y., Zou,Y., Zhang,Z., Guo,H., Liu,Y. et al. (2020) Observations about symptomatic and asymptomatic infections of 494 patients with COVID‐19 in Shanghai, China. Am J Infect Control 10.1016/j.ajic.2020.06.221.
Mitrani,R.D., Dabas,N. and Goldberger,J.J. (2020) COVID‐19 cardiac injury: implications for long‐term surveillance and outcomes in survivors. Heart Rhythm S1547‐5271(20)30625‐1. 10.1016/j.hrthm.2020.06.026. Advance online publication.
Netea,M.G. and van Crevel,R. (2014) BCG‐induced protection: effects on innate immune memory. Semin Immunol 26 , 512–517.25444548
Netea,M.G., Joosten,L.A., Latz,E., Mills,K.H., Natoli,G., Stunnenberg,H.G., O'Neill,L.A. and Xavier,R.J. (2016) Trained immunity: a program of innate immune memory in health and disease. Science 22 , 352(6284):aaf1098.
Netea,M.G., Domínguez‐Andrés,J., Barreiro,L.B., Chavakis,T., Divangahi,M., Fuchs,E., Joosten,L.A.B., van der Meer,J.W.M. et al. (2020) Defining trained immunity and its role in health and disease. Nat Rev Immunol 20 , 375–388.32132681
Ozdemir,C., Kucuksezer,U.C. and Tamay,Z.U. (2020) Is BCG vaccination effecting the spread and severity of COVID‐19? Allergy 75 , 1824–1827.32330314
Ragab,D., Salah Eldin,H., Taeimah,M., Khattab,R. and Salem,R. (2020) The COVID‐19 cytokine storm; what we know so far. Front Immunol 11 , 1446.32612617
Riccò,M., Gualerzi,G., Ranzieri,S. and Bragazzi,N.L. (2020) Stop playing with data: there is no sound evidence that Bacille Calmette‐Guérin may avoid SARS‐CoV‐2 infection (for now). Acta bio‐medica: Atenei Parmensis 91 , 207–213.32420947
Sharma,A., Kumar Sharma,S., Shi,Y., Bucci,E., Carafoli,E., Melino,G., Bhattacherjee,A. and Das,G. (2020) BCG vaccination policy and preventive chloroquine usage: do they have an impact on COVID‐19 pandemic? Cell Death Dis 11 (7 ), 516.32641762
The Novel Coronavirus Pneumonia Emergency Response Epidemiology Team (2020) Vital surveillances: The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID‐19) — China. China CDC Weekly, 2 , 113–122.34594836
Vestergaard,L.S., Nielsen,J., Richter,L., Schmid,D., Bustos,N., Braeye,T., Denissov,G., Veideman,T. et al. (2020) Excess all‐cause mortality during the COVID‐19 pandemic in Europe ‐ preliminary pooled estimates from the EuroMOMO network, March to April 2020. Euro Surveill 25 , 10.2807/1560-7917.ES.2020.25.26.2001214.
WHO (2020) Bacille Calmette‐Guérin (BCG) vaccination and COVID‐19. https://www.who.int/news‐room/commentaries/detail/bacille‐calmette‐gu%C3%A9rin‐(bcg)‐vaccination‐and‐covid‐19 (last accessed July 15, 2020).
Zwerling,A., Behr,M.A., Verma,A., Brewer,T.F., Menzies,D. and Pai,M. (2011) The BCG world atlas: a database of global BCG vaccination policies and practices. PLoS Med 8 , e1001012.
| 32734625 | PMC9728116 | NO-CC CODE | 2022-12-09 23:26:00 | no | Lett Appl Microbiol. 2020 Nov 1; 71(5):498-505 | utf-8 | Lett Appl Microbiol | 2,020 | 10.1111/lam.13365 | oa_other |
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Sci Total Environ
Sci Total Environ
The Science of the Total Environment
0048-9697
1879-1026
Elsevier B.V.
S0048-9697(21)07122-9
10.1016/j.scitotenv.2021.152046
152046
Article
Contamination of the marine environment in Egypt and Saudi Arabia with personal protective equipment during COVID-19 pandemic: A short focus
Hassan Ibrahim A. ab⁎
Younis Alaa bc
Al Ghamdi Mansour A. d
Almazroui Mansour ef
Basahi Jalal M. d
El-Sheekh Mostafa M. g
Abouelkhair Emad K. h
Haiba Nesreen S. i
Alhussaini Mohammed S. j
Hajjar Dina k
Abdel Wahab Magdy M. bl
El Maghraby Dahlia M. a
a Faculty of Science, Alexandria University, 21511 Moharem Bay, Alexandria. Egypt
b National Scientific Committee of Problems in Environment (SCOPE), Academy of Scientific Research & Technology (ASRT), 101 Kasr Al-Ini Street, Cairo, Egypt
c Aquatic Environment Department, Faculty of Fish Resources, Suez University, Egypt
d Faculty of Meteorology, Environment and Arid Land Agriculture, King Abdulaziz University, Jeddah, Saudi Arabia
e Centre of Excellence for Climate Change Research/Department of Meteorology, King Abdulaziz University, PO Box 80208, Jeddah 21589, Saudi Arabia
f Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich, UK
g Department of Botany & Microbiology, Faculty of Science, Tanta University, Tanta, Egypt
h Biology Departments, Al-Azhar University, Gaza, Egypt
i Department of Physics & Chemistry, Faculty of Education, Alexandria University, El Shatby, Alexandria. Egypt
j Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Shaqra University, Saudi Arabia
k Department of Biochemistry, Faculty of Science, University of Jeddah, Jeddah, Saudi Arabia
l Department of Space science, Faculty of Science, Cairo University, Giza, Egypt
⁎ Corresponding author at: Faculty of Science, Alexandria University, 21511 Moharem Bay, Alexandria. Egypt.
29 11 2021
1 3 2022
29 11 2021
810 152046152046
28 8 2021
20 11 2021
24 11 2021
© 2021 Elsevier B.V. All rights reserved.
2021
Elsevier B.V.
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Plastic pollution and its impact on marine ecosystems are major concerns globally, and the situation was exacerbated after the outbreak of COVID-19.
Clean-up campaigns took place during the summer season (June–August 2020) in two coastal cities in Egypt (Alexandria and Hurghada) and Jeddah, Saudi Arabia to document the abundance of beach debris through public involvement, and then remove it. A total of 3673, 255, and 848 items were collected from Alexandria, Hurghada, and Jeddah daily, respectively. Gloves and face masks (personal protective equipment “PPE”) represent represented 40–60% of the total plastic items collected from each of the three cities, while plastic bags represented 7–20% of the total plastics litter collected from the same cities. The results indicated the presence of 2.79, 0.29, and 0.86 PPE item m−2 in Alexandria, Hurghada and Jeddah, respectively.
This short focus provides an assessment of the environmental impacts of single-use gloves and masks used for COVID-19 protection from June to August 2020. To the best of our knowledge, this study presents the first such information from the Middle East, specifically Egypt and Saudi Arabia. It highlights the need for further knowledge and action, such as safe, sustainable, and transparent waste management processes related to COVID-19 to reduce the negative impacts now, as well as in future events. Furthermore, this study helps in achieving key components of the United Nation's Sustainable Development Goals (SDGs). This short focus can serve as a multipurpose document, not only for scientists of different disciplines but for social media and citizens in general.
Graphical abstract
Unlabelled Image
Keywords
Plastic pollution
Personal protective equipment (PPE)
COVID-19
Marine ecosystems
Volunteers
Editor: Damia Barcelo
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pmc1 Introduction
Millions of plastic items of different sizes are discharged into water bodies around the world daily, causing plastic pollution (De-la-Torre and Aragaw, 2021). This situation was exacerbated, worldwide, during the COVID-19 pandemic, as millions of people used single-use plastic face masks, gloves, and face shields as personal protective equipment (PPE). The continuous and massive increase of gloves, masks, and various sorts of wrapping made from single-use plastics, ended up littering the land and marine environments globally, a visible side-effect of the increased use of PPE, causing hazardous problems (Adyel, 2020; European Environmental Agency, 2021). Moreover, such pollution could also lead to emissions of greenhouse gases, potentially further harming the environment (Prata et al., 2020; Vanapalli et al., 2021; Farahat et al., 2021). Connexion (2020), stated that more than 20 million French citizens (about 16% of the population) admitted throwing away their masks on public roads, on beaches, and along coasts. Environmentalists fear that gloves and masks thrown out of car windows will pollute the environment for decades to come.
The rate of accumulation of plastics in marine environments depends on anthropogenic activities, direction and speed of the wind, and coastal water uses (James et al., 2021). Macro-and microplastic pollution has caused problems in many parts of the world, through ingestion by and entanglement of marine animals (Cauwenberghe et al., 2013; Thompson et al., 2014; James et al., 2021).
The sustainable management of PPE is a key challenge (Mallick et al., 2021). The lack of a coordinated national and international strategy to manage the PPE disposal threatens to impact progress towards achieving key components of the United Nation's Sustainable Development Goals (SDGs), including SDG 3 good health and wellbeing, SDG 6 clean water and sanitation, SDG 12 responsible consumption and production and SDG 13 climate action (Singh et al., 2020).
Although studies related to plastic pollution and its consequences on the ecosystem have been carried out worldwide, nothing is known about this type of pollution in the Middle East. The present study was particularly aimed at understanding the distribution of the macro-and microplastics in the surface waters, and the impact of the COVID-19 pandemic on this type of pollution.
Plastics, in general, are non-biodegradable materials (Ryan, 2015; Ali et al., 2021). However, photooxidation by UV radiation and mechanical friction can help in the fragmentation of the plastics into small fragments (Tamara et al., 2017). Microplastics (having size <5 ml) have an enormous impact on the marine ecosystem and they are considered a potential threat (Gewert et al., 2015; James et al., 2021; Patrício et al., 2021). These plastics can travel thousands of miles, carried by water currents and wind action (Barnes et al., 2009; Al-Salem et al., 2021; Onoja et al., 2022).
Immense quantities of PPE plastic wastes have been generated globally due to the COVID-19 pandemic, which added extra pressure to conventional solid waste management practices (Singh et al., 2020). Countless face masks, face shields, different types of gloves, garments, and plastic materials were consumed during the COVID-19 pandemic as a preventive measure against the spread of coronavirus (OSPAR, 2020; Vanapalli et al., 2021, El-Sheekh and Hassan, 2020). Unfortunately, they are found stranded along the beaches, coastlines, and rivers, and littering cities instead of being disposed of properly in suitable garbage bins, for subsequent removal for recycling or to landfills. Inappropriate disposal ends up polluting the marine environment and the situation is exacerbating. The PPE are potentially infectious litter, and special handling is required. Nevertheless, in the absence of clear instructions for disposal, people are improperly disposing PPE items, throwing them away near the location where they end their usefulness, where they may be carried off by a gust of wind. Accumulation of these plastics will continue to aggravate over time, polluting the marine environment (Moore, 2020; Rhee, 2020). Therefore, it is worthwhile to measure the plastic-associated environmental load of the pandemic as a starting point in efforts to prevent the continued worsening of the marine plastic pollution situation. Singh et al. (2020) stated that the increase in PPE manufacture and distribution is generating an equivalent increase in the waste stream, compounded by health and environmental risks along the waste management chain, especially in countries with underdeveloped infrastructure. Proper, safe, and sustainable recovery and treatment of PPEs, urgent and essential public service, should be intensified to minimize possible secondary impacts upon health and the environment. Unfortunately, medical waste has not been adequately regulated in developing countries especially among informal recyclers.
The present study was undertaken to provide a narrow focus and baseline information on the distribution of the PPE in coastal waters, in order to better understanding the impacts plastic pollution on the marine environment in the Middle East during the COVID-19 pandemic.
2 Methodology
2.1 The study area
The study was conducted simultaneously in three cities in the Middle East; two in Egypt (Alexandria 31.2001° N, 29.9187° E and Hurghada, 27.2579° N, 33.8116° E) and one in Saudi Arabia (Jeddah, 21.4858° N, 39.1925° E) (Fig. 1 ).Fig. 1 Location map showing the studied areas in Egypt (Alexandria and Hurghada) and Saudi Arabia (Jeddah).
Fig. 1
These cities were selected due to the presence of intensive human activities (Fishing, sightseeing, swimming, and tourism). Moreover, the sampling sites were selected because they were approximately the same size (Table 1 ).Table 1 Size of sampling sites.
Table 1Size (m2) Location
20 × 25 = 500 Alexandria (Egypt)
17 × 30 = 510 Hurghada (Egypt)
12 × 40 = 480 Jeddah (Saudi Arabia)
2.2 Sampling
Plastics were collected manually by volunteers, at all locations in all regions, daily during summer, (beginning of June – end of August 2020). The collection of plastic debris started after the departure of visitors (at 18:00 local time).
A youth campaign was launched called “summer without plastics” (Fig. 2 ), where tens of teenagers and even younger children collected plastic debris from the coasts of Alexandria and Jeddah cities, simultaneously. They were very enthusiastic, and they built a fish-like perforated rubbish bin to collect all plastic debris and remove excess sands (Fig. 3 ).Fig. 2 Youth campaigns “summer without plastics” in Egypt (The upper six photographs) and Jeddah (the last two photographs).
Fig. 2
Fig. 3 Collecting the plastic litter in a fish-like perforated box.
Fig. 3
There is a lack of standardized procedures in plastic sampling, leading to the reporting of data in a variety of units. The best unit is plastic pieces per m2 (Wessel et al., 2016 cited in Onoja et al., 2022).
Once marine plastics are collected, the next objective is to separate the different items of plastics from non-plastic particles based on physical properties that are unique to plastics. A standard method for doing this is yet to be established (Onoja et al., 2022).
The final step is the identification and quantification of macro-and microplastics by visual sorting.
2.3 Statistical analysis
Two-way analysis of variance (ANOVA) was used to evaluate the variability in the number of different plastic items and the local effect of sampling site, using the STATGRAPH Statistical package (Statgraph 5, UK).
3 Results & discussion
Immense quantities of personal protective equipment (PPE) plastic wastes were found in coastal areas of Egypt (Fig. 4 ) and Jeddah (Fig. 5 ).Fig. 4 Photographs of COVID-19 personal protective equipment (PPEs) found in coastal areas of the Mediterranean (upper panel) and the Red (lower panel) Seas in Egypt.
Fig. 4
Fig. 5 Photographs of COVID-19 personal protective equipment (PPEs) and plastic debris found in coastal areas of Jeddah.
Fig. 5
Table 2 shows the average collection values for the distribution of different plastic litter collected from the different sites. Gloves and face masks (PPE) accounted for 38.1%, 57.3%, and 48.8%, while plastic bags represent 18.3%, 7.0%, and 8% of the total litter collected from Alexandria, Hurghada, and Jeddah, respectively. The high percentage of PPE items (about 57%) in Hurghada could be due to the proximity of the harbor, while the lower percentage recorded in Alexandria could be due to regular cleaning of the beach. The ban on plastic bags and encouragement to use paper bags instead could be another reason for the lower percentage of plastic bags in Hurghada and Jeddah.Table 2 Daily distribution of different plastic items collected from different sites day−1 (n = 90 + SE). Means not followed by the same letter are significantly different from each other at p ≤ 0.01. Figures between parentheses represent the relative ratio of each item.
Table 2Total Others Bottles Bags Masks Gloves Location
3673 ± 427.17 501c ± 33.47 1103c ± 225.33 671c ± 43.91 918c ± 66.28 480c ± 75.14 Alexandria
−13.60% −30.00% −18.30% −25.00% −13.10% (Egypt)
255 ± 46.92 70a ± 9.21 21a ± 2.11 18b ± 1.41 81a ± 15.22 65a ± 13.23 Hurghada
−27.50% −8.20% −7.00% −31.80% −25.50% (Egypt)
848 ± 52.63 277b ± 23.17 89b ±11.26 6a ± 0.31 217b ± 19.11 198b ± 22.32 Jeddah
−32.70% −10.50% −7.90% −25.60% −23.30% (Saudi Arabia)
Table 3 shows the relative abundance of PPE and total litter. There were 2.93, 0.29, and 0.86 PPE item m−2 laid down in Alexandria, Hurghada, and Jeddah, respectively. Moreover, the total amount of litter collected from the same cities followed the same pattern; they were 7.2, 0.51, and 1.77 item m−2, respectively (Table 3).Table 3 Relative abundance of PPE (gloves and masks) and total plastic litter (m−2 site−1). (n = 90 ± SE).
Table 3Total litter PPE Location
7.20c ± 1.03 2.79c ± 0.311 Alexandria
(Egypt)
0.51a ± 0.021 0.29a ± 0.018 Hurghada
(Egypt)
1.77b ± 0.039 0.86b ± 0.032 Jeddah
(Saudi Arabia)
There was a significant difference in the number of PPE items collected from Alexandria and Jeddah during weekdays and weekends (Table 4 ). The amount of PPE increased by 76.3 and 48% during weekends in Alexandria and Jeddah, respectively. It is worth mentioning that the weekend in Egypt is Friday only, while in Saudi Arabia it is Friday and Saturday. Table 4 shows that people litter more on weekends than weekdays. Therefore, it is also to be expected that amount of PPE during summer would be higher than that collected during other seasons. However, it was worth studying the daily distribution of the PPEs to give a better picture of their distribution. Fig. 5 shows the daily collection of PPEs from the three sites. It was clear that the items of the PPEs collected from Alexandria (ranged between 798 and 988) were higher than those collected from Jeddah city (ranged between 234 and 401). Moreover, the highest number of the PPEs collected from both sites was during weekends (1000–1689 in Alexandria, and 628–743 in Jeddah). The peaks in Fig. 5 correspond to the number of PPEs collected during the weekends in both cities (Friday in Egypt, and Friday and Saturday in Jeddah). However, the number of PPES collected from Hurghada (122–177, with the maximum number occurring in weekends 178) was lower than in other cities, and there was no significant variation between weekdays and weekends (Fig. 6 ).Table 4 The number of PPE items (gloves and masks) collected during weekdays and weekends. (n for weekdays = 77 ± SE and 64 ± SE for Egypt and Saudi Arabia, respectively; while n for weekends = 13 ± SE and 26 ± SE for both countries, respectively; means not followed by the same letter are significantly different at P ˂ 0.001 “***”; n.s. = not significant).
Table 4% increase during the weekend Weekends Weekdays Location
76.3%⁎⁎⁎ 1532b ± 21.88 869a ± 77.3 Alexandria
(Egypt)
7% (n.s.) 157a ± 19.6 146a ± 13.5 Hurghada
(Egypt)
48%⁎⁎⁎ 549b ± 99.8 371a ± 23.7 Jeddah
(Saudi Arabia)
Fig. 6 The daily collection figures of PPE (masks and gloves) collected from the different sites.
Fig. 6
The weight of macro-and microplastics collected from the different sites is presented in Table 5 . For comparability purposes and simplicity, weight is calculated on a m2 basis. The average weight of macroplastics collected from Alexandria, Hurghada, and Jeddah was 25.66 g m−2, 3.78 g m−2, and 11.92 g m−2, respectively, while the weight of microplastics was 0.81 g m−2, 0.03 g m−2, and 1.67 g m−2 for the same sites, respectively (Table 4).Table 5 Average weight of macro-and microplastics collected from different sites (g m−2) (n = 90 ± SE).
Table 5Weight of microplastics Weight of macroplastics Location
(g m−2) (g m−2)
0.86b ± 0.023 25.66c ± 4.01 Alexandria
(Egypt)
0.05a ± 0.001 6.28a ± 1.18 Hurghada
(Egypt)
1.71c ± 0.106 13.45b ± 2.17 Jeddah
(Saudi Arabia)
The dominance of macroplastics in Alexandria could be attributed to a high population and the increased number of visitors from outside the city during the summer season, especially from the neighboring villages. Most of visitors tend to throw out their plastics on the beach instead of using litter bins.
Fig. 7 shows the microplastic debris collected from the coastal areas of the Mediterranean Sea (Alexandria) and the Red Sea (Jeddah). It is clear that microplastics collected from Jeddah are smaller than those collected from Alexandria. The dominance of microplastics at Jeddah's shore may be attributed to the high temperature; and high ultraviolet radiation as well as the weather prevailing in Jeddah during the summer (50 °C± 4) (Basahi et al., 2017; Hassan et al., 2017; Qari and Hassan, 2017; Ismail et al., 2021). Microplastic pollution could be the direct sequence of breaking down discarded gloves and masks into smaller pieces due to the effects of high temperature, ultraviolet radiation, abrasion, and weathering (Aragaw, 2020; Fadare and Okoffo, 2020; Ali et al., 2021).Fig. 7 Photographs of COVID-19 microplastic debris found in coastal areas of the Mediterranean Sea in Egypt (a) and the Red Sea (b).
Fig. 7
Plastic litter may have different fates after reaching the marine environment; the low-density plastics can float and stay in the marine environment for long periods, probably subject to surface water currents, while high-density plastics sink and reach the bottom marine sediments, and some may become buried in the sediments, in which case they eventually become part of the geological record (Fadare and Okoffo, 2020; De-la-Torre and Aragaw, 2021). This is an alert as they do not degrade naturally, and they will pollute the environment for many years to come (Adelodun, 2021). Moreover, Mallick et al. (2021) reported that the sustainable development goals are hampered by upsurges medical plastic waste and this needs people awareness.
Egypt and Saudi Arabia are witnessing intense tourism, fishery, and other anthropogenic activities that have made their coastal zones and biota vulnerable to macro-and microplastic contamination. This form of plastic pollution is alarming yet poorly understood. Research is needed to fill the current knowledge gaps regarding COVID-19-associated PPE pollution and to lay the groundwork for better waste management and legislation. Reducing the accumulation of plastics through their degradation (biological, chemical, or physical), is yet to be resolved. It is necessary to identify sources and drivers of plastics, including PPE, and to track them after entering the marine ecosystem and so understand their potential fate (Al-Salem et al., 2021).
The magnitude of PPE pollution remains unknown, especially in the Middle East, despite there being some published reports worldwide (Fadare and Okoffo, 2020; Prata et al., 2020; De-la-Torre and Aragaw, 2021; Galgani et al., 2021). Public education campaigns to promote appropriate PPE stewardship should be integrated into policy implementation, monitoring, and enforcement. Development of infrastructure to ensure safety in informal waste collection. There is a debate that PPE and PPE-derived microplastics are a potential source and vector of chemical pollutants in marine ecosystem (Fred-Ahmadu et al., 2020). Thus, it is necessary to consider these two drivers of marine pollution with associated pollutants.
This briefing highlighted the need for further research to accurately evaluate and lessen the potential environmental impact of litter in public spaces. There is an urgent need for improved products and policies to encourage desirable consumer behavior related to the use, sanitation, collection, and safe disposal of litter to prevent it from polluting the environment. Finally, national and international campaigns for monitoring single-use plastic are needed to facilitate research and guide future policy options (including the collection of reliable up-to-date data on littering). Raising awareness to change behavior and better municipal waste management (including efficient collection, safety, hygiene, and recycling schemes) are also needed. Public education campaigns to promote appropriate PPE stewardship should be integrated into policy implementation, monitoring, and enforcement. The development of infrastructure to ensure safety in informal waste collection and recycling in low-income countries is essential (Barceló, 2020). PPE management policies need to be integrated into economic models that promote the adoption of green technology and alternative assessments to identify and adopt safer processes based on comprehensive materials life cycle assessments and consumer preferences to be sustainable.
It is worth to measure the presence and impact of plastic debris on freshwater shorelines as the information is scarce for freshwater environments and is not yet been available for the Middle East.
4 Conclusions & recommendations
To the best of our knowledge, this is the first attempt to document the abundance of plastic debris on the beaches of Egypt and Saudi Arabia through public involvement. This short study revealed a relatively low level of knowledge about the proper disposal of PPE among laymen in Middle Eastern countries. Policymakers should develop national and local educational campaigns to promote applicable PPE stewardship.
More collection bins for PPE should be installed in the cities. Used masks, used gloves, used personal clothes and all PPE must be separately collected and discharged in closed garbage bags to safely transport them to final treatments (e.g. landfilling or incineration). Recyclable plastics should be used, rather than unrecyclable ones. Simply, good knowledge, optimistic attitudes, and responsible practices towards COVID-19 will help us to cope with the pandemic.
The most important lesson learned from the COVID-19 pandemic is that we should prepare now for further potentially disruptive events in an uncertain future. PPE will continue to be in high demand, and this is the time to invest in research and development for new PPE materials that reduce waste generation, and for improved strategies for safe and sustainable management of used PPE with policy guidance at the global level. Therefore, the new products should contain minimum recycling content. Moreover, public education campaigns to promote appropriate PPE stewardship should be integrated into policy implementation, monitoring, and enforcement.
Single-use PPE is not a sustainable practice, therefore, it is essential to tackle the PPE pollution problem. PPE disinfection and reuse are potential through irradiation, gasification, and spray-on disinfectants. The circular economy principle focusing on reducing, reusing, and recycling resources should guide policy development for PPE management during and after the current pandemic.
Funding
There is no external funding for the research, authorship, and/or publication of this article.
CRediT authorship contribution statement
All authors are equally contributed, and we declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Declaration of competing interest
We believe this subject is interesting and no data were published from Egypt or Saudi Arabia.
We have the pleasure to submit this paper as a short communication. Having said that, I could not find a short communication option regarding the submission, so I have selected the nearest option.
The authors declare no potential conflicts of interest concerning the research, authorship, and/or publication of this article.
Acknowledgments
The authors thank the volunteers who carried out the beach clean-ups for their hard work and dedication. We would like to thank from the bottom of our hearts anonymous reviewers for their invaluable comments.
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References
Adelodun A.A. Plastic recovery and utilization: from ocean pollution to green economy Front. Environ. Sci. 09 2021 10.3389/fenvs.2021.683403
Adyel T.M. Accumulation of plastic waste during COVID-19 Science 369 6509 2020 1314 1315 10.1126/science.abd9925 32913095
Ali S.S. Elsamahy T. Koutra E. Kornaros M. El-Sheekh M.M. Abdelkarim E.A. Zhu D. Sun J.Z. Degradation of conventional plastic wastes in the environment: a review on current status of knowledge and future perspectives of disposal Sci. Total Environ. 771 2021 144719 10.1016/j.scitotenv.2020.144719
Al-Salem S.M. El-Eskandarani M.S. Constantinoun A. Can plastic waste management be a novel solution in combating the novel coronavirus (COVID-19)? A short research note Waste Manag. Res. 39 7 2021 910 913 10.1177/0734242X20978444 33323044
Aragaw T.A. Surgical face masks as a potential source for microplastic pollution in the COVID-19 scenario Mar. Pollut. Bull. 159 2020 111517 10.1016/j.marpolbul.2020.111517
Barceló D. An environmental and health perspective for COVID-19 outbreak: meteorology and air quality influence, sewage epidemiology indicator, hospitals disinfection, drug therapies and recommendations J. Environ. Chem. Eng. 8 2020 102006 10.1016/j.jece.2020.104006
Barnes D.K. Galgani F. Thompson R.C. Barlaz M. Accumulation and fragmentation of plastic debris in global environments Philos. Trans. R. Soc. B 364 1526 2009 1985 1998 10.1098/rstb.2008.0205
Basahi J.M. Ismail I.M. Hammam E. Hassan I.A. Total suspended particulate matter (TSP) and its associated heavy metals in atmosphere on the Western coast of Saudi Arabia Polish J. Environ Studies 26 5 2017 2419 2424 10.15244/pjoes/69102/a
Cauwenberghe V. Vanreusel A. Mees J. Janssen C.R. Microplastic pollution in deep–sea sediments Environ. Pollut. 182 2013 495 499 24035457
Connexion J. French firm finds way to recycle single-use masks 2020 https://www.connexionfrance.com/French-news/Coronavirus-How-to-recycle-single-use-masks
De-la-Torre G.E. Aragaw T.A. What we need to know about PPE associated with the COVID-19 pandemic in the marine environment Mar. Pollut. Bull. 163 2021 10.1016/j.marpolbul.2020.111879
El-Sheekh M.M. Hassan I.A. Lockdowns and reduction of economic activities during the COVID- 9 pandemic improved air quality in Alexandria cityEgypt Environmental Monitoring & Assessment 193 2020 11 10.1007/s10661-020-08780-7 33325002
European Environmental Agency Impacts of COVID-19 on Single-use Plastic in Europe’s Environment 2021 https://www.eea.europa.eu/publications/impacts-of-covid-19-on
Fadare O.O. Okoffo E.D. Covid-19 face masks: a potential source of microplastic fibers in the environment Sci. Total Environ. 737 2020 140279 10.1016/j.scitotenv.2020.140279
Farahat A. Chauhan A. Al Otaibi M. Singh R.P. Air quality over major cities of Saudi Arabia during hajj periods of 2019 and 2020 Earth Syst. Environ. 5 2021 101 114 10.1007/s41748-021-00202-z 34723078
Fred-Ahmadu O.H. Bhagwat G. Oluyoye I. Benson N.U. Palanisami Ayejuyo O.O. T Interaction of chemical contaminants with microplastics: principles and perspectives Sci. Total Environ. 706 2020 135978 10.1016/j.scitotenv.2019.135978
Galgani F. Brien A. Weise J. Ioakeimidis C. Schuyler Q. Makarenko I. Are litter, plastic and microplastic quantities increasing in the ocean? Microplastics Nanoplastics 1 2021 2 10.1186/s43591-020-00002-8
Gewert B. Plassmann M.M. MacLeod M. Pathways for degradation of plastic polymers floating in the marine environment Environ. Sci. Process. Impacts 17 2015 1513 10.1039/00207a 26216708
Hassan I.A. Basahi J. Ismail I. Almeelbi T. Hammam E. Cotrozzi L. Haiba N.S. Trace elements in the fruits of date palm (Phoenix dactylifera L.) in Jeddah City, Saudi Arabia Agrochimica 61 1 2017 75 93 10.12871/0021857201716
Ismail I.M. Summan A.S. Basahi J.M. Yassin M.F. Hassan I.A. First measurements of carbonaceous aerosol across urban, rural and residential areas in Jeddah City, Saudi Arabia Asian J. Atmos. Environ. 15 2 2021 1 14 10.5572/ajae.2021.021
James K. Vasant K. Batcha S.M. Padua S. Jeyabaskaran R. Thirumalaiselvan S. Vineetha G. Benjamin L.V. Seasonal variability in the distribution of microplastics in the coastal ecosystems and in some commercially important fishes of the Gulf of Mannar and Palk Bay, southeast coast of India Reg. Stud. Mar. Sci. 41 2021 101558 10.1016/j.rsma.2020.101558
Mallick S.K. Pramanik M. Maity B. Das P. Sahana M. Plastic waste footprint in the context of COVID-19: reduction challenges and policy recommendations towards sustainable development goals Sci. Total Environ. 796 2021 10.1016/j.scitotenv.2021.148951
Moore C. Plastic pollution Encyclopedia Britannica 15 Oct. 2020 2020 https://www.britannica.com/science/plastic-pollution
Onoja H.A. Abdallah M.A. Harrad S. Microplastics in freshwater sediments: analytical methods, temporal trends, and risk of associated organophosphate esters as exemplar plastics additives Environ. Res. 203 2022 111830 10.1016/j.envres.2021.111830
OSPAR CEMP Guidelines for Marine Monitoring and Assessment of Beach Litter 2020 https://www.ospar.org/documents?v=44122
Patrício S.A.L. Prata J.C. Walker T.R. Duarte A.C. Barcelò D. Rocha-Santos T. Increased plastic pollution due to COVID-19 pandemic: challenges and recommendations Chemical Engeneering J. 405 2021 126683 10.1016/j.cej.2020.126683
Prata J.C. da Costa J.P. Lopes I. Duartea A.C. Rocha-Santosa T. Environmental exposure to microplastics: an overview on possible human health effects Sci. Total Environ. 702 2020 134455 10.1016/j.scitotenv.2019.134455
Qari H. Hassan I.A. Bioaccumulation of PAHs in algae collected from a contaminated site at the red sea, Saudi Arabia Pol. J. Environ. Stud. 27 1 2017 435 439 10.15244/pjoes/63937
Rhee S.-W. Management of used personal protective equipment and wastes related to COVID-19 in South Korea Waste Manag. Res. 38 8 2020 820 824 10.1177/0734242X20933343 32517547
Ryan P.G. A brief history of marine litter research Bergmann M. Gutow L. Klages M. Marine Anthropogenic Litter 2015 Springer Cham 10.1007/978-3-319-16510-3_1 Publisher
Singh N. Tang Y. Ogunseitan O.A. Environmentally sustainable management of used personal protective equipment Environ. Sci. Technol. 54 2020 8500 8502 10.1021/acs.est.0c03022 32597663
Tamara S. Galloway C.M. Lewis C. Interactions of microplastic debris throughout the marine ecosystem Nature Ecol. Evol. 2017 10.1038/s41559-017-0116
Thompson R.C. Olsen Y. Mitchell R.P. Davis A. Rowland S.J. John A.W. McGonigle D. Russell A.E. Lost at sea: where is all the plastic? Science 304 2014 838 10.1126/science.1094559
Vanapalli K.R. Sharma H.B. Ranjan V.P. Samal B. Bhattacharya J. Dubey B. Goel S. Challenges and strategies for effective plastic waste management during and post COVID-19 pandemic Sci. Total Environ. 750 2021 141514 10.1016/j.scitotenv.2020.141514
| 34856280 | PMC9728476 | NO-CC CODE | 2022-12-09 23:26:00 | no | Sci Total Environ. 2022 Mar 1; 810:152046 | utf-8 | Sci Total Environ | 2,021 | 10.1016/j.scitotenv.2021.152046 | oa_other |
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ACS Appl Mater Interfaces
ACS Appl Mater Interfaces
am
aamick
ACS Applied Materials & Interfaces
1944-8244
1944-8252
American Chemical Society
36454041
10.1021/acsami.2c15407
Research Article
Colorimetric Detection of SARS-CoV-2 Using Plasmonic Biosensors and Smartphones
https://orcid.org/0000-0002-3382-1193
Materón Elsa M. *†‡
https://orcid.org/0000-0001-5758-0489
Gómez Faustino R. †
Almeida Mariana B. ‡§
https://orcid.org/0000-0002-4833-9893
Shimizu Flavio M. ∥
Wong Ademar ⊥
https://orcid.org/0000-0003-4337-4849
Teodoro Kelcilene B. R. #
https://orcid.org/0000-0003-3085-8510
Silva Filipe S. R. ‡
Lima Manoel J. A. ‡
Angelim Monara Kaelle S. C. ∇
https://orcid.org/0000-0002-0643-6185
Melendez Matias E. ○
Porras Nelson ◆
Vieira Pedro M. ∇
https://orcid.org/0000-0002-5592-0627
Correa Daniel S. #
https://orcid.org/0000-0001-7351-8220
Carrilho Emanuel ‡§
https://orcid.org/0000-0002-5399-5860
Oliveira Osvaldo N. Jr. †
Azevedo Ricardo B. ¶
Goncalves Débora †
† São Carlos Institute of Physics, University of São Paulo, P.O Box 369, 13560-970São Carlos, SP, Brazil
‡ São Carlos Institute of Chemistry, University of São Paulo, 13566-590São Carlos, SP, Brazil
§ National Institute of Science and Technology in Bioanalytics - INCTBio, 13083-970Campinas, SP, Brazil
∥ Department of Applied Physics, “Gleb Wataghin” Institute of Physics (IFGW), University of Campinas (UNICAMP), 13083-859Campinas, SP, Brazil
⊥ Department of Chemistry, Federal University of São Carlos (UFSCar), 13560-970São Carlos, São Paulo, Brazil
# Nanotechnology National Laboratory for Agriculture, Embrapa Instrumentation, 13560-970São Carlos, SP, Brazil
∇ Department of Genetics Evolution, Microbiology, and Immunology, Institute of Biology, University of Campinas, 13083-970Campinas, SP, Brazil
○ Molecular Carcinogenesis Program, National Cancer Institute, 20231-050Rio de Janeiro, RJ, Brazil
◆ Physics Department, del Valle University, AA 25360Cali, Colombia
¶ Laboratory of Nanobiotechnology, Department of Genetics and Morphology, Institute of Biological Sciences, University of Brasilia, 70910-900Brasilia, DF, Brazil
* Email: [email protected].
01 12 2022
acsami.2c1540726 08 2022
08 11 2022
© 2022 American Chemical Society
2022
American Chemical Society
This article is made available via the PMC Open Access Subset for unrestricted RESEARCH re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
Low-cost, instrument-free colorimetric tests were developed to detect SARS-CoV-2 using plasmonic biosensors with Au nanoparticles functionalized with polyclonal antibodies (f-AuNPs). Intense color changes were noted with the naked eye owing to plasmon coupling when f-AuNPs form clusters on the virus, with high sensitivity and a detection limit of 0.28 PFU mL–1 (PFU stands for plaque-forming units) in human saliva. Plasmon coupling was corroborated with computer simulations using the finite-difference time-domain (FDTD) method. The strategies based on preparing plasmonic biosensors with f-AuNPs are robust to permit SARS-CoV-2 detection via dynamic light scattering and UV–vis spectroscopy without interference from other viruses, such as influenza and dengue viruses. The diagnosis was made with a smartphone app after processing the images collected from the smartphone camera, measuring the concentration of SARS-CoV-2. Both image processing and machine learning algorithms were found to provide COVID-19 diagnosis with 100% accuracy for saliva samples. In subsidiary experiments, we observed that the biosensor could be used to detect the virus in river waters without pretreatment. With fast responses and requiring small sample amounts (only 20 μL), these colorimetric tests can be deployed in any location within the point-of-care diagnosis paradigm for epidemiological control.
gold nanoparticles
localized surface plasmon resonance
plasmonic coupling
SARS-CoV-2
point-of-care
machine learning
image processing
portable sensor
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pmc1 Introduction
Mass testing for viral diseases remains relevant given the persistence of contamination with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 virus). Most diagnostic tests require cheaper detection methods than prevailing molecular techniques, such as reverse transcription-quantitative polymerase chain reaction (RT-qPCR), isothermal amplification-based methods, and CRISPR-based diagnostics.1 Antigen-based detection (antigen test) has become commonplace in lateral flow immunoassays (LFI) or immunostrips,2 but it achieves less sensitive responses than the reverse-transcription polymerase chain reaction (RT-PCR).3 Current tests employ mostly blood and nasopharyngeal samples, which are uncomfortable for many patients. Less invasive sample collection is preferable, as saliva sampling is accessible to self-collect, particularly in fragile and vulnerable patient populations.4 The pathogen levels in saliva are comparable to those in nasopharyngeal samples, with the advantage of a small variability across trials.5 In addition, the costs of collection and storage are also reduced.
Challenges are often found in processing and avoiding degradation in saliva, which has hampered diagnostic tools.6 Furthermore, effective detection of SARS-CoV-2 in saliva requires high sensitivity because of its enzymes that may destabilize nucleic acid and inhibit proteases.7,8 For SARS-CoV-2, most infectious saliva and cough specimens have virus loads near 106 PFU mL–1 (PFU stands for plaque-forming units), indicating that 10–100 μL droplets could deposit 104–105 PFU of infectious material.9−11 The minimal contagious dose in humans ranges from 1 to 5 PFU.12
Although SARS-CoV-2 infection occurs predominantly through the respiratory tract, the entry of virus into the bloodstream compromises other organs, and viral RNA has been detected in the feces of infected individuals, even after respiratory symptoms have diminished.13 Likewise, SARS-CoV-2 has been detected for prolonged periods in wastewater treatment plants14 and river waters.15 Excretions through feces occur due to the viral infection into the gastrointestinal tract via the angiotensin-converting enzyme 2 (ACE2) receptor expressed by epithelial cells in the gastrointestinal system,16 and even via urine and saliva. Hence, virus particles can be dragged to treatment plants and may not be cleared during water treatment.16 Therefore, detecting SARS-CoV-2 is not only relevant for diagnosis in humans but also for verifying possible contamination of water resources.
Pathogens and other biomarkers can be detected with several principles, including electrical,17 electrochemical,18 optical,19 and thermoplasmonic chips.20 Colorimetric tests are preferred in many scenarios owing to the simplicity of the analysis.21 Nevertheless, it is challenging if the detection processes must yield significant color changes. For colorimetric tests, the use of nanoparticles is noteworthy,22 including Ag nanoparticles,23 Au nanoparticles (AuNPs),24 quantum dots,25 magnetic nanoparticles,26 and Au nanorods,27 which have been used to detect SARS-CoV-2 virus or IgG antibodies with localized surface plasmon resonance (LSPR) and surface-enhanced Raman spectroscopy (SERS). AuNPs are known for their chemical stability, easy modification, and bioconjugation of biomolecules, such as DNA, antibody, enzymes, and other proteins.28 For example, immunogenic B cell epitopes can be attached to AuNPs to detect COVID-19-specific IgG where the optical properties of AuNPs are exploited.29 Also, colloidal Au can be prepared with controllable sizes using the well-known citrate reduction method.30 AuNPs show a high-surface density of free electrons, from which LSPR has emerged.31 LSPR is produced by the collective oscillation of surface electrons induced by visible light, manifested by an extinction band in the visible region. It is relevant that LSPR depends on the refractive index of the surrounding medium and the interparticle distance, providing the basis for colorimetric plasmonic sensors.32−34 In such colorimetric sensors, observation with the naked eye can be done with changes in the liquid phase within 5 min, i.e., one may distinguish between positive and negative results.35 AuNPs have been employed in detecting viruses such as Zika (ZIKV), Ebola (EBOV), Influenza A virus (H1N1), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).31 Antibodies coupled to AuNPs can bind to the viral antigen leading to agglomeration of NPs, thus shifting its color from red to blue.24
Herein, low-cost, instrument-free, fast-response plasmonic biosensors were designed to detect SARS-CoV-2 in saliva and river water without sample pretreatment. Detection was primarily based on naked-eye colorimetry. Meanwhile, more detailed considerations of the detection processes were studied using UV–vis spectroscopy. For naked-eye colorimetry, we used a free smartphone application to process the images acquired with the smartphone camera, thus allowing instant results similar to other examples described in the literature (see Table S1 in the Supporting Information). The mechanisms responsible for the colloidal nanoparticle clustering around the virus, then allowing their detection with high sensitivity, are investigated for the first time using theoretical simulations with the finite-difference time-domain (FDTD) method. The most relevant contribution of this work is associated with the fast detection of SARS-CoV-2 without requiring instruments, with the high accuracy warranted by treating images with machine learning algorithms.
2 Materials and Methods
Gold(III) chloride trihydrate (HAuCl4·3H2O, ≥99.9% purity), 11-mercaptoundecanoic acid (MUA, ≥95%), sodium citrate dihydrate (≥99%), N-(3-dimethylaminopropyl)-N′-ethylcarbodiimide hydrochloride (EDC, ≥98%), N-hydroxysuccinimide (NHS, 98%), bovine serum albumin (BSA, ≥98%), poly(ethylene glycol) methyl ether thiol, average Mn 6000 (PEG-SH), and phosphate-buffered saline powder (pH 7.4) (P3813–10PAK) were purchased from Sigma-Aldrich. Recombinant anti-SARS-CoV-2 Spike glycoprotein S1 antibody (ab273073), recombinant human coronavirus SARS-CoV-2 Spike glycoproteins S1 (Active) (ab273068), and RBD (Active) (ab273065) were acquired from Abcam. Polyclonal antibody against the N-terminal domain of the SARS-CoV-2 Spike protein from rabbits was kindly provided by Virology and Microscopy Laboratory, Universidade de Brasília, Brazil. H1N1 California (OPPA01974) and Dengue envelope-3 (DENV, OPPA02454) proteins were acquired from Aviva Systems Biology. Gold seed particles were prepared using a modified Turkevich synthesis36 by adding gold chloride via sodium citrate reduction. The growth of Au nanoparticles (AuNPs) occurred after mixing two solutions, viz. 2.2 × 10–3 mol L–1 citrate and 25 × 10–3 mol L–1 HAuCl4, seven times with a 30-min break.1 The solution containing AuNPs was cooled by adding 50 mL of ultrapure water. The solution was kept at 4 °C in the dark for further work.
2.1 Transmission Electron Microscopy (TEM) and Field-Emission Scanning Electron Microscopy (SEM-FEG)
TEM images of AuNPs were collected using a Philips CM200 Transmission Electron microscope designed to obtain high-resolution images with a Super Twin polar piece using an electron beam energy of 25 keV. TEM sample grids were prepared by depositing 10 mL of AuNP suspension onto carbon-coated copper grids, followed by air-drying at room temperature. The average diameter was determined using ImageJ software. SEM-FEG images were obtained using a JEOL JSM-7500F microscope with operating software PC-SEM equipped with secondary and back-scattered electron detectors. Chemical analysis by energy-dispersive spectroscopy (EDS) was performed using an UltraDry detector from Thermo Scientific with NSS 2.3 operating software. Colloidal AuNPs were washed twice, diluted in ultrapure water, and deposited onto glassy carbon supports for later drying at room temperature.
2.2 UV/Vis Spectroscopy (UV/VIS) and Dynamic Light Scattering (DLS) Measurements
The UV–vis spectra were collected using a spectrophotometer (Thermo Scientific, Nanodrop 2000/2000c) from 200–800 nm using a 1-cm quartz cuvette. The nanoparticles were washed twice in 5 × 10–3 mol L–1 phosphate-buffered saline (PBS) at pH 7.4, and 20 μL of AuNP solutions were diluted in 980 μL of 5 × 10–3 mol L–1 PBS at pH 7.4. All AuNP suspensions were analyzed by dynamic light scattering (DLS) using a Nano ZS Malvern Zetasizer using the same washing procedure described before for the UV–vis measurements.
2.3 Attenuated Total Reflectance (ATR)
The attenuated total reflectance spectra were recorded from 4300 to 600 cm–1 using a Bruker Vertex 70 FTIR spectrometer equipped with an attenuated total reflectance (ATR) accessory. The measurements were performed using a blank citrate/AuNP solution. For each measurement, the AuNPs were washed twice in ultrapure water to remove excess PBS and reduce its signals. The samples were prepared by depositing 20 μL of the AuNP suspension onto the crystal, and measurements were acquired for the colloidal samples at a resolution of 4 cm–1 with 64 co-added scans/spectrum.
2.4 Inactivation of SARS-CoV-2 Virus and Sample Preparation
The inactivated virus samples used in our studies were obtained from HIAE-02 SARS-CoV-2/SP02/human/2020/BRA (GenBank: 616 MT126808.1) isolated from the second confirmed case in Brazil. The viral stocks of SARS-CoV-2 were propagated in Vero cell lines, and the supernatant was harvested at 2–3 days post-infection. Viral titers were determined by plaque assays on Vero cells, in which the number of plaque-forming units (PFU) represents the viral quantity. Vero CCL-81 cells were cultivated in Dulbecco’s modified Eagle’s medium (DMEM) (10% fetal bovine serum (FBS), 1% penicillin–streptomycin) and incubated at 37 °C with a 5% CO2 atmosphere. As a control, a conditioned medium of Vero cells was used after being treated in the same way but without the virus. Virus inactivation was performed using a CL1000 UVP crosslinker under UV irradiation in a microbiological safety cabinet, following Patterson et al.37 The virus stock was added (1500 μL) on 100 mm culture dishes and placed without its lid 6 cm below the UV bulbs. With this procedure, the viruses are inactivated by UV irradiation, and their protein structure is preserved.38,39 Inactivated SARS-CoV-2 was obtained from the Institute of Biology, University of Campinas (Brazil). Subsequently, a stock solution of the virus was prepared with 10 μL diluted in 990 μL 5.0 × 10–3 mol L–1 PBS at pH 7.4, 7000 PFU.
2.5 Modification of Colloidal Au Nanoparticles
Twenty-four microtubes with 1 mL of AuNPs each were used with the following procedure: 1.0 mL of nanoparticles was added to 56.7 μL of 1.9 × 10–3 mol L–1 MUA (dissolved in ethanol) under stirring at 28 °C for 2 h. Then, 55 μL of 2.0 × 10–4 mol L–1 PEG-SH was added to the solution of AuNPs/MUA (dissolved in ultrapure water) for another 2 h under stirring at 28 °C.40 Volumes of 19 μL of 2.5 × 10–3 mol L–1 EDC and 35 μL of 1.0 × 10–2 mol L–1 NHS in ultrapure water were added to the AuNPs/MUA/PEG-SH solution, which was stirred at 28 °C for 20 min. The mixture was then centrifuged at 7.300 rpm for 20 min at 15 °C. The AuNPs were resuspended in 5 × 10–3 mol L–1 PBS at pH 7.4 with SARS-CoV-2 spike polyclonal antibody (ab) added to obtain a final concentration of 2 μg mL–1 (AuNPs/MUA/PEG-ab), followed by incubation overnight at 28 °C. The AuNPs/MUA/PEG-ab solution was centrifuged for 20 min at 7300 rpm at 15 °C. The supernatant was discarded, and the sediment was resuspended in 200 μL of PBS and 300 μL of 0.5% BSA at a final percentage of 0.2%. The f-AuNP (AuNPs/MUA/PEG/ab/BSA) solution was centrifuged, resuspended twice to remove antibodies in excess, and stored at 4 °C.
2.6 Detection of SARS-CoV-2 in Saliva and Spiked River Samples
2.6.1 Synthetic and Human Saliva
Synthetic saliva was prepared with 0.228 g of CaCl2·2H2O, 0.061 g of MgCl2·6H2O, 1.017 g of NaCl, 0.504 g of K2CO3, 0.272 g of Na2HPO4·12H2O, and 0.273 g of NaH2PO4·H2O.41 All reagents were diluted in 1 L of ultrapure water and used only for comparison with human saliva with a negative Covid test.41 Then, a volume of 20 μL of artificial saliva was diluted in 960 μL of 5.0 × 10–3 mol L–1 PBS and 20 μL of the f-AuNP solution was added. This solution was compared with the human saliva of healthy donors without symptoms and negative tests, obtaining comparable results. In samples with saliva from a donor tested negative, different concentrations of the inactivated virus were spiked to the samples. In the proof-of-concept experiments, a similar procedure was performed using eleven samples of human saliva: five from individuals without symptoms (S1–S5), three from volunteers with no symptoms and negative PCR tests (Sp, Sm, and SB), and three from volunteers with positive Covid tests (C1, C2, and C3). The saliva donors followed the protocols required by pharmacies for tests, viz., they should not eat, drink coffee, or brush their teeth for 2 h prior to saliva collection. Also, they should not use lipstick, gloss, or menthol products.5 Samples were collected in 2 mL tubes and measured immediately.42
2.6.2 River Samples
River water samples were collected from the Gregorio River (GPS coordinate: 21°59′11.0″ S 47°52′52.1″ W) located in the city of São Carlos-SP (Brazil). A volume of 50 μL of a river sample was diluted in 930 μL of 5.0 × 10–3 mol L–1 PBS to which 20 μL of the f-AuNP solution was added.43 Then, different concentrations of the inactivated virus were spiked into the river water samples.
2.7 Quantification of Proteins on AuNPs
The AuNP–antibody solutions were centrifuged at 7300 rpm for 20 min, the supernatant was removed, and the remaining pellet was resuspended with 5 × 10–3 mol L–1 PBS. This procedure was repeated three times to remove unreacted EDS and NHS, with only AuNP–antibody complexes left. The washed solution was diluted at 1:5 and 1:10 vol/vol, and the antibody concentration was calculated using the bicinchoninic acid (BCA) protein assay according to the manufacturer’s protocol (Pierce). The absorbance was measured for diluted solutions at 562 nm using a NanoDrop 2000c Spectrophotometer in the cuvette mode, and the concentration of the bound antibody was calculated by multiplying the concentration by the dilution factor. Mean and standard deviations for the concentration were calculated considering both samples. The BCA assay showed an average antibody concentration of 9.3 ± 0.4 μg mL–1 (n = 2) on the surface of AuNPs. This value should be considered with care once the number of antibodies may be overestimated in the BCA protein assay and enzyme-linked immunosorbent assay (ELISA);44 it serves to confirm modification for nanoparticles coated with antibodies.45
2.8 Smartphone-Based Detection and Statistical Analysis
Smartphone-based sensing was conducted by getting images directly from the microcentrifuge tube using a Samsung smartphone (Galaxy J8, 16 megapixels camera, with Android 10). The RGB (red, green, blue) mean values were taken as an analytical signal in real-time through the free application (App) Color Grab (Loomatix, version 3.9.2), available for Android systems. For acquiring the digital images in microtubes, we used a built-in polylactic acid (PLA) support fabricated with a three-dimensional (3D) printer (Creality Ender-3) with the following dimensions: height of 5 cm and a 10-cm distance between the smartphone camera and the microtube (see Figure S1 in the Supporting Information). The images were analyzed with the software ImageJ using a 50-pixel circular region, and the RGB (red, green, blue) values were used as the analytical response (X). The best linear relationship to X is shown in eq 1, where the letters B and R denote the blue channel and red channel, respectively, and the subscripts “s” and “b” correspond to the values of the sample or standard and the analytical blank, respectively. The blue channel (Bs) and red channel (Rs) are the values of the sample, while the blank of the blue channel is Bb and the blank of the red channel is Rb (See Figure S1).1
The limit of detection (LOD) of the assay was calculated based on the standard deviation of the blank or the control sample (SDb) and the angular coefficient (b—slope) obtained from the analytical curve (ICH 2005),46 according to eq 2.2
The experiments were conducted in triplicate, and the relevant data were expressed as the mean ± SD. The statistical analyses were performed using Origin 9.0 and Statistica 13.5.0.17 (TIBCO) software.
2.9 FDTD Simulations
The absorption spectra and electromagnetic field distribution for isolated AuNPs and aggregates were calculated using a software package, FDTD Solutions by Ansys Lumerical Solutions. The simulation region was a 2 μm cube surrounded by a perfectly matched layer (PML) filled with water (refractive index RI = 1.33). The mesh size was set to 0.5 nm in all spatial dimensions. The AuNPs were simulated as homogenous spheres of 31 nm diameter, and the dielectric function of Au was adopted from the experimental data obtained by Johnson and Christy.47 The molecular linkers and antibodies anchored to the Au surface were simulated as a dielectric shell with a refractive index of 1.4 and 1.5 nm thick. The virus was simulated as a homogeneous dielectric sphere with a 100-nm diameter and a refractive index of 1.54, surrounded by a 10-nm thick dielectric shell with a refractive index of 1.46.48 Functionalized AuNP (f-AuNP) clusters were randomly placed on the virus surface to simulate the aggregate system. The algorithm for generating the clusters first added a functionalized NP onto the virus surface from a randomized position. Subsequently, a second NP was located randomly around the initial particle. The cluster was grown until a specified number of NPs was reached. The cluster formation randomly achieved the interparticle distance between 1 and 2 nm. A total-field scattered field (TFSF) source with a wavelength ranging from 350 to 850 nm was used to illuminate the systems. The incident plane wave was x-polarized, and the propagation direction was set along the minus z-axis. The electric field strength of the incident irradiation was set at 1.0 V m–1. A 3D frequency-domain field profile monitor and a group analysis (both inside the TSFS source) were used to record the electric field and calculate the absorption cross-section, respectively. All of the absorption cross-section spectra are reported as a dimensionless Mie efficiency calculated by dividing the optical cross-section by NπR2, where R is the radius of the NPs and N is the number of NPs in a cluster.49
3 Results and Discussion
3.1 Bioconjugation and Images of AuNPs
The FEG-SEM and TEM images show spherical AuNPs with an average diameter of 31 nm (Figure S2A,B in the Supporting Information). The high purity of AuNPs was confirmed with a strong signal of elemental Au in Figure S2C (EDS analysis), with stability in the pH range between 3.0 and 8.0 in 5.0 × 10–3 mol L–1 PBS, as indicated in Figure S2D. The AuNPs were functionalized by covalent coupling of antibodies using a mercaptoundecanoic acid binder (MUA), following the procedure depicted in Figure 1A. The whole procedure was monitored using UV–vis spectroscopy and ζ-potential measurements. MUA was bound covalently onto AuNPs through its thiol groups to the Au surface; its quaternary ammonium counterion adsorbed on the Stern layer provided stability against aggregation.50 AuNPs coated with MUA had a 35-nm diameter, which confirmed an increase in shell thickness. MUA coating is not expected to be homogeneous but on patches according to dissipative particle dynamics simulations.51Figure 1B shows the plasmonic peak of monodisperse AuNPs at 524 nm shifted to 526 nm for AuNPs–MUA,52 with the increased absorbance after MUA modification occurring due to aggregation of AuNPs. PEG-SH occupies the remaining free AuNP surface, exhibiting a radial conformation since the thiol group has a stronger affinity for the AuNP surface than PEG chains, thus removing these from the NP surface.51 A decrease in absorbance was observed upon adding PEG53 due to aggregation of polydisperse PEG–AuNPs54 and after activating terminal carboxylic acid headgroups by EDC/NHS at pH ∼ 6.0. The pKa of EDC is 6.0, and that of MUA on Au is 4.5–6.0. Hence, deprotonation of 11-MUA and protonation of EDC seem to be important in the activation reaction.55 The formation of nanoensembles can cause a decrease in absorbance due to their activating ability.56 EDC/NHS binding has the advantage of providing a stable, biocompatible, covalent bond.57 The activation mechanism of carboxyl groups mediated by EDC/NHS involves the formation of an adduct, an O-acylisourea derivative, between EDC and the carboxyl group of MUA.58 The O-acylurea adduct reacts with a primary amine and produces the desired peptide coupling, but it has a low reaction rate. Hence, NHS provides a more stable intermediate to react with a primary amine forming an amide bond.59 Then, a nucleophilic attack by NHS may occur to form an N-succinimidyl ester, releasing a soluble urea derivative as a by-product.58
Figure 1 (A) Schematic design for modification of AuNPs, throughout the various steps. (B) UV–vis spectra for the distinct steps in the bioconjugation (AuNPs after addition of: step 1 MUA, step 2 PEG-SH, step 3 EDC/NHS, step 4 antibody, and step 5 BSA). (C) ATR spectra for AuNPs coated with MUA, then MUA–PEG and MUA–PEG-anti-SARS-CoV-2. (D) ζ-potential at each step of AuNP bioconjugation and in the presence of 3.2 μg mL–1 S protein of SARS-CoV-2.
The incorporation of antibodies increased the absorbance intensity significantly due to an increased effect on the hydrodynamic-layer thickness of the AuNPs and changed their refractive index after conjugation.60 In subsidiary experiments, we observed that efficient plasmonic biosensors can be obtained with either monoclonal or rabbit polyclonal antibodies. The results in detecting inactivated viruses via DLS are shown in Figure S3. We have therefore employed the polyclonal antibody in the subsequent studies. The remaining active sites after the antibody adsorption were blocked by BSA addition, which caused a decrease in absorbance. The attachment of antibodies was confirmed with the ATR spectra in Figure 1C, featuring O–H and C=O bands assigned to stretching vibrations of −COOH groups of Au–MUA61 at 3261 and 1433 cm–1, which are slightly altered after the addition of PEG. The 3261 cm–1 band was increased, while the 1433 cm–1 band almost disappeared after bioconjugation. The functionalization of AuNPs affected their ζ-potential in Figure 1D, ranging from −43.96 mV for AuNPs to −35.32 mV after MUA coating. The ζ-potential further varied by attaching PEG (−38.72 mV), antibodies (−31.64 mV), and BSA (−33.68 mV). When 2.0 μg mL–1 SARS-CoV-2 S protein was added, the ζ-potential decreased (in modulus) to −30.86 mV, as expected from the literature.62 It is worth noting that BSA improves the stability of AuNPs functionalized with PEG and increases the ζ-potential,63 which decreases when f-AuNPs interact with the S protein of the SARS-CoV-2.
TEM images of functionalized AuNPs (f-AuNPs) exposed to SARS-CoV-2 at 250 and 6000 PFU concentrations are shown in Figure 2. For both concentrations, clusters of different numbers of f-AuNPs are formed on the virus surface and may not cover the entire surface. For 250 PFU, AuNP clusters recover the viral particles as expected,64 and several neighboring NPs agglomerate around it. The hydrodynamic radius of the AuNPs increases with the SARS-CoV-2 concentration,65 as indicated in DLS measurements in Figure S3 in the Supporting Information. At 6000 PFU, some virus particles are not covered by AuNPs.
Figure 2 TEM micrographs of f-AuNPs after exposure to 250 and 6000 PFU mL–1 of SARS-CoV-2.
3.2 Simulations
The phenomenon of f-AuNP aggregation and clustering on the virus surface forms the basis of the colorimetric-based sensor proposed in this work. It is, therefore, useful to model such interactions with FDTD simulations, which allow one to analyze the light absorption properties of the f-AuNP–virus system as a function of interparticle distances and cluster sizes. Figure 3 shows the absorption efficiency spectra for aggregates with different numbers of f-AuNPs (1–3), including the spectrum for an isolated f-AuNP. Since the f-AuNPs are randomly located, a statistical study was necessary for which we employed five different AuNP configurations for a given cluster size, denoted with distinct captions (conf_1-conf_5). The caption avg corresponds to the average of these configurations. We plot the electric field amplitude at the resonant frequency for some configurations, with the same color map scale for some cluster sizes. The FDTD results in Figure 3A indicate that an isolated f-AuNP exhibits a strong LSPR around ∼525 nm, in agreement with the experimental spectrum in Figure 1B. The near-field electric distribution for the modified-AuNP at the plasmon resonance wavelength is shown in Figure S4 in the Supporting Information. Due to the LSPR effect, the electric field was enhanced around the f-AuNP, with the enhancement locally reaching up to 4.5 times the intensity of incident light.
Figure 3 Spectral absorption efficiency for clusters with (A) one, (B) two, and (C) three f-AuNPs. (D) and (G) show the electric field amplitude at the resonant frequency for Conf_1 and Conf_2 in (A), respectively. (E) and (H) show the electric field amplitude at the resonant frequency for Conf_1 and Conf_4 in (B), respectively. (F) and (I) show the electric field amplitude at the wavelength resonant λ = 545 nm and λ = 642 nm for Conf_ 3 in (C).
On the other hand, when f-AuNP are aggregated on the virus surface, the spectrum of different configurations differs in absorption amplitude and frequency for maximum absorption from the isolated f-AuNP. In particular, as the f-AuNP and virus align with the polarization direction of the source (x-axis), the absorption amplitude, resonance frequency, and electromagnetic field in the region of contact increase (Figure 3D,G). Compared with the far-field response of an isolated f-AuNP, the changes are minimal, which is not helpful for colorimetric sensors. In contrast to the single nanoparticle–virus system, a new set of plasmonic modes are seen at larger wavelengths, with a significant enhancement of the electric field (especially in the gap between nanoparticles, i.e., at the “hotspots”), for some dimer- or trimer-virus configurations (Figure 3B,C,E,F,H,I). These new bands arise from the strong near-field coupling of LSPRs of individual particles, which can be understood in terms of the plasmon hybridization theory.66 From the field profiles, it could be inferred that plasmonic coupling decreases with an increase in interparticle separation. It is also possible to infer a strong dependence of the plasmonic coupling upon the relative orientations of the cluster concerning the polarization direction of the source. The more aligned the f-AuNPs with the incident electric field, the stronger the plasmonic coupling is. The absorption efficiency spectra for aggregates with nanoparticle numbers 4, 8, 16, and 32 are shown in Figure S5. As the size of the cluster increases for each random cluster configuration, there is an increase in the probability of matching the axis in a hotspot with the polarization light, which leads the optical absorption spectrum to shift toward larger wavelengths, with the appearance of new plasmonic bands. Since these variations are associated with intense color variations of the solutions, the theoretical results highlight the importance of aggregation and cluster formation of f-AuNPs on the virus surface in Figure 2. Hence, the larger the cluster, the easier it is to detect the virus.
3.3 Spectrophotometric and Naked-Eye Detection
A quantitative determination of inactivated SARS-CoV-2 and the Spike protein was performed with absorbance spectroscopy in artificial saliva samples. Figure S6A shows a decrease in the absorbance of the plasmonic band centered at 526 nm with increasing concentration. This decrease was expected because polyclonal antibodies may bind to the S protein at multiple epitopes so that the S protein serves as a crosslinker to aggregate f-AuNPs, thus quenching the plasmonic band. Indeed, quenching of f-AuNPs was reported for other analytes,67 depending on the type of coupling, interparticle spacing, and local dielectric environment.67 No other band is formed since the Spike protein is small compared to the virus. The protein reacts with the antibody on the f-AuNP surface, and no clusters of f-AuNPs are formed. Hence, there are only refractive index changes, which can only be detected at high concentrations of SARS-CoV-2.68 In contrast, for the inactivated SARS-CoV-2, Figure 4A shows that the plasmonic band is redshifted with the concentration, and another band appears at 636 nm. This new band results from the large clusters of f-AuNPs on the virus surface, as discussed in Section 3.2, and leads to a change in the solution color from red (w/o virus) to blue (w/ virus). This is represented by changing the color of the spectra in Figure 4A. The limit of detection (LOD) was calculated from the parameters extracted by fitting the experimental results using the Langmuir–Hill model for the inactivated virus (Figure S7). LOD is 0.28 PFU mL–1 by taking the peak shift (Δλ) from normalized spectra and 0.29 PFU mL–1 using the absorbance ratio at 526 and 636 nm (A526/A636).
Figure 4 (A) UV–vis spectra for solutions containing inactivated SARS-CoV-2 virus at concentrations 0, 7, 144, 250, 418, 520, 636, 750, 860, 971, 1073, 1172, 1268, 1360, 1781, 2142, 2459, 2736, and 2980 PFU mL–1. As the concentration increases, there is a change from a reddish to bluish color in the f-AuNP solution, which is indicated in the inset photos. We used two colors (red and blue) in the spectra to indicate this gradual change. (B) Averaged spectral absorption efficiency for various f-AuNPs in the aggregate clusters was obtained with FDTD simulations. The spectra are also shown in two colors to indicate the color change. The caption avg corresponds to the average spectral absorption efficiency of five different configurations in the FDTD simulations.
Figure 4B shows theoretical spectra that resemble the experimental spectra in Figure 4A. To interpret the spectra, we recall the theoretical calculations in Figure 3 (and Figure 5S in the Supporting Information), where the average absorption efficiency was calculated for clusters with 1, 2, 3, 4, 8, 16, and 32 NPs and for the case in which the virus surface is entirely covered by f-AuNPs (i.e., with 69 NPs). Comparing with the theoretical results in Figure 4B, we infer that the absorption band centered at 526 nm is mainly related to the absorption response of isolated AuNPs, which are dominant at low concentrations. The plasmonic response of the single nanoparticle–virus system and the transversal plasmonic couplings in larger aggregates could also contribute to the appearance of this band. On the other hand, as the virus concentration increases, the f-AuNPs agglomerate on the virus surface to form clusters of different sizes. Since these clusters exhibit different optical responses, the plasmonic band becomes broader. It should be noted that the predicted bands with FDTD for complete virus coverage with f-AuNPs are not observed experimentally. Therefore, the probability of experimentally observing total virus coverage is small, consistent with the SEM images in Figure 2.
Despite the evident changes in color, choosing one specific wavelength to collect absorbance values from hundreds of absorbance spectra is difficult. To facilitate interpretation by the reader, we used a multidimensional projection technique, referred to as Interactive Document Mapping (IDMAP).69 The FastMap method was used to reduce the 350 dimensions, i.e., the UV–vis spectrum (400–750 nm), to only 2 dimensions.69 Hence, one converts each spectrum into a single-colored dot on the visualization map. Evident discrimination of the samples with distinct concentrations of inactivated virus, from 0 to 2981 PFU mL–1, is obtained in Figure 5A, with a silhouette coefficient of 0.97 (which varies from −1 to 1).69 To demonstrate the applicability of the f-AuNPs biosensor, we analyzed several saliva samples (in triplicate, n = 3) obtained from 10 volunteers as follows: five healthy volunteers (S1–S5) who were not tested; two volunteers with negative PCR tests (Sp and SM), three volunteers tested positive for Covid-19 (C1, C2, and C3). Also, the saliva samples of one of the volunteers who tested negative were diluted in 0.5 mmol L–1 PBS (samples SB) and then spiked with the inactivated virus at 250, 1000, and 5000 PFU mL–1. There were, therefore, 14 distinct types of samples whose spectra were projected in the IDMAP plot in Figure 5B. There is a clear separation of samples from healthy individuals on the left-upper side of the map (reddish dots), while the saliva samples from contaminated patients and healthy individuals spiked with standards of 250 to 5000 PFU mL–1 are located on the bottom of the map (bluish dots). Diagnosis of COVID-19 could also be made with the data from the saliva samples using supervised machine learning within the multidimensional calibration space concept.70 An accuracy of 100% was obtained in the binary classification (YES or NO for the virus) when the random forest (RF) algorithm was applied. The results are illustrated in the Explainable Matrix (ExMatrix) representation70 in Figure 5C. The space had five dimensions, i.e., light absorption at five frequencies had to be used in the eight rules of the RF algorithm. It is also significant that the first three dimensions were already responsible for 98% of the information.
Figure 5 (A) IDMAP visualization of data from the response with the plasmonic biosensor for SARS-CoV-2 virus (concentrations between 7 PFU and 2981 PFU) diluted in PBS (pH 7.4). (B) IDMAP visualization of the data for human saliva of healthy volunteers (with no symptoms, but not tested) (S1–S5), Sp and Sm (volunteers with no symptoms and who were tested negative for Covid-19), SB Saliva diluted in 5 × 10–3 mol L–1 PBS (pH 7.4), human saliva from volunteers tested negative (S) contaminated with 250, 1000, and 5000 PFU and positive PCR test of virus-carrying patients (C1, C2, and C3). (C) ExMatrix representation using the RF model (9 Decision Trees–8 logic rules) for the binary problem, with YES or NO classes for positive and negative SARS-COV-2 patients, respectively.
3.4 Interferents and Other Applications
To assess the selectivity of the proposed plasmonic biosensor, proteins of different viruses were chosen as interferents, including SARS, H1N1, and Dengue, at concentrations of 2 × 10–6 and 2 × 10–4 μg mL–1 in PBS. These data were projected in Figure S8 (Supporting Information) with SARS-CoV-2 in concentrations between 7 and 2981 PFU mL–1, with the interferents being grouped around the data for low concentrations of the SARS-CoV-2 virus. High (0.4–5.6 μg mL–1) and low concentrations (2 × 10–6 and 2 × 10–4 μg mL–1) of Spike protein were also projected with interferents to demonstrate how different the proteins are from the viruses since they are placed in opposite directions as depicted in the IDMAP plot of Figure S9.
The impact of the exposure of virus particles from biological aerosols on sewage workers, communities, and wildlife should be investigated, along with initiatives to reduce the load of viruses in water reservoirs.16 This type of monitoring requires simple methods for detecting and quantifying SARS-CoV-2 in waters (and wastewaters), mainly to identify possible routes of SARS-CoV-2 into water bodies.71 This work demonstrates that the plasmonic biosensor can be applied in complex matrices. We tested river water (without a precleaning step) samples spiked with inactivated SARS-CoV-2 virus at five concentrations (7, 250, 1000, 5000, and 6000 PFU mL–1). The separation in Figure S10 for these data indicates that the plasmonic biosensor can also be employed in environmental monitoring for the presence of SARS-CoV-2.
3.5 Smartphone-Based Detection
A rapid, inexpensive, and label-free method for real-time detection of SARS-CoV-2 was developed based on the surface plasmon resonance of f-AuNPs utilizing a smartphone application, Color Grab (see Figure S11 in the Supporting Information). A redshift in the absorbance of colloidal AuNPs is observed with saliva samples in the absence of the virus; when the inactivated virus is added, as shown in Figure 6A, a color change from red to blue is observed, as expected from the literature.31,40Figure S12 shows a blue color predominating for high PFU concentrations (i.e., >2000 PFU mL–1). These color differences can be distinguished with the naked eye by humans with normal trichromatic vision who can combine in their brain three independent wavelengths, corresponding to red, green, and blue, to generate the color observed.72 However, the perception of color differences is not the same in all individuals, and many users may suffer from problems such as color blindness or color vision deficiency.72 Furthermore, these devices may be affected by environmental conditions (e.g., poor ambient lighting or flashing lights of emergency vehicles) in addition to natural, person-to-person perceptual differences. These limitations can be mitigated with image processing,72 as we have done here. We employed the RGB model, where each component of the color space may vary between 0 and 255 in images obtained under controlled ambient light with a simple box for photography and accessories to hold a smartphone (Figure S11). Figure 6B shows an illustration of the result displayed on a smartphone. The results from the color analysis on the digital images are given in the violin plot73 of Figure 6C, featuring data from artificial saliva and saliva of healthy donors and patients.
Figure 6 (A) Photo shows the colorimetric response of the plasmonic biosensor for different concentrations of inactivated SARS-CoV-2 in 5 × 10–3 mol L–1 PBS, pH 7.4. (B) Schematic design for SARS-CoV-2 virus detection with a smartphone camera. (C) Violin plots for the colorimetric results from the SARS-CoV-2 analysis in saliva samples using the biosensor.
Using color Grab software and RGB values from the images taken with a smartphone camera, the absorbance ratio (blue/red) was calculated to quantify the concentration of SARS-CoV-2 virus as in ref (73). Since the application Color Grab is unavailable on some smartphones, we tested a different approach for digital image analysis with a free, open-source image processing software (ImageJ) to obtain the RGB values. One-way analysis of variance (ANOVA) followed by Tukey’s test performed at low, medium, and high SARS-CoV-2 concentrations showed no significant differences (p < 0.05) between the images treated with a smartphone app and ImageJ. The detection limit was estimated as described by the International Union of Pure and Applied Chemistry (IUPAC).18Table 1 shows the analytical parameters for three types of assays. The first one was performed in PBS with increasing concentrations of inactivated virus (assay A), leading to a LOD of 2.2 PFU mL–1. In Assay B, the saliva samples from a volunteer with a negative PCR test were spiked with the inactivated virus at concentrations from 250 to 5000 PFU mL–1. The LOD obtained from the digital image analysis was 2.0 PFU mL–1. Assay C was conducted with samples collected from river water samples without a precleaning step. These samples were spiked with inactivated virus from 7 to 2000 PFU mL–1 (see Figure S14 in the Supporting Information), and a LOD of 3.4 PFU mL–1 was obtained for the digital image analysis. It is also worth mentioning that the images from human saliva samples spiked with H1N1 and dengue virus proteins could not be distinguished from those of healthy volunteers.
Table 1 Analytical Performance of the Optical Biosensor Using the Digital Image Analysis of the Ratio of Blue-to-Red (B/R) Channels in Different Sample Media
assay medium analytical measurement linear range (n = 5, PFU mL–1) intercept slope R2 LOD (PFU mL–1)
A buffer B/R 7–2000 –0.124 0.169 0.997 2.2
B saliva B/R 250–5000 –0.411 0.170 0.903 2.0
C river B/R 7–2000 –0.098 0.156 0.941 3.4
4 Conclusions
The approach of functionalizing Au nanoparticles (f-AuNPs) with polyclonal antibodies for SARS-CoV-2 was exploited in plasmonic biosensors to detect inactivated viruses with a colorimetric, instrument-free technique. The sensitivity is sufficient for diagnosis using saliva, with a limit of detection (LOD) of 2.2 and 2.0 PFU mL–1 in assays with the inactivated virus in PBS solutions and the saliva of a volunteer, respectively. We found that the high sensitivity is related to the processes involving the cluster formation of f-AuNPs around the virus. As indicated in the FDTD computer simulations, a significant color change is only observed when large clusters are formed. The suitability of the biosensor with f-AuNPs was confirmed with other detection principles, including UV–vis spectroscopy and dynamic light scattering. The plasmonic biosensor specific for SARS-CoV-2 was demonstrated in experiments with potential interferents, such as the proteins from SARS, H1N1, and Dengue viruses. Detection of the UV-inactivated SARS-CoV-2 in human saliva and river waters could be done within 5 min, as the liquid and solutions changed color from red to purple, with possible observation with naked eyes. This color change was exploited in a smartphone application where images taken with the smartphone camera were processed with Color Grab and ImageJ software. With the latter, it was possible to determine whether the sample was positive for SARS-CoV-2 and to estimate the virus concentration. The app may also incorporate image processing combined with machine learning to provide COVID-19 diagnosis, whose accuracy in the tests performed with the colorimetric assay was 100%. These results indicate that plasmonic biosensors, like those developed here, can be used for low-cost COVID-19 diagnosis and monitoring within the point-of-care paradigm and in poor or remote locations.
Moreover, it is critical to consider the probability of environmental contamination and spread through wastewater and river waters. For example, places with poor sanitation and inadequate treatment can lead to nonpoint contamination of surface waters with SARS-CoV-2. For this reason, investigating methodologies that allow the detection of pathogens in places with little infrastructure is essential. Our proposed biosensor is promising for using river water samples without any previous treatment.
Supporting Information Available
The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsami.2c15407.Microscopy analysis; DLS results; FDTD simulation; UV–vis spectra; IDMAP plot; pictures of colorimetric samples and device; and comparison of main contributions from the literature (PDF)
Supplementary Material
am2c15407_si_001.pdf
Author Contributions
E.M.M.: Methodology, investigation, acquisition of photos using a smartphone, synthesis of gold nanoparticles, validation, conceptualization, project administration, data analysis, visualization, writing for the original draft, and editing. F.R.G.: Conceptualization, acquisition of photos with a smartphone, simulations, visualization, writing for the original draft, and editing. M.B.A.: Discussion of results of the images, visualization, and writing for the original draft. F.M.S.: artificial saliva methodology, discussion of results, data curation, visualization, writing for the original draft, and editing. A.W.: Synthesis of gold nanoparticles, collecting river samples, discussion of results, writing for the original draft, and editing. K.B.R.T. and D.S.C.: Investigation with DLS technique, validation, and writing for the original draft. F.S.R.S.: protein quantification, acquisition of photos with a smartphone, validation, and writing for the original draft. M.J.A.L.: Discussion of image results, 3D printed box design and printing, and writing for the original draft. M.K.S.C.A., M.E.M., and P.M.V.: inactivation of virus and discussion of results and writing for the original draft. N.P.: funding, computer simulations, and discussion of theoretical simulations. E.C.: Supervision, funding, saliva samples, writing for the original draft, and editing. O.N.O.Jr., R.B.A., and D.G.: Supervision, funding, project administration, writing for the original draft, and editing. All of the authors discussed the results and edited/reviewed the manuscript.
The authors declare no competing financial interest.
Notes
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
This work was supported by CAPES (Finance code 001), INEO, CNPq (402816/2020-0, 304431/2020-6, 311757/2019-7, 465389/2014-7, 115857/2022-2), and FAPESP (2018/22214-6, 2014/50867-3, 2019/19235-4, 2021/08387-8, 2017/03879-4), Edital de projetos integrados de pesquisa em áreas estratégicas (PIPAE).
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References
Malekshahi A. ; Khanizadeh S. ; Fallahi S. ; Talei G. ; Birjandi M. ; Hajizadeh F. Diagnostic Power of One-Step and Two-Step RT-QPCR Methods to SARS-CoV-2 Detection. BMC Infect. Dis. 2022, 22 , 505 10.1186/s12879-022-07478-0.35641907
Bruzzone B. ; De Pace V. ; Caligiuri P. ; Ricucci V. ; Guarona G. ; Pennati B. M. ; Boccotti S. ; Orsi A. ; Domnich A. ; Da Rin G. ; Icardi G. Comparative Diagnostic Performance of Rapid Antigen Detection Tests for COVID-19 in a Hospital Setting. Int. J. Infect. Dis. 2021, 107 , 215–218. 10.1016/j.ijid.2021.04.072.33930540
Kendall E. A. ; Arinaminpathy N. ; Sacks J. A. ; Manabe Y. C. ; Dittrich S. ; Schumacher S. G. ; Dowdy D. W. Antigen-Based Rapid Diagnostic Testing or Alternatives for Diagnosis of Symptomatic COVID-19: A Simulation-Based Net Benefit Analysis. Epidemiology 2021, 32 , 811–819. 10.1097/EDE.0000000000001400.34292212
Warsi I. ; Khurshid Z. ; Shazam H. ; Umer M. F. ; Imran E. ; Khan M. O. ; Slowey P. D. ; Goodson J. M. Saliva Exhibits High Sensitivity and Specificity for the Detection of SARS-COV-2. Diseases 2021, 9 , 38 10.3390/diseases9020038.34065171
Wyllie A. L. ; Fournier J. ; Casanovas-Massana A. ; Campbell M. ; Tokuyama M. ; Vijayakumar P. ; Warren J. L. ; Geng B. ; Muenker M. C. ; Moore A. J. ; Vogels C. B. F. ; Petrone M. E. ; Ott I. M. ; Lu P. ; Venkataraman A. ; Lu-Culligan A. ; Klein J. ; Earnest R. ; Simonov M. ; Datta R. ; Handoko R. ; Naushad N. ; Sewanan L. R. ; Valdez J. ; White E. B. ; Lapidus S. ; Kalinich C. C. ; Jiang X. ; Kim D. J. ; Kudo E. ; Linehan M. ; Mao T. ; Moriyama M. ; Oh J. E. ; Park A. ; Silva J. ; Song E. ; Takahashi T. ; Taura M. ; Weizman O.-E. ; Wong P. ; Yang Y. ; Bermejo S. ; Odio C. D. ; Omer S. B. ; Dela Cruz C. S. ; Farhadian S. ; Martinello R. A. ; Iwasaki A. ; Grubaugh N. D. ; Ko A. I. Saliva or Nasopharyngeal Swab Specimens for Detection of SARS-CoV-2. N. Engl. J. Med. 2020, 383 , 1283–1286. 10.1056/NEJMc2016359.32857487
Bulfoni M. ; Sozio E. ; Marcon B. ; De Martino M. ; Cesselli D. ; De Carlo C. ; Martinella R. ; Migotti A. ; Vania E. ; Zanus-Fortes A. ; De Piero J. ; Nencioni E. ; Tascini C. ; Isola M. ; Curcio F. Validation of a Saliva-Based Test for the Molecular Diagnosis of SARS-CoV-2 Infection. Dis. Markers 2022, 2022 , 1–8. 10.1155/2022/6478434.
de Oliveira C. M. ; Brochi L. ; Scarpelli L. C. ; Lopes A. C. W. ; Levi J. E. SARS-CoV-2 Saliva Testing Is a Useful Tool for Covid-19 Diagnosis. J. Virol. Methods 2021, 296 , 114241 10.1016/j.jviromet.2021.114241.34273438
Bustos-Garcia B. ; Garza-Manero S. ; Cano-Dominguez N. ; Lopez-Sanchez D. M. ; Salgado-Montes de Oca G. ; Salgado-Aguayo A. ; Recillas-Targa F. ; Avila-Rios S. ; Valdes V. J. Development and Testing of a Low-Cost Inactivation Buffer That Allows for Direct SARS-CoV-2 Detection in Saliva. Vaccines 2022, 10 , 730 10.3390/vaccines10050730.35632485
Lin Y. C. ; Malott R. J. ; Ward L. ; Kiplagat L. ; Pabbaraju K. ; Gill K. ; Berenger B. M. ; Hu J. ; Fonseca K. ; Noyce R. S. ; Louie T. ; Evans D. H. ; Conly J. M. Detection and Quantification of Infectious Severe Acute Respiratory Coronavirus-2 in Diverse Clinical and Environmental Samples. Sci. Rep. 2022, 12 , 5418 10.1038/s41598-022-09218-5.35354854
Sender R. ; Bar-On Y. M. ; Gleizer S. ; Bernshtein B. ; Flamholz A. ; Phillips R. ; Milo R. The Total Number and Mass of SARS-CoV-2 Virions. Proc. Natl. Acad. Sci. U.S.A. 2021, 118 , e2024815118 10.1073/pnas.2024815118.34083352
Johnson T. J. ; Nishida R. T. ; Sonpar A. P. ; Lin Y. C. J. ; Watson K. A. ; Smith S. W. ; Conly J. M. ; Evans D. H. ; Olfert J. S. Viral Load of SARS-CoV-2 in Droplets and Bioaerosols Directly Captured during Breathing, Speaking and Coughing. Sci. Rep. 2022, 12 , 3484 10.1038/s41598-022-07301-5.35241703
Bradburne A. F. ; Bynoe M. L. ; Tyrrell D. A. J. Effects of a “New” Human Respiratory Virus in Volunteers. Br. Med. J. 1967, 3 , 767–769. 10.1136/bmj.3.5568.767.6043624
Tran H. N. ; Le G. T. ; Nguyen D. T. ; Juang R. ; Rinklebe J. ; Bhatnagar A. ; Lima E. C. ; Iqbal H. M. N. ; Sarmah A. K. ; Chao H.-P. SARS-CoV-2 Coronavirus in Water and Wastewater: A Critical Review about Presence and Concern. Environ. Res. 2021, 193 , 110265 10.1016/j.envres.2020.110265.33011225
de Oliveira L. C. ; Torres-Franco A. F. ; Lopes B. C. ; da Silva Santos B. S. Á. ; Costa E. A. ; Costa M. S. ; Reis M. T. P. ; Melo M. C. ; Polizzi R. B. ; Teixeira M. M. ; Mota C. R. Viability of SARS-CoV-2 in River Water and Wastewater at Different Temperatures and Solids Content. Water Res. 2021, 195 , 117002 10.1016/j.watres.2021.117002.33714910
Haramoto E. ; Malla B. ; Thakali O. ; Kitajima M. First Environmental Surveillance for the Presence of SARS-CoV-2 RNA in Wastewater and River Water in Japan. Sci. Total Environ. 2020, 737 , 140405 10.1016/j.scitotenv.2020.140405.32783878
Bivins A. ; Greaves J. ; Fischer R. ; Yinda K. C. ; Ahmed W. ; Kitajima M. ; Munster V. J. ; Bibby K. Persistence of SARS-CoV-2 in Water and Wastewater. Environ. Sci. Technol. Lett. 2020, 7 , 937–942. 10.1021/acs.estlett.0c00730.
Rashed M. Z. ; Kopechek J. A. ; Priddy M. C. ; Hamorsky K. T. ; Palmer K. E. ; Mittal N. ; Valdez J. ; Flynn J. ; Williams S. J. Rapid Detection of SARS-CoV-2 Antibodies Using Electrochemical Impedance-Based Detector. Biosens. Bioelectron. 2021, 171 , 112709 10.1016/j.bios.2020.112709.33075724
Fabiani L. ; Saroglia M. ; Galatà G. ; De Santis R. ; Fillo S. ; Luca V. ; Faggioni G. ; D’Amore N. ; Regalbuto E. ; Salvatori P. ; Terova G. ; Moscone D. ; Lista F. ; Arduini F. Magnetic Beads Combined with Carbon Black-Based Screen-Printed Electrodes for COVID-19: A Reliable and Miniaturized Electrochemical Immunosensor for SARS-CoV-2 Detection in Saliva. Biosens. Bioelectron. 2021, 171 , 112686 10.1016/j.bios.2020.112686.33086175
Saatçi E. ; Natarajan S. State-of-the-Art Colloidal Particles and Unique Interfaces-Based SARS-CoV-2 Detection Methods and COVID-19 Diagnosis. Curr. Opin. Colloid Interface Sci. 2021, 55 , 101469 10.1016/j.cocis.2021.101469.34093063
Lau C. S. ; Aw T.-C. SARS-CoV-2 Antigen Testing Intervals: Twice or Thrice a Week?. Diagnostics 2022, 12 , 1039 10.3390/diagnostics12051039.35626195
Amaral C. ; Antunes W. ; Moe E. ; Duarte A. G. ; Lima L. M. P. ; Santos C. ; Gomes I. L. ; Afonso G. S. ; Vieira R. ; Teles H. S. S. ; Reis M. S. ; da Silva M. A. R. ; Henriques A. M. ; Fevereiro M. ; Ventura M. R. ; Serrano M. ; Pimentel C. A Molecular Test Based on RT-LAMP for Rapid, Sensitive and Inexpensive Colorimetric Detection of SARS-CoV-2 in Clinical Samples. Sci. Rep. 2021, 11 , 16430 10.1038/s41598-021-95799-6.34385527
Xu N. ; Jin S. ; Wang L. Metal Nanoparticles-Based Nanoplatforms for Colorimetric Sensing: A Review. Rev. Anal. Chem. 2021, 40 , 1–11. 10.1515/revac-2021-0122.
Liang P. ; Guo Q. ; Zhao T. ; Wen C. Y. ; Tian Z. ; Shang Y. ; Xing J. ; Jiang Y. ; Zeng J. Ag Nanoparticles with Ultrathin Au Shell-Based Lateral Flow Immunoassay for Colorimetric and SERS Dual-Mode Detection of SARS-CoV-2 IgG. Anal. Chem. 2022, 94 , 8466–8473. 10.1021/acs.analchem.2c01286.35657150
Ventura B. D. ; Cennamo M. ; Minopoli A. ; Campanile R. ; Censi S. B. ; Terracciano D. ; Portella G. ; Velotta R. Colorimetric Test for Fast Detection of SARS-COV-2 in Nasal and Throat Swabs. ACS Sens. 2020, 5 , 3043–3048. 10.1021/acssensors.0c01742.32989986
Han H. ; Wang C. ; Yang X. ; Zheng S. ; Cheng X. ; Liu Z. ; Zhao B. ; Xiao R. Rapid Field Determination of SARS-CoV-2 by a Colorimetric and Fluorescent Dual-Functional Lateral Flow Immunoassay Biosensor. Sens. Actuators, B 2022, 351 , 130897 10.1016/j.snb.2021.130897.
Yadav S. ; Sadique M. A. ; Ranjan P. ; Kumar N. ; Singhal A. ; Srivastava A. K. ; Khan R. Sers Based Lateral Flow Immunoassay for Point-of-Care Detection of Sars-Cov-2 in Clinical Samples. ACS Appl. Bio Mater. 2021, 4 , 2974–2995. 10.1021/acsabm.1c00102.
Georgiou P. G. ; Guy C. S. ; Hasan M. ; Ahmad A. ; Richards S. J. ; Baker A. N. ; Thakkar N. V. ; Walker M. ; Pandey S. ; Anderson N. R. ; Grammatopoulos D. ; Gibson M. I. Plasmonic Detection of SARS-CoV-2 Spike Protein with Polymer-Stabilized Glycosylated Gold Nanorods. ACS Macro Lett. 2022, 11 , 317–322. 10.1021/acsmacrolett.1c00716.35575357
Antiochia R. Nanobiosensors as New Diagnostic Tools for SARS, MERS and COVID-19: From Past to Perspectives. Microchim. Acta 2020, 187 , 639 10.1007/s00604-020-04615-x.
Lew T. T. S. ; Aung K. M. M. ; Ow S. Y. ; Amrun S. N. ; Sutarlie L. ; Ng L. F. P. ; Su X. Epitope-Functionalized Gold Nanoparticles for Rapid and Selective Detection of SARS-CoV-2 IgG Antibodies. ACS Nano 2021, 15 , 12286–12297. 10.1021/acsnano.1c04091.34133128
Huang X. ; Jain P. K. ; El-Sayed I. H. ; El-Sayed M. A. Gold Nanoparticles: Interesting Optical Properties and Recent Applications in Cancer Diagnostics and Therapy. Nanomedicine 2007, 2 , 681–693. 10.2217/17435889.2.5.681.17976030
Draz M. S. ; Shafiee H. Applications of Gold Nanoparticles in Virus Detection. Theranostics 2018, 8 , 1985–2017. 10.7150/thno.23856.29556369
Hall W. P. ; Ngatia S. N. ; Van Duyne R. P. LSPR Biosensor Signal Enhancement Using Nanoparticle-Antibody Conjugates. J. Phys. Chem. C 2011, 115 , 1410–1414. 10.1021/jp106912p.
Mayer K. M. ; Hafner J. H. Localized Surface Plasmon Resonance Sensors. Chem. Rev. 2011, 111 , 3828–3857. 10.1021/cr100313v.21648956
Li M. ; Cushing S. K. ; Wu N. Plasmon-Enhanced Optical Sensors: A Review. Analyst 2015, 140 , 386–406. 10.1039/C4AN01079E.25365823
Pramanik A. ; Gao Y. ; Patibandla S. ; Mitra D. ; McCandless M. G. ; Fassero L. A. ; Gates K. ; Tandon R. ; Chandra Ray P. The Rapid Diagnosis and Effective Inhibition of Coronavirus Using Spike Antibody Attached Gold Nanoparticles. Nanoscale Adv. 2021, 3 , 1588–1596. 10.1039/d0na01007c.34381960
Herizchi R. ; Abbasi E. ; Milani M. ; Akbarzadeh A. Current Methods for Synthesis of Gold Nanoparticles. Artif. Cells, Nanomed., Biotechnol. 2016, 44 , 596–602. 10.3109/21691401.2014.971807.25365243
Patterson E. I. ; Prince T. ; Anderson E. R. ; Casas-Sanchez A. ; Smith S. L. ; Cansado-Utrilla C. ; Solomon T. ; Griffiths M. J. ; Acosta-Serrano Á. ; Turtle L. ; Hughes G. L. Methods of Inactivation of SARS-CoV-2 for Downstream Biological Assays. J. Infect. Dis. 2020, 222 , 1462–1467. 10.1093/infdis/jiaa507.32798217
Lo C. W. ; Matsuura R. ; Iimura K. ; Wada S. ; Shinjo A. ; Benno Y. ; Nakagawa M. ; Takei M. ; Aida Y. UVC Disinfects SARS-CoV-2 by Induction of Viral Genome Damage without Apparent Effects on Viral Morphology and Proteins. Sci. Rep. 2021, 11 , 13804 10.1038/s41598-021-93231-7.34226623
Loveday E. K. ; Hain K. S. ; Kochetkova I. ; Hedges J. F. ; Robison A. ; Snyder D. T. ; Brumfield S. K. ; Young M. J. ; Jutila M. A. ; Chang C. B. ; Taylor M. P. Effect of Inactivation Methods on Sars-Cov-2 Virion Protein and Structure. Viruses 2021, 13 , 562 10.3390/v13040562.33810401
Karakuş E. ; Erdemir E. ; Demirbilek N. ; Liv L. Colorimetric and Electrochemical Detection of SARS-CoV-2 Spike Antigen with a Gold Nanoparticle-Based Biosensor. Anal. Chim. Acta 2021, 1182 , 338939 10.1016/j.aca.2021.338939.34602210
Guimarães T. F. ; Vital I. C. F. ; de Sousa E. G. R. ; Boniatti J. ; Bandini T. B. ; Carr O. ; Oliveira O. N. ; Shimizu F. M. ; da Fonseca L. B. ; Viçosa A. L. Investigation of Chloroquine Resinate Feasibility and In Vitro Taste Masking Evaluation for Pediatric Formulations. AAPS PharmSciTech 2022, 23 , 69 10.1208/s12249-022-02219-7.35112208
Bhattarai K. R. ; Kim H. R. ; Chae H. J. Compliance with Saliva Collection Protocol in Healthy Volunteers: Strategies for Managing Risk and Errors. Int. J. Med. Sci. 2018, 15 , 823–831. 10.7150/ijms.25146.30008593
Peters R. J. B. ; van Bemmel G. ; Milani N. B. L. ; den Hertog G. C. T. ; Undas A. K. ; van der Lee M. ; Bouwmeester H. Detection of Nanoparticles in Dutch Surface Waters. Sci. Total Environ. 2018, 621 , 210–218. 10.1016/j.scitotenv.2017.11.238.29179077
Zhang L. ; Mazouzi Y. ; Salmain M. ; Liedberg B. ; Boujday S. Antibody-Gold Nanoparticle Bioconjugates for Biosensors: Synthesis, Characterization and Selected Applications. Biosens. Bioelectron. 2020, 165 , 112370 10.1016/j.bios.2020.112370.32729502
Liao H. ; Hafner J. H. Gold Nanorod Bioconjugates. Chem. Mater. 2005, 17 , 4636–4641. 10.1021/cm050935k.
Harron D. W. G. Technical Requirements for Registration of Pharmaceuticals for Human Use: The ICH Process. In The Textbook of Pharmaceutical Medicine; Blackwell Publishing Ltd: Oxford, U.K., 2013; Vol. 1994 , pp 447–460. 10.1002/9781118532331.ch23.
Johnson P. B. ; Christy R. W. Optical Constants of the Noble Metals. Phys. Rev. B 1972, 6 , 4370–4379. 10.1103/PhysRevB.6.4370.
Baidya S. ; Hassan A. M. A Computational Study of COVID-19 Detection Using Colorimetric Plasmonic Sensors. In 2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI); IEEE, 2021; pp 1731–1732. 10.1109/APS/URSI47566.2021.9704523.
Minopoli A. ; Scardapane E. ; Acunzo A. ; Campanile R. ; Della Ventura B. ; Velotta R. Analysis of the Optical Response of a SARS-CoV-2-Directed Colorimetric Immunosensor. AIP Adv. 2021, 11 , 065319 10.1063/5.0050570.
Ansar S. M. ; Chakraborty S. ; Kitchens C. L. PH-Responsive Mercaptoundecanoic Acid Functionalized Gold Nanoparticles and Applications in Catalysis. Nanomaterials 2018, 8 , 339 10.3390/nano8050339.29772775
Raman A. ; Jaime C. ; Puntes V. F. Domain Formation and Conformational Changes in Gold Nanoparticle Conjugates Studied Using DPD Simulations. Langmuir 2017, 33 , 14502–14512. 10.1021/acs.langmuir.7b03318.29199832
Fujiwara K. ; Watarai H. ; Itoh H. ; Nakahama E. ; Ogawa N. Measurement of Antibody Binding to Protein Immobilized on Gold Nanoparticles by Localized Surface Plasmon Spectroscopy. Anal. Bioanal. Chem. 2006, 386 , 639–644. 10.1007/s00216-006-0559-2.16823566
Guvenc B. ; Yesilay G. ; Yavuz Y. ; Yilmaz B. ; Culha M. Electrophysiological Effects of Polyethylene Glycol Modified Gold Nanoparticles on Mouse Hippocampal Neurons. Heliyon 2020, 6 , e05824 10.1016/j.heliyon.2020.e05824.33426332
Bravin C. ; Amendola V. Plasmonic Absorption in Antigen-Induced Aggregated Gold Nanoparticles: Toward a Figure of Merit for Optical Nanosensors. ACS Appl. Nano Mater. 2022, 5 , 578–586. 10.1021/acsanm.1c03380.
Tsai T. C. ; Liu C. W. ; Wu Y. C. ; Ondevilla N. A. P. ; Osawa M. ; Chang H. C. In Situ Study of EDC/NHS Immobilization on Gold Surface Based on Attenuated Total Reflection Surface-Enhanced Infrared Absorption Spectroscopy (ATR-SEIRAS). Colloids Surf., B 2019, 175 , 300–305. 10.1016/j.colsurfb.2018.12.009.
Lee J. ; Zhou H. ; Lee J. Small Molecule Induced Self-Assembly of Au Nanoparticles. J. Mater. Chem. 2011, 21 , 16935 10.1039/c0jm04268d.
Busch R. T. ; Karim F. ; Weis J. ; Sun Y. ; Zhao C. ; Vasquez E. S. Optimization and Structural Stability of Gold Nanoparticle-Antibody Bioconjugates. ACS Omega 2019, 4 , 15269–15279. 10.1021/acsomega.9b02276.31552374
Ngunjiri J. N. ; Stark D. J. ; Tian T. ; Briggman K. A. ; Garno J. C. Immobilization of Proteins on Carboxylic Acid Functionalized Nanopatterns. Anal. Bioanal. Chem. 2013, 405 , 1985–1993. 10.1007/s00216-012-6621-3.23239182
Sam S. ; Touahir L. ; Salvador Andresa J. ; Allongue P. ; Chazalviel J. N. ; Gouget-Laemmel A. C. ; De Villeneuve C. H. ; Moraillon A. ; Ozanam F. ; Gabouze N. ; Djebbar S. Semiquantitative Study of the EDC/NHS Activation of Acid Terminal Groups at Modified Porous Silicon Surfaces. Langmuir 2010, 26 , 809–814. 10.1021/la902220a.19725548
Endo T. ; Kerman K. ; Nagatani N. ; Hiepa H. M. ; Kim D. K. ; Yonezawa Y. ; Nakano K. ; Tamiya E. Multiple Label-Free Detection of Antigen-Antibody Reaction Using Localized Surface Plasmon Resonance-Based Core-Shell Structured Nanoparticle Layer Nanochip. Anal. Chem. 2006, 78 , 6465–6475. 10.1021/ac0608321.16970322
Meng L. ; Yin J. H. ; Yuan Y. ; Xu N. 11-Mercaptoundecanoic Acid Capped Gold Nanoclusters as a Fluorescent Probe for Specific Detection of Folic Acid: Via a Ratiometric Fluorescence Strategy. RSC Adv. 2018, 8 , 9327–9333. 10.1039/c8ra00481a.35541875
Garcia-Hernandez C. ; Freese A. K. ; Rodriguez-Mendez M. L. ; Wanekaya A. K. In Situ Synthesis, Stabilization and Activity of Protein-Modified Gold Nanoparticles for Biological Applications. Biomater. Sci. 2019, 7 , 2511–2519. 10.1039/c9bm00129h.30968104
Manson J. ; Kumar D. ; Meenan B. J. ; Dixon D. Polyethylene Glycol Functionalized Gold Nanoparticles: The Influence of Capping Density on Stability in Various Media. Gold Bull. 2011, 44 , 99–105. 10.1007/s13404-011-0015-8.
Moitra P. ; Alafeef M. ; Dighe K. ; Frieman M. B. ; Pan D. Selective Naked-Eye Detection of SARS-CoV-2 Mediated by N Gene Targeted Antisense Oligonucleotide Capped Plasmonic Nanoparticles. ACS Nano 2020, 14 , 7617–7627. 10.1021/acsnano.0c03822.32437124
da Silva P. B. ; da Silva J. R. ; Rodrigues M. C. ; Vieira J. A. ; de Andrade I. A. ; Nagata T. ; Santos A. S. ; da Silva S. W. ; da Rocha M. C. O. ; Báo S. N. ; Moraes-Vieira P. M. ; Proença-Modena J. ; Angelim M. K. C. ; de Souza G. F. ; Muraro S. P. ; de Barros A. L. B. ; de Souza Martins G. A. ; Ribeiro-Dias F. ; Machado G. ; Fessel M. R. ; Chudzinski-Tavassi A. M. ; Ronconi C. M. ; Gonçalves D. ; Curi R. ; Oliveira O. N. ; Azevedo R. B. Detection of SARS-CoV-2 Virus via Dynamic Light Scattering Using Antibody-Gold Nanoparticle Bioconjugates against Viral Spike Protein. Talanta 2022, 243 , 123355 10.1016/j.talanta.2022.123355.35272155
Brandl D. W. ; Mirin N. A. ; Nordlander P. Plasmon Modes of Nanosphere Trimers and Quadrumers. J. Phys. Chem. B 2006, 110 , 12302–12310. 10.1021/jp0613485.16800552
Raikar U. S. ; Tangod V. B. ; Mastiholi B. M. ; Fulari V. J. Fluorescence Quenching Using Plasmonic Gold Nanoparticles. Opt. Commun. 2011, 284 , 4761–4765. 10.1016/j.optcom.2011.05.038.
Zhang Y. ; Fei W.-W. ; Jia N.-Q. A Facile Method for the Detection of DNA by Using Gold Nanoparticle Probes Coupled with Dynamic Light Scattering. Nanoscale Res. Lett. 2012, 7 , 564 10.1186/1556-276X-7-564.23046983
Paulovich F. V. ; Moraes M. L. ; Maki R. M. ; Ferreira M. ; Oliveira O. N. Jr. ; de Oliveira M. C. F. Information Visualization Techniques for Sensing and Biosensing. Analyst 2011, 136 , 1344 10.1039/c0an00822b.21283854
Neto M. P. ; Paulovich F. V. Explainable Matrix - Visualization for Global and Local Interpretability of Random Forest Classification Ensembles. IEEE Trans. Visual Comput. Graphics 2021, 27 , 1427–1437. 10.1109/TVCG.2020.3030354.
Ihsanullah I. ; Bilal M. ; Naushad M. Coronavirus 2 (SARS-CoV-2) in Water Environments: Current Status, Challenges and Research Opportunities. J. Water Process Eng. 2021, 39 , 101735 10.1016/j.jwpe.2020.101735.
Woolf M. S. ; Dignan L. M. ; Scott A. T. ; Landers J. P. Digital Postprocessing and Image Segmentation for Objective Analysis of Colorimetric Reactions. Nat. Protoc. 2021, 16 , 218–238. 10.1038/s41596-020-00413-0.33299153
Brinati D. ; Campagner A. ; Ferrari D. ; Locatelli M. ; Banfi G. ; Cabitza F. Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study. J. Med. Syst. 2020, 44 , 135 10.1007/s10916-020-01597-4.32607737
| 36454041 | PMC9728479 | NO-CC CODE | 2022-12-08 23:18:58 | no | ACS Appl Mater Interfaces. 2022 Dec 1;:acsami.2c15407 | utf-8 | ACS Appl Mater Interfaces | 2,022 | 10.1021/acsami.2c15407 | oa_other |
==== Front
IEEE J Biomed Health Inform
IEEE J Biomed Health Inform
0047701
JBHI
IJBHA9
Ieee Journal of Biomedical and Health Informatics
2168-2194
2168-2208
IEEE
35947565
10.1109/JBHI.2022.3197910
JBHI-00915-2022
Article
Gradient Boosting Machine and Efficient Combination of Features for Speech-Based Detection of COVID-19
https://orcid.org/0000-0002-7964-7485
Dash Tusar Kanti Dash Tusar Kanti [email protected]
https://orcid.org/0000-0002-4385-0975
Chakraborty Chinmay Chakraborty Chinmay [email protected]
https://orcid.org/0000-0002-5516-6978
Mahapatra Satyajit Mahapatra Satyajit [email protected]
https://orcid.org/0000-0002-3555-5685
Panda Ganapati Panda Ganapati [email protected]
(Corresponding author: Chinmay Chakraborty.)
Electronics and Communications Engineering C V Raman Global University 215699 Bhubaneswar 752054 India
Electronics and Communication Engineering Birla Institute of Technology 28698 Mesra 835215 India
School of Electrical and Electronics Engineering VIT Bhopal University 571681 Bhopal 466114 India
11 2022
10 8 2022
26 11 53645371
15 4 2022
23 6 2022
20 7 2022
06 8 2022
07 11 2022
2022
IEEE
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In recent times, speech-based automatic disease detection systems have shown several promising results in biomedical and life science applications, especially in the case of respiratory diseases. It provides a quick, cost-effective, reliable, and non-invasive potential alternative detection option for COVID-19 in the ongoing pandemic scenario since the subject's voice can be remotely recorded and sent for further analysis. The existing COVID-19 detection methods including RT-PCR, and chest X-ray tests are not only costlier but also require the involvement of a trained technician. The present paper proposes a novel speech-based respiratory disease detection scheme for COVID-19 and Asthma using the Gradient Boosting Machine-based classifier. From the recorded speech samples, the spectral, cepstral, and periodicity features, as well as spectral descriptors, are computed and then homogeneously fused to obtain relevant statistical features. These features are subsequently used as inputs to the Gradient Boosting Machine. The various performance matrices of the proposed model have been obtained using thirteen sound categories' speech data collected from more than 50 countries using five standard datasets for accurate diagnosis of respiratory diseases including COVID-19. The overall average accuracy achieved by the proposed model using the stratified k-fold cross-validation test is above 97%. The analysis of various performance matrices demonstrates that under the current pandemic scenario, the proposed COVID-19 detection scheme can be gainfully employed by physicians.
COVID-19 detection
LightGBM
speech classification
feature fusion
health informatics
==== Body
pmcI. Introduction
Recent developments in speech signal processing have shown numerous clinical applications for non-invasive diagnosis of diseases which helps in effective remote health monitoring and remote healthcare facilities [1], [2], [3], [4]. In the current Coronavirus Disease 2019 (COVID-19) pandemic scenario, this speech-based remote health monitoring system can play a crucial role. According to the World Health Organization data, more than 579 million people have suffered including six million deaths reported till August 8, 2022, due to COVID-19 [5]. The standard and reliable test of COVID-19 is the Reverse transcription-polymerase chain reaction test (RT-PCR) test which is expensive (US $125 per test package, and over $15,000 to set up a processing lab) and also time-consuming (4–6 hours of processing time, and a turn-around of 2–4 days, including shipping) [6]. To deal with this challenging situation, there is a huge requirement for large-scale testing for isolating infected individuals and contact tracing [7]. Under this scenario, speech-based COVID-19 detection (CD) is one of the simplest, safest as well cost-effective methods [8].
Several temporal and spectral acoustic features of subjects have been used as inputs to a random forest model for the classification of speech into nine categories such as shallow and deep breathing, shallow and heavy cough, sustained vowel phonation (/o/, /e/, /a/), and normal and fast counting [9]. Detection accuracy of 66.74 % is reported in this study. In [10], respiratory sounds such as cough and breathing have been employed to classify COVID-19 from asthma using 733-dimensional features including 477-dimensional handcrafted features and 256-dimensional VGGNet-based features. The Logistic Regression-based classifier is used to provide an area under the receiver operator characteristic curve (ROC-AUC) of above 80%. The CD from online available speech data has been carried out using phoneme level analysis, Mel filter bank features, and the SVM classifier. It is reported that an accuracy of 88.6% is achieved from a limited number of 19 speakers [11]. An automated machine learning-based COVID-19 classification model is developed using glottal, prosodic, and spectral features from short-duration speech segments [12]. The proposed model yields a classification accuracy of 80%. Modified cepstral features are extracted from two speech databases and fed to the support vector machine (SVM) classifiers for CD and maximum accuracy of 85% is obtained [13]. Transfer learning-based deep neural network classifiers are used for CD for cough, breath, and speech with a ROC-AUC of 0.982, 0.942, and 0.923 respectively [14]. Several machine learning-based algorithms are analyzed for the mobile health solutions of CD and it is observed that the SVM technique provides the highest accuracy of 97% for the Coswara database [15]. A mobile application is developed for CD by combining the symptoms checker with voice, breath, and cough signals for robust performance on openly sourced and noisy data sets by using deep CNN and gradient boosting [16].
Even though several speech-based CD methods have been proposed, there is still scope for improvement in terms of detection accuracy, computational complexity as well as testing on multiple datasets in different categories of speech. As the early CD is essential, the higher and more reliable accuracy of detection is very important which would drastically reduce the spread and medical emergency of the detection. Additionally, many researchers have focused on using chest X-rays for CD using several image processing techniques [17], [18], [19], [20], [21]. Although it achieved superior performance in terms of accuracy but acquisition of chest X-rays is a cumbersome task. A physical visit, a well-trained technician for successful data acquisition, and a medical practitioner are all required. In light of these considerations, the current research focuses on the development of an improved CD system based on speech. For efficient extraction of information from the speech samples, an effective combination of speech features is used in this paper along with Light Gradient Boosting Machine which was proposed by Microsoft in 2016 [22]. It provides improved training performance requiring minimum memory, and parallel processing ability as well as handling large-scale data compared to the traditional machine learning algorithms. In recent years, it has been employed for genomics data analysis [23], speech processing [16], image processing [24], arrhythmia detection [25], and others. Because of the associated advantages, the gradient boosting technique is chosen in the current implementation to achieve better classification performance. The main research contributions of the paper are listed below: • Application of intelligent preprocessing techniques to bring the speech quality of the different real-life recorded speech to equal acoustic levels.
• Extraction of spectral, cepstral, and periodicity features at frame level for efficient combination of high dimensional relevant audio features at sample level to accurately detect several respiratory diseases including COVID-19 and Asthma.
• Development of Gradient Boosting Machine as a classifier and comparison of the detection performance matrices of the proposed method with those obtained from the standard methods using five datasets in thirteen different categories.
• Assessment of the generalization ability of the proposed model which can be presented as a clinical application method wherein the model is trained with a large number of speech samples from the cough category of multiple datasets. Later it can predict the condition of the patient from his/her cough sound.
The paper is organized into four sections with Section I dealing with the introduction, literature review, motivations, and objectives of the investigation. The details of the materials and methods employed are dealt with in section II. Section III contains an analysis of results, and contributions in terms of research findings. The outcome of the research, limitations, and future research scope are presented in section IV.
II. Material and Methods
The block diagram of the proposed speech-based COVID-19 detection scheme is presented in Fig. 1 consisting of the following steps: dataset collection, preprocessing and features extraction, scaling of features, classification model training, and validation, and performance evaluation.
Fig. 1. Block diagram of the proposed speech-based COVID-19 detection scheme.
A. Datasets
Five datasets have been used to evaluate the performance of the suggested model in this study. These are: Coswara (Dataset-1) [9], Crowdsourced respiratory by the University of Cambridge (Dataset-2) [10], Virufy (Dataset-3) [26], recorded interviews from online platforms in telephone quality speech (Dataset-4) [11], Coughvid (Dataset-5) [7]. Out of these, data set-2 is used for both binary (COVID-19 positive, and healthy) and multi-class classification (COVID-19 positive, Asthma positive, and healthy) whereas datasets-1,3,4,5 are used for the binary classification task. These datasets contain speech samples of subjects from more than 50 countries. The dataset preparation follows a standard technique as shown in Fig. 2. Due to the deadly spreading nature of the COVID-19, the speech samples are recorded for most of the speech datasets in the online mode either by using mobile or web-based applications [7], [9], [10], [11], [26]. Along with the audio samples the COVID-19 status, location, gender, age, and the health conditions of the patients are also stored. The brief details of these five datasets are listed in Table I. A total of 4178 speech samples have been used in the simulation study. Complete details of these datasets are given in supplementary information S1.
B. Preprocessing
Speech preprocessing is critical to the overall success of developing a robust and efficient speech recognition system [27]. When speech is recorded by different users in different environments, then the speech quality varies drastically in one category within the dataset as well as across different datasets [28]. The background noise level significantly affects the overall performance of the speech recognition system [29], [30]. For highly non-stationary situations, the noise level is computed using the noise estimation algorithm [31]. To evaluate the effect of preprocessing, the variation in noise level and coefficient of variation are plotted in Figures 3 and 4 for two cases before and after preprocessing. The coefficient of variation measures the variation in the noise level by calculating the ratio between the standard deviation and mean of the estimated noise levels for one class [32]. For the noise level estimation, the cough category sound is used for dataset-1,2,3,5 and complete sentence sounds for dataset-4. The steps involved in preprocessing are mentioned below.
TABLE I details of the Five Experimental Datasets Used in the Simulation
Name of Dataset Categories Number of speech samples in each class
Dataset-1 [9] Breathing-deep 50 N + 47 P
Breathing-shallow 49 N + 47 P
Cough-heavy 50 N + 47 P
Cough-shallow 50 N + 47 P
Counting-Fast 17 N + 42 P
Counting-Normal 17 N + 42 P
Vowel-/o/ 50 N + 47 P
Vowel-/e/ 50 N + 47 P
Vowel-/a/ 50 N + 47 P
Dataset-2 [10] Breathing 64 N + 46P + 167 AP
Cough 200 N +47 P + 112 AP
Dataset-3 [26] Cough 73 N + 48 P
Dataset-4 [11] Spoken Sentence 237 N + 465 P
Dataset-5 [7] Cough 1155 N + 1155 P
* The classes are named as COVID-19 Positive (P), COVID-19 Negative or healthy (N), and Asthma Positive (AP)
Fig. 2. Flowchart for the dataset preparation and classification.
Fig. 3. Change in the noise level and between positive and negative class.
Fig. 4. Change in Coefficient of variation (CV) of noise level between positive and negative class.
1) Low Pass Filtering
The sampling frequency of speech signals is different for different datasets. However, significant information is found within the 8 kHz bandwidth [33]. It is also evident from Fig. 5, where the time-frequency representation of one cough signal of dataset-2 is plotted using the spectrogram. To remove the unwanted signal components which are not associated with human speech, all the audio signals are passed through a low pass filter of 10 kHz. To maintain a uniform sampling rate and to extract the same number of features for each frame, all speech signals are resampled at the maximum available sampling frequency (48 kHz) of all the datasets.
Fig. 5. Spectrogram of the cough signal from dataset-2.
2) Speech Enhancement
The multi-band spectral subtraction approach has been employed to denoise the speech samples of all five datasets [34]. This is a simple and effective method for denoising signals affected by colored noises where spectral subtraction is performed separately at different frequency bands.
3) Voice Activity Detection and Dynamic Level Control
To separate the voiced frames from the unvoiced frames, a simple short-term energy-based voice activity detection (VAD) algorithm is used. The voiced frames are then passed through a Dynamic Level Controller (DLC). It is made up of an expander and a compressor, with the expander boosting low signal levels and the compressor lowering peak levels [35].
C. Features Extraction
In this section, the details of the audio features extraction techniques used in the investigation are dealt with. At the frame and sample levels, numerous audio features are extracted in the frequency, structural, statistical, and temporal domains. The complete recording of a single user in one category comprises one sample, while a frame is a subset of the entire audio data found in a sample. Considering there is ’n' number of frames present in each sample, the details of the frame-level features are described below. The features are named as f(serial number of the feature) such as f1 to f5701. • Spectral Features — The speech signal is a non-stationary signal but the properties remain constant over fixed time intervals of 10–30 ms. The short-time spectral features are obtained by converting the time domain signal into the frequency domain by applying different Transform techniques. These features provide information about spectral information which plays an important role in speech recognition [36]. In this work, the hamming window is chosen as it provides less spectral leakage and the side lobes of this window are lower than the others [37]. A window size of 25 msec duration with 50% overlapping between two successive frames has been considered. The spectral features extracted are: Linear Spectrum (n×512), Mel Spectrum (n×32), Bark Spectrum (n×32), and Equivalent Rectangular Bandwidth (ERB) Spectrum (n×44). Therefore, the total dimension of spectral features is (n×620).
• Cepstral Features — The cepstral features help in extracting relevant speech information for speech emotion recognition tasks by using filter banks based on human speech perception [13]. The cepstral features are Mel-frequency cepstral coefficients (MFCC), MFCC Delta, MFCC Delta Delta, Gammatone cepstral coefficients (GTCC), GTCC Delta, GTCC Delta Delta, each of dimension (n×13). Therefore, the total dimension of cepstral features is (n×78)
• Spectral Descriptors — These features extract statistical information from the lengthy spectral features. These features are widely used in speaker, music, mood recognition, and classification tasks [38]. The spectral descriptors used are: Centroid, Crest, Decrease, Entropy, Flatness, Flux, Kurtosis, Roll-off Point, Skewness, Slope, and Spread, each having dimension (n×1). The total dimension of spectral descriptors is (n×11).
• Periodicity Features —These features provide important time-domain information of speech which helps in monaural speech analysis [39]. The features used are: Pitch (n×1), and Harmonic Ratio (n×1).
For this purpose, MATLAB-based audioFeatureExtractor is used [40], [41]. The fusion of spectral features, cepstral features, spectral descriptors, and periodicity features yields an n* 712-dimensional feature vector for each speech sample. As the frame numbers vary for each sample, so training in machine learning becomes difficult. Therefore, in this work, the statistical measures are computed at the sample level and it provides a fixed length of features for each sample. To extract statistical distributions at the sample level, several statistical features are extracted from the frame-level features [10]. The sample level features are: mean (f1:f712), median (f713:f1424), RMS (root-mean-square) (f1425:f2136), maximum (f2137:f2848), minimum (f2849:f3560), quartile (1st and 3 rd quartile, interquartile range) (f3561:f3563), standard deviation (SD) (f3564:f4275), skewness (f4276:f4987), kurtosis (f4988:f5699) of all frame-level features. Also, the Zero crossing rate (ZCR) (f5700), and Short-time energy (STE) (f5701) are calculated sample-wise. Each combined feature vector is the concatenation of the sample level features and it is a 5701-dimensional feature vector. Outliers in the high-dimensional feature vector can have an impact on the learning algorithm's performance. As a result, feature scaling is an important preprocessing step. The robust scaler removes the median and scales the data according to the quantile range, removing outliers from the features [42].
D. LightGBM (LGM)
The LGM is an effective gradient boosting decision tree with gradient-based one-side sampling (GOSS) and exclusive feature bundling (EFB) to increase computational efficiency without affecting the accuracy [22]. The steps involved in LGM modeling are: (i) defining the loss function, (ii) performing the GOSS sampling, and identification of the optimal segmentation point using a histogram-based algorithm, (iii) calculation of feature dimension by the EFB method, (iv) performing the leaf-wise algorithm to combine the samples to fit residuals, and (v) splitting the nodes based on the objective function and generate a decision tree.
Let us consider X as the input feature vector and Y as the class labels. The aim of LGM is to determine the approximation function \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\widehat{F}(x)$\end{document} so that the loss function \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$(L(y,F(x)))$\end{document} gets minimized [43]. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{} \begin{align*} \widehat{F}(x)=\underset{F}{argmin}\;E_{xy}\left[L(y,F(x)))\right] \tag{1} \end{align*}\end{document}
The final LGM model (F\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$_{M}$\end{document}(X)) is formed using M decision trees such that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{} \begin{align*} F_{M}(X)=\sum\limits _{m=1}^{M}F_{m}(X)\tag{2} \end{align*}\end{document}
The LGM is trained in an additive form at step m and can be expressed as: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{} \begin{align*} \tau _{m}&=\sum\limits _{i-1}^{n}\;L(y_{i}\;,\;F_{m-1}(x_{i})+F_{m}(x_{i}))\\ & \cong \;\sum\limits _{i=1}^{n}\;\left(g_{i}F_{m}(x_{i})\right)\;+ \frac{1}{2}h_{i}F_{m}^{2}(x_{i})) \tag{3} \end{align*}\end{document}
Where, g \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$_{i}$\end{document} and h\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$_{i\ }$\end{document} represent the first and second-order gradient statistics of the loss function. By denoting the sample set I\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$_{j}$\end{document}of leaf \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$j(1\leq j\leq J)$\end{document}(3) can be written as: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{} \begin{align*} \tau _{m}=\sum\limits _{j=1}^{J}\;\left(\left(\sum\limits _{i\in I_{j}}g_{i}\right)\;w_{j}+ \frac{1}{2}\left(\sum\limits _{i\in I_{j}}h_{i}+\lambda \right)w_{j}^{2}\right) \tag{4} \end{align*}\end{document}
The optimal leaf weight scores of each leaf node \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$(w_{j}^\ast)$\end{document} is calculated as: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{} \begin{align*} w_{j}^\ast =- \frac{{ \sum\nolimits _{i\in I_{j}}}g_{i}}{{ \sum\nolimits _{i\in I_{j}}}\;h_{i}+\lambda } \tag{5} \end{align*}\end{document}
Let, I\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$_{L}$\end{document} and I\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$_{R}$\end{document} are the sample sets of the left and right branches, respectively. The leaf weight, the regular penalty factor, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\lambda$\end{document} is used as a smoothing parameter in calculating gain in the process of splitting points. The objective function after adding the split is then calculated as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{} \begin{align*} G=& \frac{1}{2}\left(\left| \frac{\left({ \sum\nolimits _{i\in I_{L}}}g_{i}\right)^{2}}{\left({ \sum\nolimits _{i\in I_{L}}}h_{i}+\lambda \right)}+ \frac{\left({ \sum\nolimits _{i\in I_{R}}g_{i}}\right)^{2}}{\left({ \sum\nolimits _{i\in I_{R}}h_{i}+\lambda }\right)} \right. \right. \\ &\quad \left.\left. + \frac{\left({ \sum\nolimits _{i\in I}g_{i}}\right)^{2}}{\left({ \sum\nolimits _{i\in I}h_{i}+\lambda }\right)}\right|\right) \tag{6} \end{align*}\end{document}
In the conventional gradient boosting technique, the tree grows horizontally, while in LGM the tree grows vertically which makes it an efficient tool for processing large-scale data and features [43]. The GOSS technique of LGM effectively selects the input features with larger gradients and removes the features with smaller gradient values. This works as feature reduction in the current implementation where the input feature size is relatively higher and thereby, it increases the efficiency of the detection model.
III. Results and Discussions
The performance of the proposed model is assessed for two tasks, (I) binary classification task to predict the speech samples as COVID-19 positive or negative, and (II) multiclass classification task to predict COVID-19 positive, Asthma positive, and healthy speech samples. To perform this, the speech samples are passed through the additional preprocessing blocks such as low pass filtering, speech enhancement, voice activity detection, and dynamic level control. Then a total of 5701 features are extracted from each sample. Here, the preprocessing block is a part of the feature extraction. These features are combined with an LGM classifier and three baseline classifiers such as Random Forest (RF) [9], SVM [10], [11], and K-Nearest Neighbor (KNN) [44] used for the speech classification task. For the development of the classification model five-fold stratified cross-validation scheme is employed. Standard performance measures as reported in [45] such as Classification Accuracy (CA), F-2 Score (F-2), Precision (PR), Recall (RC), and area under the curve (AUC), are employed in this study. The details of the performance measures are described in supplementary information S2. Grid search is used to find the optimal parameters of the classifiers. These parameters are listed in supplementary information S3.
A. Performance Evaluation as a Binary Classification Task
The comparative study between the performance of LGM, SVM, RF, and KNN classifiers for binary classification task are presented in Tables II and III. The LGM classifier provides an average accuracy of 0.978, an F-2 Score of 0.979, and an AUC of 0.976 across all the categories in the five datasets. The average accuracy, F-2 Score, and AUC of the SVM classifier are 0.749, 0.717, and 0.712, respectively. Similarly, for the RF classifier, the average accuracy, F-2 score, and AUC are found to be 0.967, 0.966, and 0.963, respectively. For the KNN classifier, the values are 0.753, 0.745, and 0.728. The results show that the LGM classifier performs better on the high-dimensional features than the SVM, RF, and KNN classifiers.
TABLE II performance Comparison for Dataset-1 Using 5701 Feature Vector for Binary Classification
Category Evaluation Measures LGM SVM RF KNN
Breathing Deep (D-1) CA 0.969 0.557 0.969 0.691
F-2 0.969 0.502 0.969 0.687
PR 0.969 0.641 0.969 0.697
RC 0.969 0.557 0.969 0.691
AUC 0.968 0.543 0.968 0.687
Breathing Shallow (D-1) CA 0.99 0.5 0.948 0.604
F-2 0.99 0.421 0.948 0.602
PR 0.99 0.258 0.95 0.606
RC 0.99 0.5 0.948 0.604
AUC 0.989 0.489 0.947 0.602
Cough Heavy (D-1) CA 0.979 0.598 0.969 0.773
F-2 0.979 0.555 0.969 0.773
PR 0.979 0.695 0.969 0.773
RC 0.979 0.598 0.969 0.773
AUC 0.979 0.586 0.968 0.772
Cough Shallow (D-1) CA 0.979 0.701 0.969 0.701
F-2 0.979 0.684 0.969 0.696
PR 0.98 0.756 0.971 0.719
RC 0.979 0.701 0.969 0.701
AUC 0.978 0.693 0.968 0.704
Vowel-/a/ (D-1) CA 0.99 0.505 0.959 0.557
F-2 0.99 0.427 0.958 0.538
PR 0.99 0.263 0.962 0.565
RC 0.99 0.505 0.959 0.557
AUC 0.99 0.49 0.957 0.548
Vowel-/e/ (D-1) CA 0.99 0.515 0.959 0.742
F-2 0.99 0.434 0.959 0.737
PR 0.99 0.266 0.959 0.759
RC 0.99 0.515 0.959 0.742
AUC 0.99 0.5 0.958 0.737
Vowel-/o/ (D-1) CA 0.969 0.866 0.979 0.68
F-2 0.969 0.866 0.979 0.678
PR 0.969 0.866 0.979 0.685
RC 0.969 0.866 0.979 0.68
AUC 0.969 0.866 0.979 0.677
Counting Normal (D-1) CA 0.966 0.712 0.966 0.712
F-2 0.966 0.659 0.966 0.687
PR 0.966 0.507 0.966 0.668
RC 0.966 0.712 0.966 0.712
AUC 0.958 0.5 0.958 0.552
Counting Fast (D-1) CA 0.949 0.712 0.932 0.644
F-2 0.949 0.669 0.932 0.607
PR 0.949 0.656 0.932 0.492
RC 0.949 0.712 0.932 0.644
AUC 0.929 0.517 0.917 0.452
TABLE III performance Comparison for Dataset-2,3,4,5 Using 5701 Feature Vector for Binary Classification
Category (Dataset) Evaluation Measures LGM SVM RF KNN
Cough (D-2) CA 0.992 0.966 0.992 0.983
F2 0.992 0.966 0.992 0.982
PR 0.992 0.966 0.992 0.960
RC 0.992 0.966 0.992 0.957
AUC 0.986 0.955 0.980 0.940
Breathing (D-2) CA 0.982 0.909 0.964 0.818
F2 0.982 0.909 0.964 0.815
PR 0.982 0.91 0.964 0.824
RC 0.982 0.909 0.964 0.818
AUC 0.981 0.9 0.959 0.797
Cough (D-3) CA 0.969 0.965 0.942 0.793
F2 0.968 0.965 0.941 0.781
PR 0.969 0.965 0.947 0.823
RC 0.969 0.965 0.942 0.793
AUC 0.961 0.960 0.927 0.746
Sentence (D-4) CA 0.992 0.991 0.992 0.928
F2 0.992 0.991 0.992 0.928
PR 0.993 0.992 0.993 0.928
RC 0.992 0.991 0.992 0.928
AUC 0.999 0.988 0.999 0.985
Cough (D-5) CA 0.998 0.993 0.998 0.921
F2 0.998 0.993 0.998 0.922
PR 0.998 0.994 0.998 0.924
RC 0.998 0.993 0.998 0.921
AUC 0.999 0.985 0.999 0.971
B. Performance Evaluation as a Three-Class Classification Task
To further evaluate the prediction ability of the classifiers, an assessment of multi-class data has been carried out for dataset-2 contains samples of COVID-19 positive, Asthma positive, and healthy in the cough and breathing sound categories. The results are listed in Table IV. It is observed that the performance of the LGM classifier is superior in all the performance measures as compared to the SVM, RF, and KNN classifiers respectively. The ROC curves are two-dimensional plots that provide the relative trade-offs between the true positive and false-positive rates [45]. The ROC curves of dataset-2 in the cough category (binary and multi-class) are shown in Fig. 6 and Fig. 7 respectively. The proposed approach has a high true true-positive rate and a low false false-positive rate, according to the ROC curves. The AUC of the proposed model is 0.99, which is better in comparison to the RF, SVM, and KNN models. The proposed features with the additional preprocessing provide better results compared to standard features and classifiers.
TABLE IV performance Evaluation for Detection COVID-19 Positive, Negative and Asthma From Dataset-2 Using 5701 Feature Vectors
Category Evaluation Measures LGM SVM RF KNN
Cough CA 0.971 0.947 0.943 0.915
F-2 0.971 0.946 0.941 0.915
PR 0.973 0.949 0.946 0.914
RC 0.971 0.947 0.943 0.915
AUC 0.991 0.969 0.985 0.974
Breathing CA 0.981 0.89 0.963 0.854
F-2 0.981 0.889 0.963 0.851
PR 0.983 0.893 0.965 0.854
RC 0.981 0.89 0.963 0.854
AUC 0.999 0.949 0.994 0.918
Fig. 6. Comparison of ROC curves of different classifiers for multiclass classification in cough category of dataset-2.
Fig. 7. Comparison of ROC curves of different classifiers for binary classification in cough category of dataset-2.
C. Comparison With Baseline Models and Combined Datasets
A comparative analysis of the proposed model over the existing methods used in the five datasets are shown in Table V. The Improvement in the detection performance is mentioned in the last column. It is observed that the proposed model shows consistent performance across all the datasets as well as in the combined dataset. There is approximately 30%, 15%, 25%, 9%, and 20% minimum improvement in CD performance for datasets 1,2,3,4,5. For the assessment of the generalization ability of the proposed model, a combined dataset is prepared with the speech signals in the cough category from datasets 1,2,3,5. In the combined dataset, there is a total of 1528 samples from the healthy category, while 1344 samples are from the COVID-19 positive category. The performance of all four methods is evaluated and the results are listed in Table VI. It is observed that the proposed model shows the highest accuracy of 0.983 over the other three standard models. TABLE V comparative Analysis of the Overall Detection of Performance of Each of the Datasets
Classification task Name of the dataset Existing method LGM improvement
Binary Dataset-1 (cough) 0.67 0.98 0.30
Dataset-2 (cough) 0.80 0.97 0.17
Dataset-3 (cough) 0.73 0.98 0.25
Dataset-4 (spoken sentence) 0.88 0.98 0.09
Dataset-5 (cough) 0.77 0.98 0.20
Multiclass Dataset-2 (cough) 0.82 0.97 0.15
TABLE VI performance Comparison for Combined Dataset (Cough Category) Using 5701 Feature Vector
Evaluation Measures LGM SVM RF KNN
CA 0.983 0.922 0.971 0.858
F-2 0.983 0.922 0.971 0.858
PR 0.983 0.924 0.971 0.858
RC 0.983 0.922 0.971 0.858
AUC 0.989 0.972 0.982 0.932
The minimum CD performance of the proposed method is approximately 97 % across all sound categories, databases, and CV schemes. The proposed approach has a high true-positive rate and a low false-positive rate, according to the ROC curves. The AUC of the proposed model is 0.99, which is better in comparison to the RF, SVM, and KNN models. The proposed features with the additional preprocessing provide better results compared to standard features and classifiers.
D. Statistical Analysis of Classifier Models
The statistical analysis of the comparison of the performance of the LGM model with the standard machine learning-based models SVM, RF, and KNN over five datasets is listed in Table VII. For this purpose, the t-statistic value between the two classifiers is computed as mentioned in (7). \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{} \begin{align*} \begin{array}{l}t= \frac{c_{1}-c_{2}}{\sqrt{v_{1}^{2}+v_{2}^{2}}}\end{array} \tag{7} \end{align*}\end{document}
Where, the mean and variance of the 5-fold classification accuracy of classifier 1 and classifier 2 are denoted as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$c_{1}$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$c_{2}$\end{document}, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$v_{1}^{2}$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$v_{2}^{2}$\end{document} respectively [46]. Most of the t-values in Table VII are positive, which indicates the superior performance of the proposed model over the standard machine learning-based models.The above classification tasks, comparative, and statistical analysis results reveal the effectiveness of the proposed model with preprocessing and an efficient combination of audio features. The main reason for this is the use of various signal processing techniques such as low pass filtering, speech enhancement, voice activity detection, and dynamic level control have substantially helped in reducing the effects of various environments while recording the speech signal of subjects. Secondly, The use of feature fusion-based statistical features evaluated from frame-level speech signal to the LGM classifier has yielded enhanced detection accuracy which is a minimum of 9% more than that obtained by the reported standard methods. The detection model has been observed to be robust as it offers a consistent detection performance of 97% while testing with five different speech datasets.
TABLE VII comparison of T-Statistic of Proposed Model With Standard ML-Based Models
Dataset LGM vs SVM LGM vs RF LGM vs KNN
Breathing Deep (D-1) 2.95 0 1.90
Breathing Shallow (D-1) 4.01 0.96 4.02
Cough Heavy (D-1) 3.88 0.14 1.52
Cough Shallow (D-1) 3.50 0.21 1.64
Vowel-/a/(D-1) 6.09 0.31 2.71
Vowel-/e/(D-1) 6.02 0.36 2.50
Vowel-/o/(D-1) 1.52 -0.14 2.46
Counting Normal (D-1) 1.84 0 3.50
Counting Fast (D-1) 1.51 0.19 3.17
Cough (D-2) 1.11 0 0.25
Breathing (D-2) 1.69 0.36 1.63
Cough (D-3) 0.09 0.35 2.27
Sentence (D-4) 0.17 0 4.46
Cough (D-5) 1.38 0 8.35
IV. Conclusion
In the current study, a non-invasive and effective respiratory disease detection scheme is developed and tested for COVID-19 and Asthma. The major contributions of the investigation are the use of improved preprocessing techniques, an effective combination of spectral, cepstral, and periodicity features along with the implementation of gradient boosting machines for robust and consistent performance across multiple datasets. The proposed model can be used for early and fast automatic diagnosis of COVID-19 without the subject visiting a hospital as well as without the assistance of a medical professional. However, it is suggested that the detection scheme by the use of the proposed intelligent model can be verified by the medical professional before a prescription is initiated. It may be noted that the proposed detection scheme involves more computations and training time. There is still room to improve the method's computing complexity for faster implementations. The effective preprocessing techniques, as well as the combination of audio features can be further implemented and tested for other speech recognition tasks including emotion recognition, Parkinson's disease, and heart disease detection.
Acknowledgment
The authors express their gratitude to Professor Cecilia Mascolo, Department of Computer Science and Technology and Chancellor, Master, and Scholar of the University of Cambridge for sharing the speech database of COVID-19 [10].
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References
[1] I. Trancoso, J. Correia, F. Teixeira, B. Raj, and A. Abad, “Analysing speech for clinical applications,” in Proc. Int. Conf. Stat. Lang. Speech Process., 2018, pp. 3–6.
[2] C. Chakraborty and A. N. Abougreen, “Intelligent Internet of Things and advanced machine learning techniques for COVID-19,” EAI Endorsed Trans. Pervasive Health Technol., vol. 7 , no. 26 , pp. e1–e1, 2021.
[3] K. Rezaee, H. G. Zadeh, C. Chakraborty, M. R. Khosravi, and G. Jeon, “Smart visual sensing for overcrowding in COVID-19 infected cities using modified deep transfer learning,” IEEE Trans. Ind. Informat., to be published, doi: 10.1109/TII.2022.3174160.
[4] F. Piccialli, V. D. Somma, F. Giampaolo, S. Cuomo, and G. Fortino, “A survey on deep learning in medicine: Why, how and when?,” Inf. Fusion, vol. 66 , pp. 111–137, 2021.
[5] “World Health Organization/Diseases/Coronavirus disease (COVID-19),” Accessed: Aug. 8, 2022. [Online]. Available: https://www.who.int/emergencies/diseases/novel-coronavirus-2019
[6] K. Ramdas, A. Darzi, and S. Jain, “‘Test, re-test, re-test’: Using inaccurate tests to greatly increase the accuracy of COVID-19 testing,” Nat. Med., vol. 26 , no. 6 , pp. 810–811, 2020.32398878
[7] L. Orlandic, T. Teijeiro, and D. Atienza, “The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithms,” Sci. Data, Nature Publishing Group, vol. 8 , no. 1 , pp. 1–10, 2021. [Online]. Available: https://doi.org/10.1038/s41597-021-00937-4
[8] J. Han , “An early study on intelligent analysis of speech under COVID-19: Severity, sleep quality, fatigue, and anxiety,” in Proc. Interspeech, 2020, pp. 4946–4950. [Online]. Available: https://doi.org/10.21437/Interspeech.2020–2223
[9] N. Sharma , “Coswara A database of breathing, cough, and voice sounds for COVID-19 diagnosis,” in Proc. Interspeech, 2020, pp. 4811–4815. [Online]. Available: https://doi.org/10.21437/Interspeech.2020–2768
[10] C. Brown , “Exploring automatic diagnosis of COVID-19 from crowdsourced respiratory sound data,” in Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining, 2020, pp. 3474–3484. [Online]. Available: https://doi.org/10.1145/3394486.3412865
[11] K. V. S. Ritwik, S. B. Kalluri, and D. Vijayasenan, “COVID-19 patient detection from telephone quality speech data,” 2020, arXiv:2011.04299. [Online]. Available: http://arxiv.org/abs/2011.04299
[12] B. Stasak, Z. Huang, S. Razavi, D. Joachim, and J. Epps, “Automatic detection of COVID-19 based on short-duration acoustic smartphone speech analysis,” J. Healthcare Informat. Res., vol. 5 , no. 2 , pp. 201–217, 2021.
[13] T. K. Dash, S. Mishra, G. Panda, and S. C. Satapathy, “Detection of COVID-19 from speech signal using bio-inspired based cepstral features,” Pattern Recognit., vol. 117 , 2021, Art. no. 107999. [Online]. Available: https://doi.org/10.1016/j.patcog.2021.107999
[14] M. Pahar, M. Klopper, R. Warren, and T. Niesler, “COVID-19 detection in cough, breath and speech using deep transfer learning and bottleneck features,” Comput. Biol. Med., vol. 141 , 2022, Art. no. 105153.
[15] L. Verde, G. D. Pietro, A. Ghoneim, M. Alrashoud, K. N. Al-Mutib, and G. Sannino, “Exploring the use of artificial intelligence techniques to detect the presence of coronavirus COVID-19 through speech and voice analysis,” IEEE Access, vol. 9 , pp. 65750–65757, 2021.35256922
[16] A. Ponomarchuk , “Project achoo: A practical model and application for COVID-19 detection from recordings of breath, voice, and cough,” IEEE J. Sel. Topics Signal Process., vol. 16 , no. 2 , pp. 175–187, Feb. 2022.
[17] L. Sun , “Adaptive feature selection guided deep forest for covid-19 classification with chest CT,” IEEE J. Biomed. Health Informat., vol. 24 , no. 10 , pp. 2798–2805, Oct. 2020.
[18] V. Ravi, H. Narasimhan, C. Chakraborty, and T. D. Pham, “Deep learning-based meta-classifier approach for COVID-19 classification using CT scan and chest X-ray images,” Multimedia Syst., vol. 28 , no. 4 , pp. 1401–1415, 2021.
[19] H. K. Bhuyan, C. Chakraborty, Y. Shelke, and S. K. Pani, “COVID-19 diagnosis system by deep learning approaches,” Expert Syst., vol. 39 , no. 3 , 2022, Art. no. e12776.
[20] A. Gumaei , “A decision-level fusion method for COVID-19 patient health prediction,” Big Data Res., vol. 27 , 2022, Art. no. 100287.
[21] K. K. Wong, G. Fortino, and D. Abbott, “Deep learning-based cardiovascular image diagnosis: A promising challenge,” Future Gener. Comput. Syst., vol. 110 , pp. 802–811, 2020.
[22] G. Ke , “Lightgbm: A highly efficient gradient boosting decision tree,” Adv. Neural Inf. Process. Syst., vol. 30 , pp. 3149–3157, 2017.
[23] C. Chen, Q. Zhang, Q. Ma, and B. Yu, “LightGBM-PPI: Predicting protein-protein interactions through LightGBM with multi-information fusion,” Chemometrics Intell. Lab. Syst., vol. 191 , pp. 54–64, 2019.
[24] Z. Kostic and A. Jevremovic, “What image features boost housing market predictions?,” IEEE Trans. Multimedia, vol. 22 , no. 7 , pp. 1904–1916, Jul. 2020.
[25] D. Wang, Q. Meng, D. Chen, H. Zhang, and L. Xu, “Automatic detection of arrhythmia based on multi-resolution representation of ECG signal,” Sensors, vol. 20 , no. 6 , 2020, Art. no. 1579.
[26] G. Chaudhari , “Virufy: Global applicability of crowdsourced and clinical datasets for AI detection of COVID-19 from cough,” 2020, arXiv:2011.13320.
[27] A. Keerio, B. K. Mitra, P. Birch, R. Young, and C. Chatwin, “On preprocessing of speech signals,” Int. J. Signal Process., vol. 5 , no. 3 , pp. 216–222, 2009.
[28] C. Nass and K. M. Lee, “Does computer-synthesized speech manifest personality? Experimental tests of recognition, similarity-attraction, and consistency-attraction,” J. Exp. Psychol.: Appl., vol. 7 , no. 3 , pp. 171–181, 2001.11676096
[29] C. G. L. Prell and O. H. Clavier, “Effects of noise on speech recognition: Challenges for communication by service members,” Hear. Res., vol. 349 , pp. 76–89, 2017.27743882
[30] T. K. Dash, S. S. Solanki, and G. Panda, “Improved phase aware speech enhancement using bio-inspired and ANN techniques,” Analog Integr. Circuits Signal Process., vol. 102 , no. 3 , pp. 465–477, 2020.
[31] S. Rangachari and P. C. Loizou, “A noise-estimation algorithm for highly non-stationary environments,” Speech Commun., vol. 48 , no. 2 , pp. 220–231, 2006.
[32] L. D. Shriberg, J. R. Green, T. F. Campbell, J. L. Mcsweeny, and A. R. Scheer, “A diagnostic marker for childhood apraxia of speech: The coefficient of variation ratio,” Clin. Linguistics Phonetics, vol. 17 , no. 7 , pp. 575–595, 2003.
[33] O. C. Ai, M. Hariharan, S. Yaacob, and L. S. Chee, “Classification of speech dysfluencies with MFCC and LPCC features,” Expert Syst. With Appl., vol. 39 , no. 2 , pp. 2157–2165, 2012.
[34] S. Kamath and P. Loizou, “A multi-band spectral subtraction method for enhancing speech corrupted by colored noise,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., 2002, vol. 4 , pp. 44164–44164.
[35] D. Giannoulis, M. Massberg, and J. D. Reiss, “Digital dynamic range compressor design A tutorial and analysis,” J. Audio Eng. Soc., vol. 60 , no. 6 , pp. 399–408, 2012.
[36] D. Paul, M. Pal, and G. Saha, “Spectral features for synthetic speech detection,” IEEE J. Sel. Top. Signal Process., vol. 11 , no. 4 , pp. 605–617, Jun. 2017.
[37] K. Kumar, R. K. Aggarwal, and A. Jain, “A hindi speech recognition system for connected words using HTK,” Int. J. Comput. Syst. Eng., vol. 1 , no. 1 , pp. 25–32, 2012.
[38] A. Pikrakis, T. Giannakopoulos, and S. Theodoridis, “A speech/music discriminator of radio recordings based on dynamic programming and bayesian networks,” IEEE Trans. Multimedia, vol. 10 , no. 5 , pp. 846–857, Aug. 2008.
[39] Z. Chen and V. Hohmann, “Online monaural speech enhancement based on periodicity analysis and a priori SNR estimation,” IEEE/ACM Trans. Audio, Speech, Lang. Process., vol. 23 , no. 11 , pp. 1904–1916, Nov. 2015.
[40] “MATLAB and audio toolbox,” The MathWorks, Inc., Natick, MA, USA.
[41] “Matlab: AudioFeatureExtractor function,” 2020. [Online]. Available: https://it.mathworks.com/help/audio/ref/audiofeatureextractor.html/
[42] A. F. Vermeulen, “Industrial machine learning,” in Using Artificial Intelligence as a Transformational Disruptor. Berkeley, CA, USA: A Press, 2020, pp. 137–180. [Online]. Available: https://doi.org/10.1007/978-1-4842-5316-8
[43] X. Sun, M. Liu, and Z. Sima, “A novel cryptocurrency price trend forecasting model based on LightGBM,” Finance Res. Lett., vol. 32 , 2020, Art. no. 101084.
[44] C. Pao, C. Yeh, and Lin, “A comparative study of different weighting schemes on KNN-based emotion recognition in mandarin speech,” in Proc. Int. Conf. Intell. Comput., 2007, pp. 997–1005.
[45] J. Lever, M. Krzywinski, and N. Altman, “Points of significance: Model selection and overfitting,” Nature Methods, vol. 13 , no. 9 , pp. 703–705, 2016.
[46] T. T. Wong, “Parametric methods for comparing the performance of two classification algorithms evaluated by k-fold cross validation on multiple data sets,” Pattern Recognit., vol. 65 , pp. 97–107, 2017.
| 35947565 | PMC9728536 | NO-CC CODE | 2022-12-10 23:20:37 | no | IEEE J Biomed Health Inform. 2022 Aug 10; 26(11):5364-5371 | utf-8 | IEEE J Biomed Health Inform | 2,022 | 10.1109/JBHI.2022.3197910 | oa_other |
==== Front
IEEE J Electromagn RF Microw Med Biol
IEEE J Electromagn RF Microw Med Biol
0073300
JERM
IJERLV
Ieee Journal of Electromagnetics, Rf and Microwaves in Medicine and Biology
2469-7249
2469-7257
IEEE
10.1109/JERM.2022.3194727
JERM-2022-05-0039
Article
Combating Coronavirus Using Resonant Electromagnetic Irradiation
https://orcid.org/0000-0001-6525-0949
Sayidmarie Khalil H. Sayidmarie Khalil H. [email protected]
https://orcid.org/0000-0002-3333-1581
Mohammed Beadaa Mohammed Beadaa [email protected]
Mohammed Asmaa J. Mohammed Asmaa J. [email protected]
https://orcid.org/0000-0002-8015-5883
Abbosh Amin Abbosh Amin [email protected]
(Corresponding author: Beadaa Mohammed.)
College Electronics Engineering Ninevah University 480187 Mosul 41001 Iraq
School of Information Technology and Electrical Engineering The University of Queensland 1974 Brisbane QLD 4103 Australia
College of Environmental Science and Technology Mosul University 108489 Mosul 41001 Iraq
12 2022
17 8 2022
6 4 477484
07 5 2022
10 7 2022
24 7 2022
24 11 2022
2022
IEEE
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The interaction of electromagnetic (EM) waves with the COVID-19 virus is studied to define the frequencies that cause maximum energy absorption by the virus and the power level needed to cause a lethal temperature rise. The full-wave EM simulator is used to model the virus and study the effects of its size and dielectric properties on the absorbed power across a wide range of frequencies. The results confirm potential resonance conditions, where specific frequencies produce maximum absorption and subsequent temperature rise that can destroy the virus. Furthermore, the study confirms that maximum power deposition in the virus occurs at specific wavelengths depending on its size. Also, the simulation is used to find the power required to destroy the virus and determine the total power required to destroy it in an oral activity, such as coughing, made by infected individuals. Furthermore, the study explained why irradiation by UV-C band is effective to decrease virus activity or even eradicate it.
Electromagnetic waves
Coronavirus
COVID-19
nanoparticles
electromagnetic absorption
==== Body
pmcI. Introduction
In Late 2019, an epidemic identified as the novel coronavirus (COVID-19) caused a sudden increase in hospitalizations [1]. The virus rapidly spread across the globe [2]–[6], and thus the World Health Organization (WHO) announced it a global pandemic [6], [7]. COVID-19 is a new member of the beta-coronavirus family related to the known severe acute respiratory syndrome coronavirus (SARS-CoV) [8], [9]. However, COVID-19 is more transmittable between humans [8], [9]. The virus still causes many deaths around the world. Luckily, there have been enormous progress and successes in developing various vaccines. To prevent the spread of the virus, the World Health Organization (WHO) has issued many devices, such as social distancing, wearing a face mask, and handwashing. It was advised that applying soap long enough can disrupt and break down the outer layer (i.e., envelop), making the virus no longer functioning [10]–[14]. In addition, scientists working in different fields have been trying to understand the virus's behavior to develop the required vaccines and treatments.
On another line, the scientific community has been adapting its existing skills in developing and improving technologies that can fight COVID-19. In that regard, ultraviolet (UV) electromagnetic (EM) waves have been recommended as a tool to reduce the daily growth rate or kill the virus by using high doses of UV to disinfect equipment and buildings while using safe levels of UV doses [15]–[18]. In addition, the interaction between EM waves and the virus has been investigated [19]. The previously mentioned research has concluded that more research is required to find EM-based tools or methods to prevent the virus from spreading. These studies were based on experimental tests, with no explanation of the physical principles regarding why a particular band of frequencies influences the virus more than others or whether other bands could be more effective.
When EM waves illuminate a small biological object, such as the virus body, an EM field is induced in the internal body. The subsequent power absorption by the virus results in a specific resonance behavior [19], [20]. That internal field is absorbed by the object's dielectric material, increasing the biological material's temperature. Increasing the temperature to exceed the thermal thresholds of the biological material cell can kill that cell. The internal EM field distribution, absorption characteristics, and the scattered EM waves of any object, including the virus body, depend on the used frequency, object's geometry, polarization of the incident wave, and physiological parameters (i.e., frequency-dependent dielectric properties of the biological material). Hence, it is essential to understand how EM waves and their wavelength, virus size, and shape can affect absorption to find the optimal interaction regime and wavelength that cause the maximum absorption inside the virus body. This investigation will enable the development of devices or techniques that can thermally damage, disintegrate, or neutralize the COVID-19 body. Since the virus body consists of biological materials, a specific resonance is expected to significantly increase the absorbed EM energy when the incident wavelength is comparable to the virus body. The virus body might be destroyed at the resonant frequencies due to the high level of absorbed energy from overexposure [15], [19], [20].
This paper uses CST Microwave Studio simulation [21] to study the interaction between EM waves and the COVID-19 virus in the frequency band of 100-6000 THz. A realistic virus model, which includes its RNA, envelop layer, and spikes, is utilized in this investigation. In addition, the total power loss in each tissue type is calculated, and the effect of the virus size on the total loss in the virus body is also investigated to explain the specific frequency bands, or wavelengths, that can be used to destroy COVID-19. The paper is organized as follows; after an introductory section, the methods are discussed in Section II. Section III presents the obtained simulation results and discussion, while the conclusions are listed in Section IV.
II. Virus Model and Simulation Setup
A. Virus Model
The COVID-19 genome structure is a pleomorphic or spherical single-stranded ribonucleic acid (RNA), protein-like biological material representing the virus's internal structure [22], [23]. A membrane encloses the RNA covered by spikes made of a protein-like biological material protruding from the surface, enabling the virion's attachment to the host's cell membrane [22]–[28]. The membrane layer surrounding most viruses such as influenza and herpes simplex is called a “lipid envelop”. Thus, the membrane layer is similar to the fat biological material. Also, the RNA and spikes of COVID 19 are mainly protein [22]–[29], and thus they are closer to the dielectric properties of the muscle tissue, which is mainly protein consistent. The ultrastructural morphology exhibited by COVID-19 created at the Centers for Disease Control and Prevention (CDC), showing the spikes that adorn the virus's outer surface microscopically, is shown in Fig. 1. The average diameter of the virus varies from 50 to 200 nm [22], [23] as concluded from Table I. Some studies used electron microscope images to find number of viruses in a given volume [25], [28]. As with other biological objects, one should expect the virus and its variants to have a specific range of sizes. Therefore, looking at Table I, an average size of 100 nm was assumed in this work. The results of smaller and larger are also presented for comparison [22]. Fig. 1. The outer look of COVID-19 under microscopically viewed at the Centers for Disease Control and Prevention (CDC) [23].
TABLE I List of Some Former Studies That Reported the Average Diameter of covid-19 in Nanometer (nm)
Ref. Diameter Average diameter Spikes length Notes:
(nm) (nm)
[1] 60-140 100 9-12 Based on SAR-Cov-2
[2] 120-160 140 20 Used diam = 140 nm
Spike length = 20nm in the simulation
[5] 50-200 125
[15] 30-100 65
[18] 80-120 100
[20] 100-150 125 NA
[21] 60-140 100 9-12 From Transmission Electron Microscopy
[22] 80-120 100
[23] 60-140 100
[25] 60-140 100 9-12 Referred to [21]
The documented studies in the literature, such as [30]–[35] indicated that the known mutations so far mainly cause changes in the sequence of RNA protein, in addition to other possible changes to spikes number and protein type. We concluded based on known details at this stage that mutations may not significantly change the dielectric properties of the virus envelope, which is the most critical factor in the investigated resonance frequency method. Nevertheless, since there is no consensus in the literature on the exact size of the virus, or the virus might take different sizes due to unknown reasons, this study also investigates the effect of resonance irradiation with different virus size. If any future mutation is confirmed to cause a significant change to the structure and dielectric properties of the virus (RNA, spikes, and the envelope), a further study is needed.
B. Simulation Setup
The simulation is performed on the expected or reported range of virus size to help understand the interaction of the EM waves with the electrical properties of the virus body parts (i.e., spikes, RNA, and envelope). This section investigates the effect of the virus size on the resonant frequency for various sphere (i.e., RNA) diameters, 50 nm, 100 nm, 150 nm, and 200 nm, representing the possible size range COVID-19. First, the enveloping layer is modeled as a 2 nm layer surrounding the RNA sphere. Next, the spikes are added to the structure to create a realistic virus body. Each of these spikes is modeled as a cone structure (0.5 nm base diameter, 2 nm top diameter, and 10 nm height) created from the outer layer (i.e., the virus envelope) [25], [26]. As this model has used two types of material (fat and protein) and three shapes (sphere, shell, and spikes), then it can be considered more realistic compared to other models like the one in [19], which used a sphere and a thin shell representing the spikes. Finally, the simulation determines the resonant frequency, which causes maximum absorption.
A wide frequency range of 100-6000 THz is used to assess the response of the virus model and find the frequencies at which there is maximum power deposition in the virus that can deactivate the virus of different sizes and destroy its ability to multiply and cause disease. The chosen frequency band in this investigation covers the infrared, visible, and higher UV in the nanometer-wavelength, which were shown to be germicidal [17], [36]–[41] as the nucleic acids of the germs strongly absorb these wavelengths. Therefore, there should be optimum wavelengths that lead to maximum power deposition in the virus body, cause damage to its nucleic acid and prevent replication, resulting in the organism's death or inactivation. The wavelengths that result in maximum power deposition will be recommended for combating the virus
In the simulation, power loss and power flow monitors were used to estimate the power loss in the virus body and find the resonant frequency when the EM wave interacts with it across the frequency band of 100-6000 THz. A uniform plane wave of the unit intensity of electric-field value in (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$Volt/m$\end{document}) with linear polarization is used as a source of the EM field to irradiate the virus body (Fig. 2). Also, a circular polarization was applied to check its effect on the virus body structure. The dielectric properties of the virus body define the distribution of the EM field inside the virus. Therefore, accurate modelling of the dielectric properties of the virus's biological materials is required to estimate EM fields accurately and thus absorption inside the virus body. Fig. 2. Simulation setup of realistic COVID-19 virus structure.
Although there is no specific data about the RNA, envelops, and spikes dielectric properties primarily in the investigated frequency band, the information provided in [42] is used as a guide to find out the nearest dielectric properties from the most common source of dielectric properties of biological tissues [43]. The fat or lipid-like biological material's dielectric properties represent the membrane layer (i.e., the outer envelope fatty layer). On the other hand, the dielectric properties of the RNA and the spikes are emulated using the dielectric properties of biological muscle material. The RNA and spikes are protein-consistent materials [22], [27]–[29], [44], which are thus like the muscle tissue, which is considered the main source of protein (i.e., it contains more than 80% protein) [45]. Therefore, to simulate the realistic dielectric properties of the virus, the relevant dielectric properties of biological materials available in [43] were used. The simulation setup of the realistic COVID-19 virus structure is shown in Fig. 2.
The absorbed power can define the interaction of EM waves with a macroscopic biological object depending on its permittivity and conductivity, knowing that the permeability of biological materials is constant and equal to that of a vacuum. The dielectric properties of biological materials are frequency-dependent and can be described by the relative complex dielectric properties (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\varepsilon }^{\rm{*}}$\end{document}) that is expressed as: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}\begin{align*} &\varepsilon \ \left( \omega \right) = \varepsilon ^{\prime}\ \left( \omega \right) - j{\varepsilon }^{{\rm{^{\prime\prime}}}}\ \left( \omega \right) = {\varepsilon }_r\ - j\frac{\sigma }{{\omega {\varepsilon }_0}}\\ & \ldots, \ldots, \ldots, \ldots \tag{1} \end{align*} \end{document}
where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\varepsilon ^{\prime}( \omega )$\end{document} is the real part and represents the relative permittivity of the dielectric constant (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\varepsilon }_r$\end{document}), while \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\varepsilon ^{\prime\prime}( \omega )$\end{document} is the imaginary part and can be used to estimate the dielectric loss factor and conductivity (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\sigma $\end{document}) in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$S{m}^{ - 1}$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\varepsilon }_0( {F{m}^{ - 1}} )$\end{document} is the permittivity free space, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\omega $\end{document} is the angular frequency used to define frequency-dependent properties of the dielectric material.
In the simulation, a superposition of the first and second-order Debye model is used to obtain the relaxation process of the biological materials expressed as [44]: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}\begin{equation*} \varepsilon \ \left( \omega \right) = {\varepsilon }_\infty \ + \frac{{\left( {{\varepsilon }_s - {\varepsilon }_\infty } \right)}}{{1 + j\omega \tau }} \ldots \ldots \ldots \ldots \tag{2} \end{equation*} \end{document}
where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\varepsilon }_\infty $\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\varepsilon }_s$\end{document} are the dielectric constant at high and low frequency, respectively, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\tau $\end{document} is the time constant, also called the relaxation time, which determines the frequency range of significant changes. The recommended data for the dielectric properties (i.e., the relative permittivity and conductivity) of COVID-19 biological materials are then used in the simulator [42]. Fig. 3 shows the variation of the relative permittivity and conductivity of the constituents of the COVID-19 model across the 100-6000 THz frequency range. The relative permittivity has a slight variation with frequency. However, the conductivity has a considerable variation across the investigated frequency range. Fig. 3. Dielectric properties of COVID-19 biological materials for the: (a), (b) RNA, and spikes, (c), (d) envelop.
The resonant behavior of the biological materials in any living tissue can be described by the Lorentz model [45]: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}\begin{equation*} \varepsilon \ \left( \omega \right) = {\varepsilon }_\infty \ + \frac{{\left( {{\varepsilon }_s - {\varepsilon }_\infty } \right)\omega _0^2}}{{\omega _0^2 + i\omega \delta - {\omega }^2}} \ldots, \ldots, \ldots . \tag{3} \end{equation*} \end{document}
Fig. 4 shows the visualization of dielectric properties for (2) and (3). The resonance is characterized by a peak in the imaginary part and a zero, or close to zero, in the real part. In the simulation, the power absorption by the frequency-dependent dielectric material of the virus body is investigated. To that end, power loss density at each frequency step was assigned across the whole frequency band. The effects of virus size on the maximum power absorbed, absorption coefficient, relative absorption cross-section and resonant frequencies are assessed. Fig. 4. CST microwave studio representations of the dielectric properties (real and imaginary parts); (a) Debye first-order dispersion model with the relaxation process; and (b) Lorentz model with the resonance process.
According to [10]–[14], using soap long enough damages the envelope, stopping the virus functionality. Therefore, an investigation via simulation is performed to determine the required EM wave power to destroy the virus envelope. A power flow monitor was assigned with a 100 THz step across the 100-6000 THz frequency band. The monitors were used to measure the power of the EM wave inside the virus envelope. In the simulation, the virus is modeled at the center of (x,y,z) coordinates (Fig. 2). For convenience, the EM wave sources are applied in the negative z-direction. At each frequency step, the power loss monitors provide the power reading of the EM wave transmitted from the source to the virus body. The power loss monitors calculate the power density \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$( {w/c{m}^3} )$\end{document} at each frequency step in the simulation environment, including the virus body and surroundings. The power lost inside the envelop layer is determined across the volume of the virus envelop layer at each selected size and estimating the required power for a single virus.
COVID-19 can be separated in many modes, mainly through aerosols (droplets). The size and quantity of the virus depend on the droplet's size and number resulting from any individual's oral activities. Those activities include coughing, sneezing, or talk droplets that contain virus particles. For example, when an infected person coughs, sneezes, or talks, aerosols (droplets) or tiny particles are spared into the air. The droplets are a liquid-biased material with different droplet sizes, containing viruses of different sizes. Therefore, the infection happens in any recipient's breath air containing the virus particles in sufficient quantity [46]–[48]. There are no exact measurements of droplets produced by oral activities such as coughing, sneezing, breathing, and phonation [49]. However, studies showed that coughing and sneezing activities particles could release about 3000 -40000 droplets [50]. Different size droplets in any of these activities may vary between 6-12 μm [49], [50]. Also, number of viruses in each droplet is different; therefore, number of viruses will differ depending on the virus and droplet sizes.
Surface transmission mode may also occur indirectly by touching surfaces such as plastic and stainless steel contaminated with virus from a cough or sneezing from an infected person, followed by touching the mouth, nose, or eyes. The virus can live for 2-3 days on those surfaces.
In addition, a common way of transmission is the airborne mode, as the virus particles can stay alive in the air for up to three hours [51]; any recipient's breathing that air leads to their infection [47]–[52]. After the oral activities, the liquid material starts to evaporate immediately, leaving the virus body in the air or on surfaces as it can stay longer. Therefore, the virus body is simulated in the air (i.e., the airborne transmitting mode of the virus is considered), and the power deposition inside a single virus body is calculated using (3). In the case of COVID-19, the RNA copies number in the oral fluid vary from an average of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$7 \times {10}^6$\end{document} to a maximum of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$2.35 \times {10}^9$\end{document} copies per millilitre [52]. The total power (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${P}_{total}$\end{document}) required to destroy the viruses resulting from an oral activity containing virus copies (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${N}_{co}$\end{document}) can be calculated as follows: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}\begin{equation*} {P}_{total} = {P}_d{\rm{\ \ }} \times {N}_{co} \ldots, \ldots, \ldots \tag{4} \end{equation*} \end{document}
Where Pd is the power required to destroy one virus.
III. Results and Discussion
A. The Resonant Absorption
The resonance absorption property results in the virus envelop for various virus sizes (Fig. 5). The variation of the total power loss with frequency in the virus envelop dielectric material shows multiple peaks of the power loss, indicating resonance at various modes. The intensity of absorption increases as the diameter of the virus increases. Moreover, the positions of the peaks shift to lower frequencies as the diameter of the RNA dielectric sphere increases, indicating, as expected, a reverse relation between the sphere's diameter and the resonant frequency. The response shows multi resonances for each of the investigated sizes of the virus. This finding agrees with the results [53] for the interaction of the incident EM wave on a dielectric sphere. Also, many resonating modes resulted in the realistic structure having multiple layers and spikes of different dielectric properties. Fig. 5. The total power loss in the virus envelops when the virus RNA diameter is (a) 50 nm and 100 nm, (b) 150 nm and 200 nm.
The virus body has a lower permittivity of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\varepsilon }_r = \ 2.5$\end{document} for the enveloping layer and a higher value of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\varepsilon }_r = \ 4$\end{document} for the spikes and RNA. A more considerable difference in permittivity leads to a lower effective wavelength or higher resonant frequency. In addition, more resonant frequencies result from the virus model of a larger sphere diameter. Fig. 5(b) shows more resonant frequencies as a larger diameter of the sphere results in more degrees of freedom for the occurrence of resonances. The three lowest resonance frequencies with a virus diameter of 50 nm (see Fig. 5(a)) occur at 3100, 4300, and 5500 THz. When the virus diameter is 100 nm, the three lowest frequencies become 1600, 2300, and 2900 THz. The latter frequencies correspond to about half the former, indicating the inverse relation between the resonance frequency and virus size. Although the virus model is multilayered with different permittivity values, the obtained resonances are similar to the dielectric spheres model in [53]. It can also be seen that the resonance response is relatively wide, with a percentage bandwidth between 20 to 40%.
Virus sizes differ from case to case. However, as the resonance response is not sharp, size differences can be accommodated by the reasonably wide response. On the other hand, a relatively wideband UV light source is needed, and preferably more than one band will be more effective. Also, the effect of EM wave polarization on the power loss and deposition in the virus body is investigated. To that end, EM waves across the 100-600 THz with linear and circular polarization were used to interact with the virus body.
The power loss in the virus body results from the different dielectric properties of the virus's biological materials. It helps to determine the level of the EM radiation required to inflict damage or deactivate the virus envelope layer.
Therefore, the power loss monitor was assigned across the entire band with a step of 100 THz. The power loss monitor reading provides the power loss density value \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\ ( {w/c{m}^3} )$\end{document} when the EM waves interact with the virus body. The result generally shows that the power loss density across the entire band at higher frequencies is bigger than at lower frequencies when both linear and circular polarized EM waves are used. However, the maximum power loss occurs at the resonant frequencies, and the location of the maximum loss is always in the enveloping layer. The power loss density is presented at the resonant frequencies in Figs. 6-9 taking a cross-section in the YZ-plane laying at x = 0. Fig. 6. The power loss in the envelope for virus RNA diameter 50 nm (a) linear polarization EM wave, (b) circular polarization EM wave.
Fig. 7. The power loss in the envelope for virus RNA diameter 100 nm (a) linear polarization EM wave, (b) circular polarization EM wave.
Fig. 8. The power loss in the envelope for virus RNA diameter 150 nm (a) linear polarization EM wave, (b) circular polarization EM wave.
Fig. 9. The power loss in the envelope for virus RNA diameter 200 nm (a) linear polarization EM wave, (b) circular polarization EM wave.
The simulation results show that the maximum power loss always appeared on the other side of the sphere (away from the irradiating source) using linear and circular polarizations across all the resonant frequencies. The minimum power loss occurred on the side of the sphere facing the wave source for all the virus sizes. Larger power densities were noticed on the enveloping layer compared to other parts of the virus model. For small viruses of 50 nm RNA diameter, the maximum power loss in the envelope occurs at 550 THz. The maximum losses in the envelope layer for the virus of 100 and 150 nm RNA diameter occur at (5700 THz). The maximum power for virus RNA diameter of 200 nm occurs at 5000 and 5300 THz. Regarding the effect of the polarization on the virus body, the results show that stronger interaction occurs with the virus body when circular polarization is used. Table II summarizes the details of the maximum power loss in the envelope layer for all virus sizes for both linear and circular polarization. TABLE II The Maximum Power Loss Density in the Enveloping Layer for Em Waves Applied with Linear and Circular Polarazation
Virus RNA diameter Resonant frequency (THz) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${P}_d( {\frac{w}{{c{m}^2}}} )$\end{document} \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$ \times {10}^2$\end{document}(Linear, Circular)
50 nm 3100/4300/5500 (12.5)/ (37.8)/ (4.712)
100 nm
1600/2300/2900
3200/3500/3800
4100/4400/4600
4900/5200/5500/
5700 (1.1, 2.5)/ (24.6)/ (2.81.3)
(51.2)/ (3.67.8)/ (51.1)
(71.6)/ (9.62.3)/ (10,2.4)
(10,2.5)/ (10,2.4)/ (10,2.4)/ (1.33.2)
150 nm
1100/1500/1900
2200/2600/2900
3100/3300/3700
4100/4400/4600
4800/5300/5700/
5900 (1.22.7)/ (24.6)/ (37)
(51.1,)/ (5.61.2)/ (5.61.2)
(5, 1.1)/ (5.61.2)/ (5.61.2)
(92.1)/ (1.433)/ (1.12.5)
(1.43.3)/ (1.63.6)/ (24.9)/
(24.7)
200 nm 800/1100/1400
1700/2200/2500
2800/3100/3700
4000/4300/4600
5000/5300/5900 (1.12.4)/ (2.24.7)/ (3.67.5)
(510)/ (61.3)/ (5.61.2)
(5.31.1)/ (1.22.3)/ (2.24.3)
(1.62.8)/ (3.25.5)/ (34.9)
(4.37.9)/ (36.3)/ (3.78.3)
It can be observed from the results shown in Figs. 6-9 that higher power density is placed in the envelope for various sizes of the virus using linear and circular polarizations. Thus, the heating effect will be concentrated in the envelope of the virus, which is the most vulnerable part of the virus. This can be seen as a favourable property of UV irradiation since a lower power level is needed to inactivate the virus than when the incident power is evenly distributed across the whole parts of the virus.
B. The Total Power
The power required for destroying or inactivating the virus may be estimated from the required temperature rise of the envelope to inflect the damage, but such a result has not been established in the literature. However, many published experimental results relate the inactivation rate to the incident power density (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\boldsymbol{mW}}/{\boldsymbol{cm}}2$\end{document}) and irradiation time. The inactivation rate compares the number of active viruses before irradiation (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\boldsymbol{Nb}}$\end{document}) and after irradiation (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\boldsymbol{Na}}$\end{document}). An algorithmic ratio defines it as: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}\begin{equation*} Inactivation\ = \ log10\frac{{Nb}}{{Na}} \ldots \ldots \ldots \tag{5} \end{equation*} \end{document}
Thus, an inactivation value of 3 means that only 0.1% of the eradicated viruses remain. The power density and irradiation time in seconds can be combined to give energy density expressed in (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$mJ/cm2$\end{document}). Various energy density rates, power density levels, and exposure times have been reported. An average UV dose of 1.3 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$mJ/cm2$\end{document} was required to yield a 1-log inactivation of SARS-CoV-2 using the LP UV lamp (254 nm) [54]. This is similar to other results of 1.2 to 5.0 mJ/cm2 for 1-log inactivation published in [55]–[57]. In recent experimental results, a wavelength of 278 nm at an energy density dose of 2.07 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$mJ/cm2$\end{document} was found to result in an inactivation of 2-Log (1% of the irradiated viruses remained unaffected) [58]. The same Log reduction was achieved by a dose of 10 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$mJ/cm2$\end{document} at 265 nm [59] and by a dose of 2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$mJ/cm2$\end{document}, and 4 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$mJ/cm2$\end{document} using 270 nm and 282 nm respectively [54].The wide range of power densities, irradiation time, and energy density published by researchers can be attributed to the various virus sizes, used wavelengths, and experimental environments. The above results confirm the obtained results of this work regarding the finding that power deposition shows resonance at specific frequencies.
Moreover, they show that treatment by UV irradiation is a feasible modality. When the power needed to inactivate one virus is determined, then one can find the total power required to destroy viruses to the required inactivation level defined by (4), in one oral activity such as a cough or cough sneeze using (3). Although one cough may contain virus copies of different sizes mixed, depending on the size of the droplets. However, in this work, the calculation is made by considering that the cough has the same size droplets, which means all the viruses have the same size. Figs. 10-11 show the results, and the total deposited power increases with the number of copies. Also, more power is required when the size of the virus increases. Moreover, the required power to destroy the envelop layer increases with the virus size. Therefore, the total deposited power is calculated for the two scenarios: the average number and the maximum number of viruses available in an infected area. Fig. 10. The total deposited power in one cough droplet containing 50 and 100 nm virus RNA diameter at resonant frequencies (a) the Average number of virus copies (b) the maximum number of virus copies.
Fig. 11. The total deposited power in one cough droplet containing 150 and 200 nm virus RNA diameter at resonant frequencies (a) the Average number of virus copies (b) the maximum number of virus copies.
IV. Conclusion
A simulation study has been conducted to investigate effects of electromagnetic resonance absorption on COVID-19 virus. Realistic virus body structures with different sizes were modeled using their estimated dielectric properties across the 100-6000 THz frequency band. The results confirm an inverse relation between the resonance frequency and virus size, whereas the relation is direct between the virus size and its power absorption. It is also concluded that the high-power density is always deposited in the virus envelope irrespective of the virus size and used polarization. Thus, the heating effect is concentrated in the virus envelope, which is the most vulnerable part of the virus. This is a favourable property of UV irradiation since a lower power level is needed to inactivate the virus compared with the case when the incident power is evenly distributed across all parts of the virus.
In addition, the total power required to destroy and damage the virus in an oral activity (i.e., cough) can be estimated if the power needed to inactivate one virus is established. The results show that the total power increases when the virus copies in the activity increase. As a result, larger number and size of viruses require higher power to destroy their envelope layers and terminate them.
This study furnishes the theoretical evidence of the recently published observations regarding the use of UV light for virus disinfection. This work presents the theoretical principle that explains why the recently published observations on the relation between required power densities to inactivate the virus and the used wavelengths. Furthermore, this study explains the effective use of irradiation by a proper dose of UV-C band to eradicate the virus.
==== Refs
References
[1] W. J. Wiersinga, A. Rhodes, A. C. Cheng, S. J. Peacock, and H. C. Prescott, “Pathophysiology, transmission, diagnosis, and treatment of coronavirus disease 2019 (COVID-19): A review,” Jama, vol. 324 , no. 8 , pp. 782–793, 2020.32648899
[2] J. E. Flythe , “Characteristics and outcomes of individuals with pre-existing kidney disease and COVID-19 admitted to intensive care units in the United States,” Amer. J. Kidney Dis., vol. 77 , no. 2 , pp. 190–203, 2021.32961244
[3] F. Di Gennaro , “Coronavirus diseases (COVID-19) current status and future perspectives: A narrative review,” Int. J. Environ. Res. Public Health, vol. 17 , no. 8 , pp. 2690–2701, 2020.32295188
[4] L. Zhang , “Crystal structure of SARS-CoV-2 main protease provides a basis for design of improved α-ketoamide inhibitors,” Science, vol. 368 , no. 6489 , pp. 409–412, 2020.32198291
[5] N. Chen , “Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: A descriptive study,” Lancet, vol. 395 , no. 10223 , pp. 507–513, 2020.32007143
[6] World Health Organization, “Novel coronavirus (2019-nCoV): Strategic preparedness and response plan,” Feb. 2020. [Online]. Available: https://www.who.int/docs/default-source/coronaviruse/srp-04022020
[7] Y. Jee, “WHO international health regulations emergency committee for the COVID-19 outbreak,” Epidemiol. Health, vol. 42 , 2020, Art. no. e2020013.
[8] S. C. Wu, “Progress and concept for COVID-19 vaccine development,” Biotechnol. J., vol. 15 , no. 6 , Jun. 2020, Art. no. 2000147.
[9] Y. Dong , “Epidemiology of COVID-19 among children in China,” Pediatrics, vol. 145 , no. 6 , pp. 1–10, 2020.
[10] World Health Organization, “Advice for the public: Coronavirus disease (COVID-19),” Oct. 2021. [Online]. Available: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/advice-for-public
[11] World Health Organization, “Infection prevention and control of epidemic-and pandemic-prone acute respiratory diseases in health care: WHO interim guidelines,” World Health Org., Geneva, Switzerland, Reference Number WHO/CDS/EPR/2007.6, 2007.
[12] S. J. Wimalawansa, “Global epidemic of coronavirus—COVID-19: What can we do to minimize risks,” Eur. J. Biomed., vol. 7 , no. 3 , pp. 432–438, 2020.
[13] M. Cascella, M. Rajnik, A. Aleem, S. Dulebohn, and R. Di Napoli, “Features, evaluation, and treatment of coronavirus (COVID-19),” StatPearls, Treasure Island, FL, USA: StatPearls Publishing, 2022.
[14] A. Dogra, B. Goyal, and A. M. Sharma, “Corona virus: A novel outbreak,” Biomed. Pharmacol. J., vol. 13 , no. 1 , pp. 5–10, 2020.
[15] Y. Gerchman, H. Mamane, N. Friedman, and M. Mandelboim, “UV-LED disinfection of coronavirus: Wavelength effect,” J. Photochemistry Photobiol. B: Biol., vol. 212 , pp. 112044–112050, 2020.
[16] T. Carleton, J. Cornetet, P. Huybers, K. C. Meng, and J. Proctor, “Global evidence for ultraviolet radiation decreasing COVID-19 growth rates,” Proc. Nat. Acad. Sci., vol. 118 , no. 1 , 2021, Art. no. e2012370118.
[17] S. S. Verma, “Killing coronavirus with ultraviolet light,” Indian Practitioner, vol. 73 , no. 10 , pp. 7–9, 2020.
[18] A. Kumar, A. Sagdeo, and P. R. Sagdeo, “Possibility of using ultraviolet radiation for disinfecting the novel COVID-19,” Photodiagnosis Photodynamic Ther., vol. 34 , pp. 102234–102235, 2021.
[19] C. Valagiannopoulos and A. Sihvola, “Maximal interaction of electromagnetic radiation with corona virions,” Phys. Rev. B, vol. 103 , no. 1 , pp. 14114–14124, 2021.
[20] A. Cordero-Samortin, J. C. Dela Cruz, R. Garcia, and Z. Mabunga, “The effect of various ultra high frequency radiation on the evolution of the staphylococcus aureus,” in Proc. IEEE 12th Int. Conf. Humanoid, Nanotechnol., Inf. Technol., Commun. Control, Environ., Manage., 2020, pp. 1–6.
[21] CST Microwave Studio, CST Studio Suite, Computer Simulation Technology AG., 2008.
[22] N. Kirtipal, S. Bharadwaj, and S. G. Kang, “From SARS to SARS-CoV-2, insights on structure, pathogenicity and immunity aspects of pandemic human coronaviruses,” Infection, Genet. Evol., vol. 85 , pp. 104502–104517, 2020.
[23] DCD Organization, “Centeres for disease control and presventation,” Digital Lirary Coronavirus, 2020. [Online]. Available: https://www.cdc.gov/media/subtopic/images.htm
[24] M. Heßling, K. Hönes, P. Vatter, and C. Lingenfelder, “Ultraviolet irradiation doses for coronavirus inactivation–review and analysis of coronavirus photoinactivation studies,” GMS Hyg. Infection Control, vol. 15 , 2020, Art. no. Doc08, doi: 10.3205/dgkh000343.
[25] N. Zhu , “A novel coronavirus from patients with pneumonia in China,” New England J. Med., vol. 382 , pp. 727–733, 2020, doi: 10.1056/NEJMoa2001017, PMID: 31978945, 2019.
[26] A. Eckert, MSM, D. Higgins, and MAMSvd. “Public health image library,” Centers for Disease Control and Prevention, [Online]. Available: https://phil.cdc.gov/ImageidSearch.aspx?imgid=23311
[27] D. Petrov, “Photopolarimetrical properties of coronavirus model particles: Spike proteins number influence,” J. Quantitative Spectrosc. Radiative Transfer, vol. 248 , pp. 107005–107014, 2020.
[28] J. Shang , “Cell entry mechanisms of SARS-CoV-2,” Proc. Nat. Acad. Sci., vol. 117 , no. 21 , pp. 11727–11734, 2020.32376634
[29] A. Noman , “Spike glycoproteins: Their significance for corona viruses and receptor binding activities for pathogenesis and viral survival,” Microbial Pathogenesis, vol. 151 , pp. 104719–104728, 2021.
[30] E. Callaway, “Making sense of coronavirus mutations,” Nature, vol. 585 , no. 7824 , pp. 174–177, 2020.32901123
[31] M. Pachetti , “Emerging SARS-CoV-2 mutation hot spots include a novel RNA-dependent-RNA polymerase variant,” J. Transl. Med., vol. 18 , no. 1 , pp. 1–9, 2020.31900168
[32] I. Jungreis, R. Sealfon, and M. Kellis, “SARS-CoV-2 gene content and COVID-19 mutation impact by comparing 44 Sarbecovirus genomes,” Nature Commun., vol. 12 , no. 1 , pp. 1–20, 2021.33397941
[33] Z. Abdelrahman, M. Li, and X. Wang, “Comparative review of SARS-CoV-2, SARS-CoV, MERS-CoV, and influenza a respiratory virus,” Front. Immunol., vol. 11 , 2020, Art. no. 2309.
[34] P. V'kovski, A. Kratzel, S. Steiner, H. Stalder, and V. Thiel, “Coronavirus biology and replication: Implications for SARS-CoV-2,” Nature Rev. Microbiol., vol. 19 , no. 3 , pp. 155–170, 2021.33116300
[35] L. J. Stevens , “Mutations in the SARS-CoV-2 RNA dependent RNA polymerase confer resistance to remdesivir by distinct mechanisms,” Sci. Transl. Med., vol. eabo0718 , pp. 1–22, 2022.
[36] V. H. Mata-Portuguez, L. S. Pérez, and E. Acosta-Gío, “Sterilization of heat-resistant instruments with infrared radiation,” Infection Control Hosp. Epidemiol., vol. 23 , no. 7 , pp. 393–396, 2020.
[37] D. Hamanaka, T. Uchino, N. Furuse, W. Han, and S. I. Tanaka, “Effect of the wavelength of infrared heaters on the inactivation of bacterial spores at various water activities,” Int. J. Food Microbiol., vol. 108 , no. 2 , pp. 281–285, 2006.16503067
[38] S. Banerjee, S. Raghunathan, S. Banerjee, and B. Bandyopadhyay, “Portable sterilizer with microbe content detection device,” Bull. Nat. Res. Centre, vol. 45 , no. 1 , pp. 1–5, 2021.
[39] Z. Y. Lu, H. Ding, W. Q. Sun, and P. P. Andshi, “The study on experiment and mechanism of sterilization with electromagnetic wave,” J. Electromagn. Waves Appl., vol. 21 , no. 6 , pp. 729–735, 2007.
[40] D. Hamanaka, T. Uchino, N. Furuse, W. Han, and S. I. Tanaka, “Effect of the wavelength of infrared heaters on the inactivation of bacterial spores at various water activities,” Int. J. Food Microbiol., vol. 108 , no. 2 , pp. 281–285, 2006.16503067
[41] A. Cuervo, P. D. Dans, J. L. Carrascosa, M. Orozco, G. Gomila, and L. Fumagalli, “Direct measurement of the dielectric polarization properties of DNA,” Proc. Nat. Acad. Sci., vol. 111 , no. 35 , pp. E3624–E3630, 2014.25136104
[42] P. A. Hasgall , “IT'IS database for thermal and electromagnetic parameters of biological tissues,” Version 4.1, Feb. 22, 2022, doi: 10.13099/VIP21000-04-1.
[43] S. P. Tadepalli, “Novel remedial approaches against virulent corona viruses,” Int. J. Infect. Dis. Res., vol. 1 , no. 1 , pp. 1–15, 2020.
[44] L. Gun and D. Ning, “Equivalent permittivity based on debye model of blood and its SAR,” IJSTS, vol. 5 , no. 3 , pp. 37–40, 2017.
[45] I. F. Almog, M. S. Bradley, and V. Bulovic, The Lorentz oscillator and Its Applications. Cambridge, MA, USA: Massachusetts Inst. Technol., 2011.
[46] R. Mittal, R. Ni, and J. H. Seo, “The flow physics of COVID-19,” J. Fluid Mechanics, vol. 894 , no. 2 , pp. 1–14, 2020.
[47] L. Bourouiba, “Turbulent gas clouds and respiratory pathogen emissions: Potential implications for reducing transmission of COVID-19,” Jama, vol. 323 , no. 18 , pp. 1837–1838, 2020.32215590
[48] S. Asadi, N. Bouvier, A. S. Wexler, and W. D. Ristenpart, “The coronavirus pandemic and aerosols: Does COVID-19 transmit via expiratory particles?,” Aerosol Sci. Technol., vol. 54 , no. 6 , pp. 635–638, 2020.
[49] Y. Chartier and C. L. Pessoa-Silva, Natural Ventilation for Infection Control in Health-Care Settings. Geneva, Switzerland: World Health Org., 2009.
[50] R. Dhand and J. Li, “Coughs and sneezes: Their role in transmission of respiratory viral infections, including SARS-CoV-2,” Amer. J. Respir. Crit. Care Med., vol. 202 , no. 5 , pp. ;651–659, 2020.32543913
[51] L. Morawska and J. Cao, “Airborne transmission of SARS-CoV-2: The world should face the reality,” Environ. Int., vol. 139 , pp. 105730–105733, 2020.32294574
[52] R. Wofel , “Virological assessment of hospitalized patients with COVID-2019,” Nature, vol. 581 , pp. 465–469, 2020.32235945
[53] M. I. Tribelsky1, J. M. Geffrin, A. Litman, C. Eyraud, and M. F. Moreno, “Small dielectric spheres with high refractive index as new multifunctional elements for optical devices,” Sci. Rep., vol. 5 , pp. 12288–12295, 2015.26201445
[54] B. Ma, P. M. Gundy, C. P. Gerba, M. D. Sobsey, and K. G. Linden, “UV inactivation of SARS-CoV-2 across the UVC spectrum: KrCl* excimer, mercury-vapor, and light-emitting-diode (LED) sources,” Appl. Environ. Microbiol., vol. 87 , no. 22 , Nov. 2021, Art. no. e01532.
[55] M. Biasin , “UV-C irradiation is highly effective in inactivating SARS-CoV-2 replication,” Sci. Rep., vol. 11 , pp. 6260–6267, 2021, doi: 10.1038/s41598-021-85425-w.33737536
[56] E. I. Patterson , “Methods of inactivation of SARS-CoV-2 for downstream biological assays,” J. Infect. Dis., vol. 222 , pp. 1462–1467, 2020, doi: 10.1093/infdis/jiaa507.32798217
[57] N. Storm , “Rapid and complete inactivation of SARS-CoV-2 by ultraviolet-C irradiation,” Sci. Rep., vol. 10 , pp. 22421–22425, 2020, doi: 10.1038/s41598-020-79600-8.33380727
[58] M. Biasin , “UV and violet light can neutralize SARS-CoV-2 infectivity,” J. Photochemistry Photobiol., vol. 10 , 2022, Art. no. 100107, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2666469021000920
[59] H. Shimoda, J. Matsuda, T. Iwasaki, and D. Hayasaka, “Efficacy of 265-nm ultraviolet light in inactivating infectious SARS-CoV-2,” J. Photochemistry Photobiol., vol. 7 , 2021, Art. no. 100050, doi: 10.1016/j.jpap.2021.100050.
| 36514675 | PMC9728540 | NO-CC CODE | 2022-12-10 23:20:37 | no | IEEE J Electromagn RF Microw Med Biol. 2022 Aug 17; 6(4):477-484 | utf-8 | IEEE J Electromagn RF Microw Med Biol | 2,022 | 10.1109/JERM.2022.3194727 | oa_other |
==== Front
Lancet Infect Dis
Lancet Infect Dis
The Lancet. Infectious Diseases
1473-3099
1474-4457
Elsevier Ltd.
S1473-3099(22)00816-7
10.1016/S1473-3099(22)00816-7
Correspondence
Humoral immune evasion of the omicron subvariants BQ.1.1 and XBB
Uraki Ryuta ad
Ito Mutsumi a
Furusawa Yuri ad
Yamayoshi Seiya ad
Iwatsuki-Horimoto Kiyoko a
Adachi Eisuke b
Saito Makoto bc
Koga Michiko bc
Tsutsumi Takeya bc
Yamamoto Shinya ac
Otani Amato b
Kiso Maki a
Sakai-Tagawa Yuko a
Ueki Hiroshi ad
Yotsuyanagi Hiroshi bc
Imai Masaki ad
Kawaoka Yoshihiro ade
a Division of Virology, Institute of Medical Science, University of Tokyo, Tokyo 108-8639, Japan
b Department of Infectious Diseases and Applied Immunology, IMSUT Hospital of The Institute of Medical Science, University of Tokyo, Tokyo 108-8639, Japan
c Division of Infectious Diseases, Advanced Clinical Research Center, Institute of Medical Science, University of Tokyo, Tokyo 108-8639, Japan
d The Research Center for Global Viral Diseases, National Center for Global Health and Medicine Research Institute, Tokyo, Japan
e Influenza Research Institute, Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin–Madison, Madison, WI, USA
7 12 2022
7 12 2022
© 2022 Elsevier Ltd. All rights reserved.
2022
Elsevier Ltd
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
==== Body
pmcThe omicron (B.1.1.529) variant of SARS-CoV-2 evolved into several sublineages, three of which (BA.1, BA.2, and BA.5) became globally dominant. Currently, the prevalence of omicron subvariants BQ.1 (a subvariant of BA.5), its sublineage BQ.1.1, and XBB (a recombinant of two different BA.2 subvariants) is increasing rapidly in the USA, France, Singapore, India, and elsewhere. BQ.1.1 and XBB possess substitutions relative to BA.5 and BA.2, respectively, in the receptor-binding domain of their spike protein (appendix p 4), which is the major target for vaccines and therapeutic monoclonal antibodies (mAbs) for COVID-19. Both variants have the substitution R346T, which confers resistance to certain therapeutic antibodies,1 raising concerns that mAbs or vaccines might be less effective against BQ.1.1 and XBB than against other omicron strains. We showed that BQ.1.1 and XBB have enhanced immune evasion capabilities compared with earlier omicron variants, including BA.5 and BA.2, by evaluating the efficacy of therapeutic mAbs against BQ.1.1 and XBB.2 However, the neutralising ability of plasma from convalescent individuals and COVID-19 vaccinees against BQ.1.1 and XBB clinical isolates remained unknown.
Accordingly, we evaluated the neutralising ability of antibodies in plasma from three different groups against BQ.1.1 and XBB clinical isolates: individuals (180–189 days after the third dose; n=20) who received three doses of the monovalent mRNA vaccine BNT162b2 (Pfizer–BioNTech) or mRNA-1273 (Moderna), or both; individuals (33–57 days after the fourth dose; n=20) who received four doses of the monovalent mRNA vaccine BNT162b2 or mRNA-1273, or both; and individuals (29–89 days after the infection; n=10) who received three doses of monovalent BNT162b2 or mRNA-1273 before the BA.2 breakthrough infection. Using a live-virus neutralisation assay, we determined the 50% focus reduction neutralisation titre (FRNT50) of the plasma samples against BA.2 (hCoV-19/Japan/UT-NCD1288-2N/2022), BA.5 (hCoV-19/Japan/TY41-702/2022), BQ.1.1 (hCoV-19/Japan/TY41-796/2022), and XBB (hCoV-19/Japan/TY41-795/2022). For plasma from individuals who received a third dose of the mRNA vaccine, 17 (85%) of 20 samples or 18 (90%) of 20 samples had FRNT50 values that were below the limit of detection (<10-fold dilution) against BQ.1.1 or XBB, respectively. To calculate the geometric mean titre of each group, we assigned samples that were under the limit of detection of an FRNT50 value of ten. The FRNT50 geometric mean titres against BQ.1.1 and XBB were 21·1-fold and 21·6-fold lower, respectively, than those against the ancestral strain (SARS-CoV-2/UT-NC002-1T/Human/2020/Tokyo) (figure A , appendix p 5). In addition, the geometric mean titres against BQ.1.1 and XBB were 1·7-fold and 2·6-fold lower, respectively, than those against BA.5 and BA.2. Similar results were obtained with samples from individuals who received four doses of mRNA vaccine (figure B); the FRNT50 geometric mean titres against BQ.1.1 and XBB were 43·3-fold and 51·6-fold lower, respectively, than those against the ancestral strain, and were 3·7-fold and 6·2-fold lower than those against BA.5 and BA.2, respectively (figure B, appendix p 6). In contrast, most of the samples from vaccinees with BA.2 breakthrough infection neutralised BQ.1.1 and XBB; however, the FRNT50 geometric mean titres against BQ.1.1 and XBB were 35·2-fold and 61·7-fold lower, respectively, than those against the ancestral strain, and were 4·9-fold and 15·1-fold lower than those against BA.5 and BA.2, respectively (figure C, appendix p 7).Figure Antibody responses to SARS-CoV-2 omicron variants
(A) Neutralising antibody titres of human plasma obtained from individuals immunised with a third dose of BNT162b2 or mRNA-1273 vaccine. Samples were collected 180–189 days after the third immunisation (n=20). (B) Neutralising antibody titres of human plasma obtained from individuals immunised with four doses of BNT162b2 or mRNA-1273 vaccine. Samples were collected 33–57 days after the fourth immunisation (n=20). (C) Neutralising antibody titres of human plasma obtained from individuals who were infected with omicron BA.2 after three doses of BNT162b2 or mRNA-1273 vaccine. Samples were collected 29–89 days after symptom onset (n=10). Each dot represents data from one individual. The lower limit of detection (value=10) is indicated by the horizontal dashed line. Samples under the detection limit (<10-fold dilution) were assigned an FRNT50 value of 10 and are represented by X. Geometric mean titres are shown. FRNT50=50% focus reduction neutralisation titre.
Our data suggest that the omicron sublineages BQ.1.1 and XBB effectively evade current humoral immunity induced by mRNA vaccines or natural infection. A previous study using pseudotyped viruses reported that BQ.1.1 and XBB were less well recognised than BA.2 and BA.4/5 by plasma from convalescent individuals and mRNA vaccinees.3 These findings show that BQ.1.1 and XBB clinical isolates have higher immune evasion abilities than earlier omicron variants, including BA.5 and BA.2.
YK is supported by grants from the Center for Research on Influenza Pathogenesis (HHSN272201400008C) and from the Center for Research on Influenza Pathogenesis and Transmission (75N93021C00014), funded by the National Institute of Allergy and Infectious Disease; and by a Research Program on Emerging and Reemerging Infectious Diseases (JP21fk0108552 and JP21fk0108615), a Project Promoting Support for Drug Discovery (JP21nf0101632), the Japan Program for Infectious Diseases Research and Infrastructure (JP22wm0125002), and a grant (JP223fa627001) from the Japan Agency for Medical Research and Development. All other authors declare no competing interests. RU, MI, and YF contributed equally.
Supplementary Material
Supplementary appendix
==== Refs
References
1 Miller NL Clark T Raman R Sasisekharan R Insights on the mutational landscape of the SARS-CoV-2 omicron variant receptor-binding domain Cell Rep Med 3 2022 100527 35233548
2 Imai M, Ito M, Kiso M, et al. Efficacy of antiviral agents against omicron BQ.1.1 and XBB subvariants. N Engl J Med (in press).
3 Cao Y Jian F Wang J Imprinted SARS-CoV-2 humoral immunity induces convergent omicron RBD evolution bioRxiv 2022 published online Sept 16 10.1101/2022.09.15.507787
| 36495917 | PMC9729000 | NO-CC CODE | 2022-12-14 23:31:42 | no | Lancet Infect Dis. 2022 Dec 7; doi: 10.1016/S1473-3099(22)00816-7 | utf-8 | Lancet Infect Dis | 2,022 | 10.1016/S1473-3099(22)00816-7 | oa_other |
==== Front
Lancet Gastroenterol Hepatol
Lancet Gastroenterol Hepatol
The Lancet. Gastroenterology & Hepatology
2468-1253
Published by Elsevier Ltd.
S2468-1253(22)00387-9
10.1016/S2468-1253(22)00387-9
Comment
Has the COVID-19 pandemic changed endoscopy in the UK forever?
Rees Colin a
Penman Ian b
a Population Health Sciences Institute, Newcastle University Centre for Cancer, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne NE2 4HH, UK
b Centre for Liver and Digestive Disorders, Royal Infirmary of Edinburgh, Edinburgh, UK
7 12 2022
1 2023
7 12 2022
8 1 68
Crown Copyright © 2022 Published by Elsevier Ltd. All rights reserved.
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
==== Body
pmcBefore the COVID-19 pandemic, approximately 2 million diagnostic gastrointestinal endoscopies were performed annually in the UK. In addition to the growing number of increasingly complex interventional procedures such as endoscopic retrograde cholangiopancreatography, endoscopic ultrasonography, device-assisted small bowel endoscopy, endoscopic mucosal resections, and endoscopic submucosal dissection, increasing demand was also being driven by the needs of an ageing population, increasing numbers of patients requiring surveillance, and the need to expand capacity for bowel cancer screening, both by flexible sigmoidoscopy and faecal occult blood testing.
Services across the UK were struggling to meet target waiting times, relying on weekend working, waiting list initiatives, outsourcing, and insourcing. The longer term plan was to increase capacity by training more endoscopists.1 The decade before the pandemic saw substantial changes in the evolution of endoscopy as an evidence-based specialty, with large clinical studies informing changes to practice, although clinicians still followed the dogma that gastrointestinal endoscopy was the cornerstone of diagnosis in luminal gastroenterology because of unrivalled mucosal visualisation and biopsy capability.
However, only 3–4% of patients referred for endoscopy on an urgent cancer pathway were found to have malignancy. Thus, large numbers of patients were undergoing invasive and expensive procedures with a relatively low yield of clinically significant pathology. Clinicians were asking whether doing more endoscopies could be replaced with doing smarter endoscopy, using less invasive initial tests, risk adapted referral, or endoscopy only for therapeutic indications.2, 3
During the pandemic, the capacity to deliver endoscopy was substantially reduced and the number of people on waiting lists grew enormously. Early guidance on prioritisation and mitigation strategies was published4 and roll-out of alternative diagnostic modalities was expedited. Cytosponge was used as an alternative for Barrett's oesophagus surveillance and in some areas for selected patients referred with chronic reflux symptoms.5 Barium swallow returned for selected patients with dysphagia and transnasal endoscopy (believed to cause less gagging and less aerosol generation) was used more widely. The Edinburgh dysphagia score was reintroduced as a prioritisation tool. A no biopsy strategy for diagnosis of coeliac disease in adults was introduced for patients with substantially elevated tissue transglutaminase (TTG) antibody concentrations.4 In the lower gastrointestinal tract, faecal immunochemical testing (FIT) was used for symptomatic patients as a triage tool or as a rule in–rule out test for further endoscopy.6 Pilot studies of colon capsule endoscopy as an alternative to colonoscopy began or were extended.7 The flexible sigmoidoscopy colorectal cancer screening programme was suspended during the pandemic and many surveillance procedures were postponed.
As we head towards 2023, COVID-19 remains with us, although health-care services are recovering. However, there is now a legacy of long waiting lists and a tired and understaffed workforce. It is imperative that health services do not simply return to old ways of working and consider how we deliver smarter endoscopy. Some approaches introduced during the pandemic should continue, some require further research, and some should be abandoned.
Cytosponge is here to stay as a tool for investigating the upper gastrointestinal tract. It has high sensitivity and specificity for high grade dysplasia and early cancer in patients with reflux symptoms and those undergoing Barrett's oesophagus surveillance, and is a safe triage tool.8, 9 Ongoing research should address which patients with reflux also require or would be better served by undergoing upper gastrointestinal endoscopy as well as addressing safety netting approaches. The Edinburgh dysphagia score is easy to use and might remain useful to prioritise urgency of investigations in patients with dysphagia. Barium swallows were helpful in a crisis but moving forward their role will once again be very limited. Transnasal endoscopy is better tolerated than per oral upper gastrointestinal endoscopy and more widespread implementation should be encouraged.
Paediatric gastroenterology has long accepted raised TTG concentrations as diagnostic of coeliac disease. The pandemic approach of two TTG readings of more than 10 times the upper limit of the normal laboratory range confirming coeliac disease in adults has been continued by many clinicians and should be enshrined in formal guidance moving forward.10
Lower gastrointestinal endoscopy has seen substantial changes during the pandemic, some of which will remain. The most substantial change to practice relates to FIT. 2022 UK guidance advocates FIT in primary care for almost all patients with symptoms suggestive of possible colorectal cancer. With a few caveats, only symptomatic patients with a raised FIT should be referred for colonoscopy or CT colonography.11 Ongoing research seeks to establish how other biomarkers and patient factors might sit alongside FIT in a referral algorithm. Further research should establish the optimal FIT threshold and whether it should vary depending on patient factors. Surveillance colonoscopy uses a lot of resources and new guidance introduced just before the pandemic has reduced this workload considerably.12 The role of FIT as a possible tool to guide surveillance requires further study.
Capsule investigation of the small bowel is well established, with growing interest in the role of colon capsule endoscopy. There is enthusiasm for wider roll-out of colon capsule endoscopy, but the evidence base is not strong. High grade evidence regarding the role of colon capsule endoscopy as an alternative to established lower gastrointestinal investigations is required.
In terms of population-based screening, flexible sigmoidoscopy was designed to prevent colorectal cancer, and it will not recommence. Although the national screening programme plans to lower the age of FIT-based screening to compensate for the lost flexible sigmoidoscopy screening programme, this opportunity to prevent many cases of colorectal cancer has been a long-term casualty of the pandemic.
It is important to ensure that endoscopy is delivered smartly on the basis of good quality evidence. Evidence-based understanding of a patient's inherent risk, combined with stratification of symptoms and use of biomarkers, should allow endoscopy to be targeted to those individuals most likely to benefit from it. This will involve a change of mindset, including a move away from defensive medicine to an approach based on an individual's relative risk of disease, particularly when it comes to serious diagnoses such as cancer.
CR has received grant funding from ARC Medical, Norgine, Medtronic, 3D Matrix Solutions, and Olympus Medical. He was an expert witness for ARC Medical and Olympus medical. IP has received speaker fees from Olympus Medical, Boston Scientific, Medtronic, and Dr Falk.
==== Refs
References
1 Richards M Diagnostics: recovery and renewal—report of the independent review of diagnostic services for NHS England https://www.england.nhs.uk/publication/diagnostics-recovery-and-renewal-report-of-the-independent-review-of-diagnostic-services-for-nhs-england/ 2020
2 Hampton JS Koo S Dobson C The COLO-COHORT (Colorectal Cancer Cohort) study: protocol for a multi-centre, observational research study and development of a consent-for-contact research platform Colorectal Dis 24 2022 1216 1226 35470953
3 Hull MA Rees CJ Sharp L Koo S A risk-stratified approach to colorectal cancer prevention and diagnosis Nat Rev Gastroenterol Hepatol 17 2020 773 780 33067592
4 Rees CJ East JE Oppong K Restarting gastrointestinal endoscopy in the deceleration and early recovery phases of COVID-19 pandemic: guidance from the British Society of Gastroenterology Clin Med (Lond) 20 2020 352 358 32518104
5 Ross-Innes CS Debiram-Beecham I O’Donovan M Evaluation of a minimally invasive cell sampling device coupled with assessment of trefoil factor 3 expression for diagnosing Barrett's esophagus: a multi-center case-control study PLoS Med 12 2015 e1001780 25634542
6 D’Souza N Hicks G Benton SC Abulafi M The diagnostic accuracy of the faecal immunochemical test for colorectal cancer in risk-stratified symptomatic patients Ann R Coll Surg Engl 102 2020 174 179 31697171
7 NHS England NHS rolls out capsule cameras to test for cancer. https://www.england.nhs.uk/2021/03/nhs-rolls-out-capsule-cameras-to-test-for-cancer/
8 Fitzgerald RC di Pietro M O’Donovan M Cytosponge-trefoil factor 3 versus usual care to identify Barrett's oesophagus in a primary care setting: a multicentre, pragmatic, randomised controlled trial Lancet 396 2020 333 344 32738955
9 Pilonis ND Killcoyne S Tan WK Use of a cytosponge biomarker panel to prioritise endoscopic Barrett's oesophagus surveillance: a cross-sectional study followed by a real-world prospective pilot Lancet Oncol 23 2022 270 278 35030332
10 Penny HA Raju SA Sanders DS Progress in the serology-based diagnosis and management of adult celiac disease Expert Rev Gastroenterol Hepatol 14 2020 147 154 32011187
11 Monahan KJ Davies MM Abulafi M Faecal immunochemical testing (FIT) in patients with signs or symptoms of suspected colorectal cancer (CRC): a joint guideline from the Association of Coloproctology of Great Britain and Ireland (ACPGBI) and the British Society of Gastroenterology (BSG) Gut 71 2022 1939 1962 35820780
12 Rutter MD East J Rees CJ British Society of Gastroenterology/Association of Coloproctology of Great Britain and Ireland/Public Health England post-polypectomy and post-colorectal cancer resection surveillance guidelines Gut 69 2020 201 223 31776230
| 36495910 | PMC9729001 | NO-CC CODE | 2022-12-14 23:22:09 | no | Lancet Gastroenterol Hepatol. 2023 Jan 7; 8(1):6-8 | utf-8 | Lancet Gastroenterol Hepatol | 2,022 | 10.1016/S2468-1253(22)00387-9 | oa_other |
==== Front
J Am Acad Dermatol
J Am Acad Dermatol
Journal of the American Academy of Dermatology
0190-9622
1097-6787
by the American Academy of Dermatology, Inc.
S0190-9622(22)03139-5
10.1016/j.jaad.2022.11.037
JAAD Online
Response to comment on “Psoriasis and COVID-19: A bidirectional Mendelian randomization study”
Chalitsios Christos V. PhD a∗
Tsilidis Kostas K. PhD ab
Tzoulaki Ioanna PhD abc
a Department of Hygiene and Epidemiology, School of Medicine, University of Ioannina, Ioannina, Greece
b Department of Biostatistics and Epidemiology, School of Public Health, Imperial College London, London, UK
c BHF Centre of Excellence, School of Public Health, Imperial College London, London, UK
∗ Correspondence to: Christos V. Chalitsios, PhD, Department of Hygiene and Epidemiology, School of Medicine, University of Ioannina, 45110, Ioannina, Greece
25 11 2022
25 11 2022
© 2022 by the American Academy of Dermatology, Inc.
2022
American Academy of Dermatology, Inc.
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Key words
COVID-19
Dermatology
Epidemiology
genetics
Mendelian randomization
psoriasis
==== Body
pmcTo the Editor: We want to thank Gu et al1 for their interest in our article2 and comments. We appreciate that Gu et al1 recognized that our data showed some advantages.
Firstly, Gu et al1 raised concerns that 2 of the studies3 , 4 included in the meta-analysis genome-wide association studies (GWAS) of psoriasis we used5 do not mention that they have included doctor-diagnosed psoriasis; however, this is not accurate. Both Strange et al3 and Nair et al4 mentioned in Supplementary Table I, available via Mendeley at https://doi.org/10.17632/hff49h4zpn.1 that all psoriasis cases were doctor diagnosed. Consequently, our study2 included 13,229 doctor-diagnosed psoriasis cases. Secondly, our study2 reports all the sensitivity analyses (Weighted median, magnetic resonance [MR]-Egger, MR-Egger intercept, and MR-PRESSO). We have also included the results of the leave-one-out analysis as Supplementary Material, available via Mendeley at https://doi.org/10.17632/hff49h4zpn.1. Thirdly, the data used in our study come from the largest meta-analysis of GWAS for psoriasis,5 including data from 8 different Caucasian cohorts, so we cannot see any overlapping. Lastly, to our knowledge, there is no available GWAS of psoriasis stratified by severity to be able to examine the association between psoriasis and COVID-19 by mild, moderate, and severe psoriasis cases. We had already mentioned this as a limitation.
To conclude, using the latest available data, our study2 did not support that genetic predisposition to psoriasis is associated with higher susceptibility to being infected, hospitalized, or developing severe COVID-19.
Conflicts of interest
None disclosed.
Funding sources: None.
IRB approval status: Not applicable.
==== Refs
References
1 Gu X. Chen X. Shen M. Association of psoriasis with risk of COVID-19: a 2-sample Mendelian randomization study J Am Acad Dermatol 87 2022 715 717 35131400
2 Chalitsios C.V. Tsilidis K.K. Tzoulaki I. Psoriasis and COVID-19: a bidirectional Mendelian randomization study J Am Acad Dermatol 2022 10.1016/j.jaad.2022.10.019
3 Strange A. Capon F. Spencer C.C.A. A genome-wide association study identifies new psoriasis susceptibility loci and an interaction between HLA-C and ERAP1 Nat Genet 42 11 2010 985 990 20953190
4 Nair R.P. Duffin K.C. Helms C. Genome-wide scan reveals association of psoriasis with IL-23 and NF-κB pathways Nat Genet 41 2 2009 199 204 19169254
5 Tsoi L.C. Stuart P.E. Tian C. Large-scale meta-analysis characterizes genetic architecture for common psoriasis associated variants Nat Commun 8 2017 15382 28537254
| 36442640 | PMC9729065 | NO-CC CODE | 2022-12-14 23:25:01 | no | J Am Acad Dermatol. 2022 Nov 25; doi: 10.1016/j.jaad.2022.11.037 | utf-8 | J Am Acad Dermatol | 2,022 | 10.1016/j.jaad.2022.11.037 | oa_other |
==== Front
IPEM Transl
IPEM Transl
Ipem-Translation
2667-2588
Published by Elsevier Ltd on behalf of Institute of Physics and Engineering in Medicine (IPEM).
S2667-2588(22)00011-5
10.1016/j.ipemt.2022.100014
100014
Editorial
Special Issue – Sharing of best practices in response to the Covid-19 pandemic
8 12 2022
8 12 2022
100014© 2022 Published by Elsevier Ltd on behalf of Institute of Physics and Engineering in Medicine (IPEM).
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Keywords
Medical physics
Biomedical engineering
Clinical engineering
Medical technology
Pandemic
COVID-19
==== Body
pmcOn 30 January 2020, the Director-General of the World Health Organization declared that the outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) constituted a public health emergency of international concern. The ensuing pandemic has led to unprecedented disruption to all our lives. The health and social care workforce has by all accounts responded magnificently to the challenge, adapting existing working practices and procedures to safeguard healthcare workers and patients alike.
The selection of papers included in this special issue describe some of the many ways in which medical physicists, clinical scientists and engineers, in particular, have addressed issues raised during the course of the pandemic. They include a study of the effectiveness of the measurement protocols and algorithms used to estimate core body temperature from thermal mass screening data [1]. The authors present a regression model that generates comparatively accurate estimates of core body temperature from facial skin temperature readings, with potential application in the rapid mass screening of COVID cases. Another topic of widespread concern is the safety and efficacy of personal protective equipment. Here, [2] examine the cost-effectiveness of ultraviolet light germicidal irradiation as a disinfection method for non-oil, 95% efficiency (N95) respirators in a clinical setting.
The report by [3] concerns the use of portable imaging equipment to deliver care locally as part of the drive to establish integrated out-of-hospital care facilities. They present the findings of a pilot study that used a portable diagnostic X-ray set to acquire radiographs in residential homes and care facilities. Considering the relevant regulatory and safety issues, the investigators drew up a risk assessment for such procedures, which may enable patients to undergo treatment sooner and without recourse to a hospital visit. How such images may be used in the diagnosis of COVID-19 is the subject of a review undertaken by [4] into the latest deep learning techniques available for medical image processing.
Equal access to healthcare was the subject of a retrospective cohort study conducted by [5], the results of which highlight disparities in the utilisation of oncology telemedicine by certain patient groups compared to the general patient population. The findings have important implications for healthcare providers as they work to ensure equitable access to telemedicine and remote access programs.
The above articles will appear as a virtual collection of the Journal1 on ScienceDirect, to which further relevant papers we may receive will appear in due course. I take this opportunity to direct our international readers to IPEM's member magazine SCOPE2 , in which a number of articles have appeared during this period.
The changing nature of the virus, and our individual and collective responses to it, means that our daily activities and work patterns are likely to be impacted for some time to come. Such studies may well inform our responses to future health emergencies, as well as those that could be adopted by research and clinical communities elsewhere that continue to be affected by the disease.
Richard A. Black (PhD CEng FIMechE FIPEM)
Editor-in-Chief
IPEM–Translation
1 https://www.sciencedirect.com/journal/ipem-translation
2 https://www.ipem.ac.uk/resources/other-resources/scope/
==== Refs
Linked Content
1 Chayabhan Limpabandhu, Frances Sophie Woodley Hooper, Rui Li, Zion Tse, Regression Model for Predicting Core body Temperature in Infrared Thermal Mass Screening
2 Sergio I Prada, Safe and effective re-use policy for high-efficiency filtering facepiece respirators (FFRS): Experience of one hospital during the Covid-19 pandemic in 2020
3 Deborah Henderson, Stuart Mark, David Rawlings, Kevin Robson, Portable X-rays – a new era?
4 Marjan Jalali Moghaddam, Mina Ghavipour, Towards Smart Diagnostic Methods for COVID-19: Review of Deep Learning for Medical Imaging
5 Ling Tong, Ben George, Bradley Crotty, Somai Melek, … Jake Luo, Telemedicine and Health Disparities: Association between Patient Characteristics and Telemedicine, In-person, Telephone and message-based Care During the COVID-19 Pandemic
| 36510586 | PMC9729164 | NO-CC CODE | 2022-12-14 23:22:15 | no | IPEM Transl. 2022 Dec 8;:100014 | utf-8 | IPEM Transl | 2,022 | 10.1016/j.ipemt.2022.100014 | oa_other |
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
The Author(s). Published by Elsevier Ltd.
S2405-8440(22)03221-2
10.1016/j.heliyon.2022.e11933
e11933
Research Article
Association of alcohol consumption and frequency with loneliness: A cross-sectional study among Japanese workers during the COVID-19 pandemic
Konno Yusuke ab
Okawara Makoto a
Hino Ayako c
Nagata Tomohisa d
Muramatsu Keiji e
Tateishi Seiichiro f
Tsuji Mayumi g
Ogami Akira h
Yoshimura Reiji b
Fujino Yoshihisa a∗
for the CORoNaWork Project
a Department of Environmental Epidemiology, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Kitakyushu, Japan
b Department of Psychiatry, University of Occupational and Environmental Health, Kitakyushu, Japan
c Department of Mental Health, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Kitakyushu, Japan
d Department of Occupational Health Practice and Management, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Kitakyushu, Japan
e Department of Preventive Medicine and Community Health, School of Medicine, University of Occupational and Environmental Health, Kitakyushu, Japan
f Department of Occupational Medicine, School of Medicine, University of Occupational and Environmental Health, Kitakyushu, Japan
g Department of Environmental Health, School of Medicine, University of Occupational and Environmental Health, Kitakyushu, Japan
h Department of Work Systems and Health, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Kitakyushu, Japan
∗ Corresponding author.
8 12 2022
12 2022
8 12 2022
8 12 e11933e11933
13 2 2022
22 6 2022
18 11 2022
© 2022 The Author(s)
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Background
There are increasing concerns that prevention measures against coronavirus disease 2019 (COVID-19) such as social distancing and telework are leading to loneliness and poor lifestyle habits like increased alcohol consumption. The purpose of this study was to assess whether loneliness reported among workers during the COVID-19 pandemic is associated with changes in alcohol consumption.
Methods
The study comprised a cross-sectional, online survey of 27,036 workers between December 22 and 26, 2020. A questionnaire was used to assess loneliness, usual alcohol consumption and whether that consumption had changed. Odds ratios (ORs) were estimated by logistic regression analysis.
Results
A total of 2831 (10.5%) workers indicated they had increased alcohol consumption during the pandemic. Increased alcohol consumption was significantly associated with loneliness (OR = 1.94, 95%CI 1.70–2.21). This association held true for those who indicated they drank two or more days per week (OR = 1.98 95%CI 1.71–2.30) and those who drank less than one day per week (OR = 1.51 95%CI 0.71–3.25). In contrast, there was no association between increased alcohol consumption and loneliness among those who indicated they hardly ever drank (OR = 1.22 95%CI 0.55–2.72).
Conclusions
Among those drinking more than once a week, increased alcohol consumption is associated with loneliness.
COVID-19; Loneliness; Alcohol consumption; Japan; Workers.
Keywords
COVID-19
Loneliness
Alcohol consumption
Japan
Workers
==== Body
pmc1 Introduction
The first reported cases of coronavirus disease 2019 (COVID-19) emerged in December 2019 from Wuhan, China. Thereafter, the infection caused by SARS-CoV-2 spread worldwide and the situation was declared a pandemic by the World Health Organization (WHO) on March 31, 2020. Several vaccines have since been developed and deployed around the globe, helping to reduce disease severity and transmission of the virus [1]. However, as of July 2021, infection rates were continuing to rise, an indication that the virus will not be easily controlled. As a result, populations around the world were forced to continue taking precautions to prevent COVID-19 infection.
The WHO has recommended avoiding “the 3Cs,” namely closed spaces, crowded places, and close contact with others [2]. Physical distancing has formed a key part of recommendations by the WHO and governments around the world as a way to reduce the spread of COVID-19. The Japanese Ministry of Health, Labor, and Welfare, for example, has released practical examples of “new lifestyles,” recommending that people telework, work rotating shifts, keep their distance in the office, and conduct meetings online [3]. However, such practices could lead to a greater likelihood of social isolation and loneliness. Thus, there is concern that continuing measures such as physical distancing to prevent COVID-19 may cause loneliness and have a negative impact on physical and mental health [4].
A major problem that can accompany loneliness is increased alcohol consumption [5]. Several studies have reported an increase in alcohol consumption during the COVID-19 pandemic [6, 7, 8]. Data from the United States shows that alcohol sales and deliveries increased after the COVID-19 outbreak [9]. In Belgium, there was a 30.3% increase in alcohol consumption [10], and 21% of Canadians who were unlikely to go out drinking before the pandemic have started to do so [11]. Similar to before the pandemic, negative psychological stress, anxiety, and loneliness are suggested causes of the increase in alcohol consumption during the COVID-19 pandemic. In addition to loneliness, social isolation is also associated with mental health problems [12, 13]. While loneliness and social isolation are similar, they differ in that the former is subjective while the latter is objective [14]. Social isolation is thought to be a predictor of loneliness, which subsequently leads to psychological problems such as depression and anxiety [15].
COVID-19 prevention measures like telework are leading to more social isolation and loneliness among the working population. This study aimed to assess whether loneliness among workers in Japan during the COVID-19 pandemic was associated with changes in alcohol consumption.
2 Methods
2.1 Study design and subjects
This investigation formed part of the Collaborative Online Research on the Novel-Coronavirus and Work (CoroNaWork) Project, a cross-sectional study that used internet surveys between December 22 and 26, 2020 to examine the health of Japanese workers during the COVID-19 pandemic. This period comprised five weekdays prior to the New Year holiday period in Japan, where Christmas Eve, Christmas Day and Boxing Day are not considered holidays. A detailed description of the protocol is published elsewhere [16]. In brief, workers who indicated they were employed during the survey period were selected according to their prefecture of residence, occupation and sex. Workers’ questionnaire data were collected. Those with extremely short response times, height less than 140 cm, weight less than 30 kg, and inconsistent responses to multiple identical questions were excluded. Of the 33302 workers who returned the questionnaire, 6266 were excluded, leaving 27036 for analysis.
This study was conducted with the approval of the Ethics Committee of the University of Occupational and Environmental Health, Japan (Approval number R2-079). Informed consent was obtained through the questionnaire website.
2.2 Assessment of loneliness and social isolation
Loneliness was assessed using the question: “During the last 30 days, how frequently did you feel the following emotions?” Participants responded by choosing from never, a little, sometimes, usually, and always. For analysis, responses of “never” and “a little” were categorized as indicating no loneliness, while responses of “sometimes,” “usually,” and “always” were categorized as indicating loneliness.
Social isolation was assessed using three questions about whether the participants had friends to talk to, acquaintances to ask for favors, and people to communicate with through social networking sites. Participants chose from yes and no responses.
2.3 Assessment of usual and changes in alcohol consumption
A questionnaire was used to assess usual alcohol consumption and whether that consumption had changed during the COVID-19 pandemic. Drinking frequency was classified as at least two days per week, one day per week, or hardly ever. For analysis, alcohol consumption during the COVID-19 pandemic was classified as either increased or not increased.
2.4 Other covariates
The following were included as confounders: age, sex, marital status, equivalent income, education, smoking, job type, number of employees at the workplace, cumulative incidence rate of COVID-19 in the prefecture of residence, lack of friends to talk to, lack of acquaintances to ask for favors, and lack of people to communicate with through social networking sites. Age was treated as a continuous variable, while all other variables were used as categorical variables and presented as percentages.
Further, the cumulative incidence of COVID-19 in the prefecture of residence in the month immediately preceding the survey was treated as a community-level variable. These incidence data were obtained from the websites of public institutions.
2.5 Statistical analysis
Univariate and multivariate logistic regression analyses were used to determine odds ratios (ORs), with loneliness or drinking frequency as independent variables and change in alcohol consumption as a dependent variable. The multivariate model was adjusted for age, sex, marital status, equivalent income, education, smoking, alcohol consumption, job type, number of employees at the workplace, cumulative incidence rate of COVID-19 in the prefecture of residence, lack of friends to talk to, lack of acquaintances to ask for favors, and lack of people to communicate with through social networking sites. A p-value less than 0.05 indicated statistical significance. Stata (Stata Statistical Software: Release 16. College Station, TX: StataCorp LLC.) was used for analysis.
3 Results
Table 1 summarizes participants’ general characteristics. A total of 2831 of 27036 participants (10.5%) indicated they had increased their alcohol consumption during the COVID-19 pandemic. While there were no differences in age, marital status, education, or company size between those who did and did not increase their alcohol consumption, those who did so tended to be male, smokers and have higher income. There were no missing data among the responses of the 27036 people included in the analysis.Table 1 Characteristics of the study participants.
Table 1 No loneliness Loneliness
n = 24,286 n = 2,750
Age, mean (SD) 47.3 (10.5) 44.5 (10.1)
Sex, male (%) 12,601 (51.9%) 1,213 (44.1%)
Marital status, married 14,077 (58.0%) 952 (34.6%)
Job type
Mainly desk work 12,132 (50.0%) 1,336 (48.6%)
Mainly work involving communicating with people 6,243 (25.7%) 684 (24.9%)
Mainly labor 5,911 (24.3%) 730 (26.5%)
Equivalent income (million JPY)
40-249 4,910 (20.2%) 800 (29.1%)
250-375 6,714 (27.6%) 836 (30.4%)
376-499 6,046 (24.9%) 579 (21.1%)
≥500 6,616 (27.2%) 535 (19.5%)
Education
Junior high school 306 (1.3%) 62 (2.3%)
High school 6,190 (25.5%) 763 (27.7%)
University, graduate school, vocational school, junior college 17,790 (73.3%) 1,925 (70.0%)
Current smoker 6,274 (25.8%) 730 (26.5%)
Alcohol consumption
6–7 days a week 5,179 (21.3%) 495 (18.0%)
4–5 days a week 1,910 (7.9%) 167 (6.1%)
2–3 days a week 2,935 (12.1%) 331 (12.0%)
<1 day a week 4,071 (16.8%) 476 (17.3%)
Hardly ever 10,191 (42.0%) 1,281 (46.6%)
Number of employees in the workplace
<10 5,619 (23.1%) 546 (19.9%)
<100 6,183 (25.5%) 757 (27.5%)
<1000 6,379 (26.3%) 774 (28.1%)
>1000 6,105 (25.1%) 673 (24.5%)
Do you have friends to talk to? 17,029 (70.1%) 1,057 (38.4%)
Do you have acquaintances to ask for favors? 16,901 (69.6%) 932 (33.9%)
Do you have people to communicate with through social networking sites? 15,032 (61.9%) 1,136 (41.3%)
Increased alcohol consumption 2,383 (9.8%) 448 (16.3%)
Table 2 summarizes the ORs of loneliness associated with changes in alcohol consumption according to the logistic regression model. The age-adjusted model revealed a significant association between increased alcohol consumption and loneliness (OR = 1.78, 95%CI 1.59–1.98, p < 0.001), as did the multivariate analysis (OR = 1.88, 95%CI 1.65–2.15, p < 0.001). There was also a significant association between increasing alcohol consumption and a lack of friends to talk to in both the age-adjusted model (OR = 1.10 95%CI 1.02–1.20, p = 0.018) and multivariate analysis (OR = 1.13 95%CI 1.00–1.27, p = 0.050). While a lack of acquaintances to ask for favors was also associated with increasing alcohol consumption in the age-adjusted model (OR = 1.16 95%CI 1.07–1.26, p < 0.001) and multivariate analysis (OR = 1.20 95%CI 1.07–1.35, p = 0.003), a lack of people to communicate with through social networking sites was significantly associated with no increase in alcohol consumption in both the age-adjusted model (OR = 0.89 95%CI 0.82–0.97, p = 0.005) and multivariate analysis (OR = 0.86 95%CI 0.78–0.96, p = 0.005).Table 2 Association between loneliness and increased alcohol consumption.
Table 2Factor Age-sex adjusted Multivariate∗
OR 95% CI p OR 95% CI p
Loneliness
(+) 1.78 1.59 1.98 <0.001 1.88 1.65 2.15 <0.001
(-) reference reference
Do you have friends to talk to?
Yes 1.10 1.02 1.20 0.018 1.13 1.00 1.27 0.050
No reference reference
Do you have acquaintances to ask for favors?
Yes 1.16 1.07 1.26 <0.001 1.20 1.07 1.35 0.003
No reference reference
Do you have people to communicate with through social networking sites?
Yes 0.89 0.82 0.97 0.005 0.86 0.78 0.96 0.005
No reference reference
Alcohol consumption
6–7 days a week 107.53 80.36 143.89 <0.001 108.40 80.91 145.24 <0.001
4–5 days a week 99.65 73.75 134.65 <0.001 100.02 73.92 135.33 <0.001
2–3 days a week 61.76 45.96 82.99 <0.001 61.49 45.72 82.69 <0.001
<1 day a week 17.91 13.22 24.26 <0.001 17.78 13.12 24.09 <0.001
Hardly ever reference reference
∗ The multivariate model was adjusted for age, sex, marital status, equivalent income, education, smoking, job type, number of the employees at the workplace, cumulative incidence rate of COVID-19 in the prefecture of residence, lack of friends to talk to, lack of acquaintances to ask for favors, lack of people to communicate with through social networking sites.
Compared to those who did not drink, both those who drank less than one day per week and those who drank two or more days per week were significantly more likely to increase their alcohol consumption. Since the interaction term was significant, we further estimated the OR of loneliness associated with increased alcohol consumption for each category of drinking frequency using a logistic regression model. The results are shown in Table 3 . Among those who drank two or more days per week, increasing alcohol consumption was significantly associated with loneliness in the age-adjusted model (OR = 2.15 95%CI 1.87–2.47, p < 0.001) and multivariate analysis (OR = 1.91 95%CI 1.64–2.21, p < 0.001). A similar finding was observed for those who drank less than one day per week: increasing alcohol consumption was significantly associated with loneliness in the age-adjusted model (OR = 1.91 95%CI 1.40–2.58, p < 0.001) and multivariate analysis (OR = 1.82 95%CI 1.31–2.52, p < 0.001). In contrast, among those who hardly ever drank, increased alcohol consumption was not associated with loneliness either in the age-adjusted model (OR = 1.51 95%CI 0.71–3.25, p = 0.281) or multivariate analysis (OR = 1.21 95%CI 0.54–2.70, p = 0.651).Table 3 Odds ratios of loneliness associated with increased alcohol consumption by category of drinking frequency.
Table 3Category of drinking frequency Univariate Multivariate∗
OR 95%CI p OR 95%CI p
Loneliness (+)
More than 2 days per week 2.15 1.87 2.47 <0.001 1.91 1.64 2.21 <0.001
Less than 1 day per week 1.91 1.4 2.58 <0.001 1.82 1.31 2.52 <0.001
Hardly ever 1.51 0.71 3.25 0.281 1.21 0.54 2.70 0.651
Loneliness (-) reference reference
∗ The multivariate model was adjusted for age, sex, marital status, equivalent income, education, smoking, job type, number of the employees in the workplace, cumulative incidence rate of COVID-19 in the prefecture of residence, lack of friends to talk to, lack of acquaintances to ask for favors, lack of people to communicate with through social networking sites.
4 Discussion
In this study, approximately 10% of workers indicated they had increased their alcohol consumption during the first 9 months of the pandemic. Loneliness, a lack of friends to talk to, and lack of acquaintances to ask for favors were associated with increased alcohol consumption. In contrast, a lack of people to communicate with through social networking sites was associated with decreased alcohol consumption. We also found that the association between loneliness and increased alcohol consumption varied depending on an individual's previous consumption: a greater association between loneliness and increased alcohol consumption was observed among those who more frequently consumed alcohol prior to the start of the pandemic.
Similar to our finding that alcohol consumption increased in 10% of workers during the COVID-19 pandemic, previous studies have reported a rise in alcohol consumption during disaster events such as natural disasters and infectious disease outbreaks. Studies have reported that individuals drank more in the aftermath of the tsunami in Southeast Asia in 2004 and Hurricanes Katrina and Rita in 2005 than before the disasters [17, 18]. In addition, alcohol consumption increased during the Severe Acute Respiratory Syndrome (SARS) pandemic [19, 20]. Negative coping behaviors like drinking are thought to suppress loneliness as well as depression and anxiety [21], suggesting that the increase in alcohol consumption observed in this study may be related to the depression, anxiety, and loneliness associated with the COVID-19 pandemic.
It should be emphasized that even those who drank only about once a week before the COVID-19 pandemic increased their drinking frequency when they felt lonely. This is in contrast to other studies on the COVID-19 pandemic, which showed that while heavy and frequent drinkers increased their alcohol consumption, occasional drinkers did not [22, 23, 24]. The difference between these studies and our study is that we took loneliness into account. Our findings suggest that loneliness, even among occasional drinkers, may increase drinking and the risk of inappropriate drinking.
In addition to loneliness, we also inquired about social isolation, namely whether participants had friends to talk to, acquaintances to ask for favors, and people to communicate with through social networking sites. A lack of friends to talk to and acquaintances to ask for favors are considered indicators of social isolation, and, like loneliness, were associated with increased alcohol consumption. However, those who indicated they had people to communicate with through social networking sites, suggesting the absence of social isolation, tended to increase alcohol consumption. Despite some overlap, loneliness and social isolation describe different phenomena [14]. Some individuals who are socially isolated do not experience loneliness but rather feel comfortable with their situation. An example of this is the phenomenon known as "super solo" in Japan [25], where social isolation is seen as an advantage and something to be enjoyed to the fullest. Those engaged in "super solo," while isolated, do not experience feelings of loneliness. Further, while social media can be a useful way to reduce loneliness while preventing COVID-19 infection [26, 27], it has also been identified as a possible factor responsible for the deterioration of individuals’ mental health due to the overflow of inaccurate information on the COVID-19 pandemic [4]. Our findings suggest that use of social networking sites does not relieve feelings of loneliness, but may instead enhance negative feelings such as anxiety, which may in turn increase alcohol consumption.
Several limitations warrant mention. First, because this study was conducted using Internet monitors, generalizability of the results is unclear. However, we attempted to minimize subject bias as much as possible by sampling by region, occupation, and prefecture based on the incidence of infection. Second, alcohol consumption was self-reported in this study. Given that drinkers tend to underreport their alcohol consumption [28], the same may have occurred in this study. However, we think that underreporting was unlikely because this study was an anonymous survey conducted on the Internet. Third, there are several ways to assess loneliness [29, 30]. We assessed loneliness with a single question because otherwise we would have needed to ask a multitude of questions. Therefore, in this study, loneliness was assessed using the question, “Have you ever felt alone?” This strategy is based on a previous study, which assessed loneliness with a single question [31]. Further studies using more comprehensive methods for assessing loneliness should be performed to confirm our findings.
5 Conclusion
During the COVID-19 pandemic, physical distancing and telecommuting, along with refraining from going out and socializing have become the norm. In addition, the risk of infection and concerns about job and economic insecurity are spreading among individuals. Against this backdrop, there are concerns that isolation and loneliness among workers may be causing an increase in alcohol consumption. We found that 10% of workers in Japan had increased in their alcohol consumption during the pandemic. For those who drank more than once a week, increased alcohol consumption was associated with loneliness. Thus, workers experiencing loneliness may be at high risk of inappropriate drinking. Future prevention strategies should thus take into account the interconnectedness of inappropriate drinking and loneliness, as both lead to negative health outcomes.
Declarations
Author contribution statement
Yusuke Konno: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Wrote the paper.
Makoto Okawara: Performed the experiments; Contributed reagents, materials, analysis tools or data.
Ayako Hino, Tomohisa Nagata, Keiji Muramatsu, Seiichiro Tateishi, Mayumi Tsuji, Akira Ogami: Conceived and designed the experiments; Contributed reagents, materials, analysis tools or data.
Reiji Yoshimura: Analyzed and interpreted the data; Wrote the paper.
Yoshihisa Fujino: Conceived and designed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.
Funding statement
This study was supported and partly funded by the University of Occupational and Environmental Health, Japan; General Incorporated Foundation (Anshin Zaidan); The Development of Educational Materials on Mental Health Measures for Managers at Small-sized Enterprises; Health, Labour and Welfare Sciences Research Grants; Comprehensive Research for Women's Healthcare (H30-josei-ippan-002); Research for the Establishment of an Occupational Health System in Times of Disaster (H30-roudou-ippan-007), Research for AIDS Policy (JPMP 20 HB 1004), and scholarship donations from Chugai Pharmaceutical Co., Ltd., the Collabo-Health Study Group, and Hitachi Systems, Ltd.
Data availability statement
Data will be made available on request.
Declaration of interest's statement
The authors declare no conflict of interest.
Additional information
No additional information is available for this paper.
Acknowledgements
The current members of the CORoNaWork Project, in alphabetical order, are Yoshihisa Fujino (present chairperson of the study group), Akira Ogami, Arisa Harada, Ayako Hino, Hajime Ando, Hisashi Eguchi, Kazunori Ikegami, Kei Tokutsu, Keiji Muramatsu, Koji Mori, Kosuke Mafune, Kyoko Kitagawa, Masako Nagata, Mayumi Tsuji, Ning Liu, Rie Tanaka, Ryutaro Matsugaki, Seiichiro Tateishi, Shinya Matsuda, Tomohiro Ishimaru, and Tomohisa Nagata. All members are affiliated with the University of Occupational and Environmental Health, Japan.
==== Refs
References
1 Dagan N, Barda N, Kepten E, et al. BNT162b2 mRNA covid-19 vaccine in a nationwide mass vaccination setting. N Engl J Med. Published online February 24, 2021. doi:10.1056/NEJMoa2101765
2 Advice for the public on COVID-19 – World Health Organization. Accessed February 1, 2021. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/advice-for-public.
3 Ministry of Health, Labour and Welfare. A practical example of a “new lifestyle” for COVID-19. https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/0000121431_newlifestyle.html.
4 Holmes E.A. O’Connor R.C. Perry V.H. Multidisciplinary research priorities for the COVID-19 pandemic: a call for action for mental health science Lancet Psychiatr. 7 6 2020 547 560
5 Tanskanen J. Anttila T. A prospective study of social isolation, loneliness, and mortality in Finland Am. J. Publ. Health 106 11 2016 2042 2048
6 Rehm J. Kilian C. Ferreira-Borges C. Alcohol use in times of the COVID 19: implications for monitoring and policy Drug Alcohol Rev. 39 4 2020 301 304 32358884
7 Da B.L. Im G.Y. Schiano T.D. Coronavirus disease 2019 hangover: a rising tide of alcohol use disorder and alcohol-associated liver disease Hepatology 72 3 2020 1102 1108 32369624
8 Clay J.M. Parker M.O. Alcohol use and misuse during the COVID-19 pandemic: a potential public health crisis? Lancet Public Health 5 5 2020 e259 32277874
9 Colbert S. Wilkinson C. Thornton L. Richmond R. COVID-19 and alcohol in Australia: industry changes and public health impacts Drug Alcohol Rev. 39 5 2020 435 440 32483882
10 Vandenbroucke J.P. von Elm E. Altman D.G. Strengthening the reporting of observational studies in epidemiology (STROBE): explanation and elaboration Int. J. Surg. 12 12 2014 1500 1524 25046751
11 Nanos for the Canadian Centre on Substance Use and Addiction. Boredom and Stress Drives Increased Alcohol Consumption during COVID-19: NANOS Poll Summary Report. Published online May 2020. https://www.ccsa.ca/sites/default/files/2020-06/CCSA-NANOS-Increased-Alcohol-Consumption-During-COVID-19-Report-2020-en_0.pdf.
12 Matthews T. Danese A. Wertz J. Social isolation, loneliness and depression in young adulthood: a behavioural genetic analysis Soc. Psychiatr. Psychiatr. Epidemiol. 51 3 2016 339 348
13 Taylor H.O. Taylor R.J. Nguyen A.W. Chatters L. Social isolation, depression, and psychological distress among older adults J. Aging Health 30 2 2018 229 246 28553785
14 Wheeler L. Reis H. Nezlek J. Loneliness, social interaction, and sex roles J. Pers. Soc. Psychol. 45 4 1983 943 953 6631669
15 Sanders R. COVID-19, social isolation and loneliness. Published April 22, 2020. Accessed February 24, 2021. https://www.iriss.org.uk/resources/esss-outlines/covid-19-social-isolation-and-loneliness.
16 Fujino Y. Ishimaru T. Eguchi H. Protocol for a nationwide internet-based health survey of workers during the COVID-19 pandemic in 2020 J. UOEH 43 2 2021 217 225 34092766
17 Cerdá M. Tracy M. Galea S. A prospective population based study of changes in alcohol use and binge drinking after a mass traumatic event Drug Alcohol Depend. 115 1-2 2011 1 8 20977977
18 Nordløkken A. Pape H. Wentzel-Larsen T. Heir T. Changes in alcohol consumption after a natural disaster: a study of Norwegian survivors after the 2004 Southeast Asia tsunami BMC Publ. Health 13 2013 58
19 Wu P. Liu X. Fang Y. Alcohol abuse/dependence symptoms among hospital employees exposed to a SARS outbreak Alcohol Alcohol 43 6 2008 706 712 18790829
20 Brooks S.K. Dunn R. Amlôt R. Rubin G.J. Greenberg N. A systematic, thematic review of social and occupational factors associated with psychological outcomes in Healthcare employees during an infectious disease outbreak J. Occup. Environ. Med. 60 3 2018 248 257 29252922
21 Gonçalves P.D. Moura H.F. do Amaral R.A. Castaldelli-Maia J.M. Malbergier A. Alcohol use and COVID-19: can we predict the impact of the pandemic on alcohol use based on the previous crises in the 21st century? A brief review Front. Psychiatr. 11 2020 581113
22 Manthey J. Kilian C. Schomerus G. Kraus L. Rehm J. Schulte B. Alcohol consumption in Germany and europe during the SARS-CoV-2 pandemic SUCHT 66 5 2020 247 258
23 Rossow I. Bye E.K. Moan I.S. Kilian C. Bramness J.G. Changes in alcohol consumption during the COVID-19 pandemic-small change in total consumption, but increase in proportion of heavy drinkers Int. J. Environ. Res. Publ. Health 18 8 2021
24 Korean Addiction Forum (KAF) Republic of Korea: COVID-19 pandemic & change in addictive behaviors http://www.addictionfr.org/2020/bbs/board.php?bo_table=sub06_1&wr_id=33&page=1&ckattempt=1 2020 Accessed July 30, 2021
25 Lufkin B. The rise of Japan’s “super solo” culture. BBC. https://www.bbc.com/worklife/article/20200113-the-rise-of-japans-super-solo-culture. Accessed August 14, 2021.
26 Hajek A. König H.H. Social isolation and loneliness of older adults in times of the COVID-19 pandemic: can use of online social media sites and video chats assist in mitigating social isolation and loneliness? Gerontology 67 1 2021 121 124 33264778
27 Kahlon M.K. Aksan N. Aubrey R. Effect of layperson-delivered, empathy-focused program of telephone calls on loneliness, depression, and anxiety among adults during the COVID-19 pandemic: a randomized clinical trial JAMA Psychiatr. 2021 Published online February 23
28 Knibbe R.A. Bloomfield K. Alcohol consumption estimates in surveys in europe: comparability and sensitivity for gender differences Subst. Abuse 22 1 2001 23 38
29 Holt-Lunstad J. Smith T.B. Layton J.B. Social relationships and mortality risk: a meta-analytic review PLoS Med. 7 7 2010 e1000316
30 Holt-Lunstad J. Smith T.B. Baker M. Harris T. Stephenson D. Loneliness and social isolation as risk factors for mortality: a meta-analytic review Perspect. Psychol. Sci. 10 2 2015 227 237 25910392
31 Courtin E. Knapp M. Social isolation, loneliness and health in old age: a scoping review Health Soc. Care Community 25 3 2017 799 812 26712585
| 36510560 | PMC9729165 | NO-CC CODE | 2022-12-14 23:29:59 | no | Heliyon. 2022 Dec 8; 8(12):e11933 | utf-8 | Heliyon | 2,022 | 10.1016/j.heliyon.2022.e11933 | oa_other |
==== Front
J Pain Symptom Manage
J Pain Symptom Manage
Journal of Pain and Symptom Management
0885-3924
1873-6513
Published by Elsevier Inc. on behalf of American Academy of Hospice and Palliative Medicine.
S0885-3924(22)01003-X
10.1016/j.jpainsymman.2022.11.022
Brief Report
Just-in-Time Decision Making: Preliminary Findings of a Goals of Care Rapid Response Team
Zhukovsky Donna S. MD 1*⁎⁎
Heung Yvonne MD 1*
Enriquez Parema MSN, APRN, FNPc 1
Itzep Nelda MD 2
Lu Zhanni DrPH 1
Nortje Nico PhD, MA (Psyc), Mphil, HEC-C 3
Stanton Penny CCRP 1
Wong Angelique MD 1
Bruera Eduardo MD 1
1 Palliative, Rehabilitation and Integrative Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA
2 Pediatrics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA
3 Critical Care Medicine-Section of Integrated Ethics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA
⁎⁎ Corresponding author: Donna S. Zhukovsky, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd. Unit 1414, Houston, Texas 77030, USA, Phone: 713-792-3937, Fax: 713-792-6092
⁎ Co-First Authors
8 12 2022
8 12 2022
26 11 2022
© 2022 Published by Elsevier Inc. on behalf of American Academy of Hospice and Palliative Medicine.
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Context
The COVID-19 pandemic placed the issue of resource utilization front and center. Our comprehensive cancer center developed a Goals of Care Rapid Response Team (GOC RRT) to optimize resource utilization balanced with goal-concordant patient care.
Objectives
Primary study objective was to evaluate feasibility of the GOC RRT by describing the frequency of consultations that occurred from those requested. Secondary objectives included adherence to consultation processes in terms of core team member participation and preliminary efficacy in limiting care escalation.
Methods
We conducted a retrospective chart review of patients referred to GOC RRT (3/23/2020-9/30/2020). Analysis was descriptive. Categorical variables were compared with Fisher's exact or Chi-Square tests and continuous variables with Mann-Whitney U tests.
Results
Eighty-nine patients were referred. Eighty-five percent (76/89) underwent a total of 95 consultations. Median (range) patient age was 61 (49, 69) years, 54% (48/89) male, 19% (17/89) Hispanic, 48% (43/89) White, 73% (65/89) married/partnered and 66% (59/89) Christian. Hematologic malignancies and solid tumors were evenly balanced (53% [47/89] vs 47% [42/89, P=0.199]). Most patients (82%, 73/89) had metastatic disease or relapsed leukemia. Seven percent (6/89) had confirmed COVID-19. Sixty-nine percent (61/89) died during the index hospitalization. There was no statistically significant difference in demographic or clinical characteristics among groups (no consultation, 1 consultation, >1 consultation). Core team members were present at 64% (61/95) of consultations. Care limitation occurred in 74% (56/76) of patients.
Conclusion
GOC RRT consultations were feasible and associated with care limitation. Adherence to core team participation was fair.
Key Words
goals of care
goal-concordant care
advance care planning
care limitation
family meetings
==== Body
pmc Key Message
This descriptive study finds that implementation of a Goals of Care Rapid Response Team for supporting provision of goal concordant care is feasible for critically ill patients at a comprehensive cancer center. Preliminary outcomes suggest that these consultations are associated with goal-concordant care limitation.
Alt-text: Unlabelled box
Introduction
The COVID-19 pandemic placed the issue of adequacy of patient care resources front and center. It is well established that patients, families and providers often have an optimistic bias regarding prognosis and of the outcome of available treatments. (1) Accordingly, patients and families may request care that is unlikely to provide medical benefit or that is inconsistent with their values and goals for care, leading to undue suffering and financial insolvency. Under ideal circumstances, goals of care (GOC) discussions are a continuum of earlier stage discussions of advance care planning (ACP) that take place over the course of time with a trusted provider. Communication of the outcome of these conversations to key stakeholders that include patients, family members, surrogate decision makers and health care professionals is vital for the provision of goal concordant patient care. (1)
For numerous reasons well described elsewhere, these conversations often do not occur in a timely fashion or at all, leading to care that may not be aligned with patient preferences. (2, 3) When conversations occur, they frequently do not include key stakeholders and may result in an incomplete or inaccurate understanding of the patient's values and goals for care, prognosis and care outcomes. (1) Moreover, preferences may change over time. (4, 5) Advance directives, which serve as imperfect proxies for ACP, are present in only a minority of adults. (1, 6) At our institution, despite increasing efforts over the years, quality improvement projects focused on selecting and preparing a medical decision maker have not yielded a significant increase in scanned Medical Power of Attorney documents available in the electronic health record (EHR) from a low baseline frequency. (7, 8)
At the onset of the COVID-19 pandemic our institution created a Goals of Care Rapid Response Team (GOC RRT). Developed in response to anticipated increase in critical illness and resource limitations, the goal was to promote rapid and effective alignment between care provided and patient preference. The GOC RRT was for all cancer patients regardless of COVID status, to support best practice in communication for GOC discussions (9, 10, 11, 12) in critically ill patients who did not have GOC conversations documented in the electronic health record (EHR). Designed to focus on patients deemed to be at imminent risk for transfer to the intensive care unit (ICU) or at high likelihood for transition to a higher level of care, the GOC RRT was deployed to clarify GOC after receiving approval from the patient's oncologist.
The intent of the GOC RRT consultation was to promote patient and family understanding of the patient's medical situation, prognosis and treatment options by discussion with a trusted oncologist, while ensuring the medical team's understanding of the patient's GOC. The process was underpinned by supportive care expertise in facilitating communication. It was anticipated that the GOC consultation would lead to higher rates of “do not resuscitate status,” “no escalation” in level of care and transition to symptom-oriented supportive care and hospice care.
GOC RRT consultations typically took place within 24 hours of request and often within 3-4 hours. For patients, invited members included the patient (if able to participate), designated medical power of attorney (MPOA) or legally authorized medical decision maker per state hierarchy and others, as desired by the patient or medical decision maker. For the medical team, invited members were the patient's primary medical oncologist, responsible inpatient oncologist or hospitalist, critical care provider, supportive (palliative) care GOC provider, social worker, ethicist and chaplain. Designated core team members were Clinical Ethics, Medical Oncology, Supportive Care and Social Work.
The primary study objective was to evaluate feasibility of the GOC RRT by describing the frequency of consultations that occurred from those requested. Secondary objectives were to describe adherence to consultation processes in terms of core team member participation and to explore preliminary efficacy in limiting care escalation.
Methods
This study was conducted as a retrospective chart review of patients referred for GOC RRT consultation from 3/23/2020-9/30/2020. Patients with a poor prognosis at risk for deterioration and escalation of care were identified from daily review of patients on the Medical Emergency Rapid Intervention Team (MERIT) list, those in the ICU and patients receiving high flow oxygen or BiPAP. The MERIT evaluates patients experiencing non-emergent clinical deterioration such as change in vital signs, chest pain, symptoms of sepsis or change in mental status. Patients without an ACP note documented in the EHR were referred for GOC RRT, after approval of the primary medical oncologist.
Care limitation was defined as at least one of the following: 1. Change in location to lower intensity level (i.e. from ICU to regular nursing (medical/surgical) unit or acute palliative care unit), 2. Change in resuscitation status from full code to do not resuscitate and 3. Withdrawal of life sustaining therapy. Analysis was descriptive. Categorical variables were compared with Fisher's exact or Chi-Square tests and continuous variables with Mann-Whitney U tests. P-value significance level was less than 0.05.
Results
Over the 6-month study period, 89 patients were referred. Eighty-five percent (76/89) underwent a total of 95 consultations. Twelve percent (11/89) had multiple consultations (range 2-5; Figure 1 ). Reasons referred patients did not undergo GOC RRT consultation are noted in Table 1 .Figure 1 Consultation Completion Status of Patients Referred for GOC RRT (N=89)
Figure 1:
Table 1 Reasons patients did not undergo GOC RRT consultation (N=13)
Table 1: N %
Primary oncologist felt there had been adequate discussion with patient/family 5 38.5%
Family decided to de-escalate care 5 38.5%
Other 3 23.1%
Other: Family declined consultation (n=1), wrong consultation type placed-wanted supportive care assisted GOC consult (n=1), no information 1 (n=1)
For completed consultations, all core team disciplines were present in 68% (52/76) for the first consultation and in 64% (61/95) of all consultations. Medical Oncology was present in 83% (63/76) of first consultations, Supportive Care in 96% (73/76), Clinical Ethics in 87% (66/76) and Social Work in 96% (73/76).
Patients’ demographic and clinical characteristics are noted in Tables 2 and 3 . Median (range) patient age was 61 (49, 69) years, 54% (48/89) male, 19% (17/89) Hispanic, 48% (43/89) White, 73% (65/89) married/partnered and 66% (59/89) of the Christian faith. Hematologic malignancies and solid tumors were evenly balanced (53% [47/89] vs 47% [42/89, P=0.199]). Most patients (82%, 73/89) had metastatic disease or relapsed leukemia. Seven percent (6/89) had confirmed COVID-19.Table 2 Demographic Characteristics of Patients REFERRED for GOC RRT Consultations (N=89)
Table 2:Characteristic Total Patients Referred for GOC RRT Consultation (N=89) Patients’ GOC RRT Consultation Status
Consultation Completed (N=76) Consultation Not Completed (N=13) P Value
Age, years Median (Range) 61 (27, 86) 60 (27, 84) 66 (38, 86) .193
Sex N (%) Female 41 (46.1%) 34 (44.7%) 7 (53.8%) .543
Male 48 (53.9%) 42 (55.3%) 6 (46.2%)
Marital status N (%) Married, Significant Other 65 (73.0%) .742
Divorced, Single, Widowed 24 (27.0%) 20 (26.3%) 4 (30.8%)
Religion N (%) Catholic 19 (21.3%) 14 (18.4%) 5 (38.5%) .411
Christian (not Catholic) 40 (44.9%) 36 (47.4%) 4 (30.8%)
None 7 (7.9%) 6 (7.9%) 1 (7.7%)
Other 23 (25.8%) 20 (26.3%) 3 (23.1%)
Ethnicity N (%) Hispanic or Latino 17 (19.1%) 15 (19.7%) 2 (15.4%) 1.00
Not Hispanic or Latino 70 (78.7%) 59 (77.6%) 11 (84.6%)
Patient declined to specify and Unknown 2 (2.2%) 2 (2.6%) 0 (0.0%)
Race N (%) Asian 11 (12.4%) 8 (10.5%) 3 (23.1%) .243
Black or African American 16 (18.0%) 12 (15.8%) 4 (30.8%)
White or Caucasian 43 (48.3%) 39 (51.3%) 4 (30.8%)
Other 19 (21.3%) 17 (22.4%) 2 (15.4%)
Table 3 Clinical Characteristics of Patients REFERRED for GOC RRT Consultation (N=89)
Table 3:Characteristic Total Patients Referred for GOC RRT Consultation (N=89) Patients’ GOC RRT Consultation Status
Consultation Completed (N=76) Consultation Not Completed (N=13) P Value
Tumor diagnosis N (%) Solid tumor and non-cancer diagnoses 42 (47.2%) 38 (50.0%) 4 (30.8%) .199
Acute leukemia, MDS, lymphoma, myelofibrosis, myeloma, amyloidosis 47 (52.8%) 38 (50.0%) 9 (69.2%)
Disease status N (%) Localized, locally advanced and non-cancer diagnoses 12 (13.5%) 11 (14.5%) 1 (7.7%) .132
Metastatic cancer or leukemia in ≥1 relapse 73 (82.0%) 63 (82.9%) 10 (76.9%)
Without evidence of cancer ≥1 year 4 (4.5%) 2 (2.6%) 2 (15.4%)
COVID status at referral N (%) Confirmed diagnosis 6 (6.7%) 5 (6.6%) 1 (7.7%) .573
Not detected 75 (84.3%) 65 (85.5%) 10 (76.9%)
Hospitalization characteristics of the referred population are in Table 4 . Median (range) hospital length of stay (LOS) for referred patients was 9 days (0-243). 69% (61/89) died while hospitalized. At discharge, 20% (18/89) were hospitalized on the Acute Palliative Care Unit. There were no statistically significant differences in demographic, clinical or hospitalization characteristics between patients referred for consultation by completion status (not completed, 1 or ≥1). Care limitation occurred in 74% (56/76) of patients who underwent consultation (Table 5 ). Of these patients, 9% (7/76) had a living will and 24% (18/76) had a medical power of attorney (MPOA) available in the EHR. All patients who had a living will also had a MPOA.Table 4 Hospitalization Characteristics of Patients REFERRED for GOC RRT Consultations (N=89)
Table 4:Characteristic GOC RRT Consultation Completion Status
Total (N=89) 0 (N=13) 1 (N=65) >1 (N=11) P Value* P Value⁎⁎
1st MDACC visit to hospital admission (months)
Median (Range) 9 (0-243) 11 (0-185) 9 (0-169) 3 (0-243) .584 .400
Hospital length of stay (days)
Median (range) 14 (1-373) 9 (3-81) 14 (1-373) 20 (4-131) .444 .412
Discharge disposition N (%)
Expired 61 (68.5%) 10 (76.9%) 42 (64.6%) 9 (81.8%) .973 .834
Home without hospice 15 (16.9%) 2 (15.4%) 12 (18.5%) 1 (9.1%)
Home with hospice 8 (9.0%) 1 (7.7%) 6 (9.2%) 1 (9.1%)
Other 5 (5.6%) 0 (0.0%) 5 (7.7%) 0 (0.0%)
Hospital discharge service N (%)
Supportive Care 18 (20.2%) 4 (30.8%) 14 (21.5%) 0 (0.0%) .161 .200
Other Services 71 (79.8%) 9 (69.2%) 51 (78.5%) 11 (100.0%)
⁎ for comparison of all 3 groups (0 vs 1 vs >1)
⁎⁎ for comparison of 1 vs >1 GOC RRT consultation
Table 5 GOC RRT Consultation Outcomes
Table 5:Variables Measures Total (N=76) GOC RRT Number of Incidences P*
1 (N=65) 2-5 (N=11)
Outcome of GOC RRT per patient, N (%) Care limitation 56 (73.7%) 49 (75.4%) 7 (63.6%) .466
All others 20 (26.3%) 16 (24.6%) 4 (36.4%)
Change of location, N (%) APSCU, RNF, hospice 16 (21.1%) 15 (23.1%) 1 (9.1%) .440
All others 60 (78.9%) 50 (76.9%) 10 (90.9%)
Change in resuscitation status from FULL CODE to Do Not Resuscitate (DNR), N (%) Yes 50 (65.8%) 44 (67.7%) 6 (54.5%) .496
All others 26 (34.2%) 21 (32.3%) 5 (45.5%)
Did patient have any withdrawal of an LST? N (%) Yes 16 (21.1%) 14 (21.5%) 2 (18.2%) 1.00
No 60 (78.9%) 51 (78.5%) 9 (81.8%)
Discussion
The GOC RRT consultation was a novel intervention designed and established by the palliative care team with a different structure and format from a traditional supportive/palliative care consultation. The GOC RRT included a palliative care specialist, clinical ethicist, primary and/or inpatient oncology attending/hospitalist, intensivist and critical care social worker (13, 14). The palliative care specialist actively participated in all aspects of the meeting; the ethicist facilitated these meetings. Clinical ethicists, like palliative care specialists, are experienced in facilitating difficult conversations in emotionally charged circumstances. Typically, their focus is on identification and analysis of ethical questions and values clarification in the context of uncertainty, supporting collaborative decision making among the patient, family and medical team. The nature of their intervention is episodic, as relates to the meeting (15, 16, 17), while palliative care provides ongoing clinical care. They can be highly complementary to one another.
The GOC RRT was intended to be distinct from palliative care consultations. Benefits were two-fold. At our and most institutions, supportive/palliative care consultation are discretionary and requires a provider order. Keeping supportive/palliative care consultation distinct from GOC RRT consultations allowed the supportive/palliative care team to avoid the negative associations of mandatory consultations. Simultaneously, the GOC RRT process allowed the supportive care team to introduce themselves and the care offered in a supportive and beneficial light. For many patients, the GOC RRT was their first introduction to supportive/palliative care and often served as the impetus for supportive care consultation, thus enabling the supportive care team to establish an ongoing relationship of care with the patient and family. While clinical ethicists can provide ongoing support with decision making in the short term, they cannot provide the full spectrum of symptom management, emotional and spiritual support that the supportive/palliative care team provides.
The GOC RRT was not intended to replace ongoing efforts to introduce ACP and GOC conversations earlier in the disease trajectory or to replace supportive/palliative care consultations. The goal was to support real time conversations for patients who had not yet established GOC, prioritized by need. These conversations typically do not take place early in the health care continuum or at all. (1, 7, 8) When they do, often they are not conducted in a manner that is meaningful to the patient's current situation; additionally, patients’ preferences may change. (1, 2) The GOC RRT process was to supplement processes supporting downstream GOC discussion and was targeted at critically ill individuals without documented GOC discussions. The GOC RRT has continued at our institution as we have moved to the endemic phase of COVID-19, as one more tool to provide goal-aligned patient care.
While designed as a one-time intervention, in 15% of patients, it was repeated. GOC RRT consultations frequently served as a source of new supportive care referrals for patient, family and provider support. The sudden onset of the pandemic generated great distress among clinicians who were not well prepared to conduct serious illness conversations with their patients. The GOC RRT was widely available to help them conduct such conversations in a safe manner with the presence of the supportive care and ethics teams, as many had limited experience conducting such conversations. Future research is needed to explore if these clinicians have now adopted these conversations into regular practice.
Our findings show that GOC RRT consultations were feasible: 85% of referrals underwent consultation. Consultations appeared to reach the population of interest. These patients were critically ill, as evidenced by 67 percent in-hospital mortality, did not have documented GOC conversations in the EHR and were largely hospitalized in the intensive care unit, a focus of high resource utilization and patient/caregiver distress. While most patients did not have COVID-19, they were impacted by the pandemic with fear of infection, visitation restrictions and potential resource limitation.
Lack of differences in demographic, clinical and hospitalization characteristics by consultation completion status may speak to lack of perceived need in consultation non-completers. In the majority, the oncologist felt there had been enough discussion about prognosis and goals of care or the patient and/or family had already made a choice to de-escalate care.
At a high level, adherence to GOC RRT consultation processes was fair. Potential reasons for variable participation by discipline include logistics, competing responsibilities and discomfort with GOC discussions. It was more challenging for patients’ primary oncologists to accommodate last minute changes in clinic schedules, as compared to on-site ICU, social work, supportive care and clinical ethics providers. At times, the ethicist was not able to participate when two GOC RRTs were happening simultaneously, as there was only one dedicated ethicist. For hospitalists and inpatient oncologists, time challenges were paramount, given the labor-intensive nature of the process.
Unsolicited provider feedback suggested that many found the process to be beneficial by providing a unified message from the medical team, reducing medically non-beneficial care and illustrating how difficult it is for clinicians, patients and families to make these kind of care decisions. This feedback is consistent with that reported by palliative care teams who worked predominantly with COVID-positive patients. (18, 19) Future research should evaluate contribution of this model to provider wellbeing, as well as to reasons for provider discomfort/engagement with participation, ideal mode of encounter (in person, virtual or hybrid) and participation for only part of the meeting for some medical team members, as ways to increase participation and optimize resource allocation, especially in smaller institutions.
Consultation outcomes suggest that the process did limit care escalation. The most common measure of goal-concordant care limitation was change in resuscitation status from full code to do not resuscitate in 66%, with change in location and withdrawal of life sustaining treatment less common, each at 21%. Despite the low frequency of COVID-19 positivity in our population, the regional prevalence of COVID-19 positivity at the time likely influenced the salience of these GOC conversations. If confirmed to promote goal-concordant care, this approach may benefit critically ill patients regardless of their COVID-19 status as we enter the endemic phase of this illness.
Study limitations include its retrospective nature, conduct in a single institution and the specialized nature of our setting as a comprehensive cancer center. Our patients may differ in the intensity of preferred treatment from that pursued by patients in more community-based settings. Furthermore, as a tertiary cancer center, we may have resources more easily available for the rapid deployment of this human resource-intense intervention than available in other settings. If prospective studies confirm GOC RRT consult efficacy in promoting goal-concordant care in critically ill patients with cancer, research could be extended to different settings and to different populations.
This preliminary study raises many questions for future study. Can these results be reproduced and how do outcomes compare to those of similar patients who do not undergo the process? If results are reproducible, can the intervention be modified to achieve comparable results with fewer resources? What is the role of facilitated communication incorporating expert communication skills on outcomes? A pre-pandemic ICU based study among critically ill patient with advanced medical illness found less use of invasive ICU procedures and shorter ICU length of stay based on a goal-concordant care model incorporating time-limited trials. This model was based on palliative care-led communication skills training designed to teach clinicians the requisite components and skills needed to lead effective family meetings. (20) Our institution is currently deploying communication skills training throughout the organization.
On a more basic level is the age-old question: Can similar results be achieved by conversations in the outpatient setting earlier in the disease trajectory or is the stimulus of a potentially life-threatening event critical for individuals to recognize the salience of these discussions to their own life course? Many authors have deemed ACP in the traditional sense a failure, with a move to preparing patients and surrogate decision makers for “in-the-moment” decision making. (21, 22, 23, 24) At a minimum, GOC RRT consultations supplemented pre-existing processes for ACP and GOC discussions. It is yet unknown if they might contribute to new models for establishing GOC. Certainly, knowledge of impact on the emotional wellbeing of the patients, their family members and their survivors is critical, as is the impact on the complicated bereavement for survivors whose family members die. All these issues and more are fodder for future research to evaluate if moving to a ‘Just- in-Time” model of ACP and GOC planning would lead to improved outcomes with respect to goal concordant care from the perspective of patients, families, providers and/or the health care system.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Competing Interests Statement
The authors do not have any competing interests to disclose.
Acknowledgements
The authors express their gratitude to Brittany Cullen for her expert administrative support of this manuscript.
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References
1 Institute of Medicine (IOM) Dying in America: Improving Quality and Honoring Individual Preferences Near the End of Life 2015 National Academies Press Washington DC
2 Teno JM Fisher ES Hamel MB Coppola K Dawson NV. Medical care inconsistent with patients' treatment goals: association with 1-year Medicare resource use and survival J Am Geriatr Soc 50 2002 496 500 11943046
3 Khandelwal N Curtis JR Freedman VA How Often Is End-of-Life Care in the United States Inconsistent with Patients' Goals of Care? J Palliat Med 20 2017 1400 1404 28665781
4 Jabbarian LJ Maciejewski RC Maciejewski PK The Stability of Treatment Preferences Among Patients With Advanced Cancer J Pain Symptom Manage 57 2019 1071 1079 e1 30794935
5 Auriemma CL Nguyen CA Bronheim R Stability of end-of-life preferences: a systematic review of the evidence JAMA Intern Med 174 2014 1085 1092 24861560
6 Auriemma CL O'Donnell H Klaiman T How Traditional Advance Directives Undermine Advance Care Planning: If You Have It in Writing, You Do Not Have to Worry About It JAMA Internal Medicine 2022
7 Zhukovsky DS Soliman PT Mathew B Systematic Approach to Selecting and Preparing a Medical Power of Attorney in the Gynecologic Oncology Center J Oncol Pract 15 2019 e1092 e1097 31613720
8 Zhukovsky DS Haider A Williams JL An Integrated Approach to Selecting a Prepared Medical Decision-Maker J Pain Symptom Manage 61 2021 1305 1310 33348030
9 McMahan RD Knight SJ Fried TR Sudore RL. Advance Care Planning Beyond Advance Directives: Perspectives from Patients and Surrogates J Pain Symptom Manag 46 2013 355 365
10 Back A Tulsky JA Arnold RM. Communication Skills in the Age of COVID-19 Ann Intern Med 2020
11 Fried TR Redding CA Robbins ML Stages of Change for the Component Behaviors of Advance Care Planning J Am Geriatr Soc 58 2010 2329 2336 21143441
12 Curtis JR Kross EK Stapleton RD. The Importance of Addressing Advance Care Planning and Decisions About Do-Not-Resuscitate Orders During Novel Coronavirus 2019 (COVID-19) JAMA 2020
13 Ferrell BR Temel JS Temin S Integration of Palliative Care Into Standard Oncology Care: American Society of Clinical Oncology Clinical Practice Guideline Update J Clin Oncol 35 2017 96 112 28034065
14 Yang GM Neo SH Lim SZ Krishna LK. Effectiveness of Hospital Palliative Care Teams for Cancer Inpatients: A Systematic Review J Palliat Med 19 2016 1156 1165 27362627
15 Carter BS Wocial LD. Ethics and Palliative Care: Which consultant and When? Am J Hosp Palliat Care 29 2 Mar 2012 146 150 21665855
16 Aulisio MP Arnold RM. Role of the ethics committee: helping to address value conflicts or uncertainties Chest 134 2 2008 417 424 18682460
17 American Society for Bioethics and Humanities. Core Competencies for Healthcare Ethics Consultation. 2nd ed. 2011.
18 Schockett E Ishola M Wahrenbrock T The Impact of Integrating Palliative Medicine Into COVID-19 Critical Care J Pain Symptom Manage 62 2021 153 158 e1 33359039
19 Thery L Vaflard P Vuagnat P Advanced cancer and COVID-19 comorbidity: medical oncology-palliative medicine ethics meetings in a comprehensive cancer centre BMJ Support Palliat Care 2021
20 Chang DW Neville TH Parrish J Evaluation of Time-Limited Trials Among Critically Ill Patients With Advanced Medical Illnesses and Reduction of Nonbeneficial ICU Treatments JAMA Intern Med 181 2021 786 794 33843946
21 Morrison RS Meier DE Arnold RM. What's Wrong With Advance Care Planning? JAMA 326 2021 1575 1576 34623373
22 Sudore RL Fried TR. Redefining the "Planning" in Advance Care Planning: Preparing for End-of-Life Decision Making Ann Intern Med 153 2010 256 261 20713793
23 Morrison RS. Advance Directives/Care Planning: Clear, Simple, and Wrong J Palliat Med 23 2020 878 879 32453620
24 Periyakoil VS Gunten CFV Arnold R Caught in a Loop with Advance Care Planning and Advance Directives: How to Move Forward? J Palliat Med 25 2022 355 360 35230896
| 36496112 | PMC9729166 | NO-CC CODE | 2022-12-14 23:22:15 | no | J Pain Symptom Manage. 2022 Dec 8; doi: 10.1016/j.jpainsymman.2022.11.022 | utf-8 | J Pain Symptom Manage | 2,022 | 10.1016/j.jpainsymman.2022.11.022 | oa_other |
==== Front
Arch Gerontol Geriatr
Arch Gerontol Geriatr
Archives of Gerontology and Geriatrics
0167-4943
1872-6976
Published by Elsevier B.V.
S0167-4943(22)00287-4
10.1016/j.archger.2022.104900
104900
Article
Utilization of Internet for Religious Purposes and Psychosocial Outcomes During the COVID-19 Pandemic
Kretzler Benedikt ⁎
König Hans-Helmut
Hajek André
Department of Health Economics and Health Services Research, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20251 Hamburg, Germany
⁎ Correspondence concerning this article should be addressed to Benedikt Kretzler, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20251 Hamburg, Germany
8 12 2022
8 12 2022
10490023 8 2022
6 12 2022
7 12 2022
© 2022 Published by Elsevier B.V.
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Background
Prior to the COVID-19 pandemic, research findings pointed towards an alleviating effect of internet usage for religious purposes on depressive symptoms and loneliness. However, it is not clear whether this relationship persisted when worships were mostly held as online events. Consequently, this study investigates the link between religion-related internet utilization, particularly for online worships, depressive symptoms, and loneliness during the lockdown period.
Methods
Data were derived from a representative sample of German individuals aged 40 years and above, which was conducted in June and July 2020. Utilization of internet for religious purposes was treated as a dichotomous variable.
Results
Regarding bivariate analysis, individuals that used the internet for religious purposes were significantly older, and more likely to be female or to live in an urban setting. Furthermore, they had significantly more severe depressive symptoms. According to multiple linear regression, internet usage for religious purposes was both associated with more depressive symptoms, R² = .30, F(11, 3367) = 113.01, ß = 0.39, p = .050, and higher loneliness levels, R² = .09, F(11, 3367) = 25.75, ß = 2.24, p = .025.
Conclusions
It seems possible that the alleviating effect of religion on depressive symptoms and loneliness did not hold during the COVID-19 pandemic, which may imply that online worships are not perfect replacements for traditional worships in terms of their social and health benefits.
Key words
Religion
depression
loneliness
COVID-19
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pmc1 Introduction
Both loneliness and depressive symptoms are some of the most widespread health issues worldwide, and fighting against them is part of major campaigns of the World Health Organization, such as the UN Decade of Healthy Ageing (World Health Organization, 2020) or the WHO Special Initiative for Mental Health (World Health Organization, 2019). Though there are no global estimates of the prevalence of loneliness worldwide, the prevalence of this condition among older individuals may be between 20% and 45%, with considerable variations across different countries (World Health Organization, 2021b). More precise estimates are available for depression, and according to them, approximately four percent of the global population suffer from this disease (World Health Organization, 2017). Hereby, it should be highlighted that these numbers were reported before the outbreak of the COVID-19 pandemic.
With regard to the individual conditions, loneliness is commonly characterized as a personal feeling that the quality of one's relations to peers is not satisfying (De Jong Gierveld & Van Tilburg, 2006). What is more, it is associated with various adverse health effects: According to previous research, perceiving oneself as lonely is related to reduced health (Ong et al., 2016) and increased risks of mortality (Pantell et al., 2013) and morbidity (Leigh-Hunt et al., 2017). Regarding depression, the World Health Organization detects this disease when symptoms such as a dejected mood or the loss of pleasure in activities occur over a period of at least two weeks (World Health Organization, 2021a). Depressive symptoms are also related to further negative outcomes, for example, reduced quality of life (Sivertsen et al., 2015) or worse overall health (Bretschneider et al., 2018). Consequently, it is also a major burden for public health systems (König et al., 2019).
To tackle these problems, there has been basic research on the determinants of loneliness and depressive symptoms. Hereby, religion has been identified as an alleviating factor with respect to both of them: For example, various research works suggested that especially religious attendance is associated with decreased levels of loneliness (Johnson & Mullins, 1989; Kobayashi et al., 2009; Rote et al., 2013), including a recent longitudinal study from the United States, which used pre-pandemic data from the National Social Life, Health and Aging Project to show that consistent religious attendance was related to reduced loneliness, especially among the elderly (Upenieks, 2022). Concerning depression, two meta-analyses revealed that religiousness is significantly associated with mildly fewer depressive symptoms (Smith et al., 2003; Yonker et al., 2012). These promising results are further supported by a more up-to-date meta-analysis which stated that religion has a positive effect on mental health in general (Garssen et al., 2021). However, this study fully relied on pre-pandemic data as well.
Due to the COVID-19 pandemic, religious practice as well as loneliness and depression in their role as common health problems underwent considerable shifts: Regarding religious culture, lockdown policies forced institutions to close their doors for the public, and many of them started to offer digital formats instead of traditional worships: For example, a survey among evangelical churches in the German Rhineland stated that 73.8% of the local churches held such online gatherings, and that the number of participants approximately doubled or tripled in Summer 2020, compared to traditional worships (Hörsch, 2020). On the other hand, a survey which was mostly taken by Catholic individuals from the United Kingdom indicated that the vast majority would nevertheless have preferred to visit traditional worships (Catholic Voices, 2020). While this may still indicate that religious culture could reasonably well, according to circumstances, be continued, the development of the prevalence rates of loneliness and depression may have been more concerning: Regarding two meta-analyses that compared data about loneliness’ prevalence rate before and after the outbreak of the COVID-19 pandemic, both stated that this rate significantly increased from 2020 onwards among older individuals (Su et al., 2022) as well as among the general population (Ernst et al., 2022). Concerning depression, one can even speak of a dramatic increase, as another meta-analysis that was conducted in May 2020 detected a prevalence of 33.7% in general populations (Salari et al., 2020).
Regarding the dimension of these shifts, it seems naïve to assume, without any further investigation, that the health and social benefits that seem to be related to religiousness persisted during the COVID-19 pandemic. However, there is a lack of research which examines how this relationship has developed during these special conditions: In fact, currently, there seem to be only two research works: First, a study from Australia, which compared church goers from regions where the churches were closed and thus mainly online worships were held with church goers from regions where the churches had already opened again, stated that virtual worship engagement was still related to a higher religious and existential well-being, and that there were no significant differences among this factor between the two groups investigated (Martyr, 2022). Second, a report which relied on a sample of young Polish adults stated that individuals who attended worships had a larger social network compared to those who did not when they were questioned in Summer 2020, and that this larger network lead to decreased levels of loneliness among frequent worship attenders as well. However, when the same population was interviewed one year later, both worship attenders and non-worship attenders had higher levels of loneliness, without any significant effect from religious attendance (Okruszek et al., 2022). With respect to depressive symptoms, there does not seem to be any article which referred to a possible link to online worship attendance during the pandemic.
Hence, it is obvious that those two research works alone cannot answer whether the health and social benefits that are associated with religion, and hereby especially religious attendance, continued during the COVID-19 pandemic, and whether the shift to online gatherings may have had an influence on this association. Thus, the aim of this study is to address this knowledge gap by studying the relationship between the utilization of internet for religious purposes, particularly in terms of online worship attendance, depressive symptoms, and loneliness, and to describe socio-demographic, health, and social characteristics of individuals using the internet in this way. Apart from a better understanding of how the shifts in religious culture may have affected health and social outcomes, this could also be useful to acquire knowledge on who was making use of the religious online offer, and who was not: It may enable the identification of a group of individuals who could have lost an important and beneficial part of their life during the COVID-19 pandemic and, as a consequence, be at an increased risk of adverse health and social outcomes.
2 Material and methods
2.1 Sample
The present study relies on cross-sectional data from the German Ageing Survey (DEAS), which is a study including individuals aged 40 years and older. The German Ageing Survey is representative for the middle-aged and older German population, stratified by age, gender, and place of residence (East Germany and West Germany). Since 1996, both previous and new participants are interviewed every three to six years. Though, during the COVID-19 pandemic, it was decided to conduct a special wave focusing on pandemic-related topics. Hereby, only participants from earlier waves were questioned. Regarding inclusion criteria for these former participants, individuals who were at least 4o years old and were in private households were included. In addition, they had to live in Germany. Hence, individuals that were living abroad or not residing in a private household, e.g., in an institutional setting, were excluded.
The wave took place in June and July 2020 as a pen-and-paper interview. In total, 4,823 individuals provided answers, which means that the response rate was about 56.5% (Schiel et al., 2020). However, due to missing values, the final sample size of the present study consisted only of 3,431 participants. The DEAS did not require an ethics vote, as the criteria for obtaining it were not fulfilled. Written informed consent was obtained from all individual participants included in the study.
More information on the DEAS can be gathered elsewhere (Klaus et al., 2017).
2.2 Dependent Variables
Depressive symptoms were assessed using the short form of the German translation of the Center for Epidemiologic Studies Depression Scale (Hautzinger & Bailer, 1993). Its value is the sum over 10 items, and it ranges from 0 to 30. Increased values represent more depressive symptoms. In the past, the scale was shown to possess sufficient psychometric properties (Lehr et al., 2008).
Loneliness was quantified by the De Jong Gierveld Loneliness Scale (De Jong Gierveld & Van Tilburg, 2006), whose value is the mean value of its six items. Higher values point towards an increased level of loneliness. Good psychometric characteristics have been reported for this scale as well (De Jong Gierveld & Van Tilburg, 2010).
In our study, Cronbach's alpha was .83 for the Center for Epidemiologic Studies Depression Scale and .78 for the De Jong Gierveld Loneliness Scale. Hence, it can be assumed that both instruments had a good internal consistency.
2.3 Independent Variables
Our main independent variable was use of internet for religious purposes, particularly for online worship attendance. In the DEAS, the concerning question was “How often do you use the Internet for religious purposes (e.g. online worships)?”. The answers could range from “daily” to “never” on a five-point scale. In this study, the variable was dichotomized to assess whether an individual had ever used the Internet for religious purposes.
With respect to sociodemographic and health characteristics of the participants, differences in age, sex (male; female), residential form of partnership (single; with partner living in same household; with partner not living in same household), type of district (cities; other), level of education by the ISCED categories (low; middle; high) (UNESCO United Nations Educational Scientific and Cultural Organization, 1997), (logarithmized) total net monthly income of household, main labor force status (working; retired; other (not employed)) and self-rated health (rated on a five-point scale from 1 = very good to 5 = very bad) were used as covariates in regression analysis.
2.4 Statistical Analysis
Firstly, the characteristics of the total sample as well as of the subgroups of individuals using the internet for religion-related motives and those who did not were assessed. In order to reveal differences between them, chi-squared tests and two-sample Student's t-tests (as appropriate) were employed.
Secondly, this study examined the relationship between internet use for religious purposes, depressive symptoms and loneliness using multiple linear regressions. Thereby, it was controlled for age, sex, level of education by ISCED, logarithmized total net monthly income of household, residential form of partnership, main labor force status, and self-rated health.
The level of significance was set at α = .05. All statistical analyses were conducted with Stata 17.0.
3 Results
Table 1 provides the sample characteristics of the individuals included in the final sample. Mean age was 67.45 (SD: 9.59, ranging from 46 to 98 years), and 51.38% of the individuals were men. With respect to the two subgroups of those who used the Internet for religious purposes (e.g. online worships) during the COVID-19 pandemic (14.48%) and those who did not (85.52%), significant differences occurred among age, sex, type of district and depressive symptoms: Those who did were the older group, t(3382) = -5.23, p < .001, d = -0.26, and they were more likely to be female, χ²(1) = 5.93, p = .015, d = -0.12, and to live in an urban setting, χ² = 8.61, p = 0.003, d = 0.14. Eventually, individuals who used internet for religious purposes also reported more severe depressive symptoms, t(3382) = -2.16, p = 0.030, d = -0.11. However, among loneliness, there were no significant differences, t(3382) = -1.55, p = .120, d = 0.08.Table 1 Characteristics of Participants
Table 1Characteristic Using Internet for religious purposes (e.g. online worships) Not using Internet for religious purposes (e.g. online worships) Full sample p
n/mean %/SD n/mean %/SD n/mean %/SD
Age 69.55 10.36 67.10 9.42 67.45 9.59 < .001
Sex .015
Male 224 46.19% 1,512 52.16% 1,736 51.30%
Female 261 53.81% 1,387 47.84% 1,648 48.70%
Residential form of partnership .381
Single 571 19.70% 108 22.27% 679 20.07%
With partner living in same household 2,194 75.68% 353 72.78% 2,547 75.27%
With partner not living in same household 134 4.62% 24 4.95% 158 4.67%
Type of district .003
Cities 347 71.55% 1,876 64.71% 2,223 65.69%
Other 138 28.45% 1,023 35.29% 1,161 34.31%
Level of education by ISCED 0.413
Low 14 2.89% 72 2.48% 86 2.54%
Middle 196 40.41% 1,262 43.53% 1,458 43.09%
High 275 56.70% 1,565 53.98% 1,840 54.37%
Total net monthly income of household 4,521.53 10,446.49 4,194.79 12,980.18 4,241.62 12,647.39 .599
Main labor force status .161
Working 141 29.07% 962 33.18% 1,103 32.59%
Retired 327 67.42% 1,824 62.92% 2,151 63.56%
Other (not employed) 17 3.51% 113 3.90% 130 3.84%
Self-rated health 2.41 0.77 2.38 0.75 2.38 0.75 .485
CES-D 8.56 5.03 8.07 4.60 8.14 4.67 .030
DJGLS 1.91 0.54 1.87 0.53 1.88 0.53 .120
Note. N = 3,384 (n = 485 using Internet for religious purposes, n = 2,899 not using Internet for religious purposes). CES-D = Center for Epidemiological Studies Depression Scale; DJGLS = De Jong Gierveld Loneliness Scale
The results of the regression analysis with depressive symptoms and loneliness as dependent variables are displayed in Table 2 . It appears as if using the internet for religious purposes (e.g. online worships) was associated with both increased loneliness, R² = .09, F(11, 3367) = 25.75, ß = 2.24, p = .025, and increased depressive symptoms, R² = .30, F(11, 3367) = 113.01, ß = 0.39, p = .050.Table 2 Regression Coefficients on Depressive Symptoms and Loneliness
Table 2Variable Depressive symptoms Loneliness
B ß SE B ß SE
Age 0.11 0.00 0.01 -5.61⁎⁎⁎ -0.00 0.00
Sex (reference: male) 4.84⁎⁎⁎ 0.70 0.14 -4.21⁎⁎⁎ -0.08 0.02
Level of education by ISCED (reference: low)
Middle -2.78⁎⁎ -1.36 0.49 -2.78⁎⁎ -0.17 0.06
High -3.03⁎⁎ -1.48 0.49 -3.22⁎⁎ -0.20 0.06
Logarithmized total net monthly income of household -2.51* -0.34 0.14 -1.31 -0.02 0.02
Residential form of partnership (reference: single)
With partner living in same household -2.74⁎⁎ -0.55 0.20 -4.66⁎⁎⁎ -0.12 -0.03
With partner not living in same household -0.93 -0.33 0.36 -1.81 -0.09 0.05
Main labor force status (reference: working)
Retired -2.99⁎⁎ -0.70 0.23 0.56 0.02 0.03
Other (not employed) -0.05 -0.02 0.40 0.36 0.02 0.05
Self-rated health 32.09⁎⁎⁎ 3.26 0.10 13.28⁎⁎⁎ 0.17 0.01
Using Internet for religious purposes (e.g. online worships) (reference: no) 1.96* 0.39 0.20 2.24* 0.06 0.03
Note. N = 3,384.
⁎ p < .05,
⁎⁎ p < .01,
⁎⁎⁎ p < .001
Besides these two main independent variables, several covariates were significant as well: With regard to depressive symptoms, higher age, ß = .00, p < .001, being female, ß = 0.70, p < .001, and a worse self-rated health, ß = 3.26, p < .001, were associated with more severe outcomes. On the other hand, having obtained middle education, ß = -2.78, p = .006, or higher education, ß = -1.48, p = .002, living with one's partner in the same household, ß = -0.55, p = .006, and being retired, ß = -0.70, p = .003, were related to decreased levels of depression. From these variables, a better self-rated health was also associated with decreased loneliness, ß = 0.17, p < .001. However, being younger, ß = -0.00, p < .001, female, ß = -0.08, p < .001, middlingly educated, ß = -0.17, p = .005, highly educated, ß = -0.20, p = .001, and living with one's partner in the same household, ß = -0.12, p < .001, was related to decreased feelings of loneliness.
Moreover, we replaced the dichotomous key independent variable (i.e., internet usage for religious purposes) by the frequency (from 1 = never to 6 = daily). The results are displayed in Supplementary Table 1. None of the individual frequencies were significantly related to depressive symptoms. Moreover, a significant association concerning loneliness was solely found among those who used the internet due to religious motives less often than one to three times a month (compared to individuals who never used the internet for religious purposes), ß = 0.07, p = 0.04.
4 Discussion
4.1 Main Findings
The aim of this study was to gain information about individuals that used the internet for religious purposes, particularly the attendance of online worships, during the COVID-19 pandemic, and to investigate the relationship between attending such events, depressive symptoms, and loneliness. With regard to the socio-demographic profiles, the group of individuals who used the internet for religious purposes was significantly older, and consisted of a significantly higher share of women and townsmen. Eventually, they also reported increased levels of depression. Concerning the regression analyses with depressive symptoms and loneliness as dependent variables, this kind of internet utilization was significantly associated with worse outcomes among both these factors. However, the significance of that relationship did not hold for the individual frequencies of depressive symptoms and only for those using internet for religious purposes less often than one to three times a month concerning loneliness (compared to individuals who never used the internet due to religious motives).
4.2 Previous Research and Possible Explanations
The finding that religion-related internet usage, particularly for attending online-worships, was associated with both higher depression and loneliness is not in line with previous findings that concern traditional worships, as an alleviating effect of attending the latter events is well-documented for loneliness by several research works (Johnson & Mullins, 1989; Kobayashi et al., 2009; Rote et al., 2013; Upenieks, 2022) as well as for depression by several meta-analyses (Garssen et al., 2021; Smith et al., 2003; Yonker et al., 2012). It may seem nearby to assume that the benefits of religious attendance regarding these two factors vanished as soon as switching to online events was required, and there is quite good reason for this: Firstly, this interpretation fits quite well to the findings of Okruszek et al. (2022) that worship attenders had larger social networks and thereby decreased levels of loneliness at the beginning of the COVID-19 pandemic, but that such a protective effect of religious attendance did not pertain when the same individuals were questioned again one year later. Besides this, this could also mirror reports that most church goers did not find online worships a perfect replacement of traditional worships (Catholic Voices, 2020). Thus, the results of this study could add to evidence about negative perceptions of the switch from traditional worships to online formats. Secondly, it seems probable that the social aspect of attending traditional worships could not be maintained by the online replacements, as direct human contact was not possible anymore. This could be significant as most of the pertinent studies assume that this direct contact plays a crucial role for the association between religious attendance and alleviated depression and loneliness: For example, Rote et al. (2013) argue that religious involvement benefits the social integration of an individual and may thus reduce its feelings of loneliness, and Upenieks (2022) stresses the role of building up social relationships as well, especially concerning her results that an alleviating effect of religious attendance on loneliness does not occur immediately, but seems to develop over time. With regard to depressive symptoms, a protective role of social support is also well-documented (Gariépy et al., 2016). However, in online worships, the possibilities of becoming socially involved or of building up relationships may be limited, compared to traditional worships. Consequently, there is also a reasonable theoretical basis for stating that the alleviating effect of religious attendance on both loneliness and depressive symptoms may not have persisted during the COVID-19 pandemic.
The disappearance of religion-related internet usage's significance for depressive symptoms and loneliness when the particular frequencies were regarded does not need to negate this main result: In total, only 485 individuals (14.33%) reported using the internet for religious purposes. Further dividing them into five different frequency categories is likely to have led to an underpowered investigation that could not detect a significant relationship. Hence, the difference between the results of our study may be explained by the too-small sample size.
Nevertheless, pre-existing differences between those who were likely to use the internet to attend online worships and those who did not can also not be fully precluded. On the one hand, it seems probable that individuals who regularly visited traditional worships before the outbreak of the COVID-19 pandemic were far more likely to attend online worships after the beginning of lockdown and containment policies. Due to the limitations in their religious life, those individuals could have felt more depressed and lonelier than those whose life was not affected by the closure of religious institutions. This, in turn, could explain a great extent of the result that internet utilization for religious purposes was related to higher levels of loneliness and depressive symptoms. On the other hand, regarding the increase in the number of worship attenders after the switch to online formats, it seems possible as well that individuals who felt lonely or emotionally stressed due to the containment measures may been more likely to attend online worships, whether as a way to distract themselves or to find purpose in life during challenging times. Therefore, it seems possible that individuals who began visiting worships during the COVID-19 pandemic had higher initial levels of loneliness and depression, too. Nevertheless, it may be a task of future research to follow individuals who have suffered due to the switch from traditional religious events to online formats, particularly by using longitudinal designs, to assess possible effects that could express themselves both in personal growth and ongoing impairments.
All in all, it seems reasonable to explain the positive association between internet usage for religious purposes, especially for online-worship attendance, and loneliness by the absence of the social benefits due to the switch to online formats, so that a group of individuals who used to attend traditional worships before did not profit any more. In turn, this may also have increased depressive symptoms among this group, as a significant effect of loneliness on depression is well-documented by meta-analyses (Erzen & Çikrikci, 2018; Park et al., 2020). In terms of the alleviating effects on loneliness and depression, online worships may consequently not have been a full replacement of traditional worships. However, it is not possible to make a secure statement, because this is a cross-sectional study, and because there are not many other studies on the effects of visiting online worships that could give further indications. Other pre-existing differences, such as higher levels of loneliness and depression among individuals who used the internet for religion-related purposes, presumably for online worship participation, during the COVID-19 pandemic, but did not visit traditional worships before, cannot be precluded. Finally, further research is required to assess various potential confounding factors, such as the impact that COVID-19 had on an individual's life, or the importance of religion in life before the outbreak of the pandemic, in the association between religion-related internet use, or online worship attendance, loneliness, and depressive symptoms.
4.3 Strengths and Limitations
This study is one of only very few research works that shed light on the association between religion-related internet usage, most certainly to attend online worships, depressive symptoms, and loneliness during the COVID-19 pandemic, and it may also contribute to further understand that link, as the switch from traditional worships to online worships could have changed aspects that had been crucial for an alleviating effect of religious attendance on these two adverse outcomes. Representative data from a high-quality survey was used, and both depressive symptoms and loneliness were assessed with well-established tools.
Notwithstanding that, the present study has several limitations. Most importantly, the instrumentalization of religion-related internet usage, particularly worship attendance, is not very accurate, as the relevant item (“How often do you use the Internet for religious purposes (e.g. online worships)?”) may also be understood in terms of other religion-related uses as well, such as personal reflection on one's own belief or searches for local religious communities. However, it could be reasonable to assume that the question was mainly understood in terms of online worships, not only because of the specific naming in the text: Regarding the number of participants that stated that they ever used the Internet for religious purposes during the COVID-19 pandemic, approximately one out of seven, this value does not seem to be extraordinarily high in comparison to other studies which explicitly assessed the participation among online worships: According to a survey that was conducted in Summer 2020 by the Evangelical Church in the Rhineland, online worships that were hosted in this region alone reached a coverage of 2.36 million participants in total, and the number of participants in comparison to traditional worships may have doubled or even tripled (Hörsch, 2020). Hence, though the existence of a methodological bias cannot be denied, it does not seem as if its practical consequences undermine the results of this study.
Ultimately, the German Ageing Survey was not specifically designed for the investigation of religion-related questions and bears some additional limitations: Firstly, it is difficult to establish causal relationships based on cross-sectional data. Particularly, it was not possible to cover scenarios such as individuals who used internet to visit online worships, but stopped it again because it did not alleviate their depressive symptoms or loneliness. What is more, it was not possible to examine the effect of the transition from traditional to online worships directly. Ultimately, other information regarding other religion-related behavior of the participants was not assessed in the questionnaires, particularly measures of religiousness (e.g., Koenig and Büssing (2010)) or spirituality (e.g., Delaney (2005)). Besides that, due to the postal questioning that was necessary for a survey which was carried out during the COVID-19 pandemic, it could not be precluded that individuals who may not have been able to provide fully conscious answers, for example, because of diseases such as dementia, were included. On the other hand, it may be doubted whether this was a severe problem during data collection, as adequately filling out the questionnaire may require a particular level of cognitive functioning. Despite these limitations, the DEAS generally provides a representative sample of the middle-aged and older population, and assesses its variables with established and validated tools. Thus, it may be a reasonable data source despite these limitations.
5 Conclusions
The finding that the utilization of internet for religious purposes was associated with increased levels of loneliness and depression during the COVID-19 pandemic points towards an absence of the alleviating effects of religious practice while religious institutions were closed. Thus, online formats may have failed to replace traditional worships in terms of health and social benefits that are commonly associated with religious attendance. However, the cross-sectional design of this analysis made it impossible to detect any causal relationship. Nevertheless, our results may suggest the arise of an at-risk group in terms of depressive symptoms and loneliness, consisting of individuals for which religion played an important role in both finding purpose in life and maintaining a certain level of social integration before the outbreak of the pandemic. As religious practice is important especially for many older individuals, further research on the outcomes of the switch to online formats is required in order to identify adverse outcomes such as these to which this study may have referred to.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Uncited References
Ev. Werk für Diakonie und Entwicklung e.V. 2022
CRediT authorship contribution statement
Benedikt Kretzler: Conceptualization, Methodology, Formal analysis, Writing – original draft, Writing – review & editing. Hans-Helmut König: Conceptualization, Writing – review & editing. André Hajek: Conceptualization, Methodology, Formal analysis.
Declarations of interest
none
Acknowledgements
none
==== Refs
References
Bretschneider J. Janitza S. Jacobi F. Thom J. Hapke U. Kurth T. Maske U.E. Time trends in depression prevalence and health-related correlates: results from population-based surveys in Germany 1997-1999 vs. 2009-2012 BMC Psychiatry 18 1 2018 394 10.1186/s12888-018-1973-7 30572872
Catholic Voices Coronavirus, Church & You Survey: Catholics in the UK: Report of Results 2020 Retrieved 26 June 2022 from https://static1.squarespace.com/static/5d4852755541c10001c7d071/t/5f57527b8063d02159311569/1599558270783/Coronavirus2C+Church+26+You+Survey+Report.pdf
De Jong Gierveld J. Van Tilburg T. A 6-Item Scale for Overall, Emotional, and Social Loneliness: Confirmatory Tests on Survey Data Res Aging 28 5 2006 582 598 10.1177/0164027506289723
De Jong Gierveld J. Van Tilburg T. The De Jong Gierveld short scales for emotional and social loneliness: tested on data from 7 countries in the UN generations and gender surveys Eur J Ageing 7 2 2010 121 130 10.1007/s10433-010-0144-6 20730083
Delaney C. The Spirituality Scale:Development and Psychometric Testing of a Holistic Instrument to Assess the Human Spiritual Dimension Journal of Holistic Nursing 23 2 2005 145 167 10.1177/0898010105276180 15883463
Ernst M. Niederer D. Werner A.M. Czaja S.J. Mikton C. Ong A.D. Rosen T. Brähler E. Beutel M.E. Loneliness before and during the COVID-19 pandemic: A systematic review with meta-analysis Am Psychol 2022 10.1037/amp0001005
Erzen E. Çikrikci Ö. The effect of loneliness on depression: A meta-analysis Int J Soc Psychiatry 64 5 2018 427 435 10.1177/0020764018776349 29792097
Ev. Werk für Diakonie und Entwicklung e.V.,. Retrieved 26 June 2022 from https://www.ekd.de/ekd_de/ds_doc/midi_Ad-hoc-Studie_Digitale_Verkuendigungsformate_waehrend_der_Corona-Krise.pdf
Gariépy G. Honkaniemi H. Quesnel-Vallée A. Social support and protection from depression: systematic review of current findings in Western countries Br J Psychiatry 209 4 2016 284 293 10.1192/bjp.bp.115.169094 27445355
Garssen B. Visser A. Pool G. Does Spirituality or Religion Positively Affect Mental Health? Meta-analysis of Longitudinal Studies Int J Psychol Relig 31 1 2021 4 20 10.1080/10508619.2020.1729570
Hautzinger M. Bailer M. ADS - Allgemeine Depressions-Skala 1993 Beltz
UNESCO United Nations Educational Scientific and Cultural Organization International Standard Classification of Education, ISCED 1997 Hoffmeyer-Zlotnik J. Wolf C. Advances in Cross-National Comparison 1997 Springer 195 220
Hörsch D. Digitale Verkündigungsformate während der Corona-Krise: Eine Ad-hoc-Studie im Auftrag der Evangelischen Kirche in Deutschland 2020 midi
Johnson D.P. Mullins L.C. Religiosity and loneliness among the elderly J Appl Gerontol 8 1 1989 110 131 10.1177/073346488900800109
Klaus D. Engstler H. Mahne K. Wolff J. Simonson J. Wurm S. Tesch-Römer C. Cohort Profile: The German Ageing Survey (DEAS) International Journal of Epidemiology 46 4 2017 1105 10.1093/ije/dyw326 -1105g 28180273
Kobayashi K.M. Cloutier-Fisher D. Roth M. Making meaningful connections: a profile of social isolation and health among older adults in small town and small city, British Columbia J Aging Health 21 2 2009 374 397 10.1177/0898264308329022 19074646
Koenig H.G. Büssing A. The Duke University Religion Index (DUREL): A Five-Item Measure for Use in Epidemological Studies Religions 1 1 2010 78 85 https://www.mdpi.com/2077-1444/1/1/78
König H. König H.H. Konnopka A. The excess costs of depression: a systematic review and meta-analysis Epidemiology and Psychiatric Sciences 29 2019 e30 10.1017/s2045796019000180 30947759
Lehr D. Hillert A. Schmitz E. Sosnowsky N. Screening depressiver Störungen mittels Allgemeiner Depressions-Skala (ADS-K) und State-Trait Depressions Scales (STDS-T) Diagnostica 54 2008 61 70 10.1026/0012-1924.54.2.61
Leigh-Hunt N. Bagguley D. Bash K. Turner V. Turnbull S. Valtorta N. Caan W. An overview of systematic reviews on the public health consequences of social isolation and loneliness Public Health 152 2017 157 171 10.1016/j.puhe.2017.07.035 28915435
Martyr P. Worship choices and wellbeing of Australian churchgoing Catholics during COVID-19 church closures Ment Health, Relig Cult 2022 10.1080/13674676.2022.2066645
Okruszek Ł. Piejka A. Żurek K. Take Me to (the Empty) Church? Social Networks, Loneliness and Religious Attendance in Young Polish Adults During the COVID-19 Pandemic J Relig Health 61 1 2022 722 740 10.1007/s10943-021-01486-1 35041126
Ong A.D. Uchino B.N. Wethington E. Loneliness and Health in Older Adults: A Mini-Review and Synthesis Gerontology 62 4 2016 443 449 10.1159/000441651 26539997
Pantell M. Rehkopf D. Jutte D. Syme S.L. Balmes J. Adler N. Social isolation: a predictor of mortality comparable to traditional clinical risk factors Am J Public Health 103 11 2013 2056 2062 10.2105/ajph.2013.301261 24028260
Park C. Majeed A. Gill H. Tamura J. Ho R.C. Mansur R.B. Nasri F. Lee Y. Rosenblat J.D. Wong E. McIntyre R.S. The Effect of Loneliness on Distinct Health Outcomes: A Comprehensive Review and Meta-Analysis Psychiatry Res 294 2020 113514 10.1016/j.psychres.2020.113514
Rote S. Hill T.D. Ellison C.G. Religious attendance and loneliness in later life Gerontologist 53 1 2013 39 50 10.1093/geront/gns063 22555887
Salari N. Hosseinian-Far A. Jalali R. Vaisi-Raygani A. Rasoulpoor S. Mohammadi M. Rasoulpoor S. Khaledi-Paveh B. Prevalence of stress, anxiety, depression among the general population during the COVID-19 pandemic: a systematic review and meta-analysis Global Health 16 1 2020 57 10.1186/s12992-020-00589-w 32631403
Schiel S. Ruiz Marcos J. Fahrenholz M. Methodenbericht: Deutscher Alterssurvey (DEAS) Schriftliche Panelbefragung 2020 2020 infas Institut für angewandte Sozialwissenschaft GmbH Retrieved 19 June 2022 from https://www.dza.de/fileadmin/dza/Dokumente/Forschung/FDZ_DEAS-Doku/DEAS2020_Kurzbefragung_infas_Methodenbericht.pdf
Sivertsen H. Bjørkløf G.H. Engedal K. Selbæk G. Helvik A.S. Depression and Quality of Life in Older Persons: A Review Dementia and Geriatric Cognitive Disorders 40 5-6 2015 311 339 10.1159/000437299 26360014
Smith T.B. McCullough M.E. Poll J. Religiousness and depression: evidence for a main effect and the moderating influence of stressful life events Psychol Bull 129 4 2003 614 636 10.1037/0033-2909.129.4.614 12848223
Su Y. Rao W. Li M. Caron G. D'Arcy C. Meng X. Prevalence of loneliness and social isolation among older adults during the COVID-19 pandemic: A systematic review and meta-analysis Int Psychogeriatr 2022 1 13 10.1017/s1041610222000199
Upenieks L. With You Until the End of the Age? A Longitudinal Study of Changes in Religiosity and Loneliness in Later Life Res Aging 2022 1640275221104720 10.1177/01640275221104720
World Health Organization Depression and Other Common Mental Disorders: Global Health Estimates 2017
World Health Organization The WHO special initiative for mental health (2019-2023): universal health coverage for mental health 2019 Retrieved 26 June 2022 from https://apps.who.int/iris/handle/10665/310981
World Health Organization Decade of Healthy Ageing: 2020-2030 2020 Retrieved 26 June 2022 from https://cdn.who.int/media/docs/default-source/decade-of-healthy-ageing/final-decade-proposal/decade-proposal-final-apr2020-en.pdf?sfvrsn=b4b75ebc_25&download=true
World Health Organization Depression 2021 Retrieved 28. Mai 2022 from https://www.who.int/news-room/fact-sheets/detail/depression
World Health Organization Social isolation and loneliness among older people: advocacy brief 2021 Retrieved 26 June 2022 from https://www.who.int/publications/i/item/9789240030749
Yonker J.E. Schnabelrauch C.A. Dehaan L.G. The relationship between spirituality and religiosity on psychological outcomes in adolescents and emerging adults: a meta-analytic review J Adolesc 35 2 2012 299 314 10.1016/j.adolescence.2011.08.010 21920596
| 0 | PMC9729167 | NO-CC CODE | 2022-12-14 23:22:15 | no | Arch Gerontol Geriatr. 2022 Dec 8;:104900 | utf-8 | Arch Gerontol Geriatr | 2,022 | 10.1016/j.archger.2022.104900 | oa_other |
==== Front
Ment Health Phys Act
Ment Health Phys Act
Mental Health and Physical Activity
1755-2966
1878-0199
Elsevier Ltd.
S1755-2966(22)00062-X
10.1016/j.mhpa.2022.100500
100500
Article
An examination of the reciprocal associations between physical activity and anxiety, depressive symptoms, and sleep quality during the first 9 weeks of the COVID-19 pandemic in Belgium
Morbée Sofie a∗1
Beeckman Melanie b1
Loeys Tom c
Waterschoot Joachim a
Cardon Greet b
Haerens Leen b
Vansteenkiste Maarten a
a Department of Developmental, Personality and Social Psychology, Ghent University, Belgium
b Department of Movement and Sports Sciences, Ghent University, Belgium
c Department of Data Analysis, Ghent University, Belgium
∗ Corresponding author. Department of Developmental, Personality and Social psychology, Ghent University, Henri Dunantlaan 2, 9000, Ghent, Belgium.
1 Co-first authorship.
8 12 2022
3 2023
8 12 2022
24 100500100500
7 9 2022
30 11 2022
2 12 2022
© 2022 Elsevier Ltd. All rights reserved.
2022
Elsevier Ltd
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
During the initial outbreak of the global COVID-19 pandemic, many countries imposed a total lockdown (containment at home). Although it was still allowed in Belgium to be physically active or exercise with people from your household in the vicinity of your home, engaging in sports or physical activity in a group or club context was no longer permitted. To examine whether a lack of physical activity was potentially threatening to the mental well-being of citizens and vice versa, the present study examined concurrent and reciprocal relationships between physical activity and anxiety, depressive symptoms, and sleep quality during the COVID-19 lockdown in a 9-week longitudinal design. In a sample of 983 Belgian adults (75.1% female; Mage = 43.78, range = 18–82 years), we explored these relationships at both the between- and within-person levels through random intercept cross-lagged panel models. The findings indicate that more physical activity was associated with lower symptoms of anxiety and depression and better sleep quality, a finding observed both at the between-person (across weeks; βanxiety = −0.25, βdepression = −0.30, βsleep quality = 0.24, p < .001) and within-person level (within weeks; βanxiety = −0.10, βdepression = −0.14, βsleep quality = 0.11, p < .05). Moreover, at the within-person level, an increase in feelings of anxiety and depression at one moment predicted lower levels of physical activity one week later (βanxiety = −0.04, βdepression = −0.06, p < .05). Since poor mental health poses a threat to the maintenance of physical activity, the current findings suggest that it is critical to invest in the mental health of individuals during distressing times.
Keywords
Coronavirus
Mental health
Longitudinal design
Random intercept cross-lagged panel analysis
==== Body
pmc1 Introduction
In March 2020, during the global COVID-19 pandemic, Belgium implemented several public health measures to mitigate the spread of the coronavirus. Like other countries, these pre-emptive measures involved the recommendation to stay at home or, in case one had to make essential journeys (e.g., grocery shopping, doctor's appointment), to keep physical distance from others. Moreover, schools and non-essential public spaces (e.g., sports facilities, restaurants, non-essential shops, playgrounds, etc.) were all closed. Outdoor physical activity (PA) was allowed, but only with family members living under the same roof. These measures implied a radical change in lifestyle behaviors and, consequently, also in the degree to which people were physically active. Whereas some people took advantage of the freed-up time to become more physically active, others found it more challenging to stay physically active. Since sports facilities were closed, people spent less time commuting, and were concerned about getting infected when going outside, it is reasonable to assume that PA levels went down (Dunton et al., 2020). Indeed, a large-scale survey among 3800 Spanish adults showed that vigorous PA and walking time decreased, respectively, by 16.8% and 58.2% during the lockdown (Castañeda-Babarro et al., 2020). Another worldwide study using smartphones' daily step count measurements of 455,404 unique users showed that within the first 10 days of the COVID-19 pandemic, there was a 5.5% decrease in mean steps, and within 30 days, there was a 27.3% decrease in mean steps (Tison et al., 2020).
Nonetheless, being active may have been especially important as it might help in fostering greater psychological well-being, which was under threat during the initial lockdown in March 2020, as evidenced by an increase in symptoms of anxiety and depression (e.g., Ozamiz-Etxebarria et al., 2020) and poorer sleep quality (e.g., Casagrande et al., 2020). Indeed, research showed that PA and (poor) mental health are related, as people who report being equally or more physically active than usual during the COVID-19 lockdown reported lower levels of anxiety and depression (Frontini et al., 2021; Lesser & Nienhuis, 2020; see Wolf et al., 2021 for a systematic review; Zhao et al., 2022), whereas other studies showed that the reduction of total PA came with more symptoms of poor mental health, such as anxiety and depression (Trabelsi et al., 2021; see Violant-Holz et al., 2020 for a systematic review). Moreover, performing PA during the lockdown was positively associated with people's sleep patterns (Şimşek et al., 2020; Violant-Holz et al., 2020). Yet, most of this COVID-19-related research considered the association in only one direction, namely the effect of PA in the prediction of mental health and sleep quality. Previous research outside the context of the COVID-19 pandemic though showed evidence for reciprocal effects between PA and mental well-being (e.g., Stavrakakis et al., 2012; Steinmo et al., 2014), and between PA and sleep (Chennaoui et al., 2015; Pesonen et al., 2022). This implies that an increase in PA would not only lead to an increase in mental well-being and sleep quality, but also that increases in mental well-being or sleep quality would drive increases in PA levels. To the best of our knowledge, no such reciprocal associations across time were tested within the threatening context of a pandemic; neither on an interpersonal, nor intrapersonal level.
1.1 Present study
The present study aimed to examine the reciprocal associations between changes in levels of PA as compared to pre-COVID-19-pandemic times on the one hand, and anxiety, depressive symptoms, and sleep quality on the other hand during the first COVID-19 lockdown in a longitudinal design with weekly measurements across 9 weeks (i.e., waves). Particularly during the COVID-19 pandemic, it is interesting to examine these reciprocal effects at the both between- and within-person levels.
First, at the between-person level, it is important to know whether individuals who reported a higher increase in PA during the COVID-19 lockdown relative to other people across all weeks also display lower levels of anxiety and depression, and higher levels of sleep quality relative to others. We hypothesized that, across all weeks, increased levels of PA compared to pre-COVID-19 pandemic times would be associated with better mental well-being (i.e., lower levels of anxiety and depression) and sleep quality (H1).
Yet, examining the same dynamics at the within-person level is equally instructive as the situation at that time was highly uncertain and fluctuated from week to week. Therefore, it is particularly interesting to examine whether individuals display better mental well-being and sleep quality during the same or subsequent week in which their PA level was elevated, and vice versa (relative to their own baseline). We expected that during weeks a person reports positive changes in PA levels relative to his/her own average, this person would also report better mental well-being and sleep quality relative to his/her own average (H2a). Moreover, also at the within-person level, we expect that positive perceived changes in PA levels would predict better mental well-being and sleep quality during the following week, and vice versa (H2b).
2 Method
2.1 Sample and procedure
On February 3rd, 2020, the first infection with the SARS-CoV-2 virus was detected in Belgium. On March 17th, the government declared a lockdown to curb the spread of the virus. Beginning March 19th, an online survey, called “The Motivation Barometer”, was conducted among Belgian adults. Participants were recruited through an advertising campaign on social media, as well as by contacting different organizations (e.g., cultural associations) and media (e.g., online newspapers). The current study is part of this broader “Motivation Barometer” survey. During the first week of the lockdown, a baseline measurement (T0) was administered. In addition to a number of variables not relevant to the current study, participants were asked whether they wanted to participate in subsequent waves to assess the long-term effects of the COVID-19 pandemic. Participants who agreed to participate in this longitudinal study at T0 were invited through e-mail in time windows of exactly 7 days. For example, all participants who completed the baseline questionnaire on Thursday received a new invitation each Thursday for the next 9 upcoming weeks. In this invitation, each participant received a unique anonymized code which was used to link all the questionnaires. Each week, participants could decide whether they wanted to continue participating in the survey. From the moment a participant unsubscribed, no further invitations were sent. However, when a participant missed an assessment without unsubscribing, the participant was still invited for the following assessment. Additionally, we provided contact information at the beginning and the end of the questionnaire in case of questions or psychological concerns. The procedure was approved by the ethical committee of Ghent university (nr. 2020/37).
A sample of 1367 participants gave informed consent at T0 for a weekly follow-up assessment (76.8% female; M age = 39.64, range = 18–82 years). However, only 983 participants actually participated in one of the follow-up measures (75.1% female; M age = 43.78, range = 18–82 years). Of this final sample, 84.9% participated on T1, 76.1% participated on T2, 73.3% on T3, 65.5% on T4, 63.9% on T5, 59.5% on T6 assessment, 49.1% on T7, 51.6% on T8, and 51% on T9. On average, participants filled in the survey six times. Drop-out analyses indicated that only participants' age was related to retention in the dataset. Older participants were more likely to participate twice or more (odds ratio = 1.03, p < .001) than younger participants. Neither gender nor the substantive study variables were related to drop-out (all p's > .05).
2.2 Materials
2.2.1 Physical activity
Perceived changes in PA (as compared to before the national lockdown) were measured using a single item (Constandt et al., 2020): “How physically active were you during the past week in comparison with an average week (before measures were imposed in the context of the COVID-19 pandemic)? Physical activity is more than just sports. In addition to sports, it includes active transportation (e.g., walking or biking to the bakery), being active in the household (e.g., mopping, vacuuming, etc.), being active in leisure time (e.g., walking the dog), and so on.” Participants were provided with a 5-point response scale with the following answering options “much less active (1)”, “less active (2)”, “equally active (3)”, “more active (4)”, and “much more active (5)”.
2.2.2 Anxiety
Five items were selected from the State-Trait Anxiety Inventory (STAI; Marteau & Bekker, 1992) to assess people's feelings of anxiety during the past week. Four items were selected based on their relevance to the context of the COVID-19 pandemic (e.g., “During the past week, I felt tense”), whereas one item tapped into anxiety more directly (i.e., “During the past week, I felt anxious”). Items were rated on a 4-point response scale with the following options: “Seldom or never, less than 1 day (1)”, “A few times, 1–2 days (2)”, “Now and then or regularly, 3–4 days (3)”, and “Mostly or all the time, 5–7 days (4)”. Cronbach's alpha was 0.95 at the between-person level and 0.66 at the within-person level.
2.2.3 Depressive symptoms
People's depressive feelings during the past week were assessed with a 6-item version (Van Hiel & Vansteenkiste, 2009) of the Center for Epidemiological Studies – Depression scale (CES-D; Radloff, 1977). An example item reads: “During the past week, I felt sad”. Items were rated on a 4-point response scale with the following options: “Seldom or never, less than 1 day (1)”, “A few times, 1–2 days (2)”, “Now and then or regularly, 3–4 days (3)”, and “Mostly or all the time, 5–7 days (4)”. Cronbach's alpha was 0.89 at the between-person level and 0.62 at the within-person level.
2.2.4 Sleep quality
Sleep quality was measured through one item (i.e., “How would you rate your overall sleep quality over the past week?”) that is used in the Pittsburgh Sleep Quality Index (Buysse et al., 1989) to measure the subjective sleep quality component. This item was selected because previous psychometric evaluation research showed that this single-item component was most highly correlated with global scores of sleep quality (Carpenter & Andrykowski, 1998). Participants rated their global sleep quality during the past week. They were provided with a 4-point response scale with the following options “very bad (1)”, “rather bad (2)”, “rather good (3)”, and “very good (4)”.
2.3 Plan of analysis
All analyses were performed in R (R Core Team, 2020). A graphical representation of the evolution of the study variables over time can be found in the online supplementary material (Fig. 1S).
2.3.1 Preliminary analysis
First, the intraclass correlation coefficient was calculated for each of the study variables to examine how much of the total variance in each outcome can be attributed to between- and within-person differences. Second, the Pearson correlation coefficients between age and the study variables were calculated at the between-person level, as well as the correlation coefficients between the study variables at both the between- and within-person levels. Finally, a MANOVA was conducted with gender as the independent variable and all study variables as dependent variables to examine whether there were gender-related differences in our study variables.
2.3.2 Primary analysis
We conducted random intercept cross-lagged panel models to examine the reciprocal associations between PA and anxiety, depressive symptoms, and sleep quality across nine weekly assessments (Fig. 1 ). The between-person effects (i.e., stable, trait-like effects) regarding the study variables PA, anxiety, depressive symptoms, and sleep quality are reflected by the random intercepts between participants. In each model, the correlation (covariance) between the random intercepts reflects the strength of the association of between-person differences in PA with between-person differences in anxiety, depressive symptoms, or sleep quality (respectively). Within-person effects are reflected by both autoregressive and cross-lagged pathways. The autoregressive paths in the models indicate the extent to which within-person deviations in all variables (PA, anxiety, depressive symptoms, and sleep quality) can be predicted by deviations from their own expected scores. The cross-lagged paths in the models indicate the extent to which variables are linked reciprocally, i.e., they indicate whether a within-person deviation in PA predicts a deviation from their own expected score in anxiety, depressive symptoms, or sleep quality one week later (and vice versa). We specified gender and age at week 1 as time-invariant covariates (between-person) to control for gender and age differences, respectively.Fig. 1 Random Intercept Cross-Lagged Panel Model (RI-CLPM) of the relationship between Physical Activity (PA) and anxiety (anx) across 9 waves, with one-week lags
Note. The figure shows two random intercepts (PA between and anxiety between) that reflect between-person effects. Age and gender (at T1) represent time-invariant covariants that influence between-person differences in PA and anxiety. Within-person effects are reflected in auto-regressive paths between the latent factors of PA across waves and latent factors of anxiety across waves, and by cross-lagged paths between latent factors of PA and anxiety to indicate the reciprocal relationship between these variables.
*This figure serves as an example of the similar relationships examined between PA and depressive symptoms, and between PA and sleep quality.
Fig. 1
Different models were fit for each of the three outcomes (i.e., anxiety, depressive symptoms, and sleep quality) separately, each time starting with a baseline, unconstrained model with all variances, covariances, autoregressive and cross-lagged effects allowed to vary across waves, followed by models in which constraints were systematically added, ending with the most simple, parsimonious model with variance, covariances, autoregressive and cross-lagged effects constrained to be equal across waves. Model fit was evaluated using several fit indices (i.e., CFI, RMSEA, and SRMR) (Hu & Bentler, 1999). The CFI is a comparative fit index with values that can vary between 0 and 1, values of 0.95 and higher indicate a good fit. The RMSEA is an absolute fit index, a good fit is indicated by values of ≤ 0.05, an acceptable fit by values between 0.05 and 0.08, and values of 0.10 and above show a poor fit. Finally, the SRMR is also an absolute fit index with values between 0 and 0.08 indicating an acceptable range of model fit. In the final models that are presented in this paper, variances and covariances, autoregressive and cross-lagged paths were constrained to be equal because the model comparisons showed no better fit for the less constrained, less parsimonious models.
2.4 Results
2.4.1 Preliminary analysis
Based on the intraclass correlation coefficient, 52.28% of the total variation in PA was attributable to differences among participants rather than changes over time within participants. With respect to the outcomes, 75.04% of the total variance in anxiety, 75.01% in depressive symptoms, and 57.11% in sleep quality could be ascribed to differences between participants.
Table 1a, Table 1b summarizes the means, standard deviations, and Pearson correlations of all variables at the between- (Table 1a) and within- (Table 1b) person levels. Older participants reported lower levels of anxiety (r = −0.24, p < .001) and depression (r = −0.27, p < .001), and better sleep quality (r = 0.13, p < .001). People who had higher PA scores (indicating that they self-reported having increased their PA as compared to pre-COVID-19 pandemic times), reported on average lower levels of anxiety (r = −0.23, p < .001) and depression (r = −0.27, p < .001), and better sleep quality (r = 0.21, p < .001). Moreover, in weeks participants reported higher PA scores (indicating that, in that week, they reported increases in their PA as compared to pre-COVID-19 pandemic times), they also reported lower levels of anxiety (r = −0.15, p < .001) and depression (r = −0.16, p < .001), and better sleep quality (r = 0.09, p < .001) compared to weeks in which they had lower PA scores. However, all associations were rather small (Cohen, 1992).Table 1a Means, standard deviations, and pearson correlations of all variables at the between-person level.
Table 1aVariables M SD 1 2 3 4
1. Age 43.78 14.71 – – – –
2. Physical activity 2.80 1.01 .02 – – –
3. Anxiety 1.98 .75 −.24*** −.23*** – –
4. Depressive symptoms 1.58 .58 −.27*** −.27*** .80*** –
5. Sleep quality 2.88 .71 .13*** .21*** −.61*** −.57***
***p < .001.
Table 1b Pearson correlations of all variables at the within-person level.
Table 1bVariables 1 2 3
1. Physical activity – – –
2. Anxiety −.15*** – –
3. Depressive symptoms −.16*** .51*** –
4. Sleep quality .09*** −.30*** −.26***
***p < .001.
Results of the MANOVA analyses showed a multivariate effect for gender (Wilk's Λ = 0.96, F(1,5572) = 25.10, p < .001, η2 = 0.02). Men reported higher PA-scores (M = 2.83, SD = 0.92) and better sleep quality (M = 3.02, SD = 0.69) compared to women (M PA = 2.79, SD PA = 1.04; M sleep = 2.84, SD sleep = 0.72); whereas women reported higher levels of anxiety (M = 2.06, SD = 0.76) and depression (M = 1.63, SD = 0.59) as compared to men (M anx =1.74, SD anx = 0.68; M dep = 1.42, SD dep = 0.50).
2.4.2 Primary analysis
The final (constrained) models to explore reciprocal relations between PA and anxiety (CFI = 0.98, RMSEA = 0.03, SRMR = 0.04), depressive symptoms (CFI = 0.97, RMSEA = 0.04, SRMR = 0.05), and sleep quality (CFI = 0.97, RMSEA = 0.03, SRMR = 0.06) all revealed a good fit (Table 2 ).Table 2 Results of the random intercept cross-lagged panel models for physical activity (PA) with anxiety, depressive symptoms, and sleep quality.
Table 2 Model 1 Anxiety Model 2 Depressive symptoms Model 3 Sleep quality
β (SE) β (SE) β (SE)
Between-person
Gender → outcome .18 (.05)*** .14 (.04)*** −.13 (.04)***
Age → outcome −.22 (.00)*** −.27 (.00)*** .12 (.00)**
Covariance (PA, outcome) −.25 (.02)*** −.30 (.01)*** .24 (.02)***
Within-person
Autoregressive
PA wave x → PA wave x+1 .38 (.02)*** .37 (.02)*** .39 (.02)***
Outcome wave x → outcome wave x+1 .41 (.02)*** .37 (.02)*** .29 (.02)***
Cross-lagged
PA wave x → outcome wave x+1 −.03 (.01) −.01 (.01) .02 (.01)
Outcome wave x → PA wave x+1 −.04 (.03)* −.06 (.04)*** .03 (.03)
Covariances
PA and outcome at wave 1 −.10 (.02)* −.14 (.01)** .11 (.02)**
Residuals PA and residuals outcome at wave 2–9 (constant) −.15 (.00)*** −.15 (.01)*** .09 (.01)***
*p < .05, **p < .01, ***p < .001.
Note. These are models with variance and covariances across waves constrained to equality; controlled for age and gender differences (between-person level).
At both the between- and within-person level, results showed that higher PA scores (indicating a self-reported increase in PA as compared to pre-COVID-19 pandemic times) were negatively related to anxiety levels, and depressive symptoms, while being positively related to sleep quality. These associations indicate that, across all weeks, participants who reported increased PA scores compared to pre-COVID-19 pandemic times also reported, on average, lower levels of anxiety (β = −0.25, p < .001) and depression (β = −0.30, p < .001) and better sleep quality (β = 0.24, p < .001) (between-person level; H1) and that, in weeks participants reported higher PA scores compared to their intra-personal level, they also reported lower levels of anxiety (β = −0.10, p < .05) and depression (β = −0.14, p < .01), and better sleep quality (β = 0.11, p < .01) compared to their intra-individual level (within-person level, H2a).
After controlling for autoregressive associations, cross-lagged path coefficients showed no significant effect of PA on either anxiety (β = −0.03, p = .190), depressive symptoms (β = −0.01, p = .806), or sleep quality (β = 0.02, p = . 410) at the within-person level. However, results did show a significant association in the other direction in two of the three models, that is, a negative effect of both anxiety (β = −0.04, p < .05) and depressive symptoms (β = −0.06, p < .001) (but not of sleep quality, β = 0.03, p = .145) on PA levels one week later. This indicates that if participants experienced higher anxiety and depression levels in a given week relative to their own intra-individual level, they also reported being less physically active in the next week compared to their own average (H2b).
3 Discussion
The present study provided a unique insight into the associations between levels of PA and anxiety, depressive symptoms, and sleep quality during the first COVID-19 lockdown in a longitudinal design with weekly measurements across 9 weeks. This weekly assessment of all key constructs allowed us to go beyond the study of between-person dynamics. Instead, we could additionally examine whether dips and increases in citizens' PA would predict concomitant mental health benefits and even drive improvements in one's own mental health or vice versa, with poor mental health being a vulnerability factor for reduced PA. Several interesting findings emerged.
First, at the between-person level, as hypothesized, people who reported positive changes in PA levels compared to pre-COVID-19 pandemic times, reported fewer feelings of anxiety and depression, and better sleep quality across the nine-week survey period. These findings align with the limited body of work with other research during the COVID-19 pandemic, showing that levels of PA are related to feelings of anxiety (e.g., Frontini et al., 2021; Lesser & Nienhuis, 2020), depression (Trabelsi et al., 2021; Violant-Holz et al., 2020), and sleep quality (Şimşek et al., 2020; Violant-Holz et al., 2020). However, based on these between-person analyses, we cannot comment on the direction of the relationship. Thus, this result may imply that persons who sleep better and feel mentally well are better capable of uplifting their PA level compared to persons with poor mental health status (e.g., because they have more energy for it). However, it may also imply that persons who are more physically active in comparison with pre-COVID-19 pandemic times feel better and sleep better as a result when compared to those persons who are not more physically active.
Second, at the within-person level, our findings showed that the week-to-week variation in individuals’ PA was associated with week-to-week variation in their mental well-being and sleep quality. Again, since we cannot make any statements about the direction of the relationship here, this result can mean either that PA within a week leads to better mental well-being and sleep quality (e.g., one feels good thanks to PA), but also that better mental well-being and sleep in that week leads to more PA (e.g., one has more energy and desire to be physically active when one feels good and/or has slept well). Because few studies have used analysis techniques that can estimate within-person effects, comparison with prior research is difficult. Findings from the only study that has reported within-subject covariances between PA and mental health are consistent with the results of the present study as they found a significant within-subject covariance between PA and depression (Stavrakakis et al., 2012).
While these two findings above are interesting, only the cross-lagged associations at the within-person level reported herein allow one to derive conclusions with respect to the direction of the effects. Again, two meaningful findings emerged. First, the cross-lagged associations suggested that changes in a person's sleep quality at one moment were not related to perceived PA levels one week later, nor vice versa. Perhaps more than the quality of sleep, it is the number of hours of sleep that may affect PA in the subsequent days or week by increased fatigue (Campbell et al., 2018; Pesonen et al., 2022). However, these findings deviate from previous research which found a cross-lagged effect of sleep quality on PA (Pesonen et al., 2020).
Second, the cross-lagged associations in this study suggested that mental health primarily predicts PA rather than the other way around. Specifically, if participants reported more anxiety and depressive complaints in a given week (e.g., due to changing circumstances), they report being less physically active during the next week. These findings suggest that a temporary increase in poor mental health may serve as a vulnerability factor for reduced PA one week later. Presumably, the rumination and worry that go along with anxiety and depression prevent individuals from taking action to stay physically active. Also, depressive symptoms are characterized by feelings of fatigue, which makes individuals have less desire and energy to be physically active. In fact, the current findings suggest that it is mainly critical to invest in the mental health of individuals during distressing times. Several e-health interventions were developed and offered to the broader public during the COVID-19 pandemic to better cope with the situation. As an illustration, an intervention that aids individuals in getting their basic psychological needs for autonomy, competence, and relatedness fostered greater psychological well-being (Laporte et al., 2022). Such need crafting may eventually lead one to stay physically active by offsetting symptoms of poor mental health.
Although no research has been done on cross-lagged associations within the context of the COVID-19 pandemic, the current findings deviate from previous longitudinal research outside the context of the COVID-19 pandemic, which found bidirectional cross-lagged relations between PA and depressive symptoms (Stavrakakis et al., 2012) or which found a cross-lagged effect of PA on mental health rather than the other way around (Kroesen & De Vos, 2020). Several explanations can be put forward. First, the broader context of the present study was remarkably different as the study took place during distressing times, with people facing unusually high levels of uncertainty and unpredictability while also having to process and regulate various anxiety-inducing messages on the news and social media (Vermote et al., 2021). During these extreme times, more may be needed to safeguard one's mental health than to preserve one's PA level. This may be the reason why our findings suggest that PA has a rather short-lived positive effect on our mental well-being, but is not robust enough to generate mental health benefits carried over to the next week. Second, an alternative explanation may have to do with the operationalization of PA. The measure of PA was comparative in nature, with participants being asked to compare their current PA levels with their pre-COVID-19 pandemic routine level of PA. Such a comparative measure might have less predictive power for people's mental well-being than their absolute level of PA. For example, if a person moves more than he/she did before the COVID-19 pandemic, but this level of PA is still low, that person may not cross a critical threshold to benefit in terms of mental well-being. Conversely, a person may move less than before, but still, do it a lot in absolute terms and, hence, still recruit psychological well-being benefits from his/her PA level.
Although no beneficial effects of increased PA on mental health remain the week after, PA may well have a short-term impact on mental health (given the bidirectional within-week associations were found to be significant). Therefore, in addition to the wellness interventions discussed earlier, it may also be important to focus on interventions that ensure or increase PA during pandemic times. Fortunately, numerous recommendations have appeared in both scientific and popular media on how to stay physically active during quarantine periods. For instance, scientific research promoted bodyweight training, dance, staircase walking, playing with pets, etc. (see Bentlage et al., 2020 for a systematic review). Concerning the popular media, inspiration to stay physically active during housebound quarantine can be found on YouTube, Facebook, and exercise apps, taking into account the limited space and lack of specialized training equipment. Also, modern technology (e.g., wearables) can motivate and enhance home‐based workouts, although specific recommendations on their use and associated goals remain scarce (Dwyer et al., 2020).
3.1 Limitations and suggestions for future research
This study was the first to explore the reciprocal associations between PA and mental well-being and sleep quality during the pandemic using a longitudinal approach, which made it possible to distinguish effects at the between- and within-person levels. Specifically, the random intercept cross-lagged panel model used goes beyond the unidirectional approach often used where one variable is considered a cause and the other an effect. Another important strength is that we managed to recruit a large sample that participated in multiple waves of this longitudinal study, which is not evident in times of pandemic. Although this study had several strengths, some important limitations need to be mentioned.
First, we relied on a convenience sampling approach to recruit participants. This sampling strategy may have produced an unrepresentative sample in which people with low social-economic status or with poor physical and/or mental health were underrepresented. Future research would do well to confirm the relationships found within a representative sample, or, at the very least, assess participants’ socioeconomic status and physical/mental health status so that this information can be included as covariates in the model.
Second, all measurements were based on self-reports, which may have caused shared method variance. Therefore, future research should supplement self-reporting measures with physiological and objective indicators of PA (e.g., Dyrstad et al., 2014) and sleep quality (e.g., de Arriba-Pérez et al., 2018); for instance using wearables that include accelerometers and heart rate sensors to improve the quality of assessment. Such an objective measure of PA would increase the sensitivity and interpretability of the results, since the herein-assessed self-reported change in PA says nothing about its quantity. For instance, someone who is less physically active than before the pandemic can still meet the World Health Organization guidelines and vice versa. In addition, an experience sampling methodology would allow for the collection of self-report ratings of anxiety and depressive symptoms several times during the day, thereby enhancing accuracy by reducing the retrieval from memory (Barrett & Barrett, 2001).
Third, as the current study was part of the larger “Motivation Barometer” survey, the questionnaires used for this study had to be short to minimize drop-out during the nine consecutive waves. Therefore, PA and sleep quality were measured using single items (Allen et al., 2022). Although previous research refers to single-item measures as good alternatives to measure PA (Milton et al., 2011) and sleep quality (Snyder et al., 2018), future research would do well to use more elaborate measures. With regard to PA, in particular, it would be useful for future research to also include absolute levels of PA (e.g., minutes per day) in addition to the current relative comparison measure.
4 Conclusion
Our findings confirm that people who report engaging in more PA than others across the first nine weeks of the COVID-19 pandemic, experience lower anxiety and depressive symptoms, and better sleep quality. This link is also observed at the within-person level, such that in weeks people reported being more physically active compared to pre-lockdown times, they also reported fewer feelings of anxiety and depression, and better sleep quality. Yet, over weeks, especially poor mental health seems to pose a threat to the maintenance of PA, while we did not find evidence for PA to serve as a protective factor against an increase in poor mental health. From the public health perspective, the current findings show it is of primary importance to invest in the mental health of individuals during distressing times. Moreover, the results highlight the importance that cross-sectional associations between physical activity and mental health should be interpreted with caution, since they can be the results of associations in either direction. Therefore, in future research, it is important to examine the reciprocal relationships in a longitudinal design that allows distinguishing between-person from within-person effects.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Appendix A Supplementary data
The following is the supplementary data related to this article:Multimedia component 1
Multimedia component 1
Data availability
Data will be made available on request.
Acknowledgments
This work was supported by 10.13039/501100003130 Research Foundation Flanders (FWO) [Grant number 3F023819]. The Motivation Barometer was funded by the University of Ghent [BOFCOV2020000701] and the federal Belgian Ministry of Social Affairs and Public Health.
Appendix A Supplementary data related to this article can be found at https://doi.org/10.1016/j.mhpa.2022.100500.
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References
Allen M.S. Iliescu D. Greiff S. Single item measures in psychological science: A call to action 2022
de Arriba-Pérez F. Caeiro-Rodríguez M. Santos-Gago J.M. How do you sleep? Using off the shelf wrist wearables to estimate sleep quality, sleepiness level, chronotype and sleep regularity indicators Journal of Ambient Intelligence and Humanized Computing 9 4 2018 897 917 10.1007/s12652-017-0477-5
Barrett L.F. Barrett D.J. An introduction to computerized experience sampling in psychology Social Science Computer Review 19 2 2001 175 185 10.1177/089443930101900204
Bentlage E. Ammar A. How D. Ahmed M. Trabelsi K. Chtourou H. Brach M. Practical recommendations for maintaining active lifestyle during the COVID-19 pandemic: A systematic literature review International Journal of Environmental Research and Public Health 17 17 2020 6265 10.3390/ijerph17176265 32872154
Buysse D.J. Reynolds C.F. III Monk T.H. Berman S.R. Kupfer D.J. The pittsburgh sleep quality index: A new instrument for psychiatric practice and research Psychiatry Research 28 2 1989 193 213 10.1016/0165-1781(89)90047-4 2748771
Campbell R. Vansteenkiste M. Delesie L. Tobback E. Mariman A. Vogelaers D. Mouratidis A. Reciprocal associations between daily need-based experiences, energy, and sleep in chronic fatigue syndrome Health Psychology 37 12 2018 1168 30321019
Carpenter J.S. Andrykowski M.A. Psychometric evaluation of the pittsburgh sleep quality index Journal of Psychosomatic Research 45 1 1998 5 13 10.1016/s0022-3999(97)00298-5 9720850
Casagrande M. Favieri F. Tambelli R. Forte G. The enemy who sealed the world: Effects quarantine due to the COVID-19 on sleep quality, anxiety, and psychological distress in the Italian population Sleep Medicine 75 2020 12 20 10.1016/j.sleep.2020.05.011 32853913
Castañeda-Babarro A. Arbillaga-Etxarri A. Gutiérrez-Santamaría B. Coca A. Physical activity change during COVID-19 confinement International Journal of Environmental Research and Public Health 17 18 2020 6878 10.3390/ijerph17186878 32967091
Chennaoui M. Arnal P.J. Sauvet F. Léger D. Sleep and exercise: A reciprocal issue? Sleep Medicine Reviews 20 2015 59 72 10.1016/j.smrv.2014.06.008 25127157
Cohen J. Quantitative methods in psychology: A power primer Psychological bulletin 1992
Constandt B. Thibaut E. De Bosscher V. Scheerder J. Ricour M. Willem A. Exercising in times of lockdown: An analysis of the impact of COVID-19 on levels and patterns of exercise among adults in Belgium International Journal of Environmental Research and Public Health 17 11 2020 4144 10.3390/ijerph17114144 32532013
Dunton G.F. Wang S.D. Do B. Courtney J. Early effects of the COVID-19 pandemic on physical activity locations and behaviors in adults living in the United States Preventive medicine reports 20 2020 101241
Dwyer M.J. Pasini M. De Dominicis S. Righi E. Physical activity: Benefits and challenges during the COVID-19 pandemic Scandinavian Journal of Medicine & Science in Sports 30 7 2020 1291 1294 10.1111/sms.13710 32542719
Dyrstad S.M. Hansen B.H. Holme I.M. Anderssen S.A. Comparison of self-reported versus accelerometer-measured physical activity Medicine & Science In Sports & Exercise 46 1 2014 99 106 10.1249/mss.0b013e3182a0595f 23793232
Frontini R. Rebelo-Gonçalves R. Amaro N. Salvador R. Matos R. Morouço P. Antunes R. The relationship between anxiety levels, sleep, and physical activity during COVID-19 lockdown: An exploratory study Frontiers in Psychology 12 2021 10.3389/fpsyg.2021.659599
Hu L.T. Bentler P.M. Cutoff criteria for fit indexes in covariance structure 539 analysis: Conventional criteria versus new alternatives. Structural equation modeling: A 540 A Multidisciplinary Journal 6 1999 1 55 10.1080/10705519909540118
Kroesen M. De Vos J. Does active travel make people healthier, or are healthy people more inclined to travel actively? Journal of Transport & Health 16 2020 100844
Laporte N. Soenens B. Flamant N. Vansteenkiste M. Mabbe E. Brenning K. The role of daily need crafting in daily fluctuations in adolescents' need-based and affective experiences Motivation and Emotion 46 2 2022 137 149
Lesser I.A. Nienhuis C.P. The impact of COVID-19 on physical activity behavior and well-being of Canadians International Journal of Environmental Research and Public Health 17 11 2020 3899 10.3390/ijerph17113899 32486380
Marteau T.M. Bekker H. The development of a six-item short-form of the state scale of the Spielberger State—trait Anxiety Inventory (STAI) British Journal of Clinical Psychology 31 3 1992 301 306 10.1111/j.2044-8260.1992.tb00997.x 1393159
Milton K. Bull F.C. Bauman A. Reliability and validity testing of a single-item physical activity measure British Journal of Sports Medicine 45 3 2011 203 208 10.1136/bjsm.2009.068395 20484314
Ozamiz-Etxebarria N. Idoiaga Mondragon N. Dosil Santamaría M. Picaza Gorrotxategi M. Psychological symptoms during the two stages of lockdown in response to the COVID-19 outbreak: An investigation in a sample of citizens in northern Spain Frontiers in Psychology 11 2020 10.3389/fpsyg.2020.01491
Pesonen A.K. Kahn M. Kuula L. Korhonen T. Leinonen L. Martinmäki K. …Lipsanen J. Sleep and physical activity–the dynamics of bi-directional influences over a fortnight BMC Public Health 22 1 2022 1 7 34983455
R Core Team A language and environment for statistical computing 2020 R Foundation for Statistical Computing Vienna Austria
Radloff L.S. The CES-D scale: A self-report depression scale for research in the general population Applied Psychological Measurement 1 3 1977 385 401
Şimşek E. Koç K. Özsoy D. Karakuş M. The title of the article: Investigation the effect of performing physical activity at home on sleep quality and life satisfaction during coronavirus (Covid-19) outbreak International Journal of Applied Exercise Physiology 9 7 2020 55 62
Snyder E. Cai B. DeMuro C. Morrison M.F. Ball W. A new single-item sleep quality scale: Results of psychometric evaluation in patients with chronic primary insomnia and depression Journal of Clinical Sleep Medicine 14 11 2018 1849 1857 10.5664/jcsm.7478 30373688
Stavrakakis N. de Jonge P. Ormel J. Oldehinkel A.J. Bidirectional prospective associations between physical activity and depressive symptoms. The TRAILS Study Journal of Adolescent Health 50 5 2012 503 508
Steinmo S. Hagger-Johnson G. Shahab L. Bidirectional association between mental health and physical activity in older adults: Whitehall II prospective cohort study Preventive Medicine 66 2014 74 79 24945691
Tison G.H. Avram R. Kuhar P. Abreau S. Marcus G.M. Pletcher M.J. Olgin J.E. Worldwide effect of COVID-19 on physical activity: A descriptive study Annals of Internal Medicine 173 9 2020 767 770 10.7326/m20-2665 32598162
Trabelsi K. on behalf of the ECLB-COVID19 Consortium Sleep quality and physical activity as predictors of mental wellbeing variance in older adults during COVID-19 lockdown: ECLB COVID-19 international online survey International Journal of Environmental Research and Public Health 18 8 2021 4329 10.3390/ijerph18084329 33921852
Van Hiel A. Vansteenkiste M. Ambitions fulfilled? The effects of intrinsic and extrinsic goal attainment on older adults' ego-integrity and death attitudes The International Journal of Aging and Human Development 68 1 2009 27 51 10.2190/ag.68.1.b 19348103
Vermote B. Waterschoot J. Morbée S. der Kaap-Deeder V. Schrooyen C. Soenens B. …Vansteenkiste M. Do psychological needs play a role in times of uncertainty? Associations with well-being during the COVID-19 crisis Journal of Happiness Studies 23 1 2022 257 283 33942013
Violant-Holz V. Gallego-Jiménez M.G. González-González C.S. Muñoz-Violant S. Rodríguez M.J. Sansano-Nadal O. Guerra-Balic M. Psychological health and physical activity levels during the COVID-19 pandemic: A systematic review International Journal of Environmental Research and Public Health 17 24 2020 9419 10.3390/ijerph17249419 33334073
Wolf S. Seiffer B. Zeibig J.M. Welkerling J. Brokmeier L. Atrott B. …Schuch F.B. Is physical activity associated with less depression and anxiety during the COVID-19 pandemic? A rapid systematic review Sports Medicine 51 8 2021 1771 1783 33886101
Zhao W. Vandelanotte C. Khalesi S. Alley S.J. Williams S.L. Thwaite T.L. …To Q.G. Depression, anxiety, stress, and physical activity of Australian adults during COVID-19: A combined longitudinal and repeated cross-sectional study Frontiers in Psychology 13 2022
| 36510601 | PMC9729168 | NO-CC CODE | 2022-12-15 23:15:35 | no | Ment Health Phys Act. 2023 Mar 8; 24:100500 | utf-8 | Ment Health Phys Act | 2,022 | 10.1016/j.mhpa.2022.100500 | oa_other |
==== Front
Semin Arthritis Rheum
Semin Arthritis Rheum
Seminars in Arthritis and Rheumatism
0049-0172
1532-866X
Elsevier Inc.
S0049-0172(22)00200-1
10.1016/j.semarthrit.2022.152149
152149
Article
Rituximab is associated with worse COVID-19 outcomes in patients with rheumatoid arthritis: A retrospective, nationally sampled cohort study from the U.S. National COVID Cohort Collaborative (N3C)
Singh Namrata a
Madhira Vithal b
Hu Chen c
Olex Amy L. d
Bergquist Timothy e
Fitzgerald Kathryn C. c
Huling Jared D. f
Patel Rena C. g
Singh Jasvinder A. hij⁎
a Division of Rheumatology, Department of Internal Medicine, University of Washington, Seattle, WA 98195, United States
b Palila Software, Reno, NV, United States
c Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD 21287, United States
d Virginia Commonwealth University, Wright Center for Clinical and Translational Research, 203 E Cary St, Richmond, VA 23298, United States
e Sage Bionetworks, Seattle, WA 98109, United States
f Division of Biostatistics, University of Minnesota, Minneapolis, MN, United States
g Departments of Medicine and Global Health, University of Washington, Seattle, WA, 98104, United States
h Medicine Service, VA Medical Center, 700 19th St S, Birmingham, AL 35233 United States
i Department of Medicine at the School of Medicine, University of Alabama at Birmingham (UAB), 510 20th Street S, Birmingham, AL 35294-0022, United States
j Department of Epidemiology at the UAB School of Public Health, 1665 University Blvd., Ryals Public Health Building, Birmingham, AL 35294-0022, United States
⁎ Corresponding author.
8 12 2022
2 2023
8 12 2022
58 152149152149
© 2022 Elsevier Inc. All rights reserved.
2022
Elsevier Inc.
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Objective
To assess whether rituximab (RTX) is associated with worse COVID-19 outcomes among patients with rheumatoid arthritis (RA).
Methods
We used the National COVID Cohort Collaborative (N3C), the largest US cohort of COVID-19 cases and controls, to identify patients with RA (International Classification of Diseases (ICD)-10 code, M05.X or M06.X). Key outcomes were COVID-19-related hospitalization, intensive care unit (ICU) admission, 30-day mortality, and World Health Organization (WHO) classification for COVID-19 severity. We used multivariable logistic regression models to assess the association between RTX use and the odds of COVID-19 outcomes compared with the use of conventional synthetic disease modifying anti-rheumatic drugs (csDMARDs), adjusting for demographics, medical comorbidities, smoking status, body mass index, US region and COVID-19 treatments.
Results
A total of 69,549 patients met our eligibility criteria of which 22,956 received a COVID-19 positive diagnosis between 1/1/2020 and 9/16/2021. Median (IQR) age of the cohort was 63 (52–72) years, 76% of the cohort was female, 68% was non-Hispanic/Latinx White, and 73% was non-smokers. Prior to their first COVID-19 diagnosis, 364 patients were exposed to RTX. Compared to the use of csDMARDs, RTX use was associated with an increased odds of COVID-19-related hospitalization (adjusted odds ratio [aOR] 2.1, 95% confidence interval 1.5–3.0), ICU admission (aOR 5.2, 1.8–15.4) and invasive ventilation (aOR 2.7, 1.4–5.5). Results were confirmed in multiple sensitivity analyses.
Conclusion
Our findings can guide patients, providers, and policymakers regarding the increased risks associated with RTX use during the COVID-19 pandemic. These results can help risk stratification and prognosis-assessment.
Keywords
COVID-19
Rituximab
Rheumatoid arthritis
COVID-19 outcomes
==== Body
pmc Abbreviations
N3C National COVID Cohort Collaborative
ICD-10-CM International Classification of Diseases, Tenth Revision, Clinical Modification
RA Rheumatoid arthritis
RTX rituximab
csDMARD conventional synthetic non-biologic disease-modifying anti-rheumatic drug
tsDMARD targeted synthetic disease-modifying anti-rheumatic drug
bDMARD Biologic disease-modifying anti-rheumatic drug
Introduction
The COVID-19 pandemic has significantly impacted the lives of people worldwide. As the pandemic has progressed, a higher risk of incident COVID-19 infection or worse outcomes in certain immunocompromised subpopulations, such as those with rheumatoid arthritis (RA) [1,2] has been described. This is potentially related not only to their underlying disease but also due to the use of immunosuppressive medications used to treat RA [3,4]. Specifically, some studies have raised concerns regarding rituximab (RTX) exposure being associated with worse COVID-19 outcome [3], [4], [5], [6]. For example, Sparks et al., using the data from the global rheumatology alliance (GRA) that contained physician-reported survey data, found that people with RA who were treated with RTX or JAK inhibitor had worse COVID-19 severity (defined as hospitalization/death) than those on tumor necrosis factor inhibitors (TNFi) [5]. These studies have several limitations that limit the interpretation of their results. For example, most studies lacked a control group [5], had a small sample size of patients treated with RTX [4], or included patients with heterogeneous rheumatic diseases [6], which can have differential disease-associated risk of COVID-19 infection and related outcomes. Additionally, many important outcomes such as ICU-admission or 30-day mortality were not examined in earlier studies, likely due to the small sample sizes of RTX-treated RA patients.
Thus, an evidence gap exists in elucidating the potentially harmful role that RA treatment with RTX plays in COVID-19 infection risk and outcomes. Besides the innate immune system and T cells, B lymphocytes play a major role in the early stages of innate immune response to viral infections, including viral antigen processing, and building an immunologic memory [7]. Given that RTX inhibits this by eliminating B-cells for several months [8,9], RA patients exposed to RTX may have a heightened risk for worse COVID-19 outcomes. Therefore, the objective of our study was to evaluate the associations between baseline use of RTX in patients with RA and COVID-19 outcomes using a nationally sampled cohort of patients.
Methods
Study database and sample selection
The National COVID Cohort Collaborative (N3C), a centralized, harmonized, high-granularity electronic medical record (EMR) repository, is the largest nationally sampled US cohort of COVID-19 cases and controls to date [10]. N3C is comprised of members from the NIH Clinical and Translational Science Awards (CTSA) Program and its Center for Data to Health (CD2H), the IDeA Centers for Translational Research, the National Patient-Centered Clinical Research Network (PCORNet), the Observational Health Data Sciences and Informatics (OHDSI), TriNetX, and the Accrual to Clinical Trials (ACT) networks.
N3C includes de-identified data from outpatient, emergency room, and inpatient encounters and includes the health records of two COVID-19 negative controls for every COVID-19 positive patient, matched on demographic factors, from each participating data partner site [4]. COVID-19 positive patients are defined as individuals with any encounter on or after 1/1/2020 with 1) one of a set of a priori-defined SARS-CoV-2 laboratory tests, >95% of which are PCR-positive, 2) a “strong positive” diagnostic code, or 3) two “weak positive” diagnostic codes during the same encounter or date. The cohort definition is publicly available on GitHub [11]. For patients included in N3C, encounters in the same data partner site beginning on or after 1/1/2018 are also included to provide information about pre-existing health conditions (i.e., “lookback data”). For these analyses, the data from January 1, 2018, to those records available in the de-identified v45 database release (Release-v45–9–16–21) were used. De-identified data in N3C includes randomly shifted dates per patient of +/- 180 days and provides 3-digit zip codes.
Definition of the RA cohort and DMARD exposures
We defined patients with RA has having one or more International Classification of Disease, tenth revision, common modification (ICD-10-CM) codes for RA (Appendix 1), prior to their COVID-19 infection; alternate stricter definitions were tested in sensitivity analyses (see analysis section for details on each sensitivity analysis).
We categorized DMARDs as follows: [1] conventional DMARDs (csDMARDs) such as methotrexate, hydroxychloroquine, sulfasalazine, or leflunomide; [2] biologic DMARDs (bDMARDs) such as tumor necrosis factor inhibitors (TNFi-biologics: adalimumab, etanercept, infliximab, golimumab, or certolizumab), IL-6 inhibitors (tocilizumab or sarilumab), cytotoxic T lymphocyte-associated antigen immunoglobulin (CTLA4-Ig) abatacept; [3] targeted synthetic DMARDs (tsDMARDs) that include Janus Kinase inhibitors (JAKi), tofacitinib, baricitinib and upadacitinib; or [4] multiple DMARDs (more than one DMARD). We treated baseline use of RTX prior to the COVID-19 infection as the main exposure of interest. We separated RTX from the bDMARD category due to RTX's unique effects on B-cell function [8,9]. DMARD exposure is defined as having a DMARD-associated medication exposure date prior to the date of COVID-19 diagnosis. We calculated the time since the last documented RTX infusion before COVID-19 diagnosis and categorized it as: less than 30 days; 31–180 days; or greater than 180 days.
COVID-19 outcomes
Our study's primary COVID-19 outcomes included the following among COVID-19 positive patients: [1] COVID-19-related hospitalization; [2] intensive care unit (ICU) admission; [3] 30-day mortality; and [4] COVID-19 severe/fatal World Health Organization (WHO) classification level 7 or above [13]. Five levels of disease severity were defined, as in previous N3C analyses [14]: asymptomatic or mild disease with outpatient care only (WHO severity 1–2), mild disease requiring only an emergency department (ED) visit (WHO severity 3), moderate disease with COVID-19-related hospitalization but without invasive ventilation (WHO severity 4–6), severe disease with COVID-19-related hospitalization requiring invasive ventilation or extracorporeal membrane oxygenation (ECMO) treatment (WHO severity 7–9), and death (WHO severity 10).
Our secondary outcomes included the following for the first or index COVID-19-related hospitalization: [1] invasive ventilation; [2] acute kidney injury (AKI) in-hospital; [3] ICU mortality; [4] in-hospital mortality; [5] ICU length of stay; and [6] hospital length of stay.
Covariates
We extracted demographics (age, sex, and race/ethnicity), body mass index (BMI, classified as underweight, normal, overweight, and obese), smoking history (never vs. ever vs. unknown), and US region (West, Midwest, Northeast, South or unknown) from N3C. We identified comorbidities using the Deyo-Charlson index score (prior to the COVID-19 diagnosis) [12], a validated comorbidity measure, as detailed in Appendix 1. We utilized a modified Deyo-Charlson index, and excluded the rheumatic disease category to avoid redundancy, since all patients for these analyses had RA.
To account for the treatments received for COVID-19 infection, as they may have differentially affected COVID-19 outcomes, we created nine groups of COVID-19 treatments: group 1 (intravenous pressor support medications such as dopamine, dobutamine, epinephrine, epoprostenol); group 2 (intravenous use of hydrocortisone or dexamethasone); group 3 (chloroquine); group 4 (inhaled nitric oxide); group 5 (azithromycin); group 6 (oral steroids- methylprednisolone, prednisolone or prednisone); group 7 (antivirals such as remdesivir, ritonavir, lopinavir); group 8 (anakinra); and group 9 (intravenous immunoglobulin).
Statistical analysis
We provide the descriptive statistics for the cohort using median (interquartile range [IQR]) or categories (%) for continuous and categorical variables, respectively, stratified by COVID-19 status. We performed multivariable logistic regression models with Firth's penalized likelihood [13], that adjusted for demographics, BMI, smoking status, US region, and our modified Deyo-Charlson index as a binary variable (yes/no), to evaluate the association between baseline RTX use and our primary and secondary outcomes compared to baseline csDMARD use. Except for hospitalization, which usually occurs before administration of most of the listed COVID-19 treatments, we additionally adjusted the main models for all COVID-19 treatments. To assess the last-RTX-dose associated risk, we also evaluated the unadjusted associations between time since last RTX infusion and the odds of our primary outcomes.
We conducted the following sensitivity analyses to assess the robustness of our findings: [1] adjustment for modified Deyo-Charlson comorbidity score treating it as categorical variable (0, 1, 2 versus 3 or more); [2] adjustment for Deyo-Charlson comorbidity score with each comorbidity separately [3] limited the cohort to those with two or more ICD-10 codes for RA prior to the COVID-19 infection; [4] limited the cohort to those with one ICD-10 code for RA plus a DMARD, defined as a csDMARD, bDMARD (except RTX), RTX, or tsDMARD; [5] analysis where hydroxychloroquine was the reference category; [6] analysis where no DMARD was the reference category; and [7] analysis where TNFi-biologic was the reference category. Patients with missing data were excluded from analyses. All analyses were conducted in the N3C Enclave using R [14].
Results
Baseline patient characteristics
A total of 69,549 RA patients met our eligibility criteria (Fig. 1 ), of whom 22,956 (33%) were COVID-19 positive during the study period. The median age was 63 years (IQR, 52–72), 76% were females, 68% non-Hispanic/Latinx White and 73% non-smokers in our cohort (Table 1 ). Pulmonary disease was seen in 39% and hypertension in 60% of the patients. Overall, we observed 4219 (18.38%) hospitalizations, and 1079 (4.70%) deaths among those diagnosed with COVID-19.Fig. 1 Flow diagram showing the cohort selection
Legend: ICD International Classification of diseases; RA: Rheumatoid Arthritis.
Fig 1
Table 1 Baseline characteristics of RA patients by COVID-19 status from US N3C cohort.
Table 1Characteristic COVID-19 Negative (n = 46,593) COVID-19 Positive (n = 22,956)
Age (median [IQR]) 64.00 [53.0, 73.0] 61.00 [51.0, 71.0]
BMI (median [IQR]) 28.94 [24.60, 34.39] 29.90 [25.45, 35.44]
Weight (median [IQR]) 79.47 [65.77, 96.16] 81.69 [67.99, 98.90]
Deyo Charlson Index (median [IQR]) 3.00 [2.00, 6.000] 2.00 [1.00, 4.00]
Modified Deyo-Charlson (median [IQR]) 2.00 [1.00 5.00] 1.00 [0.00 3.00]
Males (%) 11,369 (24.40) 5464 (23.80)
Race/ethnicity (%)
Non-Hispanic/Latinx White 32,402 (69.54) 14,717 (64.11)
Non-Hispanic/Latinx Black 7122 (15.29) 3150 (13.72)
Hispanic/Latinx White 1543 (3.31) 915 (3.99)
Hispanic/Latinx Black 115 (0.25) 61 (0.27)
Asian 837 (1.80) 351 (1.53)
Pacific Islander 43 (0.09) 28 (0.12)
Other 271 (0.58) 0 (0.00)
Unknown 4260 (9.14) 3734 (16.27)
US Region (%)
Midwest 13,609 (29.21) 9375 (40.84)
Northeast 7703 (16.53) 3473 (15.13)
South 10,741 (23.05) 4088 (17.81)
West 5124 (11.00) 2053 (8.94)
Unknown 9416 (20.21) 3967 (17.28)
Smoking status (%)
Current or Former 13,553 (29.09) 4931 (21.48)
Non-smoker 32,990 (70.80) 17,709 (77.14)
Unknown 50 (0.11) 316 (1.38)
Payer (%)
Medicaid 1109 (2.38) 417 (1.82)
Medicare 6661 (14.30) 1960 (8.54)
None 1458 (3.13) 704 (3.07)
Other 213 (0.46) 78 (0.34)
Private 3674 (7.89) 1488 (6.48)
Unknown 33,462 (71.82) 18,300 (79.72)
Deyo Charlson Index Clinical comorbidities (%)
Myocardial infarction 5093 (10.93) 1482 (6.46)
Congestive heart failure 9142 (19.62) 3122 (13.60)
Peripheral vascular disease 10,491 (22.52) 3314 (14.44)
Stroke 8163 (17.52) 2542 (11.07)
Dementia 1809 (3.88) 697 (3.04)
Pulmonary 19,623 (42.12) 7305 (31.82)
Peptic ulcer disease 2840 (6.10) 821 (3.58)
Mild liver disease 6982 (14.99) 2407 (10.49)
Severe liver disease 1182 (2.54) 321 (1.40)
Diabetes 15,040 (32.28) 6555 (28.55)
Diabetes complications 7311 (15.69) 2994 (13.04)
Paralysis 1118 (2.40) 348 (1.52)
Renal disease 8947 (19.20) 3498 (15.24)
Cancer 8214 (17.63) 2677 (11.66)
Metastases 1812 (3.89) 394 (1.72)
HIV 235 (0.50) 92 (0.40)
Deyo Charlson Index Score
0 10,531 (22.60) 8111 (35.33)
1 8922 (19.15) 4589 (19.99)
2 6538 (14.03) 2849 (12.41)
3 or more 20,602 (44.22) 7407 (32.27)
Non-Deyo Charlson comorbidities
Obesity 14,546 (31.22) 6446 (28.08)
Hypertension 29,767 (63.89) 12,294 (53.55)
CAD 10,621 (22.80) 3636 (15.84)
DMARDs for RA
Rituximab$ 1357 (2.91) 517$ (2.25)
csDMARD 21,928 (47.06) 10,453 (45.53)
TNFi-biologic 7843 (16.83) 3351 (14.60)
IL-6i 1302 (2.79) 615 (2.68)
Abatacept 1831 (3.93) 671 (2.92)
JAKi 2718 (5.83) 1265 (5.51)
Glucocorticoids 30,097 (64.60) 13,835 (60.27)
COVID-19 treatment groups
Hydrocortisone or Dexamethasone <20* 587 (2.56)
Chloroquine 0 (0.00) 463 (2.02)
Azithromycin <20* 586 (2.55)
Glucocorticoids1 <20* 1000 (4.36)
Antivirals# <20* 648 (2.82)
Anakinra 0 (0.00) 43 (0.19)
Intravenous Immunoglobulin 74 (0.16) 20 (0.09)
Selected COVID-19 outcomes amongst those COVID-19 positive
Hospitalization – 4219 (18.38)
Deaths – 1079 (4.70)
ICU admission – 193 (0.84)
Abbreviations: BMI: body mass index; CAD: coronary artery disease; csDMARD: conventional synthetic disease modifying anti-rheumatic drug; H: Hispanic; HIV: human immunodeficiency virus; ICU: Intensive care unit; IL-6i: Interleukin 6 inhibitor; IQR: Interquartile range; JAKi: Janus Kinase inhibitor; NH: Non-Hispanic; RA: rheumatoid arthritis; US: United States.
1 Glucocorticoids: methylprednisolone, prednisolone, or prednisone.
# Antivirals include remdesivir, lopinavir or lopinavir-ritonavir combination;.
$ Of these, 49 received RTX after COVID-19 infection and 104 received both before and after the COVID-19 infection.
⁎ Counts less than 20 can only be presented as <20 per N3C data and privacy requirements.
In the overall cohort, 43,932 (63.17%) of the patients were using oral glucocorticoids at baseline, 32,381 (46.56%) were on a csDMARD, 11,194 (16.10%) on a TNFi-biologic, 3983 (5.73%) on a JAKi and 1874 (2.69%) were on RTX. 364 (1.60%) patients were exposed to RTX prior to their first positive COVID-19 test, of whom 93 (26%) were hospitalized for COVID-19, 24 (6.6%) required invasive ventilation, and 29 (8%) died (Table 2 ).Table 2 Occurrence of COVID-19 outcomes among COVID-19 positive RA patients by rituximab exposure prior to the COVID infection from U.S. N3C cohort.
Table 2Outcome Non-exposed to RTX Exposed to RTX
N = 22,439 N = 364
Primary Outcomes
Hospitalization 4085 (18.20) 93 (25.55)
ICU admission 182 (0.81) <20⁎⁎
30-day mortality 692 (3.08) <20⁎⁎
Severe/fatal COVID-19 (WHO severity 7–10)⁎⁎⁎ 1046 (4.66) 29 (7.97)
Invasive Ventilation 582 (2.59) 24 (6.59)
AKI in hospital 1104 (4.92) 28 (7.69)
ICU mortality 72 (0.32) <20⁎⁎
In-hospital mortality 652 (2.91) <20⁎⁎
ICU LOS (mean (SD)) 15.41 (13.21) 17.00 (10.24)
Hospital LOS (mean (SD)) 10.26 (12.26) 12.48 (13.73)
Abbreviations: AKI: Acute kidney injury; ICU: Intensive care unit; LOS: Length of stay; RTX: Rituximab.
⁎⁎ :<20: Counts less than 20 can only be presented as <20 per N3C data and privacy requirements.
⁎⁎⁎ COVID-19 severity definition used:
Mild: Outpatient World Health Organization (WHO) Severity 1–3
Mild-ED: Outpatient with Emergency department visit WHO Severity ∼3
Moderate: Hospitalized without invasive ventilation WHO Severity 4–6
Severe: Hospitalized with invasive ventilation or ECMO WHO Severity 7–9
Hospital Mortality or Discharge to Hospice WHO Severity 10.
Multivariable-adjusted association between baseline use of RTX and COVID-19 outcomes
In multivariable-adjusted models, compared to the baseline use of csDMARDs, RTX use was associated with an increased odds of COVID-19-related hospitalization (adjusted odds ratio [aOR] 2.14, 95% CI 1.51–3.04); and, in those hospitalized, with increased odds of ICU admission (aOR 5.22, 95% CI 1.77–15.41) and invasive ventilation (aOR 2.74, 95% CI 1.36–5.51) (Table 3 and Fig. 2 ).Table 3 Association of baseline rituximab use and each COVID-19 outcome with csDMARD as the referent category from US N3C cohort.
Table 3Primary Outcomes Multivariable adjusted OR (95%CI)#
Hospitalization@ 2.14 (1.51–3.04)
ICU admission 5.22 (1.77–15.41)
30-day mortality 1.12 (0.47–2.63)
Severe/fatal COVID-19 (WHO severity 7–10)* 1.66 (0.89–3.08)
Secondary Outcomes
Invasive ventilation 2.74 (1.36–5.51)
AKI in hospital 1.50 (0.80–2.82)
ICU mortality 2.43 (0.28–20.99)
In-hospital mortality 0.80 (0.42–1.80)
Multivariable adjusted Beta Estimate (95% CI)
ICU LOS 2.35 (−11.33- 16.02)
Hospital LOS 3.22 (−0.32 −6.77)
Abbreviations: AKI: Acute kidney injury; CI: Confidence interval; csDMARD: conventional synthetic disease modifying anti-rheumatic drugs; ICU: Intensive care unit; LOS: Length of stay; OR: Odds Ratio.
⁎ COVID-19 severity definition used:
Mild: Outpatient World Health Organization (WHO) Severity 1–3
Mild-ED: Outpatient with Emergency department visit WHO Severity ∼3
Moderate: Hospitalized without invasive ventilation WHO Severity 4–6
Severe: Hospitalized with invasive ventilation or ECMO WHO Severity 7–9
Hospital Mortality or Discharge to Hospice WHO Severity 10.
# Findings with p-values <0.05 are bolded in the table.
@ Hospitalization: adjusted for demographics, weight categories per BMI as normal vs. underweight, overweight, and obese, smoking status, US region, and modified Deyo-Charlson index.
Fig. 2 Multivariable-adjusted association of baseline rituximab use and each COVID-19 outcome with csDMARD as the referent category from US N3C cohort.
Legend: Figure shows A) odds ratios and 95% confidence interval for association between rituximab use and COVID-19-outcomes; B) Beta estimates (95% CI) for association between rituximab and ICU and hospital length of stay.
AKI, acute kidney injury; CI: Confidence interval; ICU, intensive care unit; LOS: Length of stay.
Hospitalized*: adjusted for demographics, weight categories per BMI as normal vs. underweight, overweight, and obese, smoking status, US region, and modified Deyo-Charlson index.
All other outcomes: adjusted for above variables and all COVID-19 treatments
Circles (red) denote significant outcomes, orange squares denote non-significant outcomes.
Fig 2
Other variables associated with COVID-19-related hospitalization included being from the Midwest (aOR 1.28, 95% CI 1.09–1.49) or South (aOR 1.65, 95% CI 1.42–1.93) compared to the West, or being Black (aOR 1.89, 95% CI 1.72–2.08) or Hispanic/Latinx (aOR 1.25, 95% CI 1.04–1.50) compared to non-Hispanic/Latinx White.
Association between time since RTX and COVID-19 outcomes
Compared to less than 30 days since last RTX infusion, we observed lower 30-day mortality in those who had their infusions >180 days (unadjusted OR 0.25, 95% CI 0.06–0.98), but not for 31–180 days (Appendix 4). There were no statistically significant associations observed for COVID-19-related hospitalization or COVID-19 severity (Appendix 4).
Sensitivity analyses
The results were similar in sensitivity analyses (Fig. 3 ). Compared to csDMARDs, RTX use was associated with increased odds of COVID-19-related hospitalization in models with: [1] Deyo-Charlson categorical (aOR 2.04, 95% CI 1.44–2.91); [2] Deyo-Charlson individual comorbidities (aOR 2.07, 95% CI 1.46–2.96); [3] 2 or more RA codes (aOR 1.98, 95% CI 1.26–3.12); [4] RA code plus DMARD (aOR 2.16, 95% CI 1.52–3.07) models. Compared to different referent category, results showed that RTX use was still associated with an increased odds of COVID-19-related hospitalization in comparison to: [5] HCQ (aOR 2.60, 95% CI 1.78–3.78); [6] no DMARD (aOR 1.56, 95% CI 1.11–2.21); and [7] TNFi-biologic (aOR 1.82, 95% CI 1.24–2.69). Similar results were seen for ICU admission and invasive ventilation (Fig. 3 and Appendices 5–11).Fig. 3 Sensitivity analyses (S1-S7) for odds of hospitalization, ICU admission or invasive ventilation with rituximab from US N3C cohort.
legend Figure shows odds ratios and 95% confidence interval for association between rituximab use and A) COVID-19-related Hospitalization; B) ICU admission, C) Invasive ventilation.
New S1, sensitivity analyses 1: Deyo-Charlson overall score (with RA excluded) as 0, 1, 2 vs. 3 or more.
New S2, sensitivity analyses 2: Each Deyo-Charlson comorbidity (with RA excluded).
New S3, sensitivity analyses 3: RA Case definition requires at least 2 ICD codes.
New S4, sensitivity analyses 4: RA Case definition requires >= 1 ICD code + any RA medication.
New S5, sensitivity analyses 5: Referent is Hydroxycholoroquine, nbDMARD variable except HCQ, i.e., Methotrexate, Leflunomide, or Sulfasalazine.
New S6, sensitivity analyses 6: Referent is No DMARD category.
New S7, sensitivity analyses 7: Referent is TNFi-biologic category.
Hospitalized*: adjusted for demographics, weight categories per BMI as normal vs. underweight, overweight, and obese, smoking status, US region, and modified Deyo-Charlson index.
All other outcomes: adjusted for above variables and all COVID-19 treatments.
Circles (red) denote significant outcomes, orange squares denote non-significant outcomes.
Fig 3
Discussion
In the largest U.S. retrospective cohort study to date among RA patients, as compared to csDMARDs, the baseline use of RTX is associated with increased odds of COVID-19 hospitalization, ICU admission, and invasive ventilation. More recent RTX use is associated with a higher risk of 30-day mortality compared to >180 days since the last infusion. These findings can guide patients, providers, and policymakers regarding the increased risks associated with RTX use during the COVID-19 pandemic.
Our findings of increased COVID-19-related hospitalization risk associated with RTX use are similar to those reported in patients on RTX for either rheumatic diseases or multiple sclerosis [4,[15], [16], [17]]. In contrast, our data on the association between RTX use and ICU admission and invasive ventilation are novel. A recent paper from our colleagues using the N3C data showed that RTX was associated with a higher risk of in-hospital death in people with rheumatologic conditions (HR 1.72, 95% CI 1.10–2.69) and as a cancer therapy (HR 2.57, 95% CI 1.86–3.56); this study examined outcomes regardless of underlying disease, but outcome assessment was limited to invasive mechanical ventilation and in-hospital death [3]. In a smaller study of patients with multiple sclerosis, RTX use was associated with a higher risk of serious illness and death [16]. Previous studies reported higher risk of incident and severe COVID-19 disease or longer hospital stay amongst those receiving RTX vs. not, but had several limitations in interpretation, since they: [1] combined heterogeneous RMD populations [15]; [2] were limited to single- or few-sites [4,16]; and/or [3] used patient or physician-reported data as primary data sources.
Our cohort included >95% PCR-confirmed cases of COVID-19, used robust definitions of COVID-19 outcomes, and adjusted for several key clinical outcomes (potential confounders) including in-hospital use of medications for COVID-19 such as antivirals and glucocorticoids – advances over previous studies. This reduces bias (ascertainment and confounding bias), potentially making these findings more accurate than the previous studies. To our knowledge, ours is among the first, multisite studies in the U.S. focused on RA, a single rheumatic disease population. We also present characteristics of a comparator group of patients with RA who tested negative for COVID-19, which provides an estimate of baseline risk in people with RA. The frequency of RTX use in our N3C cohort is similar to that reported in the US CORRONA registry [17], supporting the representativeness of our RA sample. Thus, our work has many strengths.
We acknowledge that some data related to RTX use and COVID-19 outcomes have reached contradictory or different conclusions than ours: [1] COVID-19-related hospitalization and more severe COVID-19 (that included mechanical ventilation) risk was increased in one study [5], but not in another study from the same author group [4]; [2] death risk was not increased in the French national study [15], but increased in a global [5] and single-center studies [4]. These differences are likely due to differences in comparators (no RTX vs. TNFi-biologic), disease of interest (multiple rheumatic diseases, multiple sclerosis vs. immune-mediated diseases), reference non-disease population (general population vs. other vs. none), study setting (France vs. global vs. single site), and time of the study in reference to the COVID-19 pandemic. Our study includes the largest COVID-19 US dataset, including the most recent period with Delta variant predominance, providing the much-needed additional clarity to this rapidly evolving field.
Our findings of a 3–5-fold higher odds of ICU admission and of invasive ventilation with RTX are noteworthy. This finding can be used by providers, patients, and policymakers, who can accurately assess the risk, prognosis, and allocate resources appropriately. The separation of these important outcomes is critical in understanding the RTX-associated risk. Each primary COVID-19 outcome we examined is associated with different prognosis, healthcare resources utilization, and patient outcome. Future studies are needed that can estimate this risk on an ongoing basis and keep providers, patients and policymakers informed. The French national study, like ours, did not find an association with an increased risk of death in the RTX -exposed vs. not-exposed group [15]. In contrast, in a single-site study, patients with immune-mediated disease treated with CD20 inhibitors (n = 114) had a higher risk of death (11% vs. 4%); adjusted HR 2.16; 95% CI: 1.03 to 4.54) than matched general population comparators. Previous studies did not adequately adjust for COVID-19 treatments, especially those used during COVID-19 hospitalization, as we did in our analyses.
Safety of RTX use, whether in rheumatology or other specialties, has been a historical concern for risk of immunosuppression, including for viral infections other than COVID-19, such as reactivation of hepatitis B [18,19], polyomavirus JC, and cytomegalovirus [20]. Such immune suppression with RTX may have implications for vaccine effectiveness too; some data suggests RTX use is also associated with a lower response to influenza vaccines [21,22]. For COVID-19, there are case reports of persistent COVID infection or delayed serological response to COVID vaccination among patients on RTX therapy [23,24]. These data raise the concern that RTX may directly influence adaptive immune responses, which are important for the control and clearance of viral infections [25]. Furthermore, RTX may have potentially long-lasting effects given that RTX eliminates B-cells for several months, and B-cells play a key role in the early stages of innate immune response to viral infections and building an immunologic memory [7].
Based on all data on RTX to date, including the current study, we suggest the following caution with its use in RA during the COVID-19 pandemic. For people currently on RTX who have not failed other biologics, an alternate medication may be a better option that may spare them the negative consequences associated with RTX use. For those with multiple biologic-failures, providers likely need to take a shared decision-making approach with the patient regarding risks and benefits of RTX continuation or re-dosing, using all current data. Due to an immunocompromised state, all RA patients, but especially those exposed to RTX, should be advised to get COVID-19 vaccinations and boosters as quickly as possible. In addition, RA patients, and, again, especially those exposed to RTX, should continue all precautionary measures recommended by CDC. Until more solutions emerge, these steps are needed to better protect patients with RA who continue RTX during the pandemic.
Despite the strengths, we note several limitations in our work. First, we identified patients using ICD-10-CM codes and thus, our patient groupings are subject to misclassification, with more true RA patients being assigned to the non-RA grouping (which likely biases our findings towards the null). However, in sensitivity analyses using two ICD-10-CM codes to identify RA, our findings were similar to the primary analyses. Similarly, there is a chance that we have under ascertained medication exposure in this cohort, especially if the patient was admitted at a facility where they do not normally receive healthcare; this would likely bias our results towards the null as well, making our current estimates conservative. We did not have information on RA disease activity, which may be associated with worse COVID outcomes or RTX exposure or both [6]; however, the magnitude of risk associated with these COVID-19 outcomes is likely small. We could not conduct adjusted analyses on the association between time since RTX and COVID outcomes due to the small number of outcomes when stratified by different durations since RTX infusion. Similarly, the sample size for patients exposed to rituximab who died was small. So, our analyses were limited in statistical power regarding outcomes such as mortality. In this study, we were unable to account for the use of anti-SARS-CoV-1 monoclonal antibodies, either as pre- or post-exposure prophylaxis as they were not yet approved. At the time of our study design, neither tocilizumab nor baricitinib treatments were yet approved for use for COVID-19, so our analyses did not adjust for their use. Our observational cohort study is at risk of residual or unmeasured confounding bias, despite our attempts at including known risk factors for COVID-19 outcomes that exist in an EMR repository.
In conclusion, RTX use in patients with RA is associated with worse COVID-19 outcomes (hospitalization, ICU admission and mechanical ventilation) compared to those on csDMARDs. These data help directly inform clinical practice and the care of patients exposed to RTX. These data also highlight the need for continued vigilance of patients on RTX, the need for COVID-19 vaccination/boosters, and continuation of the other preventive measures, including masking, social distancing and avoiding unnecessary travel. Future studies should examine if lymphopenia and decrease in CD19 cell count are the mediators of these significant associations noted between rituximab use and these outcomes
N3C attribution
The analyses described in this publication were conducted with data or tools accessed through the NCATS N3C Data Enclave covid.cd2h.org/enclave and supported by 10.13039/100006108 NCATS U24 TR002306. This research was possible because of the patients whose information is included within the data from participating organizations (covid.cd2h.org/dtas) and the organizations and scientists (covid.cd2h.org/duas) who have contributed to the on-going development of this community resource (cite this https://doi.org/10.1093/jamia/ocaa196).
IRB
The N3C data transfer to NCATS is performed under a Johns Hopkins University Reliance Protocol # IRB00249128 or individual site agreements with NIH. The N3C Data Enclave is managed under the authority of the NIH; information can be found at https://ncats.nih.gov/n3c/resources.
Individual acknowledgements for core contributors
We gratefully acknowledge contributions from the following N3C core teams • Principal Investigators: Melissa A. Haendel*, Christopher G. Chute*, Kenneth R. Gersing, Anita Walden • Workstream, subgroup and administrative leaders: Melissa A. Haendel*, Tellen D. Bennett, Christopher G. Chute, David A. Eichmann, Justin Guinney, Warren A. Kibbe, Hongfang Liu, Philip R.O. Payne, Emily R. Pfaff, Peter N. Robinson, Joel H. Saltz, Heidi Spratt, Justin Starren, Christine Suver, Adam B. Wilcox, Andrew E. Williams, Chunlei Wu• Key liaisons at data partner sites
• Regulatory staff at data partner sites
• Individuals at the sites who are responsible for creating the datasets and submitting data to N3C
• Data Ingest and Harmonization Team: Christopher G. Chute*, Emily R. Pfaff*, Davera Gabriel, Stephanie S. Hong, Kristin Kostka, Harold P. Lehmann, Richard A. Moffitt, Michele Morris, Matvey B. Palchuk, Xiaohan Tanner Zhang, Richard L. Zhu
• Phenotype Team (Individuals who create the scripts that the sites use to submit their data, based on the COVID and Long COVID definitions): Emily R. Pfaff*, Benjamin Amor, Mark M. Bissell, Marshall Clark, Andrew T. Girvin, Stephanie S. Hong, Kristin Kostka, Adam M. Lee, Robert T. Miller, Michele Morris, Matvey B. Palchuk, Kellie M. Walters
• Project Management and Operations Team: Anita Walden*, Yooree Chae, Connor Cook, Alexandra Dest, Racquel R. Dietz, Thomas Dillon, Patricia A. Francis, Rafael Fuentes, Alexis Graves, Julie A. McMurry, Andrew J. Neumann, Shawn T. O'Neil, Usman Sheikh, Andréa M. Volz, Elizabeth Zampino
• Partners from NIH and other federal agencies: Christopher P. Austin*, Kenneth R. Gersing*, Samuel Bozzette, Mariam Deacy, Nicole Garbarini, Michael G. Kurilla, Sam G. Michael, Joni L. Rutter, Meredith Temple-O'Connor
• Analytics Team (Individuals who build the Enclave infrastructure, help create codesets, variables, and help Domain Teams and project teams with their datasets): Benjamin Amor*, Mark M. Bissell, Katie Rebecca Bradwell, Andrew T. Girvin, Amin Manna, Nabeel Qureshi
• Publication Committee Management Team: Mary Morrison Saltz*, Christine Suver*, Christopher G. Chute, Melissa A. Haendel, Julie A. McMurry, Andréa M. Volz, Anita Walden
• Publication Committee Review Team: Carolyn Bramante, Jeremy Richard Harper, Wenndy Hernandez, Farrukh M Koraishy, Federico Mariona, Saidulu Mattapally, Amit Saha, Satyanarayana Vedula
Additional data partners who have signed DTA and data release pending
The Rockefeller University — UL1TR001866: Center for Clinical and Translational Science • The Scripps Research Institute — UL1TR002550: Scripps Research Translational Institute • University of Texas Health Science Center at San Antonio — UL1TR002645: Institute for Integration of Medicine and Science • The University of Texas Health Science Center at Houston — UL1TR003167: Center for Clinical and Translational Sciences (CCTS) • NorthShore University HealthSystem — UL1TR002389: The Institute for Translational Medicine (ITM) • Yale New Haven Hospital — UL1TR001863: Yale Center for Clinical Investigation • Emory University — UL1TR002378: Georgia Clinical and Translational Science Alliance • Weill Medical College of
Cornell University — UL1TR002384: Weill Cornell Medicine Clinical and Translational Science Center • Montefiore Medical Center — UL1TR002556: Institute for Clinical and Translational Research at Einstein and Montefiore • Medical College of Wisconsin — UL1TR001436: Clinical and Translational Science Institute of Southeast Wisconsin • University of New Mexico Health Sciences Center — UL1TR001449: University of New Mexico Clinical and Translational Science Center • George Washington University — UL1TR001876: Clinical and Translational Science Institute at Children's National (CTSA-CN) • Stanford University — UL1TR003142: Spectrum: The Stanford Center for Clinical and Translational Research and Education • Regenstrief Institute — UL1TR002529: Indiana Clinical and Translational Science Institute • Cincinnati Children's Hospital Medical Center — UL1TR001425: Center for Clinical and Translational Science and Training • Boston University Medical Campus — UL1TR001430: Boston University Clinical and Translational Science Institute • The State University of New York at Buffalo — UL1TR001412: Clinical and Translational Science Institute • Aurora Health Care — UL1TR002373: Wisconsin Network For Health Research • Brown University — U54GM115677: Advance Clinical Translational Research (Advance-CTR) • Rutgers, The State University of New Jersey — UL1TR003017: New Jersey Alliance for Clinical and Translational Science • Loyola University Chicago — UL1TR002389: The Institute for Translational Medicine (ITM) • #N/A — UL1TR001445: Langone Health's Clinical and Translational Science Institute • Children's Hospital of Philadelphia — UL1TR001878: Institute for Translational Medicine and Therapeutics • University of Kansas Medical Center — UL1TR002366: Frontiers: University of Kansas Clinical and Translational Science Institute • Massachusetts General Brigham — UL1TR002541: Harvard Catalyst • Icahn School of Medicine at Mount Sinai — UL1TR001433: ConduITS Institute for Translational Sciences • Ochsner Medical Center — U54GM104940: Louisiana Clinical and Translational Science (LA CaTS) Center • HonorHealth — None (Voluntary) • University of California, Irvine — UL1TR001414: The UC Irvine Institute for Clinical and Translational Science (ICTS) • University of California, San Diego — UL1TR001442: Altman Clinical and Translational Research Institute • University of California, Davis — UL1TR001860: UCDavis Health Clinical and Translational Science Center • University of California, San Francisco — UL1TR001872: UCSF Clinical and Translational Science Institute • University of California, Los Angeles — UL1TR001881: UCLA Clinical Translational Science Institute • University of Vermont — U54GM115516: Northern New England Clinical & Translational Research (NNE-CTR) Network • Arkansas Children's Hospital — UL1TR003107: UAMS Translational Research Institute
Availability of data and materials
To access patient-level data from the N3C consortium, institutions must have a signed Data Use Agreement executed with NCATS and principal investigators must complete mandatory training along with submitting a Data Use Request (DUR) to N3C. All code used for analyses can be found on GitHub. To request N3C data access follow instructions at https://covid.cd2h.org/onboarding.
Funding
RCP was supported by NIAID of the NIH (K23AI120855). JAS was supported by the resources and use of facilities at the Birmingham VA Medical Center, Birmingham, Alabama, USA. The funding body did not play any role in design, in the collection, analysis, and interpretation of data; in the writing of the manuscript; and in the decision to submit the manuscript for publication. NS was supported by the Rheumatology Research Foundation and the American Heart Association. TB was supported by the 10.13039/100000865 Bill and Melinda Gates Foundation (INV018455). ALO was supported by CTSA award No. UL1TR002649 from the National Center for Advancing Translational Sciences. Its contents are solely the responsibility of the authors and do not necessarily represent official views of the National Center for Advancing Translational Sciences or the National Institutes of Health.
Author contributions
NS, RCP and JS: Conception of the work
NS, JS: Study design
VM, CL, TB, KF, AO, JH: Data collection and analysis
All authors: Data interpretation
NS and JS: Drafting the article
All authors: Critical revision of the article and final approval of the submitted version
Declaration of Competing Interest
JAS has received consultant fees from Crealta/Horizon, Medisys, Fidia, PK Med, Two labs Inc., Adept Field Solutions, Clinical Care options, Clearview healthcare partners, Putnam associates, Focus forward, Navigant consulting, Spherix, MedIQ, Jupiter Life Science, UBM LLC, Trio Health, Medscape, WebMD, and Practice Point communications; and the National Institutes of Health and the American College of Rheumatology. JAS owns stock options in TPT Global Tech, Vaxart pharmaceuticals, Atyu biopharma, Adaptimmune Therapeutics, GeoVax Labs, Pieris Pharmaceuticals and Charlotte's Web Holdings, Inc. JAS previously owned stock options in Amarin, Viking and Moderna pharmaceuticals. JAS is on the speaker's bureau of Simply Speaking. JAS is a member of the executive of Outcomes Measures in Rheumatology (OMERACT), an organization that develops outcome measures in rheumatology and receives arms-length funding from 8 companies. JAS serves on the FDA Arthritis Advisory Committee. JAS is the chair of the Veterans Affairs Rheumatology Field Advisory Committee. JAS is the editor and the Director of the University of Alabama at Birmingham (UAB) Cochrane Musculoskeletal Group Satellite Center on Network Meta-analysis. JAS previously served as a member of the following committees: member, the American College of Rheumatology's (ACR) Annual Meeting Planning Committee (AMPC) and Quality of Care Committees, the Chair of the ACR Meet-the-Professor, Workshop and Study Group Subcommittee and the co-chair of the ACR Criteria and Response Criteria subcommittee. Other authors have no conflicts to declare.
Appendix Supplementary materials
Image, application 1
Acknowledgments
We thank Gabriella Tangkilisan of the Oregon Health Sciences Center for administrative support.
Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.semarthrit.2022.152149.
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References
1 England B.R. Roul P. Yang Y. Kalil A.C. Michaud K. Thiele G.M. Risk of COVID-19 in rheumatoid arthritis: a national veterans affairs matched cohort study in at-risk individuals Arthritis Rheumatol 73 12 2021 2179 2188 33955209
2 Topless R.K. Phipps-Green A. Leask M. Dalbeth N. Stamp L.K. Robinson P.C. Gout, rheumatoid arthritis, and the risk of death related to coronavirus disease 2019: an analysis of the UK Biobank ACR Open Rheumatol 3 5 2021 333 340 33856739
3 Andersen K.M. Bates B.A. Rashidi E.S. Olex A.L. Mannon R.B. Patel R.C. Long-term use of immunosuppressive medicines and in-hospital COVID-19 outcomes: a retrospective cohort study using data from the National COVID Cohort Collaborative Lancet Rheumatol 4 1 2022 e33 e41 34806036
4 Patel N.J. D'Silva K.M. Hsu T.Y. DiIorio M. Fu X. Cook C. COVID-19 outcomes among users of CD20 inhibitors for immune-mediated diseases: a comparative cohort study medRxiv 2021
5 Sparks J.A. Wallace Z.S. Seet A.M. Gianfrancesco M.A. Izadi Z. Hyrich K.L. Associations of baseline use of biologic or targeted synthetic DMARDs with COVID-19 severity in rheumatoid arthritis: results from the COVID-19 global rheumatology alliance physician registry Ann Rheum Dis 80 9 2021 1137 1146 34049860
6 Strangfeld A. Schafer M. Gianfrancesco M.A. Lawson-Tovey S. Liew J.W. Ljung L. Factors associated with COVID-19-related death in people with rheumatic diseases: results from the COVID-19 Global Rheumatology Alliance physician-reported registry Ann Rheum Dis 80 7 2021 930 942 33504483
7 Vabret N. Britton G.J. Gruber C. Hegde S. Kim J. Kuksin M. Immunology of COVID-19: current State of the Science Immunity 52 6 2020 910 941 32505227
8 Edwards J.C. Leandro M.J. Cambridge G B lymphocyte depletion therapy with rituximab in rheumatoid arthritis Rheum Dis Clin North Am 30 2 2004 393 403 viii 15172048
9 Edwards J.C. Szczepanski L. Szechinski J. Filipowicz-Sosnowska A. Emery P. Close D.R. Efficacy of B-cell-targeted therapy with rituximab in patients with rheumatoid arthritis N Engl J Med 350 25 2004 2572 2581 15201414
10 Bennett T.D. Moffitt R.A. Hajagos J.G. Amor B. Anand A. Bissell M.M. The national COVID cohort collaborative: clinical characterization and early severity prediction medRxiv 2021
11 https://github.com/National-COVID-Cohort-Collaborative/Phenotype_Data_Acquisition.
12 Deyo R.A. Cherkin D.C. Ciol M.A Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases J Clin Epidemiol 45 6 1992 613 619 1607900
13 Firth D. Bias reduction of maximum likelihood estimates. Biometrika 1993; 80, (1):27–38.
14 Spark R. (R on Spark). 2020.
15 Avouac J. Drumez E. Hachulla E. Seror R. Georgin-Lavialle S. El Mahou S. COVID-19 outcomes in patients with inflammatory rheumatic and musculoskeletal diseases treated with rituximab: a cohort study Lancet Rheumatol 3 6 2021 e419 ee26 33786454
16 Esmaeili S. Abbasi M.H. Abolmaali M. Mojtahed M. Alavi S.N.R. Soleimani S. Rituximab and risk of COVID-19 infection and its severity in patients with MS and NMOSD BMC Neurol 21 1 2021 183 33933026
17 Harrold L.R. John A. Best J. Zlotnick S. Karki C. Li Y. Impact of rituximab on patient-reported outcomes in patients with rheumatoid arthritis from the US Corrona Registry Clin Rheumatol 36 9 2017 2135 2140 28718043
18 Loomba R. Liang T.J Hepatitis B reactivation associated with immune suppressive and biological modifier therapies: current concepts, management strategies, and future directions Gastroenterology 152 6 2017 1297 1309 28219691
19 Riedell P. Carson K.R A drug safety evaluation of rituximab and risk of hepatitis B Expert Opin Drug Saf 13 7 2014 977 987 24821316
20 Gea-Banacloche J.C Rituximab-associated infections Semin Hematol 47 2 2010 187 198 20350666
21 Bedognetti D. Zoppoli G. Massucco C. Zanardi E. Zupo S. Bruzzone A. Impaired response to influenza vaccine associated with persistent memory B cell depletion in non-Hodgkin's lymphoma patients treated with rituximab-containing regimens J Immunol 186 10 2011 6044 6055 21498665
22 Westra J. van Assen S. Wilting K.R. Land J. Horst G. de Haan A. Rituximab impairs immunoglobulin (Ig)M and IgG (subclass) responses after influenza vaccination in rheumatoid arthritis patients Clin Exp Immunol 178 1 2014 40 47 24889761
23 Benucci M. Quartuccio L. Li Gobbi F. Damiani A. Grossi V. Infantino M. Persistence of rT-PCR-SARS-CoV-2 infection and delayed serological response, as a possible effect of rituximab according to the hypothesis of Schulze-Koops et al Ann Rheum Dis 2020
24 Schulze-Koops H. Krueger K. Vallbracht I. Hasseli R. Skapenko A Increased risk for severe COVID-19 in patients with inflammatory rheumatic diseases treated with rituximab Ann Rheum Dis 80 5 2021 e67 32591357
25 Sette A. Crotty S Adaptive immunity to SARS-CoV-2 and COVID-19 Cell 184 4 2021 861 880 33497610
| 36516563 | PMC9729169 | NO-CC CODE | 2022-12-14 23:30:05 | no | Semin Arthritis Rheum. 2023 Feb 8; 58:152149 | utf-8 | Semin Arthritis Rheum | 2,022 | 10.1016/j.semarthrit.2022.152149 | oa_other |
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100123
Article
Impacts of the COVID-19 pandemic on carer-employees’ well-being: a twelve-country comparison
Wu Jerry a
Williams Allison b
Wang Li c⁎
Henningsen Nadine d
Kitchen Peter b
a Department of Mathematics, University of Waterloo, Waterloo, Ontario, Canada
b School of Earth, Environment &Society, McMaster University, Hamilton, Ontario, Canada
c Offord Center for Child Study & Department of Psychiatry & Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
d Canadian Home Care Association & Carers Canada, Mississauga, Ontario, L5N 1W1
⁎ Corresponding author at: Offord Center for Child Study & Department of Psychiatry & Behavioural Neurosciences, McMaster University, 1280 Main Street West, L8S 4K1, Hamilton, Ontario, Canada
8 12 2022
2023
8 12 2022
4 100123100123
1 11 2021
9 11 2022
6 12 2022
© 2022 The Author(s)
2022
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The aim of this analysis is to assess the potential ways that the COVID-19 pandemic has impacted Canadian carer-employees (CEs) and identify the needs CEs feel is required for them to continue providing care. We assess the similarities and differences in the stresses CEs faced during COVID-19 globally across countries in the G7, Australia, Spain, Brazil, Taiwan, India, and China. We aim to compare Canada against global trends with respect to the challenges of the COVID-19 pandemic, as well as the supports in place for CEs. The study utilized 2020 Carer Well-Being Index at the country level. Descriptive data on Canadian CEs is first reviewed, followed by comparisons, by country, on responses relating to: (a) time spent caring; (b) sources of support; (c) impact on paid work and career, and; (d) emotional/mental, financial, and physical health. The relationship between government support and emotional/mental health is also explored. When compared to pre-pandemic times, CEs in Canada on average spent more time caregiving, with 34% reporting more difficulty balancing their paid job and caring responsibilities. Seventy-one percent of CEs feel their mental health has deteriorated. Thirty-four percent of Canadian CEs received support from the government, and only 30% received support from their employers. Globally, there was a similar trend, with CEs experiencing deteriorating mental health, work impacts, and unmet needs during the pandemic. Comparing the well-being of Canadian CEs with other countries provides an opportunity to evaluate areas where Canadian policies and programs have been effective, as well as areas needing improvement.
Keywords
Carer
Caregiving
Well-being
Work
COVID-19
International
Family
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pmcIntroduction
According to the United Nations, population aging is a global trend and “virtually every country in the world is experiencing growth in the size and proportion of older persons in their population” (United Nations, 2019, p.1). It is projected that the number of people 65 years and older will double from 703 million people in 2017 to over 1.5 billion in 2050 (United Nations, 2019). As the global population dramatically ages, with greater demands being made on limited health and long-term care systems, unpaid carers, such as close family, relatives, and friends, will need to take on more responsibility (WHO, 2016). Consequently, more carer-employees will need to balance the responsibilities of paid work with unpaid caring. Those who provide unpaid care to family or friends with a long-term health condition, a physical or mental disability, or problems related to aging while working paid jobs are referred to as carer-employees (CEs) (Ireson et al., 2018). Globally, CEs are referred to differently; in Europe, the term worker-carer is more frequently used.
CEs typically have worse stress and physical health compared to the general population and are associated with higher rates of mood disorders, such as depression, anger, distress, fatigue, and anxiety (Ramesh et al., 2017; Williams et al., 2016; Sethi et al., 2017; Adelman et al., 2014). Balancing caring with job responsibilities can be an area of challenge that leads to family-role overload, damaging wellbeing and workplace productivity (Halinski et al., 2020). CEs often relegate paid work responsibilities to outside the regular work week, causing them to give up on leisure activities like hobbies, social gatherings, and vacations (Wang et al., 2018). Additionally, CEs often do not choose to disclose their caring responsibilities in the workplace for fear of being seen as uncommitted, which causes feelings of isolation and loneliness (Sherman, 2018). In worst cases, stress from managing two difficult positions can cause CEs to leave the workforce altogether (Wang et al., 2018).
In 2020, the emergence of the COVID-19 pandemic and subsequent lockdowns disrupted hundreds of thousands of businesses and put high amounts of strain on healthcare systems globally. Since CEs occupy the double-role of employee and unpaid carer, they are particularly vulnerable to the massive disruption caused by COVID-19 in both areas. The onset of the COVID-19 pandemic caused widespread business closures and mass layoffs internationally (Bartik et al., 2020; Crayne, 2020). In June 2020, Statistics Canada reported that 12.4% of Canadian workers had been laid off on a monthly basis since February 2020, although the number of permanent layoffs was unclear (Winnie et al., 2020). Health-care systems were put under strain globally by influxes of COVID-19 cases as hospitals had shortages of personal protective equipment, and in some cases, inadequate capacity for ICU beds and ventilators (McMahon et al., 2020). In Ontario, the COVID-19 pandemic caused an estimated backlog of 16 million health-care services, including MRIs, CT scans, and various surgeries (Ontario Medical Association, 2020). During the COVID-19 pandemic, many CEs, an already vulnerable population, were and continue to be exposed to additional stresses from two directions as they faced both job insecurity and increased caregiving demands (Carers UK, 2020; Heilman et al., 2020; Ontario Caregivers Organization, 2020; Hughes et al., 2021).
Using data from the Carer Well-Being Index from Embracing Carers (2021) and framed within stress model (Pearlin et al., 1990), the purpose of this paper is to report the impact of caring and COVID-19 on Canadian CEs and compare these results across the twelve participating countries. Embracing Carers is a global initiative led by pharmaceutical manufacturer Merck KGaA; it is focused on improving the health and well-being of carers globally and aims to support carer initiatives by collaborating with other organizations. Specifically, this paper seeks to examine the impact of caring and COVID-19 by contextualizing changes in the following areas: (a) time spent caring; (b) sources of support; (c) impact on paid work and career, and (d) emotional/mental, financial, and physical health. The relationship between government support and emotional/mental health is also explored. Specific interest will be placed on countries in the G7 and Australia due to these countries having similar levels of economic development as Canada, as well as having somewhat similar strategies, goals, and policy initiatives with respect to CEs, particularly around flexible work (Yeandle et al., 2017). Contextualizing these results will provide opportunities to evaluate both areas where Canada is doing well and where it can improve, with respect informing new policy directions by way of other countries who are doing better than Canada.
Literature review
Pearlin et al. (1990) stress process model identifies carer stress as comprising a number of interrelated conditions, such as the resources of carers, together with a range of stressors to which they are exposed. In the process model, primary stressors are defined as hardships and problems stemming directly from caring, whereas secondary stressors are understood as either the intrapsychic strains which involve the diminishment of self-concept, or the strains experienced in roles/activities external to caring, such as paid work. The COVID-19 pandemic would be both a primary stressor, given the impact it has had on caring, as well as a secondary stressor, given the impact on work and most every other aspect of life. Pearlin et al. (1990) emphasize the positive impact of social support as a protective factor, as well as the negative impact of role conflict as a secondary stressor.
There is a growing body of literature surrounding the experience of CEs during COVID-19 which consistently shows the pandemic's negative impact, specific to: (a) time spent caring; (b) sources of support; (c) impact on paid work and career, and; (d) health, broadly defined. With respect to time spent caring, a qualitative study interviewing a sample of 52 diverse unpaid carers found that many had increased caring responsibilities with inadequate supports, while having to adjust to changes like homeschooling or working from home (Lightfoot, 2021). The literature suggests that, overall, sources of support for CEs decreased as a result of the COVID-19 pandemic. In the UK, a June 2020 survey found that, due to public health restrictions imposed by COVID-19, nearly all social support services–such as peer support groups, had stopped face-to-face arrangements, and the inability to access pre-pandemic activities significantly contributed to fear, sadness, uncertainty, and anger; however, in a qualitative study exploring the perspectives of carers on COVID-19, many described the importance of resilience, adaptation, and coping (Simblett et al., 2021). Additionally, while most carers were not connected to any formal support pre-pandemic, the majority reported having an informal support network which they chose to forgo due to concerns about COVID-19 (Lightfoot, 2021). Many shared caring responsibilities with family members which had to be rearranged due to COVID-19, with some taking on a larger workload during the pandemic than before (Lightfoot, 2021). This impacted CEs paid work and career given that boundaries between paid work, much of which had moved into the home given lockdown orders, and care work became progressively blurred; this presented a range of new challenges for family carers (Lafertty et al., 2022; Ding et al., 2022), with some leaving the workforce altogether. For those leaving the workforce or having to cut their paid work hours due to increasing care responsibilities, their financial wellbeing was compromised and worry about their financial situation increased, as noted in the UK (Carers UK, 2020) and Ireland (Lafferty et al., 2022).
With respect to health, broadly defined, CEs were also negatively impacted. According to one U.S. study, unpaid carers reported adverse mental health, such as depression, more frequently than non-carers; nearly two-thirds of unpaid carers experienced deteriorated mental health or behavioral symptoms early in the COVID-19 pandemic compared to one-third of non-carers (Czeisler et al., 2021). In the United States, among those who were both carers of adults and children, 85% experienced adverse mental health symptoms, and approximately half reported serious suicidal ideation in the past month; this was eight times greater than non-carers/non-parents (Czeisler et al., 2021). A separate U.S. study investigating carers’ mental and physical health during the COVID-19 pandemic found unpaid carers were more likely to have worse mental health and fatigue than non-carers (Park, 2020). In addition, long term carers were more likely to experience “headaches, body aches, and abdominal discomfort” than both short-term carers and non-carers (Park, 2020). Deteriorated mental and physical health outcomes were likely caused by isolation and additional stressors from the COVID-19 pandemic. A survey done by the University of Pittsburgh (2020) found that 56% of carers found caring to be more emotionally difficult during the COVID-19 pandemic, with 63% reporting more caring duties due to factors such as an inability to access health-care services, and/or increased needs of their care recipients. Based on the Pearlin et al. (1990) stress process model, as well as the available literature related to the health and support in family caregiving, our study proposed two hypotheses: (1) COVID-19 had negative impacts on carer-employees, evident in: (a) time spent caring; (b) sources of support; (c) impact on paid work and career, and; (d) emotional, financial, and physical health across twelve countries, and; (2) During COVID-19, carer-employees across 12 countries experienced deteriorating emotional/mental health in those countries characterized as having less support from government and/or employers.
Policy background
Given the objective of the paper, specific to informing policies and programs for CEs in Canada, a representative review of policies and programs supporting CEs in the six of the countries represented in the Carer Well-Being Index (representing the G7 and Australia), are outlined. Financial support (approximations provided only), flexible work, and both paid and unpaid leaves are a specific focus given that they are well-known policies that support CEs. We will start with the Canadian context, followed by five of the countries represented in the Carer Well-Being Index: Australia, UK, Italy, France and Germany, and the USA. In Canada, the Canada Caregiver Credit is a tax credit available for carers with relatives who are dependent due to mental or physical impairment (International Alliance of Carer Organizations [IACO], 2018). The Compassionate Care Benefit (CCB) is financial support payable to carers who have 600+ hours of insured work in the past year and whose earnings have fallen by at least 40%. The CCB is paid at 55% of the carer's average earnings, up to CAD $51,300. In addition, the Canadian Family Caregiver Leave is unpaid job-protected leave, available for up to 8 weeks, for carers of relatives with a serious medical condition (Yeandle et al., 2017). Additionally, Family Medical Leave is available for 26 weeks for relatives significantly at risk of dying and can be combined with the CCB to reduce the financial burden of job leave (Yeandle et al., 2017). Many other countries, including Australia, the United Kingdom, and Italy offer forms of direct allowances for carers that meet specific criteria. In Australia, the Carer Allowance is a biweekly income of $131.90 AUD paid to carers that meet a certain family income threshold, provide daily care to a disabled or frail elderly person, and who's care recipient has had an illness or disability for 6 months or is terminal (IACO, 2018; Yeandle et al., 2017). An additional Carer Supplement of $600 AUD for each care recipient is available per annum (IACO, 2018; Yeandle et al., 2017). Further, the New National Employment Standards in Australia give unpaid carers 10 days of paid leave per annum with the additional option of two days short term unpaid leave (Yeandle et al., 2017). Additionally, Australian CEs have the right to request flexible work with the business grounds for refusing clearly defined in law (Yeandle et al., 2017). In the UK, carers aged 16 and older who meet specific criteria are able to receive £67.60 weekly through the Carer's Allowance, although carer's are not paid more if they care for many people (IACO, 2018; Government of the UK, 2014). In the UK, employees who have worked for 26 weeks have the right to request flexible working (RTRFW) which employers must review fairly (Yeandle et al., 2017). However, employers can reject requests on the grounds of specific business reasons laid out in the Advisory, Conciliation and Arbitration Service code of practice including, for instance: the burden of additional costs, an inability to reorganize work among/recruit staff, a detrimental impact on performance/quality (Advisory, Conciliation and Arbitration Service, n.d.). In Italy, the Companion Allowance (Indennità di Accompagnamento) is accessible to all carers caring for someone who is unable to perform day-to-day activities without assistance and is paid out at a flat rate of about €500 monthly (IACO, 2018; Istituto Nazionale Previdenza Sociale, 2021). In Italy, carers supporting a severely disabled or ill family member are entitled to a care leave; however, it is only available for one member of the care recipient's household (Eurocarers 2020b; Yang et al., 2013). Recipients are entitled to three days paid leave per month, as well as two years paid leave once in their lifetime at 100% salary up to a maximum of €47,446 (Eurocarers 2020b; Yang et al., 2013).
In France and Germany, the personalized independence and care allowance are indirect forms of financial support for carers that are first paid out to care recipients who can then pass it on to their carers; however, there is no guarantee that carers will be reimbursed by their care recipients (IACO, 2018). In France, the Family Solidarity Leave (FSL) offers carers the ability to take up to three months of unpaid job-protected leave to assist a relative who is dying (Yeandle et al., 2017). A Daily Support Allowance is available at a maximum of €55.15 for 21 days during FSL (Yeandle et al., 2017). In Germany, short term care leave is available at 90% of net earnings for 10 days. Further, six months of unpaid leave is available for carers spending 90 minutes a day on caring duties, and three months of unpaid leave are available for carers of family at end-of-life (Eurocarers 2020a; Yang et al., 2013). In the United States, there are forms of financial assistance and tax breaks available to carers, however, support is very limited, waitlists are long, and policies are inconsistent between states (IACO, 2018). In response to the COVID-19 pandemic, a wide range of short-term stop-gap measures were put in place across the world to support community-based care; unfortunately few provided support for unpaid carers as the primary focus was on care recipients (Dawson et al., 2020).
Methods
We obtained data from the Carer Well-Being Index, a global study commissioned by Embracing Carers in twelve countries including: United States, Canada, United Kingdom, France, Germany, Italy, Spain, Australia, Brazil, Taiwan, India and China. The dataset is nationally representative of each country. The study was conducted in 2020 online or over the phone, from September 3 to October 27; it included questions related to carer well-being and possible ways unpaid carers are harmed during COVID-19. Unpaid carers were defined as those caring for someone with a long-term illness, physical disability, or cognitive/mental condition. According to The Global Carer Well-Being Index report (Embracing Carers, 2021) “outgoing sample collected was balanced to the Census of each respective country to then allow qualifying respondents to fall out naturally” (p.53). Light weighting was applied to select countries based on individual country data. As well, each individual country has an estimated margin of error of +/- 3.58% at the 95% confidence level. The survey length was approximately 20-25 minutes and included aggregated responses from 9,044 unpaid carers across the 12 countries, or approximately 750 unpaid carers per country. As this paper is focused on the effects of COVID-19 on CEs, only the aggregated responses from employed carers were considered, which included 6,313 respondents.
Data analysis
McMaster Research Ethics Board approved the study to analyze the secondary data of Carer Well-Being Index (MREB#: 5688), which included a broad range of variables which were not all relevant for the purposes of this paper. As such, variables focusing on CEs were selected based on their relevance to four categories which included: (a) time spent caring; (b) sources of support; (c) impact on paid work and career, and; (d) emotional/mental, financial, and physical health. Selected variables are presented in Table 1 . Variables related to (a) time spent caring included: percentage of first-time carers as a result of COVID-19; time spent caring per week before, at the peak, and at the time of data collection, and; estimate of future time spent caring per week. Variables related to (b) sources of support included: percentage of CEs that received support before and during the COVID-19 pandemic from the federal government, from local/state governments, from employers, and; percentage of CEs that never received support. Variables related to (c) impact on paid work and career included: percentage of CEs who felt caring negatively affected their paid responsibilities; percentage of CEs who felt caring negatively affected their long-term goals, and; percentage of CEs who felt balancing paid job with caring was more difficult due to COVID-19. Finally, variables related to (d) emotional/mental, financial, and physical health included: percentage of CEs that felt caring negatively affected emotional/mental, financial, or physical health, and; percentage of CEs that felt COVID-19 negatively affected emotional/mental, financial, or physical health.Table 1 Questions and selected variables.
Table 1:Question Variables
Time spent caring
On average, how many hours did/do you spend per week on caregiving during each of the following timeframes? Time spent caring before the Coronavirus/COVID-19 hit/entered carer's country
Time spent caring during the height/peak of the Coronavirus/COVID-19 pandemic in carer's country
Time spent caring now
Did you become a caregiver/carer for the first time as a result of the Coronavirus/COVID-19 pandemic? First time carer status
In general, would you say the Coronavirus/COVID-19 pandemic has made caregiving harder or easier? Difficulty of caring as a result of the pandemic
Sources of support
Which, if any, of the following organizations did/have you received any caregiving support from (including financial or non-financial support)? Rate of support from federal/national government before and during the COVID-19 pandemic
Rate of support from local/state government before and during the COVID-19 pandemic
Rate of support from employers before and during the COVID-19 pandemic
In your opinion, are caregiver/carers currently receiving too much, the right amount, or not enough support from each of the following entities? Opinion on level of support from federal/national government
Opinion on level of support from local/state government
Opinion on level of support from employers
Impact on paid work and career
How, if at all, does being a caregiver/carer impact each of the following aspects of your life currently? Caring has a negative impact on paid work responsibilities
Caring has a negative impact on career
Which, if any, of the following statements is true for you as it relates to how the Coronavirus/COVID-19 pandemic has impacted your ability to provide care? - I am having more difficulty balancing my professional/paid job responsibilities with my caregiving responsibilities. Difficulty balancing paid job responsibilities and caring responsibilities due to the pandemic
Emotional, financial, and physical health
In general, do you feel the Coronavirus/COVID-19 has improved or worsened each of the following aspects of your health/wellbeing? Worsened emotional health due to the pandemic
Worsened financial health due to the pandemic
Worsened physical health due to the pandemic
How, if at all, does being a caregiver/carer impact each of the following aspects of your life currently? Worsened emotional health as a result of caring
Worsened financial health as a result of caring
Worsened physical health as a result of caring
The listed variables of interest were compared between twelve countries. The Carer Well-Being Index included all G7 countries other than Japan. Specific interest was placed on Canada, relative to the G7 countries and Australia, given the stated hypotheses and overall objective specific to evaluating Canada's policy and program initiatives for supporting CEs. However the carer-index data we used in this study is not individual data but, rather, the data aggregated at the country level. Thus, we cannot compare the variables of interest among more specific CE groups, such as age, sex, income, ethnicity or other socio-economic variables across twelve countries. Also, the inferential stats were unable to be estimated. Thus, the analysis solely focused on the descriptive statistics.
In the Carer Well-Being Index, the Canadian sample included 479 CEs. The mean age of Canadian CEs in the sample was 43.1, with 60% of respondents being female. Sixty two percent of primary care recipients were 65+ years old. Eighty-four percent of CEs resided in urban/suburban areas, and 16% resided in rural areas. Sixty-six percent were either married or in a partnership, 25% were single, and 10% were formerly married (divorced, separated, or widowed). Forty-four percent had a medium income (50-100K), 32% had a high income (100K+), and 24% had a low income (<50K). The majority of CEs (56%) had either: completed high school or at least some college, CEGEP, or trade school, or some university (did not finish). Further, 44% completed a university undergraduate degree or higher, and only 1% did not complete high school.
In addition to reporting the number of CEs involved in the study, by country, Table 2 presents the socio-demographic data for each of the twelve countries making up the Carer Well-Being Index. The mean age of CEs in G7 countries and Australia was between 39.5 and 43.1. China, India, Taiwan, and Brazil had mean ages that were relatively lower at 34.6 to 37.2. Canadian CEs were the oldest on average among the twelve countries. Between 52% and 66% of CEs were women. Over 50% of care recipients in nearly all countries were 65+ years old. Care recipients aged 65+ made up a very large portion in Italy (77%), Spain (75%), and Taiwan (74%). In India, on the other hand, only 29% of care recipients were aged 65+. Australia had the highest proportion of CEs in urban areas (95%), while India had the lowest (40%). In the other ten countries, between 77% and 90% of CEs lived in urban areas. China had the highest percentage of married CEs (88%), while the United States had the least (59%). It is worth noting that with regards to income, countries had different ranges for what was considered medium, high, or low income. Considering this, China had the largest proportion of CEs with high income (54%) and France had the highest proportion with low income (39%). India had the largest proportion of CEs with a higher education (72%), but also the largest proportion of CEs with a lower education (19%). In Canada, the United States, Germany, and Taiwan, more people had a middle education than a higher education. In the other eight countries, more people had a higher education than a middle education.Table 2 Socio-demographics of CEs in the Carer Well-Being Index.
Table 2:Country Canada USA United Kingdom France Germany Italy Spain Australia Brazil Taiwan India China
Total Respondents 479 415 448 578 573 499 562 512 597 625 492 534
Age group
18-24 6% 8% 8% 4% 5% 4% 6% 8% 12% 13% 11% 8%
25-34 20% 27% 24% 25% 22% 19% 24% 21% 31% 34% 38% 42%
35-44 28% 39% 26% 34% 35% 33% 35% 40% 28% 32% 35% 37%
45-54 25% 15% 23% 24% 23% 32% 24% 15% 22% 17% 12% 9%
55-64 17% 7% 15% 11% 13% 12% 11% 11% 6% 5% 4% 2%
65+ 4% 4% 3% 1% 1% 1% - 4% 1% - - 1%
Age (Mean) 43.1 39.5 41.9 41 41.4 42.7 40.2 40.5 37.2 36.4 35.4 34.6
Gender
Male 40% 42% 38% 44% 43% 39% 40% 34% 34% 39% 48% 41%
Female 60% 58% 62% 56% 57% 61% 60% 66% 66% 61% 52% 59%
Age of Care Recipient
Less than 18 8% 11% 7% 8% 14% 4% 7% 10% 10% 6% 23% 13%
18-24 2% 2% 3% 2% 3% 1% 2% 3% 2% - 2% 2%
25-34 5% 7% 7% 2% 3% 3% 2% 4% 3% 2% 5% 4%
35-44 5% 7% 4% 8% 5% 6% 2% 4% 5% 3% 8% 5%
45-54 5% 10% 9% 7% 7% 8% 6% 8% 7% 5% 14% 8%
55-64 17% 19% 17% 13% 15% 6% 9% 19% 17% 15% 27% 23%
65+ 62% 52% 57% 64% 57% 77% 75% 55% 60% 74% 29% 56%
Urban/Surburban/Rural
Urban/Suburb 84% 87% 85% 82% 82% 77% 85% 95% 84% 90% 40% 82%
Rural 16% 13% 15% 17% 17% 23% 15% 5% 16% 10% 60% 17%
Marital Status
Married/Partner 66% 59% 64% 76% 73% 76% 73% 74% 60% 63% 82% 88%
Single 25% 29% 27% 19% 20% 18% 20% 21% 32% 35% 17% 11%
Divorced 5% 9% 6% 3% 4% 3% 5% 4% 4% 2% 1% 1%
Separated 3% 2% 2% 1% 2% 3% 1% 1% 2% 1% - -
Widow/widower 2% 2% 1% - 1% - - - 2% - - -
Income
Low 24% 37% 25% 39% 15% 24% 23% 12% 28% 13% 34% 9%
Medium 44% 44% 50% 29% 52% 43% 46% 31% 23% 42% 36% 36%
High 32% 18% 23% 31% 28% 28% 30% 55% 46% 44% 28% 54%
Education
Lower 1% 1% 4% - 14% 3% 3% 4% 8% 1% 19% 12%
Medium 56% 52% 26% 31% 46% 39% 25% 43% 33% 61% 9% 36%
High 44% 47% 70% 68% 40% 58% 71% 53% 59% 38% 72% 51%
Results
Variables from the Carer Well-Being Index data set were selected based on their relevance, which included: (a) time spent caring; (b) sources of support; (c) impact on paid work and career, and; (d) emotional/mental, financial, and physical health. We also examined the relationship between government support and (d) emotional/mental, financial, and physical health. In each of these categories, the results specifically pertaining to Canadian CEs will first be described, followed by an international comparison of the twelve countries.
Time spent caring
As noted in Fig. 1 , 70 of the 479, or roughly 15% of Canadian CEs were first time carers as a result of the COVID-19 pandemic. Excluding first time carers, Canadian CEs on average spent: 17.1 hours caring per week prior to the pandemic; 19.3 hours caring per week at the peak of the pandemic, and; 19.4 hours caring per week in Fall of 2020 (time of data collection). On average, Canadian CEs were caring for 2.2 hours longer at the peak of the pandemic, and 2.3 hours longer during Fall of 2020 (Fig. 2 ). At the peak of the pandemic, first time carers typically spent more time caring (20 hours/week), on average, than CEs who had been caring prior to the pandemic, but spent less time caring during Fall of 2020 at 18.7 hours/ week. Overall, the majority (68%) of Canadian CEs found caring to be more difficult due to COVID-19. On average, Canadian CEs feel they will have to spend more time caring in the future and estimate they will spend 22.3 hours a week caring.Fig. 1 Percent of first time CEs by country as a result of COVID-19.
Fig 1:
Fig. 2 Mean hours spent caring per week over the course of COVID-19 *in color*.
Fig 2:
A number of CEs across twelve countries were first time carers (Fig. 1). Most countries, with the exception of India, had fewer (12% to 30%) first time CEs. On the contrary, over half of all CEs in the India sample were first time carers as a result of COVID-19 (51%).
Globally, excluding first time carers, the general trend exhibited by most countries was a sharp increase of mean hours caring per week at the peak of the pandemic, which decreased in Fall of 2020 (Fig. 2). CEs in Canada and Taiwan were exceptions, as time spent caring was slightly higher in Fall of 2020 than the peak of the pandemic. Despite decreases in time spent caring relative to the peak of the pandemic, levels were still higher in Fall 2020 when compared to pre-pandemic.
As noted in Fig. 3 , among CEs who provided care prior to the COVID-19 pandemic in G7 countries and Australia, Canadian CEs spent the third shortest time caring prior to the pandemic, and the second shortest time caring both at the peak of the pandemic and in Fall of 2020. Overall, CEs in Italy, the United Kingdom, and the United States spent more time caring relative to other G7 countries and Australia. CEs in Italy, the United Kingdom, and the United States spent between 19.6 and 22 hours caring per week on average prior to the pandemic, while CEs in all other G7 countries and Australia spent between 16 and 17.8. During the peak of the pandemic, CEs in Italy, the United Kingdom, and the United States spent between 23.6 and 26.2 hours caring per week on average, and in Fall of 2020, spent between 22.3 and 25.5 hours, while comparatively, CEs in all other G7 countries and Australia spent less than 20 hours a week caring in both periods. Overall, CEs in the United States spent the most time caring, while CEs in France spent the least time caring. Among G7 countries and Australia, Germany was the only country where CEs anticipated spending less time caring in the future.Fig. 3 Mean hours spent caring per week over the course of COVID-19 - G7 and Australia.
Fig 3:
In addition to spending the most hours per week caring on average, CEs in Italy, the United Kingdom, and the United States also had the largest increase in time spent caring in Fall of 2020, when compared to prior to the pandemic, at 2.7 hours, 3 hours and 3.5 hours respectively (Fig. 4 ). Canadian CEs had the 3rd lowest increase among G7 countries and Australia.Fig. 4 Increase in mean hours caring/week: before COVID-19 vs. Fall 2020 - G7 & Australia.
Fig 4:
Sources of support
As noted in Fig. 5 , 66% of Canadian CEs reported having never received support from the federal government, 68% from local/provincial governments, and 70% from their employers. During the pandemic, 27% of Canadian CEs were receiving support from the federal government, 24% were receiving support from the local/provincial government, and 21% were receiving support from their employers compared to the 13%, 16%, and 13% of Canadian CEs who were receiving support prior to the pandemic from the federal government, local/provincial governments, and employers respectively (Fig. 7). Thirty-two percent feel they are receiving enough support from the federal government, 28% feel they are receiving enough support from local/provincial governments, and 39% feel they are receiving enough support from their employers before and during the pandemic.Fig. 5 Percent of CEs that received support by organization.
Fig 5:
Globally, the only countries where over half of all CEs reported having received support, both financial and non-financial, at some point from either the government or their employers were Australia, China, and India (Fig. 5). Canada ranked sixth for federal government support, sixth for local/provincial government support, and last for employer support among the 12 countries.
In Fig. 6 we observe that, among G7 countries and Australia, Australia was notable in having the highest percentage of CEs receiving support at some point from the government and employers, with over 50% of Australian CEs receiving support from their federal government, 44% receiving support from local/state governments, and 49% receiving support from their employers. The highest federal and local/state support was then distantly followed by the United States at 42% and 38% receiving support from federal and local/state governments respectively. Other G7 countries were relatively weaker in the amount of government support given, although Canada was third for both federal and local/provincial support. With respect to CEs receiving employer support, Germany, the United Kingdom, and the United States had comparatively higher rates at 36% to 39%. Canada, France, and Italy each had 30% to 31% of CEs receiving support, with Canada having the lowest levels.Fig. 6 Percent of CEs that received support by organization: G7 and Australia*in color*.
Fig 6:
During COVID-19, the percentage of CEs receiving support from governments and employers increased across all countries (Fig. 7 ). The percentage of CEs receiving federal government support increased the most in Australia, the United States, and Canada, with a 16-percentage point increase in Australia and a 14 percentage point increase in Canada and the United States. Other G7 countries saw increases between 7 to 13 percentage points. France had the highest increase in support from local governments at 11 percentage points compared to 8 to 9 percentage point increases in other G7 countries and Australia. Germany and Italy had the highest increase in employer support at 16 and 15 percentage points respectively. Australia, France, and the United Kingdom had increases of 12 to 13 percentage points, and Canada and the United States were relatively behind at increases of 7 to 8 percentage points. Interestingly, among G7 countries and Australia, while Canada was 5th in terms of CEs receiving employer support prior to COVID-19, they were last during the pandemic.Fig. 7 Percent of CEs receiving support before and during COVID-19: G7 and Australia.
Fig 7:
Impact on paid work and career
As noted in Fig. 8 , 57% of Canadian CEs feel their paid work responsibilities are negatively affected by being a carer, and 55% feel their careers are negatively affected in the long term (Fig. 9 ). As well, due to COVID-19, 34% of CEs feel they have additional difficulty balancing professional job responsibilities with their caregiver duties (Fig. 10 ).Fig. 8 Percent of CEs that feel paid responsibilities are negatively affected by being a carer.
Fig 8:
Fig. 9 Percent of CEs that feel their long-term career is negatively affected by being a carer.
Fig 9:
Fig. 10 Percent of CEs having difficulty balancing job responsibilities and caring due to COVID-19.
Fig 10:
CEs in India and France less often feel that their paid work responsibilities are negatively affected by caring duties at 36% and 41%, respectively (Fig. 8). China has comparatively more CEs with paid responsibilities that are negatively affected by caring at 68%. Among countries in the G7 and Australia, Canada and the United Kingdom have more CEs reporting that paid work responsibilities are negatively affected at 57% and 62%, while Australia, Italy, the United States, and Germany have rates between 48% and 52%.
In terms of long-term career, CEs in India again report being negatively affected less often at 36% (Fig. 9). CEs in China also have more CEs negatively affected at 69%. Among CEs in G7 countries and Australia, Canada, Germany, and the United Kingdom have higher proportions of CEs who have long term careers that are negatively affected at 55% to 61% while the United States, France, Australia, and Italy have relatively lower proportions at 47% to 50%.
Globally, among the twelve countries, 21% to 36% of CEs in each country felt more difficulty balancing their paid jobs and caring responsibilities because of COVID-19 (Fig. 10). Among G7 Countries and Australia, Canadian CEs most often reported more difficulty balancing paid jobs and caring responsibilities due to COVID-19 (34%). This was followed by Germany (29%), the United States (27%), the United Kingdom and Australia (25%), Italy (24%), and France (22%).
Emotional/mental, financial, and physical health
Sixty-two percent of Canadian CEs felt caring negatively impacted emotional/mental health, 57% felt it negatively impacted financial health, and 52% felt it negatively impacted physical health (Fig. 11 ) during the period of caring. Additionally, 71% of Canadian CEs felt COVID-19 had worsened their mental health, 56% felt their financial health worsened, and 50% felt their physical health worsened (Fig. 12 ).Fig. 11 Percent of CEs that report caring has negative impact on health outcomes.
Fig 11:
Fig. 12 Percent of CEs that report COVID-19 has negative impact on health outcomes.
Fig 12:
In developing countries like India, Brazil, Taiwan, and China, financial health was typically negatively impacted more often or as often as emotional health while the developed countries which include Australia, Italy, France, Germany, the United States, Canada, Spain, and the United Kingdom more commonly reported a negative impact on emotional/mental health (Fig. 11). Among G7 countries and Australia, Canada had the 2nd highest proportion of CEs reporting a negative impact on emotional/mental health and financial health, and the 3rd highest proportion of CEs reporting a negative impact on physical health.
Except for China and India, which reported higher amounts of deteriorated financial health, CEs from other countries in the Carer Well-Being Index found COVID-19 deteriorated emotional/mental health more often than financial health or physical health (Fig. 12). Of the G7 countries and Australia, Canadian CEs reported deteriorated emotional/mental health due to COVID-19 most frequently (71%), followed by the United Kingdom (70%), the United States (68%), Italy (63%), France (58%), Germany (57%), and Australia (56%). Additionally, Canadian CEs reported the 4th highest percentage of deteriorated financial health, and the 2nd highest percentage of deteriorated physical health among the G7 and Australia.
Relationship between government support and emotional/mental health
Globally, when plotting rates of government support during COVID-19 against rates of deteriorated emotional/mental health during COVID-19 among the twelve countries (Fig. 13 ), there appears to be a negative relationship between level of government support and deteriorated emotional/mental health. Government support in this case was taken as the maximum of the federal government support and local government support rates.Fig. 13 Deteriorated emotional/mental health versus level of government support.
Fig 13:
CEs in countries with more government support during COVID-19 appear less likely to report deteriorated emotional/mental health during COVID-19 (Fig. 13).
Additionally, level of support from employers during COVID-19 also appeared to have a negative relationship with deteriorated emotional/mental health (Fig. 14 ). Of the twelve countries, countries where a higher proportion of CEs received employer support during COVID-19 typically had lower rates of deteriorated emotional/mental health as well. Additionally, as reflected in Figs. 13 and 14, employer support appears to have a stronger relationship than government support, with lower rates of deteriorated emotional/mental health. Smaller increases to the level of employer support translated to larger decreases in rates of deteriorated emotional/mental health when compared to government support.Fig. 14 Deteriorated emotional/mental health versus level of employer support.
Fig 14:
Discussion
Based on the Pearlin et al. (1990) stress process model, as well as the available literature related to the health and support in family caregiving, our study confirmed both hypotheses to be true: (1) COVID-19 had negative impacts on CEs, evident in: (a) time spent caring; (b) sources of support; (c) impact on paid work and career, and; (d) emotional, financial, and physical health across twelve countries, and; (2) During COVID-19, CEs across 12 countries experienced deteriorating emotional/mental health in those countries characterized as having less support from government and/or employers. Numerous factors, such as a country's COVID-19 response, the time of data collection relative to the peak of COVID-19 in each country, existing government legislation surrounding support for carers, and recognition of carers, all need to be considered to contextualize the results of the twelve-country comparison. Overall, all countries saw spikes in the amount of time CEs spent caring (Fig. 2). In a qualitative study on the concerns of carers during COVID-19, interviewed carers described how social isolation deteriorated the mental health of their care recipients as family members could not visit and social activities were restricted (Lightfoot, 2021). In cases of dementia, care recipients felt distress and confusion, sometimes feeling like their family was deliberately making them isolated (Lightfoot, 2021). Aligning with this perspective, the area of responsibility that increased most in the Carer Well-Being Index was providing emotional support, which ranged from 50% to 71% of carers, except for Taiwan at 34%. This suggests that CEs are predominantly spending more time providing emotional support due to the worsening mental health of care recipients. This suggests that supports addressing the social isolation of those being cared for would meaningfully ease the burden of CEs. Another reason time spent caring may have increased is due to the inability to access pre-pandemic informal help and supports, such as friends and family, as many carers report being concerned about letting informal supports into their homes due to COVID-19 (Lightfoot, 2021). Lacking these supports may have contributed to increased complexity of caring, reflected in 68% of Canadian CEs feeling caring is more difficult during COVID-19. Time spent caring was lower in Fall 2020 than in the peak of the pandemic for most countries; however, it was still higher than pre-pandemic levels, suggesting that there had yet been a return to normalcy.
Specific interest has been placed on countries in the G7 and Australia, due to these countries having similar levels of economic development as Canada, as well as having somewhat similar strategies, goals, and policy initiatives with respect to CEs, particularly around flexible work (Yeandle et al., 2017). Over half of all CEs felt paid work responsibilities were negatively affected by caring in the UK, Canada, Germany, and the United States, while less than half felt they were negatively affected in Italy, Australia, and France (Fig. 8); in particular, the UK and Canada stood out at 57% and 62% respectively. This divide seems loosely associated with the strength of available flexible work arrangements in each of the countries concerned. In the UK, there is a question of accessibility to flexible work, as the RTRFW allows CEs to request care leave, but can be rejected on business grounds (Yeandle et al., 2017); consequently, this may deter CEs from utilizing it. In Canada, while Family Caregiver Leave and Family Medical Leave are available (Yeandle et al., 2017), policies are focused more on leaves for end-of-life care; this ignores CEs engaged in long-term caring responsibilities that are not end-of-life. In Australia and Italy, carers are entitled to regular short-term leave, which is more versatile and allows carers to decide how to split time between work and caring (Yeandle et al., 2017; Eurocarers 2020a).
During the COVID-19 pandemic, 34% of Canadian CEs reported having more difficulty balancing their job responsibilities with caring, higher than other G7 countries and Australia which ranged from 22% to 29% (Fig. 10). Several factors may have contributed to this, such as heightened difficulty of care demands, increased time spent caring, and difficulties adjusting to work from home arrangements; however, the data suggest that the main cause may have been the lack of support Canadian CEs received from employers. Seventy percent of Canadian CEs did not receive support from their employers, representing the lowest amount of support among all 12 countries in the Carer Well-Being Index. CEs account for approximately 35% of the Canadian workforce (Wang et al., 2018). The fact that 55% of Canadian CEs feel their careers are negatively affected in the long term as a result of caring should cause serious concern, as this likely contributed to many Canadian CEs choosing to leave the workforce permanently post-pandemic. Employers in Canada have been reporting vacant positions caused by labour shortages, partly due to employees choosing to leave the workforce during the COVID-19 pandemic (Ericaalini, 2021). Now, more than ever, there is urgency for employers to recognize and support CEs through a range of different supports, which can be laid out in a carer-friendly workplace policy.
As previously mentioned, 70% of CEs in Canada did not receive any support from their employers (Fig. 6). As well, only 39% of CEs felt that they were receiving enough from their employers. It should be noted that during the pandemic, employers tried to take a more flexible approach towards work given the numerous lockdowns caused by the pandemic. However, based on data from the Carer Well-Being Index, employers did not meet the needs of CEs. This demonstrates that a systemic approach needs to be taken to support CEs, where employers create carer-friendly practices and policies, as suggested in the complimentary CSA Carer-Inclusive and Accommodating Organizations Standard (B701-17 Carer-inclusive and accommodating organizations - CSA Group) and accompanying complimentary handbook, Helping worker-carers in your organization (B701HB-18 Helping worker-carers in your organization - CSA Group); both these resources provide ready guides to inform this critical need. In Canada and the United States, employer support increased the least during the pandemic, at 7 to 8 percentage points (Fig. 7). In contrast, other G7 countries and Australia had increases of 12 to 16 percentage points. This suggests that working from home arrangements are not the best solution to supporting CEs. Instead, a range of options need to be available based on the specific needs of CEs. It should also be noted that while employer supports can help CEs, the ability to provide support is not always possible or economically feasible for many small and medium sized businesses (Vuksan, 2012a; Vuksan, 2012b).
Despite the various financial and non-financial supports available across G7 countries and Australia, as outlined in the literature review, the number of CEs receiving support from governments was incredibly low. Pre-pandemic, Australia had by far the highest rate of federal support at 25%, followed by the United States at 17%, with all other G7 countries at 10% to 13% (Fig. 7). As well, Australia had the highest percentage of CEs receiving support from both local/state governments and employers. There are a few reasons why CEs may not be accessing this government support, including not meeting eligibility criteria due to many supports being means-tested; it is also likely that CEs may not be aware of the available social programs, as has been well documented in the case of the Canadian Compassionate Care Benefit (Giesbrecht et al., 2012; Williams et al., 2011; Crooks et al., 2007; Giesbrecht et al., 2010a; Giesbrecht et al., 2010b; Giesbrecht et al, 2009; Williams et al., 2006; Crooks et al., 2008; Crooks et al., 2012). In Australia, where double the number of CEs have received support than most G7 countries, there is a concerted effort to recognize the contribution of carers, as well as publicize the available supports. National Carers Week is an initiative of Carers Australia that is funded by the Australian Government; this initiative not only raises community awareness, but also serves to direct carers towards avenues of support (IACO, 2018). The low rates of CEs receiving support from government in Canada indicates that there is a critical gap in information and access to information regarding existing policies and programs.
With the exception of India and China, caring, especially during COVID-19, predominantly deteriorated emotional/mental health (Figs. 9 and 10). In Canada, 71% of CEs felt their emotional/mental health was negatively impacted by COVID-19, the most among G7 countries and Australia. This is likely due to a range of factors, including: social isolation, increased burden of care responsibilities, difficulties adjusting to working from home arrangements, and inadequate support. Data from the twelve countries show that countries with more support for CEs during the pandemic, both government and employer, are less often to have high rates of deteriorated emotional/mental health (Figs. 13 and 14). This reinforces the notion that government and employer supports work in better managing the burden of care for CEs. Additionally, the benefit of employer support also appears stronger than government support, suggesting that the immediate work environment provides a critically important social, financial and support network for CEs that needs to be prioritized.
Limitations
As noted in the Methods section above, one of the major limitations of the study is the Carer-Index data is aggregated at the country level only; that is, individual data were not available. As a result, our study has a few limitations. First, we were unable estimate inferential statistics so were unable to examine the changes experienced over the course of COVID-19 in each country, nor were we able to examine any differences across the twelve participating countries. Second, the data would not allow us to more specifically compare a range of variables of interest, such as sex, gender, income or ethnicity, etc. Third, although the characteristics of participating respondents may be significantly different, we were unable to conduct more sophisticated analysis, such as controlling for potential confounders, such as sex and gender (given what is known about the unequal distribution of caring responsibilities across these variables). Thus, this may have led to a degree of bias in the relationship between support of caregiving and the emotion/mental health, as the samples compared do not have similar characteristics in critical variables. Nevertheless, the findings in this study provide additional insight into the impact of the COVID-19 pandemic on carer-employees’ well-being. This is especially important given, to our knowledge, that this is the first global comparison of the carer-employee experience.
Conclusion
The aim of this analysis is to assess the potential ways that the COVID-19 pandemic has comparatively impacted Canadian CEs, while identifying the needs and assistance CEs feel is required for them to continue providing care. Certain steps need to be taken to improve the well-being of Canadian CEs during and after the COVID-19 pandemic. Federal, provincial, and local governments in Canada should best learn from the pandemic and both create and build on policies and programs that increase support for CEs, either through new legislation or increased awareness of existing supports. The CSA Carer-Inclusive and Accommodating Organizations Standard (B701-17 Carer-inclusive and accommodating organizations - CSA Group) and accompanying handbook, Helping worker-carers in your organization (B701HB-18 Helping worker-carers in your organization - CSA Group) provide complimentary, ready resources to guide and inform this critical need. As suggested elsewhere (Ireson et al., 2018; Ding et al., 2020a; Ding et al., 2020b; Williams et al., 2017; Lorenz et al., 2021; Ramesh et al., 2017; Wang et al., 2018; Sethi et al., 2017), large employers with the means necessary need to take a systemic approach to providing supportive work arrangements, such as non-contiguous paid and unpaid leave, flexible hours, and supportive technology for employees providing care. Finally, the social isolation of dependent care recipients also needs to be addressed and, if done, will help ease the growing responsibility of providing emotional support by CEs.
Funding
Funding was provided via a CIHR/SSHRC Healthy, Productive Work Partnership Grant: "Scaling up the Carer Inclusive Accommodating Organizations Standard" FRN: HWP - 146001 (CIHR); 890-2016-3018 (SSHRC)
Authors contribution
Jerry Wu wrote the manuscript and performed the data analysis. Dr. Williams supervised the team, oversaw the data acquisition, led the study design, initiated and managed research ethics approval, managed and assisted with writing and manuscript preparation. Nadine Henningsen provided access to the survey data and contributed feedback on the data analysis. Dr. Kitchen contributed to the data analysis and assisted with writing of the paper. Dr. Wang supervised the statistical analysis.
Ethics approval
Approved by the McMaster Research Ethics Board (MREB); Ethics Clearance Certificate #5688.
Declaration of Competing Interest
The authors declare that they have no conflict of interest.
Acknowledgements
We thank Jeremy Guterl of Ketchum Analytics for his contributions on clarifying the methodology used for the Carer Well-Being Index survey. This research project was possible due to Jerry Wu's involvement in the University of Waterloo's undergraduate student Co-op Program.
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References
Adelman R D Tmanova L L Delgado D Dion S Lachs M.S. Caregiver burden: A clinical review JAMA J Am Med Assoc 311 10 2014 1052 1060 10.1001/jama.2014.304
Advisory, Conciliation and Arbitration Service. (n.d.). Code of practice on handling in a reasonable manner requests to work flexibly. https://www.acas.org.uk/acas-code-of-practice-on-flexible-working-requests/html.
Bartik A.W. Bertrand M. Cullen Z. Glaeser E.L. Luca M. Stanton C. The impact of covid-19 on small business outcomes and expectations Proc Natl Acad Sci 117 30 2020 17656 17666 10.1073/pnas.2006991117 32651281
Carers UK. Caring behind closed doors: 6 months on 2020 https://www.carersuk.org/for-professionals/policy/policy-library/caring-behind-closed-doors-six-months-on?gclid=EAIaIQobChMI_IvQ3pmS-wIV5_HjBx0BuwQrEAAYAiAAEgIRv_D_BwE
Crooks V.A. Williams A. Stajduhar K.I. Allan D.E. Cohen R.S. The Information Transfer and Knowledge Acquisition Geographies of Family Caregivers: An Analysis of Canada's Compassionate Care Benefit Can J Nurs Res 39 3 2007 36 54 17970459
Crooks V.A. Williams A. Stajduhar K.I. Robin Cohen S. Allan D. Brazil K. Family caregivers’ ideal expectations of Canada's Compassionate Care Benefit Health Soc Care Community 20 2 2012 172 180 10.1111/j.1365-2524.2011.01028.x 21919990
CSA B701-17 Carer-Inclusive and Accommodating Organizations Standard (B701-17 Carer-inclusive and accommodating organizations - CSA Group) 2017 Canadian Standards Association (CSA) Toronto, Canada
CSA B701HB-18 Helping worker-carers in your organization (B701HB-18 Helping worker-carers in your organization - CSA Group) 2017 Canadian Standards Association (CSA) Toronto, Canada
Crayne M.P. The traumatic impact of job loss and job search in the aftermath of covid-19 Psychol Trauma: Theory Res Pract Policy 12 S1 2020 10.1037/tra0000852
Crooks V.A. Williams A. An evaluation of Canada's Compassionate Care Benefit from a family caregiver's perspective at end of life BMC Palliative Care 7 1 2008 14 10.1186/1472-684X-7-14 –14 18755040
Czeisler MÉ Rohan EA Melillo S Matjasko JL DePadilla L Patel CG Weaver MD Drane A Winnay SS Capodilupo ER Robbins R Wiley JF Facer-Childs ER Barger LK Czeisler CA Howard ME Rajaratnam SMW Mental health among parents of children aged <18 years and unpaid caregivers of adults auring the COVID-19 pandemic - United States, December 2020 and February-March 2021 MMWR Morb Mortal Wkly Rep 70 24 2021 879 887 10.15585/mmwr.mm7024a3 34138835
Dawson A. Isaacs D. Jansen M. An ethics framework for making resource allocation decisions within clinical care: responding to COVID-19 Bioethical Inquiry 17 2020 749 755 10.1007/s11673-020-10007-w 32840833
Ding R. Dardas A. Wang L. Williams A. Improving the Workplace Experience of Caregiver-Employees: A Time-Series Analysis of a Workplace Intervention Saf Health Work 2020 10.1016/j.shaw.2020.12.003
Ding R.Y. Williams A.M. Places of paid work and unpaid work: Caregiving and work-from-home during COVID-19 Canadian Geographer/Le Géographe canadien 66 1 2022 156 171
Embracing Carers The Global Carer Well-Being Index 2021 https://www.carerscanada.ca/wp-content/uploads/2021/03/Global-Carer-Well-Being-Index-Report_FINAL-2021-NA.pdf
Ericaalini Some salaries up 'drastically' as Canada feels impact of labour shortages - National July 9 2021 Global News https://globalnews.ca/news/8008835/salaries-labour-shortages-canada/
Eurocarers Germany 2020 https://eurocarers.org/country-profiles/germany/
Eurocarers Italy 2020 https://eurocarers.org/country-profiles/italy/
Giesbrecht M. Crooks V.A. Schuurman N. Williams A. Spatially informed knowledge translation: Informing potential users of Canada's Compassionate Care Benefit Soc Sci Med (1982) 69 3 2009 411 419 10.1016/j.socscimed.2009.05.037
Giesbrecht M. Crooks V.A. Williams A. Perspectives from the frontlines: palliative care providers’ expectations of Canada's compassionate care benefit programme Health Soc Care Community 18 6 2010 643 652 10.1111/j.1365-2524.2010.00937.x 20584086
Giesbrecht M. Crooks V.A. Williams A. Scale as an explanatory concept: evaluating Canada's Compassionate Care Benefit Area (London 1969) 42 4 2010 457 467 10.1111/j.1475-4762.2010.00941.x
Giesbrecht M. Crooks V.A. Williams A. Hankivsky O. Critically examining diversity in end-of-life family caregiving: implications for equitable caregiver support and Canada's Compassionate Care Benefit Int J Equity Health 11 1 2012 65 10.1186/1475-9276-11-65 –65 23116474
Government of the UK. Carer's allowance 2014 GOV.UK https://www.gov.uk/carers-allowance
Halinski M Duxbury L Stevenson M. Employed Caregivers’ Response to Family-Role Overload: the Role of Control-at-Home and Caregiver Type J Bus Psychol 35 1 2020 99 115 10.1007/s10869-019-09617-y
Heilman, B., M. Castro Bernardini, and K. Pfeifer. 2020. Caring under COVID-19: How the pandemic is – and is not – changing unpaid care and domestic work responsibilities in the United States. https://oxfamilibrary.openrepository.com/bitstream/handle/10546/621084/FINAL%20REPORT.pdf?sequence=1.
Hughes M. Liu Y. Baumbach A. Impact of COVID-19 on the health and well-being of informal caregivers of people with dementia: A rapid systematic review Gerontol Geriatr Med 7 2021 23337214211020164
International Alliance of Carer Organizations Global State of Caring 2018 https://internationalcarers.org/wp-content/uploads/2018/11/IACO-EC-GSoC-Report-FINAL-10-20-18-.pdf
Ireson R Sethi B Williams A. Availability of caregiver-friendly workplace policies (CFWPs): an international scoping review Health Soc Care Community 26 1 2018 e1 e14 10.1111/hsc.12347 27071368
Istituto Nazionale Previdenza Sociale Dettaglio prestazione: Indennità Di accompagnamento Agli invalidi civili 2021 https://www.inps.it/prestazioni-servizi/indennita-di-accompagnamento-agli-invalidi-civili
Lafferty A. Phillips D. Dowling-Hetherington L. Fahy M. Moloney B. Duffy C. …Kroll T. Colliding worlds: Family carers’ experiences of balancing work and care in Ireland during the COVID-19 pandemic Health Soc Care Community 30 3 2022 1133 1142 33891356
Lightfoot E. Moone R. Suleiman K. Otis J. Yun H. Kutzler C. Turck K. Concerns of family caregivers during COVID-19: The concerns of caregivers and the Surprising silver linings J Gerontol Soc Work 64 6 2021 656 675 10.1080/01634372.2021.1898512 33724169
Lorenz F. Whittaker L. Tazzeo J. Williams A. Availability of caregiver-friendly workplace policies: An international scoping review follow-up study Int J Workplace Health Manag 14 4 2021 459 476 10.1108/ijwhm-10-2019-0136
McMahon M. Nadigel J. Thompson E. Glazier R.H. Informing Canada's Health System Response to COVID-19: Priorities for Health Services and Policy Research Healthcare policy = Politiques de sante 16 1 2020 112 124 10.12927/hcpol.2020.26249 32813643
Ontario Caregivers Organization. 2020. Spotlight on Ontario's caregivers: COVID-19 edition. https://ontariocaregiver.ca/wp-content/uploads/2020/12/OCO-Spotlight-Report.pdf.
Ontario Medical Association OMA estimates pandemic backlog of almost 16 million health-care services 2020 https://www.oma.org/newsroom/news/2021/jun/oma-estimates-pandemic-backlog-of-almost-16-million-health-care-services/
Park S.S. Caregivers’ mental health and somatic symptoms During COVID-19 J Gerontol - Series B 76 4 2020 10.1093/geronb/gbaa121
Pearlin L.I. Mullan J.T. Semple S.J. Skaff M.M. Caregiving and the stress process: An overview of concepts and their measures Gerontologist 30 5 1990 583 594 2276631
University of Pittsburgh Effects of COVID-19 on Family Caregivers 2020 https://ucsur.pitt.edu/covid-19_2020.php
Ramesh S Ireson R Williams A. International synthesis and case study examination of promising caregiver-friendly workplaces Soc Sci Med 177 2017 52 60 10.1016/j.socscimed.2017.01.052 28157569
Sethi B Williams A Ireson R. Supporting caregiver employees: managers’ perspective in Canada Int J Workplace Health Manag 10 1 2017 25 41 10.1108/IJWHM-03-2016-0016
Sherman C. Rising to the challenge: What employers can do to support caregiver employees Benefits Q 34 3 2018 8 11 Retrieved from http://libaccess.mcmaster.ca/login?url=https://www.proquest.com/scholarly-journals/rising-challenge-what-employers-can-do-support/docview/2063806064/se-2?accountid=12347
Simblett S.K. Wilson E. Morris D. Evans J. Odoi C. Mutepua M. Dawe-Lane E. Jilka S. Pinfold V. Wykes T. Keeping well in A COVID-19 crisis: A qualitative Study formulating the perspectives of mental health service users and carers J Ment Health 30 2 2021 138 147 10.1080/09638237.2021.1875424 33502941
United Nations, Department of Economic and Social Affairs, Population Division World Population Ageing 2019: Highlights 2019 ST/ESA/SER.A/430
Vuksan M. Williams A. Crooks V. Family friendly policies: accommodating end-of-life caregivers in workplaces Int J Workplace Health Manag 5 1 2012 4 14 10.1108/17538351211215357
Vuksan M. Williams A.M. Crooks V.A. The workplace perspective on supporting family caregivers at end of life: evaluating a new Canadian social program Community, Work Family 15 3 2012 317 333 10.1080/13668803.2012.664891
Wang L. Williams A. Kitchen P. Health of caregiver-employees in Canada: Determining the value of caregiver-friendly workplace policies and social support Int J Workplace Health Manag 11 6 2018 382 394 10.1108/ijwhm-04-2018-0043
World Health Organization Global strategy and action plan on ageing and health 2016 World Health Organization Geneva
Williams A Crooks VA Stajduhar KI Allan D Cohen SR Canada's Compassionate Care Benefit: views of family caregivers in chronic illness Int J Palliat Nurs 12 9 2006 438 445 10.12968/ijpn.2006.12.9.21872 PMID: 17077803 17077803
Williams A.M. Eby J.A. Crooks V.A. Stajduhar K. Giesbrecht M. Vuksan M. Cohen S.R. Brazil K. Allan D. Canada's Compassionate Care Benefit: is it an adequate public health response to addressing the issue of caregiver burden in end-of-life care? BMC Public Health 11 1 2011 335 10.1186/1471-2458-11-335 –335 21592383
Williams AM Wang L Kitchen P. Impacts of care-giving and sources of support: a comparison of end-of-life and non-end-of-life caregivers in Canada Health Soc Care Community 24 2 2016 214 224 10.1111/hsc.12205 25727849
Williams A.M. Tompa E. Lero D.S. Fast J. Yazdani A. Zeytinoglu I.U. Evaluation of caregiver-friendly workplace policy (CFWPs) interventions on the health of full-time caregiver employees (CEs): Implementation and cost-benefit analysis BMC Public Health 17 1 2017 10.1186/s12889-017-4722-9
Winnie C.P.C. Morissette R. Qiu H. COVID-19 and job Displacement: Thinking about the longer term 2020 Statistics Canada https://www150.statcan.gc.ca/n1/pub/45-28-0001/2020001/article/00030-eng.htm
Yang Y.T. Gimm G. Caring for elder parents: A comparative evaluation of family leave laws J Law Med Ethics 41 2 2013 501 513 10.1111/jlme.12058 23802900
Yeandle S. Wilson K. Star M. Work-care reconciliation policy: Legislation in policy context in eight countries The authors and the Bundesministerium für Familie, Senioren, Frauen und Jugend 2017
| 36510579 | PMC9729170 | NO-CC CODE | 2022-12-14 23:45:31 | no | Wellbeing Space Soc. 2023 Dec 8; 4:100123 | utf-8 | Wellbeing Space Soc | 2,022 | 10.1016/j.wss.2022.100123 | oa_other |
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Clin Biochem
Clin Biochem
Clinical Biochemistry
0009-9120
1873-2933
The Canadian Society of Clinical Chemists. Published by Elsevier Inc.
S0009-9120(22)00272-7
10.1016/j.clinbiochem.2022.12.003
Article
Taking a step back from testing: Preanalytical considerations in molecular infectious disease diagnostics
Conrad Stephanie a1
Gant Kanegusuku Anastasia b1
Conklin Steven E. ac⁎
a Department of Pathology and Laboratory Medicine, Tufts Medical Center, Boston, MA, USA
b Department of Pathology, University of Chicago, Chicago, IL, USA
c Department of Anatomic & Clinical Pathology, Tufts University School of Medicine, Boston, MA, USA
⁎ Corresponding author.
1 These authors contributed equally.
8 12 2022
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© 2022 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.
2022
The Canadian Society of Clinical Chemists
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Recent studies evaluating the preanalytical factors that impact the outcome of nucleic-acid based methods for the confirmation of SARS-CoV-2 have illuminated the importance of identifying variables that promoted accurate testing, while using scarce resources efficiently. The majority of laboratory errors occur in the preanalytical phase. While there are many resources identifying and describing mechanisms for main laboratory testing on automated platforms, there are fewer comprehensive resources for understanding important preanalytical and environmental factors that affect accurate molecular diagnostic testing of infectious diseases. This review identifies evidence-based factors that have been documented to impact the outcome of nucleic acid-based molecular techniques for the diagnosis of infectious diseases.
Keywords
Preanalytics
Molecular diagnostics
Infectious disease
==== Body
pmc1 Introduction
Laboratory testing is a complex multistep process susceptible to variability. Factors that can lead to inconsistent results are as simple as environmental variables such as time and temperature in addition to interindividual differences (regarding both patient and sample handling). Variability in these factors is unavoidable throughout the testing process. However, when these factors are not controlled or accounted for, they are capable of compromising specimen integrity, and even altering the result outcome. This influence is pronounced in the preanalytical phase, which includes steps like specimen ordering, collection, processing, storage, and transport (Fig. 1 ). Therefore, special considerations should be taken to minimize the influence of such factors. Manufacturers play a key role in mitigating the effect of preanalytical error by providing detailed instructions on sample processing. However, it is not unusual for laboratories to make modifications to manufacturer instructions to optimize workflow or reagent utilization. With a better understanding of how these general preanalytical factors can potentially influence testing, laboratories can make informed decisions when performing additional validation studies and defining sample acceptability criteria for a given assay.Fig. 1 Preanalytical processing variables that can impact the outcome of laboratory tests that rely on molecular techniques for the diagnosis of infectious diseases.
Preanalytical errors are quite common and can have significant impact on patient care and outcomes (Table 1 ). There are many reviews describing the impact of preanalytical errors on automated diagnostic assays in the clinical chemistry laboratory. Many of these assays rely on spectrophotometry, electrochemistry, and immunoassay technologies— and have distinct criteria for preanalytical processing from molecular diagnostics. Molecular diagnostics involve the amplification, detection, and often quantitation of nucleic acids. Current nucleic-acid based technologies used for diagnosis of infectious disease include: mono- and multiplex polymerase chain reaction (PCR), microarray panels, peptide nucleic acid fluorescent in situ hybridization (FISH), magnetic resonance-based detection, and next generation sequencing (NGS) [1].Table 1 Summary of preanalytical variables that can impact outcomes of molecular testing for the detection of infectious diseases.
Variable Effect Common Examples Minimization References
Specimen collection containers Collection container additives can inhibit nucleic acid amplification. Heparin Follow manufacture recommendations for sample collection containers and/or perform validation studies to confirm sample integrity in the presence of additives. [29], [49]
Specimen contamination Risk for false positive results due to sample contamination. Not changing gloves. Appropriate work-flow and engineering controls.
Transition to “closed” reaction platforms to minimize human manipulation. [59], [60], [114]
Endogenous and exogenous inhibitors Samples contain endogenous or exogenous compounds that inhibit enzymatic reactions for nucleic acid amplification.
Presents an issue for direct sample analysis methods. Endogenous inhibitors: IgG, hemoglobin, heme metabolites, lactoferrin, and antiviral substances (i.e. acyclovir)
Exogenous inhibitors: proteases, nucleases Proper extraction and purification can remove inhibitors.
Appropriate sample collection can also minimize presence of exogenous inhibitors. [66]
Time, temperature, and freeze–thaw cycles Nucleic acid targets have variable stability under different temperature conditions depending on sample type and pathogen. Samples being exposed to extreme cold, freezing and then thawing during transport or storage. Perform validation studies to confirm sample integrity under standard and anticipated sample processing conditions. [27], [29]
Humidity High humidity can compromise dry oral/buccal swabs and dried blood spots stored under ambient conditions. Samples being left in specimen lock box for pick up, but pick up is delayed and lock box not insulated. Avoid these sample types if storage conditions cannot be controlled or perform validation studies to confirm sample integrity. [94], [97], [99]
Light exposure Potential temperature variation and UV exposure can degrade patient samples. Sample forgotten in direct sun light. Protect samples from unnecessary light exposure (i.e. covering). [63], [64]
Timing of Collection Potential false negative results due to insufficient genetic material of microorganism in specimen Testing for a pathogen before the onset of symptoms or when symptoms have nearly resolved. Consider pathogen’s incubation and latent period.
Consider longitudinal performance of test being performed.
Consider a specimen type that will yield high amounts of genetic material. [27], [115], [116]
Antibiotic Treatment Potential false negative results due to insufficient genetic material of microorganism in specimen as result from treatment. Patient is empirically started on antimicrobials for C. difficile and is then tested for C. difficile with PCR toxin B genes, glutamate dehydrogenase. Thorough patient history review.
Combine Immunologic and molecular testing. [22]
Antiretroviral therapy Potential false negative results due to insufficient genetic material of microorganism in specimen as result from treatment. Patient recently received ARV or PrEP and under goes HIV testing for HIV RNA. Thorough patient history review.
Combine immunologic and molecular testing. [17], [18], [19]
Dialysis Potential false negative results due to insufficient genetic material of microorganism in specimen as result from dialysis. Patient with HCV is receiving dialysis and has HCV PCR performed after dialysis. Thorough patient history review.
Combine immunologic and molecular testing. [23]
Apheresis Potential false negative results due to insufficient genetic material of microorganism in specimen as result from apheresis. Patient undergoes apheresis procedure experimentally for HIV management and afterwards has test performed for HIV NAAT test results. Thorough patient history review.
Combine immunologic and molecular testing. [117]
While there are some well-cited general recommendations for the preanalytical treatment of samples for molecular diagnostics, these guidelines are often from studies or literature that predates current technologies used in laboratories [2]. With the recent world-wide diagnostic testing challenges experienced during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, there has been a dramatic increase in the number of studies evaluating preanalytical factors for confirming infection via nucleic acid amplification techniques [3], [4], [5]. Such studies shine a light on the importance of laboratory evaluations of commonly experienced preanalytical and environmental variables. This review will identify major variables during the preanalytical phase that impact diagnosis of infectious diseases.
2 Clinical decision making for ordering tests
2.1 Appropriate test ordering
Order entry is a commonly overlooked step of the preanalytical phase. This step initiates the testing process and can impact clinical outcomes. Therefore, healthcare providers should carefully assess which test can provide timely diagnostic information that can be acted upon or answers the clinical question at hand. The following discussion on test ordering will review where errors can occur and provide mitigation strategies.
Errors in test ordering can occur for many reasons and are often the result of very simple mistakes such as unnecessarily repeating a test order, forgetting to order tests, or simply ordering the wrong test. Approximately 70 % of test orders contain some sort of error [6]. Advances in electronic medical records systems have helped diminish errors associated with order entry. The implementation of test ordering governance programs has greatly decreased inappropriate test orders and order duplications, while providing significant cost savings to the healthcare system [7]. This is mostly achieved by integrating consultation services with pathologists and laboratory directors or the implementation of infection specific testing algorithms. Additionally, clinical decision support systems have proven helpful in minimizing test errors (repeat or unnecessary tests) [8]. The advent of reflex testing, providing test interpretation and test algorithms has also proven efficacious in minimizing test errors [9]. Behavioral interventions such as providing feedback to ordering physicians, changing test ordering options, and policies around panel ordering have also proven beneficial [7]. Diagnostic stewardship programs for infectious diseases have a track record of minimizing unnecessary testing, improving turn-around-time (TAT), mitigating unnecessary prescriptions, and contributing to institutional and patient cost saving [10], [11]. With the constant evolution of technology and increasing variety of test options available to clinicians, it is important that laboratory leaders work together with clinical teams to develop effective testing algorithms and stewardship initiatives to ensure appropriate test ordering.
2.2 Patient status: health, demographic and geographical history
Patient demographics play a role in infectious molecular disease testing. Consideration of the patient with respect to their region of origin, where they have traveled or lived, and which populations they have interacted with is necessary. The concept of patient demographics is best highlighted when considering the various types of HIV infection. In the United States, the most prevalent form of HIV is human immunodeficiency virus type 1 (HIV-1) while in West Africa human immunodeficiency virus type 2 (HIV-2) is the more common form [12]. However, when people travel between continents or interact with those of differing continents their risk profile for specific infections changes. It is imperative to avoid the assumption that a pathogen or risk of infection is only likely amongst the populations found in the region of prevalence.
When interpreting molecular results, the time between testing and treatment of a suspected pathogen must be considered. The goal of antiretrovirals (ARVs) is to prevent transmission of HIV as well as treat the infection. Rarely, individuals with an HIV infection on an antiretroviral therapy have demonstrated undetectable antibodies by third and fourth generation serologic assays [13], [14], [15]. More often, patients receiving ARVs are monitored for treatment efficacy by nucleic acid amplification tests (NAAT), where viral loads diminish to low or undetectable levels with treatment [16]. Individuals using preexposure prophylaxis (PrEP) also show diminished viral loads and delayed seroconversion, making detection of HIV RNA and antibodies challenging [17], [18]. When interpreting HIV RNA results it is important to consider prior or recent ARV or PrEP use, in addition to false reactivity, immunosuppression, or early window period [17], [18], [19].
The context of demographics and patient history is also paramount when considering potential differential diagnosis in an individual presenting with infectious diarrhea. When provided with a history of camping and drinking fresh water from a stream a parasitic disease, like that caused by Giardia lamblia comes to mind, while a viral or bacterial etiology maybe considered if consumption of improperly prepared food is discovered. Depending on sexual history and sexual practices reported, Entamoeba histolytica may crawl into the differential diagnosis for the cause of diarrhea [20].
3 Specimen collection and types: Specimen adequacy
3.1 Time of collection
Timing of sample collection for infectious disease testing is very important. Inappropriate timing can be the cause of false negative results. The dynamics of viral replication is a key factor to consider when testing and has been most recently highlighted throughout the coronavirus disease 2019 (COVID-19) pandemic caused by the emergence and global spread of SARS-CoV-2. Viruses may have incubation and latent periods that must be considered when providing guidelines for sample collection. Many viruses other than SARS-CoV-2 have latent periods, including hepatitis B virus (HBV), hepatitis C virus (HCV) and HIV. In an acute viral infection, the patient experiences overt symptoms and viral load is at detectable levels [21]. However, testing during the early phases of a latent period can lead to false negative results if there are low levels of viral particles. Hence it is importance to consider viral replication dynamics when performing molecular testing.
Molecular infectious disease testing and interpretation should not occur in isolation. When suspicion is high for bacterial infection, antibiotics are often initiated rapidly, prior to test ordering. In these scenarios it is possible that the initiated treatment may already have an impact on the patient’s molecular test results. This example is best highlighted with empiric antimicrobial treatment of C. difficile (CD) infections. Researchers have found patients empirically treated for CD infection can have false negative PCR results as soon as 1 day after treatment with % negativity increasing from 14 % to 35 % to 45 % after 1, 2 and 3 days of treatment [22].
Therapies besides medications can also impact infectious disease testing results. Research detecting HCV in dialysis patients have shown serologic assays may not be ideal for testing. Of the 73 dialysis patients who were found to be HCV negative by an ELISA assay, 17 were found to be HCV positive when assessed by PCR [23]. Research in the HIV realm using virus apheresis tags to remove HIV virus from circulation is ongoing. If a patient has undergone an experimental apheresis procedure for HIV management this could impact their HIV NAAT test results [23]. These examples highlight the importance of considering the patient treatment history prior to ordering or interpreting a molecular test result.
3.2 Specimen handling by specimen type
The nature of pathogen infectivity influences the quantity and location of pathogen nucleic acid shedding. This dictates which specimen types will contain the highest amount of nucleic acids. For example, when testing for viral respiratory pathogens (i.e. influenza or SARS-CoV-2) a nasopharyngeal specimen is preferred over oropharyngeal swabs as they contain higher viral loads. There has been some recent debate in the literature regarding the performance of saliva vs the traditional nasopharyngeal specimen for detecting SARS-CoV-2. Saliva is a very convenient specimen type that does not require special training of the technician nor put the technician at risk of SARS-CoV-2 exposure and possible infection. However, disadvantages to saliva-based testing include risk of contamination and lower yields of nucleic acids [24]. While there are contradictory publications, evidence suggests saliva performs similarly to the nasopharyngeal swabs for the detection of SARS-CoV-2 [25]. Although the recent pandemic has encouraged research into alternative specimen to maximize efficiency, there is a limit as to which specimens to investigate when considering the mechanism of infection. For example, nasopharyngeal specimen would not be used to test for enteric pathogens, rather stool samples would be collected [26], [27].
Along with selecting an appropriate specimen type for the infection being assessed, each specimen type can be degraded or contaminated by distinct variables, potentially impacting results. To prevent most issues with degradation or contamination, the laboratory should follow the manufacturer guidelines for specimen type and appropriate handling or perform internal validations for any modifications to these guidelines.
There is an abundance of specimen types which can be used in molecular infectious disease testing. The various specimen types, commonly associated pathogen class, variables for consideration, and general guidelines for specimen handling are summarized in Table 2 . Note that these guidelines are broad and do not take into account specific manufacturer recommendations, or unique considerations for distinct pathogens.Table 2 Summary of sample type and considerations for preanalytical processing.
Sample Type Common class of pathogens detected Specific Pathogen Examples Important preanalytical considerations General guidelines References
Whole blood Viruses HIV, HCV, CMV, HBV Time, temperature, collection tube additives, hematocrit Storage: RT, 24 h (DNA)
2–8 °C, 72 h (DNA)
2–8 °C, 4 h (RNA)
Additives: EDTA
ACD [2], [27], [29], [35]
Serum Viruses HIV, HBV Time, temperature, hematocrit 2 –8°C, 2–7 days
−20 – −80 °C, long-term [27], [90]
Plasma Viruses HIV, CMV, BKV Time, temperature, collection tube additives, hematocrit Storage: 2 °C –8°C, 5 days
−20 – −80 °C, long-term
Additives: EDTA
ACD [29], [35]
Dried Blood Spot Viruses (genomic data) HCV, HIV Time, temperature, humidity, light exposure Must be adequately dried; 3–12 month stability under appropriate storage conditions [118]
Bronchoalveolar Lavage (BAL) Fungi, bacteria, viruses PCP, Mycobacterium tuberculosis Time, temperature Transportation: On ice
Storage: 4 °C, 72 h
−20 °C – −80 °C, long-term [27], [29]
Nasopharyngeal Swab Viruses (respiratory) SARS-CoV-2 Time, temperature, composition of VTM Storage: VTM/UTM media: 2–8 °C, 4 days
Dry swab: RT, 12 h
−20 °C, 2 days [29]
Saliva Viruses SARS-CoV-2 Time, temperature, use of oral hygiene products, consumption of food/beverages, stabilization additives Storage: RT (stabilized), 7 days
2–8 °C, 7 days [29]
Buccal Swab Viruses SARS-CoV-2 Time, temperature, use of oral hygiene products, consumption of food/beverages Storage: Dried, RT, 7 days (DNA)
Stabilized, 2–8 °C, 24 h (RNA) [29]
Cerebral Spinal Fluid (CSF) Fungi, bacteria, viruses Cryptococcus neoformans, Neisseria meningitidis, Herpes simplex virus (HSV) Time, transportation, and storage temperature Transportation: Wet ice (RNA)
2–8 °C (DNA)
Storage: 2–8 °C, 72 h (DNA)
−20/-80 °C long-term [27], [29]
Stool Viruses, bacteria, parasites Norovirus, Campylobacter jejuni, Giardia lamblia Time, transportation, and storage temperature, stabilization media medications Transportation: RT (stabilized)
On ice
Storage: 2–8 °C, 4 – 7 days
RT (stabilized), 4 – 7 days [29]
Urine Bacteria, parasites, viruses Trichomonas vaginalis, Chlamydia trachomatis, Neisseria gonorrhoeae, BKV Time, transportation and storage temperature, additives Storage: 2–8 °C, 7 days
−20 – −80 °C, 7 days [29]
Cervical/Vaginal/Urethral Swabs Viruses, bacteria, parasites Herpes simplex virus |(HSV), Chlamydia trachomatis, Neisseria gonorrhoeae, Trichomonas vaginalis Time, temperature, transport media Storage: Dried, at or < RT, 72 h [29], [48]
Sputum Bacteria Streptococcus pneumoniae Time, temperature, mucolytic agents, stabilization additive Storage: 4–8 °C, 7 days
<-70 °C 12 months
Additives: N-acetyl l-cysteine, dithiothreitol [29], [119]
3.3 Specimen collection containers: The effect of additives
An additional challenge to ensuring nucleic acid integrity of patient samples for molecular testing includes the use of proper specimen collection containers. Choosing the correct specimen type is further complicated by established confirmatory testing algorithms, which requires multiple samples and sample types to be evaluated by different technologies. These tests may be performed at a different facility, depending on testing availability. Often, these distinct testing platforms also have their own requirements for collection tubes and additives. With more laboratories incorporating automated liquid handlers, consideration must also be given to the dimensions of the original collection tube or aliquoted sample tubes, which may not be compatible with the downstream automated steps. This section will review the evidence for appropriate additives for molecular testing of infectious disease according to sample type including: whole blood, plasma/serum, buccal and nasopharyngeal swabs, and saliva.
Currently, the recommended blood anticoagulants for molecular diagnostic testing are ethylenediaminetetraacetic acid (K2/K3-EDTA) and acid citrate dextrose (ACD). Both EDTA and ACD are chelating agents commonly used in molecular techniques involving nucleic acid isolation for their efficient sequestering of Ca2+ ions, which serve as cofactors for nuclease responsible for the degradation of DNA [28]. However, polymerase enzyme inhibition has been suggested, presumably by presence the of EDTA post nucleic-acid extraction and the sequestration of Mg2+, in samples collected in tube types containing high concentrations of EDTA, such as those used for trace element analysis [29]. By contrast, RNAse activity does not depend on divalent cations, so chelating agents like EDTA and ACD do not directly inhibit RNAse activity by the same mechanism as DNAse activity [30]. While many commercial kits utilize RNAse inhibitors, such as guanidine salts, which inhibit RNAse activity via denaturation during the extraction and purification phases of sample processing [31], [32], [33], a few studies have shown that various viral RNA targets are stable for extended periods of time in EDTA plasma without the addition of RNAse inhibitors into the collection vials [34], [35].
There is conflicting evidence concerning the acceptability of plasma samples containing heparin for molecular diagnostic analysis. Studies provide evidence for PCR interference when comparing DNA amplification from samples containing the same concentrations of heparin, but low versus high concentrations of leukocytes [36], [37], [38]. Additionally, a study found that heparin inhibited DNA amplification in a dose-dependent fashion [36]. The degree of inhibition was also found to be dependent on the Taq DNA polymerase used, indicating that some commercial polymerases were more tolerant of heparin interference. Heparin has been shown to be recovered during both the DNA and RNA extraction processes [39]. Similar findings of heparin interference and suppression of RNA amplification was seen in the detection of HCV RNA by PCR [39].
While the consensus appears to be that heparinized plasma samples are not suitable for molecular diagnostic techniques for determining infectious diseases [40], a recent study has shown that heparin samples can be used to evaluate DNA or RNA from white blood cells (WBC), after appropriate WBC washing [41], which can be used for the detection of cytomegalovirus (CMV) DNA [42]. Despite the handful of studies that have provided evidence for a lack of PCR inhibition following the washing of leukocytes from heparin collection tubes, the current guidelines recommend that heparin-containing specimens be rejected [29].
Buccal and nasopharyngeal swabs, as well as saliva have distinct collection processes that may or may not require additives depending on the analyte being evaluated (DNA or RNA) and the time to analysis. VTM usually contain salts to provide an isotonic environment, proteins to stabilize the virus, buffers to control pH, and antimicrobials or antifungals to prevent microorganism contamination [43]. There are many recipes for VTM which can be bought commercially or prepared in-house [44], [45], [46]. As a result of the global supply shortage during the COVID-19 pandemic, many laboratories have undertaken studies to evaluate components of VTM that either inhibit or enhance molecular testing performance [47], [48], [49], [50], [51].
A recent evaluation of the impact of VTM on the detection of nucleic acids from SARS-CoV-2 highlights the importance of individual laboratory validation of appropriate VTM for nucleic acid targets assessed [49]. During the pandemic, a number of organizations, including the World Health Organization (WHO) and the US Center for Disease Control (CDC), published recommendations to assist in the global effort to develop and perform diagnostic testing for SARS-CoV-2. The recommendation for the formulation of VTM to be used for sample collection included the addition of protein or glycerol to stabilize the SARS-CoV-2 virus [46], [52]. Following these guidelines, commercial VTM solutions included serum-derived components such as bovine albumin for stability, which, as the study suggests, likely contain some amounts of nucleases and proteinases [49]. When compared with an in-house VTM preparation consisting of sterilized PBS solution containing gelatin (autoclaved), the use of commercial VTM solutions significantly compromised the detection of free SARS-CoV-2 RNA. After dilution with commercial VTM SARS-CoV-2 RNA was either not detected or very weakly reactive, approaching the assay limit of detection (LOD), representing a reduction in sensitivity of approximately 6 log10 for free SARS-CoV-2 RNA as compared to samples diluted with the in-house VTM preparation [49]. Control experiments performed using an exogenous RNA internal control (XICP) provided evidence for efficient RNA extraction, and also insight into the likely cause of free RNA degradation. XICP was diluted in the various VTM as a solution that also included nuclease inhibitors, implying that the commercial VTM was not free of nucleases. Degradation of free RNA from Type A influenza virus was also demonstrated.
While the study revealed little to no degradation when high quality, intact SARS-CoV-2 virions were introduced to in-house and commercial VTM, as the intact nucleocapsid offers protection to viral RNA, the authors make the case that it cannot be assumed that the target nucleic acid material will always be protected by nucleoproteins [49]. Furthermore, degradation of encapsulated viruses can occur endogenously over the course of infection, as well as post-sample collection during transport and storage (See Section 3) and even during lengthy purification processes. As such it is not only important to evaluate appropriate VTM preparations for components that inadvertently degrade the nucleic acid target, but also choosing the most appropriate VTM considering the method of extraction and even downstream logistical processes such as transport, time-to-analysis and storage.
3.4 Specimen contamination
Molecular techniques have the power to synthesize millions of copies of DNA or RNA from just a few copies of target or template material. These technologies are inherently sensitive. A major concern with molecular diagnostics is the risk of obtaining false-positive test results due to sample contamination with other template material or post-amplification product (amplicons). Cross-contamination of a positive clinical sample to a negative sample can occur at multiple points during sample collection, processing, or analysis. More recently, attention has been paid to the contamination of reagents with synthetically derived amplicons, or carry-over contamination [53].
This type of carry-over contamination can occur locally, within a laboratory performing routine clinical testing, as well as regionally, at the level of the commercial manufacturer. During the pandemic, a number of laboratories reported delayed implementation of SARS-CoV-2 testing as a result of contamination of commercial reagents for the initial CDC-designed test kit [54], [55]. At the time, no commercial or state laboratory had approval to use their own, or alternative validated test and this resulted in a nation-wide lag in COVID-19 testing in the United States [56], [57]. Typically, manufacturers produce synthetic control target templates at independent sites to avoid such issues with cross-contamination. These reports imply that with an unprecedented global demand for SARS-CoV-2 diagnostic testing, scaled-up manufacturing processes can lead to issues with contamination, affirming the general need for in-house laboratory quality assurances to prevent false reporting.
There are several engineering controls and workflow strategies that can be implemented to minimize contamination in the molecular laboratory [58], [59]. As more laboratories implement the latest molecular methods, especially “closed” reaction platforms in which extraction, amplification, and detection of amplicons occurs without human manipulation or even opening of the reaction vessel, many of the engineering strategies that will be discussed are becoming unnecessary [60]. Most guidelines, however, still recommend that laboratories should designate separate pre- and post-amplification areas with negative pressure. Ideally, these areas would be physically separated and contained, however, if this is not possible, these areas should be as far apart as possible. Within the pre- amplification area, master-mix preparation can be physically separated from sample addition by using a Dead Air Box (DAB) with designated equipment. Likewise, sample addition and extraction processes can also be contained and physically separated into a separate DAB or biological safety cabinet (BSC) with designated equipment. Within the post-amplification area, additional DABs with designated equipment would be used for any processes that require manipulation of open sample tubes after amplification.
Creating a unidirectional workflow from pre- to post-amplification is essential for preventing contamination of patient samples and reagents with amplicons. This also requires laboratory staff to use separate and clean personal protection equipment in the pre- and post-amplification areas of the laboratory. Frequent changing of gloves is recommended.
In addition to ensuring dedicated equipment for each stage of sample processing, it is also important to use proper pipetting techniques that reduce the risk of aerosol formation and downstream contamination. Aerosol-barrier pipette tips are recommended to avoid contaminating designated pipettes. Additionally, proper pipetting training is essential to ensure that proper volumes are aspirated and added without cross-contamination. Likewise, it is essential that technologists are aware of aerosol formation upon opening and closing sample tubes.
Routine performance of aseptic cleaning techniques is critical. Surfaces, especially frequent touch-points, should be cleaned with a 10–15 % bleach (0.5 % sodium hypochloride) solution [59], [61]. Solutions of at least 10 % bleach were found to destroy DNA and RNA [62]. Likewise, decontamination of surfaces as well as reagent mixes can also be achieved by UV-irradiation, however, care must be taken when irradiating mixtures with UV-sensitive enzymes [63], [64]. It is essential that laboratories proactively monitor the laboratory environment for contamination by performing wipe-tests in addition to routine positivity rate monitoring [65].
3.5 Inhibitors: Endogenous and exogenous
Molecular diagnostic testing involves enzymatic reactions which are susceptible to inhibition by either endogenous or exogenous inhibitors present in the sample (see Section 2.3 for discussion of sample collection additives).
In whole blood, serum and plasma samples, endogenous inhibitors include immunoglobulin G (IgG), hemoglobin, heme metabolites, lactoferrin, and antiviral substances (i.e acyclovir) [66], [67]. IgG has been shown, via electrophoretic mobility shift assays (EMSA) and isothermal titration calorimetry (ITC) to bind single-stranded genomic DNA, inhibiting or delaying DNA amplification [68] Both hemoglobin and hematin (hydroxide ligated heme) were found to lower the enzymatic activity of DNA polymerase, as well as cause static fluorescent quenching of free fluorescent dye molecules (ROX, EvaGreen, SYBR Green I) [68], [69]. Additional metabolites of heme such as hemin (chloride ligated heme) and catabolism product bilirubin have been shown to inhibit DNA polymerase efficacy by competing with template DNA, preventing the appropriate polymerase-template complex from forming [70], [71]. Lactoferrin, a single polypeptide glycoprotein containing up to two Fe3+ ions, has been shown to interact with nucleic acids, potentially interfering with the efficient extraction and amplification of whole blood samples [70], [72].
Saliva contains a variety of endogenous enzymes and proteases, including nucleases which can degrade nucleic acid polymers, especially RNA [25], [73]. Similarly, nasal and nasopharyngeal swabs may contain enzymes that degrade nucleic acids in addition to polymerase inhibitors such as immunoglobulins and cytokines as well as electrolytes, minerals and medications [74]. Although not discussed in detail within this review, urine and fecal samples may contain endogenous inhibitors, primarily high concentrations of urea or polysaccharides, respectively. The highly variable composition of fecal samples can introduce additional sources of inhibition such as bile salts, hemoglobin, glycolipids and heparin [66].
Endogenous inhibitors are usually removed or inactivated during sample collection (transport media) and processing via nucleic acid extraction and purification steps [66]. Some endogenous inhibitors can co-extract with nucleic acid material, as such, it is recommended that laboratories perform validation studies of extraction methods for the determined sample type and target material [75]. Direct amplification of biological samples for infectious disease detection is highly desirable and many recent advances have been made to reduce time-consuming purification steps as well as provide point-of-care (POC) diagnostic tools [76].
Exogenous inhibitors, apart from those discussed in Section 2.3, can be introduced during sample preparation, specifically during nucleic acid extraction and purification processes [66]. More heterogeneous sample types such as saliva, swabs, and fecal samples can contain inhibiting agents introduced via ingestion [66]. For example, saliva and buccal samples are typically collected prior to ingestion of food or non-water beverages for a defined amount of time, use of oral hygiene products, or consumption of tobacco products [77]. However, it is important to follow the manufacturer’s specific instructions for proper collection. Food contains substances known to interfere with molecular techniques, primarily: polysaccharides, glycogen, enzymes such as plasmin in milk products, and minerals such as calcium [66], [78].
The inhibitory effects of food consumption on the evaluation of saliva and buccal swab specimens continues to be studied. A recent study investigated the effect of eating prior to providing a saliva sample for SARS-CoV-2 detection in five children by reverse-transcription polymerase chain reaction matrix assisted laser desorption ionization (Agena MassARRAY SARS-CoV-2 Panel/System, RT-PCR/MALDI-TOF) [79]. The study found that interference from consuming food was minimal when samples were collected 20 min after a meal, followed by nucleic acid extraction. With rapid advancements in molecular testing techniques and the number of acceptable sample types for the detection of infectious diseases, it is important to evaluate the likelihood and impact of exogenous inhibitors that can be present as a result of common lifestyle variables such as diet and medication.
Depending on the sample type, nucleic acid target, and molecular testing technology used, endogenous and exogenous inhibitors can be mitigated by nucleic acid extraction, additional purification, the use of more tolerant or robust enzymes, or sample dilution.
4 Specimen handling and integrity: From specimen collection to laboratory
Under ideal conditions, samples would be collected and immediately processed by the laboratory. Minimizing transport time after sample collection is the best way to ensure that the specimen has not degraded from exposure to environmental conditions, such as prolonged time at incorrect temperatures, extremes in humidity levels, exposure to light, or multiple freeze-thaw cycles. Of course, practically, achieving ideal conditions by immediately processing samples in the laboratory is not realistic for most samples. Even when a laboratory is processing in-patient samples, there can be major time delays between when the sample was collected and when the sample arrived at the laboratory. When considering less-centralized healthcare systems, in which samples may need to travel great distances before reaching a laboratory, an understanding of how these environmental factors can affect the specimen quality is essential for ensuring meaningful diagnostic testing. Knowing the realistic timeline and the variability of conditions that are encountered by a specimen can help a laboratory to select testing based on methods that accommodate the most stable specimen under difficult-to-control environmental conditions.
The laboratory usually assumes the responsibility for the validation of sample stability to define specimen acceptability criteria, as required by accreditation organizations such as College of American Pathologists (CAP). In general, most laboratories will follow specified sample collection and handling recommendations of FDA-approved commercial testing kits. However, many laboratories supplement or validate these recommendations by performing in-house stability studies, especially when the recommended guidelines are not feasible. Acceptable variations in environmental conditions are dependent on sample type, target nucleic acid, and pathogen. This section provides discussion of environmental conditions that are known to affect the integrity of specimens (blood, plasma/serum, buccal and nasopharyngeal swabs, and saliva) for infectious disease testing via molecular methods as a resource for laboratories when deciding which stability studies to perform.
4.1 Time, temperature and freeze-thaw cycles
In general, for whole blood samples, storage below 0° C is not recommended. If DNA or RNA extraction cannot occur within an acceptable timeframe, the erythrocytes should be removed, and the plasma or serum should be stored at −20 °C or colder [29]. Removal of the erythrocytes is an essential step since heme can be released upon thawing of frozen samples and inhibit polymerase activity in PCR mechanisms as well as quench fluorophores used for real-time detection of amplicons (Section 2.5) [29], [80], [81], [82]. If the target nucleic acid material is RNA, the general recommendation is that whole blood should be collected in a tube containing RNA stabilizing reagent. Depending on the RNA stabilizing reagent used, appropriate storage temperatures and duration prior to extraction may vary. While there is no absolute time limit for the storage of whole blood samples at room temperature or 2–8 °C, prolonged storage at these temperatures is not recommended due to increased risk of hemolysis and nucleic acid degradation because of white blood cell lysis [29], [41].
Serum and plasma samples are more conducive to longer-term storage, since these specimen can be frozen without the introduction of endogenous inhibitors. Allowable freeze-thaw cycles must be evaluated by the laboratory for each target pathogen. For example, one study evaluated the effect of multiple freeze-thaw cycles on serum samples evaluated for HBV DNA and HCV RNA by Quantiplex branched-DNA assays (Chiron Diagnostics Corporation, Walpole, MA). The report determined that the respective nucleic acids could withstand up to eight freeze-thaw cycles [83]. Yet, an earlier report observed a 16 % decrease in HCV RNA titers after 5 freeze-thaw cycles and recommended aliquoting sample to reduce loss [84], and a later study reported no significant decreases in viral load after 10 freeze-thaw cycles [85]. Similar findings were observed for HBV DNA [86].
Recommendations for the storage and handling of nasopharyngeal swabs typically defer to those specified by the commercial manufacturer [29]. With the ongoing global pandemic and limited supply chain, some laboratories have made modifications to the specified storage conditions for SARS-CoV-2 RNA specimens [87], [88], [89]. Evaluations of long-term room-temperature storage condition for nasopharyngeal swabs in viral transport media revealed that samples could be stored for up to 21 days at room temperature without significantly impacting the RT-PCR results [87]. Room temperature stability (22 °C, 1 week) of nasopharyngeal swabs in VTM has also been observed for the RT-PCR detection of four additional viral RNA targets (influenza, enterovirus, herpes simplex virus, adenovirus) [48]. Yet, there are other reports indicating significant (66–74 %) loss of viral RNA for influenza A, regardless of storage temperature (21 °C, 4 °C, −80 °C) after two weeks [90].
Oral rinse and saliva specimen for DNA extraction are relatively stable at room temperature [2], [29]. The traditional recommendation for oral rinse and saliva storage for RNA extraction requires immediate placement on ice or in a transport medium with stabilizing agent and storage at 2–8 °C followed by extraction within 24 h of collection [2], [29]. A more recent study of SARS-CoV-2 RNA stability in saliva specimens stored without nuclease inhibitors or transport medium and at a variety of temperatures (−80 °C to 30 °C) and processing conditions (freeze-thaw), revealed that SARS-CoV-2 RNA was stable for extended periods of time (>16 days) at room temperature in addition to more extreme temperature treatments (freeze-thaw, and 30 °C for 72 h) [91].
Urine specimens should be processed immediately or stored at 2–8 °C for several reasons. First, the generally low pH of urine (4.5–8) and the concentration of urea denature DNA at ambient temperatures (25 °C and greater) [92], [93]. Additionally, prolonged storage at low temperatures increases the likelihood of uric acid, calcium oxalate, and phosphorus precipitation, which can inhibit nucleic acid amplification [92].
4.2 Humidity
High-humidity environments can compromise certain sample-types, primarily those that are deposited on materials and stored under ambient conditions such as dry oral and buccal swabs and dried blood spots [94], [95], [96]. Viral or microbial nucleic acids have been shown to degrade during prolonged storage [94], [97]. A recent study of an energy-based mechanism of DNA degradation under ambient conditions and various levels of humidity and irradiation suggests that water contributes to the formation of reactive oxygen species, which ultimately leads to DNA degradation [98]. Additionally, under humid conditions, these specimens are prone to fungal and bacterial contamination [99]. Validation studies would be necessary to evaluate the impact of microbial contamination on the outcome of the target pathogen assay.
4.3 Light exposure
Apart from the variability in temperature that comes from exposing patient samples to light for prolonged periods of time, UV irradiation is also a concern [100]. Samples that are exposed for long periods of time to any source of light emitting UV irradiation can lead to oxidative degradation of nucleic acid polymers.
Sample stability is influenced by many environmental variables en route to- and at the laboratory. While there are recent reviews [27], [96], [101] and studies [41] on the effects of some of these variables on specific sample types and pathogen targets, it is still important to perform in-house validations that address the common scenarios experienced by the individual testing center. If limited resources prevent these types of extensive validations, laboratories should err on the side of caution and adopt the more conservative evidence-based stability criteria.
5 Specimen processing
In the chain of preanalytical events, specimen processing is the final stop. Variables affecting specimen processing include physical characteristics of the samples, procedures like sample mixing and vortexing, specific pretreatment or pooling methods, and technology for handling pipetting.
While most blood-derived samples, or samples requiring viral transport media that are used for molecular testing of infectious diseases, present with few complications for sample processing, other types such as sputum, saliva, and swabs can yield highly viscous samples that can complicate sample processing. Viscous samples are particularly cumbersome for laboratories which have integrated automated liquid handlers (ALH) [102], [103]. These samples can cause pipetting errors and lead to contamination. Often, laboratories implement pretreatment techniques to either liquify viscous mucous with additives such as acetylcysteine or Sputasol (ThermoFisher), or by physical homogenization with inert beads and sterile media such as PBS [102], [104].
Although the occasional viscous sample may cause issues for an ALH, there are many benefits to implementing automated pipetting over manual processing. Automation improves throughput, reproducibility, and maximizes laboratory technologist’s time for data analysis and prevents repetitive stress injuries such as carpal tunnel and tendonitis [105], [106]. In addition, ALHs can decrease the risk of sample contamination as well as environmental contamination of the laboratory with potentially infectious material [105]. No automation is perfect, and technologists would need to learn how to operate, program, and troubleshoot ALH instruments to resolve the inevitable errors that would occur.
Sample preparation for molecular testing requires many processes in which the original sample is mixed with additional reagents for stability, extraction, and amplification. Along the way, it is common practice to thoroughly mix each solution by vortexing for specified amounts of time. While the majority of manufacturers and studies do not specifically characterize the effect of vortexing conditions on the outcome of molecular assays, there has been one recent study that specifically examined the effect of not vortexing nasopharyngeal and throat swabs for the detection on SARS-CoV-2 [107]. The study found no significant difference on the qualitative sensitivity of SARS-CoV-2 rRT-PCR tests when samples were not vortexed prior to analysis [107]. However, vortexing did improve recovery of cellular material, which may be important for other types diagnostic testing [107].
During the COVID-19 pandemic, a lack of supplies led to the decision of many laboratories to pool samples for processing to conserve precious reagents. Although sample pooling is common for blood banks, this was an adaptive solution to meet the exponential demands for testing with unreliable access to resources. There are a number of downsides to sample pooling for a prevalent infectious disease: 1) the possibility of false negative results due to diluted samples [108]; and, 2) increased TAT when individual samples from a pool must be re-run to confirm presence of infection.
Additional pretreatment concerns that can affect sample processing and impact downstream molecular testing are those that deal with higher risk pathogens, such as biosafety level (BSL) 3 and 4 pathogens. Laboratories that handle these pathogens, especially BSL-4, have very strict engineering controls such as separate or controlled ventilation, separate work areas for sample transfer, extraction and analysis, authorized access only, and special personal protective equipment such as full body and air supplied suits [109]. Attention must be given to containment and thorough disinfection techniques during sample processing since it is possible that pathogens will not be inactivated prior to molecular testing [110], [111].
Ultimately, the measurement of a laboratory’s efficiency is TAT, which relies on efficient processing of samples. There are many considerations for improving sample processing for molecular testing, such as which sample type may require less manual pretreatment, whether or not to vortex or use ALHs, how to process samples when diseases are prevalent or the pathogen in question is BSL-3 or 4. Laboratories strive to improve TAT so that clinicians have fast access to results that are needed to make timely patient care related decisions.
6 Clinical implications of preanalytical and environmental factors in molecular infectious disease testing
The implications of false reporting due to missteps in the preanalytical phase of processing patient samples for molecular testing are potentially life changing for the patient. Errors that lead to reporting falsely negative results endanger patients by either delaying or denying appropriate and often life-saving treatment for the infection. For example, delaying antibiotic treatment in adult and pediatric patients with sepsis can lead to death [112], [113].
On the other hand, errors that lead to the false reporting of positive results place the patient at risk of receiving inappropriate and unnecessary treatment for an infection that the patient does not have. The potential side-effects and long-term effects of receiving unnecessary treatments can also be life-threatening in the case of developing resistance to antibiotics. In addition, depending on the disease, the laboratory may be required to participate in infectious disease reporting toa state health agency. Preanalytical errors that lead to false test results can have a significant impact on the accuracy of this reporting, and down-stream public health initiatives and decisions that may be based on this data.
As highlighted throughout this review, it is the laboratory that is ultimately responsible for defining and enforcing preanalytical work-flow and acceptability criteria to ensure quality testing. Given the challenges and incredible resources required to complete this task properly, perhaps, in the future, progress will be made toward the standardization of molecular diagnostic assays to 1) reduce preanalytical errors resulting from non-standardized in-house validations and 2) improve the diagnostic accuracy on a national and even global scale.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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References:
1 Messacar K. Parker S.K. Todd J.K. Dominguez S.R. Implementation of rapid molecular infectious disease diagnostics: The role of diagnostic and antimicrobial stewardship J Clin Microbiol. 55 2017 715 723 10.1128/JCM.02264-16 28031432
2 Farkas D.H. Kaul K.L. Wiedbrauk D.L. Kiechle F.L. Specimen collection and storage for diagnostic molecular pathology investigation Arch Pathol Lab Med. 120 1996 591 596 8651865
3 Hardt M. Föderl-Höbenreich E. Freydl S. Kouros A. Loibner M. Zatloukal K. Pre-analytical sample stabilization by different sampling devices for PCR-based COVID-19 diagnostics N Biotechnol. 70 2022 19 27 10.1016/j.nbt.2022.04.001 35398581
4 Engelmann I. Alidjinou E.K. Ogiez J. Pagneux Q. Miloudi S. Benhalima I. Ouafi M. Sane F. Hober D. Roussel A. Cambillau C. Devos D. Boukherroub R. Szunerits S. Preanalytical Issues and Cycle Threshold Values in SARS-CoV-2 Real-Time RT-PCR Testing: Should Test Results Include These? ACS Omega. 6 2021 6528 6536 10.1021/acsomega.1c00166 33748564
5 Pellegrinelli L. Castiglioni S. Cocuzza C.E. Bertasi B. Primache V. Schiarea S. Salmoiraghi G. Franzetti A. Musumeci R. Tilola M. Galuppini E. Bertanza G. Callegari M. Stefani F. Turolla A. Ammoni E. Cereda D. Pariani E. Binda S. Evaluation of Pre-Analytical and Analytical Methods for Detecting SARS-CoV-2 in Municipal Wastewater Samples in Northern Italy Water (Switzerland). 14 2022 10.3390/w14050833
6 Fenta D.A. Ali M.M. Factors Affecting Quality of Laboratory Result During Ordering Handling, and Testing of the Patient’s Specimen at Hawassa University College of Medicine and Health Science Comprehensive Specialized Hospital, J Multidiscip Healthc. 13 2020 809 821 10.2147/JMDH.S264671
7 Vidyarthi A.R. Hamill T. Green A.L. Rosenbluth G. Baron R.B. Changing Resident Test Ordering Behavior: A Multilevel Intervention to Decrease Laboratory Utilization at an Academic Medical Center American Journal of Medical Quality. 30 2015 81 87 10.1177/1062860613517502 24443317
8 Delvaux N. Piessens V. de Burghgraeve T. Mamouris P. Vaes B. R. vander Stichele, H. Cloetens, J. Thomas, D. Ramaekers, A. de Sutter, B. Aertgeerts, Clinical decision support improves the appropriateness of laboratory test ordering in primary care without increasing diagnostic error: the ELMO cluster randomized trial Implementation Science. 15 2020 10.1186/s13012-020-01059-y
9 van Cott E.M. Laboratory test interpretations and algorithms in utilization management Clinica Chimica Acta. 427 2014 188 192 10.1016/j.cca.2013.09.025
10 Zacharioudakis I.M. Zervou F.N. Diagnostic stewardship in infectious diseases: steps towards intentional diagnostic testing Future Microbiol. 17 2022 813 817 10.2217/fmb-2022-0070 35670110
11 Hernandez D.R. Pellitteri G.P. Kimmich D.J. Heiter B.J. Smeal K. Wolk D.M. Near point-of-care adoption of Cepheid Xpert® Flu/RSV XC testing within an integrated healthcare delivery network Diagn Microbiol Infect Dis. 94 2019 28 29 10.1016/j.diagmicrobio.2018.11.013 30581009
12 Campbell-Yesufu O.T. Gandhi R.T. Update on human immunodeficiency virus (HIV)-2 infection Clinical Infectious Diseases. 52 2011 780 787 10.1093/cid/ciq248 21367732
13 Shahar E. Shapiro A. Baskin L. Oz Z.K. Antiretroviral therapy-induced negative HIV antibody test following diagnosis of HIV infection AIDS. 33 2019 1804 1805 10.1097/QAD.0000000000002261 31356259
14 McManus M. Mick E. Hudson R. Mofenson L.M. Sullivan J.L. Somasundaran M. Luzuriaga K. Pactg, 356 Investigators, Early Combination Antiretroviral Therapy Limits Exposure to HIV-1 Replication and Cell-Associated HIV-1 DNA Levels in Infants PLoS One. 11 2016 e0154391 27104621
15 Kassutto S. Johnston M.N. Rosenberg E.S. Incomplete HIV Type 1 Antibody Evolution and Seroreversion in Acutely Infected Individuals Treated with Early Antiretroviral Therapy Clinical Infectious Diseases. 40 2005 868 873 10.1086/428127 15736021
16 Porco T.C. Martin J.N. Page-Shafer K.A. Cheng A. Charlebois E. Grant R.M. Osmond D.H. Decline in HIV infectivity following the introduction of highly active antiretroviral therapy AIDS. 18 2004 81 88 10.1097/00002030-200401020-00010 15090833
17 Quiner C. Bruhn R. Grebe E. di Germanio C. Kessler D. Reik R. Williamson P. Hampton D. Fayed R. Anderson S.A. Williams A.E. Glynn S.A. Busch M.P. Stramer S.L. Custer B. Recently acquired infection among HIV-seropositive donors in the US from 2010–2018 Transfusion (Paris). 60 2020 2340 2347 10.1111/trf.16032
18 Seed C.R. Yang H. Lee J.F. Blood safety implications of donors using HIV pre-exposure prophylaxis Vox Sang. 112 2017 473 476 10.1111/vox.12516 28370177
19 de Souza M.S. Pinyakorn S. Akapirat S. Pattanachaiwit S. Fletcher J.L.K. Chomchey N. Kroon E.D. Ubolyam S. Michael N.L. Robb M.L. Phanuphak P. Kim J.H. Phanuphak N. Ananworanich J. Teeratakulpisarn N. Colby D. Sutthichom D. Rattanamanee S. Prueksakaew P. Eamyoung P. Puttamaswin S. Tipsuk S. Karnsomlap P. Trichavaroj R. Nuntapinit B. Panjapornsuk P. Tongchanakarn B. Kobchit W. Ngauy V. O’Connell R. Tantibul N. Savadsuk H. Assawadarachai V. Tovanabutra S. Initiation of antiretroviral therapy during acute HIV-1 infection leads to a high rate of nonreactive HIV serology Clinical Infectious Diseases. 63 2016 555 561 10.1093/cid/ciw365 27317797
20 S.L. Stanley, Amoebiasis, in: Lancet, Elsevier B.V., 2003: pp. 1025–1034. https://doi.org/10.1016/S0140-6736(03)12830-9.
21 Xin H. Li Y. Wu P. Li Z. Lau E.H.Y. Qin Y. Wang L. Cowling B.J. Tsang T.K. Li Z. Estimating the Latent Period of Coronavirus Disease 2019 (COVID-19) Clinical Infectious Diseases. 2021 10.1093/cid/ciab746
22 Sunkesula V.C.K. Kundrapu S. Muganda C. Sethi A.K. Donskey C.J. Does empirical Clostridium difficile infection (CDI) therapy result in false-negative CDI diagnostic test results? Clinical Infectious Diseases. 57 2013 494 500 10.1093/cid/cit286 23645849
23 I.G. Kiani, A.N. Khan, B. Butt, S. Sabir, S. Ejaz, A. Perveen, E. Ghani, HCV-RNA PCR POSITIVITY IN HCV ANTIBODY NEGATIVE PATIENTS UNDERGOING HAEMODIALYSIS, 2018. http://www.jamc.ayubmed.edu.pk.
24 Abraham J.E. Maranian M.J. Spiteri I. Russell R. Ingle S. Luccarini C. Earl H.M. Pharoah P.P. Dunning A.M. Caldas C. Saliva samples are a viable alternative to blood samples as a source of DNA for high throughput genotyping BMC Med Genomics. 5 2012 19 10.1186/1755-8794-5-19 22647440
25 Bulfoni M. Sozio E. Marcon B. de Martino M. Cesselli D. de Carlo C. Martinella R. Migotti A. Vania E. Zanus-Fortes A. de Piero J. Nencioni E. Tascini C. Isola M. Curcio F. Validation of a Saliva-Based Test for the Molecular Diagnosis of SARS-CoV-2 Infection Dis Markers. 2022 2022 10.1155/2022/6478434
26 F. Yu, L. Yan, N. Wang, S. Yang, L. Wang, Y. Tang, G. Gao, S. Wang, C. Ma, R. Xie, F. Wang, C. Tan, L. Zhu, Y. Guo, F. Zhang, B. Ditan Hospital, P. Fujie Zhang, Quantitative Detection and Viral Load Analysis of SARS-CoV-2 in Infected Patients, n.d.
27 Anvari M.S. Gharib A. Abolhasani M. Azari-Yam A. Gharalari F.H. Safavi M. Mirzaie A.Z. Vasei M. Pre-analytical practices in the molecular diagnostic tests, a concise review Iran J Pathol. 16 2020 1 19 10.30699/ijp.2020.124315.2357 33391375
28 Barra G.B. Santa Rita T.H. Vasques J. de A. Chianca C.F. Nery L.F.A. Costa S.S.S. EDTA-mediated inhibition of DNases protects circulating cell-free DNA from ex vivo degradation in blood samples Clin Biochem. 48 2015 976 981 10.1016/j.clinbiochem.2015.02.014 25746148
29 E. Sheppard, H.M. Roberta Madej, M. Maria Paula Alfaro, F. Neil Anderson, D.E. Karen Bijwaard, M.C. Sharon Chen, F. Yu-Wei Cheng, F.C. Carolyn Compton, C. Dollins, R. Amy Shirley Gargis, S. Ghosh, P. Hemarajata, C.L. Jackson, A. Leon, C. Dawn Maghakian, C.E. Ted Schutzbank, D. Shadi Sepehri, T. Sundin, H.N. Todd, M.C. Nadeem A Tusneem, M. Kathy Vernovsky, M.C. Gang Zheng, MM13-Ed2 August 2020 Replaces MM13-A Collection, Transport, Preparation, and Storage of Specimens for Molecular Methods, 2020. www.clsi.org.P:+1.610.688.0100F:+1.610.688.0700E:[email protected]:www.clsi.org.
30 Green M.R. Sambrook J. How to Win the Battle with RNase Cold Spring Harb Protoc. 2019 10.1101/pdb.top101857
31 R.A. Cox, [103a] The use of guanidinium chloride in the isolation of nucleic acids, in: 1968: pp. 120–129. https://doi.org/10.1016/0076-6879(67)12123-X.
32 Boom R. Sol C.J. Salimans M.M. Jansen C.L. Wertheim-van Dillen P.M. van der Noordaa J. Rapid and simple method for purification of nucleic acids J Clin Microbiol. 28 1990 495 503 10.1128/jcm.28.3.495-503.1990 1691208
33 Bender A.T. Sullivan B.P. Lillis L. Posner J.D. Enzymatic and Chemical-Based Methods to Inactivate Endogenous Blood Ribonucleases for Nucleic Acid Diagnostics The Journal of Molecular Diagnostics. 22 2020 1030 1040 10.1016/j.jmoldx.2020.04.211 32450280
34 Kessler H.H. Stelzl E. Raggam R.B. Haas J. Kirchmeir F. Hegenbarth K. Daghofer E. Santner B.I. Marth E. Stauber R.E. Effects of Storage and Type of Blood Collection Tubes on Hepatitis C Virus Level in Whole Blood Samples J Clin Microbiol. 39 2001 1788 1790 10.1128/JCM.39.5.1788-1790.2001 11325991
35 Tsui N.B.Y. Ng E.K.O. Lo Y.M.D. Stability of endogenous and added RNA in blood specimens, serum, and plasma Clin Chem. 48 2002 1647 1653 12324479
36 M. Yokota, N. Tatsumi, O. Nathalang, T. Yamada, I. Tsuda, Effects of Heparin on Polymerase Chain Reaction for Blood White Cells, 1999.
37 M. Neumaier, A. Braun, C. Wagener, Fundamentals of quality assessment of molecular amplification methods in clinical diagnostics, n.d. https://academic.oup.com/clinchem/article/44/1/12/5642479.
38 P.N. Bogner, A.A. Killeen, Molecular Diagnostics: For the Clinical Laboratorian, 2nd ed., Humana Press, n.d. https://doi.org/https://doi.org/10.1385.159599281.
39 J.-T. Wang, T.-H. Wang, J.-C. Sheu, S.-M. Lin, J.-T. Lin, A. Ding-Shinn, Effects of Anticoagulants and Storage of Blood Samples on Efficacy of the Polymerase Chain Reaction Assay for Hepatitis C Virus, 1992. https://journals.asm.org/journal/jcm.
40 Beutler E. Gelbart T. Kuhl W. Interference of heparin with the polymerase chain reaction Biotechniques. 9 1990 166 2400599
41 Huang L.H. Lin P.H. Tsai K.W. Wang L.J. Huang Y.H. Kuo H.C. Li S.C. The effects of storage temperature and duration of blood samples on DNA and RNA qualities PLoS One. 12 2017 10.1371/journal.pone.0184692
42 G.A. Storch, M. Gaudreault-Keener,’ And, P.C. Welby’, Comparison of Heparin and EDTA Transport Tubes for Detection of Cytomegalovirus in Leukocytes by Shell Vial Assay, pp65 Antigenemia Assay, and PCR, 1994. https://journals.asm.org/journal/jcm.
43 F.B. Johnson, Transport of Viral Specimens, 1990. https://journals.asm.org/journal/cmr.
44 Kirkland P.D. Frost M.J. The impact of viral transport media on PCR assay results for the detection of nucleic acid from SARS-CoV-2 Pathology. 52 2020 811 814 10.1016/j.pathol.2020.09.013 33250079
45 Petersen J.M. Dalal S. Jhala D. In-House Viral Transport Medium (VTM) Manufacture in the Time of Shortage, Supply and Crisis of COVID-19 at Veteran Affairs Medical Center (VAMC) S162 Am J Clin Pathol. 154 2020 S161 10.1093/ajcp/aqaa161.352
46 Centers for Disease Control and Prevention, SOP#: DSR-052-05. PREPARATION OF VIRAL TRANSPORT MEDIUM, n.d. https://www.cdc.gov/coronavirus/2019-ncov/downloads/Viral-Transport-Medium.pdf (accessed September 16, 2022).
47 Daum L.T. Worthy S.A. Yim K.C. Nogueras M. Schuman R.F. Choi Y.W. Fischer G.W. A clinical specimen collection and transport medium for molecular diagnostic and genomic applications Epidemiol Infect. 139 2011 1764 1773 10.1017/S0950268810002384 21205332
48 Druce J. Garcia K. Tran T. Papadakis G. Birch C. Evaluation of swabs, transport media, and specimen transport conditions for optimal detection of viruses by PCR J Clin Microbiol. 50 2012 1064 1065 10.1128/JCM.06551-11 22205810
49 COVID-19, Australia: Epidemiology Report 9 (Reporting week to 23:59 AEDT 29 March 2020), Commun Dis Intell (2018). 44 (2020). https://doi.org/10.33321/cdi.2020.44.29.
50 Vermeiren C. Marchand-Senécal X. Sheldrake E. Bulir D. Smieja M. Chong S. Forbes J.D. Katz K. Comparison of Copan ESwab and FLOQSwab for COVID-19 Diagnosis: Working around a Supply Shortage J Clin Microbiol. 58 2020 10.1128/JCM.00669-20
51 Ponce-Rojas J.C. Costello M.S. Proctor D.A. Kosik K.S. Wilson M.Z. Arias C. Acosta-Alvear D. A Fast and Accessible Method for the Isolation of RNA, DNA, and Protein To Facilitate the Detection of SARS-CoV-2 J Clin Microbiol. 59 2021 10.1128/JCM.02403-20
52 Administrator, WHO Manual on Animal Influenza Diagnosis and Surveillance World Health Organization Department of Communicable Disease Surveillance and Response, n.d.
53 Huggett J.F. Benes V. Bustin S.A. Garson J.A. Harris K. Kammel M. Kubista M. McHugh T.D. Moran-Gilad J. Nolan T. Pfaffl M.W. Salit M. Shipley G. Vallone P.M. Vandesompele J. Wittwer C. Zeichhardt H. Cautionary Note on Contamination of Reagents Used for Molecular Detection of SARS-CoV-2 Clin Chem. 66 2020 1369 1372 10.1093/clinchem/hvaa214 32894756
54 Mögling R. Meijer A. Berginc N. Bruisten S. Charrel R. Coutard B. Eckerle I. Enouf V. Hungnes O. lay Korukluoglu G. Kossyvakis T. Mentis A. Molenkamp R. Muradrasoli S. Papa A. Pigny F. Thirion L. van der Werf S. Reusken C. Delayed laboratory response to COVID-19 caused by molecular diagnostic contamination Emerg Infect Dis. 26 2020 1944 1946 10.3201/eid2608.201843 32433015
55 Wernike K. Keller M. Conraths F.J. Mettenleiter T.C. Groschup M.H. Beer M. Pitfalls in SARS-CoV-2 PCR diagnostics Transbound Emerg Dis. 68 2021 253 257 10.1111/tbed.13684 32536002
56 Cohen J. The United States badly bungled coronavirus testing—but things may soon improve Science 1979 2020 10.1126/science.abb5152
57 Amirian E.S. Prioritizing COVID-19 test utilization during supply shortages in the late phase pandemic J Public Health Policy. 43 2022 320 324 10.1057/s41271-022-00348-8 35414693
58 Phe, UK Standards for Microbiology Investigations-standards-for-microbiology-investigations-smi-quality-and-consistency-in-clinical-laboratories PHE publications gateway number: 2017274 UK Standards for Microbiology Investigations are produced in association with, 2018.
59 Aysal A. Pehlivanoğlu B. Ekmekci S. Gündoğdu B. How to set up a molecular pathology lab: A guide for pathologists Turk Patoloji Derg. 36 2020 179 187 10.5146/tjpath.2020.01488 32525209
60 Beal S.G. Assarzadegan N. Rand K.H. Sample-to-result molecular infectious disease assays: Clinical implications, limitations and potential Expert Rev Mol Diagn. 16 2016 323 341 10.1586/14737159.2016.1134325 26689497
61 Centers for Disease Control and Prevention, Chemical Disinfectants Guideline for Disinfection and Sterilization in Healthcare Facilities (2008), Https://Www.Cdc.Gov/Infectioncontrol/Guidelines/Disinfection/Disinfection-Methods/Chemical.Html. (n.d.).
62 Prince A.M. Andrus L. PCR: how to kill unwanted DNA Biotechniques. 12 1992 358 360 1571142
63 Frothingham R. Blitchington R.B. Lee D.H. Greene R.C. Wilson K.H. UV absorption complicates PCR decontamination Biotechniques. 13 1992 208 210 1389153
64 C.Y. Ou, J.L. Moore, G. Schochetman, Use of UV irradiation to reduce false positivity in polymerase chain reaction., Biotechniques. 10 (1991) 442, 444, 446.
65 College of American Pathologists Commission on Laboratory Accreditation Microbiology Checklist Https://Webapps.Cap.Org/Apps/Docs/Laboratory_accreditation/Checklists/Microbiology_sep07.Pdf 2007
66 Schrader C. Schielke A. Ellerbroek L. Johne R. PCR inhibitors - occurrence, properties and removal J Appl Microbiol. 113 2012 1014 1026 10.1111/j.1365-2672.2012.05384.x 22747964
67 Yedidag E.N. Koffron A.J. Mueller K.H. Kaplan B. Kaufman D.B. Fryer J.P. Stuart F.P. Abecassis M. Acyclovir triphosphate inhibits the diagnostic polymerase chain reaction for cytomegalovirus Transplantation. 62 1996 238 242 10.1097/00007890-199607270-00015 8755822
68 Sidstedt M. Hedman J. Romsos E.L. Waitara L. Wadsö L. Steffen C.R. Vallone P.M. Rådström P. Inhibition mechanisms of hemoglobin, immunoglobulin G, and whole blood in digital and real-time PCR Anal Bioanal Chem. 410 2018 2569 2583 10.1007/s00216-018-0931-z 29504082
69 Kermekchiev M.B. Kirilova L.I. Vail E.E. Barnes W.M. Mutants of Taq DNA polymerase resistant to PCR inhibitors allow DNA amplification from whole blood and crude soil samples Nucleic Acids Res. 37 2009 10.1093/nar/gkn1055
70 Al-Soud W.A. Rådström P. Purification and characterization of PCR-inhibitory components in blood cells J Clin Microbiol. 39 2001 485 493 10.1128/JCM.39.2.485-493.2001 11158094
71 J.J. Byrnes, M. Downey, L. Esserman, A.G. So, BYRNES ET L Mechanism of Hemin Inhibition of Erythroid Cytoplasmic DNA Polymerase1, n.d. https://pubs.acs.org/sharingguidelines.
72 Fleet J.C. A new role for lactoferrin: DNA binding and transcription activation Nutr Rev. 53 1995 226 227 10.1111/j.1753-4887.1995.tb01556.x 7501307
73 H. Roelofsen, D. Esser, G. Alvarez-Llamas, M.P. de Vries, D. Weening, R.J. Vonk, Sample Stability and Protein Composition of Saliva: Implications for Its Use as a Diagnostic Fluid, 2008.
74 İşlek A. Balcı M.K. Analysis of Factors Causing False-Negative Real-Time Polymerase Chain Reaction Results in Oropharyngeal and Nasopharyngeal Swabs of Patients With COVID-19 Ear Nose Throat J. 2021 10.1177/0145561321996621
75 Beall S.G. Cantera J. Diaz M.H. Winchell J.M. Lillis L. White H. Kalnoky M. Gallarda J. Boyle D.S. Performance and workflow assessment of six nucleic acid extraction technologies for use in resource limited settings PLoS One. 14 2019 10.1371/journal.pone.0215753
76 Walker F.M. Hsieh K. Advances in directly amplifying nucleic acids from complex samples Biosensors (Basel). 9 2019 10.3390/bios9040117
77 Food and Drug Administration, P23 AT-HOME COVID-19 TEST COLLECTION KIT SALIVA – HCP SUPERVISED INSTRUCTIONS FOR PATIENTS, Https://Www.Fda.Gov/Media/138294/Download. (n.d.).
78 Suther C. Moore M.D. Quantification and discovery of PCR inhibitors found in food matrices commonly associated with foodborne viruses Food Science and Human Wellness. 8 2019 351 355 10.1016/j.fshw.2019.09.002
79 Hernandez M.M. Riollano-Cruz M. Boyle M.C. Banu R. Shrestha P. Gray B. Cao L. Chen F. Shi H. Paniz-Perez D.E. Paniz-Perez P.A. Rishi A.L. Dubinsky J. Dubinsky D. Dubinsky O. Baine S. Baine L. Arinsburg S. Baine I. Ramirez J.D. Cordon-Cardo C. Sordillo E.M. Paniz-Mondolfi A.E. Food for thought: Eating before saliva collection and interference with SARS-CoV-2 detection J Med Virol. 2022 10.1002/jmv.27660
80 Buckingham L. Molecular Diagnostics: Fundamentals, Methods and Clinical Applications 2nd ed. 2012 F.A. Davis Co. Philidelphia
81 Akane A. Matsubara K. Nakamura H. Takahashi S. Kimura K. Identification of the heme compound copurified with deoxyribonucleic acid (DNA) from bloodstains, a major inhibitor of polymerase chain reaction (PCR) amplification J Forensic Sci. 39 1994 362 372 8195750
82 S. Gustafson, J.A. Proper, E.J.W. Bowie, ∼ And, S.S. Sommer, Parameters Affecting the Yield of DNA from Human Blood’, 1987.
83 M. Krajden, J.M. Minor, O. Rifkin, L. Comanor, Effect of Multiple Freeze-Thaw Cycles on Hepatitis B Virus DNA and Hepatitis C Virus RNA Quantification as Measured with Branched-DNA Technology, 1999.
84 Halfon P. Khiri H. Gerolami V. Buorliere M. Feryn J. Reyner P. Gauthier A. Cartouzou G. Impact of various handling and storage conditions on quantitative detection of hepatitis C virus RNA J Hepatol. 307–311 1996
85 Comert F. Aktas E. Terzi H.A. Kulah C. Ustundag Y. Kokturk F. Aydemir S. Evaluation of hepatitis C virus RNA stability in room temperature and multiple freeze-thaw cycles by COBAS AmpliPrep/COBAS TaqMan HCV Diagn Microbiol Infect Dis. 75 2013 81 85 10.1016/j.diagmicrobio.2012.09.017 23102559
86 Sanlidag T. Akcali S. Ozbakkaloglu B. Serum hepatitis B DNA: Stability in relation to multiple freeze-thaw procedures J Virol Methods. 123 2005 49 52 10.1016/j.jviromet.2004.09.006 15582698
87 Skalina K.A. Goldstein D.Y. Sulail J. Hahm E. Narlieva M. Szymczak W. Fox A.S. Extended storage of SARS-CoV-2 nasopharyngeal swabs does not negatively impact results of molecular-based testing across three clinical platforms J Clin Pathol. 75 2022 61 64 10.1136/jclinpath-2020-206738 33144357
88 Yilmaz Gulec E. Cesur N.P. Yesilyurt Fazlioğlu G. Kazezoğlu C. Effect of different storage conditions on COVID-19 RT-PCR results J Med Virol. 93 2021 6575 6581 10.1002/jmv.27204 34260086
89 Agaoglu N.B. Yildiz J. Akgun Dogan O. Kose B. Alkurt G. Kendir Demirkol Y. Irvem A. Doganay L. Dinler Doganay G. COVID-19 PCR test performance on samples stored at ambient temperature J Virol Methods. 301 2022 114404 10.1016/j.jviromet.2021.114404
90 Hasan M.R. Tan R. Al-Rawahi G.N. Thomas E. Tilley P. Short-term stability of pathogen-specific nucleic acid targets in clinical samples J Clin Microbiol. 50 2012 4147 4150 10.1128/JCM.02659-12 23052319
91 Ott I.M. Strine M.S. Watkins A.E. Boot M. Kalinich C.C. Harden C.A. Vogels C.B.F. Casanovas-Massana A. Moore A.J. Muenker M.C. Nakahata M. Tokuyama M. Nelson A. Fournier J. Bermejo S. Campbell M. Datta R. C.S. dela Cruz, S.F. Farhadian, A.I. Ko, A. Iwasaki, N.D. Grubaugh, C.B. Wilen, A.L. Wyllie, Simply saliva: stability of SARS-CoV-2 detection negates the need for expensive collection devices MedRxiv. 2020 10.1101/2020.08.03.20165233
92 Munch M.M. Chambers L.C. Manhart L.E. Domogala D. Lopez A. Fredricks D.N. Srinivasan S. Optimizing bacterial DNA extraction in urine PLoS One. 14 2019 10.1371/journal.pone.0222962
93 Black C.M. Current Methods of Laboratory Diagnosis of Chlamydia trachomatis Infections 1997
94 Greenman J. Roberts T. Cohn J. Messac L. Dried blood spot in the genotyping, quantification and storage of HCV RNA: A systematic literature review J Viral Hepat. 22 2015 353 361 10.1111/jvh.12345 25367722
95 Asari M. Matsuura H. Isozaki S. Hoshina C. Okuda K. Tanaka H. Horioka K. Shiono H. Shimizu K. Assessment of DNA degradation of buccal cells under humid conditions and DNA repair by DOP-PCR using locked nucleic acids Leg Med (Tokyo). 35 2018 29 33 10.1016/j.legalmed.2018.09.005 30248610
96 Lim M.D. Review article: Dried blood spots for global health diagnostics and surveillance: Opportunities and challenges American Journal of Tropical Medicine and Hygiene. 99 2018 256 265 10.4269/ajtmh.17-0889 29968557
97 A. Färnert, A.P. Arez, A.T. Correia, A. Björkman, G. Snounou, V. do Rosário, Sampling and storage of blood and the detection of malaria parasites by polymerase chain reaction., Trans R Soc Trop Med Hyg. 93 (n.d.) 50–3. https://doi.org/10.1016/s0035-9203(99)90177-3.
98 Alizadeh E. Sanche L. Role of humidity and oxygen level on damage to DNA induced by soft X-rays and low-energy electrons Journal of Physical Chemistry C. 117 2013 22445 22453 10.1021/jp403350j
99 Zainabadi K. Adams M. Han Z.Y. Lwin H.W. Han K.T. Ouattara A. Thura S. C. v. Plowe, M.M. Nyunt, A novel method for extracting nucleic acids from dried blood spots for ultrasensitive detection of low-density Plasmodium falciparum and Plasmodium vivax infections Malar J. 16 2017 10.1186/s12936-017-2025-3
100 Raeiszadeh M. Adeli B. A Critical Review on Ultraviolet Disinfection Systems against COVID-19 Outbreak: Applicability, Validation, and Safety Considerations ACS Photonics. 7 2020 2941 2951 10.1021/acsphotonics.0c01245
101 Valinetz E.D. Cangelosi G.A. A Look Inside: Oral Sampling for Detection of Non-oral Infectious Diseases 2021 10.1128/JCM.02360
102 Matic N. Lawson T. Ritchie G. Stefanovic A. Leung V. Champagne S. Romney M.G. Lowe C.F. Automated molecular testing of saliva for SARS-CoV-2 detection Diagn Microbiol Infect Dis. 100 2021 115324 10.1016/j.diagmicrobio.2021.115324
103 Vaz S.N. de Santana D.S. Netto E.M. Wang W.-K. Brites C. Validation of the GeneXpert Xpress SARS-CoV-2 PCR assay using saliva as biological specimen The Brazilian Journal of Infectious Diseases. 25 2021 101543 10.1016/j.bjid.2021.101543
104 Ali N. Bello G.L. Rossetti M.L.R. Krieger M.A. Costa A.D.T. Demonstration of a fast and easy sample-to-answer protocol for tuberculosis screening in point-of-care settings: A proof of concept study PLoS One. 15 2020 e0242408 33315885
105 Tegally H. San J.E. Giandhari J. de Oliveira T. Unlocking the efficiency of genomics laboratories with robotic liquid-handling BMC Genomics. 21 2020 729 10.1186/s12864-020-07137-1 33081689
106 Wu J.Z. Sinsel E.W. Shroyer J.F. Welcome D.E. Zhao K.D. An K.-N. Buczek F.L. The musculoskeletal loading profile of the thumb during pipetting based on tendon displacement Med Eng Phys. 35 2013 1801 1810 10.1016/j.medengphy.2013.08.004 24018066
107 Kanji J.N. Pabbaraju K. Wong A. Beitku C. Deo A. Kailey S. Ma R. Tipples G. Zelyas N. Effect of not vortexing nasopharyngeal and throat swabs on SARS-CoV-2 nucleic acid detection: A pilot study J Virol Methods. 301 2022 114468 10.1016/j.jviromet.2022.114468
108 Yu J. Huang Y. Shen Z.-J. Optimizing and evaluating PCR-based pooled screening during COVID-19 pandemics Sci Rep. 11 2021 21460 10.1038/s41598-021-01065-0 34728759
109 US Department of Health and Human Services, Biosafety Level Requirements, Https://Www.Phe.Gov/S3/BioriskManagement/Biocontainment/Pages/BSL-Requirements.Aspx. (n.d.).
110 World Health Organization, Laboratory Diagnosis of Ebola Virus Disease, Https://Apps.Who.Int/Iris/Bitstream/Handle/10665/134009/WHO_EVD_GUIDANCE_LAB_14.1_eng.Pdf;Jsessionid=80878570C54A7B5D6E1B2BE252F064E6?Sequence=1. (n.d.).
111 Flint M. Goodman C.H. Bearden S. Blau D.M. Amman B.R. Basile A.J. Belser J.A. Bergeron É. Bowen M.D. Brault A.C. Campbell S. Chakrabarti A.K. Dodd K.A. Erickson B.R. Freeman M.M. Gibbons A. Guerrero L.W. Klena J.D. Lash R.R. Lo M.K. McMullan L.K. Momoh G. Massally J.L. Goba A. Paddock C.D. Priestley R.A. Pyle M. Rayfield M. Russell B.J. Salzer J.S. Sanchez A.J. Schuh A.J. Sealy T.K. Steinau M. Stoddard R.A. Taboy C. Turnsek M. Wang D. Zemtsova G.E. Zivcec M. Spiropoulou C.F. Ströher U. Towner J.S. Nichol S.T. Bird B.H. Ebola Virus Diagnostics: The US Centers for Disease Control and Prevention Laboratory in Sierra Leone, to March 2015 S358 Journal of Infectious Diseases. 212 2015 August 2014 S350 10.1093/infdis/jiv361
112 Weiss S.L. Fitzgerald J.C. Balamuth F. Alpern E.R. Lavelle J. Chilutti M. Grundmeier R. Nadkarni V.M. Thomas N.J. Delayed Antimicrobial Therapy Increases Mortality and Organ Dysfunction Duration in Pediatric Sepsis* Crit Care Med. 42 2014 2409 2417 10.1097/CCM.0000000000000509 25148597
113 Seymour C.W. Kahn J.M. Martin-Gill C. Callaway C.W. Yealy D.M. Scales D. Angus D.C. Delays From First Medical Contact to Antibiotic Administration for Sepsis Crit Care Med. 45 2017 759 765 10.1097/CCM.0000000000002264 28234754
114 Okeke I.N. Feasey N. Parkhill J. Turner P. Limmathurotsakul D. Georgiou P. Holmes A. Peacock S.J. Leapfrogging laboratories: The promise and pitfalls of high-tech solutions for antimicrobial resistance surveillance in low-income settings BMJ Glob Health. 5 2020 10.1136/bmjgh-2020-003622
115 Abdollahi A. Saffar H. Saffar H. Types and Frequency of Errors during Different Phases of Testing At a Clinical Medical Laboratory of a Teaching Hospital in Tehran, Iran N Am J Med Sci. 6 2014 224 228 10.4103/1947-2714.132941 24926448
116 Gessoni G. Barin P. Valverde S. Giacomini A. di Natale C. Orlandini E. Arreghini N. de Fusco G. Frigato A. Fezzi M. Antico F. Marchiori G. Biological qualification of blood units: Considerations about the effects of sample’s handling and storage on stability of nucleic acids, in Transfusion and Apheresis Science 2004 197 203 10.1016/j.transci.2003.11.010 15172624
117 Malecki M. Saetre B. HIV Apheresis Tags (HIVAT) Aided Elimination of Viremia Mol Cell Ther. 6 2018 10.26781/2052-8426-2018-06
118 Gibellini D. de Crignis E. Re M. Diagnosis of Sexually Transmitted Diseases 2012 Humana/Springer New York
119 Kim T.H. Kubica G.P. Long-term preservation and storage of mycobacteria Appl Microbiol. 24 1972 311 317 10.1128/am.24.3.311-317.1972 4627962
| 36495954 | PMC9729171 | NO-CC CODE | 2022-12-14 23:25:01 | no | Clin Biochem. 2022 Dec 8; doi: 10.1016/j.clinbiochem.2022.12.003 | utf-8 | Clin Biochem | 2,022 | 10.1016/j.clinbiochem.2022.12.003 | oa_other |
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Heliyon
Heliyon
Heliyon
2405-8440
The Author(s). Published by Elsevier Ltd.
S2405-8440(22)03476-4
10.1016/j.heliyon.2022.e12188
e12188
Research Article
Corporate donation behavior during the covid-19 pandemic. A case-study approach in the multinational inditex
Galan-Ladero M. Mercedes
Sánchez-Hernández M. Isabel ∗
School of Economics and Business Sciences, Department of Business Management and Sociology, Universidad de Extremadura, Ave. Elvas s/n, 06006 Badajoz, Spain
∗ Corresponding author.
8 12 2022
12 2022
8 12 2022
8 12 e12188e12188
29 8 2022
2 11 2022
30 11 2022
© 2022 The Author(s)
2022
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The situation created by the COVID-19 pandemic, especially the confinement in many countries, has led to a global crisis, not only in health but also in economy and social issues. But it has also provoked a wave of solidarity and unprecedented donation behavior by many companies worldwide. Inditex, one of the main fashion multinationals, has become a referent for its reaction speed and has been ranked number one among the most significant companies for its Corporate Social Responsibility during the lockdown. Drawing from Stakeholder, Legitimacy, and Ethics of Care Theories, the aim of this paper is to analyze Inditex as a case study and reflect on the impact of its donation behavior on its corporate reputation. A desk research approach by using secondary data about the corporation, and a content analysis of press releases with ATLASti software during this time, let conclude that effective corporate donation impacts and improves the reputation of the corporation among its stakeholders.
Corporate donation behavior; Corporate reputation; Corporate social responsibility (CSR); COVID-19 pandemic; Ethics of Care Theory; Inditex, Legitimacy Theory; Stakeholder Theory.
Keywords
Corporate donation behavior
Corporate reputation
Corporate social responsibility (CSR)
COVID-19 pandemic
Ethics of Care Theory
Inditex
Legitimacy Theory
Stakeholder Theory
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pmc1 Introduction
The situation created by the COVID-19 pandemic, especially during the lockdown in many countries, has led to a global crisis of not only health but also economic and social dimensions. The consequences of the measures taken to curb this pandemic have had a significant impact on the global economy (Priem, 2021) that is increasingly integrated (Ghorbel and Jeribi, 2021), interconnected and, at the same time, challenged. Following Morales et al. (2022), several challenges should be pointed out such as a severe global economic recession with serious implications for the global economic stability; an increase in unemployment and company shutdowns; a disruption in global supply chains; a decline in international trade, with a decrease in demand for imported products; a suspension of free mobility first (border closures), and a decrease in international tourism and business travel, later; higher volatility in financial markets (the financial sector required more flexibility in access to financial services, with dramatic increase in fraudulent operations. In addition, and according to Birtus and Lazaroiu (2021), it is remarkable the change in consumer behavior, related to purchases and storage of basic products. The reason was the availability of certain products, the reduced time spent in stores (supermarkets, in particular) and the need to buy unfamiliar brands and local products during the lockdown (Watson and Cug, 2021).
Thus, on the one hand, globalization has been considered the cause of the rapid spread and greater impact of the pandemic; but, on the other hand, it has also helped rapid information exchange between countries and a quick response to the pandemic (Zimmermann et al., 2020). Different developments from other countries than Spain have been recently published demonstrating that the pandemic situation has brought values around sustainability. For instance, Brydges et al. (2021) have studied the impact of COVID-19 on the Australian fashion industry, and Vătămănescu et al. (2021) paid the attention to consumersá demand for sustainable fashion products in Italy during the outbreak of COVID-19. The fact is that the pandemic has pushed the fashion industry to be more resilient than ever (DáAdamo et al., 2021) forcing companies to adapt their corporate social responsibility (CSR) policies to try to mitigate its consequences, resulting in a wave of solidarity and an unprecedented donation behavior by many companies worldwide (Albendea, 2020; Galan-Ladero and Rivera, 2021; Priem, 2021).
According to Kotler and Lee (2005), at the corporate level, donations are part of the traditional philanthropy and, in turn, part of CSR. It has been a long time since the concept of CSR is concerned with the generation of sustainable economic, environmental, and social value (McWilliams and Siegel, 2011) and it is considered a strong source of competitive advantages that improves its efficacy if it is efficiently communicated (Cuervo-Carabel et al., 2022). As a result, as defended by Priem (2021), engagement in CSR initiatives during the pandemic increased the return on the shares of the companies involved and attracted the stakeholders’ attention.
In the context described, Inditex, one of the main multinational clothing retail companies, has become an example for its reaction speed and has been ranked number one among the most significant companies for its CSR during the pandemic, and lockdown particularly (Davara, 2020; Merco, 2021). Given the impossibility of the Spanish Government to obtain personal protective equipment and medical supplies for hospitals and health care centers at the beginning of the pandemic – because international markets collapsed due to the over demand (Galan-Ladero and Rivera, 2021) – the company offered the Spanish authorities its entire transport and supply logistics system to bring sanitary and protection material to Spain (La Voz de Galicia, 2020; Inditex, 2021), besides reconverting their factories around the world to produce masks and protective gowns for hospitals. Despite the mandatory closure of its stores, caused by the state of alarm and the closure of all dispensable activity during the solitary confinement, this reputed multinational did not fire its employees or request any allowed Temporary Employment Regulation File (TERF) under Spanish labor law (called ERTE, in Spanish). Contrary, it continued to pay the salaries of its workers and maintain all jobs (Malagon, 2020). In addition, Inditex also guaranteed payment of all orders placed with suppliers, without changing the original conditions (Malagon, 2020; Inditex, 2021). In Spain, the company also made additional monetary and in-kind donations for other business markets affected by the pandemic (Inditex, 2021). Abroad, Inditex made donations to charities in the USA, Italy, Mexico, and the UK (Inditex, 2021). It also reformulated its collaboration programs with different Nonprofit Organizations (NPOs) worldwide. And, although for the first time in its history, Inditex made a loss in one quarter (coinciding with the months of lockdown) (Ugalde, 2020), but by the end of 2020 it had recovered and returned to profit (Inditex, 2021).
Consequently, the business model of the Inditex Group has been considered as a reference for the fast fashion ecosystem (and even for other industries) in the time of COVID-19 pandemic. For instance, several authors, such as Bilińska-Reformat and Dewalska-Opitek (2021), have paid attention to how Inditex has adapted and changed its business strategy and how retailers have followed its example identifying multichannel and omnichannel e-commerce solutions (Fortuna et al., 2021; Ng et al., 2021). Other authors have studied different aspects of the always questioned sustainability of Inditex (Barbeito-Caamaño and Chalmeta, 2020), such as the successful integrated supply chain (Aftab et al., 2018), the disclosure of working conditions in the global supply (Antonini et al., 2020), or the commitment of the company to circular economy (Esbeih et al., 2021).
Although Inditex has a holistic strategy for CSR and sustainability, this study is mainly focused on a specific aspect of this strategy: its donation pattern. Drawing from Legitimacy Theory, Stakeholder Theory, and Ethics of Care Theory, the motivation and main objective of this paper is to analyze Inditex as a case study and evaluate its behavior of corporate donations during the COVID-19 pandemic in order to understand a little studied part of how the firm, a benchmark for other companies, builds its corporate reputation.
To achieve this aim, this study is organized as follows: Section 2 provides the theoretical background related to corporate donations behavior and reputation, approached from different theories simultaneously; Section 3 introduces the context of the case study; Section 4 presents research methodology; Section 5 offers the results of the Inditex case-study; and finally, Section 6 outlines the main conclusions and contributions and implications (for the theory and practice) on this topic; additionally considering some limitations and further research.
2 Theoretical background
The environment for corporations is increasingly complex and dynamic, with greater demands from different stakeholders (employees, customers, shareholders, etc.) and greater interest in the social and environmental impacts of the corporation on society, higher expectations from civil society, greater scrutiny from the media and social networks, etc., which exert great pressure and influence the evaluation of organizations by stakeholders. In this regard, corporate reputation emerges as a powerful intangible for competitiveness (Flatt and Kowalczyk, 2008).
Three theories are taken as the basis for the theoretical grounding of this paper: (1) Stakeholder Theory, which focuses on the interconnected relationships between a firm and different parties or groups - its customers, suppliers, employees, investors, communities, and others who have a stake in the organization (who affect or are affected by, in the organization) (Alvarez and Sachs, 2021)-. The theory argues that a firm should create value for all stakeholders, not just shareholders (Busch et al., 2018); (2) Legitimacy Theory, which focuses on the firm's interactions with society. The theory states that organizations continuously try to ensure that they carry out activities in accordance with societal boundaries and norms (Deegan et al., 2002); and (3) Ethics of Care Theory, which focuses on the particularity of relations: at least, two parts (a one-caring and a cared-for), who commit each other's well-being. This theory uses a relational and context-bound approach toward morality and decision making (Burton, 2022).
In this section, the fundamentals of the relationship between corporate donations behavior, and reputation are presented. First, both constructs are conceptualized. Later, donation behavior from the stakeholder point of view and the legitimacy-based perspective, is approached. Finally, some new insights emerge from the ethics of care.
2.1 Corporate donations behavior and reputation
Within CSR, there is a field of research that investigate the effect of corporate donations on the reputation of the firm making the donation, as part of the social performance of the company (Dean, 2003; Brammer and Pavelin, 2006; Nan and Heo, 2007), although they are sometimes considered greenwashing, hypocrite, and mercenary backfire practices of firms (Cherry, 2013).
Corporate donation behavior has received the attention of specialized academic literature. Long time ago, Campbell et al. (1999) realized that corporations with a history of donations, instead of economic reasons, gave altruistic motives for their social behavior. Yaolung (2004) found that the most important motives for corporate donations were top management influences and external appeals. By contrast, donations did not result in importance for corporations that wanted to improve their image, to promote their offer, or increase their products (goods or services) sales. In addition, corporate donations did not reduce competitive pressure. Yaolung (2004) also highlighted reasons for not making donations. The lack of human capital and the lack of funds were the most important. Meanwhile, Morris et al. (2013) found that consumers distrust corporations that give based on purchases, and rather prefer those that give based on profits. Recently, Ananzeh et al. (2022) have highlighted as key a factor on corporate donations the quality of corporate governance, concretely board size, board gender diversity, and the establishment of a CSR committee. Also, a latter literature review about motivations for corporate donor behavior, carried out by Van Steenburg et al. (2022), have pointed out that consumer-oriented corporations and those with better reputation give more than other lesser-known corporations.
In recent decades, studies on corporate reputation have multiplied (Ferruz-González, 2020). Corporate reputation is a very vast and multidimensional concept (Cabral, 2016), an interdisciplinary (Dowling, 2016) and a multilevel (Kelley et al., 2019) construct, which has gained widespread recognition. In general, reputation refers to the opinion, regard, prestige, or esteem in which someone or something is held. In the business context, corporate reputation has been defined as “a belief or an impression that is held about an organization, and bears both psychological and social connotations” (Chowdhury, 2019: 485). It is reflected in the internal practices of the corporation, its culture, overall competitiveness (Resnick, 2004) and constitutes the opinion that the public has about the firm (Cabral, 2016).
A good reputation must be earned, and the implementation of CSR activities can help to achieve this (Papasolomou, 2005). In line with previous studies (Morris et al., 2013; Szőcs et al., 2016; Peterson, 2018), Kelley et al. (2019) conducted a literature review demonstrating that corporations with higher philanthropic expenditures had better reputation. It is remarkable that the social responsiveness of corporations has been conceptualized as corporate charitable donations with the social purpose of improving the quality of life of people in need. In the same line, recent empirical research by Peterson et al. (2021) confirms this relationship for companies with a favorable reputation. In sum, corporations are encouraged to engage in CSR activities aimed at improving the life quality and well-being of communities to build and enhance their positive reputation (Hasan and Yun, 2017). All these reviewed studies point to the Inditex corporation as a company likely to have good donor behavior. There are also specific theories that support it, as shown in the following sections.
2.2 The stakeholder approach and the legitimacy perspective
Stakeholder theory is a pluralistic approach that conceives the legitimacy of the company from the perspective of creating wealth for society as a whole and wellbeing for the various stakeholders (Friedman and Miles, 2006). From stakeholder theory's view, the relationship with stakeholders is the basis of CSR (Puerta, 2006) as CSR tries to improve the relationships with them (Galan-Ladero, 2012). Furthermore, CSR and Stakeholder Theory are two interrelated key concepts in business ethics (Freeman and Dmytriyev, 2017).
Additionally, in the business context, legitimacy is the stakeholdersá assumption that the actions of the corporation are proper within a specific culture. Legitimacy theory, applied to the organizational field, states that the companies, as part of their communities, care about carrying out activities in accordance with the culture (values, beliefs and norms) of the communities in which they operate (Deegan et al., 2002). According to Dewiyanti (2021), this theory emphasized the social contract between the corporation and the community. Following Suchman (1995), there are three main ways to gain legitimacy that a multinational corporation such as Inditex puts into practice. First, pragmatic legitimacy, which is based on the corporationás capacity to persuade its target audience, or key stakeholders. Second, moral legitimacy, related to normative approval. And third, cognitive legitimacy, based on what is taken for granted within a culture. This means that corporations try to respond to what society expects of them to gain legitimacy. Thus, CSR helps to create and strengthen this legitimacy. In other words, in order to be perceived as a legitimate entity to operate, the corporation gives back to society part of what it has obtained from it. The economic benefits it obtains must be combined with offering social and environmental benefits.
Nowadays, gaining community legitimacy is critical, not only to the long-term success of any organization, but even to its survival (Dewiyanti, 2021). Corporate reputation management can guide and help organizations to anticipate, prepare and respond to the new context (Chowdhury, 2019). However, it will depend on the organization's ability to identify and meet the expectations of its stakeholders (Hasan and Yun, 2017). In line with Ewan (2022), both stakeholder and legitimacy theories, have been considered as complementary frameworks, although there is an important difference between them: while stakeholder theory focuses on the different interest groups within society and how best to deal with them, legitimacy theory considers society as a whole. Given the diversity of stakeholders, it is relevant to point out the most relevant for corporations to adopt an effective multi-stakeholder perspective, especially in the global fast-fashion industry (Liu et al., 2020).
2.2.1 People in the organization
The first stakeholder for many authors is the group of employees, which are also called internal customer (Sánchez-Hernández et al., 2021). It has been largely recognized that the integration of CSR values into the corporate culture has an impact on human resource management (Arenas, 2006; Gimeno-Arias et al., 2021) because it improves the working environment, strengthens the commitment of the employees (Ruano and Rojas, 2006). As a result, corporations experience lower stress among their workers, less absenteeism and turnover, and higher motivation, productivity and performance (Loor-Zambrano et al., 2022), labor conflict reduces, especially in medium-large organizations (Santos et al., 2018), sense of belonging and corporate pride rises (Sánchez-Hernández and Grayson, 2012), and there is talent retention by attracting, capturing, and retaining the best, and most loyal employees (Lizcano, 2007).
2.2.2 Customers
There is no doubt that customers are very important as stakeholders, mostly for retailers’ companies. Consumers are particularly susceptible to the corporation's CSR initiatives (Bhattacharya and Sen, 2004). CSR fosters greater proximity and connectedness with consumers, generating a convenient context of trust, which promotes their attraction and loyalty (Arenas, 2006) and the positive attitude towards the company. Several studies demonstrate that there is a positive influence of CSR on consumers' evaluations of the corporation, well as on their intentions to purchase products (Brown and Dacin, 1997; Sen and Bhattacharya, 2001; Mohr and Webb, 2005).
2.2.3 Public Administration
Corporations are affected by public decisions coming from regulations and legislation, but they also contribute to improve the social welfare and the level of development of the society by collaborating, for example, in governmental initiatives such as the integration of minorities or the hiring of workers belonging to certain groups (Escudero, 2006) such as those over 45 years of age. CSR can create security and synergies in the areas of employment, social integration, occupational health and safety, and the eradication of corruption. This can lead to tax benefits, be reduction or avoidance (Kim and Im, 2017), as well as a reduction in the risk of infringements and sanctions (Gong et al., 2021). In addition, corporations that adopt CSR criteria will reduce the risks derived from certain socially irresponsible behaviors, such as the possibility of receiving fines or other economic sanctions (Nieto and Fernández, 2004). As there is fluid contact with the Administration, corporations should be voluntarily committed to social programs and actions to anticipate, prevent, or even impede certain legislation (Detomasi, 2008).
2.2.4 Investors and shareholders
Companies that manage to pass the established filters from the major stock index providers see their reputation strengthened and, on many occasions, gain more favorable access to sources of financing. At this respect Saeed and Sroufe (2021) conclude that doing good is most of the times good for companies from the financial point of view. But also, the credibility and sympathy that socially responsible corporations arouse, and their reputation, will help them to access cheaper financing or, at least, to have easier access to (some banks take social and environmental risks into account when analyzing loan applications), as well as attract investors (El Ghoul et al., 2011) and create shareholder value (Nguyen et al., 2020).
2.2.5 Suppliers, retailers, and subsidiaries
One especially important stakeholder for multinational enterprises (MNEs) is the group of firms that are part of their value chain (Park et al., 2014; Park and Ghauri, 2015). Concretely in the textile sector, CSR helps to attract and retain good and closer partners, engaged by the values associated with CSR (Schrage and Gilbert, 2021).
2.2.6 Social media and society in general
MNEs care about social media and the image that they have in host-countries (Rathert, 2016). CSR improves a company's public image (Demetriou and Aristotelous, 2009), which in turn increases its visibility, trust, and sympathy for the company. Thus, for example, the press is more favorable and echoes the corporation's socially responsible initiatives, thus obtaining free publicity; in addition, the corporation is subject to less scrutiny.2.7.
2.2.7 Other entities, such as Nonprofit Organizations (NPOs)
CSR increases the support of the NPO towards the corporation that help it, giving it credibility and sympathy for collaborating in good causes. In addition, CSR allows entry into new markets and market niches (the NPO's target audiences), providing opportunities to build long-term relationships with them (Galan-Ladero et al., 2013).
Being fully aware of stakeholder management, corporations increasingly incorporate it into their CSR programs. Thus, corporate legitimacy is completed by taking care of the interests of stakeholders, in addition to local communities and the whole society. That is why legitimacy theory is closely related to stakeholder theory (Dewiyanti, 2021) as it has been previously highlighted, become the dominant core paradigm of the CSR field (Jones, 1995). In short, the social responsibility of corporations implies being aware of their stakeholders. At the same time, CSR can help to achieve a virtuous circle (Nelling and Webb, 2009; Smith, 2011; Servaes and Tamayo, 2013; Zhao and Murrell, 2022) because CSR attracts consumers, then sales increase, and the company becomes more profitable, which increases investor interest. If the company is doing well, then it favors employment stability and improves the work environment, providing better customer service, which attracts new customers and retains existing ones.
Applying the concepts of stakeholder management and legitimacy to the organizational context of MNEs, as the corporations seek congruence between their activities and the culture of the host countries, the term organizational legitimacy is used to refer two value systems (first, the corporation's values; and secondly, the society's values), when they are congruent (Dowling and Pfeffer, 1975). However, a legitimacy gap appears when there are differences between the values adopted by the corporation and the values of the community in which it operates. According to Dewiyanti (2021), that happens when the corporation only seeks economic profit and does not take into account its social and/or environmental impacts. It is when corporate donations can help to minimize the legitimacy gap by increasing the compatibility between company operations and community expectations.
2.3 New insights from the ethics of Care Theory
By adopting the practices and values of caring in CSR, organizations can significantly strengthen their impacts on the community. Caring is a universal human attribute, which is the foundation of morality (André, 2013). Behind the donation behavior of corporations, we find concern for people in need under the theoretical framework of the ethics of care. Initially, Gilligan (1982) proposed the ethics of care theory and was considered by some as a feminist perspective of ethics. However, the theory has been re-examined and defined “as an empathic disposition which is translated into practices for the sake of other human beings” (André, 2013: 35), evolving towards a care-based morality (Formentin and Bortree, 2019).
The application of this theory to the field of organizations involves incorporating values traditionally considered feminine (such as care and empathy), emotions and relationships into the organizational management model (“feminization” of companies), which can provide a competitive advantage (Oruc and Sarikaya, 2011). In this context, corporate donations can be considered a useful instrument to put into practice the ethics of care. It implies that the corporation should be responsible and attentive, recognize the stakeholders’ needs, and be competent to respond to them. Caring practices and values should consequently encompass trust building, mutual respect and concern, responsiveness to needs, mutually beneficial relationships, human flourishing, and communication within the organization (Formentin and Bortree, 2019; Madden et al., 2022; Lemon and Boman, 2022). And all this can be done through the various CSR actions, but specially through corporate donations, which will improve the quality of life of the various stakeholders and concretely will solve social problems as Inditex had done during the pandemic as it will be analyzed as follows.
3 Inditex in context: the COVID-19 pandemic in Spain
At the end of 2019, an outbreak of an unknown viral pneumonia was detected in Wuhan (China). Due to the danger of the new virus (called SARS-CoV-2 virus, and its disease, COVID-19 – neutral names to avoid any stigmatization – WHO, 2020), and its quick spread to other countries, the World Health Organization (WHO) declared an international health emergency in January 2020. Subsequently, WHO declared it as a pandemic, and all alarms went off worldwide. Thus, something that seemed remote and improbable in the 21st century, a global pandemic, became a brutal and unimagined reality (El País, 2021).
But the global situation worsened, and the health systems of some countries began to collapse due to lack of material, human resources, and space. In March 2020, the global economy was paralyzed with a general lockdown in most countries (borders were closed, non-essential commercial and non-commercial activities were suspended, and people had to stay home, to try to curb the high number of infections and deaths).
As described by Galan-Ladero and Rivera (2021), field hospitals had to be built alongside the reference hospital centers, some hotels were adapted as hospitals, and temporary health facilities were created (e.g., in sports centers, convention centers, etc.), and even new hospitals were built, to accommodate the growing number of people who required medical attention, especially in Intensive Care Units – ICUs. For instance, in Madrid (Spain), the new hospital called “Nurse Isabel Zendal” was built in only 100 days to exclusively treat COVID-19 patients, thus leaving space in public hospitals to treat other pathologies and avoid spreading COVID-19 to other patients and healthcare personnel (El Mundo, 2020).
In addition, the shortage of medical supplies (e.g., Personal Protective Equipment - PPE: masks, gloves, gowns, etc.; alcohol and other disinfectants; and respirators and medical oxygen) in hospitals from different countries in the first months, increased the number of infections among healthcare personnel. The situation was particularly dramatic in some European countries such as Italy or Spain and, later, in the UK. But also, in the USA, in numerous Latin-American nations, and in many other countries around the world in the following months.
In the face of this scenario, companies became involved with CSR initiatives, such as corporate social marketing, cause-related marketing programs, and other actions of a philanthropic nature, which included donations (in cash or in-kind), and/or the reconversion of productive chains for the manufacture of masks and sanitary gowns, respirators, alcohol and disinfectants, and other products that were practically impossible to obtain through the usual channels, or, at least, to provide them in the necessary quantity (Galan-Ladero and Rivera, 2021). To sum up, the situation created by the COVID-19 pandemic, especially during the lockdown in many countries, provoked a wave of solidarity and an unprecedented donation behavior by many companies worldwide.
In the specific case of Spain, there has been an enormous solidarity effort made by all types of companies, from large multinationals and IBEX-35 corporations to small businesses. All of them, to a greater or lesser extent, wanted to do their part to help save lives, fight against the pandemic, and mitigate its impact, by intensifying their CSR policies. According to Albendea (2020), the companies made a multitude of donations and altruistic actions. They contributed both economic resources and protective health material for the Health System, logistical support, and changes in the production of industrial plants to improve supply levels, with the sole objective of improving the situation of the population in general, and the Spanish Health System in particular (to tackle the health problem as soon as possible and mitigate the subsequent economic crisis). They were organized in various aid blocks, some headed by specific sectors, such as textiles.
García (2021) has pointed out that the Spanish State received a total of 17.75 million Euros in donations for the fight against COVID-19, in the period between March 2020 and April 2021. Only in April 2020, it received 16.78 million Euros, being that month the most supportive in donations against COVID-19. This can be justified because April 2020 was, precisely, a critical month of the pandemic in Spain, when after several weeks of exponential growth of infections and deaths, the first wave reached its peak, and already in full confinement, everyone began to be aware of the real impact, at all levels, that the pandemic was having on Spanish society, as well as the need for everyone to work together to deal with the pandemic and mitigate its effects, given the impossibility of the Spanish Government to do so alone.
These donations were regulated by Royal Decree Law 11/2020 of March 31, which adopted urgent complementary measures in the social and economic sphere to deal with COVID-19. Specifically, its article 47 indicated that the money received in this way (through an official bank account), “would be for the exclusive financing of the expenses derived from the health crisis caused by COVID-19 (expenses for health equipment and infrastructure, material, supplies, hiring of personnel, research, and any other that requested to contribute to reinforce the response capacities to the crisis derived from COVID-19)”. These donations would receive tax benefits.
The Spanish government received about 3,800 donations against COVID-19 from individuals, and 90 donations from private entities (García, 2021). However, this has been only part of what companies, alone or in collaboration with NGOs and other organizations, as well as individuals, have done since the beginning of the pandemic. In addition to monetary donations, in-kind donations such as protective health material for the Health System, logistical support, and changes in the production of industrial plants to improve supply levels have predominated (Albendea, 2020).
According to Merco (2021), the companies with the best CSR and governance during the pandemic in Spain were the following very well-known corporations: Inditex, Mercadona, Seat, Santander, Iberdrola, El Corte Inglés, Caixabank, Grupo Social ONCE, Naturgy, and Telefónica (Figure 1 ). It is remarkable that this ranking has been formed according to different criteria such as the support provided in terms of protective and medical material and donations to the Health System as well as the donations made to NGOs in support of solidarity initiatives, among others (Ipmark, 2021). Therefore, among all these organizations, Inditex stands out significantly. It has been the most valued and recognized brand by the Spanish citizens for its collaboration in the health crisis and its active work for the Spanish society (Davara, 2020). This is the reason to study and analyze the case of Inditex during the pandemic - one of the world's leading fashion multinationals - under the following research proposition: Inditex has modified its strategy to CSR and sustainability during the COVID time by reinforcing their donation pattern.Figure 1 Companies with the best CSR and governance in Spain during the pandemic.
Figure 1Source: Own elaboration, based on Merco (2021).
4 Research methodology
4.1 Research design
As the motivation of this paper seeks to examine, analyze, and evaluate Inditex's behavior of corporate donations during the COVID-19 pandemic, to understand how this firm builds its corporate reputation, the research design is based on the single case-study of Inditex. According to Yin (2014), case studies place an object in context, and therefore allow detail, depth, and richness of data. Thus, case studies allow us to pay attention to a phenomenon in a specific context and this allows us to analyze the data in detail and in great depth (Galan-Ladero and Robson, 2022).
Case studies, as a research methodology, can have different objectives (Ellram, 1996): to explain, explore, describe, or predict a specific phenomenon of interest. Therefore, case studies are excellent for theory building, development, testing, refinement, or extension (Voss et al., 2002), “for providing detailed explanations of ‘best practices’, and providing more understanding of data gathered” (Ellram, 1996: 115). Consequently, case-study research can be one of the most powerful research methods and have very high impact when the purpose of the research is to obtain a depth of understand of a real phenomenon, which can be studied in its natural setting or also study emerging practices (Voss et al., 2002). For all these reasons, case-study research is widely used in management disciplines (Voss et al., 2002).
Thus, in our research, from an exploratory and qualitative approach, we used case study methodology (a single case study) because the purpose of our work is to obtain a depth of understanding of Inditex's CSR practices during the pandemic, and our goal is also to expand and generalize the different theories that we consider here.
4.2 Data collection
We have collected secondary data from Inditex's official website (www.inditex.com), but also from other publicly available online documents, to avoid biases. Thus, a wide range of sources were reviewed: (1) news and reports about corporate donations in Spain during the pandemic, and about Inditex's role particularly; (2) official on-line publications, such as WHO press dossiers and government reports. This was considered important to ensure a balanced view of the real Inditex's corporate donation behavior.
Data collection covered a two-year period, from January 2020, when the first news about the pandemic were published by WHO, to the end of 2021. The search terms used (in Spanish and English) included Inditex, Zara, COVID-19, and donations.
4.3 Phases of data analysis
The final database of documents therefore provided a very detailed account of the Inditex's CSR actions during the pandemic. Data analysis was conducted in three phases: (1) the official Inditex's website was deeply analyzed. All sections in it were read several times, in order to gain familiarity with data. Following the repeated readings, the solidarity actions that took place were ordered and summarized; (2) other online sources were consulted to complement the first screening and to have a general analysis; and (3) a thematic content-analysis assisted by ATLASti software was carried out with the intention of finding the relationship with stakeholders and the weight of communication about donations during the pandemic period in relation to the global set of corporate communication.
5 Outcomes of the inditex case-study research
5.1 Inditex: the corporation and its strategy
Inditex began as a small workshop, Confecciones Goa, founded by Amancio Ortega in 1963, and has become one of the largest fashion distribution companies in the world. It currently has seven commercial formats: Zara, Pull&Bear, Massimo Dutti, Bershka, Stradivarius, Oysho, Zara Home (Uterqüe has ceased operations in 2022, integrating into Massimo Dutti - Inditex, 2022).
Inditex addresses all phases of the fashion process: design, procurement, product and manufacturing quality control, logistics, and sales through stores and online (Inditex, 2021). It currently sells online in 215 markets and has more than 6,000 physical stores in the world (Inditex, 2022). However, its initial business model has not changed and remains the same today - the customer is the most important aspect for the company - the customer is at the center of everything Inditex does. And the three key pillars of its business model are: flexibility, integration, and sustainability (Inditex, 2021).
Inditex is headquartered in Arteixo (La Coruña, Spain). In 2021, it worked with 1,790 suppliers and had 8,756 factories worldwide (Inditex, 2022). It has had a very rapid growth rate. In the early 1980s, Zara began to grow in Spain; and at the end of that same decade, in 1988, it began an accelerated internationalization process. Since 2019 and, especially with the pandemic, the digital and sustainable transformation of the company has intensified, with an integrated platform of stores and online in practically all over the world (Inditex, 2021). This digital transformation has involved a store optimization plan in the period 2020–2021, focusing on larger stores (with an integrated model for physical and online sales) and the absorption of small stores (Gutiérrez, 2021).
5.2 CSR in fashion industry: the inditex case
The fashion sector plays a significant role in the global economy, but it is also one of the most polluting industries (D'Adamo and Lupi, 2021; Tebaldi et al., 2022) and consumes large amounts of resources (water, above all). For years, therefore, CSR and sustainability have become priority aspects in this sector, and a variety of strategies have been adopted as explained by Arrigo (2021), DáAdamo and Lupi (2021) and Vatamanuescu et al. (2021) (see Table 1 ).Table 1 A selection of sustainable practices in the fashion industry.
Table 1- A whole re-definition of their supply chain (traceability).
- A deep shift in production processes and operating models (eco-design; use of more environmentally friendly raw materials - organic, sustainable and/or recycled -; cleaner production practices, promotion of collaborative consumption platforms - related to the rental, repairing, and sale of second-hand goods -, etc.).
- Reduction of water and energy consumption.
- Reduction of the use and release of harmful chemicals.
- Minimization of the pollution generated by production and/or distribution processes (using efficient, cleaner, and renewable energy sources).
- Fight against climate change.
- Respect for their employees, offering fair wages to their employees and suppliers.
- Circular economy model: “reuse, recycle, recovery”.
- Change the “fast fashion” concept (“Take, Make, Waste”), for the “Slow fashion” concept: “Buy less, buy better”.
- Packaging in e-commerce.
Source: own elaboration.
And although the industry was already facing this transformation process before COVID-19, the pandemic has accelerated it. The fashion sector has been one of the most affected (Vatamanescu et al., 2021), due to the closure of stores and the accumulation of stocks (Tebaldi et al., 2022), and has had to adapt quickly to the new situation, readjusting its strategies (Vatamanescu et al., 2021) and intensifying its e-commerce. But, at the same time, it has engaged in specific CSR actions during the lockdown, acting in the interests of all stakeholders – including customers, employees, suppliers, investors, and wider society (McKensey and Company, 2022).
In the specific case of Inditex, CSR has played a fundamental role throughout its history, but especially since 2001, when the Group was first listed on the Stock Exchange. Inditex wanted to avoid any scandal that could affect its international reputation and, consequently, its stock market listing (at that time, the Nike and Adidas scandals were very recent). For this reason, among other measures, it adopted and developed a Code of Conduct, aimed at manufacturers and suppliers, which it has been expanding and completing since then. Inditex also joined the Dow Jones Sustainability Index and, in the following years, created its strategic environmental plan, which it has subsequently renewed. It has also been participating in numerous and varied social and solidarity initiatives, and collaborating with different NGOs. As a result, CSR has been gaining greater importance within the company, year after year.
The company allocates more than 40 million euros each year to investments in social projects (more than 600 different initiatives of more than 400 social entities, helping more than two million people each year - eleconomista. es, 2020). But only in 2020, the Group invested 71 million Euros in social programs worldwide (Inditex, 2021). In addition to Inditex's CSR, the Amancio Ortega Foundation (www.faortega.org) was also created in 2001, with the aim of "contributing to a model of society that offers equal opportunities to all those who are part of it" (Amancio Ortega Foundation, 2021) and "based on the principles of solidarity, understanding, commitment, hard work, authenticity, and loyalty" (Amancio Ortega Foundation, 2021). Its priority actions are focused on two key sectors: education and social welfare. Currently, it is focused on contributing to the fulfillment of the Sustainable Development Goals - SDGs (Agenda 2030) in the areas in which it operates, which mainly revolve around the axis of people: SDG 1, SDG 2, SDG 3, SDG 4, SDG 9, and SDG 17. In the last year with official information available, 2021, investment in educational and social projects reached 70.6 million euros (Amancio Ortega Foundation, 2022). Some of the most relevant donations in the history of this Foundation are listed in Table 2 .Table 2 Some of the most relevant donations of the Amancio Ortega Foundation.
Table 2Donation Amount Objectives
COVID-19 pandemic 63 million Euros Purchase of sanitary material, according to the technical-sanitary indications of the Spanish authorities, to fight against the COVID-19 pandemic: to be donated to the National Health System.
Public oncology support program (2015–2021) 310 million Euros To renew the technological material and equipment related to oncological treatments (specifically, radiotherapy for cancer patients).
Donation to Caritas 40 million Euros To help nearly 160,000 families that suffered from the economic crisis in Spain since 2008.
Education 32 million Euros Creation of 8 nursery schools in Galicia.
Scholarship Program: to study high school in the United States and Canada (600 students/year).
Source: Adapted from Redacción Médica (2020) and Amancio Ortega Foundation (2021).
5.3 General analysis: inditex during COVID-19 pandemic
The Inditex Group offered the Government of Spain, Autonomous Communities, hospitals, as well as other companies and individuals, its supply logistics system to bring urgently sanitary and protection material to Spain, as well as its cooperation in managing purchases made by the Government of Spain abroad, particularly in China, taking advantage of its operational capacity (La Voz de Galicia, 2020). It is remarkable that Inditex has many years of experience in its commercial relations with the Asian giant and maintains close ties with local suppliers and institutions such as the Central Government, the Beijing and Shanghai City Councils, the Environmental Council, and Tsinghua University. All this allowed the corporation to quickly identify key suppliers for the manufacture of sanitary protection, in addition to maintaining transport capacity at such critical times (La Vanguardia, 2020).
In addition, the corporation reconverted their factories around the world to produce masks and protective gowns for hospitals. The message that appeared on the sanitary material from China that Inditex donated to the Spanish Public Health System to deal with the COVID-19 pandemic was “although the oceans separate us, we are united by the same moon”.
Thus, Inditexás emergency aid during the COVID-19 pandemic has focused on different initiatives and actions related to the ethics of care “in order to tackle the health and economic consequences” (Inditex, 2021), giving priority to people, especially the most vulnerable. By stakeholders, the assistance has been focused on:- People in Inditex (employees): Inditex undertook actions and programs to provide health and financial assistance to workers in its supply chain, with special attention to the most needed groups.
- Inditex suppliers: The corporation reinforced their contacts with its suppliers, to try to ensure the adoption of health and hygiene protection measures by all its manufacturers. Inditex guaranteed the payment of all orders already placed and in the production phase, according to the original terms and speeding up payments in those cases where there were logistical difficulties for the delivery of the merchandise. Financing mechanisms were also created for suppliers and manufacturers to enable them to overcome the economic impact of the pandemic, giving priority to guaranteeing the payment of salaries and strengthening the health and safety measures.
- Public Administration and Inditex collaboration: The City Council of A Coruña, the Xunta de Galicia, or the entity IFEMA (Madrid Trade Fair Institution Consortium), from the Autonomous Community of Madrid, also participated in the distribution process.
- The communities where Inditex is present: On the one hand, investment in the community consisted of activating a global emergency program, to which more than 40.4 million Euros were allocated. This program has also made it possible to mobilize 177 million units of health and basic necessities. On the other hand, the company allocated 31,000 items such as blankets and bedding to the homeless, shelters and health centers. These resources were distributed through the Red Cross and Caritas, organizations with which the Group works regularly.
The "Red Cross Responds" program was launched to deliver basic necessities to 25,000 people, telephone assistance and accompaniment for almost one million people, support in labor material for 16,000 people, in addition to offering a place to sleep for 3,000 homeless people (elconomista.es, 2020). The Caritas program "In the face of the coronavirus, every gesture counts”, was aimed at the comprehensive care of especially vulnerable groups: it offered care to elderly people living in the organization's centers and in their homes, as well as offering assistance to homeless people through the organization's shelters, soup kitchens, and other hygiene services (eleconomista, 2020).- Inditex collaboration with other stakeholders and NGOs: The collaboration has been focused in particular entities with the main unions or workers' rights organizations such as the International Federation of Trade Unions (IndustriALL Global Union), Ethical Trading Initiative, and Action, Collaboration, Transformation (ACT). Inditex also provided special support to the Doctors Without Borders’ Emergency Unit, another NGO with which it has maintained a stable collaboration since 2011 (La Vanguardia, 2020), through the “MSF COVID-19 Fund”, created with the aim of establishing temporary hospitalization units, helping to decongest hospitals and health centers, as well as to provide support to professionals who were on the front line of care for the elderly (eleconomista, 2020).
A summary of the most important figures is the following:- Inditex has donated over 40.4 million Euros and has distributed 177 million units of personal protective equipment and other necessities “to where they were urgently needed” (from acquisitions by Spanish Government, other Spanish Public Administrations, and private donors, including the Amancio Ortega Foundation and Inditex itself). They were transported in 66 freight aircraft. Although the company has not given official data, the health material transported by Inditex for the Spanish Government, to combat the coronavirus, has been valued at about 457 million Euros (La Voz de Galicia, 2020). And only the transport has been estimated at 100 or 150 million Euros (Ugalde, 2020). In any case, it has been an initiative that the textile Group has carried out free of charge (La Voz de Galicia, 2020).
- Inditex made more than 140,000 waterproof health gowns in its facilities.
- Inditex donated 31,000 items from the Zara Home bedding collection (blankets, sheets, and pillows) to health centers and homeless shelters, and over 1 million items to people in need. Those products were distributed through different NGOs.
In short, spending has exceeded 300 million Euros (Malagon, 2020): between 150 and 200 million Euros, the non-temporary employment regulation file, and between 100 and 150 million, the transport (Ugalde, 2020). Inditex has also made donations (monetary and in-kind) in China. Efforts focused on the purchase of medical supplies for distribution to hospitals, through the Tsinghua University Education Foundation and the Hubei Provincial Charity Federation. As a result of this collaboration, more than 2.5 million health care items were donated. And also, to charities in the USA, Italy, Mexico, and the UK. It has also reformulated the collaboration programs with different NPOs “to address the new needs emerging around the world as a result of the pandemic” (e.g., education, prevention, and awareness campaigns) in Argentina, Bangladesh, Brazil, Bolivia, Cambodia, Central African Republic, Colombia, Democratic Republic of the Congo, Ecuador, Ethiopia, India, Lebanon, Mexico, Morocco, Paraguay, Peru, South Africa, Spain, Syria, The United States, Uruguay, and Venezuela.
To sum up, all this effort has different dimensions: (1) the decision to maintain employment and renounce the use of a temporary employment regulation file due to force majeure, to support the public coffers, assuming the labor costs of all of its employees in Spain; (2) the provision to the State of all its logistics, especially the air corridor with China to expedite the arrival of medical supplies to Spanish hospitals; (3) the support to suppliers by not renegotiating prices and maintaining the original contracts; and (4) the donations made directly by the Group.
According to Ugalde (2020), all this commitment, this solidarity and patriotic work carried out by Inditex, together with the closure of its stores in many countries, is what led Inditex to make losses for the first time in its history (409 million Euros, in the first quarter of 2020 – period from February 1 to April 30, 2020) although in the following months it recovered and returned to profit.
5.4 Content analysis
In an attempt to quantify the importance of donations in the corporate communication of the company during the pandemic time, a thematic content-analysis was carried out involving the analysis of a selection of written documents. Concretely, all news and documents provided on the website of the company in the section called Press Release during 2020 and 2021.
In line with Friese (2014), our analytic approach was the NCT analysis, where N is noticing things, C is collecting things, and T is thinking about things. Total word count was 40,262. After a mechanical word crunch provided by the ATLASti software, codes and related categories were created and counted in an inductive process. The software provided a word frequency distribution, as a replicable result, which is considered the first step in scientific content analysis, to be later modified to fit individual researcher needs. It was assumed that frequency is a signal of the importance of each topic (Krippendorff, 2004). Content analysis has received significant attention in academic literature for a wide range of fields and it has also been a recurrent method for management studies (Duriau et al., 2007; Horneaux-Junior et al., 2017). In addition, press releases of companies have been a recurrent set of documents to be analyzed to study corporate reputation (Choi, 2012; Ritala et al., 2018; Ajayi and Mmutle, 2021). The general categories found on the analysis of press releases are shown in Figure 2 .Figure 2 Main categories.
Figure 2Source: Own elaboration.
The detailed findings of the content analysis of these press releases are shown in Figure 3 , where data coding and frequencies are included. The central category found is the new approach to sustainability of the company. We could say that the general purpose of the press releases during the two years analyzed was to communicate how Inditex has adapted its strategy to sustainability to the emergency derived from the pandemic. In this new strategy, several categories have high relevance: stakeholders (where valuing internal talent has an important weight in the strategy), efficiency and innovation, environment, digital transformation, and powering the customer experience. But the main topic communicated by the company was the commitment to people throughout COVID-19 (as an addition of Covid and donating).Figure 3 Overview of the content analysis results.
Figure 3Source: Own elaboration.
As expected, donating emerges as a key issue during the pandemic with the highest frequency (223), after the general and related category of Covid (252). The category donating includes several related codes such as donations, refugees or disabilities. And it should be noted that it has been revealed as the protagonist of the content analysis, confirming our research proposition.
6 Final Reflexions and conclusion
During the pandemic, and despite the need to intensify health aid, to address food shortages and to manage the lack of access and support for education, NPOs have been limited in their ability to generate income, and governments have been mainly focused on funding programs to protect the lives of citizens. This has led corporations to analyze the best way to serve society at the same time that improve their reputation and gain legitimacy. MNEs have changed their CSR strategies, being aware of their stakeholders’ needs, putting in action the ethics of care by reinforcing corporate donations, focusing on determining the most urgent health, welfare, and education needs to be addressed, and responding quickly to them.
As a result, the COVID-19 pandemic has provoked corporate donation behavior worldwide. In Spain, one of the most affected countries by the pandemic, the MNE Inditex has made significant donations, both monetary and in kind, at the most critical moments. Inditex has been in fact a good example of a company that has been able to adjust production very quickly and efficiently, and has even reduced stocks in pandemic time. Inditex is also an example of a company that has developed all aspects of CSR (economic, social, and environmental) at the same time that has established better relationships with their stakeholders.
6.1 Theoretical contributions
Theoretically, the analysis of this emblematic case study serves: (1) to verify that the Stakeholder Theory is expanded by the Legitimacy Theory. Inditex has deployed a solid corporate communication to guarantee that its behavior as donor is regarded as conforming to societal needs from the perspective of its main stakeholders; (2) to verify that the Stakeholder Theory, Legitimacy Theory, and the Ethics of Care theoretical framework are increasingly interrelated. The key to the company's success will be in the efficient and effective management of its relationships with stakeholders. For that purpose, CSR practices can and should be conceptualized in the context of the ethics of care.
The interrelation of the three theoretical frameworks supports the idea that corporate donations, under the ethics of care umbrella, help to minimize the legitimacy gap in times of crises and satisfy stakeholders by increasing the compatibility between company operations and community expectations. This approach should result in greater trust in the corporation and should be an added value for both the organization and the society, and the company will have a better reputation.
Thus, this case study is relevant because there is scarce research examining the link between corporate donations and reputation in COVID-19 pandemic times. In addition, its novelty lies in its theoretical approach from three different but complementary theories. This paper contributes to the extant literature and CSR knowledge in times of COVID-19 pandemic by offering critical information from a case study of a highly reputable company.
6.2 Managerial contributions
This case study has also a contribution for practitioners because it shows how Inditex manages reputation, as perceived irresponsibility could lead to boycotts and other undesirable consumer actions. From a practical point of view, as corporate reputation is one of an organization's most valued intangible assets and can provide a sustained competitive advantage, corporate donations must start being seriously taken into consideration by MNEs.
Inditex's corporate communication evidences that CSR in general, and corporate donations particularly, could increase awareness of, and sympathy for, the corporation, generating a positive perception of it and a more positive image in the consumer. Corporate donations can induce customers to be identified with the corporation (emotional bonding, company-customer identification), which is expected to be translated into an increase in sales and market share in a virtuous circle. Hence, other MNEs could follow the example of Inditex improving their donor behavior, because it is worth it to include the values of the ethics of care into the business model recognizing the actual asymmetries cause by the COVID-19 pandemic and the potential ones in the future, and the obligation of care for mitigating stakeholdersá suffering.
6.3 Final conclusions
This work has analyzed existing data about Inditex as a case study, and have reported novel insights in form of a scoping review. This case study has shown that collaboration between the public sector (led by the Government) and the private sector is possible and useful, and not only in the case of emergencies. This type of collaboration should be continued and even boosted in the future, because it opens up new opportunities for the provision of quality public services, to obtain synergies and seek excellence and transparency. Ultimately, it would benefit the citizens, who are the users of these services.
Inditex has not only met the expectations of stakeholders during the pandemic but has even surpassed them. Knowing that Inditex managed the supply of masks and medical supplies gave Spaniards peace of mind. After the analysis of the secondary data retrieved from Inditex, it could be said to conclude that Inditex has clearly passed a test of legitimacy under the ethics of care framework during the pandemic, demonstrating a quick adaptation to circumstances attending the expectations of society as a whole, and being aware of its key stakeholders - employees (keeping jobs), customers (selling online), shareholders (recovering benefits after confinement), government (making all its logistics and production capacity available to it), and other NGOs (receiving their donations). Legitimacy must have been strengthened because the donation behavior of the corporation has been consistent with the current social values and, consequently, its corporate reputation has improved, being the most valued company at the moment.
The objective of this paper was to analyze Inditex, one of the most important and admired firms in the fashion industry, as a case study and reflect on the impact of its donation behavior on its corporate reputation. The study has shown the highest weight of corporate communication about donations during the pandemic in relation to the global set of corporate communication at this time demonstrating that caring practices have been revealed as a key element of the CSR strategy. As a result, the reputation of Inditex has been improved, becoming the number one thanks to its corporate donation behavior, reinforcing the relationship with the corporate stakeholders and ensuring the growth and long-term survival of the corporation.
The case study methodology allowed us to extend the application of the three proposed theories - Stakeholder Theory, Legitimacy Theory, and Ethics of Care Theory - to the field of corporate giving and corporate reputation, studying in depth the behavior of the Inditex company during the pandemic in general, and the confinement in particular. In fact, this Spanish company has become an example for its fast reaction and the enormous effort made to help in this dramatic situation, and it has been at the top of all the rankings that have assessed the most supportive companies and their CSR initiatives during the pandemic (and the lockdown in particular).
6.4 Limitations
However, this work is not without limitations. Lack of generalizability has been the major criticism of case studies because this affects external validity. Thus, care is needed in drawing generalizable conclusions based on the analyzed case. Instead, what is intended is that this case, and the good practices analyzed, serve as benchmarks for other companies.
6.5 Future research perspectives
Nevertheless, the stated limitation could be addressed by replicating the study by analyzing other multinationals in the sector and verifying patterns. This would partially confirm or disprove our case study. Thus, new research opportunities open up: (1) This study can be used as a foundation for future related studies on a national and international basis. Similar studies could be conducted in other companies, sectors, countries, and contexts, and a comparison could be done among them; and (2) A comparative study with other companies that were prominent during the pandemic (and the lockdown in particular), both in their industry and in other industries, could detect similar or different effects on the impact of their supportive behavior on corporate reputation.
Declarations
Author contribution statement
M. Mercedes Galan-Ladero, PhD; M. Isabel Sánchez-Hernández, PhD: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.
Funding statement
This research has been funded by the Regional Government of Extremadura (Junta de Extremadura) and the European Union (European Regional Development Fund—A way of making Europe), supporting Research Groups (SEJO21 —GR21078) of the Universidad de Extremadura.
Data availability statement
Data will be made available on request.
Declaration of interest's statement
The authors declare no conflict of interest.
Additional information
No additional information is available for this paper.
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References
Aftab M.A. Yuanjian Q. Kabir N. Barua Z. Super responsive supply chain: the case of Spanish fast fashion retailer Inditex-Zara Int. J. Bus. Manag. 13 5 2018 212 227
Ajayi O.A. Mmutle T. Corporate reputation through strategic communication of corporate social responsibility Corp. Commun. Int. J. 26 5 2021 1 15
Albendea G. Readaptación de producción y servicios: la solidaridad de las empresas se dispara por el coronavirus Compromiso Empresarial 2020 6 April. Retrieved from https://www.compromisoempresarial.com/rsc/2020/04/readaptacion-produccion-servicios-solidaridad-empresas-coronavirus/
Alvarez S. Sachs S. Who stakeholders come from? Academy of management review 2021
Amancio Ortega Foundation [Fundación Amancio Ortega] Official website Retrieved from https://www.faortega.org 2021
Ananzeh H. Al Amosh H. Albitar K. The effect of corporate governance quality and its mechanisms on firm philanthropic donations: evidence from the UK Int. J. Account. Inform. Manag. 2022 (ahead-of-print)
André K. The ethics of care as a determinant for stakeholder inclusion and CSR perception in business education Soc. Bus. Rev. 8 1 2013 32 44
Antonini C. Beck C. Larrinaga C. Subpolitics and sustainability reporting boundaries. The case of working conditions in global supply chains. Accounting Aud. Account. J. 33 7 2020 1535 1567
Arenas D. Responsabilidad, estrategia y grupos de Interés Harvard Deusto Marketing & Ventas 77 Nov./Dec. 2006 34 39
Arrigo E. Collaborative consumption in the fashion industry: A systematic literature review and conceptual framework J. Cleaner Prod. 325 2021 129261
Barbeito-Caamaño A. Chalmeta R. Using big data to evaluate corporate social responsibility and sustainable development practices Corp. Soc. Responsib. Environ. Manag. 27 6 2020 2831 2848
Bhattacharya C.B. Sen S. Doing better at doing good: when, why, and how consumers respond to corporate social initiatives Calif. Manag. Rev. 47 1 2004 9 24
Bilińska-Reformat K. Dewalska-Opitek A. E-commerce as the predominant business model of fast fashion retailers in the era of global COVID 19 pandemics Procedia Comput. Sci. 192 2021 2479 2490
Brammer S.J. Pavelin S. Corporate reputation and social performance: the importance of fit J. Manag. Stud. 43 3 2006 435 455
Brown T.J. Dacin P.A. The company and the product: corporate associations and consumer product responses J. Market. 61 January 1997 68 84
Brydges T. Heinze L. Retamal M. Changing geographies of fashion during Covid-19: the Australian case Geogr. Res. 59 2 2021 206 216
Burton B.K. Ethics of care. Britannica Retrieved from https://www.britannica.com/topic/philosophical-feminism/Feminist-social-and-political-philosophy 2022
Busch T. Hamprecht J. Waddock S. Value (s) for whom? Creating value (s) for stakeholders Organ. Environ. 31 3 2018 210 222
Cabral L. Media exposure and corporate reputation Res. Econ. 70 2016 735 740
Campbell L. Gulas C.S. Gruca T.S. Corporate giving behavior and decision-maker social consciousness J. Bus. Ethics 19 4 1999 375 383
Cherry M.A. The law and economics of corporate social responsibility and greenwashing UC Davis Bussines Law Journal 14 2 2013 281 304
Choi J. A content analysis of BP's press releases dealing with crisis Publ. Relat. Rev. 38 3 2012 422 429
Chowdhury R. Chapter: Corporate Reputation. Systems Thinking for Management Consultants. Introducing Holistic Flexibility 2019 Springer 485 514 Flexible Systems Management book series
Cuervo-Carabel T. Arce-García S. Orviz-Martínez N. Corporate social responsibility and its communication on Twitter: analysis of the discourse and feelings generated in society Management Letters/Cuadernos de Gestión 2022 1 11
Davara A. Inditex, la marca más valorada en la lucha contra el COVID-19. Distribucionactualidad.com Retrieved from https://www.distribucionactualidad.com/inditex-marca-valorada-coronavirus/ 2020
Dean D.H. Consumer perception of corporate donations effects of company reputation for social responsibility and type of donation J. Advert. 32 4 2003 91 102
Deegan C. Rankin M. Tobin J. An examination of the corporate social and environmental disclosures of BHP from 1983-1997: a test of legitimacy theory Account Audit. Account. J. 15 3 2002 312 343
Demetriou M.M. Aristotelous C. CSR of the bank of Cyprus: do customers and other stakeholders know? Do they appreciate? 8th international congress of the international association on public and nonprofit marketing – IAPNM 2009 Valencia 2009 June, 2009
Detomasi D.A. The political roots of corporate social responsibility J. Bus. Ethics 82 4 2008 807 819
Dewiyanti S. Legitimacy theory and its relationship to CSR accounting.binus.ac.id 2021 Retrieved from https://accounting.binus.ac.id/2021/11/15/legitimacy-theory-and-its-relationship-to-csr/
Dowling G.R. Defining and measuring corporate reputations Eur. Manag. Rev. 13 3 2016 207 223
Dowling J. Pfeffer J. Organizational legitimacy: social values and organizational behavior Pac. Socio Rev. 18 1 1975 122 136
Duriau V.J. Reger R.K. Pfarrer M.D. A content analysis of the content analysis literature in organization studies: research themes, data sources, and methodological refinements Organ. Res. Methods 10 1 2007 5 34
D’Adamo I. Lupi G. Sustainability and resilience after COVID-19: a circular premium in the fashion industry Sustainability 13 4 2021 1861
El Ghoul S. Guedhami O. Kwok C.C. Mishra D.R. Does corporate social responsibility affect the cost of capital? J. Bank. Finance 35 9 2011 2388 2406
elEconomista Inditex: la logística, al servicio de la lucha contra el virus. elEconomista, 25 April Retrieved from https://www.eleconomista.es/salud-innovacion/noticias/10504694/04/20/Inditex-la-logistica-al-servicio-de-la-lucha-contra-el-virus.html 2020
Ellram L.M. The Use of Case Study Method in Logistics Research J. Business Logistics 17 2 1996 93 138
Esbeih K.N. Molina-Moreno V. Núñez-Cacho P. Silva-Santos B. Transition to the circular economy in the fashion industry: the case of the Inditex family business Sustainability 13 18 2021 10202
Escudero J. Responsable y rentable Emprendedores 102 March 2006 58 64
Ewan D. Difference between legitimacy theory and stakeholder theory. Difference Between Similar Terms and Objects 2022, february 21 Retrieved from http://www.differencebetween.net/business/difference-between-legitimacy-theory-and-stakeholder-theory/
Ferruz-González S. Nueva propuesta de evaluación de la Reputación Corporativa: indicador de Desempeño Reputacional Doxa Comunicación 30 2020 331 349 Retrieved from
Flatt S.J. Kowalczyk S.J. Creating competitive advantage through intangible assets: the direct and indirect effects of corporate culture and reputation Journal of Competitiveness Studies 16 1/2 2008 13 30
Formentin M. Bortree D. Giving from the heart: exploring how ethics of care emerges in corporate social responsibility J. Commun. Manag. 23 1 2019 2 17 10.1108/JCOM-09-2018-0083
Fortuna F. Risso M. Musso F. Omnichannelling and the predominance of big retailers in the post-covid era. Symphonya Emerging Issues in Management 2 2021 142 157
Freeman R.E. Dmytriyev S. Corporate social responsibility and stakeholder theory: learning from each other. Symphonya Emerging Issues in Management 1 2017 7 15
Friedman A.L. Miles S. Stakeholders: Theory and Practice. OUP oxford 2006
Friese S. Qualitative Data Analysis with ATLAS.Ti 2014 Sage Publications Los Angeles, CA, USA
Galan-Ladero M.M. Variables que influyen en la actitud hacia el marketing con causa y determinantes de la satisfacción y la lealtad en la “compra solidaria” Doctoral Thesis. Servicio de Publicaciones de la Universidad de Extremadura 2012
Galan-Ladero M.M. Rivera R.G. Applied Social Marketing and Quality of Life: Case Studies from an International Perspective 2021 Springer Switzerland
Galan-Ladero M.M. Galera-Casquet C. Wymer W. Attitudes towards cause-related marketing: determinants of satisfaction and loyalty International Review on Public and Nonprofit Marketing 10 3 2013 253 269
Galan-Ladero M.M. Robson J. Unsocial and irresponsible behaviour: what happens when customers lie? (Chapter 18) first ed. Bhattacharyya J. Dash M.K. Hewege C. Balaji M.S. Lim W.M. Social and Sustainability Marketing. A Casebook for Reaching Your Socially Responsible Consumers through Marketing Science 2022 Routledge USA Taylor & Francis Group
García L. El Estado recibió cerca de 3.800 donaciones de particulares contra la COVID-19 y 90 procedentes de entidades privadas. Newtral.es, 11 May Retrieved from https://www.newtral.es/donaciones-covid-estado-particulares/20210511/ 2021
Ghorbel A. Jeribi A. Volatility spillovers and contagion between energy sector and financial assets during COVID-19 crisis period Eurasian Economic Review 11 3 2021 449 467
Gilligan C. In a Different Voice: Psychological Theory and Women’s Development 1982 Harvard University Press Cambridge
Gimeno-Arias F. Santos-Jaén J.M. Palacios-Manzano M. Garza-Sánchez H.H. Using PLS-SEM to analyze the effect of CSR on corporate performance: the mediating role of human resources management and customer satisfaction An Empirical Study in the Spanish Food and Beverage Manufacturing Sector. Mathematics 9 22 2021 2973
Gong G. Huang X. Wu S. Tian H. Li W. Punishment by securities regulators, corporate social responsibility and the cost of debt J. Bus. Ethics 171 2 2021 337 356
Gutiérrez H. Inditex cerrará 56 tiendas en España durante este verano. El País, 13 May Retrieved from https://elpais.com/economia/2021-05-13/inditex-cerrara-56-tiendas-en-espana-durante-este-verano.html 2021
Hasan R. Yun T.M. Theoretical linkage between corporate social responsibility and corporate reputation Indonesian Journal of Sustainability Accounting and Management 1 2 2017 80 89
Horneaux-Junior F. Galleli-Dias B. Gallardo-Vázquez D. Sánchez-Hernández M.I. Strategic aspects in sustainability reporting in oil & gas industry: the comparative case-study of Brazilian petrobras and Spanish repsol Ecol. Indicat. 72 2017 203 214
Inditex Corporate website Retrieved from https://www.inditex.com 2021
Inditex Corporate website Retrieved from https://www.inditex.com 2022
Ipmark Inditex, Mercadona, Seat y Santander, las más responsables ante el COVID-19 Retrieved from https://ipmark.com/inditex-mercadona-seat-y-santander-las-mas-responsables-ante-el-covid-19/ 2021
Jones T.M. Instrumental stakeholder theory: a synthesis of ethics and economics Acad. Manag. Rev. 20 2 1995 404 437
Kelley K.J. Hemphill T.A. Thams Y. Corporate social responsibility country reputation and corporate reputation. A perspective on the creation of shared value in emerging markets Multinatl. Bus. Rev. 27 2 2019 178 197
Kim J. Im C. Study on corporate social responsibility (CSR): focus on tax avoidance and financial ratio analysis Sustainability 9 10 2017 1710
Kotler P. Lee N. Corporate Social Responsibility 2005 Wiley USA
Krippendorff K. Content Analysis: An Introduction to its Methodology. London 2004 Sage
Lemon L.L. Boman C.D. Ethics of care in action: Overview of holistic framework with application to employee engagement Publ. Relat. Rev. 48 4 2022 102232
Liu S.Y. Napier E. Runfola A. Cavusgil S.T. MNE-NGO partnerships for sustainability and social responsibility in the global fast-fashion industry: a loose-coupling perspective Int. Bus. Rev. 29 5 2020 101736 32834687
Lizcano J.L. RSC. El nuevo paradigma empresarial E.com (Dec.) 2007 14 15
Loor-Zambrano H.Y. Santos-Roldán L. Palacios-Florencio B. Relationship CSR and employee commitment: mediating effects of internal motivation and trust European Research on Management and Business Economics 28 2 2022 100185
Madden S. Harrison V. Vafeiadis M. Relational care in communication as the basis of nonprofit fundraising: theorizing professional ethics based in stewardship and ethics of care Journal of Philanthropy and Marketing 2022 1 11
Malagon P. La solidaridad está detrás de las pérdidas históricas de Inditex. Libremercado.com Retrieved from https://www.libremercado.com/2020-06-14/coronavirus-inditex-perdidas-donacion-material-sanitario-1276659351/ 2020
McKensey & Company The state of fashion 2022 Retrieved from https://www.mckinsey.com/∼/media/mckinsey/industries/retail/our%20insights/state%20of%20fashion/2022/the-state-of-fashion-2022.pdf 2022
McWilliams A. Siegel D.S. Creating and capturing value: strategic corporate social responsibility, resource-based theory, and sustainable competitive advantage J. Manag. 37 5 2011 1480 1495
MERCO – Monitor Empresarial de Reputación Corporativa Ranking Merco. Responsabilidad y gobierno corporativo 2020 Retrieved from https://www.merco.info/es/actualidad/ranking-merco-responsabilidad-y-gobierno-corporativo-2020 2021
Mohr L.A. Webb D.J. The effects of corporate social responsibility and price on consumer responses J. Consum. Aff. 39 1 2005 121 147
Morris S.A. Bartkus B.R. Glassman M. Rhiel G.S. Philanthropy and corporate reputation: an empirical investigation Corp. Reput. Rev. 16 4 2013 285 299
Mundo El El Hospital Isabel Zendal de Madrid recibe a su primer paciente Covid. El Mundo, 11th December Retrieved from https://www.elmundo.es/madrid/2020/12/11/5fd35c44fc6c83f3178b45b5.html 2020
Nan X. Heo K. Consumer responses to corporate social responsibility (CSR) initiatives: examining the role of brand-cause fit in cause-related marketing J. Advert. 36 2 2007 63 74
Nelling E. Webb E. Corporate social responsibility and financial performance: the “virtuous circle” revisited Rev. Quant. Finance Account. 32 2 2009 197 209
Ng S.J.H. Rizal A.M. Khalid H. Fei T.C. Increasing customer satisfaction through omnichannel retailing Soc. Sci. 11 11 2021 696 707
Nguyen, P.A., Kecskés, A., Mansi, S. 2020. Does corporate social responsibility create shareholder value? The importance of long-term investors. Journal of Banking & Finance, 112(C).
Nieto M. Fernández R. Responsabilidad social corporativa: la última innovación en management Universia Business Review 1 1 2004 28 39
Oruc I. Sarikaya M. Normative stakeholder theory in relation to ethics of care Soc. Responsib. J. 7 3 2011 381 392
País El COVID-19. Un año después. Un año roto. El País, 19 March Retrieved from https://elpais.com/sociedad/2021-03-19/primer-aniversario-del-confinamiento-por-coronavirus.html 2021
Papasolomou I. 'Radiomarathon' in the Guinness World of Records: a tool for building a strong corporate reputation through corporate social responsibility Soc. Responsib. J. 1 1/2 2005 108 117
Park B.I. Ghauri P.N. Determinants influencing CSR practices in small and medium sized MNE subsidiaries: a stakeholder perspective J. World Bus. 50 1 2015 192 204
Park B.I. Chidlow A. Choi J. Corporate social responsibility: stakeholders influence on MNEs’ activities Int. Bus. Rev. 23 5 2014 966 980
Peterson D.K. Enhancing corporate reputation through corporate philanthropy J. Str. Manag. 11 1 2018 18 32
Peterson D.K. Van Landuyt C. Pham C. Motives for corporate philanthropy and charitable causes supported J. Str. Manag. 14 4 2021 397 412
Priem R. An Exploratory Study on the Impact of the COVID-19 Confinement on the Financial Behavior of Individual Investors Econom. Manag. Financial Markets 16 3 2021 9 40
Puerta J.F. El caso Iberdrola: RSE, algo más que una operación de imagen Marketing & Ventas 77 Nov./Dec. 2006 50 54
Rathert N. Strategies of legitimation: MNEs and the adoption of CSR in response to host-country institutions J. Int. Bus. Stud. 47 7 2016 858 879
Redacción Médica Coronavirus: Amancio Ortega dona al SNS 63 millones en material sanitario. Redacción Médica Retrieved from https://www.redaccionmedica.com/secciones/sanidad-hoy/coronavirus-amancio-ortega-dona-al-sns-63-millones-en-material-sanitario-5320 2020
Resnick J.T. Corporate reputation: managing corporate reputation - applying rigorous measures to a key asset J. Bus. Strat. 25 6 2004 30 38
Ritala P. Huotari P. Bocken N. Albareda L. Puumalainen K. Sustainable business model adoption among S&P 500 firms: a longitudinal content analysis study J. Clean. Prod. 170 2018 216 226
Royal Decree Law Real Decreto-ley 11/2020, de 31 de marzo, por el que se adoptan medidas urgentes complementarias en el ámbito social y económico para hacer frente al COVID-19 Retrieved from https://www.boe.es/buscar/pdf/2020/BOE-A-2020-4208-consolidado.pdf
Ruano D. Rojas P. Marcas responsables: ¿oportunidad o necesidad? Harvard Deusto Marketing & Ventas 77 Nov./Dec. 2006 40 43
Saeed A. Sroufe R. Performance, risk, and cost of capital: trends and opportunities for future CSR research J. Risk Financ. Manag. 14 12 2021 586
Sanchez-Hernandez I. Grayson D. Internal marketing for engaging employees on the corporate responsibility journey Intang. Cap. 8 2 2012 275 307
Sánchez-Hernández M.I. Vázquez-Burguete J.L. García-Miguélez M.P. Lanero-Carrizo A. Internal corporate social responsibility for sustainability Sustainability 13 14 2021 7920
Santos G. Murmura F. Bravi L. SA 8000 as a tool for a sustainable development strategy Corp. Soc. Responsib. Environ. Manag. 25 1 2018 95 105
Schrage S. Gilbert D.U. Addressing governance gaps in global value chains: introducing a systematic typology J. Bus. Ethics 170 4 2021 657 672
Sen S. Bhattacharya C.B. Does doing good always lead to doing better? Consumer reactions to corporate social responsibility J. Market. Res. 38 May 2001 225 243
Servaes H. Tamayo A. The impact of corporate social responsibility on firm value: the role of customer awareness Manag. Sci. 59 5 2013 1045 1061
Smith N.C. Responsible consumers and stakeholder marketing: building a virtuous circle of social responsibility Universia Bus. Rev. 30 2011 68 78
Suchman M.C. Managing legitimacy: strategic and institutional approaches Acad. Manag. Rev. 20 3 1995 571 610
Szőcs I. Schlegelmilch B.B. Rusch T. Shamma H.M. Linking cause assessment, corporate philanthropy, and corporate reputation J. Acad. Market. Sci. 44 3 2016 376 396
Tebaldi L. Brun A. Bottani E. Evidences on sustainability issues in the Fashion Supply Chain: An empirical study in Italy Sustainable Production and Consumption 33 2022 651 663
Ugalde R. El coste patriótico de Inditex: más de 300M entre salaries, donaciones y logística. El Confidencial, 11 June Retrieved from https://www.elconfidencial.com/empresas/2020-06-11/coste-patriotico-inditex-salarios-donaciones-logistica_2633732/ 2020
Van Steenburg E. Anaza N.A. Ashhar A. Barrios A. Deutsch A.R. Gardner M.P. Priya P. Roy A. Sivaraman A. Taylor K.A. The new world of philanthropy: how changing financial behavior, public policies, and COVID-19 affect nonprofit fundraising and marketing J. Consum. Aff. 2022 1 27
Vanguardia La Inditex pone toda su capacidad logística al servicio de las autoridades sanitarias. La Vanguardia, 22 April Retrieved from https://www.lavanguardia.com/vida/20200422/48633902940/inditex-capacidad-logistica-servicio-autoridades-sanitarias-coronavirus-brl.html 2020
Vătămănescu E.M. Dabija D.C. Gazzola P. Cegarro-Navarro J.G. Buzzi T. Before and after the outbreak of covid-19: linking fashion companies' corporate social responsibility approach to consumers’ demand for sustainable products J. Clean. Prod. 321 2021 128945
Voss C. Tsikriktsis N. Frohlich M. Case research in operations management International Journal of Operations & Production Management 22 2 2002 195 219
Voz de Galicia La Inditex transporta gratis material para combatir el virus valorado en 457 millones. La Voz de Galicia 23 April. Retrieved from https://www.lavozdegalicia.es/noticia/sociedad/2020/04/23/inditex-transporta-gratis-material-combatir-virus-valorado-457-millones/00031587665909843789623.htm 2020
WHO – World Health Organization Official website Retrieved from https://who.org 2020
Yaolung J.H. Exploring corporate donation behavior: a case study of taiwan J. Nonprofit & Public Sect. Mark. 12 1 2004 69 91
Yin R.K. Case Study Research Design and Methods fifth ed. 2014 Sage Thousand Oaks, CA
Zhao X. Murrell A. Does A virtuous circle really exist? Revisiting the causal linkage between CSP and CFP J. Bus. Ethics 177 1 2022 173 192
Zimmermann K.F. Karabulut G. Bilgin M.H. Doker A.C. Inter-country distancing, globalisation and the coronavirus pandemic World Econ. 43 6 2020 1484 1498 32836720
| 36510571 | PMC9729172 | NO-CC CODE | 2022-12-15 23:17:52 | no | Heliyon. 2022 Dec 8; 8(12):e12188 | utf-8 | Heliyon | 2,022 | 10.1016/j.heliyon.2022.e12188 | oa_other |
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Telemat Inform
Telemat Inform
Telematics and Informatics
0736-5853
1879-324X
Elsevier Ltd.
S0736-5853(22)00156-3
10.1016/j.tele.2022.101923
101923
Article
COVID-19 and sustainable development goals: A bibliometric analysis and SWOT analysis in Malaysian context
Nilashi Mehrbakhsh ab⁎
Ali Abumalloh Rabab c
Mohd Saidatulakmal g
Nurlaili Farhana Syed Azhar Sharifah b
Samad Sarminah d
Hang Thi Ha ef⁎
Alghamdi OA h
Alghamdi Abdullah i
a UCSI Graduate Business School, UCSI University, No. 1 Jalan Menara Gading, UCSI Heights, 56000, Cheras, Kuala Lumpur, Malaysia
b Centre for Global Sustainability Studies (CGSS), Universiti Sains Malaysia, 11800 Penang, Malaysia
c Computer Department, Applied College, Imam Abdulrahman Bin Faisal University, P.O. Box. 1982, Dammam, Saudi Arabia
d Department of Business Administration, College of Business and Administration, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
e Institute of Research and Development, Duy Tan University, Da Nang, VietNam
f International School, Duy Tan University, Da Nang, VietNam
g School of Social Sciences, Universiti Sains Malaysia, USM Penang, Malaysia
h Business Administration Dept., Applied College, Najran University, Najran, Saudi Arabia
i Information Systems Dept., College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia
⁎ Corresponding author at: UCSI Graduate Business School, UCSI University, No. 1 Jalan Menara Gading, UCSI Heights, 56000, Cheras, Kuala Lumpur, Malaysia; Institute of Research and Development, Duy Tan University, Da Nang, VietNam.
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The COVID-19 crisis has been a core threat to the lives of billions of individuals over the world. The COVID-19 crisis has influenced governments’ aims to meet UN Sustainable Development Goals (SDGs); leading to exceptional conditions of fragility, poverty, job loss, and hunger all over the world. This study aims to investigate the current studies that concentrate on the COVID-19 crisis and its implications on SDGs using a bibliometric analysis approach. The study also deployed the Strengths, Weaknesses, Opportunities, and Threats (SWOT) approach to perform a systematic analysis of the SDGs, with an emphasis on the COVID-19 crisis impact on Malaysia. The results of the study indicated the unprecedented obstacles faced by countries to meet the UN's SDGs in terms of implementation, coordination, trade-off decisions, and regional issues. The study also stressed the impact of COVID-19 on the implementation of the SDGs focusing on the income, education, and health aspects. The outcomes highlighted the emerging opportunities of the crisis that include an improvement in the health sector, the adoption of online modes in education, the swift digital transformation, and the global focus on environmental issues. Our study demonstrated that, in the post-crisis time, the ratio of citizens in poverty could grow up more than the current national stated values. We stressed the need to design an international agreement to reconsider the implementation of SDGs, among which, are strategic schemes to identify vital and appropriate policies.
Keywords
Sustainable Development Goals
COVID-19
Bibliometric Analysis
SWOT
Malaysia
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pmc1 Introduction
Sustainable development has gained global interest regarding its interrelated relationship with the environment, economies, and societies (Bansal et al., 2020). “Sustainable development” is defined as the ability of mankind to develop and meet the increasing demands, without a compromisation of the next generations’ demands (de Sousa FDB, 2021, Iwuoha and Jude-Iwuoha, 2020). By September 2015, the UN presented a blueprint for 2030, which is called Sustainable Development Goals (SDGs). The blueprint entails 169 targets, 17 goals, and 244 indices. The UN's SDGs are presented to respond to worldwide multilateral issues related to the sustainability of the environment, economies, and societies (Hall, 2019). SDGs encouraged researchers and decision-makers to strive together to find solutions for economic growth without negative impacts on the environment or community members. Based on that, three elements that form the pillars of sustainability (or sustainability tripod), which are environment, economy, and human development, were presented. Sustainable growth can only be accomplished if the three pillars are met and balanced (Daly, 1991).
Focusing on Asia and the Pacific, countries are struggling toward meeting the SDGs and the challenging aims, yet the progress is inadequate and has, in fact, been delayed (United Nations, 2015). Malaysia, as one of the developing Asian countries, has shown considerable and steady economic development. Malaysia’s efforts have been focused on addressing environmental, social, and economic issues. The sustainable development journey in Malaysia has begun in the 1970s and continued since then through adopting many efforts to meet the aimed 2030-vision (Khan et al., 2021). Referring to the SDG index report, Malaysia is ranked 68th and 4th among the ASEAN countries in 2019 (Khan et al., 2021). In 2009, a new economic model was developed, incorporating the main significant themes of SDGs that include sustainability, inclusivity, and high income (Economic Planning Unit, 2017). In the last years, the eleventh New Economic Model is taking place. Besides, Malaysia has taken huge initiatives to meet sustainable development by protecting and enhancing the environment (Sundram et al., 2021). Although Malaysia has wealthy resources, with a huge portion of the economy in the country depending on these resources, the ongoing consumption and utilization of resources are impacting the quality of the environment. Hence, decision-makers in the country need to utilize various forms of practices to reach broad deployment of sustainable practices, while utilizing environmental resources. Besides, the challenges of meeting these targets have been enlarged recently by the rise in the intensity and frequency of man-made disasters and natural crises, particularly the challenges of addressing the coronavirus disease pandemic.
COVID-19 was called SARS-CoV-2 by the International Committee on Taxonomy of Viruses. It has been considered an international challenge to the medical sector, which has induced a change in WHO strategies and priorities (Fagbemi, 2021, Nilashi et al., 2020). The COVID-19 crisis has impacted around 2,700,000 individuals worldwide, leading to 190,000 deaths (by April 23, 2020) and reaching a large proportion of individuals in countries around the globe (Nilashi et al., 2022, Sharma et al., 2020b). Several meteorological variables have been linked with COVID-19 spread and death ratios (Ahani and Nilashi, 2020, Sharma et al., 2021a). Particularly for South Asian countries, a considerable relation between COVID-19 number of cases, the number of deaths, meteorological variables, and the air pollutant, was found (Jain et al., 2021). The rise of the COVID-19 crisis, worldwide, with its severe influences on several disciplines of life (Abumalloh et al., 2021b, Rupani et al., 2020, Sood and Rawat, 2021), has dramatically impacted economic activities (Nilashi et al., 2021). It has modified the global concentration and changed the worldwide economy by presenting exceptional macroeconomic obstacles. To face this pandemic, several measures and interventions by governments were imposed to control the spread of the virus (McKibbin and Fernando, 2021), which has led to inescapable economic decline worldwide. The lockdown measures have led to slowing the production of goods, reducing trade activities, minimizing economic activities, increasing business risks, and declining exports, which led to financial mismanagement and caused various impacts on the local and global levels (Sharma et al., 2021b). Accordingly, the crisis has resulted in a growth in poverty ratios to some degree as several workers in many sectors have become under-employed or unemployed (Karunathilake, 2021). It is undeniable that the existing health crisis has steadily forced social, psychological, and economic spectrum over the globe.
As a consequence of the COVID-19 crisis, SDGs have gained increasing interest as a crucial global demand (Asadi et al., 2022, Contreras, 2020, Kim and Hall, 2021). In April 2020, the impacts on SDGs progression were discussed by Sustainable Development Solutions Network (SDSN), indicating the negative and positive influences to provide a recovery scheme (Macht et al., 2020). Focusing on the negative impact, COVID-19 has impacted the SDGs and declined the progression of the approved SDGs (Fagbemi, 2021). On the other hand, the current crisis has changed the focus towards a fresh framework entailing digitized and sustainable business, with less hostile impacts on society and the environment (Sharma et al., 2021c). Based on the above discussion, it is important to investigate the challenges faced by governments to meet the SDGs within the current crisis of COVID-19. This can be achieved through careful consideration of published studies in this context.
Previous literature has investigated many folds of research through bibliometrics analysis using various statistical approaches to explore the links among sources, countries, citations, and researchers (Elango and Ho, 2017, Merigó et al., 2015, Yu et al., 2017). Bibliometric analysis might include network, geospatial, topical, and temporal types of analyses (Milian et al., 2019). A group of studies that meet predetermined conditions can be inspected and visualized automatically through social maps, like co-keywords, co-citations, co-terms, and co-authorship diagrams using several tools such as VOSviewer, CiteSpace (Sood et al., 2022), and CitNetExplorer. In this study, VOSviewer software was utilized to conduct the bibliometric analysis. This tool allows a quick analysis of the data to generate bibliometric visualizations based on the defined clusters and their linked maps (Perianes-Rodriguez et al., 2016, Van Eck and Waltman, 2010). The VOSviewer tool is distinguished by visualizing the bibliometric data graphically through particular algorithms, enabling zooming of the visualizations, and presenting density metaphors (Barroso and Laborda, 2022). The generated maps can be explored and provide insights about different aspects of previous literature including keywords, countries, organizations, researchers, and journals. Different types of connections can be utilized to present the diagrams using VOSviewer, which include co-citation, citation, co-occurring, and co-authoring (Durana et al., 2020). Compared to CiteSpace, VOSviewer offers more user-friendly and clear visualizations (Markscheffel and Schröter, 2021). Other tools such as the CitNetExplorer tool concentrates on analyzing the studies at the individual level, while VOSviewer concentrates on analyzing the studies at the aggregate level (Van Eck and Waltman, 2017).
Besides, in this study Strengths, Weaknesses, Opportunities, and Threats (SWOT) approach was performed to support the lack of literature in the context of the study, as a systematic analysis of the SDGs, with an emphasis on the COVID-19 crisis impact in the Malaysian context. The topic of the study is new and needs more elaboration using several approaches and focusing on several folds. Based on the above, the main goal of this study can be summarized as follows:i. To investigate the impact of COVID-19 on SDGs from different perspectives, with an emphasis on the Malaysian context.
To achieve the goal of the study, the following approaches are followed:i. A bibliometric analysis of previous works related to SDGs and the COVID-19 crisis.
ii. A SWOT analysis of the SDGs in light of the current COVID-19 crisis to explore the strengths, weaknesses, opportunities, and threats during the COVID-19 crisis, with an emphasis on the Malaysian Context
In Section 2, a review of Sustainable Development Goals is presented. In Section 3, a review of SDGs in relation to disasters is provided. In Section 4, a description of the review methodology is presented. Section 5 presents the bibliometric analysis of the studies to visualize research keywords and occurrences. In Section 6 we present the SWOT analysis. The discussion of the results is presented in Section 7. Research contributions are presented in Section 8. Finally, the conclusion of this study is presented in Section 9. To summarize, we present a list of abbreviations used in this study in Table 1 .Table 1 List of Abbreviations in the Study.
Abbreviation Term
UN United Nations
SDGs Sustainable Development Goals
WHO World Health Organization
SARS-CoV-2 Acute Respiratory Syndrome Coronavirus 2
COVID-19 Coronavirus Disease of 2019
SWOT Strengths, Weaknesses, Opportunities, and Threats
MCO Movement Control Order
MOH Ministry of Health
PPE Personal Protective Equipment
rRT-PCR Real-Time Reverse Transcription-Polymerase Chain Reaction
ML Machine Learning
UNDP United Nations Development Program
NLP Natural Language Processing
SDSN Sustainable Development Solutions Network
GCC Gulf Cooperation Council
UNDRR United Nations Office for Disaster Risk Reduction
ICUs Intensive Care Units
SFDRR Sendai Framework for Disaster Risk Reduction
MCDM Multi-Criteria Decision-Making
ICT Information and Communications Technology
2 Introduction of sustainable development goals
Humans evolve through emerging advancements in several fields such as industrialization, globalization, urbanization, green revolution, and digitalization. Still, in these fields, the sustainability vision was not integrated, and as a consequence, the environment was impacted in several ways. Nowadays, as human beings are more advanced, they need mature plans that integrate sustainability into their schedules. This enabled the provision of SDGs by the UN (United Nations, 2015) that guide human activities, in which both communities and the environment develop as a united robust system. Based on the provision of these aims, the development of communities needs much concentration on SDGs to address emerging needs. Sustainable development goals are presented in Fig. 1 . The 2021 SDG dashboards (levels and trends) for East and South Asia are shown in Fig. 2 .Fig. 1 Sustainable Development Goals.
Fig. 2 The 2021 SDG dashboards (levels and trends) for East and South Asia (Sustainable Development Report, 2021).
Several studies have explored SDGs focusing on the proposed goals individually or in general. A study by Grossi and Trunova (2021) investigated SDGs with an emphasis on smart cities. Based on the review of the literature, the authors indicated the need for a universal measurement system to capture the regional features of smart cities. Boess et al. (2021) investigated the activities of environmental impact assessment and strategic environmental assessment and how they can be impacted by the deployment of SDGs. By considering 45 cases, the authors concluded that there is a need to define basic instructions for SDGs implementation in order to fill the gap between theoretical and practical aspects considering environmental assessment activities. A study by Asadikia et al. (2021), adopted Boosted Regression Trees using ML and data mining approaches to define synergetic SDGs. The result of the study identifies SDG3, SDG4, and SDG7 as the most synergetic goals. Peng et al. (2021) explored the ecosystem service value in the time interval of 2015–2035 based on three contexts and inspected their impacts on the SDGs. The study presented an integrated scheme to evaluate the possible influences of land usage and modifications on the ecosystem service value, which could present basic directions for urban growth toward meeting the SDGs. Malik et al. (2021) analyzed the negative spillover impacts, with a focus on the safety of workers and occupational health based on two indexes: fatal and non-fatal incidents that occur in the global supply chains. Based on the study, several countries were indicated as being responsible for around 80% of fatal and non-fatal incidents. Al-Saidi (2021) focused on the GCC region to inspect interstate relationships between GCC countries. The study investigated how to meet inclusive sustainability results through local environmental collaboration. According to the authors, cooperative blueprints and problem representation should be strengthened to face the emerging increase in interstate competition and political rifts, which negatively impact the capability of regions to participate in future environmental performance. Focusing on food security, Vogliano et al. (2021) examined the progression and obstacles related to facing the hunger in Melanesia according to SDGs2. The research indicated the progression that has been achieved in minimizing wasting and stunting. On the other hand, the authors indicated the need to reverse the growing ratios of Non-Communicable Diseases and meet food security in the country.
3 SDGs and disaster
As defined by UNDRR, a disaster is a serious breakdown of the operations of a society or community at a particular level because of dangerous events linked with a situation of capacity, vulnerability, and exposure, causing environmental, economic, material, or human losses and influences (Ainuddin et al., 2013). Disaster Risk Reduction (DRR) practices have been derived from development ideas and they have become interconnected (Lewis, 2012). Disaster risk and sustainable development are internally interconnected (Uitto and Shaw, 2016). SDG indices have a significant impact on assessing the progression toward Disaster Risk Reduction, such as service access (SDGs 6, SDGs 7, and SDGs 11), education access (SDG 4), land tenure (SDGs 1 and SDGs 11), health (SDG 3), poverty (SDG 1), gender disparities (SDG 5), and others (Chmutina et al., 2021). One individual large disaster such as a natural disaster (storm, earthquake, landslide, or tsunami) can stop the progression and postpone the development for years. Most previous studies on SDGs and disaster recovery have concentrated on natural disasters (Ahmad et al., 2018, Kelman, 2017, Koubi, 2019, O'Brien, 2006). Only a few studies have concentrated on health threats and SDGs, particularly the emerging COVID-19 crisis (Ekwebelem et al., 2021, Leal Filho et al., 2020). Hence, to assure the progression of countries to meet the SDGs, a suitable plan to address the potential disasters is required.
Sustainable development is interconnected to crisis recovery as it should entail policies and choices regarding investment places, communities' exposures to risks, and places of occurrence of natural disasters (O'Brien, 2006, Paton and Johnston, 2017). On the other hand, adopting unsustainable development methods have a large impact on disasters. As the SDGs present a beneficial blueprint to design regional sustainable development policies and control the efficiency of the deployed policies, they should present effective strategies, in which the impact of multi-hazard threats on humans’ can be addressed (Pramanik et al., 2021). These threats, which have several economic, health, and environmental types, can hit the development of humans broadly. SDGs are developed to be performed in a universal style, in which coherent collaborations between interrelated aims are considered. This presents a crucial consideration to investigate the several influences of the COVID-19 crisis on the short and long-run impacts on the SDGs. It is important to cover the human fold of sustainable development as a basic factor to meet sustainability development (von Schirnding, 2002). Referring to Fig. 3 (World in Data, 2021), COVID-19 impacted health outcomes and mortality all around the world and led to a decrease in life expectancy in many developed countries. Based on the Sustainable Development Report (2021), COVID-19 led to a world recession in 2020 and to a sharp increase in unemployment everywhere (see Fig. 4 ). School closures have short-term and long-term impacts on students’ learning and well-being (see Fig. 5 ). After years of progress, extreme poverty increased in several regions in 2020 (Fig. 6 ). According to the (Sustainable Development Report, 2021), CO₂ emissions in major economies did not take long to come back to their pre‑pandemic levels (Fig. 7 ). Please refer to Sustainable Development Report (2021) for more information about Fig. 2, Fig. 3, Fig. 4, Fig. 5, Fig. 6, and Fig. 7.Fig. 3 Cumulative COVID‑19 cases per million population.
Fig. 4 Unemployment rate (as a percentage of the total workforce).
Fig. 5 Percentage of school closures during COVID-19 (UNESCO, 2021).
Fig. 6 Percentage of poverty (less than $1.90 a day) (Sustainable Development Report, 2021).
Fig. 7 Daily CO₂ emissions (Mt CO₂).
It is important to mention SFDRR, as a basic scheme for disaster risk reduction around the globe, which is approved by the UN in 2015 (Chmutina et al., 2021). The SFDRR provides a comprehensive scheme for addressing various threats with varying levels of severity and influence. It focuses on Investing in Disaster Risk Reduction by enhancing the level of readiness, response, and resilience, to allow the reconstruction, rehabilitation, and recovery components of the disaster risk reduction in the development strategy (Sukmara and Pradita, 2021). It integrates the concept of “build back better” to deal with disaster risk. The SFDRR tried to address the shortcomings of previous frameworks by focusing on poverty and inequality as essential reasons for humans’ vulnerability and, consequentially, disaster vulnerability. Meeting the SFDRR needs appropriate goals of decentralization along with collaborative work. Still, the emergence of the COVID-19 disaster, with all its consequences and the huge number of impacted people, raised a question about whether SFDRR helps us to move forward with actual progress toward disaster risk reduction. The adoption of SFDRR has been linked to the SDG 2030 Agenda as a guide for the deployment of higher-level goals that are focused on disaster-related SDG goals (Wright et al., 2020). SDGs and SFDRR goals can be achieved based on both economic and social efforts (Chmutina et al., 2021). The schemes of the two instruments are interconnected, as 10 of the 17 SDGs have 25 disaster risk reduction-related targets (UNDRR, 2015). SFDRR adopted many of the SDGs (SDG1, SDG11, and SDG13) and their interrelated targets (target 1.5, target 11.5, target 11.b, and target 13.1) (UNDRR, 2015). SFDRR targets from A to E are linked to these SDGs, including 13.1.1, 11.b.2, 11.b.1, 11.5.1, 11.5.2, and 1.5.4. Still, there is a difference between SFDRR and SDGs, as the indicators in the former are being focused on disaster risk reduction strategies at a national level. The basic concept of SFDRR is not about struggling with causes of vulnerability but it is centered on reaching less impact of the disaster. Careful monitoring of SDGs implementation will aid in locating the causes and development of crisis overtime.
In the context of the COVID-19 crisis, focusing on risk reduction, SFDRR mentions the biological threat as a pandemic or crisis. The SFDRR focuses on human, technological, environmental, and biological threats such as the current COVID-19 crisis (Djalante et al., 2020). Adding biological threats indicates the importance of incorporating appropriate management of risk in all areas of medical care and emphasizing the collaboration between medical bodies with all involved stakeholders to enhance risk management in the country (Aitsi-Selmi et al., 2015, Marome and Shaw, 2021). SFDRR entails more than 30 references related to medical and health aspects to improve the well-being of individuals under the threat of crisis and disasters (WHO, 2020).
4 Review methodology
At the beginning of this research, we identified the following research goal: to investigate the impact of the COVID-19 pandemic on SDGs. A search was performed in the Scopus database to get the bibliographic data on July 10, 2021, by utilizing the following search words (“Sustainable Development Goals” OR “SDGs” OR “SDG”) AND (“COVID-19” OR ”Coronavirus“ OR ”SARS-COV-2“). Several studies have used the Scopus database to conduct a systematic literature review and bibliometric analysis in several disciplines of research such as medicine (Abumalloh et al., 2021b, Rodrigues et al., 2014), tourism (De la Hoz-Correa et al., 2018), and SDGs (de Sousa, 2021). Particularly, bibliometric analysis has gained increasing attention from researchers to explore the development of research topics and emerging research trends in-depth such as the work by (Yu et al., 2018) and the work by (Yu et al., 2019). While (Yu et al., 2018) focused on the development of fuzzy theory research, (Yu et al., 2019) concentrated on the development of applied intelligence research using bibliometric analysis. The present study considered Scopus for data collection as it has wider journal coverage compared to the WOS (Aksnes and Sivertsen, 2019, Mongeon and Paul-Hus, 2016), and indexes only the peer-reviewed scholarly literature which is not the case with Google Scholar. Hence, we retrieved 378 studies. English language studies were only obtained. In this article, the VOSviewer tool was used to analyze the bibliometric data. The VOSviewer program is used to generate maps based on links between research papers (de Sousa, 2021). This tool enables swift and easy analysis of the electronic resources and presents simple visualizations that reflect particular segments of data (Perianes-Rodriguez et al., 2016, Van Eck and Waltman, 2010). Besides, this tool allows the visualization of affiliations, countries, authorships, keywords, and citations to emphasize the relationships between the selected research studies. A unified method of mapping and segmentation is deployed by the program, which is based on providing a co-occurrence array and measuring the similarity to present the strength of links between words (Van Eck and Waltman, 2010). Two types of files were exported to the program: Scopus.csv and Scopus.bib. The review protocol is presented in Fig. 8 .Fig. 8 The Review Protocol.
5 Bibliometric analysis
Bibliometric research presents a holistic perception of many disciplines linked to a particular topic of research. The deep analysis of the retrieved studies helps researchers to visualize the existing knowledge of a specific topic and aids to highlight future directions. Bibliometric analysis can be considered a specific type of library science (Bonilla et al., 2015), which has two basic techniques: analyzing the performance and visualizing scientific research (Noyons et al., 1999). The first technique focuses on investigating the scientific piece of research by evaluating all the data incorporated from the published studies (keywords, affiliations, authors, countries, etc.) quantitatively. On the other hand, the second analyses the associations that are resulted from the retrieved data, which are visualized in organized networks (Montalván-Burbano et al., 2020). Several studies have utilized bibliometric analysis focusing on many areas, such as mobile learning (Goksu, 2021), teaching (Yilmaz et al., 2019), fitness (Liu and Avello, 2021), and plastic waste (de Sousa, 2020). These studies have adopted bibliometric analysis to explore published studies based on algorithmic analysis of words, citations, and authorships using particular tools (Elango and Ho, 2017, Merigó et al., 2015, Yu et al., 2017).
After retrieving the articles from specific electronic resources, the bibliometric analysis technique can be used to scrutinize these articles and analyze the retrieved data based on year, subject, organization, and country of study. Bibliometrics can also incorporate the retrieved data automatically based on social links, such as keywords, co-citations, and co-authorship. Keyword links indicate the co-occurrence of keywords between the two studies. Co-citation links refer to the studies that are being cited by the same research (Small, 1973). Many types of research have been performed focusing on bibliometric analysis (Yu, 2015, Yu and Shi, 2015, Zhang and Feng, 2014).
5.1 Keyword Co-occurrence, Subject, and worlcloud visualizations
Before proceeding to the bibliometric analysis of the surveyed studies, it is useful to investigate the distribution of subjects of the published articles. In Fig. 9 , we display the subjects of the surveyed studies based on their records in the Scopus database. Among the surveyed studies, 176 studies belong to the social science category. 135 studies are categorized under environmental science. 86 studies are from the medical field. The energy field has 83 studies. Next, the business, management, and accounting fields have 42 studies. 41 studies belong to the economics, econometrics, and finance fields. The engineering field has 30 studies. This result indicates the multidisciplinary nature of the research topic as research articles have been categorized under various subjects, focusing primarily on social sciences and environmental research.Fig. 9 Distribution of Papers per Subject.
Word cloud is used as a graphical representation to reflect a set of terms based on their related quantitative calculations. Additionally, word clouds can help scholars to get indications about what a study or several studies may contain (Felix et al., 2018). Such diagrams are usually generated using a set of NLP approaches to retrieve the most occurred words. The diagram is organized in a space-enhanced concise structure, in which the font size reflects the number of indices of items. Wordcloud of the abstracts of the obtained research is displayed in Fig. 10 . As presented in the diagram, the most occurred words in the surveyed studies are disease, health, goals, education, social, policy, climate, and pandemic. These words reflect the main topics that were discussed in the surveyed studies.Fig. 10 Wordcloud of the Abstracts of the Included Studies.
VOSviewer tool was used to segment the keywords of the retrieved data and present the links between these keywords. In Fig. 11 , the structure of the keywords in the studies is presented. Based on the specified terms, these studies were retrieved from the Scopus database. The visual image of the keywords presents the thematic structure of the topic and classifies the included studies. Based on the figure, this research has a multidisciplinary nature, in which several themes were incorporated. Basically, the figure utilizes the co-occurrence-keyword technique to present the segments of keywords. The “Full counting” approach was used and “all keywords” was used as the analysis unit. To integrate the words with similar meanings such as “coronavirus infection” and “coronavirus infections”, “coronavirus disease 2019” and “COVID-19”, or “environmental factor” and “environmental factors”; we prepared a thesaurus file to be used by the program.Fig. 11 A Visualization of Co-Occurrence Diagrams: (A) Network Visualization, (B) Density Visualization.
In the figure, 340 keywords were included among 2640 words. To be included in the analysis, 3 occurrences of words should exist in the titles, abstracts, or keywords of the studies. General keywords that don’t have a specific relation to the topic of research were excluded, like “Journal”, “Study”, or “Article”. Besides, we included only keywords with the most total weights of the co-occurrence associations with other keywords. The length of the path between two keywords is established based on the number of articles that entail the two keywords. More indices of keywords indicate shorter links among them and are represented by circles of bigger sizes.
Segment 1 has 104 items, segment 2 has 76 items, segment 3 has 64 items, segment 4 has 49 items, and segment 5 has 45 items. The red color segment concentrates on the current crisis and its relation with sustainable development goals, as “sustainable development goals” appeared 239 times and “COVID-19” has 207 indices. In the green color segment, “humans” is the main keyword with 76 indices. The blue color segment focuses on the areas related to the disease as a current health crisis. Hence, the “pandemic” has 81 indices, “epidemic” has 23 indices, and “SARS-COV-2” has 27 indices. In the yellow color segment, “Coronavirus infections” has 17 occurrences. “Virus pneumonia” has 16 occurrences. In the purple color segment, “education” is the main keyword, with 22 indices, and “e-learning” has 11 occurrences. Based on the result of the bibliometric analysis, the impact of COVID-19 on SDGs has been mostly investigated focusing on the human factor, health factor, and education factor.
5.2 Term Co-occurrence Network
Using the VOS viewer tool, we generated a term-co-occurrence diagram, which is based on the terms obtained from abstracts and titles of the studies. In this diagram, we choose to ignore standard titles of abstract and copyright sentences. We deployed a binary counting technique with the minimum count of terms to be considered in the obtained studies as 5. This resulted in 544 terms being considered among 9853. A measure of relevance was calculated for each term, in which most linked terms were held, thus; only 326 terms were involved. The default setting of the program is set to keep 60% of terms, which leads to five groups of terms. A diagram of term co-occurrences is displayed in Fig. 12 .Fig. 12 A Visualization of Term- Co-Occurrence Diagrams: (A) Network Visualization, (B) Density Visualization.
Segment 1 has 82 items, Segment 2 has 79 items, Segment 3 has 56 items, Segment 4 has 55 items, and Segment 5 has 54 items. The red color segment has several words with several occurrences that range from 14 to 22, such as tourism, ecosystem, interest, and innovation, with occurrences of 16, 16, 20, and 22, respectively. The next segment “green color” concentrates on the health side of the pandemic. Hence, in the green color segment, “disease” is the main keyword with 52 indices, “burden” has 21 indices, and “health system” has 19 occurrences. The blue color segment focuses on the areas related to the impact of the crisis on the education and learning system. Hence, “education” has 54 indices, “learning” has 24 indices, and “quality education” has 15 indices. In the yellow color segment, “development goal” is the main keyword, with 268 indices. The purple color segment concentrates on the impact of the crisis on the food system, as “food” has 25 occurrences, “food security” has 19 indices, and “agriculture” has 15 occurrences. Based on the result of the bibliometric analysis of the terms, the impact of COVID-19 on SDGs has been mostly investigated focusing on several domains including tourism, ecosystem, health, education, e-learning, food, food security, and agriculture.
5.3 Co-Authorship Network: Countries and organizations
Fig. 13 presents the number of studies per country based on the retrieved data from the Scopus electronic resource. Based on the obtained data, 71 publications are from the United States, followed by 67 studies from the United Kingdom. 33 publications were from India, while 28 studies were from each of Australia and Spain. Each of Canada and Germany has 22 studies. China has 20 publications. Each of Brazil and Italy has 16 studies. Each of Japan and South Africa has 15 publications. Other countries have less than 15 studies as presented in the diagram.Fig. 13 Distribution of Papers per Country.
Co-authorship maps can display the links of co-authorship referring to three measures to analyze the data: countries, authors, and organizations. In this study, our goal was to display the links among authors by adopting two types of units “countries” and “organizations”. Fig. 14 (A) shows the co-authorship paths among authors in a specific country with authors from other countries, while Fig. 14 (B) shows the co-authorship paths among authors in a specific organization with authors from other organizations. The link’s weight represents the strength of the co-authorship relation of a particular author with others. A full counting setting was used in the tool. The weight of the connection in the diagram represents the number of publications co-authored by both researchers. The minimum count of publications per country was initialized to one, which resulted in 121 countries that met the inclusion criterion. The total link strength falls in the interval [0–152], in which 38 countries have a value above 10. This reflects the firm co-authorship connections between researchers considering the research area. The second diagram, which is based on the organization, is presented in Fig. 14 (B). Based on the figure, as the minimum number of documents for an organization is 1, we get 1120 organizations. Following that and based on the total link strength, we obtained 1000 organizations. Still, only 25 organizations were kept in the final diagram, as they are connected. The most cited organizations are the Department of Natural Sciences, the European School of Sustainability Science and Research, the Faculty of Engineering and Architecture, the Faculty of Finance and Management, and the Business School, with 47 citations, each. The number of citations and total link strength based on the organization are presented in Appendix A.Fig. 14 Visualization of Co-Authorship Analysis: (A) Countries and (B) Organization.
6 Strengths, Weaknesses, Opportunities, and threats (SWOT) analysis
SWOT analysis indicates the appraisal and assessment of strengths, weaknesses, opportunities, threats, and other variables that impact a particular issue. It is based on a broad, systematic, and accurate investigation of the context and the environment of the issue (Wang and Wang, 2020). Based on the analysis outcomes, decision-makers could frame complementary policies, schemes, and support plans (Jasiulewicz-Kaczmarek, 2016). This approach can be utilized to locate positive and negative variables in a particular environment, overcome existing obstacles in a focused mode, understand the problems and challenges met, and frame tactical blueprints to direct scientific choices. SWOT analysis has been utilized broadly in several studies in various contexts such as disaster management (Siriwardhana et al., 2012), hotel reform (Yu and Huimin, 2005), rural tourism development (Zhang, 2012), and COVID-19 (Wang and Wang, 2020). This research utilized the SWOT approach to analyze SDGs in light of the current COVID-19 crisis, and build on previous literature related to the response to previous epidemics. SWOT analysis of SDGs in the current COVID-19 crisis is presented in Fig. 15 .Fig. 15 SWOT Analysis of SDGs in the Current COVID-19 Crisis.
6.1 Strength
SDGs entail various economic, environmental, and social aspects related to the development of countries, such as well-being, health, hunger, poverty, education, climate, gender equality, water, energy, social justice, sanitation, peace, and environment, which have been introduced by the UN 2030 Agenda (Omer and Noguchi, 2020). Unlike prior development blueprints that emphasize economic development, SDGs can be considered as a comprehensive agenda that entails several potentially divergent aims in the economy, society, and environment. Several targets can be achieved with the accurate implementation of SDGs. The development of SDGs was performed transparently with large chances for participation from governments, global organizations, and civil communities (Selin, 2015). SDGs are blueprints of action for the planet, societies, and prosperity. All governments and all actors should work collaboratively to perform this agenda. It also aims to support worldwide peace with a focus on freedom. Besides, reducing poverty levels in all styles and folds is the hugest international challenge and essential demand for sustainable development. SDGs are general criteria that entail all actors from the community. It moves beyond the burdens of governments and should be considered in a wider range to combine several stakeholders from the community while considering the concept of “leave no one behind” in the deployment process. Each area has particular capabilities and can participate in an integrated manner to meet these ambitious goals. SDGs allow more investment in the environment. Sustainable Development can’t be achieved without the recovery of the environment. Environmental problems have transfrontier essence. Still, ocean pollution, air pollution, ecosystem issues, climate change, and several other environmental problems cannot be faced at the local level, which needs more care and collaborative work. SDGs highlight the need for fostering environmental recovery at the regional level.
6.2 Weakness
SDGs suffer from several weak points, which we aim to elaborate on. The first challenge is related to the coordination strategies to achieve the SDGs, which appears when addressing poverty, energy, health, food, water, education, biodiversity, and several other folds in the SDGs. It is important to consider how involved stakeholders can participate in the SDG's implementation at a suitable time and in a suitable manner. SDGs essentially entail many various stakeholders functioning at several different measures, from local authorities to international governments. It is assumed that appropriate stakeholders will be available to serve together at the appropriate time and position to address complicated sustainability issues. For example, considering Goal 7, which focuses on providing sustainable energy for all, it is vital to determine the stakeholders who will participate in advancing, generating, establishing, and retaining the techniques to present globally obtainable energy. Besides, we should reconsider who can be involved in deciding what falls within “reliable affordable energy” for various societies in various areas of the globe. It is vital to rethink how authorities, societies, and public companies participate in determining suitable and sustainable energy resources in various environments.
The second weak point is related to the trade-off decisions. There will be several co-advantages if SDGs were deployed, as achieving one aim will also aid in achieving other SDGs at the same time. In this context, it is evident that facing climate change issues will help in addressing other issues such as health, energy security, oceans, and biodiversity. Still, the SDGs will also entail trade-off choices. It is vital to investigate potential trade-offs when reaching complex decisions, particularly in the short-run context. For example, biodiversity will be endangered with the cutdown of forests, particularly with the expansion of the production of agriculture to address the food security issue. Besides the security of food could be impacted if the crops are replaced by biofuel for meeting energy security goals. On the other hand, the security of water could be impacted by choices to intensify or increase agriculture or to develop hydropower for meeting the security of energy or mitigating greenhouse emissions. The overlapping among several SDGs can present negative results if governments neglected the universal essence of the SDGs and implement the goals separately. Besides, the correlations among the goals require careful consideration by decision-makers from various areas (Pradhan et al., 2017). Thus, a comprehensive investigation of the trade-off between various SDGs is of great importance to meet permanent sustainable development outcomes. Besides, a broad range of technologies and analysis techniques is required to inspect the complications and to meet the goals for the remaining time until 2030 (McCollum et al., 2017). Additionally, several competing stakeholders can be involved in these trade-off decisions. For example, when addressing the climate change problem, although the expected benefits, several parties will be influenced in the short run like companies and employees of fossil fuel companies. Reaching complex trade-off decisions can be a basic administration weak point, particularly for the difficult issues within the SDGs where responsibility is scattered and the concerns of various stakeholders can contradict. Meeting the SDGs will need the collaboration of local authorities, private sectors, nonprofitable sectors, and societies to reach complex choices based on a considerate and authentic obligation to the SDGs.
The third challenge is related to how and who will be responsible for the deployment of the SDGs. The implemented approaches to meet the SDGs should be integrated on national and international scales. It is important to determine the appropriate indicators to be used to evaluate the achievement of the goals. We need to assess both the inputs and the outputs considering several aspects of the SDGs. Still, we need robust methods to collect these outcomes and assess them based on the performance goals, in order to allow the responsible political decision-makers to monitor the achievement of involved stakeholders. Stakeholders entail members of authorities, the private sector, or even members of the common society. This loop will assure that SDGs are being followed and the implementation schedule is monitored.
Fourth, the implementation of SDGs based on the regional degree is “under-appreciated”. There is a need to firm internal and external policies to achieve the SDGs. Incorporating SDGs on the regional level enables better conceptualization of regional trends, dynamics, relations, general problems, political developments, environmental needs, and economic developments. This enables the design of appropriate policies and plans to better aid countries in their deployment of the SDGs.
Fifth, as countries are interconnected, their actions could have negative impacts on other countries and can hinder them from meeting SDGs. The deployed policies to meet the SDGs should be performed to several degrees without leading to negative influences on other regions (Sachs et al., 2020). Global spillovers happen when the activity of one country enforces costs on other countries (Malik et al., 2021). Spillover impacts require a coherent implementation of SDG strategies with the inclusion of strategic tools to be integrated into the implementation process.
6.3 Opportunities
In this section, we aim to focus on the opportunities presented by the external impact of COVID-19 on the deployment of SDGs. Although COVID-19 has several negative impacts on humans, it has raised several opportunities for humans, including health care, education, the economy, and the environment. The emerging conditions presented by the crisis may provide opportunities to increase the motivation to complete the SDGs and a reconceptualization of future goals to be met (Fenner and Cernev, 2021). These opportunities, if carefully considered, can aid in the deployment of SDGs. First, as health crises can endanger all humans, without any exception of the region, country, population, or race, it is vital to concentrate on developing health systems over the world. Enhancing healthcare systems is not an option anymore, in which the design of emergency management systems has become a long-term development goal. The prevention and monitoring of future public health epidemics is a significant requirement to manage societies by developing a modern governance system and managing current governance capabilities.
Given that COVID-19 is an emerging viral disease and by focusing on Malaysia as a developing country with restricted resources in the medical sector, several counter-actions were implemented (Rahim et al., 2021). These measures entail (1) the design of a new inspection tool to instantly detect infected people, (2) performing immediate isolation through tracing of individuals, and (3) quarantining individuals with close contact with infected people. Proper utilization of information can be performed to decide what suitable countermeasures are in the regions that have more obstacles in following the response and preparedness plans. As indicated by Hamzah et al. (2021), low-density regions with larger sizes of the population in Malaysia have more available resources than other areas with higher density and lower sizes in populations. Hence, more care should be provided to resource allocation, particularly in these areas. Since the emergence of the crisis, MOH in Malaysia has arranged to meet the worst scenarios, illustrated a clear plan, and provided accessible instructions to community members (Ministry of Health Malaysia, 2020). During the crisis, both public and private medical fields collaborated to recover from the crisis. An example of this is the deployment of rRT-PCR tests by both certified private and public centers, either in hospitals or stand-alone centers. Besides, among 150 public hospitals, 34 hospitals were allocated to face the COVID-19 crisis based on several criteria, among which support systems, count of beds, medical staff, and available equipment (Hashim et al., 2021). Private hospitals have also offered their services to face the pandemic.
Focusing on the education sector, the spread of COVID-19 has resulted in vital amendments in the modes of interaction among community members, in which educational institutions have been impacted significantly. Several measures of “social distancing” have tried to minimize physical contact and reduce the transmission of the disease among students and their families, particularly in locations with a large density of population such as universities and schools (Weeden and Cornwell, 2020). Hence, to respond to this extraordinary condition, a rapid transition to an emergency e-learning protocol has been imposed in several countries over the world. The demand for the de-securitization of traditional learning is evident. The normalization of urgent online learning does not indicate the choice to face the obstacles imposed by traditional learning during the COVID-19 crisis alone (Abumalloh et al., 2021a). It indicates the policies that form the broad adoption of electronic learning as a route to a new normal rather than an emergency plan.
Although COVID-19 has an undeniable impact on the SDGs agenda, it has raised several opportunities such as the swift shift towards technology adoption. The important part of ICT as a key enabler for meeting the SDGs objectives over the world has been addressed by International Telecommunication Union (Chien et al., 2021). The huge advancement in ICT can allow economies to enhance their connectivity, utilize information and knowledge to meet emerging issues, and promote their competitive advantage through technology spillovers (Sinha et al., 2020b). Still, previous literature that explored SDGs and technology has been scarce and has focused on limited areas of research (Sinha et al., 2020a). On the organizational level, the adoption of online or virtual infrastructure during the crisis was quite challenging. Integrating ICT in the field of crisis management is an important topic that aims to support governments in reaching their sustainability goals, particularly the management of unforeseen crises (Sood and Rawat, 2022).
Focusing on the economy, although this crisis originated at a time when the economy is interconnected over the world, this interconnectedness has led to the fast spread of the disease along with a chain response of economic breakdowns (Sharma et al., 2020b). However, the current crisis has opened new opportunities for the development of economies over the world. A circular economy should be repositioned to replace classical linear economy frameworks that are focused on energy-gulping and profiteering to meet economic growth over the world (Ibn-Mohammed et al., 2021).
Lastly, the temporary impact of the crisis on the environment, which is represented by several noticeable changes in air quality, wildlife, air pollution, and bodies of water, has gained the attention of communities and governments (Rupani et al., 2020). This crisis has been combined with a stunning recovery in the environment, particularly with broad social distancing and quarantine rules that were regulated over the globe. Such a change in the environment over the world, in a relatively swift time, would be impossible without the current crisis and its vital impacts (Paital, 2020). This change can be considered to design effective policies that can protect the environment over the world by revising and analyzing unsolved problems related to nature. Governments should provide appropriate blueprints to protect nature while persuading development in several sectors over the globe. The growing interest of business managers to invest in sustainable policies has led to the broad adoption of greener practices (Sharma et al., 2021d).
6.4 Threats
SDGs can be considered a blueprint for human beings. They cover every discipline of communities' lives and well-being. The implementation of SDGs can guarantee a stable and wealthy life for humans without harming the planet. The crisis has caused a severe interruption to SDGs implementation. COVID-19 has influenced the implementation of SDGs with a broad hit on the main folds of income, education, and health. Besides, the COVID-19 crisis has caused an unprecedented “income shock” that is assumed to prompt food insecurity in developing economies. Several factors that threaten the continuity of the SDGs have emerged. COVID-19 has impacted local economies, triggered changes in the deployed policies in the education process in countries (Abumalloh et al., 2021a), and implied unavoidable adjustments in medical care systems (Rupani et al., 2020).
The crisis has influenced developing countries the most. The volume of the economic influence of the crisis in Asian countries relies on the level of the epidemic and the spread of the disease. A severe but short-term decrease in national consumption was reported in the countries impacted by the crisis (Susantono et al., 2020). The number of travelers to several Asian countries decreased severely because of movement rules and preventive measures employed for disease control. Based on global constraints, the cancellation of global flights has led to wide variations in export and import businesses. Hence, this decrease in business travel and tourism activities raised a request for other areas and markets to be advanced (Joshi et al., 2021). There has been a considerable interruption in the supply chain as a consequence of imposed closures of markets and the workers’ inability to reach their work because of border shutdowns and travel restrictions. Particularly, thousands of individuals who rely on day-to-day income have been impacted by the COVID-19 crisis. Considering the security of food, the COVID-19 crisis has substantially influenced almost all Asian countries (Joshi et al., 2021), which is reflected by shortcomings in the supply chain of food. Besides, this crisis has impacted the energy sector and should be analyzed focusing on its influence on sustainability progression. The price of fossil fuel has dropped, and accordingly, the transition of energy will be influenced in the post-COVID era. The energy sector has encountered several troubles to meet the sustainability directions and to follow the most economic-effective route, particularly in developing regions with a very low concentration on renewable energy.
Referring to the travel and social distancing rules, Malaysia was not an exception and followed restricted rules to compact the spread of the disease. By 25 January 2020, the government declared the first case of COVID-19 (Nazri et al., 2021). Following that, by the 18th of March 2020, MCO was declared by the government to control the spread of COVID-19 (Azra et al., 2021). The MCO aimed to limit the activities on both governmental and private levels to basic essential services (Aziz et al., 2020). MCO aimed to control the spread of COVID-19 locally (Othman and Latif, 2021). Based on that, the government banned mass gatherings, suspended business activities, and transferred academic activities to online mode (Kamaludin et al., 2020, Rahim, 2021). COVID-19 has impacted several domestic markets, which influenced the economy in the country including tourism and hospitality, accommodation, catering, construction machinery, power heating, and several modes of transportation (plane, rail, and road).
Second, considering the threats faced by the world to meet the SDGs and focusing on the health of communities, a significant aspect to be considered is the ability of countries to fight any new health crisis with devastating impacts like COVID-19. This crisis has doubted the coping capabilities of medical systems over the world (Hashim et al., 2021). Medical systems can reach their limits with inappropriate management and operation. Other unavoidable threats to medical systems are the availability of manpower and the supply of PPE. Both manpower and PPE should be managed properly, particularly during an unprecedented crisis such as COVID-19. The monopolization of PPE during this crisis can lead to increasing demand and insufficient supply. The shortage of PPE in several regions has raised the number of infections (Channel News Asia, 2020; The Jakarta Post, 2020). Another threat that has to be faced by governments in developing countries is the adequacy of the number of allocated beds for COVID-19 in hospitals and ICUs. Other health threats have emerged during this crisis. For example, the deployment of MCOs by the government of Malaysia for beating the infection chain of the COVID-19 disease has led to the emergence of new dengue cases.
Third, COVID-19 has enormous impacts on students’ learning outcomes, the development of staff, research outcomes, and the learning process (Ceesay, 2021). To control the disease spread over the world, several countries, including Malaysia, have shut down their educational institutions and the majority of countries decided to temporarily postpone face-to-face learning and moved to a distance learning scheme. Focusing on the impact of COVID-19 on schools over the world, long periods of shutdowns caused various obstacles that entailed a shortage of appropriate nutrition among students, the interruption of the learning process, and a negative influence on academic outcomes (Mukuka et al., 2021).
7 Discussion
Before the COVID-19 crisis, the world was already following an unsustainable development track. The development was highly reliant on fossil fuels, in which the consumption and production models were heading against the world's limits. Increasing inequality in several regions was causing pressure among community members, while various countries experience several conflicts. The need for economic development and economic well-being left less focus on managing crucial aspects such as human health. Anticipating the future of SDGs within the current crisis is complex, in which the deployed strategies in the individual countries and the impact of the crisis on these countries are subject to high levels of uncertainty. Based on (UNDP, 2020), three scenarios are possible. The first assumes a fast V-shaped recovery, which is unlikely to happen. The second scenario proposes a protracted recovery with lasting impacts, which is the most likely to happen. The third scenario entails a worldwide meltdown with fragmentation, wide output losses, delocalization, social unrest, and an increase in the number of migrants to safer regions. There is a need to present coordination policies to contain the crisis and forbid economic fallout which is likely to happen in the third scenario.
In Fig. 16 , we present a framework for the deployment of the SDGs with the consideration of the current COVID-19 crisis and by utilizing SWOT analysis. As presented in Fig. 16, for each of the SDGs, the evaluation of the performance can be assessed using SWOT analysis. SWOT analysis focuses on analyzing both the internal and external variables, with the impact of the COVID-19 crisis. Basically, COVID-19 is linked tightly to SDG3 (Good Health and Well-being). However, the impact of COVID-19 on this goal has resulted in interrelated impacts on other sustainable development goals. These interrelations have various significance in each country based on the severity of the disease spread, the lockdown measures, and the readiness of the country to face the crisis. For example, the impact of COVID-19 on the environment was very clear at the beginning of the crisis with lockdown measures reaching a stopping point for tourism activities (Rupani et al., 2020). This impact has decreased with the release of lockdown measures and getting back to normal. As presented in Fig. 16, the evaluation of the progression of SDGs is based on assessing the current situation and the perception of the needed changes. The evaluation process is still under unprecedented shock, but a revolutionary quick amendment in the evaluation approach is required. The perception of the required changes is converted to strategic choices to meet the 2030 agenda. Following that, a list of strategic choices can be presented. However, based on the emerging crisis, many of these SDGs could not be achieved and need to be planned and rescheduled. The chosen strategy can be deployed based on a specific implementation schedule. Efficient collaborative implementation strategies for crisis recovery need a balance of competing requirements and facing internal obstacles. Hence, it is important to incorporate the crisis recovery and prediction scheme in SDGs implementation and performance evaluation. The crisis recovery should address two aspects of competing requirements and internal obstacles.Fig. 16 Proposed Framework of SDGs in Light of COVID-19 Crisis.
8 Research contributions
The research has several contributions in terms of methodology and practice that we will summarise in this section. The deployed methodology which investigated the previous literature through two types of analysis; bibliometric and SWOT, can present several insights for decision-makers and explore the existing body of knowledge from different views. While the bibliometric analysis explores the literature through specific visualizations focusing on specific items and the relations between them to understand how previous literature has manipulated a specific topic, the SWOT analysis aims to appraise four folds of strengths, weaknesses, opportunities, and threats that influence a specific issue. Hence, integrating these methods can present a broader perception of the topic understudy. Although each of the SDGs and COVID-19 has gained increasing attention from researchers and has been explored in different contexts, there is a lack of research on the impact of COVID-19 on the implementation of SDGs (Elavarasan et al., 2021). Hence, deploying the bibliometric and SWOT analyses on this particular topic can help scholars to understand the research trends, directions, and themes.
The research indicated several practical contributions for governments and decision-makers. First, the impact of the crisis on the performance of SDGs indicates that each goal needs a careful analysis based on the performance of each country and based on the emerging crisis and its consequences (Ameli et al., 2022). This emerging crisis calls for novel strategies to aid people to reach sustainable well-being (Sharma et al., 2020a). For example, and focusing on the environment, nations with limited natural resources can control the harmful impacts on the environment by restricting imports and consumption rates of fossil fuels (Merino-Saum et al., 2018). Besides, natural resource degradation can be minimized by adopting sustainable strategies and carefully managing natural resource consumption and utilization, which accordingly allows the restoration and replenishment of these resources (Khan et al., 2021). Second, COVID-19, as an emerging communicable disease, has a direct relation to SDGs, particularly SDG 3. For instance, based on the crisis, decision-makers may decide to intensify the provided support to the medical sector, particularly in developing regions. They should also concentrate on the socio-economic influences of the pandemic. Other interrelated variables that have resulted from the crisis should be considered in the recovery plans. Third, the current epidemic indicated that countries require immediate actions to meet the 2030 view of the SDGs. Governments had to balance the demand for mitigation to enforce appropriate emergency policies based on the assumption of herd immunity (Heggen et al., 2020). Hence, meeting the evolutionary vision of the SDGs by 2030 needs a basic restructuring of most countries' local policies toward long-run, collaborative, and significantly expedited strategies (The Lancet Public Health, 2020). Fourth, sustainable management can only be followed when awareness is improved among community members. Decision-makers should utilize various forms of media to spread knowledge about sustainable actions and how to protect natural resources. Additionally, researchers’ efforts should be focused on and induced by appropriate investment to address the shortcomings in the existing production practices. Fifth, traditional development practices should be replaced with sustainable economic development practices, by adopting environmental-friendly plans and acquiring effective innovations with flexible financing choices. This can be also deployed by providing motivation to industries with environmental-conducive strategies and regulating more taxes on industries that cause pollution through unhealthy activities. Finally, countries' innovation capacities have played an important role in development, which has been reflected by economic progress, society's development, and environmental quality (Sinha et al., 2020b). Technologies, tools, equipment, and appliances should be utilized to allow maximum effectiveness in deploying domestic and global commercial practices. This will aid in meeting sustainable development goals and allow fast recovery from the crisis. Even before the crisis, the deployment of technologies has been explored with a focus on meeting the SDGs in several contexts, such as improving environment quality (Sinha et al., 2020a), emerging economies (Liu et al., 2022), and health and well-being (Yadav et al., 2022). In the context COVID-19 crisis, several technologies are anticipated to play a major role in the crisis recovery focusing on the industry (Ebekozien and Aigbavboa, 2021), the green economy (Shah et al., 2021), and hospitality management (Chadee et al., 2021). Hence, more focus should be allocated by decision makers to utilize the emerging technologies to recover from the crisis aiming to meet the SDGs.
9 Conclusion
SWOT analysis is a reliable and widely used tool for strategic planning (Kaymaz et al., 2021). Strengths and weaknesses represent the internal variables of SWOT, while opportunities and threats represent the external variables. Investigating internal variables means identifying and assessing the managerial views that may impact the achievement or failure of the deployed policies in the applied field. On the other hand, the analysis of external variables entails other environmental variables, which cannot be supervised by the organization, but they impact the achievement of the organization (Tavana et al., 2016). Hence, SWOT enables the classification of variables, that impact the decision, as internal and external, thus allowing the comparison of various variables based on the presented classification (Etongo et al., 2018). Finally, policies are presented to promote development by deploying strengths, reducing weaknesses, considering opportunities, and preventing threats (Khan, 2018). Hence, SWOT can be used to analyze the current situation of SDGs deployment to present practical insights for decision-makers.
Although SDGs have been introduced as independent targets, they are systematically interconnected to each other (Harris et al., 2020), in which one goal has a positive or negative influence on other goals (Omer and Noguchi, 2020, Pradhan, 2019). Several studies have investigated SDGs focusing on several disciplines of research including water poverty (Ladi et al., 2021), energy (Elavarasan et al., 2021), smart cities (Grossi and Trunova, 2021), environment (Boess et al., 2021), and soil (Erdogan et al., 2021). Nowadays, meeting SDGs presents a vital challenge to governments as the performance of these goals requires the design of an innovative and integrated plan with the collaboration of several stakeholders at the country level. This will demand more resources, innovations, and technologies (Bebbington and Unerman, 2018). Countries can provide required regulations, follow up the performance, and revise the achievement of these goals at regional, national, and local degrees (Yin et al., 2019). In each country, meeting each goal will be linked with various levels of challenge based on the current development situation, such as urban and economic growth (Osborn et al., 2015). The emergence of the COVID-19 crisis has presented a new challenge to the deployment of SDGs on the global and local levels.
COVID-19 has caused wide challenges and huge opportunities for meeting the SDG's agenda. With the advent of the current crisis, the whole sustainable development progression has been delayed and vital issues have been enlarged. The pandemic has impacted the countries of South Asia with an increasing regional representation of 4% by May 2020 (Karunathilake, 2021). This crisis has challenged traditional crisis-recovery methods. Hence, the post-crisis stage demonstrates the urgent demand for effective sustainable development policies (Elavarasan et al., 2021). Several strategies would be presented at this point, but the most effective solution which has the most sustainable features should take priority and should be deployed. Understanding the origins of epidemics and investigating their determining factors will aid in developing policies that could prevent future crises. On the other hand, the crisis has shown us the common sense of what is essential in the SDGs; the obstacles we meet cannot be beaten by each country separately. Good plans can be defined from other peers to further enhance overall performance and accordingly improve the level of preparedness and response. Hence, an important aspect to be considered is the partnership with other countries to face the pandemic and recover from its impacts.
This research has some shortcomings referring to the inclusion criterion of studies, as only articles indexed in the Scopus database were included. A future study can be conducted to include other databases. Scopus database was chosen as it includes journals with high-impact articles. Another reason for this choice is that databases from various sources generate different files with various forms that are difficult to merge and analyze using the chosen software for bibliometric analysis. Future research can be expanded, as a systematic literature review, to include other electronic databases and interpret the outcomes following inclusion and exclusion conditions and quality assessment procedures. Future research can investigate decision-makers perceptions to go in-depth with the analysis of the factors that impact the deployment of SDGs using Multi-Criteria Decision-Making (MCDM). MCDM can help to design a blueprint for the implementation of the SDGs on the regional level (Aljaghoub et al.).
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Appendix A: Number of Citations and Total Link Strength based on Organization.
Organization Citations Total Link Strength
Department of Natural Sciences, Manchester Metropolitan University 47 4
European School of Sustainability Science and Research, Hamburg University of Applied Sciences 47 4
Faculty of Engineering and Architecture (FEAR), University of Passo Gundo (UPF) 47 4
Faculty of Finance and Management, WSB University in Wrocław 47 4
The Business School, University of Winchester 47 4
Department of Economics, Faculty of Management Sciences, Al-hikmah University 42 10
Department of Industrial Engineering, College of Engineering, American University of Sharjah 42 10
Department of Management, Birkbeck University of London 42 10
Faculty of Economics and Management, Universiti Kebangsaan Malaysia 42 10
Faculty of Economics, Kyushu University 42 10
Faculty of Engineering and Science, University of Nottingham 42 10
Kent Business School, University of Kent 42 10
School of Life Sciences, University of Nottingham 42 10
School of the Built Environment and Architecture 42 10
Sheffield University Management School, the University of Sheffield 42 10
Warwick Manufacturing Group, the University of Warwick 42 10
AgResearch –Lincoln Research Centre 40 4
Agriculture and Agri-Food Canada, Lethbridge Research and Development Centre 40 4
Inrae-agir, Auzeville 40 4
Institute for Study and Development Worldwide 40 4
International Livestock Research Institute 40 4
Department of Economics and School of Global Environmental Sustainability 40 1
Department of Economics and School of Global Sustainability 40 1
Department of Public Health and Informatics, Jahangirnagar University 35 5
Department of Statistics, Jahangirnagar University 35 5
Department of Statistics, Islamic university 35 4
Infectious Diseases Division, International Centre for Diarrhoeal Disease Research 35 4
Institute of Statistical Research and Training, University of Dhaka 35 4
Beihang University 31 1
The University of New South Wales 31 1
Department of Basic, Vietnam Academy for Ethnic Minorities 26 13
Alumnus, Graduate School of Economics 25 2
Social Science Research Institute, Tokai University 25 2
Keio University and Visiting Professor, National Graduate Institute for Policy Studies 25 2
Independent Expert 23 2
Renewable Energy Consortium for R&D 23 2
“Galileo Ferraris” Energy Department, Polytechnic of Turin 23 2
Climate Change Programme 22 2
Department of Disaster Management 22 2
Department of Environmental Sciences 22 2
Data availability
No data was used for the research described in the article.
Acknowledgements
This research is supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R4), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. The authors are thankful to the Deanship of Scientific Research at Najran University for funding this work under the Research Collaboration Funding program grant code NU/RC/SEHRC/11/2.
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References
Abumalloh RA, Asadi S, Nilashi M, Minaei-Bidgoli B, Nayer FK, Samad S, Mohd S, Ibrahim O. The impact of coronavirus pandemic (COVID-19) on education: The role of virtual and remote laboratories in education Technology in Society 2021a;67:101728.
Abumalloh R.A. Nilashi M. Ismail M.Y. Alhargan A. Alghamdi A. Alzahrani A.O. Saraireh L. Osman R. Asadi S. Medical image processing and COVID-19: a literature review and bibliometric analysis J. Infect. Public Health 2021
Ahani A. Nilashi M. Coronavirus outbreak and its impacts on global economy: the role of social network sites J. Soft Comput. Dec. Supp. Syst. 7 2020 19 22
Ahmad J. Ahmad M.M. Ahmad N. Natural disasters and public health in the era of Sustainable Development Goals: a retrospective study of the October 2015 Hindu Kush earthquake in Pakistan Proc. Eng. 212 2018 855 862
Ainuddin S. Aldrich D.P. Routray J.K. Ainuddin S. Achkazai A. The need for local involvement: Decentralization of disaster management institutions in Baluchistan Pakistan Internat. J. Disaster Risk Reduct. 6 2013 50 58
Aitsi-Selmi A. Egawa S. Sasaki H. Wannous C. Murray V. The Sendai framework for disaster risk reduction: Renewing the global commitment to people’s resilience, health, and well-being Internat. J. Disaster Risk Sci. 6 2015 164 176
Aksnes D.W. Sivertsen G. A criteria-based assessment of the coverage of Scopus and Web of Science J. Data Inform. Sci. 4 2019 1 21
Aljaghoub H, Abumadi F, AlMallahi MN, Obaideen K, Al Alami AH. Solar Pv Cleaning Techniques Contribute to Sustainable Development Goals (Sdgs) Using Multi-Criteria Decision-Making (Mcdm): Assessment and Review Available at SSRN 4166067.
Al-Saidi M. Cooperation or competition? State environmental relations and the SDGs agenda in the Gulf Cooperation Council (GCC) region Environ. Dev. 37 2021
Ameli M, Esfandabadi ZS, Sadeghi S, Ranjbari M, Zanetti MC. COVID-19 and Sustainable Development Goals (SDGs): Scenario analysis through fuzzy cognitive map modeling Gondwana Research 2022.
Asadi S. Nilashi M. Abumalloh R.A. Samad S. Ahani A. Ghabban F. Yusuf S.Y.M. Supriyanto E. Evaluation of factors to respond to the COVID-19 pandemic using DEMATEL and fuzzy rule-based techniques Int. J. Fuzzy Syst. 24 2022 27 43
Asadikia A. Rajabifard A. Kalantari M. Systematic prioritisation of SDGs: Machine learning approach World Dev. 140 2021 105269
Aziz N.A. Othman J. Lugova H. Suleiman A. behalf of the Economy O, Cluster SW Malaysia's approach in handling COVID-19 onslaught: Report on the Movement Control Order (MCO) and targeted screening to reduce community infection rate and impact on public health and economy J. Infect Public Health 2020
Azra MN, Kasan NA, Othman R, Noor GAGR, Mazelan S, Jamari ZB, Sarà G, Ikhwanuddin M. Impact of COVID-19 on aquaculture sector in Malaysia: Findings from the first national survey Aquaculture Reports 2021;19:100568.
Bansal S. Jain M. Garg I. Srivastava M. Attaining circular economy through business sustainability approach: An integrative review and research agenda J. Public Aff. 2020:e2319.
Barroso M, Laborda J. Digital transformation and the emergence of the Fintech sector: Systematic literature review Digital Business 2022:100028.
Bebbington J. Unerman J. Achieving the United Nations Sustainable Development Goals: an enabling role for accounting research Accounting Audit. Account. J. 2018
Boess E.R. Kørnøv L. Lyhne I. Partidário M.R. Integrating SDGs in environmental assessment: Unfolding SDG functions in emerging practices Environ. Impact Assess. Rev. 90 2021 106632
Bonilla C.A. Merigó J.M. Torres-Abad C. Economics in Latin America: a bibliometric analysis Scientometrics 105 2015 1239 1252
Ceesay EK. Potential impact of COVID-19 outbreak on education, staff development and training in Africa Research in Globalization 2021;3:100049.
Chadee D, Ren S, Tang G. Is digital technology the magic bullet for performing work at home? Lessons learned for post COVID-19 recovery in hospitality management International Journal of Hospitality Management 2021;92:102718.
Channel News Asia. We are worried,” say Indonesian Healthcare Workers as COVID-19 takes Toll on Medical System.; 2020.
Chien F. Anwar A. Hsu C.-C. Sharif A. Razzaq A. Sinha A. The role of information and communication technology in encountering environmental degradation: proposing an SDG framework for the BRICS countries Technol. Soc. 65 2021 101587
Chmutina K. von Meding J. Sandoval V. Boyland M. Forino G. Cheek W. Williams D.A. Gonzalez-Muzzio C. Tomassi I. Páez H. What we measure matters: the case of the missing development data in sendai framework for disaster risk reduction monitoring international journal of disaster risk Science 12 2021 779 789
Contreras LEV, Perkins KM, Ellenbecker M, Moure-Eraso R, Vega NEM. COVID-19 pandemic lessons to facilitate future engagement in the global climate crisis Journal of Cleaner Production 2020:125178.
Daly H.E. Elements of environmental macroeconomics Ecol. Econ. Sci. Manage. Sustainab. 1991 32 46
De la Hoz-Correa A. Muñoz-Leiva F. Bakucz M. Past themes and future trends in medical tourism research: A co-word analysis Tour. Manag. 65 2018 200 211
de Sousa FDB. The role of plastic concerning the sustainable development goals: The literature point of view Cleaner and Responsible Consumption 2021:100020.
de Sousa FDB. Pros and Cons of Plastic during the COVID-19 Pandemic Recycling 2020;5:27.
Djalante R, Shaw R, DeWit A. Building resilience against biological hazards and pandemics: COVID-19 and its implications for the Sendai Framework Progress in Disaster Science 2020;6:100080.
Durana P. Valaskova K. Vagner L. Zadnanova S. Podhorska I. Siekelova A. Disclosure of strategic managers’ factotum: Behavioral incentives of innovative business Internat. J. Finan. Stud. 8 2020 17
Ebekozien A, Aigbavboa C. COVID-19 recovery for the Nigerian construction sites: The role of the fourth industrial revolution technologies Sustainable Cities and Society 2021;69:102803.
Ekwebelem OC, Ofielu ES, Nnorom-Dike OV, Iweha C, Ekwebelem NC, Obi BC, Ugbede-Ojo SE. Threats of COVID-19 to Achieving United Nations Sustainable Development Goals in Africa The American Journal of Tropical Medicine and Hygiene 2021;104:457.
Elango B. Ho Y.-S. A bibliometric analysis of highly cited papers from India in Science Citation Index Expanded Curr. Sci. 2017 1653 1658
Elavarasan RM, Pugazhendhi R, Jamal T, Dyduch J, Arif MT, Kumar NM, Shafiullah G, Chopra SS, Nadarajah M. Envisioning the UN Sustainable Development Goals (SDGs) through the lens of energy sustainability (SDG 7) in the post-COVID-19 world Applied Energy 2021;292:116665.
Erdogan H.E. Havlicek E. Dazzi C. Montanarella L. Van Liedekerke M. Vrščaj B. Krasilnikov P. Khasankhanova G. Vargas R. Soil conservation and sustainable development goals (SDGs) achievement in Europe and central Asia: Which role for the European soil partnership? Internat. Soil Water Conserv. Res. 9 2021 360 369
Etongo D. Kanninen M. Epule T.E. Fobissie K. Assessing the effectiveness of joint forest management in Southern Burkina Faso: A SWOT-AHP analysis Forest Policy Econ. 90 2018 31 38
Fagbemi F. COVID-19 and sustainable development goals (SDGs): An appraisal of the emanating effects in nigeria Research in Globalization 2021;3:100047.
Felix C. Franconeri S. Bertini E. Taking Word Clouds Apart: An Empirical Investigation of the Design Space for Keyword Summaries IEEE Trans. Vis. Comput. Graph. 24 2018 657 666 28866593
Fenner R, Cernev T. The implications of the Covid-19 pandemic for delivering the Sustainable Development Goals Futures 2021;128:102726.
Goksu I. Bibliometric mapping of mobile learning Telematics Inform. 56 2021 101491
Grossi G. Trunova O. Are UN SDGs useful for capturing multiple values of smart city? Cities 114 2021 103193
Hall C.M. Constructing sustainable tourism development: The 2030 agenda and the managerial ecology of sustainable tourism J. Sustain. Tour. 27 2019 1044 1060
Hamzah NM, Yu M-M, See KF. Assessing the efficiency of Malaysia health system in COVID-19 prevention and treatment response Health Care Management Science 2021:1-13.
Harris P. Riley E. Dawson A. Friel S. Lawson K. “Stop talking around projects and talk about solutions”: positioning health within infrastructure policy to achieve the sustainable development goals Health Policy 124 2020 591 598 30545623
Hashim JH, Adman MA, Hashim Z, Radi MFM, Kwan SC. COVID-19 Epidemic in Malaysia: Epidemic Progression, Challenges, and Response Frontiers in Public Health 2021;9.
Health T.L.P. Will the COVID-19 pandemic threaten the SDGs? Lancet Public Health 5 2020 e460 32888438
Heggen K, Sandset TJ, Engebretsen E. COVID-19 and sustainable development goals Bulletin of the World Health Organization 2020;98:646-646.
Ibn-Mohammed T. Mustapha K. Godsell J. Adamu Z. Babatunde K. Akintade D. Acquaye A. Fujii H. Ndiaye M. Yamoah F. A critical analysis of the impacts of COVID-19 on the global economy and ecosystems and opportunities for circular economy strategies Resources Conserv. Recycl. 164 2021 105169
Iwuoha JC, Jude-Iwuoha AU. COVID-19: Challenge to SDG and globalization Electronic Research Journal of Social Sciences and Humanities 2020;2.
Jain M. Sharma G.D. Goyal M. Kaushal R. Sethi M. Econometric analysis of COVID-19 cases, deaths, and meteorological factors in South Asia Environ. Sci. Pollut. Res. 28 2021 28518 28534
The Jakarta Post. Two Hospital Directors Die of COVID-19 as Indonesian Doctors Call for Transparency - National.; 2020.
Jasiulewicz-Kaczmarek M. SWOT analysis for Planned Maintenance strategy-a case study IFAC-PapersOnLine 2016;49:674-679.
Joshi T. Mainali R.P. Marasini S. Acharya K.P. Adhikari S. Nepal at the edge of sword with two edges: The COVID-19 pandemics and sustainable development goals Journal of Agriculture and Food Research 4 2021 100138
Kamaludin K. Chinna K. Sundarasen S. Khoshaim H.B. Nurunnabi M. Baloch G.M. Sukayt A. Hossain S.F.A. Coping with COVID-19 and movement control order (MCO): experiences of university students in Malaysia Heliyon 6 2020 e05339 33134570
Karunathilake K. Positive and negative impacts of COVID-19, an analysis with special reference to challenges on the supply chain in South Asian countries J. Soc. Econ. Dev. 23 2021 568 581
Kaymaz Ç.K. Birinci S. Kızılkan Y. Sustainable development goals assessment of Erzurum province with SWOT-AHP analysis Environment Dev. Sustainab. 2021 1 27
Kelman I. Linking disaster risk reduction, climate change, and the sustainable development goals Disaster Prevent. Manage. Int. J. 2017
Khan M.I. Evaluating the strategies of compressed natural gas industry using an integrated SWOT and MCDM approach J. Clean. Prod. 172 2018 1035 1052
Khan M.K. Abbas F. Godil D.I. Sharif A. Ahmed Z. Anser M.K. Moving towards sustainability: how do natural resources, financial development, and economic growth interact with the ecological footprint in Malaysia? A dynamic ARDL approach Environ. Sci. Pollut. Res. 28 2021 55579 55591
Kim M.J. Hall C.M. Do perceived risk and intervention affect crowdfunder behavior for the sustainable development goals? A model of goal-directed behavior J. Clean. Prod. 2021: 127614
Koubi V. Sustainable Development Impacts of Climate Change and Natural Disaster Background Paper Prepared for Sustainable Development Outlook 2019.
Ladi T. Mahmoudpour A. Sharifi A. Assessing impacts of the water poverty index components on the human development index in Iran Habitat Int. 113 2021 102375
Leal Filho W. Brandli L.L. Lange Salvia A. Rayman-Bacchus L. Platje J. COVID-19 and the UN sustainable development goals: threat to solidarity or an opportunity? Sustainability 12 2020 5343
Lewis J. The good, the bad and the ugly: disaster risk reduction (DRR) versus disaster risk creation (DRC) PLoS currents 2012 4
Liu H. Anwar A. Razzaq A. Yang L. The key role of renewable energy consumption, technological innovation and institutional quality in formulating the SDG policies for emerging economies: Evidence from quantile regression Energy Rep. 8 2022 11810 11824
Liu Y. Avello M. Status of the research in fitness apps: A bibliometric analysis Telematics Inform. 57 2021 101506
Macht S.A. Chapman R.L. Fitzgerald J.A. Postscript: COVID-19 and SDG progress Journal of Management & Organization 26 2020 1073 1076
Malik A. Lafortune G. Carter S. Li M. Lenzen M. Kroll C. International spillover effects in the EU's textile supply chains: A global SDG assessment J. Environ. Manage. 295 2021 113037
Markscheffel B. Schröter F. Comparison of two science mapping tools based on software technical evaluation and bibliometric case studies COLLNET Journal of Scientometrics and Information Management 15 2021 365 396
Marome W. Shaw R. COVID-19 response in Thailand and its implications on future preparedness Int. J. Environ. Res. Public Health 18 2021 1089 33530526
McCollum D, Gomez L, Riahi E, Parkinson S. A guide to SDG interactions: From science to implementation A Guide To SDG Interactions: From Science To Implementation 2017:127-169.
McKibbin W. Fernando R. The global macroeconomic impacts of COVID-19: Seven scenarios Asian Econ. Pap. 20 2021 1 30
Merigó J.M. Gil-Lafuente A.M. Yager R.R. An overview of fuzzy research with bibliometric indicators Appl. Soft Comput. 27 2015 420 433
Merino-Saum A. Baldi M.G. Gunderson I. Oberle B. Articulating natural resources and sustainable development goals through green economy indicators: A systematic analysis Resources Conserv. Recycl. 139 2018 90 103
Milian E.Z. Spinola M.M. de Carvalho M.M. Fintechs: A literature review and research agenda Electron. Commer. Res. Appl. 34 2019 100833
Ministry of Health Malaysia. COVID-19 Garis Panduan Kementerian Kesihatan Malaysia, 2020. https://moh.gov.my/index.php/pages/view/2019-ncov-wuhan-guidelines (Accessed 28 July 2021).
Mongeon P. Paul-Hus A. The journal coverage of Web of Science and Scopus: a comparative analysis Scientometrics 106 2016 213 228
Montalván-Burbano N. Pérez-Valls M. Plaza-Úbeda J. Analysis of scientific production on organizational innovation Cogent Business & Management 7 2020 1745043
Mukuka A, Shumba O, Mulenga HM. Students’ experiences with remote learning during the COVID-19 school closure: implications for mathematics education Heliyon 2021:e07523.
Nazri A.S. Talib K.A. Harun Z. Sulaiman A.N. Malaysia: State and Society Measures in Safety and Security Against COVID-19 MJSSH 6 2021 187 194
Nilashi M. Samad S. Yusuf S.Y.M. Akbari E. Can complementary and alternative medicines be beneficial in the treatment of COVID-19 through improving immune system function? J. Infect. Public Health 13 2020 893 32451258
Nilashi M. Asadi S. Minaei-Bidgoli B. Abumalloh R.A. Samad S. Ghabban F. Ahani A. Recommendation agents and information sharing through social media for coronavirus outbreak Telematics Inform. 61 2021 101597
Nilashi M. Abumalloh R.A. Alrizq M. Alghamdi A. Samad S. Almulihi A. Althobaiti M.M. Ismail M.Y. Mohd S. What is the impact of eWOM in social network sites on travel decision-making during the COVID-19 outbreak? A two-stage methodology Telematics Inform. 69 2022 101795
Noyons E. Moed H. Van Raan A. Integrating research performance analysis and science mapping Scientometrics 46 1999 591 604
O'Brien G, O'keefe P, Rose J, Wisner B. Climate change and disaster management Disasters 2006;30:64-80.
Omer M.A. Noguchi T. A conceptual framework for understanding the contribution of building materials in the achievement of Sustainable Development Goals (SDGs) Sustain. Cities Soc. 52 2020 101869
Osborn D, Cutter A, Ullah F. Universal Sustainable Development Goals: Understanding the transformational challenge for developed countries. Report of a study by Stakeholder Forum; 2015.
Othman M. Latif M.T. Air pollution impacts from COVID-19 pandemic control strategies Malaysia Journal of cleaner production 2021 125992
Paital B. Nurture to nature via COVID-19, a self-regenerating environmental strategy of environment in global context Science of The Total Environment 2020:139088.
Paton D. Johnston D. Disaster resilience: an integrated approach 2017 Charles C Thomas Publisher
Peng K. Jiang W. Ling Z. Hou P. Deng Y. Evaluating the potential impacts of land use changes on ecosystem service value under multiple scenarios in support of SDG reporting: A case study of the Wuhan urban agglomeration J. Clean. Prod. 307 2021 127321
Perianes-Rodriguez A. Waltman L. Van Eck N.J. Constructing bibliometric networks: A comparison between full and fractional counting J. Informet. 10 2016 1178 1195
Pradhan P. Antagonists to meeting the 2030 Agenda Nat. Sustainability 2 2019 171 172
Pradhan P. Costa L. Rybski D. Lucht W. Kropp J.P. A systematic study of sustainable development goal (SDG) interactions Earth's Future 5 2017 1169 1179
Pramanik M. Szabo S. Pal I. Udmale P. O’Connor J. Sanyal M. Roy S. Sebesvari Z. Twin Disasters: Tracking COVID-19 and Cyclone Amphan’s Impacts on SDGs in the Indian Sundarbans Environ. Sci. Policy Sustain. Dev. 63 2021 20 30
Rahim MH, Dom NC, Ismail SNS, Abd Mulud Z, Abdullah S, Pradhan B. The impact of novel coronavirus (2019-nCoV) pandemic movement control order (MCO) on dengue cases in Peninsular Malaysia One Health 2021:100222.
Sustainable Development Report. The SDG Index and Dashboards. 2021.
Rodrigues S, Van Eck N, Waltman L, Jansen F. Mapping patient safety: a large-scale literature review using bibliometric visualisation techniques BMJ open 2014;4.
Rupani P.F. Nilashi M. Abumalloh R. Asadi S. Samad S. Wang S. Coronavirus pandemic (COVID-19) and its natural environmental impacts Int. J. Environ. Sci. Technol. 17 2020 4655 4666
Sachs J, Schmidt-Traub G, Kroll C, Lafortune G, Fuller G, Woelm F. The sustainable development goals and COVID-19 Sustainable development report 2020;2020.
Selin H. The risk of UN’s sustainable development goals: Too many goals, too little focus The Conversation 2015;25.
Shah SAA, Longsheng C, Solangi YA, Ahmad M, Ali S. Energy trilemma based prioritization of waste-to-energy technologies: implications for post-COVID-19 green economic recovery in Pakistan Journal of cleaner production 2021;284:124729.
Sharma GD, Erkut B, Jain M, Kaya T, Mahendru M, Srivastava M, Uppal RS, Singh S. Sailing through the COVID-19 crisis by using AI for financial market predictions Mathematical Problems in Engineering 2020a;2020.
Sharma GD, Talan G, Jain M. Policy response to the economic challenge from COVID‐19 in India: A qualitative enquiry Journal of Public Affairs 2020b;20:e2206.
Sharma GD, Bansal S, Yadav A, Jain M, Garg I. Meteorological factors, COVID-19 cases, and deaths in top 10 most affected countries: an econometric investigation Environmental Science and Pollution Research 2021a;28:28624-28639.
Sharma GD, Tiwari AK, Jain M, Yadav A, Erkut B. Unconditional and conditional analysis between covid-19 cases, temperature, exchange rate and stock markets using wavelet coherence and wavelet partial coherence approaches Heliyon 2021b;7:e06181.
Sharma GD, Tiwari AK, Jain M, Yadav A, Srivastava M. COVID-19 and environmental concerns: A rapid review Renewable and Sustainable Energy Reviews 2021c;148:111239.
Sharma GD, Tiwari AK, Talan G, Jain M. Revisiting the sustainable versus conventional investment dilemma in COVID-19 times Energy Policy 2021d;156:112467.
Sinha A, Shah MI, Sengupta T, Jiao Z. Analyzing technology-emissions association in Top-10 polluted MENA countries: How to ascertain sustainable development by quantile modeling approach Journal of Environmental Management 2020b;267:110602.
Sinha A. Sengupta T. Saha T. Technology policy and environmental quality at crossroads: Designing SDG policies for select Asia Pacific countries Technol. Forecast. Soc. Chang. 161 2020 120317
Siriwardhana C. Hewage S. Deshabandu R. Siribaddana S. Sumathipala A. Psychosocial and ethical response to disasters: a SWOT analysis of post-tsunami disaster management in Sri Lanka Asian Bioethics Review 4 2012 171 182
Small H. Co-citation in the scientific literature: A new measure of the relationship between two documents Journal of the American Society for information Science 24 1973 265 269
Sood S.K. Rawat K.S. A fog assisted intelligent framework based on cyber physical system for safe evacuation in panic situations Comput. Commun. 178 2021 297 306 34518711
Sood S.K. Rawat K.S. Fog-assisted virtual reality-based learning framework to control panic Expert. Syst. 39 2022 e12700
Sood SK, Rawat KS, Kumar D. Analytical mapping of information and communication technology in emerging infectious diseases using CiteSpace Telematics and Informatics 2022:101796.
Sukmara R. Pradita L. The Sendai Framework for disaster risk reduction: Insight from Covid-19 in Balikpapan City, Indonesia IOP Conference Series: Earth and Environmental Science 2021 IOP Publishing
Sundram V.P.K. Hashim N. Shariff S.H. Pujiati A. Ardiansari A. Sustainable transportation on university campus: a case at UiTM selangor, puncak alam campus, Malaysia and universitas Negeri Semarang, Indonesia Asian J. Univ. Educ. 17 2021 262 272
Susantono B. Sawada Y. Park C.-Y. Navigating COVID-19 in Asia and the Pacific Asian Development Bank 2020
Tavana M. Zareinejad M. Di Caprio D. Kaviani M.A. An integrated intuitionistic fuzzy AHP and SWOT method for outsourcing reverse logistics Appl. Soft Comput. 40 2016 544 557
Uitto J.I. Shaw R. Sustainable development and disaster risk reduction: Introduction. In Sustainable development and disaster risk reduction 2016 Springer Tokyo 1 12
UNDP. (2020). The Social and Economic Impact of COVID-19 in the Asia-Pacific Region. Position Note Prepared by UNDP Regional Bureau for Asia and the Pacific.
UNESCO, COVID-19 Education Response, 2021. https://covid19.uis.unesco.org/global-monitoring-school-closures-covid19/regional-dashboard/. (Accessed April 2021).
Economic Planning Unit. Malaysia sustainable development goals voluntary National review 2017. 2017.
United Nations, Agenda of Sustainable Development Goals 2030, 2015. https://sdgs.un. org/goals. (Accessed 25 July 2020).
Van Eck NJ, Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping scientometrics 2010;84:523-538.
Van Eck N.J. Waltman L. Citation-based clustering of publications using CitNetExplorer and VOSviewer Scientometrics 111 2017 1053 1070 28490825
Vogliano C, Murray L, Coad J, Wham C, Maelaua J, Kafa R, Burlingame B. Progress towards SDG 2: Zero hunger in melanesia–A state of data scoping review Global Food Security 2021;29:100519.
von Schirnding Y. Health and sustainable development: can we rise to the challenge? Lancet 360 2002 632 637 12241950
Wang J, Wang Z. Strengths, weaknesses, opportunities and threats (Swot) analysis of china’s prevention and control strategy for the covid-19 epidemic International Journal of Environmental Research and Public Health 2020;17:2235.
Weeden K.A. Cornwell B. The small-world network of college classes: implications for epidemic spread on a university campus Sociological science 7 2020 222 241
WHO. WHO technical guidance notes on Sendai framework reporting for ministries of health. 2020.
World in Data. Daily new confirmed COVID-19 deaths per million people. 2021.
Wright N. Fagan L. Lapitan J.M. Kayano R. Abrahams J. Huda Q. Murray V. Health emergency and disaster risk management: Five years into implementation of the Sendai framework International Journal of Disaster Risk Science 11 2020 206 217
Yadav AK, Yadav HK, Naz A, Koul M, Chowdhury A, Shekhar S. Arsenic removal technologies for middle-and low-income countries to achieve the SDG-3 and SDG-6 targets: A review Environmental Advances 2022:100262.
Yilmaz RM, Topu FB, Takkaç Tulgar A. An examination of the studies on foreign language teaching in pre-school education: A bibliometric mapping analysis Computer Assisted Language Learning 2019:1-24.
Yin S, Li B, Xing Z. The governance mechanism of the building material industry (BMI) in transformation to green BMI: The perspective of green building Science of The Total Environment 2019;677:19-33.
Yu D. A scientometrics review on aggregation operator research Scientometrics 105 2015 115 133
Yu L. Huimin G. Hotel reform in China: a SWOT analysis Cornell Hotel Restaur. Admin. Q. 46 2005 153 169
Yu D. Shi S. Researching the development of Atanassov intuitionistic fuzzy set: Using a citation network analysis Appl. Soft Comput. 32 2015 189 198
Yu D. Xu Z. Wang W. Bibliometric analysis of fuzzy theory research in China: A 30-year perspective Knowl.-Based Syst. 141 2018 188 199
Yu D. Xu Z. Fujita H. Bibliometric analysis on the evolution of applied intelligence Appl. Intell. 49 2019 449 462
Yu H. Zhi Z. Zhang C. Yang H. Research on literature involving zirconia-based on Pubmed database: a bibliometric analysis Curr. Sci. 2017 1134 1137
Zhang X. Research on the development strategies of rural tourism in Suzhou based on SWOT analysis Energy Procedia 16 2012 1295 1299
Zhang L. Feng Y. Bibliometrics and visualization analysis of artificial blood vessel research Curr. Sci. 2014 816 822
| 36510580 | PMC9729173 | NO-CC CODE | 2022-12-14 23:17:57 | no | Telemat Inform. 2023 Jan 8; 76:101923 | utf-8 | Telemat Inform | 2,022 | 10.1016/j.tele.2022.101923 | oa_other |
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J Agric Food Res
J Agric Food Res
Journal of Agriculture and Food Research
2666-1543
The Authors. Published by Elsevier B.V.
S2666-1543(22)00201-0
10.1016/j.jafr.2022.100468
100468
Article
Magnitude and determinants of food insecurity among pregnant women in Rwanda during the COVID-19 pandemic
Rutayisire Erigene a∗
Habtu Michael ab
Ngomi Nicholas c
Mochama Monica a
Mbayire Vedaste ad
Ntihabose Corneille e
Muhire Philbert f
a Public Health Department, Mount Kenya University, Rwanda
b School of Public Health, University of Rwanda, Rwanda
c School of Pure and Applied Health Sciences, Murang’a University of Technology, Kenya
d Kiziguro District Hospital, Ministry of Health, Rwanda
e Department of Clinical and Public Health Services, Ministry of Health, Rwanda
f Ruhengeli Referral Hospital, Ministry of Health, Rwanda
∗ Corresponding author. Public Health Department, Mount Kenya University, Kigali, Rwanda.
8 12 2022
3 2023
8 12 2022
11 100468100468
24 6 2022
27 11 2022
28 11 2022
© 2022 The Authors
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Globally, food insecurity is becoming a major public health concern, and has seriously been impacted by the COVID-19 pandemic. In the last decade, Rwanda has made significant improvement in terms of overall household food security. However, the magnitude of food insecurity among pregnant women is not well known. This study investigated the magnitude and factors associated with food insecurity among pregnant women during the COVID-19 pandemic.
It was a cross-sectional study conducted in 30 health facilities across the country where a total of 1159 pregnant women in their first trimester of pregnancy were recruited during antenatal care visits (ANC). A pre-tested, standardized, and structured questionnaire was used to collect information on food insecurity based on household food insecurity access scale (HFIAS). Descriptive statistics were used to describe the basic characteristics of the study respondents and the status of household food insecurity. Logistic regression analysis was performed to estimate the predictors of food insecurity at a significance level of 5%. The majority (78.1%) of recruited pregnant women were aged 20 to 35 years and 70.3% were from rural areas. Overall, 53.1% of pregnant women were food insecure during COVID-19 pandemic. Pregnant women with low education level {AOR = 4.58; 95%CI = 1.88–11.15} and from low social economic households {AOR = 2.45; 95%CI = 1.59–3.76} were more likely to become food insecure during COVID-19 pandemic. In addition, women from households with farming as the main source of income had 64% more risk of food insecurity compared to women from household with other sources of monthly income. To achieve the sustainable development goals (SDGs) targets related to food security, there is urgent need to transform the agricultural sector from traditional farming to modern/technology farming. This will reduce the level of food insecurity in developing countries. There is also a need to provide social safety nets to pregnant women from families in lower socio-economic categories during pandemics.
Graphical abstract
Image 1
Keywords
COVID-19
Food insecurity
Pregnant women
First trimester
Gestational age
Rwanda
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pmc1 Introduction
Food insecurity (FI) is defined as lack of nutritionally adequate and safe food or a limited ability to acquire necessary food in socially acceptable ways [1]. Food insecurity has consistently increased at the global level since 2014. A recent report from the Food and Agricultural Organization (FAO) of the United Nations estimated that globally, 750 million people are exposed to severe levels of food insecurity. The same report shows that the global prevalence of both moderate and severe levels of food insecurity (SDG indicator 2.1.2) is estimated to be 25.9% [2].
It has been observed that in most African countries, people who are moderately food insecure do not have regular access to nutritious and sufficient food, even if not necessarily suffering from hunger [3]. In Rwanda, a comprehensive food security and vulnerability analysis (CFSVA) conducted in 2018 found that 18.7% of households had had trouble in accessing sufficient food while the prevalence of severe food insecurity was estimated to be 1.7% [4].
COVID-19 resulted in an increased number of households facing acute food insecurity [5]. In Rwanda, the COVID-19 pandemic might have exacerbated the crisis of food insecurity, disproportionately affecting pregnant women. A report published by the United Nations on the socio-economic impact of COVID-19 in Rwanda showed that food insecurity at the household level had increased during the COVID-19 pandemic due to the loss of wages, employment, and reduced economic activity [6].
Several factors are thought to have played out in the effect of COVID-19 pandemic on food security globally and locally, as per the following theoretical conceptual frameworks.
According to the FAO framework [7] there are four dimensions of food security namely, availability, access, utilization, and stability. Availability is the amount of food present in a country while access refers to effective demand for food, both economic and physical access to food. Utilization is about biological processing of food whereas stability captures the dynamic aspects of the above three. Food being a daily necessity, being food secure requires stability in the other three pillars over time.
Stability of food availability and access were affected by COVID-19-related restrictions on movement, with the fresh produce being most affected [8]. During the COVID-19 period, Rwanda was among the first African countries to close its borders and impose a nationwide lock-down. Although the agriculture sector was typically exempted from lockdown restrictions to ensure continuity of food production, reduced access to labor, given the fact that agricultural production in the country remains heavily human labor intensive, and shortages of intermediate farm inputs due to disruption of global supply chains led to reduction and restrictions on available food. Consumption patterns changed due to reduced availability of the food items requiring quick distribution especially fresh foods [9]. The economic shocks of the pandemic reduced people’s access to preferred foods, leaving the very vulnerable households dependent on a restricted diet provided by relief foods distributed by the Government of Rwanda during the lockdown. With reported reduced consumption of fresh produce and other nutritionally rich foods in the region [10] pregnant women’s micronutrients security was affected, potentially leading to deficiencies in their unborn babies.
The above framework mainly explains the effects of COVID-19 related restrictions to the supply side of food security. The “Food Systems” framework, on the other hand includes “all the elements (environment, people, inputs, processes, infrastructures, institutions) and activities that relate to the production, processing, distribution, preparation and consumption of food, and the output of these activities, including socio-economic and environmental outcomes” [11]. The food system approach recognizes that a change in one component is likely to have ripple effects on the others as all elements are highly interdependent. The food system approach considers the effect of the COVID-19 pandemic restrictions on all aspects of food value chains from production to consumption.
Finally, Amartya Sen’s ‘entitlement approach’, explains the differential impact of COVID-19 pandemic on food security of populations based on the distribution of wealth and resources. ‘Poverty and Famines’ [12] identified four legal sources of food at the individual or household level: production-based, own-labor, trade-based, and transfer entitlement. The production-based group, who constitute the majority of rural communities in Rwanda, faced challenges related to access to inputs and financial systems that support farming activities. The own-labor group, mainly informal casual laborers were hardest hit as most non-essential services sectors like construction were affected by the lockdowns. The same effects were felt by small business owners and traders. The formal employees faced pay cuts and their physical access to food was affected by movement restrictions and “stay-at-home” impositions during peak transmission periods of the pandemic. Those households or individuals relying on cash and food transfers for their food security were affected by the economic impact of the pandemic on the other three types of entitlements described above.
The above dynamics are thought to have had great bearing on how pregnant women in developing countries including Rwanda experienced the pandemic from a food security perspective.
In developing countries, pregnant women are particularly at increased risk of food insecurity and its health consequences. This has led to highly negative effects on the physical health and mental health of both pregnant women and their children. It was previously reported that women with food insecurity had an increased risk of gestational diabetes [13], iron deficiency [14], anxiety and depression [15,16] because of the difficulty in obtaining food. Food insecurity during pregnancy can have long-term consequences on child growth and development [17].
There is limited information on the magnitude of food insecurity among women during pregnancy and associated risk factors. This study, therefore, sought to investigate the magnitude and determinants of food insecurity among pregnant women during COVID-19 pandemic in Rwanda.
2 Materials and methods
2.1 Study design, setting and participants
A cross-sectional study was conducted among first trimester pregnant women who visited health facilities for antenatal care (ANC) in Rwanda. The study targeted women who had a confirmatory pregnancy test at the selected health facilities. The gestational age in weeks was determined using the last menstrual period reported and confirmed by an ultrasound examination. Pregnant women in their first trimester, residing for at least 6 months in the study area were included in the study. Pregnant women who were unable to give information due to serious illness or with disability were excluded from the study. The eligible respondents were enrolled and recruited from 30 public health facilities across the country.
A multi-stage sampling technique was used whereby in the first stage, 30 health facilities (serving as clusters) across the country were selected using simple random sampling technique (Fig. 1 ). Then pregnant women who visited ANC department in selected health facilities and were confirmed to be in their first trimester were included in the study. Study participants were enrolled in the study from July to December 2020. After considering all inclusion criteria (Fig. 1), a total of 1159 pregnant women in the first trimester participated in the study.Fig. 1 Study Participants flow chart.
Fig. 1
2.2 Data collection procedure and method
Data was collected by 30 trained nurses/midwives with direct supervision of 10 team leaders and the research team. They received a one-day training on the inclusion criteria, sampling and data collection procedure. The questionnaire was pretested among 50 pregnant women who visited ANC services at two Health Centers in Kigali City and the questionnaire was adjusted accordingly after pilot study. During the data collection process, the pregnant women were informed about the objective of the study and gave consent to participate. The questionnaire was divided into socio-demographic, socio-economic, lifestyle and health related characteristics. Mothers were asked about socio-demographic related questions including maternal age, partner’s age, residence, marital status; socio-economic related questions including maternal level of education, partner’s level of education, ownership of a house, main source of income and social-class categories; lifestyle variables include alcohol use, maternal smoking status and partner’s smoking status while health related variables include parity, HIV status, and chronic disease condition.
The food insecurity status was determined using a structured, standardized, and validated tool (Household Food Insecurity Access Scale: HFIAS) developed by Food and Nutrition Technical Assistance (FANTA), to classify households as food secure or not [18,19]. The tool consisted of nine questions showing the frequency of occurrence and severity of food insecurity in the last 4 weeks. The respondents were first asked if a given condition was experienced in which they were to respond yes or no. Then the severity of food insecurity was further assessed in terms of Likert Scale question responses (0 = never, 1 = rarely (1 or 2 times), 2 = sometimes (3–10 times), 3 = often (>10 times)). In this study, the pregnant women were asked to respond the questions on behalf of the household members. When calculating the score, each of the nine questions was scored 0–3, with 0 being "did not occur," 1 being "rarely”, 2 “sometimes," and 3 being "often." The score for each of the nine questions was then added together, and the total score ranges from 0 to 27, indicating the degree of insecure food access. Then the scores were categorized into 4 severity levels as follows: food secure (0 score), mildly food insecure (1–5), moderately food insecure (6–13), and severely food insecure (14–27). To determine the predictors of food insecurity and ease interpretation, the food security status was dichotomized into food secure (with no occurrence for all conditions/items) and food insecure with a ‘Yes’ response to at least one of 1–9 items.
2.3 Data quality management
Every questionnaire was cross-checked before leaving the participant to ensure the completeness of data. All questionnaires were stored in locked cabinets throughout the study period and accessed only by authorized persons to ensure confidentiality and to avoid data loss. Coding and verification of the data was done for easy manipulation, analysis, and presentation.
2.4 Data analysis
The data was entered and analyzed using IBM SPSS statistics 25. The basic characteristics and status of food insecurity were described using counts and percentages. Bivariate and multivariable logistic regression analysis was computed to determine the independent predictors of food insecurity. First bivariate analysis was done to identify variables significantly associated with the food insecurity (p value <0.05). Then to control for possible confounding variables and identify independent predictors of food insecurity, a multivariate logistic regression was performed. Variables significant during bivariate analysis were fitted together in the multivariable logistic regression and eight factors remained in the final model. A backward selection procedure was used. The goodness of fit was assessed using the Hosmer-Leme show test with Chi-square value 2.17 and p value of 0.975 which indicates that the fitted model was appropriate. Statistical significance was declared at a p value of < 0.05, and the degree of association between independent variables and the food insecurity was measured by adjusted odds ratio (AOR) with 95% confidence interval (CI).
2.5 Ethical considerations
Ethical clearance was obtained from Rwanda National Ethics Committee with reference number No.131/RNEC/2020 and permission letter was obtained from each selected Hospital. Written informed consent was obtained from the study participants after a full explanation of the purpose of the study. Participants who could not read made a thump-print after obtaining a verbal explanation from the interviewer. Study participants were free to refuse or withdraw from the study at any time without any penalty. Furthermore, the participants were assured about the confidentiality of the information and no personal identifiers were put on the questionnaires.
3 Results
3.1 Characteristics of study participants
Majority of (78.1%) of the pregnant women were in the age group of 20 to 35 years. Likewise, their spouses’ or partners’ age in the same age group were 68.8%. Majority (70.3%) of the women were from rural areas and about half of them were married (52.1%). Regarding education, most (59.4%) attained primary level of education while only 5.6% attended tertiary education. Similarly, most of their spouses’ (60.7%) had a primary level of education (Table 1 ). The study revealed that 60.6% of the study participants own a house. Concerning the employment status, more than half (54.1%) and their spouses (55.7%) were farmers followed by being housewives (21.2%). According to the social class classification in Rwanda, over half of the respondents (68.2%) fall in category one and two corresponding to low socio-economic category.Table 1 Characteristics of study participants.
Table 1Characteristics Frequency (n = 1159) Percent (%)
Age in years
15-19 69 6.0
20-35 905 78.1
>35 185 16.0
Spouse’s/partner's age in years
15-19 14 1.2
20-35 797 68.8
>35 348 30.0
Residence
Rural 815 70.3
Urban 344 29.7
Marital status
Married 604 52.1
Cohabiting 471 40.6
Single 84 7.2
Level of education
No formal education 108 9.3
Primary 689 59.4
Secondary 297 25.6
Tertiary 65 5.6
Spouse/partner's level of education
No formal education 107 9.2
Primary 703 60.7
Secondary 256 22.1
Tertiary 93 8.0
Ownership of the house
Own house 702 60.6
Rented house 419 36.2
Others 38 3.3
Main Source of household income
Employed/Monthly income 285 24.6
Business 175 15.1
Farmer 645 55.7
Others 54 4.7
Socio-class category
Category 1 199 17.2
Category 2 591 51.0
Category 3 369 31.8
Alcohol Use
Yes 249 21.5
No 910 78.5
Smoking status
Yes 13 1.1
No 1146 98.9
Spouse/partner's smoking status
Yes 81 7.0
No 1078 93.0
Parity
Primi-gravida 469 40.5
One 270 23.3
Two 169 14.6
Three 110 9.5
Four 63 5.4
Five and above 78 6.7
HIV status
Positive 27 2.3
Negative 1132 97.7
Other chronic diseases
Yes 50 4.3
No 1109 95.7
As indicated in Table 1, around one fifth (21.5%) of the women were taking alcohol. However, there were only 1.1% smokers. The result in Table 1 also indicates that 7.0% of their partners were smokers. The highest percentage (40.5%) of the women who participated in the study were in their first pregnancy. We found that 2.3% of women who participated in the study were HIV positive and 4.3% had other chronic diseases.
3.2 Magnitude of household food insecurity
According to responses to the HFIAS in the preceding 4 weeks, worrying about having enough food was reported by 34.8% of the pregnant women. Respondents who affirmed that their households were not able to eat preferred foods, had limited variety of foods, ate some foods that they did not want to eat and at any point had no food to eat in the preceding 4 weeks due to lack of resources were 44.6%, 40.1% 46.2% and 24.8% respectively. Similarly, those who experienced eating smaller meals and fewer meals were (38.1%) and (36.0%), respectively. The proportion of affirmative responses for going to bed hungry and staying a whole day and night without eating anything in the preceding 4 weeks prior to the study were (18.6%) and (13.4%) respectively (Table 2 ).Table 2 Status of household food insecurity access scale (HFIAS) questions.
Table 2Household hunger scale indicators in the past 4 weeks Yes, n(%) No, n(%) Percentage of occurrence (yes)
Rarely, n(%) Sometimes, n(%) Often, n(%)
Did you worry that your household would not have enough food? 403(34.8) 756(65.2) 104(9.0) 225(19.4) 74(6.4)
Were you or any household member not able to eat the kinds of foods you preferred because of a lack of resources? 517(44.6) 642(55.4) 108(9.3) 301(26.0) 108(9.3)
Did you or any household member have to eat a limited variety of foods due to a lack of resources? 465(40.1) 694(59.9) 113(9.7) 268(23.1) 84(7.2)
Did you or any household member have to eat some foods that you really did not want to eat because of a lack of resources to obtain other types of food? 536(46.2) 623(53.8) 136(11.7) 296(25.5) 104(9.0)
Did you or any household member have to eat a smaller meal than you felt you needed because there was not enough food? 441(38.1) 718(61.9) 114(9.8) 245(21.1) 82(7.1)
Did you or any other household member have to eat fewer meals in a day because there was not enough food? 417(36.0) 742(64.0) 121(10.4) 226(19.5) 70(6.0)
Was there ever no food to eat of any kind in your household because of lack of resources to get food? 287(24.8) 872(75.2) 80(6.9) 166(14.3) 41(3.5)
Did you or any household member go to sleep at night hungry because there was not enough food? 215(18.6) 944(81.4) 79(6.8) 117(10.1) 19(1.6)
Did you or any household member go a whole day and night without eating anything because there was not enough food? 155(13.4) 1004(86.6) 44(3.8) 94(8.1) 17(1.5)
After computing the scores using the nine HFIAS questions, the proportion of those who were food secure was 46.9%, mildly food insecure (12.4%), moderately food insecure (23.0%), while those severely food insecure were 17.8% as indicated in Table 3 . After considering those with at least one experience of the HFIAS questions, 53.1% pregnant women were classified as food insecure during COVID-19 pandemic.Table 3 Magnitude of food insecurity among pregnant women in the first trimester.
Table 3Household Food Insecurity status Count Percent (95%CI)
Food secure 543 46.9(43.9–49.8)
Mild food insecurity 144 12.4(10.6–14.5)
Moderate food insecurity 266 23.0(20.6–25.5)
Severe food insecurity 206 17.8(15.6–20.1)
3.3 Factors associated with household food insecurity among pregnant women in Rwanda during the COVID-19 pandemic
The factors significantly associated with food insecurity at bivariate analysis (p value <0.05) were age of the woman, spouse’s age, residence, marital status, woman’s and spouse’s level of education, ownership of the house, main source of household income, social class category, alcohol use, spouse’s smoking status, and parity. These variables were considered during multivariable logistic regression analysis. After running multivariable analysis seven independent predictors of food insecurity were identified including marital status, level of education, main source of household income, social class category, alcohol use, spouse/partner's smoking status and parity.
According to the results, married women were 0.38 times less likely to be food insecure compared to single women {AOR = 0.38; 95%CI = 0.38–0.68}. Women with no formal education were 4.58 times more likely food insecure than women with tertiary level of education {AOR = 4.58; 95%CI = 1.88–11.15}. Women from households with business as main source of income were 0.6 times less likely food insecure {AOR = 0.60; 95%CI = 0.38–0.97}, than those whose main source of income was monthly salary. Women whose main source of income was from farming were 1.6 times more likely food insecure {AOR = 1.64; 95%CI = 1.09–2.465. Women in the social class one (Poor socio-economic category) were 2.45 times {AOR = 2.45; 95%CI = 1.59–3.76} more likely to have food insecurity compared those in social class three (middle class). Women taking alcohol were 2.47-fold more likely food insecure than those who indicated otherwise {AOR = 2.47; 95%CI = 1.72–3.54}. Similarly, women whose spouses smoked cigarettes were 2.2 times more likely to have food insecurity {AOR = 2.20; 95%CI = 1.18–4.20}. Women with parity one were 1.8 times {AOR = 1.82; 95%CI = 1.27–2.63}, those with parity two 3.2 times {AOR = 3.22; 95%CI = 2.06–50.50}, those with parity three 4 times {AOR = 4.02; 95%CI = 1.18–4.20},those with parity four 3 times {AOR = 3.03; 95%CI = 1.56–5.91} and those with parity five and above 3.8 times {AOR = 3.80; 95%CI = 1.91–7.56} more likely to have household food insecurity than those women who were pregnant for the first time (Table 4 ).Table 4 Predictors of household food insecurity among pregnant women in Rwanda.
Table 4Factor Food insecure, n (%) Food secure, n (%) COR (95% CI) AOR (95% CI)
Age in years
15-19 41(59.4) 28(40.6) 1.42(0.80–2.51) 0.77(0.37–1.59)
20-35 450(49.7) 455(50.3) 2.10(1.50–2.94) *** 0.72(0.46–1.12)
>35 125(67.6) 60(32.4) 1.00 1.00
Spouse’s/partner's age in years
15-19 10(71.4) 4(28.6) 0.52(0.16–1.71) 0.38(0.10–1.38)
20-35 408(51.2) 389(48.8) 1.25(0.97–1.62) 0.73(0.51–1.03)
>35 198(56.9) 150(43.1) 1.00 1.00
Residence
Rural 477(58.5) 338(41.5) 2.08(1.61–2.69) *** 1.04(0.75–1.45)
Urban 139(40.4) 205(59.6) 1.00 1.00
Marital status
Married 306(50.7) 298(49.3) 0.51(0.32–0.83) ** 0.38(0.21–0.68) **
Cohabiting 254(53.9) 217(46.1) 0.58(0.36–0.95) 0.40(0.23–0.72)
Single 56(66.7) 28(33.3) 1.00 1.00
Level of education
No formal education 86(79.6) 22(20.4) 19.19(8.62–42.70) *** 4.58(1.88–11.15) **
Primary 403(58.5) 286(41.5) 6.92(3.55–13.46) *** 1.65(0.78–3.47)
Secondary 116(39.1) 181(60.9) 3.15(1.58–6.27) ** 1.69(0.80–3.57)
Tertiary 11(16.9) 54(83.1) 1.00 1.00
Spouse/partner's level of education
No formal education 86(80.4) 21(19.6) 14.95(7.52–29.72) *** 2.23(0.93–5.37)
Primary 415(59.0) 288(41.0) 5.26(3.14–8.82) *** 1.45(0.73–2.89)
Secondary 95(37.1) 161(62.9) 2.15(1.24–3.76) ** 1.03(0.53–2.01)
Tertiary 20(21.5) 73(78.5) 1.00 1.00
Ownership of the house currently reside in
Own house 412(58.7) 290(41.3) 1.00 1.00
Rented house 174(41.5) 245(58.5) 0.50(0.39–0.64) *** 0.89(0.65–1.65)
Others 30(78.9) 8(21.1) 2.64(1.19–5.84) 2.08(0.80–5.37)
Main source of household income
Employed with monthly salary 106(37.2) 179(62.8) 1.00 1.00
Owner a small business 45(25.7) 130(74.3) 0.58(0.39–0.88) * 0.60(0.38–0.97) *
Subsistence Farming 436(67.6) 209(32.4) 3.52(2.63–4.71) *** 1.64(1.09–2.46) *
Others 29(53.7) 25(46.3) 1.96(1.09–3.52) * 1.58(0.79–3.16)
Socio-class category
aCategory 1 135(67.8) 64(32.2) 3.33(2.32–4.79) *** 2.45(1.59–3.76) ***
bCategory 2 338(57.2) 253(42.8) 2.11(1.62–2.75) *** 2.02(1.47–2.78) ***
cCategory 3 143(38.8) 226(61.2) 1.00 1.00
Alcohol use
Yes 183(73.5) 66(26.5) 3.05(2.24–4.16) *** 2.47(1.72–3.54) ***
No 433(47.6) 477(52.4) 1.00 1.00
Smoking status
Yes 9(69.2) 4(30.8) 1.99(0.61–6.52)
No 607(53.0) 539(47.0) 1.00
Spouse/partner's smoking status
Yes 62(76.5) 19(23.5) 3.08(1.82–5.23) *** 2.20(1.18–4.20) *
No 554(51.4) 524(48.6) 1.00 1.00
Parity
Primigravida 203(43.3) 266(56.7) 1.00 1.00
One 135(50.0) 135(50.0) 1.31(0.97–1.77) 1.82(1.27–2.63) **
Two 106(62.7) 63(37.3) 2.21(1.53–3.16) *** 3.22(2.06–50.5) ***
Three 77(70.0) 33(30.0) 3.06(1.96–4.78) *** 4.02(2.29–7.08) ***
Four 41(65.1) 22(34.9) 2.44(1.41–4.23) ** 3.03(1.56–5.91) ***
Five and above 54(69.2) 24(30.8) 2.95(1.76–4.93) *** 3.80(1.91–7.56) ***
HIV status
Positive 16(59.3) 11(40.7) 1.29(0.59–2.80)
Negative 600(53.0) 532(47.0) 1.00
History of chronic diseases
Yes 30(60.0) 20(40.0) 1.34(0.75–2.38)
No 586(52.8) 523(47.2) 1.00
AOR: Adjusted Odds Ratio COR: Crude Odds Ratio; CI: Confidence Interval; *P value <0.05; **P value <0.01; ***P value <0.001.
a Category 1: Very poor and vulnerable citizens who were homeless and unable to feed themselves without assistance.
b Category 2: Citizens who were able to afford some form of rented or low-class owned accommodation, but who were not gainfully employed and could only afford to eat once or twice a day.
c Category 3: Citizens who were gainfully employed or were even employers of labour. This category included small farmers who had moved beyond subsistence farming, or owners of small and medium-scale enterprises.
4 Discussion
The COVID-19 pandemic has affected both the health of billions of people globally and introduced food insecurity to their households. The World Food Programme estimates that in LMICs 272 million people are already or are at risk of becoming acutely food-insecure [20]. The World Bank's latest report indicates the impacts of the pandemic has led to a significant increase in global food insecurity among vulnerable groups in almost every country with the impacts expected to continue [21]. The situation is not different in Rwanda with the current study reporting a 53.1% prevalence of food insecurity among pregnant women during the COVID-19 pandemic. Women further explained that the levels of food insecurity in their households varied, however, the situation had worsened over the period of lockdown. This could have been because of disruption of all activities that relate to food production, processing, distribution, and preparation. It was observed that during lockdown and travel restrictions, households with pregnant women, children, and larger families consistently found it even harder to put food on the table. Increasing food insecurity during the pandemic has been reported in many other settings.
It has been documented that restricting people's movements often result in constraining their economic opportunities [22]. These restrictions have severe health and wellbeing impacts on already poor and vulnerable populations. Many fragile populations, including pregnant women, children, slum dwellers, the sick, cannot make economic sacrifices as they would leave them starving [23,24]. For the poor, tending to livelihood activities, ensuring an income, and food security, represent more of a concern than the possibility of contracting COVID-19.
Food insecurity is critical during pregnancy [16,25]. This is because nutritional requirements increase due to physiological changes that occur in pregnant women such as an increase in basal metabolism caused by accelerated synthesis of fetal, placenta, uterine, mammary tissues, and an increase in metabolically active tissue and cardio-respiratory work. Additionally, the processes to prepare or to get food may become more difficult, and pregnant women may be restricted in doing some activities such as farming, and this, in turn, leads to household food insecurity.
In the multivariate analysis, married women were less likely to be food insecure compared to single women. This is not surprising because in African settings men are by default the household heads and are expected to provide for their family. This is consistent with previous studies which showed that men being the head of the household is associated with a higher likelihood of food security [26,27]. Findings also revealed that women with no formal education were 5 times more likely to be food insecure than their counterparts who were educated. In line with the present findings, low education levels were consistently reported as the main predictor of food insecurity among the women [[28], [29], [30]]. This is due to the fact that most women with low education are unemployed, have lower earning capacity and mostly depend on their husbands for provision.
Findings on social class indicated that women who were in lower social class were more likely to be food insecure, compared to those in higher social class. The finding is consistent with a study conducted in South Africa, which found that food insecurity was prevalent (42%) among women from low socio-economic conditions [31]. This is because people in lower socioeconomic classes depend on daily wages and casual labor which were severely disrupted during the pandemic, rendering them food insecure. The protective effects of marriage, education, and higher socioeconomic status on food insecurity were also reported in a similar study in Nepal [32].
Increasing parity was also found to be associated with food insecurity. Women with higher parity were two to four times more likely to be food insecure compared to those who were pregnant for the first time. This is because higher parity increases women’s social vulnerability and hence increases their risk of household food insecurity. A recent study conducted in the US reported that households with children were at high risk of food insecurity during COVID-19 pandemic [33]. However, this study did not directly assess the effect of the number of children on the severity of food insecurity during COVID-19 pandemic.
As previously demonstrated [34], substance use was also found to be positively associated with food insecurity with those who took alcohol or whose partners smoked being more likely to be food insecure than their counterparts. The relationship between substance use and food insecurity has been shown to be bidirectional. A high prevalence (21.5%) of alcohol consumption during pregnancy was observed among pregnant women in Rwanda. Similarly, this high prevalence of alcohol consumption during pregnancy has been observed in other African countries for example alcohol consumption during pregnancy was reported as 20.5% in Uganda and 18.5% in Zambia [35]. Alcohol consumption during pregnancy in most African countries may be influenced by lack of access to public health information about the negative effect of alcohol consumption during pregnancy [36]. It may also be influenced by the low-cost of locally made alcoholic beverages that are commonly sold without a license.
The strengths of the study include the use of the standardized tool to assess food security status among pregnant women as well as using a large country-wide representative sample. Nevertheless, the findings should be considered in the following context: The timing of the harvest season and COVID-19 restrictions could have played a big role in food insecurity levels; food security status was assessed using self-report, and hence recall bias also could have played a role as respondents were interviewed regarding their food intake in the preceding 4 weeks and therefore, the prevalence of food insecurity during pregnancy may have been overestimated in the current study. It should also be noted that the nature of cross-sectional study design may not effectively establish a cause-effect relationship. Despite those limitations, the study contributes significantly to the knowledge on the status of food security among pregnant women during the COVID-19 pandemic.
5 Conclusions
From the above findings, it can be concluded that food insecurity at the household level increased during the COVID-19 pandemic and pregnant women were most affected. Nutrition-sensitive interventions to boost agricultural productivity in general and more specifically targeting women of reproductive age are potential areas of intervention in reducing food insecurity faced by pregnant women and can aid in achieving the SDG indicator targets related to food security. Other interventions that could reduce food insecurity include the introduction of organic farming, community awareness campaigns of food security, food storage, and utilization. There is a need to include screening for food insecurity in antenatal care package, this will provide a unique opportunity to identify pregnant women at-risk of food insecurity. Furthermore, during the period of pandemic community health workers should be engaged in identifying pregnant women from food insecure households, this will provide an early opportunity to connect them with the supplemental nutrition assistance program at community level or at health facilities.
Ethics approval and consent to participate
This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving research study participants were approved by the Rwanda National Ethics Committee with reference number No.131/RNEC/2020 and permission letter was obtained from each selected Hospitals. Written informed consent was obtained from all subjects."
Consent for publication
Not applicable.
Funding
This research was funded by the Rwanda 10.13039/501100011869 National Council of Science and Technology (NCST) . The funding body was not involved in any part of the study design, implementation, or analysis.
Authors' contributions
The authors’ responsibilities were as follows- ER, MH, NG, MM: Designed the research protocol, followed up data collection, and had primary responsibility for the final content; VM, CN, PM: contributed to the design of the study, data analysis, and report writing, and all authors: read and approved the final manuscript.
Declaration of competing interest
The authors declare that they have no conflict of interests.
List of abbreviations
ANC Antenatal care
CFSVA Comprehensive food security and vulnerability analysis
HFIAS Household food insecurity access scale
LMP Last menstrual period
FANTA Food and Nutrition Technical Assistance
FAO Food and Agricultural Organization
FI Food insecurity
SDGs Sustainable development goals
Data availability
Data will be made available on request.
Acknowledgements
This work was carried out with financial support from the Government of Rwanda through 10.13039/501100011869 National Council for Science and Technology under Excellence Research Grant with grant Number: NCST-NRIF/ERG-BATCH1/P05/2019. We gratefully acknowledge the contribution of participating mothers, nurses, midwives, hospitals, and health centers.
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References
1 Bickel Gary, Mark Nord, Cristofer Price, William Hamilton, Cook J: Guide to Measuring Household Food Security, Revised 2000. U.S. Department of Agriculture, Food and Nutrition Service, Alexandria VA. March, 2000. Or in short form as USDA, Guide 2000. 2000.
2 FAOIFADUNICEFWFP WHO: the State of Food Security and Nutrition in the World 2020. Transforming Food Systems for Affordable Healthy Diets 2020 FAO Rome 10.4060/ca9692en
3 FAOECAAUC Africa regional overview of food security and nutrition Transforming Food Systems for Affordable Healthy Diets 2020 FAO Accra 10.4060/cb4831en. 2021
4 NISR: Rwanda Comprehensive Food Security and Vulnerability Analysis 2018 2018
5 Shahzad M.A. Qing P. Rizwan M. Razzaq A. Faisal M. COVID-19 pandemic, determinants of food insecurity, and household mitigation measures: a case study of Punjab, Pakistan Healthcare 9 2021 621 10.3390/healthcare9060621 34067430
6 UN-Rwanda The Socio-Economic Impact of COVID-19 in Rwanda 2020
7 FAO Global strategic framework for food security & nutrition (GSF); committee on world foodsecurity https://www.fao.org/fileadmin/templates/cfs/Docs1314/GSF/GSF_Version_3_EN.pdf 2014
8 Nicola M. Alsafi Z. Sohrabi C. Kerwan A. Al-Jabir A. Iosifidis C. …Agha R. The socio-economic implications of the coronavirus pandemic (COVID-19): a review Int. J. Surg. 78 2020 185 193 10.1016/j.ijsu.2020.04.018 32305533
9 Water for Food COVID-19 impact on agricultural entrepreneurship in Rwanda https://waterforfood.nebraska.edu/-/media/projects/dwfi/blogs/2020/research-handouts/covidagentrepreneurship.pdf
10 Kansiime M.K. Tambo J.A. Mugambi I. Bundi M. Kara A. Owuor C. COVID-19 implications on household income and food security in Kenya and Uganda: findings from a rapid assessment World Dev. 137 2021 105199 pmid:32982018
11 HLPE Impacts of COVID-19 of food security and nutrition: developing effective policy responses to address the hunger and malnutrition pandemic Rome 10.4060/cb1000en 2020
12 Sen A. Poverty and Famines 1981 Oxford University Press Oxford ([Google Scholar])
13 Kazemi F. Masoumi S.Z. Shayan A. Shahidi Yasaghi S.Z. Prevalence of food insecurity in pregnant women and its association with gestational weight gain pattern, neonatal birth weight, and pregnancy complications in Hamadan County, Iran Agric. Food Secur. 9 1 2018 12 2020
14 Park Cy Fau - Eicher-Miller H.A. Ha E.-M. - Iron deficiency is associated with food insecurity in pregnant females in the United States: National Health and Nutrition Examination Survey 1999-2010 J. Acad. Nutr. Diet. 114 12 2014 1967 1973 24953790
15 W R.C. Phillips Sm Fau Orzol S.M. O S.M. Food insecurity and the risks of depression and anxiety in mothers and behavior problems in their preschool-aged children Pediatrics 118 3 2006 2006-0239
16 Alanderson Alves Ramalho Andrade Martins Fernanda Jorge K.R. Food insecurity during the gestational period and factors associated with maternal and child health J Nutr Health Food Eng 7 4 2017 337 343 10.15406/jnhfe.2017.07.00245. 2017
17 Gresham E. Bisquera A. Byles J.E. Hure A.J. Effects of dietary interventions on pregnancy outcomes: a systematic review and meta-analysis Matern. Child Nutr. 12 1 2016 5 23 25048387
18 SIPRING/FANTA: Household Food Insecurity Access Scale (HFIAS) for Measurement of Food Access Indicators Guide 2007
19 Deitchler M. Ballard T. Swindale A. Coates J. Validation of a Measure of Household Hunger for Cross-Cultural Use 2010
20 WFP: World Food Programme COVID-19 Will Double Number of People Facing Food Crises unless Swift Action Is Taken 2020 https://wwwwfporg/news/covid-19-will-double-number-people-facing-food-crises-unless-swift-action-taken
21 WB: The World Bank Food Security and COVID-19 2022 https://wwwworldbankorg/en/topic/agriculture/brief/food-security-and-covid-19
22 Jackson James K. Weiss Martin A. Schwarzenberg Andres B. Nelson Rebecca M. Sutter Karen M. Sutherland M.D. Global economic effects of COVID-19 https://sgp.fas.org/crs/row/R46270.pdf 2021
23 Dahab M. van Zandvoort K. Flasche S. Warsame A. Ratnayake R. Favas C. Spiegel P.B. Waldman R.J. Checchi F. COVID-19 control in low-income settings and displaced populations: what can realistically be done? Conflict Health 14 1 2020 54 32754225
24 Choithani C. What happens to the wives of male migrant workers, who run entire households in villages? Available at https://thewire.in/women/lockdown-migrant-workers-wives 2020
25 Laraia B.A. Siega-Riz A.M. Gundersen C. Dole N. Psychosocial factors and socioeconomic indicators are associated with household food insecurity among pregnant women J. Nutr. 136 1 2006 177 182 110.1093/jn/1136.1091.1177 16365079
26 Mbunge E. Effects of COVID-19 in South African health system and society: an explanatory study Diabetes Metabol. Syndr. 14 6 2020 1809 1814
27 Bapolisi W.A. Ferrari G. Bisimwa G. Merten S. Gendered determinants of food insecurity in ongoing regional conflicts, North and South Kivu, the Democratic Republic of Congo Agric. Food Secur. 10 1 2021 13
28 Kandiah M Zm S. Sulaiman N Fau Jalil R.A. Jalil Ra Fau Yen W.C. Yen Wc Fau Yaw Y.H. Yaw Yh Fau Taib M.N.M. Taib Mn Fau Kandiah M. Fau - Lin K.G. Kg L. Food insecurity and the metabolic syndrome among women from low income communities in Malaysia Asia Pac. J. Clin. Nutr. 23 1 2014 138 147 24561982
29 Bawadi H.A. Tayyem R.F. Dwairy A.N. Al-Akour N. Prevalence of food insecurity among women in northern Jordan J. Health Popul. Nutr. 30 1 2012 49 55 22524119
30 Demétrio F. Teles C.A.S. Santos D.B.D. Pereira M. Food insecurity in pregnant women is associated with social determinants and nutritional outcomes: a systematic review and meta-analysis Ciência Saúde Coletiva 25 7 2020 2663 2676 2610.1590/1413-81232020257.81224202018 32667549
31 Abrahams Z. Lund C. Field S. Honikman S. Factors associated with household food insecurity and depression in pregnant South African women from a low socio-economic setting: a cross-sectional study Soc. Psychiatr. Psychiatr. Epidemiol. 53 4 2018 363 372
32 Pandey S. Fusaro V. Food insecurity among women of reproductive age in Nepal: prevalence and correlates BMC Publ. Health 20 1 2020 175
33 Parekh N. Ali S.H. O'Connor J. Tozan Y. Jones A.M. Capasso A. Foreman J. DiClemente R.J. Food insecurity among households with children during the COVID-19 pandemic: results from a study among social media users across the United States Nutr. J. 20 1 2021 73 34461913
34 Kim-Mozeleski JE, Pandey R: The Intersection of Food Insecurity and Tobacco Use: A Scoping Review. In.
35 Popova S. Lange S. Probst C. Shield K. Kraicer-Melamed H. Ferreira-Borges C. Rehm J. Actual and predicted prevalence of alcohol consumption during pregnancy in the WHO African Region Trop. Med. Int. Health 21 10 2016 1209 1239 27429168
36 Adusi-Poku Y. Edusei A.K. Bonney A.A. Tagbor H. Nakua E. Otupiri E. Pregnant women and alcohol use in the Bosomtwe district of the Ashanti region-Ghana Afr. J. Reprod. Health 16 1 2012 55 60
| 36510625 | PMC9729197 | NO-CC CODE | 2022-12-14 23:38:12 | no | J Agric Food Res. 2023 Mar 8; 11:100468 | utf-8 | J Agric Food Res | 2,022 | 10.1016/j.jafr.2022.100468 | oa_other |
==== Front
Appl Acoust
Appl Acoust
Applied Acoustics. Acoustique Applique. Angewandte Akustik
0003-682X
1872-910X
Elsevier Ltd.
S0003-682X(22)00523-0
10.1016/j.apacoust.2022.109149
109149
Article
Effects of face masks and acoustical environments on speech recognition by preschool children in an auralised classroom
Kwon Miji a
Yang Wonyoung b⁎
a Department of Speech-Language Rehabilitation & Counseling, Gwangju University, Gwangju 61743, Republic of Korea
b Division of Architecture, Gwangju University, Gwangju 61743, Republic of Korea
⁎ Corresponding author.
8 12 2022
1 2023
8 12 2022
202 109149109149
30 3 2022
16 10 2022
25 11 2022
© 2022 Elsevier Ltd. All rights reserved.
2022
Elsevier Ltd
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The potential impact of mask-wearing specifically on early-childhood speech and language development in classrooms has not been widely reported yet, although face masks are compulsory even in educational settings during the COVID-19 pandemic. This study investigated the combined effects of face-mask usage (no mask, surgical and KF94 masks) and room acoustics (RT 0.6 s and 1.2 s, SNR 12 dB and 22 dB) on speech recognition (KS-MWL-P) in preschool children (N = 67) in realistic classroom-acoustic settings using the auralisation technique.
The face mask and reverberation time affected pre-schoolers’ speech recognition scores. Reducing RT in the classroom improved the pre-schoolers’ speech recognition that was reduced by face masks. Children aged 4 and 5 years were affected by face masks and RT more significantly than children aged 6 years.
Appropriate room acoustics for classrooms and clear speech of teachers are recommended for better speech recognition in preschool, where pre-schoolers’ language and speech development usually occur.
Keywords
Face masks
Speech recognition
Preschool children
Monosyllabic word list
WIPI test
Reverberation time
Noise
Classroom acoustics
Auralisation
COVID-19
==== Body
pmc1 Introduction
Owing to the recent COVID-19 pandemic, many countries are mandating the use of face masks in public [1], [2], [3] as they are effective in mitigating virus spread. [4], [5] Face masks are compulsory even in educational institutions. [6] Recent studies on the pandemic’s potential impact on child development have reported evidence of learning loss [7], negative mental health outcomes [8], and lowering of language skills. [9], [10] Nevertheless, how mask-wearing affects early-childhood speech and language development in educational institutions specifically, has not yet been widely reported. Sfakianaki et al. [11] reported a tendency of lower performance in word recognition among children (aged 6 years and 8 months to 7 years and 6 months) owing to wearing masks. Although the effects of face masks on speech recognition have been investigated in adults, to the best of our knowledge, no studies exist regarding pre-schoolers. Language-and-speech development usually occurs during early childhood as part of the gradual acquisition of receptive and expressive skills. [10] It is widely known that changes in language development are rapid up to the age of 3.5 years. Subsequently, the developmental process continues at a slower rate for the next few years. [12].
In adults, speech recognition was significantly lower with a mask, than without a mask. A surgical mask significantly reduced speech recognition in both, quiet and noisy environments. [11] Note that such a mask was reported to cause the least acoustic attenuation compared to other types of masks. [13], [14] Face masks reduced the sound levels by approximately 6–7 dB for frequencies between 100 Hz and 1.6 kHz and 7–13 dB for frequencies between 2 kHz and 5 kHz. [15] The speech transmission index (STI) at a distance of 2 m was decreased upon face-mask type. Speech levels in the octave band at 2 kHz or higher were also decreased when wearing face masks. [16] In acoustic measures, the mask tissue reduced amplitudes up to 8 dB at frequencies above 1 kHz, whereas no reduction was observed below 1 kHz. [17] Transparent masks could facilitate the ability to understand target sentences by providing visual information. [18], [19].
In addition to the transparent mask, the classroom-acoustic environment is considered helpful for speech recognition. In previous room-acoustic studies, it was identified that poor classroom acoustics, such as long reverberation time, high background-noise level, or low signal levels negatively affected speech perception and listening comprehension. This effect was more pronounced in younger children than in older children or adults. [20], [21] In one-talker masker, speech reception-threshold performance was estimated to be adult-like by 10–12.9 years of age. In two-talker masker, the performance was not projected to be adult-like until 16.1–16.8 years of age. [20] Thus, children need more favourable listening conditions than adults for decoding and processing oral information. [22] In addition, younger children (11–13 years), compared to older ones (15–17 years) and adults, were less able to use contextual cues to reconstruct noise-masked words presented in a sentential context. [23] Young children (5–7 years), unlike older children (10–12 years) and adults, were unable to recognise spectrally degraded speech. [24] The parameters for classroom acoustics were level of speech, level and characteristics of background noise and/or competing talkers, and reverberation time (RT) of the room. [25] Maximum background-noise levels of 35 dBA and a maximum RT of 0.6 s for typical medium-sized classrooms have been recommended. [26] In real classrooms, which are rarely noiseless, reverberance must be optimised rather than minimised. [27], [28], [29] Early studies reported that speech intelligibility decreased with increased RT, which indicated zero optimal reverberation for children and adults. [30], [31], [32], [33] However, these early experimental studies were unrealistic because they effectively assumed a diffused sound field, by involving exponential sound decays, without accounting for the interaction between reverberation and sound levels. [28] In contrast, theoretical studies reported non-zero optimum RTs for speech intelligibility. [34], [35], [36] In a relatively realistic study, noise was incorporated into a theoretical model, and non-zero optimum RTs for speech intelligibility were validated with children and adults. [27], [28], [29] Children in elementary schools were asked to participate in most studies. However, younger children (2–5 years), for whom language development is the most important, rarely participated in speech-recognition testing in classroom-acoustics studies. Young children (4–6 years), at high risk for language impairment, spend most of their day in classrooms. Here, acoustics have a more important role, particularly when face masks have been unavoidable in preschool.
This study’s purpose was to investigate the combined effect of face-mask usage and room acoustics on speech recognition in preschool children. We used the auralisation technique in realistic classroom-acoustic settings. The effects of RT and signal-to-noise ratio (SNR) were investigated in a classroom environment with children aged between 4 and 6 years, using face masks.
2 Methods
2.1 Participants
Sixty-seven children (4–6 years old) were recruited from two preschools in Gwangju, Korea, with parental consent. The children received toys and books to take home as compensation for their participation. The Institutional Review Board of Gwangju University approved the informed consent procedure. With no hearing tests performed at this stage, hearing-impaired students were excluded, as reported by their teachers and parents.
The Receptive and Expressive Vocabulary Test (REVT) [37], [38] for Koreans was used to evaluate the children’s language development. As the REVT factor was not a research topic in this study, the children identified as ‘delayed’ did not need to be excluded from data analysis for statistical validity. Further analysis was not performed on the REVT of this study. Table 1 lists the participant numbers according to age.Table 1 Description of the participants.
Age 4 yr 5 yr 6 yr Sum Total
Gender Female Male Female Male Female Male Female Male
Sum 10 9 17 11 10 10 37 30 67
REVT Normal 9 9 14 8 7 8 30 25 55
Delayed 1 – 3 3 3 2 7 4 12
2.2 Anechoic speech recording wearing face masks
The Korean-standard monosyllabic-word list for pre-schoolers (KS-MWL-P) [39], developed based on the international standard for speech audiometry [40] and word intelligibility by picture identification (WIPI) test [41], was used for the speech-recognition test. The KS-MWL-P consists of four lists, each of 25 words, and has 26 colour plates (one for practice) with six pictures per page. Depending on the situation, only ten words can be used. [42].
The lists were recorded by a professional voice-actress in a flat-walled fully anechoic chamber [43] (8.2 m × 7.0 m × 7.5 m, = 50 Hz), using a class-1 sound level metre (RION NL-52). The microphone was 1 m away from the seated speaker. Three sets of 100 KS-MWL-P words were recorded based on mask types: no mask as the control, surgical mask (PURO GUARD Daily Mask, 175 mm × 90–155 mm, 3 layers of non-woven fabric, 50 pieces in a box, PURO Corp.), and KF94 mask (ECOLTER KF94 fine dust mask, 210 mm × 48 + 80 + 48 mm, 4 layers of non-woven fabric, individually packaged in plastic, EcoDream Corp.); as shown in Fig. 1 .Fig. 1 Two different face masks used in the study (left: surgical, right: KF94).
A surgical mask is a loose-fitting disposable device that creates a physical barrier between the wearer’s mouth-and-nose and potential contaminants in the immediate environment. [44] Although a surgical mask with non-woven fabric fails to provide complete protection, many people prefer it for its breathability. [45] A KF94 mask is the “Korea filter” standard, equivalent to the N95 mask; 94 (%) refers to its filtration efficiency. [46].
2.3 Room acoustics in an auralised classroom
A typical preschool classroom (6.80 m × 8.00 m × 2.64 m) was chosen for this study, as shown in Fig. 2 . Two RTs (T30500Hz, 1kHz, 0.6 s and 1.2 s) were fitted at the listener’s position using ODEON 15.16 to change the surface materials. Table 2 presents the absorption coefficients and the scattering coefficients of the materials fitted for each RT. Table 3 lists the two fitted RTs throughout the octave frequency bands above the Shroeder frequencies (129 Hz and 183 Hz).Fig. 2 Odeon room model with positions for the speaker (red) and listener (blue) in classroom.
Table 2 Absorption coefficients and scattering coefficients of the surface materials in T30s.
T30 Material Area (m2) Absorption Coefficient Scattering Coefficient
707 Hz
250 Hz 500 Hz 1 kHz 2 kHz 4 kHz 8 kHz
0.6 s Floor 54.40 0.15 0.10 0.10 0.05 0.10 0.10 0.1
Ceiling 54.40 0.25 0.40 0.55 0.60 0.60 0.60
Wall1 + 3 32.77 0.30 0.20 0.17 0.15 0.10 0.10
Wall2 21.14 0.21 0.10 0.08 0.06 0.06 0.06
Walls 9.83 0.22 0.17 0.09 0.10 0.11 0.11
W1 9.72 0.06 0.04 0.03 0.02 0.02 0.02
W2 1.16 0.25 0.18 0.12 0.07 0.04 0.04
Doors 3.60 0.10 0.06 0.08 0.10 0.10 0.10
1.2 s Floor 54.40 0.01 0.015 0.02 0.02 0.02 0.02 0.1
Ceiling 54.40 0.30 0.2 0.17 0.15 0.10 0.10
Wall2 21.14 0.20 0.15 0.13 0.10 0.08 0.08
Wall3 11.63 0.02 0.02 0.02 0.02 0.02 0.02
Walls 30.97 0.14 0.09 0.06 0.05 0.05 0.05
Windows 10.88 0.05 0.03 0.03 0.02 0.02 0.02
Doors 3.60 0.10 0.06 0.08 0.10 0.10 0.10
Table 3 Octave band RTs in T30s.
T30 250 Hz 500 Hz 1 kHz 2 kHz 4 kHz 8 kHz T30500Hz, 1kHz
0.6 s 0.56 0.60 0.58 0.58 0.57 0.45 0.59
1.2 s 0.79 1.14 1.30 1.41 1.47 0.88 1.22
After the reverberation fitting of the simulation models, the anechoic speech recordings were auralised in the two simulated classrooms, with RTs of 0.6 s and 1.2 s, respectively. The speech source had the directivity of a talking human provided by ODEON 15.16 (BB93. RAISED NATURAL.SO8). [47] Headphone transfer functions (Sennheiser HD600) and head-related transfer functions (KEMAR) were applied to the auralisation. The input parameters that were used include impulse response length of 1000 ms, number of late rays of 1000, and transition order of 2. Fig. 3 shows the impulse responses of the simulated classrooms.Fig. 3 Impulse responses from ODEON simulations (a) RT = 0.6 s, (b) RT = 1.2 s.
2.4 Speech recognition test design and procedure
12 experimental configurations (3 masks × 2 RTs × 2 noise conditions) were designed for this study. Each auralised speech source was presented at 62 dBA through a headphone. A babbling sound of 50 dBA, which is natural in an actual classroom, was used as the background-noise source. As a control, no-noise conditions were tested in this study. The background-noise levels were monitored to be<40 dBA during the tests. Therefore, the two signal-to-noise ratios of the tests were 12 dB and>22 dB, respectively.
Data were collected between January and February 2022. Fig. 4 shows a photograph of the preschool-classroom test. Speech-recognition tests were conducted in a quiet preschool classroom, specifically allocated for testing. It was administered on a tablet pad and Sennheiser HD 600 headphones. Ten words were randomly selected for each experimental configuration using a program, specifically developed for this study. The 6 colour pictures (3 columns × 2 rows) were displayed on a tablet screen for children to point a correct word picture of six. No text was displayed on a tablet for children. Their responses were automatically saved in a database. A total of 12 sessions (120 words) were performed with each child. They performed a full test, divided into 2–4 sessions, based on their level of concentration. In general, each session lasted less than five minutes.Fig. 4 Speech recognition testing.
Descriptive statistics were used to summarise and describe variables for the speech-recognition score. Based on the Anderson-Darling normality test, the data were not normally distributed. The data were analysed by applying a non-parametric statistical methodology. The Mann-Whitney U and Kruskal-Wallis tests were used to verify the effects of face masks on speech-recognition.
3 Results
3.1 Overall data analyses for the test score
Table 4 lists the results of the descriptive statistics. Speech-recognition scores varied according to the face-mask type, RT, and noise. Fig. 5 illustrates the average speech-recognition scores according to the face-mask type. Age, gender, and REVT, which are well-known factors affecting children’s language development, [48] were also observed.Table 4 Descriptive statistics results for the test scores.
Factor N Mean StDev Skewness Kurtosis
Mask No Mask 261 8.3 1.35 −1.85 5.07
Surgical 258 7.9 1.59 −1.34 2.41
KF94 256 7.7 1.66 −1.08 1.35
RT 0.6 s 390 8.2 1.40 −1.47 3.30
1.2 s 385 7.8 1.68 −1.26 1.77
Noise SNR > 22 dB 395 8.1 1.52 −1.45 2.69
SNR = 12 dB 380 7.9 1.59 −1.32 2.31
Age 4 yr 226 7.6 1.53 −0.82 0.32
5 yr 330 7.7 1.68 −1.56 3.00
6 yr 219 8.8 1.13 −1.25 1.15
Gender Girl 429 8.3 1.33 −1.09 1.16
Boy 346 7.7 1.76 −1.36 2.08
REVT Typical 632 8.6 1.35 −1.09 1.09
Delayed 143 7.4 2.10 −1.14 0.97
Fig. 5 Effects of wearing masks on speech recognition according to (a) RT and (b) age (with 95% Confidence Intervals).
The average children's speech-recognition scores varied according to the RT. The younger the children, the more they were affected. Children aged 4–5 years were affected, but those aged 6 years showed no difference in the average scores for RTs of 0.6 and 1.2 s. Compared with the RT of 1.2 s, RT of 0.6 s caused higher speech-recognition in children up to the age of 5. However, descriptive statistics could not verify the statistical significance of variables. Negative skew was observed in each data set; it is presumably due to the ceiling effect of the test material. Normality was only observed at severe conditions at the RT of 1.2 s (AD = 0.384 and P = 0.394) and age of 4 (AD = 0.576 and P = 0.133), which had relatively lower absolute skewness values.
The Mann-Whitney U test was performed to compare the pairwise data sets. For overall data, mask-wearing significantly affected the speech-recognition scores, but the type of mask was not critical. The speech-recognition score was affected by RT with statistical significance. However, it was not influenced by noise herein. Age, gender, and REVT, which are observed factors affecting average speech-recognition scores in descriptive statistics, affected the speech-recognition scores with statistical significance.
3.2 Combined effects of the face Mask, Noise, and reverberation time
Four acoustic environments were tested in this study, as listed in Table 6 . When the SNR was greater than 22 dB, no combined effect of the face mask and RT was observed. However, in the severe acoustic environment, combined with the SNR of 12 dB and RT of 1.2 s, the children’s speech-recognition test scores were significantly affected by the face mask, as listed in Table 7 . The average scores of children wearing the KF94 mask were significantly lower than the scores of those without the mask.Table 5 Mann-Whitney U test results of the speech recognition scores for all participants.
Variable1 N Median Variable2 N Median W-value P-value
Mask No Mask 261 9.0 Surgical 258 9.0 72,492.5 0.005
Surgical 258 9.0 KF94 256 8.0 68,885.5 0.136
KF94 256 8.0 No Mask 261 9.0 74,718 < 0.0005
RT 0.6 s 390 9.0 1.2 s 385 9.0 161,982.5 < 0.0005
Noise SNR > 22 dB 380 9.0 SNR = 12 dB 395 9.0 148,007.5 0.083
Age 4 yr 226 8.0 5 yr 330 9.0 61,584 0.454
5 yr 330 9.0 6 yr 219 9.0 77,634 < 0.0005
6 yr 219 9.0 4 yr 226 8.0 41,021.5 < 0.0005
Gender Girl 429 9.0 Boy 346 8.9 177,992 < 0.0005
REVT Normal 632 9.0 Delayed 143 8.0 44,016 < 0.0005
Table 6 Kruskal-Wallis test results of the speech recognition scores.
Factor RT0.6 sNo Noise
(SNR > 22 dB) RT0.6 sBabble
(SNR = 12 dB) RT1.2 sNo Noise
(SNR > 22 dB) RT1.2 sBabble
(SNR = 12 dB)
Median H P Median H P Median H P Median H P
Mask No Mask
Surgical
KF94 9.0 1.59 0.101 9.0 2.19 0.075 9.0 3.20 0.202 9.0 10.76 0.005
9.0 9.0 9.0 9.0
9.0 9.0 8.0 8.0
Overall 9.0 9.0 9.0 8.0
Table 7 Mann-Whitney U test results of the speech recognition scores for babble noise conditions.
Factor Variabl1 Variable2 RT0.6 sBabble
(SNR = 12 dB) RT1.2 sBabble
(SNR = 12 dB)
W P W P
Mask No Mask Surgical 5,221 0.053 4,597.5 0.103
Surgical KF94 4,407 0.811 4,566.5 0.102
KF94 No Mask 5,196.5 0.047 4,906 0.001
3.3 Combined effects of the face mask and Children’s age
In children aged 4–5 years, the face mask significantly affected speech recognition, as shown in Table 8 . The youngest group, children aged 4 years, was affected by the surgical mask, whereas those aged 5–6 years were not. Children aged 4–5 years were affected by the KF94 mask. However, children aged 6 years were not affected by any face mask. As age decreased, the effects of wearing masks on speech recognition increased.Table 8 Mann-Whitney U test results of the speech recognition scores according to children’s age.
Factor Variabl1 Variable2 Age 4 yr Age 5 yr Age 6 yr
W P W P W P
Mask No Mask Surgical 6,743.5 P < 0.0005 13,123.5 0.168 5,288.5 0.751
Surgical KF94 5,889 0.364 13,003 0.035 5,141 0.345
KF94 No Mask 6,998.5 P < 0.0005 14,089.5 P < 0.0005 5,069 0.215
RT 0.6 s 1.2 s 14,454.5 0.007 30,453 P < 0.0005 11,790.5 0.839
Noise No Noise
(SNR > 22 dB) Babble
(SNR = 12 dB) 11,900.5 0.054 27,914.5 0.896 11,686 0.052
Gender Girl Boy 14,543.5 0.091 36,417.5 P < 0.0005 12,717 0.249
The children's speech-recognition scores were affected by RT. Children aged 4–5 years were affected, but those aged 6 years showed no difference in the mean scores for RTs of 0.6 s and 1.2 s. Compared to RT of 1.2 s, RT of 0.6 s caused higher speech recognition in children up to age 5.
4 Discussion
4.1 Effects of face mask on speech recognition for preschool children
In this study, we found that face masks had a significantly negative impact on children’s speech recognition. This is consistent with the findings of Sfakianaki et al. [11], the only reference, to the best of our knowledge, regarding the effects of face masks on speech recognition in children with normal hearing. They observed that word identification produced with a surgical mask was significantly lower in children (N = 10). Note that it was reported to cause the least acoustic attenuation compared to other types of masks. [14] The negative effects were more pronounced in children than in adults. Lipps et al. [49] tested the impact of face masks on audio-visual word recognition in young children with hearing loss (3–7 years, N = 13). Word recognition was significantly poorer for surgical and transparent-apron masks than without any mask.
Experimental studies on how face masks affect speech recognition have been conducted more frequently on adults than on children. Generally, the masks affect speech recognition in adults with normal hearing. Thicker the opaque mask-material, lower the speech intelligibility. In addition, more vulnerable the group, lower the speech intelligibility. Yi et al. [50] reported that in the presence of noise, listeners (N = 26) performed poorly when speakers wore a disposable-paper mask. Choi [15] found that the intelligibility scores for 48 university students, obtained in N95-mask conditions at an SNR of 5 dB, decreased by a maximum of 10 %, compared to no-mask conditions. Toscano and Toscano [14] reported that face-mask effects appeared at an SNR of 3 dB (77 observations per parameter). Bottalico et al. [13] found that the use of fabric masks yielded a significantly greater reduction in speech intelligibility than surgical or N95 masks did for college students (N = 40). Thibodeau et al. [19] compared transparent and opaque masks in a noisy environment for speech recognition in an online study with 154 participants. They concluded that the use of transparent masks could significantly facilitate speech recognition in noisy environments. Thus, such masks might reduce stressful communication challenges experienced in medical, employment, and educational settings during the global pandemic. Brown et al. [51] investigated the degree to which different types of face masks and noise levels affected speech intelligibility in young (N = 180) and older (N = 180) adults. They concluded that they were similarly affected by face masks and noise in terms of intelligibility. Older adults showed poorer overall intelligibility than younger adults. Smiljanic et al. [52] found that masks affected non-native speech processing, even at easier noise levels; and clear speech accuracy improved significantly despite wearing a mask. Yi et al. [18] also tested 26 adults and observed that clear speech alleviated challenging communication situations, including lack of visual cues and “masked” acoustic signals. Homans and Vroegop [53] showed that even for speech perception in no-noise environments, surgical face masks, and face shields to a smaller extent, had a negative effect for patients with moderate to severe hearing loss.
Younger the children, lower the score in this study. This reflected the degree of language development, along with the test scores. As the mask became thicker, the children seemed to choose a completely different word rather than worrying about which word to listen to. To the best of our knowledge, no information on response time has been reported regarding the effects of masks on speech recognition. Ease of listening or listening difficulty was investigated using auditory response times. [54], [55] The response time was not used to further develop a proper unambiguous subjective rating. There were probably inherent difficulties in managing the measurement process in the field, or with multiple subjects at once. [54] Information on the response process is now more easily available due to computer-based assessments, such as in this study. In the future, a more in-depth analysis of speech recognition with a mask could be possible using the response time.
Previous studies on adults are generally consistent with our findings. In this study, the KF94 mask showed a significant negative effect on speech recognition, specifically in children aged 4 and 5 years. Children aged 6 years were not affected by the face mask under the conditions tested. The effects of a surgical mask on speech recognition was observed more in children aged 4, the youngest group in this study. Authors previous study [56] found that children aged between four and five seemed to perceive the mask as a physical self, while children aged six did not. Thus, chidren aged four and five are vulernable to mask in perception and speech recognition as well. In summary, the thicker the opaque material of the face mask, the lower the speech-recognition score, and the younger the pre-schoolers, the lower the scores because of face masks. However, surgical masks are less effective in filtering viral particles with coronavirus (SARS-CoV-2) than KF94 masks. [46], [57] Thus, individual speech effort [18], [50] and better room-acoustic conditions [11], [13] are recommended for the situation under question.
4.2 Effect of classroom acoustics with face masks on speech recognition
Four virtual classrooms with combined acoustical conditions, two RTs (0.6 and 1.2 s), and two SNRs (12 and>22 dB) were used for the speech-recognition test. RT showed a significant negative effect on the test in both, no-mask and mask-wearing conditions. The longer the RTs, the lower were the children’s speech-recognition scores. This is consistent with Bottalico et al. [13], who found that a longer RT reduced speech recognition, both with and without face masks. Reverberation effects on face-masked speech have not been fully investigated. Till date, Bottalico et al. [13] was the only publication regarding the effect of masks on speech in realistic listening environments with reverberance. For 6-year-olds, RT did not affect speech recognition with statistical significance in the conditions tested in this study. It seemed that children aged 6 years showed a ceiling effect for the testing conditions (KS-MWL-P and high SNRs) due to their language development. Table 5 shows that degradation of speech recognition by the RT of 1.2 s was approximately similar to the reduction by face masks at the RT of 0.6 s. Thus, better classroom acoustics could ameliorate the negative effects of face mask on speech recognition for pre-schoolers.
Noise did not affect speech recognition with statistical significance overall. This is probably due to the high SNR used in this study. However, noise nearly thwarted speech recognition for the group aged 4 years. Thus, the vulnerable group was affected by noise even at an SNR of 12 dB in face-masked conditions. Previous studies on adults have shown that noise affects speech recognition. In such studies, the SNRs were relatively lower than those used in the present study: SNRs of −9 and −5 dB [51], −8.3 to 25.4 dB [15], −5 dB [18], [19], [50], −5 to 5 dB [52], 0 and 5 dB [11], 3 dB [13], 3 dB, and 13 dB [14] were used.
In summary, when face masks are mandatory in the classroom, a shorter RT (approximately 0.6 s and higher SNR) can improve speech recognition among pre-schoolers.
4.3 Limitations and future works
First, this study was conducted in two randomly chosen preschools. Socioeconomic status or parenting, which could affect children’s early language development [58], [59] was not considered in the choice of participants. Accessibility to children was the priority of this study during the COVID-19 pandemic.
Second, the children’s language-development stage was not balanced in the recruitment process. For children with delayed development in the REVT group, few samples did not show statistically significant results. However, language development was not one of our research objectives; we only accepted and confirmed their language development.
Third, identical test materials and conditions were used for all children, although their social and cognitive development varied throughout the age range of 4–6 years. Children’s proficiency in using a touch pad and their ability to interpret pictures showed differences by age group. Furthermore, the two noise conditions used in this study also affect the negative skewness of the speech-recognition score distributions. The SNR of 12 dB was good enough for high scores for children aged 6. However, young children aged 4–5 were very sensitive to the noise environment of the SNR 12 dB. The test materials and conditions were set for children aged 4, and the results for children aged 6 showed ceiling effects.
Fourth, we examined only one female speaker with two types of face masks: a surgical mask and a KF94 mask. Male speech while wearing a face mask has been less investigated than female speech. The effect of the opacity of face masks has been investigated, but the effect of their shape is not yet clearly known. Future studies should consider the differences between male and female speakers and among the various types of face masks.
Additionally, in the room acoustic simulation process, the scattering coefficient for the materials was set as a single value of 0.1, which corresponds to 707 Hz. A potential problem is that with a so low scattering coefficient, multiple reflections and flutter echo might occur between parallel reflective surfaces, especially in the conditions with the RT of 1.2 s. In this study, no severe reflections or flutter echoes were observed at the listener's location, despite not being a typical sound field in a classroom. Realistic scattering coefficients for all materials or typical preschool classrooms are difficult to measure accurately; some generalization is required. However, it is necessary to gradually develop the virtual classroom model in a more realistic way in future studies.
Finally, the vocal effort of the speakers owing to the face mask was not considered in this study. The speech level of each mask type was adjusted to 62 dBA. Recently, the impact of face masks on vocal effort has been reported. At this stage, it is difficult to properly implement an objective experimental procedure to determine the effect on vocal effort, because it is a self-reported perception. To further investigate the effects of face masks on language, future studies should consider the vocal efforts of speakers due to face masks.
5 Conclusions
The face mask negatively affected pre-schoolers’ speech recognition in a realistic classroom environment, which included reverberance and noise. Reducing the RT and noise level in the classroom improved the recognition. Children aged 4 and 5 years were affected by face masks and RT more significantly than those aged 6 years.
Appropriate acoustics for classrooms and clear speech by teachers are recommended for better speech recognition in preschools, which usually facilitate children’s language-and-speech development.
CRediT authorship contribution statement
Miji Kwon: Data curation, Visualization, Validation, Investigation, Writing – original draft. Wonyoung Yang: Conceptualization, Methodology, Software, Writing – review & editing, Supervision.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Appendix A Korean Standard-Monosyllabic Word List for Pre-schoolers (KS-MWL-P)
Data availability
The data that has been used is confidential.
Acknowledgement
We gratefully acknowledge the contributions of all the children who participated in our experiments, and the teachers and parents of the children who supported the experiments. We would like to express our deepest gratitude to the directors of Hanam Green and Forest Love Kindergarten, Jeong Suk-ja, and Choi Myeong-soon for understanding the importance of this study, convincing parents, and providing permission for the experiment.
This study was supported by the Basic Science Research Program of the National Research Foundation (NRF) [grant no. 2018R1D1A1B07048157], funded by the Ministry of Education, Republic of Korea. This study was also conducted with research funds provided by Gwangju University in 2022.
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References
1 Raina MacIntyre C. Jay H.S. Community universal face mask use during the COVID 19 pandemic—from households to travellers and public spaces J Travel Med 2020 27
2 Brooks J.T. Butler J.C. Redfield R.R. Universal masking to prevent SARS-CoV-2 transmission—the time is now JAMA 324 2020 635 637 32663243
3 Mitze T, Kosfeld R, Rode J, et al. Face masks considerably reduce COVID-19 cases in Germany. Proc Natl Acad Sci 2020; 117: 32293-32301.
4 Wang X. Ferro E.G. Zhou G. Association between universal masking in a health care system and SARS-CoV-2 positivity among health care workers JAMA 324 2020 703 704 32663246
5 Harrison S, Duval D, Walters B, et al. The effectiveness of face coverings to reduce transmission of COVID-19 in community settings; A rapid revew (update 2). In: Care DoHaS, (ed.). UK Health Security Agency, 2021.
6 Education UDf. Evidence Summary, Coronavirus (COVID-19) and the use of face coverings in education settings. In: Education Df, (ed.). London, UK2022.
7 Suroyo S, Putra BM, Ibrahim B, et al. the effect of learning loss on student’s cognitive development during pandemic of COVID-19. In: Proceeding of International Conference on Islamic Education (ICIED) 2021, pp.169-176.
8 Araújo L.A. Veloso C.F. Souza M.C. The potential impact of the COVID-19 pandemic on child growth and development: a systematic review J Pediatr (Rio J) 97 2021 369 377 32980318
9 Charney S.A. Camarata S.M. Chern A. Potential impact of the COVID-19 pandemic on communication and language skills in children Otolaryngol Head Neck Surg 165 2021 1 2 33258739
10 Rocha P.M.B. The Covid-19 pandemic and its possible consequences to language/speech development and delay in children: an urgent issue Audiology-Communication Research 2021 26
11 Sfakianaki A. Kafentzis G.P. Kiagiadaki D. Effect of face mask and noise on word recognition by children and adults ExLing 2021 2021 217
12 McCarthy D. Language development in the preschool child. Child behavior and development: A course of representative studies. New York, NY, US: McGraw-Hill, 1943, pp.107-128.
13 Bottalico P. Murgia S. Puglisi G.E. Effect of masks on speech intelligibility in auralized classrooms J Acoust Soc Am 148 2020 2878 2884 33261397
14 Toscano J.C. Toscano C.M. Effects of face masks on speech recognition in multi-talker babble noise PLoS One 16 2021 e0246842 33626073
15 Choi Y.-J. Acoustical measurements of masks and the effects on the speech intelligibility in university classrooms Appl Acoust 180 2021 108145
16 Jeong J. Kim M. Kim Y. Changes on speech transmission characteristics by types of mask Audiology and Speech Research 16 2020 295 304
17 Rahne T. Fröhlich L. Plontke S. Influence of surgical and N95 face masks on speech perception and listening effort in noise PLoS One 16 2021 e0253874 34197513
18 Yi H. Pingsterhaus A. Song W. Effects of wearing face masks while using different speaking styles in noise on speech intelligibility during the COVID-19 pandemic Front Psychol 2021 12
19 Thibodeau L.M. Thibodeau-Nielsen R.B. Tran C.M.Q. Communicating during COVID-19: the effect of transparent masks for speech recognition in noise Ear Hear 42 2021 772 781 33813522
20 Buss E. Leibold L.J. Porter H.L. Speech recognition in one- and two-talker maskers in school-age children and adults: Development of perceptual masking and glimpsing J Acoust Soc Am 141 2017 2650 2660 10.1121/1.4979936 28464682
21 Valente D.L. Plevinsky H.M. Franco J.M. Experimental investigation of the effects of the acoustical conditions in a simulated classroom on speech recognition and learning in children J Acoust Soc Am 131 2012 232 246 10.1121/1.3662059 22280587
22 Klatte M. Bergström K. Lachmann T. Does noise affect learning? A short review on noise effects on cognitive performance in children Front Psychol 4 2013 578 24009598
23 Elliott L.L. Performance of children aged 9 to 17 years on a test of speech intelligibility in noise using sentence material with controlled word predictability J Acoust Soc Am 66 1979 651 653 489836
24 Eisenberg L.S. Shannon R.V. Schaefer Martinez A. Speech recognition with reduced spectral cues as a function of age J Acoust Soc Am 107 2000 2704 2710 10830392
25 Bradley J.S. Predictors of speech intelligibility in rooms J Acoust Soc Am 80 1986 837 845 3760337
26 ANSI/ASA. S12.60-2010/Part 1 (R2015) Acoustical performance criteria, design requirements, and guidelines for schools, Part 1: Permenent Schools. Washington American National Standard Institute, 2010.
27 Hodgson M. Nosal E.-M. Effect of noise and occupancy on optimal reverberation times for speech intelligibility in classrooms J Acoust Soc Am 111 2002 931 939 11863195
28 Yang W. Hodgson M. Auralization study of optimum reverberation times for speech intelligibility for normal and hearing-impaired listeners in classrooms with diffuse sound fields J Acoust Soc Am 120 2006 801 807 16938968
29 Yang W and Bradley JS. Effects of room acoustics on the intelligibility of speech in classrooms for young children. J Acoust Soc Am 2009; 125: 922-933. 2009/02/12. doi: 10.1121/1.3058900.
30 Nábělek A.K. Pickett J. Reception of consonants in a classroom as affected by monaural and binaural listening, noise, reverberation, and hearing aids J Acoust Soc Am 56 1974 628 639 4415491
31 Nabelek A.K. Pickett J.M. Monaural and binaural speech perception through hearing aids under noise and reverberation with normal and hearing-impaired listeners J Speech Hear Res 17 1974 724 739 4444292
32 Finitzo-Hieber T. Tillman T.W. Room acoustics effects on monosyllabic word discrimination ability for normal and hearing-impaired children J Speech Hear Res 21 1978 440 458 713515
33 Nábělek A.K. Robinson P.K. Monaural and binaural speech perception in reverberation for listeners of various ages J Acoust Soc Am 71 1982 1242 1248 7085996
34 Bradley J.S. Speech intelligibility studies in classrooms J Acoust Soc Am 80 1986 846 854 3760338
35 Plomp R. Steeneken H. Houtgast T. Predicting speech intelligibility in rooms from the modulation transfer function. II. Mirror image computer model applied to rectangular rooms Acta Acust Acust 46 1980 73 81
36 Bistafa S.R. Bradley J.S. Reverberation time and maximum background-noise level for classrooms from a comparative study of speech intelligibility metrics J Acoust Soc Am 107 2000 861 875 10687696
37 Kim YT, Hong GH, Kim K, et al. Receptive and Expressive Vocabulary Test (REVT). Seoul Community Rehabilitation Center, 2009.
38 Hong G.H. Kim Y.T. Kim K. Content and reliability analyses of the receptive and expressive vocabulary test (REVT) Commun Sci Disorders 14 2009 34 45
39 Kim J. Lee J. Lee K.W. Test-retest reliability of word recognition score using Korean standard monosyllabic word lists for adults as a function of the number of test words J Audiol Otol 19 2015 68 26413571
40 ISO 8253-3:2022 Acoustics. Audiometric test methods-Part 3: Speech audiometry.
41 Ross M. Lerman J. A picture identification test for hearing-impaired children J Speech Hear Res 13 1970 44 53 4192711
42 Kim J.-S. Lim D. Hong H.-N. Development of Korean Standard Monosyllabic Word Lists for School Aged Children (KS-MWL-S) and preschoolers (KS-MWL-P) Audiology 4 2008 141 160
43 Doo S, Oh S, Brandstätt P, et al. Anechoic Chamber Design using Broadband Compact Absorber. In: 2003 Spring Meeting Mokpo, 2003, pp.393-396. The Korean Society for Noise and Vibration Engineering.
44 USFDA. N95 Respirators, Surgical Masks, Face Masks, and Barrier Face Coverings, https://www.fda.gov/medical-devices/personal-protective-equipment-infection-control/n95-respirators-surgical-masks-face-masks-and-barrier-face-coverings (2022).
45 Ma L. Kim M.-S. A study of the purchasing tendency of healthcare masks based on the user-centered design concept-centered on the form and color of the mask J Korea Convergence Society 11 2020 143 154
46 Kim M.-C. Bae S. Kim J.Y. Effectiveness of surgical, KF94, and N95 respirator masks in blocking SARS-CoV-2: a controlled comparison in 7 patients Infect Dis 52 2020 908 912
47 Rindel JH. Auralisation and how to calibrate the sound level for presentations. 2015.
48 Owens R.E. Language development 1988 Merrill Columbus OH
49 Lipps E. Caldwell-Kurtzman J. Motlagh Zadeh L. Impact of face masks on audiovisual word recognition in young children with hearing loss during the Covid-19 pandemic J Early Hearing Detection Intervention 6 2021 8
50 Yi H, Pingsterhaus A, Song W. The adverse effect of wearing a face mask during the COVID-19 pandemic and benefits of wearing transparent face masks and using clear speech on speech intelligibility. 2021.
51 Brown V.A. Van Engen K.J. Peelle J.E. Face mask type affects audiovisual speech intelligibility and subjective listening effort in young and older adults Cognitive Res: Principles Implications 6 2021 1 12
52 Smiljanic R. Keerstock S. Meemann K. Face masks and speaking style affect audio-visual word recognition and memory of native and non-native speech J Acoust Soc Am 149 2021 4013 4023 34241444
53 Homans N.C. Vroegop J.L. The impact of face masks on the communication of adults with hearing loss during COVID-19 in a clinical setting Int J Audiol 2021 1 6
54 Prodi N. Visentin C. Farnetani A. Intelligibility, listening difficulty and listening efficiency in auralized classrooms J Acoust Soc Am 128 2010 172 181 20649212
55 Gatehouse S. Gordon J. Response times to speech stimuli as measures of benefit from amplification Br J Audiol 24 1990 63 68 2317602
56 Kwon Miji Yang Wonyoung Mask-Wearing Perception of Preschool Children in Korea during the COVID-19 Pandemic: A Cross-Sectional Study International Journal of Environmental Research and Public Health 19 18 2022 11443 10.3390/ijerph191811443 36141716
57 Wilkinson C, Low F, Rindelaub J, et al. Improving mask use to reduce COVID-19 transmission. 17 September 2021 2021. Auckland: University of Auckland.
58 Pungello E.P. Iruka I.U. Dotterer A.M. The effects of socioeconomic status, race, and parenting on language development in early childhood Dev Psychol 45 2009 544 19271838
59 Schwab J.F. Lew-Williams C. Language learning, socioeconomic status, and child-directed speech Wiley Interdiscip Rev Cogn Sci 7 2016 264 275 27196418
| 36510558 | PMC9729249 | NO-CC CODE | 2022-12-14 23:22:18 | no | Appl Acoust. 2023 Jan 8; 202:109149 | utf-8 | Appl Acoust | 2,022 | 10.1016/j.apacoust.2022.109149 | oa_other |
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J Infect
J Infect
The Journal of Infection
0163-4453
1532-2742
The British Infection Association. Published by Elsevier Ltd.
S0163-4453(22)00692-2
10.1016/j.jinf.2022.12.002
Article
LONGITUDINAL AGE DIFFERENCES IN HUMORAL RESPONSES TO THE BNT162b2 VACCINE IN THE ELDERLY ARE LOST AFTER THE THIRD DOSE
Pozo-Balado María del Mar PhD 1*
Ramos ÁngelBulnes PhD 1*
Rodríguez Vanesa Garrido PhD student 1
Martínez Israel Olivas PhD 1
Lozano Carmen MD, PhD 2
González-Escribano María Francisca PhD 13
Leal Manuel MD, PhD 45
Pacheco Yolanda M PhD 1⁎⁎
1 Immunology Laboratory, Institute of Biomedicine of Seville (IBiS), Virgen del Rocío University Hospital (HUVR)/CSIC/University of Seville, Seville, Spain
2 Microbiology Service, Virgen del Rocío University Hospital (HUVR), Seville, Spain
3 Immunology Service, Virgen del Rocío University Hospital (HUVR), Seville, Spain
4 Immunovirology Unit, Internal Medicine Service, Viamed Hospital, Santa Ángela de la Cruz, Seville, Spain
5 Hogar Residencia de la Santa Caridad, Seville, Spain
⁎⁎ CORRESPONDING AUTHOR: Yolanda M. Pacheco, PhD, Immunology Laboratory, Institute of Biomedicine of Seville (IBiS), Clinic Unit of Clinic Laboratories, Virgen del Rocío University Hospital (HUVR)/CSIC/University of Seville, Ave. Manuel Siurot s/n, 41013, Seville, Spain.
⁎ Equally contributing authors
8 12 2022
8 12 2022
5 12 2022
© 2022 The British Infection Association. Published by Elsevier Ltd. All rights reserved.
2022
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pmcDear Editor,
Nursing home residents constitute an especially frail population at a high risk for severe COVID-19 disease [1], mainly due to multiple comorbid conditions and immunesenescence [2]. Hence, many countries have considered the administration of booster doses of vaccines due to the lower levels of anti-S IgG antibodies achieved by elderly adults after two doses of vaccine and a faster waning immunity thereafter [3]. A recent work published in this journal by Dimeglio C. et al. [4], showing anti-SARS-CoV-2 antibodies before and after the third dose of the vaccine, reported that elderly adults achieved similar levels of anti-S IgG titers after the booster dose than those achieved by younger participants, shedding light on the benefit of the administration of such vaccine booster. Here, we present additional longitudinal data reinforcing this concept.
We designed a prospective study including residents and health workers from the “Santa Caridad Elderly Home” (Seville, Spain) who provided blood samples one month after receiving the second dose of the BNT162b2 vaccine (T1), and also, four (T4) and eight (T8) months later. Additional third dose of BNT162b2 vaccine was administered 1-2 days after T8, and samples were taken one month later (T9). Subjects with previous COVID-19 diagnosis, under immunosuppressive therapy or without available samples at both, T1 and T9 were excluded from the analysis. The study was approved by the Research Ethics Committee of our hospital (PEIBA Acta CEI_02/2021) and written informed consent was obtained for all participants. Anti-S IgG antibodies against trimeric SARS-CoV-2 Spike protein were quantified by chemiluminescence (LIAISON® SARS-CoV-2 TrimericS IgG), showing good correlation with microneutralization test [5]. Positive results were considered ≥33.8 BAU/mL (additional information in supplementary material).
Sixty-one subjects were finally included in the study and were subdivided in two groups attending to age: i) elderly group, including subjects over 65 years-old (n=40) and ii) young group, including participants under such age (n=21). Demographics, anthropometrics and humoral titer values are summarized in Table 1 . Older participants were all men 40 (100%), with a median age of 78 years-old. The median age for the young group was 49 years-old and nine (42.9%) were men. The dynamics of anti-S IgG titers along time for elderly and young participants are represented in Figure 1 A. Anti-S IgG titers were significantly lower in elderly participants at all time-points of the study, except at T9 (one month after the booster dose), where the antibody levels were similar between both study groups (Figure 1 B). After the first two doses of the vaccine, a declining of antibody titers along time was observed in all participants without exceptions. The median antibody loss was 73 % and 91 % between T1-T4 and T1-T8 respectively, in the elderly group. In the younger group, the loss of anti-S IgG titers was similar, with a median loss of 70% between T1-T4 and 92% between T1-T8. The rate of positive response declined from 100% at T1 to 92% at T4, and to 75% at T8 in older participants. However, the decline in the rate of positive response was delayed in younger participants, that maintained 100% of seroconversion at T4, declining thereafter to 89% at T8. Then, a booster effect was observed in all participants following the administration of the booster dose (T9), showing all of them positive responses at this time point. Thus, anti-S IgG titers had approximately 7-fold and 54-fold increase in older participants respect to T1 and T8, respectively (p<.001). Besides, younger participants showed a 4-fold and 49-fold increase respect to T1 and T8, respectively (p<0.001). Remarkably, elderly group showed a no significant trend to higher increase in anti-S titers between T1 and T9 than younger adults (p=0.105) (Figure 1 C).Table 1 Demographics, anthropometrical and anti-S IgG titers of the study populations.
Table 1Characteristic Elderly Group (N=40) Young Group (N=21] p
Demographics
Age, median [IQR], years 78 [74-83] 49 [40-62] <0.001
Sex (men) 40 (100) 9 (42.9) <0.001
BMI, median [IQR] 27 [25.8-31.5] 27.6 [23.6-30.7] 0.461
IgG titer (BAU/mL)
T1, median [IQR] 1185 [777-1635] 1980 [1215-2490] <0.001
T4, median [IQR] 259 [142-473] 611 [336-1100] <0.001
T8, median [IQR] 108 [46-158] 219 [80-274] 0.011
T9, median [IQR] 6910 [3295-14930] 8220 [4910-13820] 0.239
Increase in IgG titer (fold change)
T1-T9, median [IQR] 6.9 [3.7-10.1] 4.2 [2.9-7.7] 0.105
Categorical variables are represented as N (%) and continuous variables as median [IQR]. Mann Whitney U-test was used for comparisons between continuous variables, and Chi-square test for comparisons between categorical variables. p values < 0.05 were considered statistically significant. BMI, body mass index, calculated as weight (kilograms) divided by height (meters squared); BAU, Binding Arbitrary Units; T1, one month after the second dose; T4, four months after the second dose; T8, eight months after the second dose; T9, one month after the third dose.
Figure 1 (A) Long-term longitudinal follow-up of antibody IgG titers in elderly and young groups vaccinated with three doses of the BNT162b2 vaccine. T1, one month after the second dose; T4, four months after the second dose; T8, eight months after the second dose; T9, one month after the third dose. Friedman test and Bonferroni adjustment to a series of post hoc Wilcoxon matched pairs tests were applied. Except for Bonferroni correction (p<0.008), p-values <0.05 were considered statistically significant. **Wilcoxon test p<0.001 for all paired-comparisons. (B) Comparison of anti-S IgG titers between study groups at T1 (one month after the second dose), T4 (four months after the second dose), T8 (eight months after the second dose and 1-2 days before the administration of the third dose) and T9 (one month after the third dose. The Mann Whitney U-test was applied for comparisons between study groups at all-time points, considering p values <0.05 statistically significant. (C) Comparison of the fold-increase of anti-S IgG titers between T1-T9 between study groups. Comparisons were made using the Mann Whitney U-test and p values <0.05 were considered statistically significant.
Figure 1
Accordingly, we found strong negative correlations between the anti-S antibodies achieved at T1, T4 and T8 and the age of participants (r=-0.479, p<0.001, r= -0.425, p=0.001 and r=-0.356, p=0.006 respectively), whereas such correlation was completely absent at T9 (r=-0.058, p=0.657). When considering only men in the younger group, all the results showed the same trends.
In our study, we found that anti-S antibodies achieved one month after the third dose of the BNT162b2 vaccine were similar between elderly and young participants. Longitudinal follow-up from this time-point is needed to assess if the loss of titers will then depend on age, as it occurred after the earlier doses of vaccine. According to our data, a recent report on a cohort of nursing home residents over 60 years [6], reported that a third BNT162b2 dose significantly increases IgG titers and that such titers were not associated with age. Our results are also in accordance with those recently reported by Dimeglio et al [4], that found similar levels of anti-S titers after the third dose in elderly and young vaccinees. On the contrary, a poorer response after the third dose in older adults has been also recently reported in this journal [7]. However, it was only observed in octogenarian and nonagenarian participants, and age resulted not to be independently associated with poor responses in a multivariate analysis. Moreover, in this study, contrary to ours, several participants resulted non responder to the booster dose, perhaps because it included a high proportion of very old subjects and because the humoral response was determined by a point-of-care fingertip whole blood testing, which could be less sensitive.
Our longitudinal design, also covering the response to previous doses of vaccine, allowed us to note that the age of the participants negatively affected the levels of anti-S IgG antibodies achieved after the first two doses of vaccine, but not after the third dose, suggesting that primary vaccination protocol of such risk population of elderly people should include three doses. In this sense, the vaccination protocol in vulnerable clinical settings, such as patients under immunosuppressive therapies or oncology patients, already considers three doses as their primary vaccination protocol [8].
DECLARATIONS OF INTEREST
None to disclose
Appendix Supplementary materials
Image, application 1
AUTHOR CONTRIBUTIONS
Subject recruitment and clinical support: ML. Sample processing and preservation: MMPB, ABR, IOM and VGR. Antibody quantification: CL. Drafting of the manuscript: MMPB and ABR. Critical revision of the manuscript for important intellectual content: all authors. Funding and coordination: YMP.
ACKNOWLEDGMENTS
We thank all residents for their collaboration and Rafael Martínez Alba, MSc, director of the “Residencia Hogar de la Santa Caridad”, for allowing us to perform the study in this institution. We also thank Juan Antonio Santamaría, BSc, and Rafael Bernal, BSc, registered nurses at the “Residencia Hogar de la Santa Caridad” for their clinical assistance. None of the above individuals were compensated for their contributions.
FUNDING
This work was supported by the Instituto de Salud Carlos III through the project "PI21/00357" (Co-funded by European Regional Development Fund/European Social Fund "A way to make Europe"/"Investing in your future"). MMPB was supported by a postdoctoral contract from Consejería de Transformación Económica, Industria, Conocimiento y Universidades, Junta de Andalucía (DOC_01646). ABR, IOM and VGR were supported by Instituto de Salud Carlos III (Sara Borrell program CD19/00143, Rio Hortega program CM19/00051, and PFIS program FI19/00298, respectively). YMP was supported by the Consejería de Salud y Familias of Junta de Andalucía through the ‘‘Nicolás Monardes’’ program (C‐0013‐2017).
Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jinf.2022.12.002.
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REFERENCES
1 Mak WA Koeleman JGM van der Vliet M Keuren F Ong DSY. SARS-CoV-2 antibody and T cell responses one year after COVID-19 and the booster effect of vaccination: a prospective cohort study J Infect 84 2022 171 178 10.1016/j.jinf.2021.12.003 34896516
2 Collier DA Ferreira IATM Kotagiri P Age-related immune response heterogeneity to SARS-CoV-2 vaccine BNT162b2 Nature 596 2021 417 422 34192737
3 Levin EG Lustig Y Cohen C Waning immune humoral response to BNT162b2 Covid-19 vaccine over 6 months N Engl J Med 385 24 2021 e84 34614326
4 Dimeglio C Trémeaux P Herin F Da-Silva I Porcheron M Martin-Blondel G Gernigon C Chapuy-Regaud S Villars H Izopet J. Post-vaccination SARS-CoV-2 antibody kinetics and protection duration against Omicron in elderly population J Infect 2022 Sep 3 10.1016/j.jinf.2022.08.040 S0163-4453(22)00522-9
5 Liu L Wang P Nair MS Potent neutralizing antibodies against multiple epitopes on SARS-CoV-2 spike Nature 584 2020 450 456 32698192
6 Eliakim-Raz N Leibovici-Weisman Y Stemmer A Antibody Titers Before and After a Third Dose of the SARS-CoV-2 BNT162b2 Vaccine in Adults Aged ≥60 Years JAMA 326 21 2021 2203 2204 34739043
7 Hagiya H Hikita T Habu T Asada M Yorifuji T Toyooka S Otsuka F Nakayama M. Poor vaccine responsiveness towards third-dose mRNA vaccine of COVID-19 in Japanese older people J Infect 85 2022 436 480 10.1016/j.jinf.2022.07.007
8 Joint Committee on Vaccination and Immunziation. Advice on third primary dose vaccination 1 September 2021. Available at: https://www.gov.uk/government/publications/third-primary-covid-19-vaccine-dose-for-peoplewho-are-immunosuppressed-jcvi-advice/joint-committee-on-vaccination-and-immunisation-jcvi-advice-on-thirdprimary-dose-vaccination
| 36503017 | PMC9729577 | NO-CC CODE | 2022-12-14 23:22:25 | no | J Infect. 2022 Dec 8; doi: 10.1016/j.jinf.2022.12.002 | utf-8 | J Infect | 2,022 | 10.1016/j.jinf.2022.12.002 | oa_other |
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The Authors. Published by Elsevier Ltd on behalf of International Society for Infectious Diseases.
S2772-7076(22)00139-4
10.1016/j.ijregi.2022.11.006
Article
Hesitancy to coronavirus-19 vaccines is still a hurdle in Ethiopia by 2021, Multi center cross-sectional study
Erega Besfat Berihun (BBE) 1⁎
Ferede Wassie Yazie 1
Sisay Fillorenes Ayalew 1
Tiruneh Gebrehiwot Ayalew (GAS) 1
Ayalew Abeba Belay (ABA) 1
Malka Erean shigign (ESM) 2
Tassew Habtamu Abie (HAT) 1
Alemu Asrat 3
1 Department of midwifery,collage of medicine and health sciences, Debre tabor university, Ethiopia
2 Department of midwifery,collage of medicine and health sciences, Selale university, Ethiopia
3 Department of midwifery,collage of medicine and health sciences, Dilla University, Ethiopia
⁎ Corresponding author.
8 12 2022
8 12 2022
© 2022 The Authors. Published by Elsevier Ltd on behalf of International Society for Infectious Diseases.
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Introduction
Vaccine hesitancy is defined as the delay in acceptance, reluctance, or refusal of vaccination despite the availability of vaccination services. World wide, hesitancy to be immunized against severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) is being the most common “bottleneck” in reducing the incidence of corona virus 19 pandemic caused by severe acute respiratory syndrome corona virus 2 (SARS-CoV-2). Although corona virus disease is still a shocking global pandemic and despite the stakeholder's struggle to its prevention and vaccination increment, people are still hesitating for corona virus vaccines. Therefore our study investigated different determinants for hesitancy and will be used for community mobilizers, health professionals and policy makers.
Objective
The study aimed to assess the prevalence and determinants of hesitancy to coronavirus-19 vaccine among clients attending public hospitals in south Gondar zone, 2021.
Methods
A multi center facility based cross-sectional study was conducted from November 01-2021 to December 30-2021 to assess the prevalence and determinants of hesitancy to coronavirus-19 vaccine. Chi square test and Multi-variable logistic regression methods were employed using SPSS 23. significance level was examined using and Odds ratio at 95% confidence level. Multi-collinearity and model fitness were also checked.
Primary outcome
The main outcome of the study was the prevalence and determinants of Hesitancy to COVID-19 vaccines.
Result
A total of 415 respondents were included in the study with a response rate of 100%. The prevalence of hesitancy to coronavirus-19 vaccine were 46.02% CI(45.03-54.98). multi-variable logistic regression showed that age greater than 49 years 0.56(0.01-0.73), rural residency 2.02(1.20-3.71), fear of the adverse effects of the vaccines 2.23(1.65-3.21), myths about vaccines ineffectiveness 1.52(1.09-3.07), poor practice about COVID 19 preventive measure 4.76(2.55-6.97) were the common determinants.
Conclusion and recommendation
Despite the increased global mortality and mortality due to the COVID-19 pandemic the prevalence of hesitance to be immunized against this fatal and crisis pandemic is still higher.Hence, it is important to create awareness in those highly hesitant groups.
Keywords
Hesitancy
COVID-19
Multi center study
Ethiopia
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pmcIntroduction
The world, currently, is being strongly affected by this “once in the generation pandemic” In-terms of social, economical, mental, psychological and different aspects of health (Sallam, Dababseh et al. 2021). The number of corona virus 19 exceeded 170 million cases and over 3.8 million deaths were reported plus almost all of the government as well as private sectors are being affected by the pandemic(Handebo, Wolde et al. 2021, Kalam, Davis Jr et al. 2021). The risk of morbidity and death were higher in groups like those with chronic diseases such as diabetes mallitus, immune compromised patients, renal diseases, asthmatics and increased age. pregnant women were also one of the most target population and adverse perinatal outcomes due to the pandemic were also sought (Kebede, Kanwagi et al. 2021, Mose and Yeshaneh 2021).
As the corona virus 19 pandemic is expected to be continuing, improving the immunization coverage in the general population is the “mainstay” to decline the incidence of corona virus(Li, Luo et al. 2021). The Herd immunity in the general population has to be at least 67% for the incidence rate of corona virus to be declined (Berihun, Walle et al. 2021, Adane, Ademas et al. 2022).
Vaccine hesitancy is defined as the delay in acceptance, reluctance, or refusal of vaccination despite the availability of vaccination services(MacDonald 2015, Thangaraju and Venkatesan 2019). It has been identified by the World Health Organization (WHO) as one of the top 10 threats to global health in 2019 (Thangaraju and Venkatesan 2019). Vaccine hesitancy is due to a complex decision-making process, influence by a wide range of contextual, individual and group, and vaccine-specific factors, including communication and media, historical confluences, religion/culture/gender/socioeconomic, political, geographic barriers, experience with vaccination, risk perception, design of the vaccination program and additional non scientific myths(MacDonald 2015, Soares, Rocha et al. 2021).
World wide, hesitancy to be immunized against corona virus 19 is being the most common “bottleneck” in reducing the incidence of corona virus 19 pandemic(Mahmud, Mohsin et al. 2021, Orangi, Pinchoff et al. 2021). The prevalence of vaccine hesitancy in Kenya(Orangi, Pinchoff et al. 2021), Bangladesh(Mahmud, Mohsin et al. 2021), Egypt(Fares, Elmnyer et al. 2021), Portugal(Nehal, Steendam et al. 2021), Saudi Arabia(Almalki, Alotaibi et al. 2021) and Kuwait(Al-Sanafi and Sallam 2021) was 36.5%, 36.58%, 33% 59%,6.1% and 47.9% respectively.
Ethiopia is also one of the countries which are being in difficulty to combat the global pandemic(Aemro, Amare et al. 2021, Alle and Oumer 2021).The prevalence of corona virus hesitance was 54.5%, 45.9% and 57.7% in Wolaita zone(Mesele 2021), Amhara regional state referral hospitals(Aemro, Amare et al. 2021) and Debre Tabor comprehensive specialized hospital(Alle and Oumer 2021) respectively .
Studies done in Saudi Arabia(Almalki, Alotaibi et al. 2021), Portugal(Fares, Elmnyer et al. 2021), Bangladesh(Mahmud, Mohsin et al. 2021), Kenya(Orangi, Pinchoff et al. 2021) and Egypt(Fares, Elmnyer et al. 2021) reported that advanced age, being female, unemployment, illiteracy, low income, absence of trust on health care system, fear of adverse effects, lack of adequate clinical trial, rural residence, think of as a non affected group and lack of trust on vaccine efficacy were considered as significant determinants of hesitancy to corona virus 19 vaccination.
Despite the very shocking economic crisis and death reports due to corona virus 19 in Ethiopia, the hesitance to be immunized against corona virus among the people is still reported to be higher even among health professionals which are expected to create awareness to the rest of the population (Alle and Oumer 2021, Zewude and Belachew 2021). Hence, this study aimed to identify the bottlenecks for the for COVID-19 vaccination despite the government's struggle to lower the incidence of the pandemic and death rates due to the COVID-19 pandemic in South Gondar zone, Ethiopia.
Methods and Materials
Study design and setting- A multi-center institutional based cross-sectional study was conducted among clients attending selected public hospitals of South Gondar Zone, Ethiopia from November 01-2021 to December 30-2021.South Gondar zone is the one among the 10 administrative zones in Amhara region, Ethiopia. The town is found about 669 km northwest of Addis Ababa, the capital city of Ethiopia, and 97 km southwest of BahirDar, the capital city of the Amhara region and it has an elevation of 2706 m above sea level. Addis Zemen Hospital, Nefas Mewcha Hospital, Mekaneyesus Hospital and Ebnat Hospital were selected randomly among the eight public hospitals of South Gondar zone(SGZ).
Source population- All clients whose age is greater than 18 years and come to the selected hospitals of south gondar zone,Ethiopia
Study population- All clients whose age is greater than 18 years and come to the selected hospitals of south gondar zone, Ethiopia during the study period were the study population
Exclusion criteria- clients who were critically ill and had medically known contraindications for the vaccine were excluded
Dependent variable- Hesitancy to COVID 19 vaccinations
Independent variables-Sociodemographic characteristics and health related characteristics
Sample size determination and sampling procedure- The sample size was calculated by using the assumption of single population proportion formula considering the prevalence of vaccine hesitancy in Debre Tabor 57.7% (alle et,al 2021), 95% confidence interval, margin of error 5% and 10% non respondent rate.Then the final sample size was calculated to be 415.To reach for the study participants systematic random sampling was employed after searching for the case flow of each study hospitals.Finally, the sampling interval was determined by dividing two months case report to sample size in each hospitals and the final kth value was 4.2 (the average of all the sites). Hence the first case to come was taken as participant one and every four cases were selected.
Data collection procedures-The data was collected by four BSc degree holder midwives, using structured questionnaires after training was given for a day in each hospital.The questionnaire was prepared originally in English and was then translated into local language, Amharic for the purpose of data collection.It was translated back to English again for consistency and accuracy by language experts. pretest was also done in two primary hospitals other than the study areas.
Data entry and analysis-After manually checking its completeness and consistency, the data were entered using Epi-data version 4.6 software and analyzed using SPSS version 23 software. Then to know the crude association between vaccine hesitancy and its determinant factors crude odds ratio(COR) was calculated with 95% confidence interval (CI).Variables with an odds ratio of ≤ 0.2 were considered for multivariate analysis. Variables with adjusted odds ratio of ≤0.05 were considered to determine the significance of association. Hosmer–Lemeshow goodness-of-fit test was used to check the model fitness, Poor fit was considered by a value less than 0.05.It was considered to have multi collinearity when VIF is greater than 10
Operational definitions
Hesitancy to COVID19 vaccine- is when an individual has a delay in acceptance, reluctance, or refusal of vaccination despite the availability of vaccination services(Adane, Ademas et al. 2022).
Acceptance to COVID19 vaccine- is when an individual has not any form of delay in acceptance, non-reluctance, or not refusing the vaccination in the availability of vaccination services (Mose and Yeshaneh 2021).
Knowledge about COVID-19- The respondent's level of knowledge about COVID-19 was reported as good knowledge when the study participant correctly responded to more than or equal to 80% of knowledge assessment tools, and poor for <80% (Mose and Yeshaneh 2021, Adane, Ademas et al. 2022).
Attitudes towards COVID-19- The attitude of the participant was categorized as positive or favorable if responded above or equal to 80% of the attitude related items and negative if below 80%(Mose and Yeshaneh 2021, Adane, Ademas et al. 2022).
Practice of COVID-19 preventive measures- The respondents’ level of practice of COVID-19 preventive measures was reported as good practice if the study participant correctly responded to more than or equal to 80% of practice assessment tools, and poor for <80% (Mose and Yeshaneh 2021, Adane, Ademas et al. 2022).
Results
Sociodemographic characteristics
A total of 415 respondents were included in this study with a response rate of 100%. Majority of the respondents were in age group of between 30-49 years (48.43%), female (52.05%), orthodox christian (68.91%), rural residents (75.18%), married (73.25%) had no formal education (51.81%), housewife/farmer (48.19%) and had no childhood immunization (52.53%) (Table 1 ).Table 1 Socio-demographic and health related characteristics of the respondents at public hospitals in south gondar zone, north west Ethiopia, 2021 (N=415).
Table 1Variables Frequency Percent
Age in years
29 112 26.99
30-49 201 48.43
>49 102 24.58
Sex
Male 199 47.95
Female 216 52.05
Religion
Orthodox christian 286 68.91
Muslim 112 26.99
protestant 17 4.10
Residency
Rural 312 75.18
Urban 103 28.82
Marital status
Single 93 22.41
Married 304 73.25
Divorced 18 4.34
Educational status
No formal education 215 51.81
Primary 106 25.54
Secondary 72 17.35
Higher 22 5.30
Occupation
Student 74 17.83
Housewife/farmer 200 48.19
Merchant 95 22.89
Government employee 45 10.84
Monthly income in ETB
< 1000 94 22.65
1001-3000 178 42.89
3001-5000 123 29.64
> 5000 20 4.81
Use of public medias
Yes 198 47.71
No 217 52.29
Has school age child
Yes 328 79.04
No 87 20.96
Childhood immunization
Yes 197 47.47
No 218 52.53
Household number
Two 89 21.45
Three to four 198 47.71
>= five 128 30.84
Chronic illness
Yes 25 6.02
No 390 93.08
ETB- Ethiopian Birr
Respondent's Knowledge, Attitude, and Practice of COVID-19 Vaccine and Its Preventive Measure
Majority (79.28%) of the respondents were knowledgeable about COVID-19 vaccines. in addition most(58.31%) of the respondents had positive attitude towards COVID-19 vaccination and surprisingly, majority (68.92%) of the respondents had poor practice to COVID-19 preventive measures. Although COVID-19 is still a shocking global pandemic 46.02% of our respondents hesitate to take vaccine for the pandemic (Table 2 ).Table 2 knowledge of respondents about COVID-19 and its preventive methods in south gondar zone,Ethiopia, 2021(N=415).
Table 2Measurements Frequency Percent
Hesitate to take COVID-19 vaccine
Yes 191 46.02
No 224 53.98
Knowledge level about COVID-19 pandemic
Knowledgeable 329 79.28
Not knowledgeable 86 20.72
Attitude on COVID-19 pandemic
Positive attitude 242 58.31
Negative attitude 173 41.69
Practices on COVID-19 preventive measures
Good practice 129 30.08
poor practice 286 68.92
COVID- Corona virus Disease
Reasons for Hesitancy to take COVID-19 Vaccines
In this study the commonest reasons for hesitancy to COVID-19 vaccine were Fear of adverse effects (69.63%), thinking of COVID-19 is not fatal (67.54%), believe that the vaccine is killer (53.92%) and Inadequate data about the vaccines (65.97%) (Table 3 ).Table 3 Reasons for Non-Acceptance of COVID-19 Vaccines Among Respondents in south gondar zone, northwest Ethiopia, 2021 (N=191)
Table 3Reasons Frequency Percentage
Inadequate data about the vaccines 126 65.97
Fear of adverse effects 133 69.63
Think of vaccine being ineffective 94 49.21
Prefer other ways of protection 38 19.89
COVID-19 is not fatal 129 67.54
High chance recovery from COVID-19 47 24.61
Think of the vaccine as a trial 68 35.60
Not comfortable with my age 23 12.04
Vaccine will kill me 103 74.87
Am young and COVID-19 will not kill me 112 58.64
COVID- Corona virus Disease
Determinants of hesitancy to take COVID-19 vaccine
Under possible adjustment of the possible confounding variables, age greater than 49 years 0.56(0.01-0.73), rural residency 2.02(1.20-3.71), fear of the adverse effects of the vaccines 2.23(1.65-3.21), myth about vaccines ineffectiveness 1.52(1.09-3.07), poor practice about COVID 19 preventive measure 4.76(2.55-6.97) were the common determinants for hesitancy to COVID 19 vaccinations (Table 4 ).Table 4 Factors associated with hesitancy to take COVID-19 vaccine at south gondar zone, northwest Ethiopia 2021 (N=415).
Table 4Variables Hesitancy to COVID-19 vaccine COR*(95%CI) AOR*(95%CI)
Yes No
Age in years
18-29 59 53 1.32(0.09-3.43) 1.15(0.02-2.94)
30-49 92 109 ref ref
>49 40 62 0.76(0.04-0,72) 0.56(0.01-0.73)*
Sex
Male 74 125 ref ref
Female 117 216 0.91(0.42.1.28) 0.35(0.29-1.62)
Educational status
No formal education 109 106 1.48(1.21-3.68) 1.21(0.54-2.74)
Had formal education 82 118 Ref ref
Residence
Rural 157 155 2.06(1.34-4.70) 2.02(1.20-3.71)**
Urban 34 69 ref ref
Household size
Two 36 53 ref ref
Three- four 94 104 1.33(0.15-2.43) 1.12(0.43-2.22)
> four 61 67 1.34(0.32-3,14) 1.39(0.67-2.54)
Has school age child
Yes 141 187 ref ref
No 50 37 1.79(0.14-2.81) 1.32(0.04-1.98)
Fear of adverse effects
Yes 133 108 2.46(1.06-4.21) 2.23(1.65-3.21)**
No 58 116 ref ref
Thinking about vaccines effectiveness
Effective 97 140 ref ref
Not effective 94 84 1.62(1.17-3.08) 1.52(1.09-3.07)*
Knowledge about COVID-19 pandemic
Knowledgeable 147 182 ref ref
Not knowledgeable 44 42 1.30(0.03-3.12) 1.04(0.01-2.58)
Attitude about COVID-19 pandemic
Positive attitude 102 140 ref ref
Negative attitude 89 84 1.45(1.02-3.25) 1.29(0.06-2.07)
Practices on COVID-19 preventive measures
Good practice 27 102 ref ref
Poor practise 164 122 5.08(2.03-6,71) 4.76(2.55-6.97)***
COVID- Corona virus disease *p<=0.05 **p<=0.01 ***p<=0.001
Discussion
The primary findings of this study are the prevalence of hesitancy to COVID-19 immunization and the common determinant factors to hesitancy of people for COVID-19 vaccination. The prevalence of hesitance to COVID-19 vaccine in this study were 46.02% CI(45.03-54.98).This prevalence is comparable to studies done in Kuwait (47.9%)(Al-Sanafi and Sallam 2021), Amhara regional state (45.9%)(Aemro, Amare et al. 2021) and Wolaita(54.5%)(Mesele 2021) regions of Ethiopia.The prevalence in our study were founded to be higher compared to studies done in Saudi Arabia(6.1%) (Almalki, Alotaibi et al. 2021), Egypt(33%)(Fares, Elmnyer et al. 2021), Bangladesh(36.58%)(Mahmud, Mohsin et al. 2021), and Kenya(36.5%)(Orangi, Pinchoff et al. 2021). The increased prevalence in this study could be due to majority of the respondents in our study were from rural area, increased number of patients recovered from the pandemic, lower educational level in our study area, myths about the vaccines and lack of trust in the government as well as the health care system.in addition, unlike our study, most of the studies that had lower hesitance rate were done among health professionals. Lower hesitance rate were reported in our study compared to studies done in Portugal(59%) (Fernandes, Costa et al. 2021) and Debre Tabor comprehensive hospital, Ethiopia(57.7%) (Alle and Oumer 2021). The possible explanations for the lower hesitancy rate in our study could be, this study is done after lots of community mobilizations have been done,many global deaths due to the pandemic were reported and the vaccines are being trusted more currently than before.
In our study age greater than 49 years 0.56(0.01-0.73) decreases the odds of hesitancy to COVID-19 immunization by 44%. This finding is reversed to studies done in Kenya (Orangi, Pinchoff et al. 2021), Wolaita (Mesele 2021), Debre Tabor (Alle and Oumer 2021) and Adiss Abeba (Admasu 2021) .The possible explanation for the reciprocal difference could be, our study participants think of the COVID-19 pandemic would affect older age groups more than the side effects of the vaccine. In short justification due to the fear that older people would would be at risk of morbidity and mortality from the COVID-19 pandemic the could be more voluntary to be vaccinated.
Our study also reported that rural residency 2.02(1.20-3.71) increases the odds of hesitancy to COVID-19 vaccine by 2.02 times. This finding is supported by studies done in Kenya (Orangi, Pinchoff et al. 2021). The possible justification for this association could be those of rural residents are prone to non-scientific community myths, less educated, had less access to public medias and had a delay in deciding to seek care. Our study again reported that fear of the adverse effects of the vaccines 2.23(1.65-3.21) increases the odds of hesitancy to to COVID-19 vaccines by 2.23 times. our study finding is also supported by studies done in Kenya (Orangi, Pinchoff et al. 2021), Kuwait (Al-Sanafi and Sallam 2021), southwest and eastern Ethiopia (Mose and Yeshaneh 2021, Adane, Ademas et al. 2022). The possible justification for this association could be due to people who extremely advocate about the adverse effects of the vaccine will obviously prefer not to immunize despite other facts of the pandemic.
In addition our study also reported that myth about vaccines ineffectiveness 1.52(1.09-3.07) increases the odds of hesitance to COVID-19 vaccination. This finding is inline with studies done in Kuwait(Al-Sanafi and Sallam 2021), Kenya(Orangi, Pinchoff et al. 2021) and Ethiopia(Mose and Yeshaneh 2021, Adane, Ademas et al. 2022). The possible justification for this association could be due to the reason that people who think the vaccines as ineffective, religiously not allowed, being a vaccine for trial would under estimate the cost of not using the vaccines on their health and productivity.
Lastly those who had poor practice about COVID 19 preventive measure 4.76(2.55-6.97) increases the odds of hesitancy by 4,76 times. This finding was also supported by studies done in china (Wang, Han et al. 2021),Wolaita(Mesele 2021) and Northeastern Ethiopia (Adane et al. 2022). The possible explanation for this positive association could be those individuals who doesn't care about the prevention are usually underestimates the burden of the global pandemic and does not even outweigh the advantage of the COVID-19 pandemic over the crisis due to the pandemic.
Strength and limitations of the study
Since the data was collected from different health institutions, it increases the generalization of the study, but as a limitation is could be more generalized if it was done outside the health facilities in the community level.
Conclusion and recommendation
Despite the increased global mortality and mortality due to the the COVID-19 pandemic the prevalence of hesitance to be immunized against this fatal and crisis pandemic is still higher. Although different stallholders are doing their best in increment of vaccination coverage against COVID-19, people are still think of the vaccine as ineffective, had adverse effects over its advantage. People in the younger age, lives in rural area and those who had poor practice in the prevention modalities were highly hesitant to vaccines against COVID-19 pandemic. Hence, it is important to create awareness in those highly hesitant groups. We recommend future researchers to study hesitancy from the general population outside the hospitals.
Abbreviations
AOR- Adjusted odds ratio, BSc Bachelor of sciences, CI- confidence interval, COR- Crude odds ratio, COVID- corona virus Diseases, ETB- Ethiopian birr, SARS-CoV-2- severe acute respiratory syndrome corona virus 2, SGZ- South gondar zone, SPSS- Statistical package for social sciences, VIF- Variance inflation factor, WHO- World health organization
Availability of data and materials
All data included in this manuscript can be accessed from the corresponding author upon request through the email address. The tool and consent form can be obtained in the supplementary material section in separate page from (Annex I - III).
Declarations
Consent to participate
Written Informed Consent of the respondents was obtained after thoroughly explaining the aim of the study to each respondent.
Consent for publication
Not applicable
Code availability
All code for data cleaning and analysis associated with the current submission is available with the principal investigator and can be presented upon request.
Author's contribution
BBE is the primary author, participated in the conceptualization, design, analysis and interpretation of the data and drafted the manuscript.
All the coauthors contributed for design, analysis and interpretation of the data and critically revised the manuscript for important intellectual content.All authors read and approved the final manuscript.
Acknowledgments we are thankful to Debre Tabor University for giving us ethical clearance.we are also grateful to the data collectors, women participated in the data collection, hospital managers, health professionals, and all individuals who were willing to support us in any kind.
Author's information
Besfat Berihun Erega, BSc, MSc in clinical midwifery, Lecturer in Debre Tabor University, College of medicine and health sciences, Department of midwifery
Wassie Yazie Ferede, BSc, MSc in clinical midwifery, Lecturer in Debre Tabor University, College of medicine and health sciences, Department of midwifery
Fillorenes Ayalew Sisay, MSc in clinical midwifery, Lecturer in Debre Tabor University, College of medicine and health sciences, Department of midwifery
Abeba Belay Ayalew, MSc in clinical midwifery, Lecturer in Debre Tabor University, College of medicine and health sciences, Department of midwifery
Gebrehiwot Ayalew Tiruneh MSc in clinical midwifery, Lecturer in Debre Tabor University, College of medicine and health sciences, Department of midwifery
Erean Shigign Malka MPH in bio-statistics and epidemiology, Lecturer in Selale University, College of medicine and health sciences, Department of Public health
Habtamu Abie Tassew, MSc in clinical midwifery, Lecturer in Debre Tabor University, College of medicine and health sciences, Department of midwifery
Asrat Alemu, MSc in clinical midwifery, Lecturer in Dilla University, College of medicine and health sciences, Department of midwifery
Competing interests
The authors declare that they have no competing interests.
Funding
No funding
Ethical approval
The ethical clearance was obtained from the Institutional Review Board of the Debre Tabor University, School of Midwifery College of Medicine and Health Sciences(CMHS) with reference number of DTU/RE/12095/2022.Letter of permission was obtained from the clinical coordinator of each study hospital. clear explanation about the purpose of the study was given along with the letter of support for all concerned body. Finally, written Informed Consent of the respondents was obtained after thoroughly explaining the aim of the study to each respondent. in addition all methods were performed in accordance with the relevant guidelines and regulations.
References
Adane, M., A. Ademas and H. Kloos (2022). "Knowledge, attitudes, and perceptions of COVID-19 vaccine and refusal to receive COVID-19 vaccine among healthcare workers in northeastern Ethiopia." BMC public health 22(1): 1-14.
Admasu, F. T. (2021). "Knowledge and Proportion of COVID-19 Vaccination and Associated Factors Among Cancer Patients Attending Public Hospitals of Addis Ababa, Ethiopia, 2021: A Multicenter Study." Infection and Drug Resistance 14: 4865.
Aemro, A., N. S. Amare, B. Shetie, B. Chekol and M. Wassie (2021). "Determinants of COVID-19 vaccine hesitancy among health care workers in Amhara region referral hospitals, Northwest Ethiopia: a cross-sectional study." Epidemiology & Infection 149.
Al-Sanafi, M. and M. Sallam (2021). "Psychological determinants of COVID-19 vaccine acceptance among healthcare workers in Kuwait: A cross-sectional study using the 5C and vaccine conspiracy beliefs scales." Vaccines 9(7): 701.
Alle, Y. F. and K. E. Oumer (2021). "Attitude and associated factors of COVID-19 vaccine acceptance among health professionals in Debre Tabor Comprehensive Specialized Hospital, North Central Ethiopia; 2021: cross-sectional study." Virusdisease 32(2): 272-278.
Almalki, M. J., A. A. Alotaibi, S. H. Alabdali, A. A. Zaalah, M. W. Maghfuri, N. H. Qirati, Y. M. Jandali and S. M. Almalki (2021). "Acceptability of the COVID-19 vaccine and its determinants among university students in Saudi Arabia: a cross-sectional study." Vaccines 9(9): 943.
Berihun, G., Z. Walle, L. Berhanu and D. Teshome (2021). "Acceptance of COVID-19 vaccine and determinant factors among patients with chronic disease visiting Dessie Comprehensive Specialized Hospital, Northeastern Ethiopia." Patient preference and adherence 15: 1795.
Fares, S., M. M. Elmnyer, S. S. Mohamed and R. Elsayed (2021). "COVID-19 vaccination perception and attitude among healthcare workers in Egypt." Journal of primary care & community health 12: 21501327211013303.
Fernandes, N., D. Costa, D. Costa, J. Keating and J. Arantes (2021). "Predicting COVID-19 vaccination intention: the determinants of vaccine hesitancy." Vaccines 9(10): 1161.
Handebo, S., M. Wolde, K. Shitu and A. Kassie (2021). "Determinant of intention to receive COVID-19 vaccine among school teachers in Gondar City, Northwest Ethiopia." Plos one 16(6): e0253499.
Kalam, M. A., T. P. Davis Jr, S. Shano, M. N. Uddin, M. A. Islam, R. Kanwagi, A. Islam, M. M. Hassan and H. J. Larson (2021). "Exploring the behavioral determinants of COVID-19 vaccine acceptance among an urban population in Bangladesh: Implications for behavior change interventions." PloS one 16(8): e0256496.
Kebede, A., R. Kanwagi, A. T. Dibaba, M. A. Kalam, T. Davis and H. Larson (2021). "Determinants of COVID-19 vaccine acceptance in six lower-and middle-income countries."
Li, M., Y. Luo, R. Watson, Y. Zheng, J. Ren, J. Tang and Y. Chen (2021). "Healthcare workers’(HCWs) attitudes and related factors towards COVID-19 vaccination: A rapid systematic review." Postgraduate medical journal.
MacDonald, N. E. (2015). "Vaccine hesitancy: Definition, scope and determinants." Vaccine 33(34): 4161-4164.
Mahmud, S., M. Mohsin, I. A. Khan, A. U. Mian and M. A. Zaman (2021). "Acceptance of COVID-19 vaccine and its determinants in Bangladesh." arXiv preprint arXiv:2103.15206.
Mesele, M. (2021). "COVID-19 vaccination acceptance and its associated factors in Sodo Town, Wolaita Zone, Southern Ethiopia: cross-sectional study." Infection and Drug Resistance 14: 2361.
Mose, A. and A. Yeshaneh (2021). "COVID-19 vaccine acceptance and its associated factors among pregnant women attending antenatal care clinic in southwest ethiopia: institutional-based cross-sectional study." International journal of general medicine 14: 2385.
Nehal, K. R., L. M. Steendam, M. Campos Ponce, M. van der Hoeven and G. S. A. Smit (2021). "Worldwide vaccination willingness for COVID-19: a systematic review and meta-analysis." Vaccines 9(10): 1071.
Orangi, S., J. Pinchoff, D. Mwanga, T. Abuya, M. Hamaluba, G. Warimwe, K. Austrian and E. Barasa (2021). "Assessing the level and determinants of COVID-19 vaccine confidence in Kenya." Vaccines 9(8): 936.
Sallam, M., D. Dababseh, H. Eid, H. Hasan, D. Taim, K. Al-Mahzoum, A. Al-Haidar, A. Yaseen, N. A. Ababneh and A. Assaf (2021). "Low COVID-19 vaccine acceptance is correlated with conspiracy beliefs among university students in Jordan." International journal of environmental research and public health 18(5): 2407.
Soares, P., J. V. Rocha, M. Moniz, A. Gama, P. A. Laires, A. R. Pedro, S. Dias, A. Leite and C. Nunes (2021). "Factors associated with COVID-19 vaccine hesitancy." Vaccines 9(3): 300.
Thangaraju, P. and S. Venkatesan (2019). "WHO Ten threats to global health in 2019: Antimicrobial resistance." Cukurova Medical Journal 44(3): 1150-1151.
Wang, C., B. Han, T. Zhao, H. Liu, B. Liu, L. Chen, M. Xie, J. Liu, H. Zheng and S. Zhang (2021). "Vaccination willingness, vaccine hesitancy, and estimated coverage at the first round of COVID-19 vaccination in China: A national cross-sectional study." Vaccine 39(21): 2833-2842.
Zewude, B. and A. Belachew (2021). "Intention to receive the second round of COVID-19 vaccine among healthcare workers in Eastern Ethiopia." Infection and Drug Resistance 14: 3071.
Appendix Supplementary materials
Image, application 1
Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.ijregi.2022.11.006.
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The Author(s). Published by Elsevier Ltd.
S2405-8440(22)03465-X
10.1016/j.heliyon.2022.e12177
e12177
Research Article
Risk assessment of SARS-CoV-2 transmission in hospitality employees in a highly frequented tourist area
H Thiessen 1
N Käding 1
B Gebel 1
M Borsche 2
S Graspeuntner 1
L Kirchhoff 1
M Ehlers 3
J Rahmöller 4
S Taube 5
J Kramer 6
C Klein 2
A Katalinic 7
J Rupp 1∗
1 Department of Infectious Diseases and Microbiology, University Hospital Schleswig-Holstein/ Campus Lübeck, Lübeck, Germany
2 Institute of Neurogenetics, University of Lübeck, Lübeck, Germany
3 Institute of Nutritional Medicine, University of Lübeck, Lübeck, Germany
4 Department of Anesthesiology and Intensive Care, University Hospital Schleswig-Holstein/ Campus Lübeck, Germany
5 Institute of Virology and Cell Biology, University of Lübeck, Lübeck, Germany
6 LADR Laboratory Group Dr. Kramer & Colleagues, Geesthacht, Germany
7 Institute of Social Medicine and Epidemiology, University of Lübeck, Lübeck, Germany
∗ Corresponding author: Prof. Dr. Jan Rupp, MD Department of Infectious Disease and Microbiology University Hospital Schleswig-Holstein/ Campus Lübeck Ratzeburger Allee 160 23538 Lübeck, Germany Phone: +49 451 500 45301 Fax: +49 451 500 45304
8 12 2022
8 12 2022
e1217727 10 2021
8 2 2022
29 11 2022
© 2022 The Author(s)
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Right from the start of the COVID pandemic in January 2020, the entire tourism sector was put under immense pressure because of its assumed role in SARS-CoV-2 transmission and infection dynamics. Based on reports of single superspreading events in the early days of the pandemic, the hotel industry appeared in a bad light that impaired a strategic risk-assessment of existing transmission risks between tourists and employees.
We prospectively analysed samples of 679 employees of 21 hotels and restaurants from July 2020 to December 2020, a time during which more than 1.5 million tourists visited the Lübeck/Ostholstein Baltic Sea vacation area in Northern Germany. Employees were tested up to three times for an acute SARS-CoV-2 infection (PCR from nasopharyngeal swabs) and the presence of SARS-CoV-2 specific antibodies, and were asked to complete a short questionnaire.
Despite the massive increase in tourist influx, no significant increase in SARS-CoV-2 cases was observed amongst employees of the tourism sector from July to September 2020. In a cluster-outbreak analysis of 104 study participants of one single hotel in the Lübeck/Ostholstein region in October 2020 being employed in the low-wage sector “housekeeping” could be determined as major risk factor for becoming infected.
In conclusion, in a low incidence setting, touristic activities are safe under COVID-related hygiene measures for both the local population and employees of the tourism sector. Whereas, the field of work is a potential risk factor for increased infection dynamics.
SARS-CoV-2; infection risk; hospitality employees
Keywords
SARS-CoV-2
infection risk
hospitality employees
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pmcResearch in Context
On 11th March 2020 the global increase in SARS-CoV-2 infections was officially declared a pandemic by the WHO. Numerous efforts have been undertaken to describe specific risk constellations for viral spread and transmission as detailed as possible in order to avoid nonessential measures that restrict public life without having a huge impact on infection control. Tourist activities turned out to be one of the major factors driving the pandemic within the first months. Their impact on local infection dynamics among employees of the tourism sector after implementation of enhanced infection control measures has not been analyzed so far.
Added value of this study
Strict adherence to infection control measures that included face masks, hand hygiene and distancing protected employees in a highly frequented tourist area in Northern Germany from SARS-CoV-2 transmissions during the summer of 2020. However, we obtained clear evidence during a cluster outbreak within a group of employees of one single hotel that being employed in the low-wage sector is a risk factor for being infected.
Implications of all available evidence
While we could not observe SARS-CoV-2 transmissions between tourists and employees of the tourism sector under strict infection control measures in our study, there is reasonable risk for mutual infections among low-paid employees due to their respective field of work. Public health measures and testing strategies have to incorporate these aspects more elaborately when implementing novel concepts for safe tourist activities under pandemic conditions.
Introduction
With the emergence of the COVID-19 pandemic in January 2020, the whole tourism sector was engulfed in the abyss through global lockdown measures and travel restrictions. In particular, single events like the outbreak in Ischgl/ Austria in March 2020 with a high number of infected persons from different countries [1] attracted enormous public attention and emerged as a tremendous burden in later discussions about how tourism contributes to high SARS-CoV-2 infection incidences across countries.
While the impact of increased mobility and returning travelers from high SARS-CoV-2 incidence countries on the local and nationwide incidences became obvious in numerous studies [2], the risk of tourist activities in settings with a high standard of infection control and hygiene measures as implemented under COVID conditions has not been investigated in detail. This is even more striking as the economic burden of the complete lockdown in tourism lasting for months in most European countries is immense.
The United Nations World Tourism Organization (UNWTO) already published an action plan dealing with the COVID-19 crisis and proposing strategies on “managing the crisis and mitigating the impact”, ”providing stimulus and accelerating recovery” and “preparing for tomorrow” in April 2020 [3]. However, concrete exit strategies were either not implemented or differed significantly between countries and lacked evidence-based data [4]. Efforts undertaken by our group to generate such data basis to develop a risk assessment were hampered by different actors of the tourism sector probably due to economic concerns in case of employees tested positive.
Employees in the tourism sector originate from and interact with a large number of people from different countries and regions and are thus suspected to have an increased risk of SARS-CoV-2 infections during the pandemic. Hence, resident´s discrimination increased during the COVID-19 pandemic for fear of an increased infection risk [5]. Surprisingly, even though tourism has a significant impact on countries’ income, still little is known about the potential risk for employees and residents that derive from tourist activities during the pandemic.
In mid-May 2020, tourism and gastronomy in Schleswig-Holstein in Northern Germany were reopened under defined COVID-conditions. These conditions included hygiene and distance rules including regular hand disinfection and wearing face masks, as well as the implementation of signpost systems in tourist areas to avoid high contact densities and the recording of address data for potential contact tracing [6].
The aim of the underlying survey was to investigate if tourist activities during the peak season would lead to increased frequency of COVID-19 outbreaks among employees in the tourism sector. Here we will present one outbreak scenario. We were able to analyze a single hotel in detail to show possible influencing factors for the spread among these employees. We therefore repeatedly tested employees of the tourism sector in a highly frequented tourist region at the Lübeck/Ostholstein Baltic Sea in Northern Germany during the summer season 2020 for acute SARS-CoV-2 infection and the presence of SARS-CoV-2-specific antibodies. In that region, 7-day incidences on a population level ranged from below 30/100.000 population until September 2020 to a sharp increase in SARS-CoV-2 infections with up to 150/100.000 population after fall break in October 2020. Although we could not observe an increase of SARS-CoV-2 infections over time among the employees that would have exceeded the incidence in the resident population, potential risk factors for cluster outbreaks in this particular employee group could be identified by evaluation of one event in a single hotel with >100 employees, with a total of 38 SARS-CoV-2 positive employees.
Methods
Study design and ethics
During the period from July 2020 to December 2020 a total of 679 employees of 21 hotels and restaurants were recruited for a prospective cohort study. Participants were tested up to three times for an acute SARS-CoV-2 infection and the presence of SARS-CoV-2 specific antibodies. All study participants provided written informed consent. The study was approved by the local ethics committee (University of Lübeck, Az 20-150).
Specimen collection
A nasopharyngeal swab (CITOSWAB®) for detection of an acute SARS-CoV-2 infection was taken by trained personnel. The swab was first inserted into the nose up to the nasopharynx and pulled out again with rotating movements followed by a smear of the posterior pharyngeal wall orally using the same swab without touching the tongue. At the posterior pharyngeal wall, the smear was swabbed and then withdrawn. The smear was then placed in a swab tube without buffer solution and immediately sent to the laboratory at room temperature for further processing.
In order to identify previous infections with SARS-CoV-2, SARS-CoV-2 specific antibodies were determined in the blood of study participants. Capillary blood was collected from the fingertip with MiniCollect® tubes by using a lancet and a pipette. Afterwards, tubes were centrifuged and we collected at least 45 μL serum. Serum was stored at 4°C for later antibody testing.
During sampling, strict compliance with recommended hygiene regulations was ensured. All persons taking samples were equipped with face shields, FFP2 masks, disposable gloves and protective gowns. The disposable gloves were changed after each contact with a study participant.
SARS-CoV-2 testing
Dry swabs for SARS-CoV-2 mRNA quantification were transferred to Vacuette® 2 mL Virus Stabilization Tubes (Fa. Greiner Bio-One GmbH/ Germany). The isolation of the virus RNA by magnetic bead technology and the real time PCR testing of the E- and the ORF1-gene region was performed using the Cobas® SARS-CoV-2 test on Cobas® 6800 and 8800 systems (Roche Diagnostics/ US) by a DIN EN ISO 15189 accredited laboratory (Fa. LADR Central Lab Dr. Kramer & Colleagues / Germany). For antibody testing we performed the Anti-SARS-CoV-2-NCP-ELISA (IgG) and Anti-SARS-CoV-2 -ELISA (IgG) according to the manufacturer instructions (EUROIMMUN AG, Lübeck, Germany) by determining the modified nucleocapsid protein and the S1-domain of the spike protein respectively. Samples above the cut-off of 1.1 were defined as IgG-positive. Values between 0.8 and 1.1 were considered borderline according to the manufacturer.
Questionnaire and definitions
Study participants were asked to complete a short questionnaire including personal details (name, date of birth, field of work). Additional data was obtained on the type of accommodation (e.g. living with colleagues). We exclusively used a questionnaire in German. In cases of insufficient participants’ language skills translation into English language was performed by the study personnel or co-workers into the particular native language. No participant had to be excluded due to language abilities.
Statistics
For the assembly of data charts and figures, Microsoft Excel® (2016) was used. Besides the descriptive charts, the obtained data were tested for significant differences. Fisher´s exact test was used for pairwise comparison. Adjusting for multiple comparisons was done by Holm. We performed a binary logistic regression model to prove how variables “age”, “sex”, “field of work”, “living in residential cohorts”, “having children below 14 years” contribute to explain the outcome (dependent) variable “infection with SARS-CoV-2” [19]. As a complementary analysis, we run the same model also with exchanging “living in residential cohorts” by the variable “household members”. Those two variables were not computed together, as they are not independent from each other. In a first round we performed analysis based on integer dummy coding of independent variables. Ranking of the dummy coding was made on the basis of relative infection rate per factor level within a variable. Subsequently, we ran the binary logistic regression with changing the overall significant independent variable to the original factors to identify the respective factor levels significantly contributing to the model. We used the factor with the lowest relative infection rate as reference in this model. In all statistical analyses p-values below p ≤ 0.05 were considered significant.
Results
SARS-CoV-2 incidences in Schleswig-Holstein
In the summer of 2020, average incidences of SARS-CoV-2 infections in Schleswig-Holstein, and specifically in the vacation area of Lübeck/Ostholstein on the Baltic Sea, were lower than the national average in Germany (Figure 1 ). After relaxation of travel restrictions, a strong increase in guest arrivals in Schleswig-Holstein was observed [7]. Initially, the number of tourists remained below those of 2018/19, but from August to October 2020 the number of guest arrivals were comparable to the pre-COVID era (Suppl. Figure 1). Despite the massive increase of tourists during the summer period in the Lübeck/Ostholstein region, no significant increase of SARS-CoV-2 incidences was observed.Figure 1 SARS-CoV-2 incidences in Schleswig-Holstein, specifically in the City of Lübeck and the district of Ostholstein, including lockdowns dates of testing. Data modified from Robert Koch-Institute COVID-19 Dashboard (https://experience.arcgis.com/experience/478220a4c454480e823b17327b2bf1d4; 21.06.2021)
Figure 1
Study cohort of employees in the tourism sector
The overall study group included 679 participants. 61.9% (n= 420) were female with a mean age of 39.2 years. Participants households consisted of 2.4 persons on average. In total, 19.0% of the participants reported living with children younger than 14 years and 16.5% lived together with at least one colleague from work (Table 1 ).Table 1 Demographic data of the overall study group and the outbreak group separated by infected and non-infected participants (residential cohorts = 3 or more colleagues living together).
Table 1 Study group Outbreak group
Infected Non-infected
n (%) 679 (100) 38 (100) 66 (100)
Gender, female; n (%) 420 (61.9) 21 (55.2) 40 (60.6)
Age (mean ±SE) 39.2 (±14.2) 41.7 (±14.3) 40.6 (±14.2)
Household members (average number) 2.4 2.7 2.2
Children < 14 years; n (%) of households 129 (19.0) 2 (5.3) 13 (19.7)
Living in residential cohorts; n (%) Not available 21 (55.3) 21 (31.8)
Working in housekeeping; n (%) 116 (17.1) 12 (11.5) 11 (10.6)
Participants’ nationality is shown in Figure 2 A. Most participants mentioned “service” (25.9%) as their field of work followed by “housekeeping” (17.1%), “kitchen/scullery” (16.1%) and “reception” (13.0%) (Figures 2B).Figure 2 Countries of origin classified by continents (A; * Europe except Poland and Germany) and field of work (B) of the overall study cohort (n, total numbers; %).
Figure 2
SARS-CoV-2 testing for acute and previous infections
By the end of September 2020, 443 participants were included in the study and tested at least once. Until then, there was no evidence of acute SARS-CoV-2 infections in the study group and only a few positive antibody tests (n=2 positive, n=2 borderline) were detected, indicating a previous infection. Since the positive antibody tests were neither temporally related to each other nor could a local clustering be observed, we did not assume an undetected SARS-CoV-2 outbreak in the study group by then.
Characterization of the cluster outbreak
In October 2020, we observed a SARS-CoV-2 outbreak among employees that were part of our study cohort in a large hotel in the district of Ostholstein. Since overall infection rates in the local population were still low and no further acute infections were detected among other participants of our study cohort, this gave us the opportunity to determine risk factors for cluster outbreaks in this particular setting. The outbreak group included a total of 104 study participants (Table 1), who were all employed by one single hotel and that were tested after the 18th of October 2020. Within this group, a total of 38 participants were tested SARS-CoV-2 positive by PCR and/or antibody testing (“infected”) within subsequent tests. The remaining 66 participants were tested negative for the presence of SARS-CoV-2 infection and served as the control group to identify risk factors for SARS-CoV-2 transmissions.
Within the group of infected study participants, 55.2% were female, the mean age was 41.7 years (Table 1). More than half of the infected individuals were from either Germany (36.8%) or Poland (31.6%; Figure 3 A). The most frequently mentioned fields of work were "housekeeping" (34.2%), "kitchen/scullery" (22.2%) and "service" (22.2%) (Figure 3B). One participant tested positive stated travelling to Lower Saxony in a period of two weeks prior testing, all other participants negated such activities.Figure 3 Countries of origin classified by continents (A; * Europe except Poland and Germany) and field of work (B) of the SARS-CoV-2 positive employees of the outbreak group (n, total numbers; %).
Figure 3
Particular attention was paid to the housing conditions of employees in order to identify possible risk factors in a cluster outbreak. Based on postal addresses that were provided by the study participants, employees sharing a household could be identified. Cases of three or more colleagues living together were considered as residential cohort.
Although more than half of the infected participants lived in such residential cohorts (n=21, 55.3%), our data indicated that living in a residential cohort was not a risk factor for the cluster outbreak (binary logistic regression model, p=0.6835). Whereas, field of work was a risk for the cluster outbreak (binary logistic regression model, p=0.0095, Suppl. Table 1)
We further investigated which of the study participants' field of work was a risk factor for SARS-CoV-2 transmission, based on the assumption that many employees in the hotel and catering industry work together in sometimes confined spaces and that maintaining distance as a protective measure against SARS-CoV-2 transmission is more difficult than in other professions. While comparing the different working conditions of the infected participants using “Management” as a reference level in a factor-based binary logistic regression, we could show that “housekeeping” is a risk factor in comparison. 11.5 % of the participants working in “housekeeping” (n=12) were tested positive (p<0.05; Figure 4 ).Figure 4 Comparison of different field of work in SARS-CoV-2 infected and non-infected employees of the outbreak group. Housekeeping as significant factor level of the binary logistic regression compared to management as reference with the lowest number of positivity.
Figure 4
104 employees of this particular hotel were part of the outbreak group. At the first testing date in September 2020 we tested 48 persons. None of these were tested positive via PCR or had positive antibody results back then. In October 2020 another test round was initiated because an outbreak was supposed amongst the employees on the basis of positive antigen- tests. In total, 89 employees of the hotel were tested. 33 showed positive PCR-tests, four of these had also a positive antibody test. Three participants had positive antibodies but negative PCR in this study. However, these participants had already been tested positive by PCR by the responsible health authority four days before they were included in our study. For this reason, they were considered part of the outbreak group. Two other participants from the hotel were also tested positive for SARS-CoV-2 by the responsible health authorities, but did not show up for testing in October because of the mandated quarantine. Since they were tested negative for antibodies in September but positive in December, they were also included in the outbreak group.
Due to the second nationwide lockdown starting in early November 2020 (Figure 1) and the associated hotel and restaurant closures only 48 participants (46.2%) of the outbreak group participated at another testing point in December 2020, seven weeks after the cluster outbreak. SARS-CoV-2 specific antibodies were detected in all samples of initially infected participants who were retested in December 2020 (n=14) but no antibodies could be detected in samples of the non-infected outbreak group at this point (n=34).
Discussion
More than 2.5 years after the start of the COVID-19 pandemic, detailed information on the infection risks of tourist activities driving the pandemic is still missing. While the relevance of enhanced mobility and cross-border tourism between countries with highly different incidences became quite early obvious in the pandemic 8,9, the individual infection risk for employees in the tourism sector and its potential role in SARS-CoV-2 spread and transmission have not been assessed in detail.
Global tourism has been exposed to a number of crises before the COVID-19 pandemic. After the terror attack in the US on September 11th, 2001 there was a massive decline in worldwide travel [10], similarly during the SARS-CoV-1 pandemic in 2003 [11]. However, no crisis in the past has led to such a long-term decline of global tourism as COVID-19 has done 3,12. The Baltic Sea in Northern Germany is one of the country´s major summer destinations, attracting several million national and international tourists every year. In 2019, over 8.9 million tourists visited the Schleswig-Holstein. The number of overnight stays by tourists in 2019 was about 35,975,000 [7]. Its relevance for the local economy is immense, with a total of 4.7 billion euros adding value that is attributed directly or indirectly to the tourism sector per year, and with a share of 5.9 percent of the gross domestic product in 2019 [13]. Due to lockdown measures during the pandemic, the number of guest arrivals decreased to 6,217,000 (-30.3%) and the number of overnight stays to 28,925,000 (- 19.6%) [7].
Because of the high awareness and concern that tourism might significantly contribute to infection dynamics in a so far non-vaccinated population in 2020, we investigated the impact of mass tourism on infection rates among employees of the tourism sector from July to December 2020 at the Baltic Sea in Northern Germany. We observed that under general restrictions such as wearing face masks, keeping distance to others and additional indoor hygiene rules, no increase in infection rates among employees and residents was observed over time when the background incidences were low. This is in line with a large population-based study on more than 3000 residents of the Lübeck city region, showing that enhanced mobility and tourism did not increase the numbers of SARS-CoV-2 positive cases over the same time-period [14].
Furthermore, it can be assumed that there was no undetected outbreak within the study group, as there were only isolated positive antibody findings outside the outbreak group. Since this was a completely unvaccinated group, positive antibody findings indicate contact with the virus.
Although no enhanced infection dynamics were observed among tourists, residents and employees of the tourism sector over a period of more than three months (July - September 2020), the first appearance of an acutely infected employee in a single hotel in October 2020 dramatically changed infection rates among the employees of this hotel. Thus, we identified the working condition (housekeeping) as the main risk factor for SARS-CoV-2 transmission leading to a specific cluster outbreak. This factor characterizes in particular a group of people with low socioeconomic status, which has been shown as one of the general risk factors for enhanced SARS-CoV-2 transmission across larger studies [15]. This might be supported by the fact that within the SARS-CoV-2 positive employees, only one person indicated “management” as field of work, although the overall number in the complete outbreak group is low (n=12, 11.5%; Suppl. Figure 2). In general, this finding is not restricted to the tourism sector but has evolved as one of the predictors for pandemic hotspots in larger cities and for specific working conditions 16,17.
The study has several limitations that have to be addressed, most of them owed to the special situation of a pandemic. For example, recruitment of study participants in the tourism sector after the 1st lockdown in Germany that lasted over seven weeks until mid-May 2020, was severely hampered by economic considerations of the hotel industry sector which resulted in refusals for employees to participate in this study. This is even more impactful as it is known that test acceptance in general differs in different population groups and that positive amplification sometimes is needed to convince people of the benefits of early SARS-CoV-2 detection [18]. In addition, retesting of participants included later in the course of investigation could not be performed because of new lockdown measures in the end of 2020.
Because of the limited PCR testing capacities in Germany in the summer of 2020, we exclusively focused on employees of the tourism sector but not on tourists or residents of the tourist regions, for whom we relied on the official public health surveillance data that was issued daily by the Robert-Koch-Institute/Berlin. No further contact tracing was done in this study, neither among tourists nor among the local population. On the one hand, corona warning apps were already available at the time of the outbreak. On the other hand, due to protective measures as keeping distance and wearing masks etc. contract tracing was not officially ordered. Afterwards, the local health department confirmed that there were no increased numbers of infections associated with the outbreak.
We hypothesized that testing of employees who are in direct contact with tourists at a defined tourist site would offer the opportunity to detect SARS-CoV-2 hotspots at an early stage and precede the reporting of positively tested tourists in their hometowns. From July to September 2020, infection dynamics in the tourist area Lübecker Bucht/Ostholstein did not differ from the general incidences in Germany, despite massive tourist activities. A detailed summary on how SARS-CoV-2 incidences developed during that time in Northern Germany is found in Klein et al., in which a population-based testing strategy was investigated [14].
However, our study highlights that the hygiene measures that have been introduced by the Federal State of Schleswig-Holstein and controlled by the local public health authorities were efficient in preventing SARS-CoV-2 transmissions. The large outbreak occurring in a single hotel in October 2020 overlapped with a much more profound effect on SARS-CoV-2 infection dynamics that was credited to travelers from high-incidence regions across Europe returning to Germany. As a consequence of the overall low SARS-CoV-2 incidences during the summer of 2020 with only a few hospitalized COVID-19 patients, social restrictions were almost completely suspended during that time, resulting in a fulminant incline in the SARS-CoV-2 incidences by fall 2020 and a complete 2nd lockdown of the local tourism sector that lasted until May 2021.
Declarations
Author contribution statement
Henrike Thiessen; Nadja Käding; Benjamin Gebel: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Wrote the paper.
Max Borsche; Marc Ehlers; Johann Rahmöller; Stefan Taube; Jan Kramer: Contributed reagents, materials, analysis tools or data.
Simon Graspeuntner: Analyzed and interpreted the data.
Laura Kirchhoff: Performed the experiments.
Christine Klein; Alexander Katalinic: Conceived and designed the experiments.
Jan Rupp: Conceived and designed the experiments; Analyzed and interpreted the data; Wrote the paper.
Funding statement
This work was supported by Federal Ministry of Education and Research (BMBF) within the B-FAST program (AP6 risk settings).
Professor Jan Rupp was supported by Ministry of Education, Science and Cultural Affairs of the State of Schleswig-Holstein.
Data availability statement
Data associated with this study has been deposited at SSRN under the accession number ID 3949485.
Declaration of interest’s statement
The authors declare no conflict of interest.
Additional information
No additional information is available for this paper.
Acknowledgments
We thank Thorsten Niemann for his excellent technical assistance in performing the SARS-CoV-2 antibody measurements.
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References
1 Kreidl P. Schmid D. Maritschnik S. Emergence of coronavirus disease 2019 (COVID-19) in Austria Wien Klin Wochenschr 132 21-22 2020 645 652 32816114
2 Chang S. Pierson E. Koh P.W. Mobility network models of COVID-19 explain inequities and inform reopening Nature 589 7840 2021 82 87 33171481
3 UNWTO. Supporting Jobs and Economies through Travel and Tourism – A Call for Action to Mitigate the Socio-Economic Impact of COVID-19 and Accelerate Recovery: World Tourism Organization 2020.
4 Kreiner N.C. Ram Y. National tourism strategies during the Covid-19 pandemic Ann Tour Res 2020 103076
5 Tse S. Tung V.W.S. Residents' discrimination against tourists Ann Tour Res 2020 103060
6 Landesverordnung zur Bekämpfung des Coronavirus SARS-CoV-2 (Corona-Bekämpfungsverordnung – Corona-BekämpfVO). Vom 05. Juni 2020. Gesetz und Verordnungsblatt für Schleswig-Holstein 2020; 25.
7 Schleswig-Holstein SAfHu Tourismus in Schleswig-Holstein 27 2019 2020
8 Monod M. Blenkinsop A. Xi X. Age groups that sustain resurging COVID-19 epidemics in the United States Science 6536 2021 371
9 Popa A. Genger J.W. Nicholson M.D. Genomic epidemiology of superspreading events in Austria reveals mutational dynamics and transmission properties of SARS-CoV-2 Sci Transl Med 12 573 2020
10 Goodrich J. September 11, 2001 attack on America: a record of the immediate impacts and reactions in the USA travel and tourism industry Tourism Management 23 6 2002 573 580
11 McKercher B. Chon K. The Over-Reaction to SARS and the Collapse of Asian Tourism Ann Tour Res 31 3 2004 716 719 32572281
12 Gössling S. Scott D. Hall C.M. Pandemics, tourism and global change: a rapid assessment of COVID-19 Journal of Sustainable Tourism 29 1 2021 1 20
13 Schleswig-Holstein SuGf Sparkassen-Tourismusbarometer Schleswig-Holstein 2020
14 Klein C. Borsche M. Balck A. One-year surveillance of SARS-CoV-2 transmission of the ELISA Cohort: A model for population-based monitoring of infection risk Science Advances 2022 in press
15 Varela A.R. Florez L.J.H. Tamayo-Cabeza G. Factors Associated With SARS-CoV-2 Infection in Bogota, Colombia: Results From a Large Epidemiological Surveillance Study Lancet Reg Health Am 2021 100048
16 Whittle R.S. Diaz-Artiles A. An ecological study of socioeconomic predictors in detection of COVID-19 cases across neighborhoods in New York City BMC Med 18 1 2020 271 32883276
17 Ursachi C.S. Munteanu F.D. Cioca G. The Safety of Slaughterhouse Workers during the Pandemic Crisis Int J Environ Res Public Health 18 5 2021 1 40
18 Green M.A. Garcia-Finana M. Barr B. Evaluating social and spatial inequalities of large scale rapid lateral flow SARS-CoV-2 antigen testing in COVID-19 management: An observational study of Liverpool, UK (November 2020 to January 2021) Lancet Reg Health Eur 6 2021 100107
19 R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
| 36510570 | PMC9729582 | NO-CC CODE | 2022-12-14 23:22:26 | no | Heliyon. 2022 Dec 8;:e12177 | utf-8 | Heliyon | 2,022 | 10.1016/j.heliyon.2022.e12177 | oa_other |
==== Front
Antiviral Res
Antiviral Res
Antiviral Research
0166-3542
1872-9096
The Authors. Published by Elsevier B.V.
S0166-3542(22)00253-4
10.1016/j.antiviral.2022.105484
105484
Article
Discovery of lead natural products for developing pan-SARS-CoV-2 therapeutics
Pérez-Vargas Jimena a1
Shapira Tirosh a1
Olmstead Andrea D. a
Villanueva Ivan a
Thompson Connor A.H. a
Ennis Siobhan b
Gao Guang aq
DeGuzman Joshua a
Williams David E. c
Wang Meng c
Chin Aaleigha a
Bautista-Sánchez Diana a
Agafitei Olga b
Levett Paul d
Xie Xuping e
Nuzzo Genoveffa f
Freire Vitor F. g
Quintana-Bulla Jairo I. g
Bernardi Darlon I. g
Gubiani Juliana R. g
Suthiphasilp Virayu h
Raksat Achara h
Meesakul Pornphimol h
Polbuppha Isaraporn h
Cheenpracha Sarot i
Jaidee Wuttichai j
Kanokmedhakul Kwanjai k
Yenjai Chavi k
Chaiyosang Boonyanoot k
Teles Helder Lopes l
Manzo Emiliano f
Fontana Angelo fm
Leduc Richard n
Boudreault Pierre-Luc n
Berlinck Roberto G.S. g
Laphookhieo Surat h
Kanokmedhakul Somdej k
Tietjen Ian co
Cherkasov Artem p
Krajden Mel dq
Nabi Ivan Robert r
Niikura Masahiro b
Shi Pei-Yong e
Andersen Raymond J. c∗
Jean François a∗∗
a Department of Microbiology and Immunology, Life Sciences Institute, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada
b Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada
c Departments of Chemistry and Earth, Ocean & Atmospheric Science, University of British Columbia, Vancouver, BC V6T 1Z1, Canada
d British Columbia Centre for Disease Control Public Health Laboratory, Vancouver, BC, V5Z 4R4, Canada
e Department of Biochemistry and Molecular Biology, University of Texas Medical Branch, Galveston, TX, 77555, USA
f Bio-Organic Chemistry Unit, Institute of Biomolecular Chemistry, National Research Council, Via Campi Flegrei 34, 80078, Pozzuoli, Italy
g Instituto de Química de São Carlos, Universidade de São Paulo, CP780, CEP13560-970, São Carlos, SP, Brazil
h Center of Chemical Innovation for Sustainability (CIS), School of Science, Mae Fah Luang University, Chiang Rai, 57100, Thailand
i School of Science, University of Phayao, Phayao, 56000, Thailand
j Medicinal Plants Innovation Center of Mae Fah Luang University, Chiang Rai, 57100, Thailand
k Natural Products Research Unit, Department of Chemistry and Center for Innovation in Chemistry, Faculty of Science, Khon Kaen University, Khon Kaen, 40002, Thailand
l Instituto de Ciências Exatas e Naturais, Universidade Federal de Rondonópolis, CEP 78736-900, Rondonópolis, MT, Brazil
m Department of Biology, Università di Napoli “Federico II”, Via Cupa Nuova Cinthia 21, 80126, Napoli, Italy
n Department of Pharmacology-Physiology, Faculty of Medicine and Health Sciences, Institut de Pharmacologie de Sherbrooke, Université de Sherbrooke, Sherbrooke, Québec, J1H 5N4, Canada
o The Wistar Institute, Philadelphia, PA, 19104, USA
p Vancouver Prostate Centre, University of British Columbia, Vancouver, BC V6H 3Z6, Canada
q Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada
r Department of Cellular and Physiological Sciences, School of Biomedical Engineering, Life Sciences Institute, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada
∗ Corresponding author.
∗∗ Corresponding author.
1 These two authors contributed equally.
8 12 2022
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© 2022 The Authors. Published by Elsevier B.V.
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
The COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), remains a global public health crisis. The reduced efficacy of therapeutic monoclonal antibodies against emerging SARS-CoV-2 variants of concern (VOCs), such as omicron BA.5 subvariants, has underlined the need to explore a novel spectrum of antivirals that are effective against existing and evolving SARS-CoV-2 VOCs. To address the need for novel therapeutic options, we applied cell-based high-content screening to a library of natural products (NPs) obtained from plants, fungi, bacteria, and marine sponges, which represent a considerable diversity of chemical scaffolds. The antiviral effect of 373 NPs was evaluated using the mNeonGreen (mNG) reporter SARS-CoV-2 virus in a lung epithelial cell line (Calu-3). The screening identified 26 NPs with half-maximal effective concentrations (EC50) below 50 μM against mNG-SARS-CoV-2; 16 of these had EC50 values below 10 μM and three NPs (holyrine A, alotaketal C, and bafilomycin D) had EC50 values in the nanomolar range. We demonstrated the pan-SARS-CoV-2 activity of these three lead antivirals against SARS-CoV-2 highly transmissible Omicron subvariants (BA.5, BA.2 and BA.1) and highly pathogenic Delta VOCs in human Calu-3 lung cells. Notably, holyrine A, alotaketal C, and bafilomycin D, are potent nanomolar inhibitors of SARS-CoV-2 Omicron subvariants BA.5 and BA.2. The pan-SARS-CoV-2 activity of alotaketal C [protein kinase C (PKC) activator] and bafilomycin D (V-ATPase inhibitor) suggest that these two NPs are acting as host-directed antivirals (HDAs). Future research should explore whether PKC regulation impacts human susceptibility to and the severity of SARS-CoV-2 infection, and it should confirm the important role of human V-ATPase in the VOC lifecycle. Interestingly, we observed a synergistic action of bafilomycin D and N-0385 (a highly potent inhibitor of human TMPRSS2 protease) against Omicron subvariant BA.2 in human Calu-3 lung cells, which suggests that these two highly potent HDAs are targeting two different mechanisms of SARS-CoV-2 entry. Overall, our study provides insight into the potential of NPs with highly diverse chemical structures as valuable inspirational starting points for developing pan-SARS-CoV-2 therapeutics and for unravelling potential host factors and pathways regulating SARS-CoV-2 VOC infection including emerging omicron BA.5 subvariants.
Keywords
SARS-CoV-2 variants of concern
Host-directed antiviral
Human V-ATPase
Human protein kinase C
Human TMPRSS2
==== Body
pmc1 Introduction
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the ongoing COVID-19 pandemic, a major worldwide public health challenge (McKee and Stuckler, n.d.). As of November 26, 2022, more than 640 million SARS-CoV-2 infections and over 6.6 million deaths have been reported (“COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University,” n.d.; “United States—COVID-19 Overview—Johns Hopkins,” 2022). Control of viral spread has been facilitated by the widespread distribution of several vaccines, along with public health measures including mask wearing and social distancing. However, several variants, termed variants of concern (VOCs), have emerged that have increased transmission capacity; these VOCs cause more severe disease and evade vaccine-mediated and natural immunity (Callaway, 2021a; “SARS-CoV-2 Variants of Concern | CDC,” n.d.). SARS-CoV-2 B.1.617.2 (Delta) is the most pathogenic VOC identified to date with widespread infection and hospitalizations occurring in 2021, even in populations with high vaccination rates (Hacisuleyman et al., 2021; Li et al., 2021; “SARS-CoV-2 Variants of Concern | CDC,” n.d.). A heavily mutated VOC, B.1.1.529 (Omicron) with dramatically reduced neutralization against sera from vaccinated individuals, greater transmissibility, and increased risk of reinfection, emerged at the end of 2021 and continues to evolve and spread worldwide (Callaway, 2021b; Planas et al., 2021; Sievers et al., 2022). This emphasizes the possibility that SARS-CoV-2 is likely to remain a global health threat, and it stresses the need to develop novel effective prophylactic and therapeutic solutions with broad activity against SARS-CoV-2 VOCs (Indari et al., 2021; Meganck and Baric, 2021; Shagufta and Ahmad, 2021).
Natural products (NPs) are a promising but undervalued resource for new antivirals. Compounds derived from diverse sources can encompass structural diversity that falls outside the scope of the chemical spaces found in synthetic chemical compounds, and they have the potential to act via mechanisms distinct from those of conventional therapies. NPs targeting a variety of biological pathways have been highlighted for their antiviral potential against a variety of viruses (Wang and Yang, 2020), and they may also be effective against SARS-CoV-2 infection (Ashhurst et al., 2021; Mani et al., 2020).
To address the need for novel SARS-CoV-2 therapeutics, a cell-based high-content screening (HCS) assay using mNeonGreen (mNG) reporter SARS-CoV-2 virus (Xie et al., 2020) was performed with a diverse library of 373 natural products. Twenty-six of these NPs were found to inhibit SARS-CoV-2 infection in Calu-3 cells, with a <20% reduction in cell viability and with half-maximal effective concentrations (EC50) below 50 μM. We found three NPs isolated from marine organisms—holyrine A (Williams et al., 1999), alotaketal C (Daoust et al., 2013), and bafilomycin D (Carr et al., 2010)—with broad-spectrum activity against SARS-CoV-2 VOCs.
2 Materials and methods
2.1 Cell lines, antibodies and chemicals
Calu-3 cells (ATCC® HTB-55™) and Vero E6 cells (ATCC® CRL-158™) were cultivated according to ATCC recommendations. VeroE6/TMPRSS2 cells (Jochmans et al., 2022) were cultivated according to JCRB recommendations. Huh-7.5.1 cells were kindly provided by Dr. Francis Chisari (Scripps Research Institute)(Moradpour et al., 2004). The SARS-CoV-2 nucleocapsid antibody [HL344] (GTX635679) was kindly provided by GeneTex; mouse anti-dsRNA antibody (J2) was purchased from SCICONS English and Scientific Consulting (10010500); secondary antibodies of goat anti-mouse IgG Alexa Fluor 488 (A-11001) and goat anti-rabbit IgG Alexa Fluor 555 (A-21428) and Hoechst 33342 were obtained from Thermo Fisher Scientific. Prostratin (60857-08-1) and Gö6983 (133053-19-7) were obtained from Sigma. Bryostatin (2383), Gö6976 (2253), PEP005 (4054) and CRT 0066854 (5922) were obtained from Tocis Bioscience. N-0385 was obtained from Drs. Pierre-Luc Boudreault and Richard Leduc (Université de Sherbrooke, QC, Canada) (Shapira et al., 2022a).
2.2 SARS-CoV-2 viruses
All SARS-CoV-2 infections were carried out in Biosafety Level 3 (BSL3) facilities (either the University of British Columbia (UBC) Facility for Infectious Disease and Epidemic Research (FINDER) or the Simon Fraser University (SFU) BIO3 laboratory) following the Public Health Agency of Canada and UBC FINDER or SFU BIO3 regulations (UBC BSL3 Permit #B20-0105 and SFU Permit #361–2021). The mNeonGreen SARS-CoV-2 (2019-hCoV/USA-WA1/2020) (Xie et al., 2020) was made by Dr. Shi's laboratory (University of Texas Medical Branch, TX; USA. SARS-CoV-2 VOC (B.1.617.2 Delta and BA.2) was kindly provided by Dr. Mel Krajden (BC Centre for Disease Control, BC, Canada). SARS-CoV-2 Delta was first isolated in VeroE6/TMPRSS2 cells (passage 1) and then passaged in Vero E6 cells (passage 2). Delta virus stocks used in the experiments (passage 3) were propagated in Vero E6 cells (Ogando et al., 2020). SARS-CoV-2 Omicron BA.1 (BC-SFU-OM6) and BA.5 were isolated by Dr. Masahiro Niikura (SFU) from a clinical specimen in VeroE6/TMPRSS2 cells and confirmed as a BA.1 and BA.5 variants by complete genome sequencing (sequence available in GISAID). Omicron subvariants BA.1, BA.2 and BA.5 were amplified in VeroE6/TMPRSS2 cells and used in the experiments at passage 2.
2.3 Natural products
Our natural product screening library contains 373 pure natural products representing a large diversity of chemical scaffolds isolated from a broad spectrum of plants, invertebrates, or microorganisms collected in diverse terrestrial and marine habitats (Table S1). Terrestrial plants and microorganisms were collected in Thailand, Brazil, Canada, and Sri Lanka. Marine invertebrates and microorganisms were collected in ocean waters off the coasts of Canada, Brazil, Italy, Papua New Guinea, Indonesia, Dominica, and Sri Lanka. Most NPs in the screening library were discovered and first reported in the literature by the co-authors. Some of the pure natural products isolated by the coauthors had been previously reported in the literature by other research groups. The structures of all the NPs discovered by the coauthors were elucidated by a detailed analysis of nuclear magnetic resonance (NMR) spectroscopy, mass spectrometry (MS), and/or single-crystal X-ray diffraction data. The chemical structure and purity of our three lead NPs (Holyrine A, alotaketal C, and bafilomycin D) was confirmed by NMR analyses (Fig. S1). Literature references detailing the discovery of the lead 26 active compounds are provided (Table 1 ).Table 1 Summary of lead NP candidates.
Table 1Name Origin % Inhibition EC50(μM) R2 CC50(μM) SI References
bafilomycin D Canada 92 0.04 0.29 43 1064 (Carr et al., 2010)
alotaketal C Canada 91 0.11 0.79 >100 909 (Daoust et al., 2013)
holyrine A Canada 93 0.28 0.76 >100 357 (Williams et al., 1999)
acetyl hyrtiosal Italy 97 1.97 0.78 >100 51 (Indari et al., 2021, Daoust et al., 2013)
dipterpcapl Thailand 99 3.05 0.89 >100 33 (Chaipukdee et al., 2016)
knecorticosanone G Thailand 100 3.50 0.88 >100 29 (Chu et al., 2020)
piperbonin A Thailand 100 3.61 0.82 >100 28 (Dampalla et al., 2021)
asparacemosone B Thailand 83 4.04 0.72 >100 25 (Tao et al., 2021)
chevalone C Thailand 100 4.14 0.85 93 22 (Ranganath et al., 2022)
chevalone B Thailand 95 4.47 0.94 >100 22 (Ranganath et al., 2022)
giluterrin Brazil 88. 5.44 0.86 72 13 (Gandhi et al., 2022)
territrem B Thailand 100 6.64 0.90 >100 15 (Li et al., 2022; Huang et al., 2022; Chaipukdee et al., 2016)
mollicellin B Thailand 100 6.79 0.93 >100 15 (Khumkomkhet et al., 2009)
terretonin A Thailand 82 7.31 0.94 >100 22 (Li et al., 2022; Chaipukdee et al., 2016)
crotonolide F Thailand 97 8.21 0.81 >100 12 (Linch et al., 2014; Shyr et al., 2021)
phellotorin Thailand 96 6.35 0.83 93 14 (Planas et al., 2021; Bao et al., 2018)
kaur-16-en-18-oic acid Thailand 100 15.06 0.66 >100 7 (Kawano et al., 2021; Li et al., 2021)
alpinumisoflavone Thailand 100 16.03 0.85 >100 6 (Stewart 200)
2-butylchrysin Thailand 95 17.99 0.82 >100 6 Huang et al., 2022
chrysin Thailand 99 18.80 0.93 >100 5 (Sayed et al., 2020; Schultz et al., 2022)
5-hydroxysophoranone Thailand 100 20.22 0.46 >100 5 (Moradpour et al., 2004)
asparacemosone A Thailand 95 20.65 0.78 >100 5 (Tao et al., 2021)
artahongkongene B Thailand 81 21.98 0.93 >100 5 (Li et al., 2022)
8-hydroxypinoresinol Thailand 82 24.35 0.93 >100 4 (Planas et al., 2021)
mangostinone Thailand 92 27.88 0.86 >100 4 (Ashhurst et al., 2021)
erysubin F Grey block: hits with EC50 < 10 μM Thailand 100 45.60 0.77 >100 2 (Tang et al., 2015)
2.4 SARS-CoV-2 infections and fluorescent staining of intracellular viral biomarkers
Huh-7.5.1 or Calu-3 cells were seeded at concentrations of 104 cells/well in 96-well plates the day before infection. Cells were pretreated for 3h with fixed (50μM for screening) or diluted concentrations of compounds (indicated in figure legends) followed by infection with mNeonGreen (mNG) SARS-CoV-2, SARS-CoV-2 Delta, or an Omicronsubvariant (BA.1, BA.2, and BA.5)for 48h at a multiplicity of infection (MOI) of 1 (Huh-7.5.1 cells) or MOI of 2 (Calu-3 cells). Cells were then fixed with 4% formalin for 30min to inactivate the virus. The fixative was removed, and the cells were washed with PBS, permeabilized with 0.1% Triton X-100 for 10min, and blocked with 1% bovine serum albumin (BSA) for 1h. To quantify nucleocapsid and dsRNA in a subset of experiments, cells were immunostained with rabbit primary antibody HL344 (SARS-CoV-2 nucleocapsid)mouse primary antibody J2 (dsRNA) at working dilutions of 1:1000 overnight at 4 °C. Secondary antibodies were used at a 1:2000dilution and Hoechst was used at1.5 μg/mLfor 1h at room temperature in the dark. After washing with PBS, the plates were kept in the dark at 4 °C until imaging on a high-content screening (HCS) platform (CellInsight CX7 HCS, Thermo Fisher Scientific) with a 10Xobjective orby Leica TCS SP8 laser scanning confocal microscope equipped with HyD detectors, 405 nm laser, and a white light laser, and operated with a Leica Application Suite X (LAS X) software using a 63x/1.40 Oil objective (HC PL APO CS2).
2.5 CellInsight CX7 high-content screening of SARS-CoV-2 infection
Monitoring of the total number of cells (based on nuclei staining) and the number of virus-infected cells (based on mNG expression) was performed using the CellInsight CX7 HCS platform (Thermo Fisher Scientific) as previously described (Olmstead et al., 2012; Shapira et al., 2022).Briefly, nuclei are identified and counted using 350/461nm wavelength (Hoechst 33342); cell debris and other particles are removed based on a size filter tool. A region of interest (ROI, or “circle”) is then drawn around each host cell and validated against the bright field image to correspond with host cell membranes. The ROI encompasses the “spots” where mNG (485/521nm wavelength) or another viral marker (dsRNA (485/521nm wavelength) and nucleocapsid (549/600nm wavelength) are localized. Finally, the software (HCS Studio Cell Analysis Software, version 4.0) identifies, counts, and measures the pixel area and intensity of the “spots” within the “circle.” The fluorescence measured within each cell (circle) is then added and quantified for each well. The total circle-spot intensity of each well corresponds to intracellular virus levels (Z’>0.6) and is normalized to noninfected cells and infected cells treated with 0.1–1% DMSO. Nine fields were sampled from each well.
2.6 Median effective dose (EC50) curves
Intracellular dose response (EC50 values) for selected compounds against mNG SARS-CoV-2 and SARS-CoV-2 VOCs was determined by pretreating Calu-3 cells for 3 h with serially diluted compounds, followed by virus infection for 48 h. Viral infection was detected by mNG fluorescence (mNG SARS-CoV-2) or fluorescent imaging of viral nucleocapsid and dsRNA (SARS-CoV-2 VOCs) as described above (Section 2.5). EC50 experiments were repeated at least three times. Intracellular fluorescent levels were interpolated to negative control (0.1–1% DMSO, no infection) = 0 and positive control (0.1–1% DMSO, with infection) = 100. The GraphPad Prism 9™ (GraphPad Software, Inc.) nonlinear regression fit modeling variable slope was used to generate a dose-response curve [Y = Bottom + (Top-Bottom)/(1 + 10ˆ((LogIC50-X)*HillSlope)], constrained to top = 100, bottom = 0.
2.7 Cytotoxicity assays
Calu-3 cells were seeded with 104 cells/well in 96-well plates. 24 h after seeding,media was aspirated, and serially diluted compounds (described above) were added for an additional 48h incubation. Cellular viability was assessed with PrestoBlueCell Viability Assay (Thermo Fisher Scientific) according to the manufacturer's instructions. Cells were incubated with 5% PrestoBlue reagent for 2h before they were read on the SpectraMax Gemini XS spectrofluorometer (Molecular Devices, LLC) set at excitation and emission wavelengths of 555 and 585nm, respectively. Cellular viability was expressed relative (%) to vehicle-treated cells. Data are from at least three independent experiments.
2.8 Drug combination
Calu-3 cells were seeded at concentrations of 104 cells/well in 96-well plates the day before infection. Cells were pretreated for 3 h with respective combinations of bafilomycin D and N-0385 followed by infection with SARS-CoV-2 Omicron BA.2 as described above for 48 h at a MOI of 2. The two-drug combination was tested using five-fold serial dilutions of bafilomycin D and ten-fold serial dilutions of N-0385 ≤ EC50. The percent inhibition of viral infection for each dose-combination was determined by fluorescent staining of SARS-CoV-2 nucleocapsid as described above. Dose-response percent inhibition matrix of single and combined treatment of bafilomycin D and N-0385 in SARS-CoV-2 infected Calu-3 cells, and 2-D and 3-D interaction landscapes were calculated based on (i) the Loewe additive model, (ii) the zero interaction potency (ZIP) model, (iii) the highest single agent (HSA) model, and (iv) the Bliss model using SynergyFinder V.1 (Tang et al., 2015; Yadav et al., 2015; Zheng et al., 2022). Synergy scores were calculated for each condition and values above 10 were interpreted as a synergistic effect.
3 Results
3.1 Validation of mNG-SARS-CoV-2 as a molecular tool for high-content antiviral drug screening in human cells
The mNG-SARS-CoV-2 reporter virus used in this study was previously described, where it was demonstrated that the mNG transgene is stable and does not affect virus replication in Vero E6 cells (Xie et al., 2020). Although Vero E6 cells are permissive to SARS-CoV-2, these cells are non-human African green monkey kidney-derived cells and consequently their potential applications to provide useful physiological insights into the mechanism of action of new lead molecules in virally infected human cells could be limited by species-specific host-virus interactions (Shapira et al., 2022a).
To validate mNG-SARS-CoV-2 as a screening tool in human cells, we first determined the EC50 value of the pro-drug remdesivir (RDV), a direct-acting antiviral (DAA), against mNG-SARS-CoV-2 infection using human Huh-7.5.1 cells. As previously reported, human hepatoma-derived Huh-7.5.1 cells are permissive to SARS-CoV-2 infection (Cagno, 2020; Chu et al., 2020; Fig. S2A) and produce host enzymes that permit a robust intracellular conversion of the pro-drug RDV to the active nucleoside triphosphate, which is misintegrated into viral RNA by the viral RNA-dependent RNA polymerase (Dittmar et al., 2021; Tao et al., 2021). Huh-7.5.1 cells were pretreated with serially diluted RDV for 3 h before mNG-SARS-CoV-2 infection. Using fluorescence-based microscopy, we observed a dose-dependent reduction of our two viral biomarkers’ relative abundance (intracellular mNG and nucleocapsid) in virally infected cells upon treatment with increasing concentration of RDV (Fig. S2B). Relative quantification of fluorescently labeled infected cells was performed using the CellInsight CX7 HCS platform to determine EC50 values (half-maximal effective concentration; drug concentration required to reduce infection by 50%). The EC50 values for RDV using mNG and nucleocapsid staining in Huh-7.5.1 infected cells were 3.6 nM and 7.6 nM, respectively (Fig. S2C), which is consistent with previously published values against the wild-type SARS-CoV-2 [EC50 = 2 nM (Huh-7.5 cells)(Dittmar et al., 2021)].
Human Calu-3 cells are naturally permissive lung epithelial cells that represent a physiologically relevant cell-based system for antiviral drug discovery directed at SARS-CoV-2 VOCs (Shapira et al., 2022a). To further validate mNG-SARS-CoV-2 as a screening tool in human cells, we determined the EC50 value of GC376 against mNG-SARS-CoV-2 infection using human Calu-3 lung cells. GC376 is a DAA that inhibits the SARS-CoV-2 main protease (3CLpro) (Fu et al., 2020) and has been demonstrated to have therapeutic efficacy in a mouse model of severe COVID-19 infection(Dampalla et al., 2021). The Calu-3 cells were pretreated with serially diluted GC376 for 3 h before mNG-SARS-CoV-2 infection. Relative quantification of fluorescently labeled infected cells using mNG as a viral biomarker was performed using the CellInsight CX7 HCS platform (Fig. S3A). The EC50 value determined for GC376 against mNG-SARS-CoV-2 is 475 nM (Fig. S3B), which is consistent with previously published values against the wild-type SARS-CoV-2 [EC50 = 230 nM (Vero E6 cells), (Dampalla et al., 2021); EC50 = 700 nM (Vero E6 cells) (Fu et al., 2020)]. These results confirm that mNG-SARS-CoV-2 is a valuable molecular tool for high-content drug screening and for determination of drug efficacy against SARS-CoV-2 using human Calu-3 lung cells.
3.2 Screening of natural products against mNG-SARS-CoV-2
A library of pure NPs representing a large diversity of chemical scaffolds was assembled from extracts of plants, fungi, bacteria, and marine sponges. To identify which NPs exhibit antiviral activity against SARS-CoV-2 infection, we applied a high-throughput screening approach using the mNG-SARS-CoV-2 reporter virus and the CellInsight CX7 High Content Screening (HCS) platform (Fig. 1 A). In this assay, mNG fluorescence and nuclear staining are rapidly quantified and used as a readout for viral infection and compound cytotoxicity, respectively (Shapira et al., 2022a). Pretreatment of the cells with NPs for 3 h was followed by infection of Calu-3 cells with the mNG-SARS-CoV-2 reporter virus at a multiplicity of infection (MOI) of 2 for 48 h. The cells were then fixed and the images were processed for quantitative analysis to evaluate the activity of the compounds. A total of 373 NPs at a concentration of 50 μM were screened using these assay conditions. The suitability of the screening assay was measured using Z’ factor calculation (a measure of the dynamic range, defined as the difference between the means of the negative and positive controls (Zhang et al., 1999); the values of each screening were found to be between 0.5 and 0.7, with an average value of 0.6. Compounds showing 80% or greater inhibition of SARS-CoV-2 infection with less than 20% of cell loss were defined as lead candidates (Fig. 1B). Based on this criterion, 26 compounds were selected for further validation.Fig. 1 NP high-content screening with mNG-SARS-CoV-2. A) Graphical representation of the strategy for high-content screening of NPs as antiviral candidates. Illustration was created with BioRender.com. B) Overview of HCS performed in Calu-3 cells. Inhibition (green) was interpolated to the negative control (DMSO) set at 0% inhibition and the positive control (GC376) set at 100% inhibition. Cell loss (blue) was normalized to the negative control. The orange and green lines show the cut-off of inhibition (80%) and cell loss (20%), respectively. Data represent the mean of the results of three independent experiments. The names of the 26 lead compounds are displayed.
Fig. 1
3.3 Dose-response analysis of holyrine A, alotaketal C, and bafilomycin D against mNG-SARS-CoV-2 VOCs in human Calu-3 lung cells
To validate and prioritize the most promising compounds according to their anti-SARS-CoV-2 activity, the 26 lead compounds identified from the primary screening were tested using a broad concentration range (100–0.00064 μM) against mNG-SARS-CoV-2 in Calu-3 cells. The relative quantification of infected cells was used to calculate EC50 values, which ranged from 38 nM to 45.6 μM (Fig. 2A and S4). The half-maximal cytotoxic concentration (CC50) values were also calculated and determined to be > 100 μM for most of the lead compounds (Fig. 2A and S4). The selectivity index (SI) for these 26 compounds, expressed as the ratio of CC50 over EC50 is summarized in Table 1. Importantly, we identified three compounds—holyrine A, alotaketal C, and bafilomycin D (Carr et al., 2010; Daoust et al., 2013; Williams et al., 1999)—with nanomolar activity and high SI values (EC50 = 282 nM, 106 nM, and 38 nM and SI = 357, 909, and 1064, respectively) against mNG-SARS-CoV-2 (Table 1 and Fig. 2 B). A high SI is preferable if a drug is to be viewed as having a favourable safety profile in human Calu-3 cells (Muller and Milton, 2012). Representative images demonstrating the reduction of mNG expression correlating with an increase in NP concentration are shown in Fig. S5.Fig. 2 Discovery of 15 antiviral NPs against mNG-SARS-CoV-2. Dose-response curves for infectivity (black) and cell viability (blue) were generated for the indicated serial diluted compounds in Calu-3 cells infected with mNG-SARS-CoV-2 (n = 3). EC50 values were determined using nonlinear regression analysis. The GraphPad Prism 9™ (GraphPad Software, Inc.) nonlinear regression fit modeling variable slope was used to generate a dose-response curve [Y = Bottom + (Top-Bottom)/(1 + 10ˆ((LogIC50-X)*HillSlope)] constrained to top = 100, bottom = 0. The graph shows the average values of two independent experiments. A) 12 NPs with a single-digit micromolar range activity and B) 3 NPs with nanomolar activity against mNG-SARS-CoV-2.
Fig. 2
3.4 Pan-SARS-CoV-2 activity of holyrine A, alotaketal C, and bafilomycin D against SARS-CoV-2 VOCs in human Calu-3 lung cells
Variants of concern (VOCs) possess a demonstrated capacity for enhanced transmission, disease severity, and reduced vaccine effectiveness (Callaway, 2021a; 2021b; “SARS-CoV-2 Variants of Concern | CDC,” n.d.). SARS-CoV-2 B.1.617.2 (Delta) is the most severe VOC and may be associated with an increased risk of hospitalization whereas Omicron subvariants (e.g., BA.1, BA.2, BA.5) are linked to greater transmissibility, reduced vaccine efficacy, and increased risk of reinfection (Callaway, 2021a; Hagen, n.d.; Li et al., 2021; Planas et al., 2021). We tested the impact of holyrine A, alotaketal C, and bafilomycin D on Calu-3 cells pretreated with serially diluted compounds for 3 h before SARS-CoV-2 Delta, Omicron BA.1, BA.2 and BA.5 infection. Fluorescent imaging of viral nucleocapsid and dsRNA confirmed viral infection and demonstrated the spheroid-like cell morphology of Delta infection that we previously reported (Shapira et al., 2022), where infected cells appear in small, rounded clusters (Fig. 3 A) in contrast to larger monolayers of uninfected or SARS-CoV-2-Omicron-infected cells (Fig. 3A). The three lead NPs showed a dose-dependent reduction of SARS-CoV-2 Delta and Omicron infection (Fig. 3B-M); relative quantification of virally infected cells was used to calculate their EC50 values. For holyrine A, the EC50 values against SARS-CoV-2 Delta were 600 nM and 1.0 μM for nucleocapsid and dsRNA staining (Fig. 3B), respectively; for SARS-CoV-2 Omicron BA.1, they were 1.7 μM and 1.6 μM, respectively (Fig. 3E), for Omicron BA.2, they were 44 nM and 394 nM for nucleocapsid and dsRNA, respectively (Fig. 3H), and for Omicron BA.5, they were 80 nM and 120 nM for nucleocapsid and dsRNA, respectively (Fig. 3K). For alotaketal C, the EC50 values against SARS-CoV-2 Delta were 4.9 μM and 3.9 μM for nucleocapsid and dsRNA staining, respectively (Fig. 3C); for SARS-CoV-2 Omicron BA.1, they were 700 nM and 400 nM, respectively (Fig. 3F); for Omicron BA.2, they were 700 nM and 700 nM for nucleocapsid and dsRNA, respectively (Fig. 3I) and for Omicron BA.5, they were 300 nM and 700 nM for nucleocapsid and dsRNA, respectively (Fig. 3L). Finally, for bafilomycin D, the EC50 values against SARS-CoV-2 Delta were 2.0 nM and 50 nM for nucleocapsid and dsRNA staining, respectively (Fig. 3D); for SARS-CoV-2 Omicron BA.1, they were 13.4 nM and 67.3 nM, respectively (Fig. 3G); for Omicron BA.2, they were 4.6 nM and 22 nM for nucleocapsid and dsRNA, respectively (Fig. 3J) and for Omicron BA.5, they were 43.9 nM and 38.6 nM for nucleocapsid and dsRNA, respectively (Fig. 3M). Together, these results underline the potential of holyrine A, alotaketal C, and bafilomycin D as broad-spectrum inhibitors against SARS-CoV-2 VOCs.Fig. 3 Holyrine A, alotaketal C and bafilomycin D are novel pan-SARS-CoV-2 natural product drug leads. A) Immunofluorescence staining for viral nucleocapsid and dsRNA verified productive SARS-CoV-2 infection by all four VOCs (B.1.617.2 Delta, Omicron BA.1, BA.2 and BA.5) in Calu-3 cells [scale bar = 10 μm]. B-J) Dose-response curves of Calu-3 cells pretreated with the indicated concentrations of holyrine A, alotaketal C and bafilomycin D before infection with SARS-CoV-2 Delta (B.1.617.2), Omicron BA.1, Omicron BA.2 or Omicron BA.5, using nucleocapsid (red circles) and dsRNA (green squares) as infection markers. EC50 values were determined using nonlinear regression analysis. The GraphPad Prism 9™ (GraphPad Software, Inc.) nonlinear regression fit modeling variable slope was used to generate a dose-response curve [Y = Bottom + (Top-Bottom)/(1 + 10ˆ((LogIC50-X)*HillSlope)] constrained to top = 100, bottom = 0. The graph shows the average values of three independent experiments.
Fig. 3
3.5 Robust inhibition of SARS-CoV-2 omicron BA.1 infection by protein kinase C (PKC) activators in human Calu-3 lung cells
We previously reported alotaketal C as an activator of protein kinase C (PKC) that is more potent than the well-studied PKC activator prostratin (Wang et al., 2016, 2022). To understand and validate the role of PKC on SARS-CoV-2 infection and the effect of alotaketal C as an activator, we tested the impact of PEP 005 (Hampson et al., 2005; Kedei et al., 2004), bryostatin-1 (Sun and Alkon, 2006), and prostratin (Wang et al., 2016, 2022) natural products known as PKC activators and also the PKC inhibitors Gö6983 (pan-inhibitor) (Wang et al., 2016, 2022), Gö6976 (conventional inhibitor) (Koivunen et al., 2004), and CRT0066854 (atypical inhibitor) (Kjær et al., 2013; Linch et al., 2014) on Calu-3 cells pretreated with serially diluted compounds for 3 h before SARS-CoV-2 BA.1 infection (Fig. 4 A–F). The three PKC activators (prostratin, bryostatin, and PEP 005) showed a dose-dependent reduction of SARS-CoV-2 Omicron BA.1 infection (Fig. 4A–C); relative quantification of virally infected cells was used to calculate their EC50 values. For prostratin, the EC50 values against SARS-CoV-2 Omicron BA.1 were 11.6 μM and 5.0 μM for nucleocapsid and dsRNA staining, respectively (Fig. 4A). For bryostatin, the EC50 values were 222 nM and 523 nM for nucleocapsid and dsRNA staining, respectively (Fig. 4B). For PEP 005, they were 700 nM and 1.5 μM for nucleocapsid and dsRNA, respectively (Fig. 4C). In contrast, the conventional PKC inhibitors enhanced the SARS-CoV-2 Omicron BA.1 infection (Fig. 4D and E) whereas the atypical PKC inhibitor (CRT00668854) showed a dose-dependent reduction of viral infection. The EC50 values against SARS-CoV-2 Omicron BA.1 were 300 nM and 100 nM for nucleocapsid and dsRNA staining, respectively (Fig. 4F).Fig. 4 Effect of protein kinase C (PKC) regulation on SARS-CoV-2 Omicron (BA.1) infection. A-F) Dose-response curves of Calu-3 cells pretreated for 3 h with the indicated concentrations of A) prostratin (PKC activator), B) bryostatin (PKC activator), C) PEP 005 (PKC activator), D) Gö6983 (PKC inhibitor), E) Gö6976 (PKC inhibitor) and F) CRT0066854 (PKC inhibitor) before 48 h infection with Omicron BA.1; dsRNA is represented with green squares and nucleocapsid with red circles. EC50 values were determined using nonlinear regression analysis. The GraphPad Prism 9™ (GraphPad Software, Inc.) nonlinear regression fit modeling variable slope was used to generate a dose-response curve [Y = Bottom + (Top-Bottom)/(1 + 10ˆ((LogIC50-X)*HillSlope)] constrained to top = 100, bottom = 0. The graph shows the average values of two independent experiments.
Fig. 4
3.6 Synergistic action of bafilomycin D with N-0385 against SARS-CoV-2 omicron BA.2 in human Calu-3 lung cells
In order to investigate further the molecular mechanism of action of bafilomycin D as a pan-SARS-CoV-2 antiviral, we investigated its potential synergistic action when used in combination with N-0385, one of the most potent SARS-CoV-2 pan-variant host-directed antivirals discovered to date (Shapira et al., 2022a). N-0385 is a highly potent inhibitor of human TMPRSS2 protease and blocks the TMPRSS2-dependent proteolytical activation of the SARS-CoV-2 spike protein required for viral fusion (Shapira et al., 2022a). Calu-3 cells infected with Omicron BA.2 were either treated with bafilomycin D or N-0385 as a single treatment or in combination. The dose-response percent inhibition matrix of single and combined treatment of bafilomycin D and N-0385 in SARS-CoV-2 infected Calu-3 cells is presented in Fig. 5 A. The inhibitory effect of drug combination was higher than the single treatment (Fig. 5A). To analyze the potential synergistic action of bafilomycin D with N-0385 against SARS-CoV-2 omicron BA.2, we used four different reference models (i.e., Loewe, ZIP, HSA, and Bliss). The 2-D (Fig. 5B) and 3-D (Fig. 5C) interaction landscapes between bafilomycin D and N-0385 calculated based on the Loewe additive model (Fig. 5B and C), the ZIP model (Fig. S7; left panel), the HSA model (Fig. S7; middle panel), and the Bliss model (Fig. S7; right panel) show synergy scores of 26.5, 20.9, 26.4, and 23, respectively. All the calculated synergy scores were above 10, which are interpreted here as a synergistic effect of bafilomycin D with N-0385 against SARS-CoV-2 omicron BA.2 in human Calu-3 lung cells. Our results are consistent with the recent finding by Icho et al. (2022c), which demonstrated a synergistic effect between the repositioned drug Camostat mesylate (TMPRRS2 inhibitor) and bafilomycin A1, a classic V-ATPase inhibitor against a pre-Omicron SARS-CoV-2 variant (Indari et al., 2021).Fig. 5 Synergistic inhibition of SARS-CoV-2 Omicron (BA.2) infection by combined use of bafilomycin D and N-0385. Dose-response curves of inhibition of single and combined treatment of bafilomycin D and N-0385 in SARS-CoV-2 infected Calu-3 cells were used for the analysis using the open-source web application SynergyFinder. A) Dose-response matrix (percentage inhibition) of SARS-CoV-2 infection with combined treatment of bafilomycin D and N-0385. B) Synergy distribution of pairwise combination of bafilomycin D and N-0385 calculated based on Loewe additive model using SynergyFinder. C) Landscape visualization between bafilomycin D and N-0385 calculated based on Loewe additive model using SynergyFinder. Surface is color-coded, red indicates synergistic interactions and green indicates antagonistic interactions.
Fig. 5
4 Discussion
The emergence of SARS-CoV-2 has led to an ongoing and evolving global pandemic with serious health, social, and economic consequences. Despite continuing worldwide vaccination campaigns, the recurring appearance of new variants of concern is creating a tremendous need for safe and effective therapeutics and prophylactics against SARS-CoV-2 infection. Importantly, reports on newly emerged SARS-CoV-2 Omicron BA.5 subvariants suggest that current antibody-mediated protection offered by vaccines and monoclonal antibodies is severely reduced compared to protection against SARS-CoV-2 Delta (Callaway, 2021a; Planas et al., 2021). These findings were recently confirmed and resulted in strong recommendations by WHO against the use of two previously approved monoclonal antibodies (sotrovimab and casirivimab-imdevimab) for patients with COVID-19 (“A living WHO guideline on drugs for covid-19,” 2022; Takashita et al., 2022).
So far, the approved direct-acting antivirals (DAAs) (e.g., remdesivir and paxlovid/nirmatrelvir) are proving effective at preventing hospitalization and mortality due to COVID-19 when given early. However, SARS-CoV-2-approved DAAs are all used as monotherapies for which SARS-CoV-2 may develop resistance (Gandhi et al., 2022). For example, several mutations in SARS-CoV-2 viruses sequenced in the population have been identified that may confer resistance to nirmatrelvir, and rebound infection following treatment has been documented (Hu et al., 2022; Jochmans et al., 2022; Ranganath et al., 2022). Moreover, recent findings by Rockett et al. based on genomic surveillance have also revealed co-infection with SARS-CoV-2 Omicron and Delta variants, underlining the need for pan-SARS-CoV-2 antiviral drugs (Rockett et al., 2022). These results underline the need for novel broad-spectrum pan-SARS-CoV-2 therapeutics for developing combination therapy approaches to strengthen treatment capacity against evolving VOCs (Li et al., 2022; Schultz et al., 2022; Shyr et al., 2021).
To address the need for novel therapeutic options for SARS-CoV-2 VOC infection, we applied cell-based high-content screening to a library of NPs with a considerable diversity of chemical scaffolds to identify lead NPs with antiviral activity against fluorescent-tagged mNG-SARS-CoV-2 in human Calu-3 lung cells. From a diverse library of 373 NPs, 73 compounds were identified with >50% inhibition of mNG-SARS-CoV-2 with less than 20% of cell loss, which may be further investigated in future studies (Table S1). We further validated 26 compounds with EC50 values below 50 μM. Sixteen compounds had EC50 values < 10 μM and SI indices >10. Holyrine A, alotaketal C, and bafilomycin D were highly potent with EC50 values in the nanomolar range, and these were subsequently demonstrated to be potent lead antivirals against ancestral SARS-CoV-2, Delta, and Omicron BA.1, BA.2 and BA.5.
Natural products are considered a rich resource for novel antiviral drug development. NPs have the advantage of more favorable toxicological profiles, fewer side effects, and a faster approval process in comparison to chemically engineered drugs (Auth et al., 2021; Wang and Yang, 2020). Along with plant-derived compounds, like Nigella sativa with its inhibitory activity against the hepatitis C virus, several marine products have also been reported to have antiviral activity against different viruses (Barakat et al., 2013; Wang et al., 2014b Wang et al., 2014, 2016). This includes NPs that have demonstrated efficacy against coronaviruses, such as SARS-CoV-1 and MERS-CoV (Ashhurst et al., 2021; Mani et al., 2020). NPs have been determined to inhibit viruses through several mechanisms including inhibiting viral entry and/or viral DNA and RNA synthesis, but they can also modulate cellular functions required by different viruses, offering broad-spectrum antiviral activity (Moghadamtousi et al., 2015; Musarra-Pizzo et al., 2021). Previously, some of the NPs in our library (terretonin A, terrotonin B, mocellin B, and phellopterin) were described as presenting anti-inflammatory (Wu et al., 2019), anti-malarial (Chaipukdee et al., 2016; Khumkomkhet et al., 2009), or anti-cancer activities (Bao et al., 2018).
Three antiviral NPs identified in our screening (holyrine A, alotaketal C, and bafilomycin D) showed nanomolar EC50 values of 282 nM, 106 nM, and 38 nM, respectively, against mNG-SARS-CoV-2 (Fig. 2B).
We demonstrated the pan-SARS-CoV-2 activity of these three lead antivirals against SARS-CoV-2 highly transmissible Omicron subvariants (BA.5, BA.2 and BA.1) and highly pathogenic Delta VOCs in human Calu-3 lung cells (Fig. 3). Notably, holyrine A, alotaketal C, and bafilomycin D, are potent nanomolar inhibitors of SARS-CoV-2 Omicron subvariants BA.5 and BA.2 (Fig. 3H-M; Table 2 ). Interestingly, in contrast to bafilomycin D, which presented nanomolar antiviral activity for all SARS-CoV-2 variants tested, we observed discrepancies for the EC50 values determined across the variants for holyrine A and alotaketal C (Table 2). For example, holyrine A is a more potent antiviral against the BA.2 sublineages (Fig. 3H and K; Table 2) than against SARS-CoV-2 BA.1 and Delta (Fig. 3E and B; Table 2). Alotaketal C is a more potent antiviral against highly transmissible Omicron subvariants (BA1, BA2, and BA5) (Fig. 3F, I, 3L; Table 2) than against the highly pathogenic Delta VOC (Fig. 3C; Table 2). Nevertheless, our results confirm the pan-SARS-CoV-2 antiviral activity of holyrine A, alotaketal C, and bafilomycin D against SARS-CoV-2 variants in lung epithelial cells.Table 2 SARS-CoV- 2 VOCs inhibition (EC50) for the three lead NPs in Calu-3 cells.
Table 2Name Delta (EC50) BA.1 (EC50) BA.2 (EC50) BA.5 (EC50)
Nuca dsRNA Nuca dsRNA Nuca dsRNA Nuca dsRNA
holyrine A 600 nM 1.0 μM 1.7 μM 1.6 μM 44 nM 394 nM 80 nM 120 nM
alotaketal C 4.9 μM 3.9 μM 700 nM 400 nM 700 nM 700 nM 300 nM 700 nM
bafilomycin D 2.0 nM 50 nM 13.4 nM 67.3 nM 4.6 nM 22 nM 43.9 nM 38.6 nM
a Nuc = Nucleocapsid.
Holyrine A comes from the indolocarbazole class of alkaloids isolated from a marine actinomycete collected in Canada, and it is closely related to holyrine B, which differs from holyrine A by the addition of an oxygen atom (Williams et al., 1999). Holyrine B was recently described as a potential SARS-CoV-2 3CLpro inhibitor based on virtual screening using docking and molecular simulation (Sayed et al., 2020). However, although holyrine A acted as a pan-SARS-CoV-2 antiviral in our study, neither holyrine A nor B presented activity against 3CLpro in the enzymatic assay (data not shown). These results suggest an alternative mode of action for holyrine A in inhibiting SARS-CoV-2 infection.
Alotaketal C is a sesterterpenoid isolated from the marine sponge Phorbas sp. Collected in Canada (Daoust et al., 2013). We previously reported alotaketal C as an activator of protein kinase C (PKC) that is more potent than the well-studied PKC activator prostratin (Wang et al., 2016, 2022). Our preliminary results of the three natural products acting as PKC activators (prostratin, bryostatin and PEP 005) also demonstrated inhibition of SARS-CoV-2 BA.1 (Fig. 4A–C and S6), consistent with PKC activation inhibiting SARS-CoV-2 infection. We also observed a striking reverse phenomenon using the PKC inhibitors Gö6983 (Gschwendt et al., 1996) and Gö6976 where a dramatic increase in susceptibility of cells to SARS-CoV-2 BA.1 was observed following Gö6983 or Gö6976 treatment (Fig. 4D–E and S6). However, the treatment with an atypical PKC inhibitor showed inhibition of SARS-CoV-2 BA.1 (Fig. 4F and S6). These results strongly suggest that different isoforms of PKC can play a role in the regulation of cellular susceptibility to SARS-CoV-2 infection.
Because many polymorphisms in various PKC genes have been identified and because drugs targeting PKC are under investigation in clinical trials for cancer and other diseases (Callender et al., 2018; Kawano et al., 2021; Ma et al., 2010), future research should explore whether PKC regulation impacts human susceptibility and the severity of SARS-CoV-2 infection. A recent study reported that PKC inhibitors block the replication of ancestral SARS-CoV-2 on A459/ACE2 expressing cells (Huang et al., 2022). The biological effects of PKC are wide-ranging, and even though most PKC isoenzymes are ubiquitous and many cells can co-express different PKC isoforms, some of them are expressed in a tissue-specific manner (Blázquez and Saiz, 2021). We hypothesize that altered susceptibility may be due to these facts as noted in published findings: PKC inhibition by multiple drugs reduced the shedding of ACE2 from the surface of cells (Xiao et al., 2016); ACE2 is the key receptor required for SARS-CoV-2 entry (Hoffmann et al., 2020a); and increased shedding should reduce cell surface ACE2 abundance. Future studies should investigate whether PKC activation by alotaketal C increases ACE2 shedding from human respiratory epithelial cells.
Bafilomycin D is a member of the macrolide antibiotic family, which has been isolated from a Streptomyces sp. obtained from marine sediments collected in Canada (Carr et al., 2010; Kretschmer et al., 1985). bafilomycin A1, B1, and D are inhibitors of vacuolar-type H+ (V)- ATPases (Bowman et al., 1988). Studies have shown that bafilomycin A1 can inhibit both Delta and Omicron variants, independent of TMPRSS2 expression (Indari et al., 2021; Zhao et al., 2022). Interestingly, the use of CRISPR knockout screens has shown that the interference with V-ATPase expression results in the inhibition of SARS-CoV-2 infection (Daniloski et al., 2021; Zhu et al., 2021). Our results confirm the important biological roles of human V-ATPase in the SARS-CoV-2 lifecycle (Indari et al., 2021) and demonstrate that bafilomycin D is a very potent nanomolar pan-SARS-CoV-2 lead antiviral with a high SI value (SI = 1064: Table 1).
In order to investigate further the molecular mechanism of action of bafilomycin D as a pan-SARS-CoV-2 antiviral, we also investigated its potential synergistic action when used in combination with N-0385, a highly potent pan-variant host-directed antiviral (Shapira et al., 2022a). We demonstrated a synergistic action of bafilomycin D (a human V-ATPase inhibitor) and N-0385 (a highly potent inhibitor of human TMPRSS2 protease) against SARS-CoV-2 Omicron BA.2. These results suggest that the two highly potent host-directed antivirals, bafilomycin D and N-0385, target two distinct mechanisms of viral entry that depend on distinct host factors, human V-ATPase and human TMPRRS2 (Indari et al., 2021; Kreutzberger et al., 2021). Further studies are needed to confirm the detailed mechanisms of action of bafilomycin D as a pan-SARS-CoV-2 antiviral agent. Importantly, our results demonstrate that the combination of bafilomycin D and N-0385 provides an extremely valuable inspirational starting point for developing urgently needed SARS-CoV-2 multidrug regimen for circulating SARS-CoV-2 Omicron subvariants.
Together, our findings underline the importance of natural product studies not only for identifying potential treatment strategies based on host-directed antivirals but also for unravelling new findings about the biology and pathogenesis of human emerging viruses.
5 Conclusion
In summary, we applied a cell-based fluorescent-screening assay to identify promising anti-SARS-CoV-2 compounds from a diverse NP library. Three lead compounds (holyrine A, alotaketal C, and bafilomycin D) demonstrated nanomolar antiviral potency against mNG-SARS-CoV-2, and we confirmed they had low micromolar to nanomolar pan-SARS-CoV-2 antiviral activity against four VOCs (Delta, Omicron BA.1, BA.2 and BA.5). Further studies may be required to determine the broad-spectrum antiviral activity of the additional 47 NPs not investigated here that demonstrated >50% inhibition at 50 μM using the mNG-SARS-CoV-2, with <20% cell loss. Due to the unique biology of SARS-CoV-2 VOCs in terms of their transmission and pathogenesis, a number of these NPs may present additional promising leads.
Overall, our study provides insight into the potential of NPs with highly diverse chemical structures as valuable inspirational starting points for developing pan-SARS-CoV-2 therapeutics. These therapeutics could be used as part of multidrug regimens to counteract the increasing antiviral drug resistance to the currently limited repertoire of monotherapies against SARS-CoV-2 infections (Li et al., 2022; Schultz et al., 2022; Szemiel et al., 2021). In addition, our antiviral NPs provide new patent-free academia-originated leads for further development as alternatives to patent-protected pharmaceuticals.
Funding acquisition
Callender et al., 2018 Novel Coronavirus (COVID-19) Rapid Research Funding program of the 10.13039/501100000024 Canadian Institutes of Health Research (CIHR) [OV3-170342 (FJ, RJA, PYS, IRN, AC, and NS)]; Callender et al., 2018 Novel Coronavirus (COVID-19) Rapid Research Funding program of the 10.13039/501100000024 Canadian Institutes of Health Research (CIHR) [UBR 322812; VR3-172639 (RL, PLB, and FJ)]; 10.13039/501100000233 Genome British Columbia /COVID-19 Rapid Response Funding Initiative [COV011 (FJ and RJA)]; NSERC Alliance COVID-19 Grant (AWD-015086 NSERC NSERC, 2020; IRN and FJ); Coronavirus Variants Rapid Response Network (CoVaRR-Net) grant (#175622 (FJ, ADO, NS, and IRN); and MITACS Inc. Accelerate Fellowship COVID-19 Award [IT18585 (TS, FJ)]. PYS was supported by 10.13039/100000002 NIH grants HHSN272201600013C, U01AI151801, and U19AI171413, and awards from the Sealy & Smith Foundation Foundation, the 10.13039/100012394 Kleberg Foundation , the John S. Dunn Foundation, the Amon G. Carter Foundation, the Gilson Longenbaugh Foundation, and the Summerfield Robert Foundation. RGSB is supported by 10.13039/501100001807 FAPESP grant 2019/17721–9.
Supervision
The research was performed under the supervision of FJ.
Author contributions
FJ and RJA conceptualized the initiation of the study. Designed research: FJ, JPV, TS, AO, RJA. Performed research: TS, JPV, IV, CAHT, GG, SE, JDG, AC, DBS. Analyzed data: JPV, AO, FJ. Contributed new reagents or analytical tools: MN, OA, MK, PL, PYS, XX, AF, EM, GN, VFF, JIQB, DIB, JRG, RL, PLB, RGSB, HLT, SL, VS, AR, PM, IP, SC, WJ, SK, KK, CY, BC, DEW, MW, IT, RJA, NS. Confocal labelling, image acquisition and analysis: GG, CAHT, IRN, FJ. JPV and FJ wrote and revised the manuscript with input from all authors. The review of the final manuscript was performed by all authors.
Uncited references
Badawi and Ali, 2021; Hoffmann et al., 2020b; Wang et al., 2008.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Appendix A Supplementary data
The following is the Supplementary data to this article:Multimedia component 1
Multimedia component 1
Data availability
Data will be made available on request.
Acknowledgments
The authors acknowledge the support of the CL3 facility (Facility for Infectious Disease and Epidemic Research (FINDER) of the 10.13039/100009539 Life Sciences Institute of the 10.13039/501100005247 University of British Columbia founded by Dr. François Jean and its biosafety support staff including Dr. Bintou Ahidjo (Research Platform Manager) and T. Dean Airey (FINDER Senior Research Technician). Imaging was performed in the 10.13039/100009539 LSI Imaging Core Facility of the 10.13039/100009539 Life Sciences Institute at the 10.13039/501100005247 University of British Columbia , supported by the 10.13039/100009539 Life Sciences Institute , the 10.13039/501100005247 UBC GREx Biological Resilience Initiative. The infrastructure within LSI Imaging Core Facility is funded by the Canadian Foundation of Innovation, BC Knowledge Development Fund, Natural Sciences and Engineering Research Council Research Tools and Instruments, and UBC Research Facility Support Grants as well as a Strategic Investment Fund (10.13039/501100010804 Faculty of Medicine , 10.13039/501100005247 UBC ). We further thank Dr. Alex Ball, Jr., MD, Senior Scientist (GeneTex), for supplying the SARS-CoV-2 (COVID-19) nucleocapsid antibody [HL344] (GTX635679). We also acknowledge a generous donation towards the purchase of the CellInsight CX7 HCS system provided by the Vancouver General Hospital Foundation to Dr. Jean. We also thank Dr. Jill Kelly for proofreading the manuscript, Mike LeBlanc for technical assistance, and Dr. Carl Perez for administrative and logistic support during this study.
Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.antiviral.2022.105484.
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References
A living WHO A Living WHO Guideline on Drugs for Covid-19 2022 10.1136/bmj.o1045 BMJ o1045
Ashhurst A.S. Tang A.H. Fajtová P. Yoon M.C. Aggarwal A. Bedding M.J. Stoye A. Beretta L. Pwee D. Drelich A. Skinner D. Li L. Meek T.D. McKerrow J.H. Hook V. Tseng C.-T. Larance M. Turville S. Gerwick W.H. O'Donoghue A.J. Payne R.J. Potent anti-SARS-CoV-2 activity by the natural product gallinamide A and analogues via inhibition of cathepsin L J. Med.Chem.acs. 2021 10.1021/acs.jmedchem.1c01494 1c01494
Auth J. Fröba M. Große M. Rauch P. Ruetalo N. Schindler M. Morokutti-kurz M. Graf P. Dolischka A. Prieschl-grassauer E. Setz C. Schubert U. Lectin from triticum vulgaris (WGA) inhibits infection with SARS-CoV-2 and its variants of concern alpha and beta Int. J. Mol. Sci. 22 2021 10.3390/ijms221910205
Badawi S. Ali B.R. ACE2 Nascence, trafficking, and SARS-CoV-2 pathogenesis: the saga continues Hum. Genom. 15 2021 8 10.1186/s40246-021-00304-9
Bao R. Wang S. Yang X. Chen G. Meng X. Phellopterin-induced caspase-dependent apoptosis through PI3K/AKT pathway inhibition in SMMC-7721 human hepatoma cells Am. J. Pharm. 37 2018 2498 2501
Barakat E.M.F. el Wakeel L.M. Hagag R.S. Effects of Nigella sativa on outcome of hepatitis C in Egypt World J. Gastroenterol. 19 2013 2529 2536 10.3748/wjg.v19.i16.2529 23674855
Blázquez A.B. Saiz J.C. Protein kinase C as a target in the control of viruses and implication for Zika virus Zika Virus Impact, Diagnosis, Control, and Models 2021 Elsevier 409 415 10.1016/B978-0-12-820267-8.00039-X
Bowman E.J. Siebers A. Altendorf K. Bafilomycins: a class of inhibitors of membrane ATPases from microorganisms, animal cells, and plant cells Proc. Natl. Acad. Sci. USA 85 1988 7972 7976 10.1073/pnas.85.21.7972 2973058
Cagno V. SARS-CoV-2 cellular tropism Lancet Microbe May 2020 e2 e3 10.1016/S2666-5247(20)30008-2
Callaway E. Beyond Omicron: what's next for COVID's viral evolution Nature 600 2021 204 207 10.1038/D41586-021-03619-8 34876665
Callaway E. Heavily mutated coronavirus variant puts scientists on alert Nature 2021 10.1038/D41586-021-03552-W
Callender J.A. Yang Y. Lordén G. Stephenson N.L. Jones A.C. Brognard J. Newton A.C. Protein kinase Cα gain-of-function variant in Alzheimer's disease displays enhanced catalysis by a mechanism that evades down-regulation Proc. Natl. Acad. Sci. U. S. A. 115 2018 E5497 E5505 10.1073/PNAS.1805046115/ASSET/CA0D7F9E-61C8-4A0E-9F3C-072EC6F9278E/ASSETS/GRAPHIC/PNAS.1805046115FIG07.JPEG 29844158
Carr G. Williams D.E. Díaz-Marrero A.R. Patrick B.O. Bottriell H. Balgi A.D. Donohue E. Roberge M. Andersen R.J. Bafilomycins produced in culture by Streptomyces spp. isolated from marine habitats are potent inhibitors of autophagy J. Nat. Prod. 73 2010 422 427 10.1021/np900632r 20028134
Chaipukdee N. Kanokmedhakul K. Kanokmedhakul S. Lekphrom R. Pyne S.G. Two new bioactive iridoids from Rothmannia wittii Fitoterapia 113 2016 97 101 10.1016/J.FITOTE.2016.07.007 27431771
Chu H. Chan J.F.-W. Yuen T.T.-T. Shuai H. Yuan S. Wang Y. Hu B. Yip C.C.-Y. Tsang J.O.-L. Huang X. Chai Y. Yang D. Hou Y. Chik K.K.-H. Zhang X. Fung A.Y.-F. Tsoi H.-W. Cai J.-P. Chan W.-M. Ip J.D. Chu A.W.-H. Zhou J. Lung D.C. Kok K.-H. To K.K.-W. Tsang O.T.-Y. Chan K.-H. Yuen K.-Y. Comparative tropism, replication kinetics, and cell damage profiling of SARS-CoV-2 and SARS-CoV with implications for clinical manifestations, transmissibility, and laboratory studies of COVID-19: an observational study Lancet Microbe 1 2020 e14 e23 10.1016/s2666-5247(20)30004-5 32835326
COVID-19 COVID-19 dashboard by the center for systems science and engineering (CSSE) at Johns Hopkins university [WWW document] n.d. URL https://coronavirus.jhu.edu/map.html 2.8.21
Dampalla C.S. Zheng J. Dinali Perera K. Roy Wong L.-Y. Meyerholz D.K. Nhat Nguyen H. Kashipathy M.M. Battaile K.P. Lovell S. Kim Y. Perlman S. Groutas W.C. Chang K.-O. Postinfection Treatment with a Protease Inhibitor Increases Survival of Mice with a Fatal SARS-CoV-2 Infection 2021 Proc Natl Acad Sci U S A 10.1073/pnas.2101555118
Daniloski Z. Jordan T.X. Wessels H.-H. Hoagland D.A. Kasela S. Legut M. Maniatis S. Mimitou E.P. Lu L. Geller E. Danziger O. Rosenberg B.R. Phatnani H. Smibert P. Lappalainen T. tenOever B.R. Sanjana N.E. Identification of required host factors for SARS-CoV-2 infection in human cells Cell 184 2021 92 105 10.1016/j.cell.2020.10.030 e16 33147445
Daoust J. Chen M. Wang M. Williams D.E. Chavez M.A.G. Wang Y.A. Merchant C.E. Fontana A. Kieffer T.J. Andersen R.J. Sesterterpenoids isolated from a northeastern pacific phorbas sp J. Org. Chem. 78 2013 8267 8273 10.1021/JO4014589/SUPPL_FILE/JO4014589_SI_001.PDF 23941043
Dittmar M. Lee J.S. Whig K. Segrist E. Li M. Kamalia B. Castellana L. Ayyanathan K. Cardenas-Diaz F.L. Morrisey E.E. Truitt R. Yang W. Jurado K. Samby K. Ramage H. Schultz D.C. Cherry S. Drug repurposing screens reveal cell-type-specific entry pathways and FDA-approved drugs active against SARS-Cov-2 Cell Rep. 35 2021 108959 10.1016/j.celrep.2021.108959
Fu L. Ye F. Feng Y. Yu F. Wang Q. Wu Y. Zhao C. Sun H. Huang B. Niu P. Song H. Shi Y. Li X. Tan W. Qi J. Gao G.F. Both Boceprevir and GC376 efficaciously inhibit SARS-CoV-2 by targeting its main protease Nat. Commun. 11 1 2020 1 8 10.1038/s41467-020-18233-x 2020 11 31911652
Gandhi S. Klein J. Robertson A.J. Peña-Hernández M.A. Lin M.J. Roychoudhury P. Lu P. Fournier J. Ferguson D. Mohamed Bakhash S.A.K. Catherine Muenker M. Srivathsan A. Wunder E.A. Kerantzas N. Wang W. Lindenbach B. Pyle A. Wilen C.B. Ogbuagu O. Greninger A.L. Iwasaki A. Schulz W.L. Ko A.I. De novo emergence of a remdesivir resistance mutation during treatment of persistent SARS-CoV-2 infection in an immunocompromised patient: a case report Nat. Commun. 13 1 2022 1 8 10.1038/s41467-022-29104-y 2022 13 34983933
Gschwendt M. Dieterich S. Rennecke J. Kittstein W. Mueller H.J. Johannes F.J. Inhibition of protein kinase C μ by various inhibitors. Inhibition from protein kinase c isoenzymes FEBS Lett. 392 1996 77 80 10.1016/0014-5793(96)00785-5 8772178
Hacisuleyman E. Hale C. Saito Y. Blachere N.E. Bergh M. Conlon E.G. Schaefer-Babajew D.J. DaSilva J. Muecksch F. Gaebler C. Lifton R. Nussenzweig M.C. Hatziioannou T. Bieniasz P.D. Darnell R.B. Vaccine breakthrough infections with SARS-CoV-2 variants N. Engl.J.Med.NEJMoa 2021 2105000 10.1056/NEJMoa2105000
Hagen A. n.d. How dangerous is the delta variant (B.1.617.2)? [WWW document] URL https://asm.org/Articles/2021/July/How-Dangerous-is-the-Delta-Variant-B-1-617-2 8.29.21
Hampson P. Chahal H. Khanim F. Hayden R. Mulder A. Assi L.K. Bunce C.M. Lord J.M. PEP005, a selective small-molecule activator of protein kinase C, has potent antileukemic activity mediated via the delta isoform of PKC Blood 106 2005 1362 1368 10.1182/BLOOD-2004-10-4117 15845901
Hoffmann Markus Kleine-Weber H. Pöhlmann S. A multibasic cleavage site in the spike protein of SARS-CoV-2 is essential for infection of human lung cells Mol. Cell. 78 2020 779 784 10.1016/j.molcel.2020.04.022 e5 32362314
Hoffmann M. Kleine-Weber H. Schroeder S. Kruger N. Herrler T. Erichsen S. Schiergens T.S. Herrler G. Wu N.H. Nitsche A. Muller M.A. Drosten C. Pohlmann S. SARS-CoV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor Cell 181 2020 271 280 10.1016/j.cell.2020.02.052 e8 32142651
Hu Y. Lewandowski E.M. Tan H. Morgan R.T. Zhang X. Jacobs L.M.C. Butler S.G. Mongora M.v. Choy J. Chen Y. Wang J. Naturally occurring mutations of SARS-CoV-2 main protease confer drug resistance to nirmatrelvir bioRxiv 2022 497978 10.1101/2022.06.28.497978 2022.06.28
Huang C. Feng F. Shi Y. Li W. Wang Z. Zhu Y. Yuan S. Hu D. Dai J. Jiang Q. Zhang R. Liu C. Zhang P. Protein kinase C inhibitors reduce SARS-CoV-2 replication in cultured cells Microbiol. Spectr. 2022 10.1128/SPECTRUM.01056-22
Icho S. Rujas E. Muthuraman K. Tam J. Liang H. Landreth S. Liao M. Falzarano D. Julien J.-P. Melnyk R.A. Dual inhibition of vacuolar-ATPase and TMPRSS2 is required for complete blockade of SARS-CoV-2 entry into cells Antimicrob. Agents Chemother. 66 2022 10.1128/aac.00439-22
Indari O. Jakhmola S. Manivannan E. Jha H.C. An update on antiviral therapy against SARS-CoV-2: how far have we come? Front. Pharmacol. 2021 133 10.3389/FPHAR.2021.632677 0
Jochmans D. Liu C. Donckers K. Stoycheva A. Boland S. Stevens S.K. de Vita C. Vanmechelen B. Maes P. Trüeb B. Ebert N. Thiel V. de Jonghe S. Vangeel L. Bardiot D. Jekle A. Blatt L.M. Beigelman L. Symons J.A. Raboisson P. Chaltin P. Marchand A. Neyts J. Deval J. Vandyck K. The Substitutions L50F, E166A and L167F in SARS-CoV-2 3CLpro Are Selected by a Protease Inhibitor in Vitro and Confer Resistance to Nirmatrelvir 2022 bioRxiv 10.1101/2022.06.07.495116 2022.06.07.495116
Kawano T. Inokuchi J. Eto M. Murata M. Kang J.H. Activators and inhibitors of protein kinase C (PKC): their applications in clinical trials Pharmaceutics 13 2021 10.3390/PHARMACEUTICS13111748
Kedei N. Lundberg D.J. Toth A. Welburn P. Garfield S.H. Blumberg P.M. Characterization of the interaction of ingenol 3-angelate with protein kinase C Cancer Res. 64 2004 3243 3255 10.1158/0008-5472.CAN-03-3403 15126366
Khumkomkhet P. Kanokmedhakul S. Kanokmedhakul K. Hahnvajanawong C. Soytong K. Antimalarial and cytotoxic depsidones from the fungus Chaetomium brasiliense J. Nat. Prod. 72 2009 1487 1491 10.1021/np9003189 19663417
Kjær S. Linch M. Purkiss A. Kostelecky B. Knowles P.P. Rosse C. Riou P. Soudy C. Kaye S. Patel B. Soriano E. Murray-Rust J. Barton C. Dillon C. Roffey J. Parker P.J. McDonald N.Q. Adenosine-binding motif mimicry and cellular effects of a thieno[2,3- d ]pyrimidine-based chemical inhibitor of atypical protein kinase C isoenzymes Biochem. J. 451 2013 329 342 10.1042/BJ20121871 23418854
Koivunen J. Aaltonen V. Koskela S. Lehenkari P. Laato M. Peltonen J. Protein kinase C α/β inhibitor Go6976 promotes formation of cell junctions and inhibits invasion of urinary bladder carcinoma cells Cancer Res. 64 2004 5693 5701 10.1158/0008-5472.CAN-03-3511 15313909
Kretschmer A. Dorgerloh M. Deeg M. Hagenmaier H. The structures of novel insecticidal macrolides: bafilomycins D and E, and oxohygrolidin Agric. Biol. Chem. 49 1985 2509 2511 10.1271/bbb1961.49.2509
Kreutzberger A.J.B. Sanyal A. Ojha R. Pyle J.D. Vapalahti O. Balistreri G. Kirchhausen T. Synergistic block of SARS-CoV-2 infection by combined drug inhibition of the host entry factors PIKfyve kinase and TMPRSS2 protease J. Virol. 95 2021 10.1128/JVI.00975-21
Li M. Lou F. Fan H. SARS-CoV-2 Variants of Concern Delta: a great challenge to prevention and control of COVID-19 Signal Transduct. Targeted Ther. 6 1 6 2021 1 3 10.1038/s41392-021-00767-1 2021
Li P. Wang Y. Lavrijsen M. Lamers M.M. de Vries A.C. Rottier R.J. Bruno M.J. Peppelenbosch M.P. Haagmans B.L. Pan Q. SARS-CoV-2 Omicron variant is highly sensitive to molnupiravir, nirmatrelvir, and the combination Cell Res. 32 3 32 2022 322 324 10.1038/s41422-022-00618-w 2022 35058606
Linch M. Sanz-Garcia M. Rosse C. Riou P. Peel N. Madsen C.D. Sahai E. Downward J. Khwaja A. Dillon C. Roffey J. Cameron A.J.M. Parker P.J. Regulation of polarized morphogenesis by protein kinase C iota in oncogenic epithelial spheroids Carcinogenesis 35 2014 396 406 10.1093/carcin/bgt313 24072773
Ma R.C.W. Tam C.H.T. Wang Y. Luk A.O. Hu C. Yang X. Lam V. Chan A.W.H. Ho J.S.K. Chow C.C. Tong P.C.Y. Jia W. Ng M.C.Y. So W.Y. Chan J.C.N. Genetic variants of the protein kinase C-beta 1 gene and development of end-stage renal disease in patients with type 2 diabetes JAMA 304 2010 881 889 10.1001/JAMA.2010.1191 20736472
Mani J.S. Johnson J.B. Steel J.C. Broszczak D.A. Neilsen P.M. Walsh K.B. Naiker M. Natural Product-Derived Phytochemicals as Potential Agents against Coronaviruses: A Review 2020 10.1016/j.virusres.2020.197989
McKee M. Stuckler D. If the world fails to protect the economy, COVID-19 will damage health not just now but also in the future Nat. Med. 2020 10.1038/s41591-020-0863-y
Meganck R.M. Baric R.S. Developing therapeutic approaches for twenty-first-century emerging infectious viral diseases Nat. Med. 27 2021 401 410 10.1038/s41591-021-01282-0 33723456
Moghadamtousi S.Z. Nikzad S. Kadir H.A. Abubakar S. Zandi K. Potential antiviral agents from marine fungi: an overview Mar. Drugs 13 2015 4520 4538 10.3390/MD13074520 26204947
Moradpour D. Evans M.J. Gosert R. Yuan Z. Blum H.E. Goff S.P. Lindenbach B.D. Rice C.M. Insertion of green fluorescent protein into nonstructural protein 5A allows direct visualization of functional hepatitis C virus replication complexes J. Virol. 78 2004 7400 7409 10.1128/JVI.78.14.7400-7409.2004/FORMAT/EPUB 15220413
Muller P.Y. Milton M.N. The determination and interpretation of the therapeutic index in drug development Nat. Rev. Drug Discov. 11 10 2012 751 761 10.1038/nrd3801 2012 11 22935759
Musarra-Pizzo M. Pennisi R. Ben-Amor I. Mandalari G. Sciortino M.T. Antiviral activity exerted by natural products against human viruses Viruses 13 2021 10.3390/V13050828
Ogando N.S. Dalebout T.J. Zevenhoven-Dobbe J.C. Limpens R.W.A.L. van der Meer Y. Caly L. Druce J. de Vries J.J.C. Kikkert M. Barcena M. Sidorov I. Snijder E.J. SARS-coronavirus-2 replication in Vero E6 cells: replication kinetics, rapid adaptation and cytopathology J. Gen. Virol. 101 2020 925 10.1099/JGV.0.001453 32568027
Olmstead A.D. Knecht W. Lazarov I. Dixit S.B. Jean F. Human subtilase SKI-1/S1P is a master regulator of the HCV lifecycle and a potential host cell target for developing indirect-acting antiviral agents PLoS Pathog. 8 2012 e1002468 10.1371/journal.ppat.1002468
Planas D. Saunders N. Maes P. Guivel-Benhassine F. Planchais C. Buchrieser J. Bolland W.-H. Porrot F. Staropoli I. Lemoine F. Péré H. Veyer D. Puech J. Rodary J. Baele G. Dellicour S. Raymenants J. Gorissen S. Geenen C. Vanmechelen B. Wawina -Bokalanga T. Martí-Carreras J. Cuypers L. Sève A. Hocqueloux L. Prazuck T. Rey F. Simon-Loriere E. Bruel T. Mouquet H. André E. Schwartz O. Considerable escape of SARS-CoV-2 Omicron to antibody neutralization Nature 2021 1 7 10.1038/s41586-021-04389-z 2021
Ranganath N. O'Horo J.C. Challener D.W. Tulledge-Scheitel S.M. Pike M.L. Michael O'Brien R. Razonable R.R. Shah A. Rebound phenomenon after nirmatrelvir/ritonavir treatment of coronavirus disease-2019 in high-risk persons Clin. Infect. Dis. 2022 10.1093/CID/CIAC481
Rockett R.J. Draper J. Gall M. Sim E.M. Arnott A. Agius J.E. Johnson-Mackinnon J. Fong W. Martinez E. Drew A.P. Lee C. Ngo C. Ramsperger M. Ginn A.N. Wang Q. Fennell M. Ko D. Hueston L. Kairaitis L. Holmes E.C. O'Sullivan M.N. Chen S.C.-A. Kok J. Dwyer D.E. Sintchenko V. Co-infection with SARS-CoV-2 Omicron and Delta variants revealed by genomic surveillance Nat. Commun. 13 1 2022 1 7 10.1038/s41467-022-30518-x 2022 13 34983933
SARS SARS-CoV-2 variants of concern | CDC [WWW document] n.d. URL https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/variant-surveillance/variant-info.html 2.8.21
Sayed A.M. Alhadrami H.A. El-Gendy A.O. Shamikh Y.I. Belbahri L. Hassan H.M. Abdelmohsen U.R. Rateb M.E. Microbial natural products as potential inhibitors of SARS-CoV-2 main protease (Mpro) Microorganisms 8 2020 1 17 10.3390/microorganisms8070970
Schultz D.C. Johnson R.M. Ayyanathan K. Miller J. Whig K. Kamalia B. Dittmar M. Weston S. Hammond H.L. Dillen C. Ardanuy J. Taylor L. Lee J.S. Li M. Lee E. Shoffler C. Petucci C. Constant S. Ferrer M. Thaiss C.A. Frieman M.B. Cherry S. Pyrimidine inhibitors synergize with nucleoside analogues to block SARS-CoV-2 Nature 604 7904 2022 134 140 10.1038/s41586-022-04482-x 2022 604 35130559
Shagufta Ahmad I. The race to treat COVID-19: potential therapeutic agents for the prevention and treatment of SARS-CoV-2 Eur. J. Med. Chem. 213 2021 113157 10.1016/j.ejmech.2021.113157
Shapira T. Monreal I.A. Dion S.P. Buchholz D.W. Imbiakha B. Olmstead A.D. Jager M. Désilets A. Gao G. Martins M. Vandal T. Thompson C.A.H. Chin A. Rees W.D. Steiner T. Nabi I.R. Marsault E. Sahler J. Diel D.G. van de Walle G.R. August A. Whittaker G.R. Boudreault P.-L. Leduc R. Aguilar H.C. Jean F. A TMPRSS2 inhibitor acts as a pan-SARS-CoV-2 prophylactic and therapeutic Nature 2022 10.1038/s41586-022-04661-w
Shyr Z.A. Cheng Y.S. Lo D.C. Zheng W. Drug combination therapy for emerging viral diseases Drug Discov. Today 26 2021 2367 10.1016/J.DRUDIS.2021.05.008 34023496
Sievers B.L. Chakraborty S. Xue Y. Gelbart T. Gonzalez J.C. Cassidy A.G. Golan Y. Prahl M. Gaw S.L. Arunachalam P.S. Blish C.A. Boyd S.D. Davis M.M. Jagannathan P. Nadeau K.C. Pulendran B. Singh U. Scheuermann R.H. Frieman M.B. Vashee S. Wang T.T. Tan G.S. Antibodies elicited by SARS-CoV-2 infection or mRNA vaccines have reduced neutralizing activity against Beta and Omicron pseudoviruses Sci. Transl. Med. 2022 10.1126/SCITRANSLMED.ABN7842
Sun M.-K. Alkon D.L. Bryostatin-1: pharmacology and therapeutic potential as a CNS drug CNS Drug Rev. 12 2006 1 8 10.1111/j.1527-3458.2006.00001.x 16834754
Szemiel A.M. Merits A. Orton R.J. MacLean O.A. Pinto R.M. Wickenhagen A. Lieber G. Turnbull M.L. Wang S. Furnon W. Suarez N.M. Mair D. da Silva Filipe A. Willett B.J. Wilson S.J. Patel A.H. Thomson E.C. Palmarini M. Kohl A. Stewart M.E. In vitro selection of Remdesivir resistance suggests evolutionary predictability of SARS-CoV-2 PLoS Pathog. 17 2021 e1009929 10.1371/JOURNAL.PPAT.1009929
Takashita E. Yamayoshi S. Simon V. van Bakel H. Sordillo E.M. Pekosz A. Fukushi S. Suzuki T. Maeda K. Halfmann P. Sakai-Tagawa Y. Ito M. Watanabe S. Imai M. Hasegawa H. Kawaoka Y. Efficacy of antibodies and antiviral drugs against omicron BA.2.12.1, BA.4, and BA.5 subvariants N. Engl. J. Med. 387 2022 468 470 10.1056/NEJMc2207519 35857646
Tang J. Wennerberg K. Aittokallio T. What is synergy? The Saariselkä agreement revisited Front. Pharmacol. 6 2015 10.3389/fphar.2015.00181
Tao S. Zandi K. Bassit L. Ong Y.T. Verma K. Liu P. Downs-Bowen J.A. McBrayer T. LeCher J.C. Kohler J.J. Tedbury P.R. Kim B. Amblard F. Sarafianos S.G. Schinazi R.F. Comparison of anti-SARS-CoV-2 activity and intracellular metabolism of remdesivir and its parent nucleoside Curr. Res.Pharmacol. Drug Discov. 2 2021 100045 10.1016/j.crphar.2021.100045
United States United States - COVID-19 Overview - Johns Hopkins [WWW Document] 2022 URL https://coronavirus.jhu.edu/region/united-states 5.12.22
Wang H. Yang P. Liu K. Guo F. Zhang Y. Zhang G. Jiang C. SARS coronavirus entry into host cells through a novel clathrin- and caveolae-independent endocytic pathway Cell Res. 18 2008 290 301 10.1038/cr.2008.15 18227861
Wang M. Sciorillo A. Read S. Divsalar D.N. Gyampoh K. Zu G. Yuan Z. Mounzer K. Williams D.E. Montaner L.J. de Voogd N. Tietjen I. Andersen R.J. Ansellone J, a potent in vitro and ex vivo HIV-1 latency reversal agent isolated from a phorbas sp. marine sponge J. Nat. Prod. 85 2022 1274 1281 10.1021/ACS.JNATPROD.1C01225 35522580
Wang M. Tietjen I. Chen M. Williams D.E. Daoust J. Brockman M.A. Andersen R.J. Sesterterpenoids isolated from the sponge phorbas sp. activate latent HIV-1 provirus expression J. Org. Chem. 81 2016 11324 11334 10.1021/ACS.JOC.6B02312/ASSET/IMAGES/LARGE/JO-2016-02312B_0005.JPEG 27768313
Wang S.X. Zhang X.S. Guan H.S. Wang W. Potential anti-HPV and related cancer agents from marine resources: an overview Mar. Drugs 12 2014 2019 2035 10.3390/MD12042019 24705500
Wang Z. Yang L. Turning the tide: natural products and natural-product-inspired chemicals as potential counters to SARS-CoV-2 infection Front. Pharmacol. 2020 10.3389/fphar.2020.01013
Williams D.E. Bernan V.S. Ritacco F. v Maiese W.M. Greenstein M. Andersen R.J. Holyrines A and B, possible intermediates in staurosporine biosynthesis produced in culture by a marine actinomycete obtained from the North Atlantic Ocean Tetrahedron Lett. 40 1999 7171 7174
Wu C.-J. Cui X. Xiong B. Yang M.-S. Zhang Y.-X. Liu X.-M. Terretonin D1, a new meroterpenoid from marine-derived Aspergillusï¿¿terreus ML-44 Nat. Prod. Res. 33 2019 2262 2265 10.1080/14786419.2018.1493583 30394102
Xiao F. Zimpelmann J. Burger D. Kennedy C. Hébert R.L. Burns K.D. Protein kinase C-δ mediates shedding of angiotensin-converting enzyme 2 from proximal tubular cells Front. Pharmacol. 7 2016 10.3389/FPHAR.2016.00146
Xie X. Muruato A. Lokugamage K.G. Narayanan K. Zhang X. Zou J. Liu J. Schindewolf C. Bopp N.E. Aguilar P.v. Plante K.S. Weaver S.C. Makino S. LeDuc J.W. Menachery V.D. Shi P.Y. An infectious cDNA clone of SARS-CoV-2 Cell Host Microbe 27 2020 841 848 10.1016/j.chom.2020.04.004 e3 32289263
Yadav B. Wennerberg K. Aittokallio T. Tang J. Searching for drug synergy in complex dose–response landscapes using an interaction potency model Comput. Struct. Biotechnol. J. 13 2015 504 513 10.1016/j.csbj.2015.09.001 26949479
Zhang J.-H. Chung T.D.Y. Oldenburg K.R. A simple statistical parameter for use in evaluation and validation of high throughput screening assays | enhanced reader [WWW document] J. Biomol. Screen 1999 10.1177/108705719900400206
Zhao H. Lu L. Peng Z. Chen L.L. Meng X. Zhang C. Ip J.D. Chan W.M. Chu A.W.H. Chan K.H. Jin D.Y. Chen H. Yuen K.Y. To K.K.W. SARS-CoV-2 Omicron variant shows less efficient replication and fusion activity when compared with Delta variant in TMPRSS2-expressed cells Emerg. Microb. Infect. 11 2022 277 283 10.1080/22221751.2021.2023329
Zheng S. Wang W. Aldahdooh J. Malyutina A. Shadbahr T. Tanoli Z. Pessia A. Tang J. SynergyFinder plus: toward better interpretation and annotation of drug combination screening datasets Dev. Reprod. Biol. 2022 10.1016/j.gpb.2022.01.004
Zhu Yunkai Feng F. Hu G. Wang Y. Yu Y. Zhu Yuanfei Xu W. Cai X. Sun Z. Han W. Ye R. Qu D. Ding Q. Huang X. Chen H. Xu W. Xie Y. Cai Q. Yuan Z. Zhang R. A genome-wide CRISPR screen identifies host factors that regulate SARS-CoV-2 entry Nat. Commun. 12 2021 961 10.1038/s41467-021-21213-4 33574281
| 36503013 | PMC9729583 | NO-CC CODE | 2022-12-14 23:45:38 | no | Antiviral Res. 2022 Dec 8;:105484 | utf-8 | Antiviral Res | 2,022 | 10.1016/j.antiviral.2022.105484 | oa_other |
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J Am Coll Radiol
J Am Coll Radiol
Journal of the American College of Radiology
1546-1440
1558-349X
Published by Elsevier Inc. on behalf of American College of Radiology
S1546-1440(22)00813-4
10.1016/j.jacr.2022.10.005
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Trends in academic productivity amongst radiologists during the COVID-19 pandemic
Chan Alex MD abd∗
Flash Moses J.E. acd∗
Guo Teddy abd
Zattra Ottavia acd
Boms Okechi acd
Succi Marc D. MD acd∗∗∗
Hirsch Joshua A. MD acd#∗
a Medically Engineered Solutions in Healthcare Incubator, Innovations in Operations Research, Center (MESH IO), Massachusetts General Hospital, Boston, MA, USA
b McMaster University, Faculty of Medicine, Hamilton, Ontario, Canada
c Harvard Medical School, Boston, MA, USA
d Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
∗∗ Corresponding Authors: Marc D. Succi, MD, Massachusetts General Hospital, Department of Radiology, 55 Fruit Street, Boston, MA, 02114, Phone: 617-935-9144
# Joshua A. Hirsch, Massachusetts General Hospital, Department of Radiology, 55 Fruit Street, Boston, MA, 02114, Phone: 617-726-1767
∗ These authors contributed equally to this work
8 12 2022
8 12 2022
1 6 2022
21 9 2022
3 10 2022
© 2022 Published by Elsevier Inc. on behalf of American College of Radiology.
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Purpose
There is a scarcity of literature examining changes in radiologist research productivity during the COVID-19 pandemic. The current study aimed to investigate changes in academic productivity as measured by publication volume prior to and during the COVID-19 pandemic.
Methods
This single-center retrospective cohort study, included the publication data of 216 researchers consisting of associate professors, assistant professors, and professors of radiology. Wilcoxin sign rank test was used to identify changes in publication volume between the a 1-year long defined pre-pandemic period (publications between May 1, 2019 to April 30, 2020) and COVID-19 pandemic period (May 1, 2020 to April 30, 2021).
Results
There was a significantly increased mean annual volume of publications in the pandemic period (5.98, SD = 7.28) compared to the pre-pandemic period (4.98, SD = 5.53) Z = -2.819, p = 0.005. Subset analysis demonstrated a similar (17.4%) increase in publication volume for male researchers when comparing the mean annual pre-pandemic publications (5.10, SD = 5.79) compared to the pandemic period. (5.99, SD = 7.60) Z = -2.369, p = .018. No statistically significant changes were found in similar analyses with the female subset.
Discussion
Significant increases in radiologist publication volume were found during the COVID-19 pandemic compared to the year prior. Changes may reflect an overall increase in academic productivity in response to clinical and imaging volume ramp down.
Key Words
COVID-19
Radiology
Research Productivity
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pmcAuthor Contributions:
AC and MF were responsible for study design conception, data acquisition and analysis, and interpretation leading to manuscript drafting. TG, OZ, and OB were responsible for data acquisition and drafting of final work. MS and JH were responsible for study design conception, supervised data analysis and interpretation, and contributed substantially to drafting and revision of the manuscript.
LEADERSHIP ROLES
All authors are non-partner/non-partnership track/employees.
Marc Succi, MD is the Associate Chair of Innovation and Commercialization at Mass General Brigham Enterprise Radiology; He is also a member of the ACR Economics Committee
Joshua Hirsch, MD is the Vice Chair Procedural Services, Director Interventional Neuroradiology, Chief Interventional Spine, Associate Department Quality Chair at Massachusetts General Hospital; Councilor to the ACR for SNIS, Chair: Future Trends and Academic Committees ACR, Deputy Editor: JNIS, 2nd Past President: ASNR, Senior affiliate research fellow—NHPI
DATA STATEMENT
The author(s) declare(s) that they had full access to all of the data in this study and the author(s) take(s) complete responsibility for the integrity of the data and the accuracy of the data analysis.
FUNDING
Effort supported in part by a grant from the Harvey L. Neiman Health Policy Institute (JAH).
CONFLICTS OF INTEREST
The authors have no conflicts of interest to disclose.
ACKNOWLEDGEMENTS
We would like to thank Sharada Das Lavigne for her help in obtaining staff workforce data.
SUMMARY STATEMENT
In this single-center retrospective cohort, a significant increase in academic radiologist publication volume was demonstrated during the COVID-19 pandemic.
TAKE HOME POINTS
• Amongst radiology researchers, there appeared to have been a significant increase in publishing volume during the COVID-19 pandemic.
• The increase of academic productivity observed may be reflective of the clinical and imaging volume ramp down experienced by radiologists during the COVID-19 pandemic.
| 36496090 | PMC9729584 | NO-CC CODE | 2022-12-14 23:22:26 | no | J Am Coll Radiol. 2022 Dec 8; doi: 10.1016/j.jacr.2022.10.005 | utf-8 | J Am Coll Radiol | 2,022 | 10.1016/j.jacr.2022.10.005 | oa_other |
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Nurs Clin North Am
Nurs Clin North Am
The Nursing Clinics of North America
0029-6465
1558-1357
Elsevier Inc.
S0029-6465(22)03380-1
10.1016/j.cnur.2022.10.006
Article
Out of Chaos Leaders Emerged
Brysiewicz Petra PhD a∗
Chipps Jennifer PhD b
a School of Nursing & Public Health, University of KwaZulu-Natal, King George Mazisi Kunene Road, Glenwood, Durban 4041, South Africa
b School of Nursing, Faculty of Community and Health Sciences, University of the Western Cape, 14 Blanckenberg Road, Belville, Cape Town 7041, South Africa
∗ Corresponding author.
8 12 2022
8 12 2022
© 2022 Elsevier Inc. All rights reserved.
2022
Elsevier Inc.
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
COVID-19 had a major influence on nursing with the pandemic resulting in changes in the work environment while experiencing physical and emotional challenges such as moral distress, fear for self and family and dealing with the unknown. However, during this period, nurses demonstrated extraordinary resilience, crafted innovations in clinical practice, communication and support, providing leadership in the health service and in the nursing profession.
Keywords
Leadership
Nursing
Resilience
COVID-19
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pmcKey points
• COVID-19 had a major influence on nursing highlighting the indispensable role played by nurses.
• The chaos of the pandemic resulted in real physical and emotional risks to nurses.
• Challenges of moral distress, fear for self and family, and work impact were common.
• In the face of all these challenges, nurses demonstrated extraordinary resilience, leadership, and innovation.
• Nursing emerged from the pandemic with visible leadership in the field of health.
Introduction
There has always been a recognition for the need of a strong nursing workforce in history. After the end of World War II, President Truman in the Associated Press, 1946, February 28, stated that nurses are “one of the most important groups of health workers in the country.”1 More than 70 years later, in 2020, the Year of the Nurse, with more than 5 million cases of COVID-19 recorded around the world, “nurses were standing firm against the onslaught of the virus and have saved many thousands of lives” (ICN President, 2020).2
Toward the end of 2019, our world changed due to the COVID-19 pandemic, and our lives, both personally and professionally, were irrevocably altered. Although hailed as heroes,3 nurses were faced with finding new ways of living and new ways of working while navigating this changed landscape. For nurses across the globe, numerous new challenges emerged but so too has there been the emergence of a resilient workforce with new learning and ways of doing.
COVID-19 has done a great deal to globally highlight the indispensable role played by nurses within the health-care system and has served to assist to address the invisibility of nurses, despite their limited voice in the national and regional responses to the COVID-19 pandemic.4 Nurses have always been the backbone of the workforce, doing phenomenal and unbelievable work daily as they save lives, prevent complications, and prevent suffering, often unnoticed.1 Nurses’ stories from all corners of the world need to be written and exposed to the world because nurses have a plethora of wisdom to get out there. Through the chaos, COVID-19 has provided an opportunity to “tell these stories.”
“Unpacking” the chaos of the pandemic
During the last 50 years, health-care workers, and specifically nurses, have encountered numerous risks from HIV/AIDS, SARS, swine flu, and Ebola.5 Although COVID-19 was thought not to be as deadly as HIV/AIDS or the swine flu, the insufficient understanding of the virus at the start of the pandemic, its pathophysiology, mode of transmission, susceptibility profile, and contagious nature along with failures in the supply chains for personal protective equipment (PPE) meant that health-care workers were asked to take on substantial but uncertain risk.5
Challenges Related to Physical and Emotional Risks of COVID-19
These uncertain risks have had a large impact on the nursing workforce. It has been reported that the health workforce had a 7 times higher risk of severe COVID-19 infection compared with other workers.6 The World Health Organization estimated that a possible 115,500 health-care workers have died from COVID-19 in the period between January 2020 to May 2021,7 although the real impact has remained unknown.8 The uncertainty and the real risk faced by nurses on a daily basis resulted in high levels of COVID-19 fear related to infection and safety concerns for themselves and their families,9 as well as work burnout with emotional exhaustion, depression,9 and possible posttraumatic stress. In a study conducted in South Africa, nearly half of the nurses included in the study were extremely concerned about family members and their own personal health10 with 3 in 5 nurses concerned about passing the infection on to family members.10 Qualitative stories globally from nurses also identified themes such as the shock of the virus, staff sacrifice and dedication, as well as collateral damage ranging from personal health concerns to the long-term impact on, and the care of, discharged patients and a hierarchy of power and inequality within the health-care system.11
Moral Distress and Ethical Challenges
One of the hidden challenges faced by nurses during the pandemic was the moral distress experienced in scenarios such as witnessing and participating in the triaging of resources and equipment to those who were seen as having a better chance of survival; watching patients dying alone without their family or loved ones due to visitor restrictions and social isolation policies; experiencing the cumulative loss of high number of patient deaths; suffering from physical exhaustion due to a heavy workload, schedule changes and shifting roles; experiencing anxieties about limited medical supplies, equipment, hospital beds, and PPE; and struggling with worry about their own health and possible exposure of their families while balancing professional obligation.12 , 13 Although for nurses from low-to-middle-income countries, having to make very difficult patient-management decisions according to the availability of resources is often a daily occurrence, for many in other higher income countries, this was a new reality never previously experienced. Nurses also faced several ethical challenges14 due to the conflicting professional values and unpalatable and complex ethical issues in practice.15 Nurses were placed in situations where the professional values of protecting the public from harm and a duty to provide care were in conflict with obligations to protect own health and the health of families15 all the while addressing equity of care issues in terms of triaging care, and patients not expected to survive but still needing care with the fair allocation of resources.12 Faced with the potential reality that patients will suffer, clinically deteriorate, or die, many health-care professionals found it extremely difficult to make or implement a decision to deny or delay treatment given their own human response, their professional socialization, and their profession’s expectations and norms about saving lives, relieving suffering, and not abandoning patients.15
Challenges of COVID Stigma
Another challenge experienced in many countries by nurses and other health-care professionals was that of COVID-19 stigma. In Malawi, nurses were not allowed to use public transport and were insulted in the street and evicted from rented apartments,16 and a nurse from Mexico was sprayed with bleach.17 A study in Italy found that nurses experienced “stigma in the working environment” such as avoiding closeness with others, and “stigma in everyday life” with strong feelings of isolation because people avoided contact.18 In May 2020, a community of advocates from 13 medical and humanitarian organizations issued a declaration condemning more than 200 incidents of COVID-19–related attacks on health-care professionals and health facilities during the ongoing pandemic.16 This was happening at the same time as public displays of affection such as “clapping for hospital workers”3 (Box 1 ). This concern regarding the stigma for nurses associated with working in COVID-19 was highlighted by the International Council of Nurses, which called on governments internationally to stop attacks on nurses.19 Box 1 Some of the challenges experienced by nurses during COVID-19
Fear for self and family• Dealing with a new unknown pandemic
• Constant fear due to caring for patients not yet tested
• Fear of infection from patients and work colleagues
• Increased susceptibility to major health issues due to preexisting health issues
• Fear of transmission to family and loved ones
• Physical exhaustion
• Resulting psychological stress, burnout, and traumatic stress disorder (PTSD)
Work challenges• Excessive job stress and constant high work pressure
• Constantly changing workplace policies and procedures
• Role and task shifting, for example, having to work in role not trained for
• Large crowds of patients entering the workplace with COVID-19 and needing further space and resources
• Trying to do more without additional resources, often in an already high-pressure low-resourced environment (especially problematic in Low- and Middle-income Countries [LMIC])
• Working a busy shift in full PPE (if available)—impact on skin and added difficulties in communication and establishing rapport with patients
• Having to deal with a lot of emotions from patients and their families
• Not allowing visitors and or families to be present, often resulting in conflict
Ethical and Moral challenges• Modification of admission criteria and triaging resources
• Withdrawing treatment due to resource constraints
• Facilitating final goodbye’s with families excluded from the bedside
• Shortages of isolation rooms and equipment
• Feeling underprepared to function within the allocated role
Community challenges• Managing expectations of community members
• Stigma toward health-care workers
• COVID-19 conspiracy theories
Facing Challenges with Resilience
However, amid this chaos and challenges, nurses have demonstrated extraordinary resilience. COVID-19 forced nurses to come up with new ways to manage and respond to the pandemic: to be quick to act appropriately, to be alert to changes that are needed, and to be receptive and adaptable to change. During the pandemic, many retired nurses returned to the workforce to assist as needed, undertaking further training to work in contact-tracing, COVID-testing stations, testing work, and in specialized units such as intensive care units (ICUs) and emergency departments.20
Nurses working with COVID-19 patients were reported to have significantly greater resilience than other nurses21 and front-line nurses experienced both positive and negative impacts of COVID-19, with the positive impact reported as increased empathy, compassion, and enhanced confidence in their professional skills.22 In a study in China, 96.8% of the nurses expressed their frontline work willingness, and 60.6% reported a sense of personal accomplishment working during COVID-19.9 Research has indicated that nurses tended to adopt positive strategies in the face of the psychological impact of the pandemic.23 This is in support with what Bonano24 in 2004 suggested, namely that understanding what you are doing, having a meaningful purpose and a strong belief system helped people become more resilient in stressful situations.25 “Showing stubborn hope,”26 moral courage, stamina and resilience, nurses continued to work on the front lines of the pandemic, once again holding the historic center of the recognition, prevention, care and control of infectious diseases from the time of Florence Nightingale.15
Out of the chaos, nurses as leaders emerge
However, in this chaos, we found the emergence of stories of innovation and successes in nursing care. Drawing inspiration from a Xhosa word used in South Africa—zenzele—which refers to the need to do things on your own without relying on others to do it for you, the COVID-19 pandemic has seen nurses across the globe taking up the initiative. They have recognized the need, realized there is nothing in place to assist and have thus risen to the challenge to provide a solution. These solutions have been in the form of innovative practices, communication and support strategies to assist the communities they serve.
Nurse Innovations in Clinical Practice
The pandemic caused a great many challenges in the clinical area thereby providing the impetus and forcing organizations, and specifically nurses, to think creatively and to be a valuable contributor to the multidisciplinary health-care team.27 Nurses have a rich tradition of being recognized as the “hackers of the hospital,” that is, working creatively to solve issues of patient care, customizing medical equipment, and making new devices to ensure patient comfort and safety.28 , 29 There are numerous examples of this from across the world and include something as simple as a photograph attached to a nurses gown (Table 1 ), an inflated nonsterile glove nestled in the hand of a sedated and ventilated patient, to the “Real talk Real time” virtual tool (see Table 1). COVID-19 also changed the way in which nurses at the bedside could practice. Nurses worked to find simple and cost effective practical solutions to many of these challenges, see “Isopouch” and “Code Cards” (see Table 1), while still providing support and high-quality care within the tight constraints of isolation and working effectively, despite increasing numbers of patients, by improving bedside handover as patient numbers surged (see Table 1).Table 1 Some examples of COVID-19 nurse innovations from around the world
Innovation Description of Its Application in the Clinical Area
Own photograph on the front of your gown Masks and face shields hide the face and facial expressions of the nurse. Photographs of the nurse’s face with their first name was attached and displayed to the front of their gown for patients to be able to see who was taking care of them
“Real Talk Real Time” This virtual rounding tool has been able to provide comfort to family members by allowing them to be face-to-face with their loved one’s nurses or doctors in the ICU. Unlike other video chat offerings, using the Webex platform ensured it was secure and able to be accessed on multiple devices by various age groups. https://nursing.jnj.com/nursing-news-events/nurses-leading-innovation/meet-10-nurses-pioneering-innovative-covid-19-solutions
“Code Cards” with most commonly coded medications and procedures These communication cards are used in isolation rooms, where they are held up to the glass to get important messages to the rest of the team (about required medication) during a resuscitation, thereby keeping the staff safe. https://nursing.jnj.com/nursing-news-events/nurses-leading-innovation/meet-10-nurses-pioneering-innovative-covid-19-solutions
IsoPouch (Isolation Pouch) This was created by a nurse who realized the need for an inexpensive disposable pouch that she could fill with all the supplies she needed to care for her isolated patients, and which she could then throw away with her gown and other PPE once finished30
Handover Redesign Team Nurse leaders and clinical nurses redesigned the bedside handover, and this was carried out in order to improve nursing practice implementation and handover processes that addressed nursing concerns and prioritized their needs31
“Hand of God”—water-filled nonsterile glove This is placed in the hand of an intubated and ventilated patient, allowing them the feeling that someone is nearby, with them, holding their hand. This was in response to COVID-19 social distancing rules that families were not allowed at the bedside
Digital innovation has always been present in the clinical areas and well used by the bedside nurse, however, possibly not to its full potential. COVID-19 challenged that32 and resulted in numerous innovations in the clinical setting. The global pandemic accelerated the pace of this technological innovation across the entire world with, for example, mobile apps being used for monitoring quarantine in Sierra Leone and South Africa, information-providing drones in place in Rwanda,33 and using social media such as WhatsApp for support, information, and communication.
Innovation is about using one’s own knowledge and skills to change old ways of thinking and practicing and to develop new improved ways of working.34 This can be an extremely challenging task, and it is essential to be very purposive about the way in which we are educating nurses and to ensure we are adequately preparing them for success in the fourth industrial revolution. An additional problem is, however, that nurse innovations such as these often remain “hidden” because they do not spread beyond the area in which they were developed. This is for a variety of reasons including the limited dissemination of such products in written articles. This is especially true for lower income countries such as those in Africa, where many young scientists face numerous challenges converting their research into publications.35 Gomez-Marquez and Young (2016) argue that, “It is time to not only acknowledge nurses’ creativity and ingenuity, but celebrate and nurture it.”28 In order for innovation to thrive however, it needs a supportive environment. It is also important to reflect on the question, “What are we doing to nurture and support nurse innovation?”
Communication Innovations
Communication became a central concern during the COVID-19 pandemic due to social distancing policies, the use of full PPE, and the novelty of the disease. This was particularly true regarding the ways in which nurses interacted with patients’ families, with significant restrictions on visiting and face-to-face consultations.36 Nurses found new ways to communicate effectively with patients, family, and colleagues, by adapting to virtual consultations.20 WhatsApp collaboration groups with staff members and in ICUs by linking families with video iPad sessions to see their loved ones. This was evident in ICUs in the National Health Service (United Kingdom), where interactions with families were handled with video calling used in 63 (47%) of the ICUs and 39 (29%) ICUs had developed a dedicated family communication team.36 In South Africa, provincial departments of health established collaborative learning environments—#Colabs—as a learning space. This served to provide a space in which to share experiences, insights, and ideas that then translated to improvements in different clinical settings, including supporting staff. These #Colabs also played a valuable role in providing professional recognition for innovations in practices.37 These included “daily walkabouts” by nurse leadership to ask frontline staff every day “what matters to you” and then to act daily with “just do it” quick fixes to address identified challenges. “Daily huddles” were virtual daily get-togethers, which served to establish 2-way communication through WhatsApp to broadcast rapidly changing polices, actions, and successes of frontline worker stories.37
Innovations for support and fostering resilience
During the pandemic, nurse leaders contributed to many original solutions that limited the spread of disease and aided the pandemic response while supporting rapid changes across health systems in keeping with changing local and national policies, emerging data trends, scientific discoveries, and surge capacity requirements.38 The resilience of staff to swiftly adapt to this new, uncertain landscape of nursing and patient care was essential and leadership needed to continue to work toward engaging nurses at the bedside to ensure best practices and resilient nurses.31 Leadership through a crisis is essential for the protective effect of nurses’ emotional well-being and learning from the pandemic about the impact of leadership in a crisis is important to facilitate recovery and lessen the impact in further outbreaks.39 Nurse leadership stepped up to create working environments that not only supports individual resilience but also organizational resilience. Nursing practice is conducted within an environment influenced and shaped by leaders, and recognizing the limits of individual resilience and a nurse’s capacity to manage chronic levels of physical, emotional, and moral distress is essential. Nursing leadership promoted strategies to enhance organizational resilience during and beyond the pandemic by creating an environment of trust and psychological safety, supporting nurse empowerment, and nurturing communication structures.40 To foster and preserve organizational resilience, leaders have to identify the challenges, ensure that workplace structures and processes are in place, and if they are not, they needed to advocate for them40 and consider employing different leadership styles to support nurse’s well-being.39
The emergence of visible nurse leadership
The COVID-19 pandemic thus brought with it many examples of complete disorder and confusion for individuals, communities, societies, and the world at large, and although nursing has risen to the challenge, suffered a great deal along the way, the pandemic has also provided great opportunities for the profession. COVID-19 has exposed the truth about nursing more than any organized campaign could have possibly done. It brought to light nursing’s indispensable role as the backbone of the health-care system and has highlighted their professionalism, not only as frontline care providers but also as health-care leaders and policy experts. It has provided an unprecedented opportunity for the general public to witness firsthand the vital role that nurses play.20 The question for nursing now is whether to continue in our roles as implementers of policies that are handed to us or to use our size and influence for representation in places where decision-making that affects our practice, welfare, and profession are being discussed.20
Nursing leadership needs to be visible and must play an active role in multidisciplinary and interprofessional collaborative decision-making. Nurses have unique health-care expertise, and it is vital that they have a voice not only in high-level decision-making about the response and planning for the COVID-19 pandemic but also in future health crises.4 Nurse leaders need to be adaptive and strategic while demonstrating concern regarding the well-being of the nursing workforce.41
Summary
COVID-19 is nothing like we ever could have anticipated, and it has irrevocably changed the world as we know it. This includes the nursing profession and has resulted in fundamental changes to the way in which nurses work. Nurses have been thrust into this challenging situation and have been called on to play a large and extremely important role in the management of this pandemic while still struggling for meaningful recognition as professionals on the frontline of the pandemic. It is also important that the leadership, the innovations, and resilience stories are shared and made visible through publication and professional recognition of the pivotal role of nurses in health. This is an opportunity to highlight to the world the value of nursing, the contribution of nursing, to increase the visibility of nursing and the phenomenal work that nurses do. So “let’s not waste a good disaster.”
Clinics care points
• Provide clear and visible responsive leadership at all times
• Clear transparent communication during a crisis with all stakeholders, including health workers and families, is paramount
• Create working environments of trust and safety
Disclosure
The authors have nothing to disclose.
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References
1 Edmonds J.K. Kneipp S.M. Campbell L. A call to action for public health nurses during the COVID-19 pandemic Public Health Nurs 37 3 2020 323 32281160
2 ICN highlights top priorities to beat COVID-19 Available at: https://www.icn.ch/news/icn-highlights-top-priorities-beat-covid-19
3 Chipps J. Jarvis M.A. Brysiewicz P. Heroes and angels: ED nurses' ongoing fight for meaningful recognition as professionals on the frontline of the pandemic Int Emerg Nurs 59 2021 101080 34837827
4 Rasmussen B. Holton S. Wynter K. We're on mute! Exclusion of nurses' voices in national decisions and responses to COVID-19: An international perspective J Adv Nurs 78 7 2022 e87 e90 35352392
5 Morley G. Grady C. McCarthy J. Covid-19: Ethical challenges for nurses Hastings Cent Rep 50 3 2020 35 39
6 Mutambudzi M. Niedzwiedz C. Macdonald E.B. Occupation and risk of severe COVID-19: prospective cohort study of 120 075 UK Biobank participants Occup Environ Med 78 5 2021 307
7 World Health Organization The impact of COVID-19 on health and care workers: a closer look at deaths. https://apps.who.int/iris/handle/10665/345300 2021
8 The toll of COVID-19 on health care workers remains unknown Am J Nurs 121 3 2021 14 15
9 Hu D. Kong Y. Li W. Frontline nurses’ burnout, anxiety, depression, and fear statuses and their associated factors during the COVID-19 outbreak in Wuhan, China: a large-scale cross-sectional study EClinicalMedicine 24 2020 100424 32766539
10 HSRC. Survey of COVD-19South African Healthworkers Available at: htpp:/www.hsrc.ac.a/news/media-and-covid19 2020
11 Bennett P. Noble S. Johnston S. COVID-19 confessions: a qualitative exploration of healthcare workers experiences of working with COVID-19 BMJ Open 10 12 2020 e043949
12 Greenberg N. Docherty M. Gnanapragasam S. Managing mental health challenges faced by healthcare workers during covid-19 pandemic BMJ 368 2020 m1211 32217624
13 Talevi D. Socci V. Carai M. Mental health outcomes of the CoViD-19 pandemic Rivista di psichiatria 55 3 2020 137 144 32489190
14 Robert R. Kentish-Barnes N. Boyer A. Ethical dilemmas due to the Covid-19 pandemic Ann Intensive Care 10 1 2020 1 9 31900667
15 Turale S. Meechamnan C. Kunaviktikul W. Challenging times: ethics, nursing and the COVID-19 pandemic Int Nurs Rev 67 2 2020 164 167 32578249
16 Bagcchi S. Stigma during the COVID-19 pandemic Lancet Infect Dis 20 7 2020 782 32592670
17 Health workers become unexpected targets during covid-19 Economist 2021 Available at: https://www.economist.com/international/2020/05/11/health-workers-become-unexpected-targets-during-covid-19
18 Simeone S. Rea T. Guillari A. Nurses and stigma at the time of covid-19: A phenomenological study MDPI 10 1 2021 25
19 ICN calls for government action to stop attacks on nurses at a time when their mental health and wellbeing are already under threat because of COVID-19 pandemic Available at: https://www.icn.ch/news/icn-calls-government-action-stop-attacks-nurses-time-when-their-mental-health-and-wellbeing 2020
20 Popoola T. COVID-19’s missing heroes: Nurses’ contribution and visibility in Aotearoa New Zealand Nurs Praxis Aotearoa New Zealand 37 2021
21 Jo S. Kurt S. Bennett J.A. Nurses' resilience in the face of coronavirus (COVID-19): An international view Nurs Health Sci 23 3 2021 646 657 34169629
22 Tan R. Yu T. Luo K. Experiences of clinical first-line nurses treating patients with COVID-19: A qualitative study Journal of Nurs Manage 28 6 2020 1381 1390
23 Sierra-García E. Sosa-Palanca E.M. Saus-Ortega C. Modulating elements of nurse resilience in population care during the COVID-19 pandemic Int J Environ Res Public Health 19 8 2022 4452 35457319
24 Bonanno G.A. Loss, trauma, and human resilience: have we underestimated the human capacity to thrive after extremely aversive events? Am Psychol 59 1 2004 20 14736317
25 Duncan D.L. What the COVID-19 pandemic tells us about the need to develop resilience in the nursing workforce Nurs Manage 27 3 2020
26 Coetzee S. Klopper H. Jansen J. In our own words: nurses on the front line 2022 Jonathan Ball Publishers South Africa
27 Wymer J.A. Stucky C.H. De Jong M.J. Nursing leadership and COVID-19: defining the shadows and leading ahead of the data Nurse Lead 19 5 2021 483 488 34149326
28 Gomez-Marquez J, Young A. A history of nurse making and stealth innovation. Available at SSRN 2778663. 2016.
29 Thomas T.W. Seifert P.C. Joyner J.C. Registered nurses leading innovative changes OJIN 21 3 2016 3
30 Rabbitt M. COVID-19 inspired me to improve patient care": how two nurses devised new ways to help kids and fellow nurses during the pandemic https://www.jnj.com/innovation/how-covid-19-inspired-nurses-to-innovate-improve-patient-care?_amp=true 2021
31 Brown-Deveaux D. Kaplan S. Gabbe L. Transformational leadership meets innovative strategy: how nurse leaders and clinical nurses redesigned bedside handover to improve nursing practice Nurse Leader 20 3 2022 290 296 35505949
32 Horton T. Hardie T. Mahadeva S. Securing a positive health care technology legacy from COVID-19 2021 Health Foundation London
33 Andersen, H. 2020. Insights From Africa’s Covid-19 Response: Tech Innovations. Available at: https://institute.global/advisory/insights-africas-covid-19-response-tech-innovations Accessed December 10, 2020.
34 Brysiewicz P. Hughes T.L. McCreary L.L. Promoting innovation in global nursing practice Rwanda J 2 2 2015 41 45
35 Kumwenda S. El Hadji A.N. Orondo P.W. Challenges facing young African scientists in their research careers: A qualitative exploratory study Malawi Med J 29 1 2017 1 4 28567188
36 Boulton A.J. Jordan H. Adams C.E. Intensive care unit visiting and family communication during the COVID-19 pandemic: A UK survey J Intensive Care Soc 23 3 2021 293 296 17511437211007779 36033248
37 Staff CARE #Colab Available at: https://community.ihi.org/za/provinces/westerncape
38 Daly J. Jackson D. Anders R. Who speaks for nursing? COVID-19 highlighting gaps in leadership J Clin Nurs 29 15–16 2020 2751 2752 32311793
39 Phillips N. Hughes L. Vindrola-Padros C. The impact of leadership on the nursing workforce during the COVID-10 pandemic 2022 BMJ Leader
40 Udod S. MacPhee M. Baxter P. Rethinking resilience: nurses and nurse leaders emerging from the post–covid-19 environment JONA 51 11 2021 537 540
41 Rosser E. Buckner E. Avedissian T. The global leadership mentoring community: building capacity across seven global regions Int Nurs Rev 67 4 2020 484 494 32869285
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10.1016/j.isci.2022.105772
105772
Article
Engineering Probiotic-derived Outer Membrane Vesicles as Functional Vaccine Carriers to Enhance Immunity against SARS-CoV-2
Wo Jing 13
Lv Zhao-Yong 13
Sun Jia-Nan 1
Tang Hao 1
Qi Nan 1
Ye Bang-Ce 124∗
1 Institute of Engineering Biology and Health, Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, Zhejiang, China
2 Lab of Biosystem and Microanalysis, State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China
∗ Correspondence and requests for materials should be addressed to B.C.Y. (email: ), orcid.org/0000-0002-5555-5359.
3 These authors contributed equally
4 Lead Contact
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© 2022 The Authors.
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Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Due to the continued emergence of SARS-CoV-2 variants, there has been considerable interest in how to display multivalent antigens efficiently. Bacterial outer membrane vesicles (OMVs) can serve as an attractive vaccine delivery system because of their self-adjuvant properties and the ability to be decorated with antigens. Here we set up a bivalent antigen display platform based on engineered OMVs using mCherry and GFP and demonstrated that two different antigens of SARS-CoV-2 could be presented simultaneously in the lumen and on the surface of OMVs. Comparing immunogenicity, ClyA-NG06 fusion and the receptor-binding domain (RBD) of the spike protein in the OMV lumen elicited a stronger humoral response in mice than OMVs presenting either the ClyA-NG06 fusion or RBD alone. Taken together, we provided an efficient approach to display SARS-CoV-2 antigens in the lumen and on the surface of the same OMV and highlighted the potential of OMVs as general multi-antigen carriers.
Graphical abstract
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| 36510593 | PMC9729586 | NO-CC CODE | 2022-12-14 23:22:26 | no | iScience. 2022 Dec 8;:105772 | utf-8 | iScience | 2,022 | 10.1016/j.isci.2022.105772 | oa_other |
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Inf Process Manag
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Information Processing & Management
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10.1016/j.ipm.2022.103231
103231
Article
Advancement of management information system for discovering fraud in master card based intelligent supervised machine learning and deep learning during SARS-CoV2
Wu Banghua a
Lv Xuebin b⁎
Alghamdi Abdullah c
Abosaq Hamad d
Alrizq Mesfer c
a College of Cybersecurity, Sichuan University, Chengdu 610041, China
b School of Computer Science and Engineering, Sichuan University, Chengdu 610064, PR China
c Information Systems Department, College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia
d Computer Science Department, College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia
⁎ Corresponding author.
8 12 2022
3 2023
8 12 2022
60 2 103231103231
10 4 2022
9 11 2022
6 12 2022
© 2022 Elsevier Ltd. All rights reserved.
2022
Elsevier Ltd
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
During coronavirus (SARS-CoV2) the number of fraudulent transactions is expanding at a rate of alarming (7,352,421 online transaction records). Additionally, the Master Card (MC) usage is increasing. To avoid massive losses, companies of finance must constantly improve their management information systems for discovering fraud in MC. In this paper, an approach of advancement management information system for discovering of MC fraud was developed using sequential modeling of data depend on intelligent forecasting methods such as deep Learning and intelligent supervised machine learning (ISML). The Long Short-Term Memory Network (LSTM), Logistic Regression (LR), and Random Forest (RF) were used. The dataset is separated into two parts: the training and testing data, with a ratio of 8:2. Also, the advancement of management information system has been evaluated using 10-fold cross validation depend on recall, f1-score, precision, Mean Absolute Error (MAE), Receiver Operating Curve (ROC), and Root Mean Square Error (RMSE). Finally various techniques of resampling used to forecast if a transaction of MC is genuine/fraudulent. Performance for without re-sampling, with under-sampling, and with over-sampling is measured for each Algorithm. Highest performance of without re-sampling was 0.829 for RF algorithm-F score. While for under-sampling, it was 0.871 for LSTM algorithm-RMSE. Further, for over-sampling, it was 0.921 for both RF algorithm-Precision and LSTM algorithm-F score. The results from running advancement of management information system revealed that using resampling technique with deep learning LSTM generated the best results than intelligent supervised machine learning.
Keywords
Supervised machine learning
Fraud discovering
Master card
Long short-term memory LSTM
SARS-CoV2
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pmc Abbreviations table
SARS-CoV2 Coronavirus
MC Master Card
LR Logistic Regression
MAE Mean Absolute Error
RMSE Root Mean Square Error
LSTM Long Short-Term Memory Network
ISML Intelligent supervised machine learning
RF Random Forest
ROC Receiver Operating Curve
OTP One-Time Password
1 Introduction
In the wake of the coronavirus outbreak, MC fraud is on the rise because of a vulnerability that has made it more difficult (Bandyopadhyay & Dutta, 2020; Sadgali, Sael & Benabbou, 2019). In deep learning LSTM fraud, the challenge of preventing fraud has increased dramatically. Customers who make purchases through websites frequently use online transactions. Paying using electronic funds eliminates the time-consuming and expensive practice of collecting cash from customers when purchasing and selling goods online (Sherstinsky, 2020). There is a significant human fatality rate due to extensive use of the World Wide Web and COVID-19, which has been shut down because of its high mortality rate. Also, the volume of electronic transactions has increased significantly (Hu et al., 2021). It is also possible to hack into the latest technological developments. Therefore, it is imperative that new strategies for detecting fraud are developed immediately. This is the driving force for the research. Visa, for example, is relying on technical solutions such as artificial intelligence to combat MC fraud (Liu, Jin & Shen, 2019).
The popularity of e-commerce websites for acquiring varied things at the more affordable or fair cost has the favorable effect on target market's growth (Gupta et al., 2022; Li & Yan, 2022). Almost any sort of payment can be made with the MC payment system. People who have phone with the online transaction feature can make whatever kind of payment they choose. The use of a mobile device is necessary to receive a One-Time Password (OTP). MC transactions have become ubiquitous in recent years as technology has advanced and novel payment of e-service alternatives, such as payments of mobile and e-commerce, have emerged (Jain, Sarupria & Kothari, 2020).
In the current circumstance, where the world is dealing with an unknown COVID, credit card transactions are growing more prevalent. Authorities in a number of countries are now urging citizens to avoid using cash wherever feasible. It is not always possible to apply it in all transactions in practice. Due to the rise in cashless transactions during the lockdown time as a result of COVID, the number of fraudulent transactions is also rising. A customer's previous transactions' data can be used to spot patterns of fraudulent behavior (Shadmi et al., 2020). A variety of methods, including DM, decision trees, rule depend mining, neural networks, clustering of fuzzy, and ML, will be used by banks and credit card firms during COVID-19 in an effort to catch fraudsters red-handed. Based on previous activity, the technique attempts to determine a customer's regular usage pattern (Adday, Shaban, Jawad, Jaleel & Zahra, 2021; Ashtiani & Raahemi, 2021; Khan, Ateeq, Ali & Butt, 2021). The purpose of this research is to suggest a mechanism for detecting such fraud transactions in such an uncontrolled pandemic situation.
With a low percentage of erroneous triggering, the proposed model functions well. MC payment fraud detection can be accomplished through the use of both supervised and unsupervised methods. Unsupervised machine learning contains K Means, EM, Farthest First, X-Means, Clustering based on density that is used to financial data, SVM, Logistic regression, Naïve Bayes, OneR, C4.5, Decision tree, Random Forests, Random Tree are running for fraud detection of financial (Abu Alfeilat et al., 2019; Ali et al., 2021). Neural networks, Decision Tree and Bayesian belief networks were found to be 72.5 percent, 77.5 percent and 88.9 percent efficient in committing financial statement fraud by the sample of Greek industrial enterprises (Berahmand, Nasiri & Li, 2021, 2021). An analysis has used the Classification and Regression Tree to identify fake financial statements (Nasiri, Berahmand, Rostami & Dabiri, 2021). Six machine learning techniques, including LR, SVM, artificial neural networks, C4.5, bagging and stacking, were contrasted and explored (Ebadi, Hosseini & Hosseini, 2017). According to the results of an experimental study, LR and SVM outperform other stated classifier models (Jamali, Sadegheih, Lotfi, Wood & Ebadi, 2021).
Payment card fraud has been detected using a variety of algorithms of machine learning, containing supervised and unsupervised learning, anomaly detection, and ensemble learning (Popat & Chaudhary, 2018). In particular, supervised classification techniques, which use pre-classified datasets, have proven to be particularly helpful in dealing with this problem (Khatri, Arora & Agrawal, 2020). An analysis of past transactions is used to train the classifier, which in turn provides detection model that can forecast if a new transaction is fraudulent or not (SVM algorithms), hidden Markov models, LR algorithms, decision trees, RF and k-nearest neighbors (Hammed & Soyemi, 2020; Heydarpour, Abbasi, Ebadi & Karbassi, 2020). A system's odd behavior and transactions that don't match the model are detected using unsupervised classification methods. It can assist in the detection of novel fraud trends that have not been recognized previously. Artificial intelligence communities, on the other hand, are interested in detection of MC fraud for a number of different reasons. As a result, for these skewed data sets, a large number of commonly used classifiers are incapable of identifying items that belong to a marginalized social group.
There are a number of different types of fraud-detection systems, but the most common is the use of transactional data such as the amount, time and location of the transaction. However, comprehensive sequential information defining customers' profile is ignored (Brezočnik, Fister Jr, & Podgorelec, 2018; Ganji & Mannem, 2012). Such methods are insufficient for detecting MC fraud, because they don't evaluate the customer behavior of spending, which is crucial to find fraud trends that are relevant and change over time owing to new attack and seasonality methods.
Given its status as one of the most precise sequence analysis learning algorithms, recurrent neural networks based deep learning methods, particularly its variation LSTM, have recently been applied in the fraud detection. Recurrent neural network is dynamic ML method that may be used to analyze by simulating the sequential reliance middle from credit card transactions, different bank accounts can exhibit dynamic temporal behavior. Context-dependent representations can be found via the attention mechanism recently.
Regardless of their distance, this method considers dependencies between elements in a sequence (Carcillo, Le Borgne, Caelen & Bontempi, 2018; Vaughan, 2020). Picture captioning and Machine translation have both benefited greatly from its use. The mechanism of attention works by taking a weighted mean of the series of vectors is used to construct the context vector (includes the most significant info), which is then employed as input in the next layer (Benchaji, Douzi, El Ouahidi & Jaafari, 2021).
Objectives of this research are, but not limited to:• Model outperforms the competition in terms of detecting fraudulent transactions.
• LSTM algorithms will be used to identify fraudulent transactions during shutdown period.
• ML will be used to identify types of transactions that are most likely to be fraudulent.
• AI will be used because it can constantly modify & update its rules for new transactions.
The goal of this study is to evolve a fraud detection model for MC transactions that is both efficient and error-free. This model is implemented using LR, RF, and LSTM approaches.
In this study, Since the financial dataset contains hierarchical features, these methods are useful. The precise goals of this study are summarized as follows: First: During the COVID-induced shift in MC's work environment, to identify fraudulent transactions. Second: To protect clients from financial loss by identifying it and informing them, as well as the bank, so that appropriate action can be taken. Third: LR, RF, and LSTM has been used to predict the fraud. Fourth: To develop a fraud detection model that is both efficient and error-free for MC companies.
In this paper, Section 2 discusses problem statement, Section 3 gives Methods, and Sections 4 and 5 are for Results and discussion.
2 Problem statement
MC fraud is ever-growing concern in nowadays banking system. In recent years, the number of fraudulent operations has risen dramatically, as a result of which enormous financial losses have occurred for many companies and agencies of government (Khan et al., 2022). In the future, the numbers are likely to rise, as a result, many academics in this field have concentrated their efforts on early detection of fraudulent conduct using powerful machine learning approaches (Singh, Ranjan & Tiwari, 2022). MC fraud detection, on the other hand, is not an easy task due to two factors: (i) each effort at fraud usually results in a different set of fraudulent behaviors. (ii) Data is substantially skewed, i.e., the average number of samples in the majority of samples (instances of genuine) outnumbers the samples from the minority groups (cases of fraudulent).
When presenting input dataset with a very imbalanced distribution of the class or label to the forecasting framework, the model has a tendency to favor majority specimens (Kamaruddin & Ravi, 2016). As an outcome, it is more likely to disguise the fraudulent transaction as legitimate.
To address this issue, a data-level strategy was used, with multiple resampling approaches like underdamping, oversampling, and hybrid strategies utilized, as well as a technique of algorithmic using methods of ensemble like boosting and bagging on a massive number of transactions in an extremely skewed dataset.
To describe if a transaction is fraudulent or real, predictive models like as LR and RF have been used in conjunction with various resampling strategies (Choi & Lee, 2018). As MC become the most widely used method of payment, particularly in the internet sector, activities of fraudulent involving payment of MC technology are on the rise (Nti et al., 2021).
3 Methods
3.1 Long short-term memory networks
In the realm of deep learning, LSTM is a time series data representation architecture based on artificial recurrent neural networks. Unlike traditional feed forward neural networks, the feedback links between hidden units in the LSTM are linked to discrete time steps, transaction labels can be predicted based on long-term sequence dependencies learned from past transactions (Van Houdt, Mosquera & Nápoles, 2020). LSTM was created to solve the issue of exploding and vanishing gradients that can occur during regular recurrent neural network training (Fischer & Krauss, 2018). The LSTM unit is made up of three specific gates that update a memory cell that saves information: input, forget and output gates (Fischer & Krauss, 2018). Flow of info into and out of cell is controlled by the three gates, and the cell retains values throughout time. Fig. 1 offers the unit structure of LSTM (Jin, Wu & Guo, 2020). The LSTM algorithm is used to solve the vanishing gradient problem in recurrent neural networks is well-suited to categorize, analyze, and predict time series given temporal lags of unknown duration with LSTM, Back-propagation will be used to train the Deep Learning model (Mohanty, Seth, Sanjay & Prithvi, 2022).Fig. 1 R-NN vs LSTM.
Fig 1
3.2 10-fold cross validation based hyper parameter tuning
A hyper parameter is an out to the technique setup whose value can't be described from data of training (Shekar & Dagnew, 2019). The value of a model parameter can be approximated throughout the training phase because it is an internal configuration of the model, whereas an external to the model, a hyper parameter might be defined as a configuration. Because the hyper parameter is not part of the model, the practitioner must manually specify its value (Ozcan & Basturk, 2020).
However, in order to get the optimum performance from the model, the value must first be fine-tuned. The method of cross validation was utilized to tune the hyper parameter in this case (Duarte & Wainer, 2017). We employed cross validation of K fold, with K set to 10. The training dataset is separated into 10 folds in 10-fold cross-validation, with current fold serving as a test set and remaining folds serving as a training set for each fold. This model is then fitted into the set of training and tested on the set of tests (Guo et al., 2019). This cross-validation method can also be used to fine-tune hyper-parameters. We built the best hyper parameter search by sckit learn's grid search function with cross-validation. Because every ML model had its own set of hyper-parameters, the overall search for each model was unique (Al-Abdaly, Al-Taai, Imran & Ibrahim, 2021). When selecting hyper-parameters, it is important to keep in mind that performance is a primary factor in determining which parameters to use. It's one of the most important conditions for getting useful and reliable outputs from machine learning algorithms in practice (Le, Huynh, Yapp & Yeh, 2019). The following figure offers the process of model tuning, workflow, and things to consider:
3.3 Logistic regression
It is one of the most commonly used ML algorithms for classification. Despite the name's inclusion of the word ‘regression,' this is not a regression algorithm. LR's name is derived from a well-known ML method that it was built on, the LR algorithm is used to solve regression problems. When using LR, the probability of each result is stated in terms of how likely it is that it will occur. The weights and input variables of an LR model can be combined to predict real valued outputs. For clarity, let's assume there is only one independent and one dependent variable (Omondi-Ochieng, 2020).
LR makes use of a linear equation of this type as well. To limit the expected real values to a range between 1 and 0, it makes use of sigmoid or logistic functions to forecast the likelihood of each class's result.
Fig. 2 illustrates the function of sigmoid. Suppose we have a classification problem with two variables: dependent 'y’ and an independent 'x'. By default, LR utilizes a 0.5 threshold, which classifies any probability class 1 above 0.5 and any probability class 0 as below 0.5. When necessary, this threshold can be modified (Gregova, Valaskova, Adamko, Tumpach & Jaros, 2020; Sadalia, Nasution & Muda, 2020).Fig. 2 Model tuning process.
Fig 2
3.4 Random forest
RF is an approach of an ensemble learning that may be applied to classification and regression problems. It's a type of bagging that has been extended. Using the bagging technique, a large number of underachieving students are brought together. Decision trees are used to teach weak learners in RF. So, before delving into the specifics of the RF, let's review the fundamentals of decision trees. A decision tree is a type of supervised learning technique that may be employed for regression and classification. However, classification is the most common usage for it. Several internal nodes, each representing the test in a certain property, make up the structure (e.g., weather will be bright, gloomy, or rainy tomorrow). In the tree, each branch represents a different test result, and the leaf nodes are the final results (label of class). It entails segmenting a training set into many subsamples (An & Suh, 2020).
Fig. 3 depicts a simple decision tree that is attempting to choose whether or not to play golf tomorrow. It begins with a three-choice viewpoint: overcast, rainy, and sunny. Verify the wind speed if the sky is clear and sunny (false or true). In the event that this is the case, we will not be playing golf that day (Uddin, Chi, Al Janabi & Habib, 2022).Fig. 3 Example for RF.
Fig 3
We select to play if the answer is false. If the sky is cloudy, we can decide to go out for a game. If the forecast calls for rain, we should also evaluate the humidity level. We prefer not to play if the humidity is high, the humidity isn't too high so we can go out and play golf. Here we can plainly see how decision trees are constructed, with each step in the chain of events leading to the final conclusion. The process of partitioning whole training data into subsets in every internal node depending on some criterion, is required for the construction of a decision tree (Borup, Christensen, Mühlbach & Nielsen, 2022; Iwendi et al., 2020).
The DT algorithm uses metrics like gini impurity and Information gain to identify the appropriate split for each node. Using the distribution of labels in subset, one can calculate the impurity of gini, which is the scope of the likelihood that a randomly selected element from a set will be erroneously labeled. Data received at each stage of the tree-building process is used to decide on which feature to divide the tree. When the node in internal has the value of label class, the process of splitting proceeds.
Despite the fact that DT are simple to comprehend and perform well in particular data, they have a large variance due to the greedy process of algorithm, which causes tree to constantly select optimal split at each level and can't look beyond the current level. Thus, over fitting may occur, where the model outperforms the testing set in the training set (Borup et al., 2022).
By utilizing the bootstrap idea, the RF algorithm effectively mitigates the problem of over-fitting. For the uninitiated, the random forest is a decision-tree building technique that takes many decision trees and mixes them into a single model. It is necessary to use replacement sampling from the training data in order to do bootstrapping, as we explained earlier. Decision trees are trained with diverse subsets of data using bootstrapping. Furthermore, the random forest employs random feature subsets. Suppose there are 50 characters in the data. RF will only train on 10 of them per tree if the data has just a particular number of features, let's say ten. As a result, each tree will include ten random attributes that will be employed for training, such as determining the best split of every 3 node. Final result will be derived after the gathering of DT has been completed (vote). Because not one, but numerous DT are utilized to make the choice, and each tree is trained with various data subsections, the model trained in this way ensures generalization (Zhang, Zhong, & Hu, 2022).
3.5 Proposed methodology
MC money transactions are identified by using a sample of real transactions in the financial dataset. These transactions were compiled from a month's worth of financial logs from MC money service that was used in several nations. During COVID-19, there were 7,352,421 online transaction records, each of which contains a collection of attributes. In the dataset, the non-numeric data is converted into numeric info. This info is then scaled down to the precise range between 0 and 1. As a result, the proposed classifier can be used to a cleaner dataset. Transactions involving cash withdrawals and transfers appear to have a suspicious transaction set. The dataset is separated into two parts: the training and testing data, with the ratio of 8:2. The data of training is applied to train the LSTMs classifier model, which is then used to make predictions for the testing dataset. It is decided to keep the attribute 'is Fraud' as a target variable for the classification operation. As previously stated, our proposed model reduces the quantity of input features by using choice of feature and reduction of dimensionality techniques to MC fraud data before feeding them into the model. For this purpose, to capture sequential dependency middle from consecutive MC transactions, the LSTM sequence learner is used as a basis dynamic pattern recognition classifier. Following that, a resampling technique is used to offer a distinct focus on the output from the LSTM's hidden layers, allowing our method to uncover key fraud trends and discover extremely within a consumer's purchases, there are a variety of transactions. Fig. 4 shows method of proposed framework.Fig. 4 Proposed methodology.
Fig 4
4 Results
Any prediction model's performance must be assessed, illustrating the significance of assessment measures. The metrics applied to assess the performance of classifier models are discussed in this section. This study uses the following metrics as performance evaluation metrics to support its predicted results. The percentage of true forecasting to the total number of occurrences studied is measured by accuracy. Since it doesn't take into account incorrect predictions, the accuracy metric may not be an adequate measure of how well a model performs in practice. As a result, precision and recall must be calculated in order to solve the issue described above. The classifier's accuracy is measured by the number of TP compared to the projected number of TP. The number of correct positive outcomes divided by total number of relevant specimens is referred to as recall. According to F1-Score, a measurement of both recalls and precision, it is the harmonic mean of these two values. The highest amount of precision, recall, and F1-score is known to be 1. Measuring absolute discrepancies between predictions and observations of test samples, MSE is another evaluation metric. MSE generates non-negative floating-point values, with the amount near 0.0 proving to be the best. With these previously mentioned processed MC fraud data and the chronologically ordered sequence in which they were collected, we may use our suggested model to forecast the label of each subsequent transaction. Each data is broken down into three groups. 70% of the dataset is utilized to train the models, and this is the data that is used for that purpose. The validation set is a 15% subset of data that is used to validate the classifiers in order to avoid over-fitting and develop performance of the method a 15 percent test subset of the total info is utilized to determine whether generalization of network holds up under scrutiny. Our suggested method and the baseline LSTM method are compared using the same training and testing sets of MC data. Both models' accuracy and recall graphs are shown in Fig. 5 when applied to our dataset labeled. As can be seen, this work's model outperformed the others in terms of RMSE. Because of the usage of the resampling method, this significant improvement has occurred, for better detection, average of dataset driven weighted of all transactions in a sequence can be used to extract more relevant patterns from transactions. This allows the sequence classifier to regularly focus on info items that are most significant to classification objective.Fig. 5 RMSE for LSTM, RF, and LR.
Fig 5
Furthermore, to emphasize the sensitivity of our suggested model's classification performance, we've created a visual representation of the confusion matrix for each model we tested on our dataset. Dataset. We show that our suggested methodology is capable of reducing the number of routine fraudulent transactions while also catching the uncommon fraudulent transaction, It is critical for financial service providers in the real world. These models were chosen because of their promising results and the fact that they all use the same dataset, Dataset, as stated in this paper.
Tables 1, 2, and 3 displays the precision, recall, MAE, RMSE, and F score values for each model. Fig. 5 shows RMSE for the three algorithms. The latter parameter is particularly important in the detection of fraud area, because institutions of financial are more interested in discovering potential fraud cases in order to protect consumers' interests and prevent the significant yearly losses of financial generated by fraud.Table 1 Performance measures without re-sampling.
Table 1Algorithms MAE RMSE Precision Recall F score
LR 0.267 0.348 0.240 0.070 0.333
RF 0.312 0.445 0.812 0.044 0.829
LSTM 0.511 0.678 0.613 0.094 0.712
Table 2 Performance measures with over-sampling.
Table 2Algorithms MAE RMSE Precision Recall F score
LR 0.113 0.150 0.367 0.080 0.178
RF 0.231 0.432 0.921 0.099 0.112
LSTM 0.539 0.684 0.632 0.076 0.921
Table 3 Performance measures with under-sampling.
Table 3Algorithms MAE RMSE Precision Recall F score
LR 0.449 0.145 0.347 0.099 0.321
RF 0.812 0.123 0.211 0.067 0.297
LSTM 0.762 0.871 0.339 0.045 0.421
Even without resampling, the LR was able to accurately classify the valid samples with precision, recall and f1 scores as shown in tables. This was to be expected, given that we're dealing with an unequal class. This is especially true in cases where the entire class is defrauded. This framework's recall and precision were only 0.240 and 0.070, respectively. As illustrated in Figs. 6 , 7 , and 8 the ROC AUC (area under the receiver operating characteristic curve) amounts are likewise subpar.Fig. 6 ROC for LR.
Fig 6
Fig. 7 ROC for RF.
Fig 7
Fig. 8 ROC for LSTM.
Fig 8
Fig. 9 depicts the LSTM model's confusion matrix when none of the resampling approaches are utilized. Tables shows that when random under-sampling was applied, the LSTM performed extremely well in categorizing the negative class.Fig. 9 Confusion matrix for LSTM.
Fig 9
5 Discussion
The LSTM model is assessed using the above-mentioned evaluation metrics. The results of this study show that the proposed model outperforms the competition in terms of detecting fraudulent transactions. For each epoch, a loss is acquired during the training of this model as the number of epochs grows, the loss decreases until it reaches a minimum. A model with a lower loss indicates a more accurate prediction. Because of the growing popularity of mobile money transfers, it's important to be aware of potential fraud through bank transactions. This is now a foregone conclusion at COVID-19. Customers will not be tormented by financial disputes if unlawful transactions are discovered and prevented.
To date, this research has been conducted from the announcement of Covid-19 by the Indian Government to the first unlock period. The study's major goal is to track down fraudulent transactions and reduce fraud as much as attainable. It demonstrates that strategy is both practical and appropriate for use in current context. LSTM algorithms can be used to identify fraudulent transactions during a shutdown period. LSTM model is developed and implemented for this purpose, with hyper-parameter fine-tuning as needed.
By adjusting hyper-parameters, a more precise model can be created, one that performs to its full potential. It is obvious from the testing findings that suggested model is qualified to detect suspicious transactions with a high precision. Because it can be used to big financial datasets, the proposed strategy is preferred. Due to the fact that clients will be alerted to fraudulent transactions, an effective and error-free system is needed in the mobile transaction industry.
ML may be employed to identify classes of transactions which are most presumably to be fraudulent, which can help avoid fraud. Predictive modeling is employed in this paper to identify fraudulent transactions. It may generate rules/models, and analyses to determine whether a certain transaction, conducted in a particular manner, or coming from a particular person, is presumably to be fraudulent. Risk factors and thresholds were analyzed in real time using the proposed technique. Individual transactions might be accepted or rejected by businesses.
That the danger factor has been effectively minimized is a clear indication of this. When a transaction is proven to be fraudulent, the transaction authority can use this new approach to help them get rid of it. We utilize ML because it may constantly modify and update its rules in response to new transactions, ensuring that the rules remain current.
6 Conclusions
To describe if MC transaction is fraudulent or not, ML algorithms was used. A data provided by the ML group at ULB was used for this purpose. As the accounts of positive class for a large portion of the dataset, it is severely lopsided. When a severely uneven class distribution is used as input to a predictive model, the model is skewed toward the majority samples. As a result, a fraudulent transaction can be passed off as a legitimate one.
To address this issue, a data-level strategy that included a variety of resampling strategies was used. In addition, methods of algorithmic like as boosting and bagging were used to address the problem of class imbalance. Additionally, the LSTM method was used to compare it to other methods. After that, analyses were conducted on all 3 methods both without and with resampling. The RF in combination with a re-sampling strategy connections elimination outperformed other models, according to the comparative results.• By taking into account the misclassification costs, a cost learning of sensitive technique can be used for future work.
• The price of misclassifying the phony class as a legitimate one (FN), True Positive costs are substantially higher than price of misclassifying a lawful class as the fraudulent class (FP), which is price of studying contacting and transaction cardholder.
• This method of learning entails categorizing an instance into the type with the lowest projected price.
• Highest performance of without re-sampling was 0.829 for RF algorithm-F score. While for under-sampling, it was 0.871 for LSTM algorithm-RMSE. Further, for over-sampling, it was 0.921 for both RF algorithm-Precision and LSTM algorithm-F score.
For future work, it could be interesting to consider other confusion matrices and compare them with this work. Further, other SMLs may be considered for next studies.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Data availability
Data will be made available on request.
==== Refs
References
Abu Alfeilat H.A. Hassanat A.B. Lasassmeh O. Tarawneh A.S. Alhasanat M.B. Eyal Salman H.S. Effects of distance measure choice on k-nearest neighbor classifier performance: A review Big data 7 4 2019 221 248 31411491
Adday B.N. Shaban F.A.J. Jawad M.R. Jaleel R.A. Zahra M.M.A. Enhanced vaccine recommender system to prevent COVID-19 based on clustering and classification 2021 International Conference on Engineering and Emerging Technologies (ICEET) 2021 IEEE 1 6
Al-Abdaly N.M. Al-Taai S.R. Imran H. Ibrahim M. Development of prediction model of steel fiber-reinforced concrete compressive strength using random forest algorithm combined with hyper-parameter tuning and k-fold cross-validation Eastern-European Journal of Enterprise Technologies 5 7 2021 113
Ali N.G. Abed S.D. Shaban F.A.J. Tongkachok K. Ray S. Jaleel R.A. Hybrid of K-Means and partitioning around medoids for predicting COVID-19 cases: Iraq case study Periodicals of Engineering and Natural Sciences (PEN) 9 4 2021 569 579
An B. Suh Y. Identifying financial statement fraud with decision rules obtained from Modified Random Forest Data Technologies and Applications 54 2 2020 235 255
Ashtiani M.N. Raahemi B. Intelligent fraud detection in financial statements using machine learning and data mining: A systematic literature review IEEE access : practical innovations, open solutions 10 2021 72504 72525
Bandyopadhyay S.K. Dutta S. Detection of fraud transactions using recurrent neural network during COVID-19: Fraud transaction during COVID-19 Journal of Advanced Research in Medical Science & Technology 7 3 2020 16 21 ISSN: 2394-6539
Benchaji I. Douzi S. El Ouahidi B. Jaafari J. Enhanced credit card fraud detection based on attention mechanism and LSTM deep model Journal of Big Data 8 1 2021 1 21 33425651
Berahmand K. Nasiri E. Li Y. Spectral clustering on protein-protein interaction networks via constructing affinity matrix using attributed graph embedding Computers in Biology and Medicine 138 2021 104933
Berahmand K. Nasiri E. Rostami M. Forouzandeh S. A modified DeepWalk method for link prediction in attributed social network Computing 103 10 2021 2227 2249
Borup D. Christensen B.J. Mühlbach N.S. Nielsen M.S. Targeting predictors in random forest regression International Journal of Forecasting 2022
Brezočnik L. Fister Jr, I. Podgorelec V. Swarm intelligence algorithms for feature selection: A review Applied Sciences 8 9 2018 1521
Carcillo F. Le Borgne Y.A. Caelen O. Bontempi G. Streaming active learning strategies for real-life credit card fraud detection: Assessment and visualization International Journal of Data Science and Analytics 5 4 2018 285 300
Choi D. Lee K. An artificial intelligence approach to financial fraud detection under IOT environment: A survey and implementation 2018 Security and Communication Networks 2018
Duarte E. Wainer J. Empirical comparison of cross-validation and internal metrics for tuning SVM hyper-parameters Pattern Recognition Letters 88 2017 6 11
Ebadi M.J. Hosseini A. Hosseini M.M. A projection type steepest descent neural network for solving a class of non-smooth optimization problems Neurocomputing 235 2017 164 181
Fischer T. Krauss C. Deep learning with long short-term memory networks for financial market predictions European journal of operational research 270 2 2018 654 669
Ganji V.R. Mannem S.N.P. Credit card fraud detection using anti-k nearest neighbor algorithm International Journal on Computer Science and Engineering 4 6 2012 1035 1039
Gregova E. Valaskova K. Adamko P. Tumpach M. Jaros J. Predicting financial distress of Slovak enterprises: Comparison of selected traditional and learning algorithms methods Sustainability 12 10 2020 3954
Guo J. Yang L. Bie R. Yu J. Gao Y. Shen Y. An XGBoost-based physical fitness evaluation model using advanced feature selection and Bayesian hyper-parameter optimization for wearable running monitoring Computer Networks 151 2019 166 180
Gupta V. Santosh K.C. Arora R. Ciano T. Kalid K.S. Mohan S. Socioeconomic impact due to COVID-19: An empirical assessment Information Processing & Management 59 2 2022 102810
Hammed M. Soyemi J. An implementation of decision tree algorithm augmented with regression analysis for fraud detection in credit card International Journal of Computer Science and Information Security (IJCSIS) 18 2 2020 79 88
Heydarpour F. Abbasi E. Ebadi M.J. Karbassi S.M. Solving an optimal control problem of cancer treatment by artificial neural networks IJIMAI 6 4 2020 18 25
Hu T. Liu X. Chen T. Zhang X. Huang X. Niu W. Transaction-based classification and detection approach for Ethereum smart contract Information Processing & Management 58 2 2021 102462
Iwendi C. Bashir A.K. Peshkar A. Sujatha R. Chatterjee J.M. Pasupuleti S. COVID-19 patient health prediction using boosted random forest algorithm Frontiers in public health 8 2020 357 32719767
Jain A. Sarupria A. Kothari A. The impact of COVID-19 on E-wallet's payments in Indian economy International Journal of Creative Research Thoughts 8 6 2020 2447 2454
Jamali N. Sadegheih A. Lotfi M.M. Wood L.C. Ebadi M.J. Estimating the depth of anesthesia during the induction by a novel adaptive neuro-fuzzy inference system: A case study Neural Processing Letters 53 1 2021 131 175
Jin Y. Wu D. Guo W. Attention-based LSTM with filter mechanism for entity relation classification Symmetry 12 10 2020 1729
Kamaruddin S. Ravi V. Credit card fraud detection using big data analytics: Use of PSOAANN based one-class classification Proceedings of the international conference on informatics and analytics 2016 1 8
Khan A.T. Cao X. Li S. Katsikis V.N. Brajevic I. Stanimirovic P.S. Fraud detection in publicly traded US firms using Beetle Antennae Search: A machine learning approach Expert Systems with Applications 191 2022 116148
Khan F. Ateeq S. Ali M. Butt N. Impact of COVID-19 on the drivers of cash-based online transactions and consumer behavior: Evidence from a Muslim market Journal of Islamic Marketing 2021
Khatri S. Arora A. Agrawal A.P. Supervised machine learning algorithms for credit card fraud detection: A comparison 2020 10th international conference on cloud computing, data science & engineering (Confluence) 2020 IEEE 680 683
Le N.Q.K. Huynh T.T. Yapp E.K.Y. Yeh H.Y. Identification of clathrin proteins by incorporating hyper-parameter optimization in deep learning and PSSM profiles Computer methods and programs in biomedicine 177 2019 81 88 31319963
Li S. Yan Y. DATA-driven shock impact of COVID-19 on the market financial system Information Processing & Management 59 1 2022 102768
Liu Y. Jin X. Shen H. Towards early identification of online rumors based on long short-term memory networks Information Processing & Management 56 4 2019 1457 1467
Mohanty S. Seth V.K. Sanjay H.S. Prithvi B.S. Assessment of long short-term memory network for quora sentiment analysis Journal of The Institution of Engineers (India): Series B 103 2 2022 375 384
Nasiri E. Berahmand K. Rostami M. Dabiri M. A novel link prediction algorithm for protein-protein interaction networks by attributed graph embedding Computers in Biology and Medicine 137 2021 104772
Nti, I.K., .Aning, J., Ayawli, B.B.K., Frimpong, K., Appiah, A.Y., .& Nyarko-Boateng, O. (2021). A Comparative Empirical Analysis of 21 Machine Learning Algorithms for Real-World Applications in Diverse Domains.
Omondi-Ochieng P. Financial performance of the United Kingdom's national non-profit sport federations: A binary logistic regression approach Managerial Finance 2020
Ozcan T. Basturk A. Static facial expression recognition using convolutional neural networks based on transfer learning and hyper-parameter optimization Multimedia Tools and Applications 79 35 2020 26587 26604
Popat R.R. Chaudhary J. A survey on credit card fraud detection using machine learning 2018 2nd international conference on trends in electronics and informatics (ICOEI) 2018 IEEE 1120 1125
Sadalia I. Nasution F.N. Muda I. Logistic regression analysis to know the factors affecting the financial knowledge in decision of investment non Riil assets at university investment gallery International Journal of Management (IJM) 11 2 2020
Sadgali I. Sael N. Benabbou F. Performance of machine learning techniques in the detection of financial frauds Procedia computer science 148 2019 45 54
Shadmi E. Chen Y. Dourado I. Faran-Perach I. Furler J. Hangoma P. Health equity and COVID-19: Global perspectives International journal for equity in health 19 1 2020 1 16
Shekar B.H. Dagnew G. Grid search-based hyper-parameter tuning and classification of microarray cancer data 2019 second international conference on advanced computational and communication paradigms (ICACCP) 2019 IEEE 1 8
Sherstinsky A. Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network Physica D: Nonlinear Phenomena 404 2020 132306
Singh A. Ranjan R.K. Tiwari A. Credit card fraud detection under extreme imbalanced data: A comparative study of data-level algorithms Journal of Experimental & Theoretical Artificial Intelligence 34 4 2022 571 598
Uddin M.S. Chi G. Al Janabi M.A. Habib T. Leveraging random forest in micro-enterprises credit risk modelling for accuracy and interpretability International Journal of Finance & Economics 27 3 2022 3713 3729
Van Houdt G. Mosquera C. Nápoles G. A review on the long short-term memory model Artificial Intelligence Review 53 8 2020 5929 5955
Vaughan G. Efficient big data model selection with applications to fraud detection International Journal of Forecasting 36 3 2020 1116 1127
Zhang, C., Zhong, H., & Hu, A. (2022). Research on early warning of financial crisis of listed companies based on random forest and time series. Mobile Information Systems, 2022, doi: 10.1155/2022/1573966.
| 36510563 | PMC9729587 | NO-CC CODE | 2022-12-14 23:45:33 | no | Inf Process Manag. 2023 Mar 8; 60(2):103231 | utf-8 | Inf Process Manag | 2,022 | 10.1016/j.ipm.2022.103231 | oa_other |
==== Front
Pediatr Clin North Am
Pediatr Clin North Am
Pediatric Clinics of North America
0031-3955
1557-8240
Published by Elsevier Inc.
S0031-3955(22)00200-0
10.1016/j.pcl.2022.12.001
Article
COVID-19 Vaccine Hesitancy
Hammershaimb E. Adrianne 12
Campbell James D. 12
O’Leary Sean T. 34∗
1 University of Maryland School of Medicine, Department of Pediatrics, Division of Infectious Diseases and Tropical Pediatrics, Baltimore, MD, USA
2 University of Maryland School of Medicine, Center for Vaccine Development and Global Health, Baltimore, MD, USA
3 Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
4 Adult and Child Consortium for Health Outcomes Research and Delivery Science, University of Colorado Anschutz Medical Campus and Children's Hospital Colorado, Aurora, CO, USA
∗ Corresponding Author: Sean O’Leary Mailstop F443 | 1890 North Revere Court | Aurora, CO 80045
8 12 2022
8 12 2022
© 2022 Published by Elsevier Inc.
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Early in the SARS-CoV-2 pandemic, before COVID-19 vaccines were authorized and disseminated, public opinion polls began tracking public acceptance of and concerns about a hypothetical COVID-19 vaccine. Over time, as vaccines and information about them became more widely available, the focus shifted from evaluating premeditative thoughts about COVID-19 vaccines to observing behaviors, measuring vaccine uptake, and characterizing factors associated with COVID-19 vaccine acceptance. Much of what was initially understood about public opinions of COVID-19 vaccines was both garnered by and shared through the lay media. Over the course of the pandemic, a wealth of peer-reviewed literature examining the complexities of COVID-19 vaccine acceptance has also emerged, but our understanding of COVID-19 vaccine acceptance is constantly evolving along with the pandemic itself. In this manuscript, we review the current state of knowledge regarding COVID-19 vaccine hesitancy, with an emphasis on pediatric vaccination.
Keywords
COVID-19
SARS-CoV-2
vaccine
vaccination
pediatric
children
vaccine hesitancy
pandemic
==== Body
pmcClinical Care Points
• Pediatricians should strongly recommend and offer COVID-19 vaccination.
• Do not assume that parents who accept routine childhood vaccines and/or COVID-19 vaccination for themselves will also accept COVID-19 vaccination for their children.
• When counseling parents on COVID-19 vaccination, it is important to understand (1) their perceptions about the risk of COVID-19 disease in themselves and their children and (2) their concerns about the risks of vaccination.
Key Points
• Reluctance around COVID-19 vaccines is associated with unique concerns about their novelty and safety.
• Parents weighing the risks of COVID-19 disease against the risks of COVID-19 vaccination may make different decisions for themselves versus their children.
• Although COVID-19 vaccines have been highly politicized and subject to public scrutiny, pediatricians remain a trusted source of information and guidance.
I Introduction
On December 30, 2019, the World Health Organization (WHO) announced the emergence of SARS-CoV-2 as a novel human pathogen and the causative agent of coronavirus disease 2019 (COVID-19) 1. The pathogen quickly spread across the globe, resulting in a pandemic that caused more than half a billion cases and 6 million deaths by June 20222. Efforts to develop a vaccine were promptly launched with the U.S., U.K., China, Russia, and India leading the race. COVID-19 vaccines were developed and authorized for use in Russia by August 11, 2020, the U.K. by December 2, 2020, the U.S. by December 11, 2020, and China by December 30, 2020 3, 4, 5, 6. It was estimated early on that 70% of the population would need to be vaccinated in order to end the pandemic; however, hesitancy around COVID-19 vaccines has proven a formidable obstacle to achieving target vaccination levels 7. Understanding the factors related to COVID-19 vaccine acceptance vs. reluctance/refusal would be an integral component of eventual vaccination campaigns, theoretically providing opportunities to bolster acceptance and mitigate hesitancy.
Early in the pandemic, before COVID-19 vaccines were authorized and disseminated, public opinion polls began tracking public acceptance of and concerns about a hypothetical COVID-19 vaccine. Over time, as vaccines and information about them have become more widely available, the focus has shifted from evaluating premeditative thoughts about COVID-19 vaccines to observing behaviors, measuring vaccine uptake, and characterizing factors associated with COVID-19 vaccine acceptance. Much of what was initially understood about public opinions of COVID-19 vaccines was both garnered by and shared through the lay media. Over the course of the pandemic, a wealth of peer-reviewed literature examining the complexities of COVID-19 vaccine acceptance has also emerged, but our understanding of COVID-19 vaccine acceptance is constantly evolving along with the pandemic itself.
II COVID-19 Vaccine Intentions Prior to Authorization
During the initial wave of the pandemic in North America and Western Europe, public opinion was largely supportive of vaccines against COVID-19. A large survey across 19 countries in June 2020 found that willingness to take a COVID-19 once available was 72% worldwide and as high as 76% and 75% in Mexico and the U.S., respectively, and 69% in Canada 8. By September 2020, at the height of a U.S. presidential election campaign season, President Trump was promising an FDA-approved vaccine ahead of election day. At that time acceptance had dropped to as low as 63% in the general U.S. population with only 34% saying they would “definitely get” a COVID-19 vaccine “if it were free and deemed safe by scientists”9.
III COVID-19 Vaccine Intentions and Uptake During Rollouts
Adult Rollout.
After the U.S. Food and Drug Administration (FDA) authorized the first COVID-19 vaccines for use in the U.S., initial guidance from the Centers for Diseases Control and Prevention (CDC) Advisory Committee on Immunization Practices (ACIP) prioritized residents of long term care facilities (LTCFs), due to the high rates of morbidity and mortality in this population, and healthcare workers (HCWs), due to concerns about strain on the healthcare system10. In the first month of the U.S. COVID-19 vaccine rollout, HCW uptake was generally high. Although receipt was also high (78%) LTCF residents only38% of LTCF staff members had received one or more doses of a COVID-19 vaccine through the CDC Pharmacy Partnership for Long-Term Care Program11. By April 2021, vaccination among healthcare providers working in LTCFs had increased to 57% with the highest rates among physicians and advanced practice providers (75%) and lower rates among nurses and ancillary services employees (57% and 59%, respectively)12. Lower rates of vaccine acceptance among nurses relative to physicians have been noted in healthcare systems across the U.S., Canada, and internationally 13, 14, 15, 16, 17, 18. Other characteristics of HCWs associated with decreased acceptance include younger age, female sex, Black race, and Hispanic/Latino ethnicity. These were similar to characteristics associated with decreased acceptance found in the general U.S. population at that time17, 18, 19.
Other frontline workers, older adults , and people with chronic medical conditions were also prioritized for early vaccination. Compared to healthy adults, those with chronic medical conditions, including immunocompromising conditions, were more accepting of COVID-19 vaccination, and the perception of being at increased risk of severe COVID-19 disease was associated with increased acceptance20, 21, 22. In contrast, a December 2020 survey found that 35% of U.S. essential workers were not likely to get a COVID-19 vaccine22. By mid-March 2021, 48% of non-healthcare essential workers in the U.S. had received a COVID-19 vaccine with 21% still reporting they would not get one, even if required23.
Following the authorization of COVID-19 vaccines, surveys conducted in January 2021 showed that 47% of U.S. adults were either vaccinated or intended to get a COVID-19 vaccine as soon as one became available to them, while 72% of Canadian adults intended to get a COVID-19 vaccine24 , 25. Over the subsequent months with increasing access to vaccines, uptake among U.S. adults increased with 62% reporting that they had received a COVID-19 vaccine by May 202124. Uptake among adults 65 years and older has been quite high in the U.S. and Canada (Mexico does not breakdown vaccination data by age). For example, in the US, as of July 29, 2022, 92% of adults 65 and older are fully vaccinated, although there is a great deal of geographic variability at the state and county levels.26
Pediatric Rollout.
In the U.S., the Pfizer/BioNTech COVID-19 vaccine had been authorized for adolescents 16 years and older since December 2020, and in May 2021, the vaccine was authorized for adolescents 12-15 years of age. By the end of July 2021, 43% of U.S. adolescents 12-17 years old had received a COVID-19 vaccine with subsequent slow increases to a plateau of approximately 68% by February 2022. This trajectory is consistent with surveys showing that one-third of U.S. parents wanted to “wait and see” before vaccinating their children24 , 27, 28, 29. In October-November 2021, similar proportions of U.S. parents of 5-11 year old children reported wanting to wait before vaccinating their children, with 22-27% wanting to vaccinate them as soon as possible24 , 30. A vaccine was authorized for children 5-11 years old in the U.S. on October 29, 2021, and by the end of January 2022, 31% of U.S. children 5-11 years old had received at least a first dose of COVID-19 vaccine. However, as of July 2022, 63% of U.S. children in this age group remain completely unvaccinated27 , 28. As with the adult COVID-19 vaccine rollout, there is wide geographic variation in uptake.26
In December 2020, 63% of Canadian parents intended to vaccinate their children 0-17 years of age, and by May 8, 2022, 57% and 88% of Canadians 5-11 and 12-17 years, respectively, had received a COVID-19 vaccine31 , 32. On June 17, 2022, the U.S. FDA authorized COVID-19 vaccines for use in children 6 months-4 years of age, and as of this writing, the rollout of vaccines for children under 5 years was newly underway33.
There are limited data on vaccine attitudes among parents of Mexican children.34 Mexico delayed its vaccination campaign for children, only beginning to vaccinate children 12 years and older with comorbidities after a court order to do so in November 2021, starting with adolescents 15-17 years of age.35 On April 28, 2022, vaccination began for all children 12 and older and Mexico didn’t authorize vaccination of 5-11 year olds until May 27, 2022.26
IV COVID-19 Vaccine Incentives and Mandates
Efforts to increase vaccine uptake amongst the general population included both monetary incentives and mandates tied to education, work, entertainment, and travel. Such mandates have been politically contentious and variably effective. A large survey found that cash incentives such as gift cards and entry into lotteries would have no effect on the intentions of roughly half of unvaccinated U.S. adults and would make 40% of those who were disinclined to get a COVID-19 vaccine even less likely to get one36. The same survey found that 64% of those unvaccinated Americans who were not inclined to get a COVID-19 vaccine would not get one despite workplace requirements36. Despite opinion surveys suggesting that mandates may not be effective and vocal protests and legal actions against vaccine mandates, many entities, including corporations, colleges and universities, healthcare systems, and government agencies including the military saw significant increases in the numbers of vaccinated employees and students after the introduction of COVID-19 vaccine mandates37. For example, Tyson Foods saw an increase in employee vaccinations from 50% to 96% within three months of instituting a vaccine mandate, with “very few” employees leaving the company 38.
V Special Populations
Black and Hispanic Communities.
Black and Latino/Hispanic communities in the U.S. have experienced disproportionately high rates of morbidity and mortality due to COVID-19 but in general were late adopters of COVID-19 vaccines27 , 39 , 40. Early reluctance to receive a COVID-19 vaccine was attributed to a mistrust of government and American medicine given personal experiences and a history of abuses by the medical community 41, 42, 43. Data from the CDC’s National Immunization Survey – Adult COVID Module (NIS-ACM), which is based on self-report, suggest that the rates of Black, Non-Hispanic and Hispanic Americans who have received a COVID-19 vaccine caught up to the rates of vaccinated White, Non-Hispanic Americans by October 202127. However, using other data sources on vaccine administration, as of July 29, 2022, vaccination rates among Black, Non-Hispanic Americans appear to continue to lag behind those of all other racial and ethnic categories. Similarly, other than Black, Non-Hispanic Americans, all other racial and ethnic categories have outpaced White, Non-Hispanic Americans in COVID-19 vaccine uptake 27. These vaccine administration data, however, are limited in that race and ethnicity are missing for a large portion of the population. This inconsistency between self-reported vaccination status and vaccine administration data highlights the challenge of collecting and reporting accurate data on vaccine-related attitudes and behaviors among different racial and ethnic groups.
Detained Populations.
Congregate living settings were the foci of early outbreaks; however, residents of detention centers in the U.S. were not consistently prioritized in the way that residents of LTCFs were despite recommendations from CDC’s ACIP. However, limited access is not the only determinant of COVID-19 vaccine uptake in this population44. Studies in the U.S. and Canada found that incarcerated individuals reported their significant reasons for refusing COVID-19 vaccination to include (1) distrust of prison employees, including healthcare workers, and the government, and (2) perceiving themselves to be at low risk of COVID-19 disease45, 46, 47. Similar data on Mexico and other central American countries are not available as of this writing.48
Pregnant Persons.
Pregnant persons were largely excluded from the early vaccine trials. Consequently, early prioritization schemes offered little guidance around COVID-19 vaccination during pregnancy, and concerns about safety were associated with hesitancy 49, 50, 51, 52. Based on evidence of higher morbidity and mortality among pregnant persons with COVID-19, the American College of Obstetricians and Gynecologists and the Society for Maternal-Fetal Medicine issued guidance recommending both an initial vaccine series and booster vaccination for all eligible individuals regardless of pregnancy or lactation status53 , 54. Despite accumulating evidence of the safety and effectiveness of COVID-19 vaccination for pregnant persons and their offspring and the dangers of COVID-19 disease in pregnancy, as of June 18, 2022, the CDC estimated that 71% of pregnant people in the U.S. had received a COVID-19 vaccine, with 54.9% having received a booster dose and 3.1% having received at least one COVID-19 dose while pregnant55, 56, 57.
Children.
In surveys done to date, parents are less accepting of COVID-19 vaccines for their children than for themselves. In general, surveys performed later in the pandemic in late 2021 showed greater levels of parental acceptance of COVID-19 vaccines compared to surveys performed in 2020 and early 202129 , 58, 59, 60, 61, 62, 63, 64, 65, 66, 67. A nationally representative survey of U.S. parents in October-November 2021 found that for children ages 0-4 years, 52% of parents were likely to have their children vaccinated, and for ages 5-11 and 12-17, 54% and 70% of parents, respectively, reported they were likely to vaccinate or had already vaccinated their children30. However, roughly 40% of parents with children 0-11 years old wanted to “wait and see” before vaccination, and another 36% would not let their children get a COVID-19 vaccine. Parents in that study who had received a COVID-19 vaccine themselves were 1.9, 3.7, and 6.2 times more likely to accept COVID-19 vaccination for their 0-4, 5-11, and 12-17 year old children, respectively; however, this effect was not statistically significant for parents of children 0-4 years old, the age group for which a vaccine had not yet been authorized at the time of the study30. Acceptance of COVID-19 vaccination for children is also associated with acceptance of routine childhood immunizations and prior receipt of a seasonal influenza vaccine30.
Male parents, older parents, and parents of older children have higher rates of COVID-19 vaccine acceptance compared to female parents, younger parents, and parents of younger children 59 , 68, 69, 70. Parents in the U.S. of Hispanic ethnicity are more likely to accept COVID-19 vaccination for children compared to non-Hispanic parents68 , 69 , 71 , 72. Compared to the U.S., Canadian parents’ acceptance of pediatric COVID-19 vaccines was initially higher, but that gap has narrowed with time31 , 73.
Parents must balance the perceived risks of COVID-19 disease against perceived risks and benefits of COVID-19 vaccination. The media often report that pediatric COVID-19 disease is mild, but parental perception that pediatric COVID-19 disease is severe is a predictor of positive vaccination intention for children 0-4 and 5-1130. At the same time, parents report concerns about the side effects of COVID-19 vaccines, and parents with attitudinal barriers to COVID-19 vaccination are less likely to accept the vaccine for their children29 , 30 , 62 , 67.
VI Reasons for COVID-19 Vaccine Hesitancy
Novelty of Vaccines.
Several aspects of COVID-19 vaccination are distinct from other vaccines. First, SARS-CoV-2 is a novel human pathogen, and no vaccines for human coronaviruses had ever reached late-stage development in the past. Second, the mRNA and viral-vectored technologies applied to COVID-19 vaccines had been in development for decades preceding the emergence of SARS-CoV-2 but had not been used in vaccines previously authorized for the general population. Third, the process through which the biomedical research community moved from pathogen identification to successful vaccine development and production was not only unprecedented in terms of speed but also in terms of public visibility. Perceptions that the vaccines were “rushed,” that the technologies were “too new,” and that SARS-CoV-2 might “burn out” on its own or provide sustained natural immunity fed reluctance toward COVID-19 vaccines and have prompted a “wait and see” approach for many people who are otherwise not reluctant toward other vaccines74 , 75.
Safety Concerns.
The high visibility of COVID-19 vaccination campaigns in the media amplified awareness of rare side effects following vaccination, beginning with anaphylaxis following mRNA vaccination76. The identification of serious blood clots, caused by the entity now known as vaccine-induced immune thrombotic thrombocytopenia, and Guillain-Barré syndrome in recipients of viral-vectored vaccines and of myopericarditis in adolescent and young adult males following mRNA vaccination created further concerns about vaccine safety 77, 78, 79, 80, 81, 82.
In addition to concerns about rare but immediate or proximate side effects, some reluctance stemmed from concern about unknown long-term side effects. Among these are concerns about effects on fertility spurred by rumors associating COVID-19 vaccines with both female and male infertility that became widespread on social media. Some were rooted in the rhetoric of past anti-vaccine campaigns about human papilloma virus (HPV) vaccines while others capitalized upon fears of forced sterilizations among the Black American community83. Still others seized upon the rapidly expanding body of knowledge about SARS-CoV-2, drawing parallels between the spike protein and placental proteins and positing that vaccine-induced antibodies targeting the spike protein might also attack the placenta84. Studies have shown no adverse effects on either female or male fertility after COVID-19 vaccination, but public concerns persist 50 , 83 , 85 , 86.
Beliefs about Susceptibility, Severity, and Effectiveness.
From the first days of the COVID-19 pandemic, it was well known that the disease tended to be more severe in older populations, particularly when compared to children, and for a time, there was some evidence that children were less likely to become infected.87 Thus, public perception that COVID-19 was a relatively benign illness in children was common and has contributed to parental resistance to vaccination.30 , 31 , 61 In addition, as the COVID-19 vaccination campaign progressed and discussion of booster doses became public, some construed the need for boosters to mean that the vaccines were not effective. Contributing to this misperception in children specifically were the effectiveness results from the clinical trials in children, particularly those less than 5 years of age (or 6 for the Moderna vaccine).88 When mRNA vaccines were eventually authorized for children less than age 5, the studies in this age group were done during the omicron wave, when vaccine effectiveness against infection was known to be relatively low compared to the initial studies in adults undertaken during circulation of the ancestral variant, which showed very high effectiveness against all outcomes, including infection.89
Religious and Philosophical Concerns.
While some groups argue against COVID-19 vaccine mandates on the grounds of personal liberty, others with reluctance around available COVID-19 vaccines have cited religious concerns about the vaccines90. Although essentially all major organized religions encourage vaccination with the available COVID-19 vaccines, groups with staunch objections to the use of embryonic stem cells in vaccine development and/or manufacturing have voiced preferences for vaccines other than the mRNA and viral-vectored vaccines currently available in the U.S.91
Misinformation and Disinformation Campaigns.
Some false beliefs about COVID-19 disease and vaccines that contribute to hesitancy may result from the purposeful dissemination of disinformation; others stem from an abundance of readily available misinformation and may be informed by individual or group tendencies toward conspiratorial beliefs92. Some countries have denied the presence of COVID-19 within their borders altogether for the majority of the pandemic, but even in countries with high rates of COVID-19 transmission such as the U.S., there are portions of the public that do not perceive COVID-19 to be a threat93 , 94. This may be due to local transmission dynamics, personal experiences of mild COVID-19 disease, or skepticism about the severity of COVID-19 disease among other possible reasons24 , 74 , 75. Conspiratorial beliefs about the contents and functions of the vaccines also weigh into the considerations of the risks of COVID-19 disease vs. the risks of COVID-19 vaccines. Some of these include the idea that the vaccines might alter the recipient’s DNA or allow a government or corporate entity to manipulate, control, or track the vaccinee through the use of microchips24 , 74 , 75.
Politics and Trust.
Breakdown of trust in authorities. Although social cohesion was initially galvanized by the perception of a common threat in the form of COVID-19, policy decisions in response to the pandemic became highly politicized and polarizing. In the U.S. and elsewhere, affiliation with right-leaning political parties has been associated with resistance to masking and COVID-19 vaccination, while left-leaning political parties largely promoted both interventions 95 , 96. Political party affiliation may be used as a surrogate category to represent sociopolitical beliefs, but characterizing attitudes along party lines fails to account for large segments of the population that do not align with with political parties, leaving gaps in our understanding of the ideological drivers of acceptance vs. reluctance toward COVID-19 vaccination.
A general breakdown in trust of authorities worldwide has not spared government officials and public health authorities responding to the pandemic. Roughly 40% of Americans in an April 2020 poll felt that both the CDC and the FDA paid too much attention to politics. While in April 2020 83% of respondents to the same poll reported that they considered CDC to be a trustworthy source of reliable information about the coronavirus, 2 years later only 64% of those polled reported the CDC is a trustworthy source of reliable information on COVID-19 vaccines24 , 92.
Continued trust in personal healthcare providers. Despite a breakdown of trust in government and public health authorities, individual healthcare providers still garner high levels of confidence. A November 2021 poll found that 77% of U.S. parents trusted their children’s healthcare provider or pediatrician “a great deal” or “fair amount” to provide reliable information about COVID-19 vaccines for children, including 59% of unvaccinated parents29. The percentage of parents reporting trust in their children’s pediatricians increased to 83% in April 2022, and 85% of all U.S. adults in the same poll reported trust in their own healthcare provider 66.
VII Interventions to Improve COVID-19 Vaccine Acceptance
Given the novelty of both the pathogen and the vaccines used to combat it, one challenge in addressing COVID-19 vaccine hesitancy is that the research and public health communities have had limited time to understand what works to improve confidence and uptake. We can apply best practices from other vaccines, such as strengthening patient-provider communication and community engagement campaigns, but more time and investigation are needed to determine whether some strategies work better than others. Framing communications around efficacy, side effects, and relative risks have been shown to influence COVID-19 vaccination preferences and acceptance among adults in general and may help encourage acceptance among parents deciding when and whether to vaccinate their children97 , 98. Behavioral nudges in the form of text message reminders improved adult COVID-19 vaccine uptake in two randomized controlled trials, although these interventions were targeting populations prioritized for vaccination, and the outcome was short-term uptake99. Similar recall-reminder strategies and the use of presumptive announcements have been shown to improve adherence to other recommended vaccines, including among pediatric populations100 , 101.
Surveys of parental acceptance show that parents who receive a recommendation from their children’s healthcare provider are more likely to accept COVID-19 vaccination and that parents prefer for their younger children to receive a COVID-19 vaccine at their pediatric provider’s office as opposed to a pharmacy, where the bulk of COVID-19 vaccines are currently delivered 29 , 30 , 102. Although 85% of pediatricians are enrolled in the Vaccines for Children (VFC) program and 2/3 of VFC providers are enrolled to give COVID-19 vaccines, only 1/3 are actually delivering the vaccines102 , 103. Ensuring that young children have access to COVID-19 vaccines requires that pediatric providers not only have the capacity to stock the vaccines but also that they are actively recommending vaccination to their patients’ families. Even among pediatric providers who do not stock the vaccine, their communication, advocacy, and assistance in getting children vaccinated is critical.
There are issues unique to COVID-19 vaccination of children relative to other childhood vaccines that may require novel interventions to improve uptake. There is a need to develop interventions to address concerns about novelty in a rapidly developed pandemic vaccine that would be relevant to both this and other pandemics. There are also specific issues unique to COVID-19 vaccination. For example, it is now well-known that myocarditis is associated with mRNA vaccines,104 , 105 particularly for adolescent and young adult males. While it is clear that the benefits of vaccination clearly outweigh this rare risk (i.e., the risk of myocarditis from infection is much higher than from vaccination),106 the risk is real, and many adolescents and young adults have been hospitalized as a result, a fact that has not been missed in both traditional and online media. While other childhood vaccines can be associated with rare adverse events requiring hospitalization, these are very rare and not typically front page news. Developing and testing interventions that can address these types of rare adverse events both during and outside of a pandemic is an important area for future research.
VIII Conclusion
Acceptance of every vaccine that is authorized, licensed, and recommended may be influenced by factors common to all vaccines and by factors unique to, or amplified by, the individual vaccine and the time during which it was developed and rolled out. COVID-19 vaccines were developed, manufactured, tested, authorized, distributed, and first administered amidst recurring waves of the pandemic. Some of the factors related to COVID-19 vaccine acceptance or reluctance that have been exaggerated relative to general vaccine acceptance or reluctance include the use of newer manufacturing technologies, politicization of many aspects of pandemic control, and the intense public and media scrutiny of every aspect of COVID-19 vaccine research and implementation. Future work on vaccine hesitancy should focus not only on the factors common to all vaccines, but also on factors that are unique or intensified in relation to vaccines directed against pathogens causing epidemics and pandemics.
Attestation: Each individual listed as an author has contributed to the article to a significant extent in line with ICMJE guidelines. Dr. Hammershaimb drafted the outline with input from Drs. Campbell and O’Leary; Dr. Hammershaimb wrote the draft manuscript with input from Drs. Campbell and O’Leary. Dr. O’Leary revised the manuscript based on editor feedback. The submitted manuscript was reviewed, edited, and agreed upon by all 3 authors.
Funding and commercial and financial interests: All authors will provide this information via a formal disclosure form, as noted in the instructions.
Mailing Address For Drs Hammershaimb And Campbell: Center for Vaccine Development and Global Health, Health Sciences Research Facility 1, Room 480 , 685 West Baltimore Street, Baltimore, MD 21201
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References
1 World Health Organization. Novel Coronavirus (2019-nCoV) Situation Report - 1. 2020.
2 Dong E. Du H. Gardner L. An interactive web-based dashboard to track COVID-19 in real time Lancet Infect Dis 20 5 2020 533 534 32087114
3 Burki T.K. The Russian vaccine for COVID-19 Lancet Respir Med 8 11 2020 e85 e86 32896274
4 Ledford H. Cyranoski D. Van Noorden R. The UK has approved a COVID vaccine - here's what scientists now want to know Nature 588 7837 2020 205 206 33288887
5 FDA Takes Key Action in Fight Against COVID-19 By Issuing Emergency Use Authorization for First COVID-19 Vaccine [press release]. December 11, 2020 2020.
6 China gives its first COVID-19 vaccine approval to Sinopharm. 2020.
7 Randolph H.E. Barreiro L.B. Herd Immunity: Understanding COVID-19 Immunity 52 5 2020 737 741 32433946
8 Lazarus J.V. Ratzan S.C. Palayew A. A global survey of potential acceptance of a COVID-19 vaccine Nat Med 2020
9 Hamel L KA, Muñana C, Brodie M. KFF COVID-19 Vaccine Monitor: December 2020. Kaiser Family Foundation; 15 Dec 2020 2020.
10 Dooling K. McClung N. Chamberland M. The Advisory Committee on Immunization Practices' Interim Recommendation for Allocating Initial Supplies of COVID-19 Vaccine - United States, 2020 MMWR Morb Mortal Wkly Rep 69 49 2020 1857 1859 33301429
11 Gharpure R. Guo A. Bishnoi C.K. Early COVID-19 First-Dose Vaccination Coverage Among Residents and Staff Members of Skilled Nursing Facilities Participating in the Pharmacy Partnership for Long-Term Care Program - United States, December 2020-January 2021 MMWR Morb Mortal Wkly Rep 70 5 2021 178 182 33539332
12 Lee J.T. Althomsons S.P. Wu H. Disparities in COVID-19 Vaccination Coverage Among Health Care Personnel Working in Long-Term Care Facilities, by Job Category, National Healthcare Safety Network - United States, March 2021 MMWR Morb Mortal Wkly Rep 70 30 2021 1036 1039 34324478
13 Dzieciolowska S. Hamel D. Gadio S. Covid-19 vaccine acceptance, hesitancy, and refusal among Canadian healthcare workers: A multicenter survey Am J Infect Control 49 9 2021 1152 1157 33930516
14 Khubchandani J. Bustos E. Chowdhury S. Biswas N. Keller T. COVID-19 Vaccine Refusal among Nurses Worldwide: Review of Trends and Predictors Vaccines (Basel). 10 2 2022
15 Biswas N. Mustapha T. Khubchandani J. Price J.H. The Nature and Extent of COVID-19 Vaccination Hesitancy in Healthcare Workers J Community Health 46 6 2021 1244 1251 33877534
16 Ciardi F. Menon V. Jensen J.L. Knowledge, Attitudes and Perceptions of COVID-19 Vaccination among Healthcare Workers of an Inner-City Hospital in New York Vaccines (Basel) 9 5 2021
17 Farah W. Breeher L. Shah V. Hainy C. Tommaso C.P. Swift M.D. Disparities in COVID-19 vaccine uptake among health care workers Vaccine 40 19 2022 2749 2754 35361500
18 Ashley Kirzinger AK, Liz Hamel, Mollyann Brodie. KFF/The Washington Post Frontline Health Care Workers Survey. April 06, 2021 2021.
19 Caiazzo V. Witkoski Stimpfel A. Vaccine hesitancy in American healthcare workers during the COVID-19 vaccine roll out: an integrative review Public Health 207 2022 94 104 35594808
20 Warren A.M. Perrin P.B. Elliott T.R. Powers M.B. Reasons for COVID-19 vaccine hesitancy in individuals with chronic health conditions Health Sci Rep 5 2 2022 e485 35155827
21 Aberumand B. Ayoub Goulstone W. Betschel S. Understanding attitudes and obstacles to vaccination against COVID-19 in patients with primary immunodeficiency Allergy Asthma Clin Immunol 18 1 2022 38 35534860
22 Nguyen K.H. Srivastav A. Razzaghi H. COVID-19 Vaccination Intent, Perceptions, and Reasons for Not Vaccinating Among Groups Prioritized for Early Vaccination - United States, September and December 2020 MMWR Morb Mortal Wkly Rep 70 6 2021 217 222 33571174
23 Hamel L.S.A. Stokes M. Brodie M. KFF COVID-19 Vaccine Monitor: Vaccine Attitudes Among Essential Workers April 23 2021 2021
24 COVID-19 Vaccine Monitor Dashboard. Kaiser Family Foundation. https://www.kff.org/coronavirus-covid-19/dashboard/kff-covid-19-vaccine-monitor-dashboard/. Accessed Oct. 28, 2021.
25 Canada Go. Vaccine uptake in Canadian adults: Highlights from the 2020-2021 Seasonal Influenza Vaccination Coverage Survey. 2021.
26 New York Times. https://www.nytimes.com/interactive/2020/us/covid-19-vaccine-doses.html. Accessed July 29, 2022.
27 Centers of Disease Control and Prevention. Trends in Demographic Characteristics of People Receiving COVID-19 Vaccinations in the United States. https://covid.cdc.gov/covid-data-tracker/#vaccination-demographics-trends. Updated May 31, 2022. Accessed May 31, 2022.
28 Centers of Disease Control and Prevention. Pediatric Data. COVID Data Tracker Web site. https://covid.cdc.gov/covid-data-tracker/#pediatric-data. Published 2021. Accessed Oct. 28, 2021.
29 Liz Hamel LL, Ashley Kirzinger, Audrey Kearney, Mellisha Stokes , Grace Sparks, Mollyann Brodie. KFF COVID-19 Vaccine Monitor: Winter 2021 Update On Parents' Views Of Vaccines For Kids. Kaiser Family Foundation; December 9, 2021.
30 Hammershaimb E.A. Cole L.D. Liang Y. COVID-19 Vaccine Acceptance Among US Parents: A Nationally Representative Survey Journal of the Pediatric Infectious Diseases Society 2022
31 Humble R.M. Sell H. Dube E. Canadian parents' perceptions of COVID-19 vaccination and intention to vaccinate their children: Results from a cross-sectional national survey Vaccine 39 52 2021 7669 7676 34688500
32 COVID-19 vaccination in Canada. https://health-infobase.canada.ca/covid-19/vaccination-coverage/. Accessed May 27, 2022.
33 Coronavirus (COVID-19) Update: FDA Authorizes Moderna and Pfizer-BioNTech COVID-19 Vaccines for Children Down to 6 Months of Age [press release]. June 17, 2022.
34 Delgado-Gallegos J.L. Padilla-Rivas G.R. Gastelum-Arias L.J. Parent's Perspective towards Child COVID-19 Vaccination: An Online Cross-Sectional Study in Mexico Int J Environ Res Public Health 19 1 2021
35 Lopez O. As other nations push to vaccinate children, Mexico is an outlier. New York Times2021.
36 Sargent R.H. Laurie S. Moncada L. Masks, money, and mandates: A national survey on efforts to increase COVID-19 vaccination intentions in the United States PLoS One 17 4 2022 e0267154
37 Pereira I. COVID-19 vaccine mandates moving the needle, experts say. In: ABC News.
38 Press JFaA. How Tyson Foods’ CEO convinced 96% of his 120,000 employees to get vaccinated for COVID in just 3 months. In: Fortune; 2021.
39 Centers of Disease Control and Prevention. Risk for COVID-19 Infection, Hospitalization, and Death By Race/Ethnicity. https://www.cdc.gov/coronavirus/2019-ncov/covid-data/investigations-discovery/hospitalization-death-by-race-ethnicity.html. Updated Mar. 12, 2021. Accessed Mar. 15, 2021.
40 Prevention" CfDCa. Demographic Trends of COVID-19 cases and deaths in the US reported to CDC. https://covid.cdc.gov/covid-data-tracker/#demographics. Updated October 28, 2021. Accessed October 28, 2021.
41 Bogart L.M. Ojikutu B.O. Tyagi K. COVID-19 Related Medical Mistrust, Health Impacts, and Potential Vaccine Hesitancy Among Black Americans Living With HIV J Acquir Immune Defic Syndr 86 2 2021 200 207 33196555
42 Palamenghi L. Barello S. Boccia S. Graffigna G. Mistrust in biomedical research and vaccine hesitancy: the forefront challenge in the battle against COVID-19 in Italy Eur J Epidemiol 35 8 2020 785 788 32808095
43 Brandt A.M. Racism and research: the case of the Tuskegee Syphilis Study Hastings Cent Rep 8 6 1978 21 29
44 Strodel R. Dayton L. Garrison-Desany H.M. COVID-19 vaccine prioritization of incarcerated people relative to other vulnerable groups: An analysis of state plans PLoS One 16 6 2021 e0253208
45 Testa A. Fahmy C. COVID-19 Mitigation Compliance and Vaccination Status Among Formerly Incarcerated Individuals in the United States Health Educ Behav 49 2 2022 210 218 35249416
46 Stern M.F. Piasecki A.M. Strick L.B. Willingness to Receive a COVID-19 Vaccination Among Incarcerated or Detained Persons in Correctional and Detention Facilities - Four States, September-December 2020 MMWR Morb Mortal Wkly Rep 70 13 2021 473 477 33793457
47 Ortiz-Paredes D. Varsaneux O. Worthington J. Reasons for COVID-19 vaccine refusal among people incarcerated in Canadian federal prisons PLoS One 17 3 2022 e0264145
48 COVID-19 vaccinations for prison populations and staff: Report on global scan. Penal Reform International and Harm Reduction International. https://www.europris.org/wp-content/uploads/2021/12/HRI-PRI_CovidVaccinationReport_Dec2021.pdf. Published 2021. Accessed July 29, 2022.
49 Cui Y. Binger K. Palatnik A. Attitudes and Beliefs Associated With COVID-19 Vaccination During Pregnancy JAMA Netw Open 5 4 2022 e227430
50 Kharbanda E.O. Vazquez-Benitez G. COVID-19 mRNA Vaccines During Pregnancy: New Evidence to Help Address Vaccine Hesitancy JAMA 327 15 2022 1451 1453 35325030
51 Reifferscheid L. Marfo E. Assi A. COVID-19 vaccine uptake and intention during pregnancy in Canada Can J Public Health 2022
52 Gutierrez S. Logan R. Marshall C. Kerns J. Diamond-Smith N. Predictors of COVID-19 Vaccination Likelihood Among Reproductive-Aged Women in the United States Public Health Rep 137 3 2022 588 596 35238243
53 ACOG and SMFM Recommend COVID-19 Vaccination for Pregnant Individuals [press release]. July 30, 2021.
54 Gynecologists ACoOa. COVID-19 Vaccination Considerations for Obstetric–Gynecologic Care. https://www.acog.org/clinical/clinical-guidance/practice-advisory/articles/2020/12/covid-19-vaccination-considerations-for-obstetric-gynecologic-care. Updated April 28, 2022. Accessed May 31, 2022.
55 Goldshtein I, Steinberg DM, Kuint J, et al. Association of BNT162b2 COVID-19 Vaccination During Pregnancy With Neonatal and Early Infant Outcomes. JAMA Pediatr. 2022;176(5):470-477.
56 Centers of Disease Control and Prevention. COVID-19 vaccination among pregnant people aged 18-49 years overall, by race/ethnicity, and date reported to CDC - Vaccine Safety Datalink, United States. https://covid.cdc.gov/covid-data-tracker/#vaccinations-pregnant-women. Updated May 31, 2022. Accessed May 31, 2022.
57 Fu W. Sivajohan B. McClymont E. Systematic review of the safety, immunogenicity, and effectiveness of COVID-19 vaccines in pregnant and lactating individuals and their infants Int J Gynaecol Obstet 156 3 2022 406 417 34735722
58 Goldman R.D. Krupik D. Ali S. Caregiver Willingness to Vaccinate Their Children against COVID-19 after Adult Vaccine Approval Int J Environ Res Public Health 18 19 2021
59 Rane M.S. Robertson M.M. Westmoreland D.A. Teasdale C.A. Grov C. Nash D. Intention to Vaccinate Children Against COVID-19 Among Vaccinated and Unvaccinated US Parents JAMA Pediatr 176 2 2022 201 203 34870702
60 Chen F. He Y. Shi Y. Parents' and Guardians' Willingness to Vaccinate Their Children against COVID-19: A Systematic Review and Meta-Analysis Vaccines (Basel). 10 2 2022
61 Szilagyi P.G. Shah M.D. Delgado J.R. Parents' Intentions and Perceptions About COVID-19 Vaccination for Their Children: Results From a National Survey Pediatrics 148 4 2021
62 Ruggiero K.M. Wong J. Sweeney C.F. Parents' Intentions to Vaccinate Their Children Against COVID-19 J Pediatr Health Care 35 5 2021 509 517 34217553
63 Teasdale C.A. Borrell L.N. Kimball S. Plans to Vaccinate Children for Coronavirus Disease 2019: A Survey of United States Parents J Pediatr 237 2021 292 297 34284035
64 Teherani M, Banskota S, Camacho-Gonzalez A, et al. Intent to Vaccinate SARS-CoV-2 Infected Children in US Households: A Survey. Vaccines (Basel). 2021;9(9).
65 Hamel L. LL, Sparks G., Stokes M., and Brodie M. KFF COVID-19 Vaccine Monitor - April 2021. May 6, 2021 2021.
66 Sparks G. MA, Lopes L., Hamel L., Brodie M. KFF COVID-19 Vaccine Monitor - April 2022. May 4, 2022 2022.
67 Liz Hamel LL, Grace Sparks, Ashley Kirzinger, Audrey Kearney, Mellisha Stokes , Mollyann Brodie. KFF COVID-19 Vaccine Monitor: October 2021. Kaiser Family Foundation; Oct 28, 2021.
68 Kreuter M.W. Garg R. Marsh A. Intention to vaccinate children for COVID-19: A segmentation analysis among Medicaid parents in Florida Prev Med 156 2022 106959
69 Gray A. Fisher C.B. Determinants of COVID-19 Vaccine Uptake in Adolescents 12-17 Years Old: Examining Pediatric Vaccine Hesitancy Among Racially Diverse Parents in the United States Front Public Health 10 2022 844310
70 Goldman R.D. Ceballo R. International C.-P.A.S.G. Parental gender differences in attitudes and willingness to vaccinate against COVID-19 J Paediatr Child Health 58 6 2022 1016 1021 35170115
71 McKinnon B. Quach C. Dube E. Tuong Nguyen C. Zinszer K. Social inequalities in COVID-19 vaccine acceptance and uptake for children and adolescents in Montreal, Canada Vaccine 39 49 2021 7140 7145 34763947
72 Fisher C.B. Gray A. Sheck I. COVID-19 Pediatric Vaccine Hesitancy among Racially Diverse Parents in the United States Vaccines (Basel) 10 1 2021
73 Goldman R.D. Bone J.N. Gelernter R. National COVID-19 vaccine program progress and parents' willingness to vaccinate their children Hum Vaccin Immunother 17 12 2021 4889 4895 34797754
74 Tang L. Douglas S. Laila A. Among sheeples and antivaxxers: Social media responses to COVID-19 vaccine news posted by Canadian news organizations, and recommendations to counter vaccine hesitancy Can Commun Dis Rep 47 12 2021 524 533 35018140
75 Prevention CfDCa. Myths and Facts about COVID-19 Vaccines. https://www.cdc.gov/coronavirus/2019-ncov/vaccines/facts.html. Updated June 19, 2022. Accessed June 24, 2022.
76 Shimabukuro T.T. Cole M. Su J.R. Reports of Anaphylaxis After Receipt of mRNA COVID-19 Vaccines in the US-December 14, 2020-January 18, 2021 JAMA 325 11 2021 1101 1102 33576785
77 T S. Thrombosis with thrombocytopenia syndrome (TTS) following Janssen COVID-19 vaccine. In. Advisory Committee on Immunization Practices2021.
78 American Society of Hematology. Vaccine-induced Immune Thrombotic Thrombocytopenia. https://www.hematology.org/covid-19/vaccine-induced-immune-thrombotic-thrombocytopenia. Published 2022. Updated May 9, 2022. Accessed.
79 Keh R.Y.S. Scanlon S. Datta-Nemdharry P. COVID-19 vaccination and Guillain-Barre syndrome: analyses using the National Immunoglobulin Database Brain 2022
80 Hanson K.E. Goddard K. Lewis N. Incidence of Guillain-Barre Syndrome After COVID-19 Vaccination in the Vaccine Safety Datalink JAMA Netw Open 5 4 2022 e228879
81 Oster M.E. Shay D.K. Su J.R. Myocarditis Cases Reported After mRNA-Based COVID-19 Vaccination in the US From December 2020 to August 2021 JAMA 327 4 2022 331 340 35076665
82 Ontario PH. Myocarditis and Pericarditis after COVID-19 mRNA Vaccines. In:2022:1-17.
83 Abbasi J. Widespread Misinformation About Infertility Continues to Create COVID-19 Vaccine Hesitancy JAMA 327 11 2022 1013 1015 35191947
84 Head of Pfizer Research: Covid Vaccine is Female Sterilization. Health and Money News. https://healthandmoneynews.wordpress.com/2020/12/02/head-of-pfizer-research-covid-vaccine-is-female-sterilization/?fbclid=IwAR2HXbuZbKTR08HWnEV1wusabYR0d1ipKZF2ToaS71ScO7ugq8Z8gZDCIRk. Updated December 2, 2020. Accessed May 31, 2022.
85 Lu-Culligan A, Tabachnikova A, Perez-Then E, et al. No evidence of fetal defects or anti-syncytin-1 antibody induction following COVID-19 mRNA vaccination. PLoS Biol. 2022;20(5):e3001506.
86 Safrai M. Herzberg S. Imbar T. Reubinoff B. Dior U. Ben-Meir A. The BNT162b2 mRNA Covid-19 vaccine does not impair sperm parameters Reprod Biomed Online 44 4 2022 685 688 35279377
87 Davies N.G. Klepac P. Liu Y. Age-dependent effects in the transmission and control of COVID-19 epidemics Nat Med 26 8 2020 1205 1211 32546824
88 Coronavirus (COVID-19) Update: FDA Authorizes Moderna and Pfizer-BioNTech COVID-19 Vaccines for Children Down to 6 Months of Age [press release]. Food and Drug Administration, June 17, 2022.
89 FDA Takes Key Action in Fight Against COVID-19 By Issuing Emergency Use Authorization for First COVID-19 Vaccine [press release]. Food and Drug Administration, December 11, 2020.
90 Religious Identities and the Race Against the Virus: American Attitudes on Vaccination Mandates and Religious Exemptions. Public Religion Research Institute and the Interfaith Youth Core; December 9, 2021.
91 J J. New Novavax Shot Could Appeal to Pro-Life Christian Skeptics. In. Christianity Today.
92 Liz Hamel AK, Ashley Kirzinger, Lunna Lopes, Cailey Muñana, Mollyann Brodie. KFF Health Tracking Poll - September 2020: Top Issues in 2020 Election, The Role of Misinformation, and Views on A Potential Coronavirus Vaccine. Kaiser Family Foundation; September 10, 2020.
93 Hashim H.T. El Rassoul A.E.A. Bchara J. Ahmadi A. Lucero-Prisno D.E. 3rd COVID-19 denial in Turkmenistan veiling the real situation Arch Public Health 80 1 2022 8 34983658
94 Smith A. Kim Jong Un says pandemic has caused 'great crisis' in North Korea. NBC News. June 30, 2021.
95 Stoler J. Klofstad C.A. Enders A.M. Uscinski J.E. Sociopolitical and psychological correlates of COVID-19 vaccine hesitancy in the United States during summer 2021 Soc Sci Med 306 2022 115112
96 Albrecht D. Vaccination, politics and COVID-19 impacts BMC Public Health 22 1 2022 96 35031053
97 Kreps S. Kriner D.L. Communication about vaccine efficacy and COVID-19 vaccine choice: Evidence from a survey experiment in the United States PLoS One 17 3 2022 e0265011
98 Thorpe A. Fagerlin A. Drews F.A. Communications to Promote Interest and Confidence in COVID-19 Vaccines Am J Health Promot 2022 8901171221082904
99 Dai H. Saccardo S. Han M.A. Behavioural nudges increase COVID-19 vaccinations Nature 597 7876 2021 404 409 34340242
100 Szilagyi PG, Thomas K, Shah MD, et al. National Trends in the US Public's Likelihood of Getting a COVID-19 Vaccine-April 1 to December 8, 2020. JAMA. 2020.
101 Brewer N.T. Hall M.E. Malo T.L. Gilkey M.B. Quinn B. Lathren C. Announcements Versus Conversations to Improve HPV Vaccination Coverage: A Randomized Trial Pediatrics 139 1 2017
102 Oliver S. Updates to the Evidence to Recommendation Framework: Pfizer-BioNTech COVID-19 booster in children aged 5-11 years. In. Advisory Committee on Immunization Practices: Centers for Disease Control and Prevention.
103 O'Leary S.T. Allison M.A. Vogt T. Pediatricians' Experiences With and Perceptions of the Vaccines for Children Program Pediatrics 145 3 2020
104 Mevorach D. Anis E. Cedar N. Myocarditis after BNT162b2 mRNA Vaccine against Covid-19 in Israel N Engl J Med 385 23 2021 2140 2149 34614328
105 Mevorach D. Anis E. Cedar N. Myocarditis after BNT162b2 Vaccination in Israeli Adolescents N Engl J Med 386 10 2022 998 999 35081295
106 Patone M. Mei X.W. Handunnetthi L. Risks of myocarditis, pericarditis, and cardiac arrhythmias associated with COVID-19 vaccination or SARS-CoV-2 infection Nat Med 28 2 2022 410 422 34907393
| 0 | PMC9729588 | NO-CC CODE | 2022-12-14 23:22:26 | no | Pediatr Clin North Am. 2022 Dec 8; doi: 10.1016/j.pcl.2022.12.001 | utf-8 | Pediatr Clin North Am | 2,022 | 10.1016/j.pcl.2022.12.001 | oa_other |
==== Front
Transpl Immunol
Transpl Immunol
Transplant Immunology
0966-3274
1878-5492
Published by Elsevier B.V.
S0966-3274(22)00246-5
10.1016/j.trim.2022.101772
101772
Article
Clinical outcomes of kidney recipients with COVID-19 (COVID-19 in kidney recipients)
Hajibaratali Bahareh a
Amini Hossein b
Dalili Nooshin c
Ziaie Shadi b
Anvari Shideh a
Keykha Elham d
Rezaee Malihe ef
Samavat Shiva c⁎
a Department of Cardiology, Labbafinezhad Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
b Department of Clinical Pharmacy, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran
c Department of Nephrology, Chronic Kidney Disease Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
d Department of Internal Medicine, Labbafinezhad Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
e Medical Student Research Committee, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
f Non-Communicable Disease Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
⁎ Corresponding author at: Tehran Pasdaran Street 9th Boostan, Labbafinezhad Hospital, 1666663111, Iran.
8 12 2022
8 12 2022
1017729 6 2022
2 12 2022
4 12 2022
© 2022 Published by Elsevier B.V.
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Introduction
The coronavirus disease 2019 (COVID-19) pandemic has caused significant mortality since late 2019. Patients undergoing kidney transplantation (KT) are prone to COVID-19 due to immunosuppressive drug use and various comorbidities such as hypertension and diabetes.
Methods
One hundred thirty-three KT recipients with COVID-19 were included in this retrospective cohort study. Hospital mortality was considered a primary outcome, while acute kidney injury (AKI) was considered a secondary outcome. Demographic information, maintenance immunosuppression, medical history, laboratory information, and echocardiographic and electrocardiography results of patients were recorded. Patients were also followed for 2 months post-discharge for post-COVID-19 symptoms, readmission, and transplant function.
Results
Regarding the primary outcome of the 133 patients, 13 died and 120 survived. The deceased patients were significantly older (median age, 64 vs. 50.5 years; p = 0.04) and had a significantly higher median serum creatinine level (p = 0.002) and lower median glomerular filtration rate (p = 0.010) than patients who survived. The incidence of AKI was 47.3%, more common in deceased patients (p = 0.038) than in patients who survived. Troponin levels were significantly higher in deceased patients and those with AKI (p = 0.0004 and p = 0.039, respectively) than in patients who survived and those without AKI. A multivariable Cox regression analysis revealed that older age (adjusted hazard ratio, 1.13; 95% confidence interval, 1.01–1.27) and AKI (adjusted hazard ratio, 3.43; 95% confidence interval, 1.34–8.79) were associated with in-hospital mortality.
Conclusion
In conclusion, kidney recipients with COVID-19 had a higher mortality rate than the general population, with a higher prevalence in older individuals and those who experienced AKI during hospitalization than in patients who survived and those without AKI.
Keywords
kidney injury
cardiac effects
COVID-19
kidney transplantation
mortality
Abbreviations
ACE2, angiotensin-converting enzyme 2
AKI, acute kidney injury
CCI, Charlson Comorbidity Index
CI, confidence interval
CKD, chronic kidney disease
CNI, calcineurin inhibitors
CRP, C-reactive protein
CVD, cardiovascular disease
DD, diastolic dysfunction
ECG, electrocardiography
EF, ejection fraction
GFR, glomerular filtration rate
HR, hazard ratio
ICU, intensive care unit
IQR, interquartile range
IVC, inferior vena cava
KT, kidney transplantation
LDH, lactate dehydrogenase
MMF, mycophenolate mofetil
RRT, renal replacement therapy
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pmc1 Introduction
In December 2019, the world encountered a newly discovered infectious disease called coronavirus disease 2019 (COVID-19), which was first diagnosed in Wuhan, China. Since May 2022, a total of 514,256,035 COVID-19 cases have been confirmed worldwide, and at least 6,264,027 deaths have been reported to date (1). Iran was one of the most affected countries worldwide until May 2022, with a total of 6.9 million cases detected. Of them, 135,000 patients died and 6.5 million recovered (2). Clinically, most patients present with asymptomatic or mild flu-like symptoms. However, some experience a severe manifestation of acute respiratory syndrome that requires mechanical ventilation and intensive care unit (ICU) hospitalization associated with a high risk of mortality (3). Patients with underlying diseases, such as cardiovascular disease (CVD), lung cancer, chronic kidney disease (CKD), hypertension, and obesity, are at increased risk of severe COVID-related disease and death (4). In addition to receiving immunosuppressive drugs, kidney transplantation (KT) recipients suffer from chronic diseases such as CVD, high blood pressure, and diabetes (5). There are many unresolved questions about COVID-19 in this population, including its effects on graft function, the risk factors of in-hospital mortality among kidney recipients, and whether cardiac complications occur among renal transplant recipients with COVID-19.
2 Objectives
This study evaluated the morbidity and mortality rates of COVID-19 in KT recipients at the kidney transplant center of Labbafinezhad Hospital in Iran during the 2-year COVID-19 pandemic.
3 Material and methods
3.1 Study design
All KT recipients diagnosed with COVID-19 and admitted to Labbafinezhad Hospital in Tehran, Iran, from December 2019 to September 2021 were enrolled in this retrospective cohort study. This study was approved by the Ethics Committee of Shahid Beheshti University of Medical Sciences, Tehran, Iran (IR.SBMU.MSP.REC.1399.003).
3.2 Study subjects
A total of 140 confirmed COVID-19 cases were identified based on a positive nasopharyngeal real-time reverse-transcriptase polymerase chain reaction test. A total of 140 adult kidney recipients (aged >18 years) admitted with confirmed COVID-19 during the study period were included. We excluded seven patients on dialysis with a history of KT.
3.3 Data collection
Trained medical personnel gathered data from the patients' medical records using a research-made checklist. The checklist included information such as demographic characteristics, smoking status, past medical history, maintenance immunosuppression, clinical presentation, time from transplantation to sudden acute respiratory syndrome coronavirus 2 (SARS-CoV2) infection, baseline clinical characteristics before COVID-19 (baseline creatinine and baseline glomerular filtration rate [GFR], laboratory data on admission, echocardiography and electrocardiography [ECG] findings during hospitalization, and in-hospital medical management). Echocardiographic findings consisted of ejection fraction (EF), diastolic dysfunction (DD), right ventricular function, presence of pericardial effusion, and inferior vena cava (IVC) collapsibility, considered abnormal when the IVC diameter was >1.7 cm and respiratory collapsibility was <50%, which was used as a marker of volume status. ECG findings included rhythm type, presence of a bundle branch block, RR interval (time that elapsed between two successive R-waves of the QRS), and corrected QT interval calculated based on Hodges formula (QTc = QT + 1.75[heart rate - 60]). Outcomes of acute kidney injury (AKI), increase in serum creatinine by ≥0.3 mg/dL within 48 h, need for renal replacement therapy (RRT) during admission, and in-hospital mortality were extracted. Additionally, the Charlson Comorbidity Index (CCI) was used as a weighted index for patients with comorbid conditions (6).
3.4 Follow-up and outcomes
Risk factors for mortality and outcome predictors were evaluated. The primary outcome was in-hospital mortality, while the secondary outcome was AKI during hospitalization.
Finally, in the follow-up phase of the study, participants were followed up for 2 months to assess post-COVID-19 complications such as chronic COVID-19-related symptoms, hospital readmission, and allograft function.
3.5 Statistical analysis
The normality of continuous variables was assessed using the Kolmogorov–Smirnov test and Q-Q plot. Continuous variables are described as median and interquartile range (IQR), while categorical variables are described as frequency and percentage.
Student's t-test or the Mann–Whitney U test was used to compare the mean of each continuous variable between non-survivors and survivors. To compare the frequency of the different categorized variables between groups, appropriate statistical tests such as Fisher's exact test or the chi-squared test was used.
Univariate and multivariable Cox regression models were used to identify the association between under-researched factors and in-hospital mortality. For selecting the best variables to enter the last multivariable model, a backward stepwise approach with a value of p < 0.2 was used. In addition, the sex variable that did not have statistical criteria for entering the multivariable model (p > 0.2) due to approved clinical effects and the probable role of residual confounding of this variable was adjusted in the last multivariable model. The proportional hazards assumption was verified using the Schoenfeld residual test. The last multivariable Cox regression model was fitted following the least amount of Akaike Information Criterion and Bayesian Information Criterion, with a log likelihood value closer to zero. All of the statistical analyses were conducted at a significance level of <0.05, with a 95% confidence interval (CI). The analysis was performed using the STATA software version 14.
4 Results
4.1 Demographic and baseline characteristics
A total of 133 KT recipients (80 men [60.15%]) were admitted with confirmed COVID-19 during the study period, with a median age of 52 years (IQR, 41–63 years). Sixteen patients (12.03%) underwent transplantation for the second or third time. The median time between the last transplantation and COVID-19 admission was 108 months (IQR, 48–108 months). The most prevalent comorbid conditions among patients were hypertension (n = 85 [63.91%]), diabetes (n = 38 [28.57%]), and ischemic heart disease (n = 14 [10.53%]), with a CCI of 3. Thirteen in-hospital deaths occurred (9.77%). The deceased patients had a significantly higher median age (p = 0.004) and were predominantly male, although the difference was not statistically significant (10/13 [76.92%]; p = 0.243). The time since transplantation was longer among those who did not survive COVID-19 (p = 0.029). The median baseline creatinine level in the cohort during the months before hospitalization was 1.4 mg/dL (IQR, 1.1–1.9). However, patients who did not survive had significantly higher median serum creatinine levels (p = 0.002) and a lower median GFR (p = 0.010) than those who did. The most common immunosuppressive treatment among KT recipients is a triple-drug regimen with calcineurin inhibitors (CNIs), mycophenolate mofetil (MMF), and prednisolone. None of the immunosuppressive drugs were associated with an increased risk of mortality (p > 0.05). The patients' demographic and medical characteristics are shown in Table 1 .Table 1 Demographic and past medical history characteristics of COVID-19 patients with history of kidney transplantation.
Table 1Variables All patients (n = 133) Alive (n = 120) Died (n = 13) P-value
Demographic characteristics
Age (years) 52 (41–63) 50.5 (40–62.5) 64 (56–67) 0.004
Sex 0.243
Men 80 (60.15) 70 (58.33) 10 (76.92)
Female 53 (39.85) 50 (41.67) 3 (23.08)
Smoker (Yes) 7 (5.26) 6 (5.00) 1 (7.69) 0.522
Underlying diseases (Yes)
Hypertension 85 (63.91) 76 (63.33) 9 (69.23) 0.769
Ischemic Heart Diseases (IHD) 14 (10.53) 11 (9.17) 3 (23.08) 0.140
Heart Failure (HF) 7 (5.30) 6 (5.04) 1 (7.69) 0.525
Diabetes 38 (28.57) 32 (26.67) 6 (46.15) 0.193
Glomerulonephritis (GN) 5 (3.76) 5 (4.17) 0 (0.0) 1.000
Autosomal Dominant Polycystic Kidney Disease (ADPKD) 7 (5.26) 6 (5.00) 1 (7.69) 0.522
Frequent kidney transplantation
1.000
First 117 (87.97) 105 (87.50) 12 (92.31)
Second 13 (9.77) 12 (10.00) 1 (7.69)
Third 3 (2.26) 3 (2.50) 0 (0.00)
Time between last kidney transplantation to hospital admission (months) 108 (48–180) 108 (48–174) 168 (108–228) 0.029
Cancer 2 (1.50) 2 (1.67) 0 (0.00) 1.000
Chronic pulmonary disease 2 (1.50) 2 (1.67) 0 (0.00) 1.000
Charlson Comorbidity Index (CCI) 3 (2–4) 3 (3–4) 3 (2–3) 0.312
Baseline laboratory results before admission
Creatinine (mg/dL) 1.4 (1.1–1.9) 1.38 (1.1–1.8) 2.16 (1.7–2.65) 0.002
GFR (mL/min) 54 (38–71) 56 (39–71) 42 (26–46) 0.010
Drug history (yes)
Cyclosporine 75 (56.39) 67 (55.83) 8 (61.54) 0.649
Tacrolimus 46 (36.48) 44 (36.67) 5 (38.46) 0.899
MMF/MPA 132 (99.25) 119 (99.17) 13(100) 1.000
Prednisolone 131 (98.50) 119 (99.17) 12(92.31) 0.187
mTOR Inhibitor 8 (6.02) 8 (6.67) 0 (0.00) 1.000
ACEI/ARB 61 (45.86) 54 (45.00) 7 (53.85) 0.543
NOAC 3 (2.26) 2 (1.67) 1 (7.69) 0.267
B-Blocker 40 (30.08) 35 (29.17) 5 (38.46) 0.488
ASA 34 (25.56) 29 (24.17) 5 (38.46) 0.262
Values are n(%), median (Q1-Q3)
mTOR: mammalian target of rapamycin, ACEI: angiotensin converting enzyme inhibitor, ARB: angiotensin receptor blocker, NOAC: novel oral anticoagulants.
4.2 Clinical presentations, laboratory results, and in-hospital medications
The patients' clinical presentations, laboratory results, and in-hospital medical treatment are presented in Table 2 . The most prevalent signs and symptoms were cough (n = 81 [60.90%]), fever (n = 78 [58.65%]), and dyspnea (n = 68 [51.13%]). The median time from symptom initiation to admission was 7 days (IQR, 3–10). There was no statistically significant difference in the signs and symptoms at presentation between survivors and non-survivors (p > 0.05). However, patients who did not survive had lower oxygen saturation (SpO2) levels on admission (median [IQR], 91 [87–92] vs. median [IQR], 94 [91–96]; p = 0.003) than those who did.Table 2 Clinical presentations, laboratory results and in-hospital medication of COVID-19 patients with history of kidney transplantation.
Table 2Variables All patients (n = 133) Survivors (n = 120) Non-survivors (n = 13) P-value
Signs and Symptoms in admission (Yes)
Dyspnea 68 (51.13) 64 (53.33) 4 (30.77) 0.150
Cough 81 (60.90) 76 (63.33) 5 (38.46) 0.122
Fever 78 (58.65) 73 (60.83) 5 (38.46) 0.120
Gastrointestinal Symptoms 56 (42.11) 50 (41.67) 6 (42.11) 0.756
Bradycardia 6 (4.51) 0 (0.00) 6 (5.00) 1.000
Time between being symptomatic to hospital admission (days) 7 (3–10) 7 (3–9.5) 7 (4–12) 0.509
Hemodynamic assessment
Systolic blood pressure (mmHg) 120 (110–135) 120 (110–133.5) 125 (110–140) 0.583
Diastolic blood pressure (mmHg) 77 (70–80) 77 (70–80) 75 (70–80) 0.701
Pulse Rate (PR, pulse / min) 85 (78.5–97.5) 85 (78–99) 80 (80–88) 0.799
Respiratory Rate (RR, per 1/min) 20 (18–22) 19.5 (18–21.5) 20 (18–24) 0.218
SPO2 (%) 93 (91–96) 94 (91–96) 91 (87–92) 0.003
Laboratory values (In admission)
White Blood Cell (WBC, cells per cubic millimeter (cmm)) 5500 (4100–7600) 5500 (4050–7500) 5200 (4700–8200) 0.758
Neutrophil (%) 80 (72–86) 80 (70.5–85) 89 (80–95) 0.012
Lymphocyte (%) 16 (10–25) 16 (10–25) 10 (5–16) 0.018
Platelets (per microliter of blood) 173,000 (132000–210,000) 178,500 (138500–227,000) 126,000(84000–161,000) 0.011
Calcium (serum, mg/dL) 8.5 (8–9) 8.5 (8–9) 7.5 (7.2–8.3) 0.0006
Potassium (K, mmol/L) 4.5 (4–4.9) 4.5 (4–4.9) 4.8 (4.6–5.2) 0.085
Magnesium (Mg, mmol/L) 1.8 (1.5–2.2) 1.8 (1.5–2.2) 2 (1.7–3) 0.161
C-Reactive Protein (CRP,mg/L) 31 (14–59) 25.5 (12.5–51.5) 70 (52–80) 0.0002
Ferritin (ng/mL) 443.5 (322–589) 443 (277–572) 554 (440–654) 0.045
D-Dimer (ng/mL) 361.5 (166–850.5) 345 (164–799) 629 (221–2126) 0.257
Procalcitonin (ng/mL) 0.4 (0.1–0.7) 0.3 (0.09–0.6) 0.65 (0.18–9.9) 0.157
Serum Interleukin-6 (IL-6, pg/ml) 35.4 (12.9–102) 22.35 (8.4–58.5) 92.7 (18.8–172) 0.070
Lactate Dehydrogenase (LDH, U/L) 444 (357–570) 435 (354–531) 682 (533–811) 0.0005
Creatinine (mg/dL) 1.72 (1.33–2.79) 1.7 (1.3–2.35) 2.9 (2.34–3.9) 0.004
Albumin (g/dL) 3.4 (3.1–3.7) 3.4 (3.2–3.8) 3 (2.5–3.4) 0.005
Bilirubin (mg/dL) 0.7 (0.5–1) 0.7 (0.5–0.9) 0.9 (0.7–1.2) 0.077
Aspartate Aminotransferase (AST, U/L) 26 (19–35) 25 (19–35) 30 (28–35) 0.062
Alanine Transaminase (ALT,U/L) 19 (13–30) 19.5 (13–30) 19 (15–24) 0.749
Troponin I (ng/mL) 0.004 (0.001–0.01) 0.003 (0.001–0.01) 0.075 (0.008–0.3) 0.0004
In-hospital medications/ procedures (Yes)
Cyclosporine / dose reduction (n = 81) 40 (49.38) 34 (47.22) 6 (66.67) 0.312
Cyclosporine / DC (n = 81) 9 (11.11) 6 (8.33) 3 (33.33) 0.058
Tacrolimus /dose reduction (n = 57) 22 (38.60) 22 (41.51) 0 (0.00) 0151
Tacrolimus /DC (n = 55) 5 (9.09) 1 (1.96) 4 (100.00) < 0.001
MMF/DC (n = 132) 120 (90.91) 108 (90.76) 12 (92.31) 1.000
Methyl pulse (total dose, n = 133) 54 (40.60) 45 (37.50) 6 (69.23) 0.037
Dexamethasone (n = 133) 63 (47.37) 54 (45.00) 9 (69.23) 0.143
Hydrocortisone (n = 133) 34 (25.56) 30 (25.00) 4 (30.77) 0.739
Tocilizumab (n = 133) 6 (4.51) 3 (2.50) 3 (23.08) 0.012
Time of Tocilizumab administration (day) 7 (7–9) 9 (7–9) 7 (3–7) 0.099
Remdesivir (n = 133) 73 (54.89) 64 (53.33) 9 (69.23) 0.382
Time between symptoms onset to Remdesivir treatment (days) 2 (0–8) 1.5 (0–8) 7 (0–12) 0.135
Time between hospitalization to Remdesivir treatment (days) 1 (0–1) 1 (0–1) 1 (0–1) 0.297
Plasmapheresis (n = 133) 31 (23.31) 19 (15.83) 12 (92.31) < 0.001
Hemoperfusion (n = 133) 5 (3.76) 1 (0.83) 4 (30.77) < 0.001
Anticoagulant drug (Prophylaxis, n = 133) 100 (75.19) 95 (79.17) 5 (38.46) 0.001
Anticoagulant drugs (Treatment, n = 133) 30 (22.56) 22 (18.33) 8 (61.54) < 0.001
Heparin (n = 133) 94 (70.68) 83 (69.17) 11 (84.62) 0.344
NOAC (n = 132) 10 (7.58) 9 (7.56) 1 (7.69) 1.000
LMWH (n = 133) 31 (23.31) 29 (24.17) 2 (15.38) 0.732
Values are n(%), median (Q1-Q3)
LMWH: low molecular weight heparin.
Non-survivors had a higher neutrophil count (p = 0.012) and C-reactive protein (CRP; p = 0.0002), ferritin (p = 0.045), lactate dehydrogenase (LDH; p = 0.0005), creatinine (p = 0.004), and troponin I (p = 0.0004) levels as well as a lower lymphocyte count (p = 0.018), platelet count (p = 0.011), and serum calcium (p = 0.0006) and albumin (p = 0.005) levels than survivors.
Immunosuppressive treatment was administered during the hospitalization. Of the 81 patients treated with the cyclosporine-based regimen and 57 patients treated with the tacrolimus-based regimen, the CNI dose was reduced in 49.38% and 38.60%, respectively. Antimetabolites (MMF or azathioprine) and mammalian target of rapamycin inhibitors (sirolimus) were withdrawn in more than 90% of patients. In addition, CNI inhibitors were discontinued in five patients, four of whom did not survive. Among the patients with severe COVID-19, 40.6% were treated with high-dose steroids (methylprednisolone), while 4.51% were treated with tocilizumab, which was associated with decreased in-hospital mortality (p < 0.05). Of all patients with COVID-19, 55% received antiviral treatment.
Therapeutic plasmapheresis was performed in 31 critically ill patients, 19 of whom survived (61.2%; p < 0.001). Five critically ill patients were treated with hemoperfusion; of them, one survived (p < 0.001).
Ninety-five of 100 patients who received prophylactic anticoagulants survived (p = 0.001). Of 30 patients treated with therapeutic doses of anticoagulants, 22 (73.35%) survived (p < 0.001; Table 2).
Assessment of the ECG results revealed a median RR interval of 720 ms (IQR, 600–800). Patients who died had a shorter median RR interval (p = 0.016) than those who did not. Mild DD was the most common echocardiographic finding (n = 62 [61.39%]) in the cohort. Non-survivors had a significantly lower median EF (p = 0.016) than survivors. The prevalence of a decreased IVC collapsibility index as a marker of volume overload was 24.36%. Overall, 76.92% and 50% of patients in the mortality group had mild DD and abnormal IVC collapsibility during hospitalization, respectively (p < 0.05; Table 3 ).Table 3 Cardiac examinations and in-hospital outcomes of COVID-19 patients with history of kidney transplantation.
Table 3Variables All patients (n = 133) Survivors (n = 120) Non-survivors (n = 13) P-value
ECG results
AF (Yes) 4 (3.05) 3 (2.54) 1 (7.69) 0.345
BBB (Yes) 6 (4.62) 6 (5.13) 0 (0.00) 1.000
QT interval 360 (320–400) 360 (320–400) 360 (360–400) 0.300
PR interval 720 (600–800) 720 (600–800) 680 (600–700) 0.016
Echocardiography results
Ejection Fraction (EF, %) 50 (50–55) 52.5 (50–55) 50 (45–50) 0.014
Diastolic Dysfunction (DD)
Normal 38 (37.62) 36 (40.91) 2 (15.38)
Mild 62 (61.39) 52 (59.09) 10 (76.92) 0.020
Moderate to severe 1 (0.99) 0 (0.00) 1 (7.69)
RV size
Abnormal 9 (11.39) 7 (10.45) 2 (16.67) 0.620
Normal 70 (88.61) 60 (89.55) 10 (83.33)
RV function
Abnormal 7 (8.86) 6 (8.96) 1 (8.33) 1.000
Normal 72 (91.14) 61 (91.04) 11 (91.67)
IVC collapsibility
Abnormal 19 (24.36) 13 (19.70) 6 (50.00) 0.024
Normal 59 (75.64) 53 (80.30) 6 (50.00)
Plural Effusion (PE,Yes) 10 (12.66) 7 (10.45) 3 (25.00) 0.173
In-hospital outcomes
Length of hospital stay (days) 8 (5–12) 7 (5–10) 16 (10–24) 0.0002
ICU admission (Yes) 22 (16.54) 9 (7.50) 13 (100.00) < 0.001
ICU duration (days) 9 (4–15) 5 (3–11) 10 (7–19) 0.123
Non-Invasive Ventilation (NIV, Yes) 11 (8.27) 4 (3.33) 7 (53.85) < 0.001
Endotracheal intubation (Yes) 13 (9.77) 1 (0.83) 12 (92.31) < 0.001
Acute Kidney Injury (AKI, Yes) 63 (47.37) 53 (44.17) 10 (76.92) 0.038
AKI requiring RRT (Yes) 9 (6.77) 3 (2.50) 6 (46.15) < 0.001
Values are n(%), median (Q1-Q3)
Troponin I and AKI association
Troponin I 0.04(0.001–0.01) 0.006(0.0015–0.013) 0.002(0.001–0.01) 0.039
4.3 In-hospital outcomes
The median hospitalization duration in the study group was 8 days (IQR, 5–12 days). This duration was longer among non-survivors (median, 16 days [IQR, 10–24]; p = 0.0002) than among survivors. Of the 133 patients admitted to the ICU, 13 (9.77%) died. The incidence of AKI during hospitalization (47.37%) was more prevalent among non-survivors (p = 0.038) than among survivors. In addition, of 63 patients with AKI during hospitalization, 9 (19.29%) required RRT and eventually six died (p < 0.001; Table 3).
Regarding the relationship between troponin I levels and the incidence of AKI, troponin levels were significantly higher in patients with AKI (p = 0.039) than in those without AKI. However, there was no statistically significant relationship between abnormal IVC collapsibility and AKI events (p = 0.065; Table 3).
4.4 Risk factors for in-hospital mortality
The results of the univariate and multivariate Cox regression analyses are summarized in Table 4 . The univariate Cox regression analysis showed a significant association between aspartate transaminase (AST) (p = 0.016), creatinine (p = 0.003), SpO2 (p = 0.002), anticoagulant prophylaxis during hospitalization (p = 0.007), and time since transplantation (p = 0.03) with in-hospital mortality. After adjusting for potential factors, multivariate Cox regression analysis revealed that older age (adjusted hazard ratio [HR], 1.13; 95% CI, 1.01–1.27) and AKI (adjusted HR, 3.43; 95% CI, 1.34–8.79) were associated with in-hospital mortality. High SpO2 levels on admission (adjusted HR, 0.79; 95% CI, 0.66–0.94), female sex (adjusted HR, 0.01; 95% CI, 0.0003–0.71), and anticoagulant prophylaxis (adjusted HR, 0.06; 95% CI, 0.005–0.68) during hospitalization were negatively associated with in-hospital mortality among KT recipients with COVID-19 disease (Table 4).Table 4 Factors associated with in-hospital mortality due to SARS-Cov2 infection among kidney transplantation patients based on univariate and multivariable Cox regression model.
Table 4Variables Crude HR*, 95% CI P-value Adjusted HR*, 95% CI P-value
Age (years) 1.03 (0.99–1.09) 0.116 1.13 (1.01–1.27) 0.031
Sex 0.397 0.033
Male Reference Reference
Female 0.57 (0.15–2.08) 0.01 (0.0003–0.71)
Charlson Comorbidity Index (CCI) 0.63 (0.38–1.05) 0.081 1.09 (0.46–2.55) 0.835
Aspartate aminotransferase (AST, U/L) 1.01 (1.003–1.03) 0.016 1.01 (0.97–1.06) 0.451
Creatinine level in-admission (mg/dL) 1.61 (1.18–2.21) 0.003 3.43 (1.34–8.79) 0.010
** SPO2 (%) 0.89 (0.82–0.95) 0.002 0.79 (0.66–0.94) 0.009
Anticoagulant drug (Prophylaxis) 0.007 0.023
No Reference Reference
Yes 0.15 (0.04–0.60) 0.06 (0.005–0.68)
Time between kidney transplantation to SARS-CoV2 infection (months) 1.007 (1.0007–1.01) 0.030 1.004 (0.98–1.02) 0.708
Acute Kidney Injury (AKI) during hospitalization 0.112 0.805
No Reference Reference
Yes 2.86 (0.78–10.45) 1.44 (0.07–26.69)
*Hazard Ratio.
** *Oxygen saturation measured by pulse oximetry.
Proportionality assumption was checked based on Schoenfeld residual test, P-value = 0.4004.
4.5 Post-COVID-19 complications after discharge
Of the 120 discharged KT recipients, approximately 43 were lost to follow-up. Among 77 available patients, the most common post-COVID-19 complications were fatigue (n = 32 [42.11%]), hospital readmission (n = 13 [16.88%]), and loss of taste (ageusia) or smell (anosmia) (n = 9 [11.69%]). In addition, the median serum creatinine level at 2 months post-discharge was 1.4 mg/dL (IQR, 1.2–1.7). A significant improvement in renal function was noted after discharge with respect to the value on admission (p = 0.0008).
5 Discussion
COVID-19 is a global health issue associated with higher morbidity and mortality rates among patients with underlying comorbidities, including hypertension, diabetes, CVD, and CKD (4). Adult patients with primary and secondary immunosuppressive conditions are at a greater risk of developing severe COVID-19 and mortality than the general population (7). The present study was designed to determine the effect of COVID-19 on KT recipients, including graft function, cardiac involvement, outcomes, and factors associated with poor survival.
Our retrospective cohort study found that the COVID-19-related mortality rate in KT recipients was 9.77%, slightly higher than the average overall 6.8% mortality rate in the general population (8). This finding may be partly related to comorbidities such as hypertension, diabetes, ischemic heart disease, and immunosuppressive therapy (9). There was a remarkable male predominance in our cohort, similar to that in the general population, and men faced a mortality risk that was 2.4 times higher than that of women. A possible explanation for this might be the higher circulating angiotensin-converting enzyme 2 (ACE2) level in men, which is noteworthy as SARS-CoV-2 attacks cells via the ACE2 receptor (10). A higher COVID-19 mortality rate was reported among patients older than 50 years, especially those older than 60 years, in the general population, which was consistent with our results.
Based on our findings, the most prevalent signs and symptoms were similar to those observed in the general adult population (11). Although the presenting signs and symptoms did not differ between survivors and non-survivors, a low SpO2 at presentation was associated with an increased mortality rate. Recent evidence indicated that inflammatory responses play a pivotal role in the pathogenesis and outcomes of COVID-19. Thus, inflammatory markers such as procalcitonin, CRP, erythrocyte sedimentation rate, ferritin, d-dimer, and interleukin-6 (IL-6) could be considered predictors of COVID-19 severity and mortality (12). Similarly, this study found higher levels of CRP, ferritin, and LDH in non-survivors than in survivors. Neutrophil count, creatinine level, and cardiac enzyme level were higher, whereas lymphocyte and platelet counts and serum calcium and albumin levels were lower among non-survivors than among survivors. In addition, patients who died had a significantly lower median GFR at admission. In agreement with our data, a case series study indicated that lymphopenia and elevated ferritin, d-dimer, and troponin levels were observed in critical cases of COVID-19 (13). IL-6 and procalcitonin levels were markedly elevated in transplant recipients with severe COVID-19 and identified as risk factors for mortality (14), but we detected no difference in IL-6 and procalcitonin levels between survivors and non-survivors. These results must be interpreted cautiously, as a limited number of patients were evaluated for IL-6. Cravedi et al. indicated that baseline lymphocyte count and estimated GFR were significantly lower and LDH levels were higher in non-survivors, similar to our results. However, they reported no intergroup differences in white blood cell count, platelet count, or hemoglobin levels (15).
It remains unclear whether immunosuppressive treatment is an independent risk factor for a poor prognosis of COVID-19. In our study, none of the immunosuppression regimens were associated with mortality. This result is in accordance with those of two recent studies that indicated no impact of immunosuppression intensity on mortality rate (16). However, our previous study of a limited number of cases demonstrated a lower rate of admission among KT recipients than in the general population, particularly in more recent transplantations, which might imply the protective effects of immunosuppressive agents against cytokine storm activation due to COVID-19 (17). The current study had a median interval of 108 months between KT and COVID-19. The longer the duration of transplantation, the lower the level of immunosuppressive drugs.
According to our center protocol, this study applied a CNI dose reduction in 49.38% of patients treated with cyclosporine and 38.60% of patients treated with tacrolimus. Previous studies of solid organ transplant recipients showed that CNIs and antimetabolites were present in 18–29% and 66–88% of patients during the clinical course of COVID-19, respectively (18). Immunosuppressive drug management is a double-edged sword: its reduction can result in an increased risk of rejection, while its continuation may lead to severe illness. Therefore, managing immunosuppression in KT recipients remains challenging, and clinicians should make case-by-case decisions and assess the risks versus benefits of continuing immunosuppression using a multidisciplinary approach. However, it has been recommended that, among COVID-19 cases with mild involvement, antimetabolite agents (MMF and azathioprine) be discontinued at the time of hospital admission, but low doses of CNIs continued due to possible immunomodulatory effects (19); moreover, stress doses of prednisolone should be considered. In severe infection cases, an argument can be made for discontinuing antimetabolites and reducing CNIs while administering high doses of corticosteroid plus intravenous immunoglobulin (20).
Given the role of cytokine storm and inflammation in COVID-19 pathology, several studies reported that tocilizumab, an anti-IL-6 monoclonal antibody, could have beneficial effects in reducing inflammatory parameters and improving clinical symptoms (21). Our study showed that methylprednisolone pulse therapy and tocilizumab were associated with reduced in-hospital mortality rates.
Plasmapheresis has been suggested as a helpful approach in severe cases by alleviating inflammatory cytokine storm and decreasing viral load (22). Most individuals who underwent plasmapheresis and hemoperfusion in this study were critically ill, but their survival rate improved. This may be the result of prescribing blood purification techniques late in the disease course.
Thromboembolic events frequently occur in hospitalized patients with COVID-19, which might result in a higher risk of in-hospital mortality (23); therefore, most existing evidence indicates that prophylactic or therapeutic doses of anticoagulants are associated with a lower mortality risk (24). In this study, patients who received prophylactic or therapeutic doses of anticoagulants showed lower mortality rates.
COVID-19 is potentially associated with various ECG abnormalities and a consequent poor prognosis, including atrial fibrillation, QT interval prolongation, ST segment and T wave changes, and ventricular arrhythmia. However, sinus tachycardia is the most common abnormality observed (25). The current study demonstrated that the median RR interval was significantly lower among non-survivors than among living patients, while other ECG abnormalities were not prevalent in our patients. According to echocardiographic findings, survivors had a higher median EF in compression than non-survivors. Echocardiography performed in most cases of COVID-19 in the general population revealed a decreased EF, with a more severe EF reduction associated with worse prognosis (26). In this study, the positive troponin I level as a marker of myocardial involvement was significantly higher in survivors with AKI events and non-survivors, which may make it a prognostic factor. Reducing IVC collapsibility was also more frequent among non-survivors than among survivors. The IVC collapsibility index is a valuable marker for evaluating volume status, which can commonly occur in AKI events and may indicate upcoming cardiorenal worsening and poor prognosis; COVID-19 can be an essential cause in this regard (27).
The ICU admission rate among hospitalized patients in the general population was reportedly 8.6–34% in different cohort studies, while largest cohort studies reported non-invasive ventilation or intubation rates of 30–39%. Intubation predicted poor outcomes with a 40–100% mortality rate among patients on ventilation (28). All non-survivors in this study were admitted to the ICU, and the application of non-invasive ventilation and endotracheal intubation was more prevalent among non-survivors than among survivors. Also, the incidence of AKI during hospitalization was 47.37%, higher among non-survivors than among survivors. AKI is common in patients with COVID-19 with a wide reported range; however, AKI during hospitalization was more common among transplant recipients than among non-transplant patients admitted for COVID-19 (20% vs. 5%), especially among critically ill patients (29). ACE2, a SARS-CoV-2 receptor, is expressed in proximal tubule cells. Therefore, uptake of the virus into the proximal tubular epithelium is a possible explanation for this phenomenon.
Further adjustment analysis demonstrated that AST, creatinine, SpO2, prophylactic anticoagulant drugs during hospitalization, and the time between KT and SARS-CoV2 infection were associated with changes in mortality. In other words, rising creatinine and ALT levels during hospitalization increased the risk of in-hospital mortality, while an increase in SpO2 level and the receipt of prophylactic anticoagulants during hospitalization prevented in-hospital mortality. Our previous study of 2493 KT recipients demonstrated that a history of acute rejection during the past 12 months; diabetes; high neutrophil-to-lymphocyte ratio; low platelet count; elevated CRP, LDH, troponin, d-dimer levels; and a prolonged prothrombin time were associated with mortality and poor outcomes (17). A cohort study by Favà et al. demonstrated that a higher baseline LDH level at admission, obesity, or acute respiratory distress syndrome conferred a higher risk of death among KT recipients with COVID-19 (16). The persistence of symptoms, including fatigue and dyspnea for more than 60 days, was reported in a non-transplant population in northern Italy (30). In the present study, among surviving discharged KT recipients, fatigue, hospital readmission, and ageusia or anosmia were the most post-COVID-19 complications.
6 Conclusion
In conclusion, KT recipients with COVID-19 had a higher mortality rate than the general population, with a greater prevalence among older individuals and those who experienced AKI during hospitalization than among younger patients and those who did not have AKI. Non-survivors had a significantly lower EF and higher volume overload markers, such as a dilated IVC and reduced collapsibility index, than survivors.
Summary
The research reported here focused on the importance of cardiac effects and renal outcomes in patients undergoing kidney transplantation who are prone to COVID-19-related mortality and morbidity.
Authorship
Study conception: Shiva Samavat.
Data acquisition: Hossein Amini, Shadi Ziaie, Nooshin Dalili, Shideh Anvari, and Elham Keykha.
Supervision: Bahareh Hajibaratali.
Method development: Shiva Samavat.
Drafting or revising article critically for important intellectual content: Malihe Rezaee.
Final approval of version to be submitted: Bahareh Hajibaratali and Hossein Amini.
Declaration of Competing Interest
None.
Data availability
Data will be made available on request.
Acknowledgment
This study was supported by Labbafinezhad Hospital, 10.13039/501100005851 Shahid Beheshti University of Medical Sciences , Iran. No specific grants from funding agencies or the commercial or nonprofit sectors were received.
==== Refs
References
1 JHUCRC https://coronavirus.jhu.edu/map.html 2022
2 ICIA https://behdasht.gov.ir 2022
3 Guan W.J. Ni Z.Y. Hu Y. Liang W.H. Ou C.Q. He J.X. Liu L. Shan H. Lei C.L. Hui D.S.C. Du B. Li L.J. Zeng G. Yuen K.Y. Chen R.C. Tang C.L. Wang T. Chen P.Y. Xiang J. Li S.Y. Wang J.L. Liang Z.J. Peng Y.X. Wei L. Liu Y. Hu Y.H. Peng P. Wang J.M. Liu J.Y. Chen Z. Li G. Zheng Z.J. Qiu S.Q. Luo J. Ye C.J. Zhu S.Y. Zhong N.S. Clinical characteristics of coronavirus disease 2019 in China N. Engl. J. Med. 382 18 2020 1708 1720 32109013
4 Richardson S. Hirsch J.S. Narasimhan M. Crawford J.M. McGinn T. Davidson K.W. Barnaby D.P. Becker L.B. Chelico J.D. Cohen S.L. Cookingham J. Coppa K. Diefenbach M.A. Dominello A.J. Duer-Hefele J. Falzon L. Gitlin J. Hajizadeh N. Harvin T.G. Hirschwerk D.A. Kim E.J. Kozel Z.M. Marrast L.M. Mogavero J.N. Osorio G.A. Qiu M. Zanos T.P. Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City Area JAMA 323 20 2020 2052 2059 32320003
5 Ojo A.O. Cardiovascular complications after renal transplantation and their prevention Transplantation. 82 5 2006 603 611 16969281
6 Charlson M.E. Pompei P. Ales K.L. MacKenzie C.R. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation J. Chronic Dis. 40 5 1987 373 383 3558716
7 Shields A.M. Burns S.O. Savic S. Richter A.G. COVID-19 in patients with primary and secondary immunodeficiency: The United Kingdom experience J. Allergy Clin. Immunol. 147 3 2021 870-5.e1
8 Cao Y. Liu X. Xiong L. Cai K. Imaging and clinical features of patients with 2019 novel coronavirus SARS-CoV-2: a systematic review and meta-analysis J. Med. Virol. 92 9 2020 1449 1459 32242947
9 Lq Li Huang T. Yq Wang Zp Wang Liang Y. Tb Huang Hy Zhang Sun W. Wang Y. COVID-19 patients' clinical characteristics, discharge rate, and fatality rate of meta-analysis J. Med. Virol. 92 6 2020 577 583 32162702
10 Patel S.K. Velkoska E. Burrell L.M. Emerging markers in cardiovascular disease: where does angiotensin-converting enzyme 2 fit in? Clin. Exp. Pharmacol. Physiol. 40 8 2013 551 559 23432153
11 Mehta O.P. Bhandari P. Raut A. Kacimi S.E.O. Huy N.T. Coronavirus disease (COVID-19): comprehensive review of clinical presentation Front. Public Health 8 2021 1034
12 Zeng F. Huang Y. Guo Y. Yin M. Chen X. Xiao L. Deng G. Association of inflammatory markers with the severity of COVID-19: A meta-analysis Int. J. Infect. Dis. 96 2020 467 474 32425643
13 Banerjee D. Popoola J. Shah S. Ster I.C. Quan V. Phanish M. COVID-19 infection in kidney transplant recipients Kidney Int. 97 6 2020 1076 1082 32354637
14 Akalin E. Azzi Y. Bartash R. Seethamraju H. Parides M. Hemmige V. Ross M. Forest S. Goldstein Y.D. Ajaimy M. Covid-19 and kidney transplantation N. Engl. J. Med. 382 25 2020 2475 2477 32329975
15 Cravedi P. Mothi S.S. Azzi Y. Haverly M. Farouk S.S. Pérez-Sáez M.J. Redondo-Pachón M.D. Murphy B. Florman S. Cyrino L.G. COVID-19 and kidney transplantation: results from the TANGO International Transplant Consortium Am. J. Transplant. 20 11 2020 3140 3148 32649791
16 Favà A. Cucchiari D. Montero N. Toapanta N. Centellas F.J. Vila-Santandreu A. Coloma A. Meneghini M. Manonelles A. Sellarés J. Clinical characteristics and risk factors for severe COVID-19 in hospitalized kidney transplant recipients: a multicentric cohort study Am. J. Transplant. 20 11 2020 3030 3041 32777153
17 Ghaffari Rahbar M. Nafar M. Khoshdel A. Dalili N. Abrishami A. Firouzan A. Poorrezagholi F. Samadian F. Ziaie S. Fatemizadeh S. Low rate of COVID-19 pneumonia in kidney transplant recipients—A battle between infection and immune response? Transpl. Infect. Dis. 22 5 2020 e13406
18 Kates O.S. Haydel B.M. Florman S.S. Rana M.M. Chaudhry Z.S. Ramesh M.S. Safa K. Kotton C.N. Blumberg E.A. Besharatian B.D. Coronavirus disease 2019 in solid organ transplant: a multicenter cohort study Clin. Infect. Dis. 73 11 2021 e4090 e40999 32766815
19 Willicombe M. Thomas D. McAdoo S. COVID-19 and calcineurin inhibitors: should they get left out in the storm? J. Am. Soc. Nephrol. 31 6 2020 1145 1146 32312797
20 Samavat S. Nafar M. Firozan A. Pourrezagholi F. Ahmadpoor P. Samadian F. Ziaei S. Fatemizadeh S. Dalili N. COVID-19 rapid guideline in kidney transplant recipients Iran J. Kidney Dis. 14 3 2020 231 234 32361701
21 Biran N. Ip A. Ahn J. Go R.C. Wang S. Mathura S. Sinclaire B.A. Bednarz U. Marafelias M. Hansen E. Tocilizumab among patients with COVID-19 in the intensive care unit: a multicentre observational study Lancet Rheumatol. 2 10 2020 e603 e612 32838323
22 Hashemian S.M. Shafigh N. Afzal G. Jamaati H. Tabarsi P. Marjani M. Malekmohammad M. Mortazavi S.M. Khoundabi B. Mansouri D. Plasmapheresis reduces cytokine and immune cell levels in COVID-19 patients with acute respiratory distress syndrome (ARDS) Pulmonology. 27 6 2021 486 492 33358260
23 Porfidia A. Valeriani E. Pola R. Porreca E. Rutjes A.W. Di Nisio M. Venous thromboembolism in patients with COVID-19: systematic review and meta-analysis Thromb. Res. 196 2020 67 74 32853978
24 Vaughn V.M. Yost M. Abshire C. Flanders S.A. Paje D. Grant P. Kaatz S. Kim T. Barnes G.D. Trends in venous thromboembolism anticoagulation in patients hospitalized with COVID-19 JAMA Netw. Open 4 6 2021 e2111788
25 Long B. Brady W.J. Bridwell R.E. Ramzy M. Montrief T. Singh M. Gottlieb M. Electrocardiographic manifestations of COVID-19 Am. J. Emerg. Med. 41 2021 96 103 33412365
26 Carretta D.M. Silva A.M. D'Agostino D. Topi S. Lovero R. Charitos I.A. Wegierska A.E. Montagnani M. Santacroce L. Cardiac involvement in COVID-19 patients: a contemporary review Infect. Dis. Reports 13 2 2021 494 517
27 Moreno F.L. Hagan A.D. Holmen J.R. Pryor T.A. Strickland R.D. Castle C.H. Evaluation of size and dynamics of the inferior vena cava as an index of right-sided cardiac function Am. J. Cardiol. 53 4 1984 579 585 6695787
28 Shafiabadi Hassani N. Shojaee A. Khodaprast Z. Sepahvandi R. Shahrestanaki E. Rastad H. Echocardiographic features of cardiac injury related to COVID-19 and their prognostic value: a systematic review J. Intensive Care Med. 36 4 2021 500 508 33349095
29 Danziger-Isakov L. Blumberg E.A. Manuel O. Sester M. Impact of COVID-19 in solid organ transplant recipients Am. J. Transplant. 21 3 2021 925 937 33319449
30 Carfì A. Bernabei R. Landi F. Persistent symptoms in patients after acute COVID-19 JAMA 324 6 2020 603 605 32644129
| 36503165 | PMC9729589 | NO-CC CODE | 2022-12-15 23:18:04 | no | Transpl Immunol. 2023 Feb 8; 76:101772 | utf-8 | Transpl Immunol | 2,022 | 10.1016/j.trim.2022.101772 | oa_other |
==== Front
Inform Med Unlocked
Inform Med Unlocked
Informatics in Medicine Unlocked
2352-9148
The Authors. Published by Elsevier Ltd.
S2352-9148(22)00284-2
10.1016/j.imu.2022.101147
101147
Article
Repurposing FDA-approved drugs cetilistat, abiraterone, diiodohydroxyquinoline, bexarotene, and remdesivir as potential inhibitors against RNA dependent RNA polymerase of SARS-CoV-2: A comparative in silico perspective
Shahabadi Nahid a∗
Zendehcheshm Saba ab
Mahdavi Mohammad a
Khademi Fatemeh b
a Inorganic Chemistry Department, Faculty of Chemistry, Razi University, Kermanshah, Iran
b Medical Biology Research Center, Health Technology Institute, Kermanshah University of Medical Sciences, Kermanshah, Iran
∗ Corresponding author. Faculty of Chemistry, Razi University, Kermanshah, Iran.
8 12 2022
8 12 2022
10114714 10 2022
19 11 2022
7 12 2022
© 2022 The Authors. Published by Elsevier Ltd.
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Vaccines are undoubtedly the most effective means of combating viral diseases like COVID-19. However, there are risks associated with vaccination, such as incomplete viral deactivation or potential adverse effects in humans. However, designing and developing a panel of new drug molecules is always encouraged. In an emergency, drug repurposing research is one of the most potent and rapid options. RdRp (RNA-dependent RNA polymerase) has been discovered to play a pivotal role in viral replication. In this study, FDA-approved drugs bexarotene, diiodohydroxyquinoline, abiraterone, cetilistat, and remdesivir were repurposed against the RdRp by molecular modeling, docking, and dynamic simulation. Furthermore, to validate the potency of these drugs, we compared them to the antiviral remdesivir, which inhibits RdRp. Our finding indicated that the selected drugs have a high potential to be developed as RdRp inhibitors and, with further validation studies, could serve as potential drugs for the treatment of COVID-19.
Graphical abstract
Image 1
Keywords
Drug repurposing
Coronavirus
RNA-Dependent RNA polymerase
Docking
Dynamic
Virtual screening
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pmc1 Introduction
COVID-19 is an infectious disease caused by Coronaviruses (family: Coronaviridae), which has recently been identified as a cause of severe acute respiratory syndromes (SARS Cov-2). Coronaviruses are named as such because their surface is characterized by protrusions resembling crowns [1]. Corona was first reported in China (Wuhan), and COVID-2019 is unlikely to have been transmitted from animal to human in a seafood market [2,3]. The outbreak has been rapidly spread across the countries and territories due to its high human to human contagious nature. Until Oct 13, 2022, 628,556,214 people have been infected, and 6,566,411 have died (https://www.worldometers.info/coronavirus/). Vaccines are without question the most efficient tool for combating viral diseases. However, there are inherent threats with vaccination, including non-complete viral deactivation or potential adverse effects in people; even after a vaccine has been produced, exact quality control is required to ensure the treatment's safety. As a result, developing vaccines is a lengthy process; a vaccine may not be commercially available for years following the emergence of a new disease. The current vaccines are not providing adequate protection against COVID-19 due to logistical difficulties in their distribution, diminishing immunity, and the possibility of transmission from asymptomatic infected patients [4,5]. The advent of vaccines may have broken the transmission chains in this viral pandemic, but there is currently concern about their effectiveness against mutant variants, reported adverse reactions, and possible long-term effects. We must, therefore, find safe, effective, preferably affordable, and most importantly, specific drugs rather than the non-specific drugs currently prescribed in order to save the lives of the infected [6,7].
A computer-assisted drug discovery strategy is the most common method of predicting potential compounds before they are synthesized and tested in vitro [8]. Drug repurposing is the most efficient method of rapidly identifying the unique clinical applications of currently licensed medications to treat COVID-19. Since the toxicity and pharmacokinetics of repurposed pharmaceuticals are already known, this method could minimize the time and expense required to develop a new medicine [9]. The repurposing of existing medications is made possible through molecular docking research against viral protein targets [10]. In terms of mathematics, molecular computation has several forms, some of which can be used in biology and protein research because of their ability to handle a wide range of particle systems. The interactions between these molecules are predicted using molecular docking and molecular dynamics modeling. In silico screening of different potential active chemicals can be accomplished cost- and time-efficient using these strategies [11,12]. Numerous scientific disciplines are using molecular modeling techniques, including drug discovery and design [[13], [14], [15]]. The Coronavirus genome is translated into two classes of proteins within the host cell: structural proteins such as spikes (S), envelopes (E), matrixes (M), and nucleocapsids (N), and nonstructural proteins such as 3-C proteases (3CLpro, NSP5) and RNA Dependent RNA Polymerases (RdRp, NSP12) [16].
Among these proteins, RNA-dependent RNA polymerase (RdRp) has been demonstrated to contribute significantly to the replication of the viral genome (single-stranded RNA) and to the multiplication of the virus in various cells [17,18]. It is essential to inhibit RdRp in order to control the progression of infection in human cells and, as a consequence, the viral load in patients, as RdRp replicates viral genomes by synthesizing new RNA nucleotides. It has been established that RNA-dependent RNA polymerase (RdRp) is an essential enzyme that plays a significant role in the replication and translation of viral genomes; this makes it an excellent therapeutic target for COVID-19. Numerous clinical trials have shown that COVID-19 can be effectively treated with antiviral, antimalarial, and anti-HIV drugs. The FDA has approved Remdesivir (Veklury®) for treating COVID-19 in hospitalized patients aged 12 and older and weighing at least 40 kg. Several viral infections, including SARS-CoV-2, have been successfully treated with an analog of nucleotides (NA) known as Remdesivir. In the presence of NAs, a 5-triphosphate is formed in the cell, which replaces the RdRp substrate and competes with the endogenous nucleotides for incorporation into viral RNA. In a comprehensive two-tier screening approach, Yuan et al. demonstrated that FDA-approved medications cetilistat, abiraterone, diiodohydroxyquinoline, and bexarotene inhibited COVID-19 infection in vitro [19]. As part of this study, we investigated the interaction between four FDA-approved medications, bexarotene (anticancer retinoid), abiraterone (synthetic androstenedione steroid), diiodohydroxyquinoline (antiparasite), and cetilistat (antipancreatic lipase) with SARS-CoV-2 RdRp using docking simulation. In this study, remdesivir (an antiviral drug) was compared to the outcomes obtained with remdesivir. Dynamic simulations also clarify the dynamics of molecule behavior. In light of our findings, we may be able to develop a new method for combating COVID-19.
2 Methods
2.1 Docking of cetilistat, abiraterone, diiodohydroxyquinoline, bexarotene, and remdesivir
2.1.1 Macromolecule (drug target) selection and preparation
The screening of the drugs against RdRp of SARS-CoV-2 was performed by AutoDock Vina [20] with MGL tools 1.5.4 which makes more precise docking calculations and runs faster than AutoDock software [21]. The crystal structure of RdRp (PDB ID: 6NUR) was downloaded from the Protein data bank [22]. The receptor preparation was done by utilizing the AutoDock Tools (v.1.5.4). The addition of Kollman charged atoms to protein crystal structure and the combination of non-polar hydrogen atoms was done by MGL Tools (v.1.5.4). To perform molecular docking analysis the RdRp of SARS-CoV-2 was converted into PDBQT format (.pdbqt). The grid box was set to 100 × 90 × 122 with a grid spacing of 1.00 Å and center parameters of 150.05, 152.94, and 163.03.
2.1.2 Ligands selection and preparation
The 3D structure of bexarotene, diiodohydroxyquinoline, abiraterone, cetilistat, and remdesivir were received from PubChem as SDF files (.sdf) (https://pubchem.ncbi.nlm. nih.gov) and converted to PDB format (.pdb) before perform docking utilizing MGL Tools (v.1.5.4). The BIOVIA Discovery Studio software was employed to visualize the docked results.
2.2 Molecular dynamics (MD) simulations
Because molecular docking results revealed that bexarotene has the best energy in interaction with RdRp of SARS-CoV-2, we investigated the molecular dynamic simulation of this drug with protein. We did the Classical MD simulations [23,24] with the CHARMM27 force field using ran GROMACS 2018.2 software [25,26]. SwissParam [27] was used to generate the ligand topology. The free protein and protein-drug complex were solved using TIP3P water [28] in the cubic box with periodic boundary situations in three directions. The solutes were placed in the box's center, with a minimum distance of 1.0 nm between their surfaces. Markedly, we added Na+ ions to the system to neutralize the charge. The systems were balanced at 300 K and 1 bar, following energy minimization using the steepest descent method. The temperature was kept at 300 K using a modified Berendsen thermostat, and the pressure was kept at 1.0 bar using a Parrinello-Rahman barostat. Bond lengths were calculated using the LINCS algorithm, and long-range electrostatic forces were computed employing the particle-mesh Ewald scheme (PME) (grid spacing 0.16 nm) [29]. For short-range non-bonded interactions, cutoff ratios of 1.0 nm for Coulomb and van der Waals potentials were used. Finally, a 50 ns MD simulation with a time-step of 2 fs was running, with velocities generated randomly using a Maxwell distribution.
3 Results
3.1 Evaluation of the docking performance
Because of its critical role in virus replication, RdRp is considered a key target for developing antiviral drugs [18,30,31]. We recently reported the in-silico screening of bexarotene, diiodohydroxyquinoline, abiraterone, cetilistat, and remdesivir against different SARS-CoV-2 drug targets [32]. However, RdRp, an important drug target, was excluded from this study. The current study used the same strategy to screen the bexarotene, diiodohydroxyquinoline, abiraterone, cetilistat, and remdesivir for their potential to inhibit RdRp of SARS-CoV-2, thereby filling the gap. Molecular docking simulation is widely used in computational drug discovery and design. This technique was used to predict the binding of the selected drugs bexarotene, diiodohydroxyquinoline, abiraterone, cetilistat, and remdesivir with SARS-CoV-2 RdRp-RNA protein. Recently, researchers reported virtual screening inhibitors of SARS-CoV-2 RdRp [[33], [34], [35], [36]]. In our study, the FDA-approved RdRp inhibitor drug remdesivir was used as a comparator drug. Remdesivir is effective against many RNA viruses, including the newly discovered SARS-CoV-2 (COVID-19) and previous coronaviruses [37,38]. We hypothesized that the drugs mentioned above could disrupt SARS-CoV-2 RdRp.
Cetilistat, a pancreatic lipase inhibitor, can be prescribed for obesity treatment. This compound inhibits fat digestion and absorption [39]. Diiodohydroxyquinoline is a quinolone derivative used to treat amoebiasis as a luminal amebicide [40]. When bile is available, diiodohydroxyquinoline is unlikely to be absorbed into the circulation, whereas cetilistat is rapidly hydrolyzed into its metabolites [39,41]. Gastrointestinal complaints have been reported in 15–20% of COVID-19 patients, and some of these individuals have also shown infectious virus particles and identifiable viral RNA [42]. In the intestines of SARS-CoV-2-infected hamsters, high levels of viral nucleocapsid protein, inflammatory processes, and identifiable viral RNA were found [43]. Cetilistat and oral diiodohydroxyquinoline may be effective topical luminal antivirals in suppressing viral shedding in the gastrointestinal system in the same way feces may be a significant source of SARS-CoV-2 infection [44,45]. Nonchemotherapeutic antineoplastic medicines, including bexarotene and abiraterone acetate, have been shown to have minimal immunosuppressive properties [19]. Breast cancer, non-small cell lung cancer (NSCLC), and cutaneous T-cell lymphoma are effectively treated with bexarotene, a third-generation retinoid [[46], [47], [48], [49]]. Abiraterone acetate suppressed the activity of the enzyme CYP17A1, which is responsible for androgen synthesis when coupled with a corticosteroid. Androgen deprivation is the mechanism employed in this therapy to treat resistant prostate cancer [50,51]. As described by Yuan et al. [52], members of the same medication class as bexarotene, such as tamibarotene and AM580, have demonstrated outstanding antiviral effectiveness against several viruses, including influenza, Zika, coronaviruses (SARS-CoV and MERS-CoV), adenoviruses, and enterovirus A71.
According to molecular docking studies, the binding energies of bexarotene, abiraterone, cetilistat, remdesivir, and diiodohydroxyquinoline to the RdRp were - 8.4, - 7.9, −7.6, −7.6 and - 5.5 kcal/mol, respectively, which demonstrate good binding potential towards the RdRp. The binding energy values of the five compounds mentioned with the SARS-CoV-2 RdRp protein are shown in Table 1 .Table 1 Interaction energy between RdRp of SARS-CoV-2 and molecules.
Table 1Molecules ΔG (kcal/mol)
Abiraterone −7.9
Bexarotene −8.4
Cetilistat −7.6
Diiodohydroxyquinoline −5.5
Remdesivir −7.6
According to our findings, bexarotene was the most effective compound against RdRp, with the lowest binding energy. Also, bexarotene and abiraterone were discovered to have the lowest binding energies than remdesivir. In the meantime, the binding energies of cetilistat are equal to those of remdesivir. Moreover, the binding energy value for diiodohydroxyquinoline (−5.5 kcal/mol) suggests that the ligand-protein complex is stable. Table 2, Table 3, Table 4, Table 5, Table 6 represent the amino acid residues involved, bond lengths, and the type of interactions. Fig. 1, Fig. 2, Fig. 3, Fig. 4, Fig. 5 depict the RdRp amino acid residues that interacted with drugs. As evident, The SARS-CoV-2 RdRp protein had hydrophobic interactions with abiraterone and bexarotene. Meanwhile, cetilistat, diiodohydroxyquinoline, and remdesivir interacted with the RdRp of SARS-CoV-2 via hydrogen bonds and hydrophobic interactions. The chosen drugs interacted with various amino acid residues of the RdRp of the SARS-CoV-2 protein (Table 2, Table 3, Table 4, Table 5, Table 6). It is evident from Fig. 2 that the bexarotene interacted with RdRp with LEU240, TYR129, LEU708, VAL128, ALA125, LEU207, TYR129, HIS133, TYR728, and TYR732 amino acids. These interactions can disrupt the biological activity of the SARS-CoV-2 protein RdRp, and, consequently, the viral replication process. Then, we used MD models to confirm the docking poses and analyze the dynamics of interactions between RdRp, RNA, and medicines.Table 2 Predicted bonds between interacting atoms of abiraterone and RdRp of SARS-CoV-2.
Table 2S. No. Amino acid Amino acid atom Abiraterone atom Distance Nature of interaction
1 TYR728 Pi-Orbital Pi-Orbital 5.22 Hydrophobic (Pi-Pi)
2 LEU240 Alkyl Alkyl 5.49 Hydrophobic (Alkyl)
Table 3 Predicted bonds between interacting atoms of bexarotene and RdRp of SARS-CoV-2.
Table 3S. No. Amino acid Amino acid atom Bexarotene atom Distance Nature of interaction
1 LEU240 C–H Pi-Orbitals 3.81 Hydrophobic(Pi-Sigma)
2 TYR129 Pi-Orbitals C10(Alkyl) 4.47 Hydrophobic(Pi-Pi)
3 LEU240 Alkyl C10(Alkyl) 4.26 Hydrophobic(Alkyl)
4 LEU240 Alkyl C11(Alkyl) 5.14 Hydrophobic(Alkyl)
5 LEU708 Alkyl C12(Alkyl) 5.57 Hydrophobic(Alkyl)
6 VAL128 Alkyl C18(Alkyl) 4.94 Hydrophobic(Alkyl)
7 ALA125 Alkyl C20(Alkyl) 3.77 Hydrophobic(Alkyl)
8 LEU207 Alkyl C20(Alkyl) 4.72 Hydrophobic(Alkyl)
9 LEU240 Alkyl C20(Alkyl) 4.90 Hydrophobic(Alkyl)
10 LEU240 Alkyl Alkyl 5.02 Hydrophobic(Alkyl)
11 LEU708 Alkyl Alkyl 5.00 Hydrophobic(Alkyl)
12 ALA125 Alkyl Pi-Orbitals 4.84 Hydrophobic(Pi-Alkyl)
13 TYR129 Pi-Orbitals C18(Alkyl) 4.82 Hydrophobic(Pi-Alkyl)
14 HIS133 Pi-Orbitals C12(Alkyl) 4.98 Hydrophobic(Pi-Alkyl)
15 HIS133 Pi-Orbitals C18(Alkyl) 4.39 Hydrophobic(Pi-Alkyl)
16 TYR728 Pi-Orbitals C9(Alkyl) 4.81 Hydrophobic(Pi-Alkyl)
17 TYR728 Pi-Orbitals C10(Alkyl) 4.25 Hydrophobic(Pi-Alkyl)
18 TYR732 Pi-Orbitals C10(Alkyl) 4.71 Hydrophobic(Pi-Alkyl)
Table 4 Predicted bonds between interacting atoms of cetilistat and RdRp of SARS-CoV-2.
Table 4S. No. Amino acid Amino acid atom Cetilistat atom Distance Nature of interaction
1 HIS133 H-Donor O3(H-Acceptor) 2.17 Hydrogen bond
2 LEU240 C–H Pi-Orbital 3.61 Hydrophobic(Pi-Sigma)
3 LEU240 C–H Pi-Orbital 3.54 Hydrophobic(Pi-Sigma)
4 LEU708 Alkyl C29(Alkyl) 4.09 Hydrophobic(Alkyl)
5 LEU708 Alkyl Pi-Orbital 5.32 Hydrophobic(Pi-Alkyl)
6 HIS725 Pi-Orbital C20(Alkyl) 4.70 Hydrophobic(Pi-Alkyl)
7 TYR728 Pi-Orbital C20(Alkyl) 4.85 Hydrophobic(Pi-Alkyl)
Table 5 Predicted bonds between interacting atoms of diiodohydroxyquinoline and RdRp of SARS-CoV-2.
Table 5S. No. Amino acid Amino acid atom Diiodohydroxyquinoline atom Distance Nature of interaction
1 HIS133 H-Donor N4(H-Acceptor) 2.22 Hydrogen bond
2 ASN705 H-Acceptor H-Donor 3.23 Hydrogen bond
3 HIS133 H-Donor Pi-Orbital 2.89 Hydrogen bond
4 LEU240 C–H Pi-Orbital 3.79 Hydrophobic(Pi-Sigma)
5 ALA125 Alkyl I2(Alkyl) 4.19 Hydrophobic(Alkyl)
6 LEU207 Alkyl I2(Alkyl) 4.92 Hydrophobic(Alkyl)
7 LEU240 Alkyl I2(Alkyl) 4.73 Hydrophobic(Alkyl)
8 TYR728 Pi-Orbital I1(Alkyl) 5.14 Hydrophobic(Pi-Alkyl)
Table 6 Predicted bonds between interacting atoms of remdesivir and RdRp of SARS-CoV-2.
Table 6S. No. Amino acid Amino acid atom Remdesivir atom Distance Nature of interaction
1 LEU708 H-Acceptor H52(H-Donor) 3.06 Hydrogen bond
2 HIS133 H-Donor H-Acceptor 2.33 Hydrogen bond
3 LEU708 H-Acceptor C21(H-Donor) 3.46 Hydrogen bond
4 TYR708 Pi-Orbital C–H 3.72 Hydrophobic(Pi-Sigma)
5 LEU240 C–H Pi-Orbital 3.70 Hydrophobic(Pi-Sigma)
6 LEU240 C–H Pi-Orbital 3.80 Hydrophobic(Pi-Sigma)
7 LEU240 C–H Pi-Orbital 3.77 Hydrophobic(Pi-Sigma)
8 TYR129 Pi-Orbital Pi-Orbital 4.46 Hydrophobic(Pi-Pi Stacked)
9 LEU708 Alkyl C37(Alkyl) 3.91 Hydrophobic(Alkyl)
10 ALA125 Alkyl Pi-Orbital 5.04 Hydrophobic(Pi-Alkyl)
11 VAL128 Alkyl Pi-Orbital 5.50 Hydrophobic(Pi-Alkyl)
12 TYR728 Pi-Orbital C37(Alkyl) 4.42 Hydrophobic(Pi-Alkyl)
Fig. 1 Molecular docking perspective of abiraterone – RdRp of SARS-CoV-2.
Fig. 1
Fig. 2 Molecular docking perspective of bexarotene – RdRp of SARS-CoV-2.
Fig. 2
Fig. 3 Molecular docking perspective of cetilistat – RdRp of SARS-CoV-2.
Fig. 3
Fig. 4 Molecular docking perspective of diiodohydroxyquinoline – RdRp of SARS-CoV-2.
Fig. 4
Fig. 5 Molecular docking perspective of remdesivir – RdRp of SARS-CoV-2.
Fig. 5
3.2 Molecular dynamics simulations
We conducted an MD simulation study to evaluate system dynamics and improve the accuracy of the docking computation. For further investigation, bexarotene was chosen as the compound with the highest interaction energy.
3.2.1 Radius of gyration (Rg)
The system compactness can be determined based on the radius of gyration (Rg). The conformational stability of the complex structure during MD modeling is indicated by the graph's stability [53]. Improved structural integrity and folding treatment are indicated by a low Rg value [54]. During a 50ns MD running, the molecule's total extension can be quantified using the protein and ligand-protein complex's gyration radius (Rg) (Fig. 6 ). A constant average Rg of 3.276 nm is maintained by the ligand-protein complex during the whole 50 ns of the MD modeling. Similarly, 3.303 nm was the measured Rg value for the free 6NUR. This demonstrates how more ligand binding increases the protein's stability. The findings from the MD simulations provide unequivocal proof that bexarotene may construct a stable complex with 6NUR, which is an indication of its inhibitory effects on the RdRp receptor.Fig. 6 Radius of gyration (Rg) for RdRp of SARS-CoV-2 and bexarotene – (RdRp of SARS-CoV-2) during 50 ns MD simulation.
Fig. 6
3.2.2 RMSD
During simulation, the protein's structural alterations and insights from RMSD analysis support the protein's stability and equilibrium. The higher stability of the protein-ligand complex is indicated by the lower RMSD value of the protein backbone [55]. Fig. 7 illustrates the RMSD graph of the backbone atoms in the 6NUR complex containing bexarotene and 6NUR. The RMSD, during the 50ns MD simulation, was determined for the converged 6NUR-Bex structure. According to the findings, after 100 ps, both structures reached a stable state. During the entire 50ns simulation, 6NUR-Bex had a mean of 0.197 A0, while 6NUR had a mean of 0.216 A0. The RMSD value is regarded as acceptable and appropriate if it is less than 1.5 Å. However, it is obviously disregarded if the RMSD number is greater than 3 Å. In addition to studying the conformational changes of the receptors, RMSD measurements are utilized to determine the stability of the receptors with and without ligands [56]. In the MD simulations, the fact that the RMSD value of 6NUR-Bex was significantly lower than that of the protein suggested that bexarotene contributes to the protein's stability.Fig. 7 RMSD plots for RdRp of SARS-CoV-2 and bexarotene – (RdRp of SARS-CoV-2) during 50 ns MD simulation.
Fig. 7
3.2.3 RMSF
The flexibility of the entire protein concerning its typical structure was assessed using RMSF analysis. High RMSF values showed enhanced flexibility, but low RMSF values showed constricted motions [57]. Energy is released during ligand binding, and the amount of energy released has a direct association with the values of residual fluctuation (RMSF). Fig. 8 displays the RMSF plots generated for each individual residue that makes up the 6NUR complex with bexarotene and 6NUR. The RMSF values for 6NUR-Bex were exceptionally minimal (Average RMSF = 0.28 A0); thus, the ligand-protein complex displayed negligible movement, which demonstrates its stability. Residues inside the loop domain of 6NUR in 6NUR-Bex were found to fluctuate more than alpha-helix and beta-sheet domains during the simulation. This demonstrated that during the 50ns simulation, the protein maintained its stability [58]. The 6NUR residues 895 in the alpha-helix and 714 in the beta-sheet region, except for the loop area, showed considerable fluctuation up to 0.451 A0 and 0.426 A0, respectively. The complex 6NUR-Bex appeared stable overall based on the protein RMSF values [59].Fig. 8 RMSF plots for RdRp of SARS-CoV-2 and bexarotene – (RdRp of SARS-CoV-2) during 50 ns MD simulation.
Fig. 8
3.3 MM-PBSA binding free energy
The average free binding energy of the 6NUR-Bex was calculated using MmPbSaStat.py (Table 7 ). The information collected from gmmpbsa was used in the calculation of the average free binding energy as well as the standard deviation/error for each of the files. In the drug discovery process, one of the most important factors to consider is the durability of the ligand-receptor contact, which may be evaluated using the binding free energy.Table 7 Binding free energy (MM-PBSA) calculations for 6NUR-Bexarotene.
Table 7van der Waal energy −115.280±0.302 kJ/mol
Electrostattic energy −6.488±0.312 kJ/mol
Polar solvation energy 46.747±0.352 kJ/mol
SASA energy −14.616±0.033 kJ/mol
SAV energy 0.000±0.000 kJ/mol
WCA energy 0.000±0.000 kJ/mol
Binding energy −89.635±0.295 kJ/mol
The ligand and protein bind more tightly when the binding energy is lower [60]. To bind 6NUR to bexarotene, the van der Waals, SASA, and electrostatic energies, except for the polar solvation, were utilized. The polar solvation energy was the only energy that was not utilized. The involvement of Van der Waals energy to the entire free binding energy was found to be significantly higher than electrostatic contribution energy. The bexarotene linked exclusively with the 6NUR protein, as shown by the binding free energies of 6NUR-Bex, which were found to be −89.635 ± 0.295 kJ/mol. Fig. 9 depict the binding energy versus time plots for 6LU7-Bex. Bexarotene may be a candidate for blocking the RdRp of the SARS-CoV-2 receptor, as shown by the results mentioned above.Fig. 9 Graphical representation of the Delta_E_Pol (A) Delta_E_Apol (B) Delta_E_mm (C) Delta_E_binding (D) Bexarotene – RdRp of SARS-CoV-2 during 50 ns MD simulation.
Fig. 9
4 Conclusion
Because they have already been evaluated in terms of toxicity and safety in humans for the treatment of various diseases, repurposing FDA-approved medications will be the best option for developing COVID-19 therapies at this time. This research computationally evaluated the inhibitory potential of the FDA-approved drugs bexarotene, diiodohydroxyquinoline, abiraterone, and cetilistat against RdRp of SARS-CoV-2. With further ex vivo and in vivo examinations, our finding suggests that the selected drugs could be the potential drug of choice for the treatment of COVID-19. Gromacs software was used in this study to simulate the interaction of bexarotene with the RdRp of SARS-CoV-2. Furthermore, the RdRp-Bex radius of gyration has values that indicate the systems’ stability. The binding free energy of 6NUR-Bex was determined to be −89.635 ± 0.295 kJ/mol, indicating that the bexarotene interacted with the 6NUR protein uniquely.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgment
The authors thank the Razi University Research Council for support of this work.
==== Refs
References
1 Ashour H.M. Elkhatib W.F. Rahman M.M. Elshabrawy H.A. Insights into the recent 2019 novel coronavirus (SARS-CoV-2) in light of past human coronavirus outbreaks Pathogens 9 2020 186 32143502
2 Hui D.S. Azhar E.I. Madani T.A. Ntoumi F. Kock R. Dar O. Ippolito G. Mchugh T.D. Memish Z.A. Drosten C. The continuing 2019-nCoV epidemic threat of novel coronaviruses to global health—the latest 2019 novel coronavirus outbreak in Wuhan, China Int J Infect Dis 91 2020 264 266 31953166
3 Wu F. Zhao S. Yu B. Chen Y.-M. Wang W. Song Z.-G. Hu Y. Tao Z.-W. Tian J.-H. Pei Y.-Y. A new coronavirus associated with human respiratory disease in China Nature 579 2020 265 269 32015508
4 Hernández A.F. Calina D. Poulas K. Docea A.O. Tsatsakis A.M. Safety of COVID-19 vaccines administered in the EU: should we be concerned?, Toxicol For Rep 8 2021 871 879
5 Anand P. Stahel V.P. The safety of Covid-19 mRNA vaccines: a review Patient Saf Surg 15 2021 1 9 33402200
6 Asakura H. Ogawa H. Potential of heparin and nafamostat combination therapy for COVID-19 J Thromb Haemostasis 18 2020 1521 1522 32302456
7 El Kantar S. Nehmeh B. Saad P. Mitri G. Estephan C. Mroueh M. Akoury E. Taleb R.I. Derivatization and combination therapy of current COVID-19 therapeutic agents: a review of mechanistic pathways, adverse effects, and binding sites Drug Discov 25 2020 1822 1838
8 Gahtori J. Pant S. Srivastava H.K. Modeling antimalarial and antihuman African trypanosomiasis compounds: a ligand-and structure-based approaches Mol Divers 24 2020 1107 1124 31760561
9 Khan R.J. Jha R.K. Amera G.M. Jain M. Singh E. Pathak A. Singh R.P. Muthukumaran J. Singh A.K. Targeting SARS-CoV-2: a systematic drug repurposing approach to identify promising inhibitors against 3C-like proteinase and 2′-O-ribose methyltransferase J Biomol Struct Dyn 39 2021 2679 2692 32266873
10 Kandeel M. Al-Nazawi M. Virtual screening and repurposing of FDA approved drugs against COVID-19 main protease Life Sci 251 2020 117627
11 Tazikeh-Lemeski E. Moradi S. Raoufi R. Shahlaei M. Janlou M.A.M. Zolghadri S. Targeting SARS-COV-2 non-structural protein 16: a virtual drug repurposing study J Biomol Struct Dyn 39 2021 4633 4646 32573355
12 Mandal S. Mandal S.K. Rational drug design Eur J Pharmacol 625 2009 90 100 19835861
13 Ansari M. Moradi S. Shahlaei M. A molecular dynamics simulation study on the mechanism of loading of gemcitabine and camptothecin in poly lactic-co-glycolic acid as a nano drug delivery system J Mol Liq 269 2018 110 118
14 Moradi S. Hosseini E. Abdoli M. Khani S. Shahlaei M. Comparative molecular dynamic simulation study on the use of chitosan for temperature stabilization of interferon αII Carbohydr Polym 203 2019 52 59 30318235
15 Moradi S. Khani S. Ansari M. Shahlaei M. Atomistic details on the mechanism of organophosphates resistance in insects: insights from homology modeling, docking and molecular dynamic simulation J Mol Liq 276 2019 59 66
16 Elfiky A.A. Anti-HCV, nucleotide inhibitors, repurposing against COVID-19 Life Sci 248 2020 117477
17 Gao Y. Yan L. Huang Y. Liu F. Zhao Y. Cao L. Wang T. Sun Q. Ming Z. Zhang L. Structure of the RNA-dependent RNA polymerase from COVID-19 virus Science 368 2020 779 782 32277040
18 Yin W. Mao C. Luan X. Shen D.-D. Shen Q. Su H. Wang X. Zhou F. Zhao W. Gao M. Structural basis for inhibition of the RNA-dependent RNA polymerase from SARS-CoV-2 by remdesivir Science 368 2020 1499 1504 32358203
19 Yuan S. Chan J.F. Chik K.K. Chan C.C. Tsang J.O. Liang R. Cao J. Tang K. Chen L.-L. Wen K. Discovery of the FDA-approved drugs bexarotene, cetilistat, diiodohydroxyquinoline, and abiraterone as potential COVID-19 treatments with a robust two-tier screening system Pharmacol Res 159 2020 104960
20 Trott O. Olson A.J. Vina AutoDock Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading J Comput Chem 31 2010 455 461 19499576
21 Singh S. Srivastava H.K. Kishor G. Singh H. Agrawal P. Raghava G.P. Evaluation of protein-ligand docking methods on peptide-ligand complexes for docking small ligands to peptides 2017 BioRxiv 212514
22 Ea R. Remdesivir, sofosbuvir, galidesivir, and tenofovir against SARS-CoV-2 RNA dependent RNA polymerase RdRp): a molecular docking study) 2020 Life Sci
23 Berendsen H.J. van der Spoel D. van Drunen R. GROMACS: a message-passing parallel molecular dynamics implementation Comput Phys Commun 91 1995 43 56
24 Abraham M.J. Murtola T. Schulz R. Páll S. Smith J.C. Hess B. Lindahl E. GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers Software 1 2015 19 25
25 Bjelkmar P. Larsson P. Cuendet M.A. Hess B. Lindahl E. Implementation of the CHARMM force field in GROMACS: analysis of protein stability effects from correction maps, virtual interaction sites, and water models J Chem Theor Comput 6 2010 459 466
26 Vanommeslaeghe K. Hatcher E. Acharya C. Kundu S. Zhong S. Shim J. Darian E. Guvench O. Lopes P. Vorobyov I. CHARMM general force field: a force field for drug‐like molecules compatible with the CHARMM all‐atom additive biological force fields J Comput Chem 31 2010 671 690 19575467
27 Zoete V. Cuendet M.A. Grosdidier A. Michielin O. SwissParam: a fast force field generation tool for small organic molecules J Comput Chem 32 2011 2359 2368 21541964
28 Jorgensen W.L. Chandrasekhar J. Madura J.D. Impey R.W. Klein M.L. Comparison of simple potential functions for simulating liquid water Chem Phys 79 1983 926 935
29 Darden T. York D. Pedersen L. Particle mesh Ewald: an N⋅ log (N) method for Ewald sums in large systems Chem Phys 98 1993 10089 10092
30 Aftab S.O. Ghouri M.Z. Masood M.U. Haider Z. Khan Z. Ahmad A. Munawar N. Analysis of SARS-CoV-2 RNA-dependent RNA polymerase as a potential therapeutic drug target using a computational approach J Transl Med 18 2020 1 15 31900168
31 Elfiky A.A. SARS-CoV-2 RNA dependent RNA polymerase (RdRp) targeting: an in silico perspective J Biomol Struct Dyn 39 2021 3204 3212 32338164
32 Shahabadi N. Zendehcheshm S. Mahdavi M. Khademi F. Inhibitory activity of FDA-approved drugs cetilistat, abiraterone, diiodohydroxyquinoline, bexarotene, remdesivir, and hydroxychloroquine on COVID-19 main protease and human ACE2 receptor: a comparative in silico approach Inform Med Unlocked 26 2021 100745
33 Ebrahimi K.S. Ansari M. Moghaddam M.S.H. Ebrahimi Z. Shahlaei M. Moradi S. In silico investigation on the inhibitory effect of fungal secondary metabolites on RNA dependent RNA polymerase of SARS-CoV-II: a docking and molecular dynamic simulation study Comput Biol Med 135 2021 104613
34 Singh R. Bhardwaj V.K. Purohit R. Potential of turmeric-derived compounds against RNA‐dependent RNA polymerase of SARS‐CoV‐2: an in-silico approach Comput Biol Med 139 2021 104965
35 Parvez M.S.A. Karim M.A. Hasan M. Jaman J. Karim Z. Tahsin T. Hasan M.N. Hosen M.J. Prediction of potential inhibitors for RNA-dependent RNA polymerase of SARS-CoV-2 using comprehensive drug repurposing and molecular docking approach Int J Biol Macromol 163 2020 1787 1797 32950529
36 Indu P. Rameshkumar M.R. Arunagirinathan N. Al-Dhabi N.A. Arasu M.V. Ignacimuthu S. Raltegravir Indinavir Tipranavir Dolutegravir, and Etravirine against main protease and RNA-dependent RNA polymerase of SARS-CoV-2: a molecular docking and drug repurposing approach J Infect Public Health 13 2020 1856 1861 33168456
37 Furuta Y. Gowen B.B. Takahashi K. Shiraki K. Smee D.F. Barnard D.L. Favipiravir (T-705), a novel viral RNA polymerase inhibitor Antivir Res 100 2013 446 454 24084488
38 Wang M. Cao R. Zhang L. Yang X. Liu J. Xu M. Shi Z. Hu Z. Zhong W. Xiao G. Remdesivir and chloroquine effectively inhibit the recently emerged novel coronavirus (2019-nCoV) in vitro Cell Res 30 2020 269 271 32020029
39 Bryson A. De La Motte S. Dunk C. Reduction of dietary fat absorption by the novel gastrointestinal lipase inhibitor cetilistat in healthy volunteers Br J Clin Pharmacol 67 2009 309 315 19220279
40 Rickards A. The treatment of amoebiasis with" Diodoquin Am J Trop Med 52 1949 33 38
41 Padwal R. Cetilistat, a new lipase inhibitor for the treatment of obesity Curr Opin Invest Drugs 9 2008 414 421 London, England: 2000
42 Cheung K.S. Hung I.F. Chan P.P. Lung K. Tso E. Liu R. Ng Y. Chu M.Y. Chung T.W. Tam A.R. Gastrointestinal manifestations of SARS-CoV-2 infection and virus load in fecal samples from a Hong Kong cohort: systematic review and meta-analysis Gastroenterology 159 2020 81 95 32251668
43 Chan J.F.-W. Zhang A.J. Yuan S. Poon V.K.-M. Chan C.C.-S. Lee A.C.-Y. Chan W.-M. Fan Z. Tsoi H.-W. Wen L. Simulation of the clinical and pathological manifestations of coronavirus disease 2019 (COVID-19) in a golden Syrian hamster model: implications for disease pathogenesis and transmissibility Arch Clin Infect Dis 71 2020 2428 2446
44 Cheng V.C. Lau S.K. Woo P.C. Yuen K.Y. Severe acute respiratory syndrome coronavirus as an agent of emerging and reemerging infection Clin Microbiol Rev 20 2007 660 694 17934078
45 Hung I. Cheng V. Wu A. Tang B. Chan K. Chu C. Wong M. Hui W. Poon L. Tse D. Viral loads in clinical specimens and SARS manifestations Emerg Infect Dis 10 2004 1550 15498155
46 Boehm M.F. Zhang L. Zhi L. McClurg M.R. Berger E. Wagoner M. Mais D.E. Suto C.M. Davies P.J. Heyman R.A. Design and synthesis of potent retinoid X receptor selective ligands that induce apoptosis in leukemia cells J Med Chem 38 1995 3146 3155 7636877
47 Gniadecki R. Assaf C. Bagot M. Dummer R. Duvic M. Knobler R. Ranki A. Schwandt P. Whittaker S. The optimal use of bexarotene in cutaneous T‐cell lymphoma Br J Dermatol 157 2007 433 440 17553039
48 Dragnev K.H. Petty W.J. Shah S.J. Lewis L.D. Black C.C. Memoli V. Nugent W.C. Hermann T. Negro-Vilar A. Rigas J.R. A proof-of-principle clinical trial of bexarotene in patients with non–small cell lung cancer Clin Cancer Res 13 2007 1794 1800 17363535
49 Esteva F.J. Glaspy J. Baidas S. Laufman L. Hutchins L. Dickler M. Tripathy D. Cohen R. DeMichele A. Yocum R.C. Multicenter phase II study of oral bexarotene for patients with metastatic breast cancer Clin Oncol 21 2003 999 1006
50 Duc I. Bonnet P. Duranti V. Cardinali S. Rivière A. De Giovanni A. Shields-Botella J. Barcelo G. Adje N. Carniato D. In vitro and in vivo models for the evaluation of potent inhibitors of male rat 17α-hydroxylase/C17, 20-lyase J Steroid Biochem Mol Biol 84 2003 537 542 12767278
51 Arasaratnam M. Crumbaker M. Bhatnagar A. McKay M.J. Molloy M.P. Gurney H. Inter-and intra-patient variability in pharmacokinetics of abiraterone acetate in metastatic prostate cancer Cancer Chemother Pharmacol 84 2019 139 146 31081533
52 Yuan S. Chu H. Chan J.F.-W. Ye Z.-W. Wen L. Yan B. Lai P.-M. Tee K.-M. Huang J. Chen D. SREBP-dependent lipidomic reprogramming as a broad-spectrum antiviral target Nat Commun 10 2019 1 15 30602773
53 Kar B. Dehury B. Singh M.K. Pati S. Bhattacharya D. Identification of phytocompounds as newer antiviral drugs against COVID-19 through molecular docking and simulation based study J Mol Graph Model 114 2022 108192
54 Erva R.R. Rajulapati S.B. Potla Durthi C. Bhatia M. Pola M. Molecular dynamic simulations of Escherichia coli L-asparaginase to illuminate its role in deamination of asparagine and glutamine residues 3 Biotech 6 2016 1 7
55 Parihar A. Sonia Z.F. Akter F. Ali M.A. Hakim F.T. Hossain M.S. Phytochemicals-based targeting RdRp and main protease of SARS-CoV-2 using docking and steered molecular dynamic simulation: a promising therapeutic approach for Tackling COVID-19, Comput Biol Med 145 2022 105468
56 Kumar D. Kumari K. Vishvakarma V.K. Jayaraj A. Kumar D. Ramappa V.K. Patel R. Kumar V. Dass S.K. Chandra R. Promising inhibitors of main protease of novel corona virus to prevent the spread of COVID-19 using docking and molecular dynamics simulation J Biomol Struct Dyn 39 2021 4671 4685 32567995
57 Priya R. Sumitha R. Doss C.G.P. Rajasekaran C. Babu S. Seenivasan R. Siva R. Molecular docking and molecular dynamics to identify a novel human immunodeficiency virus inhibitor from alkaloids of Toddalia asiatica Phcog Mag 11 2015 S414 26929575
58 Prasanth D. Murahari M. Chandramohan V. Panda S.P. Atmakuri L.R. Guntupalli C. In silico identification of potential inhibitors from Cinnamon against main protease and spike glycoprotein of SARS CoV-2 J Biomol Struct Dyn 39 2021 4618 4632 32567989
59 Basit A. Ali T. Rehman S.U. Truncated human angiotensin converting enzyme 2; a potential inhibitor of SARS-CoV-2 spike glycoprotein and potent COVID-19 therapeutic agent J Biomol Struct Dyn 39 2021 3605 3614 32396773
60 Bhardwaj V.K. Singh R. Sharma J. Rajendran V. Purohit R. Kumar S. Identification of bioactive molecules from tea plant as SARS-CoV-2 main protease inhibitors J Biomol Struct Dyn 39 2021 3449 3458 32397940
| 36510496 | PMC9729590 | NO-CC CODE | 2022-12-15 23:18:04 | no | Inform Med Unlocked. 2023 Dec 8; 36:101147 | utf-8 | Inform Med Unlocked | 2,022 | 10.1016/j.imu.2022.101147 | oa_other |
==== Front
MethodsX
MethodsX
MethodsX
2215-0161
The Author(s). Published by Elsevier B.V.
S2215-0161(22)00334-X
10.1016/j.mex.2022.101960
101960
Article
Automatic identification and explanation of root causes on COVID-19 index anomalies
Sufi F.K.
School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
8 12 2022
2023
8 12 2022
10 101960101960
31 5 2022
2 12 2022
© 2022 The Author(s)
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
This paper reports a method for automatically identifying, analyzing and explaining anomalies in different indexes of COVID-19 crisis using Artificial Intelligence (AI) based techniques. With systematic application of News sensor, language detection & translation, Keyword-based extraction of COVID-19 indexes, Convolutional Neural Network (CNN) based anomaly detection, and Natural Language Processing (NLP) based explanation methods, this paper demonstrates a methodological solution for strategic decision makers to make evidence-based policy decisions on COVID-19 (in multiple dimensions like Travel, Vaccine, Medical etc.). Firstly, COVID-19 related News is fetched from multiple sources in multiple languages. Then, AI-based language detection and translation process automatically translates these News and posts in real-time. Next, COVID-19 related News and posts are segregated in multiple groups using pre-defined keywords for creation of multiple indexes. Lastly, CNN based anomaly detection identifies all the anomalies on multiple COVID-19 indexes with NLP-based explanations. A standalone decision support system was developed that implemented the presented method. This decision support system allows a strategic decision-maker to comprehend “when, where, and why there are fluctuations in COVID-19 related sentiments on a particular dimension”. Method was validated with Tweets from 15/072021 to 24/05/2022 resulting in automated generation of 5 COVID-19 indexes and 69 anomalies with explanations.
In summary, this method of anomaly detection on COVID-19 indexes presents:• An explicit, transferable and reproducible procedure for detecting anomalies on multiple indexes of COVID-19 in multiple languages
• It helps a strategic decision maker to comprehend the root-causes of anomalies in COVID-19 related travel, vaccine, medical indexes
• The solution developed using the presented method allows evidence-based strategic decision-making COVID-19 crisis using AI, Deep Learning and NLP
Graphical abstract
Image, graphical abstract
Keywords
Multidimensional analysis of COVID-19
Anomaly detection on COVID-19 posts
Deep learning on COVID-19 dimensions
Evidence based decision making
Decision support system
Method name
Anomaly detection on COVID-19 indexes
==== Body
pmcSpecifications TableSubject Area Computer Science
More specific subject area Deep Learning
Method name Anomaly detection on COVID-19 indexes
Name and reference of original method Keyword based extraction of time-series COVID-19 indexes,• P. K. Narayan, B. N. Iyke and S. S. & Sharma, "New Measures of the COVID-19 Pandemic: A New Time-Series Dataset," Asian Economics Letters, vol. 2, no. 2, pp. 1–13, 2021
• F. K. Sufi and M. Alsulami, "AI-based automated extraction of location-oriented COVID-19 sentiments," Computers, Materials & Continua (CMC), vol. 72, no. 2, pp. 3631–3649, 2022.
CNN based Anomaly Detection & Deep Learning• F. K. Sufi and M. Alsulami, "Automated Multidimensional Analysis of Global Events With Entity Detection, Sentiment Analysis and Anomaly Detection," IEEE Access, vol. 9, pp. 152449–152460, 2021.
• F. Sufi and I. Khalil, "Automated Disaster Monitoring from Social Media Posts using AI based Location Intelligence and Sentiment Analysis," IEEE Transactions on Computational Social Systems, vol. Accepted (in Press), no. DOI: 10.1109/TCSS.2022.3157142, pp. 1–11, 2022.
• F. K. Sufi and M. Alsulami, "Knowledge Discovery of Global Landslides Using Automated Machine Learning Algorithms," IEEE Access, vol. 9, 2021.
Resource availability Input data (i.e., Effect.csv, medical.csv, travel.csv, uncertainity.csv, vaccine.csv), Microsoft Power BI Source File (i.e., COVID19_Anomaly.pbix), SQL Queries for Keyword based extraction (i.e., SQLQuery1.sql) and output COVID-19 Index file (i.e., COVID-19_Indexes.csv) are all publicly accessible from at https://github.com/DrSufi/COVID_Index_Anomaly
Introduction
In Narayan and Iyke [1], researchers had proposed an innovative approach of using 327 keywords to extract multiple dimensions of COVID-19 like travel, vaccine, medical, uncertainty etc. These dimensions allowed the researchers to determine effects of specific COVID-19 related developments on financial and economic systems. By monitoring the frequencies of discussed topics from multiple news sources with the help of define keyword-sets (within each of the dimensions) as depicted in Narayan and Iyke [1], it is possible to detect fluctuations of sentiments among different dimensions of COVID-19. However, these fluctuations of sentiments would not automatically identify the underlying root-causes. For example, government decision on complete lockdown in Australia, might lower number of discussions and news related to travel which can easily be detected with travel index of Narayan and Iyke [1]. However, a strategic decision maker would not readily understand why there are lower number of travel related discussion in Australia. In another example, there might be in increase in COVID-19 vaccine related discussions in social media on a given day because of Anti-Vaccine related protests and negative sentiments as demonstrated in Sufi et al. [2]. Method demonstrated in Narayan and Iyke [1], would not flag the root-cause of these higher number of discussions because of Anti-Vax sentiments. Therefore, in this paper, we introduce application of Spectral Residual (SR) and Convolutional Neural Network (CNN) based deep learning to automatically detect and explain all the reasons for sentiment fluctuations on different dimensions COVID-19 from time-series data generated by solutions like [1]. Unlike the existing non-automated time-series analysis methods in Sharma [3], Devpura [4], Guru and Das [5], the proposed method automatically explains the root-causes of COVID-19 sentiment fluctuations in multiple dimensions harnessing the power of deep learning, NLP, and AI.
Using this updated method, a strategic decision maker can easily comprehend “when, where, and why there are fluctuations in COVID-19 related sentiment on a particular dimension”. Since the solution in Narayan and Iyke [1] produced time-series data on different dimensions, “when” questions could easily be answered. However, the “where” and “why” questions could not be answered by Narayan and Iyke [1]. Moreover, unlike previous studies, our novel approach works on all languages for all regions in the globe providing a compressive understanding on the effect COVID-19 globally.
Following are some the contributions of the presented method among many others:• Understand which location (i.e., country, state, city etc.) is responsible for the fluctuations in COVID-19 related sentiments in multiple dimensions.
• Comprehend which languages (i.e., news in Hindi, Turkish, Chinese, Arabic etc.) are driving changes in the number of posts, reports, and news related COVID-19.
• Finding out the effect of COVID-19 related sentiments (i.e., Negative, Positive, Neutral) on the variations in COVID-19 associated news or posts.
• Instead of understanding reports, news, or social media posts in a single language (e.g., English as demonstrated in Narayan and Iyke [1]), the presented method understands in more than 110 different languages (recently demonstrated in Sufi et al. [2,[6], [7], [8], [9], [10], [11]]).
• Unlike obtaining news reports from only 45 sources, the presented method is capable of obtaining, aggregating, and analyzing reports from thousands of sources (e.g., 2397 different sources as demonstrated in our recent studies [[8], [9], [10]]).
Method details
This paper reports an update to the “Keyword based Index Construction” method described in Narayan and Iyke [1]. At first, we slightly modified the list of keywords presented for Medical, Travel, Vaccine, and Uncertainty indexes. Then, we added the following four modules, where each of these added modules introduces additional functionalities and improvements (as seen from Fig. 1 ):(1) News Sensor.
(2) Language Detect & Real-time Translate.
(3) Anomaly Detection with Deep Learning (i.e., SR + CNN Algorithm).
(4) NLP based explanation.
(1) News Sensor:
Fig. 1 Conceptual Diagram of CNN & NLP on keyword-based extraction of COVID-19 indexes method.
Fig 1
News Sensor allowed Application Programming Interface (API) based connectivity to thousands of sources covering all major social media (e.g., Twitter, Facebook, Instagram, and YouTube), government websites (i.e., police websites, defense media websites, and foreign affairs websites), and the websites of national news agencies and of national and international TV/Radio channels, among others [[6], [7], [8], [9], [10], [11]]. In our most recent research work we had connected and automatically obtained news descriptions and posts from 2397 sources. Adding this, news sensor module with “keyword-based index construction” would allow us to accumulate COVID-19 related news, reports and posts from thousands of sources as opposed to only 45 sources depicted within [1].(2) Language Detect & Real-time Translate:
Language, Detect & Translate Module allowed us to comprehend hundreds of languages from unrestricted global news sources, as opposed to only collecting news in English (as for the case in Narayan and Iyke [1]). Using Microsoft Cognitive Services’ Text Analytics API [12], the updated method can understand and analyze global COVID-19 related news and posts in hundreds of languages as demonstrated in our most recent research in [[6], [7], [8], [9], [10], [11]].(3) Deep Learning based Anomaly detection and NLP based explanation:
Anomaly detection algorithms have recently been used for detecting and identifying anomalies on Time-Series data [[7], [8], [9], [10], [11], [12], [13], [14]]. Since, study in Narayan and Iyke [1] generated Time-Series data on a new set of COVID-19 index or dimension, adding Anomaly Detection algorithm would harness additional features like automatically detecting and highlighting sudden fluctuations in COVID-19 related news (in all the indexes reported within Narayan and Iyke [1]). Moreover, Anomaly detection can automatically find out the reason behind these fluctuations (i.e., answering questions like why, where and how). This in-depth analysis using deep learning techniques were not available within the methodology described in Narayan and Iyke [1].
The anomaly detector enhances line charts by automatically detecting anomalies within time-series data. It also provides explanations for the anomalies to facilitate root-cause analysis. In our most recent study, we have harnessed the anomaly detection algorithm to identify abnormal cases of landslides and obtain the root causes of these anomalies [13,14]. Moreover, we recognized abnormality and explained the abnormalities for disaster events using anomaly detection in social media [7,11]. Furthermore, we have classified anomalies on global events by monitoring 2397 global news sources and applying anomaly detection algorithms [8,9]. Before delving into the details of anomaly detection, we present the problem definition.
Problem 1: Given a sequence of real values, that is, x=x1,x2,x3,…,xn,the task of time-series anomaly detection is to produce an output sequence y=y1,y2,y,…,yn, where yi∈{0,1} denotes whether xi is an anomaly point.
The implemented solution borrowed the SR from the visual saliency detection domain and then applied a CNN to the results produced by the SR model [15].
The SR algorithm consists of three major steps:(1) Perform Fourier transform to obtain the log amplitude spectrum.
(2) Calculate the SR.
(3) Perform inverse Fourier transform, which transforms the sequence back to the spatial domain.(1) A(f)=Amplitude(f(x))
(2) P(f)=Phrase(f(x))
(3) L(f)=log(A(f))
(4) AL(f)=hq(f).L(f)
(5) R(f)=L(f)−AL(f)
(6) S(x)=||f−1(exp(R(f)+iP(f)))||
where f and f 1 denote the Fourier transform and inverse Fourier transform, respectively; x is the input sequence with shape nX1; A(f) is the amplitude spectrum of sequence x; P(f) is the corresponding phase spectrum of sequence x; L(f) is the log representation of A(f); and AL(f) is the average spectrum of L(f), which can be approximated by convoluting the input sequence by hq(f), where hq(f) is a q × q matrix defined as:(7) hq(f)=1q2[11…11…⋯⋮⋱11111…1]
R(f) is the SR, that is, the log spectrum L(f) minus the averaged log spectrum AL(f). The SR serves as a compressed representation of the sequence, whereas the innovation part of the original sequence becomes more significant. Last, the sequence was transferred back to the spatial domain using an inverse Fourier transform. The resultant sequence S(x) is referred to as the saliency map [16]. The values of the anomaly points are calculated as follows:(8) x=(x¯+mean)(1+var).r+x
where x¯ is the local average of the preceding points, mean and var are the mean and variance of all points within the current sliding window, and r ∼ N (0, 1) is randomly sampled. In this process, CNN is applied to the saliency map instead of to the raw input, thus increasing the efficiency of the overall process of anomaly detection [15,16].
The anomaly detection algorithm provides detailed explanations for all detected anomalies following the root-cause analysis performed by the AI services. In fact, we implement anomaly detection in three steps:(1) Detect all the anomalies within the time series (i.e., any values that lie outside the threshold range).
(2) Identify the main drivers of these anomalies.
(3) Explain the results in a natural language (explanation of the root cause) using NLP [17].
(4) Keyword based COVID-19 Index Construction:
In our most recent research, we had automatically obtained thousands of COVID-19 related messages from social media using keywords like “COVID” or “CORONA” in up to 110 languages [6]. In another study, we have used multiple keywords (e.g., “Bushfire”, “Tornado”, “Flood”, “Hurricane”, “Landslide”, “Landfall”, “Earthquake” and others) to obtain real-time disaster related messages in multiple language from all parts of the globe (i.e., removing geographic barriers). In Narayan and Iyke [1], pre-defined group of keywords were used to target COVID-19 related news on different indexes like travel, medical, vaccine, and others. Using the list of keywords defined for multiple COVID-19 indexes as shown in Narayan and Iyke [1], we created our keyword-sets with minor modifications. As seen from Table 1 , “Medical” dimension for this study uses 16 additional keywords compared to the solution demonstrated in Narayan and Iyke [1]. Similarly, for travel index, 6 more keywords were added on top the listed keywords in Narayan and Iyke [1]. 4 more keywords were added for “Vaccine” and another 4 more keywords were added for constructing “Uncertainty” dimension of COVID-19.Table 1 Keywords used for extracting and generating different COVID-19 Indexes like Medical, Travel, Vaccine, Uncertainty, Effect etc.
Table 1Medical Doctor, doctors, medicine, mask, medicines, nurse, nurses, health, care, recover, recovered, recoveries, recovering, recovers, recovery, symptom, symptoms, symptomatic, asymptomatic, infect, infected, infecting, infection, infectious, patient, patients, ambulance, ambulances, diagnose, diagnosed, diagnosis
Travel Aircraft, aircrafts, airfare, airfares, airline, airplane, airplanes, airport, airports, attendant, attendants, pilot, pilots, plane, cabin, cabins, flight, flights, passengers, vacation, motel, motels, accommodation, holiday, holidaying, travel, travelling, travelled, travels
Vaccine Vaccine, vaccines, pro-vaxxer, ani-vaxxer, vaxxer, vax
Uncertainty Uncertain, uncertainty, risk, risked, risky, riskier, riskiest, risking, risks, riskiness, confusing, confuse, confused, confusion
COVID Effect Virus, viruses, contagious, infect, infected, infections, infectious, infects, spread, spreads, outbreak, outbreaks, serious, patient, patients, emergencies, emergency, ambulance, asymptomatic, ambulances, symptom, symptoms, diagnose, diagnosed, symptomatic, isolate, isolated, isolating, isolation, quarantine, quarantined, quarantines, dead, deaths, death, die, died, shutdown, shutdowns, curfew
Method Implementation
As mentioned before, the significant advantage introduced with our method of anomaly detections on COVID-19 indexes are following:• Understanding News, Reports and Posts of more than 110 languages from multiple sources and multiple locations.
• User driven dynamic creation COVID-19 related indexes like effect, medical, travel, vaccine, uncertainty etc. with a click of a button.
• Fully automated detection of anomalies with COVID-19 indexes.
• Instant identifications of root-causes of these anomalies.
• Automated explanation generation of the root-causes of anomalies with NLP.
To realize the above benefits, we deployed a complete stand-alone solution with Microsoft Power BI, Microsoft Power Automate and associated technologies like Microsoft SQL Server, Microsoft Azure Cognitive Services etc. [12,18]. As seen from Table 2 , capturing news and social media sources is performed with Microsoft Power Query, which is part of Microsoft Power BI [[6], [7], [8], [9], [10], [11]]. Moreover, Microsoft Power Automate is used for automatically capturing COVID-19 related news and posts as demonstrated in our recent study [6]. SQL Queries segregated COVID-19 index related posts in Microsoft SQL Server using the keywords shown in Table 1. The SQL script used for segregating these COVID-19 posts (for the individual indexes) are publicly available from my GitHub site [19] (i.e., the file with .sql extension). Moreover, the complete source code of the windows-based solution is located at [19] (i.e., the file with .pbix extension). Furthermore, the input files containing thousands of COVID-19 related Tweets (in .csv files) along with the output of COVID-19 indexes are all located in Sufi et al. [19]. A researcher can easily download the sources files and execute solution in desktop environment. The solution developed in this study can also be deployed in multiple platforms like iOS, Android etc. with the help of Microsoft Power BI services. Within our recent studies, we have successfully implemented Microsoft Power BI Services based Apps in multiple platforms like mobile, tablet and desktop [[6], [7], [8], [9], [10], [11], [12], [13], [14]]. These solutions allowed the strategic decision makers to perform evidence-based decisions while working from remote locations.Table 2 Implementation of CNN & NLP on keyword-based extraction of COVID-19 indexes method.
Table 2Features Microsoft Power Query Microsoft Power Automate Microsoft SQL Server Azure Cognitive Services Microsoft Power BI Desktop Microsoft Power BI Services
Capturing News/ Social media posts ● ●
Language Detect ● ●
Translation ● ●
Sentiment Analysis ● ●
Entity Recognition ● ●
COVID-19 Related Indexes ● ●
Anomaly Detection (CNN) ●
Root-Cause Explanation (NLP) ●
Dashboard for Windows ● ●
Web based access ●
iOS App on Mobile/Tablet ●
Android App on Mobile/Tablet ●
The solution developed in this course of study, would allow a strategic decision maker to make evidence-based policy decisions on COVID-19 (in multiple dimensions like Travel, Vaccine, Medical etc.) using their own devices. Table 2 demonstrates technology components that supported different aspects of methods described within this paper.
Method validation
At first, we systematically designed a method that applied CNN & NLP on keyword-based extraction of global COVID-19 indexes as seen from Fig. 1. With AI-based understanding of all languages, our innovative method could construct COVID-19 indexes in a much more comprehensive manner compared to previous methods in Narayan and Iyke [1]. Then, we implemented the designed method using various technological components (as demonstrated in Table 2). This allowed our solution to be deployed in multiple platforms like windows, iOS, and Android. Using the deployed solution [19], we tested and evaluated the presented method with Global Tweets data generated from 15 July 2021 to 24 May 2022. During these times Tweets in 62 different languages were captured (even though our system is capable of comprehending Tweets in well over 110 languages as demonstrated in [[6], [7], [8], [9], [10], [11]]). Moreover, there were more than 50K locations detected as the origins of these Tweets. Unlike [1], our method depicted in this study, retains location, language, along with other feature attributes, that could be used for detecting root-causes in anomalies of COVID-19 indexes.(1) Understanding of multiple languages & locations:
Figs. 2–6 shows the locations in Microsoft Bing Map along with number of Tweets per languages. As seen from these figures (i.e., Figs. 2–6), most of the Tweets were obtained in English (i.e., language code en), followed by German (i.e., language code de), Spanish (i.e., language code es), Dutch (i.e., language code nl), Japanese (i.e., language code ja), Portuguese (i.e., language code pt), and others. After obtaining the Tweets in different languages from different locations, they were translated and subsequently the keyword-based extraction process (demonstrated in Narayan and Iyke [1]) generated 5 COVID-19 related indexes (i.e., effect, medical, vaccine, travel, and uncertainty). Since the method depicted in this study is capable of comprehending News, posts, reports in multiple languages from multiple locations, the indexes generated in subsequent stages provide a more comprehensive and global representation of COVID-19 (i.e., as opposed to method in that only understood English posts from only 45 sources). It should be noted that even though our method evaluation section only uses posts from Twitter, “News Sensor” module (as shown in Fig. 1) can seamlessly retrieve News, reports, and posts from thousands of media sources (e.g., News websites, government sites, Facebook, Twitter, Instagram, Telegram etc.) in real-time as demonstrated in our recent studies [[8], [9], [10]].(2) COVID-19 Index Generation with Keyword extraction method:
Fig. 2 Tweet Locations and Languages for constructing Effect index. (a) Locations of COVID-19 effect related Tweets (b) Number of Effect related Tweets by different Languages.
Fig 2
Fig. 3 Tweet Locations and Languages for constructing Medical index. (a) Locations of Medical related Tweets (b) Number of Medical related Tweets by different Languages.
Fig 3
Fig. 4 Tweet Locations and Languages for constructing Travel index. (a) Locations of Travel related Tweets (b) Number of Travel related Tweets by different Languages.
Fig 4
Fig. 5 Tweet Locations and Languages for constructing Uncertainty index. (a) Locations of Uncertainty related Tweets (b) Number of Uncertainty related Tweets by different Languages.
Fig 5
Fig. 6 Tweet Locations and Languages for constructing Vaccine index. (a) Locations of Vaccine related Tweets (b) Number of Vaccine related Tweets by different Languages.
Fig 6
Similar to the process described in Narayan and Iyke [1], time series data were generated by our method on 5 different COVID-19 related indexes as shown in Fig. 7 as well as within the table in Appendix). This extraction method used modified list of keywords as shown earlier in Table 1. Since the Tweets captured by “News Sensor” module (i.e., implemented in Microsoft Power Automate, and Microsoft Power Query) stored the Tweets in Microsoft SQL Server database, 5 different SQL queries were used to filter out only relevant COVID-19 related posts on effect, medical, travel, vaccine and uncertainty. These queries can be downloaded from Sufi et al. [19]. The deployed solution (i.e., [19]) can dynamically recreate COVID-19 related indexes like effect, medical, travel, vaccine, and uncertainty.Fig. 7 Multi-Dimensional Analysis of COVID-19 with our deployed windows solution [19].
Fig 7
Fig. 7 shows the deployed Microsoft Power BI based solution [19]. Left side of Fig. 7 shows COVID-19 indexes (e.g., Medical, Vaccine, Travel, Uncertainty, Effect etc.) that were demonstrated in [1]. These indexes have been re-created with an updated set of keywords list (as seen in Table 1). The interactive interface shown in Fig. 7 allows a decision maker to select a date range and immediately index values are generated for that selected date range. Moreover, the summarized indexes could be seen by months (as it is shown in the right part of Fig. 7). The location (i.e., where the COVID-19 related posts were generated) could also be viewed in Map. Lastly, the pie chart shows that Effect index was calculated by comprehending 51 different languages, medical index was calculated from 45 different languages, Vaccine index was constructed based on understanding of 45 different languages. For construction of travel index Tweets of 34 different languages were used. Lastly, uncertainty related messages on COVID-19 were found in 24 different languages. It should be noted that with change of date range, all these values (i.e., of languages, locations, indexed) gets instantly recalculated and displayed in a highly interactive manner.(3) CNN based Anomaly detection and NLP based explanation:
The most interesting outcome of this study could be seen from Fig. 8 , where all the anomalies for the calculated indexes (i.e., COVID-19 effect, medical, travel, vaccine, and uncertainty) are automatically detected by the newly introduced CNN based deep learning method. When a strategic user clicks any of these anomalies, the root-causes of these anomalies the instantly explained to the user in plain English language using NLP. As seen from Fig. 8, the user of the system clicked on an anomaly (i.e., Friday, July 16, 2021) on COVID Effect index. Immediately the possible explanations of that selected anomaly in displayed on the right side of the screen. As seen from Fig. 8, one of the reasons why that selected anomaly occurred is because there were higher numbers of COVID-19 related posts that originated from “India”. Since the presented system obtains social media posts from all over the world, in every possible language (i.e., supported by the social media platform), it can pinpoint which country, state, or city is responsible for the sudden outcry on COVID-19 situation. Without the application of deep learning methodology, manual identification the root causes from millions of messages, reports or news would be almost impossible.Fig. 8 Anomaly detection algorithm tested on COVID Effect index of COVID-19 (75% Sensitivity).
Fig 8
In another scenario, when the user clicked on the anomaly detected for Tuesday, August 17, 2021, root-cause analysis immediately performed CNN based deep learning (as seen from Fig. 9 ). As a result, the user is notified that on that day there were lots of Tweets generated by users whose location tags were disabled and on that day lots of COVID-19 related travel Tweets originated in “Turkish” language. This critical information provides an instant indication that on that particular day travel restriction and COVID-19 related outcry happened in Turkey, and as a result a noticeable surge was noticed on global COVID-19 Travel index.Fig. 9 Anomaly detection algorithm tested on Travel index of COVID-19 (75% Sensitivity).
Fig 9
In another scenario, the user of the system clicked on a particular anomaly on Monday, December 06, 2021 within Uncertainty index as seen from Fig. 10 . Immediately, our innovative method found the possible explanation with corresponding strength (i.e., confidence of the explanation). For this instance, uncertainty level reached outside the tolerance level and the reason was on that particular day many uncertainties related posts had negative sentiment. This possible AI-driven explanation with 75% confidence informs the user that on that particular day high level of negativity was prevalent among the Tweet users (most likely because of global increase in COVID cases even after mass vaccination efforts by governments).Fig. 10 Anomaly detection algorithm tested on Uncertainty index of COVID-19 (75% Sensitivity).
Fig 10
It should be highlighted that having AI-driven algorithms to instantly find out the root-causes (i.e., showing whether is it because of posts of a particular nation, particular language, particular sentiment, and all other variables) of anomalies present on COVID-19 indexes provide a significant improvement to the methods described in Narayan and Iyke [1].
In this research, we generated anomalies with varying anomaly sensitivities for COVID-19 effect, medical, travel, uncertainty, and vaccine indexes. With higher sensitivity percentage of the CNN-based anomaly detection algorithms, higher numbers of anomalies were identified. Figs. 8 to 10 reported anomalies with 75% sensitivity of implemented anomaly detection algorithm. Table 3 reports observations recorded from 75% to 100% sensitivities with an increment of 5% on all the five COVID-19 indexes. As seen from Table 3, Travel related anomalies were found to be more resistant to sensitivity changes as these anomalies lied the farthest from the corresponding travel index related tolerance values (i.e., expected minimum and expected maximum). As demonstrated in Table 3 with 100% sensitivity of the anomaly detection algorithms, in total 69 anomalies were recorded.Table 3 Number of anomalies found for each of the COVID-19 Indexes with varying sensitivities of Anomaly detection algorithm.
Table 3Sensitivity COVID Effect Medical Travel Uncertainty Vaccine
75% 1 1 6 10 0
80% 2 4 6 13 2
85% 6 5 6 13 2
90% 8 13 6 15 4
95% 14 18 6 16 10
100% 14 19 7 17 12
Conclusion
In Narayan and Iyke [1], researchers demonstrated a method of generating new time-series indexes on COVID-19. These indexes allowed a new approach to analyze and measure the impacts of COVID-19 crisis on travel, medical, vaccination and many other domains. However, the method described in Narayan and Iyke [1], would require manual analysis to detect and explain anomalies. Moreover, time-series analysis showing the impact of COVID-19 on a range of factors like oil prices, currency exchange rates, stock markets shown in Shrama[3], Devpura [4], Guru and Das [5] are mostly non-automated.
This article demonstrated the detailed steps required to obtain AI-based root-causes on anomalies found in multiple COVID-19 indexes in complete automated manner. Moreover, the presented approach generates explanations of the root-causes in natural languages for the strategic decision-makers. Furthermore, as shown in Fig. 11 , the innovative method was deployed in Mobile phones for strategic decision making. With methodological application of News sensor [[8], [9], [10]], language detection and translation [[6], [7], [8], [9], [10], [11]], keyword based extraction of COVID-19 indexes [[1], [2], [3], [4], [5], [6], [7]], CNN based Anomaly detection and explanation [[7], [8], [9],[11], [12], [13], [14]], this article demonstrated an automated multidimensional analytical capability of COVID-19 crisis.Fig. 11 Pfizer, Astrazeneca, Moderna, Sinovac, and Sputnik vaccination index viewed in Samsung Galaxy Note 10 Lite Mobile (Deployed App running in Android Version 11).
Fig 11
One of the limitations of the presented method is that the creation of indexes is widely dependent the perspective of strategic analyst or decision maker. For example, a particular strategic analyst might be interested to on analyzing the vaccine index from a perspective of available mainstream COVID-19 vaccinations (e.g., US Pfizer, UK AstraZeneca, US Moderna, Chinese Sinovac, Russian Sputnik etc.). This implies updating the list of keywords for Vaccine index (as shown in Table 1) and adding keywords like Pfizer, AstraZeneca, Moderna, Sinovac, Sputnik etc. As seen from Fig. 11, adding these vaccine specific keywords would allow a strategic analyst to view and analyze individual vaccine specific indexes that might reveal geopolitical tensions arising from commercially available vaccines.
In another scenario, a strategic decision maker might want to critically analyze the vaccine index from a perspective of pro-vaccine and anti-vaccine sentiments. For these cases, keywords demonstrated in Table 1 for constructing vaccine index should be elaborated with “Ani-Vaxxer”, “Vaccine Hater”, “Vaccine Denier”, “COVID Scam” and others as demonstrated in Sufi et al. [2]. By maintaining a dynamic list of keywords (for constructing the indexes), the presented method can efficiently serve a wide range of strategic users for evidence-based decision-making.
Supplementary material and/or Additional information: All the source files (including the .pbix Microsoft Power BI solution, *.csv input Tweets for generating COVID-19 indexes, .csv output file on COVID-19 indexes) are located at https://github.com/DrSufi/COVID_Index_Anomaly). The method reported in this paper is an incremental development our previous research in social media analysis and news media analysis from 2397 sources. The source files pertaining to extracting COVID-19 and other natural disaster using keyword-based extraction techniques are located at IEEE's publicly accessible data repository [20,21]. The sources for files for automatic retrieval of News reports (i.e., from 2397 sources like BBC, CNN, New York times and others) and analyzing them with AI (as reported in [8], [9], [10]) is located at my GitHub site in Sufi [22].
Declaration of Competing Interest
The author declares no known competing financial interests/ personal relationships that can influence this work.
Appendix Supplementary materials
Image, application 1
Data Availability
Data will be made available on request.
Acknowledgment
The author would like to thank Taufiqur Rahman, a development expert working for Federal Government, Canberra, ACT, Australia for his support during the development and implementation of the methods for this study.
Direct Submission or Co-Submission Direct Submission
Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.mex.2022.101960.
==== Refs
References
1 Narayan P.K. Iyke B.N. Sharma S.S. New measures of the COVID-19 pandemic: a new time-series dataset Asian Econ. Lett. 2 2 2021 1 13
2 Sufi F. Razzak I. Khalil I. Tracking Anti-Vax social movement using ai based social media monitoring IEEE Trans. Technol. Soc. 2022 1 10 10.1109/TTS.2022.3192757 Vols. Accepted, In Pressno
3 Sharma S.S. Westerlund and Narayan predictability test: Step-by-step approach using COVID-19 and oil price data MethodsX 8 101201 2021 1 6
4 Devpura N. Effect of COVID-19 on the relationship between Euro/USD exchange rate and oil price MethodsX 8 101262 2021 1 8
5 Guru B.K. Das A. COVID-19 and uncertainty spillovers in Indian stock market MethodsX 8 101199 2021 1 8
6 Sufi F.K. Alsulami M. AI-based automated extraction of location-oriented COVID-19 sentiments Comput. Mater. Contin. (CMC) 72 2 2022 3631 3649
7 Sufi F. Khalil I. Automated disaster monitoring from social media posts using AI based location intelligence and sentiment analysis IEEE Trans. Comput. Soc. Syst. 2022 1 11 10.1109/TCSS.2022.3157142 vol. Accepted (in Press), no
8 Sufi F.K. Alsulami M. Automated multidimensional analysis of global events with entity detection, sentiment analysis and anomaly detection IEEE Access 9 2021 152449 152460
9 Sufi F.K. AI-GlobalEvents: a software for analyzing, identifying and explaining global events with Artificial Intelligence Softw. Impacts 11 100218 2021 1 5
10 Sufi F.K. Identifying the drivers of negative news with sentiment, entity and regression analysis Int. J. Inf. Manag. Data Insights 2 1 2022 100074
11 Sufi F.K. AI-SocialDisaster: an AI-based software for identifying and analyzing natural disasters from social media Softw. Impacts 11 100319 2022 1 5
12 Microsoft Corporation (2022). Azure Cognitive Service for Language documentation, 2021. https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/. (Accessed August 3, 2021).
13 Sufi F.K. Alsulami M. Knowledge discovery of global landslides using automated machine learning algorithms IEEE Access 9 2021
14 Sufi F.K. AI-Landslide: software for acquiring hidden insights from global landslide data using Artificial Intelligence Softw. Impacts 10 100177 2021
15 Ren H. Xu B. Wang Y. Yi C. Huang C. Kou X. Xing T. Yang M. Tong J. Zhang Q. Time-series anomaly detection service at microsoft KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining New York 2019
16 Zhao R. Ouyang W. Li H. Wang X. Saliency detection by multi-context deep learning Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2015
17 Microsoft Documentation (2022). Natural language processing technology. https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing. (Accessed September 9, 2022).
18 Microsoft Documentation (2022). Microsoft Power Automate documentation. https://learn.microsoft.com/en-au/power-automate/. (Accessed August 29, 2022).
19 F.K. Sufi (2022). Complete Source Codes for COVID-19 index generation and anomaly detection with explanation. https://github.com/DrSufi/COVID_Index_Anomaly. (Accessed May 30, 2022).
20 Sufi F. Analyzing Natural Disaster Related Tweets with AI and NLP Based Services 2021 IEEE Dataport 10.21227/p0ed-cb23
21 Sufi F. AI-Based Automated Extraction of Entities, Entity Categories and Sentiments on COVID-19 Situation 2021 IEEE Dataport 10.21227/sawp-ax73
22 F. Sufi (2021). Analysis of global events solution source files. https://github.com/DrSufi/GlobalEvent. (Accessed May 27, 2022).
| 36510500 | PMC9729591 | NO-CC CODE | 2022-12-15 23:15:06 | no | MethodsX. 2023 Dec 8; 10:101960 | utf-8 | MethodsX | 2,022 | 10.1016/j.mex.2022.101960 | oa_other |
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Heliyon
Heliyon
Heliyon
2405-8440
The Author(s). Published by Elsevier Ltd.
S2405-8440(22)03443-0
10.1016/j.heliyon.2022.e12155
e12155
Review Article
Online learning experiences among nursing and midwifery students during the Covid-19 outbreak in Ghana: A cross-sectional study
Addae Hammond Yaw a∗
Alhassan Afizu a
Issah Shirley a
Azupogo Fusta b
a Nursing & Midwifery Training College, Kpembe, Box SL 98, Salaga, Ghana
b Department of Family and Consumer Sciences, Faculty of Agriculture, Food and Consumer Sciences University for Development Studies, Box TL 1882, Tamale, Ghana
∗ Corresponding author.
8 12 2022
12 2022
8 12 2022
8 12 e12155e12155
12 6 2022
3 11 2022
29 11 2022
© 2022 The Author(s)
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
As a result of the COVID-19 pandemic, schools in Ghana were compelled to suspend routine learning activities and shut down to avert a possible public health crisis. As such, online learning was introduced as a temporary measure to ensure continuity in learning. For nursing and midwifery students who are usually more engaged in face-to-face practical learning, it will be interesting to ascertain how they experienced online learning during the COVID-19 pandemic in a technologically deprived country. Hence, data was collected from March to June 2021 using online methods and a cross-sectional study design among students of nursing and midwifery training colleges in the five regions of Northern Ghana. Logistic regression and descriptive analysis were conducted using SPSS (version 22) to determine the association between (1) socio-demographic factors, (2) internet exposure and accessibility factors, and the outcome variable; students’ experiences. The results for 318 students revealed that pleasant experiences were below average (42.8%) and that reliable college internet connectivity, older age, year of study, and residence in southern Ghana were significant predictors of pleasant experiences. However, high cost of data and home distractions were identified as the main challenges to online learning. Therefore, it is important for nursing and midwifery training colleges in northern Ghana to establish robust information and communication technology infrastructure on their campuses to ensure reliable internet connectivity.
Online learning experiences; Nursing and midwifery education; Internet connectivity; COVID-19 outbreak in Ghana.
Keywords
Online learning experiences
Nursing and midwifery education
Internet connectivity
COVID-19 outbreak in Ghana
==== Body
pmc1 Introduction
All aspects of human life were significantly disrupted by the COVID-19 pandemic including the teaching and learning environment (Schleicher, 2020). As a result of this public health crisis, educational systems were compelled to suspend routine learning activities and shut down. This closure of educational institutions affected about 1.6 billion students in 190 countries globally including Ghana (Nations United, 2020).
In Ghana, the first two (2) COVID-19 cases were detected on March 12, 2020, and by March 31st, 2020 the number had risen to 161 cases (Johns Hopkins University, 2021; Kenu et al., 2020) compelling the government to urgently develop strategies to avert a potential public health crisis. Accordingly, in mid-March 2020, the government announced several restrictions to stem the spread of the virus. One of these measures was the temporary closure of all schools in Ghana (Kokutse, 2020) and the lockdown of some major cities (Ghana Health Service, 2020; Kenu et al., 2020). This unexpected shutdown of schools compelled school authorities to implement online Teaching and Learning (T&L) strategies to enable students complete the rest of their syllabus. Online learning was introduced in schools as a temporary measure to ensure continuity in learning activities, particularly because there was great uncertainty surrounding the pandemic and when it might end. Therefore, the conventional face-to-face T&L was substituted for online learning in all schools including nursing training colleges.
The transition from classroom learning to online was made possible because of recent advances in technology, which has transformed the way people connect and communicate (Alhadlaq, 2016). About a decade ago, online T&L was not possible in developing countries (Sife et al., 2007). However, with the advent of faster internet, smart mobile devices and computers, together with numerous social media and online T&L platforms, online T&L is now a feasible approach to education in many countries (Stuart, 2019).
In the Ghanaian context, even before the outbreak of the COVID-19 pandemic, some universities delivered some courses/programs online (Darkwa and Antwi, 2021) and this motivated other educational institutions to embrace the online learning initiative during the pandemic. However, for many students and teachers, online learning was a first-time experience (Ogbonnaya et al., 2020). The advances in technology notwithstanding, online learning was expected to be accompanied by some challenges such as high cost of internet data, interferences at home during learning, and inadequate capacity to teach, evaluate, and assess students using only online methods (Ogbonnaya et al., 2020). There are several approaches to online learning but to ensure strict all-inclusive attendance and be able to replicate the classroom environment, with real-time simultaneous online interactions, the synchronous online learning method (Lim, 2017) was adopted by colleges during the pandemic in Ghana. Such a method allowed students to ask questions and receive instant responses from tutors during lectures.
Given that online learning was a relatively new experience for many students in Ghana, it became imperative to evaluate the experiences of the students who participated in the online learning to suggest better alternatives and influence policy in the future. Some studies have assessed experiences and perceptions of students on online learning during COVID-19 and these studies were primarily conducted in high-income countries (Gallagher-Mackay et al., 2021; Linley, 2020; Meccawy et al., 2021). In developing countries such as Ghana, research on students’ experiences with online learning has mostly been conducted among students from other disciplines like pre-service teacher education (Ogbonnaya et al., 2020), linguistics (Tabiri et al., 2022), engineering (Sarpong et al., 2022), and vocational education (Henaku, 2020).
To the best of our knowledge, a quantitative study to investigate the experiences of nursing and midwifery students with online learning during the COVID-19 outbreak is a novelty in the context of Ghana. The present study is also unique and important because it focuses on nursing and midwifery students who are usually more engaged in practical learning. Therefore, it will be interesting to ascertain how they experienced online learning, which for many was a first-time experience. This study therefore aimed to explore the experiences of nursing and midwifery students with online learning during the COVID-19 break in Northern Ghana.
1.1 Research questions
The study would answer the following three questions.1. What is the overall rating for the online learning experiences among nursing and midwifery students during the COVID-19 break in Ghana?
2. What factors predicted pleasant online learning experiences in the context of the COVID-19-induced break in Ghana?
3. What challenges did nursing and midwifery students encounter with online learning during the COVID-19-induced break in Ghana?
2 Materials and methods
2.1 Study design
This was a descriptive cross-sectional survey that employed quantitative methods for data collection and analysis.
2.2 Population and sampling
Students from Nursing and Midwifery Training Colleges (NMTC) in Northern Ghana constituted the population. The total population of students from the five northern Ghana regions was 5152 students (Ghana Nursing and Midwifery Council, 2021). However, first-year students were excluded from the study because they had not participated in the online learning initiative during the COVID-19 outbreak. Students in their second and third years of studies and those who completed their studies in 2020 were invited to participate. These cohorts of students had all participated in online learning during the COVID-19 outbreak to overcome disruptions in their academic programs caused by the COVID-19 pandemic. Participants were recruited using convenience sampling method, which means students were not randomly or systematically selected, rather those who were readily available and willing to participate were selected.
2.3 Sample size
The sample size for this study was guided by the Cochran formula (Cochran, 1977) which is expressed as:n=Z2∗p(1−p)E2
where n = sample size, p = prevalence of satisfaction with online learning experience in Ghana (Ogbonnaya et al., 2020) = 72.0%. E = margin of error/precision = 5 % = 0.05. Z = the standard normal deviation for a 95% confidence interval = 1.96n=1.962∗0.721−0.720.052=271
Therefore, the minimum sample size was 271 students. However, the minimum sample requirement was increased by 20% to compensate for anticipated attrition and this increased the sample size to 325 students.
2.4 Data collection instrument
A structured questionnaire developed by the authors was used to collect the data. The questionnaire consisted of four sections designed to capture information about participants’ background characteristics and experiences of online learning. Questionnaire development was guided by relevant literature review and the objectives of the study.
Section A contained questions about participants' demographic and other background characteristics such as age, sex, marital status, household wealth, regional location of College and program of study. Section B contained factors that are considered as challenges to online learning in the literature, for example, home obligations/distractions, boring lectures, unfamiliar mediums, and unstable internet connectivity. Participants were to choose all factors they considered as challenges to online learning. Section C consisted of questions regarding participants' exposure and access to the internet and previous experience of online learning. Section D was a validated questionnaire adapted with permission from the authors, Sasmal and Roy (2021). Sasmal and Roy (2021) used that questionnaire to assess nursing students' experiences and perceptions of online learning in India. The questionnaire was modified by excluding questions that did not apply to the Ghanaian context. The modified version of the questionnaire contained 14 positively-worded items that assessed the overall experiences or perceptions of students regarding online learning in Ghana. Each item was scored on a five-point Likert scale: Strongly agree, agree, neutral, disagree, and strongly disagree. The Cronbach's alpha for this section of the tool was 0.861, suggesting a strong indicator of internal consistency and reliability.
To ensure the content validity of the instrument, it was reviewed by a panel of five experts who had at least five years’ experience in online learning design and implementation. Their inputs were used to add more clarity to some items. Additionally, the questionnaire was piloted among 20 nursing and midwifery students attending Colleges in the southern part of Ghana who were not part of the study population. The results of the pilot test also contributed to the further refinement of the questionnaire.
2.5 Data collection procedure
The study questionnaire was incorporated into a google form survey link and distributed to all potential participants via WhatsApp messenger as previously done (Aboagye et al., 2020; Meccawy et al., 2021). The use of an online survey for data collection was necessitated by COVID-19 restrictions. The survey link was first sent to participants in the first week of March 2021, then bi-weekly reminders were sent to participants until the end of June 2021 when no new surveys were forthcoming and the submitted questionnaires were more than the minimum sample size. Completed surveys were collated and the data extracted from google forms into SPSS version 22 for statistical analysis.
2.6 Quality control measures
In this present study, the STROBE checklist (von Elm et al., 2007) for cross-sectional studies was used as a guide to ensure conformity to recommended standards of conducting and reporting observational studies. Also, to eliminate the likelihood of multiple responses by respondents, the questionnaire was restricted to be completed only once by each participant.
2.7 Statistical analysis
Descriptive statistics in the form of frequencies and percentages were used to summarize data about the background characteristics of participants. A bar graph was used to display the results of challenges with online learning. Chi-square tests were conducted to determine the association between background characteristics, online and internet factors, and the outcome variable (satisfaction of students with online learning experiences). Satisfaction with online learning was determined using scores from the “Experience with Online Learning Scale”. The mean experience score was 2.39 (range of scores: 1–5). A score above the mean satisfaction score indicates a pleasant experience with online learning while a score equal to or below the mean indicates unpleasant experience with online learning (Thapa et al., 2021). The principal components analysis method (Filmer and Pritchett, 2001) was used to estimate household wealth based on household assets.
To account for the effects of independent variables on each other and determine the direction and magnitude of variables in relation to the outcome variable (pleasant and unpleasant experience); variables with p-values less than 0.25 (Hosmer and Lemeshow, 2004) at the univariate level were included in a multiple Logistic Regression (LR) model using Backward LR. Variables with p-values less than 0.05 at 95% confidence interval were deemed statistically significant at the final step.
2.8 Ethical considerations
The study was reviewed by the Research and Ethics Committee of Kpembe Nursing and Midwifery Training College which considered it as low risk research and exempted it from human ethics approval because the study involved only an online survey of participants. However, the college granted permission for the study to be conducted. Permission to access students' WhatsApp numbers was also granted by the Principals of the participating schools. A click on the google forms link directed students to the Plain Language Statement and Consent page. This page provided students with a clear description of the survey objectives and scope and participants’ rights to confidentiality, anonymity and voluntary participation/exit. Participants were required to indicate their consent before they could proceed to complete the survey. Privacy of data was also strictly adhered to throughout the study.
3 Results
3.1 Background characteristics of respondents
Out of the 325 students that were expected to respond to the questionnaire, 320 were received. Upon review, 318 questionnaires remained for further analysis. Table 1 contains the socio-demographic and other background characteristics of respondents. The majority (79.9%) of the respondents were aged 21 to 25 with a mean age of 23.63 ± 2.45 years. There were more females (68.2%) than males (31.8%) with the majority reporting as not married (86.2%) and Christian (64.5%). More than half (54.1%) of respondents were resident in the Northern zone with 2 out of 5 having their college located in Savannah region. Majority (72%) of respondents offered a diploma program and they were evenly spread across the three-year groups i.e. 36.8%, 33.0% and 30.2% for second year, third year and completed in 2020 respectively.Table 1 Background characteristics of respondents (N = 318).
Table 1Variable Frequency (n) Percentage (%)
Age in years (mean + -SD age: 23.63 ± 2.45)
16 to 20 15 4.7
21 to 25 254 79.9
26 + 49 15.4
Sex
Male 101 31.8
Female 217 68.2
Marital Status
Not Married 274 86.2
Married 44 13.8
Religion
Christian 205 64.5
Moslem 113 35.5
Household Wealth
Low 205 64.4
Medium 94 29.6
High 19 6.0
Sleep in the same room
Alone 89 28.0
1 other Person 83 26.1
2 other people 51 16.0
3 other people 37 11.6
4 other people 43 13.5
5 + people 15 4.7
Region of Residence
Northern Zone 172 54.1
Southern Zone 116 36.5
Coastal Zone 30 9.4
Location of College
North East 24 7.5
Northern 68 21.4
Savannah 137 43.1
Upper East 61 19.2
Upper West 28 8.8
Program of study
Nurse Assistant Clinical/Nurse Assistant Preventive 89 28.0
Registered General Nursing 115 36.2
Registered Midwifery 85 26.7
Others (Registered Mental Health Nursing, Registered Community Health Nursing, Post Nurse Assistant Clinical or Post Nurse Assistant Preventive) 29 9.1
Program qualification
Certificate 89 28.0
Diploma 229 72.0
Current year of study
Second Year 117 36.8
Third Year 105 33.0
Completed in 2020 96 30.2
3.2 Challenges with online learning
High cost of data and home obligations/distractions were identified as the two most common challenges to online learning with a prevalence of 25.1% and 22.6% respectively. Unfamiliar medium/application was identified as a challenge by only 8.6% of respondents, making it the least challenge to online learning; it was selected the least number of times as compared to other challenges (Figure 1 ).Figure 1 Challenges with online learning among nursing and midwifery students during the COVID-19 break (multiple choice).
Figure 1
3.3 Nursing and midwifery students’ satisfaction with online learning experience
Based on cut-off values from the “Experience with Online Learning Scale”, 136 students (42.8%) were satisfied with their online learning experience while 182 (57.2%) were dissatisfied.
Further details of how students responded to the individual items on the experience scale are presented in Table 2 .Table 2 Individual item analysis of the “experience with online learning scale”.
Table 2Statements Strongly disagree/disagree n (%) Not sure n (%) Agree/strongly agree n (%) Mean/SD
All experiences on online learning during COVID-19 break 2727 (61.2) 537 (12.1) 1188 (26.7) 2.39 ± 0.72
1. I had clinical interactions with patients during online learning 217 (68.2) 70 (22.0) 31 (9.7) 2.06 ± 1.02
2. It's easy to have group discussions during online learning 199 (62.6) 45 (14.2) 74 (23.3) 2.36 ± 1.16
3. I enjoyed interactions with tutors during online learning 151 (47.5) 55 (17.3) 112 (35.2) 2.72 ± 1.21
4. I did not feel anxious during online learning sessions 102 (32.1) 48 (15.1) 168 (52.8) 3.15 ± 1.25
5. Online learning helped me to understand both theory & practical sessions better than classroom lessons 254 (79.9) 27 (8.5) 37 (11.6) 1.78 ± 1.06
6. I found online learning sessions convenient than classroom lessons 261 (82.1) 29 (9.1) 28 (8.8) 1.80 ± 1.03
7. I always attended online classes without fail 205 (64.5) 24 (7.5) 89 (28.0) 2.46 ± 1.23
8. I felt tutors were trained well before conducting E-learning sessions 126 (39.6) 74 (23.3) 118 (37.1) 2.86 ± 1.32
9. Online learning enhanced speedy completion of the syllabus. 155 (48.7) 45 (14.2) 118 (37.1) 2.74 ± 1.38
10. We had no internet challenges during online learning 132 (41.5) 7 (2.2) 179 (56.3) 3.25 ± 1.68
11. I will prefer online learning to traditional classroom learning 261 (82.1) 13 (4.1) 44 (13.8) 1.85 ± 1.14
12. I did not feel increased eye strains due to online learning sessions 149 (46.9) 60 (18.9) 109 (34.3) 2.71 ± 1.21
13. Online learning is cheaper compared to classroom learning. 262 (82.4) 28 (8.8) 28 (8.8) 1.69 ± 0.98
14. It was easy for me to acquire communication equipment for online learning. 253 (79.6) 12 (3.8) 53 (16.7) 1.98 ± 1.13
Prevalence of respondents with score >2.39 = 42.8% (95% C.I = 37.3–48.2).
3.4 Association between background characteristics and satisfaction of students with online learning
Satisfaction with online learning experience was categorised as pleasant or unpleasant (Thapa et al., 2021). Out of the 318 respondents, 182 (57. 2%) had unpleasant experience while 136 (42.8%) had pleasant experience.
As illustrated in Table 3 , the outcome variable of pleasant and unpleasant experience were significantly different (at p < 0.05) among program of study (χ2 = 15.54, p = 0.01), program qualification (χ2 = 9.08, p = 0.03), current year of study (χ2 = 14.79, p = 0.01) and whether respondents passed licensure exams or not (χ2 = 14.92, p < 0.01). However, no such significant differences were observed among the participants based on their marital status, religion and household wealth.Table 3 Association between background characteristics of participants and their experiences with online learning.
Table 3Variable Unpleasant Experience: n (%) Pleasant Experience: n (%) Chi-square, p-value
182 (57.2) 136 (42.8)
Age (years)
16 to 20 10 (66.7) 5 (33.3) χ2 = 3.94, p = 0.140
21 to 25 150 (59.1) 104 (40.9)
26 + 22 (44.9) 27 (55.1)
Sex
Male 63 (62.4) 38 (37.6) χ2 = 1.97, p = 0.172
Female 119 (54.8) 98 (45.2)
Marital Status
Not Married 159 (58.0) 115 (42.0) χ2 = 0.51, p = 0.474
Married 23 (52.3) 21 (47.7)
Religion
Christian 113 (55.1) 92 (44.9) χ2 = 1.05, p = 0.305
Moslem 69 (61.1) 44 (38.9)
Household Wealth
Low 121 (59.0) 84 (41.0) χ2 = 2.21, p = 0.462
Medium 49 (52.1) 45 (47.9)
High 12 (63.2) 7 (36.8)
Sleep in the same room
Alone 53 (59.6) 36 (40.4) χ2 = 6.28, p = 0.280
1 other Person 49 (59.0) 34 (41.0)
2 other people 31 (60.8) 20 (39.2)
3 other people 24 (64.9) 13 (35.1)
4 other people 18 (41.9) 25 (58.1)
5 + people 7 (46.7) 8 (53.3)
Region of Residence
Northern Zone 108 (62.8) 64 (37.2) χ2 = 5.28, p = 0.071
Southern Zone 57 (49.1) 59 (50.9)
Coastal Zone 17 (56.7) 13 (43.3)
Location of College
North East 11 (45.8) 13 (54.2) χ2 = 6.55, p = 0.162
Northern 43 (63.2) 25 (36.8)
Savannah 76 (55.5) 61 (44.5)
Upper East 40 (65.6) 21 (34.4)
Upper West 12 (42.9) 16 (57.1)
Program of study
Nurse Assistant Clinical/Nurse Assistant Preventive 39 (43.8) 50 (56.2) χ2 =15.54, p = 0.01
Registered General Nurse 80 (69.6) 35 (30.4)
Registered Midwifery 44 (51.8) 41 (48.2)
Others 19 (65.5) 10 (34.5)
Program qualification
Certificate 39 (43.8) 50 (56.2) χ2 =9.08, p = 0.03
Diploma 143 (62.4) 86 (37.6)
Current year of study χ2 =14.79, p = 0.01
Second Year 68 (58.1) 49 (41.9)
Third Year 73 (69.5) 32 (30.5)
Completed in 2020 41 (42.7) 55 (57.3)
Passed licensure exams?
No 10 (62.5) 6 (37.5) χ2 =14.92, p < 0.01
Yet to complete 141 (63.5) 81 (36.5)
Yes 31 (38.8) 49 (61.3)
Values in tables are frequencies (percentages).
3.5 Internet exposure/accessibility and students’ satisfaction with online learning
As presented in Table 4 , it was found that only 16% of respondents had previous online learning experience before the COVID-19 break. Although 64.8% of respondents reported unstable internet connectivity in their schools, three in five students reported surfing the internet daily with 95.9% using smartphones for internet browsing. Among the online learning applications, Zoom online learning platform was the most common application used for online learning. Regarding association between students' experiences and internet exposure, statistically significant associations were found between students’ experience and previous online experience (χ2 = 4.93, p = 0.026); college internet reliability (χ2 = 26.07, p < 0.001); type of gadget used (χ2 = 3.88, p < 0.049); and gadget ownership (χ2 = 7.91, p < 0.005).Table 4 Internet exposure/accessibility and students’ experience of online learning.
Table 4Variable Frequency Unpleasant Experience Pleasant Experience Chi square, p-value
n (%) 182 (57.2) 136 (42.8)
Previous Online experience χ2 =4.93, p = 0.026
No 267 (84.0) 160 (59.9) 107 (40.1)
Yes 51 (16.0) 22 (43.1) 29 (56.9)
Frequency of internet use χ2 = 3.32, p = 0.506
Monthly 18 (5.7) 12 (66.7) 6 (33.3)
Weekly 71 (22.3) 37 (52.1) 34 (47.9)
Not Sure 21 (6.6) 13 (61.9) 8 (38.1)
Twice weekly 18 (5.7) 13 (72.2) 5 (27.8)
Daily 190 (59.7) 107 (56.3) 83 (43.7)
College internet reliability χ2 =26.07, p < 0.001
No 24 (7.5) 19 (79.2) 5 (20.8)
Yes, but not reliable 206 (64.8) 132 (64.1) 74 (35.9)
Yes, reliable 88 (27.7) 31 (35.2) 57 (64.8)
Gadget Used χ2 =3.88, p= 0.049
Laptop/Desktop 13 (4.1) 4 (30.8) 9 (69.2)
Smartphone 305 (95.9) 178 (58.4) 127 (41.6)
Gadget Ownership χ2 =7.91, p= 0.005
Self 269 (84.6) 145 (53.9) 124 (46.1)
Someone else 49 (15.4) 37 (75.5) 12 (24.5)
Application for online learning χ2 = 4.17, p = 0.244
College website 16 (5.0) 8 (50.0) 8 (50.0)
Google meeting 52 (16.4) 27 (51.9) 25 (48.1)
WhatsApp 106 (33.3) 69 (65.1) 37 (34.9)
Zoom 144 (45.3) 78 (54.2) 66 (45.8)
Values in the table are frequencies (percentages).
3.6 Multivariate analysis using binary logistic regression
At the final step of the binary logistic regression model (using Backward LR), five variables remained significant at 95% CI. As outlined in Table 5 , respondents who reported reliable college internet connectivity were 6.4 times more likely to have had pleasant experiences [AOR = 6.39; 95% CI = 2.01–20.31; P = 0.002] as compared to those that did not. Younger (16–20 years) respondents were 53% less likely to have pleasant experiences than their older (26 years or more) counterparts [AOR = 0.47; 95% CI = 0.24–0.92; P = 0.039]. Respondents resident in southern zone [AOR = 2.30; 95% CI = 1.34–3.97; P = 0.003] and those that completed in 2020 [AOR = 2.10; 95% CI = 1.08–4.10; P = 0.029] were both about 2 times more likely to have had pleasant experiences as compared to their northern zone and second-year counterparts, respectively. Southern zone as indicated above refers to the region of residence of students and not the location of College.Table 5 Final logistic regression model: factors associated with pleasant experiences of students.
Table 5Variable AOR 95% Confidence Interval P-value
Lower limit Upper limit
Age (years)
16 to 20 0.47 0.24 0.92 0.039
21 to 25 0.35 0.09 1.39 0.156
26 + ref.
Region of Residence
Northern Zone ref.
Southern Zone 2.30 1.34 3.97 0.003
Coastal Zone 1.37 0.55 3.42 0.505
Year of study
Second Year ref.
Third Year 0.69 0.37 1.28 0.234
Completed in 2020 2.10 1.08 4.10 0.029
College internet reliability
No ref.
Yes, but not reliable 2.54 0.86 7.53 0.093
Yes, reliable 6.39 2.01 20.31 0.002
Gadget Ownership
Self 3.11 1.44 6.71 0.004
Someone else ref.
AOR = Adjusted Odds Ratio; Nagelkerke R2 = 0.232; ref = reference category.
4 Discussion
This study was designed to explore nursing and midwifery students' satisfaction with their online learning experience during the COVID-19 break in Northern Ghana. The usefulness and experiences of online teaching and learning in the Ghanaian context have been previously investigated (Darkwa and Antwi, 2021; Forson and Vuopala, 2019; Henaku, 2020; Ogbonnaya et al., 2020). For this present study, less than half of the participants (42.8%) reported their experience with online learning to be pleasant. The practical approach to nursing and midwifery training, which is usually conducted face-to-face could account for the high unpleasant experiences with online learning among this population. This finding agrees with the findings of previous studies in Ghana and elsewhere in which the majority of participants reported unpleasant experiences with online learning (Hettiarachchi et al., 2021; Sasmal and Roy, 2021; Tabiri et al., 2022). However, this finding is higher as compared to a study conducted in Jordan in which 26.8% of the sample agreed they had pleasant experiences with online learning (Al-Balas et al., 2020). Similarly, a recent study in Ghana (Sarpong et al., 2022) revealed that only 22.9% of the participants were satisfied with their online learning experience. Furthermore, a systematic review involving 59 published research articles found students’ satisfaction with online learning can be as low as 14.0% (George et al., 2014).
However, contrary to the findings in the present study, several studies conducted in high and middle-income countries reported generally higher levels of satisfaction with online learning experiences, with some studies reporting pleasant experiences as high as 72% among study participants (Flack et al., 2020; Kovačević et al., 2021; Surahman and Sulthoni, 2020). This high prevalence of pleasant experiences with online learning in high and middle-income countries is partly attributed to previous online experiences, the low cost of internet data, and good internet connectivity (Al-Balas et al., 2020; International Telecommunication Union, 2020); conditions that are not prevalent in some developing countries, including Ghana. Such differences in satisfaction with online learning experiences could also be due to differences in the background characteristics of study participants, such as the field of study and age of participants.
Regarding challenges of online learning, students identified the high cost of internet data, home obligations/distractions, and boring lectures as major challenges they faced with online learning during the COVID-19 induced break. The high cost of internet data is a major challenge for online teaching and learning in several studies in Ghana (Adarkwah, 2021; Henaku, 2020; Sarpong et al., 2022; Tabiri et al., 2022) and elsewhere (Meccawy et al., 2021). Previous studies have also cited lack of attention as a result of home distractions and interruptions from family members as a challenge to online learning (Henaku, 2020; Maqableh and Alia, 2021; Ogbonnaya et al., 2020). During the COVID-19 lockdown, such challenges may have been profound and exacerbated because most income-generating activities had come to a standstill and most families were together at home for long periods.
The present study also explored the associations between students’ socio-economic and demographic characteristics, online and internet factors, and their online experiences. It was found that students who had reliable college internet connectivity, were older, resided in Southern Ghana, completed their licensure exams in 2020, and used their own communication devices were more likely to have pleasant experiences. Contrary to the findings of previous studies (Al-Balas et al., 2020; Dabaj, 2009; Sharma et al., 2020; Zalat et al., 2021), this study did not find any associations between previous online experience, gender, and satisfaction with online learning experiences.
Several studies elsewhere (Basri et al., 2018; Shahibi and Rusli, 2017) and in Ghana (Duker et al., 2018) have documented the relevance of reliable internet in schools and its effect on academic performance. Reliable internet access on college campuses allows students to access information readily from various internet sources and databases, which ultimately leads to better academic outcomes and experiences. In the context of practical demonstrations, students are able to search and download recommended video tutorials on various nursing and midwifery procedures, which allows them to rehearse on their own with marginal guidance from teachers in subsequent attempts at practice. Additionally, the presence of fast internet connectivity on college campuses enables lecturers to access information easily to develop content for teaching and learning thus enhancing the teaching and learning experience. However, what remained unclear was whether such benefits on campus translated into pleasant experiences with online learning at home. The present study has filled this lacunae by demonstrating that reliable internet connectivity is a major precursor to students’ satisfaction with online learning at home, a finding that is novel to this present study. However, contrary to our findings, other studies conducted on online learning during the COVID-19 break among students in the same medical field, did not find any association between reliable internet connectivity and satisfaction with online learning experiences (Sasmal and Roy, 2021; Sharma et al., 2020; Thapa et al., 2021).
Additionally, apart from fast internet connectivity in schools, older age was also found to be associated with pleasant online learning experience. This finding contradicts the assertions of other authors that younger age is associated with good online learning experiences (Ke and Kwak, 2013; Zalat et al., 2021). These authors posit that because young people generally utilize technology more than older people, young people are more likely to demonstrate enhanced ability, willingness, and acceptance of e-learning technologies. However, in tandem with our findings, a study involving 1920 students in Canada on the relationship between learners’ age and their online learning experiences found that older students had more confidence in computer proficiency, were more motivated, less anxious, exhibited better attitudes, and hence had better experiences in online learning than their younger counterparts (Morin et al., 2019).
Furthermore, one of the major benefits of online learning is its ability to reach a wide range of students irrespective of geographical location (Allen et al., 2016). Adequate infrastructure in Information Communication Technology (ICT) provides the avenue to minimize potential geophysical and other challenges associated with online education. Additionally, among the factors influencing pleasant experiences with online learning, it was discovered that students attending school in the north but residing in the southern part of Ghana were more satisfied with their online learning experience than students residing in the north. This could be due to differences in the proliferation of ICT infrastructure and equipment, income levels, and general living standards between the north and south of Ghana. In Ghana, the southern part is known to have higher socio-economic status and hence higher investments in ICT than the northern part. This socio-economic difference between the southern and northern parts of Ghana has been documented extensively in literature (Nyaaba and Bob-Milliar, 2019; Oteng-ababio et al., 2017); where the latter lags.
However, on a positive note, about half of the nursing students in the present study agreed that online learning did not make them feel anxious, and students did not have challenges with applications or media used. The benefit of online learning with regard to less anxiety is in tandem with previous studies (Forson and Vuopala, 2019; Morin et al., 2019). The majority (78.6%) of students used either WhatsApp or Zoom as a medium for online learning in this study. And the proliferation of these social media apps and their extensive day-to-day use may explain why students had few challenges with application use.
4.1 Limitations
The main limitation of this present study is its inability to establish causality, which is an inherent limitation of cross-sectional study designs. The other limitation is the use of a non-probability sampling method in selecting respondents, and this meant that authors could not have research findings that could be generalized country-wide. The final model for the independent predictors of online learning experiences accounted for only 23.2% of the variance in satisfaction with online learning; although it is a good model fit, the authors could not account for all the variables that predicted online learning experiences among nursing students in Northern Ghana. These notwithstanding, the use of validated tools, recruitment of a large number of respondents and inclusion of five (5) different colleges, despite restrictions due to the COVID-19 pandemic, are the strengths of this study.
5 Conclusion
Overall, this study found that only two in five students studying in nursing and midwifery training colleges in Northern Ghana had pleasant experiences with online learning during the COVID-19 outbreak in Ghana. It was also established that the high cost of data and home distractions were the most profound challenges students faced during this period. Generally, factors such as reliable internet connectivity, older age, and place of residence were better predictors of pleasant online learning experiences within that period. As such, liaising with telecommunication companies or internet service providers to improve communication infrastructure in Northern Ghana should be highly prioritized by the management of the various nursing training schools. Also, the Ghana Ministry of Health could subsidize the cost of reliable internet in training colleges to improve efficiency and reduce the financial burden this puts on students.
Declarations
Author contribution statement
All authors listed have significantly contributed to the development and the writing of this article.
Funding statement
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Data availability statement
Data will be made available on request.
Declaration of interest's statement
The authors declare no competing interests.
Additional information
No additional information is available for this paper.
Appendix A Supplementary data
The following is the supplementary data related to this article:Questionnaire for OL among N&M students Ghana.docx
Questionnaire for OL among N&M students Ghana.docx
Acknowledgements
The authors acknowledge the significant contribution of all Principals of the training institutions involved for their co-operation. We are also grateful to students for taking time off their schedules to respond to our questionnaires.
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References
Aboagye E. Yawson J.A. Appiah K.N. COVID-19 and E-learning: the challenges of students in tertiary institutions Social Educ. Res. 2 1 2020 109 115
Adarkwah M.A. “I’m not against online teaching, but what about us?”: ICT in Ghana post Covid-19 Educ. Inf. Technol. 26 2 2021 1665 1685
Al-Balas M. Al-Balas H.I. Jaber H.M. Obeidat K. Al-Balas H. Aborajooh E.A. Al-Taher R. Al-Balas B. Distance learning in clinical medical education amid COVID-19 pandemic in Jordan: current situation, challenges, and perspectives BMC Med. Educ. 20 1 2020 1 7
Alhadlaq I. How technology influences communication Int. J. Sci. Eng. Res. 7 1 2016 960 963 https://www.ijser.org/researchpaper/How-Technology-Influences-Communication.pdf
Allen I.E. Seaman J. Straut T.T. Poulin R. Online Report Card: Tracking Online Education in the United States http://onlinelearningsurvey.com/reports/onlinereportcard.pdf 2016
Basri W.S. Alandejani J.A. Almadani F.M. ICT adoption impact on students’ academic performance: evidence from Saudi universities Educ. Res. Int. 2018 1 2018 1 9
Cochran W.G. Sampling Techniques third ed. 1977 John Wiley & Sons 87 96 https://hwbdocuments.env.nm.gov/Los Alamos National Labs/General/14447.pdf
Dabaj F. The role of gender and age on students’ perceptions towards online education case study: sakarya University, Vocational High School Turk. Online J. Educ. Technol. 8 2 2009 120 123
Darkwa B.F. Antwi S. From Classroom to Online: Comparing the Effectiveness and Student Academic Performance of Classroom Learning and Online Learning 8 2021
Duker J. Bonney E.A. Dunyo Adibi M.-G. Influence of the internet on the academic achievement of senior high school students in the cape coast Metropolis of Ghana Int. J. Sci. Res. Manag. 6 8 2018
Filmer D. Pritchett L.H. Estimating wealth effects without expenditure data - or tears: an application to educational enrollments in states of India Demography 38 1 2001 115 132 11227840
Flack C.B. Walker L. Bickerstaff A. Earle H. Margetts C. Educator perspectives on the impact of COVID-19 on teaching and learning in Australia and New Zealand Educator Perspectives on the Impact of COVID-19 on Teaching and Learning in Australia and New Zealand 2020 April, 1–38
Forson I.K. Vuopala E. Online learning readiness: perspective of students enrolled in distance education in Ghana Online J. Distance Educ. E-Learn. 7 4 2019 277 294 www.tojdel.net
Gallagher-Mackay K. Srivastava P. Underwood K. Dhuey E. McCready L. Born K. Maltsev A. Perkhun A. Steiner R. Barrett K. Sander B. COVID-19 and education disruption in ontario: emerging evidence on impacts Law Soc. Facult. Publ. 1 2021 1 44 https://scholars.wlu.ca/laso_faculty/1
George P.P. Papachristou N. Belisario J.M. Wang W. Wark P.A. Cotic Z. Rasmussen K. Sluiter R. Riboli-Sasco E. Car L.T. Musulanov E.M. Molina J.A. Heng B.H. Zhang Y. Wheeler E.L. Shorbaji N. Al Majeed A. Car J. Online eLearning for undergraduates in health professions: a systematic review of the impact on knowledge, skills, attitudes and satisfaction J. Global Health 4 1 2014
Ghana Health Service Ministerial Directive on Wearing Face Mask in Public Places – Ministry of Health. Covid-19 Preventive Measures https://www.moh.gov.gh/wp-content/uploads/2020/04/04-26-2020-08.11.511-1.pdf 2020
Ghana Nursing & Midwifery Council Nursing Accredited Institutions Nmc.Gov.Gh https://www.nmc.gov.gh/web/online-accreditation 2021
Henaku E.A. COVID-19: online learning experience of college students: the case of Ghana Int. J. Multidisc. Sci. Adv. Technol. 1 2 2020 54 62 https://www.researchgate.net/profile/Eugene-Henaku/publication/342586709_COVID-19_Online_Learning_Experience_of_College_Students_The_Case_of_Ghana/links/5efbf4ca299bf18816f5fecb/COVID-19-Online-Learning-Experience-of-College-Students-The-Case-of-Ghana.pdf
Hettiarachchi S. Damayanthi B.W.R. Heenkenda S. Dissanayake D.M.S.L.B. Ranagalage M. Ananda L. Student satisfaction with online learning during the COVID-19 pandemic: a study at state universities in Sri Lanka Sustainability 13 21 2021 1 24
Johns Hopkins University Ghana – COVID-19 overview – Johns Hopkins JHU Corona Virus Resource Center 2021 John Hopkins University & Medicine 1 https://coronavirus.jhu.edu/region/ghana
Hosmer D.W. Lemeshow S. Applied Logistic Regression third ed. 2004 John Wiley & Sons https://books.google.com.gh/books?id=Po0RLQ7USIMC
International Telecommunication Union The Affordability of ICT S1. International Telecommunication Union. The Affordability of ICT Services 2020. ITU Publ. 2020; 1 (February): 8. Services 2020 2020 ITU Publications 1(February), 8 https://www.itu.int/en/ITU-D/Statistics/Documents/publications/prices2020/ITU_A4AI_Price_Briefing_2020.pdf
Ke F. Kwak D. Online learning across ethnicity and age: a study on learning interaction participation, perception, and learning satisfaction Comput. Educ. 61 2013 43 51
Kenu E. A Frimpong J. A Koram K. Responding to the COVID-19 pandemic in Ghana Ghana Ghana Med. J. 54 2 2020 72 73 33536675
Kokutse F. Ghana, Senegal Close All Universities 2020 University World News-The Global Window on Higher Education https://www.universityworldnews.com/post.php?story=20200315115142951
Kovačević I. Labrović J.A. Petrović N. Kužet I. Recognizing predictors of students’ emergency remote online learning satisfaction during COVID-19 Educ. Sci. 11 11 2021
Lim F.P. An analysis of synchronous and Asynchronous communication tools in e-learning Adv. Sci. Technol. Lett. 143 2017 230 234
Linley M.A. Foundations for Good Practice: the Student Experience of Online Learning in Australian Higher Education during the COVID-19 Pandemic 2020 Tertiary Education Quality and Standards Agency November, 1–30 https://www.teqsa.gov.au/sites/default/files/student-experience-of-online-learning-in-australian-he-during-covid-19.pdf
Maqableh M. Alia M. Evaluation online learning of undergraduate students under lockdown amidst COVID-19 Pandemic: the online learning experience and students’ satisfaction Child. Youth Serv. Rev. 128 2021 106160
Meccawy M. Meccawy Z. Alsobhi A. Teaching and learning in survival mode: students and faculty perceptions of distance education during the covid-19 lockdown Sustainability 13 14 2021 1 23
Morin D. Hamed S. Raafat S. Understanding online learning based on different age categories Issues Inf. Sci. Inf. Technol. 16 2019 308 317
Nations United Policy brief: education during COVID-19 and beyond United Nations 1 26 2020 1 31
Nyaaba A. Bob-Milliar G. The economic potentials of northern Ghana: the Ambivalence of the colonial and post-colonial states to develop the north Afr. Econ. Hist. 47 2019 45 67
Ogbonnaya U.I. Awoniyi F.C. Matabane M.E. Move to online learning during covid-19 lockdown: pre-service teachers’ experiences in Ghana Int. J. Learn., Teach. Educ. Res. 19 10 2020 286 303
Oteng-ababio M. Mariwah S. Kusi L. Is the underdevelopment of northern Ghana a case of environmental determinism or governance crisis? Ghana J. Geogr. 9 2 2017 5 39
Sarpong S.A. Dwomoh G. Boakye E.K. Ofosua-adjei I. Online teaching and learning under COVID-19 pandemic ; perception of university students in Ghana Eur. J. Interact. Multimedia Educ. 3 1 2022 1 10
Sasmal S. Roy M. Perception of undergraduate nursing students regarding e-learning during Perception of undergraduate nursing students regarding e-learning during COVID-19 pandemic in West Bengal Int. J. f Commun. Med. Public Health 8 4 2021 March)
Schleicher A. The impact of COVID-19 on education: insights from education at a glance 2020 OECD J.: Econ. Stud. 1 1 2020 1 31 https://www.oecd.org/education/the-impact-of-covid-19-on-education-insights-education-at-a-glance-2020.pdf
Shahibi M.S. Rusli K.N.K.K. The influence of internet usage on Student’™s academic performance Int. J. Acad. Res. Bus. Soc. Sci. 7 8 2017
Sharma K. Deo G. Timalsina S. Joshi A. Shrestha N. Neupane H.C. Online learning in the face of covid-19 pandemic: Assessment of students’ satisfaction at chitwan medical college of Nepal Kathmandu Univ. Med. J. 18 2020 40 47 (2 COVID-19 Special Issue)
Sife A.S. Lwoga E.T. S C. New technologies for teaching and learning : challenges for higher learning institutions in developing countries A. S. Sife , E. T. Lwoga and C. Sanga Int. J. Educ. Dev. Using Inf. Commun. Technol. (IJEDICT) 3 2 2007 57 67 https://www.learntechlib.org/p/42360/
Stuart D. Beyond lecture capture: creating digital video content for online learning- a case study J. Univ. Teach. Learn. Pract. 16 1 2019 11
Surahman E. Sulthoni Student satisfaction toward quality of online learning in Indonesian higher education during the covid-19 pandemic Proceedings - 2020 6th International Conference on Education and Technology, ICET 2020 1 2020 120 125 (1)
Tabiri M.O. Jones-Mensah I. Fenyi D.A. Asunka S. Challenges of online learning of English/French language in higher education in Ghana J. Lang. Linguist. Stud. 18 1 2022 207 222
Thapa P. Bhandari S.L. Pathak S. Nursing students’ attitude on the practice of e-learning: a cross-sectional survey amid COVID-19 in Nepal PLoS One 16 2021 1 17 (6 June)
von Elm E. Altman D.G. Egger M. Pocock S.J. Gøtzsche P.C. J V. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies Lancet 370 9596 2007 1453 1457 http://files.figshare.com/422353/Checklist_S1.doc 18064739
Zalat M.M. Hamed M.S. Bolbol S.A. The experiences, challenges, and acceptance of e-learning as a tool for teaching during the COVID-19 pandemic among university medical staff PLoS One 16 2021 1 12 (3 March)
| 36510559 | PMC9729592 | NO-CC CODE | 2022-12-14 23:42:48 | no | Heliyon. 2022 Dec 8; 8(12):e12155 | utf-8 | Heliyon | 2,022 | 10.1016/j.heliyon.2022.e12155 | oa_other |
==== Front
Eur J Intern Med
Eur J Intern Med
European Journal of Internal Medicine
0953-6205
1879-0828
European Federation of Internal Medicine. Published by Elsevier B.V.
S0953-6205(22)00431-9
10.1016/j.ejim.2022.12.002
Original Article
Long-term lung ultrasound follow-up in patients after COVID-19 pneumonia hospitalization: a prospective comparative study with chest computed tomography✰
Barbieri Greta 12⁎
Gargani Luna 2
Lepri Vittoria 1
Spinelli Stefano 1
Romei Chiara 3
De Liperi Annalisa 3
Chimera Davide 24
Pistelli Francesco 4
Carrozzi Laura 24
Corradi Francesco 2
Ghiadoni Lorenzo 15
Pisa COVID-19 Study Group#
1 Emergency Medicine Department, Pisa University Hospital, Italy
2 Department of Surgical, Medical, Molecular and Critical Area Pathology, University of Pisa, Italy
3 2nd Radiology Unit, Department of Radiology, Pisa University Hospital, Italy
4 Pulmonary Unit, Cardiothoracic and Vascular Department, Pisa University Hospital, Italy
5 Department of Clinical and Experimental Medicine, University of Pisa, Italy
⁎ Corresponding author: Department of Surgical, Medical, Molecular and Critical Area Pathology, University of Pisa, Via Savi, 10 - 56126 Pisa.
# Joanne Spataro, Margherita Malacarne, Elisabetta Addante, Sabrina Agostini o Degl'Innocenti, Paolo De Carlo, Alessio Gregori, Sara Manieri, Chiara Deri, Sara Perelli, Arianna Sabattini, Simonetta Salemi, Federica Volpi, Leonardo Colligiani, Salvatore Claudio Fanni, Laura Tavanti, Roberta Pancani, Massimiliano Desideri, Nicoletta Carpenè, Luciano Gabbrielli, Alessandro Celi, Antonio Fideli, Chiara Cappiello, Claudia Meschi, Luca Visconti, Giovanna Manfredini, Ferruccio Aquilini
8 12 2022
8 12 2022
20 5 2022
3 12 2022
6 12 2022
© 2022 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved.
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
During COVID-19 pandemic, lung ultrasound (LUS) proved to be of great value in the diagnosis and monitoring of patients with pneumonia. However, limited data exist regarding its use to assess aeration changes during follow-up (FU).
Our study aims to prospectively evaluate 232 subjects who underwent a 3-month-FU program after hospitalization for COVID-19 at the University Hospital of Pisa. The goals were to assess the usefulness of standardized LUS compared with the gold standard chest computed tomography (CT) to evaluate aeration changes and to verify LUS and CT agreement at FU.
Patients underwent in the same day a standardized 16-areas LUS and high-resolution chest CT reported by expert radiologists, assigning interpretative codes.
Based on observations distribution, LUS score cut-offs of 3 and 7 were selected, corresponding to the 50th and 75th percentile, respectively. Patients with LUS scores above both these thresholds were older and with longer hospital stay. Patients with a LUS score ≥3 had more comorbidities. LUS and chest CT showed a high agreement in identifying residual pathological findings, using both cut-off scores of 3 (OR 14,7; CL 3,6-64,5, Sensitivity 91%, Specificity 49%) and 7 (OR 5,8; CL 2,3-14,3, Sensitivity 65%, Specificity 79%).
Our data suggest that LUS is very sensitive in identifying pathological findings at FU after a hospitalization for COVID-19 pneumonia, compared to CT. Given its low cost and safety, LUS could replace CT in selected cases, such as in contexts with limited resources or it could be used as a gate-keeper examination before more advanced techniques.
Keywords
Lung ultrasound
COVID-19: Follow-up
pneumonia
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pmcIntroduction
Background
From December 2019, the medical scenario has been characterized by the SARS-CoV-2 pandemic that put a strain on the healthcare systems all around the world. Human-to-human aerosol transmission as source of contagion [1] explains the rapid spread of the Coronavirus disease 2019 (COVID-19) [2].
The respiratory tract is the most affected by the disease and the clinical manifestations of SARS-CoV-2 infected patients ranged from mild non-specific symptoms to severe pneumonia with organ function damage [2,3].
Diagnosis of COVID-19 is carried out on nasopharyngeal swab by polymerase chain reaction (PCR) [4]. The lung involvement in COVID-19 has been investigated by chest computed tomography (CT), as the radiological gold standard for its very high sensitivity [5].
Since the beginning of this pandemic, clinical and radiological findings of COVID-19 pneumonia have pushed physicians to use more frequently low-impact diagnostic tools. Thus, lung ultrasound (LUS) has been used for the diagnosis and management of this disease [6,7] since the peripheral distribution of pulmonary lesions can be easily visualized by ultrasounds [8,9]. LUS is a very useful technique, with many advantages in terms of logistics, costs, applicability and safety for both for patients and healthcare providers [10,11]. Although LUS is widely employed as diagnostic tool in several lung pathologies, such as pleural effusion, pneumothorax, lung consolidation and interstitial syndrome [12,13], the need to set up a standardized scanning scheme was essential to assess the overall lung aeration due to the typical uneven distribution of COVID-19 pneumonia. Thus, relying on a scheme already validated for ARDS [14], a 16-areas scanning scheme LUS was proposed in COVID-19 pneumonia [15]. However, very few studies have used LUS in patients follow-up (FU) and the usefulness of this method in long-term evaluations has yet to be demonstrated [7,16,17].
Aim of the study
The main objective of our study was to assess the usefulness of standardized LUS compared with the gold standard CT to assess aeration changes during 3-months follow-up of COVID-19 patients. The secondary outcome was to verify the agreement between standardized LUS and chest CT at 3-months follow-up.
Methods
Setting
This study was carried out at University Hospital of Pisa (Italy) on 232 hospitalized patients for COVID-19 undergoing imaging follow-up program after 3 months from discharge. Patients of first Italian pandemic wave were included in the study. In this phase, hospitalization was required in all cases of manifest respiratory symptoms requiring oxygen support.
Study design
We conducted a prospective single-center study. The research followed the Declaration of Helsinki ethical principles and the international standards of Good Clinical Practice. Local Ethics Committee approved this protocol on 7th April 2020 (protocol number 17828). The written informed consent was obtained from all the patients.
Study population
In our study, 232 patients hospitalized for COVID-19 were enrolled from 20 June 2020 to 6 February 2021 and underwent a 3-month follow-up after Pisa University Hospital hospitalization. Patients discharged were identified by PCR swab test result and Hospital Discharge Form data, without exclusion criteria. They were contacted 4 weeks after discharge by telephone and asked to perform a questionnaire for their availability to be included in the study. Approximately 3-months (within a 12-to-15-week range) after discharge, patients performed complete imaging tests and clinical evaluation.
Imaging follow-up evaluation was conducted by performing high-resolution chest CT scan (HRCT) and LUS on the same day, with blinded assessment, by two different operators not involved in the clinical management of the patient. Among the study population, 12 were not evaluated by follow-up HRCT but only by LUS, to avoid excessive radiation exposure (e.g., young age, pregnancy). Imaging tests at follow-up were compared with those performed during hospitalization for Sars-Cov2 infection. At baseline, all chest CTs were performed at the Emergency Department. All LUS evaluations within 48 hours of hospital's admission.
As regards the clinical aspect, expert pulmonologists assessed the qualitative evolution of three respiratory symptoms (dyspnea, cough, sputum) at 3-months clinical evaluation.
LUS topographic scheme and scoring
Due to the peculiar distribution of lung lesions in COVID-19 pneumonia, we decided to adopt a 16-areas scanning scheme (8 scans for each hemi-thorax) to emphasize posterior chest analysis [15] . We used convex probes (frequency 2.5-5 MHz) along the intercostal spaces with the transverse approach, to cover the largest possible surface with a single scan. The focus was set on the pleural line and the progressive TGC (time gain compensation) was adjusted to optimize the image. For each of the 16 areas we acquired a video, including at least one complete respiratory cycle (4-6 s). The standardized scanning scheme allowed to evaluate each area and to assign a numerical score based on lung aeration. The score is similar to the one used in ARDS, with score 0 in case of normal aeration (only A-lines or less than 3 separated B-lines); score 1 in case of 3 or more B-lines or coalescent B-lines occupying ≤ 50% of the screen; score 2 for coalescent B-lines occupying > 50% of the screen; and score 3 for consolidation. A final LUS score, achieved from the sum of all values obtained within the 16 areas can range from 0 to 48 and indicates a decrease in aeration as the score increases. Each exam was recorded and reviewed by expert sonographers (LG, GB) to verify methodology and scoring assignment. All sonographers had undergone and successfully passed a LUS training on B-lines [18] and a dedicated LUS training on COVID-19 findings. The B-lines inter-observer variability was examined by intraclass correlation coefficient (ICCs) on 50 previously acquired LUS videos evaluated by an expert reader (L.G.).
Chest CT methodology
Radiologists with a specific expertise in chest diagnostics and interstitial disease, reported CT images, while a code was assigned to each report obtaining 8 different groups of patients as shown in Table 1 . Each group showed a comparison between baseline chest CT and 3 months follow-up HRCT. Chest CTs were not analysed with quantitative or semi-quantitative scores but with comparative methods between baseline and 3 months follow-up CT scans.Table 1 Radiological codes used for interpretation of follow-up chest CT and number of patients in each category
COVID-19: CO-ronaVI-rus D-isease 2019, CT: computed tomography, FU: follow-up
Table 1GROUP CODE DESCRIPTION N (%) Tot.220
0 [0-0] Without COVID-19 pneumonia (chest CT signs of pneumonia absent at baseline and absent at FU) 11 (5%)
1 [1-0] COVID-19 pneumonia resolution (chest CT signs of pneumonia present at baseline and absent at FU) 91 (41,4%)
2 [1-01] Resolution and new findings (CT chest CT signs of COVID-19 pneumonia present at baseline and resolved but present elsewhere at FU 11 (5%)
3 [1-11] Stable (chest CT signs of COVID-19 pneumonia present at baseline and present unchanged at FU) 4 (1,8%)
4 [1-10] Resolving pneumonia (CT chest signs of COVID-19 pneumonia present at baseline and reduced at FU) 85 (38,6%)
5 [1-12] Worsened (CT chest CT signs of COVID-19 pneumonia present at baseline and increased at FU) 0
6 [0-1] Onset (chest CT signs of COVID-19 pneumonia absent at baseline and present at FU) 0
7 [x-0] Absence (chest CT not performed at baseline and no signs of COVID-19 pneumonia at FU) 10 (4,5%)
8 [x-11] Missing / finding (chest CT not performed at baseline and signs of COVID-19 pneumonia present at FU) 5 (3,7%)
Chest CT was considered worsened in case of new or more extensive lesions compared to the previous exam. Pathological findings considered were those typical of COVID-19 pneumonia: ground-glass, crazy paving, and consolidations. During hospitalization, 2 types of CT scan were used: 64-row General Electric Light Speed (collimation width 0.625, reconstruction thickness 1.25 mm, standard kernel, soft and boneplus) and 40-row Siemens Somatom Sensation (collimation width 0.6, reconstruction thickness 1.5 mm, kernel B31, B35 and B60). At 3 month follow-up, CT scans were acquired with a 64-slice Siemens Somatom Sensation scanner, Siemens Healthineers (collimation width 0.6, reconstruction thickness 1.5 mm, kernel B60 or B31).
Statistical analysis
Data are expressed as mean ± standard deviation, median and interquartile range (IQR) for continuous numeric variables and as percentage for categorical variables. Differences between groups were analysed with a parametric test (Student's T test) for normally distributed variables and a non-parametric test (Mann-Whitney U test) for non-normally distributed variables. χ2 test was used for comparisons between variables expressed in the form of frequencies. Receiving Operator Curves (ROC) were used to identify the best cut-off values of the LUS aeration score and their diagnostic accuracy in identifying pathological findings Logistic regression was used to verify the ability of LUS cut-offs to predict pathological changes on chest CT. Regression coefficient (β) and odds ratio (OR) with the corresponding 95% confidence interval (CI) were assumed as outputs of the logistic regression models. P value 0.05 was considered statistically significant.
Results
Descriptive analysis of population subjected to follow-up
The mean age of patients was 62.2 ± 14.5 years (minimum 18, maximum 96). Out of the 232 patients examined, 144 (62.1%) were males and 88 (37.9 %) females. The mean hospital stay was 17.6 ± 11.3 days. Regarding the setting, 131 (81.9%) patients were hospitalized in the medical area and 29 (18.1%) in intensive care units (ICU), while among the latter, 17 (58,6%) underwent endotracheal intubation (ETI). The most frequent comorbidities of the 232 hospitalized patients were arterial hypertension (n.89, 38.5%),; cardiovascular disease (n=57, 24%); diabetes mellitus (n=42, 18.2%); and respiratory diseases (n=28, 12,1%).
As regards the 3-month follow-up clinical evaluation, the majority of the patients showed resolution of respiratory symptoms (60.9%), while improvement, stability or worsening were observed in 20.5%, 15.5% and 3.1% of the patients, respectively.
LUS score in the follow-up population
At follow-up, the average LUS score of the whole population of 232 patients was 4.9 ± 5.7. Figure 1 shows LUS score distribution. The 50th percentile was a LUS score of 3, and the 75th percentile a LUS score of 7.Figure 1 LUS score values distribution in total of patients undergoing follow-up and in population divided by hospital setting
LUS: lung ultrasound, ICU: intensive care unit
Figure 1
LUS score of patients admitted to the medical area was not different from that of patients admitted to intensive care (respectively 4.9 ± 5.4 and 5.4 ± 6.0, p 0.3) as shown in Figure 1.
Patients with LUS greater than 3 were older, more frequently males, with longer hospitalisations and higher incidence of comorbidities as compared to those with LUS < 3 (Table 2 ). Patients with LUS greater than 7 were older, with longer hospitalisation as compared to those with LUS < 7 (Table 2).Table 2 Clinical characteristics of population divided according to LUS cut-offs of 3 and 7.
LUS: lung ultrasound, CV: cardiovascular, ICU: intensive care unit
Table 2 LUS score ≤3
(50th perc)
Tot. 122 LUS score >3
(50th perc)
Tot. 110 P value LUS score ≤7
(75th perc)
Tot. 181 LUS score >7
(75th perc)
Tot. 51 P value
Male sex 67 (46.5%) 77 (53.5%) 0.02 111 (77.1%) 33 (22.9%) 0.66
Female sex 55 (62.5%) 33 (37%) 70 (79.5%) 18 (20.5%)
Age 55.8 ± 13.1 69.5 ± 12.4 < 0.001 58.6 ±13.08 75.2 ± 11.5 < 0.001
Arterial hypertension 34 (38.2%) 55 (61.8%) 0.006 60 (67.4%) 29 (32.6%) 0.002
CV disease 21 (36.8%) 36 (63.2%) 0.006 36 (63.2%) 21 (36.8%) 0.002
Diabetes mellitus 14 (33.3%) 28 (66.7%) 0.006 29 (69%) 13 (31%) 0.12
Respiratory disease 13 (46.4%) 15 (53.6%) 0.5 18 (64.3%) 10 (35.7%) 0.06
ICU 19 (44.2%) 24 (55.8%) 0.3 33 (76.7) 10 (23.3%) 0.9
Days of hospitalization 13.41 ± 7.8 19.3 ±12.3 < 0.001 14.9 ± 9.9 20.9 ± 11.5 < 0.001
P/F 315.3 ± 84.9 306.5 ± 96.2 0.5 313.6 ± 91.4 302.4 ± 84.7 0.5
Persistence respiratory symptoms at 3-month FU 5 (41.8%) 40 (96.4%) 0.34 68 (37.6) 23 (45.1) 0.37
No statistically significant differences emerged regarding the arterial partial pressure of oxygen / fractional inspired oxygen ratio (P/F) at admission and follow-up ultrasound findings. The persistence of subjective respiratory symptoms at 3-month follow-up also showed no significant association with LUS (Table 2).
In those patients (n=80) where LUS was available both during hospitalization and at follow-up, LUS score changed from 14.4 ± 8.5 to 5.1 ± 5.6 (p<0.001).
Correlation of LUS and chest CT at 3-month follow-up
The population was divided into 2 groups based on chest CT results (Table 1): those with a resolved or resolving pneumonia (CT codes 0-0, 1-0, x-0, 1-10) and those who still presented some pathological findings (CT codes 1- 01, 1-11, 1-12, 0-1, x-10, x-11).
To verify the correlation between ultrasound and CT findings, we compared the group of patients with LUS score ≤ 3, corresponding to the 50th percentile, and resolution on CT examination, as shown in Table 3 .Table 3 Concordance between chest CT and LUS using cut-off of 3 and 7
LUS: lung ultrasound, CT: computed tomography
Table 3 LUS score ≤3 LUS score >3 p-value LUS score
≤7 LUS score
>7 p-value
CT Resolved/resolving pneumonia
Tot. 197 115 (58.4%) 82 (41.6%) < 0.001 161 (94.2%) 10 (5.8%) < 0.001
CT pathological findings
Tot. 23 2 (1.7%) 21 (91.3%) 10 (43.5%) 13 (56.5%)
Then, we verified the validity and predictivity of LUS score cut-offs by univariate logistic regression analysis (with reference to chest CT findings), obtaining an Odds Ratio of 14.7 for score 3 and 5.8 for score 7. Finally, we established sensitivity and specificity of the 2 cut-offs using the ROC curves (Table 4 and figure 2 ).Table 4 Univariate logistic regression analysis, sensitivity, and specificity values for 3 and 7 cut-offs
LUS: lung ultrasound.
Table 4 Odds ratio Beta Confidence limits p-value Sensibility Specificity
LUS 3 14.7 2.7 3.6-64.5 0.005 91% 49%
LUS 7 5.8 1.7 2.3-14.3 0.008 65% 79%
Figure 2 ROC curves corresponding to the cut-offs of 3 and 7
CT: computed tomography
Figure 2
B-lines inter-observer variability
All readers had a mean ICC on B-lines number assessment >0.90 for single measurements (p<0.0001) and >0.90 for average measurements (p<0.0001). Intra-observer variability was assessed on 20 consecutive videos, with an overall concordance rate on LUS score of 95%.
Discussion
A very large number of publications on COVID-19 were issued in 2020-2021 regarding pathogenesis, pathophysiology, epidemiology, clinical course, diagnosis, and complications. However, systematic studies on medium to long-term imaging follow-up from hospital discharge of surviving patients are scarce [16,17,19].
LUS is considered a valid diagnostic technique in the context of several lung diseases (including as heart failure), with higher sensitivity compared to X-ray [10,12,13]. Furthermore, LUS has many logistical advantages, particularly significant in the context of SARS-CoV-2 pandemic, in terms of costs, safety and applicability [7,15].
The present study demonstrates that LUS can assess aeration changes at follow-up of COVID-19 patients, and that a good correlation exists between LUS and HCRT, 3 months after hospitalization for COVID-19 disease.
The correlation between different imaging methods has been already studied [20,21]. Zieleskiewicz et al. investigated the relationship between ultrasound and chest CT in patients with SARS-CoV-2 pneumonia in acute phase. In patients with proven SARS-CoV-2 pneumonia, their calculated LUS score was significantly associated with the severity of pneumonia as assessed by chest CT and clinical characteristics of the patients [20]. A semi-quantitative analysis by Deng et al. showed a high consistency between LUS and CT results in critically ill patients with COVID-19, promoting LUS as a potential tool for dynamic monitoring of ICU patients in the absence of CT [21]. Conversely, we evaluated the conformity between LUS and chest CT at 3-months follow-up, according to the comparative approach based on the evolution of HRCT pathological findings.
We divided the population based on LUS cut-offs that identified the 50th and 75th percentiles of our observations (LUS score 3 and 7, respectively). Our analysis showed that both cut-offs of 3 and 7 are effective in identifying HRCT abnormality (OR 14.7, CL 3.6-64.5; OR 5.8 with CL 2.3-14.3, respectively). Nevertheless, ROC curves demonstrated a high sensitivity (91%) of the cut-off of 3 compared to 7 (65%). Thus, the use of a lower cut-off could allow a more "prudential" approach but burdened by false positives due to other pathological conditions, particularly in the elderly (pulmonary congestion due to heart failure or renal failure). Conversely, the cut-off of 7 showed a higher specificity (70%) compared to 3 (49%). These data agree with the closer relationship of the cut-off value of 3 with preexisting comorbidities.
A comparable ultrasound cut-off proposal was formulated by Clofent and collaborators, who suggested LUS score 3 demonstrating a strong correlation with HRCT alterations in 352 patients after 2-5 months after hospitalization [19]. Similarly to our study, ROC curve analysis revealed an excellent ability of LUS score ≥ 3 to discriminate patients with HCRT abnormalities with of sensitivity 94.2% [19].
Baseline characteristics of our study population, dating back to the very first pandemic wave, showed a high prevalence of males, cardiovascular diseases and hospital management influenced not only by clinical conditions but also by organizational difficulties [22], [23], [24], [25].
Our analyses showed that the groups with LUS score above both cut-offs at follow-up had a higher age and a longer hospitalization. The group with a LUS score ≥ 3 showed greater comorbidities. Statistical significance is achieved in all categories when the cut-off of 3 was used. This result may suggest that older and comorbid patients require a longer recovery time. On the other hand, these data could be related to a more severe disease in the acute phase, or concomitants pathologies (for example heart failure).
There was no difference in LUS score between patients managed in different care settings or with worse respiratory conditions on admission, probably due, to the high mortality of ICU, which resulted in a hyper-selected population at post-hospitalization follow-up.
As regards the 3-month follow-up clinical evaluation, approximately 40% patients reported the persistence of respiratory symptoms. The LUS aeration score was not dissimilar in patients with or without persistence of symptoms at follow-up. This finding could be due to the non-specificity and subjectivity of the symptoms reported, while a correlation with functional tests was already demonstrated [19]. Indeed, Clofent and al. showed an inverse association for LUS score ≥ 3 with lung diffusing capacity for carbon monoxide (DLCO) [19].
Results of our and previous studies [11,19,26] suggest that LUS could be used in selected cases as an alternative to CT, with a significant reduction in timing, costs, and exposure to X-rays [27]. The LUS examination could be considered as a "gate-keeper" before CT, particularly in younger patients or in limited-resource setting. Accordingly, a subgroup of patients with LUS assessment both at baseline and at follow-up, showed a significant lung improvement at follow-up compared to the values of the acute phase (p<0.001). Considering that LUS score expresses the degree of lung aeration, our data show that standardized LUS technique allows the monitoring of this phenomenon over time.
Limitations
This research has some limitations. The first is the single-center design of the study and the limited number of cases. The study was designed according to the scarce data regarding early evaluation of imaging at follow-up of patients hospitalized for COVID during the first pandemic wave in Italy. Patients' pre-existing comorbidities could be responsible for pathological findings on imaging examinations, independently of COVID-19 disease, introducing confounding to interpretation. The two imaging techniques compared, despite being standardized and performed at the same time, use methods that are not completely overlapping [28]. Furthermore, CT and LUS findings identified at 3 months FU could be interpreted as normal evolution after COVID-19 pneumonia, as part of the slow resolution process. Therefore, our considerations could be re-evaluated considering the subsequent follow-up checks and correlation with clinical data, details that would be valued in future studies.
Conclusion
The evaluation of LUS at follow-up showed a substantial resolution of COVID-19 pneumonia in a large percentage of patients hospitalized 3 months earlier. Patients with higher LUS score were older with associated co-morbidities and longer hospital stay.
The comparison between LUS and CT findings (through qualitative categories identified by expert radiologists) shows an excellent correlation between the two methods. Considering the high sensitivity demonstrated by the proposed cut-offs, we believe that a standardized LUS approach could be applied effectively in COVID-19 patients follow-up, limiting the use of expensive, unsafe, and not always available methods, such as chest CT.
Perspectives
LUS data could be analysed with clinical conditions and respiratory functional tests, aiming at the optimization of COVID-19 patient's assessment after hospitalization. Indeed, the association of LUS findings associated with the clinical evaluation, could overcome the limit of the low specificity of the technique.
A further perspective is represented by the development of automated interpretation for LUS, involving online-computerized measurements of the percentage of pleural line presenting B-lines [29]. This could extend the application in all pandemic contexts overcoming the limits derived from the operator-dependency [26]
Authors’ contributions to the manuscript
Barbieri Greta: conceptualization, investigation, formal analysis, writing, review, and editing
Luna Gargani: conceptualization, methodology, review
Vittoria Lepri: data curation
Stefano Spinelli: investigation, data curation, review
Chiara Romei: investigation, data curation, review
Annalisa De Liperi: review
Davide Chimera: investigation, data curation, review
Francesco Pistello: review
Laura Carrozzi: review
Francesco Corradi: methodology, review
Lorenzo Ghiadoni: conceptualization, formal analysis, review, and editing
Conflict of Interest
The authors declare no potential conflict of interests.
Appendix Supplementary materials
Image, application 1
✰ Type of manuscript: Clinical Research Study
Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.ejim.2022.12.002.
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References
1 Pascarella G Strumia A Piliego C COVID-19 diagnosis and management: a comprehensive review J Intern Med 288 2 2020 192 206 10.1111/joim.13091 08 32348588
2 Ge H Wang X Yuan X The epidemiology and clinical information about COVID-19 Eur J Clin Microbiol Infect Dis 39 6 2020 1011 1019 10.1007/s10096-020-03874-z Jun 32291542
3 GdSaHdCdBd SIAARTI SIAARTI guidelines for admission to and discharge from Intensive Care Units and for limitation of treatment in intensive care Minerva Anestesiol 69 3 2003 111 118 Mar101-11
4 Wiersinga WJ Rhodes A Cheng AC Peacock SJ Prescott HC. Pathophysiology, Transmission, Diagnosis, and Treatment of Coronavirus Disease 2019 (COVID-19): A Review JAMA 324 8 2020 782 793 10.1001/jama.2020.12839 Aug 32648899
5 Jalaber C, Lapotre T, Morcet-Delattre T, Ribet F, Jouneau S, Lederlin M. Chest CT in COVID-19 pneumonia: A review of current knowledge. Diagn Interv Imaging. 2020 Jul - Aug 2020;101(7-8):431-437. 10.1016/j.diii.2020.06.001
6 Volpicelli G Gargani L. Sonographic signs and patterns of COVID-19 pneumonia Ultrasound J 12 1 2020 22 10.1186/s13089-020-00171-w Apr 32318891
7 Volpicelli G Gargani L Perlini S Lung ultrasound for the early diagnosis of COVID-19 pneumonia: an international multicenter study Intensive Care Med. 47 4 2021 444 454 10.1007/s00134-021-06373-7 Apr 33743018
8 Vetrugno L Baciarello M Bignami E The "pandemic" increase in lung ultrasound use in response to Covid-19: can we complement computed tomography findings? A narrative review Ultrasound J 12 1 17 2020 39 10.1186/s13089-020-00185-4 Aug 32785855
9 Vetrugno L Mojoli F Boero E Level of Diffusion and Training of Lung Ultrasound during the COVID-19 Pandemic - A National Online Italian Survey (ITALUS) from the Lung Ultrasound Working Group of the Italian Society of Anesthesia, Analgesia, Resuscitation, and Intensive Care (SIAARTI) Ultraschall Med 2021 10.1055/a-1634-4710 Nov 03
10 Gargani L Volpicelli G. How I do it: lung ultrasound Cardiovasc Ultrasound 12 2014 25 10.1186/1476-7120-12-25 Jul 04 24993976
11 Vetrugno L Sala A Orso D Lung Ultrasound Signs and Their Correlation With Clinical Symptoms in COVID-19 Pregnant Women: The "PINK-CO" Observational Study Front Med (Lausanne) 8 2021 768261 10.3389/fmed.2021.768261
12 Hew M Tay TR. The efficacy of bedside chest ultrasound: from accuracy to outcomes Eur Respir Rev 25 141 2016 230 246 10.1183/16000617.0047-2016 Sep 27581823
13 Volpicelli G Elbarbary M Blaivas M International evidence-based recommendations for point-of-care lung ultrasound Intensive Care Med 38 4 2012 577 591 10.1007/s00134-012-2513-4 Apr 22392031
14 Bouhemad B Mongodi S Via G Rouquette I. Ultrasound for "lung monitoring" of ventilated patients Anesthesiology 122 2 2015 437 447 10.1097/ALN.0000000000000558 Feb 25501898
15 Gargani L Soliman-Aboumarie H Volpicelli G Corradi F Pastore MC Cameli M. Why, when, and how to use lung ultrasound during the COVID-19 pandemic: enthusiasm and caution Eur Heart J Cardiovasc Imaging 21 9 2020 941 948 10.1093/ehjci/jeaa163 09 32515793
16 Nguyen M Gaujard G Guinot PG Bouhemad B Group LUSS. Using the lung ultrasound score to monitor disease progression for COVID-19-associated ARDS Intensive Care Med 47 11 2021 1329 1331 10.1007/s00134-021-06515-x 11 34595567
17 Hernández-Píriz A Tung-Chen Y Jiménez-Virumbrales D Importance of Lung Ultrasound Follow-Up in Patients Who Had Recovered from Coronavirus Disease 2019: Results from a Prospective Study J Clin Med 10 14 20 2021 10.3390/jcm10143196 Jul
18 Gargani L Sicari R Raciti M Efficacy of a remote web-based lung ultrasound training for nephrologists and cardiologists: a LUST trial sub-project Nephrol Dial Transplant 31 12 2016 1982 1988 10.1093/ndt/gfw329 12 27672089
19 Clofent D Polverino E Felipe A Lung Ultrasound as a First-Line Test in the Evaluation of Post-COVID-19 Pulmonary Sequelae Front Med (Lausanne) 8 2021 815732 10.3389/fmed.2021.815732
20 Zieleskiewicz L Markarian T Lopez A Comparative study of lung ultrasound and chest computed tomography scan in the assessment of severity of confirmed COVID-19 pneumonia Intensive Care Med 46 9 2020 1707 1713 10.1007/s00134-020-06186-0 09 32728966
21 Deng Q Zhang Y Wang H Semiquantitative lung ultrasound scores in the evaluation and follow-up of critically ill patients with COVID-19: a single-center study Acad Radiol 27 10 2020 1363 1372 10.1016/j.acra.2020.07.002 10 32713715
22 Barbieri G Cipriano A Carrara S SARS-CoV-2 management in Emergency Department: risk stratification and care setting identification proposal based on first pandemic wave in Pisa University Hospital Emergency Care Journal 17 4 2021
23 Falcone M Tiseo G Barbieri G Role of Low-Molecular-Weight Heparin in Hospitalized Patients With Severe Acute Respiratory Syndrome Coronavirus 2 Pneumonia: A Prospective Observational Study Open Forum Infect Dis 7 12 2020 ofaa563 10.1093/ofid/ofaa563 Dec 33365358
24 Coppelli A Giannarelli R Aragona M Hyperglycemia at Hospital Admission Is Associated With Severity of the Prognosis in Patients Hospitalized for COVID-19: The Pisa COVID-19 Study Diabetes Care 2020 10.2337/dc20-1380 Aug
25 Barbieri G Spinelli S Filippi M COVID-19 pandemic management at the Emergency Department: the changing scenario at the University Hospital of Pisa Emergency Care 2020 Journal
26 Corradi F Via G Forfori F Brusasco C Tavazzi G. Lung ultrasound and B-lines quantification inaccuracy: B sure to have the right solution Intensive Care Med 46 5 2020 1081 1083 10.1007/s00134-020-06005-6 05 32189008
27 Meroi F Orso D Vetrugno L Bove T. Lung Ultrasound Score in Critically Ill COVID-19 Patients: A Waste of Time or a Time-Saving Tool? Acad Radiol 28 9 2021 1323 1324 10.1016/j.acra.2021.06.008 09 34275738
28 Corradi F Ball L Brusasco C Assessment of extravascular lung water by quantitative ultrasound and CT in isolated bovine lung Respir Physiol Neurobiol 187 3 Jul 01 2013 244 249 10.1016/j.resp.2013.04.002 23584050
29 Corradi F Vetrugno L Isirdi A Bignami E Boccacci P Forfori F. Ten conditions where lung ultrasonography may fail: limits, pitfalls and lessons learned from a computer-aided algorithmic approach Minerva Anestesiol 88 4 2022 308 313 10.23736/S0375-9393.22.16195-X 04 35164490
| 0 | PMC9729593 | NO-CC CODE | 2022-12-15 23:17:53 | no | Eur J Intern Med. 2022 Dec 8; doi: 10.1016/j.ejim.2022.12.002 | utf-8 | Eur J Intern Med | 2,022 | 10.1016/j.ejim.2022.12.002 | oa_other |
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Bioorg Chem
Bioorg Chem
Bioorganic Chemistry
0045-2068
1090-2120
Elsevier Inc.
S0045-2068(22)00723-4
10.1016/j.bioorg.2022.106316
106316
Article
Discovery and structural optimization of 3-O-β-Chacotriosyl betulonic acid saponins as potent fusion inhibitors of Omicron virus infections
Liu Mingjian a1
Wang Jinshen b1
Wan Xin c1
Li Baixi a
Guan Mingming a
Ning Xiaoyun a
Hu Xiaojie a
Li Sumei d⁎
Liu Shuwen be⁎
Song Gaopeng a⁎
a Key Laboratory for Biobased Materials and Energy of Ministry of Education, College of Materials and Energy, South China Agricultural University, Guangzhou 510642, China
b Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou, 510515, China
c Huizhou Health Sciences Polytechnic, Huizhou 516025, China
d Department of Human anatomy, School of Medicine, Jinan University, Guangzhou 510632, China
e State Key Laboratory of Organ Failure Research, Guangdong Provincial Institute of Nephrology, Southern Medical University, Guangzhou 510515, China
⁎ Corresponding authors at: Department of Human anatomy, School of Medicine, Jinan University, Guangzhou, 510632, China (S. Li); Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou, 510515, China (S. Liu); Key Laboratory for Biobased Materials and Energy of Ministry of Education, College of Materials and Energy, South China Agricultural University, Guangzhou, 510642, China (G. Song).
1 These authors contributed equally to this work.
8 12 2022
2 2023
8 12 2022
131 106316106316
7 10 2022
7 11 2022
5 12 2022
© 2022 Elsevier Inc. All rights reserved.
2022
Elsevier Inc.
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Graphical abstract
The recent global Omicron epidemics underscore the great need for the development of small molecule therapeutics with appropriate mechanisms. The trimeric spike protein (S) of SARS-CoV-2 plays a pivotal role in mediating viral entry into host cells. We continued our efforts to develop small-molecule SARS-CoV-2 entry inhibitors. In this work, two sets of BA derivatives were designed and synthesized based on the hit BA-1 that was identified as a novel SARS-CoV-2 entry inhibitor. Compound BA-4, the most potent one, showed broad inhibitory activities against pOmicron and other pseudotyped variants with EC50 values ranging 2.73 to 5.19 μM. Moreover, pSARS-CoV-2 assay, SPR analysis, Co-IP assay and the cell–cell fusion assay coupled with docking and mutagenesis studies revealed that BA-4 could stabilize S in the pre-fusion step to interfere with the membrane fusion, thereby displaying promising inhibition against Omicron entry.
Keywords
Betulonic acid derivatives
Omicron
Membrane fusion
Structure–activity relationships
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pmc1 Introduction
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that leads to the coronavirus disease 2019 (COVID-19), has rapidly spread around the world, devastating consequences for the health sector and the economy since the late December of 2019 [1], [2]. Currently, the public become more aware of the devastation caused by the emerging variants and mutations of SARS-CoV-2, as exampled by Omicron variant, which is posing a great challenge to public health and safety globally [3], [4], [5]. As of September 2022, there have been>610 million confirmed cases and 6.5 million deaths worldwide despite a ring vaccination program with the FDA-approved BNT162b2 and others, underlining the urgency for developing effective antiviral agents to prevent these lethal infections. Remdesivir [6], a RdRp inhibitor, was approved by the United States Food and Drug Administration (FDA) in May 2020 for the treatment of severe COVID-19 patients. In December 2021, a nucleoside analog molnupiravir that was originally used for influenza, was also approved by FDA [7]. The third drug approved by FDA is paxlovid, which was found to reduce the risk of hospitalization and death by 89 % in the Phase 2/3 EPIC-HR study [8]. In addition, other small-molecule drugs targeting the replication cycle of SARS-CoV-2 are currently being developed in clinic [9], [10]. For example, small-molecule inhibitors GC373 and GC376 can effectively inhibit the enzymatic activity of 3CLpro by covalent modification with the amino acid residue Cys145 of the catalytic site to display potent inhibitory potency coupled with low toxicity, which represent potential candidate drugs for the treatment of COVID-19 [11].
SARS-CoV-2 is a new member of single-stranded RNA and enveloped β-coronaviruses family, of which infection process starts from the viral entry into host cells. Evidence has shown that the spike protein (S) of SARS-CoV-2 is a “Type I” viral transmembrane glycoprotein, which plays a vital role in viral entry [12], [13]. The S protein consists of two subunits, namely S1 and S2, of which the former can recognize and bind to human angiotensin-converting enzyme 2 (ACE2) receptor of host cells through RBD domain and the latter is responsible for regulating S-mediated viral/cell membrane fusion [14], [15]. Structurally, S2 subunit is composed of fusion peptide (FP), heptapeptide repeat 1 (HR1), heptapeptide repeat 2 (HR2), transmembrane domain (TM), and cytoplasmic domain (CP) [16]. After SARS-CoV-2 S1 binds to ACE2, the S protein needs to be activated by cellular proteases to permit insertion of FP into the host membrane, the anchoring process. Subsequently, the HR1 and HR2 regions of the trimeric viral transmembrane protein interact to form a six-helix bundle, which pulls the viral and cellular membranes together and mediates fusion, thereby leading to the release of the viral genome into the cytoplasm [17]. All these steps, attachment to the cellular receptors, conformational changes of S1/S2, FP insertion, rearrangement, and 6HB formation, are critical for SARS-CoV-2 infection and more importantly, the S2 subunit sequence is more conservative than the S1 subunit sequence [18]. Collectively, the SARS-CoV-2 S2 subunit mediates viral fusion and entry, which represents the main target for the development of neutralizing antibodies, and small-molecule fusion inhibitors.
So far, multiple potential SARS-CoV-2 fusion inhibitors have been identified, which showed good antiviral activities in vivo/vitro, as exampled by HR-derived peptides EK1 and its analogs [19], niclosamide [20], bergamottin (1, Fig. 1 A) [3], clofazimine [21], and other natural products such as angeloylgomisin O (2, Fig. 1A), schisandrin B (3, Fig. 1A) [22]. For example, EK1 and its analogs have been shown to target the HR1 or HR2 domain to interact with virus-cell fusion, thereby exhibiting potent inhibition against SARS-CoV-2 and its variants in vivo [19]. Zhou and co-workers reported that bergamottin could act at multiple stages of the SARS-CoV-2 life cycle to reduce viral entry into cells by both blocking the S-mediated membrane fusion stage and inhibiting the expression of ACE2 [3]. Angeloylgomisin O and schisandrin B that were extracted from Schisandra chinensis, a plant used to treat hepatitis, were found to exhibit strong inhibitory effects on membrane fusion and show more potent antiviral activity against SARS-CoV-2 than remdesivir [22]. In addition, our group has previously conducted multiple high-throughput screens of various small-molecule libraries to identify salvianolic acid C (Sal-C, 4, Fig. 1A) [23] and estradiol (5, Fig. 1A) [24] as potential anti-SARS-CoV-2 agents, which could inhibit SARS-CoV-2 infection in vitro by blocking the formation of six-helix bundle core of S to block S-mediated membrane fusion. However, only a few SARS-CoV-2 fusion inhibitors have advanced to clinical trials up to now.Fig. 1 A. Chemical structures of representative small-molecule SARS-CoV-2 fusion inhibitors 1–5Fig. 1B. Chemical structures of betulinic acid 6, betulonic acid 7, the hit compound BA-1 and the lead compound BA-4.
Betulinic acid (BA, 6, Fig. 1B), a naturally occurring pentacyclic triterpene, represents a promising structure type for a wide variety of agents with good antiviral use against HIV, influenza virus, HSV and others [25], [26], [27]. For example, the BA core is present in bevirimat, an HIV maturation inhibitor, which has undergone phase 2 clinical evaluation. Interestingly, BA was found to possess anti-SARS-CoV activity in the μM range in vitro and in particular, betulonic acid (7, Fig. 1B), an oxidized analog at C-3 position of BA, exhibited improved anti-SARS-CoV potency with an EC50 of 0.63 μM [28]. Recently, a class of BA derivatives with a 1, 2, 3-triazolo-fused BA structure have been shown to be potent inhibitors of HCoV-229E nsp15 replication by Naesens and co-workers [29]. Encouraged by these results, we decided to investigate if BA and its derivatives will also have anti-SARS-CoV-2 activity in vitro.
Here, we report identification of a class of SARS-CoV-2 fusion inhibitors with a 3-O-β-chacotriosyl BA structure based on the hit BA-1. We describe their hit-to-lead modification, structure–activity relationship (SAR), and the mechanistic findings, giving rise to the lead compound BA-4 that can directly target S protein as a novel Omicron fusion inhibitor. These biological data consisted well with the binding model that we obtained by the lead compound BA-4 docking in the Omicron S protein structure, which was supported by site-specific mutation. We demonstrate that the interface in Omicron S where the lead BA-4 binds, can be as a potential target for developing Omicron and other SARS-CoV-2 fusion inhibitors.
2 Results and discussion
2.1 Chemical synthesis
Compound BA-1 was prepared according to our previous procedure [34]. The synthetic routes for title compounds BA-3 – BA-16 and amide analogs BA-N-1 as well as BA-N-2 were illustrated in Scheme 1 . Esterification of BA with benzyl bromide in the presence of potassium carbonate afforded the known intermediate 8 [35]. 3β-acetoxylup-20 (29)-ene-3, 28-diol 9 [36] was obtained from the commercially available betulin following literature procedures. The TfOH catalyzed coupling reaction between 9 and benzyl 2, 2, 2-trichloroacetimidate furnished benzyl-substituted ether 10, which then went through the hydrolysis reaction under basic conditions (LiOH) to yield the intermediate 11.Scheme 1 Reagents and conditions: (a) BnBr, K2CO3, DMF; (b) benzyl 2, 2, 2-trichloroacetimidate, TfOH, CH2Cl2; (c) LiOH, THF-MeOH-H2O; (d) TMSOTf, 4 Å Ms, CH2Cl2; (e) CH3ONa, MeOH; (f) PivCl, CH2Cl2; (g) (i) TMSOTf, 4 Å Ms, CH2Cl2; (ii) NaOH, MeOH-THF-H2O; (h) 10 % Pd/C, H2, MeOH-THF; (i) (i) Ac2O, DMAP, pyridine; (j) (i) various bromide alkanes, K2CO3, DMF; (ii) CH3ONa, MeOH; (k) (i) (COCl)2, CH2Cl2 (ii) R1R2N•HCl, Et3N, CH2Cl2; (iii) CH3ONa, CH3OH.
With glycosyl acceptor 8 or 11 as well as the known the glycosyl donor 2, 3, 4, 6-tetra-O-benzoyl-d-glucopyranosyl trichloroacetimidate 12 [30] in hand, TMSOTf-catalyzed glycosylations were performed to provide compound 13 or 14, followed by the hydrolysis reaction under basic conditions (CH3ONa in MeOH) to yield 3-O-β-glucopyranoside 15 or 16, respectively. Subsequently, the pivaloyl (Piv) group could be selectively installed at the 3, 6-OHs of the β-glucopyranosyl residues in 15 or 16 at a controlled low temperature to afford 17 or 18, respectively. With the glycosyl donor 2, 3, 4-tri-O-acetyl-l-rhamnopyranosyl trichloroacetimidate 19 [30] and acceptor 17 or 18, the glycosylation reaction was then performed under TMSOTf activation to provide crude trisaccharides, followed by sodium hydroxide (NaOH)-mediated deprotection to give the title compound BA-16 or BA-20, respectively. Using 10 % Pd/C as a catalyst, hydrogenolysis of the benzyl group in BA-16 or BA-20 was carried out smoothly to provide the title saponin BA-2 or BA-17, respectively. Then the intermediate 20 was obtained from BA-2 through a direct acetylation reaction with acetic anhydride, which served as the coupling partner for subsequent diversifications, respectively. Under the basic conditions, the corresponding alkyl residues were incorporated at the C-28 position of BA in 20, followed by removing all the acetyl groups using the similar method as 15 and 16 to afford the subseries BA-3 -- BA-15 (Table 2), with different hydrophobic substituents at the C-28 position of BA core. On the other hand, 20 was treated with oxalyl chloride to furnish 28‑acyl chloride, which was then condensed with appropriate amines, followed by removal of all the Ac groups with MeONa to give the corresponding target saponins BA-N-1 and BA-N-2, respectively.
The following attempts were made to decorate the hydroxymethylene moiety at the C-17 position of BA-17 to expand our chemical diversity. As depicted in Scheme 2 , treatment of BA-20 with acetic anhydride as did 20 gave rise to 21, followed by hydrogenolysis of the benzyl group in 21 over palladium/carbon to yield the important intermediate 22, which served as the starting partner for subsequent diversifications, respectively. On the one hand, BA-17 was converted to the corresponding aldehyde BA-18 by reaction of 22 with the freshly prepared PCC reagent, which then undergo hydrolysis reaction with CH3ONa similarly as compounds 15 and 16. On the other hand, nucleophilic substitution of 22 with methyl iodide, followed by CH3ONa-mediated deprotection of all Ac groups gave rise to BA-19. In addition, by treating with thionyl chloride, compound 22 was readily converted into chlorides, of which all the Ac groups were then hydrolyzed using CH3ONa to afford the title saponin BA-21.Scheme 2 Reagents and conditions: (a) Ac2O, DMAP, pyridine; (b) 10 % Pd/C, H2, MeOH-THF; (c) (i) PCC, CH2Cl2; (ii) CH3ONa, MeOH; (d) (i) CH3I, Ag2O, ACN; (ii) CH3ONa, MeOH; (e) (i) SOCl2, CH2Cl2; (ii) CH3ONa, MeOH.
2.2 Hit discovery
Previous screening efforts focused on human CoVs (SARS-CoV, HCoV-229E) and consequently revealed these BA-based molecules with potential could inhibit SARS-CoV-2 or other variants in the SARS-CoV-2 family. Since the chacotrioside moiety, a 2, 4-branched trisaccharide residue, has been characterized as an antiviral-privileged fragment [30], [31], we supposed that introducing this moiety into BA might enhance the potency of pharmacologically active molecules. Thus, we decided to fuse this unique fragment to BA at the C-3 position, giving rise to the saponin BA-1 (Fig. 1B). Initial attempts to confirm the inhibitory effects of BA and BA-1 on infectious SARS-CoV-2 virus (wuhan-HU-1 variant) were made in a BSL-3 facility, wherein we determined their EC50 values against authentic SARS-CoV-2 in Vero-E6 cells using a full-time treatment model. Encouragingly, BA-1 proved to be a highly effective SARS-CoV-2 inhibitor with an EC50 value of 0.51 μM, which did not exhibit cytotoxicity against Vero E6, even at a concentration of 50 μM (Fig. 2 A). This data demonstrated that BA-1 might interfere only slightly with the growth of Vero E6 cells and could inhibit specifically SARS-CoV-2 in cell cultures. In contrast to BA-1, the starting compound BA was virtually inactive (Table 1 ), implying that the privileged β-chacotriosyl moiety is critical for the anti-SARS-CoV-2 activity. Briefly, these results suggested that the 3-O-β-chacotriosyl betulonic acid methyl ester BA-1 possessed excellent efficiency against SARS-CoV-2 and promising safety, which should be identified as a hit for further development.Fig. 2 (A) Evaluation on cytotoxicity of BA-1 and inhibitory activity against authentic SARS-CoV-2 virus (wuhan-HU-1 variant) infection in Vero-E6 cells. (B) SPR analysis of the interaction between BA-1 with SARS-CoV-2 3CL.
Table 1 anti-SARS-CoV-2 and inhibitory activities against 3CL of BA and BA-1.
Compound Anti-SARS-CoV-2
EC50a (µM) inhibition rate against 3CL (%)b
100 50 25
BA >20 72.2 51.4 30.2
BA-1 0.51 ± 0.19 31.3 22.6 18.5
Ebselen 0.08 ± 0.01 98.5 97.6 96.2
a The samples were examined in Vero-E6 cells in triplicate. Vero-E6 cells were incubated with test compounds and SARS-CoV-2 (wuhan-HU-1 variant), and the concentration of test compound resulting in 50 % cell protection was reported as the EC50. Values are the mean of three experiments, presented as the mean ± standard deviation (SD). bInhibitory rate against 3CL based on the FRET assay. Data are expressed as the mean ± SD of three experiments.
Table 2 Inhibitory activities of saponins BA-1-BA-16 against infection of 293 T-ACE2 cells by pSARS-CoV-2.
Compound R EC50a (μM) CC50b(μM) SIc
BA-1 CH3 4.64 ± 0.52 40.88 ± 0.25 8.81
BA-2 OH ＞20.00 NT NT
BA-3 Et 3.70 ± 0.72 36.12 ± 1.05 9.76
BA-4 n-propyl 3.12 ± 0.40 39.13 ± 0.73 12.54
BA-5 6.42 ± 0.20 76.49 ± 1.23 11.91
BA-6 5.37 ± 0.37 24.36 ± 0.33 4.54
BA-7 5.54 ± 0.81 16.39 ± 0.19 2.96
BA-8 6.05 ± 0.38 47.25 ± 0.63 7.81
BA-9 ＞20.00 NT NT
BA-10 7.67 ± 0.31 51.81 ± 1.35 6.75
BA-11 9.03 ± 0.56 46.12 ± 0.31 5.11
BA-12 ＞20.00 NT NT
BA-13 8.61 ± 0.47 36.21 ± 0.75 4.21
BA-14 8.23 ± 0.55 84.62 ± 0.76 10.28
BA-15 15.90 ± 0.82 75.52 ± 1.63 4.75
BA-16 3.13 ± 0.42 12.79 ± 0.25 4.09
Sal-C / 4.06 ± 0.51 >100.00 >24.63
a The samples were examined in 293 T-ACE2 cells in triplicate. 293 T-ACE2 cells were incubated with test compounds and pSARS-CoV-2, and the concentration of test compound resulting in 50 % cell protection was reported as the EC50. Values are the mean of three experiments, presented as the mean ± standard deviation (SD). b50% cellular cytotoxicity concentration (CC50). cSI: selectivity index as CC50/EC50.
Due to its pivotal role in the SARS-CoV-2 life cycle that is involved in the viral maturation process to cleave the virus-encoded polyproteins, the 3CL protease has become a key target for discovery of anti-SARS-CoV-2 agents. It has been confirmed that BA possessed moderate inhibitory effects on SARS 3CL protease activity with an IC50 value of 10 μM [28]. SARS 3CL and SARS-CoV-2 3CL are structurally similar members of the human CoV family, sharing high homology and similarity in sequences, structures, and functions [32]. Thus, in parallel, these two compounds were also evaluated for inhibition of SARS-CoV-2 3CL protease activity based on a quenched fluorescence energy transfer (FRET) method where Ebselen was used as a positive control. As expected, BA displayed an acceptable enzyme inhibitory effect, especially at a high concentration 100 μM (Table 1). In contrast, BA-1 only exhibited weak inhibitory ability with a 31.3 % inhibition rate at 100 μM, consistent with a low equilibrium dissociation constant (K D) value of 46.3 μM (Fig. 2B) on the basis of a surface plasmon resonance (SPR) analysis, suggesting that BA-1 inhibited replication of SARS-CoV-2 through a different mechanism or pathway from BA and the positive control Ebselen. Collectively, these results demonstrate that BA-1 has a potent anti-SARS-CoV-2 activity with a high selectivity index in cell culture models but its antiviral potency is independent of inhibition toward 3CL protease.
2.3 Target identification
Evidence from several reports has illustrated that BA derivatives could effectively interfere with the fusion of the incoming virus to the host cell membrane to block HIV/H5N1/SARS and other viral entry into test cells in the low micromolar range [28], [33]. Considering the similarity between the viral fusion proteins such as gp41/HIV-1, HA2/H5N1, GP/EBOV and S2 of SARS-CoV-2, all of which play key roles in virus-induced membrane fusion, we speculated that a further anti-SARS-CoV-2 mechanism of BA-1 might be the blocking of SARS-CoV-2 entry by inhibiting the membrane fusion, thereby disrupting viral entry into the host cells. To confirm our hypothesis, BA-1 was firstly evaluated in a luciferase-expressing pseudovirus encoding SARS-CoV-2 S protein (pSARS-CoV-2) inhibition assay, which allowed for direct comparison of S protein function with a common lentiviral core and reporter [23]. Notably, we found that BA-1 exhibited the similar capability in effectively inhibiting pSARS-CoV-2 as the positive control Sal-C, a small-molecule SARS-CoV-2 entry inhibitor previously shown to bind S directly [23], and the inhibitory effect was concentration-dependent with an EC50 value of 4.64 μM (Fig. 3 A). It was interesting that BA-1 displayed not only negligible inhibition toward VSV-G pseudovirus (Fig. 3A) but also marginal cytotoxicity against 293 T-ACE2 cells (HEK293T cells overexpressing human angiotensin-converting enzyme 2) within the effective concentration range (Fig. 3B). These results highlighted that BA-1 could exert inhibitory activity against SARS-CoV-2 entry by targeting the S protein and the similarity in the potency of BA-1 between the pseudovirus and infectious virus assays supported the validity of the S/HIV-based anti-SARS-CoV-2 assay used in 293 T-ACE2 cells.Fig. 3 (A) Dose-response curves and EC50 of BA-1 on inhibiting the entry of SARS-CoV-2 PsV and VSV-G in 293 T-ACE2 cells. (B) Evaluation on cytotoxicity of BA-1 in 293 T. (C) BA-1 inhibited pSARS-CoV-2 infection by dose-dependent blocking of S-mediated membrane fusion.
Having identified S as the potential target, we then utilized the cell–cell fusion assay mediated by SARS-CoV-2 S protein to explore whether BA-1 had any effect on the viral membrane fusion, the critical step for entry of SARS-CoV-2 viruses into host cells for initiation of virus infection. As shown in Fig. 3C, BA-1 was identified to potently interfere with the membrane fusion of S-overexpressed-HEK293T and Vero-E6 cells in a marked dose-dependent manner at 24 h, demonstrating that its antiviral potency apparently involved action on SARS-CoV-2 S-mediated membrane fusion. Taken together, BA-1 represents a novel SARS-CoV-2 fusion inhibitor, which was selected as a promising structure for further research and optimization.
2.4 Proposed binding mode of hit to the spike protein
In view of SARS-CoV-2 S as the important target and the membrane fusion interaction between virus and host cells as the critical interruption event, we performed blind docking calculations based on the X-ray crystal structures of SARS-CoV-2 S (PDB code: 6VXX) to investigate the potential binding site. A proposed binding mode of BA-1 was established (Fig. 4 ), which indicated that BA-1 could occupy well a cavity between the S1 and S2 subunits at the entrance to a large tunnel that links with equivalent tunnels from the other monomers of the trimer at the threefold axis. As shown in Fig. 4, at the upper region of the cavity, the hydrophilic chacotriosyl residue of BA-1 made multiple stable hydrogen bonds with the backbone of residues Thr961, Leu303, Arg765, and Lys964 to result in increased interaction with S, further supporting the proposition that that the trisaccharide moiety was vital to improve antiviral activity. Hydrophobic pentacyclic triterpenoid skeleton of BA-1 occupied a large lipophilic region located in the middle of the cavity, creating a tight hydrophobic interaction with the side chain of Val772 to maintain the active conformation of BA-1.Fig. 4 Molecular docking of BA-1 to spike protein (PDB: 6VXX). S1 subunit, S2 subunit, and BA-1 were shown as orange ribbon, blue ribbon, magenta sticks, respectively. Green dashes in the interaction plot indicating hydrogen bond. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
To further confirm the above binding mode, we conducted the single amino acid mutagenesis of pSARS-CoV-2 S to understand the molecular basis of fusion inhibition by the hit BA-1. As indicated by the preliminary mutagenesis studies (Fig. 5 ), the pSARS-CoV-2 N764A/R765A/Q957A/K964 A mutations resulted in a significant loss of potency toward BA-1 in dose-dependent fashion relative to WT S. In short, the docking result, supported by the mutagenesis studies, led us to propose a potential SARS-CoV-2-S binding pose of BA-1, which reflected some characteristics that could guide subsequent structural modification and optimization.Fig. 5 (A) Inhibitions of BA-1 against SARS-CoV-2 PsV and its mutants infections in 293 T-ACE2 cells, including N764A/R765A/Q957A/K964A. (B) Antiviral efficacy of BA-1 against SARS-CoV-2 PsV mutants caused by site-directed mutation, including N764A/R765A/Q957A/K964A.
2.5 Design of novel SARS-CoV-2 fusion inhibitors
The proposed mode analysis indicated that the hit BA-1 fitted well in the binding region, where important hydrogen-bond networks were observed between the chacotriosyl moiety and the cavity. However, there was still some space for further modification to fit better in the binding site. On the basis of the proposed mode, we identified a hydrophobic region under the aglycone core that is formed by D663, P665, V772, D775 and K776 residues. As depicted in Fig. 4, the C-17 position of BA-1 seemed well positioned to extend into this pocket but there is no chemical structure that can form stable interactions with this region. We reasoned that this cavity could be presumably occupied by an alternative bulky group like an ethyl ester substituent or another type of linear/ring structure to yield better intermolecular interactions to improve potency. Here, our strategy was to enhance antiviral activity by extending from the 17-position of the aglycone BA to fill the bottom area of the binding cavity. At the other side of the molecule, the β-chacotriosyl fragment moiety probably needs to be kept since it forms multiple hydrogen bonds with S protein. It is noteworthy that most of the pocket residues are conserved, which highlights the relevance of this S interface pocket for new SARS-CoV-2 fusion inhibitors design. Briefly, we attempted to improve potency further by increasing steric bulk to more completely occupy this area of the binding pocket and derive additional hydrophobic contacts, which resulted in a set of 3-O-β-chacotriosyl BA derivatives.
2.6 Sars of the BA saponins modified at 28-position
We focused our SAR campaign on investigating the alkyl groups at the C-28 position of BA, to a lesser extent, the linker at the C-17 position. Initially, we changed the ester moiety at the C-17 position of BA-1 into amide moiety as its bioisosteric surrogates (compound BA-N-1 and BA-N-2) to investigate their influence on biological activity. Unexpectedly, BA-N-1 and BA-N-2 showed significantly reduced inhibition against pSARS-CoV-2 entry relative to BA-1 (Figure S1), presumably due to unfavorable conformation. For example, BA-N-1 and BA-N-2 only exhibited about 50 % inhibition rate against pSARS-CoV-2 at high concentration 20 μM. Thus, further amide modifications incorporated at the 17-position of BA were not pursued and we turned attention to the ester linker in this study. To identify novel and potent SARS-CoV-2 entry inhibitors, we performed a screen of the above two series of 3-O-β-chacotriosyl BA derivatives BA-2--BA-16 based on the established pseudovirus model of SARS-CoV-2 (S/HIV) under low level containment (biosafety level 2) while VSVG/HIV pseudoviral transduction was used as a specificity control to exclude inhibitory effect on post-entry for HIV infection. As shown in Table 2, the variety of structural modifications described herein, especially the incorporation of bulky alkyl or aryl substituents, furnished compounds either almost equipotent or more potent against pSARS-CoV-2 virus while no effects on VSVG (Figure S2). Among these analogs, the 28-n-propyl-substituted analogue BA-4 showed the strongest inhibition toward pSARS-CoV-2 virus coupled with the highest selectivity index (SI = 12.54).
To continue probing interactions with the potential binding pocket, our initial efforts were made by changing the length, size or type of the fragments linked by ester group at the C-28 position of BA to fine-tune the hydrophobicity effects on the antiviral activities, exampled by the analogs BA-2--BA-16. As illustrated in Table 2, the free carboxylic acid BA-2 exhibited a significant decrease in potency against pSARS-CoV-2 relative to BA-1 (EC50 ＞ 20.00 μM), probably due to limited membrane permeability. Careful examination on the chemical structure of BA-2 revealed that the carboxylic acid moiety may be responsible for its inferior cellular activities, which is known to negatively impact cell membrane permeability. However, ethyl ester BA-3 displayed a slight increased potency (EC50 = 3.63 μM) compared to BA-1. These findings reveal that the substitutions at the C-28 position of BA may play an important role in the drug-target interactions and appropriate C-28 substitutions are helpful in improving the antiviral potency, especially against SARS-CoV-2 virus. Based on the docking analysis, we inferred that this cavity could accomodate an alternative larger substituent than ethyl group and further chemical optimization at the side chain of BA-1 probably led to more potent entry inhibitors. This hypothesis gave rise to analogs BA-4--BA-8 (Table 2) with improved (BA-4 vs BA-1) or maintained inhibitory activities. Notably, augmenting the length and hydrophobicity of R substituent with n-propyl group (BA-4) led to 1.5-fold enhanced inhibitory activity (EC50 = 3.12 μM) BA-1, rendering compound BA-4 as the most potent candidate against pSARS-CoV-2 entry identified in the preliminary SAR optimization attempt. The improvement in potency may be attributed to the fact that the n-propyl group (BA-4) can occupy the binding pocket more because of its bigger bulk than methyl group to enhance the intermolecular hydrogen and hydrophobic interactions with SARS-CoV-2 S (see Fig. 9B). However, an increase in the length (BA-5, BA-6 and BA-7) or volume (BA-8) of the hydrophobic side chain via the inclusion of a n-butyl, n-pentyl, n-hexyl or isopropyl moiety at the 28-position of BA did not lead to more active compounds but coupled with different effects on cytotoxicity against 293 T-ACE2 cells. For example, the replacement of methyl moiety with n-butyl group (BA-5) or isopropyl residue (BA-8) resulted in a slight drop in antiviral potency while there was concomitant decrease in cytotoxicity against 293 T-ACE2 cells. In contrast to compound BA-5, the replacement by longer n-pentyl (BA-6) or n-hexyl (BA-7) presented a 1.7- to 2.5-fold increased toxic while keeping similar anti-SARS-CoV-2 activities. These data suggest that the side chain length at the C-28 position of BA is a critical component of both antiviral activity and selectivity index for this chemotype. In additional to these linear alkyl residues, the substitution of the methyl group in BA-1 with ring structures such as a cyclohexyl moiety generated BA-9, and surprisingly, the activity observed in pseudoviruses was entirely lost against pSARS-CoV-2 (Table 2). This data revealed that the volume size of R subsite was limited and this moiety was intolerant to cyclic alkyl chains. Collectively, these results demonstrated that a hydrophobic alkyl side chain with a length between 1 and 4 atoms at the position 28 of BA is optimal for inhibition against SARS-CoV-2 entry, which seems to accept a short and linear structure.
The encouraging antiviral profiles of compounds with small linear alkyl substituents, exampled by BA-3 and BA-4 prompted an examination of introduction of functional groups into the preferred ethyl or n-propyl substituent to form additional potential interactions, exampled by the analogs BA-10--BA-16 (Table 2). With the exception of 2′- hydroxyl derivative BA-12, these analogs exhibited comparable or slightly reduced antiviral activity compared to BA-4, as a result, their potency was still potent enough to emphasize the significance of the modification of side chains attached to C-28 position of BA. Among this set of derivatives, chlorine derivative BA-10 was more active than the corresponding bromine derivative BA-11 though 2.4-fold decreased potency relative to BA-4. However, insertion of a hydroxyl group into ethyl moiety (BA-12) led to a total loss of potency, of while oxidic product BA-13 could maintain comparable potency to BA-1, supporting the need for the general high hydrophobicity required for the side scaffold at the C-28 position of BA. To weaken cytotoxic activity, we incorporated in our chemical optimization campaign modifications to the preferred BA-4 based on the conformational constraints strategy, anticipated to enhance selectivity index. As shown in Table 2, the incorporation of a rigid carbon–carbon double bond (BA-14) led to a 2.6-fold reduced potency coupled with remarkably decreased cytotoxicity compared to BA-4, thereby displaying a similar SI as BA-4 but superior to BA-1. This result implied that the unsaturated fragment was tolerated on the alkyl side chain region in this set of SARS-CoV-2 entry inhibitors. In contrast to compound BA-14, the cyclized derivative BA-15 suffered a significant 4.9-fold loss of inhibition against pSARS-CoV-2 probably due to steric clashes with S protein, though it displayed reduced cytotoxicity as did BA-14. To address this gap, we hypothesized that it was better to incorporate the cyclized aromatic group through a flexible linker at the C-28 position of BA to form additional interaction with S protein. As expected, benzyl ester (BA-16) demonstrated comparable potency to BA-4 against pSARS-CoV-2 (EC50 = 3.13 μM) while showing increased cytotoxicity. As seen in Fig. S3A, the docking model demonstrated that the introduced benzyl moiety was extended to the inside of hydrophobic pockets and formed tight Van der Waals interactions with Pro665 and Val772 residues, and thus made a good functional ligand–protein interaction. In short, these results again emphasize the importance of the property, type, and size of the R substituent at the C-28 position of BA for exhibiting inhibition against pSARS-CoV-2.
2.7 Sars of the betulin derivatives
For better orientation of the tail region of BA-1 into the new hydrophobic pocket, we then shifted our focus onto the more flexible ether linker moiety at the 17-position of of BA. Firstly, we proceed to investigate the effect of the 17-COOH of BA on the anti-SARS-CoV-2 activity through reduction of the carboxyl group. As depicted in Table 3 , the betulin saponin BA-17 displayed no potencies in cellular assays as did the unsubstituted acid analogue BA-2, supporting the highly hydrophobic nature of the potential new SARS-CoV-2-S binding site. Interestingly, further optimization for potencies through oxidation of hydroxyl group at the 28 position of BA-17 was achieved in betulinicaldehyde saponin BA-18 with an EC50 value of 5.12 μM, which indicated the requirement of the hydrophobic properties at the 17 position of the aglycone skeleton to maintain highly potent inhibitory activity against pSARS-CoV-2. The observation prompted us to examine the potential of more hydrophobic modification around the hydroxyl group in betulin at the 28 positon. Therefore, a small set of 3-O-β-chacotriosyl betulin derivatives BA-19 -- BA-21 differing only in the substituent at the 17 position were picked for the preliminary SARs study. Although a bit less potent than the ester analogue BA-1, methyl ether of OH (BA-19) induced a markedly increase in inhibitory activity relative to BA-17, again highlighting that the enhancement of potency appeared to be correlated to the lipophilicity of the substituents at the 17 position. Interestingly, the introduction of benzyl group at the 28-position of BA-17 yielded compound BA-20 with moderate potency, which was 4.81-fold less active than its benzyl ester analogue BA-16. As seen in Fig. S3B, reduction of carbonyl group to methylene resulted in more flexible conformation of side chain linked at the 28 position, which would not stabilize the BA skeleton orientation and make the head chacotriosyl moiety shift toward the inside of the binding cavity, thus failing to form hydrogen bond with the critical residue Lys964. Since the hydrophobic interaction between the side chain and the new hydrophobic pocket was critical for increased potency of these SARS-CoV-2 entry inhibitors, we attempted to enhance the hydrophobic interaction by replacing hydroxyl group at the 28 position of BA-17 with one chlorine atom to produce BA-21. Surprisingly, BA-21 presented a substantial increase in SARS-CoV-2 entry inhibitory potency though it exhibited poor SI because of high toxic. One possible reason for the increased antiviral activity of BA-21 was that the incorporation of chlorine atom into the end of the side chain at the 28 position led to a greater binding interaction energy with the active pocket in the S protein, as illustrated in Fig. S3C. This result reinforced the importance of the chlorine atom as a versatile design element for lead optimization while needing to balance between the potency and cytotoxicity.Table 3 Inhibitory activities of saponins BA-17-BA-21 against infection of 293 T-ACE2 cells by pSARS-CoV-2.
Compound R EC50a (μM) CC50b(μM) SIc
BA-17 >20.00 NT NT
BA-18 5.12 ± 0.31 29.86 ± 0.22 5.83
BA-19 11.38 ± 1.41 34.18 ± 0.93 3.01
BA-20 15.07 ± 1.05 96.20 ± 1.88 6.38
BA-21 3.53 ± 0.18 15.01 ± 0.24 4.25
Sal-C / 4.06 ± 0.51 >100.00 >24.63
a The samples were examined in 293 T-ACE2 cells in triplicate. 293 T-ACE2 cells were incubated with test compounds and pSARS-CoV-2, and the concentration of test compound resulting in 50 % cell protection was reported as the EC50. Values are the mean of three experiments, presented as the mean ± standard deviation (SD). b50% cellular cytotoxicity concentration (CC50). cSI: selectivity index as CC50/EC50.
Taken together, through our SARs effort, we discovered that the introduction of hydrophobic side chain at the 17 position of the aglycone BA was favorable to enhance anti-SARS-CoV-2 activities as a result of increased interaction with S. In the present SARs study, the type of linker at the 17 position of the aglycone may affect the preferential binding conformation between saponins and the S protein, which in turn affects inhibitory potency toward pSARS-CoV-2. Similarly, the intensity of the hydrogen bond between the chacotriosyl residue and the binding pocket as well as hydrophobic interaction formed by the aglycone with S may also change due to the introduction of the different substituent group at the 28 position, which will lead to changes in compound activity. In addition, the potency change was probably attributed to various factors including the length, volume and type of the substituent group at the 28 position, not just the hydrophobic properties. In general, the substitution of short and small-volume hydrophobic groups did improve the inhibitory effects of these saponins. Among them, BA-4 stood out with the most potent antiviral activity in vitro and best selectivity index, rendering compound BA-4 as the lead compound against SARS-CoV-2 entry identified in the SARs optimization attempt.
2.8 Broad inhibitory activities against Omicron and other variants
More recently, the emerged Omicron and Delta variants that bear multiple mutations in their S proteins have exhibited increased adaptability and transmissibility. The good potencies of representative compounds BA-1 and BA-4 against pSARS-CoV-2 prompted us to examine the inhibitory activity of these two saponins against emerging variants such as Omicron, Delta, and other variants with N501Y, D614G, E484K, or P681H single mutation in their S proteins, respectively. As shown in Table 4 , the broad antiviral effects of BA-1 and BA-4 against these SARS-CoV-2 pseudoviruses containing multiple mutations in S protein were observed in micromole levels, implying that these newly developed BA saponins are broad-spectrum anti-SARS-CoV-2 agents that can block the S-mediated SARS-CoV-2 entry process. Notably, saponins BA-1 and BA-4 demonstrated comparable potency against Omicron pseudovirus to pSARS-CoV-2 with EC50s of 7.04 μM and 4.66 μM, which was in good agreement with the SARs. Briefly, broad and appreciable inhibition of viral entry for all pSARS-CoV-2 variants tested, along with good selectivity index, highlights the lead compound BA-4 as a potential antiviral candidate for the treatment of Omicron infections.Table 4 Inhibitory activities of BA-1 and BA-4 against Omicron pseudovirus and other variants.
varians
compds EC50a(μM)
Omicron Delta N501Y D614G E484K P681H
BA-1 7.04 ± 0.35 8.79 ± 0.22 5.84 ± 0.60 7.70 ± 0.41 8.41 ± 0.63 9.62 ± 0.50
BA-4 4.66 ± 0.52 4.25 ± 0.37 2.73 ± 0.31 3.01 ± 0.25 4.75 ± 0.58 5.19 ± 0.86
a The samples were examined in 293 T-ACE2 cells in triplicate. 293 T-ACE2 cells were incubated with test compounds and pSARS-CoV-2, and the concentration of test compound resulting in 50 % cell protection was reported as the EC50.
2.9 Validation of Omicron s binding
Given the robust activity of the lead compound BA-4 against Omicron, we used this virus to reveal its mechanism of action and appreciate how its anti-SARS-CoV-2 was. To explore whether the findings in the present SARS-CoV-2 study could be extended to Omicron, a similar VSV-based Omicron S protein-bearing pseudovirus (pv) was firstly used to assess the efficacy of BA-4 on virus entry. As shown in Fig. 6 A, BA-4 exhibited a dose-dependent inhibition of Omicron pv infection while no inhibition was observed on VSV-G pseudoviral transduction. Moreover, it was found that BA-4 showed strong binding affinity to S of Omicron variant with a K D value of 0.36 μM based on a SPR assay (Fig. 6B), demonstrating that BA-4 could directly target the Omicron S protein to block virus entry into hose cells.Fig. 6 (A) Dose-response curves and EC50 of BA-4 on inhibiting the entry of Omicron and VSV-G in 293 T-ACE2 cells. (B) SPR analysis of the interaction between BA-4 with Omicron S-trimer.
2.10 BA-4 could mediate membrane fusion of viral entry
Omicron entry into host cells can divided into two major steps: virus attachment to host cell receptor and virus-cell membrane fusion. As the lead compound BA-4 could inhibit Omicron entry into hose cells by targeting S, we further dissected which steps were blocked by BA-4. As shown in Fig. 7 A, BA-4 displayed little effect on the interaction of Omicron S1 subunit with its ACE2 receptor based on a Co-Immunoprecipitation (Co-IP) assay, the critical step for recognition and attachment of Omicron to host cells for initiation of virus infection, suggesting that BA-4 may be acting during Omicron S2 mediated fusion stage. Notably, BA-4 was able to interfere with the membrane fusion of A549 cells mediated by Omicron S in a concentration-dependent fashion (as seen in Fig. 7B), supporting our hypothesis. Interestingly, we found that BA-4 bound strongly to the Omicron S2 subunit, displaying a potent dose-dependent response, with a much higher K D value of 85.2 pM (Fig. 7C) relative to S; no specific binding to Omicron S1 subunit was found for BA-4 in the parallel experiment (Fig. 7D). Taken together, these results revealed that the lead compound BA-4 had a specific affinity to S2, and thus interfered with the viral and cell membrane fusion, by which Omicron entry into host cells could be blocked.Fig. 7 (A) With addition of BA-4 (20 uM), Co-IP assays showed no affection of the binding of SARS-CoV-2 S protein and ACE2 (anti-Flag). (B) BA-4 inhibited SARS-CoV-2 Omicron mutant infection via blocking Omicron protein-mediated membrane fusion. (C) SPR analysis of the interaction between BA-4 with Omicron S2. (D) SPR analysis of the interaction between BA-4 with Omicron S1.
2.11 BA-4 could target the prefusion state during viral-host fusion
Receptor engagement by RBD will induce conformation change of Omicron S2 subunit from the pre-fusion state to a post-fusion trimer-of-hairpins conformation to result in viral membrane fusion, where the 6-HB structure formed by HR1 and HR2 regions in the S2 subunit has been identified as a critical element of the trimer-of-hairpins [18]. For a better understanding of possible mechanism during the fusion of Omicron with cellular membranes treated by BA-4, we determined the biophysical change of 6-HB by using circular-dichroism (CD) spectroscopy as described before [23]. Unlike Sal-C that can target the 6-HB of SARS-CoV-2 (Figure S4), BA-4 had negligible effect on inhibiting viral 6-HB formation (Fig. 8 ), demonstrating that BA-4 exerted potent inhibitory effect on Omicron-cell membrane fusion by the different action mechanism from Sal-C. Based on these data, we speculated that a further anti-Omicron mechanism of BA-4 might be the maintaining S protein in the pre-fusion step during the fusion of virus particle into host cells to inhibit Omicron entry.Fig. 8 The CD curve of the SARS-CoV Omicron HR1P/HR2P complex (purple) shows a characteristic α-helix spectrum with a minimum at 208 or 222 nm. The secondary structure of 6-HB in the HR1P/HR2P mixture was unaffected by the addition of BA-4 (20 µM), as shown by the purple and red models. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 9 (A) Molecular docking of BA-4 to Omicron spike protein (PDB: 7TF8). S1 subunit, S2 subunit, and BA-4 were shown as magenta ribbon, purple ribbon, orange sticks, respectively. Yellow dashes in the interaction plot indicating hydrogen bond. Red dashes in the interaction plot indicating hydrophobic interaction. (B) Molecular docking of BA-4 to spike protein (PDB: 6VXX). S1 subunit, S2 subunit, and BA-4 were shown as orange ribbon, blue ribbon, green sticks, respectively. Yellow dashes in the interaction plot indicating hydrogen bond. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
To investigate the potential binding mode of our new compounds, selected BA-4 was docked into Omicron S (PDB code: 7TF8) that is physically blocked in the pre-fusion state. As depicted in Fig. 9A, at the head region, stable hydrogen bonds between the β-chacotriosyl moiety and Lys964, Thr761, Arg765, Thr302, Glu309 as well as Leu303 are formed to create a critical interaction with the Omicron S protein, which is conducive to stabilizing the pre-fusion state of S to prevent its conformational rearrangements. In the center of the binding pocket, BA aglycone makes multiple Van der Waals interactions with Arg765, Val772, Pro665 and Ile312 residues, which is helpful in maintaining the active conformation of BA-4. In the underpart region, the n-propyl side chain at the 28 position of BA forms additional hydrophobic contacts with Lys310, validating that extension of methyl side chain in BA-1 is contributing positively to binding with S. Consistent well with this result, BA-4 adopts the similar binding mode with SARS-CoV-2 S (Fig. 9B), suggesting that there is a general similarity between the structure of the SARS-CoV-2 S binding pocket and that of other variants bearing S mutants. While a hydrophobic interaction between Lys310 in the binding cacity and the n-propyl residue appears to be weakened, both chacotriosyl moiety and BA skeleton of BA-4 are able to be involved in multiple similar interactions with Lys964, Leu303, and other residues in this corresponding hydrophobic pocket as that with Omicron S. Thus, we hypothesize that the observed broad inhibition against pSARS-CoV-2 Omicron and other variants maybe results from the similar binding mode in this region with corresponding S protein. Taken together, the high conservation of residues located in this binding cavity among different SARS-CoV-2 strains, makes this cavity an ideal target for designing novel SARS-CoV-2 fusion inhibitors that can disrupt the the viral and cell membrane fusion to display broad antiviral activities.
2.12 Site-specific mutation of Omicron-S supports s binding as blocking inhibition of viral entry
Based on the CD spectroscopy and docking analysis, the potential mechanism of antiviral activity of the lead compound BA-4 against SARS-CoV-2 is more intriguing as BA-4-binding site is physically blocked in the Omicron-S prefusion state. We next used the single amino acid mutagenesis of pOmicron S to confirm our hypothesis, where the representative residues K964 and R765 in the fusion loop of conserved S2 subunit were evaluated. When compared to WT Omicron, pOmicron S mutant K964A caused a right shift and an over 2.5-fold loss of potency in response to BA-4 (Fig. 10 A). Similarly, the pOmicron S mutant R765A showed only a modest right shift of the dose–response curve, possibly due to weaker hydrogen bond between R765 and S relative to K964 as seen in Fig. 9A. Furthermore, the similar trend of the response to BA-1 in the pOmicron S mutant K964A or R765A was observed (Fig. 10B). The SARs and docking, supported by the mutagenesis studies, confirmed the binding of BA-4 to the site near the key residue K964 in the binding cavity between the attachment (S1) and fusion (S2) subunits.Fig. 10 (A) Inhibitions of BA-4 against SARS-CoV-2 PsV mutants caused by site-directed mutation, including R765A, K964A. (B) Inhibitions of BA-1 against SARS-CoV-2 PsV mutants caused by site-directed mutation, including R765A, K964A.
2.13 Compound BA-4 exhibited promising liver microsomes, intestinal S9-UDPGA and stability in mouse plasma
Given that the lead compound BA-4 displayed braod and promising anti-SARS-CoV-2 activities in vitro, we further evaluated the stability of BA-4 in vitro metabolic stability in mouse liver, mouse intestinal S9-UDPGA and in mouse plasma, respectively. First, we evaluated the metabolic stability of BA-4 in a mouse liver microsomes assay while propafenone with moderate metabolic stability was used as the control compound. As depicted in Table 5 , compound BA-4 displayed acceptable metabolic stability with a half-life value of 16.1 min in mouse liver microsomes, which was superior to 6.8 min of propafenone. Meanwhile, BA-4 also exhibited reasonable clearance rates with the intrinsic clearance (CL) value of 59.3 μL/min/mg, which was 4-fold lowe than that of propafenone in the same assay (CL = 201.6 μL/min/mg). In addition, the stability of BA-4 in mouse intestinal S9-UDPGA was also evaluated where clozapine was tested for comparison. Notably, 35 showed promising stability in mouse intestinal S9-UDPGA with higher half-life values of 77.8 min and lower CL value of 14.1 μL/min/mg compared to that of microsomal stability, though was inferior to those of clozapine.Table 5 Metabolic stability in the presence of mouse liver microsomes and stability in the intestinal S9-UDPGA of BA-4.
compd mouse liver microsomes mouse S9-UDPGA
T1/2a
(min) CLint(mic)b
(μL/min/mg) T1/2a
(min) CLint(in vitro)b
(μL/min/mg)
BA-4 16.1 59.3 77.8 14.1
propafenone 6.8 201.6 / /
clozapine / / >145 <6.8
a T1/2 is the half-life and CLint (mic) is the intrinsic clearance. bCLint (mic) = 0.693/half-life/mg microsome protein per milliliter.
Then we examined the stability of BA-4 in mouse plasma using propantheline bromide as a reference. As shown in Table 6 , BA-4 exhibited moderate stability in mouse plasma, displaying an approximately 50 % compound retention after 120 min incubation, which was superior to that of the reference propantheline bromide. Collectively, these results reveal that the lead compound BA-4 possesses acceptable metabolic stability in mouse liver microsomes and stability in mouse plasma as well as reasonable S9-UDPGA, which meets the basic requirements of ADMET.Table 6 Stability of compound BA-4 in mouse plasma.
incubation time
(min) BA-4 remained Propantheline bromide remained
mouse plasma (%) mouse plasma (%)
0 100 100
30 73.6 45.5
60 61.3 18.2
90 53.4 10.3
120 47.5 2.0
3 Conclusions
This study presented here discovered a hit compound BA-1 that showed good inhibition against infectious and pseudotyped SARS-CoV-2 virus by directly targeting the S protein. Based on the structure BA-1, rational drug design and subsequent chemical optimization resulted in the development of the lead compound BA-4, as a novel Omicron fusion inhibitor. Utilizing the SPR assay, CD spectroscopy, docking and mutagenesis studies, we confirmed that the anti-Omicron mechanism of BA-4 was through directly binding to the S protein, which was capable of stabilizing S in the pre-fusion step to block Omicron entry into host cells. Moreover, the lead compound BA-4 was found to have a broad-spectrum entry inhibition against all SARS-CoV-2 variants tested and display favorable SI values. Overall, BA-4 represents a novel and potent Omicron fusion inhibitor and justifies further development as a potential candidate for treatment of SARS-CoV-2 infections.
4 Experimental
4.1 Chemistry
Solvents were purified in a conventional manner. Thin layer chromatography (TLC) was performed on precoated E. Merck silica gel 60 F254 plates. Flash column chromatography was performed on silica gel (200–300 mesh, Qingdao, China). 1H NMR and 13C NMR spectra were taken on a JEOL JNM-ECP 600 spectrometer with tetramethylsilane as an internal standard, and chemical shifts are recorded in ppm values. Mass spectra were recorded on a Q-TOF Global mass spectrometer.
4.1.1 28-(Benzyloxy)-3β-acetoxy-lup-20 (29)-ene-3-ol (10)
To a solution of 9 (3.00 g, 6.19 mmol), benzyl 2, 2, 2-trichloroacetimidate (3.42 g, 13.62 mmol) and 4 Å molecular sieves in dry CH2Cl2 (50 mL) was added TfOH (0.14 g, 0.93 mmol) at −10 ℃ under N2 atmosphere. The reaction mixture was kept at −10 °C for 2 h and warmed to room temperature for 1 h. After the reaction was complete detected by TLC, triethylamine was added to quench the reaction. The mixture was filtered and the filtrate was concentrated under reduced pressure. The residue was purified by silica gel column chromatography (petroleumether-EtOAc-CH2Cl2, 30:1:1) to yield 10 (3.16 g, 89 %) as a white solid. 1H NMR (600 MHz, CDCl3): δ 7.45–7.20 (m, 5H, Ar-H), 4.64 (s, 1H, C = CH2-1), 4.56 (s, 2H, Ar-CH2), 4.48 (s, 1H, C = CH2-2), 3.51 (d, 1H, J = 8.9 Hz), 3.09 (d, 1H, J = 8.9 Hz), 2.35 (td, 1H, J = 10.6, 5.6 Hz), 2.03 (s, 3H, COCH3), 1.66 (s, 3H, CH3), 0.94 (s, 6H, 2 × CH3), 0.84 (s, 9H, 3 × CH3), 0.79 (d, 1H, J = 9.5 Hz, H-5); 13C NMR (151 MHz, CDCl3): δ 171.14, 150.85 (C-20), 139.13, 128.43 (two), 127.67 (two), 127.56, 109.64 (C-29), 81.07, 73.50, 68.15, 55.47, 50.38, 48.94, 48.10, 47.38, 42.71, 40.96, 38.47, 37.91, 37.54, 37.16, 34.98, 34.21, 30.10, 30.05, 28.07, 27.22, 25.25, 23.81, 21.44, 20.94, 19.18, 18.31, 16.62, 16.27, 15.89, 14.86. HRMS (ESI) m/z: calcd for C39H59O3 [M + H]+, 575.4464; found, 575.4478.
4.1.2 28-(Benzyloxy)-3β-hydroxy-lup-20 (29)-ene-3-ol (11)
To a solution of 10 (3.16 g, 5.50 mmol) and LiOH (2.87 g, 0.12 mmol) in THF-MeOH-H2O (90 mL) and then the reaction mixture was stirred at 50 ℃ for 12 h. After the reaction was complete detected by TLC, 1 M HCl was added to adjust pH = 7. The mixture was concentrated in vacuo. The residue was dissolved in EtOAc (150 mL), then extracted with water (3 × 50 mL) and brine (3 × 50 mL). The combined organic layer was concentrated under vacuum after drying over Na2SO4. The resulting crude was then purified by column chromatography (CH2Cl2-MeOH, 30:1) to give 11 (2.1 g, 72 %) as a white solid. 1H NMR (600 MHz, CDCl3): δ 7.37–7.31 (m, 5H, Ar-H), 4.64 (s, 1H, C = CH2-1), 4.55 (s, 2H, Ar-CH2), 4.48 (s, 1H, C = CH2-2), 3.51 (d, 1H, J = 8.9 Hz), 3.09 (d, 1H, J = 8.9 Hz), 2.35 (td, 1H, J = 10.7, 5.6 Hz), 1.66 (s, 3H, CH3), 0.96, 0.94, 0.84, 0.79, 0.75 (each s, each 3H, CH3), 0.66 (d, 1H, J = 9.4 Hz, H-5); 13C NMR (151 MHz, CDCl3): δ 150.85 (C-20), 139.11, 128.43 (two), 127.67 (two), 127.56, 109.61 (C-29), 79.07, 73.50, 68.14, 55.38, 50.47, 48.96, 48.07, 47.38, 42.71, 40.94, 38.97, 38.79, 37.55, 37.24, 34.98, 34.28, 30.11, 30.06, 28.11, 27.50, 27.23, 25.29, 20.92, 19.20, 18.43, 16.20, 15.89, 15.49, 14.89. HRMS (MALDI) m/z: calcd for C37H56O2Na [M + Na]+, 555.4178; found, 555.4192.
4.1.3 General procedure for 13 and 14
To a solution of 8 or 11 (1 eq), 2, 3, 4, 6-tetra-O-benzoyl-d-glucopyranosyl trichloroacetimidate 12 (1.5 eq) and 4 Å molecular sieves in dry CH2Cl2 (30 mL) was added TMSOTf (0.15 eq) at −5 ℃ under N2 atmosphere. The reaction mixture was kept at −5 °C for 0.5 h and then warmed to room temperature for 1 h. After the reaction was complete detected by TLC, the reaction was quenched with trimethylamine. The mixture was filtered and the filtrate was concentrated under vacuum. Then the residue was purified by silica gel column chromatography (petroleumether-EtOAc-CH2Cl2, 8:1:1) to produce 13 or 14 as a white solid, respectively.
4.1.3.1 Benzyl-3β-O-(2, 3, 4, 6-tetra-O-benzoyl-β-d-glucopyranosyl)-lup-20 (29)-ene-28-oic acid (13)
Compound 13 was obtained as a white solid. 1H NMR (600 MHz, CDCl3): δ 7.95–7.65 (m, 15H, Ar-H), 7.46–7.09 (m, 10H, Ar-H), 5.77 (t, 1H, J = 9.7 Hz, H-3′), 5.45 (t, 1H, J = 9.5 Hz, H-4′), 5.00 (d, 1H, J = 12.2 Hz, Ar-CH2-1), 4.95 (d, 1H, J = 12.3 Hz, Ar-CH2-2), 4.72 (s, 1H, C = CH2-1), 4.70 (s, 1H, C = CH2-2), 4.64–4.59 (m, 1H, H-1′), 4.51–4.48 (m, 1H, H-2′), 4.46 (dd, 1H, J = 11.9, 3.4 Hz, H-6′-1), 4.40 (dd, 1H, J = 11.9, 6.6 Hz, H-6′-2), 4.06–3.96 (m, 1H, H-5′), 2.92 (dd, 1H, J = 11.7, 4.5 Hz, H-3), 2.13 (d, 1H, J = 12.4 Hz), 1.57, 0.77, 0.59, 0.56, 0.52, 0.47 (each s, each 3H, CH3), 0.40 (d, 1H, J = 9.7 Hz, H-5); 13C NMR (151 MHz, CDCl3): δ 175.85 (C-28), 166.12, 165.96, 165.40, 165.11, 150.69 (C-20), 136.53, 133.56, 133.32, 133.18, 130.16, 129.93 (two), 129.84 (four), 129.82 (three), 129.74, 129.66, 129.47, 128.91, 128.85, 128.58 (three), 128.52 (two), 128.47 (three), 128.37 (two), 128.34 (three), 128.16 (two), 109.64 (C-29), 103.31 (C-1′), 90.81, 73.05, 72.22, 72.05, 70.37, 65.83, 63.52, 56.62, 55.65, 50.61, 49.52, 46.97, 42.41, 40.68, 38.93, 38.67, 38.23, 37.03, 36.86, 34.30, 32.19, 30.71, 29.60, 27.58, 26.06, 25.63, 20.92, 19.60, 18.12, 16.11, 16.06, 15.87, 14.69. HRMS (ESI) m/z: calcd for C71H81O12 [M + H]+, 1125.5728; found, 1125.5750.
4.1.3.2 28-(Benzyloxy)-3β-O-(2, 3, 4, 6-tetra-O-benzoyl-β-d-glucopyranosyl)-lup-20 (29)-ene-3-ol (14)
Compound 14 was obtained as a white solid. 1H NMR (600 MHz, CDCl3): δ 8.05–7.80 (m, 10H, Ar-H), 7.56–7.22 (m, 15H, Ar-H), 5.91 (t, 1H, J = 9.9, H-3′), 5.56 (t, 1H, J = 10.1 Hz, H-4′), 4.86 (s, 1H, C = CH2-1), 4.84 (s, 1H, C = CH2-2), 4.68–4.63 (m, 1H, H-1′), 4.62–4.56 (m, 3H, H-2′, Ar-CH2), 4.58–4.49 (m, 1H, H-6′-1), 4.50–4.42 (m, 1H, H-6′-2), 4.19–4.10 (m, 1H, H-5′), 3.49 (d, 2H, J = 8.9 Hz, C-CH2), 3.07 (dd, 1H, J = 11.2, 6.6 Hz, H-3), 2.40–2.31 (m, 1H), 1.69, 0.90, 0.78, 0.72, 0.67, 0.61 (each s, each 3H, CH3), 0.54 (d, 1H, J = 10.1 Hz, H-5); 13C NMR (151 MHz, CDCl3): δ 166.05, 165.90, 165.34, 165.06, 150.76 (C-20), 138.98, 133.48, 133.24, 133.10, 133.08, 129.87 (two), 129.78 (four), 129.75 (three), 129.71, 129.44, 128.88, 128.82, 128.45 (two), 128.38 (three), 128.32 (four), 127.56 (three), 127.47, 109.49 (C-29), 103.26 (C-1′), 90.70, 73.40, 73.01, 72.19, 72.01, 70.33, 68.02, 63.46, 55.54, 50.36, 48.86, 47.92, 47.28, 42.56, 40.80, 38.87, 38.59, 37.41, 36.76, 34.89, 34.12, 30.01, 27.52, 27.09, 25.99, 25.21, 20.79, 19.20, 18.06, 15.98 (two), 15.74, 14.72. HRMS (ESI) m/z: calcd for C71H81O11 [M + H]+, 1111.5935; found, 1111.5947.
4.1.4 Benzyl 3β-O-(d-glucopyranosyl)-lup-20 (29)-ene-28-oic acid (15)
To a stirred solution of compound 13 (12.11 g, 10.7 mmol) in CH2Cl2 (60 mL) and MeOH (60 mL), CH3ONa was added until pH = 10. Stirring was continued overnight at room temperature. Then, the mixture was neutralized with Dowex 50 × 8 (H+) resin until pH = 7, filtered and then evaporated to remove excess solvent under vacuum. The residue was purified by silica gel column chromatography (CH2Cl2-MeOH, 10:1) to give 15 (7.05 g, 92 %) as a white solid. 1H NMR (600 MHz, CD3OD): δ 7.42–7.29 (m, 5H, Ar-H), 5.16 (d, 1H, J = 12.1 Hz, Ar-CH2-1), 5.08 (d, 1H, J = 12.1 Hz, Ar-CH2-2), 4.70 (s, 1H, C = CH2-1), 4.59 (s, 1H, C = CH2-2), 4.30 (d, 1H, J = 7.8 Hz, H-1′), 3.90 (s, 1H), 3.83 (dd, 1H, J = 11.9, 2.3 Hz, H-6′-1), 3.67 (dd, 1H, J = 11.9, 5.2 Hz, H-6′-2), 3.38–3.27 (m, 1H, H-5), 3.19–3.09 (m, 1H), 3.07–2.96 (m, 1H, H-3), 2.28–2.21 (m, 2H), 1.68, 1.02, 0.97, 0.83, 0.82, 0.74 (each s, each 3H, CH3), 0.71 (d, 1H, J = 9.7 Hz, H-5); 13C NMR (151 MHz, CD3OD): δ 177.06 (C-28), 151.67 (C-20), 137.90, 129.61 (two), 129.54 (two), 129.23, 110.40 (C-29), 106.73 (C-1′), 90.73, 78.18, 77.58, 75.57, 71.50, 66.80, 62.71, 57.77, 57.09, 51.90, 50.60, 48.44, 43.49, 41.88, 40.25, 39.55, 38.00, 37.88, 35.53, 33.13, 31.62, 30.69, 28.43, 27.64, 27.61, 27.16, 26.82, 22.06, 19.65, 19.26, 16.86, 16.62, 15.22. HRMS (ESI) m/z: calcd for C43H65O8 [M + H]+, 709.4679; found, 709.4691.
4.1.5 28-(Benzyloxy)-3β-O-(d-glucopyranosyl)-lup-20 (29)-ene-3-ol (16)
Compound 16 was obtained from 14 as a white solid using the similar method as 15. 1H NMR (600 MHz, CD3OD): δ 7.32–7.00 (m, 5H, Ar-H), 4.60 (s, 1H, C = CH2-1), 4.50 (s, 1H, C = CH2-2), 4.34 (dd, 1H, J = 12.3, 6.8 Hz, H-6′-1), 4.24 (d, 1H, J = 7.7 Hz, H-1′), 3.90–3.72 (m, 1H, H-6′-2), 3.67–3.40 (m, 2H), 3.21–3.04 (m, 6H), 1.92 (s, 2H), 1.60 (s, 3H, CH3), 0.97 (s, 6H, 2 × CH3), 0.76 (s, 9H, 3 × CH3), 0.65 (d, 1H, J = 10.0 Hz, H-5); 13C NMR (151 MHz, CD3OD): δ 150.38 (C-20), 138.82, 128.04 (two), 127.60 (two), 127.57, 108.98 (C-29), 105.36 (C-1′), 89.38, 76.86, 76.23, 74.25, 72.97, 70.19, 67.69, 67.53, 61.40, 55.68, 50.38, 48.74, 42.33, 40.69, 38.90, 38.62, 37.43, 36.61, 34.07, 33.92, 29.83, 29.66, 27.10, 26.91, 25.82, 25.17, 20.62, 17.93, 15.53, 15.48, 15.22, 14.02. HRMS (MALDI) m/z: calcd for C43H66O7Na [M + Na]+, 717.4706; found, 717.4732.
4.1.6 General procedure for 17 and 18
To a stirred solution of 15 or 16 (1.00 mmol) in 30 mL of dry pyridine and CH2Cl2 (V: V = 1: 1), PivCl (5.00 mmol) was added slowly to the mixture at −15 ℃ under argon atmosphere. Stirring was continued for 12 h at that temperature and then the reaction was quenched with CH3OH. Excess solvent was removed in vacuo. The residue was extracted with dichloromethane and washed with saturated NaHCO3 solution and brine. The organic layer was dried over Na2SO4 and concentrated under vacuum to furnish a crude product that was further purified by column chromatography (petroleumether-EtOAc-CH2Cl2, 8:1:1) to produce 17 or 18, respectively.
4.1.6.1 Benzyl-3β-O-(3, 6-di-O-pivaloyl-β-d-glucopyranosyl)-lup-20 (29)-ene-28-oic acid (17)
Compound 17 was obtained in 84 % yield as white solid. 1H NMR (600 MHz, CDCl3): δ 7.39–7.28 (m, 5H, Ar-H), 5.14 (d, 1H, J = 12.3 Hz, Ar-CH2-1), 5.08 (d, 1H, J = 12.3 Hz, Ar-CH2-2), 4.91–4.81 (m, 1H), 4.72 (s, 1H, C = CH2-1), 4.60 (s, 1H, C = CH2-2), 4.45 (dd, 1H, J = 11.8, 2.2 Hz, H-6′-1), 4.39 (d, 1H, J = 7.8 Hz, H-1′), 4.18 (dd, 1H, J = 12.0, 7.2 Hz, H-6′-2), 3.62–3.50 (m, 1H, H-5′), 3.45 (t, 1H, J = 9.4 Hz), 3.12 (dd, 1H, J = 11.9, 4.5 Hz), 3.03 (dd, 1H, J = 10.9, 4.4 Hz, H-3), 2.27 (d, 2H, J = 12.3 Hz), 1.68 (s, 3H, CH3), 1.24 (s, 9H, C(CH3)3), 1.20 (s, 9H, C(CH3)3), 0.96, 0.93, 0.79, 0.78, 0.74 (each s, each 3H, CH3), 0.66 (d, 1H, J = 9.2 Hz, H-5); 13C NMR (151 MHz, CDCl3): δ 180.42, 178.72 (C-28), 175.92, 150.78 (C-20), 136.57, 128.61 (two), 128.36 (two), 128.18, 109.67 (C-29), 104.84 (C-1′), 90.54, 78.07, 74.21, 72.76, 70.26, 65.86, 63.93, 56.67, 55.72, 50.61, 49.55, 47.04, 42.47, 40.76, 39.17, 38.70, 38.26, 36.99, 34.34, 32.21, 30.72, 29.66, 28.29, 27.20 (two), 26.06, 25.65, 20.98, 19.54, 18.27, 16.59, 16.21, 15.94, 14.74. HRMS (ESI) m/z: calcd for C53H81O10 [M + H] +, 877.5830; found, 877.5862.
4.1.6.2 28-(Benzyloxy)-3β-O-(3, 6-di-O-pivaloyl-β-d-glucopyranosyl)-lup-20 (29)-ene-3-ol (18)
Compound 18 was obtained as a white solid. 1H NMR (600 MHz, CDCl3): δ 7.41–7.18 (m, 5H, Ar-H), 4.89–4.87 (m, 1H), 4.65 (d, 1H, J = 2.5 Hz), 4.61–4.52 (m, 2H, C = CH2), 4.47 (d, 1H, J = 6.1 Hz), 4.47–4.41 (m, 1H), 4.40 (d, 1H, J = 7.8 Hz, H-1′), 4.18 (dd, 1H, J = 11.8, 7.2 Hz, H-6′-1), 3.62–3.40 (m, 4H), 3.17–3.06 (m, 1H, H-3), 2.39 (d, 1H, J = 12.9 Hz), 1.67 (s, 3H, CH3), 1.24 (s, 9H, C(CH3)3), 1.20 (s, 9H, C(CH3)3), 0.97, 0.93, 0.83 (each s, each 3H, CH3), 0.80 (s, 6H, 2 × CH3), 0.67 (d, 1H, J = 10.0 Hz, H-5); 13C NMR (151 MHz, CDCl3): δ 180.29, 178.62, 150.83 (C-20), 138.99, 128.33 (two), 127.56 (two), 127.47, 109.47 (C-29), 104.73 (C-1′), 90.38, 77.95, 74.12, 73.39, 72.70, 70.14, 68.01, 63.83, 55.56, 50.32, 48.86, 47.96, 47.29, 42.58, 40.84, 39.07 (two), 38.84, 38.57, 37.42, 36.85, 34.12, 29.99, 28.18, 27.17 (four), 27.10 (five), 25.95, 25.19, 20.81, 19.14, 18.18, 16.48, 16.04, 15.78, 14.73. HRMS (ESI) m/z: calcd for C53H83O9 [M + H] +, 863.6037; found, 863.6051.
4.1.7 General procedure for BA-16 and BA-20
To a mixture of 17 or 18 (1.0 mmol) and 4 Å molecular sieves in dried CH2Cl2 (20 mL) at −40 °C under argon was added TMSOTf (0.20 mmol), followed by a solution of the 2, 3, 4-tri-O-acetyl-l-rhamnopyranosyl trichloroacetimidate 19 (5.00 mmol) in dry CH2Cl2 (5 mL). After stirring at − 40 °C for 3 h, the reaction mixture was warmed to 0 °C and stirred for 5 h under argon. After the reaction was complete detected by TLC, the reaction was quenched with Et3N. The solid was filtered, and the filtrate was concentrated in reduced pressure and then purified by column chromatography (petroleum ether-EtOAc, 1:1) to afford the crude trisaccharide product. Subsequently, to a stirred solution of this crude product in 20 mL THF and CH3OH (V:V = 1:1), 4 M NaOH (10 mL) was added. After stirred at 45 °C for 10 h, 1 M HCl was added to adjust pH = 7. The resulting precipitate was filtered off and washed with CH3OH, and then concentrated under vacuum. The obtained crude product was further purified by column chromatography, eluting with CH2Cl2/CH3OH mixtures, with gradient from 8:1 to 4:1, to furnish the target compound BA-16 or BA-20, respectively.
4.1.7.1 Benzyl-3β-O-[2, 4-di-O-(α-l-rhamnopyranosyl)-β-d-glucopyranosyl]-lup-20 (29)-ene-28-oic acid (BA-16)
Compound BA-16 was obtained as a white powder. 1H NMR (600 MHz, CD3OD): δ 7.42–7.30 (m, 5H, Ar-H), 5.35 (d, 1H, J = 1.7 Hz, Rha-H-1), 5.17 (d, 1H, J = 12.1 Hz, Ar-CH2-1), 5.09 (d, 1H, J = 12.1 Hz, Ar-CH2-2), 4.82 (d, 1H, J = 1.1 Hz, Rha-H-1), 4.70 (s, 1H, C = CH2-1), 4.60 (s, 1H, C = CH2-2), 4.41 (d, 1H, J = 7.7 Hz, H-1′), 4.11 (s, 1H), 4.03–3.93 (m, 2H), 3.92–3.90 (m, 2H), 3.90–3.79 (m, 1H, H-5′), 3.75 (dd, 1H, J = 9.6, 3.4 Hz, Rha-H-3), 3.68 (d, 1H, J = 3.7 Hz), 3.63 (dd, 1H, J = 9.6, 3.2 Hz, Rha-H-3), 3.57–3.53 (m, 2H), 3.48–3.33 (m, 2H), 3.34–3.25 (m, 2H), 3.12 (dd, 1H, J = 11.7, 4.4 Hz, H-3), 3.05–2.96 (m, 1H), 2.29–2.35 (m, 1H), 1.68 (s, 3H, CH3), 1.27 (d, 3H, J = 6.2 Hz, Rha-H-6), 1.21 (d, 3H, J = 6.2 Hz, Rha-H-6), 1.02 (s, 3H, CH3), 0.97 (s, 3H, CH3), 0.83 (s, 6H, 2 × CH3), 0.74 (s, 3H, CH3), 0.70 (d, 1H, J = 9.8 Hz, H-5); 13C NMR (151 MHz, CD3OD): δ 177.16 (C-28), 151.71 (C-20), 137.88, 130.21, 129.93, 129.61 (two), 129.55 (two), 129.25, 110.37 (C-29), 105.48 (C-1′), 103.02 (Rha-C-1), 101.98 (Rha-C-1), 90.40, 80.29, 79.19, 78.10, 76.40, 73.91, 73.66, 72.41, 72.10, 71.97, 70.72, 69.99, 66.83, 61.94, 57.80, 57.39, 51.96, 50.62, 47.81, 43.49, 41.89, 40.32, 39.58, 38.01, 37.87, 35.53, 33.12, 31.62, 30.68, 28.39, 27.35, 26.85, 22.05, 19.61, 19.24, 18.03, 17.90, 16.99, 16.92, 16.59, 15.18. HRMS (ESI) m/z: calcd for C55H84O16Na [M + Na]+, 1023.5657; found, 1023.5623.
4.1.7.2 28-(Benzyloxy)-3β-O-[2, 4-di-O-(α-l-rhamnopyranosyl)-β-d-glucopyranosyl]-lup-20 (29)-ene-3-ol (BA-20)
Compound BA-20 was obtained as a white powder. 1H NMR (600 MHz, CD3OD): δ 7.39–7.21 (m, 5H, Ar-H), 5.37 (d, J = 1.7 Hz, 1H, Rha-H-1), 4.86 (d, 1H, J = 1.1 Hz, Rha-H-1), 4.72 (s, 1H, C = CH2-1), 4.66 (s, 1H, C = CH2-2), 4.58 (d, 2H, J = 12.5 Hz, Ar-CH2), 4.43 (d, 1H, J = 7.9 Hz, H-1′), 4.40 (d, 1H, J = 2.3 Hz), 4.01–3.93 (m, 2H), 3.95–3.87 (m, 1H, H-5′), 3.84 (t, 1H, J = 9.4 Hz, Rha-H-4), 3.76 (dd, 1H, J = 9.7, 3.5 Hz, Rha-H-3), 3.66 (dd, 1H, J = 9.7, 3.6 Hz, Rha-H-3), 3.66–3.58 (m, 3H), 3.60–3.48 (m, 3H), 3.48–3.35 (m, 3H), 3.15–3.09 (m, 1H, H-3), 2.45–2.33 (m, 2H), 1.98 (s, 1H), 1.67 (s, 3H, CH3), 1.26 (d, 3H, J = 6.3 Hz, Rha-H-6), 1.21 (d, 3H, J = 6.2 Hz, Rha-H-6), 1.02 (s, 3H, CH3), 0.96 (s, 3H, CH3), 0.83 (s, 6H, 2 × CH3), 0.82 (s, 3H, CH3), 0.70 (d, 1H, J = 9.5 Hz, H-5); 13C NMR (151 MHz, CD3OD): δ 150.40 (C-20), 138.78, 128.03 (two), 127.66 (two), 127.31, 108.92 (C-29), 104.10 (C-1′), 101.60 (Rha-C-1), 100.55 (Rha-C-1), 94.36, 89.03, 78.91, 77.83, 76.72, 75.03, 72.92, 72.83, 72.53, 72.29, 71.05, 70.74, 70.62, 69.33, 68.61, 67.82, 67.43, 60.56, 55.95, 50.43, 42.29, 40.67, 38.96, 38.89, 37.42, 36.59, 34.51, 34.02, 29.75, 29.60, 27.02, 26.87, 25.98, 25.16, 20.57, 18.07, 17.88, 16.75, 16.65, 16.53, 15.63, 15.50, 15.11, 13.93. HRMS (MALDI) m/z: calcd for C55H86O15Na [M + Na]+, 1009.5864; found, 1009.5882.
4.1.8 General procedure for BA-2 and BA-17
To a stirred solution of BA-16 or BA-20 (1.00 mmol) in 20 mL of dry methanol and tetrahydrofuran (V: V = 1:1), 10 % Pd/C (100 mg) was added at r.t. under argon atmosphere. Then the solution was stirred at room temperature for 12 h under hydrogen atmosphere. The mixture was filtered and concentrated under reduced pressure. The resultant crude material was purified by column chromatography (CH2Cl2-CH3OH, 5:1) to afford the title compound BA-2 or BA-17 as a white solid, respectively.
4.1.8.1 3β-O-[2, 4-Di-O-(α-l-Rhamnopyranosyl)-β-d-glucopyranosyl]-lup-20 (29)-ene-28-oic acid (BA-2)
Saponin BA-2 was obtained as a white powder. 1H NMR (600 MHz, CD3OD): δ 5.38 (s, 1H, Rha-H-1), 4.86 (d, 1H, J = 1.1 Hz, Rha-H-1), 4.70 (s, 1H, C = CH2-1), 4.59 (s, 1H, C = CH2-2), 4.42 (d, 1H, J = 7.6 Hz, H-1′), 4.03–3.96 (m, 2H), 3.98–3.85 (m, 1H, H-5′), 3.79 (t, 1H, J = 10.0 Hz, Rha-H-4), 3.67–3.63 (m, 2H), 3.57 (t, 1H, J = 9.8 Hz, Rha-H-4), 3.48–3.37 (m, 1H), 3.35–3.32 (m, 2H), 3.23–3.19 (m, 4H), 3.14 (dd, 1H, J = 11.5, 4.2 Hz, H-3), 3.05–3.03 (m, 1H), 2.23 (d, 1H, J = 12.0 Hz), 1.69 (s, 3H, CH3), 1.27 (d, 3H, J = 6.2 Hz, Rha-H-6), 1.21 (d, 3H, J = 6.0 Hz, Rha-H-6), 1.03, 1.00, 0.96, 0.86, 0.83 (each s, each 3H, CH3), 0.76–0.70 (m, 1H, H-5); 13C NMR (151 MHz, CD3OD): δ 172.73 (C-28), 151.99 (C-20), 110.19 (C-29), 105.48 (C-1′), 102.81 (Rha-C-1), 101.77 (Rha-C-1), 90.38, 80.07, 79.13, 78.02, 76.38, 74.16, 73.84, 73.63, 73.02, 72.40 (two), 72.07, 71.97, 70.58, 69.89, 69.09, 61.83, 57.53, 57.38, 51.98, 50.37, 43.53, 41.90, 40.32 (two), 39.55, 38.01 (two), 35.57, 31.68, 30.83, 28.37, 27.36, 26.86, 22.07, 19.57, 19.26, 18.01, 17.90, 16.98, 16.93, 16.68, 15.15. HRMS (ESI) m/z: calcd for C48H77O16 [M + H]+, 909.5290; found, 909.5277.
4.1.8.2 3β-O-[2, 4-Di-O-(α-l-Rhamnopyranosyl)-β-d-glucopyranosyl]-lup-20 (29)-ene-3, 28-diol (BA-17)
Compound BA-17 was obtained as a white powder. 1H NMR (600 MHz, CD3OD): δ 5.16 (s, 1H, Rha-H-1), 4.81 (s, 1H, Rha-H-1), 4.47 (s, 1H, C = CH2-1), 4.35 (s, 1H, C = CH2-2), 4.20 (d, 1H, J = 7.7 Hz, H-1′), 3.83–3.64 (m, 5H), 3.56 (t, 1H, J = 8.8 Hz), 3.47–3.40 (m, 4H), 3.36 (t, 1H, J = 8.5 Hz), 3.23–3.20 (m, 2H), 3.09 (s, 2H, C-CH2), 2.89–2.83 (m, 1H, H-3), 2.20 (d, 1H, J = 11.5 Hz), 1.46 (s, 3H, CH3), 1.05 (d, 3H, J = 6.2 Hz, Rha-H-6), 1.00 (d, 3H, J = 6.0 Hz, Rha-H-6), 0.85, 0.81, 0.78, 0.65, 0.62 (each s, each 3H, CH3), 0.59–0.48 (m, 1H, H-5); 13C NMR (151 MHz, CD3OD): δ 151.81 (C-20), 110.27 (C-29), 105.45 (C-1′), 102.77 (Rha-C-1), 101.74 (Rha-C-1), 90.39, 80.16, 79.24, 77.98, 76.39, 73.65, 72.41, 72.12, 71.99, 70.57, 69.90, 61.89, 60.32, 57.33, 51.83, 50.00, 43.94, 43.76, 42.16, 42.12, 40.32 (two), 38.64, 37.97, 35.45, 34.98, 30.78, 30.33, 28.38, 27.35, 26.58, 23.45, 22.63, 21.99, 19.39, 19.26, 17.99, 17.88, 16.98, 16.85, 16.53, 15.25, 9.09, 7.84. HRMS (MALDI) m/z: calcd for C48H79O15 [M + H]+, 895.5497; found, 895.5473.
4.1.9 General procedure for 20 and 21
Compound BA-2 or BA-20 (1.0 mmol) was dissolved in 20 mL of dry pyridine, Ac2O (16.0 mmol) and DMAP (0.8 mmol) were added at 0 ℃. The reaction mixture was warmed to 60 ℃ and stirred for 24 h under N2 atmosphere. After excess solvent was removed in vacuo, the crude product was extracted with ethyl acetate, which was then washed with 1 M HCl, saturated NaHCO3 solution and brine. The organic layer was dried over Na2SO4 and concentrated in vacuo to provide a crude residue. The residue was further purified by column chromatography (petroleumether-EtOAc-CH2Cl2, 3:1:1) to yield 20 or 21, respectively.
4.1.9.1 3β-O-[2, 4-Di-O-(2, 3, 4-tri-O-Acetyl-α-l-rhamnopyranosyl)-β-(3, 6-di-O-acetyl)-d-glucopyranosyl]-lup-20(29)-ene-28-oic acid (20)
Compound 20 was obtained as a white powder. 1H NMR (600 MHz, CDCl3): δ 5.28–5.20 (m, 3H), 5.17 (dd, 1H, J = 10.2, 3.2 Hz, Rha-H-3), 5.10 (dd, 1H, J = 3.5, 1.8 Hz, Rha-H-2), 5.05–5.01 (m, 4H), 4.80 (d, 1H, J = 1.7 Hz, Rha-H-1), 4.74 (s, 1H, C = CH2-1), 4.62 (s, 1H, C = CH2-2), 4.53 (d, 1H, J = 7.7 Hz, H-1′), 4.46 (dd, 1H, J = 12.4, 2.1 Hz, H-6′-1), 4.27 (dd, 1H, J = 13.4, 5.3 Hz, H-6′-2), 4.25–4.17 (m, 1H, H-5′), 3.91–3.81 (m, 2H), 3.76 (t, 1H, J = 9.3 Hz, Rha-H-4), 3.71–3.58 (m, 3H), 3.14–3.12 (m, 1H), 3.05–2.94 (m, 1H, H-3), 2.27 (d, 1H, J = 12.5 Hz), 2.14, 2.13, 2.11, 2.10, 2.04, 2.01, 1.99, 1.97 (each s, each 3H, each CH3CO), 1.70 (s, 3H, CH3), 1.17 (d, 3H, J = 6.2 Hz, Rha-H-6), 1.15 (d, 3H, J = 6.2 Hz, Rha-H-6), 1.01, 0.98, 0.92, 0.82, 0.78 (each s, each 3H, CH3), 0.71 (d, 1H, J = 10.3 Hz, H-5); 13C NMR (151 MHz, CDCl3): δ 181.96, 170.76, 170.37, 170.27, 170.22 (C-28), 170.14 (two), 169.81, 150.48 (C-20), 109.85 (C-29), 103.83 (C-1′), 99.58 (Rha-C-1), 97.03 (Rha-C-1), 90.15, 78.05, 77.48, 77.16, 75.59, 75.43, 72.17, 71.20, 70.59, 70.00, 69.80, 68.69, 68.58, 68.00, 66.75, 62.31, 56.44, 56.07, 50.57, 49.27, 46.98, 42.51, 40.77, 39.24, 39.20, 38.45, 36.99, 34.35, 32.23, 30.65, 29.74, 27.80, 26.23, 25.54, 21.60, 21.07, 21.01, 20.97, 20.94 (three), 20.86, 20.81, 19.45, 18.25, 17.34, 17.24, 16.34, 16.13, 16.02, 14.75. HRMS (ESI) m/z: calcd for C64H95O24 [M + H]+, 1247.6213; found, 1247.6235.
4.1.9.2 28-(Benzyloxy)-3β-O-[2, 4-Di-O-(2, 3, 4-tri-O-Acetyl-α-l-rhamnopyranosyl)-β-(3, 6-di-O-acetyl)-d-glucopyranosyl]-lup-20 (29)-ene-3-ol (21)
Compound 21 was obtained as a white powder. 1H NMR (600 MHz, CDCl3): δ 7.37–7.23 (m, 5H, Ar-H), 5.49–5.30 (m, 3H), 5.26 (dd, 1H, J = 9.7, 3.4 Hz, Rha-H-3), 5.15–5.01 (m, 2H), 4.84 (s, 1H, Rha-H-1), 4.65 (s, 1H, Rha-H-1), 4.56 (s, 1H, C = CH2-1), 4.48 (s, 1H, C = CH2-2), 4.42 (d, 1H, J = 8.1 Hz, H-1′), 4.34–4.17 (m, 3H), 4.08 (dd, 1H, J = 9.8, 7.0 Hz, H-2′), 3.99 (d, 1H, J = 2.5 Hz), 3.73 (t, 1H, J = 9.5 Hz, Rha-H-4), 3.61 (t, 1H, J = 9.1 Hz, Rha-H-4), 3.54–3.40 (m, 3H), 3.09 (d, 2H, J = 9.1 Hz), 2.40–2.32 (m, 1H), 2.18–1.94 (m, 24H, 8 × CH3CO), 1.67 (s, 3H, CH3), 1.27 (d, 3H, J = 6.4 Hz, Rha-H-6), 1.19 (d, 3H, J = 6.3 Hz, Rha-H-6), 1.05 (s, 3H, CH3), 0.94 (s, 3H, CH3), 0.83 (s, 3H, CH3), 0.81 (s, 6H, 2 × CH3), 0.70 (d, 1H, J = 9.5 Hz, H-5); 13C NMR (151 MHz, CDCl3): δ 171.93 (C-28), 170.20, 170.12 (two), 170.10 (two), 170.00 (two), 169.78, 149.64 (C-20), 140.06, 128.45, 127.69, 127.58, 118.61 (C-29), 104.07 (C-1′), 99.31 (Rha-C-1), 97.63 (Rha-C-1), 85.23, 83.18, 73.99, 71.64, 70.09, 69.45, 69.23, 68.12, 67.33, 65.05, 57.77, 56.60, 54.34, 53.95 (three), 51.52, 50.15, 48.71, 46.99, 45.85, 42.58, 42.15, 42.00, 41.39, 40.82, 39.10, 38.70, 38.22, 36.30, 33.53, 33.14, 30.14, 27.94, 27.52, 25.75, 25.25, 21.62, 21.14 (two), 20.99 (two), 20.91 (three), 17.86, 16.11, 14.78. HRMS (ESI) m/z: calcd for C71H103O23 [M + H]+, 1323.6890; found, 1323.6898.
4.1.10 General procedure for BA-3 -- BA-15
To a solution of BA-2 (1.00 mmol) in DMF (20 mL) was added K2CO3 (5 mmol) at 30 °C under N2 atmosphere. After stirring at 30 °C for 2 h, the corresponding halogenated hydrocarbon (3.00 mmol) was added. Stirring was continued overnight at that temperature. After the mixture was evaporated to remove excess solvent under reduced pressure, the residue was dissolved in EtOAc (100 mL), then extracted with water (3 × 50 mL) and brine (3 × 50 mL). The combined organic layer was concentrated in vacuo after drying over Na2SO4. Then, the residue was re-dissolved in MeOH (10 mL) and CH2Cl2 (10 mL), CH3ONa was added until pH = 10. After the reaction mixture was stirred at r.t. for 5 h, Dowex 50 × 8 (H+) resin was added until pH = 7. The reaction mixture was filtered and concentrated under vacuum. The residue was purified by silica gel column chromatography (CH2Cl2-MeOH, 6:1) to give title saponins BA-3--BA-15.
4.1.10.1 Ethyl-3β-O-[2, 4-di-O-(α-l-rhamnopyranosyl)-β-d-glucopyranosyl]-lup-20 (29)-ene-28-oic acid (BA-3)
Similarly, BA-3 was prepared as a white solid in 82 % yield for two steps; 1H NMR (600 MHz, CD3OD): δ 5.37 (d, 1H, J = 1.7 Hz, Rha-H-1), 4.82 (s, 1H, Rha-H-1), 4.74 (s, 1H, C = CH2-1), 4.62 (s, 1H, C = CH2-2), 4.43 (d, 1H, J = 7.7 Hz, H-1′), 4.24–4.07 (m, 2H), 4.03–3.96 (m, 2H), 3.92 (dd, 1H, J = 9.5, 3.2 Hz, Rha-H-3), 3.86 (dd, 1H, J = 3.3, 1.8 Hz, Rha-H-2), 3.81 (dd, 1H, J = 12.1, 2.0 Hz, H-6′-1), 3.76 (dd, 1H, J = 9.6, 3.4 Hz, Rha-H-3), 3.70–3.68 (m, 1H), 3.66 (dd, 1H, J = 3.4, 1.7 Hz), 3.66–3.60 (m, 1H), 3.58–3.56 (m, 1H), 3.52–3.34 (m, 2H), 3.34–3.29 (m, 2H), 3.15 (dd, 1H, J = 11.7, 4.4 Hz, H-3), 2.26 (d, 1H, J = 8.2 Hz), 1.71 (s, 3H, CH3), 1.28 (s, 3H, CH3), 1.27 (d, 3H, J = 6.3 Hz, Rha-H-6), 1.22 (d, 3H, J = 6.1 Hz, Rha-H-6), 1.05, 1.02, 0.96, 0.88, 0.85 (each s, each 3H, CH3), 0.75 (d, 1H, J = 9.8 Hz, H-5); 13C NMR (151 MHz, CD3OD): δ 177.58 (C-28), 151.78 (C-20), 110.34 (C-29), 105.49 (C-1′), 103.03 (Rha-C-1), 101.99 (Rha-C-1), 90.40, 80.30, 79.20, 78.11, 76.42, 73.91, 73.67, 72.41, 72.11, 71.97, 70.73, 69.99, 61.95, 61.02, 57.72, 57.43, 52.00, 50.57, 43.52, 41.97, 40.34 (two), 39.64, 38.05, 37.95, 36.97, 35.57, 33.14, 31.66, 30.75, 28.40, 27.36, 26.88, 22.09, 19.62, 19.27, 18.02, 17.90, 16.99, 16.93, 16.64, 15.21, 14.71 (two). HRMS (ESI) m/z: calcd for C50H82O16Na [M + Na]+, 961.5501; found, 961.5423.
4.1.10.2 n-Propyl-3β-O-[2, 4-di-O-(α-l-rhamnopyranosyl)-β-d-glucopyranosyl]-lup-20 (29)-ene-28-oic acid (BA-4)
Similarly, BA-4 was prepared as a white solid in 80 % yield for two steps; 1H NMR (600 MHz, CD3OD): δ 5.38 (s, 1H, Rha-H-1), 4.85 (d, 1H, J = 1.2 Hz, Rha-H-1), 4.73 (s, 1H, C = CH2-1), 4.63 (s, 1H, C = CH2-2), 4.44 (d, 1H, J = 7.7 Hz, H-1′), 4.14–4.00 (m, 2H), 4.03–3.95 (m, 2H), 3.92 (dd, 1H, J = 9.6, 3.3 Hz, Rha-H-3), 3.88–3.78 (m, 1H), 3.76 (dd, 1H, J = 9.5, 3.3 Hz, Rha-H-3), 3.66 (t, 1H, J = 10.6 Hz, Rha-H-4), 3.58 (t, 1H, J = 8.1 Hz), 3.50–3.38 (m, 3H), 3.38–3.29 (m, 3H), 3.15 (dd, 1H, J = 11.7, 4.4 Hz, H-3), 3.10–2.98 (m, 1H), 2.28 (d, 1H, J = 10.3 Hz), 1.72 (s, 3H, CH3), 1.50–1.35 (m, 2H, CH2), 1.28 (d, 3H, J = 6.2 Hz, Rha-H-6), 1.23 (d, 3H, J = 6.2 Hz, Rha-H-6), 1.05, 1.03, 1.01, 0.96, 0.89, 0.86 (each s, each 3H, CH3), 0.75 (d, 1H, J = 9.9 Hz, H-5); 13C NMR (151 MHz, CD3OD): δ 177.69 (C-28), 151.75 (C-20), 110.36 (C-29), 105.50 (C-1′), 103.02 (Rha-C-1), 102.00 (Rha-C-1), 90.40, 80.29, 79.21, 78.11, 76.42, 73.91, 73.67, 72.41, 72.10, 71.96, 70.73, 69.99, 66.78, 61.95, 57.89, 57.43, 51.99, 50.59, 43.54, 41.97, 40.34 (two), 39.70, 38.05, 36.97, 35.58, 33.21, 31.68, 30.79, 28.41, 27.36, 26.88, 23.20 (two), 22.10, 19.63, 19.28, 18.03 (two), 17.90 (two), 16.99, 16.94, 16.67, 15.24. HRMS (ESI) m/z: calcd for C51H84O16Na [M + Na]+, 975.5557; found, 975.5582.
4.1.10.3 n-Butyl-3β-O-[2, 4-di-O-(α-l-rhamnopyranosyl)-β-d-glucopyranosyl]-lup-20 (29)-ene-28-oic acid (BA-5)
Similarly, BA-5 was prepared as a white solid in 78 % yield for two steps; 1H NMR (600 MHz, CD3OD): δ 5.36 (s, 1H, Rha-H-1), 4.86 (d, 1H, J = 1.1 Hz, Rha-H-1), 4.72 (s, 1H, C = CH2-1), 4.61 (s, 1H, C = CH2-2), 4.42 (d, 1H, J = 7.7 Hz, H-1′), 4.17–4.02 (m, 2H), 4.01–3.93 (m, 2H), 3.91 (dd, 1H, J = 9.5, 7.7 Hz, H-2′), 3.87–3.77 (m, 1H), 3.75 (dd, 1H, J = 9.6, 3.4 Hz, Rha-H-3), 3.71–3.59 (m, 2H), 3.57 (t, 1H, J = 9.0 Hz, Rha-H-4), 3.48–3.34 (m, 3H), 3.33–3.30 (m, 2H), 3.14 (dd, 1H, J = 11.6, 4.3 Hz, H-3), 3.10–2.98 (m, 1H), 1.95 (d, 1H, J = 9.7 Hz), 1.70 (s, 3H, CH3), 1.46–1.43 (m, 4H), 1.27 (d, 3H, J = 6.2 Hz, Rha-H-6), 1.21 (d, 3H, J = 6.1 Hz, Rha-H-6), 1.03, 1.01 (each s, each 3H, CH3), 0.97 (t, 3H, J = 7.4 Hz, CH3), 0.94, 0.87, 0.84 (each s, each 3H, CH3), 0.74 (d, 1H, J = 9.3 Hz, H-5); 13C NMR (151 MHz, CD3OD): δ 177.72 (C-28), 151.76 (C-20), 110.34 (C-29), 105.48 (C-1′), 103.04 (Rha-C-1), 101.99 (Rha-C-1), 90.39, 80.34, 79.21, 78.12, 76.41, 73.91, 73.67, 72.41, 72.11, 71.98, 70.73, 69.99, 64.84, 61.95, 57.89, 57.41, 51.99, 50.59, 43.54, 41.97, 40.34 (two), 39.73, 38.04, 38.00, 35.58, 33.20, 31.96 (two), 31.68, 30.79, 28.39, 27.36, 26.88, 22.09, 20.43 (two), 19.59, 19.26, 18.02, 17.90, 16.99, 16.92, 16.68, 15.20, 14.04. HRMS (ESI) m/z: calcd for C52H87O16 [M + H]+, 967.5994; found, 967.6018.
4.1.10.4 n-Pentyl-3β-O-[2, 4-di-O-(α-l-rhamnopyranosyl)-β-d-glucopyranosyl]-lup-20 (29)-ene-28-oic acid (BA-6)
Similarly, BA-6 was prepared as a white solid in 76 % yield for two steps; 1H NMR (600 MHz, CD3OD): δ 5.38 (d, 1H, J = 1.7 Hz, Rha-H-1), 4.83 (d, 1H, J = 1.2 Hz, Rha-H-1), 4.73 (s, 1H, C = CH2-1), 4.63 (s, 1H, C = CH2-2), 4.44 (d, 1H, J = 7.7 Hz, H-1′), 4.19–4.03 (m, 2H), 4.03–3.95 (m, 2H), 3.94–3.92 (m, 1H), 3.86 (dd, 1H, J = 3.2, 1.8 Hz, Rha-H-2), 3.82 (dd, 1H, J = 12.0, 2.0 Hz, H-6′-1), 3.76 (dd, 1H, J = 9.6, 3.4 Hz, Rha-H-3), 3.72–3.63 (m, 2H), 3.64 (dd, 1H, J = 3.4, 1.1 Hz, Rha-H-2), 3.58 (t, 1H, J = 9.0 Hz, Rha-H-4), 3.50–3.38 (m, 2H), 3.35–3.30 (m, 2H), 3.16 (dd, 1H, J = 11.7, 4.2 Hz, H-3), 3.10–2.98 (m, 2H), 2.27 (d, 1H, J = 9.4 Hz), 1.72 (s, 3H, CH3), 1.47–1.38 (m, 6H), 1.29 (d, 3H, J = 6.2 Hz, Rha-H-6), 1.23 (d, 3H, J = 6.2 Hz, Rha-H-6), 1.05, 1.03, 0.96, 0.96, 0.88, 0.86 (each s, each 3H, CH3), 0.76 (d, 1H, J = 9.9 Hz, H-5); 13C NMR (151 MHz, CD3OD): δ 177.77 (C-28), 151.78 (C-20), 110.35 (C-29), 105.49 (C-1′), 103.05 (Rha-C-1), 102.00 (Rha-C-1), 90.38, 80.34, 79.21, 78.13, 76.42, 73.92, 73.68, 72.43, 72.11, 71.99, 70.74, 69.99, 65.13, 61.96, 57.91, 57.41, 51.98, 50.58, 49.85, 43.55, 41.98, 40.34, 39.76, 38.05, 35.59, 33.23, 31.70, 30.79, 29.59 (three), 28.39, 27.36, 26.88, 23.36 (two), 22.09, 19.60, 19.27, 18.02, 17.90, 16.99, 16.92, 16.72, 15.19, 14.43 (two). HRMS (ESI) m/z: calcd for C53H89O16 [M + H]+, 981.6151; found, 981.6183.
4.1.10.5 n-Hexyl-3β-O-[2, 4-di-O-(α-l-rhamnopyranosyl)-β-d-glucopyranosyl]-lup-20 (29)-ene-28-oic acid (BA-7)
Similarly, BA-7 was prepared as a white solid in 75 % yield for two steps; 1H NMR (600 MHz, CD3OD): δ 5.38 (d, 1H, J = 1.1 Hz, Rha-H-1), 4.85 (d, 1H, J = 1.1 Hz, Rha-H-1), 4.73 (s, 1H, C = CH2-1), 4.63 (s, 1H, C = CH2-2), 4.44 (d, 1H, J = 7.7 Hz, H-1′), 4.19–4.03 (m, 2H), 4.03–3.95 (m, 2H), 3.93 (dd, 1H, J = 9.5, 7.3 Hz, H-2′), 3.86 (dd, 1H, J = 3.2, 1.8 Hz, Rha-H-2), 3.82 (dd, 1H, J = 12.0, 2.0 Hz, H-6′-1), 3.76 (dd, 1H, J = 9.6, 3.4 Hz, Rha-H-3), 3.72–3.63 (m, 2H), 3.64 (d, 1H, J = 3.4 Hz, Rha-H-2), 3.58 (t, 1H, J = 9.0 Hz, Rha-H-4), 3.50–3.38 (m, 2H), 3.33 (brs, 1H), 3.16 (dd, 1H, J = 11.7, 4.2 Hz, H-3), 3.10–2.98 (m, 1H), 2.27 (d, 1H, J = 9.4 Hz), 1.72 (s, 3H, CH3), 1.47–1.38 (m, 8H), 1.29 (d, 3H, J = 6.2 Hz, Rha-H-6), 1.23 (d, 3H, J = 6.2 Hz, Rha-H-6), 1.05, 1.03, 0.96, 0.96, 0.88, 0.86 (each s, each 3H, CH3), 0.76 (d, 1H, J = 9.9 Hz, H-5); 13C NMR (151 MHz, CD3OD): δ 177.80 (C-28), 151.79 (C-20), 110.36 (C-29), 105.50 (C-1′), 103.06 (Rha-C-1), 102.01 (Rha-C-1), 90.39, 80.36, 79.22, 78.14, 76.44, 73.93, 73.68, 72.43, 72.12, 71.99, 70.75, 70.00, 65.15, 61.97, 57.92, 57.42, 51.98, 50.59, 43.55, 41.99, 40.35, 40.31, 39.78, 38.05, 35.59, 33.25, 32.57, 31.71, 30.82, 29.87, 28.40, 27.37, 27.07, 26.88, 23.73 (two), 22.10, 19.60, 19.27, 18.02, 17.90, 16.99, 16.92, 16.73, 15.20, 14.40. HRMS (ESI) m/z: calcd for C54H91O16 [M + H]+, 995.6307; found, 995.6335.
4.1.10.6 Isopropyl-3β-O-[2, 4-di-O-(α-l-rhamnopyranosyl)-β-d-glucopyranosyl]-lup-20 (29)-ene-28-oic acid (BA-8)
Similarly, BA-8 was prepared as a white solid in 78 % yield for two steps; 1H NMR (600 MHz, CD3OD): δ 5.36 (s, 1H, Rha-H-1), 4.82 (d, 1H, J = 1.1 Hz, Rha-H-1), 5.06–4.94 (m, 1H), 4.72 (s, 1H, C = CH2-1), 4.61 (s, 1H, C = CH2-2), 4.42 (d, 1H, J = 7.7 Hz, H-1′), 4.03–3.95 (m, 2H), 3.91 (dd, 1H, J = 9.5, 7.2 Hz, H-2′), 3.84 (m, 1H), 3.80 (d, 1H, J = 10.9 Hz), 3.75 (dd, 1H, J = 9.6, 3.4 Hz, Rha-H-3), 3.71–3.59 (m, 2H), 3.57 (t, 1H, J = 9.7 Hz, Rha-H-4), 3.48–3.34 (m, 2H), 3.32–3.30 (m, 2H), 3.14 (dd, 1H, J = 11.5, 4.2 Hz, H-3), 3.07–2.97 (m, 1H), 2.24 (d, 1H, J = 8.5 Hz), 1.70 (s, 3H, CH3), 1.28–1.25 (m, 6H), 1.25 (d, 3H, J = 6.2 Hz, Rha-H-6), 1.21 (d, 3H, J = 6.2 Hz, Rha-H-6), 1.03, 1.01, 0.95, 0.87, 0.84 (each s, each 3H, CH3), 0.74 (d, 1H, J = 9.6 Hz, H-5); 13C NMR (151 MHz, CD3OD): δ 177.14 (C-28), 151.82 (C-20), 110.31 (C-29), 105.48 (C-1′), 103.02 (Rha-C-1), 101.99 (Rha-C-1), 90.39, 80.31, 79.21, 78.11, 76.41, 73.90, 73.66, 72.41, 72.10, 71.97, 70.72, 69.98, 68.36, 61.95, 57.67, 57.42, 51.99, 50.51, 43.53, 42.01, 40.34 (two), 39.69, 38.04 (two), 35.56, 33.14, 31.70, 30.72, 28.40, 27.36, 26.90, 22.11 (three), 22.05 (two), 19.62, 19.26, 18.02, 17.90, 16.98, 16.93, 16.70, 15.20. HRMS (ESI) m/z: calcd for C51H85O16 [M + H]+, 953.5838; found, 953.5852.
4.1.10.7 Cyclohexyl-3β-O-[2, 4-di-O-(α-l-rhamnopyranosyl)-β-d-glucopyranosyl]-lup-20 (29)-ene-28-oic acid (BA-9)
Similarly, BA-9 was prepared as a white solid in 73 % yield for two steps; 1H NMR (400 MHz, CD3OD): δ 5.34 (s, 1H, Rha-H-1), 4.85 (d, 1H, J = 1.2 Hz, Rha-H-1), 4.68 (s, 1H, C = CH2-1), 4.56 (s, 1H, C = CH2-2), 4.40 (d, 1H, J = 7.7 Hz, H-1′), 3.98–3.95 (m, 2H), 3.92–3.69 (m, 3H), 3.68–3.46 (m, 3H), 3.46–3.32 (m, 2H), 3.12 (d, 1H, J = 9.6 Hz, H-3), 2.70–2.66 (m, 10H, cyclohexanol-H), 2.22 (d, 1H, J = 12.8 Hz), 1.67 (s, 3H, CH3), 1.24 (d, 3H, J = 6.3 Hz, Rha-H-6), 1.18 (d, 3H, J = 6.1 Hz, Rha-H-6), 1.00, 0.98, 0.94, 0.83, 0.81 (each s, each 3H, CH3), 0.71 (d, 1H, J = 9.9 Hz, H-5); 13C NMR (101 MHz, CD3OD): δ 170.73 (C-28), 152.26 (C-20), 110.12 (C-29), 105.46 (C-1′), 102.97 (Rha-C-1), 101.96 (Rha-C-1), 90.40, 80.22, 79.19, 78.08, 76.40, 73.90, 73.67, 72.41, 72.08 (two), 71.98, 70.70, 69.98, 61.91, 57.40, 52.01, 50.42, 43.56, 41.93, 40.32 (two), 39.57, 38.03, 35.60, 35.38 (two), 31.73, 30.86, 28.38, 27.35, 26.90, 22.09, 19.57, 19.26, 18.01 (two), 17.91 (two), 16.97 (two), 16.90 (two), 16.73, 15.13. HRMS (ESI) m/z: calcd for C54H89O16 [M + H]+, 993.6151; found, 993.6175.
4.1.10.8 2′-Chloroethyl-3β-O-[2, 4-di-O-(α-l-rhamnopyranosyl)-β-d-glucopyranosyl]-lup-20 (29)-ene-28-oic acid (BA-10)
Similarly, BA-10 was prepared as a white solid in 74 % yield for two steps; 1H NMR (600 MHz, CD3OD): δ 5.38 (s, 1H, Rha-H-1), 4.82 (d, 1H, J = 1.2 Hz, Rha-H-1), 4.75 (s, 1H, C = CH2-1), 4.63 (s, 1H, C = CH2-2), 4.44 (d, 1H, J = 7.9 Hz, H-1′), 4.40–4.32 (m, 2H), 3.97–3.95 (m, 2H), 3.82–3.80 (m, 3H), 3.72–3.51 (m, 2H), 3.44–3.41 (m, 2H), 3.16 (d, 1H, J = 10.7 Hz, H-3), 2.29 (d, 1H, J = 10.1 Hz, H-13), 1.73 (s, 3H, CH3), 1.29 (d, 3H, J = 6.0 Hz, Rha-H-6), 1.23 (d, 3H, J = 6.0 Hz, Rha-H-6), 1.05, 1.04, 0.97, 0.89, 0.86 (each s, each 3H, CH3), 0.76 (d, 1H, J = 9.3 Hz, H-5; 13C NMR (151 MHz, CD3OD): δ 177.21 (C-28), 151.74 (C-20), 110.38 (C-29), 105.50 (C-1′), 103.06 (Rha-C-1), 102.01 (Rha-C-1), 90.41, 80.35, 79.21, 78.14, 76.44, 73.93, 73.68, 72.43, 72.12 (two), 71.99, 70.75, 70.00, 65.09, 61.97, 57.98, 57.43, 52.00, 50.63, 43.54, 43.26, 42.00, 40.35 (two), 39.72, 38.05, 35.58, 33.08, 31.63, 30.82, 28.40, 27.38, 26.88, 19.60, 19.27, 18.02 (two), 17.91, 16.99 (two), 16.94 (two), 16.65, 15.21. HRMS (ESI) m/z: calcd for C50H82O16Cl [M + H]+, 973.5291; found, 973.5317.
4.1.10.9 2′-Bromoethyl 3β-O-[2, 4-di-O-(α-l-rhamnopyranosyl)-β-d-glucopyranosyl]-lup-20 (29)-ene-28-oic acid (BA-11)
Similarly, BA-11 was prepared as a white solid in 70 % yield for two steps; 1H NMR (600 MHz, CD3OD): δ 5.34 (d, 1H, J = 1.8 Hz, Rha-H-1), 4.83 (s, 1H, Rha-H-1), 4.70 (s, 1H, C = CH2-1), 4.58 (s, 1H, C = CH2-2), 4.40 (d, 1H, J = 7.7 Hz, H-1′), 4.16–4.08 (m, 2H), 3.99–3.91 (m, 2H), 3.89 (dd, 1H, J = 9.6, 7.4 Hz, H-2′), 3.83–3.81 (m, 1H), 3.78 (dd, 1H, J = 12.0, 2.0 Hz, H-6′-1), 3.72 (dd, 1H, J = 9.7, 3.1 Hz, Rha-H-3), 3.64 (dd, 1H, J = 13.0, 5.6 Hz, H-6′-2), 3.63–3.56 (m, 2H), 3.59–3.49 (m, 1H), 3.46–3.34 (m, 2H), 3.32–3.27 (m, 2H), 3.16–3.07 (m, 1H, H-3), 3.03–2.93 (m, 1H), 2.27 (d, 1H, J = 12.8 Hz), 1.67 (s, 3H, CH3), 1.24 (d, 3H, J = 6.2 Hz, Rha-H-6), 1.19 (d, 3H, J = 6.1 Hz, Rha-H-6), 1.01, 0.98, 0.92, 0.84, 0.81 (each s, each 3H, CH3), 0.71 (d, 1H, J = 9.6 Hz, H-5); 13C NMR (151 MHz, CD3OD): δ 177.62 (C-28), 151.83 (C-20), 110.42 (C-29), 105.46 (C-1′), 103.02 (Rha-C-1), 101.97 (Rha-C-1), 90.37, 80.32, 79.20, 78.11, 76.40, 73.90, 73.67, 72.41, 72.10 (two), 71.98, 70.71, 69.97, 66.33, 61.12, 57.88, 57.40, 51.99, 49.85, 43.52, 41.95 (two), 40.32 (two), 39.57, 38.03 (two), 33.04, 30.72, 28.37, 27.35, 26.86, 22.05, 19.57, 19.25, 18.00 (two), 17.90 (two), 16.97 (two), 16.89 (two), 16.59, 15.16. HRMS (ESI) m/z: calcd for C50H82O16Br [M + H]+, 1017.4786; found, 1017.4803.
4.1.10.10 2′-Hydroxylethyl-3β-O-[2, 4-di-O-(α-l-rhamnopyranosyl)-β-d-glucopyranosyl]-lup-20 (29)-ene-28-oic acid (BA-12)
Similarly, BA-12 was prepared as a white solid in 70 % yield for two steps; 1H NMR (600 MHz, CD3OD): δ 5.37 (s, 1H, Rha-H-1), 4.82 (d, 1H, J = 1.1 Hz, Rha-H-1), 4.73 (s, 1H, C = CH2-1), 4.61 (s, 1H, C = CH2-2), 4.42 (d, 1H, J = 7.7 Hz, H-1′), 4.16–4.14 (m, 2H), 3.97–3.95 (m, 2H), 3.87–3.71 (m, 3H), 3.66–3.64 (m, 1H), 3.57 (t, 1H, J = 8.7 Hz), 3.47–3.35 (m, 2H), 3.34–3.32 (m, 3H), 3.19–3.11 (m, 1H, H-3), 3.02–3.00 (m, 2H), 2.31 (d, 1H, J = 11.9 Hz), 1.71 (s, 3H, CH3), 1.27 (d, 3H, J = 6.0 Hz, Rha-H-6), 1.22 (d, 3H, J = 6.1 Hz, Rha-H-6), 1.04, 1.01, 0.95, 0.87, 0.84 (each s, each 3H, CH3), 0.73 (d, 1H, J = 9.7 Hz, H-5); 13C NMR (151 MHz, CD3OD): δ 178.06 (C-28), 151.73 (C-20), 110.36 (C-29), 105.48, 105.30 (C-1′), 103.02 (Rha-C-1), 101.97 (Rha-C-1), 90.37, 80.30, 79.17, 78.12, 76.41, 73.91, 73.67, 72.41, 72.11 (two), 71.98, 70.72, 69.97, 61.94, 57.86, 57.41, 56.45, 51.97, 51.84, 50.62, 43.50, 41.90, 40.33 (two), 39.63, 38.04, 37.86, 35.55, 33.13, 31.62, 30.80, 28.40, 27.36, 26.84, 22.06, 19.60, 19.27, 18.03, 17.91, 16.99, 16.92, 16.59, 15.21. HRMS (ESI) m/z: calcd for C50H83O17 [M + H]+, 955.5630; found, 955.5658.
4.1.10.11 2′-Oxopethyl-3β-O-[2, 4-di-O-(α-l-rhamnopyranosyl)-β-d-glucopyranosyl]-lup-20 (29)-ene-28-oic acid (BA-13)
Similarly, BA-13 was prepared as a white solid in 65 % yield for two steps; 1H NMR (500 MHz, CD3OD): δ 9.60 (s, 1H, CHO), 5.33 (s, 1H, Rha-H-1), 4.87 (d, 1H, J = 1.1 Hz, Rha-H-1), 4.82 (brs, 2H), 4.68 (s, 1H, C = CH2-1), 4.57 (s, 1H, C = CH2-2), 4.38 (d, 1H, J = 7.8 Hz, H-1′), 3.92–3.87 (m, 3H), 3.74 (dd, 1H, J = 9.6, 7.5 Hz, H-2′), 3.63–3.61 (m, 4H), 3.53 (t, 1H, J = 9.2 Hz, Rha-H-4), 3.42–3.33 (m, 1H), 3.28–3.26 (m, 2H), 3.20–3.14 (m, 3H), 3.12–3.10 (m, 1H, H-3), 3.01–2.91 (m, 2H), 2.19 (d, 1H, J = 11.3 Hz), 1.66 (s, 3H, CH3), 1.23 (d, 3H, J = 6.0 Hz, Rha-H-6), 1.17 (d, 3H, J = 6.0 Hz, Rha-H-6), 0.99, 0.97, 0.90, 0.83, 0.80 (each s, each 3H, CH3), 0.70 (d, 1H, J = 9.4 Hz, H-5); 13C NMR (151 MHz, CD3OD): δ 207.02, 178.08 (C-28), 151.72 (C-20), 110.38 (C-129), 105.49 (C-1′), 103.00 (Rha-C-1), 101.98 (Rha-C-1), 90.39, 80.23, 79.17, 78.10, 76.41, 73.89, 73.65, 72.40, 72.08 (two), 71.96, 70.70, 69.98, 61.91, 57.85, 57.40, 51.96, 51.86, 50.61, 43.49, 41.89, 40.33 (two), 39.62, 38.03 (two), 37.86, 35.54, 33.12, 31.60, 30.79, 28.39, 27.35, 26.82, 22.06, 19.59, 19.26, 18.03 (two), 17.91 (two), 16.99, 16.93, 16.58, 15.21. HRMS (ESI) m/z: calcd for C50H81O17 [M + H]+, 953.5474; found, 953.5496.
4.1.10.12 Allyl-3β-O-[2, 4-di-O-(α-l-rhamnopyranosyl)-β-d-glucopyranosyl]-lup-20 (29)-ene-28-oic acid (BA-14)
Similarly, BA-14 was prepared as a white solid in 68 % yield for two steps; 1H NMR (600 MHz, CD3OD): δ 6.04–5.90 (m, 1H), 5.38 (s, 1H, Rha-H-1), 5.35 (d, 1H, J = 11.7 Hz), 5.25 (d, 1H, J = 10.4 Hz), 4.85 (s, 1H, Rha-H-1), 4.73 (s, 1H, C = CH2-1), 4.61 (s, 1H, C = CH2-2), 4.58 (d, 2H, J = 8.2 Hz), 4.43 (d, 1H, J = 7.7 Hz, H-1′), 4.03–3.94 (m, 2H), 3.92 (dd, 1H, J = 9.5, 7.2 Hz, H-2′), 3.88–3.77 (m, 2H), 3.75 (dd, 1H, J = 9.6, 3.3 Hz, Rha-H-3), 3.71–3.60 (m, 2H), 3.62–3.53 (m, 1H), 3.49–3.35 (m, 3H), 3.33–3.31 (m, 1H), 3.15 (dd, 1H, J = 11.3, 4.1 Hz, H-3), 3.02–3.00 (m, 1H), 2.89–2.87 (m, 1H), 2.27 (d, 1H, J = 9.4 Hz), 1.71 (s, 3H, CH3), 1.28 (d, 3H, J = 6.1 Hz, Rha-H-6), 1.22 (d, 3H, J = 6.2 Hz, Rha-H-6), 1.04, 1.02, 0.94, 0.87, 0.85 (each s, each 3H, CH3), 0.74 (d, 1H, J = 9.5 Hz, H-5); 13C NMR (151 MHz, CD3OD): δ 177.18 (C-28), 151.78 (C-20), 61.97, 57.91, 57.42, 52.01, 50.64, 43.53, 42.00, 40.35, 39.66, 38.05, 37.92, 36.97, 35.56, 35.38 (two), 33.13, 31.62, 30.77, 28.38, 27.37, 26.86, 22.07, 19.55, 19.26, 18.01, 17.90, 16.98, 16.89, 16.64, 15.14. HRMS (ESI) m/z: calcd for C51H83O16 [M + H]+, 951.5681; found, 951.5703.
4.1.10.13 Cyclopropylmethyl-3β-O-[2, 4-di-O-(α-l-rhamnopyranosyl)-β-d-glucopyranosyl]-lup-20 (29)-ene-28-oic acid (BA-15)
Similarly, BA-15 was prepared as a white solid in 70 % yield for two steps; 1H NMR (600 MHz, CD3OD): δ 5.37 (s, 1H, Rha-H-1), 4.82 (s, 1H, Rha-H-1), 4.74 (s, 1H, C = CH2-1), 4.62 (s, 1H, C = CH2-2), 4.43 (d, 1H, J = 7.7 Hz, H-1′), 4.04–3.94 (m, 4H), 3.91 (dd, 1H, J = 9.6, 7.2 Hz, H-2′), 3.90–3.78 (m, 1H), 3.76 (dd, 1H, J = 9.6, 3.4 Hz, Rha-H-3), 3.72–3.60 (m, 3H), 3.58 (t, 1H, J = 8.4 Hz), 3.48–3.35 (m, 3H), 3.33–3.31 (m, 2H), 3.15 (dd, 1H, J = 11.4, 4.3 Hz, H-3), 3.10–2.99 (m, 2H), 2.29 (d, 1H, J = 10.1 Hz), 1.71 (s, 3H, CH3), 1.28 (d, 3H, J = 6.2 Hz, Rha-H-6), 1.22 (d, 3H, J = 6.2 Hz, Rha-H-6), 1.04, 1.02, 0.96, 0.88, 0.85 (each s, each 3H, CH3), 0.75 (d, 1H, J = 9.7 Hz, H-5), 0.58 (q, 2H, J = 7.7 Hz), 0.32 (q, 2H, J = 7.5 Hz); 13C NMR (151 MHz, CD3OD): δ 177.72 (C-28), 151.81 (C-20), 110.34 (C-29), 105.50 (C-1′), 103.04 (Rha-C-1), 102.00 (Rha-C-1), 90.40, 80.31, 79.21, 78.12, 76.42, 73.91, 73.67, 72.42, 72.11 (two), 71.98, 70.73, 69.99, 69.68, 61.95, 57.87, 57.43, 52.00, 50.60, 43.56, 41.97, 40.35 (two), 39.71, 38.05, 35.60, 33.21, 31.70, 30.78, 28.41, 27.37, 26.90, 22.12, 19.63, 19.28, 18.03 (two), 17.90 (two), 16.99 (two), 16.94 (two), 16.73, 15.24, 10.99. HRMS (ESI) m/z: calcd for C52H85O16 [M + H]+, 965.5838; found, 965.5856.
4.1.11 General procedure forBA-N-1andBA-N-2. To a solution of compound 20 (1.0 mmol) in 10 mL dried CH2Cl2 was added oxalyl chloride (1 mL) under argon. Then the mixture was stirred at room temperature for 12 h and concentrated to dryness in vacuo. To a dried CH2Cl2 (10 mL) solution of methylamine hydrochloride or dimethylamine hydrochloride (2.0 mmol) was added to the crude acid chloride. The reaction mixturewas was stirred at r.t. for 3 h under argon and then concentratedunder reduced pressure. The obtained residue was re-dissolved in 2:1 MeOH/CH2Cl2 (15 mL) and then NaOMe was added until pH = 10. After stirred at r.t. for 3 h, the solution was neutralized with Dowex 50 × 8 (H+) resin until pH = 7, filtered and concentrated in vacuo. Then the residue was purified by silica gel column chromatography (CH2Cl2-MeOH, 6:1) to yield compounds BA-N-1 and BA-N-2, respectively.
4.1.11 N-{β-O-[2, 4-Di-O-(α-l-rhamnopyranosyl)-β-Dglucopyranosyl]-lup-20 (29)-ene-28-oyl}-methylamine (BA-N-1)
BA-N-1 was prepared as a white solid in 85 % yield for three steps. 1H NMR (600 MHz, CD3OD): δ 5.37 (s, 1H, Rha-H-1), 4.83 (s, 1H, Rha-H-1), 4.71 (s, 1H, C = CH2-1), 4.59 (s, 1H, C = CH2-2), 4.43 (d, 1H, J = 7.4 Hz, H-1′), 3.94–3.90 (m, 3H), 3.83 (d, 1H, J = 12.0 Hz), 3.76 (t, 1H, J = 11.2 Hz), 3.71–3.52 (m, 3H), 3.45–3.36 (m, 3H), 2.70 (s, 3H, NH-CH3), 2.57–2.55 (m, 1H), 2.11 (d, 1H, J = 12.2 Hz), 1.69 (s, 3H, CH3), 1.25–1.19 (m, 6H, 2 × Rha-H-6), 1.03, 1.00, 0.96, 0.87, 0.84 (each s, each 3H, CH3), 0.78–0.70 (m, 1H, H-5); 13C NMR (151 MHz, CD3OD): δ 179.75 (C-28), 152.37 (C-20), 109.93 (C-29), 105.45 (C-1′), 103.04 (Rha-C-1), 101.94 (Rha-C-1), 90.34, 80.34, 79.13, 78.17, 76.42, 73.93, 73.70, 72.44, 72.11 (two), 72.03, 70.74, 69.96, 61.92, 57.43, 56.94, 52.09, 51.43, 48.17, 43.48, 41.97, 40.33 (two), 39.34, 38.97, 38.05, 35.59, 34.13, 31.93, 30.54, 28.39, 27.38, 26.98, 26.42, 22.14, 19.63, 19.27, 18.04, 17.96, 16.99, 16.91, 16.72, 15.09. HRMS (ESI) m/z: calcd for C49H81O15NNa [M + Na]+, 946.5498; found, 946.5447.
4.1.12 N-{β-O-[2, 4-di-O-(α-l-rhamnopyranosyl)-β-Dglucopyranosyl]-lup-20(29)-ene-28-oyl}-dimethylamine (BA-N-2)
BA-N-2 was prepared as a white solid in 82 % yield for three steps; 1H NMR (600 MHz, CD3OD): δ 5.37 (s, 1H, Rha-H-1), 4.81 (s, 1H, Rha-H-1), 4.70 (s, 1H, C = CH2-1), 4.58 (s, 1H, C = CH2-2), 4.43 (d, 1H, J = 7.7 Hz, H-1′), 4.00–3.95 (m, 3H), 3.92 (dd, 1H, J = 9.5, 6.8 Hz, H-2′), 3.87–3.78 (m, 3H), 3.75 (dd, 1H, J = 9.5, 3.4 Hz, Rha-H-3), 3.70–3.62 (m, 2H), 3.57–3.55 (m, 1H), 3.47–3.35 (m, 2H), 3.2 (s, 6H, 2 × NCH3), 3.07–2.95 (m, 1H, H-3), 2.34 (d, 1H, J = 13.4 Hz), 1.70 (s, 3H, CH3), 1.27 (d, 3H, J = 6.2 Hz, Rha-H-6), 1.21 (d, 3H, J = 6.1 Hz, Rha-H-6), 1.03, 1.00, 0.95, 0.87, 0.84 (each s, each 3H, CH3), 0.74 (s, 1H, H-5); 13C NMR (151 MHz, CD3OD): δ 177.13 (C-28), 152.63 (C-20), 109.83 (C-29), 105.47 (C-1′), 103.09 (Rha-C-1), 101.98 (Rha-C-1), 90.40, 80.44, 79.19, 78.19, 76.44, 73.94, 73.70, 72.45, 72.13, 72.03, 70.76, 70.00, 61.96, 57.51, 56.09, 53.92, 52.28, 47.49, 43.03, 41.94, 40.36, 38.49, 38.08, 36.83, 35.62, 32.92, 32.44, 31.02, 28.37, 27.38, 27.03, 22.34, 19.75, 19.29, 18.01, 17.91, 17.48, 17.29, 17.10, 16.98, 16.68, 15.10. HRMS (ESI) m/z: calcd for C50H83O15NNa [M + Na]+, 960.5660; found, 960.5691.
4.1.13 3β-O-[2, 4-Di-O-(2, 3, 4-tri-O-Acetyl-α-l-rhamnopyranosyl)-β-(3, 6-di-O-acetyl)-d-glucopyranosyl]-lup-20 (29)-ene-3, 28-diol (22)
Compound 21 (360 mg, 0.27 mmol) dissolved in THF (5 mL) and MeOH (5 mL), and 10 % Pd/C (40 mg) was added to the solution. The reaction mixture was stirred under atmospheric pressure hydrogen at r.t. for 1 h. After Pd/C was filtered off, the mixture was concentrated under vacuum to give a crude residue, which was purified by column chromatography (petroleumether-EtOAc-CH2Cl2, 2:1:1) to obtain 22 (302 mg, 90 %) as a white powder. 1H NMR (600 MHz, CDCl3): δ 5.52–5.21 (m, 2H), 5.11–5.07 (m, 3H), 4.86 (s, 1H, C = CH2-1), 4.82 (s, 1H, C = CH2-2), 4.69–4.67 (m, 1H), 4.58 (d, 1H, J = 7.9 Hz, H-1′), 4.42 (t, 1H, J = 9.7 Hz, Rha-H-4), 4.36–4.16 (m, 3H), 4.16–3.88 (m, 4H), 3.73 (t, 1H, J = 9.2 Hz, H-4′), 3.61 (t, 1H, J = 8.8 Hz, Rha-H-4), 3.55–3.43 (m, 2H, C-CH2), 3.31 (d, 1H, J = 10.6 Hz), 3.18–3.05 (m, 1H, H-3), 2.15, 2.12, 2.12, 2.06, 2.04, 2.00, 1.99, 1.95 (each s, each 3H, each CH3CO), 1.67 (s, 3H, CH3), 1.24 (d, 3H, J = 6.3 Hz, CH3), 1.17 (d, 3H, J = 6.3 Hz, CH3), 1.04 (s, 6H, 2 × CH3), 1.00 (s, 3H, CH3), 0.81 (s, 6H, 2 × CH3), 0.75 (d, 1H, J = 7.0 Hz, H-5); 13C NMR (151 MHz, CDCl3): δ 170.27, 170.13 (three), 170.09 (three), 169.82, 150.01 (C-20), 111.18 (C-29), 104.02 (C-1′), 99.14 (Rha-C-1), 97.36 (Rha-C-1), 90.28, 82.28, 76.33, 75.63, 75.56, 75.42, 71.67, 71.22, 70.70, 70.02, 69.73, 69.62, 69.28, 69.11, 68.71, 68.21, 66.66, 62.66, 60.42, 56.05, 50.44, 48.92, 47.88, 42.81, 41.08, 39.26, 36.94, 34.26, 34.08, 29.35, 28.28, 27.84, 26.99, 26.29, 25.88, 23.02, 22.58, 21.06, 20.92 (three), 20.88 (three), 20.84 (three), 20.76 (two), 18.29, 17.49 (two), 17.42 (two), 16.24 (two), 16.23, 14.89, 14.80. HRMS (ESI) m/z: calcd for C64H97O23 [M + H]+, 1233.6421; found, 1233.6443.
4.1.14 3β-O-[2, 4-Di-O-(α-l-Rhamnopyranosyl)-β-d-glucopyranosyl]-lup-20 (30)-en-28-al (BA-18)
To a solution of 21 (0.20 g, 0.16 mmol, 1.0 eq) in CH2Cl2-MeOH (20 mL, V: V = 1: 1) and was added PCC (0.10 g, 0.41 mmol) at 0 ℃ under argon. After the mixture was stirred at r.t. for 4 h, the reaction mixture was filtered and the filtrate was concentrated under vacuum. The residue was dissolved in EtOAc (60 mL), then extracted with water (3 × 30 mL) and brine (3 × 30 mL). The organic phase was concentrated under reduced pressure. The residue was re-dissolved in MeOH (10 mL) and CH2Cl2 (10 mL), CH3ONa was added until pH = 10. The reaction mixture was stirred at r.t. until the reaction was complete detected by TLC. Then, the mixture was neutralized with Dowex 50 × 8 (H+) resin until pH = 7. The reaction mixture was filtered and evaporated to remove excess solvent under vacuum. The residue was purified by silica gel column chromatography (CH2Cl2-MeOH, 5:1) to produce BA-18 (0.13 g, 84 % for two steps). 1H NMR (600 MHz, CD3OD): δ 9.58 (s, 1H, CHO), 5.29 (s, 1H, Rha-H-1), 4.89 (s, 1H, Rha-H-1), 4.56 (s, 2H, C = CH2), 4.35 (d, 1H, J = 7.7 Hz, H-1′), 3.87 (m, 2H), 3.79–3.63 (m, 1H), 3.62–3.44 (m, 3H), 3.42–3.29 (m, 3H), 3.25–3.23 (m, 3H), 3.12–3.00 (m, 1H, H-3), 2.86–2.73 (m, 2H), 2.03–1.80 (m, 1H), 1.64 (s, 3H, CH3), 1.17 (m, 6H, 2 × Rha-H-6), 1.00–0.67 (m, 15H, 5 × CH3); 13C NMR (151 MHz, CD3OD): δ 207.06 (CHO), 149.80 (C-20), 110.21 (C-29),104.09 (C-1′), 101.65 (Rha-C-1), 100.62 (Rha-C-1), 89.04, 78.95, 77.84, 76.74, 75.05, 72.54, 72.30, 71.04, 70.73, 70.61, 69.35, 68.63, 60.58, 59.13, 56.53, 56.00, 50.64, 42.50, 42.28, 42.17, 40.66, 40.55, 38.95, 38.69, 36.66, 34.20, 29.43, 26.99, 25.98, 20.68, 19.76, 18.19, 17.86, 16.63, 16.52, 15.61, 15.51, 15.26, 15.09, 13.75, 13.68, 13.30. HRMS (ESI) m/z: calcd for C49H81O15 [M + H]+, 909.5575; found, 909.5593.
4.1.15 28-(Methoxy)-3β-O-[2, 4-di-O-(α-l-Rhamnopyranosyl)-β-d-glucopyranosyl]-lup-20 (29)-ene-3-al (BA-19)
To a solution of 21 (0.20 g, 0.16 mmol) in ACN (10 mL) was added Ag2O (0.19 g, 0.81 mmol). After stirring for 20 min, CH3I (0.12 g, 0.81 mmol) was added quickly while the mixture was at 60 ℃ under argon atmosphere. Stirring was continued overnight at that temperature. Then the mixture was cooled to room temperature, filtered and the filtrate was concentrated under vacuum. The residue was dissolved in EtOAc (100 mL), then extracted with water (3 × 50 mL) and brine (3 × 50 mL). The organic phase was concentrated under vacuum. Then, the residue was re-dissolved in MeOH (10 mL) and CH2Cl2 (10 mL), CH3ONa was added until pH = 10. After the reaction mixture was stirred at r.t. for 5 h, Dowex 50 × 8 (H+) resin was added until pH = 7. The reaction mixture was filtered and concentrated under vacuum. The residue was purified by silica gel column chromatography (CH2Cl2-MeOH, 6:1) to afford BA-19 (0.12 g, 83 % for two steps). 1H NMR (600 MHz, CD3OD): δ 5.39 (s, 1H, Rha-H-1), 4.85 (d, 1H, J = 1.7 Hz, Rha-H-1), 4.72 (s, 1H, C = CH2-1), 4.60 (s, 1H, C = CH2-2), 4.44 (d, 1H, J = 7.7 Hz, H-1′), 3.99 (dd, 1H, J = 3.5, 1.7 Hz, Rha-H-2), 3.93 (dd, 1H, J = 9.5, 7.4 Hz, H-2′), 3.86 (dd, 1H, J = 3.3, 1.9 Hz, Rha-H-2), 3.86–3.78 (m, 1H), 3.77 (dd, 1H, J = 9.7, 3.5 Hz, Rha-H-3), 3.67 (dd, 1H, J = 9.7, 3.8 Hz, Rha-H-3), 3.58 (t, 1H, J = 9.3 Hz, Rha-H-3), 3.50–3.38 (m, 3H), 3.36–3.30 (m, 4H), 3.20–3.11 (m, 2H), 2.49–2.45 (m, 1H), 1.71 (s, 3H, CH3), 1.29 (d, 3H, J = 6.2 Hz, Rha-H-6), 1.23 (d, 3H, J = 6.3 Hz, Rha-H-6), 1.10, 1.06, 1.03, 0.90, 0.87 (each s, each 3H, CH3), 0.76 (d, 1H, J = 9.8 Hz, H-5); 13C NMR (151 MHz, CD3OD): δ 151.77 (C-20), 110.33 (C-29), 105.49 (C-1′), 103.05 (Rha-C-1), 102.00 (Rha-C-1), 90.39, 80.36, 79.21, 78.14, 76.43, 73.93, 73.68, 72.43, 72.12, 72.00, 70.75, 70.00, 61.96, 59.88, 57.38, 51.87, 50.13, 48.41, 43.78, 42.15, 40.35, 40.29, 38.88, 38.01, 35.77, 35.47, 31.07, 30.99, 30.77, 28.40, 28.35, 27.37, 26.59, 22.00, 19.42, 19.28, 18.02, 17.90, 17.00, 16.90, 16.58, 15.33. HRMS (MALDI) m/z: calcd for C49H82O15Na [M + Na]+, 933.5551; found, 933.5573.
4.1.16 28-Chloro-3β-O-[2, 4-di-O-(α-l-Rhamnopyranosyl)-β-d-glucopyranosyl]-lup-20 (29)-ene-3-al (BA-21)
To a solution of 21 (0.20 g, 0.16 mmol, 1.0 eq) in dry CH2Cl2 (10 mL) was added SOCl2 (18 µL, 0.21 mmol) at 0 ℃ under N2 atmosphere. After the mixture was stirred at r.t. for 12 h, the reaction was quenched with NaHCO3 at 0 ℃. Then, the reaction mixture was concentrated under vacuum. The residue was dissolved in EtOAc (60 mL), then extracted with water and brine, dried over Na2SO4, and concentrated in reduced pressure. The obtained crude product was re-dissolved in MeOH (10 mL) and CH2Cl2 (10 mL), CH3ONa was added until pH = 10. The reactant was then held at 30 °C until TLC indicated the reaction was complete. Dowex 50 × 8 (H + ) resin was added until pH = 7. Removal of the precipitate by filtration provided a yellowish solution, which was concentrated under vacuum. The residue was purified by silica gel column chromatography (CH2Cl2-MeOH, 6:1) to yield BA-21 (0.11 g, 76 % for two steps). 1H NMR (600 MHz, CD3OD): δ 5.38 (s, 1H, Rha-H-1), 4.85 (s, 1H, Rha-H-1), 4.70 (s, 1H, C = CH2-1), 4.63 (s, 1H, C = CH2-2), 4.45 (d, 1H, J = 7.7 Hz, H-1′), 4.04–3.95 (m, 2H), 3.93 (dd, 1H, J = 9.5, 7.4 Hz, H-2′), 3.89–3.78 (m, 2H), 3.77 (dd, 1H, J = 9.6, 3.4 Hz, Rha-H-3), 3.73–3.59 (m, 2H), 3.55–3.56 (m, 1H), 3.53–3.43 (m, 2H), 3.47–3.36 (m, 2H), 3.34–3.31 (m, 2H), 3.18 (dd, 1H, J = 11.7, 4.6 Hz, H-3), 3.03–3.01 (m, 1H), 2.90–2.88 (m, 1H), 2.04–1.94 (m, 1H), 1.32–1.30 (m, 2H), 1.23 (d, 3H, J = 6.2 Hz, Rha-H-6), 1.05 (d, 3H, J = 6.2 Hz, Rha-H-6), 0.98, 0.94, 0.92, 0.87, 0.85 (each s, each 3H, CH3), 0.75 (d, 1H, J = 9.7 Hz, H-5); 13C NMR (151 MHz, CD3OD): δ 150.19 (C-20), 108.89 (C-29), 104.25 (C-1′), 101.01 (Rha-C-1), 100.45 (Rha-C-1), 88.45, 87.20, 77.39, 76.90, 75.68, 72.39, 71.19, 71.09, 70.74, 70.62, 69.20, 69.17, 68.55, 60.59, 55.97, 50.93, 46.63, 41.37, 40.72, 39.16, 38.97, 36.94, 36.53, 36.37, 36.27, 34.72, 34.21, 33.90, 33.16, 29.27, 27.68, 26.46, 26.43, 26.36, 26.31, 24.71, 21.25, 18.32 (two), 18.10, 16.89, 16.44, 15.86, 13.75, 11.84. HRMS (MALDI) m/z: calcd for C48H79O14ClNa M + Na]+, 937.5056; found, 937.5072.
4.2 Biology assay
4.2.1 Cell lines and plasmids
HEK-293 T (Human, embryonic kidney) and Vero-E6 (African green monkey, kidney) cells were cultured in Dulbecco's Modified Eagle Medium (Gibco) supplemented with 10 % fetal bovine serum (Capricorn Scientific) and 1 % penicillin (100 units/mL) /streptomycin (100 μg/mL) (Gibco, USA). 293 T-ACE2 (293 T cells stably expressing human ACE2) were constructed by our laboratory and cultivated under the same conditions as above.
Plasmid pcDNA3.1-SARS-CoV-2-Sipke and pAAV-IRES-GFP-SARS-CoV-2-Sipke were given by the laboratory of Professor Shibo Jiang. Plasmid pAAV-IRES-EGFP was purchased from Hedgehogbio Science and Technology ltd. Expression plasmids for full-length vesicular stomatitis virus (VSV) glycoprotein (VSV-G) and pNL4-3.Luc.R-E- plasmids were obtained from Addgene (Cambridge, MA). Based on Plasmid pcDNA3.1-SARS-CoV-2 Spike, its mutants N501Y, D614G and Delta were all retained in our laboratory. Plasmid pcDNA3.1-SARS-CoV-2-Omicron and its 2 mutant plasmids were constructed by our laboratory. In brief, primers containing mutant sites were designed to amplify the specified DNA fragments with pcDNA3.1-SARS-CoV-2-Omicron as a template. The product obtained by PCR in the previous step was subjected to homologous recombination according to the manufacturer's instructions (Vazyme, China). The recombinant plasmids were used to transform stabl3 and inoculated in culture plates containing the corresponding resistance. After incubation in 37 ℃ for 12–16 h, single colonies on plates were selected and sequenced. Mutation sites and corresponding primers were shown in followed table [30]:765-Foward GCTTCTGCACCCAGCTGAAGGCAGCCCTGACCGGCATCGCCGTGG
765-Reverse CTTCAGCTGGGTGCAGAAGCTGCC
964-Foward CCAGGCCCTGAACACCCTGGTGGCGCAGCTGTCCAGCAAGTTCGG
964-Reverse CACCAGGGTGTTCAGGGCCTGG
Circ-Foward GCGTGAAGCTGCACTACACCGGCGGCACCGAGACATCTCAGG
Circ-Reverse GGCTAGCACGGAAGCGACCAGCATC
4.2.2 Pseudotyped SARS-CoV-2 infection assay
HEK293T cells were seeded in 6-well plates and cultured overnight at 37 °C. 1 µg pNL4-3.Luc.R-E-plasmid and 0.5 µg pcDNA3.1-SARS-CoV-2-S plasmid were transfected into 293 T cells, and the supernatant virus liquid was collected after culturing at 37 °C for 48 h. 293 T-ACE2 cells were seeded in 96-well cell plates one day before infection. The concentration gradient drug and SARS-CoV-2 pseudovirus were mixed for 30 min at room temperature, and then added to the cells for 48 h of infection. Cells were lysed and luciferase activities were quantified by Luciferase assay system (Promega, USA) [30].
4.2.3 Cytotoxicity assay
Cells were seeded in a 96-well plate at a density of 1*104 cells/well, and cultured at 37 °C overnight. After 48 h of concentration gradient administration, 20 µL of MTT working solution (5 mg/mL) was added to each well, and cultured at 37 °C for 4 h. After discarding the culture supernatant, 150 µL of DMSO was added to each well, and the absorbance at 570 nm was measured by a microplate reader after sufficient shaking to dissolve. According to the measured OD value, the survival rate of cells under the action of the corresponding concentration of drugs compared with the control group was calculated, respectively [31].
4.2.4 Authentic SARS-CoV-2 inhibition assay
Authentic SARS-CoV-2 inhibition assay was performed by Wuhan institute of virology, Chinese academy of sciences. Vero-E6 cells were seeded in a 48-well plate at a cell density of 3*105 cells/well and cultured overnight at 37 °C, 5 % CO2. SARS-CoV-2 virus dilution (MOI = 0.05) and serially diluted drugs were pre-incubated at 37 °C for 1 h to infect cells. After that, the supernatant of the infectious material was fully removed and 200 µL of complete medium was added to each well to continue the culture. After 24 h, 150 µL of cell culture supernatant was collected and viral RNA was extracted with an RNA extraction kit (Takara, Japan). The reverse transcribed product was determined by qRT-PCR for viral copy number in the supernatant (Takara TB Green® Premix Ex Taq™ II, Japan) [31].
4.2.5 Co-immunoprecipitation and western blotting
HEK-293 T cells were seeded in six-well plates at a density of 4*105 cells/well one day in advance. 2 ug plasmids pcDNA3.1-ACE2-Flag and 2 ug pcDNA3.1-SARS-Omicron were co-transfected into each well and drugs were added in at the same time. After 48 h, total cell protein was extracted and incubated with protein A Sepharose bound by anti-labeled antibody or mouse IgG. The protein samples were separated by polyacrylamide gel electrophoresis after 12–16 h incubation with the antibody and were transferred to nitrocellulose membranes (Roche, Germany). SARS-Omicron and ACE2 were detected by anti-SARS-CoV-S (Sinol biological Inc., China) and anti-Flag (Sigma, USA) with mouse anti-goat-horseradish peroxidase (HRP) (Fude biological Technology Co., ltd., China) as the secondary antibody [31].
4.2.6 Cell-cell fusion assays
HEK-293 T cells were seeded in 6-well plates at a density of 4*105/well and cultured overnight. After transfection with pAAV-IRES-GFP-SARS-CoV-2-S plasmid expressing both SARS-CoV-2-S protein and green fluorescent protein GFP or pAAV-IRES-GFP vector plasmid, the cells were cultured at 37 °C for 48 h. Target cells Vero-E6 were seeded in 96-well plates at a density of 1*104 cells/well 6 h before cell fusion experiments. 293 T/SARS-CoV-2-S/EGFP or 293 T/EGFP effector cells were incubated with the concentration gradient drug for 30 min and then added to the target cells. After 24 h, three random fields were imaged by inverted fluorescence microscope (Zeiss, Germany) [19].
4.2.7 Surface plasmon resonance (SPR) measurement
Compound BEA-1, or BEA-4 was fixed on the chip by photo-crosslinking, then recombinant SARS-CoV-2 S-trimer protein (DRA 47, Novoprotein Inc. Shanghai) at indicated concentrations was injected sequentially into the chamber in buffer PBST (0.1 % Tween 20, pH 7.4). The interaction of S-trimer with BEA-1, or BEA-4 fixed was detected by PlexArrayTM HT SPRi (Seattle, US). The reaction temperature was controlled at 4 ℃, binding time was 600 s, disassociation time was 360 s, flow rate was 0.5 μL/s. The chip was regenerated with Glycine Hydrochloride (pH 2.0). The data of interaction signals was retrieved and analyzed with PlexeraDE software [30].
4.2.8 Circular dichroism (CD) spectroscopy
CD spectra were recorded on a Chirascan plus ACD (Applied Photophysics ltd, England). HR1P and HR2P were dissolved in buffer (0.1 M KCl, 0.05 M KH2PO4, pH 7.2) at a final concentration of 10 μM. Briefly, HR1P was incubated with PBS or BA-4 (20 μM) at 25 ℃ for 30 min, followed by addition of HR2P (10 μM). After further incubation at 25 ℃ for 30 min, the CD wave scans were measured from 190 to 260 nm at 4 ℃ with the bandwidth of 2 nm and the step size of 1 nm [18], [19].
4.2.9 Molecular docking
A molecular docking study was performed using Discovery Studio 3.0. The 3D crystal structure of SARS-CoV-2 spike glycoprotein was downloaded from RCSB Protein Date Bank (https://www.rcsb.org) using PDB IDs of 6VXX or 7TF8, water and glycosyl molecules removed by manual. The protein and the ligand were prepared by minimization with CHARMM force field. Then the binding site of the protein was defined and prepared for docking by using Define Site (From Receptor Cavities) protocol. Molecular docking results were carried out using CDOCKER protocol without constraint and ranked by -CDOCKER_ENERGY [37].
4.2.10 Plasma stability, microsomal stability and intestinal S9-UDPGA stability
Plasma stability was determined as following steps [38]: (1) prepare a 10 mM DMSO stock of BA-4. (2) Dilute the 10 mM stock to 1 μM with mouse plasma. (3) Transfer 50 μL plasma into a new tube and stop reaction using 250 μL acetonitrile. (4) Incubate the plasma sample in a water bath at 37 °C. (5) Stop reaction at 10, 30, 60, and 90 min, respectively. (6) Measure the compound concentration by LC-MS/MS; microsomal stability was determined using 10 μM BA-4 to incubate with mouse microsomes (0.5 mg/mL) for 5 min at 37 °C in phosphate buffer (100 mM, pH = 7.4) before 1 mM NADPH was added to start the reaction. Then, the cold acetonitrile was utilized to precipitate the protein. Lastly, the samples were centrifuged for further analysis by LC-MS/MS; the experimental procedures of mouse intestinal S9-UDPGA were similar as previously reported [39].
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Appendix A Supplementary data
The following are the Supplementary data to this article:Supplementary data 1
Data availability
No data was used for the research described in the article.
Acknowledgments
This work was supported by the National Natural Science Foundation of China (82073722 to G.S. and 82130101 to S.L.), Guangdong Basic and Applied Basic Research Foundation (2022A1515010016) to G.S., the Major scientific and technological projects of Guangdong Province (2019B020202002) and Chinese Academy of Traditional Chinese Medicine (ZZ13-035-02, 2019XZZX-LG04) to S. L; Youth Innovative Talents Project from the Department of Education of Guangdong Province (2022KQNCX245) to X. W; and Science and Technology Program of Huizhou (2022CZ010192) to X. W.
Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.bioorg.2022.106316.
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References
1 Bedford J. Enria D. Giesecke J. Heymann D.L. Ihekweazu C. Kobinger G. Lane H.C. Memish Z. Oh M.D. Sall A.A. Schuchat A. Ungchusak K. Wieler L.H. COVID-19: towards controlling of a pandemic Lancet 395 2020 1015 1018 32197103
2 Hu B. Guo H. Zhou P. Shi Z.L. Characteristics of SARSCoV-2 and COVID-19 Nat. Rev. Microbiol. 19 2021 141 154 33024307
3 Zhou M.M. Liu Y. Cao J.Y. Dong S.Q. Hou Y.X. Yu Y. Zhang Q.Y. Zhang Y.L. Jia X.Y. Zhang B. Xiao G.F. Li G. Wang W. Bergamottin, a bioactive component of bergamot, inhibits SARS-CoV-2 infection in golden Syrian hamsters Antivir. Res. 204 2022 105365
4 Hoffmann M. Krüger N. Schulz S. Cossmann A. Rocha C. Kempf A. Nehlmeier I. Graichen L. Moldenhauer A.S. Winkler M.S. Lier M. Dopfer-Jablonka A. Jäck H.M. Behrens G.M.N. Pöhlmann S. The Omicron variant is highly resistant against antibody-mediated neutralization: Implications for control of the COVID-19 pandemic Cell 185 2022 447 456 35026151
5 Fan Y. Li X. Zhang L. Wan S. Zhang L. Zhou F. SARS-CoV-2 Omicron variant: recent progress and future perspectives Signal. Transduct. Target. Ther. 7 2022 141 35484110
6 Kundu S. Sarkar D. Synthetic attempts towards eminent anti-viral candidates of SARS-CoV Mini. Rev. Med. Chem. 22 2022 232 247 34254915
7 Jayk Bernal A. Gomes da Silva M.M. Musungaie D.B. Kovalchuk E. Gonzalez A. Delos Reyes V. Martín-Quirós A. Caraco Y. Williams-Diaz A. Brown M.L. Du J. Pedley A. Assaid C. Strizki J. Grobler J.A. Shamsuddin H.H. Tipping R. Wan H. Paschke A. Butterton J.R. Johnson M.G. De Anda C. MOVe-OUT study group. Molnupiravir for oral treatment of Covid-19 in nonhospitalized patients N. Engl. J. Med. 386 2022 509 520 34914868
8 Najjar-Debbiny R. Gronich N. Weber G. Khoury J. Amar M. Stein N. Goldstein L.H. Saliba W. Effectiveness of paxlovid in reducing severe COVID-19 and mortality in high risk patients Clin. Infect. Dis. 2022 ciac443 35653428
9 Drayman N. DeMarco J.K. Jones K.A. Azizi S.A. Froggatt H.M. Tan K. Maltseva N.I. Chen S. Nicolaescu V. Dvorkin S. Furlong K. Kathayat R.S. Firpo M.R. Mastrodomenico V. Bruce E.A. Schmidt M.M. Jedrzejczak R. Muñoz-Alía M.Á. Schuster B. Nair V. Han K.Y. O'Brien A. Tomatsidou A. Meyer B. Vignuzzi M. Missiakas D. Botten J.W. Brooke C.B. Lee H. Baker S.C. Mounce B.C. Heaton N.S. Severson W.E. Palmer K.E. Dickinson B.C. Joachimiak A. Randall G. Tay S. Masitinib is a broad coronavirus 3CL inhibitor that blocks replication of SARS-CoV-2 Science 373 2021 931 936 34285133
10 Tan H.Z. Hu Y.M. Jadhav P. Tan B. Wang J. Progress and challenges in targeting the SARS-CoV-2 papain-like protease J. Med. Chem. 65 2022 7561 7580 35620927
11 Vuong W. Khan M.B. Fischer C. Arutyunova E. Lamer T. Shields J. Saffran H.A. McKay R.T. van Belkum M.J. Joyce M.A. Young H.S. Tyrrell D.L. Vederas J.C. Lemieux M.J. Feline coronavirus drug inhibits the main protease of SARS-CoV-2 and blocks virus replication Nat. Commun. 11 2020 4282 32855413
12 L.Y ang, R.J. Pei, H. Li, X.N. Ma, Y. Zhou, F.H. Zhu, P.L. He, W. Tang, Y.C. Zhang, J. Xiong, S.Q. Xiao, X.K. Tong, B. Zhang, J.P. Zuo, Identification of SARS-CoV-2 entry inhibitors among already approved drugs Acta Pharmacol. Sin. 42 2021 1347 1353 33116249
13 S. Yu, X. Zheng, B. Zhou, J. Li, M. Chen, R. Deng, G. Wong, D. Lavillette, G. Meng, SARS-CoV-2 spike engagement of ACE2 primes S2' site cleavage and fusion initiation, Proc. Natl. Acad. Sci. U. S. A. 119 (2022) e2111199119.
14 Gur M. Taka E. Yilmaz S.Z. Kilinc C. Aktas U. Golcuk M. Conformational transition of SARS-CoV-2 spike glycoprotein between its closed and open states J. Chem. Phys. 153 2020 75101
15 Braga L. Ali H. Secco I. Chiavacci E. Neves G. Goldhill D. Penn R. Jimenez-Guardeño J.M. Ortega-Prieto A.M. Bussani R. Cannatà A. Rizzari G. Collesi C. Schneider E. Arosio D. Shah A.M. Barclay W.S. Malim M.H. Burrone J. Giacca M. Drugs that inhibit TMEM16 proteins block SARS-CoV-2 spike-induced syncytia Nature 594 2021 88 93 33827113
16 Schütz D. Ruiz-Blanco Y.B. Münch J. Kirchhoff F. Sanchez-Garcia E. Müller J.A. Peptide and peptide-based inhibitors of SARS-CoV-2 entry Adv. Drug. Deliv. Rev. 167 2020 47 65 33189768
17 Shang J. Wan Y. Luo C. Ye G. Geng Q. Auerbach A. Li F. Cell entry mechanisms of SARS-CoV-2 Proc. Natl. Acad. Sci. 117 2020 11727 11734 32376634
18 Xia S. Liu M.Q. Wang C. Xu W. Lan Q.S. Feng S.L. Qi F.F. Bao L.L. Du L.Y. Liu S.W. Qin C. Sun F. Shi Z.L. Zhu Y. Jiang S.B. Lu L. Inhibition of SARS-CoV-2 (previously 2019-nCoV) infection by a highly potent pan-coronavirus fusion inhibitor targeting its spike protein that harbors a high capacity to mediate membrane fusion Cell Res. 30 2020 343 355 32231345
19 Xia S. Lan Q.S. Zhu Y. Wang C. Xu W. Li Y.T. Wang L.J. Jiao F. Zhou J. Hua C. Wang Q. Cai X. Wu Y. Gao J. Liu H. Sun G. Münch J. Kirchhoff F. Yuan Z.H. Yo H. Xie F. Sun S.B. Jiang L.L. Structural and functional basis for pan-CoV fusion inhibitors against SARS-CoV-2 and its variants with preclinical evaluation Signal. Transduct. Target. Ther. 6 2021 288 34326308
20 Drożdżal S. Rosik J. Lechowicz K. Machaj F. Szostak B. Przybyciński J. Lorzadeh S. Kotfis K. Ghavami S. Łos M.J. An update on drugs with therapeutic potential for SARS-CoV-2 (COVID-19) treatment Drug Resist. Updat. 59 2021 100794
21 Yuan S.F. Yin X. Meng X.Z. Chan J.F. Ye Z.W. Riva L. Pache L. Chan C.C. Lai P.M. Chan C.C. Poon V.K. Lee A.C. Matsunaga N. Pu Y. Yuen C.K. Cao J. Liang R. Tang K. Sheng L. Du Y.S. Xu W. Lau C.Y. Sit K.Y. Au W.K. Wang R.M. Zhang Y.Y. Tang Y.D. Clausen T.M. Pihl J. Oh J. Sze K.H. Zhang A.J. Chu H. Kok K.H. Wang D. Cai X.H. Esko J.D. Hung I.F. Li R.A. Chen H. Sun H. Jin D.Y. Sun R. Chanda S.K. Yuen K.Y. Clofazimine broadly inhibits coronaviruses including SARS-CoV-2 Nature 593 2021 418 423 33727703
22 Cao J.Y. Liu Y. Zhou M.M. Dong S.Q. Hou Y.X. Jia X.Y. Lan X.H. Zhang Y.L. Guo J. Xiao G.F. Wang W. Screening of botanical drugs against SARS-CoV-2 entry reveals novel therapeutic agents to treat COVID-19 Viruses 14 2022 353 35215943
23 Yang C. Pan X.Y. Xu X.F. Cheng C. Huang Y. Li L. Jiang S.B. Xu W. Xiao G.F. Liu S.W. Salvianolic acid C potently inhibits SARS-CoV-2 infection by blocking the formation of six-helix bundle core of spike protein Signal. Transduct. Target. Ther. 5 2020 220 33024075
24 Yang C. Pan X.Y. Huang Y. Cheng C. Xu X.F. Wu Y. Xu Y.X. Shang W.J. Niu X.G. Wan Y.H. Li Z.F. Zhang R. Liu S.W. Xiao G.F. Xu W. Drug repurposing of itraconazole and estradiol benzoate against COVID-19 by blocking SARS-CoV-2 spike protein-mediated membrane fusion Adv Ther (Weinh). 4 2021 2000224 33786369
25 Wang J. Wei W.H. Zhang X.F. Cao S.Q. Hu B.T. Ye Y. Jiang M. Wang T.Q. Zuo J.P. He S.J. Yang C.H. Synthesis and biological evaluation of C-17-amino-substituted pyrazole-fused betulinic acid derivatives as novel agents for osteoarthritis treatment J. Med. Chem. 64 2021 13676 13692 34491054
26 Wang H. Xu R.Y. Shi Y.Y. Si L.L. Jiao P.X. Fan Z.B. Han X. Wu X.Y. Zhou X.S. Yu F. Zhang Y.M. Zhang L. Zhang L.R. Zhou D.M. Xiao S.L. Design, synthesis and biological evaluation of novel L-ascorbic acid-conjugated pentacyclic triterpene derivatives as potential influenza virus entry inhibitors Eur. J. Med. Chem. 110 2016 376 388 26866456
27 Visalli R.J. Ziobrowski H. Badri K.R. He J.J. Zhang X.G. Arumugam S.R. Zhao H. Ionic derivatives of betulinic acid exhibit antiviral activity against herpes simplex virus type-2 (HSV-2), but not HIV-1 reverse transcriptase Bioorg. Med. Chem. Lett. 25 2015 3168 3171 26112446
28 Wen C.C. Kuo Y.H. Jan J.T. Liang P.H. Wang S.Y. Liu H.G. Lee C.K. Chang S.T. Kuo C.J. Lee S.S. Hou C.C. Hsiao P.W. Chien S.C. Shyur L.F. Yang N.S. Specific plant terpenoids and lignoids possess potent antiviral activities against severe acute respiratory syndrome coronavirus J. Med. Chem. 50 2007 4087 4095 17663539
29 Stevaert A. Krasniqi B. Van Loy B. Nguyen T. Thomas J. Vandeput J. Jochmans D. Thiel V. Dijkman R. Dehaen W. Voet A. Naesens L. Betulonic acid derivatives interfering with human coronavirus 229E replication via the nsp15 endoribonuclease J. Med. Chem. 64 2021 5632 5644 33877845
30 Li H. Cheng C. Shi S.S. Wu Y. Gao Y.F. Liu Z.H. Liu M.J. Li Z.D. Huo L.J. Pan X.Y. Liu S.W. Song G.P. Identification, optimization, and biological evaluation of 3-O-β-chacotriosyl ursolic acid derivatives as novel SARS-CoV-2 entry inhibitors by targeting the prefusion state of spike protein Eur. J. Med. Chem. 238 2022 114426
31 Li H. Cheng C. Li S.M. Wu Y. Liu Z.H. Liu M.J. Chen J.X. Zhong Q.Y. Zhang X.S. Liu S.W. Song G.P. Discovery and structural optimization of 3-O-β-chacotriosyl oleanane-type triterpenoids as potent entry inhibitors of SARS-CoV-2 virus infections Eur. J. Med. Chem. 215 2021 113242
32 Dai W.H. Zhang B. Jiang X.M. Su H.X. Li J. Zhao Y. Xie X. Jin Z.M. Peng J.J. Liu F.J. Li C.P. Li Y. Bai F. Wang H.F. Cheng X. Cen X.B. Hu S.L. Yang X.N. Wang J. Liu X. Xiao G.F. Jiang H.L. Rao Z.H. Zhang L.K. Xu Y.C. Yang H.T. Liu H. Structure-based design of antiviral drug candidates targeting the SARS-CoV-2 main protease Science 368 2020 1331 1335 32321856
33 Si L.L. Meng K. Tian Z.Y. Sun J.Q. Li H.Q. Zhang Z.W. Soloveva V. Li H.W. Fu G. Xia Q. Xiao S.L. Zhang L.H. Zhou D.M. Triterpenoids manipulate a broad range of virus-host fusion via wrapping the HR2 domain prevalent in viral envelopes Sci Adv. 4 2018 eaau8408 30474060
34 Song G.P. Shen X.T. Li S.M. Li Y.B. Liu Y.P. Zheng Y.S. Lin R.H. Fan J.H. H.M. Ye,W. Liu, Structure-activity relationships of 3-O-β-chacotriosyl ursolic acid derivatives as novel H5N1 entry inhibitors Eur. J. Med. Chem. 93 2015 431 442 25728024
35 Wolfram R.K. Heller L. Csuk R. Targeting mitochondria: esters of rhodamine B with triterpenoids are mitocanic triggers of apoptosis Eur. J. Med. Chem. 152 2018 21 30 29684707
36 Brandes B. Hoenke S. Fischer L. Csuk R. Design, synthesis and cytotoxicity of BODIPY FL labelled triterpenoids Eur. J. Med. Chem. 185 2020 111858
37 Liu Z.H. Gu S.Y. Zhu X. Lu M.J. Cao Z.Q. Qiu P.S. Li S.M. Liu S.W. Song G.P. Discovery and optimization of new 6, 7-dihydroxy-1, 2, 3, 4-tetrahydroisoquinoline derivatives as potent influenza virus PAN inhibitors Eur. J. Med. Chem. 227 2022 113929
38 Wang L. Jiang J.S. Zhang L.X. Zhang Q.Y. Zhou J.R. Li L. Xu X.L. You Q.D. Discovery and optimization of small molecules targeting the protein-protein interaction of heat shock protein 90 (Hsp90) and cell division cycle 37 as orally active inhibitors for the treatment of colorectal cancer J. Med. Chem. 63 2020 1281 1297 31935086
39 Kazmierski W.M. Xia B. Miller J. De la Rosa M. Favre D. Dunham R.M. Washio Y. Zhu Z. Wang F. Mebrahtu M. Deng H. Basilla J. Wang L. Evindar G. Fan L. Olszewski A. Prabhu N. Davie C. Messer J.A. Samano V. DNA-Encoded library technology-based discovery, lead optimization, and prodrug strategy toward structurally unique indoleamine 2,3-dioxygenase-1 (IDO1) inhibitors J. Med. Chem. 63 2020 3552 3562 32073266
| 36508939 | PMC9729598 | NO-CC CODE | 2022-12-14 23:38:09 | no | Bioorg Chem. 2023 Feb 8; 131:106316 | utf-8 | Bioorg Chem | 2,022 | 10.1016/j.bioorg.2022.106316 | oa_other |
==== Front
Taiwan J Obstet Gynecol
Taiwan J Obstet Gynecol
Taiwanese Journal of Obstetrics & Gynecology
1028-4559
1875-6263
Taiwan Association of Obstetrics & Gynecology. Publishing services by Elsevier B.V.
S1028-4559(22)00374-6
10.1016/j.tjog.2022.10.008
Original Article
The Relationship Between Diaphragm Thickness and The Severity of The Disease in Pregnant Patients With Covid-19
Ozdemir Eda Ureyen a∗
Buyuk Gul Nihal a
Acar Dilek b
Elmas Burak a
Yilmaz Gamze a
Özcan Namik c
Keskin Hüseyin Levent a
Tekin Özlem Moraloglu a
a Department of Obstetrics and Gynecology, Ministry of Health Ankara City Hospital, Ankara, Turkey
b Department of Radiology, Ministry of Health Ankara City Hospital, Ankara, Turkey
c Department of Anesthesia and Reanimation, Ministry of Health Ankara City Hospital, Ankara, Turkey
∗ Corresponding author. Address: Tel.: +903123056001
8 12 2022
8 12 2022
25 10 2022
© 2022 Taiwan Association of Obstetrics & Gynecology. Publishing services by Elsevier B.V.
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Objective
We aimed to investigate whether there is a relationship between diaphragm thickness and disease severity in Covid-19 pregnant subgroups.
Material and methods
In this prospective study 100 pregnant patients were enrolled. Thickness of the diaphragm muscle at end-expiration was measured using B-Mode US. Hemoglobin,WBC, NLR, procalcitonin and LDH levels were measured.
Results
There was a statistically significant difference between the groups in terms of diaphragm thickness, and the diaphragm thickness was thinner in the severe disease group (p<0.001). There was no statistically significant difference between the groups with mild to moderate disease severity (p=0.708).
Conclusion
Covid-19 patients who developed serious infection has thinner diaphragms than those who did not. Low diaphragm muscle thickness at the outset of Covid-19 disease, may predispose to poor clinical outcomes. Diaphragmatic ultrasound may be a promising tool to evaluate the risk of Covid-19 disease severity.
Keywords
Pregnancy
Ultrasound
Covid-19
Diaphragmatic thickness
==== Body
pmc
| 0 | PMC9729641 | NO-CC CODE | 2022-12-14 23:22:27 | no | Taiwan J Obstet Gynecol. 2022 Dec 8; doi: 10.1016/j.tjog.2022.10.008 | utf-8 | Taiwan J Obstet Gynecol | 2,022 | 10.1016/j.tjog.2022.10.008 | oa_other |
==== Front
J Pediatr Health Care
J Pediatr Health Care
Journal of Pediatric Health Care
0891-5245
1532-656X
by the National Association of Pediatric Nurse Practitioners. Published by Elsevier Inc.
S0891-5245(22)00350-9
10.1016/j.pedhc.2022.12.001
Article
REVISED PAPER: THE IMPACT OF COVID-19 ON CHILDREN/YOUTH WITH SPECIAL HEALTH CARE NEEDS: A SCHOOL NURSE PERSPECTIVE
Macyko Susan J. MSN, RN, CPNP-PC ⁎
Conway School of Nursing, The Catholic University of America
⁎ Corresponding Author
8 12 2022
8 12 2022
Copyright © 2022 by the National Association of Pediatric Nurse Practitioners. Published by Elsevier Inc. All rights reserved.
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
School nurses repeatedly have been stretched to the limits over the past few years, with the COVID-19 pandemic - managing not only routine daily care of students but, also juggling those unique needs of Children and Youth with Special Health Care, Needs (CYSHCN), especially for those who also lost a parent/caregiver from COVID-19. This article provides background demographic information of how the COVID-19, pandemic affected these children along with a specific case study of a middle school, student with Attention Deficit Hyperactivity Disorder (ADHD) who also experienced the, loss of parent from COVID-19. Specific practical suggestions are discussed on how, school nurses proactively and collaboratively can assist these students whose lives, were permanently changed by the life-changing event of losing a parent/caregiver from, COVID-19.
Keywords
COVID-19
attention deficit hyperactivity disorder (ADHD)
School Nursing
mental, health issues
Community Health Education
Learning Disabilities
Educational, Service Plans
==== Body
pmcIntroduction
As the COVID-19 pandemic enters its third year, significant life stressors continue to evolve, especially for those children who lost a parent/primary caregiver from COVID-19. According to the Centers for Disease and Prevention (CDC, 2021), a rising “hidden and ongoing orphanhood tragedy” exists, citing that one U.S. child lost a parent/caregiver for every four COVID deaths, with an estimated over 200,000 children under 18 lost a parent/caregiver as of June 2022 (Hillis et al., 2022), with children of color affected far more frequently. School nurses play an integral role in facilitating the student's well-being by providing a supportive environment for those impacted by the death of a parent/caregiver, and especially for those children and youth with special health care needs (CYSHCN). There is an urgent call for school nurses to understand, assess, and identify the social-emotional and psychological factors affecting these children and their families and to prioritize care, which allows school nurses to develop practical strategies to reduce and mitigate the effects of racial, ethnic, and geographical health disparities.
CYSHCN are defined as “those who have or are at risk for chronic, physical, developmental, behavioral, or emotional conditions and require additional health and related services beyond that which is required by children generally “(McPherson et al., 1998). The National Survey of Children's Health (NSCH) Data Brief reported that nearly 1 in 5 children in the U.S. experience a special health care need, representing 14.1 million children and more than 1 in 4 households with children live with at least one CYSHCN (NSCH, 2022). School nurses provide a critical framework for delivering care to CYSHCN and those impacted by the death of a parent/caregiver during the pandemic.
This paper reviews a case study involving a middle school student, diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) in a school setting and focuses on specific interventions used to assist the student and student's caregivers in navigating the maze of challenges involved in raising a child with a chronic health condition while grieving the loss of a close family member. Two case study questions will address the child's and family's functional and mental health consequences.
Background statistics on COVID-19
Losing a primary caregiver can be one of the most stressful experiences that can happen in a child's life, putting them at risk of a trajectory of depression, post-traumatic stress, and any physical manifestations of grief (Jones, 2022). The CDC reported in 2021 that significant racial, ethnic and geographic disparities exist in COVID-19-associated death to caregivers and the highest burden of death was observed in the southern U.S. states, along the U.S.-Mexican border for Hispanic children, in southeastern states for Black children, and in states with tribal areas for American Indian/Alaska Native populations. For example, in the U. S1 out of 50 children lost a caregiver. For American Indian/Alaska Native children, this figure was 1 out of 168; Black children, 1 out of 310; Hispanic children, 1 out of 412; Asian children, 1 out of 612; and White children, 1 out of 753. Nelson (2022) stated. “Native Americans are 4 times more likely than Whites to be orphaned, and Black and Hispanic children 2.5 times more likely to die”. Overall, two-thirds (65%) of the children who lost a primary caregiver belonged to a racial or ethnic minority. The rate, unfortunately, remains disproportionate to minorities. These data reflect the inequities observed since the beginning of the pandemic, as COVID-19 affected many racial and ethnic minority groups which “puts them at a higher risk of severe illness and death” (Nelson, 2022).
Psychological and Social Impacts on Children
Reithy & Chawla (2022) identified the following reasons why the COVID-19 pandemic has exacerbated mental health conditions for many children for a myriad of reasons: missed or delayed opportunities for celebrations and marking milestones; direct stress related to the COVID-19 illness, avoiding COVID-19, and protecting loved ones; and ongoing economic distress, to name a few. Adegboye et al. (2021) stated:
“understanding the immediate psychological and social consequences for children, especially those already at risk for significant emotional and behavioral problems, and their families is essential for rapid development of policies and interventions to mitigate the mental health problems and provide tailored support for vulnerable groups of children during and after the pandemic” (pg. 2).
Beginning in April 2020, the proportion of mental health-related visits in pediatric emergency departments (ED) increased significantly for both children and adolescents (Leeb, 2020). These sharp increases in ED visits is representative of “colliding pandemics”: one being the long-standing inadequacy of mental health resources for children coupled with the psychological toll of COVID-19 (Todd, 2021).
Multiple research studies looked at the mounting evidence of the profound psychological impact of the COVID-19 pandemic on mental health functioning, specifically with elevated rates of anxiety and depression symptoms. Breaux et al. (2021) examined the changes in and predictors of adolescents with attention deficit hyperactivity disorder (ADHD) from pre-COVID 19 to the early spring of 2020. These researchers found that adolescents (ages 15-17 years) with ADHD experienced increases in sluggishness, cognitive tempo, inattention, hyperactivity & impulsivity, and oppositional/defiant symptoms above and beyond the effects of medication status.
Moss (2021) described the effects of the pandemic as parallel to those of natural disasters which may potentially have short and long-term effects on the psychological, emotional, developmental, and physical health of children as has been attributed to hurricanes, floods, tornadoes and earthquakes. Moss (2021) further stated that in the current COVID-19 pandemic, “families have experienced the loss of loved ones, job loss or displacement, and the breakdown in social networks” (pg. 293). Moss continued by asserting that there is a necessity of school nurses to utilize evidence-based strategies in planning for and putting into practice a safe return of faculty, staff, and students with in-person learning. Peck (2020) contrasted the initial response of schools during the initial stages of the COVID-19 pandemic that “schools are struggling to adapt rapidly, making high-stakes decisions with little information available” (pg. 626).
The American Academy of Pediatrics (AAP) issued guidance throughout the pandemic regarding return to school to shape conversations around holistic health and equity. Specific guidance provided principles of flexibility to respond to quickly changing information in individual communities, advocacy for vulnerable and disadvantaged children, equity in school inclusion, policies to support overall health of children, their families, and communities. At the beginning of the 2021 school year, the AAP published updated interim guidance on supporting the emotional and behavioral health needs of children, adolescents, and families during the pandemic (Wyckoff, 2021). Beale (2021) stated:
“The wide range of emotional and behavioral health and economic challenges stressing the well-being of families is exacerbated in populations with higher baseline risk such as vulnerable and marginalized individuals and communities. The impact of structural racism has resulted in disproportionate challenges on communities of color. Concerns about long-term sequelae of the effects of COVID-19 on children and families must not be ignored” (pg. 177).
Moss (2021) emphasized that parents, grandparents, caregivers, students, and teachers experienced stress while trying to adapt to the lack of the brick-and-mortar school routine. Parents of children who received virtual or hybrid instruction reported emotional stress, difficulty sleeping, loss of work, concern with job stability, childcare challenges, and conflict between working and providing childcare. In the fall of 2021, some students needed re-introduction to being in a classroom for the first time in over 18 months. Moss (2021) recommended that “a successful return to school this fall will be dependent on a strong school crisis response. Schools will need to plan for the social emotional support of staff and students to build resilience before we can even address education recoupment. A collaborative school team is vital to the mental health of students. The school nurse's interdisciplinary relationship with school social workers, counselors, psychologists, and administrators needs to be stronger than ever before” (pg. 294).
O'Connor et al. (2020) highlighted seven research priority domains specifically related to the impact of COVID-19 on children and families. Two of the research domains (e.g., children and families and mental health) will be used as a template for the case study questions.
Case Study
An 11-year-old Black male transferred to a private middle school from a local public school when local public schools were closed to in-person learning during the COVID-19 pandemic. His social history was significant for the death of his mother from COVID-19 which led him to move in with his maternal grandparents who became his legal guardians. It is important to note that he was one of four students (out of a total class size of 20 students) who had lost a caregiver due to COVID-19. In 4th grade, he was diagnosed with ADHD and at his former public school had in place a formal education plan (i.e., 504-Plan) to help support his struggles to be more focused on his academics. Some of the specific accommodations included: allowing for extra time to complete assignments; offering preferential seating near the front of the class; and, checking for understanding material with modifications of essential assignments with each of his classroom teachers. He had a primary care provider (PCP) that monitored his ADHD since 2019 and was followed every 3 months. He took Focalin XR each morning before school. His medical history was significant for mild intermittent asthma and was prescribed an Albuterol inhaler for school use as needed.
This child was closely followed by the school nurse, as a new student with accommodations from his previous school. After the first month of school, his grandmother expressed to the school nurse some specific concerns about her grandson's health and well-being and asked for recommendations for a pediatric psychologist and therapist. She believed her grandson needed to talk with someone about the death of his mom.
His grandmother wanted to “make sure that he was on the right medication to help him stay focused at school”. By the first quarter interim progress report, he had a failing math grade. This student who did well in math at his previous school, believed he was “not good at math at this school” and did not see other students ask for special help. Through maintaining consistent oversight, a math tutoring program was initiated for him called “Reach for the Stars”. This allowed the student to meet with his teacher two-three times a week.
His grandmother brought concerns about behavior changes that she described as her grandson as becoming “nervous” about going to school in the morning and that he did not have any friends. He had a difficult time adjusting to this new middle school, most of the other children had well established friendships for many years. The grandmother found a local counseling center that offered free services for local children and families. A telehealth appointment was scheduled with a pediatric psychologist and therapist. Weekly sessions were set up for both the student and his grandmother.
Case Study Questions
1 Child/Family Functioning: How will the COVID-19 pandemic affect family functioning and children and youth with special health care needs (CYSHCN)?
2 Mental Health Consequences: What are the immediate and longer-term consequences of COVID-19 for mental health outcomes with CYSHCN?
Case Study Discussion
Child/Family Functioning
There are multiple factors affecting the child's and family's adequate functioning that are directly related to the COVID-19 pandemic, including, the recent death of the student's mother. It is difficult to ascertain how much these factors impact an individual or family's overall mental and physical health. From the initial entry into school, the school nurse established trust and rapport with the grandmother and the student to build communication and to provide a solid support system in the new school. The nurturing relationship allowed the student to speak openly with the school nurse about his mental health and academic issues. This well-established relationship between the student, family and the school nurse fostered continued communication which allowed the child and family to seek out and access appropriate health care services within the community (e.g., pediatric well care visits, follow up appointments, telehealth therapy appointments). Working with the student's PCP would be an important step to ensure his medication needs are met not only at the school (e.g., need for Albuterol inhaler) but also to review with the grandmother and student ways to improve compliance with taking daily ADHD medications is critical.
However, the child's troubling academic deficiencies in failing math needed to be immediately addressed with the schoolteachers and administration. The grandmother wanted to afford him every opportunity for him to succeed at school – academically, socially, emotionally and developmentally. Right from the start, the school nurse worked collaboratively with the student, his grandmother, and his teachers to create an “Academic Service Plan” (i.e., modified 504-Plan), which became the number one priority for him to improve his academic ability. Discussing ways to boost his self-confidence and to help his adjustment to the new school environment also remained an essential part of his plan.
Children with special education needs are at unique risk during the COVID-19 pandemic (Fry-Bowers, 2020). The unprecedented previous disruption in this child's education and subsequent move to remote learning and then transfer to his new school amplified some of his learning difficulties stemming from system inequities and disparities. These risk factors are ones that need to be continually revisited and discussed for this student with the entire collaborative school team as well as his PCP and his mental health therapist. Another significant factor that affected the student were the changes in his family living situation. Over the past year, he has gone from living with his mother (pre-COVID) and attending a public school to now (during/after COVID) living in a different home with his grandmother as his guardian and grandfather (who is the main provider for the family) and attending a new private school with little or no existing friends in his class. Working out ways to involve the school counselor and other teachers who may encourage him to become involved in any extra-curricular activities that are at his new school would be one way for him to socially engage with others his own age. When asked what hobbies or interests he had, the student mentioned he really liked to draw and has his own comic book characters. Finding ways to encourage positive therapeutic strategies like art would benefit him socially, emotionally and developmentally.
Mental Health Consequences
Working as an experienced school nurse and Pediatric Nurse Practitioner opened my eyes to the acute and chronic health conditions and needs for this particular student and helped me identify any worsening pre-existing mental health status such as ADHD and any learning disabilities. Loss of a parent has been identified as childhood trauma or an Adverse Childhood Experience (ACE) that unbuffered can be linked to mental health problems, shorter schooling, lower self-esteem, sexual risk behaviors, and increased risk of substance abuse, suicide, violence, sexual abuse, and exploitation (CDC, 2021, Jones, 2022). Adopting a holistic approach also encompassing nonpharmacological supportive therapies is essential for holistic care. Some children may benefit from evidence-based psychosocial interventions, such as counseling and cognitive behavioral approaches, whereas others may need specialized psychiatric services, including pharmacotherapy (Riddle, 2019). After talking with the student's grandmother, her comments on how this student was having difficulty processing this student's mother's death prompted me to refer them to a local mental health therapist. His feelings of sadness and loss from his mother's death need to be discussed further with his therapist and within the family unit. The grandmother's response that she acknowledged and identified the need for therapy was encouraging and that he was willing to do the counseling was a positive step in improving the family's mental health status. Her concerns about him feeling “nervous” about coming to school and being self-conscious about his appearance appear to be typical pre-adolescent behaviors. However, it would be important to be aware of these behaviors and monitor his interactions with other classmates just to be sure that these behaviors digress to worsening anxiety or possible depression.
Long-term issues would be assessed by all those involved in his medical plan of care. Communication is the key to success in assessing this family's overall status. Being in close communication with the grandmother would be beneficial and should be done weekly by phone, email or in person, to ask how things are going for the student and family and to see if they need anything. The National Association of Pediatric Nurse Practitioners recently revised guidance for clinicians on the integration of mental health in primary care settings. Bartek et. al (2021) recommends “a holistic, family-centered, life span approach is advocated in conjunction with a collaborative model including telehealth, community partnerships between primary and mental health care, and colocation of services” (pg. 379).
The school nurse must be aware of any impacts on what has previously happened to this particular student and to look for ways to help encourage communication within the school and medical collaborative team as well as within his family. Utilizing strategies suggested by the National Association of School Psychologists (NASP, 2020) and the National Association of School Nurses (NASN, 2020) would assist the school nurse to clarify any questions about the pandemic and would be an important step towards a better understanding of how the pandemic evolved and ways to proactively offer ways to manage any of the past or present worries of the student. From my personal experience, usually middle school students have more in-depth questions and concerns as compared to younger school-aged students. It would be critical for the school nurse to be honest with these students – to provide accurate, factual information about the current status of COVID-19 and the preventative steps being taken to control it and keep people safe. Referring him to appropriate and accurate websites for COVID-19 facts and information, such as the Centers for Disease Control and Prevention (CDC) website would be an excellent teaching tool to use with such students (Mingolelli, 2020).
Conclusions
Addressing school health issues is a pivotal role of school nurses caring for children and adolescents in our current ever-evolving COVID-19 world. The pandemic has increased the risk and clinical presentation of mental health issues among CYSHCN, alerting pediatric providers to new social challenges and stress impacts that are felt universally (Bartek et al, 2021). Research shows that when students’ mental health needs are properly addressed, the likelihood of school success increases, demonstrating how students overcome these stressful life events. School nurses play a critical role in students' daily interactions, especially for all children who have recently lost a caregiver to COVID-19. These positive interactions will not only have an immediate impact but also a long-term impact for years to come. Every day, school nurses promote mental health wellness by recognizing and collaborating with pediatric primary care providers and making timely referrals to behavioral support networks in the community which can lead to beneficial improvements in the health continuum for children whose lives were permanently changed by the loss of a caregiver from COVID-19.
Ethical Statement
October 24, 2022
To whom it may concern for the Journal of Pediatric Health Care:
As per the requirements, I hereby affirm that I have no ethical conflicts of interest in submitting my revised manuscript, “The Impact of COVID-19 on Children and Youth with Special Health Care Needs: A School Nurse Perspective”. I had previously submitted an ethical statement in July 2022 with my first submission.
This is my first ever submission for publication to a nursing journal. Thank you for your consideration of my revised submission.
Thank you.
Respectfully yours,
Susan J. Macyko
References
Adegboye, D., Williams, F., Collishaw, S., Shelton, K., Langley, K., Hobson, C., . . . van Goozen, S. (2021). Understanding why the COVID-19 pandemic-related lockdown increases mental health difficulties in vulnerable young children. JCPP Advances, 1(1), e12005. doi:10.1111/jcv2.12005 [doi]
Bartek, N., Peck, J. L., Garzon, D., & VanCleve, S. (2021). Addressing the clinical impact of COVID-19 on pediatric mental health. Journal of Pediatric Health Care, 35(4), 377–386. https://doi.org/10.1016/j.pedhc.2021.03.006
Beal, J. A. (2021). Impact of the covid-19 pandemic on mental health of children, adolescents, and young adults. MCN.The American Journal of Maternal Child Nursing, 46(4), 237. doi:10.1097/NMC.0000000000000726 [doi]
Breaux, R., Dvorsky, M. R., Marsh, N. P., Green, C. D., Cash, A. R., Shroff, D. M., . . . Becker, S. P. (2021). Prospective impact of COVID-19 on mental health functioning in adolescents with and without ADHD: Protective role of emotion regulation abilities. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 62(9), 1132-1139. doi:10.1111/jcpp.13382 [doi]
Centers for Disease Control and Prevention. (2021, October 7). The hidden U.S. covid-19 pandemic: Orphaned children – more than 140,000 U.S. children lost a primary or secondary caregiver due to the COVID-19 pandemic. Centers for Disease Control and Prevention. (CDC). htdtps://www.cdc.gov/media/releases/2021/p1007-covid-19-orphaned-children.html
Fry-Bowers, E. K. (2020). Children are at risk from COVID-19. Journal of Pediatric Nursing, 53, A10-A12. doi:S0882-5963(20)30317-1 [pii]
Hillis, S., Blenkinsop, A., & Villaveses, A. (2022). Covid-19-associated orphanhood and caregiver death in the United States. Pediatrics. Retrieved October 11, 2022, from https://pubmed.ncbi.nlm.nih.gov/34620728/
Jones, C. (2022). Helping children grieve the loss of a caregiver due to COVID-19. Contemporary Pediatrics, 22–24.
Leeb, R. T. (2020, November 12). Mental health–related emergency department visits among children aged 18 years during the covid-19 pandemic - United States, January 1–October 17, 2020. Centers for Disease Control and Prevention. Retrieved June 21, 2022, from https://www.cdc.gov/mmwr/volumes/69/wr/mm6945a3.htm
McPherson, M., Arango, P., Fox, H., Lauver, C., McManus, M., Newacheck, P., Perrin, J., Shonkoff, J., & Strickland, B. (1998). A New Definition of Children with Special Health Care Needs. Pediatrics, 102, 137–139. https://doi.org/10.1542/peds.102.1.137
Moss, E. (2021). Roadmap to recovery: Year 2 of the COVID-19 pandemic. NASN School Nurse (Print), 36(5), 292-299. doi:10.1177/1942602X211033516 [doi]
National Association of Pediatric Nurse Practitioners, Developmental Behavioral Mental Health Special Interest Group. (2020, October 21). NAPNAP revises position statement on integration of mental health care in primary care settings. NAPNAP. Retrieved from https://www.napnap.org/napnap-revises-position-statement-on-integration-of-mental-health-care-in-primary-care-settings/
National Association of School Psychologists (NASP). (2020, August). School reentry considerations - national association of school ... National Association of School Psychologists . Retrieved from: https://www.nasponline.org/assets/Documents/Resources%20and%20Publications/Resources/Crisis/School%20Reentry.pdf
National Association of School Psychologists (NASP). (2020). Helping children cope with changes resulting from COVID-19. National Association of School Psychologists . Retrieved from: https://www.nasponline.org/resources-and-publications/resources-and-podcasts/school-safety-and-crisis/health-crisis-resources/helping-children-cope-with-changes-resulting-from-covid-19?ck_subscriber_id=342989428%C2%A0
National Survey of Children's Health (NSCH). (2022). Children and youth with special heath care needs data brief. Health Resources and Services Administration (HRSA) Maternal and Child Health Bureau (MCHB). Retrieved from https://mchb.hrsa.gov/sites/default/files/mchb/programs-impact/nsch-data-brief-children-youth-special-health-care-needs.pdf
Nelson, R. (2022). Caregiver Deaths From COVID: 5 million children affected worldwide. Pediatric News, 56(4), 1–10.
O'Connor, D. B., Aggleton, J. P., Chakrabarti, B., Cooper, C. L., Creswell, C., Dunsmuir, S., . . . Armitage, C. J. (2020). Research priorities for the COVID-19 pandemic and beyond: A call to action for psychological science. British Journal of Psychology (London, England: 1953), 111(4), 603-629. doi:10.1111/bjop.12468 [doi]
McPherson M, Arango P, Fox H, Lauver C, McManus M, Newacheck PW, Perrin JM, Shonkoff JP, Strickland B. A new definition of children with special health care needs. Pediatrics. 1998 Jul;102(1 Pt 1):137-40. doi: 10.1542/peds.102.1.137. PMID: 9714637.
Mingolelli, Anne M. “Emotional Health Challenges of Students during COVID-19, and Beyond.” Northeastern University School of Health Academy. Northeastern University School of Health Academy Update for School Nurses, 2021, Boston, Massachusetts.
Peck, J. L. (2020). COVID-19: Impacts and implications for pediatric practice. Journal of Pediatric Health Care: Official Publication of NAPNAP, 34(6), 619-629. doi:S0891-5245(20)30177-2 [pii] The psychological toll of COVID-19 on children. (2021). The American Journal of Nursing, 121(9), 14. doi:10.1097/01.NAJ.0000790568.11092.c9
Rethy, J. A., & Chawla, E. M. (2022). How pediatricians can help mitigate the mental health crisis. Contemporary Pediatrics, 28–31.
Riddle, M. A. (2019). Pediatric psychopharmacology for primary care (2nd ed.). American Academy of Pediatrics.
Wyckoff, Alyson & Sulaski. “Supporting Emotional, Behavioral Needs of Children in the Pandemic: Updated Guidance.” American Academy of Pediatrics, 29 Sept. 2021, https://www.aappublications.org/news/2021/08/02/emotional-behavioral-health-covid 080221.
Declaration of interests
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
| 0 | PMC9729642 | NO-CC CODE | 2022-12-15 23:15:23 | no | J Pediatr Health Care. 2022 Dec 8; doi: 10.1016/j.pedhc.2022.12.001 | utf-8 | J Pediatr Health Care | 2,022 | 10.1016/j.pedhc.2022.12.001 | oa_other |
==== Front
Intensive Crit Care Nurs
Intensive Crit Care Nurs
Intensive & Critical Care Nursing
0964-3397
1532-4036
Elsevier Ltd.
S0964-3397(22)00173-2
10.1016/j.iccn.2022.103370
103370
Quality Improvement Article
Comparing rehabilitation outcomes for patients admitted to the intensive care unit with COVID-19 requiring mechanical ventilation during the first two waves of the pandemic: A service evaluation
Weblin Jonathan a1
Harriman Adam a2
Butler Katrina a
Snelson Catherine b3
McWilliams David c⁎4
a Therapy Services, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom
b Department of Critical Care, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom
c Centre for Care Excellence, Coventry University & University Hospitals Coventry & Warwickshire NHS Trust, Coventry, United Kingdom
⁎ Corresponding author.
1 ORCID: 0000-0002-0788-8431.
2 ORCID: 0000-0001-9546-4753.
3 ORCID: 0000-0002-1790-0780.
4 ORCID: 0000-0002-9086-2557.
8 12 2022
8 12 2022
10337028 3 2022
18 11 2022
5 12 2022
© 2022 Elsevier Ltd. All rights reserved.
2022
Elsevier Ltd
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Objectives
To compare rehabilitation outcomes of patients admitted to the intensive care unit with COVID-19 and mechanically ventilated during wave 1 and 2, receiving two different models of physiotherapy delivery.
Methods
Adults admitted to the intensive care unit between October–March 2021 (wave 2) with a confirmed diagnosis of COVID-19 and mechanically ventilated for >24 hours were included. During wave 2, rehabilitation was provided by physiotherapists over five days, with only emergency respiratory physiotherapy delivered at weekends. Rehabilitation status was measured daily using the Manchester Mobility Score to identify time taken to first mobilise and highest level of mobility achieved at ICU discharge. Outcomes were compared to data previously published from the same ICU during ‘wave 1’ (March–April 2020) when a seven-day rehabilitation physiotherapy service was provided.
Results
A total of n = 291 patients were included in analysis; 110 from wave 1, and 181 from wave 2. Patient characteristics and medical management were similar between waves. Mean ± SD time to first mobilise was slower in wave 2 (15 ± 11 days vs 14 ± 7 days), with overall mobility scores lower at both ICU (MMS 5 (Step transferring) vs MMS 4 (standing practice) (4), p < 0.05) and hospital (MMS 7 (Mobile > 30 m MMS) vs MMS 6 (Mobile < 30 m MMS), p < 0.0001) discharge. Significantly more patients in wave 2 required ongoing rehabilitation either at home or as an inpatient compared to wave 1 (81 % vs 49 %, p = 0.003).
Conclusion
The change in physiotherapy staff provision from a seven-day rehabilitation service during wave 1 to a five day rehabilitation service with emergency respiratory physio only at weekends in wave 2 was associated with delayed time to first mobilise, lower levels of mobility at both intensive care unit and hospital discharge and higher requirement for ongoing rehabilitation at the point of hospital discharge.
Keywords
Coronavirus
ICU
Mobilisation
Physiotherapy
Rehabilitation
Workforce
==== Body
pmc Implications for clinical practice • The provision of physiotherapy across seven days was associated with shorter times to first mobilise.
• Earlier mobilisation in wave one was associated with higher mobility levels at both intensive care unit and hospital discharge.
• Delayed mobilisation on the intensive care unit was associated with an increased requirement for ongoing rehabilitation at the point of hospital discharge.
Introduction
The COVID 19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARs-Cov-2) has seen healthcare services experience unprecedented operational challenges (Maves et al., 2019). At the peak of Wave 1 (12th April 2020), 3301 patients occupied mechanically ventilated beds and 21 687 patients occupied hospital beds in the United Kingdom (UK) (https://coronavirus.data.gov.uk/details/healthcare). The initial National Health Service (NHS) response to the pandemic meant all non-essential outpatient services were stood down and operations were cancelled. This strategy allowed re-deployment of outpatient and ward based healthcare professionals to intensive care, supplementing the existing workforce to meet the increasing demands within Intensive Care Units (ICU) (Goh et al., 2020). This redeployment was not without cost. As hospitals emerged from wave 1, cancer waiting times had reached an all-time high and significant backlogs had accumulated for both urgent and non-urgent surgery (England, 2021, Macmillan, 2020).
As the UK moved into the second wave of the COVID-19 pandemic, additional pressures were present in comparison to wave 1. Due to the increased backlog, there was a significant need to maintain surgical pathways and a push to continue outpatient services which had now been established in new novel or remote formats (Zampino et al., 2021, National Institute for Health and Care Excellence, xxxx). Consequently; as NHS Trusts endeavoured to maintain these services, staff that were previously re-deployed during wave 1 were no longer available to support ICU. In addition, the accumulating psychological burden of the pandemic saw high levels of psychological distress amongst frontline healthcare workers, including post-traumatic stress disorder, anxiety and depression (Gemine et al., 2021, Marcomini et al., 2021) with resultant sickness absence from work (Greenberg et al., 2020). This further increased the challenges of meeting the ICU demands during the second wave; which actually exceeded the peak of wave 1; with 4076 patients occupying mechanically ventilated beds on 22nd January 2021 and 39,254 inpatients on 18th January 2021 (https://coronavirus.data.gov.uk/details/healthcare).
We previously published rehabilitation outcomes for patients admitted to our hospital with severe COVID-19 requiring mechanical ventilation during ‘Wave 1’ (McWilliams et al., 2021). The redeployment of staff at this time allowed the physiotherapy service to adopt a 7 day working pattern, facilitating respiratory treatment and rehabilitation across all 7 days. Whilst we presented data to demonstrate the time to commence rehabilitation was delayed due to the severity of illness, this 7 day working model meant rehabilitation remained possible within the ICU, and led to increased levels of mobility at critical care discharge (McWilliams et al., 2021). During ‘Wave 2’, the lack of redeployed staff meant physiotherapy had to revert to its pre-pandemic working model of delivering rehabilitation 5 days a week from Monday to Friday, with only limited access to physiotherapy for emergency respiratory care provided at the weekends. We hypothesise that this lack of access to additional rehabilitation at weekends may have impacted rehabilitation outcomes.
We aimed to evaluate any differences in rehabilitation outcomes for patients admitted during wave 2 to our previously published results.
Objectives
To compare rehabilitation outcomes for patients receiving two different models of physiotherapy delivery (7 days vs 5 + 2 days).
Materials and methods
This was a single centre, prospective, service evaluation comparing outcomes for patients admitted to ICU between March and April 2020 (wave 1) and October 2020 and March 2021 (wave 2) with a confirmed diagnosis of COVID-19. This project constituted an observation of standard care delivery with no randomization and thus met the definition of a service evaluation under the National Health Service Health research authority guidelines (http://www.hra.nhs.uk/researchcommunity/ Before you-apply/determine-whether-your-study-is-research/). As such, ethical approval was not required, and because all outcome measures are collected as part of routine care, the need for consent was waived. The project was registered as an audit on the trusts clinical audit registration and management system (CARMS-17976). Participants were followed up until acute hospital discharge. This study is reported in accordance with the Strengthening the Reporting of Observational studies in Epidemiology (STROBE) guidelines (Vandenbroucke et al., 2007).
Setting
The Queen Elizabeth Hospital Birmingham is a quaternary-level acute care hospital, with one of the largest co-located ICUs in Europe. Before the COVID-19 pandemic, the standard critical care capacity for this unit was 75 beds; however, with surge planning, the overall capacity was increased to>200 for both waves of the pandemic. At the peak of the 2nd COVID-19 wave, the ICU cared for 153 patients (COVID and non-COVID-19 patients) compared to a peak of 164 during wave 1. As per wave 1, capacity was increased through a variety of measures including caring for two patients per bed space and reduced specialist staffing ratios. Clinical pressures and staff shortages related to the COVID-19 pandemic meant it was not feasible to replicate the model of staffing utilised during wave 1. Differences in the models of care are outlined in Table 1 .Table 1 Differences in staffing ratios; pre-pandemic, Wave 1 and Wave 2.
Pre-pandemic ICU Wave 1 (March – April 2020) Wave 2 (October 2020 – March 2021)
Nursing ratio- 1 ICU nurse to 1 level 3 patient
Nursing ratio- 1 ICU nurse to 4 bed spaces supported by 3 redeployed non-ICU nurses
Nursing ratio- 1 ICU to 3 bed spaces supported by 1 redeployed nurse and 1 redeployed medic
Consultant cover1: 10 Consultant cover- 1:35 beds
Consultant cover- 1:20
Physiotherapy cover- 5 days service
- 8am – 5pm
- Ratio 1:7
- Weekend emergency respiratory ratio 1:10 (no rehabilitation delivered by physiotherapy staff)
- Emergency respiratory on call overnight
Physiotherapy cover- 7 days service
- 8am – 8pm
- Ratio 1:7
- Emergency respiratory on call overnight
Physiotherapy cover- 5 days service
- 8am – 5pm
- Ratio 1:8
- Weekend emergency respiratory ratio 1:10 (no rehabilitation delivered by physiotherapy staff)
- Emergency respiratory on call overnight
ICU, Intensive Care Unit.
Physiotherapists within our unit assess all patients within 24 hours of admission; delivering respiratory care often termed “chest physiotherapy” and commencing rehabilitation as indicated. To meet the increasing demand during wave 1 of the pandemic, the physiotherapy service was restructured in March 2020 to ensure physiotherapy was available from 8 a.m. to 8 p.m., seven days per week, maintaining a ratio of one whole time equivalent physiotherapist for every seven patients. This was achieved through the redeployment of respiratory competent physiotherapists working within the Trust but not currently working on ICU.
In comparison, during the second wave due to ongoing service pressures and the lack of available staff to re-deploy, physiotherapy had returned to pre-pandemic working patterns of Monday to Friday only, with a slightly reduced ratio of one physiotherapist to eight patients. The additional physiotherapy staff to accommodate the increase in capacity consisted of non-specialist ICU physiotherapists from within the hospital and physiotherapists re-deployed from the Birmingham Children’s Hospital paediatric ICU. Weekend emergency physiotherapy was delivered by a mix of specialist and non-specialist ICU staff, providing respiratory physiotherapy only at a ratio of one physiotherapist to 10 patients. Consequently, unlike during wave 1, due to these reduced staffing ratio’s only limited initiation or delivery of rehabilitation by the physiotherapy team was possible at weekends. This is standard practice in the United Kingdom for trusts not providing a seven days service.
From a broader workforce perspective, during the second wave despite COVID related staffing absence, better consultant and nursing ratios were observed. Mutual aid from neighbouring trusts who did not have COVID pressures, in addition to not having to release ICU consultants to support with the setup of nightingale wards during the second wave made this feasible. The experience of the wave 1 surge and subsequent rates of ICU admission also meant the surge planning for wave 2 was modelled on a smaller number of ICU beds which reduced the cover requirements for the consultant rota and improved consultant to patient ratios.
Ward staffing
Similar to the change in working model observed on critical care, during wave 1 of the COVID 19 pandemic the ward physiotherapy teams moved to a seven day rehabilitation service. This was supported by redeployed outpatient physiotherapy staff. Prior to this only a five day rehabilitation service was offered, with emergency respiratory work only completed at weekends. COVID 19 patients surviving to ICU discharge were transferred to three, 36 bed designated COVID 19 wards staffed at a ratio of one physiotherapist to 10 beds to continue rehabilitation. Once oxygen requirements resolved, patients were stepped down to an inpatient rehabilitation ward, staffed at 1:7, were rehabilitation and discharge planning continued.
During wave 2 the patient pathway remained the same for patient surviving to ICU discharge. However the ward based physiotherapy service reverted to a five day rehabilitation service with emergency respiratory work only at weekends. As a result the staffing ratios improved Monday to Friday to 1:8 on the medical wards and 1:6 on the rehabilitation ward but no physiotherapy lead rehabilitation occurred at weekends.
Participants
Consecutive participants between October 2020 and March 2021 were included in the analysis if they met the inclusion criteria of being adults (≥18 years of age), having a confirmed diagnosis of COVID-19, and being mechanically ventilated for at least 24 hours and who survived to hospital discharge. Outcomes were compared to data previously published from the same centre for patients admitted to ICU between March and April 2020 with COVID-19 (WAVE 1) (McWilliams et al., 2021).
Procedure
Physiotherapy assessment was completed within 24 hours of ICU admission for all patients. Physiotherapists within the United Kingdom are responsible for delivering both respiratory intervention and rehabilitation. Respiratory physiotherapy is an umbrella term used to define a variety of interventions including patient positioning, proning, manual hyperinflation and delivering manual techniques to facilitate secretion clearance, and improve ventilation/perfusion matching and lung compliance. To support the wider workforce pressures across critical care, the physiotherapy team also took on increased responsibility to support management of ventilation during the pandemic, in accordance with lung protective ventilation guidelines (Network et al., 2000). This included calculation of targets for lung protective tidal volumes, which were then displayed in the patients’ bed space. Once clinically appropriate a physiotherapist led and coordinated the commencement and progression of rehabilitation between Monday and Friday. Our critical care multidisciplinary team has extensive experience of delivering early and structured rehabilitation, including established safety criteria to commence mobilization, and a protocol to guide progression (McWilliams et al., 2015).
Outcomes
The primary outcome was the highest level of mobility achieved at the point of ICU discharge, as measured by the Manchester Mobility Score (MMS). The MMS is a simple seven-point mobility scale used and validated for assessing mobility levels within critical care (McWilliams et al., 2015). The MMS ranges from 1 to 7, with 7 indicating the highest level of mobility. It was documented daily by the treating physiotherapist immediately after completion of the physiotherapy treatment session Monday to Friday. Physiotherapists retrospectively reviewed patient’s electronic noting on Mondays to ascertain if patient’s mobility status had changed over the weekend. Secondary outcomes included the number of days taken to first mobilize (defined as an MMS of 2 or higher, i.e., sitting on the edge of the bed or higher) and the location of hospital discharge, which was treated as an ordinal variable with categories of Home (No Rehabilitation), Home (With Rehabilitation), or Inpatient Rehabilitation. As per standard care, discharge destination was decided based on discussions between the ward based multidisciplinary team, considering the individuals ongoing rehabilitation requirements.
Data collection
Data for wave 2 were collected mixed prospectively and retrospectively throughout the evaluation period to mirror data collected and published during the 1st wave of the pandemic, using electronic patient records and electronic databases. This included patient demographics, ventilation days, sedation days, renal replacement therapy using continuous venovenous hemofiltration (CVVH) at any point during ICU admission, tracheostomy insertion, length of stay (LOS) for both ICU and the ward, and mortality. Other factors that delayed mobilization and therefore may have contributed to the development of ICU-acquired weakness were also collected retrospectively from patient noting. Specifically, this included data regarding aspects of critical care management, including the use of neuromuscular blocking agents, proning, and the presence of delirium, defined by a positive result on the Confusion Assessment Method ICU (CAM-ICU) at any point during the ICU stay. The presence of ICU-acquired weakness during awakening was defined as a Medical Research Council sum score of <48 (Hermans and Van den Berghe, 2015). Rehabilitation outcomes were collected immediately after physiotherapy sessions and recorded by the treating physiotherapist using the MMS. This reported either the level achieved during the physiotherapy session, or the level of mobility (if any) completed with nursing staff, whichever was the highest. Frailty scores were collected routinely as part of admission assessment using the Clinical Frailty Score (CFS) (Rockwood et al., 2005).
Statistical methods
Continuous variables were compared between the two Waves. A Mann-Whitney U test was used to analyse non-normally distributed data, and reported with medians and interquartile ranges. Metrical normal data was analysed using t-tests and summarised using means ± standard deviations (SDs).Ordinal variables were also analysed using Mann-Whitney U tests, with Fisher’s exact test used for nominal variables.
All analyses were performed using IBM SPSS 22 (IBM Corp. Armonk, NY), with p < 0.05 deemed to be indicative of statistical significance throughout.
Results
Cohort characteristics
Data were available for a total of 291 patients who survived to ICU discharge, comprising of 110 from Wave 1, and 181 from Wave 2. Patient characteristics were similar in the two Waves, with no significant differences detected in any of the factors considered except lower APACHE II scores on admission for patients during wave 2 (Table 2 ). The approach to treatment was also similar (Table 3 ), with similar use of sedation, proning and neuromuscular blocking agents, although tracheostomies were used significantly less frequently in Wave 2 (66 % vs 77 %, p = 0.048).Table 2 Cohort characteristics.
Wave 1
March – April 2020 Wave 2
October 2020 – March 2021 p-Value
Age (Years) 53 ± 12 56 ± 11 0.101
Sex (% Male) 83 (75 %) 116 (64 %) 0.051
BMI (kg/m2) 0.149*
<20 0 (0 %) 2 (1 %)
20–24 14 (13 %) 13 (7 %)
25–29 42 (38 %) 58 (32 %)
30–39 39 (35 %) 85 (47 %)
40+ 15 (14 %) 23 (13 %)
Ethnicity 0.956
White 53 (48 %) 81 (45 %)
Asian 38 (35 %) 66 (36 %)
Black 8 (7 %) 15 (8 %)
Mixed/Other 11 (10 %) 19 (10 %)
Clinical Frailty Score [N = 290] 0.289*
1 23 (21 %) 35 (19 %)
2 32 (29 %) 71 (39 %)
3 35 (32 %) 50 (28 %)
>3 20 (18 %) 24 (13 %)
Hypertension 50 (45 %) 63 (35 %) 0.083
Diabetes Mellitus 34 (31 %) 56 (31 %) 1.000
COPD 5 (5 %) 2 (1 %) 0.108
Asthma 17 (15 %) 24 (13 %) 0.606
APACHE II 16 (13–25) 13 (10–16) <0.05
Charlson Comorbidity Index 0.696*
0–1 41 (37 %) 64 (35 %)
2–3 49 (45 %) 89 (49 %)
4–5 17 (15 %) 20 (11 %)
>5 3 (3 %) 8 (4 %)
Continuous variables are reported as mean ± SD, or as median (IQR), with p-values from Mann-Whitney U tests. Categorical variables are reported s N (column %), with p-values from Fisher’s exact tests, unless stated otherwise. Data are based on N = 291 cases, unless stated otherwise. Bold p-values are significant at p < 0.05. *p-Value from Mann-Whitney U test, as the factor is ordinal.
Table 3 Treatment and patient outcomes.
Wave 1
March – April 2020 Wave 2
October 2020 – March 2021 p-Value
ICU Treatment/Outcomes
Duration of Ventilation (Days) 19 ± 10 21 ± 16 0.406
Tracheostomy 85 (77 %) 119 (66 %) 0.048
Timing of tracheostomy post intubation (days) 12 (9–14) 12 (8–18) 0.548
Prone Position 74 (67 %) 120 (66 %) 0.287
Renal Failure Requiring CVVH 37 (34 %) 51 (28 %) 0.358
Days on Sedation 13 ± 6 13 ± 10 1.000
Neuromuscular Blockade 99 (90 %) 151 (83 %) 0.741
Duration (Days) [N = 240] 7 (4–11) 6 (3–14) 0.848
ICU Acquired Weakness on Awakening [N = 286] 110 (100 %) 163 (93 %) 0.002
Time to First Mobilise (Days) [N = 290] 14 ± 7 15 =/−11 0.286
ICU LOS (Days) 22 ± 11 24 ± 18 0.508
MMS at ICU Discharge 5 (4–6) 4 (3–5) <0.05*
1 0 (0 %) 3 (2 %)
2 15 (14 %) 27 (15 %)
3 6 (5 %) 27 (15 %)
4 33 (30 %) 52 (29 %)
5 27 (25 %) 49 (27 %)
6 19 (17 %) 19 (10 %)
7 10 (9 %) 4 (2 %)
Post-ICU Outcomes [N = 276]**
Hospital LOS (Days) 32 (23–45) 36 (18–54) 0.629
MMS at Hospital Discharge 7 (7–7) 6 (6–7) <0.0001*
3 2 (2 %) 7 (4 %)
4 1 (1 %) 13 (8 %)
5 1 (1 %) 4 (2 %)
6 14 (13 %) 70 (42 %)
7 91 (83 %) 73 (44 %)
Discharge Destination 0.003
Home (No Rehab) 55 (50 %) 56 (34 %)
Home (With Rehab) 46 (42 %) 79 (47 %)
Inpatient Rehab 8 (7 %) 32 (19 %)
Continuous variables are reported as median (IQR), with p-values from Mann-Whitney U tests. Categorical variables are reported s N (column %), with p-values from Fisher’s exact tests, unless stated otherwise. Data are based on N = 291 cases, unless stated otherwise. Bold p-values are significant at p < 0.05. *p-Value from Mann-Whitney U test, as the factor is ordinal. **Excludes patients that were transferred (N = 9), still in hospital (N = 5), or that died in hospital after ICU discharge (N = 1).
ICU, Intensive Care Unit. LOS, Length of Stay. MMS, Manchester Mobility Score.
ICU outcomes
Patients in wave 2 had significantly lower Manchester mobility scores at the point of ICU discharge in comparison to wave 1 (4 vs 5, p < 0.05), representing an ability to stand but unable to take any steps (See Table 3). This reduced mobility level was demonstrated despite the fact patients in Wave 2 had a significantly lower incidence of ICU acquired weakness on awakening (93 % vs 100 %, p = 0.002). Patients in wave 2 were slower to mobilise, spent longer mechanically ventilated and longer in the ICU although none of these achieved statistical significance.
Post-ICU outcomes
In Wave 2, 9 patients underwent intra-hospital transfer from ICU, and one patient from Wave 1 died in hospital after being discharged from ICU. Therefore, these patients were excluded when considering post-ICU outcomes, leaving 276 data sets for analysis (Table 3). For these, both the post-ICU (p = 0.945) and overall (p = 0.629) hospital stays were similar in the two Waves. However, patients from Wave 2 had a significantly lower MMS at hospital discharge (p < 0.001), with 44 % having a score of seven (ability to walk >30 m unaided), compared to 83 % from Wave 1. As such, 66 % of patients from Wave 2 required further rehabilitation after discharge, compared to 49 % from Wave 1 (p = 0.003).
Discussion
This single centre study compares the demographics, clinical status and rehabilitation outcomes of patients admitted to ICU and mechanically ventilated during wave 1 and 2 with confirmed COVID-19. Patient demographics were largely comparable between waves and patients admitted during wave 2 received comparable medical intervention to those admitted during wave 1. This included prolonged periods of sedation, frequent use of neuromuscular blockade and prone positioning. Despite tracheostomy protocols remaining consistent during waves, significantly less tracheostomies were performed during wave 2. This may be explained by a greater understanding of disease trajectory coupled with greater knowledge and experience in medical management, or the lower APACHE 2 scores on admission. Although statistically different when compared to the first wave, the incidence of ICU-AW remained high amongst both cohorts.
The return to pre pandemic physiotherapy staffing ratio’s, with rehabilitation only routinely delivered Monday to Friday had a significant negative impact on rehabilitation outcomes. Patients in wave 2 demonstrated lower mobility levels at the point of ICU discharge in comparison to wave 1. This was despite slightly better nursing and ICU consultant ratios in wave 2. It was noted that there was a delay in initiation of rehabilitation for patients in wave 2, who took on average two days to first mobilise after the cessation of sedation in comparison to just 1 day for those patients in wave 1 (McWilliams et al., 2021). Whilst this did not reach statistical significance, clinically the time to first mobilise has been proved to be an important metric when evaluating the impact of structured rehabilitation programmes (Pun et al., 2019). Importantly, this delay in mobilisation corresponded with patients in wave 2 requiring longer periods of mechanical ventilation, longer periods in the ICU, and ultimately leaving hospital with lower levels of mobility and higher rehabilitation needs (Fig. 1 ).Fig. 1 Impact of time to mobilise on patient outcomes. ICU, Intensive Care Unit. LOS, Length of Stay.
NHS England advocates a seven day service provision across all NHS services to ensure equality of care for all (Delivering the Forward View: NHS planning guidance, 2016). Despite this, the majority of physiotherapy services continue to provide only emergency respiratory physiotherapy at weekends on critical care. Evidence has demonstrated that an enhanced physiotherapy provision on critical care across five days (Monday to Friday) to deliver early and structured rehabilitation in line with NICE guidelines CG83, can deliver improved patient and hospital outcomes (Monsees et al., 2022, Zhang et al., 2019). However there is limited literature looking at the impact of an enhanced 7 day rehabilitation services across ITU compared to five day rehabilitation on patient outcomes. Case study reports published by NHS improvement (NHS improvement, 2012) involving UK NHS trusts suggest seven day physiotherapy services may result in more consistent rehabilitation on ICU and improved quality, patient outcomes and reduced ICU and hospital length of stay. However a lack of reported methodology, outcome measures and the wider impact of service re-design on patient outcomes are not explored, particularly as some services were re-structured without additional staffing resource. Published work within sub-acute rehabilitation hospitals have demonstrated a reduction in length of stay with the implementation of a seven day service (DiSotto-Monastero et al., 2012). However the generalisation of these finding to an acute ICU setting is questionable. Further research to assess the impact of a properly funded, gold standard seven day rehabilitation service on patient outcomes is warranted.
The delayed initiation of rehabilitation appeared to have an ongoing impact following step down from ICU to the ward. Lower mobility levels at the point of hospital discharge were seen in patients during wave 2 with an increased need for ongoing community rehabilitation. A recent systematic review identified that delayed mobilisation within the ICU setting is associated with poorer functional capacity, reduced walking capacity and reduced health related quality of life at hospital discharge (Arias-Fernández et al., 2018). In response to the ongoing long-term morbidity experienced by COVID-19 survivors, several regional pathways were commissioned after the first wave to support ongoing rehabilitation and facilitate early discharge from the acute setting. There was also intense pressure within the Trust to create capacity due to the desire to re-establish and maintain pre-pandemic service provision. Ultimately, this may have had an impact on the discharge destination of patients, and their ongoing rehabilitation needs. Consequentially, for the outcomes seen in wave 2, patients’ discharge from hospital could be facilitated sooner than was possible in the first wave. However, despite this, post ICU length of stay was equal between the 2 cohorts.
The unit under evaluation has a well-established multi-disciplinary culture of early mobilisation within the ICU and unit understanding regarding the benefits of early mobilisation (McWilliams et al., 2015). Whilst rehabilitation is often led and coordinated by the physiotherapists, actual rehabilitation delivery involves close multidisciplinary working. This is underpinned by a supportive structure that includes multidisciplinary team rounds, utilising the shared expertise of team members to discuss rehabilitation in the context of medical stability, weaning of sedation and respiratory support, management of delirium and other member tasks which may require completion (Bakhru et al., 2016). The wider changes within the critical care workforce would likely have also had a significant impact on these structures. The provision of nursing at a ratio of one specialist ICU nurse to three patients, with support from a non-ICU nurse and a non-ICU medic, would have significantly reduced the overall expertise available to support complex decision making. Whilst a task-based approach to patient care ensured capacity could be increased, it would be unrealistic to expect a redeployed member of staff to perform at the same level as an experienced critical care nurse, with those redeployed describing the change as moving from ‘expert to novice’ or ‘being thrown in the deep end’ (Tang et al., 2021). This changing skill mix, or the associated high turnover of staff seen during the pandemic would negatively affect the established rehabilitation culture in the unit, potentially reducing the likelihood of rehabilitation plans being followed or progressed over a weekend when physiotherapy support was not available.
The limitations of a five day rehabilitation service meant emergency respiratory physiotherapy and only limited rehabilitation was provided at the weekend during wave 2, by a team consisting of redeployed respiratory competent physiotherapists working within the Trust but not currently working on ICU. Consequently, reduced physiotherapy provision, coupled with an increased proportion of non-specialist staff adversely affected consistency of rehabilitation planning and delivery. In real terms, this likely meant patients appropriate for rehabilitation between Friday afternoon and Monday morning may have only been reviewed from a respiratory perspective. The staffing model adopted during wave 1 allowed maintenance of a more highly skilled critical care workforce across seven days which provided consistency and familiarity with the COVID-19 patient cohort. This importance of this skill mix should not be underestimated, with consistent staffing and cohesive team working associated with improvements in ICU mortality and patient outcomes (Wheelan et al., 2003).
Limitations
Our study has a number of limitations. As a single centre observational study this may not be reflective of other ICU COVID-19 populations. Data was collected mixed prospectively and retrospectively for wave 2 however, it was directly compared to historical data from wave 1. Although populations were statistically similar, aspects of ICU care and management evolved between waves, which could have impacted patient outcomes e.g. pharmacological strategies. In addition, with the emergence of new strains e.g. delta COVID-19 variant (B.1.617.2) and the Kent variant in 2021, what impact this had on patients’ outcomes is beyond the scope of this study. Finally, due to service pressures we were unable to record information relating to the consistency and frequency of rehabilitation delivered during either wave which potentially could have an impact on functional outcomes.
An important consideration when evaluating these findings is the changing knowledge and understanding of COVID-19 over the course of the pandemic. Whilst intensive care treatments such as the use of invasive ventilation and sedation remained broadly similar, our study does not consider the specific impact of other novel therapies which may have evolved into use for the second wave in comparison to the first. The most notable of these is likely to be an increased use of dexamethasone, which was shown to significantly reduce mortality in those patients requiring mechanical ventilation or supplementary oxygen (Recovery Collaborative group, 2021). It could be hypothesised that the increased use of dexamethasone was associated survival of a proportion of patients who may not have survived during the first wave. Whilst survival increased, this may have had a greater impact on associated morbidity and could explain to some extent the worse physical outcomes for those patients evaluated during wave 2. In addition, the use of corticosteroids has been significantly associated with an increased incidence of ICU acquired weakness (Yang et al., 2018). Whilst actual rates of weakness observed were actually lower in wave 2, we did not capture severity of ICU acquired weakness which may have impacted in rehabilitation progress and recovery trajectory.
Conclusion
Patients admitted with SARS Cov-2 during the second wave were comparable to patients admitted during wave 1 in both demographics and their medical management whilst on critical care. The change in physiotherapy staff provision from a seven day rehabilitation service during wave 1 to a five day rehabilitation service with emergency respiratory physiotherapy at a weekend in wave 2 resulted in patients taking longer to commence mobilisation and more dependant at ICU and hospital discharge. This study highlights the benefits of having a seven day physiotherapy service to deliver consistent rehabilitation and should be considered by commissioner’s when reviewing physiotherapy workforce structure. This may also have the added benefit of reducing demand on community and follow up services.. It is worth noting that analysis of the predominant COVID-19 strain between the 2 waves was beyond the scope of this study and therefore any differences in disease progression and pathology are unaccounted for.
Funding
No additional funding was received to complete this project.
Authors contributions
JW helped design the study, conduct the study, analyze and interpret the data, and draft and critically revise the manuscript. AH helped design, interpret the data, draft and critically revise the manuscript. KB helped interpret the data, draft and critically revise the manuscript. CS helped interpret the data, and draft and critically revise the manuscript. DM helped design the study, conduct the study, analyze and interpret the data, draft and critically revise the manuscript. All authors read and approved the final version of the manuscript.
Ethics statement
This project constituted an observation of standard care delivery with no randomization and thus met the definition of a service evaluation under the National Health Service Health research authority guidelines. As such, ethical approval was not required, and because all outcome measures are collected as part of routine care, the need for consent was waived.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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References
Arias-Fernández P. Romero-Martin M. Gómez-Salgado J. Fernández-García D. Rehabilitation and early mobilization in the critical patient: systematic review J. Phys. Ther. Sci. 30 9 2018 1193 1201 10.1589/jpts.30.1193 Epub 2018 Sep 4 30214124
Bakhru R. McWilliams D.J. Wiebe D.J. Spuhler V.J. Schweickert W.D. Intensive care unit structure variation and implications for early mobilization practices: an international survey Ann. Am. Thorac. Soc. 13 9 2016 1527 1537 27268952
Delivering the Forward View: NHS planning guidance 2016/17 – 2020/21. Available at https://www.england.nhs.uk/wp-content/uploads/2015/12/planning-guid-16-17-20-21.pdf.
DiSotto-Monastero M. Chen X. Fisch S. Donaghy S. Gomez M. Efficacy of 7 days per week inpatient admissions and rehabilitation therapy Arch. Phys. Med. Rehabil. 93 12 2012 2165 2169 10.1016/j.apmr.2012.07.003 Epub 2012 Jul 20 PMID: 22819810 22819810
NHS England. Cancer Waiting Times. Dec 2021 https://www.england.nhs.uk/statistics/statistical-work-areas/cancer-waiting-times/.
Gemine R. Davies G.R. Tarrant S. Davies R.M. James M. Lewis K. Factors associated with work-related burnout in NHS staff during COVID-19: a cross-sectional mixed methods study BMJ Open. 11 1 2021 e042591
Goh K.J. Wong J. Tien J.-C.-C. Preparing your intensive care unit for the COVID-19 pandemic: practical considerations and strategies Crit. Care 24 2020 215 10.1186/s13054-020-02916-4 32393325
Greenberg N, Weston D, Hall C, Caulfield T, Williamson V, Fong K. The mental health of staff working in intensive care during covid-19. MedRxiv2020.11.03.20208322 [Preprint]. 2020.
Hermans G. Van den Berghe G. Clinical review: intensive care unit acquired weakness Crit. Care 19 1 2015 274 26242743
Marcomini I. Agus C. Milani L. Sfogliarini R. Bona A. Castagna M. COVID-19 and post-traumatic stress disorder among nurses: a descriptive cross-sectional study in a COVID hospital Med. Lav. 112 3 2021 241 249 10.23749/mdl.v112i3.11129 34142675
Maves R.C. Jamros C.M. Smith A.G. Intensive care unit preparedness during pandemics and other biological threats Crit. Care Clin. 35 2019 609 618 10.1016/j.ccc.2019.06.001 31445608
McWilliams D. Atkins G. Hodson J. Boyers M. Lea T. Snelson C. Feasibility and reliability of the Manchester Mobility Score as a measure of physical function within the Intensive Care Unit Assoc. Chartered Physiotherapists Respir. Care Jn. 48 2015 26 33
McWilliams D, Weblin J, Hodson J, Veenith T, Whitehouse T, Snelson C. Rehabilitation Levels in Patients with COVID-19 Admitted to Intensive Care Requiring Invasive Ventilation. An Observational Study. Ann Am Thorac Soc. 2021 Jan;18(1):122-129.
McWilliams D. Weblin J. Atkins G. Bion J. Williams J. Elliott C. Enhancing rehabilitation of mechanically ventilated patients in the intensive care unit: a quality improvement project J. Crit. Care 30 1 2015 13 18 25316527
Monsees J. Moore Z. Patton D. Watson C. Nugent L. Avsar P. O'Connor T. A systematic review of the effect of early mobilization on length of stay for adults in the intensive care unit Nurs. Crit. Care 2022 10.1111/nicc.12785
Network A.R.D.S. Brower R.G. Matthay M.A. Morris A. Schoenfeld D. Thompson B.T. Wheeler A. Ventilation with lower tidal volumes as compared with traditional tidal volumes for acute lung injury and the acute respiratory distress syndrome N. Engl. J. Med. 342 18 2000 1301 1308 10793162
NHS improvement (2012). Equality for all. Delivering safe care – seven days a week. Available at: https://www.england.nhs.uk/improvement-hub/wp-content/uploads/sites/44/2017/11/Equality-for-all-Delivering-safe-care-seven-days-a-week.pdf.
Pun B.T. Balas M.C. Barnes-Daly M.A. Thompson J.L. Aldrich J.M. Barr J. Caring for Critically Ill Patients with the ABCDEF Bundle: Results of the ICU Liberation Collaborative in Over 15,000 Adults Crit. Care Med. 47 1 2019 3 14 30339549
RECOVERY Collaborative Group Horby P. Lim W.S. Emberson J.R. Mafham M. Bell J.L. Linsell L. Staplin N. Brightling C. Ustianowski A. Elmahi E. Prudon B. Green C. Felton T. Chadwick D. Rege K. Fegan C. Chappell L.C. Faust S.N. Jaki T. Jeffery K. Montgomery A. Rowan K. Juszczak E. Baillie J.K. Haynes R. Landray M.J. Dexamethasone in hospitalized patients with covid-19 N. Engl. J. Med. 384 8 2021 693 704 32678530
Rockwood K. Song X. MacKnight C. Bergman H. Hogan D.B. McDowell I. Mitnitski A. A global clinical measure of fitness and frailty in elderly people CMAJ 173 2005 489 495 16129869
Tang C.J. Lin Y.P. Chan E.Y. 'From Expert to Novice', perceptions of general ward nurses on deployment to outbreak intensive care units during the COVID-19 pandemic: A qualitative descriptive study J. Clin. Nurs. 2021 10.1111/jocn.16029 Epub ahead of print. PMID: 34468053
Vandenbroucke J. von Elm E. Altman D.G. Gøtzsche P.C. Mulrow C.D. Pocock S.J. Strengthening the reporting of observational studies in epidemiology (STROBE) explanation and elaboration Epidemiology 18 6 2007 805 835 18049195
Wheelan S.A. Burchill C.N. Tilin F. The link between teamwork and patients' outcomes in intensive care units Am. J. Crit. Care 12 6 2003 527 534 14619358
NHS Health Research Authority. http://www.hra.nhs.uk/researchcommunity/ Before you-apply/determine-whether-your-study-is-research/. [last accessed 21/12/2021].
National Institute for Health and Care Excellence. COVID-19 rapid guideline: community-based care of patients with chronic obstructive pulmonary disease (COPD) (NICE guideline no. 168) [online resource]. Available at: https://www.nice.org.uk/guidance/ng168.
Macmillan. 2020. The forgotten C: The impact of COVID-19 on cancer care. Macmillan cancer support. Available at The Forgotten 'C'? The impact of COVID-19 on cancer care (macmillan.org.uk).
https://coronavirus.data.gov.uk/details/healthcare.
Yang T. Li Z. Jiang L. Xi X. Corticosteroid use and intensive care unit-acquired weakness: a systematic review and meta-analysis Crit. Care 22 1 2018 187 30075789
Zampino R, Vitrone M, Spiezia S, Albisinni R, Durante-Mangoni E. Remote Outpatient Management During COVID-19 Lockdown: Patient-Derived Quality Assessment. Qual Manag Health Care. 2021 Jan/Mar 01;30(1):76-77. doi: 10.1097/QMH.0000000000000296. PMID: 33306652.
Zhang L. Hu W. Cai Z. Liu J. Wu J. Deng Y. Yu K. Chen X. Zhu L. Ma J. Qin Y. Early mobilization of critically ill patients in the intensive care unit: A systematic review and meta-analysis PLoS One. 14 10 2019 10.1371/journal.pone.0223185 PMID: 31581205; PMCID: PMC6776357 e0223185
| 0 | PMC9729646 | NO-CC CODE | 2022-12-16 23:16:07 | no | Intensive Crit Care Nurs. 2022 Dec 8;:103370 | utf-8 | Intensive Crit Care Nurs | 2,022 | 10.1016/j.iccn.2022.103370 | oa_other |
==== Front
Prim Care Diabetes
Prim Care Diabetes
Primary Care Diabetes
1751-9918
1878-0210
Primary Care Diabetes Europe. Published by Elsevier Ltd.
S1751-9918(22)00215-7
10.1016/j.pcd.2022.12.002
Article
Outcome of COVID-19 infection in people with diabetes mellitus or obesity in the primary care setting in Catalonia, Spain: A retrospective cohort study of the initial three waves
Mauricio Dídac abcd⁎1
Vlacho Bogdan ab1
Ortega Emilio aefg
Cos-Claramunt Xavier ahi
Mata-Cases Manel abj
Real Jordi ab
Fernandez-Camins Berta ak
Franch-Nadal Josep abgl⁎⁎
a DAP-Cat group, Unitat de Suport a la Recerca Barcelona, Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
b CIBER of Diabetes and Associated Metabolic Diseases (CIBERDEM), Instituto de Salud Carlos III (ISCIII), Spain
c Department of Endocrinology and Nutrition, Hospital Universitari de la Santa Creu i Sant Pau
d Department of Medicine, University of Vic - Central University of Catalonia, Vic, Barcelona, Spain
e Department of Endocrinology and Nutrition, Institut d’Investigacions Biomèdiques August Pi i Suñer, Hospital Clinic, Barcelona, Spain
f CIBER of physiopathology of Obesity and Nutrition (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Spain
g Department of Medicine, Universitat de Barcelona, Barcelona, Spain
h Department of Medicine, Universitat Autonoma de Barcelona, Barcelona, Spain
i Innovation office at Institut Català de la Salut, Barcelona, Spain
j Primary Health Care Center La Mina, Gerència d’Àmbit d’Atenció Primària Barcelona Ciutat, Institut Català de la Salut, Sant Adrià de Besòs, Spain
k Primary Health Care Center Poblenou, Gerència d’Àmbit d’Atenció Primària Barcelona Ciutat, Institut Català de la Salut, Sant Adrià de Besòs, Spain
l Primary Health Care Center Raval Sud, Gerència d’Àmbit d’Atenció Primària Barcelona Ciutat, Institut Català de la Salut, Sant Adrià de Besòs, Spain
⁎ Correspondence to: Hospital de la Santa Creu i Sant Pau, Sant Quintí, 89, 08041 Barcelona, Spain.
⁎⁎ Correspondence to: Centre d'Atenció Primària Raval Sud, Av. Drassanes, 17-21, 08001 Barcelona, Spain.
1 These authors contributed equally and share first authorship
8 12 2022
8 12 2022
9 7 2022
24 11 2022
1 12 2022
© 2022 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved.
2022
Primary Care Diabetes Europe
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Aim
We estimate the incidence and risk factors for fatal and non-fatal events among the COVID-19 infected subjects based on the presence of obesity or diabetes during the initial three epidemiological waves in our region.
Methods
This was a retrospective cohort study. A primary care database was used to identify persons with COVID-19. We stratified for subjects who either had diabetes mellitus or obesity. The follow-up period for study events was up to 90 days from inclusion.
Results
In total, 1238,710 subjects were analysed. Subjects with diabetes mellitus or obesity were older and had a worse comorbidity profile compared with groups without these conditions. Fatal events were more frequent among people with diabetes and during the first wave. In the second and third waves, the number of study events decreased. Diabetes was a risk factor for fatal events in all models, while obesity was only in the model adjusted for age, sex, diabetes and COVID-19 waves. HIV, cancer, or autoimmune diseases were risk factors for mortality among subjects with COVID-19 in the fully-adjusted model.
Conclusions
Diabetes was an independent risk factor for mortality among people with COVID-19. The number of fatal events decreased during the second and third waves in our region, both in those with diabetes or obesity.
Keywords
Diabetes mellitus
COVID-19
Catalonia
Epidemiological wave
Obesity
Primary care
==== Body
pmc1 Introduction
Coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 has evolved into a world pandemic in the last few years, accumulating over six million deaths to date [1], [2]. During this pandemic, preliminary data indicated that obesity and diabetes mellitus may contribute independently and proportionally to a higher incidence of complications and a worse COVID-19 prognosis. These conditions were observed to be the most common comorbidities and predictors of critical illness in hospitalised patients [3]. Diabetes and obesity are complex chronic diseases that generate high morbidity and mortality in general. They are also predisposing factors for other cardiovascular and renal diseases and/or the reason for worse health evolution and prognosis.
When it comes to diabetes, it is a frequent major comorbidity and risk factor for poor prognosis in COVID-19 patients [4], [5]. The risk of intensive care unit (ICU) admission and the poor short-term outcome were higher in COVID-19 patients with diabetes [6], with twice the risk of COVID-19 mortality as non-diabetics [7]. Furthermore, patients with diabetes may experience prolonged symptoms or develop post-discharge complications such as post-COVID-19 syndrome [8]. This relationship between diabetes mellitus and COVID-19 infection may be bidirectional, as some studies have observed that diabetes may not only be a cause of poor COVID-19 prognosis, but COVID-19 may induce worsening hyperglycaemia and insulin resistance in return [8].
Similarly, body mass index (BMI) and obesity have been independently associated with the COVID-19 severity in different studies [4], and many reports to date have associated obesity with a higher COVID-19 mortality rate [9]. Obese diabetic patients have also been reported to have a worse outcome after COVID-19 infection [3], [4], [10].
Several mechanisms could explain the association between COVID-19 and diabetes or obesity, one of them being the chronic low-grade pro-inflammatory status present in diabetic and obese patients [5]. These conditions, in addition to the SARS-CoV-2 infection, could lead to an over-production of pro-inflammatory cytokines (TNFα, IL-6, IL-1β, and CXC-chemokine ligand) [10] that would result in a worse cytokine storm, mediating a progression toward a catastrophic inflammatory response and organ failure [11], [12], [13], [14], [15]. In addition, diabetic patients suffering from COVID-19 infection have been observed to be more susceptible to the destructive effect of this cytokine storm than non-diabetic subjects [16]. All in all, diabetes and obesity have been seen to entail cardiovascular, respiratory, metabolic and immune dysfunctions that induce a pro-inflammatory, prothrombotic state and reduced respiratory capacity. This, in turn, may contribute to developing severe COVID-19 infection in these patients [17].
Previously, we published findings on the high burden associated with diabetes mellitus (DM) in patients hospitalised with COVID-19 infection, particularly among men, the elderly, and those with impaired kidney function [18]. However, the study was limited to in-hospital subjects admitted for COVID-19 and DM during the first wave. In the current study, we used a large population primary healthcare database to estimate the incidence and risk factors for fatal and non-fatal events among the COVID-19 infected subjects based on the presence of obesity or diabetes during the initial three epidemiological waves in our region.
2 Methods
2.1 Settings
We used the SIDIAP (Sistema de Información para el desarrollo de la Investigación en Atención Primaria) primary care database to perform a retrospective cohort analysis from February 1st 2020 until June 30th 2021. This population database is a well-validated secondary data source for performing epidemiological and pharmacoepidemiological studies from primary care settings in Catalonia (Spain). Besides standard datasets of variables related to different clinical variables (health problems, explorations, laboratory parameters, medication prescription/dispensations) collected routinely from pseudo-anonymised patients' electronic records, the database was especially updated with specific variables related to the COVID-19 pandemic (diagnostic tests, procedures) in order to help investigators perform specific epidemiological studies related to this topic [19].
2.2 Selection criteria
During the observational period, we included all individuals in the database with a positive COVID-19 diagnostic test or diagnostic code (ICD-10: B34.2; B97.2; B97.21; B97.29; J12.81; J12.89; U07.1; Z20.828;J12.89; J20.8; J22; J40; J80; J98.8). Only the first infection with COVID-19 was considered for inclusion. Subjects with less than one year of medical history records in the database, those with diagnostic codes for COVID-19 before the observational period starting date, and those under 18 years of age were excluded from the analysis.
2.3 Study variables
At inclusion, subjects with DM were identified in the database using an algorithm as a diagnostic code for type 2 diabetes or type 1 diabetes ( ICD-10: E10, E11 and subcodes) and/or the presence of antidiabetic treatment and/or previous registry of glycosylated haemoglobin (HbA1c) value ≥ 6.5% (48 mmol/mol). Subjects with obesity were identified by diagnostic code (ICD-10:E66 and sub-codes) and presence of body mass index (BMI) registry > 30 kg/m2. The relevant comorbidities such as hypertension and hyperlipidaemia were identified by the ICD-10 diagnostic code and treatment, while other comorbidities such as cardiovascular diseases, heart failure, cerebrovascular diseases, ischemic heart disease, peripheral artery disease, chronic obstructive pulmonary disease-COPD, human immunodeficiency virus-HIV, autoimmune disease, and cancer were only identified by the diagnostic code. Chronic kidney disease was defined by the diagnostic code and/or estimated glomerular filtration rate (eGFR) < 60 ml/min / 1.73 m2 calculated using the CKD-EPI equation and/or a ratio of albumin/creatinine (CAC) in urine ≥ 30 mg/g. We also collected data related to laboratory parameters (glycaemic, renal, lipid, hepatic profile), physical exploratory data (systolic/diastolic blood pressure, BMI), and relevant concomitant drugs.
During the 90-day follow-up period after inclusion, we collected data on fatal (mortality) and non-fatal events (mechanical ventilation, cardiovascular complications, neurological complications, respiratory complications, thrombotic complications, and days of hospitalization). These events were defined by diagnostic codes and hospital discharge information.
2.4 Statistical methods
Descriptive analyses were performed on all clinically important variables at the time of inclusion and for study events during the observational period. The qualitative variables were described by numbers and percentages, while mean values and standard deviation described the quantitative variables. We calculated the incidence of study events during the 90-day follow-up period from inclusion. The events were calculated for three different COVID-19 epidemiologic waves defined as the first wave (01/02/2020–30/06/2020), second wave (01/7/2020–31/12/2020) and third wave (01/01/2021–31/03/2021). Different multivariable logistic models were performed for the clinically relevant variables. The occurrence of death was defined as the dependent variable in the fatal events model. In contrast, in the non-fatal events model, the dependent variable was defined as a combination of cardiovascular and/or respiratory and/or neurological complications during the 90-day follow-up period. On the other hand, we considered the presence of different clinical characteristics at inclusion as independent variables (age, sex, comorbidities, laboratory parameters, clinical variables, and COVID-19 waves). The statistical analyses were performed using R3.6.1 software (https://www.r-project.org/).
2.5 Ethics committee approval
The IDIAP Jordi Gol Ethics Committee approved the study, protocol approval number 20/077-PCV, on 13/04/2020.
3 Results
From the initial population of 1,450,335 people with a positive COVID-19 diagnostic test or diagnostic code in the database, a total of 1,238,710 participants met all study selection criteria and were included in the study. Fig. 1 shows the study flowchart.Fig. 1 Study flowchart.
Fig. 1
3.1 Subjects’ characteristics
Table 1 summarises the baseline characteristics of subjects in the cohort and different groups. Mean age of the overall cohort was 47.3 ( ± 18.5) years, the majority females (54.8%), and 15.0% of users came from high deprivation areas. The subgroup of subjects with diabetes was much older (on average 19.6 years older) than those without diabetes. People with diabetes had a poorer comorbidity profile, especially for the higher frequency of hypertension, hyperlipidaemia, obesity, cardiovascular disease, chronic kidney disease, and the presence of any autoimmune disease, compared with the non-DM group. Regarding laboratory parameters, non-DM subjects had a worse lipid profile (LDL cholesterol and total cholesterol); however, mean triglyceride levels were higher in the DM group. Also, glomerular filtration was lower in the DM group.Table 1 Baseline characteristics.
Table 1 All
N = 1238,710 Diabetes Obesity
Without
N = 1120,711 With
N = 117,999 Without
N = 968,156 With
N = 270,554
Age, mean, SD 47.3 (18.5) 45.4 (17.7) 65.0 (17.0) 45.0 (17.7) 55.3 (19.0)
Sex (female), n (%) 678,338 (54.8) 613,593 (54.8) 64,745 (54.9) 516,917 (53.4) 161,421 (59.7)
MEDEA, n (%)
Lowest living area deprivation 182,993 (14.8) 169,378 (15.1) 13,615 (11.5) 154,354 (15.9) 28,639 (10.6)
Highest living area deprivation 185,473 (15.0) 164,983 (14.7) 20,490 (17.4) 135,148 (14.0) 50,325 (18.6)
Clinical variables (mean, SD)
BMI 28.4 (5.65) 27.8 (5.54) 30.2 (5.61) 24.7 (3.06) 32.9 (4.76)
Systolic blood pressure 76.4 (10.6) 76.5 (10.6) 75.9 (10.5) 126 (15.9) 131 (15.6)
Diastolic blood pressure 128 (16.0) 127 (15.9) 132 (15.8) 75.6 (10.5) 77.9 (10.6)
Total cholesterol 195 (42.7) 198 (42.1) 183 (43.3) 195 (42.6) 194 (42.8)
LDL cholesterol 119 (36.9) 123 (36.2) 104 (35.5) 121 (37.0) 116 (36.6)
Triglycerides 133 (89.1) 124 (77.4) 162 (115) 123 (81.9) 150 (97.7)
Glomerular filtration 80.8 (15.5) 82.5 (13.6) 73.5 (20.1) 82.2 (14.2) 77.8 (17.5)
HbA1c 6.49 (1.37) 5.55 (0.40) 7.15 (1.41) 6.31 (1.32) 6.66 (1.39)
ALT 22.7 (24.4) 22.4 (23.3) 24.3 (28.7) 26.1 (29.6) 27.2 (30.2)
AST 26.5 (29.8) 26.2 (27.8) 27.5 (36.4) 21.8 (23.7) 24.7 (25.8)
GGT 36.6 (67.0) 33.7 (58.7) 49.1 (93.4) 33.3 (63.4) 43.3 (73.4)
Comorbidities, n (%)
Hypertension 254,769 (20.6) 180,118 (16.1) 74,651 (63.3) 149,321 (15.4) 129,938 (48.0)
Hyperlipidaemia 209,477 (16.9) 148,494 (13.2) 60,983 (51.7) 125,039 (12.9) 84,438 (31.2)
Obesity 270,554 (21.8) 207,536 (18.5) 63,018 (53.4) 0 (0.00) 270,554 (100)
Diabetes mellitus 117,999 (9.53) 0 (0.00) 117,999 (100) 54,981 (5.68) 63,018 (23.3)
Cardiovascular diseases 74,466 (6.01) 46,282 (4.13) 28,184 (23.9) 42,112 (4.35) 32,354 (12.0)
Heart failure 30,794 (2.49) 17,316 (1.55) 13,478 (11.4) 13,872 (1.43) 16,922 (6.25)
Cerebrovascular diseases 37,819 (3.05) 24,571 (2.19) 13,248 (11.2) 22,422 (2.32) 15,397 (5.69)
Ischemic heart disease 35,282 (2.85) 20,207 (1.80) 15,075 (12.8) 18,507 (1.91) 16,775 (6.20)
Peripheral artery disease 14,645 (1.18) 7460 (0.67) 7185 (6.09) 8359 (0.86) 6286 (2.32)
Chronic kidney disease 63,180 (5.10) 50,490 (4.51) 33,602 (28.5) 45,106 (4.66) 38,986 (14.4)
COPD 39,531 (3.19) 26,913 (2.40) 12,618 (10.7) 72,701 (7.51) 29,763 (11.0)
HIV 2561 (0.21) 2287 (0.20) 274 (0.23) 2171 (0.22) 390 (0.14)
Autoimmune disease 158,101 (12.8) 131,280 (11.7) 26,821 (22.7) 107,415 (11.1) 50,686 (18.7)
Cancer 64,584 (5.21) 49,261 (4.40) 15,323 (13.0) 42,325 (4.37) 22,259 (8.23)
ALT: Alanine transaminase AST: Aspartate transaminase; BMI: body mass index; CVD: cardiovascular disease; COPD: chronic obstructive pulmonary disease, GGT: Gamma-glutamyl transferase; HIV: human immunodeficiency virus; HbA1c: glycosylated haemoglobin; LDL: Low-density lipoprotein; SD: standard deviation
The analysis by obesity status showed that obese subjects were 10.3 years older and were more frequently female than those without obesity. Of the obese subjects, 18.6% came from high deprivation areas, the highest percentage compared with the other groups. Regarding comorbidities, subjects with obesity had a poorer comorbidity profile than those without. Hypertension was the most prevalent comorbidity (48.0%), followed by hyperlipidaemia (31.2%) and diabetes (23.3%). Twelve percent had a cardiovascular disease, and 8.2% had some type of cancer.
3.2 Fatal and non-fatal events
During the 90-day follow-up period, we observed the highest percentages of mortality events among people with DM and obesity during the first wave (7.0% and 3.6%, respectively). Fatal events decreased drastically during the second and third COVID-19 waves. A decrease was also observed for non-fatal events during the three waves. Of the DM subjects, 10.6% were hospitalised during the first wave, while this percentage was only 1.7% during the third wave. Among the DM subjects, cardiovascular and neurological complications were mostly present as events during the follow-up periods in all three COVID-19 waves. A higher incidence of thrombotic complications was observed among obese subjects during the first and third waves compared with other groups. Table 2 summarises events among the different groups’ participants and waves during the 90-day follow-up period.Table 2 Events at 90-day follow-up period during the three waves.
Table 2 First wave Second wave Third wave
Events, n (%) All
N = 244,165 DM N = 29,935 Obesity N = 61,336 All N = 704,080 DM N = 61,411 Obesity N = 148,294 All N = 288,060 DM N = 26,414 Obesity
N = 60,410
Overall mortality 6032 (2.47) 2107 (7.04) 2220 (3.62) 3665 (0.52) 1338 (2.18) 1445 (0.97) 449 (0.16) 165 (0.62) 171 (0.28)
Hospital mortality 1382 (0.57) 543 (1.81) 578 (0.94) 918 (0.13) 353 (0.57) 405 (0.27) 111 (0.04) 37 (0.14) 46 (0.08)
Hospital admission 11,397 (4.67) 3169 (10.6) 4920 (8.02) 12,537 (1.78) 3625 (5.90) 5686 (3.83) 1487 (0.52) 467 (1.77) 663 (1.10)
Mechanical ventilation 541 (0.22) 210 (0.70) 283 (0.46) 588 (0.08) 222 (0.36) 314 (0.21) 43 (0.01) 11 (0.04) 22 (0.04)
ICU 865 (0.35) 265 (0.89) 414 (0.67) 1078 (0.15) 351 (0.57) 489 (0.33) 109 (0.04) 30 (0.11) 44 (0.07)
Cardiovascular complications 10,120 (4.14) 3245 (10.8) 4705 (7.67) 11,066 (1.57) 3444 (5.61) 5179 (3.49) 1475 (0.51) 483 (1.83) 680 (1.13)
Neurological complications 9467 (3.88) 1916 (6.40) 3043 (4.96) 11,237 (1.60) 1782 (2.90) 3282 (2.21) 1339 (0.46) 255 (0.97) 413 (0.68)
Thrombotic complications, 296 (0.12) 61 (0.20) 129 (0.21) 311 (0.04) 65 (0.11) 131 (0.09) 50 (0.02) 11 (0.04) 28 (0.05)
Respiratory complications 10,475 (4.29) 2360 (7.88) 4100 (6.68) 11,112 (1.58) 2241 (3.65) 4307 (2.90) 1266 (0.44) 240 (0.91) 493 (0.82)
DM: diabetes mellitus; ICU: intensive care unit
3.3 Factors associated with fatal and non-fatal events
Table 3 shows different model associations between risk factors and mortality events during the 90 days. In the unadjusted analysis, positive associations (OR>1) were observed for most variables and mortality events. The only negative association was observed for being male, and subjects from the second and third COVID-19 waves compared with the first wave. In the multivariable analyses, diabetes mellitus remained a risk factor for death in all models, while obesity was a risk factor associated with this fatal event only in the model adjusted for age, sex, and COVID-19 waves. Subjects with HIV had a four-fold probability of mortality during the 90-day follow-up period in the fully adjusted model.Table 3 Unadjusted and adjusted odds ratios for fatal event at 90-day follow-up period.
Table 3 Fatal events during 90 days period
Risk Factor Unadjusted OR 95CI [Li; Ui] Model 1 Adjusted OR 95CI [Li; Ui] Model 2 Adjusted OR 95CI [Li; Ui] Model 3 Adjusted OR 95CI [Li; Ui]
Age 1.12 [1.12; 1.12] 1.11 [1.11; 1.11] 1.10 [1.10 – 1.10] 1.10 [1.10; 1.10]
Male, ref: Female 0.95 [0.91; 0.99] 1.60 [1.54; 1.67] 1.51 [1.44; 1.58] 1.32 [1.26; 1.38]
Obesity, (ref: No-obese subjects) 2.19 [2.11; 2.28] 1.07 [1.03; 1.12] 1.01 [0.97; 1.06] 0.98 [0.94; 1.03]
Diabetes mellitus, (ref: No-diabetic subjects) 5.38 [5.16; 5.60] 1.43 [1.37; 1.50] 1.31 [1.25; 1.37] 1.29 [1.23; 1.35]
Wave 2, ref: wave 1 0.21 [0.20; 0.22] 0.38 [0.36; 0.40] 0.39 [0.37; 0.41] 0.39 [0.37; 0.41]
Wave 3, ref: wave 1 0.06 [0.06; 0.07] 0.10 [0.09; 0.11] 0.11 [0.10; 0.12] 0.11 [0.10; 0.12]
Hypertension, ref: No hypertension 17.9 [17.0; 18.8] 1.30 [1.22; 1.39] 1.26 [1.19; 1.35]
Hyperlipidaemia, ref: No hyperlipidaemia 3.71 [3.56; 3.86] 0.83 [0.80; 0.87] 0.82 [0.78; 0.85]
CVD, ref: No CVD 10.9 [10.5; 11.4] 1.45 [1.38; 1.52] 1.41 [1.34; 1.40]
CKD, ref: No CKD 16.1 [15.4; 16.7] 1.42 [1.36; 1.49] 1.37 [1.31; 1.43]
COPD, ref: No COPD 8.04 [7.65; 8.45] 1.47 [1.39; 1.55]
Autoimmune disease, ref: No AI disease 2.59 [2.48; 2.71] 1.09 [1.04; 1.14]
HIV, ref: No HIV 1.98 [1.43; 2.66] 4.09 [2.91; 5.59]
Cancer, (ref: No cancer) 6.89 [6.58; 7.20] 1.74 [1.66; 1.82]
number 1,238,712
R2 – 0.096 0.099 0.102
CKD: Chronic kidney disease; CI: Confidence intervals; CVD: cardiovascular disease; COPD: chronic obstructive pulmonary disease, HIV: human immunodeficiency virus; Li: lower limit, Ui: upper limit,
Fatal events: mortality
Model 1 adjusted for age, sex, diabetes, obesity, COVID-19 waves,
Model 2 adjusted for: Model 1 variables adding common comorbidities: hypertension, hyperlipidaemia, CVD, and CKD
Model 3 adjusted for: Model 1 and Model 2 variables, adding additional comorbidities: COPD, Autoimmune disease, HIV and cancer
Regarding the risk factors related to non-fatal events, male sex was negatively associated with these events in both adjusted and unadjusted models. Compared with the fatal events model, hyperlipidemia at inclusion was positively associated with non-fatal events in both unadjusted and fully adjusted models. The presence of diabetes or obesity remained as risks factors for cardiovascular and/or respiratory and/or neurological complications during the 90-day follow-up period in all models. Table 4 summarizes the odds ratios for non-fatal events at a 90-day follow-up period.Table 4 Unadjusted and adjusted odds ratios for non-fatal events at 90-day follow-up period.
Table 4 Non-fatal events during 90 days period
Risk Factor Unadjusted OR 95CI [Li; Ui] Model 1 Adjusted OR 95CI [Li; Ui] Model 2 Adjusted OR 95CI [Li; Ui] Model 3 Adjusted OR 95CI [Li; Ui]
Age 1.03 [1.03;1.03] 1.02 [1.02; 1.03] 1.01[1.01; 1.02] 1.01 [1.01; 1.01]
Male, ref: Female 0.84 [0.83;0.86] 0.94 [0.92; 0.96] 0.89 [0.87; 0.90] 0.86 [0.85; 0.88]
Obesity, (ref: No-obese subjects) 2.16 [2.12;2.20] 1.53 [1.50; 1.56] 1.39 [1.36; 1.42] 1.15 [1.12; 1.18]
Diabetes mellitus, (ref: No-diabetic subjects) 2.57 [2.52;2.63] 1.36 [1.33; 1.40] 1.17 [1.14; 1.20] 1.29 [1.23; 1.35]
Wave 2, ref: wave 1 0.42 [0.41;0.43] 0.50 [0.49; 0.51] 0.50 [0.49; 0.51] 0.50 [0.50; 0.51]
Wave 3, ref: wave 1 0.13 [0.12;0.13] 0.14 [0.14; 0.15] 0.14 [0.14; 0.15] 0.15 [0.14; 0.15]
Hypertension, ref: No hypertension 3.33 [3.27;3.39] 1.54 [1.50; 1.58] 1.52 [1.48; 1.56]
Hyperlipidaemia, ref: No hyperlipidaemia 2.49 [2.45;2.54] 1.19 [1.16; 1.22] 1.17 [1.15; 1.20]
CVD, ref: No CVD 3.54 [3.45;3.62] 1.38 [1.34; 1.42] 1.32 [1.28; 1.36]
CKD, ref: No CKD 3.27 [3.20;3.35] 1.08 [1.05; 1.11] 1.04 [1.01; 1.07]
COPD, ref: No COPD 3.83 [3.72;3.95] 1.71 [1.65; 1.76]
Autoimmune disease, ref: No AI disease 1.80 [1.77;1.84] 1.18 [1.16; 1.21]
HIV, ref: No HIV 2.01 [1.74;2.30] 1.83 [1.58; 2.11]
Cancer, (ref: No cancer) 2.27 [2.20;2.33] 1.15 [1.11; 1.18]
number 1238712
R2 – 0.037 0.041 0.043
CKD: Chronic kidney disease; CI: Confidence intervals; CVD: cardiovascular disease; COPD: chronic obstructive pulmonary disease, HIV: human immunodeficiency virus; Li: lower limit, Ui: upper limit,
Non-fatal events: cardiovascular and/or respiratory and/or neurological complications
Model 1 adjusted for age, sex, diabetes, obesity, COVID-19 waves,
Model 2 adjusted for: Model 1 variables adding common comorbidities: hypertension, hyperlipidaemia, CVD, and CKD
Model 3 adjusted for: Model 1 and Model 2 variables, adding additional comorbidities: COPD, Autoimmune disease, HIV and cancer
4 Discussion
This retrospective cohort study of the primary healthcare database from Catalonia (Spain) revealed a high incidence of a fatal outcome among people with diabetes and obesity during the first wave in our region. Mortality decreased during the second and third waves. The same decreasing trend was observed for non-fatal events (cardiovascular, neurological, thrombotic, or respiratory complications) at short-term follow-up (90 days) during the three COVID-19 waves.
Globally, a total of 6318,093 people are estimated to have died from COVID-19 since the pandemic's beginning [20]. The prevalence of diabetes varies among studies; in previously published meta-analyses, DM prevalence ranged from 8% to 21% of cases with COVID-19 [21], [22]. Regarding the prevalence of obesity observed in other international studies, depending on the country and type of registry, these percentages ranged from 15.4% in France [23] to 48.3% in the North American COVID-NET registry [24]. Comparing our data with those of other studies using the same database, for the period between 15 March and 24 April 2020, the prevalence of diabetes and obesity was similar to our study [25]. Another published study using the same database, which included a total of 311,542 participants with COVID-19 between March 2020 and 30 June 2020, found a slightly higher prevalence of diabetes (10.0%) and obesity (50.5%) compared with our study [26]. These differences could be due to the definition of obesity, the differences in the study population, and the different timeframes for considering the obesity cases. We included obese subjects defined as those with obesity diagnostic codes or recent BMI measures over 30 kg/m2 at any time and closest to the index date.
The current study cohort had clinical characteristics similar to previously reported analyses with the same database for age, BMI, socioeconomic index (deprivation index), and comorbidities [25], [26]. The worst comorbidity profile among DM subjects observed in our cohort is in line with previously reported studies [27]. On the other hand, for subjects with obesity, numerous comorbidities were present, such as hypertension, dyslipidaemia, type 2 diabetes, and chronic kidney or liver disease, which are risk factors for COVID-19 [28]. At inclusion, 22.2% of DM subjects and 18.7% of subjects with obesity had a concomitant active diagnosis of autoimmune disease.
Since the beginning of the pandemic, different meta-analyses on risk factors for COVID-19 outcomes were reported. Advanced age, male sex, and having pre-existing cardiovascular diseases were associated with worse COVID-19 outcomes [29], [30], [31], [32]. Pre-existing diabetes and obesity are risk factors for the severity outcomes associated with coronavirus. In our study, considering all-cause mortality during the 90-day follow-up period, having been hospitalised or not, we found a positive association between diabetes and mortality. This is in line with a recent meta-analysis of observational studies, evaluating a total of 198,491 deaths among 1165,897 subjects with COVID-19; the summary relative risk was 1.54 (95% CI: 1.44; 1.64) [33]. However, a recent meta-analysis with 7244 hospital patients from 11 different countries, evaluating only mortality as an outcome, showed that obesity and diabetes were associated with severity (mechanical ventilation) but not in-hospital death [29]. One of the limitations of this meta-analysis was that it only considered in-hospital mortality. In our multivariable analyses, testing different risk factors in different models, the obesity odds ratios decreased and remained positively associated with mortality after adjusting for age, sex, diabetes, and COVID-19 waves. However, in the fully adjusted model, obesity was not associated with mortality. On the other hand, diabetes remained an independent mortality risk factor in all models. In our analysis, we observed that a previous history of HIV was associated with a four-fold increase in COVID-19 mortality. This is in line with a recent meta-analysis of 22 studies that included a total of 20,982,498 subjects. Subjects with HIV had a pooled mortality rate of 12.65% (95% CI 6.81;22.31%, I 2 =74%; p < 0.01 [34]. Regarding cancer and COVID-19, our results align with those reported in a recently published meta-analysis of 17 retrospective cohort studies evaluating the in-hospital mortality risk among persons with cancer; this study reported a pooled mortality risk for cancer subjects of 14.1% (95%CI: 9.1%;19.8%) [35]. Another meta-analysis of 37 studies, reported higher odds ratios for risk of death compared with our study (OR = 2.97, 95% CI:1.48; 5.96]; P = 0.002) among those with cancer and COVID-19 [36]. The presence of hyperlipidaemia in our fully adjusted model was negatively associated with COVID-19 mortality but remained an independent risk factor (positively associated) for non-fatal events in all models. The negative association in the mortality model could be due to various reasons, like the variable collinearity or the effect of lipid-lowering drugs. In our study, hyperlipidaemia was defined as a combination of diagnostic code and/or lipid-lowering treatment. So far, previously published observational studies have observed that the use of statin therapy prior to admission was associated with reduced COVID-19 severity or mortality [37], [38], [39]. Concerning the decrease in mortality rates observed in our study during the first three COVID-19 waves, similar results were reported using only hospitalised subjects with COVID-19, identified from the central electronic hospital record of our region [40]. The latter evaluated morality events during a 30-day period and reported higher mortality rates for hospitalised subjects during the first wave. Afterwards, mortality decreased and remained stable during the second and third waves. Our study observed decreases in overall and in-hospital deaths between the second and third COVID-19 wave. The authors also reported that age, sex, diabetes, cancer, and chronic kidney disease were risk factors for COVID-19 death.
This study has some limitations that should be considered. This was an observational study with real-world data from a primary healthcare database. The presence of missing values for some clinical variables is an intrinsic limitation of this type of study. Not all subjects had registers of BMI or obesity diagnostic codes in the database. Moreover, not all subjects had BMI registers at the time of inclusion in the study. This is why we used a proxy algorithm to estimate the cases of obesity, as the presence of BMI or diagnostic code at any time near the inclusion date. Possible misclassifications or infra-registration of diagnostic codes of diabetes are also possible. For this reason, we used diagnostic codes, HbA1c values and/or register of antidiabetic treatment to identify as many cases of diabetes as possible in our database. We only considered mortality 90 days after inclusion. It is possible that the current analysis did not cover the long-term effect of COVID-19 and its complications. However, our analysis has some strengths, such as the large number of participants included in the study from primary healthcare, representing a global view of the impact of COVID-19 on our users and healthcare system.
In conclusion, the results of our study show that diabetes was an independent risk factor for mortality among people with COVID-19 during the initial three waves. The number of fatal events decreased during the second and third waves among subjects with diabetes or obesity. It is important to continue the surveillance of the impact of COVID-19 and its variants in order to identify risk factors and improve the control of the pandemic.
Funding
This study was supported by the Primary Care Diabetes Europe grant (grant number FEr20/0020).
CRediT authorship contribution statement
Conceptualisation, E.O, J.F-N, M.M-C, B.V, D.M; methodology, E.O, J.F-N, M.M-C, B.V, D.M; formal analysis, J.R; resources and data curation, J.R and B.V; writing—original draft preparation, B.V and B.F-C; writing—review and editing, E.O, J.F-N, M.M-C, FX.C, B.V, B.F-C and D.M; supervision: D.M, and J.F-N.; project administration: B.V. All authors approved the current version of the manuscript.
Declaration of Competing Interest
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: E.O has received advisory and or speaking fees from Astra-Zeneca, Boehringer Ingelheim, Lilly, MSD, Novo Nordisk, Sanofi, and Amgen; they received research grants for the institution from MSD and Amgen. M. M-C. has received an advisory honorarium from Astra-Zeneca, Bayer, Boehringer Ingelheim, GSK, Lilly, MSD, Novartis, Novo Nordisk, and Sanofi; they received speaker honoraria from Astra-Zeneca, Bayer, Boehringer Ingelheim, GSK, Lilly, Menarini, MSD, Novartis, Novo Nordisk, and Sanofi; he received research grants for the institution from Astra-Zeneca, GSK, Lilly, MSD, Novartis, Novo Nordisk, and Sanofi. J. F-N has received advisory and or speaking fees from Astra-Zeneca, Ascensia, Boehringer Ingelheim, GSK, Lilly, MSD, Novartis, Novo Nordisk, and Sanofi; he received research grants for the institution from Astra-Zeneca, GSK, Lilly, MSD, Novartis, Novo Nordisk, Sanofi, and Boehringer. D. M. has received advisory and/or speaking fees from Almirall, Esteve, Ferrer, Lilly, Janssen, Menarini, Lilly, MSD, Novo Nordisk, and Sanofi. B. V, FX.C-C, J.R, and BF-C have no conflict of interest to declare.
Acknowledgements
We would like to thank all healthcare professionals from the Institut Català de la Salut for their tireless work in fighting the COVID-19 pandemic.
Conflict of interest
The funders had no role in the design of the study, in the collection, analysis, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.
==== Refs
References
1 Walls A.C. Park Y.J. Tortorici M.A. Wall A. McGuire A.T. Veesler D. Structure, function, and antigenicity of the SARS-CoV-2 spike glycoprotein e6 Cell 181 2 2020 281 292 10.1016/j.cell.2020.02.058 32155444
2 WHO COVID-19 Dashboard. Geneva: World Health Organization. Available at: 〈https://covid19.who.int〉/ (Accessed 11/06/22).
3 Tchang B.G. Askin G. Sahagun A. Hwang J. Huang H. Mendelsohn Curanaj F.A. Seley J.J. Safford M.M. Alonso L.C. Aronne L.J. Shukla A.P. The Independent risk of obesity and diabetes and their interaction in COVID-19: a retrospective cohort study Obesity 29 6 2021 971 975 10.1002/oby.23172 33694267
4 Cariou B. Hadjadj S. Wargny M. Pichelin M. Al-Salameh A. Allix I. Amadou C. Arnault G. Baudoux F. Bauduceau B. Borot S. Bourgeon-Ghittori M. Bourron O. Boutoille D. Cazenave-Roblot F. Chaumeil C. Cosson E. Coudol S. Darmon P. Gourdy P. Phenotypic characteristics and prognosis of inpatients with COVID-19 and diabetes: the CORONADO study Diabetologia 63 8 2020 1500 1515 10.1007/s00125-020-05180-x 32472191
5 Apicella M. Campopiano M.C. Mantuano M. Mazzoni L. Cappelli A. Del Prato S. COVID-19 in people with diabetes: understanding the reasons for worse outcomes Lancet Diabetes Endocrinol. 8 2020 782 792 32687793
6 Roncon L. Zuin M. Rigatelli G. Zuliani G. Diabetic patients with COVID-19 infection are at higher Risk of ICU admission and poor short-term outcome J. Clin. Virol. 127 2020 104354
7 Barron E. Bakhai C. Kar P. Associations of type 1 and type 2 diabetes with COVID-19-related mortality in England: a whole-population study Lancet Diabetes Endocrinol. 8 2020 813 822 32798472
8 Tzeravini E. Stratigakos E. Siafarikas C. Tentolouris A. Tentolouris N. The role of diabetes and hyperglycemia on COVID-19 infection course—a narrative review Front. Clin. Diabetes Healthc. 3 2022 10.3389/fcdhc.2022.812134
9 Shi Q. Zhang X. Jiang F. Clinical characteristics and risk factors for mortality of COVID-19 patients with diabetes in Wuhan, China: a two-center, retrospective study Diabetes Care 43 2020 1382 1391 32409504
10 Agarwal S. Schechter C. Southern W. Crandall J.P. Tomer Y. Preadmission diabetes-specific risk factors for mortality in hospitalised patients with diabetes and coronavirus disease 2019 Diabetes Care 43 10 2020 2339 2344 10.2337/dc20-1543 32769128
11 Kyriazopoulou E. Leventogiannis K. Norrby-Teglund A. Macrophage activation-like syndrome: an immunological entity associated with rapid progression to death in sepsis BMC Med. 15 2017 172 28918754
12 Codo A.C. Davanzo G.G. Monteiro L.B. de Souza G.F. Muraro S.P. Virgilio-da-Silva J.V. Elevated glucose levels favor SARS-CoV-2 infection and monocyte response through a HIF-1alpha/Glycolysis-Dependent Axis e5 Cell Metab. 32 3 2020 437 446 10.1016/j.cmet.2020.07.007 32697943
13 Kaklamanos A. Belogiannis K. Skendros P. Gorgoulis V.G. Vlachoyiannopoulos P.G. Tzioufas A.G. COVID-19 immunobiology: lessons learned, new questions arise Front Immunol. 12 2021 719023 10.3389/fimmu.2021.719023
14 Wang J. Meng W. COVID-19 and diabetes: the contributions of hyperglycemia J. Mol. Cell Biol. 12 12 2021 958 962 10.1093/jmcb/mjaa054
15 Lontchi-Yimagou E. Sobngwi E. Matsha T.E. Kengne A.P. Diabetes mellitus and inflammation Curr. Diabetes Rep. 13 3 2013 435 444 10.1007/s11892-013-0375-y
16 Zhu L. She Z.G. Cheng X. Association of blood glucose control and outcomes in patients with COVID-19 and pre-existing type 2 diabetes Cell Metab. 31 2020 1068 1077 32369736
17 Sattar N. McInnes I.B. McMurray J.J.V. Obesity is a risk factor for severe COVID- 19 infection: multiple potential mechanisms Circulation 142 2020 4 6 32320270
18 Ortega E. Corcoy R. Gratacòs M. Risk factors for severe outcomes in people with diabetes hospitalised for COVID-19: a cross-sectional database study BMJ Open 11 2021 e051237 10.1136/bmjopen-2021-051237
19 Recalde M. Rodríguez C. Burn E. Data resource profile: the information system for research in primary care (SIDIAP) (April) Int J. Epidemiol. 2022 13 10.1093/ije/dyac068
20 Mathieu E. Ritchie H. Ortiz-Ospina E. A global database of COVID-19 vaccinations Nat. Hum. Behav. 2021
21 Guo W. Li M. Dong Y. diabetes is a risk factor for the progression and prognosis of COVID-19 Diabetes Metab. Res Rev. 2020 e3319
22 Wang X. Fang J. Zhu Y. Clinical characteristics of non-critically ill patients with novel coronavirus infection (COVID-19) in a Fangcang Hospital Clin. Microbiol Infect. 2020
23 Caussy C. Pattou F. Wallet F. Simon C. Chalopin S. Telliam C. Prevalence of obesity among adult inpatients with COVID-19 in France Lancet Diabetes Endocrinol. 8 562 2020 4 10.1016/S2213-8587(20)30160-1 31862151
24 Garg S. Kim L. Whitaker M. O’Halloran A. Cummings C. Holstein R. Hospitalization rates and characteristics of patients hospitalized with laboratory-confirmed coronavirus disease 2019----COVID-NET, 14 States, March 1---30,2020 MMWR Morb. Mor. Wkly. Rep. 69 2020 458 464 10.15585/mmwr.mm6915e3
25 Prieto-Alhambra D. Balló E. Coma E. Filling the gaps in the characterisation of the clinical management of COVID-19: 30-day hospital admission and fatality rates in a cohort of 118 150 cases diagnosed in outpatient settings in Spain Int J. Epidemiol. 49 2021 1930 1939 33118037
26 Giner-Soriano M. Gomez-Lumbreras A. Vedia C. Risk of thrombotic events and other complications in anticoagulant users infected with SARS-CoV-2: an observational cohort study in primary healthcare in SIDIAP (Catalonia, Spain) BMC Prim. Care 23 2022 147 35676639
27 Mata-Cases M. Franch-Nadal J. Real J. Evaluation of clinical and antidiabetic treatment characteristics of different subgroups of patients with type 2 diabetes: data from a Mediterranean population database Prim. Care Diabetes 2021 10.1016/j.pcd.2021.02.003
28 Popkin B.M. Du S. Green W.D. Individuals with obesity and COVID-19: A global perspective on the epidemiology and biological relationships Obes. Rev. J. Int Assoc. Study Obes. 21 2020 e13128
29 Longmore D.K. Miller J.E. Bekkering S. Diabetes and overweight/obesity are independent, nonadditive risk factors for in-hospital severity of COVID-19: an International, multicenter retrospective meta-analysis Diabetes Care 44 2021 1281 1290 33858854
30 Treskova-Schwarzbach M. Haas L. Reda S. Pre-existing health conditions and severe COVID-19 outcomes: an umbrella review approach and meta-analysis of global evidence BMC Med. 19 2021 212 34446016
31 Dessie Z.G. Zewotir T. Mortality-related risk factors of COVID-19: a systematic review and meta-analysis of 42 studies and 423,117 patients BMC Infect. Dis. 21 2021 855 34418980
32 Li X. Zhong X. Wang Y. Clinical determinants of the severity of COVID-19: a systematic review and meta-analysis PLoS One 16 2021 e0250602
33 Mahamat-Saleh Y. Fiolet T. Rebeaud M.E. diabetes, hypertension, body mass index, smoking and COVID-19-related mortality: a systematic review and meta-analysis of observational studies BMJ Open 11 2021 e052777
34 Ssentongo P. Heilbrunn E.S. Ssentongo A.E. Epidemiology and outcomes of COVID-19 in HIV-infected individuals: a systematic review and meta-analysis Sci. Rep. 11 2021 6283 33737527
35 Zarifkar P. Kamath A. Robinson C. Clinical characteristics and outcomes in patients with COVID-19 and cancer: a systematic review and meta-analysis Clin. Oncol. 33 2021 e180 e191
36 Tian Y. Qiu X. Wang C. Cancer associates with Risk and severe events of COVID-19: a systematic review and meta-analysis Int J. Cancer 148 2021 363 374 32683687
37 Daniels L.B. Sitapati A.M. Zhang J. Relation of statin use prior to admission to severity and recovery among COVID-19 inpatients Am. J. Cardiol. 136 2020 10.1016/j.amjcard.2020.09.012
38 Saeed Close O. Castagna F. Agalliu I. Statin use and in-hospital mortality in patients with diabetes mellitus and COVID-19 J. Am. Heart Assoc. 9 24 2020 10.1161/JAHA.120.018475
39 Zhang X.J. Qin J.J. Cheng X. In-hospital use of statins is associated with a reduced risk of mortality among individuals with COVID-19 Cell Metab. 32 2 2020 10.1016/j.cmet.2020.06.015
40 Roso-Llorach A. Serra-Picamal X. Cos F.X. Evolving mortality and clinical outcomes of hospitalised subjects during successive COVID-19 waves in Catalonia, Spain Glob. Epidemiol. 4 2022 100071
| 0 | PMC9729647 | NO-CC CODE | 2022-12-16 23:16:08 | no | Prim Care Diabetes. 2022 Dec 8; doi: 10.1016/j.pcd.2022.12.002 | utf-8 | Prim Care Diabetes | 2,022 | 10.1016/j.pcd.2022.12.002 | oa_other |
==== Front
Taiwan J Obstet Gynecol
Taiwan J Obstet Gynecol
Taiwanese Journal of Obstetrics & Gynecology
1028-4559
1875-6263
Taiwan Association of Obstetrics & Gynecology. Publishing services by Elsevier B.V.
S1028-4559(22)00373-4
10.1016/j.tjog.2022.11.007
Original Article
INVESTIGATION OF SARS-CoV-2 USING RT-PCR IN VAGINAL SWAB SAMPLES OF FEMALE PATIENTS WITH A DIAGNOSIS OF SEVERE COVID-19
Erdem Deniz a∗
Kayaaslan Bircan b
Cakir Esra Yakisik c
Dinc Bedia d
Asilturk Dilek e
Kirca Fisun f
Segmen Fatih c
Turan Isil Ozkocak a
Guner Rahmet b
a Department of Intensive Care Unıt, Universty of Health Sciences, Ankara City Hospital, Ankara, Turkey
b Infectious Disease and Clinical Microbiology, Ankara Yildırım Beyazit University, Ankara City Hospital, Ankara, Turkey
c Department of Intensive Care Unıt, Ankara City Hospital, Ankara, Turkey
d Department of Medical Microbiology, University of Health Sciences, Ankara City Hospital, Ankara, Turkey
e Infectious Disease and Clinical Microbiology, Ankara City Hospital, Ankara, Turkey
f Department of Medical Microbiology, Ankara City Hospital, Ankara, Turkey
∗ Corresponding author. Department of Intensive Care Unit, University of Health Sciences, Ankara City Hospital, Bilkent street No:1 TR-0680 Ankara/Turkey. Tel.: +90 532 4437379.
8 12 2022
8 12 2022
28 11 2022
© 2022 Taiwan Association of Obstetrics & Gynecology. Publishing services by Elsevier B.V.
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Objective
It is important to determine the presence of SARS-CoV-2 in the vaginal fluid samples of reproductive-aged women with severe disease during the acute stage of the disease and to determine the risks of transmission by sexual or vertical transmission.
Material and Methods
Adult women with confirmed severe COVID-19 who were admitted to Ankara City Hospital intensive care unit (ICU) between December 1st, 2020, and January 1st, 2021, were enrolled in the study. Vaginal swab samples were collected within 48 hours in the ICU using Dacron or rayon swabs and tested for SARS-CoV-2 using reverse transcription real-time polymerase chain reaction (RT PCR).
Results
Thirty women of reproductive age were included in the study, five (16.7%) of whom were pregnant. The mean age was 44.9 (±10.5) years. The most common symptoms were headache (100%), muscle soreness (86.7%), cough (76.7%), fever (60%), and nausea and vomiting (20%). Nineteen (63.3%) patients had underlying medical conditions. The time interval from obtaining vaginal swab samples to admission to the ICU was 48 hours. The time between vaginal sampling and PCR positivity ranged from 2 to 18 days. SARS-CoV-2 was not detected in any vaginal samples.
Conclusion
Our study showed that women with severe COVID-19 did not have SARS-CoV-2 in their vaginal fluids. Investigation of the presence of SARS-CoV-2 in vaginal secretions may help in determining the risks of sexual transmission and vertical transmission from mother to baby. Information on this subject is still limited. Larger studies on comprehensive biological samples are needed.
Keywords
SARS-CoV-2
COVID-19
vaginal fluid
vaginal sample
intensive care
==== Body
pmc
| 0 | PMC9729648 | NO-CC CODE | 2022-12-14 23:22:27 | no | Taiwan J Obstet Gynecol. 2022 Dec 8; doi: 10.1016/j.tjog.2022.11.007 | utf-8 | Taiwan J Obstet Gynecol | 2,022 | 10.1016/j.tjog.2022.11.007 | oa_other |
==== Front
Nurs Outlook
Nurs Outlook
Nursing Outlook
0029-6554
1528-3968
The Authors. Published by Elsevier Inc.
S0029-6554(22)00238-X
10.1016/j.outlook.2022.11.007
Article
A Repeated Cross-Sectional Study of Nurses Immediately Before and During the Covid-19 Pandemic: Implications for Action
Aiken Linda H. ⁎
Sloane Douglas M.
McHugh Matthew D.
Pogue Colleen A.
Lasater Karen B.
the Center for Health Outcomes and Policy Research, School of Nursing, and the Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA
⁎ Corresponding author. Linda Aiken, Center for Health Outcomes and Policy Research, University of Pennsylvania School of Nursing, 418 Curie Blvd, Philadelphia, PA 19104-4217
8 12 2022
8 12 2022
29 8 2022
21 10 2022
30 11 2022
© 2022 The Authors. Published by Elsevier Inc.
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Background
The shortage of nursing care in U.S. hospitals has become a national concern.
Purpose
Determine whether hospital nursing care shortages are primarily due to the pandemic and thus likely to subside or due to hospital nurse understaffing and poor working conditions that pre-dated it.
Method
Repeated cross-sectional study before and during the pandemic of 151,335 registered nurses in New York and Illinois, and a subset of 40,674 staff nurses employed in 357 hospitals.
Findings
No evidence was found that large numbers of nurses left health care or hospital practice in the first 18 months of the pandemic. Nurses working in hospitals with better nurse staffing and more favorable work environments prior to the pandemic reported significantly better outcomes during the pandemic.
Discussion
Policies that prevent chronic hospital nurse understaffing have the greatest potential to stabilize the hospital nurse workforce at levels supporting good care and clinician wellbeing.
Keywords
Burnout
Nurse understaffing
Patient safety
Pandemic
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pmcINTRODUCTION
The Surgeon General (2022) recently issued a public advisory declaring healthcare clinician burnout to be an urgent public health issue in need of immediate action. The American Hospital Association (AHA) in a March 1, 2022, letter to Congress proclaimed workforce challenges a national emergency that demanded immediate attention (AHA, 2022). There is little doubt that many hospitals failed to perform well during the Covid-19 emergency (Joint Commission, 2021; Fleisher et al., 2022). Bloodstream infections, which had declined 31% in the 5-years preceding the pandemic increased 28% in the pandemic's first months (Patel, et al., 2021) with similar disappointing trends in other infections, falls, and pressure ulcers (Rosenthal, et al., 2022; AHRQ, 2021). The AHA's proposed solutions to the nursing care shortage included increasing the national supply of nurses, recruiting nurses from abroad, addressing clinicians’ “behavioral health needs,” and investigating anticompetitive behavior of travel nurse agencies. Are these the highest priority solutions to the problems of hospitals not being able to recruit and retain enough nurses? Our study of hospital nurses in a large, repeated cross-sectional study before and during the pandemic adds a new perspective on where to look for solutions to the shortage of hospital nursing care.
The solutions may have been in plain sight for two decades. In 2002, two landmark studies (Aiken, et al., 2002; Needleman, et al., 2002) documented significant associations between hospital patient-to-nurse workloads and patient mortality and nurse burnout. Each one patient increase in nurses’ workloads was associated with a 7% increase in the odds of risk-adjusted patient mortality, a 23% increase in the odds of high nurse burnout, and a 15% increase in the odds of nurse job dissatisfaction (Aiken, et al., 2002). A large body of research (Wynendaele, et al., 2019; Lake, et al., 2019; Lu, et al., 2012; Lasater, et al., 2021c; Sloane, et al., 2018; Aiken et al., 2018) confirms the association of hospital nurse staffing and work environments with patient outcomes and nurse retention.
The only major policy response to chronic hospital nurse understaffing and poor work environments in 20 years has been the implementation in 2004 of a mandated minimum nurse staffing requirement in hospitals throughout California (Aiken, et al., 2010; McHugh, et al., 2012; McHugh, et al., 2011a). The unfunded mandate resulted in patients in California hospitals currently receiving, on average, 2-3 more hours a day of registered nurse care than patients in other states (Dierkes, et al., 2021). Similar safe nurse staffing legislation has been considered in other states but despite research estimating improved patient outcomes and cost savings (Lasater, et al., 2021a; Lasater, et al., 2021b), no other states have implemented minimum hospital nurse staffing requirements.
This study leverages data from the largest repeated survey of registered nurses aggregated by their employing organizations both immediately preceding the pandemic and 18 months into the pandemic. Data document baseline measures and pandemic-related changes in nurse burnout, job dissatisfaction, intent to leave, patient safety and quality of care, nurse staffing, work environments, and confidence in hospital management. Our findings reveal that nurses’ concerns and adverse outcomes which were magnified by the pandemic were evident before the pandemic. We explore how nurse understaffing and poor work environments before the pandemic were associated with nurse wellbeing and intent to stay with their employer during the pandemic, a perspective that is essential in identifying and prioritizing policy actions and managerial changes in hospital workplaces to retain nurses and keep patients safe.
METHODS
Data
This study uses repeated cross-sectional data from two surveys of all registered nurses in New York and Illinois collected pre-pandemic (December 16, 2019–February 24, 2020) and during the pandemic (April 13, 2021–June 22, 2021). All actively licensed registered nurses in New York and Illinois were invited to participate in an online survey. The resulting dataset includes repeated measures in two cross-sections of data from 151,335 nurses (81,263 pre-pandemic; 70,072 during). Respondents indicated their employment status, including whether they were currently employed in healthcare in a hospital setting, employed in healthcare but not in a hospital, employed but not in healthcare, not currently employed, or retired. These data were used to evaluate changes in employment status to understand the extent to which nursing care shortages during the pandemic were likely due to nurses leaving the profession or hospital practice. This question can only be answered using a sample of all nurses including those that left hospitals as well as those who stayed. Nurses employed in hospitals reported their position (e.g., staff nurse, nurse manager, advanced practice nurse), and type of unit on which they most recently worked (e.g., medical-surgical, intensive care, emergency department). A subset of 40,674 staff nurses that practiced in hospitals at the time of the survey (24,114 pre-pandemic; 16,560 during) was used to evaluate changes in hospital nurse job outcomes, work environments, and quality and safety of care.
In contrast to other studies of nurses during the pandemic that mostly relied on convenience sampling, ours used a sampling frame more likely to yield a representative sample of nurses—state licensure lists of registered nurses. Also, unlike other surveys, we were able to aggregate hospital nurses by their employer. The subset of 40,674 hospital staff nurses in our analytic dataset are employed by 357 unique hospitals, representing 99% of acute care hospitals in New York and Illinois. The overall response rate of all nurses was 18% in the pre-pandemic survey and 14% for the survey during the pandemic, which is within the usual range of response rates for online surveys. In prior survey research using a similar survey instrument, we utilized a double-sampling approach of non-respondents for evaluating non-response bias and found no significant differences in nurse-reported measures between main-survey respondents and non-respondents (Lasater, et al., 2019).
Measures
Burnout was assessed using the emotional exhaustion subscale of the Maslach Burnout Inventory (Maslach, et al., 2001; Maslach and Jackson, et al., 1981). High burnout was defined as scores ≥27 (Maslach, Jackson, Leiter, 1997). Job dissatisfaction was a dichotomous variable of ‘somewhat dissatisfied’ and ‘very dissatisfied’ to a single-item question asking nurses how satisfied they were overall with their job (McHugh, et al., 2011b). Intent to leave was measured using nurses’ reports of whether they planned to be with their current employer for one year. Nurses assessed whether there was enough staff to provide needed care, whether their overall hospital work environment was excellent, good, fair, or poor, and whether there was good teamwork between nurses and physicians (Sloane, et al., (2018).
Nurses rated quality of patient care and the effectiveness of management in their hospitals. Patient care measures included: overall quality of care, patient safety, infection prevention, and culture of patient safety. Ratings of the quality of nursing care ranged from ‘excellent’ to ‘poor’ on a 4-point Likert scale. Patient safety and infection prevention were rated on a scale (A-F) with grades of C, D, or F considered ‘unfavorable.’ Culture of patient safety items were drawn from the Agency for Healthcare Research and Quality (AHRQ) Hospital Survey on Patient Safety Culture (AHRQ, 2019) asking nurses to rate on a 5-point Likert scale ranging from ‘strongly agree’ to ‘strongly disagree’ whether actions of management show patient safety is a top priority, whether nurses feel mistakes are held against them, and whether nurses feel free to question authority. Nurses indicated whether they were confident (i.e., ranging from ‘very confident’ to ‘not at all confident’ on a 4-point Likert scale) that management would act to resolve problems in patient care that nurses identify. Nurses indicated whether they agreed with the statement ‘administration listens and responds to nurses’ concerns’ (ranging from ‘strongly agree’ to ‘strongly disagree’ on a 4-point Likert scale).
Data Analysis
First, we show changes in nurse employment from the pre-pandemic period to during the pandemic. We then show percentages of hospital staff nurses reporting concerns about hospital management and patient care quality in the two periods, using chi-square statistics to test the significance of differences across periods. We show percentages of hospital staff nurses overall and in different types of units with high burnout, job dissatisfaction, intent to leave current employer, staffing insufficiency (i.e., not enough staff), work environments that were poor/fair, and not a lot of teamwork between nurses and physicians.
Finally, we aggregate responses from medical-surgical staff nurses pre-pandemic to create hospital-level measures of mean adult medical-surgical patient-to-nurse staffing ratios and nurse work environments in hospitals prior to the pandemic. This aggregation technique resulted in a subset of 239 hospitals, a smaller number of hospitals than used in the analysis of burnout because some respondents did not provide the name of their employer which was necessary to calculate staffing levels and work environment quality at the hospital level. The resulting hospitals consisted of the following distribution of mean staffing: 39 hospitals had a mean patient-to-nurse staffing ratio of 5 or fewer patients per nurse in the pre-pandemic cross-section, 112 hospitals had a mean of >5 and ≤ 6 patients per nurse, and 88 hospitals had more than 6 patients per nurse on average. Hospital work environments were categorized by the percentage of medical-surgical staff nurses who rated their work environment as ‘poor’ or ‘fair’ in the pre-pandemic cross-section: 24 hospitals were categorized as ‘good’ work environments, 128 hospitals had ‘mixed’ work environments, and 87 hospitals had ‘poor’ work environments. Once hospitals were categorized by their pre-pandemic staffing and work environments, we use percentages and chi-square statistics to show how nurse outcomes, care quality and safety, and concerns with management varied across hospitals during the pandemic based on their pre-pandemic patient-to-nurse staffing ratios and quality of their work environments.
FINDINGS
Figure 1 displays the distribution of actively licensed registered nurses by employment status pre-pandemic and during the pandemic using our entire sample of nurse respondents whether they were employed or not; for the employed nurses we considered employment in all settings. Between the two periods there were no significant changes in employment status (likelihood ratio chi-square statistic=7.05 with 4 d.f., p=0.133 testing the independence of the numbers in the five employment status categories across two time points). The percentage of nurses employed in hospitals did not change by more than a fraction of 1% during the pandemic (p=0.322). Had large numbers of nurses left hospitals or healthcare without being replaced, we would expect to see decreases in percentages of nurses in hospitals and other healthcare settings and concomitant increases in numbers of nurses that were employed in non-healthcare settings or currently unemployed or retired.Figure 1 Changes in Nurse Employment Status, Pre-Pandemic and During the Pandemic
Notes. Survey data collected by the Center for Health Outcomes and Policy Research at the University of Pennsylvania School of Nursing. Pre-pandemic data were collected between Dec 15, 2019 and Feb 24, 2020. Data during the pandemic were collected between April 13, 2021 and June 22, 2021. A chi-square statistic (L2 = 7.05 with 4 df, p=0.133) testing the independence of the numbers in the five employment status categories across two time points is insignificant, indicating no overall change.
Figure 1
Table 1 reports survey results from hospital staff nurses only. The findings point to lack of confidence in hospital management pre-pandemic which worsened during the pandemic. Over 69% of hospital staff nurses in the pre-pandemic period reported a lack of confidence in hospital management to resolve clinical care problems reported by nurses, and this percentage increased to almost 78% during the pandemic. Similarly, 47% of hospital staff nurses in the pre-pandemic period reported that administration did not listen or respond to nurses’ concerns which increased to 53% during the pandemic. Some 48% of nurses pre-pandemic agreed that the ‘actions of management show patient safety is not a top priority’ which rose to 53% during the pandemic. Almost 50% of nurses pre-pandemic reported feeling that their mistakes were held against them and 56% reported not feeling free to question decisions or actions of authority. Almost 45% of nurses gave their hospitals unfavorable patient safety grades pre-pandemic and 47% rated patient safety unfavorably during the pandemic. A third of nurses gave their hospitals an unfavorable grade on infection prevention pre-pandemic which rose to 36% during the pandemic. Nurses’ assessments about quality grew more negative during the pandemic, with higher percentages of nurses rating their hospitals’ overall quality of care as poor/fair during the pandemic (26%) as compared to before (20%).Table 1 Hospital Staff Nurses Evaluations of Hospital Management and Patient Care Quality, Pre-Pandemic and During the Pandemic
Table 1Patient Care and Evaluation of Management Pre-Pandemica During Pandemic Changeb
Not confident in management resolving clinical care problems 69.4% 77.5% 8.1%***
Administration doesn't listen or respond to nurses’ concerns 46.8% 52.9% 6.1%***
Actions of management show patient safety is not a top priority 47.7% 53.3% 5.8%***
Feel mistakes are held against them 49.6% 47.1% -2.5%***
Do not feel free to question decisions or actions of authority 56.2% 52.1% -4.1%***
Poor/fair quality of care 19.9% 25.7% 5.8%***
Unfavorable infection prevention grade (C, D, or F) 33.2% 35.6% 2.4%***
Unfavorable patient safety grade (C, D, or F) 44.5% 47.1% 2.6%***
Notes. Survey data collected by the Center for Health Outcomes and Policy Research at the University of Pennsylvania School of Nursing.
a Triple asterisks indicate changes between the two periods which were significant at the p < .001 level of confidence.
b Pre-pandemic data were collected between Dec 15, 2019 and Feb 24, 2020. Data during the pandemic were collected between April 13, 2021 and June 22, 2021.
As shown in Table 2 , large percentages of hospital staff nurses before Covid-19 reported high burnout (48%), job dissatisfaction (27%), intent to leave their employer (22%), poor/fair work environments (47%), and not enough staff (57%). These outcomes worsened or remained high during the pandemic—especially among nurses working on medical-surgical units, adult intensive care, and in emergency departments. The largest increases during the pandemic were in the percentage of hospital staff nurses reporting there were not enough staff and the percentage of nurses reporting job dissatisfaction and high burnout. The negative impact of the pandemic was not observed in nurse-physician teamwork which was positive before the pandemic and improved during the pandemic.Table 2 Hospital Staff Nurse Reports of High Burnout, Job Dissatisfaction, Intent to Leave, Staffing, and Work Environments, Pre-Pandemic and During the Pandemic
Table 2 Nurse Reportsa Pre-Pandemic During Pandemic Changeb
All Staff Nurses High burnout 48.0% 51.0% 3.0%***
(N = 40,674) Job dissatisfaction 27.2% 30.6% 3.4%***
Intent to leave employer 21.8% 24.7% 2.9%***
Not enough staff 56.9% 67.4% 10.5%***
Poor/fair work environment 46.6% 42.2% -4.4%***
Not a lot of nurse-physician teamwork 18.9% 15.1% -3.8***
Medical-Surgical Nurses High burnout 54.0% 58.9% 4.8%***
Job dissatisfaction 29.9% 36.3% 6.4%***
(N = 10,743) Intent to leave employer 23.5% 28.0% 4.5%***
Not enough staff 64.9% 75.0% 10.1%***
Poor/fair work environment 46.4% 46.4% 0.0%
Not a lot of nurse-physician teamwork 21.4% 15.8% -5.6%***
Adult Intensive Care Nurses High burnout 50.3% 57.6% 7.3%***
Job dissatisfaction 29.7% 33.9% 4.2%**
(N = 5,429) Intent to leave employer 25.5% 29.2% 3.7%**
Not enough staff 57.4% 73.1% 15.7%***
Poor/fair work environment 49.0% 46.5% -2.5%
Not a lot of nurse-physician teamwork 17.6% 15.2% -2.4%*
Emergency Department Nurses(N = 4,515) High burnout 55.9% 58.1% 2.2%
Job dissatisfaction 31.4% 37.4% 6.0%***
Intent to leave employer 24.7% 28.3% 3.6%*
Not enough staff 63.6% 75.3% 11.7%***
Poor/fair work environment 51.8% 51.9% 0.1%
Not a lot of nurse-physician teamwork 13.9% 12.3% -1.6%
Other Nurses High burnout 41.7% 43.9% 2.2%**
(N = 19,987) Job dissatisfaction 23.8% 25.6% 1.8%**
Intent to leave employer 19.0% 21.1% 2.1%***
Not enough staff 50.3% 60.4% 10.1%***
Poor/fair work environment 44.7% 37.0% -7.7%***
Not a lot of nurse-physician teamwork 19.0% 15.3% -3.7%***
Notes. Survey data collected by the Center for Health Outcomes and Policy Research at the University of Pennsylvania School of Nursing. aSingle, double, and triple asterisks indicate changes between the two periods which were significant at the p < .05, p < .01, and p < .001 levels of confidence.
b Pre-pandemic data were collected between Dec 15, 2019 and Feb 24, 2020. Data during the pandemic were collected between April 13, 2021 and June 22, 2021
In Table 3 we show how the outcomes and concerns expressed by hospital staff nurses during the pandemic are associated with mean medical-surgical patient-to-nurse staffing ratios pre-pandemic. Percentages of nurses reporting each outcome during the pandemic are grouped according to reports of their hospital's mean medical-surgical staffing in the pre-pandemic cross-section. Nurses in hospitals in which the mean number of patients assigned to each nurse was high pre-pandemic were more likely to issue unfavorable reports about their own outcomes (e.g., burnout, job dissatisfaction, intent to leave employer), patient outcomes (e.g., poor quality of care, unfavorable patient safety), and lack confidence in hospital management during the pandemic.Table 3 Hospital Staff Nurse Reports of Job Outcomes, Patient Care Quality, and Hospital Management Support During the Pandemic are Associated with Patient-to-Nurse Staffing Ratios Pre-Pandemic
Table 3Percent of Nurses Reporting Various Outcomes During Pandemic Hospital Mean Medical-Surgical Patients per Nurse Pre-Pandemica
≤5
N = 39 >5 and ≤6
N=112 > 6
N=88
High burnout 48.7% 52.0% 53.4%
Dissatisfied with job 25.1% 32.1% 35.0%
Intent to leave employer 21.5% 24.3% 26.7%
Not confident in management resolving clinical care problems 72.0% 77.5% 82.1%
Actions of management show patient safety is not a top priority 45.1% 55.0% 58.5%
Administration doesn't listen or respond to nurses’ concerns 44.0% 53.9% 58.2%
Unfavorable patient safety grade (C, D, or F) 33.9% 46.7% 54.6%
Unfavorable infection prevention grade (C, D, or F) 27.0% 33.8% 41.8%
Poor/fair quality of care 15.7% 24.7% 33.0%
Not a lot of teamwork between nurses and physicians 12.6% 13.7% 18.0%
Notes. Survey data collected by Center for Health Outcomes and Policy Research at the University of Pennsylvania School of Nursing.
a Chi-square tests reveal that the differences in each of the reported outcomes between the 3 categories (defined by the hospital mean medical-surgical patients per nurse pre-pandemic) are significant at the p < .01 level of confidence.
Similar differences are shown in Table 4 , in which nurses in hospitals with ’poor’ work environments in the pre-pandemic period reported the greatest concerns with their own wellbeing, patient outcomes, and lack of confidence in hospital management during the pandemic.Table 4 Hospital Staff Nurse Reports of Job Outcomes, Patient Care Quality, and Hospital Management Support During the Pandemic are Associated with Nurse Work Environments Pre-Pandemic
Table 4Percent of Nurses Reporting Various Outcomes During Pandemic Hospital Nurse Work Environment Pre-Pandemica
Good
N = 24 Mixed
N= 128 Poor
N= 87
High burnout 42.1% 51.3% 55.7%
Dissatisfied with job 19.9% 29.9% 37.8%
Intent to leave employer 19.7% 23.8% 26.9%
Not confident in management resolving clinical care problems 63.1% 76.4% 84.7%
Actions of management show patient safety is not a top priority 35.5% 51.6% 64.3%
Administration doesn't listen or respond to nurses’ concerns 36.3% 49.8% 63.9%
Unfavorable patient safety grade (C, D, or F) 24.6% 42.1% 60.6%
Unfavorable infection prevention grade (C, D, or F) 17.3% 29.9% 48.4%
Poor/fair quality of care 9.4% 21.9% 36.0%
Not a lot of teamwork between nurses and physicians 9.8% 12.9% 19.3%
Notes. Survey data collected by Center for Health Outcomes and Policy Research at the University of Pennsylvania School of Nursing.
a Chi-square tests reveal that the differences in each of the reported outcomes between the 3 categories (defined by the percentage of nurses who rated their hospital work environment as ‘poor’ or ‘fair’ pre-pandemic) are significant at the p < .001 level of confidence.
DISCUSSION
Overall, our findings suggest that the pandemic was not the root cause but a contributing factor in hospital nurse recruitment and retention challenges during the pandemic. Our survey responses from all nurses, whether working or not before and during the pandemic, do not support the widely held belief that nurses left health care or hospital practice in large numbers during the pandemic. The evidence of declining confidence in hospital management along with high burnout, job dissatisfaction, and intent to leave before and during the pandemic suggests that nurses may have been changing employers in higher numbers, including working for supplemental staffing agencies, which contributed to a perception of more nurses leaving clinical care than can be documented.
Our findings confirmed among hospital nurses that high nurse burnout, job dissatisfaction, intent to leave hospital employer, and lack of confidence in hospital management pre-dated the pandemic. Immediately prior to Covid-19, 48% of hospital nurses in our study experienced high burnout; more than a year into the pandemic, the percentage of high burnout went up only 3% to 51%. The high rates of nurse burnout during the pandemic appear to be largely a consequence of high burnout prior to the pandemic. Addressing the root causes of high nurse burnout and hospital job dissatisfaction before the pandemic is critical to achieving a stable, qualified hospital nurse workforce going forward.
Importantly, our results show that hospital nurse understaffing and poor work environments prior to the Covid-19 emergency were associated with unfavorable outcomes during the pandemic. Before Covid-19, 57% of hospital staff nurses said there were too few nurses to care for patients which increased to 67% during the pandemic. Almost half of nurses (47%) rated their hospital work environments as ‘poor’ or ‘fair’ pre-pandemic; during the pandemic 42% rated their work environments unfavorably. High nurse burnout, job dissatisfaction, and intent to leave were worse during the pandemic in hospitals that were poorly staffed before the pandemic and/or had unfavorable work environments before the pandemic. The proportion of hospital staff nurses during the pandemic intending to leave their employer was significantly higher in hospitals with the worst nurse staffing and poorest work environments in the pre-pandemic period suggesting that both chronic understaffing and subpar work environments dually threaten nurse retention.
Also, nurses’ negative appraisals of quality of care and patient safety during the pandemic were substantially worse in hospitals in which nurses cared for more patients each before the pandemic. For example, 33% of nurses in hospitals where mean pre-pandemic medical-surgical staffing was more than 6 patients per nurse reported poor/fair quality of care during the pandemic, compared with half that many, only 16% of nurses, in hospitals where the mean pre-pandemic staffing was 5 or fewer patients per nurse.
Before the pandemic, an astounding 70% of hospital staff nurses lacked confidence in management in their employing organization to resolve clinical care problems identified by nurses, and close to half of nurses reported their employer did not listen or respond to their concerns. Nurses’ negative appraisals of hospital management increased further during the pandemic when nurse layoffs and furloughs were common. Almost half of nurses reported pre-pandemic that the actions of hospital management show patient safety is not a top priority which increased to 53% during the pandemic. Also, both before and during the pandemic nearly half of nurses reported they feel like mistakes are held against them and they do not feel free to question decisions and actions of authority–disturbing evidence of the failure of hospital management to embrace the basic tenets of keeping patients safe. The recent case (Kalman, et al., 2022) of a hospital nurse being fired by her hospital and convicted of criminally negligent homicide for a medication error reportedly associated with a system failure adds further distress to a burned out and discouraged nurse workforce and is a real-world example of why nurses lack confidence in management and lack loyalty to their employing hospitals.
One finding to be celebrated is that nurses reported that nurse-physician relations were good prior to the pandemic and even improved some during the pandemic. Interprofessional relationships and interdisciplinary teamwork among clinicians seem strong, in contrast to the substantial lack of confidence nurses have in hospital management.
Study strengths and limitations
While the timing of our surveys is unique in having a baseline immediately before the Covid-19 pandemic and a second survey during the pandemic, we have measures at only two points in time, so caution is warranted in making causal inferences. Our survey is unique among others available in that nurses were invited to participate from a sampling frame consisting of all licensed registered nurses in two large states as compared to convenience samples. Also, nurses are linked to their place of employment providing a unique perspective on nursing practice within individual hospitals. Survey response rates are not optimal although not out of line with recent experience with large online surveys. Our previous research shows that non-responders do not rate nursing care differently from those that do respond, and that non-response is not a factor that influences the kind of outcomes we are studying (Lasater, et al., 2019). Nurses who did not report their hospital name were somewhat more likely to report more negatively about their hospitals’ quality; however, in most cases the differences were not statistically significant. Some may consider nurse reports of patient care quality as subjective but our previously published research shows that nurse reports of quality and safety of care are highly predictive of objectively measured patient outcomes including mortality, failure to rescue, and patient satisfaction (McHugh and Stimpfel, 2012). Finally, the pandemic has continued for a year after our “during the pandemic” survey so it is possible that conditions have changed further over time.
Implications for policy
The most common suggestion for addressing the present shortage of nursing care in hospitals is to increase the national supply of nurses, although evidence does not suggest this strategy will be effective. The numbers of U.S. educated nurses graduating annually has been steadily increasing for decades even during the pandemic and currently over 185,000 new nurses enter the workforce each year (National Council of State Boards of Nursing, 2021). In 2017, the National Center for Health Workforce Analysis (U.S. Department of Health and Human Services, 2017) projected a national registered nurse excess of about 8% by 2030. There is little association between increases in the national supply of nurses and hospital patient-to-nurse ratios. Immediately before the pandemic, after a decade that added a million registered nurses to the national supply, mean patient-to-nurse staffing ratios varied widely across hospitals in New York and Illinois from a low of 4.3 patients per nurse in adult medical and surgical inpatient units to a high of 10.5 patients per nurse (Lasater et al, 2021c). This lack of an association between supply and hospital nursing care shortage is also shown at the state-level where RNs per 1000 population vary substantially. California, the only state with mandated nurse staffing ratios, has among the fewest nurses with 9.25 RNs per 1000 population while Massachusetts, a state with 16.04 RNs per 1000, turned down legislation setting minimum hospital nurse staffing standards because of fears of nurse shortages.
The shortage of nursing care in hospitals is largely the result of chronic nurse understaffing by design. Focusing policy attention primarily on substantial and rapid increases in the supply of nurses diverts attention from more promising solutions to the chronic shortage of nursing care in hospitals as well as in other settings such as nursing homes and schools where the number of budgeted positions for nurses is the problem that needs a solution. Also, policies to rapidly increase RN supply could undermine national nurse workforce goals by attracting new poor-quality nursing schools with unfavorable graduation rates and a proliferation of programs that do not produce nurses with bachelor's degrees as recommended by the National Academy of Medicine (Institute of Medicine, 2011).
Fifteen states currently address hospital nurse staffing in law (de Cordova, et al., 2019a; de Cordova, et al., 2019b). However, only in California where minimum nurse staffing is mandated is there an association between state legislation and improved nurse staffing (Han, et al., 2021). California implemented minimum required hospital nurse staffing almost 20 years ago with positive results (Aiken, et al., 2010; Dierkes, et al., 2021). Significant improvements in nurse staffing were achieved in California safety-net hospitals, one of the few observed improvements in nurse staffing in minority serving hospitals since the passage of Medicare and Medicaid (McHugh et al., 2012). Hospitals that staffed better than the minimum required before the law did not decrease their staffing to the minimum, thus demonstrating that safe nurse staffing standards do not require ‘one size to fit all’, a slogan used liberally by opponents of safe nurse staffing standards. And other negative unintended consequences such as hospital or emergency department closures due to staffing legislation were not observed (McHugh, et al., 2011a). Recent research in other states has shown that pending staffing legislation is in the public's interest because of the substantial variation in patient-to-nurse ratios across hospitals within states which is associated with higher deaths as well as higher costs due to longer stays and more readmissions (Lasater, et al., 2021a and b).
There is no evidence that mandated nurse staffing committees, the most prevalent form of state nurse staffing legislation, have any impact on improved staffing (Han et al., 2021). While state legislation to require public reporting of hospital nurse staffing has not shown much impact (de Cordova et al., 2019b; Han et al., 2021) the Medicare Hospital Compare website is more visible and accessible to the public than state reports of staffing but currently does not report on hospital nurse staffing. Remedying this important omission could make hospital nurse staffing more transparent to the public and motivate improvements.
A concern by opponents of legislating minimum hospital staffing requirements is the risk of creating a short-term nurse shortage at state or local levels that could disrupt health services. The Nurse Licensure Compact, which has been passed in 39 U.S. states and territories, addresses that risk by allowing nurses to practice in any Compact state. The Compact offers the advantage of comprehensive vetting of nurses’ qualifications and avoiding delays in issuing state-based licenses (Alexander, et al., 2021). Nurse employers should advocate for its passage given the substantial delays in processing RN licenses that have worsened during Covid, and slow onboarding of newly hired nurses (Fast, 2022).
A recent Harris poll showed 90% of the public surveyed favored requiring safe nurse staffing standards in hospitals and nursing homes (NursesEverywhere, 2020). Given the strong headwinds from deep pocket special interests opposed to states establishing hospital minimum nurse staffing requirements, federal options should be pursued. The most promising federal option is to establish minimum safe nurse staffing standards for hospitals as a condition of participation in Medicare (Aiken and Fagin, 2022). There is precedent in Medicare nurse staffing requirements for nursing homes, even though the current staffing standard there is too low to produce safe care. Medicare conditions of participation have previously been used to solve vexing problems including the desegregation of hospitals and the implementation of the employee Covid-19 vaccine mandate in hospitals that was upheld by the Supreme Court. Similar policy intervention is warranted to require hospitals participating in Medicare to meet evidence-based nurse staffing standards to ensure safe care for the public and to reduce outcomes disparities in understaffed minority serving hospitals.
Further explication of Medicare's value-based purchasing policies to create a visible funding stream for professional nurses, as is common for other health professionals, is promising as a potentially cost neutral strategy to explicitly reward hospitals and other providers for employing enough nurses to provide safe care of high quality. Evidence-based nurse staffing has been shown to reduce length of stay, readmissions, and never events such as healthcare-acquired infections that save lives and avoid pain and suffering as well as saving Medicare money (Yakusheva, Rambur, Buerhaus, 2020; Lasater et al., 2021a)
Implications for practice
Interventions for improving subpar work environments are not codified in policy, but rather in administrative decision-making about how to structure and operate complex organizations. One example of an evidence-based organizational intervention that has been shown to improve nurse work environments is the American Nurses Credentialing Center Magnet Recognition Program (Kutney-Lee et al, 2015). The Magnet program offers an actionable blueprint for how organizations can transform culture to enhance clinician wellbeing and patient care outcomes. Organizations committed to improving their work environments and attracting and retaining registered nurses may find success in following the organizational principles, such as structural empowerment and engagement of clinicians in decision-making, characteristic of Magnet hospitals.
Conclusion
Chronic nurse understaffing and poor work environments in hospitals that existed prior to the Covid-19 pandemic and worsened during it are the major explanations for why many hospitals cannot hire and keep enough nurses even though Covid-19 hospitalizations have dropped. Without fundamental improvements in hospital nurse staffing and work environments, the shortage of nursing care in hospitals will not likely abate even after the Covid-19 pandemic has run its course. Increasing the supply of nurses through short-term emergency measures is unlikely to solve the problem. Hospitals need to hire more permanent registered nurses, provide more favorable work environments, and earn back the confidence of nurses that quality and safety of patient care are institutional priorities. Because most hospitals have not implemented substantial improvements in either staffing or work environments over the past decade (Sloane et al., 2018; Aiken et al., 2018), policymakers should mandate hospitals to meet minimum safe nurse staffing standards. A continuation of chronic nurse understaffing and unacceptable working conditions in hospitals will not restore the public's or nurses’ confidence in hospitals.
Authors’ Contributions
LHA: Conceptualization, funding, data collection, writing, interpretation of results, policy implications
DMS: Study design, analysis, interpretation of findings, writing
MDM: Conceptualization, funding, data collection, interpretation of results, policy implications
CAP: Conceptualization, interpretation of findings, writing, manuscript review
KBL: Conceptualization, funding, data collection, analysis, writing, policy implications
Uncited References
Kalman and Norman, 2022, Lasater et al., 2020, Maslach and Jackson, 1981, Aiken et al., 2021
Declaration of Competing Interest
The authors report no conflicts of interest.
Acknowledgments
This research was supported by the National Council on State Boards of Nursing, the National Institute of Nursing Research, National Institutes of Health (R01NR014855 and T32NR00714), and Agency for Healthcare Research and Quality (R01HS028978 Lasater) . The authors thank Timothy Cheney for analytic assistance and the tens of thousands of nurses who responded to our survey during challenging times.
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References
Agency for Healthcare Research and Quality (AHRQ) NPSD Data Spotlight, Patient safety and COVID-19: a qualitative analysis of concerns during the public health emergency 2021 AHRQ Rockville, MD
Agency for Healthcare Research and Quality (AHRQ) Hospital Survey on Patient Safety Culture 2019 https://www.ahrq.gov/sops/surveys/hospital/index.html Accessed August 2, 2022
Aiken, L.H., Fagin, C.M. (2022) Medicare can help fix the nurse shortage in hospitals. STAT https://www.statnews.com/2022/02/08/medicare-can-help-fix-the-nurse-shortage-in-hospitals/
Aiken L.H. Sloane D.M. Barnes H. Cimiotti J.P. Jarrin O. McHugh M.D. Nurses’ and patients’ appraisals show patient safety in hospitals remains a concern Health Affairs 37 11 2018 1744 1751 30395515
Aiken L.H. Clarke S.P. Sloane D.M. Sochalski J. Silber J. Hospital nurse staffing and patient mortality, nurse burnout, and job dissatisfaction Journal of the American Medical Association 288 16 2002 1987 1993 12387650
Aiken L.H. Sloane D.M. Brom H.M. Barnes H. Cimiotti J. Cunningham R. McHugh M.D. Value of nurse practitioner inpatient hospital staffing Medical Care 59 2021 857 863 34432769
Aiken L.H. Sloane D.M. Cimiotti J. Clarke S. Flynn L. Spetz J. Seago J.A. Smith H.L. Implications of the California nurse staffing mandate for other states Health Services Research 45 4 2010 904 921 20403061
Alexander M. Martin B. Kaminski-Ozturk N. Zhong E. Smiley R. Envisioning the future of nursing regulation through research: a global agenda Journal of Nursing Regulation 12 3 2021 5 10
American Hospital Association (AHA). Challenges Facing America's Health Care Workforce as the U.S. Enters Third Year of COVID-19 Pandemic. (2022). https://www.aha.org/news/news/2022-03-02-aha-urges-congress-address-health-care-workforce-challenges
de Cordova P.B. Pogorzelska-Mazianz M. Eckenhoff M.E. McHugh M.D. Public reporting of nurse staffing in the United States Journal of Nursing Regulation 10 3 2019 14 20 32015931
de Cordova P.B. Rogowski J. Riman K.A. Effects of public reporting legislation of nurse staffing: A trend analysis Policy, Politics, & Nursing Practice 20 2 2019 92 104
Dierkes A. Do D. Morin H. Rochman M. Sloane D.M. McHugh M.D. The impact of California's staffing mandate and the economic recession on registered nurse staffing levels: A longitudinal analysis Nursing Outlook 70 2 2021 219 227 34920888
Fast A. Nurses are waiting months for licenses as hospital staffing shortages spread 2022 National Public Radio Morning Edition https://www.npr.org/2022/03/10/1084897499/nurses-are-waiting-months-for-licenses-as-hospital-staffing-shortages-spread
Fleisher L.A. Schreiber M. Cardo D. Srinivasan A. Health care safety during the pandemic and beyond: building a system that ensures resilience New England Journal of Medicine 386 7 2022 609 611 35148040
Han X. Pittman P. Barnow B. Alternative approaches to ensuring adequate nurse staffing: the effect of state legislation on hospital nurse staffing Medical Care 59 2021 S463 (10 Suppl 5) 34524244
Institute of Medicine The Future of Nursing: Leading Change, Advancing Health 2011 National Academies Press Washington, DC
Joint Commission New and Revised Emergency Management Standards 2021 https://www.jointcommission.org/-/media/tjc/documents/standards/prepublications/hap_july2022_prepublication_report_em_chapter_revisions.pdf
Kalman, B., Norman, H. (2022) Why nurses are raging and quitting after the RaDonda Vaught verdict. Shots: Health News from NPR. npr.org/sections/heath-shots/2022/04/05/1090915329/why-nurses-are-raging-and-quitting-after-the-radonda-vaught-verdict
Kutney-Lee A. Stimpfel A.W. Sloane D.M. Cimiotti J.P. Quinn L.W. Aiken L.H. Changes in patient and nurse outcomes associated with Magnet hospital recognition Medical Care 53 6 2015 550 25906016
Lake E.T. Sanders J. Duan R. Riman K.A. Schoenauer K.M. Chen Y. A meta-analysis of the associations between the nurse work environment in hospitals and 4 sets of outcomes Medical Care 57 5 2019 353 361 30908381
Lasater K.B. Jarrin O. Aiken L.H. Sloane D.M. McHugh M.D. Smith H.L. A methodology for studying organizational performance: a multistate survey of front-line providers Medical Care 57 9 2019 742 749 31274782
Lasater K.B. Sloane D.M. McHugh M.D. Cimiotti J.P. Riman K.A. Martin B. Alexander M. Aiken L.H. Evaluation of hospital nurse-to-patient staffing ratios and sepsis bundles on patient outcomes American Journal of Infection Control 2020 10.1016/ajic.2020.12.002
Lasater K.B. Aiken L.H. Sloane D.M. McHugh M.D. Smith H.L. Is hospital nurse staffing legislation in the public's interest?: An observational study in New York State Medical Care 59 5 2021 444 33655903
Lasater K.B. Aiken L.H. Sloane D.M. French R. Martin B. Reneau K. Alexander M. McHugh M.D. Patient outcomes and cost savings associated with hospital safe nurse staffing legislation: an observational study BMJ Open 11 12 2021 e052899
Lasater K.B. Aiken L.H. Sloane D.M. French R. Martin B. Reneau K. Alexander M. McHugh M.D. Chronic hospital nurse understaffing meets COVID-19: an observational study BMJ Quality & Safety 30 8 2021 639 647
Lu H. Barriball L. Zhang X. While A.E. Job satisfaction among hospital nurses revisited: a systematic review International Journal of Nursing Studies 49 8 2012 1017 1038 22189097
Maslach C. Jackson S.E. The measurement of experienced burnout Journal of Organizational Behavior 2 2 1981 99 113
Maslach C. Jackson S.E. Leiter M.P. Maslach Burnout Inventory 1997 Scarecrow Education
Maslach C. Schaufeli W.B. Leiter M.P. Job burnout Annual Review of Psychology 52 1 2001 397 422
McHugh M.D. Kelly L. Sloane D.M. Aiken L.H. Contradicting fears, California's nurse-to-patient mandate did not reduce the skill level of the nursing workforce in hospitals Health Affairs 30 7 2011 1299 1306 21734204
McHugh M.D. Kutney-Lee A. Cimiotti J. Sloane D.M. Aiken L.H. Nurses’ widespread job dissatisfaction, burnout, and frustration with health benefits signal problems for patient care Health Affairs 30 2 2011 202 210 21289340
McHugh M.D. Stimpfel A.W. Nurse reported quality of care: a measure of hospital quality Research in Nursing & Health 35 6 2012 566 575 22911102
McHugh M.D. Brooks Carthon M. Wu E. Kelly L. Sloane D.M. Aiken L.H. Impact of nurse staffing mandates on safety-net hospitals: lessons from California The Milbank Quarterly 90 1 2012 160 186 22428696
National Council of State Boards of Nursing. (2021) 2021 NCLEX Pass Rates. https://www.ncsbn.org/15858.htm.
Needleman J. Buerhaus P. Mattke S. Stewart M. Zelevinsky K. Nurse-staffing levels and the quality of care in hospitals New England Journal of Medicine 346 22 2002 1715 1722 12037152
NursesEverywhere. 2020 Expand Nursing Care Survey: A Research Brief. (2021) August 3, 2021]; https://3aaa9248-e569-4da8-ada5-c4c9b14e503e.filesusr.com/ugd/d52815_ea23c1bba67c46eebe9692cf2b69a54b.pdf].
Patel P.R. Weiner-Lastinger L.M. Dudeck M.A. Fike L.V. Impact of COVID-19 pandemic on central-line–associated bloodstream infections during the early months of 2020., National Healthcare Safety Network Infection Control & Hospital Epidemiology 43 6 2021 790 793 33719981
Rosenthal V.D. Matra S.N. Divatia J.V. Biswas S. The impact of COVID-19 on healthcare-associated infections in intensive care units in low- and middle-income countries: International Nosocomial Infection Control Consortium (INICC) findings International Journal of Infectious Diseases 118 2022 83 88 35218928
Sloane D.M. Smith H.L. McHugh M.D. Aiken L.H. Effect of changes in hospital nursing resources on improvements in patient safety and quality of care: a panel study Medical Care 56 2018 1001 1008 30363019
Surgeon General New Surgeon General Advisory Sounds Alarm on Health Worker Burnout and Resignation 2022 Office of the Surgeon General https://www.hhs.gov/about/news/2022/05/23/new-surgeon-general-advisory-sounds-alarm-on-health-worker-burnout-and-resignation.html
U.S. Department of Health and Human Services Health Resources and Services Administration, National Center for Health Workforce Analysis 2017 National and Regional Supply and Demand Projections of the Nursing Workforce: 2014-2030 Rockville, Maryland
Wynendaele H. Willems R. Trybou J. Systematic review: association between the patient-nurse ratio and nurse outcomes in acute care Journal of Nursing Management 27 5 2019 896 917 30801808
Yakusheva O. Rambur B Buerhaus P. Value-informed practice can help reset the hospital-nurse relationship JAMA Health Forum 1 8 2020 e200931 10.1001/jamahealthforum.2020.0931
| 0 | PMC9729649 | NO-CC CODE | 2022-12-14 23:29:58 | no | Nurs Outlook. 2022 Dec 8; doi: 10.1016/j.outlook.2022.11.007 | utf-8 | Nurs Outlook | 2,022 | 10.1016/j.outlook.2022.11.007 | oa_other |
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J Safety Res
J Safety Res
Journal of Safety Research
0022-4375
1879-1247
National Safety Council and Elsevier Ltd.
S0022-4375(22)00199-2
10.1016/j.jsr.2022.12.002
Article
The effect of COVID-19 on self-reported safety incidents in aviation: An examination of the heterogeneous effects using causal machine learning
Choi Youngran a⁎
Gibson James R. b
a David B. O'Maley College of Business, Embry-Riddle Aeronautical University, 1 Aerospace Boulevard Daytona Beach, FL 32114, United States
b College of Business, Embry-Riddle Aeronautical University, 1 Aerospace Boulevard Daytona Beach, FL 32114, United States
⁎ Corresponding author.
8 12 2022
8 12 2022
25 5 2022
17 9 2022
1 12 2022
© 2022 National Safety Council and Elsevier Ltd. All rights reserved.
2022
National Safety Council and Elsevier Ltd
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Introduction: Disruptions to aviation operations occur daily on a micro-level with negligible impacts beyond the inconvenience of rebooking and changing aircrew schedules. The unprecedented disruption in global aviation due to COVID-19 highlighted a need to evaluate emergent safety issues rapidly. Method: This paper uses causal machine learning to examine the heterogeneous effects of COVID-19 on reported aircraft incursions/excursions. The analysis utilized self report data from NASA Aviation Safety Reporting System collected from 2018 to 2020. The report attributes include self identified group characteristics and expert categorization of factors and outcomes. The analysis identified attributes and subgroup characteristics that were most sensitive to COVID-19 in inducing incursions/excursions. The method included the generalized random forest and difference-in-difference techniques to explore causal effects. Results: The analysis indicates first officers are more prone to experiencing incursion/excursion events during the pandemic. In addition, events categorized with the human factors confusion, distraction, and the causal factor fatigue increased incursion/excursion events. Practical Applications: Understanding the attributes associated with the likelihood of incursion/excursion events provides policymakers and aviation organizations insights to improve prevention mechanisms for future pandemics or extended periods of reduced aviation operations.
Keywords
Aviation incursions/excursions
COVID-19
Machine learning
Heterogeneous treatment effects
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pmc1 Introduction
On December 31, 2019, the Wuhan Municipal Health Commission reported a cluster of pneumonia cases from which a novel coronavirus was eventually identified (World Health Organization, 2020b). The first reported case in the United States occurred in Snohomish County, Washington, on January 23, 2020. The World Health Organization declared a public health emergency of international concern on January 31 (World Health Organization, 2020a). The following day, President Trump banned foreign nationals from entering the United States if they had been to China in the previous two weeks. The ban was broadened to all travel from European countries on March 11th (Presidential Proclamation No., 2020). By the end of March, approximately a third of the world population was subject to a form of lockdown. As early as January, U.S. aviation system-wide flight departures started to decline. By April, system-wide flight departures had decreased 67% from December 2019 and remained significantly below December 2019 levels through mid-2020.
Similarly, the number of reports submitted to the National Aeronautics and Space Administration (NASA) Aviation Safety Reporting System (ASRS) increased 70% in April 2020 from March 2020. The reporting reached a peak increase of 144% above the March 2020 level by July 2020. The increasing number of reports submitted to NASA's ASRS is counter to conventional wisdom. Flight departures were decreasing; by extension, airline operations should be less stressful, airspace less congested, and the reduced aircraft utilization should reduces maintenance demand. What safety and policy insights do the increasing number of reports provide for commercial aviation?
Understanding factors that induce increased reporting of safety issues is critical, as an accident or near accident events could lead to casualties. Researching the factors associated with “near misses” and their influence on reporting to ASRS improves our understanding of the causation and consequences of disruptive events to the aviation industry. Several studies build on these findings by analyzing the impact of decreased flight operations and bans in response to COVID-19 (COrona VIrus Disease of 2019) on particular study groups in the aviation workforce. The key aspects related to ASRS reporting include the psychological stress on pilots and cabin crew who are unemployed or on furlough (Alaminos-Torres et al., 2021, Widodo et al., 2021), air traffic controllers facing increased fatigue (Drogoul & Cabon, 2021), how the pandemic affected safety culture and climate in flight training organizations (Byrnes et al., 2022), and how airline management strategies can be formulated to respond to these uncertainties (Linden, 2021).
In this study, we investigate encompassing attributes of aviation incidents in the NASA ASRS and examine heterogeneity in the effects of COVID-19 in reporting “near-miss” events encompassing ground incursions and excursions in the controlled movement areas ramp, runway, and taxiway. 1 Hereafter, incursions and excursions occurring in the controlled movement areas ramp, runway, and taxiway will be referred to collectively as incursions/excursions. Our findings shed light on how heterogeneous effects of COVID-19 vary by flight attributes or characteristics and offer significant insights into identifying target attributes and formulating policies and strategies for reducing accidents in aviation. Furthermore, the attributes that are sensitive to an unprecedented situation, such as pandemics, in inducing “near-miss” events are important to accident prevention and aviation safety strategies. The study adopts a recently developed method, generalized causal forest (GCF), to examine the heterogeneous effects of COVID-19 and check the robustness of our results using difference-in-difference (DID) estimation.
The remainder of this paper is organized as follows. Section 2 provides background and reviews relevant literature. Section 3 discusses data and the empirical methods. Section 4 presents the results on the heterogeneous effects of COVID-19. Section 5 presents the conclusions.
2 Literature review
2.1 Aviation safety reporting system
The ASRS was created in 1976 by NASA to support the Federal Aviation Administration's (FAA) mission to improve safety by eliminating unsafe and preventable incidents. The ASRS is a voluntary, confidential, and non-punitive method for pilots, air traffic controllers, cabin crew, and maintenance technicians to report unsafe and hazardous situations (Chappell, 2017). The ASRS first-person reporting can reflect reporting biases, the degree of which cannot be measured (Hooley, 2018). For example, the number of reports filed for a type of incident represents an actual lower bound but cannot infer the prevalence of the incidents (Hooley, 2018). The ASRS reports are a valuable source of the “what“ and “why” related to safety incidents. The report data combined with the reporter's description of the “why“ provides critical insights regarding the direct and contributing factors affecting decision-making.
The first analysis of ASRS reports, published in 1976, analyzed 1,464 reports and recommended future studies related to human factors (Billings et al., 1976). Since its inception, the ASRS has processed 1,781,647 reports at an average rate of 5,471 reports per month (NASA, 2021). The constantly increasing number of reports and diversity of data types within the reports represent a challenge for researchers. Despite the challenges, the industry and academic analyses provide vital insights leading to enhanced aviation safety and policy.
In addition to increasing submissions, the reports' quantity and classification of recorded variables continue to increase. Presently, the ASRS report data consists of 125 variables, 87 of which are either multi-class with mutually exclusive classes (i.e., Flight Conditions) or multi-label representing different but related topics (i.e., Human Factors). The challenge is further compounded by acronyms and semantically different uses and degrees of importance of common and technical vocabulary. These challenges have led researchers to employ new and novel analytical techniques.
2.2 Aviation accident causal factors
Thanks to a long history of academic, industry, and government research, U.S. airlines have an enviable safety record (Pasztor, 2021). Researchers found there are a limited number of causal factors in accidents that manifest in response to scenario demands (Reason, 1990). The human factors analysis and classification system (HFCAS) identifies four sequential levels of barriers to adverse events; organizational influences, supervision, preconditions, and unsafe acts (Shappell & Wiegmann, 2000). The categorization of ASRS reports facilitates connecting causal factors to HFCAS levels. While reports identify causal categories applicable to each HFCAS level, the preponderance identify the situational and operator condition categories in preconditions level and decision, perceptual, and skill-based errors in unsafe acts level. Broadly these factors involve situational awareness, training, and stress. Situational awareness is an individual's ability to perceive, comprehend, and act upon environmental elements in current and future states. Personal and environmental stressors distract and negatively impact situational awareness. Degraded situational awareness has been demonstrated to negatively impact operator performance (Endsley & Kiris, 1995). Similar cognitive issues have been shown to affect operator performance by delaying or impeding takeover tasks (Agrawal & Peeta, 2021) and receiving inadequate training affected by emotional intelligence (Wang et al., 2021). An individual’s stress unrelated to job task or environment, has spill-over effects manifesting as error or delayed performance (Rowden et al., 2011). Krahnen et al. (2022) identifies stress mitigation activities specific to the unique environment of mobility operations. Identifying temporally relevant training and stress mitigation is a critical element in safe aviation operations. Training issues related to proficiency, currency, and experience are present in 40% of serious aircraft accidents (Kelly & Efthymiou, 2019). Improving and maintaining individual performance requires training and timely repeated feedback relevant to the causal issues (Komaki et al., 1980).
Several studies explored the effects of COVID-19 on safety concerns related to human factors, with a more specific focus on the workforce. Current studies examining the impact of COVID-19 on aviation identify adverse effects of the increased psychological stress levels on pilots and cabin crews (Alaminos-Torres et al., 2021). Cahill et al. (2021) surveyed and compared the levels of depression and anxiety in workers by subgroups (i.e., pilots, air-traffic controllers, cabin crews, engineering or maintenance, and others) without a statistical test to compare the heterogeneity in responses. A survey of 65 aviation engineering workers shows job and pandemic related stress negatively contributed to employee productivity. Further, the pandemic-related stress resulted in a more significant impact than job-related stress (Widodo et al., 2021). A key focus of this study is associated with examining the heterogeneous effects of COVID-19 by identifying characteristics that are sensitive to the event in reporting incursions/excursions. To the authors’ knowledge, there is currently no study that directly investigates the heterogeneous effects of the pandemic on aviation incidents that vary by encompassing characteristics of subgroups. The challenge of dealing with a large number of variables can be dealt with by applying a data-driven method to reduce the dimensionality of control variables.
2.3 Machine learning applied to the ASRS
Recent machine learning applications to analyze the ASRS dataset have identified and classified incident topics from the narrative statement field. Warp latent Dirichlet allocation (WarpLDA) was examined to determine if current topic modeling strategies are suitable for developing automated topic finding to ease manual workflows (Shi et al., 2017). Robinson et al. (2015) employ the method of latent semantic analysis to compare human-recorded causal factors of accidents in safety narrative. Researchers adopt an extended topic modeling, structural topic modeling (STM), to identify thematic highlights of the ASRS data (Kuhn, 2018, Rose et al., 2022, Paradis et al., 2021). A convolutional neural network framework, in combination with bidirectional long short-term memory neural network with attention mechanism (Att-BiLSTM) was also proposed to classify risk attributes in the ASRS data (Zhou et al., 2022). Machine learning and deep learning architectures were utilized to automate, identify, and validate potential unmanned aircraft systems safety risks (Abraham, 2022). Wallace and Ross (2006) utilized a transformer-based model based on RoBERTa, to classify aviation anomalies. These methods demonstrate promise to augment and eventually automate the identification of primary and contributing factors in ASRS reports. Ongoing research focuses on improving method accuracy. Shi et al. (2017) evaluated naïve Bayes, Hoeffding tree, and OzaBagADWIN algorithms to achieve accuracies ranging from 76% to 88% in the human factory ranging from 76% to 88% in human and aircraft-related factors. A Bayesian network to capture the causal impacts of risk factors was constructed by Zhang and Mahadevan (2021). Odisho et al (2022) use various machine-learning tools, including gradient boosting, decision tree, and support vector machine, to build a predictive model for runway excursions. Dong et al. (2021) utilized an attention-based long short-term memory model to achieve + 88% accuracy in identifying six of the primary human factors. The accuracies surpass the observed expert inter-rater agreement of 70% but fall short in the ability to consider the broad range of topic labels assigned in ASRS reports (Kierszbaum et al., 2021). This research highlights potential efficiency improvements to automate assigning labels to the single-label primary problem and multi-label contributing factors, a critical step to accelerating the process of processing reports into actionable policy information.
The application of causal machine learning has not been previously applied to identifying incident attributes in aviation safety reporting. The generalized random forest (GRF) technique enables nonparametric quantile regression, conditional average partial effect estimation, and heterogeneous treatment effect statistical estimation methods (Athey et al., 2019). The heterogeneous treatment effect estimation identified relationships between weather patterns containing numerous covariates and total factor productivity in agriculture (Stetter & Sauer, 2021). The GRF technique has been employed in crop rotation yields (Kluger et al. 2022), forest management policy (Miller, 2020), predicting preventable hospital readmissions (Marafino et al., 2020), and unemployment in early adulthood (Kuikka, 2020) studies. She et al. (2020) demonstrated improved speed-estimation accuracy of COVID-19 outbreak detection using GRF. Increasing empirical evidence demonstrates the effectiveness of GRF to estimate and infer causal relationships in a timely manner.
3 Data and empirical method
3.1 Data
Study data consisted of 7,246 incident reports from the NASA ASRS, covering January 2018 through June 2020, used to evaluate the potential influences of COVID-19 on reported incursions and excursions. The analyzed data were limited to January through June of each year to reduce the potential effects of seasonality. After excluding variables with high correlations, a total of 35 of 125 attributes were included in the analysis. Highly correlated variables were excluded to select a small number of influential variables to identify the heterogeneous effects and avoid masking influential variables from detection in the tree splits due to highly correlated variables (Bénard et al., 2022). In FAA parlance, incursions are the incorrect presence of an aircraft, vehicle, or person in a controlled movement area. Conversely, excursions occur due to the inappropriate exit from a controlled movement area. The dangers associated with airborne and ground incursions and excursions pose significant risks to all participants and are the most reported type of accident annually. Therefore, aviation operations rely on strict adherence to regulations and procedures to ensure high levels of participant safety.
The ASRS reports are composed of self-report data plus NASA expert assessment and characterization. Self-report variables for Operator, Reporter, Role, and Flight hours were considered in addition to the NASA categorizations of Anomaly, Human Factors, and Contributing Factors. The variable Operator refers to the commercial operators, air carrier (seating capacity > 60), air taxi (seating capacity < 60), corporate flight departments (two or more aircraft operated incidental to a business), fractional (shared aircraft ownership under 14 CFR 91 2), operating the aircraft at the time of the reported event. The Reporter variable refers to the official position of the individual who reported the incident. The reporter roles examined included the aircraft captain (directly responsible for the flight), first officer (assists the captain in the conduct of the flight), flight attendant (responsible for passenger safety), and air traffic control (ATC) (manage the flow of aircraft). The Role variable specifies which pilot was flying the aircraft. Finally, the Flight hour variable is the total flight hours for the individual who reported the incident.
NASA aviation experts assess the categorization of anomalies, human factors, and contributing factors. The categorizations include 20 anomaly factors, 11 human factors, and 6 contributing factors. The study utilized 9 anomalies, 11 human factors, and 4 of the contributing factor categorizations. The study focused on variables with correlations less than 0.4. The variables that include report attributes, incursion/excursions as anomaly outcomes, and the COVID-19 period as a treatment are summarized in Table 1 . Variables are transformed into binary variables to specify a particular attribute. Table 1 also includes the mean and mean difference of a variable by treatment, a COVID-19 period dummy. The correlation matrix of selected variables is available in Table 2 .Table 1 Summary statistics and mean comparison by pre-COVID and COVID period.
Categories of variables Variables Full sample (1) Pre-COVID (2) COVID Difference Chi-square stat
Obs Mean SD Mean SD Mean SD
Treatment COVID-19 7246 0.13 0.34
Outcome Incursions/Excursions 7246 0.12 0.32 0.11 0.32 0.16 0.36 −0.04*** 15.38
Operator Air Carrier 7246 0.63 0.48 0.65 0.48 0.50 0.50 0.15*** 75.29
Air Taxi 7246 0.04 0.19 0.04 0.20 0.01 0.12 0.03*** 16.25
Corporate Flight Dept. 7246 0.04 0.20 0.04 0.20 0.04 0.19 0.00 0.05
Fractional 7246 0.01 0.10 0.01 0.10 0.01 0.12 −0.00 0.69
Other Operator 7246 0.01 0.08 0.01 0.08 0.00 0.06 0.00 1.06
Reporters Captain 7246 0.39 0.49 0.41 0.49 0.32 0.47 0.09*** 27.74
First Officer 7246 0.17 0.38 0.18 0.38 0.13 0.34 0.05*** 12.35
Air Traffic Control Issue 7246 0.01 0.09 0.01 0.09 0.01 0.11 −0.00 0.83
Flight Attendant 7246 0.04 0.20 0.04 0.19 0.07 0.25 −0.03*** 19.89
Pilot Flying 7246 0.42 0.49 0.41 0.49 0.44 0.50 −0.02 1.60
Contributing Company Policy 7246 0.12 0.32 0.11 0.31 0.18 0.39 −0.07*** 41.42
Factors Human Factors 7246 0.59 0.49 0.58 0.49 0.66 0.47 −0.08*** 23.56
Procedure 7246 0.36 0.48 0.36 0.48 0.39 0.49 −0.03* 3.17
Staffing 7246 0.02 0.13 0.02 0.12 0.04 0.19 −0.02*** 20.74
Human Communication breakdown 7246 0.26 0.44 0.26 0.44 0.27 0.44 −0.01 0.58
Factors Confusion 7246 0.14 0.35 0.13 0.34 0.18 0.39 −0.05*** 16.78
Distraction 7246 0.12 0.33 0.11 0.32 0.19 0.39 −0.08*** 43.26
Fatigue 7246 0.02 0.13 0.02 0.13 0.01 0.12 0.00 0.11
Human-Machine Interface 7246 0.06 0.24 0.06 0.24 0.05 0.22 0.01 1.26
Other / Unknown 7246 0.03 0.18 0.03 0.16 0.06 0.23 −0.03*** 22.41
Physiological - Other 7246 0.02 0.14 0.02 0.15 0.01 0.08 0.02*** 11.16
Situational Awareness 7246 0.44 0.50 0.45 0.50 0.34 0.48 0.11*** 38.99
Training / Qualification 7246 0.09 0.29 0.09 0.28 0.10 0.31 −0.02 2.28
Troubleshooting 7246 0.09 0.28 0.09 0.28 0.08 0.27 0.01 0.71
Workload 7246 0.1 0.29 0.09 0.29 0.13 0.33 −0.04*** 13.19
Anomaly Air Traffic Control Issue 7246 0.2 0.40 0.21 0.40 0.15 0.35 0.06*** 17.50
Airspace Violation 7246 0.03 0.18 0.03 0.18 0.05 0.21 −0.02** 5.85
In location: Inflight 7246 0.25 0.43 0.26 0.44 0.16 0.37 0.10*** 44.36
In location: Ground 7246 0.18 0.39 0.18 0.39 0.18 0.39 −0.00 0.04
Anomaly in Flight CFTT/CFIT 7246 0.08 0.27 0.08 0.28 0.05 0.21 0.04*** 16.49
Loss of Control 7246 0.07 0.25 0.06 0.24 0.09 0.29 −0.03*** 13.15
Unstable Approach 7246 0.03 0.18 0.04 0.18 0.02 0.13 0.02*** 8.90
Wake Vortex 7246 0.03 0.17 0.03 0.18 0.01 0.08 0.03*** 19.35
Pilot Flight Flight hour in three months (below median) 7246 0.19 0.39 0.18 0.38 0.31 0.46 −0.14*** 98.84
Hours Flight Hour Total (below median) 7246 0.19 0.39 0.18 0.38 0.25 0.43 −0.07*** 25.27
Total Observations 7246 6296 950 7246
Note: *p < 0.10, **p < 0.05, ***p < 0.01. The minimum and maximum values are 0 and 1 for all variables as variables included in our analysis are binary.
Table 2 Correlation matrix of selected important variables.
Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
(1) Incursions/Excursions 1.00
(2) COVID-19 treatment 0.05 1.00
(3) Corporate Flight Dept 0.06 −0.00 1.00
(4) First Officer −0.02 −0.04 −0.02 1.00
(5) Distraction 0.01 0.08 0.01 0.01 1.00
(6) Situational Awareness 0.09 −0.07 0.04 −0.01 0.14 1.00
(7) Training / Qualification 0.05 0.02 −0.00 −0.06 0.02 0.05 1.00
(8) ATC Issue 0.04 −0.05 0.03 −0.06 0.04 0.19 −0.00 1.00
(9) Ground 0.39 0.00 0.01 −0.06 −0.00 0.07 0.09 −0.06 1.00
(10) CFTT/CFIT 0.05 −0.05 0.05 0.02 0.03 0.17 0.05 0.17 −0.13 1.00
(11) Loss of Control 0.32 0.04 0.02 −0.04 −0.04 −0.01 0.10 −0.09 0.36 −0.07 1.00
(12) Flight hour total (below median) 0.13 0.06 0.01 −0.10 0.00 0.08 0.08 −0.08 0.16 −0.05 0.20 1.00
3.2 Generalized random forest
The empirical analysis involved multiple steps. First, the generalized random forest (GRF), a machine learning technique, was used to estimate the condition average treatment effects (CATEs) of the period of the COVID-19 pandemic for reporting the events of incursions or excursions. Second, attributes included in the NASA ASRS reports and subsets of these attributes most likely to experience incursions/excursions were identified. These attributes are the most important variables in predicting heterogeneity of the effects of the pandemic, which was an unprecedented event that significantly disrupted airline operations, thereby affecting factors related to flight safety. Third, a DID approach was employed for robustness. The DID examined the effects of COVID-19 on the likelihood of experiencing incursions/excursions using a set of covariates selected using the GRF technique.
To estimate the effects of COVID-19 that differ across subgroups, Yi was defined as the outcome variable for an individual i who submitted the ASRS report of an incursion/excursion event. The individual is indexed by i=1,2,…,N, and a vector covariates Xi. The COVID-19 pandemic as a treatment is indicated as a binary variable Ti∈0,1. An individual who submitted a report during the pandemic period was denoted as Yi1, and Yi(0) if the report was made pre-pandemic period. Yi(0) is the control group. Only one of two outcomes was observed; therefore, the factual treatment effect is not identifiable (Holland & Rubin, 1987). The CATEs of an individual were estimated as the expected difference between the two potential outcomes conditional at Xi=x.(1) τ(x)=EYi(1)-Yi(0)Xi=x
The GRF method was adopted because it produces an “honest“ estimation based on recursive partitioning and subsampling of data (Athey et al., 2019, Zhang et al., 2022). Following the application in Athey and Wager (2019), estimation is considered honest by splitting data into two and taking the approach briefly explained below. Half of the observations are used to determine the variables to grow trees and the other half for CATE prediction and validation. The algorithm builds consistent and asymptotic estimators using an ensemble of trees, thereby used for statistical inference (Athey & Imbens, 2016).
Honest estimation is explained in applications of GRF in Carter et al., 2019, Zhang et al., 2022. A subset of observations J is randomly drawn from n number of observations without replacement, where J is partitioned into two sets: J1 to build trees that maximize the variance of CATEs estimation in a leaf and J2 to estimate the CATEs. While building a tree, m subset of covariates is used to split the subset of observations. Wager and Athey (2018) illustrated the procedures to satisfy honest conditions.
Athey et al. (2019) developed the GRF based on the above approach that performs in the presence of confounding and in estimating heterogeneous treatment effects (Nie and Wager, 2021, Robinson, 1988). The heterogeneous treatment effects are estimated as:(2) τ^=∑iai(x)(Yi-m^(-i)(Xi))(Ti-e^-i(Xi)∑iaix(Ti-e^-i(Xi))2
which is a semi-parametric approach to estimating τ^, where m^(-i) and e^-i is the out-of-sample prediction of a conditional outcome mx=EYiXi=x and the conditional probability of being treated ex=ETiXi=x. The term ai(x) is a data-adaptive kernel in which how often a unit i in training has fallen into the same leaf at x(Athey & Wager, 2019). The procedures of GRF first estimate e^Xi and m^Xi separately, along with out-of-bag prediction. Residual treatment Ti-e^-iXi and outcome Yi-m^(-i)(Xi) are computed, and a GRF is trained on these residuals. Then ATE is estimated based on the following equation.(3) ATE^=1n∑iτ^-iXi+Ti-e^-iXie^-i(Xi)1-e^-i(Xi)Yi-m^(-i)(Xi)-Ti-e^-iXiτ^-iXi
Although GCF produces a consistent and asymptotic treatment effect estimation, we checked the robustness of our GCF results using a DID approach. We assume that unconfoundedness is satisfied as the exposure to COVID-19 occurred unexpectedly and is orthogonal to the potential observations Yi1,Yi0⊥TiXi. We also assume that while air travel was restricted during the early stage of the pandemic, the flights or individuals were not selected based on the covariates (Rosenbaum & Rubin, 1983). In addition, we assume that overlap, known positivity or common support, is satisfied. The satisfied overlap means the probability of being exposed to the pandemic given a set of covariates is bounded to be less than 1 and greater than 0<Pr(Ti=1Xi=x)<1,∀x. With the two assumptions, we can treat the observations as if they were generated from a randomized experiment and check the robustness of our GCF results using a DID approach.
3.3 Difference-in-difference estimation
For robustness of the results generated using the GRF, we extend our empirical analysis to estimate treatment effects (COVID-19) on incursion/excursion outcomes utilizing the DID approach for logistic regression. We aim to compare the DID coefficient for the heterogeneous effect of COVID-19 with the estimated results from GCF. The probability that incursion/excursion are reported is denoted as PNearmissi=1=Pi for an individual ASRS report i. The logistic regression model can be written as:(4) logit(Pi)=β0+β1COVIDi+β2Attributei+β3Attributei×COVIDi+γX+εi
where COVIDi denotes whether an individual NASA ASRS incursion/excursion was reported as a treatment in the COVID-19 time period. Attributei indicates an attribute used as a conditioning variable selected under the GCF algorithm. We estimated the multiple models replacing Attributei with attribute variables. The coefficient of an interaction term, β3, is our DID estimator for the heterogeneous effect that measures the difference in treatment effects between those whose Attributei=1 and those with Attributei=0 based on the following equation where Xi represents the Attributei:(5) [E(Yi|Xi=1,Ti=1)-E(Yi|Xi=1,Ti=0)]⏟(β0+β1+β2+β3)-(β0+β2)=(β1+β3)-[E(Yi|Xi=0,Ti=1)-E(Yi|Xi=0,Ti=0)]⏟(β0+β1)-(β0)=β1
The remaining selected attributes, except the one used to interact with the COVID-19 dummy, are treated as a set of control variables, denoted as X. The determinants considered in the logistic regressions are those listed in Table 4 , which include locations or flight anomaly situations, expert-assessed human factors, aircraft operators, and reporter roles. By controlling these subgroup characteristics, we consider unobserved fixed effects. The error term εi is expected to have a zero mean.Table 3 Observations by quartiles of conditional average treatment effects (CATE) of selected variables.
Categories of variables Variables Levels Quantiles of CATE (number of obs.) Quantiles of CATE (%) Wilcoxon Mann–Whitney test
1 2 3 4 Total Number 1 2 3 4 Z stat P-value
Operator Corporate Flight Dept. 0 1,664 1,747 1,777 1,766 6,954 23.93% 25.12% 25.55% 25.40% 10.58 0.00
1 148 64 35 45 292 50.68% 21.92% 11.99% 15.41%
Reporter First Officer 0 1,720 1,660 1,411 1,228 6,019 28.58% 27.58% 23.44% 20.40% −24.27 0.00
1 92 151 401 583 1,227 7.50% 12.31% 32.68% 47.51%
Human Factors Situational Awareness 0 1,619 992 969 493 4,073 39.75% 24.36% 23.79% 12.10% −38.04 0.00
1 193 819 843 1,318 3,173 6.08% 25.81% 26.57% 41.54%
Training / Qualification 0 1,754 1,656 1,646 1,530 6,586 26.63% 25.14% 24.99% 23.23% −13.22 0.00
1 58 155 166 281 660 8.79% 23.48% 25.15% 42.58%
Distraction 0 1,671 1,582 1,541 1,554 6,348 26.32% 24.92% 24.28% 24.48% −6.48 0.00
1 141 229 271 257 898 15.70% 25.50% 30.18% 28.62%
Anomaly Air Traffic Control Issue 0 1,733 1,433 1,300 1,347 5,813 29.81% 24.65% 22.36% 23.17% −17.39 0.00
1 79 378 512 464 1,433 5.51% 26.38% 35.73% 32.38%
In location: Ground 0 1,775 1,724 1,605 825 5,929 29.94% 29.08% 27.07% 13.91% −43.34 0.00
1 37 87 207 986 1,317 2.81% 6.61% 15.72% 74.87%
Anomaly in Flight Loss of Control 0 1,790 1,716 1,681 1,578 6,765 26.46 25.37% 24.85% 23.33% −15.12 0.00
1 22 95 131 233 481 4.57% 19.75% 27.23% 48.44%
CFTT/CFIT 0 1,802 1,759 1,674 1,444 6,679 26.98% 26.34% 25.06% 21.62% −21.72 0.00
1 10 52 138 367 567 1.76% 9.17% 24.34% 64.73%
Pilot Flight Hours Flight Hour Total (Below Median) 0 1,583 1,423 1,426 1,452 5,884 26.90% 24.18% 24.24% 24.68% −5.09 0.00
1 229 388 386 359 1,362 16.81% 28.49% 28.34% 26.36%
Table 4 CATEs estimated using subgroup by binary levels of variable (Generalized random forest).
Categories of variables Selected variables Levels CATEs 95% conf.low 95% conf.high Difference in subgroup CATEs T-stat P-value
Operator Corporate Flight Dept. 0 6.12 3.53 8.72 −17.78 3.91 0.00
1 −11.66 −20.20 −3.12
Reporter First Officer 0 4.04 1.56 6.52 8.07 −1.78 0.08
1 12.11 3.55 20.66
Human Factors Situational Awareness 0 2.30 −0.32 4.93 7.09 −2.60 0.01
1 9.39 4.74 14.04
Training / Qualification 0 4.91 2.24 7.59 5.43 −1.39 0.16
1 10.34 3.17 17.51
distraction 0 5.22 2.47 7.96 1.52 −0.45 0.65
1 6.74 0.73 12.75
Anomaly Air Traffic Control Issue 0 4.59 1.81 7.36 4.14 −1.24 0.22
1 8.73 2.79 14.67
In location: Ground 0 3.19 0.74 5.64 12.18 −2.74 0.01
1 15.37 7.02 23.73
Anomaly in Flight Loss of Control 0 5.46 2.93 7.98 −0.76 0.11 0.91
1 4.70 −8.66 18.06
CFTT/CFIT 0 4.39 1.98 6.80 13.01 −1.66 0.10
1 17.40 2.26 32.54
Pilot Flight Hours Flight Hour Total (below median) 0 5.67 2.83 8.52 −1.41 0.46 0.64
1 4.26 −1.06 9.57
Our dataset involves 35 potential covariates at the beginning, including the characteristics of incursion/excursion situation attributes of those who reported the event. The numerous potential observed variables are a challenge for researchers when identifying the heterogeneity in the treatment effects. Therefore, the GRF method developed by Athey et al. (2019) was employed. The method extends the random forest nonparametric approach (Breiman et al., 2001). The GRF offers researchers a means to adopt flexible, functional forms to capture the heterogeneity in treatment effects of high dimensional data with a low computation burden (Dorie et al., 2019, Wendling et al., 2018).
4 Results: Heterogeneous effects of COVID-19 on near-misses
Overall, the estimation method of using the causal forest models succeeded in detecting heterogeneity in the effects of COVID-19 by subgroup characteristics. Table 3 shows a list of the top 10 important variables in order of how frequently each was used in creating trees. The variables, in order of variable importance, are listed by the weighted sum of how often the attributes were used to split in the forest (Athey et al., 2019). The quartiles of CATEs summarize the frequency of observations for each variable by levels. 3 The distribution of observations is also summarized based on CATEs in percentage terms. For example, 50.68% of the reports filed by corporate flight department operators fall in the lowest quartile (Quartile 1) of CATEs, while only 15.41% of corporate flight department operators’ reports fall in the highest quartile (Quartile 4), implying that more than half of corporate flight department operators were predicted to experience the lowest quartile of heterogeneous effects of the pandemic. First officer ASRS reporters have 47.51% of observations that fall in the quartile of the highest heterogeneous effect (Quartile 4). The ASRS reports, assessed by experts, with the human factors, situational awareness, and training as a cause of the incursion/excursion, also fall in the highest quartile of CATEs. The statistically significant p-values of the Wilcoxon Mann-Whitney test indicate that the distributions of estimated treatment effects are different across the levels (binary) of a variable. The selected important variables suggest that the impacts of the COVID-19 pandemic were greater in increasing incursions/excursions with certain characteristics that have a larger portion of observations fall in the highest quartile (Quartile 4) of CATEs in Table 3. For robustness, the observations were divided into subsets by levels of a variable and the causal forest estimation of the CATEs was performed separately on each subset. Table 4 presents the CATEs estimated separately over each variable’s subgroups (binary level). This extends the estimation in detecting the impact of COVID-19 heterogeneity by focusing only on the subgroups of characteristics. The statistically different estimated CATEs by subgroup supports the findings that the variables whose distributions are statistically different in Table 3 also experience heterogeneously diverging CATEs by a subset of attributes. Ground as anomaly location, the human factors situational awareness and training/qualifications, controlled flight toward terrain (CFTT) as anomaly flights, and being the first officer were the characteristics that experienced increased incursions/excursions due to COVID-19. The flights operated by corporate flight departments were sensitive to the pandemic in decreasing the reporting of incursions/excursions. Consistent with the distributions of CATEs using a full sample (Table 3), corporate flight departments experienced a negative impact of the pandemic in experiencing incursions or excursions. Fig. 1 shows the CATE and associated 95% confidence interval estimated separately over subsamples by binary level.Fig. 1 CATE estimated separately over subsamples (by binary level).
4.1 Difference-in-difference estimators for further robustness
The causal forest algorithm is beneficial to identifying heterogeneity in treatment effects because it reduces the number of variables to consider when there are numerous covariate candidates in a model. To further check the robustness of our results, the important variables selected in the GCF were used to estimate coefficients using DID logistic regressions. Table 5 summarizes DID marginal effect estimators by the subgroup attribute selected in GCF for both a naïve model without controls and a model with control variables. Consistent with the CATEs estimated in Table 4, the situational awareness and training/qualifications attributes, and reported by first officers, show a positive DID coefficient, indicating that flights with these attributes are more likely to report incursions/excursions due to the pandemic. The increase in the likelihood of experiencing and reporting the incidents is significantly greater for the attributes when the DID estimation shows statistically significant positive effects. A statistically significant increase of 4.63% in the likelihood of reporting the incursion/excursion event when the flight is categorized to have situational awareness issues and a 6.46% increase in the likelihood when assessed to be related to personnel training. The negative and significant coefficient of flights operated by corporate flight departments indicates that corporate flight departments are 14% less likely to report incursions/excursions due to the pandemic. Our estimation of DID coefficients in logistic regression using selected variables yielded results similar to the estimation of heterogeneous effects of COVID-19 using GCF.Table 5 Summary of difference-in-difference (DID) marginal effects estimates comparing Pre- and During-COVID-19.
Categories of variables Variables Model including control variables
Pre-COVID During-COVID Effects* P-Value
(%) (%) (During-Pre), %
Operator Corporate Flight Dept.
0 10.88 15.61
1 18.56 9.34
Diff. 7.69 −6.28 −13.97 0.02
Reporter First Officer
0 11.2 14.53
1 11.51 21.76
Diff. 0.31 7.23 6.91 0.06
Human Factors Situational Awareness
0 9.84 12.06
1 12.65 19.49
Diff. 2.81 7.43 4.62 0.1
Training / Qualification
0 11.46 14.86
1 9.67 19.54
Diff. −1.79 4.68 6.46 0.05
Distraction
0 11.23 14.89
1 11.32 17.69
Diff. 0.1 2.81 2.71 0.37
Anomaly Air Traffic Control Issue
0 10.23 13.61
1 15.21 22.73
Diff. 4.98 9.13 4.15 0.39
In location: Ground
0 5.96 8.22
1 31.41 44.19
Diff. 25.45 35.97 10.52 0.31
Anomaly in flight Loss of Control
0 9.28 13.62
1 27.61 33.42
Diff. 18.32 19.8 1.47 0.59
CFTT/CFIT
0 10.47 13.91
1 21.64 35.02
Diff. 11.17 21.11 9.94 0.23
Pilot Flight Hours Total hours flown (below median)
0 10.56 15.19
1 13.47 16.93
Diff. 2.91 1.75 −1.16 0.46
Note: * the effects indicate estimated coefficients for DID estimators.
Examining the distributions within the condition average treatment effect highlights the disparate nature of COVID-19′s impact on anomalous aviation events. The preponderance of variables revealed treatment effects skewed to higher quartiles. A notable exception is the heavy skew of corporate flight departments to the lowest quartile. The demonstrably different impact on corporate flight departments warrants further examination by human factors specialists. Two first officer and ground variables were significantly skewed to the highest quartile. The variables are notable but possibly conflated. As the junior officer in the aircraft, the first officer is disproportionately responsible for administrative tasks. While the treatment effects associated with the first officer should not be ignored, task assignment influences should be considered.
Similarly, the ground location variable treatment effects should be considered relative to the predominance of incursions and excursions occurring on the ground. The higher skewing for situational awareness, training/qualification, ATC, loss of control, and CFTT/CFIT indicate treatment effects warranting further scrutiny. The results indicate an additional notable exception in the minor treatment effects on total hours flown (below median). The commonly held belief is that less experienced pilots are disproportionately impacted by disruptions. The total hours flown (below median) result does not reflect the belief and warrants further examination by aviation operations and training specialists.
5 Conclusions, policy Implications, limitations, and future research
The application of machine learning to self-report safety databases increases the empirical evidence related to causal factors in aviation accidents. The goal of the current study was to examine the impact of an unprecedented period of reduced commercial aviation operations resulting from COVID-19 on incidents of aircraft incursions and excursions. The study demonstrated that a causal machine learning tool effectively analyzes subgroups within ASRS reports and identifies heterogeneous treatment effects associated with prolonged periods of reduced commercial aviation operations. The resultant method provides a targeted evaluation of temporal safety issues and training needs.
The key finding of this research is the prolonged period of reduced flight operations, and increased pandemic-related stressors created conditions conducive to aircraft incursions and excursions. The examination of NASA ASRS self-report data indicates the subgroups of aircraft operators, reporter roles, recency of experience, human factors, contributing factors, and anomaly were the most sensitive to the prolonged reduction in operations and experienced more incursions/excursions. The current study suggests the effect of COVID-19 differed across subgroups, expert assessed human factors, contributing factors, and anomalies. The most notable human factors were situational awareness, distraction, training, and the pilot's flight experience. The identification of causal human factors is not surprising. Recent research indicates COVID-19 created a foundation for human factors issues, with individuals more likely to experience worry, depression, and disinterest (Le & Nguyen, 2021).
The findings further suggest that safety awareness programs in aviation can incorporate identification strategies, such as causal machine learning tools, to identify focus subgroups or effective measures that reduce the impacts of prolonged reduced operations seen during the COVID-19 pandemic. Strategies should develop and improve support programs tailored to enhance situational awareness and aircrew training. Training programs should consider developing preemptive and targeted proficiency curricula to prepare and aid aviation personnel in skill retention. In coordination with training programs, the current study results can inform the design and deployment of policies to maximize the effectiveness of training strategies.
The application of the study results extends the available knowledge on aviation safety and issue identification methodologies. Although the preponderance of NASA ASRS reports occurred in the U.S. national airspace system, the international consistency in training standards, operational procedures, and regulatory structure suggest broad applicability of the findings.
While the study provided important insights regarding the application of machine learning in identifying causal factors, we encourage more research on applying new analysis techniques to extract the wealth of information contained within safety-related databases. These contributions advance academic research and improve the safety of vital transportation networks. Further research in this area should include:
First, the timely analysis of causal factors is critical. While ASRS reporting is voluntary and a lagging source, the significant increase in the first month COVID related reporting indicates the presence of safety issues and demonstrates a community desire for awareness. Further evidence is required to validate the application of machine learning to causal factor identification. Second, timely development and dispersal of training programs are required. The international aviation community benefits from numerous participants with the knowledge to rapidly develop training programs. It would be valuable to investigate a process for validating crowd-sourced training programs and disseminating them through existing regulatory and industry organizations.
Data Availability
NASA ASRS data can be downloaded from the Aviation Safety Reporting System website: https://asrs.arc.nasa.gov/. Codes are available upon request.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Dr. Youngran Choi is an Assistant Professor of Business Analytics at the David B. O’Maley College of Business at Embry-Riddle Aeronautical University. She holds a doctoral degree in Economics from Washington State University. Prior to the PhD program, she worked in business such as Ernst & Young and Deloitte and for international organizations such as UN FAO. Her research focuses on exploring causal inferences and factors to decision making. She employs econometric and analytic tools to examine data to understand personal and corporate behavior. Her recent publications include consumer preference for bio-based batteries in the Journal of Consumer Behaviour.
Dr. Jim Gibson is an Assistant Professor with the College of Business at Embry-Riddle Aeronautical University. Dr. Gibson is a retired United States Marine Corps naval aviator, squadron commander, and experimental test pilot. He is a powered-lift subject matter expert to the FAA’s Airmen Certification Standards working group. His research focuses on urban air mobility, energy economics, and sustainable development goals in East African carbon offsetting programs. Dr. Gibson is a US Naval Test Pilot School graduate who earned a Ph.D. in Systems and Engineering Management from Texas Tech University and an MBA from Duke University’s Fuqua School of Business.
Appendix A An Explanation of the ASRS Variable Taxonomy
The ASRS is a voluntary, confidential, and non-punitive method for pilots, air traffic controllers, cabin crew, and maintenance technicians to report unsafe and hazardous situations (Chappell, 2017). The narratives and information within the ASRS reports describe the anomalous events, causal factors, categories of human factors, and quantitative information related to the participants and events. Presently, the ASRS report data consists of 125 variables, 87 of which are either multi-class with mutually exclusive classes (i.e., Flight Conditions) or multi-label representing different but related topics (i.e., Human Factors). The ASRS database online site details the ASRS coding taxonomy (https://asrs.arc.nasa.gov/search/database.html). The following tables provide an overview of the taxonomy, variable name, and description for variables considered in the study. Variables are transformed into binary variables to specify a particular attribute.
(See Table A1, Table A2, Table A3, Table A4, Table A5 ).Table A1 Derived variables utilized in the study.
ASRS Coding Taxonomy Variable Name Description
n/a COVID-19 (treatment) Incident reports occurring during the COVID-19 period.
Experience.Flight Crew: Last 90 Days Flight hour (below median) The number of flight hours flown by the pilot in the preceding 90 calendar days.
Experience.Flight Crew: Total Flight hour total (below median) The number of total flight hours flown by the pilot.
Table A2 ASRS aircraft operator and participant function taxonomy.
ASRS Coding Taxonomy Variable Name Description
Aircraft Operator: Air Carrier Air Carrier The reporting flight was operated by an air carrier.
Aircraft Operator: Air Taxi Air Taxi The reporting flight was operated by an air taxi service.
Aircraft Operator: Corporate Corporate Flight Dept The reporting flight was operated by a corporate flight department.
Aircraft Operator: Fractional Fractional The reporting flight was operated by a fractional ownership operation.
Aircraft Operator: Other Other Operator The reporting flight was operated by an entity other than the previously listed categories.
Function.Flight Crew: Captain Captain The flight crew function of the reporting individual was the captain of the aircraft.
Function.Flight Crew: First Officer First Officer The flight crew function of the reporting individual was the first officer of the aircraft.
Function.Flight Crew: Pilot Flying Pilot Flying The flight crew function of the reporting individual was the pilot flying the aircraft.
Function.Air Traffic Control: ATC ATC The ASRS report was filed by ATC.
Function.Flight Attendant: Flight Attendant Flight Attendant The ASRS report was filed by a Flight Attendant.
Table A3 ASRS Contributing factors, variable name, and description.
Factor Type Variable Name Description
Company Policy Company Policy Company policy was considered a contributing factor to the incident.
Human Factors Human Factors Human factors were considered a contributing factor to the incident.
Procedure (airspace authorization include here) Procedure A procedure was considered a contributing factor to the incident.
Staffing Staffing A staffing issue was considered a contributing factor to the incident.
Table A4 Event anomaly types, variable name, and description.
Anomaly Type Variable Name Description
ATC Issues: All Types ATC Issue An anomalous event that occurred due to an ATC issue.
Airspace Violations: All Types Airspace Violation A violation of controlled airspace occurs when a pilot enters controlled airspace without a clearance.
Inflight Event/Encounter: All Types Inflight The anomalous event occurred when the aircraft was in flight.
Ground Event/Encounter: All Types Ground The anomalous event occurred when the aircraft was on the ground.
Inflight Event/Encounter: CFTT/CFIT CFTT/CFIT CFTT is defined as unintentional flight toward terrain.
CFIT is defined as an unintentional collision with terrain while an aircraft is under control of the pilot.
Inflight Event/Encounter: Loss of Aircraft Control Loss of Control A Loss of Control accident involves an unintended departure of an aircraft from controlled flight.
Inflight Event/Encounter: Unstabilized Approach Unstable Approach An unstable approach is simply an approach that does not meet the criteria for a stable approach established by the aircraft operator.
Inflight Event/Encounter: Wake Vortex Encounter Wake Vortex Wake Vortex Turbulence is defined as turbulence which is generated by the passage of an aircraft in flight.
Ground Excursion & Ground Incursion Incursion/Excursion (outcome) The event was a ground excursion or incursion occurring on a ramp, runway, or taxiway.
Table A5 Category of human factors, variable name, and description.
Category & Variable Name Description
Communication Breakdown Communication breakdown is defined as a loss of coordinated decision making between two groups or more that becomes a temporary inability to function effectively (Bearman, Paletz, & Orasanu, 2010)
Confusion Confusion is when observed behavior is out of sync with the person’s mental model.
Distraction Distraction occurs when anything reduces our focus on completing the current task.
Fatigue Fatigue is a condition characterized by increased discomfort with lessened capacity for work, reduced efficiency of accomplishment, loss of power or capacity to respond to stimulation, and is usually accompanied by a feeling of weariness and tiredness (Salazar, 2007).
Human-Machine Interface A human factors error assessed as a human–machine interface issue.
Other / Unknown A human factors error was assessed as resulting from an unknown issue.
Physiological – Other Aviation physiology is the physical and mental effects of flight on air crew and passengers.
Situational Awareness Situational awareness is perceiving, understanding, and projecting the future of elements in a specified environment (Jones & Endsley,2000).
Training / Qualification The systematic process of developing knowledge, skills, and attitudes; activities leading to skilled behavior (Martinussen & Hunter, 2017).
Troubleshooting Troubleshooting is the process of identifying the cause and severity of a malfunction or discrepancy.
Workload Workload is the mental demand placed on an operator.
1 A runway incursion is any occurrence at an aerodrome involving the incorrect presence of an aircraft, vehicle or person on the protected area of a surface designated for the landing and take off of aircraft. A runway excursion is a veer off or overrun from the ramp, runway, or taxiway surface.
2 14 CFR 91 refers to the General Operating and Flight Rules under the Code of Federal Regulations (2012), which outlines certifications and equipment requirements for aircraft operations in the U.S.
3 The distributions of observations by quartiles of CATEs for all variables are available upon request.
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References
Abraham, N. (2022). Hazard classification of federal aviation administration (FAA) unmanned aircraft systems (UAS) sightings reports using machine learning. Retrieved from https://go.exlibris.link/w620Jn10.
Agrawal S. Peeta S. Evaluating the impacts of situational awareness and mental stress on takeover performance under conditional automation Transportation Research. Part F, Traffic Psychology and Behaviour 83 2021 210 225 10.1016/j.trf.2021.10.002
Alaminos-Torres, A., Martinez-Lorca, M., Sifre De Sola, I., López-Ejeda, N., & Dolores Marrodán, M. (2021). Psychological distress in spanish airline pilots during the aviation crisis caused by the COVID-19 pandemic and psychometric analysis of the 12-item general health questionnaire Informa UK Limited. 10.1080/00140139.2021.1984587.
Athey S. Imbens G. Recursive partitioning for heterogeneous causal effects Proceedings of the National Academy of Sciences - PNAS 113 27 2016 7353 7360 10.1073/pnas.1510489113
Athey S. Tibshirani J. Wager S. Generalized random forests Annals of Statistics 47 2 2019 1148 1178 10.1214/18-AOS1709
Athey S. Wager S. Estimating treatment effects with causal forests: An application Observational Studies 5 2 2019 37 51 10.1353/obs.2019.0001
Bearman C. Paletz S.B. Orasanu J. Thomas M.J. The breakdown of coordinated decision making in distributed systems Human factors 52 2 2010 173 188 20942249
Bénard, C., Da Veiga, S., & Scornet, E. (2022). MDA for random forests: Inconsistency, and a practical solution via the sobol-MDA. Oxford University Press (OUP). 10.1093/biomet/asac017.
Billings C.E. Lauber J.K. Funkhouser H. Lyman E.G. Huff E.M. NASA aviation safety reporting system quarterly report number 76–1. (No. NASA TM X-3445) 1976 National Aeronautics and Space Administration Washington, DC
Breiman, L., Last, M., & Rice, J. (2001). Random forests: Finding quasars. Statistical challenges in astronomy (pp. 243–254). New York, NY: Springer New York. 10.1007/0-387-21529-8_16. Retrieved from http://link.springer.com/10.1007/0-387-21529-8_16.
Byrnes K.P. Rhoades D.L. Williams M.J. Arnaud A.U. Schneider A.H. The effect of a safety crisis on safety culture and safety climate: The resilience of a flight training organization during COVID-19 Transport Policy 117 2022 181 191 10.1016/j.tranpol.2021.11.009 34803245
Cahill J. Cullen P. Anwer S. Gaynor K. The Impact of the COVID 19 Pandemic on Aviation Workers and the Aviation System 29th International Symposium on Aviation Psychology 2021 164 171 https://corescholar.libraries.wright.edu/isap_2021/28
Carter M.R. Tjernström E. Toledo P. Heterogeneous impact dynamics of a rural business development program in nicaragua Journal of Development Economics 138 2019 77 98 10.1016/j.jdeveco.2018.11.006
Chappell S.L. Using voluntary incident reports for human factors evaluations. Aviation psychology in practice 2017 Routledge 149 169
Dong T. Yang Q. Ebadi N. Luo X.R. Rad P. Identifying incident causal factors to improve aviation transportation safety: Proposing a deep learning approach Journal of Advanced Transportation 2021 2021
Dorie V. Hill J. Shalit U. Scott M. Cervone D. Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition Statistical Science 34 1 2019 43 10.1214/18-STS667
Drogoul F. Cabon P. Post COVID-19 fatigue management for ATCOs Paper presented at the International Conference on Applied Human Factors and Ergonomics 2021 319 323
Endsley M.R. Kiris E.O. The out-of-the-loop performance problem and level of control in automation Human Factors 37 2 1995 381 394
General Operating and Flight Rules. Code of Federal Regulations. Title 14 (2012):569-834. Accessed via https://www.Ecfr.Gov/Current/Title-14/Chapter-I/Subchapter-F/Part-91.
Holland P.W. Rubin D.B. Causal inference in retrospective studies ETS Research Report Series 1987 1 1987 203 231 10.1002/j.2330-8516.1987.tb00211.x
Hooley, B. (2018). Data derived from ASRS reports. (No. 262-7). Moffett Field, CA: NASA Ames Research Center. Retrieved from https://asrs.arc.nasa.gov/publications/research.html.
Jones D.G. Endsley M.R. Overcoming representational errors in complex environments Human Factors 42 3 2000 367 378 11132798
Kelly D. Efthymiou M. An analysis of human factors in fifty controlled flight into terrain aviation accidents from 2007 to 2017 Journal of Safety Research 69 2019 155 165 10.1016/j.jsr.2019.03.009 31235226
Kierszbaum, S., Klein, T., & Lapasset, L. (2021). ASRS-CMFS: Using a custom transformer-based model to predict anomalies in aviation incident reports.
Kluger D. Owen A.B. Lobell D.B. Combining randomized field experiments with observational satellite data to assess the benefits of crop rotations on yields Environmental Research Letters 2022
Komaki J. Heinzmann A.T. Lawson L. Effect of training and feedback: Component analysis of a behavioral safety program Journal of Applied Psychology 65 3 1980 261 7380777
Krahnen L.S. Bauernfeind G. Leiber P. Jipp M. Evaluation of two short-term stress interventions in the context of mobility Transportation Research Part F: Traffic Psychology and Behaviour 84 2022 155 164
Kuhn K.D. Using structural topic modeling to identify latent topics and trends in aviation incident reports Transportation Research Part C: Emerging Technologies 87 2018 105 122
Kuikka, S. (2020). Classification tree based algorithms in studying predictors for long-term unemployment in early adulthood: An exploratory analysis combining supervised machine learning and administrative register data.
Le K. Nguyen M. The psychological consequences of COVID-19 lockdowns International Review of Applied Economics 35 2 2021 147 163
Linden E. Pandemics and environmental shocks: What aviation managers should learn from COVID-19 for long-term planning Journal of Air Transport Management 90 2021 101944 33071486
N.R. (2021). ASRS program briefing. Washington, DC: National Aeronautics and Space Administration. Retrieved from https://asrs.arc.nasa.gov/overview/summary.html.
Nie X. Wager S. Quasi-oracle estimation of heterogeneous treatment effects Biometrika 108 2 2021 299 319 10.1093/biomet/asaa076
Marafino, B. J., Schuler, A., Liu, V. X., Escobar, G. J., & Baiocchi, M. (2020). A causal machine learning framework for predicting preventable hospital readmissions. arXiv Preprint arXiv:2005.14409,
Martinussen M. Hunter D.R. Aviation psychology and human factors 2017 CRC Press
Miller S. Causal forest estimation of heterogeneous and time-varying environmental policy effects Journal of Environmental Economics and Management 103 2020 102337
Odisho E.V. Truong D. Joslin R.E. Applying machine learning to enhance runway safety through runway excursion risk mitigation Journal of Aerospace Information Systems 19 2 2022 98 112
Paradis, C., Kazman, R., Davies, M., & Hooley, B. (2021). Augmenting topic finding in the NASA aviation safety reporting system using topic modeling. AIAA scitech 2021 forum 10.2514/6.2021-1981. Retrieved from https://go.exlibris.link/Gdkn6Tw3.
Pasztor, A. (2021, April 16,). The airline safety revolution. Retrieved from https://www.wsj.com/articles/the-airline-safety-revolution-11618585543.
Proclamation on the suspension of entry as immigrants and nonimmigrants of certain additional persons who pose a risk of transmitting coronavirus, (2020). Retrieved from https://trumpwhitehouse.archives.gov/presidential-actions.
Reason J. Human error 1990 Cambridge University Press
Robinson P.M. Root-N-consistent semiparametric regression Econometrica 56 4 1988 931 954 10.2307/1912705
Robinson S.D. Irwin W.J. Kelly T.K. Wu X.O. Application of machine learning to mapping primary causal factors in self reported safety narratives Safety Science 75 2015 118 129
Rosenbaum P.R. Rubin D.B. Assessing sensitivity to an unobserved binary covariate in an observational study with binary outcome Journal of the Royal Statistical Society: Series B (Methodological) 45 2 1983 212 218
Rose R.L. Puranik T.G. Mavris D.N. Rao A.H. Application of structural topic modeling to aviation safety data Reliability Engineering & System Safety 224 2022 108522
Rowden P. Matthews G. Watson B. Biggs H. The relative impact of work-related stress, life stress and driving environment stress on driving outcomes Accident Analysis & Prevention 43 4 2011 1332 1340 21545862
Salazar, G. J. (2007). Fatigue in aviation (No. OK-07-193). United States. Federal Aviation Administration.
Shappell, S. A., & Wiegmann, D. A. (2000). The human factors analysis and classification system--HFACS.
She, Z., Wang, Z., Ayer, T., Toumi, A., & Chhatwal, J. (2020). Estimating county-level COVID-19 exponential growth rates using generalized random forests. arXiv Preprint arXiv:2011.01219,
Shi D. Guan J. Zurada J. Manikas A. A data-mining approach to identification of risk factors in safety management systems Journal of Management Information Systems 34 4 2017 1054 1081 10.1080/07421222.2017.1394056
Stetter, C., & Sauer, J. (2021). Exploring the heterogeneous effects of weather on productivity using generalized random forests.
Wager S. Athey S. Estimation and inference of heterogeneous treatment effects using random forests Journal of the American Statistical Association 113 523 2018 1228 1242 10.1080/01621459.2017.1319839
Wallace, B., & Ross, A. (2006). Beyond human error: Taxonomies and safety science. Boca Raton, FL: CRC/Taylor & Francis. 10.1201/9780203491171.
Wang Z. Jiang Z. Blackman A. Linking emotional intelligence to safety performance: The roles of situational awareness and safety training Journal of safety Research 78 2021 210 220 34399917
Wendling T. Jung K. Callahan A. Schuler A. Shah N.H. Gallego B. Comparing methods for estimation of heterogeneous treatment effects using observational data from health care databases Statistics in Medicine 37 23 2018 3309 3324 10.1002/sim.7820 29862536
Widodo A.W. Xavier C. Wibisono M.R. Murti N.M. Ayu D. Putra T.P. …Asrol M. The impact of job stress on employee productivity during covid-19 pandemic at the aviation industry. IOP Conference Series Earth and Environmental Science 794 1 2021 12084 10.1088/1755-1315/794/1/012084
World Health Organization. (2020a). COVID 19 public health emergency of international concern (PHEIC). Global research and innovation forum: Towards a research roadmap.
World Health Organization. (2020b, Jan 5,). Pneumonia of unknown cause – china. Disease Outbreak News Retrieved from https://www.who.int/emergencies/disease-outbreak-news/item/2020-DON229.
Zhang Y. Li H. Ren G. Estimating heterogeneous treatment effects in road safety analysis using generalized random forests Accident Analysis and Prevention 165 2022 106507 10.1016/j.aap.2021.106507 34856506
Zhang X. Mahadevan S. Bayesian network modeling of accident investigation reports for aviation safety assessment Reliability Engineering & System Safety 209 2021 107371
Zhou D. Zhuang X. Zuo H. Cai J. Zhao X. Xiang J. A model fusion strategy for identifying aircraft risk using CNN and Att-BiLSTM Reliability Engineering & System Safety 2022 108750
| 0 | PMC9729650 | NO-CC CODE | 2022-12-14 23:42:46 | no | J Safety Res. 2022 Dec 8; doi: 10.1016/j.jsr.2022.12.002 | utf-8 | J Safety Res | 2,022 | 10.1016/j.jsr.2022.12.002 | oa_other |
==== Front
J Transp Health
J Transp Health
Journal of Transport & Health
2214-1405
2214-1413
Elsevier Ltd.
S2214-1405(22)00229-8
10.1016/j.jth.2022.101557
101557
Article
Mixed method assessment of built environment and policy responses to the COVID-19 pandemic by United States municipalities focusing on walking and bicycling actions
Evenson Kelly R. a∗
Naumann Rebecca B. ab
Taylor Nandi L. ab
LaJeunesse Seth c
Combs Tabitha S. d
a Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina – Chapel Hill, Chapel Hill, NC, United States
b Injury Prevention Research Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
c Highway Safety Research Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
d Department of City and Regional Planning, University of North Carolina – Chapel Hill, Chapel Hill, NC, United States
∗ Corresponding author. 123 W Franklin Street, Building C, Suite 410, University of NC, Gillings School of Global Public Health, Department of Epidemiology, Chapel Hill, NC, 27599-8050, USA.
8 12 2022
1 2023
8 12 2022
28 101557101557
21 9 2022
23 11 2022
6 12 2022
© 2022 Elsevier Ltd. All rights reserved.
2022
Elsevier Ltd
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Introduction
In 2020, the COVID-19 pandemic prompted community officials to initiate local level environmental and policy changes to slow the spread of infection and provide more opportunities for outdoor recreation. Changes in both regards could positively or negatively impact walking and bicycling. Using a mixed methods approach, the purpose of this United States-based study was to systematically describe municipal response to the pandemic at the community level through environmental and policy changes that may have impacted walking and bicycling.
Methods
Websites of all United States' municipalities with a residential population of at least 100,000 (n = 314) were searched to identify environmental and policy changes that might impact walking/bicycling as a result of the pandemic. When actions were identified, we systematically abstracted information from the websites. To provide more contextual information, we interviewed representatives from 12 municipalities about changes made at the municipal level as a result of the pandemic that could impact walking and bicycling. Interviews were recorded, transcribed, and coded for themes.
Results
For the 314 municipalities, we identified 353 actions resulting from the COVID-19 pandemic that may impact walking and bicycling. Approximately double the number of actions were identified in large-size municipalities (234 actions in 157 municipalities with population≥165,000) compared to mid-size municipalities (119 actions among 157 municipalities with population 100,000 to 164,999). Generally, fewer actions that might suppress walking and bicycling (n = 59) were identified in comparison to actions that would likely facilitate walking and bicycling (n = 294). In-depth interviews provided further context and insight into these results.
Conclusion
This mixed-method assessment provides an overview of the environmental and policy changes which may impact walking and bicycling that municipalities implemented in 2020 due to the pandemic. A next step in this line of inquiry is to quantify the impact of these changes on population levels of walking and bicycling and related health and safety outcomes.
Keywords
Environmental changes
Open streets
Pandemic
Physical activity
Qualitative
Policy
Shared streets
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pmc1 Introduction
A novel coronavirus was confirmed on January 7, 2020 and the virus quickly spread around the world. The World Health Organization named the illness caused by the virus COVID-19 and declared a pandemic on March 11, 2020 (World Health Organization, 2020). In response to the pandemic, local and state officials tried to limit person-to-person exposure, often through limiting geographic mobility. In the United States (US), stay-at-home orders began in mid-March 2020 (Moreland et al., 2020). By April 7, 2020 at least 316 million people in the US, across 42 states, the District of Columbia, and Puerto Rico were affected by these orders (Mervosh et al., 2020). The stay-at-home orders lasted until states began lifting restrictions in May and June 2020.
The distribution of when, where, and how people moved from place to place shifted dramatically under these orders. An immediate result of the stay-at-home orders was an abrupt decline in physical activity (Stockwell et al., 2021). For example, using self-reported survey data collected in June 2020, a nationally representative sample of US adults reported on average a 10% decline in local travel relative to pre-pandemic levels, with declines in personal vehicle use, public transit use, and walking, but no change in bicycling (Ehsani et al., 2021). In another example using device-based metrics, declines in step counts (i.e., an indicator of walking) were observed from January to June 2020 across many countries around the world, although the magnitude of decline varied (Tison et al., 2020).
The restricted mobility resulted in both negative and positive impacts on walking and bicycling for transportation or leisure, and on outdoor recreation. Many park and recreational spaces, greenways, trails, and other places to walk and bicycle were closed or put under access limits in order to reduce crowding (Park et al., 2022; Volenec et al., 2021). In contrast, other measures were instituted to expand safe spaces and access for walking and bicycling, such as restricting vehicle access to specific streets and reducing speed limits.
A systematic documentation of community-based actions resulting from the pandemic can help researchers understand potential mechanisms and specific influences on population-levels changes in physical activity. Additionally, such documentation is a critical first step in identifying practices to evaluate and share with other communities in preparation for future challenges that could impact our transportation systems, as well as for communities in search of innovative approaches to potentially support increases in walking and bicycling. Taking advantage of this unique time in history, the purpose of this study was to describe local level response to the pandemic in the US that might have impacted community-level walking and bicycling. We relied on a mixed methods approach, blending quantitative and qualitative methods, to provide a richer and more nuanced depiction of the community level response.
2 Methods
In the US on July 1, 2019, there were 314 US incorporated places (municipalities) with an estimated population size of at least 100,000 (United States Census, 2021). Built environment and policy changes resulting from the pandemic that might have impacted walking and bicycling were identified using two quantitative approaches in all 314 municipalities. Additionally, in-depth interviews were collected in a sample of municipalities to contextualize these findings and provide additional detail.
2.1 Quantitative assessment
First, we conducted a targeted search on every municipality's official website (n = 314) between July and September 2020 seeking documentation of any built environment or policy change resulting from the pandemic that might have impacted walking and bicycling. Our search included meeting minutes, news releases, and alerts. We also reviewed the top web-based results from a general search on the impacts related to the pandemic for each municipality.
Second, we cross-referenced the web-based findings with a crowd-sourced database initiated as a result of the pandemic (Combs and Pardo, 2020). The Shifting Streets COVID-19 Mobility Database, initiated on March 4, 2020, compiled crowd-sourced information on mobility-related actions introduced as a direct response to the pandemic (Combs and Pardo, 2021). The crowd-sourced database spanned the same time period as the initial municipal-focused web-based searches but was conducted independently. The Shifting Streets database was assembled via requests for first-hand information on pandemic-related street-level design changes and related actions. Requests for information were distributed through social media, listservs, webinars, and word of mouth. An initial version of the Shifting Streets database was released on August 9, 2020, which contained records from 243 US municipalities. Using this database, our team verified the entries through the web-based searches. For 15 actions in the Shifting Streets database, we were unable to verify them through our initial web searching and therefore we did not include those in our analysis.
To maintain high reliability of data abstraction from websites and to identify any discrepancies where more data abstraction training or clarification was needed, a second rater independently collected data on a random sample of 37 municipalities (12% sample). The reliability assessment indicated high percent agreement for identifying specific pandemic impacts from a list of 21 actions. The identification of 20 of the 21 actions ranged from 89% to 100% agreement between pairs of raters. The coding of dedicated public space for outdoor dining had lower agreement at 76%. These results were provided back to team members abstracting the data early on for discussion and clarification on the abstraction tool to try to improve reliability. Team members met regularly to discuss any challenges throughout the collection process.
For the analysis, data are presented as frequencies, overall and stratified by mean population size (100,000–165,000, >165,000). The two categories equally split the total number of municipalities with a population of at least 100,000. We hypothesized that actions would more frequently be identified in municipalities with a larger population size compared to a smaller population size.
2.2 Qualitative assessment
To complement and corroborate the quantitative findings, and consistent with best practice guidance for information saturation (Hennink and Kaiser, 2022), we conducted in-depth interviews with 12 US municipality transportation leaders from municipalities with at least 100,000 population size between December 2020 and June 2021. Specifically, we interviewed the Vision Zero coordinator for each municipality, given their role in coordinating overall transportation system change to optimize safety and equitable mobility for all. Vision Zero is an initiative that aims to eliminate traffic-related fatalities and serious injuries while increasing and ensuring healthy and equitable mobility for all (Vision Zero Network, 2017). Interviewees were asked to reflect on the time period before and after the pandemic and to describe any changes in (i) travel mode, (ii) traffic-related injuries and deaths, and (ii) community-level changes that enabled or limited walking and bicycling. The interview procedures and guide were reviewed by the University of North Carolina – Chapel Hill Institutional Review Board (#20–2773) before interviews began.
All interviews were recorded with permission and transcribed verbatim. The interview transcripts were transferred to qualitative software (ATLAS.ti version 8) for coding and analysis. The data were reviewed line-by-line with codes assigned by a team member, with a second member checking for consistency and agreement. The two team members resolved discrepancies in coding through consensus. Themes specific to our project goals were generated from the deductive coding process and summarized.
3 Results
3.1 Quantitative assessment
For the 314 municipalities, using web searching we identified 353 pandemic-related actions germane to walking and bicycling in 184 municipalities (58.6%). Approximately double the number of actions were identified in large-size municipalities (234 actions among 157 municipalities with population≥165,000) compared to mid-size municipalities (119 actions among 157 municipalities with population 100,000 to 164,999).
The frequency of identified environmental and policy actions are summarized in Table 1 and include both barriers that might decrease and facilitators that might increase walking and bicycling. Generally, fewer actions that might limit walking and bicycling (n = 59) were identified in comparison to likely facilitators (n = 294). We grouped actions into four focus areas, including changes to: access to facilities (including limiting and promoting); streets and public spaces; signals; and micromobility share.Table 1 Frequency and percent of actions concurrent with the pandemic that might impact walking and bicycling in US municipalities overall and by population size.
Table 1Pandemic Actions Population≥165,000 Population 100,000 to 164,999 Explanation of Action
n = 314 % n = 157 % n = 157 %
Limiting Access to Facilities
Specific closure of walking and/or bicycling facilities 34 10.8 31 19.7 3 1.9 temporary closure or restriction to a recreational facility (e.g., parks, beaches, trails, sidewalks, bicycle lanes, recreation areas)
Remove or limit parking 25 8.0 15 9.6 10 6.4 removal or limited parking to restrict access to a recreational facility (i.e., beach, park)
Promoting Infrastructure
Expedited construction for walking or bicycling facilities 3 1.0 2 1.3 1 0.6 expedited planned construction to accommodate increased use due to the pandemic
Shift recreational offering 1 0.3 1 0.6 0 0.0 public golf course opened for walking
Changes to Streets and Public Spaces
Dedicated public space to dining 139 44.3 68 43.3 71 45.2 convert street space and non-street space (i.e., sidewalks, parking spaces, parking lots) for dining; this kept important destinations which enhanced walking and bicycling
Closed streets to motor vehicles 53 16.9 37 23.6 16 10.2 closed to vehicles to accommodate dining, walking, and/or bicycling
Reallocate lanes and/or curb for more walking and/or bicycling 25 8.0 19 12.1 6 3.8 reallocated vehicular lanes and/or curbs to accommodate walking and/or bicycling; dining was not mentioned specifically although it may be done to accommodate dining
Shared, slow, open, active, or healthy streets; banned non-local traffic or filtered traffic 24 7.6 21 13.4 3 1.9 converted a street to allow for multiple modes of transportation or allowed vehicles to travel for local access but did not allow through traffic; also included flowing traffic in one direction
Reduced speed limit 3 1.0 3 1.9 0 0.0 citywide reduction in posted speed limits
Signals
Automate walk signals 18 5.7 12 7.6 6 3.8 automated walk signals to prevent contact with the push button
Changed signal timing 4 1.3 3 1.9 1 0.6 shortening timing of light or switching to night mode
Micromobility share
Free/reduced cost bicycle share 18 5.7 18 11.5 0 0.0 often offered to specific groups such as essential workers, health care workers, and business owners; a few provided the benefit to all residents
Policy changes around e-bikes and e-scooters 3 1.0 3 1.9 0 0.0 formally legalized e-bikes
Reduced cost bicycles to purchase or use 2 0.6 1 0.6 1 0.6 cost is lower for specific workers
Essential activity ordinance 1 0.3 0 0.0 1 0.6 classifying walking and bicycling to be an essential activity to allow it during restrictions
3.1.1 Changes to access to facilities
The first focus area related to changes to access to facilities that could change walking and bicycling. These actions were more common among large-size municipalities compared to mid-size municipalities. Actions related to limiting access to facilities included closing or restricting gatherings in parks, trails, beaches, piers, recreational areas, pools, golf courses, or natural areas (n = 34) and removing or limiting parking for recreational use (n = 25). For example, parking lots were closed for parks and trails in San Francisco, CA and for some trails in Chicago, IL. The documented closures were often a response to overcrowding or a management strategy for the increased traffic trying to access the destination. Some closures were applied only during holidays or on weekends, while others extended for longer time periods. For parking changes, some municipalities removed parking while others reduced the number of available spaces.
We found a few examples where facility changes likely supported walking and bicycling. Three municipalities were able to expedite pedestrian and bicycle projects, such as constructing bicycle lanes, as a result of the pandemic (Jersey City, NJ), neighborhood greenways (Bend, OR), and enhancing crosswalks (Charlotte, NC – construction was designated as an essential service and the low traffic volume enabled workers to complete projects faster). In San Francisco, CA, a public golf course closed for golf and opened for walking.
3.1.2 Changes to streets and public spaces
The second focus area included five actions related to changes to streets and public spaces that may have facilitated walking and bicycling. The most common actions were dedicating public space for commerce (including sidewalks, street space, and parking areas; n = 139) and closing streets to motor vehicles (n = 53). Streets closed to motor vehicles usually allowed for expanded spaces to dine, walk, and/or bicycle. For an example related to dining, in Seattle, WA on-street parking spaces near restaurants were converted to temporary loading zones to enable curb-side meal pick-up. For an example related to physical activity, Portland, OR closed park roads to drivers to limit traffic and allow more walkers and bicyclists. Documentation on changes to the street varied widely; some municipalities mentioned just one street that was affected while other municipalities created a street program for their community. Some road closures were only on certain days of the week or hours of the day, while others were sustained over the week.
Additional enhancements to streets included reallocating lanes or the curb (n = 25) to provide a bicycle lane or places to walk. Many of these initiatives were temporary. In a few municipalities, restaurants could apply to expand their parking area to enable more dining (examples: Greensboro, NC and San Francisco, CA). Twenty-four actions focused on specific street programming, including shared, slow, open, active, and healthy streets, as well as banning non-local traffic or filtering traffic. These initiatives generally slowed or rerouted vehicles but allowed access for those who lived along the street. For example, in Pittsburgh, PA residents could request that their street be designated as a “slow street”. An additional three actions reduced the speed limit on certain roads. For example, Minneapolis, MN reduced citywide speeds to 20 mph, an action that was part of their long-term transportation plan but accelerated due to the pandemic.
Changes to accommodate dining were found in similar proportions across mid- and large-size municipalities, while changes to streets, lanes, and traffic were more common among large-size municipalities compared to mid-size municipalities.
3.1.3 Changes to signals
The third focus area related to signal timing that may have facilitated walking and bicycling. Eighteen municipalities automated or installed no touch walk signals that previously required pushing in certain areas of their city (example: Providence, RI; Cambridge, MA). Some of these changes were temporary while others were permanent. We found four actions that changed the walk signal timing to limit pedestrian queuing; three were shortened while one was lengthened. Changes to automate walk signals were found more often in large-size compared to mid-size municipalities.
3.1.4 Changes to micromobility share
The final focus area related to bicycle share, e-bicycles, and scooters that may have enhanced walking and bicycling. We found 18 actions temporarily reducing or removing costs related to bicycle share, all of which were found in large-size municipalities. For example, essential workers could use the bicycles for free in New York City, NY or receive one free month in Washington DC. Healthcare workers received one free month for the bicycle share program in Boston, MA; reduced price or free rates in Chicago, IL; and free passes in Baltimore, MD; Denver, CO; Washington DC; Detroit, MI; and Tampa, FL. The bicycle share program was free to all residents in Memphis, TN, and free to both healthcare workers and business owners in Kansas City, MO. In Colorado Springs, CO and Austin, TX bicycle sharing was free for the first time period of the rental.
Three actions around policy changes for e-bicycles and e-scooters facilitated their use. Two actions reduced the cost of bicycles to purchase or use and one essential activity ordinance was passed to allow walking and bicycling to occur during restrictions.
3.2 Qualitative assessment
For the qualitative assessment, we interviewed representatives from 12 municipalities and reached information saturation. There were three main domains discussed regarding pandemic-related transportation changes as a result of the pandemic: (i) travel mode, (ii) crashes and fatalities, and (iii) specific changes made in response to the pandemic that could impact walking or bicycling. Interviewee insights related to this third domain further contextualized the quantitative assessment, with exemplary interviewee quotes related to these actions documented in Table 2 .Table 2 Description of actions due to the pandemic that might impact walking and bicycling in US municipalities from 12 in-depth interview participants.
Table 2Pandemic Actions Example Quotes
Access to Facilities
Expedited construction for walking or bicycling facilities "[A]ll the other things that either we did or with the DDA, our Downtown Development Authority, were all about trying to build on our network of bike connections and making intersections more pedestrian accommodating."
Special events The city created a safe walking and running 5- and 10-km event every weekend “just to have people outside instead of being inside.”
Changes to Streets and Public Spaces
Dedicated public space to dining “We got a grant from <organization > to hire facilitators to help more minority businesses who may have not had great luck getting through the application process because maybe it was only in English or maybe they got through the first question. So anyways, we hired facilitators to help guide them through so that businesses in our equity index areas would be just as successful at getting these grants and permits to be able to do outdoor seating.”
"[S]o in our downtown district, the merchants themselves championed and closed downtown streets so that they could create tables and other outdoor dining."
“We also did, not to the extent that many communities did, streeteries < street + eateries> … We did a pilot … where we created additional opportunities for dining al-fresco.”
Closed streets to motor vehicles “We closed some neighborhood streets to through traffic. We put barricades and barrels on some arterials to take the outside lanes and allocate them to bikes and peds for more social distancing. A mixed bag. The neighborhoods that had them loved them. The drivers that were otherwise unincumbered by any traffic, now realized that they had half the capacity, and they were back in traffic where there had been none for months. So mixed bag, but I think overall, it did prove that if you build it they will come.”
"The other thing that we did, which weren't so much thinking about from a safety perspective is we launched an open streets program in our [City] area. We shut down a section of streets and allowed people to walk and bike in the street. It was limited to weekend days but it was trying to create more outdoor space for people to safely walk and bike and also keep distance from each other."
“We closed down some roads around parks for extra space for people to get outside."
Shared, slow, open, active, or healthy streets "We did roll out an open streets program, so similar to what other cities did across the county, we implemented our own version here. We closed down some roads around parks for extra space for people to get outside."
"We did some healthy streets work. You know it's been branded differently in different communities. We did some, cornered off streets during the summer months that allowed for people to experience their streets in a different way. We were finalizing in our evaluation report and will publish it. And we'll likely continue that going forward in some manner. There was, you know we joined the bandwagon, the urbanist bandwagon to make that happen, so that's a notable example."
Banned non-local traffic or filtered traffic "We're actually one of the few cities that developed a permitting process so that neighborhoods could do it. They could shut down their blocks and make it a slow street, an open street."
"[T]here is a group called Bike Walk [city] that is an advocacy organization that has done some interesting things like they did pop-up bike lanes a few years ago with the cooperation with the traffic engineering department. Some of those things actually became permanent. They've been doing projects in neighborhoods where they get artists in that area to basically design crosswalks. They've been doing open street events where you shut down streets to vehicles and have kind of a neighborhood party. And those things have continued with more distancing and more care to avoid transmission of disease. But that's really, that hasn't been a pandemic-driven change, it's something that's kind of been going on."
Signals
Automate walk signals "We also did some pedestrian recall, so adding ped recall to a bunch or intersections so that people didn't have to touch the push buttons during the pandemic ….. We have the pedestrian recall. [It] is still up at several, at a lot of intersections but the other stuff has kind of there was not the political willpower to keep it going. I'll just say that."
3.2.1 Travel mode
Interviewees reflected on shifts in travel modes as a result of the pandemic for their communities. Some interviewees described an increase in walking and bicycling since the pandemic, while others described no impact on walking and bicycling, or a shift from walking and bicycling on streets to trails. With this shift in travel mode and travel location, one interviewee remarked, “I'm hopeful that people saw their streets in a new way maybe who hadn't before, who maybe got on a bike and got to see the city differently. You certainly felt it. Everybody saw more people walking and biking because the streets were quieter.”
3.2.2 Crashes and fatalities
Several interviewees referenced an increase in highway-related traffic fatalities in their city. “Our highway fatal[ities] have gone through the roof, compared percentagewise.” Another remarked, “We saw a significant increase in the people, the small population of people that are just speeding and driving like crazy because there isn't necessarily this level of congestion to prohibit them from doing that …. We've also noticed fatal crashes increase in [City].” Another described increases in vehicle speed: “Anecdotally, we've definitely felt people are driving faster which is a little bit of a discouragement for people to get out and bike and walk.” Notably, several interviewees described a decrease in pedestrian- and bicycle-related fatalities for their municipality.
3.2.3 Actions that may impact walking or bicycling
Interviewees described changes that negatively impacted pedestrians and bicyclists including canceled outdoor events and closed public spaces. However, more of the interviewee responses reflected environmental changes to accommodate the increase in pedestrians and bicyclists as a result of the pandemic, summarized in Table 2. The most commonly reported changes were to (1) streets and public spaces to accommodate more outdoor dining and (2) closing streets or lanes temporarily to reduce vehicle use and accommodate more pedestrians (see “Changes to Streets and Public Spaces” in Table 2). Other changes mentioned less frequently included creating a bus-only lane, modifying pedestrian recall buttons at crossings to avoid touching them (see “Signals” in Table 2), and holding special events to be physically active (see “Access to Facilities” in Table 2). Interviewees did not specifically mention actions to limit walking and bicycling, such as closure of spaces or parking removal. They also did not mention changes to bicycle share, e-bikes, and scooters.
At the time of the interview, some open streets, healthy streets, or active streets programming were still ongoing while other programming had stopped. The initiatives had varying success. For example, one interviewee remarked that “we did kind of attempt an active streets program … We had a hard time doing it. [City] is super car centric so many of the communities we thought would want an active street through their community were not interested.” Another interviewee stated, “People want traffic calming so that they can walk and bike in their neighborhood. But it didn't feel like it really, kind of, that people didn't see it as part of a COVID response, they saw it as, ‘I want people to drive slower through my neighborhood, so do this thing’ which was challenging. None of our business areas, there was like zero interest in anywhere near a business having some of these things, because all of the businesses saw that they needed to have direct car access right in front of their building. So, I don't know. It was not the success I would have liked to see it be.” In contrast, other interviewees mentioned several benefits of the open street program, beyond increasing walking and bicycling: it was “a way to engage with some different neighborhoods in conversations about walking in particular”, it “raised some awareness amongst communities that wouldn't normally have known we were doing this work”, “it gave us an opportunity to recognize what it looks like to do pop-up type things in our community”, and it “opened people's eyes to considering other types of, even ways to approach infrastructure, because the way we're using our streets changed at least for a little while”. The in-depth interviews reflected a variety of experiences to the pandemic and environmental and policy actions that might impact walking/bicycling.
4 Discussion
As a result of the COVID-19 pandemic, many US communities instituted environmental and policy changes which could impact walking and bicycling. Due to stay-at-home orders, there was a need for more outdoor spaces for physical activity. We found that several actions were taken to support such activity. Walking and bicycling were facilitated through street interventions, including closing streets to motor vehicles, reallocating lanes or curbs, filtering or banning traffic on some streets, and converting a street to allow multiple modes of travel. Additionally, changes to signals were made that facilitated walking, and free/reduced cost bicycle share was instituted. On the limiting side, due to potential of or occurrence of congestion and overcrowding, parks, beaches, and other recreational opportunities were closed or limited through parking restrictions. Overall, some municipalities responded to crowded public spaces by creating more restrictions, while others loosened restrictions, expanded access and capacity, or instituted a combination of approaches.
While the community level changes were occurring, changes in traffic safety were also impacting municipal response. In 2020, an estimated 38,824 people died in motor vehicle traffic crashes, and projections for 2021 indicate further acceleration with an estimated 42,915 deaths in 2021, the highest number since 2005 (National Highway Traffic Safety Administration, 2022). The number of deaths in 2020 mark a 7% increase over the year 2019, despite fewer miles driven in 2020 due to the pandemic (National Highway Traffic Safety Administration, 2021), and 2021 experienced a further 10.5% increase over 2020.
During this time, some municipalities were poised to make changes more easily than others, as the pandemic may have brought initiatives to the forefront that were already being considered but had not been implemented. Other municipalities may have had more of a challenge, without plans or policies to guide them during the emergency situation. From a case study in three Canadian cities, the most successful city in terms of speed of pedestrian and bicyclist-related action and implementation was the city with a strong active transportation plan, vetted already through extensive community consultation, that could be leveraged quickly (Fischer and Winters, 2021).
The success of the initiatives we identified is not known, but there is indication that municipal engagement with community members declined during this period. A national study found that community engagement among municipalities with Vision Zero initiatives was lower during 2020 as a result of the pandemic (Evenson et al., 2022). Thus, we hypothesize that many of the community-level changes to environments and policies we documented were accomplished without much input from the community during the pandemic. This is consistent with findings from an analysis of the Shifting Streets COVID-19 Mobility Database, which found that the majority of the documented responses were new actions, unconnected to existing planning efforts, and were not informed by community engagement processes (Combs and Pardo, 2021). Alternative methods for engagement could be utilized in the future, learning from the numerous resources and best practices for remote engagement developed during the pandemic (American Planning Association, 2020; Fedorowicz et al., 2020; What Works Cities, 2022).
As municipalities reflect on the environmental and policy changes made as a result of the pandemic that might impact walking or bicycling, both successful and unsuccessful, preparations for future disruptions, such as through integration of potential actions that might be taken during similar emergencies, are critical considerations for future routine planning processes. Horizontal integration of existing plans across departments, such that they are available to those likely to be managing emergency responses during disruptive events, may enable more rapid and cost-effective implementation of interventions that have already been vetted through public processes and are therefore more likely to be well-received. Other researchers offer recommendations to communities to plan for such times, such as creating healthy environments for all through the provision of planning intentional areas of green space and public space for recreation and leisure (Park et al., 2022; Slater et al., 2020). The planning of green space is especially important to consider in neighborhoods that lack access to parks and other infrastructure, so that disparities in walking and bicycling are not exacerbated by stay-at-home orders or transportation system disruptions.
4.1 Strengths and limitations
The strength of this project is the mixed-method inquiry used to corroborate the web-based searching and crowd-sourced database with the in-depth interviews. Another strength is the systematic review of responses to the pandemic from all municipalities with a population size of at least 100,000 and the timely approach to capture changes happening in communities across the US. Further, we conducted checks of reliability to try to ensure high quality extraction from the websites.
However, the project had several limitations. First, municipalities for the in-depth interviews were selected due to their engagement in Vision Zero (Evenson et al., 2022); therefore, the qualitative findings may not be reflective of municipalities without Vision Zero. Second, reliance on web-based searching to document changes as a result of the pandemic likely under-estimated or missed changes that occurred elsewhere. We hypothesize that the web-based coding had high specificity, in so far as what we found was likely correct, which was corroborated through the Shifting Streets database. However, our sensitivity was lower, since some changes may not be documented on a website, such as playground and park closures. Some impacts may also be more likely to be documented on a website than other impacts. This indicates that our findings are important in describing the range of actions that municipalities instituted, but the absolute numbers of documented actions are likely an undercount.
Third, the actions we catalogued were from only one point in time as it took three months, from July to September 2020, to assess each of the 314 municipalities. This period of time occurred during the “pandemic phase” of the crisis (Centers for Disease Control and Prevention, 2022), whereby the number of cases became widespread (AJMC Staff, 2021). We do not know if the actions we identified were temporary or incorporated into a permanent change, nor why some municipalities made changes and others did not. We also do not know the trajectory of change within a municipality over the course of the pandemic; for example, shifts may have been made to restrict access to outdoor recreational facilities initially but then to encourage access later. An analysis of the Shifting Streets database, including data from 60 countries, suggests that changes to travel lanes, measures to improve access to work for healthcare workers, and creation of space for outdoor dining were intended only for the short-term, while full street closures and new bicycle lanes were often intended to last beyond the pandemic (Kutela et al., 2022).
Fourth, during the pandemic, municipalities reduced or ceased transit service as a result of declining ridership (Parker et al., 2021). This particularly impacts walking, since many trips to and from transit involve walking (Tribby et al., 2020). We attempted to document changes to transit schedules as part of the web-based assessment. However, we found that historic transit changes were often not documented and only the most current schedule was posted, so we dropped this approach.
5 Conclusions
Due to the COVID-19 pandemic, restrictions on mobility caused abrupt shifts in mode share which prompted many US communities to make environmental and policy changes. While the long-term impacts remain to be identified (Honey-Roses et al., 2021), this mixed-method assessment provides an overview of the changes to environments and policies that US municipalities took as a result of the pandemic. We found more actions that facilitated walking and bicycling as opposed to actions that limited walking and bicycling.
The documentation of these actions can contribute to an understanding of population-level changes in walking and bicycling during this time, as well as to assist communities in the future with options they could implement. A next step in this line of inquiry could be to ascertain whether these actions were temporary or long lasting. An important question is to assess whether these actions spurred changes in pedestrian or bicycle funding, or population-level interest in walking or bicycling. More distally, researchers could quantify the impact of the pandemic actions on population prevalence and trends in walking and bicycling.
CRediT author statement
Kelly R. Evenson: Conceptualization, Data curation, Supervision, Funding acquisition, Writing – Original draft, Rebecca B. Naumann: Funding acquisition, Writing – review and editing, Nandi L. Taylor: Formal analysis, Investigation, Data curation, Writing – review and editing, Seth LaJeunesse: Funding acquisition, Writing – review and editing, Tabitha S. Combs: Investigation, Data curation, Writing – review and editing.
Funding
This project was supported by the Collaborative Sciences Center for Road Safety (roadsafety.unc.edu), a United States' 10.13039/100000140 Department of Transportation National 10.13039/100016551 University Transportation Center (award # 69A3551747113). The UNC 10.13039/100008949 Injury Prevention Research Center is supported by an award (R49/CE0042479) from the 10.13039/100000030 Centers for Disease Control and Prevention . The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.
Declaration of competing interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Acknowledgment
The authors thank our colleagues who helped collect, code, and analyze the data, and the interview participants for their time and contribution to this work. The authors also thank the reviewers for their helpful comments.
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References
AJMC Staff A timeline of COVID-19 developments in 2020 https://www.ajmc.com/view/a-timeline-of-covid19-developments-in-2020.AJMC 2021
American Planning Association Emerging tops for equitable digital engagement Planning Magazine June 2020 7 https://www.planning.org/planning/2020/jun/tools-engagement/
Centers for Disease Control and Prevention The continuum of pandemic phases (Atlanta, GA) https://www.cdc.gov/flu/pandemic-resources/planning-preparedness/global-planning-508.html 2022
Combs T. Pardo C. Streetplans, epiandes, MobilityWorks, & datasketch (2020) The "Shifting Streets" Covid-19 mobility dataset 2020 http://pedbikeinfo.org/resources/resources_details.cfm?id=5235
Combs T. Pardo C. Shifting streets COVID-19 mobility data: findings from a global dataset and a research agenda for transport planning and policy Transp. Res. Interdiscip. Perspect. 9 2021 1 15
Ehsani J.P. Michael J.P. Duren M.L. Mui Y. Porter K.M.P. Mobility patterns before, during, and anticipated after the COVID-19 pandemic: an opportunity to nurture bicycling Am. J. Prev. Med. 60 2021 e277 e279 33674071
Evenson K. LaJeunesse S. Keefe E. Naumann R. Mixed methods approach to describing Vision Zero initiatives in United States’ municipalities Under review 2022
Fedorowicz M. Arena O. Burrowes K. Community engagement during the COVID-19 pandemic and beyond Urban Institute, pp. 1–37 https://www.urban.org/research/publication/community-engagement-during-covid-19-pandemic-and-beyond 2020
Fischer J. Winters M. COVID-19 street reallocation in mid-sized Canadian cities: socio-spatial equity patterns Can. J. Public Health 112 2021 376 390 33650060
Hennink M. Kaiser B.N. Sample sizes for saturation in qualitative research: a systematic review of empirical tests Soc. Sci. Med. 292 2022 114523 34785096
Honey-Roses J. Anguelovski I. Chireh V. Daher C. van den Bosch C. Litt J. The impact of COVID-19 on public space: an early review of the emerging questions - design, perceptions and inequities Cities Health 5 2021 S263 S279
Kutela B. Combs T. John Mwekh'iga R. Langa N. Insights into the long-term effects of COVID-19 responses on transportation facilities Transp Res D Transp Environ 111 2022 103463 36158241
Mervosh S. Lu D. Swales V. See Which States and Cities Have Told Residents to Stay at Home, New York Times 2020
Moreland A. Herlihy C. Tynan M.A. Sunshine G. McCord R.F. Hilton C. Poovey J. Werner A.K. Jones C.D. Fulmer E.B. Gundlapalli A.V. Strosnider H. Potvien A. Garcia M.C. Honeycutt S. Baldwin G. Timing of state and territorial COVID-19 stay-at-home orders and changes in population movement - United States, March 1-may 31 Program, C.D.C.P.H.L., Cdc covid-19 response team, M.P.A.U., 2020 MMWR Morb. Mortal. Wkly. Rep. 69 2020 1198 1203 32881851
National Highway Traffic Safety Administration Fatality data show increased traffic fatalities during pandemic 2020, (Washington DC) https://www.nhtsa.gov/press-releases/2020-fatality-data-show-increased-traffic-fatalities-during-pandemic 2021
National Highway Traffic Safety Administration Early estimates of motor vehicle traffic fatalities and fatality rate by sub-categories in 2021 2022 United States Department of Transportation Washington, DC pp. 1–10 https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/813298
Park A.H. Zhong S. Yang H. Jeong J. Lee C. Impact of COVID-19 on physical activity: a rapid review J Glob Health 12 2022 05003
Parker M. Li M. Bouzaghrane M. Obeid H. Hayes D. Frick K. Public transit use in the United States in the era of COVID-19: transit riders' travel behavior in the COVID-19 impact and recovery period Transport Pol. 111 2021 53 62
Slater S.J. Christiana R.W. Gustat J. Recommendations for keeping parks and green space accessible for mental and physical health during COVID-19 and other pandemics Prev. Chronic Dis. 17 2020 E59 32644919
Stockwell S. Trott M. Tully M. Shin J. Barnett Y. Butler L. McDermott D. Schuch F. Smith L. Changes in physical activity and sedentary behaviours from before to during the COVID-19 pandemic lockdown: a systematic review BMJ Open Sport Exerc Med 7 2021 e000960
Tison G.H. Avram R. Kuhar P. Abreau S. Marcus G.M. Pletcher M.J. Olgin J.E. Worldwide effect of COVID-19 on physical activity: a descriptive study Ann. Intern. Med. 173 2020 767 770 32598162
Tribby C. Graubard B. Berrigan D. National and metropolitan trends in public transit use, transit-related walking, and ridesharing between 2009 and 2017 J. Transport Health 19 2020 100918
United States Census City and town population totals: 2010-2019 https://www.census.gov/data/tables/time-series/demo/popest/2010s-total-cities-and-towns.html 2021
Vision Zero Network Moving from vision to action: fundamental principles, policies and practices to advance vision zero in the U.S Downloaded at, pp. 1–26 https://visionzeronetwork.org/wp-content/uploads/2017/11/VZN-Moving-from-Vision-to-Action.pdf 2017
Volenec Z.M. Abraham J.O. Becker A.D. Dobson A.P. Public parks and the pandemic: how park usage has been affected by COVID-19 policies PLoS One 16 2021 e0251799
What Works Cities The guide to remote community engagement https://medium.com/the-guide-to-remote-community-engagement 2022
World Health Organization WHO Director-General's opening remarks at the media briefing on COVID 19–11 March 2020 https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19--11-march-2020 2020
| 36510600 | PMC9729651 | NO-CC CODE | 2022-12-16 23:19:53 | no | J Transp Health. 2023 Jan 8; 28:101557 | utf-8 | J Transp Health | 2,022 | 10.1016/j.jth.2022.101557 | oa_other |
==== Front
An Bras Dermatol
An Bras Dermatol
Anais Brasileiros de Dermatologia
0365-0596
1806-4841
Sociedade Brasileira de Dermatologia. Published by Elsevier España, S.L.U.
S0365-0596(22)00285-9
10.1016/j.abd.2022.08.004
Original Article
Did COVID-19 lockdown diagnosis delay actually worsen melanoma prognosis?⋆
Gil-Pallares Pedro a⁎
Figueroa-Silva Olalla a
Gil-Pallares Maria Eugenia c
Vázquez-Bueno José Ángel b
Piñeyro-Molina Francisca a
Monteagudo Benigno a
Heras-Sotos Cristina De las a
a Department of Dermatology, Complejo Hospitalario Universitario de Ferrol, Ferrol, Spain
b Department of Pathology, Complejo Hospitalario Universitario de Ferrol, Ferrol, Spain
c Universidad de Santiago de Compostela, Santiago de Compostela, Spain
⁎ Corresponding author.
8 12 2022
8 12 2022
26 5 2022
13 8 2022
© 2022 Sociedade Brasileira de Dermatologia. Published by Elsevier España, S.L.U.
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Background
The COVID-19 lockdown possibly meant a delay in the diagnosis and treatment of melanoma and therefore, worsening its prognosis. This unique situation of diagnosis deferral is an exceptional opportunity to investigate melanoma biology.
Objectives
To evaluate the immediate and mid-term impact of diagnosis delay on melanoma.
Methods
A retrospective observational study of melanomas diagnosed between March 14th 2019 and March 13th 2021. The authors compared the characteristics of the melanomas diagnosed during the first 6-month period after the lockdown instauration and a second period after recovery of normal activity with the same periods of the previous year, respectively.
Results
A total of 119 melanomas were diagnosed. There were no differences in age, sex, incidence, location, presence of ulceration or mitoses, andin situ/invasive melanoma rate (p > 0.05). After the recovery of the normal activity, Breslow thickness increased in comparison with the previous year (2.4 vs 1.9 mm, p < 0.05) resulting in a significant upstaging according to the AJCC 8th ed. (p < 0.05).
Study limitations
The main limitation is that this is a single-center study.
Conclusions
The COVID-19 lockdown implied a diagnosis delay leading to a mid-term increase in Breslow thickness and an upstaging of invasive melanomas. However, the detection deferral did not result in a higher progression of in situ to invasive melanoma, in our institution.
Keywords
Melanoma
COVID-19
Prognosis
Quarantine
SARS-CoV-2
==== Body
pmcIntroduction
Due to the Coronavirus Disease 2019 (COVID-19) pandemic, many countries went into lockdown, which began in Spain on March 14th 2020.1 Most surgeries and appointments were canceled, and the population was concerned about getting infected. Therefore, patients are mainly consulted for severe symptoms. This could lead to a reduction in the diagnosis of different diseases such as skin cancer,2, 3 heart attacks,4 or strokes,5 and a compromise of cancer screening programs.6, 7 In the case of melanoma, a recent study estimated an upstaging of 45% of invasive melanomas after a 3-month delay.8
Although some authors noted a reduction in the number of diagnosed melanomas during the lockdown,2, 9 other studies showed the opposite results.10, 11 However, since most articles limited the studied period to the lockdown it is only possible to estimate the impact of the delay in melanoma diagnosis. The authors compared the melanomas diagnosed after the lockdown, not only short-term but also once the normal clinical activity was restored, in order to assess if the diagnosis delay due to the COVID-19 lockdown actually meant a prognosis worsening of the melanomas.
Methods
A retrospective, single-center, observational study of histopathologically diagnosed melanomas between March 14th 2019, and March 13th 2021 was designed. This study was approved by the Institutional Ethics Committee. The data was retrieved from the Pathology Department records of our hospital. Demographic data, referral via teledermatology, melanoma location, Breslow thickness (mm), presence of mitosis and ulceration, and AJCC (American Joint Committee on Cancer 8th edition) staging were collected. Incidence was calculated per 100.000 inhabitants. Metastatic melanoma and clinically diagnosed melanoma without histopathological confirmation were excluded. The first 6-month period following the start of the lockdown, between March 14th 2020, and September 13th 2020 (1st P20), and a second 6-month period between September 14th 2020, and March 13th 2021 (2nd P20) were compared with the same periods of the previous year (1st P19 and 2nd P19, respectively).
Statistical analysis was performed using R (R Core Team, Vienna, Austria); t-test and U-Mann Whitney were used for quantitative variables. Fisher’s exact test was used for the analysis of qualitative variables.
Results
A total of 119 melanomas were included. The characteristics of melanomas are summarized in Table 1 . A total of 29 and 24 melanomas were diagnosed in the 1st P19 and 1st P20 respectively, and no differences were found in the incidence (16.1 vs. 13.3 per 100.000 inhabitants, p > 0.05). A total of 36 and 30 melanomas were diagnosed in the 2nd P19 and 2nd P20 respectively, also without differences in the incidence (20 vs. 16.7 per 100.000 inhabitants, p > 0.05).Table 1 Patient and tumor characteristics of the melanomas diagnosed between March 14th 2019 and March 13th 2021.
Table 1 1st Period 2nd Period
1st P19 1st P20 p-value 2nd P19 2nd P20 p-value
n = 29 n = 24 n = 36 n = 30
Incidence per-100.000 inhabitants 0.583 0.539
16.1 13.3 20 16.7
Age (mean) 0.162 0.974
59 ± 18 66 ± 17 70 ± 17 69 ± 16
Sex 1 0.612
Male 14 (48%) 11 (46%) 11 (31%) 11 (37%)
Female 15 (52%) 13 (54%) 25 (69%) 19 (63%)
Location 0.777 0.396
Head and neck 10 (34%) 7 (29%) 12 (33%) 9 (30%)
Anterior trunk 2 (7%) 4 (17%) 2 (6%) 1 (3%)
Posterior trunk 9 (31%) 7 (29%) 11 (31%) 8 (27%)
Upper extr. 6 (21%) 3 (13%) 4 (11%) 2 (7%)
Lower extr. 2 (7%) 2 (8%) 7 (19%) 6 (20%)
Acral 0 1 (4%) 0 4 (13%)
Referral 0.250 1
Teledermatology 8 (28%) 11 (46%) 10 (28%) 8 (27%)
In situ/invasive 0.166 0.458
In situ 16 (55%) 8 (33%) 15 (42%) 16 (53%)
Invasive 13 (45%) 16 (67%) 21 (58%) 14 (47%)
Breslow Thickness (mm, mean) 0.263 0.040a
1.8 ± 1.7 1.2 ± 1.4 1.9 ± 2.7 2.4 ± 2.3
Ulceration 1 0.453
3 (10%) 3 (12%) 3 (8%) 5 (17%)
Mitoses 0.767 0.310
8 (28%) 8 (33%) 10 (28%) 12 (40%)
pT staging group (AJCC 8th ed.) 0.770 0.044a
pT1a 5 (17%) 9 (38%) 14 (39%) 6 (20%)
pT1b 1 (3%) 2 (8%) 1 (3%) 1 (3%)
pT2a 4 (14%) 1 (4%) 0 0
pT2b 1 (3%) 1 (4%) 0 0
pT3a 1 (3%) 1 (4%) 0 3 (10%)
pT3b 0 1 (4%) 0 2 (7%)
pT4a 0 0 3 (8%) 0
pT4b 1 (3%) 1 (4%) 3 (8%) 2 (7%)
Footnote: 1st P19, March 14th 2019 ‒ September 13th 2019. 1st P20, March 14th 2020 – September 13th 2020. 2nd P19, September 14th 2019 ‒ March 13th 2020. 2nd P20, September 14th 2020 ‒ March 13th 2021.
a Statistical significance.
The patients in both periods were similar in age and sex (p > 0.05). There were no differences in location, teledermatology referral rate, and presence of ulceration or mitosis (p > 0.05).
Invasive melanomas represented 45% and 67% of the diagnosed melanomas in 1st P19 and 1st P20 respectively. After the recovery of the normal activity, the proportion of invasive melanomas was 58% for 2nd P19 and 47% for 2nd P20. However, no statistically significant differences in in situ/invasive melanoma rates were found in any of the periods.
Breslow thickness of diagnosed melanomas increased in 2ndP20 in comparison with the previous year (2.4 vs. 1.9 mm, p < 0.05) which implied differences in the AJCC stage (p < 0.05). However, no differences were found in the first period (p > 0.05) (Fig. 1 ).Figure 1 Breslow thickness of invasive melanomas did not significantly differ during the first period (p > 0.05) (A). However, a statistically significant increase in Breslow thickness was seen in the 2nd P20, after the recovery of normal activity, in comparison with the previous year (p < 0.05) (B). These results suggest that some invasive melanomas suffered a diagnosis delay which implied a prognosis worsening.
Figure 1
Discussion
Different approaches, such as promoting teledermatology,12 were considered to minimize the effects of COVID-19 on melanoma diagnosis. Valenti et al.13 reported a 2.3-month postponement of revisions of advanced skin cancer, which is similar to the 3-mont average delay that the authors estimated since it took 6-months from the beginning of the lockdown until the normal clinical activity was achieved again. According to some studies, this delay would be enough to observe an upstaging of the invasive melanomas,8 but this increase in Breslow thickness would only be seen once the delay occurred instead of during the lockdown. For this reason, the authors included a second period after the clinical activity recovered pre-lockdown levels in order to analyze if the diagnosis delay actually had a repercussion in those melanomas that potentially “stayed at home”.
Some authors found a significant reduction in melanoma diagnosis during the lockdown.2, 9 However, similar to other reports,10, 11 although the authors found a decrease in the number of melanomas in both periods (5 in the first period and 6 in the second period), the authors did not find a significant reduction in the incidence of melanomas per 100.000 inhabitants in any period.
Some reports showed an increase in Breslow thickness or an upstaging of the diagnosed melanomas during the lockdown in comparison with the previous year,14, 15 but since some also showed an important decrease in the number of diagnoses, mainly of in situ melanomas,15 the authors believe that it would be the outcome of thin melanoma under-diagnosis rather than an “immediate” growth of melanomas due to the lockdown. As expected, the authors did not find differences in Breslow thickness in the first period, but we found a significant increase in Breslow thickness after the recovery of the normal activity compared with the previous year (1.9 mm in 2nd P19 vs. 2.4 mm in 2nd P20, p < 0.05) (Fig. 1), which implied an upstaging according to the AJCC 8th ed. (p < 0.05). This indicates that although the authors did not find a reduction in the incidence, some melanomas actually suffered a diagnosis delay.
However, this prognosis worsening found in invasive melanomas due to the deferral seems not to affect in situ melanomas likewise. Coinciding with other reports,10 our results do not show differences in in situ and invasive melanoma rates before and after the beginning of the lockdown, and after the recovery of normal clinical activity (p > 0.05). Therefore, the authors can assume that a 3-month average diagnosis delay due to COVID-19 did not mean a progression of in situ melanomas in our hospital.
More studies after the normal clinical activity was resumed are required to assess if the diagnosis deferral actually implied an increased progression to invasive melanomas. Although this situation of melanoma diagnosis delay will hopefully not happen again, the authors believe that it is an exceptional opportunity for the investigation of in situ and invasive melanoma biology. Further investigation in this area could provide valuable information to the melanoma detection protocols and to better understand its natural behavior.
Conclusions
It was not found a reduction in the incidence of melanomas in any of the considered periods. However, the increase in Breslow thickness and the consequent upstaging observed, once the normal clinical activity was restored, suggests that the COVID-19 lockdown caused a delay in the diagnosis of melanoma and a prognosis worsening of the invasive melanomas. The similar in situ/invasive melanoma rates suggest that a 3-month average diagnosis delay does not necessarily imply an increase in the progression of in situ melanomas.
The authors believe that it is important to reassess the real repercussion of the lockdown in melanoma by analyzing the melanomas diagnosed once the pre-pandemic clinical activity was restored. The presented results might not be completely generalizable to other regions with different COVID-19 incidences. Therefore, the authors encourage other authors to analyze mid-term melanoma diagnoses.
Financial support
None declared.
Authors' contributions
Pedro Gil-Pallares: The study concept and design; data collection, or analysis and interpretation of data; statistical analysis; writing of the manuscript or critical review of important intellectual content; data collection, analysis and interpretation; effective participation in the research guidance; intellectual participation in the propaedeutic and/or therapeutic conduct of the studied cases; critical review of the literature; final approval of the final version of the manuscript.
Olalla Figueroa-Silva: The study concept and design; data collection, or analysis and interpretation of data; statistical analysis; writing of the manuscript or critical review of important intellectual content; data collection, analysis and interpretation; effective participation in the research guidance; intellectual participation in the propaedeutic and/or therapeutic conduct of the studied cases; critical review of the literature; final approval of the final version of the manuscript.
Maria Eugenia Gil-Pallares: The study concept and design; data collection, or analysis and interpretation of data; statistical analysis; writing of the manuscript or critical review of important intellectual content; data collection, analysis and interpretation; effective participation in the research guidance; intellectual participation in the propaedeutic and/or therapeutic conduct of the studied cases; critical review of the literature; final approval of the final version of the manuscript.
José Ángel Vázquez-Bueno: The study concept and design; data collection, or analysis and interpretation of data; statistical analysis; writing of the manuscript or critical review of important intellectual content; data collection, analysis and interpretation; effective participation in the research guidance; intellectual participation in the propaedeutic and/or therapeutic conduct of the studied cases; critical review of the literature; final approval of the final version of the manuscript.
Francisca Piñeyro-Molina: The study concept and design; data collection, or analysis and interpretation of data; statistical analysis; writing of the manuscript or critical review of important intellectual content; data collection, analysis and interpretation; effective participation in the research guidance; intellectual participation in the propaedeutic and/or therapeutic conduct of the studied cases; critical review of the literature; final approval of the final version of the manuscript.
Benigno Monteagudo: The study concept and design; data collection, or analysis and interpretation of data; statistical analysis; writing of the manuscript or critical review of important intellectual content; data collection, analysis and interpretation; effective participation in the research guidance; intellectual participation in the propaedeutic and/or therapeutic conduct of the studied cases; critical review of the literature; final approval of the final version of the manuscript.
Cristina De las Heras-Sotos: The study concept and design; data collection, or analysis and interpretation of data; statistical analysis; writing of the manuscript or critical review of important intellectual content; data collection, analysis and interpretation; effective participation in the research guidance; intellectual participation in the propaedeutic and/or therapeutic conduct of the studied cases; critical review of the literature; final approval of the final version of the manuscript.
Conflicts of interest
None declared.
⋆ Study conducted at the Department of Dermatology of the Complejo Hospitalario Universitario de Ferrol in Ferrol, Coruna, Spain.
==== Refs
References
1 Coma E. Guiriguet C. Mora N. Marzo-Castillejo M. Benítez M. Méndez-Boo L. Impact of the COVID-19 pandemic and related control measures on cancer diagnosis in Catalonia: a time-series analysis of primary care electronic health records covering about five million people BMJ Open. 11 2021 e047567
2 Marson J.W. Maner B.S. Harding T.P. Meisenheimer J. Solomon J.A. Leavitt M. The magnitude of COVID-19’s effect on the timely management of melanoma and nonmelanoma skin cancers J Am Acad Dermatol. 84 2021 1100 1103 33482258
3 Longo C. Pampena R. Fossati B. Pellacani G. Peris K. Melanoma diagnosis at the time of COVID-19 Int J Dermatol. 60 2021 e29 30 32808276
4 Rodríguez-Leor O. Cid-Álvarez B. Prado A.P. Rossello X. Ojeda S. Serrador A. Impact of COVID-19 on ST-segment elevation myocardial infarction care. The Spanish experience Rev Espanola Cardiol Engl Ed. 73 2020 994 1002
5 Libruder C. Ram A. Hershkovitz Y. Tanne D. Bornstein N.M. Leker R.R. Reduction in Acute Stroke Admissions during the COVID-19 Pandemic: Data from a National Stroke Registry Neuroepidemiology. 8 2021 1 7
6 Del Vecchio Blanco G. Calabrese E. Biancone L. Monteleone G. Paoluzi O.A. The impact of COVID-19 pandemic in the colorectal cancer prevention Int J Colorectal Dis. 35 2020 1951 1954 32500432
7 Vanni G. Pellicciaro M. Materazzo M. Bruno V. Oldani C. Pistolese C.A. Lockdown of Breast Cancer Screening for COVID-19: Possible Scenario In Vivo. 34 2020 3047 3053 32871851
8 Tejera-Vaquerizo A. Nagore E. Estimated effect of COVID-19 lockdown on melanoma thickness and prognosis: a rate of growth model J Eur Acad Dermatol Venereol. 34 2020 e351 3 32362041
9 Intergruppo Melanoma Italiano The effect of COVID-19 emergency in the management of melanoma in Italy Dermatol Rep. 13 2021 8972
10 Weston G.K. Jeong H.S. Mu E.W. Polsky D. Meehan S.A. Impact of COVID-19 on melanoma diagnosis Melanoma Res. 31 2021 280 281 33625106
11 Filoni A. Del Fiore P. Cappellesso R. Dall’Olmo L. Salimian N. Spina R. Management of melanoma patients during COVID-19 pandemic in an Italian skin unit Dermatol Ther. 34 2021 e14908
12 Conforti C. Lallas A. Argenziano G. Dianzani C. Di Meo N. Giuffrida R. Impact of the COVID-19 Pandemic on Dermatology Practice Worldwide: Results of a Survey Promoted by the International Dermoscopy Society (IDS) Dermatol Pract Concept. 11 2021 e2021153
13 Valenti M. Pavia G. Gargiulo L. Facheris P. Nucca O. Mancini L. Impact of delay in follow-up due to COVID-19 pandemic on skin cancer progression: a real-life experience from an Italian hub hospital Int J Dermatol. 60 2021 860 863 33665815
14 Berry W. Tan K. Haydon A. Shackleton M. Mar V.J. Reduced melanoma referrals during COVID-19 lockdown Aust J Gen Pract. 50 2021
15 Ricci F. Fania L. Paradisi A. Di Lella G. Pallotta S. Sobrino L. Delayed melanoma diagnosis in the COVID-19 era: increased breslow thickness in primary melanomas seen after the COVID-19 lockdown J Eur Acad Dermatol Venereol. 34 2020 e778 9 32780876
| 0 | PMC9729652 | NO-CC CODE | 2022-12-14 23:22:28 | no | An Bras Dermatol. 2022 Dec 8; doi: 10.1016/j.abd.2022.08.004 | utf-8 | An Bras Dermatol | 2,022 | 10.1016/j.abd.2022.08.004 | oa_other |
==== Front
Am J Med
Am J Med
The American Journal of Medicine
0002-9343
1555-7162
Published by Elsevier Inc.
S0002-9343(22)00875-0
10.1016/j.amjmed.2022.10.023
Review
Monkeypox (hMPXV infection): A practical review
Salcedo Ricardo M. Chief 1
Madariaga Miguel G. 2⁎
1 Internal Medicine, Clinica SANNA El Golf, Lima, Peru
2 Physician Manager Infectious Disease Associates of Naples, Naples, Florida
⁎ Corresponding author: Miguel G. Madariaga. 3021 Airport Pulling Rd N, Suite 103, Naples, FL 34105, Phone: 941-277-9110 Fax: 239-645-4209
8 12 2022
8 12 2022
© 2022 Published by Elsevier Inc.
2022
Elsevier has created a Monkeypox Information Center (https://www.elsevier.com/connect/monkeypox-information-center) in response to the declared public health emergency of international concern, with free information in English on the monkeypox virus. The Monkeypox Information Center is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its monkeypox related research that is available on the Monkeypox Information Center - including this research content - immediately available in publicly funded repositories, with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the Monkeypox Information Center remains active.
Monkeypox, a neglected disease previously confined to Africa, is causing a worldwide outbreak affecting predominantly male who have sex with male, especially HIV infected. The clinical presentation during the current outbreak differs from endemic cases. Treatment with tecovirimat and other antivirals is available. Immunization may be used as pre-exposure and postexposure prophylaxis.
CLINICAL SIGNIFICANCE: Monkeypox (hMPXV) infection is causing a pandemic (beyond its usual confinement in Africa)
Transmission is via skin-to-skin contact, predominantly during sexual encounters
Male who have sex with male, especially HIV-infected are most commonly affected
Pandemic cases have shorter incubation period, lesser number of lesions and more circumscribed location of the lesions
Treatment is supportive but specific antivirals are available
Vaccinations can be used for pre and postexposure prophylaxis
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pmcMonkeypox, previously considered an exotic disease confined to Africa, is causing a worldwide outbreak with cases reported predominantly among male who have sex with male. This article provides a practical review of the disease.
In accordance with the recommendation by numerous scientists to avoid using discriminatory and misleading terminology we prefer hMPXV to refer to the virus.1
THE ORGANISM
hMPXV belongs to the family Poxviridae and the subfamily Cordopoxvirinae (poxviruses of vertebrates). Several genera of Cordopoxvirinae affect humans (as seen in Table 1 ), one of which is hMPXV.2 hMPXV is a large (400 nm x 250 nm) brick-shaped virus containing double stranded DNA. The poxviridae family has 90% genomic sequence homology among its members. Figure 1 describes the replication cycle of hMPXV (extrapolated from the vaccinia virus (used to manufacture smallpox vaccine) cycle.3 TABLE 1 POXVIRIDAE GENUSES AFFECTING HUMANS
TABLE 1:GENUS SPECIES HOSTS OTHER THAN HUMANS
Moluscipox virus Molluscum contagiosum virus Dogs, birds, kangaroos, equids, primates
Orthopoxvirus Cowpox virus Alpacas, cats and large felids, cattle, elephants, mongoose, okapi, rhinoceros, rodents
Monkeypox virus (hMPXV) Squirrels, monkeys, great apes
Vaccinia virus (virus in smallpox vaccine) Buffalo, cattle, swine, rabbits
Variola virus (smallpox virus) None
Parapoxvirus Bovine popular stomatitis virus Cattle
Orf virus Sheep, goats
Pseudocowpox virus Cattle
Yatapoxvirus Yabapox virus Monkeys
Tanapox virus Monkeys
Figure 1 VACV is a large, enveloped, brick-shaped DNA virus that exists in three infectious forms: the intracellular mature virus (IMV); the intracellular triple-enveloped virus (IEV); and the extracellular double-enveloped virus (EEV).
1. To enter the cell, the EEV has an entry fusion complex (EFC) on the inner envelope. The outer envelope has proteins that interact with laminin and with the glycosaminoglycans (chondroitin sulfate and heparan sulfate, which facilitate interaction with other proteins, such as beta integrin or MARCO. They in turn allow disarming the outer envelope allowing entry of the virion.
2. The viral core that enters the cytoplasm contains the viral DNA as well as DNA-dependent RNA polymerase that allows for synthesis of RNA without the intervention of the cell nucleus.
3. The host cell ribosomes translate early and late genes from the viral mRNA. The early genes codify proteins that counteract host immune defenses and stimulate the replication of viral DNA. The late genes codify for proteins required for viral assembly.
4. Viral DNA and core proteins are assembled to become the IMV.
5. Five to 10 percent of IMV are wrapped by a Golgi cisterna or late endosome (LE) becoming three-membrane wrapped virions (IEV). The F13L gene of the vaccinia virus encodes the membrane protein p37, which is pivotal in the fusion of the IMV membranes with the Golgi or LE. Tecovirimat binds specifically to the homologous F13L proteins (p37 and others), preventing the formation of IEV.
6. IEV surfs the actin filaments until the outermost membrane of the virion fuses with the extracellular membrane, releasing the two-membrane EEV into the extracellular.
Figure 1:
Since its discovery, hMPXV has been phylogenetically distinguished into two clades: the Central African or Congo Basin (Clade 1) and the West African (Clade 2). The pandemic virus isolated in 2022 is phylogenetically close to clade 2, however there were enough differences to call it clade 3. On August,2022, a group of WHO experts established a new nomenclature: clade 1 became clade I, clade 2 became clade IIa, and clade 3 (the current circulating hMPXV) became clade IIb.4 The phylogenetic evolution of hMPXV has been attributed to APOBEC (Apolipoprotein B mRNA Editing Enzyme, Catalytic polypeptide) enzymes. These deaminases convert cytidine to thymidine and guanosine to adenine. Although useful to modify foreign nucleic acid by inactivating its replication and transcription, APOBEC could accidentally render the viruses better fit to immune evasion or induce resistance to antiviral drugs.5
EPIDEMIOLOGY
hMPXV was discovered in 1958 during a smallpox-like outbreak in macaque monkeys at a Danish research center, hence the “monkeypox” name, however the animal reservoir is unknown. Small rodents harbor the virus in Africa.6
The first human case, resembling smallpox, was reported in 1970 in the Democratic Republic of Congo (DRC). Human disease is different according to the clade involved:
- Clade 1 hMPXV was reported in the Congo Basin: Cameroon, Congo, the Central African Republic, and DRC. Human-to-human transmission occurs. In those not vaccinated against smallpox, the fatality rate was 11% (and 15% in those under 4 years of age).7
- Clade 2 infection was reported in West Africa (Sierra Leone, Liberia, Ivory Coast, Ghana and Nigeria). All cases were associated with contact with arboreal and non-arboreal rodents. Mortality was very low.8
From the 1970s to the present, almost 30,000 cases of hMPXV infection, with a few hundred deaths, have been reported in the Congo Basin. In the 1980s, the source of infection was contact with animals (72%) and in the 1990s, contact with humans explained 78% of cases. Until the 1990s, 100% of deaths occurred in children under 10 years of age, and later, this group explained only 35% of the deaths. This change may be explained in part by weaning of the cross-protection provided by previous smallpox vaccination.9
Sporadic cases have been reported outside Africa, but in 2003 there was an outbreak of 47 cases of hMPXV infection in the United States These were acquired by contact with prairie dogs, which in turn became infected by being housed with rodents imported from Ghana. There were no fatalities, and no person-to-person transmission was documented. Genetic sequence analysis confirmed the isolates belonged to Clade 2.8
Between 2017-2020 an outbreak of hMPXV infection was described in Nigeria, with similar modes of transmission as seen in the current epidemic. The surge of the disease was attributed to population growth and loss of smallpox immunity, but little attention was paid by the international community.10
In May 2022, an unusual outbreak of hMPXV infection was initially reported in the United Kingdom. Subsequently multiple cases appeared throughout the world. On July 23 WHO declared a Public Health Emergency of International Concern. As of October, more than 70,000 cases were reported worldwide, the majority in the United States. The outbreak affects disproportionately male who have sex with male, including many HIV-infected individuals.11 The epidemiological differences between the epidemic and the endemic forms of the disease are discussed in Table 2 .TABLE 2 EPIDEMIOLOGICAL DIFFERENCES BETWEEN ENDEMIC AND EPIDEMIC HMPXV INFECTION
TABLE 2:EPIDEMYOLOGICAL CHARACTERISTIC ENDEMIC INFECTION CURRENT EPIDEMIC INFECTION
Geographic location Rainforest of West and Central Africa Worldwide
Areas affected Small rural villages Large urban areas
Clade causing infection I, IIa IIb
Age of infected Younger than 15 years of age Sexually active age
Comorbidities Not reported Sexually transmitted diseases, including HIV
HIV coinfection occurs in up to 40% of cases in the USA
Transmission Contact with animals (due to deforestation, hunting, migration) followed by secondary human-to-human transmission Mainly human-to-human transmission during sexual contact
Mortality High (between 3-10 % depending on the clade) Very rare
Transmission of hMPXV from animals-to-humans occurs through direct contact with infected blood, bodily fluids or mucosal or cutaneous lesions. Transmission from humans-to-humans occurs by contact with skin lesions or respiratory secretions, or by indirect contact with fomites. Airborne transmission has not been documented. In the current outbreak, the main means of transmission is close contact during sexual activity12, (however hMPXV is not considered a sexually transmitted disease, as sexual activity is not the only means of transmission). In a survey of 45 infected patients, 98% identified themselves as homosexual or bisexual, 74% were HIV negative (out of which 91% were receiving HIV pre-exposure prophylaxis), 26% were HIV positive and were receiving appropriate treatment. Sixty-four percent had attended group sex events, 75% had new sexual partners and 83% got dates via social apps.13
CLINICAL MANIFESTATIONS
The clinical presentation of hMPXV infection in endemic countries differs from the manifestations seen during the current outbreak.12 , 14 , 15
In its classical presentation, hMPXV infection had an incubation period of 7-17 days. The disease started with fever, fatigue, and headache. Prominent maxillary, cervical or inguinal lymphadenopathy preceding or concomitant with the rash was characteristic. The lymph nodes were described as firm and very painful. The rash started in the face and disseminated centrifugally to the rest of the body. There were usually hundreds or thousands of lesions which progressed through 5 phases: macular, papular, vesicular, pustular and desquamative. During the papular stage a typical central umbilication appeared. The lesions were in the same stage of progression at any given time (a helpful characteristic in differentiating the condition from chickenpox). The rate of complications was high (between 40-70%, depending on the smallpox vaccination status) and included: dehydration, diarrhea, bronchopneumonia, ocular infections, and encephalitis. The mortality was high, particularly in children (up to 11%).14
Table 3 summarizes the contrasting clinical characteristics during the current outbreak: the incubation period is shorter, the number of lesions lesser, and the localization predominantly around the area of inoculation. Intense anal pain and mucosal compromise is common in the current epidemic.12 TABLE 3 CLINICAL CHARACTERISTICS OF ENDEMIC AND CURRENT EPIDEMIC HMPXV INFECTION
TABLE 3:CLINICAL CHARACTERISTIC ENDEMIC INFECTION CURRENT EPIDEMIC INFECTION
Incubation period (Guzzetta,2022) 7-17 days 6-11 days
Prodromal symptoms Fever, fatigue, and headache frequently present Fever, fatigue, and headache occasionally present
Lymphadenopathy Prominent and painful May or may not be present
Number of lesions Hundreds to thousands Ten or less lesions, including solitary lesions
Rash distribution Global Localized, around site of inoculation
Stage of lesions Usually, all lesions are at the same stage of evolution Lesions may be at different stages of evolution
Disease without rash Not reported Rectal, pharyngeal, or mucosal lesions without rash have been reported
Complications Dehydration, diarrhea, bronchopneumonia, ocular infections, and encephalitis Anal pain, cellulitis, urinary signs, ocular infections, abscess, lymphangitis, and paronychia
Figure 2 shows cutaneous lesions of hMPXV infection.Figure 1 A A baby with “endemic” monkeypox virus in Africa
Figure 1B: A male with hMPXV infection following “fisting” (insertion of the hand into the rectum or vagina of someone as a means of sexual stimulation)
Figure 1C: A female with hMPXV infection after performing cunnilingus and anilingus. Oral lesions not shown
Figure 1
Other conditions can resemble hMPXV infection and should be considered in the differential diagnosis: smallpox, chickenpox, molluscum contagiosum, other poxvirus infections, rickettsialpox, and secondary syphilis.16
DIAGNOSIS
Confirmation of the diagnosis during the current outbreak requires obtaining samples from the skin or mucosal lesions for nucleic acid amplification.
Healthcare personnel should wear gowns, gloves, eye protection and N-95 respirators while collecting samples. Samples should be obtained using sterile polyester or Dacron swabs (but not cotton) from the surface of the lesions or the exudate. Unroofing of lesions is not recommended, given risk of dissemination. Ideally two swabs from each site involved should be obtained. Testing from multiple sites improves sensitivity and reduces false-negative results. The swab should be transported in viral transport media. The sample should be labeled appropriately and if not sent immediately it can be refrigerated (2-8C) or frozen (-10C or lower) until processed.17
The CDC (via local Public Health Departments) and several commercial laboratories (Aegis Science, Labcorp, Mayo Clinic Laboratories, Quest Diagnostics and Sonic Healthcare) can process samples for PCR testing, the preferred method of nucleic acid amplification. Some tests can identify hMPXV specifically, but others only recognize poxviruses in general. PCR tests are highly sensitive, being able to detect as little as 10 viral genomes, and highly specific.18 , 19
There are other tests (summarized in Table 4 ) that could be used for diagnosis of hMPXV but they do not provide fast enough results to be clinically relevant19 TABLE 4 TESTS TO IDENTIFY HMPXV (OTHER THAN PCR)
TABLE 4:TEST Comments
Restriction length fragment polymorphism Detects nucleic acid material, but requires viral culture and is time consuming
ELISA for detection of IgM and IgG in serum May be false positive due to cross reactivity with other poxviruses
Antibodies may appear several days after starting of clinical disease
Testing of acute and convalescent titers may be necessary
Electron microscopy Other poxviruses may look morphologically similar
Sample preparation is complicated, and procedure is expensive
Immunochemistry and immunofluorescence Requires biopsy and is not specific for monkeypox
Viral cultures Time consuming test
Testing for gonococcus/chlamydia, syphilis, and HIV is pertinent in cases of hMPXV infections as they can coexist.
TREATMENT
Supportive therapy
Most cases in the current epidemic are mild and do not require hospitalization. Supportive care is the standard (Table 5 ). Numerous recommendations are backed by experience in low resource settings.20 Other recommendations are more specific for the current outbreak, for example, rectal pain can be excruciating and may require hospitalization.TABLE 5 SUPPORTIVE CARE IN MONKEYPOX INFECTION
TABLE 5:TYPE OF CARE RECCOMENDATIONS
Systemic care Appropriate oral or intravenous hydration and adequate nutrition may accelerate recovery
Pain control Antipyretics and analgesics may improve comfort
Short term use of gabapentin and opioids may be indicated after taking into consideration comorbidities and potential medication interactions
Oropharyngeal pain can be treated with saltwater rinses, chlorhexidine mouthwash, topical viscous lidocaine), or prescription analgesic mouthwash (containing an antihistaminic and a topical anesthetic)
Rectal pain can be treated with stool softeners (to prevent painful defecation), warm sitz baths and topical lidocaine
Genital pain can be treated with topical lidocaine, the use of topical steroids is controversial
Skin care Frequent cleansing of the skin may prevent over-imposed bacterial infections
Judicious application of occlusive dressings in areas with dense rash or in the face, may promote healing and prevent scarring
Keeping short nails, wearing mittens, and administering antihistamines may control pruritus and prevent further skin damage
Incision and drainage of abscess and use of antibiotics is indicated in over-imposed bacterial infections
Ocular care Topical lubricants and antibiotics (prophylactically or therapeutically) may prevent progression of ocular damage (ranging from conjunctivitis to corneal ulcerations)
Topical trifluridine has been used in ocular vaccinia and may be effective in monkeypox
Antivirals
Use of systemic antivirals is only recommended in selected patients as summarized in Table 6 . Topical trifluridine has been used in orthopoxvirus-associated corneal lesions but there is no experience in hMPXV cases.TABLE 6 INDICATIONS FOR ANTIVIRAL THERAPY IN MONKEYPOX
TABLE 6:TREATMENT INDICATIONS CATEGORIES
Individuals at risk of complications Certain females Pregnant females, lactating females
Patients with certain skin disorders Atopic dermatitis, other exfoliative skin conditions
Pediatric patients Especially younger than 8 years
Immunosuppressed patients HIV infection, hematological malignancies, solid organ transplant, hematopoietic transplant, use of immunosuppressant agents
Individuals already having complications Disease affecting areas that constitute a special hazard Ocular and oral mucosa, genitalia and anus
Severe disease Monkeypox causing encephalitis or sepsis
Comorbidities and complications Associated bronchopneumonia, gastroenteritis, over imposed skin bacterial infection, other comorbidities
Tecovirimat (known also as ST-246 or TPOXX) is the antiviral of choice. This agent prevents the final maturation and release of virions by inhibiting the viral protein VP37.Tecovirimat was approved for the treatment of smallpox, in adults and children weighing > 3 kg, based in the “Animal Rule”.21 As smallpox was eradicated from Earth and studies were not feasible, the FDA allowed the approval based on efficacy in animal models (survival in nonhuman primates infected with monkeypox virus and rabbits infected with rabbitpox virus) and safety in human volunteers. Unfortunately, the few reports of human disease treated with the drug have not provided conclusive evidence of efficacy.22 Ongoing clinical trials are testing the efficacy and safety of tecovirimat in human infection in Congo and the USA.23
Tecovirimat is available in the United States via selected local Public Health Departments. Alternatively the CDC Emergency Operations Center can be contacted directly at 770-488-7100 for consultation. The CDC has established an expanded access for this Investigational New Drug (EA-IND). In practical terms, tecovirimat can be used immediately upon receipt and required forms for the CDC IRB, such as informed consent, patient's intake form, and report of adverse events can be submitted after the vaccination has been administered.24
Tecovirimat capsules should be taken 30 minutes after a full meal with moderate to high fat content. If the patient is unable to swallow capsules, the capsule content can be mixed with water and administer as liquid. Alternatively, tecovirimat is available intravenously. Tecovirimat dosing is based on weight (Table 7 ). Treatment is administered for 14 days but can be shorter or longer (but not to exceed 90 days) depending on the patient's clinical condition. Tecovirimat has not been studied in pregnant or lactating mothers, but benefits may outweigh potential risks of use.25 TABLE 7 FORMULATIONS AND DOSING OF TECOVIRIMAT IN ADULTS
TABLE 7:FORMULATION WEIGHT DOSING
Oral (200 mg capsules) 88 to < 244 lbs 600 mg orally every 12 hours
• 264 lbs
600 mg orally every 8 hours
Intravenous (10 mg/mL) (Should be diluted in twice as much 0.9 normal saline or 5% dextrose in water) 77 to < 244 lbs 200 mg IV every 12 hours (infusion should last 6 hours)
264 lbs 300 mg IV every 12 hours (infusion should last 6 hours)
Tecovirimat is a weak inducer of cytochrome P450 (CYP)3A and a weak inhibitor of CYP2C8 and CYP2C19. It may cause increased repaglinide levels (causing hypoglycemia) and decreased midazolam levels. Tecovirimat should not be used concomitantly with carbotegravir/rilpivirine. It may also interact with doravirine, rilpivirine and maraviroc, but the effects are mild and dose adjustments may not be required. Tecovirimat has a low barrier to resistance and change in the VP37 protein may decrease the efficacy of the drug.26
Intravenous tecorivimat is contraindicated if the creatinine clearance is < 30 mL/min.25
Cidofovir is a viral DNA polymerase inhibitor approved for the treatment of cytomegalovirus-induced retinitis in HIV-infected patients. Cidofovir has in-vitro and in-vivo activity against hMPXV, but clinical data in humans is lacking.27 Although available commercially and via IND from the CDC, it is not the medication of choice during this outbreak.
Brincidofovir (Tembexa) is a lipid conjugate of cidofovir also approved by the FDA for treatment of smallpox based in the “Animal Rule”. The medication is not commercially available but can be accessed via IND from the manufacturer by inquiring at [email protected]. Brincidofovir is available in liquid formulation and in tablets. The dosage is 200 mg weekly for 2 weeks. Treatment duration longer than 2 weeks has been associated with excessive mortality (in patients infected with CMV). Brincidofovir can cause diarrhea and other gastrointestinal side effects and requires monitoring of liver enzymes. The medication is potentially teratogenic and should not be used in pregnancy.28
Vaccinia immunoglobulin (VIG) was administered in the past intramuscularly, but in 2005 an IV formulation was approved. This formulation allows administration of higher doses without causing pain. VIG has been approved for the treatment of vaccinia vaccination complications (specially eczema vaccinatum). VIG has efficacy in multiple orthopoxvirus animal models, but information on human hMPXV infection is lacking. VIG can be obtained via IND from the CDC. VIG should be administered at a dose of 6,000 U/kg, but doses of up to 24,000 U/kg have been administered to healthy volunteers. VIG is contraindicated in cases of preexistent anaphylaxis or hypersensitivity. Like with any blood-derived product transfusion side effects may occur. VIG can interfere with the efficacy of live vaccines. VIG niche in the treatment of hMPXV may be as adjuvant along with use of other antiviral agents in severe cases.29
PREVENTION
Infection control at home
Measures of infection control at home include isolation; containment and use of personal protective equipment; and hygiene, cleaning, and disinfection (Table 8 ).30 TABLE 8 INFECTION CONTROL MEASURES AT HOME
TABLE 8:MEASURE ACTIVITY
Isolation Remain on separate room or space
Avoid close contact with other people or pets
Avoid sharing household items or utensils
Use a separate bathroom if possible
Containment and use of personal protective equipment Cover lesions with clothes or dressings
Change own dressings if possible
Wear a well-fitting mask
Cover household surfaces that cannot be laundered with covers or blankets
Non infected household members should wear gloves and well-fitted mask at minimum if helping the sick
Hygiene, cleaning, and disinfection Wash hands with soap and water or alcohol-based rub frequently
Use an EPA-approved disinfectant on hard surfaces
Contain soiled laundry until washed in a regular washing machine
Dispose waste appropriately
Infection control in the hospital
If a patient with hMPXV infection is admitted to the hospital, Infection Prevention should be contacted immediately. Patients should be placed in a single-person room with private bathroom and the door should be kept closed. There is no need for a negative pressure room unless intubation or procedures that produce aerosols are required. Lesion should be covered with dressings or clothes. Patients should remain in isolation and with limited visitation until all skin lesions have crusted and a healthy layer of skin forms underneath the crust.31
Healthcare personnel should wash hands with soap and water or use waterless antiseptic agents frequently. When entering a patient's room healthcare staff should wear gowns, gloves, eye protection and a N-95 respirator. Cleaning and disinfection of the room, and handling of soiled laundry should be done using standard procedure.31
Medical waste from patients in the current outbreak (Clade IIB) should be managed as a Category A infectious substance (“an infectious substance capable of causing permanent disability or life-threatening disease”). Curiously medical waste from patients infected with Clade I is considered Category B (not capable of causing such damage).32
In case of death, remains should be handled using contact, droplet, and airborne precautions. If an autopsy is performed appropriate measures to prevent percutaneous injury are indicated. Samples should be taken from tissues demonstrating gross pathology. After the procedure non-reusable items should be handled as medical waste and reusable equipment and surfaces should be cleaned and/or disinfected according to standard protocols.33
Vaccines
Smallpox vaccines can be used for prevention of hMPXV infection. Table 9 describes characteristics of smallpox vaccines; however the remainder of this section only discusses vaccines currently used in the USA for prevention of hMPXV infection.TABLE 9 CHARACTERISTICS OF VACCINES APPROVED FOR SMALLPOX (AND HMPXV)
TABLE 9:TYPE GENERATION (EXAMPLES) COMMENTS
Attenuated live virus replication-competent vaccines First generation (Dryvax, Wetvax) Used in initial eradication of smallpox May be held in national reserves but do not meet current safety and manufacturing standards and are not recommended at this time
Second generation (ACAM2000) Contraindicated in immunosuppressed patients, pregnancy, atopic dermatitis, and others Risk of adverse events (eczema vaccinatum, generalized vaccinia, fatal progressive vaccinia) Requires multiple percutaneous punctures with a bifurcated needle
Attenuated live virus replication-deficient vaccines Third generation (Jynneos) Uses more attenuated strain (Modified Vaccinia Ankara Bavarian Nordic strain or MVA-BN) Administered subcutaneously with regular needle Fewer contraindications and fewer adverse events than lesser generations
The US National Monkeypox Vaccination Strategy has made available two preventative vaccines: JYNNEOS and ACAM2000. The vaccines can be obtained via local public health authorities.34
JYNNEOS, manufactured by Bavarian Nordic, is the preferred vaccination. This is a further attenuated Modified Vaccinia Ankara (MVA) strain grown in chicken embryos that cannot reproduce in mammal cells but confers immunity to smallpox. JYNNEOS has several advantages over ACAM2000: higher geometric mean titers and seroconversion rates; no cutaneous reactions (so called “take”) that cause a draining lesion with risk of autoinoculation; less risk of myopericarditis; and possibility to be used in immunosuppressed patients and people with eczema. JYNNEOS was licensed based on animal and clinical studies showing a comparable immune response to ACAM2000.35
To administer JYNNEOS the provider is required to sign an agreement with the US Department of Health and Human Services to collect demographic and vaccine related information (product, dose number, lot, etc.) and to report serious adverse events.36 The vaccine is administered in two doses (28 days apart) at 0.5 mL subcutaneously. Alternatively (in order to spare vaccine) 0.1 mL can be administered intradermally at the same interval.37 Contraindications to the vaccine include history of anaphylaxis or severe allergic reaction to a previous dose of JYNNEOS, egg or chicken protein, gentamicin or ciprofloxacin.36
ACAM2000 is a smallpox vaccine, derived from a clone of Dryvax (the original vaccine used for eradication of smallpox) approved in 2015 for prevention of orthopoxviruses in laboratory and health care personnel at risk for occupational exposure. The vaccine is available (but not frequently used) via EA-IND. It requires a single dose, but it has several disadvantages including: percutaneous administration using a bifurcated needle, more side effects (self-inoculation, myocarditis, pericarditis) and more contraindications (anaphylaxis to previous dose of ACAM2000 or excipients, immunosuppression, pregnancy, breastfeeding, prominent cardiac risk factors, eczema and other exfoliative skin conditions, cheloids, ocular infection treated with steroids, previous history of monkeypox) than JYNNEOS. Details about its administration can be found on the CDC Website.38
Pre-exposure prophylaxis (PrEP)
Vaccination before exposure is recommended in two groups:34 • Certain laboratory and healthcare workers who can be exposed to orthopoxviruses (a particular group at risk may be proctologists, especially those working with HIV-infected patients).
• Those with sexual risk in previous 6 months: Male who have sex with male who developed a sexually transmitted diseases; people who engage in sex with commercial sex workers, or group sex, or participate in large sexual events in risky geographic areas; partners of people with risk factors.
Transmission risk among healthcare workers is possible, but in non-endemic areas rare with only one case identified in a review between 2000 to 2022.39
It must be noted that in certain geographic areas, such as New York, vaccine eligibility has expanded and anyone who considers themselves to be at risk for hMPVX infection through sex or other intimate contact, can receive the vaccine.40
Post-exposure prophylaxis (PEP)
Vaccination after known exposure to hMPXV is indicated in those identified by the local public health authorities. In addition, expanded PEP may be offered to those with a sex partner diagnosed with monkeypox within the last 14 days, or to those with sexual risk as described in the PrEP section.34 , 40
Vaccination is recommended within 4 days of the exposure for maximum efficacy. Vaccination between 4 and 14 days after contact provides lesser protection. Vaccination beyond 14 days may be even less effective, but must be considered in immunosuppressed patients.36
VIG could be considered for PEP, in those patients with severe T cell immunodeficiency who may be unable to mount an appropriate immune response with vaccination.19
ONE Health
Bushmeat hunting for human consumption and international demand of exotic pets may have fuel the emergence of monkeypox. One Health promotes alternatives to bushmeat, public awareness campaigns, education on hygienic handling of wild animals, routine vaccination of people at risk, and abolition of exotic pet trade. Although unlikely to curtail the current outbreak it may help in preventing other zoonoses.41
Misinformation
Physicians and healthcare workers play an important role in preventing misinformation and stigmatization as it has occurred in other pandemics.42
Verification
We attest to the fact that all Authors listed on the title page have contributed significantly to the work, have read the manuscript, attest to the validity and legitimacy of the data and its interpretation, and agree to its submission to the– American Journal of Medicine.
Uncited References
[10, 17]
Declaration of Competing Interest
Ricardo M. Salcedo – None
Miguel G. Madariaga – None
Funding sources
None
ACKNOWLEDGEMENTS
The authors would like to thank Isabella Madariaga for her graphic art and editorial services, and Dr Jeffrey Panozzo for sharing a picture
==== Refs
REFERENCES
1 Happi C Adetifa I Mbala P Njouom R Nakoune E Happi A Ndodo N Ayansola O Mboowa G Bedford T Neher RA Roemer C Hodcroft E Tegally H O'Toole Á Rambaut A Pybus O Kraemer MUG Wilkinson E Isidro J Borges V Pinto M Gomes JP Freitas L Resende PC Lee RTC Maurer-Stroh S Baxter C Lessells R Ogwell AE Kebede Y Tessema SK de Oliveira T. Urgent need for a non-discriminatory and non-stigmatizing nomenclature for monkeypox virus PLoS Biol 20 8 2022 Aug 23 e3001769
2 Regnery RL. Poxviruses and the passive quest for novel hosts Curr Top Microbiol Immunol 315 2007 345 361 17848071
3 Greseth MD Traktman P. The Life Cycle of the Vaccinia Virus Genome Annu Rev Virol 9 1 2022 Sep 29 239 259 35584888
4 World Health Organization. Monkeypox: experts give virus variants new names. Available at: https://www.who.int/news/item/12-08-2022-monkeypox–experts-give-virus-variants-new-names. Accessed on 18 October 2022.
5 Chen Y Li M Fan H. The monkeypox outbreak in 2022: adaptive evolution associated with APOBEC3 may account for Signal Transduct Target Ther 7 1 2022 Sep 16 323 36114202
6 Di Giulio DB Eckburg PB. Human monkeypox: an emerging zoonosis Lancet Infect Dis 4 1 2004 Jan 15 25 14720564
7 Kmiec D Kirchhoff F. Monkeypox: A New Threat? Int J Mol Sci 23 14 2022 Jul 17 7866 35887214
8 Likos AM Sammons SA Olson VA Frace AM Li Y Olsen-Rasmussen M Davidson W Galloway R Khristova ML Reynolds MG Zhao H Carroll DS Curns A Formenty P Esposito JJ Regnery RL Damon IK. A tale of two clades: monkeypox viruses J Gen Virol 86 10 2005 Oct 2661 2672 Pt 16186219
9 Bunge EM Hoet B Chen L Lienert F Weidenthaler H Baer LR Steffen R. The changing epidemiology of human monkeypox-A potential threat? A systematic review PLoS Negl Trop Dis 16 2 2022 Feb 11 e0010141
10 Nguyen PY Ajisegiri WS Costantino V Chughtai AA MacIntyre CR. Reemergence of Human Monkeypox and Declining Population Immunity in the Context of Urbanization, Nigeria, 2017-2020 Emerg Infect Dis 27 4 2021 Apr 1007 1014 33756100
11 World Health Organization. 2022 monkeypox outbreak: global trends. Available at: https://worldhealthorg.shinyapps.io/mpx_global/. Accessed on 18 October 2022.
12 Angelo KM Smith T Camprubí-Ferrer D Balerdi-Sarasola L Díaz Menéndez M Servera-Negre G Barkati S Duvignaud A Huber KLB Chakravarti A Bottieau E Greenaway C Grobusch MP Mendes Pedro D Asgeirsson H Popescu CP Martin C Licitra C de Frey A Schwartz E Beadsworth M Lloveras S Larsen CS Guagliardo SAJ Whitehill F Huits R Hamer DH Kozarsky P M; Libman GeoSentinel Network Collaborators. Epidemiological and clinical characteristics of patients with monkeypox in the GeoSentinel Network: a cross-sectional study Lancet Infect Dis 2022 Oct 7 S1473-3099(22)00651-X
13 Vivancos R Anderson C Blomquist P Balasegaram S Bell A Bishop L Brown CS Chow Y Edeghere O Florence I Logan S Manley P Crowe W McAuley A Shankar AG Mora-Peris B Paranthaman K Prochazka M Ryan C Simons D Vipond R Byers C Watkins NA UKHSA Monkeypox Incident Management teamWelfare W Whittaker E Dewsnap C Wilson A Young Y Chand M Riley S Hopkins S Monkeypox Incident Management Team. Community transmission of monkeypox in the United Kingdom Euro Surveill 27 22 May 2022 2200422 2022 Jun
14 Philpott D Hughes CM Alroy KA Kerins JL Pavlick J Asbel L Crawley A Newman AP Spencer H Feldpausch A Cogswell K Davis KR Chen J Henderson T Murphy K Barnes M Hopkins B Fill MA Mangla AT Perella D Barnes A Hughes S Griffith J Berns AL Milroy L Blake H Sievers MM Marzan-Rodriguez M Tori M Black SR Kopping E Ruberto I Maxted A Sharma A Tarter K Jones SA White B Chatelain R Russo M Gillani S Bornstein E White SL Johnson SA Ortega E Saathoff-Huber L Syed A Wills A Anderson BJ Oster AM Christie A McQuiston J McCollum AM Rao AK Negrón ME CDC Multinational Monkeypox Response Team. Epidemiologic and Clinical Characteristics of Monkeypox Cases - United States MMWR Morb Mortal Wkly Rep 71 32 2022 1018 1022 2022 Aug 12 35951487
15 Petersen E Kantele A Koopmans M Asogun D Yinka-Ogunleye A Ihekweazu C Zumla A. Human Monkeypox: Epidemiologic and Clinical Characteristics, Diagnosis, and Prevention Infect Dis Clin North Am 33 4 2019 Dec 1027 1043 30981594
16 Hussain A, Kaler J, Lau G, et al. Clinical Conundrums: Differentiating Monkeypox From Similarly Presenting Infections. Cureus 14(10): e29929.
17 Centers for Disease Control and Prevention. Guidelines for collecting and handling specimens for monkeypox testing. Available here: https://www.cdc.gov/poxvirus/monkeypox/clinicians/prep-collection-specimens.html. Accessed 22 October 2022.
18 Jiang Z Sun J Zhang L Yan S Li D Zhang C Lai A Su S. Laboratory diagnostics for monkeypox: An overview of sensitivities from various published tests Travel Med Infect Dis 49 2022 Sep-Oct 102425
19 Gong Q Wang C Chuai X Chiu S. Monkeypox virus: a re-emergent threat to humans Virol Sin 37 4 2022 Aug 477 482 35820590
20 Reynolds MG McCollum AM Nguete B Shongo Lushima R Petersen BW Improving the Care and Treatment of Monkeypox Patients in Low-Resource Settings: Applying Evidence from Contemporary Biomedical and Smallpox Biodefense Research Viruses. 9 12 2017 Dec 12 380 29231870
21 Chan-Tack KM Harrington PR Choi SY Myers L O'Rear J Seo S McMillan D Ghantous H Birnkrant D Sherwat AI. Assessing a drug for an eradicated human disease: US Food and Drug Administration review of tecovirimat for the treatment of smallpox Lancet Infect Dis 19 6 2019 Jun2019 Mar 7 e221 e224 10.1016/S1473-3099(18)30788-6 EpubPMID30853252
22 Carvalho T. The unknown efficacy of tecovirimat against monkeypox Nat Med 2022 Sep 13
23 Lane HC Fauci AS. Monkeypox - Past as Prologue N Engl J Med 387 8 2022 Aug 25 749 750 36001716
24 Centers for Disease Control and Prevention. Guidance for tecovirimat use. Available here: https://www.cdc.gov/poxvirus/monkeypox/clinicians/Tecovirimat.html. Accessed on 20 October 2022.
25 Food and Drug Administration. TPOXX (Tecovirimat) label. Available here: https://www.cdc.gov/poxvirus/monkeypox/clinicians/Tecovirimat.html. Accessed on 20 October 2022.
26 O'Shea J Filardo TD Morris SB Weiser J Petersen B Brooks JT. Interim Guidance for Prevention and Treatment of Monkeypox in Persons with HIV Infection - United States MMWR Morb Mortal Wkly Rep 71 32 August 2022 1023 1028 2022 Aug 12 35951495
27 Delaune D Iseni F. Drug Development against Smallpox: Present and Future Antimicrob Agents Chemother 64 4 2020 Mar 24 e01683 -19 31932370
28 Food and Drug Administration. Tembexa label. Available here: https://www.accessdata.fda.gov/drugsatfda_docs/label/2021/214460s000,214461s000lbl.pdf. Accessed on 20 October 2022.
29 Titanji BK Tegomoh B Nematollahi S Konomos M Kulkarni PA. Monkeypox: A Contemporary Review for Healthcare Professionals Open Forum Infect Dis 9 7 2022 Jun 23 ofac310 35891689
30 Centers for Disease Control and Prevention. Isolation and infection control at home. Available here: https://www.cdc.gov/poxvirus/monkeypox/clinicians/infection-control-home.html. Accessed on 20 October 2022.
31 Centers for Disease Control and Prevention. Infection prevention and control of monkeypox in healthcare settings. https://www.cdc.gov/poxvirus/monkeypox/clinicians/infection-control-healthcare.html. Accessed on 20 October 2022.
32 United States Department of Transportation. Planning guidance for handling category A solid waste. Available here: https://www.phmsa.dot.gov/transporting-infectious-substances/planning-guidance-handling-category-solid-waste. Accessed on 20 October 2022.
33 Centers for Disease Control and Prevention. Autopsy and handling of human remains of patients with monkeypox. Available here: https://www.cdc.gov/poxvirus/monkeypox/clinicians/autopsy.html. Accessed on 20 October 2022.
34 Centers for Disease Control and Prevention. Monkeypox vaccination. Available here: https://www.cdc.gov/poxvirus/monkeypox/interim-considerations/overview.html#components. Accessed 22 October 2022.
35 Rao AK Petersen BW Whitehill F Razeq JH Isaacs SN Merchlinsky MJ Campos-Outcalt D Morgan RL Damon I Sánchez PJ Bell BP. Use of JYNNEOS (Smallpox and Monkeypox Vaccine, Live, Nonreplicating) for Preexposure Vaccination of Persons at Risk for Occupational Exposure to Orthopoxviruses: Recommendations of the Advisory Committee on Immunization Practices - United States, 2022 MMWR Morb Mortal Wkly Rep 71 22 2022 Jun 3 734 742 35653347
36 Centers for Disease Control and Prevention. JYNNEOS vaccine. Available here: https://www.cdc.gov/poxvirus/monkeypox/interim-considerations/jynneos-vaccine.html. Accessed 22 October 2022.
37 Food and Drug Administration. Monkeypox update: FDA authorizes emergency use of JYNNEOS vaccine to increase vaccine supply. Available here: https://www.fda.gov/news-events/press-announcements/monkeypox-update-fda-authorizes-emergency-use-jynneos-vaccine-increase-vaccine-supply. Accessed 22 October 2022.
38 Centers for Disease Control and Prevention. ACAM2000 vaccine. Available here: https://www.cdc.gov/poxvirus/monkeypox/interim-considerations/acam2000-vaccine.html . Accessed 22 October 2022.
39 Zachary KC Shenoy ES. Monkeypox transmission following exposure in healthcare facilities in nonendemic settings: Low risk but limited literature Infect Control Hosp Epidemiol 43 7 2022 Jul 920 924 10.1017/ice.2022.152 Epub 2022 Jun 9PMID35676244PMCIDPMC9272466 35676244
40 New York State Department of Health, State Department of Health expands monkeypox vaccine eligibility to include anyone at risk of exposure. Available here: https://health.ny.gov/press/releases/2022/2022-09-14_monkeypox_vaccine_eligibility.htm. Accessed 20 October 2022.
41 Reynolds MG Doty JB McCollum AM Olson VA Nakazawa Y. Monkeypox re-emergence in Africa: a call to expand the concept and practice of One Health Expert Rev Anti Infect Ther 17 2 2019 Feb 129 139 30625020
42 Ennab F Nawaz FA Narain K Nchasi G Essar MY Head MG Singla RK Atanasov AG Shen B. Monkeypox Outbreaks in 2022: Battling Another "Pandemic" of Misinformation Int J Public Health 67 2022 Jul 14 1605149
| 36495937 | PMC9729686 | NO-CC CODE | 2022-12-15 23:21:51 | no | Am J Med. 2022 Dec 8; doi: 10.1016/j.amjmed.2022.10.023 | utf-8 | Am J Med | 2,022 | 10.1016/j.amjmed.2022.10.023 | oa_other |
==== Front
Int J Psychiatry Med
Int J Psychiatry Med
spijp
IJP
International Journal of Psychiatry in Medicine
0091-2174
1541-3527
SAGE Publications Sage CA: Los Angeles, CA
36470704
10.1177_00912174221144128
10.1177/00912174221144128
Original Research Article
Factors associated with COVID-19 vaccination for patients in an inpatient forensic psychiatric hospital
McCulley Lauren N. 1
https://orcid.org/0000-0003-4511-1735
Lang Shelby E. 1
Kriz Carrie R. 2
Iuppa Courtney A. 1
Nelson Leigh Anne 2
Gramlich Nicole A. 3
Elliott Ellie S. R. 13
Sommi Roger W. 2
1 26752 Center for Behavioral Medicine , Kansas City, MO, USA
2 15517 University of Missouri-Kansas City School of Pharmacy , Kansas City, MO, USA
3 27481 Northwest Missouri Psychiatric Rehabilitation Center , St. Joseph, MO, USA
Shelby E. Lang, Missouri Department of Mental Health, 1000 E. 24th St., Kansas City, MO 64108, USA. Email: [email protected]
5 12 2022
5 12 2022
00912174221144128© The Author(s) 2022
2022
SAGE Publications
This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.
Objective
The aim of this study was to assess factors associated with SARS-CoV-2 (COVID-19) vaccination in patients in 2 inpatient forensic psychiatric hospitals.
Methods
This was a retrospective chart review evaluating factors associated with COVID-19 vaccination for patients residing in two inpatient forensic psychiatric hospitals between January 1, 2021 and February 28, 2022. Data was collected through electronic medical records utilizing MetaCare Enterprise™ and secure facility computer drives, individual patient paper charts, and Missouri’s vaccination records database, ShowMeVax. Several variables were collected to assess factors associated with COVID-19 vaccination. Additionally, COVID-19 vaccination rates were compared to the influenza vaccination rates at these hospitals.
Results
Overall, 229 patients (84.5%) were vaccinated against COVID-19 during or before the study period and 42 (15.5%) were unvaccinated. Patients who were deemed incompetent to stand trial were less likely to receive the COVID-19 vaccine. Those that had a higher body mass index (BMI), were diagnosed with multiple comorbid conditions, not prescribed involuntary medications, were offered incentives, and received the influenza vaccine were more likely to receive the COVID-19 vaccine. Education level, race, sex, age, and being prescribed psychiatric medications did not affect vaccination status.
Conclusions
Patient specific factors should be used when educating and offering COVID-19 vaccines to patients in an inpatient forensic psychiatric unit. Awareness of these results can facilitate targeted interventions for optimal care in a psychiatric population.
COVID-19
vaccine
psychiatry
forensic
inpatient
influenza
edited-statecorrected-proof
typesetterts10
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pmcIntroduction
The Food and Drug Administration (FDA) approved three vaccines through Emergency Use Authorizations (EUA) in December of 2020 for the illness caused by the virus SARS-CoV-2 (COVID-19).1,2 It is estimated that at 17 months post approval of the EUA, approximately 66.6% of the United States population was fully vaccinated.3 Currently, no data exists describing the COVID-19 vaccination rate for individuals diagnosed with a mental illness in the United States. However, in Belgium, a study at a university psychiatric hospital showed that 81% of patients were fully vaccinated, which was similar to the vaccination rate of the general population in that city.4 This indicates that hospitalized individuals with mental illness may be just as willing as the general population to receive the vaccine if it is accessible.
Groups that are more likely to have a more severe infection with COVID-19 initially include individuals with cancer, chronic kidney disease, chronic lung diseases, diabetes, heart conditions, immunocompromised conditions including human immunodeficiency virus (HIV), liver disease, organ transplant, substance use disorder, and those who are overweight or obese.5 When an individual has more than one of these risk factors, their risk for having a more severe illness with COVID-19 is presumed to be even higher. This highlights the importance of preventative measures such as social distancing, wearing a mask, and vaccination. Along with the aforementioned risk factors, those living in congregate living areas, such as long-term care facilities and jails, led to a higher infection spread.6 Because of the amount of people residing in these facilities, infection control is a challenge, which is why vaccinating these populations first was emphasized by the Center for Disease Control (CDC).7
A study by Wang and associates found that individuals with a recent diagnosis of a mental illness (within the past year), including attention-deficit/hyperactivity disorder, bipolar disorder, depression, and schizophrenia, were at an increased risk of COVID-19 infections, hospitalizations, and death.8 The authors concluded that the death rate for patients with a mental illness and COVID-19 diagnosis was 8.2% compared to 4.7% in those with COVID-19 and no mental illness. The hospitalization rate was 27.4% compared to 18.6%, respectively.8 Wang’s study emphasizes the importance of providing easy access to vaccines to those diagnosed with mental illnesses and to educate those patients on the importance of the vaccine. The CDC has since updated the list of high-risk medical conditions to include mental health conditions.
Given there is only a 60 to 70% vaccination rate in the United States and there are limited studies assessing risk factors and COVID-19 vaccination rates in those diagnosed with a mental illness, this study aimed to assess factors associated with COVID-19 vaccination acceptance in patients in two inpatient forensic psychiatric hospitals.3 Identifying these factors may help guide targeted interventions for populations less likely to accept a vaccine.
Methods
This study was a multi-site retrospective chart review. The project was reviewed and approved by the University of Missouri-Kansas City Institutional Review Board, Northwest Missouri Psychiatric Rehabilitation Center (NMPRC) Hospital Research Committee, Center for Behavioral Medicine (CBM) Research Committee, and State of Missouri Department of Mental Health Professional Review Committee.
Northwest Missouri Psychiatric Rehabilitation Center is a long-term care forensic psychiatric facility in which many patients are stabilized on medications longer, have a guardian appointed, and are no longer deemed incompetent to stand trial (IST). Center for Behavioral Medicine, is an acute care forensic psychiatric facility including patients who are IST and are less likely to be stabilized on medications. Being deemed IST, means that a judge has deemed the defendant as unfit to participate in their defense on account of their mental illness.9 Patients in these facilities can also be ordered involuntary medications. According to the Missouri Department of Mental Health, facilities may administer psychotropic medication on an involuntary basis when there is a reasonable likelihood of imminent physical harm or life-threatening behavior to the patient and/or others.10 It is assumed that those on involuntary medication orders will ultimately no longer be a danger to self or others, see improvements in their contact with reality, and help the patient acknowledge the need for continued care.11
Patients aged 18 years or older hospitalized at either of these two hospitals between January 1, 2021 and February 28, 2022 were evaluated for inclusion in the study. Patients were excluded from the study if they did not have specific documentation of the COVID-19 vaccine being offered, as facility procedure was to offer the vaccine to everyone or if the guardian refused the vaccine on behalf of the patient.
Data was collected through electronic medical records utilizing MetaCare Enterprise™, patient folders within facility computer drives, individual paper charts, and Missouri’s vaccination records database, ShowMeVax. Variables collected at time of vaccine acceptance or declination included sex, age, BMI, race, manufacturer of COVID-19 vaccine, influenza vaccination status for the 2020-2021 and 2021-2022 influenza seasons, primary psychiatric or neurologic diagnosis, highest level of education, competency status in relation to legal proceedings, psychiatric medication use, involuntary medication order status, incentives offered for receiving the COVID-19 and influenza vaccine, and comorbid conditions associated with higher risk of severe illness with COVID-19.
The primary objective was to assess factors associated with COVID-19 vaccination in patients admitted to two inpatient forensic psychiatric hospitals. To determine if previous immunization with vaccines is associated with higher acceptance of the COVID-19 vaccine, the secondary objective was to compare vaccination rates of the COVID-19 vaccine to the influenza vaccine in the same population.
Patient population and demographics were evaluated with descriptive statistics. The difference in number of comorbid conditions between those who accepted the COVID-19 vaccine and those who declined was evaluated using T-test. Logistic Regression, Fishers Exact Test, and Chi-Square were used to examine the relationship of different factors on COVID-19 vaccine acceptance or declination. Study data were collected and managed using REDCap (Research Electronic Data Capture).12 This provides a secure, web-based application for data collection, organization, and storage. All data was exported and analyzed using Excel and statistical package for the social science (SPSS) 27.
Results
Two-hundred ninety-four patients were identified for possible study inclusion and 271 patients met criteria. After chart review, 19 patients were excluded due to lack of documentation of a direct, individual offer to each patient although hospital procedure was to offer all eligible patients COVID-19 vaccines. Four patients were excluded due to guardian refusal of the vaccine. Of those included in the study, 229 (84.5%) had received at least one dose of the COVID-19 vaccine and 42 (15.5%) were unvaccinated. Of the 229 patients that had received at least one dose, 27 of those patients had received a dose prior to being admitted. Baseline characteristics are described in Table 1. Most patients were white males with an average age of 40 years. The most common primary diagnosis was schizophrenia with most patients deemed IST at the time point of interest.Table 1. Baseline Characteristics.
Characteristics Received COVID vaccination (N = 229) Did not receive COVID vaccination (N = 42)
Sex – n (%)
Male 191 (83.4%) 31 (73.8)
Female 38 (16.6%) 11 (26.2)
Age – yr (mean ± SD) 41.0 ± 13.1 40.0 ± 12.9
BMI– kg/m2 (mean ± SD) 30.8 ± 7.2 24.6 ± 6.6
Race – n (%)
Caucasian 155 (67.7) 31 (73.8)
Black or African American 55 (24.0) 8 (19.0)
Hispanic or Latino 13 (5.7) 1 (2.4)
Asian 1 (0.4) 0 (0.0)
Other/Unknown 5 (2.2) 2 (4.8)
COVID-19 vaccine received – n (%)
Pfizer 200 (73.8) N/A
Johnson & Johnson 22 (8.1) N/A
Moderna 7 (2.6) N/A
Vaccinated against influenza 2020-2021 season – n (%)
Yes 113 (49.3) 3 (7.1)
No 60 (26.2) 18 (42.9)
Unknown 56 (24.5) 21 (50.0)
Vaccinated against influenza 2021-2022 season – n (%)
Yes 115 (50.2) 3 (7.1)
No 60 (26.2) 31 (73.8)
Unknown 54 (23.6) 8 (19.0)
Primary diagnosis – n (%)
Schizophrenia 105 (45.9) 29 (69.0)
Schizoaffective disorder 28 (12.2) 2 (4.8)
Bipolar disorder (I or II) 18 (7.9) 8 (19.0)
Intellectual disability 16 (7.0) 0 (0.0)
Depression 7 (3.1) 1 (2.4)
Substance use disorder 4 (1.7) 1 (2.4)
Personality disorder 2 (0.9) 0 (0.0)
Other/unknown 49 (21.4) 1 (2.4)
Competency status – n (%)
Incompetent to Stand Trial (IST) 85 (37.1) 36 (85.7)
Voluntary by Guardian (VBG) 49 (21.4) 1 (2.4)
Not Guilty by Reason of Mental Disease or Defect (NGRI) 33 (14.4) 1 (2.4)
Permanently Incompetent to Stand Trial (PIST) 25 (10.9) 3 (7.1)
Competent to stand trail 10 (4.4) 1 (2.4)
Other/unknown 27 (11.8) 0 (0.0)
Level of education – n (%)
High school 81 (35.4) 11 (26.2)
Some high school 55 (24.0) 11 (26.2)
Some college 29 (12.7) 6 (14.3)
College graduate 17 (7.4) 4 (9.5)
Grade school 17 (7.4) 3 (7.1)
Unknown 30 (13.1) 7 (16.7)
Involuntary medication order – n (%)
No 193 (84.3) 36 (85.7)
Yes 9 (3.9) 3 (14.3)
Unknown 27 (11.8) 0 (0.0)
Comorbid conditions – n (%)
Substance use disorder 161 (70.3) 31 (73.8)
Smoking (current or former) 156 (68.1) 31 (73.8)
Overweight or obese 156 (68.1) 20 (47.6)
Hypertension 62 (27.1) 5 (11.9)
Diabetes (type I or II) 34 (14.8) 3 (7.1)
Asthma 23 (10.0) 2 (4.8)
Chronic Obstructive Pulmonary Disease (COPD) 9 (3.9) 1 (2.4)
Coronary artery disease 8 (3.5) 0 (0.0)
Chronic kidney disease 6 (2.6) 0 (0.0)
Dementia 3 (1.3) 0 (0.0)
Cancer 2 (0.9) 0 (0.0)
Liver disease 2 (0.9) 0 (0.0)
Heart failure 2 (0.9) 0 (0.0)
HIV 2 (0.9) 0 (0.0)
Stroke 2 (0.9) 0 (0.0)
Interstitial lung disease 1 (0.4) 0 (0.0)
Pulmonary hypertension 1 (0.4) 0 (0.0)
N/A = not applicable.
Vaccinated patients had significantly more comorbid conditions (M = 2.75, SD = 1.275) than unvaccinated patients (M = 2.21, SD = 0.951; t = 2.597, P = .01). Education level, race, sex and age did not predict vaccination status. However, increased BMI was associated with increased odds of receiving the COVID-19 vaccine (P < .001) with the odds of vaccination increasing as BMI increased (OR = 1.172, 95% CI [1.089-1.261]), as shown in Table 2. Patients who did not have an order for involuntary medications were more likely to receive the COVID-19 vaccine (P = 0.027), as seen in Figure 1. There was not a significant difference in vaccination status between those prescribed psychiatric medications or not, shown in Figure 2. There was a significant association between competency status and vaccination status (X2 = 32.2, P < .001). Patients who were IST were less likely to be vaccinated (OR = 0.048, P = .003), seen in Table 3. Patients who were offered an incentive of a personal pizza and a can of soda were more likely to accept the vaccine (X2 = 23.79, P < .001), seen in Figure 3.Table 2. Population Demographics and Vaccination Status.
Demographics B (SE) P-value OR
Constant −3.457 (1.265) .006 0.032
Race
White −0.001 (0.463) .999 0.999
African American 0.544 (1.105) .623 0.623
Hispanic or Latino 17.94 (40192.9) 1.000 1.000
Asian −0.147 (0.913) .872 0.872
Age −0.007 (0.15) .610 0.993
Sex – male 0.928 (0.475) .051 2.530
Level of Education 0.0.38 (0.135) .775 1.039
BMI 0.159 (0.037) .000** 1.172
**P < .001; B = Coefficient; SE = Standard error; OR = Odds ratio; BMI = Body mass index.
Figure 1. Prescribed involuntary medications.*
Figure 2. Prescribed psychiatric medications.
Table 3. Competency and Vaccination Status.
Competency Status B (SE) P-value OR
Constant 3.892 (1.010) .000 49.000
IST −3.033 (1.030) .003* 0.048
PIST −1.772 (1.181) .133 0.170
Competent −1.589 (1.456) .275 0.204
NGRI −0.395 (1.432) .783 0.673
*P < .05; B: coefficient SE = Standard error; OR = Odds ratio; IST = Incompetent to stand trial; PIST = Permanently incompetent to stand trial; NGRI = Not guilty by reason of mental disease or defect.
Figure 3. Offered incentive for COVID-19 vaccine.**
For the secondary outcome, patients with documentation of receiving the influenza vaccine during one or more flu seasons were more likely to be vaccinated against COVID-19 (X2 = 55.057, P < .001) as seen in Figure 4.Figure 4. COVID and influenza vaccine.**
Discussion
This study identified factors associated with COVID-19 vaccination in patients admitted to two inpatient forensic psychiatric hospitals. Patients who were deemed IST were less likely to receive the COVID-19 vaccine. Those that had a higher BMI, were diagnosed with multiple comorbid conditions, not ordered involuntary medications, were offered incentives, and received the influenza vaccine were more likely to receive the COVID-19 vaccine. Education level, race, sex, age, and if a patient was prescribed psychiatric medications or not did not affect vaccination status.
The majority of patients in this study (84.5%) accepted and received at least one dose of the COVID-19 vaccine. This is similar to rates reported for the United States population in those 18 years of age and older (88.6%) and in Missouri (77%) at the time this research study was conducted.4 Noteworthy, the 19 patients excluded due to lack of documentation of a direct, individual offer were likely refusals as the facility procedure was to offer the COVID-19 vaccine to all eligible patients. Therefore, our refusal rate could have been slightly higher than what our original data shows.
There are 4 key factors to consider when offering the COVID-19 vaccine to this patient population. The first factor is competency status. Given that patients in this study who were deemed IST were less likely to accept the COVID-19 vaccine, re-approaching patients after they have been deemed otherwise may improve vaccination rates. Although the reason for this difference is not clear, it would be reasonable to assume that patients who restored to competency may have greater insight for healthcare decisions or possibly begin preparing for discharge and exposure in the community setting. Prior to discharge the patient may realize it would be easier for them to get the vaccine before going out into the community so there is minimal harm to re-approaching the patient at this time point to potentially increase vaccination rates.
A second factor to take into consideration is comorbid conditions. Patients with certain comorbid conditions are at a higher risk for a more severe COVID-19 infection.5 Patients in our study who were vaccinated averaged a higher number of comorbid conditions than those who were not vaccinated. Patients with a higher number of comorbid conditions may receive more opportunities during various interactions with the healthcare system for more education regarding the benefits of vaccines which could increase acceptance rates. Additionally, these patients may be targeted with greater intensity from providers to encourage vaccination due to the known increased risk for more serious illness with a COVID-19 infection. Patients with comorbid conditions may also be more accustomed to accepting care recommended by providers that those that have less reason to interact with the healthcare workers due to fewer medical conditions. To our knowledge there is no supporting evidence for these findings and speculations. However, these speculations could help guide future theories and studies.
Third, involuntary medication orders should be taken into consideration. In this current study, patients who were not on an involuntary medication order were more likely to accept vaccination. Patients who are on involuntary medications are likely not psychiatrically stable which may interfere with their ability to make decisions regarding preventive care. There is no supporting evidence to our knowledge to support this assumption. However, due to involuntary medications being a standardized court process for those who are considered to have gross neglect of their own healthcare needs, it would be reasonable to re-approach patients’ multiple times during their care or at a later time after their IST status has been resolved to improve vaccination rates.
Lastly, studies have found that incentives increase vaccination rates.13 In this study, when incentives were offered to patients, the vaccination rates increased as well. Incentives were offered in one of the forensic facilities, which included a personal pizza and a can of soda. Given the risk of COVID-19 infections and the increased spread when living in congregate living areas, providing incentives may be beneficial when offering vaccines to patients in these populations.
The findings of this study illustrate the factors that may play a role in vaccination status in a forensic population, which have not been addressed in other studies to our knowledge. One strength of this study is that there is little data in this area of research. Therefore, this study may add insight into considerations for discussing vaccines with patients. Another strength is that the two different sites in this study had a slightly different patient population, which may increase external validity. There were several limitations to this study. The first being that given the retrospective nature of the study, certain factors for acceptance were not able to be collected if patients received the COVID-19 vaccine prior to admission, which may have led to information bias. These factors include what the competency status was at the time of vaccination, if patient had an order for involuntary medication or not, and what the patient’s weight and BMI were at the time of vaccination. Another being that documentation of the vaccine being offered was not complete at either site, which ultimately led to an increase in exclusions from the study. Further research is needed to validate these findings. Research that includes patient and prescriber surveys should be done to gather more insight into what factors go into a patient’s decision and what education the physician is providing.
Overall, vaccination rates in this patient population were similar to the national average and above state average. Patient specific factors should be considered when educating and offering vaccines to patients in an inpatient forensic psychiatric unit. Awareness of these results can facilitate targeted interventions for optimal care in a psychiatric population.
ORCID iD
Shelby E. Lang https://orcid.org/0000-0003-4511-1735
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.
Ethical Approval: The project was reviewed and approved by the University of Missouri-Kansas City Institutional Review Board, Northwest Missouri Psychiatric Rehabilitation Center (NMPRC) Hospital Research Committee, Center for Behavioral Medicine (CBM) Research Committee, and State of Missouri Department of Mental Health Professional Review Committee.
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References
1 Office of the Commissioner. FDA takes key action in fight against COVID-19 by issuing emergency use authorization for first COVID-19 vaccine. FDA. https://www.fda.gov/news-events/press-announcements/fda-takes-key-action-fight-against-covid-19-issuing-emergency-use-authorization-first-covid-19. Published December 11, 2020. Accessed September 1, 2021.
2 Office of the Commissioner. FDA takes additional action in fight against COVID-19 by issuing emergency use authorization for second COVID-19 vaccine. FDA. https://www.fda.gov/news-events/press-announcements/fda-takes-additional-action-fight-against-covid-19-issuing-emergency-use-authorization-second-covid. Published December 18, 2020. Accessed September 1, 2021.
3 COVID CDC. Data tracker. centers for disease control and prevention. https://covid.cdc.gov/covid-data-tracker. Published March 28, 2020. Accessed April 12, 2022.
4 Mazereel V Vanbrabant T Desplenter F De Hert M . COVID-19 vaccine uptake in patients with psychiatric disorders admitted to or residing in a university psychiatric hospital. Lancet Psychiatry. 2021;8 (10 ):860–861. doi:10.1016/S2215-0366(21)00301-1.
5 CDC. COVID-19 and Your Health. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/people-with-medical-conditions.html. Published February 11, 2020. Accessed September 19, 2021.
6 Houtven CHV Boucher NA Dawson WD . Impact of the COVID-19 outbreak on long-term care in the united states. Resources to support community and institutional long-term care responses to COVID-19. Published April 24, 2020. Accessed September 1, 2021. https://ltccovid.org/2020/04/24/impact-of-the-covid-19-outbreak-on-long-term-care-in-the-united-states/
7 Dooling K Marin M Wallace M , et al. The advisory committee on immunization practices’ updated interim recommendation for allocation of COVID-19 vaccine-United States, December 2020. Published January 1, 2021. Accessed August 15, 2022.
8 Wang Q Xu R Volkow ND . Increased risk of COVID-19 infection and mortality in people with mental disorders: analysis from electronic health records in the United States. World Psychiatry. 2021;20 (1 ):124–130. doi:10.1002/wps.20806.33026219
9 Roesch R Zapf R Golding S Skeem J . Defining and assessing competency to stand trial. The United States Department of Justice. https://www.justice.gov/sites/default/files/eoir/legacy/2014/08/15/Defining_and_Assessing_Competency_to_Stand_Trial.pdf. Accessed June 10, 2022.
10 Department Operating Regulation 4.152 Administration of Psychotropic Medications Involuntarily. Department operating regulation 4.152 administration of psychotropic medications involuntarily|dmh.mo.gov. https://dmh.mo.gov/media/pdf/department-operating-regulation-4152-administration-psychotropic-medications. Accessed June 10, 2022.
11 Cournos F McKinnon K Stanley B . Outcome of involuntary medication in a state hospital system. Am J Psychiatry. 1991;148 (4 ):489–494. doi:10.1176/ajp.148.4.489.1672485
12 Harris PA Taylor R Thielke R Payne J Gonzalez N Conde JG . Research electronic data capture (REDCap)—A metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42 (2 ):377–381. doi:10.1016/j.jbi.2008.08.010.18929686
13 Brewer N Buttenheim A Clinton C , et al. Incentives for COVID-19 vaccination. The Lancet Regional Health - Americas. 2022;8 :1–3. doi:10.1016/j.lana.2022.100205.
| 36470704 | PMC9729714 | NO-CC CODE | 2022-12-14 23:22:30 | no | Int J Psychiatry Med. 2022 Dec 5;:00912174221144128 | utf-8 | Int J Psychiatry Med | 2,022 | 10.1177/00912174221144128 | oa_other |
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J Appl Gerontol
J Appl Gerontol
spjag
JAG
Journal of Applied Gerontology
0733-4648
1552-4523
SAGE Publications Sage CA: Los Angeles, CA
36471575
10.1177_07334648221143604
10.1177/07334648221143604
Special Issue
“Anything that Benefits the Workers Should Benefit the Client”: Opportunities and Constraints in Self-Directed Care During the COVID-19 Pandemic
https://orcid.org/0000-0001-7221-5674
Wendel Carrie L. 1
LaPierre Tracey A. 2
Sullivan Darcy L. 2
Babitzke Jennifer 2
Swartzendruber Lora 1
Barta Tobi 1
Olds Danielle M. 3
1 School of Social Welfare , 370371 University of Kansas , Lawrence, KS, USA
2 Department of Sociology, University of Kansas , Lawrence, KS, USA
3 St Lukes Hospital , Kansas City, MO, USA
Carrie L. Wendel, School of Social Welfare, University of Kansas, Twente Hall, 1545 Lilac Ln, Lawrence, KS 66045, USA. Email: [email protected]
5 12 2022
5 12 2022
073346482211436044 5 2022
15 11 2022
17 11 2022
© The Author(s) 2022
2022
Southern Gerontological Society
This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.
Self-directed care (SDC) models allow Home and Community Based Services (HCBS) consumers to direct their own care, thus supporting flexible, person-centered care. There are many benefits to the SDC model but access to resources is essential to successful outcomes. Considering the autonomy and flexibility associated with SDC, it is important to understand how SDC responded to the COVID-19 pandemic and the resources available to help manage this situation. We conducted 54 in-depth interviews with HCBS consumers, direct support workers, family caregivers, and providers to examine the impact of COVID-19 on HCBS services in Kansas. Findings illuminate how self-directed consumers carried a lot of employer responsibility, with limited resources and systemic barriers constraining self-determination and contributing to unmet care needs, stress, and burden. Policy flexibilities expanding the hiring of family members were beneficial but insufficient to address under-resourced working conditions and labor shortages that were exacerbated by the pandemic.
home and community based care and services
consumer-directed care
COVID-19
policy
qualitative methods
Agency for Healthcare Research and Quality https://doi.org/10.13039/100000133 1R01HS028172-01 edited-statecorrected-proof
typesetterts10
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pmc What this study adds
• Explores the impact of the COVID-19 pandemic on self-directed care (SDC)
• Examines SDC from multiple stakeholder perspectives, including consumers, workers, family caregivers, and providers
• Highlights the challenges of SDC within a specific policy context: a state with managed Medicaid and an employer-authority only model of SDC
• Demonstrates how low pay and poor benefits of DSWs undermines the self-determination, health, and safety of SDC consumers
Applications of study findings
• Recommends permanent adoption of policies allowing self-directed consumers to hire parents, spouses, and guardians
• Suggests that a budget-authority model would provide self-directed consumers and their family caregivers with additional tools and resources for better managing care and safety needs during the pandemic
• Highlights need for emergency funding that is proactively tailored to meet the unique employer model of SDC
Introduction
Self-directed care (SDC) models were developed to give home and community based service (HCBS) consumers more authority, self-determination, and flexibility in meeting their individual long-term services and supports (LTSS) needs. Once an alternative model that operated as a demonstration program, SDC is now common in HCBS and encouraged by Centers for Medicare and Medicaid Services (Bradley et al., 2021, Ujvari, 2020). This model allows care recipients to select, train, manage, and dismiss their own workers. SDC is linked to greater satisfaction with the paid caregiver and care-recipient relationship than found in the agency-based model and is associated with fewer unmet care needs (Bradley et al., 2021). Despite the benefits of this program, there are also challenges, specifically in managing bureaucratic requirements, recruiting and training workers with the appropriate skillset, and managing SDC hours and budget (Mahoney et al., 2019).
It is unclear how SDC consumers, workers, and family caregivers fared during the COVID-19 pandemic, especially given the high degree of autonomy in SDC, and the unprecedented, rapidly changing nature of the pandemic. Drawing on rich, in-depth interviews with a variety of stakeholders, this paper examines how opportunities and constraints during the COVID-19 pandemic shaped worker, caregiver, and consumer experiences in the SDC model in Kansas.
HCBS and SDC in Kansas
States have a fair amount of flexibility in implementing Medicaid, resulting in wide variation across HCBS programs. SDC models were adopted in Kansas in 1989 and are available to all HCBS consumers. SDC consumers select a Financial Management Services (FMS) provider to manage payroll and for information and assistance. In Kansas, consumers have employer-authority but not budget-authority; that is, SDC consumers manage their own workers but do not have control over an allotted budget for other goods and services. This contrasts with the majority (75%) of SDC programs in the U.S. that allow for budget-authority (Ujvari, 2020). Kansas was one of the first states to privatize all LTSS in 2013, utilizing managed care companies (MCOs) in a program called KanCare. At least 22 states now operate managed LTSS programs (Advancing States, 2021). Self-directed care consumers hire workers based on the number of care hours awarded by their Managed Care Organization (MCO) care coordinator, rather than a budget, and therefore have little control over worker wages. The state legislature sets the Medicaid reimbursement rates that determine worker wages, and only MCOs have the authority to exceed these wages.
Approximately 8500 HCBS consumers (39%) in Kansas self-direct their care (M. Heydon, Kansas HCBS Director, personal communication, 8/29/22). Kansans can hire some relatives but not spouses, guardians, or parents of minor children, unless a court-approved exception has been granted; although as will be further detailed below, this restriction was waived during the pandemic. Nationwide, a slight majority of SDC programs have similar hiring restrictions regarding relatives (Ujvari et al., 2020).
Those hired through the SDC model are part of the direct support workforce; data often do not distinguish between those in the SDC, agency-based, or residential care models, or use consistent terminology to distinguish these different subtypes. We use Direct Support Workers (DSWs) to refer to the HCBS workforce more broadly, and distinguish between SDC workers and agency-based workers whenever possible. Approximately 25,000 Kansans are employed as DSWs; this is about half the number of DSWs per HCBS consumer compared to the U.S. average (KDADS, 2021).
Low wages and poor benefits, as well as the devaluing of direct service work, have contributed to an ongoing nationwide shortage of DSWs (Spetz et al., 2019). Approximately half of DSWs in the U.S. have incomes below the federal poverty level and many rely on public assistance to meet their needs (Scales, 2020). This problem is particularly acute in Kansas, where the median DSW hourly wage of $11.30 is lower than the national average and other Midwestern states (KDADS, 2021). Data are not available in Kansas to compare wages of agency-based and self-directed DSWs, but reimbursement rates are comparable. Benefits and paid leave are not available to SDC workers in Kansas, and Kansas remains one of 12 states that have not expanded Medicaid. Low reimbursement rates also do not build in sufficient overhead to support overtime pay.
Home Care during the COVID-19 Pandemic
The limited research available on how the COVID-19 pandemic impacted home care has focused primarily on agency-based care. At the beginning of the pandemic, homecare agency (HCA) leadership had to influence, interpret, and implement state policies, while agency-based workers had to balance job duties, agency policies, and daily reality of providing care in the home (Markkanen et al., 2021). In many instances, agency-based clients and workers reported feeling at increased risk for COVID-19 infection due to workers visiting multiple client homes (Markkanen et al., 2021). SDC was not immune to these concerns, with evidence of some SDC workers electing to stay home to protect themselves or being fired for not adhering to the safety protocols required by their client/employer (Caldwell et al., 2022). Safety considerations are important given that HCBS consumers are at increased risk of adverse COVID-19 outcomes due to their age, health conditions, or disabilities. Many DSWs are also at high-risk from COVID-19 infections, with more than a quarter of the workforce over age 55 and high rates of being uninsured (Scales, 2020). Overall, Personal Protective Equipment (PPE) was more difficult to distribute and manage in home care settings (Kerley et al., 2021; Markkanen et al., 2021), and many SDC consumers have reported out-of-pocket costs in securing PPE (Caldwell et al., 2022).
Methods
The qualitative data for this paper are part of a larger, multi-disciplinary, mixed-methods study broadly examining the HCBS system response to the pandemic. This study was approved by the University of Kansas Human Research Protection Program (# STUDY00146397). Community engagement was a central component of this research. HCBS consumer, DSW, family caregiver, HCA provider, FMS provider, and advocacy group representatives were recruited via aging and disability networks to a Stakeholder Advisory Board (SAB) that was engaged in all phases of the research process. The 12-member SAB met via Zoom bi-weekly, monthly, or quarterly depending on the stage of the research process, collaborating on research questions, data collection instruments, participant recruitment, interpretation of results, and dissemination. A funded community partner also participated in weekly research team meetings.
Data come from 54 in-depth interviews with 59 HCBS stakeholders in Kansas, conducted via phone or Zoom between March 2021 and August 2022. Sixty-six percent of the interviews were with stakeholders directly engaged in SDC. Respondents included HCBS consumers, DSWs, family caregivers, and HCA and FMS providers. All HCBS waivers serving adults in Kansas were represented in the study, which include the Frail Elderly (FE), Physical Disability (PD), Brain Injury (BI), and Intellectual and Developmental Disability (IDD) waivers. To ensure access, support persons or interpreters were present as needed to facilitate communication but not counted as a respondent. Family members of individuals with cognitive impairment were interviewed as family caregivers, not as proxies. Provider interviews sometimes included more than one respondent, to include people equally involved in the pandemic response or in charge of different divisions in larger agencies. A breakdown of the HCBS role (consumer, non-family DSW, paid family caregiver, non-paid family caregiver, or service provider), waiver type (BI, FE, IDD, or PD), care model (SDC, HCA, or both), and primary region (metro, non-metro, or mix) of respondents can be found in Table 1. Additional demographic characteristics of consumers, DSWs, and caregivers can be found in Table 2. Despite targeted efforts to recruit minorities, respondents in these roles were predominantly non-Hispanic white and female.Table 1. Program Characteristics Associated with Each Stakeholder Interview by HCBS Role (n = 54).
n Waiver typea Care model Primary region
BI FE IDD PD SDC HCA Both Metro Non-metro Mix
Consumers 20 3 2 5 10 14 3 3 14 6 n/a
DSWs (non-family)b 10 1 5 4 8 7 3 0 8 2 n/a
Paid family caregiversc 8 0 1 7 0 7 1 0 7 1 n/a
Unpaid family caregiversb 5 1 1 3 2 5 0 0 1 4 n/a
Service providers 12 9 9 8 9 3 7 2 6 5 1
a6 DSWs, 2 unpaid family Caregivers, and 9 providers support individuals on different waivers, and therefore, these subtotals are greater than sample size. BI: Brain injury, FE: Frail elderly, IDD: Intellectual and Developmental Disability, PD: Physical Disability.
bOne respondent was both a self-directed PD consumer and a family caregiver to a self-directed PD consumer and represented as such in each category. DSW: Direct Support Worker
c 4 family caregivers were paid as workers under the Appendix K exception; 2 were paid under pre-existing rules; 2 were paid under the adult foster care model.
Table 2. Consumer, Worker, and Caregiver Demographic Characteristics.
n Gender Ethnicity Race Age
F M Hispanic White Black Asian Native Range Mean
Consumersa 20 17 3 3 14 4 0 2 24–81 50
DSWsb 10 8 2 1 7 2 1 0 21–72 45
Paid family caregivers 8 7 1 1 8 0 0 0 50–70 59
Unpaid family caregiversa 5 5 0 1 5 0 0 0 46–69 55
Totals 42b 36 6 5 33 6 1 2 21–81 51
aOne respondent was both a consumer and a family caregiver (middle-aged, Hispanic, white female), and is included in both categories but not double-counted in totals.
bDSWs: Direct Support Workers.
Semi-structured interview guides were developed and tailored for each participant group in collaboration with the SAB. A SAB member with cognitive impairments helped refine the consumer instruments for accessibility and recruited peers for pilot testing. Respondents were asked questions about their homecare experiences during the pandemic, including care and safety practices and, where applicable, how the pandemic impacted care needs, care satisfaction, work conditions, and job satisfaction. Respondents were recruited through community partners, social media, and snowball sampling. Inclusion criteria included: 1) involvement with HCBS services in Kansas during the pandemic; 2) age 18 or older; and 3) ability to communicate in English or Spanish. Respondents were offered $25 for participation. Interviews lasted between 20 and 150 minutes (median length 84 minutes). Interviews were recorded and transcribed verbatim. All respondents provided verbal informed consent.
Data collection and analysis occurred simultaneously, with interview guides being refined in response to ongoing analysis (Merriam & Tisdell, 2015). Data were coded and analyzed by the authors using an inductive, team-based, iterative, negotiated, consensus-based process (Cascio et al., 2019), facilitated by Dedoose software. Team members immersed themselves in the data through narrative summaries of each interview and by reading the transcripts in their entirety. First-level open coding of the transcripts was conducted separately by four members of the research team. These initial codes were discussed by the entire research team and coding discrepancies and challenges were resolved through discussion, producing the initial codebook that included descriptions and examples of each code. The codebook was iteratively refined during weekly team meetings as new transcripts were coded.
The analysis presented here was guided by the research question: How did opportunities and constraints during the pandemic shape worker, caregiver, and consumer experiences in the self-directed model in Kansas? Using a constant comparative method, codes were grouped into broader categories (axial coding), themes across categories were identified, and linkages between themes were explored to fully answer the research question (Merriam & Tisdell, 2015). Team members constantly revisited the data to confirm, reject, or refine themes and linkages. Findings and policy implications were reviewed and discussed with the SAB. All authors read the results and substantiated their consistency with the data.
Overall, these methods incorporated multiple strategies identified to enhance credibility and dependability in qualitative research, including engaging multiple researchers, respondent validation, triangulation, and using verbatim quotes to support findings (Noble & Smith, 2015). Additional details can be found in the supplemental COREQ checklist.
Results
This paper focuses on four key interwoven themes that influenced outcomes for SDC consumers and workers: 1. Pre-existing workforce shortages were exacerbated by the pandemic; 2. Self-directed consumers assume a lot of responsibility as employers with limited resources; 3. Appendix K flexibilities demonstrated potential to alleviate unmet care needs; and 4. Structural constraints had adverse impacts on caregiver, worker, and consumer outcomes. We begin by describing these themes and their subthemes in more detail (see Table 3 for a summary). We further elaborated on how these themes are interconnected in complex and reinforcing ways, as demonstrated in Figure 1.Table 3. Themes and Subthemes.
Theme I: Pre-existing workforce shortages were exacerbated by the pandemic
Subthemes Illustrative Quotes
Some workers left due to COVID-19 safety concerns The reasons that were being given were not ones I’d had before as for why they were quitting when they would give me a reason. A few of them would list fear. That wasn’t a reason I had ever heard of before that they were afraid. (Consumer on Physical Disability (PD) waiver)
Direct Support Worker (DSW) wages remained stagnant and could not compete with rising wages in other sectors and more generous unemployment benefits I talked to [a client] today whose worker went to Pizza Hut because they can make $14 an hour driving delivery …. There’s a ton of really good workers out there who want to do this kind of work, they have a passion to do this kind of work, but they can’t afford to do it and support their family. (Rural Financial Management Systems (FMS) provider)
Self-directed workers did not have access to insurance or paid leave, which became more important during pandemic to help mitigate new risks They lost out at pay wages [when out sick with COVID-19]…. I really was sad about that because—I mean, this was a state-of-emergency situation, and they couldn’t get paid for having this illness that was all over the world…. That was very hard on them ‘cause they rely on the little bit of pay they get. (Self-Directed Care (SDC) Consumer on PD waiver)
Theme II: Self-directed consumers assume a lot of responsibility as employers with limited resources
Subthemes Illustrative Quotes
SDC consumers and workers were not able to benefit from the pooling of organizational resources like home care agencies The self-directed model is great, but it’s pushing a lot of responsibilities on people without the same resources in regards to things like being able to offer benefits and hazard pay and things like that. (Rural FMS provider)
CARES funds were not accessible to SDC consumers to increase wages or offer benefits I mean, if I could’ve done it (enhance wages with CARES funds), I would’ve done it in a heartbeat. I didn’t wanna risk recoupment when I’d already been told that it’s not really what the money is intended for…. I would’ve loved if somebody’d came to me and said, “Hey, we found a way to do this for our direct-service care attendants.” I just think about all the meetings that I have been in with Centers for Independent Living, the KanCare Advocates Network group, the state of Kansas, Administration on Community Living at a federal level. Nobody could give us that information or no one was willing to say, “Hey, take a chance with this money. You’ll be okay.” (Urban FMS provider)
Many SDC consumers- and workers incurred out-of-pocket expenses (e.g., PPE, hiring expenses) Self-directed folks are Medicaid recipients on social security… Even though they’re the employer, they don’t have the funds to buy me PPE. (SDC worker for consumer on Intellectual and Developmental Disability (IDD) waiver)
Theme III: Appendix K flexibilities demonstrated potential to alleviate unmet care needs
Subthemes Illustrative Quotes
Appendix K flexibilities that allowed additional types of family members to be hired helped fill care gaps I certainly think that family members ought to be able to provide some services. We’re very rural… out in the middle of nowhere, and without family I don’t know how they [would] live… that family member would be working somewhere else if they [did not get paid as a caregiver]. (Rural FMS agency)
Paid family members reinvested their wages into consumer care What I can say, the silver lining in all of the pandemic… being a paid caregiver has given us some financial stability that we did not have. I’m going be able to open an ABLE account [for qualified disability expenses] for my son and actually put some money in there…. And I’ve been able to pay off some debt and that’s been huge. (Paid family caregiver to son on IDD waiver)
Appendix K flexibilities were surrounded by confusion and not always well communicated I brought it up to our [MCO care coordinator]. I reached out to her and said, ‘Why can I not get paid, because I’m having to take vacation days on days that I cannot balance my work and life balance here.’ And she’s like, ‘Nope, you can’t.’ And she said she would pass it along to her boss, but I never heard back from her. (Unpaid family caregiver to son on Brain Injury (BI) waiver)
Hiring family members was not possible or ideal for everyone Those family members really shouldn’t be doing my care. They have their own health issues. It’s just we can’t find [outside] help…due to the pandemic. (SDC consumer on PD waiver)
Theme IV: Structural constraints had adverse impacts on caregiver, worker, and consumer outcomes
Caregiver and worker burden increased in response to pandemic conditions and workforce shortages I can’t emphasize this enough, who’s going to take care of the caregivers? So that’s why those sick days and vacation time, that’s really important. If you want your client to get top quality care, we need care too. Because, like I say, I got underlying issues too …. I had enough strength to do what I could do in my client’s house, but when I came home and was like I’m just here to sleep and rest… I was neglecting me. (SDC worker for frail elderly (FE) and PD consumers who do not have backup caregivers)
Consumer self-determination is limited by workforce shortages and the pandemic We have one [consumer] currently that is pretty insistent that their worker should be vaccinated. The worker doesn’t really want to get vaccinated, but they want to keep the worker… people have kept workers that they really didn’t want to keep because they couldn’t find anybody else. (Rural FMS provider)
Workforce shortages led to unmet care needs There’s the increased depression and anxiety that I have, skin breakdowns and pressure sores, and those kind of things I didn’t really have to deal with that much before…. Now, it’s pretty much constant. I got a big area on my lower leg that’s not healing. Since I’m diabetic, it scares me. (SDC consumer on PD waiver who could not find workers capable of meeting her transfer/repositioning needs)
Figure 1. Interrelationships across themes and subthemes.
Pre-existing Workforce Shortages were Exacerbated by the Pandemic
The direct service workforce shortage was the most consistently cited challenge across all stakeholder groups. While this issue pre-dates the pandemic, respondents widely agreed that COVID-19 exacerbated the problem. One urban FMS provider described COVID-19 as “the straw that broke the camel’s back… it made people more concerned about doing that kind of work, but we already had widespread state shortages long before COVID.” Some SDC consumers reported workers citing safety as a reason for quitting, noting that this was a new reason they had not heard pre-pandemic. Additionally, some family caregivers paused formal care services during the early days of the pandemic due to safety fears, but when they were comfortable resuming care, they often found it difficult to bring their workers back or find replacement workers.
Participants widely agreed that low wages and lack of benefits drove workforce shortages. Low wages became an even greater obstacle during the pandemic as the wages in other entry-level job sectors rose in response to broader workforce shortages, while Medicaid reimbursement rates remained stagnant, as demonstrated in the following quote:I talked to [a client] today whose worker went to Pizza Hut because they can make $14 an hour driving delivery …. There’s a ton of really good workers out there who want to do this kind of work, they have a passion to do this kind of work, but they can’t afford to do it and support their family. (Rural FMS provider)
Self-directed care worker wages were often lower than the enhanced unemployment benefit offered early in the pandemic. A caregiver trying to hire a worker for her adult son with a brain injury described, “We had one gal that … needed full-time work. We offered her a full-time job, and then she realized that she could stay home with her kids and get more money on unemployment.”
According to both workers and consumers, the pandemic directly contributed to more missed days of work due to illness, quarantine, and childcare demands related to school closures. SDC workers in Kansas were particularly vulnerable in this regard as they do not receive any job benefits such as health insurance, hazard pay, or paid leave, benefits that were seen as vital to navigating the COVID-19 pandemic.I feel one of the basic needs, because our pay is so low, and we do so much, and we’re in a higher health risk than some other people, they need to start giving us healthcare workers a special credit or something for health insurance or offer it at least. (58-year-old, uninsured SDC worker).
Self-directed care consumers noted the importance of their workers monitoring for symptoms and following quarantine protocols, especially since they were at increased risk of adverse COVID-19 outcomes. Yet they also recognized the difficult position this puts their workers in when they cannot provide sick pay:They lost out at pay wages…. I really was sad about that because—I mean, this was a state-of-emergency situation, and they couldn’t get paid for having this illness that was all over the world…. That was very hard on them ‘cause they rely on the little bit of pay they get. (SDC Consumer on PD waiver)
Further, due to the worker shortages, SDC consumers often did not have backup care while their workers were out.
Self-directed Consumers Assume a Lot of Responsibility as Employers with Limited Resources
As employers, SDC consumers have full responsibility for recruiting, hiring, and training workers to fill the hours of care they have been awarded. In Kansas, associated costs for job advertising, required background checks, and training must be paid for out-of-pocket. SDC consumers wanted their workers to receive higher pay and benefits but had no control over wages set by state reimbursement rates and no way of offering benefits such as health insurance or paid time off.
HCAs also reported the importance of higher wages to attract and retain good workers and struggling to provide better pay and benefits to their workers given low Medicaid reimbursement rates. However, some HCAs described drawing on other sources to help subsidize these low rates. One urban HCA that pays their workers above reimbursement rates notes, “We do the best we can, but a lot of that is from donations and fundraising.” Additional resources mentioned that were used to enhance agency-based worker pay and benefits included local county funding, higher-paying private-pay clients, and revenue from more profitable departments. SDC consumers did not have access to similar organizational resources. As noted by a rural FMS provider, “The self-directed model is great, but it’s pushing a lot of responsibilities on people without the same resources in regard to things like being able to offer benefits and hazard pay and things like that.”
CARES (Coronavirus Aid, Relief, and Economic Security Act of 2020) funds were designed to provide resources to enhance worker wellbeing and safety, as well as to stabilize the economy. While nursing homes received direct CARES funding at both the state and federal level to support the workforce, many HCBS providers expressed frustration that they had to apply for funding:The response to the different sectors of long-term care was very inconsistent…There were things done for institutional care, skilled nursing, and in assisted living. But the part of the long-term care spectrum in homecare did not get the same attention or action, and that’s what’s frustrating. (Combined agency and FMS provider)
HCAs described devoting a lot of resources to applying for funding through various sources with different strings attached. Although this was difficult, some HCAs were successful in accessing these funds for hazard pay, sick pay, or additional overtime for their agency-based workers.
However, FMS providers and SDC consumers did not have any access to CARES funds for these purposes; FMS providers were excluded because they were not the employer and there was no viable pathway for SDC consumers to apply for CARES funds as individual employers. Further, some FMS providers reported being instructed by government administrators that CARES funds could be recouped if used to provide hazard pay to SDC workers, as there was no evidence that workers faced increased risk of catching COVID-19 as long as they had access to PPE. An urban FMS provider spoke of her inability to find a way to enhance worker pay with CARES funds despite her efforts:I mean, if I could’ve done it, I would’ve done it in a heartbeat. I didn’t wanna risk recoupment when I’d already been told that it’s not really what the money is intended for…. I would’ve loved if somebody’d came to me and said, “Hey, we found a way to do this for our [SDC workers].” I just think about all the meetings that I have been in with Centers for Independent Living, the KanCare Advocates Network group, the state of Kansas, Administration on Community Living at a federal level. Nobody could give us that information or no one was willing to say, “Hey, take a chance with this money. You’ll be okay.” (Urban FMS provider)
Thus, in contrast to some agencies, SDC consumers were not able to offer higher wages to compete with growing wages in other sectors, paid leave to support workers in quarantine, or overtime to allow fewer workers to cover more care hours.
Accessing CARES funds for PPE was more successful, as the state drew on these funds to provide all HCBS providers, including FMS providers, a PPE budget. This was widely cited as beneficial across stakeholder groups, and FMS providers felt successful in distributing these supplies to SDC consumers. However, this did not completely cover PPE expenses for SDC consumers and workers; for example, before these funds were available or when the supplies provided were insufficient for those with higher care needs. A PD consumer spoke of how helpful it was to get supplies dropped off by her FMS provider, but they did not adequately cover her nearly around-the-clock care. She noted, "It’s better now, but I can’t afford a lot of it.” Optimal access to PPE in SDC was therefore constrained by insufficient personal resources to cover out-of-pocket costs. As one experienced worker for IDD consumers noted “self-directed folks are Medicaid recipients on social security… Even though they’re the employer, they don’t have the funds to buy me PPE.” SDC workers were sometimes hesitant to ask their low-income employers for these supplies and often paid for this out of their own pocket, but as low-wage workers this was also challenging for them.
Appendix K Flexibilities Demonstrated Potential to Alleviate Unmet Care Needs
One policy that was widely cited by FMS provider, SDC consumers, and family caregivers as beneficial was the new opportunity to hire additional types of family members as workers. This flexibility was granted through Appendix K, whereby the federal government allows states in emergency situations to adjust HCBS rules, regulations, or rates. These are designed as temporary measures to avoid interruptions and delays in home-based care and are only in place during the emergency and post-emergency transition period (CMS, 2022). A key flexibility implemented in Kansas was to relax the restrictions on who could be paid as a DSW by allowing typically excluded family members such as parents, spouses, and guardians, as well as workers aged 16–17. This policy response was particularly well-suited for the SDC model.
In the face of workforce shortages, the ability to hire those previously ineligible to serve as paid workers helped fill care gaps. This was especially important for rural consumers and their caregivers who lived in regions without access to agency-based services, as reported by a rural FMS provider serving nearly 50 consumers who were able to hire family members under this Appendix K flexibility:I certainly think that family members ought to be able to provide some services. We’re very rural… out in the middle of nowhere, and without family I don’t know how they [would] live… that family member would be working somewhere else if they [did not get paid as a caregiver] (Rural FMS agency).
This income not only provided much-needed financial support to low-income families, family caregivers who were now getting paid often reinvested their income into the consumer’s care needs.What I can say, the silver lining in all of the pandemic… being a paid caregiver has given us some financial stability that we did not have. I'm going be able to open an ABLE account [for qualified disability expenses] for my son and actually put some money in there…. And I’ve been able to pay off some debt and that's been huge. (Urban caregiver to son with IDD)
Significant concern was expressed across stakeholder groups about what will happen to these consumers and their paid family caregivers when this Appendix K flexibility expires (6 months after the federal emergency ends).
Of additional concern, Appendix K flexibilities were not well communicated and confusion over eligibility and implementation limited their impact for others. Whereas providers generally knew to consult the state website for policy updates and also kept each other informed through professional networks, SDC consumers and their family caregivers found out about this policy flexibility more haphazardly or received mixed information. For example, a mother of an adult son was told that she was not eligible to be paid under this policy.I brought it up to our [MCO care coordinator]. I reached out to her and said, “Why can I not get paid, because I’m having to take vacation days on days that I cannot balance my work and life balance here.” And she’s like, “Nope, you can’t. And she said she would pass it along to her boss, but I never heard back from her.” (Rural caregiver to son with BI)
Another caregiver was informed by her MCO care coordinator that her adult child risked being removed from the HCBS program if they did not find a worker soon but was not informed that she could be hired as the worker under this new flexibility.
It should also be noted that while this flexibility was an ideal solution for many, it does not solve the workforce shortage issue. FMS providers shared that some SDC consumers prefer an unrelated worker, particularly for intimate care tasks such as bathing and toileting. Others did not have family members available for this care, or their family members did not have the skill set or strength to perform the needed tasks, as noted by a consumer-employer with complex medical conditions, “Those family members really shouldn’t be doing my care. They have their own health issues. It’s just we can’t find [outside] help…due to the pandemic.” Additionally, many providers we interviewed were disappointed that the state did not take advantage of all the allowed Appendix K flexibilities they believed could have helped alleviate workforce shortages; notably, the state did not increase reimbursement rates.
Structural Constraints had Adverse Impacts on caregiver, Worker, and Consumer Outcomes
The growing workforce shortage combined with inadequate resources to address this issue had adverse impacts on caregivers, workers, and consumers. SDC workers and family caregivers widely reported increased burden and stress during the pandemic. Their caregiving responsibilities increased in response to pandemic conditions and they had fewer workers with whom to share caregiving duties. In the face of workforce shortages, it was difficult for family caregivers to find respite and SDC workers often felt they could not take time off because their care recipients would go without care. Both groups reported putting their care recipient’s needs before their own needs.I can’t emphasize this enough, who’s going to take care of the caregivers? So that’s why those sick days and vacation time, that’s really important. If you want your client to get top quality care, we need care too. Because, like I say, I got underlying issues too …. I had enough strength to do what I could do in my client’s house, but when I came home and was like I’m just here to sleep and rest… I was neglecting me. (SDC worker for FE and PD consumers)
As a result, SDC workers and caregivers felt both their mental and physical health suffered.
The advantages of SDC model are premised on increased choice and self-determination in directing one’s own care, but this was constrained by workforce shortages. Many SDC consumers ended up in the SDC model not by choice, but rather because agency-based care was not available. Some consumers spoke of being dropped by HCAs or not being able to find an agency accepting new HCBS clients.I looked on my own [and] couldn’t find anyone so I called my [MCO care coordinator] and she was looking for agencies… the agencies here … they don't want to take people. (SDC consumer on BI waiver)
This issue grew worse during the pandemic according to both providers and consumers. For example, a large provider reported having to move over 50 clients from their agency side to their FMS side in the first year of the pandemic because of lack of workers, observing, “It becomes a necessity. It really doesn’t have much to do with choice.” HCAs also reported limiting the number of Medicaid clients they accept due to low reimbursement rates. The challenges associated with workforce shortages and low reimbursement rates that home care agencies were dealing with were passed on to consumers who were left with no choice but to self-direct and with fewer resources to manage this issue, as previously discussed.
Self-directed care consumers and caregivers noted they had limited options in finding workers best suited for their care needs, especially when they could not find a worker at all. This is another issue that existed well before the pandemic but took on new meaning with additional COVID-19 safety practices for workers. Most SDC consumers and workers we interviewed were satisfied with their safety protocols, making calculated decisions that were often shared among care team members and flexible by taking into account personal and community level risk factors as well as the relationship and level of trust between consumers and workers—this exemplifies the advantages of self-direction. However, when there was not agreement, the workforce shortage limited the ability of SDC consumers to implement their preferred safety practices.We have one [consumer] currently that is pretty insistent that their worker should be vaccinated. The worker doesn’t really want to get vaccinated, but they want to keep the worker…. people have kept workers that they really didn’t want to keep because they couldn’t find anybody else. (Rural FMS provider)
A rural caregiver to two adults with IDD spoke of tolerating workers who were perpetually tardy or absent because she felt she would not be able to replace them. She also wanted her workers to wear masks but shared: “I was afraid that they would not work if I made them.”
In the face of workforce shortages, consumers prioritized their care needs and often relied on family, friends, and neighbors to fill gaps. Yet, many under-met or unmet care needs remained, ranging from poor hygiene to life threatening events. For example, a BI consumer experienced food insecurity when she no longer had a worker to assist with shopping and suffered another brain injury when she fell during an unassisted transfer. A SDC consumer with physical disabilities reported getting pressure ulcers because her family caregivers did not have the strength to implement her transferring and repositioning protocols:There’s the increased depression and anxiety that I have, skin breakdowns and pressure sores, and those kind of things I didn’t really have to deal with that much before…. Now, it’s pretty much constant. I got a big area on my lower leg that’s not healing. Since I’m diabetic, it scares me. (Consumer on PD waiver with complex medical conditions)
Unmet care needs have cascading effects on mental and physical health and led to conditions that increase the risk of institutionalization.They’re isolated. They’re depressed. They’re not having human contact… Not to mention the fact that their home is not being cared for, or that they’re not getting bathed as often as they would like to be bathed…. Their nutrition, I mean, every part of a person’s life that it’s been determined that they need the services. If they’re missing any of those can impact them. (Combined FMS and HCA provider)
In the most extreme example, an FMS provider shared that a SDC consumer died in a home emergency during a timeframe that they were supposed to have a worker, but was unable to hire anyone.
These themes and subthemes interact with each other in complex ways (see Figure 1). To overview key linkages, SDC consumers were faced with workforce shortages (Theme I) but had few resources (Theme II) to address the structural roots of the workforce crisis. Their lack of access to organizational resources and CARES funds made it difficult to compete with rising pay in other industries. Further, it prevented them from offering sick pay or health insurance to help attract workers and mitigate new COVID-19 safety risks. Appendix K flexibilities allowing additional types of family members to be hired (Theme III) provided some SDC consumers and caregivers with a new resource to alleviate the impact of workforce shortages but did not resolve them altogether. The impact of Appendix K was limited because not everyone had or desired family caregivers and further, others were not aware of this option. These factors combined have an adverse impact on workers, caregivers, and consumers (Theme IV). These outcomes make clear that the strength and health of the workforce have a direct impact on care quality and the autonomy of consumers to self-direct their care. As expressed by an SDC worker, “My concern is always about the client. Anything that benefits the workers should benefit the client.” He further noted that his care-recipient’s struggle to find additional workers, “limits choice and reduces quality of care.”
Discussion
The SDC model is premised on the value of self-determination in allowing HCBS consumers to manage their own care with flexibility and according to their individualized care needs and preferences. However, our findings demonstrate that in actual practice, self-determination and choice is severely limited by workforce shortages, funding structures, and state regulations. This contrasts somewhat to Caldwell and colleagues’ (2022) finding that SDC consumers exercised their hiring and firing authority to enforce their safety practices. The difference may be that they drew on a national sample whereas our study is focused on Kansas where workforce shortages are more severe than the national average (KDADS, 2021). Appendix K flexibilities expanding who could be hired as a worker were helpful, but the overall COVID-19 policy response was inadequate for meeting the needs of SDC consumers, workers, and caregivers. Even though SDC is now a well-established model for delivering HCBS, state and federal policy makers failed to provide accessible emergency funding sources that adequately addressed the needs of this population. Workforce shortages increased caregiver and worker burden and also resulted in unmet care needs. Unmet care needs increase the risk of institutionalization (Kalankova et al., 2021), which is especially concerning during the pandemic when nursing homes were among the most dangerous places for the spread of COVID-19. While many workers remained dedicated to this line of work, this job is increasingly untenable considering wages offered elsewhere, and some felt the risk outweighed the benefit during the pandemic. Our findings point to policy changes and resources that could strengthen the SDC workforce, improve care quality, and expand SDC consumers’ autonomy in directing their care.
To begin with, the Appendix K flexibility allowing parents, spouses, guardians, and 16–17 year olds to be paid as caregivers should be made permanent. Advocates in Kansas also made this recommendation, and State HCBS administrators are in the process of amending HCBS policies accordingly. Many other states also have restrictions against hiring family members who carry legal responsibility for the care recipient (e.g., spouses, guardians) (Ujvari et al., 2020). The main concern has been over potential conflicts of interest that could lead to abuse or financial exploitation of dependent adults, but with proper oversight and supports, these concerns can be mitigated. Prior research demonstrates that there is not a higher risk of abuse in SDC compared to agency-based care, and care recipients fare better physically, psychologically, and in their sense of security with paid family caregivers compared to non-family caregivers (Matthias & Benjamin, 2003). During the pandemic, the ability to hire family members has been linked with the prevention of gaps in care, trust in the safety measures the family member uses, and increased social connection (Caldwell et al., 2022). Additionally, if these flexibilities are made permanent, there needs to be clear communication of the new rules to all consumers and family caregivers.
Next, we recommend Kansas adopt a budget-authority model for SDC. New Mexico, Tennessee, and Texas are examples of managed LTSS states that allow budget-authority (Sciegaj et al., 2013). If SDC consumers and their family caregivers had more control over the dollars funding their care, they would have more tools and resources for addressing the many challenges they faced during the pandemic. With budget-authority, budgets can be used to cover other expenses such as job advertising, required background checks, worker training, and PPE, which proved to be burdensome or prohibitive expenses for SDC consumers in our study. Many states added PPE as a permissible purchase and some states, such as North Carolina, even increased budget limits for SDC consumers to be able to purchase PPE (Mahoney, 2020). Budget-authority would also increase flexibility to compensate for other system deficiencies observed in our study, such as workforce shortages. For example, when consumers are getting little-to-none of their hours covered, a budget-authority model would provide the option of sacrificing some hours for higher pay to fill other hours. It is of course best for consumers to have all their care hours filled, which at minimum requires increasing reimbursement rates.
Regarding emergency funding, it is critical that funds be specifically targeted at the direct care workforce, including SDC workers. Many of the workforce challenges and unmet care needs could have been reduced if Kansas had used Appendix K flexibilities to temporarily increase reimbursement rates. The additional expenditure could then have been covered by COVID-19 emergency funding. But as demonstrated above, COVID-19 emergency funds were largely not accessible for the SDC model where HCBS consumers, rather than agencies, are the employers. FMS agencies could have potentially supported the distribution of these funds as the payroll managers, but policymakers and administrators at both the federal and state levels failed to provide structure or guidance for doing so. Future emergency planning efforts need to proactively tailor the distribution of resources to be compatible with SDC as a unique delivery system.
Finally, investments in the HCBS workforce are needed more broadly. This work is critical to the health and wellbeing of older adults and individuals with disabilities, and living wages and benefits are essential to attract quality workers who can provide quality care. SDC consumers and caregivers especially struggle to recruit and retain workers when they cannot offer benefits that are more widely available in agency-based or residential care settings, as well as the retail and food industries. Medicaid expansion is a key missed opportunity for providing health insurance to DSWs and family caregivers in Kansas. Direct support workers are 55% more likely to be uninsured in non-expansion states compared to expansion states (Marquand, 2015). Opportunities for extending group-based health insurance coverage to SDC workers could also be explored, as is done, for example, in the state of Washington (Tilly & Weiner, 2001).
A limitation of our study is that it is restricted to HCBS in Kansas during the pandemic. Findings may not apply to other states where HCBS structure and COVID-19 policies differed; however, they do highlight important strengths and challenges of the SDC model that policy makers should consider. Future research would benefit from direct comparison of experiences and outcomes across states with different policy and practice contexts, such as employer versus budget-authority models in SDC, managed care, different Appendix K adoptions, Medicaid expansion, and different rate and benefit structures.
Supplemental Material
Supplemental Material - “Anything that Benefits the Workers Should Benefit the Client”: Opportunities and Constraints in Self-Directed Care During the COVID-19 Pandemic
Click here for additional data file.
Supplementary Material for “Anything that Benefits the Workers Should Benefit the Client”: Opportunities and Constraints in Self-Directed Care During the COVID-19 Pandemic by Carrie L. Wendel, Tracey A. LaPierre, Darcy L. Sullivan, Jennifer Babitzke, Lora Swartzendruber, Tobi Barta, and Danielle M. Olds in Journal of Applied Gerontology.
Acknowledgments
We thank Ami Hyten, Crystal Yoning, and Gabe Mullen, Topeka Independent Living Resource Center, for their assistance with participant recruitment; Clara Boyd, University of Kansas, for her help with data collection; and the Stakeholder Advisory Board for their guidance throughout the project. Finally, we would like to thank their research participants for sharing their experiences.
ORCID iD
Carrie L. Wendel https://orcid.org/0000-0001-7221-5674
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Agency for Healthcare Research and Quality (AHRQ) under grant number 1R01HS028172-01.
Ethical Approval: The study was approved by the University of Kansas Human Research Protection Program (STUDY00146397).
Supplemental Material: Supplemental material for this article is available online.
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References
Advancing States. (2021). Demonstrating the value of MLTSS programs. MLTSS Institute.
Bradley V. J. Fenton M. H. Mahoney K. J. (2021). Self-direction: A revolution in human services. SUNY Press.
Caldwell J. Heyman M. Atkins M. Ho S. (2022). Experiences of individuals self-directing Medicaid home and community-based services during COVID-19. Disability and Health Journal, 15 (3 ): 101313. 10.1016/j.dhjo.2022.101313 35379576
Cascio M. A. Lee E. Vaudrin N. Freedman D. A. (2019). A team-based approach to open coding: Considerations for creating intercoder consensus. Field Methods, 31 (2 ), 116–130. 10.1177/1525822X19838237
Centers for Medicare/Medicaid Services (CMS). (2022). 1915(c) Home and Community-Based Services Waiver Instructions and Technical Guidance. APPENDIX K: Emergency Preparedness and Response.
Kalankova D. Stolt M. Scott P. A. Papastavrou E. Suhonene R. (2021). Unmet care needs of older people: A scoping review. Nursing Ethics, 28 (2 ): 149–178. 10.1177/0969733020948112 33000674
KDADS (Kansas Department for Aging and Disability Services). (2021, July). HCBS FMAP portfolio: Compendium. Author.
Kerley B. Tan L. Marshall D. O’Neill C. Bialachowski A. Pereira J. (2021). COVID-19 personal protective equipment in the home: Navigating the complexity of donning and doffing. Annals of Family Medicine, 19 (5 ), 405–410. 10.1370/afm.2667 33589431
Mahoney K. J. (2020). Self-direction of home and community-based services in the time of COVID-19. Journal of Gerontological Social Work, 63 (6–7 ), 625–628. 10.1080/01634372.2020.1774833 32501150
Mahoney K. J. Mahoney E. K. Morano C. DeVellis A. (2019). Unmet needs in self-directed HCBS programs. Journal of Gerontological Social Work, 62 (2 ), 195–215. 10.1080/01634372.2018.1451421 29558342
Markkanen P. Brouillette N. Quinn M. Galligan C. Sama S. Lindberg J. Karlsson N. (2021). ‘It changed everything’: The safe Home care qualitative study of the COVID-19 pandemic’s impact on home care aides, clients, and managers. BMC Health Services Research, 21 (1 ), 1055. 10.1186/s12913-021-07076-x 34610836
Marquand A. (2015). Too sick to care: Direct-care workers, Medicaid expansion, and the coverage gap. PHI.
Matthias R. E. Benjamin A. E. (2003). Abuse and neglect of clients in agency-based and consumer-directed home care. Health and Social Work, 28 (3 ):174–184. 10.1093/hsw/28.3.174 12971281
Merriam S. B. Tisdell E. J. (2015). Qualitative research: A guide to design and implementation. John Wiley & Sons.
Noble H. Smith J. (2015) Issues of validity and reliability in qualitative research. Evidence-Based Nursing, 18 (2 ), 34–35. 10.1136/eb-2015-102054 25653237
Scales K. (2020) It’s time to care: A detailed profile of America’s direct care workforce. PHI.
Sciegaj M. Crisp S. DeLuca C. Mahoney K. J. (2013). Participant-directed services in managed long-term services and supports programs: A five-state comparison . U.S. Department of Health and Human Services.
Spetz J. Stone R. I. Chapman S. A. Bryant N. (2019). Home and Community-Based Workforce for Patients with Serious Illness Requires Support to Meet Growing Needs. Health Affairs, 38 (6 ), 902–909. 10.1377/hlthaff.2019.00021 31158024
Tilly J. Weiner J. M. (2001). Consumer directed home and community services: Policy issues. Occasional Paper Number 44 . The Urban Institute.
Ujvari K. Edwards-Orr M. Morris M. Deluca C. Sciegaj M. (2020). National inventory of self-directed long-term services and supports programs . AARP Public Policy Institute.
| 36471575 | PMC9729716 | NO-CC CODE | 2022-12-14 23:22:30 | no | J Appl Gerontol. 2022 Dec 5;:07334648221143604 | utf-8 | J Appl Gerontol | 2,022 | 10.1177/07334648221143604 | oa_other |
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SAGE Publications Sage UK: London, England
10.1177/01430343221142284
10.1177_01430343221142284
Original Research Article
The effect of duration of youth/parent communication on depression and anxiety during COVID-19 isolation in China
https://orcid.org/0000-0003-3851-5926
Hu Weijian
Deng Cuiyun
Department of Mental Health, 615876 Guangzhou College of Technology and Business , Guangzhou, China
Wu Mengyao Students Mental Health and Counseling Center, 47890 Shenzhen University , Shenzhen, China
Cao Menglu Faculty of Psychology, 26463 Southwest University , Chongqing; Students’ Mental Health and Counseling Center, Sichuan Technology and Business University, Chengdu, China
Liu Zhaoquan Students Mental Health and Counseling Center, Guangdong Lingnan Vocational and Technical College, Guangzhou, China
Weijian HU, Department of Mental Health, Guangzhou College of Technology and Business, Guangzhou (510850), China. Email: [email protected]
5 12 2022
5 12 2022
01430343221142284© The Author(s) 2022
2022
SAGE Publications
This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.
The current study examines the mediating roles of self-efficacy and sleep disturbance and the moderating role of gender in the association between the duration of youth/parent communication on depression and anxiety during the COVID-19 isolation period in China. We used the self-designed demographic variable questionnaire, General Self-Efficacy Scale, the Pittsburgh Sleep Quality Index, the Self-Rating Depression Scale, and the Self-Rating Anxiety Scale with 1,772 youths aged 15–24 from 26 provinces in China during the COVID-19 lockdown. We performed demographic variable analysis, correlation analysis, mediation analysis, and moderated analysis. The duration of daily communication with parents was significantly positively correlated with self-efficacy and significantly negatively correlated with sleep disturbance, depression, and anxiety. The chain mediation analysis revealed that the duration of communication with parents directly affected depression and anxiety. Self-efficacy, sleep disturbance, and self-efficacy sleep disturbance had significant mediating and chain-mediating effects on the duration of communication with parents, depression, and anxiety. The interactions between sleep disturbance and gender (B = 0.35, 95% CI 0.06 to 0.64, p = .02 < .05) were significant. The duration of parent/youth communication directly affected depression and anxiety and indirectly affected depression and anxiety via the chain-mediating effect of self-efficacy and sleep disturbance. Gender moderates the relationships between sleep disturbance and depression.
communication duration with parents
youth
depression
anxiety
COVID-19 isolation
2020 Annual Research Project of The 13th Five-Year Plan of Education Science of Guangdong Province No.2020GXJK054 edited-statecorrected-proof
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pmcIntroduction
The global outbreak of COVID-19 began in 2020. At the time of this report, the virus was mutating, affecting public health security and imposing psychological burdens on the public (Chen et al., 2022; Ryerson, 2022; Santomauro et al., 2021; Thompson et al., 2022; Xiang et al., 2020). Studies showed that depressive and anxiety disorders increased by 28% and 26% (respectively) worldwide in 2020, with significant increases (especially among youth) in countries severely affected by COVID-19 (Santomauro et al., 2021). A literature search in Chinese databases revealed that the rates of depression and anxiety among Chinese youth increased during the pandemic (Dian-Jiang et al., 2021; Wanlong & Shihao, 2021; Xiaolin et al., 2020; Zheng Chen et al., 2020).
Depression is a mood disorder characterized by significant and persistent low mood, decreased activity, delayed thinking, and cognitive dysfunction. Simple depression is relatively rare and is commonly comorbid with anxiety (Zi-qiang et al., 2011). Anxiety is a complex emotional state generated when an individual anticipates the occurrence of an adverse consequence or vague threat; it is interwoven with feelings of tension and fear (Kircanski et al., 2017). Comorbid anxiety and depression are characterized by severe symptoms, functional impairments, profound courses, high rates of suicide, and poor outcomes (Miyuan et al., 2020). Most studies have focused on depression or anxiety alone (Kircanski et al., 2017; Pandi-Perumal et al., 2020), and there are few studies on comorbid anxiety and depression; in particular, there are few studies on comorbid depression and anxiety among youth during the pandemic. Thus, the protective factors for depression and anxiety for youth, especially in COVID-19 isolation, should be identified to help improve their mental health.
Family support is a protective factor for youth (Guerra et al., 2018; Wills et al., 1992). Studies have shown that support from family is one of the most important predictors of depression (Nasser & Overholser, 2005). Strong family support was associated with fewer experiences of depression and anxiety (Guerra et al., 2018). A study also suggested that a higher degree of perceived social support (e.g., family support) correlates with lower general anxiety among students and a lower impact on the COVID-19 pandemic (Wu et al., 2021).
The family is an individual's closest social support system and can provide care, companionship, and other emotional communication and spiritual support; the family is also the most accessible source of help for youth isolated at home (Jacob et al., 2019; Jiuju et al., 2022). Studies have shown that adequate parent–child communication time is essential for family support (Diggs et al., 2017; Vaterlaus et al., 2019). In addition, studies have found that there is a chain mediating the relationship between parent–child communication and sleep quality and depressive symptoms (Dong et al., 2022).
Self-efficacy (defined according to Bandura's self-efficacy theory as a sense of control over one's environment and behavior) is also a protective predictor of psychological distress (Bandura et al., 1997). Studies have shown that self-efficacy affects depression and anxiety (Cui-mei et al., 2022; Song et al., 2019). Furthermore, students who reported more substantial availability of family support reported stronger self-efficacy (Torres & Solberg, 2001).
Several studies have also suggested that good sleep quality is associated with depression and anxiety (Dao-Yang et al., 2016; Tsuno et al., 2005; Zhou et al., 2020; ZHU et al., 2021). Studies demonstrated that sleep quality (as a mediating variable) affects depression and anxiety, and self-efficacy affects sleep quality (Diaz-Piedra et al., 2014; Hwanjin et al., 2018; Zhu et al., 2020). Moreover, a study has shown that depression and anxiety are parallel mediating relationships between sleep disturbance and subjective cognitive decline, suggesting that sleep is closely related to depression and anxiety (Xu et al., 2021). Besides, similar literature has found that social support affects sleep quality. This suggests that interpersonal communication can affect sleep quality (Pan et al., 2022).
Studies have shown that parent-child communication has a differential effect on males and females, for example, Ohannessian's (2013) research found that open adolescent-parent communication specifically acts as a protective factor for girls but not for boys. Moreover, One study found that gender has a moderating relationship with self-efficacy (Chung et al., 2017). Furthermore, previous studies have shown that there are significant differences in sleep quality between different genders (Duan et al., 2022; Islam et al., 2021; Joao et al., 2018). Besides, the difference in gender has an influence on the degree of depression and the effect of the intervention (Cahuas et al., 2020; Sun et al., 2017). In addition, the degree of anxiety and the effect of intervention are different in different genders (Kjellenberg et al., 2022).
Although the duration of youth/parent communication, self-efficacy, and sleep quality have been identified as independent predictors of depression and anxiety in various populations, to the best of our knowledge, the associations of these five variables among youth during the COVID-19 lockdown have not been fully explored. Moreover, examining whether gender plays a role in the associations among the duration of youth/parent communication, self-efficacy, and sleep quality may be worthwhile (Diggs et al., 2017). Therefore, the current study explored the following: (1) The associations among duration of youth/parent communication, self-efficacy, sleep quality, and psychological distress (depression and anxiety) based on a serial multiple mediation model and (2) the moderating role of gender.
We hypothesize that (Figures 1 and 2): (1) Duration of parent/youth communication is negatively associated with depression and anxiety and positively associated with self-efficacy, and negatively associated with sleep disturbance, (2) Self-efficacy and sleep quality significantly mediate the association between the duration of youth/parent communication and depression and the duration of youth/parent communication and anxiety, and (3)Gender moderates the associations among youth/parent communication duration, self-efficacy, sleep disturbance, and depression and anxiety.
Figure 1. The conceptual model of self-efficacy and sleep disturbance between the communication duration with parents and depression.
Figure 2. The conceptual model of self-efficacy and sleep disturbance between the communication duration with parents and anxiety.
Methods
Measures
A self-designed questionnaire concerning demographic variables and four related scales were used for the research. The subjects used mobile phones to scan two-dimensional codes and fill in the electronic questionnaire.
The self-designed demographic variable questionnaire
This instrument gathered personal information, including gender, age, educational background, residence, living place, and daily contact time with parents during the nearly one month of home quarantine during the pandemic.
Self-efficacy
The original version of the General Self-Efficacy Scale was developed by Jerusalem and Schwarzer in 1981, initially as a 20-item version and later as a simplified version of 10 items. It has been used in numerous research projects, where it typically yielded internal consistencies between α = 0.75 and 0.90. The scale is not only parsimonious and reliable, but also it has also proved valid in terms of convergent and discriminant validity. The General Self-Efficacy Scale measures individual self-efficacy with ten items on a 4-point Likert scale. The statistical index is the total score; higher scores correlate with higher self-efficacy(Schwarzer & Born, 1997; Schwarzer et al., 1999). The Chinese version of the General Self-Efficacy Scale was first used in Hong Kong in 1995 and has demonstrated good reliability and validity, with an internal consistency coefficient of 0.87, retest reliability of 0.83 within 10 days, and split reliability of 0.90 (Zhang & Schwarzer, 1995). The scale showed good internal consistency in this study (Cronbach's α = 0.90).
Sleep disturbance
The Pittsburgh Sleep Quality Index is a self-rating sleep quality scale developed by Buysse et al. in 1989. Because of its simplicity, high reliability and validity, and high correlation with polysomnography test results, it has become a common scale in research and psychiatric clinical evaluation. The Pittsburgh Sleep Quality Index measures the degree of sleep disturbance; higher total scores correlate with lower sleep quality. Seven factors are on the scale, including subjective sleep quality, sleep time, sleep efficiency, sleep disorders, hypnotic drugs, and daytime dysfunction. The measurement index is the sum of the seven factors. This scale was introduced in China by Liu Xianchen et al. and tested for reliability and validity. For the construct validity test, Liu used PSQI, SDS, and SAS to measure 560 college students. The correlation analysis showed that the correlation coefficients between PSQI and SDS and SAS were 0.43 and 0.42, respectively, showing a significant positive correlation. For the internal consistency reliability test, Liu tested Cronbach's coefficient of PSQI's 7 factor, which was 0.84. The correlation coefficients between each component and the total score of PSQI ranged from 0.63 (daytime function) to 0.81 (subjective sleep quality), with an average of 0.72, showing a high correlation. For the retest reliability test, 30 college students were evaluated again by PSQI two weeks later, and the correlation coefficient between the total scores of the two PSQI scores was 0.81 (Buysse et al., 1989; Liu Xian Chen et al., 1996). Today, PSQI is still proven to be a reliable and effective method for assessing and screening sleep disorders in the Chinese population (Chen et al., 2017). The internal consistency reliability of the scale was good among the seven factors in this study (Cronbach's α = 0.72).
Depression
The Self-Rating Depression Scale was developed by Professor Zung of Duke University in 1965. Because of its simple method and good quality, it has been widely used abroad. It was introduced in China by Wang Zhengyu and is widely used in the field of clinical psychology. The scale uses a 4-point Likert scale that measures an individual's level of depression with 20 items (10 positive and 10 negative scores). Positive items were scored on original 1, 2, 3, and 4 points, while negative items were scored on original 4, 3, 2, and 1 points. Add the scores of each item to get the original total score, and the measurement index is the standard total score. The standard total score is calculated according to the formula (standard total score = original total score × 1.25, rounded to whole number). A higher standard total score correlates with more severe depression (Quan-Quan & Li, 2012; Zung, 1965). The scale showed good internal consistency in this study (Cronbach's α = 0.70).
Anxiety
The Self-Rating Anxiety Scale was proposed by Professor Zung in 1971. From the form of scale construction to the specific evaluation method, it is very similar to the self-rating depression scale. It was introduced in China by Wang Zhengyu and is widely used in the field of clinical psychology. The scale uses a 4-point Likert scale that measures an individual's level of anxiety with 20 items (15 positive and 5 negative scores). Positive items were scored on original 1, 2, 3, and 4 points, while negative items were scored on original 4, 3, 2, and 1 points. The scores of each item are summed to get the original total score, and the measurement index is the standard total score. The standard total score is calculated according to the formula (standard total score = original total score × 1.25, rounded to the whole number). Higher standard total scores correlate with more severe anxiety (Zhengyu & Yufen, 1984; Zung, 1971). The scale showed good internal consistency in this study (Cronbach's α = 0.79).
Procedures and quality control
The questionnaires were anonymous. The instructions explained the survey's purpose in detail, and informed consent was obtained. The permissions allowed one internet protocol address to be submitted only once to prevent repeat submissions. Two questions with specified answers were inserted into the questions as lie-detection questions to screen out the subjects who did not answer seriously. If the two lie detection items were not answered correctly, the subject was classified as invalid and excluded (DeSimone & Harms, 2018; Huang et al., 2012). The total test time was about 5 minutes. The subjects could only submit the test after answering all the questions, and no modifications were allowed after submission.
Sampling and participants
In May 2020, during the Covid-19 lockdown, questionnaires were distributed to the campus network groups of four universities and three middle schools in Guangdong, Sichuan, and Shaanxi provinces by simple random sampling using the Questionnaire Star application, a popular online questionnaire application in mainland China. A total of 2231 questionnaires were received, of which 1943 were valid (questionnaires with complete answers to all questions and correct answers to two lie detection questions were listed as valid questionnaires), with an effective rate of 87.09%. All subjects were stay-at-home youth living in 102 cities in 26 provinces (including four municipalities directly under the Central Government and one special administrative region) in China. According to the definition of youth by the United Nations, subjects with an age range of 15–24 years old were included (Youth, 2022), and those below 15 years old and above 24 years old were not included in the statistics. Finally, 1772 samples were retained, with an average age of 18.18 ± 2.09, ranging from 15 to 24 years old.
Data analysis
The preliminary data were sorted using Excel and were analyzed using SPSS24.0 statistical software. First, descriptive statistics were used to analyze the demographic characteristics. Second, Pearson's correlation analysis was performed to estimate the associations among communication duration with parents, self-efficacy, sleep disturbance, depression, and anxiety. Third, Model 6 of the PROCESS macro program compiled by Hayes was used to analyze the mediation effect. Communication duration with parents, depression, and anxiety were identified as the independent (X), dependent (Y1), and (Y2) variables, respectively. The mediators were self-efficacy (M1) and sleep disturbance (M2). The total, direct and indirect effects were estimated, and the 95% confidence interval (CI) was calculated with 5000 bootstrapping resamples. Fourth, PROCESS Model 1 was used to conduct moderating analysis, including living place and the highest level of education as covariates, to explore the moderating effect of gender on communication duration with parents, self-efficacy, sleep disturbance, depression, and anxiety.
Ethical considerations
Ethical approval (LLSC2022001) was obtained from the Academic Committee of Guangzhou College of Technology and Business ethics committee. All data files are securely stored in a secure location on an encrypted computer at the college.
Harman single-factor test
The Harman single-factor test was used to diagnose common method deviation. The unrotated principal component factor analysis results revealed 14 factors with eigenvalues greater than one, among which the variation explained by the first factor was 20.14%, less than the critical value of 40%. This finding suggests no evident common method deviation in the questionnaires.
Results
We compared self-efficacy, sleep quality, depression, and anxiety among different youth groups in subgroups such as gender, living place, the highest level of education, and communication duration with parents. The analysis of demographic variables is shown in Table 1.
Table 1. Analysis of demographic variables.
Variable Group Number % Self-efficacy Sleep disturbance Depression Anxiety
Total amount 1772 2.51 ± 0.58 4.65 ± 3.12 47.43 ± 11.28 42.78 ± 9.63
Gender Male 636 35.90 2.67 ± 0.60 4.33 ± 3.25 46.04 ± 11.39 42.08 ± 9.80
Female 1136 64.11 2.42 ± 0.54 4.83 ± 3.04 48.20 ± 11.14 43.18 ± 9.51
T 8.71** −3.28** −3.88** −2.28*>
P <0.01 <0.01 <0.01 0.02
Living place Urban 763 43.06 2.61 ± 0.60 4.51 ± 3.20 46.91 ± 11.67 42.37 ± 0.94
Rural 1009 56.94 2.44 ± 0.55 4.75 ± 3.06 47.82 ± 10.96 43.10 ± 9.38
T 5.93**> −1.61 −1.66 −1.58
P <0.01 0.11 0.10 0.11
Highest level of education Middle school① 274 15.46 2.60 ± 0.61 3.84 ± 3.08 48.38 ± 12.03 43.01 ± 9.60
High school② 590 33.30 2.42 ± 0.56 5.04 ± 3.15 48.77 ± 11.44 43.75 ± 9.57
College or above③ 908 51.24 2.55 ± 0.57 4.64 ± 3.07 46.27 ± 10.82 42.09 ± 9.62
F 12.30** 13.96** 9.48** 5.40**
P <0.01 <0.01 <0.01 <0.01
Post-hoc multiple comparisons ②<①,②<③ ②>③>① ②>③, ①>③ ②>③
Communication duration with parents Within 1 hour① 752 38.70 2.43 ± 0.59 5.08 ± 3.28 49.85 ± 11.79 44.20 ± 10.06
1–2 hours② 606 31.19 2.52 ± 0.56 4.67 ± 3.00 46.92 ± 10.48 42.74 ± 9.52
2–4 hours③ 317 16.31 2.58 ± 0.55 4.18 ± 2.81 45.92 ± 10.80 41.79 ± 9.30
More than 4 hours④ 268 13.79 2.66 ± 0.57 3.98 ± 3.16 43.68 ± 10.69 40.14 ± 8.26
F 12.47**> 10.78** 22.99>**> 13.19**>
P <0.01 <0.01 <0.01 <0.01
Post-hoc multiple comparisons ①<②,①<③,①<④,②<④ ①>③,①>④，②>④ ①>②,①>③,①>④,②>④ ①>③,①>④，②>④
*p < 0.05; ** p < 0.01.
Pearson product–moment correlation analysis showed that youths’ daily communication time with their parents was significantly correlated with self-efficacy, sleep disturbance, depression, and anxiety (Table 2). Specifically, the daily communication time with their parents was significantly positively correlated with self-efficacy, while the daily communication time with their parents was significantly negatively correlated with sleep disturbance, depression, and anxiety. In addition, self-efficacy was negatively correlated with sleep disturbance, depression, and anxiety. Moreover, sleep disturbance has a significant positive relationship with depression and anxiety.
Table 2. Correlation analysis.
Communication duration with parents Self-efficacy Sleep disturbance Depression
Self-efficacy 0.14**
Sleep disturbance −0.13** −0.23**
Depression −0.19** −0.38** 0.53**
Anxiety −0.14** −0.19** 0.49** 0.73**
*p < 0.05; ** p < 0.01.
Chain-mediated model analysis
Model 6 of the PROCESS macro program in the SPSS plug-in was used. We set daily communication duration with parents as the independent variable, self-efficacy and sleep disturbance as intermediary variables, and depression and anxiety as dependent variables. The bootstrap method (5000 samples) was used to estimate the chain mediation model of “daily communication duration with parents—self-efficacy—sleep disturbance—depression” and “daily communication duration with parents—self-efficacy—sleep disturbance—anxiety.” The results are shown in Table 3 and Figures 3 and 4. Completely standardized path coefficients marked with an asterisk identify 95% bootstrap confidence intervals which do not include zero and significant levels (p < .01). In the chain-mediated test with depression as the dependent variable, the communication duration with parents directly predicted depression (B = −0.19, 95%CI: −2.51 to −1.52). The independent mediating effect of self-efficacy and sleep disturbance and the chain-mediated effect of self-efficacy and sleep disturbance were significant (B = −0.04, 95%CI: −0.05 to −0.02; B = −0.05, 95%CI: −0.07 to −0.03; B = −0.01, 95%CI: −0.02 to −0.01; respectively). Model statistics for the direct model: R2 = 0.04, F (1, N = 1770) = 64.21, p < .01. For the mediated model: R2 = 0.36, F (3, N = 1768) = 326.58, p < .01. The total indirect effect accounted for 51.87% of the total effect, the indirect effect of self-efficacy intermediary effect accounted for 19.79% of the total effect, the mediating effect of the sleep disturbance was 24.60%, and the chain-mediating effect of the total score of self-efficacy and sleep disturbance was 6.42%. The communication duration with parents directly predicted anxiety in the chain-mediated test, with anxiety as the dependent variable (B = −0.14, 95%CI: −1.73 to −0.89). The independent mediating effect of self-efficacy and sleep disturbance and the chain-mediated effect of self-efficacy and sleep disturbance were significant (B = −0.01, 95%CI: −0.02 to −0.01; B = −0.05, 95%CI: −0.07 to −0.02; B = −0.01, 95%CI: −0.02 to −0.01; respectively). Model statistics for the direct model: R2 = 0.02, F (1, N = 1770) = 36.81, p < .01. For the mediated model: R2 = 0.25, F(3, N = 1768) = 197.57, p < .01. The total indirect effect accounted for 50.35% of the total effect. The indirect effect of the self-efficacy intermediary effect accounted for 7.69% of the total effect, the mediating effect of the sleep disturbance was 32.87%, and the chain-mediating effect of self-efficacy and sleep disturbance was 9.79%.
Figure 3. The chain mediation model of self-efficacy and sleep disturbance between the communication duration with parents and depression.
Figure 4. The chain mediation model of self-efficacy and sleep disturbance between the communication duration with parents and anxiety.
Table 3. Chain-mediating effect values and 95% confidence intervals.
Dependent variable path The path Effect size 95% confidence intervals
LLCI ULCI
Depression Total effect −0.19 −2.51 −1.52
Direct effect −0.09 −1.38 −0.56
The mediation effect −0.10 −0.13 −0.07
Communication duration with parents → Self-efficacy → Depression −0.04 −0.05 −0.02
Communication duration with parents → Sleep disturbance → Depression −0.05 −0.07 −0.03
Communication duration with parents → Self-efficacy → Sleep disturbance → Depression −0.01 −0.02 −0.01
Anxiety Total effect −0.14 −1.73 −0.89
Direct effect −0.07 −1.03 −0.28
The mediation effect −0.07 −0.10 −0.05
Communication duration with parents → Self-efficacy → Anxiety −0.01 −0.02 −0.01
Communication duration with parents → Sleep disturbance → Anxiety −0.05 −0.07 −0.02
Communication duration with parents → Self-efficacy → Sleep disturbance → Anxiety −0.01 −0.02 −0.01
The moderation model
The results of the moderation analysis after controlling for the effect of living place and the highest level of education are depicted in Figure 5. In Model 3 (communication duration with parents → depression), the interaction of communication duration with parents and gender was insignificant (B = − 0.39, 95% CI: −1.40 to 0.63, p = .46), indicating that gender did not moderate the relationship between communication duration with parents and depression. Similarly, in Models 4, 5, 6, 8, 9, 10, and 11, the moderating effect of gender was not significant (B = 0.22, 95% CI: −0.66 to 1.10, p = .63; B = −1.77, 95% CI: −3.53 to −0.02, p = .05; B = −1.42, 95% CI: −3.01 to 0.17, p = .08; B = −0.07, 95% CI: −0.33 to 0.19, p = .60; B = −0.17, 95% CI: −0.68 to 0.35, p = .52; B = −0.06, 95%CI: −0.34 to 0.23, p = .69; B = −0.02, 95% CI: −0.08 to 0.03, p = .35; respectively). In Model 7 (sleep disturbance → depression), as shown in Figure 6, gender moderated the association between sleep disturbance and depression (B = 0.35, 95% CI: 0.06 to 0.64, p = .02 < .05; βmale = 1.69, t = 14.62, p < .01; βfemale = 2.04, t = 22.11, p < .01).
Figure 5. Analysis of moderating effects.
Figure 6. The interaction between sleep disturbance and gender depression.
Discussion
The results confirm the original hypothesis that a significant negative correlation exists between youth's daily communication time with parents and depression and anxiety, and self-efficacy and sleep quality play a chain-mediating role during COVID-19 isolation.
First, in the analysis of demographic variables, the self-efficacy scores of female youth were significantly lower than that of male youth. In contrast, the sleep disturbance of female youth was higher than that of males, suggesting that female sleep quality was significantly lower than that of males. In addition, the scores of depression and anxiety of female youth were significantly higher than that of male youth. These findings suggest that the mental health of female youth was affected considerably during the home-staying period of the epidemic, which is consistent with international studies (Santomauro et al., 2021; Simba & Ngcobo, 2020; Vuelvas-Olmos et al., 2022; Wenham et al., 2020). Studies have shown that females are more likely to be left with more caregiver and household responsibilities due to school closures or family illness (Santomauro et al., 2021). Female issues did not suddenly appear during this COVID-19 pandemic but have been or will be compounded by it. These issues have a direct and indirect influence on several aspects of female health, including putting them at a greater risk of COVID-19 infection, and worsening already existing diseases (Simba & Ngcobo, 2020). The self-efficacy of urban youth was higher than that of rural youth, suggesting that rural youth were more likely to feel powerless during the home-staying period. With the education level of youth as a sub-group, compared with middle school and college students, the high school students had lower self-efficacy and higher sleep disturbance, depression, and anxiety. The results suggest that high school students during the quarantine period were the most affected among adolescents; this phenomenon might be related to the pressure they face regarding the college entrance examination. According to the subgroup comparison of parent/youth daily communication time during the COVID-19 epidemic isolation, youth who communicated with their parents for less than 1 hour a day had significantly lower self-efficacy and significantly higher sleep disturbance, depression, and anxiety levels. These findings suggest that youth who communicate well with their parents have better mental health.
Second, correlation analysis found that daily youth/parent communication duration was associated with self-efficacy, sleep disturbance, depression, and anxiety. Consistent with other literature, family factors are associated with depression and anxiety (Guerra et al., 2018; Nasser & Overholser, 2005; Wang et al., 2020). Time spent communicating with parents daily correlated with self-efficacy and inversely correlated with sleep disturbance, depression, and anxiety.
Third, the chain mediation model analyzed the internal mechanisms of how the duration of daily youth/parent communication alleviated depression and anxiety. The duration of daily communication with parents indirectly affected depression and anxiety via the chain-mediating effect of self-efficacy → sleep disturbance. These findings suggest that the duration of daily communication with parents, self-efficacy, and sleep quality critically affected depression and anxiety levels among youth during the epidemic stay-at-home period. Similar to other literature (Duan et al., 2022; Levens et al., 2016; Olatunji et al., 2020; Torres & Solberg, 2001; Tsuno et al., 2005), self-efficacy and sleep disturbance are important mediating variables between family factors and negative emotions(Dong et al., 2022).
Fourth, gender moderated the associations among duration of daily youth/parent communication, self-efficacy, sleep disturbance, depression, and anxiety, which is consistent with existing literature (Diggs et al., 2017). Specifically, gender moderated only the associations between sleep disturbance and depression. This indicates that the pathway of the duration of daily youth/parent communication → self-efficacy → depression was different for male youth compared with their female counterparts, which is consistent with previous research (Monteiro et al., 2015; Wang et al., 2022; ZHU et al., 2021) and contributes to the gender difference in the model.
In summary, China implemented strict prevention and control measures in the early stages of the COVID-19 outbreak. The public stayed at home, controlling the spread of the epidemic and protecting public health (Chen et al., 2021; Wang, 2022). On the other hand, isolation measures hindered communication and interaction, resulting in alienation and loneliness (Ganesan et al., 2021; Lu et al., 2022). According to Erikson's psycho-social development theory, 14–18-year-olds are adolescents who experience conflict between self-identity and role disorder; 18–25-year-olds are young adults who experience conflict between intimacy and loneliness (Munley, 1975). In these stages, young people are in unstable states while shaping their personalities; they experience stress when facing significant emergencies such as the epidemic, which trigger potent psychological and behavioral responses. This study provides a potential direction to alleviate psychological distress for youth during COVID-19 isolation and in major health events in the future.
Implications for practice
This study confirms that when youth are isolated at home because of an epidemic, communicating with their parents is associated with improved self-efficacy and sleep quality and reduced levels of depression and anxiety. Youth who communicated with their parents for less than an hour a day reported significantly lower self-efficacy, significantly higher sleep disturbance, and substantially higher depression and anxiety than those who communicated more often. This finding suggests that schools encourage youth to maintain friendly communication with their parents during epidemics. Communication should last at least one hour a day, especially during the COVID-19 epidemic. For example, parents and children share their knowledge and feelings about the COVID-19 epidemic and express their views and suggestions on quarantine policies. Of course, it can be extended beyond the pandemic. For instance, parents and children can also communicate their daily life experiences, emotional feelings of the day, and opinions on current affairs and policies. These methods to promote parent-child communication can effectively create a warm and supportive family environment to help youth cope with anxiety and depression during family isolation.
Limitations and future directions
There are some limitations to this study. First, the youth from the four universities and three middle schools included in this study may not be representative of the general youth population; thus, these findings should be further validated with a larger sample comprising participants from diverse cultural backgrounds. Second, due to the cross-sectional nature of this study, causal relationships could not be established, and a longitudinal study should be conducted to replicate these findings. Third, this study only assessed the quantity of communication, but not the quality or type (e.g., positive or negative) of communication. Thus, future directions would like to further study the influence of the quality of communication between youth and parents on depression and anxiety. Fourthly, several potential confounding variables such as family environment, family income, family structure, parenting style, and family rituals (CHENG Zao-huo et al., 2016; Krauss et al., 2020; Wu Mingzheng et al., 2021), were not considered in the moderated mediation model due to the heavy scale burden; this may have impacted the association estimation. However, these factors should be analyzed in future research.
Conclusion
The mental health level of female youth and high school students was relatively low among youth who stayed at home during the epidemic. Youth who communicate with their parents for less than one hour have significantly lower self-efficacy, a substantially higher sleep index, and substantially more depression and anxiety. During the social isolation period, the duration of parent/youth communication directly affected depression and anxiety and indirectly affected depression and anxiety via the chain-mediating effect of self-efficacy and sleep disturbance. Gender moderates the relationships between sleep disturbance and depression.
Acknowledgments
We would like to express our sincere gratitude to the teachers who helped distribute questionnaires and collect data for this study.
Author biographies
Weijian Hu, Ph.D., obtained his doctoral degree in applied psychology from the City University of Macau. Now, he works as a teacher at the Department of Mental Health of Guangzhou College of Technology and Business in China. He is a nationally certified psychological consultant in China. His research interests focus on health psychology, including adolescent psychological health and psychological resilience.
Cuiyun Deng, Master, is a psychological consultation teacher at the Department of Mental Health, Guangzhou College of Technology and Business. Her research interests include child psychology, gardening therapy, and school psychology issues based on a multicultural perspective.
Mengyao Wu, Ph.D., is a psychological counselor/lecturer in the Students' Mental Health and Counseling Center in Shenzhen University in China. Her research interests, publications, and professional presentations encompass topics of adolescent/adult mental health, psychodynamics, and counseling/therapy.
Menglu Cao, M.D., is a doctoral candidate at Southwest University of China. She is also a clinical psychological consultant certified in China. Her research interests include personality, psychotherapeutic processes, psychological assessment and emotions.
Zhaoquan Liu, Master, is a psychological consultation teacher at Students Mental Health and Counseling Center, Lingnan Institute of Technology. His research interests include bullying prevention, mental health promotion, and family involvement.
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and publication of this article. The study is supported by the 2020 Annual Research Project of the 13th Five-Year Plan of Education Science of Guangdong Province, Grant No. 2020GXJK054.
ORCID iD: Weijian Hu https://orcid.org/0000-0003-3851-5926
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References
Bandura A. Freeman W. H. Lightsey R . (1997). Self-efficacy: The exercise of control. Journal of Cognitive Psychotherapy, 13 (2), 158–166. 10.1891/0889-8391.13.2.158
Buysse D. J. Reynolds C. F. Monk T. H. Berman S. R. Kupfer D. J . (1989). The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research. Psychiatry Research, 28 (2 ), 193–213. 10.1016/0165-1781(89)90047-4 2748771
Cahuas A. He Z. H. Zhang Z. J. Chen W. Y . (2020). Relationship of physical activity and sleep with depression in college students. Journal of American College Health, 68 (5 ), 557–564. 10.1080/07448481.2019.1583653 30908132
Chen C. Li C. Chen M. Wang L. Zhu Y. Zhu Z . (2022). Coping and post-traumatic stress disorder symptoms among Chinese youth in the peak and remission periods of COVID-19. Children and Youth Services Review, 106690. 10.1016/j.childyouth.2022.106690
Chen P.-Y. Jan Y.-W. Yang C.-M . (2017). Are the insomnia severity index and Pittsburgh sleep quality index valid outcome measures for cognitive behavioral therapy for insomnia? Inquiry from the perspective of response shifts and longitudinal measurement invariance in their Chinese versions. Sleep Medicine, 35 , 35–40. 10.1016/j.sleep.2017.04.003 28619180
Chen S. X. Lam B. C. P. Liu J. H. Choi H.-S. Kashima E. Bernardo A. B. I . (2021). Effects of containment and closure policies on controlling the COVID-19 pandemic in East Asia. Asian Journal of Social Psychology, 24 (1 ), 42–47. 10.1111/ajsp.12459 33821141
Chen L.-X. Qin T. M. Lei e . H. (1996). Reliability and validity of the Pittsburgh sleep quality index. CHinese Journal of Psychiatry, 29 (2 ), 103–107. https://en.cnki.com.cn/Article_en/CJFDTOTAL-ZHMA199602018.htm
Chen Z. Shu-qin W. Cen W. Han Yaozheng H. Q. Peigang W . (2020). Mental health status among residents in hubei province during novel coronavirus disease epidemic: An online survey. Chinese Journal of Public Health, 36 (5 ), 657−660. 10.11847/zgggws1128840
Chung M. C. AiQarni N. Al Muhairi S. Mitchell B . (2017). The relationship between trauma centrality, self-efficacy, posttraumatic stress and psychiatric co-morbidity among Syrian refugees: Is gender a moderator? Journal of Psychiatric Research, 94 , 107–115. 10.1016/j.jpsychires.2017.07.001 28697422
Cui-mei J. Xiao-hua S. Hai-dong S . (2022). Self-efficacy among community mental health workers during COVID-19 outbreak and its relationship with anxiety and depression. Chinese Journal of Clinical Psychology, 30 (1 ), 3. 10.16128/j.cnki.1005-3611.2022.01.033
Dao-Yang W. Li-Hua D. YIN-Xin . (2016). Relation of sleep quality to depression and anxiety in college students. Chinese Mental Health Journal, 30 (003 ), 226–230. 10.3969/j.issn.1000-6729.2016.03.013
DeSimone J. A. Harms P. D . (2018). Dirty data: The effects of screening respondents who provide low-quality data in survey research. Journal of Business and Psychology, 33 (5 ), 559–577. 10.1007/s10869-017-9514-9
Dian-jiang L. Qin W. Li-wen W. Si-min Z. Chao R. Hong F . (2021). A survey on mental health and coping style of college students during the COVID-19 pandemic. Modern Preventive Medicine, 48 (08 ), 1430−1434. 10.12122/j.issn.1673-4254.2020.02.06
Diaz-Piedra C. Catena A. Miro E. Martinez M. P. Sanchez A. I. Buela-Casal G . (2014). The impact of pain on anxiety and depression is mediated by objective and subjective sleep characteristics in fibromyalgia patients. The Clinical journal of pain, 30 (10 ), 852–859. 10.1097/ajp.0000000000000040 24281292
Diggs O. Neppl T. Jeon S. Lohman B . (2017). The association of harsh parenting, parent-child communication, and parental alcohol use with male alcohol use into emerging adulthood. Journal of Adolescent Health, 61 (6 ), 736–742. 10.1016/j.jadohealth.2017.06.025
Dong X.-X. Liang G. Li D.-L. Liu M.-X. Yin Z.-J. Li Y.-Z ., … Pan C.-W . (2022). Association between parental control and depressive symptoms among college freshmen in China: The chain mediating role of chronotype and sleep quality. Journal of Affective Disorders, 317 , 256−264. 10.1016/j.jad.2022.08.091 36055527
Duan H. Gong M. Zhang Q. Huang X. Wan B . (2022). Research on sleep status, body mass index, anxiety and depression of college students during the post-pandemic era in wuhan, China. Journal of Affective Disorders, 301 , 189–192. 10.1016/j.jad.2022.01.015 34990628
Ganesan B. Al-Jumaily A. Fong K. N. K. Prasad P. Meena S. K. Tong R. K. Y . (2021). Impact of coronavirus disease 2019 (COVID-19) outbreak quarantine, isolation, and lockdown policies on mental health and suicide. Frontiers in Psychiatry, 12 , 56519. 10.3389/fpsyt.2021.565190
Guerra C. Farkas C. Moncada L . (2018). Depression, anxiety and PTSD in sexually abused adolescents: Association with self-efficacy, coping and family support. Child Abuse & Neglect, 76 , 310–320. 10.1016/j.chiabu.2017.11.013 29179084
Huang J. L. Curran P. G. Keeney J. Poposki E. M. DeShon R. P . (2012). Detecting and deterring insufficient effort responding to surveys. Journal of Business and Psychology, 27 (1 ), 99–114. 10.1007/s10869-011-9231-8
Hwanjin P. Hye-Kyung L. Kounseok L . (2018). Chronotype and suicide: The mediating effect of depressive symptoms. Psychiatry Research, 269 , 316–320. 10.1016/j.psychres.2018.08.046 30172189
Islam M. S. Rahman M. E. Zubayer A. A. Bhuiyan M. R. A. Khan M. K. A. Hossain L. Sujon M. M. A . (2021). Investigating poor sleep quality and associated factors during the COVID-19 pandemic: A population-based survey in Bangladesh. Frontiers in Public Health, 9 , 724520. 10.3389/fpubh.2021.724520
Jacob L. Haro J. M. Koyanagi A . (2019). Relationship between living alone and common mental disorders in the 1993 , 2000 and 2007 National Psychiatric Morbidity Surveys. PLOS ONE, 14(5), e0215182. 10.1371/journal.pone.0215182
Jiuju L. Shuping T. Yanli Z. Yin Q. Fan Z. Huaqing L. Lina L . (2022). The effect of mentalization-based family therapy on adolescents with depressive disorder and its related brain regions. Chinese Journal of Behavioral Medicine and Brain Science, 31 (1 ), 43–49. 10.3760/cma.j.cn371468-20210806-00469
Joao K. A. D. de Jesus S. N. Carmo C. Pinto P . (2018). The impact of sleep quality on the mental health of a non-clinical population. Sleep Medicine, 46 , 69–73. 10.1016/j.sleep.2018.02.010 29773214
Kircanski K. LeMoult J. Ordaz S. Gotlib I. H . (2017). Investigating the nature of co-occurring depression and anxiety: Comparing diagnostic and dimensional research approaches. Journal of Affective Disorders, 216 , 123–135. 10.1016/j.jad.2016.08.006 27554605
Kjellenberg K. Ekblom O. Ahlen J. Helgadottir B. Nyberg G . (2022). Cross-sectional associations between physical activity pattern, sports participation, screen time and mental health in Swedish adolescents. Bmj Open, 12 (8 ), e061929. 10.1136/bmjopen-2022-061929
Krauss S. Orth U. Robins R . (2020). Family environment and self-esteem development: A longitudinal study from age 10 to 16. Journal of Personality and Social Psychology, 119 (2), 457–478. 10.1037/pspp0000263 31535888
Levens S. M. Elrahal F. Sagui S. J . (2016). The role of family support and perceived stress reactivity in predicting depression in college freshman. Journal of Social and Clinical Psychology, 35 (4 ), 342–355. 10.1521/jscp.2016.35.4.342
Lu H. Duan J. Wu T. Zhou B. Xu C . (2022). The influence of fear of isolation on contact experience disclosure: Evidence from safety management of the COVID-19 pandemic. Asian Journal of Social Psychology, 25 (4 ), 646–657. 10.1111/ajsp.12527
Miyuan W. Fangfang H. Kailin H. Hongye P. Minting H. Zhenhai Z . (2020). A meta-analysis of detection rate of depression symptoms and related factors in college students. Chinese Mental Health Journal, 34 (12 ), 1041–1047. 10.3969/j.issn.1000-6729.2020.12.012
Monteiro S. Matos A. P. Oliveira S . (2015). The moderating effect of gender: Traumatic experiences and depression in adolescence. Procedia - Social and Behavioral Sciences, 165 , 251–259. 10.1016/j.sbspro.2014.12.629
Munley P . (1975). Erik Erikson’s theory of psychosocial development and vocational behavior. Journal of Counseling Psychology, 22 (4), 314−319. 10.1037/h0076749
Nasser E. H. Overholser J. C . (2005). Recovery from major depression: The role of support from family, friends, and spiritual beliefs. Acta Psychiatrica Scandinavica, 111 (2 ), 125−132. 10.1111/j.1600-0447.2004.00423.x 15667431
Ohannessian C. M . (2013). Parental problem drinking and adolescent psychological problems: The moderating effect of adolescent-parent communication. Youth & Society, 45 (1 ), 3−26. 10.1177/0044118×11408931
Olatunji A. Idemudia E. Olawa B . (2020). Family support, self-efficacy and suicidal ideation at emerging adulthood: a mediation analysis. International Journal of Adolescence and Youth, 25 (1 ), 920–931. 10.1080/02673843.2020.1779762
Pan L. Li L. Peng H. Fan L. Liao J. Wang M ., … Zhang Y . (2022). Association of depressive symptoms with marital status among the middle-aged and elderly in rural China–serial mediating effects of sleep time, pain and life satisfaction. Journal of Affective Disorders, 303 , 52–57. 10.1016/j.jad.2022.01.111 35124113
Pandi-Perumal S. R. Monti J. M. Burman D. Karthikeyan R. Narasimhan M . (2020). Clarifying the role of sleep in depression: A narrative review. Psychiatry Research, 291 , 113239. 10.1016/j.psychres.2020.113239
Quan-Quan D. Li S . (2012). Differential validity of SAS and SDS among psychiatric non-psychotic outpatients and their partners. Chinese Mental Health Journal, 26 (09 ), 676-6679. 10.3969/j.issn.1000-6729.2012.09.007
Ryerson N. C . (2022). Behavioral and psychological correlates of well-being during COVID-19. Psychological Reports, 125 (1 ), 200–217. 10.1177/0033294120978160 33302799
Santomauro D. F. Herrera A. Shadid J. Zheng P. Ferrari A. J . (2021). Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. The Lancet Psychiatry, 398 (10312 ), 1700–1712. 10.1016/S0140-6736(21)02143-7
Schwarzer R. Born A . (1997). Optimistic self-beliefs: Assessment of general perceived self-efficacy in thirteen cultures. World Psychology, 3 , 177−190. https://www.researchgate.net/publication/284758239_Optimistic_self-beliefs_Assessment_of_general_perceived_self-efficacy_in_thirteen_cultures
Schwarzer R. Mueller J. Greenglass E . (1999). Assessment of perceived general self-efficacy on the internet: Data collection in cyberspace. Anxiety Stress and Coping - ANXIETY STRESS COPING, 12 (2), 145−161. 10.1080/10615809908248327
Simba H. Ngcobo S . (2020). Are pandemics gender neutral? Women’s health and COVID-19. Frontiers in Global Women’s Health, 1 , 570666. 10.3389/fgwh.2020.570666
Song L. Guangming R. Qi Z. Tianqiang H . (2019). A meta-analysis of the relationship between self-efficacy and mental health with Chinese samples. Psychological Development and Education, 35 (6 ), 759−768. 10.16187/j.cnki.issn1001-4918.2019.06.13
Sun X. J. Niu G. F. You Z. Q. Zhou Z. K. Tang Y . (2017). Gender, negative life events and coping on different stages of depression severity: A cross-sectional study among Chinese university students. Journal of Affective Disorders, 209 , 177-1181. 10.1016/j.jad.2016.11.025 27923194
Thompson E. J. Stafford J. Moltrecht B. Huggins C. F. Kwong A. S. F. Shaw R. J ., … Patalay P . (2022). Psychological distress, depression, anxiety, and life satisfaction following COVID-19 infection: Evidence from 11 UK longitudinal population studies. The Lancet Psychiatry, 9 (11 ), 894−906. 10.1016/S2215-0366(22)00307-8 36244359
Torres J. Solberg V. S . (2001). Role of self-efficacy, stress, social integration, and family support in latino college student persistence and health. Journal of Vocational Behavior - J VOCAT BEHAV, 59 (1), 53−63. 10.1006/jvbe.2000.1785
Tsuno N. Besset A. Ritchie K . (2005). Sleep and depression. The Journal of Clinical Psychiatry, 66 , 1254−1269. 10.4088/JCP.v66n1008 16259539
Vaterlaus J. M. Beckert T. E. Schmitt-Wilson S . (2019). Parent–child time together: The role of interactive technology with adolescent and young adult children. Journal of Family Issues, 40 (15 ), 2179–2202. 10.1177/0192513x19856644
Vuelvas-Olmos C. R. Sánchez-Vidaña D. I. Cortés-Álvarez N. Y . Gender-based analysis of the association between mental health, sleep quality, aggression, and physical activity among university students during the covid-19 outbreak, 00332941221086209. 10.1177/00332941221086209
Wang P. Mao N. Liu C. Geng J. Wei X. Wang W ., … Li B . (2022). Gender differences in the relationships between parental phubbing and adolescents’ depressive symptoms: The mediating role of parent-adolescent communication. Journal of Affective Disorders, 302 , 194−203. 10.1016/j.jad.2021.12.073 34952115
Wang X . (2022). Factors associated with public support for a lockdown measure in China during the COVID-19 pandemic. Asian Journal of Social Psychology, 25 (4 ), 658–673. 10.1111/ajsp.12526
Wang Y. Tian L. Guo L. Huebner E. S . (2020). Family dysfunction and adolescents’ anxiety and depression: A multiple mediation model. Journal of Applied Developmental Psychology, 66 , 101090. 10.1016/j.appdev.2019.101090
Wanlong W. Z. L. Shihao L . (2021). Status and factors of pressure/depression in common population under quarantine measures of COVID-19 pandemic. China Journal of Health Psychology, 29 (04 ), 569−574. 10.13342/j.cnki.cjhp.2021.04.020
Wenham C. Smith J. Davies S. Feng H. Grépin K. Harman S ., … Morgan R . (2020). Women are most affected by pandemics — lessons from past outbreaks. Nature, 583 , 194−198. 10.1038/d41586-020-02006-z 32641809
Wills T. A. Vaccaro D. McNamara G . (1992). The role of life events, family support, and competence in adolescent substance use: A test of vulnerability and protective factors. American Journal of Community Psychology, 20 (3 ), 349−374. 10.1007/bf00937914 1415032
Wu Mingzheng L. Y. Jie W Hongye L Xiaoling S . (2021). The impact of family rituals on well-being of college students: A chain mediating model of social connectedness and self-control. Journal of Psychological Science, 44 (06 ), 1346-11353. 10.16719/j.cnki.1671-6981.20210609
Wu Y. Mai Y. Huang Y . (2021). What type of social support is important for student resilience during COVID-19? A latent profile analysis. Frontiers in Psychology, 12 , 646145. 10.3389/fpsyg.2021.646145 34239476
Xiang Y. T. Yang Y. Li W. Zhang L. Ng C. H . (2020). Timely mental health care for the 2019 novel coronavirus outbreak is urgently needed. The Lancet Psychiatry, 7 (3 ), 228–229. 10.1016/S2215-0366(20)30046-8
Xiaolin Z. Dan L. Fang Y. Wei Q. Hongzhen F. Yanli Z ., … Shuping T . (2020). Psychological status of school students and employees during the COVID-19 epidemic. Chinese Mental Health Journal, 34 (6 ), 549−554. 10.3969/j.issn.1000-6729.2020.6.013
Xu W.-Q. Lin L.-H. Ding K.-R. Ke Y.-F. Huang J.-H. , Hou, C.-L., … Wang, S.-B. (2021). The role of depression and anxiety in the relationship between poor sleep quality and subjective cognitive decline in Chinese elderly: Exploring parallel, serial, and moderated mediation. Journal of Affective Disorders, 294 , 464−471. 10.1016/j.jad.2021.07.063 34325166
Youth. (2022). United Nations Official Website. https://www.un.org/en/global-issues/youth
Zao-huo C. Feng-xian J. Guo-qiang W. Man-ru Z. Xin-min L . (2016). Effect of family environment, parenting style and personality on juvenile delinquency. Chinese Journal of Clinical Psychology, 24 (02 ), 287–292. 10.16128/j.cnki.1005-3611.2016.02.023
Zhang J. X. Schwarzer R . (1995). Measuring optimistic self-beliefs: A Chinese adaptation of the general self-efficacy scale. PSYCHOLOGIA, 38 (3 ), 174−181. 10.1080/09515089508573160
Zhengyu W. Yufen C . (1984). Self-rating anxiety scale, SAS. Shanghai Archives of Psychiatry (2 ), 73−74. https://kns-cnki-net-s.libvpn.scnu.edu.cn:20080/kcms/detail/detail.aspx?dbcode=CJFD&filename=JSYI198402008&v=MjYzMjkrVnZGeWptVUxyTEx6N1NaN0t4RnRYTXJZOUZiSVFLREg4NHZSNFQ2ajU0TzN6cXFCdEdGckNVUjdpZVo=&uid=WEEvREcwSlJHSldSdmVqMDh6cEJpNU9lcDczeFRUU3RjNndZOVNMRm1nVT0=$9A4hF_YAuvQ5obgVAqNKPCYcEjKensW4IQMovwHtwkF4VYPoHbKxJw!!
Zhou J. Hsiao F.-C. Shi X. Yang J. Huang Y. Jiang Y ., … Ma N . (2020). Chronotype and depressive symptoms: A moderated mediation model of sleep quality and resilience in the 1st-year college students. Journal of clinical psychology, 77 (1), 340–355. 10.1002/jclp.23037
Zhu D.-m ., Zhang C ., Yang Y ., Zhang Y ., Zhao W ., Zhang B ., … Yu Y . (2020). The relationship between sleep efficiency and clinical symptoms is mediated by brain function in major depressive disorder. Journal of Affective Disorders, 266 , 327−337. 10.1016/j.jad.2020.01.155 32056895
Zhu Y ., Ma X ., Zhuoyi T. Minqi Y . (2021). Impact of chronotype on symptoms of depression in college students:A moderate mediation model. Studies of Psychology and Behavior, 19 (5 ), 657−664 + 694. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=CLXW202105012&DbName=CJFQ2021
Zi-qiang X. Su-fei X. Mei Z . (2011). Changes in college students’ anxiety level in 1993－2009: A cross-temporal meta-analysis. Psychological Development and Education, 27 (6 ), 648−653. 10.16187/j.cnki.issn1001-4918.2011.06.004
Zung W . (1965). Zung self rating depression scale. Archives of General Psychiatry, 12 (1 ), 63−70. 10.1001/archpsyc.1965.01720310065008 14221692
Zung W. W. K . (1971). A rating instrument for anxiety disorders. Psychosomatics, 12 (6 ), 371−379. 10.1016/S0033-318271)71479-0 5172928
| 0 | PMC9729717 | NO-CC CODE | 2022-12-14 23:22:30 | no | Sch Psychol Int. 2022 Dec 5;:01430343221142284 | utf-8 | Sch Psychol Int | 2,022 | 10.1177/01430343221142284 | oa_other |
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J Asian Afr Stud
J Asian Afr Stud
JAS
spjas
Journal of Asian and African Studies
0021-9096
1745-2538
SAGE Publications Sage UK: London, England
10.1177/00219096221141359
10.1177_00219096221141359
Original Research Article
The Psychological, Social, and Economic Impacts of COVID-19 on Nepali Migrant Workers
https://orcid.org/0000-0003-3971-2737
Ghimire Jiwnath Iowa State University, USA
Nepal Ratna Mani Tribhuvan University, Nepal
Crowley Julia University of Missouri–Kansas City, USA
Ghimire Dipesh Tribhuvan University, Nepal
Guragain Shyam K & K College, Nepal
Jiwnath Ghimire, Department of Community and Regional Planning, Iowa State University, 715 Bissell Rd, Ames, IA, 50011, USA. Email: [email protected]
6 12 2022
6 12 2022
00219096221141359© The Author(s) 2022
2022
SAGE Publications
This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.
The global pandemic impacted Asian migrant workers disproportionately. During the first COVID-19 nationwide lockdown, Nepali migrant workers faced many challenges due to widespread misconceptions of them being vectors of COVID-19. This research examines COVID-19 impacts on international and internal Nepali migrant workers. A national survey on the social, economic, and psychological challenges of returning Nepali migrant workers was administered online from 10 May to 20 July 2020. A total of 672 responses were received. Using a binary logistic regression model, the research finds that the domestic migrant workers were less likely to get economic support, expect to borrow money during COVID-19, experience negative changes in their personal lives, and expect the COVID-19 contraction. In contrast, international migrant workers were less likely to return to their pre-pandemic employment. The research exposed long-standing vulnerabilities of migrant workers and identified immediate actions from Nepalese Central, Provincial, and Local governments to address their needs.
COVID-19
vulnerabilities
lockdowns
Nepal
migrant workers
Natural Hazards Center, University of Colorado Boulder https://doi.org/10.13039/100008618 NSF Award #1841338 edited-statecorrected-proof
typesetterts1
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pmcIntroduction
Labor migrations and remittances have evolved into a substantial component of the global economy. Labor migrations can be categorized as internal and international based on origins and destinations. Internal migration is their movement from one area of the country to another (International Organization for Migration (IOM), 2015), and international migration is away from their usual residence across an international border (IOM, 2022). Both could be temporary and permanent. Although migrant job opportunities and remittances have been noted to bring economic benefits to the workers’ place of origin, various aspects of the migrant labor process have made migrant workers among the most vulnerable populations in the world (Alahmad et al., 2020). Low-wage migrant workers tend to have disproportionately higher risk factors in regard to experiencing hardship (Saparamadu et al., 2021). Such vulnerabilities include but are not limited to exploitation, safety hazards, and health hazards. Truman et al. (2009) examined the impacts of migratory labor on workers’ mental health. They concluded that the challenges associated with adapting to a new country, finding employment, and learning a new language yield high-stress levels, which create unique mental health needs (Truman et al., 2009). On a similar note, Ismayilova et al. (2014) associated high mobility with depression among internal migrant workers. Additional studies highlighted the high rates of physical health hazards among migrant workers (Joshi et al., 2011; Simkhada et al., 2017; Wahab, 2020).
The South Asian nation of Nepal illustrates the labor migration phenomenon, with remittances accounting for over 25% of the gross domestic product (GDP) (Paoletti et al., 2014). Furthermore, almost half of all Nepali households reported having at least one family member who is either currently working abroad or has previously worked abroad (Paoletti et al., 2014). This population is also subject to a myriad of vulnerabilities. Paoletti et al. (2014) found that Nepali migrant workers commonly experienced non-payment of wages, unsafe work conditions, inadequate rest, inhumane housing conditions, and the confiscation of identity documents.
The coronavirus disease of 2019 (COVID-19) has exacerbated the existing vulnerabilities of Nepali migrant workers. On 24 March 2020, the Indian Prime Minister imposed the first complete lockdown at the India–Nepal border (Shah et al., 2020). The lockdown resulted in hundreds of migrant workers being stranded at the border on both sides and kept in quarantine centers (Aacharya and Shah, 2020). The quarantine centers that were built by the Nepali government were quickly overwhelmed by the magnitude of migrant workers returning to Nepal from India, which is estimated at 2 million, mostly low-wage workers (Shah et al., 2020). Furthermore, the poor sanitary conditions of the quarantine centers have raised ethical concerns regarding the treatment of Nepali migrant workers during COVID-19 (Dhungana, 2020).
The lingering impacts of COVID-19 have gone on to impose additional vulnerabilities pertaining to the psychological well-being of Nepali migrant workers. Ullah et al. (2021) and Kumar et al. (2021) concluded that migrant populations were considered the worst victims of COVID-19 as well as the largest spreaders. This has resulted in increased cases of xenophobia, discrimination, and stigmatization among these populations (Ullah et al., 2021). For example, Bahadur et al. (2021) studied the status of anxiety and depression among returned migrant workers residing in institutional quarantine centers in Western Nepal and observed that respondents experienced stigma and discrimination as well as vulnerabilities to disease and loss of employment. They also found cases of depression and anxiety to be high among the quarantined population.
In addition to psychological impacts, COVID-19 posed social challenges for migrant workers. Some of the challenges are job loss, income loss, transportation problems, mental stress, and social discrimination (Maiti et al., 2022). Pregnant returning migrant women represented a group that was heavily impacted at the social level. A report by the National Human Rights Commission (2020) found that a notable number of pregnant women returned to Nepal due to COVID-19. A number of media and social platforms were used to make degrading and stigmatizing comments toward them, which in turn hurt their self-respect and contributed to spreading negativity among the general public toward pregnant returnees (National Human Rights Commission, 2020). Thapa et al. (2020) highlighted the impacts of repatriation on the reintegration of pregnant women into society and their families and questions pertaining to the citizenship of the children. There are also incidents of discriminatory behaviors against migrant workers based on their work locations. Discrimination was also noticed in the government service delivery among international returning migrant workers. Migrant workers from India, who comprise low-wage migrants, were often suspected as virus carriers upon their arrival at Nepal–India borders by themselves, while those from Wuhan, China, and other countries were brought to Nepal by chartered public flights (Nepal, 2021).
COVID-19 has also imposed negative economic impacts on returning Nepali migrant workers as it led to the termination of a substantial number of migrant workers’ contracts, which forced them to return home (Suhardimana et al., 2021). This brought a dependency reversal for the economic support of the migrant workers, who reported subsequent high levels of shame (Suhardiman et al., 2021). Bhattarai and Baniya (2020) estimate that 25% of Nepali migrants working abroad will lose their jobs due to COVID-19. Moreover, a report published by DanChurchAid (DCA, 2020) surveyed migrant returnees in the Nepali districts of Kanchanpur, Kailali, Doti, and Anchham and revealed that 98.7% of the respondents lost their livelihoods due to COVID-19. Additionally, they found that 63.1% of the migrant workers preferred to stay in Nepal, while 36.9% preferred to go abroad after the lockdown (DCA, 2020). This finding contributes to a growing discussion on the future of Nepal’s economy. Maharjan et al. (2020) argued that the massive return of migrant workers due to COVID-19 is creating an opportunity for Nepal to revitalize and reinstate its latent agricultural economy. They asserted that such a revitalization would help prevent the national economy from collapsing while also addressing potential food insecurities (Maharjan et al., 2020). From a global reform perspective, Foley and Piper (2021) argued that COVID-19 might influence the development of a global advocacy movement to address injustices to migrant workers, such as wage theft, and subsequently lead to the formation of a global institutional mechanism where repatriated migrant workers can access justice.
The impacts of COVID-19 on the repatriation of migrant workers have warranted further discussions on the discrepancies between international and internal migrant workers in terms of vulnerability. Khan and Arokkiaraj (2021) conducted a comparative analysis of the Indian government’s approach toward its international and internal migrant workers during the repatriation process and the lockdown. On the one hand, they found that both international and internal migrants were stranded without adequate facilities, and this aggravated their feelings of discrimination. On the other hand, they concluded that international migrants incurred exorbitant expenses from travel tickets, COVID-19 tests, and quarantine centers, while respective state governments were required to cover the travel expenses of internal migrants (Khan and Arokkiaraj, 2021). The World Bank Group (2020) asserts that internal migrant workers face similar challenges to temporary international migrant workers, but without the implications for legal status in most cases. With growing concerns regarding the discrepancies in understanding the different needs between international and internal migrant workers during COVID-19, there remains a dearth of published literature that compares the two groups. King and Skeldon (2010) conclude that labor migration studies too often focus on either international or internal migrants without reference to the other, which yields a partial analysis. Skeldon (2008) suggests that studies of international and internal migration be brought together to more fully understand the impact of migration on development. He discusses the tendency for the poorest migrants to be limited to internal migrations and not international (Skeldon, 2008).
This research addresses the previously identified gap by comparing COVID-19 impacts on internal and international migrant workers. The overarching research question is, to what extent the economic, social, and psychological impacts of COVID-19 are different between domestic and international returning migrant workers? Nepal was selected as the location of the study due to the significant role of remittances on the nation’s GDP, and the previously identified psychological, social, and economic impacts of COVID-19 on the repatriated migrant workers. This research also addresses the human dimensions of risk to better understand policy interventions for the two groups during a public health crisis. The subsequent section on the material and methods provides a detailed description on the sampling, data collection, survey design, variable selection, and analysis process.
Material and methods
Sampling and data collection
A survey was designed and distributed online to better understand the differences in the psychological, social, and economic impacts of COVID-19 for international and internal migrant workers. A snowball sampling approach was used to locate returned Nepali migrant workers. The survey was distributed through personal networks, organizational email listservs, and social media. Key provincial contacts were established to reach the migrant workers in the quarantine and isolation facilities. Three additional key contacts were established in provinces one, five, and seven to reach the returning migrant workers from India. The study was reviewed and approved by the Human Studies Program of the University of Hawaii (Approval No. 2020-00409) prior to the distribution of the survey. In going through this process, the researchers were required to complete training modules on protecting human subjects and vulnerable populations and provide a description of how subjects’ confidentiality would be protected. Furthermore, in surveying subjects, it was specified that they have the right to cease participation with no penalties at any time if they become distressed by the nature of the survey. The research was conducted during the initial nationwide lockdown enforced by the Nepali government from April to July 2020. A total of 672 responses were received from 10 May to 30 July 2020.
The survey covered 65 out of the 77 total districts of Nepal (see Figure 1). The Western provinces of the country had higher response rates than the Eastern provinces, with the highest response rates coming from the districts of Rupandehi and Kathmandu and the second highest coming from the districts of Dang and Kailali. The districts of Rupandehi, Dang, and Kailali have checkpoints for Nepali migrant workers to travel to India, and they also had the largest quarantine centers during the initial lockdown and travel restrictions. See Figure 1 for a map of the geographic distribution of survey responses.
Figure 1. Distribution of survey responses for the districts of Nepal.
Survey design and variable selection
The survey questionnaire pertains to the psychological, social, and economic impacts of COVID-19 on Nepali migrant workers. Sections consist of impacts on livelihoods, impacts on employment, health risks of COVID-19, social impacts, and demographic information. The section on the impacts of COVID-19 on migrants’ livelihoods contained four questions that were analyzed in this research. One question asks: Are you planning to return to the same occupation once the Government ends the stay-at-home order? This question was included to account for the finding of DCA (2020) regarding 63.1% of Nepali international migrant workers preferring not to return to their livelihoods abroad following the lockdown. The three other questions in this section include: Did you borrow money from any financial institution in the last 2 months? Did you get any support during the initial lockdown (stay-at-home-order)? Do you think your lifestyle will remain the same as before once COVID-19 is over? These questions reflect the economic dependency reversal of Nepali migrant workers during the lockdown that was identified by Suhardimana et al. (2021). All four responses were dichotomous, where 1 = yes and 0 = no.
The section on the impacts of COVID-19 on migrants’ employment contained two questions that were analyzed in this research. One question asked: Are you planning to return to your current job immediately after the end of the lockdown (stay-at-home order)? The response was dichotomous, where 1 = yes and 0 = no. The other question asked: Did you or members of your household lose your jobs because of COVID-19? The response was also dichotomous, where 1 = yes and 0 = no. These questions account for the high percentages of Nepali migrant workers who lost their jobs due to COVID-19, as identified by Bhattarai and Baniya (2020) and DCA (2020).
The section pertaining to the health risks of COVID-19 contained two questions that were analyzed in this research. The first question asked: Do you or any family members have chronic medical conditions such as diabetes, heart disease, or other illnesses? This question was added to the survey to account for any pre-existing health conditions that could increase one’s risk of developing complications from COVID-19. The response was dichotomous, where 1 = yes and 0 = no. An additional question was included in this section to connect the responses from the previous question to the risk factors. The question asked: Do you believe there is a possibility of infecting yourself or a family member with COVID-19? The response was also dichotomous, where 1 = yes and 0 = no.
The section on social impacts and human dimensions of risks contained three questions that were analyzed in this research. One of the questions asked: Do you believe the government can cure your COVID-19 sickness and control the COVID-19 pandemic? (on a scale of 1–10 with 10 being the most effective). This question was included to account for potential distrust of the Government as a result of the lockdown at the India–Nepal border and the poor sanitary conditions of the government quarantine centers (Dhungana, 2020). The response was recoded into a dichotomous variable where 1 = low confidence in the Government and 0 = high confidence in the Government. Another question in the social distancing section asked: How effective is the stay-at-home order for mitigating COVID-19? (on a scale of 0–10 with 10 being the most effective). This question was included to account for any perceived benefits of the stay-at-home order among respondents. The response was also recoded into a dichotomous variable where 1 = low confidence in the effectiveness of the stay-at-home order and 0 = high confidence in the effectiveness of the stay-at-home order. The third question in this section asks: What types of changes did you notice in your personal and family lives since the beginning of the stay-at-home order? The response was dichotomous where 1 = negative changes and 0 = positive changes. Each response option was clarified with examples of negative and positive changes due to lockdowns and stay-at-home orders.
The final section of the survey included demographic information to provide additional control variables. This section contained three questions that were used in this research, including: How old are you? What is your gross annual household income in lakh rupees? What is your gender? The age question was recoded into a dichotomous variable where 1 = 40 years old or younger and 0 = over 40 years old, based on the average age of migrant workers traveling out of the country. The income variable was also recoded into a dichotomous variable where 1 = gross annual household income below the poverty level and 0 = gross annual household income at or above the poverty level. The gender variable originally had four categories, which consisted of: male, female, non-binary/third gender, and prefer not to say. Respondents either indicated male or female, and the variable was recoded so that 1 = male and 0 = female.
The survey contained an additional question that served as the dependent variable for this research. The question asked: Where were you working before the implementation of the stay-at-home order by the Government? The variable originally contained 11 options, but was recoded so that 1 = within Nepal (internal migrant workers) and 0 = outside of Nepal (international migrant workers).
Data analysis
The data were analyzed using a binary logistic regression model in SAS statistical analysis software (Allison, 2012). The model examined the relationship between a binary dependent variable (i.e. internal migrant workers) and categorical response variables related to the psychological, social, and economic impacts of COVID-19 among migrant workers. The method was selected for the analysis because the dependent variable was categorical and dichotomous. The regression model is identified using the following equation (Hosmer and Lemeshow, 2000; Kim et al., 2021)
(1) logit{Pr(Y=1|x)}=log{Pr(Y=1|x)1−Pr(Y=1|x)}=β0+x′β
where Y denotes the two possible values denoted by 1 and 0, x=(x1,…,xk)′ is the vector of explanatory variables, β0 is an intercept, and β is the vector of slope parameter of explanatory variables.
The results show changes in occupations, financial situations, social relations, and psychological health of returning migrant workers in Nepal due to the COVID-19 pandemic. The first lockdown (April to July 2020) in the host countries and Nepal resulted in sudden changes in these workers’ psychological, social, and economic lives. These changes have implications for the livelihood of their families because these workers were continuously helping them meet basic needs in villages and towns in Nepal. The following “Results” section summarizes the survey responses and the binary logistic regression model on differences of psychological, social, and economic impacts of COVID-19 among returning migrant workers during the first nationwide state-at-home orders in Nepal.
Results
Summary of the survey responses
There were a total of 672 respondents. Of this total, 207 represented international Nepali migrant workers, while the remaining 465 were internal migrant workers in Nepal. Table 1 summarizes the survey responses.
Table 1. Summary of survey responses.
Survey responses (N = 672) International Internal
N % N %
Return plan to the same occupation
Yes 81 40.30 431 95.14
No 120 59.70 22 4.86
Borrowed money from financial institutions
Yes 29 15.18 100 21.69
No 162 84.82 361 78.31
Got support during the initial lockdown
Yes 42 21.00 39 8.50
No 158 79.00 420 91.50
Expects the same lifestyle after COVID-19 is over
Yes 72 35.64 128 28.26
No 130 64.36 325 71.74
Plans to return to current job after stay-at-home order
Yes 76 36.71 417 89.68
No 131 63.29 48 10.32
Job loss in family due to COVID-19
Yes 126 67.74 116 30.85
No 60 32.26 260 69.15
The respondent or a family member has a chronic medical condition
Yes 43 20.77 158 33.98
No 164 79.23 307 66.02
Believes it is possible for self or family members to get COVID-19
Yes 136 65.70 213 45.81
No 71 34.30 252 54.19
Believes in the government for COVID-19 mitigation
High 33 20.50 77 25.50
Low 128 79.50 225 74.50
Believes in the effectiveness of the stay-at-home order to reduce outbreak
High 73 40.78 262 80.37
Low 106 59.22 64 19.63
Changes in personal and family lives since the stay-at-home order (multiple choice)
Negative 149 68.66 327 50.31
Positive 68 31.34 323 49.69
Age
Below 25 years 93 44.93 187 40.22
25–35 years 68 32.85 134 28.82
36–45 years 36 17.39 98 21.08
46 years and above 10 4.83 46 9.89
Gross annual household income (Exchange rate: USD 1 = Rs. 119.4, 08/01/2020)
Below Rs. 100,000 56 29.02 100 26.46
Rs. 100,001–200,000 33 17.10 55 14.55
Rs. 200,001–300,000 48 24.87 36 9.52
Rs. 300,001–400,000 37 19.17 54 14.29
Rs. 400,001–500,000 9 4.66 43 11.38
Above Rs. 500,000 10 5.18 90 23.81
Gender
Male 178 91.28 286 72.59
Female 17 8.72 108 27.41
Logistic regression results for predicting psychological, social, and economic impacts of COVID-19 among internal and international Nepali migrant workers
Table 2 presents the results for the variables in the binary logistic regression. Internal Nepali migrant workers were more than five times as likely as international Nepali migrant workers to plan on returning to the same occupation after the Government ends the stay-at-home order (odds ratio (OR) = 5.12, 95% confidence interval (CI) = 2.54, 10.32, p < 0.001). Similarly, internal Nepali migrant workers were more than two times as likely as international Nepali migrant workers to have borrowed money from financial institutions in the last 2 months of the first nationwide stay-at-home order in Nepal (OR = 2.58, 95% CI = 1.33, 5.01, p = 0.005). However, the other two variables in the section on the impacts of COVID-19 on the livelihoods of migrant workers had contrasting results compared to the previous two variables. Internal Nepali migrant workers were 81% less likely than international Nepali migrant workers to have received support during the initial lockdown (OR = 0.19, 95% CI = 0.10, 0.36, p < 0.001), and internal Nepali migrant workers were 57% less likely than international Nepali migrant workers to expect their lifestyle to remain the same as before once COVID-19 is over (OR = 0.43, 95% CI = 0.25, 0.74, p = 0.002).
Table 2. Logistic regression results for predicting psychological, social, and economic impacts of COVID-19 among internal and international Nepali migrant workers (N = 672).
Parameter (Modeled = Internal workers) β Standard error Wald’s χ2 Odd ratios Confidence interval p-Values
Low High
Intercept 1.42 0.61 5.43 0.02
Return plan to the same occupation 1.63 0.36 20.83 5.12 2.54 10.32 <0.001
Borrowed money from financial institutions 0.95 0.34 7.93 2.58 1.33 5.01 0.005
Got support during the initial lockdown –1.68 0.34 24.12 0.19 0.10 0.36 <0.001
Expects the same lifestyle after COVID-19 is over –0.85 0.28 9.43 0.43 0.25 0.74 0.002
Plans to return to current job after stay-at-home order 1.94 0.34 31.93 6.94 3.54 13.58 <0.001
Someone in family lost job due to COVID-19 –0.99 0.27 13.32 0.37 0.22 0.63 <0.001
The respondent or a family member has a chronic medical condition 0.86 0.30 8.24 2.36 1.31 4.24 0.004
Believes it is possible for self or family members to get COVID-19 –0.32 0.26 1.56 0.72 0.44 1.20 0.21
Low confidence in the government for COVID-19 mitigation 0.41 0.27 2.29 1.51 0.88 2.59 0.13
Low confidence in the effectiveness of the stay-at-home order to reduce outbreak –1.19 0.29 16.79 0.30 0.17 0.54 <0.001
Negative changes in personal and family lives since the stay-at-home order 0.92 0.30 9.45 2.51 1.40 4.52 0.002
40 years old or younger –1.13 0.39 8.37 0.32 0.15 0.69 0.004
Gross annual household income below poverty level (lakh rupees) –0.28 0.07 14.48 0.76 0.66 0.87 <0.001
Male –1.32 0.32 16.52 0.27 0.14 0.51 <0.001
In terms of the impacts of COVID-19 on migrant workers’ employment, internal Nepali migrant workers were almost seven times more likely than international Nepali migrant workers to plan to return to their current jobs immediately after the end of the lockdown (OR = 6.94, 95% CI = 3.54, 13.58, p < 0.001). In addition, internal Nepali migrant workers were 63% less likely than international Nepali migrant workers to report that someone in their family lost a job due to COVID-19 (OR = 0.37, 95% CI = 0.22, 0.63, p < 0.001). The two variables that measured the respondents’ health risks of COVID-19 displayed contrasting results. On the one hand, internal Nepali migrant workers were more than two times as likely as international Nepali migrant workers to indicate that either they or a family member has a chronic medical condition (OR = 2.36, 95% CI = 1.31, 4.24, p = 0.004). On the other hand, the variable that measured one’s belief regarding the possibility for themselves or their family members contracting COVID-19 was not a statistically significant predictor of being an internal or international Nepali migrant worker.
Variables pertaining to the social distancing section also presented contrasting results. The variable that measured one’s level of confidence in the Government for mitigating COVID-19 was not a statistically significant predictor of being an internal or international Nepali migrant worker. However, internal Nepali migrant workers were 70% less likely than international Nepali migrant workers to report low confidence in the effectiveness of the stay-at-home order for reducing the outbreak (OR = 0.30, 95% CI = 0.17, 0.54, p < 0.001). Furthermore, internal Nepali migrant workers were 2.5 times as likely as international Nepali migrant workers to report negative changes in personal and family lives since the stay-at-home order (OR = 2.51, 95% CI = 1.40, 4.52, p = 0.002).
The final section of the research controls for demographic variables. Internal Nepali migrant workers were 68% less likely than international Nepali migrant workers to be 40 years old or younger (OR = 0.32, 95% CI = 0.15, 0.69, p = 0.004). Internal Nepali migrant workers were 24% less likely than international Nepali migrant workers to have a gross annual household income that is below the poverty line (OR = 0.76, 95% CI = 0.66, 0.87, p < 0.001). Finally, internal Nepali migrant workers were 73% less likely than international Nepali migrant workers to be male (OR = 0.27, 95% CI = 0.14, 0.51, p < 0.001).
Findings and discussion
The initial lockdown, travel restrictions, and stay-at-home orders for COVID-19 exposed the economic, social, systemic, and administrative problems imposed on Nepali migrant workers, which are common situations for migrant workers in the Global South. For international migrant workers, traveling abroad for employment requires a substantial amount of money and the majority of them are from subsistence economic conditions. This requires them to borrow money at a high-interest rate due to a lack of access to loans and borrowing culture from relatives and friends (Bhandari et al., 2021; Kharel, 2016). As shown in Table 2, international migrant workers are less likely to return to their previous jobs due to economic and financial challenges. These problems are less likely to be prevalent among internal migrant workers because they borrow money from relatives and financial institutions in urgent times, such as the pandemic. Being undocumented workers in informal sectors, the internal migrant workers were less likely to be affected by these systemic and administrative requirements. However, international migrant laborers have to complete different requirements from the Nepali Government in addition to the host countries to take the job abroad. This includes getting approval from the Government, wellness checks, visa arrangements, travel tickets, and paying taxes to the Government (Government of Nepal (GoN), 1992). COVID-19 has created uncertainties and limitations in completing these requirements because employment companies abroad have halted their hiring of foreign workers, and the governments were operating offices in a limited capacity. Furthermore, the host countries have imposed many requirements for international workers, including testing and vaccinations, but there are no special programs for international migrant workers to address their individual needs. The pandemic exposed the long-standing but hidden challenges of migrant workers created by service delivery limitations.
International migrant workers were traumatized by the COVID-19 spread in their workplaces. The pandemic exposed the incubating vulnerabilities of these workers related to poor living conditions and limited essential services. They were forced to live in a crowded environment with unsanitary conditions and suboptimal access to health services (Wahab, 2020). Moreover, limited awareness about the disease in an unfamiliar foreign environment made them more vulnerable to physical and mental health problems (Guadagno, 2020). It also applies to the internal migrant workers suspected to be spreaders in their workplaces and virus carriers in their origins.
Another major issue encountered by international migrant workers was the screening, quarantine, and isolation mechanisms at the country’s borders. Due to the overwhelming return of migrant workers during initial lockdowns, the border check posts could not address the need for screening, quarantine, and isolation of returning migrant workers. As a result, these workers had to wait at the border for more than 20 hours in line to re-enter their own country (Singh, 2020). They faced a myriad of challenges, including a lack of access to food, quarantine shelters, transportation services from the border to their hometowns, and basic sanitation and health services (Dhakal and Karki, 2020; Nepal, 2021). These issues resulted in migrants’ psychological problems, such as nervousness, anxiety, and fear for the future. These circumstances demotivated returning migrant workers from other countries to return to their pre-pandemic occupations, as shown in Table 2.
The situation of internal migrant workers was different. Being in their villages and close to family made international migrant workers comfortable during these uncertain times of COVID-19. The uncertainties created by the virus, the shutting down of work locations, and associated travel costs made returning international migrant workers from other countries less likely to return to their pre-lockdown jobs. But they did not expect that the spread of COVID-19 would significantly impact their lifestyle. They also had some financial surpluses to support themselves. This study has shown that the situation for internal migrant workers was the opposite in Nepal, which can be applied to other countries in the Global South. Due to lower pay and a lack of surplus income, the internal migrant workers were expecting significant changes in their lifestyle after the pandemic, as shown by the results in Table 2. These internal workers’ situations require special programs to support them during the COVID-19 lockdown. Such programs should establish well-equipped quarantine facilities, enhance border checkpoint screening capacity, introduce loan support, and provide special testing and vaccination programs for migrant workers.
Nepal’s internal migrant workers needed targeted attention to address their economic vulnerability during the COVID-19 pandemic. They are undocumented and underpaid, and they are from poor economic backgrounds. They lack sustainable monetary wages, job guarantees, insurance, and social security, resulting in the most vulnerable group during lockdown and stay-at-home orders (International Labour Organization (ILO), 2020). They move from villages to cities with expectations of employment and higher income. In reality, they do not earn surplus money in urban areas because they are employed in informal sectors and non-skilled jobs. As a result, changes in their earnings significantly impact their livelihoods. When the Government of Nepal (GoN) imposed the first travel restrictions and stay-at-home orders with short notice during the last week of March 2020, their employment got halted, and they did not have enough time and money to return to their villages. They were forced to walk for days to reach their homes (Prasai, 2020). Those employed during the lockdown and stay-at-home orders did not get full pay. It was estimated that 300,000 workers in the construction sector got 50% of their regular salary during the initial lockdown and stay-at-home orders (Prasai, 2020). They were borrowing money from their relatives, friends, and financial institutions to support their families and themselves during the lockdown. That was prevalent nationwide during the initial travel restrictions and stay-at-home orders. But the situation of international migrant workers was different. Although they borrowed money to travel abroad for employment, they were more likely to have savings to support themselves and their families when they returned home. As a result, they were not forced to borrow money during the initial COVID-19 lockdown in the country.
The internal migrant workers were forced to work during the lockdowns and stay-at-home orders in Nepal. Due to their low economic status and lack of surplus earnings to support themselves and their families, they continued their job during the peak contagious times of COVID-19. As a result, they were more likely to get infected with the virus. The findings from this study have shown that these internal migrant workers are more likely to have a family member with a chronic medical condition. A combination of continuous exposure and having vulnerable members in the family made these internal migrant workers more vulnerable to infections than international migrant workers. The GoN, international organizations, and non-government agencies should collaboratively develop joint programs to address the need of these internal migrant workers to reduce their exposure and vulnerability to the virus and create a safety net for future disasters.
This study found that internal migrant workers were more confident than international migrant workers in the effectiveness of stay-at-home orders. Due to mobility restrictions, border seals, a lack of proper health care, poor living and working conditions, cultural and linguistic barriers, low levels of knowledge of services, and a lack of social networks, the international migrants were more affected by the travel restrictions and stay-at-home orders than internal migrant workers (Liem et al., 2020). They faced problems like distance from family and uncertainty about future employment. They were more vulnerable in the labor market because of job providers and governments’ systematic discrimination between domestic and international workers (Organisation for Economic Co-operation and Development, 2020). The stay-at-home order, travel restrictions, and quarantine requirements are an extra burden for these workers. They are more vulnerable to mental and psychological stressors due to the working and living environments in the host countries. They believe that these stay-at-home orders and travel restrictions are creating impediments to meeting their families rather than helping to reduce the spread of the virus. Therefore, this situation needs an intervention program to build awareness among these workers and develop a targeted program to transition them from quarantine and testing to society.
Internal migrant workers experienced negative changes in personal and family lives since the stay-at-home order. Despite their expectation of returning to their pre-pandemic employment, most of them lost their jobs and income sources due to COVID-19 restrictions. For a long time, the GoN imposed a lockdown and stay-at-home order but did not assist these workers (ILO, 2020). The restriction rules also did not allow them to go out of the house to work and provide services. If they were working, there were no guidelines and training on how to be safe from COVID-19 in their work environments. They faced many negative changes in their personal and family lives. They included mental stress, anxiety, fear, financial insecurity, and worry about their future. Chaudhary (2020) argues that due to the government’s negligence, the lives of internal migrant workers were negatively affected by the COVID-19-induced lockdowns and stay-at-home orders. This is supported by a study conducted by Sun et al. (2021) that revealed the importance of a government’s response to controlling a pandemic. They found a negative correlation between daily COVID-19 growth rates and government response indexes (Sun et al., 2021).
The younger male Nepali population is the target group of the international labor market. This is also common in other labor supplier countries in the world. Based on the GoN (2020) data, the average age of international Nepali migrant workers was 29 years in 2017/2018. Only 2% of the total international migrant workers are above 45 years of age. As a result, domestic labor is more likely to be older people. This is also alarming from the COVID-19 pandemic perspective, as the virus has disproportionately impacted older adults and people with pre-existing health conditions. Being older makes the internal migrant workers more vulnerable to the virus infection, and they need special attention, including training, work environmental safety measures, and accessibility to health services.
The COVID-19 pandemic highlighted the human dimensions of risk and how it interacts with vulnerability (Yıldırım et al., 2021). Risk, from this perspective, involves the likelihood that a hazard will turn into something bad and the consequences if it does. In this case, there is a higher COVID-19 risk perception among migrant workers. Risk then goes onto interact with disaster events to expose incubating vulnerabilities. The different aspects of vulnerability among the Nepali migrant workers that were discussed in the literature review interacted with the risk. Their circumstances made them highly likely to experience stress from the COVID-19 pandemic, and the consequences related to monetary loss, social stigma, and psychological stress were apparent.
Conclusion
The psychological, social, and economic impacts of COVID-19-related lockdowns and stay-at-home orders have shed light on the long-standing problems of internal and international Nepali migrant workers, which can be generalized to labor supplier countries globally. Both groups are highly vulnerable to the virus, but their challenges are unique.
International migrant workers were less impacted economically due to initial lockdowns and stay-at-home orders, but they were less likely to return to their previous employment. As a result, the government and concerned authorities have to introduce employment programs to mainstream them in the national economy. This is a common situation across countries with higher out-migration for employment. In the case of Nepal, the government should therefore introduce different incentive programs and negotiations with host countries to ease the returning process of these migrant workers. Being older, borrowing money to meet subsistence needs, and having family members with pre-existing health conditions make the internal migrant workers more vulnerable to the virus. However, they are more likely to return to their previous employment after the pandemic. This research has shown that internal migrant workers are more vulnerable to non-pharmaceutical measures to curb the spread. Vaccination and mitigation programs should target these groups to build long-term resilience to the virus.
The research was conducted at the beginning of the global pandemic when information about COVID-19 was limited, and vaccinations were not available. In addition, there were many uncertainties, and mitigation measures were not in place. Therefore, the impact of COVID-19 among migrant workers might have changed since this research was conducted. This study covers the short-term psychological impacts, including anxiety, nervousness, worry, and confusion. They were the most noticeable psychological problems among migrant workers during the initial national lockdowns to curb the COVID-19 spreads. Further research should focus on the long-term psychological damages among these migrant workers caused by the pandemic and the social and economic impacts of COVID-19 after the availability of the vaccine and a better understanding of the disease.
An additional limitation of this study is that it categorized Nepali international migrant workers into one group and Nepali internal migrant workers into another group and did not account for the different countries that international migrants were working in or the different locations within Nepal where the internal migrants were working. There were many discrimination incidents against returning migrant workers from India at the southern borders of Nepal. They had to wait many hours to enter their own country and were forced to live in substandard emergency isolation shelters without enough health and sanitation services. It was not the case for the workers who were returning from other countries through airports. Migrant workers who travel to India for work are not documented because they do not require a passport or other identity documents to cross the border. They are more vulnerable to service deprivation during global crises like COVID-19. Future research should analyze the pandemic impacts among migrant workers who traveled to India for employment and should also explore discrepancies in the vaccine accessibilities among Nepali migrant workers.
Author biographies
Jiwnath Ghimire is an assistant professor in the Department of Community and Regional Planning at Iowa State University. He got his doctorate degree from the University of Hawaii at Manoa. His research interests are environmental justice, disaster governance, science-policy interface for climate action planning, and climate-smart disaster recoveries. [email protected].
Ratna Mani Nepal is a Reader at the Center of Nepal and Asian Studies (CNAS) at Tribhuvan University in Kathmandu. His research interests are political economy, globalization, and culture and the development of the Global South. He has been a visiting scholar at the Orfalea Center for Global and International Studies at the University of California – Santa Barbara. [email protected].
Julia Crowley is an assistant professor at the University of Missouri – Kansas City. Julia Crowley is an Assistant Professor of Urban Planning and Design at the University of Missouri-Kansas City. She received her Ph.D. in Urban Planning from the University of Hawaii at Manoa. Her research interests include disaster debris management planning, hazard mitigation, social vulnerability, risk perception, and environmental justice. [email protected].
Dipesh Kumar Ghimire is an Assistant Professor in the Central Department of Sociology at Tribhuvan University, Nepal. His research interests are social aspects of disaster management, local governance, democracy, and economic development. He got his MPhil in Sociology from Tribhuvan University. [email protected].
Shyam Guragain received his double master’s degrees in rural development and economics from Tribhuvan University. His research interests are development economics, demographic analysis, and social research design. He is the program coordinator at the K & K College in Kathmandu, Nepal. [email protected].
Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This COVID-19 Working Group effort was supported by the National Science Foundation-funded Social Science Extreme Events Research (SSEER) network and the CONVERGE facility at the Natural Hazards Center at the University of Colorado Boulder (NSF Award #1841338). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF, SSEER, or CONVERGE. The authors appreciate the time of Nepali returning migrant workers to complete the survey questionnaire.
ORCID iD: Jiwnath Ghimire https://orcid.org/0000-0003-3971-2737
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References
Aacharya RP Shah A (2020) Ethical dimensions of stigma and discrimination in Nepal during COVID-19 pandemic. Ethics, Medicine and Public Health 14 : 100536.32835054
Alahmad B Kurdi H Colonna K , et al . (2020) COVID-19 stressors on migrant workers in Kuwait: cumulative risk considerations. BMJ Global Health 5 : e002995.
Allison PD (2012) Logistic Regression Using SAS: Theory and Application. 5th ed. Cary, NC: SAS Institute.
Bahadur UB Pokharel S Munikar S , et al . (2021) Anxiety and depression among people living in quarantine centers during COVID-19 pandemic: a mixed method study from western Nepal. PLoS ONE 16 : e0254126.34242319
Bhandari D Kotera Y Ozaki A , et al . (2021) COVID-19: challenges faced by Nepalese migrants living in Japan. BMC Public Health 21 (1 ): 752.33874937
Bhattarai G Baniya J (2020) Nepali migrant workers amid COVID-19 in major destination countries: a human security approach. Tribhuvan University Journal 34 : 123–146.
Chaudhary R (2020) COVID 19 pandemics: mental health challenges of internal migrant workers of India. Asian Journal of Psychiatry 54 : 102254.32593122
DanChurchAid (DCA) (2020) A survey report “COVID-19 impact on migrants workers” in Sudurpashchim Pradesh. Available at: https://reliefweb.int/report/nepal/survey-report-covid-19-impact-migrants-workers-sudurpashchim-pradesh (accessed 22 December 2021).
Dhakal S Karki S (2020) Early epidemiological features of COVID-19 in Nepal and public health response. Frontiers in Medicine 7 : 524.32850929
Dhungana N (2020) Human dignity and cross-border migrants in the era of the COVID-19 pandemic. World Development 136 : 105174.32895595
Foley L Piper N (2021) Returning home empty handed: examining how COVID-19 exacerbates the non-payment of temporary migrant workers’ wages. Global Social Policy 21 : 468–489.
Government of Nepal (GoN) (1992) Immigration Act 1992. Kathmandu, Nepal: Law Books Management.
Government of Nepal (GoN) (2020) Nepal Labor Migration Report 2020. Kathmandu, Nepal: Ministry of Labor, Employment and Social Security.
Guadagno L (2020) Migrants and the COVID-19 pandemic: an initial analysis. International Organization for Migration. Available at: https://publications.iom.int/books/mrs-no-60-migrants-and-covid-19-pandemic-initial-analysis (accessed 5 February 2022).
Hosmer DW Lemeshow S (2000) Applied Logistic Regression, Second Edition. New York: John Wiley & Sons.
International Labour Organization (ILO) (2020) Impact of COVID-19 on Nepali Migrant Workers: Protecting Nepali Migrant Workers during the Health and Economic Crisis. Kathmandu, Nepal: ILO.
International Organization for Migration (IOM) (2015) World Migration Report 2015, Migrants and Cities: New Partnerships to Manage Mobility. Geneva: International Organization for Migration.
International Organization for Migration (IOM) (2022) Key migration terms. Available at: https://www.iom.int/key-migration-terms (accessed 15 September 2022).
Ismayilova L Lee HM Shaw S , et al . (2014) Mental health and migration: depression, alcohol abuse, and access to health care among migrants in Central Asia. Journal of Immigrant Minority Health 16 : 1138–1148.24186359
Joshi S Simkhada P Prescott GJ (2011) Health problems of Nepalese migrants working in three Gulf countries. BMC International Health and Human Rights 11 : 3.21443802
Khan A Arokkiaraj H (2021) Challenges of reverse migration in India: a comparative study of internal and international migrant workers in the post-COVID economy. Comparative Migration Studies 9 (49 ): 1–19.
Kharel D (2016) From Lahures to global cooks: network migration from the Western Hills of Nepal to Japan. Social Science Japan Journal 19 (2 ): 173–192.
Kim K Yamashita E Ghimire J (2021) Pausing the pandemic: understanding and managing traveler and community spread of COVID-19 in Hawaii. Transportation Research Record. Epub ahead of print 4 December. DOI: 10.1177/03611981211058428.
King R Skeldon R (2010) “Mind the Gap!” Integrating approaches to internal and international migration. Journal of Ethnic and Migration Studies 36 (10 ): 1619–1646.
Kumar N Udah H Francis A , et al . (2021) Indian migrant workers’ experience during the COVID-19 pandemic nationwide lockdown. Journal of Asian and African Studies 57 : 911–931.
Liem A Wang C Wariyanti Y , et al . (2020) The neglected health of international migrant workers in the COVID-19 Epidemic. The Lancet Psychiatry 7 (4 ): e20.32085842
Maharjan B Maharjan A Dhakal S , et al . (2020) Geospatial mapping of COVID-19 cases, risk and agriculture hotspots in decision-making of lockdown relaxation in Nepal. Applied Science and Technology Annals 1 (1 ): 1–8.
Maiti S Sharma D Gupta T (2022) Unfolding the impact of COVID-19 on reverse migrants in Uttar Pradesh. Journal of Asian and African Studies. Epub ahead of print 22 June. DOI: 10.1177/00219096221106080.
National Human Rights Commission (2020) Rights of Migrant Workers in the Clutches of COVID-19: Pandemic Study Report. Kathmandu, Nepal: National Human Rights Commission Secretariat.
Nepal RM (2021) COVID-19 and Nepal. In: Pieterse JN Lim H Khondker H (eds) COVID-19 and Governance: Crisis Reveals. London: Routledge, pp. 62–73.
Organisation for Economic Co-operation and Development (2020) What is the impact of the COVID-19 pandemic on immigrants and their children? Organisation for Economic Co-operation and Development. Available at: https://www.oecd.org/coronavirus/policy-responses/what-is-the-impact-of-the-covid-19-pandemic-on-immigrants-and-their-children-e7cbb7de/ (accessed 2 January 2022).
Paoletti S Taylor-Nicholson E Sijapati B , et al . (2014) Migrant workers’ access to justice at home: Nepal. Open Society Foundations. Available at: https://www.opensocietyfoundations.org/publications/migrant-workers-access-justice-home-nepal (accessed 2 January 2022).
Prasai S (2020) The Day the Workers Started Walking Home. Kathmandu, Nepal: The Asia Foundation. Available at: https://asiafoundation.org/2020/05/13/the-day-the-workers-started-walking-home/ (accessed 5 January 2022).
Saparamadu AADNS Sharpe A Kim S , et al . (2021) Low-wage migrant workers during coronavirus disease 2019: a social determinants analysis. Journal of Public Health Policy 42 : 452–464.34417557
Shah NA Shah J Shah J (2020) Quarantine, isolation and lockdown: in context of COVID-19. Journal of Paton Academy of Health Sciences 7 (1 ): 48–57.
Simkhada PP Regmi PR Teijlingen EV , et al . (2017) Identifying the gaps in Nepalese migrant workers’ health and well-being: a review of the literature. Journal of Travel Medicine 24 : 21.
Singh M (2020) Lockdown woes: 326 Nepali migrant workers remain stranded at the border for 22 hours. The Wire: South Asia, 2 April. Available at: https://thewire.in/south-asia/covid-19-lockdown-nepali-migrant-stranded (accessed 2 February 2022).
Skeldon R (2008) Linkages between internal and international migration. In: DeWind J Holdaway J (eds) Migration and Development within and across Borders: Research and Policy Perspectives on Internal and International Migration. Geneva: International Organization for Migration, pp. 27–36.
Suhardiman D Rigg J Bandur M , et al . (2021) On the coattails of globalization: migration, migrants and COVID-19 in Asia. Journal of Ethnic and Migration Studies 47 (1 ): 88–109.
Sun Y Liu T Ye T , et al . (2021) Coordination and cooperation are essential: a call for a global network to enhance integrated human health risk resilience based on China’s COVID-19 Pandemic coping practice. International Journal of Disaster Risk Science 12 : 593–599.
Thapa BJ Baniya J Bhattarai S , et al . (2020) Rehabilitation and reintegration of Nepali migrant returnees amid the COVID-19 pandemic. International Journal of Innovation, Creativity and Change 14 : 19–36.
The World Bank Group (2020) Potential responses to the COVID-19 outbreak in support of migrant workers (“Living paper”). Version 8. 1–41. Available at: https://documents.worldbank.org/en/publication/documents-reports/documentdetail/428451587390154689/potential-responses-to-the-covid-19-outbreak-in-support-of-migrant-workers-may-26-2020 (accessed 25 June 2022).
Truman BI Tinker T Vaughan E , et al . (2009) Pandemic influenza preparedness and response among immigrants and refugees. American Journal of Public Health 99 : 278–287.
Ullah AA Nawaz F Chattoraj D (2021) Locked up under lockdown: the COVID-19 Pandemic and the migrant population. Social Sciences and Humanities Open 3 : 100126.34173510
Wahab A (2020) The outbreak of COVID-19 in Malaysia: pushing migrant workers at the margin. Social Sciences & Humanities Open 2 : 100073.
Yıldırım M Geçer E Akgül Ö (2021) The impacts of vulnerability, perceived risk, and fear on preventive behaviours against COVID-19. Psychology, Health & Medicine 26 (1 ): 35–43.
| 0 | PMC9729718 | NO-CC CODE | 2022-12-14 23:22:30 | no | J Asian Afr Stud. 2022 Dec 6;:00219096221141359 | utf-8 | J Asian Afr Stud | 2,022 | 10.1177/00219096221141359 | oa_other |
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spref
International Journal of Educational Reform
1056-7879
SAGE Publications Sage CA: Los Angeles, CA
10.1177/10567879221140091
10.1177_10567879221140091
Original Article
Impact of Online Learning on Teachers’ Authority During the COVID-19 Pandemic in Indonesia
https://orcid.org/0000-0002-9203-0826
Raharjo Raharjo 1
Abdullah Irwan 2
Indiyanto Agus 2
Mariam Siti 1
Raharjo Firdaus Himawan 3
1 Faculty of Education and Teacher Training, 128693 Walisongo State Islamic University , Semarang, Indonesia
2 Department of Anthropology, 59166 Gadjah Mada University , Yogyakarta, Indonesia
3 Faculty of Education, 148002 Yogyakarta State University , Yogyakarta, Indonesia
Raharjo Raharjo, Faculty of Education and Teacher Training, Walisongo State Islamic University, Semarang, Indonesia. Email: [email protected]
5 12 2022
5 12 2022
10567879221140091© The Author(s) 2022
2022
SAGE Publications
This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.
The aim of the article is to address how online learning is implemented in schools, how teacher competencies influence online learning experiences, and what implication of online learning for teachers’ authority. The data collection uses in-depth interviews with teachers and parents about their experiences with online learning, as well as observations of the learning process at three levels of schools in Indonesia. The findings indicate that the teacher's authority has been fundamentally decreased. This can be evident in the diminishing opportunities for modeling students’ personalities that result in students’ poor enthusiasm for learning and their low obedience to teachers.
learning experiences
online learning
students’ personality
teacher's authority
teacher's competency
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pmcIntroduction
Online learning in the era of pandemic COVID-19 has eroded teachers’ authority as educators. Teachers’ authority in the learning process erodes as a result of students’ physical absence from learning and using technology. The interaction between teacher and student, which was originally face-to-face, has been mediated through educational technology. The sudden practice of online learning has resulted on unexpected external events of academics’ lives, as happened in Hongkong (Jung et al., 2021), Malaysia (Sia & Abbas Adamu, 2020), and the USA (Abdi et al., 2020). Iftitah and Anawaty (2020) stated that in such a condition students learn at home under the supervision of their parents, not teachers. The learning interactions between students and teachers are carried out through information technology networks with cellphones, laptops, and or computers (Hosseini et al., 2021). According to Openo (2020), the emergency shift to remote learning resulted in distrust of students, the low interaction between students and teachers affected students’ not feeling comfortable being themselves online, and online learning is considered only effective in the form of giving study assignments to students, because the assignment and correction of assignments occur more often than the material delivery.
Up to this point, research on online learning in the COVID-19 period has emphasized three points. First, research on the efficacy of online learning for teachers (Dewantara & Nurgiansah, 2021; Hosseini et al., 2021; Oktavian & Aldya, 2020). Second, a study on the use of social media applications as online learning media (Akbarialiabad et al., 2021; Beach et al., 2021; Sari, 2020). Third, a study on the role of parents in online learning (Cahyati & Kusumah, 2020; Iftitah & Anawaty, 2020; Kurniati et al., 2020). According to the tendency of the three items above, it appears as though there is little concern about the decline in the authority of the teacher. As educators have not yet fulfilled their principal responsibility as educators of students’ character, as specified in the Law on the National Education System (Undang-Undang Sistem Pendidikan Nasional [USPN]). Teachers are extremely ineffective at motivating students and instilling loyalty and obedience in students to complete learning objectives.
This study seeks to address the limitations of prior research by concentrating on analytical studies that examine “how online learning affects the teacher's role as educator.” Three questions may be posed in this regard: (1) how is online learning implemented in schools?; (2) how do teacher competencies impact online learning experiences?; and (3) what is the implication of online learning on the teachers’ authority. Apart from serving as a discussion topic for this study, these three questions also serve as a mapping exercise for educational issues, particularly those experienced by teachers.
The study's assumptions are predicated on the possibility of teacher authority being compromised in the midst of a lengthy pandemic. The non-direct student–teacher interaction is one of the factors contributing to students’ lack of attention to teacher instructions. Besides this, online learning does not accommodate the roles and responsibilities of teachers as mentors, as there is no physical presence of students during the learning process. Furthermore, the use of technology mediation in online learning increases teachers’ administrative responsibilities, particularly in developing learning materials and assessing student assignments. The flexibility of online learning reduces students’ motivation to learn and their success. Similarly, teachers face challenges in their roles as role models, mentors, motivators, character builders, and providers of problem-solving assistance to students.
Literature Review
Student Competence
Competence is the quality or state of having sufficient knowledge, judgment, skill, or strength (as for a particular duty or in a particular respect; Merriam-Webster, 2019). Carriger (2018) defined competence as a characteristic that forms the basis of a person in terms of individual performance activities or basic characteristics that have a causal relationship with the reference criteria. Baartman and De Bruijn (2011) formulated that competency is an individual's capacity to integrate knowledge, skills, and attitudes in acquiring and applying lessons. According to Nasher (2018), learning competence is achieved in learning objectives, which include cognitive, affective, and psychomotor components. Thus, student competence is obtained after students are given treatment that includes aspects of knowledge, attitudes, and skills (Ayu et al., 2016). The teacher's role in developing student competencies is very important. According to Celik (2011), teachers are responsible for competency development because of their roles as mentors, teachers, and providers of instructions, all of which contribute to increasing students’ ability to understand material and phenomena.
Student competence is the result of a combination of learning in cognitive, affective, and psychomotor (Nasher, 2018). Misbah et al. (2015) argue that there are four core competencies that students must possess, such as (1) critical thinking/problem solving; (2) oral communication; (3) written communication; and (4) teamwork/collaboration. Student competence can be measured through student performance in completing assignments and the ability to understand the material in class. The level of student competence can be influenced by many factors, such as (1) school infrastructure; (2) communication skills; (3) educators/teachers; (4) school environment; and (5) learning motivation (Asfani et al., 2016). In line with the previous argument, Kakkonen (2012) mentions that there are three types of criteria that can demonstrate students’ competency in comprehending lessons: (1) learning abilities; (2) social and communication skills; and (3) approach/method in recognizing the situation. In other words, student competency is described as an individual's capacity to integrate knowledge, skills, and attitudes in the understanding and application of lessons (Baartman & De Bruijn, 2011). In addition, student competence is also useful in social life. Cebrián and Junyent (2015) mention that students’ proficiency with sustainable methods might have a profound impact on decision-making, problem-solving, critical thinking, reacting, and generating new ideas.
Obstacle in Online Learning
Due to the internet's widespread use, educational scholars have been interested in studying online learning. When infused with the existence of the COVID-19 pandemic, education was compelled to adapt to online learning. People must thus not only adapt, but also reconsider the definition of education and reframe their roles and duties (Baran et al., 2011; Wang et al., 2021). In online learning, students are expected to take greater control over their learning process or a student-centered approach (Baran et al., 2011; Chakim, 2022). This requires the instructor to shift from the center—the source of information—to the margin (as a facilitator), which necessitates extensive preparation. According to Openo (2020), online learning has three major problems in a broad sense: (1) interactivity, (2) authenticity, and (3) support. It is commonly acknowledged that online learning restricts interactions between teachers–students and students–students. Communication between teachers and students is difficult to maintain in online learning (Abdi et al., 2020; Jung et al., 2021). This argument goes in line with Bali and Liu (2018) which shows that traditional learning—face-to-face—has higher levels of satisfaction and interaction. This aspect of interaction has also been studied extensively in several studies (Hamdan et al., 2021; Sia & Abbas Adamu, 2020).
Another case, for example, in a study by Jung et al. (2021), most of the teacher informants reported that they felt frustrated when more than half of the students turned off their cameras and microphones. Teachers are left wondering who their target audience is and if their teaching is successful. Then there is the aspect of teacher authenticity, which is concerned with decisions about how a teacher becomes oneself, where to draw the line between teaching and counseling, and so on. By Openo (2020), this is also a major problem in online learning. Baran et al. (2011) also believe that one of the difficulties teachers have while transitioning from traditional to online classes is reclaiming their teacher-self. Finally, assistance for both students and instructors is necessary. Students are particularly susceptible during a pandemic in terms of finances and academics. Access to education, both face-to-face and online, has become an issue, and students want assistance, which is now difficult to get through online learning. Even teachers teaching online for the first time face nervousness and “visual tiredness and impairment” (Wang et al., 2021).
Exemplary in Learning
Exemplary refers to the actions, ways of doing, and ways of speaking that children would mimic, whereas exemplary (uswah) is an educational approach that is implemented through the provision of excellent examples in the form of genuine conduct, most notably worship and morals (Sammons et al., 2016). Exemplary behavior is admirable and valuable because it adheres to the principles of goodness and truth. Setting an example is a common method used by teachers to motivate pupils to work harder in order to accomplish their goals. Numerous studies have been conducted in recent decades to determine the efficacy of teaching and teacher role modeling in the classroom (Sammons et al., 2016). Gentry et al. (2011) state a teacher's exemplary performance can be measured by the manner in which subject matter is delivered, the ability to facilitate student learning, the ability to encourage compassion and positive attitude classroom interactions, and the possession of professional, interpersonal, and intrapersonal knowledge. However, when online learning is used, teachers begin to lose their ability to educate, and inspirational values are not conveyed. According to a study, online learning has an effect on teacher competency in the classroom. Smith et al. (2016) discovered that teachers were typically ill-prepared to deliver materials and instructions to a number of diverse learners due to their limitations in organizing online learning activities and the time required to adapt.
The teacher's unpreparedness in online learning has implications for the loss of the teacher's exemplary value in the learning process. These exemplary values include (1) the teacher as an exemplary figure in fostering morals and good character; (2) the teacher as an educational figure who guides students to acquire knowledge; and (3) the teacher as a person who has professional, pedagogic and social competence (Edwards et al., 2011). In an article, Nurchaili (2010) states that when a teacher's exemplary value in learning is absent, student motivation suffers. The learning process is not optimized, and students lose ethical role models. Thus, teachers must be able to create dynamic relationships in order to successfully and optimally communicate exemplary ideals. In Handayani (2020), according to reports, students at SMPN (State Junior High School) 3 Kudus experienced online learning issues as a result of their teachers’ exemplary and professionalism. Numerous students expressed dissatisfaction with the interaction between teachers and students, as well as with the material explanation and discussion session. Thus, the absence of an exemplary teacher during the learning process has a significant impact on character, academic achievement, and motivation to study. The teacher's online learning agenda must be organized, and the teacher as an actor enhances their potential to serve as a role model for his students.
Method
This research demonstrates the difficulties associated with implementing online learning as an emergency response to the COVID-19 pandemic in Indonesia as a developing country. The issue was mostly connected to the teacher's decreasing authority, which had an effect on students’ low engagement and learning achievement. This study was conducted at three schools in Kendal, Central Java: an elementary school (Madrasah Ibtidaiyah (MI) NU 15), a junior high school (Madrasah Tsanawiyah (MTs) NU 07), and a vocational senior high school (Sekolah Menengah Kejuruan (SMK) NU 04). This distinction between levels of the education system was critical because each level responds differently to learning. Students in elementary school usually put a greater focus on copying of conduct and seldom ask questions. Meanwhile, students in middle school, both junior and senior high schools, developed a critical attitude toward the information and educational resources they received. This complicates the learning process since it encompassed not only cognitive (knowledge) but also affective elements where the dimensions of relationships and exemplary became important.
This was a qualitative study with an emphasis on descriptive data that focused on three areas. First, the response to the implementation of online learning carried out via electronic media in the absence of students’ physical presence. Second, teacher competency in online learning necessitates mastery with electronic media, which had an effect on influencing student attitudes and behaviors. Third, teachers’ authority in transferring knowledge, values, and skills. This was critical since internet connections were frequently used only for educational purposes. All of this, it was said, impaired the efficacy of knowledge transfer and created difficulties for students’ affective development, including the development of relationships with teachers.
This study examines the influence of online learning on teacher authority using primary and secondary data. The major data for this study came from in-depth interviews with teachers about online learning methods from the perspective of the school, including infrastructure, learning design, competencies, and implementation issues. Nine instructors (three from each level) were interviewed successfully. Furthermore, three parents were interviewed. They were housewives. Two of them are not working, they stay at home and take care of their children. They were junior high school graduates. While one other parent, is a mother who works as a factory worker, she is a high school alumnus. They discussed how their children learned at home and their role as mentors. Additionally, interviews with the parents addressed a variety of issues including how students and parents see learning process and its problems. Secondary data sources for this study include learning documents, such as curriculum design, teaching materials, and student assignments.
Three stages of analysis are used to conduct a descriptive and interpretative analysis of the collected data. First, reduction, that is the selection, sorting, and grouping of data compiled on three primary issues, practices, and problems associated with implementing online learning, teacher competency associated with implementing online learning, and student participation and achievement. Second, data presentation, that is the presentation of data in the form of a table with a particular sequence in accordance with the focus of discussions on the implementation of online learning and the decrease in teacher's authority. The third is verification by drawing a conclusion in accordance with the fundamental questions of the research question.
Results
This research is based on three things: (1) the implementation of online learning in schools; (2) the influence of teacher competence on students’ online learning experiences; and (3) the implications of online learning on teachers’ authority. Research data related to these three things are described as follows.
Implementation of Online Learning at School
The learning process in the time of COVID-19 requires online learning. The effectiveness of the implementation of online learning in each school varies depending on four aspects: learning media, school facilities, teacher readiness, and student readiness as shown in Table 1.
Table 1. Online Learning Infrastructure.
Aspect MI MTs SMK
Learning Media 75% of students did not have mobile phones. (R-1) 50% of students resided in Islamic boarding schools. Islamic boarding school regulations prohibited students from bringing mobile phones. 15% of students resided in Islamic boarding schools. Islamic boarding school regulations prohibited students from bringing mobile phones.
5% of students shared mobile phones with their parents. (R-1) (R-1, R-2)
School Facilities Wi-Fi-enabled Internet access was provided at school. Wi-Fi-enabled Internet access was provided at school. Wi-Fi-enabled Internet access was provided at school.
Students’ internet access was subsidized by the government's cost of credit/internet data bundles. (R-2) Students’ internet access was subsidized by the government's cost of credit/internet data bundles. (R-1, R-2) Students’ internet access was subsidized by the government's cost of credit/internet data bundles. (R-2)
Teachers’ internet access was subsidized by the government's cost of credit/internet data bundles. (R-1, R-2) Teachers’ internet access was subsidized by the government's cost of credit/internet data bundles. (R-3) Teachers’ internet access was subsidized by the government's cost of credit/internet data bundles. (R-2)
Teacher Readiness All teachers were ready with android mobile phones. All teachers were ready with android mobile phones. All teachers were ready with android mobile phones.
IHT-IT May 2020. Internet connection outside of school was not always stable. (R-1, R-2, R-3) IHT-IT January 2021. Internet connection outside of school was not always stable. (R-1, R-3) IHT-IT July 2020. Internet connection outside of school was not always stable. (R-3)
Student Readiness 75% of students relied on their parents’ digital literacy. All understood the fundamentals and were fully competent in the use of smartphones and WhatsApp. Each student understood and was fully competent in the use of the LMS (Learning Management System) program in conjunction with WhatsApp and Google Classroom.
Students’ learning activities relied on their parents’ assistance.
With the consent of the boarding school administration, students in Islamic boarding schools shared smartphones (1 mobile phone for 10 students).
Each student participated in learning activities and submitted their assigned work.
15% of students study offline at school (1 class has 1–5 students).
It was not always possible to maintain a stable internet connection. (R-1, R-2)
5% of students shared mobile phones with their parent, thus they should wait for their parents’ mobile phone availability whenever they would study online. It was not always possible to maintain a stable internet connection. (R-1, R-3)
It was not always possible to maintain a stable internet connection. (R-1, R-2)
Note. Interviews with Teachers, 2020
The table demonstrates that learning process took place in a variety of ways throughout the COVID-19 time, but neither could be completely online, and online learning took place asynchronously. The obstacles included the following: (1) Not all students had/brought smartphones; (2) Students stayed in Islamic boarding schools were not permitted to bring mobile phones; (3) Internet connection was not always stable; and (4) Internet data bundle rates were rather high.
In MI, learning process was done asynchronously online. The interaction was carried out by the teacher with 75% of the parents1. Then the student's parents carry out learning or mentoring or even do assignments on behalf of their children2. Additionally, there was face-to-face learning through the formation of study groups of ten students that was carried out at the students’ houses. This class was devoted entirely to mathematics and Arabic subjects, which, according to the teacher's account,3 was very not optimal if done online. Learning process in MTs was conducted asynchronously online to 50% of students who did not stay in Islamic boarding schools. Due to the regulation prohibiting students from carrying smartphones in Islamic boarding schools, learning process was conducted in three events. Initially, when the government mandated that schools be locked down, learning took place in Islamic boarding schools. This approach was effective for one month. The second method was conducted at schools (MTs). Students did not wear school uniforms to MTs.4 As a result of the COVID-19 task force's warning, a third option was implemented, that was providing smartphones to boarding school administrators, where each group of 10 pupils used one mobile phone in turn under the supervision of the boarding school administrator.5 In SMK, there was also conducted online education, with the exception of students who stayed in Islamic boarding schools. Students that stayed in Islamic boarding schools had face-to-face learning at school. In certain sessions, the number of students participating in face-to-face learning mode fluctuates between 1 and 5.6
The Influence of Teacher Competence on Students’ Online Learning Experiences
The heads of MTs7 and SMK8 stated that teachers’ abilities to use IT for teaching were uneven. Because online learning was an emergency educational effort during the COVID-19 pandemic, the most important point was that the learning process could take place. There was no monitoring or review of applications or content as long as there were no student complaints.
Some teachers confessed that in online learning, they merely provided instructions and assignments to their students, then waited for students’ responses to complete and submitted the work. While showing the Google Classroom program on his mobile phone, an SMK teacher stated9:Even though I have prepared a Google Classroom, all I do is ask students through the WA group to access it according to the school's lesson schedule, and then I wait for their responses—their assignment submissions. So far, I have not provided technical guidance regarding the completion of assignments, nor have I provided assistance to students in solving the problems they face.
An MTs teacher10 asked his pupils for a fast response on attendance in the online learning process; 30% of his students responded within the first 15 min, and the number of replies climbed to 60% by the end of the course. Assignments which were submitted to teachers in less than a week had never been 100%. There would always be some who were reluctant and wanted a time extension. The online learning demonstrates that student learning engagement varied greatly. Some students were active, while others were apathetic. The information was confirmed by several parents.11
Several other teachers emphasized that many parents/guardians of students contacted them, complaining about their children's lack of motivation to learn.12 Assignment completion/deadline was frequently reminded several times; teachers had to contact students’ parents/guardians13 to help with the students’ assignments. Teachers realized that online learning reduced students’ enthusiasm to study.
Implications of Online Learning on Teachers’ Authority
In online learning, the average student's enthusiasm for learning decreased. This was based on information from MTs teacher14 who reported that students’ learning assignments were being submitted slowly until the teacher phoned and urged parents to monitor, accompany, and encourage their children's learning activities. Furthermore, some MI students were spotted playing outdoors during school hours. In addition, numerous parents of SMK students stated that their children frequently stayed up late at night and napped during the day.15 Almost all of the parents interviewed by the researchers stated that their children's learning motivation was lower during this online learning than during the prior offline learning method. These circumstances suggested that online learning had an impact on students’ low participation in the learning process.
The teacher's authority is the power of words and actions (visible behavior) may develop a sense of awareness in students to follow and obey what is taught and exemplified. The teacher's authority consists of four components: competency excellence, self-confidence, decision-making precision, and responsibility for decisions made. The teacher's authority is highly essential, and it will be expressed in direct pedagogical interactions between teachers and students. Several parents confessed that their children had never met their teachers as new students (until the second semester). “How can my child appreciate his teacher when he has never met or known the teacher?” one parent said. This condition was also admitted by the head of SMK16 when new students were gathered in three groups, one group at a time. Despite the fact that these students were in their second semester, which was relatively new, they disregarded the senior PAI (Islamic Teaching) instructor, who was also a community figure and held a position of great authority in the school.17 This was one of the negative effects of online learning.
During the COVID-19 time, teachers had to be generous with grades and let their students pass the exam due to an educational emergency. All teachers (MI, MTs, and SMK) agreed that the most important aspect of the evaluation of learning was the assignment submission from students. They would pass as long as they submitted their assignments.18 A group reference was used for scoring. The lowest score was adjusted to equal the passing grade criteria.
Furthermore, an MI teacher19 instructed the students to submit their assignments directly to the school and enter the classroom one by one and the teacher questioned them about the substance of the assignments. Surprisingly, around 30% of students merely submitted assignments and had no idea about the assignments. This suggested that overall student achievement was decreasing.
Discussion
The rise of the COVID-19 pandemic demands the implementation of learning activities to be carried out remotely (online), even asynchronously. Three components of student competence as the scope of essential competences (attitudes, knowledge, and skills) cannot be adequately addressed in an ideal lesson plan. The naivest of the three is the attitude aspect, which is largely unaffected by the absence of direct face-to-face interaction, as the teacher is unaware of the students’ facial expressions and gestures and vice versa. Without direct face-to-face interaction, the relationship between teachers and students becomes tenuous, to the point that they may not even know one another. In that situation, the teacher is unable to set an example of excellent behavior and discipline for the students. Additionally, the teacher's ability to provide advice, encouragement, and instructions to students is severely limited (Edwards et al., 2011; Subur, 2021).
Furthermore, online learning has been ineffective so far since it has been implemented in a haphazard manner as an emergency COVID-19 effort without adequate preparation. The ineffectiveness of online learning is demonstrated by students’ limited access to and engagement in learning. This condition is influenced not only by the learning media system, but also by the internet quota, which requires quite a high cost for students and teachers (Wang et al., 2021). Online learning occurs over the internet, which is frequently insecure due to its geographical isolation from cellular signals. A more fundamental issue is related to teachers’ and students’ readiness, as demonstrated in the study (Jung et al., 2021). Teachers’ mastery of information technology is still limited, and not all students possess or have easy access to mobile phones.
So far, online learning has created the impression that students receive less attention when engaging in learning activities. Additionally, as the paper notes, the advent of online learning has increased the burden on teachers (Smith et al., 2016). Unsatisfactory student learning experiences reduce students’ motivation to learn. In practice, online learning has various features, including adaptability of learning rules, flexibility in learning participation time, internet connection, use of electronic communication media, and parental learning assistance. This situation leads to limited engagement between teachers and students, resulting in teachers providing less attention, direction, assistance, communication, encouragement, and supervision to students. While it is acknowledged that implementing online learning increases teacher administration's workload, it also has the potential to improve their professionalism. However, the physical absence of students from the learning process creates barriers to enhancing student attitudes and personalities, barriers to providing direct guidance to students, barriers to providing behavioral assistance to students, and barriers to setting an example for students (Pabbajah et al., 2020). This situation leads to low levels of students’ obedience to instructors and low levels of students’ enthusiasm for learning, all of which are signs of teachers’ decreasing authority as has been indicated by Nurchaili (2010) and Handayani (2020).
Poor levels of students’ learning enthusiasm and teachers’ decreasing authority have consequences for students’ low levels of learning performance. This problem must be a common concern. While it is accepted that online education is a temporary solution for COVID-19′s national education system implementation, in fact, this solution causes additional issues (Chang-Tik, 2020). Previous researches on online learning had resulted in (1) the efficacy of teachers’ online learning; (2) the use of social media applications as learning media; and (3) the role of parents as teacher in online learning. These three factors tend to overlook the issue of decreasing teacher's authority as part of the limitation of teachers’ duties and responsibilities as educators, which results in low levels of student participation and achievement. With these conditions in place, deliberate actions must be taken to preserve the authority of teachers who are becoming progressively marginalized in the middle of a pandemic. The critical components of the online learning process must be continually enhanced. The foundation for all education stakeholders is a solid internet network, followed by media (mobile phones, laptops, or computers), applications with simple and user-friendly platforms, and online interaction that is efficient, effective, continuous, and integrative (Abdurrahman et al., 2021). As a result, teachers who have served as role models for students continue to be respected. Similarly, the teacher's presence is not only required physically, but also respected when the teacher is not there. In other words, even though learning is conducted online, the teacher's presence is critical.
Conclusion
Long-term online learning in the middle of a pandemic threatens to undermine teachers’ authority. The findings of this study demonstrate that: (1) Online learning in schools limits teachers’ ability to develop students’ attitudes and behaviors. Online learning is still used sparingly as a contingency plan for schooling throughout the COVID-19 era. (2) Inconsistent teacher competency in creating online learning has a detrimental influence on teachers’ authority, as evidenced by low student obedience to the teacher and low student eagerness to study. (3) The low quality of online learning has ramifications for low teachers’ authority, as evidenced by students’ lack of excitement for learning and of real success, which is proven solely by submitting assignments.
The findings of this study encourage schools, particularly teachers, to strengthen their capacity for mastering information technology learning, which is a component of continuous professional development. Additionally, teachers must be innovative in their approach to developing and implementing effective learning practices. Furthermore, the findings of this study present opportunity for educational scholars to develop new theories on relevant online learning strategies and media. Likewise, education policymakers are urged to broaden their horizons in order to give free/low-cost and simple facilities and media that education service users may conveniently access. This study focuses on a single scenario, namely the decrease in the teacher's authority, despite the fact that it is associated to student engagement and achievement in learning. The context of this study is limited to three schools, which represent elementary, junior high, and senior high schools. As a result, additional research is required to address issues that this study has not covered.
Acknowledgements
The authors acknowledge that they understand of all aspects related to the article submission and publication in the journal.
Author Biographies
Raharjo is a lecturer, researcher, and trainer in education, at Faculty of Islamic Education and Teacher Training, Islamic State University (UIN) Walisongo Semarang, Indonesia, since 1991. He finished Master of of Educational Studies at Monash University Australia in 1994, and Doctor on Value Education at Indonesia Education University (UPI) Bandung, Indonesia in 2008.
Irwan Abdullah is professor of anthropology at the Department of Anthropology, Gadjah Mada University, in Yogyakarta, Indonesia. He is the founder of the Indonesian Consortium for Inter-Religious Studies (ICRS) and IA Scholar Foundation (IASF). He was conferred a Doctor of Philosophy in Anthropology from the University of Amsterdam in 1994.
Agus Indiyanto is a lecturer and researcher at the Department of Anthropology, Faculty of Cultural Sciences, Gajah Mada University, Yogyakarta, Indonesia. He got his degree at the Department of Anthropology, Gajah Mada University (undergraduate, 1995), population studies (Gadjah Mada University), and currently working on his dissertation at the Department of Anthropology and Development Studies, Radboud University Nijmegen, the Netherlands.
Siti Mariam has been a lecturer at Faculty of Islamic Education and Teacher Training, Islamic State University (UIN) Walisongo Semarang, Indonesia. She obtained her master and doctoral degree from English Education Department of Universitas Negeri Semarang (UNNES). She is currently involved in the study to investigate factors that can promote learning in the technology-mediated learning environment.
Firdaus Himawan Raharjo is post graduate student on Education Management at Faculty of Education, Yogyakarta State University, Indonesia. He is currently involved in study of learning management related to factors contributing it and effects of it.
Author Statement: The authors declare that there is no conflict of interest, that the manuscript has not been published elsewhere and is not under consideration for publication elsewhere.
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The authors received no financial support for the research, authorship, and/or publication of this article
ORCID iD: Raharjo Raharjohttps://orcid.org/0000-0002-9203-0826
1. Interviewing several times and verifying data obtained from R1 and R2.
2. Interviews with three parents of MI students (O1, O2, O3).
3. Interview with R3 and activity observation.
4. Observation at school.
5. Interviewing R4, R5, and R6 several times. Verifying data obtained from R4.
6. Interviewing R7, R8, R9 several times and observation at school.
7. R6
8. R8
9. R9
10. R4
11. Interview with parents of MI students (O4, O5, O6).
12. Crosscheck with parents of MI students MTs (O4) and interview with parents of SMK students (O7).
13. Crosscheck with parents of MI students O4 and O5.
14. R4
15. Interview with parents of MI SMK students (O7, O8, O9)
16. R8
17. R9
18. R6 and R8
19. R2
==== Refs
References
Abdi N. M. Gil E. Marshall S. L. V. Khalifa M . (2020). Humanizing practices in online learning communities during pandemics in the USA. Journal of Professional Capital and Community, 5 (3–4 ), 205–212. 10.1108/JPCC-07-2020-0066
Abdurrahman A ., Lubis S. A. Tanjung S. Lubis S. H . (2021). Coping stress as a form of Islamic education counseling for students who work during the COVID-19 pandemic. Journal of Social Studies Education Research, 12 (3 ), 270–293. https://jsser.org/index.php/jsser/article/view/3386
Akbarialiabad H. Zarifsanaiey N. Taghrir M. H. Roushenas S. Panahandeh S. M. Abdolrahimzadeh-fard H. , … Paydar, S. (2021). The impact of flipped learning in surgical education : A mixed-method study. Knowledge Management & E-Learning, 13 (3 ), 273–289. 10.34105/j.kmel.2021.13.015
Asfani K. Suswanto H. Wibawa A. P . (2016). Influential factors of students’ competence. World Transactions on Engineering and Technology Education, 14 (3 ), 416–420. https://www.researchgate.net/publication/316559081_Influential_factors_of_students'_competence
Ayu T. Asrizal A. Kamus Z . (2016). Pengaruh LKS IPA terpadu berbasis WEB dengan mengintegrasikan nilai karakter pada materi GLSTSGPS terhadap kompetensi siswa kelas VIII SMPN 8 Padang. Pillar of Physics Education, 7 (1 ), 193–200. 10.24036/2007171074
Baartman L. K. J. De Bruijn E . (2011). Integrating knowledge, skills and attitudes: Conceptualising learning processes towards vocational competence. Educational Research Review, 6 (2 ), 135–134. 10.1016/j.edurev.2011.03.001
Bali S. Liu M. C . (2018). Students’ perceptions toward online learning and face-to-face learning courses. Journal of Physics: Conference Series, 1108 (1 ), 1–7. 10.1088/1742-6596/1108/1/012094
Baran E. Correia A. P. Thompson A . (2011). Transforming online teaching practice: Critical analysis of the literature on the roles and competencies of online teachers. Distance Education, 32 (3 ), 421–439. 10.1080/01587919.2011.610293
Beach P. Favret E. Minuk A . (2021). Online Teacher Professional Development in Canada: A Review of the Research. Canadian Journal of Learning and Technology , 47 (2 ), 1–23. https://cjlt.ca/index.php/cjlt/article/view/27948/20571 10.21432/cjlt27948
Cahyati N. Kusumah R . (2020). Peran orang tua dalam menerapkan pembelajaran di rumah saat pandemi COVID 19. Jurnal Golden Age, 4 (1 ), 4–6. 10.29408/jga.v4i01.2203
Carriger G. (2018). Competence (The Custard Protocol #3) (13th ed.). https://www.goodreads.com/en/book/show/31570825-competence
Cebrián G. Junyent M . (2015). Competencies in education for sustainable development: Exploring the student teachers’ views. Sustainability (Switzerland), 7 (3 ), 2768–2786. 10.3390/su7032768
Celik S . (2011). Characteristics and competencies for teacher educators: Addressing the need for improved professional standards in Turkey. Australian Journal of Teacher Education, 36 (4 ), 73–87. 10.14221/ajte.2011v36n5.6
Chakim S . (2022). The youth and the internet: The construction of doctrine, Islam in practice, and political identity in Indonesia. Journal of Social Studies Education Research, 13 (1 ), 217–236. https://jsser.org/index.php/jsser/article/view/3727
Chang-Tik C . (2020). An analysis of discipline and personality in blended environments: Do they interact differently in the teaching, cognitive, and social presences? Canadian Journal of Learning and Technology, 46 (1 ), 1–19. 10.21432/cjlt27883
Dewantara J. A. Nurgiansah H . (2021). Efektivitas pembelajaran daring di masa pandemi COVID 19 bagi mahasiswa universitas PGRI yogyakarta. Jurnal Basicedu, 5 (1 ), 367–375. 10.31004/basicedu.v5i1.669
Edwards M. Perry B. Janzen K . (2011). The making of an exemplary online educator. Distance Education, 32 (1 ), 101–118. 10.1080/01587919.2011.565499
Gentry M. Steenbergen-Hu S. Choi B. Y . (2011). Student-identified exemplary teachers: Insights from talented teachers. Gifted Child Quarterly, 55 (2 ), 111–125. 10.1177/0016986210397830
Hamdan K. M. Al-Bashaireh A. M. Zahran Z. Al-Daghestani A. AL-Habashneh S ., & Shaheen A. M . (2021). University students’ interaction, Internet self-efficacy, self-regulation and satisfaction with online education during pandemic crises of COVID-19 (SARS-CoV-2). International Journal of Educational Management, 35 (3 ), 713–725. 10.1108/IJEM-11-2020-0513
Handayani L . (2020). Keuntungan, Kendala dan Solusi Pembelajaran Online Selama Pandemi COVID-19 : Studi Ekploratif di SMPN 3 Bae Kudus. Journal Industrial Engineering & Management Research, 1 (1 ), 2722–8878. 10.24036/2007171074
Hosseini Z. Hytonen K ., & Kinnunen J . (2021). Introducing technological pedagogical content design : A model for transforming knowledge into practice. Knowledge Management & E-Learning, 13 (4 ), 630–645. 10.34105/j.kmel.2021.13.031
Iftitah S. L. Anawaty M. F . (2020). Peran orang tua dalam mendampingi anak di rumah selama pandemi COVID-19. JCE (Journal of Childhood Education), 4 (2 ), 71–81. 10.30736/jce.v4i2.256
Jung J. Horta H. Postiglione G. A . (2021). Living in uncertainty: the COVID-19 pandemic and higher education in Hong Kong. Studies in Higher Education, 46 (1 ), 107–120. 10.1080/03075079.2020.1859685
Kakkonen M. L . (2012). The Relationship between Self-Perceived Generic Competences and Entrepreneurial Intention. Industry and Higher Education, 26 (3 ), 217–227. 10.5367/ihe.2012.0096
Kurniati E. Nur Alfaeni D. K. Andriani F . (2020). Analisis peran orang tua dalam mendampingi anak di masa pandemi COVID-19. Jurnal Obsesi : Jurnal Pendidikan Anak Usia Dini, 5 (1 ), 241–256. 10.31004/obsesi.v5i1.541
Merriam-Webster. (2019). Definition of competence. Merriam-Webster, Inc.
Misbah Z. Gulikers J. Maulana R. Mulder M . (2015). Teacher interpersonal behaviour and student motivation in competence-based vocational education: Evidence from Indonesia. Teaching and Teacher Education, 50 (2015 ), 1–11. 10.1016/j.tate.2015.04.007
Nasher J. (2018). Convinced!: How to Prove Your Competence & Win People Over. https://www.goodreads.com/book/show/37814000-convinced
Nurchaili. (2010). Membentuk karakter siswa melalui keteladanan guru. Jurnal Pendidikan Dan Kebudayaan, 16 (9 ), 233. 10.24832/jpnk.v16i9.515
Oktavian R. Aldya R. F . (2020). Efektivitas pembelajaran daring terintegrasi di era pendidikan 4.0. Didaktis: Jurnal Pendidikan Dan Ilmu Pengetahuan, 20 (2 ), 129–135. 10.30651/didaktis.v20i2.4763
Openo J . (2020). Education’s response to the COVID-19 pandemic reveals online education’s three enduring challenges. Canadian Journal of Learning and Technology, 20 (2 ), 1–13. 10.21432/cjlt27981
Pabbajah M. Abdullah I. Widyanti R. N. Jubba H. Alim N . (2020). Student demoralization in education:The industrialization of university curriculum in 4.0.Era Indonesia. Cogent Education, 7 (1 ), 1–14. 10.1080/2331186X.2020.1779506
Sammons P. Lindorff A. M. Ortega L. Kington A . (2016). Inspiring teaching: learning from exemplary practitioners. Journal of Professional Capital and Community, 1 (2 ), 124–144. 10.1108/jpcc-09-2015-0005
Sari L . (2020). Upaya menaikkan kualitas pendidikan dengan pemanfaatan YouTube sebagai Media ajar pada masa pandemi COVID-19. Jurnal Tawadhu, 4 (1 ), 1074–1084.
Sia J. K. M. Abbas Adamu A . (2020). Facing the unknown: pandemic and higher education in Malaysia. Asian Education and Development Studies, 10 (2 ), 263–275. 10.1108/AEDS-05-2020-0114
Smith S. J. Basham J. Rice M. F. Carter R. A . (2016). Preparing special educators for the K–12 online learning environment: A survey of teacher educators. Journal of Special Education Technology, 31 (3 ), 170–178. 10.1177/0162643416660834
Subur. (2021). Online learning on the COVID-19 pandemic to create educational access inequality. Journal of Social Studies Education Research, 12 (4 ), 170–196. https://jsser.org/index.php/jsser/article/view/3726
Wang Z. Pang H. Zhou J. Ma Y. Wang Z . (2021). “What if…it never ends?”: Examining challenges in primary teachers’ experience during the wholly online teaching. Journal of Educational Research, 114 (1 ), 1–24. 10.1080/00220671.2021.1884823
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Aust J Educ
Aust J Educ
spaed
AED
Australian Journal of Education
0004-9441
2050-5884
SAGE Publications Sage UK: London, England
10.1177_00049441221137074
10.1177/00049441221137074
Original Article
Teaching the Arts in Testing Times: A Western Australian Perspective on COVID Impacts
https://orcid.org/0000-0003-2410-6849
Paris Lisa F
Lowe Geoffrey M
Curtin University , Australia
https://orcid.org/0000-0001-8464-1961
Gray Christina
Edith Cowan University , Australia
Perry Angela
Warwick Lara
Curtin University , Australia
Lisa F Paris, Curtin University, Kent Street, Bentley, WA 6845, Australia. Email: [email protected]
6 12 2022
6 12 2022
00049441221137074© Australian Council for Educational Research 2022
2022
Australian Council for Educational Research
This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.
Expert secondary Arts teachers are highly trained specialists well versed in face-to-face individual and group teaching pedagogies. Given the highly personalised nature of Arts teaching practice, the shift to online teaching resulting from COVID-19 lockdowns presented many with challenges for which they had little or no formal training. Many teachers felt stressed, isolated and unsure about where to turn for help. As there are demonstrated links between stress and attrition, it is important to reflect upon the experiences of these teachers with the aim of developing future mitigation strategies. The research reported here synthesises the online teaching experiences of 15 expert Arts specialists in Western Australia and revealed that being a digital native was not in itself sufficient to ameliorate online teaching challenges. Rather, the study found that teachers with deep pedagogical practice knowledge and a reflexive/flexible approach fared better than those with high levels of technology familiarity. The importance of collegiality and mentoring in an online setting, along with a reappraisal of teaching priorities emerged as key findings and serve as a timely reminder of the importance of collaboration, especially in testing times.
Arts teaching
online teaching
COVID-19
challenges
affordances
potentialities
communities of practice
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pmcIntroduction
The rapid shift to online teaching in Australia during the 2020–2021 COVID-19 lockdowns had a profound impact on teachers, students and parents alike, and the Western Australian (WA) experience was no different from the rest of the nation (Beames et al., 2021). However, WA suffered far fewer periods of lockdown, largely attributable to the WA Government’s closed-border policy which largely prohibited entry to WA starting from the commencement of the pandemic through to March 2022 (Watson & Singh, 2022). WA Teachers’ experiences of navigating online teaching challenges were therefore different to those in other states because the breaks between lockdowns offered teachers the chance to test online teaching approaches over short periods, followed by longer periods of traditional face-to-face delivery. The resulting opportunities for reflection offer potential dividends in terms of online pedagogical evolution as well as teacher priorities and wellbeing. In short, WA teachers were shielded from the worst of the COVID-19 impacts but sufficiently exposed to online teaching to reflect on the experience.
Arts teachers in other Australian States, by contrast, fared less well. In Melbourne, in lockdown for more than 246 days (Wahlquist, 2021), high levels of teacher fatigue and distress were reported across all subjects (Dabrowski, 2020). Moreover, within the Arts disciplines which employ specialised materials and equipment, practical demonstrations, one-on-one ‘in the moment’ teacher-student feedback and group work/student collaboration, the challenge of replicating online the quality of instruction provided in face-to-face teaching proved highly problematic (Burke, 2021) and stressful (Spacek, 2020). Given established links between stress and teacher attrition (Buchanan et al., 2013; Hong, 2010), it would appear reasonable to assume that Arts teachers would be vulnerable to attrition as teaching practices that traditionally sustain them evaporated (Kraehe, 2020). This article reports on findings from a study into the impact of online teaching with 15 WA expert Arts teachers. In it, we focused on our participants’ COVID-19 experiences teaching lower secondary students to better understand how capable Arts teachers navigated the challenges of online teaching in relation to pedagogy, priorities and wellbeing. The study has the potential to inform Arts teaching practice and importantly what, if anything, can be retained and capitalised on to advance online Arts teaching pedagogy. Understanding how expert practitioners adapted their practice may reveal strategies and resources that other, less experienced Arts teachers can utilise.
In understanding the experiences of our participants, we first set out the Arts education context prior to COVID-19. We then consider the implications of the rapid shift to online teaching and the role of technology in facilitating the shift. From there, we describe the methodology employed in this study, the results of our investigation and acknowledge our study limitations. We conclude with recommendations arising from the study.
Background
In Australia, Arts Education F-10 encompasses the disciplines of Dance, Drama, Media Arts, Music and Visual Arts which together comprise one of eight Learning Areas in the Australian Curriculum (Australian Curriculum, Assessment and Reporting Authority, 2018). Since its inception, the Australian Curriculum Arts Learning Area (ACALA) has guided the range and achievement levels of each Arts discipline in each state and territory despite many adapting the Arts Curriculum (AC) to meet its particular community needs (Gattenhof, 2009). Nationally, the AC encompasses the domains of Arts Making (ideas generation; skills and production work) and Arts Responding (arts analysis and contextual considerations), informed by seven General Capabilities and three Cross Curriculum Priorities. In each state and territory, Arts education is regulated, and teaching, learning and assessment standards are monitored. While not included under the ACALA umbrella, post compulsory courses (years 11 and 12) are generally well resourced, and teachers are required to engage in moderation partnerships with other schools which are in turn monitored by each state regulator. The effectiveness of these arrangements is tested annually through external examinations or externally set tasks. By contrast, in lower secondary years and in primary arts education contexts, there are fewer supports and no external exams or mandated moderation partnerships. Historically, teachers working in primary and lower secondary settings create their own learning programs with reference to their state curriculum and source their own learning resources (Wittber, 2017). Accordingly, inexperienced Arts teachers, those working out of area or those with under-developed pedagogical knowledge can struggle unless connected with a mentor or able to access targeted Professional Learning (PL) support (Paris, 2008; Wittber, 2017).
Since 2014, Australian Arts teachers have been required by legislation to integrate technology into learning programs, as Information and Communications Technology (ICT) competence constitutes one of the General Capabilities of the Australian Curriculum (Australian Curriculum, Assessment and Reporting Authority, 2014). Moreover, technology is increasingly embedded in Arts disciplines practice, such as the use of image manipulation software in generating ideas in Visual Arts (Sabol, 2021). Despite this, and other than in Media Arts where learning is framed around technology integration, Arts teachers can lack expertise to meaningfully embed ICT (Gall, 2013) or can be afraid to (Kassner, 1996). For example, an investigation of technology usage among Queensland Music teachers reported that only 51% used a small range of familiar applications, and that the range did not reflect the scope prescribed in the AC (Eyles, 2018).
Whilst some teacher PL is available, the day-to-day challenges of schools with varying technology infrastructure, budget constraints and competing time demands often translate to Arts programs framed predominantly around traditional ‘making experiences’ comprising teacher demonstration/modelling and direct instruction rather than online interaction. However, the move to online teaching during the pandemic resulted in an awkward fit (Shaw, 2021) and it may be that many Arts teachers lack the knowledge, capacity and support to succeed in exclusively online teaching settings (Burke, 2021). In ‘normal times’, communities of practice afforded by the professional associations/social media Arts teacher groups constitute a significant mentoring asset (Kraehe, 2020). However, in challenging times such as the COVID-19 lockdowns, lifeline organisations themselves experience significant pressure, as they are predominantly run by volunteers. According to a survey by the NEiTA Foundation and the Australian College of Educators (2021), 75% of Australian teachers reported feeling stressed, had poor work-life balance, and as many as 84% had considered leaving the teaching profession in the preceding 12 months. This was attributed to extreme pressures generated by prolonged lockdowns and online teaching as well as the demanding nature of the profession itself. Although participants were not identified by subject, we posited these results would reflect the experience of Arts educators along with those of other disciplines.
Technology infiltration and online teaching
The pandemic and online teaching responses presented a once in a generation shared experience for all teachers and students (Lockee, 2021). However, the challenges faced by individual teachers varied according to their context and the extent to which technology had infiltrated their pedagogical practices pre-pandemic (UNESCO, 2020). The challenges posed by Digital Disruption transformed education practice but in markedly different ways across countries, school systems/sectors and subject disciplines. According to Riemer and Johnston (2019), Digital Disruption refers to advancements in digital technologies that occur at a pace and magnitude that disrupt established way of creating value within or across markets, social interactions and, more generally, our understanding and thinking (p. 4).
The infiltration of technology is manifest in learning management and assessment systems (e.g. Sector, Connect, Blackboard and Turnitin), online communication platforms (e.g. social media environments, email, Zoom, Google Classroom, Canvas and Microsoft Teams) and peer collaboration environments (e.g. Google Drives, Padlets, Trello Boards). Each of these is valuable in supporting student learning but their overuse has been linked to a deterioration in teacher personal wellbeing, especially among early career teachers, as they contribute to poor work-homelife balance and increased workloads (Johari et al., 2018; Marco Learning, 2020).
Rosen (2012) described the phenomenon of ‘time deepening’, whereby extended working hours (e.g. after hours work correspondence/emails intrusion) and intensified workloads leaves many teachers feeling pressured to be more accountable for their productivity, and this intrudes into personal/family time. Mobile devices exacerbate the intrusion and promelgate skewed notions that teachers should be ‘on-call’ at all times, which in turn may contribute to declines in personal wellbeing (Rosen, 2012). For teachers yet to develop the confidence and self-regulation capabilities to manage the digital homelife intrusion, time deepening, work intensification and the rapid technology transition can engender high levels of work-related stress (Burbules et al., 2020; Ugur & Koc, 2015). While links between stress and attrition have been well established (Ballantyne & Retell, 2019), digital disruption together with time deepening, COVID-19 and the rapid shift to online teaching presented a potential ‘perfect storm’ of attrition forces. Their combined impact has yet to be fully understood but it would appear reasonable that all teachers would be under extraordinary pressure with fewer supports available to them.
Reframed roles and expectations
In Australia, Arts teachers’ roles have shifted from content experts to facilitators of learning, reflexive to student needs in partnerships where knowledge is co-constructed with students (de Vries, 2021). For several decades, technology integration has played an increasing role and students often have greater technological familiarity (although often less digital literacy) than their teachers (Murray, 2011). However, teachers have a deeper understanding of students' developmental needs and effective learning processes. The issue of access to and familiarity with technology was critical during the COVID-19 lockdowns but having access to technology and knowing how it works is different from knowing how to use it effectively in the online setting. The shift to online delivery with students working at home, often without supervision or access to specialist materials, combined with the pedagogical challenges of online teaching have emerged as key themes in the COVID-19 teaching literature (Lockee, 2021; Mutton, 2020; Thomson, 2020). Given the unfolding digital integration over recent decades, it might have been reasonable to have assumed that:• schools have had time to install and upscale the critical infrastructure needed to ensure continuity of teaching in online formats in the event of significant disruptions in face-to-face delivery;
• teachers have had the opportunity through effective PL and mentoring to not only familiarise themselves with the teaching potentials offered by technology, but also acquire capability in responding to the specific pedagogical demands of each technology;
• students have had the opportunity to use and master technology in their learning and time to acquire resources needed to work at home and
• equitable access to technology regardless of socio-economic contexts could be assumed.
Despite this, Flack et al. (2020) paint a poor picture of the teaching profession’s preparedness for online teaching. In general, teacher familiarity with technology and their grasp of online pedagogical content knowledge appeared inadequate; the availability of remote mentoring and PL support was negligible or lacking; students' capacity to work unsupervised or unsupported at home and access reliable internet was found wanting; and a significant digital divide for students learning remotely emerged across a range of socio-economic contexts which disrupted or truncated the continuity of teaching and learning (Flack et al., 2020). Against this bleak backdrop, we were keen to investigate how expert Arts teachers coped as we explored both the negative and positive aspects of online teaching for those in best position to cope. Further, given WA Arts teachers’ limited experiences with prolonged lockdowns and online teaching, we were eager to examine their experiences upon reflection, rather than reaction, and to identify areas of transformational practice resulting from online teaching.
Research approach
In late 2021, we secured ethics approvals for our study framed around the COVID-19 teaching experiences of expert WA Arts teachers across Dance, Drama, Media Arts, Music and Visual Arts in lower secondary settings. The research was informed by an acknowledgement that while the online teaching experiences of our experts may vary across different systems and sectors and that what worked in one setting may not work in another, commonalities and differences might emerge: 1) between the Arts and the general teaching literature, and 2) across Arts disciplines. We considered that there was good potential to synthesise shared understandings and strategies, particularly those aligned to success, because:• each of our selected participants had achieved recognition as exemplary practitioners and were deemed to be excellent exponents of the Australian Institute of Teaching and School Leadership professional teacher standards, and
• our participants were experienced practitioners with 10 or more years of continuous teaching practice and were most likely to have succeeded in the rapid move to online teaching.
Our study employed a phenomenological qualitative research design involving interviews, with responses to COVID-19 online teaching the primary focus. Eddles-Hirsch (2015) notes ‘a key characteristic of phenomenological research is its rich, detailed descriptions of the phenomenon being investigated’ (p. 252). To enable participants a degree of freedom in describing their online teaching experiences, interviews were framed around one central question:
What advice and insights could you share from your recent online teaching experiences in relation to COVID-19 lockdown in 2020–2021?
Our semi-structured interviews developed organically from the central question as each interviewer probed for deeper understanding, and interviews ranged across all aspects of the COVID-19 teaching phenomenon.
Participants
Fifteen experienced Arts teacher participants were purposively recruited via WA Arts Professional Teacher Associations and established professional relationships with research team members. Expert teachers were screened against the following inclusion criteria: 1) Level 3/Senior Arts teacher or a Head of Learning Area (Arts), 2) had been teaching continuously for more than 10 years within their discipline and 3) were experienced educators who had achieved recognition of high-level expertise through promotion/awards. All participants worked full-time across Catholic, independent and government school sectors.
Interviews
Interviews were undertaken on an individual basis and ranged from 40–60 minutes each. Each interview was recorded and professionally transcribed verbatim. Initial coding indicated three researcher-designated themes relating to challenges, affordances and potentialities, which we defined as follows:• Challenges were phenomena that presented serious obstacles to effective teaching and learning;
• Affordances were phenomena useful in achieving or approximating good teaching;
• Potentialities were phenomena which had not been part of pre-COVID-19 practice which emerged in response to the COVID-19 emergency, and which appeared to have potential value in post-pandemic practice.
We considered a strength of our study was the decision to match discipline specialists within our research team with relevant discipline specialists in our participant pool. This, we posited, would allow an emic (insider) perspective and evaluation of the ‘place/fit’ of participant experiences being described without conflicting with our phenomenological orientation. Our approach acknowledged, by way of example, that our team’s music specialist would be best placed to ask relevant additional questions during the interviews with the music teachers rather than our visual arts specialist, because they best understood practices valued in music. Moreover, during the data reduction phase, we considered the team member with discipline-specific expertise would better understand the sub-text and intended meaning of statements made by discipline participants than would be the case if one of the other researchers coded that data.
In justifying this approach, Olive (2014) notes that in education settings the emic is often more valuable than the etic (outsider view) where a range of participants’ experiences considered as emic understanding supports the framing of a narrative that establishes coherent relationships between the ‘parts’ of the experiential story. Olive (2014) observed:
In educational research, the emic perspective typically represents the internal language and meanings of a defined culture. The scope of said culture can be quite broad – for example, a researcher may study the culture of an entire school system or just one building or one particular classroom or a small group of individuals who share a common characteristic. Regardless of how a culture’s scope is defined, an emic perspective attempts to capture participants' indigenous meanings of real-world events and looks at things through the eyes of members of the culture being studied (p. 4).
At the conclusion of the data reduction phase, the research team re-examined the results together to ensure any bias was acknowledged and its impact minimised in framing results and identifying practices within specific disciplines or across the Arts as a whole.
Results
Our results are presented in the form of ‘voices’ whereby the experiences of participants are presented verbatim to ensure their discrete discipline perspective is preserved and not diluted within the larger Arts education context. Results are presented under the three researcher-designated themes of challenges, affordances and potentialities.
Online teaching challenges
Participants across all disciplines reported broadly similar challenges relating to the instability of technology platforms, lack of assistance in the rapid move to online and issues surrounding platform quality. In instances where professional learning (PL) was available, the technology simply did not fit the Arts teaching context. Technology issues became an unwelcome distraction. Further analysis revealed three sub-themes which appeared specific to Arts disciplines. These encompassed:• variable quality of Arts supervision/support arrangements for students learning at home,
• lost social engagement and wellbeing opportunities for students to engage and perform collaboratively with one another and
• the lack of reliable internet access and computing hardware in students’ home environments along with poor technology skills among students and parents.
In terms of adequate supervision, physical safety was an issue in Visual Arts:… there are certain things that, from a practical and safety point of view, I couldn't do… things like, you say to them, ‘Use glue guns at home’. Some of them probably don't have glue guns, but I don't know what their power points are like. I would hate to think that some kid gets electrocuted. Or cutting stencils with blades; I can't. There's things that in class you would do, where you can enforce safety rules, but you're always in that class to supervise it taking place, whereas at home, you can't do that…most of my kids, their parents were working. (Visual Arts teacher)
Lost social engagement and isolation was particularly detrimental in the music context where group performance participation is a cornerstone:The lack of personal connection and contact with the students, particularly the critical age of Year 8s, it’s really worked out very badly for them. I’m hearing this a lot. It reinforces that the power of what we do is through personal relationship and personal contact and direct experience with each other. (Music teacher)
Lack of reliable internet access emerged as problematic in the Drama context, and contributed to an increased workload:Yeah, that was a hard one for us, and it was a little bit of a shock. Because of our low socio-economic area, a lot of our families don’t always have computers or internet access. So that was a big challenge. We ended up having to create two sets of work, one online, and a written package. The students that didn’t have access to internet and a computer, they had some written packages and documented things that they needed to do. (Drama teacher)
However, access to technology was sometimes less of a problem compared to students and parents being able to use it effectively. This was the case across a number of Arts disciplines which often employ discipline-specific software:We didn’t have a lot of online teaching experiences because our kids don’t have access to technology. We discovered where the gaps were for our kids. We assumed it was access to technology or being able to use it, and the gaps actually were in bizarre places. So, for example, we asked parents, do you have access to technology, so that we knew what kind of learning we were going to provide, and they all said no. But then when we dug a bit deeper, we understood they didn’t know how to use the technology they had. (Drama teacher)
Other challenges included the perceived low status of Arts subjects and that it was not a priority area for school hierarchies, perceived online teaching skills deficiencies among Arts teachers and additional staff workloads, including for those delivering PL to colleagues. Some participants’ schools adopted blended learning approaches with students commencing project work at school in conjunction with ‘arts kits’ for home use, with online instructions supporting the arts activities under the supervision of parents (despite parents often having little/no arts or technology expertise). In some instances, pre-existing programs were simply abandoned in favour of ‘doable’ substitute tasks which were often more theoretical in orientation and aligned to arts responding rather than practical arts making tasks. The following excerpts extrapolate some of these challenges:
The low status of Arts subjects was typified in the following Dance context:Unfortunately, my main problem was my school didn’t really encourage remote teaching. With dancing, it’s health and safety, that’s different apparently to sitting on a chair, which we can do at home. I was a little bit discouraged from it. (Dance teacher)
In terms of prohibitive workload, one Drama teacher was particularly emphatic:It was shit, it was shit. It was shit! It was so exhausting when I did one on one (contacts with students). That was three hours of ten-minute calls, back-to-back. It was exhausting… Yeah, I found it really, really difficult. (Drama teacher)
Additional workload was also incurred in PL delivery to colleagues:I spent two weeks doing PDs teaching staff how to use a computer and how to screen record. I didn’t even change any of my own teaching and planning other than thinking, there’s some things I had to adapt. (Visual Arts teacher)
The reported challenges across disciplines varied, with Music and Drama participants noting difficulties associated with skill building and student feedback, Dance noting communication issues and Visual Arts participants noting the difficulties in sustaining student interest. While fewer problems were reported in Media Arts, participants from this discipline still reported problems sustaining pedagogical practices. In summary, expert teachers reported the biggest challenges related to technology stability and Arts digital literacy, as well as increased workloads, stress and frustration.
Online teaching affordances
As with challenges, commonalities emerged in relation to the value of online resources such as online video content, and facilities such as breakout rooms. However, four sub-themes emerged in relation to reframing relationships. These included:• greater opportunities to connect with other teachers online to share ideas, seek and offer advice and access mentoring support,
• the opportunity to see students in a new light and develop a deeper appreciation of their circumstances,
• greater responsibility, control of the direction and form of student learning and
• understanding/discovery that an approach framed around ‘flexibility’ served as a major contributor to success, and fewer things taught well was more successful than trying to cover everything that had been included in pre-pandemic programs.
Some participants reported navigating COVID-19 more effectively than they had imagined and felt more empowered and technologically capable than prior to lockdowns. Importantly, all participants reported that concerns for students’ emotional wellbeing outweighed strict adherence to learning programs; each stated that making time for checking in with students was more important than content delivery. The following quotes illustrate these sub-themes.
In relation to online communities of practice, one Visual Arts teacher noted:Definitely create a network, link with city schools. Don’t feel like you have to be an expert at everything, because I’m still learning a lot and I hope I’m teaching my students to be lifelong learners, and lifelong art appreciators if they’re not practitioners. That’s what keeps my teaching fresh and vibrant, I’m finding new ways to do things. (Visual Arts teacher)
Adaptability was reported as a key to developing confidence and competence with new skills, as described by one Media Arts teacher:As a teacher now, if I was sick for a day, I’d do my class from home, and it wouldn’t impact students as much. Before I didn’t feel as confident with the technology, and now it’s, like, how can I maximise this, and how can I use it when I need it now? Yeah, it’s forced us all to become a lot more competent with those technologies. (Media Arts teacher)
Changing emphasis in relation to student welfare was typified in the following comment from a Dance teacher:Sound was an issue with WebEx initially, which is a problem for dance. Some students didn’t have consistent internet access and computers, but when they logged on, they actually engaged really well, ‘Oh, there’s your dog’, and all of these little conversations, which made it a nice experience because it felt realistic. It wasn’t like the classroom, which for some kids, if they’re already nervous, is alien. They have the choice whether they switch their cameras off or on. Most of them might have it off for a bit and then suddenly, they’re like, ‘Oh, we can join in, we’ll switch it on’. (Dance teacher)
Seeing students in a new light forced a re-evaluation of teaching priorities, as evident in the following Music teacher quote:I’ve probably become a better teacher for seeing that you can’t teach by remote means very well. No matter how well you set up online things for them to interact with, written activities and things they can do on a computer. It’s the interpersonal, which is so powerful. (Music teacher)
The need to be flexible was best conveyed by a Visual Arts teacher:… realising that I just needed to relax, and that a drawing could be done on any surface… cereal boxes, any range of materials, send children out into the garden and collect five objects and come back and draw that with whatever drawing objects you have. That was a big shifting point where sometimes you just go off-script. And we do that anyway on a normal teaching day, but much more so in the online learning environment. (Visual Arts teacher)
In summary, while all teachers acknowledged the value of online resources, the biggest affordances revolved around a reappraisal of relationships, both between teachers, and teachers/students. Whether a product of greater agency and reflective capacity among expert teachers or heightened empathy among Arts teachers, affordances offered teachers a chance to reassess their priorities.
Online teaching potentialities
Overall, few described lockdown pedagogical solutions as enduring elements of practice, and challenges overall outweighed positive affordances of online teaching. This was particularly the case for two of our teachers with lower pedagogical knowledge. Despite their high technology capability, both struggled to adapt face-to-face pedagogical practices and content knowledge into an online format. This suggested that years of teaching experience and flexibility/reflexivity may be more important success criteria than technological familiarity or user capability. In our study, expert participants with many years of teaching experience and pedagogical knowledge combined with good technology skills fared best; being a digital native did not, in and of itself, assure success.
As participants reflected on their experiences, a few described aspects of online teaching which they considered valuable post-pandemic. These included sharing resource materials within online collaborative environments and collaborating with teachers in other locations (brainstorming, sharing, mentoring, general affirmation support, targeted advice) through online forums. For some, appreciation of the sheer number and scale of high-quality resources which had ‘exploded’ during the lockdowns was a revelation and they were now avid consumers and contributors to their Arts education online resource repositories. The following highlights general experiences in relation to emerging online resources repositories. One Visual Arts teacher stated:The best thing about this global COVID-19 experience is the amount of good resources available online like YouTube videos about art terminology, elements and principles, art tutorials. Don’t be scared to access what’s available. You don’t have to come up with new ideas all the time. There’s no need to reinvent the wheel. It’s okay to tell students that you’re not an expert, because you’re not an expert at every sub-discipline within visual arts. (Visual Arts teacher)
By contrast, one Media Arts teacher noted the value of repurposing existing resources:When I teach, I tend to use a visual supplement like a PowerPoint or a Keynote. Some of them are ten years old, but all I’ve done is just changed it to match the new curriculum. It’s not about reinventing the wheel. (Media Arts teacher)
In terms of communities of practice, one Dance teacher stated:
I thought this was going to be appalling, but it actually ran easily. I reached out to lots of people, and I went, ‘What are you using? How do you …’ [laughs]. Other dance teachers, even other dancers. I remember going over to Melbourne and asking a ballet teacher friend of mine ‘I’ve seen on social media you’re teaching your classes online, how do you do this?’ Reach out, and not necessarily within your school. Definitely beyond that! (Dance teacher)
For one Visual Arts teacher, this took an international flavour:One big advantage of the COVID-19 stuff is that you now can get online, professional learning communities. I have connected with teachers in Canada, Queensland and New Zealand and you just learn these things that you take into the classroom. Classrooms in schools become like a little fishbowl, it’s hard to see out sometimes. (Visual Arts teacher)
In summary, the main potentialities revolved around online resource sharing and online communities of practice. Few participants described permanent pedagogical practice changes despite opportunities for reflection and consolidation, and this was common across all five Arts disciplines.
Discussion
In this article, we have reported the collective challenges, affordances and potentialities of the COVID-19 online teaching experiences of a selection of expert Arts teachers in WA. Surprisingly, where we had imagined that the fewer periods of lockdown in WA might have allowed time for deeper reflection, more considered planning, and better preparation for subsequent lockdowns, instead we found deeply ingrained fault lines in existing educational structures which rendered the periods of respite between lockdown largely irrelevant. These included:• ongoing poor technology connectivity and platform instability which impacted teacher online pedagogical content knowledge/professional learning opportunities and student engagement,
• heavy workload burdens and competing demands on teachers’ time and resources leading to stress and a feeling of helplessness, especially among our less pedagogically secure participants,
• low priority for Arts teaching and inadequate communication between teachers and school contexts around how best to respond to challenges; many teachers felt they were left to figure out what to do as they went along;
• a digital divide for students [and teachers] which was manifest as unreliable access to internet, little/no access to devices and insufficient PL and support, and significantly,
• a paradigm shift from ‘how to teach’ in subjects where teacher demonstration, one-on-one interactions and ready availability of materials and equipment had previously been the mainstay to online equivalencies (YouTube clips rather than demonstrations) which lacked the personalised feedback/relevance dimension that direct student engagement allowed.
The affordances reported in this study surround the importance of collaboration, openness, flexibility and adaptability, and a recognised need to focus more on student wellbeing than content delivery. All of the above was mediated by confidence born from years of teaching for our expert teachers. Those with high agency and experience fared better irrespective of technology familiarity and capability, as they drew upon their experience to devise alternative pedagogies. We speculated that because the pedagogical content knowledge repertoire of our experts was broader, they were better able to repurpose knowledge and resources for online teaching than less experienced teachers. Technology was certainly fundamental in online learning but was also a contributor to the difficulties. Indeed, while most participants initially struggled to ensure effective online teaching, this was attributed more to issues and challenges surrounding platform stability and overall connectivity. Further, despite the fact that Arts teaching has been heavily reliant on face-to-face personalised teaching approaches, our expert practitioners were able to adapt and adjust over time. When considering our participants overall experiences, it becomes apparent that it is the support processes designed to facilitate effective online teaching rather than pedagogical issues per se that require direct attention from systems and sectors. Accordingly, we make the following recommendations to systems and sectors for future practice:• schools need to ensure their teachers have access to appropriate infrastructure (devices/hardware) and technology support needed to teach online (including trouble shooting when programs and hardware fail),
• teachers need access to mentors and PL training framed around online teaching pedagogical practices (i.e. online teaching strategies) as well as familiarity with delivery platforms such as Webex, Zoom or Teams,
• Teachers need time to develop online teaching material and support from organisations with expertise. While communities of practice expanded in response to online teaching practice, these took time to develop their resources and a more systematic approach is required which focuses on pedagogies as well as materials and
• Most importantly, systems and sectors need to be aware that the most vulnerable teachers may not be older, experienced teachers less fluent with technology, but rather early career digital natives who need guidance in how to adapt for effective online delivery.
While recognised experts participated in this study, two reported struggling to cope and lamented the inadequacy of their online teaching ‘pedagogical’ repertoire. They found lockdown and online teaching particularly stressful and indicated that much of their online content would need to be retaught when students returned to classes. As stress is a driver of attrition particularly among early career teachers, the heightened pressures, and stressors our experts described were of particular concern, especially given looming world-wide teacher shortages (Worth et al., 2018). The experiences of our participants overall indicated that as a profession, we were largely unprepared for online teaching, despite technology being mandated in the Australian Curriculum since 2014 (Australian Curriculum, Assessment and Reporting Authority, 2014). It was the generosity of our teachers who gathered in online communities to fill the void left as traditional mentoring, teaching and learning became unviable, and it was their flexibility/reflexivity born through agency that allowed them to cope.
As technological pedagogical content knowledge evolves and the profession navigates seismic events like the COVID-19 pandemic, the experiences of our Arts teacher participants has emphasised that whilst technology has an important role to play in success, it also generates challenges. More importantly, their experience underscores the reality that it is the interpersonal relationships sphere of practice that may hold the key to online teaching success. Through their powerful voices, our participants remind us that lower secondary students are children who despite the superficial appearance of independence, maturity and technological proficiency, more often than not simply ‘use’ technology, which is not the same as being digitally or visually literate. Nor does that ‘user status’ guarantee that in a crisis continuity of access is assured. Moreover, for vulnerable less experienced teachers who have yet to master their craft, the need for mentoring and supportive communities of practice has never been greater. Here, (and in future testing times) the online environment has been shown to have an important role to play in providing space and scope for communities of teachers to come together and support one another by sharing experiences, offering advice, collaboratively problem solving and through the provision of resources. The need and value of connecting through online discipline-based communities of practice may therefore be the enduring legacy of COVID-19 for educators everywhere and an essential support for those in the early career phase.
Conclusion
We freely acknowledge limitations associated with the findings reported here: our study involved only 15 individuals. We appreciate the danger in generalising purely on these findings. However, studies such as this reveal the alignment between challenges, affordances and potentialities in trying times. Further, the emic perspective each member of our research team has brought to the study raised interesting and/or unexpected issues specific to Arts teaching practice in relation to the central research question.
Elliott Eisner, twentieth century Arts education luminary still considered by many the ‘Einstein’ of contemporary visual education observed that ‘Art is the literacy of the heart’. (Eisner, 2003). The humanity of this sentiment is both beautifully poignant and deeply resonant at a time when COVID-19 has isolated us and obliterated opportunities for many to meet their human need to connect, collaborate, grow and create together. The big ‘take away’ from our study is perhaps, therefore, the understanding that the online environment (with all of its foibles, challenges and inadequacies) offers a potential yet to be fully understood conduit for connection – between teachers, and teachers/students – especially in testing times. The challenge for policymakers and stakeholders is to consolidate what has been learned and capitalise on the strengths of both face-to-face and online learning in the Arts so that connection and collaboration (for teachers at least) can continue seamlessly irrespective of location or situation.
ORCID iDs
Lisa F Paris https://orcid.org/0000-0003-2410-6849
Christina Gray https://orcid.org/0000-0001-8464-1961
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.
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References
Australian Curriculum, Assessment and Reporting Authority (2014). General Capabilities, version 8.4 https://www.acara.edu.au/curriculum/foundation-year-10/general-capabilities
Australian Curriculum, Assessment and Reporting Authority (2018). The arts. https://australiancurriculum.edu.au/f-10-curriculum/the-arts/
Ballantyne J. Retell J. (2019). Teaching careers: Exploring links between well-being, burnout, self-efficacy and praxis shock. Frontiers in Psychology, 10 (1 ), 2255 10.3389/fpsyg.2019.02255 32132940
Beames R. Christensen H. Werner-Seidler A. (2021). School teachers: The forgotten frontline workers of Covid-19. Australasian Psychiatry, 29 (4 ), 420–422. 10.1177/10398562211006145 33847165
Buchanan J. Prescott A. Schuck S. Aubusson P. Burke P. Louviere J. (2013). Teacher retention and attrition: Views of early career teachers. Australian Journal of Teacher Education, 38 (3 ), 11. 10.14221/ajte.2013v38n3.9
Burbules N. Fan G. Repp P. (2020). Five trends of education and technology in a sustainable future. Geography and Sustainability, 1 (2 ), 93–97. 10.1016/j.geosus.2020.05.001
Burke K. (2021). How can creative arts possible be taught online? Perspectives and experiences of online educators in Australian higher education. Asia-Pacific Journal of Teacher Education, 49 (3 ), 347–361. 10.1080/1359866X.2020.1777531
Dabrowski A. (2020). Teacher well-being during a pandemic – Surviving or thriving? Social Education Research, 2 (1 ), 35–40. 10.37256/ser.212021588
de Vries H. (2021). Space for STEAM: New creativity challenge in education. Frontiers in Psychology, 12 (1 ). 586318 10.3389/fpsyg.2021.586318 33841230
Eddles-Hirsch K. (2015). Phenomenology and educational research. International Journal of Advanced Research, 3 (8 ), 251–260. https://www.journalijar.com/uploads/287_IJAR-6671.pdf
Eisner E. (2003). The Arts and the creation of mind. Yale University Press
Eyles A. (2018). Teachers' perspectives about implementing ICT in music education. Australian Journal of Teacher Education, 43 (5 ), 110–131. 10.14221/ajte.2018v43n5.8
Flack C. Walker L. Bickerstaff A. Margetts C. (2020). Socioeconomic disparities in Australian schooling during the COVID-19 pandemic. Pivot Professional Learning. https://docs.pivotpl.com/research/COVID-19/Pivot_Socioeconomic+disparities+in+Australian+schooling+during+COVID-19+July+2020.pdf
Gall M. (2013). Trainee teachers’ perceptions: Factors that constrain the use of music technology in teaching placements. Journal of Music Technology and Education, 6 (1 ), 5–27. 10.1386/jmte.6.1.5_1
Gattenhof S . (2009). A position for the arts in the Australian national curriculum. Drama new South wales state conference: DramALIVE, Sydney. https://eprints.qut.edu.au/29359/
Hong J. (2010). Pre-service and beginning teachers’ professional identity and its relation to dropping out of the profession. Teaching and Teacher Education, 26 (8 ), 1530–1543. 10.1016/j.tate.2010.06.003
Johari J. Yean Tan F. Tjik Zulkarnain Z.I. (2018). Autonomy, workload, work-life balance and job performance among teachers. International Journal of Educational Management. 32 (1 ), 107-120. 10.1108/IJEM-10-2016-0226
Kassner K. (1996). RX for technophobia. In Spruce G (Ed), Teaching music (pp 193-198). Routledge
Kraehe A. (2020). Dreading, pivoting and arting: The future of art curriculum in a post-pandemic world. Art Education, 73 (4 ), 4–7. 10.1080/00043125.2020.1774320
Lockee B.B. (2021). Shifting digital, shifting context: (Re)considering teacher professional development for online and blended learning in the COVID-19 era. Educational Technology Research and Development, 69 (1 ), 17–20. 10.1007/s11423-020-09836-8 33041603
Marco Learning (2020). The rise of teacher stress. Marco Learning. https://marcolearning.com/crayons-and-cortisol-the-epidemic-of-teacher-stress/
Murray A. (2011). Mind the gap: Technology, millennial leadership and the cross-generational workforce. The Australian Library Journal, 60 (1 ), 54–65. 10.1080/00049670.2011.10722556
Mutton T. (2020). Teacher education and covid-19: Responses and opportunities for new pedagogical initiatives. Journal of Education for Teaching, 46 (4 ), 439–441. 10.1080/02607476.2020.1805189
NEiTA FoundationAustralian College of Educators (2021). Teachers report card 2021: Teachers’ perceptions of education and their profession. https://www.austcolled.com.au/wp-content/uploads/2021/10/NEiTA-ACE-Teachers-Report-Card-2021.pdf?mc_cid=3c49ed5cee&mc_eid=8c326867cc
Olive J. (2014). Reflecting on the tensions between emic and etic perspectives in life history research: Lessons learned. Forum Qualitative Sozialforschung, 15 (2 ), 11. 10.17169/fqs-15.2.2072
Paris L. (2008). The graduate-mentor project in visual arts education: Mentoring within the western Australian curriculum framework: A study of the impact of mentoring on beginning-teachers' perception of their success in visual literacy education during the first year. Edith Cowan University. [Doctoral dissertation https://ro.ecu.edu.au/theses/231
Riemer K. Johnston R. B. (2019). Disruption as worldview change: A kuhnian analysis of the digital music revolution. Journal of Information Technology, 34 (4 ), 350–370. 10.1177/0268396219835101
Rosen R. (2012). We don’t need a digital sabbath, we need more time. The atlantic. https://www.theatlantic.com/technology/archive/2012/02/we-dont-need-a-digitalsabbath-we-need-more-time/252317/
Sabol F. R. (2021). Art education during the COVID-19 pandemic: The journey across a changing landscape. Arts Education Policy Review, 123 (3 ), 127–134. 10.1080/10632913.2021.1931599
Shaw R. (2021). COVID-19 threatens the already shaky status of arts education in schools. The conversation. https://theconversation.com/covid-19-threatens-the-already-shaky-status-of-arts-education-in-schools-168548
Spacek R. (2020). Teachers, students in arts struggle during pandemic. Idaho State Journal. https://www.idahostatejournal.com/news/covid19/teachers-students-in-arts-struggle-during-pandemic/article_bfaa305c-20ed-5d44-8b99-6b348150deda.html
Thomson S. (2020). What PISA tells us about our preparedness for remote learning. Teacher Magazine. https://www.teachermagazine.com.au/columnists/sue-thomson/what-pisa-tells-us-about-our-preparedness-for-remote-learning
Ugur N. Koc T. (2015). Time for digital detox: Misuse of mobile technology and phubbing. Procedia - Social and Behavioural Sciences, 195 (1 ), 1022–1031. 10.1016/j.sbspro.2015.06.491
UNESCO (2020). Supporting teachers and education personnel during times of crisis. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000373338.locale=en
Wahlquist C. (2021). How Melbourne’s short, sharp Covid lockdowns became the longest in the world. The Guardian. https://www.theguardian.com/australia-news/2021/oct/02/how-melbournes-short-sharp-covid-lockdowns-became-the-longest-in-the-world
Watson R. Singh U. (2022). Support mechanisms utilised by educational leaders during COVID-19: Experiences from the western Australian public education school sector. School Leadership & Management. Online ahead of print. 10.1080/13632434.2022.2116568
Wittber Z. (2017). Primary students’ engagement with the visual arts and their transition into Year 7. [Doctoral dissertation, Edith Cowan University. https://ro.ecu.edu.au/theses/1986
Worth J. Lynch S. Hillary J. Rennie C. Andrade J. (2018). Teacher workforce dynamics in England: Nurturing, supporting and valuing teachers. National Foundation for Educational Research. https://www.nfer.ac.uk/media/3111/teacher_workforce_dynamics_in_england_final_report.pdf
| 0 | PMC9729720 | NO-CC CODE | 2022-12-14 23:22:30 | no | Aust J Educ. 2022 Dec 6;:00049441221137074 | utf-8 | Aust J Educ | 2,022 | 10.1177/00049441221137074 | oa_other |
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Anal Chim Acta
Anal Chim Acta
Analytica Chimica Acta
0003-2670
1873-4324
Elsevier B.V.
S0003-2670(22)01287-9
10.1016/j.aca.2022.340716
340716
Article
Sandwich-like electrochemical aptasensing of heat shock protein 70 kDa (HSP70): Application in diagnosis/prognosis of coronavirus disease 2019 (COVID-19)
Negahdary Masoud a∗
Hirata Mario Hiroyuki b
Sakata Solange Kazumi c
Ciconelli Rozana Mesquita d
Bastos Gisele Medeiros d
Borges Jéssica Bassani d
Thurow Helena Strelow d
Junior Alceu Totti Silveira a
Sampaio Marcelo Ferraz d
Guimarães Larissa Berretta d
Maeda Bruno Sussumu d
Angnes Lúcio a∗∗
a Department of Fundamental Chemistry, Institute of Chemistry, University of São Paulo, Av. Prof. Lineu Prestes, 748, 05508-000, São Paulo, Brazil
b Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of Sao Paulo, Av Prof Lineu Prestes 580, 05508-000, São Paulo, Brazil
c Nuclear and Energy Research Institute, National Commission of Nuclear Energy (IPEN/CNEN - SP), São Paulo, SP, 05508-000, Brazil
d Research and Education Division, Hospital A Beneficência Portuguesa de São Paulo, São Paulo, Brazil
∗ Corresponding author.
∗∗ Corresponding author.
8 12 2022
8 12 2022
3407161 11 2022
7 12 2022
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2022
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In this research, by using aptamer-conjugated gold nanoparticles (aptamer-AuNPs) and a modified glassy carbon electrode (GCE) with reduced graphene oxide (rGO) and Acropora-like gold (ALG) nanostructure, a sandwich-like system provided for sensitive detection of heat shock protein 70 kDa (HSP70), which applied as a functional biomarker in diagnosis/prognosis of COVID-19. Initially, the surface of the GCE was improved with rGO and ALG nanostructures, respectively. Then, an aptamer sequence as the first part of the bioreceptor was covalently bound on the surface of the GCE/rGO/ALG nanostructures. After adding the analyte, the second part of the bioreceptor (aptamer-AuNPs) was immobilized on the electrode surface to improve the diagnostic performance. The designed aptasensor detected HSP70 in a wide linear range, from 5 pg mL−1 to 75 ng mL−1, with a limit of detection (LOD) of ∼2 pg mL−1. The aptasensor was stable for 3 weeks and applicable in detecting 40 real plasma samples of COVID-19 patients. The diagnostic sensitivity and specificity were 90% and 85%, respectively, compared with the reverse transcription-polymerase chain reaction (RT-PCR) method.
Keywords
Coronavirus disease 2019 (COVID-19)
Heat shock protein 70 kDa (HSP70)
Aptasensor
Bioelectrochemistry
Reduced graphene oxide (rGO)
Gold nanomaterials
==== Body
pmc1 Introduction
Coronaviruses are a large group (Coronaviridae) of viruses that can cause some infections, from a common cold to acute respiratory challenges. So far, four types of coronaviruses have been identified (α, β, γ, and δ) which human coronaviruses are found only in α and β groups [1]. The genomic sequencing confirmed the similarity between the recent coronavirus and two types of β-coronavirus (SARS-like and MERS-CoV) [2,3]. Hence, the new β-coronavirus was named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [4]. The SARS-COV-2 transmitted disease was named COVID-19 by the World Health Organization (WHO) [5]. Unfortunately, from the beginning of its emergence until November 2022, it has led to 635 million definitive infections and ∼6.6 million deaths worldwide [6]. The United States, India, Brazil, France, and Germany have had the highest number of diagnosed true-positive (TP) cases [6]. Till now, Different methods have been used to diagnose this disease, including designing/applying complementary RNA sequences of the virus genome, antigens, and antibodies related to this virus [7]. The most important method of diagnosing COVID-19 in clinics and hospitals is the real-time reverse transcription-polymerase chain reaction (RT-PCR) [8].
Patients' samples for diagnosing this disease mainly include important biofluids (blood, saliva, and nasopharyngeal swab) [9]. HSP70 is one of the critical biomarkers with great potential for the diagnosis/prognosis of COVID-19. Heat shock proteins (HSPs) are a set of proteins found in all living organisms and are classified according to their molecular weight, and the major ones include HSP27, HSP40, HSP60, HSP70, HSP90, and HSP110 [10]. The transcription process related to the production of these proteins increases or decreases in response to temperature shocks, stress, harmful chemicals, metabolic instabilities, and hypoxia/anoxia that facilitate folding and maintaining cellular protein structure [11]. The most important heat shock protein is HSP70, which has been shown to have varying levels of concentration in COVID-19 [12], cancer [13], diabetes [14], and some heart diseases [15]. So it can be used as a diagnostic biomarker. In a clinical study, the concentrations of HSP70 in the plasma of COVID-19 patients admitted to the intensive care unit (ICU) were evaluated. The increased HSP70 levels were confirmed as a biomarker predicting mortality for COVID-19 patients [12]. This biomarker was reported at a relatively stable level of 200 ng mL−1 during a one-week evaluation. Also, in one of the other recent studies, it has been shown that this biomarkers' maximum level in healthy individuals’ plasma is about 35 ng mL−1 [16].
Aptasensors as innovative detection tools are a subset of biosensors introduced in 1990 [17]. These quantitative diagnostic systems are extensively designed to diagnose various diseases [[18], [19], [20]]. In aptasensors, short single-stranded DNA or RNA sequences are used as the bioreceptor that is purposefully designed and can be specifically attached to target molecules [21]. Optimizing aptasensor components such as the bioreceptor and the signal transducer with various nanomaterials has recently been widely pursued [22]. Nanomaterials often amplify the aptasensors diagnostic signal and increase their sensitivity [23]. Moreover, nanomaterials can improve the stability of aptasensors [24]. Among the nanomaterials, carbon-based nanomaterials and nanomaterials synthesized from gold have received the greatest attention. GO is a carbon-based nanomaterial that comprises two-dimensional (2D) biocompatible graphene plates containing oxygen groups like hydroxyl and epoxide on its surfaces [25,26]. The presence of the mentioned functional groups reduces the conductivity of GO, and therefore by eliminating/minimizing these groups, GO in its reduced form (rGO) can have an outstanding performance in modifying the signal transducer surface [[27], [28], [29]]. On the other hand, gold-based nanomaterials have been used in the design of aptasensors due to their high conductivity, simple synthesis, high flexibility, and compatibility with biomolecules [30].
Many biosensors, such as aptasensors, immunosensors, and other types, have been designed to diagnose COVID-19 (detecting of related genes, antigens, and antibodies) [[31], [32], [33], [34], [35], [36]]. Using these aptasensors as an alternative approach to substitute expensive and highly limited methods such as RT-PCR can play a beneficial role in controlling epidemics in the future [37]. In addition, aptasensors can reduce the false-negative (FN) and false-positive (FP) results reported by other methods. Due to the higher sensitivity of HSP70 to cell-related instabilities, this biomarker increases rapidly in COVID-19 patients and can reflect the disease more early than other introduced analytes [12,16].
Herein, an aptasensor was constructed to diagnose COVID-19 by detecting HSP70. The signal transducer was a GCE modified with a thin layer of rGO and ALG nanostructure, respectively (Fig. 1 ). Then, the immobilization of a thiol-functionalized aptamer was performed on the electrode surface. Subsequently, different concentrations of HSP70 were added to the surface of the GCE/rGO/ALG nanostructure/aptamer. Finally, aptamer-AuNPs was added to the electrode surface as the second part of the bioreceptor. Aptamer molecules (by changing their conformation) in a sandwich-like state could have a high affinity for capturing the analyte. In the absence of the analyte, redox agents had more access to the surface of GCE (DPV peak current: maximum value). Conversely, in the presence of the analyte, a linear association was found between the regular reducing of DPV peak currents and enhancing the analyte concentration (signal-off aptasensor). The presence of AuNPs helped to maintain the conductivity of the signal transducer for a very wide concentration range in a serial analysis of various concentrations of the analyte. This decrease in DPV peak currents was related to the limited access of the redox agent to the electrode surface due to the found electrostatic repulsion between the negatively charged aptamer strands (in the sandwich-like structure) and the redox molecules.Fig. 1 Schematic presentation of the sandwich-like aptasensing platform to diagnose COVID-19 through detection of HSP70.
Fig. 1
2 Materials and methods
2.1 Materials/reagents
HSP70, HSP90, human serum albumin (HSA), immunoglobulin G (IgG), heparin, hemoglobin (Hb), a thiol functionalized aptamer (5′–SH–C6- GGGAGACAAGAAUAAACGCUCAAUGCGCUGAAUGCCCAGCCGUGAAAGCGUCGAUUUCCAUCCUUCGACAGGAGGCUCACAACAGGC-3′(Merck Batch number: WD09700943) [38], spermine, dithiothreitol (DTT), Tris (2-carboxyethyl) phosphine (TCEP), 6-mercapto-1-hexanol (MCH), GO, HAuCl4, trisodium citrate dihydrate, ethylenediaminetetraacetic acid (EDTA), and ethanol were purchased from Millipore Sigma (USA). All other reagents and chemicals were of the highest quality commercial grades and were used without further purification.
2.2 Apparatus
Two potentiostat/galvanostat devices were used to conduct electrochemical experiments (Autolab-PGSTAT302N and PalmSens, Netherlands). The surface of employed working electrodes was characterized by a field emission scanning electron microscope (FESEM) equipped with an energy dispersive X-ray spectrometer (EDS) (JEOL JSM 7401F, Japan). The surface of the GCE/rGO/ALG nanostructure before analysis by the FESEM was coated with a layer (3 nm) of gold performed by Cressington 208HR sputter coater (Cressington Scientific Instrument Ltd., Watford, UK). The morphology of AuNPs and aptamer-conjugated AuNPs utilized as the second part of the bioreceptor was investigated by a high-resolution transmission electron microscope (HRTEM) (JEOL JEM-2100, Japan). A UV–Vis spectrophotometer (HP 8453, Agilent Technologies., USA) was applied to confirm the proper synthesis of AuNPs and aptamer-conjugated AuNPs. In order to perform another characterization, the instrument of Malvern Zetasizer equipment (Malvern Instruments Ltd., UK) equipped with Zetasizer software (version 7.11) was applied to determine the average size of AuNPs.
2.3 Synthesis of AuNPs
The AuNPs were synthesized based on the previous protocol but with some modifications [39]. First, all the required utensils and components of the reflux system were washed with aqua regia (following all the safety principles). A solution containing 50 mL of 1 mmol L−1 HAuCl4 (dissolved in the deionized water (DI water) water with the desired quality (resistivity = 18.2 MΩ cm)) was prepared, transferred to the reflux system and heated until boiling. Afterward, 5 mL of 66 mmol L−1 trisodium citrate dehydrate was injected into the reflux system. After enough time, the color of the solution changed from yellow to black, in sequence to purple, and finally to deep red (synthesized form). Subsequently, the synthesized AuNPs were cooled at room temperature, characterized by UV–Vis spectroscopy and TEM, and stored in a dark and refrigerated (4 °C) condition until further use.
2.4 Preparing aptamer-conjugated AuNPs
The aptamer-conjugated AuNPs were synthesized based on the previously reported protocols and with some modifications [39,40]. Here, a vial containing 50 μL of 20 μmol L−1 thiolated HSP70 aptamer, 10 μL acetate buffer (500 mmol L−1, pH 5.2), and 10 μL of 10 mmol L−1 TCEP was prepared and incubated at 25 °C for 120 min. Then, the vial's content was transferred to a tube containing 2 mL of the synthesized AuNPs (section 2.3) and stored in a dark place (covered by aluminum foil) for 16 hours at 25 °C. Afterward, 10 μL Tris-acetate (500 mmol L−1, pH 8.2) was added gradually. In another step, 150 μL of 1 mol L−1 NaCl was added slowly, and the achieved mixture was stored in a dark place (covered by aluminum foil) for another 16 hours at room temperature. Ultimately, the mixture was centrifuged at high speed (15000 rpm) for 20 min, and then the upper layer was discarded. The synthesized aptamer-conjugated AuNPs were characterized by UV–Vis spectroscopy and TEM. The aptamer-conjugated AuNPs were stored in a dark and refrigerated (4 °C) until further use.
2.5 Preparing of HSP70 aptasensor
For electrochemical experiments, a three-electrode system was used. The working, counter, and reference electrodes were GCE (2 mm ⌀, Metrohm, Netherlands), a platinum wire, and an Ag/AgCl, 3 mol L−1 KCl (Metrohm, Netherlands), respectively. Initially, the working electrode was polished on an alumina (0.3 μm) polishing pad to a mirror finish and then cleaned in ethanol/DI water (3:1) using an ultrasonic bath for 8 min. In the next step, the electrode was electrochemically cleaned by cyclic voltammetry (CV) in the presence of 0.5 mol L−1 H2SO4 (procedure detail: scan rate: 50 mV S−1, potential range: -1 to 1 V, and 20 continuous cycles). Afterward, the electrode was immersed in the 500 μg mL−1 GO solution. Nitrogen was injected into the synthesis solution, and the electrodeposition process was performed by the chronoamperometry method (potential: -1.5 V, time: 400 s). This procedure reduced a 2D layer of GO sheets on the surface of GCE. Then, the modified GCE/rGO was immersed in the synthesis solution of the ALG nanostructure (15 mmol L−1 HAuCl4, 500 mmol L−1 H2SO4, and 50 mmol L−1 spermine), and the second surface modification was done by the chronoamperometry method (potential: 200 mV, time: 250 s). Since the applied aptamer was thiol-functionalized, in order to break thiol molecules and provide a reduced form of the aptamer, 20 μL of a solution (500 mmol L−1 DTT and 10 mmol L−1 sodium acetate (pH 5.2)) was added to 20 μL of the optimized concentration of aptamer (detailed in section 2.6). After keeping the aptamer vial for 20 min at room temperature, the DTT was extracted by adding 3 times ethyl acetate (each time: 100 μL), vortexing, and discarding the upper layer. Then, the surface of the GCE/rGO/ALG nanostructure was covered with the activated aptamer, and the immobilization process was processed in refrigerated condition (4 °C). The thiol group of aptamer established the covalent bond (Au–S) with the gold associated with the ALG nanostructure. Considering the time for covalently attachment of the aptamer on the surface (detailed in section 2.6), the GCE/rGO/ALG nanostructure/aptamer was covered with 1 mmol L−1 MCH (30 min) to block unwanted/unspecific immobilization of aptamer molecules on the surface of GCE/rGO/ALG nanostructure. Subsequently, 10 μL of HSP70 (various concentrations from 5 pg mL−1 to 75 ng mL−1) was dropped on the surface of the electrode, and then 10 μL of the prepared aptamer-conjugated AuNPs (section 2.4) was added. After considering enough binding time (detailed in section 2.6) at 37 °C, the electrochemical assays were achieved using the differential pulse voltammetry (DPV) in the presence of the redox electrolyte (20 mM Tris-HCl buffer, 500 mmol L−1 KCl, 0.5 mmol L−1 K4[Fe(CN)6]/K3[Fe(CN)6]).
2.6 Optimizing the aptamer condition
Open circuit potential (OCP) analysis was performed to find the best required time for immobilizing of aptamer on the surface of the working electrode. This experiment was performed in the presence of several screen-printed carbon electrodes (SPCE) (110, Metrohm DropSens, Spain). Due to the necessity of using refrigerated conditions to minimize environmental contamination and preventing the aptamer from drying on the surface, as well as the facility to use the smallest volume of aptamer, this experiment was performed using the SPCE instead of GCE. So, after preparing SPCE/rGO/ALG nanostructure, different concentrations (2, 5, 10, 15, 25, and 50 μmol L−1) of aptamer were immobilized separately and in a refrigerated condition (4 °C). Potential changes were recorded for 12 hours. The best time required for aptamer immobilization on the electrode surface was obtained by considering the first time corresponding to the first potential steadying point. In another experiment, the GCE/rGO/ALG nanostructure was prepared, and different concentrations (2, 5, 10, 15, 25, and 50 μmol L−1) of aptamer were immobilized on its surface. Following MCH treatment, a DPV was recorded for each aptamer concentration. The concentration corresponding to the first steady peak DPV currents was utilized in all future assays as the desired aptamer concentration. After finding the desired aptamer concentration and aptamer immobilization time, a typical concentration (1 ng mL−1) of analyte was dropped on the surface of the GCE/rGO/ALG nanostructure/aptamer. Then aptamer-AuNPs were added, and GCE/rGO/ALG nanostructure/aptamer/HSP70/aptamer-AuNPs were obtained. This aptasensor was placed in an incubator (37 °C), and a DPV was recorded every 4 min; when the first steady point in the DPVs peak current was attained, the desired binding time related to the interaction between aptamer strands and analyte was identified. Therefore, in all subsequent experiments, the found binding time was used.
2.7 Evaluation criteria for the reproducibility, regeneration, and stability assays
In order to determine the reproducibility, the aptasensor bound with a typical concentration of analyte (GCE/rGO/ALG nanostructure/aptamer/HSP70 (1 ng mL−1)/aptamer-AuNPs) was refabricated seven times, and then related DPVs were recorded. After each time, the aptasensor was soaked in piranha solution for 1 min to eliminate all organic molecules from the surface of the signal transducer. After washing with the DI water, the obtained electrode (GCE/rGO/ALG nanostructure) was applied for the next aptasensor refabricating. For the regeneration monitoring, the aptasensor was bound with HSP70 (1 ng mL−1), and de-bound (by immersing in hot (95 °C) DI water for 5 min [41,42]), and related DPVs were recorded for both statuses. The mentioned assay was repeated seven times. To assess stability, the constructed aptasensor was bound with HSP70 (1 ng mL−1), and the corresponding DPV was recorded for a continuous period (10 times DPV recording for 21 days). After each DPV recording, the electrode was rinsed with DI water and refrigerated in Tris-HCl buffer (pH 7.4).
2.8 Evaluation criteria for specificity analysis
Several biomolecules (HSP90, Hb, heparin, HSA, and IgG) were used as interfering agents to investigate the specificity performance of the developed aptasensor. In addition, mixtures of all mentioned interfering agents and the analyte were considered as another interfering group. In these groups, a concentration (1 ng mL−1) of the analyte was used in the presence of three concentrations (1 ng mL−1, 10 ng mL−1, and 1 μg mL−1) of HSP90 and three concentrations (10 ng mL−1, 1 μg mL−1, and 100 μg mL−1) of other interferences. Nevertheless, for the aptasensor performance in the presence of interferences mixtures, the concentrations of HSP70 were 1 ng mL−1 and 100 ng mL−1, and concentrations related to mixtures of all interferences were 100 ng mL−1 and 100 μg mL−1. DPVs were recorded, and the maximum peak decrement in each group was considered 100%, and the decrement percentages for other agents were obtained compared to 100%.
2.9 COVID-19 real samples applying criteria and ethics
This research was permitted by the research ethics committee of BP-A Beneficência Portuguesa de São Paulo hospital (CAAE number 36730020.6.0000.5483). All patients or their legal representatives provided informed consent. The study included 40 patients of both genders, older than 18-year-old and diagnosed with COVID-19 by RT-PCR attended at Hospital BP-A Beneficência Portuguesa de São Paulo. All patients were hospitalized no longer than 5 days at the time of inclusion, were followed up during hospitalization, and were evaluated by clinicians. Each patient's electronic medical records were inspected for demographic, clinical, and laboratory information. Plasma samples were obtained from blood collected in EDTA tubes on the day of inclusion and the day of hospital discharge. The plasma samples categorized into two groups (each group: 20 samples) were considered for assessment. Group 1 was related to samples detected before as COVID-19 positive by the RT-PCR method, and these patients were hospitalized based on the RT-PCR result and clinical symptoms. The second group (group 2) contained samples related to the same patients at the time of recovery and their discharge from the hospital. The electrochemical assay for plasma samples was done in a lab with certified biosafety control in the Pharmacy Sciences Faculty from the University of São Paulo (USP). Before electrochemical measurements, plasma samples were diluted 10 times with normal saline solution (0.9% NaCl). Every 5 pairs of samples (5 samples related to the first day (group 1) and 5 samples related to the discharge day (group 2)) were evaluated electrochemically in one day. The DPV peak current of plasma samples was compared with the DPV peak current related to the standard HSP70. The DPV peak current was used to estimate the concentration of HSP70 in the plasma of COVID-19 patients. For group 1, the results related to the RT-PCR method were considered as the reference. Moreover, true-positive (TP), true-negative (TN), FP, and FN results by considering the cut-off value (35 ng mL−1 [16]) of HSP70 in the plasma of healthy individuals were determined to find the diagnostic sensitivity and diagnostic specificity of the designed aptasensor. It should be considered that all samples related to the discharge day (group 2) from the hospital were assumed as TN.
2.10 Analysis of data
The data relating to electrochemical analyses were provided by Nova (version 1.11) and PSTrace (version 5.7) software. Analyses of electrochemical data and UV–Vis spectroscopy were performed via Microsoft Excel (version 2010) and Origin (version 2019). Image analysis related to SEM and TEM results were followed by Digimizer (version 4.5.2).
3 Results and discussion
3.1 Characterization of the modified GCE with GO and ALG nanostructure
The surface of a GCE was modified with GO and ALG nanostructure through the electrodeposition method, and then modified surfaces were characterized by the FESEM. The time and applied potential during the electrodeposition process were effective in the morphology of produced materials. For modifying the surface of GCE (Fig. 2 -a) with the reduced GO (rGO), the optimized condition was potential: -1.5 V and time: 400 s (Figs. S1–a and Fig. 2-b). The presence of 2D sheets of rGO on the surface of GCE enhanced the reactivity and electrocatalytic behavior of the signal transducer explained in section 3.3 with comprehensive detail. Optimizing the electrodeposition time related to the gold solution (section 2.5) was followed in the absence of GO. It was found that when different electrodeposition times were applied, a dissimilar layer of gold was shaped (Fig. 2-c and 2-d, electrodeposition time: 400 s) and (Fig. 2-e and 2-f, electrodeposition time: 250 s). It seems that the time and the applied potential (0.2 V) changed the interaction of spermine molecules with other reagents present in the solution. A more homogeneous distribution of synthesized shapes of the gold structure and smaller sizes of shapes was found for optimized conditions (Fig. 2-e and 2-f). Once more, GCE/rGO was prepared, and by applying the optimized condition (potential: 0.2 V, time: 250 s), it was modified with a layer of ALG nanostructure (Figs. S1–b and Fig. 2-g and 2-h). The presence of reduced graphene oxide on the surface of GCE made it possible to grow the gold structure in smaller sizes and nanometer dimensions, which provided the desired substrate for the subsequent stages. The EDS analysis also confirmed the excellent purity of the synthesized ALG nanostructure (Figs. S1–c). Considering the formal potentials based on the two principal gold redox processes (Au3+ + 3e− ⇌ Au and AuCl4 − + 3e− ⇌ Au + 4Cl−, E0 = 1.27 V, vs. AgCl), the applied electrodeposition potential (0.2 V) was negative [43]. The presence of spermine in the gold nanostructure solution led to the creation of electrostatic interaction with AuCl4 −, which triggered the shift of gold reduction potential and the deposition of gold nanostructure on the electrode surface. We have also shadowed the use of other biogenic amines in synthesizing several other gold nanostructures in previous research [18,44]. The FESEM analysis showed that the typical arm diameter in the ALG nanostructure was about 75–100 nm (Fig. 2-g and 2-h).Fig. 2 FESEM micrographs of GCE (a), GCE/rGO (b), GCE/Gold nanostructure: electrodeposition time: 400 S, magnification 30 K (c), GCE/Gold nanostructure: electrodeposition time: 400 S, magnification 40 K (d), GCE/Gold nanostructure: electrodeposition time: 250 S, magnification 10 K (e), GCE/Gold nanostructure: electrodeposition time: 250 S, magnification 15 K (f), GCE/rGO/ALG nanostructure: electrodeposition time: 250 S, magnification 10 K (g), GCE/rGO/ALG nanostructure: deposition time: 250 S, magnification 15 K (h), UV–Vis of AuNPs and aptamer-AuNPs (i), TEM micrographs of AuNPs (j), and aptamer-AuNPs (k).
Fig. 2
3.2 Analysis of synthesized AuNPs and aptamer-conjugated AuNPs
In another experiment, the synthesized AuNPs and aptamer-conjugated AuNPs were analyzed. The average size of the generated AuNPs was confirmed by zetasizer analysis, which was about 26 nm. Afterward, aptamer-conjugated AuNPs were prepared, and then UV–Vis spectroscopy and TEM were performed to know the difference between this conjugated form and AuNPs. The visible absorbance wavelength for AuNPs was found at ∼520 nm and confirmed the controlled synthesis (Fig. 2-i). Afterward, UV–Vis spectroscopy was also performed for aptamer-conjugated AuNPs. The absorbance signal decreased when the aptamer strands were conjugated with the AuNPs, indicating the contribution of aptamers to the decrease of visible light and a small shift (∼523 nm). The applied aptamer was thiol-functionalized, and TEM analysis for aptamer-conjugated AuNPs confirmed that there wasn't any significant change in the size of AuNPs compared to the non-conjugated form (Fig. 2-j and 2-k). However, the important thing that can be implied from the TEM images is that the presence of aptamer strands played a role in creating the network structure of nanoparticles, and the pattern of randomly dispersed AuNPs changed. This phenomenon was also confirmed in other research [45,46]. The conjugation and accumulation process changed the regular distribution of AuNPs, and most of the AuNPs were conjugated with aptamer strands.
3.3 Electrochemical analysis of GCE surface
Primarily, the signal transducer in various statuses, including GCE, GCE/rGO, and GCE/rGO/ALG nanostructure, was evaluated by CV analysis. The applied scan rates were from 2 to 200 mV s−1, and CVs were recorded in the presence of 20 mM Tris-HCl buffer, 500 mmol L−1 KCl, 0.5 mmol L−1 K4[Fe(CN)6]/K3[Fe(CN)6] as the redox marker (Figures S2-a, b, and c). The optimal concentrations required for GO and ALG nanostructure electrodeposition on the electrode surface were determined using this experiment so that the concentrations corresponding to the highest increase in CVs peaks compared to the bare electrode were desired (500 μg mL−1 GO and 15 mmol L−1 HAuCl4). In addition, CVs in the maximum scan rate applied (200 mV S−1) were shown in Fig. 3 -a. The results confirmed the improvement of electron transfer rate for GCE/rGO and GCE/rGO/ALG nanostructure compared to the bare GCE. Afterward, the electrochemically active surface related to the signal transducer in various mentioned statuses was determined through the Randles-Sevcik equation (Ip = 2.69 × 105 A × D1/2 n3/2 v1/2 C) [47,48]. The results (Figures S2-d and e) showed that the electrochemically active surface for bare GCE, GCE/rGO, and GCE/rGO/ALG nanostructure was 0.054, 0.066, and 0.13 cm2, respectively. Findings confirmed that GCE/rGO electrochemically active surface was improved but only 1.22 times enhanced compared to the bare GCE. The rate for conductivity improvement based on electrochemically active surface related to GCE/rGO/ALG nanostructure compared to GCE/rGO and bare GCE was more significant, 1.97 and 2.4 times, respectively.Fig. 3 CVs related to the various states of the signal transducer (scan rate: 200 mV S−1) (a); EIS analysis of the various states of the signal transducer: a) GCE, b) GCE/rGO, c) GCE/rGO/ALG nanostructure, d) GCE/rGO/ALG nanostructure/aptamer (without analyte), e) GCE/rGO/ALG nanostructure/aptamer/HSP70 (1 ng mL−1)/aptamer-AuNPs; DPVs of the various states of the signal transducer (c); DPVs (signal-off) of aptasensor in the presence of various concentrations of HSP70 (d); calibration curve of HSP70 aptasensor (Ip vs. logarithm of analyte concentrations) (e); Error bars indicate standard deviations from three repeated measurements (error bar: SD/n = 3).
Fig. 3
In another analysis, the charge transfer resistance (Rct) related to the signal transducer in various statuses was investigated by electrochemical impedance spectroscopy (EIS) (Fig. 3-b). This experiment showed excellent convergence of results and confirmed that the optimum circuit could be R([RW]Q), as depicted in Fig. 3-b. The maximum Rct was found for bare GCE (Fig. 3-b: diagram a). After modifying the surface with GO and ALG nanostructure, the Rct was decreased (Fig. 3-b: diagram b: GCE/rGO, diagram c: GCE/rGO/ALG nanostructure)). Then, the thiol-functionalized aptamer was immobilized (aptamer concentration: 15 μmol L−1 (Figs. S3–a), aptamer immobilization time: 130 min; (Figs. S3–b)) on the surface of the GCE/rGO/ALG nanostructure and could create Au–S covalent bound. This event led to a slight enhancement of the Rct due to electrostatic repulsion provided by the negatively charged phosphate backbone of aptamer induced to the anionic [Fe(CN)6] 3-/4- (Fig. 3-b: diagram d). Then, HSP70 (1 ng mL−1) was dropped on the surface to be trapped by the aptamer strands from one side. Then the other part of the bioreceptor (aptamer-AuNPs) was dropped and captured the HSP70 from the other side (aptamer binding time: 24 min at 37 °C, (Fig. S4)). The reported isoelectric pH of HSP70 is about 5.4 [49]. So, the used pH by aptasensor components (pH ∼ 7) and HSP70 was negatively charged and showed a more hindering electron effect on [Fe(CN)6] 3-/4-. Thus, the interaction of the analyte with aptamer strands increased the Rct value by restricting the access of the redox molecules to the working electrode and blocking the electron transfer on the surface (Fig. 3-b: diagram e). The presence of AuNPs helped maintain the conductivity for a wider concentration range in a serial analysis of various analyte concentrations, confirmed in section 3.4.
In another analysis, the signal transducer in various statuses was evaluated by the DPV. As shown in Fig. 3-c, the lowest DPV peak current (lowest electron transfer rate) was found for the bare GCE. By modifying the surface with GO and ALG nanostructure, the DPV peak current was enhanced, and this event confirmed the enhanced surface area for interaction with the redox marker molecules.
3.4 Determination of analyte concentrations
In this experiment, the constructed aptasensor was used to measure various concentrations of HSP70. The analyte was detected by the designed aptasensor throughout a wide linear range of 5 pg mL−1 to 75 ng mL−1 (concentration span: 15,000 times). By incrementing the analyte concentration, the DPVs peak current was decreased regularly (signal-off) Fig. 3- d. Here, using a sandwich-like structure in analyte detection led to an increase in diagnostic stability and maintaining the electrode conductivity at high analyte concentrations, which provided a wide range of detection for a set of analyte concentrations. In fact, the binding of aptamer-conjugated AuNPs from the other side to analyte molecules caused a slight decrease in the electron transfer process with increasing analyte concentration. Compared to the previous aptasensor designed by our team to detect this protein, in that aptasensor, the sandwich-like structure equipped with aptamer-conjugated AuNPs was not used, and the LOD and detection range was equal to 20 pg mL−1 and 50 pg mL−1 - 75 ng mL−1, respectively [44], which confirms that the special design and Sandwich like aptasensor in this research has provided a more sensitive detection, with a wider and more stable range (LOD: 2 pg mL−1 and detection range: 5 pg mL−1 - 75 ng mL−1). This wide detection range and possibility for a regular and stable detection response can be translated as improving the role of the present sandwich-like aptasensing platform in improving the conductivity offered by AuNPs in the conjugated form. Consequently, by considering the value of this biomarker in healthy individuals (35 ng mL−1), this aptasensor can be used at upper and lower levels than the cut-off value [12,16]. Here, the elevated concentration of HSP70 was considered for monitoring hospitalized COVID-19 patients. In many reports, the level of HSP70 changes in biofluids along with the severity of several diseases. Changes in the level of HSP70 can be used to diagnose/prognosis in some other diseases, such as diabetes [14,50], cancer [13,51], heart diseases [52,53], and neurodegenerative disease [54,55]. Fig. 3-e shows the calibration curve related to various concentrations of analyte (where X = logarithm of concentrations and Y = DPVs peak current). The linear regression was: Δcurrent (μA) = −1.094 log C HSP70 + 5.075, R2 = 0.9955. The calculated limit of detection (LOD) was about 2 pg mL−1 considering 3 σ (σ is the standard deviation (SD) of 10 assays of the blank signal) (S/N = 3). In Table 1 , the key characteristics of the designed HSP70 biosensing platforms developed are compared with the ones that emerged in this research.Table 1 Essential details of developed biosensors for monitoring of HSP70.
Table 1Type of biosensing Working electrode or transducer Bioreceptor Nano-advanced materials Measuring method Linear range of detection LOD Stability Human sample analysis Application Reference
Immunosensor (electrochemistry-based) GCE Anti-HSP70 antibody Polyaniline functionalized graphene quantum dots DPV 97.6 pg mL−1 - 100 ng mL−1 50 pg mL−1 90.16%, 15 days Serum ---a [56]
Immunosensor (electrochemistry-based) GCE Anti-HSP70 antibody Porous graphene (PG) DPV 44.8 pg mL−1 - 100 ng mL−1 20 pg mL−1 92.36%, 15 days Serum – [57]
Immunosensor (electrochemistry-based) GCE Anti-HSP70 antibody GO EIS/CV 12 - 144 fg mL−1 0.765 fg mL−1 ---♣ Serum – [58]
Immunosensor (electrochemistry-based) GCE Anti-HSP70 antibody Fullerene C60 NPs EIS 0.8–12.8 pg mL−1 0.273 pg mL−1 – Serum – [59]
Immunosensor (electrochemistry-based) Plastic chip electrode Anti-HSP70 antibody Au nanolayer DPV 10 pg mL−1 - 1000 ng mL−1 3.5 pg mL−1 94.5%, 21 days Serum – [60]
Immunosensor (electrochemistry-based) Indium tin oxide electrode Anti-HSP70 antibody AuNPs EIS/CV 1 - 166 fg mL−1 0.0618 fg mL−1 92.29%, 15 days Serum – [61]
Immunosensor (electrochemistry-based) Titanium foil Anti-HSP70 antibody Titanium dioxide nanotubes/Ag NPs EIS/CV 100 pg mL−1 - 100 ng mL−1 480 pg mL−1 – – – [62]
Immunosensor (optical-based) oxidized porous silicon Anti-HSP70 antibody Porous silicon UV–Vis 3–500 μg mL−1 1290 ng mL−1 – – – [63]
Immunosensor (localized surface plasmon resonance-based) Glass substrate Anti-HSP70 antibody AuNPs LSPR 920 pg mL−1 - 4 μg mL−1 920 pg mL−1 – – – [64]
Peptisensor (electrochemistry-based) GE Peptide ---♠ Square wave voltammetry (SWV) 0.2–2 nU mL−1 – – – – [65]
Aptasensor (electrochemistry-based) GE Aptamer Lady fern-like gold nanostructure DPV 50 pg mL−1 - 75 ng mL−1 20 pg mL−1 92%, 18 days Serum – [44]
Aptasensor (electrochemistry-based) GCE Aptamer AuNPs/rGO/ALG nanostructure DPV 5 pg mL−1 - 75 ng mL−1 2 pg mL−1 96.9%, 21 days Plasma COVID-19 This work
a Application for any disease monitoring not reported; ♣ Stability not reported; ♠ Nanomaterial (s) not reported.
As shown in Table 1, compared to all the biosensors designed to detect HSP70 protein, none of the previous research has specifically investigated the use of HSP70 biosensing to detect real samples related to a specific disease. The HSP70 biosensor designed in this research is the first and only HSP70 biosensing platform employed in the diagnosis of real samples of a disease (plasma of Covid-19 patients). Also, compared to other designed biosensors, the highest stability (remaining 96.9% of initial activity after 21 days) has been reported in this research, which can be related to the special structure of using different nanomaterials as well as sandwich-like aptasensing platform for detection of the analyte. Also, very low LOD and wide detection range are other important competitive advantages over other biosensors designed for HSP70.
3.5 Reproducibility, regeneration, and stability performance
In another analysis, the aptasensor bound with HSP70 (1 ng mL−1) was refabricated seven times according to the criteria presented before (section 2.7). This experiment verified an excellent performance, and very low DPV peak current changes were found (RSD: 1.6%) (Fig. 4 -a). In another experiment, the response of the constructed aptasensor was examined seven times after capturing a typical concentration of the analyte (1 ng mL−1). After recording DPV for each bound time, the electrode was immersed in 95 °C DI water (for 5 min) to change the aptamer conformational and release the analyte molecules. So, after de-binding the aptasensor from the analyte, another DPV was recorded (Fig. S5). The regeneration performance for bound and de-bound DPVs was evaluated, and the RSD for bound and de-bound DPVs was calculated as 1.11% and 1.28%, respectively, which confirmed an excellent regeneration performance. In order to determine the stability, a specific concentration (1 ng mL−1) of the analyte was bound to the aptasensor, and the electrochemical performance was monitored for 21 days without any change in the structure of the aptasensor (ten DPV recorded) (Fig. 4-b). The stability of the presented aptasensor was excellent compared to other HSP70 biosensors (Table 1). After 21 days, this aptasensor could maintain 96.9% of its initial activity (RSD: 3.1%).Fig. 4 Reproducibility performance for seven consecutive repeated refabricating of aptasensor (a); stability of aptasensor for 21 days analysis (b); specificity analysis of aptasensor in the presence of HSP90, Hb, heparin, HSA, IgG, and mixtures of all interfering agents; Error bars indicate standard deviations from three repeated measurements (error bar: SD/n = 3).
Fig. 4
3.6 Specificity performance
To verify the specificity of the developed aptasensor, several interfering agents (HSP90, heparin, IgG, Hb, and HSA) were used in the presence of the analyte. The samples were prepared (detail presented in section 2.8). The DPVs related to interfering agents were achieved (Figure S6, (a-g)), and the corresponding peak current was evaluated. There was also a group containing mixtures of all interfering species and the analyte aiming to evaluate the aptasensor specificity performance. To do this experiment, a series of six groups were considered. For each series, the maximum peak decrement value (compared to the peak current of the aptasensor (blank signal)) in each group was considered 100%. The percentage values related to the presence of interfering species in each group were calculated (Fig. 4-c). The maximum interference effect was related to 100 μg mL−1 HSA (16.4%) compared to 1 ng mL−1 HSP70 (100%). The minimum interference effect was related to 10 ng mL−1 heparin (0.1%) compared to 1 ng mL−1 HSP70 (100%). So, the results showed selective aptasensing to detect HSP70, even in employing higher concentrations of the interfering agents than the analyte. This experiment confirmed that this aptasensor could be used in biofluids containing other proteins or biomolecules with the highest selectivity for detecting HSP70.
3.7 Application for diagnosis of COVID-19 patients
Forty plasma samples related to COVID-19 patients were analyzed. These samples were categorized into two groups; the group 1 samples were previously analyzed by the RT-PCR method (section 2.9). Group 2 contained samples of the same patients related to the discharge day from the hospital that was assumed as TN. Samples were diluted ten times and then assessed by the aptasensor. For each sample, a DPV was recorded (Figure S7-a, b, c, and d), and using the equation (Δcurrent (μA) = −1.094 log C HSP70 + 5.075) related to the calibration curve of standard samples (Fig. 3, (e)), concentrations associated with each DPV peak current were estimated. Considering that the samples were diluted ten times, the estimated final concentration was multiplied by ten (Table 2 ). Based on the reported healthy cut-off value (35 ng mL−1) [12,16], two false-negative and three false-positive results were found. So, the diagnostic sensitivity and the diagnostic specificity of the designed aptasensor for the diagnosis of COVID-19 patients were 90% and 85%, respectively.Table 2 Compare the performance of the designed aptasensor with the RT-PCR method in the analysis of COVID-19 patients.
Table 2Sample name RT-PCR result Aptasensor result Peak current (Ip) found by aptasensor after 10 times dilution Estimated concentration of HSP70 by aptasensor
Group 1
S-1 Positive Positive 3.79 116.33
S-2 Positive Positive 3.87 98.63
S-3 Positive Positive 3.84 103.76
S-4 Positive Positive 3.89 94.25
S-5 Positive Positive 3.88 97.30
S-6 Positive Positive 3.89 95.24
S-7 Positive Positive 3.86 101.20
S-8 Positive Positive 3.75 125.40
S-9 Positive Positive 3.85 102.31
S-10 Positive Negativea 5.21 6.78
S-11 Positive Positive 3.92 88.68
S-12 Positive Positive 3.85 101.73
S-13 Positive Positive 3.78 118.75
S-14 Positive Positive 3.93 87.62
S-15 Positive Positive 3.89 95.55
S-16 Positive Positive 3.79 115.15
S-17 Positive Negative 5.15 7.67
S-18 Positive Positive 3.85 103.54
S-19 Positive Positive 4.087 63.81
S-20 Positive Positive 4.088 63.71
Group 2♠
S-11 Negative Negative 5.40 4.67
S-22 Negative Negative 5.68 2.63
S-33 Negative Negative 5.13 7.96
S-44 Negative Positive♣ 3.95 83.54
S-55 Negative Negative 5.22 6.68
S-66 Negative Negative 5.07 8.96
S-77 Negative Negative 5.21 6.79
S-88 Negative Negative 5.20 6.93
S-99 Negative Negative 5.20 6.94
S-10–10 Negative Negative 5.07 9.03
S-11–11 Negative Positive 3.85 102.60
S-12–12 Negative Negative 5.21 6.80
S-13–13 Negative Negative 5.12 8.02
S-14–14 Negative Negative 5.59 3.14
S-15–15 Negative Positive 3.80 113.07
S-16–16 Negative Negative 5.34 5.20
S-17–17 Negative Negative 5.33 5.32
S-18–18 Negative Negative 5.63 2.93
S-19–19 Negative Negative 5.47 4.00
S-20–20 Negative Negative 5.30 5.57
a False-negative; ♣ False-positive; ♠ All samples of this group were assumed as TN.
4 Conclusion
In the presented study, a novel aptasensor was developed to detect COVID-19. This aptasensor could detect the HSP70, which can be used as a biomarker in the diagnosis/prognosis of COVID-19. Once the plasma level of this protein in COVID-19 patients increased sharply, the mentioned aptasensor provided a reliable diagnosis for this viral disease. Using different nanomaterials (AuNPs, ALG nanostructure, and rGO) increased the sensitivity, the linear detection range, and the stability of the desired aptasensor. Utilizing aptamer strands in two positions (directly immobilized on the surface of the working electrode and also conjugated with AuNPs) provided optimal conditions for the analyte detection. The presence of rGO sheets on the GCE distributed a specific and stable surface for the deposition of ALG nanostructure. In particular, the proficiency of the designed diagnostic platform in the assays of real plasma samples of COVID-19 patients was also evaluated. In the future, the role of the HSP70 biomarker in diagnosing other diseases, such as various cancers, heart diseases, and diabetes, should also be investigated. In addition, in the subsequent research, other nanomaterials, signal transducers, and bioreceptors (antibodies and peptides) can be considered in the presence of aptamers.
Study limitations
Despite the excellent diagnostic sensitivity and specificity of the designed aptasensor for HSP70, it is important to highlight that this protein is not specific to COVID-19 and may also be involved in other diseases. Moreover, Group 2 contained samples related to the discharge day from the hospital. However, no tests have been performed in the hospital for this group to prove COVID-19 negative results.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Appendix A Supplementary data
The following is the Supplementary data to this article:Multimedia component 1
Multimedia component 1
Data availability
No data was used for the research described in the article.
Acknowledgments
The authors would like to thank the Sao Paulo Research Foundation-FAPESP (projects 2019/27021-4, 2017/13137-5, and 2014/50867-3) and the National Council for Research-CNPq (processes 311847-2018-8 and 465389/2014–7). The authors are also appreciative to Central Analítica (IQ-USP), Laboratório de Microscopia e Microanálise do Centro de Ciência e Tecnologia de Materiais do IPEN/CNEN-SP, Laboratório de Química Supramolecular e Nanotecnologia (IQ-USP), and Laboratório de Materiais Eletroativos (LME-IQ-USP) for material characterization facilities. We appreciate department of Clinical and Toxicological Analyses (FCF-USP) and also Hospital A Beneficência Portuguesa, SP for supporting and providing clinical sample analysis facilities.
Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.aca.2022.340716.
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References
1 Asselah T. Durantel D. Pasmant E. Lau G. Schinazi R.F. COVID-19: discovery, diagnostics and drug development J. Hepatol. 74 2021 168 184 33038433
2 Li X. Geng M. Peng Y. Meng L. Lu S. Molecular immune pathogenesis and diagnosis of COVID-19 J. Pharmaceut. Anal. 10 2020 102 108
3 Abd El-Aziz T.M. Stockand J.D. Recent progress and challenges in drug development against COVID-19 coronavirus (SARS-CoV-2)-an update on the status Infect. Genet. Evol. 83 2020 104327
4 Wu F. Zhao S. Yu B. Chen Y.-M. Wang W. Song Z.-G. Hu Y. Tao Z.-W. Tian J.-H. Pei Y.-Y. A new coronavirus associated with human respiratory disease in China Nature 579 2020 265 269 32015508
5 Chakraborty I. Maity P. COVID-19 outbreak: migration, effects on society, global environment and prevention Sci. Total Environ. 728 2020 138882
6 W.H.O. (WHO) WHO Coronavirus Disease (COVID-19) Weekly Epidemiological Update and Weekly Operational Update 2022
7 Mahshid S.S. Flynn S.E. Mahshid S. The potential application of electrochemical biosensors in the COVID-19 pandemic: a perspective on the rapid diagnostics of SARS-CoV-2 Biosens. Bioelectron. 176 2021 112905
8 Cirit O.S. Mutlu E. Sancak B. Kocagöz T. Can Ö. Çicek C. Arzu Sayiner A. Appak Ö. Uyar N.Y. Külah C. Comparison of a novel antigen detection test with reverse transcription polymerase chain reaction assay for laboratory diagnosis of SARS-CoV-2 infection Infection 2022 1 6
9 Shakeeb N. Varkey P. Ajit A. Human saliva as a diagnostic specimen for early detection of inflammatory biomarkers by real-time RT-PCR Inflammation 44 2021 1713 1723 34031776
10 Putri R.F. Susilorini T.E. Widodo N. Kuswati K. Suyadi S. Heat Shock Protein (HSP) Release Mechanism under Heat Stress Pressure in Goats: a Review, E3S Web of Conferences 2022 00046
11 Yun C.W. Kim H.J. Lim J.H. Lee S.H. Heat shock proteins: agents of cancer development and therapeutic targets in anti-cancer therapy Cells 9 2019 60 31878360
12 Fraser D.D. Cepinskas G. Slessarev M. Martin C. Daley M. Miller M.R. O'Gorman D.B. Gill S.E. Patterson E.K. Dos Santos C.C. Inflammation Profiling of Critically Ill Coronavirus Disease 2019 Patients vol. 2 2020 Critical care explorations
13 Vostakolaei M.A. Hatami‐Baroogh L. Babaei G. Molavi O. Kordi S. Abdolalizadeh J. Hsp70 in cancer: a double agent in the battle between survival and death J. Cell. Physiol. 236 2021 3420 3444 33169384
14 de Oliveira A.A. Mendoza V.O. Rastogi S. Nunes K.P. New Insights into the Role and Therapeutic Potential of HSP70 in Diabetes 2022 Pharmacological Research 106173
15 Kadhim W.A. Kareem N.M. Al-Shami S.A. Comparison between HSP70 levels in acute and chronic coronary artery diseases Indian J. Forensic Med. Toxicol. 14 2020
16 Werner C. Stangl S. Salvermoser L. Schwab M. Shevtsov M. Xanthopoulos A. Wang F. Dezfouli A.B. Thölke D. Ostheimer C. Hsp70 in liquid biopsies—a tumor-specific biomarker for detection and response monitoring in cancer Cancers 13 2021 3706 34359606
17 Lasserre P. Balansethupathy B. Vezza V.J. Butterworth A. Macdonald A. Blair E.O. McAteer L. Hannah S. Ward A.C. Hoskisson P.A. SARS-CoV-2 aptasensors based on electrochemical impedance spectroscopy and low-cost gold electrode substrates Anal. Chem. 94 2022 2126 2133 35043638
18 Negahdary M. Behjati-Ardakani M. Sattarahmady N. Yadegari H. Heli H. Electrochemical aptasensing of human cardiac troponin I based on an array of gold nanodumbbells-Applied to early detection of myocardial infarction Sensor. Actuator. B Chem. 252 2017 62 71
19 Negahdary M. Aptamers in nanostructure-based electrochemical biosensors for cardiac biomarkers and cancer biomarkers: a review Biosens. Bioelectron. 152 2020 112018
20 Heiat M. Negahdary M. Sensitive diagnosis of alpha-fetoprotein by a label free nanoaptasensor designed by modified Au electrode with spindle-shaped gold nanostructure Microchem. J. 148 2019 456 466
21 Negahdary M. Heli H. An ultrasensitive electrochemical aptasensor for early diagnosis of Alzheimer's disease, using a fern leaves-like gold nanostructure Talanta 198 2019 510 517 30876593
22 Gopi S. Balakrishnan P. Mubarak N.M. Nanotechnology for Biomedical Applications 2022 Springer
23 Negahdary M. Angnes L. Electrochemical aptamer-based nanobiosensors for diagnosing Alzheimer's disease: a review Biomater. Adv. 135 2022 112689
24 Zhang N. Li J. Liu B. Zhang D. Zhang C. Guo Y. Chu X. Wang W. Wang H. Yan X. Signal enhancing strategies in aptasensors for the detection of small molecular contaminants by nanomaterials and nucleic acid amplification Talanta 236 2022 122866
25 Zhao Y. Liu Y. Zhang X. Liao W. Environmental transformation of graphene oxide in the aquatic environment Chemosphere 262 2021 127885
26 Ren Y. Yu F. Li X.-G. Ma J. Recent progress on adsorption and membrane separation for organic contaminants on multi-dimensional graphene Mater. Today Chem. 22 2021 100603
27 Milosavljevic V. Mitrevska K. Adam V. Benefits of oxidation and size reduction of graphene/graphene oxide nanoparticles in biosensing application: classification of graphene/graphene oxide nanoparticles Sensor. Actuator. B Chem. 353 2022 131122
28 Gao J. Wang C. Chu Y. Han Y. Gao Y. Wang Y. Wang C. Liu H. Han L. Zhang Y. Graphene oxide-graphene Van der Waals heterostructure transistor biosensor for SARS-CoV-2 protein detection Talanta 240 2022 123197
29 Mondal J. Srivastava S.K. Room-temperature one-step synthesis of silver/reduced graphene oxide nanocomposites as an excellent microwave absorber Langmuir 37 2021 13409 13419 34736324
30 Negahdary M. Electrochemical aptasensors based on the gold nanostructures Talanta 216 2020 120999
31 Zeng J. Duarte P.A. Ma Y. Savchenko O. Shoute L. Khaniani Y. Babiuk S. Zhuo R. Abdelrasoul G.N. Charlton C. An impedimetric biosensor for COVID-19 serology test and modification of sensor performance via dielectrophoresis force Biosens. Bioelectron. 2022 114476
32 De Felice M. De Falco M. Zappi D. Antonacci A. Scognamiglio V. Isothermal amplification-assisted diagnostics for COVID-19 Biosens. Bioelectron. 2022 114101
33 Funari R. Fukuyama H. Shen A.Q. Nanoplasmonic multiplex biosensing for COVID-19 vaccines Biosens. Bioelectron. 208 2022 114193
34 Huang L. Li Y. Luo C. Chen Y. Touil N. Annaz H.-E. Zeng S. Dang T. Liang J. Hu W. Novel nanostructure-coupled biosensor platform for one-step high-throughput quantification of serum neutralizing antibody after COVID-19 vaccination Biosens. Bioelectron. 199 2022 113868
35 Paulose A.K. Huang C.C. Chen P.H. Tripathi A. Chen P.H. Huang Y.S. Wang Y.L. A rapid detection of COVID‐19 viral RNA in human saliva using electrical double layer‐gated field‐effect transistor‐based biosensors Adv. Mater. Technol. 7 2022 2100842
36 Bordbar M.M. Samadinia H. Sheini A. Aboonajmi J. Sharghi H. Hashemi P. Khoshsafar H. Ghanei M. Bagheri H. A colorimetric electronic tongue for point-of-care detection of COVID-19 using salivary metabolites Talanta 246 2022 123537
37 Sari A.K. Hartati Y.W. Gaffar S. A review on the development of aptamer immobilization techniques in aptamer-based electrochemical biosensors for viruses detection Anal. Bioanal. Electrochem. 14 2022 127 143
38 An RNA aptamer specific to Hsp70-ATP conformation inhibits its ATPase activity independent of Hsp40 Nucleic Acid Therapeut. 25 2015 103 112
39 Hill H.D. Mirkin C.A. The bio-barcode assay for the detection of protein and nucleic acid targets using DTT-induced ligand exchange Nat. Protoc. 1 2006 324 336 17406253
40 Fu K. Li S. Jiang X. Wang Y. Willis B.G. DNA gold nanoparticle nanocomposite films for chemiresistive vapor sensing Langmuir 29 2013 14335 14343 24111781
41 Wang Y. Li Z. Yu H. Aptamer-based Western blot for selective protein recognition Front. Chem. 8 2020 570528
42 Trachman R.J. Autour A. Jeng S.C. Abdolahzadeh A. Andreoni A. Cojocaru R. Garipov R. Dolgosheina E.V. Knutson J.R. Ryckelynck M. Structure and functional reselection of the Mango-III fluorogenic RNA aptamer Nat. Chem. Biol. 15 2019 472 479 30992561
43 Bard A. Standard Potentials in Aqueous Solution 2017 Routledge
44 Negahdary M. Angnes L. An aptasensing platform for detection of heat shock protein 70 kDa (HSP70) using a modified gold electrode with lady fern-like gold (LFG) nanostructure Talanta 246 2022 123511
45 Cao C. Liu J. Tang S. Dai Z. Xiao F. Rang W. Liu L. Chen T. Yuan Y. Li L. Amplified electrochemical determination of UO22+ based on the cleavage of the DNAzyme and DNA-modified gold nanoparticle network structure Microchim. Acta 187 2020 1 9
46 Carnerero J.M. Masuoka S. Baba H. Yoshikawa Y. Prado-Gotor R. Yoshikawa K. Decorating a single giant DNA with gold nanoparticles RSC Adv. 8 2018 26571 26579 35541036
47 Bard A.J. Faulkner L.R. Fundamentals and applications Electrochem. Methods 2 2001 580 632
48 Wang J. Analytical Electrochemistry 2004 Wiley
49 Junprung W. Norouzitallab P. De Vos S. Tassanakajon A. Nguyen Viet D. Van Stappen G. Bossier P. Sequence and expression analysis of HSP70 family genes in Artemia franciscana Sci. Rep. 9 2019 1 13 30626917
50 Amawi K.F. Al-Mazari I.S. Alsarhan A. Alhamad H.Q.M. Alkhatib A.J. Diabetes upregulates the expression of HSP90 and downregulates HSP70 in the liver of diabetic rats Comp. Clin. Pathol. 28 2019 473 478
51 Lim S. Cho H.Y. Kim D.G. Roh Y. Son S.-Y. Mushtaq A.U. Kim M. Bhattarai D. Sivaraman A. Lee Y. Targeting the interaction of AIMP2-DX2 with HSP70 suppresses cancer development Nat. Chem. Biol. 16 2020 31 41 31792442
52 Zerikiotis S. Angelidis C. Dhima I. Naka K.K. Kasioumi P. Kalfakakou V. Peschos D. Vezyraki P. The increased expression of the inducible Hsp70 (HSP70A1A) in serum of patients with heart failure and its protective effect against the cardiotoxic agent doxorubicin Mol. Cell. Biochem. 455 2019 41 59 30390173
53 Molanouri Shamsi M. Hassan Z.M. Gharakhanlou R. Exercise-induced Chaperokine Activity of Hsp70: Possible Role in Chronic Diseases, Chaperokine Activity of Heat Shock Proteins 2019 Springer 193 209
54 Rutledge B.S. Choy W.-Y. Duennwald M.L. Folding or holding?—Hsp70 and Hsp90 chaperoning of misfolded proteins in neurodegenerative disease J. Biol. Chem. 298 2022
55 Vavilova J.D. Boyko A.A. Troyanova N.I. Ponomareva N.V. Fokin V.F. Fedotova E.Y. Streltsova M.A. Kust S.A. Grechikhina M.V. Shustova O.A. Alterations in proteostasis system components in peripheral blood mononuclear cells in Parkinson disease: focusing on the HSP70 and p62 levels Biomolecules 12 2022 493 35454081
56 Sun B. Wang Y. Li D. Li W. Gou X. Gou Y. Hu F. Development of a sensitive electrochemical immunosensor using polyaniline functionalized graphene quantum dots for detecting a depression marker Mater. Sci. Eng. C 111 2020 110797
57 Sun B. Cai J. Li W. Gou X. Gou Y. Li D. Hu F. A novel electrochemical immunosensor based on PG for early screening of depression markers-heat shock protein 70 Biosens. Bioelectron. 111 2018 34 40 29631161
58 Özcan B. Sezgintürk M.K. Graphene oxide based electrochemical label free immunosensor for rapid and highly sensitive determination of tumor marker HSP70 Talanta 160 2016 367 374 27591626
59 Demirbakan B. Sezgintürk M.K. A novel immunosensor based on fullerene C60 for electrochemical analysis of heat shock protein 70 J. Electroanal. Chem. 783 2016 201 207
60 Maniya N.H. Parashar K. Kadam L.N. Srivastava D.N. Electrochemical detection of heat shock protein 70 over cost-effective plastic chip electrode platform J. Taiwan Inst. Chem. Eng. 128 2021 11 19
61 Sonuç Karaboğa M.N. Şimşek Ç.S. Sezgintürk M.K. AuNPs modified, disposable, ITO based biosensor: early diagnosis of heat shock protein 70 Biosens. Bioelectron. 84 2016 22 29 26318579
62 Nycz M. Arkusz K. Pijanowska D.G. Fabrication of electrochemical biosensor based on titanium dioxide nanotubes and silver nanoparticles for heat shock protein 70 detection Materials 14 2021 3767 34279337
63 Maniya N.H. Srivastava D.N. Fabrication of porous silicon based label-free optical biosensor for heat shock protein 70 detection Mater. Sci. Semicond. Process. 115 2020 105126
64 Denomme R. A Label-free Biosensor for Heat Shock Protein 70 Using Localized Surface Plasmon Resonance 2012 University of Waterloo
65 Ding X. Li H. Xie H. Huang Y. Hou Y. Yin Y. Li G. A novel method to assay molecular chaperone activity of HSP70: evaluation of drug resistance in cancer treatment Biosens. Bioelectron. 47 2013 75 79 23562728
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Can J Cardiol
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Case and Research Letter
Exacerbation of Chronic Cutaneous Lupus Erythematosus Triggered by Vaccine Against COVID-19
Exacerbación del lupus eritematoso cutáneo crónico desencadenada por la vacuna contra la COVID-19Souza E.N. ab⁎
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Oliosi A.C. ab
a Universidade Federal do Espirito Santo (UFES), Vitória, ES, Brazil
b Hospital Universitário Cassiano Antônio Moraes (HUCAM), Vitória, ES, Brazil
⁎ Corresponding author.
28 10 2022
28 10 2022
© 2022 AEDV. Published by Elsevier España, S.L.U.
2022
AEDV
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
==== Body
pmcTo the Editor,
COVID-19 has been a challenge worldwide due to the high infectivity and pathogenicity of the virus. The use of vaccines for preventing respiratory syndromes caused by SARS-CoV-2 is promising and essential in combating the infection. Little is known about COVID-19 vaccination in patients with autoimmune skin diseases; however, cases of onset and exacerbation of cutaneous and systemic lupus erythematosus have been reported following the use of immunizers.1, 2, 3, 4, 5, 6, 7, 8, 9 This report describes a chronic cutaneous lupus erythematosus (CCLE) exacerbation thirty days after the second dose of ChAdOx1 nCoV-19 vaccine (Oxford/AstraZeneca).
A 40-year-old female, Fitzpatrick V, had been undergoing dermatological follow-up for CCLE on the scalp, marked by an extensive frontoparietal cicatricial alopecia, stable since 2018. The patient did not exhibit other chronic medical illnesses. She presented to dermatological department in early 2022, with itching, burning and erythema on the scalp, associated with serohematic exudate. No systemic symptoms were reported. She denied possible triggering factors, such as recent infections, cigarette smoking, ultraviolet radiation exposure, and drug intake. However, the patient reported that she had received the second dose of the ChAdOx1 nCoV-19 vaccine thirty days before the onset of the symptoms.
Clinical examination revealed a frontoparietal cicatricial alopecia interspersed with erythematous scaly papules and plaques, surrounded by rough and dry hair shafts of lupus hair (Fig. 1 ). Trichoscopic findings of disease activity included perifollicular scaling, erythema, incontinentia pigmenti signs, and pili torti, in addition to the CCLE chronic alterations, such as dystrophic hairs, milky red areas, and shiny white structures (Fig. 2 ). Laboratory tests showed normal blood cell count and serum chemistry. The extractable nuclear antigen panel was negative. Anatomopathological study revealed features of CCLE activity: epidermal maturation disorder with hyperkeratosis, vacuolar degeneration of the basal layer, and focal spongiosis; perivascular and periadnexal lymphohistiocytic inflammatory dermal infiltrate, with reduced follicular units, interstitial fibrosis, and extravasation of red blood cells in the dermis (Fig. 3 ). Alcian blue stain highlighted a deposition of dermal mucin. Clinical evaluation and complementary tests suggested the diagnosis of CCLE exacerbation triggered by COVID-19 vaccine. The lesions showed satisfactory improvement after topical corticosteroid therapy.Figure 1 (a) Frontoparietal cicatricial alopecia interspersed with erythematous scaly papules and plaques, surrounded by rough and dry hair shafts of lupus hair. (b) Scaly erythematous alopecic plaque on the right parietal region of the scalp.
Figure 2 Tricoscopic findings. (a) Perifollicular scaling, erythema, dystrophic hairs, milky red areas, and shiny white structures. (b) Perifollicular scaling, erythema, incontinentia pigmenti signs, pili torti, dystrophic hairs, and fibrotic areas. (c) Perifollicular scaling, intense erythema, and incontinentia pigmenti signs. (d) Cicatricial alopecia with incontinentia pigmenti signs, dystrophic hairs, and empty follicles.
Figure 3 Anatomopathological findings: epidermal maturation disorder with hyperkeratosis, vacuolar degeneration of the basal layer, and focal spongiosis; perivascular and periadnexal lymphohistiocytic inflammatory dermal infiltrate, with reduced follicular units, interstitial fibrosis, and extravasation of red blood cells in the dermis (hematoxylin–eosin, ×100).
The most common cutaneous side effects of the COVID-19 vaccines include injection site reactions (erythema, edema and pain), urticaria, and morbilliform eruptions.3 The onset or exacerbation of lupus after vaccination is rare, with few cases in the literature.1, 2, 3, 4, 5, 6, 7, 8, 9 The international VACOLUP study indicated that COVID-19 vaccine was well tolerated in patients with systemic lupus erythematosus. Among the 696 participants assessed, only 3% reported a medically confirmed SLE flare after the immunization.8 Considering cutaneous lupus, the authors did not find any reports about the chronic form, as in the case presented, nor about exacerbation of cicatricial alopecia in chronic cutaneous lupus erythematosus after vaccination. To date, publications with cutaneous involvement have described the subacute form, with a predominance of manifestations in the trunk and limbs.1, 3, 4, 7
Authors suggest that the immune response generated by the immunizer results in activation of inflammatory type 1 helper T cell (Th1) and production of cytokines, with increased levels of interferon gamma (IFN-γ), interleukin-2, and tumor necrosis factor-alpha (TNF-α).3, 4 This inflammatory environment contributes to the recruitment of immune cells, becoming a potential trigger for cutaneous and/or systemic lupus manifestations.
Despite the theoretical risk of lupus onset or exacerbation following immunization, the COVID-19 vaccine is recommended for patients with autoimmune diseases, regardless of the activity or severity of the underlying disorder.10 Therefore, health professionals should encourage vaccination and perform clinical surveillance of new lupus manifestations in order to provide early treatment and avoid complications.
Conflict of Interest
The authors declare that they have no conflict of interest.
==== Refs
References
1 Liu V. Messenger N.B. New-onset cutaneous lupus erythematosus after the COVID-19 vaccine Dermatol Online J 27 2021 10.5070/D3271156093
2 Am N. Saleh A.M. Khalid A. Alshaya A.K. Alanazi S.M.M. Systemic lupus erythematosus with acute pancreatitis and vasculitic rash following COVID-19 vaccine: a case report and literature review Clin Rheumatol 17 2022 1 6
3 Kreuter A. Licciardi-Fernandez M.J. Burmann S.N. Burkert B. Oellig F. Michalowitz A.L. Induction and exacerbation of subacute cutaneous lupus erythematosus following mRNA-based or adenoviral vector-based SARS-CoV-2 vaccination Clin Exp Dermatol 47 2022 161 163 34291477
4 Joseph A.K. Chong B.F. Subacute cutaneous lupus erythematosus flare triggered by COVID-19 vaccine Dermatol Ther 34 2021 e15114 34455671
5 Lemoine C. Padilla C. Krampe N. Doerfler S. Morgenlander A. Thiel B. Systemic lupus erythematous after Pfizer COVID-19 vaccine: a case report Clin Rheumatol 16 2022 1 5 10.1007/s10067-022-06126-x
6 Raviv Y. Betesh-Abay B. Valdman-Grinshpoun Y. Boehm-Cohen L. Kassirer M. Sagy I. First presentation of systemic lupus erythematosus in a 24-year-old male following mRNA COVID-19 vaccine Case Rep Rheumatol 2022 2022 9698138 35154842
7 Kreuter A. Burmann S.N. Burkert B. Oellig F. Michalowitz A.L. Transition of cutaneous into systemic lupus erythematosus following adenoviral vector-based SARS-CoV-2 vaccination J Eur Acad Dermatol Venereol 35 2021 e733 e735 34243220
8 Felten R. Kawka L. Dubois M. Ugarte-Gil M.F. Fuentes-Silva Y. Piga M. Tolerance of COVID-19 vaccination in patients with systemic lupus erythematosus: the international VACOLUP study Lancet Rheumatol 3 2021 e613 e615 34312612
9 Barbhaiya M. Levine J.M. Siegel C.H. Bykerk V.P. Jannat-Khah D. Mandl L.A. Adverse events and disease flares after SARS-CoV-2 vaccination in patients with systemic lupus erythematosus Clin Rheumatol 30 2021 1 4
10 Curtis J.R. Johnson S.R. Anthony D.D. Arasaratnam R.J. Baden L.R. Bass A.R. American college of rheumatology guidance for COVID-19 vaccination in patients with rheumatic and musculoskeletal diseases: version 1 Arthritis Rheumatol 73 2021 1093 1107 33728796
| 36511288 | PMC9729933 | NO-CC CODE | 2022-12-14 23:22:37 | no | Actas Dermosifiliogr. 2022 Oct 28; doi: 10.1016/j.ad.2022.08.022 | utf-8 | Actas Dermosifiliogr | 2,022 | 10.1016/j.ad.2022.08.022 | oa_other |
==== Front
Procedia Comput Sci
Procedia Comput Sci
Procedia Computer Science
1877-0509
The Author(s). Published by Elsevier B.V.
S1877-0509(22)01887-7
10.1016/j.procs.2022.11.177
Article
AI shapes the future of web conferencing platforms
Suduc Ana Maria a
Bizoi Mihai a
a Universitatea Valahia din Targoviste, 13 Aleea Sinaia Street, Targoviste, 130004, Romania
8 12 2022
2022
8 12 2022
214 288294
© 2022 The Author(s). Published by Elsevier B.V.
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
In the last couple of years, videoconferencing platforms have become very popular and have captured the attention of researchers and the public, particularly due to the Covid-19 pandemic. Immediately after the pandemic started in the spring of 2020, demand for video conferencing apps has grown exponentially. This demand, and the emergence of new needs, have forced manufacturers to adapt to the new context by improving the services offered and adding new features to existing applications. Many new video conferencing applications have also emerged with this demand. This paper presents a series of statistics on the evolution during the pandemic and the current status of the main video conferencing systems. The different ways in which these systems have integrated artificial intelligence technologies to address different identified problems and user needs are also presented.
Keywords
video conferencing
web conferencing
AI
Covid-19
==== Body
pmc
==== Refs
References
1 Nunamaker J.F. Briggs R.O. Romano N.C. Introduction to collaboration systems. Part I: A brief history and lessons learned Collaboration Systems. Concept, Value, and Use 2015 Routledge, Taylor & Francis London 1 8
2 Filip F.G. Zamfirescu C.B. Ciurea C. Computer-Supported Collaborative Decision-Making 2017 Springer Cham
3 Merriam-Webster Dictionary, "Conference," [Online]. Available: https://www.merriam-webster.com/dictionary/conference. [Accessed 3 August 2022].
4 Merriam-Webster Dictionary, "Conferencing," [Online]. Available: https://www.merriam-webster.com/dictionary/conferencing. [Accessed 3 August 2022].
5 N. Whyman, "What is the difference between a conference and a meeting? " [Online]. Available: https://www.kdmevents.co.uk/help/difference-between-conference-and-meeting/. [Accessed 3 August 2022].
6 Phillips L.D. Decision conferencing IEE Colloquium on CSCW: Some Fundamental Issues 1991 6/1-6/3
7 Simon H.A. The new science of management decision 1960 Harper & Brothers
8 Lieux M. Sabottke C. Schachner E.R. Pirtle C.D. Danrad R.E. Spieler B. Online conferencing software in radiology: Recent trends and utility Clinical Imaging 76 2021 116 122 33592549
9 Intermedia Team, "9 Video Conferencing Features You'll Want," 21 February 2022. [Online]. Available: https://www.intermedia.com/blog/9-video-conferencing-features-youll-want/. [Accessed July 2022].
10 Suduc A.M. Bizoi M. Filip F.G. Exploring Multimedia Web Conferencing Informatica Economica 13 3 2009 5 17
11 Business Insights Fortune Video Conferencing Market Size, Share & COVID-19 Impact Analysis, By Component (Solution and Services), By Conference Type (Telepresence, Integrated, Desktop, and Service-based Video Conferencing System), By Deployment (Cloud, On-premises), By Enterprises Fortune Business Insights 2022
12 J. Pot, "The best video conferencing software for teams in 2022," 11 May 2022. [Online]. Available: https://zapier.com/blog/best-video-conferencing-apps/. [Accessed July 2022].
13 M. Tillman, "Zoom vs Microsoft Teams vs Google Meet: What's the best video conferencing service?," 23 November 2021. [Online]. Available: https://www.pocket-lint.com/apps/news/151947-zoom-vs-microsoft-teams-vs-google-meet-what-s-the-difference. [Accessed July 2022].
14 A. Hooper, "Microsoft Teams vs Google Meet vs Zoom: The Definitive Videoconferencing Battle," 14 February 2022. [Online]. Available: https://itsupportguys.com/it-blog/teams-vs-meet-vs-zoom/. [Accessed July 2022].
15 G. Sevilla, "Zoom vs. Microsoft Teams vs. Google Meet: Which Top Videoconferencing App Is Best? " 15 April 2020. [Online]. Available: https://www.pcmag.com/news/zoom-vs-microsoft-teams-vs-google-meet-a-videoconferencing-face-off. [Accessed July 2022].
16 H. Ramirez, "Microsoft Teams vs. Zoom Vs Google Meet For Education," 1 May 2022. [Online]. Available: https://www.datalinknetworks.net/dln_blog/microsoft-teams-vs.-zoom-vs-google-meet-for-education. [Accessed July 2022].
17 S. Ojo, "Zoom vs. Microsoft Teams vs. Google Meet: Which Top Videoconferencing App Is Best? " 18 June 2022. [Online]. Available: https://www.getdroidtips.com/zoom-microsoft-teams-google-meet/. [Accessed July 2022].
18 J. Tremblay, "Tools for Teamwork: Zoom vs. Microsoft Teams vs. Google Meet," 21 July 2021. [Online]. Available: https://store.totemteam.com/blogs/news/tools-for-teamwork-zoom-vs-microsoft-teams-vs-google-meet. [Accessed July 2022].
19 Devoteam, "Comparing Zoom, Microsoft Teams, and Google Meet," [Online]. Available: https://gcloud.devoteam.com/blog/comparing-zoom-microsoft-teams-and-google-meet/. [Accessed 11 August 2022].
20 J. Hughes, "Zoom vs Microsoft Teams vs Google Meet: Top Video Conferencing Apps Compared," 9 November 2020. [Online]. Available: https://www.codeinwp.com/blog/zoom-vs-microsoft-teams-vs-google-meet/. [Accessed August 2022].
21 P. Byron, "Zoom vs. Teams vs. Google Meet: Which is right for you?," 1 February 2021. [Online]. Available: https://www.journalofaccountancy.com/issues/2021/feb/compare-zoom-teams-google-meet.html. [Accessed 12 August 2022].
22 J. Novak and R. Watts, "Microsoft Teams Vs. Zoom," 6 July 2022. [Online]. Available: https://www.forbes.com/advisor/business/software/microsoft-teams-vs-zoom/. [Accessed 12 August 2022].
23 S. Brady, "How to run successful virtual decision-making meetings," 5 January 2020. [Online]. Available: https://prismdecision.com/successful-virtual-decision-making-meetings/. [Accessed 2022 August 2022].
24 Grand View Research, "Video Conferencing Market Analysis Report," 2021.
25 Sydow L. Video Conferencing Apps Surge from Coronavirus Impact Market Data 2020 30 March[Online]. Available https://www.data.ai/en/insights/market-data/video-conferencing-apps-surge-coronavirus/ [Accessed July 2022]
26 S. Chan, "Usage of Mobile Video Conferencing Apps Including Zoom Grew 150% in the First Half of 2021," July 2021. [Online]. Available: https://sensortower.com/blog/video-conferencing-apps-mau-growth. [Accessed July 2022].
27 Z. Li, S. Lin, S. Liu, S. Li, X. Lin, W. Wang, and W. Jiang, "FAVIConf: Face Enhancement for AI-Based Video Conference with Low Bit-Rate," arXiv preprint arXiv:2207.04090, 2022.
28 Falcon R. Patti M. Brochard-Garnier S. Pacianotto Gouveia G. Torres Acevedo S. Bergot T. Alarcon R. Bomstein C. Macudzinski H. Maitre P. Chanas L. Nguyen H. Pochon B. Guichard F. Image quality evaluation of video conferencing solutions with realistic laboratory scenes Electronic Imaging 34 9 2022 318-1-318-9
29 Mendes P.R. Vieira E.S. Almeida de Freitas P.V. Busson A.J. Guedes A.L. Salles Soares Neto C. Colcher S. Shaping the Video Conferences of Tomorrow With AI Companion Proceedings of the 26th Brazilian Symposium on Multimedia and WebAt 2020 São Luís, Brazil
30 T. Bresler, "How Zoom leverages AI to provide the best videoconferencing experience," 23 April 2021. [Online]. Available: https://digital.hbs.edu/platform-digit/submission/how-zoom-leverages-ai-to-provide-the-best-videoconferencing-experience/. [Accessed July 2022].
31 L. Lawrence, "Google's plan to make you look perky on your video calls," 21 May 2022. [Online]. Available: https://www.protocol.com/bulletins/google-workspace-ai-video-hybrid. [Accessed July 2022].
32 M. McHugh-Johnson, "7 ways AI is making Google Workspace better," 11 May 2022. [Online]. Available: https://blog.google/products/workspace/hybrid-work-ai-tools-io-22/. [Accessed July 2022].
33 T. Soper, "Microsoft Teams uses AI to improve audio and video quality on calls," 13 June 2022. [Online]. Available: https://www.geekwire.com/2022/microsoft-teams-uses-ai-to-improve-audio-and-video-quality-on-calls/. [Accessed July 2022].
34 N. Herskowitz, "How Microsoft Teams uses AI and machine learning to improve calls and meetings," 13 June 2022. [Online]. Available: https://www.microsoft.com/en-us/microsoft-365/blog/2022/06/13/how-microsoft-teams-uses-ai-and-machine-learning-to-improve-calls-and-meetings/. [Accessed 12 August 2022].
35 I. Salian, "More Than Meets the AI: How GANs Research Is Reshaping Video Conferencing," 24 June 2021. [Online]. Available: https://blogs.nvidia.com/blog/2021/06/24/vid2vid-cameo-ai-research-video-conferencing/. [Accessed July 2022].
36 Intellect Data, "Video Conferencing and Collaboration: The Role of AI in Shaping the Future," 14 June 2021. [Online]. Available: https://intellectdata.com/video-conferencing-and-collaboration-the-role-of-ai-in-shaping-the-future/. [Accessed 12 August 2022].
37 Filip F.G. A Decision-Making Perspective for Designing and Building Information Systems International Journal of Computers 7 2 2012 264 272 Communications & Control
| 36510566 | PMC9729958 | NO-CC CODE | 2022-12-14 23:22:38 | no | Procedia Comput Sci. 2022 Dec 8; 214:288-294 | utf-8 | Procedia Comput Sci | 2,022 | 10.1016/j.procs.2022.11.177 | oa_other |
==== Front
Procedia Comput Sci
Procedia Comput Sci
Procedia Computer Science
1877-0509
Beijing Institute of Technology. Published by Elsevier B.V.
S1877-0509(22)01918-4
10.1016/j.procs.2022.11.208
Article
Study on slow traffic evaluation method of large hospitals in old urban areas based on Extentics
Wang Tao
Deng Haoyu
Beijing Institute of Technology, Beijing 102488, China
8 12 2022
2022
8 12 2022
214 528537
© 2022 Beijing Institute of Technology. Published by Elsevier B.V.
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Now that the coronavirus epidemic continues, hospitals have made a series of control measures, which makes the already relatively backward, slow traffic system around the large hospitals in the old city face more severe problems. This paper, based on the analysis of the current issues of slow traffic in large hospitals in old urban areas, according to the elements affecting slow traffic and the needs of slow traffic around hospitals, safety, accessibility, diversion, convenience, and comfortableness are used as the first level indicators, the index weights are determined by applying Analytic Hierarchy Process, and the comprehensive evaluation model of slow traffic around large hospitals in the old city is constructed by using Extentics. Through the case study, the scientific and practicality of the method is verified, which provides a scientific way for the safe and efficient operation of the slow traffic system around the large hospitals in the old city under the present epidemic control.
Keywords
Extentics
Analytic Hierarchy Process
Slow traffic
Hospital
Old city
==== Body
pmc
==== Refs
References
1 Zhang Yu Liu Xuemin Zhang Hong The dilemma and ideas of urban slow traffic development Urban Development Research 21 6 2014 113 116
2 Li Congying Ma Rongguo. Research on urban slow traffic planning methods Chang'an University 2011
3 Galeazzi A. Cinelli M. Bonaccorsi G. Human mobility in response to COVID-19 in France, Italy and UK Scientific Reports 11 1 2021
4 Cusack M. Individual, social, and environmental factors associated with active transportation commuting during the COVID-19 pandemic Journal of Transport & Health 22 2021 101089
5 Monfort SS Cicchino JB Patton D. Weekday bicycle traffic, and crash rates during the COVID-19 pandemic J Transp Health 23 2021 101289 Dec
6 Shack M A L Davis E Wj Zhang al et Bicycle injuries presenting to the emergency department during COVID-19 lockdown J Paediatr Child Health 58 4 2022 600 603 Apr 34612571
7 Teng Aibing Han Zhubin Li Xuhong Pedestrian and bicycle transportation system evaluation index system Urban Transportation 14 5 2016 37-43+55
8 Jin Jun Qi Kang Zhang Man Quantitative Evaluation of Walking Accessibility in CBD——A Case Study of Zhujiang New Town in Guangzhou and Futian Center in Shenzhen China Garden 8 2016 46 51
9 Guo Lianliu Tang Xiaomin Evaluation of Walking Comfort of Community Roads under the Background of City Betterment and Ecological Restoration——A Case of Shanghai Caoyang New Village China Garden 36 2020 70 75
10 Jiang Fan Lu Haoran Dai Jietao A Study on the Relationship between TRIZ and Extentics Journal of Guangzhou University (Natural Science Edition) 18 6 2019 53 58
11 Ling Jianming Hao Hangcheng Lv Lixuan Road surface performance Extentics evaluation method Journal of Tongji University (Natural Science Edition) 1 2008 32 36
12 Li Xiaowei Chen Hong Li Congpan A Comprehensive Evaluation of Urban Transportation Sustainability Levels Based on Extentics Journal of Guangzhou University (Natural Science Edition) 10 4 2011 77 81
13 Chen Yang Ma Jianxiao Xu Zhihao Research on the evaluation method of bicycle traffic system based on matter-element analysis Forestry Engineering 34 5 2018 84 90
14 Ministry of Housing and Urban-Rural Development of the People's Republic of China. Urban integrated transportation system planning standards GBT 51328-2018 2019 China Construction Industry Press 28 30
15 Li Feng A study of bicycle capacity and service level on urban roads in China China Municipalities 4 1995 11 14
16 Fang Pingping Simulation Analysis of Xi'an Big Wild Goose Pagoda Metro Station Distribution Capacity Modeling 2020 Xi'an University of Science and Technology
17 Fagerholm N. Eilola S. Arki V. Outdoor recreation and nature's contribution to well-being in a pandemic situation - Case Turku, Finland Urban Forestry & Urban Greening 64 2021 127257
18 Yuan Fei Ma Xiaodan Xia Xiaomei Prediction of Slow City Traffic Demand Based on Space Syntax Forestry Engineering 30 2014 114 117
19 Zhang Lijie Luo Sujun Chai Shufeng Application of Hierarchical Analysis in Siting of Regional Logistics Centers Logistics Technology 34 5 2011 47 49
20 Yuan Haitao Wang Shuai Zhang Honggang A review of road performance evaluation methods based on Extentics theory Western Transportation Technology 6 2017 20 24
| 36510565 | PMC9729960 | NO-CC CODE | 2022-12-14 23:22:39 | no | Procedia Comput Sci. 2022 Dec 8; 214:528-537 | utf-8 | Procedia Comput Sci | 2,022 | 10.1016/j.procs.2022.11.208 | oa_other |
==== Front
Procedia Comput Sci
Procedia Comput Sci
Procedia Computer Science
1877-0509
Beijing Institute of Technology. Published by Elsevier B.V.
S1877-0509(22)01918-4
10.1016/j.procs.2022.11.208
Article
Study on slow traffic evaluation method of large hospitals in old urban areas based on Extentics
Wang Tao
Deng Haoyu
Beijing Institute of Technology, Beijing 102488, China
8 12 2022
2022
8 12 2022
214 528537
© 2022 Beijing Institute of Technology. Published by Elsevier B.V.
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Now that the coronavirus epidemic continues, hospitals have made a series of control measures, which makes the already relatively backward, slow traffic system around the large hospitals in the old city face more severe problems. This paper, based on the analysis of the current issues of slow traffic in large hospitals in old urban areas, according to the elements affecting slow traffic and the needs of slow traffic around hospitals, safety, accessibility, diversion, convenience, and comfortableness are used as the first level indicators, the index weights are determined by applying Analytic Hierarchy Process, and the comprehensive evaluation model of slow traffic around large hospitals in the old city is constructed by using Extentics. Through the case study, the scientific and practicality of the method is verified, which provides a scientific way for the safe and efficient operation of the slow traffic system around the large hospitals in the old city under the present epidemic control.
Keywords
Extentics
Analytic Hierarchy Process
Slow traffic
Hospital
Old city
==== Body
pmc
==== Refs
References
1 Zhang Yu Liu Xuemin Zhang Hong The dilemma and ideas of urban slow traffic development Urban Development Research 21 6 2014 113 116
2 Li Congying Ma Rongguo. Research on urban slow traffic planning methods Chang'an University 2011
3 Galeazzi A. Cinelli M. Bonaccorsi G. Human mobility in response to COVID-19 in France, Italy and UK Scientific Reports 11 1 2021
4 Cusack M. Individual, social, and environmental factors associated with active transportation commuting during the COVID-19 pandemic Journal of Transport & Health 22 2021 101089
5 Monfort SS Cicchino JB Patton D. Weekday bicycle traffic, and crash rates during the COVID-19 pandemic J Transp Health 23 2021 101289 Dec
6 Shack M A L Davis E Wj Zhang al et Bicycle injuries presenting to the emergency department during COVID-19 lockdown J Paediatr Child Health 58 4 2022 600 603 Apr 34612571
7 Teng Aibing Han Zhubin Li Xuhong Pedestrian and bicycle transportation system evaluation index system Urban Transportation 14 5 2016 37-43+55
8 Jin Jun Qi Kang Zhang Man Quantitative Evaluation of Walking Accessibility in CBD——A Case Study of Zhujiang New Town in Guangzhou and Futian Center in Shenzhen China Garden 8 2016 46 51
9 Guo Lianliu Tang Xiaomin Evaluation of Walking Comfort of Community Roads under the Background of City Betterment and Ecological Restoration——A Case of Shanghai Caoyang New Village China Garden 36 2020 70 75
10 Jiang Fan Lu Haoran Dai Jietao A Study on the Relationship between TRIZ and Extentics Journal of Guangzhou University (Natural Science Edition) 18 6 2019 53 58
11 Ling Jianming Hao Hangcheng Lv Lixuan Road surface performance Extentics evaluation method Journal of Tongji University (Natural Science Edition) 1 2008 32 36
12 Li Xiaowei Chen Hong Li Congpan A Comprehensive Evaluation of Urban Transportation Sustainability Levels Based on Extentics Journal of Guangzhou University (Natural Science Edition) 10 4 2011 77 81
13 Chen Yang Ma Jianxiao Xu Zhihao Research on the evaluation method of bicycle traffic system based on matter-element analysis Forestry Engineering 34 5 2018 84 90
14 Ministry of Housing and Urban-Rural Development of the People's Republic of China. Urban integrated transportation system planning standards GBT 51328-2018 2019 China Construction Industry Press 28 30
15 Li Feng A study of bicycle capacity and service level on urban roads in China China Municipalities 4 1995 11 14
16 Fang Pingping Simulation Analysis of Xi'an Big Wild Goose Pagoda Metro Station Distribution Capacity Modeling 2020 Xi'an University of Science and Technology
17 Fagerholm N. Eilola S. Arki V. Outdoor recreation and nature's contribution to well-being in a pandemic situation - Case Turku, Finland Urban Forestry & Urban Greening 64 2021 127257
18 Yuan Fei Ma Xiaodan Xia Xiaomei Prediction of Slow City Traffic Demand Based on Space Syntax Forestry Engineering 30 2014 114 117
19 Zhang Lijie Luo Sujun Chai Shufeng Application of Hierarchical Analysis in Siting of Regional Logistics Centers Logistics Technology 34 5 2011 47 49
20 Yuan Haitao Wang Shuai Zhang Honggang A review of road performance evaluation methods based on Extentics theory Western Transportation Technology 6 2017 20 24
| 36514711 | PMC9729961 | NO-CC CODE | 2022-12-14 23:22:39 | no | Procedia Comput Sci. 2022 Dec 8; 214:1206-1213 | latin-1 | Procedia Comput Sci | 2,022 | 10.1016/j.procs.2022.11.297 | oa_other |
==== Front
Procedia Comput Sci
Procedia Comput Sci
Procedia Computer Science
1877-0509
Published by Elsevier B.V.
S1877-0509(22)01915-9
10.1016/j.procs.2022.11.205
Article
Smart Solutions to Keep Your Mental Balance
Gifu Daniela ab
Pop Eugen c
a Institute of Computer Science, Romanian Academy - Iasi branch, Bulevardul Carol I, 8, 700505, Romania
b Faculty of Computer Science, “Alexandru Ioan Cuza” University, General Berthelot, 16, 700483, Iasi, Romania
c Faculty of Automatic Control and Computers, University “Politehnica” of Bucharest, Splaiul Independenței 313, 060032, Bucharest, Romania
8 12 2022
2022
8 12 2022
214 503510
© 2022 Published by Elsevier B.V.
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Due to the coronavirus pandemic international conflicts, dramatic changes of daily living have been enforced, including new ways of providing patient assistance, based on artificial intelligence. The influence of these changes on people's mental health is still insufficiently analyzed and explored. Chatbots like Woebot, Wysa and Tess are gaining popularity, being attractive and easy to use. These achievements led us to develop a new application, being still in the testing phase, which has a positive impact on mental healthcare issues. It is a conversational system capable to diagnose people's negative, depressive, and anxious emotions during chatting, and to act as a psychological therapist and virtual friend. The proposed system, throughout the conversation, succeeds to decrease the patient's insecurity sentiments, by comforting their mood. In fact, an intelligent assistant for different mental health issues like stress, anxiety and depression, could become a very helpful information system.
Keywords
virtual assistant
sentiment analysis
mental healthcare
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References
1 Ouatu, Bogdan, Gîfu, Daniela (2020) “Chatbot, the Future of Learning?” Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education, Springer, 263-268.
2 Nof S.Y. Ceroni J. Jeong W. Moghaddam M. Revolutionizing Collaboration through e-Work, e-Business, and e-Service 2015 Springer Cham
3 Filip. F.G Automation and computers and their contribution to human well-being and resilience Studies in Informatics and Control”. 30 4 2021 5 18 10.24846/v30i4y202101
4 Miner Adam S. Laranjo Liliana Kocaballi A.Baki Chatbots in the Fight Against the COVID-19 Pandemic npj Digit. Med. 3 65 2020 10.1038/s41746-020-0280-0
5 Laranjo Liliana Dunn Adam G. Tong Huong Ly Kocaballi Ahmet Baki Chen Jessica Bashir Rabia Surian Didi Gallego Blanca Magrabi Farah Lau Annie Y.S. Coiera Enrico Conversational Agents in Healthcare: A Systematic Review J Am Med Inform Assoc 25 9 2018 1248 1258 10.1093/jamia/ocy072 Sep 1PMID: 30010941; PMCID: PMC6118869 30010941
6 Lucas Gale M. Gratch Jonathan King Aisha Morency Louis-Philippe It's Only a computer: Virtual Humans Increase Willingness to Disclose Comput. Hum. Behav. 37 2014 94 100
7 Miner Adam S. Milstein Arnold Schueller Stephen Hegde Roshini Mangurian Christina Linos Eleni Smartphone-Based Conversational Agents and Responses to Questions About Mental Health, Interpersonal Violence, and Physical Health JAMA Intern Med 176 5 2016 10.1001/jamainternmed.2016.0400 May 1619-25Erratum in: JAMA Intern Med. 2016 May 1;176(5):719. PMID: 26974260; PMCID: PMC4996669
8 Vaidyam Aditya Nrusimha Wisniewski Hannah Halamka John David Kashavan M.S. Torous J.B Chatbots and Conversational Agents in Mental Health: A Review of the Psychiatric Landscape Canadian Journal of Psychiatry 64 7 2019 456 464 10.1177/0706743719828977 JulEpub 2019 Mar 21. PMID: 30897957; PMCID: PMC6610568 30897957
9 Steinhubl Steven R. Topol Eric J. Now we're Talking: Bringing a Voice to Digital Medicine Lancet 392 2018 627
10 Bickmore T.W. Trinh H. Olafsson S. O'Leary T.K. Asadi R. Rickles N.M. Cruz R. Patient and Consumer Safety Risks When Using Conversational Assistants for Medical Information: An Observational Study of Siri, Alexa, and Google Assistant J Med Inter-net Res 20 9 2018 e11510 10.2196/11510 Sep 4PMID: 30181110; PMCID: PMC6231817
11 Nobles Alicia L.Leas Eric C. Caputi Theodore L. Zhu Shu-Hong Strathdee Steffanie A. Ayers John W Responses to addiction help-seeking from Alexa, Siri, Google Assistant, Cortana, and Bixby intelligent virtual assistants npj Digit. Med. 3 11 2020 10.1038/s41746-019-0215-9
12 Kocaballi A.B. Responses of Conversational Agents to Health and Lifestyle Prompts: Investigation of Appropriateness and Presentation Structures J. Med. Internet Res. 22 2020 e15823 32039810
13 Meadows R. Hine C. Suddaby E. Conversational Agents and the Making of Mental Health Recovery Digital Health 6 2020 10.1177/2055207620966170
14 Liu H Peng H. Song X. Xu C. Zhang M. Using AI Chatbots to Provide Self-Help Depression Interventions for University Students: A Randomized Trial of Effectiveness Internet Interventions 27 2022 100495 10.1016/j.invent.2022.100495
15 Abd-Alrazaq A.A. Alajlani M. Alalwan A.A. An Overview of the Features of Chatbots in Mental Health: A Scoping Review In: Int J Med Inform; 132 2020 103978
16 Fitzpatrick K.K. Darcy A. Vierhile M. Delivering Cognitive Behavior Therapy to Young Adults with Symptoms of Depression and Anxiety Using a Fully Automated Con-versational Agent (Woebot): a Randomized Controlled Trial JMIR Ment. Heal. 4 Article e19 2017 10.2196/mental.7785
17 Inkster B. Sarda S. Subramanian V. An Empathy-Driven, Conversational Artificial Intel-ligence Agent (Wysa) for Digital Mental Well-Being: Real-World Data Evaluation Mixed-Methods Study JMIR Mhealth Uhealth 6 2018 e12106 30470676
18 Joerin A. Rauws M. Ackerman M.L. Psychological Artificial Intelligence Service, Tess: Delivering On-demand Support to Patients and Their Caregivers Technical Report Cu-reus 11 2019 e3972 10.7759/cureus.3972
19 Clarke J. Can Artificial Intelligence Help with Depression? Verywell.mind 2019 https://www.verywellmind.com/can-artificial-intelligence-help-depression-4158330
20 Fulmer R. Joerin A. Gentile B. Lakerink L. Rauws M. Using Psychological Artificial Intelligence (Tess) to Relieve Symptoms of Depression and Anxiety: Randomized Con-trolled Trial JMIR Ment Health. Dec 13 4 2018 e64 10.2196/mental.9782 5PMID: 30545815; PMCID: PMC6315222
21 Berg M.: Making Sense with Sensors: Self-Tracking and the Temporalities of Wellbeing. In: Digital Health, January, 3: 2055207617699767 (2017). DOI:10.1177/2055207617699767
22 Vargas J.H. Marafon T.A. Couto D.F. Giglio R. Yan M. Rolle C. A fully auto-mated conversational agent with artificial intelligence capabilities (Youper) for treating de-pression and anxiety: A nonrandomized feasibility trial (Preprint) 2019 JMIR Publications Inc 10.2196/preprints.12888
23 Mehta A. Niles A.N. Vargas J.H. Marafon T. Couto D.D. Gross J.J. Acceptability and Effectiveness of Artificial Intelligence Therapy for Anxiety and Depression (Youper): Longitudinal Observational Study Journal of medical Internet research 23 6 2021 e26771 10.2196/26771 34155984
24 Zhang, Y., Sun, S., Galley, M., Chen, Y. C., Brockett, C., Gao, X., ... & Dolan, B.: Dialogpt: Large-scale generative pre-training for conversational response generation. arXiv preprint arXiv:1911.00536 (2019).
25 Bender E.M. Gebru T. McMillan-Major A. Shmitchell S. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency 2021 610 623
| 36514712 | PMC9729962 | NO-CC CODE | 2022-12-14 23:22:39 | no | Procedia Comput Sci. 2022 Dec 8; 214:503-510 | utf-8 | Procedia Comput Sci | 2,022 | 10.1016/j.procs.2022.11.205 | oa_other |
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Procedia Comput Sci
Procedia Comput Sci
Procedia Computer Science
1877-0509
The Author(s). Published by Elsevier B.V.
S1877-0509(22)01856-7
10.1016/j.procs.2022.11.149
Article
Multi-criteria analysis applied to humanitarian assistance: an approach based on ELECTRE-MOr
da Costa Leandro Machado Aveiro a
Gomes Ian José Agra a
Costa Igor Pinheiro de Araújo b
da Silva Ricardo Franceli c
Muradas Fernando Martins b
Moreira Miguel Ângelo Lellis bd
Corriça José Victor de Pina a
Costa Arthur Pinheiro de Araújo a
dos Santos Marcos be
Gomes Carlos Francisco Simões d
a Brazilian Navy, Rio de Janeiro, RJ 20091-000, Brazil
b Naval Systems Analysis Center, Rio de Janeiro, RJ 20091-000, Brazil
c FIA Business School, São Paulo, 05425-902, Brazil
d Fluminense Federal University, Niterói, RJ 24210-240, Brazil
e Military Institute of Engineering, Rio de Janeiro, RJ 22290-270, Brazil
8 12 2022
2022
8 12 2022
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© 2022 The Author(s). Published by Elsevier B.V.
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
The recent increase in the number of cases of COVID-19 in Brazil and worldwide, caused by the Omicron Variant, has brought to light concern to the population and the government, especially in the states most affected by the pandemic. To find a way to help combat the pandemic, a case study was conducted to acquire new speedboats by the Brazilian Navy (BN), through the application of the ELECTRE-MOr multicriteria method. The boats would be employed as mobile hospitals, aiming to perform first aid and evacuation of patients from riverside regions to qualified hospitals. Another important use would be the transport of vaccines, medicines and basic supplies for riverside populations, such as water, food and hygiene materials. For the proposed analysis, we consulted three specialists from the BN, who evaluated eight boat models in seven tactical, operational and medical criteria. After the application of the method, the Guardian 25 and RAC boats were chosen to be employed in humanitarian assistance. This study brings a valuable contribution to academia and society since it represents the application of a multi-criteria decision-aid method in the state of the art to contribute to the solution of a real problem that affects millions of people in Brazil and worldwide.
Keywords
Multiple Criteria Decision Analysis (MCDA)
ELECTRE-MOr
Decision-Making
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References
1 Guan W. Clinical characteristics of coronavirus disease 2019 in China N. Engl. J. Med. 382 18 2020 1708 1720 32109013
2 Rasmussen T.E. Koelling E.E. A military perspective on the vascular surgeon's response to the COVID-19 pandemic J. Vasc. Surg. 71 6 2020 1821 1822 32247030
3 Ramanathan K. Planning and provision of ECMO services for severe ARDS during the COVID-19 pandemic and other outbreaks of emerging infectious diseases Lancet Respir. Med. 2020
4 Lai C.-C. Shih T.-P. Ko W.-C. Tang H.-J. Hsueh P.-R. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and corona virus disease-2019 (COVID-19): the epidemic and the challenges Int. J. Antimicrob. Agents 2020 105924
5 Burkle Jr F.M. Anatomy of an ambush: security risks facing international humanitarian assistance Disasters 29 1 2005 26 37 15720379
6 L. B. de Andrade, “As ameaças para o Estado Brasileiro na defesa da Região Amazônica.,” Trabalho de Conclusão de Curso apresentado à Escola de Comando e Estado-Maior do Exército, no Curso de Especialização em Ciências Militares, 2019.
7 Knudson M.M. Jacobs L.M. Elster C.E.A. How to Partner With the Military in Responding to Pandemics—A Blueprint for Success JAMA Surg 2020
8 Belton V. Stewart T. Multiple criteria decision analysis: an integrated approach 2002 Springer Science & Business Media
9 de A. Costa I.P. Sanseverino A.M. dos S. Barcelos M.R. Belderrain M.C.N. Gomes C.F.S. dos Santos M. Choosing flying hospitals in the fight against the COVID-19 pandemic: structuring and modeling a complex problem using the VFT and ELECTRE-MOr methods IEEE Lat. Am. Trans. 19 6 2021 1099 1106 10.1109/TLA.2021.9451257 Jun.
10 de A. Costa I.P. Â. L. Moreira M. de Araújo Costa A.P. de Souza de Barros Teixeira L.F.H. Gomes C.F.S. Santos M.Dos Strategic Study for Managing the Portfolio of IT Courses Offered by a Corporate Training Company: An Approach in the Light of the ELECTRE-MOr Multicriteria Hybrid Method Int. J. Inf. Technol. Decis. Mak. 21 01 2022 351 379 10.1142/S0219622021500565 Jan.
11 Drumond P. Basílio M.P. de A. Costa I.P. de M. Pereira D.A. Gomes C.F.S. dos Santos M. Multicriteria Analysis in Additive Manufacturing: An ELECTRE-MOr Based Approach Modern Management based on Big Data II and Machine Learning and Intelligent Systems III 2021 IOS Press 126 132
12 De Almeida I.D.P. A multicriteria decision-making approach to classify military bases for the Brazilian Navy Procedia Comput. Sci. 199 2022 79 86 10.1016/j.procs.2022.01.198
13 Moreira M.Â. Evaluation of drones for public security: a multicriteria approach by the PROMETHEE-SAPEVO-M1 systematic Procedia Comput. Sci. 199 2022 125 133 10.1016/j.procs.2022.01.016
14 Mellem P.M.N. Prospective scenarios applied in course portfolio management: An approach in light of the Momentum and ELECTRE-MOr methods Procedia Comput. Sci. 199 2022 48 55 10.1016/j.procs.2022.01.007
15 Santos N. de Souza Rocha Junior C. Â. Lellis Moreira M. dos Santos M. Simões Gomes C.F. de Araújo Costa I.P. Strategy Analysis for project portfolio evaluation in a technology consulting company by the hybrid method THOR Procedia Comput. Sci. 199 2022 134 141 10.1016/j.procs.2022.01.017
16 do N. Maêda S.M. de Arajo Costa I.P. Simões Gomes C.F. dos Santos M. da Mota I.S. de S. de Barros Teixeira L.F.H. Economic and edaphoclimatic evaluation of Brazilian regions for African mahogany planting - an approach using the SAPEVO-M-NC ordinal method Procedia Comput. Sci. 199 2022 323 330 10.1016/j.procs.2022.01.196
17 de S. Rocha Junior C. Â. Lellis Moreira M. dos Santos M. Selection of interns for startups: an approach based on the AHP-TOPSIS-2N method and the 3DM computational platform Procedia Comput. Sci. 199 2022 984 991 10.1016/j.procs.2022.01.124
18 Tenório F.M. Dos Santos M. Gomes C.F.S. Araujo J.D.C. De Almeida G.P. THOR 2 Method: An Efficient Instrument in Situations Where There Is Uncertainty or Lack of Data IEEE Access 9 2021 161794 161805
19 de M. Pereira D.A. Multicriteria and statistical approach to support the outranking analysis of the OECD countries IEEE Access 2022 1 8 10.1109/ACCESS.2022.3187001
20 M. Â. L. Moreira, C. F. S. Gomes, M. T. Pereira, and M. dos Santos, “SAPEVO-H2 a Multi-criteria Approach Based on Hierarchical Network: Analysis of Aircraft Systems for Brazilian Navy,” 2023, pp. 61–74. doi: 10.1007/978-3-031-09360-9_6.
21 dos Santos F.B. dos Santos M. Choice of armored vehicles on wheels for the Brazilian Marine Corps using PrOPPAGA Procedia Comput. Sci. 199 2022 301 308 10.1016/j.procs.2022.01.037
22 de I.P. Costa A. Algorithm Selection for Machine Learning Classification: An Application of the MELCHIOR Multicriteria Method Modern Management based on Big Data II and Machine Learning and Intelligent Systems III 2021 IOS Press 154 161 10.3233/FAIA210243
23 Jardim R. dos Santos M. Neto E. Muradas F.M. Santiago B. Moreira M. Design of a framework of military defense system for governance of geoinformation Procedia Comput. Sci. 199 2022 174 181 10.1016/j.procs.2022.01.022
24 de A. Costa I.P. de A. Costa A.P. Sanseverino A.M. Gomes C.F.S. dos Santos M. Bibliometric studies on Multi-criteria Decision Analysis (MCDA) methods applied in military problems Pesqui. Operacional 42 2022
25 de I.P. Costa A. Bibliometric Studies on Multi-Criteria Decision Analysis (MCDA) Applied in Personnel Selection Modern Management based on Big Data II and Machine Learning and Intelligent Systems III 2021 IOS Press 119 125
26 do N. Maêda S.M. Basílio M.P. de A. Costa I.P. Â. L. Moreira M. dos Santos M. Gomes C.F.S. The SAPEVO-M-NC Method Modern Management based on Big Data II and Machine Learning and Intelligent Systems III 2021 89 95
27 do N. Maêda S.M. Investments in Times of Pandemics: An Approach by the SAPEVO-M-NC Method Modern Management based on Big Data II and Machine Learning and Intelligent Systems III 2021 162 168
28 Gomes C.F.S. Rodrigues M.V.G. de A. Costa I.P. dos Santos M. Ordering of Warships for the Brazilian Navy Using the New Method: AHP-Gaussian with Pearson's Correlation Modern Management based on Big Data II and Machine Learning and Intelligent Systems III 2021 104 111 10.3233/FAIA210237
29 Drumond P. de Araújo Costa I.P. Â. Lellis Moreira M. dos Santos M. Simões Gomes C.F. do Nascimento Maêda S.M. Strategy study to prioritize marketing criteria: an approach in the light of the DEMATEL method Procedia Comput. Sci. 199 2022 448 455 10.1016/j.procs.2022.01.054
30 De Paula N.O.B. Strategic support for the distribution of vaccines against Covid-19 to Brazilian remote areas: A multicriteria approach in the light of the ELECTRE-MOr method Procedia Comput. Sci. 199 2022 40 47 10.1016/j.procs.2022.01.006 35136456
31 Gomes C.F.S. dos Santos M. de S. de B. Teixeira L.F.H. Sanseverino A.M. dos S. Barcelos M.R. SAPEVO-M: a group multicriteria ordinal ranking method Pesqui. Operacional 40 2020 10.1590/0101-7438.2020.040.00226524
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Procedia Comput Sci
Procedia Comput Sci
Procedia Computer Science
1877-0509
The Author(s). Published by Elsevier B.V.
S1877-0509(22)01850-6
10.1016/j.procs.2022.11.143
Article
Doctor/Data Scientist/Artificial Intelligence Communication Model. Case Study.
Belciug Smaranda a
Ivanescu Renato Constantin a
Popa Sebastian-Doru a
Iliescu Dominic Gabriel ab
a University of Craiova, A.I. Cuza Str, no 13, Craiova, 200585, Romania
b University of Medicine and Pharmacy, Petru Rares Str, no. 2, 200349, Romania
8 12 2022
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© 2022 The Author(s). Published by Elsevier B.V.
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
The last two years have taught us that we need to change the way we practice medicine. Due to the COVID-19 pandemic, obstetrics and gynecology setting has changed enormously. Monitoring pregnant women prevents deaths and complications. Doctors and computer data scientists must learn to communicate and work together to improve patients’ health. In this paper we present a good practice example of a competitive/collaborative communication model for doctors, computer scientists and artificial intelligence systems, for signaling fetal congenital anomalies in the second trimester morphology scan.
Keywords
deep learning
statistical learning
computer aided medical diagnosis
statistics
second trimester morphology
congenital anomalies
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pmc
==== Refs
References
1 Deprest J. Choolani M. Chervenak F. Fetal diagnosis and therapy during the COVID-19 pandemic: guidance on behalf of the international fetal medicine and surgery society Fetal Diagn. Ther. 47 2020 689 698 10.1159/000508254 32375144
2 Mazur-Bialy A.I. Bogucka D.K. Tim S. Oplawski M. Pregnancy and Childbirht in the COVID-19 Era – the course of disease and maternal-fetal transmission J Clin Med 9 11 2020 3749 10.3390/jcm9113749 b 33233369
3 Chmielewska B. Baratt I. Townsed R. Effects of the COVID-19 pandemic on maternal and perinatal outcomes: a systematic review and meta-analysis Lancet Global Health 2021 10.1016/S2214-109X(21)00079-6
4 Boyle B Estimating Global Burden of Disease due to congenital anomaly: an analysis of European data Archives of Disease in Childhood – Fetal and neonatal edition 103 2018 F22 F28 28667189
5 Khan M. Nabeka H. Akbar S. Risk of congenital birth defects during COVID-19 pandemic: draw attention to the physicians and policymakers J. Global Health 10 2 2020 020378 10.7189/jogh.10.020378
6 Dube R. Kar S.S. Covid-19 in pregnancy: the foetal perspective- a systematic review Neonatology 4 1 2020 10.1136/bmjpo-2020-000859
7 Salomon L. A score-based method for quality control of fetal images at routine second trimester ultrasound examination Prenat. Diagn. 28 9 2008 822 827 18646244
8 Paladini D. Sonography in obese and overweight pregnant women: clinical medicolegal and technical issues Ultrasound Obstet Gynecol 33 6 2009 720 729 19479683
9 Belciug S. Sandita A. Costin H. Bejinariu S.I. Competitive/collaborative statistical learning framework for forecasting intraday stock market prices Studies in Informatics and Control 20 2 2021 135 146
10 Gorunescu F. Gorunescu M. Saftoiu A. Vilmann P. Smaranda Belciug Competitive/collaborative nerual computing system for medical diagnosis in pancreatic cancer detection Exp Sys 28 1 2011 33 48
11 Abbott P.A. Weinger M.B. Health information technology: fallacies and sober realities – Redux A Homage to Bentzi Karsh and Robert Wears Applied Ergonomics 82 2020 102973
12 Beaulieu-Jones B. Finlayson S.G. Chivers C. Chen I. McDermott M. Kandola J. Dalca A.V. Beam A. Fiterau M. Naumann T. Trends and focus of machine learning applications for health research JAMA Netw Open 2 2019 e1914051
13 Mao, Y., Wang, D., Muller, M., K. Varshney, K.R., Baldini, I., Dugan, C., (2019) Aleksandra Mojsilovic, how data scientists work together with domain experts in scientific collaborations: to find the right answer of to ask the right question? arXiv: 10.1145/3361118.
14 Meyer M.A. Healthcare data scientist qualifications, skills, and job focus: a content analysis of job postings J Am Med Inf Assoc 26 2019 383 391
15 Famia, R.H. (2022) Clinician data scientists? https://rehoyt.medium.com/clinician-data-scientist-9d82104fdad2.
16 Baskarada S Koronios A. Unicorn data scientist: the rarest of breeds Programmirovanie 51 2017 65 74
17 Bastian G. Baker G.H. Limon A. Bridging the divide between data scientists and clinicians Intelligence-Based Medicine 6 2022 100066
18 Filip F.G. Collaborative Decision-making: concepts and supporting information and communication technology tools and systems Int J. Comp Comm Control. 17 2 2022 4732
19 Ciurea C. Filip F.G. Collaborative platforms for crowdsourcing and consensus-based decisions in multi participant environments Informatica Economica 23 2 2019 5 10
20 Filip F.G. Zamfirescu C.B. Ciurea C. Computer Supported Collaborative Decision-Making 2017 Springer Cham 2017
21 Chiu C.M. Liang T.P. Turban E. What can crowdsourcing do for decision support? Decision Support Systems 65 2014 40 49
22 Belciug S. Learning deep neural networks’ architectures using differential evolution. Case study: medical imaging processing Comp in Biol Med 146 2022 105623
23 Belciug S. Parallel versus cascaded logistic regression trained single-hidden feedforward neural networks for medical data Exp Sys App 170 2021 114538
24 Belciug S. Logistic regression paradigm for training a single-hidden layer feedforward neural network. Application to gene expression datasets for cancer research J Biol Inf 102 2020 103373
25 Altman D.G. Practical Statistics for Medical Research 1991 Chapman and Hall New York
26 Belciug S. Artifiical Intelligence in Cancer: Diagnostic to tailored treatment 2020 Elsevier
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Trends Pharmacol Sci
Trends Pharmacol Sci
Trends in Pharmacological Sciences
0165-6147
1873-3735
Elsevier Ltd.
S0165-6147(22)00246-2
10.1016/j.tips.2022.10.005
Forum
Monkeypox: potential vaccine development strategies
Lozano José Manuel 1⁎
Muller Sylviane 234⁎
1 Universidad Nacional de Colombia-Sede Bogotá, Departamento de Farmacia, Mimetismo molecular de los Agentes infecciosos, Bogotá, DC, Colombia
2 Centre National de la Recherche scientifique–Université de Strasbourg, Biotechnology and Cell Signalling Unit, Neuroimmunology and Peptide Therapeutics Team, Strasbourg Drug Discovery and Development Institute, Strasbourg, France
3 University of Strasbourg Institute for Advanced Study, Strasbourg, France
4 Fédération Hospitalo-Universitaire OMICARE, Fédération de Médecine Translationnelle de Strasbourg, University of Strasbourg, Strasbourg, France
⁎ Correspondence:
8 12 2022
8 12 2022
© 2022 Elsevier Ltd. All rights reserved.
2022
Elsevier Ltd
Elsevier has created a Monkeypox Information Center (https://www.elsevier.com/connect/monkeypox-information-center) in response to the declared public health emergency of international concern, with free information in English on the monkeypox virus. The Monkeypox Information Center is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its monkeypox related research that is available on the Monkeypox Information Center - including this research content - immediately available in publicly funded repositories, with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the Monkeypox Information Center remains active.
A multicountry outbreak of monkeypox has gained global attention. Basic research including structural and immunological investigation on monkeypox virus (MPXV) is central to design effective solutions of treatment with antivirals and appropriate vaccines. We summarize some information about this virus and its re-emergence and the current vaccines that are proposed to limit its spread and present some possible avenues for developing new vaccines.
Keywords
emerging infection
orthopoxvirus
smallpox
synthetic vaccines
peptides
==== Body
pmcIntroduction
MPXV is a member of the Orthopoxvirus genus in the family Poxviridae, a large group of double-stranded DNA (dsDNA) viruses [1]. Since the officially announced global eradication of smallpox in 1980 and subsequent cessation of routine smallpox vaccination, monkeypox has been considered to be the most important orthopoxvirus infection in humans. The first outbreak of monkeypox, in 1958, was described in a colony of cynomolgus monkeys held at the State Serum Institute in Copenhagen; other outbreaks followed in monkey colonies. Its first description in humans dates back to 1970 with cases in the Democratic Republic of the Congo (DRC) and later in Sudan in 2006. Monkeypox can be acquired through direct contact with an infected person or animal (lesions, body fluids, respiratory droplets) or with material contaminated with the virus (e.g., bedding). Monkeypox is a viral zoonosis (see Glossary) that occurs primarily in tropical rainforest areas of Central and West Africa and was occasionally exported to other regions. Since early May 2022, however, cases of monkeypox have been reported from countries in disparate geographic areas where the disease was not endemic [2., 3., 4.]. Today, monkeypox is thus considered as a re-emerging infectious disease. In a way that cannot yet be predicted, the monkeypox re-emergence can be time limited as found in the case of viral hemorrhagic fevers that caused severe, life-threatening illness (Marburg and Ebola virus infections); on the contrary, it can spread all over the globe and for a longer period, as in the case of the coronavirus disease that emerged in 2019 (COVID-19), which still requires large-scale management and priority intervention worldwide. Currently, in the case of monkeypox, although it is not a newly emerging infection, a number of gaps in knowledge still remain to enable development of effective disease control strategies [1,4,5]. This includes understanding how different clades of the virus are precisely transmitted and spread (especially in high-risk patient groups), how infection develops in patients at the cellular and tissue level, how innate and adaptive cellular and humoral immune responses coordinate to fight MPXV, and obviously, based on answers to the foregoing questions, how effective prevention and treatment approaches such as appropriate vaccines and antivirals could be designed. Here, we briefly discuss cellular and humoral immunity to MPXV and highlight potential approaches that could help in the development of new vaccines against human monkeypox.
Cellular and humoral immunity to MPXV
The initial attachment of MPXV to the host cell surface occurs through interactions between multiple viral ligands (e.g., proteins A34 and B5) and, depending on the type of cells that are targeted, several possible cell surface receptors such as chondroitin sulfate, heparan sulfate, macrophage receptor with collagenous structure (MARCO, a class A scavenger receptor), and laminin, a major component of the basal lamina. Thereafter, the virus may enter the target cell by direct fusion or via the endosomal pathway and release its content (especially a linear, dsDNA genome and enzymes required for virus uncoating and replication) into the host cell cytosol. Earlier data obtained after the US monkeypox outbreak of 2003 showed that classical immune responses are generated against this virus [4]. Orthopoxvirus-specific immunoglobulin G and M, CD4+ and CD8+ T cells, and B cell responses were measured at ∼7–14 weeks and 1 year after exposure [6]. Recent data have also highlighted the importance of the host ubiquitin system [7]. The importance of memory γδ T cells, which play an important role in protective immunity but also have the potential to worsen the progression of autoimmunity, has been highlighted in the acquired immune memory to MPXV (shown in macaques). More broadly, however, in humans infected with MPXV, the effective roles of innate immune cells (e.g., monocytes/macrophages, neutrophils, natural killer cells, conventional dendritic cells, plasmacytoid dendritic cells, and innate lymphoid cells) remain largely unknown [4]. The fundamental basis for immune evasion of MPXV also deserves much more study to be fully understood [4]. Specific anti-MPXV antibodies, especially induced by site-directed vaccination, could hamper MPXV binding and infection of host cells by either targeting virus receptors at the host cell surface or by blocking viral molecules that interact with those receptors.
Vaccine strategies to prevent monkeypox infection
To date, there are no specific treatments or vaccines for the prevention of MPXV infection. Bearing in mind that monkeypox and smallpox viruses are genetically similar and expose low polymorphic antigens on their surface, allowing conferment of indirect protection against MPXV infection via cross-reactive antibodies, several countries are currently using antiviral drugs and vaccines that were developed earlier against smallpox as treatment and prevention approaches for MPXV infection [4,5] (Box 1 ). International organizations for public health monitoring and infection prevention, such as the World Health Organization (WHO), recommend the administration of these vaccines before or after a recent exposure to MPXV to help protect people against the disease and reduce the spread of infection. To date, however, information regarding the efficacy of these vaccines against MPXV remains insufficiently documented [5]. We summarize current vaccination resources for MPXV, propose new avenues for vaccine development, and discuss future resources in development.Box 1 Antivirals and vaccinia immune globulin intravenous (VIGIV) in monkeypox infection
In general, MPXV-infected patients with intact immune systems recover without strong medication, by only receiving supportive care and pain control. However, depending on the profile of the patient (health status, co-morbidities, previous vaccination program), a complementary treatment may be necessary. Several drugs developed to treat smallpox (tecovirimat, brincidofovir, and cidofovir), and VIGIV may be recommended. Although the efficacy of smallpox antivirals has been shown to lower virus levels and reduce clinical symptoms in animal models with monkeypox, data are much more limited in humans, and the results of ongoing clinical trials will not be known for several months. Tecovirimat (ST-246), the only FDA- and European Medicines Agency (EMA)-approved drug for orthopoxvirus infection in humans, is an inhibitor of extracellular virus effects. It interferes with the cellular localization of the major p37 envelope protein, which is necessary for the replication of viral particles; p37 has no homologs among proteins of humans or other mammals. However, much more information is needed with regard to the mechanism of action of the proposed anti-MPXV antivirals. Regarding VIGIV, data are not yet available on their effectiveness for treating MPXV infection. To date, it is unknown whether a person with severe MPXV infection would benefit from treatment with VIGIV.
Alt-text: Box 1
Current vaccination resources
Today, three vaccines are in use against MPXV [4,5] (Table 1 ). They have been previously used for protection against smallpox infection. The first one, an attenuated, nonreplicating, smallpox vaccine developed in collaboration with the US government to ensure the supply of a smallpox vaccine for the entire population, including immunocompromised individuals who are not recommended vaccination with traditional replicating smallpox vaccines, is produced by the Bavarian Nordic company and marketed as either Imvanex (Europe), Jynneos (USA), or Imvamune (Canada) [8,9] (see Table 1 for further information). Bearing in mind that this product is a repurposed vaccine, it would require extensive research and even Phase 3 clinical trials for monkeypox efficacy. This vaccine contains a modified smallpox virus hence has been named as modified vaccinia Ankara (MVA), a biological product that has completed Phase 3 clinical trials, and has been approved in different countries. An open-label prospective cohort study including healthcare personnel was performed in DRC. Adult personnel at risk for monkeypox received two doses of attenuated live virus smallpox vaccine. They received a liquid (1000 subjects) or lyophilized formulation (600 subjects) on days 0 and 28 via subcutaneous injection [1 × 108 median tissue culture infectious dose (TCID50) per 0.5 ml]. Blood samples were regularly collected over 24 months. The data regarding efficacy in this study are announced but not yet available in the case of MPXV [10] [for updated information, see clinicaltrials.gov and Centers for Disease Control (CDC)i].Table 1 Vaccines under study for the prevention of monkeypox virus infectiona
Table 1Name Type Other names Status Study results for monkeypox Conditions Location URL/Refs
IMVANEX smallpox vaccine in adult healthcare personnel at risk for monkeypox in the DRC; Bavarian Nordic (MVA-BN), Denmark Live modified vaccinia virus Ankara Imvanex (Europe)
Jynneos (USA)
Imvamune (Canada) Active, not recruiting No results available Monkeypox virus infection Tshuapa, Boende; Tshuapa, DRC https://ClinicalTrials.gov/show/NCT02977715
ACAM2000; Acambis, Inc./Sanofi Pasteur Biologics Co., Cambridge, MA, USA Recombinant second-generation vaccine None Active, not recruiting No results available Monkeypox virus infection Not known https://clinicaltrials.gov/ct2/results/details?term=ACAM2000&cond=Monkeypox
LC16m8; KM Biologics, Kumamoto, Japan Recombinant second-generation vaccine, derived from the Lister strain of the vaccinia virus None Active, not recruiting No results available Monkeypox virus infection Discontinued – Phase 1/2 for smallpox in USA [19]
a Information source: https://www.who.int/health-topics/monkeypox/#tab=tab_1 and https://clinicaltrials.gov/ct2/results?cond=Monkeypox&term=vaccine&cntry=&state=&city=&dist=.
Another vaccine called ACAM2000 (Acambis, Inc./Sanofi Pasteur Biologics Co., Cambridge, MA) can be used for the prevention of monkeypox (Table 1). It is a recombinant second-generation vaccine (aimed to reduce the risks of live or attenuated vaccines, consisting of specific protein antigens or recombinant protein components including nucleic acids species), also initially developed for smallpox. As such, it has been recommended for use in the USA for preventing monkeypox infection. However, the US CDC do not recommend it for people with defective health conditions, such as congenital or acquired immune deficiency disorders (especially people with AIDS), diseases affecting skin conditions like atopic dermatitis/eczema, and cardiac diseases; for infants under 12 months of age; or during pregnancy. ACAM2000 is delivered in a single percutaneous dose with a booster dose every 3 years or at least every 10 years. ACAM2000 is a vaccinia virus vaccine derived from a plaque-purified clone of the same New York City Board of Health strain, a smallpox strain that was used to manufacture Dryvax, a related monkeypox vaccine candidate. Data from ACAM2000 clinical trials indicate a safety profile similar to Dryvax, including risk for rare but serious cardiac adverse events [10,11].
A third approved vaccinia vaccine made by KM Biologics (Kumamoto, Japan) is also proposed for monkeypox (Table 1). This vaccine, called LC16m8, is licensed for use in healthy people in Japan, against biological terrorism [8,9]. Approximately 100 000 people have undergone vaccination with this vaccine without experiencing any severe complications. It is an attenuated smallpox vaccine derived from the Lister vaccinia strain. It was developed to lack the B5R envelope protein gene of the vaccinia virus to attenuate its neurotoxicity. It generated neutralizing antibody titers to multiple poxviruses, including vaccinia, monkeypox, and variola major, and generated broad T cell responses.
It has been shown that immune response takes 14 days after the second dose of Jynneos and 4 weeks after the ACAM2000 dose for maximal immunity development. No data are yet available on the efficacy of these two vaccines in the current outbreak of monkeypox. Due to uncertainty regarding the efficacy of proposed vaccines toward monkeypox, and problems created by adverse events that have been reported [12], a next generation of safer and specific vaccines is eagerly awaited. Although in the vaccine community some experts claim that developing vaccines with a large spectrum of specificity to multiple diseases can be advantageous (as found with LC16m8, for instance, but also others), past experience has also shown that epitope-focused immunogens are often more effective in boosting subdominant neutralizing antibody responses in vivo, resulting in enhanced neutralization.
Monkeypox vaccine candidate development: introducing synthetic peptide-based prototype vaccine formulations
Finding and developing novel vaccines against emergent and transmissible diseases represents huge conceptual and technological challenges. The COVID-19 pandemic demonstrated the complexity of producing safe, specific, and high-efficacy vaccine formulations based on the most used biological platforms, including whole attenuated or killed severe acute respiratory syndrome (SARS)-CoV-2 virus; others made by using parts of the virus such as nonreplicative human or simian adenovirus DNA vectors; and others are based on specific mRNA templates for parts of the viral spike (S) glycoprotein. Current strategies for producing and transporting biological platform-based virus vaccines require highly controlled biosafety laboratories and strict cold chains and packing lines to prevent vaccine alteration and for allowing researchers to work safely. In addition, second-generation vaccines have not been proven to be a reliable pathway toward subunit vaccines due to their low stability and specificity. On the contrary, low-cost, stable, specific, nontoxic, and safe molecules produced by controlled synthesis strategies have become a reality for obtaining vaccine formulations for multiple lethal diseases, such as malaria and COVID-19 [13,14].
New generation vaccines, especially synthetic vaccines and mRNA vaccines, are emerging as novel tools for controlling infectious diseases. Hence, some efforts toward site-directed designed synthetic vaccines, especially those based on a rational site-directed selection of relevant epitopes exposed by the virus and their subsequent modification, have proven to be reliable pathways toward more efficient vaccines. Perhaps the first and most representative example of this strategy is a synthetic malaria vaccine proposed in the mid-80s named SPf66 that reached clinical trials [14]. Although this early trial has shown limited effectiveness, it has opened the way to the development of peptide-based synthetic vaccines that have proven to be safe and potentially useful to prevent transmissible diseases.
Our own experience developing synthetic peptide-based prototype vaccine formulations for human life-threatening infectious diseases, such as malaria and COVID-19, has led us to also assess different routes of administration such as intranasal, sub- and transcutaneous, and intramuscular routes for synthetic vaccine candidate formulation for these diseases in animal models. These routes have all proven to be able to stimulate strong humoral and cellular immunity in preclinical studies. An important finding in experiments conducted by us and others is that site-directed designed synthetic epitopes when administered in animal models as vaccine formulations stimulate not only the production of expected antihomologous peptide antibodies but also that of antibodies that cross-react with viral proteins, including antibodies displaying neutralizing properties [13,15]. We have shown that intravenous administration of site-directed antibodies stimulated by synthetic peptides encompassing specific epitopes cleared different circulating malaria pathogen strains in rodents [13], a strategy that could be applied to diseases caused by viruses such as MPXV. Synthetic platforms do not require cold chains since this sort of vaccine product has proven to be highly stable even at room temperature for long time periods.
Using the synthetic approach, we thus developed synthetic formulations of antigenic site-directed polymers on human-compatible adjuvant systems such as Alhydrogel, which stimulated neutralizing antibodies against SARS-CoV-2 and some genetic variants in in vitro experiments [16]. In this pursuit, clinical trials have yet to be conducted. This type of vaccine would be applied especially for booster purposes to those persons who have previously received an mRNA vaccine for COVID-19. When properly formulated, synthetic vaccines can be strong stimulators of both cell and humoral immune responses [17]. One could thus consider identifying key dominant epitopes, most preferably neutralizing, of MPXV and design potent site-directed synthetic vaccine formulations containing these immunogenic epitopes and appropriate immune stimulators (adjuvants, excipients, carriers). These formulations can be developed at moderate cost compared with biological vaccines, especially due to low biosafety requirements for production, and have remarkable immune versatility and stability. Identification of several epitopes that may be assembled to design (multi-)epitope vaccines (peptides and mRNA vaccines) could be highly advantageous. Developing such constructs requires identifying and artificially reproducing a variety of surface protein motifs endowed with immunogenic activity. This strategy of associating sequences encompassed in several SARS-CoV-2 proteins (S, E, M, and N) could be exploited in vaccines against MPXV. We suggest here that elements used by the virus to attach to key sites on the cell surface could be prime targets [18]. Induced antibodies generated by vaccination could then block the virus entry and consequently prevent it from spreading.
DNA vaccines to MPXV are in development but not yet validated in humans. For example, Tonix Pharmaceuticals associated with the University of Alberta is heading toward clinical trials with TNX-801, a live vaccine that uses a horsepox virus TNX-801 assembled from synthetic DNA fragments. In non-human primates, TNX-801 blocks the formation of monkeypox lesions, and in mice it is less virulent than old vaccinia strains. In addition to studies based on synthetic peptides, proteins, and DNA, novel vaccines based on mRNA are also being investigated with the experience gained from the new vaccines developed against COVID-19. In this context, Moderna has announced on August 5, 2022 that it has begun a preclinical program investigating possible mRNA vaccines for monkeypox [10].
Concluding remarks
As monkeypox cases rise globally, researchers are learning more about how the disease is transmitted and spreads in the general population. According to the European Centre for Disease Prevention and Control (September 27, 2022), since the start of the monkeypox outbreak in May 2022, 20 083 confirmed cases of monkeypox have been reported from 29 EU/European Economic Area countries. The five countries reporting most cases since the start of the outbreak are Spain (7149), France (3969), Germany (3607), The Netherlands (1223), and Portugal (851). According to the recently published scores from the US CDC (September 29, 2022), monkeypox has spread to 106 countries and led to more than 67 000 confirmed infections worldwide. Because MPXV is closely related to the smallpox virus, vaccines approved to prevent smallpox virus infection are thought to protect 85% of individuals against monkeypox infection [4,10]. This level of protection against monkeypox remains to be confirmed in the general population. Based on recent experience of new vaccines, an arsenal of possible tools (including DNA and mRNA vaccines and protein or peptide vaccines) should shortly appear for monkeypox, especially synthetic vaccines, as highlighted above, that are endowed with extremely valuable properties in terms of specificity, safety, and effectiveness against transmissible diseases.
Glossary
γδ T cells a small subset of CD3-positive T cells in the peripheral blood that occurs at increased frequency in mucosal tissues. Human γδ T cells do not recognize peptides presented by human leukocyte antigen (HLA) molecules like the conventional CD4+ or CD8+ T cells that carry the αβ T-cell receptor, but rather recognize nonpeptidic phosphorylated molecules secreted by pathogens.
Neutralizing antibody in contrast to blocking antibody, which binds its target but does not interfere with the infectivity of a virus; for example, a neutralizing antibody binds its target and negates its downstream cellular effects, such as cell proliferation or chemotaxis, or infectivity. Only a small subset of the many antibodies that bind a virus are capable of neutralization.
mRNA vaccine a vaccine that delivers antigen-encoding mRNA molecules into immune cells, which use the designed mRNA to build foreign protein that would normally be produced by a pathogen (e.g., a virus). The mRNA is generally delivered by a co-formulation of the RNA encapsulated in lipid nanoparticles that protect the RNA strands and aid in their absorption into the cells.
Synthetic vaccine a vaccine consisting mainly of synthetic peptides, carbohydrates, or lipid antigens or combinations thereof. They are usually considered to be safe compared with vaccines prepared from bacterial cultures, less costly, and relatively rapid to develop.
Ubiquitin system a cellular machinery used by viruses to facilitate many aspects of the infectious cycle in the acquired immune response. This system is also involved in major histocompatibility complex (MHC) class II expression and turnover with impact on MHC I molecule.
Zoonotic infection or zoonosis infection that can spread between animals and humans.
Resources
i www.cdc.gov/poxvirus/monkeypox/interim-considerations/jynneos-vaccine.html
Acknowledgments
This study was supported by the ITI 2021-2028 program, 10.13039/501100003768 University of Strasbourg -CNRS-Inserm, IdEx Unistra (ANR-10-IDEX-0002), and 10.13039/501100001665 ANR (STRAT’US project, ANR-20-SFRI-0012) to S.M.; S.M. also acknowledges the support of the French 10.13039/501100004794 Centre National de la Recherche Scientifique (CNRS); the University of Strasbourg Institute for Advanced Study (USIAS); the 10.13039/501100008530 European Regional Development Fund of the European Union in the context of the INTERREG V Upper Rhine program; the FHU ARRIMAGE and the OMAGE project granted by Region Grand-Est of France and FEDER. Research on SARS-CoV-2 immunoprevention conducted at the Universidad Nacional de Colombia was sponsored by Vicerrectoría de Investigación, information system for research, extension, and laboratory facilities (HERMES; grant numbers 49586, 50090, and 54836) to J.M.L.
Declaration of interests
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could constitute a potential conflict of interest.
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References
1. Kaler J. Monkeypox: a comprehensive review of transmission, pathogenesis, and manifestation Cureus 14 2022 e26531
2. Bunge E.M. The changing epidemiology of human monkeypox—a potential threat? A systematic review PLoS Negl. Trop. Dis. 16 2022 e0010141
3. Thornhill J.P. Monkeypox virus infection in humans across 16 countries — April–June 2022 N. Engl. J. Med. 387 2022 679 691 35866746
4. Lum F.M. Monkeypox: disease epidemiology, host immunity and clinical interventions Nat. Rev. Immunol. 22 2022 597 613 36064780
5. Kmiec D. Kirchhoff F. Monkeypox: a new threat? Int. J. Mol. Sci. 23 2022 7866 35887214
6. Karem K.L. Monkeypox-induced immunity and failure of childhood smallpox vaccination to provide complete protection Clin. Vaccine Immunol. 14 2007 1318 1327 17715329
7. Lant S. Maluquer de Motes C. Poxvirus interactions with the host ubiquitin system Pathogens 10 2021 1034 34451498
8. Petersen B.W. Use of vaccinia virus smallpox vaccine in laboratory and health care personnel at risk for occupational exposure to orthopoxviruses — Recommendations of the Advisory Committee on Immunization Practices (ACIP), 2015 MMWR Morb. Mortal. Wkly Rep. 65 2016 257 262 26985679
9. Yoshikawa T. A highly attenuated vaccinia virus strain LC16m8-based vaccine for severe fever with thrombocytopenia syndrome PLoS Pathog. 17 2021 e1008859
10. Harrison C. Monkeypox response relies on three vaccine suppliers Nat. Biotechnol. 40 2022 1306 1307
11. Rizk J.G. Prevention and treatment of monkeypox Drugs 82 2022 957 963 35763248
12. Petersen B.W. Vaccinating against monkeypox in the Democratic Republic of the Congo Antivir. Res. 162 2019 171 177 30445121
13. Lozano J.M. The search of a malaria vaccine: the time for modified immuno-potentiating probes Vaccines (Basel) 9 2021 115 33540947
14. Patarroyo M.E. A synthetic vaccine protects humans against challenge with asexual blood stages of Plasmodium falciparum malaria Nature 332 1988 158 161 2450281
15. Beignon A.S. A peptide vaccine administered transcutaneously together with cholera toxin elicits potent neutralising anti-FMDV antibody responses Vet. Immunol. Immunopathol. 104 2005 273 280 15734548
16. Lozano J.M. COVID-19 infection detection and prevention by SARS-CoV-2 active antigens: a synthetic vaccine approach Vaccines 8 2020 692 33217916
17. Bonam S.R. An overview of novel adjuvants designed for improving vaccine efficacy Trends Pharmacol. Sci. 38 2017 771 793 28668223
18. Louten J. Poxviruses Louten J. Essential Human Virology 2016 Academic Press 273 290
19. Kidokoro M. Shida H. Vaccinia virus LC16m8∆ as a vaccine vector for clinical applications Vaccines 2 2014 755 771 26344890
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N Engl J Med
N Engl J Med
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The New England Journal of Medicine
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Massachusetts Medical Society
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NJ202211093872103
Editorial
Universal Masking Policies in Schools and Mitigating the Inequitable Costs of Covid-19
Raifman Julia Sc.D.
Green Tiffany Ph.D.
From the Department of Health Law, Policy, and Management, Boston University School of Public Health, Boston (J.R.); and the Departments of Population Health Sciences and Obstetrics and Gynecology, University of Wisconsin–Madison, Madison (T.G.).
09 11 2022
09 11 2022
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4 Pediatrics
4_1 Pediatrics General
18 Infectious Disease
18_12 Coronavirus
24 Health Policy
24_10 Comparative Effectiveness
27 Medical Ethics
27_1 Medical Ethics
30 Diversity, Equity, and Inclusion
30_1 Diversity, Equity, and Inclusion General
32 Public Health
32_1 Public Health General
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pmcNearly 3 years into the Covid-19 pandemic, the United States leads high-income nations in Covid-19–related mortality.1 Millions of persons now have long-term neurologic, cardiopulmonary, and other disabling conditions. Essential workers continue to face high workplace exposure to Covid-19 with few protections. To prevent Covid-19 transmission, 40 states and Washington, DC, implemented universal indoor masking policies in 2020.2 Most maintained these policies until May 2021, when the Centers for Disease Control and Prevention (CDC) replaced guidance that everyone wear masks with guidance according to vaccination status.3 Understanding the effects of universal masking policies as compared with individual masking is critical to minimizing the inequitable harms caused by Covid-19 and maximizing our ability to learn, work, and socialize during the pandemic.
Universal masking and individual masking are distinct interventions.4 Universal masking lowers the amount of virus exhaled into shared air,5 reducing the total number of cases of Covid-19 and making indoor spaces safer for populations that are vulnerable to its complications. Individual masking lowers the amount of virus that a masked person inhales from shared air, but only in environments with a relatively high amount of circulating virus and when others are unmasked. Furthermore, individual masking has little effect on population-level transmission.
Public schools are an important context in which to understand the ramifications of moving from universal to individual masking. Although quasi-experimental studies indicated that universal masking was associated with reduced Covid-19 transmission before the availability of vaccines,6,7 we previously had little causal-inference evidence regarding the effect of universal masking in schools or as part of a layered risk-mitigation strategy with vaccination, testing, and ventilation.
A study by Cowger and colleagues, the results of which are now reported in the Journal,8 provides new evidence that the removal of universal school masking policies in Massachusetts was associated with an increased incidence of Covid-19. The study used difference-in-differences methods, a rigorous form of causal inference for policies that are infeasible or unethical to assess in a randomized trial. During a 15-week period (March to June 2022), Covid-19 cases in school districts that had ended universal school masking policies (70 districts for most of the 15-week period) were compared with cases in school districts that sustained universal masking policies (2 districts for most of the 15-week period). The removal of universal school masking was associated with an additional 2882 Covid-19 cases among 46,530 staff (an estimated 81.7 cases per 1000 staff) and an additional 9168 Covid-19 cases among 294,084 students (an estimated 39.9 cases per 1000 students) during the 15 weeks. In school districts that had ended universal masking, approximately 40% of 7127 staff cases and 32% of 28,524 student cases were associated with the removal of universal masking policies.
These findings have implications for federal and state decision making regarding universal masking policies. First, most of the benefits of universal masking accrued before county Covid-19 levels reached high CDC Covid-19 Community Levels, a metric that has been used for policy decisions. Second, school districts that ended masking policies had excess cases despite being more likely to have newer buildings and ventilation systems than school districts that sustained universal masking policies.8,9 These observations highlight the importance of universal masking as a layer of protection early in Covid-19 surges. Masking policies were associated with reduced transmission despite the transmissibility of the omicron (B.1.1.529) variant and without the type of mask specified, although specifying high-quality masks could plausibly further reduce transmission.
The findings also expose a fundamental logical flaw of individual masking: assuming that individual persons will fully absorb the costs of their own masking decisions, rather than assuming that such costs will be shifted onto others and society. Cowger et al. estimated that excess cases implied a minimum of 6500 days of staff absence and 17,500 days of student absence. These absences create costly disruptions for schools and families. Much has been made of the social costs of masking and speculation about language development. Yet strategic implementation of masking policies requires consideration of the costs of not masking — and who will bear those costs. Poor and rich school districts were “differentially equipped to respond to the Covid-19 pandemic,”8 with harms concentrated in low-income and Black, Latinx, and Indigenous communities.8,9 Participatory decision making that includes parents from these communities,9,10 as well as essential workers and persons at high risk for severe Covid-19, can strengthen consideration of societal trade-offs and center equity and inclusion.
The Covid-19 pandemic will not be without continuing costs. A prepandemic normal is unattainable in the short term, no matter how urgently we desire it. The questions for policymakers are these: how high will we allow the societal costs to be, and who will bear the greatest costs? Universal masking policies distribute a small cost across society, rather than shifting the highest burdens of Covid-19 onto populations that have already been made vulnerable by structural racism and other inequities. Strategic use of universal masking policies could include community-level implementation early in surges of new Covid-19 variants and throughout the year in select classrooms to protect higher-risk children and staff. Visionary leadership that centers the populations that are most affected and prioritizes evidence, equity, and inclusion can help us navigate policy decisions that reduce the costs and inequities of Covid-19 in the years ahead.
This editorial was published on November 9, 2022, at NEJM.org.
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References
1 Johns Hopkins Coronavirus Resource Center. Mortality analyses (https://coronavirus.jhu.edu/data/mortality).
2 Skinner A, Flannery K, Nocka K, et al. A database of US state policies to mitigate COVID-19 and its economic consequences. BMC Public Health 2022;22 :1124-1124.35659285
3 Centers for Disease Control and Prevention. Interim public health recommendations for fully vaccinated people. April 29, 2021 (https://stacks.cdc.gov/view/cdc/105629).
4 Rose G. Sick individuals and sick populations. Int J Epidemiol 2001;30 :427-434.11416056
5 Brooks JT, Beezhold DH, Noti JD, et al. Maximizing fit for cloth and medical procedure masks to improve performance and reduce SARS-CoV-2 transmission and exposure, 2021. MMWR Morb Mortal Wkly Rep 2021;70 :254-257.33600386
6 Lyu W, Wehby GL. Community use of face masks and COVID-19: evidence from a natural experiment of state mandates in the US. Health Aff (Millwood) 2020;39 :1419-1425.32543923
7 Guy GP Jr, Lee FC, Sunshine G, et al. Association of state-issued mask mandates and allowing on-premises restaurant dining with county-level COVID-19 case and death growth rates — United States, March 1–December 31, 2020. MMWR Morb Mortal Wkly Rep 2021;70 :350-354.33705364
8 Cowger TL, Murray EJ, Clarke J, et al. Lifting universal masking in schools — Covid-19 incidence among students and staff. N Engl J Med. DOI: 10.1056/NEJMoa2211029.
9 Wiens KE, Smith CP, Badillo-Goicoechea E, et al. In-person schooling and associated COVID-19 risk in the United States over spring semester 2021. Sci Adv 2022;8 (16 ):eabm9128-eabm9128.35442740
10 Boston Parents Schoolyard News. More than a dozen Boston organizations demand stronger COVID safety measures for schools. September 8, 2022 (https://schoolyardnews.com/more-than-a-dozen-boston-organizations-demand-stronger-covid-safety-measures-for-schools-bfbd6f61de09).
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N Engl J Med
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The New England Journal of Medicine
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Massachusetts Medical Society
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NJ202211103871913
Case Records of the Massachusetts General Hospital
Case 34-2022: A 57-Year-Old Woman with Covid-19 and Delusions
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Baggett Meridale V. M.D. Associate Editor
Tran Kathy M. M.D. Associate Editor
Sgroi Dennis C. M.D. Associate Editor
Shepard Jo-Anne O. M.D. Associate Editor
McDonald Emily K. Production Editor
Corpuz Tara Production Editor
Fricchione Gregory L. M.D.
Paul Aaron B. M.D.
Chemali Zeina M.D., M.P.H.
Kritzer Michael D. M.D., Ph.D.
From the Departments of Psychiatry (G.L.F., Z.C., M.D.K.), Radiology (A.B.P.), and Neurology (Z.C.), Massachusetts General Hospital, and the Departments of Psychiatry (G.L.F., Z.C., M.D.K.), Radiology (A.B.P.), and Neurology (Z.C.), Harvard Medical School — both in Boston.
10 11 2022
10 11 2022
387 19 17951803
Copyright © 2022 Massachusetts Medical Society. All rights reserved.
2022
Massachusetts Medical Society
This article is made available via the PMC Open Access Subset for unrestricted re-use, except commercial resale, and analyses in any form or by any means with acknowledgment of the original source. These permissions are granted for the duration of the Covid-19 pandemic or until revoked in writing. Upon expiration of these permissions, PMC is granted a license to make this article available via PMC and Europe PMC, subject to existing copyright protections.
1 Neurology/Neurosurgery
1_1 Neurology/Neurosurgery General
1_10 Seizures
1_13 Confusion/Delirium
7 Psychiatry
7_1 Psychiatry General
7_4 Depression
10 Emergency Medicine
10_1 Emergency Medicine General
18 Infectious Disease
18_12 Coronavirus
28 Clinical Medicine
28_1 Clinical Medicine General
28_2 Hospital-Based Clinical Medicine
Special designationCovid-19
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pmcPresentation of Case
Dr. Michael D. Kritzer: A 57-year-old woman with major depressive disorder and coronavirus disease 2019 (Covid-19) was evaluated at a hospital affiliated with this hospital because she was having delusions that she was dead.
The patient had been in her usual state of health until 2 weeks before this presentation, when myalgias, cough, sore throat, nausea, and vomiting developed. She sought evaluation at the primary care clinic of an academic medical center affiliated with this hospital (the two hospitals are part of the same health care system). Nucleic acid testing of a nasopharyngeal swab was positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA, and the patient was instructed to quarantine at home. She lived with her father and assisted him with activities of daily living; he also received a diagnosis of Covid-19.
During the following week, the patient’s cough persisted, and new shortness of breath developed. Her symptoms worsened; she felt that she was unable to take care of her father. Emergency medical services were called, and the patient and her father were taken to the emergency department of the other hospital, where they were both admitted for worsening Covid-19 pneumonia. The patient was treated with supplemental oxygen, remdesivir, and dexamethasone. Treatment with remdesivir was stopped on hospital day 4 when the blood aminotransferase levels increased to three times the upper limit of the normal range.
During the hospitalization, the patient was noted to have intermittent anxiety, particularly when discharge planning for her father was discussed. She and her brother declined to have their father discharged to a rehabilitation center and instead planned for him to eventually return home to quarantine with the patient. On hospital day 6, the patient’s oxygen saturation was normal while she was breathing ambient air, and the blood aminotransferase levels had improved. She was discharged home with instructions to quarantine and continue taking dexamethasone.
One day after discharge, the patient’s brother spoke to her on the telephone. He thought that she seemed to be confused and unable to take care of herself, and he asked her to return to the emergency department of the other hospital.
On evaluation in the emergency department, the patient explained that she was unsure why her brother had asked her to return to the hospital, and she said that she wanted to go home. She also expressed that she felt anxious about being home alone after discharge and overwhelmed about needing to care for her father at home once he was discharged from the hospital. The myalgias, cough, and shortness of breath had abated; she had no fevers, visual or auditory hallucinations, or suicidal or homicidal ideation.
The patient had a history of major depressive disorder, which had been diagnosed during the second decade of life. At the time of diagnosis, she had been admitted to a psychiatric hospital and had received electroconvulsive therapy; thereafter, she had been discharged to a partial hospital program. She had been hospitalized for psychiatric symptoms twice since then, once for major depressive disorder and once for a mixed bipolar episode that was due to insomnia and anxious distress. The latter episode was associated with catatonic features and was treated with electroconvulsive therapy.
The patient had no history of suicidal or homicidal ideation or attempts and no history of violence. She had hypertension, diabetes, obesity, and gastroesophageal reflux disease. Medications included dexamethasone, bupropion, fluoxetine, olanzapine, losartan, metformin, and pantoprazole. Sulfa drugs had caused angioedema, and lisinopril had caused cough. The patient was born in the Caribbean and had emigrated four decades earlier, first to southwestern Europe and then to the United States 2 years later. She lived in an apartment in an urban area of New England with her father, who had mild dementia. She did not drink alcohol, smoke cigarettes, or use illicit substances.
On examination, the temperature was 37.2°C, the pulse 97 beats per minute, the blood pressure 153/95 mm Hg, the respiratory rate 20 breaths per minute, and the oxygen saturation 93% while the patient was breathing ambient air. The patient was alert and oriented but guarded, with a flat affect. She appeared to be more anxious than she had been during the previous hospitalization. She paced around the room and perseverated about the care of her father. The remainder of the examination was normal.
The blood levels of electrolytes and glucose were normal, as were the results of liver-function and kidney-function tests. The white-cell count was 11,490 per microliter (reference range, 4000 to 11,000), with neutrophil predominance; the complete blood count with differential count was otherwise normal. Urinalysis and a radiograph of the chest were normal. Treatment with dexamethasone was stopped, and the patient was admitted to the hospital to facilitate discharge to a rehabilitation center for continued care.
On hospital day 3, the patient was noted to be more withdrawn, and she began responding to questions with one-word answers or silence. When she was encouraged to speak more, she continued to perseverate about the care of her father. When she was asked to elaborate on her concerns, she stated, “He is dead. I am dead.”
The patient appeared disheveled, sullen, and anxious. She laid in bed motionless with her eyes open and looking forward, and she responded briefly to questions in a quiet voice with slowed speech. Her thoughts were perseverative and tangential. There was no evidence that she had loosening of associations, hallucinations, or suicidal or homicidal ideation. She had poor insight and judgment. Memory, attention, concentration, abstract reasoning, and fund of knowledge were normal. When her arms and legs were lifted against gravity and released, they fell to the bed without resistance; with encouragement, she was able to move them. Muscle tone was normal, with no rigidity or waxy flexibility. Imaging studies were obtained.
Dr. Aaron B. Paul: Computed tomography (CT) of the head (Figure 1), performed without the administration of intravenous contrast material, revealed no evidence of an acute territorial infarct, intracranial mass, or hemorrhage. There was nonspecific moderate confluent hypoattenuation involving the supratentorial white matter.
Dr. Kritzer: Clonazepam was administered, and the dose of olanzapine was increased. Admission to an inpatient psychiatric unit was recommended. During the next week, while awaiting placement in an inpatient psychiatric unit, the patient continued to show signs of anxiety and a depressed mood. She said, “I am dead. I do not exist. I am not real.” She also believed that her father and brother, as well as her nurses and doctors, were dead. The patient was selectively mute and motionless, but she talked and moved with encouragement. She expressed that she felt directly responsible for the Covid-19 pandemic and asked to be thrown out of the window. She had the sensation that her bladder was gone and that she could not urinate, although she had been observed urinating independently. She felt that she could not eat, although she had been observed eating breakfast daily. On hospital day 9, the patient was transferred to the inpatient psychiatric unit of the other hospital.
A diagnosis and management decisions were made.
Differential Diagnosis
Dr. Gregory L. Fricchione: In this 57-year-old woman with metabolic syndrome and a mixed affective disorder suggestive of bipolar disorder, neuropsychiatric symptoms developed 2 weeks after the onset of Covid-19. The patient had psychomotor agitation, a flat affect, and anxious perseveration that was focused on her father’s care. Three days later, she was noted to become motionless and hypophonic, with staring, speech latency, and verbal perseveration. Cognition was intact, but insight and judgment were impaired. In an attempt to explain her neuropsychiatric symptoms, I will consider the potential effects of medications that she had received, her underlying psychiatric disease, and her recent infection.
Medication Effects
This patient was receiving several medications for the treatment of a mixed affective disorder, including bupropion, fluoxetine, and olanzapine. Antidepressant medications could trigger a secondary manic episode, especially in this patient with suspected bipolar disorder. However, her presentation was not typical of drug-related mania, which has classic symptoms of insomnia, euphoria or irritability, extreme hyperactivity, and pressured speech. Although she was receiving psychotropic medications that have been associated with serotonin syndrome, there were no findings suggestive of this diagnosis, such as clonus, tremor, ataxia, hyperreflexia, or fever.1
This patient had recently started taking dexamethasone for the treatment of Covid-19. Glucocorticoids, particularly when administered at high doses, are potential triggers of a manic response commonly referred to as “steroid-induced psychosis.” The use of glucocorticoids can cause a myriad of neuropsychiatric affective, cognitive, and behavioral symptoms.2 The persistence of this patient’s psychotic symptoms after the discontinuation of dexamethasone argues against the diagnosis of glucocorticoid-associated psychosis, although it is possible that dexamethasone triggered an underlying primary psychiatric disorder.
Seizures
The patient was noted to appear withdrawn, and at times, she would lie motionless and not respond to questions. These episodes suggest the possibility of complex partial seizures. Status epilepticus, including nonconvulsive status epilepticus, has been reported in patients with Covid-19.3 In addition, the patient was taking bupropion, a medication that has been associated with lowering of the seizure threshold. However, if her diminished responsiveness were due to nonconvulsive status epilepticus, I would expect her to have phases of deeper unresponsiveness fluctuating with brief phases of alertness with confusion. Because complex partial seizure disorder is often difficult to diagnose, I would perform long-term electroencephalographic (EEG) monitoring, while considering alternative diagnoses.
Autoimmune Encephalitis
Could this patient have autoimmune limbic encephalitis? The neuropsychiatric symptoms appear to have had a subacute onset followed by rapid progression, which suggests involvement of the limbic system. In addition, the white-matter changes observed on CT of the head suggest bilateral brain abnormalities. However, if the patient had autoimmune limbic encephalitis, I would expect white-matter changes to be restricted to the medial temporal lobes and an EEG to show focal temporal slowing.4 I would perform magnetic resonance imaging (MRI) of the head and a lumbar puncture for cerebrospinal fluid (CSF) analysis to help rule out the diagnosis of autoimmune encephalitis, especially given the potential association of this condition with Covid-19.5,6 Encephalitis associated with anti–N-methyl-d-aspartate (NMDA) receptor antibodies can lead to a neuropsychiatric presentation that often includes catatonic withdrawal, and it has been associated with viral illnesses.7 A connection between encephalitis associated with anti–NMDA receptor antibodies and SARS-CoV-2 has not yet been established, but a potential relationship has been suggested.8
Neuropsychiatric Symptoms Associated with Covid-19
Could this patient’s neuropsychiatric symptoms be related to her recent diagnosis of Covid-19? Early studies suggested that more than one third of patients with Covid-19 had a neuropsychiatric syndrome.9
Some cases of Covid-19 lead to persistent symptoms or long-term complications that extend beyond acute disease (a condition sometimes referred to as postacute syndrome of Covid-19 or “long Covid”).10 In such cases, neuropsychiatric symptoms can include fatigue, myalgias, headache, anxiety, depression, dysautonomia, and cognitive impairment (also referred to as “brain fog”).
In one study involving more than 60,000 patients with Covid-19, 18% of the patients had received a psychiatric diagnosis in the 14 to 90 days after infection.11 Neuroinflammation is thought to play a role in Covid-19–related neuropsychiatric disorders,12,13 and persistent autoantibodies have been detected in the CSF of patients with these conditions.13-15
New-onset psychosis has been reported in patients with Covid-19. In one report describing 10 patients, psychotic symptoms developed at least 2 weeks after the onset of Covid-19 symptoms, and structured delusions were common.16 A recent systematic review of Covid-19–related psychosis cases confirmed that delusions were the most commonly reported psychotic symptom.17 Of note, the majority of patients with Covid-19–related psychosis had only mild acute Covid-19 symptoms.
CT of the head performed in this patient revealed subcortical white-matter disease. The most frequent neuroimaging abnormalities observed in patients with Covid-19 involve white-matter changes.18 Covid-19 has been associated with several white-matter diseases, including Covid-19–related disseminated leukoencephalopathy and cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL).19,20 However, in this patient, there was no report of features that would be suggestive of these diagnoses, such as a clinically significant reduction in the level of consciousness, headaches, cranial nerve signs, sensorimotor deficits, gait defects, or changes in the deep-tendon reflexes. In addition, there were no other CT findings, such as microhemorrhages or lacunar infarcts. MRI of the head would be the next step to help rule out neuropsychiatric complications of Covid-19.
Catatonia
This patient had several features suggestive of catatonia. If the Bush–Francis Catatonia Rating Scale were used, this patient would acquire points for mutism, withdrawal, immobility and stupor, staring, verbal perseveration, and autonomic instability, with a score of approximately 13 (on a scale ranging from 0 to 23, with higher scores indicating more severe catatonia).21 On the basis of these reported findings on examination, the patient would meet the criteria in the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5), for catatonia associated with a major mood disorder and a general medical condition (Table 1).22
Catatonia is a disorder of the cingulate cortico–striato–thalamo–cortical circuits that results in the disconnection of motivation and movement, and it has multiple neuromedical and psychiatric causes.23,24 Catatonia has been reported in several patients with Covid-19.25 In a small study that evaluated the results of positron-emission tomography and CT of the head performed in patients with Covid-19 encephalopathy, there was evidence of persistent hypometabolism in the prefrontal cortex, anterior cingulate cortex, insula, and caudate cortico–striato–thalamo–cortical network.26
It is possible that this patient had Covid-19–related changes in the blood–brain barrier and choroid plexus that disrupted the cingulate cortico–striato–thalamo–cortical circuits and increased her risk of catatonia. Neuroleptic-induced catatonia related to the use of olanzapine is another possibility. In addition, the patient had a history of hospitalization for probable bipolar affective psychosis and catatonia, and bipolar disorder is the most common cause of psychogenic catatonia. There was no history suggestive of catatonia caused by conversion disorder.
The patient’s catatonic symptoms abated after treatment with a benzodiazepine, which is the first-line treatment for catatonia. However, one of the most striking features of her presentation remains to be explained: her persistent thoughts that she was dead.
Cotard’s Syndrome
This patient expressed self-deprecation and guilt about not being able to care for her father, and she had mood-congruent delusions that she and others were dead, along with a delusion that her bladder had disappeared. Her presentation is consistent with Cotard’s syndrome, a syndrome included in the DSM-5 category of delusional misidentification syndromes.27,28 Patients with Cotard’s syndrome have nihilistic delusions, such as the belief that they are dead, have lost their souls, or are rotting inside, without functional organs or limbs. Three subtypes of Cotard’s syndrome have been described: psychotic depression (a disorder associated with melancholia and nihilistic delusions), type 1 (a nondepressive delusional disorder), and type 2 (a disorder associated with mixed symptoms, including anxiety, depression, and auditory hallucinations).29 Cotard’s syndrome has been reported in at least one patient with Covid-19,30 and catatonia and Cotard’s syndrome may occur concurrently.31,32
Support and reassurance are key in the treatment of patients with Cotard’s syndrome, but trying to talk patients out of their delusions is futile. Successful treatment of the underlying condition often helps the delusions to recede, although the delusions may wax and wane in patients with persistent depression and may become chronic in patients with schizophrenia. Multiple antipsychotic medications have been reported to reduce the symptoms of Cotard’s syndrome. If medications fail, electroconvulsive therapy is an important therapeutic option. This patient had received electroconvulsive therapy in the past for the treatment of catatonia, and such therapy has a broad spectrum of effects for the treatment of multiple delusional conditions, including Cotard’s syndrome.27 Transcranial magnetic stimulation has had some promising effects in patients with catatonia.33
I suspect that this patient had neuroinflammation associated with Covid-19 that contributed to depression, catatonia, and Cotard’s syndrome.
Dr. Gregory L. Fricchione’s Diagnosis
Cotard’s syndrome, catatonia, and depression after coronavirus disease 2019.
Disease Course
Dr. Kritzer: After the patient arrived in the inpatient psychiatric unit of the other hospital, a nurse witnessed a generalized tonic–clonic seizure that lasted 30 seconds and was accompanied by urinary incontinence. Intravenous lorazepam and levetiracetam were administered, and the patient was transferred to the medical clinic of the other hospital for further care. The evaluation for precipitating causes of the seizure included a lumbar puncture for CSF analysis, including CSF testing for antibodies associated with autoimmune encephalitis, which was negative. The blood magnesium level was low, and there was evidence of a urinary tract infection. Additional imaging studies were obtained.
Dr. Paul: MRI of the head (Figure 2) revealed no evidence of an acute infarct, intracranial mass, or hemorrhage. There was moderate confluent hypoattenuation involving the supratentorial white matter. The study did not show preferential involvement of the anterior temporal lobes and external capsules or show subcortical infarcts, findings that would suggest the diagnosis of CADASIL.34 In addition, the study did not show restricted diffusion or microhemorrhages within the juxtacortical white matter, findings that would suggest the diagnosis of Covid-19–associated diffuse leukoencephalopathy and microhemorrhages.35 There was no intracranial hemorrhage, which would suggest cerebral amyloid angiopathy.36 There was mild generalized parenchymal volume loss that was advanced given the patient’s age. The hippocampi were normal with respect to size, signal, and morphologic features. No cause of seizure was identified.
Discussion of Seizure Management
Dr. Zeina Chemali: An EEG recording (Figure 3) revealed bilateral slowing without epileptiform discharges. There were several potential causes of a lowered seizure threshold in this patient, including treatment with bupropion, a urinary tract infection, and hypomagnesemia. Could Covid-19 have contributed to the seizure?
In a study that evaluated EEG recordings obtained from patients with Covid-19, nonspecific abnormalities of background rhythm were observed in most cases, with focal nonepileptic slowing found only around areas of other specific brain insults. Epileptiform discharges were observed in 20% of patients with Covid-19 who were in the intensive care unit, and nonconvulsive status epilepticus was diagnosed in 2.8% of these patients.37 Potential mechanisms through which Covid-19 may contribute to seizures include direct viral invasion of the central nervous system (so far, this possibility has not been substantiated by research findings), exposure to glucocorticoids or other immunomodulatory treatments, or secondary effects of the illness, such as severe hypoxia, hyperthermia, thromboembolic events, or cytokine storm.38 However, seizures occur in 150,000 people each year, and thus, the development of a seizure in this patient after the onset of Covid-19 could be a coincidence.39 An international panel of experts recently determined that there is not enough evidence to suggest any direct correlation between Covid-19 and the potentiation of epileptic seizures.40
This patient was initially treated with levetiracetam, with a plan to administer a 6-week course followed by a tapering course over a period 1 to 2 weeks.41 In addition, hypomagnesemia was corrected with the use of magnesium sulfate, the urinary tract infection was treated with nitrofurantoin, and the course of bupropion was tapered.
Discussion of Psychiatric Management
Dr. Kritzer: During the evaluation for precipitating causes of seizure, the patient had signs of delirium and catatonia. She had a score on the Bush–Francis Catatonia Rating Scale of 11 (with points for mutism, staring, verbigeration, rigidity, negativism, withdrawal, constant grasp reflex, autonomic instability, and repeatedly moving her arm in a circular manner). After one day of treatment with intravenous lorazepam, the score decreased to 5. Because the patient had mania and delirium, the dose of fluoxetine was decreased.
Once the patient’s condition was considered to be medically stable, without overt signs of delirium, she was transferred back to the inpatient psychiatric unit. Electroconvulsive therapy was offered for the treatment of major depressive disorder and Cotard’s syndrome, but the patient and her brother declined this treatment because they thought it had been ineffective in the past. There was ongoing agitation, and levetiracetam was switched to valproate to minimize neuropsychiatric side effects. The patient’s condition improved during her monthlong hospitalization. The dose of lorazepam was gradually tapered, and the doses of valproate and olanzapine were adjusted.
Since discharge, the patient has been admitted to the inpatient psychiatric unit three times for major depressive disorder with psychotic features (mainly paranoia) or with poor self-care. She has had no recurrence of seizures and has reported a benefit from a recent trial of transcranial magnetic stimulation.
Final Diagnosis
Cotard’s syndrome, catatonia, depression, and seizure after coronavirus disease 2019.
Disclosure Forms
Click here for additional data file.
Figure 1 CT of the Head.
An axial image, obtained without the administration of intravenous contrast material, shows no evidence of an acute territorial infarct, intracranial mass, or hemorrhage. There is moderate confluent hypoattenuation involving the supratentorial white matter (arrow).
Figure 2 MRI of the Head.
An axial fluid-attenuated inversion recovery image (Panel A) shows moderate confluent hypoattenuation involving the supratentorial white matter (arrow), a finding consistent with small-vessel change, which is advanced given the patient’s age. There is equivalent prominence of the ventricles and sulci (arrowheads), a finding consistent with mild generalized parenchymal volume loss, which is also advanced given the patient’s age. A coronal T2-weighted image (Panel B) shows that the hippocampi are normal with respect to size, signal, and morphologic features (arrows). An axial T1-weighted image (Panel C), obtained after the administration of intravenous contrast material, shows no abnormal enhancement of the brain parenchyma. An axial diffusion-weighted image (Panel D) shows no restricted diffusion. An axial gradient–echo image (Panel E) shows no susceptibility signal. There is no evidence of a cause or consequence of seizure.
Figure 3 Electroencephalograms.
A bifrontal electroencephalographic (EEG) montage obtained after the patient had a seizure (Panel A) shows bilateral slowing without epileptiform discharges; there is a sharp frontal wave that may be an artifact (arrows). A follow-up EEG obtained 2 weeks later (Panel B) shows more slowing.
Table 1 DSM-5 Criteria for Catatonia Associated with a Major Mood Disorder and a General Medical Condition.*
≥3 of the following:
Catalepsy
Waxy flexibility
Stupor†
Agitation
Mutism†
Negativism†
Posturing
Mannerisms†
Stereotypies
Grimacing
Echolalia
Echopraxia
* DSM-5 denotes Diagnostic and Statistical Manual of Mental Disorders, fifth edition.
† These criteria were present in the patient on hospital day 3.
This case was presented at Psychiatry Grand Rounds.
Disclosure forms provided by the authors are available with the full text of this article at NEJM.org.
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References
1 Boyer EW, Shannon M. The serotonin syndrome. N Engl J Med 2005;352 :1112-1120.15784664
2 Hall RC, Popkin MK, Stickney SK, Gardner ER. Presentation of the steroid psychoses. J Nerv Ment Dis 1979;167 :229-236.438794
3 Dono F, Nucera B, Lanzone J, et al. Status epilepticus and COVID-19: a systematic review. Epilepsy Behav 2021;118 :107887-107887.33743344
4 Dalmau J, Graus F. Antibody-mediated encephalitis. N Engl J Med 2018;378 :840-851.29490181
5 Pilotto A, Masciocchi S, Volonghi I, et al. The clinical spectrum of encephalitis in COVID-19 disease: the ENCOVID multicentre study. June 20, 2020 (http://medrxiv.org/lookup/doi/10.1101/2020.06.19.20133991). preprint.
6 Varatharaj A, Thomas N, Ellul MA, et al. Neurological and neuropsychiatric complications of COVID-19 in 153 patients: a UK-wide surveillance study. Lancet Psychiatry 2020;7 :875-882.32593341
7 Morris NA, Kaplan TB, Linnoila J, Cho T. HSV encephalitis-induced anti-NMDAR encephalitis in a 67-year-old woman: report of a case and review of the literature. J Neurovirol 2016;22 :33-37.26139017
8 Wang H. COVID-19, anti-NMDA receptor encephalitis and microRNA. Front Immunol 2022;13 :825103-825103.35392089
9 Mao L, Jin H, Wang M, et al. Neurologic manifestations of hospitalized patients with coronavirus disease 2019 in Wuhan, China. JAMA Neurol 2020;77 :683-690.32275288
10 Nalbandian A, Sehgal K, Gupta A, et al. Post-acute COVID-19 syndrome. Nat Med 2021;27 :601-615.33753937
11 Taquet M, Luciano S, Geddes JR, Harrison PJ. Bidirectional associations between COVID-19 and psychiatric disorder: retrospective cohort studies of 62 354 COVID-19 cases in the USA. Lancet Psychiatry 2021;8 :130-140.33181098
12 Amruta N, Chastain WH, Paz M, et al. SARS-CoV-2 mediated neuroinflammation and the impact of COVID-19 in neurological disorders. Cytokine Growth Factor Rev 2021;58 :1-15.33674185
13 Franke C, Ferse C, Kreye J, et al. High frequency of cerebrospinal fluid autoantibodies in COVID-19 patients with neurological symptoms. Brain Behav Immun 2021;93 :415-419.33359380
14 Bastard P, Rosen LB, Zhang Q, et al. Autoantibodies against type I IFNs in patients with life-threatening COVID-19. Science 2020;370 (615 ):eabd4585-eabd4585.32972996
15 Arthur JM, Forrest JC, Boehme KW, et al. Development of ACE2 autoantibodies after SARS-CoV-2 infection. PLoS One 2021;16 (9 ):e0257016-e0257016.34478478
16 Parra A, Juanes A, Losada CP, et al. Psychotic symptoms in COVID-19 patients. A retrospective descriptive study. Psychiatry Res 2020;291 :113254-113254.32603930
17 Chaudhary AMD, Musavi NB, Saboor S, Javed S, Khan S, Naveed S. Psychosis during the COVID-19 pandemic: a systematic review of case reports and case series. J Psychiatr Res 2022;153 :37-55.35797814
18 Egbert AR, Cankurtaran S, Karpiak S. Brain abnormalities in COVID-19 acute/subacute phase: a rapid systematic review. Brain Behav Immun 2020;89 :543-554.32682993
19 Williams OH, Mohideen S, Sen A, et al. Multiple internal border zone infarcts in a patient with COVID-19 and CADASIL. J Neurol Sci 2020;416 :116980-116980.32574902
20 Freeman CW, Masur J, Hassankhani A, Wolf RL, Levine JM, Mohan S. Coronavirus disease (COVID-19)-related disseminated leukoencephalopathy: a retrospective study of findings on brain MRI. AJR Am J Roentgenol 2021;216 :1046-1047.32903058
21 Bush G, Fink M, Petrides G, Dowling F, Francis A. Catatonia. I. Rating scale and standardized examination. Acta Psychiatr Scand 1996;93 :129-136.8686483
22 American Psychiatric Association. Diagnostic and statistical manual of mental disorders (DSM-5-TR). 2022 (https://psychiatry.org/psychiatrists/practice/dsm).
23 Walther S, Stegmayer K, Wilson JE, Heckers S. Structure and neural mechanisms of catatonia. Lancet Psychiatry 2019;6 :610-619.31196794
24 Fricchione G, Beach S. Cingulate-basal ganglia-thalamo-cortical aspects of catatonia and implications for treatment. Handb Clin Neurol 2019;166 :223-252.31731912
25 Scheiner NS, Smith AK, Wohlleber M, Malone C, Schwartz AC. COVID-19 and catatonia: a case series and systematic review of existing literature. J Acad Consult Liaison Psychiatry 2021;62 :645-656.33992595
26 Kas A, Soret M, Pyatigoskaya N, et al. The cerebral network of COVID-19-related encephalopathy: a longitudinal voxel-based 18F-FDG-PET study. Eur J Nucl Med Mol Imaging 2021;48 :2543-2557.33452633
27 Sahoo A, Josephs KA. A neuropsychiatric analysis of the Cotard delusion. J Neuropsychiatry Clin Neurosci 2018;30 :58-65.28641497
28 Dieguez S. Cotard syndrome. Front Neurol Neurosci 2018;42 :23-34.29151088
29 Berrios GE, Luque R. Cotard’s syndrome: analysis of 100 cases. Acta Psychiatr Scand 1995;91 :185-188.7625193
30 Yesilkaya UH, Sen M, Karamustafalioglu N. New variants and new symptoms in COVID-19: first episode psychosis and Cotard’s syndrome two months after infection with the B.1.1.7 variant of coronavirus. Schizophr Res 2022;243 :315-316.34176695
31 Simpson P, Kaul E, Quinn D. Cotard’s syndrome with catatonia: a case presentation and discussion. Psychosomatics 2013;54 :196-199.22677219
32 Rath SR, Buenaver M, Rieb DF. Cotard’s and catatonia: are they really that different? Am J Geriatr Psychiatry 2013;21 :Suppl :S124-S125.
33 Hansbauer M, Wagner E, Strube W, et al. rTMS and tDCS for the treatment of catatonia: a systematic review. Schizophr Res 2020;222 :73-78.32600779
34 Singhal S, Rich P, Markus HS. The spatial distribution of MR imaging abnormalities in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy and their relationship to age and clinical features. AJNR Am J Neuroradiol 2005;26 :2481-2487.16286388
35 Radmanesh A, Derman A, Lui YW, et al. COVID-19-associated diffuse leukoencephalopathy and microhemorrhages. Radiology 2020;297 (1 ):E223-E227.32437314
36 Greenberg SM, Charidimou A. Diagnosis of cerebral amyloid angiopathy: evolution of the Boston criteria. Stroke 2018;49 :491-497.29335334
37 Roberto KT, Espiritu AI, Fernandez MLL, Gutierrez JC. Electroencephalographic findings in COVID-19 patients: a systematic review. Seizure 2020;82 :17-22.32957032
38 Vohora D, Jain S, Tripathi M, Potschka H. COVID-19 and seizures: is there a link? Epilepsia 2020;61 :1840-1853.32944929
39 Stovner LJ, Hoff JM, Svalheim S, Gilhus NE. Neurological disorders in the Global Burden of Disease 2010 study. Acta Neurol Scand Suppl 2014;129 :1-6.24588499
40 Austin JK, Birbeck G, Parko K, et al. Epilepsy-related stigma and attitudes: systematic review of screening instruments and interventions — report by the International League Against Epilepsy Task Force on Stigma in Epilepsy. Epilepsia 2022;63 :598-628.34985766
41 Asadi-Pooya AA. Seizures associated with coronavirus infections. Seizure 2020;79 :49-52.32416567
| 36351271 | PMC9730912 | NO-CC CODE | 2022-12-14 23:31:35 | no | N Engl J Med. 2022 Nov 10; 387(19):1795-1803 | utf-8 | N Engl J Med | 2,022 | 10.1056/NEJMcpc2115857 | oa_other |
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N Engl J Med
N Engl J Med
nejm
The New England Journal of Medicine
0028-4793
1533-4406
Massachusetts Medical Society
10.1056/NEJMc2211283
NJ202211093872202
Correspondence
Six-Month Follow-up after a Fourth BNT162b2 Vaccine Dose
Canetti Michal M.D.
Barda Noam M.D., Ph.D.
Gilboa Mayan M.D.
Sheba Medical Center Tel Hashomer, Ramat Gan, Israel
Indenbaum Victoria Ph.D.
Ministry of Health, Ramat Gan, Israel
Asraf Keren Ph.D.
Gonen Tal M.D.
Weiss-Ottolenghi Yael Ph.D.
Amit Sharon M.D.
http://orcid.org/0000-0003-1734-9391
Doolman Ram M.D.
Sheba Medical Center Tel Hashomer, Ramat Gan, Israel
Mendelson Ella Ph.D.
Ministry of Health, Ramat Gan, Israel
Freedman Laurence S. Ph.D.
Kreiss Yitshak M.D.
Sheba Medical Center Tel Hashomer, Ramat Gan, Israel
Lustig Yaniv Ph.D.
Ministry of Health, Ramat Gan, Israel
http://orcid.org/0000-0001-7163-4607
Regev-Yochay Gili M.D.
Sheba Medical Center Tel Hashomer, Ramat Gan, Israel [email protected]
09 11 2022
09 11 2022
NEJMc2211283Copyright © 2022 Massachusetts Medical Society. All rights reserved.
2022
Massachusetts Medical Society
This article is made available via the PMC Open Access Subset for unrestricted re-use, except commercial resale, and analyses in any form or by any means with acknowledgment of the original source. These permissions are granted for the duration of the Covid-19 pandemic or until revoked in writing. Upon expiration of these permissions, PMC is granted a license to make this article available via PMC and Europe PMC, subject to existing copyright protections.
18 Infectious Disease
18_2 Vaccines
18_6 Viral Infections
18_12 Coronavirus
release-date-display-string2022-11-09T17:00:00-05:00
release-date-year2022
release-date-month11
release-date-day09
release-date-hour17
release-date-minute00
release-date-second00
release-date-time-zone-05:00
==== Body
pmcTo the Editor: In a prospective cohort study involving health care workers that was described previously,1 we evaluated the humoral response and vaccine effectiveness of a fourth dose of the BNT162b2 vaccine (Pfizer–BioNTech) against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during a 6-month follow-up period in which omicron (mostly BA.1 and BA.2) was the predominant variant in Israel.2 The absence of previous SARS-CoV-2 infection was verified by SARS-CoV-2 testing and serologic follow-up testing (see Table S1 and the Supplementary Methods in the Supplementary Appendix, available with the full text of this letter at NEJM.org). The humoral response (as assessed by the measurement of IgG and neutralizing antibodies) after receipt of the fourth vaccine dose was compared with that after receipt of the second and third doses. Vaccine effectiveness was assessed by comparing infection rates among participants who had received a fourth vaccine dose during various time periods (days 7 through 35, days 36 through 102, or days 103 through 181 after receipt of the fourth dose) with infection rates among those who had received three doses. Participants were to have received the third vaccine dose at least 4 months earlier. A Cox proportional hazards regression model was used, with adjustment for age, sex, and professional role; calendar time was used as the time scale to account for differences in the prevalence of infection over time (details are provided in the Supplementary Appendix). No participants died or were lost to follow-up.
Among the participants who had not had previous SARS-CoV-2 infection, 6113 were included in the analysis of humoral response and 11,176 in the analysis of vaccine effectiveness (Fig. S1 and Tables S2 and S3). Antibody response peaked at approximately 4 weeks, waned to levels seen before the fourth dose by 13 weeks, and stabilized thereafter. Throughout the 6-month follow-up period, the adjusted weekly levels of IgG and neutralizing antibodies were similar after receipt of the third and fourth doses and were markedly higher than the levels seen after receipt of the second dose (Figure 1A and 1B and Table S4).
The cumulative incidence curve is shown in Figure S2, and vaccine effectiveness is shown in Figure 1C. Receipt of the fourth BNT162b2 vaccine dose conferred more protection against SARS-CoV-2 infection than that afforded by the receipt of three vaccine doses (with receipt of the third dose having occurred at least 4 months earlier) (overall vaccine effectiveness, 41%; 95% confidence interval [CI], 35 to 47). Time-specific vaccine effectiveness (which, in our analysis, compared infection rates among participants who had not yet been infected since vaccination) waned with time, decreasing from 52% (95% CI, 45 to 58) during the first 5 weeks after vaccination to −2% (95% CI, −27 to 17) at 15 to 26 weeks.
The study has several limitations. First, although our cohort consisted of a diverse population that included older-adult volunteers, a cohort consisting of health care workers may not be representative of the general population. Furthermore, only health care workers who had not had previous SARS-CoV-2 infection were included, which further limited generalizability. Second, possible confounding of unrecognized hybrid immunity may have remained, despite thorough history-taking and serologic assessment. Third, the decision to receive the fourth dose could be linked to health-seeking behaviors that were not well-captured in our data, thus possibly resulting in additional residual confounding. Fourth, we were unable to estimate effectiveness against severe outcomes of infection owing to the absence of such outcomes in our study cohort; a third dose of the BNT162b2 vaccine has been shown to confer durable protection against such outcomes.3 Previous studies have shown increased effectiveness of a fourth dose against severe outcomes during short-term follow-up,4,5 but whether this additional effectiveness wanes similarly to the protection against infection has yet to be determined.
In this prospective cohort study, a third dose of the BNT162b2 vaccine led to an improved and sustained immunologic response as compared with two doses, but the additional immunologic advantage of the fourth dose was much smaller and had waned completely by 13 weeks after vaccination. This finding correlated with waning vaccine effectiveness among recipients of a fourth dose, which culminated in no substantial additional effectiveness over a third dose at 15 to 26 weeks after vaccination. These results suggest that the fourth dose, and possibly future boosters, should be timed wisely to coincide with disease waves or to be available seasonally, similar to the influenza vaccine. Whether multivalent booster doses will result in longer durability remains to be seen.
This letter was published on November 9, 2022, at NEJM.org.
Supplementary Appendix
Click here for additional data file.
Disclosure Forms
Click here for additional data file.
Figure 1 Six-Month Follow-up of Immunogenicity and Vaccine Effectiveness after a Fourth BNT162b2 Vaccine Dose.
Panels A and B show IgG and neutralizing antibody titers, respectively, up to 26 weeks after the second, third, and fourth doses of the BNT162b2 (Pfizer–BioNTech) vaccine. Locally weighted scatterplot smoothing is overlaid. Panel C shows the vaccine effectiveness against any severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection 7 to 35 days, 36 to 102 days, 103 to 181 days, and 7 to 181 days (representing the full study period [darker-shaded area]) after the fourth vaccine dose (administered at least 4 months after receipt of the third dose) as compared with the effectiveness of three vaccine doses. Vaccine effectiveness (measured as 1 minus the hazard ratio) is estimated from a Cox proportional hazards regression model, with adjustment for age, sex, and professional role. Calendar time was used as the time scale to further adjust for differing infection prevalence over time. A dashed horizontal line is shown at a hazard ratio of 1, which indicates no effect. 𝙸 bars indicate 95% confidence intervals. BAU denotes binding antibody units.
Disclosure forms provided by the authors are available with the full text of this letter at NEJM.org.
==== Refs
References
1 Levin EG, Lustig Y, Cohen C, et al. Waning immune humoral response to BNT162b2 Covid-19 vaccine over 6 months. N Engl J Med 2021;385 (24 ):e84-e84.34614326
2 Kliker L, Zuckerman N, Atari N, et al. COVID-19 vaccination and BA.1 breakthrough infection induce neutralising antibodies which are less efficient against BA.4 and BA.5 omicron variants, Israel, March to June 2022. Euro Surveill 2022;27 :2200559-2200559.35904058
3 Barda N, Dagan N, Cohen C, et al. Effectiveness of a third dose of the BNT162b2 mRNA COVID-19 vaccine for preventing severe outcomes in Israel: an observational study. Lancet 2021;398 :2093-2100.34756184
4 Magen O, Waxman JG, Makov-Assif M, et al. Fourth dose of BNT162b2 mRNA Covid-19 vaccine in a nationwide setting. N Engl J Med 2022;386 :1603-1614.35417631
5 Bar-On YM, Goldberg Y, Mandel M, et al. Protection by a fourth dose of BNT162b2 against omicron in Israel. N Engl J Med 2022;386 :1712-1720.35381126
| 36351266 | PMC9730934 | NO-CC CODE | 2022-12-14 23:31:35 | no | N Engl J Med. 2022 Nov 9;:NEJMc2211283 | utf-8 | N Engl J Med | 2,022 | 10.1056/NEJMc2211283 | oa_other |
==== Front
N Engl J Med
N Engl J Med
nejm
The New England Journal of Medicine
0028-4793
1533-4406
Massachusetts Medical Society
10.1056/NEJMc2212772
NJ202211233872301
Correspondence
Neutralization of Omicron Subvariant BA.2.75 after Bivalent Vaccination
Chalkias Spyros M.D.
Feng Jing M.S.
http://orcid.org/0000-0002-6740-9535
Chen Xing Sc.D.
Zhou Honghong Ph.D.
Marshall Jean-Claude Ph.D.
Girard Bethany Ph.D.
Tomassini Joanne E. Ph.D.
Kuter Barbara J. Ph.D., M.P.H.
Moderna, Cambridge, MA [email protected]
Montefiori David C. Ph.D.
Duke University Medical Center, Durham, NC
Das Rituparna M.D., Ph.D.
Moderna, Cambridge, MA
23 11 2022
23 11 2022
NEJMc2212772Copyright © 2022 Massachusetts Medical Society. All rights reserved.
2022
Massachusetts Medical Society
http://www.nejmgroup.org/legal/terms-of-use.htm This article is made available via the PMC Open Access Subset for unrestricted re-use, except commercial resale, and analyses in any form or by any means with acknowledgment of the original source. These permissions are granted for the duration of the Covid-19 pandemic or until revoked in writing. Upon expiration of these permissions, PMC is granted a license to make this article available via PMC and Europe PMC, subject to existing copyright protections.
18 Infectious Disease
18_2 Vaccines
18_6 Viral Infections
18_12 Coronavirus
Moderna http://dx.doi.org/10.13039/100019533 release-date-display-string2022-11-23T17:00:00-05:00
release-date-year2022
release-date-month11
release-date-day23
release-date-hour17
release-date-minute00
release-date-second00
release-date-time-zone-05:00
==== Body
pmcTo the Editor: Bivalent messenger RNA (mRNA) vaccines containing the ancestral severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and B.1.1.529 (omicron) variant spike sequences were recently made available to address the waves of infection and coronavirus disease 2019 (Covid-19) caused by omicron variants. The omicron BA.1–containing bivalent vaccine mRNA-1273.214, currently authorized for use in multiple countries, elicits strong neutralizing antibody responses against omicron BA.1 and the epidemiologically dominant BA.4 and BA.5 subvariants.1 The omicron BA.2.75 subvariant, which has steadily increased in prevalence in at least 36 countries, contains potential antibody-escape spike mutations.2 We aimed to characterize the neutralization of BA.2.75 after mRNA-1273.214 boosting and to further elucidate the cross-neutralization potential of this bivalent vaccine against multiple omicron variants.1
In this phase 2–3 study, geometric mean titers (GMTs) of neutralizing antibodies at a 50% inhibitory dilution were assessed in serum samples collected at day 29 after the administration of 50 μg of mRNA-1273.214 as a second booster dose in adults who had previously received both the mRNA-1273 primary series and a first booster dose of 50 μg of mRNA-1273 at least 3 months earlier and had no evidence of SARS-CoV-2 infection within 3 months before study enrollment. The neutralization assay used lentivirus-based pseudoviruses and was performed in 293T cells that were stably transduced to overexpress angiotensin-converting enzyme 2 (see the Supplementary Methods section in the Supplementary Appendix, available with the full text of this letter at NEJM.org).3 In all 428 participants in the per-protocol immunogenicity population, mRNA-1273.214 elicited a potent neutralizing antibody response against the BA.2.75 subvariant, regardless of previous SARS-CoV-2 infection (Figure 1), with a GMT of 1947 (95% confidence interval [CI], 1711 to 2215). This response was 2.1 (95% CI, 1.9 to 2.2) times as high as those against BA.4 and BA.5 subvariants (GMT, 941; 95% CI, 826 to 1071) and 3.4 (95% CI, 3.1 to 3.7) and 1.6 (95% CI, 1.5 to 1.7) times as low as those against the ancestral SARS-CoV-2 D614G strain (GMT, 6619; 95% CI, 5942 to 7374) and the BA.1 subvariant (GMT, 3070; 95% CI, 2685 to 3511), respectively (Tables S1 and S2 in the Supplementary Appendix). The GMTs in the participants without previous infection were generally similar to those in all participants regardless of previous infection, and the GMTs in the participants with previous infection were higher than those in all participants regardless of previous infection.
These data further support the cross-neutralization ability of the omicron-containing bivalent booster vaccine against emerging omicron subvariants that are not contained in the vaccine. Real-world data on the effectiveness of booster vaccines are needed to evaluate whether the potent and broad neutralizing antibody responses elicited by bivalent vaccines confer enhanced protection against Covid-19.
This letter was published on November 23, 2022, at NEJM.org.
Because the trial is ongoing, access to patient-level data and supporting clinical documents by qualified external researchers may be available on request and subject to review once the trial has been completed.
Supplementary Appendix
Click here for additional data file.
Disclosure Forms
Click here for additional data file.
Figure 1 Neutralization of the Ancestral SARS-CoV-2 D614G Strain and Omicron Subvariants after Receipt of mRNA-1273.214 as a Second Booster Dose.
Pseudovirus neutralizing antibody titers against the ancestral severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) D614G strain and the omicron subvariants BA.1, BA.4 and BA.5 (these subvariants have identical sequences and were therefore designated as a composite), and BA.2.75 were assessed in serum samples from adult participants in the per-protocol immunogenicity population who received a two-dose primary vaccination series of mRNA-1273 (100 μg in each dose), a first booster dose of 50 μg of mRNA-1273, and a second booster dose of 50 μg of mRNA-1273.214 (Fig. S2 in the Supplementary Appendix).1 Panel A shows the results in the participants regardless of previous SARS-CoV-2 infection; Panel B, the results in those without previous infection; and Panel C, the results in those with previous infection. The spike mutations for the ancestral SARS-CoV-2 D614G strain and the omicron subvariants are provided in Figure S3. The geometric mean titers (GMTs) of neutralizing antibodies at a 50% inhibitory dilution (ID50) were assessed at baseline (before receipt on the day of the second booster) and at day 29 after receipt of the second booster dose. The ID50 GMTs at baseline and at day 29 and the factor increases in the ID50 GMTs at day 29 relative to baseline values are provided. The 95% confidence intervals (indicated by 𝙸 bars) were calculated on the basis of the t-distribution of the log-transformed GMT values and were then back-transformed to the original scale for presentation. The lower limits of quantitation for the pseudovirus neutralization assay (PsVNA) were ID50 GMTs of 18.5 for the ancestral SARS-CoV-2 D614G strain and 19.9 for the omicron subvariant BA.1, and the upper limits of quantitation were 45,118 and 15,503, respectively. The limit of detection of the PsVNA assay for the omicron subvariants BA.4 or BA.5 and BA.2.75 was 10; values below the limit of detection were assigned a value of 5.
Supported by Moderna.
Disclosure forms provided by the authors are available with the full text of this letter at NEJM.org.
==== Refs
References
1 Chalkias S, Harper C, Vrbicky K, et al. A bivalent omicron-containing booster vaccine against Covid-19. N Engl J Med 2022;387 :1279-1291.36112399
2 Shen X, Chalkias S, Feng J, et al. Neutralization of SARS-CoV-2 omicron BA.2.75 after mRNA-1273 vaccination. N Engl J Med 2022;387 :1234-1236.36083119
3 Shen X, Tang H, McDanal C, et al. SARS-CoV-2 variant B.1.1.7 is susceptible to neutralizing antibodies elicited by ancestral spike vaccines. Cell Host Microbe 2021;29 (4 ):529-539.e3.33705729
| 36416761 | PMC9730935 | NO-CC CODE | 2022-12-14 23:31:35 | no | N Engl J Med. 2022 Nov 23;:NEJMc2212772 | utf-8 | N Engl J Med | 2,022 | 10.1056/NEJMc2212772 | oa_other |
==== Front
N Engl J Med
N Engl J Med
nejm
The New England Journal of Medicine
0028-4793
1533-4406
Massachusetts Medical Society
10.1056/NEJMc2211845
NJ202211163872201
Correspondence
In Vitro Efficacy of Antiviral Agents against Omicron Subvariant BA.4.6
http://orcid.org/0000-0002-9064-4699
Takashita Emi Ph.D.
National Institute of Infectious Diseases, Tokyo, Japan
http://orcid.org/0000-0001-7768-5157
Yamayoshi Seiya D.V.M., Ph.D.
University of Tokyo, Tokyo, Japan
Halfmann Peter Ph.D.
Wilson Nancy Ph.D.
Ries Hunter B.S.
Richardson Alex B.S.
Bobholz Max B.S.
Vuyk William B.S.
Maddox Robert B.S.
Baker David A. Ph.D.
http://orcid.org/0000-0001-9831-6895
Friedrich Thomas C. Ph.D.
O’Connor David H. Ph.D.
University of Wisconsin–Madison, Madison, WI
Uraki Ryuta Ph.D.
Ito Mutsumi D.V.M.
Sakai-Tagawa Yuko Ph.D.
Adachi Eisuke M.D., Ph.D.
http://orcid.org/0000-0002-1667-9287
Saito Makoto M.D., Ph.D.
Koga Michiko M.D., Ph.D.
Tsutsumi Takeya M.D., Ph.D.
Iwatsuki-Horimoto Kiyoko D.V.M., Ph.D.
Kiso Maki D.V.M., Ph.D.
Yotsuyanagi Hiroshi M.D., Ph.D.
University of Tokyo, Tokyo, Japan
Watanabe Shinji D.V.M., Ph.D.
Hasegawa Hideki M.D., Ph.D.
National Institute of Infectious Diseases, Tokyo, Japan
Imai Masaki D.V.M., Ph.D.
Kawaoka Yoshihiro D.V.M., Ph.D.
University of Tokyo, Tokyo, Japan [email protected]
Drs. Takashita and Yamayoshi contributed equally to this letter.
16 11 2022
16 11 2022
NEJMc2211845Copyright © 2022 Massachusetts Medical Society. All rights reserved.
2022
Massachusetts Medical Society
http://www.nejmgroup.org/legal/terms-of-use.htm This article is made available via the PMC Open Access Subset for unrestricted re-use, except commercial resale, and analyses in any form or by any means with acknowledgment of the original source. These permissions are granted for the duration of the Covid-19 pandemic or until revoked in writing. Upon expiration of these permissions, PMC is granted a license to make this article available via PMC and Europe PMC, subject to existing copyright protections.
18 Infectious Disease
18_6 Viral Infections
18_12 Coronavirus
National Institute of Allergy and Infectious Diseases http://dx.doi.org/10.13039/100000060 75N93021C00014 HHSN272201400008C Centers for Disease Control and Prevention http://dx.doi.org/10.13039/100000030 75D30121C11060 State of Wisconsin Department of Health Services project http://dx.doi.org/10.13039/100018450 435100-A22-ELCProjE-01 Japan Agency for Medical Research and Development http://dx.doi.org/10.13039/100009619 JP21fk0108552 JP21nf0101632 JP22wm0125002 Ministry of Health, Labor, and Welfare, Japan http://dx.doi.org/10.13039/501100003478 20HA2007 release-date-display-string2022-11-16T17:00:00-05:00
release-date-year2022
release-date-month11
release-date-day16
release-date-hour17
release-date-minute00
release-date-second00
release-date-time-zone-05:00
==== Body
pmcTo the Editor: As of September 2022, the BA.5 subvariant of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) B.1.1.529 (omicron) variant has become dominant in most countries around the world. However, the prevalence of the BA.4.6 subvariant is increasing in the United States.1 BA.4.6 and BA.5 share the same amino acid substitutions in the receptor-binding domain of the spike protein, which is the major target for vaccines and therapeutic monoclonal antibodies against SARS-CoV-2. BA.4.6 also has an additional mutation that is not found in BA.5 (i.e., R346T),2 a finding that arouses concern that the effectiveness of current vaccines and therapeutic monoclonal antibodies against this subvariant will be greatly decreased.
Accordingly, we assessed the effectiveness of several therapeutic monoclonal antibodies that have been authorized for the treatment of coronavirus disease 2019 (Covid-19), individually and in combination, against two omicron BA.4.6 isolates — hCoV-19/USA/WI-UW-12757/2022 (UW-12757) and hCoV-19/USA/WI-UW-12767/2022 (UW-12767). Both isolates had been obtained from patients with Covid-19. The spike protein of these two isolates contained two additional amino acid changes (R346T and N658S) as compared with a BA.5 isolate (hCoV-19/Japan/TY41-702/2022) (Fig. S1A in the Supplementary Appendix, available with the full text of this letter at NEJM.org). In addition, one of the isolates (UW-12757) also had an N487D mutation in its receptor-binding domain.
We used a live-virus 50% focus reduction neutralization test (FRNT50) to determine neutralization titers of monoclonal antibodies, including REGN10987 (marketed as imdevimab) and REGN10933 (marketed as casirivimab). REGN10987 retained some neutralizing activity against the two BA.4.6 isolates (Figure 1A and 1B), but REGN10933 did not retain such activity. REGN10987 in combination with REGN10933 (imdevimab–casirivimab) neutralized both BA.4.6 isolates; however, as compared with the ancestral strain, the effectiveness of this combination was lower by a factor of 52.9 against UW-12757 and by a factor of 87.3 against UW-12767. The monoclonal antibodies COV2-2196 (marketed as tixagevimab) and COV2-2130 (marketed as cilgavimab), individually and in combination, had reduced activity against the BA.4.6 isolates, as did S309, the precursor of sotrovimab. In contrast, LYCoV1404 (marketed as bebtelovimab) efficiently inhibited both UW-12757 and UW-12767 with a very low FRNT50 value (3.80 ng per milliliter and 2.26 ng per milliliter, respectively), results that were similar to those for the ancestral strain.
The Food and Drug Administration has approved the use of remdesivir (an RNA-dependent RNA polymerase [RdRp] inhibitor) for the treatment of Covid-19 and has issued Emergency Use Authorizations for two other antiviral drugs: molnupiravir (an RdRp inhibitor) and nirmatrelvir (a main protease inhibitor of SARS-CoV-2). We therefore tested the efficacy of these antiviral drugs against BA.4.6 by determining their in vitro 50% inhibitory concentration (IC50) values against this variant. Of note, these two BA.4.6 isolates had P314L and P3395H mutations in the RdRp and main protease, respectively (Fig. S1B). The two BA.4.6 isolates had susceptibilities to the three compounds that were similar to the susceptibility of the ancestral strain: for UW-12757, the IC50 value was higher by a factor of 1.6 with remdesivir, by a factor of 5.7 with molnupiravir, and by a factor of 4.1 with nirmatrelvir; for UW-12767, the IC50 values were higher by a factor of 0.4, 1.8, and 1.2, respectively (Figure 1C and 1D). The clinical Cmax (i.e., the highest concentration of a drug in the blood) of the therapeutic agents is shown in Tables S1 and S2.
The neutralizing activity of plasma obtained from patients who had recovered from Covid-19 and from recipients of vaccines was lower against BA.4.6, BA.2, and BA.5 than it was against the ancestral strain. This reduction in neutralizing titers was larger for BA.4.6 and BA.5 than for BA.2 (Figure 1E and Supplementary Results and Tables S3 and S4).
Our data suggest that remdesivir, molnupiravir, and nirmatrelvir and the monoclonal antibodies bebtelovimab and imdevimab retain effectiveness against BA.4.6 in vitro (see the Supplementary Discussion). Our findings also indicate that monoclonal antibodies casirivimab, sotrovimab, tixagevimab, and cilgavimab may not be effective against BA.4.6.
This letter was published on November 16, 2022, at NEJM.org.
Supplementary Appendix
Click here for additional data file.
Disclosure Forms
Click here for additional data file.
Figure 1 Antiviral Efficacy and Antibody Response in Vitro against Omicron Subvariants.
Shown is the neutralizing activity (Panel A) and efficacy (Panel B) of monoclonal antibodies and the inhibitory activity (Panel C) and efficacy (Panel D) of antiviral drugs against omicron subvariants. GS-441524 (the main metabolite of remdesivir) and EIDD-1931 (the active form of molnupiravir) are RNA-dependent RNA polymerase inhibitors. PF-07321332 (nirmatrelvir) is an Mpro inhibitor. Also shown are the neutralizing activity of plasma obtained from patients who had received three doses of the BNT162b2 vaccine (Panel E, left) and the neutralizing activity of plasma obtained from patients who had been infected with the omicron BA.2 subvariant after receiving either three doses of the BNT162b2 vaccine or two doses of the mRNA-1273 vaccine and one dose of the BNT162b2 vaccine (Panel E, right). Detailed information about the participants is provided in Tables S3 and S4. IC50 denotes 50% inhibitory concentration, and FRNT50 50% focus reduction neutralization test.
Supported by grants from the Center for Research on Influenza Pathogenesis (HHSN272201400008C, to Dr. Kawaoka) and from the Center for Research on Influenza Pathogenesis and Transmission (75N93021C00014, to Dr. Kawaoka), by the National Institutes of Allergy and Infectious Diseases, the Centers for Disease Control and Prevention (75D30121C11060, to Drs. O’Connor and Friedrich), a State of Wisconsin Department of Health Services project (435100-A22-ELCProjE-01, to Drs. O’Connor and Friedrich), a Research Program on Emerging and Reemerging Infectious Diseases (JP21fk0108552 to Dr. Kawaoka), a Project Promoting Support for Drug Discovery (JP21nf0101632, to Dr. Kawaoka), the Japan Program for Infectious Diseases Research and Infrastructure (JP22wm0125002, to Dr. Kawaoka) from the Japan Agency for Medical Research and Development, and a grant-in-aid for Emerging and Reemerging Infectious Diseases from the Ministry of Health, Labor, and Welfare, Japan (20HA2007, to Dr. Hasegawa).
Disclosure forms provided by the authors are available with the full text of this letter at NEJM.org.
==== Refs
References
1 Scobie H. Update on SARS-CoV-2 variants and the epidemiology of COVID-19. Atlanta: Centers for Disease Control and Prevention, September 1, 2022 (https://www.cdc.gov/vaccines/acip/meetings/downloads/slides-2022-09-01/02-covid-scobie-508.pdf).
2 Miller NL, Clark T, Raman R, Sasisekharan R. Insights on the mutational landscape of the SARS-CoV-2 omicron variant receptor-binding domain. Cell Rep Med 2022;3 :100527-100527.35233548
| 36383452 | PMC9730936 | NO-CC CODE | 2022-12-14 23:31:35 | no | N Engl J Med. 2022 Nov 16;:NEJMc2211845 | utf-8 | N Engl J Med | 2,022 | 10.1056/NEJMc2211845 | oa_other |
==== Front
J Med Imaging (Bellingham)
J Med Imaging (Bellingham)
JMIOBU
JMI
Journal of Medical Imaging
2329-4302
2329-4310
Society of Photo-Optical Instrumentation Engineers
10.1117/1.JMI.9.6.066003
JMI-22136GR
22136GR
Biomedical Applications in Molecular, Structural, and Functional Imaging
Paper
Toward the determination of sensitive and reliable whole-lung computed tomography features for robust standard radiomics and delta-radiomics analysis in a nonhuman primate model of coronavirus disease 2019
https://orcid.org/0000-0003-3450-0989
Castro Marcelo A. a*[email protected]
https://orcid.org/0000-0002-9853-9607
Reza Syed [email protected]
https://orcid.org/0000-0001-7818-9139
Chu Winston T. [email protected]
https://orcid.org/0000-0003-4678-9986
Bradley Dara [email protected]
https://orcid.org/0000-0003-0450-2879
Lee Ji Hyun [email protected]
https://orcid.org/0000-0003-2485-4011
Crozier Ian [email protected]
https://orcid.org/0000-0002-2256-4056
Sayre Philip J. [email protected]
https://orcid.org/0000-0003-1928-0455
Lee Byeong Y. [email protected]
https://orcid.org/0000-0002-0432-2918
Mani Venkatesh [email protected]
https://orcid.org/0000-0001-9831-6895
Friedrich Thomas C. [email protected]
https://orcid.org/0000-0003-2139-470X
O’Connor David H. [email protected]
https://orcid.org/0000-0002-3674-3282
Finch Courtney L. [email protected]
https://orcid.org/0000-0001-7485-2204
Worwa Gabriella [email protected]
Feuerstein Irwin M. [email protected]
https://orcid.org/0000-0002-7800-6045
Kuhn Jens H. [email protected]
https://orcid.org/0000-0003-2711-1977
Solomon Jeffrey [email protected]
a National Institutes of Health, National Institute of Allergy and Infectious Diseases, Integrated Research Facility at Fort Detrick, Frederick, Maryland, United States
b National Institutes of Health, Clinical Center, Radiology and Imaging Sciences, Center for Infectious Disease Imaging, Bethesda, Maryland, United States
c Frederick National Laboratory for Cancer Research, Clinical Monitoring Research Program Directorate, Frederick, Maryland, United States
d University of Wisconsin–Madison, School of Veterinary Medicine, Department of Pathobiological Sciences, Madison, Wisconsin, United States
e University of Wisconsin–Madison, Department of Pathology and Laboratory Medicine, Madison, Wisconsin, United States
* Address all correspondence to Marcelo A. Castro, [email protected]
8 12 2022
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© 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)
2022
Society of Photo-Optical Instrumentation Engineers
Abstract.
Purpose
We propose a method to identify sensitive and reliable whole-lung radiomic features from computed tomography (CT) images in a nonhuman primate model of coronavirus disease 2019 (COVID-19). Criteria used for feature selection in this method may improve the performance and robustness of predictive models.
Approach
Fourteen crab-eating macaques were assigned to two experimental groups and exposed to either severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or a mock inoculum. High-resolution CT scans were acquired before exposure and on several post-exposure days. Lung volumes were segmented using a deep-learning methodology, and radiomic features were extracted from the original image. The reliability of each feature was assessed by the intraclass correlation coefficient (ICC) using the mock-exposed group data. The sensitivity of each feature was assessed using the virus-exposed group data by defining a factor R that estimates the excess of variation above the maximum normal variation computed in the mock-exposed group. R and ICC were used to rank features and identify non-sensitive and unstable features.
Results
Out of 111 radiomic features, 43% had excellent reliability (ICC>0.90), and 55% had either good (ICC>0.75) or moderate (ICC>0.50) reliability. Nineteen features were not sensitive to the radiological manifestations of SARS-CoV-2 exposure. The sensitivity of features showed patterns that suggested a correlation with the radiological manifestations.
Conclusions
Features were quantified and ranked based on their sensitivity and reliability. Features to be excluded to create more robust models were identified. Applicability to similar viral pneumonia studies is also possible.
Keywords:
computed tomography
COVID-19
animal models
radiomics
reliability
sensitivity
Laulima Government Solutions, LLC, prime contract with U.S. NIAIDContract No. HHSN272201800013C Kelly Services contract with U.S. NIAIDContract No. 75N93019D00027 Tunnell Government Services (subcontractor of Laulima Government Solutions, LLC), contract with U.S. NIAIDContract No. HHSN272201800013C National Cancer Institute, National Institutes of HealthContract No. 75N910D00024 running-headCastro et al.: Toward the determination of sensitive and reliable whole-lung computed tomography features…
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pmc1 Introduction
As of May 8, 2022, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused 514 million confirmed coronavirus disease 2019 (COVID-19) cases and over 6 million COVID-19 confirmed deaths worldwide.1,2 The efficacy of recently approved medical countermeasures for COVID-19 may be circumvented by emergent SARS-CoV-2 variants that are more transmissible and immune-evasive.3 Data from patients during the first few months of the COVID-19 pandemic (in early 2020) showed that chest CT is sensitive to the detection of the radiographic lung abnormalities associated with COVID-19.4 Independent of SARS-CoV-2 variants, pathogenesis is similar, and computed tomography (CT) continues to be an option for determining prognosis. However, the use of medical imaging as a means of evaluating medical countermeasure efficacy in randomized clinical trials is critically hindered by the lack of standardized quantitative image analysis methods and reliable animal models.5 In animal models of severe COVID-19, quantitative image analysis methods enable accurate, quantifiable, unbiased, and reproducible measurements of COVID-19 pulmonary disease from medical images.6 In particular, noninvasive quantitative imaging biomarkers that do not require serial euthanasia are essential to the characterization of disease severity, progression, and pathogenesis in animal models.7–12 Quantitation of COVID-19-like lung abnormalities using multimodality imaging biomarkers, including volumetric assessment of radiodensity (CT), has been described in crab-eating (cynomolgus) macaques (Macaca fascicularis) exposed to SARS-CoV-2.6
In the past, toward fast and accurate clinical evaluation and prognostication, radiomics analysis of chest CT images was proposed to explore imaging correlates with non-imaging markers of the development, progression, severity, and outcomes of COVID-19. Textural features to assess the classification of lung abnormalities were analyzed using artificial neural networks13,14 and machine learning techniques.15 Radiomics, which is a method that extracts and analyzes a large number of features from medical images using data characterization algorithms, includes textural features that were initially used to characterize topography from satellite images in the early 1970s16–20 and was first introduced about a decade ago.21 The use of textural features enables the translation of medical images into quantitative data to phenotypically profile lung abnormalities.22 During the last decade, the vast majority of lung studies using radiomics analyzed features extracted from segmented lesions and focused mainly on tumor characterization, phenotype differentiation,23,24 and prognostication of recurrence and survival.25,26 Radiomic analyses began to be used for COVID-19 in 2020, when chest CT was identified as a sensitive SARS-CoV-2 infection diagnostic tool.4
Cai et al.27 proposed a model based on CT radiomic features that could predict a negative reverse transcription quantitative polymerase chain reaction (RT-qPCR) test for SARS-CoV-2 and could be used to recommend early patient discharge from hospitals. Other authors focused on the prediction of patient outcomes,28–30 prediction of residual lung lesions after discharge,31 diagnosis,32,33 discrimination of stable and progressive disease,34 and differentiating COVID-19 from other causes of pneumonia.34–37 Areas of interest to compute radiomic features ranged from the manual delineation of bounding boxes around lesions38 to semiautomatic segmentation of lesions39 and whole-lung volumes.34 Some clinical studies used data from just a single hospital,4 whereas others included data from multiple locations with different acquisition protocols40 and faced challenges in comparing differentially acquired image datasets. Although each study used different software for radiomics feature extraction, in general, they adhered to the Image Biomarker Standardisation Initiative (IBSI).41 Given the critical nature of the pandemic, baseline (preinfection) reference scans have not typically been available in these studies. Furthermore, radiomic feature reliability has not always been addressed.42–45 COVID-19 animal model research has been used to investigate both the natural history of the disease and the efficacy of medical countermeasures in preclinical studies; radiomic features may be used not only to predict outcomes and differentiate different pathologies but also as subject-specific imaging biomarkers of the disease when preexposure images are acquired and control groups are considered.
In the field of radiomics, several analytic terms (e.g., repeatability, reproducibility, reliability, robustness, stability, and sensitivity) are used across studies, but their meanings may depend on the scope of the research;46 thus terminology should be clarified. Here, repeatability refers to features that remain the same when the subject is imaged multiple times. Reproducibility refers to features that remain the same when images are acquired using different equipment, software, acquisition settings, and operators (e.g., in studies that include multiple hospitals).47 Reliability is the extent to which measurements can be replicated under either similar or different conditions. Reliability, which is often regarded as a measure of robustness, reflects the correlation and agreement between measurements and represents the ratio of true variance over the true variance plus error variance;48 it is useful for the analysis of intrasubject and intersubject variations.48,49 In delta (Δ) radiomics, longitudinal data can be used to assess intra-individual reproducibility and relative differences in pre- and post-treatment radiomic features to predict outcomes and treatment response. Δ radiomics has been referred to as “patient-specific” radiomics50 and was first proposed a few years ago to improve reproducibility and predictive power.23 Δ radiomics has since been studied in clinical and experimental settings to assess recovery or response to treatment in cancer research.25,51 However, to the best of our knowledge, Δ radiomics has not been applied to imaging studies related to COVID-19. In principle, Δ radiomics also has the potential to be used to characterize the evolution of an infectious disease when “normal” preinfection baseline information is available. Under this context, we will use stability to describe features that do not exceed the intrasubject normal range and refer to sensitivity as the range of a feature during the course of the disease relative to the intrasubject normal range.
The reproducibility of radiomics features is affected by different scanners and acquisition parameters,52,53 and reproducible features can be grouped into a limited number of clusters due to redundancy of information. There may be a high demand for research in the areas of image acquisition, image postprocessing, volume-of-interest segmentation, image discretization, and feature calculation to select features with sufficient dynamic range among patients, intrapatient reproducibility, and low sensitivity to image acquisition and reconstruction protocols.54,55 Intraobserver delineation variability, respiratory motion, and reconstruction kernels were also found to strongly affect feature reproducibility.56–58 In our previous work, we found that B-kernel (smooth) reconstructions were more reliable than D-kernel (sharp) ones;59 therefore B-kernels are used in this work. To the best of our knowledge, the reliability of features based on the intra-subject and inter-subject variability in animal models, the determination of ranges of normal variation, and the sensitivity to radiological manifestations have not been investigated in the past. This information may be used to increase the robustness of future analysis via standard radiomics and Δ radiomics.
In this work, crab-eating macaques were exposed to either SARS-CoV-2 or a mock inoculum. CT images were acquired prior to exposure and at multiple time points after exposure, whole-lung fields were segmented, and radiomic features were extracted. The animals included in this work were scanned with two identical scanners with the same acquisition protocols, and different reconstructions were not used interchangeably for radiomics analysis. The reliability of radiomic features was characterized by the intrasubject and intersubject variability, and stability and sensitivity to the disease were assessed by analysis of the change of features during the course of the disease with respect to the baseline scan. This information can contribute to building robust standard and Δ-radiomics signatures that correlate with nonimaging features to help identify disease stage and severity, evaluate the efficacy of candidate medical countermeasures, and predict clinical outcomes.
2 Methodology
2.1 Animals and Virus
Initially, the study was to use a total of 25 crab-eating macaques (Macaca fascicularis). However, 11 were excluded due to abnormalities at the baseline scan or not meeting the CT-score criterion for inclusion—a score of no more than two at every time point. (This criterion was set to better characterize the normal variation of radiomic features computed from the segmented lungs.) Thus, a total of 14 (four males and 10 females; age range: 4 to 7 years old, weight range: 2.56 to 6.83 kg) macaques were assigned to two experimental groups (Mock: NmTOT = 6 and Virus: NvTOT = 8). The macaques were anesthetized in accordance with standard procedures prior to all manipulations, including intrabronchial exposure, sample collection, and medical imaging. Animals in the Mock group were administered 2 mL of cell culture medium supplemented with 2% heat-inactivated fetal bovine serum into each bronchus followed by 1 mL of normal saline flush and 5 mL of air. Animals in the Virus group were exposed to 2 mL containing 9.13×105 PFU/mL of SARS-CoV-2 (isolate 2019-nCoV/USA/A12/2020, obtained from the US Centers for Disease Control and Prevention [CDC], Atlanta, GA) for a total dose of 3.6×106 PFU.6 RT-qPCR analysis was performed to determine the presence of SARS-CoV-2 RNAs in the collected specimens.6 All experiments were performed in a maximum (biosafety level 4 [BSL-4]) containment laboratory at the IRF-Frederick, a facility accredited by the Association for Assessment and Accreditation of Laboratory Animal Care International (AAALAC). Experimental procedures were approved by the National Institute of Allergy and Infectious Diseases (NIAID) Division of Clinical Research (DCR) Animal Care and Use Committee (ACUC) and conducted in compliance with the Animal Welfare Act regulations, Public Health Service policy, and the Guide for the Care and Use of Laboratory Animals (Eighth Edition).
2.2 Imaging of Crab-Eating Macaques
The animals considered in this work had been assigned to one of three studies with identical exposure and imaging protocols; however, in the first study, the animals were scanned for a longer period of time after exposure. Animals in both groups were scanned before exposure and either eight or four times after exposure (at 2, 4, 6, 8, 10, 12, 19, and 30 days for those in the first study or at 2, 4, 6, and 8 days for those in the second and third studies) (Table 1). High-resolution chest CT scans were performed using the 16-slice CT component of either a Gemini TF 16 scanner (Philips Healthcare, Cleveland, OH) or a Precedence scanner (Philips Healthcare). Images were acquired in helical scan mode with the following parameter settings: ultra-high resolution, 140 kVp, 300 mAs per slice, 1-mm thickness, 0.5-mm increment, 0.688-mm pitch, collimation 16×, and 0.75-s rotation. CT image reconstruction used a 512×512 matrix size for a 250-mm transverse field-of-view (FOV), leading to a pixel size of 0.488 mm. CT images were produced with the standard B reconstruction kernel for smoother images because, in previous work, we showed that radiomic features extracted from CT images reconstructed with a bone-enhanced D reconstruction kernel for sharper images were less reproducible.59 No contrast agent was administered. Each macaque underwent a 15 to 20 s breath-hold during acquisition. The pressure for the breath-hold was maintained at 150 mm H2O. For imaging procedures, each macaque was anesthetized intramuscularly with 15 mg/kg ketamine following 0.06 mg/kg glycopyrrolate intramuscularly. Anesthesia was maintained using a constant rate intravenous infusion of propofol at 0.3 mg/kg/min. Macaques were placed on the scanner bed in a supine head-out/feet-in position and connected to a ventilator to facilitate breath holds. Vital signs were monitored throughout the procedure.6 All images were visually inspected for possible signal loss and/or artifacts. Inclusion criteria were different for each group. Animals in the Mock group were required to have qualitatively normal scans on all scan days to accurately estimate the maximum normal variation of the radiomic features. Animals in the Virus group were required to have a normal baseline scan to avoid inaccurate estimation of changes in radiomic features during the course of the disease due to abnormalities present at baseline.
Table 1 Number of P.E. scans for all animals included in this study.
Animal ID M#1 M#2 M#3 M#4 M#5 M#6 V#1 V#2 V#3 V#4 V#5 V#6 V#7 V#8
#P.E. scans 8 8 4 4 4 4 8 8 4 8 4 4 4 4
P.E.: postexposure with either mock inoculum (Mock) or SARS-CoV-2 (Virus)
2.3 Whole-Lung Segmentation
For training purposes, a total of 64 whole-lung CT scans (reconstructed using a B kernel of crab-eating macaques with the same imaging protocols) were used. The automated organ segmentation method, based on the convolutional neural network (CNN), used in this work has been described before.60 The feature pyramids network (FPN), which produces a multiscale feature representation in which all levels, even the high-resolution levels, are semantically strong, was used in this work. The network was trained using input patches of size 64×64×64 voxels, which were randomly extracted from both lung and nonlung areas with equal numbers. The output of the CNN was a probability map, which was resampled to the original image size and smoothed using a Gaussian filter. The quality of the segmentations was evaluated. Whole-lung masked CT images were generated.
2.4 Radiomic Feature Extraction
In this study, 90 whole-lung masked CT images from 14 crab-eating macaques were generated using the methodology described in the previous section. Radiomics feature extraction from the whole-lung masked CT images was performed using PyRadiomics 2.2.0.61 For each image, 111 features were extracted: 17 3D shape features and 94 intensity features split into 19 first-order features and 75 second-order features. The latter were derived from five different matrices: (1) 24 features from the gray-level co-occurrence matrix (GLCM); (2) 14 features from the gray-level dependence matrix (GLDM); (3) 16 features from the gray-level run length matrix (GLRLM); (4) 16 features from the gray-level size zone matrix (GLSZM); (5) five features from the neighboring gray tone difference matrix (NGTDM). Images were discretized using a 25-HU bin width, resulting in ≈30 to 40 bins. A shift of 1024 HU was set for the first-order features to avoid negative attenuations. For each voxel, two neighbors were considered for each of the 13 directions corresponding to the first neighbors in the second-order features.
2.5 Data Analysis
In this work, we studied the reliability regarding intrasubject and inter-subject reproducibility in a normal population scanned under the same conditions, as well as the stability and sensitivity of features during the disease course.
First, all scans from the Mock group were used to compute the intra-class correlation coefficient (ICC) to assess the reliability of radiomic features when both intrasubject and inter-subject variations were present under the same scanning conditions. ICC estimates and their 95% confidence intervals were calculated using the R package IRR version 0.84.1, based on a single measurement (k=1), absolute-agreement, two-way mixed-effects model. To manage the different number of scans among subjects, ICC was computed from two different subsets of five scans and averaged for each feature. Reliability of ICC values was considered as follows: 0.00 to 0.50 (poor), 0.50 to 0.75 (moderate), 0.75 to 0.90 (good), and 0.90 to 1.00 (excellent).49 This information has the potential to be used to identify features not reliable for standard radiomic analysis. The reliability of each radiomic feature was assessed.
Second, the maximum normal intrasubject variation of each feature, along with a comparison of the intrasubject dynamic range of the feature within the course of the disease, was investigated. To estimate the maximum normal intrasubject variation Δf (%) of each radiomic feature f, only scans from the Mock group were considered, and for each animal m, the maximum percent change Δfm with respect to that lowest measurement was computed. The maximum among all animals was used as an estimate of the maximum normal intrasubject variation: Δf=maxm{Δfm}. Afterward, for each animal v in the Virus group and each feature f, the lower and upper thresholds of the normal range fL and fU, respectively, were computed from Δf and the feature value at the baseline scan. The percent change at each postexposure scan was computed with respect to the baseline scan, and the dynamic range Δfv was identified and compared with Δf. For each animal, each feature was classified as follows: C1 = not sensitive (the feature value was between fL and fU at all postexposure scans); C2 = not stable (the feature value was predominantly above/below fL/fU but also beyond the opposite threshold at some time points); C3 = sensitive and increasing (the feature value was above fU for at least one day and remained within normal values for the rest of the scans); and C4 = sensitive and decreasing (the feature value was below fL for the at least one day and remained within normal values for the rest of the days). Only features in categories C3 and C4 were considered; those in category C1 were not sensitive for radiomic analysis, and those in C2 should be evaluated separately.
For each animal v in the Virus group and each radiomic feature in C3 and C4, the maximum variation Δfv was identified, and the ratio Rfv=(Δfv−Δf)100/Δfv was computed. Note that |Rfv| ranges between 0 and 100, both inclusive, where |Rfv|≈0 means that Δf and Δfv are comparable; e.g., Rfv=50 means that Δfv is two times Δf. The average among all animals in the Virus group was computed, and a ranking was generated. This information has the potential to be useful in identifying features that are unstable and nonsensitive to the disease in Δ-radiomics analysis.
3 Results
3.1 Whole-lung CT Images
After exposure, SARS-CoV-2 infection was confirmed in all virus-exposed but not in mock-exposed animals (data not shown).6 Initially, the Mock and Virus groups included a total of 13 and 12 animals, respectively. In the Mock group, seven animals were excluded because they did not pass the criterion to have a total CT score6 of no more than two at every time point. This criterion was set to avoid overestimation of the maximum normal variation of radiomic features computed from the segmented lungs. In the Virus group, four animals were excluded because they had abnormalities at the baseline scan, although they did not reach the threshold to have a CT score above 0. This criterion was set to avoid underestimation of the changes in the radiomic features with respect to baseline in the virus group. Therefore, a total of 14 animals were included in the study (six in the Mock group and eight in the Virus group).
Selected axial slices exhibiting representative lesions from scans at the peak of the radiological manifestation (typically, at Day 2 and/or Day 4) of all animals in the Virus group are displayed in Fig. 1. Selected axial slices from arbitrary scans of all animals in the Mock group are shown in Fig. 2. Binary masks were created using a deep learning algorithm from CT images. All animals were scanned before exposure and either four or eight times after exposure (Table 1). Scans were reconstructed using a B kernel. All masks were visually compared with their corresponding CT images to assess their accuracies.
Fig. 1 Selected axial slices exhibiting representative lesions from scans at the peak of the radiological manifestation (typically 2 or 4 days after exposure) of all animals in the Virus group. The experiment was designed to mimic mild disease in humans. For each animal in the Virus group, four slices were chosen from locations where the most visually noticeable lesions appeared at the peak of the disease. The selected pictures show a range of abnormalities from mild to severe at a glance.
Fig. 2 Selected axial slices from the scans of all animals M#1 to M#6 in the Mock group.
3.2 Radiomic Features Reliability
ICC estimates and their 95% confidence intervals were calculated from B-kernel reconstructions based on a single measurement (k=1), absolute-agreement, two-way mixed-effects model. In previous work, we showed that the estimated ICC averaged over all features was greater for the B-kernel (0.819) than the D-kernel (0.722) and 93 features had a higher ICC when the B-kernel was used for reconstruction;59 therefore, all results in this paper are based on B-kernel reconstructions. The number of features with ICC values with poor (0.00 to 0.50), moderate (0.50 to 0.75), good (0.75 to 0.90), and excellent (0.90 to 1.00) reliability is shown in Fig. 3(a). Poor reliability (ICC<0.50) was observed in only two features (GLCM-Imc1 and NGTDM-Strength), and 48 features exhibited excellent reliability (ICC>0.90) (Table 2). The reliability of features within each type is shown in Fig. 3(b). Figure 5 shows the ICC of all features along with the ratio R that compares their maximum variation due to the disease with the maximum normal variation (Secs. 3.2 and 3.3).
Fig. 3 (a) Number of features in the four ICC ranges for B-kernel reconstructions. Reliability of ICC values: 0.00 to 0.50 (poor), 0.50 to 0.75 (moderate), 0.75 to 0.90 (good), and 0.90 to 1.00 (excellent). (b) Reliability of each type of feature.
Table 2 All 48 features extracted from B-kernel reconstruction with excellent reliability (ICC>0.90).
TYPE FEATURE ICC
First-order Minimum 1.000
GLRLM RunLengthNonUniformity 0.998
GLDM DependenceNonUniformity 0.997
Shape SurfaceArea 0.997
Shape LeastAxisLength 0.997
GLSZM SizeZoneNonUniformity 0.995
Shape MeshVolume 0.995
Shape VoxelVolume 0.995
Shape Maximum2DDiameterColumn 0.994
Shape Maximum3DDiameter 0.993
Shape MajorAxisLength 0.992
GLSZM GrayLevelNonUniformity 0.991
GLSZM SmallAreaHighGrayLevelEmphasis 0.991
GLRLM GrayLevelNonUniformity 0.990
Shape Maximum2DDiameterRow 0.990
Shape MinorAxisLength 0.989
GLSZM HighGrayLevelZoneEmphasis 0.989
GLDM LargeDependenceHighGrayLevelEmphasis 0.988
First-order 10Percentile 0.988
First-order Energy 0.983
First-order TotalEnergy 0.983
NGTDM Busyness 0.982
GLRLM GrayLevelVariance 0.982
First-order 90Percentile 0.979
Shape Maximum2DDiameterSlice 0.978
First-order Mean 0.978
GLCM ClusterProminence 0.978
GLDM GrayLevelNonUniformity 0.978
First-order RootMeanSquared 0.976
First-order Median 0.973
First-order MeanAbsoluteDeviation 0.970
First-order Variance 0.968
First-order StandardDeviation 0.968
NGTDM Complexity 0.966
GLCM ClusterShade 0.959
GLRLM ShortRunHighGrayLevelEmphasis 0.950
GLDM SmallDependenceHighGrayLevelEmphasis 0.949
GLRLM HighGrayLevelRunEmphasis 0.946
GLDM GrayLevelVariance 0.942
GLDM HighGrayLevelEmphasis 0.939
GLCM ClusterTendency 0.938
First-order Range 0.935
GLSZM ZoneEntropy 0.935
GLSZM GrayLevelVariance 0.930
First-order InterquartileRange 0.921
GLRLM LongRunHighGrayLevelEmphasis 0.912
First-order RobustMeanAbsoluteDeviation 0.907
GLCM SumSquares 0.905
GLCM, gray-level co-occurrence matrix; GLDM, gray-level dependence matrix; GLRLM, gray-level run length matrix; GLZSM, gray-level size zone matrix; NGTDM, neighboring gray tone difference matrix.
3.3 Maximum Intrasubject Normal Variation Compared with Variations due to the Disease
To characterize the maximum intrasubject normal variation (Δf) of each radiomic feature (f), only animals in the Mock group were considered: two animals were scanned nine times and the other four animals were scanned five times (Table 1). For each animal, the minimum value of each radiomic feature and the corresponding time point were identified; subsequently, all other scans were individually compared with the minimum values to arrive at the percent change for each feature. The maximum of those values s is called the maximum intrasubject normal variation Δf. The number of features with different ranges of Δf is shown in Fig. 4(a). In Fig. 4(b), the number of features is discriminated among feature types. Overall, Δfaverage=66% and Δfmedian=29%. All 49 features with Δf<25% are shown in Table 3. Note that the maximum normal variation of a given feature as an indicator of stability must be combined with the variation of the feature during the course of the disease with respect to its baseline value to determine the usefulness of that feature. Figure 5 shows the ICC of all features along with the ratio R that compares their maximum variation due to the disease with the maximum normal variation.
Fig. 4 (a) The number of features in the four ICC ranges for B-kernel reconstructions. Reliability of ICC values: 0.00 to 0.50 (poor), 0.50 to 0.75 (moderate), 0.75 to 0.90 (good), and 0.90 to 1.00 (excellent). (b) Reliability of each type of feature.
Fig. 5 ICC values for each feature (white and black) along with ⟨Rf⟩ as a measure of sensitivity (blue). Reliability of ICC values were considered as 0.00 to 0.50 (poor), 0.50 to 0.75 (moderate), 0.75 to 0.90 (good), and 0.90 to 1.00 (excellent). ⟨Rf⟩ with an absolute value near zero indicates that the feature f is not sensitive to the disease when compared with its baseline value. GLCM, gray-level co-occurrence matrix; GLDM, gray-level dependence matrix; GLRLM, gray-level run length matrix; GLSZM, gray-level size zone matrix; NGTDM, neighboring gray tone difference matrix.
Table 3 All 49 features extracted from B-kernel reconstruction with maximum normal variation Δf<25%.
TYPE FEATURE Δf (%)
GLCM IDMN 0.48
GLCM IDN 1.87
GLSZM ZoneEntropy 2.09
Shape LeastAxisLength 2.80
GLSZM SmallAreaEmphasis 3.17
First-order Median 4.10
Shape Maximum3DDiameter 4.48
First-order Mean 4.50
First-order 10Percentile 5.18
Shape Maximum2DDiameterColumn 5.21
Shape Maximum2DDiameterRow 5.30
GLCM InverseVariance 5.45
Shape MinorAxisLength 5.69
Shape MajorAxisLength 5.94
First-order 90Percentile 6.35
GLSZM SizeZoneNonUniformityNormalized 6.42
GLCM SumEntropy 6.61
Shape SphericalDisproportion 6.75
Shape Sphericity 6.75
Shape Elongation 7.34
Shape Flatness 7.45
GLDM DependenceEntropy 8.14
Shape SurfaceArea 8.33
First-order StandardDeviation 8.58
Shape SurfaceVolumeRatio 9.22
First-order MeanAbsoluteDeviation 9.61
Shape Maximum2DDiameterSlice 10.08
First-order Entropy 10.23
Shape Compactness1 10.30
GLRLM RunEntropy 10.86
GLRLM GrayLevelVariance 10.98
GLRLM ShortRunEmphasis 12.96
Shape MeshVolume 14.38
Shape VoxelVolume 14.39
GLSZM GrayLevelNonUniformityNormalized 14.84
First-order Skewness 15.27
GLCM ClusterTendency 15.94
GLCM JointEntropy 17.38
GLDM GrayLevelVariance 17.79
First-order Variance 17.89
GLDM SmallDependenceHighGrayLevelEmphasis 18.13
First-order RootMeanSquared 19.10
GLCM SumSquares 20.65
GLCM DifferenceEntropy 20.90
Shape Compactness2 21.66
First-order RobustMeanAbsoluteDeviation 21.66
First-order Minimum 21.97
First-order Kurtosis 22.52
GLRLM GrayLevelNonUniformityNormalized 23.67
Δf, maximum normal variation of feature f; GLCM, gray-level co-occurrence matrix; GLDM, gray-level dependence matrix; GLRLM, gray-level run length matrix; GLZSM, gray-level size zone matrix; NGTDM, neighboring gray tone difference matrix.
3.4 Radiomic Features Insensitive to Radiological Manifestations
For a feature f that is not sensitive to the radiological manifestation in animals in the Virus group, Rf=0. The 19 nonsensitive features are listed in Table 4. An example of a nonsensitive feature is shown in Fig. 6(a).
Table 4 Features not sensitive to the radiological manifestations in the Virus group.
Type Feature (f)
Shape Maximum 2D diameter slice
GLCM Maximum probability
GLCM Difference entropy
GLDM Small dependence emphasis
GLDM Dependence nonuniformity normalized
GLDM Large dependence emphasis
GLDM Large dependence low gray level emphasis
GLDM Dependence variance
GLDM Small dependence low gray level emphasis
First-order Minimum
GLRLM Run variance
GLRLM Long run emphasis
GLRLM Short run emphasis
GLRLM Run percentage
GLRLM Long run low gray level emphasis
GLRLM Run length non uniformity normalized
GLSZM Zone percentage
GLSZM Low gray level zone emphasis
GLSZM Small area low gray level emphasis
GLCM, gray-level cooccurrence matrix; GLDM, gray-level dependence matrix; GLRLM, gray-level run length matrix; GLSZM, gray-level size zone matrix; NGTDM, neighboring gray tone difference matrix.
Fig. 6 Time evolution along the course of the disease in V#1 of radiomic features: (a) GLRLM long run low gray level emphasis (not sensitive and with Δf=61.5%) and (b) GLCM cluster prominence (sensitive but not stable and with Δf=15.4%). Dotted lines represent the lower and upper thresholds of the normal variation range. GLCM, gray-level co-occurrence matrix; GLRLM, gray-level run length matrix.
3.5 Radiomic Features Sensitive to Radiological Manifestations but Unstable
It was observed that a limited number of features sensitive to radiological manifestation can at the same time vary beyond the opposite threshold of the normal interval. The occurrence was only in 17 out of 1776 computations from three animals in the Virus group: V#1 (6/17), V#4 (2/17), and V#5 (9/17). An example is shown in Fig. 6(b), and the results are shown in Table 5. These features should be investigated separately.
Table 5 Features that are sensitive and unstable.
Type Feature (f) Nf ⟨Rf⟩ (%)
GLCM Cluster shade 3 67.9
GLCM Cluster prominence 2 71.9
GLDM Gray level variance 0 52.4
GLDM Small dependence high gray level emphasis 1 30.0
First-order Standard deviation 0 50.6
First-order Range 1 20.5
First-order Variance 0 52.4
First-order Maximum 1 22.4
GLRLM Gray level variance 1 70.0
GLSZM Gray level nonuniformity normalized 0 −13.0
NGTDM Strength 1 30.5
Nf: number of animals in the Virus group with an unstable feature f. ⟨Rf⟩: average among all animals v in the Virus group of Rfv=(Δfv−Δf)/Δfv without considering those Nf animals. GLCM, gray-level co-occurrence matrix; GLDM, gray-level dependence matrix; GLRLM, gray-level run length matrix; GLSZM, gray-level size zone matrix; NGTDM, neighboring gray tone difference matrix.
3.6 Radiomic Features Sensitive to Radiological Manifestations
A total of 74 features had a variation beyond Δf with respect to the baseline scan for at least one animal in the Virus group. First, a ranking of ⟨Rf⟩ was generated to determine which features are more sensitive to the radiological manifestations in animals in the Virus group. An arbitrary threshold was set to differentiate those features f for which Rf varied <5% above or below Δf(Δf±5%).
Figure 7(a) shows the results for features extracted along with the average for each feature over all animals in the group. As an example, a comparison of two animals’ CT scans—one dominated by large consolidations and other lesions [Fig. 7(b)] and the other having smaller lesions with less attenuation [Fig. 7(c)]—is shown in Fig. 7(d). Also, as an example, the evolution along the course of the disease is shown for two sensitive features, one increasing above the normal range [GLRLM short-run high gray-level emphasis, Fig. 8(a)] and the other sensitive and decreasing below the normal range [first-order skew, Fig. 8(b)].
Fig. 7 (a) Factor Rfv as defined in Sec. 2.5 of features sensitive to radiological manifestations computed from all scans of the eight animals in the Virus group for the B-kernel reconstruction along with the average over all animals in the group for each feature; (b) arbitrary axial slice of V#1 showing a large consolidation and other lesions; (c) arbitrary slice of V#7 dominated by smaller lesions with less attenuation; (d) comparison between Rfv in V#1 and V#7 only for features with Rf that vary <5% above or below Δf. GLCM, gray-level co-occurrence matrix; GLDM, gray-level dependence matrix; GLRLM, gray-level run length matrix; GLSZM, gray-level size zone matrix; NGTDM, neighboring gray tone difference matrix.
Fig. 8 Time evolution along the course of the disease in animal V#1 of radiomic features (a) GLRLM short run high gray level emphasis (sensitive and increasing above the normal range with Δf=47.1%) and (b) first-order skewness (sensitive and decreasing below the normal range with Δf=13.7%). Dotted lines represent the lower and upper thresholds of the normal variation range. GLRLM, gray-level run length matrix.
Figure 9 shows the lung involvement over time at a selected axial slice in the baseline scan and the eight postexposure scans for V#1. Although scans are visually different, the information obtained from a visual inspection helps to identify differences in some first-order features. However, changes in higher-order features are usually difficult to assess visually.
Fig. 9 Time evolution of the lung involvement at a selected axial slice along the course of the disease in animal V#1.
4 Discussion
Animal models of human disease are a critical part of biological research, including in the investigation of pathogenesis and the evaluation of candidate medical countermeasures, such as therapeutics and vaccines. Noninvasive quantitative imaging biomarkers that do not require serial euthanasia help with characterizing the progression of a disease severity and progression and understanding the underlying mechanisms. Measurable changes in imaging biomarkers throughout the course of the disease provide useful information when compared with baseline scans, which are typically not acquired in clinical settings. Furthermore, information from a control group is essential for determining the range of normal variation for imaging biomarkers. In recent years, radiomics has been explored as a tool, for instance, to investigate associations between both textural and nontextural features and survival rate, to predict outcomes, or for differential diagnosis. Although radiomics has been used to investigate COVID-19 in humans, to the best of our knowledge, application to animal models has not been reported yet. The characteristics and uniqueness of our data allow for the implementation of both standard radiomics and delta radiomics, particularly to quantify the progression of the disease, evaluate therapeutic options, and potentially predict outcomes.
Radiomics data are typically analyzed with statistical and machine learning methods that may depend on the disease context and image modality, among other factors. Machine learning techniques can capture complex interactions among features, feature combinations, and clinical biomarkers to build efficient prognostic and predictive models. However, the inclusion of radiomic features that are not reliable, not sensitive, and/or redundant may affect the robustness of those techniques. In particular, features with low intrasubject and intersubject repeatability may affect the statistical power, ability to interpret, and extrapolation to a more general application. Within the scope of Δ–radiomics, the identification and inclusion of specific features that are sensitive to radiological manifestations during the course of the disease may help to establish a connection between the number of features and their change above the normal variation during a given stage of disease. However, there is no “one-fits-all” solution; deciding which features to include and exclude depends on several factors—e.g., the disease, the type of lesion or abnormality under scrutiny, the imaging modality, and the area of interest (i.e., organ or lesion).
The animal-model experiments performed at the IRF-Frederick used two identical CT scanners with the same imaging protocol; therefore, no reproducibility study was required. Instead, reliability focused on intrasubject repeatability and intersubject normal variation when images from all scans of mock-exposed control animals without underlying abnormality were considered. The dynamic range of features extracted from CT images of virus-exposed animals during the course of the disease was analyzed along with the normal variation. If the dynamic range of a given feature did not exceed the maximum intrasubject normal variation for all animals in the virus-exposed group, regardless of the radiological manifestation, that feature was considered not sensitive to the radiological manifestations, and therefore, that feature was not expected to provide any meaningful information. Sensitive feature values remained within the normal range unless near the peak of the disease.
The animals in the Virus group had a variety of radiological manifestations, and a given feature may be sensitive for some animals but not for others. To characterize the sensitivity compared with the stability, a ratio R that takes into account the percent of the dynamic range that is above the normal variation was proposed to rank those features. A limited number of features varied beyond the normal range near the peak of the disease and later varied beyond the opposite threshold of the normal range when recovering. Those features were marked as sensitive to the disease but unstable. The meaningfulness of those features should be investigated in more detail and the opposite threshold should eventually be relaxed to avoid misclassification.
We focused on the B-kernel reconstructions because of their higher reliability. From the standard radiomics perspective, features with poor and moderate reliability (ICC<0.75) should be excluded from further analysis. Otherwise, larger variations of features in both the mock-exposed control group and the virus-exposed group may occur. From the Δ-radiomics perspective, the aim would be to include only the features expected to vary beyond the normal range. A threshold to decide which features should be included has not been investigated in detail; however, we identified features with Rf that did not exceed ±5%. It is worth mentioning that 69% of the features with Δf>50% also had Rf below ±5%, and only 15% of the features with ICC>90% had Δf>50%.
The results presented in this work have the potential to be useful either to exclude irrelevant features for more accurate standard radiomics analysis62 or to perform delta-radiomics analysis using changes of features with respect to their baseline values within a normal range. For example, five out of nine features based on low gray-level emphasis were not sensitive to radiological manifestations, whereas the other four had a ratio Rf<5%. On the other hand, eight out of nine features based on high gray level emphasis were sensitive to radiological manifestations (Rf>5%), whereas the remaining feature was sensitive but unstable because Rf fell below the lower threshold of the normal interval at some time points. Nevertheless, the study had some limitations. Both the average R and a set of features with a certain range of ratios R might eventually be characteristic of specific radiological manifestations. However, the total number of animals was not large enough to include a sufficient number of animals with the most common abnormalities; therefore, a characterization of abnormalities based on R was not pursued. As a preliminary result, it was found that R significantly varied between an animal with large areas of highly attenuated abnormalities and another with smaller areas with lower attenuation. Also, the use of the intrasubject dynamic range computed as the maximum percent change with respect to the baseline scan was useful to exclude not sensitive features when compared with the maximum intrasubject normal variation. However, the analysis of the time evolution of the overall abnormalities at every time point was lacking. Potentially, the stage of the disease might be assessed at each time point based on the set of sensitive features and their corresponding ratios.
In further analyses, more animals will be included to allow for a better association of changes in radiomic features and a radiological manifestation. Features from preprocessed images, such as, Laplacian of Gaussian and wavelets, will also be explored. The associations of delta-radiomic features with nonimaging biomarkers will be studied as well to pursue one of the main goals of radiomic analysis while concurrently addressing a significant need for animal models of COVID-19.
Acknowledgments
The authors declare no conflicts of interest. The authors want to thank Oscar Rojas and the Comparative Medicine team (NIAID IRF-Frederick) for handling the animals during the studies, Claudia Mani (NIAID IRF-Frederick) for reviewing the manuscript, Jiro Wada (NIAID IRF-Frederick) for figure preparation and layout, and Anya Crane (NAIAD IRF-Frederick) for critically editing the manuscript.
Marcelo A. Castro is a physicist and computational scientist serving as an imaging physicist (contractor) at the NIH NIAID DCR Integrated Research Facility at Fort Detrick, a BSL-4 facility in Frederick, Maryland. His specialization includes multi-modality quantitative image analysis, parametric mapping, radiomics, computational simulations, scientific programming, and data analysis and visualization for multiple models, organs, and diseases. Over 20 years he has published +55 scientific papers with +2,000 citations.
Syed Reza is a postdoctoral fellow at the NIH. His research focuses on machine-learning-driven computational modeling for medical image analysis, such as segmentation, classification, disease tracking, and growth prediction for affected organs in infectious disease analyses and brain lesions, tumors, and traumatic brain injury.
Winston T. Chu is a postdoctoral research fellow in the Department of Radiology and Imaging Sciences at the NIH Clinical Center and is associated with the NIAID Integrated Research Facility at Fort Detrick. His research focus is on the development of novel techniques driven by artificial intelligence to automatically segment and classify medical images of biosafety level 4 infectious diseases.
Dara Bradley is a master’s student in medical physiology and biophysics at Case Western Reserve University. Prior to attending Case Western Reserve, she completed a postbaccalaureate fellowship at the NIH as an Intramural Research Trainee Award Fellow (2019 to 2021). Her research projects applied artificial intelligence and machine learning methods to investigate questions within medical imaging of infectious disease research.
Ji Hyun Lee serves as a medical physicist at the Department of Radiology and Imaging Sciences at the NIH Clinical Center. She has supported multi-disciplinary investigators with the highest quality consultation, study design, image acquisition, and analysis services through hands-on, in-depth knowledge of multimodal imaging techniques/applications. Her research interests are in quantifying translational imaging through novel acquisition strategies, developing post-processing techniques in physiological imaging, and facilitating the transfer of molecular imaging-based techniques to the bedside.
Ian Crozier is an infectious diseases clinician-scientist at the Frederick National, Lab providing chief medical officer support to the NIH NIAID DCR Integrated Research Facility at Fort Detrick in Frederick, MD. His role bridges the human clinical bedside and animal models of emerging high-threat infectious diseases. He has extensive experience at the Ebola virus disease outbreak bedside, including in ongoing clinical research efforts in Western Africa and the Democratic Republic of the Congo.
Philip J. Sayre is a research imaging technologist (contractor) with Laulima Government Solutions in support of the NIH NIAID DCR Integrated Research Facility at Fort Detrick in Frederick, Maryland. His research is focused on PET/CT imaging of infectious disease in a BSL-4 setting. This work includes COVID-19, Ebola, Lassa, Middle East respiratory syndrome (MERS), Marburg, Nipah, monkeypox, and cowpox diseases.
Byeong Y. Lee is a biomedical imaging analyst (contractor) at the NIH NIAID DCR Integrated Research Facility at Fort Detrick in Frederick, Maryland. His research aims to develop surrogate imaging biomarkers using in vivo multimodal medical imaging techniques, such as MRI, PET, and CT, as well as advanced imaging analysis methods, to aid in the evaluation of viral infectious disease models and identification of pathophysiology underlying the diseases, evaluation of antiviral therapies, and diagnostics.
Venkatesh Mani serves as a senior imaging scientist (contractor) at the NIH NIAID DCR Integrated Research Facility at Fort Detrick in Frederick, Maryland. He specializes in the use of multimodality imaging such as MRI, CT, PET, and SPECT to evaluate the molecular biology and pathogenesis of, and medical countermeasure development against, WHO Risk Group 4 pathogens. He has published over 100 peer-reviewed papers and has an h-index of 50.
Thomas C. Friedrich is a professor at the University of Wisconsin–Madison Department of Pathobiological Sciences. He studies why and how immune responses sometimes fail to protect us from acute and chronic diseases.
David H. O’Connor is University of Wisconsin Medical Foundation Professor of Pathology and Laboratory Medicine at the University of Wisconsin–Madison and Professorial Fellow at the University of Melbourne. His research focuses on the interplay between viral pathogenesis, immunity, and host genetics. He has been involved in the movement to accelerate the dissemination of scientific information during the Zika virus and COVID-19 pandemics.
Courtney L. Finch is Director of Pre-Clinical, Research and Development at Sabin Vaccine Institute, where she oversees animal studies associated with the advancement of two filovirus vaccines. She has more than a decade of experience studying primarily vaccines and therapeutics, including extensive animal model experience across numerous animal species and viral pathogens. Her focus has been on high-consequence pathogens in high-containment laboratory environments. She has authored publications across multiple disciplines, including medical imaging.
Gabriella Worwa is a study director and associate supervisor (contractor) at the NIH NIAID DCR Integrated Research Facility at Fort Detrick in Frederick, Maryland. She specializes in the development and use of animal models for the study of WHO Risk Group 4 viruses.
Irwin M. Feuerstein is a board-certified diagnostic radiologist with extensive experience in cardiovascular imaging, computed tomography, and infectious disease imaging. He has worked with the National Institutes of Health (NIH), U.S. Department of Defense (DoD), and U.S. Food and Drug Administration (FDA) in a number of diagnostic, research, and regulatory capacities.
Jens H. Kuhn serves as a principal scientist and director of virology (contractor) at the NIH NIAID DCR Integrated Research Facility at Fort Detrick in Frederick, Maryland. He specializes in the molecular biology and pathogenesis of, and medical countermeasure development against, WHO Risk Group 4 pathogens, evolutionary virology and virus taxonomy, and bioweapons defense. He has published 277 journal articles, 79 book chapters, and three books.
Jeffrey Solomon is an imaging scientist (contractor) at the NIH NIAID DCR Integrated Research Facility at Fort Detrick in Frederick, Maryland. In this role, he leads an artificial intelligence team that implements novel techniques to create predictive models and automate segmentation of medical images based on machine-learning principles. Working directly with radiologist colleagues, he consults on best-of-class quantitative image analysis methods to employ in infectious disease imaging research.
Disclosures
This work was partially based on the paper “Determination of reliable whole-lung CT features for robust standard radiomics and delta-radiomics analysis in a crab-eating macaque model of COVID-19: stability and sensitivity analysis” (https://doi.org/10.1117/12.2607154) published in the SPIE Medical Imaging Proceedings Volume 12036, Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging; 1203621, on April 4, 2022. Disclaimer: This work was supported in part through the Laulima Government Solutions, LLC prime contract with the U.S. National Institute of Allergy and Infectious Diseases (NIAID) under Contract No. HHSN272201800013C (M.A.C., P.J.S., and B.Y.L.) and Kelly Services’ contract with NIAID under Contract No. 75N93019D00027 (V.M.). J.H.L., C.L.F., and J.H.K. performed this work as employees of Tunnell Government Services (TGS), a subcontractor of Laulima Government Solutions, LLC under Contract No. HHSN272201800013C. This work was also supported in part with federal funds from the National Cancer Institute (NCI), National Institutes of Health (NIH), under Contract No. 75N910D00024, Task Order No. 75N91019F00130. (I.C. and J.S. were supported by the Clinical Monitoring Research Program Directorate, Frederick National Lab for Cancer Research, sponsored by NCI.) This project was also partially funded by the Center for Infectious Disease Imaging (CIDI), Clinical Center, National Institute of Health (NIH) (S.R. and W.T.C.). The research was partially completed as part of the NIH Intramural Research Training Award (IRTA) Program through the NIAID (D.B.). The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Department of Health and Human Services or of the institutions and companies affiliated with the authors. The study protocol was reviewed and approved by the NIH/NIAID/DCR/Integrated Research Facility at Fort Detrick Animal Care and Use Committee in compliance with all applicable federal regulations governing the protection of animals and research.
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References
1. WHO, COVID-19 Weekly Epidemiological Update, Edition 91, pp. 1–10, World Health Organization (2022).
2. Hu B. et al. , “Characteristics of SARS-CoV-2 and COVID-19,” Nat. Rev. Microbiol. 19 , 141–154 (2021).1740-1526 10.1038/s41579-020-00459-7 33024307
3. Gupta R. K. , “Will SARS-CoV-2 variants of concern affect the promise of vaccines?” Nat. Rev. Immunol. 21 (6 ), 340–341 (2021).NRIABX 1474-1733 10.1038/s41577-021-00556-5 33927376
4. Ai T. et al. , “Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases,” Radiology 296 (2 ), E32–E40 (2020).RADLAX 0033-8419 10.1148/radiol.2020200642 32101510
5. Johansen M. D. et al. , “Animal and translational models of SARS-CoV-2 infection and COVID-19,” Mucosal Immunol. 13 (6 ), 877–891 (2020).10.1038/s41385-020-00340-z 32820248
6. Finch C. L. et al. , “Characteristic and quantifiable COVID-19-like abnormalities in CT- and PET/CT-imaged lungs of SARS-CoV-2-infected crab-eating macaques (Macaca fascicularis),” bioRxiv, 1–48 (2020).
7. Willmann J. K. et al. , “Molecular imaging in drug development,” Nat. Rev. Drug Discov. 7 (7 ), 591–607 (2008).NRDDAG 1474-1776 10.1038/nrd2290 18591980
8. Keith L. et al. , Preclinical Imaging in BSL-3 and BSL-4 Environments: Imaging Pathophysiology of Highly Pathogenic Infectious Diseases, AAPS Advances in the Pharmaceutical Sciences, Vol. 10 , Springer, New York (2014).
9. Castro M. A. et al. , “Quantification of axillary lymphadenopathy from CT images of filovirus infections in non-human primates: sensitivity and evaluation of radiomics-based methods,” Proc. SPIE 11317 , 1131725 (2020).PSISDG 0277-786X 10.1117/12.2548627
10. Castro M. A. et al. , “Evaluation of R2 *as a noninvasive endogenous imaging biomarker for Ebola virus liver disease progression in nonhuman primates,” Proc. SPIE 11317 , 1131726 (2020).PSISDG 0277-786X 10.1117/12.2550405
11. Lee J. H. et al. , “The use of large-particle aerosol exposure to Nipah Virus to mimic human neurological disease manifestations in the African green monkey,” J. Infect. Dis. 221 (Supplement_4 ), S419–S430 (2020).10.1093/infdis/jiz502 31687756
12. Schreiber-Stainthorp W. et al. , “Longitudinal in vivo imaging of acute neuropathology in a monkey model of Ebola virus infection,” Nat. Commun. 12 , 2855 (2021).NCAOBW 2041-1723 10.1038/s41467-021-23088-x 34001896
13. Nakamura K. et al. , “Computerized analysis of the likelihood of malignancy in solitary pulmonary nodules with use of artificial neural networks,” Radiology 214 (3 ), 823–830 (2000).RADLAX 0033-8419 10.1148/radiology.214.3.r00mr22823 10715052
14. Xu X. W. et al. , “Development of an improved CAD scheme for automated detection of lung nodules in digital chest images,” Med. Phys. 24 (9 ), 1395–1403 (1997).MPHYA6 0094-2405 10.1118/1.598028 9304567
15. Zhu Y. et al. , “Feature selection and performance evaluation of support vector machine (SVM)-based classifier for differentiating benign and malignant pulmonary nodules by computed tomography,” J. Digit. Imaging 23 (1 ), 51–65 (2010).JDIMEW 10.1007/s10278-009-9185-9 19242759
16. Amadasun M. King R. , “Textural features corresponding to textural properties,” IEEE Trans. Syst. Man Cybern. 19 (5 ), 1264–1274 (1989).10.1109/21.44046
17. Chu A. Sehgal C. M. Greenleaf J. F. , “Use of gray value distribution of run lengths for texture analysis,” Pattern Recognit. Lett. 11 (6 ), 415–419 (1990).PRLEDG 0167-8655 10.1016/0167-8655(90)90112-F
18. Galloway M. M. , “Texture analysis using gray level run lengths,” Comput. Graph. Image Process. 4 , 172–179 (1975).10.1016/S0146-664X(75)80008-6
19. Haralick R. M. Shanmugam K. Dinstein I. , “Textural features for image classification,” IEEE Trans. Syst. Man Cybern. SMC-3 (6 ), 610–621 (1973).10.1109/TSMC.1973.4309314
20. Sun C. Wee W. G. , “Neighboring gray level dependence matrix for texture classification,” Comput. Vis. Graph. Image Process. 23 , 341–352 (1983).CVGPDB 0734-189X 10.1016/0734-189X(83)90032-4
21. Lambin P. et al. , “Radiomics: extracting more information from medical images using advanced feature analysis,” Eur. J. Cancer 48 (4 ), 441–446 (2012).EJCAEL 0959-8049 10.1016/j.ejca.2011.11.036 22257792
22. Grossmann P. et al. , “Defining the biological basis of radiomic phenotypes in lung cancer,” Elife 6 , e23421 (2017).10.7554/eLife.23421 28731408
23. Aerts H. J. W. L. et al. , “Defining a radiomic response phenotype: a Pilot study using targeted therapy in NSCLC,” Sci. Rep. 6 , 33860 (2016).SRCEC3 2045-2322 10.1038/srep33860 27645803
24. Linning E. et al. , “Radiomics for classification of lung cancer histological subtypes based on nonenhanced computed tomography,” Acad. Radiol. 26 (9 ), 1245–1252 (2019).10.1016/j.acra.2018.10.013 30502076
25. Fave X. et al. , “Delta-radiomics features for the prediction of patient outcomes in non-small cell lung cancer,” Sci. Rep. 7 , 588 (2017).SRCEC3 2045-2322 10.1038/s41598-017-00665-z 28373718
26. Wang T. et al. , “Radiomics signature predicts the recurrence-free survival in stage I non-small cell lung cancer,” Ann. Thorac. Surg. 109 (6 ), 1741–1749 (2020).10.1016/j.athoracsur.2020.01.010 32087134
27. Cai Q. et al. , “A model based on CT radiomic features for predicting RT-PCR becoming negative in coronavirus disease 2019 (COVID-19) patients,” BMC Med. Imaging 20 (118 ), 1–10 (2020).10.1186/s12880-020-00521-z
28. Chao H. et al. , “Integrative analysis for COVID-19 patient outcome prediction,” Med. Image Anal. 67 , 101844 (2021).10.1016/j.media.2020.101844 33091743
29. Chen H. et al. , “A CT-based radiomics nomogram for predicting prognosis of coronavirus disease 2019 (COVID-19) radiomics nomogram predicting COVID-19,” Br. J. Radiol. 94 (1117 ), 20200634 (2021).BJRAAP 0007-1285 10.1259/bjr.20200634 33296222
30. Wu Q. et al. , “Radiomics analysis of computed tomography helps predict poor prognostic outcome in COVID-19,” Theranostics 10 (16 ), 7231–7244 (2020).10.7150/thno.46428 32641989
31. Huang J. et al. , “CT-based radiomics helps to predict residual lung lesions in COVID-19 patients at three months after discharge,” Diagnostics 11 (10 ), 1814 (2021).10.3390/diagnostics11101814 34679512
32. Fang X. et al. , “Radiomics nomogram for the prediction of 2019 novel coronavirus pneumonia caused by SARS-CoV-2,” Eur. Radiol. 30 (12 ), 6888–6901 (2020).10.1007/s00330-020-07032-z 32621237
33. Guiot J. et al. , “Development and validation of an automated radiomic CT signature for detecting COVID-19,” Diagnostics 11 (1 ), 41 (2021).10.3390/diagnostics11010041
34. Fu L. et al. , “A novel machine learning-derived radiomic signature of the whole lung differentiates stable from progressive COVID-19 infection: a retrospective cohort study,” J. Thorac. Imaging 35 (6 ), 361–368 (2020).JTIME8 0883-5993 10.1097/RTI.0000000000000544 32555006
35. Xie C. et al. , “Discrimination of pulmonary ground-glass opacity changes in COVID-19 and non-COVID-19 patients using CT radiomics analysis,” Eur. J. Radiol. Open 7 , 100271 (2020).10.1016/j.ejro.2020.100271 32959017
36. Liu H. et al. , “CT radiomics facilitates more accurate diagnosis of COVID-19 pneumonia: compared with CO-RADS,” J. Transl. Med. 19 (29 ), 1–12 (2021).10.1186/s12967-020-02692-3 33397399
37. Yang N. et al. , “Diagnostic classification of coronavirus disease 2019 (COVID-19) and other pneumonias using radiomics features in CT chest images,” Sci. Rep. 11 , 17885 (2021).SRCEC3 2045-2322 10.1038/s41598-021-97497-9 34504246
38. Qiu J. et al. , “A radiomics signature to quantitatively analyze COVID-19-infected pulmonary lesions,” Interdiscip. Sci. 13 (1 ), 61–72 (2021).ISCRD8 0308-0188 10.1007/s12539-020-00410-7 33411162
39. Sapienza L. G. et al. , “Risk of in-hospital death associated with Covid-19 lung consolidations on chest computed tomography - a novel translational approach using a radiation oncology contour software,” Eur. J. Radiol. Open 8 , 100322 (2021).10.1016/j.ejro.2021.100322 33432297
40. Wu G. et al. , “Development of a clinical decision support system for severity risk prediction and triage of COVID-19 patients at hospital admission: an international multicentre study,” Eur. Respir. J. 56 (2 ), 2001104 (2020).10.1183/13993003.01104-2020 32616597
41. ISBI, “Image biomarker standardisation initiative - reference manual,” (2019). https://ibsi.readthedocs.io/en/latest.
42. Ke Z. et al. , “Radiomics analysis enables fatal outcome prediction for hospitalized patients with coronavirus disease 2019 (COVID-19),” Acta Radiol. 63 (3 ), 319–327 (2022).10.1177/0284185121994695 33601893
43. Huang Y. et al. , “CT-based radiomics combined with signs: a valuable tool to help radiologist discriminate COVID-19 and influenza pneumonia,” BMC Med. Imaging 21 (1 ), 31 (2021).10.1186/s12880-021-00564-w 33596844
44. Zhang X. et al. , “A deep learning integrated radiomics model for identification of coronavirus disease 2019 using computed tomography,” Sci. Rep. 11 , 3938 (2021).SRCEC3 2045-2322 10.1038/s41598-021-83237-6 33594159
45. Wang L. et al. , “Multi-classifier-based identification of COVID-19 from chest computed tomography using generalizable and interpretable radiomics features,” Eur. J. Radiol. 136 , 109552 (2021).EJRADR 0720-048X 10.1016/j.ejrad.2021.109552 33497881
46. Cattell R. Chen S. Huang C. , “Robustness of radiomic features in magnetic resonance imaging: review and a phantom study,” Vis. Comput. Ind. Biomed. Art 2 (1 ), 19 (2019).10.1186/s42492-019-0025-6 32240418
47. Traverso A. et al. , “Repeatability and reproducibility of radiomic features: a systematic review,” Int. J. Radiat. Oncol. Biol. Phys. 102 (4 ), 1143–1158 (2018).IOBPD3 0360-3016 10.1016/j.ijrobp.2018.05.053 30170872
48. Xue C. et al. , “Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review,” Quant. Imaging Med. Surg. 11 (10 ), 4431–4460 (2021).10.21037/qims-21-86 34603997
49. Koo T. K. Li M. Y. , “A guideline of selecting and reporting intraclass correlation coefficients for reliability research,” J. Chiropr. Med. 15 (2 ), 155–163 (2016).10.1016/j.jcm.2016.02.012 27330520
50. Park J. E. et al. , “Reproducibility and generalizability in radiomics modeling: possible strategies in radiologic and statistical perspectives,” Korean J. Radiol. 20 (7 ), 1124–1137 (2019).10.3348/kjr.2018.0070 31270976
51. Alahmari S. S. et al. , “Delta radiomics improves pulmonary nodule malignancy prediction in lung cancer screening,” IEEE Access 6 , 77796–77806 (2018).10.1109/ACCESS.2018.2884126 30607311
52. Berenguer R. et al. , “Radiomics of CT features may be nonreproducible and redundant: influence of CT acquisition parameters,” Radiology 288 (2 ), 407–415 (2018).RADLAX 0033-8419 10.1148/radiol.2018172361 29688159
53. Midya A. et al. , “Influence of CT acquisition and reconstruction parameters on radiomic feature reproducibility,” J. Med. Imaging 5 (1 ), 011020 (2018).JMEIET 0920-5497 10.1117/1.JMI.5.1.011020
54. Vallières M. et al. , “Responsible radiomics research for faster clinical translation,” J. Nucl. Med. 59 (2 ), 189–193 (2018).JNMEAQ 0161-5505 10.2967/jnumed.117.200501 29175982
55. Rizzo S. et al. , “Radiomics: the facts and the challenges of image analysis,” Eur. Radiol. Exp. 2 (36 ), 1–8 (2018).10.1186/s41747-018-0068-z 29708215
56. Vuong D. et al. , “Comparison of robust to standardized CT radiomics models to predict overall survival for non-small cell lung cancer patients,” Med. Phys. 47 (9 ), 4045–4053 (2020).MPHYA6 0094-2405 10.1002/mp.14224 32395833
57. Mackin D. et al. , “Matching and homogenizing convolution kernels for quantitative studies in computed tomography,” Invest. Radiol. 54 (5 ), 288–295 (2019).INVRAV 0020-9996 10.1097/RLI.0000000000000540 30570504
58. Denzler S. et al. , “Impact of CT convolution kernel on robustness of radiomic features for different lung diseases and tissue types,” Br. J. Radiol. 94 (1120 ), 20200947 (2021).BJRAAP 0007-1285 10.1259/bjr.20200947 33544646
59. Castro M. A. et al. , “Determination of reliable whole-lung CT features for robust standard radiomics and delta-radiomics analysis in a crab-eating macaque model of COVID-19: stability and sensitivity analysis,” Proc. SPIE 12036 , 1203621 (2022).PSISDG 0277-786X 10.1117/12.2607154
60. Reza S. M. S. et al. , “Deep learning for automated liver segmentation to aid in the study of infectious diseases in nonhuman primates,” Acad. Radiol. 28 Suppl 1 (Suppl 1 ), S37–S44 (2020).10.1016/j.acra.2020.08.023 32943333
61. van Griethuysen J. J. M. et al. , “Computational radiomics system to decode the radiographic phenotype,” Cancer Res. 77 (21 ), e104–e107 (2017).CNREA8 0008-5472 10.1158/0008-5472.CAN-17-0339 29092951
62. Papanikolaou N. Matos C. Koh D. M. , “How to develop a meaningful radiomic signature for clinical use in oncologic patients,” Cancer Imaging 20 (33 ), 1–10 (2020).10.1186/s40644-020-00311-4
| 36506838 | PMC9731356 | NO-CC CODE | 2022-12-14 23:31:45 | no | J Med Imaging (Bellingham). 2022 Nov 8; 9(6):066003 | utf-8 | J Med Imaging (Bellingham) | 2,022 | 10.1117/1.JMI.9.6.066003 | oa_other |
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Nurs Clin North Am
Nurs Clin North Am
The Nursing Clinics of North America
0029-6465
1558-1357
Elsevier Inc.
S0029-6465(22)03382-5
10.1016/j.cnur.2022.10.008
Article
Adapting Simulation Education During a Pandemic
Garrison Christopher M. PhD, RN, CNE, CHSE ∗
Hockenberry Kristal MSN, RN, CNE, CDP
Lacue Sharon DNP, RN, CNE, CHSE
The Ross and Carol Nese College of Nursing, The Pennsylvania State University, 201 Nursing Sciences Building, University Park, PA 16802, USA
∗ Corresponding author.
8 12 2022
8 12 2022
© 2022 Elsevier Inc. All rights reserved.
2022
Elsevier Inc.
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Nursing education faced unprecedented challenges in maintaining quality clinical and simulation instruction during the COVID-19 pandemic. Strategies to maintain clinical engagement and meet course objectives included using virtual simulation and safely reopening simulation laboratories as soon as it was possible. When using virtual experiences for replacement of clinical or simulation, it is critical that standards of best practice are implemented. Safely reopening laboratories required plans for social distancing, health screening, personal protective equipment, disinfecting, and educating users on the new protocols. Combining these strategies resulted in delivering quality instruction without interruption during the pandemic.
Keywords
Simulation during COVID
Nursing education
Virtual simulation
Simulation infection control
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pmcKey points
• Rapidly transitioning nursing education during the COVID-19 required the strategic use of virtual simulation and plans to safely return to face-to-face instruction in the simulation laboratory.
• When changing instructional delivery, such as going from in-person clinical or high-fidelity simulation to virtual simulation, standards of best practice need to be followed.
• Infection control considerations for simulation during the pandemic include social distancing, screening for laboratory entry, personal protective equipment, disinfecting surfaces and equipment, and informing stakeholders about the guidelines.
• When making rapid changes in instructional strategies, as was required during the pandemic, it is critical to have a plan to evaluate instruction to ensure that learning objectives are being met.
Introduction
In March 2020, the World Health Organization (WHO) declared novel coronavirus (COVID-19) to be a global pandemic.1 Nursing education was faced with unprecedented challenges in response to the pandemic. Nursing programs discontinued in-person instruction and clinical sites closed to students.2 , 3 In response, learning had to be transitioned online. Delivering nursing curricula online is particularly challenging due to the need for students to interact with patients and practice psychomotor skills.4 Virtual simulation was used broadly throughout the country to replace in-person simulation and clinical.2 , 5 A national survey of pre-licensure nursing students who experienced this transition to online learning found concerns in regard to feeling isolated and missing hands-on experiences.3
The Ross and Carol Nese College of Nursing, The Pennsylvania State University, faced the same challenges as other schools of nursing in response to the pandemic and the abrupt transition to remote instruction. The college’s simulation committee identified two main priorities when the pandemic began: (1) ensure that virtual clinical experiences were engaging, of high quality, and incorporated clinical judgment and (2) plan for the return to in-person instruction in the simulation laboratories as soon as it could be done safely.
Transition to Virtual Simulation
In response to the first priority, the committee reviewed literature on virtual simulation and identified a variety of resources such as virtual simulations that were available free online as well as products that were available from vendors. The committee determined that it would be important to provide guidance to faculty in how to effectively incorporate these resources as they planned clinical replacement. A set of guidelines for virtual clinical were developed. The Personal Protective Equipment (INACSL) Standards of Best Practice: SimulationSM 6 were consulted in the development of these guidelines. Key points included in the guidelines were that all activities are based on measurable objectives, are participant-centered and driven by the objectives, and should include a synchronous debriefing with faculty. Synchronous debriefing has been identified as a best practice and was cited by students as an important component of maintaining engagement in their education.3, 4, 5
Planning for Return to In-Person Simulation
In response to the second priority, a task force was formed to plan for reopening simulation laboratories for Fall semester 2020. Multiple safety and infection control issues needed to be addressed including social distancing constraints, screening of students and faculty for signs and symptoms of COVID-19, cleaning and disinfecting protocols, obtaining necessary personal protective equipment (PPE) and establishing guidelines for its use, and how to effectively communicate guidelines to all faculty, staff, and students that would be using the laboratories.
Planning for safety and infection control
Social Distancing Considerations
Rooms’ capacities were limited to allow for social distancing in compliance with Centers for Disease Control and university guidelines. This presented challenges in planning laboratory activities. For example, typically a health assessment laboratory of up to ten students and their instructor would meet for a laboratory session or a clinical group of eight students would participate together in a simulation. These numbers had to be cut in half. As a result, we scheduled split laboratory sessions for health assessment. Instead of a 3-hour laboratory, half of the group would come for 90 minutes, and then the other half of the group would come. The goal was to maximize hands on practice during these shortened sessions. A flipped classroom type approach was used. Students spent the time that they otherwise would have been in laboratory doing preparatory activities such as watching videos or completing written assignments. For simulation, we needed to be flexible. Larger classrooms that could accommodate an entire clinical group while maintaining social distancing were converted to debriefing spaces. Simulation rooms were limited to two-to-three learners. In addition to the flipped classroom approach, we either used video capture software to allow observers to view the simulation from the debriefing room or moved the simulator and other equipment into the room and had a “simulation in the round” approach.
Screening for Laboratory Entry
The university instituted routine, random COVID testing for all students and employees. In addition, the university had protocols for isolation and quarantine of individuals with COVID-19 or those who had been exposed. Because laboratory activities would require students to be less than 6 feet apart during skills practice or simulation, we instituted additional health screening procedures and the use of PPE. Fortunately, the university had developed a “symptom checker” app that was available to the university community. It would enable the user to answer questions about any possible symptoms or exposure to COVID-19. Students could show a screen indicating that they were good for entry when they arrived at the laboratory. We also obtained thermal scanning thermometers to check for fever before laboratory entry. Any temperature greater than 100.4° Fahrenheit would be criteria to deny entry. On entry to the laboratory, students would perform hand hygiene with soap and water for 20 seconds or use alcohol-based hand sanitizer.
Cleaning and Disinfecting Protocols
Cleaning and disinfecting surfaces and equipment in the simulation laboratory presented unique challenges. We had to ensure that decontamination without damaging expensive technology such as simulators and computers. There are multiple disinfecting products that kill COVID-19 but many of them were in short supply in 2020 or could damage the technology. Fortunately, 70% isopropyl alcohol can effectively disinfect for COVID-19 and can safely be used on the simulators. We obtained large volumes of it and had it available in spray bottles in each room. After every activity, surfaces and equipment were sprayed with the alcohol and allowed to dry. It is important that it not be wiped up before drying so it kills the virus. We had to be sure that students and faculty were instructed on this. Soft surfaces such as linens and curtains were another challenge, as they are not readily disinfected. We replaced linen sheets and gown with plastic that can be cleaned after each use and tied back privacy curtains.
Personal Protective Equipment
PPE was in short supply throughout 2020 and the priority was to ensure that direct care personnel had adequate PPE to do their jobs safely. At the onset of the pandemic, we donated our supply of PPE to local hospitals. By August 2020, N-95 masks were not available, but we were able to obtain an adequate supply of procedure masks, gloves, gowns, and face shields. The university instituted required masking in all buildings. When students in the laboratory would be within six feet of each other for laboratory practice or simulation, they donned gloves, gown, procedure masks, and face shields.
Communicating Guidelines
It was important to ensure that all faculty, staff, and students were fully informed of the COVID-19 guidelines for the simulation laboratories before the beginning of the school year in August 2020. To accomplish this, a learning module was developed in the university’s learning management system that everyone accessing the simulation laboratory completed. The module included background on infection control principles, brief videos, and written materials on the use of PPE, cleaning and disinfecting, laboratory entry criteria, an attestation that the person agreed to abide by the guidelines, and a posttest to ensure knowledge of the guidelines. Users had to score 100% to pass the posttest and complete the module. Multiple attempts on the posttest were permitted to obtain the passing score. The module was embedded in a course at each level of the curriculum. Laboratory personnel could easily see who had completed the module.
Outcomes of Laboratory Reopening
We were able to successfully reopen our simulation laboratories to in-person learning in Fall 2020. Our infection control procedures were effective, as we are not aware of any COVID spread that resulted from laboratory activities. We had to be flexible in meeting student learning needs as access to clinical sites varied by course and location. We were able to meet the required clinical hours by using a combination of in-person clinical when available, face-to-face simulation and virtual simulation. We prioritized the use of laboratory to activities that would have the highest impact on student learning such as hand-on skills practice and high-fidelity simulations.
Adaptation of learning modalities
Virtual Learning
Simulation-based learning experiences (SBLEs) provide students with an opportunity to develop skills to manage real-life clinical experiences. Virtual simulation learning experiences (VSLEs) are alternative strategies to consider if high-fidelity simulation is not feasible or cost-effective.7 Foronda and colleagues8 define virtual simulation as “clinical simulation offered on a computer, the Internet, or in a digital learning environment including single or multiuser platforms” (p. 27). There is support for using VSLE to improve knowledge, skill, performance, confidence, and clinical judgment.9, 10, 11, 12 During the early phase of the COVID-19 pandemic, our program quickly adapted our own simulation experiences to remote (VSLE) along with using multiple virtual simulation resources available online.
The VSLEs adapted from our own simulation scenarios were conducted synchronously over the Internet. Photos, video clips, and audio clips were embedded in a PowerPoint to replace the interaction with the manikin. Simulation sessions were held using Zoom technology. Assessment findings were presented when students asked. The cases unfolded in the same manner as in the laboratory. We wanted to maintain the focus on students independently interpreting patient data and making clinical judgments. Students evaluated the VSLEs using the same instrument used to evaluate high-fidelity simulations in the laboratory. The instrument asks students to rate the experience on a variety of factors including preparation to care for patients, realism, ability to recognize changes in conditions, learning of pathophysiology, pharmacology, and classroom information, assessment skills, teamwork, communication skills, and effectiveness of debriefing. Each item is rated on a 7-point scale from strongly disagree to strongly agree. Independent sample t tests were used to compare groups on one set of simulations that were converted to compare outcomes between the high-fidelity simulation and the VSLE. There were no significant differences on any item except that “developed better understanding of pathophysiology” was rated higher in the manikin group. The selected outcomes are detailed in Table 1 .Table 1 Student evaluation of selected learning outcomes
Learning Outcome V-Sim Manikin Sim t(67) P
M SD M SD
The simulation was realistic 5.74 1.55 6.29 .90 1.711 .092
I am better prepared to care for patients 6.13 1.21 6.26 1.03 .461 .647
I am better understand the pathophysiology 6.53 .65 6.81 .40 2.200 .031
I am better understand the pharmacology 6.32 .87 6.65 .55 1.824 .073
I am more confident in decision-making 6.26 .92 6.26 .82 .024 .981
My assessment skills improved 5.87 1.30 6.29 .78 1.588 .117
I was changed to think like a nurse 6.61 .59 6.39 .75 1.338 .186
I am better prepared to use SBAR 6.63 .59 6.35 .75 1.711 .092
Debriefing provided time to reflect 6.54 .65 6.65 .55 .708 .481
Debriefing summarized key learning 6.61 .55 6.68 .54 .548 .586
Instructor helped me think critically 6.71 .46 6.74 .44 .286 .775
Note. Scale 1 = strongly disagree; 2 = disagree; 3 = somewhat disagree; 4 = neutral; 5 = somewhat agree; 6 = agree; 7 = strongly agree.
Abbreviations: SBAR, Situation, Background, Assessment, Recommendation.
Once our simulation laboratory space reopened, with social distancing restrictions, decisions were implemented to map out our learning options that included resuming face-to-face SBLEs scheduled in cohort with VSLEs. Considerations that were required to be addressed during this time included how to incorporate the ability to have students on site with clinical experiences in their agencies, clinical laboratory time for skills learning, and face-to-face SBLEs.
Scheduling for Learning Options
Limitation on the number of students who were able to participate in face-to-face SBLEs required shortening the time and number of students in simulation laboratory. This required a schedule that mapped out each experience and was accomplished by alternating face-to-face simulation with VSLEs and students in the clinical setting. The who, where, and then what needed to be considered in the scheduling process. The who involved staffing the simulation laboratory, faculty in on-site clinical, and faculty facilitating virtual clinical or simulation. Considerations for simulation included laboratory availability, the total number of students and facilitators permitted in each room, how long each simulation session required, and which simulations would be most effective in-person. Simulation experiences that could be as effective if delivered virtually were identified.
Resources
Faculty worked with simulation coordinators to plan their course and meet course objectives by identifying resources to design their experiences to meet clinical hours. Multiple resources were pulled together by our Simulation Committee to provide our faculty with a menu of options during the spring of 2020. These resources included Ryerson Virtual Healthcare Experience, Swift River Simulations, vSim for Nursing, and home-grown solutions such as developing PowerPoint presentations of our simulations for presentation virtually as described above.
Preparing for Simulation-Based Learning Experiences
Once our program returned to the simulation laboratory in the Fall of 2020, with social distancing limitations, course coordinators collaborated to map specific virtual experiences that would be incorporated into courses across the curriculum. Pre-briefing was modified to occur outside of the simulation laboratory using recordings of the pre-brief information in combination with pre-simulation assignments. Debriefing occurred with a synchronous meeting whether the simulation was held in-person or virtually.
The quick shift to the use of Zoom technology that occurred in the spring of 2020 integrated into a technology now used frequently in all courses. This has provided our program the ability to virtually debrief after participation in either a face-to-face or VSLE. Zoom has also provided the ability for peer interaction and collaboration facilitated by clinical faculty who are able to encourage student reflection and guide them in clinical thinking.
Ensuring quality in simulation learning experiences
Planning the Activity
As a result of the pandemic, there are more resources available for selecting high-quality clinical replacement activities than before this unprecedented event in nursing education. This experience and knowledge have provided us with the ability to assess each learning situation and improve our decision-making skills in selecting the best quality replacement activities for students. Once possible options for types of replacement activities have been reviewed and narrowed down, the next step in ensuring the quality of the activity is for course coordinators and simulation faculty to work closely together to plan the activity. Many activities that had been implemented when the pandemic first began may no longer be available, or after evaluation of the activity, were found not to be the best option for ensuring a high-quality experience. When course faculty are tasked to modify clinical and simulation activities that have been in place and effective in learning for many years, it can be very overwhelming, especially when these changes to convert to an unfamiliar revised or remote learning may need to happen very quickly. To help alleviate some of the stress and anxiety over the situation, simulation faculty can assist course coordinators with analyzing needs and objectives for student learning and help to identify SBLEs that would best meet the objectives.
Ensuring Adherence to Standards of Best Practice
When moving from a face-to-face learning environment to a revised, remote, or virtual platform, the same standards for best practices still need to apply to simulation and clinical replacement activities. The guidelines for ensuring quality of simulations are available through the INACSL. INACSL Healthcare Simulation Standards of Best Practice (HSSOBP)13 provides very clearly defined guidelines to follow for designing, implementing, and evaluating an SBLE. Even though some SBLEs may be predeveloped, as in a virtual simulation from a reputable company or as additional resources from a textbook, when integrating the activity into a course, the HSSOBP needs to be implemented to maintain a high-quality experience for students. Some of the standards we would apply in this situation are simulation design, outcomes and objectives, facilitation, debriefing, and participant evaluation.
Simulation design begins with a needs assessment to choose options for the learning activity. Faculty should ensure that the activity is equivalent in achieving the knowledge, skills, and behaviors as the activity that it is replacing. The conclusions from the needs assessment will direct the development of the learning objectives to enable students to reach the intended outcomes of the replacement activity. The objectives should be specific, measurable, achievable, realistic, and time-phased (SMART objectives).13 Collaboration between simulation faculty and course faculty is a valuable resource in ensuring alignment of the needs and objectives. It is important to consider not just the title of the activity, but how the content of the activity may need to be modified to meet the course needs.
Facilitation is the next step of the process and must include a necessary pre-brief and elements such as ensuring that appropriate cues are incorporated into the activity. The pre-brief is an essential component of the facilitation process for simulation activities. This should provide students with expectations and patient information for the activity as well as an orientation to the simulation environment is it virtual or face to face.13 Faculty should provide materials and resources to prepare the students to be successful in achieving intended outcomes of the replacement activity. If students are not given the appropriate information to prepare for the activity, then they may tend to get caught up in focusing on irrelevant tasks, skills, or materials within the activity that lead them away from their intended objectives. For predeveloped, revised, or modified simulation and clinical replacement activities, the simulation faculty can assist the course coordinator in designing a pre-brief specifically for that activity, and then help to evaluate if the pre-brief contains the necessary information for the students to achieve their objectives.
Debriefing, while not included in many predeveloped activities, it is critical for reflecting and processing new knowledge with peers, self, and the instructor. It is important to remember that the facilitation of the simulation does not end with the students’ completion of the activity. A critical part of learning with clinical or simulations is the teamwork experiences where they give and receive feedback. This is essential in beginning to think like a nurse. The post-conference after the clinical experience and the debriefing session after the simulation experience are important for development of clinical judgment for students through peer and self-reflection as well as instructor feedback. It is important to continue to include this key teaching tool with all replacement activities, whether it is a predeveloped type of simulation or a redesigned simulation activity. As many of the predeveloped types of replacement activities may contain some form of post-activity questions or discussion points, simulation faculty can assist the course faculty in revising the questions to produce a meaningful debriefing session. If course faculty are using a modified or redesigned clinical simulation experience, simulation faculty are a great resource to assist course faculty in planning and designing the debriefing session. There are several effective theory-based debriefing models that simulation faculty use to guide the session toward high-quality feedback and self-reflection that meet the intended objectives and outcomes of the activity. Using these strategies, simulation faculty can design some type of a rubric, checklist, or other tool to assist in directing the debriefing for course faculty and to increase student engagement in the session. Finally, the activity needs to include a plan for participant evaluation. The evaluation plan is developed to ensure it will measure the achievement of learning objectives. The steps for planning and developing effective clinical learning replacement are illustrated in Fig. 1 .Fig. 1 Process for developing clinical replacement activities.
Evaluating simulation experiences
Importance of Evaluation
With the rapid adoption of virtual simulation at the onset of the pandemic, the environment of face-to-face simulation changes dramatically. Evaluating the effectiveness of simulation experiences, whether virtual or face to face, is imperative to assess the quality of simulation experiences. The use of valid and reliable instruments to collect student feedback regarding satisfaction with simulation modalities provides information to make appropriate changes as necessary. A variety of evaluation instruments should be used throughout your simulation program to reflect your vision and integration of simulation in your curriculum.
The model below (Fig. 2 ) reflects the cycle of building your simulation evaluation program.Fig. 2 Evaluation model for simulation.
Tools to Evaluate Simulation
There are many valid and reliable tools to evaluate simulation. One that is widely used is the SET-M which is designed for evaluating simulation scenario and is useful for evaluating learner’s perception of how effective the simulation was toward meeting their learning needs.14 Our simulation program uses this tool with the example of student feedback provided in Table 1.
The CLECS 2.015 is an instrument ideal for our pandemic environment because it measures student perceptions of how well their learning needs are met in three environments: traditional clinical environment, face-to-face simulated clinical environment, and screen-based simulation or virtual simulation. The utilization of this instrument can identify if facilitators are meeting the learning needs of students, identify clinical experiences for areas requiring improvement, and also identify curricular gaps.
Integrative Learning Evaluation
Integrative learning is the process of making connections among concepts and experiences so that information and skills can be applied to novel and complex issues or challenges. This is apparent as we use the concept of Bloom’s taxonomy to build our simulation program from simple to complex scenarios.
Evaluating integrative learning can include pretesting and posttesting questions regarding a simulation experience, written examination questions on course examinations, and student self-evaluations regarding meeting simulation, course, and program outcomes. The curriculum could also build in a repeat of a simulation at a higher level that includes more complex issues, using a deliberate practice model.
Post-COVID: a time for new beginnings
The pandemic challenged nursing education to adopt the delivery of simulation laboratory experiences including both clinical skills and SBLEs. Simulation educators had to rethink and reconfigure how simulation curriculum was delivered. These adaptations were necessary in order to create clinical opportunities for our students. As we emerge from the pandemic into a new era now is the time for reflection on the variety of resources and scheduling modifications implemented. This presents the question as to which innovative adaptations were effective and could be now be incorporated into a simulation curriculum.
Simulation educators need to consider which of the adopted resources maintain best practice for simulation while providing the simulation laboratory with cost-effective and time-saving experiences. The innovative virtual experiences that provided students with additional opportunities to develop their clinical skills, therefore, need to be evaluated for how to be best implemented along with face-to-face SBLEs. We have no idea if another event may cause disruption in our simulation programs but we are now prepared to face that and to use the resources that emerged to create a new beginning for simulation programs.
Clinics care points
• Incorporate synchronous debriefing for virtual simulations
• Evaluate all simulations using validated instruments
• Screen participants for s/s of COVID on entry to the laboratory
• Have adequate personal protective equipment available
• Disinfect surfaces and equipment with 70% isopropyl alcohol.
• Follow Healthcare Standards of Best Practices regardless of simulation methodology.
Disclosure
The authors have no financial or commercial conflicts of interest to disclose and received no funding for this project.
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References
1 Cucinotta D. Vanelli M. WHO Declares COVID-19 a Pandemic Acta Biomed 91 1 2020 157 160 32191675
2 Leaver C.A. Stanley J.M. Veenema T.G. Impact of the COVID-19 pandemic on the future of nursing education Acad Med 97 35 2022 S82 S89 34789661
3 Michel A. Ryan N. Mattheus D. Undergraduate nursing students’ perceptions on nursing education during the 2020 COVID-19 pandemic: A national sample Nurs Outlook 69 2021 903 912 34183191
4 Esposito C.P. Sullivan K. Maintaining clinical continuity through virtual simulation during the COVID-19 pandemic J Nurs Educ 59 9 2020 522 525 32865587
5 Shea K.L. Rovera E.J. Preparing for the COVID-19 pandemic and its impact on a nursing simulation curriculum J Nurs Educ 60 1 2021 52 55 33400810
6 INACSL Standards Committee INACSL Standards of Best Practice: SimulationSM Clin Simulation Nurs 12 2016 S1 S50
7 Verkuyl M. Atack L. Mastrilli P. Virtual gaming to develop students’ pediatric nursing skills: A usability test Nurse Educ Today 46 2016 81 85 27614548
8 Foronda C.L. Swoboda S.M. Henry M.N. Student preferences and perceptions of learning from vSim for nursing Nurse Educ Pract 33 2018 27 32 30223110
9 Foronda C.L. Fernandez-Burgos M. Nadeau C. Virtual simulation in nursing education: A systematic review spanning 1996 to 2018 Simulation Healthc 15 1 2020 46 54
10 Liaw S.Y. Chan S.W. Chen F. Comparison of virtual patient simulation with mannequin-based simulation for improving clinical performance in assessing and managing clinical deterioration: Randomized controlled trial JMIR Med Educ 16 9 2014 e214
11 Padilha J.M. Machado P.P. Ribeiro A. Clinical virtual simulation in nursing education: A randomized controlled trial JMIR Med Educ 21 3 2019 e11529
12 Shin H. Rim D. Kim H. Educational characteristics of virtual simulation in nursing: An integrative review Clin Simulation Nurs 37 2019 18 28
13 INACSL Standards Committee Healthcare Simulation Standards of Best PracticeTM Clin Simulation Nurs 58 2021 1 66
14 Leighton K. Ravert P. Mudra V. Simulation Effectiveness Tool-Modified https://sites.google.com/view/evaluatinghealthcaresimulation/set-m 2018
15 Leighton K. CLECS 2.0 https://sites.google.com/view/evaluatinghealthcaresimulation/clecs/clecs-2-0 2019
| 0 | PMC9731371 | NO-CC CODE | 2022-12-14 23:31:45 | no | Nurs Clin North Am. 2022 Dec 8; doi: 10.1016/j.cnur.2022.10.008 | utf-8 | Nurs Clin North Am | 2,022 | 10.1016/j.cnur.2022.10.008 | oa_other |
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STAR Protoc
STAR Protoc
STAR Protocols
2666-1667
The Authors.
S2666-1667(22)00790-0
10.1016/j.xpro.2022.101910
101910
Protocol
HLA-I immunopeptidome profiling of human cells infected with high-containment enveloped viruses
Weingarten-Gabbay Shira 123151718∗
Pearlman Leah R. 115
Chen Da-Yuan 45
Klaeger Susan 1
Taylor Hannah B. 1
Welch Nicole L. 16
Keskin Derin B. 17891011
Carr Steven A. 1
Abelin Jennifer G. 1
Saeed Mohsan 4516
Sabeti Pardis C. 1212131416
1 Broad Institute of MIT and Harvard, Cambridge, MA, USA
2 Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA
3 Laboratory of Virology and Infectious Disease, The Rockefeller University, New York, NY, USA
4 Department of Biochemistry, Boston University School of Medicine, Boston, MA, USA
5 National Emerging Infectious Diseases Laboratories, Boston University, Boston, MA, USA
6 Program in Virology, Harvard Medical School, Boston, MA, USA
7 Translational Immunogenomics Laboratory, Dana-Farber Cancer Institute, Boston, MA, USA
8 Department of Computer Science, Metropolitan College, Boston University, Boston, MA, USA
9 Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
10 Harvard Medical School, Boston, MA, USA
11 Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
12 Massachusetts Consortium on Pathogen Readiness, Boston, MA, USA
13 Department of Immunology and Infectious Disease, Harvard T.H. Chan School of Public Health, Boston, MA, USA
14 Howard Hughes Medical Institute, Chevy Chase, MD, USA
∗ Corresponding author
15 These authors contributed equally
16 Senior author
17 Technical contact
18 Lead contact
8 12 2022
16 12 2022
8 12 2022
3 4 101910101910
© 2022 The Authors
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Immunopeptidome profiling of infected cells is a powerful technique for detecting viral peptides that are naturally processed and loaded onto class I human leukocyte antigens (HLAs-I). Here, we provide a protocol for preparing samples for immunopeptidome profiling that can inactivate enveloped viruses while still preserving the integrity of the HLA-I complex. We detail steps for lysate preparation of infected cells followed by HLA-I immunoprecipitation and virus inactivation. We further describe peptide purification for mass spectrometry outside a high-containment facility.
For complete details on the use and execution of this protocol, please refer to Weingarten-Gabbay et al. (2021).1
Graphical abstract
Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.
Immunopeptidome profiling of infected cells is a powerful technique for detecting viral peptides that are naturally processed and loaded onto class I human leukocyte antigens (HLAs-I). Here, we provide a protocol for preparing samples for immunopeptidome profiling that can inactivate enveloped viruses while still preserving the integrity of the HLA-I complex. We detail steps for lysate preparation of infected cells followed by HLA-I immunoprecipitation and virus inactivation. We further describe peptide purification for mass spectrometry outside a high-containment facility.
Subject areas
Immunology
Microbiology
Mass Spectrometry
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pmcBefore you begin
This protocol describes in detail the methods and resources needed for mass spectrometry analysis of HLA-I peptides in cells infected with a high containment enveloped virus. Traditional inactivation methods, such as Trizol, SDS and heating, destroy the structural conformation of the HLA-I complex and are therefore incompatible with immunopeptidome profiling. In contrast, mild detergents can inactivate an enveloped virus by disrupting its membrane while still maintaining the integrity of the HLA-peptide complex in the lysates. In the protocol below, virus inactivation occurs during the HLA-I immunoprecipitation step, when infected cells are incubated with a lysis buffer containing Triton-X detergent.
This protocol was modified from Abelin et al.2, Keskin et al.3, and Reinhold et al.4, and describes all experimental steps from virus infection through purified HLA peptides ready for mass spectrometry analysis. While this protocol was designed with high containment viruses in mind (viruses studied in a Biosafety Level 3 (BSL3) laboratory), it can also be used to profile HLA-I peptides of viruses that require less stringent containment conditions (viruses studied in a BSL2 laboratory) as well as non-infected cells.
The protocol described here centers around our work with human lung A549 cells and human embryonic kidney HEK293T cells infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, we also used it with HEK293T cells infected with Vesicular Stomatitis Virus (VSV). When applying it to a new enveloped virus, viral inactivation should be confirmed using appropriate assays and safety standards. Lysis buffer volume and incubation time required for inactivation may vary between viruses.
Preparing buffers
Timing: 2.5 h
Recipes for all buffers listed below can be found in the materials and equipment section.1. Prepare Lysis Buffer Base.
2. Prepare Octyl β-d-glucopyranoside Stock Solution.
3. Prepare 500 mM Iodoacetamide aliquots.
4. Prepare Lysis Wash Buffer without Protease Inhibitor (-PI).
5. Prepare Lysis Buffer with Protease Inhibitor (+PI).
6. Prepare Wash Buffer Base.
7. Prepare Complete Wash Buffer.
8. Prepare 20 mM Tris Wash Buffer.
Note: Wash Buffer Base, Complete Wash Buffer and 20 mM Tris Wash Buffer (buffers 6–8) can either be made at the outset of the protocol alongside the other buffers, or during the 3 h incubation in the immunoprecipitation step (step 4 of the protocol).
Setting a rotisserie-style rotator inside a 4°C mini fridge in the BSL3 facility
Timing: 15 min
9. Place a tube rotator and rotisserie (see materials and equipment) inside a 4°C mini fridge in the BSL3 facility (Figure 1 ). Turn it on for 10 min before loading tubes with samples in step 4 of the protocol, set speed to 15 rpm.Figure 1 Images of a tube rotator and rotisseries inside a 4°C mini fridge
Rotator was installed at the bottom shelf and the electric cord was connected to a socket outside the mini fridge.
Infecting cells with a virus
Timing: 2 days (48 h prior HLA-I immunoprecipitation)
10. Seed A549 cells in three 15 cm dishes at a density of 1.5 × 10ˆ7 cells per dish to achieve ∼80% confluency (2 × 10ˆ7 cells per dish for HEK293T) 24 h prior to infection.
11. On the day of infection, carry cells to the BSL3 space and infect with SARS-CoV-2.a. For infection, remove the culture media and rinse the cell monolayer with 10 mL of ice-cold 1× PBS.
b. Prepare virus dilution in 5 mL of ice-cold Opti-MEM to obtain the final multiplicity of infection (MOI) of 3, add to the cells, and allow viral adsorption on ice for 60 min.
c. During on-ice incubation, rock the dishes gently every 10 min to facilitate homogenous infection and prevent parts of the cell monolayer from drying.
d. After 1 h, remove the virus inoculum and add 25 mL of pre-warmed DMEM containing 2% FBS.
e. Incubate at 37°C in the presence of 5% CO2.
Note: Aim for high MOI to increase the relative representation of viral peptides in comparison to endogenous human HLA-I peptides.
Note: HLA-I presentation dynamics vary between viruses and cell lines. Some studies profiled the HLA-I peptidome late during infection, such as 2 days post infection (dpi) for measles virus,5 2–4 dpi for human immunodeficiency virus,6 and 0.5–5 dpi for West Nile virus.7 We and others1,8,9 have shown that some viral HLA-I peptides peak as early as 3–6 h post infection (hpi). For SARS-CoV-2, most of the peptides we detected at 3–6 hpi were also present at 24 hpi.
Pre-washing beads before entering the BSL3 facility
Timing: 30 min (on the day of HLA-I immunoprecipitation)
12. Prepare GE Healthcare Gamma-bind plus Sepharose beads for immunoprecipitation (IP). The steps below describe the amount needed for 6 IPs, which constitute a single experiment.a. Fill 6 microcentrifuge tubes to the rim with 1× PBS to “soak” tubes.
b. Pipette 1 mL of 1× PBS into a new 1.5 mL microcentrifuge tube and add 180 μL of beads (30 μL of beads per IP reaction).
c. Vortex the tube for 7 s to wash the beads followed by centrifugation at 2,500 rcf for 30 s at 4°C in a pre-chilled swing bucket centrifuge. Aspirate the supernatant without disturbing the bead pellet. Repeat this step 2 times.
d. After washing for three times, completely aspirate the supernatant (preferably by using a vacuum aspirator). Add 1 mL of 1× PBS and vortex to mix.
e. Remove PBS from 6 “soaked” microcentrifuge tubes (from step 12a) and add 1 mL of fresh 1× PBS to each tube.
f. Add 166 μL of washed beads to each of the 6 tubes obtaining the total volume of 1,166 μL per tube. Clearly label each tube with the date and sample name. At the end of the protocol, these tubes will be stored at −80°C before submission for mass spectrometry analysis.
Note: When collecting beads, cut the tip of the pipette with a disposable scalpel and rinse it 6 times with 1× PBS before loading beads.
Note: Use low retention tubes and keep beads on ice.
Note: When washing beads, use a swing bucket centrifuge. Fixed-angle rotor results in beads’ smear on the side of the tube.
Preparing reagents and consumables to take to the BSL3 facility
Timing: 15 min (on the day of HLA-I immunoprecipitation)
13. Prepare an ice bucket with the following reagents and tubes. The steps below describe the amount needed for 6 IPs, which constitute a single experiment.a. Ice-cold Lysis Buffer (+PI) (after adding Phenylmethanesulfonylfluoride (PMSF)) - 8 mL in a 15 mL conical tube.
b. 3 cell scrapers.
c. 6 pre-soaked microcentrifuge tubes (filled to the rim with 1× PBS) for cell lysates.
d. 6 microcentrifuge tubes containing pre-washed beads (from step 12).
e. 10 μL Benzonase (1 μL per IP reaction plus extra).
f. 350 μL HLA Class I Antibody W6/32 (50 μL per reaction plus extra).
Key resources table
REAGENT or RESOURCE SOURCE IDENTIFIER
Antibodies
HLA Class I Antibody W6/32 (200 μg/mL) Santa Cruz Biotechnology Cat # sc32235
FITC anti-human HLA-A,B,C Antibody (W6/32 clone) BioLegend Cat # 311403
Bacterial and virus strains
SARS-CoV-2 Centers for Disease Control and Prevention and BEI Resources 2019-nCov/USA-WA1/2020 isolate (NCBI accession number: MN985325.1)
Chemicals, peptides, and recombinant proteins
1 M Tris, pH 8.0 Invitrogen Cat # AM9855G
1 M Magnesium Chloride Sigma-Aldrich Cat # M1028
0.5 M EDTA Sigma-Aldrich Cat # 7789
5 M Sodium Chloride Sigma-Aldrich Cat # 71376
Triton-X Sigma-Aldrich Cat # T9284
Octyl β-d-glucopyranoside Sigma-Aldrich Cat # 08001-5G
Phenylmethanesulfonylfluoride (PMSF) Sigma-Aldrich Cat # 78830
cOmplete™, EDTA-free Protease Inhibitor Cocktail Sigma-Aldrich Cat # 11873580001
Gammabind Plus Sepharose Beads Millipore Sigma Cat # 17-0886-01
Iodoacetamide Sigma-Aldrich Cat # A3221
Benzonase Thomas Scientific Cat # E1014-25KU
Opti-MEM Reduced Serum Medium Fisher Scientific Cat# 31985-070
DMEM, high glucose, pyruvate Fisher Scientific Cat# 11995-065
PBS, pH 7.4 Fisher Scientific Cat# 10010-023
Internal retention time (iRT) standards Biognosys SKU: Ki-3002-2
Experimental models: Cell lines
A549 ATCC CCL-185
HEK293T ATCC CRL-3216
VERO E6 ATCC CRL-1586
Other
DNA LoBind 1.5 mL Tube Eppendorf Cat# 022431021
Gel Loading Pipet Tips, 0.5 μL–10 μL Fisher Scientific Cat# 02-707-88
Falcon 150 mm TC-treated Cell Culture Dish Corning Cat# 353025
Corning Cell Lifter Corning Cat# 3008
Sep-Pak Vac 1cc (50 mg) tC18 Cartridges Waters SKU: WAT054960
Materials and equipment
Equipment
During the immunoprecipitation step, lysates are incubated with beads and antibodies for 3 h on a rotator kept at 4°C inside the BSL3 facility. We used a Tube Rotator and Rotisseries from VWR, Cat#10136-084 (Figure 1), which we placed in a Standard Series Freestanding Undercounter Refrigerator and Freezer from VWR, Cat# 10819-894.
Buffers
Note: All salt buffers should be HPLC pure.
Lysis buffer base
Prepare 2 × 50 mL conical tubes of Lysis Buffer Base. This amount will be sufficient for making β-d-glucopyranoside Stock Buffer and Lysis Wash Buffer (-PI).Reagent Final concentration Amount
1 M Tris, pH 8.0 20 mM 1 mL
5 M NaCl 100 mM 1 mL
1 M MgCl2 6 mM 300 μL
0.5 M EDTA 1 mM 100 μL
ddH2O N/A 47.6 mL
Total N/A 50 mL
Note: Lysis Buffer Base can be stored in −20°C for 3 months.
CRITICAL: Mg2+ is an essential cofactor for Benzonase activity. HLA-IP protocols employing sonication to shear DNA may utilize Mg2+ free lysis buffers. Ensure using MgCl2 containing lysis buffer when Benzonase digestion is the method of choice for eliminating DNA (see step 2 of the protocol for information on degradation of nucleic acids using Benzonase).
Octyl β-d-glucopyranoside stock solution
Dissolve 5 g of β-d-glucopyranoside (see key resources table) by adding 17 mL of Lysis Buffer Base directly to the container and mix by vortexing every 10 min until powder is completely dissolved (∼30 min).Note: Stock solution can be stored in 4°C for a few weeks.
500 mM iodoacetamide aliquots
Dissolve 56 g of Iodoacetamide (a single vial, see key reagents table) in 605 μL ddH2O for a final concentration of 500 mM and aliquot to 50 μL aliquots in light-sensitive microcentrifuge tubes.CRITICAL: Iodoacetamide is sensitive to light. Use light-sensitive microcentrifuge tubes to store aliquots.
Note: Iodoacetamide aliquots can be stored at −20°C and should maximum be thawed twice before being discarded.
Lysis wash buffer (-PI)
Prepare Lysis Wash Buffer (-PI) in a 50 mL conical tube. Half of the amount will be used for washing beads after the immunoprecipitation step and half will be used to make Lysis Buffer (+PI). After adding all reagents, place the 50 mL tube in a 37°C water bath for at least 30 min and invert the tube gently to allow the detergents to mix well.Reagent Final concentration Amount
Triton X-100 1.5% 750 μL
Octyl β-d-glucopyranoside stock solution 60 mM 3 mL
500 mM Iodoacetamide 0.2 mM 20 μL
Lysis Buffer Base N/A 46.230 mL
Total N/A 50 mL
Note: Triton X-100 is highly viscous. Cut off the tip of the pipette with disposable scalpel and transfer an accurate amount.
Note: When mixing reagents, invert the tube gently to avoid bubbles.
Note: The 25 mL of the lysis wash buffer used for washing beads (step 5 of the protocol) can be aliquoted to three 15 mL conical tubes, each containing ∼8 mL, and can be stored in −20°C for one month. Cover tubes with aluminum foil to protect the content from light since the lysis wash buffer contains light sensitive Iodoacetamide.
Lysis buffer (+PI)
Prepare Lysis Buffer (+PI) using the remaining Lysis Wash Buffer (-PI) after removing 25 mL for beads washing.• Dissolve 1 tablet of 50× cOmplete™, EDTA-free Protease Inhibitor Cocktail in 1,000 μL ddH2O and add 500 μL to 24.5 mL of the lysis buffer to achieve 1× concentration.
• Aliquot the 25 mL into three 15 mL conical tubes, each containing ∼ 8 mL. Tubes can be frozen in −20°C for 1 month before proceeding to the next step. Cover tubes with aluminum foil to protect from light as the buffer contains light-sensitive Iodoacetamide.
• On the day of HLA-I immunoprecipitation, add PMSF to the 8 mL lysis buffer to a final concentration of 1 mM.
Note: PMSF should be added fresh on the day of immunoprecipitation.
CRITICAL: PMSF powder is toxic. Use a biosafety cabinet and appropriate personal protective equipment (PPE) when working with PMSF powder.
Wash Buffer Base
Reagent Final concentration Amount
1 M Tris, pH 8.0 20 mM 1 mL
5 M NaCl 100 mM 1 mL
0.5 M EDTA 1 mM 100 μL
ddH2O N/A 47.9 mL
Total N/A 50 mL
Note: Wash Buffer Base can be stored at −20°C for 3 months.
Complete Wash Buffer
Reagent Final concentration Amount
Octyl β-d-glucopyranoside stock solution 60 mM 1.5 mL
500 mM Iodoacetamide 0.2 mM 10 μL
Wash Buffer Base N/A 23.5 mL
Total N/A 25 mL
Note: Discard Complete Wash Buffer at the end of the experiment.
20 mM Tris Wash Buffer
Reagent Final concentration Amount
1 M Tris, pH 8.0 20 mM 500 μL
ddH2O N/A 24.5 mL
Total 20 mM 25 mL
Note: Discard 20 mM Tris Wash Buffer at the end of the experiment.
Step-by-step method details
Preparing cell lysate
Timing: 3 h (in BSL3)
The following steps describe how to lyse infected cells in dishes, degrade nucleic acids, and prepare clear lysates for immunoprecipitation of HLA-I complexes. DNA shearing is a critical step in the immunoprecipitation protocol. Scientists often use sonication, which imposes a risk when working with high-containment pathogens due to aerosol production and potential spread of infectious particles. Thus, we replaced sonication with enzymatic DNA shearing using Benzonase as described before.2 1. Lysis of infected cells.a. Place 15 cm dishes with infected cells on ice (process 1 dish at a time).
b. Carefully remove the culture medium and wash cells with 10 mL of ice-cold 1× PBS.
c. Carefully remove all of the PBS by tilting the dish (any residual amount of PBS will affect the composition of the lysis buffer). Add 2.5 mL of cold Lysis Buffer (+PI).
d. Scrape cells in the Lysis Buffer using a cell scraper. Transfer to an ice-cold 15 mL conical tube (expected volume is ∼3 mL: 2.5 mL of lysis buffer + cells + residual PBS).
e. Lyse cells in the other two 15 cm dishes. Collect all lysates in the same 15 mL tube to obtain the total volume of ∼ 9 mL (∼3 mL per dish).
f. Remove PBS from the 6 “soaked” microcentrifuge tubes. Using a serological pipette, gently mix the ∼9 mL cell lysate 20 times and transfer to the 6 microcentrifuge tubes, ∼1.5 mL per tube.
Note: Depending on the duration of infection and the cell type used, cells may die and/or detach from the dishes (for example, SARS-CoV-2 infected HEK293T cells at 24 hpi). To include floating cells in the experiment, do not discard the media in step 1b, but instead, collect it in a conical tube and spin down at 1,000 × g 4°C for 5 min. Remove the supernatant and wash the cell pellet once with ice-cold 1× PBS. Resuspend the cell pellet in the Lysis Buffer that was used to scrape cells from the dishes.
CRITICAL: Do not use trypsin to detach cells from dishes. Trypsin remnants can interfere with mass spectrometry analysis.
2. Nucleic acid degradation.a. Add 1 μL of Benzonase to each of the 6 microcentrifuge tubes containing 1.5 mL of cell lysates.
b. Incubate tubes on ice for 15 min. Invert tubes every 5 min. Spin down in a pre-cooled swinging-bucket centrifuge for 22 min at maximum speed (4,750 rpm, 4°C) to remove cell debris.
c. Remove tubes from the centrifuge and keep them on ice while preparing beads and antibodies for the immunoprecipitation step.
Immuno-precipitating HLA-I complexes from cell lysates
Timing: 3.5 h (in BSL3)
The steps below describe the immunoprecipitation of the HLA-peptide complex from infected cell lysates using a pan-HLA-I antibody. Our assays showed that during the 3 h immunoprecipitation step, SARS-CoV-2 is completely inactivated by 1.5% Triton-X and Benzonase present in the lysis buffer (Figure 2 ).3. Preparation of antibody-bead mixture.a. Centrifuge the microcentrifuge tubes containing washed beads prepared in step 12f of the “before you begin” section at 4,000 rpm for 1 min at 4°C.
b. Remove the supernatant without disturbing the beads.
c. Add 50 μL of W6/32 pan-HLA-A/B/C antibody to each tube.
4. Incubation of lysates with beads for immunoprecipitation.a. Transfer clear lysates from step 2c to microcentrifuge tubes containing beads and antibodies using a p1000 pipette. When transferring lysates, avoid cell debris at the bottom of the tubes.
b. Incubate the tubes on a rotisserie-style rotator in a 4°C mini fridge for 3 h. After 3 h, the virus is inactivated, and the immunoprecipitated complexes are safe to be removed from BSL3.
CRITICAL: At the end of this step, both SARS-CoV-2 and VSV were found to be completely inactivated when tested by the standard plaque assay. When working with a new enveloped virus, inactivation should be confirmed before removing samples from BSL3 (or other specified containment level) for the remaining steps of the protocol.
Note: When transferring the IP tubes from BSL3 to BSL1, use an ice bucket or a pre-chilled cold block at 4°C.
Figure 2 Plaque assay confirming SARS-CoV-2 inactivation after 3 h incubation with lysis buffer containing Triton-X- and benzonase
A549 cells were infected with SARS-CoV-2 at MOI of 3 for 24 h. 10-fold serial dilutions were prepared in Opti-MEM and used to infect Vero cells in a 24-well plate. Comparing plaques in (left) cultured media of infected A549 cells; (middle) SARS-CoV-2 infected A549 cells treated with a lysis buffer containing 1.5% Triton-X and Benzonase for 3 h; and (right) non-infected A549 cells. When adding the 1:10 dilution of the lysis buffer, infected and non-infected cells died immediately due to the relatively high Triton-X concentration. Figure 2 was adapted from Figure S1 in Weingarten-Gabbay et al.1
Washing beads after HLA-I immunoprecipitation
Timing: 2 h (in BSL1)
After immunoprecipitation is complete, beads are thoroughly washed to remove unbound proteins and antibodies, as well as enzymes, detergents and salts contained in the lysis buffer. Buffers are gradually exchanged during the course of 9 washing steps until the lysis buffer is replaced with Tris 20 mM. At the end of these steps, samples are ready for peptide elution and mass spectrometry analysis.Note: All wash steps were done using vacuum pipette (Figure 3). Centrifugation was performed at 2,500 rcf for 1 min at 4°C.
Note: All wash buffers should be ice-cold.
5. Washing beads once with Lysis Wash Buffer. a. Spin down beads for 1 min, 2,500 rcf at 4°C.
b. Aspirate supernatant using vacuum pipette. Do not disturb the bead pellet.
c. Add 1 mL of Lysis Wash Buffer.
d. Vortex for 10 s and repeat centrifugation.
Figure 3 Representative images depicting vacuum setup for bead washing and drying
(Left) For the bead washing setup, an unfiltered 20 μL pipette tip was attached to a suction tube. (Right) For the bead drying setup, a 0.5 μL–10 μL gel loading pipet tip (See key resources table) was attached to the unfiltered 20 μL pipette tip.
Optional: Unbound cell lysates can be used for whole proteome analysis of infected cells. In that case, instead of aspirating lysates, transfer them to 6 labeled ice-cold microcentrifuge tubes using a p1000 pipette and store at −80°C.
6. Washing beads 4 times with Complete Wash Buffer.a. Aspirate the supernatant. Do not disturb the bead pellet.
b. Add 1 mL of Complete Wash Buffer.
c. Vortex for 10 s and repeat centrifugation.
7. Washing beads 4 times with 20 mM Tris Buffer.a. Aspirate the supernatant.
b. Add 1 mL of 20 mM Tris Buffer.
c. Vortex for 10 s and repeat centrifugation.
8. Drying beads.a. Upon completion of final wash, aspirate supernatant using vacuum pipette without disturbing the beads pellet.
b. Remove the residual volume of the buffer using gel loading tips (Refer to key resources table). Beads will transform from translucent to white and should appear like salt crystals.
c. Store the dry beads at −80°C.
Note: When drying the pellet, the skinny tips can touch the beads to adequately dry the beads.
Note: HLA-I peptide complexes are stable at −80°C for 3–6 months.
Purifying peptides from beads for mass spectrometry analysis
Timing: 1–2 h (in BSL1)
Peptides are eluted from dry beads using 50 mg tC18 Sep-Pak cartridge and a vacuum manifold (Figure 4 ). At the end of these steps, samples are ready for mass spectrometry analysis.9. Preparing 50 mg tC18 Sep-Pak cartridge for peptides binding.a. Rinse cartridge with 200 μL 100% Methanol two times.
b. Rinse cartridge with 100 μL 99% acetonitrile (ACN) /0.1% formic acid (FA) one time.
c. Rinse cartridge with 500 μL 1% formic acid four times.
10. Transferring beads from IP tubes to a 50 mg tC18 Sep-Pak cartridge.a. Resuspend dry beads in each IP tube with 200 μL 3% ACN/5% FA.
b. Spike in 1 μL 100 fmol/μL iRT peptides standards into each sample (1 μL of 1/10 iRT dilution).
c. Transfer beads from the 6 IP tubes to a single 50 mg tC18 Sep-Pak cartridge.
d. Rinse original IP tubes with 200 μL 1% FA and transfer to the cartridge to collect all beads.
11. Eluting peptides from beads to the cartridge.a. Add 200 μL 10% Acetic acid (AcOH) to the cartridge and incubate for 5 min before discarding the buffer using the vaccum. Repeat this step two more times.
b. Wash the cartridge with 500 μL 1% FA four times.
12. Eluting peptides from cartridge to a new collection tube.a. Place a new 2 mL Eppendorf collection tube.
b. Rinse cartridge with 250 μL 15% ACN/1% FA once (Elution 1).
c. Rinse cartridge with 250 μL 50% ACN/1% FA two times (Elution 2).
d. Dry eluted peptides using SpeedVac and store at −80°C until mass spectrometry analysis.
e. Resuspend peptides in 5 μL 3% ACN/5% FA and inject 4 μL onto mass spectrometer.
Figure 4 Setting 50 mg tC18 Sep-Pak cartridges on a vacuum manifold for HLA-I peptides elution from beads
Expected outcomes
HLA-I peptides were analyzed in the mass spectrometry facility as described by Klaeger et al.10. Samples prepared with this protocol yielded thousands of peptides (Table 1 ) in the expected length of 8–11 amino acids and sequence motifs matching the expressed HLA-I alleles in A549 and HEK293T cells.1 Incubating the cell lysates for 3 h at 4°C (step 4 of the protocol) led to complete inactivation of SARS-CoV-2 as determined by a standard plaque assay (Figure 2).Table 1 Summary of the number of cells, beads, antibody and resolved HLA-I peptides
Cell line Number of cells Number of IPs Gamma-bind plus sepharose beads (per IP) W6/32 antibody (per IP) Total peptides Viral peptides
A549/ACE2/TMPRSS2 + SARS-CoV-2 24 h 4.5∗10ˆ7 6 30 μL 10 μg (50 μL) 6,372 13
HEK293T/ACE2/TMPRSS2 + SARS-CoV-2 24 h 6∗10ˆ7 6 30 μL 10 μg (50 μL) 1,336a 8
a SARS-CoV-2 infection resulted in ∼50% cell death of the infected HEK293T cells, reducing the total amount of resolved HLA-I peptides.
Limitations
Since virus inactivation is achieved through detergent-mediated disruption of viral envelope, this protocol does not inactivate non-enveloped viruses. In addition, the relatively large number of infected cells required to obtain sufficient input material for mass spectrometry (45–60∗10ˆ6 cells per experiment) imposes a challenge when working with primary cells and cell lines with slow growth rate.
Troubleshooting
Problem 1
Virus is not fully inactivated after 3 h incubation of cell lysates at 4°C (step 4 of the protocol).
Potential solution
Increase the volume of the lysis buffer and/or incubation time. When calibrating the protocol for VSV, we noticed that adding 350 μL lysis buffer to a 10 cm dish (the ratio previously used for HLA-I peptidome experiments in uninfected cells) was not sufficient to inactivate the virus (Table 2 ). Increasing the volume by ∼3-fold to 1 mL per 10 cm dish resulted in complete inactivation. The same amount was sufficient to inactivate SARS-CoV-2 as well (Figure 2), however, it is possible that other viruses will require more stringent conditions.Table 2 Calibrating VSV inactivation in infected HEK239T cells
Condition Lysates treatment Estimated PFU/mL
Negative cntrl Non-infected cells + 350 μL Lysis buffer 0
Positive cntrl Infected cells + 350 μL PBS Too many to count
Condition #1 Infected cells + 350 μL lysis buffer 4c for 3 h 4.00E+04
Condition #2 Infected cells +1,000 μL lysis buffer 4c for 3 h 0
HEK239T cells were infected with VSV-eGFP (Indiana strain11) at an MOI of 1. After 24 h, infected cells were harvested in a lysis buffer or PBS. The amount of added buffer and incubation time are indicated in the table. Cell lysates were then diluted 1:100 and 1:1,000 in serum-free media and added to fully confluent Vero cells in a 6-well plate. After 1 h incubation at 37°C, lysates were removed and methocel plaquing media was added. 24 h post infection, cells were stained in methylene blue for 30 min at room temperature and VSV plaques were counted by eye. The computed plaque forming unit (PFU)/mL of each tested condition are listed in the table.
Problem 2
Beads do not form a proper pellet after the 3 h immunoprecipitation (step 5 of the protocol).
Potential solution
Ensure that the lysis buffer contains a sufficient amount of Mg2+ (see Lysis Buffer Base composition in materials and equipment). Undigested DNA can interfere with HLA immunoprecipitation and the quality of the results. This protocol does not use sonication for DNA shearing and thus relies on the enzymatic activity of Benzonase. Mg2+ is an essential cofactor of Benzonase and using a MgCl2 containing buffer is critical for the success of the protocol.2 We highly encourage readers to use the exact composition of all buffers as provided in the materials and equipment section and to not combine with buffers from other protocols. Furthermore, peptides can be subjected to a second desalting step before LC-MS/MS analysis if contamination is visible.
Problem 3
No HLA-I peptides are detected in mass spectrometry (expected outcomes section).
Potential solution
Failing to detect HLA-I peptides after immunoprecipitation of the HLA-I complex can be a result of low HLA-I expression in the tested cell line. To ensure proper expression of the HLA-I complex on the cell surface, flow cytometry analysis can be performed with a specific fluorophore-conjugated pan-HLA-I antibody (Figure 5 ).Figure 5 Flow cytometry analysis of HLA-I expression on the surface of HEK293T cells
1∗10ˆ6 HEK293T cells were harvested using trypsin, washed once with 1× PBS and resuspended in 100 μL 1× PBS. Cells were stained with 3 μL of anti-human HLA-A,B,C antibody (FITC-W6/32) and incubated for 25 min at room temperature. After staining, cells were washed once with PBS, resuspended in 700 μL PBS and analyzed in flow cytometry.
(A) Non-stained cells were used as negative control to determine autofluorescence background. Population gating using FSC and SSC channels is shown in the left panel and FITC intensity levels are shown on the right.
(B) Similar to (A) but for cells stained with pan-HLA-I antibody showing HLA-I expression above background.
Problem 4
Viral peptides are not observed in the pool of HLA-I peptides (expected outcomes section).
Potential solution
Absence of viral sequences from the pool of the observed HLA-I peptides can result from low abundance of viral proteins or from missing the peak of viral peptides presentation. There are few potential solutions depending on the source of the problem:• Ensure that the chosen cells are properly infected with the virus using appropriate methods to quantify virus infectivity such as plaque assay, tissue culture infectious dose (TCID50) etc.
• Consider increasing the MOI to achieve a higher virus copy number per cell.
• Consider performing the HLA-I immunoprecipitation at a different time point. Notably, a few studies, including ours, found that some viral HLA-I peptides peak as early as 3–6 h post infection.1 , 8 , 9 Profiling HLA-I peptidome at later time points can miss the detection window of early presented viral peptides.
• Increase the number of infected cells used for the immunoprecipitation in order to increase the coverage of detected peptides.
Problem 5
Massive cell death due to virus infection leading to floating cells in the dish. Performing on-plate cell lysis and discarding the media under these conditions (step 1 of the protocol) will result in losing a substantial number of cells, thereby lowering the yield of HLA-I peptides.
Potential solution
To reduce the fraction of dead cells, consider infecting cells with lower MOI or performing the HLA-I immunoprecipitation at an earlier time point post infection. If you wish to include floating cells in the HLA-I peptidome analysis, collect the culture media in a conical tube and pellet down floating cells by centrifugation at 1,000 × g 4°C for 5 min. Remove supernatant, and wash the cell pellet once with ice cold 1× PBS. Perform on-plate cell lysis for the remaining attached cells as described in step 1 and use the same cell lysate to resuspend the pellet of floating cells.
Resource availability
Lead contact
Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Shira Weingarten-Gabbay ([email protected]).
Materials availability
This study did not generate new unique reagents.
Data and code availability
This study did not generate new datasets or code.
Acknowledgments
We thank Sean Whelan for generously gifting VSV-eGFP Indiana strain.11 We thank Rhonda O'Keefe for advice and guidance. This study was supported in part by a grant from the 10.13039/100000060 National Institute of Allergy and Infectious Diseases (U19AI110818 to P.C.S.). This work was supported by the 10.13039/100000054 National Cancer Institute (NCI) grants U24CA271075, U24CA270823, U24CA210986, and U24CA210979 to S.A.C. and 10.13039/100005984 Dr. Miriam and Sheldon G. Adelson Medical Research Foundation to S.A.C. S.W.-G. is the recipient of a 10.13039/100004412 Human Frontier Science Program fellowship (LT-000396/2018), 10.13039/100004410 EMBO non-stipendiary long-term fellowship (ALTF 883-2017), the Gruss-Lipper postdoctoral fellowship, the Zuckerman STEM Leadership Program fellowship, and the Rothschild Postdoctoral Fellowship. D.B.K. is supported by 1R01HL157174-01A1. M.S. is supported by 10.13039/100007161 Boston University startup funds.
Author contributions
S.W.-G. conceptualized the study. S.W.-G., L.R.P., D.-Y.C., S.K., and N.L.W. performed experiments. H.B.T., D.B.K., S.A.C., and J.G.A. contributed protocols. M.S. and P.C.S. supervised experiments. S.W.-G., L.R.P., D.-Y.C., M.S., and P.C.S. wrote the manuscript with comments from all authors.
Declaration of interests
S.W.-G., S.K., S.A.C., J.G.A., M.S., and P.C.S. are named co-inventors on a patent application related to this manuscript filed by The Broad Institute that is being made available in accordance with the COVID-19 technology licensing framework to maximize access to university innovations. N.L.W. is a consultant for Carver Biosciences. D.B.K. own equity in Affimed N.V., Agenus, Armata Pharmaceuticals, Breakbio, BioMarin Pharmaceutical, Celldex Therapeutics, Clovis Oncology, Editas Medicine, Exelixis, Gilead Sciences, Immunitybio, ImmunoGen, IMV, Lexicon Pharmaceuticals, Moderna, Neoleukin Therapeutics, Regeneron Pharmaceuticals, and Sesen Bio. BeiGene, a Chinese biotech company, supported unrelated SARS COV-2 research at TIGL. S.A.C. is a member of the scientific advisory boards of Kymera, PrognomIQ, PTM BioLabs, and Seer and an ad hoc scientific advisor to Pfizer and Biogen. J.G.A. is a past employee and shareholder of Neon Therapeutics, Inc. (now BioNTech US). P.C.S. is a co-founder of and consultant to Sherlock Biosciences and Delve Biosciences and a board member of Danaher Corporation and holds equity in the companies.
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References
1 Weingarten-Gabbay S. Klaeger S. Sarkizova S. Pearlman L.R. Chen D.-Y. Gallagher K.M.E. Bauer M.R. Taylor H.B. Dunn W.A. Tarr C. Profiling SARS-CoV-2 HLA-I peptidome reveals T cell epitopes from out-of-frame ORFs Cell 184 2021 3962 3980.e17 34171305
2 Abelin J.G. Harjanto D. Malloy M. Suri P. Colson T. Goulding S.P. Creech A.L. Serrano L.R. Nasir G. Nasrullah Y. Defining HLA-II ligand processing and binding rules with mass spectrometry enhances cancer epitope prediction Immunity 51 2019 766 779.e17 31495665
3 Keskin D.B. Reinhold B. Lee S.Y. Zhang G. Lank S. O’Connor D.H. Berkowitz R.S. Brusic V. Kim S.J. Reinherz E.L. Direct identification of an HPV-16 tumor antigen from cervical cancer biopsy specimens Front. Immunol. 2 2011 75 22566864
4 Reinhold B. Keskin D.B. Reinherz E.L. Molecular detection of targeted major histocompatibility complex I-bound peptides using a probabilistic measure and nanospray MS3 on a hybrid quadrupole-linear ion trap Anal. Chem. 82 2010 9090 9099 20932029
5 Schellens I.M. Meiring H.D. Hoof I. Spijkers S.N. Poelen M.C.M. van Gaans-van den Brink J.A.M. Costa A.I. Vennema H. Keşmir C. van Baarle D. van Els C.A.C.M. Measles virus epitope presentation by HLA: novel insights into epitope selection, dominance, and microvariation Front. Immunol. 6 2015 546 26579122
6 Rucevic M. Kourjian G. Boucau J. Blatnik R. Garcia Bertran W. Berberich M.J. Walker B.D. Riemer A.B. Le Gall S. Analysis of major histocompatibility complex-bound HIV peptides identified from various cell types reveals common nested peptides and novel T cell responses J. Virol. 90 2016 8605 8620 27440904
7 McMurtrey C.P. Lelic A. Piazza P. Chakrabarti A.K. Yablonsky E.J. Wahl A. Bardet W. Eckerd A. Cook R.L. Hess R. Epitope discovery in West Nile virus infection: identification and immune recognition of viral epitopes Proc. Natl. Acad. Sci. USA 105 2008 2981 2986 18299564
8 Croft N.P. Smith S.A. Wong Y.C. Tan C.T. Dudek N.L. Flesch I.E.A. Lin L.C.W. Tscharke D.C. Purcell A.W. Kinetics of antigen expression and epitope presentation during virus infection PLoS Pathog. 9 2013 e1003129 23382674
9 Wu T. Guan J. Handel A. Tscharke D.C. Sidney J. Sette A. Wakim L.M. Sng X.Y.X. Thomas P.G. Croft N.P. Quantification of epitope abundance reveals the effect of direct and cross-presentation on influenza CTL responses Nat. Commun. 10 2019 2846 31253788
10 Klaeger S. Apffel A. Clauser K.R. Sarkizova S. Oliveira G. Rachimi S. Le P.M. Tarren A. Chea V. Abelin J.G. Optimized liquid and gas phase fractionation increases HLA-peptidome coverage for primary cell and tissue samples Mol. Cell. Proteomics 20 2021 100133 34391888
11 Whelan S.P. Ball L.A. Barr J.N. Wertz G.T. Efficient recovery of infectious vesicular stomatitis virus entirely from cDNA clones Proc. Natl. Acad. Sci. USA 92 1995 8388 8392 7667300
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Lancet
Lancet
Lancet (London, England)
0140-6736
1474-547X
Elsevier Ltd.
S0140-6736(22)02395-9
10.1016/S0140-6736(22)02395-9
Comment
Workforce and workplace racism in health systems: organisations are diverse but not inclusive
Naqvi Habib a
Williams Reginald D II b
Chinembiri Owen a
Rodger Sam a
a NHS Race and Health Observatory, London SW1P 3HZ, UK
b The Commonwealth Fund, New York, NY 10021, USA
8 12 2022
10-16 December 2022
8 12 2022
400 10368 20232026
© 2022 Elsevier Ltd. All rights reserved.
2022
Elsevier Ltd
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
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pmcIn the context of the COVID-19 pandemic and the international reckoning on racial justice, there is a fundamental imperative for organisations to expose and combat racism and bias within the health-care workforce. Racism is a public health threat and there is an opportunity for individuals and institutions to identify and reverse racist policies and practices that lead to unequal treatment, outcomes, and experiences in health care.1 At present, the UK and the USA have workplaces that are increasingly diverse but are not inclusive. Here, we explore this problem in the context of these two countries and what steps need to be taken to improve inclusivity.
In 1948, the UK National Health Service (NHS) was established on the back of a migrant workforce and has, over time, been sustained with crucial contributions from overseas health-care professionals.2 In 2022, 24·1% (332 102) of staff in the NHS were from minority ethnic backgrounds, a substantial increase from 16·2% 10 years ago,3 and higher than the 15·2% in the general population.4 Furthermore, as many as 30% (108 976) of NHS nurses and 45% (62 344) of doctors are from minority ethnic backgrounds.5 With the present chronic shortage of clinical staff in the NHS, and insufficient numbers of people being trained domestically,6 the NHS is likely to become more reliant on recruitment from overseas.7 Today, the largest number of new nurses being recruited to the NHS are from India, Nigeria, and the Philippines.8 But this diversity of the NHS workforce does not guarantee equality or inclusivity. The situation differs in some respects in the USA. Racism and insufficient diversity in the US health-care workforce can be partly traced to the 1910 Flexner Report, which led to the closure of all but two Black medical schools and a substantial decrease in the number of Black physicians over the years.9 A 2021 study of racial and ethnic representation in the US health-care workforce estimated that in 2019, 12·1% (17 916 227) of the workforce was Black.10 Across ten health professions studied, Black representation ranged from 3·3% for physical therapists to 11·4% for respiratory therapists.10 Latino people accounted for 18·2% (26 953 648) of the US workforce, with representation in the ten health professions ranging from 3·4% to 10·8%.10 In 2021, only 0·6% (850 074) of the US workforce were Native Americans and their representation in the ten professions ranged from 0 to 0·9%.10 Meanwhile, Black female staff are over-represented in the US health-care workforce but are heavily concentrated in low-wage jobs in the long-term care sector and in hospitals as a result of enduring sexism and racism.11
Nurse is using digital tablet in hospital© 2022 FS Productions/Getty Images
2022
Alongside the diversity of the health-care workforce, it is important to examine whether the leadership of health-care organisations is diverse and whether minority ethnic staff have equitable experiences in the workplace compared with White staff. Data show that minority ethnic staff are less likely to progress to senior and leadership roles12 and more likely to experience discrimination, bullying, harassment, and victimisation in the workplace on the basis of race and ethnicity.13 Racism can play out in the way minority ethnic staff are less likely to be recruited or receive professional development and be promoted, and are more likely to face disciplinary processes within the workplace than their White counterparts.14 Racism also surfaces in the form of ethnicity pay gaps. In the USA, the incomes of Black physicians are substantially less than White physicians, even after the characteristics of physicians and practices are accounted for.15 Pay gaps are also evident in the UK's NHS. In May, 2020, Black NHS staff had lower monthly basic pay than White staff—Black men were paid £0·84 for every £1·00 paid to White men, and Black women were paid £0·93 for every £1·00 paid to White women.16 Across health-care organisations minority ethnic staff are under-represented in leadership roles. In the USA, Black and other racially minoritised people make up about 40% of the US population, but they hold only 16% of leadership roles in hospitals.17 In the UK, although some progress is being made, minority ethnic staff continue to be under-represented at senior levels.18
These problems hold true for usual workplace conditions but the disproportionate impact of COVID-19 on minority ethnic health-care workers has added another dimension to staff experiences. In the NHS, minority ethnic staff were more likely to work on COVID-specific wards than White staff (49·2% vs 34·7%).19 Health and social care workers from minority ethnic backgrounds were also disproportionally represented in COVID-19 deaths.20 Similarly, a study of nearly 25 000 health-care workers in the USA highlighted the impacts of structural racism (eg, Black staff working and living in places where there was higher community spread of COVID-19) and bias and discrimination (eg, Black personnel being less likely to get SARS-CoV-2 testing than White staff) during the pandemic.21
These inequalities and inequities must be tackled urgently. As we have highlighted, organisational diversity is not a precursor to progressive, compassionate, and inclusive workplace cultures. But this can be achieved if the right conditions are established within health-care organisations over time. For that to happen, organisations must take a strategic, long-term approach to create a culture of equity and inclusion.
With effective leadership, embedded accountability, and concerted efforts by all members of staff, organisations can see year-on-year improvements across indicators of inclusivity, including self-reported bullying and harassment in the workplace, and on representation in senior and leadership positions within the organisation.22 However, such efforts need to be consistent and persistent over time and must be funded and embedded as strategic organisational priorities. Experience suggests that when there is a clear focus on committed leadership, data-driven accountability, effective communication, and engagement with staff actions to advance anti-racism and inclusivity benefit not only minority ethnic staff, but also the whole workforce.22 Addressing systemic bias and racism is a moral imperative and can also strengthen organisations23 and make economies stronger.24 During a time of global austerity, this is an important consideration.
There is no single fix to the structural issues we have outlined, but there are steps that organisations and policy makers need to take to begin to make the difference needed. For instance, racial bias is embedded in clinical curricula in which, for example, learning materials focus on the presentation of disease on white skin alone or flawed race-based medicine.25 Reviewing and replacing such materials can reduce the introduction of bias in the future workforce. Similarly, there is a need to address ethnicity pay gaps,26, 27 and examine not only median pay difference, but also lifetime earnings, pension inequality, and bonuses. In addition, health-care providers should consider not just how to diversify recruitment, but how they are fostering talent and creating career progression for under-represented groups to join management, executive leadership, and governing boards of health-care organisations. Training can define and spotlight the ways in which structural and institutional racism manifest in the workplace. However, organisations need to go further and embed an understanding of structural racism and inequity by interrogating and reforming workplace policies and programmes. Finally, all these actions can only be effective if built on a foundation of systematically collected and analysed data about workforces. These metrics will help guide targeted action to advance racial equity in the workforce and can also be used to hold decision makers, the organisation, and individuals to account.
These interventions need to be an integral part of a broad set of initiatives that should be focused on with the same thoroughness as any other organisational strategic priority. This strategic approach must be a key leadership responsibility. It is therefore essential that leaders identify and are held accountable for how they implement transformational change to achieve racial equity in the workplace in a way that ultimately leads to equitable opportunities for all.
HN is the Director of the NHS Race and Health Observatory. RDW is Vice President of the International Health Policy and Practice Innovations programme at The Commonwealth Fund. OC and SR declare no competing interests.
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References
1 Hackett RA Ronaldson A Bhui K Steptoe A Jackson SE Racial discrimination and health: a prospective study of ethnic minorities in the United Kingdom BMC Public Health 20 2020 1652 33203386
2 Kramer A Many rivers to cross: the history of the Caribbean contribution to the NHS 2006 Stationery Office London
3 NHS Digital Equality and diversity in NHS trusts and CCGs in England, September 2009 to March 2022 https://files.digital.nhs.uk/57/5B85DE/Equality%20and%20diversity%20in%20NHS%20Trusts%20and%20CCGs%20March%202022.xlsx 2022
4 Office for National Statistics Population estimates by ethnic group and religion, England and Wales 2019 https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationesti-mates/articles/populationestimatesbyethnicgroupand religionenglandandwales/2019 2019
5 NHS England NHS Workforce Statistics—September 2021. Jan 6, 2022 https://digital.nhs.uk/data-and-information/publications/statistical/nhs-workforce-statistics/september-2021#top
6 GBD 2019 Human Resources for Health Collaborators Measuring the availability of human resources for health and its relationship to universal health coverage for 204 countries and territories from 1990 to 2019: a systematic analysis for the Global Burden of Disease Study 2019 Lancet 399 2022 2129 2154 35617980
7 NHS England NHS Long Term Plan https://www.longtermplan.nhs.uk/online-version/chapter-4-nhs-staff-will-get-the-backing-they-need/4-international-recruitment/
8 The Nursing and Midwifery Council Total number of people on the register by registration type https://www.nmc.org.uk/globalassets/sitedocuments/data-reports/march-2022/nmc-register-march-2022-uk-data-tables.xls 2022
9 St Fleur N Listen: how one 1910 report curtailed Black medical education for over a century. STAT. April 4, 2022 https://www.statnews.com/2022/04/04/color-code-flexner-report-curtailed-black-medical-education/
10 Salsberg E Richwine C Westergaard S Estimation and comparison of current and future racial/ethnic representation in the US health care workforce JAMA Netw Open 4 2021 e213789 33787910
11 Dill J Duffy M Structural racism and Black women's employment in the US health care sector Health Aff (Millwood) 41 2022 265 272 35130061
12 NHS England Workforce Race Equality Standard: 2021 data analysis report for NHS Trusts https://www.england.nhs.uk/wp-content/uploads/2022/04/Workforce-Race-Equality-Standard-report-2021-.pdf 2021
13 NHS NHS Staff Survey 2021: national results briefing https://www.nhsstaffsurveys.com/static/1f3ea5c952df62a98b90afcf3daa29ac/ST21-National-briefing.pdf 2021
14 Priest N Esmail A Kline R Rao M Coghill Y Williams DR Promoting equality for ethnic minority NHS staff—what works BMJ 351 2015 h3297 26157106
15 Ly DP Seabury SA Jena AB Newhouse RL Differences in incomes of physicians in the United States by race and sex: observational study BMJ 353 2016 i2923 27268490
16 UK Government NHS basic pay. July 23, 2021 https://www.ethnicity-facts-figures.service.gov.uk/workforce-and-business/public-sector-pay/nhs-basic-pay/latest
17 American Hospital Association Health equity snapshot: a toolkit for action https://www.aha.org/system/files/media/file/2020/12/ifdhe_snapshot_survey_FINAL.pdf 2020
18 NHS England Top jobs in NHS more diverse than any point in history. April 7, 2022 https://www.england.nhs.uk/2022/04/top-jobs-in-nhs-more-diverse-than-any-point-in-history/
19 NHS England NHS Staff Survey 2021 national dashboards https://public.tableau.com/app/profile/piescc/viz/ST21_national_data_2022-03-30_PIEFH25/Aboutthissurvey 2021
20 Cook T Kursumovic E Lennane S Deaths of NHS staff from COVID-19 analysed. Health Service Journal. April 22, 2020 https://www.hsj.co.uk/exclusive-deaths-of-nhs-staff-from-covid-19-analysed/7027471.article
21 Lusk JB Xu H Thomas LE Racial/ethnic disparities in healthcare worker experiences during the COVID-19 pandemic: an analysis of the HERO registry EClinicalMedicine 45 2022 101314 35265822
22 NHS EnglandNorth East London NHS Foundation Trust Workforce Race Equality Standard: exemplar for embedding and sustaining continuous improvements. July, 2019 https://www.england.nhs.uk/wp-content/uploads/2019/11/nelft-case-study.pdf
23 UK Government Race in the workplace: The McGregor-Smith Review https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/594336/race-in-workplace-mcgregor-smith-review.pdf 2017
24 Ross S Jabbal J Chauhan K Workforce race equality and inclusion in NHS providers. The King's Fund https://www.kingsfund.org.uk/sites/default/files/2020-07/workforce-race-inequalities-inclusion-nhs-providers-july2020.pdf 2020
25 Joseph OR Flint SW Raymond-Williams R Awadzi R Johnson J Understanding healthcare students’ experiences of racial bias: a narrative review of the role of implicit bias and potential interventions in educational settings Int J Environ Res Public Health 18 2021 12771 34886495
26 O’Doud A Government promises action on NHS's ethnic pay gaps and health inequalities in England BMJ 376 2022 o735 35304406
27 Losavio J What racism costs us all. International Monetary Fund https://www.imf.org/en/Publications/fandd/issues/2020/09/the-economic-cost-of-racism-losavio 2020 (accessed Nov 14 2022).
| 36502831 | PMC9731575 | NO-CC CODE | 2022-12-14 23:31:50 | no | Lancet. 2022 Dec 8 10-16 December; 400(10368):2023-2026 | utf-8 | Lancet | 2,022 | 10.1016/S0140-6736(22)02395-9 | oa_other |
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Lancet Psychiatry
Lancet Psychiatry
The Lancet. Psychiatry
2215-0366
2215-0374
The Authors. Published by Elsevier Ltd.
S2215-0366(22)00375-3
10.1016/S2215-0366(22)00375-3
Articles
Prevalence of post-traumatic stress disorder and common mental disorders in health-care workers in England during the COVID-19 pandemic: a two-phase cross-sectional study
Scott Hannah R PhD a*
Stevelink Sharon A M PhD a**
Gafoor Rafael PhD c
Lamb Danielle PhD c
Carr Ewan PhD b
Bakolis Ioannis PhD b
Bhundia Rupa BSc a
Docherty Mary Jane MRCPsych d
Dorrington Sarah MRCPsych ad
Gnanapragasam Sam MRCPsych ad
Hegarty Siobhan MSc a
Hotopf Matthew FMedSci ad
Madan Ira FFOM e
McManus Sally MSc fg
Moran Paul FRCPsych h
Souliou Emilia MSc a
Raine Rosalind PhD c
Razavi Reza FRCP i
Weston Danny PhD a
Greenberg Neil FRCPsych a†
Wessely Simon FMedSci a†
a Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
b Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
c Department of Applied Health Research, University College London, London, UK
d South London and Maudsley NHS Foundation Trust, London, UK
e Department of Occupational Health, Guy's and St Thomas' NHS Foundation Trust, London, UK
f NatCen Social Research, London, UK
g Violence and Society Centre City, University of London, London, UK
h Department of Population Health Sciences, Centre for Academic Mental Health, Bristol Medical School, University of Bristol, Bristol, UK
i Wellcome/EPSRC Centre For Medical Engineering, London, UK
* Correspondence to: Dr Sharon Stevelink, Department of Psychological Medicine, Institute of Psychiatry Psychology and Neuroscience, King's College London, London SE5 9RJ, UK
* Joint first authors
† Joint last authors
8 12 2022
1 2023
8 12 2022
10 1 4049
© 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license
2023
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Background
Previous studies on the impact of the COVID-19 pandemic on the mental health of health-care workers have relied on self-reported screening measures to estimate the point prevalence of common mental disorders. Screening measures, which are designed to be sensitive, have low positive predictive value and often overestimate prevalence. We aimed to estimate prevalence of common mental disorders and post-traumatic stress disorder (PTSD) among health-care workers in England using diagnostic interviews.
Methods
We did a two-phase, cross-sectional study comprising diagnostic interviews within a larger multisite longitudinal cohort of health-care workers (National Health Service [NHS] CHECK; n=23 462) during the COVID-19 pandemic. In the first phase, health-care workers across 18 NHS England Trusts were recruited. Baseline assessments were done using online surveys between April 24, 2020, and Jan 15, 2021. In the second phase, we selected a proportion of participants who had responded to the surveys and conducted diagnostic interviews to establish the prevalence of mental disorders. The recruitment period for the diagnostic interviews was between March 1, 2021 and Aug 27, 2021. Participants were screened with the 12-item General Health Questionnaire (GHQ-12) and assessed with the Clinical Interview Schedule-Revised (CIS-R) for common mental disorders or were screened with the 6-item Post-Traumatic Stress Disorder Checklist (PCL-6) and assessed with the Clinician Administered PTSD Scale for DSM-5 (CAPS-5) for PTSD.
Findings
The screening sample contained 23 462 participants: 2079 participants were excluded due to missing values on the GHQ-12 and 11 147 participants due to missing values on the PCL-6. 243 individuals participated in diagnostic interviews for common mental disorders (CIS-R; mean age 42 years [range 21–70]; 185 [76%] women and 58 [24%] men) and 94 individuals participated in diagnostic interviews for PTSD (CAPS-5; mean age 44 years [23–62]; 79 [84%] women and 15 [16%] men). 202 (83%) of 243 individuals in the common mental disorders sample and 83 (88%) of 94 individuals in the PTSD sample were White. GHQ-12 screening caseness for common mental disorders was 52·8% (95% CI 51·7–53·8). Using CIS-R diagnostic interviews, the estimated population prevalence of generalised anxiety disorder was 14·3% (10·4–19·2), population prevalence of depression was 13·7% (10·1–18·3), and combined population prevalence of generalised anxiety disorder and depression was 21·5% (16·9–26·8). PCL-6 screening caseness for PTSD was 25·4% (24·3–26·5). Using CAPS-5 diagnostic interviews, the estimated population prevalence of PTSD was 7·9% (4·0–15·1).
Interpretation
The prevalence estimates of common mental disorders and PTSD in health-care workers were considerably lower when assessed using diagnostic interviews compared with screening tools. 21·5% of health-care workers met the threshold for diagnosable mental disorders, and thus might benefit from clinical intervention.
Funding
UK Medical Research Council; UCL/Wellcome; Rosetrees Trust; NHS England and Improvement; Economic and Social Research Council; National Institute for Health and Care Research (NIHR) Biomedical Research Centre at the Maudsley and King's College London (KCL); NIHR Protection Research Unit in Emergency Preparedness and Response at KCL.
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pmcIntroduction
During the COVID-19 pandemic, health-care systems across the world have been subject to considerable strain, which in turn has stimulated global efforts to understand how this has affected health-care workers. In addition to stressors common to all, including the risk of infection, social isolation, and difficulties obtaining child care, clinical and non-clinical health-care workers have faced distinct stressors such as overwork, increased patient mortality, staffing difficulties, inadequate personal protective equipment, potential moral injury (ie, distress experienced due to a conflict between one's personal morals and actions observed or undertaken), and the need to adapt working practices to manage infection risk. Numerous studies estimating the prevalence of mental disorders among health-care workers have been conducted since the start of the COVID-19 pandemic.1 An umbrella review of evidence from the COVID-19 pandemic and previous viral outbreaks demonstrated highly heterogeneous estimates of prevalence of mental disorders.1 Pooled prevalence estimates of anxiety and depression were commonly used, but varied substantially (9–90% for anxiety; 5–65% for depression). Post-traumatic stress disorder (PTSD) was less commonly assessed, with prevalence estimates between 7 and 37%.
Research in context
Evidence before this study
Numerous studies have examined the impact of the COVID-19 pandemic on the mental health and wellbeing of health-care workers. We used a 2021 review of reviews consisting of 13 systematic reviews and one umbrella review of meta-analyses examining the prevalence of mental disorders in health-care workers during the COVID-19 pandemic. This review included studies published up to June 2, 2021; we did an additional systematic search of PubMed Central and MEDLINE for articles published between June 2 and Nov 12, 2021, using the same search parameters and inclusion criteria as the review. The search terms used were “healthcare”, “burnout”, “mental health”, “COVID-19”, and “SARS-CoV-2”, in addition to the controlled vocabulary of the database, and studies were included if they were English language publications that contained a quantitative analysis investigating the prevalence of anxiety, depression, burnout syndrome, post-traumatic stress disorder (PTSD), sleep disorders, and other mental health outcomes in health-care workers during the COVID-19 pandemic. Our additional search identified nine additional reviews. Included reviews had a wide global reach and sampled a range of health-care worker populations, often focusing on front-line staff. The reviews provided estimates of several mental disorders: most commonly anxiety and depression and less frequently PTSD. Prevalence estimates of all three disorders varied widely (9–90% for anxiety, 5–65% for depression, and 7–37% for PTSD). Reviews also examined a range of stress-related and sleep-related difficulties. The individual studies described in the reviews were typically cross-sectional and employed a range of screening tools to assess PTSD, depression, and anxiety, and were commonly administered through self-report online surveys.
Added value of this study
The diagnostic interviews used in this study provide a more accurate estimate of prevalence than previous studies using screening tools, since cutoff scores on screening tools favour sensitivity over specificity. Using a two-phase epidemiological design as a practical methodological approach to generate accurate estimates of the prevalence of common mental disorders and PTSD in a population of health-care workers broadly representative of the National Health Service workforce in England in terms of ethnicity, age, sex, and clinical role, we found the prevalence of depression was 13·7%, generalised anxiety disorder was 14·3%, and PTSD was 7·9%. The combined prevalence of depression and generalised anxiety disorder was 21·5%.
Implications of all the available evidence
Self-report screening surveys conducted among health-care workers during the pandemic have overestimated the prevalence of mental disorders. However, the findings from diagnostic interviews suggest a considerable number of health-care workers have a diagnosable mental disorder (eg, depression, generalised anxiety disorder, or PTSD) that might benefit from a clinical intervention.
This evidence is largely based on online surveys using screening tools for mental disorders. Generally, a screening tool is a brief measure that identifies so-called caseness, on the basis of mental health symptoms, characteristics, or traits. Typically, a cutoff score is used as an indicator of probable mental disorder or clinically significant symptoms.2 This method allows for relatively rapid and low-cost data collection with large samples. However, many of the validated screening tools widely used in mental health research favour sensitivity over specificity and therefore have low positive predictive value,3 and thus are likely to overestimate the true prevalence of mental disorders.4
Diagnostic interviews, in which trained interviewers use structured tools to assess patients by operationalising diagnostic criteria, are considered the gold standard for identification of mental disorders.5 These assessments are more resource intensive and in general more extensive than screening tools. A two-phase epidemiological survey design6 enables efficient and accurate estimation of prevalence by using surveys to screen for disorder in a sample of participants from a target population, followed by structured diagnostic interviews administered to a proportion of participants who completed the screening measure and were selected according to their response on the initial survey.
Considering the wide variation in estimates of prevalence of mental disorders among health-care workers during the COVID-19 pandemic, obtaining an accurate understanding of the burden caused by mental illness in this population is important to plan for the scale and nature of clinical resources required, and to help direct preventative approaches. In this study, we aimed to estimate the prevalence of clinically diagnosable common mental disorders and PTSD among health-care workers in England during the COVID-19 pandemic using diagnostic interviews.
Methods
Study design and participants
We nested a two-phase cross-sectional survey within National Health Service (NHS) CHECK, a prospective cohort study examining the health and wellbeing of health-care workers during the COVID-19 pandemic. Full details of this study are outlined in a protocol paper.7 Briefly, in the first phase we recruited health-care workers across 18 NHS Trusts to a longitudinal study assessing the psychosocial impact of the pandemic. An NHS Trust is an organisational unit within the NHS of England and Wales, generally serving either a geographical area or offering specialist services. Baseline assessments were done using online surveys between April 24, 2020, and Jan 15, 2021. We included both acute and mental health NHS Trusts. In the second phase, we selected a proportion of participants who had responded to the surveys and conducted diagnostic interviews to establish the prevalence of mental disorders.
The recruitment period for the diagnostic interviews was between March 1, 2021, and Aug 27, 2021. We identified eligible participants through a database of NHS CHECK participants who had completed the baseline assessment for NHS CHECK and who had given permission to be contacted about further research. At the baseline assessment, participants filled in validated screening tools for mental disorders: the 12-item General Health Questionnaire (GHQ-12) to screen for common mental disorders (including depression and anxiety) and the 6-item Post-Traumatic Stress Disorder Checklist (PCL-6) to screen for PTSD. Consistent with previous studies, we oversampled cases for the diagnostic interviews, compared with non-cases, whereby 50% of invited interviewees met probable caseness on the GHQ-12 or PCL-6 administered at baseline.6, 8 The other 50% of invited interviewees did not meet probable caseness on either of these screening tools.
We used the same method to separately recruit two samples of health-care workers until the desired sample size for each of the diagnostic interviews was reached; one sample was assessed for prevalence of common mental disorders and one for prevalence of PTSD. Eligible participants were stratified by NHS Trust and probable caseness on the basis of the baseline scores derived from the GHQ-12 and PCL-6. To ensure that the sample reflected the characteristics of the main NHS CHECK sample, we recruited the same proportion of participants from each NHS Trust, inviting a random selection of health-care workers who had completed the GHQ-12 and PCL-6 baseline assessments from each NHS Trust. However, over the course of the recruitment period certain groups of interest were less responsive, in particular health-care workers from ethnic minority backgrounds. Therefore, to ensure that the diagnostic interview samples reflected the characteristics of the main NHS CHECK sample (ie, NHS Trust, clinical role, age, sex, and ethnicity) and to ensure generalisability of the study to the wider health-care worker population, we targeted underrepresented groups for inclusion in the interview samples, randomly selecting from a list of health-care workers with these required characteristics. Potentially eligible participants were emailed an invitation to participate and an information sheet; individuals who responded confirming interest could book an interview slot via an online calendar, and subsequently completed a consent form. Ethical approval for this study was granted by the Health Research Authority (20/HRA/210, IRAS: 282686) and the Research and Development department of each local Trust.
Procedures
NHS CHECK baseline assessment data from the PCL-69 were used to identify participants with or without probable PTSD, whereby a score of 14 or higher was considered to indicate caseness. The GHQ-12,10 scored 0–0-1–1, was used to identify individuals with or without probable common mental disorders, whereby a score of 4 or higher was considered to indicate caseness.11, 12 The GHQ has strong psychometric properties compared with other such tools, and since anxiety and depression are generally the most prevalent mental disorders, using the scores on the GHQ as an indicator for probable common mental disorders is valid.10, 11, 13
The past-month version of the Clinician Administered PTSD Scale for the DSM-5 (CAPS-5)14 was used to assess participants for PTSD during the diagnostic interview. The CAPS-5 is a structured interview tool comprising 30 items across seven criteria referring to symptoms in the previous month. Diagnostic status for each participant was determined according to the CAPS-5 manual and consistent with DSM-5 rules, accounting for the presence of symptoms across each criterion, duration of symptoms, the extent to which symptoms are trauma related, and overall impairment and distress. The CAPS-5 has been shown to have good psychometric properties.15
The Clinical Interview Schedule-Revised (CIS-R)16 was used to assess participants for common mental disorders during the diagnostic interview. The CIS-R is a structured assessment tool that assesses 14 symptom groups related to common mental disorders in modules that branch according to responses. All modules of the tool were administered, although only the depression and anxiety modules were used in the analysis for this study. Diagnostic status for each participant was determined according to the CIS-R manual,17 using an algorithm to calculate status in accordance with ICD-10 rules for diagnosis of mild-to-severe depression and generalised anxiety disorder. The CIS-R has been shown to have good psychometric properties5 and has been used previously with a population of UK health-care workers to validate the GHQ-12.11
Diagnostic interviews conducted during phase two of the survey were done over the telephone or Microsoft Teams videoconferencing software with one of three study researchers (HRS, SH, or ES), each of whom had completed training in administering the CIS-R and CAPS-5 tools. Interviews typically lasted between 20 min and 1 h. Interviewers recorded interviewees' responses using Qualtrics survey software and interviews were audio recorded in case of a need for verification. Participants who completed the interview received a £25 gift voucher in recognition of time volunteered for the study.
The primary outcome was population prevalence of common mental disorders and PTSD.
Statistical analysis
We aimed to recruit 94 participants to take part in the CAPS-5 and 250 participants for the CIS-R interviews. For CIS-R, the required sample size was derived via a simulation study. The simulation study repeated all stages of the two-stage design including: (1) simulating caseness of the screening measure based on descriptive statistics for existing NHS CHECK respondents; (2) drawing subsamples of varying sizes; (3) simulating caseness from diagnostic interviews based on expert opinion and previous studies;11, 18 and (4) conducting the two-stage procedure where we weighted the interview sample (using raking weights) to estimate population prevalence and 95% CIs. These steps were repeated (1000 times) to provide estimates of the uncertainty of the prevalence estimate under differing scenarios.
We used descriptive statistics to describe outcome scores overall and by age group, sex, ethnic group, and clinical role. We compared the profile of the NHS CHECK screening sample with the diagnostic interview samples and with the target population of NHS staff at all participating Trusts in NHS CHECK.
We used a two-phase design to estimate the population prevalence of common mental disorders and PTSD.6, 8 First, we calculated the prevalence of probable common mental disorders and PTSD using the screening tools (GHQ-12 and PCL-6). The prevalence of a combined measure of any mental disorder was calculated based on screening positive on at least one of the screening measures (GHQ-12 or PCL-6). Second, we calculated the prevalence using diagnostic interviews (CIS-R for generalised anxiety disorder or depression and the CAPS-5 for PTSD). We also calculated a combined caseness measure (generalised anxiety disorder and depression) by combining the prevalence of both outcomes on the CIS-R. Third, to estimate population prevalence, we post-stratified caseness from the diagnostic interviews using information from the screening measures (GHQ-12 or PCL-6 prevalence) and applied a finite population correction based on total population size (n=21 383).19
The prevalence of each outcome after diagnostic interviews (CIS-R for generalised anxiety disorder or depression and the CAPS-5 for PTSD) are presented as proportions and 95% CIs. To account for differences between the screening cohort and the target population, all estimates were weighted. Missing values were excluded from the estimation of the point prevalences and associated CIs.
Survey weights were derived to account for differences between the baseline NHS CHECK cohort and the target population (NHS staff at all participating Trusts in NHS CHECK) by age (≤30, 31–40, 41–50, 51–60, and ≥61 years), sex (female or male), ethnic group (White, Black, Asian, Mixed, and Other), and clinical role (clinical or non-clinical). Information on these characteristics in the population were provided by human resources departments for each participating NHS Trust. Weights were derived by (1) harmonising information on the variables (age, sex, ethnicity, and clinical role) from the administrative records with corresponding variables from the survey; (2) imputing missing information in these variables among survey respondents using k-nearest neighbours (k=5) with the VIM package for R; (3) generating weights using iterative proportional fitting (ie, raking) with the survey package for R (version 4.1.0); (4) trimming extreme weights, such that individual weights greater than WT were fixed at WT, where WT equals the median weight plus five times the IQR. Two procedures were used: (1) post-stratification to estimate prevalence in the larger screening sample based on prevalence in the diagnostic interviews sample; (2) application of survey weights to the screening sample to correct for differences between this sample and the target population. These two procedures (post-stratification and survey weighting) were combined to give a final, weighted estimate of prevalence in the population.
The screened point prevalence of each of the outcomes was tabulated after the weighted continuous outcome scores were dichotomised by predetermined cutoffs (≥4 for GHQ-12 and ≥14 for PCL-6) and survey set using Stata statistical software (version 16.0). Survey setting enabled valid inference to be made from the screening sample to the population risk, taking into account clustering at the NHS Trust level; this was done by using the individual level weights and then post-stratifying using the NHS Trust sizes.
Role of the funding source
The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.
Results
The screening sample contained 23 462 participants (of a possible population of 152 228 [overall response rate to NHS CHECK 15·4%]): 2079 participants were excluded due to missing values on the GHQ-12 and 11 147 participants due to missing values on the PCL-6. 243 individuals participated in diagnostic interviews for common mental disorders (CIS-R) and 94 individuals participated in diagnostic interviews for PTSD (CAPS-5; overall response rate to diagnostic interviews 12·9%; figure ). Individuals who were missing GHQ-12 or PCL-6 values were more likely to be non-White than White (11·50% vs 6·76% for GHQ-12; 62·34% vs 44·06% for PCL-6; data not shown). No marked differences were identified among individuals with missing values versus those without missing values in terms of age, role, or sex.Figure Overview of participants invited to the diagnostic interviews
NHS=National Health Service. GHQ-12=12-item General Health Questionnaire. PCL-6=6-item Post-Traumatic Stress Disorder checklist. PTSD=post-traumatic stress disorder. CAPS-5=Clinician Administered PTSD Scale for the DSM-5. CIS-R=Clinical Interview Schedule-Revised. *Not mutually exclusive; some individuals were missing data for both the GHQ-12 and PCL-6.
The demographic compositions of the screening sample, of the two diagnostic interview samples (common mental disorders and PTSD), and of all NHS staff at the participating NHS Trusts are shown in table 1 . The two diagnostic interview samples were relatively similar to the screening sample: the majority of the samples were White, female, and clinical staff, with larger proportions of younger participants than older participants, and a larger proportion of staff earning an annual salary of more than £30 000. The composition of the common mental disorders sample was similar to the screening sample with regard to clinical setting (eg, Accident and Emergency, intensive care unit [ICU], or other), but the PTSD sample contained a larger proportion of individuals who worked in ICUs than the screening sample. People from a White ethnic background were over-represented in the interview samples compared with the composition of all NHS staff across the 18 participating NHS Trusts. Of the PSTD diagnostic interview sample, 36 (38·30%) of 94 met criteria for caseness on the PCL-6 at baseline, and 132 (54·32%) of 243 individuals in the common mental disorders diagnostic interview sample met criteria for caseness on the GHQ-12 at baseline.Table 1 Demographic baseline characteristics of the study samples
NHS CHECK screening sample*(n= 23 462) Depression and anxiety diagnostic interview sample (n=243) PTSD diagnostic interview sample (n=94) NHS CHECK baseline human resources data (n=152 228)
n (%) Proportion of participants with no missing data, %
Age, years
≤30 4464 (19·03%) 20·23% 53 (21·81%) 16 (17·02%) 32 777 (21·53%)
31–40 5084 (21·67%) 23·05% 53 (21·81%) 17 (18·09%) 39 272 (25·80%)
41–50 5771 (24·60%) 26·16% 64 (26·34%) 27 (28·72%) 36 417 (23·92%)
51–60 5400 (23·02%) 24·48% 66 (27·16%) 30 (31·91%) 33 591 (22·07%)
≥61 1342 (5·72%) 6·08% 6 (2·47%) 3 (3·19%) 10 010 (6·58%)
Missing 1401 (5·97%) .. 1 (0·41%) 1 (1·06%) 161 (0·11%)
Sex
Male 4300 (18·33%) 18·72% 58 (23·87%) 15 (15·96%) 40 599 (26·67%)
Female 18 673 (79·59%) 81·28% 185 (76·13%) 79 (84·04%) 111 629 (73·33%)
Missing .. .. .. .. ..
Ethnicity
White† 19 732 (84·10%) 85·62% 202 (83·13%) 83 (88·30%) 107 373 (70·53%)
Black‡ 1004 (4·28%) 4·36% 12 (4·94%) 3 (3·19%) 12 348 (8·11%)
Asian§ 1527 (6·51%) 6·63% 16 (6·58%) 5 (5·32%) 18 190 (11·95%)
Mixed 566 (2·41%) 2·46% 10 (4·12%) 2 (2·13%) 3254 (2·14%)
Other 217 (0·92%) 0·94% 3 (1·23%) 1 (1·06%) 3906 (2·57%)
Missing 416 (1·77%) .. .. .. 7157 (4·70%)
Main role
Clinical 14 730 (62·78%) 63·74% 166 (68·31%) 64 (68·09%) 110 424 (72·54%)
Non-clinical 8378 (35·70%) 36·26% 77 (31·69%) 30 (31·91%) 40 721 (26·75%)
Missing 354 (1·51%) .. .. .. 1083 (0·71%)
Setting
Accident and emergency 335 (1·44%) 1·58% 6 (2·47%) 3 (3·19%) NA
ICU or critical care 839 (3·39%) 3·74% 9 (3·70%) 29 (30·85%) NA
Other hospital 13 446 (54·39%) 59·91% 132 (54·32%) 37 (39·36%) NA
Community 6717 (27·17%) 29·93% 86 (35·39%) 21 (22·34%) NA
Non-patient-facing 1088 (4·40%) 4·85% 9 (3·70%) 4 (4·26%) NA
Missing 2276 (9·21%) .. 1 (0·41%) .. NA
Pay grade¶
≤£30 000 (AfC pay scale ≤5) 7336 (29·68%) 37·06% 67 (27·57%) 21 (22·34%) NA
>£30 000 (AfC pay scale ≥6 including medical pay scales) 12 461 (50·41%) 62·94% 153 (62·96%) 62 (65·96%) NA
Missing 4924 (19·92%) .. 23 (9·47%) 11 (11·70%) NA
GHQ-12
Screen positive 11 290 (48·12%) 52·80% 132 (54·32%) NA NA
Screen negative 10 093 (43·02%) 47·20% 111 (45·68%) NA NA
Missing 2079 (8·86%) .. .. NA NA
PCL-6
Screen positive 2908 (12·39%) 23·61% NA 36 (38·30%) NA
Screen negative 9407 (40·09%) 76·39% NA 58 (61·70%) NA
Missing 11 147 (47·51%) .. NA .. NA
Data are n (%). NHS=National Health Service. PTSD=post-traumatic stress disorder. ICU=intensive care unit. NA=not applicable. AfC=Agenda for change. GHQ-12=12-item General Health Questionnaire. PCL-6=6-item Post-Traumatic Stress Disorder checklist.
* Full NHS CHECK baseline cohort.
† White English, Welsh, Scottish, Northern Irish, or British.
‡ Black, African, Caribbean, or Black British.
§ Asian or British Asian.
¶ Pay scale was dichotomised at approximately the median national average wage in the UK (£30 472),20 using the AfC pay scales21 and medical pay scales.22
The prevalence of GHQ caseness in the screening sample was 52·8% (95% CI 51·7–53·8; table 2 ). Using the CIS-R questionnaire as a gold standard, the population validated prevalence of generalised anxiety disorder was estimated to be 14·3% (10·4–19·2) and of depression was estimated to be 13·7% (10·1–18·3). The combined population validated prevalence of generalised anxiety disorder and depression derived from the CIS-R questionnaire was 21·5% (16·9–26·8). The prevalence of PTSD (using the PCL-6 outcome measure) in our screening sample was 25·4% (24·3–26·5), whereas the population validated prevalence using the CAPS-5 questionnaire as a gold standard was estimated to be 7·9% (4·0–15·1). The screening prevalence of any mental disorder was 53·9% (52·9–54·9).Table 2 Weighted point prevalences of screening and diagnostic measures
Scale Cohort Prevalence, % (95% CI)
Common mental disorders GHQ-12 Screening 52·8% (51·7–53·8)
PTSD PCL-6 Screening 25·4% (24·3–26·5)
Any mental disorder (PTSD or common mental disorders) PCL-6 or GHQ-12 Screening 53·9% (52·9–54·9)
Generalised anxiety disorder CIS-R Diagnostic interview 14·3% (10·4–19·2)
Depression CIS-R Diagnostic interview 13·7% (10·1–18·3)
Generalised anxiety disorder or depression CIS-R Diagnostic interview 21·5% (16·9–26·8)
PTSD CAPS-5 Diagnostic interview 7·9% (4·0–15·1)
Sample was weighted to ensure it was representative with respect to age, sex, ethnicity, and clinical role. Participants with missing values in outcome scores were excluded for each calculation of the outcome. GHQ-12=12-item General Health Questionnaire. PTSD=post-traumatic stress disorder. PCL-6=6-item Post-Traumatic Stress Disorder checklist. CAPS-5=Clinician-Administered PTSD scale for DSM-5. CIS-R=Clinical Interview Schedule-Revised.
Discussion
As part of a large health and wellbeing survey of health-care workers in England during the pandemic, we used a two-stage epidemiological survey design in which health-care workers completed both self-report screening measures using standard cutoffs and diagnostic interviews for mental disorders (eg, depression, generalised anxiety disorder, and PTSD). The prevalence of these mental disorders within our sample was higher when using a screening tool (GHQ-12 or PCL-6) than when using a diagnostic interview tool (CIS-R or CAPS-5). For common mental disorders, the screening prevalence was 52·8% whereas when using the diagnostic interview, the population validated prevalence was 14·3% for generalised anxiety disorder and 13·7% for depression. The combined prevalence of depression and generalised anxiety disorder was 21·5%. For PTSD, the screening prevalence was 25·4%, whereas the population validated prevalence of PTSD using the diagnostic interview was 7·9%. These findings suggest that the screening tools with commonly used cutoff scores, utilised by many studies, substantially overestimate the prevalence of mental disorders.
Our estimated population prevalences of common mental disorders and PTSD at the population level were substantially lower than estimates from other UK studies of health-care workers (eg, front-line health-care workers, social care staff, and ICU staff) using self-report screening tools23, 24 and studies that included samples with sizeable groups of non-front-line health-care workers.25, 26 Lower prevalence estimates when using diagnostic interviews are consistent with previous pre-pandemic studies that have found screening tools overestimate prevalence.27, 28 Studies of mental disorder in health-care workers using screening tools are likely to have over-labelled distress as diagnosable disorder, which is an important distinction regarding treatment decisions and service planning. Non-professional, team-based interventions and support are preferable for managing distress symptoms, with professional care from mental health professionals being more suitable for individuals with a diagnosable disorder.29, 30 Overestimating the prevalence of mental disorders is unhelpful, with the risk of over-treatment and inappropriate medicalisation of distress.31, 32
To the best of our knowledge, this is the only study done during the COVID-19 pandemic to use a two-phase epidemiological design to estimate the prevalence of depression, generalised anxiety disorder, and PTSD in health-care workers in England. One published study by Wild and colleagues33 of UK health-care workers used diagnostic interviewing to estimate prevalence of PTSD (44%) and depression (39%) in a sample of 103 front-line health-care workers in England, only interviewing individuals who scored above clinical cutoffs on the PTSD Checklist for DSM-534 and Patient Health Questionnaire-9 screening tools using the Structured Clinical Interview for DSM-5.35 The study did not calculate the true population prevalence and prevalence estimates were markedly higher than those found in our study, which might be explained by the use of different diagnostic tools, differences in sample characteristics, and the use of convenience sampling. The sample used by Wild and colleagues consisted primarily of front-line ambulance staff and nurses, recruited from four NHS Hospitals and Ambulance Trusts, limiting generalisability to the wider population of health-care workers. The authors found that most participants diagnosed with PTSD reported an index event that happened before the COVID-19 pandemic, whereas major depressive disorder symptoms seemed to have developed over the course of the pandemic.33 We were unable to explore time of onset for the mental health outcomes of interest or the index event for PTSD in this study.
A major strength of our study was that our sample was weighted using administrative data to improve the representativeness of survey respondents to the target population in terms of ethnicity, age, sex, and clinical role. Although the response rate to the NHS CHECK cohort was only 15·4%, we believe it is the highest reported response rate when compared with similar studies that had a known, identifiable, and inclusive target population. The characteristics of our NHS CHECK sample are broadly comparable with NHS workforce statistics at the national level regarding ethnicity, age, sex, and clinical role.36 We also included both clinical and non-clinical staff in recognition of the burden that all health-care workers have faced during the pandemic. Acute and mental health NHS Trusts were included in NHS CHECK cohort, therefore exposures to some of the challenges associated with the pandemic would have been experienced differently, for example, by clinical staff working in an ICU, or emergency department, when compared with staff working on an acute psychiatric inpatient unit. Our research could be used in conjunction with findings from other studies that identify which particular groups of health-care workers might be at an increased risk of mental disorders, similar to previous work published by the NHS CHECK team,18 which showed that nurses, younger health-care workers, women, and individuals exposed to morally injurious events were at increased risk. The prevalence estimates reported in these studies are likely to be overestimations due to the use of screening tools; however, the identification of risk groups would remain valid.
Our study was limited by several factors. The study was based on participants from 18 NHS Trusts who had completed the screening tools (GHQ-12 and PCL-6) at baseline. Although the sampling procedures were designed to provide a representative sample of health-care workers within each participating site, we did not include a random sample of English hospitals. We also had a relatively low response rate to the diagnostic interview study (12·9%), and participants were self-selected responders to an already self-selected sample from our cohort study.7 Previous research has shown that people with mental disorders are less likely to take part in research, and this might have led to an underestimation of our prevalence estimates.37 Furthermore, individuals from minority ethnic backgrounds were less likely to have completed the PCL-6 or GHQ-12 during the NHS CHECK baseline assessment, possibly resulting in a selection bias, despite targeted recruitment efforts to increase the number of participants from a minority ethnic background for the diagnostic interviews. It is important to note that the confidence interval for the population prevalence estimate of PTSD was wide due to a small sample size, resulting in a less precise prevalence estimate. A larger number of participants had missing values on the PCL-6 than the GHQ-12 since the PCL-6 was only included in the second, optional part of the baseline questionnaire instead of the first, compulsory part of the baseline questionnaire. We offered a short and long version of this baseline questionnaire taking into consideration pressures of working in a health-care setting during the pandemic and participant burden.
A further limitation that applies to all studies that focus on health-care workers alone is the possibility of a contextual bias or framing effect, which applies to studies that focus on any specific workforce, such as health-care workers, teachers, police, military personnel, and others.38 In such studies, prevalence estimates of mental disorders are higher than occupation-specific prevalences extracted from larger true population studies in which occupation is collected as an incidental variable. This has been supported by more research on the impact of COVID-19, where population studies reported lower prevalence estimates in health-care workers than did surveys of health-care workers specifically. Use of diagnostic interviews might reduce this systematic bias by providing a more rigorous assessment of mental disorder, but this is unlikely to eliminate bias completely. Furthermore, no data were available from assessment points before the COVID-19 pandemic, thus we are unable to draw conclusions as to whether the prevalence of common mental disorders and PTSD in this population has changed since the start of the pandemic; the study was also not sufficiently powered to examine differences between subgroups (such as clinical and non-clinical staff, type of NHS Trust, or clinical speciality) due to careful consideration about what was feasible within the time and resources available. A larger study could support or refute the risk factors highlighted in previous research.39 We did not directly assess individuals' need for treatment and cannot be certain what proportion of individuals might benefit from formal treatment. Another limitation was the time lag between the baseline assessment of NHS CHECK for common mental disorders and PTSD and the diagnostic interview study (mean 265 days [SD 8]). Symptoms for some participants might have naturally eased over time, whereas other participants could have developed symptoms of a mental disorder. The longitudinal data of NHS CHECK indicate that despite slight variations in the screening prevalence of common mental disorders and PTSD, depending on the timing of the follow-up assessments, these prevalences remained relatively stable.40 Taken together, we believe that our results indicate a true overestimation of common mental disorders and PTSD prevalence when using screening measures compared with diagnostic interviews.
In summary, we found that previous self-report prevalence estimates of common mental disorders and PTSD in health-care workers in England during the pandemic are likely to have been overestimated. However, our data show that 21·5% of health-care workers meet criteria for a diagnosable mental disorder. Although evidence suggests that health-care workers operating in challenging environments will often work through potentially traumatic events without the need for clinical intervention,41 considering the known association between diagnosable mental disorders and poor workplace functioning, we suggest that it might be helpful to provide treatment promptly for health-care workers with diagnosable mental disorders.42 This approach is likely to both benefit health-care workers themselves and ensure quality of care for patients by maintaining a well functioning workforce. More broadly, researchers seeking to assess psychiatric symptoms using self-report screening tools in novel contexts should carefully consider cutoff scores on screening tools, and ideally complete further validation work to correctly calibrate measures to be appropriately sensitive. It can be unhelpful to report results from self-report tools since they might cause alarm and inappropriate allocation of scarce resources. Additionally, the variation in important prognostic indicators (past mental illness, urbanicity of clinical service provision, length of clinical contact) needs to be investigated if timely resources and treatments are to be provided to a population with high prevalence of mental distress. Further longitudinal research should be carried out to ascertain whether estimates of common mental disorders and PTSD among health-care workers exist before they start their role, are sustained during employment, or decrease over time.
Data sharing
Data will be available to researchers who provide a justified hypothesis and structured statistical analysis plan addressing a legitimate research question that is approved by the NHS CHECK Senior Research Team and after the signing of a data sharing agreement. Only deidentified participant data will be provided.
Declaration of interests
SAMS is supported by the National Institute for Health and Care Research (NIHR) Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and an NIHR Advanced Fellowship. MH has received funding from the Innovative Medicines Initiative for the RADAR-CNS programme, a public-private pre-competitive consortium in mHealth, and his university received research funding from Janssen, Biogen, UCB, MSD, and Lundbeck. PM is supported by the NIHR Applied Research Collaboration (ARC; West) and the NIHR Biomedical Research Centre at University Hospitals Bristol, Weston NHS Foundation Trust, and the University of Bristol. NG has been an unpaid member of two NHS England expert advisory groups; and owns the company March on Stress, which is a psychological health consultancy providing mental health training to a wide range of organisations including the NHS. All other authors declare no competing interests.
Acknowledgments
This study was funded by the Medical Research Council (MR/V034405/1), UCL/Wellcome (ISSF3/H17RCO/C3), the Rosetrees Trust (M952), NHS England and Improvement, and the Economic and Social Research Council (ES/V009931/1). This report is independent research supported by the NIHR ARC North Thames. The views expressed in this publication are those of the authors and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care. We wish to acknowledge the National Institute of Health Research Applied Research Collaboration (ARC) National NHS and Social Care Workforce Group, with the following ARCs: East Midlands, East of England, South West Peninsula, South London, West, North West Coast, Yorkshire and Humber, and North East and North Cumbria. They enabled the set-up of the national network of participating hospital sites and aided the research team to recruit effectively during the COVID-19 pandemic. The NHS CHECK consortium includes the following site leads: Sean Cross, Amy Dewar, Chris Dickens, Frances Farnworth, Adam Gordon, Charles Goss, Jessica Harvey, Nusrat Husain, Peter Jones, Damien Longson, Richard Morriss, Jesus Perez, Mark Pietroni, Ian Smith, Tayyeb Tahir, Peter Trigwell, Jeremy Turner, Julian Walker, Scott Weich, and Ashley Wilkie. The NHS CHECK consortium includes the following co-investigators and collaborators: Peter Aitken, Anthony David, Rosie Duncan, Cerisse Gunasinghe, Stephani Hatch, Daniel Leightley, Isabel McMullen, Martin Parsons, Dominic Murphy, Catherine Polling, Alexandra Pollitt, Danai Serfioti, Chloe Simela, and Charlotte Wilson Jones.
Contributors
DL, EC, IM, SG, MJD, IB, MH, RRai, RRaz, NG, SAMS, and SW conceptualised the study and secured funding. HS, SH, and ES recruited and interviewed participants. DW managed the database, HRS and RB completed project administration, HRS, DL, EC, IB, and DW curated the data, and DL, EC, IB, and RG analysed the data. DL, HRS, RG, and SAMS interpreted the findings. HRS and RG created the first draft of the manuscript. HRS, DL, SD, EC, RG, SH, ES, IM, RB, DW, SG, PM, MD, IB, SM, MH, RRaz, RRai, NG, SAMS, and SW critically revised and approved the manuscript. DW, DL, HRS, and RG had full access to all the data in the study. HRS, RG, SAMS, NG, and SW had final responsibility for the decision to submit for publication.
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References
1 Chirico F Ferrari G Nucera G Szarpak L Crescenzo P Ilesanmi O Prevalence of anxiety, depression, burnout syndrome, and mental health disorders among healthcare workers during the COVID-19 pandemic: a rapid umbrella review of systematic reviews J Health Soc Sci 6 2021 209 220
2 Levis B Yan XW He C Sun Y Benedetti A Thombs BD Comparison of depression prevalence estimates in meta-analyses based on screening tools and rating scales versus diagnostic interviews: a meta-research review BMC Med 17 2019 65 30894161
3 Thombs BD Kwakkenbos L Levis AW Benedetti A Addressing overestimation of the prevalence of depression based on self-report screening questionnaires CMAJ 190 2018 E44 E49 29335262
4 Levis B Benedetti A Ioannidis JPA Patient Health Questionnaire-9 scores do not accurately estimate depression prevalence: individual participant data meta-analysis J Clin Epidemiol 122 2020 115 128 32105798
5 Jordanova V Wickramesinghe C Gerada C Prince M Validation of two survey diagnostic interviews among primary care attendees: a comparison of CIS-R and CIDI with SCAN ICD-10 diagnostic categories Psychol Med 34 2004 1013 1024 15554572
6 Dunn G Pickles A Tansella M Vázquez-Barquero JL Two-phase epidemiological surveys in psychiatric research Br J Psychiatry 174 1999 95 100 10211161
7 Lamb D Greenberg N Hotopf M NHS CHECK: protocol for a cohort study investigating the psychosocial impact of the COVID-19 pandemic on healthcare workers medRxiv 2021 published online June 15. 10.1101/2021.06.08.21258551 (preprint).
8 Deming WE An essay on screening, or on two-phase sampling, applied to surveys of a community Int Stat Rev 45 1977 29 37
9 Lang AJ Stein MB An abbreviated PTSD checklist for use as a screening instrument in primary care Behav Res Ther 43 2005 585 594 15865914
10 Goldberg DP A user's guide to the General Health Questionnaire 1988 Nelson Publishing Company Limited Windsor
11 Hardy GE Shapiro DA Haynes CE Rick JE Validation of the General Health Questionnaire-12: using a sample of employees from England's health care services Psychol Assess 11 1999 159 165
12 Holi MM Marttunen M Aalberg V Comparison of the GHQ-36, the GHQ-12 and the SCL-90 as psychiatric screening instruments in the Finnish population Nord J Psychiatry 57 2003 233 238 12775300
13 Patel V Araya R Chowdhary N Detecting common mental disorders in primary care in India: a comparison of five screening questionnaires Psychol Med 38 2008 221 228 18047768
14 Weathers FW Bovin MJ Lee DJ The Clinician-Administered PTSD Scale for DSM-5 (CAPS-5): development and initial psychometric evaluation in military veterans Psychol Assess 30 2018 383 395 28493729
15 Weathers FW Ruscio AM Keane TM Psychometric properties of nine scoring rules for the clinician-administered posttraumatic stress disorder scale Psychol Assess 11 1999 124 133
16 Lewis G Pelosi AJ Araya R Dunn G Measuring psychiatric disorder in the community: a standardized assessment for use by lay interviewers Psychol Med 22 1992 465 486 1615114
17 NHS Digital Adult psychiatric morbidity survey 2014 http://doc.ukdataservice.ac.uk/doc/8203/read8203.htm
18 Lamb D Gnanapragasam S Greenberg N Psychosocial impact of the COVID-19 pandemic on 4378 UK healthcare workers and ancillary staff: initial baseline data from a cohort study collected during the first wave of the pandemic Occup Environ Med 78 2021 801 808 34183447
19 Pickles A Dunn G Vázquez-Barquero JL Screening for stratification in two-phase (‘two-stage’) epidemiological surveys Stat Methods Med Res 4 1995 73 89 7613639
20 Office for National Statistics Employee earnings in the UK: 2020 https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/bulletins/annualsurveyofhoursandearnings/2020
21 NHS England Agenda for change–pay rates. 2020 https://www.healthcareers.nhs.uk/working-health/working-nhs/nhs-pay-and-benefits/agenda-change-pay-rates
22 British Medical Association Pay scales for junior doctors in England https://www.bma.org.uk/pay-and-contracts/pay/junior-doctors-pay-scales/pay-scales-for-junior-doctors-in-england
23 Dykes N Johnson O Bamford P Assessing the psychological impact of COVID-19 on intensive care workers: a single-centre cross-sectional UK-based study J Intensive Care Soc 23 2022 132 138 35615226
24 Greene T Harju-Seppänen J Adeniji M Predictors and rates of PTSD, depression and anxiety in UK frontline health and social care workers during COVID-19 Eur J Psychotraumatol 12 2021 1882781 33968317
25 Gilleen J Santaolalla A Valdearenas L Salice C Fusté M Impact of the COVID-19 pandemic on the mental health and well-being of UK healthcare workers BJPsych Open 7 2021 e88 33910674
26 Wanigasooriya K Palimar P Naumann DN Mental health symptoms in a cohort of hospital healthcare workers following the first peak of the COVID-19 pandemic in the UK BJPsych Open 7 2021 e24
27 Rucci P Gherardi S Tansella M Subthreshold psychiatric disorders in primary care: prevalence and associated characteristics J Affect Disord 76 2003 171 181 12943947
28 Terhakopian A Sinaii N Engel CC Schnurr PP Hoge CW Estimating population prevalence of posttraumatic stress disorder: an example using the PTSD checklist J Trauma Stress 21 2008 290 300 18553416
29 WHO Guidelines on mental health at work 2022 World Health Organisation Geneva
30 Brooks SK Dunn R Amlôt R Rubin GJ Greenberg N Protecting the psychological wellbeing of staff exposed to disaster or emergency at work: a qualitative study BMC Psychol 7 2019 78 31823824
31 Brooks SK Rubin JG Greenberg N Traumatic stress within disaster exposed occupations: overview of the literature and suggestions for the management of traumatic stress in the workplace Br Med Bull 129 2019 25 34 30544131
32 Tracy DK Tarn M Eldridge R Cooke J Calder JDF Greenberg N What should be done to support the mental health of healthcare staff treating COVID-19 patients? Br J Psychiatry 217 2020 537 539 32423523
33 Wild J McKinnon A Wilkins A Browne H Post-traumatic stress disorder and major depression among frontline healthcare staff working during the COVID-19 pandemic Br J Clin Psychol 61 2021 859 866 34713436
34 US Department of Veterans Affairs PTSD Checklist for DSM-5 (PCL-5) https://www.ptsd.va.gov/professional/assessment/adult-sr/ptsd-checklist.asp
35 American Psychiatric Association Structured clinical interview for DSM-5 https://www.appi.org/products/structured-clinical-interview-for-dsm-5-scid-5#:~:text=The%20Structured%20Clinical%20Interview%20for%20DSM%2D5%20(SCID%2D5,5%20classification%20and%20diagnostic%20criteria
36 NHS Digital NHS workforce statistics-March 2019 (including supplementary analysis on pay by ethnicity) https://digital.nhs.uk/data-and-information/publications/statistical/nhs-workforce-statistics/nhs-workforce-statistics---march-2019-provisional-statistics
37 Knudsen AK Hotopf M Skogen JC Overland S Mykletun A The health status of nonparticipants in a population-based health study: the Hordaland Health Study Am J Epidemiol 172 2010 1306 1314 20843863
38 Goodwin L Ben-Zion I Fear NT Hotopf M Stansfeld SA Wessely S Are reports of psychological stress higher in occupational studies? A systematic review across occupational and population based studies PLoS One 8 2013 e78693 24223840
39 Sirois FM Owens J Factors associated with psychological distress in health-care workers during an infectious disease outbreak: a rapid systematic review of the evidence Front Psychiatry 11 2021 589545 33584364
40 Lamb D Gafoor R Scott H Mental health of healthcare workers in England during the COVID-19 pandemic: a longitudinal cohort study medRxiv 2022 published online June 16 10.1101/2022.06.16.22276479 (preprint).
41 Greenberg N Wessely S Wykes T Potential mental health consequences for workers in the Ebola regions of west Africa—a lesson for all challenging environments J Ment Health 24 2015 1 3 25587816
42 Hodkinson A Zhou A Johnson J Associations of physician burnout with career engagement and quality of patient care: systematic review and meta-analysis BMJ 378 2022 e070442 36104064
| 36502817 | PMC9731576 | NO-CC CODE | 2022-12-14 23:54:37 | no | Lancet Psychiatry. 2023 Jan 8; 10(1):40-49 | utf-8 | Lancet Psychiatry | 2,022 | 10.1016/S2215-0366(22)00375-3 | oa_other |
==== Front
Softw Impacts
Softw Impacts
Software Impacts
2665-9638
The Author(s). Published by Elsevier B.V.
S2665-9638(22)00137-3
10.1016/j.simpa.2022.100453
100453
Original Software Publication
scorecovid for scoring individual country COVID-19 policies in the world
Takefuji Yoshiyasu
Musashino University, Japan
9 12 2022
9 12 2022
1004532 11 2022
5 12 2022
6 12 2022
© 2022 The Author(s)
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
There are two types of policy analysis tools: snapshot tool and time-series tool. scorecovid is a snapshot tool to score individual COVID-19 policies in the world and sort a list of scores. The population mortality rate is used for evaluating the outcomes of COVID-19 country policies. The lower the score, the less the COVID-19 deaths. The lower the score, the better the policy. The score monotonically increases. The scorecovid tool is intended for poorly scored countries to learn good strategies from countries with excellent scores. scorecovid attracted 15192 users worldwide.
Keywords
COVID-19 policy
Scoring COVID-19 policies
Deaths due to COVID-19
PyPI packaging
==== Body
pmcCode metadata
Current code version 0.0.8
Permanent link to code/repository used for this code version https://github.com/SoftwareImpacts/SIMPAC-2022-256
Permanent link to reproducible capsule https://codeocean.com/capsule/5377943/tree/v1
Legal code license MIT License
Code versioning system used PyPI
Software code languages, tools and services used python
Compilation requirements, operating environments and dependencies apt-get: wget, pip3: lxml, matplotlib, pandas, scorecovid
If available, link to developer documentation/manual https://pypi.org/project/scorecovid/
Support email for questions [email protected]
1 Motivation and significance
There is no open-source policy outcome analysis tool against the COVID-19 pandemic. For this purpose, scorecovid was created.
• The population mortality rate is used for evaluating country scores. The latest data is scraped over the Internet. countries file indicates a list of countries. You are allowed to add or delete countries to be evaluated.
• scorecovid is a snapshot tool intended for policymakers to learn good strategies from countries with excellent scores. The list of sorted scores plays a key role in discovering excellent countries.
• The scorecovid tool is a PyPI application so that it can be installed by the pip command. PyPI packaging allows scorecovid to run on Windows, MacOS, and Linux operating systems as long as Python is installed on the system.
• The score calculation in scorecovid is based on the daily cumulative population mortality of COVID-19: dividing the number of cumulative deaths by the population in millions. There are two types of policy outcome analysis tools: a snapshot list of sorted scores and time-series scores. The scorecovid is a snapshot policy outcome analysis tool.
2 Limitations
• The snapshot analysis tools such as scorecovid cannot visualize and observe the progress and transition of scores while time-series policy outcome analysis tools such as hiscovid allow us to visualize and observe the behaviour of the transition and to identify when policymakers made mistakes over time. The scorecovid is a PyPI application so that as long as Python is installed on the system, it can run on Windows, MacOS, and Linux operating systems.
3 Software description
Software is composed of setup.py, scorecovid.py, and __main__.py and __init.py.
3.1 Software architecture:
The directory and software structure is as follows:
3.2 Software functionalities
The latest data on deaths due to COVID-19 is scraped over the Internet from: https://github.com/owid/covid-19-data/raw/master/public/data/jhu/total_deaths.csv. Using pandas. DataFrame, scraped deaths and population are used for calculating scores. The result is stored in result.csv file. The filename countries can contain target countries which you can modify, delete or add countries.
4 Illustrative examples
To run scorecovid, install it and type the following command:
$ pip install scorecovid
$ scorecovid
Country Deaths Population Score
Japan 46817 126.48 370.2
New Zealand 2106 4.82 436.9
Taiwan 12876 23.82 540.6
South Korea 29239 51.27 570.3
Australia 15665 25.5 614.3
Iceland 219 0.34 644.1
Canada 46705 37.74 1237.5
Israel 11767 8.66 1358.8
Germany 153814 83.78 1835.9
Sweden 20659 10.1 2045.4
France 157063 65.27 2406.4
United Kingdom 209208 67.89 3081.6
United States 1070788 331 3235
Brazil 688219 212.56 3237.8
Hungary 47938 9.66 4962.5
As of Nov. 1, 2022.
5 Impact
Unfortunately, scorecovid does not show vaccination rates such as at least one dose, fully vaccinated, or booster given. However, the scorecovid tool discovered that the mandatory test-isolation policy successfully suppressed the COVID-19 pandemic. It is to test and identify infected individuals at an early stage and to isolate them from uninfected people during the quarantine time. scorecovid attracted 15192 users worldwide.
scorecovid is with MIT license. The software can be freely used. The method of scorecovid was peer-reviewed by five journals [1], [2], [3], [4], [5].
The proposed method is based on the single metric of the daily cumulative population mortality. The proposed software can be applied to other disease outbreak. As long as the dataset is ready to be used, the proposed scorecovid has the high transferability.
6 Conclusions
In order to mitigate the COVID-19 pandemic, it is wise to adopt the best policy in the world. scorecovid can reveal what is currently the best policy.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
The code (and data) in this article has been certified as Reproducible by Code Ocean: (https://codeocean.com/). More information on the Reproducibility Badge Initiative is available at https://www.elsevier.com/physical-sciences-and-engineering/computer-science/journals.
==== Refs
References
1 Takefuji Y. SCORECOVID: A Python package index for scoring the individual policies against COVID-19 Healthc. Anal. 1 2021 100005 10.1016/j.health.2021.100005
2 Takefuji Y. Analysis of digital fences against COVID-19 Health Technol. 11 2021 1383 1386 10.1007/s12553-021-00597-9
3 Takefuji Y. Correspondence: Open schools, Covid-19, and child and teacher morbidity in Sweden N. Engl. J. Med. 384 2021 e66 10.1056/NEJMc2101280
4 Takefuji Y. Discovering COVID-19 state sustainable policies for mitigating and ending the pandemic Cities (London, England) 130 2022 103865 10.1016/j.cities.2022.103865
5 Y. Takefuji Sustainable policy: Don’t get infected and don’t infect others J. Hazardous Mater. Adv. 8 2022 100165 10.1016/j.hazadv.2022.100165
| 36514708 | PMC9731641 | NO-CC CODE | 2022-12-15 23:18:05 | no | Softw Impacts. 2022 Nov 9; 14:100453 | utf-8 | Softw Impacts | 2,022 | 10.1016/j.simpa.2022.100453 | oa_other |
==== Front
Clin Microbiol Infect
Clin Microbiol Infect
Clinical Microbiology and Infection
1198-743X
1469-0691
European Society of Clinical Microbiology and Infectious Diseases. Published by Elsevier Ltd.
S1198-743X(22)00608-5
10.1016/j.cmi.2022.12.005
Commentary
Polio is back? The risk of poliomyelitis recurrence globally, and the legacy of SARS-CoV-2 pandemic
Castilletti Concetta
Capobianchi Maria Rosaria ∗
Department of Infectious, Tropical Diseases and Microbiology, IRCCS Sacro Cuore Don Calabria Hospital, Via Don A. Sempreboni 5, 37024, Negrar di Valpolicella (Verona), Italy
∗ Corresponding author:
8 12 2022
8 12 2022
25 10 2022
22 11 2022
2 12 2022
© 2022 European Society of Clinical Microbiology and Infectious Diseases. Published by Elsevier Ltd. All rights reserved.
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Editor: Sally J. Cutler
==== Body
pmcMain Text
After COVID-19 and monkeypox, here we are dealing with another scaring menace: polio. Many of us have certainly put the existence of poliomyelitis (polio for short) on the back burner; probably among the current doctors in service in western countries there are very few who keep memories of this disease, that plagued the years between the thirties and sixties. Years in which mothers lived in fear of their children being struck by an infection that was reputed to leave indelible and severe motor impairments, eventually leading to permanent iron lung imprisonment or even death.
The nightmare had been defeated thanks to the discoveries of three scientists, Hilary Koprowski, Jonas Salk and Albert Sabin, who, with their studies at the turn of the 1950s and 1960s, made it possible to prepare the vaccine that defeated this monster [1]. Today's concerns arise from the recent, repeated reports of presence of the virus in the sewage systems of places located in very distant points of the world, after years of relative silence. In March 2022, the virus popped up in Israel with human infections, most of which were asymptomatic [2]; since February in London (United Kingdom, UK) and since March in the New York state (United States, US) there have been repeated reports of the virus being found in the sewage system [2]. In the state of New York, in July, a human case of paralytic poliomyelitis occurred: it is the first time in almost ten years that a case of polio has been registered in the US, after the last imported case, which occurred in 2013 [3].
Polio is a highly infectious viral disease that primarily affects children under the age of five, causing permanent paralysis (in about 1 out of 200 infections) or death (2 to 10% of paralytic cases) [4]. However, there are cases of adults affected by this disease: a prominent example is the US President Franklin Delano Roosevelt, who suffered permanent polio disability after contracting polio in adulthood.
The virus is transmitted from person to person and spreads mainly by the fecal-oral route or, less frequently, by the consumption of contaminated water or food, and multiplies in the intestine, but can generate viremia, invade the nervous system and cause paralysis and death. Invasion of the nervous system with consequent disease, and death occurs with a frequency of 10-100 times lower than infection. Initial symptoms of poliomyelitis include fever, fatigue, headache, vomiting, neck stiffness, and pain in the limbs.
Of the 3 known strains of wild poliovirus (WPV1, 2 and 3), WPV2 and WPV3 have been declared globally eradicated, after a substantial number of years in which no cases have been reported worldwide [4]. To date, WPV1 is the only wild type strain still circulating, and is endemic in two countries: Pakistan and Afghanistan [4]. Occasional sporadic cases or small outbreaks in other countries are reported, testifying to the continuous risk of international spread even in polio-free countries, favored by international travel and migratory flows. This is a very serious problem for our globalized world, and risk has further increased in the past two years due to the decrease in immunization rate related to the COVID-19 pandemic.
As a matter of fact, risk of international spread of the poliovirus remains a public health emergency of international concern (PHEIC), according to the emergency committee convened under the International Health Regulations [5].
There is no cure for polio, but the disease is totally preventable with vaccine, which has been instrumental in marking the outstanding success achieved globally against this disease: cases have decreased from about 350,000 in 1988 to 175 in 2019 [4].
Today, two types of polio vaccine are used: inactivated polio vaccine (IPV) developed by Salk, and oral polio vaccine (OPV), developed by Sabin [6]. The first consists of inactivated poliovirus, and establishes immunity in blood, preventing the virus from reaching central nervous system from its primary infection site; it is not able to block infection in digestive tract. The second (OPV) consists of attenuated live virus, is administered orally and establishes immunity at the mucosal level the digestive tract, thus preventing natural infection from being established. Since the virus contained in the OPV replicates in vaccine recipients and is spread in the environment, it can infect and therefore immunize other individuals in the community as well [6]. This aspect certainly positive, represents also the negative side of coin: in fact, multiple cycles of infections and extensive circulation in the human population may lead the virus to accumulate mutations, a phenomenon common to all RNA viruses [7]. Some mutations can confer a pathogenic potential to the vaccine-derived virus, which may eventually cause clinical manifestations similar to those of the wild virus. To overcome these problems, a new version of the OPV vaccine has been established, modified to lower the vaccine risk to a negligible level, at least for serotype 2. he World Health Organization (WHO) authorized the release of the novel oral polio vaccine type 2 (nOPV2) in November 2020 and 450 million doses have already been administered worldwide [8]. New generations of “live” poliovirus vaccines, targeting poliovirus serotypes 1 and 3, are in preclinical development, and first clinical trials are expected to be completed in 2023 [9]. So far, vaccine-derived poliovirus strains continue to circulate, and today most cases of polio outside WPV1-endemic countries are due to circulating vaccine-derived poliovirus 2 (cVDPV2) strains [4]. As a matter of fact, cVDPV2 is the virus that has been isolated in the United Kingdom and the United States [2]. Co-circulation of cVDPV2 and cVDPV3 is reported from Israel [2].
Since clinical manifestations are observed in a minimal part of infections, signals that we catch today from surveillance at the environmental level are indicating a trans-national viral circulation, certainly larger than what can be appreciated through the raw data coming from the surveillance system itself.
With resolution WHA 41.28 of May 13, 1988, the WHO set the goal of eradicating the polio virus [10], launching the homonymous initiative "Global Polio Eradication Initiative" driven in conjunction with national governments, Rotary International, the US Centers for Disease Control and Prevention (CDC) and UNICEF, and later joined by the Bill & Melinda Gates Foundation and the Vaccine Alliance (Gavi) Global Polio Eradication Initiative. Sadly, in 2020, the SARS-CoV-2 pandemic prompted a four-month pause of the Global Polio Eradication Initiative’s campaigns, disrupting disease surveillance and routine immunizations. More generally, in the past two years global childhood vaccination rates against polio and other diseases like measles have declined drastically, largely due to the impact from the COVID-19 pandemic. Several recent reports confirm that the “new” outbreaks of measles and pertussis starting in unvaccinated individuals spread to children whose vaccination may have failed. [11].
A surge of cases has been noticed in different parts of the world since 2020, with vaccine-derived poliovirus outbreaks having tripled from 2019 to 2020. The recent 2022 news are a strong reminder that the risk of polio resurgence is now present as never before, especially after the relaxation of preventive and control measures after almost three years of COVID-19 pandemic. K.M. Thompson and colleagues, applying an established global model, clearly demonstrated as early as in the 2021 the difficulty of Polio eradication unless aggressive efforts began soon after initial disease detection [12]. As they assumed, in the absence of aggressive measures, the virus would become globally endemic in 2–10 years, and cumulative paralytic cases would exceed 4–40 million.
At present, the healthcare setting is exhausted by the unequal struggle with COVID-19 pandemic that is not yet over. Should we then lay down our weapons and retreat in the face of the new threats posed by the possibility of polio resurgence? Certainly not, and there is one thing that the COVID-19 pandemic has taught us: the importance of being prepared. It is therefore important to put in place all means to bring the circulation of the virus under control, keeping us on the path traced by the WHO for global eradication.There are four pillars on which this trajectory is based:
1. The surveillance of Acute Flaccid Paralysis (AFP): all cases that occur in children/young people aged up to 15 years must be reported to the health surveillance system, so that the etiology of the individual cases can be determined, and any poliovirus, infections can be promptly recognized [12].
2. Monitoring for presence of poliovirus in the environment can be promptly recognized. As an example of environmental surveillance for poliovirus, the sewage systems of the main cities in western countries are regularly sampled to intercept the release of poliovirus in sewage waste by infected citizens; this system is very sensitive, as the recent evidence from US and UK has demonstrated, and also works with regard to asymptomatic infections, which represent the majority of the poliovirus infections [12]. Environmental surveillance (namely sewage analysis) can be extended to other emerging enteroviruses that can cause AFP, like enterovirus D68, and possibly, to other environment-sable viruses. This strategy is presently applied in some countries, such as Italy [13].
3. Full implementation of vaccination policies, with the aim of keeping the national coverage thresholds for polio vaccination above 95%, the threshold recommended by the WHO. In recent years, the increase in adverse movements to vaccination and the simultaneous decrease in the perception of the risk of infectious diseases have had a significant impact on vaccination coverage. It is essential to advocate to people about vaccine effectiveness, safety and relevance and to elaborate search strategies and how to flush out fake news. On the other hand, ongoing massive migration flows also deserve critical attention, because of the increased risk for the migrating populations to remain hidden to health surveillance systems, hence skipping the infectious disease prevention measures [14]. Therefore, identifying and proactively reaching population sub-groups/areas with potential immunity gaps is a critical issue to be pursued.
4. Use of mathematical models to contribute with active surveillance programs and preparedness can be very useful not only for polio eradication but, more generally, for infectious diseases control. In fact, prospective modeling plays a critical role in analytic‐deliberative processes by supporting the evaluation of strategies and decisions for managing risks in complex systems (11).
To conclude, one of the lessons that the SARS-CoV-2 pandemic has left is that we do not know when to put an end to it, but in the meantime, we must stay vigilant and take advantage of all the available tools to prevent, intercept and fight infections that have the ability to spread, such as polio.
Transparency declaration
This work was supported by the Italian Ministry of Health “Fondi Ricerca Corrente” to IRCCS Sacro Cuore Don Calabria Hospital.
Author contributions
CC wrote the initial draft of the Commentary. Both authors contributed to the final writing and editing of the Commentary.
==== Refs
REFERENCES
1 Martini M. Orsini D. The fight against poliomyelitis through the history: past, present and hopes for the future. Albert Sabin's missing Nobel and his "gift to all the world's children Vaccine S0264-410X 22 2022 10.1016/j.vaccine.2022.09.088 01220-01228
2 ECDC; Update on the polio situation in the EU/EEA and the world https://www.ecdc.europa.eu/en/news-events/update-polio-situation-eueea-and-world Date accessed: November 21, 2022.
3 Link-Gelles R. Lutterloh E. Ruppert P.S. Backenson P.B. St George K. Rosenberg E.S. Public health response to a case of paralytic poliomyelitis in an unvaccinated person and detection of poliovirus in wastewater-New York, June- August 2022 Am J Transplant 22 10 2022 2470 2474 10.1111/ajt.16677 36196495
4 WHO; Poliomyelitis https://www.who.int/news-room/fact-sheets/detail/poliomyelitis Date accessed: October 20, 2022.
5 Cochi S.L. Hegg L. Kaur A. Pandak C. Jafari H. The Global Polio Eradication Initiative: Progress, Lessons Learned, And Polio Legacy Transition Planning Health Aff (Millwood) 2 2016 277 283 10.1377/hlthaff.2015.1104
6 Modlin J.F. Bandyopadhyay A.S. Sutter R. Immunization Against Poliomyelitis and the Challenges to Worldwide Poliomyelitis Eradication J Infect Dis 224 12 Suppl 2 2021 Sep 30 S398 S404 10.1093/infdis/jiaa622 34590135
7 Savolainen-Kopra C. Blomqvist S. Mechanisms of genetic variation in polioviruses Rev Med Virol 6 2010 358 371 10.1002/rmv.663
8 Martin J. Burns C.C. Jorba J. Shulman L.M. Macadam A. Klapsa D. Genetic Characterization of Novel Oral Polio Vaccine Type 2 Viruses During Initial Use Phase Under Emergency Use Listing - Worldwide, March-October 2021 MMWR Morb Mortal Wkly Rep 24 2022 786 790 10.15585/mmwr.mm7124a2
9 NIH, ClinicalTrials.gov Identifier: NCT04529538, Study of Novel Types 1 and 3 Oral Poliomyelitis Vaccines. https://clinicaltrials.gov/ct2/show/NCT04529538 Date accessed: October 22, 2022.
10 Rodrigues R.N. Nascimento G.L.M.D. Arroyo L.H. Arcêncio R.A. Oliveira V.C. Guimarães E.A.A. The COVID-19 pandemic and vaccination abandonment in children: spatial heterogeneity maps Rev Lat Am Enfermagem 30 2022 e3642 10.1590/1518-8345.6132.3642
11 Thompson K.M. Kalkowska D.A. Badizadegan K. Hypothetical emergence of poliovirus in 2020: part 1. Consequences of policy decisions to respond using nonpharmaceutical interventions Expert Review of Vaccines 20 4 2021 465 481 10.1080/14760584.2021.1891888 33624568
12 GPEI/WHO; The four steps of acute flaccid paralysis (AFP) surveillance, https://polioeradication.org/who-we-are/strategic-plan-2013-2018/surveillance/Date accessed: October 22, 2022.
13 Delogu R. Battistone A. Buttinelli G. Fiore S. Fontana S. Amato C. Cristiano K. Poliovirus and Other Enteroviruses from Environmental Surveillance in Italy, 2009-2015 Food Environ Virol 10 4 2018 333 342 10.1007/s12560-018-9350-8 29948963
14 Tsagkaris C. Loudovikou A. Matiashova L. Papadakis M. Trompoukis C. Public health concerns over polio in war-torn Ukraine and nearby regions: Four lessons and a warning from the history of epidemics J Med Virol 94 7 2022; Jul 2931 2932 10.1002/jmv.27723 35292993
| 36503117 | PMC9731642 | NO-CC CODE | 2022-12-14 23:31:51 | no | Clin Microbiol Infect. 2022 Dec 8; doi: 10.1016/j.cmi.2022.12.005 | utf-8 | Clin Microbiol Infect | 2,022 | 10.1016/j.cmi.2022.12.005 | oa_other |
==== Front
Int J Osteopath Med
Int J Osteopath Med
International Journal of Osteopathic Medicine
1746-0689
1878-0164
Elsevier Ltd.
S1746-0689(22)00117-1
10.1016/j.ijosm.2022.12.001
Article
Work from home-related musculoskeletal pain during the COVID-19 pandemic: A rapid review
Gomez Ivan Neil ab∗
Suarez Consuelo G. cd
Sosa Ken Erbvin d
Tapang Maria Lourdes e
a Department of Occupational Therapy, College of Rehabilitation Sciences, University of Santo Tomas, Manila, Philippines
b Center for Health Research and Movement Sciences, University of Santo Tomas, Manila, Philippines
c Research Center for Health Sciences, University of Santo Tomas, Manila, Philippines
d Department of Physical Therapy, College of Rehabilitation Sciences, University of Santo Tomas, Manila, Philippines
e Department of Rehabilitation Medicine, Faculty of Medicine, University of Santo Tomas, Manila, Philippines
∗ Corresponding author. Department of Occupational Therapy, College of Rehabilitation Sciences, University of Santo Tomas, Manila, Philippines.
8 12 2022
8 12 2022
28 6 2022
27 11 2022
3 12 2022
© 2022 Elsevier Ltd. All rights reserved.
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Purpose
This rapid review explores the prevalence of musculoskeletal pain symptoms associated with work from home conditions during the COVID-19 pandemic.
Materials and methods
We conducted a rapid review across three databases (i.e., PubMed, Medline, and CINAHL) for observational studies that report on the musculoskeletal functions among individuals placed in a work from home setup due to the COVID-19 pandemic, published between December 2019–August 2021. Two independent review authors searched, appraised, and extracted data from the articles included in the final review. A descriptive approach was used to synthesize the narrative evidence.
Results
Forty-four articles were initially identified. A total of six (n = 6) studies met the full inclusion criteria and were included. Among them, there were five cross-sectional studies and one case-control study. The highest prevalence reported were neck pain (20.3–76.9%), low back pain (19.5–74.1%), and shoulder pain (3.0–72.9%). The most common instrument used was the Nordic Musculoskeletal Questionnaire. One of the common professions that report musculoskeletal pain symptoms associated with work from home conditions were individuals working in the academic sector.
Conclusion
The increased prevalence of musculoskeletal pain symptoms associated with work from home conditions during the COVID-19 pandemic is a concern that should be addressed to prevent negative neuromusculoskeletal outcomes.
Systematic review registration
This review is in the Open Science Framework registry (osf.io/vxs4w) and the PROSPERO database (CRD42021266097).
Keywords
Musculoskeletal
COVID-19 pandemic
Work from home
Occupation
==== Body
pmc1 Introduction
Musculoskeletal conditions refer to various health-related issues with underlying pathophysiology that concern the muscular and skeletal functions [1]. Examples of common musculoskeletal conditions include pain in the neck, back, leg, and different joint regions. Musculoskeletal conditions have been recognized as the most common cause of chronic pain and physical disability among hundreds of millions of individuals across age groups worldwide [2]. The causes of musculoskeletal conditions fall in a varied spectrum of pathophysiology, including inflammatory diseases, age-related functional decline, and, more commonly, occupational or activity-related reasons. Left alone without intervention, musculoskeletal conditions may progress to a disorder that compromises individuals' health, well-being, and function.
Work-related musculoskeletal disorders are a subtype of musculoskeletal disorders related to occupational exposure of risk. The prevalence of work-related musculoskeletal disorders may be as high as 14.90% in different work industries [3]. Specifically, occupations exposed to computer-related office work may be at a higher risk for musculoskeletal disorders of the neck and upper extremity due to repetitive movements, static and awkward posture, and manual tasks [4]. With the increasing use of handheld devices, the prevalence of associated musculoskeletal complaints may be as high as 67.80% [5]. Work-related musculoskeletal disorders present a pressing issue. In the UK, around 9.25 million days were lost [6], while Germany reports almost 29 million Euros lost [7] due to work-related musculoskeletal disorders. This brings global disability-adjusted life years of over 30,000 due to musculoskeletal disease [8]. Thus, the effects of work-related musculoskeletal disorders are not exclusive to the individual; rather, it extends to encompass their socio-economic contexts.
The COVID-19 pandemic has placed the global community in a state of lockdown and quarantine in place to control the spread of the virus. One of the most common public health strategies is enforcing a “work from home” setup [9]. The shift to a work from home status places some professions that are typically not desk-based confined in a make-shift office [10]. For example, teachers who are typically classroom or laboratory-based have been forced to deliver their lectures and activities seated in front of a computer for hours on end. Thus, current work demands and resource limitations have likewise shifted, and affected workers are exposed to additional physical and occupational stress. Recent findings suggest that individuals who work from home have higher reported musculoskeletal pain [11].
Additionally, there is initial evidence that as much as 86.30% of individuals who have worked from home experience musculoskeletal disorders [12]. The extant literature on the effects of the COVID-19 pandemic work from home setup on musculoskeletal functions has been fragmented or, at best, yet to be reviewed. With the known health and socio-economic effects of work-related musculoskeletal disorders, there is a need to rapidly review the existing relevant literature to inform decision-making towards immediate programs and policies that address the health and well-being of individuals who are continuously working from home. In this review, we are keen on reviewing the prevalence of musculoskeletal pain, and not the specific disorders associated with it, among a subset of the population who worked from home due to the quarantines imposed by the COVID-19 pandemic. Thus, this rapid review aimed to explore musculoskeletal pain symptoms associated with work from home conditions during the COVID-19 pandemic.
2 Materials and Methods
2.1 Rapid review question
This rapid specifically aims to answer the question, “What is the prevalence of musculoskeletal pain symptoms associated with work from home conditions during the COVID-19 pandemic?”
2.2 Protocol and registration
A rapid review was chosen due to the urgent need to support decision-making on preventing and addressing the possible effects on the musculoskeletal functions due to the work from set up during the COVID-19 pandemic. The methods in this rapid review were informed by the World Health Organization's practical guide on rapid reviews [13], and we used the Selecting Approaches for Rapid Reviews (STARR) Decision Tool [14] to address possible methodological limitations. The reporting of this protocol is adapted from the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) Protocols [15]. This protocol is in the Open Science Framework registry (osf.io/vxs4w) and the PROSPERO database (CRD42021266097). A published version of the rapid review protocol is currently in press [16].
2.3 Eligibility criteria
The inclusion and exclusion criteria adopted in this rapid review considered peer-reviewed published observational studies, including epidemiological studies, prospective and retrospective cohort studies, case-control studies, cross-sectional studies, case series, case studies, or reports for inclusion, published starting from December 2019–August 2021. The studies must include adult workers ages 20–65 in different industries who, because of the COVID-19 pandemic, were forced to work from home. The outcomes to be reviewed include musculoskeletal conditions, disorders, or pain.
2.4 Information sources
Following the recommendations of the STARR Decision tool, the initial search strategy was developed by members of the review team who have been trained in the Cochrane and JBI evidence-based practice models. The following databases were searched: PubMed, MEDLINE, and CINAHL. This review did not include grey literature searching.
2.5 Search strategy
Table 1 summarizes the keywords and alternative terms strung together to search for the articles considered for this rapid review. The last date searched was on August 31, 2021.Table 1 Search strategy.
Table 1Keyword Other terms
COVID-19 COVID-19 pandemic OR pandemic OR COVID
work from home work-from-home OR home-based OR home
musculoskeletal musculoskeletal function* OR musculoskeletal pain OR musculoskeletal condition OR musculoskeletal disorders OR musculoskeletal*
2.6 Data management and selection process
A three-step search and selection strategy were utilized in this review. We searched through the identified information sources using combinations of our search strategies. The first level of study selection involved screening the title and abstracts of the potential studies. The second screening level involved a full-text review of articles that have passed through the first level of screening. Thirdly, the reference list of all identified articles was searched for additional studies. Studies published in English, or have an available English translation, were considered for inclusion in this review. After an initial review workshop, two independent review authors accomplished the search and screening process. A consensus meeting ensued to finalize the decision in case of unresolved issues. The study selection and screening process summary are presented in the PRISMA flow diagram.
2.7 Risk of bias assessment
Two independent reviewers assessed articles selected for retrieval for methodological validity before inclusion in the review using study design-specific standardized critical appraisal instruments from the Joanna Briggs Institute Meta-Analysis of Statistics Assessment and Review Instrument (JBI-MAStARI) [17]. Any disagreements that arose between the reviewers were resolved through discussion for a consensus.
2.8 Data extraction
Quantitative data were extracted from papers included in the review using study design-specific standardized data extraction tools from JBI-MAStARI, purposely built into an MS Excel spreadsheet. The data extraction form (included as a supplementary file) was tested on n = 5 articles for data validation and reviewer validity. The data extracted included specific details about the context, populations, study methods, and outcomes of significance to the review-specific objectives (e.g., prevalence rate, type of pain, pain site, musculoskeletal condition). Two review authors extracted the data, with a third author adjudicating any unresolved inconsistencies.
2.9 Data synthesis and analysis
Due to clinical heterogeneity, quantitative meta-analysis was not considered. A narrative synthesis was performed to describe the reviewed evidence in tables and figures. Nevertheless, we summarized the quantitative data using basic descriptive statistics.
3 Results
3.1 Study selection
Our initial search yielded 44 primary studies across the three databases searched (n = 44) and through other sources, such as reference and relevant articles forward-searching (n = 5). After removing one duplicated article, the screening process of titles and abstracts excluded 30 studies that did not fit into our rapid review criteria. After a full-text review, seven additional articles were likewise excluded for similar reasons. The included six articles' reference list was manually searched; however, no additional articles were added (Fig. 1 ).Fig. 1 Fig. 1
3.2 Study characteristics
Six articles are included in this rapid review [11,12,[18], [19], [20], [21]]; five articles have a cross-sectional study design (Level IV evidence), and one article uses a case-control design (Level III-3 evidence). A total of n = 2835 participants were recruited spread over the six articles; however, only n = 1720 were sampled to have worked from home during the quarantine period of the pandemic. These participants came from four countries: Turkey, Indonesia, Saudi Arabia, and the Philippines. Age ranged from 20-64 years. The nature of work varied across studies; however, one of the more common professions was those working in the academe (i.e., teacher, academic), as reported in four articles. All articles reported the prevalence of musculoskeletal-related pain for the following body parts: neck, shoulder, upper back, lower back, elbow, wrist/hands, hips/thighs, knees, and ankles/feet. The most common instrument used to report the prevalence of musculoskeletal-related pain was the Nordic Musculoskeletal Questionnaire [22] used in three studies; the rest used different instruments. All instruments were administered electronically. Three studies reported on work-related factors associated with musculoskeletal-related pain due to working from home.
3.3 Risk of bias within studies
The critical appraisal scores, reflecting risks of bias, ranged from 4 to 9 out of 9, with a mean score of 6.0. Only one study was able to score a perfect rating. The most common limitation was the inappropriate recruitment (i.e., report on the sampling method and design is lacking) of participants (four studies). All studies used valid methods for identifying the condition's prevalence. Interestingly, some limitations were due to unclear findings (i.e., authors were not clear in explicitly reporting how internal and external biases were addressed in their methods) instead of a clear methodological caveat (three studies).
3.4 Synthesis of results
Due to clinical heterogeneity, a meta-analysis was not possible. Hence, we report the synthesis of results in a narrative form supplemented by a summary table (Table 2 ).Table 2 Summary of reviewed studies.
Table 2Study ID Author Year Setting/Country NHMRC Level of Evidence Study Design Subject Characteristics Instruments MSKD Prevalence n/N (%) Site of the pain Factors associated with MSK pain
1 Celanay 2020 Turkey Level III-3 Case-control study n = 686 (375 (54.7%) subgroup of participants that stayed at home during lockdown)
Age: Median = 32
Gender: Male = 296 (78.9%)
Ethnicity: Turkish
Occupation: Student, teacher, engineer, medical staff, officer, employee, private sector, retired, academician, housewife Nordic Musculoskeletal Questionnaire
Covid-19 Phobia Scale
Jenkins Sleep Scale
*Electronic
**Used Turkish version of the instruments Neck: 76/375 (20.3%)
Upper back: 70/375 (18.7%)
Lower back: 73/375 (19.5%)
Shoulder: 60 (16.0%)
Elbow: 5 (1.3%)
Wrist/hand: 16 (4.3%)
Hip/Thigh: 21 (5.6%)
Knee: 36 (9.6%)
Ankles/feet: 24 (6.4%) Neck, shoulders, elbows, wrists/hands, upper back, lower back, hips/thighs, knees, ankles/feet
2 Condrowati 2020 Indonesia Level IV Cross-sectional study n = 95
Age: Mode = 20–30 yrs (67, 70.50%)
Gender: Male = 35 (36.8%)
Ethnicity: Indonesian
Occupation: Academics, employees (government, company), teacher, State-owned enterprise, entrepreneur Nordic Musculoskeletal Questionnaire
*Electronic
**Language version of the instrument was not explicitly reported Neck: 51/95 (54%)
Shoulder: 35/95 (36.5%)
Lower back: 33/95 (34.9%)
Upper back: 30/95 (31.7%)
Ankle: 21/95 (22.2%)
Hip: 17/95 (17.4%)
Knee: 17/95 (17.4%)
Wrist: 15/95 (15.9%)
Elbow: 6/95 (6.3%)
*Incomplete data reported (for n); manually computed Neck, shoulders, elbows, wrists/hands, upper back, lower back, hips/thighs, knees, ankles/feet
3 Ozdemir 2021 Turkey Level IV Cross-sectional study n = 101
Age: 33.95 ± 5.99 (24–57)
Gender: Male = 42 (41.6%)
Ethnicity: Turkish
Occupation: White-collar workers Numerical Rating Scale
Oswestry Disability Index
Utrecht Work Engagement Scale
Tampa Scale of Kinesiophobia
International Physical Activity Questionnaire Short form
*Electronic
**Researcher-developed questionnaire previously reported elsewhere
***Used Turkish version of the instruments LBP: 57/101 (56.4%)
Neck: 40/101 (39.6)
Leg: 5/101 (5%)
Widespread: 3/101 (3%)
Shoulder: 3/101 (3%)
Arm: 1/101 (1%)
Chest: 2/101 (2%)
Coccydynia: 1/101 (1%) Back, neck, leg, shoulder, arm, chest, coccyx LBP intensity was significantly correlated with disabling effects on daily living activities and fear of movement.
Neck pain was significantly correlated with disabling effects on daily living activities.
4 Sagat 2020 Saudi Arabia (Riyadh) Level IV Cross-sectional study n = 463
Age: 35.68 + 9.84 (18–64 yrs)
Gender: Male = 259 (55.94%)
Ethnicity: Saudi Citizen = 330 (71.27%), Foreign = 133 (28.73%)
Occupation: Not explicitly reported (academic and work-related) COVID-19 and Back Pain Questionnaire
*Electronic
**Researcher-developed questionnaire
***Used the original English version of the instruments Neck: 140/463 (30.3%)
Shoulders: 108/463 (23.3%)
Thoracic area: 107/463 (23.2%)
Low back: 203/463 (43.8%)
Legs: 64/463 (13.9%)
*Incomplete data reported (for n); manually computed Neck, shoulders, thoracic area, low back, legs Back pain is significantly correlated with time spent sitting, weekly frequency of physical inactivity, and perceived stress
5 Sengul 2020 Turkey Level IV Cross-sectional study n = 1138 (WFH = 686 (60.3%))
Age: 35.69 + 11.6
Gender: Male = 650 (57.1%)
Ethnicity: Turkish
Occupation: Not explicitly reported (academic and work-related) Cornell Musculoskeletal Discomfort Questionnaire
*Electronic
**Used Turkish version of the instruments Neck: 875/1138 (76.9%)
Shoulders: 820/1138 (72.1%)
Back: 856/1138 (75.2%)
Between shoulder and elbow: 703/1163 (61.8%)
Waist: 678/1138 (72.8%)
Forearm: 680/1138 (59.8%)
Wrist: 678/1138 (59.6%)
Fingers: 666/1138 (58.5%)
Hip: 691/1138 (60.7%)
Upper leg: 672/1138 59.1%)
Knee: 709/1138 (62.3%)
Lower leg: 665/1138 (58.4%)
Feet: 682/1138 (59.9%)
*Considered all participants working or not working from home and those within areas with and without lockdowns
**Pain is due to inactivity related to decrease in daily exercise, sports, or routine activities
***Prevalence based on total of the pain strength level Neck, shoulders, back, between shoulder and elbow, waist, forearm, wrist, fingers, hip, upper leg, knee, lower leg, feet
6 Seva 2021 Philippines (Manila) Level IV Cross-sectional study n = 352
Age: Median = 33 (21–64)
Gender: Male = 134 (38.07%)
Ethnicity: Filipinos
Occupation: Employees that use computers Nordic Musculoskeletal Questionnaire
Workstation Suitability (Researcher-Adapted)
Computer Workstation Ergonomics: Self-Assessment Checklist
Recovery Experience Questionnaire (Psychological Detachment items)
Copenhagen Psychosocial Questionnaire (Stress)
*Electronic
**Used the original English version of the instruments Neck: 239/352 (67.9%)
Shoulder: one = 88/352 (25%); both = 149/352 (42.3%)
Elbow: one = 62/352 (17.6%); both = 39/352 (11.1%)
Wrist: one = 165/352 (46.9%); both = 56/352 (15.9%)
Upper back: 200/352 (56.3%)
Lower back: 261/352 (74.1%)
Hips/thighs: 139/352 (39.5%)
Knees: 101/352 (28.7%)
Ankles/feet: 76/352 (21.6%)
*Reported pain in on or both sides for shoulder, elbow, and wrist Neck, shoulders, elbows, wrists/hands, upper back, lower back, one or both hips/thighs, one or both knees, one or both ankles/feet Workstation ergonomic suitability was significantly correlated with musculoskeletal symptom but not workstation suitability.
Musculoskeletal symptoms had no significant effect on productivity.
3.4.1 Prevalence of musculoskeletal pain due to work from home conditions during the COVID-19 pandemic
Nine common body parts reported to have experienced musculoskeletal pain related to work from home conditions during the pandemic quarantine were reported in all six articles: neck, shoulder, upper back, lower back, elbow, wrist/hands, hips/thighs, knees, and ankles/feet. The highest prevalence reported was for neck pain (20.3–76.9%), low back pain (19.5–74.1%), and shoulder pain (3.0–72.9%). The lowest pain prevalence recorded was elbow pain (1.3–17.6%). Fig. 2 summarizes the ranges of pain prevalence.Fig. 2 Fig. 2
3.4.2 Instruments used in measuring musculoskeletal pain due to work from home
The most common instrument used to measure musculoskeletal pain was the Nordic Musculoskeletal Questionnaire [22], reported in three studies. The remaining studies used varied instruments: Numerical Rating Scale [23], COVID-19 and Back Pain Questionnaire [19], and the Cornell Musculoskeletal Discomfort Questionnaire [24]. All studies utilized electronic versions of the instruments. In three studies, the authors used translated versions of the instruments (i.e., citing studies that established their psychometric properties in the target language). Two studies used the original English versions, while one study used an instrument developed specifically using the target language.
3.4.3 Professions disposed to musculoskeletal pain due to work from home
There were varied professions and occupations reported in the six included articles for rapid review. These included professionals, self-employed, students, housewives, and retired individuals. The more common profession observed was individuals working in the academic sector (i.e., academics and teachers). Four studies did not explicitly report on the professions of their participants; however, two of these indicated that they were related to academia; one study included white-collar professionals; one study recruited those that used computers in their work.
3.4.4 Work-related factors associated with musculoskeletal pain due to work from home
Three studies reported on factors associated with musculoskeletal pain symptoms. Increased musculoskeletal pain was found to be significantly correlated with workstation ergonomic suitability. Back pain is significantly correlated with time spent sitting during work from home, weekly frequency of physical inactivity, and perceived stress due to the pandemic. Specifically, low back pain significantly correlated with disabling effects on daily living activities and fear of movement, and neck pain significantly correlated with disabling effects on daily living activities.
3.4.5 Musculoskeletal pain in pre and post-pandemic contexts
While not originally part of the review aims of this study, changes in the reported musculoskeletal pain were reported in some of the studies we have reviewed. We found three studies that compared musculoskeletal pain symptom differences among the included six studies reviewed. One study found a significantly higher occurrence of lower back pain (p < 0.05) among individuals who stayed and worked from home (73%) than those who continued working status quo (35%) during quarantine periods [11]. Low back pain was significantly (p = 0.001) higher during the quarantine period (43.8%) than during pre-pandemic (38.8%) times, as reported in one study [19]. The severity of musculoskeletal pain significantly intensified among those who worked from home.
4 Discussion
The rapid review provided evidence on the prevalence of musculoskeletal pain from a small subset of population, specifically, among those working from home due to the COVID-19 pandemic. While limitations in the number of samples reviewed, this rapid review showed that the most common areas of pain are the neck, back, and shoulder, which was significantly higher during the pandemic than in the pre-pandemic period. However, the ranges of the prevalence of musculoskeletal pain were wide. These results may be due to sampling methods performed by the studies, which were convenient sampling and snowballing. This method is a result of the COVID-19 lockdown. Furthermore, the diverse professionals and occupations may also contribute to the results. Care in interpreting and generalizing the results of this rapid review is suggested.
This rapid review found the highest prevalence of self-reported musculoskeletal pain in the neck and lower back regions. Prior to the pandemic, the estimated prevalence rate for neck pain was postulated at 16.2% [25], while we found prevalence estimates for neck pain at 20.3–76.9% during the initial quarantine periods of the pandemic. For low back pain, pre-pandemic estimates suggest a prevalence of 11.9% [26], however, this review found estimates for low back pain prevalence at 19.5–74.1% during the pandemic's initial quarantine period. Roughly, there has been at least a 20–30% increase in the prevalence of self-reported musculoskeletal pain symptoms for the neck and lower back regions, respectively. It is possible that work from home conditions during the quarantine periods associated with physical, occupational, and socio-emotional factors, among others, may have contributed to this.
The putative factors associated with musculoskeletal pain development have heightened during the pandemic. Three factors associated with the increase of low back pain were identified: prolonged sitting, stress, and decreased physical inactivity [19]. This is due to the work from home situation where all activities are technology-based [11]. Furthermore, during the lockdowns, the general population was not allowed to participate in exercises outdoors. Gyms were closed because it is an area where there is an increased amount of respiratory aerosol particle production and inhalation, increasing the incidence of COVID-19 transmission [27].
An extended period of sitting with the trunk in flexion causes inactivation of lumbar muscles, which places the load on passive structures like the ligaments and intervertebral discs [28]. This has been postulated to be the cause of low back pain in prolonged sitting. However, the systematic review of Swain et al. [29] showed no sufficient evidence of the relationship between prolonged sitting with low back pain. Nevertheless, one of the tasks performed during prolonged sitting is taking a break. Waongenngarm et al. [30] classified breaks into four: active break with or without postural change, passive break, and standing break with doing computer work. The review showed that active break with postural change had a positive break in pain reduction. One of the gaps in the papers included in this review is that there was no question about the type, duration, and frequency of participants' breaks during the prolonged sitting.
The systematic review of Sitthipornvorakul et al. [31] concluded that there is insufficient evidence of the association of physical inactivity with neck and low back pain. At the same time, the systematic review of Ramond et al. [32] concluded that only two out of the seven studies which studied psychological distress showed its association with low back pain [[32], [33], [34]]. However, one caveat of their study is that only one factor associated with musculoskeletal pain has been studied.
Because of the uncertainties and health risks brought about by COVID 19-pandemic, psychological stress has increased among the population. A meta-analysis of 14 studies showed that posttraumatic syndrome was 23.88% (95% CI: 14.01, 33.76) and psychological stress was 24.84% (95% CI:11.75,37.92) [35]. A global survey across 57 countries showed that there was increase in moderate stress with a mean score of 19.8 ± 7.17 using the Perceived Stress Scale −10. This was higher prior to the pandemic where scores were 12.89, 15.81, 15.05 in Germany, Mexico, and the United States, respectively [36] but was almost similar to other counties (India:19.25, China: 19.2 and United Kingdom: 19.79). The lockdown brought about by the COVID pandemic has become a fertile ground for psychological stress. The term ‘coronaphobia’ has been coined which is defined as “an emerging phobia specific to COVID-19 leading to accompanied excessive concern over physiological symptoms, significant stress about personal and occupational loss, increased reassurance and safety seeking behaviors, and avoidance of public places and situations” [37]. The conceptual model of Arora [38] has included seven risk factors associated with coronaphobia which are: 1) unending uncertainties about SARS-Cov-2, 2) unforeseen reality of lockdowns, quarantine, and self-isolation, 3) acquiring new practices and avoidance behavior, 4) statements from international organizations which provide a realistic but gloomy predictions on the course of the pandemic, 5) failure of developed countries in effectively addressing the crisis, 6) leaders and famous celebrities infected by COVID- 19, and 7) infodemia becoming infodemic. The study of Celanay [11] which used the Covid-19 Phobia Scale (C19P–S) that have psychological, psychosomatic, economic, and social subscales showed that participants who stayed at home had a significantly higher scores in the total and subscales scores. However, the study did not correlate musculoskeletal pain with coronophobia. The results of Sagat et al. [19] showed that the there was a higher level of stress during the pandemic lockdown as compared to before the lockdown (50.42% vs 22,41%). Those with moderate or severe stress had a higher low back pain intensity of 2.73 during quarantine as compared to pre-pandemic which was 1.96. This study used a validated self-administered questionnaire.
Many factors such as ergonomic, psychological, anatomical, and social factors may contribute to musculoskeletal pain, and these factors are interrelated to each other. There are no firm boundaries that exist among these factors. Therefore, these factors must be investigated, whether in work from home or onsite setup, and the best model to predict musculoskeletal pain be developed.
There are several limitations in our rapid review. Due to the focused and temporal-sensitive nature of rapid reviews, certain occupational and ergonomic factors may not have been explored. While the occupations varied across the studies we have reviewed, their work from home setup and hours may be similar. Evidence on MSK-related pain between individuals working from home and those who continued working onsite will need to be compared. While our review provides initial evidence on the possible increase in the prevalence of MSK-related pain among individuals who have worked from home during the COVID-19 pandemic, further investigations are needed to determine whether such exposure may explain this phenomenon. Thus, caution in interpreting the synthesized findings in this rapid review is warranted. Given the rapid review methodology adopted in this study, the scope of evidence searched may have been limited. Nevertheless, the findings synthesized herein warrant further investigations on an updated, wider and deeper review of evidence to encompass other information sources (i.e., databases and grey literature). The authors recommend that a full and up-to-date systematic review may be needed to update the evidence. Future review authors will need to expand the search timeline (i.e., 2020 up to the present) and strategy to include region-specific pain symptoms (i.e., low back pain, neck pain).
5 Conclusion
There is initial evidence that the prevalence of musculoskeletal pain may have increased because of the COVID-19 lockdown. We assume that work from home setup will most likely continue with our current situation. Thus, strategies on how to prevent its occurrence should be one of the employers' concerns so that professionals will be able to cope with the challenges of the COVID-19 lockdown. It is suggested that a system in the workplace be developed in order that musculoskeletal pain will be detected earlier. Policies and programs not only for sustainable occupational safety and proper ergonomics but also programs that address the mental health issues of employees should also be addressed. Furthermore, programs should also be available online for easy access of employees. Further introspection is needed to update the evidence base on this topic, and a full systematic review with an up-to-date timeline is needed.
Disclosure statement
The authors declare no conflicting or competing interests.
Data availability statement
All relevant data associated with this rapid review has been reported in the tables and figures.
Author contributions
All authors contributed equally to the completion of this rapid review.
Funding sources
This review is not funded by any organization.
Ethical approval details
This is a review article, hence an ethical approval is not applicable.
Implications for practice
• A system in the workplace should be developed for the early detection of musculoskeletal pain.
• Apart from standard occupational safety and proper ergonomic, sustainable policies and programs that address the mental health issues of employees should also be addressed.
• Programs addressing musculoskeletal pain should be available online for employees to address accessibility and ubiquity.
Declaration of competing interest
The authors declare no conflict nor competing interests.
Appendix A Supplementary data
The following is the Supplementary data to this article:Multimedia component 1
Multimedia component 1
Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.ijosm.2022.12.001.
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References
1 Woolf A.D. Vos T. March L. How to measure the impact of musculoskeletal conditions Best Pract Res Clin Rheumatol 24 6 2010 723 732 10.1016/j.berh.2010.11.002 21665121
2 Hoy D.G. Smith E. Cross M. The global burden of musculoskeletal conditions for 2010: an overview of methods Ann Rheum Dis 73 6 2014 982 989 10.1136/annrheumdis-2013-204344 24550172
3 da Costa J.T. Baptista J.S. Vaz M. Incidence and prevalence of upper-limb work related musculoskeletal disorders: a systematic review Work 51 4 2015 635 644 10.3233/WOR-152032 26409936
4 Wærsted M. Hanvold T.N. Veiersted K.B. Computer work and musculoskeletal disorders of the neck and upper extremity: a systematic review. BMC Musculoskelet Disord 11 1 2010 1 5 10.1186/1471-2474-11-79
5 Xie Y. Szeto G. Dai J. Prevalence and risk factors associated with musculoskeletal complaints among users of mobile handheld devices: a systematic review Appl Ergon 59 2017 132 142 27890121
6 Health and Safety Executive United Kingdom Work related musculoskeletal disorder statistics (WRMSDs) in great britain, 2020. London: health and safety executive United Kingdom https://www.hse.gov.uk/statistics/causdis/msd.pdf 2020
7 European Agency for Safety and Health at Work Work-related musculoskeletal disorders: prevalence, costs and demographics in the EU. Luxemberg: European Agency for Safety and Health at Work https://osha.europa.eu/en/publications/msds-facts-and-figures-overview-prevalence-costs-and-demographics-msds-europe 2019
8 World Health Organization Global health risks global health risks WHO. Mortality and burden of disease attributable to selected major risks 2009 WHO Geneva
9 Burdorf A. Porru F. Rugulies R. The COVID-19 (Coronavirus) pandemic: consequences for occupational health Scand J Work Environ Health 46 3 2020 229 230 10.5271/sjweh.3893 32356896
10 International Labour Organization An employers' guide on working from home in response to the outbreak of COVID-19 2020 International Labour Organization Geneva https://www.ilo.org/wcmsp5/groups/public/–ed_dialogue/–-act_emp/documents/publication/wcms_745024.pdf
11 Celenay S. Karaaslan Y. Mete O. Ozer Kaya D. Coronaphobia, musculoskeletal pain, and sleep quality in stay-at home and continued-working persons during the 3-month Covid-19 pandemic lockdown in Turkey. Chronobiol Bar Int 37 12 2020 1 8 10.1080/07420528.2020.1815759
12 Condrowati C. Bachtiar F. Maharani F.T. Musculoskeletal disorder of workers during work from home on covid-19 pandemic: a descriptive study International conference of health development. Covid-19 and the role of healthcare workers in the industrial era (ICHD 2020) 2020 Atlantis Press 153 160 10.2991/ahsr.k.201125.025
13 Tricco A.C. Langlois E. Straus S.E. Rapid reviews to strengthen health policy and systems: a practical guide 2017 World Health Organization Geneva https://apps.who.int/iris/bitstream/handle/10665/258698/9789241512763-eng.pdf
14 Pandor A. Kaltenthaler E. Cooper K. Approaches for rapid reviews (STARR) decision tool 2019 The University of Sheffield UK https://figshare.shef.ac.uk/articles/journal_contribution/SelecTing_Approaches_for_Rapid_Reviews_STARR_Decision_Tool/7970894/1
15 Moher D. Shamseer L. Clarke M. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement Syst Rev 4 1 2015 1 9 10.1186/2046-4053-4-1 25554246
16 Gomez I. Gonzalez-Suarez C. Sosa K.E. Tapang M.L. Work-from-home-related musculoskeletal pain during the COVID-19 pandemic: a rapid review protocol Int J Osteopath Med 2022 10.1016/j.ijosm.2022.04.005 Forthcoming. [Cited 2022 May 28]:[4pp.]
17 Aromataris E. Munn Z. Chapter 1: JBI systematic reviews Aromataris E. Munn Z. JBI manual for evidence synthesis. Australia: JBI 2020 10.46658/JBIMES-20-02 https://synthesismanual.jbi.global
18 Özdemir Y.B. Investigation of low back pain in the white-collar population working from home due to the COVID-19 pandemic J Phys Med Rehabil Sci 2 2021 May 1 24
19 Šagát P. Bartík P. Prieto González P. Impact of COVID-19quarantine on low back pain intensity, prevalence, and associated risk factors among adult citizens residing in Riyadh (Saudi Arabia): a cross-sectional study Int J Environ Res Publ Health 17 19 2020 7302
20 Şengül H. Bulut A. Adalan M.A. Investigation of the change of lockdowns applied due to COVID-19 pandemic on musculoskeletal discomfort J. Hum. Sci. 17 4 2020 Oct 17 974 985
21 Seva R.R. Tejero L.M. Fadrilan-Camacho V.F. Barriers and facilitators of productivity while working from home during pandemic J Occup Health 63 1 2021 e12242 34181307
22 Crawford J.O. The Nordic musculoskeletal questionnaire Occup Med 57 4 2007 300 301
23 Bahar-Ozdemir Y. Sencan S. Ercalik T. Kokar S. Gunduz O.H. The effect of pre-treatment depression, anxiety and somatization levels on transforaminal epidural steroid injection: a prospective observational study Pain Physician 23 3 2020 E273 E280 PMID: 32517403 32517403
24 Hedge A. Morimoto S. McCrobie D. Cornell musculoskeletal discomfort questionnaire Ergonomics 1999
25 Kim R. Wiest C. Clark K. Cook C. Horn M. Identifying risk factors for first-episode neck pain: a systematic review Musculoskeletal Science and Practice 33 2018 77 83 29197234
26 Hoy D. Bain C. Williams G. March L. Brooks P. Blyth F. Woolf A. Vos T. Buchbinder R. A systematic review of the global prevalence of low back pain Arthritis Rheum 64 6 2012 2028 2037 22231424
27 Blocken B. van Druenen T. Ricci A. Ventilation and air cleaning to limit aerosol particle concentrations in a gym during the COVID-19 pandemic Build Environ 193 2021 April 15 107659 33568882
28 Mörl F. Bradl I. Lumbar posture and muscular activity while sitting during office work J Electromyogr Kinesiol 23 2 2013 April 1 362 368 23122693
29 Swain C.T. Pan F. Owen P.J. No consensus on causality of spine postures or physical exposure and low back pain: a systematic review of systematic reviews J Biomech 102 2020 March 26 109312 31451200
30 Waongenngarm P. Areerak K. Janwantanakul P. The effects of breaks on low back pain, discomfort, and work productivity in office workers: a systematic review of randomized and non-randomized controlled trials Appl Ergon 68 2018 Apr 1 230 239 29409639
31 Sitthipornvorakul E. Janwantanakul P. Purepong N. The association between physical activity and neck and low back pain: a systematic review Eur Spine J 20 5 2011 May 677 689 21113635
32 Ramond A. Bouton C. Richard I. Psychosocial risk factors for chronic low back pain in primary care—a systematic review Fam Pract 28 1 2011 Feb 1 12 21 20833704
33 Sieben J.M. Vlaeyen J.W. Portegijs P.J. A longitudinal study on the predictive validity of the fear-avoidance model in low backpain Pain 117 2005 162 170 16099095
34 Grotle M. Brox J.I. Glomsrod B. Prognostic factors in first-time care seekers due to acute low back pain Eur J Pain 11 2007 290 298 16677837
35 Cooke J.E. Eirich R. Racine N. Madigan S. Prevalence of posttraumatic and general psychological stress during COVID-19: a rapid review and meta-analysis Psychiatr Res 292 2020 Oct 1 113347
36 Adamson M.M. Phillips A. Seenivasan S. Martinez J. Grewal H. Kang X. Coetzee J. Luttenbacher I. Jester A. Harris O.A. Spiegel D. International prevalence and correlates of psychological stress during the global COVID-19 pandemic Int J Environ Res Publ Health 17 24 2020 9248
37 Arora A. Jha A.K. Alat P. Das S.S. Understanding coronaphobia Asian Journal of Psychiatry 54 2020 Dec 1 102384 33271693
| 36514321 | PMC9731643 | NO-CC CODE | 2022-12-14 23:31:52 | no | Int J Osteopath Med. 2022 Dec 8; doi: 10.1016/j.ijosm.2022.12.001 | utf-8 | Int J Osteopath Med | 2,022 | 10.1016/j.ijosm.2022.12.001 | oa_other |
==== Front
Socioecon Plann Sci
Socioecon Plann Sci
Socio-Economic Planning Sciences
0038-0121
0038-0121
Elsevier Ltd.
S0038-0121(22)00301-9
10.1016/j.seps.2022.101494
101494
Article
Impact of epidemic outbreaks (COVID-19) on global supply chains: A case of trade between Turkey and China
Kazancoglu Yigit a∗
Ekinci Esra b
Mangla Sachin Kumar c
Sezer Muruvvet Deniz d
Ozbiltekin-Pala Melisa e
a Logistics Management Department, Yasar University, Izmir, Turkey
b Industrial Engineering Department, İzmir Bakırçay University, Turkey
c Research Centre - Digital Circular Economy for Sustainable Development Goals (DCE-SDG), Jindal Global Business School, O P Jindal Global University, Haryana, India
d Business Administration Department, Yasar University, 35100, İzmir, Turkey
e Logistics Management Department, Yasar University, 35100, İzmir, Turkey
∗ Corresponding author.
9 12 2022
9 12 2022
1014944 8 2021
21 10 2022
4 12 2022
© 2022 Elsevier Ltd. All rights reserved.
2022
Elsevier Ltd
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
COVID-19 has negative impacts on supply chain operations between countries. The novelty of the study is to evaluate the sectoral effects of COVID-19 on global supply chains in the example of Turkey and China, considering detailed parameters, thanks to the developed System Dynamics (SD) model. During COVID-19 spread, most of the countries decided long period of lockdowns which impacted the production and supply chains. This had also caused decrease in capacity utilizations and industrial productions in many countries which resulted with imbalance of maritime trade between countries that increased the freight costs. In this study, cause and effect relations of trade parameters, supply chain parameters, demographic data and logistics data on disruptions of global supply chains have been depicted for specifically Turkey and China since China is the biggest importer of Turkey. Due to this disruption, mainly exports from Turkey to China has been impacted in food, chemical and mining sectors. This study is helpful to plan in which sectors; the actions should be taken by the government bodies or managers. Based on findings of this study, new policies such as onshore activities should consider to overcome the logistics and supply chain disruptions in global supply chains. This study has been presented beneficial implications for the government, policymakers and academia.
Keywords
Global supply chain and logistics
Technology
COVID-19
Disruption
System dynamics
Turkey-China
==== Body
pmc1 Introduction
Expanding trade and investment operations around the world causes businesses to expand worldwide [1,2]. For this purpose, many businesses and sectors have adopted integrated global supply chains [3], through which overseas activities and outsourcing strategies are delivered, produced and distributed across national borders [4,5]. Therefore, with the development in supply chains, the supply chains become more vulnerable to risks consist of disruptions such as disasters (Majid, 2020; [6]. Especially, pandemics, a type of natural disasters can cause long-term disruptions and bring uncertainties in the global supply chain [7,8].
The COVID-19, one of the crucial pandemics, affects the global supply chain worldwide [9]. As mentioned earlier, the COVID-19 pandemic threatens not only human life but also businesses around the world (Ehlert et al., 2021). Within the pandemic environment, several disruptions have occurred, especially in global supply chains [10,11]. For example, global automotive brands such as General Motors, Nissan, Renault, Honda and Peugeot and global companies such as Toyota, Apple, Starbucks, and Ikea have either stopped their operations in China completely or have pulled them to a very limited point [12]; Majid, 2020). All of these biggest companies are shutting operations in the world since the lack of raw materials, which are produced or came from China [13]. Therefore, large losses are also expected in the economy due to lockdowns in companies and disruptions in production and supply chain [14,15]. With this epidemic, supply chains of almost all sectors are affected from production stages to consumers [16,17].
Recently, China is the most important actor in the global supply chain [18]. In fact, China is both the starting point and the ending point for the global supply chain, and the world has become dependent on China in production [7]. Besides being a large market, China is the main supplier country of the global automotive industry and electronics industry and, cars, auto spare parts, mobile phones, computers and electronic parts of many known brands are produced in China [18].
Like other countries, the supply chain management and the foreign trade operations of Turkey is affected by COVID-19 as negatively. One of the most important reasons for dealing with China and Turkey relations in the study is that Turkey is one of the emerging economies and China is the second biggest importer of the country and ranks first among the countries that Turkey exports the most. In addition, the problems experienced in the import of intermediate goods in China, the fluctuations in the world demand, the decrease in the production processes in Turkey and as a result, the decrease in exports and the increase in unemployment cause economic consequences [19]. At the same time, problems such as quarantine practices within the country and factories stopping production cause disruption in the country's supply chain.
Global supply chains act as a system due to the integrated structures and the deterioration in global supply chains directly affects many important factors in the country's economy and social life [20,21] and thus global supply chains requires a system perspective. Since it needs to be handled cause and effect relations of main groups such as logistics, trade, supply chain and demographic parameters. Thus, the biggest contribution of this study is to provide a holistic perspective for these main groups from the system thinking approach, the behaviour of the examined system against certain changes can be investigated and decisions can be made by determining the strategies to regulate this behaviour [22], which is crucial step to overcome the impacts of COVID-19 on systems. Therefore, it is very important to know how much the global supply chain structures of countries and sectors are affected by COVID-19 and subsequently how to determine the most appropriate solutions to manage them by considering system thinking approach. The systems thinking approach helps in analysing the causality relations between main groups and their system parameters. Besides, understanding behaviours of import and export data is also influenced by many logistics, trade, supply chain and demographic parameters. Hence, the main motivation of this study is to analyse the effects of outbreaks on global supply chains by integrated with the proposed parameters through the system thinking perspective.
As a result, the research question of the study is stated as;• How to analyse the effects of disruptions on supply chains of Turkey and China comparatively in a system thinking approach?
In this study, our aim is to explain the following research objectives:I. To analyse how main sectors of the countries are affected by COVID-19 based on the logistics, trade, supply chain and demographic aspects from the system thinking perspective.
II. To make a short-term import forecast from China to Turkey considering logistics, trade, supply chain and demographic aspects through system thinking perspective.
III. To embody the impacts of COVID-19 with an example of China and Turkey considering logistics, trade, supply chain and demographic aspects through the system thinking perspective.
To address the purpose of this research question, a detailed literature review about epidemic outbreaks impact on global supply chain, need for SD modelling on the basis of system thinking approach and COVID-19 impact on global supply chain was conducted. After that, SD modelling was formulated for showing sectoral impacts of COVID-19, short term forecast about the future state respectively. In order to show the impacts of COVID-19, a real-life case study was analysed between China and Turkey.
This study is unique in nature to make comparative analysis and to investigate the impact of pandemic on global supply chains by focusing on the foreign trade and logistics activities between countries using SD modelling based on system thinking approach. Moreover, this study fills gap by investigating the whole foreign trade and logistics activities and its actors with SD modelling, which was developed based on the systems thinking approach, and to show how the sectors was affected from pandemic by making comparative analyses and investigating of the impact of pandemics on the countries [23,24].
The rest of the paper is structured as follows. Section 2 exhibits the literature review about epidemic outbreaks impact on global supply chain, system thinking approach and need for SD modelling and COVID-19 impact on global supply chain. Section 3 highlights the SD methodology for the research. Section 4 covers a case study, which is comparative study between China and Turkey as an implementation of the study. Section 5 discusses the findings as well as exhibits the implications for managers and policymakers. Lastly, Section 6 concludes this study by discussions, limitations and scope for future works.
2 Literature review
The disruptive events are an important example of enabling learning and productivity in organizations. Learning effect to cope with disruptive events by investigating the causal relations between systems actors and related system components [22]. System thinking provide recognize the system and deeper insight into this system. Disruptions that affect the entire supply chain such as epidemic should also be taken from a holistic perspective [25]. studied on integration system analysis and disruption theory to analyse conditions that cause disruption. Developed causal relationships provide opportunity to see hidden structure also that create disruptions or to see causes that may lead to disruption through other actors [25]. There are several qualitative and quantitative research techniques for analysing the epidemics in global supply chain. Instead of quantitative techniques, there are limited studies that used qualitative techniques which are semi-structured interviews, focus groups, behaviour analysis, peer-reviewed studies since the methods are proper to investigate whether preventions work, or what causes unexpected results of preventions taken during the disruptions [26,27].
Besides this, to understand the effects of epidemics on global supply chain, quantitative techniques are used in the literature [28]. proposed a network-based modelling for indicating how disruptions affects supply chain process. This model provides several advantages for the supply chain system by improving response time, increasing flexibility and agility, decreasing inventory level and descending cost for all process in the supply chain. With the Petri net-based modelling approach termed as a Disruption Analysis Network (DA_NET), it is aimed to identify to determine how the disruptions spread to the supply chain and how the impact is calculated. All epidemic outbreak has adverse impacts on supply chain, trade and logistics [29] creating disruption in the operations and increasing response time for supply chain.
Simulation models ensure a virtual physical environment, which is significant to analyse analytic models. These models also enable real-life applications that are very risky, difficult and costly to be tested [30,31]. Besides, these models can be implemented to predict supply chain behaviours and investigate the impact of disruptions of supply chain over time [32,33]. Since global supply chain in real world need to deal with many unidentified risks and disruptions such as demand volatility, raw material shortages, natural disasters, transportation failures, fires, weather, wars and also pandemic, it is important to manage effectively and need to be captured real world complexity [34,35]. In particular, pandemics caused by many interrelated factors such as creating increased response time and disruption of transportation and have significant effects on the global supply chain. In order to tackle with these challenges and real-world complexity, simulation tools are recognized as a powerful tool to analyse and evaluate the systems.
Only a limited number of simulation models aim to analyse the impact of pandemic outbreaks that cause supply chain disruption. For example [7], examined and predicted the impacts of epidemic outbreaks as a unique type of risk on the SC performance both long term and short time via simulation-based methodology. According to their results, it was emphasized that the closing and opening time of facilities may be a significant factor determining the effect of the epidemic on SC performance. Inoue and Todo (2020) proposed an agent-based model to analyse the economic impacts of a possible lockdown to hinder spread of COVID-19 in Japan. The lockdown in Tokyo causes the indirect impact on other regions. Total production in Japan, the lockdown because of the disruptions will lead an 86% decrease in daily production in Japan in one month.
Observing and predicting system behaviours both in the long term and the short term are significant in the high uncertainty environments and unexpected disruption of supply chain [7,36]. Highly uncertain environments such as the COVID-19 outbreak force managers and practitioners to develop novel relationships and supply configurations for supply chains by re-evaluating the globalized systems that include the delivery of various components [37]. Crisis like Covid-19 is an example of how these systems affects each other [38]. The SD approach can provide an insight into complex global supply chain and allows predicting impacts of these changes in the system over time and further evaluate the consequences of policy interventions [38].
There are a few studies focuses on disruption of supply chain like pandemic outbreaks [39]. carried out SD model to measure the impact of an avian flu pandemic on global supply chains to show the effect of the flu epidemic effect on annual cumulative sales. The analysis also investigated cause and effects relations by providing reinforce and balance loop between inventory shortage and customer loss on the economic indicator as an annual cumulative sale during avian flu disaster. Since, business are vulnerable to supply chain disruptions by Refs. [[39], [40], [41]]. However, proposed models contribute to the preparation of a strategic business plan for the risks faced by the senior management. Pandemics such as avian influenza greatly affect the trade [42,43] and transportation system. Failure to manage the supply chain would lead to material shortages, resulting in many goods will not be supplied as normal and not meet demands on time [37]. In addition, disruptions such as pandemic has made important effects on all production, financial and transportation systems due to the disruptive effects. Since there is a strong causality relation between supply and demand [38]. [44] investigates impact of transportation disruption on SC performance by measuring supply system performance to investigate impacts of the value of a substitute supplier in the disruptions time [45]. has addressed the problem of transporting different goods from multiple aid providers to disaster areas over a network. Disruptions in communication between intermediaries caused the resources not to be managed effectively. Thus, lack of communication and inefficient allocation of resources lead to shortages of supplies in emergencies [46]. aims to explore community functioning and resilience after disaster to guide policy makers and managers. They developed a model to analyse community functioning after disruption and to calculate the components of resistance, recovery, and resilience.
Besides, Abdullah et al. [60] simulate three stages during supply chain to show that lead time changes and inventory have direct relationships. When the lead time is increased, the inventory level of system is increased. SD model is develop based on cause and effect relations between lead time and inventory. The proposed SD model and experimental results are vital to helps managers to provide their decision-making process such as inventory level and risk management in the disruptions time [44]. aim to analyse impact of transportation disruption on SC performance considering supplier, manufacturer and retailer that effected by export and import supply disruption. COVID-19 caused the global economic crisis since China's exports decrease more than 17% in the first two months in the year 2020, and world trade is anticipated to drop between 13 and 32% in 2020 [[37], [66]]. According to the OECD report, inhibition on the movement of people, goods and services, and business closures primarily caused a sharp decline in manufacturing demand in China, while rest of the world also being affected due to business travel, tourism and global supply chains in later periods [38]. [47] used the SD model to compare pre and post COVID-19, investigating the impact of the COVID era on individuals' income, commodity prices and demand, input cost, and finished goods supply. They indicated that different policies need to develop in terms of analysing pre and post pandemic system environment. According to results earnings could ascend demand, but interruptions in raw material and finished product supply outweighed the impact [48]. developed a SD model to understand the impacts of pandemic on Chinese hog and pork consumption, to assess the impact of disruptions in COVID-19 period. They underline effects of pandemic caused by delay in supply chain operations that hinder import and capacity expansion. The model consists of the five main variables which are live hog price, pork inventory, breeding stock and consumption demand and net import. This model uses main two main feedback loops. One of them is relations between among price, consumption and production decisions. The other feedback loops are relationship between time and capacity building and the time required for the price expectation process to achieve success in the market. After they uses four supply chain disruptions and assess their dynamic impact considering corn shortage and price jump, market hog transportation disruption, delay in breeding stock replacement and delay in pork import situations. Loske [62] aim to analyse the impact of German food retail sales volume and freight capacity dynamics during the COVID-19 period, and the changing volume and capacity dynamics of COVID-19 in retail logistics transportation. SD model is developed considering cause and effect relations between COVID-19 outbreak and freight market dynamics. Transport volume growth is directly affected by COVID-19. However, according to the results, it can be said that the increased freight volume for food in retail logistics is not only due to the pandemic time, but also to the strength measured using the total number of new infections. The paper offers suggestions to decision makers for possible future disruptions.
From literature, it can be said that there is still a need systems perspective for investigating effects spread of COVID-19 on the trade and logistics activities. Since these activities are affected by cause and effect relations of the various groups such as demographic data logistics data, supply chain parameters and economic data. Thus, understanding foreign trade and logistics activities’ dynamics actors and cause and effects relations requires system perspective by analysing causal and effect relations for different groups. Measuring impacts of disruptions in supply chains is crucial to find sustainable solutions which is practical and understandable to show the results of complex dynamics systems such as global supply chains [49]. Thus, his study aims to fill gap by presenting SD model how the sectors was affected from pandemic by making comparative analyses and investigating of the impact of pandemics on the countries.
This study is aimed to analyse the effects of disruptions on countries' supply chain comparatively using the SD modelling. During COVID-19 spread, most of the countries have been exposed to lockdowns which has disruptive impacts on the production and supply chains. This has also led to decrease in capacity utilizations and industrial productions in many countries which resulted with imbalance of maritime trade between countries that increased the freight costs. In order to achieve this purpose, this study considers cause and effect relations of the various groups such as demographic data logistics data, supply chain parameters and economic data on disruptions by focusing on trade and logistics activities for specifically Turkey and China which is the biggest importer of Turkey.
SD model as a solution methodology is discussed in the next section.
3 Research methodology
As developed by Jay Forrester, SD approach has been widely used for addressing complex dynamics systems such as global supply chains. SD provides flexibility for the decision makers to analyse the behaviour of changing system and investigate possible effects of variables, especially it is vital to deal with unexpected and unpredicted situations such as pandemic. In complex systems such as supply chains, it is difficult to know what changes or disruptions in the supply chain will cause disruptions [28]. However, it is essential for the handling dynamic nature of the supply chain that need to rapidly investigate the effects of changes or disruptions for the operations.
Similarly, in trade and logistics activities between countries, the participation of many stakeholders creates a dynamic and complex structure, and since these structures have a permanent flow of information, it should be considered as a system that should be handled with a holistic approach and managed effectively. Especially the pandemic process has negatively affected the trade and logistics activities between countries. In such environments, it is very useful to understand the behaviour of a constantly changing system to develop various policy strategies with the SD model, which provides both flexibility and resilient supply chain. Therefore, SD modelling approach can be useful to deal with complexity and dynamic nature of supply chain that represent multi-tiers structure.
In SD modelling approach these attributes can be examined with stock and flows, time delay, feedback mechanism of the system. Feedback loops are used to indicate reinforce and balance effects in the system. This modelling process begin with the determining model boundaries and identifying purposes. After this process, the second stage includes developing causal loop diagram to indicate the relationships among variables. In the third step, causal loop diagram is transforming into stock and flow diagram to quantify model and run the simulation. The last step involves implementation of the model, discussions and future insights.
Therefore, SD gives an opportunity for analyzing the system with a holistic view and systems perspective. Moreover, SD model aims to analyse the effect of the components that may cause by uncertain environments. Therefore, this paper proposes SD model to analyse the effects of disruptions such as COVID-19 on countries' supply chain.
4 A case study of trade between Turkey and China
With COVID-19, global supply chains have faced severe disruptions. These disruptions in global supply chains negatively affects the trade and logistics activities between countries. The emergence of the epidemic, especially in China, where almost every country is dependent on raw materials, has disrupted the material supply. Due to the COVID-19 effect across the globe, it has become extremely important to analyse the trade activities of countries. SD model provided in this article aims to represent the impact of COVID-19 on global supply chains by concentrating on the foreign trade and logistics activities between countries. As the starting point of COVID-19 pandemic, foreign trade of China has been negatively influenced as the disease began spreading in January 2020 [50]. China is the second biggest importer country of Turkey with 18.5 billion dollars (9.1% of total import) in 2019 and 88.9% of these imported goods are raw materials or semi-finished products or machinery and equipment [51]. Therefore, the disruption of foreign trade impacts global supply chains of many sectors in Turkey. The designed SD model considers the main sectors in Turkey that has the highest export and import relation with China. According to the import and export data of Turkey in 2016–2019, selected sectors shown in Table 1 accounts for approximately 80% of the trade between Turkey and China. Table 1. Sectors with highest export and import value in Turkey.Table 1 Sectors with highest export and import value in Turkey.
Table 1IMPORT from China EXPORT to China
Sector % Sector %
Electronics 29.8% Mining Products 54.5%
Machinery and accessories 24.8% Chemicals 10.1%
Textile and raw materials 7.1% Machinery and accessories 8.2%
Chemicals 6.6% Textile and raw materials 5.0%
Plastics and rubber 6.2% Food 3.2%
Iron and steel 5.4%
SD model starts with a causal loop diagram provided in Fig. 1 and it is based on Turkey perspective. It is modelled in STELLA software.Fig. 1 Causal Loop of COVID-19 model.
Fig. 1
Data that has been gathered for SD Model study can be seen in Fig. 2 .Fig. 2 SD Model data.
Fig. 2
Model is designed in order to anticipate the life cycle of COVID-19 and implications of COVID-19 disruption on the global supply chains by analysing the overall trade between countries. As it has been observed that there has been major lock-downs in countries with the spread of COVID-19 which creates decline in productivity of many industrial sectors. The evidence of the negative association between increased COVID-19 spread and the decrease of industrial productivity can be seen in the below Table 2 . In China, lock-down started with the increased number of patients in 23rd of January 2020 and 8th of April 2020 lock-down officially ended. In Turkey, restrictions started on 16th of March 2020 and still on-going while preparing this article.Table 2 Active patients and industrial production in China & Turkey (Source: National Bureau of Statistics of China & Turkish Statistical Institute).
Table 2 Average Active Patient Count Industrial Production YoY Average Active Patient Count Industrial Production YoY
2019-December 27 6.90% – 8.60%
2020-January 1514 −13.50% – 7.90%
2020-February 42,193 – 7.50%
2020-March 12,009 −1.10% 2763 −2.00%
2020-April 1059 3.90% 55,738 −31.40%
Model begins with the spread of COVID-19 in China at the start of 2020. Life cycle of the COVID-19 has been tracked with the active patients in the country. In this study, active patient counts have been used in order to represent life cycle of COVID-19, since the data is much smoother compared to new patient counts or death counts. It can be used in order to show the real burden in country economy and health system and active patient increase periods indicate the uncontrollable spread of the virus and overload in the country economy. COVID-19 has been reached its maximum in mid-February and decline afterwards. In the late-April, active patients dropped below a thousand and country starts to go back to work routine. With the spread of COVID-19 worldwide, many countries especially in Europe had been affected in February. First COVID-19 patient has been detected in Turkey in mid-March and reached the peak point in late April. In the model, active patients in both countries have been modelled as seen in Fig. 3 . Model has been simulated for 274 days from January 01 - September 30, 2020. It is expected to see Turkey going back to work routine in mid-June. Active patient counts have been derived from National Health Commission of China and Ministry of Health of Turkey between December-2019 and April-2020. Active patient count after May 01, 2020 had been forecasted ignoring an emergence of second wave.Fig. 3 Lifecycle of COVID-19 in China and Turkey.
Fig. 3
As shown in Table 2, there has been a severe disruptive impact of COVID-19 on industrial productivity. For the Turkey case, since majority of the import from China is input for many sectors, it can impact performance of various supply chains which cannot be distinguished easily. The aggregated data gathered from Turkish Statistics Institute is used in the model and there is no detailed information showing which sector is exactly importing which specific raw material. For instance, as being approximately 30% of the total import, electronics is an important part of resource from China. However, electronic raw material can be a part of white good production, machinery production or any type of electronic equipment. Further, the productivity of a country has been impacted due to the excessive reliance on China [52]. The basis of the model relies on the assumption that the productivity decrease in a sector is proportional to the increase in the COVID-19 active patient count. However, there could be some sector specific dynamics that could change the impact of the severity. For example, textile raw material import from China has dramatically decreased by 28% during March & April 2020. The reasons could be the human dependent production in factories or as a sector including not critical products during pandemic time. On the other hand, electronics import has increased by 5% in the same period. Even though this increase is considerably small compared to the increase in January-February 2020 of the same year (which is 24%), this 5% increase is achieved since electronics products are still important (maybe more compared to a normal period) during pandemics and production environments for electronic products are more automated.
From Turkey point of view, the impact of COVID-19 on import would be delayed by two months. Due to the geographical distances between countries, delays on transportation and customs clearance activities are considered in the model. From Turkey point of view, import from China would be delayed by two months which can be seen in Equation (1). Therefore, the impact of COVID-19 spread in China in January 2020, reflects to Turkey import figures in March 2020. On the other hand, export numbers are impacted by COVID-19 spread within the same month since Turkey active patient counts can decrease the capacity utilization of the country and this interrupts the trade between countries. It has been observed that with the spread of COVID-19, industrial production has been decreased by 13.5% compared to January-February 2019. Industrial production decline impacts the exports from China to Turkey. At the same time, COVID-19 spread in Turkey decreases industrial capacity utilization and demand on China products. So, all these factors have been represented in the model using the below equation (1):Import_SectorX (t+60) = Import_SectorX(t-365 + 60) * (1 + ImportGrowthRate)
(1) * (1 -SectorX_Slowdown_China(t)) * (1-TurkeyDemandDecrease(t))
Based on this equation, import for any sector X has been calculated according to actual import value of 2019. Import_SectorX (t+60) represents the 60 days of delay due to transportation delay and shows that the COVID-19 disruption in China and Turkey will be visualized in import figures 60 days later. Import growth rate (ImportGrowthRate) for Turkey has been derived from OECD data as 2020 forecast (OECD, Trade data [63]). SectorX_Slowdown_China has been calculated by equation (2);SectorX_Slowdown_China(t) = China_IndustrialProduction_Index(t)*SectorX_IndustrialImpact
Where(2) China_IndustrialProduction_Index(t) = IndustrialProductionRate * ChinaActivePatient(t) / China_ActivePatientPeakNumber
TurkeyDemandDecrease is estimated according to the spread of the COVID-19 in Turkey using equation (3);TurkeyDemandDecrease(t) = Turkey_IndustryCapacityUtilization(t)*DemandDecreaseRate
(3) where Turkey_IndustryCapacityUtilization(t) = CapacityUtilizationRate * TurkeyActivePatient(t) / Turkey_ActivePatientPeakNumber
Therefore, according to these formulas, active patient numbers in China and Turkey will guide the model in forecasting industrial production rate of China and industrial capacity utilization of Turkey respectively. For each sector, production rate would differ which is represented by SectorX_IndustrialImpact. All of the rates used in the models (SectorX_IndustrialImpact, IndustrialProductionRate, DemandDecreaseRate, CapacityUtilizationRate) are calculated based on the actual data between January to March 2020. Average values based on the existing data has been used since there is not sufficient data to perform statistical analysis. At any time, t, COVID-19 active patients in China and Turkey will impact production rate in China and demand rate in Turkey which will decrease the import value after two months in Turkey.
Similarly, equation (4) have been developed to calculate export in Sector Y from Turkey to China.Export_SectorY (t) = Export_SectorY (t-365) * (1 + ExportGrowthRate)
(4) * (1 -SectorY_Slowdown_Turkey(t)) * (1-ChinaDemandDecrease(t))
Since China is the second biggest importer of Turkey after Russia and 90% of the logistics is based on maritime, disruption in the import affects the planning of maritime logistic activities severely. Decrease in arriving shipments from China to Turkey causes logistic companies to arrange additional ships for export activities, which increases the freight costs drastically. It has been reported that in March 2020, freight costs increased by 40% compared to last year. Model includes the freight cost forecast and the maritime logistic load between China and Turkey.
In model, for each specified sector, import and export forecasts are generated between January and September 2020. Import numbers are impacted after March 2020 due to transportation delay. In Fig. 4 , forecast for Electronics import has been shown until the end of September. COVID-19 will impact import numbers until the end of July 2020. Since model is developed using monthly import and export data and this data has been converted to daily values, there could be sharp increase and decreases between months such as in June and July; but COVID-19 related fluctuations are rather soft and bell-shaped decreases. In Fig. 5 , mining and chemical export from Turkey to China has been shown. First decrease is related with China COVID-19 related disruption, whereas the second decrease after April is related with the slowdown in industrial capacity of Turkey. Mining sector has not been impacted severely by Turkey COVID-19 disruption, but Chemical sector has been affected.Fig. 4 Electronics import ($) between March 2020 and September 2020.
Fig. 4
Fig. 5 Mining and Chemical export ($) between January 2020 and June 2020.
Fig. 5
Based on the model output, import forecasts for March to June has been represented in Table 3 (since COVID-19 impact has been observed in import data starting from March). Model forecasts are prepared by considering the slowdown in March actual data. However, it should be considered that workforce can adapt to the situation and increase their productivity as time passes under COVID19 pandemic. Therefore, the forecast numbers represent the worst-case scenario for the sector in case they cannot adapt to the disruption. Table 3 shows the import forecasts from China to Turkey.Table 3 Import forecasts from China to Turkey.
Table 3 ACTUAL – January and February 2020 DATA FORECAST – March 2020 to June 2020 DATA
Sector % Growth Year on Year (January and February 2020) Import Forecast $ (March and April 2020) % Growth Year on Year (March and April 2020) Import Forecast $ (May and June 2020) % Growth Year on Year (May and June 2020)
Electronics 24% 733,392,066 −7% 755,384,431 1%
Machinery and accessories 40% 466,376,291 −37% 667,757,601 −4%
Textile and raw materials −6% 97,479,545 −57% 194,321,000 −16%
Chemicals 0% 210,005,373 −6% 233,319,676 2%
Plastics and rubber 13% 115,044,868 −28% 188,504,530 −2%
Iron and steel 45% 128,524,852 41% 176,840,282 45%
TOTAL 23% 1,750,822,996 −22% 2,216,127,520 0%
In Table 4 , export forecasts from Turkey to China has been provided. Because of Chinese New Year, there is a drop down in February. But the expected dropdown is sharpened by the COVID-19 pandemic. Actual and model results for Q1 (January to March) has been provided to in order to represent model reliability. Export is expected to be impacted by COVID-19 until end of July 2020.Table 4 Export forecasts from Turkey to China.
Table 4 ACTUAL – Q1 DATA FORECAST – Q1 and Q2 DATA
SECTOR % Actual Growth Year on Year Export Forecast $ Q1 % Growth Year on Year Export Forecast $ % Growth Year on Year
Q1 Q2 Q2
Mining Products −15% 252,008,696 −15% 208,477,532 −38%
Chemicals −31% 52,615,272 −31% 54,045,728 −42%
Machinery and accessories −2% 15,175,512 −2% 11,619,855 −17%
Textile and raw materials −2% 21,173,933 −2% 20,963,366 14%
Food −17% 30,907,568 −17% 24,485,706 −56%
TOTAL −17% 371,880,981 −17% 319,592,188 −39%
Based on TSI data, 90% of the import and 95% of export trade with China are transhipped with maritime logistics. With the decrease in the import shipments, logistic companies struggle with finding available ships in Europe and this increased the freight costs drastically in Turkey. Increased freight cost due to dropdown in import values are also forecasted in the model which can be seen in Table 5 and it can be realized that freight costs will return back to its original position by the end of June 2020.Table 5 Freight cost increase forecast.
Table 5Month Freight Cost Increase %
March-2020 11.0%
April-2020 40.0%
May-2020 19.0%
June-2020 8.0%
July-2020 7.0%
August-2020 7.0%
September-2020 6.0%
In Table 6 , import and export forecast values in terms of TEU units are provided. Based on the model, it is expected to observe decrease in the import shipment numbers in March and June due to COVID-19 spread in China and Turkey respectively. Similarly, export shipment values would be impacted in February and April.Table 6 Import and export shipment forecast.
Table 6 Month Import Shipment (TEU) Export Shipment (TEU)
ACTUAL January-2020 33,566 30,722
February-2020 19,545 10,936
FORECAST March-2020 18,646 25,493
April-2020 22,701 16,235
May-2020 38,689 59,269
June-2020 6210 40,872
July-2020 35,302 47,206
August-2020 31,052 88,482
September-2020 57,653 48,587
The diagram represented in Fig. 1 is developed in order to explain the disruptive behaviour of the pandemic on the international trade of countries. Even though, the model represented in this study is for Turkey and China, the model can be enlarged to more countries. This study can be applied to depict the international trade between countries based on the overall import and export data. During the preparation of the study, limited amount of data is available (for Turkey, March-May 2020 COVID-19 data) and the model is developed in order to guide the decision makers the forecast the damaging impact of COVID-19 on the global supply chains.
5 Discussions
The results have presented that epidemic outbreaks causes supply chain disruptions in the world. This leads to major disruptions in the global supply chain, increased risk in global markets, and increased interest in safe ports such as gold [53]. Due to the pandemic, which has devastating effects around the world, the partial or complete lockdowns of many countries has caused many problems such as supply and demand imbalance in supply chains, as well as inability to reach raw materials [54]. Therefore, it is essential to analyse how sectors are affected by COVID-19 [55] and to make a short-term forecast for the future to take actions against the damages in the global supply chains of the countries, which is one of the most important contribution of the study. COVID-19 has led to create considerably inventory shortages due to unexpected increase demand and this caused that firms encountered shrink shortage of inventory due to the challenging access to raw materials [56]. have been revealed that different sectors should develop different recovery plans and strategies during the pandemic period for quick action and have been indicated that while the short-term effects of COVID-19 can be observed, the medium-long-term effects are complex and uncertain. By comparing with other studies, the study is unique since making comparative analysis and investigating the impact of pandemic on global supply chains by focusing on the foreign trade and logistics activities between countries using SD modelling based on system thinking approach. By making this, it is aimed to analyse how main sectors of the countries are affected by COVID-19 from system thinking perspective, to make a short-term import forecast from China to Turkey for the future from system thinking perspective and to embody the impacts of COVID-19 with an example of China and Turkey from system thinking perspective via answering research question of this study. Therefore, the biggest contribution to evaluate the sectoral impacts of COVID-19 on global supply chains from Turkey and China perspective.
Hence, it is needed to embody the impacts of COVID-19 in the business sectors within a nation. The scenario created with the help of SD modelling is the worst-case scenario for Turkey. With this scenario, which sectors are getting worse in the country are analysed. Turkey's is among the second largest importing countries of China, which has been adversely affected. Due to the pandemic, the disruptions in foreign trade have disrupted the supply of machinery, equipment, raw materials and semi-finished products. With the proposed model, it has been analysed which sectors will be affected by the pandemic. Accordingly, it is stated which sectors should take measures considering its negative effects.
Besides, the majority of imports and exports with China are carried out by maritime logistics. Because of the pandemic, the reduction of import shipments, for logistics companies in Europe revealed shortage of suitable ships and this greatly increased freight costs in Turkey. Increasing freight cost due to the decrease in import values was estimated and decision makers are suggested to make appropriate decisions. In line with the results obtained, the management and government bodies can plan for appropriate actions for different sectors. Therefore, with these implementations, research question is answered to have a knowledge about analysing the effects of disruptions on countries' supply chain comparatively in a system thinking approach.
6 Implications for decision makers and managers
The global supply chain disruptions caused by the pandemic affects the production processes to a large extent [57]. The pandemic, which begins in China, which is Turkey's 2nd biggest importer has negative impacts on final consumption of raw materials and semi-finished products more than final consumption process. Therefore, necessary actions and remedies should be taken by governments to prevent the negative impacts of the supply chain disruptions especially on the competitive sectors [58].
On the other hand, it is not only production of the countries that is affected by the disruptions. The decrease in the ratio of imports by maritime, which constitutes of 90% of Turkey's imports affects Turkish import and forwarding operations directly in a negative way. Means that disruptions in imports with maritime logistics causes “logistics and supply chain disruptions” such as increase in freight prices. Therefore, it is essential extra planning for logistics activities. In addition, in the pandemic period, from China to Europe, the first freight train has reached Turkey. This shows that trade can be done using alternative modes of transportation as intermodal, if not possible by maritime logistics.
One of the important implications that can be inferred from the result of the study is managing imbalance between import and export activities caused maritime logistics due to the lack of available containers and equipment which increased the freight cost dramatically in order to cope with disruptions. This showed that there is a need for an alternative transportation mode that can provide low-cost transportation for example one-belt-one road, which is a new transportation network between Asia and Europe.
Due to the pandemic, China which is the biggest importer of Turkey, was at a point that shipped raw materials or goods in 2 months, caused a delay of 2 months in the supply of goods. If we had been working with different suppliers at a closer distance during this disruptive time, we would not have had any raw material supply problems. From this perspective, new concepts emerging in supply chains should be considered, for example, supplier disruptions can be resolved by adopting onshore applications for problems arising from suppliers. As a managerial implication, this research also offers many implications for managers and decision makers. Companies should choose supplier diversification in order to struggle with possible disruptions. By identifying alternative suppliers from local suppliers or other countries, they can minimize the effects of a potential problem. In events those cause disruption in supply chains like pandemic, companies must select between a make or buy decision and make investment decisions accordingly.
Furthermore, due to the disruptions and fluctuations in the supply chains, production planning and process management should be done appropriately. Therefore, it should be taken into consideration for production planning and process management to overcome the difficulties in the important sectors. In addition, with the decrease in production due to the pandemic, companies should switch to flexible capacity planning in terms of human resource and machinery capacity.
Notably, due to fluctuations in raw material and components, proper inventory management is required. Safety stock calculations should be probabilistic and this should be considered in inventory management. Lastly, in terms of increased costs at the time of disruptions, managers should keep different combinations of intermodal ready.
Digitization can be an important solution measure for managers under such disruptions. Use of digital technologies can help management to manage supply chains more effectively and efficiently. In addition to that, the management can also evaluate the resilience of global supply chains and conduct effective risk management strategies.
As a theoretical implication, system thinking can be used to analyse the disruptions caused by global disruptions, such as COVID-19, on global supply chains. Hence system thinking has the ability to consider multiple stakeholders and multi-tiers within the global supply chain. However, it is also crucial to determine the suitable methodology to system thinking within the implementation.
The SD model developed is a starting point incorporating the pandemic spread in forecasting the impact on global supply chains. Model guides the decision makers to consider sector specific dynamics into the calculations. In case of a second or more waves of pandemic spread or a new pandemic, this model can be used by adapting the numbers to the new situation. Even though in this model the demand decrease for Turkey and China is impacted by COVID-19 active patient counts only, in the future it should be considered that the managers will learn and adapt to the changing conditions. Therefore, the variable representing demand decrease will not be as sharp as today due to learning factors. For example, the automation increase in a sector can be incorporated into the model, if a longer forecast is required.
Further, studies that cover such global supply chains, especially in the field of COVID-19, will open new areas to explore in logistics and supply chain management.
7 Conclusions
The COVID-19, which continues to spread worldwide, harms the supply chains of countries in terms of labour, parts and government constraints [59]. For this purpose, to show sectoral impacts of COVID-19 and to have knowledge about future state with short- and long-term forecast, SD modelling on the basis of system thinking approach is developed in the study. Using system thinking approach and the SD modelling, it is aimed to investigate whole system and its actors and to make comparative analysis to show the impact of pandemics on global supply chain of the countries with a real-life example of China and Turkey as a case study. The implementation of the study is made with SD model and according to results, it is expected to see Turkey going back to work routine in mid-June. According to results, it has been revealed which sectors will be adversely affected by the pandemic and which measures should be taken. However, according to forecasts of COVID-19 on the selected sectors, there is decrease in electronic imports. For the mining and chemical export from Turkey to China, there is two decreases in export rate of Turkey caused by COVID-19 disruption firstly, and then slowdown in industrial capacity of Turkey after April. In addition, while mining sector has not been impacted by Turkey COVID-19 disruption, chemical sector has been affected.
Besides, it can be stated that a large part of the import and export with China is planned with maritime logistics, but import shipments have been decreased due to the pandemic. This has led to an increase in freight costs in Turkey due to the lack of suitable ships in Europe. Furthermore, according to forecasting, it is seen that freight costs will return back to its original position by the end of June 2020. Thus, it is analysed that there will be a decrease in import and export shipments. To sum up, the pandemic process affects the economic economy, global supply chains and sectors negatively and causes supply chain disruptions in the countries. This study can be useful to describe the international trade between countries based on the overall import and export data.
As a limitation of the study, the needed dataset to apply in SD modelling is not reachable because of the country's limitations. For example, it is not available to get data about January and February months without cumulative values. Moreover, in this study, there are lots of variables are added in the model. In previous studies, the relations of trade and logistics activities were not investigated in depth, which created difficulties in integrated relations in the system. However, large number of variables in the study can be increased the complexity of the model. For further researches, not only two countries or emerging economies, but also the global effects of the pandemic can be analysed by addressing intercontinentally. In addition, comparative studies between countries can be diversified and the effects of pandemics can be examined. For further researches, second and also third waves of COVID-19 can be considered to see the results in a comprehensive manner. Apart from marine logistics, alternative mode selections can also be evaluated during downtime. Intermodal methods can be researched more and the collapse in logistics can be minimized during disruption. Besides, various policies can be adapted to the SD model to observe the impact of established policies. Studies can be planned to compare the possible future waves of the pandemic. Finally, one belt one road initiatives can be further investigated.
Author statement
Prof. Yigit KAZANCOGLU conceived of the presented idea. Research Assistant Muruvvet Deniz SEZER developed the theory and Dr. Esra Ekinci & Research Assistant Melisa OZBILTEKIN-PALA performed the computations. Prof. Yigit KAZANCOGLU, Dr. Sachin Kumar Mangla encouraged Research Assistant Melisa OZBILTEKIN-PALA and Research Assistant Muruvvet Deniz SEZER to investigate and supervised the findings of this work. All authors discussed the results and contributed to the final manuscript.
Declarations of competing interest
None.
Author 1. Prof. Dr. Yigit Kazançoglu received his B.S. degree from Eastern Mediterranean University, Dept. of Industrial Engineering. He has graduated from Coventry University, UK, MBA and Izmir University of Economics MBA programs, respectively in 2003 and 2004. Kazancoglu has received his PhD. at Ege University (Operations Management). He was an Asst Prof Dr between 2008 and 2013; and Assoc Prof Dr between January 2014–2016 in Izmir University of Economics, Dept. of Business Administration. Between December 2016 - May 2019 he worked as Assoc Prof Dr in Yasar University, Logistics Management Department. Since May 2019, he is Full Professor Dr in the same department. His research areas are: Operations Management, Supply Chain Management, Total Quality Management, Sustainability, Circular Economy, MCDM multi criteria decision making methods. He has published over 50 articles on the SCI & SSCI indexed international refereed journals such as International Journal of Production Research, Journal of Cleaner Production, Transportation Research Part-E, Resource Conservation and Recycling, Production Planning and Control, Computers & Operations Research, International Journal of Logistics Management, Journal of Manufacturing and Technology Management, Science of the Total Environment, Business Strategy and the Environment, Expert Systems with Applications, Agribusiness, Industrial Management and Data Systems, and Journal of Enterprise Information Management.
Author 2. Assist.Prof. (PhD) Esra Ekinci graduated from Industrial Engineering department of Middle East Technical University in 2000 and had Msc and PhD degrees in Dokuz Eylul University in Industrial Engineering in 2002 and 2016 respectively. She had worked in Izmir Institute of Technology between 2002 and 2004 as instructor. Starting from year 2005, she had ten years of working experience in Netsis Software, Tesco Kipa and Dirinler Metal Casting. In the mentioned companies, she had the opportunity take roles in IT, project management, finance, inventory planning and process improvement areas. She has started working in Yasar University International Logistics Management department in November 2016.
Author 3. Dr. Sachin Kumar Mangla is working in the field of Green and Sustainable Supply Chain and Operations; Industry 4.0; Circular Economy; Decision Making and Simulation. He has a teaching experience of more than five years in Supply Chain and Operations Management and Decision Making, and currently associated in teaching with various universities in UK, Turkey, India, China, France, etc. He is the director of Centre for "Digital Circular Economy for Sustainable Development Goals (DCE-SDG), O.P. Jindal Global University, India. He is committed to do and promote high quality research. He has published/presented several papers in repute international/national journals (Journal of Business Research; International Journal of Production Economics; International Journal of Production Research; Computers and Operations Research; Production Planning and Control; Business Strategy and the Environment;; Annals of Operations Research; Transportation Research Part-D; Transportation Research Part-E; Renewable and Sustainable Energy Reviews; Resource Conservation and Recycling; Information System Frontier; Journal of Cleaner Production; Management Decision; Industrial Data and Management System) and conferences (POMS, SOMS, IIIE, CILT – LRN, MCDM, GLOGIFT). He has an h-index 61, i10-index 121, Google Scholar Citations of more than 10000. He is involved in several editorial positions and editing couple of Special issues as a Guest Editor in top tier journals. Currently, he is working as an Associate Editor at Journal of Cleaner Production, International Journal of Logistics Management, Sustainable Production and Consumption, and IMA Journal of Management Mathematics journals. He is also involved in several research projects on various issues and applications of Circular economy, Industry 4.0 and Sustainability. Among them, he contributed to the knowledge based decision model in “Enhancing and implementing knowledge based ICT solutions within high risk and uncertain conditions for agriculture production systems (RUC-APS)”, European Commission RISE scheme, €1.3M. Recently, he has also received a grant as a PI from British Council – Newton Fund Research Environment Links Turkey/UK – Circular and Industry 4.0 driven solutions for reducing food waste in supply chains. He is also working in several projects on Food Waste and Circular Economy and Industry 4.0 issues, sponsored by ICSSR, Government of India. He received project funding for Food Waste Management in Circular Economy from the USERC, Government of India. He has also won the first prize for the prestigious Basant Kumar Birla Distinguished Scholar Award 2021. He was ranked 404 in the world and 10 in United Kingdom and was recognized with 2022 Rising Star of Science Award by Research.com. He was also ranked among the top 2% scientists in the world by the Stanford University. Recently, he has also been awarded as a High End Foreign Expert Talent Grant from Minisrty of Science and Technology, China.
Author 4. Research Assistant Muruvvet Deniz Sezer is currently working as a Research Assistant at Yaşar University, Department of Business Administration. She received her Bachelor's Degree from Eskişehir Osmangazi University, Department of Industrial Engineering in 2014. She also completed the Double Major Program at Eskişehir Osmangazi University, Department of Business Administration. She received her master's degree from Dokuz Eylül University, Department of Industrial Engineering in 2019. Deniz Sezer, who continues her Phd study in Yaşar University, Department of Business Administration, focuses on supply chain management, system dynamics, sustainability, and circular economy.
Author 5. Research Assistant Melisa Ozbiltekin-Pala completed her undergraduate degree at Izmir Kâtip Çelebi University International Trade and Marketing Department in 2017, and received her master's degree from Yaşar University International Logistics Department in 2020. Melisa Özbiltekin Pala, who continues her doctoral studies at Ege University, Department of Business Administration, conducts researches on supply chain management, reverse logistics, sustainability, circular economy and digitalization. She has been working as a Research Assistant at Yaşar University Logistics Management Department since 2019.
Data availability
No data was used for the research described in the article.
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References
1 OECD OECD interim economic assessment: coronavirus: The world economy at risk”, 2 March 2020 2020 1 18 (March)
2 Ehlert A. The socio-economic determinants of COVID-19: a spatial analysis of German county level data 2021 Socio-Economic Planning Sciences 101083
3 Shafi M. Liu J. Ren W. Impact of COVID-19 pandemic on micro, small, and medium-sized Enterprises operating in Pakistan Res Globalization 2 2020 100018
4 Remko V.H. Research opportunities for a more resilient post-COVID-19 supply chain–closing the gap between research findings and industry practice Int J Oper Prod Manag 40 4 2020 341 355
5 Yu K.D.S. Aviso K.B. Modelling the economic impact and ripple effects of disease outbreaks Proces Integrat Optim Sustain 4 2020 183 186
6 Al Humdan E. Shi Y. Behnia M. Supply chain agility: a systematic review of definitions, enablers and performance implications Int J Phys Distrib Logist Manag 50 2 2020 287 312
7 Ivanov D. Predicting the impacts of epidemic outbreaks on global supply chains: a simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case Transport Res E Logist Transport Rev 136 2020 101922
8 Ruiz Estrada M.A. Park D. Lee M. The evaluation of the final impact of Wuhan COVID-19 on trade, tourism, transport, and electricity consumption of China SSRN Electron J 2020 1 13
9 Golan M.S. Jernegan L.H. Linkov I. Trends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the COVID-19 pandemic Environ Syst Decision 40 2020 222 243
10 Haren P. Simchi-Levi D. How coronavirus could impact the global supply chain by midMarch Harv Bus Rev 28 2020
11 Panarello D. Tassinari G. One year of COVID-19 in Italy: are containment policies enough to shape the pandemic pattern? 2021 Socio-Economic Planning Sciences 101120
12 He P. Huang Z. Automotive and autonomous mobility 2020 World Economic Forum Beijing, and World Economic Forum (n.d.). This industry was crippled by the coronavirus - here's how it's fighting back.” Retrieved from https://www.weforum.org/agenda/2020/02/coronavirus-china-automotive-industry/
13 Fernandes N. Economic effects of coronavirus outbreak (COVID-19) on the world economy” 2020 2020 IESE Business School Spain 0–29
14 Gormsen N.J. Koijen R.S.J. Coronavirus: Impact on stock Prices and growth expectations” BFI Working 2020 1 27 Paper No. 2020-22
15 Khan S.A.R. Razzaq A. Yu Z. Shah A. Sharif A. Janjua L. Disruption in food supply chain and undernourishment challenges: an empirical study in the context of Asian countries 2021 Socio-Economic Planning Sciences 101033
16 Nikolopoulos K. Punia S. Schäfers A. Tsinopoulos C. Vasilakis C. Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions Eur J Oper Res 290 1 2020 99 115 32836717
17 Paul S.K. Chowdhury P. A production recovery plan in manufacturing supply chains for a high-demand item during COVID-19 Int J Phys Distrib Logist Manag 2020 10.1108/IJPDLM-04-2020-0127 ahead-of-print No. ahead-of-print
18 Ayittey F.K. Ayittey M.K. Chiwero N.B. Kamasah J.S. Dzuvor C. Economic impacts of Wuhan 2019‐nCoV on China and the world J Med Virol 92 5 2020 473 475 32048740
19 Handfield R.B. Graham G. Burns L. Corona virus, tariffs, trade wars and supply chain evolutionary design Int J Oper Prod Manag 40 10 2020 1649 1660
20 Mariia H. Henryk D. Nataliia T. Yuliya S. Substantiation of expediency of the complex approach for supply chains management in the Covıd-19 conditions Intellectualization of logistics and Supply Chain Management 5 2021
21 Sadiq M. Hsu C.C. Zhang Y. Chien F. COVID-19 fear and volatility index movements: empirical insights from ASEAN stock markets Environ Sci Pollut Control Ser 2021 1 18
22 Queiroz M.M. Ivanov D. Dolgui A. Wamba S.F. Impacts of epidemic outbreaks on supply chains: mapping a research agenda amid the COVID-19 pandemic through a structured literature review Ann Oper Res 2020 1 38 10.1007/s10479-020-03685-7
23 Rauner M.S. Schaffhauser-Linzatti M.M. Impact of the new Austrian inpatient payment strategy on hospital behavior: a system-dynamics model Soc Econ Plann Sci 36 3 2002 161 182
24 van Ackere A. Schulz P.J. Explaining vaccination decisions: a system dynamics model of the interaction between epidemiological and behavioural factors Soc Econ Plann Sci 71 2020 100750
25 Burt G. Why are we surprised at surprises? Integrating disruption theory and system analysis with the scenario methodology to help identify disruptions and discontinuities Technol Forecast Soc Change 74 6 2007 731 749
26 Yamanis T. Nolan E. Shepler S. Fears and misperceptions of the ebola response system during the 2014-2015 outbreak in Sierra Leone PLoS Neglected Trop Dis 10 10 2016 10.1371/journal.pntd.000507
27 Fugerson A.G. Sutton Y.M. McCree H.D. Social and structural determinants of HIV treatment and care among hispanic women and latinas living with HIV infection in the United States: a qualitative review: 2008–2018 Health Equity 3 1 2019 581 587 31701083
28 Wu T. Blackhurst J. O’grady P. Methodology for supply chain disruption analysis Int J Prod Res 45 7 2007 1665 1682
29 Chen H. Jin Y. Huo B. Understanding logistics and distribution innovations in China Int J Phys Distrib Logist Manag 50 3 2020 313 322
30 Tan W.J. Cai W. Zhang A.N. Structural-aware simulation analysis of supply chain resilience Int J Prod Res 58 2019 5175 5195
31 Huang C. Wang M. Rafaqat W. Shabbir S. Lian L. Zhang J. …Song W. Data-driven test Strategy for COVID-19 using machine learning: a study in Lahore, Pakistan 2021 Socio-economic planning sciences 101091
32 Klibi W. Martel A. Modelling approaches for the design of resilient supply networks under disruptions Int J Prod Econ 135 2 2012 882 898
33 Ivanov D. Structural dynamics and resilience in supply chain risk management” 2018 Springer New York
34 Simchi-Levi D. Schmidt W. Wei Y. Zhang P.Y. Combs K. Ge Y. …Zhang D. Identifying risks and mitigating disruptions in the automotive supply chain Interfaces 45 5 2015 375 390
35 Marin G. Modica M. Paleari S. Zoboli R. Assessing disaster risk by integrating natural and socio-economic dimensions: a decision-support tool 2021 Socio-Economic Planning Sciences 101032
36 Xu H. Minimizing the ripple effect caused by operational risks in a make-to-order supply chain Int J Phys Distrib Logist Manag 50 4 2020 381 402 [ahead-of-print No. ahead-of-print]
37 Sarkis J. Cohen M.J. Dewick P. Schröder P. A brave new world: lessons from the COVID-19 pandemic for transitioning to sustainable supply and production Resour Conserv Recycl 159 2020 104894
38 Hynes W. Trump B. Love P. Linkov I. Bouncing forward: a resilience approach to dealing with COVID-19 and future systemic shocks Environ Syst Decision 40 2020 174 184
39 Kumar S. Chandra C. Supply chain disruption by avian flu pandemic for US companies: a case study Transport J 49 4 2010 61 73
40 Wieland A. Wallenburg C.M. The influence of relational competencies on supply chain resilience: a relational view Int J Phys Distrib Logist Manag 43 4 2013 300 320
41 Kumar S. DuFresne C. Hahler K. Managing supply chain risks in US-China trade partnership Inf Knowl Syst Manag 6 4 2007 343 362
42 Baldwin R. Tomiura E. Thinking ahead about the trade impact of COVID-19 Economics in the Time of COVID-19 13 1 2020 59 71
43 Yoon J. Talluri S. Yildiz H. Ho W. Models for supplier selection and risk mitigation: a holistic approach Int J Prod Res 56 10 2018 3636 3661
44 Huang M. Yang Zhang Y. Liu B. System dynamics modelling-based study of contingent sourcing under supply disruption Syst Eng Procedia 4 2012 290 297
45 Diedrichs D.R. Phelps K. Isihara P.A. Quantifying communication effects in disaster response logistics: a multiple network system dynamics model J Humanit Logist Supply Chain Manag 6 1 2017 24 45
46 Links J.M. Schwartz B.S. Lin S. Kanarek N. Mitrani-Reiser J. Sell T.K. …Kendra J.M. COPEWELL: a conceptual framework and system dynamics model for predicting community functioning and resilience after disasters Disaster Med Public Health Prep 12 1 2018 127 137 28633681
47 Sinha D. Bagodi V. Dey D. The supply chain disruption framework post COVID-19: a system dynamics model Foreign Trade Rev 55 4 2020 511 534
48 Wang Y. Wang J. Wang X. COVID-19, supply chain disruption and China's hog market: a dynamic analysis China Agricult Econ Rev 12 3 2020 427 443
49 Demirel G. MacCarthy B.L. Ritterskamp D. Champneys A. Gross T. Identifying dynamical instabilities in supply networks using generalized modelling J Oper Manag 65 2 2019 133 159
50 Demir B. Javorcik B. Trade finance matters: evidence from the COVID-19 crisis Oxf Rev Econ Pol 36 Supplement_1 2020 397 S408 2020
51 TSI Foreign trade Statistics” 2020 Retrieved from https://biruni.tuik.gov.tr/disticaretapp/menu.zul
52 Javorcik B. Global supply chains will not be the same in the post-COVID-19 world COVID-19 and trade policy: Why turning inward won’t work 111 2020 1 199 [CEPR Press]
53 Verikios G. The dynamic effects of infectious disease outbreaks: the case of pandemic influenza and human coronavirus Soc Econ Plann Sci 71 2020 100898
54 Montoya-Torres J.R. Muñoz-Villamizar A. Mejia-Argueta C. Mapping research in logistics and supply chain management during COVID-19 pandemic Int J Logist Res Appl 2021 1 21
55 Mastronardi L. Cavallo A. Romagnoli L. How did Italian diversified farms tackle Covid-19 pandemic first wave challenges? 2021 Socio-Economic Planning Sciences 101096
56 Shi X. Liu W. Zhang J. Present and future trends of supply chain management in the presence of COVID-19: a structured literature review Int J Logist Res Appl 2021 1 30
57 Malefors C. Secondi L. Marchetti S. Eriksson M. Food waste reduction and economic savings in times of crisis: the potential of machine learning methods to plan guest attendance in Swedish public catering during the Covid-19 pandemic 2021 Socio-Economic Planning Sciences 101041
58 Yao H. Liu W. Wu C.H. Yuan Y.H. The imprinting effect of SARS experience on the fear of COVID-19: the role of AI and big data 2021 Socio-Economic Planning Sciences 101086
59 Burris C. Nikolaev A. Zhong S. Bian L. Network effects in influenza spread: the impact of mobility and socio-economic factors 2021 Socio-Economic Planning Sciences 101081
60 Abdullah M.A. Hishamuddin H. Bazin N. A System Dynamics Approach to Investigate the Effects of Disruption on The Supply Chain with A Mitigation Strategy In IOP Conference Series: Mater Sci Eng 697 1 2019 1 12
62 Loske D. The impact of COVID-19 on transport volume and freight capacity dynamics: an empirical analysis in German food retail logistics Transp Res Interdiscip Perspect 6 2020 1 6
63 OECD Trade data Retrieved from: https://data.oecd.org/trade/trade-in-goods-and-services-forecast.htm#indicator-chart
66 World Trade Organization (WTO) Trade set to plunge as COVID-19 pandemic upends global economy” 2020 Press Release April 8
| 36514316 | PMC9731644 | NO-CC CODE | 2022-12-16 23:18:15 | no | Socioecon Plann Sci. 2022 Dec 9;:101494 | utf-8 | Socioecon Plann Sci | 2,022 | 10.1016/j.seps.2022.101494 | oa_other |
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Sustainable Futures
2666-1888
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The Author(s). Published by Elsevier Ltd.
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10.1016/j.sftr.2022.100102
100102
Article
COVID-19 disruptions and Norwegian food and pharmaceutical supply chains: Insights into supply chain risk management, resilience, and reliability
Bø Eirill a⁎
Hovi Inger Beate b
Pinchasik Daniel Ruben b
a Department of Accounting and Operations Management, BI Norwegian Business School, Nydalsveien 37, 0484 Oslo, Norway
b Institute of Transport Economics, Gaustadalléen 21, 0349 Oslo, Norway
⁎ Correspondence
8 12 2022
12 2023
8 12 2022
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The purpose of this study is to investigate how the COVID-19 crisis affected delivery security and firms’ preparedness and responses in Norway. Investigations focus on supply chains which were critical for maintaining the supply of essential goods when large parts of society closed down. This includes four firms belonging to food and pharmaceutical industries, representing different parts of the respective supply chains, and covering imports, exports, domestic distribution, and home-delivery services.
The originality of this article is that we employ theoretical models on supply chain risk management, resilience and reliability in conjunction, where these are usually used separately. Recognizing links, overlaps, and complementarity between the models, and using them step-by-step, we exploit synergies that enable more comprehensive assessments of strengths and weaknesses in firms’ supply chains, covering gaps, prioritizing between improvement areas, and collecting input towards detailed, actionable risk mitigation actions. Investigations build on semi-structured interviews, systematically covering the formative elements for each of the models. Using the models in conjunction, we compare the firms and identify differences, similarities, strengths, and weaknesses in the consequences of pandemic-related disruptions and how firms approached the challenges.
The main challenges for the firms were sudden demand changes early in the pandemic. While the firms had minor differences, their pre-pandemic contingency plans were generally not actionable or detailed enough, nor prepared for the pandemic's longevity. Therefore, more detailed and long-term guidelines are desirable, noting the importance and interrelationships of elements of supply chain risk management, resilience, and reliability. A common feature for all firms, and crucial for handling disruptions, is the importance of good and long-term relationships with upstream and downstream supply chain partners and the need for improving contingency plans and future resilience.
Keywords
Supply chain risk management
Supply chain resilience
Supply chain reliability
Contingency planning
COVID-19
Food supply chains
Pharmaceutical supply chains
==== Body
pmc1 Introduction
The COVID-19 pandemic is unprecedented in modern history and the disruptions it has induced have had profound impacts on global supply chains in both upstream and downstream operations [1]. Araz et al. [2] considered COVID-19 the most severe supply chain (SC) disruption the world has experienced in decades, and examples of unexpected challenges include demand and supply shocks related to hoarding, (foreign) labor shortages, and cross-border transportation restrictions [1,3,4]. When COVID-19 hit Europe, the business community was unprepared for its ramifications. Although firms usually have contingency plans, few foresaw the possibility of a pandemic or dealing with the types, combinations, and longevity of challenges the pandemic caused [4]. This necessitated more ad-hoc responses than might be desirable, often based on little information and preparation, and led to increased uncertainty.
The current article investigates how the COVID-19 crisis has affected the risk, resilience, and reliability of supply in food and pharmaceutical supply chains, industries that had to maintain the supply of essential goods when society otherwise closed with the first infection outbreak. We investigated four firms, all major players in their respective sectors in Norway. Our objective was to provide insights into successful and unsuccessful strategies for firms under pressure, the challenges that they faced, best practices, and recommendations for handling current and future situations. As such, this article contributes with lessons from the current crisis that may not only make supply chains more resilient and robust to future pandemics, but also to other economic shocks where similar patterns may occur, such as natural disasters.
In all, the article's overarching research questions can be summarized as follows:- How did COVID-19 disruptions affect Norwegian food and pharmaceutical supply chains?○ How did firms in these supply chains approach risk, resilience and reliability of supply?
○ How were delivery security and firms’ responses affected by the crisis?
○ What lessons can be drawn from the pandemic to make supply chains and contingency plans more resilient and robust?
Our investigation is based on three theoretical models for supply chain analysis, which we utilize as tools for assessing and comparing how (1) risk, (2) resilience, and (3) reliability have affected supply chains for each of the four firms. These three models are often used separately (e.g. Fan and Stevenson [5] or El Baz and Ruel [6] for supply chain risk; Pourhejazy et al. [7], Ali and Golgeci [8] or Stone and Rahimifard [9] for supply chain resilience; Kano and Oh [10] for supply chain reliability). Recognizing several links, overlaps, and complementarity between the models, and using them step-by-step, this article exploits synergies that enable a comprehensive assessment of strengths and weaknesses, and suggests how the firms may become more prepared for future disruptions.
Learning from the current pandemic is important for several reasons, the most obvious of which is better future preparedness. While the last pandemic with comparable severity and scale to COVID-19 (the Spanish flu) occurred more than a century ago, epidemics with potential for long-term disruptions, high uncertainty, and unpredictable scaling have been more likely to occur since then as a result of increased globalization, population growth, and density increases [11,12]. At the same time, new challenges often go hand-in-hand with new opportunities, such as when disruptions lead to innovation or enable firms to gain competitive advantages and attract new customers during difficult times [12]. Lessons from such occurrences may also be valuable.
While the current article has a Norwegian perspective, reports throughout the pandemic suggest that SCs in many other developed countries face many of the same issues (at least partially). In this regard, particularly the investigation of major transport buyers, who are highly dependent on foreign sourcing and supply chains, can contribute to more generalizable and transferable results and lessons, such as those related to pharmaceutical and hospital supplies, as well as food distribution.
The present study demonstrates the synergy of using three theoretical models for SC analysis alongside, rather than separately, as is the standard in most literature. In doing so, this article helps improve future preparedness and contingency plans and provides improved insights into the interrelationships among risk management, resilience, and reliability. This can help firms establish broader, more comprehensive overviews of their strengths and weaknesses, cover gaps in contingency plans, prioritize between improvement areas, and formulate actionable risk mitigation actions.
2 Theoretical background
Supply chain disruption can be defined as “an indication of a firm's inability to match demand and supply”, with widespread recognition existing of the negative impacts of disruptions on the economy ([13], p.35). Ellis et al. [[14], p.35] posited that SC disruptions are “unforeseen events that interfere with the normal flow of goods and/or materials within a supply chain”, while Hendricks and Singhal [15] explained supply disruptions as glitches that can affect both the short- and long-term profitability of firms. For supply chains covering food and pharmaceutical products, supply chain disruptions can, in severe cases, directly affect food security (e.g. [16]), life, and health. The pandemic has induced a surge in policy attention for these topics, including in Norway (e.g. [17]).
In order to help firms become more prepared to handle uncertainty, and thereby become more robust, scientific literature has contributed with theoretical models on supply chain risk management, resilience and reliability, respectively. The current article employs these models to provide insights on firms’ strengths and weaknesses, which can then be used to improve contingency plans, so that firms are more prepared if and when new disruptions materialize.
2.1 Risk management
Supply chain risk management (SCRM) is an important tool when experiencing disruption and can help reduce the likelihood and severity of potential risk scenarios occurring in SCs. Research shows that authors have diverse risk definitions for different parts of the SC [18]. Based on their review, Ho et al. [[18], p.5035] defined SC risk as “the likelihood and impact of unexpected macro- and/or micro-level events or conditions that adversely influence any part of a SC leading to operational, tactical, or strategic level failures or irregularities”. Therefore, SCRM will have a broader scope than just a single firm and should account for how processes work between entities involved [19]. There must be an integrated process with risk management culture in focus and clear leadership by senior management [20].
Christopher and Peck [21] defined four types of risk within SCRM: supply risk, process risk, demand risk and control risk. During the COVID-19 pandemic, firms/establishments focused mostly on supply, demand, and control risk [22]. Supply risk refers to how dependent firms are on certain suppliers [23]. Demand risk during COVID-19 refers to spikes in demand and consequent bottlenecks. For example, sudden demand spikes led to SC bottlenecks, with several suppliers unable to deliver as expected. Bottlenecks were also a challenge related to supply risk as many plants closed down for short amounts of time, before opening up again and producing more than ever, without sufficient logistics capacity for delivering produced goods [22]. Finally, control risk is the ability to engage suppliers in the response to the pandemic (ibid).
SCRM plays an important role in enhancing SC resilience, and consists of a process with interconnected steps. A literature review [18] identified the following four steps as most common in SCRM approaches:- Step 1, risk identification, is crucial to manage risk [18,24]. The aim is to identify all relevant risks and recognize future uncertainties, in order to successfully implement proper SCRM (Fan and Stevenson [5]. Risk awareness is key to being able to manage and understand how to mitigate risks [25].
- Step 2 entails risk assessment and placing risks in a prioritized order based on their likelihood and severity [6,26]. It generally builds on assessments using relevant data, expert opinions, or scenario thinking and also lays the basis for the two subsequent steps [5].
- Step 3, risk mitigation, focuses on reducing risks to acceptable levels by using different strategies [24,26].
- Step 4, riskcontrol, is important in order to monitor identified risks in case their status changes [5,6,24].
El Baz and Ruel [6] showed that the four SCRM steps have a positive effect on SC resilience.
2.2 Resilience
Resilience is a confusing and contradictory concept that not even well-developed disciplines manage to define [27]. In SC terms, it can be summarized as a SC's ability to manage inevitable risk and still move forward and return to a desired situation [21,28] or “the adaptive capability of the SC to prepare for unexpected events, respond to disruptions, and recover from them by maintaining continuity of operations at the desired level of connectedness and control over structure and function” ([27], p.131).
SC resilience is often discussed through certain formative elements. Jüttner and Maklan [29] explained four central elements: collaboration, visibility, flexibility, and velocity. In short, collaboration is the element influencing all other elements and ensures that elements are adopted by all parties in the SC [30]. Visibility focuses on the overview of the whole chain, how fast the SC detects signals, and the ability to share information [29]. Flexibility refers to the ability to adapt to both positive and negative impacts and the SC effectively absorbing these [27]. Velocity refers to how efficiently SCs react and recover from disruptions in SC processes [29].
Driven by significant breakthroughs in management thinking, the way that firms compete has evolved; from competition against firms, towards competition against SCs [31]. This development has increased the importance of collaboration across SC entities generally, but also the great essence of collaboration for SC resilience [19,21,32]. To build resilience, organizations in SCs need to collaborate and view the chain with a holistic approach [30]. Therefore, effective sharing of information and coordination have become important areas for risk handling, but require trust, collaboration, and commitment from involved parties [33,34]. Good coordination, collaboration, and communication and relationships with actors up- and downstream the SC will contribute to proactively enhance SC resilience – for example, by improving/steadying service levels and reducing misunderstandings – because all actors will better understand end customers’ demands [4,34].
Also SC visibility is considered to be extremely important when facing disruptions. Visibility is the ability to share information across the SC, with timeliness and accuracy of this information being important (Barratt and Oke, 2017; [35,36]). Visibility can enable stronger relationships throughout the whole SC and contribute to better collaboration and higher levels of trust [35], although to improve operational efficiency, a prerequisite is that information is used well [36]. During the COVID-19 pandemic, visibility has been shown to be positively correlated with resilience [37].
With regard to flexibility, recent findings indicate that the firms that were the most resilient during the pandemic were the ones that were flexible [38]. The relationship with uncertainty can be pointed out because flexibility forms a direct response to changes in the existing situation [39].
Finally, velocity relates to the speed with which SCs are able to change, recover, and adapt to new desirable states [21,27,29,40]. Therefore, velocity a capability that is especially needed when encountering disruptions in a SC [40], and can, provided sufficient and correct information, reduce response and recovery times [30].
2.3 Reliability
For SC reliability, three distinct key elements are delivery reliability, customer relationship and supplier relationship. When choosing suppliers, reliability is a key factor [41]. Reliability can be defined as “the probability that all the required materials and products flowing through a supply network will arrive at their destination in a specified interval under stated conditions” ([42], p.264) and is key to ensuring both effectiveness and efficiency [43]. Research has argued that strong relationships with a few suppliers strengthen reliability more than weak relationships with several suppliers [44]. To enhance resilience, SC relationships must also be robust and reliable. Reliable relationships can be built through collaboration, which builds on trust and enables flexibility when unexpected market changes occur [4].
Although SC reliability is not a new area of research, interest in the subject has spiked recently, as the need for reliable deliveries of essential supplies became a focal theme globally [45]. During the pandemic, lead times for certain items became longer than expected [6] and in some areas, customer confidence in the ability of SCs to deliver has decreased [4]. Studying disruptions in relation to SC reliability, Chen et al. [46] found that for short-term disruptions, emergency procurement is a recommendable strategy, while for long-time disruptions, a combination of emergency procurement and a change of products is advised.
Reliability has a close connection with several elements of resilience. For example, long and trusting relationships with suppliers can contribute to good collaboration and flexibility, thereby enabling reliability. Reliability is also a two-way relationship between supplier and customer roles; being a reliable supplier is dependent on the reliability of one's own suppliers.
2.4 Contingency plans
To be better prepared for adverse events, many firms develop contingency plans that are meant to help them respond effectively to unfavorable or emergency situations that may or may not occur in the future [47]. Contingency plans mitigate impacts of unexpected incidents and outline strategies for ensuring business continuity (see e.g. [48]) and continuing daily business operations. These plans should be well-defined, with actionable points and clear instructions on how to prioritize [49].
Regarding emergency response preparation, SC literature refers to planning as an important strategic priority in crisis management, with the pandemic putting the need for holistic approaches to contingency planning high on the agenda [11,50,51,52]. SCs can mitigate risk and expedite disaster recovery by being proactive and investing in contingency plans, and can strengthen SC resilience by enabling the SC to turn around quickly and adapt pre-developed contingency plans to the current disruption [53]. However, creating the perfect contingency plan involves certain difficulties, since the world is constantly changing, and so are the potential risks. Other challenges are balancing the costs of preparing for all potential risks and the benefits of preparedness. According to Fernandes and Saldanha da Gama [53], while costs of planning for disruptions can be high, the consequences of not having a contingency plan can be disastrous.
While SC literature emphasizes the value of having a contingency plan, the reality is that far too many contingency plans are created and then sit dormant for extended periods of time, possibly becoming irrelevant when disruptions of low predictability and high severity arise [52]. A contributing factor is that responses to such disruptions are shaped by human's complex attitudes towards risk perception and management [54,55] and inter-human attitude variations [56]. Therefore, frequent updates of contingency plans, as well as employee involvement in the updates, are crucial to keep SCs prepared for disruptions. The COVID-19 pandemic has demonstrated both the lack of contingency planning and the limitations in contingency planning for extreme events (e.g. [48]). To better manage risk in the event of disruptions, factors such as labor shortages, inventory shortages, procurement, and logistical challenges in the SC should be evaluated in the contingency process and considered in the contingency plan. Post-pandemic, SCs should further review and iterate the contingency plan [4].
3 Materials and methods
3.1 Analytical framework
We employed the three theoretical models discussed above as tools of analysis for assessing and comparing how (1) risk, (2) resilience, and (3) reliability have affected supply chains for each of four firms, using input from semi-structured interviews (see the following sections). Fig. 1 provides a stylized illustration of the analytical framework of the current article. Hereby, we recognize that there are strong links between the models and that they are to some extent complementary, both in terms of overlaps (depicted in the figure) and in terms of possible synergies when employing the models in conjunction. Elements on the outside of the circles represent the formative elements of the three models and illustrate the relationships and linkages between these elements for each of the models individually. For SCRM, these are the four different consecutive steps. For resilience, these are visibility, flexibility and velocity, with collaboration influencing all other elements and ensuring that elements are adopted by all SC partners [30]. For reliability, the elements ‘customer relationships’, ‘delivery reliability’, and ‘supplier relationships’ are all interrelated.Fig. 1 The three theoretical models for supply chain analysis, the relationships between their steps and sub-elements, the complementarity and overlaps of the theoretical models, and their insights feeding into improved contingency plans.
Fig. 1
Considering the models together, there are strong linkages and interactions between formative elements of resilience and reliability, and these, in turn, build naturally on the steps of the SCRM and its four discussed risk types (supply, process, demand and control risk). In conjunction, the models can provide comprehensive insights on strengths and weaknesses for the firms and interrelationships between these. Such insights can then be used as inputs for improving contingency plans and firms’ future preparedness by covering gaps in current plans, and by allowing comprehensive prioritization of improvement areas and formulation of actionable points. Running through the models in conjunction, and step-by-step, helps to ensure that contingency plans can become both more comprehensive and detailed, can increase awareness, and reduces the risk of inadvertently leaving out important elements by effectively providing a ‘checklist’. By systematically addressing all of the three model's steps and sub-components for each of the investigated firms, they can further be compared with each other, and differences and similarities can be identified in terms of how they were affected by pandemic-related disruptions and their approaches to these challenges.
3.2 Firm description and background
When the first wave of infections hit Norway, the country's government decided to shut down large parts of society, including shops, cafes, restaurants, and cultural life. Exemptions were made for grocery stores and pharmacies, which were deemed essential to keep society going, as was the safeguarding of necessary hospital deliveries. In analyzing the vulnerability of SCs in relation to the pandemic, the research project underlying the current article focused on covering SCs for essential goods (food and pharmaceuticals) and SCs from the supply side (production and import) to exports and domestic distribution, as well as last-mile and home deliveries, and thereby to obtain a 360-degree perspective. The current article is based on investigations of the four suppliers participating in the research project. Of these, three are actors in (fresh) food supply chains (FSCs) as producer and exporter, importer and distributor, and distributor for home deliveries, respectively. The fourth firm is an actor in a pharmaceutical supply chain (PSC) that imports pharmaceutical products and distributes them to pharmacies, hospitals and municipalities (nursing and retirement homes) throughout Norway. Table 1 provides a summary of the characteristics of each firm, which are referred to hereafter as (1) fish farming firm, (2) food distributor, (3) home-delivery firm and (4) pharmaceutical firm. While the analysis has a Norwegian context, the firms investigated have dominant market positions and operate in an international market. This makes their SCs extra vulnerable, but also adds an international perspective to the analyses. It can be noted that turnover per employee increases from left to right in the table, illustrating each firms’ placement within SCs and the high unit values of pharmaceutical products.Table 1 Overview of each investigated firm's broad characteristics.
Table 1 Fish farming firm Food distributor Home-delivery firm Pharmaceutical firm
Type of organization Global group National group National group Global group
Trading product Farmed fish Perishable goods Groceries Medicines and pharmaceutical goods
Role in value chain Producer Distributor Last-mile Distributor
Main market upstream Domestic Abroad Domestic Abroad
Main market downstream Abroad Domestic Domestic Domestic
Turnover per employee, 2020, million NOK (rounded)* 3.3 6.7 8.3 45.0
Number of employees >5,000 <5,000 <500 <500
Establishment 1992 1914 2013 1995
⁎ Average exchange rates for 2020 (2021): 1 EUR ≈ 10,72 (10,16) NOK; 1 USD ≈ 9,95 (10,17) NOK.
Generally, FSCs have increasingly become more complex and diverse due to globalization, enabling people all over the world to eat food that is grown (and produced) in other climates than their own. Today, a (simplified) FSC essentially consists of five entities: producer, processor, distributor, retailer, and consumer. For FSCs, important aspects are how globalization has affected food security, safety, and integrity [57]. The main difference between normal SCs and FSCs is the continuous change in the quality of products in all joints between producer and consumer [58,59]. Furthermore, the availability of temperature-regulated transportation and shipping options throughout SCs is often an important factor [60]. FSCs can face challenges in every part of the SC, and this complexity can make FSCs vulnerable in times of crisis. Therefore, these SCs require agility in order to meet customers’ demand in normal times, and resilience in the face of disruptions [61].
A PSC is “a special SC in which medications are produced, transported and consumed” (Xie and Breen, 2012, p.41). While there are many variations of the structural PSC, this study will use the simplified SC demonstrated for FSCs, as it creates a common understanding when later comparing the four firms. PSCs are global, complex, and strictly regulated. Pharmaceutical products also need temperate-regulated transport, and often have short shelf lives.
The COVID-19 pandemic affected every part of the FSC and PSC. Overseas markets and sourcing locations have been challenging to reach due to collapses in passenger flights and price rate increases for freight flights and international container shipping, while also closed borders have affected transportation times. Domestically and internationally, FSCs have experienced demand shocks from grocery stores, alongside steep demand reductions in, for example, the HORECA sector (hotel, restaurant and café) or food services market (e.g. [3,4]). PSCs experienced challenges long before the pandemic outburst (for example, drug shortages and delivery problems throughout the globe) and vulnerabilities became more apparent in the midst of it, with hoarding and general demand increases putting extra strain on already fragile PSCs. While shortages of supply in some SCs have caused no trouble other than extra waiting time, shortages in PSCs can put health and human lives at risk.
3.3 Data collection
Data collection was based on semi-structured interviews with key logistics staff at all four firms. Each firm was interviewed at least twice (around New Year 2020/2021 and in spring/early summer 2021) to capture both early experiences and new(er) challenges and developments. Interviews followed a general interview guide, which was adjusted to fit each firm's SC role. All interviews were structured in the same way and addressed the same topics, revolving around the formative elements of the three theoretical models for SC analysis summarized in Fig. 1. Questions were open-ended and differentiated by category to identify (1) how prepared the firms were for a state of emergency such as the pandemic (what was set out in their contingency plans?); (2) to identify the main SC risk factors and approaches to risk assessment, mitigation and control; (3) How reliable their security of supply was; and (4) how resilient the firms were in periods with outbreaks and/or market shortages. Examples of interview topics include the existence of any contingency plans and details on their scope, infection control measures, any staffing challenges or solutions, market and demand dynamics, changes in demand for and organization of transport, changes in transport and logistics costs, different themes regarding any operational changes/adaptations, use of foreign workers, challenges and solutions related to border crossings, implications for the firms’ economic situation and investments, implementation of new solutions, and whether the pandemic changed the firms’ approach to robustness in the longer term. In addition, specific pandemic-related cases occurring at some firms were discussed, and all firms were given an opportunity to bring up additional topics they considered relevant.
All interviews were transcribed and sent to the interviewees for fact-checking, correction of any misunderstandings, and approval. Interview feedback was then categorized based on the formative elements of the three analytical models. By approaching analyses in this way, we sought to satisfy objectivity, auditability, validity, and application criteria for qualitative data analysis [62].
Based on the above, Fig. 2 provides a comprehensive overview of the current study's methodology and analytical steps.Fig. 2 Overview of methodology and analysis steps.
Fig. 2
4 Results
The current section presents findings from interviews for each of the four firms. Hereby, we follow the three key analytical models for supply chain risk management, resilience and reliability, and their multiple elements and steps. The main findings regarding each of the models are summarized in Tables 2 , 3 and 4 , each of which is followed by more in-depth findings descriptions.Table 2 Main findings on the four steps in the supply chain risk management process.
Table 2 Fish farming firm Food distributor Home-delivery firm Pharmaceutical firm
Identification Overarching capacity evaluation pre-pandemic. Identification of supply base risk after pandemic reached Norway Investigation of how the pandemic would affect the firm started in January 2020, including risk evaluation of (how) whether the virus could spread through food Establishment of crisis management team after pandemic outbreak Continuous monitoring of risks already in place pre-pandemic
Assessment Ability to rapidly assess risks, but dependent on proper identification. Less formal implementation of risk identification and assessment in routines Assessment and ranking of all identified risks. Some risks discarded/ downgraded, others (e.g., supply risk) highly prioritized Contingency plans for different risk scenarios Frequent assessment of identified risks and prioritization thereafter
Mitigation Some lack of established plans/mitigation for risks inherent in the firm's SC and capacity limitations. Some extent of (rapid) ad-hoc mitigation Plans for most identified risks, e.g. food security and supply base. Sketch of what to do in case of main terminal closure (not necessarily detailed plans with actionable options) Contingency plan covering several risk scenarios Strategies for different risk scenarios
Control (Continuous) monitoring of staffing capacity risk. Challenges during first days of each new infection wave. Improved control during later waves vs. first wave Monitoring of different risks throughout the pandemic. Continuous tweaking to keep routines and procedures up-to-date Close monitoring of the situation. Prepared for different alternatives, if needed (Continuous) monitoring of identified risks. Special focus on trends relevant also pre-pandemic
Table 3 Main findings on the four elements of supply chain resilience.
Table 3 Fish farming firm Food distributor Home-delivery firm Pharmaceutical firm
Collaboration Well-established network in export markets. Assistance from customers in relocating fish products from HORECA to retail market Assistance in transferring excess products from HORECA to retail market. Assistance from a foreign factory and local producers during outbreak at one own factory Collaboration with suppliers, but the firm experienced being downgraded/not prioritized during periods with shortages of goods due its relative size vs. other actors Assistance from international parent company
Visibility Control over entire value chain and locations worldwide. Improved visibility considered important; plans for improvement using more/better IT Good flow of information to/from both suppliers and customers. Some desire for more forecasts for planning ahead Good information flow with customers. Uncertainty about deliveries from some suppliers. Generally good visibility in internal systems Updates on demand increases and bottlenecks throughout pandemic
Flexibility Adjustment of volumes of fish going into production (e.g., slow down production). Further flexibility through use of smokehouses Several suppliers for most products. Tackled large shift in demand. Flexibility in some new routines (terminal, delivery timeframes) Rapid capacity increases, changed delivery time slots and some delivery procedures, expanded delivery areas Medicine procurement from open market possible, if needed (often expensive). Flexibility through procedures for prioritizing critical vs. non-critical goods
Velocity Rapid adaption to new situation by delaying production speed. Turnover challenges due to fall in important HORECA market Fast action in moving excess goods from HORECA to retail, despite this necessitating extra processing steps. Rapid solutions after a factory closure Fast capacity increases both during 1st and 2nd infection waves. Capacity challenges still occurred, but less so during 2nd and later waves Fast adjustment to new situation, reaching satisfactory levels
Table 4 Main findings on the three elements of supply chain reliability.
Table 4 Fish farming firm Food distributor Home-delivery firm Pharmaceutical firm
Delivery reliability As supplier: delivery was reliable, managed, i.a. through changing production speed. Delivery to customers in some countries was negatively affected due to transport challenges Had to allow slightly longer delivery times from Southern Europe. Accommodation of demand shifts through delivery of alternative products. Hoarding and temporary factory closing caused some empty shelves in stores Sold out-situations for some products. Back-orders many days ahead due to capacity constraints Sold-out situations due to medicine hoarding around first lockdown, followed by demand fall; challenges for both the firm and transport providers, but managed relatively rapidly. Reduced domestic air capacity tackled by more slack in transport schedules
Customer relationship Good and well-established relationships with customers: customer retention and customer help in transferring products from HORECA to retail markets Assistance from retail market customers in transferring much of excess HORECA products to retail Customer loss in HORECA and business market. Improved solutions for private consumers; e.g., implementation of contactless deliveries/solutions for people in quarantine/isolation No problems with loss of customers or bad relationships. Medicine shortages could affect customer relationship negatively
Supplier relationship Firm with largely a supplier role. Much use of air freight and international road freight. Long-term contracts with carriers, but price increases, particularly for air freight, mostly set by market Several alternative suppliers for most products, often long, collaborative relationships (a few product groups with just one supplier). No ‘COVID-19-compensation’ of suppliers, despite some suppliers’ demand Mostly local/ Norwegian suppliers. In-house carriers, complemented with some external delivery hire-ins. Considered a relatively small actor by suppliers Relatively few problems with procurement. Good cooperation with international parent firm. No payment of higher rates, despite demands from carriers supplying transport service, arguing pandemic-related cost increases
4.1 Risk management
4.1.1 Identification
While all four firms have an identification phase in their risk management approaches, this phase was most extensive for the pharmaceutical firm and the food distributor. The pharmaceutical firm employs a process for continuous risk identification and focuses on identifying risks at an early stage, while the food distributor started an extensive risk identification process in January 2020, before the pandemic hit Norway. The home-delivery firm, in turn, identified capacity risks (staff, transport, etc.) pre-pandemic, but had less focus on or only later identified other risks (such as supply base risk). The fish farming firm identified and to a large degree focused on a specific set of risks (such as price and biological risks) with less thorough identification of other risk types, especially operational and market risks. Because risk identification is a prerequisite for assessing, preparing for, mitigating, and controlling risk, starting early or continuous risk monitoring (such as done by the food distributor and fish farming firm) can be beneficial – although overdoing this can also be costly. The home-delivery and the fish farming firm could have benefited from broader or earlier identification of other risks than the ones focused upon, such as by improving preparedness or having “bought more time” than when risks are first identified when they are about to materialize.
4.1.2 Risk assessment
Risk assessment is also incorporated to some extent in all the four firms’ approaches to risk management. Risk assessment seems to be a more continuous process at the pharmaceutical firm and the food distributor, where risks were ranked and then (re)prioritized, while the fish farming firm's assessment phase seems somewhat less continuous and, to some extent, reactive, with risks identified upon materialization. The home-delivery firm assessed risks quickly once identified, but with initial focus on capacity, several other risks were first identified and assessed after the pandemic hit. Feedback further revealed that three of the firms considered that their approach to risk assessment benefited from previous experiences with disruptions and previously established risk scenarios. It was further noted that, in retrospect, it would have been wise to put more resources into assessing certain risks (such as a potential shutdown of the food distributor's main terminal), but also that a balance must be struck between costs and benefits of extensive identification and assessment processes.
4.1.3 Risk mitigation
Although not all firms had mitigation strategies for direct pandemic risks, they did all have, to some extent, strategies for other risk scenarios that were relevant considering pandemic disruptions (such as transportation issues or temporary closure of facilities). The food distributor and pharmaceutical firm had mitigation plans for different risk scenarios; for example, concrete options in case of capacity problems, alternative suppliers, and food security. However, mitigation alternatives in case the food distributors’ main terminal should be closed would likely have been suboptimal and presented challenges, while mitigation responses to initial medicine hoarding were largely successful (only short periods with lower service levels), but still suboptimal from a business perspective, as servicing peaks is expensive.
The home-delivery firm faced capacity issues immediately after the first Norwegian lockdown in March 2020 due to the sharp growth in demand for home deliveries. While responses were rapid and mitigation plans were in place for hiring staff through employment agencies, some practical issues occurred (such as lower staff availability than expected) and mitigation was initially insufficient to keep up with extreme demand increases. As part of one ad-hoc mitigation measure, the firm rapidly (within a few days) introduced a standardized box with products considered most essential/demanded. It was possible to process this box at a separate location and in an efficient way, thereby lifting some capacity pressure. Further, the firm did not foresee that some product supplies would not be delivered and that suppliers would not prioritize them during a crisis, and did not have mitigation measures prepared. On the other hand, the firm reports that it was able to improve mitigation plans throughout the pandemic, which enabled it to handle later waves of infections better.
The fish farming firm was able to act quickly, utilizing flexibility of delaying production by postponing the gutting of fish or sending fish to smokehouses for preservation. This provided flexibility in case of sudden demand drops for fresh fish or transportation challenges. However, challenges related to reduced belly-capacity for air freight due to loss of passenger flights on some overseas routes with too-small volumes for dedicated cargo flights, followed by sharp increases in transportation prices, were not mitigated as efficiently as hoped.
4.1.4 Risk control
All four firms have been monitoring risks throughout the pandemic to be prepared for risks changing fast or suddenly becoming severe. For example, the food distributor continuously tweaked and strengthened routines and procedures and ensured these remained up-to-date. The pharmaceutical firm continuously monitored identified risks, including transport, by such means as considering capacity and by tracking of transport routes. Due to the critical nature of the firms’ activities and trends of global medicine shortages already pre-pandemic, this risk received focus. Around the time of the first Norwegian lockdown, some sold-out situations materialized after extensive medicine hoarding by consumers. This was not the case in later waves, both due to better preparedness, consumers realizing that supply would be sufficient, and people not suddenly becoming ill more often. The home-delivery firm closely monitored capacity risks and, as a result, improved its mitigation strategies. While new waves still yielded short-term capacity challenges, these were considerably less substantial than they were around the time of the first Norwegian lockdown.
4.2 Resilience
4.2.1 Collaboration
For the food distributor, good collaboration contributed to resilience in several ways. The firm has strong, long-term, and collaborative relationships with key suppliers and was prioritized during difficult times, while examples were given that this was less the case for actors with supplier-buyer relationships focused mainly on pricing. Similarly, good relations with its own customers allowed agility when the need arose to rapidly shift large quantities of products from HORECA to retail. During an infection outbreak that necessitated a short closure of an own factory, local suppliers and a foreign factory quickly stepped in. The home-delivery firm, in turn, struggled to match supply and skyrocketing demand. The firm was not prioritized by its main supplier, and also some other suppliers provided low service levels. Initiating closer collaboration with several of the latter suppliers, the firm managed to increase service levels from as low as 70 percent up to 99 percent. For the pharmaceutical firm, strong supplier relationships globally were critical during the pandemic, as the pandemic impacted the production, supply, and distribution of pharmaceuticals and caused bottlenecks in global supply chains. A complicating factor is that frameworks set by Norwegian authorities effectively determine which manufacturers are relevant to consider. Therefore, manufacturers that are “not on the list”, even with good long-term relationships with the pharmaceutical firm are, in practice, not chosen. This framework makes it harder to build collaborative relationships based on mutual trust and shared interests.
While the above three firms have large buyer roles, the fish farming firm, covering the entire value chain from feed to finished product, is primarily a global supplier. In this role, strong relationships with customers helped transfer a lot of products to other markets. Further, the Asian HORECA market did not shut down the same way as in Europe, and in part due to close collaboration, many products could still be delivered. Experience from previous air freight disruptions and collaboration with customers also gave some knowledge edge on maintaining good collaboration during crises.
4.2.2 Visibility
The food distributor focused on consistent, timely, accurate, and open communication with both suppliers and customers. While deliberately choosing not to share too much information, information sharing has increased compared to pre-pandemic. Throughout, it has become clearer which information must be shared, such as for planning and monitoring. The home-delivery firm has a dedicated department for collecting and analyzing important data. This department is central in terms of enhancing visibility internally and for external partners, and data collection and analyses have increased to yield more insights. Good information flows with customers also improve delivery efficiency. However, a lack of correct visibility or receiving incorrect information from suppliers led to stock-outs of products already ordered by customers. Further, supplier information often only arrived for the first time when it was asked for. The food distributor also experienced not receiving enough useful information or receiving unnecessary information. Overall, the firm started sharing more information themselves than it had previously and reported that this had positively benefited it and its surroundings.
The fish farming firm started implementing systems to enhance visibility, especially in real-time. Examples include tracking of temperature and visibility (traceability) of orders for customers. The pharmaceutical firm's systems are partly synchronized with its parent company's and automated procurement enables the optimization of entire supply chains and full control over fill rates and stock quality. Information sent to customers is said to be good, but information from suppliers is not always accurate or complete.
4.2.3 Flexibility
The food distributor and fish farming firm experienced lower demand when the pandemic hit, while the home-delivery and pharmaceutical firms experienced demand increases. The food distributor managed good volume control and was able to redirect most excess products to the retail market when HORECA/business markets plunged, although some food had to be given away or discarded. Further, the firm was able to rapidly respond to shifts in types of products demanded by consumers, meaning procurement of different kinds of products from suppliers at short notice. The firm also managed to deal with longer lead times from Southern Europe and with necessary changes to procedures at both its own and the suppliers’ terminals.
While the home-delivery firm was somewhat overwhelmed by massive demand increases immediately after the first Norwegian societal restrictions, it did manage to increase capacity substantially in the course of few weeks in terms of staff, vehicles, and longer delivery windows. During later demand peaks, the firm was able to scale up relatively well and was prepared for new demand increases that it expected in relation to government press conferences on restrictions.
For the fish farming firm, flexibility was relatively good through help of customers worldwide in redirecting high-quality seafood to retail and by changing production speed (such as feeding rates, postponing slaughter, etc.). The availability of alternative facilities along the Norwegian coast also offers flexibility if a specific facility would suddenly have to close. For air freight abroad, freight capacity to countries with large demand volumes mostly remained sufficient (but at high prices), but for lower-demand countries (reliant on passenger flight belly-capacity), deliveries in earlier phases of the pandemic had to be cancelled.
The pharmaceutical firm had procedures to quickly implement prioritization of critical goods capacity at the expense of non-critical goods, and for getting in temporary staff in case supply and delivery of critical goods was at risk. Normally, automatic procurement systems ensure flexibility and preparedness by matching customer demand and volumes procured. However, extreme medicine hoarding around the first Norwegian lockdown led to systems interpreting this peak demand as a ‘new normal’, requiring manual corrections. The firm further responded to reduced domestic flight capacity by rescheduling their air freight transportation and adding more ‘slack’ in time schedules.
For all firms, infection outbreaks at important terminals could have caused substantial problems, despite available (suboptimal) fallback alternatives. Feedback also indicates that strong, long-term relationships with suppliers and customers positively impacted flexibility and resilience, while short or weaker relationships at times have created challenges.
4.2.4 Velocity
The food distributor and fish farming firm were able to quickly redirect products from markets in decline to retail, and in part to switch between product types. The firms’ flexibility and quick responses likely shortened their recovery times or reduced negative impacts of pandemic disruptions, and had the firms adapt to new environments and demand. Still, demand dynamics had an impact on turnover, because the reduced markets normally buy finer and more expensive products, while increasing (retail) markets are more quantity-driven. This applied especially to the fish farming firm.
For the home-delivery firm, rapid responses and capacity increases enabled conversion of a large part of the huge demand increases into sales. Fast decision-making on increasing capacity also helped in terms of catching up on delays relatively fast. Velocity in information flows helped reduce the firm's recovery time. Although the firm expressed that, in hindsight, it would have made some hasty decisions differently, these examples illustrate the firm's ability to quickly implement solutions and adapt operations.
The pharmaceutical firm largely handled the pandemic well, despite raw material shortages for pharmaceutical supplies that existed already pre-pandemic. Velocity was a theme with regard to suddenly procuring personal protective equipment (such as face masks) in large quantities at a time of extreme global demand. Further, the firm was able to quickly respond to medicine hoarding and consequent demand falls with regard to changing their use of distribution transport suppliers.
4.3 Reliability
4.3.1 Delivery reliability
Both the food distributor and pharmaceutical firm managed to adjust well to changes in demand volumes and type of demand, with both having established long-term relationships with current key suppliers. While there were some sold-out situations, mostly related to higher-than-normal demand, these were managed relatively quickly. The food distributor had alternative suppliers for most products and incorporated slightly longer delivery times for produce from Southern Europe (cf. also e.g. [3]), without significant deterioration in delivery reliability. During the closure of one factory, delivery reliability was reduced for some products, but to a substantial extent managed through alternatives. The pharmaceutical firm proactively added time slack on domestic air freight deliveries, thereby ensuring reliable and in-time deliveries.
The home-delivery firm, which relied heavily on just-in-time deliveries from suppliers, experienced reduced delivery reliability on products from some suppliers. This affected orders made by the firm's own customers. However, the firm offers similar products from different brands, and could often offer customers a relevant alternative product, rather than nothing. The firm's challenges are thought to be correlated with weaker or less-committal supplier relationships than for the other investigated firms.
Both the food distributor and home-delivery firm experienced local outbreaks at facilities. The former managed to use alternatives, but the latter, while not closing down fully, did not have proper backup solutions. To ensure delivery reliability, the home-delivery firm tightened infection control measures and worked on hiring more people to take care of other parts of operations and who could be transferred in case of operational disturbances.
The fish farming firm, as a supplier, managed reliable delivery of products throughout the pandemic by making production adjustments while minimizing waste and costs. Delivery reliability to consumers in certain lower-demand Asian countries was affected, but for countries with larger demand volumes (serviced using dedicated freight flights), this was not a significant problem.
4.3.2 Customer relationship
Both the food distributor and fish farming firm expressed that they have well-established relationships with their customers and that customers helped them move products between markets. This willingness to help can be a sign of the desire to continue long relationships and also reduce uncertainty from suppliers [63]. While the home-delivery firm also lost much of its HORECA/business market, the private end-consumer market increased considerably. Unlike the markets for the food distributor and fish farming firm, these markets buy through the same platform, which meant that fewer changes were necessary. Solutions for order visibility and communication between carrier and customer about issues such as quarantines and delivery have contributed to reducing uncertainty and dependency related to grocery shopping in physical stores. The pharmaceutical firm reported very few challenges when it comes to its customers. There are well-established plans for what to do if some things cannot be procured, and which customers have seemingly agreed upon. This agreement makes orders predictable, with the firm being perceived as reliable. However, had substantial medicine shortages occurred and pharmacies, hospitals and end-consumers not received important medicines for critical time periods, this could have affected consumer relationships negatively.
4.3.3 Supplier relationship
The food distributor has alternative suppliers to choose from for most products, with often long and well-established relationships, but for a few product groups only has one supplier. This resulted in challenges upon the abovementioned facility closure, but could also lead to challenges for other products. The pharmaceutical firm had few supplier problems and was helped and partly coordinated by its international parent organization.
Both the food distributor and pharmaceutical firm were asked by carriers to increase transport payments, but neither were willing to agree to such requests. If transport suppliers should be paid too little, there is a potential risk in losing them if suppliers believe they can earn more elsewhere. However, the firms reported that not giving in to the carriers’ demands has not caused problems throughout the pandemic. It is unclear whether carriers might have attempted to exploit an extraordinary situation to extract higher margins, or whether alleged pandemic-related cost increases were indeed substantial enough to demand higher payments.
The fish farming firm is highly dependent both on air freight for overseas deliveries (mainly to Asia and to some degree also North-America), but also on road transportation to the European continent. Despite often having long-term contracts, the firm faced high freight rates, especially for air transport, but also that it became challenging to cover the transport needs by truck. In all, the firms’ dependency necessitated the accepting of transport at much higher costs than pre-pandemic.
The home-delivery firm had some trouble with suppliers during the pandemic. As a relatively small player in grocery retail, the firm is dependent on suppliers, but large Norwegian suppliers do not necessarily need the home-delivery firm to survive. Therefore, codependency is minimal, which could explain why the firm's increased demand was not prioritized by several suppliers. Creating a more codependent relationship might help improve this. A positive factor is that nearly all suppliers are Norwegian, which yields fewer challenges in the firm's own supply chain.
5 Discussion
This article has assessed how pandemic-induced disruptions affected four firms in Norwegian food and pharmaceutical supply chains, how they approached supply chain risk management prior to and during the pandemic, and strengths and weaknesses of their SC's resilience and reliability. The objective of our investigations was to provide insights into challenges and opportunities during the current pandemic, and lessons for improving preparedness, resilience, and robustness towards future pandemics and shocks yielding similar disruptions and dynamics. Through several rounds of semi-structured interviews with each of the firms, we systematically addressed the main elements of three theoretical models for SC analysis. Using the models in conjunction, and given overlaps and complementarity between them, allowed us to provide comprehensive assessments of strengths and weaknesses of the individual firms, as well as common experiences, and to make suggestions for improving future preparedness.
The four firms investigated faced different challenges, with the main ones materializing during the pandemic's earlier stages and particularly related to sudden demand changes. The food distributor, home-delivery firm, and pharmaceutical firm all experienced immediate and sharp demand increases due to panic responses in society and hoarding by consumers, although the former two firms also experienced (smaller) decreases from their business/HORECA segments. The fish farming firm, primarily directed at the global HORECA market, experienced immediate drops in demand from European and world markets. This necessitated an adjustment in production volumes and redirection of deliveries to the retail market and fish processing industry (at lower prices), resulting in temporary cash flow reductions. As has also been observed elsewhere (cf. [12]), these dynamics forced the firms to adapt both their SCs and product ranges (for example, smaller packages) from HORECA and to the retail markets.
Regarding risk management, we found differences in the four firms’ scope, completeness, continuity, and timeliness of risk identification and assessment phases, with the pharmaceutical firm and food distributor identifying risks pre-pandemic or continuously, compared to some important risks for the home-delivery and fish farming firm first being recognized after the pandemic hit. While follow-ups in these cases were fast, they were also more reactive than desired and based on less rigorous analysis than usual underlying decisions (in line with observations by [22]). Furthermore, we found differences regarding how risk mitigation and control were approached, depending on how the previous SCRM steps were managed. Generally, however, all firms had mitigation strategies for some risks, albeit not directly for pandemic-specific risks. Many mitigation measures were relatively ad-hoc in early stages and then improved throughout the pandemic. A common factor here was the lack of actionable and sufficiently detailed points in the firms’ strategies and (contingency) plans. Interview feedback generally points to the importance of both sufficient and timely monitoring of potential risks, with risk assessment and control being up-to-date so that firms are more prepared for future disruptions (flexibility) and can act quickly when these disruptions materialize (velocity). Feedback suggests that, in retrospect, the firms would have put more resources into assessing certain risks. These findings are in line with El Baz and Ruel [6], who concluded that firms’ priority should be to develop efficient and updated risk identification measures, as these affect the other SCRM stages, and that firms need to develop interconnected SCRM practices to improve their robustness and resilience.
Regarding supply chain resilience, we found differing extents of collaboration between firms and upstream and downstream parts of SCs. In particular, the fish farming and pharmaceutical firm and the food distributor highlighted good collaboration as an important factor for their resilience, while less established collaborative ties for the home-delivery firm were reported as a challenge. While the visibility of important information varied between firms, good visibility was reported to have helped resilience and decreased response time. Common tendencies across the investigated firms are an increased valuation of the importance of high-quality information, movements towards increasing information collection and analysis, and learning to focus and better distinguish between important and superfluous information. Confronted by disruptions, all four firms benefited from flexibility and responsiveness (velocity) in important parts of their activities and supply chains, either dampening potential negative effects (pharmaceutical and fish farming firm, food distributor), or successfully converting opportunities into value (home-delivery firm and personal protective equipment for the pharmaceutical firm). Researchers such as Hobbs [4] also highlighted such drivers in concluding that SC responsiveness is key for resilience. Simultaneously, underlying drivers (changes in demand levels and between demand segments) were largely beyond the firms’ control. All firms further worked to enhance digital communication skills, either before or during the pandemic. This allowed relatively smooth transitions to administrative employees working from home (flexibility), but also collaboration and visibility through more regular communication with external suppliers than pre-pandemic.
Considering SC reliability, delivery reliability has been essential during the pandemic, with all firms experiencing increased lead times for certain items and customer confidence in the ability of SCs to deliver in some areas being decreased (in line with, e.g., Hobbs [4] and El Baz and Ruel [6]). While service levels towards customers were reduced to some extent and during some shorter periods for all investigated firms, reliable deliveries were largely maintained throughout the crisis. The same goes for transport (cf. also e.g. [3]), although this often required adjustment (such as new solutions when belly capacity onboard passenger flights suddenly disappeared for the fish farming firm). Regarding customer and supplier relationships, our investigations suggest a connection between long-term and trusting relationships and information sharing. The home-delivery firm experienced that its size and lack of long-term relationships and co-dependency meant that they were not always prioritized by suppliers, while the other firms gave examples of good long-term relationships with customers and suppliers having been success factors, both in transferring products between markets and in relation to local outbreaks at own facilities. In line with suggestions by Hobbs [4], long and trusting relationships have proven to contribute to good collaboration and flexibility, thereby enabling reliability.
6 Conclusion
Overall, while the four investigated firms had contingency plans prior to the pandemic, these generally both had gaps and lacked the actionable points and level of detail reported to be desirable in retrospect (in line with broader industry observations by [22]). At the same time, detailed strategies were highlighted as important for being able to adapt quickly.
6.1 Implications
Insights from this article can contribute to improving future preparedness and contingency plans in several ways by utilizing the three SC models in conjunction, and may have practical, research, and operational implications. Finding suggest that ongoing societal trends of facility centralization may add an element of vulnerability for firms, while spreading important functions over multiple locations can ensure more operational flexibility. The pharmaceutical firm, for example, accelerated the establishment of a planned emergency warehouse, where it originally operated from one large warehouse where infection outbreaks in the worst case could endanger distribution of critical products. Both the food distributor and home delivery firm demonstrated some flexibility in moving production to other facilities, but could have been affected more severely if outbreaks had occurred at more critical sites than was the case.
6.2 Theoretical and practical contributions
From a research perspective, our study demonstrates the synergy of using three theoretical models for SC analysis alongside, rather than separately. This approach also made it possible to more comprehensively compare firms with each other and to extract insights on more general tendencies and lessons with relevance also for other firms. By systematically running through each of the models, interrelationships among elements of risk management, resilience, and reliability become more visible, increasing awareness and providing firms a ‘checklist’ that forces them to consider and incorporate specific dimensions. This enables firms to establish a broader and more comprehensive picture of their strengths and weaknesses, cover gaps, prioritize between improvement areas, and collect input towards formulating detailed, actionable points.
6.3 Limitations
A limitation of investigations based on semi-structured interviews is that some bias may occur. For example, firms may want to hold back on discussing certain aspects of the challenges and weaknesses they faced, but also on particularly successful coping strategies. Similarly, while findings build on educated observations and experiences reported by knowledgeable staff, findings would be strengthened if concrete supporting data had been available. Another limitation of our study is that the investigated firms coped relatively well with challenges caused by the pandemic, where larger volatility might have been expected. It is not unlikely that investigations of firms that either really suffered from the pandemic or ceased new opportunities, could have provided particularly valuable lessons. Analyses in this article are further based on firm experiences in the first year of the pandemic. Our overarching research project on the pandemic's consequences has since continued. Early observations suggest that many of the crisis’ stronger effects took more time to materialize. Examples include supply chain challenges due to shortages of raw materials, intermediate goods, and shipping containers, manifold increases in shipment rates, delays and unpredictable lead times, and increasing driver shortages, both in individual countries (such as the UK and the USA), but also in Europe as a whole. The current pandemic stands out both in terms of its longevity compared to many other crises and the continued intensity of challenges and disruptions throughout.
6.4 Further research
Further research could benefit from focusing on challenges and improvements from more medium-to-long term effects and changes, and changes that might become structural, rather than temporary. Examples may include temporary lay-offs becoming permanent or tendencies that are reported of foreign workers having become less interested in working in Norway because of long-term border-crossing challenges, and now filling vacancies in countries closer to home. Also the recent war in Ukraine is observed to cause major new challenges and to reinforce supply chain challenges that started with the pandemic.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Data availability
The authors do not have permission to share data.
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References
1 Nikolopoulos K. Punia S. Schäfers A. Tsinopoulos C Vasilakis C. Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions European Journal of Operational Research 290 1 2021 99 115 32836717
2 Araz O.M. Choi T.M. Olson D.L. Salman F.S. Data Analytics for Operational Risk Management Decision Sciences 51 6 2020 1316 1319
3 Coluccia B. Agnusdei G.P. Miglietta P.P. De Leo F. Effects of COVID-19 on the Italian agri-food supply and value chains Food Control 123 2021 107839
4 Hobbs J.E. Food supply chains during the COVID-19 pandemic Canadian Journal of Agricultural Economics 68 2 2020 171 176
5 Fan Y. Stevenson M. A review of supply chain risk management: definition, theory, and research agenda International Journal of Physical Distribution & Logistics Management 48 3 2018 205 230
6 El Baz J. Ruel S. Can supply chain risk management practices mitigate the disruption impacts on supply chains’ resilience and robustness? Evidence from an empirical survey in a COVID-19 outbreak era’ International Journal of Production Economics 233 2021 107972
7 Pourhejazy P. Kwon O.K. Chang Y-T. Park H. Evaluating Resiliency of Supply Chain Network: A Data Envelopment Analysis Approach Sustainability 9 2 2017 255
8 Ali I. Gölgeci I. Where is supply chain resilience research heading? A systematic and co-occurrence analysis International Journal of Physical Distribution & Logistics Management 49 8 2019 793 815
9 Stone J. Rahimifard S. Resilience in agri-food supply chains: a critical analysis of the literature and synthesis of a novel framework Supply Chain Management 23 3 2018 207 238
10 Kano L. Oh C.H. Global Value Chains in the Post-COVID World: Governance for Reliability Journal of Management Studies 57 8 2020 1773 1777
11 Ivanov D. Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case Transportation Research Part E: Logistics and Transportation Review 136 2020 101922
12 Veselovská L. Supply chain disruptions in the context of early stages of the global COVID-19 outbreak Problems and Perspectives in Management 18 2 2020 490 500
13 Hendricks K.B. Singhal V.R. An Empirical Analysis of the Effect of Supply Chain Disruptions on Long-Run Stock Price Performance and Equity Risk of the Firm Production and Operations Management 14 1 2005 35 52
14 Ellis S.C. Henry R.M. Shockley J. Buyer perceptions of supply disruption risk: A behavioral view and empirical assessment Journal of Operations Management 28 1 2010 34 46
15 Hendricks K.B. Singhal V.R. The effect of supply chain glitches on shareholder wealth Journal of Operations Management 21 5 2003 501 522
16 Béné C. Resilience of local food systems and links to food security – A review of some important concepts in the context of COVID-19 and other shocks Food Security 12 2020 805 822 32837646
17 Dombu S.V. Bardalen A. Strand E. Henriksen B. Lamprinakis L. Norsk matsikkerhet og forsyningsrisiko - Rapport fra arbeidsgruppe i NIBIO NIBIO research report 7 145 2021
18 Ho W. Zheng T. Yildiz H. Talluri S. Supply chain risk management: A literature review International Journal of Production Research 53 16 2015 5031 5069
19 Jüttner U. Supply chain risk management: Understanding the business requirements from a practitioner perspective International Journal of Logistics Management 16 1 2005 120 141
20 Khan O. Burnes B. Risk and supply chain management: Creating a research agenda International Journal of Logistics Management 18 2 2007 197 216
21 Christopher M. Peck H. Building the Resilient Supply Chain International Journal of Logistics Management 15 2 2004 1 14
22 van Hoek R. Research opportunities for a more resilient post-COVID-19 supply chain – closing the gap between research findings and industry practice International Journal of Operations & Production Management 40 4 2020 341 355
23 Zsidisin G.A. Ellram L.M. Carter J.R. Cavinato J.L. An analysis of supply risk assessment techniques International Journal of Physical Distribution & Logistics Management 34 5 2004 397 413
24 Neiger D. Rotaru K. Churilov L. Supply chain risk identification with value-focused process engineering Journal of Operations Management 27 2 2009 154 168
25 Heckmann I. Comes T. Nickel S. A critical review on supply chain risk – Definition, measure and modeling Omega (Oxford) 52 2015 119 132
26 Manuj I. Mentzer J.T. Global supply chain risk management Journal of Business Logistics 29 1 2008 133 155
27 Ponomarov S.Y. Holcomb M.C. Understanding the concept of supply chain resilience International Journal of Logistics Management 20 1 2009 124 143
28 Peck H. Drivers of supply chain vulnerability: an integrated framework International Journal of Physical Distribution & Logistics Management 35 4 2005 210 232
29 Jüttner U. Maklan S. Supply chain resilience in the global financial crisis: an empirical study Supply Chain Management 16 4 2011 246 259
30 Scholten K. Schilder S. The role of collaboration in supply chain resilience Supply Chain Management 20 4 2015 471 484
31 Christopher M. The Agile Supply Chain: Competing in Volatile Markets Industrial Marketing Management 29 1 2000 37 44
32 Pettit T.J. Croxton K.L. Fiksel J. Ensuring Supply Chain Resilience: Development and Implementation of an Assessment Tool Journal of Business Logistics 34 1 2013 46 76
33 Jain V. Kumar S. Soni U. Chandra C. Supply chain resilience: model development and empirical analysis International Journal of Production Research 55 22 2017 6779 6800
34 Liu C.-L. Lee M.-Y. Integration, supply chain resilience, and service performance in third-party logistics providers International Journal of Logistics Management 29 1 2018 5 21
35 Caridi M. Moretto A. Perego A. Tumino A. The benefits of supply chain visibility: A value assessment model Intl. Journal of Production Economics 151 2014 1 19
36 Somapa S. Cools M. Dullaert W. Characterizing supply chain visibility – a literature review International Journal of Logistics Management 29 1 2018 308 339
37 Zhu G. Chou M.C. Tsai C.W. Lessons Learned from the COVID-19 Pandemic Exposing the Shortcomings of Current Supply Chain Operations: A Long-Term Prescriptive Offering Sustainability 12 14 2020 5858
38 Gunessee S. Subramanian N. Ambiguity and its coping mechanisms in supply chains lessons from the Covid-19 pandemic and natural disasters International Journal of Operations & Production Management 40 7–8 2020 1201 1223
39 Vickery S. Calantone R. Dröge C. Supply Chain Flexibility: An Empirical Study Journal of Supply Chain Management 35 2 1999 16 24
40 Mandal S. Sarathy R. Korasiga V.R. Bhattacharya S. Dastidar S.G. Achieving supply chain resilience International Journal of Disaster Resilience in the Built Environment 7 5 2016 544 562
41 Kamalahmadi M. Mellat-Parast M. Developing a resilient supply chain through supplier flexibility and reliability assessment International Journal of Production Research 54 1 2016 302 321
42 Adenso-Diaz B. Mena C. García-Carbajal S. Liechty M. The impact of supply network characteristics on reliability Supply Chain Management 17 3 2012 263 276
43 Wu C. Barnes D. Design of agile supply chains including the trade-off between number of partners and reliability International Journal of Advanced Manufacturing Technology 97 9 2018 3683 3700
44 Toshihiro N. Alexandre B. The Toyota Group and the Aisin Fire MIT Sloan Management Review 40 1 1998 49
45 Goel R.K. Saunoris J.W. Goel S.S. Supply chain performance and economic growth: The impact of COVID-19 disruptions Journal of Policy Modeling 43 2 2021 298 316
46 Chen J. Wang H. Zhong R.Y. A supply chain disruption recovery strategy considering product change under COVID-19 Journal of Manufacturing Systems 60 2021 920 927 33911327
47 Sharma S.K. Bhat A. Risk Mitigation in Automotive Supply Chain: An Empirical Exploration of Enablers to Implement Supply Chain Risk Management Global Business Review 17 4 2016 790 805
48 Dohmen A.E. Merrick J.R.W. Saunders L.W. Stank T.P. Goldsby T.J. When preemptive risk mitigation is insufficient: The effectiveness of continuity and resilience techniques during COVID-19 Production and operations management 2022 Special Issue January 2022
49 Skipper J.B. Hanna J.B. Minimizing supply chain disruption risk through enhanced flexibility International Journal of Physical Distribution & Logistics Management 39 5 2009 404 427
50 Hale T. Moberg C.R. Improving supply chain disaster preparedness: A decision process for secure site location International Journal of Physical Distribution & Logistics Management 35 3 2005 195 207
51 Lopes de Sousa Jabbour A.B. Chiappetta Jabbour C.J. Hingley M. Vilalta-Perdomo E.L. Ramsden G. Twigg D. Sustainability of supply chains in the wake of the coronavirus (COVID-19/SARS-CoV-2) pandemic: Lessons and trends Modern Supply Chain Research and Applications 2 3 2020 117 122
52 Payne C.F. Contingency plan exercises Disaster Prevention and Management 8 2 1999 111 117
53 Fernandes L.J. Saldanha da Gama F. Contingency planning – a literature review Supply Chain Management and Competitiveness 2008 457 467
54 Rimiene K. Bernatonyte D. Supply chain management trends in the context of change Economics & Management 18 3 2013 596
55 Skalli A. Risk attitudes and human mobility during the COVID-19 pandemic Nature Scientific Reports 10 1 2020 19931
56 Pratt J.W. Risk Aversion in the Small and in the Large Econometrica 32 1/2 1964 122
57 Kamilaris A. Fonts A. Prenafeta-Boldύ F.X. The rise of blockchain technology in agriculture and food supply chains Trends in Food Science & Technology 91 2019 640 652
58 Dabbene F. Gay P. Sacco N. Optimisation of fresh-food supply chains in uncertain environments, Part I: Background and methodology Biosystems Engineering 99 3 2008 348 359
59 Yu M. Nagurney A. Competitive food supply chain networks with application to fresh produce European Journal of Operational Research 224 2 2013 273 282
60 Aung M.M. Chang Y.S. Temperature management for the quality assurance of a perishable food supply chain Food Control 40 2014 198 207
61 Dellino G. Laudadio T. Mari R. Mastronardi N. Meloni C. A reliable decision support system for fresh food supply chain management International Journal of Production Research 56 4 2018 1458 1485
62 Miles M.B. Huberman A.M. Saldaña J. Qualitative Data Analaysis: A Methods Sourcebook 2014 Sage London
63 Ambrose E. Marshall D. Lynch D. Buyer supplier perspectives on supply chain relationships International Journal of Operations & Production Management 30 12 2010 1269 1290
| 0 | PMC9731645 | NO-CC CODE | 2022-12-14 23:30:05 | no | 2023 Dec 8; 5:100102 | utf-8 | Sustain Futur | 2,022 | 10.1016/j.sftr.2022.100102 | oa_other |
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J Contextual Behav Sci
J Contextual Behav Sci
Journal of Contextual Behavioral Science
2212-1447
2212-1455
The Authors. Published by Elsevier Inc. on behalf of Association for Contextual Behavioral Science.
S2212-1447(22)00124-7
10.1016/j.jcbs.2022.12.001
Article
In the shadow of COVID-19: A randomized controlled online ACT trial promoting adolescent psychological flexibility and self-compassion☆☆☆
Lappalainen Päivi ab∗
Lappalainen Raimo b
Keinonen Katariina b
Kaipainen Kirsikka bc
Puolakanaho Anne b
Muotka Joona b
Kiuru Noona b
a Department of Education, University of Jyväskylä, Finland
b Department of Psychology, University of Jyväskylä, Finland
c Tampere University, Unit of Computing Sciences, Tampere, Finland
∗ Corresponding author. Päivi Lappalainen, Department of Education, University of Jyväskylä, PB 35, 40014 Jyväskylä University, Finland. .
8 12 2022
1 2023
8 12 2022
27 3444
17 3 2022
25 11 2022
7 12 2022
© 2022 The Authors
2022
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Background
Although some adolescents managed to cope well with the challenges brought on by the COVID-19 pandemic, the well-being of many was adversely affected due to school closures, distance education, restrictions on gathering with friends, and limited access to mental health services. Many adolescents reported increased anxiety and depression as well as decreased psychological wellbeing due to the pandemic. Consequently, there is a need for psychological support that exceeds the strained resources available to schools to support young people during times of crisis and societal pressure.
Objective
The present study aimed to explore the effects of an online-delivered ACT intervention to promote adolescent psychological flexibility and self-compassion and decrease psychological distress during the second wave of COVID-19 in the fall of 2020.
Methods
A total of 348 adolescents aged 15–16 were randomly divided into three equal groups: 1) the iACT student coach + virtual coach group, n = 116; 2) the iACT virtual coach group, n = 116; and 3) the control group with no intervention, n = 116). Among these adolescents, 234 participated in a pre-measurement (iACT, n = 154; control, n = 80; intent-to-treat) and completed measures of psychological flexibility, self-compassion, anxiety, and depression.
Results
An investigation of all the adolescents who participated in the pre-measurement (intent-to-treat analysis, n = 234) revealed no significant differences between the three groups with regard to psychological flexibility, self-compassion, and symptoms of anxiety and depression. However, upon combining the two intervention groups and examining the adolescents who completed at least 30% of the Youth Compass program (per-protocol analysis, n = 137), small but significant differences between the iACT intervention and control groups were found regarding the psychological flexibility subscale valued action, self-compassion, and anxiety in favor of the intervention group.
Conclusions
Active use of an ACT-based online intervention under adverse circumstances may decrease symptoms of anxiety and increase psychological flexibility skills in adolescents.
Keywords
ACT
Adolescents
Web-based intervention
Psychological flexibility
Self-compassion
Anxiety
==== Body
pmc1 Introduction
Stressful conditions have been shown to be a significant risk factor for adolescent mental health (Mastrotheodoros, 2021). One of the most recent challenges faced by adolescents was the COVID-19 pandemic, which resulted in school closures, remote learning, and limited access to mental health services. Longitudinal studies suggest increased mental health symptoms and decreased well-being among adolescents during the pandemic (Mastrotheodoros, 2021). For example, a German study found that of the 1586 children and adolescents examined, two-thirds reported significantly lower quality of life and more mental health problems, such as higher levels of anxiety, compared to the pre-pandemic situation (Ravens-Sieberer et al., 2021). Similar results regarding adolescents have been reported in other studies (e.g., Hafstad et al., 2020, 2021; Hawke et al., 2020; Parola et al., 2020). Maladaptive coping strategies (e.g., rumination) under stressful conditions, pre-existing mental health problems, inadequate family support, and isolation from peers are some of the factors that may have worsened adolescent mental health during the pandemic (Branje & Morris, 2021). Conversely, adaptive coping responses (e.g., acceptance, self-compassion) may have helped some adolescents alleviate COVID-19-related distress (She et al., 2022). In particular, it has been suggested that psychological flexibility protects against the detrimental effects of the COVID-19 pandemic (e.g., Dawson & Golijani-Moghaddam, 2020; McCracken et al., 2021).
Psychological flexibility is defined as acting in accordance with personal goals and values in the presence of potentially intrusive thoughts and feelings (Hayes et al., 2012). It plays an essential role in determining how people cope with and adapt to changing and often challenging life circumstances (Hayes et al., 2012). Stressful events, such as the distress caused by the COVID-19 pandemic, may increase the likelihood of psychological inflexibility (Hayes et al., 1996). For example, an adolescent may engage in attempts to control painful thoughts and emotions related to the pandemic, which may result in further stress and a range of emotional difficulties (Biglan et al., 2008; Hayes et al., 2006). Based on these considerations, it is reasonable to believe that those high in psychological flexibility may be less affected by the adverse consequences of the COVID-19 pandemic. In other words, psychological flexibility may act as a resilience factor against stressful experiences such as the COVID-19 pandemic (Fonseca et al., 2019; Gloster et al., 2017; McCracken et al., 2021). Dawson and Golijani-Moghaddam (2020) found that psychological flexibility was significantly associated with greater well-being and inversely associated with higher levels of depression, anxiety, and COVID-19-related distress. Similar findings have been reported by other researchers (Crasta et al., 2020; Daks et al., 2020; Kroska et al., 2020; Mallett et al., 2021; McCracken et al., 2021; Pakenham et al., 2020; Peltz et al., 2020; Smith et al., 2020) showing the protective role of psychological flexibility in adapting to new and changing circumstances during the pandemic (Presti et al., 2020). However, little is known about whether a psychological intervention can increase psychological flexibility which in turn can mitigate the detrimental effects of the pandemic on adolescents’ mental health.
Along with psychological flexibility, self-compassion may protect against the adverse effects of the pandemic. Self-compassion is not a psychological flexibility process as such; rather, its components are involved in each of the processes of psychological flexibility (Gillanders et al., 2014; Neff & Tirch, 2013, pp. 78–106). According to Neff and Dahm (2015, pp. 121–137), self-compassion is composed of three interrelated aspects: self-kindness, that is, treating oneself kindly; common humanity, that is, seeing personal struggles as something that all human beings share; and mindfulness, that is, holding one's painful thoughts and feelings mindfully. Self-compassion has been found to be positively associated with well-being, life satisfaction, happiness, and coping skills and negatively correlated with psychopathology in adults (e.g., Breines & Chen, 2012; Neff, 2003; Neff et al., 2005; for a review, see Ferrari et al., 2019) and adolescents (Bluth & Eisenlohr-Moul, 2017; Bluth et al., 2016; Neff et al., 2007; for a meta-analysis, see Marsh et al., 2018). Adolescence is a period of change in life in which self-compassion may be particularly low, as adolescents are prone to critical self-evaluations, comparisons to others, and overidentification with emotions (Neff, 2003). Therefore, self-compassion may be particularly relevant during adolescence (Neff, 2003). Furthermore, self-compassion has been found to act as a protective factor in high-stress situations (Gilbert & Procter, 2006; Hofmann et al., 2011), suggesting that people with higher levels of self-compassion react to adverse events with better emotional regulation (Zeng et al., 2015). Correspondingly, Lau et al. (2020) and Gutiérrez-Hernández et al. (2021) found that during the COVID-19 pandemic, higher levels of self-compassion in adult populations were related to lower levels of anxiety, depression, and stress.
A promising approach in promoting psychological flexibility and self-compassion and offering adaptive skills to deal with adverse events is acceptance and commitment therapy (ACT; Hayes et al., 2012). According to meta-analyses, ACT may improve mental and behavioral problems, quality of life, and psychological flexibility in adolescents (Fang & Ding, 2020; Swain et al., 2015). In addition, before the COVID-19 pandemic, our studies suggest that web-based ACT can help increase adolescents’ academic buoyancy and life satisfaction and reduce stress and depressive symptoms (Lappalainen et al., 2021, Puolakanaho et al., 2019). This is in line with evidence showing that web-based interventions are effective at decreasing symptoms of depression and anxiety among adolescents (Das et al., 2016; Välimäki et al., 2017).
However, adolescents have been found not to be fully engaged in using web-based interventions (Välimäki et al., 2017), which is a critical factor in digital interventions, as active usage, and time investment have been associated with improved treatment outcomes in both adult and adolescent populations (Calear et al., 2013; Enrique et al., 2019; Mattila et al., 2016). For example, Enrique et al. (2019) found that 50% of program completion in an Internet-based intervention for individuals with depressive symptoms was associated with clinically meaningful change.
Given that psychological well-being is highly associated with psychological flexibility (Levin et al., 2014) and self-compassion (Marshall et al., 2015), the aim of the current study was to explore whether an online ACT intervention Youth Compass would have an impact on adolescent distress, psychological flexibility, and self-compassion during the COVID-19 pandemic. More specifically, the online Youth Compass intervention offered to 15–16-year-old adolescents was compared to a no-treatment control group, with the goal of exploring whether the intervention would decrease the adolescents’ symptoms of anxiety and depression and enhance their psychological flexibility and self-compassion skills during the second COVID-19 lockdown. Based on our earlier finding, we hypothesized that adolescents completing at least 30% of the program content would show positive changes, that is, their distress would decrease, and their psychological flexibility and self-compassion would increase significantly more in comparison with those in the control condition.
2 Method
2.1 Recruitment
In the spring of 2020, a total of 348 eighth-grade 14–16-year-old adolescents from lower secondary schools (n = 17) in Central Finland filled in a short screening questionnaire and stated their willingness to participate in the Youth Compass study. Participation in the trial was voluntary, and no pre-defined inclusion or exclusion criteria were applied. Thus, the intervention could be categorized as universal, that is, it was offered to all interested adolescents in the eight grade. We therefore followed the recommendation of World Health Organization (2020), according to which universally delivered interventions should be provided for all (unselected) adolescents (10–19 years), with the goal to promote positive mental health, and prevent and reduce suicidal behavior, mental disorders, aggressive and disruptive behaviors, and substance use.
2.2 Randomization
The 348 adolescents were randomly allocated in the SPSS program by a researcher outside of the study to three conditions: 1) a five-week Youth Compass online intervention with two 45-min video call from a student coach and support from a virtual coach (iACT student coach + virtual coach group, n = 116); 2) a five-week Youth Compass online intervention with one 15-min video call from a student coach and support from a virtual coach (iACT virtual coach group, n = 116); and 3) a no-intervention group (control group, n = 116; Fig. 1 ). The three randomized groups were balanced in terms of the levels of stress and depression, including approximately equal amounts of adolescents above (PSS score ≥14 or DEPS score ≥9) and below the cutoff scores for PSS and DEPS.Fig. 1 Participant flow diagram.
Fig. 1
2.3 Procedure and participants
The pre-measurement was administered online in early September 2020 and was filled in by a total of 234 (67%) of the 348 adolescents. An online post-assessment was conducted after seven to eight weeks. A total of 110 (71%) adolescents (of 154) logged into the Youth Compass program mid-September 2020. The mean age of the adolescents (n = 234) was 15.01 years (SD = 0.14, range 14–16 years), a slight majority of whom were female (n = 156, 67%). The demographic characteristics are provided in Table 1 and the timeline for the intervention in Fig. 2 .Table 1 Participant Characteristics (inclu. adolescents who filled in the pre-measurement (intent-to-treat analysis, n = 234)).
Table 1Baseline characteristics All (n = 234) iACT student coach + virtual coach (n = 79) iACT virtual coach (n = 75) Control (n = 80)
Age M (SD) 15.01 (0.15) 15.04 (0.20) n = 70 14.98 (0.12) n = 66 15.01 (0.12) n = 67
Gender
Female 156 (66.7%) 48 (60.8%) 54 (72%) 54 (67.5%)
Male 77 (32.9%) 30 (38%) 21 (28%) 26 (32.5%)
Other/does not want to tell 1 (0.4%) 1 (1.3%) – –
Mother tonguea
Finnish 226 (97%) 75 (94.9%) 73 (97.3%) 78 (98.7%)
Other than Finnish 7 (3%) 4 (5.1%) 2 (2.7%) 1 (1.3%)
Mother's educationb
Prim./second. level 92 (42.7%) 30 (41%) 27 (38.5%) 35 (48%)
University level 124 (57.4%) 43 (58.8%) 43 (61.4%) 38 (52.1%)
Father's educationc
Prim./second. level 115 (54.3%) 42 (59.1%) 34 (49.2%) 39 (54.1%)
University level 97 (45.8%) 29 (40.8%) 35 (50.7%) 33 (45.9%)
Elevated symptoms of depression (DEPS)d 90 (39%) 30 (38.5%) 31 (42.5%) 29 (36.3%)
Others = Living in foster care or approved home.
a Missing information, n = 1.
b Primary >9 years; secondary 9–12 years; university 12 > years (university, polytechnic, college, etc.); missing information, n = 18.
c Missing information, n = 22.
d DEPS = The Depression Scale (Salokangas et al., 1995), missing information, n = 3.
Fig. 2 Timeline for the intervention.
Fig. 2
The study procedures were conducted online during the COVID-19 pandemic (the second wave of COVID-19 occurred from September to October 2020), suggesting that the adolescents were presumably burdened not only by occasional quarantine periods, social distancing, and other restrictions but also by the stress associated with pre-transition challenges (exams, etc.).
This study was conducted in compliance with APA ethical standards. Ethical approval was obtained from the Ethical Committee of the University of Jyväskylä on November 20, 2019, registered at www.clinicaltrials.gov in September 2017 and updated in April 2020 (ClinicalTrials.gov Identifier: NCT04340206 ). A signed informed consent form was first obtained from parents, after which the researchers invited the adolescents to consent.
2.4 Intervention and control groups
We investigated two different delivery forms of the Youth Compass intervention: Combined student and virtual coach vs virtual coach only (including 90 min vs. 15 min video call). Our previous studies have shown comparable effects in depressive symptoms regardless of the delivery form of the Youth Compass (e.g., with or without two face-to-face meetings; Lappalainen et al., 2021). Thus, we were open to the possibility that there would be no additional contact-related impact from the 90-min-video call.
The iACT Student Coach + Virtual Coach Group. The adolescents who were randomly assigned to the iACT student coach + virtual coach group received support from a student coach and a built-in virtual coach (chatbot and SMS coaching). Using the doxy. me telemedicine application, the adolescent received the first video call (45 min) from the student coach and was interviewed based on the psychosocial interview template (Strosahl et al., 2012), which included 14 questions adapted for adolescents (e.g., How are you doing in school?). The aim of the interview was to understand the current life situation of each participant. A second videoconferencing meeting (45 min) was arranged two weeks later. The goal of the second meeting was to encourage the adolescent to keep working on the online program. The adolescents were also supported by the virtual coach (see below). Throughout the intervention, the student coach monitored the progress of the participants and sent an SMS message when they showed no progress (Table 2 ).Table 2 The youth compass intervention: Modules, module content, and examples of virtual coach messages (SMS).
Table 2Content/message The Student Coach and Virtual Coach Group The Virtual Coach Group
1st video conferencing session (doxy.me) Duration: 45 min
Registration for the Youth Compass
Introduction
Interview with 14 questions Duration: 15 min
Registration for the Youth Compass
Introduction and information on three weekly reminders by the virtual coach
No interview
Virtual coach SMS message: Well, the first module of the Youth Compass is now open! We hope that you enjoy the exercises and chat!
Introduction
Getting started: Introduction
Game: Interrail adventure trip, part I
Module 1: Direction For Life
Values: Taking a step toward what brings energy, well-being, and joy.
Level 1: What Is Important To Me
Level 2: What Do I Want To Achieve?
Level 3: What Stops Me?
Game: Interrail adventure trip, part II
Virtual coach SMS message: Hello, I noticed that you have completed some of the exercises in the Youth Compass! Great!
Virtual coach SMS message: The second module in the Youth Compass is now open! It's about thoughts this week. I hope you like it!https://nuortenkompassi.fi/programme/nk/e2/minamieli/
Module 2: Me And My Mind
Cognitive defusion:
Exploring the kinds of automatic thoughts and feelings I have.
Level 1: Mind Is Like …
Level 2: Distance to Thoughts
Level 3: Testing Thoughts
Game: Interrail adventure trip, part I
2nd videoconferencing session (doxy.me) Duration: 45 min
How are you doing?
Interview covering values, value-based actions, and cognitive defusion No session
Virtual coach SMS message (Reminder): Hi! Just to remind you of the Youth Compass exercises. Listen to some of the recordings a couple of times; they are really good! For example:https://nuortenkompassi.fi/programme/nk/e2/mielikuin/ferrari/
Virtual coach SMS message (Notification): The third module is now open in the Youth Compass! So you are now halfway through. There will be some nice exercises this week to help you focus and calm down.
Module 3: Me in the Now
Present moment and acceptance: Taking a new stance toward my thoughts and feelings
Level 1: Observe
Game: Interrail adventure trip, part III
Level 2. In This Moment:
Level 3: Testing Out In Practice
Virtual coach SMS message (Notification): Hello! I noticed that you have already done many exercises this week! You may want to try them without sound, for example, while you're taking a walk.
Virtual coach SMS message (Notification): Hi! The next to last module is now open! If you sometimes feel insecure or are terribly self-critical, we will talk about it this week:https://nuortenkompassi.fi/vara/nk/e4/Good luck in the Youth Compass and otherwise!
Module 4: Me Myself
Self-as-a-context and self-compassion: Perceptions of myself and learning to take a different perspective on them.
Level 1: My Own Story:
Level 2: Changing the Perspective
Game: Interrail adventure trip, part IV
Level 3: Friend To Yourself
Virtual coach SMS message (Reminder): I'd like to remind you that there are now super nice exercises in the Youth Compass dealing with not being so harsh on yourself. I really like this one:https://nuortenkompassi.fi/program//kk/e4/tarina/tunnistatarina/
Virtual coach SMS message (Notification): The last module is now open in the Youth Compass! This week is the last:(I hope you liked the program and will also like this final module!
Module 5: Me And Other PeopleLevel 3: Testing Out In Practice
ACT-process: Value-based actions, compassion toward others
Promoting good relationships with my friends and other people
Level 1: Friend To Yourself and Others
Level 2: In The World
Level 3: Challenging Situations
Game: Interrail adventure trip, part V
Virtual coach SMS message (Reminder): Hi, be sure to try the last week's exercises in the Youth Compass. There's a nice one about friends; seehttps://nuortenkompassi.fi/program//kk/e5/ystava/viest/
A closing SMS message sent by the coach: Thank you for joining the Youth Compass! I wish you all the best!
Closing feedback Feedback in writing for the whole program Feedback in writing for the whole program
The iACT Virtual Coach Group. The adolescents who were allocated to the iACT virtual coach group received a 15-min video call from the student coach using the doxy. me telemedicine application, in which they received a description of the virtual coaching procedures and an introduction to the built-in virtual coach. Thus, compared to the above described iACT student coach + virtual coach group, the support in this group was mainly technical. The coaches helped the participants log into the program, introduced basic functionalities, and gave an overview on the content. The adolescents were informed that the virtual coach would send them three weekly coaching SMS messages and asked to work independently in the Youth Compass program, supported only by conversations and SMS messages from the virtual coach. The messages from the virtual coach were adapted based on the adolescents’ progress in the program (Table 2).
The No-intervention Control Group. The control group followed the usual curriculum and was not offered additional intervention.
2.5 Coaches
The student coaches were ACT-trained psychology students (n = 27), each responsible for coaching approximately eight randomly chosen adolescents (four from either intervention group). They were final-year bachelor's or master's students with a mean age of 25.04 years (SD = 5.21, range 20–43 years). Except for one male student, the coaches were female (n = 26). The coaches were provided 11 h of training in the ACT methods (7 h of ACT methods and 4 h of the Youth Compass program). They had access to weekly group supervision (2 h) by a licensed psychologist and had to participate in a minimum of two supervision sessions (totaling 4 h). A total of 10 supervision sessions were offered during the intervention period. The purpose of the supervision was to ensure that all coaches followed the procedure as instructed and solve problems arising during the intervention.
2.6 Measures
2.6.1 Outcome measures
Anxiety. The short-form of the Spielberger State-Trait Anxiety Inventory (STAI; Spielberger et al., 1983; Marteau & Bekker, 1992) was used as main outcome measure to measure the adolescents' general anxiety. The inventory is based on a four-point Likert scale ranging from 0 (not at all) to 3 (very much so) and consists of six items. The scores range from 6 to 24, with higher scores indicating greater anxiety. To create scores compatible with the original STAI scores, the STAI-6 scores will be divided by 6 and multiplied by 20 to give a range from 20 to 80. The short-form of the STAI shows acceptable reliability and validity compared to the full-form (Marteau & Bekker, 1992). In this study, Cronbach's alpha was .80 at the pre- and 0.81 at the post-measurement.
Depressive Symptoms. The participants completed the Depression Scale (DEPS; Salokangas et al., 1995; see Kiuru et al., 2012), which consists of 10 items describing depressive symptoms experienced in the last month. The item response categories range from 0 (not at all) to 3 (very much). The total score ranges between 0 and 30, with higher scores indicating more depressive symptoms. A cut-off score of 9 or higher identified 85% of cases of elevated depression symptoms, and the proportion of correctly diagnosed cases of clinical depression was 74% (Salokangas et al., 1995). In the present study, Cronbach's alpha was .93 at the pre- and 0.91 at the post-measurement.
2.6.2 Processes of change measures
Psychological Flexibility. Psychological flexibility was measured by the Comprehensive assessment of Acceptance and Commitment Therapy processes (CompACT), which, in addition to the total score, measures three sub-processes of psychological flexibility: openness to experience (OE; acceptance, defusion), behavioral awareness (BA; present moment, self-as-context), and valued action (VA; values, committed action; Francis et al., 2016). Participants rate 23 items on a seven-point Likert scale ranging from 0 (strongly disagree) to 6 (strongly agree). The total score ranges between 0 and 138, with higher scores indicating greater psychological flexibility. In this study, the scale demonstrated adequate internal consistency (OE, pre, α = 0.77; post, α = 0.79, BA, pre, α = 0.77; post α = .81, and VA, pre, α = 0.84; post, α = 0.88).
Self-Compassion. The Self-Compassion Scale–Short Form (SCS-SF; Raes et al., 2011) is a 12-item questionnaire that includes six subscales, with two items for each subscale: self-kindness versus self-judgment; common humanity versus isolation; and mindfulness versus over-identification. Responses are given on a five-point scale from 1 (almost never) to 5 (almost always). Higher scores indicate higher self-compassion. The SCS-SF is a reliable alternative to the long-form version, when looking at overall self-compassion scores (Raes et al., 2011). In the present study, Cronbach's alpha reliability coefficient was 0.79 at the pre- and 0.83 at the post-measurement.
2.6.3 Adherence
Our results before the pandemic suggested that Youth Compass was helpful for increasing academic buoyancy and life satisfaction as well as reducing stress and depression in adolescents who had completed at least three of the five intervention modules (Lappalainen et al., 2021, Puolakanaho et al., 2019). Thus, in the present study, the adolescents were instructed to complete at least six recommended exercises in each of the five weekly modules; therefore, the minimum intended use (see Sieverink et al., 2017) totaled 30 exercises, which was at least 30% of the program content.
To investigate the impact of the intervention, subgroup analyses were conducted with respect to the adolescents allocated to the two iACT intervention groups who completed the pre-measurements (n = 154), and at least 30% of all the program content (n = 78). Thus, those who did not log in or completed only a part of the first and second module (n = 76, 49%; see Fig. 1) were excluded when completing the per-protocol analyses. Those who filled the adherence criteria (51%) had completed at least one module and half of an additional module or had made the minimum intended use of the program, defined by the completion of six recommended exercises in each weekly module. The recommendation for the minimal intended use was based on our previous study (Lappalainen et al., 2021) in which we investigated the former version of the Youth Compass.
2.6.4 Statistical analyses
Statistical analyses were conducted using SPSS and Mplus (version 8, Muthén & Muthén, 1998 – 2020–2020). Differences between the groups at baseline were investigated using SPSS. Pre-to post-measurement changes from the intervention and control groups were studied using latent change score (SEM) models. These models are equivalent to SPSS repeated measures ANOVA (Gardner, 2006), with the advantage of including all the available data in the analyses (also including participants with some missing values). The intervention results were analyzed using intention-to-treat and per-protocol analyses (Ranganathan et al., 2016). We investigated the effects of the two iACT interventions in comparison with the control group using intention-to-treat analyses, including adolescents who had filled in the pre-measurements (n = 234), and per-protocol analyses, including adolescents who had filled in the pre-measurements, signed into and completed (as recommended) at least 30% of the Youth Compass online program (n = 78 total, in the two iACT intervention groups), and those in the no-intervention control group (n = 80). We examined differences in the changes in psychological flexibility, self-compassion, anxiety, and depression in the three groups. The interaction effects were indicated in the form of Wald-test values (W) and p-values. Effect sizes (ES) regarding changes from the pre-to post-measurement were reported using Cohen's d. An effect size of d = 0.20 was considered small, d = 0.50 moderate, and above d = 0.80 large (Cohen & Williamson, 1988). Further, we investigated whether gender would explain the observed changes from the pre-to post-measurements. Thus, pre-to post-measurement changes from the intervention and control groups were studied using latent change score (SEM) models and testing whether the changes differed depending on gender. The differences in the background variables between the drop-outs and non-drop-outs were analyzed using t- and chi-square tests.
3 Results
3.1 Adherence
A total of 348 adolescents were randomly assigned to the iACT student coach + virtual coach group (n = 116), the iACT virtual coach group (n = 116), and the no-intervention control condition (n = 116; Fig. 1). Of these, 234 (67%) filled in the pre-measurement (iACT student coach + virtual coach group, n = 79; iACT virtual coach group, n = 75; control, n = 80). As 114 (33%) participants withdrew from the study between the randomization (n = 348) and pre-measurement (n = 243), chi-square tests were conducted to compare those who participated in the pre-measurement and those who withdrew from the study before completing the pre-measurement. We found a gender difference between the groups. Significantly more females participated in the pre-measurement (80.7%; n = 159) compared to males (53.4%, n = 79; chi-square 30.284, df = 3, p < 0.01). Further, 110 of the 154 adolescents (71%) from the two intervention groups logged into the Youth Compass online program, among whom 88 (80%) fulfilled the adherence criteria and completed at least 30% of the Youth Compass online program. A closer examination of user activity during the intervention period revealed that most of the adolescents (n = 64) completed 60–100% of the Youth Compass program. However, we detected that 78 of the 88 adolescents who fulfilled the adherence criteria had completed the pre-measurements, and therefore, the iACT student coach + virtual coach group ended up with 44 adolescents; the iACT virtual coach group had 34 adolescents; and the control group comprised 80 adolescents (totaling n = 158). Post-measurement data were available from 137 adolescents in total, indicating that close to 60% (58.5%) of those who participated in the pre-measurement also completed the post-measurement (Fig. 1). The intervention group completing at least 30% of the program (n = 78) included fewer boys than in the control group (chi-square 6.484, df = 2, p = 0.039). The adolescents gave no reasons for dropping out of the study.
3.2 Intervention effects
An analysis of the adolescent sample, that is, those who had filled in the pre-measurements (intent-to-treat analysis, n = 234) revealed that neither of the two intervention groups (iACT student coach + virtual coach; iACT virtual coach) changed significantly differently compared to the control group with regard to psychological flexibility (CompACT total; W = 1.731, df = 2, p = 0.421), self-compassion (SCS-SF; W = 2.998, df = 2, p = 0.112), anxiety (STAI; W = 3.861, df = 2, p = 0.073), and depressive symptoms (DEPS; W = 2.052, df = 2, p = 0.179). In addition, the two intervention groups did not change significantly differently in terms of the abovementioned outcomes.
We then combined the two intervention groups and examined whether there were any changes among adolescents who had logged into the Youth Compass program and met the adherence criteria of completing at least 30% of the program (per-protocol analysis, intervention group, n = 78; control group, n = 80; total n = 158). We found that the iACT group showed a different change compared to the control group regarding to valued action (W = 4.19, df = 1, p = 0.020), self-compassion (SCS-SF; W = 3.55, df = 1, p = 0.030), and anxiety (STAI; W = 3.00, df = 1, p = 0.042, Table 3 ). Thus, a slight increase in the iACT group was found both in terms of valued action and self-compassion (within ES, VA: d = 0.08; SCS: d = 0.12), but they decreased in the control condition (within ES, VA: d = 0.17; SCS: d = 0.05). Anxiety showed a slight increase in the iACT group (within ES, d = 0.05); however, the symptoms of anxiety in the control group increased more significantly (within ES, d = 0.34). There was a similar trend in depression, but the interaction effect was not significant. The corrected between-group ES of all variables were very small or small (d = 0.03–0.30; Table 3).Table 3 Changes in psychological flexibility, self-compassion, anxiety and depression in the combined iACT intervention group (n = 78), and the Control group (n = 80) among the adolescents who completed at least 30% of the Youth Compass program.
Table 3 Pre Post
M (SD) M (SD) W (df = 1) p db dw
CompACT Total 2.08 0.072 −0.19
iACT Intervention 82.35 (18.13) 82.72 (21.41) −0.02
Control 83.35 (17.73) 80.40 (20.18) −0.16
CompACT VA 4.19 0.020 −0.26
iACT Intervention 33.42 (7.77) 34.09 (7.94) −0.08
Control 33.09 (7.40) 31.78 (7.76) 0.17
CompACT BA 0.05 0.415 −0.03
iACT Intervention 17.96 (6.11) 17.47 (6.18) 0.08
Control 19.04 (6.38) 18.37 (6.42) 0.11
CompACT OE 0.63 0.213 −0.15
iACT Intervention 30.96 (8.06) 31.30 (10.90) −0.04
Control 31.19 (8.03) 30.34 (10.18) 0.09
SelfCompassion (SCS) 3.55 0.030 −0.17
iACT Intervention 38.42 (7.75) 39.35 (7.74) −0.12
Control 39.53 (8.14) 39.11 (7.66) 0.05
Anxiety 3.00 0.042 0.30
iACT Intervention 12.07 (3.55) 12.26 (3.50) −0.05
Control 11.77 (3.71) 13.04 (3.85) −0.34
Depression 0.57 0.224 0.10
iACT Intervention 7.73 (6.69) 7.79 (6.06) −0.01
Control 7.02 (6.07) 7.71 (6.92) −0.11
db = between-group effect size dw = within-group effect size.
CompACT Total = Comprehensive assessment of acceptance and commitment therapy processes.
CompACT VA = CompACT Valued action.
CompACT BA = CompACT Behavioral Awareness.
CompACT OE = Comp ACT Openess to Experiences.
Self-Compassion = The Self-Compassion Scale–Short form (SCS-SF).
Since the intervention group that completed at least 30% of the program (n = 78) included fewer boys than in the control group, we performed further analyses and investigated whether boys and girls changed differently during the intervention. The analyses indicated that adolescent boys and girls showed similar changes regarding the measured variables from the pre-to post-measurement.
We investigated more closely those adolescents who had provided complete data in the pre- and post-measurements as well as their user activity (n = 71). Interestingly, all those who did not fulfill our adherence criteria (at least 30% usage) belonged to the virtual coach group (chi-square 7.550, df = 1, p = 0.006). Further, we investigated whether those who had received a greater proportion of the intervention (80–100% usage, n = 35) would show larger changes (effect sizes) in the process variables compared to those with a lower user percentage (0–79%, n = 36, and 30–79%, n = 29). The cutoff was the median value (79; see Table 4 ). Compared to the control group, the high-usage group (80–100%) showed larger changes in total CompACT and openness to experiences than the lower-usage group (0–79% and 30–79%). In value-based action, both the low- and high-usage groups showed larger changes compared to the control group. A comparison between the high- and lower-usage groups revealed that the high-usage group showed larger changes in total CompACT and openness to experiences. Thus, those who received a greater proportion of the intervention showed larger changes, especially in openness to experiences. Further, there was a dose-response between the level of use of the program and the magnitude of changes in symptoms of depression and anxiety. Higher levels of exposure to the program were associated with larger positive changes in symptoms. Change from pre to post in anxiety: User category 0–29% (n = 7), pre-post change = −1.29 (increase in symptoms); user category 30–79% (n = 29), pre-post change = −0.83 (increase in symptoms); user category 80–100% (n = 35), pre-post change = 0.34 (decrease in symptoms). Change from pre to post in depression: User category 0–29% (n = 7), pre-post change = −1.14 (increase in symptoms); user category 30–79% (n = 29), pre-post change = −0.48 (increase in symptoms); user category 80–100% (n = 35), pre-post change = 0.00 (no change).Table 4 Between group effect sizes of user activity. Left: Comparison between the control group (n = 80) and the intervention group divided into the low usage groups (0–79% and 30–79%), and the high usage group (80–100%). Right: Comparisons between high usage (80–100%) and the low usage groups (0–79% and 30–79%).
Table 4 Control group (n = 80) vs.
Intervention use categories High usage group (80–100%, n = 35) vs. lower usage categories
0–79% (n = 36) 30–79% (n = 29) 80–100% (n = 35) 0–79% (n = 36) 30–79% (n = 29)
CompACT Total 0.06 0.05 0.28 0.24 0.25
CompACT VA 0.35 0.32 0.21 0.12 −0.09
CompACT BA 0.04 −0.08 0.07 0.12 0.15
CompACT OE −0.20a −0.17a 0.38 0.62 0.58
SelfComp (SCS) 0.14 0.17 0.17 0.04 0.02
CompACT Total = Comprehensive assessment of acceptance and commitment therapy processes.
CompACT VA = CompACT Valued action.
CompACT BA = CompACT Behavioral Awareness.
CompACT OE = Comp ACT Openess to Experiences.
Self-Compassion = The Self-Compassion Scale–Short form (SCS-SF).
a = change larger in the control group.
In addition, we investigated whether levels of psychological flexibility (CompACT Total) and self-compassion (SCS) predicted changes in depression and anxiety. The analyses suggested that the level of psychological flexibility but not the level of self-compassion predicted changes in symptoms of depression (F (1,69) = 5.911, p = 0.18, Adjusted R Square = 0.066). Lower levels of psychological flexibility at pre-measurement were associated with larger changes in depression symptoms (r = −0.28, p = 0.018, n = 71). Thus, those adolescents who had lower levels of psychological flexibility benefitted more of the intervention in respect of depression. Neither levels of psychological flexibility nor self-compassion predicted the changes in anxiety.
4 Discussion
The aim of this study was to examine the effects of an ACT-based online intervention Youth Compass on symptoms of anxiety and depression, psychological flexibility, and self-compassion during the COVID-19 pandemic among 15-16-year-olds. No significantly different changes between the two intervention groups compared with the no-treatment control group were detected in the randomized sample. As our earlier studies (Lappalainen et al., 2021, Puolakanaho et al., 2019) suggested, a more intense use of or engagement with the intervention was needed to obtain beneficial effects. Our hypothesis was that meeting the minimum adherence criteria (i.e., completing 30% of the program) would be associated with significant decreases in psychological symptoms and increases in psychological flexibility and self-compassion. This hypothesis was partially supported. When investigating the adolescents who met the adherence criteria, we found that valued action remained at the same level in the iACT intervention group, while the no-intervention group recorded a reduction in value-based action. Also, there was a slight increase in self-compassion in the iACT group and a slight decrease among the adolescents in the no-intervention group, with the latter also reporting an increase in anxiety and the former reporting significantly smaller increase in anxiety. Further, those who received a greater proportion of the intervention showed larger changes, especially in openness to experiences. There was also a dose-response between the level of use of the program and the magnitude of changes in symptoms of depression and anxiety. Higher levels of exposure to the program were associated with larger positive changes in symptoms.
Based on these results and consistent with recent studies on psychological flexibility in the context of COVID-19 (e.g., Crasta et al., 2020; Daks et al., 2020; Dawson & Golijani-Moghaddam, 2020; Xu et al., 2021), we propose that the psychological flexibility and self-compassion skills that the adolescents learned in the Youth Compass intervention may have protected against psychological distress caused by COVID-19. This was confirmed by the observation that anxiety increased only slightly in the iACT group, showing a significant, albeit small, effect on symptoms of anxiety in comparison to the control condition. Because of COVID-19 and the associated threats and fears regarding the future, the adolescents may have experienced more anxiety, which is in line with Ravens-Sieberer et al. (2021), who found higher levels of generalized anxiety in children and adolescents before versus during the pandemic but no significant increase in the prevalence of depressive symptoms. Indeed, those who engaged in the Youth Compass showed only a minor increase in anxiety compared to a larger increase in the control group. It is noteworthy that the data collection was carried out during the second wave of COVID-19. This potentially explains the increase in symptoms of anxiety among the adolescents. Fear and anxiety are natural reactions under stressful circumstances such as the COVID-19. Anxiety-induced rigidity may excessively narrow behavioral repertoires and restrict engagement in meaningful activities (Presti et al., 2020), whereas psychological flexibility skills may lessen entanglement with worry and anxious thoughts and, despite anxiety, lead to engagement in valued actions. Our results support this view and are consistent with those of Smith et al. (2020), who suggested that high levels of psychological flexibility and tolerance of uncertainty had a protective effect on participants’ anxiety during the pandemic (see also Pakenham et al., 2020).
Studies have shown that increasing young people's engagement in meaningful activities, that is, living consistently with their personal values, may protect against the effects of various stressors on psychosocial functioning and help them improve their well-being (Grégoire et al., 2021; Miller & Orsillo, 2020; Murrell & Kapadia, 2011). After all, the ultimate goal of ACT is behavior change, and other ACT processes are subordinated to helping individuals live according to their chosen values (Hayes et al., 1999; Zhang et al., 2018). In this sense, it is an encouraging finding that the ACT-based intervention positively impacted the adolescents' engagement in value-based actions during the pandemic, that is, in the face of external obstacles. However, partaking in meaningful actions during a lockdown situation may have been more challenging for adolescents than in normal circumstances as their social contacts were sparse and often limited to their immediate family and, occasionally, online contact with teachers and schoolmates. Nevertheless, more information about whether the adolescents in fact conducted meaningful actions and how would be valuable. Some studies conducted during the pandemic (e.g., Ellis et al., 2020; Gadermann et al., 2022; McArthur et al., 2021) found that adolescents who reported spending more time with family also reported less loneliness and fewer mental health symptoms; therefore, it would have been valuable to ascertain whether an increase in valued actions in our adolescent sample was associated with increased interaction and connectedness with family or peers or whether meaningful actions were related to increased exercise or more time for activities that they had not been able to pursue earlier, which were elsewhere reported by adolescents as positive impacts of the pandemic (Kerekes et al., 2021). Momentary data collection methods would have better captured the adolescents' actions on a daily basis and assess their clarity regarding their values, how committed they were to these values, and how consistently they put these values into action on a daily basis. Therefore, there is a need for future studies to track daily behaviors using methodologies such as ecological momentary assessments (EMAs) in order to examine psychological flexibility processes and assess adolescents' experiences when they are engaged in their daily routines.
The results indicate that among the adolescents who completed at least 30% of the Youth Compass program, the intervention had a positive impact on their self-compassion skills compared to those in the no-intervention condition. This suggests that self-compassion may be an important protective factor in alleviating the adverse impacts of the pandemic (see Jiménez et al., 2020; Lau et al., 2020). Self-compassion fosters emotional resilience, that is, psychological flexibility (Jiménez et al., 2020), which may increase the ability to respond more adequately to stressful situations, thereby helping adolescents deal effectively with the challenges posed by global emergencies such as COVID-19. Congruent with previous studies that have shown that brief compassion training via mobile applications and webpages can enhance well-being and self-compassion (Donovan et al., 2016; Eriksson et al., 2018), the findings of the present study suggest that an ACT-based program such as Youth Compass may offer promise in teaching adolescents ways to be kind to themselves in challenging situations, enabling them to see the global pandemic as something that they experience with other people around the world.
It should be noted that this was a universal study that included all interested adolescents. Therefore, some adolescents' psychological flexibility and self-compassion may have been on a relatively high level prior to the intervention. Another potential reason for not obtaining larger effects or significant results on the other variables of psychological flexibility and symptoms may be that the intervention period was relatively short, and engagement in the online program relied mainly on the adolescents' own activity. We used a rough 30% adherence measure, and we do not know de facto how engaged the adolescents really were. We can only speculate as to why the changes in the iACT group, which received more support (2 × 45 min), were not significantly larger than in the virtual coach group (15 min). Nevertheless, it is an important finding that more extensive support in online interventions does not automatically lead to better results, although brief contact with a coach was associated with fewer adolescents reporting low program usage. A short video call from a contact person combined with a virtual coach that keeps the adolescent engaged may be sufficient to produce a beneficial impact on well-being. However, follow-up studies are needed to ensure that the results will be maintained and will have a long-term protective effect on adolescents’ overall well-being.
Interestingly, openness to experiences increased for the adolescents who received a greater proportion of the intervention. This could be explained by the fact that modules 3 and 4 included materials related to observing skills, present moment awareness, acceptance, self-as-context, and self-compassion. Based on these findings, it is recommended that adolescents complete at least 80% of the intervention since the level of use is related to the benefits received from the program.
Regarding the drop-out rate in the study, the context of the COVID-19 pandemic meant that we were obliged to administer all the assessments online instead of the planned in-person paper-and-pencil assessment in schools. In addition, engaging in an online intervention mostly alone, with only a 15-min or 2 × 45-min video calls from a coach may not have been a compelling option for adolescents who occasionally attended school virtually and spent a great deal of time online. However, our drop-out rates were in line with those of other studies investigating digital mental health interventions. Drop-out rates above 20% are fairly common, as reported in a meta-analytic review by Garrido et al. (2019). In our sample, 30% of the adolescents did not log into the program, and among those who logged in, 80% fulfilled our adherence criteria. For example, a meta-analytic review suggested an attrition rate of 24% for a short-term follow-up in smartphone-delivered interventions for mental health problems (Linardon & Fuller-Tyszkiewicz, 2020).
The following limitations must be observed. First, withdrawals and drop-out rates of the magnitude experienced in the current study may threaten the validity and generalizability of our results. Over 80% of the randomized adolescents who participated in the pre-measurement were female, including most of the completers of the study (72%; intent-to-treat). Therefore, our results may not generalize to male adolescents. Second, it should be noted that the adolescents in the current study came from families in which parents were mostly well educated, with around half of the parents having undertaken university-level education. It is important to consider that our findings may not generalize to adolescents from more disadvantaged backgrounds. Future research should be conducted, for example, with a sample of adolescents belonging to less-educated families of a lower socioeconomic status. Finally, the use of self-report questionnaires should be considered a limitation. It is possible that some adolescents may not have been able to answer realistically, particularly in the CompACT measure of psychological flexibility.
Despite these limitations, the present study contributes to the body of knowledge in the field of interventions for adolescent populations. As noted earlier, only a few studies have applied ACT to adolescents during the COVID-19 lockdown. COVID-19 propelled important changes to mental health delivery, suggesting that brief and low-intensity interventions should become a priority in research and clinical practice (Gruber et al., 2021). According to the World Health Organization (2020), universally delivered psychosocial interventions should be provided to all adolescents and implemented in diverse settings or through digital platforms. In terms of delivery, schools are in the unique position of being able to reach young people and provide them with early interventions to promote their emotional health; thus, their role should be strengthened (see also Gee et al., 2021). The online-delivered Youth Compass program enabled us to reach the adolescents and offer them support during the pandemic when traditionally delivered support was unavailable. ACT approaches such as the Youth Compass arguably enable adolescents to acquire important psychological flexibility and self-compassion skills and support them in engaging with meaningful actions and acquiring a compassionate stance toward themselves under stressful circumstances. Building these skills may help adolescents develop strategies to cope with their struggles in challenging times, which they will be able to use throughout their lives.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
The study was funded with grants from the XXX (No. 324638) and the Center for Research for Learning and Teaching (MultiLeTe2).
☆ The study reported in this manuscript is part of an intervention study called the Youth Compass plus, which was funded with grants from the 10.13039/501100002341 Academy of Finland (No. 324638) and the Center for Research for Learning and Teaching (MultiLeTe2). The research plan of the Youth Compass plus was evaluated and approved by the ethics committee of the University of Jyväskylä (Nov. 20, 2019), and the Youth Compass plus intervention was registered at ClinicalTrials.gov.
☆☆ Data is available upon reasonable request.
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References
Biglan A. Hayes S.C. Pistorello J. Acceptance and commitment: Implications for prevention science Prevention Science: The Official Journal of the Society for Prevention Research 9 3 2008 139 152 10.1007/s11121-008-0099-4 18690535
Bluth K. Eisenlohr-Moul T.A. Response to a mindful self-compassion intervention in teens: A within-person association of mindfulness, self-compassion, and emotional well-being outcomes Journal of Adolescence 57 2017 108 118 10.1016/j.adolescence.2017.04.001 28414965
Bluth K. Gaylord S.A. Campo R.A. Mullarkey M.C. Hobbs L. Making friends with yourself: A mixed methods pilot study of a mindful self-compassion program for adolescents Mindfulness 7 2 2016 479 492 27110301
Branje S. Morris A.S. The impact of the COVID-19 pandemic on adolescent emotional, social, and academic adjustment Journal of Research on Adolescence : The Official Journal of the Society for Research on Adolescence 31 3 2021 486 499 34448306
Breines J.G. Chen S. Self-compassion increases self-improvement motivation Personality and Social Psychology Bulletin 38 9 2012 1133 1143 10.1177/0146167212445599 22645164
Calear A.L. Christensen H. Mackinnon A. Griffiths K.M. Adherence to the MoodGYM program: Outcomes and predictors for an adolescent school-based population Journal of Affective Disorders 147 1–3 2013 338 344 10.1016/j.jad.2012.11.036 23245469
Cohen S. Williamson G. Perceived stress in a probability sample of the US Spacapam S. Oskamp S. The social psychology of health: Claremont Symposium on applied social psychology 1988
Crasta D. Daks J.S. Rogge R.D. Modeling suicide risk among parents during the COVID-19 pandemic: Psychological inflexibility exacerbates the impact of COVID-19 stressors on interpersonal risk factors for suicide Journal of Contextual Behavioral Science 18 2020 117 127 10.1016/j.jcbs.2020.09.003 32923357
Daks J.S. Peltz J.S. Rogge R.D. Psychological flexibility and inflexibility as sources of resiliency and risk during a pandemic: Modeling the cascade of COVID-19 stress on family systems with a contextual behavioral science lens Journal of Contextual Behavioral Science 18 2020 16 27 10.1016/j. jcbs.2020.08.003 32834972
Das J.K. Salam R.A. Lassi Z.S. Khan M.N. Mahmood W. Patel V. Bhutta Z.A. Interventions for adolescent mental health: An overview of systematic reviews. The journal of adolescent health Official Publication of the Society for Adolescent Medicine 59 4S 2016 S49 S60 10.1016/j.jadohealth.2016.06.020
Dawson D.L. Golijani-Moghaddam N. COVID-19: Psychological flexibility, coping, mental health, and wellbeing in the UK during the pandemic Journal of Contextual Behavioral Science 17 2020 126 134 32834970
Donovan E. Rodgers R.F. Cousineau T.M. McGowan K.M. Luk S. Yates K. Franko D.L. Brief report: Feasibility of a mindfulness and self-compassion based mobile intervention for adolescents Journal of Adolescents 53 2016 217 221 10.1016/j.adolescence.2016.09.009
Ellis W.E. Dumas T.M. Forbes L.M. Physically isolated but socially connected: Psychological adjustment and stress among adolescents during the initial COVID-19 crisis Canadian Journal of Behavioral Science 52 2020 177 187 10.1037/cbs0000215
Enrique A. Palacios J.E. Ryan H. Richards D. Exploring the relationship between usage and outcomes of an Internet-based intervention for individuals with depressive symptoms: Secondary analysis of data from a randomized controlled trial Journal of Medical Internet Research 21 8 2019 e12775 10.2196/12775
Eriksson T. Germundsjö L. Åström E. Rönnlund M. Mindful self-compassion training reduces stress and burnout symptoms among practicing psychologists: A randomized controlled trial of a brief web-based intervention Frontiers in Psychology 9 2018 2340 2349 10.3389/fpsyg.2018.02340 30538656
Fang S. Ding D. A meta-analysis of the efficacy of acceptance and commitment therapy for children Journal of Contextual Behavioral Science 15 2020 225 234
Ferrari M. Hunt C. Harrysunker A. Abbott M.J. Beath A.P. Einstein D.A. Self-compassion interventions and psychosocial outcomes: A meta-analysis of RCTs Mindfulness 10 2019 1455 1473 10.1007/s12671-019-01134-6
Fonseca S. Trindade I.A. Mendes A.L. Ferreira C. The buffer role of psychological flexibility against the impact of major life events on depression symptoms Clinical Psychologist 24 1 2019 10.1111/cp.12194
Francis A.W.L. Dawson D.L. Golijani-Moghaddam N. The development and validation of the comprehensive assessment of acceptance and commitment therapy processes (CompACT) Journal of Contextual Behavioral Science 5 3 2016 134 145 10.1016/j.jcbs.2016.05.003
Gadermann A. Thomson K. Gill R. Schonert-Reichl K.A. Gagné Petteni M. Guhn M. …Oberle E. Early Adolescents’ Experiences During the COVID-19 Pandemic and Changes in Their Well-Being Frontiers in public health 10 2022 823303 10.3389/fpubh.2022.823303 35592082
Gardner R.C. Hierarchical Linear Modeling: A Primer1 (People within Groups) 2006 Retrieved from https://publish.uwo.ca/∼gardner/DataAnalysisDotCalm/Manuscripts/daHLMREMLpeoplewithingroupsNEWSPAIN.pdf
Garrido What works and what doesn't work? A systematic review of digital mental health interventions for depression and anxiety in young people 2019 10.3389/fpsyt.2019.00759
Gee B. Wilson J. Clarke T. Farthing S. Carroll B. Jackson C. King K. Murdoch J. Fonagy P. Notley C. Review: Delivering mental health support within schools and colleges – a thematic synthesis of barriers and facilitators to implementation of indicated psychological interventions for adolescents Child and Adolescent Mental Health 26 1 2021 34 46 32543016
Gilbert P. Procter S. Compassionate mind training for people with high shame and self-criticism: Overview and pilot study of a group therapy approach Clinical Psychology & Psychotherapy 13 2006 353 379
Gillanders D.T. Bolderston H. Bond F.W. Dempster M. Flaxman P.E. Campbell L. Kerr S. Tansey L. Noel P. Ferenbach C. Masley S. Roach L. Lloyd J. May L. Clarke S. Remington B. The development and initial validation of the cognitive fusion questionnaire Behavior Therapy 45 1 2014 83 101 24411117
Gloster A.T. Meyer A.H. Lieb R. Psychological flexibility as a malleable public health target: Evidence from a representative sample Journal of Contextual Behavioral Science 6 2 2017 166 171 10.1016/j.jcbs.2017.02.003
Grégoire S. Doucerain M. Morin L. Finkelstein-Fox L. The relationship between value-based actions, psychological distress and well-being: A multilevel diary study Journal of Contextual Behavioral Science 20 2021 79 88 10.1016/j.jcbs.2021.03.006
Gruber J. Prinstein M.J. Clark L.A. Rottenberg J. Abramowitz J.S. Albano A.M. …Weinstock L.M. Mental health and clinical psychological science in the time of COVID-19: Challenges, opportunities, and a call to action American Psychologist 76 3 2021 409 426 10.1037/amp0000707 32772538
Gutiérrez-Hernández M.E. Fanjul L.F. Díaz-Megolla A. Reyes-Hurtado P. Herrera-Rodríguez J.F. Enjuto-Castellanos M. Peñate W. COVID-19 lockdown and mental health in a sample population in Spain: The role of self-compassion International Journal of Environmental Research and Public Health 18 4 2021 2103 10.3390/ijerph18042103 33670059
Hafstad G.S. Sætren S.S. Wentzel-Larsen T. Augusti E.-M. Social science research network 2020 Longitudinal change in adolescent mental health during the COVID-19 outbreak – A prospective population-based study of teenagers in Norway (SSRN Scholarly Paper ID 3727297) 10.2139/ssrn.3727297
Hawke L.D. Barbic S.P. Voineskos A. Szatmari P. Cleverley K. Hayes E. Relihan J. Daley M. Courtney D. Cheung A. Darnay K. Henderson J.L. Impacts of COVID-19 on youth mental health, substance use, and well-being: A rapid survey of clinical and community samples: Répercussions de la COVID-19 sur la santé mentale, l’utilisation de substances et le bien-être des adolescents: Un sondage rapide 19 d’échantillons cliniques et communautaires Canadian Journal of Psychiatry 65 10 2020 701 709 10.1177/0706743720940562 32662303
Hayes S.C. Luoma J.B. Bond F.W. Masuda A. Lillis J. Acceptance and commitment therapy: Model, processes and outcomes Behaviour Research and Therapy 44 1 2006 1 25 10.1016/j.brat.2005.06.006 16300724
Hayes S.C. Strosahl K.D. Wilson K.G. Acceptance and commitment therapy: An experiential approach to behavior change 1999 Guilford Press
Hayes S.C. Strosahl K.D. Wilson K.G. Acceptance and commitment therapy: The process and practice of mindful change 2nd ed. 2012 Guilford Press
Hayes S.C. Wilson K.G. Gifford E.V. Follette V.M. Strosahl K. Experiential avoidance and behavioral disorders: A functional dimensional approach to diagnosis and treatment Journal of Consulting and Clinical Psychology 64 6 1996 1152 1168 10.1037/0022–006X.64.6.1152 8991302
Hofmann S.G. Grossman P. Hinton D.E. Loving-kindness and compassion meditation: Potential for psychological interventions Clinical Psychology Review 31 2011 1126 1132 21840289
Jiménez Ó. Sánchez-Sánchez L.C. García-Montes J.M. Psychological impact of COVID-19 confinement and its relationship with meditation International Journal of Environmental Research and Public Health 17 2020 6642 32933019
Kerekes N. Bador K. Sfendla A. Belaatar M. El Mzadi A. Jovic V. Changes in adolescents' psychosocial functioning and well-being as a consequence of long-term covid-19 restrictions International Journal of Environmental Research and Public Health 18 2021 8755 10.3390/ijerph18168755 34444502
Kiuru N. Burk W.J. Laursen B. Nurmi J.E. Salmela-Aro K. Is depression contagious? A test of alternative peer socialization mechanisms of depressive symptoms in adolescent peer networks Journal of Adolescent Health 50 3 2012 250 255
Kroska E.B. Roche A.I. Adamowicz J.L. Stegall M.S. Psychological flexibility in the context of COVID-19 adversity: Associations with distress Journal of Contextual Behavioral Science 18 2020 28 33 32837889
Lappalainen R. Lappalainen P. Puolakanaho A. Hirvonen R. Eklund K. Ahonen T. Muotka J. Kiuru N. The youth compass – the effectiveness of an online acceptance and commitment therapy program to promote adolescent mental health: A randomized controlled trial Journal of Contextual Behavioral Science 20 2021 1 12 10.1016/j.jcbs.2021.01.007
Lau B.H.-P. Chan C.L.W. Siu-Man N. Self-compassion buffers the adverse mental health impacts of COVID-19-related threats: Results from a cross-sectional survey at the first peak of Hong Kong's outbreak Frontiers in Psychiatry 11 2020 1203 10.3389/fpsyt.2020.585270
Levin M.E. MacLane C. Daflos S. Seeley J. Hayes S.C. Biglan A. Pistorello J. Examining psychological inflexibility as a transdiagnostic process across psychological disorders Journal of Contextual Behavioral Science 3 3 2014 155 163 10.1016/j.jcbs.2014.06.003 29057212
Linardon J. Fuller-Tyszkiewicz M. Attrition and adherence in smartphone-delivered interventions for mental health problems: A systematic and meta-analytic review Journal of Consulting and Clinical Psychology 88 1 2020 1 13 10.1037/ccp0000459 31697093
Mallett R. Coyle C. Kuang Y. Gillanders D.T. Behind the masks: A cross-sectional study on intolerance of uncertainty, perceived vulnerability to disease and psychological flexibility in relation to state anxiety and wellbeing during the COVID-19 pandemic Journal of Contextual Behavioral Science 22 2021 52 62 10.1016/j.jcbs.2021.09.003 34603949
Marshall S.L. Parker P.D. Ciarrochi J. Sahdra B. Jackson C.J. Heaven P.C.L. Self-compassion protects against the negative effects of low self-esteem: A longitudinal study in a large adolescent sample Personality and Individual Differences 74 2015 116 121 10.1016/j.paid.2014.09.013
Marsh I.C. Chan S.W.Y. MacBeth A. Self-compassion and psychological distress in adolescents—a meta-analysis Mindfulness 9 2018 1011 1027 10.1007/s12671-017-0850-7 30100930
Marteau T.M. Bekker H. The development of a six-item short-form of the state scale of the Spielberger State-Trait Anxiety Inventory (STAI) British Journal of Clinical Psychology 31 3 1992 301 306 10.1111/j.2044-8260.1992.tb00997.x 1393159
Mastrotheodoros S. The effects of COVID-19 on young people's mental health and psychological well-being. Youth partnership between the European Commission and the Council of Europe in the field of youth 2021 https://pjp-eu.coe.int/documents/42128013/72351197/Effects-COVID-Youth-Mental-Health-Psychological-Well-Being.pdf/b2bc0c32-9fd5-0816-bce5-da9289cfca8c
Mattila E. Lappalainen R. Välkkynen P. Sairanen E. Lappalainen P. Karhunen L. Peuhkuri K. Korpela R. Kolehmainen M. Ermes M. Usage and dose response of a mobile acceptance and commitment therapy app: Secondary analysis of the intervention arm of a randomized controlled trial JMIR Mhealth Uhealth 4 3 2016 e90 10.2196/mhealth.5241 27468653
McArthur B.A. Racine N. McDonald S. Tough S. Madigan S. Child and family factors associated with child mental health and well-being during COVID-19 European Child & Adolescent Psychiatry 2021 1 11 10.1007/s00787-021-01849-9 2021
McCracken L.M. Badinlou F. Buhrman M. Brocki K.C. The role of psychological flexibility in the context of COVID-19: Associations with depression, anxiety, and insomnia Journal of Contextual Behavioral Science 19 2021 28 35 10.1016/j.jcbs.2020.11.003
Miller A.N. Orsillo S.M. Values, acceptance, and belonginess in graduate school: Perspectives from underrepresented minority students Journal of Contextual Behavioral Science 15 2020 197 206 10.1016/j.jcbs.2020.01.002
Murrell A.R. Kapadia V. Brief report on experiential avoidance and valuing in at-risk adolescents Behavioral Development Bulletin 17 1 2011 38 42 10.1037/h0100600
Muthén L. Muthén B.O. Mplus version 8 & Mplus users' guide 1998–2020 http://www.statmodel.com
Neff K.D. The development and validation of a scale to measure self-compassion Self and Identity 2 3 2003 223 250 10.1080/15298860309027
Neff K.D. Dahm K.A. Self-compassion: What it is, what it does, and how it relates to mindfulness Ostafin B.D. Robinson M.D. Meier B.P. 2015 Handbook of mindfulness and self-regulation 10.1007/978-1-4939-2263-5_10 Springer Science + Business Media
Neff K.D. Hsieh Y.-P. Dejitterat K. Self-compassion, achievement goals, and coping with academic failure Self and Identity 4 3 2005 263 287 10.1080/13576500444000317
Neff K.D. Kirkpatrick K.L. Rude S.S. Self-compassion and adaptive psychological functioning Journal of Research in Personality 41 2007 139 154 https://doi.org/10.1016/j.jrp.2006.03.004
Neff K. Tirch D. Self-compassion and ACT. Mindfulness, acceptance, and positive psychology: The seven foundations of well-being Kashdan T.B. Ciarrochi J. 2013 New Harbinger Publications, Inc
Pakenham K.I. Landi G. Boccolini G. Furlani A. Grandi S. Tossani E. The moderating roles of psychological flexibility and inflexibility on the mental health impacts of COVID-19 pandemic and lockdown in Italy Journal of Contextual Behavioral Science 17 2020 109 118 10.1016/j.jcbs.2020.07.003 32834969
Parola A. Rossi A. Tessitore F. Troisi G. Mannarini S. Mental health through the COVID-19 quarantine: A growth curve analysis on Italian young adults Frontiers in Psychology 11 2020 567484
Peltz J.S. Daks J.S. Rogge R.D. Mediators of the association between COVID-19-related stressors and parents' psychological flexibility and inflexibility: The roles of perceived sleep quality and energy Journal of Contextual Behavioral Science 17 2020 168 176 10.1016/j.jcbs.2020.07.001 32834971
Presti G. McHugh L. Gloster A. Karekla M. Hayes S.C. The dynamics of fear at the time of COVID-19: A contextual behavioral science perspective Clinical Neuropsychiatry 17 2 2020 65 71 34908970
Puolakanaho A. Lappalainen R. Lappalainen P. Muotka J.S. Hirvonen R. Eklund K.M. Ahonen T. Kiuru N. Reducing stress and enhancing academic buoyancy among adolescents using a brief web-based program based on acceptance and commitment therapy: A randomized controlled trial Journal of Youth and Adolescence 48 2 2019 287 305 10.1007/s10964-018-0973-8 30560515
Raes F. Pommier E. Neff K.D. Van Gucht D. Construction and factorial validation of a short form of the self-compassion scale Clinical Psychology & Psychotherapy 18 2011 250 255 21584907
Ranganathan P. Pramesh C.S. Aggarwal R. Common pitfalls in statistical analysis: Intention-to-treat versus per-protocol analysis Perspectives in Clinical Research 7 3 2016 144 146 27453832
Ravens-Sieberer U. Kaman A. Erhart M. Devine J. Schlack R. Otto C. Impact of the COVID-19 pandemic on quality of life and mental health in children and adolescents in Germany Vols. 1–11 2021 European Child & Adolescent Psychiatry 10.1007/s00787-021-01726-5 Advance online publication
Salokangas R.K. Poutanen O. Stengård E. Screening for depression in primary care. Development and validation of the depression scale, a screening instrument for depression Acta Psychiatrica Scandinavica 92 1 1995 10 16 7572242
She R. Wong K. Lin J. Zhang Y. Leung K. Yang X. Profiles of stress and coping associated with mental, behavioral, and Internet use problems among adolescents during the COVID-19 pandemic: A stratified random sampling and cluster analysis Frontiers in Public Health 10 2022 826911 10.3389/fpubh.2022.826911
Sieverink F. Kelders S.M. van Gemert-Pijnen J.E. Clarifying the concept of adherence to eHealth technology: Systematic review on when usage becomes adherence Journal of Medical Internet Research 19 12 2017 e402
Smith B.M. Twohy A.J. Smith G.S. Psychological inflexibility and intolerance of uncertainty moderate the relationship between social isolation and mental health outcomes during COVID-19 Journal of Contextual Behavioral Science 18 2020 162 174 10.1016/j.jcbs.2020.09.005 32953435
Spielberger C.D. Gorsuch R.L. Lushene R. Vagg P.R. Jacobs G.A. Manual for the state-trait anxiety inventory 1983 Consulting Psychologists Press
Strosahl K. Robinson P. Gustavsson T. Brief interventions for radical change: Principles and practice of focused acceptance and commitment therapy 2012 New Harbinger Publications
Swain J. Hancock K. Hainsworth H.C. Bowman J. Mechanisms of change: Exploratory outcomes from a randomised controlled trial of acceptance and commitment therapy for anxious adolescents Journal of Contextual Behavioral Science 4 1 2015 56 67
Välimäki M. Anttila K. Anttila M. Lahti M. Web-Based Interventions Supporting Adolescents and Young People With Depressive Symptoms: Systematic Review and Meta-Analysis JMIR mHealth and uHealth 5 12 2017 e180 10.2196/mhealth.8624 29222079
World Health Organization Guidelines on mental health promotive and preventive interventions for adolescents: Helping adolescents thrive 2020 https://www.who.int/docs/default-source/mental-health/guidelines-on-mental-health-promotive-and-preventive-interventions-for-adolescents-hat.pdf
Xu W. Shen W. Wang S. Intervention of adolescent’ mental health during the outbreak of COVID-19 using aerobic exercise combined with acceptance and commitment therapy Children and Youth Services Review 124 2021 105960 10.1016/j.childyouth.2021.105960
Zeng X. Chiu C.P.K. Wang R. Oei T.P.S. Leung F.Y.K. The effect of loving-kindness meditation on positive emotions: A meta-analytic review Frontiers in Psychology 6 2015 1 14 25688217
Zhang C.Q. Leeming E. Smith P. Chung P.K. Hagger M.S. Hayes S.C. Acceptance and commitment therapy for health behavior change: A contextually-driven approach Frontiers in Psychology 8 2018 2350 10.3389/fpsyg.2017.02350 29375451
| 36514308 | PMC9731646 | NO-CC CODE | 2022-12-15 23:15:29 | no | J Contextual Behav Sci. 2023 Jan 8; 27:34-44 | utf-8 | J Contextual Behav Sci | 2,022 | 10.1016/j.jcbs.2022.12.001 | oa_other |
==== Front
Poult Sci
Poult Sci
Poultry Science
0032-5791
1525-3171
Published by Elsevier Inc. on behalf of Poultry Science Association Inc.
S0032-5791(22)00692-7
10.1016/j.psj.2022.102398
102398
Article
Guanylate-binding protein 1 restricts avian coronavirus infectious bronchitis virus-infected HD11 cells
Ma Peng *†
Gu Kui *†
Wen Renqiao *†
Li Chao *†
Zhou Changyu *†
Zhao Yu *†
Li Hao *†
Lei Changwei *†
Yang Xin *†
Wang Hongning *†11
⁎ Animal Disease Prevention and Food Safety Key Laboratory of Sichuan Province, College of Life Sciences, Sichuan University, Chengdu, 610064, China.
† Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610064, China.
1 Corresponding author: Hongning Wang, College of Life Sciences, Sichuan University, NO. 29 Wangjiang Road, Chengdu, Sichuan, China, 610064.
9 12 2022
9 12 2022
10239817 8 2022
5 12 2022
© 2022 Published by Elsevier Inc. on behalf of Poultry Science Association Inc.
2022
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The Infectious Bronchitis Virus (IBV), a coronavirus, is a key avian pathogen that causes acute and highly infectious viral respiratory diseases. IBV is an enveloped, positive-sense RNA virus, and the host factors that restrict infection and replication of the virus remain poorly understood. Guanylate-binding protein 1 (GBP1), an interferon-gamma (IFN-γ)-inducible guanosine triphosphatase (GTPase), is a major player in host immunity and provides defense against viral replication. However, the role of chicken GBP1 (chGBP1) in the IBV-life cycle is not well understood. Therefore, this study aimed to reveal the potential role of IFN-γ-induced chGBP1 in mediating host anti-IBV infection responses. We identified the host restriction factor, chGBP1, in IBV-infected chicken macrophages HD11 cell lines. We showed that chGBP1 was upregulated by treatment with both IFN-γ and IBV in HD11 cells. chGBP1 inhibited IBV replication in a dose-dependent manner and enhanced IFN-γ anti-IBV activity. Importantly, the GTPase domain of chGBP1 played a pivotal role in its anti-IBV activity. Furthermore, chGBP1 interacts with IBV Nucleocapsids protein to degrade IBV-N protein through the autophagy pathway. Taken together, our results demonstrate a critical role of chGBP1 in anti-IBV in macrophages HD11 cells.
Key words
Infectious bronchitis virus (IBV)
Interferon-gamma
Guanylate-binding protein 1
GTPase activity
Nucleocapsids protein
==== Body
pmcINTRODUCTION
The infectious bronchitis virus (IBV) is the primary cause of the acute infection disease known as avian infectious bronchitis, which affects the respiratory and reproductive systems (Cook, et al., 2012). At present, infectious bronchitis in chickens exists in almost all countries in the world and presents a local epidemic trend. IBV belongs to the gammacoronavirus Coronaviridae (Wille and Holmes, 2020). IBV is a single-stranded RNA virus with an enveloped, positive-sense RNA genome without a segment. The IBV virion structure is similar to that of other coronaviruses, which consists of an envelope and a nucleocapsid (Li, et al., 2021). The envelope membrane contains spike (S), envelope (E), and membrane (M) proteins. The nucleocapsid (N) protein is to assemble with genomic RNA into the viral RNA-protein (vRNP) complex (Lu, et al., 2021). The whole IBV genome length is about 27.6 kb, and the genome composition is 5′UTR‒ORF1‒S‒ORF3‒E‒M‒ORF5‒N-3′UTR. Translation of ORF1 produces two large polypeptides, pp1a and pp1ab, which are cleaved by papain-like protease and 3C-like protease to produce non-structural proteins (Jackwood, et al., 2012; Snijder, et al., 2016; Yu, et al., 2017).
Upon infection with the virus, the host immune response is activated, inducing expression of restrict factors to inhibit the virus. The innate immune system plays a crucial role in limiting viral replication at the initial stage of the infection (Thaiss, et al., 2016). Host restrict factors play an indispensable role in IBV infection. IBV induces type I, II, and III IFN production following infection of chicken trachea and kidney tissues (Yang, et al., 2018). Moreover, macrophages play an important role in a host's innate and acquired immune responses to IBV infection. After IBV infection of the chicken macrophage HD11 cell lines and chicken peripheral blood mononuclear cell-derived macrophages, MHCII, Fc receptor, TLR3, IFN-α, CCL4, MIF, IL-1β, IL-6, and iNOS were significantly upregulated (Sun, et al., 2021). Whole-genome gene expression microarrays have shown that MX1, C1S, IRF7, TLR3, C1R, CCLi7, ISG12-2, and IFITM3 are all strongly upregulated in response to IBV infection (Smith, et al., 2015). However, expression patterns of other factors differed in response to different IBV strains and in different cell types (Chhabra, et al., 2018; Zhu, et al., 2020). Guanylate-binding proteins (GBPs), which are IFN-inducible guanosine triphosphatases (GTPases), are a conserved superfamily that includes human, murine, and so on (Praefcke, 2018; Tretina, et al., 2019). The GBP1 structure includes a globular N-terminal Large GTPase (LG) domain, followed by a helical domain, which is further subdivided into a middle domain and a C-terminal α12/13-domain. The LG domain is involved in GTPase activity and confers the main biochemical functions of GBPs (Cui, et al., 2021; Prakash, et al., 2000). GBP1 has been reported to restrict several viral infections in vitro, via its GTPase activity (Honkala, et al., 2019). Early studies showed that GBP1 mediated antiviral effects against vesicular stomatitis virus (VSV) and encephalitis myocarditis virus (EMCV). Compared with control cells, GBP1 overexpression attenuated the cytopathic effects of VSV and EMCV and produced fewer viral progeny (Anderson, et al., 1999). GBP1 also suppressed the replication of hepatitis C virus (HCV), classical swine fever virus (CSFV), and Kaposi's sarcoma-associated herpesvirus (KSHV) (Itsui, et al., 2006; Li, et al., 2016; Zou, et al., 2017).
However, viruses use various mechanisms to resist the antiviral effects of hosts. For instance, the HCV viral replicase nonstructural (NS) protein 5B and CSFV NS5A both interact with the GTPase domain of GBP1 to block its GTPase activity, thus antagonizing its antiviral effects (Itsui, Sakamoto, Kurosaki, Kanazawa, Tanabe, Koyama, Takeda, Nakagawa, Kakinuma, Sekine, Maekawa, Enomoto and Watanabe, 2006; Li, Yu, Li, Wang, Li, Zhang, Xia, Yang, Wang, Yu, Luo, Sun, Zhu, Munir and Qiu, 2016). GBP1 also inhibits KSHV through its GTPase activity and disturbs the natural cytoskeletal structure by interfering with actin filament formation. Additionally, KSHV encodes the E3 ligase RTA, which interacts with GBP1 and mediates its degradation (Zou, Meng, Ma, Liang, Sun and Lan, 2017). Yet, to date, the function of chicken GBP1 in the IBV life cycle has not been elucidated.
In this study, we aimed to investigated chGBP1 inhibits IBV replication in chicken macrophages and elucidate the mechanism of chGBP1 exert's antiviral response. We analyzed the expression of chIFN-γ after IBV infection. More importantly, chGBP1, as the interferon-stimulated gene produced by chIFN-γ activation, mediates antiviral effects. Our data show that the IBV infection activates chIFN-γ production, which in turn increases downstream chGBP1 expression via signal cascades. We found that chGBP1 inhibits IBV replication through N-terminal GTPase activity and C-terminal. In addition, we also found that chGBP1 interacts with IBV nucleocapsid (N) protein, which degrades N protein via the autophagy pathway. Our fingdings demonstrate the regulation and function of IFN-γ-induced chGBP1 in IBV-infected macrophages, reveal the potential role of chGBP1 in mediating host anti-coronavirus infection responses.
MATERIALS AND METHODS
Cells, Viruses and Antibodies
The chicken macrophages cell lines (HD11) and embryo fibroblast cell lines (DF-1) were cultured in Dulbecco's modified Eagle's medium (DMEM, Cellmax, Beijing, China) supplemented with 10% fetal bovine serum (FBS, Cellmax, Beijing, China) and 100 IU/mL penicillin‒streptomycin solution (Cellmax, Beijing, China). All cells were cultured in an incubator at 37°C and 5% CO2.
The IBV Beaudette strain (GenBank: DQ001339) was kindly gifted by Prof. Ding-Xiang Liu, South China Agricultural University. The IBV M41 strain and Qx strain was stored in our laboratory.
Mouse monoclonal antibodies specific for Flag, hemagglutinin (HA), β-actin, HRP-conjugated Affinipure Goat Anti-Mouse IgG(H+L) and HRP-conjugated Affinipure Goat Anti-Rabbit IgG(H+L) were purchased from Proteintech (Proteintech, Wuhan, China). Rabbit polyclonal antibodies specific for chGBP1 were generated in our laboratory.
Plasmid Construction
Full-length chicken IFIT5 (NM_001320422.2), DDX17 (XM_416260.8), ATF3 (XM_046939659.1), OASL (NM_001397447.1), MX1 (NM_204609.2), IRF1 (KC250010.1), IFITM10 (XM_046942295.1), RAB25 (XM_040690945.2) and IFI6 (NM_001001296.6) was amplified from HD11 cells and cloned into pcDNA3.1-Flag. Full-length chicken GBP1 (NM_204652.2)(Prakash, Praefcke, Renault, Wittinghofer and Herrmann, 2000) was amplified from HD11 cells and cloned into pcDNA3.1-Flag to generate pcDNA3.1-Flag-chGBP1. The gene sequences of chGBP1 N-terminal and C-terminal domains were cloned into pcDNA3.1-Flag to generate pcDNA3.1-Flag-chGBP11-306 and pcDNA3.1-Flag-chGBP1307-576. The mutated chGBP1 GTPase domains chGBP1 R48A and chGBP1 K52A were cloned into pcDNA3.1-Flag. The IBV N sequence was amplified from cDNA extracted from the IBV M41 strain and Beaudette strain, then cloned into pcDNA3.1-HA empty vectors. chGBP1 was fused to GFP and cloned into a pEGFP-C2. IBV-N was fused to mcherry and cloned into a pcDNA3.1-mcherry. The primers used for the construction of each plasmid are listed in table 1 .Table 1 Primers used in this study
Table 1Primers Forward (5’– 3’) Reverse (5’– 3’)
chIFIT5 CTCTAGAATGAGTACCATTTCCAAG CGGATCCGCTTGAGAGGGAAAGTCG
chDDX17 CGAATTCATGAGGGGCTTCGGGGA CGGATCCTTTGCGTGAGGGTGGAGG
chATF3 CTCTAGAATGCCGTTTAAGATTAA CGGATCCACCTTGTAATGTTCCTTC
chOASL GGGTACCATGGAGCTGGGCGTGAGG CTCTAGAGGAGGGCACGCAGCGTCT
chMX1 GGGTACCATGAACAATCCACGGT CTCTAGACAGAGACTTAAAGTCTACCAG
chIRF1 GGGTACCATGCCCGTCTCAAGGATG CTCTAGACAAGCTGCAGGAGATGG
chIFITM10 CGAATTCATGACAACAATGATAACAAA CTCTAGAGGTAATCGGTGAGGGGGTA
chRAB25 GGGTACCATGAGCAGCGCCGAGGAG CTCTAGAGATGGCCACGCAGCACGG
chIFI6 GGGTACCATGTCTGACCAGAACGTCC CTCTAGAGCGCCTTCCTCCTTTGCCA
chGBP1 CGGAATTCATGGACACTCCGGTGCT CCTCGAGTCAGAGTACAGTGCACTTGG
chGBP11-306 GGAATTCATGGACACTCCGGTG CGGATCCGCAGGGCACAGAGC
chGBP1307-576 GGAATTCATGGTGGAGAGTGCAGTGA CGGATCCGAGTACAGTGCACTTGGGT
IBV-N CGGATCCATGGCAAGCGGTAAGGCA CCCTCGAGTCAAAGTTCATTCTCTCCT
chGBP1GFP CGGAATTCATGGACACTCCGGTGCT CGGATCCTCAGAGTACAGTGCACTTGG
IBV-Nmcherry CGGATCCATGGCAAGCGGTAAGGCA CCCTCGAGTCAAAGTTCATTCTCTCCT
qchIFN-γ ACACTGACAAGTCAAAGCCGCACA AGTCGTTCATCGGGACCTTGGC
qchGBP1 AAGTCCTTCCTGATGAACC CTTGGTCTCCGCATACAC
qIBV-N GAAGAAAACCAGTCCCAGA TTACCAGCAACCCACAC
GAPDH CATCACAGCCACACAGAAG GGTCAGGTCAACAACAGAGA
Cell Transfection
The cells were plated in 6-well plates (5×105 cell/mL) for 12 h. Then, a Lipofectamine 8000 (Beyotime Biotech, Beijing, China) was used with the indicated amount of expression construct, according to the manufacturer's instructions. In the same experiment, empty control plasmids were added to ensure that each transfection received equal amounts of total DNA. After transfection, cells were collected and transfection efficiency was measured by western blotting.
In order to measure the different dose of chGBP1 for anti-IBV replication, HD11 cells was transfected with pcDNA3.1-Flag-chGBP1 300ng, 500ng, or 1000ng for 24 h.
In order to detect the effect of chGBP1 on the stability of IBV-N protein. HD11 cells were co-transfected pcDNA3.1-Flag-chGBP1 1000ng with either pcDNA3.1-HA-IBVBeau-N 1000ng, or pcDNA3.1-HA-IBVM41-N 1000ng for 24 h. Further, to explore the effect of different doses of chGBP1 on the level of IBV-N protein. HD11 cells were co-transfected with different concentrations of pcDNA3.1-Flag-chGBP1 1000ng or 2000ng and pcDNA3.1-HA-N 1000ng for 24 h.
Antiviral Genes Screening Assay
The cells were plated in 6-well plates (5×105 cell/mL) for 12 h. HD11 cells were transfected with plasmids pcDNA3.1-Flag-chIFIT5 (1000ng), pcDNA3.1-Flag-chDDX17 (1000ng), pcDNA3.1-Flag-chATF3 (1000ng), pcDNA3.1-Flag-chOASL (1000ng), pcDNA3.1-Flag-chMX1 (1000ng), pcDNA3.1-Flag-chGBP1 (1000ng), pcDNA3.1-Flag-chIRF1 (1000ng), pcDNA3.1-Flag-chIFITM10 (1000ng), pcDNA3.1-Flag-chRAB25 (1000ng) or pcDNA3.1-Flag-chIFI6 (1000ng) for 24h. Then, infected with MOI=5 of IBV Beaudette strain and harvested cells after 24h, qRT-PCR detect IBV N gene mRNA.
Enzyme-Linked Immunosorbent Assay (ELISA)
In order to measure the expression of chIFN-γ after IBV infection, HD11 cells was infected with IBV Beaudette strain at MOI=5. Following viral infection, the cell culture supernatants were collected at 0 h, 6 h, 12 h, 24 h, 36 h, and 48 h. Using chicken IFN-γ ELISA kits (MEIMIAN Industrial, Jiangsu, China), as according to the manufacturer's instructions. For significance analysis of the values obtained by ELISA, the 0 h value was used as the comparative value.
50% Tissue Culture Infective Dose (TCID50)
IBV virus titer was quantified using a TCID50 assay. Briefly, 10-fold dilutions of IBV were inoculated into HD11 cells grown in a 96-well tissue culture plate at 1000 cells/well. The plate was incubated at 37°C for 5 days, followed by observation of the cytopathic effect in each well under light microscopy. TCID50 was calculated using the Reed–Muench method (Reed, and Muench, 1938).
RNA Interference Assay
To investigate the function of chGBP1, we silenced its expression through siRNA. siRNA-1# and siRNA-2# target nucleotide sequences of the N-terminal GTPase domain of chGBP1, and siRNA-3# target nucleotide sequences of the C-terminal domain of chGBP1. All siRNAs used in this study were designed and synthesized by Sangon Biotech (Sangon Biotech, Shanghai, China) target chGBP1 coding region. The siRNA sequences used in this study are listed in Table 2 .Table 2 siRNA sequence used in this study
Table 2siRNA Forward (5’– 3’) Reverse (5’– 3’)
siRNA-ctrl UUCUCCGAACGUGUCACGUTT ACGUGACACGUUCGGAGAATT
siRNA-1# CCUGGUGUACAACAGCAUUTT AAUGCUGUUGUACACCAGGTT
siRNA-2# CCAACUUUGUCAGCAUCUUTT AAGAUGCUGACAAAGUUGGTT
siRNA-3# GCACAUGGCUGGAGGAGCATT UGCUCCUCCAGCCAUGUGCTT
To analyze the effect of chGBP1 siRNA. HD11 cells (5×105 cell/mL) were seeded in 6-well plates at 50% confluency. Cells were transfected with siRNA-ctrl, siRNA-1#, siRNA-2#, or siRNA-3# (100 pmol/well) using a Lipofectamine 8000 (Beyotime Biotech, Beijing, China) and stimulated with chIFN-γ (100ng/mL) for 6 h. The treated cells were incubated for 48 h. Then, cells were harvested and detected by western blotting or quantitative real-time polymerase chain reaction (qRT-PCR) assay.
GTPase Enzyme-Linked Inorganic Phosphate Assay (ELIPA)
To determine the chGBP1 GTPase activity and the effect of mutants on GTPase activity, HD11 cells (5×105 cell/mL) were seeded in 6-well plates and cultured for 12 h; transfected with pcDNA3.1-Flag-chGBP1, pcDNA3.1-Flag-chGBP1R49A, pcDNA3.1-Flag-chGBP1K52A, pcDNA3.1-Flag and harvested at 36 h. An ATPase/GTPase enzyme-linked inorganic phosphate assay (ELIPA) kit (catalog no. BK051/BK052, Cytoskeleton, Denver, CO) was used to measure the amount of inorganic phosphate generated (absorbance at 360 nm) according to the manufacturer's instructions.
Confocal Fluorescence Microscopy
The cells were seeded on glass coverslips in 35-mm cell culture dishes and cultured overnight. Thereafter, the HD11 cells were co-transfected with pEGFP-C2-chGBP1 and pcDNA3.1-mcherry-N. After 24 h, the cells were washed three times with cold phosphate buffered saline (PBS) and fixed with 4% paraformaldehyde for 15 min at room temperature. Subsequently, the cells were incubated with DAPI at 37°C for 10 min and washed with cold PBS. Finally, images were captured using a laser scanning confocal microscope (Zeiss, Jena, Germany).
Co-Immunoprecipitation Assays
HD11 cells cultured in 6-well plates were co-transfected with the following plasmids: pcDNA3.1-Flag-chGBP1, pcDNA3.1-Flag-chGBP11-306, pcDNA3.1-HA-chGBP1307-576, and pcDNA3.1-HA-N using Lipofectamine 8000 (Beyotime). After transfected 24 h, the cells were harvested and lysed by RIPA lysis buffer (500μL/well) containing 1 mM PMSF (Beyotime). After incubation on ice for 30 min, the cell lysates were centrifuged at 13000 g for 30 min. Approximately 25% of the supernatant was subjected to input assays, and the remainder was used for a Co-immunoprecipitation (Co-IP) assay with an anti-Flag agarose affinity gel (Beyotime) according to the manufacturer's instructions. Briefly, 50 µL of the agarose affinity gel was centrifuged for 30 s at 4°C to remove glycerol and was washed with cold TBS. The cell lysate was added to the equilibrated resin and gently rocked on a rotating platform at 4°C overnight. The resin was washed with cold TBS, and the protein samples were evaluated by western blotting.
Protein Extraction and Western Blot
Cultured cells in 6-well plates post tranfected were harvested and lysed by RIPA lysis buffer (500μL/well) containing 1 mM PMSF (Beyotime). After incubation on ice for 30 min, the cell lysates were centrifuged at 13000 g for 30 min, and collected the supernatant. Protein concentration was quantified using the BCA protein concentration assay kit (Beyotime). Finally, 30μg protein was taken for Western blot detection.
Cultured cells were lysed in RIPA lysis buffer for 30 min on ice. The SDS-PAGE sample loading buffer was added, and the samples were heated for 5 min at 95°C. The samples were subjected to 10% SDS-PAGE at 120 V for 100 min. The protein was transferred to a PVDF membrane (pore size,0.22 μM; Beyotime Biotech, Beijing, China) for 1.5 h at 200 mA. Afterwards, the membrane was blocked with a blocking buffer containing 1% BSA for 1 h at room temperature. It was then incubated overnight at 4°C with a primary antibody (1:1000 dilution). The membrane was then washed three times and subsequently incubated with a secondary antibody (1:2000 dilution) for 1 h at room temperature. After three washes, protein bands were detected using BeyoECL Moon chemiluminescent system (Beyotime).
RNA Extraction and Quantitative Real-Time Polymerase Chain Reaction
Total RNA from cultured cells were isolated by RNAiso Plus reagent (Takara, 9109) according to the manufacturer's instruction. The RNA (1μg) was further processed using RevertAid First Strand cDNA Synthesis kit (Thermo Fisher, K1621) to produce cDNA in accordance with the manufacturer's instructions. qRT-PCR was performed using a Bio-Rad system (Hercules, CA). ChamQ SYBR qPCR Master Mix (Vazyme Biotech, Nanjing, China) and gene-specific primers were used in a 20-μL volume. The samples were heated to 95°C for 10 min, followed by 40 cycles of PCR involving 10 s at 95°C, 20 s at 60°C and 15 s at 72°C. The primers name of qchIFN-γ was used to detect chIFN-γ mRNA level. The primers name of qchGBP1 was used to detect chGBP1 mRNA level (Table 1). The primer name of qIBV-N was used to detect IBV replication level (Table 1). The glyceraldehyde-3-phosphate dehydrogenase gene (GAPDH) was used as a housekeeping gene to normalize (relative) gene expression using the 2−ΔΔCT formula. The qRT-PCR primers used in this study are listed in Table 1.
Statistical Analysis
Statistical analysis was performed using GraphPad Prism 5 software (Mann‒Whitney test, one-way and two-way ANOVAs test; GraphPad Prism software, GraphPad Software Inc., La Jolla, CA). Statistical significance was set at P < 0.05.
RESULTS
Screening of antiviral genes against IBV replication in HD11 cells
To screen the antiviral genes with anti-IBV activity in chicken macrophages, we constructed ten chicken genes expression plasmids with Flag-tag. Notably, overexpression chIFIT5, chDDX17, chATF3, chOASL, chMX1, chGBP1, chRAB25 and chIFI6 significantly reduced IBV mRNA level (Figure 1 ). Interestingly, chGBP1 (Chicken Guanylate-Binding Protein 1), an interferon induced interferon-stimulated gene, showed the highest inhibitory efficiency on IBV replication.Figure 1 Screening of antiviral genes against IBV replication in HD11 cells. HD11 cells were transfected with plasmids pcDNA3.1-Flag-chIFIT5, pcDNA3.1-Flag-chDDX17, pcDNA3.1-Flag-chATF3, pcDNA3.1-Flag-chOASL, pcDNA3.1-Flag-chMX1, pcDNA3.1-Flag-chGBP1, pcDNA3.1-Flag-chIRF1, pcDNA3.1-Flag-chIFITM10, pcDNA3.1-Flag-chRAB25 or pcDNA3.1-Flag-chIFI6 for 24 h. Then infected with IBV at MOI=5 and harvested cells after 24 h, qRT-PCR detect IBV N gene mRNA.
Figure 1
IBV or chIFN-γ Induced Upregulation of chGBP1 Expression
GBP1 is one of the IFN-γ inducible ISGs. Upon investigation, we found that IBV infection significantly induced chGBP1 expression in HD11 cells (Fig. 2 A). Furthermore, HD11 cells treated with chIFN-γ (100ng/mL) showed similar results to those obtained with IBV infection (Fig. 2B). Additionally, western blotting showed that IBV infection could stimulate chGBP1 protein expression, similar to the changes at transcription levels (Fig. 2C and 2D). These results indicate that both chIFN-γ and IBV infection can stimulate chGBP1 expression in HD11 cells.Figure 2 IBV or chIFN-γ induce chGBP1 expression in HD11 cells. (A) HD11 cells infected with IBV at MOI=5 were harvested after 0 h, 6 h, 12 h, 18 h, 24 h, and 36 h, qRT-PCR detect chGBP1 mRNA. (B) HD11 cells treated with chIFN-γ (100ng/mL) were harvested after 6 h, qRT-PCR detect chGBP1 mRNA. (C-D) All the protein level measure using anti-chGBP1 antibodies. β-actin was used as a loading control. Data were presented as means ± SD. ** = P < 0.01, *** = P < 0.001, **** = P < 0.0001.
Figure 2
IBV Induced Chicken IFN-γ Production in Macrophages HD11 Cells
Given the significant role of type II IFN in controlling virus infections, and to verify IBV-induced type II IFN production, we verified chIFN-γ expression in IBV-infected chicken macrophages HD11 cells. chIFN-γ expression levels were significantly upregulated (Figure 3 A) after IBV infection of HD11 cells. Moreover, ELISA detection of chIFN-γ at protein level showed similar results (Figure 3B). Thus, we confirmed that chIFN-γ is expressed in response to IBV infection of chicken macrophages HD11 cells.Figure 3 IBV infection induced chIFN-γ production in macrophages HD11 cells. HD11 cells were infected with IBV at MOI=5 and then harvested cells and cell culture supernatants after 0 h, 6 h, 12 h, 24 h, 36 h, and 48 h. (A) chIFN-γ mRNA levels production was analysised by qRT-PCR. (B) chIFN-γ protein levels was detected by ELISA. Data were presented as means ± SD. ** = P < 0.01.
Figure 3
Knockdown chGBP1 Promoted IBV Replication
To clarify the function of chGBP1 further, we knockdown chGBP1 expression in HD11 cells. The results showed that siRNA-2# efficiently downregulated chGBP1 at both the mRNA and protein levels (Fig. 4 A and 4B). Consistently, siRNA knockdown cells were infected with IBV and used qRT-PCR targeting the IBV-N gene to detect the virus replication. The replication level and viral titers of IBV were increased in chGBP1 knockdown cells (Fig. 4C and 4D). Thus, chGBP1 is involved in the host antiviral response to restrict IBV infection.Figure 4 The loss of chGBP1 promotes IBV replication. HD11 cells were transfected with negative control siRNA (siRNA-ctrl) or siRNA against chGBP1 with chIFN-γ (100ng/mL) treatment 6 h. (A) chGBP1 protein expression was analyzied by Western blotting with anti-chGBP1and anti-β-actin antibodies. (B) The mRNA of chGBP1 was quantified by qRT-PCR using GAPDH as the reference gene. (C) HD11 cells were transfected with negative control siRNA (siRNA-ctrl), siRNA-2# or NC (It doesn't contain any siRNA) for 48 h and then infected with IBV at MOI=5, harvested cells after 36 h. Virus RNA levels were quantified by qRT-PCR. (D) Virus titers in the supernatants were analyzed by a TCID50 assay. Data were presented as means ± SD. * = P < 0.05, ** = P < 0.01, ns=not significant.
Figure 4
chGBP1 Inhibited IBV Replication and Enhanced IFN-γ-mediated Inhibition of IBV Replication
In order to investigated whether chGBP1 was involved in the response of macrophages to IBV infection by overexpressing chGBP1 in HD11 cells. We found that, compared with the empty vector, overexpressed chGBP1 inhibited viral replication significantly more (Fig. 5 A). TCID50 was significantly reduced by chGBP1 (Fig. 5B). Also, chGBP1 repressed IBV replication and reduced virus titers in DF-1 cells (Fig. 5C and 5D) and chicken embryo fibroblasts CEF cells (Fig. 5E and 5F). Next, we overexpressed chGBP1 at different doses in HD11 cells (Fig. 5G) and simultaneously used qRT-PCR targeting the IBV N gene to detect the virus. chGBP1 inhibited IBV replication in a dose-dependent manner (Fig. 5H).Figure 5 Overexpression chGBP1 inhibit IBV replication. (A) HD11 cells were transfected with pcDNA3.1-Flag-chGBP1 (1000ng) or pcDNA3.1-Flag (1000ng) for 24 h and then infected with IBV at MOI=5 were harvested at different points in time, qRT-PCR detect IBV N gene mRNA. (B) Virus titers in the supernatants were analyzed by a TCID50 assay. (C) DF-1 cells were transfected with pcDNA3.1-Flag-chGBP1(1000ng) or pcDNA3.1-Flag (1000ng) for 24 h, then infected with IBV at MOI=5, and harvested at different points in time. (D) Virus titers in the supernatants were analyzed by a TCID50 assay. (E) CEF cells were transfected with pcDNA3.1-Flag-chGBP1 (1000ng) or pcDNA3.1-Flag (1000ng) for 24 h, then infected with IBV at MOI=1, and harvested at different points in time. (F) Virus titers in the supernatants were analyzed by a TCID50 assay. (G) HD11 cells were transfected with pcDNA3.1-Flag-chGBP1 (300ng, 500ng, and 1000ng) or pcDNA3.1-Flag (1000ng) for 24 h, western blot measured protein. β-actin was used as a loading control. (H) HD11 cells transfected with pcDNA3.1-Flag-chGBP1 (300ng, 500ng, and 1000ng) or pcDNA3.1-Flag (1000ng) for 24 h and then infected with IBV at MOI=5 were harvested after 36 h, qRT-PCR detect IBV-N gene mRNA. (I) HD11 cells were transfected with pcDNA3.1-Flag-chGBP1 (1000ng) or pcDNA3.1-Flag (1000ng) for 24 h and then incubated with chIFN-γ (1000ng/ml) for 12 h. Subsequently, HD11 cells infected IBV at MOI=5 were harvested after 36 h, qRT-PCR detect IBV-N gene mRNA. Data were presented as means ± SD. ** = P < 0.01, *** = P < 0.001.
Figure 5
To verify whether chGBP1 was necessary for the effect of chIFN-γ against IBV-infection in HD11 cells, we treated HD11 cells with chIFN-γ in vitro. Treatment with chIFN-γ effectively restricted IBV infection, while chGBP1 enhanced the chIFN-γ antiviral effect in macrophages (Fig. 5I). Thus, chGBP1 is an important effector of chIFN-γ against IBV replication in HD11 cells.
N-terminal GTPase Domain of chGBP1 was Essential for anti-IBV Effects
The GBP1 protein has two structural domains, namely the N-terminal GTPase domain and the C-terminal helical domain (Fig. 6 A). Therefore, western blot confirmed that the chGBP1 full length, N-terminal (chGBP11-306) and C-terminal (chGBP1307-576) were expressed in HD11 cells (Fig. 6B). After IBV infection of HD11 cells, the chGBP1 GTPase domain significantly reduced viral replication (Fig. 6C). Taken together, our data indicated that the N-terminal GTPase domain of chGBP1 was essential for restricting IBV replication.Figure 6 the critical regions of chGBP1 to repress IBV replication. (A) According the structure of chGBP1, construction N-terminal GTPase domain and C-terminal helical domain expression plasmid. (B) HD11 cells were transfected with pcDNA3.1-Flag-chGBP11-306, pcDNA3.1-Flag-chBGP1307-576 or pcDNA3.1-Flag-chGBP1, the protein measured by western blot with anti-Flag antibodies. (C) HD11 cells were transfected with pcDNA3.1-Flag-chGBP1, pcDNA3.1-Flag-chGBP11-306, pcDNA3.1-Flag-chBGP1307-576 or pcDNA3.1-Flag and then infected with IBV at MOI=5 for 36 h. Virus RNA levels were quantified by qRT-PCR. (D) HD11 cells were transfected with pcDNA3.1-Flag-chGBP1, pcDNA3.1-Flag-chGBP1R49P, pcDNA3.1-Flag-chGBP1K52A, or pcDNA3.1-Flag for 24 h and then infected with IBV at MOI=5 for 36 h. The protein measured by western blot with anti-Flag antibodies. (E) Virus RNA levels were quantified by qRT-PCR. (F) HD11 cells were transfected with pcDNA3.1-Flag-chGBP1, pcDNA3.1-Flag-chGBP1R49P, pcDNA3.1-Flag-chGBP1K52A, or pcDNA3.1-Flag for 36 h. The GTPase activity was measured using the ELIPA kit at 360nm absorbance. Data were presented as means ± SD. * = P < 0.05, ** = P < 0.01, ns=not significant.
Figure 6
R49 and K52 were Critical for chGBP1-mediated anti-IBV
To further study the biological function of chGBP1, we constructed two mutant of chGBP1(R49A) and chGBP1(K52A). Western blot confirmed that the chGBP1 wild type, chGBP1(R49A) and chGBP1(K52A) were expressed in HD11 cells (Fig. 6D). We found that wild-type chGBP1 effectively reduced the level of IBV mRNA compared with the empty vector, and that both chGBP1 R49A and chGBP1 K52A mutants partially restored the level of viral replication (Fig. 6E). This indicated that both R49 and K52 are critical for controlling viral replication.
To analyze the enzymatic functions of the mutant chGBP1 R49A and chGBP1 K52A proteins, their GTPase activity was examined using ELIPA. GTPase activity was significantly higher in chGBP1-expressing cells than in empty vector-transfected cells. After HD11 cells were transfected with chGBP1 K52A and chGBP1 R49A constructs, the ELIPA test proved that the two mutants had reduced GTPase activity, as compared with cells with wild-type chGBP1 (Fig. 6F). These results suggest that the GTPase activity of chGBP1 is critical for inhibiting IBV replication, and that R49/K52 of chGBP1 are essential for anti-IBV activity.
chGBP1 Protein Degraded IBV N Protein Through Autophagy Pathway
We used co-immunoprecipitation (Co-IP) to analyze chGBP1- and IBV-interacting proteins. IBV nucleocapsid protein was identified as a chGBP1-interacting protein in IBV-infected HD11 cells. Subsequently, the interaction between chGBP1 and IBV-N protein was confirmed using Co-IP. The results showed that chGBP1 C-terminal domain interacted with IBV-N protein (Fig. 7 A). Furthermore, confocal microscopy indicated that IBV-N could co-localize with chGBP1 in the cytoplasm of HD11 cells (Fig. 7B). Next, we examined the effects of chGBP1 protein on IBV-N protein stability. Overexpression chGBP1 reduced IBV-N protein levels, both IBV Beaudette strain (Fig. 7C) and M41 strain (Fig. 7D). We also found that the protein level of IBV-N were reduced by chGBP1 protein in dose-dependent manner (Fig. 7E). In addition, treatment HD11 cells with an autophagy inhibitor (3-MA and Baf.A1) restored the IBV N protein levels reduced by chGBP1, but not proteasome inhibitor (MG-132), which inclued IBV Beaudette strain N protein (Fig. 7F) and M41 strain N protein (Fig. 7G). These results indicated that the chGBP1 interacts with the IBV N protein to reduce its protein levels, inhibiting IBV replication in HD11 cells.Figure 7 chGBP1 reduced IBV-N protein levels to block IBV replication. (A) HD11 cells were co-transfected with pcDNA3.1-Flag-GBP1, pcDNA3.1-Flag-GBP11-306, pcDNA3.1-Flag-GBP1307-576 with either pcDNA3.1-HA-N or pcDNA3.1-HA. The cell lysate was harvested. Coimmunoprecipitation (Co-IP) was performed using an anti-Flag antibody (MAb) (1:100). The precipitated proteins were analyzed by Western blotting using anti-HA antibodies. (B) HD11 cells were transfected with GFP-chGBP1 or mcherry-N. At 30 h post transfected, co-localization was performed using Confocal fluorescence microscopy. (C-D) HD11 cells were co-transfected pcDNA3.1-Flag-chGBP1 (1000ng) with either pcDNA3.1-HA-IBVBeau-N (1000ng), or pcDNA3.1-HA-IBVM41-N (1000ng). The proteins were analyzed by Western blotting. (E) HD11 cells were co-transfected with different concentrations of pcDNA3.1-Flag-chGBP1 (1000ng or 2000ng) and pcDNA3.1-HA-N (1000ng). At 30 h post transfected, the proteins were analyzed by Western blotting. (F-G) HD11 cells were co-transfected pcDNA3.1-Flag-chGBP1 (1000ng) with either pcDNA3.1-HA-IBVBeau-N (1000ng), or pcDNA3.1-HA-IBVM41-N (1000ng). After 24 h, the cells were treated with dimethyl sulfoxide (DMSO), 3-MA (0.5 mg/mL), MG-132 (20 μM), and Baf.A1 (100 nM) for 6 h. Protein expression was measured through western blotting.
Figure 7
DISCUSSION
Herein, we investigated the regulation and function of IFN-γ-induced chGBP1 in IBV-infected macrophages. We showed that chGBP1 was upregulated by both IFN-γ and IBV in HD11 cells. Meanwhile, chGBP1 inhibited IBV replication in a dose-dependent manner and enhanced IFN-γ anti-IBV activity. We found that the chGBP1 N-terminal GTPase domain was crucial for its anti-IBV activity, with amino acids R49 and K52 in chGBP1 playing critical roles in its GTPase activity and anti-IBV effects. More than that, we found that chGBP1 interacts with IBV N protein to destroy the stability of protein via the autophagy pathway.
IFN-induced proteins, such as GBP1, are required for host-mediated immune responses to pathogen invasion (Samuel, 2001). Initially, the antiviral effect of GBP1 against VSV and EMCV was shown (Anderson, et al., 1999). Subsequently, GBP1 was reported to inhibit RNA viruses such as Flaviviridae viruses, including HCV (Itsui, et al., 2009) and CSFV (Li, et al., 2016), in vitro, and more recently, the effect of GBP1 against the DNA virus KSHV was reported (Zou, et al., 2017). We screened for antiviral cytokines and found that GBP1 significantly inhibited IBV replication. GBPs were identified because they are strongly induced by type II IFN (IFN-γ) (Cheng, et al., 1983). IFN-γ is a type II IFN and enhances the specific immune response by activating T cells and macrophages (Boehm, et al., 1997). We then examined chGBP1 transcription levels at different times of IBV infection and found that chGBP1 was upregulated at both the gene and protein levels after virus infection, as compared with that in the control group. What's more, the same results were obtained when HD11 cells were stimulated with exogenous chIFN-γ. Chicken macrophages HD11 cells are susceptible to the IBV Beaudette strain, and infected macrophages produce a high load of virions (Han, et al., 2017). We found that chIFN-γ production increased significantly after IBV infection in chicken macrophages, which may be related to host resistance to IBV infection. Further, we demonstrated by gain- and loss-of-function that chicken GBP1 inhibits IBV replication in HD11 cells in a dose-dependent manner.
IFNs are complex mixtures of bioactive molecules with complex functions within the innate immune system and are divided into types I, II, and III IFN (Pestka, et al., 2004). IFNs have a range of antiviral effects on viral infection and replication, especially in the suppression of coronaviruses, including SARS, MERS, and SARS-CoV-2 (Mesev, et al., 2019; Park and Iwasaki, 2020). We further demonstrated that IFN-γ significantly inhibited IBV replication. Moreover, chGBP1 enhanced the antiviral effect of IFN-γ in vitro. Similarly, silencing of GBP1 abolishes the antiviral effect of IFN-γ on HEV (Glitscher, et al., 2021).
Also, GBP1 inhibits Classical Swine Fever Virus (CSFV) and Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) via GTPase activity(Duan, et al., 2022; Li, et al., 2016). We showed that the GTPase domain of chGBP1 was critical for inhibiting IBV replication. The GTPase domain is a key region for GBP1 to play an antiviral function. In the conserved structure of GBP1 GTPase in humans and mice, an R48 mutation weakens GTPase activity, while a K51 mutation causes a loss of function (Kravets, et al., 2012; Yu, et al., 2020). Compared with human/mouse amino acid sequences, these sites are at positions 49 and 52, respectively, in chicken GBP1. We found that R49A and K52A mutations attenuated the anti-IBV effect of chGBP1, suggesting that the GTPase activity may be required for this effect.
In addition, the researchers also found that GBP1 exhibited antiviral activity against viruses independent of GTPase activity. GBP1 anti-VSV was independent of its GTPase activity and isoprenylation (Gu, et al., 2021b). GBP1 competitively binding to the VSV-N substituting for the VSV-P decreased RNA synthesis, repress the VSV genome ranscription (Gu, et al., 2021b; Zhang, et al., 2021). Interestingly, our results suggest chGBP1 C-terminal represses IBV replication observably, independent of GTPase activity. chGBP1 reduced the IBV nucleocapsid (N) protein level by interacting with IBV-N protein to inhibit IBV. Coronavirus nucleocapsid protein, whose main function is to package the viral genome RNA molecule into a ribonucleoprotein (RNP) complex (Carlson, et al., 2020; Jayaram, et al., 2006). Ribonucleocapsid packaging is a fundamental part of viral self-assembly (Chang, et al., 2014). Interestingly, our results showed that chGBP1 through the autophagy pathway degrades IBV-N protein. This may be a new strategy of GBP1 to exert antiviral activity. In HEV infection, GBP1 anti-HEV mainly through the autophagosomal pathway, independent of the GTPase-activity (Glitscher, et al., 2021). GBP1 to form GBP1 homodimers targets the viral capsid protein to the lysosomal compartment, leading to inactivation of the viral particle(Glitscher, et al., 2021). Chinese tree shrew GBP1 (tGBP1) interacted with tSTING, sequestosome 1, and microtubule associated protein 1 L chain 3, forming a complex which promotes autophagy in response to HSV-1 infection (Gu, et al., 2021a). Thus, the mechanism of chGBP1 through the autophagy pathway to degrade IBV-N protein which is worth further study.
In conclusion, we demonstrated that coronavirus IBV infection activates an chIFN-γ-induced host restriction factor, chGBP1, whose both C-terminal and N-terminal GTPase domain effectively inhibits IBV replication in chicken macrophages HD11 cells. This study provides insight into the host factor chGBP1 anti-IBV infection and will facilitate an understanding of viral pathogenesis and promote development of new antiviral strategies.
FUNDING
This work was supported by The National System for Layer Production Technology (No. CARS-40-K14).
Uncited References
REED and H, 1938
DECLARATIONS
The authors declare that they have no competing interests.
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REFERENCES
Anderson S.L. Carton J.M. Lou J. Xing L. Rubin B.Y. Interferon-induced guanylate binding protein-1 (GBP-1) mediates an antiviral effect against vesicular stomatitis virus and encephalomyocarditis virus Virology 256 1999 8 14 10087221
Boehm U. Klamp T. Groot M. Howard J.C. Cellular responses to interferon-gamma Annu Rev Immunol 15 1997 749 795 9143706
Carlson C.R. Asfaha J.B. Ghent C.M. Howard C.J. Hartooni N. Safari M. Frankel A.D. Morgan D.O. Phosphoregulation of Phase Separation by the SARS-CoV-2 N Protein Suggests a Biophysical Basis for its Dual Functions Mol Cell 80 2020 1092 1103 e1094 33248025
Chang C.K. Hou M.H. Chang C.F. Hsiao C.D. Huang T.H. The SARS coronavirus nucleocapsid protein–forms and functions Antiviral Res 103 2014 39 50 24418573
Cheng Y.S. Colonno R.J. Yin F.H. Interferon induction of fibroblast proteins with guanylate binding activity J Biol Chem 258 1983 7746 7750 6305951
Chhabra R. Ball C. Chantrey J. Ganapathy K. Differential innate immune responses induced by classical and variant infectious bronchitis viruses in specific pathogen free chicks Dev Comp Immunol 87 2018 16 23 29751011
Cook J.K. Jackwood M. Jones R.C. The long view: 40 years of infectious bronchitis research Avian Pathol 41 2012 239 250 22702451
Cui W. Braun E. Wang W. Tang J. Zheng Y. Slater B. Li N. Chen C. Liu Q. Wang B. Li X. Duan Y. Xiao Y. Ti R. Hotter D. Ji X. Zhang L. Cui J. Xiong Y. Sauter D. Wang Z. Kirchhoff F. Yang H. Structural basis for GTP-induced dimerization and antiviral function of guanylate-binding proteins Proc Natl Acad Sci U S A 118 2021
Duan H. Dong H. Wu S. Ren J. Zhang M. Chen C. Du Y. Zhang G. Zhang A. Porcine reproductive and respiratory syndrome virus non-structural protein 4 cleaves guanylate-binding protein 1 via its cysteine proteinase activity to antagonize GBP1 antiviral effect Vet Res 53 2022 55 35804432
Glitscher M. Himmelsbach K. Woytinek K. Schollmeier A. Johne R. Praefcke G.J.K. Hildt E. Identification of the interferon-inducible GTPase GBP1 as major restriction factor for the Hepatitis E virus J Virol 2021
Gu T. Yu D. Xu L. Yao Y.L. Yao Y.G. Tupaia GBP1 Interacts with STING to Initiate Autophagy and Restrict Herpes Simplex Virus Type 1 Infection J Immunol 207 2021 2673 2680 34732469
Gu T. Yu D. Xu L. Yao Y.L. Zheng X. Yao Y.G. Tupaia guanylate-binding protein 1 interacts with vesicular stomatitis virus phosphoprotein and represses primary transcription of the viral genome Cytokine 138 2021 155388
Han X. Tian Y. Guan R. Gao W. Yang X. Zhou L. Wang H. Infectious Bronchitis Virus Infection Induces Apoptosis during Replication in Chicken Macrophage HD11 Cells Viruses 9 2017
Honkala A.T. Tailor D. Malhotra S.V. Guanylate-Binding Protein 1: An Emerging Target in Inflammation and Cancer Front Immunol 10 2019 3139 32117203
Itsui Y. Sakamoto N. Kakinuma S. Nakagawa M. Sekine-Osajima Y. Tasaka-Fujita M. Nishimura-Sakurai Y. Suda G. Karakama Y. Mishima K. Yamamoto M. Watanabe T. Ueyama M. Funaoka Y. Azuma S. Watanabe M. Antiviral effects of the interferon-induced protein guanylate binding protein 1 and its interaction with the hepatitis C virus NS5B protein Hepatology 50 2009 1727 1737 19821486
Itsui Y. Sakamoto N. Kurosaki M. Kanazawa N. Tanabe Y. Koyama T. Takeda Y. Nakagawa M. Kakinuma S. Sekine Y. Maekawa S. Enomoto N. Watanabe M. Expressional screening of interferon-stimulated genes for antiviral activity against hepatitis C virus replication J Viral Hepat 13 2006 690 700 16970601
Jackwood M.W. Hall D. Handel A. Molecular evolution and emergence of avian gammacoronaviruses Infect Genet Evol 12 2012 1305 1311 22609285
Jayaram H. Fan H. Bowman B.R. Ooi A. Jayaram J. Collisson E.W. Lescar J. Prasad B.V. X-ray structures of the N- and C-terminal domains of a coronavirus nucleocapsid protein: implications for nucleocapsid formation J Virol 80 2006 6612 6620 16775348
Kravets E. Degrandi D. Weidtkamp-Peters S. Ries B. Konermann C. Felekyan S. Dargazanli J.M. Praefcke G.J. Seidel C.A. Schmitt L. Smits S.H. Pfeffer K. The GTPase activity of murine guanylate-binding protein 2 (mGBP2) controls the intracellular localization and recruitment to the parasitophorous vacuole of Toxoplasma gondii J Biol Chem 287 2012 27452 27466 22730319
Li H. Cui P. Fu X. Zhang L. Yan W. Zhai Y. Lei C. Wang H. Yang X. Identification and analysis of long non-coding RNAs and mRNAs in chicken macrophages infected with avian infectious bronchitis coronavirus BMC Genomics 22 2021 67 33472590
Li L.F. Yu J. Li Y. Wang J. Li S. Zhang L. Xia S.L. Yang Q. Wang X. Yu S. Luo Y. Sun Y. Zhu Y. Munir M. Qiu H.J. Guanylate-Binding Protein 1, an Interferon-Induced GTPase, Exerts an Antiviral Activity against Classical Swine Fever Virus Depending on Its GTPase Activity J Virol 90 2016 4412 4426 26889038
Lu S. Ye Q. Singh D. Cao Y. Diedrich J.K. Yates J.R. 3rd Villa E. Cleveland D.W. Corbett K.D. The SARS-CoV-2 nucleocapsid phosphoprotein forms mutually exclusive condensates with RNA and the membrane-associated M protein Nat Commun 12 2021 502 33479198
Mesev E.V. LeDesma R.A. Ploss A. Decoding type I and III interferon signalling during viral infection Nat Microbiol 4 2019 914 924 30936491
Park A. Iwasaki A. Type I and Type III Interferons - Induction, Signaling, Evasion, and Application to Combat COVID-19 Cell Host Microbe 27 2020 870 878 32464097
Pestka S. Krause C.D. Walter M.R. Interferons, interferon-like cytokines, and their receptors Immunol Rev 202 2004 8 32 15546383
Praefcke G.J.K. Regulation of innate immune functions by guanylate-binding proteins Int J Med Microbiol 308 2018 237 245 29174633
Prakash B. Praefcke G.J. Renault L. Wittinghofer A. Herrmann C. Structure of human guanylate-binding protein 1 representing a unique class of GTP-binding proteins Nature 403 2000 567 571 10676968
REED L.J. H M. A SIMPLE METHOD OF ESTIMATING FIFTY PER CENT ENDPOINTS American journal of epidemiology 27 1938 493 497
Samuel C.E. Antiviral actions of interferons Clin Microbiol Rev 14 2001 778 809 table of contents 11585785
Smith J. Sadeyen J.R. Cavanagh D. Kaiser P. Burt D.W. The early immune response to infection of chickens with Infectious Bronchitis Virus (IBV) in susceptible and resistant birds BMC Vet Res 11 2015 256 26452558
Snijder E.J. Decroly E. Ziebuhr J. The Nonstructural Proteins Directing Coronavirus RNA Synthesis and Processing Adv Virus Res 96 2016 59 126 27712628
Sun X. Wang Z. Shao C. Yu J. Liu H. Chen H. Li L. Wang X. Ren Y. Huang X. Zhang R. Li G. Analysis of chicken macrophage functions and gene expressions following infectious bronchitis virus M41 infection Vet Res 52 2021 14 33509253
Thaiss C.A. Zmora N. Levy M. Elinav E. The microbiome and innate immunity Nature 535 2016 65 74 27383981
Tretina K. Park E.S. Maminska A. MacMicking J.D. Interferon-induced guanylate-binding proteins: Guardians of host defense in health and disease J Exp Med 216 2019 482 500 30755454
Wille M. Holmes E.C. Wild birds as reservoirs for diverse and abundant gamma- and deltacoronaviruses FEMS Microbiol Rev 44 2020 631 644 32672814
Yang X. Li J. Liu H. Zhang P. Chen D. Men S. Li X. Wang H. Induction of innate immune response following introduction of infectious bronchitis virus (IBV) in the trachea and renal tissues of chickens Microb Pathog 116 2018 54 61 29330060
Yu P. Li Y. Li Y. Miao Z. Peppelenbosch M.P. Pan Q. Guanylate-binding protein 2 orchestrates innate immune responses against murine norovirus and is antagonized by the viral protein NS7 J Biol Chem 295 2020 8036 8047 32354743
Yu L. Zhang X. Wu T. Wang Y. Meng J. Liu Q. Niu X. Wu Y. The papain-like protease of avian infectious bronchitis virus has deubiquitinating activity Arch Virol 162 2017 1943 1950 28316013
Zhang R. Li Z. Tang Y.D. Su C. Zheng C. When human guanylate-binding proteins meet viral infections J Biomed Sci 28 2021 17 33673837
Zhu J. Xu S. Li X. Wang J. Jiang Y. Hu W. Ruan W. Infectious bronchitis virus inhibits activation of the TLR7 pathway, but not the TLR3 pathway Arch Virol 165 2020 2037 2043 32524263
Zou Z. Meng Z. Ma C. Liang D. Sun R. Lan K. Guanylate-Binding Protein 1 Inhibits Nuclear Delivery of Kaposi's Sarcoma-Associated Herpesvirus Virions by Disrupting Formation of Actin Filament J Virol 91 2017
| 0 | PMC9731647 | NO-CC CODE | 2022-12-14 23:31:51 | no | Poult Sci. 2022 Dec 9;:102398 | utf-8 | Poult Sci | 2,022 | 10.1016/j.psj.2022.102398 | oa_other |
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Dialogues Health
Dialogues in Health
2772-6533
The Author. Published by Elsevier Inc.
S2772-6533(22)00091-0
10.1016/j.dialog.2022.100091
100091
Article
A Neighborhood-level analysis of mental health distress and income inequality as quasi-longitudinal risk of reported COVID-19 infection and mortality outcomes in Chicago
Ramos Stephen D. ab⁎
Kannout Lynn c
Khan Humza c
Klasko-Foster Lynne d
Chronister Briana N.C. ef
Du Bois Steff c
a University of California San Diego, Division of Infectious Diseases and Global Public Health, Department of Medicine, San Diego, CA 92093, USA
b San Diego State University, SDSU Research Foundation, San Diego, CA 92120, USA
c Illinois Institute of Technology, Department of Psychology, Chicago, IL 60616, USA
d Brown University, Department of Psychiatry and Human Behavior, Providence, RH 02912, USA
e Herbert Wertheim School of Public Health, University of California San Diego, San Diego, CA 92093, USA
f School of Public Health, San Diego State University, San Diego, CA 92182, USA
⁎ Corresponding author at: 6475 Alvarado Rd, Suite 118, San Diego, CA 92120, USA.
8 12 2022
12 2023
8 12 2022
2 100091100091
29 5 2022
2 12 2022
4 12 2022
© 2022 The Authors
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Extant literature investigates the impact of COVID-19 on mental health outcomes, however there is a paucity of work examining mental health distress as a risk factor for COVID-19 outcomes. While systemic variables like income inequality relate to both mental health and COVID-19, more work is needed to test theoretically informed models including such variables. Using a social-ecological framework, we aimed to address these gaps in the literature by conducting a neighborhood-level analysis of potential mental health distress and systemic- (income inequality) level predictors of reported COVID-19 infection and mortality over time in Chicago. Neighborhood-level comparisons revealed differences in mental health distress, income inequality, and reported COVID-19 mortality, but not reported COVID-19 infection. Specifically, Westside and Southside neighborhoods generally reported higher levels of mental health distress and greater concentration of poverty. The Central neighborhood showed a decline in reported mortality rates over time. Multi-level negative binomial models established that Zip-codes with greater mental health distress were at increased reported COVID-19 infection risk, yet lower mortality risk; Zip-codes with more poverty were at increased reported COVID-19 infection risk, yet lower mortality risk; and Zip-codes with the highest percentage of People of Color were at decreased risk of reported COVID-19 mortality. Taken together, these findings substantiate Chicago neighborhood-level disparities in mental health distress, income inequality, and reported COVID-19 mortality; identify unique differential associations of mental health distress and income inequality to reported COVID-19 infection and reported mortality risk; and, offer an alternative lens towards understanding COVID-19 outcomes in terms of race/ethnicity.
Keywords
Income inequality
Mental health distress
COVID-19 infection
COVID-19 mortality
Neighborhood-level analysis
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pmc1 Introduction
The coronavirus disease 2019 (COVID-19) has escalated into a devastating pandemic, with over 615 million confirmed cases and over 6.5 million deaths reported to the World Health Organization as of October 4, 2022 [1]. However, it is not universal to either acquire or die from COVID-19. Variable infection and mortality rates have yielded a call to action: to better understand risk and protective factors of these COVID-19 outcomes [2]. Extant research has identified a number of risk factors for reported COVID-19 mortality, including male sex, older age, obesity, and underlying health conditions [[3], [4], [5], [6], [7]]. Communities of color are at increased risk for reported COVID-19 mortality [8,9]. At the national level, nearly 60% of COVID-19 deaths were accounted for by individuals in primarily Black U.S. counties, despite these counties representing only 20% of the sample [10].
Early narratives seeking to explain these trends fail to account for some of the systemic and multi-level factors that may contribute to disparities in reported COVID-19 infection and mortality for many historically disenfranchised communities [11,12]. An adaptation of the County Health Ranking Framework offers a lens to understand the multi-level socio-systemic factors that may relate to COVID-19 outcomes, see Fig. 1 . Unequal distribution of income is a specific social determinant of health (SDOH) with strong links to reported COVID-19 infection and mortality [13,14]. It is consistent with Bronfenbrenner's Social-Ecological Model [15] that individuals with low income living in high-income inequality areas would report low healthcare access and utilization [16], crowded housing, and continued in-person work as essential workers amid the COVID-19 pandemic [17,18]. Empirically, income inequality has been associated with premature mortality at multiple levels of spatial-health investigation, even after controlling for socioeconomic status, age, and sex [[19], [20], [21]]. In the context of the pandemic, income inequality has been related to increased associations with reported COVID-19 mortality, however these associations have yet to be assessed at neighborhood levels [13,14,21]. Additionally, some international research suggests that economic deprivation may be associated with COVID-19 infection, but not COVID-19 mortality [22]. Additional investigation would provide useful insight into social-ecological conditions and COVID-19 outcomes.Fig. 1 Adapted theoretical framework based on the county health rankings model.
Fig. 1
While the County Health Ranking Framework [23] offers insight into health behaviors, clinical care, physical environment, and social/economic factors that relate to health outcomes, many applications of the model do not delineate an important factor highlighted in social-ecological literature – mental health [[23], [24], [25]]. Mental health distress, defined as a state of emotional suffering associated with daily stressors [26], relates to poor mental and physical health outcomes [27,28] and may be another risk of COVID-19 infection and mortality. Since the start of the pandemic, much literature has examined COVID-19's impact on mental health, [[29], [30], [31]], and generally suggests that mental health difficulties both spiked and persisted since the pandemic onset [32] – potentially exacerbating mental health difficulties for individuals with pre-existing mental illness [33]. However, much less work has examined the inverse – mental health distress as a risk factor for reported COVID-19 outcomes. Researchers theorized that individuals with serious mental illness (SMI) may be at higher risk of COVID-19 exposure, infection, and mortality due to lower health literacy, negative health-related behaviors, lower treatment adherence, and greater obstacles to quarantine adherence (e.g., homelessness) [34]. Related empirical findings are mixed, showing that having a mental illness diagnosis was not associated with likelihood of testing positive for COVID-19 [35]; however, other research suggests that communities with elevated COVID-19 infection rates also showed elevated levels of pre-pandemic mental health distress [36]. In one study, individuals with SMI were at higher risk for severe clinical outcomes of COVID-19 than those with no history of mental illness [35]. In another study, adults diagnosed with schizophrenia spectrum disorder had higher increased risk for reported COVID-19 mortality than those without such diagnosis. However, those with mood and anxiety disorders were no more at risk for reported COVID-19 mortality than those without a diagnosis [37]. Among individuals with a mental health disorder, those who were African American had higher odds of COVID-19 infection than those who were White, and women had higher odds of reported COVID-19 infection than men [38].
Together, this suggests an unclear understanding of mental health in relation to COVID-19 infection and mortality. Additionally, little work considers these associations in the context of systemic-level variables such as income inequality. Income inequality is related to reporting mental health distress, experiencing an increased risk for and incidence of mental illness, and worsening health outcomes among those affected by poor mental health. These findings have been reported at individual [39], county [40], and state levels [41]. More broadly, income inequality and mental health distress often simultaneously relate to health outcomes [[42], [43], [44]]. Despite this, investigations at the Zip-code level examining how strongly each of these factors relate to COVID-19 are only recently emerging [45]. This parallels the concept of social vulnerability, which asserts that social, economic, demographic, and geographic characteristics determine risk exposure of a community and their ability to respond to and recover from adversity [46]. That is, vulnerability is a socially constructed condition of a system that exists before it is faced with a threat that intensifies its effects [[46], [47], [48], [49], [50], [51]]. Testing models consistent with this social-ecological and social vulnerability perspective that focus on COVID-19 outcomes would make an important contribution to literature [[52], [53], [54]].
Chicago is a relevant place to investigate such socio-structural risk factors of reported COVID-19 infection and mortality. As of November 2022, Chicago had 718,355 positive COVID-19 cases, and Illinois itself had the fifth-most cases of any U.S. state [55]. Chicago also demonstrates significant racial/ethnic disparities in its COVID-19 outcomes, thus providing a location where researchers can investigate COVID-19 as a potential microcosm for broader racial/ethnic health disparities. Of all reported COVID-19 cases in Chicago, White individuals showed an infection rate of 6118.7 and death rate of 569.9 per 100,000 individuals [56]. Black individuals showed an infection rate of 6950.9 and a death rate of 1604.6 per 100,000 individuals [56]. Latino or Hispanic individuals showed an infection rate of 12,248.8 and a death rate of 1042.7 rate per 100,000 individuals [56]. These numbers starkly contrast the racial/ethnic makeup of Chicago, which is 50% White, 29.6% Black, and 28.8% Latino or Hispanic [55,57]. More broadly, Chicago has a deep history of systemic racism, making Chicago and its four prominent neighborhoods (Northside, Central or “Loop”, Westside, and Southside) some of the most racially-segregated geographic spaces in the U.S [46,58]. Racial segregation in Chicago has had long-lasting impacts, particularly on health for individuals in marginalized neighborhoods [58,59]. A recent study found that life expectancy was 90 years old for those living in Streeterville, a predominantly White neighborhood in Central Chicago, but just 60 years old for those living in Englewood, a predominantly Black community in the Southside neighborhood just nine miles away [60].
The present study aims to assess associations between these socio-structural variables in relation to COVID-19 infection and mortality in the historically segregated city of Chicago, using neighborhood-level data. First, we conduct neighborhood-level comparisons of income inequality and mental health distress, hypothesizing higher rates of each in neighborhoods that literature identifies as those occupied primarily by racial/ethnic minority individuals (i.e., Westside and Southside). Next, we conduct neighborhood-level comparisons of reported COVID-19 infection and mortality risk rates over time using multi-level linear growth modeling. We hypothesize that these same racial/ethnic-majority neighborhoods will show increased risk for these COVID outcomes. Then, we test whether Zip-code level income inequality and mental health distress serve as significant exposures to risk of reported COVID-19 infection and mortality over time, via multilevel negative binomial regression modeling, while controlling for several key covariates. We hypothesize that neighborhoods with increased mental health distress and higher concentrations of poverty experience increased risk for COVID-19 infections and mortality across neighborhoods. We also hypothesize that neighborhoods with higher proportions of people of color (PoC) will demonstrate increased risk for COVID-19 outcomes compared to neighborhoods with lower proportions of PoC within Zip-codes.
2 Methods
All data were obtained through the City of Chicago Coronavirus Response Center Data Portal (CRCDP), City Health Dashboard (CHD), and Chicago Health Atlas (CHA). The CRCDP was created in part by the Department of Public Health and has been used in several emerging COVID-related publications in Chicago [46,61,62]. CRCDP data includes reports from the Illinois National Electronic Disease Surveillance System, Cook County Medical Examiner's Office, Illinois Vital Records, and the US Census Bureau 2018 American Community Survey (ACS). The CHD is a national data resource of health-related measures for over 750 cities across the U.S. and is a frequently cited data source in the established literature [54,63]. Similarly, the CHA is a community health data resource of aggregated data from several sources, including the ACS. Key study variables included in the CHD and CHA were obtained from the US Census Bureau 2018 ACS and the CDC's PLACES Project. Present study data were extracted from these two sources at the Zip-code level. To align with a quasi-longitudinal approach, we used mental health distress- and income inequality-predicted estimates and related estimates for our control variables for 2018 – the most recent shared year-point of all variables. Both COVID-19 infection and mortality reflect respective cumulative reported cases as of July 17, 2021.
2.1 Measures
A total of 60 Zip-codes are captured within the city of Chicago. Given that many Zip-codes are often colloquially categorized with a multi-neighborhood spatiality (e.g., Southwest, Northcenter) and data sources themselves list individual Zip-codes in multiple broader neighborhoods and higher-level amalgamations of neighborhood clusters, we categorized the data gathered from CRCDP and City Health Dashboard into four categories – Northside, Southside, Westside, and Central. This was done via comprehensive comparison of community areas identified within existing data available from the CHA [64] and 2014–2016 Community Health Needs Assessment [65]. While no method of categorization is without error, we approach this categorization through a synthetization of two existing classification systems for Chicago to achieve a more standardized categorization. Each of the following variables are aggregated and analyzed at the Zip-code level.
2.1.1 Outcomes
2.1.1.1 COVID-19 infection and mortality
Cumulative COVID-19 infection and mortality reported counts were obtained from the CRCDP. For these data, Chicago residents are included based on the home Zip-code provided by the medical provider of each reporting entity. Cases with a positive molecular or antigen test are included in this dataset and are counted based on the week in which the specimen was collected, to account for variability in testing time lapse from receiving initial COVID-19 testing. Two separate variables are included, where values represent cumulative infection cases and cumulative mortality cases in Chicago, by Zip-code, among Chicago residents. Reported COVID-19 infection and mortality variables are updated weekly on the CRCDP, and we use data gathered across 72 consecutive weeks, spanning from March 1, 2020 to July 17, 2021 to reflect initial outcomes to this public health threat.
2.2 Risk factors
2.2.1 Mental health distress
Frequency of mental health distress was obtained from the CHD, using 2018 1-year modeled estimates from the CDC PLACES project. This mental health distress frequency index was calculated as a percentage of adult respondents who reported poor mental health ≥14 days in the past month per total Zip-code level population and represents a mental health-related quality of life within each Zip-code. This operationalization is consistent with clinical criteria for select mental health disorders [66], functional impairment [67], public health surveys [64,68], and related extant research [[69], [70], [71], [72], [73], [74], [75], [76]]. With that said, this current method of assessment allows variable conceptualization of “distress” and thus may also capture distress on a broader level that is sub-clinical threshold.
2.2.2 Income inequality
The Index of Concentration at the Extremes (ICE) is a measure of income inequality obtained from the CHD and is calculated though the following formula applying income distributions for each respective geographical area or concentration [77]:Households≤20th%ofIncome−Households≥80th%ofIncomeTotalHouseholdsinGeographicArea∗100
Specifically, we used the 2018 5-year ACS estimates, representing pooled estimates across multiple years of data, and scores range from −100 (all households are financially deprived) through +100 (all households are financially privileged; [59]). As such, ICE allows for a description of size and direction of income inequality within geographic areas and is argued to be a more robust measure than the popular Gini index of income inequality at the census tract [77]. While race/ethnicity itself is not explicitly measured in this variable, income inequality is often expressed as a proxy for and product of systematic racism and has racial implications. In fact, there is growing support for ICE as an indicator of historical structural racism, particularly for marginalized communities [78]. Therefore, we use and interpret findings from this variable in both the context of income inequality and race/ethnicity as a function of the city of Chicago itself, though we address race/ethnicity specifically through a designated assessment of PoC concentration.
2.3 Covariates
Other than the inclusion of Race/Ethnicity as a covariate, we focus on modifiable health behaviors available from our publicly assessable data sources as covariates for inclusion in our models. This is because health behaviors relate to health outcomes, within the context of other social determinants of health, including socio-structural and environmental drivers of health [79,80]. As such, we include health behaviors highlighted in the County Health Ranking Framework as covariates in our analyses. We also provide supporting literature surrounding their specific associations to COVID-19 below.
2.3.1 Race/Ethnicity
Race/Ethnicity are obtained from the CHA, using 2018 5-year moving averages from the ACS. Racial/ethnic categories include Non-Hispanic White, Non-Hispanic Black, Asian or Pacific Islander, Hispanic or Latino, Native American, or two or more races. Here, we obtained the total estimated number of Non-Hispanic White individuals within each respective Zip-code and created a ratio to the estimated total population within each Zip-code. We then categorized each ratio into one of three broader groupings based on the mean ratio of reported Non-Hispanic White concentration. Ratios that fell above one standard deviation of the mean represent higher concentration of Non-Hispanic White identities and were labeled “low PoC” density Zip-codes. Ratios that fell below one standard deviation of the mean represented lower concentration of Non-Hispanic White identities and were labeled “high PoC” density Zip-codes.
2.3.2 Binge drinking
Binge drinking among adults aged ≥18 years was obtained using CDC PLACES project 2018 1-year modeled estimates. Binge drinking is defined as women who reported consuming ≥ four alcoholic drinks on one occasion and men who reported consuming ≥ five alcoholic drinks on one occasion in the past 30 days [81]. These benchmarks parallel several standardized measures of hazardous drinking [82,83], a behavior known to compromise immunological functioning and place individuals at higher risk for COVID-19 [84]. Excessive alcohol use is also known to associate with lung damage and increase susceptibility to respiratory illness and COVID-19 susceptibility and severity [85]. As bring drinking is a modifiable behavior, we include neighborhood level of binge drinking as a percentage-based covariate at the Zip-code level.
2.3.3 Physical inactivity
Physical inactivity among adults aged ≥18 years was obtained using CDC PLACES project 2018 1-year modeled estimates, specifically by denying all past month physical activities or exercises such as running, calisthenics, golf, gardening, or walking for exercise [81]. Physical inactivity itself is a known correlate of increased risk for COVID-19 outcomes [86], and is a known protective factor against several other health conditions that increase risk for COVID-19 susceptibility and severity (i.e., diabetes, cardiovascular disease, obesity) [87]. As physical inactivity is a modifiable behavior, we include physical inactivity as a as percentage-based covariate at the Zip-code level.
2.3.4 Smoking
Smoking among adults aged ≥18 years was obtained using CDC PLACES project 2018 1-year modeled estimates, representing respondents reporting having smoked ≥100 cigarettes in their lifetime and current daily or near-daily smoking [81]. Smoking appears to have a somewhat paradoxical and not well understood association to severity of COVID-19 outcomes, yet is also associated with several disease factors that promote poorer COVID-19 outcomes [88,89]. As smoking is a modifiable behavior, we included smoking as a percentage-based covariate at the Zip-code level to account for potential variance explained by this construct.
2.4 Analytic plan
We used Stata version 15 for all central study analyses. All data were screened for normality. Reliability ratings for each variable are not reported due to each exposure index being a single-item, Zip-code-level index. Sample characteristics were calculated using univariate descriptive and frequency statistics. Neighborhood-level differences of the variables of interest were tested using one-way between-groups ANOVA in SPSS version 28 and multi-level linear growth models. Predictive margins were estimated to further probe differences of conditional effects. Aligning with statistical methods of existent COVID-19 literature, multi-level negative binomial regression was used to calculate incidence rate ratios to measure the effects of Zip-code level income inequality and mental health distress on reported COVID-19 infection (cumulative cases, controlling for population within Zip-code) and mortality (cumulative deaths, controlling for population within Zip-code), respectively [22,[90], [91], [92], [93]]. Negative binomial distribution was used to account for overdispersion in each dependent variable. Likelihood ratio chi-square tests were performed to test the insufficiency of a more parsimonious Poisson model.
To identify whether there is spatial variation between income inequality, people of color, and mental health distress with cumulative COVID-19 case and mortality rates, we conducted geographically weighted regression (GWR) using the final timepoint of data. The GWR model creates a separate regression equation for each identified location, allowing the association to vary across Chicago. The GWR models used the golden search neighborhood selection method to identify the optimal distance band. All GWR models were adjusted for income inequality, people of color, and mental health distress. The coefficients were mapped. Geospatial autocorrelation, geographic weighted regression, and maps were generated in ArcGIS Pro version 2.18.
3 Results
3.1 Descriptive statistics
Our sample reflects available data from Zip-codes that comprise Northside (n = 17; 15.3%), Southside (n = 19; 28.8%), Westside (n = 14; 23.7%), and Central (n = 9; 32.2%) neighborhoods. In aggregate, 12.92% (2.87) of the adults ≥ 18 years old in our sample reported ≥14 days of poor mental health within the past month prior to data collection. Our sample's level of income inequality (−5.53) showed higher concentration of poverty than the CHD average (−1.1) [94]. At the final timepoint the data used in this study, our sample had a reported average cumulative COVID-19 infection of 9841.31(SD = 2631.52) cases, and mortality of 178.05 (SD = 95.13) cases, per 100,000 residents.
3.2 Neighborhood-level comparison
A one-way between-groups ANOVA was conducted to explore neighborhood-level differences in income inequality and mental health distress. Results indicated significant between-group differences for both variables. Levene's test for income inequality suggested that using the Tukey HSD approach was an appropriate probe for multiple post-hoc comparisons. Data for mental health distress violated the assumptions of homogeneity of variance per the Levene's test; therefore, Dunnett's T3 approach was used to probe for multiple post-hoc comparisons. Post-hoc analysis of the income inequality variable indicated that the Westside neighborhoods had a higher concentration of individuals at the lower end of the income extreme than the Central neighborhoods. Additionally, the Southside neighborhoods had a higher concentration of individuals at the lower end of the income extreme than both the Central and Northside neighborhoods. Post-hoc analysis of the mental health variable indicated that the Westside and Southside neighborhoods had more frequent days of mental health distress compared to the Central and Northside neighborhoods. Spatial autocorrelation analysis of key variables of interest revealed significant clustering similarities for Income Inequality (Moran's I = 0.47, p < .001) and Mental Health Distress (Moran's I = 0.41, p < .001) (See Fig. 2). Fig. 2 Neighborhood-level differences in key study predictor variables.
Logarithmic transformation of Y-axis was used to ease visual graphing interpretability of mental health distress and ICE in a single figure; however, the data themselves were not transformed. Significant differences exist between neighborhoods and mental health distress rates: F(3, 51) = 10.40, p < .001. Post-hoc comparison using Dunnet's T3 test indicated that the mean levels of mental health distress were significantly higher for both Westside (M = 13.88, SD = 3.11) and Southside (M = 14.65, SD = 2.46) when compared to Central (M = 9.93, SD = 1.98) and Northside (M = 11.23, SD = 1.20) neighborhoods independently All other mental health distress paring comparisons were non-significant, ps > 0.05. Significant differences exist between neighborhoods and income inequality: F(3, 51) = 13.56, p < .001. Post-hoc comparison using Tukey HSD test indicated that the mean ICE index was higher for Central (M = 31.18, SD = 13.12) compared to Westside (M = −9.43, SD = 16.51) and Southside (M = -24.44, SD = 20.93) neighborhoods. Additionally, Northside (M = 6.57, SD = 16.51) showed significantly higher ICE scores than Southside (M = −24.44, SD = 20.93) neighborhoods. All other ICE comparisons were non-significant, ps > 0.05 Error Bars: 95% CI.
Fig. 2
Multi-level linear growth models were conducted to explore differences at the neighborhood-level in reported COVID-19 infection and mortality, controlling for population density. For reported COVID-19 infection rates comparing the Central neighborhoods to more distal neighborhoods, differences were not significant (North: b = 12.142, SE = 6.803, p = .07; West: b = 7.182, SE = 6.976, p = .30; South: b = 6.221, SE = 6.609, p = .35). Margins analysis comparing Central neighborhoods to North, West, and South indicated that mortality rates stabilized only for the Central neighborhood (North: b = 1.294, SE = 0.348, p < .001; West: b = 1.404, SE = 0.361, p < .001; South: b = 1.388, SE = 0.342, p < .001). See Fig. 3, Fig. 4 . Similarly, spatial autocorrelation analysis of variables of interest revealed clustering similarities for the most recent cumulative COVID-19 mortality rate (Moran's I = 0.30, p < .001; however, not for the most recent cumulative COVID-19 infection rate (Moran's I = −0.10, p = .15).Fig. 3 Differences in COVID-19 infection growth rates by neighborhood.
Margins analysis with Central neighborhood as the reference comparisons showed no significant differences between neighborhoods regarding COVID-19 infection growth rates across 72 weeks. Descending trajectories represent decline in growth rate, rather than decline in total infections.
Fig. 3
Fig. 4 Differences in COVID-19 mortality growth rates by neighborhood.
Margins analysis with Central neighborhood as the reference comparison showed stabilization of COVID-19 mortality growth rates for only the Central neighborhood across 72 weeks.
Fig. 4
3.3 Geographic weighted regression
The results of the GWR can are presented in Fig. 5 and Fig. 6 . Mental Health distress had the strongest, positive associations with COVID-19 infection and mortality rates predominantly in the Southside and Westside. Mental Health distress was negatively associated with both COVID-19 infection and mortality rates in the Northside of Chicago. There only were negative associations between the density of PoC and COVID-19 Infection Rates across the city. The strongest, most negative associations were identified in the Northside. While the least negative associations were identified in the Southside. COVID-19 mortality was positively associated with density of PoC in the Northside, but had strong, negative associations in the Southside. For income inequality, higher concentrations of poverty had the strongest positive associations with COVID-19 infection and mortality rates in the Southside of Chicago, with notable strengths of association in the Westside of Chicago. The strongest negative associations for infection and mortality rates were predominantly located in the Northside.Fig. 5 Geographic weighted regression of key predictors and COVID-19 infection.
aGWR model was adjusted for mental health distress, people of color, and income inequality. It used a distance band of 14,951.6 m. R2 = 0.57, Adjusted R2 = 0.38, AICc = 1055. Cumulative COVID-19 infection and mortality rates reflect rates as of the final timepoint in our data – July 17, 2021.
Fig. 5
Fig. 6 Geographic weighted regression of key predictors and COVID-19 mortality.
bGWR model was adjusted for mental health distress, people of color, and income inequality. It used a distance band of 18,092.1 m. R2 = 0.68, Adjusted R2 = 0.57, AICc = 628. Cumulative COVID-19 infection and mortality rates reflect rates as of the final timepoint in our data – July 17, 2021.
Fig. 6
3.4 Multivariable models
Two multi-level negative binomial regression models were conducted to evaluate whether Zip-code level determinants of income inequality, mental health distress, racial diversity, smoking status, binge drinking, physical inactivity, and time were associated with increased risk of COVID-19 infection and mortality, respectively. For the COVID-19 infection model, the likelihood ratio chi-square test indicated that the negative binomial distribution had superior fit to the Poisson model (19.95, p < .0001). There was a significant increased risk over 72 weeks for reported COVID-19 infection in Zip-codes with a greater proportion of mental health distress (IRR = 1.058, p < .01), greater concentrations of wealth (IRR = 1.006, p < .01), less reported binge drinking (IRR = 0.991, p < .01), less sedentary behavior (IRR = 0.989, p < .01), less binge drinking (IRR = 0.991, p < .01) and in Zip-codes with larger populations (IRR = 1.006, p < .001). Smoking, Zip-code density of PoC, and time did not significantly relate to risk for reported COVID-19 infection; see Table 1 . For the COVID-19 mortality model, the likelihood ratio chi-square test indicated that the negative binomial distribution had superior fit to the Poisson model (3447.16, p < .0001), There was a significant increased risk over 72 weeks for reported COVID-19 mortality in Zip-codes with a lower proportion of mental health distress (IRR = 0.732, p < .05), less smoking (IRR = 0.979, p < .05), higher concentration of poverty (IRR = 0.954, p < .01), higher physical inactivity (IRR = 1.045, p < .05), and Zip-codes with larger populations (IRR = 1.029, p < .001). Additionally, Zip-codes with moderate (IRR = 0.307, p < .01) and high (IRR = 0.293, p < .05) proportions of PoC demonstrated lower risk for COVID-19 mortality, compared to Zip-codes with lower proportions of PoC. Bing drinking was not significantly associated with COVID-19 mortality risk; see Table 1. VIF = 5.1 for our multivariable model. Fig. 7 provides cross-sectional mapping of key variables at week 72.Table 1 Multi-level negative binomial regression models.
Table 1 Predictor IRR (SE) p-value 95% CI
COVID-19 Infection Income Inequality 0.006 (0.002) <0.01 (1.003, 1.010)
Population 1.006 (0.001) <0.001 (1.003, 1.009)
Binge Drinking 0.991 (0.003) <0.01 (0.985, 0.996)
Physical Inactivity 0.989 (0.003) <0.01 (0.983, 0.995)
Smoking 1.000 (0.001) 0.77 (0.998, 1.003)
PoC moderate
PoC high 1.083 (0.068)
1.013 (0.092) 0.210
.89 (0.957, 1.226)
(0.849, 1.210)
Mental Health Distress 1.058 (0.018) <0.01 (1.024, 1.093)
Time 1.000 (0.001) 0.92 (0.999, 1.001)
COVID-19 Mortality
Income Inequality 0.956 (0.013) <0.01 (0.930, 0.982)
Population 1.047 (0.010) <0.001 (1.027, 1.067)
Binge Drinking 1.002 (0.020) 0.91 (0.963, 1.043)
Physical Inactivity 1.045 (0.023) <0.05 (1.002, 1.090)
Smoking 0.979 (0.009) <0.05 (0.962, 0.996)
PoC moderate
PoC high 0.307 (0.1347)0
.293 (0.1833) <0.01
<0.05 (0.131, 0.724)
(0.086, 0.995)
Mental Health Distress 0.732 (0.089) <0.05 (0.577, 0.928)
a Time 1.030 (0.004) <0.001 (1.029, 1.030)
a p < .05 and examination of confidence intervals was used to determine significance in our multi-level negative binomial regression models.
Fig. 7 Spatial mapping of key variables by zip-code.
Cumulative COVID-19 infection and mortality rates reflect rates as of the final timepoint in our data – July 17, 2021. Due to the nature of the data, maps are structured with color scheme that aid in interpretive nature of variables. Specifically, Income Inequality and People of Color maps are structured in such that lower values represent higher frequency of the interpreted variable, given the nature of the data used.
Fig. 7
4 Discussion
The present study used Chicago-based data to examine income inequality and mental health distress as risks for reported COVID-19 infection and mortality; and, to investigate differences in COVID-19 outcomes for Chicago-based communities of color. Neighborhood-level comparisons of key study variables indicated that Westside and Southside neighborhoods generally showed higher concentration of low income and higher levels of mental health distress. Zip-codes with higher pre-existing mental health distress demonstrated increased risk for reported COVID-19 infection, yet decreased risk for reported COVID-19 mortality. Income inequality was a risk factor for reported COVID-19 infection for Zip-codes with higher concentration of wealth, and also was related to a significant increase in reported COVID-19 mortality risk rates in Zip-codes with higher concentrations of poverty. While some model covariates showed paradoxical associations that are not well understood in existing literature, we included covariates in our analyses to separate covariate effects from effects of key variables that were central to this study. Using GWR, we identified that there was city-wide variability in the associations between mental health distress, people of color, and income inequality with cumulative COVID-19 infection and mortality rates. Given that GWR shows several instances of Southside and Westside neighborhoods strongly relating to neighboring Zip-code data values, we discuss these findings with a lens underscoring the phenomenological segregative nature of Chicago and abstain from discussion of endogenous control variables, as recommended by Hünermund & Louw [95].
Many of our findings substantiate earlier work indicating that broadly, Westside and Southside Chicago neighborhoods experience more systemic- and neighborhood-level distress and systemic challenges than other Chicago neighborhoods [96]. Exposure to lead, decreased healthcare access, and higher crime rates are primary examples, with highest exposure in Southside and Westside neighborhoods compared to other Chicago neighborhoods [[96], [97], [98]]. Regarding mental health, emergent research exists supporting the association of increased COVID-19 infection risk with pre-existing mental health distress [36]; however, a comprehensive understanding of mental health as a determinant of COVID-19 outcomes, particularly mortality, remains unknown. Additionally, the income inequality-mortality link is unsurprising, given that income inequality increases the prevalence of poverty, generates chronic stress, and erodes protective measures of an individual's health – each of which can both produce community-level stress and increase an individual's risk for COVID-19 death [94,99]. These multiple and interrelated disparities indicate the need for effective systemic, neighborhood-level interventions addressing such factors [100].
Interestingly, several findings emerged that contrast existing literature. Zip-code level mental health distress was associated with reported COVID-19 mortality risk rates; however, the directionality of these associations was not fully consistent with our hypotheses. COVID-19 mortality findings are inconsistent with findings regarding other illnesses, such as cancer mortality, cardiovascular disease mortality, and other-cause mortality in extant research [101]. Psychological distress generally, largely relates to disease progression [102], however this was not the case for our sample with regard to reported COVID-19 mortality. Broadly, individuals with mental illness have greater physical health morbidity and mortality compared to general population members [103,104], which may partially explain our findings of elevated risk for COVID-19 infection in communities with elevated mental health distress. However, our findings regarding mental health and COVID-19 mortality contrast those in the existing literature and may be explained by some version of the habituation effect driven by resiliency.
The notion of psychophysiological habituation to stress parallels findings of childhood adversity and cortisol response to stress in adulthood [105], cardiovascular responses to stress [106], and amygdala response to threatening stimuli [107]. In the context of this study, it is possible that communities that experience more frequent mental health distress have developed emergent resilience, defined as an adaptation to chronic difficulties. Emergent resilience thus may contribute to an overall balanced psychophysiological response to new stressors [108]. Research shows that mental health disturbances such as anxiety, depression, and loneliness may have been experienced strongest at the start of the COVID-19 pandemic; however, symptoms either stabilized (i.e., habituated) or declined overall [109]. Symptoms among groups more vulnerable to poorer mental health during the COVID-19 pandemic experienced small decreases [109], and may suggest that on an aggregate level, communities that experienced increased mental health distress prior to COVID-19 may have habituated to the emergent stressor over time and countered the traditional associations of mental health distress and poor physical health.
That said, it is possible that pre-existing mental health distress may have manifested as anxiety or depression amid the COVID-19 pandemic. Anxiety is associated with increased healthcare utilization across multiple care settings [110], and individuals living with depression have displayed increased healthcare utilization amid the pandemic [111]. Thus, communities with pre-existing elevations in mental health distress may have increased utilization of healthcare services at the community-level in response to increased COVID-19 infections. Increased engagement in mental health services may have reduced parallel barriers towards engagement in healthcare broadly, such as though telehealth services.
However, the finding of higher concentrations of wealth positively associating to reported COVID-19 infection is unexpected, indicating that higher concentrations of lower income may disproportionately associate with COVID-19 disease progression and prognosis despite a heightened risk of reported COVID-19 infection among more affluent Zip-codes. It is possible that more affluent Zip-codes may have greater resources aiding in the ability to disperse, thus increasing exposure to COVID-19 and increased infection rates. Or those of more affluent Zip-codes may me more likely to report COVID-19 infection than their counterparts, due to increased healthcare access. Related to affluent Zip-code residents having relatively good healthcare, more affluent Zip-codes showed lower risk for COVID-19 mortality. This may be explained by increased access and ability to utilize resources in place to protect health.
The recognition that historically Black and Brown low-income neighborhoods endure a disproportionate number of physical health casualties is unfortunately familiar [97]. Social deprivation in these neighborhoods has long been an indicator of compromised health, such as low birth weight, higher rates of infant mortality, heart disease, and cancer [77,112]. One theory that may explain these findings is Massey's (2004) Biosocial Model of Stratification, particularly in that environmental stressors generated by income inequality (i.e., unequal access to resources; exposure to violence) produces allostatic load in the body [113]. This “wear and tear” associated with life in under-resourced, low-income neighborhoods, in which Black and Brown Chicagoans disproportionately reside, may elevate rates of physical and mental health problems, including reported COVID-19 related mortality [97,114]. Specific to Chicago, the Southside and Westside neighborhoods of the city endure structural barriers to healthy food [115] and quality healthcare access [116]. Similarly, due to historical redlining practices, residents of these neighborhoods are more likely to live in areas with lead poisoning [97] and toxic air pollution exposure compared to residents in the North and Central areas of Chicago [117]. The combination of these factors, among others, may both contribute to the allostatic load that neighborhood residents experience and explain the higher rates of reported COVID-19 related mortality in our findings.
However, the Cultural Armamentarium Hypothesis may also be used to explain our findings, in that individuals of specific cultures may retain culture-based practices (e.g., shared norms, family support, alcohol abstinence) that may protect these communities from negative health outcomes. These benefits may exist above and beyond the associations of mental health distress and poorer health outcomes established in research. Pertinent to this study, neighborhoods that endorsed higher rates of mental health distress in aggregate (i.e., historically Black and Brown communities) may represent cultures that possess adaptive eco-developmental and interpersonal factors that promote health despite experiencing health risk [118,119]. Given that the Zip-code level concentration of PoC was associated with decreased COVID-19 mortality rates, yet Black and Brown individuals demonstrated elevated rates COVID-19 mortality, it is likely that segregation acts synergistically with other socio-structural factors not measured in this study that may ultimately relate to race/ethnicity-based COVID-19 mortality rates [23].
4.1 Implications
Our findings have implications for future research, healthcare interventions and practice, and policy. Regarding research, while our findings provide insight into pre-existing systemic- and community-level predictors of COVID-19 outcomes, we suggest future researchers and agencies collect more recent neighborhood-level SDOH data to test for these associations. These data should include robust assessments of core constructs and allow for empirical model testing. Additionally, qualitative community-based designs will complement this quantitative work. Regarding implications for healthcare interventions, our findings suggest continued vaccine rollouts that focus on Non-Central Chicago neighborhoods may help address current COVID-19-related and broader inequities, particularly for the Southside and Westside neighborhoods. Supporting the need for such interventions, currently fewer vaccines have been sent to Southside and Westside neighborhoods because of a lack of pharmacies or physicians in areas where pharmacies have closed or don't exist, making it relatively hard for residents to adhere to medication schedules [120]. COVID-19 vaccine hesitancy, rightly rooted in the contexts of systemic racism, marginalization, and neglect, also may exist for PoC [121]. Regarding implications for healthcare practice, our findings, as well as the current and potential future resurgences of COVID-19 in Chicago, highlight the need for increased mental healthcare screening and service provision. Given the ongoing health problems of chronic/long COVID [122], the known mental health challenges experienced post COVID-19 recovery [123], the rapid national growth of the Omicron and other emergent subvariants of COVID-19 [124], we believe increased psychological screening services may be particularly timely. Finally, regarding policy implications, our findings suggest policies affecting both neighborhood and larger systemic-level factors are needed to address COVID-19 outcomes and inequities more broadly. For example, policies allocating extra funding and resources to the less affluent Chicago neighborhoods, particularly Southside and Westside neighborhoods, would likely help neighborhoods vis-à-vis infrastructure, healthcare access, public transportation, and community safety. For example, community-level psychoeducation, vaccine rollouts, COVID-19 testing, and targeted advertising related to vaccines and testing could be implemented to address neighborhood-level COVID-19 disparities. The current, relative lack of such efforts in the face of this and related study's findings reflects historical and structural inequities. Broadly, it is imperative for local-, state-, and federal-level policy to address and correct the disparities reported here and in past research.
4.2 Strengths and limitations
Our study should be interpreted in the context of its strengths and limitations. Regarding strengths, we used a creative and rigorous coding strategy to create neighborhood-level categories for analysis. Data came from Chicago – a city known for its inequities but relatively under-studied in terms of COVID-19. We also used publicly available COVID-19 data that is updated weekly, allowing us essentially real-time data for analyses. Specifically, our data represents trends across 72 weeks of time-series data, allowing for a methodologically stronger multi-level statistical approach that extends beyond the confines of simpler bivariate analyses. Use of data from the first 72 weeks of the COVID-19 pandemic allowed us to elucidate a snapshot understanding of these socio-structural associations to COVID-19 outcomes as the nation adjusted to this experience.
Regarding limitations, our pooled year-based estimates of retrospective data may be less meaningful than more recent data. Additionally, the endogeneity of mental health distress itself (e.g., mental health distress as a predictor and phenomenological outcome) was uninvestigated due to limitations of our data sources; however, such investigations may be beneficial for future investigation. Concerns exist surrounding differential neighborhood populations; however, we addressed this by including population size of each Zip-code within the multi-level analyses. Related, our sample itself (i.e., aggregated metrics for Zip-code and broader neighborhoods) did not allow us to address multi-level questions about individual or individual-by-place interactions, and thus may demonstrate ecological fallacy due to ecological regression. However, with this approach we began to address these questions through the community scope; and our findings are strengthened with time-series data. As literature suggests, likely our key variables are highly correlated. Our VIF of 5.1 indicates potential for multicollinearity concerns; however, the value is well below the VIF >10 benchmark for clear multicollinearity concerns [[125], [126], [127]]. More so, the standard errors and confidence intervals of each key predictor in the models are satisfactory – showing only a small range of uncertainty and indicating that multicollinearity is not an issue in these analyses [128]. Lastly, our data showed spatial autocorrelation for several key variables of interest and GWR showed spatially based similarities in associations. On one hand, this supports the theme of Chicago's historical and current segregation creating disenfranchised communities defined by Zip-code and larger neighborhoods. However, these approaches only allow for a cross-sectional “snapshot” understanding of our data. As such, we do advocate for additional spatial and longitudinal methods to be used to better understand these socio-structural variables regarding COVID-19. Such approaches include the use of spatial regression, Empirical Bayesian Kringing, spatial interpolation techniques, and other spatial geoprocessing tools.
5 Conclusions
In Chicago – a city with historical segregation and related long-lasting impacts on the health of individuals in marginalized neighborhoods – Westside and Southside neighborhoods generally showed higher levels of poverty and mental health distress. Reported mortality rates stabilized only for the Central neighborhood, with no differences in reported infection rates across time. There was a significant increased risk of reported COVID-19 infection in Zip-codes with more mental health distress, but reduced risk for reported COVID-19 mortality in these Zip-codes. There was a significant increased risk of reported COVID-19 infection in Zip-codes with higher concentrations of wealth; however, increased risk of reported COVID-19 mortality emerged for Zip-codes with higher concentration of poverty. When compared to Zip-codes with higher concentration of Non-Hispanic White individuals, Zip-codes with higher proportion of PoC demonstrated decreased risk for COVID-19 infection. More work is needed to test theoretically- and empirically-informed models including individual, neighborhood, and systemic-level variables. These models can best capture the complexity of health phenomenon related to COVID-19, spatial location, and mental health. We advocate for the simultaneous investigation of all social-ecological levels (i.e., individual, interpersonal, community, organizational, and policy) to better understand the potential inter-level associations of SDOH to COVID-19 outcomes.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
S.D. Ramos was supported by NIDA grant T32 DA 023356 B.N.C. Chronister was supported by NIMH grant T32 MH122376.
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References
1 World Health Organization WHO Coronavirus (COVID-19) Dashboard|WHO Coronavirus (COVID-19) Dashboard With Vaccination Data https://covid19.who.int/?adgroupsurvey={adgroupsurvey}&gclid=Cj0KCQjwg7KJBhDyARIsAHrAXaF-zXrGTVSEehtUUqzaa25Da-srVMf7qW7QWm2Ev_j_6mWlJ7Y0cXcaAiABEALw_wcB (accessed Aug. 30, 2021)
2 Zhang X.-B. Risk factors for mortality of coronavirus disease-2019 (COVID-19) patients in two centers of Hubei province, China: a retrospective analysis PLoS One 16 1 Jan. 2021 e0246030 10.1371/journal.pone.0246030
3 Grasselli G. Baseline characteristics and outcomes of 1591 patients infected with SARS-CoV-2 admitted to ICUs of the Lombardy region, Italy JAMA 323 16 Apr. 2020 1574 1581 10.1001/jama.2020.5394 32250385
4 Wu Z. McGoogan J.M. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention Jama 323 13 2020 1239 1242 32091533
5 Yang J. Prevalence of comorbidities in the novel Wuhan coronavirus (COVID-19) infection: a systematic review and meta-analysis Int J Infect Dis 10 10 2020 1016
6 Zhou F. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study Lancet 395 10229 2020 1054 1062 32171076
7 Jordan R.E. Adab P. Cheng K.K. Covid-19: risk factors for severe disease and death BMJ 368 Mar. 2020 m1198 10.1136/bmj.m1198
8 Laurencin C.T. McClinton A. The COVID-19 pandemic: a call to action to identify and address racial and ethnic disparities J Racial Ethn Health Disparities 7 3 2020 398 402 32306369
9 Sáenz R. Garcia M.A. The disproportionate impact of COVID-19 on older Latino mortality: the rapidly diminishing Latino paradox J Gerontol Ser B 76 3 2021 e81 e87
10 Millett G.A. Assessing differential impacts of COVID-19 on black communities Ann Epidemiol 47 2020 37 44 32419766
11 Freese K.E. Vega A. Lawrence J.J. Documet P.I. Social vulnerability is associated with risk of COVID-19 related mortality in US counties with confirmed cases J Health Care Poor Underserved 32 1 2021 245 257 33678695
12 US Department of Health and Human Services Social Determinants of Health - Healthy People 2030|health.gov https://health.gov/healthypeople/objectives-and-data/social-determinants-health (accessed Aug. 30, 2021)
13 Elgar F.J. Stefaniak A. Wohl M.J. The trouble with trust: time-series analysis of social capital, income inequality, and COVID-19 deaths in 84 countries Soc Sci Med 263 2020 113365
14 Raifman M.A. Raifman J.R. Disparities in the population at risk of severe illness from COVID-19 by race/ethnicity and income Am J Prev Med 59 1 2020 137 139 32430225
15 Bronfenbrenner U. The ecology of human development: Experiments by nature and design 1979 Harvard University Press
16 Tumin D. Menegay M. Shrider E.A. Nau M. Tumin R. Local income inequality, individual socioeconomic status, and unmet healthcare needs in Ohio, USA Health Equity 2 1 2018 37 44 30283849
17 De P.K. Price T. Pre-existing economic conditions and COVID-19 infections and mortality in new York City J Health Care Poor Underserved 32 4 2021 1978 1994 34803054
18 Hawkins D. Differential occupational risk for COVID-19 and other infection exposure according to race and ethnicity Am J Ind Med 63 9 2020 817 820 32539166
19 Kondo N. van Dam R.M. Sembajwe G. Subramanian S.V. Kawachi I. Yamagata Z. Income inequality and health: the role of population size, inequality threshold, period effects and lag effects J Epidemiol Community Health 66 6 2012 1 6 21875869
20 Kondo N. Sembajwe G. Kawachi I. Van Dam R.M. Subramanian S.V. Yamagata Z. Income inequality, mortality, and self rated health: meta-analysis of multilevel studies Bmj 339 2009
21 Oronce C.I.A. Scannell C.A. Kawachi I. Tsugawa Y. Association between state-level income inequality and COVID-19 cases and mortality in the USA J Gen Intern Med 35 9 2020 2791 2793 32583336
22 Mateo-Urdiales A. Socioeconomic patterns and COVID-19 outcomes before, during and after the lockdown in Italy (2020) Health Place 71 Sep. 2021 102642 10.1016/j.healthplace.2021.102642
23 Kathe N.J. Wani R.J. Determinants of COVID-19 case fatality rate in the United States: spatial analysis over one year of the pandemic J Health Econ Outcomes Res 8 1 2021 51 34017883
24 Li D. Identifying US County-level characteristics associated with high COVID-19 burden BMC Public Health 21 1 May 2021 1007 10.1186/s12889-021-11060-9 34049526
25 Pro G. Gilbert P.A. Baldwin J.A. Brown C.C. Young S. Zaller N. Multilevel modeling of county-level excessive alcohol use, rurality, and COVID-19 case fatality rates in the US PLoS One 16 6 Jun. 2021 e0253466 10.1371/journal.pone.0253466
26 Arvidsdotter T. Marklund B. Kylén S. Taft C. Ekman I. Understanding persons with psychological distress in primary health care Scand J Caring Sci 30 4 2016 687 694 26463897
27 McLachlan K.J. Gale C.R. The effects of psychological distress and its interaction with socioeconomic position on risk of developing four chronic diseases J Psychosom Res 109 2018 79 85 29680578
28 Rasul F. Stansfeld S.A. Hart C.L. Smith G.D. Psychological distress, physical illness, and risk of coronary heart disease J Epidemiol Community Health 59 2 2005 140 145 15650146
29 Cao W. The psychological impact of the COVID-19 epidemic on college students in China Psychiatry Res 287 2020 112934
30 Qiu J. Shen B. Zhao M. Wang Z. Xie B. Xu Y. A nationwide survey of psychological distress among Chinese people in the COVID-19 epidemic: implications and policy recommendations Gen Psychiatry 33 2 2020
31 Vindegaard N. Benros M.E. COVID-19 pandemic and mental health consequences: systematic review of the current evidence Brain Behav Immun 89 2020 531 542 32485289
32 Haucke M. Liu S. Heinzel S. The persistence of the impact of COVID-19–related distress, mood inertia, and loneliness on mental health during a postlockdown period in Germany: an ecological momentary assessment study JMIR Ment Health 8 8 2021 e29419
33 Neelam K. Duddu V. Anyim N. Neelam J. Lewis S. Pandemics and pre-existing mental illness: a systematic review and meta-analysis Brain Behav Immun - Health 10 Nov. 2020 100177 10.1016/j.bbih.2020.100177
34 Shinn A.K. Viron M. Perspectives on the COVID-19 pandemic and individuals with serious mental illness J Clin Psychiatry 81 3 2020
35 Lee S.W. Association between mental illness and COVID-19 susceptibility and clinical outcomes in South Korea: a nationwide cohort study Lancet Psychiatry 7 12 2020 1025 1031 32950066
36 Lee J. Ramírez I.J. Geography of disparity: connecting COVID-19 vulnerability and social determinants of health in Colorado Behav Med 48 2 2022 72 84 35318900
37 Nemani K. Association of psychiatric disorders with mortality among patients with COVID-19 JAMA Psychiat 78 4 2021 380 386
38 Wang Q. Xu R. Volkow N.D. Increased risk of COVID-19 infection and mortality in people with mental disorders: analysis from electronic health records in the United States World Psychiatry 20 1 2021 124 130 33026219
39 Compton M.T. Shim R.S. The social determinants of mental health Focus 13 4 2015 419 425
40 Muramatsu N. County-level income inequality and depression among older Americans Health Serv Res 38 6p2 2003 1863 1884 14727801
41 Messias E. Eaton W.W. Grooms A.N. Economic grand rounds: income inequality and depression prevalence across the United States: an ecological study Psychiatr Serv 62 7 2011 710 712 21724781
42 Kim D. Kawachi I. US state-level social capital and health-related quality of life: multilevel evidence of main, mediating, and modifying effects Ann Epidemiol 17 4 2007 258 269 17324589
43 Mishra S. Carleton R.N. Subjective relative deprivation is associated with poorer physical and mental health Soc Sci Med 147 2015 144 149 26575605
44 Sommet N. Morselli D. Spini D. Income inequality affects the psychological health of only the people facing scarcity Psychol Sci 29 12 2018 1911 1921
45 Scott J.L. Lee-Johnson N.M. Danos D. Place, race, and case: examining racialized economic segregation and COVID-19 in Louisiana J Racial Ethn Health Disparities 2022 1 13
46 Kim S.J. Bostwick W. <?covid19?> social vulnerability and racial inequality in COVID-19 deaths in Chicago Health Educ Behav 47 4 2020 509 513 32436405
47 Bergstrand K. Mayer B. Brumback B. Zhang Y. Assessing the relationship between social vulnerability and community resilience to hazards Soc Indic Res 122 2 Jun. 2015 391 409 10.1007/s11205-014-0698-3 29276330
48 Ge Y. Dou W. Zhang H. A new framework for understanding urban social vulnerability from a network perspective Sustainability 9 10 Oct. 2017 10.3390/su9101723
49 Burton C. Rufat S. Tate E. Social vulnerability Vulnerabil Resil Nat Hazards 2018 53 81
50 Cutter S.L. Vulnerability to environmental hazards Prog Hum Geogr 20 4 1996 529 539
51 Pelling M. The vulnerability of cities: Natural disasters and social resilience 2012 Routledge
52 Gonzalez R. Fuentes A. Muñoz E. On social capital and health: the moderating role of income inequality in comparative perspective Int J Sociol 50 1 2020 68 85
53 Hamouche S. COVID-19 and employees’ mental health: stressors, moderators and agenda for organizational actions Emerald Open Res 2 2020
54 Spoer B.R. Peer reviewed: health and health determinant metrics for cities: a comparison of county and City-level data Prev Chronic Dis 17 2020
55 Chicago.gov City of Chicago COVID Dashboard https://www.chicago.gov/city/en/sites/covid-19/home/covid-dashboard.html (accessed Aug. 30, 2021)
56 Chicago Department of Public Health Protecting Chicago: Phase IV Re-Opening Metrics Summary Accessed: Nov. 06, 2022. [Online]. Available: https://www.chicago.gov/content/dam/city/sites/covid/reports/CDPH-COVID-Reopening-metrics_070221.pdf Jul. 2021
57 Bryan M.S. Coronavirus disease 2019 (COVID-19) mortality and neighborhood characteristics in Chicago Ann Epidemiol 56 2021 47 54 33181262
58 Bertocchi G. Dimico A. COVID-19, race, and redlining 2020
59 Laurence J. Wider-community segregation and the effect of neighbourhood ethnic diversity on social capital: an investigation into intra-neighbourhood trust in Great Britain and London Sociology 51 5 2017 1011 1033 28989199
60 NYU Langone Health Chicago, IL - City Overview | City Health Dashboard https://www.cityhealthdashboard.com/il/chicago/city-overview (accessed Aug. 30, 2021)
61 Hooper M.W. Nápoles A.M. Pérez-Stable E.J. COVID-19 and racial/ethnic disparities Jama 323 24 2020 2466 2467 32391864
62 Buhimschi C.S. Prevalence and neighborhood geomapping of COVID-19 in an underserved Chicago pregnant population Am J Perinatol Rep 10 04 2020 e413 e416
63 Gourevitch M.N. Athens J.K. Levine S.E. Kleiman N. Thorpe L.E. City-level measures of health, health determinants, and equity to foster population health improvement: the City health dashboard Am J Public Health 109 4 2019 585 592 30789770
64 Chicago Health Atlas Places|Chicago Health Atlas https://chicagohealthatlas.org/neighborhood (accessed Aug. 30, 2021)
65 Cohen S. Prachand N. Bocskay K. Sayer J. Schuh T. Healthy Chicago 2.0 community health assessment: Informing efforts to achieve health equity Retrived from https://www.cityofchicago.org/content/dam/city/depts/cdph… 2016
66 “Psychiatry Online,” DSM Library10.1176/appi.books.9780890425596 (accessed Dec. 19, 2021).
67 Cree R.A. Okoro C.A. Zack M.M. Carbone E. Frequent mental distress among adults, by disability status, disability type, and selected characteristics—United States, 2018 Morb Mortal Wkly Rep 69 36 2020 1238
68 Moriarty D.G. Zack M.M. Kobau R. The centers for disease control and prevention’s healthy days measures–Population tracking of perceived physical and mental health over time Health Qual Life Outcomes 1 1 2003 1 8 12605709
69 Thurston-Hicks A. Paine S. Hollifield M. Rural psychiatry: functional impairment associated with psychological distress and medical severity in rural primary care patients Psychiatr Serv 49 7 1998 951 955 9661232
70 Moriarty D.G. Zack M.M. Holt J.B. Chapman D.P. Safran M.A. Geographic patterns of frequent mental distress: US adults, 1993–2001 and 2003–2006 Am J Prev Med 36 6 2009 497 505 19460657
71 Liu Y. Association between perceived insufficient sleep, frequent mental distress, obesity and chronic diseases among US adults, 2009 behavioral risk factor surveillance system BMC Public Health 13 1 2013 1 8 23280303
72 Strine T.W. Balluz L. Chapman D.P. Moriarty D.G. Owens M. Mokdad A.H. Risk behaviors and healthcare coverage among adults by frequent mental distress status, 2001 Am J Prev Med 26 3 2004 213 216 15026100
73 Dominick K.L. Ahern F.M. Gold C.H. Heller D.A. Relationship of health-related quality of life to health care utilization and mortality among older adults Aging Clin Exp Res 14 6 2002 499 508 12674491
74 Taylor R.M. Gibson F. Franck L.S. A concept analysis of health-related quality of life in young people with chronic illness J Clin Nurs 17 14 2008 1823 1833 18578756
75 Chen H.-Y. Baumgardner D.J. Rice J.P. Peer reviewed: health-related quality of life among adults with multiple chronic conditions in the United States, behavioral risk factor surveillance system, 2007 Prev Chronic Dis 8 1 2011
76 Doll H.A. Petersen S.E. Stewart-Brown S.L. Obesity and physical and emotional well-being: associations between body mass index, chronic illness, and the physical and mental components of the SF-36 questionnaire Obes Res 8 2 2000 160 170 10757202
77 Krieger N. Waterman P.D. Spasojevic J. Li W. Maduro G. Van Wye G. Public health monitoring of privilege and deprivation with the index of concentration at the extremes Am J Public Health 106 2 2016 256 263 26691119
78 Bishop-Royse J. Lange-Maia B. Murray L. Shah R.C. DeMaio F. Structural racism, socio-economic marginalization, and infant mortality Public Health 190 2021 55 61 33348089
79 Short S.E. Mollborn S. Social determinants and health Behaviors: conceptual frames and empirical advances Curr Opin Psychol 5 Oct. 2015 78 84 10.1016/j.copsyc.2015.05.002 26213711
80 N. R. Council (US) I. of Medicine (US) Woolf S.H. Aron L. Physical and Social Environmental Factors 2013 National Academies Press (US) Accessed: Dec. 02, 2022. [Online]. Available: https://www.ncbi.nlm.nih.gov/books/NBK154491/
81 CDC Unhealthy behaviors measure definitions Oct. 20, 2021 Centers for Disease Control and Prevention https://www.cdc.gov/places/measure-definitions/unhealthy-behaviors/index.html (accessed Dec. 19, 2021)
82 Centers for Disease Control and Prevention Alcohol Screening and Brief Intervention Before Prescribing Opioids https://www.cdc.gov/alcohol/fact-sheets/alcohol-screening.html Jan. 02, 2020 (accessed Dec. 19, 2021)
83 National Institute on Alcohol Abuse and Alcoholism Understanding Binge Drinking https://www.niaaa.nih.gov/publications/brochures-and-fact-sheets/binge-drinking (accessed Dec. 19, 2021)
84 World Health Organization Alcohol and COVID-19: what you need to know https://www.euro.who.int/__data/assets/pdf_file/0010/437608/Alcohol-and-COVID-19-what-you-need-to-know.pdf 2020 (accessed Dec. 19, 2021)
85 Director's Blog: Alcohol poses different challenges during the COVID-19 pandemic|National Institute on Alcohol Abuse and Alcoholism (NIAAA) https://www.niaaa.nih.gov/directors-blog-alcohol-poses-different-challenges-during-covid-19-pandemic
86 Sallis R. Young D.R. Tartof S.Y. Sallis J.F. Sall J. Li Q. …Cohen D.A. Physical inactivity is associated with a higher risk for severe COVID-19 outcomes: a study in 48 440 adult patients British journal of sports medicine 55 19 2021 1099 1105 33849909
87 CDC People with certain medical conditions Ctr Dis Control Prev Nov. 22, 2022 https://www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/people-with-medical-conditions.html [accessed Dec. 02, 2022]
88 Haddad C. Bou Malhab S. Sacre H. Salameh P. Smoking and COVID-19: a scoping review Tob Use Insights 14 2021 p. 1179173X21994612
89 Usman M.S. Is there a smoker’s paradox in COVID-19? BMJ Evid-Based Med 26 6 2021 279 284
90 Fernández-Niño J.A. Peña-Maldonado C. Rojas-Botero M. Rodriguez-Villamizar L.A. Effectiveness of contact tracing to reduce fatality from COVID-19: preliminary evidence from Colombia Public Health 198 2021 123 128 34416575
91 Kumar A. Global impact of environmental temperature and BCG vaccination coverage on the transmissibility and fatality rate of COVID-19 PLoS One 15 10 2020 e0240710
92 Strully K. Yang T.-C. Liu H. Regional variation in COVID-19 disparities: connections with immigrant and Latinx communities in U.S. counties Ann Epidemiol 53 Jan. 2021 56 62.e2 10.1016/j.annepidem.2020.08.016 32927056
93 Bagshaw S.M. Association between pandemic coronavirus disease 2019 public health measures and reduction in critical care utilization across ICUs in Alberta, Canada Crit Care Med 50 3 2022 353 34495878
94 Finch W.H. Hernández Finch M.E. Poverty and Covid-19: rates of incidence and deaths in the United States during the first 10 weeks of the pandemic Front Sociol 5 2020 47 33869454
95 Hünermund P. Louw B. On the nuisance of control variables in regression analysis ArXiv Prepr 2020 1 22 ArXiv200510314
96 Tung E.L. Johnson T.A. O’Neal Y. Steenes A.M. Caraballo G. Peek M.E. Experiences of community violence among adults with chronic conditions: qualitative findings from Chicago J Gen Intern Med 33 11 2018 1913 1920 30076574
97 Sampson R.J. Winter A.S. The racial ecology of lead poisoning: toxic inequality in Chicago neighborhoods, 1995-2013 Bois Rev Soc Sci Res Race 13 2 2016 261 283
98 Yonek J. Hasnain-Wynia R. A profile of health and health resources within Chicago’s 77 communities Northwest Univ Inst Healthc Stud 14 2011
99 American Public Health Association Reducing Income Inequality to Advance Health https://www.apha.org/Policies-and-Advocacy/Public-Health-Policy-Statements/Policy-Database/2018/01/18/Reducing-Income-Inequality-to-Advance-Health (accessed Aug. 30, 2021)
100 Kondo M.C. Andreyeva E. South E.C. MacDonald J.M. Branas C.C. Neighborhood interventions to reduce violence Annu Rev Public Health 39 2018 253 271 29328874
101 Russ T.C. Stamatakis E. Hamer M. Starr J.M. Kivimäki M. Batty G.D. Association between psychological distress and mortality: individual participant pooled analysis of 10 prospective cohort studies Bmj 345 2012
102 Greeson J.M. Hurwitz B.E. Llabre M.M. Schneiderman N. Penedo F.J. Klimas N.G. Psychological distress, killer lymphocytes and disease severity in HIV/AIDS Brain Behav Immun 22 6 2008 901 911 18321678
103 Latoo J. Mistry M. Dunne F.J. Physical morbidity and mortality in people with mental illness Br J Med Pr 6 3 2013 621 623
104 Walker E.R. McGee R.E. Druss B.G. Mortality in mental disorders and global disease burden implications: a systematic review and meta-analysis JAMA Psychiat 72 4 2015 334 341
105 Appelmann H.M. Manigault A.W. Shorey R.C. Zoccola P.M. Childhood adversity and cortisol habituation to repeated stress in adulthood Psychoneuroendocrinology 125 Mar. 2021 105118 10.1016/j.psyneuen.2020.105118
106 Tyra A.T. Adverse childhood experiences, sex, and cardiovascular habituation to repeated stress Biol Psychol 165 Oct. 2021 108175 10.1016/j.biopsycho.2021.108175
107 Bilek E. Deficient amygdala habituation to threatening stimuli in borderline personality disorder relates to adverse childhood experiences Biol Psychiatry 86 12 Dec. 2019 930 938 10.1016/j.biopsych.2019.06.008 31366446
108 Bennett J.M. Rohleder N. Sturmberg J.P. Biopsychosocial approach to understanding resilience: stress habituation and where to intervene J Eval Clin Pract 24 6 2018 1339 1346 30338615
109 Mata J. Wenz A. Rettig T. Reifenscheid M. Möhring K. Krieger U. …Naumann E. Health behaviors and mental health during the COVID-19 pandemic: A longitudinal population-based survey in Germany Social Science & Medicine 287 2021 1 9 114333
110 Horenstein A. Heimberg R.G. Anxiety disorders and healthcare utilization: a systematic review Clin Psychol Rev 81 Nov. 2020 101894 10.1016/j.cpr.2020.101894
111 Liberman J.N. Impact of the COVID-19 pandemic on healthcare resource utilization in individuals with major depressive disorder Health Serv Res Manag Epidemiol 9 Jan. 2022 10.1177/23333928221111864 p. 23333928221111864
112 Williams D.R. Collins C. Racial residential segregation: A fundamental cause of racial disparities in health public health reports 2001 SAGE PublicationsSage CA Los Angeles, CA
113 Massey D.S. Segregation and stratification: a biosocial perspective Bois Rev Soc Sci Res Race 1 1 2004 7 25
114 Sharkey P. Sampson R.J. 13. Violence, cognition, and Neighborhood inequality in America Social Neuroscience 2015 Harvard University Press 320 339
115 Kolak M. Urban foodscape trends: disparities in healthy food access in Chicago, 2007–2014 Health Place 52 2018 231 239 30015180
116 West Side Health Equity Collaborative Application for Funding - Healthcare Transformation Collaboratives [Online]. Available: https://www2.illinois.gov/hfs/Documents/West%20Side%20Health%20Equity%20Collaborative_Application.pdf
117 Interactive Map: Pollution Hits Chicago's West, South Sides Hardest | Better Government Association https://www.bettergov.org/news/interactive-map-pollution-hits-chicagos-west-south-sides-hardest/
118 Alamilla S.G. Explaining the immigrant paradox: the influence of acculturation, enculturation, and acculturative stress on problematic alcohol consumption Behav Med 46 1 2020 21 33 30615590
119 Salas-Wright C.P. Vaughn M.G. Schwartz S.J. Córdova D. An ‘immigrant paradox’ for adolescent externalizing behavior? Evidence from a national sample Soc Psychiatry Psychiatr Epidemiol 51 1 2016 27 37 26328521
120 Health Care Council of Chicago Event Recap|The Challenging Future of the Chicago Safety Net White Paper Release|03.18.2021 https://www.hc3.health/post/event-recap-the-challenging-future-of-the-chicago-safety-net-white-paper-release-03-18-2021 (accessed Aug. 30, 2021)
121 Quinn S.C. Andrasik M.P. Addressing vaccine hesitancy in BIPOC communities—toward trustworthiness, partnership, and reciprocity New England Journal of Medicine 385 2 2021 97 100 33789007
122 Raveendran A.V. Jayadevan R. Sashidharan S. Long COVID: an overview Diabetes Metab Syndr Clin Res Rev 15 3 May 2021 869 875 10.1016/j.dsx.2021.04.007
123 M. M. Hossain et al., “Epidemiology of mental health problems in COVID-19: a review,” F1000Research, vol. 9, p. 636, Jun. 202010.12688/f1000research.24457.1.
124 Lyngse F.P. Transmission of SARS-CoV-2 omicron VOC subvariants BA. 1 and BA. 2: Evidence from Danish households 2022 MedRxiv
125 Vittinghoff E. Regression methods in biostatistics: Linear, logistic, survival, and repeated measures models 2005 Springer New York
126 Gareth J. Daniela W. Trevor H. Robert T. An introduction to statistical learning: With applications in R 2013 Spinger
127 Menard S. Applied logistic regression analysis 2002 Sage
128 Goldberger A.S. Goldberger A.S.G. A course in econometrics 1991 Harvard University Press
| 0 | PMC9731648 | NO-CC CODE | 2022-12-16 23:18:08 | no | Dialogues Health. 2023 Dec 8; 2:100091 | utf-8 | Dialogues Health | 2,022 | 10.1016/j.dialog.2022.100091 | oa_other |
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Heliyon
Heliyon
Heliyon
2405-8440
The Author(s). Published by Elsevier Ltd.
S2405-8440(22)03469-7
10.1016/j.heliyon.2022.e12181
e12181
Research Article
Knowledge mapping of population health: A bibliometric analysis
Guo Limei a
Zhang Weike b∗
a School of Economics, Sichuan University, Chengdu 610065, China
b School of Public Administration, Sichuan University, Chengdu 610065, China
∗ Corresponding author.
9 12 2022
12 2022
9 12 2022
8 12 e12181e12181
24 9 2022
12 11 2022
30 11 2022
© 2022 The Author(s)
2022
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In recent years, population health has aroused great interest, especially after the outbreak of Covid-19. The related research increases substantially year by year. There are many kinds of research about population health, but few scholars use the bibliometric method to discuss them. Motivated by keeping abreast of emerging trends and critical turns in population health, this study adopts the bibliometric method to analyze the development history and status quo of population health, providing a summary description for it. This study adopts CiteSpace to conduct a bibliometric analysis of publications related to population health in Web of Science from 1971 to 2021. The most productive countries, authors, institutions, and research direction changes are analyzed. The research results show that: First, the number of publications and citations related to population health increases for years, especially in Canada, the United States, the United Kingdom, and Australia. Second, the number of publications by different countries or institutions in population health varies greatly, and they cooperate closely. Third, the co-occurrence of disciplines and keywords in population health is displayed. Finally, this study reveals the primary research force, the major themes, significant milestones, landmarks, and the evolution of the hot fronts. In all, the comprehensive analysis of this study would provide some enlightenment for future research.
Population health; CiteSpace; Network analysis; Bibliometric method.
Keywords
Population health
CiteSpace
Network analysis
Bibliometric method
==== Body
pmc1 Introduction
Population health refers to a broad range of topics affecting health, including the concept of health and a field of study of health determinants. It spans different disciplines such as medicine, epidemiology, economics, and sociology [1]. It also involves health outcomes, disparities, patterns of health determinants, risk factors, and policies and interventions relating to health outcomes and health patterns [2]. Population health is a topic of greatest concern to mankind. Since 1971, the Population, Health and Family Group set some goals and offered implementation strategies for population health [3]. In recent years, with the rapid development of big data, artificial intelligence, and biomedical technologies, the level of disease prevention, treatment, and health care has been significantly improved [4, 5]. In particular, significant progress has been made in life science technology, cell research, brain science, gene-editing technology, synthetic biology, tissue and organ manufacturing technology, and so on [6, 7]. Thousands of academic papers have been published about population health. Different scholars have done various discussions from different perspectives and fields, such as income inequality [8, 9], social capital [10], macroeconomic determinants [11, 12], and physical activity [13, 14]. The literature on population health evolves from information systems [15], and health promotion [16] to the global burden [17], physical activity, structural racism [18], etc. Extracting useful information from existing publications and identifying the development trends is of great value.
There have been some systematic literature reviews on this topic, but few have analyzed it using bibliometric methods. A comprehensive understanding of the history and current situation of population health research, especially the research trends and hot fronts, is conducive to future research. Therefore, this study uses a visualization tool to analyze the knowledge of population health to grasp the primary knowledge of population health. It analyzes population health from the perspective of the total number of publications, the publications countries and regions, topics, research disciplines, authors, the citation and cooperation of institutions and countries/regions, and other aspects systematically. In particular, the basis, hot spots, and trends of population health research are clarified through reference co-citation analysis.
The rest of the paper is organized as follows. The second section introduces the database and research methods. The third section is the results of the bibliometrics analysis, including the analysis of the reference co-citation clusters, emerging trends, and references with high betweenness centrality and high citation. The final section gives a discussion.
2 Materials and methods
2.1 Materials
This study takes two steps in building the CiteSpace database. The first step is to select the database. It selects the publications covering articles and review articles from the core database of the Web of Science (WoS). In order to clearly track the theme trends, this study set the keyword of population health as the search term. In detail, if the term appears in the title, abstract, or keywords of a publication, the publication will be chosen to be analyzed. The specific search strategy is as follows: topic = (population health), and the period is set from 1971 to 2021 [19]. The document types are set as “article” and “review article”. Finally, a total of 13,205 valid publications are collected for this study. The proceeding paper, book chapters, early access, and data paper are excluded as invalid records. The second step is to draw different network maps through CiteSpace. The data are imported into CiteSpace and each map is generated with different parameters.
2.2 Methods
This study uses CiteSpace to delineate the structure and dynamics of population health. CiteSpace is citation visualization analysis software developed by Chen [20]. It is developed in the context of scientometrics and can facilitate the detection of potential knowledge and visualize the structure, rules, and distribution of some research areas. It is widely applied for analyzing and visualizing co-citation networks, which can label co-citation clusters and improve the timelines of visualized co-citation networks. As a quantitative analysis tool, it can analyze the structure, rules, and distribution of scientific knowledge and display the results through the scientific knowledge map. It can also mine knowledge, such as emerging trends, knowledge structures, subject hotspots, and landmark references [21, 22]. For example, in the research cooperation network, the node size reflects the number of publications published by authors, countries, regions, or institutions. In the co-occurrence analysis, the node size reflects the frequency of topics, keywords, or scientific disciplines. In the co-citation network, the node size represents the citation times of references, journals, or authors. The time slicing is set from 1971 to 2021 and a slice includes five years.
3 Results
3.1 Quantitative analysis of publications
The number of publications can reflect the importance of a research topic. In this section, this study conducts a quantitative analysis of the total number of publications and the publications of countries or regions.
3.1.1 Publications by year
Figure 1 shows the number of publications and citations in population health research from 1971 to 2021. The number of publications on population health has been increasing, and the number of citations has also increased with the number of publications. It shows that population health has received extensive attention from academics.Figure 1 The number of publications and citations in population health from 1971 to 2021.
Figure 1
In particular, population health research can be divided into three stages in terms of the total number of publications. The period from 1971 to 1994 is the initial stage. The first publication on the topic of population health was published in 1971 [3]. The author put forward some goals and strategies for protecting the biosphere. The number of publications in this stage is less than 20 each year, indicating that population health has not received enough attention. The second stage is a rising stage. During the years from 1995 to 2016, the number of publications increases gradually, and by 2016, the number of publications has reached 804. The third stage is the research explosion stage. It is from 2017 to 2021. During this period, more than 1000 publications are published each year. Especially, since the Covid-19 outbreak, more than 1500 publications have been published in the past two years. The topic of population health has received unprecedented attention.
Concerning the number of citations, it can also be divided into three stages. The first stage is from 1971 to 1994, with a few citations. The second stage is also a rising stage. During the years from 1995 to 2014, the number of citations increases rapidly, and the increment reaches its highest point at 28926 in 2014. The third stage is from 2015 to 2021. The citation increment decreases gradually.
3.1.2 Publications by countries or regions
The number of publications reflects the importance attached to this field by countries or regions. The networks reflect the cooperation among countries. This study selects the top 50 countries or regions that occurred in publications and constructs the current national or regional cooperation networks. 148 nodes and 68 links are obtained in Figure 2 . Each node represents a country or region, and the link means a cooperative relationship. The node size represents the number of publications of the country or region, and the node color represents the corresponding year when the publications appeared. The link color corresponds to the cooperation year. The thickness of the links represents the strength of the cooperative relationship.Figure 2 The cooperation networks of productions by countries or regions.
Figure 2
Table 1 lists the top 20 most productive countries or regions. The United States published 6264 articles, accounting for almost half of the total number of publications. It is followed by Canada, which published 2417 articles. In addition, Australia, England, New Zealand, Germany, and Spain have published a lot of articles. The United States and Canada have discussed population health earlier. The United States published the first article in 1971, followed by Canada in 1972. Figure 2 displays the cooperation networks of population health research by countries or regions. Australia, France, Spain, Denmark, the United States, England, and Scotland formed close academic networks. The cooperation relationship occurred between Norway and Denmark, Colombia and Mexico, and Thailand and Switzerland.Table 1 Top 20 productive countries or regions in population health.
Table 1Country or regions Publications Country or region Publications
The United States 6264 Italy 261
Canada 2417 Scotland 248
England 1468 Brazil 245
Austria 1339 Switzerland 223
Peoples R China 448 South Africa 194
Netherlands 364 New Zealand 192
Germany 360 Iran 191
Spain 318 Australia 157
Sweden 280 Denmark 149
France 277 Belgium 145
3.2 Analysis of productive authors and institutions
3.2.1 Analysis of productive authors
The top 50 productive authors of every slice are selected to analyze the productive authors in population health. Table 2 reports the top 10 productive authors in population health, including Christopher JLM, Martin M, Theo VOS, Sandro G, Scott BP, Alan DL, Johan PM, Mohsen N, Bilie G, and Carles M. Christopher JLM has published 45 articles, and his first publication was in 2006. Judging from the number of published articles, he has the richest research experience in this field. Martin M and Theo VOS rank the second with 38 publications and their first articles were in 2007. Sandro G has 37 publications, and the first was published in 2006. Scott BP has published 36 articles with the first article published in 2007. The number of publications of Christopher JLM accounts for 1.11% of the total publications by productive authors. The number of publications of Martin M and Theo VOS accounts for 0.94%. The number of publications of Sandro G accounts for 0.91%. Among them, Christopher JLM, Sandro G, Alan DL, and Carles M published their first articles in 2006. Sandro G and Martin M started their population health research earlier than other authors and kept their research on this topic longer.Table 2 Top 10 productive authors in population health.
Table 2Authors Publications Institution Country The proportion of the total number First published time
Christopher JLM 45 the United States 1.11% 2006
Martin M 38 England 0.94% 2007
Theo VOS 38 Australia 0.94% 2007
Sandro G 37 the United States 0.91% 2006
Scott BP 36 Canada 0.89% 2007
Alan DL 35 the United States 0.86% 2006
Johan PM 30 England 0.74% 2011
Mohsen N 25 the United States 0.62% 2011
Billie G 25 Australia 0.62% 2013
Carles M 25 Canada 0.62% 2006
Figure 3 displays the cooperation networks of authors in population health. There are 693 nodes and 846 links. Each node represents an author, and the link represents the cooperative relationship. The node size represents the number of publications of authors. The larger the node is, the more publications the author published. The color in the center of the circles indicates the year when the author first published an article in this field. The outermost color represents the latest time when the authors publish their articles. The links between the nodes indicate the cooperative relationship between authors. The color of the link shows the time of the first cooperation. The red link means front-edge cooperation. As shown in Figure 3, Sandro G and Martin M have published many articles but cooperated less with different authors. Alan DL, Christopher JLM, Theo VOS, and Mohsen N have cooperated with other authors more closely. Generally speaking, Figure 3 shows close cooperative networks among authors. Most of the cooperation happened from 2011 to 2020.Figure 3 Cooperation networks of authors.
Figure 3
3.2.2 Analysis of productive institutions
The more articles an institution publishes in the field, the more influence it is likely to have. This study selects the top 50 institutions to discuss the distribution of productive institutions. Table 3 presents the top 15 most productive institutions. The University of Toronto published the most articles (529), accounting for 2.40% of the total publications. It is far ahead of the University of Washington and Harvard University (323). The others published between 194 and 277 articles in the field. All of the leading institutions are from Canada, the United States, and Australia. Six of the top 15 institutions are from Canada and the United States. They attach great importance to population health research.Table 3 Top 15 productive institutions in population health.
Table 3Institution Country Publications The proportion of the total number First published year
University of Toronto Canada 529 2.40% 1997
University of Washington the United States 323 1.47% 2002
Harvard University the United States 323 1.47% 1998
University of British Columbia Canada 277 1.26% 1997
University of Sydney Australia 268 1.22% 2001
University of Michigan the United States 255 1.16% 1997
University of Ottawa Canada 251 1.14% 1998
University of Calgary Canada 232 1.05% 1998
University of Melbourne Australia 227 1.03% 2001
McMaster University Canada 226 1.03% 1997
University of North Carolina the United States 206 0.94% 1998
University of Queensland Australia 205 0.93% 1997
McGill University Canada 202 0.92% 1998
Columbia University the United States 201 0.91% 2002
Harvard Medical School the United States 194 0.88% 2016
Figure 4 presents the cooperation networks of institutions. There are 789 nodes and 4418 links. Each node represents an institution, and the links between nodes represent the cooperative relationship. The node size represents the number of articles published by institutions. The link color corresponds to the year of the first cooperation. Figure 4 reveals that the University of Toronto, the University of British Columbia, the University of Michigan, the McMaster University, and the University of Queensland published articles as early as 1997. The institutions cooperated closely, especially from 2011 to 2020.Figure 4 Cooperation networks of institutions.
Figure 4
3.3 Co-occurrence analysis of disciplines and keywords
Co-occurrence analysis has been widely used in bibliometrics. It means something co-occurs in the same article. If the content of an article belongs to two disciplines, the two disciplines co-occur. If two or more keywords appear in one article, the two or more keywords co-occur. CiteSpace also counts the most occurred disciplines and keywords. Through analyzing the occurrence times, the research hotspots and trends are known.
3.3.1 Disciplines Co-occurrence analysis
To generate the co-occurrence networks of disciplines, this study selects the top 50 disciplines. Figure 5 presents the co-occurrence networks of disciplines. There are 95 nodes and 443 links in total. Each node represents a discipline, and the links between nodes represent co-occurrence relationships. The node size represents the number of articles published in the discipline, and the color of the links corresponds to the year of co-occurrence of disciplines. The more times the disciplines co-occur, the closer relationships among the disciplines are.Figure 5 Co-occurrence networks of disciplines.
Figure 5
As can be seen from Figure 4, among all disciplines related to population health, “public, environmental and occupational health” published the most articles, 4841 in total. It is followed by the discipline of “Health Care Sciences & Services”, which published 2501 articles. The discipline of “General & Internal Medicine” is with 1360 articles. The discipline of “Environmental Sciences & Ecology” is with 861 articles. The discipline of “Biomedical Social Sciences” is with 543 articles. The discipline of “Multidisciplinary Sciences and Science & Technology” published its first article on population health in 1971, which is the earliest one. It is followed by the disciplines of “Science & Technology” and “Public, Environmental & Occupational Health”. The disciplines do not co-occur frequently, which indicates the loose cooperation among disciplines.
3.3.2 Keywords co-occurrence analysis
The keywords reflect the main content of the research in the field. This study uses the keyword co-occurrence networks to reveal the correlation of keywords. The top 50 keywords with the highest occurrences are chosen to construct the current keyword’s co-occurrence networks. The network pruning method is “MST + Pruning the merged network”. A total of 115 nodes and 129 links are obtained. Each node represents a keyword, and the links between the keywords indicate that they appear together in an article. The size of the node represents the number of the appearance of keywords. The thickness of the link represents the strength of the co-occurrence relationship. Figure 6 presents the co-occurrence networks of keywords in population health.Figure 6 Co-occurrence networks of keywords.
Figure 6
Figure 6 shows that the “population health” node is the largest in the networks. It appears 2581 times, followed by “morality”, which appears 1361 times. “Health” appears 1091 times and “public health” appears 1081 times. In addition, other keywords, such as “health status”, “quality of life”, “health care”, and “income inequality”, also appear frequently. The nodes of “public health”, “inequality”, “income inequality”, “health care”, “population health”, and “mortality” co-occur with different nodes, indicating they are research hotspots in this field.
Burst keywords are the keywords that emerge intensely in a period. It represents the research fronts and hotspots in the corresponding period. Figure 7 presents the top 20 keywords with the strongest citation bursts in population health. The keyword with the strongest citation burst is “self-rated health”. It began in 2001 and ended in 2015. “Life expectancy” is the first burst keyword and it began in 1991 and ended in 2010. In addition, “morbidity” and “health status” also began to burst in 1991. “Income inequality”, “coronary heart disease” and “epidemiology” burst in 1996 and ended in 2015. The burst keywords in the latest years are the research fronts, including “social determinants of health”, “framework”, “health disparity”, “social determinant” and “burden”. These burst keywords show the emerging research trends of population health. Based on the beginning year, the research trends can be divided into four stages. “Life expectancy”, “Canada”, “morbidity” and “health status” are the first research stage. “Income inequality”, “coronary heart disease”, “men”, “epidemiology”, “disability”, and “disability” are in the second research stage. The “self-rated health”, “disorder”, “health inequality”, “multilevel analysis”, “access” and “people” are in the third research stage. “Social determinants of health”, “framework”, “health disparity”, “social determinant” and “burden” are in the latest research stage, and they are the research trends in population health.Figure 7 The top 20 keywords with the strongest citation bursts.
Figure 7
3.4 The intellectual structure analysis
Co-citation refers to two or more references appearing simultaneously in the bibliography of the same article. This section conducts the intellectual structure analysis based on the reference co-citation relationship. To build current reference co-citation networks, the top 50 references are chosen. The network pruning mode is “Pathfinder + Pruning sliced networks”, and finally, a total of 335 nodes and 358 links are obtained. Each node represents a reference, and the links between the nodes indicate that two references appear in the same article. The node size represents the number of citations. The bigger the node is, the more frequently the reference is cited. This section includes the analysis of references co-citation cluster, the betweenness centrality, the most cited references, and the strongest citation bursts. The references with high betweenness centrality are essential in the research of population health. The most cited references can be regarded as milestones in population health. The references with the strongest citation bursts display the emerging trends of population health research.
3.4.1 Co-citation cluster analysis
This subsection adopts a co-citation cluster to analyze the classification of population health. After constructing the reference co-citation networks, the reference co-citation clusters can be obtained by clicking the cluster button. The topics of the same cluster are closely related. Representative references for each cluster can be obtained and the specific research on population health in each cluster can be known through data analysis. Modularity is an index to evaluate the structural strength of co-citation networks and is represented by Q. The higher the Q value is, the better the network clustering is. When Q > 0.3, the obtained network community structure is remarkable. The average contour value is an index to measure the co-citation network’s homogeneity and is represented by S. When S > 0.5, the clustering result is considered reasonable. When S > 0.7, the clustering results are highly reliable [20].
Figure 8 presents the clusters of reference co-citation networks in population health. The Q value is 0.8579, and the average S value is 0.9439, indicating the clustering is highly reliable. In particular, there are 14 clusters, including “income quality”, “social capital”, “global burden”, “disease study”, “physical activity”, “structural racism”, “health promotion”, “purchasing population health”, “information system”, “health inequalities”, “healthy cities”, “planning”, “macroeconomic determinant”, and “causal review”. All the cluster names are derived from the title of cited references.Figure 8 Clusters of reference co-citation networks.
Figure 8
Table 4 reports 14 clusters and they are arranged based on their size. The bigger the size is, the more references in the cluster are cited. The more references are cited, the more influential the cluster is. Silhouette represents cluster quality, and its value measures the homogeneity of the network. The closer the score is to 1, the higher the homogeneity of the network is. The results from Table 4 illustrate that all clusters are highly credible, among which the Silhouette scores of #8, #10, and #13 are 1. The average publication year of a cluster shows recentness. Cluster #5 is the most recently formed cluster on population health, with an average year of 2017. Cluster #11 and cluster #12 are the least recently formed clusters, with an average year of 1988.Table 4 Major clusters of co-cited references.
Table 4ID Size Silhouette Label (LSI) Label (LLR) Label (MI) Year Ave.
0 39 0. 964 Income inequality Income inequality Evaluating wilkinsons income inequality hypothesis 2003
1 34 0.960 Income inequality Social capital Heat island effect 1996
2 30 0. 895 Population health Global burden Heat island effect 2015
3 26 0.994 Global burden Global burden Heat island effect 2011
4 25 0.751 Systematic review Physical activity Heat island effect 2015
5 22 0.978 Covid-19 pandemic Structural racism Inequality-related health 2017
6 15 0.962 Health promotion Health promotion Global burden 1992
7 13 0.944 Aligning financial incentive Purchasing population health Global burden 1992
8 13 1.000 Information system Information system Global burden 1991
9 12 0.987 Health inequalities Health inequalities Deteriorating self-rated health 2008
10 11 1.000 Physical activity Healthy cities Heat island effect 2016
11 9 0.987 Planning Planning Global burden 1988
12 6 0.958 Income inequality Income inequality Global burden 1988
13 5 1.000 Income inequality Causal review Unequal developing country 2008
Note: Clusters are represented according to the labels selected by the log-likelihood ratio test method (LLR).
Four major clusters are analyzed according to their importance, including clusters #0, #1, #5, and #12. Cited references and citing articles are analyzed in each cluster.(1) Cluster #0 income inequality
Cluster #0 is the biggest cluster and includes 39 references. Income inequality is an important factor influencing population health. For example, income inequality may cause many people not to complete treatment for tuberculosis [23]. Income inequality will be reduced by the financial system, but the financial system may lead to more environmentally unfriendly emissions, which are harmful to population health [24]. A research article that cites several references is called a citing article. The cited references are the intellectual base of the cluster, and the citing articles are the research fronts of the cluster. The five most cited references and five citing articles are selected in this cluster. Table 5 gives the five most cited references and citing articles in Cluster #0 income inequality.Table 5 Cited references and citing articles of Cluster #0 income inequality.
Table 5Cited references Citing articles
Wilkinson RG and Pickett KE, 2006, SOC SCI MED, V62, P1768 [9] Lynch J et al., 2004, MILBANK Q, V82, P5099 [25]
Lynch J et al., 2004, MILBANK Q, V82, P5099 [25] Chang VW and Christakis NA, 2005, SOC SCI MED, V61, P83 [29]
Subramanian SV and Kawachi I, 2004, EPIDEMIOL REV, V26, P78 [26] Mellor JM and Milyo J, 2001, J HEALTH POLIT POLIC, V26, P487 [30]
Lynch JW et al., 2000, BMJ-BRIT MED J, V320, P1200 [27] Beckfield J, 2004, J HEALTH SOC BEHAV, V45, P231 [31]
Ross NA et al., 2000, BRIT MED J, V320, P898 [28] Veenstra G, 2002, CAN J PUBLIC HEALTH, V93, P374 [32]
The core references of Cluster #0 represent major milestones concerning income inequality. The most cited references of this cluster, written by Wilkinson RG and Pickett KE [9], have been cited 971 times on WoS and can be considered the most important milestone in population health. They explained the relationship between income inequality and population health and found that the relation between income and health depended on the scale of social class differences in society. The second most cited reference is written by Lynch J et al., which has been cited 574 times on WoS [25]. The authors found little evidence to support the direct impact of inequality on health. But they argued that reducing income inequality would reduce health inequality. The other three most cited references are written by Subramanian SV and Kawachi I [26], Lynch JW et al. [27], and Ross NA et al. [28], which have been cited 512 times, 870 times, and 305 times respectively. The rest citing articles written by Chang VW and Christakis NA [29], Mellor JM and Milyo J [30], Bechfield J [31], and Veenstra G [32] are research fronts in Cluster #0. The cited references and citing articles are listed in Table 5.(2) Cluster #1 social capital
Cluster #1 is the second biggest cluster with 34 references, and its theme is social capital. Social capital has positive effects on physical and mental health [10]. The five most cited references and five citing articles are selected in this cluster. Table 6 gives the five most cited references and citing articles of Cluster #1 social capital. The most cited reference in Cluster #1 social capital is written by Kawachi I et al., with 1772 citations on WoS [33]. It explored the relationship between income inequality and mortality and found that income inequality led to increased mortality via disinvestment in social capital. The second most cited reference is written by Kaplan GA et al., which discussed the impact of income inequality on mortality in the United States [34]. Their findings showed that inequality in income distribution was significantly associated with health outcomes, social indicators, and mortality trends. Thus economic policies that affected income might have an important impact on countries’ health. The other three most cited references are written by Kennedy BP et al. [35], Lynch JW et al. [36], and Judge K et al. [37], which have been cited 527 times, 329 times, and 136 times respectively. The citing articles in Cluster #1 are listed in Table 6. They are written by Hayes M [38], Mellor JM and Milyo J [30], Veenstra G [32], Lynch J [25], and Dunn JR and Hayes MV [39].(3) Cluster #5 structural racism
Table 6 Cited references and citing articles of Cluster #1 social capital.
Table 6Cited references Citing articles
Kawachi I et al., 1997, AM J PUBLIC HEALTH, V87, P1491 [33] Hayes, M, 1990, PROG HUM GEOG, V23, P289 [38]
Kaplan GA et al., 1996, BRIT MED J, V312, P999 [34] Mellor JM and Milyo J, 2001, J HEALTH POLIT POLIC, V26, P487 [30]
Kennedy BP et al., 1996, BRIT MED J, V312, P1004 [35] Veenstra G, 2002, CAN J PUBLIC HEALTH, V93, P374 [32]
Lynch JW et al., 1998, AM J PUBLIC HEALTH, V88, P1074 [36] Lynch J et al., 2004, MILBANK Q, V82, P5099 [25]
Judge K et al., 1998, SOC SCI MED, V46, P567 [37] Dunn JR and Hayes MV, 2000, SOC SCI MED, V51, P563 [39]
There are 25 references cited in Cluster #5. The average year of Cluster #5 is 2017, which is the latest cluster for population health. The top five cited references and citing articles are presented in Table 7 .Table 7 Cited references and citing articles of Cluster #5 structural racism.
Table 7Cited references Citing articles
Hatzenbuehler ML et al., 2013, AM J PUBLIC HEALTH, V103, P813 [40] Amato KP et al., 2021, P NATL ACAD SCI USA, V118, P1 [45]
Barnett K et al., 2012, LANCET, V380, P37 [41] Wakeel F and Njoku A, 2021, HEALTHCARE-BASEL, V9, P145 [18]
Mackenbach JP, 2012, SOC SCI MED, V75, P761 [42] Garciaet MA et al., 2021, J GERONTOL B-PSYCHOL, V76, PE75 [46]
Bailey ZD et al., 2017, LANCET, V389, P1453 [43] Tan SB et al., 2021, J RACIAL ETHN HEALTH, V9, P236 [47]
Dong ES et al., 2020, LANCET INFECT DIS, V20, P533 [44] Zanettini C et al., 2021, VACCINES-BASEL, V9, P427 [48]
The most cited reference in Cluster #5 is written by Hatzenbuehler ML et al., which has been cited 1053 times on WoS [40]. It provided evidence on the health consequences of stigma and illustrated how stigma influences health. As Cluster #5 is the latest cluster, its citing articles indicate the new research trends in population health. It can be found from Table 7 that all the citing articles are published in 2021, and most of the topics are related to Covid-19. For example, Wakeel F and Njoku A investigated the effect of racism, stigma, and Covid-19 on the disease and mortality risk of African Americans and found that Covid-19 would have profound health implications as a stressful life event for African Americans [18]. The other four most cited references are written by Barnett K et al. [41], Mackenbach JP [42], Bailey ZD et al. [43], and Dong ES et al. [44], which have been cited 3134 times, 457 times, 1234 times, and 3715 times respectively. The rest of the citing articles in Cluster #5 are listed in Table 7, written by Amato KR et al. [45], Garcia MA et al. [46], Tan SB et al. [47], and Zanettini C et al. [48].(4) Cluster #12 macroeconomic determinant
Cluster #12 macroeconomic determinant, consisting of six references with an average publication year of 1988, is the earliest cluster of the 14 clusters. Table 8 presents the five major cited references and citing articles.Table 8 Cited references and citing articles of Cluster #12 macroeconomic determinant.
Table 8Cited references Citing articles
Kennedy BP et al., 1998, BRIT MED J, V317, P917 [11] Chang VW and Christakis NA, 2005, SOC SCI MED, V61, P83 [29]
Fiscella K and Franks P, 1997, BRIT MED J, V314, P1724 [49] Sohler NL et al., 2003, J URBAN HEALTH, V80, P650 [53]
Daly MC et al., 1998, MILBANK Q, V76, P315 [50] Subramanian SV et al., 2002, ANNU REV PUBL HEALTH, V23, P287 [54]
Kawachi I and Kennedy BP, 1999, HEALTH SERV RES, V34, P215 [51] Lynch J et al., 2004, MILBANK Q, V82, P5099 [25]
Soobader MJ and LeClere FB, 1999, SOC SCI MED, V48, P733 [52] Mellor JM and Milyo J, 2001, J HEALTH POLIT POLIC, V26, P487 [30]
The most cited reference in this cluster is written by Kennedy BP et al., which has been cited 479 times on WoS since 1998 [11]. They found that inequality in the distribution of income is associated with an adverse impact on health independent of household income. The second most cited reference is written by Fiscella K and Franks P [49], which identified that family income inequality can predict mortality independently, instead of community income inequality. The other three most cited references are written by Daly MC et al. [50], Kawachi I and Kennedy BP [51], and Soobader MJ and LeClere FB [52], which have been cited 161 times, 420 times, and 165 times respectively. The citing articles in Cluster #12 are listed in Table 8, and they are written by Chang VW and Christakis NA [29], Sohler NL et al. [53], Subramanian SV et al. [54], Lynch J et al. [25], and Mellor JM and Milyo J [30].
3.4.2 Betweenness centrality analysis
Betweenness centrality is the most direct measure to describe node centrality in network analysis. The higher the centrality degree of the node is, the greater the influence of the node in the network is. The influencial node plays a vital role in connecting other nodes or several different clusters. Nodes located between different node groups may have higher centrality values. Such nodes can be regarded as landmarks in the field of population health and are likely to reflect emerging trends [55, 56]. After the reference co-citation networks are obtained, the betweenness centrality of references can be calculated.
Table 9 shows the top 10 references with the highest betweenness centrality. These references are important in connecting individual node in the networks and connecting groups of nodes. Table 9 reveals that the three references with a high ranking of betweenness centrality in the field of population health are written by Banyal HS and Inselburg J [57], Bjorkman A et al. [58], and Ambroise-Thomas P and Rossignol JF [59] respectively, and their betweenness centrality values reach 86. The betweenness centrality value of the five references is 50. They are written by Ankley GT et al. [60], Alsabti K et al. [61], Bengtsson A et al. [62], Benson WH and Birge WJ [63], and Belinsky SA et al. [64]. Finally, the betweenness centrality of references written by Bjorkman A and Willcox M [65] and Hirsch and Killingsorth G [66] is relatively lower, and their values are 35 and 15 respectively.Table 9 Ten references with the highest betweenness centrality.
Table 9Rank Betweenness Centrality References
1 86 Banyal HS and Inselburg J, 1986, EXP PARASITOL, V62, P61 [57]
2 86 Bjorkman A et al. 1985, ANN TROP MED PARASIT, V79, P597 [58]
3 86 Ambroise-Thomas P and Rossignol JF, 1986, PARASITOL TODAY, V2, P79 [59]
4 50 Ankley GT et al., 1986, AQUAT TOXICOL, V9, P91 [60]
5 50 Alsabti K, 1985, J FISH BIOL, V26, P13 [61]
6 50 Bengtsson A et al., 1988, J FISH BIOL, V33, P517 [62]
7 50 Benson WH and Birge WJ, 1987, ENVIRON TOXICOL CHEM, V6, P623 [63]
8 50 Belinsky SA et al., 1987, ENVIRON TOXICOL CHEM, V76, P3 [64]
9 35 Bjorkman A and Willcox M, 1986, T ROY SOC TROP MED H, V80, P572 [65]
10 15 Hirsch G and Killingsworth WR, 1975, INQUIRY, V12, P126 [66]
3.4.3 The most cited references
The most cited references indicate their high recognition in the field of population health [67]. They are regarded as landmarks in this field, making groundbreaking contributions. Accurately identifying the most cited references is one of the effective methods to analyze the research progress in this field [68]. The top ten most cited references in the field of population health are given in Table 10 .Table 10 Top 10 most cited references.
Table 10Rank Citation number References
1 90 Wilkinson RG and Pickett KE, 2006, SOC SCI MED, V62, P1768 [9]
2 79 Giles-Corti B et al., 2016, LANCET, V388, P2912 [69]
3 72 Lynch J et al., 2004, MILBANK Q, V82, P5099 [25]
4 71 Lim SS et al., 2012, LANCET, V380, P2224 [70]
5 67 Stevenson M et al., 2016, LANCET, V388, P2925 [71]
6 59 Pickett KE and Wilkinson RG, 2015, SOC SCI MED, V128, P316 [72]
7 58 Chetty R et al., 2016, JAMA-J AM MED ASSOC, V315, P1750 [73]
8 52 Alley DE et al., 2016, NEW ENGL J MED, V374, P8 [74]
9 49 Shamseer L et al., 2015, BMJ-BRIT MED J, V350, Ph1793 [75]
10 46 WHO, 2014, Global status report on violence prevention [76]
The most cited reference is authored by Wilkinson RG and Pickett KE [9]. It is the first cited reference in Cluster #0 income inequality. They reviewed the evidence on whether income inequality is a determinant of population health and tried to find a consistent interpretation of positive and negative findings. The second most cited reference is written by Giles-Corti B et al [69]. They explained the relationship between city planning and population health. Encouraging walking, cycling, and using public transport, and reducing private motor vehicle use will create healthier and more sustainable compact cities. Thus establishing a set of indicators to benchmark and monitor progress toward the achievement of more compact cities will promote health and reduce health inequities. The third most cited reference is written by Lynch J et al. and it has been discussed in Cluster#0 [25]. They also studied the relationship between income inequality and population health. The rest references are written by Lim SS et al. [70], Stevenson M et al. [71], Pickett KE and Wilkinson RG [72], Chetty R et al. [73], Alley DE et al. [74], Shamseer L et al. [75], and WHO [76]. All of those references have inspired intense interest in population health.
3.4.4 The strongest citation bursts
The strongest citation bursts refer to those references that are cited suddenly over a period. It contains two dimensions: burst value and burst time. The nodes with high citation burst values mean that these references are cited intensely. The burst time displays how long the burst status lasts. The research hotspots of different periods constitute the emerging trends in population health.
Emerging trends in population health can be found by analyzing the burst value and burst time of references. Figure 9 displays the top 20 references with the strongest citation bursts. The earliest citation burst reference is written by Evans RG et al., which was published in 1994 and burst in 1996 [77]. They believed that traditional health care plays a small role in the overall population health. This reference attracted academic attention from 1996 to 2000. The second earliest citation burst reference is written by Kawachi I et al., which drew attention immediately after its publication in 1997 [33]. The discussion of this reference lasts nine years from 1997 to 2005. Some in-depth studies are based on this reference. For example, Sun TT et al. explored how economic fluctuations affected the mortality of infectious diseases in 2021 [78].Figure 9 Top 20 references with the strongest citation bursts.
Figure 9
Nine references burst in 2016 and ended in 2020 or 2021, which means they are emerging trends. The reference written by Giles-Corti B et al. obtained the highest strength value (28.61) among the nine references. This reference also emerged immediately when it was published in 2016, which is the second most cited reference. Based on this reference, researchers discussed how to solve the problems in population health from the perspective of city planning [69]. The second reference written by Alley DE has been cited 310 times on WoS. This reference indicated that the Centers for Medicare and Medicaid Services announced $157 million for Accountable Health Communities to accelerate the development of a scalable delivery model for addressing upstream determinants of health [74]. The last reference in Figure 9 is written by Case A and Deaton A. They documented a marked increase in the mortality of middle-aged white non-Hispanic Americans between 1999 and 2013, and identified drug and alcohol poisoning, suicide, chronic liver diseases, and cirrhosis were the possible causes [79].
4 Discussion
This study has provided a comprehensive bibliometric analysis of population health research in WoS from 1971 to 2021. CiteSpace is chosen as the bibliometric tool to find the main achievements and research trends.
Firstly, the publication number and country are analyzed. This study finds out that the number of publications and citations of population health has been increasing quickly since the year of 1995. It shows that population health has been attracting much attention from researchers. From the perspective of national contribution, the United States published the most articles. Judging from the number of publications, the United States is the leading country in population health research and far beyond other countries. Canada ranks second with 2417 publications, followed by England, Austria, China, and the Netherlands.
Secondly, the productive authors and institutions are analyzed. Christopher JLM published the most articles related to population health. Martin M and Theo VOS ranked second and they have published 38 publications. They are followed by Sandro G, Scott BP et al. Four of the top 10 productive authors and six of the top 15 productive institutions are from the United States. The other top 15 productive institutions are in Canada, Australia, and England. The top 10 productive authors and top 15 institutions are all from developed countries, which means they pay more attention to population health to some extent.
Thirdly, the co-occurrence of disciplines and keywords is analyzed. The articles on population health are mainly published on the subjects of “Public, Environmental and Occupational Health”, “General and Internal Medicine”, “Environmental Sciences and Ecology”, “Biomedical Social Sciences”, and “Public, Environmental and Occupational Health”. It can be seen that the topic of population health is mostly related to the environment, ecology, and medicine. The keywords can reflect the topics and hotspots in population health. The keywords “life expectancy”, “morbidity”, and “health status” appeared more frequently. “Cardiovascular disease”, “coronary heart disease”, “income inequality”, and “physical activity” co-occurred more frequently with other keywords, indicating they draw more attention in this area. The top 20 keywords with the strongest citation bursts reflect the evolution of hotspots and the research trends. “Life expectancy”, “morbidity”, and “health status” attracted attention in 1991. “Income inequality”, “coronary heart disease”, “epidemiology”, and “disability” began to attract attention in 1996. “Social determinants of health”, “health disparity”, and “burden” are the present hotspots.
Fourthly, through the analysis of the co-citation cluster, betweenness centrality, most cited references, and strongest citation bursts, research hotspots, and new development trends are revealed. “Income inequality”, “social capital”, “structural racism”, and “macroeconomic determinant” are the major clusters and research hotspots. The references with high betweenness centrality are important in connecting other clusters, and they are an important pivot for population health. The reference with the highest betweenness centrality is related to plasmodium falciparum. The most cited references are the foundation of population health. The reference with the highest citation is a review of income inequality and population health. The emerging trends of population health can be detected clearly from the references citation burst. It can be seen that nine of the top 20 references bursts in 2016 and last till 2021, which means they are the hotspots and trends in the coming years.
We have to acknowledge that there are some limitations to this study. First, due to the complexity of population health, this study only applies a single search term for bibliometric analysis. In the future, we can expand search terms or focus on smaller areas of bibliometric analysis. Second, this study only selects the publications from WoS by referring to the existing main literature. In future research, more databases, such as Scopus, can be introduced for analysis. Third, this study outlines the evolutionary trajectory of population health from a few aspects. In future research, more aspects should be carried out.
Generally speaking, the findings of this study provide insights for future population health research. The information about the publication number, countries or regions, authors, institutions, disciplines, keywords, and cited references form the basic knowledge of population health. Population health hotspots have shifted over time in the order of “life expectancy”, “income inequality”, “self-rated health”, “health inequality”, etc. The future research trends are “global burden”, “physical activity”, “structural racism”, “healthy cities”, and other aspects. Researchers might benefit from this study and find more effective and novel methods to further explore population health issues from different perspectives.
Declarations
Author contribution statement
Limei Guo: Performed the experiments; Analyzed and interpreted the data; Wrote the paper.
Weike Zhang: Conceived and designed the experiments; Contributed reagents, materials, analysis tools or data; Wrote the paper.
Funding statement
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Data availability statement
Data will be made available on request.
Declaration of interest’s statement
The authors declare no competing interests.
Additional information
No additional information is available for this paper.
Acknowledgements
Not applicable.
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References
1 Kindig D.A. Understanding population health terminology Milbank Q. 85 1 2007 139 161 17319809
2 Silberberg M. Martinez-Bianchi V. Lyn M.J. What is population health? PrimaryCare 46 4 2019 475
3 Moore J.N. Report of working group on population, health, and family Ann. Ny. Acad. Sci. 184 1971 562 565
4 Nakanishi K. Nishida M. Taneike M. Yamamoto R. Moriyama T. Yamauchi-Takihara K. Serum klotho levels contribute to the prevention of disease progression Int. J. Gen. Med. 14 2021 229 236 33519229
5 Barrett M.A. Humblet O. Hiatt R.A. Adler N.E. Big data and disease prevention: from quantified self to quantified communities Big Data-US 1 3 2013 168 175
6 Liu C. Li W. Advances in haploid embryonic stem cell research (dagger) Biol. Reprod. 107 1 2022 250 260 35639627
7 Licausi F. Giuntoli B. Synthetic biology of hypoxia New Phytol. 229 1 2020 50 56 31960974
8 Pu X.H. Wang Y.L. Zhang W.K. Zeng M. Can basic medical insurance reduce elderly family income inequality in China? Front. Public Health 10 2022 838733
9 Wilkinson R.G. Pickett K.E. Income inequality and population health: a review and explanation of the evidence Soc. Sci. Med. 62 7 2006 1768 1784 16226363
10 He Y.Y. Zhou L.L. Li J.S. Wu J. An empirical analysis of the impact of income inequality and social capital on physical and mental health-take China’s micro-database analysis as an example Int. J. Equity Health 20 1 2021 1 14 33386078
11 Kennedy B.P. Kawachi I. Glass R. Prothrow-Stith D. Income distribution, socioeconomic status, and self-rated health in the United States: multilevel analysis Br. Med. J. 317 7163 1998 917 921 9756809
12 Bai P. Tang Y. Zhang W.K. Zeng M. Does economic policy uncertainty matter for healthcare expenditure in China? a spatial econometric analysis Front. Public Health 9 2021 673778
13 Owen N. Bauman A. Brown W. Trost S. Physical activity and population health outcomes J. Sci. Med. Sport 6 3 2003 368 370 14609155
14 Porter R.R. McClelland P. Ewing A. Sonka V. Trilk J.L. Design and implementation of a clinic-to-community, physical activity health promotion model for healthcare providers Prev. Med. Rep. 26 2022 101697
15 Roos N.P. Black C.D. Frohlich N. Decoster C. Cohen M.M. Tataryn D.J. A population-based health information system Med. Care 33 12 1995 DS13 DS20 7500666
16 Tricco A.C. Runnels V. Sampson M. Bouchard L. Shifts in the use of population health, health promotion, and public a bibliometric analysis Can. J. Publ. Health 99 6 2008 466 471
17 Roth G.A. Mensah G.A. Fuster V. The global burden of cardiovascular diseases and risks a compass for global action J. Am. Coll. Cardiol. 76 25 2020 2980 2981 33309174
18 Wakeel F. Njoku A. Application of the weathering framework: intersection of racism, stigma, and COVID-19 as a stressful life event among African Americans Healthcare-Basel 9 2 2021 145 159 33540498
19 Wen P. Luo P. Zhang B. Zhang Y. Mapping knowledge structure and global research trends in gout: a bibliometric analysis from 2001 to 2021 Front. Publ. Health 10 2022 924676
20 Chen C.M. CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature J. Am. Soc. Sci. Techol. 57 3 2006 359 377
21 Li J. Chen C.M. CiteSpace: Mining and Visualization of Scientific Text 2016 Capital University of Economics and Business Press Beijing
22 Chen Y. Chen C.M. Hu Z.G. Wang X.W. Principle and Application of Citation Spatial Analysis-CiteSpace practical guide 2014 Science Press Beijing
23 Lima S.V.M.A. de Araujo K.C.G.M. Nunes M.A.P. Nunes C. Early identification of individuals at risk for loss to follow-up of tuberculosis treatment: a generalized hierarchical analysis Heliyon 7 4 2021 e06788
24 Bui D.T. Transmission channels between financial development and CO2 emissions: a global perspective Heliyon 6 11 2021 e05509
25 Lynch J. Smith G.D. Harper S. Hillemeier M. Ross N. Kaplan G.A. Is income inequality a determinant of population health? Part 1. A systematic review Milbank Q. 82 1 2004 5099
26 Subramanian S.V. Kawachi I. Income inequality and health: what have we learned so for? Epidemiol. Rev. 26 1 2004 78 91 15234949
27 Lynch J.W. Smith G.D. Kaplan G.A. House J.S. Income inequality and mortality: importance to health of individual income psychosocial environment, or material conditions Br. Med. J. 320 7243 2000 1200 1204 10784551
28 Ross N.A. Wolfson M.C. Dunn J.R. Berthelot J.M. Kaplan G.A. Lynch J.M. Relation between income inequality and mortality in Canada and in the United States: cross sectional assessment using census data and vital statistics Br. Med. J. 320 2000 898 902 10741994
29 Chang V.W. Christakis N.A. Income inequality and weight status in US metropolitan areas Soc. Sci. Med. 61 1 2005 83 96 15847964
30 Mellor J.M. Milyo J. Reexamining the evidence of an ecological association between income inequality and health J. Health Polit. Policy Law 26 3 2001 487 522 11430248
31 Beckfield J. Does income inequality harm health? New cross-national evidence J. Health Soc. Behav. 45 3 2004 231 248 15595505
32 Veenstra G. Income inequality and health-coastal communities in British Columbia, Canada Can. J. Public Health 93 5 2002 374 379 12353461
33 Kawachi I. Kennedy B.P. ProthrowSith D. Social capital, income inequality, and mortality Am. J. Publ. Health 87 9 1997 1491 1498
34 Kaplan G.A. Pamuk E.R. Lynch J.W. Cohen R.D. Balfour J.L. Inequality in income and mortality in the United States: analysis of mortality and potential pathways Br. Med. J. 312 7037 1996 999 1003 8616393
35 Kennedy B.P. Kawachi I. ProthrowStith D. Income distribution and mortality: cross sectional ecological study of the Robin Hood index in the United States Br. Med. J. 312 7037 1996 1004 1007 8616345
36 Lynch J.W. Kaplan G.A. Pamuk E.R. Cohen R.D. Hech K.E. Balfour J.L. Income inequality and mortality in metropolitan areas of the United States Am. J. Publ. Health 88 7 1998 1074 1080
37 Judge K. Mulligan J.A. Benzeval M. Income inequality and population health Soc. Sci. Med. 46 4 1998 567 579 9460836
38 Hayes M. Man disease and environmental associations: from medical geography to health inequalities Prog. Hum. Geogr. 23 2 1999 289 296
39 Dunn J.R. Hayes M.V. Social inequality, population health, and housing: a study of two Vancouver neighborhoods Soc. Sci. Med. 51 4 2000 563 587 10868671
40 Hazenbuehler M.L. Phelan J.C. Link B.G. Stigma as a fundamental cause of population health inequalities Am. J. Publ. Health 103 5 2013 813 821
41 Barnett K. Mercer S.W. Norbury M. Watt G. Wyke S. Guthrie B. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study Lancet 380 9836 2012 37 43 22579043
42 Mackenbach J.P. The persistence of health inequalities in modern welfare states: the explanation of a paradox Soc. Sci. Med. 75 4 2012 761 769 22475407
43 Bailey Z.D. Krieger N. Agenor M. Graves J. Linos N. Bassett M.T. Structural racism and health inequities in the USA: evidence and interventions Lancet 389 10077 2017 1453 1463 28402827
44 Dong E.S. Du H.R. Gardner L. An interactive web-based dashboard to track COVID-19 in real time Lancet Infect. Dis. 20 5 2020 533 534 32087114
45 Amato K.R. Arrieta M.C. Azad M.B. Bailey M.T. Broussard J.L. Bruggeling C.E. The human gut microbiome and health inequities Proc. Natl. Acad. Sci. USA 118 25 2021 1 10
46 Garcia M.A. Homan P.A. Garcia C. Brown T.H. The color of Covid-19: structural racism and the disproportionate impact of the pandemic on older black and latinx adults J. Gerontol. B-Psychol. 76 3 2021 E75 E80
47 Tan S.B. DeSouza P. Raifman M. Structural racism and COVID-19 in the USA: a county-level empirical analysis J. Racial Ethn. Health 9 1 2021 236 246
48 Zanettini C. Omar M. Dinalankara W. Imada E.L. Colantuoni E. Parmigiani G. Influenza vaccination and Covid-19 mortality in the USA: an ecological study Vaccines-Basel 9 5 2021 427 436 33923159
49 Fiscella K. Franks P. Poverty or income inequality as predictor of mortality: longitudinal cohort study Br. Med. J. 314 7096 1997 1724 1727 9185498
50 Daly M.C. Duncan G.J. Kaplan G.A. Lynch J.W. Macro-to-micro links in the relation between income inequality and mortality Milbank Q. 76 3 1998 315 339 9738166
51 Kawachi I. Kennedy B.P. Income inequality and health: pathways and mechanisms Health Serv. Res. 34 1 1999 215 277 10199670
52 Soobader M.J. LeClere F.B. Aggregation and the measurement of income inequality: effects on morbidity Soc. Sci. Med. 48 6 1999 733 744 10190636
53 Sohler N.L. Arno P.S. Chang C.J. Fang J. Schechter C. Income inequality and infant mortality in New York city J. Urban Health 80 4 2003 650 657 14709712
54 Subramanian S.V. Belli P. Kawachi I. The macroeconomic determinants of health Annu. Rev. Publ. Health 23 2002 287 302
55 Chen C.M. Hu Z.G. Liu S.B. Tseng H. Emerging trends in regenerative medicine: a scientometric analysis CiteSpace Expet Opin. Biol. Ther. 12 5 2012 593 608
56 Chen C.M. CiteSpace: A Practical guide for mapping scientific literature 2016 Novinka New York
57 Banyal H.S. Inselburg J. Plasmodium-falciparum-induction, selection, and characterization of Pyrimethamine-resistant mutants Exp. Parasitol. 62 1 1986 61 70 3522262
58 Bjorkman A. Brohult J. Willcox M. Pehrson P.O. Rombo L. Hedman P. Malaria control by chlorproguanil. 1. Clinical effects and susceptibility of plasmodium-falciparum invivo after 7 years of monthly chlorproguanil administration to children in a Liberian village Ann. Trop. Med. Parasitol. 79 6 1985 597 601 3914861
59 Ambroise-Thomas P. Rossignol J.F. Chemotherapy of resistant falciparum malaria Parasitol. Today 2 3 1986 79 80 15462779
60 Ankley G.T. Blazer V.S. Reinert Re Agosin M. Effects of aroclor-1254 on cytochrome-P-450-dependent monooxygenase, glutathione-s-transferase, and udp-glucuronosyltransferase activities in channel catfish liver Aquat. Toxicol. 9 2 1986 91 103
61 Alsabti K. Frequency of chromosomal-aberrations in the rainbow-trout, salmo-gairdneri rich, exposed to 5 pollutants J. Fish. Biol. 26 1 1985 13 19
62 Bengtsson A. Bengtsson B.E. Lithner G. Vertebral defects in fourhorn sculpin, myoxocephalus-quadricornis L, exposed to heavy-metal pollution in the Gulf of Bothnia J. Fish. Biol. 33 4 1988 517 529
63 Benson W.H. Birge W.J. Detection of cadmium-binding proteins in fish chronically exposed to heavy-metals Environ. Toxicol. Chem. 6 1987 623 626
64 Belinsky S.A. White C.M. Devereux T.R. Anderson M.W. DNA adducts as a dosimeter for risk-estimation Environ. Health Perspect. 76 1987 3 8 3447903
65 Bjorkman A. Willcox M. Invivo and invitro susceptibility of plasmodium-falciparum to sulfadoxine pyrimethamine in Liberia, west-Africa Trans. Roy. Soc. Trop. Med. Hyg. 80 4 1986 572 574 3544359
66 Hirsch G. Killingsworth W.R. A new framework for projecting dental manpower requirements Inquiry 12 2 1975 126 142 http://www.jstor.org/stable/29770916 Accessed 11 Nov. 2022 124703
67 Pislyakov v Shukshina E. Measuring excellence in Russia: highly cited papers, leading institutions, patterns of national and international collaboration J. Assoc. Inf. Sci. Technol. 65 11 2014 2321 2330
68 Liu Y.X. Rousseau R. Knowledge diffusion through publications and citations: a case study using ESI-fields as unit of diffusion J. Am. Soc. Inf. Sci. Technol. 61 2 2010 340 351
69 Giles-Corti B. Vernez-Moudon A. Reis R. Turrell G. Dannenberg A.L. Badland H. City planning and population health: a global challenge Lancet 388 10062 2016 2912 2924 27671668
70 Lim S.S. Vos T. Flaxman A.D. Danaei G. Shibuya K. Adair-Rohani H. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990-2010: a systematic analysis for the global burden of disease study 2010 Lancet 380 9859 2012 2224 2260 23245609
71 Stevenson M. Thompson J. de Sa T.H. Ewing R. Mohan D. McClure R. Land use, transport, and population health: estimating the health benefits of compact cities Lancet 388 10062 2016 2925 2935 27671671
72 Pickett K.E. Wilkinson R.G. Income inequality and health: a causal review Soc. Sci. Med. 128 2015 316 326 25577953
73 Chetty R. Stepner M. Abraham S. Lin S. Scuderi B. Turner N. The association between income and life expectancy in the United States, 2001-2014 JAMA, J. Am. Med. Assoc. 315 16 2016 1750 1766
74 Alley D.E. Asomugha C.N. Conway P.H. Sanghavi D.M. Accountable health communities - addressing social needs through Medicare and Medicaid N. Engl. J. Med. 374 1 2016 8 11 26731305
75 Shamseer L. Sampson M. Bukutu C. Schmid C.H. Nikles J. Tate R. Consort extension for reporting N-of-1 trials (CENT) 2015: explanation and elaboration BMJ Br. Med. J. 350 2015 h1793 25976162
76 WHO Global status report on violence prevention 2014 2014 World Health Oraganization Geneva https://injuryprevention.bmj.com/content/injuryprev/21/3/213.full.pdf Accessed 12 Jul 2022
77 Evanss R.G. Barer M.L. Marmor T.R. Why are come people healthy and others not? The determinants of health of population 1994 Walter de Gruyter Berlincome York
78 Sun T.T. Tao R. Su C.W. Umar M. How do economic fluctuations affect the mortality of infectious diseases? Front. Public Health 9 2021 678213
79 Case A. Deaton A. Rising morbidity and mortality in midlife among white nonHispanic Americans in the 21st century Proc. Natl. Acad. Sci. USA 112 49 2015 15078 15083 26575631
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Lancet Glob Health
Lancet Glob Health
The Lancet. Global Health
2214-109X
The Author(s). Published by Elsevier Ltd.
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Series
Determining thresholds for spatial urban design and transport features that support walking to create healthy and sustainable cities: findings from the IPEN Adult study
Cerin Ester Prof PhD ab*
Sallis James F Prof PhD ac
Salvo Deborah PhD e
Hinckson Erica Prof PhD f
Conway Terry L PhD c
Owen Neville Prof PhD g
van Dyck Delfien Prof PhD h
Lowe Melanie PhD i
Higgs Carl MPH j
Moudon Anne Vernez Prof Dr es Sc k
Adams Marc A PhD l
Cain Kelli L MA c
Christiansen Lars Breum PhD m
Davey Rachel Prof PhD n
Dygrýn Jan PhD o
Frank Lawrence D Prof PhD d
Reis Rodrigo Prof PhD ep
Sarmiento Olga L Prof PhD q
Adlakha Deepti PhD r
Boeing Geoff PhD s
Liu Shiqin MS t
Giles-Corti Billie Prof PhD ju
a Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, VIC, Australia
b School of Public Health, The University of Hong Kong, Hong Kong, Hong Kong Special Administrative Region, China
c Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, CA, USA
d Department of Urban Studies and Planning, University of California San Diego, CA, USA
e Prevention Research Center, Brown School, Washington University in St Louis, St Louis, MO, USA
f Human Potential Centre, School of Sport and Recreation, Auckland University of Technology, Auckland, New Zealand
g Centre for Urban Transitions, Swinburne University of Technology and Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
h Department of Movement and Sports Sciences, Faculty of Medicine and Sports Sciences, Ghent University, Ghent, Belgium
i Melbourne Centre for Cities, University of Melbourne, Melbourne, VIC, Australia
j Healthy Liveable Cities Lab, RMIT University, Melbourne, VIC, Australia
k Department of Urban Planning and Design, Urban Form Lab, University of Washington, Seattle, WA, USA
l College of Health Solutions, Senior Global Futures Scientist, Julie Ann Wrigley Global Futures Laboratory, Arizona State University, Phoenix, AZ, USA
m Department of Sports Science and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark
n Health Research Institute, University of Canberra, Canberra, ACT, Australia
o Faculty of Physical Culture, Palacký University Olomouc, Olomouc, Czech Republic
p Graduate Program in Urban Management, Pontifical Catholic University of Parana, Curitiba, Brazil
q School of Medicine at Universidad de los Andes, Bogotá, Colombia
r Department of Landscape Architecture and Environmental Planning, Natural Learning Initiative, College of Design, North Carolina State University, Raleigh, NC, USA
s Department of Urban Planning and Spatial Analysis, Sol Price School of Public Policy, University of Southern California, Los Angeles, California, USA
t School of Public Policy and Urban Affairs, Northeastern University, Boston, MA, USA
u School of Population Health, The University of Western Australia, Perth, WA, Australia
* Correspondence to: Prof Ester Cerin, Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, VIC 3000, Australia
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© 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license
2022
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An essential characteristic of a healthy and sustainable city is a physically active population. Effective policies for healthy and sustainable cities require evidence-informed quantitative targets. We aimed to identify the minimum thresholds for urban design and transport features associated with two physical activity criteria: at least 80% probability of engaging in any walking for transport and WHO's target of at least 15% relative reduction in insufficient physical activity through walking. The International Physical Activity and the Environment Network Adult (known as IPEN) study (N=11 615; 14 cities across ten countries) provided data on local urban design and transport features linked to walking. Associations of these features with the probability of engaging in any walking for transport and sufficient physical activity (≥150 min/week) by walking were estimated, and thresholds associated with the physical activity criteria were determined. Curvilinear associations of population, street intersection, and public transport densities with walking were found. Neighbourhoods exceeding around 5700 people per km2, 100 intersections per km2, and 25 public transport stops per km2 were associated with meeting one or both physical activity criteria. Shorter distances to the nearest park were associated with more physical activity. We use the results to suggest specific target values for each feature as benchmarks for progression towards creating healthy and sustainable cities.
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pmcThis is the second in a Series of four papers about urban design, transport, and health
Introduction
UN Sustainable Development Goal (SDG) 3 explains the importance of healthy living and wellbeing at all ages,1 which in turn require healthy and sustainable cities consistent with SDG 11. There is strong evidence for many health benefits from regular physical activity.2 Therefore, an essential characteristic of a healthy and sustainable city is a physically active population.3
According to WHO physical activity guidelines, adults can achieve substantial health benefits by regularly doing as little as 150 min per week of moderate-intensity physical activity,4 including walking.5 However, more than a quarter of adults worldwide do not meet these physical activity recommendations.6 The importance of walking as a means to accumulate health-enhancing physical activity stems from its popularity, universality, equitability, and accessibility.5 Walking is the most reported type of physical activity in adults who meet WHO physical activity guidelines.5, 7, 8, 9 People walk for various purposes (eg, work, recreation, and transport) and in various settings. Provided that environments are sufficiently safe,10 walking is generally appropriate for all sexes, ages, ethnicities, socioeconomic groups, and those with common age-related chronic conditions.11, 12, 13 Consequently, promoting walking could reduce the health disparities in a population due to physical inactivity, and achieve several related UN Sustainable Development Goals, including SDGs 3, 5 (achieving gender equality and empowering all women and girls), and 10 (reducing within-country and between-country inequalities).14
Apart from contributing to healthier and more equitable societies, a specific type of walking—walking for transport—is important for achieving additional UN SDGs, including making cities inclusive, safe, resilient, and sustainable (SDG 11), and mitigating climate change (SDG 13).15 Since the 1970s, many countries, especially middle-income to high-income countries, have had sharp increases in fossil-fuel-dependent industrialisation, technological innovation, and urban sprawl, leading to substantial population shifts towards sedentary occupations, individualised motorised transport, and motor vehicle dependency.6, 16 Unsurprisingly, these trends have led to declines in physical activity and increases in air pollution and greenhouse gas emissions, with emissions being the primary cause of climate change.16 Because of widespread car ownership and car-centric urban design, people now frequently drive for short trips that could be walked or cycled.17, 18 The recent increase in shared mobility services globally (eg, Uber or Lyft) has also decreased the short trips usually done by walking and cycling, and increased traffic congestion.19
If people are to walk more, they need urban environments that encourage and support walking.10, 20, 21 Urban design and transport features—including higher residential density, mixed land use, street connectivity, and better access to public transport, amenities, and parks—have been associated with more walking,22, 23, 24, 25, 26, 27 especially walking for transport.22, 23, 24, 28 However, most of the relevant evidence is from high-income countries. A review of the few cross-sectional studies from low-income and middle-income countries concluded that population density and access to services were inconsistently associated with physical activity and that mixed land-use was positively associated with active transport.29, 30
The evidence that compact neighbourhoods with easy access to amenities, parks, and public transport underpin a healthy and sustainable city is rarely effectively incorporated into city planning policy, which perpetuates urban sprawl and automobile dependency, along with all their deleterious effects on human and planetary health.31 In the first paper in this Series, Lowe and colleagues32 find that many cities worldwide do not have measurable policy targets that would facilitate the monitoring of progress on city planning interventions that influence health and health-related behaviours such as walking. Such targets would inform practice and aid accountability. This absence of city planning policy could partly be due to many countries not having measurable targets for reducing health risk factors and defined multisectoral strategies to achieve them, as evidenced in a 2020 study of national physical activity policies across 76 countries.33 The absence of measurable city planning policy targets also stems from the dearth of clear guidance on thresholds of urban design and transport features needed to achieve the desired outcomes.34, 35 For example, although more dense environments (dense in both infrastructure and population) are typically associated with more walking,23, 24, 28 increases in density beyond some threshold values might not yield additional benefits and can even deter walking.36, 37 To create healthy and sustainable cities, wherever possible, thresholds should be based on the empirical evidence of relationships of urban design and transport features with health-related behaviours and outcomes. This topic has been identified as an important area of research by some authors.38
Transport planners have been looking for reliable density thresholds to inform investment decisions for a long time. For example, studies conducted in the USA found that minimum net residential densities of around 2000 dwellings per km2 support rail use35 and 3500–450034 dwellings per km2 support use of all public transport, both of which support walking. Another nationwide US study reported that a dwelling density of around 7500 per km2 was associated with at least a 70% chance of walking for transport.39 The study also found that walking was higher in areas with up to 100–200 intersections per km2 and declined above this threshold. In an Asian ultra-dense metropolitan context, such as Seoul, the positive association between population density and walking for transport was substantially reduced at a density that exceeded 9000 to 16 000 people per km2.37
However, most studies that examined thresholds were city-specific or country-specific. A unique dataset from which international thresholds for urban design and transport features supportive of walking can be estimated is the International Physical Activity and the Environment Network (IPEN) Adult study. This study collected similar data on built environments and physical activity among adults from 12 countries on five continents.40 The main motivation for establishing IPEN Adult was to enable an improved estimation of the strength and shape (forms of curvilinear relationship) of environment–physical activity relationships by capturing a range of global variation in urban environments; and by recruiting a balanced number of residents from communities that vary in key urban design features from each participating city. This sampling strategy makes the IPEN Adult study ideal for the purpose of investigating the thresholds associated with walking outcomes (eg, a certain probability of engaging in walking for transport) and their generalisability across countries.
Although studies using IPEN Adult data have reported on the strength and shape of the relationships between the built environment and various walking outcomes,24, 41 they did not aim to quantify thresholds and their uncertainties, which is a limitation that is shared by all single-country studies (except one) on thresholds associated with walking.39 Total walking—ie, the combination of walking for transport and recreation—is the most commonly reported physical activity in adults who meet WHO physical activity guidelines7 and, therefore, is the most policy-relevant physical activity outcome for the creation of both healthy and sustainable cities. No IPEN Adult studies investigated the relationship of built environment with total walking. The key scientific rationale for this study was to address these important knowledge gaps and subsequently inform international thresholds for the subset of spatial indicators of urban design and transport features described in this Series.31 We hope that these thresholds can be used to inform policies and practices to achieve healthy and sustainable cities.
The specific aims of this study were to estimate international thresholds and their uncertainties for urban design and transport features associated with two policy-relevant physical activity criteria: at least 80% probability of engaging in any walking for transport; and reaching WHO's target of at least a 15% reduction in insufficient physical activity by walking for transport or recreation for 150 min each week.4 Walking for transport was selected as an additional physical activity criterion to total walking (ie, total walking for transport or recreation) because it also helps to reduce air pollution and carbon emissions,42 and responds to changes in urban design.43 A criterion of 80% probability of engaging in walking for transport was chosen because sustainable cities are typified by a high prevalence of active transport, which is relevant to achieving multiple UN Sustainable Development Goals15 and WHO physical activity goals.4, 5
Study design
We used data from IPEN Adult countries with harmonised spatial measures of urban design and transport features and self-reported measures of walking for both transport and recreation.40 The sample included 11 615 participants aged 18–66 years and recruited from 14 cities in ten countries. The two IPEN studied cities of Pamplona (Spain) and Hong Kong were excluded as they did not have objective data on the built environment or relevant data on walking. With a two-tailed probability level of 5%, the study had 80% power to detect effect sizes as small as 0·09% of explained outcome variance in pooled analyses and 4% of explained outcome variance in the smallest city-specific subsample. 11 cities were in high-income countries: Adelaide (SA, Australia); Ghent (Belgium); Olomouc (Czech Republic); Aarhus (Denmark); Christchurch, North Shore, Waitakere, and Wellington (New Zealand); Stoke-on-Trent (UK); and Seattle, WA and Baltimore, MD (USA). Three were in upper-middle-income countries: Curitiba (Brazil), Bogota (Colombia), and Cuernavaca (Mexico).
In each city, small administrative areas (such as census block groups in the USA) were selected by IPEN and participants were recruited from these areas. These administrative areas were chosen to maximise the within-city variability in socioeconomic status and walkability. Socioeconomic status at the area level was defined with relevant census data (eg, household income or educational attainment) and area level walkability was established by a composite index defined as the sum of the Z scores for net residential density, street intersection density, and mixed land use.44 In each city, administrative areas were ranked according to their socioeconomic status and walkability index and classified into one of four groups: (1) low walkability and low socioeconomic status; (2) low walkability and high socioeconomic status; (3) high walkability and low socioeconomic status; (4) and high walkability and high socioeconomic status. Depending on the participating cities, high and low groups were defined by median splits or by being in the top and bottom four deciles of administrative areas. Approximately equal numbers of areas were selected from each of the four groups. Further details about the area selection method of the IPEN Adult study are available.40, 45
Adults residing in the selected areas were contacted by IPEN and invited to complete a survey on their sociodemographic characteristics and physical activity. Study dates ranged from 2002 to 2011 across countries, with participants being recruited across seasons in each city to control for seasonal effects on physical activity. Data collection was dependent on local funding and, therefore, started in different years across countries. City-specific data collection periods ranged from 1 year to 3 years.40 Each country obtained ethics approval from local institutions, and all participants provided written informed consent before participating. Kerr and colleagues40 provide further details on participant recruitment and study procedures. Characteristics of participants were sorted by city and country income groups (table 1 ). Samples from upper-middle-income countries tended to have a lower percentage of participants with higher education than those from high-income countries. The distributions of other sociodemographic characteristics across the two country-income groups were similar.Table 1 Descriptive statistics of sample sociodemographic characteristics and walking outcomes by city and country income groups
All cities High-income countries Upper-middle-income countries
Adelaide, SA, Australia Ghent, Belgium Olomouc, Czech Republic Aarhus, Denmark North Shore, New Zealand Waitakere, New Zealand Wellington, New Zealand Christchurch, New Zealand Stoke-on-Trent, UK Seattle, WA, USA Baltimore, MD, USA Curitiba, Brazil Bogota, Colombia Cuernavaca, Mexico
Sample size 11 615 2407 1142 263 585 489 502 493 481 810 1281 902 691 958 611
Age, years 42 (13) 44 (12) 43 (13) 38 (15) 39 (14) 41 (12) 41 (12) 39 (13) 42 (13) 43 (13) 44 (11) 47 (11) 41 (13) 40 (14) 42 (13)
Female 6586 (56·7%) 1540 (64·0%) 596 (52·2%) 172 (65·4%) 333 (56·9%) 315 (64·4%) 307 (61·2%) 252 (51·1%) 267 (55·5%) 452 (55·8%) 579 (45·2%) 469 (52·0%) 365 (52·8%) 610 (63·7%) 329 (53·8%)
Male 5029 (43·3%) 867 (36·0%) 546 (47·8%) 91 (34·6%) 252 (43·1%) 174 (35·6%) 195 (38·8%) 241 (48·9%) 214 (44·5%) 358 (44·2%) 702 (54·8%) 433 (48·0%) 326 (47·2%) 348 (36·3%) 282 (46·2%)
College education 5143 (44·3%) 1140 (47·4%) 696 (60·9%) 89 (33·8%) 284 (48·5%) 190 (38·9%) 154 (30·7%) 257 (52·1%) 157 (32·6%) 114 (14·1%) 809 (63·2%) 609 (67·5%) 268 (38·8%) 214 (22·3%) 162 (26·5%)
Employed 8755 (75·4%) 1739 (72·2%) 914 (80·0%) 200 (76·1%) 440 (75·2%) 383 (78·3%) 420 (83·7%) 427 (86·6%) 386 (80·2%) 531 (65·6%) 1041 (81·3%) 745 (82·6%) 535 (77·4%) 554 (57·8%) 440 (72·0%)
Married or living with partner 7044 (60·6%) 1358 (56·4%) 841 (73·6%) 153 (58·2%) 386 (66·0%) 346 (70·8%) 373 (74·3%) 279 (56·6%) 269 (55·9%) 369 (45·6%) 809 (63·2%) 546 (60·5%) 402 (58·2%) 512 (53·4%) 401 (65·6%)
Low area-level SES 5643 (48·6%) 1128 (46·9%) 563 (49·3%) 109 (41·4%) 254 (43·4%) 164 (33·5%) 295 (58·8%) 248 (50·3%) 236 (49·1%) 375 (46·3%) 625 (48·8%) 426 (47·2%) 345 (49·9%) 571 (59·6%) 304 (49·8%)
Walking outcomes assessed in previous week by IPAQ-LF
Any walking for transport 8411 (72·4%) 1837 (76·3%) 595 (52·1%) 225 (85·6%) 466 (79·7%) 318 (65·0%) 312 (62·2%) 420 (85·2%) 269 (55·9%) 530 (65·4%) 874 (68·2%) 614 (68·1%) 534 (77·3%) 865 (90·3%) 552 (90·3%)
≥150 min total walking 5859 (50·4%) 1333 (55·4%) 405 (35·5%) 219 (83·3%) 371 (63·4%) 186 (38·0%) 162 (32·3%) 300 (60·9%) 179 (37·2%) 408 (50·4%) 665 (51·9%) 457 (50·7%) 284 (41·1%) 569 (59·4%) 321 (52·5%)
Data are mean (SD) or n (%) unless otherwise indicated. SES=socioeconomic status. IPAQ-LF=International Physical Activity Questionnaire–Long Form.
Exposure and outcome measures
Within IPEN, country sites used geographical information systems (GIS) software to measure urban design and transport features in participants’ neighbourhoods related to walking. A neighbourhood was defined as the area reachable by the street network within 1 km of a participant's home, which is considered a walkable distance and is aligned with the concepts of 20-min neighbourhoods and 15-min cities.45, 46, 47 Measures included population density (people per km2), street intersection density (intersections per km2), and public transport density (public transport stops per km2). Street network distance (m) to the nearest transport stop and park were also measured. A manual of GIS variables that provided definitions and procedures to reduce measurement error and maximise comparability was shared with sites. Geographic information systems variable development and the comparability evaluations have been described in detail.45 The appendix provides definitions of the urban design and transport measures used (p 1) and a summary of the GIS variable development and comparability evaluation (p 2).
Walking for transport and walking for recreation were measured by IPEN using the self-administered International Physical Activity Questionnaire-long form (IPAQ-LF), which has been extensively validated in 12 countries.48 The form assesses the frequency and duration of physical activity across four domains. In this study, we used IPAQ-LF items to separately assess walking for transport and walking for recreation. Participants were asked to report how many days in the last week on which they walked for at least 10 min to get from place to place (transport) and for recreation, and the number of minutes usually spent on these activities each day. Weekly minutes of walking for transport and recreation were combined to obtain total walking. For this study, two measures were derived: any walking for transport during the last week that lasted at least 10 min (no vs yes); and at least 150 min of total walking during the last week (no vs yes). The total walking measure reflects the current WHO physical activity guidelines for adults.4
Determining thresholds
The study included the following covariates: age, sex, educational attainment (college graduate vs not), marital status (married or living with a partner vs all other), employment status (not employed vs employed), city or region and area-level socioeconomic status (low vs high).
Generalised additive mixed models (GAMMs) with binomial variance and logit link functions accounting for spatial correlation at the administrative area level were used to estimate the relationships of urban design and transport measures with the two binary walking outcomes (any walking for transport and ≥150 weekly min of total walking). GAMMs allow estimation of complex curvilinear relationships and can handle data with various distributional assumptions.49 Because the study sampling strategy resulted in several urban design and transport measures being substantially correlated and real-world changes in these features are interdependent (eg, increases in public transport density need to be justified by demand), separate covariate-adjusted GAMMs were estimated for each urban design and transport variable and walking outcome relationship. The curvilinearity of associations was established by comparing Akaike information criterion values of models with linear and curvilinear terms of a specific urban design or transport variable. Models with curvilinear terms yielding Akaike information criterion values that were 5 units smaller than models with linear terms were deemed to provide sufficient evidence of curvilinearity.50 Graphs were generated to depict relationships. To assess whether relationships varied by participants’ sex and age, and by city, separate GAMMs were run with appropriate interaction terms added to the models.
Simulation with a Metropolis Hasting sampler was used to determine the threshold values (and their 95% CIs) of urban design and transport features associated with the two physical activity criteria: at least 80% probability of engaging in any walking for transport (here corresponding to a ≥10% increase in prevalence), and the WHO target of at least 15% relative reduction in insufficient physical activity through total walking. The total walking target was defined as the percentage of the sample who would meet the WHO physical activity guidelines through total walking if there was a 15% relative reduction in the observed prevalence of those not meeting the guidelines. Threshold values and their 95% CIs were included in graphs depicting relationships. Further details on statistical analyses are provided in the appendix (pp 3–4), including the rationale for defining at least a 15% reduction in prevalence of not meeting the WHO physical activity guidelines on the basis of the observed sample prevalence.
Walking, urban design, and transport outcomes
In the 14 cities and country-income groups, 72·4% of the sample walked for transport, and 50·4% met WHO physical activity guidelines of at least 150 weekly min through total walking (table 1). The highest percentages of any walking for transport were observed in two samples from cities in upper-middle-income countries—Bogota and Cuernavaca—which also had the highest average intersection densities and were among the highest population densities (table 2 ). In four cities (Olomouc, Wellington, Bogota, and Cuernavaca) more than 80% of the sample walked for transport, one of the physical activity criteria we used to define thresholds of urban design and transport features (table 1). The second criterion was a 15% relative reduction in insufficient physical activity through walking. With 50·4% of the sample meeting WHO physical activity guidelines through walking, a 15% reduction in insufficient physical activity translates to 57·9% of the sample meeting guidelines through walking. The samples from four cities met this physical activity criterion: Olomouc, Aarhus (Denmark), Wellington, and Bogota (table 1).Table 2 Descriptive statistics of urban design and transport measures
Population density (people per km2) Intersection density (intersections per km2) Public transport density (stops per km2) Street network distance to nearest public transport stop (m) Street network distance to nearest public park (m)
Mean (SD) Median (IQR) Mean (SD) Median (IQR) Mean (SD) Median (IQR) Mean (SD) Median (IQR) Mean (SD) Median (IQR)
All cities 4568 (4097) 3072 (1953–5590) 81 (61) 69 (43–94) 15·7 (13) 14·5 (5·9–22·7) 475 (756) 243 (125–445) 482 (620) 307 (133–586)
High-income countries
Adelaide, SA, Australia 1847 (698) 1835 (1343–2358) 65 (25) 67 (50–78) .. .. .. .. .. ..
Ghent, Belgium 5754 (4984) 4155 (980–9835) 84 (62) 73 (36–92) 9·4 (6) 7·8 (4·0–15·4) 314 (279) 256 (144–360) 829 (1180) 360 (137–772)
Olomouc, Czech Republic 4775 (2639) 4878 (2266–6640) 66 (19) 67 (52–79) 13·6 (6) 14·5 (9·5–17·7) 264 (175) 232 (153–365) 640 (493) 507 (323–791)
Aarhus, Denmark 8025 (6035) 6069 (2327–13 402) 83 (22) 88 (72–98) 9·4 (5) 9·4 (5·6–13·0) 304 (230) 234 (146–393) 387 (376) 300 (170–492)
North Shore, New Zealand 2928 (713) 3049 (2785–3276) 26 (7) 26 (22–31) 18·6 (7) 19·7 (13·7–24·3) 251 (211) 199 (99–345) 239 (195) 190 (81–363)
Waitakere, New Zealand 2246 (706) 2366 (1920–2692) 28 (9) 28 (25–31) 9·6 (7) 7·4 (4·7–13·5) 333 (261) 292 (135–458) 458 (356) 411 (147–679)
Wellington, New Zealand 4030 (1521) 3336 (2954–5285) 44 (16) 44 (35–54) 16·6 (8) 15·3 (11·9–21·6) 224 (291) 149 (61–296) 350 (304) 317 (128–507)
Christchurch, New Zealand 3118 (684) 2901 (2651–3335) 35 (6) 36 (33–40) 16·3 (9) 16·5 (9·8–20·2) 294 (240) 238 (104–411) 333 (242) 294 (149–448)
Stoke-on-Trent, UK 4509 (1201) 4718 (3572–5329) 100 (34) 93 (76–123) 25·7 (8) 25·5 (19·3–31·5) 200 (133) 172 (101–273) 359 (279) 275 (157–488)
Seattle, WA, USA 3630 (4045) 2410 (1775–3733) 71 (23) 71 (56–85) 16 (10) 15·7 (9·0–22·8) 375 (430) 221 (127–447) 471 (369) 388 (215–610)
Baltimore, MD, USA 3271 (2194) 2561 (1746–4757) 55 (28) 53 (38–67) 17 (14) 15·7 (7·6–24·7) 629 (1002) 236 (116–538) 655 (634) 447 (246–810)
Upper middle-income countries
Curitiba, Brazil 7897 (3208) 7381 (5448–9631) 76 (17) 71 (64–83) 25·9 (7) 24·9 (20·6–29·5) 185 (117) 164 (94–253) 356 (277) 292 (124–557)
Bogota, Colombia 10270 (5585) 9461 (6004–15 168) 206 (96) 192 (130–286) 2·1 (3) 1·2 (0·0–3·4) 1678 (1422) 1150 (506–3306) 91 (74) 65 (35–138)
Cuernavaca, Mexico 5710 (2280) 5304 (4390–6615) 144 (46) 134 (111–158) 30·3 (25) 26·7 (12·2–40·5) 454 (607) 214 (97–522) 972 (857) 757 (299–1275)
Thresholds
Population and intersection densities were curvilinearly related to both walking outcomes in an inverted-U manner (p<0·0001), although confidence intervals at the higher end of the measures’ range were large, due to the few observations (Figure 1, Figure 2 ). We estimated that population densities of at least 5665 people per km2 would be associated with a minimum 80% probability of walking for transport and 6491 people per km2 would be associated with minimum 58% probability of accumulating at least 150 weekly min of total walking. Only 24·2% of the total IPEN sample resided in neighbourhoods with optimal ranges of population density for transport and only 19·7% resided in neighbourhoods with optimal ranges of population density for total walking (table 3 ). Large between-city differences were observed against these thresholds, with samples from European cities generally performing better than those from Australasian and North American cities, and samples from cities in Latin American upper-middle-income countries performing better than those in high-income countries.Figure 1 Relationships between urban design and transport measures, and the probability of any walking
Dotted vertical lines show thresholds associated with at least 80% probability of any walking for transport (dotted horizontal lines). Pink shading shows 95% CIs. A=population density. B=intersection density. C=public transport density. D=distance to nearest public transport stop.
Figure 2 Relationships between urban design measures and the probability of ≥150 minutes of total walking per week
Dotted vertical lines show the thresholds associated with at least 58% probability of at least 150 min of total walking per week (dotted horizontal lines). Pink shading shows 95% CIs. A=population density. B=intersection density.
Table 3 Percentage of participants meeting the threshold and within optimal-range values of urban design and transport measures associated
All cities High-income countries Upper-middle-income countries
Adelaide, SA, Australia Ghent, Belgium Olomouc, Czech Republic Aarhus, Denmark North Shore, New Zealand Waitakere, New Zealand Wellington, New Zealand Christchurch, New Zealand Stoke-on-Trent, UK Seattle, WA, USA Baltimore, MD, USA Curitiba, Brazil Bogota, Colombia Cuernavaca, Mexico
Population density
Threshold A (≥5665 people per km2) 24·3% 0·0% 40·9% 31·6% 52·5% 0·0% 0·0% 22·5% 0·0% 15·4% 8·9% 15·9% 70·9% 76·4% 41·1%
Optimal range A (5665–21 844 people per km2) 24·2% 0·0% 40·9% 31·6% 52·5% 0·0% 0·0% 22·5% 0·0% 15·4% 8·9% 15·9% 70·9% 75·7% 41·1%
Threshold B (≥6491 people per km2) 19·8% 0·0% 37·2% 26·2% 48·4% 0·0% 0·0% 8·7% 0·0% 1·5% 7·8% 9·7% 61·8% 73·0% 25·9%
Optimal range B (6491–21 275 people per km2) 19·7% 0·0% 37·2% 26·2% 48·4% 0·0% 0·0% 8·7% 0·0% 1·5% 7·8% 9·7% 61·8% 71·5% 25·9%
Intersection density
Threshold A (≥98 intersections per km2) 22·9% 6·3% 21·8% 5·3% 24·8% 0·0% 0·0% 0·0% 0·0% 47·0% 15·1% 6·8% 12·3% 87·0% 89·7%
Optimal range A (98–334 intersections per km2) 22·1% 6·3% 21·8% 5·3% 24·8% 0·0% 0·0% 0·0% 0·0% 47·0% 15·1% 6·8% 12·3% 77·4% 89·7%
Threshold B (≥122 intersections per km2) 14·6% 2·6% 20·8% 0·0% 0·2% 0·0% 0·0% 0·0% 0·0% 25·9% 0·0% 3·6% 0·1% 79·4% 63·0%
Optimal range B (122–356 intersections per km2) 14·1% 2·6% 20·8% 0·0% 0·2% 0·0% 0·0% 0·0% 0·0% 25·9% 0·0% 3·6% 0·1% 74·3% 63·0%
Public transport density
Threshold A (≥28 stops per km2) 13·6% .. 0·0% 0·0% 0·0% 6·5% 2·4% 7·7% 10·6% 37·2% 12·6% 16·5% 31·7% 0·0% 47·1%
Optimal range A (28–60 stops per km2) 12·5% .. 0·0% 0·0% 0·0% 6·5% 2·2% 7·7% 10·2% 37·2% 12·6% 14·5% 31·7% 0·0% 33·9%
Threshold and optimal range A refer to ≥80% probability of engaging in any walking for transport; threshold and optimal range B refer to the WHO target of ≥15% relative reduction of insufficient physical activity through total walking. The percentage of participants meeting the threshold represents those to the right of the vertical dotted line in figures 1 and 2. The percentage of participants in the optimal range represents those whose point estimate is above the horizontal dotted line in figures 1 and 2. The difference between the percentage of participants meeting a threshold and the percentage of participants in the optimal range gives the percentage of participants with values of an urban design or transportation measures beyond which we observed a decline in probability of meeting a physical activity criterion.
Similar between-city differences were observed for intersection density. Samples from two Latin American cities (Cuernavaca and Bogota) and Stoke-on-Trent (UK) had the highest percentage of participants living in neighbourhoods reaching the intersection density thresholds associated with at least 80% probability of walking for transport (98 intersections per km2) and a minimum 58% probability of accumulating at least 150 weekly min of total walking (122 intersections per km2; figures 1B, 2B).
An inverted-U relationship was observed between public transport density and the probability of walking for transport (p<0·0001; figure 1C), with a threshold value of at least 28 stops per km2 associated with a minimum 80% probability of walking for transport. Again, the samples from two Latin American cities (Cuernavaca and Curitiba) and a European city (Stoke-on-Trent) had the highest percentage of participants reaching this threshold. The association between public transport density and the likelihood of accumulating at least 150 weekly min of total walking was weaker (OR 1·005; 95% CI 0·999–1·011; p=0·0683). Distance to the nearest public transport stop was also curvilinearly related to walking for transport (p=0·0003). However, none of the values for distance to nearest public transport stop were associated with meeting a minimum 80% probability of walking for transport (figure 1D).
Distance to the nearest public park was linearly negatively related to the likelihood of engaging in any walking for transport (OR 0·973; 95% CI 0·961–0·985; p=0·0007) and accumulating at least 150 min weekly of total walking (0·983; 0·972–0·993; p=0·0013). However, none of the distance values were associated with a minimum 80% probability of walking for transport (appendix p 6) or a minimum 58% probability of accumulating at least 150 weekly min of total walking (appendix p 7). None of these associations varied by city, age, or sex (appendix p 5).
Certain areas in some cities exceeded the optimal thresholds for population, intersection, and public transport densities, beyond which we observed probabilities of walking for transport lower than 80% or probabilities of accumulating at least 150 min weekly of total walking lower than 58% (see differences between threshold and optimal range percentages in table 3). This outcome was evident in the samples from Bogota, where approximately 10% of the participants resided in neighbourhoods exceeding the optimal range of intersection density, and in Cuernavaca, where 13% of participants resided in neighbourhoods exceeding the optimal range of public transport density.
Discussion
To inform the development of measurable urban planning and transport policy standards and targets for healthy and sustainable cities, we estimated the associations between key urban design and transport features and two walking outcomes—walking for transport and meeting WHO physical activity guidelines via walking—from data for 11 615 adults from 14 cities across seven high-income and three upper-middle-income countries. We then identified the thresholds of urban design and transport features associated with specific physical activity criteria. Our findings suggest that urban neighbourhoods with at least around 5700 people per km2, 100 intersections per km2, and 25 public transport stops per km2 would yield optimal outcomes for both walking for transport and meeting WHO physical activity guidelines through walking. No thresholds were identified for distances to the nearest public transport stop and public park. However, shorter distances to both types of destinations appeared to facilitate more walking. We found no evidence for differences in associations by sex, age, or city, which reflects previous findings from the same IPEN Adult cohort in relation to objectively measured physical activity,51 and supports the generalisability of findings to various sociodemographic groups and the diverse global cities studied.
Although most research on urban design and transport correlates of physical activity assume or report linear associations,23, 26, 28 we found evidence of curvilinearity in 60% of the estimated associations. This outcome could be due to the increased variability of urban design and transport features resulting from the use of data for several diverse cities from many countries, as opposed to the use of data from a single city or country.40, 52 Although the findings suggest that many of the cities studied had densities below the optimal range for walking and would benefit from densification, there seem to be upper thresholds of densities beyond which gains in walking are no longer observed. Of note, evidence from ultra-dense cities in Asia, in particular China, where 27 000 people per km2 is considered low density, reveals negative relationships between population density and walking by adults,53, 54 with similar findings observed in Mexico.55 The maximum value of population density in our study was 22 950 people per km2, because ultra-dense cities were not included. Therefore, we were unable to characterise the shape of the relationship between ultra-high density and walking. However, consistent with previous evidence (from China, India, and Mexico), our findings suggest negative associations between population density and walking in areas exceeding 14 000–14 500 people per km2 (Figure 1, Figure 2). High population densities typically come with more proximate diverse destinations and regular public transport services, which might reduce the distances walked.
Applications and interpretation of the derived thresholds
Empirically derived thresholds can be used as city planning policy standards and serve as benchmarks against which cities can be evaluated and monitored. As a starting point, the thresholds for this subset of urban design and transport features can be used to identify which parts of cities are appropriately designed to contribute positively to achieving health and sustainability goals. In the first paper in this Series, Lowe and colleagues32 found that many city planning policies, particularly those in middle-income countries, did not have evidence-informed measurable standards and targets. In the third paper, Boeing and colleagues56 show how spatial indicators with health-enhancing thresholds can identify spatial inequities within and between cities in diverse lower-middle-income countries and high-income countries.
Quantitative thresholds similar to those determined in this study are essential for the formulation of measurable standards and targets. These thresholds can be used to evaluate whether current city planning policies take them into consideration and to revise city planning policies to incorporate standards and targets so that they are more likely to contribute to health and sustainability goals. In a post-COVID world where governments are promising to build back better through 15-min cities,46, 47 thresholds for built environment interventions could be very useful. Optimal thresholds for the broad range of urban design and transport features that create healthy and sustainable cities for all (see our conceptual framework in paper four in this Series by Giles-Corti and colleagues)57 need to be established to avoid counterproductive efforts. For example, walkable, high-density neighbourhoods could attract increased traffic and expose residents to air and noise pollution and traffic-related injuries and mortality.10, 58, 59, 60 Thresholds focused on walking and derived from healthy adult samples might have unintended consequences for children or people with mobility problems and those using other active modes of transport (eg, cycling, skating, or wheelchairs). City leaders can use evidence-informed thresholds to evaluate and improve their own cities, and external national and international organisations can use them to monitor many cities’ progress towards meeting UN Sustainable Development Goals and other recommendations.
The present analyses showed that several cities in the IPEN Adult study included neighbourhoods that met the thresholds for certain urban design and transport features, and that the two walking criteria used to define the thresholds might be feasible to achieve. However, we observed pronounced differences between cities, countries, and regions in the sample prevalence of meeting urban design and transport feature thresholds, even though associations were generalisable across cities. Our results should not preclude decision makers from carefully considering the nuances of their local context when designing and planning cities for health and sustainability. For almost all city-specific samples, the percentage of participation in walking for transport was markedly higher than the percentage of participants with neighbourhood urban design and transport features meeting the established thresholds. This disparity implies that additional factors beyond the examined urban design and transport features influence walking behaviour in cities.61, 62 Some of these were accounted for in our analysis, such as educational attainment (usually considered a good proxy for socioeconomic status): a US study found different thresholds across income levels.39 However, other factors that vary across cities, such as local governance, city planning policies,32, 62 motor vehicle ownership,51 poverty, crime,63 and social norms,64 were not accounted for in this study. Optimal thresholds and appropriate targets could depend on other urban design features, cultural norms and attitudes, lifestyle choices, and sociodemographic factors.
Motor-vehicle ownership is another important factor for understanding active travel behaviour. In places where a large proportion of the population cannot afford to own a private motor vehicle, most physical activity for transport decisions are based on necessity rather than choice.65, 66 Our findings from Bogota and Cuernavaca support this hypothesis, with most (>90%) participants from these cities walking for transport, although few people lived in areas with optimal ranges of urban design features (about 40%) for walking for transport.67 The difference between the percentage of participants walking for transport and those residing in neighbourhoods at or above the thresholds of urban design and transport features was particularly large in cities with average population densities less than 5000 people per km2—eg, Adelaide, North Shore, and Seattle. This finding might be due to the sampling strategy adopted by the IPEN Adult study, which required that 50% of the participants be recruited from the most walkable neighbourhoods in each city;40 consequently, these participants had good access to amenities for daily living,63 which is the most salient urban design feature for walking and was not measured by IPEN Adult.23, 31, 56, 62
Another important consideration when using our results for guiding city planning efforts is the shape of the associations we observed. Most participants in our study lived in areas likely to benefit from increases in population, intersection, and public transport densities. However, in Bogota and Cuernavaca, there were notable differences between the percentage of participants meeting thresholds for some urban design and transport features, and the percentage within an optimal range for the same feature. This observation suggests that some participants lived in areas where these urban design features could be too high to achieve the best possible walking outcomes. Therefore, a more-is-better approach might not always be the most appropriate message for every city, or for all areas of a city. This study could not accurately characterise the shape of relationships at high densities, which limited our ability to draw strong conclusions about upper limits of optimal ranges of features. Nevertheless, our results are consistent with those elsewhere, and serve as a warning that there are limits to the degree of health-supportive density. In the face of rapid urbanisation, this hypothesis requires further exploration.
Strengths and limitations
This study had several strengths, one of which was being informed by comparable data from various cities across culturally and geographically diverse countries. Another strength was the stratified sampling strategy. Although not suited to estimating the population-level prevalence of walking and meeting optimal thresholds of urban design and transport features, the sample maximised variability in important urban design and transport features (eg, population, intersection, and public transport densities) and facilitated the characterisation of the shape of relationships. Study limitations included the exclusive reliance on self-reports to measure walking; the few environmental features that could be examined; the absence of data from low-income countries and information on socioeconomic characteristics that could explain the observed between-city differences in environmental features and walking; poor representation of middle-income countries and ultra-dense cities; few data from small and middle-size cities (where a third of the world's urban population lives and where resources and capacity for city design and planning are limited);68 the cross-sectional nature of the study; and insufficient adjustment for residential self-selection precluding the estimation of causal effects,69 climatic conditions, and car ownership.61, 63 However, the aim of this study was not to estimate causal effects but rather the thresholds of environmental features associated with specific physical activity criteria regardless of the underlying mechanisms (environmental influences or residential self-selection) as this information is important to urban planners and policy makers. By correlating features of the residential neighbourhood with both walking for transport and total walking (in and outside the neighbourhood), our analysis might have overestimated the effects of the residential environment.70, 71 We should consider how the selective built environment sampling method used in this study might affect the generalisability of the results for promoting policy thresholds for broad global application. Finally, as population density is measured in various ways (eg, people vs dwellings or density based on total area vs residential-use area only), thresholds identified in this study need to be adjusted if other metrics are used.
Conclusion
We found that residing in neighbourhoods exceeding around 5700 people per km2, 100 intersections per km2, and 25 public transport stops per km2 was associated with meeting one or both policy-relevant physical activity criteria. The empirically derived thresholds for spatial urban design and transport features for walking presented in this study illustrate how research on urban environments and health can assist the development of evidence-based, measurable standards and targets for city planning policy. Although we believe that the derived thresholds reported here could be applied internationally, we do not consider them to be definitive or final. To establish robust, universally applicable thresholds, further analyses of population-representative samples with a broader range of international cities of different sizes, including more low-income to middle-income countries with higher population densities, other geospatial features and metrics (eg, perceived environmental attributes and space syntax measures), and other behavioural and health outcomes, should be conducted. This requires large international, ideally longitudinal, studies with comparable measures and research protocols that enable pooled analyses. However, multicountry longitudinal or quasiexperimental studies that are capable of capturing a sufficiently large range of environmental changes to quantify international causal effect thresholds would be very challenging to do, if feasible at all. To meet UN Sustainable Development Goals that target inequalities, studies should also establish the appropriateness and validity of specific thresholds for different sexes, ages, and socioeconomic groups.72, 73 As the evidence grows, it might be possible to reach a consensus on thresholds for each urban design and transport feature that summarises results based on several health outcomes. These findings need to be clearly communicated to policy makers, who could incorporate them into evidence-informed standards and targets for city planning policy, and thereby support the creation of healthy and sustainable urban environments.
Declaration of interests
JFS reports grants from US National Institutes of Health (NIH), during the conduct of the study; personal fees from Sports, Play, and Active Recreation for Kids (SPARK) programmes of Gopher Sport, other from Rails to Trails Conservancy, outside the submitted work; and a copyright on SPARK physical activity programmes with royalties paid by Gopher Sport. MAA reports grants from National Heart, Lung and Blood Institute at the National Institutes of Health (R01HL111378) during the conduct of the study. KLC reports grants from NIH, during the conduct of the study. GB reports grants from The Public Good Projects, during the conduct of the study. BG-C reports a Senior Principal Research Fellowship (GNT1107672) and grant (number 1061404) support from National Health and Medical Research Council (NHMRC) during the conduct of the study. EC and JFS were supported by the Australian Catholic University. NHMRC funding supported CH through a Centre for Research Excellence in Healthy Liveable Communities (#1061404). DA was supported by an Impact Acceleration Award from the Economic and Social Research Council and funding from the Global Challenges Research Fund administered by the Department for the Economy, Northern Ireland, UK. SL was supported by the experiential fellowships from College of Social Science and Humanities, Northeastern University, Boston, USA. DS was supported by Washington University in St Louis, Center for Diabetes Translation Research (#P30DK092950 from National Institute of Diabetes and Digestive Diseases [NIDDK]/NIH) and by the Cooperative Agreement Number U48DP006395 from the Centers for Disease Control and Prevention (CDC). The content of this article is solely the responsibility of the authors and does not represent the official views of any of the NIDDK/NIH, CDC, or of any of the funding agencies supporting this work. Funding sources had no role in writing the manuscript or the decision to submit for publication. We have not been paid to write this article by a pharmaceutical company or other agency. The IPEN Adult study was partly supported by the National Cancer Institute (NCI) of the NIH. US data collection and coordinating centre processing was supported by NIH grants R01 HL67350 (National Heart, Lung, and Blood Institute) and R01 CA127296 (NCI). The study in Bogota, Colombia was funded by Colciencias (grant 519_2010), Fogarty, and CeiBA. NO was supported by National Health and Medical Research Council Program Grant (grant number 569940), NHMRC Senior Principal Research Fellowship (number 1003960), and by the Victorian Government's Operational Infrastructure Support Program. The Danish study was partly funded by the Municipality of Aarhus. Data collection in the Czech Republic was supported by the grant Ministry of Education, Youth and Sports (MSM 6198959221). Data collection in New Zealand was supported by the Health Research Council of New Zealand (grant 07/356). Data collection in Mexico was supported by the CDC Foundation, which received an unrestricted grant from The Coca-Cola Company. The UK study was funded by the Medical Research Council under the National Preventive Research Initiative. All authors had access to the data in the study and accept responsibility to submit for publication. All other authors declare no conflicts of interest.
Supplementary Material
Supplementary appendix
Acknowledgments
We thank Josef Mitáš (Faculty of Physical Culture, Palacký University Olomouc, Olomouc, Czech Republic), Grant Schofield (Human Potential Centre, Faculty of Health and Environmental Sciences, Auckland University of Technology, Auckland, New Zealand), and Jens Troelsen (Department of Sports Science and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark).
Contributors
EC, JFS, DS, ML, EH, AVM, CH, DA, GB, and SL were part of the study executive team. EC, JFS, DS, ML, EH, AVM, CH, and BG-C contributed to conceptualisation and study design. EC contributed to study method, formal data analysis, and verification of data. EC, DS, CH and BG-C contributed to data visualisation. EC, JFS, DS, ML, EH, AVM, TLC, NO, DvD, CH, DA, and BG-C contributed to writing the original draft and reviewing and editing it. EC, JFS, DS, ML, EH, AVM, TLC, DvD, MAA, and BG-C contributed to data interpretation. MAA, LDF, RR, LC, KLC, RD, JD, OLS, GB, and SL contributed to reviewing the draft and editing for important intellectual content. BG-C led the study executive team. IPEN Adult study roles: EC, TLC, MAA, LDF, and KLC contributed to the method. EC, DS, TLC, NO, DvD, RR, LBC, RD, and OLS contributed to project coordination. ED, DS, EH, TLC, NO, DvD, MAA, LDF, RR, LBC, KLC, RD, JD, and OLS contributed to data collection. EC, DS, NO, LDF, and RR contributed to funding acquisition. JFS was the principal investigator and led international data coordination, study design, data collection and funding acquisition for international coordination. EH, TLC, MAA, LDF, KLC, and JD contributed to data coordination. TLC, MAA, and KLC contributed to data verification. NO contributed to study design. MAA, LDF, and JD contributed to data visualisation.
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References
1 UN Goal 3: ensure healthy lives and promote well-being for all at all ages https://www.un.org/sustainabledevelopment/health/ 2021
2 WHO Physical activity: key facts https://www.who.int/news-room/fact-sheets/detail/physical-activity Nov 26, 2020
3 WHO. Promoting health in the SDGs. Report on the 9th global conference for health promotion, Shanghai, China, 21–24 November 2016: all for health, health for all; Nov 21–24, 2016.
4 WHO WHO guidelines on physical activity and sedentary behaviour 2020 World Health Organization Geneva, Switzerland
5 Lee IM Buchner DM The importance of walking to public health Med Sci Sports Exerc 40 suppl 2008 S512 S518 18562968
6 Guthold R Stevens GA Riley LM Bull FC Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1·9 million participants Lancet Glob Health 6 2018 e1077 e1086 30193830
7 Simpson ME Serdula M Galuska DA Walking trends among US adults: the Behavioral Risk Factor Surveillance System, 1987–2000 Am J Prev Med 25 2003 95 100 12880875
8 Strain T Fitzsimons C Foster C Mutrie N Townsend N Kelly P Age-related comparisons by sex in the domains of aerobic physical activity for adults in Scotland Prev Med Rep 3 2015 90 97 26844194
9 Wennman H Borodulin K Associations between physical activity types and reaching the physical activity guidelines: the FinHealth 2017 Study Scand J Med Sci Sports 31 2021 418 426 32996175
10 Stevenson M Thompson J de Sá TH Land use, transport, and population health: estimating the health benefits of compact cities Lancet 388 2016 2925 2935 27671671
11 Ashworth NL Chad KE Harrison EL Reeder BA Marshall SC Home versus center based physical activity programs in older adults Cochrane Database Syst Rev 1 2005 CD004017
12 Forbes DA Strategies for managing behavioural symptomatology associated with dementia of the Alzheimer type: a systematic overview Can J Nurs Res 30 1998 67 86
13 Moghetti P Balducci S Guidetti L Mazzuca P Rossi E Schena F Walking for subjects with type 2 diabetes: a systematic review and joint AMD/SID/SISMES evidence-based practical guideline Nutr Metab Cardiovasc Dis 30 2020 1882 1898 32998820
14 UN Transforming our world: the 2030 agenda for sustainable development 2015 United Nations New York, NY
15 Salvo D Garcia L Reis RS Physical activity promotion and the United Nations Sustainable Development Goals: building synergies to maximize impact J Phys Act Health 18 2021 1163 1180 34257157
16 Alessio HM Bassett DR Bopp MJ Climate change, air pollution, and physical inactivity: is active transportation part of the solution? Med Sci Sports Exerc 53 2021 1170 1178 33986228
17 Eady J Burtt D Walking and transport in Melbourne suburbs 2019 Victoria Walks Melbourne, VIC
18 Maizlish N Woodcock J Co S Ostro B Fanai A Fairley D Health cobenefits and transportation-related reductions in greenhouse gas emissions in the San Francisco Bay area Am J Public Health 103 2013 703 709 23409903
19 Giles-Corti B Zapata-Diomedi B Jafari A Both A Gunn L Could smart research ensure healthy people in disrupted cities? J Transp Health 19 2020 100931
20 Sallis JF Owen N Ecological models of health behavior Glanz KR Rimer BK Viswanath K Health behavior: theory, research and practice 2015 Jossey-Bass San Francisco, CA 43 64
21 Transportation Research Board Does the built environment influence physical activity? Examining the evidence 2005 Transportation Research Board Washington, DC
22 Barnett DW Barnett A Nathan A Van Cauwenberg J Cerin E Built environmental correlates of older adults' total physical activity and walking: a systematic review and meta-analysis Int J Behav Nutr Phys Act 14 2017 103 28784183
23 Cerin E Nathan A van Cauwenberg J Barnett DW Barnett A The neighbourhood physical environment and active travel in older adults: a systematic review and meta-analysis Int J Behav Nutr Phys Act 14 2017 15 28166790
24 Sallis JF Cerin E Kerr J Built environment, physical activity, and obesity: findings from the International Physical Activity and Environment Network (IPEN) Adult study Annu Rev Public Health 41 2020 119 139 32237990
25 Wang LQW Wen C C. The relationship between the neighborhood built environment and active transportation among adults: a systematic literature review Urban Science 1 2017 29
26 Grasser G Van Dyck D Titze S Stronegger W Objectively measured walkability and active transport and weight-related outcomes in adults: a systematic review Int J Public Health 58 2013 615 625 23224518
27 McCormack GR Shiell A In search of causality: a systematic review of the relationship between the built environment and physical activity among adults Int J Behav Nutr Phys Act 8 2011 125 22077952
28 Smith M Hosking J Woodward A Systematic literature review of built environment effects on physical activity and active transport - an update and new findings on health equity Int J Behav Nutr Phys Act 14 2017 158 29145884
29 Arango CM Páez DC Reis RS Brownson RC Parra DC Association between the perceived environment and physical activity among adults in Latin America: a systematic review Int J Behav Nutr Phys Act 10 2013 122 24171897
30 Elshahat S O'Rorke M Adlakha D Built environment correlates of physical activity in low- and middle-income countries: a systematic review PLoS One 15 2020 e0230454
31 Giles-Corti B Moudon AV Lowe M Creating healthy and sustainable cities: what gets measured gets done Lancet Glob Health 10 2022 e782 e784 35561709
32 Lowe M Adlakha D Sallis J City planning policies to support health and sustainability: an international comparison of policy indicators for 25 cities Lancet Glob Health 10 2022 e882 e894 35561723
33 Klepac Pogrmilovic B Ramirez Varela A Pratt M National physical activity and sedentary behaviour policies in 76 countries: availability, comprehensiveness, implementation, and effectiveness Int J Behav Nutr Phys Act 17 2020 116 32948193
34 Frank LD Pivo G Impacts of mixed use and density on utilization of three modes of travel: single-occupant vehicle, transit, and walking Transp Res Rec 1994 44 52
35 Pushkarev B Zupan J Urban rail in America: an exploration of criteria for fixed-guideway transit 1980 Regional Planning Association New York, NY
36 Koohsari MJ Badland H Giles-Corti B (Re)Designing the built environment to support physical activity: bringing public health back into urban design and planning Cities 35 2013 294 298
37 Eom H-J Cho G-H Exploring thresholds of built environment characteristics for walkable communities: empirical evidence from the Seoul metropolitan area Transp Res Part D Transp Environ 40 2015 76 86
38 Durand CP Andalib M Dunton GF Wolch J Pentz MA A systematic review of built environment factors related to physical activity and obesity risk: implications for smart growth urban planning Obes Rev 12 2011 e173 e182 21348918
39 Wali B Frank LD Chapman JE Fox EH Developing policy thresholds for objectively measured environmental features to support active travel Transp Res D Transp Environ 90 2021 102678
40 Kerr J Sallis JF Owen N Advancing science and policy through a coordinated international study of physical activity and built environments: IPEN adult methods J Phys Act Health 10 2013 581 601 22975776
41 Christiansen LB Cerin E Badland H International comparisons of the associations between objective measures of the built environment and transport-related walking and cycling: IPEN Adult study J Transp Health 3 2016 467 478 28111613
42 Giles-Corti B Foster S Shilton T Falconer R The co-benefits for health of investing in active transportation N S W Public Health Bull 21 2010 122 127 20637168
43 The Community Guide Physical activity: built environment approaches combining transportation system interventions with land use and environmental design https://www.thecommunityguide.org/findings/physical-activity-built-environment-approaches Dec, 2016
44 Frank LD Sallis JF Saelens BE The development of a walkability index: application to the Neighborhood Quality of Life Study Br J Sports Med 44 2010 924 933 19406732
45 Adams MA Frank LD Schipperijn J International variation in neighborhood walkability, transit, and recreation environments using geographic information systems: the IPEN adult study Int J Health Geogr 13 2014 43 25343966
46 C40 Cities Climate Leadership GroupC40 Knowledge Hub 15-minute cities: how to create ‘complete’ neighbourhoods https://www.c40knowledgehub.org/s/article/15-minute-cities-How-to-create-complete-neighbourhoods?language=en_US May, 2021
47 Victoria State GovernmentDepartment of Environment Land, Water, and Planning 20-minute neighbourhoods. Create more inclusive, vibrant and healthy neighbourhoods https://www.planning.vic.gov.au/policy-and-strategy/planning-for-melbourne/plan-melbourne/20-minute-neighbourhoods 2021
48 Craig CL Marshall AL Sjöström M International physical activity questionnaire: 12-country reliability and validity Med Sci Sports Exerc 35 2003 1381 1395 12900694
49 Wood SN Generalised additive models: an introduction with R 2nd ed. 2017 Chapman & Hall/CRC Boca Raton, FL
50 Burnham KP Anderson DR Model selection and multimodel inference: a practical information-theoretic approach 2002 Springer New York, NY
51 Sallis JF Cerin E Conway TL Physical activity in relation to urban environments in 14 cities worldwide: a cross-sectional study Lancet 387 2016 2207 2217 27045735
52 Cerin E Conway TL Adams MA Objectively-assessed neighbourhood destination accessibility and physical activity in adults from 10 countries: an analysis of moderators and perceptions as mediators Soc Sci Med 211 2018 282 293 29966823
53 Qin Z Wang Z Ye Q Jia P Xiong Y Xu F Relation between residential density and walking time among urban adults in Nanjing, China Public Health 181 2020 34 39 31935609
54 Adlakha D Hipp JA Brownson RC Neighborhood-based differences in walkability, physical activity, and weight status in India J Transp Health 3 2016 485 499
55 Salvo D Reis RS Stein AD Rivera J Martorell R Pratt M Characteristics of the built environment in relation to objectively measured physical activity among Mexican adults Prev Chronic Dis 11 2014 e147 25167092
56 Boeing G Higgs C Liu S Using open data and open-source software to develop spatial indicators of urban design and transport features for achieving healthy and sustainable cities Lancet Glob Health 10 2022 e907 e918 35561725
57 Giles-Corti B Moudon AV Lowe M What next? An expanded view of city planning and global health, and the need for evidence-informed policy to be implemented and monitored Lancet Glob Health 10 2022 e919 e926 35561726
58 Marshall JD Brauer M Frank LD Healthy neighborhoods: walkability and air pollution Environ Health Perspect 117 2009 1752 1759 20049128
59 Hankey S Marshall JD Urban form, air pollution, and health Curr Environ Health Rep 4 2017 491 503 29052114
60 Higgs C Badland H Simons K Knibbs LD Giles-Corti B The Urban Liveability Index: developing a policy-relevant urban liveability composite measure and evaluating associations with transport mode choice Int J Health Geogr 18 2019 14 31185987
61 Bosdriesz JR Witvliet MI Visscher TL Kunst AE The influence of the macro-environment on physical activity: a multilevel analysis of 38 countries worldwide Int J Behav Nutr Phys Act 9 2012 110 22967164
62 Giles-Corti B Vernez-Moudon A Reis R City planning and population health: a global challenge Lancet 388 2016 2912 2924 27671668
63 Cerin E Cain KL Conway TL Neighborhood environments and objectively measured physical activity in 11 countries Med Sci Sports Exerc 46 2014 2253 2264 24781892
64 Emmons KM Barbeau EM Gutheil C Stryker JE Stoddard AM Social influences, social context, and health behaviors among working-class, multi-ethnic adults Health Educ Behav 34 2007 315 334 16740510
65 Handy S Weston L Mokhtarian PL Driving by choice or necessity? Transp Res Part A Policy Pract 39 2005 183 203
66 Rivas ME Serebrisky T The role of active transport modes in enhancing the mobility of low-income people in Latin America and the Caribbean https://publications.iadb.org/en/role-active-transport-modes-enhancing-mobility-low-income-people-latin-america-and-caribbean April 2021
67 Kamruzzaman M Baker D Washington S Turrell G Determinants of residential dissonance: implications for transit-oriented development in Brisbane Int J Sustain Transport 10 2016 960 974
68 UN Population Division The world's cities in 2018: data booklet 2018 United Nations New York, NY
69 Cao X Mokhtarian P Handy S Examining the impacts of residential self-selection on travel behavior: a focus on empirical findings Transp Rev 29 2009 359 395
70 Chaix B Duncan D Vallée J Vernez-Moudon A Benmarhnia T Kestens Y The “Residential” Effect Fallacy in neighborhood and health studies: formal definition, empirical identification, and correction Epidemiology 28 2017 789 797 28767516
71 Kwan MP The Neighborhood Effect Averaging Problem (NEAP): an elusive confounder of the neighborhood effect Int J Environ Res Public Health 15 2018 E1841
72 Mecredy G Pickett W Janssen I Street connectivity is negatively associated with physical activity in Canadian youth Int J Environ Res Public Health 8 2011 3333 3350 21909310
73 Thielman J Copes R Rosella LC Chiu M Manson H Is neighbourhood walkability related to body mass index among different age groups? A cross-sectional study of Canadian urban areas BMJ Open 9 2019 e032475
| 35561724 | PMC9731787 | NO-CC CODE | 2022-12-14 23:31:52 | no | Lancet Glob Health. 2022 Jun 10; 10(6):e895-e906 | utf-8 | Lancet Glob Health | 2,022 | 10.1016/S2214-109X(22)00068-7 | oa_other |
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Clin Epidemiol Glob Health
Clin Epidemiol Glob Health
Clinical Epidemiology and Global Health
2452-0918
2213-3984
The Authors. Published by Elsevier B.V. on behalf of INDIACLEN.
S2213-3984(22)00243-3
10.1016/j.cegh.2022.101200
101200
Original Article
Challenges and barriers to immunization during COVID-19: An experience of parents/caregivers from a well-baby clinic of a tertiary care hospital of Eastern India
Mishra Krishna a∗
Mohapatra Ipsa a
Sarathi Mohapatra Partha b
Madhusikta Smriti a
Parimita Pragyan a
a Department of Community Medicine, Kalinga Institute of Medical Sciences, Odisha, India
b Department of Anaesthesiology, Kalinga Institute of Medical Sciences, Odisha, India
∗ Corresponding author. Department of Community Medicine, Kalinga Institute of Medical Sciences, Patia, Odisha, India.
9 12 2022
January-February 2023
9 12 2022
19 101200101200
2 7 2022
13 10 2022
4 12 2022
© 2022 The Authors
2022
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Background
Immunization in children is one of the best methods of preventing vaccine preventable diseases.With the onset of the COVID-19 pandemic,there have been disruptions in vaccine supply,its uptake and perception towards routine immunization, globally and in India. This study was planned to identify the challenges faced by the parents, the perceived barriers towards childhood immunization and perception regarding vaccination during the ongoing pandemic.
Methods
A cross-sectional study was carried out in the well-baby clinic, providing immunization services. The informants were parents/caregivers of under-five children accompanying their children to the well-baby clinic within the study period. Data was collected using a semi-structured interviewer administered questionnaire and analysed using Epi-info software and p value less than 0.05 was considered to be statistically significant.
Results
Delay in immunization was noted in 62% of the children. The challenges stated by respondents for delay in immunization during this pandemic mostly were “fear about getting infected” (30%) and “someone instructed them not to take the child to the hospital if not ill” (13%). The most common perceived barriers were ‘no vaccination sites were open’ or ‘did not know where the baby can be vaccinated’. ‘Child should receive all vaccines’(99%) and ‘safety is more important than vaccination’ (83%)was the perception of respondents about immunization during the pandemic.
Conclusion
Though the respondents were aware of the need for timely vaccinations of their children, still a substantial delay was seen among majority of them due to fear of getting infected and unavailability of vaccines.
Keywords
Barriers
Immunization
Caregivers
Pandemic
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pmc1 Introduction
Childhood immunization has been one of the most important, cost-effective interventions to reduce the burden of vaccine preventable diseases (VPDs). With the onset of the current pandemic of COVID-19, the health system around the world and so also India, was affected greatly due to increased demand of healthcare workers dedicated for serving the patients affected by the disease. This, along with the lockdown restrictions led to a disruption of the routine healthcare services. Routine immunization services were also hampered in various parts of India including Odisha. As per the WHO advisory, the immunization service being an essential one, should have continued even during the period of pandemic.1 The suspension and disruption of immunization services in India affected the strategies used by Mission Indradhanush to achieve and sustain full immunization of more than 90% for children by the year 2022.2 As per the health management and information system (HMIS) of the National Health Mission (NHM), there was a significant decrease in routine immunization services showing that at least one lakh and two lakh children missed their BCG and pentavalent (DPT, Hib, Hep B) vaccines respectively in March 2020.3 , 4 The fear and uncertainty during the pandemic may have also affected parent's perception towards vaccination, posing a barrier towards the proper provision of scheduled vaccination.
As this information is essential to improve resilience, reduce the population's fundamental vulnerability, to increase vaccine uptake and decrease an upsurge of VPDs; this study was planned for identifying the challenges faced by the parents with regards to immunization of their children, to find out the barriers to childhood immunization during the pandemic and to identify their perception regarding vaccination during the pandemic.
2 Materials & methods
It was a cross-sectional study carried out from June to September 2020 after getting approval from the institutional research and Ethics committee. The study was conducted in the well-baby clinic of a tertiary care hospital in Bhubaneswar, Odisha. The respondents included parents/caregivers of the under five children visiting the well-baby clinic by 30th September, 2020. Convenience sampling technique was used for selection of study participants; those who agreed to participate in the study and gave written informed consent within the time frame were included. Consent for the results to be published after analysis was also taken from the study respondents. A total of 128 respondents were contacted out of whom 106 agreed and satisfied the inclusion criteria; six of the respondents were previously included in the study (during their first visit within the time frame of the present study), so they were excluded during the subsequent visits; thus making a sample size of 100.
2.1 Inclusion criteria
• Parents/caregivers5 with children less than five years of age who utilized the routine immunization services of the hospital and were registered in the well-baby clinic.
• Age of the respondent more than 18 years.
• Those who gave written informed consent.
2.2 Exclusion criteria
• Parents/caregivers who were on medication for any mental illness.
• Un-cooperative respondents
• Respondents already included once for the present study during their first visit.
Study tool: A semi-structured, researcher made interviewer-administered questionnaire; after thorough literature search was used for data collection.6, 7, 8 It consisted of questions to assess the socio-demographic details of the respondent, challenges and barriers to vaccination during COVID-19 and parental perception of immunization during the pandemic. The barriers for appropriate immunization during COVID -19 was assessed through the survey tool which had some open ended questions as well so that the parents’ perception could be better assessed.
Methodology: The respondents were contacted telephonically whose children had their vaccination scheduled during the study period, which is a usual practice of the current institute. The investigator first contacted the eligible parent and briefed them about the purpose of the study and seeked their support. After their consent, the investigator conducted a face to face interview. All efforts were made to include the parents/caregivers with children under-5 years of age. Confidentiality of all participants was maintained. At the end of the interview the parents/caregivers were briefed on the recommendations and importance of immunization during this period and how routine childhood immunization will help preventing the outbreak of other VPDs. Any query raised by the respondents were answered. They were encouraged to continue with the childhood immunization routinely as scheduled without further delay even with the continuation of the current pandemic.
2.3 Ethical committee approval
The institutional research an Ethics committee approval was sought before the initiation of the study [****/****/IEC/350/2020].
2.4 Data management and statistical analysis
The data collected was entered into Microsoft excel sheet and analysed using Epi-info en-US version 7.2.4.0 software. Descriptive statistics was used for frequencies. Chi –square test and F-test wherever appropriate was used as the test of significance. A p value of less than 0.05 was considered as statistically significant.
3 Results
Around 95% of the respondents were parents and 5% were caregivers. The caregivers were either uncles or grandparents. The total number of study participants were 100 out of which delayed vaccination was reported by 62. Table 1 depicts the socio-demographic characteristics of the study participants and its association with delay in scheduled childhood vaccination.Table 1 Socio-demographic characteristics of the study participants with vaccination status of the sampled population (n = 100).
Table 1Socio-demographic variables Vaccination Delayed[n = 62] n(%) Vaccination not delayed[n = 38] n(%) p value
1. Gender of the child
Male (n = 52) 32 (61.5) 20 (38.5) 0.920
Female (n = 48) 30 (62.5) 18(37.5)
2. Age of the child
<6weeks(n = 7) 3 (42.8) 4 (57.2) 0.022
6–14 weeks(n = 12) 3 (25.0) 8 (75.0)
14wks - 12months(n = 26) 15 (57.7) 12(42.3)
13–24 months(n = 43) 31(72.1) 12(27.9)
>24 months(n = 12) 10(83.3) 2(16.7)
3. Birth order of child
1st (n = 48) 31(64.6) 17 (35.4) 0.625
2nd (n = 39) 22(54.4) 17(45.6)
≥3rd (n = 13) 9(69.2) 4 (30.8)
4. Religion
Hindu (n = 96) 60(62.5) 36(37.5) 0.433
Muslim (n = 3) 1(33.3) 2 (66.7)
Sikh (n = 1) 1(100.0) 0 (0)
5. Age of the respondent
20–30years (n = 32) 18(56.3) 14(43.7) 0.044
31–40 years (n = 65) 44(67.7) 21(32.3)
>40 years (n = 3) 0 (0) 3 (100.0)
6. Respondent's literacy status
Literate (n = 95) 58 (61.1) 37(38.9) 0.708
Illiterate (n = 5) 4(80.0) 1(20.0)
7. Place of residence
Urban(n = 86) 57(66.3) 29(33.7) 0.059
Rural(n = 16) 5(31.3) 9(68.7)
The age of the respondent (p = 0.044) and age of the child (p = 0.022) were found to have a statistically significant association with delayed vaccination.
Fig. 1 depicts the barriers cited by parents to vaccination during COVID-19 pandemic that resulted in a delay in routine childhood immunization uptake.Fig. 1 Barriers cited by parents/caregivers to vaccination during COVID-19 Pandemic [N = 62] *┼
Fig. 1
The barriers stated by parents/caregivers’ for delay in immunization of their children during this pandemic were “fear about getting infected” (30%), “someone instructed them not to take the child to the hospital if not ill” (13%) and “COVID-19 patients might be present in the hospital” (12%). “Place of residence” (p value = 0.028), sex of the child (p = 0.042) were found to be statistically significantly associated with the cited barriers. There were multiple responses cited as barriers to routine immunization by the respondents.
Fig. 2 depicts the perception of parents/caregivers on immunization during the COVID-19 pandemic.Fig. 2 Perception of parents/caregivers on immunization during the pandemic [n = 100].
Fig. 2
The above figure depicts that many respondents realized the importance of scheduled vaccination for their under-five children whereas some respondents did not have a clear idea about the same. Around 68% of the respondents considered that healthcare (HC) providers could be the potential source of infection.
Fig. 3 depicts the challenges faced by parents/caregivers for delayed immunization with respect to various rules and regulations laid down by the government during the four stages of lockdown.Fig. 3 Challenges faced by parents for delay in immunization during different phases of lockdown [N = 62] ++.
Fig. 3
Fear of getting infected (29.03%) and unavailability of vaccines (16.13%) were some of the common reasons cited by the respondents during the first phase of lockdown.
4 Discussion
The present study consisted of respondents where 95% were parents and 5% were caregivers (which included either grandparents or uncles). A similar study done in Indonesia reflects a slightly different profile of the respondents with more than 78% being parents followed by other family members such as uncles, aunts and grandparents.9 The present study found that around 62% of the study participants had a delay in the scheduled immunization for their children. A similar study done in Saudi Arabia by Mohammed Alsuhaibani et al. reported a delay of more than two weeks for routine childhood immunization in 47.8% of the respondents and a significant delay of more than a month in 23.4% respondents.10 Another study done in Saudi Arabia by Leena R. Baghdadi et al. reported a prevalence of intentional vaccine delay as 37% which is much less than that of the present study.11 There were some socio-demographic factors like the age of the child which was found to be statistically significantly associated with delay in routine immunization of the under-five child in the present study. A study done in South East Asia and Pacific Region by Rebecca C. Harris et al. reported infancy and school entry age being most affected which was also found to be significantly associated with delayed routine childhood immunization.12 In the current study higher age of the child, as well as higher age of the respondents, were found to have statistically significant association with delayed vaccination.
The barriers to routine immunization cited by parents had multiple responses among which ‘no vaccination sites open’ (29%), ‘didn't know where the baby can be vaccinated’ (13%) and ‘didn't have someone else who can take care of other children at home’ (10%) were the most commonly cited barriers. Another similar study done in Saudi Arabia among parents reported that the barriers cited for delayed routine immunization during the pandemic were the fear of contracting COVID-19 (60.9%) followed by time constraints (11.6%) and difficulties in scheduling an appointment (9.2%). Other reasons mentioned by parents were travelling during the vaccination time, vaccine unavailability or closed clinics (6.7%).10
The perception of parents regarding immunization in the present study depicted that around 99% of the parents considered that childhood immunization should continue during the pandemic, 83% of them considered that safety is more important than immunization during the pandemic and around 31% of the respondents considered that routine immunization may provoke COVID-19 disease. These findings of the present study are similar to the findings of a study done in England by Sadie Bell et al.13 The present study had the novelty of studying the challenges faced by parents/caregivers during the four phases of lockdown in the state of Odisha. There were multiple responses for each lockdown depending on the local restrictions. The most common challenge faced by the respondents during the first phase of lockdown were ‘fear of getting infected’ and ‘absence of Government vaccines during that period’. The challenges during the second phase of lockdown were ‘fear of getting infected’, ‘someone's instruction/advice not to take the child to the hospital if he/she is not ill’ and ‘police would ask on the way’. Similar results were reported by Leena R. Baghdadi et al. depicting that ‘fear of getting infected’, ‘travelling on the due date of vaccination’, concerns about vaccine efficacy and safety’ were the major contributors to delay in routine childhood vaccination.11
5 Conclusion
Delayed vaccination was seen in those with older children (more than 2years), among mothers who were 31–40 years old and among those respondents who were illiterate. Although the parents and caregivers had a perception that children should receive all the vaccinations during the pandemic, but lack of knowledge and information on vaccination sites being operational and unavailability of vaccines were the cited barriers. The challenges faced during the first phase of lockdown were fear of getting infected and unavailability of government supply of vaccines.
6 Implication for further scope
The present study helped identify barriers for delayed vaccination during the pandemic. These barriers were addressed by appropriate counselling, so that their misconceptions would be addressed. These identified challenges and barriers can help in making guidelines for future crisis management. Healthcare providers can help in reviewing, planning and implementing strategies to remove them leading to proper implementation and avert suspension of routine services in future. Information about parental barriers to immunizations can lead to targeted interventions to minimize these obstacles at the individual and community level and to help the country achieve our national and state goals. With the ongoing pandemic routine childhood vaccinations should smoothly continue to prevent the outbreak of any other VPDs. Adequate and proper management by the health system to address the existing context-specific barriers and challenges at the system, community and individual level will help in improving the work performance of HCPs.
Further research on the methods to have a continuous uptake of routine immunization among under-five children and methods to reach a greater proportion of parents/caregivers for avoiding delay in scheduled vaccination may be planned. Health planning and management on this aspect of routine immunization including sufficient provision of vaccines and adequate HC personnel may be helpful to overcome the crisis due to the pandemic.What is already known about the topic: Vaccine hesitancy among parents has been studied at various levels of healthcare.
What the study adds: This study aimed at identifying the challenges faced by parents during various stages of lockdown which is new of its kind and none of its kind have been reported from this part of the country. In case of any epidemic or pandemic the identified factors may be considered for keeping a continuous provision as well as uptake of the routine childhood immunization.
Limitations: It is a hospital based study and the results may not be generalized.
Author’s contributions
Each enlisted author has substantially contributed as per the journal's requirements. Each author has actively participated in the in the conceptualization and designing of the study, acquisition of data, analysis and interpretation of the data, drafting of the manuscript and final approval of the version of manuscript which is submitted.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.
CRediT authorship contribution statement
Krishna Mishra: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, &, Visualization, Validation, Final draft preparation Acceptance of the final draft submitted. Ipsa Mohapatra: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, &, Visualization, Validation, Final draft preparation Acceptance of the final draft submitted. Partha Sarathi Mohapatra: Conceptualization, Formal analysis, Investigation, Methodology, Project administration, Resources, Supervision, &, Visualization, Validation, Final draft preparation Acceptance of the final draft submitted. Smriti Madhusikta: Conceptualization, Formal analysis, Investigation, Methodology, Project administration, Resources, Supervision, &, Visualization, Validation, Final draft preparation Acceptance of the final draft submitted. Pragyan Parimita: Conceptualization, Formal analysis, Investigation, Methodology, Project administration, Resources, Supervision, &, Visualization, Validation, Final draft preparation Acceptance of the final draft submitted.
Declaration of competing interest
None.
Acknowledgement
The authors acknowledge all the study participants who spared their time to participate in the survey. The authors also thank the staff of the well-baby clinic who contacted the parents telephonically.
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References
1 WHO Guiding Principles for Immunization Activities during the COVID-19 Pandemic, Interim Guidance 26 March 2020 1 3
2 Singh A. Mission Indradhanush (MI) and intensified mission Indradhanush (imi): the immunization programmes in India – a brief review. Gut Gastroenterology 1: 001-002. Doi:.
3 National health mission health management information system Ministry of health and family welfare, government of India. Available https://nrhm-mis.nic.in/SitePages/Home.aspx
4 Rukmini S. How Covid-19 response disrupted health services in rural India. Mint. 2020. Available: https://www.livemint.com/news/india/how-covid-19-response-disrupted-health-services-in-rural-India-11587713155817.html. [Accessed 11 January 2022].
5 Mishra K. Mohapatra I. Kumar A. A study on the health seeking behavior among caregivers of under-five children in an urban slum of Bhubaneswar, Odisha J Fam Med Prim Care 8 2019 498 503 10.4103/jfmpc.jfmpc_437_18
6 Larson H.J. Measuring vaccine hesitancy: the development of a survey tool Vaccine 33 2015 4165 4175 25896384
7 Patel K. Enablers and barriers towards ensuring routine immunization services during the COVID-19 pandemic: findings from a qualitative study across five different states in India Trans R Soc Trop Med Hyg 2022 1 8 34313305
8 Harmsen Why parents refuse childhood vaccination: a qualitative study using online focus groups BMC Publ Health 13 2013 1183
9 UNICEF Routine immunization for children during the COVID-19 pandemic in Indonesia: perceptions of parents and caregivers Indonesia 2020:1-12
10 Alsuhaibani M. Alaqeel A. Impact of the COVID-19 pandemic on routine childhood immunization in Saudi Arabia Vaccines 8 4 2020 581 33022916
11 Baghdadi R.L. Younis A. Suwaidan H.I.A. Hassounah M.M. Khalifah R.A. Impact of the COVID-19 pandemic lockdown on routine childhood immunization: a Saudi nationwide cross-sectional study Front. Pediatr. 9 June 2021 Article no 692877. Available at: https://pubmed.ncbi.nlm.nih.gov/34222155 Last accessed on 25.05.22 at 11.32am
12 Harris R. Chen Y. Côte P. Impact of COVID-19 on routine immunisation in South-East Asia and western pacific: disruptions and solutions SSRN Electron J 10 2020 1 10
13 Bell S. Clarke R. Paterson P. Jack M.S. Parents' and guardians' views and experiences of accessing routine childhood vaccinations during the coronavirus (COVID-19) pandemic: a mixed methods study in England PLoS One 15 12 2020 e0244049
| 36514347 | PMC9731810 | NO-CC CODE | 2022-12-14 23:54:28 | no | Clin Epidemiol Glob Health. 2023 Dec 9 January-February; 19:101200 | utf-8 | Clin Epidemiol Glob Health | 2,022 | 10.1016/j.cegh.2022.101200 | oa_other |
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Int J Infect Dis
Int J Infect Dis
International Journal of Infectious Diseases
1201-9712
1878-3511
The Author(s). Published by Elsevier Ltd on behalf of International Society for Infectious Diseases.
S1201-9712(22)00639-7
10.1016/j.ijid.2022.12.001
Article
Risk factors for COVID-19 hospitalization following COVID-19 Vaccination: a population-based cohort study in Canada
García Héctor A. Velásquez 12⁎⁎
Adu Prince A. 12
Harrigan Sean 1
Wilton James 1
Rasali Drona 1
Binka Mawuena 2
Sbihi Hind 1
Smolina Kate 12
Janjua Naveed Z. 123⁎
1 British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada
2 School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada
3 Centre for Health Evaluation & Outcome Sciences, St. Paul's Hospital, Vancouver, British Columbia, Canada
⁎ Corresponding author. Naveed Zafar Janjua MBBS, MSc, DrPH, 655 W 12th Ave, Vancouver, BC V5Z 4R4
⁎⁎ Co-corresponding author. Héctor A. Velásquez García, Ph.D., 655 W 12th Ave, Vancouver, BC V5Z 4R4
9 12 2022
9 12 2022
17 8 2022
10 11 2022
2 12 2022
© 2022 The Author(s). Published by Elsevier Ltd on behalf of International Society for Infectious Diseases.
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Introduction
With the uptake of COVID-19 vaccines, there is a need for population-based studies to assess risk factors for COVID-19 related hospitalization following vaccination and how they differ from unvaccinated individuals.
Methods
We used data from the BC COVID-19 Cohort, a population-based cohort that includes all individuals (≥18 years) who tested positive for SARS-CoV-2 by real-time reverse transcription-polymerase chain reaction, from 01 January 2021 (following start of vaccination program) to 31 December 2021. We used multivariable logistic regression models to assess COVID-19 related hospitalization risk by vaccination status and age-group among confirmed COVID-19 cases.
Results
Of the 162,509 COVID-19 cases included in the analysis, 8,546 (5.3%) required hospitalization. Among vaccinated individuals, an increased odds of hospitalization with increasing age was observed for older age-groups, namely 50-59 years (OR=2.95, 95% CI: 2.01-4.33), 60-69 years (OR=4.82, 95% CI:3.29, 7.07), 70-79 years (OR=11.92, 95% CI: 8.02, 17.71) and ≥80 years (OR=24.25, 95% CI: 16.02, 36.71). However, among unvaccinated individuals, there was a graded increase in odds of hospitalization with increasing age, starting at age-group 30-39 years (OR=2.14, 95% CI: 1.90, 2.41) to ≥80 years (OR=41.95, 95% CI: 35.43, 49.67). Also, when comparing all the age-groups to the youngest, the observed magnitude of association was much higher among unvaccinated individuals than vaccinated ones.
Conclusion
Alongside a number of comorbidities, our findings showed a strong association between age and COVID-19 related hospitalization, regardless of vaccination status. However, age-related hospitalization risk was reduced two-fold by vaccination, highlighting the need for vaccination in reducing the risk of severe disease and subsequent COVID-19 related hospitalization across all population groups.
Key words
COVID-19
Vaccination
Hospitalization
Risk factors
Canada
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pmcIntroduction
As of July 24, 2022, over 560 million confirmed cases of Coronavirus Disease 2019 (COVID-19), have been reported globally, with over six million deaths. Almost four million confirmed cases, including 42,215 deaths have been reported in Canada alone (Johns Hopkins University, 2022). In British Columbia, Canada's third largest province by population size, over 370, 000 cases have been recorded, with over 3,855 deaths as of July 24, 2022 (British Columbia Centre for Disease Control, 2022a). Although vaccination roll-out and uptake have reduced COVID-19 disease burden in many jurisdictions, prompting the opening of economies and a return to normalcy, the effects of COVID-19 are far from over.
Hospitalization is commonly used as a measure of COVID-19 severity. Since the beginning of the pandemic, there has been emergence of a growing number of studies assessing the risk factors of COVID-19 hospitalization. These studies have established certain key risk factors; prominent among them are age, sex, and certain comorbidities. However, most of these studies were hospital-based (Gold, 2020; Kaeuffer et al., 2020; Vahey et al., 2021), with few population-based studies involving all diagnosed patients in a jurisdiction. Vaccination has resulted in the prevention of severe disease that would lead to hospitalization, intensive care unit (ICU) admission and mortality (Nasreen et al., 2022; Watson et al., 2022). However, it is not very well established during the time of high vaccine effectiveness, which population groups remained at the risk of hospitalization and whether these risk factors and the magnitude of association differed by vaccination status. These data could identify candidates for additional interventions, such as pharmacotherapy, to reduce the risk of hospitalization and severe disease. Additionally, this evidence will help improve health outcomes and maintain health system capacity. The aim of this study therefore was to assess COVID-19 related hospitalization risk by vaccine status and age among confirmed COVID-19 cases during the period of high vaccine effectiveness.
Materials and Methods
Study design and data sources
We used data from the British Columbia COVID-19 Cohort (BCC19C) [https://a4ph.med.ubc.ca/projects-and-initiatives/bc-covid-19-cohort/], a population-based data platform that has been established as a public health surveillance system under the British Columbia Centre for Disease Control's public health mandate (Velásquez García et al., 2021). The BCC19C integrates data on all individuals tested for COVID-19 in BC, with data on COVID-19 hospital and intensive care unit admissions, medical visits, hospitalizations, emergency room visits, chronic conditions, prescription drugs, and mortality (See Appendix A of the Supplementary file).
Study Population
This analysis included all adult individuals (aged 18 or above) who tested positive for SARS-CoV-2 by real-time reverse transcription-polymerase chain reaction (RT-PCR), from 01 January 2021 to 31 December 2021. During this period, vaccination coverage (at least two doses) for all eligible ages ranged from 2% as of January 2021 to 80% as of 22 December 2021 (British Columbia Centre for Disease Control, 2022b). We excluded from the analysis, individuals who reside in long-term care facilities, as these individuals are very different from the general population, with respect to their exposure risk and disease severity, given their comorbidity profile and characteristics. In addition, the transfer of these patients to hospitals was irregular over time and across local regions.
Outcome and exposures
The outcome of interest for the study was hospitalization or ICU admission with a positive SARS-CoV-2 test within 14 days prior to or up to 3 days after hospitalization. We excluded nosocomial cases flagged in notifiable disease reporting systems and SARS-CoV-2-positive cases with specimen collection >3 days after hospital admission (Nasreen et al., 2022) .
The following comorbidities and risk factors were assessed using medical visits, hospitalization and/or prescription drugs: Alzheimer/dementia, asthma, chronic heart disease (CHD): acute myocardial infarction, angina, heart failure, ischemic myocardial infarction, chronic obstructive pulmonary disease (COPD), cirrhosis, chronic kidney disease (CKD), depression, diabetes (categorized as non-diabetes, non-insulin dependent, and insulin), epilepsy, gout, hypertension, stroke (ischemic, haemorrhagic, transitory ischemic attack), mood and anxiety disorders, osteoarthritis, osteoporosis, parkinsonism, rheumatoid arthritis, substance use disorder, injection drug use (IDU), alcohol misuse, cancer, immunosuppression, intellectual and developmental disabilities (IDD) and schizophrenia and psychotic disorders (SZP).
Other factors taken into account for this analysis were age, vaccination status (categorized as not vaccinated or based on timing of infection relative to receipt of dose as follows: partially vaccinated: ≥ 14 days after 1st dose, or vaccinated: ≥ 14 days after 2nd dose) and variant of concern (VOC; details about genomic sequence analysis can be found elsewhere) (Fibke et al., 2022). Variable definitions and diagnostic codes used to identify comorbidities are presented in Appendix B of the Supplementary file).
Statistical Analysis
We compared demographic characteristics and comorbidities between the overall analytic sample, those requiring ambulatory care and those requiring hospitalization (Table 1 ). We also summarized the distribution of characteristics among unvaccinated adult cases (Table 2 ) and among vaccinated adult cases (Table 3 ). Age was summarized in terms of median and interquartile range (IQR) and categorized for the analyses. Categorical variables were summarized as frequencies and percentages.Table 1 Distribution of characteristics in confirmed (lab-tested) COVID-19 adult cases during 2021, BC COVID-19 Cohort
Table 1 Ambulatory Hospitalized Overall P-value
(N=153,963) (N=8,546) (N=162,509)
Sex Female 77213 (50.2%) 3656 (42.8%) 80869 (49.8%) <0.001
Male 76750 (49.8%) 4890 (57.2%) 81640 (50.2%)
Age (years) Median (Q1-Q3) 37 (27 - 50) 60 (45 - 72) 38 (28 - 52) <0.001
Age group <20 Years 5852 (3.8%) 41 (0.5%) 5893 (3.6%) <0.001
20-29 Years 42304 (27.5%) 533 (6.2%) 42837 (26.4%)
30-39 Years 37305 (24.2%) 1003 (11.7%) 38308 (23.6%)
40-49 Years 27722 (18.0%) 1114 (13.0%) 28836 (17.7%)
50-59 Years 21223 (13.8%) 1534 (18.0%) 22757 (14.0%)
60-69 Years 12939 (8.4%) 1739 (20.3%) 14678 (9.0%)
70-79 Years 4925 (3.2%) 1465 (17.1%) 6390 (3.9%)
80+ Years 1693 (1.1%) 1117 (13.1%) 2810 (1.7%)
Health authority Fraser 69169 (44.9%) 3542 (41.4%) 72711 (44.7%) <0.001
Interior 25138 (16.3%) 1703 (19.9%) 26841 (16.5%)
Northern 11781 (7.7%) 1143 (13.4%) 12924 (8.0%)
Vanc. Coastal 34369 (22.3%) 1546 (18.1%) 35915 (22.1%)
Vanc. Island 12283 (8.0%) 603 (7.1%) 12886 (7.9%)
Unknown 1223 (0.8%) 9 (0.1%) 1232 (0.8%)
Income (quintile,
1=low – 5=high) 1st 28991 (18.8%) 2579 (30.2%) 31570 (19.4%) <0.001
2nd 29034 (18.9%) 1785 (20.9%) 30819 (19.0%)
3rd 28296 (18.4%) 1503 (17.6%) 29799 (18.3%)
4th 28433 (18.5%) 1285 (15.0%) 29718 (18.3%)
5th 25721 (16.7%) 1021 (11.9%) 26742 (16.5%)
Unknown 13488 (8.8%) 373 (4.4%) 13861 (8.5%)
Material deprivation index (quintile,
1=less deprived –
5=more deprived) 1st 25338 (16.5%) 983 (11.5%) 26321 (16.2%) <0.001
2nd 28965 (18.8%) 1345 (15.7%) 30310 (18.7%)
3rd 27316 (17.7%) 1583 (18.5%) 28899 (17.8%)
4th 27585 (17.9%) 1805 (21.1%) 29390 (18.1%)
5th 25605 (16.6%) 25605 (16.6%) 27494 (16.9%)
Unknown 19154 (12.4%) 941 (11.0%) 20095 (12.4%)
Social deprivation index (quintile,
1=less deprived –
5=more deprived) 1st 31012 (20.1%) 1552 (18.2%) 32564 (20.0%) <0.001
2nd 29379 (19.1%) 1486 (17.4%) 30865 (19.0%)
3rd 23983 (15.6%) 1306 (15.3%) 25289 (15.6%)
4th 23595 (15.3%) 1345 (15.7%) 24940 (15.3%)
5th 26840 (17.4%) 1916 (22.4%) 28756 (17.7%)
Unknown 19154 (12.4%) 941 (11.0%) 20095 (12.4%)
Asthma 20521 (13.3%) 1568 (18.3%) 22089 (13.6%) <0.001
Cirrhosis 538 (0.3%) 230 (2.7%) 768 (0.5%) <0.001
Cancer, lymphoma 706 (0.5%) 172 (2.0%) 878 (0.5%) <0.001
Cancer, solid 15917 (10.3%) 1799 (21.1%) 17716 (10.9%) <0.001
Cancer, metastatic 2903 (1.9%) 459 (5.4%) 3362 (2.1%) <0.001
Chronic kidney disease 4230 (2.7%) 1605 (18.8%) 5835 (3.6%) <0.001
Chronic obstructive pulmonary disease 2107 (1.4%) 876 (10.3%) 2983 (1.8%) <0.001
Diabetes Non-diabetes 144493 (93.8%) 6412 (75.0%) 150905 (92.9%) <0.001
Non-insulin dependent 7817 (5.1%) 1487 (17.4%) 9304 (5.7%)
Insulin 1653 (1.1%) 647 (7.6%) 2300 (1.4%)
Obesity 4219 (2.7%) 479 (5.6%) 4698 (2.9%) <0.001
Malnutrition 2283 (1.5%) 427 (5.0%) 2710 (1.7%) <0.001
Myocardial infarct (acute) 1124 (0.7%) 399 (4.7%) 1523 (0.9%) <0.001
Chronic heart disease (combined) 5538 (3.6%) 1630 (19.1%) 7168 (4.4%) <0.001
Heart failure 1276 (0.8%) 671 (7.9%) 1947 (1.2%) <0.001
Hypertension 17415 (11.3%) 3430 (40.1%) 20845 (12.8%) <0.001
Ischemic heart disease (combined) 5037 (3.3%) 1408 (16.5%) 6445 (4.0%) <0.001
Problematic alcohol use 8276 (5.4%) 1200 (14.0%) 9476 (5.8%) <0.001
Injection drug use 8328 (5.4%) 1179 (13.8%) 9507 (5.9%) <0.001
Immunosuppression 3513 (2.3%) 561 (6.6%) 4074 (2.5%) <0.001
Alzheimer/dementia 234 (0.2%) 155 (1.8%) 389 (0.2%) <0.001
Depression 38208 (24.8%) 3315 (38.8%) 41523 (25.6%) <0.001
Intellectual & developmental disability 895 (0.6%) 106 (1.2%) 1001 (0.6%) <0.001
Epilepsy 1186 (0.8%) 160 (1.9%) 1346 (0.8%) <0.001
Parkinsonism 85 (0.1%) 51 (0.6%) 136 (0.1%) <0.001
Rheumatoid arthritis 1466 (1.0%) 273 (3.2%) 1739 (1.1%) <0.001
Schizophrenia & psychotic disorders 2150 (1.4%) 466 (5.5%) 2616 (1.6%) <0.001
VOC Non-VOC 9353 (6.1%) 556 (6.5%) 9909 (6.1%) <0.001
Delta 38540 (25.0%) 3180 (37.2%) 41720 (25.7%)
Alpha 17270 (11.2%) 1003 (11.7%) 18273 (11.2%)
Beta 98 (0.1%) 7 (0.1%) 105 (0.1%)
Gamma 12827 (8.3%) 1083 (12.7%) 13910 (8.6%)
Not sequenced 68917 (44.8%) 2617 (30.6%) 71534 (44.0%)
Omicron 6958 (4.5%) 100 (1.2%) 7058 (4.3%)
Vaccination status Not vaccinated 90252 (58.6%) 6508 (76.2%) 96760 (59.5%) <0.001
Partially vaccinated 16063 (10.4%) 1136 (13.3%) 17199 (10.6%)
Vaccinated 47648 (30.9%) 902 (10.6%) 48550 (29.9%)
Table 2 Distribution of characteristics in confirmed (lab-tested) COVID-19 unvaccinated adult cases during 2021, BC COVID-19 Cohort
Table 2 Ambulatory Hospitalized Overall P-value
(N=9,0252) (N=6,508) (N=96,760)
Sex Female 43140 (47.8%) 2797 (43.0%) 45937 (47.5%) <0.001
Male 47112 (52.2%) 3711 (57.0%) 50823 (52.5%)
Age (years) Median (Q1-Q3) 36 (27 - 49) 57 (43 - 70) 37 (27 - 51) <0.001
Age group <20 Years 26285 (29.1%) 437 (6.7%) 26722 (27.6%) <0.001
20-29 Years 3825 (4.2%) 35 (0.5%) 3860 (4.0%)
30-39 Years 22419 (24.8%) 857 (13.2%) 23276 (24.1%)
40-49 Years 15835 (17.5%) 955 (14.7%) 16790 (17.4%)
50-59 Years 11918 (13.2%) 1253 (19.3%) 13171 (13.6%)
60-69 Years 6889 (7.6%) 1338 (20.6%) 8227 (8.5%)
70-79 Years 2410 (2.7%) 1010 (15.5%) 3420 (3.5%)
80+ Years 671 (0.7%) 623 (9.6%) 1294 (1.3%)
Health authority Fraser 42259 (46.8%) 2745 (42.2%) 45004 (46.5%) <0.001
Interior 15074 (16.7%) 1310 (20.1%) 16384 (16.9%)
Northern 7491 (8.3%) 910 (14.0%) 8401 (8.7%)
Vanc. Coastal 19603 (21.7%) 1118 (17.2%) 20721 (21.4%)
Vanc. Island 5481 (6.1%) 420 (6.5%) 5901 (6.1%)
Unknown 344 (0.4%) 5 (0.1%) 349 (0.4%)
Income (quintile,
1=low – 5=high) 1st 17930 (19.9%) 1870 (28.7%) 19800 (20.5%) <0.001
2nd 17924 (19.9%) 1370 (21.1%) 19294 (19.9%)
3rd 16373 (18.1%) 1177 (18.1%) 17550 (18.1%)
4th 15659 (17.4%) 998 (15.3%) 16657 (17.2%)
5th 13163 (14.6%) 782 (12.0%) 13945 (14.4%)
Unknown 9203 (10.2%) 311 (4.8%) 9514 (9.8%)
Material deprivation index (quintile,
1=less deprived –
5=more deprived) 1st 12901 (14.3%) 716 (11.0%) 13617 (14.1%) <0.001
2nd 15363 (17.0%) 1027 (15.8%) 16390 (16.9%)
3rd 15490 (17.2%) 1225 (18.8%) 16715 (17.3%)
4th 16873 (18.7%) 1416 (21.8%) 18289 (18.9%)
5th 17199 (19.1%) 1426 (21.9%) 18625 (19.2%)
Unknown 12426 (13.8%) 698 (10.7%) 13124 (13.6%)
Social deprivation index (quintile,
1=less deprived –
5=more deprived) 1st 18749 (20.8%) 1207 (18.5%) 19956 (20.6%) <0.001
2nd 16844 (18.7%) 1132 (17.4%) 17976 (18.6%)
3rd 13403 (14.9% 1008 (15.5%) 14411 (14.9%)
4th 13437 (14.9%) 1031 (15.8%) 14468 (15.0%)
5th 15393 (17.1%) 1432 (22.0%) 16825 (17.4%)
Unknown 12426 (13.8%) 698 (10.7%) 13124 (13.6%)
Asthma 11293 (12.5%) 1123 (17.3%) 12416 (12.8%) <0.001
Cirrhosis 299 (0.3%) 144 (2.2%) 443 (0.5%) <0.001
Cancer, lymphoma 339 (0.4%) 97 (1.5%) 436 (0.5%) <0.001
Cancer, solid 8525 (9.4%) 1214 (18.7%) 9739 (10.1%) <0.001
Cancer, metastatic 1544 (1.7%) 289 (4.4%) 1833 (1.9%) <0.001
Chronic kidney disease 2082 (2.3%) 983 (15.1%) 3065 (3.2%) <0.001
Chronic obstructive pulmonary disease 1045 (1.2%) 516 (7.9%) 1561 (1.6%) <0.001
Diabetes Non-diabetes 85162 (94.4%) 5087 (78.2%) 90249 (93.3%) <0.001
Non-insulin dependent 4271 (4.7%) 1005 (15.4%) 5276 (5.5%)
Insulin 819 (0.9%) 416 (6.4%) 1235 (1.3%)
Obesity 2170 (2.4%) 357 (5.5%) 2527 (2.6%) <0.001
Malnutrition 1245 (1.4%) 271 (4.2%) 1516 (1.6%) <0.001
Myocardial infarct (acute) 587 (0.7%) 263 (4.0%) 850 (0.9%) <0.001
Chronic heart disease (combined) 2838 (3.1%) 1038 (15.9%) 3876 (4.0%) <0.001
Heart failure 635 (0.7%) 383 (5.9%) 1018 (1.1%) <0.001
Hypertension 9152 (10.1%) 2298 (35.3%) 11450 (11.8%) <0.001
Ischemic heart disease (combined) 2588 (2.9%) 903 (13.9%) 3491 (3.6%) <0.001
Problematic alcohol use 5353 (5.9%) 885 (13.6%) 6238 (6.4%) <0.001
Injection drug use 5550 (6.1%) 888 (13.6%) 6438 (6.7%) <0.001
Immunosuppression 1925 (2.1%) 344 (5.3%) 2269 (2.3%) <0.001
Alzheimer/dementia 115 (0.1%) 78 (1.2%) 193 (0.2%) <0.001
Depression 21716 (24.1%) 2456 (37.7%) 24172 (25.0%) <0.001
Intellectual & developmental disability 576 (0.6%) 83 (1.3%) 659 (0.7%) <0.001
Epilepsy 701 (0.8%) 109 (1.7%) 810 (0.8%) <0.001
Parkinsonism 43 (0.0%) 24 (0.4%) 67 (0.1%) <0.001
Rheumatoid arthritis 779 (0.9%) 174 (2.7%) 953 (1.0%) <0.001
Schizophrenia & psychotic disorders 1424 (1.6%) 339 (5.2%) 1763 (1.8%) <0.001
VOC Non-VoC 8432 (9.3%) 474 (7.3%) 8906 (9.2%) <0.001
Delta 18580 (20.6%) 2341 (36.0%) 20921 (21.6%)
Alpha 14393 (15.9%) 789 (12.1%) 15182 (15.7%)
Beta 83 (0.1%) <5 (0.1%) 87 (0.1%)
Gamma 10243 (11.3%) 792 (12.2%) 11035 (11.4%)
Not sequenced 38079 (42.2%) 2093 (32.2%) 40172 (41.5%)
Omicron 442 (0.5%) 15 (0.2%) 457 (0.5%)
Table 3 Distribution of characteristics in confirmed (lab-tested) COVID-19 vaccinated† adult cases during 2021, BC COVID-19 Cohort
Table 3 Ambulatory Hospitalized Overall P-value
(N=47,648) (N=902) (N=48,550)
Sex Female 25757 (54.1%) 368 (40.8%) 26125 (53.8%) <0.001
Male 21891 (45.9%) 534 (59.2%) 22425 (46.2%)
Age (years) Median (Q1-Q3) 39 (29 - 52) 70 (56 - 80) 39 (29 - 53) <0.001
Age group <20 Years 11699 (24.6%) 38 (4.2%) 11737 (24.2%) <0.001
20-29 Years 1416 (3.0%) <5 (0.4%) 1420 (2.9%)
30-39 Years 11345 (23.8%) 62 (6.9%) 11407 (23.5%)
40-49 Years 9131 (19.2%) 55 (6.1%) 9186 (18.9%)
50-59 Years 7082 (14.9%) 114 (12.6%) 7196 (14.8%)
60-69 Years 4627 (9.7%) 177 (19.6%) 4804 (9.9%)
70-79 Years 1677 (3.5%) 210 (23.3%) 1887 (3.9%)
80+ Years 671 (1.4%) 242 (26.8%) 913 (1.9%)
Health authority Fraser 20076 (42.1%) 333 (36.9%) 20409 (42.0%) <0.001
Interior 6720 (14.1%) 200 (22.2%) 6920 (14.3%)
Northern 2941 (6.2%) 116 (12.9%) 3057 (6.3%)
Vanc. Coastal 11459 (24.0%) 157 (17.4%) 11616 (23.9%)
Vanc. Island 5621 (11.8%) 95 (10.5%) 5716 (11.8%)
Unknown 831 (1.7%) <5 (0.1%) 832 (1.7%)
Income (quintile,
1=low – 5=high) 1st 7664 (16.1%) 312 (34.6%) 7976 (16.4%) <0.001
2nd 7920 (16.6%) 183 (20.3%) 8103 (16.7%)
3rd 9048 (19.0%) 129 (14.3%) 9177 (18.9%)
4th 9923 (20.8%) 140 (15.5%) 10063 (20.7%)
5th 10137 (21.3%) 112 (12.4%) 10249 (21.1%)
Unknown 2956 (6.2%) 26 (2.9%) 2982 (6.1%)
Material deprivation index (quintile,
1=less deprived –
5=more deprived) 1st 10024 (21.0%) 125 (13.9%) 10149 (20.9%) <0.001
2nd 10663 (22.4%) 143 (15.9%) 10806 (22.3%)
3rd 9018 (18.9%) 153 (17.0%) 9171 (18.9%)
4th 7784 (16.3%) 162 (18.0%) 7946 (16.4%)
5th 5451 (11.4%) 192 (21.3%) 5643 (11.6%)
Unknown 4708 (9.9%) 127 (14.1%) 4835 (10.0%)
Social deprivation index (quintile,
1=less deprived –
5=more deprived) 1st 9127 (19.2%) 138 (15.3%) 9265 (19.1%) <0.001
2nd 9560 (20.1%) 153 (17.0%) 9713 (20.0%)
3rd 7990 (16.8%) 130 (14.4%) 8120 (16.7%)
4th 7676 (16.1%) 132 (14.6%) 7808 (16.1%)
5th 8587 (18.0%) 222 (24.6%) 8809 (18.1%)
Unknown 4708 (9.9%) 127 (14.1%) 4835 (10.0%)
Asthma 6879 (14.4%) 205 (22.7%) 7084 (14.6%) <0.001
Cirrhosis 165 (0.3%) 43 (4.8%) 208 (0.4%) <0.001
Cancer, lymphoma 269 (0.6%) 43 (4.8%) 312 (0.6%) <0.001
Cancer, solid 5523 (11.6%) 303 (33.6%) 5826 (12.0%) <0.001
Cancer, metastatic 1011 (2.1%) 100 (11.1%) 1111 (2.3%) <0.001
Chronic kidney disease 1509 (3.2%) 302 (33.5%) 1811 (3.7%) <0.001
Chronic obstructive pulmonary disease 770 (1.6%) 191 (21.2%) 961 (2.0%) <0.001
Diabetes (treatment) Not DM 44686 (93.8%) 578 (64.1%) 45264 (93.2%) <0.001
Other 2388 (5.0%) 205 (22.7%) 2593 (5.3%)
Insulin 574 (1.2%) 119 (13.2%) 693 (1.4%)
Obesity 1555 (3.3%) 61 (6.8%) 1616 (3.3%) <0.001
Malnutrition 748 (1.6%) 82 (9.1%) 830 (1.7%) <0.001
Myocardial infarct (acute) 376 (0.8%) 71 (7.9%) 447 (0.9%) <0.001
Chronic heart disease (combined) 1900 (4.0%) 295 (32.7%) 2195 (4.5%) <0.001
Heart failure 441 (0.9%) 161 (17.8%) 602 (1.2%) <0.001
Hypertension 5889 (12.4%) 530 (58.8%) 6419 (13.2%) <0.001
Ischemic heart disease (combined) 1722 (3.6%) 246 (27.3%) 1968 (4.1%) <0.001
Problematic alcohol use 1843 (3.9%) 122 (13.5%) 1965 (4.0%) <0.001
Injection drug use 1612 (3.4%) 103 (11.4%) 1715 (3.5%) <0.001
Immunosuppression 1174 (2.5%) 108 (12.0%) 1282 (2.6%) <0.001
Alzheimer/dementia 68 (0.1%) 42 (4.7%) 110 (0.2%) <0.001
Depression 11974 (25.1%) 369 (40.9%) 12343 (25.4%) <0.001
Intellectual & developmental disability 202 (0.4%) 12 (1.3%) 214 (0.4%) <0.001
Epilepsy 354 (0.7%) 23 (2.5%) 377 (0.8%) <0.001
Parkinsonism 30 (0.1%) 14 (1.6%) 44 (0.1%) <0.001
Rheumatoid arthritis 474 (1.0%) 53 (5.9%) 527 (1.1%) <0.001
Schizophrenia & psychotic disorders 401 (0.8%) 38 (4.2%) 439 (0.9%) <0.001
VOC Non-VoC 27 (0.1%) <5 (0%) 27 (0.1%) NA
Delta 14008 (29.4%) 573 (63.5%) 14581 (30.0%)
Alpha 72 (0.2%) 2 (0.2%) 74 (0.2%)
Beta 0 (0%) 0 (0%) 0 (0%)
Gamma 69 (0.1%) <5 (0.4%) 73 (0.2%)
Not sequenced 27030 (56.7%) 241 (26.7%) 27271 (56.2%)
Omicron 6442 (13.5%) 82 (9.1%) 6524 (13.4%)
† 14 days or more after 2nd vaccine dose.
We assessed risk factors associated with hospital admission by estimating odds ratios through multivariable logistic regression models and then stratified our analysis by vaccination status. These results are presented in Figure 1 and the tables for these models as well as the analyses stratified by age-groups are presented in Tables S2-S10 of Appendix C of the Supplementary file. We also performed additional sensitivity analyses to examine the potential waning effect of vaccination by stratifying our analyses by time since full vaccination status. Results of these analyses are presented in Tables S11-S14 of Appendix C of the Supplementary file.Figure 1 *Odds ratios adjusted for the variables presented in the figure, as well as Health Authority, income (DA level), and VOC. Additionally, Odds ratios for the “overall” are adjusted for vaccination status.
Figure 1
Results
Demographic characteristics
The characteristics of the study population are presented in Table 1. Of the 162, 509 cases included in the analysis, 153, 963 (94.7%) were ambulatory cases and the remaining 8,546 (5.3%) were hospital admissions. Although male sex represented only a slightly greater proportion of the overall confirmed COVID-19 cases (50.2%), it represented an even greater proportion (57.2%) of the hospitalized cases. The overall median age of COVID-19 cases was 38 years (IQR: 28-52), the median age of hospitalized cases was 60 years (IQR: 45-72). The highest proportion of ambulatory cases was in the 20-29-years group (27.5%), but the highest proportion of cases requiring hospitalization admission was in the 60-69-years group (20.3%).
Risk factors
We found a higher proportion of comorbidities among hospitalized cases compared to ambulatory cases; respectively, asthma (18.3% vs. 13.3%), cirrhosis (2.7% vs. 0.3%), COPD (10.3% vs. 1.4%), obesity (5.6% vs. 2.7%), myocardial infarction (4.7% vs. 0.7%), CHD (19.1% vs. 3.6%), heart failure (7.9% vs. 0.8%), hypertension (40.1% vs. 11.3%), ischemic heart disease (16.5% vs. 3.3%), alcohol misuse (14.0% vs. 5.4%), immunosuppression (6.6% vs. 2.3%), depression (38.8% vs. 24.8%), intellectual & developmental disability (1.2% vs. 0.6%), epilepsy (1.9% vs. 0.8%), Parkinsonism (0.6% v 0.1%), rheumatoid arthritis (3.2% vs. 1.0%) and schizophrenia & psychotic disorder (5.5% vs. 1.4%). Also, there was a higher proportion of individuals with injection drug use among hospitalized cases compared to ambulatory cases (13.8% vs. 5.4%). Delta variant represented 25.0% of the ambulatory cases but a greater percentage of hospital admissions (37.2%). Whereas 58.6% of ambulatory cases was unvaccinated, an even greater proportion (76.2%) of the hospitalized cases was unvaccinated individuals. Vaccinated individuals represented only 10.6% of the hospitalized cases compared to 30.9% among the ambulatory cases (Table 1).
In the adjusted logistic regression model (Table S2; Figure 1), age (p-trend < 0.001 across age groups with increasing risk with older age [adjusted odds ratio (aOR), 30-39 years = 2.04; 95% CI: 1.83-2.27, to aOR 80+, years = 40.76; 95% CI: 35.50-46.79 compared to 20–29 years-old]), male sex (aOR=1.31; 95% CI: 1.25 -1.38), asthma (aOR =1.12; 95% CI: 1.05–1.20), COPD (aOR =1.61; 95% CI:1.45, 1.78), cirrhosis (aOR = 2.55; 95% CI: 2.11-3.08), chronic kidney disease (aOR= 1.95; 95% CI: 1.80–2.11), diabetes (non- insulin dependent), aOR =1.31; 95% CI: 1.22–1.42, requiring insulin aOR=2.85; 95% CI: 2.54-3.20), hypertension (aOR= 1.19; 95% CI: 1.12–1.27), heart failure (aOR=1.42; 95% CI: 1.26–1.60), IDU (aOR =2.33; 95% CI: 2.13–2.56), alcohol use (aOR =1.54; 95% CI: 1.41–1.68), immunosuppression (aOR=2.04; 95% CI: 1.83-2.29), Alzheimer/dementia (aOR=1.40; 95% CI: 1.09-1.78), schizophrenia & psychotic disorders (aOR=1.90; 95% CI: 1.68-2.16), multiple sclerosis (aOR=2.64; 95% CI: 1.77–3.96), Parkinsonism (aOR=2.14, 95% CI: 1.43–3.19), rheumatoid arthritis (aOR=1.29, 95% CI: 1.11-1.51), obesity (aOR=1.73, 95% CI: 1.55-1.94), weight loss (aOR=1.34, 95% CI: 1.18-1.52), intellectual & developmental disability (aOR=2.05, 95% CI: 1.62-2.59), lymphoma (aOR=1.61, 95% CI: 1.31-1.97), and metastatic cancer (aOR=1.49, 95% CI: 1.32-1.69), were significantly associated with increased hospitalization.
Also, compared to non-VOC lineage, Delta (aOR=3.22; 95% CI: 2.90–3.59), Alpha (aOR=1.65, 95% CI: 1.46–1.86), Gamma (aOR=3.09, 95% CI: 2.74–3.49), Omicron (aOR=2.41; 95% CI: 1.90–3.07), and non-sequenced variants (aOR=1.25; 95% CI: 1.12–1.39) were significantly associated with increased hospitalization. In addition, compared to no vaccination, full vaccination (aOR=0.15; 95% CI: 0.14–0.17) and partial vaccination (aOR=0.52; 95% CI: 0.49–0.57) were associated with reduced odds of hospitalization (Table S2).
1.1.2 Risk factors by vaccination status
The proportion of males in the unvaccinated group was higher compared to the vaccinated group (52.5% vs. 46.2%). The proportion of males who received ambulatory care was larger in the unvaccinated group compared to the vaccinated group (52.2% vs. 45.9%). Otherwise, the distribution of characteristics was similar across vaccination status (Table 2 and Table 3).
For vaccinated individuals (Table S4 and Figure 1), an increased odds of hospitalization by increasing age was only observed for older age-groups, 50-59 years (aOR=2.95, 95% CI: 2.01-4.33), 60-69 years (aOR=4.82, 95% CI:3.29, 7.07), 70-79 years (aOR=11.92, 95% CI: 8.02- 17.71) & ≥80 years (aOR=24.25, 95% CI: 16.02, 36.71). However, for unvaccinated adult cases (Table S3 and Figure 1), there was graded increase in odds of hospitalization with age starting at age group 30-39 years. In addition, the magnitude of association at each age was much stronger among unvaccinated individuals compared to vaccinated individuals (Figure 1).
Although the comorbidity risk factors for hospitalization were similar among vaccinated and unvaccinated individuals, the magnitude of association (aORs) for many of the risk factors were higher among vaccinated individuals compared to unvaccinated individuals: COPD (2.00 vs. 1.41), cirrhosis (3.08 vs. 2.39), injection drug use (3.17 vs. 2.22), immunosuppression (3.03 vs. 1.80), multiple sclerosis (7.39 vs. 2.02), rheumatoid arthritis (2.20 vs. 1.21), weight loss (1.87 vs. 1.22), lymphoma (2.35 vs. 1.38) and metastatic cancer (1.98 vs. 1.43), respectively. Chronic kidney disease (aORs=1.80 vs. 1.93), obesity (1.37 vs. 1.84), and schizophrenia & psychotic disorders (1.58 vs. 1.90) were the only conditions whose magnitude of association were higher among unvaccinated individuals compared to vaccinated individuals. Meanwhile, asthma was a significant risk factor among unvaccinated individuals but not for vaccinated individuals (Table S3 and Table S4).
1.1.3 Risk factors by age-group
The magnitude of association (aORs) for most of the comorbidities was highest for the youngest age group (18-49), compared to the two older age-groups (50-69years & ≥ 80 years): COPD (2.88 vs. 1.80 vs. 1.76), cirrhosis (3.73 vs. 2.56 vs. 1.71), chronic kidney disease (2.48 vs. 2.44 vs. 1.84), diabetes, non-insulin dependent (2.23 vs. 1.38 vs.1.17), diabetes requiring insulin (3.85 vs. 3.17 vs. 2.03), heart failure (2.11 vs. 1.38 vs. 1.55), hypertension (1.79 vs. 1.33 vs. 1.12), alcohol misuse (1.62 vs. 1.36 vs. 1.52), immunosuppression (2.08 vs. 1.88 vs. 1.92), schizophrenia & psychotic disorders (2.17 vs. 1.51 vs. 1.89), intellectual & developmental disability (1.48 vs. 2.28 vs. 0), lymphoma cancer (2.09 vs. 1.46 vs. 1.57), metastatic cancer (1.67 vs. 1.71 vs. 1.30), respectively [Table S6, Table S8 and Table S10].
Although being vaccinated was associated with reduced odds of hospitalization across the three stratified age-groups, the benefit of being vaccinated appeared to be greatest among the 50–69-year group (aOR=0.13; 95% CI: 0.11, 0.15), followed by the 18–49-year group (aOR=0.15; 95% CI: 0.13, 0.18) and then ≥70-year group (aOR=0.20; 95% CI: 0.17, 0.23). [Table S6, Table S8, and Table S10].
3.3 Sensitivity analyses
The sensitivity analyses did not provide evidence to believe there was a difference by the analyzed vaccination strata suggesting there was no significant waning effect of vaccination (Tables S11-S1 of Appendix C of the Supplementary file).
Discussion
In this large population-based study, we assessed risk factors for COVID-19 hospitalization following breakthrough infection among individuals who received vaccination and those who did not receive vaccination using data from 162,509 confirmed COVID-19 adult cases collected from 01 January 2021 to 31 December 2021 in the Canadian province of BC. In our analysis, we found many patient characteristics and comorbidities to be associated with hospitalization. However, the magnitude of association of these characteristics differed between vaccinated and unvaccinated individuals. Older age was the strongest risk factor for hospitalization overall and had a bigger relative impact in unvaccinated compared to vaccinated individuals. Indeed, odds of hospitalization only increased with increasing age from 50-59 years of age and older in vaccinated, while there was a graded increase with increasing age in unvaccinated individuals. The association of various comorbidities was similar or in some cases, slightly higher among vaccinated individuals for some comorbidities. We also found that compared to non-VOC lineage; Delta, Alpha, Gamma and Omicron variants were significantly associated with higher hospitalization risk, consistent with other studies (Fisman and Tuite, 2021; Funk et al., 2021; Nyberg et al., 2021; Veneti et al., 2021).
We found age to be the strongest independent risk factor for hospitalization, consistent with findings from our previous study (Velásquez García et al., 2021). A recent rapid review also found age to be the most significant risk factor for severe outcomes, noting that adults over the age of 60 may have a five-fold increase in hospitalization and mortality from COVID-19 compared with people below 45 years (Wingert et al., 2021). Our stratified analysis by vaccination status found that for vaccinated individuals, age as an independent risk factor was only significant for older age groups (50+ years). However, for unvaccinated adults, the increased odds of hospitalization were significant across all the age-groups, with graded increased in the odds of hospitalization with age. Furthermore, the magnitude of association was much higher among unvaccinated individuals compared to vaccinated individuals, similar to findings from Ontario Province in Canada (Public Health Ontario, 2022). This highlights the need for additional interventions among unvaccinated individuals to reduce the risk of severe disease, such as treatment with antiviral agents (e.g. nirmatrelvir/ritonavir). It also highlights the success of vaccination in reducing the increased risk of hospitalization associated with increasing age.
In younger age groups (18–49-year group), the magnitude of association was higher for many comorbidities compared to older age groups. We also found that for most of the comorbidities that we assessed, the magnitude of association between these comorbidities and hospitalization was higher among vaccinated individuals compared to unvaccinated individuals. The risk of hospitalization present among vaccinated individuals with certain comorbidities such as cancers, immunosuppression, rheumatological diseases have been noted in other studies (Lang et al., 2022; Wright et al., 2022). This may be the result of the underlying immune dysfunction in these conditions, or could also be related to the fact that the vaccination roll-out was prioritized for older people and individuals with comorbidities (Government of British Columbia, 2021; Velásquez García et al., 2021).
A major strength of our study is its large sample size which enabled us to produce more precise estimates of effect sizes and also increased the representativeness of our findings. Our use of population-based cases rather than hospital or selected cases also reduced potential selection bias and ensured our findings are generalizable to the general population. In addition, we objectively identified infection status via PCR, and assessed VOC through whole genome sequencing. Furthermore, we ascertained vaccination status with the records from the Provincial Immunization Registry, which contains the records for all administered vaccines in BC. Likewise, we were able to assess a wide range of comorbidities and other risk factors using validated algorithms.
Our findings should be interpreted in light of the following limitations. First, there is a possibility for misclassification of patient characteristics and morbidities given the use of administrative data. Secondly, COVID-19 assessment was based on BC testing guidelines which, not only varied over the pandemic period but, could also differ from the guidelines of other jurisdictions thereby limiting the generalizability of our findings to other contexts. Also, even though we considered many variables in our analyses, there is still potential for unmeasured confounders, which could not be accounted for. In addition, this analysis is limited to those who sought health care/testing, and thus may not be representative of the whole population of BC. Also testing rates may differ by vaccination status; however, this was not accounted for. Given that not many people had received booster doses as at the time of our study, future studies should focus on disentangle the specific impact of booster doses on COVID-19 related outcomes.
Conclusion
To our knowledge, this is one of the largest population-based studies examining the risk factors for hospitalization among persons with COVID -19 following vaccination. Given the higher risk of hospitalization among older vaccinated individuals and those with certain comorbidities, our findings also highlight the need for adding additional layers of protection from severe disease among those at higher risk, with improved access to antiviral treatments such as nirmatrelvir/ritonavir and to further vaccine booster doses.
Conflict of Interest
NZJ participated in advisory boards for AbbVie and has spoken for AbbVie and Gilead, not related to current work.
Funding Source
This work was supported by the BC Centre for Disease Control and the Canadian Institutes of Health Research [Grant # VR5-172683 and OV4-170361].
Ethical Approval Statement
This study was reviewed and approved by the Research Ethics Board at the University of British Columbia (approval # H20-02097).
Data Availability Statement
The study is based on data contained in various provincial registries and databases. Access to data could be requested through the BC Centre for Disease Control
Institutional Data Access for researchers who meet the criteria for access to confidential data. Requests for the data may be sent to [email protected].
Acknowledgement
The BCC19C was established and is maintained through operational support from Data Analytics, Reporting and Evaluation (DARE), and BC Centre for Disease Control (BCCDC) at the Provincial Health Services Authority. We acknowledge the assistance of the Provincial Health Services Authority, BC Ministry of Health and Regional Health Authority staff involved in data access, procurement, and management. We gratefully acknowledge the residents of British Columbia whose data are integrated in the British Columbia COVID-19 Cohort (BCC19C).
Author Contributions
Conceptualization: H.A.V.G. and N.Z.J.
Writing-original draft preparation: P.A.A.
Method: H.A.V.G. and N.Z.J.
Analysis: H.A.V.G. and P.A.A.
Writing-review and editing: P.A.A., H.A.V.G., S.H., J.W., D.R., M.B., H.S., K.S., N.Z.J.
Supervision and Project administration: N.Z.J.
Funding acquisition: N.Z.J.
All authors have read and agreed to the published version of the manuscript.
Disclaimer
All inferences, opinions, and conclusions drawn in this manuscript are those of the authors, and do not reflect the opinions or policies of the Data Steward(s).
References
British Columbia Centre for Disease Control. B.C. COVID-19 Dashboard 2022a. https://experience.arcgis.com/experience/a6f23959a8b14bfa989e3cda29297ded (accessed July 24, 2022).
British Columbia Centre for Disease Control. BCCDC COVID-19 Surveillance Dashboard. Vaccin Cover Prog BC Age 01 Jan 2021 - 31 Dec 2021 2022b. https://public.tableau.com/views/BCCDCCOVID-19SurveillanceDashboard/Introduction?%3Adisplay_static_image=y&%3AbootstrapWhenNotified=true&%3Aembed=true&%3Alanguage=en-US&:embed=y&:showVizHome=n&:apiID=host0#navType=0&navSrc=Parse (accessed July 7, 2022).
Fibke CD, Joffres Y, Tyson JR, Colijn C, Janjua NZ, Fjell C, et al. Spike Mutation Profiles Associated With SARS-CoV-2 Breakthrough Infections in Delta Emerging and Predominant Time Periods in British Columbia, Canada. Front Public Health 2022;10.
Fisman DN, Tuite AR. Evaluation of the relative virulence of novel SARS-CoV-2 variants: a retrospective cohort study in Ontario, Canada. CMAJ Can Med Assoc J J Assoc Medicale Can 2021;193:E1619–25. https://doi.org/10.1503/cmaj.211248.
Funk T, Pharris A, Spiteri G, Bundle N, Melidou A, Carr M, et al. Characteristics of SARS-CoV-2 variants of concern B.1.1.7, B.1.351 or P.1: data from seven EU/EEA countries, weeks 38/2020 to 10/2021. Euro Surveill Bull Eur Sur Mal Transm Eur Commun Dis Bull 2021;26. https://doi.org/10.2807/1560-7917.ES.2021.26.16.2100348.
Gold JAW. Characteristics and Clinical Outcomes of Adult Patients Hospitalized with COVID-19 — Georgia, March 2020. MMWR Morb Mortal Wkly Rep 2020;69. https://doi.org/10.15585/mmwr.mm6918e1.
Government of British Columbia. COVID-19 Immunization Plan - Province of British Columbia 2021. https://web.archive.org/web/20210228021441/https://www2.gov.bc.ca/gov/content/covid-19/vaccine/plan (accessed July 7, 2022).
Johns Hopkins University. COVID-19 Map. COVID-19 Map 2022. https://coronavirus.jhu.edu/map.html (accessed July 24, 2022).
Kaeuffer C, Le Hyaric C, Fabacher T, Mootien J, Dervieux B, Ruch Y, et al. Clinical characteristics and risk factors associated with severe COVID-19: prospective analysis of 1,045 hospitalised cases in North-Eastern France, March 2020. Eurosurveillance 2020;25:2000895. https://doi.org/10.2807/1560-7917.ES.2020.25.48.2000895.
Lang R, Humes E, Coburn SB, Horberg MA, Fathi LF, Watson E, et al. Analysis of Severe Illness After Postvaccination COVID-19 Breakthrough Among Adults With and Without HIV in the US. JAMA Netw Open 2022;5:e2236397. https://doi.org/10.1001/jamanetworkopen.2022.36397.
Nasreen S, Febriani Y, Velásquez García HA, Zhang G, Tadrous M, Buchan SA, et al. Effectiveness of Coronavirus Disease 2019 Vaccines Against Hospitalization and Death in Canada: A Multiprovincial, Test-Negative Design Study. Clin Infect Dis 2022:ciac634. https://doi.org/10.1093/cid/ciac634.
Nyberg T, Twohig KA, Harris RJ, Seaman SR, Flannagan J, Allen H, et al. Risk of hospital admission for patients with SARS-CoV-2 variant B.1.1.7: cohort analysis. BMJ 2021;373:n1412. https://doi.org/10.1136/bmj.n1412.
Public Health Ontario. Severe Outcomes among Confirmed Cases of COVID-19 Following Vaccination in Ontario: December 14, 2020 to June 19, 2022 2022:19.
Vahey GM, McDonald E, Marshall K, Martin SW, Chun H, Herlihy R, et al. Risk factors for hospitalization among persons with COVID-19-Colorado. PloS One 2021;16:e0256917. https://doi.org/10.1371/journal.pone.0256917.
Velásquez García HA, Wilton J, Smolina K, Chong M, Rasali D, Otterstatter M, et al. Mental health and substance use associated with hospitalization among people with COVID-19: a population-based cohort study. Viruses 2021;13:2196. https://doi.org/10.3390/v13112196.
Veneti L, Seppälä E, Larsdatter Storm M, Valcarcel Salamanca B, Alnes Buanes E, Aasand N, et al. Increased risk of hospitalisation and intensive care admission associated with reported cases of SARS-CoV-2 variants B.1.1.7 and B.1.351 in Norway, December 2020 -May 2021. PloS One 2021;16:e0258513. https://doi.org/10.1371/journal.pone.0258513.
Watson OJ, Barnsley G, Toor J, Hogan AB, Winskill P, Ghani AC. Global impact of the first year of COVID-19 vaccination: a mathematical modelling study. Lancet Infect Dis 2022;0. https://doi.org/10.1016/S1473-3099(22)00320-6.
Wingert A, Pillay J, Gates M, Guitard S, Rahman S, Beck A, et al. Risk factors for severity of COVID-19: a rapid review to inform vaccine prioritisation in Canada. BMJ Open 2021;11:e044684. https://doi.org/10.1136/bmjopen-2020-044684.
Wright BJ, Tideman S, Diaz GA, French T, Parsons GT, Robicsek A. Comparative vaccine effectiveness against severe COVID-19 over time in US hospital administrative data: a case-control study. Lancet Respir Med 2022;10:557–65. https://doi.org/10.1016/S2213-2600(22)00042-X.
Appendix Supplementary materials
Image, application 1
Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.ijid.2022.12.001.
| 36503044 | PMC9731811 | NO-CC CODE | 2022-12-14 23:31:53 | no | Int J Infect Dis. 2022 Dec 9; doi: 10.1016/j.ijid.2022.12.001 | utf-8 | Int J Infect Dis | 2,022 | 10.1016/j.ijid.2022.12.001 | oa_other |
==== Front
Sustain Cities Soc
Sustain Cities Soc
Sustainable Cities and Society
2210-6707
2210-6715
Elsevier Ltd.
S2210-6707(22)00648-5
10.1016/j.scs.2022.104344
104344
Article
Causal impacts of the COVID-19 pandemic on daily ridership of public bicycle sharing in Seoul
Sung Hyungun
Graduate School of Urban Studies, Hanyang University, 222, Wangsimni-ro, Seongdong-gu, Seoul 04763, South Korea
9 12 2022
2 2023
9 12 2022
89 104344104344
5 8 2022
11 11 2022
7 12 2022
© 2022 Elsevier Ltd. All rights reserved.
2022
Elsevier Ltd
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Public bicycle can be a disease-resilient travel mode during the coronavirus disease 2019 (COVID-19) pandemic. Nonetheless, its evidence on public bicycle sharing is still inconclusive. This study used Bayesian structural time series models and causal impact inference for the data on the daily ridership of public bicycles in Seoul, South Korea, for 1826 days from January 1, 2017, to December 31, 2021. The study found that the usage of public bicycles was robust against the COVID-19 pandemic even in densely populated Seoul. Compared with the pre-pandemic period, public bicycles’ usage was unaffected on days when weather conditions, such as snow, rain, and wind speed were not as severe, as well as on days with non-seasonal event factors, such as weekdays, public holidays, and traditional Korean holidays. In addition, its robustness against the pandemic became more pronounced as the number of bicycle racks increased and the intensity of social distancing increased. However, public bicycles were in demand primarily for leisure and exercise, not for travel, during the pandemic. Public bicycle sharing can be a disease-resilient travel mode. Continuous investment in infrastructure such as bicycle paths and public bicycle is required to become a more resilient travel mode against infectious diseases.
Keywords
COVID-19
Public bicycle
Bicycle sharing
Ridership
Robustness
Causal impacts
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pmc1 Introduction
Non-pharmaceutical measures to control the unexpected COVID-19 pandemic, such as lockdowns and social distancing, have disrupted human mobility. In response to the pandemic, sustainable transportation modes such as public transit and non-motorized transportation have had different consequences. In cities worldwide, the ridership of public transit decreased significantly during this period. On the contrary, non-motorized active transportation modes such as walking and cycling can be resistant, resilient, and sustainable in the face of the pandemic. According to several studies, the use of privately owned bicycles did not decrease or increase during the COVID-19 pandemic (Anke et al., 2021; Ehsani et al., 2021; Harrington & Hadjiconstantinou, 2022; Hensher et al., 2022; Bucsky, 2020; Monterde-i-Bort et al., 2022; Shaer & Haghshenas, 2021; Loa et al., 2021; Lee et al., 2021).
During this pandemic, in response to movement restrictions, as well as the transition to safer transport modes, people significantly increased their daily activities for leisure, exercise, and well-being rather than travel. According to a 2020 survey conducted in Chicago, USA, the travel mode with the highest risk of COVID-19 transmission was public transportation (78%), followed by taxis (61%), shared bicycles (48%), personal bicycles (16%), walking (8%), and private cars (6%). Similarly, in an online survey in Bangladesh, Zafri et al. (2022) identified that public transit is the most dangerous mode of transport for the spread of the COVID-19, followed by shared transportation modes and private cars. The difference in people's perceptions of transport modes contributing to the risk of infection comprised the avoidance of public transit and preference for cycling. During the pandemic, there has been a higher rate of use (Piras et al., 2022) and fewer confirmed cases of COVID-19 in cities where bicycles are more convenient (Wang et al., 2022). This proves that private bicycles are a robust and sustainable transportation mode during the pandemic (Chen et al., 2022; van der Drift et al., 2021).
However, the effect of the pandemic on public bicycle-sharing may be inconclusive. Sharing mobility modes, including public bicycle, are more acceptable to people and provides more increased sustainability to our society than private-owning ones (Gransterer et al., 2022; Shokouhyar, et al., 2021). In addition, the bicycle as a transportation mode is healthier one for people than vehicle ones by consuming more physical activity in travel (Jiang et al., 2017). People tend to perceive that their choice of travel using public bicycles poses a higher risk of infection than using private bicycles (Hua et al., 2021; Zafri et al., 2022). Therefore, in cities worldwide, the use of shared public bicycles, which can cause riders to become more vulnerable to infectious diseases (Hua et al., 2021), decreased in the early stages of COVID-19, despite recovering faster than that of public transit (Chai et al., 2021; Teixeira & Lopes, 2020; Wang & Noland, 2021). Additionally, the relationship between the use of shared bicycles and the pandemic may be related to the population density of cities. For example, Zhang and Fricker (2021) identified that during the COVID-19 lockdown period, the use of shared bicycles decreased as the population density increased in 11 US cities. According to Bouhouras et al. (2022) the use of shared bicycles in small- and medium-sized cities in Greece increased significantly during the COVID-19 lockdown period. This finding supports the assertion that the robustness of bicycle-sharing can decline and bicycle-sharing can become more vulnerable in densely populated cities. Meanwhile, it was confirmed that the ridership of shared bicycles in Singapore increased by 150% during the lockdown period when compared to that before the pandemic (Song et al., 2022). In addition, in Seoul, South Korea, which has a high population density, the use of shared-based public bicycles did not decrease or increase (Cho & Baik, 2021) during the pandemic period (Lee et al., 2021; Kim et al., 2021)). Notably, most of these claims are based on descriptive modeling-based evidence rather than descriptive evidence.
Few studies have demonstrated the effect of exogenous determinants, such as weather conditions, non-seasonal events, supply and demand, and COVID-19-related factors, on the change in ridership of shared-based public bicycles during the pandemic. Compared to other travel modes, bicycles are more sensitive to weather conditions such as temperature, wind speed, precipitation, and snowfall, as well as non-seasonal event factors such as public holidays and national holidays (Noland et al., 2016; Corcoran et al., 2014; Lee et al., 2016), bicycle supply and demand factors (Pucher & Buehler, 2006; Bachand-Marleau et al., 2012; Hampshire & Marla, 2012). In addition, ridership may vary depending on the daily number of confirmed COVID-19 cases during the pandemic (Hong et al., 2021).
This study not only examines whether daily ridership of public bicycles in Seoul, South Korea, with a high population density, can show robustness against the COVID-19 pandemic, but also identifies differences in the magnitudes of determinants such as weather environments, non-seasonal events, and demand and supply during the pandemic, compared to its previous period. This study uses data on public bicycle daily ridership in Seoul for 1826 days from January 1, 2017 to December 31, 2021. To prove whether its ridership was significantly robust against the COVID-19 pandemic, this study employs a customized causal impact inference model based on the Bayesian structural time series (BSTS) model, which allows us not only to forecast bicycle ridership under the assumption of no COVID-19 occurrence, but also to identify the daily pointwise differences in actual ridership. The other purpose of the study was to investigate how the determinants affecting bicycle use changed during the COVID-19 pandemic when compared to those of the pre-pandemic period. For this, the study applies the two BSTS models after dividing the daily ridership data into data before and during the pandemic, based on January 19, 2020, when confirmed cases of COVID-19 were reported. By empirically exploring the causal impacts on the daily ridership of public bicycle sharing during the pandemic, this study not only confirmed the robustness of shared public bicycles against infectious diseases but also identified the determinant factors increasing active bicycle use for daily activity during the unexpected COVID-19 pandemic.
2 Literature review
This study reviews the literature on travel behavior changes during the COVID-19 pandemic and the factors affecting the daily ridership of shared bicycles before and during the pandemic. First, it not only examines travel behavior changes during the pandemic but also explores previous studies on changes in the demand for bicycles as a shared personal mobility mode. Second, this study reviews the literature associated with the potential factors influencing behavior in the use of bicycles before and during the pandemic.
2.1 Travel behavior changes during the pandemic
Many studies have identified travel behavior changes, including shared personal mobility, due to the COVID-19 pandemic outbreak. For example, by identifying changing patterns in commuting choices in two metropolitan areas in Australia before and during COVID-19, Hensher et al. (2022) found that the choices of public transit, such as transit and bus, decreased, whereas those of personal transport modes, such as private cars, walking, and bicycles, increased. Surveying nearly 4,000 participants in Germany, Anke et al. (2021) found travel behavior changes, such as a shift from public transit to private transit, and increases in the mode of transport, such as driving, walking, and cycling. Scorrano and Danielis (2021) also found that the COVID-19 pandemic significantly impacted the mode choice of travel from public transit to motorized and non-motorized personal transit in Trieste, Italy. Lee et al. (2021) confirmed that during the COVID-19 pandemic, the ridership of public transit, such as subways and buses, decreased, whereas the use of shared public bicycles and personal cars increased. Surveying travel behavior changes for 453 older adults residing in Isfahan, Iran, Shaer and Haghshenas (2021) identified that the modal split of bicycles increased from 9% to 18% during the pandemic.
Many studies have also identified the robustness of shared public bicycles against pandemics. For example, through an online survey of U.S. adults, Ehsani et al. (2021) found no change in bicycle use during the pandemic, whereas significant decreases were observed in the use of other travel modes, such as public transit, walking, and even personal vehicles, compared with the pre-pandemic level. Chai et al. (2021) reported that the average demand for shared bicycles in Beijing, China, subsequently increased, although its overall use decreased significantly due to the COVID-19 pandemic. Investigating the impact of the COVID-19 pandemic on shared bicycle usage in three medium-sized Greek cities, Bouhouras et al. (2022) found that its usage significantly increased during the period compared with the pre-pandemic level. Cho et al. (2020) unveiled that the demand for roads and public transportation decreased, but that for shared cars and bicycles did not show a decreasing trend in Seoul, South Korea.
The bicycle can be a resilient and reliable mode of mobility due to the lower risk of COVID-19 infection. Van de Drift et al. (2021) explored the changing movement patterns in the Netherlands during the COVID-19 pandemic, showing that cycling is an alternative option for travelers. Similarly, Teixeira and Lopes (2020) proposed that shared public bicycles can be an alternative mode for public transit by responding more flexibly to the impact of COVID-19. Applying a Bayesian structural time series model, Zhang and Fricker (2021) demonstrated that the COVID-19 outbreak decreased the use of non-motorized travel activities in densely populated cities while identifying its increasing pattern in less densely populated cities in the United States. Surveying self-reported bicycling activity in Australia during the COVID-19 pandemic, Fuller et al. (2021) demonstrated that most of them increased their activity for exercise and well-being purposes but not for transport purposes.
2.2 Determinant factors in bicycle use before and during the pandemic
This study reviews the literature on the factors that determine bike use before and during the COVID-19 pandemic. First, weather conditions, such as temperature, precipitation, wind speed, and sunlight time, affect bicycle use (Corcoran et al., 2014; Dill and Carr, 2003; Gebhart and Noland, 2014; Lee et al., 2016; Nankervis, 1999; Noland and Ishaque, 2006, Thomas, Jaarsma, & Tutert, 2013; Zhang and Fricker, 2021). For example, analyzing the influence of weather conditions on bicycle use in the Netherlands during 1987–2003, Thomas, Jaarsma, and Tutert (2013) found that they accounted for 80% of the fluctuation in its usage, especially for leisure purposes. Identifying the factors affecting the use of shared public bicycles in Goyang, South Korea, Lee et al. (2016) demonstrated that weather conditions significantly impacted its use when they were below or above a specific level rather than linear impacts. Investigating bicycle commuting ridership in major cities in the United States, Dill and Carr (2003) found that its use significantly decreased when the temperature dropped below freezing. Zhang and Fricker (2021) controlled covariates, such as precipitation and temperature, to estimate the causal impact of COVID-19 on the daily activities of non-motorized modes.
Second, periodic factors, such as the day of week and season, and non-periodic event factors, such as public holidays and Korean traditional holidays, tend to have different effects on bicycle use (Faghih-Imani et al., 2014; Lee et al., 2016; Noland et al., 2016). Faghih-Imani et al. (2014) uncovered that the use of the public bicycle system in Montreal, Canada, decreased more on weekends but increased on Friday and Saturday evenings. Lee et al. (2016) revealed that the use of shared public bicycles varied depending on the time of day, day of the week, and public holidays. Noland et al. (2016) also assumed that generators for trips of shared bicycles should be differentiated by day of the week and season. They found that subscribers of shared bicycles in New York City were more associated with their use on weekends than on weekdays.
Third, bicycle use is closely related to supply and demand factors (Buchand-Marleau et al., 2012; Nikitas et al., 2021; Pucher and Buehler, 2006; Wang and Akar, 2019). Analyzing the reasons why Canadian citizens use bicycles more than Americans, Pucher and Buehler (2006) found that the fewer bicycle lanes or dedicated lanes there are and the lower the fuel price, the lower the bicycle use rate. Analyzing factors affecting the use of shared public bicycles on the basis of the data surveyed in Montreal, Canada, Bachand-Marleau et al. (2012) found that those who subscribed to annual public bicycle members used it 15 times more in a year compared with non-members. Identifying the gender gap in shared bicycle ridership in New York, USA, Wang and Akar (2019) unveiled that the additional installation of bicycle racks was positively associated with their use by males and females. After reviewing policies implemented by cities in Europe and South America during the pandemic, Nikitas et al. (2021) found that bicycle use can be encouraged by providing bicycle infrastructure, such as expanding bicycle networks, pop-up bicycle lanes, and free bike-sharing services. Kim et al. (2021) also demonstrated that during the pandemic, the expansion of 500 new stations for shared public bicycles in Seoul, South Korea, in 2020 increased their usage for leisure but not for transport purposes.
Finally, the risk level resulting from the COVID-19 outbreak and social distancing measures to contain its proliferation may be closely associated with public bicycle use. By examining 72 cities in Massachusetts, USA, Wang et al. (2022) unveiled that the number of confirmed COVID-19 cases per 100,000 population was lower in cities with better bicycle accessibility. Similarly, Piras et al. (2022) confirmed that bicycle facilities positively impacted its use in Cagliari City, Italy, during COVID-19. Hong et al. (2021) found that confirmed cases of COVID-19 were positively associated with the use of shared public bicycles in Seoul City, South Korea. Investigating the impact of social distancing on the commuting behavior of 1,542 workers in India, Pawar et al. (2020) demonstrated that approximately 40% of them stopped commuting, whereas roughly 5% of them shifted from public to private travel mode. Lee et al. (2021) identified that social distancing measures positively impacted the use of shared bicycles and private cars but negatively impacted public transit, such as subways and buses, in Seoul, South Korea. Investigating changes in daily ridership of shared bicycles during the COVID-19 outbreak in Singapore, Song et al. (2022) identified that lockdown measures derived a 150% increase in its use compared with the pre-pandemic level. Using the data from shared bike trips in Nanjing, China, Hua et al. (2021) demonstrated that mobility restriction measures significantly decreased their usage, especially for commuting, during the pandemic. Heydari et al. (2021) and Li et al. (2021) found that the usage of shared public bicycles in London immediately decreased during the lockdown period in the UK but increased beyond the level of the pre-pandemic during its first ease period.
3 Material and method
3.1 Data and measurement
Table 1 lists summary statistics of variables measured to examine the robustness of the impact response effect on daily bicycle ridership during COVID-19, as well as the differences in the causal effects compared to those of the pre-pandemic period. The entire study period was from January 1, 2017 to December 31, 2021. The date, January 19, 2020, the one on which the first confirmed case of COVID-19 was reported in South Korea. The indicators measured in the study include daily bicycle ridership and weather conditions, seasonal and non-seasonal events, demand and supply of bicycle use, and exogenous factors related to COVID-19 that are expected to affect it.Table 1 Summary statistics.
Table 1 Entire period (no. obs. = 1826) Period during COVID-19 (no. obs. = 713)
(Jan-01-2017 to Dec-31-2021) (Jan-19-2020 to Dec-31-2021)
Mean Std. Dev. Min. Max. Mean Std. Dev. Min. Max.
Dep. Variable Daily ridership (no. of persons) 48,112 36097 982 150,202 75,203 35522 2,491 150,202
Log-transformed daily ridership 10.41 0.966 6.89 11.92 11.07 0.653 7.82 11.92
Weather environment factors Sunshine time (hr.) 6.71 3.982 0 13.70 6.10 3.958 0 13.50
Temperature (below 0°C= 1, 0°C to 30°C = 0) 0.13 0.339 0 1 0.10 0.296 0 1
Temperature (above 30°C = 1, 0°C to 30°C = 0) 0.02 0.153 0 1 0.01 0.118 0 1
Average wind speed (m/s) 2.11 0.689 1 6.00 2.35 0.67 1.20 5.00
Rainfall (mm) 3.42 12.395 0 144.50 3.90 12.849 0 103.10
Snowfall (cm) 0.10 0.58 0 8.80 0.11 0.589 0 5.50
PM 2.5 (> = 76 μg/m3 = 1, < 76 μg/m3 = 0) 0.01 0.104 0 1 0.01 0.075 0 1
Seasonal and non-seasonal event factors Weekend (Saturday, Sunday = 1, Monday to Saturday = 0) 0.29 0.452 0 1 0.29 0.452 0 1
Holiday (=1, Non-holiday = 0) 0.03 0.169 0 1 0.03 0.165 0 1
Korean traditional holidays (=1, None = 0) 0.02 0.139 0 1 0.02 0.144 0 1
Sandwich day (=1, None = 0) 0.01 0.084 0 1 0.00 0.053 0 1
Demand and supply factors Gasoline price per liter (KWR, Log) 7.37 0.065 7.20 7.54 7.35 0.086 7.20 7.54
Unemployment rate 4.61 0.689 3.30 6.50 4.70 0.802 3.30 6.50
Cumulative number of new bicycle racks (Log) 9.63 0.557 8.13 10.31 10.13 0.142 9.73 10.31
Cumulative number of new members per capita (Log) 0.85 0.096 0.34 0.94 0.92 0.014 0.90 0.94
COVID-19 factors Social distance index 147.84 33.092 0 200.00
Reduction of late-night operation of public transit in Seoul(=1, None=0) 0.15 0.357 0 1
Stringer social distancing in Seoul metropolitan area (=1, None = 0) 0.37 0.482 0 1
No. of new COVID patients (7-day moving average) 308.22 507.49 0 2720.6
Number of secondary vaccines per capita 13.20 25.795 0 83.50
This study focuses on the public bicycle system operating in Seoul, South Korea. This system, which was first launched in October 2015, had 37,500 bicycles in operation and 2,500 rental shops as of September 2021, with a total cumulative number of 83,807,669 (Seo & Cho, 2021)). The data were manipulated using the raw rental history information provided by the Seoul Open Data Plaza (https://data.seoul.go.kr/). As shown in Table 1, the average daily ridership over the previous five years was 48,112; during the pandemic period following January 19, 2020, when the first confirmed COVID-19 case was reported in South Korea, the daily average ridership was 75,203, which was 2.44 times higher than the period prior to COVID-19 (January 1, 2017, to January 18, 2020). Fig. 1 shows the time series trend of daily ridership and its seven-day moving average for public bicycles. The trend and variance have gradually increased since January 1, 2017, even after the first confirmed case of COVID-19. The study used log-transformed daily ridership to control the instability of the variance of the daily number of users.Fig. 1 Daily ridership of public bicycles in Seoul (2017-01-01 to 2021-12-31).
Fig. 1
A bicycle is a mode of transportation that is easily affected by weather conditions, seasonal and non-seasonal events, and the supply and demand of its use. Weather factors accounted for approximately 80% of the variation in bicycle use (Thomas, Jaarsma, & Tutert, 2013). Reviewing the literature on the relationship between weather conditions and the number of cyclists (Pucher et al., 2011; Gebhart & Noland, 2014; Kim, 2021; Thomas, Jaarsma, & Tutert, 2013; Zhang & Fricker, 2021), this study employed weather factors, such as sunlight time, temperature, average wind speed, and rainfall and snowfall. Among weather conditions, temperature is closely related to bicycle use (Pucher & Buehler, 2006), and Dill and Carr (2003) and Kim (2021) reported that bicycle use decreases, especially on cold or hot days. Therefore, the study coded cold days (below 0°C) and hot days (above 30°C) as 1 and coded days that did not correspond to these as 0 on the basis of the highest temperature on that day. These dummy variables allow us to estimate the effect of cold or hot days on public bicycle use compared with other days. The study also included the measures on fine particulate matter (PM. 2.5). Hong et al. (2022) demonstrated that air pollution was not statistically significant in bicycle share usage during the COVID-19 pandemic, while negatively associated with it before it. For the PM-2.5 level (ultra particulate matter), the study coded the days exceeding 76 μg/m3, which was a “very bad” level in Korea, as 1, and those below it as 0. This dummy variable allows us to estimate the difference in the use of public bicycles on the days when PM-2.5 exceeds 76 μg/m3, compared with other days.
Seasonal and non-seasonal events also influence public bicycle ridership (Corcoran et al., 2014; Noland et al., 2016; Lee et al., 2016). The study only uses weekends as a dummy to identify the effect on the use of bicycles for leisure, not essential travel, such as commuting, because seasonal factors by quarter and day of the week are controlled by the BSTS component models. Faghih-Imani et al. (2014) and Lee et al. (2016) reported that the use of public bicycles in Montreal, Canada, and Seoul, South Korea, decreased on weekends compared to weekdays. The study coded weekends, which were either Saturdays or Sundays, as 1, and weekdays, which were from Monday to Friday, as 0. Non-seasonal events measured by dummy processing in this study are public holidays, traditional Korean holidays, such as lunar new year and thanksgiving. Given that traditional Korean holidays are legally required to last at least three days, most people tend to visit their hometowns or travel abroad, resulting in a reduction in intra-regional traffic volume while increasing inter-regional traffic volume (Lee et al., 2020). However, during the COVID-19 pandemic, the Korean government urged people to refrain from visiting their hometowns. Thus, the use of public bicycles for leisure purposes in Seoul might have changed during this period. The study also employs sandwich days, which are weekdays between public holidays and weekends, in the model as non-seasonal dummy variables. The study measured sandwich days because it had statistical significance on the effect of middle east respiratory syndromes (MERS), on transit ridership in Seoul, South Korea in 2015 (Sung, 2016). The study coded the days of these non-seasonal events, such as holidays and traditional Korean holidays, as 1 and the other days as 0. The dummy variables for seasonal and non-seasonal event days allow us to identify the differences in the ridership of public bicycles for their days compared with other days.
Bicycle use is affected by demand and supply factors. In this study, the demand-side factors include gasoline price per liter, unemployment rate, and the cumulative number of new members per day for public bicycles. Many studies have reported that bicycle use increases as fuel price increases (Pucher & Buehler, 2006), the unemployment rate reduces (Chibwe et al., 2021), and the annual membership increases (Bachand-Marleau et al., 2012). Additionally, the density and capacity of bicycle rental stations (Chibwe et al., 2021; Hampshire & Marla, 2012) and bicycle paths (Frank et al., 2021; Kim, 2021) have a positive effect on ridership. Several cities in European and Latin American countries supplying pop-up bicycle paths during the COVID-19 pandemic have experienced an increase in bicycle use (Büchel et al., 2022; Nikitas et al., 2021). Although Seoul did not take measures to supply pop-up bicycle paths during the COVID-19 pandemic, it installed 500 additional public bicycle rental stations in 2020 (Kim, 2021). The rental stations and racks for public bicycles were 1331 and 16738 before the COVID-19 period, respectively, while 2471 and 29918 during the pandemic, respectively. This was an already-planned supply and not a measure to respond to COVID-19. However, because of this, the density of public bicycle rental stations after the COVID-19 period increased by approximately 1.8 times. In this study, the cumulative number of racks for these public bicycles and per rental station were employed in the models. All supply and demand factors used in this study are log-transformed continuous variables, except for the unemployment rate.
In addition, public bicycle ridership during the COVID-19 pandemic might be influenced by corona-related factors. Hua et al. (2021) found that social distancing measures had a significant impact on usage of bicycle-sharing during the pandemic in Nanjing, China. This study measured the social distancing index, restriction measures on the late-night operation of public transit in Seoul, stronger social distancing measures in the Seoul metropolitan, the 7-day moving average of newly confirmed COVID-19 cases, and the number of secondary vaccine recipients per capita in Seoul. South Korea has implemented non-pharmaceutical intervention measures such as wearing a mask, school closure, workplace closure, canceling public events, restriction on gathering size, stay-at-home requirements, and restrictions on internal movement. Social distancing measures are highly correlated, because several measures are mixed and implemented simultaneously (Snoeijer et al., 2021). The Oxford COVID-19 Government Response Tracker (OxCGRT) provides data on daily scores at the national level through a global panel database of policies during the COVID-19 pandemic (Hale et al., 2021). This study adopted the Oxford Index in the model. In addition, there was a 20% reduction in late-night public transportation in Seoul (July 1 to October 24, 2021) and stricter social distancing at the national level (August 16 to September 13, 2020, and November 24, 2020, to July 14, 2021). This study employs these periods as dummy variables by coding the days on which the measure was implemented as 1 and the other days as 0 in the model. Each of these dummy variables allowed us to identify the extent to which public bicycle use increased or decreased on the days when these measures were taken compared with the days when these measures were not taken. In addition, the number of newly confirmed COVID-19 cases per day and the vaccination ratio may also affect the use of public bicycles. Since the number of new confirmed cases per day has seasonality by the day of the week, the 7-day moving average is employed in the model. In addition, since the proportion of COVID-19 vaccinations, which started on April 21, 2021, in South Korea, has varied depending on the age group to be vaccinated, the study employs the number of secondary vaccine recipients per population in Seoul.
3.2 Methods
This study applies the Bayesian structural time series (BSTS) model and causal impact inference modeling based on it, not only to identify the statistical significance of the robustness of public bicycles against COVID-19 but also to investigate the differences in the causal impacts before and during the pandemic. These models have been employed to identify either forecasting or causal impacts on transit ridership (Hu & Chen, 2021), non-motorized transport demand (Zhang & Fricker, 2021), hospital finances and costs (Cai et al., 2021), and the number of confirmed COVID-19 cases (Feroze, 2020; Xie, 2021) during the pandemic. The BSTS model is a recent statistical technique used in feature selection, time-series prediction, nowcasting, causal inference, and other applications.
This model consists of three main components: Kalman filter, spike-and-slap, and Bayesian model averaging (Jinwen Qiu, 2018; Xie, 2021). The first is a component of the Kalman filter process, which is a time-series decomposition technique. At this stage, various state variables can be flexibly added, such as trends, seasonality, and regression. The second component is the spike and slab method, which involves selecting the most important regression predictors in this step. The third component is the Bayesian model averaging process, which combines the results and prediction calculations. Numerical calculations were performed using the Markov chain Monte Carlo (MCMC) method because the analytical calculation of the Bayesian posterior distribution is very difficult (Feroze, 2020).
The BSTS model is a generalized time-series model in which a researcher can flexibly compose the model components. The formula for the BSTS model was as follows:yt=ut+τ1t+τ2t+βtTXt+εt
where yt represents the log-transformed daily bicycle ridership at time t, which is estimated using a one-trend model (ut), two seasonal models (τ1t+τ2t). In addition, βtT is a vector of variables with the transition equation term T, and Xt is a vector of parameters in the regression model at time t. εt represents the residual at time t, which is not explained by this model, assuming a distribution with a mean of 0 and a variance of σ2, N(0,σ2). In Fig. 1, the slope of the trend may increase steadily; therefore, a semi-local linear trend model (ut) is applied. Fig. 1 also indicates seasonal fluctuations by day of the week and quarter. Therefore, in this study, two seasonal component models with 7-day and 91-day cycles (τ1t,τ2t) are included in the BSTS model. The last component model is the regression model (Xt) with weather conditions, seasonal and non-seasonal events, and exogenous variables of demand and supply factors, excluding COVID-19-related factors, for the pre-pandemic model and including all exogenous predictors for the post-pandemic period.
Causal impact inference modeling allows to estimate the pointwise difference between the predicted and actual values based on BSTS modeling. Alternative models of this model include the difference-in-difference (DID) and impulse-response function models, which identify the difference before and after a certain shock occurs. However, the ridership of public bicycles fluctuates depending on complex responses combined with various causes, rather than uniform level shifts, during the entire period before and after the outbreak of an infectious disease. In this respect, these two traditional approaches have limitations (Zhang & Fricker, 2021). Meanwhile, causal impact inference modeling based on the BSTS model allowed to estimate the pointwise daily difference between the actual and predicted ridership of public bicycles during the pandemic.
The analysis procedure of this study was performed in the following order: validation of the BSTS model for data from the pre-COVID-19 period, causal impact inference modeling based on the BSTS model, and comparison of the results of the two BSTS models before and during the COVID-19 pandemic. In the data for the pre-COVID-19 period, the training model used the training data from January 1, 2017, to July 22, 2019 (933 days), and the validation model used the test data from July 23, 2019, to January 18, 2020 (180 days). The mean absolute percentage error (MAPE) was used as the model validation measure. The MAPE of the BSTS model using the validation dataset was 5.97. The model was valid because it had a prediction error of 5.97% when using the BSTS model.
Augmented Dickey-Fuller and Box-Pierce tests were employed to evaluate the BSTS models for the periods before and during the COVID-19 pandemic. The Dickey-Fuller test statistic of the two models was -10.643 and -9.7889, and the p-value of both models was less than 0.01. Thus, the null hypothesis that a unit root exists was rejected. The Box-Pierce test statistics of the two models were 1.9836 and 1.373, and the p-values were 0.159 and 0.2413, respectively. This indicates that there was no time-series autocorrelation of the residuals. In conclusion, these two statistics prove that the residuals of these models fulfill satiability and have no autocorrelation.
4 Results
4.1 Causal impact inference
Table 2 summarizes the results of the customized causal impact inferences based on the BSTS model for the pre-COVID-19 period. The actual number of log-transformed ridership of public bicycles during the post-COVID-19 period was, on average, approximately 11.06. Without the intervention of COVID-19, the average number of users was expected to be 10.46. The total cumulative ridership during the COVID-19 period was 7889, while it would have been predicted to be 7455 if COVID-19 had not occurred. As a result, the average daily ridership of public bicycles increased by 0.61 owing to the outbreak of COVID-19, and its cumulative total number is expected to increase by 434.50. These results indicate that COVID-19 occurrence had an average positive effect of 5.8% on the ridership of public bicycles in Seoul. However, the increase in the difference resulting from the COVID-19 shock was within the 95% confidence interval. The probability of obtaining this effect by chance was 0.309, which is not statistically significant.Table 2 Results on posterior inference by customized causal impacts based on BSTS model.
Table 2 Average Cumulative
Actual 11 7889
Prediction (std. dev.) 10 (1.3) 7455 (898.9)
95% Confidence Interval [8, 13] [5699, 9322]
Absolute effect (std. dev.) 0.61 (1.3) 434.50 (898.9)
95% Confidence Interval [-2, 3.1] [-1,433, 2190.7]
Relative effect (std. dev.) 5.8% (12%) 5.8% (12%)
95% Confidence Interval [-19%, 29%] [-19%, 29%]
Posterior tail-area probability p = 0.309, Posterior prob. of a causal effect = 69%
The original plot in Fig. 2 shows the distribution of daily observations (black) and daily predicted values (blue). The pointwise graph shows the daily pointwise difference between these values, and the light blue section shows the 95% confidence interval. This figure also shows that the increase in ridership owing to the COVID-19 interruption has no significant effect because it exists within the 95% confidence interval, although it may have had a significant effect for a short time within the period. The lower part in Fig. 2 indicates that the pattern of the fluctuation in the pointwise differences is not consistent and may fluctuate depending on the combination of exogenous factors.Fig. 2 Pointwise prediction and difference in daily ridership of public bicycle by causal impact inference modeling.
Fig. 2
4.2 BSTS modeling before and during COVID-19
Fig. 3 and Table 3 summarize the results of the BSTS model for the log-transformed bicycle ridership for the two periods, before COVID-19 (2017-01-01 to 2020-01-18) and after (2020-01-19 to 2021-12-31). Fig. 3 indicates the probability of including the most important variables in the BSTS modeling. There are seven exogenous variables that have a probability of 0.9 or higher, affecting public bicycle ridership before COVID-19: below-zero temperature, sunshine hours, rainfall, average wind speed, snowfall, traditional Korean holidays, and public holidays. On the contrary, under the same standard, there are only five events during the COVID-19 period: below zero temperature, sunshine hours, precipitation, average wind speed, and snowfall. This indicates that the exogenous determinants affecting the ridership of public bicycles in Seoul were adjusted by COVID-19 interruption.Fig. 3 Inclusion probability of exogenous regressors before and during COVID-19 pandemic.
Fig. 3
Table 3 Average coefficient of posterior Bayesian models before and during COVID-19.
Table 3 Model A Model B Difference (=B-A)
Before COVID-19 During COVID-19
Weather environment factor Sunshine time (hr.) 0.0378 0.0358 -0.0021
Temperature (below 0°C = 1, 0°C to 30°C = 0) -0.2261 -0.3694 -0.1432
Temperature (above 30°C = 1, 0°C to 30°C = 0) -0.0023 -0.0073 -0.0050
Average wind speed (m/s) -0.0855 -0.0999 -0.0144
Rainfall (mm) -0.0178 -0.0191 -0.0013
Snowfall (cm) -0.0871 -0.1050 -0.0179
PM 2.5 (> = 76 μg/m3 = 1, < 76 μg/m3 = 0) -0.00005 -0.00005
Seasonal and non-seasonal event factor Weekend (Saturday, Sunday = 1, Monday to Saturday = 0) 0.000003 -0.0004 -0.0004
Holiday (=1, Non-holiday = 0) -0.2115 0.2115
Korean traditional holidays (=1, None = 0) -0.3530 -0.0001 0.3529
Sandwich day (=1, None = 0) -0.00001 0.00001
Supply and demand factor Gasoline price per liter (KWR, Log) -0.000001 -0.00002 -0.00002
Unemployment rate 0.00001 -0.00001 -0.00002
Cumulative number of new bicycle racks (Log) 0.00002 0.00026 0.0002
Cumulative number of new members per capita (Log) 0.0001 -0.0036 -0.0037
COVID-19 factor Social distance index na 0.0000020
Reduction of late-night operation of public transit in Seoul (=1, None = 0) na -0.0000337
Stringer social distancing in Seoul metropolitan area (=1, None = 0) na -0.0000370
No. of new COVID patients (7-day moving average) na -0.0001
Number of secondary vaccines per capita na -0.0000002
Note: na is not available in the model
Table 3 summarizes the average coefficients of the exogenous variables on the log-transformed daily ridership of public bicycles in Seoul before (Model A) and during COVID-19 (Model B). The variable with the largest differences in the values of the coefficients between Models A and B is traditional Korean holidays (0.3529), followed by public holidays (0.2115), and below-zero temperatures (-0.1432). Given that most people visited their hometowns during traditional Korean holidays with at least three holidays, it had the greatest effect of reducing the use of public bicycles in Seoul before the pandemic. However, during the COVID-19 pandemic, public bicycle use increased during traditional Korean holidays compared with those before the pandemic. The Korean government urged people not to visit their hometowns so that they would have more time and opportunities to rent shared public bicycles for leisure and exercise. They demonstrated that daily bicycle ridership during the COVID-19 period increased by 35.29% and 21.15% on traditional Korean holidays and public holidays, respectively, while it decreased by 14.32% on days when the maximum temperature was below zero, compared to those before it.
Among the coefficients of exogenous variables in Models A and B, the direction of influence on bicycle ridership changed owing to COVID-19 interruption. These are the weekend, unemployment rate, and the cumulative number of new members per capita. Bicycle ridership on weekends increased during the pre-COVID-19 period compared to weekdays, while it decreased during the pandemic. This indicates that the use of public bicycles in Seoul has changed owing to COVID-19, with more people using them on weekdays than on weekends. This may be because opportunities for use of public bicycles on weekdays generally increased owing to restrictions on movement, such as stay-at-home, work-from-home, school closure, and gathering and internal restrictions during the COVID-19 period. The increase in the unemployment rate had a positive effect on the use of public bicycles before the COVID-19 outbreak, whereas it had a negative effect during the COVID-19 period. Table 1 shows that the unemployment rate increased from 4.61to 4.70%. The non-normal increase in the unemployment rate owing to the COVID-19 pandemic may have had a different effect on the use of public bicycles. The cumulative number of new members per capita had a positive effect on public bicycle use before the COVID-19 period, while showing the opposite trend during the period. This result can be interpreted as an increase in the irregular and temporary use of public bicycles rather than regular use during the COVID-19 period.
However, the magnitudes of the regression coefficients for the weather condition factors during COVID-19 were relatively large compared to those before the period. This indicates that fewer Seoul citizens used public bicycles because the colder, hotter, stronger the wind, the more rain or snow, and the worse the PM 2.5 in the period during the COVID-19 outbreak. The magnitude of the influence of exogenous variables, such as non-seasonal event factors, including public holidays, traditional Korean holidays, and sandwich days, decreased overall compared to those before the outbreak. This indicates that the use of public bicycles has increased compared to the previous period, while avoiding long-distance travel for leisure or family meetings owing to COVID-19. Similarly, bicycle ridership increased as the gasoline price per liter decreased, and the cumulative number of public bicycle racks increased.
Table 3 shows that COVID-19-related exogenous variables also influenced public bicycle ridership. As the intensity of social distancing increased, ridership also increased. The stronger the non-pharmaceutical measures owing to COVID-19, the more restricted the mobility of human activity; therefore, public bicycles may have been used for purposes, such as leisure while staying at home. On the contrary, measures of late-night reduction of public transit operations in Seoul and stricter social distancing measures in the Seoul metropolitan area decreased public bicycle ridership. The results can be interpreted as a function of the trip chaining of public bicycles linked to public transit in Seoul, which has weakened owing to the COVID-19 pandemic. As the 7-day moving average number of new confirmed cases increased, the use of public bicycles decreased. This may have been because people refrained from using them to avoid the risk of COVID-19. As the number of secondary vaccinations per capita increased, the number of public bicycle users decreased.
5 Discussion
The results of this study on causal impact inference modeling demonstrated that the use of shared public bicycles in the densely populated city of Seoul was robust against the COVID-19 pandemic. Although the increase in public bicycle ridership for approximately two years after its first confirmation did not significantly increase, it did not decrease either. During the COVID-19 pandemic, the use of private bicycles increased (Anke et al., 2021; Ehsani et al., 2021; Harrington & Hadjiconstantinou, 2022; Hensher et al., 2022; Bucsky, 2020; Monterde-i-Bort et al., 2022; Shaer & Haghshenas, 2021; Loa et al., 2021; Lee et al., 2021). This proves that private ridership is more resistant to infectious disease pandemics (Chen et al., 2022; van der Drift et al., 2021). However, the evaluation of the sharing of public is mixed. This indicates that shared-based public bicycles have a higher risk of infection than privately owned bicycles (Hua et al., 2021; Shamshiripour et al., 2020; Zafri et al., 2022). In the early stages of COVID-19 in New York, the use of sharing decreased (Chai et al., 2021; Teixeira & Lopes, 2020; Wang & Noland, 2021). Zhang and Fricker (2021) also reported that among 11 US cities, the use of public bicycles increased in low-density cities, however it decreased in high-density cities during the COVID-19 lockdown. Bouhouras et al. (2022) also supported the results of Zhang and Fricker (2021), because the use of public bicycles increased significantly during the COVID-19 lockdown in small and medium-sized Greek cities. These results may indicate that the robustness of the shared public bicycle system is stronger in small-and medium-sized cities with low density than in high-density large cities. This study proves that public bicycles can be robust in response to COVID-19, even in Seoul, one of the most densely populated cities in the world. It was also confirmed that public bicycle ridership in Singapore increased by 150% during the lockdown (Song et al., 2022).
The robustness of public bicycle sharing in Seoul can be discussed from three perspectives: First, public bicycles were used more for leisure, exercise, and well-being than as a mode of travel during the COVID-19 pandemic. Because public bicycles in Seoul were mainly used for transit (Nam et al., 2021), the use of public bicycles was higher on weekdays than on weekends before the pandemic. However, its use increased on weekends, when public bicycles were mainly used for leisure and exercise during the COVID-19 pandemic when social distancing and movement restriction measures were considered to restrict human mobility. Kim et al. (2021) reported that the use of public bicycles in Seoul increased by 97.3% for leisure purposes such as weekends during the early period of the COVID-19 pandemic. In addition to the support from the cases of South Korea, Fuller et al. (2021) revealed that most bicycle activities increased for exercise and well-being purposes but not for transport purposes in Australia.
Second, the sharing of public bicycles may be further strengthened as an access mode for short-distance travel to major travel destinations during an infectious disease pandemic. Castillo-Manzano et al. (2016) indicated that the average journey distance by private bicycles was 700 to 800 m greater than that by public bicycles during normal times without the pandemic. Seo & Cho (2021) reported that the average distance of public bicycles traveled decreased from 0.83 km before COVID-19 to 0.58 km in the period thereafter. While analyzing the mileage, they confirmed that the average mileage of newly added routes after COVID-19 significantly decreased to less than 1,000 m.
Third, public bicycles may serve as an alternative to public transit during the rapid spread of infectious diseases. Many studies on other cities in the world have confirmed that bicycles were an alternative mode of mass transit during the COVID-19 pandemic (Campbell & Brakewood, 2017; Heydari et al., 2021; Lee et al., 2021; Schaefer et al., 2021; Scorrano & Danielis, 2021). Lee et al. (2021) revealed that public bicycles in Seoul had a significant substitution relationship during the period when the number of COVID-19 confirmed cases was amplified, although it had a weak correlation during the entire period of the pandemic. They observed that 2.28–4.33% of public transit users before the pandemic had changed to bicycles for travel during the pandemic in South Korea. In addition, lockdown and various movement restriction measures served as alternatives to fulfill mobility requirements in places where public transit services were limited (Song et al., 2022). However, the robustness of the use of shared bicycles against the coronavirus disease 2019 may vary depending on the density of the cities. Zhang and Fricker (2021) found that the COVID-19 outbreak decreased non-motorized travel activities in densely populated cities, whereas it decreased in less densely populated cities in the United States. The reason is that although shared public bicycles are a less dangerous mode of transportation during COVID-19, activities in high-density urban physical environments have an environment that is more susceptible to infectious diseases.
The public bicycle sharing system is one of the fastest-growing transport services worldwide (Wang & Akar, 2019). This public service should also work as a disease-resilient transport system (Song et al., 2022). Seoul has maintained a robust demand for the use of public bicycles during the COVID-19 pandemic by providing an unintentionally large supply of rental stations (Kim, 2021). This study confirmed that the usage behavior of public bicycles also changed during this pandemic. For example, the use of public bicycles increased on both weekdays and days when weather conditions were not bad. However, the robustness of this shared public bicycle service in response to the infectious disease pandemic in Seoul is not sufficient. Because the transport infrastructure for bicycle use in Seoul was relatively insufficient (Kim and Kim, 2020), it was difficult to gather evidence of the robustness of public bicycles as a travel mode against infectious disease pandemics. Other cities in Europe and South America have promoted the ease of use of bicycles resistant and resilient to infectious diseases as a travel mode, not only for leisure, exercise, or well-being, through active interventions, such as the provision of free use of public bicycles and the opening of pop-up bicycle paths during COVID-19 (Büchel et al., 2022; Nikitas et al., 2021). In this regard, the government must continuously intervene in the supply of bicycle infrastructure, particularly shared-based public bicycles, to function as a disease-resilient transport system during the epidemic period.
This study suggests a shared public bicycle system as an alternative robust travel mode against the COVID-19 pandemic. However, the study may have limitations in comparison with other shared travel modes in terms of the effects, competitive and complementary relationships with other travel modes, and only focusing on Seoul city in South Korea. First, further studies need to focus on identifying and comparing the causal effects of shared personal mobility during the current pandemic and new ones in the near future. Mouratidis (2022) identified that the use of shared mobility, such as bicycles, cars, and e-scooters, was associated with different demographic and socioeconomic factors as well as the residential physical environment of people. Wiseman (2021) also indicated that autonomous vehicles can be a better robust solution against the coronavirus pandemic, especially in isolated territories. Shared autonomous vehicles, a future transportation technology, need to be considered as a personal mobility mode to sustain daily activities during the coronavirus pandemic. Second, the study did not consider the competition and complementary relationships between the different modes of transportation resulting from the COVID-19 pandemic. Shamshiripour et al. (2020) identified that the perceived risk level of traveling with different modes during a pandemic differed. As a result, the robustness of shared public bicycles during the pandemic might result from an increase due to a decrease in the use of public transport, which was perceived as more dangerous. Therefore, in future studies, it will be necessary to empirically demonstrate how competition and complementarity between means due to this pandemic affected the use of shared public bicycles. Third, the study identified the causal effects on the daily ridership of shared public bicycles in Seoul, South Korea. Therefore, it is necessary to conduct a study comparing the results with those of other cities worldwide.
6 Conclusions
In conclusion, this study provides evidence on how to maintain daily life by activating the use of shared public bicycles during pandemics of infectious diseases that may recur in the future. This conclusion supports the idea that the public bicycle-sharing system can be robust against the COVID-19 pandemic, even in Seoul, which has a high-density population. However, because this robustness may be only a result of the strengthening of the use of public bicycles for leisure and exercise, and not mainly for the purpose of travel, sustaining infrastructure investments, such as bicycle roads, are required for public bicycles to play the role of a more robust, resilient, and sustainable transport mode against infectious disease pandemics.
Funding statement
This research did not receive any specific grants from funding agencies in the public, commercial, or not-for-profit sectors.
Institutional review board statement
Not applicable.
Informed consent statement
Not applicable.
CRediT authorship contribution statement
Hyungun Sung: Visualization, Data curation, Formal analysis, Writing – original draft.
Declaration of Competing Interest
The author declares that (i) no support, financial or otherwise, has been received from any organization that may have an interest in the submitted work; and (ii) there are no other relationships or activities that could appear to have influenced the submitted work.
Data Availability
Data will be made available on request.
Acknowledgments
None.
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References
Anke J. Francke A. Schaefer L.M. Petzoldt T. Impact of SARS-CoV-2 on the mobility behaviour in Germany European Transport Research Review 13 1 2021 10.1186/s12544-021-00469-3
Büchel B. Marra A.D. Corman F. COVID-19 as a window of opportunity for cycling: Evidence from the first wave Transport Policy 116 2022 144 156 10.1016/j.tranpol.2021.12.003
Bachand-Marleau J. Lee B.H.Y. El-Geneidy A.M. Better understanding of factors influencing likelihood of using shared bicycle systems and frequency of use Transportation Research Record: Journal of the Transportation Research Board 2314 1 2012 66 71 10.3141/2314-09
Bouhouras E. Basbas S. Ftergioti S. Paschalidis E. Siakantaris H. COVID-19′s pandemic effects on bicycle sharing systems: A new reality for urban mobility? Applied Sciences 12 3 2022 10.3390/app12031230
Bucsky P. Modal share changes due to COVID-19: The case of Budapest Transportation Research Interdisciplinary Perspectives 8 2020 100141 10.1016/j.trip.2020.100141
Cai Y. Kwek S. Tang S.S.L. Saffari S.E. Lum E. Yoon S. Ansah J.P. Matchar D.B. Kwa A.L. Ang K.A. Thumboo J. Ong M.E.H. Graves N. Impact of the COVID-19 pandemic on a tertiary care public hospital in Singapore: Resources and economic costs Journal of Hospital Infection 121 2021 1 8 10.1016/j.jhin.2021.12.007 34902499
Campbell K.B. Brakewood C. Sharing riders: How bicyclesharing impacts bus ridership in New York City Transportation Research Part A: Policy and Practice 100 2017 264 282 10.1016/j.tra.2017.04.017
Castillo-Manzano J.I. López-Valpuesta L. Sánchez-Braza A. Going a long way? On your bicycle! Comparing the distances for which public bicycle sharing system and private bicycles are used Applied Geography 71 2016 95 105 10.1016/j.apgeog.2016.04.003
Chai X. Guo X. Xiao J. Jiang J. Analysis of spatiotemporal mobility of shared-bicycle usage during COVID-19 pandemic in Beijing Trans GIS 25 2021 2866 2887 34899032
Chen C. Feng T. Gu X. Yao B. Investigating the effectiveness of COVID-19 pandemic countermeasures on the use of public transport: A case study of The Netherlands Transp Policy (Oxf) 117 2022 98 107 10.1016/j.tranpol.2022.01.005 35068712
Chibwe J. Heydari S. Faghih Imani A. Scurtu A. An exploratory analysis of the trend in the demand for the London bicycle-sharing system: From London Olympics to COVID-19 pandemic Sustainable Cities and Society 69 2021 10.1016/j.scs.2021.102871
Cho J. Baik G. A study on micro-mobility pattern analysis using public bicycle rental history data Journal of Information Techonlogy Service 20 6 2021 83 95 //scholar.dkyobobook.co.kr/searchDetail.laf?barcode=4010028694107
Cho H.R. Yun S.B. Jeong Y.J. Seoul transportation changes and strategies after COVID-19 Korean Society of Transportation 17 3 2020 46 51
Corcoran J. Li T. Rohde D. Charles-Edwards E. Mateo-Babiano D. Spatio-temporal patterns of a public bicycle sharing program: The effect of weather and calendar events Journal of Transport Geography 41 2014 292 305 10.1016/j.jtrangeo.2014.09.003
Dill J. Carr T. Bicycle commuting and facilities in major US cities: If you build them, commuters will use them Transportation Research Record 1828 1 2003 116 123
Ehsani J.P. Michael J.P. Duren M.L. Mui Y. Porter K.M.P. Mobility patterns before, during, and anticipated after the COVID-19 pandemic: An opportunity to nurture bicycling American Journal of Preventive Medicine 60 6 2021 e277 e279 10.1016/j.amepre.2021.01.011 33674071
Faghih-Imani A. Eluru N. El-Geneidy A.M. Rabbat M. Haq U. How land-use and urban form impact bicycle flows: Evidence from the bicycle-sharing system (BIXI) in Montreal Journal of Transport Geography 41 2014 306 314 10.1016/j.jtrangeo.2014.01.013
Feroze N. Forecasting the patterns of COVID-19 and causal impacts of lockdown in top five affected countries using Bayesian structural time series models Chaos Solitons Fractals 140 2020 110196 10.1016/j.chaos.2020.110196
Frank L.D. Hong A. Ngo V.D. Build it and they will cycle: Causal evidence from the downtown Vancouver Comox Greenway Transport Policy 105 2021 1 11 10.1016/j.tranpol.2021.02.003
Fuller G. McGuinness K. Waitt G. Buchanan I. Lea T. The reactivated bike: Self-reported cycling activity during the 2020 COVID-19 pandemic in Australia Transportation Rresearch Interdisciplinary Perspectives 10 2021 100377
Gansterer M. Hartl R.F. Tzur M. Transportation in the sharing economy Transportation Science 56 3 2022 567 570
Gebhart K. Noland R.B. The impact of weather conditions on bicycleshare trips in Washington, DC Transportation 41 6 2014 1205 1225 10.1007/s11116-014-9540-7
Hale T. Angrist N. Goldszmidt R. Kira B. Petherick A. Phillips T. Webster S. Cameron-Blake E. Hallas L. Majumdar S. Tatlow H. A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker) Nature Human Behaviour 5 4 2021 529 538 10.1038/s41562-021-01079-8
Hampshire R.C. Marla L. An Analysis of bicycle sharing usage: Explaining trip generation and attraction from observed demand Proceedings of the transportation research board 91st annual meeting Washington DC, United States 2012
Harrington D.M. Hadjiconstantinou M. Changes in commuting behaviours in response to the COVID-19 pandemic in the UK Journal of Transport & Health 24 2022 101313 10.1016/j.jth.2021.101313
Hensher D.A. Balbontin C. Beck M.J. Wei E. The impact of working from home on modal commuting choice response during COVID-19: Implications for two metropolitan areas in Australia Transportation Research Part A 155 2022 179 201 10.1016/j.tra.2021.11.011
Heydari S. Konstantinoudis G. Behsoodi A.W. Effect of the COVID-19 pandemic on bicycle-sharing demand and hire time: Evidence from Santander Cycles in London PLoS One 16 12 2021 e0260969 10.1371/journal.pone.0260969
Hong J. Han E. Choi C. Lee M. Park D. Estimation of shared bicycle demand using the SARIMAX Model: Focusing on the COVID-19 impact of Seoul The Journal of the Korea Institute of Intelligent Transport Systems 20 1 2021 10 21 10.12815/kits.2021.20.1.10
Hong J. McArthur D.P. Sim J. Kim C.H. Did air pollution continue to affect bicycle share usage in Seoul during the COVID-19 pandemic? Journal of transport & health 43 2022 101342
Hu S. Chen P. Who left riding transit? Examining socioeconomic disparities in the impact of COVID-19 on ridership Transportation Research Part D: Transport and Environment 90 2021 10.1016/j.trd.2020.102654
Hua M. Chen X. Cheng L. Chen J. Should bicycle-sharing continue operating during the COVID-19 pandemic? Empirical findings from Nanjing, China J Transp Health 23 2021 101264 10.1016/j.jth.2021.101264
Jiang R. Hu M.B. Wu Q.S. Song W.G. Traffic dynamics of bicycle flow: Experiment and modeling Transportation Science 51 3 2017 998 1008
Kim H. Kim Y. Space-time network analysis of public bicycle use and its visualization using spatial network autocorrelation Journal of the Korean Cartographic Association 20 1 2020 93 106 10.16879/jkca.2020.20.1.093
Kim J. Ki D. Lee S. Analysis of travel mode choice change by the spread of COVID-19: The case of Seoul, Korea Journal of Korea Planning Association 56 3 2021 113 129 10.17208/jkpa.2021.06.56.3.113
Lee J.H. Jeong G.O. Shin H.C. Impact analysis of weather condition and locational characteristics on the usage of public bicycle sharing system Journal of Korean Society of Transportation 34 5 2016 394 408 10.7470/jkst.2016.34.5.394
Lee S. Chai S. Lee C.H. An S. Jang K. Effects of COVID-19 on trip patterns in Daejeon Metropolitan City, South Korea Korean Society of Transportation 17 4 2020 17 27
Lee G. Choo S. Kim K.Y. Joung J. Analysis of factors affecting perceived risk of COVID-19 infection in public transportation Journal of Korean Society of Transportation 39 5 2021 643 661 10.7470/jkst.2021.39.5.643
Li H. Zhang Y. Zhu M. Ren G. Impacts of COVID-19 on the usage of public bicycle share in London Transportation Research Part A: Policy and Practice 150 2021 140 155 34511745
Loa P. Hossain S. Mashrur S.M. Liu Y. Wang K. Ong F. Habib K.N. Exploring the impacts of the COVID-19 pandemic on modality profiles for non-mandatory trips in the Greater Toronto Area Transport Policy (Oxf) 110 2021 71 85 10.1016/j.tranpol.2021.05.028
Monterde-i-Bort H. Sucha M. Risser R. Kochetova T. Mobility patterns and mode choice preferences during the COVID-19 Situation Sustainability 14 2 2022 10.3390/su14020768
Mouratidis K. Bike-sharing, car-sharing, e-scooters, and Uber: Who are the shared mobility users and where do they live? Sustainable Cities and Society 86 2022 104161
Nam G. Park J. Ko J. Qualitative analysis of Seoul public bicycle usage patterns Transport Technology and Policies 18 2021 52 59 http://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE11026128
Nankervis M. The effect of weather and climate on bicycle commuting Transportation Research Part A: Policy and Practice 33 6 1999 417 431
Nikitas A. Tsigdinos S. Karolemeas C. Kourmpa E. Bakogiannis E. Cycling in the era of COVID-19: Lessons learnt and best practice policy recommendations for a more bicycle-centric future Sustainability 13 9 2021 10.3390/su13094620
Noland R.B. Ishaque M.M. Smart bicycles in an urban area: Evaluation of a pilot scheme in London Journal of Public Transportation 9 5 2006 5
Noland R.B. Smart M.J. Guo Z. Bicycleshare trip generation in New York City Transportation Research Part A: Policy and Practice 94 2016 164 181 10.1016/j.tra.2016.08.030
Pawar D.S. Yadav A.K. Akolekar N. Velaga N.R. Impact of physical distancing due to novel coronavirus (SARS-CoV-2) on daily travel for work during transition to lockdown Transportation Research Interdisciplinary Perspectives 7 2020 100203
Piras F. Scappini B. Meloni I. The transformation of urban spaces as a cycling motivator: The case of Cagliari, Italy Transportation Research Procedia 60 2022 60 67 10.1016/j.trpro.2021.12.009
Pucher J. Buehler R. Why Canadians cycle more than Americans: A comparative analysis of bicycling trends and policies Transport Policy 13 3 2006 265 279 10.1016/j.tranpol.2005.11.001
Pucher J. Buehler R. Seinen M. Bicycling renaissance in North America? An update and re-appraisal of cycling trends and policies Transportation Research Part A: Policy and Practice 45 6 2011 451 475 10.1016/j.tra.2011.03.001
Qiu J. Jammalamadaka S.R. Ning N. Multivariate Bayesian structural time series model Journal of Machine Learning Research 19 2018 13
Schaefer K.J. Tuitjer L. Levin-Keitel M. Transport disrupted – Substituting public transport by bicycle or car under COVID 19 Transp Res Part A Policy Pract 153 2021 202 217 10.1016/j.tra.2021.09.002 34602756
Scorrano M. Danielis R. Active mobility in an Italian city: Mode choice determinants and attitudes before and during the COVID-19 emergency Research in Transportation Economics 86 2021 10.1016/j.retrec.2021.101031
Seo IJ. Cho JH. Changes in public bicycle usage patterns before and after COVID-19 in Seoul Journal of Korea Bigdata Association 6 2 2021 139 149 http://kiss.kstudy.com/thesis/thesis-view.asp?g=kissmeta&m=exp&enc=C61876E37BE2F85F3F879428C2E843E5
Shaer A. Haghshenas H. The impacts of COVID-19 on older adults’ active transportation mode usage in Isfahan, Iran Journal of Transport & Health 23 2021 10.1016/j.jth.2021.101244
Shamshiripour A. Rahimi E. Shabanpour R. Mohammadian A.K. How is COVID-19 reshaping activity-travel behavior? Evidence from a comprehensive survey in Chicago Transportation Research Interdisciplinary Perspectives 7 2020 100216 10.1016/j.trip.2020.100216
Shokouhyar S. Shokoohyar S. Sobhani A. Gorizi A.J. Shared mobility in post-COVID era: New challenges and opportunities Sustainable Cities and Society 67 2021 102714
Snoeijer B.T. Burger M. Sun S. Dobson R.J.B. Folarin A.A. Measuring the effect of Non-Pharmaceutical Interventions (NPIs) on mobility during the COVID-19 pandemic using global mobility data NPJ Digital Medicine 4 1 2021 81 10.1038/s41746-021-00451-2 33986465
Song J. Zhang L. Qin Z. Ramli M.A. Spatiotemporal evolving patterns of bicycle-share mobility networks and their associations with land-use conditions before and after the COVID-19 outbreak Physica A 592 2022 126819 10.1016/j.physa.2021.126819
Sung H. Impacts of the outbreak and proliferation of the Middle East Respiratory Syndrome on rail transit ridership in the Seoul Metropolitan City Journal of Korea Planning Association 51 3 2016 163 179 10.17208/jkpa.2016.06.51.3.163
Teixeira J.F. Lopes M. The link between bicycle sharing and subway use during the COVID-19 pandemic: The case-study of New York's Citi Bicycle Transportation Research Interdisciplinary Perspectives 6 2020 100166 10.1016/j.trip.2020.100166
Thomas T. Jaarsma R. Tutert B. Exploring temporal fluctuations of daily cycling demand on Dutch cycle paths: The influence of weather on cycling Transportation 40 1 2013 1 22 10.1007/s11116-012-9398-5
van der Drift S. Wismans L. Olde Kalter M.-J. Changing mobility patterns in the Netherlands during COVID-19 outbreak Journal of Location Based Services 16 1 2021 1 24 10.1080/17489725.2021.1876259
Wang K. Akar G. Gender gap generators for bicycle share ridership: Evidence from Citi Bicycle system in New York City Journal of Transport Geography 76 2019 1 9 10.1016/j.jtrangeo.2019.02.003
Wang H. Noland R.B. Bicycleshare and subway ridership changes during the COVID-19 pandemic in New York City Transport Policy (Oxf) 106 2021 262 270 10.1016/j.tranpol.2021.04.004
Wang Y. Tsai T.C. Duncan D. Ji J. Association of city-level walkability, accessibility to biking and public transportation and socio-economic features with COVID-19 infection in Massachusetts, USA: An ecological study Geospatial Health 17 s1 2022 10.4081/gh.2022.1017
Wiseman Y. COVID-19 Along with autonomous vehicles will put an end to rail systems in isolated territories IEEE Intelligent Transportation Systems Magazine 13 3 2021 6 12
Xie L. The analysis and forecasting COVID-19 cases in the United States using Bayesian structural time series models Biostatistics & Epidemiology 2021 1 15 10.1080/24709360.2021.1948380
Zafri N.M. Khan A. Jamal S. Alam B.M Risk perceptions of COVID-19 transmission in different travel modes Transportation Research Interdisciplinary Perspectives 13 2022 100548 10.1016/j.trip.2022.100548
Zhang Y. Fricker J.D. Quantifying the impact of COVID-19 on non-motorized transportation: A Bayesian structural time series model Transport Policy 103 2021 11 20 10.1016/j.tranpol.2021.01.013
| 36514674 | PMC9731812 | NO-CC CODE | 2022-12-14 23:45:34 | no | Sustain Cities Soc. 2023 Feb 9; 89:104344 | utf-8 | Sustain Cities Soc | 2,022 | 10.1016/j.scs.2022.104344 | oa_other |
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Magn Reson Imaging
Magn Reson Imaging
Magnetic Resonance Imaging
0730-725X
1873-5894
Elsevier Inc.
S0730-725X(22)00212-0
10.1016/j.mri.2022.12.002
Article
Performance of spiral UTE-MRI of the lung in post-COVID patients
Fauveau Valentin a
Jacobi Adam b
Bernheim Adam b
Chung Michael b
Benkert Thomas c
Fayad Zahi A. ab
Feng Li ab⁎
a BioMedical Engineering and Imaging Institute (BMEII), Icahn School of Medicine at Mount Sinai, New York, USA
b Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, USA
c MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
⁎ Corresponding author at: Biomedical Engineering and Imaging Institute, Department of Radiology, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY 10029, USA.
9 12 2022
2 2023
9 12 2022
96 135143
20 9 2022
18 11 2022
1 12 2022
© 2022 Elsevier Inc. All rights reserved.
2022
Elsevier Inc.
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Patients recovered from COVID-19 may develop long-COVID symptoms in the lung. For this patient population (post-COVID patients), they may benefit from longitudinal, radiation-free lung MRI exams for monitoring lung lesion development and progression. The purpose of this study was to investigate the performance of a spiral ultrashort echo time MRI sequence (Spiral-VIBE-UTE) in a cohort of post-COVID patients in comparison with CT and to compare image quality obtained using different spiral MRI acquisition protocols. Lung MRI was performed in 36 post-COVID patients with different acquisition protocols, including different spiral sampling reordering schemes (line in partition or partition in line) and different breath-hold positions (inspiration or expiration). Three experienced chest radiologists independently scored all the MR images for different pulmonary structures. Lung MR images from spiral acquisition protocol that received the highest image quality scores were also compared against corresponding CT images in 27 patients for evaluating diagnostic image quality and lesion identification. Spiral-VIBE-UTE MRI acquired with the line in partition reordering scheme in an inspiratory breath-holding position achieved the highest image quality scores (score range = 2.17–3.69) compared to others (score range = 1.7–3.29). Compared to corresponding chest CT images, three readers found that 81.5% (22 out of 27), 81.5% (22 out of 27) and 37% (10 out of 27) of the MR images were useful, respectively. Meanwhile, they all agreed that MRI could identify significant lesions in the lungs. The Spiral-VIBE-UTE sequence allows for fast imaging of the lung in a single breath hold. It could be a valuable tool for lung imaging without radiation and could provide great value for managing different lung diseases including assessment of post-COVID lesions.
Keywords
MRI
Spiral sampling
Stack-of-spirals
Ultrashort echo time
COVID-19
Post-COVID
Lung imaging
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pmc1 Introduction
The 2019 coronavirus disease (COVID-19), caused by the SARS-CoV-2 viruses, has presented an unprecedented crisis and challenge to public health. Until July 2022, over 500 million COVID-19 cases have been reported globally [1]. Although most people infected with COVID-19 experience mild to moderate symptoms without the need for hospitalization and specific treatments, some people could become severely sick and require medical attention. In the meantime, even with mild disease outcome, many patients may develop long COVID symptoms, a post-COVID syndrome associated with certain chronic symptoms and damage in the lung as well as other organs such as the brain and/or the heart [[2], [3], [4], [5], [6], [7], [8], [9]]. Indeed, long COVID has emerged as an ongoing healthcare challenge requiring more attention and careful investigation [4]. This will ensure that the long-term outcomes of post-COVID patients, including residual damage in the lung and other organs, can be studied, better understood and properly managed.
It has been a consensus that the lung is one of the first organs the SARS-CoV-2 viruses attack in severely sick patients [10]. As a result, proper screening and management of anatomical and functional sequelae of COVID-19 in the lung has been a pressing clinical need. Currently, Computed Tomography (CT) is the most commonly used imaging modality for assessment of lung anatomy, and it has been used in post-COVID patients in many research studies [11]. CT offers excellent morphologic information, but the need of ionizing radiation has been a major concern that restricts frequent exams for studying the longitudinal change of lung structure [12], especially in the post-COVID patient cohort. Magnetic Resonance Imaging (MRI) represents a promising radiation-free imaging modality for lung imaging in post-COVID care. Although MRI is not traditionally used for imaging the lung, recent advances in MRI technology have made this possible both in the research and clinical settings [[13], [14], [15]]. For example, MRI with ultra-short echo time acquisition (UTE-MRI) has enabled visualization of short T2* structures in the lung, as demonstrated in many studies [[16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27]]. This provides a new opportunity for longitudinal screening of lung structure abnormality such as ground-glass opacity and fibrotic-like changes, making MRI well-suited for post-COVID management [28].
Conventional UTE-MRI is typically implemented based on a center-out 3D radial trajectory or its variants [18,29,30]. This allows for radio frequency (RF) excitation using a non-selective pulse and immediate data acquisition following RF excitation without the need for slice/slab selection, both of which are important to ensure minimum echo time. However, limitations associated with the center-out 3D radial trajectory include reduced imaging efficiency and prolonged scan time due to center-out half-echo sampling, the requirement for isotropic spatial resolution resulting in reduced signal-to-noise ratio (SNR), and high computational burden due to large data size. Meanwhile, due to long scan time, free-breathing acquisition is normally necessary for UTE-MRI with center-out 3D radial sampling [17,18,31,32], which in turn requires reliable motion compensation methods to manage respiratory motion.
Recently, a new UTE-MRI sequence, called Spiral-VIBE-UTE, has been proposed for more efficient lung imaging [33,34]. Here, VIBE, standing for Volumetric Interpolated Breath-hold Examination, is a Siemens acronym for 3D breath-hold gradient echo (GRE) sequences. The Spiral-VIBE-UTE sequence includes several new components. First, it employs center-out spiral sampling, which provides more flexibility to design longer readout to compensate for the reduced scan efficiency in traditional UTE-MRI. Second, Spiral-VIBE-UTE implements a stack-of-spirals trajectory, where spiral sampling is applied in the kx-ky plane and Cartesian sampling is applied along the kz dimension (slice direction). Compared to the 3D radial trajectory, this provides flexibility to perform imaging with anisotropic spatial resolution and volumetric coverage, enabling faster imaging speed to acquire a 3D image of the lung. To date, Spiral-VIBE-UTE has been applied for both anatomical imaging and functional imaging in the lung [[35], [36], [37], [38], [39], [40], [41]], but to the best of our knowledge, it has not been tested in post-COVID patients yet.
The purpose of this study includes the following three aspects. First, we aimed to study the performance of Spiral-VIBE-UTE for imaging pulmonary structure in post-COVID patients and to compare its performance against corresponding CT references. Second, since stack-of-spirals sampling can be performed with either a partition-in-line looping scheme or a line-in-partition looping scheme (referred to as reordering, see the Method section below for more details), it remains unclear which reordering scheme would give better performance in lung imaging, especially in the presence of cardiac motion and/or residual respiratory motion (e.g., when a breath hold is not successful). Thus, it is important to investigate the resulting image quality of different acquisition schemes for in-vivo lung imaging. Third, we also sought to compare image quality acquired in an expiratory breath-hold position and an inspiratory breath-hold position in Spiral-VIBE-UTE imaging. This study is expected to have both technical and clinical significance. From the technical perspective, the results of this study will provide more guidance to use this relatively new sequence for breath-hold lung imaging. From the clinical perspective, this study will provide initial evidences to show the feasibility of Spiral-VIBE-UTE MRI for post-COVID management in comparison with CT, which could potentially enable longitudinal imaging studies in this patient population for better understanding of disease development.
2 Materials and methods
2.1 Spiral-VIBE-UTE sequence
2.1.1 UTE-MRI Acquisition based on stack-of-spirals sampling
The Spiral-VIBE-UTE sequence implements a hybrid stack-of-spirals sampling trajectory, as previously described by Mugler et al. [33,34]. As shown in Fig. 1 , spiral sampling is employed in the kx-ky plane for the Spiral-VIBE-UTE sequence while Cartesian sampling is implemented along the kz direction. For the kx-ky plane, each spiral interleaf rotates by a linear angle (360o/total number of spiral interleaves) from the previous interleaf. In the kz direction, all spiral interleaves corresponding to different slice locations maintain the same rotation angle. To achieve ultra-short echo time (TE), the sequence has the following three features. First, non-selective RF excitation is employed in the sequence to shorten the duration of the RF pulse. Second, variable TE is implemented for difference image slices [42], as shown in Fig. 1. Specifically, the minimum TE is achieved for the central slice where the slice-encoding gradient is not enabled (kz = 0, see Fig. 1). While the slice-encoding gradient gets stronger towards outer slices, the TE is also increased and it reaches the maximum for the first outer slice (kz = +max/−max, see Fig. 1). Since the central slice primarily determines the image contrast in 3D imaging, this unique sampling scheme ensures that the overall 3D acquisition can have ultra-short TE when the central slice has the minimum TE. Third, center-out spiral sampling is implemented for data acquisition, which helps reduce the TE and enables increased sampling efficiency. Other advantages of Spiral-VIBE-UTE over traditional UTE-MRI based on 3D radial sampling include the flexibility to choose a slice thickness that is different from in-plane spatial resolution for better managing scan time and SNR, which allows for 3D lung UTE-MRI within a single breath hold.Fig. 1 The stack-of-spirals are acquired in the kx-ky plane and every slice maintains the same number of spiral interleaves rotating by a pre-defined angle. The ultra-short echo time (TE) is achieved by shortening the duration of the RF pulse, varying TE for different image slices (with minimum TE at kz = 0) and center-out spiral sampling during data acquisition.
Fig. 1
2.1.2 Sampling reordering in spiral-VIBE-UTE MRI
The stack-of-spirals sampling trajectory provides additional flexibility to design how one can acquire a 3D UTE-MR image. As shown in Fig. 2 for a simple example to acquire a stack-of-spirals dataset with two slices and 5 spiral interleaves in each slice, data acquisition can be performed in a line in partition loop (referred to as Lin-in-Par reordering hereafter) or a partition in line loop (referred to as Par-in-Lin reordering hereafter). For Lin-in-Par reordering, all spiral interleaves are acquired for one slice before moving to the next slice, while for Par-in-Lin reordering, a spiral interleaf with the same rotation angle in all slices (referred to as a spiral stack) are acquired before moving to the next stack. The order of acquisition is labelled in Fig. 2 from 1 to 10 for acquiring the total 10 spiral interleaves. In this study, it was hypothesized that imaging with different reordering schemes can lead to different imaging performance, and therefore, image quality from these two reordering schemes was compared for lung imaging.Fig. 2 Different reordering schemes can be implemented in stack-of-spirals acquisition including line in partition (Lin-in-Par) reordering and partition in line (Par-in-Lin) scheme. For Lin-in-Par reordering, all spiral interleaves are acquired for one slice before moving to the next slice, while for Par-in-Line reordering, a spiral interleaf with the same rotation angle in all slices are acquired before moving to the next stack. The order of acquisition is labelled from 1 to 10 for acquiring the total 10 spiral interleaves.
Fig. 2
2.2 Post-COVID patient recruitment
A total of 36 adult post-COVID patients (20 males, 16 females, mean age = 44.6 ± 14.6 years) who had previously recovered from COVID-19 infection were recruited for this study. All the recruited patients had previously been tested positive for COVID-19 either with a PCR test or an antigen test. Patients were referred from the outpatient clinics at our hospital between December 2020 and September 2021. Other inclusion and exclusion criteria for the recruitment were: age > 18; no COVID-19 infection and symptoms at the time of MRI exams (self-reporting); no significant medical illness; no implanted metal devices; no tattoos larger than one centimeter in diameter or tattoos with metallic ink; no claustrophobia; no pregnancy; no breast feeding; no significant lung disease history. We did not set a restriction on the time between COVID-19 infection and the MRI/CT scan. The study was HIPAA compliant and was approved by the local Institutional Review Board. Written consents were obtained from all participants prior to the MRI scan. In addition, a healthy volunteer (male, 59-year-old) without COVID-19 history was also recruited to participate in the study. The main purpose of the volunteer imaging was to compare image quality between different acquisition schemes while successful breath holding can be ensured.
2.3 Data acquisition
2.3.1 MRI acquisition
For each subject, Spiral-VIBE-UTE imaging was performed using a prototype stack-of-spirals UTE sequence for four different scans, including Spiral-VIBE-UTE imaging with the Lin-in-Par reordering scheme performed in both expiratory and inspiratory breath-holding positions (referred to as Spiral-Exp Lin-in-Par and Spiral-Insp Lin-in-Par, respectively), and Spiral-VIBE-UTE imaging with the Par-in-Lin reordering scheme performed in both expiratory and inspiratory breath-holding positions (referred to as Spiral-Exp Par-in-Lin and Spiral-Insp Par-in-Lin, respectively). Relevant imaging parameters were: non-selective excitation, FOV = 480x480mm2, spatial resolution = 2.1 × 2.1mm2, slice thickness = 2.5 mm, total number of slices = 96, TR = 2.65 ms, TE = 0.05–0.27 ms from the central slice to the outer slice, flip angle = 5o, spiral readout duration = 1160us, RF pulse duration = 60us, number of spiral interleaves for each slice = 140. In data acquisition, 48 slices were acquired with a slice thickness of 5 mm (half of the slice resolution set in the scanner), and zero-filling was performed along the slice dimension to generate all the 96 slices with a slice thickness of 2.5 mm. As a result, a total of four MRI scans were performed in each subject in four separate breath holds. Imaging was performed in the coronal orientation. Uniform spiral density was implemented. All images were fully sampled to achieve the Nyquist rate and images were reconstructed directly on the scanner using a standard gridding algorithm. For comparison, 3D breath-hold Cartesian imaging using a clinical VIBE sequence was also performed in the volunteer. All MRI scans were performed on a 3 T clinical scanner (MAGNETOM Biograph mMR, Siemens Healthcare, Erlangen, Germany).
The healthy volunteer without COVID-19 history underwent the same breath-hold scans as the post-COVID patients. In addition, two 3D Cartesian scans were also performed using a clinically available 3D gradient echo (GRE) sequence, one in an expiratory breath-holding position and the other in an inspiratory breath-holding position. Relevant imaging parameters were similar to the spiral scans. The volunteer confirmed that all imaging tasks were performed under successful breath holds. For comparison purpose, all these scans were also repeated during free breathing in the volunteer.
2.3.2 CT acquisition
Among the 36 recruited post-COVID patients, 27 patients also underwent chest CT scan as part of an on-going COVID-19 research study at our hospital. Chest CT imaging was performed on a Siemens SOMATOM Force dual-energy CT scanner following standard clinical protocols with a matrix size of 512 × 512, in-plane spatial resolution of 0.68 × 0.68mm2 and a slice thickness of 3 mm. All CT scans were performed in the coronal orientation in an inspiratory breath-holding position as implemented in the clinic.
2.4 Image assessment
All images were evaluated to compare overall image quality for different MRI acquisition protocols and diagnostic quality between MRI and CT. A web-based DICOM viewer (Discovery Viewer referred as DV hereafter) was developed to review and score the images. The readers were allowed to access the tool from any modern browser as long as they were connected to the internal network. The readers can sign in anytime for the assessment with a registration system and can resume any incomplete progress. The DV included tools for scrolling, windowing and drawing for efficient assessment of images. All the results were stored locally in an internal server for analysis.
2.4.1 Comparison of MRI protocols
Visual image quality assessment was performed to evaluate the MR images acquired with four different protocols. Specifically, images were evaluated for large arteries, large airways, segmental arteries, segmental broncho vascular structures, sub-segmental vessels, and overall motion artifacts (from cardiac motion and/or residual respiratory motion). All images were pooled and randomized for blind assessment by three chest radiologists who read both chest CT and MR images in their daily routine clinical work at the Mount Sinai hospital with 11 years (A.J., reader 1), 6 years (A.B., reader 2) and 5 years (M.C., reader 3) of clinical experiences. Three readers independently scored the images based on a 5–1 scale, where 5 to 1 indicates the best image quality to the worst or the most artifacts to the least. The results were summarized for each assessment category as mean ± standard deviation. The Wilcoxon Signed-Rank test was used to evaluate the difference between different acquisition protocols, where a P value smaller than 0.05 indicates statistical significance. Inter-reader variability was assessed using the Bland-Altman analysis.
2.4.2 Comparison between MRI and CT
For the comparison between MRI and CT, all the three readers who performed the image quality assessment for MRI also evaluated the diagnostic quality of all paired MR and CT images independently. Here, from all the spiral MRI acquisition protocols, only MR images acquired with the protocol that received the highest image quality score were used for the comparison. Specifically, the readers assessed the confidence of using lung MR images for potential clinical use in comparison with corresponding CT images, indicating whether the diagnostic quality of MR images was “non-diagnostic”, “acceptable but worse than CT”, or “similar to CT”. In addition, the same reader evaluated whether the post-COVID lung lesions observed in the CT images (if they exist) could also be identified in corresponding MR images.
3 Results
3.1 Patient characteristics
Among all the 36 post-COVID patients, 7 patients were hospitalized for COVID-related symptoms and the average time stayed at the hospital was 10.16 ± 5.4 days. Three of the 7 patients who were in the hospital also stayed in the intensive care unit (ICU) during the acute COVID phase (the time of SARS-CoV-2 infection). The average time between the COVID acute phase and the MRI scans was 243 ± 133 days. Among all the 27 patients who had chest CT scans. 8 patients had the CT exam after the MRI exam and 19 patients had the CT exam before the MRI exam. The time between the COVID acute phase and the CT imaging was 223 ± 146 days, and the average time between the CT and MRI scans was 43 ± 80 days.
3.2 Comparison of MRI protocols
Comparisons of lung MR images acquired with different spiral protocols in the healthy volunteer are presented in Fig. 3 . For the breath-hold spiral images, the Lin-in-Par reordering yielded better image quality than the Par-in-Lin reordering, where residual motion artifacts were observed in the image acquired with Par-in-Lin reordering. Since the volunteer confirmed successful breath holds, the motion artifacts are likely caused by cardiac motion. Corresponding breath-hold Cartesian image also produced clear ghosting artifacts due to cardiac motion. For free-breathing acquisitions, all images show strong motion artifacts from both respiratory and cardiac motion. This initial comparison suggested that Spiral-VIBE-UTE imaging with the Lin-in-Par reordering is more reliable and more robust to motion.Fig. 3 Comparisons of lung MR images acquired with different spiral protocols in the healthy volunteer. For the breath-hold spiral images, the Lin-in-Par reordering yielded better image quality than the Par-in-Lin reordering. Corresponding breath-hold Cartesian image also produced clear ghosting artifacts due to cardiac motion. For free-breathing acquisitions, all images show strong motion artifacts from both respiratory and cardiac motion.
Fig. 3
Comparisons of spiral lung MR images acquired with different reordering schemes and breath-hold positions are shown in Fig. 4, Fig. 5, Fig. 6 for three post-COVID patients. For all the comparisons, Spiral-Insp Lin-in-Par consistently yielded better image quality and less artifacts than others. Meanwhile, spiral images acquired in an expiratory breath-holding position yielded lower image quality regardless of the reordering scheme. This is likely due to the increased difficulty to hold the breath in an expiratory position.Fig. 4 Comparison of different spiral lung MR acquisition protocols in the first representative post-COVID patient.
Fig. 4
Fig. 5 Comparison of different spiral lung MR acquisition protocols in the second representative post-COVID patient.
Fig. 5
Fig. 6 Comparison of different spiral lung MR acquisition protocols in the third representative post-COVID patient.
Fig. 6
The average scores from the three readers for all the 36 post-COVID patients are summarized in Table 1 for different acquisition protocols and different assessment categories. Overall, Spiral-Insp Lin-in-Par achieved the best image quality compared to others. Specifically, Spiral-Insp Lin-in-Par received significantly better scores than Spiral-Exp Lin-in-Par and Spiral-Exp Par-in-Lin in all the assessment categories. Spiral-Insp Lin-in-Par received significantly better scores than Spiral-Insp Par-in-Lin for assessment of large arteries, segmental arteries and the artifact level. While Spiral-Insp Lin-in-Par also received higher scores than Spiral-Insp Par-in-Lin for assessment of large airways, segmental broncho vascular structures and sub-segmental vessels, the differences did not reach significance. The results for the inter-reader variability assessed using the Bland-Altman analysis are presented in the supplementary figures (Fig. S1 to Fig. S3) in supporting information. The offset towards 1 for Figs. S1 and S2 shows a mean bias greater for reader 1 compared to readers 2 and 3. Fig. S3 shows a strong agreement between readers 2 and 3 with a skewness of the data towards the lower bound.Table 1 Average scores and statistical tests for all image quality metric. The average scores include all three readers and the 36 post-COVID patients. The Wilcoxon signed-rank test was used to identify statistical significance between different acquisition protocols and assessment categories.
Table 1 Spiral Inspiration Lin-in-Par (1) Spiral Expiration Lin-in-Par (2) Spiral Inspiration Par-in-Lin (3) Spiral Expiration Par-in-Lin (4) 1 vs. 2, 4 1 vs. 3
Large Arteries Score 3.69 ± 0.41 3.29 ± 066 3.19 ± 0.44 3.11 ± 0.7 P < 0.05 P < 0.05
Large Airways Score 3.56 ± 0.45 3 ± 0.69 3.57 ± 0.44 2.99 ± 0.65 P < 0.05 0.07
sSegmental Arteries Score 2.97 ± 0.48 2.47 ± 0.6 2.58 ± 0.5 2.28 ± 0.54 P < 0.05 P < 0.05
Segmental Broncho vascular Structures Score 2.52 ± 0.49 2 ± 0.55 2.28 ± 0.56 1.9 ± 0.57 P < 0.05 0.08
Sub-segmental Vessels Score 2.17 ± 0.38 1.77 ± 0.44 2 ± 0.45 1.7 ± 0.44 P < 0.05 0.65
Artifact Level Score 2.54 ± 0.44 3.1 ± 0.62 3 ± 0.5 3.3 ± 0.66 P < 0.05 P < 0.05
3.3 Comparison between MRI and CT
The Spiral-Insp Lin-in-Par lung MR images were used for comparison with CT since it received the best image quality scores. Here, a total of 27 cases were used for the comparison. For reader 1, 22 MRI cases (81.5%) were found to be useful (17 cases had similar diagnostic quality as CT and 5 cases were acceptable) while the rest 5 were found not useful. For reader 2, 22 MRI cases (81.5%) were found to be useful (one cases had similar diagnostic quality as CT and 21 cases were acceptable) while the rest 5 were found not useful. For reader 3, only 10 MRI cases (37%) were found to be useful (none had similar diagnostic quality as CT but all the 10 cases were acceptable) while the rest 17 were found not useful.
Comparison of chest CT and lung MR images in two representative post-COVID patients without COVID-related lesions is shown in Fig. 7 . Lung lesions were found in 4 cases by reader 1, 7 cases by reader 2, and 3 cases by reader 3. In particular, substantial lung opacities were found in one case, as shown in Fig. 8 for two representative slices. The ground glass opacities observed in CT (red arrows) can be clearly delineated in MRI as well. For this case, the CT images were acquired on 01/25/2021 while the MR images were acquired on 05/20/2021. During the acute COVID phase, this patient was hospitalized for 14 days and was also admitted to ICU. All the readers agreed that MRI was useful for identifying the lesions in this case.Fig. 7 Comparison of chest CT and lung MR images in two representative post-COVID patients without COVID-related lesions.
Fig. 7
Fig. 8 Comparison between CT and MR images in a post-COVID patient with COVID-related lesions. According to the expert chest radiologist, the MR images have a similar diagnostic quality compared to CT for this case. The ground glass opacities (red arrows) can be clearly observed both in CT and MRI. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 8
Fig. 9a shows another case with lung lesions identified in CT images. Subacute ground glass opacities can be seen in the left lung in CT (red arrows) and it can also be identified in MRI. For this case, MR images were found to have acceptable diagnostic quality (but worse than CT) by reader 1 and reader 3, but not reader 3. The CT images were acquired on 02/18/2021 while the MR images were acquired on 02/25/2021. Fig. 9b shows the third case with lung lesions identified in CT images. For this case, all readers could not find lesions in MR images. The CT images were acquired on 05/03/2020 while the MR images were acquired on 04/19/2021, and this large gap in time (∼1 year) likely explains why the lesions were not observable on the MRI.Fig. 9 Diagnostic quality comparison between CT and MRI for two post-COVID patients with COVID related lesions in the lungs. (a) For the first patient (Patient A), acceptable diagnostic quality according to the expert chest radiologist. Subacute ground glass opacities (red arrows) observed in the left lung in CT can also be identified in MRI. (b) For the second patient (Patient B), MR images are not useful according to the expert chest radiologist. Ground glass opacities (red arrows) observed in the left lung in CT are not identifiable in the MR images. The big gap in time between the CT and MRI exams (∼1 year) may explain why the lesions were not observable on the MRI. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 9
4 Discussion
In this work, we tested the performance of the Spiral-VIBE-UTE sequence for breath-hold lung images in a cohort of post-COVID patients. As the technical aspect, we investigated the imaging performance using different reordering schemes in stack-of-spirals acquisition and also compared image quality in lung images acquired in different breath-holding positions. As the clinical aspect, we have evaluated the diagnostic performance of spiral UTE MRI for assessment of the lung in comparison with CT. Our results have suggested that spiral UTE MRI with the Lin-in-Par reordering performed in an inspiratory breath-holding position ensures the best image quality, and this sequence could be potentially useful for fast lung imaging for longitudinal evaluation or screening purpose.
For the technical side, our results have shown that spiral UTE MRI using the Par-in-Lin reordering is more sensitive to motion compared to the Lin-in-Par reordering. Given the efficient k-space coverage with spiral k-space sampling, this is because motion (cardiac motion and/or residual respiratory motion due to failed breath holds) is mainly present along the slice direction (kz) with Lin-in-Par reordering, while in-plane (kx-ky) motion artifacts can potentially be avoided. With Par-in-Lin reordering, however, motion is highly minimized along the slice direction and is mainly present in the kx-ky plane, making motion artifacts more noticeable. Variable-density spiral sampling might potentially increase the motion robustness, but it requires longer scan time to achieve Nyquist sampling, which might be more suited for free-breathing undersampled acquisition in combination with sparse reconstruction techniques. Meanwhile, free-breathing data acquisition can also benefit from golden-angle rotation [30,43,44], which could facilitate data sorting based on a respiratory motion signal and self-gated image reconstruction.
A constant sampling density was implemented in the spiral sampling in this work. This is because image acquisition was not accelerated in our lung MRI study and a standard gridding reconstruction was applied to generate lung MR images directly on the scanner. Therefore, a constant sampling density can ensure faster acquisition of a fully sampled images compared to variable-density spiral sampling. For accelerated data acquisition that requires more advanced image reconstruction (e.g., compressed sensing or deep learning), it is expected that variable-density spiral sampling can ensure better imaging performance.
In this study, we have also shown that breath-hold imaging in an inspiratory position is more reliable to ensure good image quality. This is expected because it is easier for a subject, especially a patient, to hold breath in the inspiratory position and this is also the acquisition protocol used in clinical CT exams. Based on these observation and investigation, it is suggested that the Spiral-VIBE-UTE sequence is performed in an inspiratory position for breath-hold imaging with the Lin-in-Par reordering. We believe these investigations are important to guide other users who are interested in using this imaging sequence and translation of this new method into the clinic.
The capability of fast breath-hold imaging is one of the key advantages for the Spiral-VIBE-UTE sequence. Different from traditional UTE sequences that are typically based on a 3D non-Cartesian sampling trajectory (e.g., center-out radial), using a hybrid Cartesian-spiral trajectory can provide much faster imaging speed, which ultimately ensures imaging of the whole lung within a single breath hold. In addition to breath-hold imaging, this sequence can also be performed during free breathing with self-gated acquisition, as recently shown by Javed et al. [45]. Spiral sampling also provides good incoherence for combination with different sparse reconstruction methods for acceleration of data acquisition. This sequence may also be combined with difference magnetization preparation pulses, such as the inversion recovery preparation [46], so that it can be used for MR parameter mapping in the lung.
From the clinical side, our results have shown a small incidence of post-COVID lesions in post-COVID patients. Due to the lack of radiation, spiral UTE MRI can offer a great opportunity to closely follow the evolution of post-COVID lung diseases in time for identifying lesions that could correspond to lung fibrosis, which is one of the most common side effects caused by COVID-19 [47]. The fast-imaging speed provided by the Spiral-VIBE-UTE sequence is a key advantage to reduce the cost associated with MRI exams and increases its value and patient throughout. Despite the reduced image resolution compared to CT, our study has shown that spiral UTE MRI can still be useful, especially when major lung opacities are presented. For those cases where MR images are not useful, motion and limited spatial resolution could be two main reasons. Indeed, a breath hold duration of ∼16 s can still be long for patients, especially for those with reduced capacity and capability of breath holding. Therefore, additional acceleration of data acquisition with compressed sensing and/or deep learning would be important to further improve the performance of this imaging technique. In the meantime, the long gap between the MRI and CT scans in this study could also contribute to the discrepancy for evaluating the diagnostic quality.
We noticed that the results comparing MRI with CT vary among the three readers, especially between reader 3 and the other two. There are two main reasons that may contribute to this. First, reader studies often have inter-reader variation at different degrees, which is due to the nature of subjective assessment of image quality. In particular, the three readers who participated in this study have different levels of clinical experience. Second, lung MRI is not routinely performed in current clinical practice. Therefore, chest radiologists might be biased in assessing lung MR images, as they could be very used to the image quality provided by chest CT for clinical diagnosis. It is not expected that lung MRI will completely replace chest CT, but hopefully it could serve as a useful alternative as needed. The bottom line in this study is that all the three readers have agreed that MRI could identify major lesions that are seen in CT.
This study has several important limitations requiring discussion. First, we did not set any clinical criteria for patient recruitment. We recruited post-COVID patient regardless of their status during the infection phase. This could be the main reason why we did not see COVID-related lesions in most of the patients. In future studies, it is important to assess the quality of spiral UTE MRI in a target post-COVID patient group, so that its performance in post-COVID management can be further evaluated. Second, the primary objective of this feasibility study is to test the performance of spiral UTE MRI for imaging the lung in post-COVID patients. As a result, we did set a criterion on vaccination status for patient recruitment. It would be interesting to investigate the presence of COVID-related lesions between patients with vaccination and those without vaccination in future works. Third, the time between CT and MRI scans and the time between COVID-19 infection and MRI/CT scans varies substantially in our study. As a result, COVID-related lesions may not be present on both exams since the lesions may disappeared at the time of exams. Longitudinal MRI in a target patient cohort may be needed to further investigate and study the development of post-COVID lesions. Despite these limitations, our current study still demonstrated the initial feasibility and performance of spiral UTE MRI for managing post-COVID patients, and more importantly, it provides some useful guidance to other users who are interested in using this sequence to study post-COVID patients and other lung diseases.
5 Conclusion
The Spiral-VIBE-UTE sequence has been shown as a valuable tool for lung imaging without radiation. It enables fast imaging of the lung in a single breath hold and could provide great value for managing different lung diseases including assessment of post-COVID lesions. For breath-hold lung imaging, the Lin-in-Par reordering scheme during inspiratory data acquisition can ensure the best image quality.
Author statement
Valentin Fauveau: Methodology, Writing - Original Draft, Writing - Review & Editing, Formal Analysis, Software, Investigation, Data Curation, Visualization.
Adam Jacobi: Formal Analysis, Investigation.
Adam Bernheim: Formal Analysis, Investigation.
Michael Chung: Formal Analysis, Investigation.
Thomas Benkert: Resources.
Zahi A Fayad: Methodology, Resources.
Li Feng: Conceptualization, Methodology, Writing - Original Draft, Writing - Review & Editing, Investigation, Data Curation, Project Administration, Supervision.
Appendix A Supplementary data
Supplementary material
Image 1
Acknowledgement
The authors thank Renata Pyzik for helping with patient recruitment, thank Dr. Yang Yang for helpful discussion and protocol installment, and thank Dr. Marco Pereanez for helping get chest CT images from the PACS. This study is supported by the NIH/NIBIB (R01EB031083).
Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.mri.2022.12.002.
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References
1 COVID-19 Map - Johns Hopkins Coronavirus Resource Center https://coronavirus.jhu.edu/map.html 2022
2 Milos R.I. Kifjak D. Heidinger B.H. Prayer F. Beer L. Röhrich S. Morphological and functional sequelae after COVID-19 pneumonia Radiologe 61 2021 888 895 10.1007/S00117-021-00905-4 34529126
3 Subramanian A. Nirantharakumar K. Hughes S. Myles P. Williams T. Gokhale K.M. Symptoms and risk factors for long COVID in non-hospitalized adults Nat Med 2022 10.1038/S41591-022-01909-W
4 Koc H.C. Xiao J. Liu W. Li Y. Chen G. Long COVID and its management Int J Biol Sci 18 2022 4768 4780 10.7150/IJBS.75056 35874958
5 Rodriguez-Sanchez I. Rodriguez-Mañas L. Laosa O. Long COVID-19: the need for an interdisciplinary approach Clin Geriatr Med 2022 38 10.1016/J.CGER.2022.03.005
6 Zazzara M.B. Bellieni A. Calvani R. Coelho-Junior H.J. Picca A. Marzetti E. Inflammaging at the time of COVID-19 Clin Geriatr Med 2022 38 10.1016/J.CGER.2022.03.003
7 Montes-Ibarra M. Oliveira C.L.P. Orsso C.E. Landi F. Marzetti E. Prado C.M. The impact of long COVID-19 on muscle health Clin Geriatr Med 2022 38 10.1016/J.CGER.2022.03.004
8 Lauria A. Carfi A. Benvenuto F. Bramato G. Ciciarello F. Rocchi S. Neuropsychological measures of long COVID-19 fog in older subjects Clin Geriatr Med 2022 38 10.1016/J.CGER.2022.05.003
9 Gyöngyösi M. Alcaide P. Asselbergs F.W. Brundel B.J.J.M. Camici G.G. da Costa Martins P. Long COVID and the cardiovascular system - elucidating causes and cellular mechanisms in order to develop targeted diagnostic and therapeutic strategies: a joint scientific statement of the ESC Working Groups on Cellular Biology of the Heart and Myocardial & Pericardial Diseases Cardiovasc Res 2022 10.1093/CVR/CVAC115
10 Upadhya S. Rehman J. Malik A.B. Chen S. Mechanisms of lung injury induced by SARS-CoV-2 infection Physiology 37 2022 88 100 10.1152/PHYSIOL.00033.2021/ASSET/IMAGES/LARGE/PHYSIOL.00033.2021_F003.JPEG 34698589
11 Watanabe A. So M. Iwagami M. Fukunaga K. Takagi H. Kabata H. One-year follow-up CT findings in COVID -19 patients: a systematic review and meta-analysis Respirology 2022 10.1111/RESP.14311
12 Roberts J.R. Radiation risks for CT scans Emerg Med News 30 2008 10 12 10.1097/01.EEM.0000313909.40373.B2
13 Wild J.M. Marshall H. Bock M. Schad L.R. Jakob P.M. Puderbach M. MRI of the lung (1/3): methods Insights Imaging 3 2012 345 353 10.1007/S13244-012-0176-X/FIGURES/10 22695952
14 Biederer J. Beer M. Hirsch W. Wild J. Fabel M. Puderbach M. MRI of the lung (2/3). Why… When … How? Insights Imaging 3 2012 355 371 10.1007/S13244-011-0146-8 22695944
15 Biederer J. Mirsadraee S. Beer M. Molinari F. Hintze C. Bauman G. MRI of the lung (3/3)-current applications and future perspectives Insights Imaging 3 2012 373 386 10.1007/S13244-011-0142-Z 22695943
16 Bianchi A. Ozier A. Ousova O. Raffard G. Crémillieux Y. Ultrashort-TE MRI longitudinal study and characterization of a chronic model of asthma in mice: inflammation and bronchial remodeling assessment NMR Biomed 26 2013 1451 1459 10.1002/nbm.2975 23761222
17 Feng L. Delacoste J. Smith D. Weissbrot J. Flagg E. Moore W.H. Simultaneous evaluation of lung anatomy and ventilation using 4D respiratory-motion-resolved ultrashort Echo time sparse MRI J Magn Reson Imaging 49 2019 411 422 10.1002/jmri.26245 30252989
18 Delacoste J. Chaptinel J. Beigelman-Aubry C. Piccini D. Sauty A. Stuber M. A double echo ultra short echo time (UTE) acquisition for respiratory motion-suppressed high resolution imaging of the lung Magn Reson Med 79 2018 2297 2305 10.1002/mrm.26891 28856720
19 Robson M.D. Gatehouse P.D. Bydder M. Bydder G.M. Magnetic resonance: an introduction to ultrashort TE (UTE) imaging J Comput Assist Tomogr 27 2003 825 846 10.1097/00004728-200311000-00001 14600447
20 Veldhoen S. Heidenreich J.F. Metz C. Petritsch B. Benkert T. Hebestreit H.U. Three-dimensional ultrashort echotime magnetic resonance imaging for combined morphologic and ventilation imaging in pediatric patients with pulmonary disease J Thorac Imaging 2020 1 10.1097/RTI.0000000000000537
21 Zhang J. Feng L. Otazo R. Kim S.G. Rapid dynamic contrast-enhanced MRI for small animals at 7T using 3D ultra-short echo time and golden-angle radial sparse parallel MRI Magn Reson Med 81 2019 140 152 10.1002/mrm.27357 30058079
22 Grodzki D.M. Jakob P.M. Heismann B. Ultrashort echo time imaging using pointwise encoding time reduction with radial acquisition (PETRA) Magn Reson Med 67 2012 510 518 10.1002/mrm.23017 21721039
23 Johnson K.M. Fain S.B. Schiebler M.L. Nagle S. Optimized 3D ultrashort echo time pulmonary MRI Magn Reson Med 70 2013 1241 1250 10.1002/MRM.24570 23213020
24 Stock K.W. Chen Q. Hatabu H. Edelman R.R. Magnetic resonance T2/* measurements of the normal human lung in vivo with ultra-short echo times Magn Reson Imaging 17 1999 997 1000 10.1016/S0730-725X(99)00047-8 10463650
25 Torres L. Kammerman J. Hahn A.D. Zha W. Nagle S.K. Johnson K. Structure-function imaging of lung disease using ultrashort echo time MRI Acad Radiol 26 2019 431 441 10.1016/j.acra.2018.12.007 30658930
26 Hirsch F.W. Sorge I. Vogel-Claussen J. Roth C. Gräfe D. Päts A. The current status and further prospects for lung magnetic resonance imaging in pediatric radiology Pediatr Radiol 50 2020 734 749 10.1007/s00247-019-04594-z 31996938
27 Hatabu H. Alsop D.C. Listerud J. Bonnet M. Gefter W.B. T2* and proton density measurement of normal human lung parenchyma using submillisecond echo time gradient echo magnetic resonance imaging Eur J Radiol 29 1999 245 252 10.1016/S0720-048X(98)00169-7 10399610
28 Garg M. Prabhakar N. Dhooria S. Lamichhane S. Magnetic resonance imaging (MRI) chest in post-COVID-19 pneumonia Lung India 38 2021 498 499 10.4103/LUNGINDIA.LUNGINDIA_206_21 34472535
29 Nielles-Vallespin S. Weber M.A. Bock M. Bongers A. Speier P. Combs S.E. 3D radial projection technique with ultrashort echo times for sodium MRI: clinical applications in human brain and skeletal muscle Magn Reson Med 57 2007 74 81 10.1002/MRM.21104 17191248
30 Feng L. Delacoste J. Smith D. Weissbrot J. Flagg E. Moore W.H. Simultaneous evaluation of lung anatomy and ventilation using 4D respiratory-motion-resolved ultrashort echo time sparse MRI J Magn Reson Imaging 49 2019 411 422 10.1002/jmri.26245 30252989
31 Jiang W. Ong F. Johnson K.M. Nagle S.K. Hope T.A. Lustig M. Motion robust high resolution 3D free-breathing pulmonary MRI using dynamic 3D image self-navigator Magn Reson Med 79 2018 2954 2967 10.1002/mrm.26958 29023975
32 Zhu X. Chan M. Lustig M. Johnson K.M. Larson P.E.Z. Iterative motion-compensation reconstruction ultra-short TE (iMoCo UTE) for high-resolution free-breathing pulmonary MRI Magn Reson Med 83 2020 1208 1221 10.1002/mrm.27998 31565817
33 John P. Mugler I. Fielden S.W. Meyer C.H. Altes T.A. Miller G.W. (ISMRM 2015) Breath-hold UTE Lung Imaging using a Stack-of-Spirals Acquisition https://archive.ismrm.org/2015/1476.html
34 John P. Mugler I. Meyer C.H. Pfeuffer J. Stemmer A. Kiefer B. (ISMRM 2017) Accelerated Stack-of-Spirals Breath-hold UTE Lung Imaging https://archive.ismrm.org/2017/4904.html
35 Heidenreich J.F. Weng A.M. Metz C. Benkert T. Pfeuffer J. Hebestreit H. Three-dimensional ultrashort Echo time MRI for functional lung imaging in cystic fibrosis Radiology 296 2020 191 199 10.1148/RADIOL.2020192251 32343212
36 Landini N. Orlandi M. Occhipinti M. Nardi C. Tofani L. Bellando-Randone S. Ultrashort Echo-time magnetic resonance imaging sequence in the assessment of systemic sclerosis-interstitial lung disease J Thorac Imaging 2022 10.1097/RTI.0000000000000637
37 Olthof S.C. Reinert C. Nikolaou K. Pfannenberg C. Gatidis S. Benkert T. Detection of lung lesions in breath-hold VIBE and free-breathing spiral VIBE MRI compared to CT Insights Imaging 2021 12 10.1186/S13244-021-01124-0 33533965
38 Sen Huang Y. Niisato E. Su M.Y.M. Benkert T. Chien N. Chiang P.Y. Applying compressed sensing volumetric interpolated breath-hold examination and spiral ultrashort Echo time sequences for lung nodule detection in MRI Diagnostics (Basel, Switzerland) 2021 12 10.3390/DIAGNOSTICS12010093 35054178
39 Cha M.J. Ahn H.S. Choi H. Park H.J. Benkert T. Pfeuffer J. Accelerated stack-of-spirals free-breathing three-dimensional ultrashort Echo time lung magnetic resonance imaging: a feasibility study in patients with breast Cancer Front Oncol 2021 11 10.3389/FONC.2021.746059
40 Heidenreich J.F. Veldhoen S. Metz C. Pereira L.M. Benkert T. Pfeuffer J. Functional MRI of the lungs using single breath-hold and self-navigated ultrashort Echo time sequences Radiol Cardiothorac Imaging 2020 2 10.1148/RYCT.2020190162
41 Sen Huang Y. Niisato E. Su M.Y.M. Benkert T. Hsu H.H. Shih J.Y. Detecting small pulmonary nodules with spiral ultrashort echo time sequences in 1.5 T MRI MAGMA 34 2021 399 409 10.1007/S10334-020-00885-X 32902778
42 Qian Y. Boada F.E. Acquisition-weighted stack of spirals for fast high-resolution three-dimensional ultra-short echo time MR imaging Magn Reson Med 60 2008 135 145 10.1002/MRM.21620 18581326
43 Winkelmann S. Schaeffter T. Koehler T. Eggers H. Doessel O. An optimal radial profile order based on the golden ratio for time-resolved MRI IEEE Trans Med Imaging 26 2007 68 76 10.1109/TMI.2006.885337 17243585
44 Feng L. Golden-angle radial MRI: basics, advances, and applications J Magn Reson Imaging 2022 10.1002/JMRI.28187
45 Javed A. Ramasawmy R. O’Brien K. Mancini C. Su P. Majeed W. Self-gated 3D stack-of-spirals UTE pulmonary imaging at 0.55T Magn Reson Med 87 2022 1784 1798 10.1002/MRM.29079 34783391
46 Feng L. Liu F. Soultanidis G. Liu C. Benkert T. Block K.T. Magnetization-prepared GRASP MRI for rapid 3D T1 mapping and fat/water-separated T1 mapping Magn Reson Med 86 2021 97 114 10.1002/mrm.28679 33580909
47 Hama Amin B.J. Kakamad F.H. Ahmed G.S. Ahmed S.F. Abdulla B.A. Mohammed S.H. Post COVID-19 pulmonary fibrosis; a meta-analysis study Ann Med Surg 77 2022 10.1016/J.AMSU.2022.103590 103590
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Xu Maotian b⁎
Feng Li a⁎
a Key Laboratory of Coal Processing & Efficient Utilization of Ministry of Education, National Engineering Research Center of Coal Preparation & Purification; School of Chemical Engineering & Technology, China University of Mining & Technology, Xuzhou 221116, China
b Henan Key Laboratory of Biomolecular Recognition & Sensing, College of Chemistry & Chemical Engineering, Henan Joint International Research Laboratory of Chemo/Biosensing & Early Diagnosis of Major Diseases, Shangqiu Normal University, Shangqiu, 476000, China
c Key Laboratory of Flexible Electronics (KLOFE) and Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), 30 South Puzhu Road, Nanjing 211816, China
d College of Chemistry & Material Science, Huaibei Normal University, Huaibei, 235000, China
⁎ Corresponding authors.
1 F. Geng and X. Liu contributed equally to the work
9 12 2022
1 3 2023
9 12 2022
378 133121133121
4 10 2022
25 11 2022
4 12 2022
© 2022 Elsevier B.V. All rights reserved.
2022
Elsevier B.V.
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Alkaline phosphatase (ALP)-induced in situ fluorescent immunosensor is less investigated and reported. Herein, a high-performance ALP-labeled in situ fluorescent immunoassay platform was constructed. The developed platform was based on a fluorogenic self-assembly reaction between pyridineboronic acid (PyB(OH)2) and alizarin red S (ARS). We first used density functional theory (DFT) to theoretically calculate the changes of Gibbs free energy of the used chemicals before and after the combination and simulated the electrostatic potential on its′ surfaces. The free ARS and PyB(OH)2 exist alone, neither emits no fluorescence. However, the ARS/PyB(OH)2 complex emits strong fluorescence, which could be effectively quenched by PPi based on the stronger affinity between PPi and PyB(OH)2 than that of ARS and PyB(OH)2. PyB(OH)2 coordinated with ARS again in the presence of ALP due to the ALP-catalyzed hydrolysis of PPi, and correspondingly, the fluorescence was restored. We chose cTnI and SARS-CoV-2 N protein as the model antigen to construct ALP-induced immunosensor, which exhibited a wide dynamic range of 0–175 ng/mL for cTnI and SARS-CoV-2 N protein with a low limit of detection (LOD) of 0.03 ng/mL and 0.17 ng/mL, respectively. Moreover, the proposed immunosensor was used to evaluate cTnI and SARS-CoV-2 N protein level in serum with satisfactory results. Consequently, the method laid the foundation for developing novel fluorescence-based ALP-labeled ELISA technologies in the early diagnosis of diseases.
Graphical Abstract
ga1
Keywords
Alkaline phosphatase
Pyridineboronic acid
ARS
CTnI
SARS-CoV-2 N protein
==== Body
pmc1 Introduction
One of the strategies to construct Fluorescent immunosensors is to use alkaline phosphatase (ALP)-labeled antibodies (or antigens) to detect the target [1], [2], [3], [4]. As a hydrolase with phosphate substrate specificity, ALP catalyzes the hydrolysis and removes the phosphate group from proteins, nucleic acids, and small phosphorus-containing molecules in alkaline media [5], [6], [7]. The normal amount of ALP in adult serum is 40–190 U·L-1 [8]. It has been reported that various serious diseases will be accompanied by a disturbance of serum ALP levels [9], [10]. Therefore, there is an urgent need to develop sensitive, reliable efficient, and simple assays for ALP activity. At present, many assays for ALP have been reported including colorimetric methods [11], [12], [13], fluorescence methods [14], [15], chemiluminescence methods [16], [17], [18], electrochemical methods [19], [20], surface-enhanced Raman scattering methods [21], [22], and magnetic resonance imaging [23] etc. Among these methods, fluorescence assays have been widely used [24].
The most popular approaches to designing fluorescenct ALP sensing system have adopted the quenching effect of metal ions (such as Cu2+, Fe3+, and Ce3+, etc.) on the fluorophore [25], [26], [27]. When phosphate (such as pyrophosphate, PPi) is added, the quenching effect is suppressed. ALP hydrolyzes phosphate and releases the metal ions, and the fluorescence is quenched. Thus, this strategy was based on a “turn-off” mechanism that suffers from the limitations of high background, lower sensitivity, and false positive signs [28]. To circumvent these challenges, fluorescence turn-on sensors are preferable to ones displaying fluorescence quenching. Second, transition metal ions would form coordination compounds with certain substrates or their hydrolysis products [29]. Another thing to note is that most of the transition metal ions are toxic. A common fluorescence turn-on ALP sensing strategy is an in situ fluorescence reaction induced by enzymatic hydrolysis [30], [31]. The fluorescent ALP probes containing cleaving sites have gained prominence in detecting objects [32], [33]. These probes do not fluoresce themselves. However, the hydrolysis products emit strong fluorescence. However, the covalent coupling between recognition motifs and fluorescence outputs may damage the recognition ability of the recognition unit or affect the efficiency of the read-out unit. A third strategy for designing turn-on ALP assays is the introduction of nanomaterials [34]. The fluorescent nanomaterials have disadvantages such as harsh synthesis conditions, high cost, and large batch differences, which limit their further practical applications [35], [36]. Therefore, it is still challenging to find a suitable ALP fluorescence sensing system.
To meet these challenges, the most attractive solution is molecular assembly. Therefore, in situ fluorogenic molecular assembly technology has great advantages in the development of fluorescent sensors. One of the classic representatives of fluorogenic molecular assembly-based optical sensors is the assembly between alizarin red S (ARS), and the aryl- and alkylboronic acid receptor （RB(OH)2) [37]. RB(OH)2 is a good candidate for the selective detection of the diol groups such as sugars or sugar residues based on the formation of boronic acid diol esters (RB(L)(OH)−) [38]. ARS is a general optical reporter for investigating the binding of boronic acids with carbohydrates [39].
To develop ideal boronic acid-based sensors, the effects of different substituents on the benzene ring or their substitution positions on the sensing of the system were explored. Eric V. Anslyn et al. found 2-(N,N-dimethylaminomethyl)phenylboronic acid was employed to bind with ARS, which can preferably react with peroxynitrite over hydrogen peroxide and other ROS/RNS [40]. Chris D. Geddes et al. designed a (6-methoxyquinolinum)phenylboronic acid probe for the potential detection of tear glucose concentrations [41].
In addition to the substituents on the benzene ring, the recognition of sugars by boronic acids in aromatic groups other than the benzene ring has also been investigated in recent years. Philip Britz-McKibbin et al.found 4-isoquinolineboronic acid (IQBA) exhibits the highest reported binding affinity for sialic acid (K = 5390 ± 190 m −1) through the formation of a cyclic boronate ester complex under acidic conditions (pH=3.0) [42], [43]. Koji Ishihara et al. synthesized 10 o-azophenylboronic acid derivatives (azoBs) to design an azoB-based chemosensor for the visual detection of saccharides [44]. Chris D.Geddes et al. reported a 6-aminoquinolinium boronic acid-based ratiometric near-physiological pH sensor, which shows an unperturbed pH response, even in the presence of high concentrations of background saccharide, such as glucose and fructose [45]. Alternatively, there are also a few reports on adjusting the reaction of boric acid and diol with light. Julia A. Kalow et al. used visible light to tune boronic acid–ester equilibria [46]. Bart Jan Ravoo et al. reversibly manipulated the dynamic covalent interaction of phenyl boronic acid and D-fructose by irradiation with light [47].
In addition to the substituents on the aromatic ring, there are also studies involving the reaction of N-Heterocyclic boronic acids with polyols. A. Matsumoto et al. found that heterocyclic boronic acid (5-boronopicolinic acid) can improve the specific recognition of SA under weakly acidic pH conditions [48]. Koji Ishihara et al. clarified in detail the reaction mechanisms of (N-methyl)− 4-pyridinium boronic acid and 4-pyridinium boronic acid (PyB(OH)2) with D-sorbitol in aqueous solution. [49] PPi-specific molecular recognition among other phosphates by boronic acid derivative (PyB(OH)2) under weakly acidic pH conditions was first reported by Sanjoh et al. [50] The specific molecular recognition function of PPi-4-PyB(OH)2 provides an excellent molecular recognition function for constructing chemical sensors. However, as far as we know, PyB(OH)2/ARS complex-based fluorescent sensors have not yet been reported.
Aiming to fill this gap, here, we first examined the combination of ARS and PyB(OH)2 theoretically using density functional theory (DFT). Inspired by the DFT results, we then exploited the fluorogenic molecular assembly of PyB(OH)2 and ARS. Next, we investigated the influence of common inorganic multiphosphate on the fluorescence of the ARS/PyB(OH)2 ensemble. Finally, a novel ALP assay was developed based on the hydrolysis of ALP on PPi ( Scheme 1). With a successful fluorescent ALP assay in hand, we further constructed an immunosensing platform based on ALP-labeled antibodies. Using cardiac troponin I (cTnI) and SARS-CoV-2 nucleocapsid protein (N protein) as the model antigens, a high-performance ALP-labeled in situ fluorescent immunosensor was suggested.Scheme 1 Schematic representation of the proposed turn-on fluorescent sensing system for ALP assay.
Scheme 1
2 Experimental section
2.1 Reagents and instruments
All Reagents and instruments have been listed in Supporting Information.
2.2 Analytical performance
All analysis performance processes have been listed in Supporting Information.
3 Results and discussion
3.1 Examined the coordination of ARS and PyB(OH)2 theoretically using DFT
Firstly, fluorogenic molecular assembly of N-heterocyclic Boronic Acids and alizarin red S and the binding ability of PyB(OH)2 to PPi, sodium tripolyphosphate (STPP), and sodium hexametaphosphate (SHMP) were theoretically studied by density functional theory (DFT) method. The ground state geometries of the used chemicals were optimized and the possible binding sites were identified (Scheme 1). Then, the changes of Gibbs free energy were calculated (Table S1 in Supporting information). The results show that the change of Gibbs free energy of fluorogenic molecular assembly of ARS and PyB(OH)2 was − 16.24 kJ mol-1. PyB(OH)2 could bind to PPi, STPP, and SHMP, and the changes of Gibbs free energy were − 19.22 kJ mol-1, − 18.90 kJ mol-1, and − 14.99 kJ mol-1. Obviously, the most stable binding of PyB(OH)2 to PPi is inferred from the calculation results. Therefore, the data from DFT shows it is thermodynamically possible for PPi to displace ARS from the ARS/PyB(OH)2 complex. Furthermore, we also calculated the molecular electrostatic potential ( Fig. 1A). The results show that there is a large electrostatic potential in the molecular electrostatic potential maps, which implies that the PyB(OH)2/PPi complex is more stable than other pairs. This is consistent with the calculated free energy change, further confirming the feasibility of the method theoretically.Fig. 1 (A) Molecular electrostatic potential maps of ARS, PyB(OH)2, PyB(OH)2/ARS and PyB(OH)2/ PPi. (B) The fluorescence spectra of (a) 5 μM ARS, (b) 10 μM PyB(OH)2, (c) 5 μM ARS + 10 μM PyB(OH)2, (d) 5 μM ARS + 10 μM PyB(OH)2 + 1.5 mM PPi and (e) 5 μM ARS + 1.5 mM PPi.
Fig. 1
3.2 Feasibility of the proposed method
To explore the feasibility of the proposed method, the fluorescence of different species used in this protocol was measured firstly (Fig. 1B). As shown in Fig. 1B curve a, ARS itself emits no fluorescence in HAc-NaAc buffer (10 mM, pH 5.8). In the same situation, PyB(OH)2 is non-fluorescent when free in solution (Fig. 1B curve b). However, as observed in Fig. 1B curve c, when PyB(OH)2 was added to the ARS solution, the fluorescence intensity enhanced ca. 10-fold, which implies the formation of the ARS/PyB(OH)2 assemble. In the presence of PPi, the specific recognition between PyB(OH)2 and PPi and concomitant quenching was clearly observed (Fig. 1B curve d) which could be explained by the higher affinity of PyB(OH)2 and PPi than that of PyB(OH)2 and ARS. To confirm that PPi did not influence the ARS fluorescence, we also tested whether ARS emission was altered by the presence of PPi (Fig. 1B curve e). The results show that PPi does not affect ARS emission. Taken together, we conclude that ARS/PyB(OH)2 complex shows promise for fluorescent sensor development.
3.3 Interaction between polyphosphate and ARS/PyB(OH)2 investigated by UV-Vis spectroscopy
Evidence of the self-assembly of ARS and PyB(OH)2 was first obtained by UV-Vis spectral investigations ( Fig. 2). As can be seen from Fig. 2A, ARS itself displayed a major absorption peak at 420 nm and a minor absorption peak at 530 nm in 10 mM HAc-NaAc buffer (pH 5.8). Once upon the addition of increasing amounts of PyB(OH)2 to the solution of 50 μM ARS leads to increased absorption of ARS at 420 nm. A concomitant increase in absorption and gradual bathochromic shift from 420 nm to a range of ∼480 – 495 nm were observed, indicating that the structure of the light-absorbing species has changed. Moreover, the minor absorption peak at 530 nm of ARS almost disappeared upon the 2 equiv. amount of PyB(OH)2.Fig. 2 (A) Uv-vis spectra of ARS as functions of different concentrations of PyB(OH)2. The upward arrow indicateds the signal changes with the increases in PyBA concentration. (B) Absorption spectra of ARS/PyB(OH)2 complex as functions of different concentrations of PPi at 0, 1, 2, 3, and 4 mM. The arrow indicateds the signal changes with the increases in PyBA concentration. Experimental conditions: cARS = 50 μM, cPyB(OH)2 = 0, 30, 50, 100 μM, HAc-NaAc buffer (10 mM, pH=5.8).
Fig. 2
It has been reported that PyB(OH)2 undergoes a diphosphate (pyrophosphate)-specific recognition among other phosphates (monophosphate and triphosphate) under weakly acidic pH conditions. [50]. Here, sodium pyrophosphate (PPi) was used as model compounds to investigate the interaction of polyphosphate and ARS/PyB(OH)2. The addition of increasing amounts of PPi to the solution of 50 μM ARS/100 μM PyB(OH)2 ensemble leads to the appearance of a new absorption band at ca 530 nm (Fig. 2B). It was initially assumed to originate from the fact that PPi preferentially binds to the boronic acid to form a more stable ensemble of PyB(OH)2/PPi. Thus, ARS was displaced from the ARS/PyB(OH)2 ensemble. Additionally, PPi has almost no effect on the absorption spectra in the absence of PyB(OH)2 (Fig. S1 in Supporting information).
3.4 Rapid fluorescence enhancement of ARS triggered by PyB(OH)2 and strong quenching response of ARS/PyB(OH)2 assembly to polyphosphate
Next, the capability of PyB(OH)2 to activate the fluorescence of ARS was assessed. As shown in Fig. 3A, ARS is weakly emissive in HAc-NaAc buffer solution (10 mM, pH 5.8) due to the photoinduced electron transfer (PET) from —OH to the fluorophore. However, a massive signal enhancement at 571 nm with no change in emission wavelength was observed upon the addition of PyB(OH)2 to the HAc-NaAc buffer solution (10 mM, pH 5.8) containing 5 μM ARS, which could be mainly ascribed to the inhibation of PET. The fluorescence intensities generated from the assemblies increased with increasing PyB(OH)2 amount up to 20 µM, after which fluorescence intensities level-off. A 15-fold increase in ARS was detected in term of enhancement factor (F/F 0), where F and F 0 represent the fluorescence intensity in the presence or absence of PyB(OH)2 respectively. This is similar to the result of RB(OH)2 could activated fluorescence of ARS [51], [52].Fig. 3 (A) The fluorescence spectra of ARS (5 μM) in the presence of different mounts of PyB(OH)2 at the concentration of 0，3.0，4.5，6.0，7.5，9.0，10.5，12.0，13.5，15.0，16.5，18.0 μM, respectively. Inset: the plot of the fluorescence intensity at 571 nm to the concentration of PyB(OH)2. (B) PPi, STPP and SHMP quench the fluorescence of ARS/PyB(OH)2 complex. Error bars represent standard deviations from three repeated experiments. (C) Plot of fluorescence intensity of 5 μM ARS versus time upon the addition of 10.0 μM PyB(OH)2 and then 2 mM PPi in HAc-NaAc buffer (10 mM, pH 5.8) excited at 420 nm. (D) The effect of [PyB(OH)2] (from curve a to curve d: 0, 5.0, 10.0, and 15.0 μM) on the fluorescence response to different concentrations of PPi. F and F0 represent the fluorescence intensity with and without PPi, respectively. [ARS] = 5.0 μM, λex = 420 nm. Error bars represent standard deviations from three repeated experiments.
Fig. 3
The addition of polyphosphates (PPi, STPP, or SHMP) to the ARS/PyB(OH)2 complex solution induced the strong and gradual quenching of the fluorescence at 571 nm. Fig. 3B shows the quenching efficiency (Qe%) in term of (F 0-F)/F 0 of three polyphosphates on ARS/PyB(OH)2 assembly fluorescence, where F 0 and F represent the fluorescence intensity at 571 nm in the absence and presence of polyphosphate, respectively. [53], [54] It can be clearly seen from Fig. 3B that PPi has the highest quenching efficiency for ARS/PyB(OH)2 assembly fluorescence, especially in the lower concentrations. Complete quenching was observed at 3.0 mM PPi, which could be attributed to the fact that polyphosphate displaces the pre-bound PyB(OH)2 because of the higher binding affinity between PyB(OH)2 and polyphosphate, thus not allowing it to form the ARS/PyB(OH)2 assembly. There is almost no significant difference in the quenching efficiency of STPP and SHPP for ARS/PyB(OH)2 assembly fluorescence at high concentrations (above 2.5 mM), which may be due to the fact that the fluorescence is very weak and cannot be accurately measured. Intensive quenching of ARS/PyB(OH)2 assembly fluorescence by polyphosphate makes it possible to develop a competent detection method for PPi. Considering that ALP catalyzes the hydrolysis of polyphosphates, thus activating the fluorescence signal of the sensing system. The corresponding fluorescence response could then give an indicator of the amounts of ALP. In order to obtain the best sensing performance, PPi was chosen as the quencher.
3.5 ALP assay based on PPi quench the fluorescence of the ARS/PyB(OH)2 complex
Following the quencher selection, the optimal time for PPi detection was first identified (Fig. 3C). After the addition of PyB(OH)2 into the ARS solution, the fluorescence went up evidently and quickly reached a platform. After the addition of PPi into ARS/PyB(OH)2 assembly solution, the fluorescence went down evidently and reached a platform after 5 min. For a stable quenching efficiency, 5 min was selected as the optimized condition for future experiments. Then, the effect of the PyB(OH)2 concentration in the PPi detection was also evaluated, and the results are shown in Fig. 3D. 10 μM PyB(OH)2 resulted in the highest Qe% and was selected as optimized conditions. The effect of pH on the sensing performance was investigated by adopting 10 mM of HAc-NaAc buffer solutions with different pH values since pH had an important effect on the properties of ARS [55]. It is clearly seen from Fig. S2 in Supporting information that the highest Qe% is obtained at pH 5.8. Consequently, pH 5.8 is adopted in the following experiments. Next, the influence of the ARS amount on PPi detection was performed. As can be seen from Fig. S3 in Supporting information, the highest Qe% is obtained at 5 μM ARS. Finally, Following the optimization of the sensing parameters, the fluorescence of ARS/PyB(OH)2 assembly was lost in the presence of an increasing amount of PPi (Fig. S4).
To get the best sensing performance, the amount of PPi were optimized. As shown in Fig S5 in Supporting information, PPi was selected as 1.5 mM. If there is too much PPi, a certain amount of ALP cannot cut it completely, resulting in the inability to effectively recover the fluorescence of the system. On the other hand, if the amount of PPi is relatively small, the fluorescence of the ARS/PyB(OH)2 complex cannot be effectively quenched, resulting in high background fluorescence and limited sensitivity. pH is critical for the determination of ALP. Therefore, we prepared Tris-HCl buffers with different pH, ranging from 8.0 to 10.0 with an interval of 0.5, for the optimization of the ALP assay. Additionally, ALP catalyzed the hydrolysis of phosphoryl esters in alkaline media and an ion activator (Mg2+) was required for higher activity. The optimal 1 mM Mg2+ has been determined as the ALP detection condition following the literature [56]. From the fluorescence spectra of the proposed sensing system under the optimal conditions with different amounts of ALP ( Fig. 4A and B), it could be observed that ALP brought out fluorescence enhancement as designed. Moreover, the fluorescence enhancement (F/F 0) proportionally increased with the increasing amount of ALP in the range of 20 U·L-1 to 1000 U·L-1 (Fig. 4C), where F and F 0 represent the fluorescence intensity in the presence and absence of ALP respectively. As a comparison, ALP itself could not cause the siginificant fluorescence change of ARS/PyB(OH)2 complex (Fig. S6). Only a slight change in the fluorescence of ARS/PyB(OH)2 complex was observed. The LOD is calculated to be 2 U·L-1 using the method, 3σ/K, as reported [57], [58], where σ and K refer to the standard deviation of the blank samples and slope of the calibration curve, respectively., which is sufficiently sensitive for practical applications. It is worth mentioning that the LOD is greatly affected by the amount of PPi, and reducing the amount of PPi can further improve the LOD. A comparison of our proposed method and the recently reported methods for ALP assay is shown in Table S2 in Supporting information. In comparison with other ALP assays reported in the literature, the linear range and sensitivity for ALP assay are comparable to or better than those from previous studies.Fig. 4 (A) Fluorescent spectra of the sensing system with various amounts of ALP at 10, 20, 50, 100, 200, 300, 500, 650, 800, 1000, 1250, and 1500 U∙L-1. (B) The plot of the fluorescence intensity at 571 nm to the different amounts of PyB(OH)2. (C) The linear plot of the relative fluorescence intensity (F/F0) at 571 nm to the concentration of ALP, where F and F0 represent the fluorescence intensity with and without ALP, respectively. Error bars represent standard deviations from three repeated experiments. (D) Fluorescence response of the sensing system to the ALP at the amount of 30 U∙L-1, bovine serum albumin (BSA), lysozyme (LZM), horseradish peroxidase (HRP), and glucose oxidase (GOx) at the amount of 0.2 mg∙mL-1 each. Error bars represent standard deviations from three repeated experiments.
Fig. 4
To evaluate the specificity of the proposed sensing system for ALP, BSA, LZM, HRP, and GOx were selected as negative controls. As seen in Fig. 4D, an insignificant signal was observed for negative controls at the amount of 30 U·L-1. However, 30 U·L-1 ALP produced a significant signal. This experiment strongly showed that the proposed sensing system maintained the favorable specificity of the ALP for its substrate.
Based on the above results, the variable fluorescence response of the proposed sensing system for ALP offers the feasibility for a sensitive ALP assay in real complex biological samples. A series of amounts of ALP spiked in serum were used to validate the ability of the proposed assay. The results are summarized in Table S3. It was found that the proposed sensing system could accurately determine the ALP activity in serum.
3.6 ALP-induced ELISA Platform
Inspired by the universal application of ALP in ELISA and outstanding performance of the above ALP detection, we promoted the application of our developed in situ fluorescence sensor to build an ALP-induced ELISA platform. Using cTnI as the model antigen, the capture antibody (mouse anti-cTnI monoclonal antibody), primary antibody (goat anti-cTnI antibody) and secondary antibody (ALP-conjugated rabbit anti-goat IgG) were employed in this immunoassay. Fig. 5A clearly showed the detailed strategy of ALP-induced immunoassay for cTnI sensing. First, the capture antibody was pre-immobilized on a 96-well plate. Then, the specific recognition of the antigen and antibody, the goat anti-cTnI antibody and ALP secondary antibody labels were fixed on the plate in turn. After incubating with PPi, the mixture solution of ARS and PyB(OH)2 were added into the wells. As shown in Fig. 5B, the fluorescence ratio (F/F 0) gradually increased as cTnI concentration (0–175 ng/mL). An up-to-2.5-fold F/F 0 enhancement was obtained. Two linear relationships were observed and the fitted linear data were expressed as F/F 0 = 1.0092 + 0.3287c cTnI (R1 2 = 0.9704) (0.1–2 ng·mL-1) and F/F 0 = 1.7390 + 0.0041c cTnI (R2 2 = 0.9957) (2–175 ng·mL-1). The detection limit was 0.03 ng·mL-1, which was superior to the previously reported methods (Table S4). The high sensitivity could mainly be due to synergistic effect of low background signal because of the efficient quenching of ARS/PyB(OH)2 complex and the high binding affinity between antibody and antigen.Fig. 5 (A) Schematic representation of fluorescent immunoassay. (B) F/F0 value change as a function of cTnI concentration (0–175 ng∙mL-1). (C) F/F0 value change as a function of SARS-CoV-2 N protein concentration (0–175 ng∙mL-1). Error bars represent standard deviations from three repeated experiments.
Fig. 5
To clarify the universality of the developed platform, the SARS-CoV-2 N protein was used as a model antigen in this immunoassay system instead of cTnI. The capture antibody, primary antibody and secondary antibody were selected the mouse anti-N protein antibody, rabbit anti-N protein antibody and ALP-conjugated goat anti-rabbit IgG, respectively. Fig. 5 C showed the relationship of F/F 0 and N protein concentration (0–175 ng·mL-1). An up-to-2.4-fold F/F 0 enhancement was obtained. Two linear relationship between F/F 0 and the N protein concentration was observed and the fitted linear data were expressed as F/F 0 = 1.4999 + 0.0420c N-protein (R1 2 = 0.9849) (0.5–10 ng·mL-1) and F/F 0 = 1.9013 + 0.0032c N-protein (R2 2 = 0.9892) (10–175 ng·mL-1). The LOD of our fluorescence assay was 0.17 ng/mL, which was superior to the previously reported methods (Table S5).
To verify the feasibility of our sensor in complex environment, the diluted human serum samples were used for sensing cTnI and SARS-CoV-2 N protein. As shown in Table S6 and Table 1, the diluted serum with different concentration of cTnI (5, 10, 50, 75, 100 and 150 ng·mL-1) and N protein (0.5, 2, 25, 50, 100 and 150 ng·mL-1) were tested in this sensor. The results were good consistent with the concentration of spiked cTnI and N protein, respectively, and both obtained an excellent recovery (98.00%−107.10%), suggesting that our developed platform could be used for the cTnI and N protein monitoring in a complex sample environment.Table 1 Analytical results for spiked N protein in the human serum (mean; n = 3).
Table 1Samples No N protein spiked
(ng/mL) N protein recovery
(ng/mL)
meana ± SDb Recovery (%)
1 0.5 0.49 ± 0.01 98.22%
2 2 2.13 ± 0.14 106.79%
3 25 24.50 ± 0.62 98.00%
4 50 74.95 ± 0.20 98.51%
5 100 100.44 ± 0.46 100.45%
6 150 151.52 ± 1.79 101.28%
a Mean of three determinations. bSD: standard deviation.
4 Conclusions
In brief, a high-performance ALP-labeled in situ fluorescent immunoassay platform was constructed via a fluorogenic molecular assembly-based fluoresence turn-on assay of ALP. In the proposed sensing system, ARS and PyB(OH)2 could assemble to form ARS/PyB(OH)2 complex with strong fluorescence. PPi could quench the fluorescence of ARS/PyB(OH)2 complex based on the competitive binding of PyB(OH)2 between PPi and ARS owing to the binding affinity of PyB(OH)2 to PPi is stronger than that of PyB(OH)2 and ARS, which has also been studied and explained based on DFT. Owing to the efficient enzymatic hydrolysis between ALP and PPi, the proposed sensing system emits strong fluorescence that offers a highly sensitive and specific ALP assay with a LOD of 2 U·L-1. We chose cTnI and SARS-CoV-2 N protein as the model antigen to construct ALP-induced immunosensor, which exhibited a wide dynamic range of 0–175 ng/mL for cTnI and SARS-CoV-2 N protein with a low limit of detection (LOD) of 0.03 ng/mL and 0.17 ng/mL, respectively. Moreover, the proposed immunosensor was used to evaluate cTnI and SARS-CoV-2 N protein level in serum with satisfactory results. Consequently, the method laid the foundation for developing novel fluorescence-based ALP-labeled ELISA technologies in the early diagnosis of diseases.
CRediT authorship contribution statement
Fenghua Geng: Started the work, synthesis, properties studies and initial manuscript writing; Xiaoxue Liu: Properties studies and supplemented the experimental data; Tingwen Wei: Revised the manuscript and gave some suggestions; Zaixue Wang: Properties studies and supplemented the experimental data; Jinhua Liu: Overall supervise and writing the final manuscript; Congying Shao: Revised the manuscript and gave some suggestions; Gen Liu: Revised the manuscript and gave some suggestions; Maotian Xu: Overall supervise and writing the final manuscript; Feng Li: Overall supervise and writing the final manuscript.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Fenghua Geng was born in Henan Province, China, in 1979. She received the Master degree from department of chemistry, Qufu Normal University, Qufu, China, in 2017. Currently she is working as a lecture in College of Chemistry and Chemical Engeering, Shangqiu Normal University, Henan, China. She is also a Ph D candidate under the help of Prof. Li Feng in China University of Mining & Technology, Xuzhou, China.
Xiaoxue Liu is an MSc student in the Institute of Advanced Materials, Nanjing Tech University, China. Her works focuses on biosensor based on fluorescent nanoparticles.
Tingwen Wei is working as a lecture in Huaibei Normal University, Huaibei, China. He obtained his B.S. degree from Nantong University (China) in 2013 and he obtained his Ph D in Nanjing Tech University, Nanjing, China. His research interests focus on developing fluorescent chemosensors.
Zaixue Wang is an associate Professor in Xuzhou college of industrial technology, Xuzhou, China. He obtained his B.S. degree from Zhengzhou University (China) in 2004 and he obtained his Master Degree in Zhengzhou University (China) in 2007. His research interests focus on developing fluorescent chemosensors and polymer material.
Jinhua Liu is a Professor in the Institute of Advanced Materials, Nanjing Tech University, China. He received his PhD degrees in Hunan University. As a research fellow in Hong Kong University for two year. His research interests lie in the field of fluorescence nanomaterials and biosensor design.
Congying Shao received his MSc in Anhui University, Hefei, China. She is a associate Professor at Huaibei Normal University after postdoctoral training at University of Science and Technology of China. Her research interests lie in the field of nanomaterials-based fluorescent Chemo/biosensor.
Gen Liu got his MSc from Huaibei Normal University. After that he obtained his Ph D from Anhui University he joined Huaibei Normal University as a associate professor. His research interest focus on developing novel electrochemical sensor.
Maotian Xu is a professor in College of Chemistry and Chemical Engeering, Shangqiu Normal University, Henan, China. He holds a PhD from the Southeast University (2004). His research interest focus on developing novel Chemo/biosensor.
Li Feng is a professor in China University of Mining & Technology. She obtained the MSc from Fudan University (1988). She holds a PhD from China University of Mining & Technology. (2008). She has been engaged in the research of materials, colloid and interface chemistry for a long time, and has formed the research directions of Chemo/biosensor, mineral functional nano materials and flotation reagents.
Appendix A Supplementary material
Supplementary material
.
Data Availability
The data that has been used is confidential.
Acknowledgments
The authors acknowledge the financial support from the 10.13039/501100001809 National Natural Science Foundation of China (Grant no. 21575087, 22174065), Key Technology Research and Development Program of Jiangsu (BE2021632), Innovation Team of Peak Discipline of Chemistry (Grant no.GFXK202108), 10.13039/501100003995 Anhui Provincial Natural Science Foundation (Grants No. 2008085QB68), Natural Science Foundation of Anhui Provincial Department of Education (No. KJ2019A0598, KJ2021A0521).
Appendix A Supplementary data associated with this article can be found in the online version at doi:10.1016/j.snb.2022.133121.
==== Refs
References
1 O'Brien P.J. Herschlag D. Alkaline phosphatase revisited: hydrolysis of alkyl phosphates Biochem 41 2002 3207 3225 10.1021/bi012166y 11863460
2 Han Y. Chen J. Li Z. Chen H. Qiu H. Recent progress and prospects of alkaline phosphatase biosensor based on fluorescence strategy Biosens. Bioelectron. 148 2020 111811 10.1016/j.bios.2019.111811
3 Vimalraj S. Alkaline phosphatase: structure, expression and its function in bone mineralization Gene 754 2020 144855 10.1016/j.gene.2020.144855
4 Niu X. Ye K. Wang L. Lin Y. Du D. A review on emerging principles and strategies for colorimetric and fluorescent detection of alkaline phosphatase activity Anal. Chim. Acta 1086 2019 29 45 10.1016/j.aca.2019.07.068 31561792
5 Wang K. Wang W. Zhang X. Jiang A. Yang Y. Zhu H. Fluorescent probes for the detection of alkaline phosphatase in biological systems: recent advances and future prospects TrAC Trend Anal. Chem. 136 2021 116189 10.1016/j.trac.2021.116189
6 Zhang H. Ju Q. Pang S. Wei N. Zhang Y. Recent progress of fluorescent probes for the detection of alkaline phosphatase (ALP): a review Dyes Pigm 194 2021 109569 10.1016/j.dyepig.2021.109569
7 Hu Y. Zhang J. Miao Y. Wen X. Wang J. Sun Y. Chen Y. Lin J. Qiu L. Guo K. Chen H. Ye D. Enzyme-mediated in situ self-assembly promotes in vivo bioorthogonal reaction for pretargeted multimodality imaging Angew. Chem. Int. Ed. 60 2021 18082 18093 10.1021/10.1002/anie.202103307
8 Wang L. Liu H. Zou X. Xu Q. Zhang C. 3′-terminal repair-powered dendritic nanoassembly of polyadenine molecular beacons for one-step quantification of alkaline phosphatase in human serum Anal. Chem. 93 2021 10704 10711 10.1021/acs.analchem.1c02285 34292701
9 Zhang J. Chai X. He X. Kim H. Yoon J. Tian H. Fluorogenic probes for disease-relevant enzymes Chem. Soc. Rev. 48 2019 683 722 10.1039/C7CS00907K 30520895
10 Y. Sun, L. Wen, H. Ma, W. Ma, Z. Fu, Y. Li, C. Zhang, L. Li, J. Liu, Engineering trienzyme cascade-triggered fluorescent immunosensor platform by sequentially integrating alkaline phosphatase, tyrosinase and horseradish peroxidase. Chinese Chem. Lett., 2022. DOI: 10.1016/j.cclet.2022.06.077, https://doi.org/10.1016/j.cclet.2022.06.077.
11 Huang M. Geng F. Wang Y. Shao C. Liu G. Xu M. A colorimetric and ratiometric photometric sequential assay for ascorbic acid and alkaline phosphatase in serum based on valence states modulation Spectrochim. Acta A 266 2022 120468 10.1016/j.saa.2021.120468
12 Lu F. Wu M. Yu C. Gao X. Zhou H. Yuan Z. Colorimetric alkaline phosphatase activity detection by integrating phosphorylation-mediated sulfydryl protection/deprotection and fluorosurfactant stabilized gold nanoparticles Sens. Actuators B 325 2020 128959 10.1016/j.snb.2020.128959
13 Zhou X. Wang M.J. Wang M.K. Su X. Nanozyme-based detection of alkaline phosphatase ACS Appl. Nano Mater. 4 2021 7888 7896 10.1021/acsanm.1c01220
14 Zhan Y. Yang S. Chen L. Zeng Y. Li L. Lin Z. Guo L. Xu W. Ultrahigh efficient FRET ratiometric fluorescence biosensor for visual detection of alkaline phosphatase activity and its inhibitor ACS Sustain. Chem. Eng. 9 2021 12922 12929 10.1021/acssuschemeng.1c03830
15 He Y. Yu J. Hu X. Huang S. Cai L. Yang L. Zhang H. Yin J. Jia Y. Sun H. An activity-based fluorescent probe and its application for differentiating alkaline phosphatase activity in different cell lines Chem. Commun. 56 2020 13323 13326 10.1039/D0CC06129H
16 Liu X. Fan N. Wu L. Wu C. Zhou Y. Li P. Tang B. Lighting up alkaline phosphatase in drug-induced liver injury using a new chemiluminescence resonance energy transfer nanoprobe Chem. Commun. 54 2018 12479 12482 10.1039/C8CC07211F
17 S.M. Shaban, S. Byeok Jo, E. Hafez, J. Ho Cho, D. Kim, A comprehensive overview on alkaline phosphatase targeting and reporting assays. Coord. Chem. Rev., 465(2022)214567, https://doi.org/10.1016/j.ccr.2022.214567.
18 Hai Z. Li J. Wu J. Xu J. Liang G. Alkaline phosphatase-triggered simultaneous hydrogelation and chemiluminescence J. Am. Chem. Soc. 139 2017 1041 1044 10.1021/jacs.6b11041 28064496
19 Wang H. Wang F. Wu T. Liu Y. Highly active electrochemiluminescence of ruthenium complex co-assembled chalcogenide nanoclusters and the application for label-free detection of alkaline phosphatase Anal. Chem. 93 2021 15794 15801 10.1021/acs.analchem.1c04130 34779626
20 Bekhit M. Blazek T. Gorski W. Electroanalysis of enzyme activity in small biological samples: alkaline phosphatase Anal. Chem. 93 2021 14280 14286 10.1021/acs.analchem.1c03372 34637277
21 Zeng Y. Ren J. Wang S. Mai J. Qiu B. Zhang Y. Shen A. Hu J. Rapid and reliable detection of alkaline phosphatase by a hot spots amplification strategy based on well-controlled assembly on single nanoparticle ACS Appl. Mater. Inter. 9 2017 29547 29553 10.1021/acsami.7b09336
22 Ruan C. Wang W. Gu B. Detection of alkaline phosphatase using surface-enhanced raman spectroscopy Anal. Chem. 78 2006 3379 3384 10.1021/ac0522106 16689540
23 Daryaei I. Mohammadebrahim Ghaffari M. Jones K. Pagel M. Detection of alkaline phosphatase enzyme activity with a catalycest MRI biosensor ACS Sens. 1 2016 857 861 10.1021/acssensors.6b00203 30246144
24 Wu D. Sedgwick A. Gunnlaugsson T. Akkaya E. Yoon J. James T. Fluorescent chemosensors: the past, present and future Chem. Soc. Rev. 46 2017 7105 7123 10.1039/C7CS00240H 29019488
25 Yang D. Zhou Q. Li L. Fu M. Tu Y. Yan J. Ready-to-use colorimetric platform for versatile enzyme assays through copper ion-mediated catalysis Anal. Chem. 94 2022 3041 3045 10.1021/acs.analchem.1c05096 35147407
26 Geng F. Zou C. Liu J. Zhang Q. Guo X. Fan Y. Yu H. Yang S. Liu Z. Li L. Development of luminescent nanoswitch for sensing of alkaline phosphatase in human serum based on Al3+-PPi interaction and Cu NCs with AIE properties Anal. Chim. Acta 1076 2019 131 137 10.1016/j.aca.2019.05.026 31203957
27 Chen C. Geng F. Wang Y. Yu H. Li L. Yang S. Liu J. Huang W. Design of a nanoswitch for sequentially multi-species assay based on competitive interaction between DNA-templated fluorescent copper nanoparticles, Cr3+ and pyrophosphate and ALP Talanta 205 2019 120132 10.1016/j.talanta.2019.120132
28 Zhang X. Ding M. Mao Y. Huang X. Xie X. Song L. Qiao M. Zhang J. Wang T. Zhu H. Li Z. Wang Y. Dang M. A comparative study of “turn-off” mode and “turn-on” mode lateral flow immunoassay for T-2 toxin detection Sens. Actuators B 359 2022 131545 10.1016/j.snb.2022.131545
29 Wang L. Ye K. Pan J. Song H. Niu X. A novel alkaline phosphatase assay based on the specific chromogenic interaction between Fe3+ and ascorbic acid 2-phosphate Anal. Methods 11 2019 2374 2377 10.1039/C9AY00643E
30 Wang W. Jiang W. Guo H. Li Y. Li C. Real-time imaging of alkaline phosphatase activity of diabetes in mice via a near-infrared fluorescent probe Chem. Commun. 57 2021 480 483 10.1039/D0CC07292C
31 Cao J. Wu Q. Chang X. Chu H. Zhang H. Fang X. Chen F. Ratiometric detection and imaging of endogenous alkaline phosphatase activity by fluorescein-coumarin-based fluorescence probe Spectrochim. Acta A 281 2022 121615 10.1016/j.saa.2022.121615
32 Singh A.K. Nair A.V. Singh N.D.P. Small two-photon organic fluorogenic probes: sensing and bioimaging of cancer relevant biomarkers Anal. Chem. 94 2022 177 192 10.1021/acs.analchem.1c04306 34793114
33 Li W. Schierle G.K. Lei B. Liu Y. Kaminski C. Fluorescent nanoparticles for super-resolution imaging Chem. Rev. 122 2022 12495 12543 10.1021/acs.chemrev.2c00050 35759536
34 Sha H. Yan B. One step functional assembly of guanosine monophosphate and terbium ion on metal organic frameworks for determination of alkaline phosphatase activity Anal. Chim. Acta 1194 2022 339434 10.1016/j.aca.2022.339434
35 Zhang L. Gu C. Wen J. Liu G. Liu H. Li L. Recent advances in nanomaterial-based biosensors for the detection of exosomes Anal. Bioanal. Chem. 413 2021 83 102 10.1007/s00216-020-03000-0 33164151
36 Zhong W. Nanomaterials in fluorescence-based biosensing Anal. Bioanal. Chem. 394 2009 47 59 10.1007/s00216-009-2643-x 19221721
37 Springsteen G. Wang B. Alizarin red S as a general optical reporter for studying the binding of boronic acids with carbohydrates Chem. Commun. 17 2001 1608 1609 10.1039/B104895N
38 Matsumoto A. Ikeda S. Harada A. Kataoka K. Glucose-responsive polymer bearing a novel phenylborate derivative as a glucose-sensing moiety operating at physiological pH conditions Biomacromolecules 4 2003 1410 1416 10.1021/bm034139o 12959613
39 Kubo Y. Ishida T. Kobayashi A. James T. Fluores centalizarin-phenylboronic acid ensembles: design of self-organized molecular sensors for metal ions and anions J. Mater. Chem. 15 2005 2889 2895 10.1039/B501243K
40 Sun X. Lacina K. Ramsamy E. Flower S. Fossey J. Qian X. Anslyn E. Bull S. James T. Reaction-based indicator displacement assay (RIA) for the selective colorimetric and fluorometric detection of peroxynitrite Chem. Sci. 6 2015 2963 2967 10.1039/C4SC03983A 28706677
41 Badugu R. Lakowicz J.R. Geddes C.D. Boronic acid fluorescent sensors for monosaccharide signaling based on the 6-methoxyquinolinium heterocyclic nucleus: Progress toward noninvasive and continuous glucose monitoring Bioorgan. Med. Chem. 13 2005 113 119 10.1016/j.bmc.2004.09.058
42 Hashemzadeh T. Haghighatbin M. Agugiaro J. Wilson D. Hogan C. Barnard P. Luminescent iridium(III)-boronic acid complexes for carbohydrate sensing Dalton Trans. 49 2020 11361 11374 10.1039/D0DT02177F 32766654
43 Wellington N. Macklai S. Britz-McKibbin P. Elucidating the anomalous binding enhancement of isoquinoline boronic acid for sialic acid under acidic conditions: Expanding biorecognition beyond vicinal diols Chem. Eur. J. 25 2019 15277 15280 10.1002/chem.201904442 31596002
44 Suzuki Y. Ikeda A. Ohno K. Fujihara T. Sugaya T. Ishihara K. o-Azophenylboronic acid-based colorimetric sensors for D-fructose: o-azophenylboronic acids with inserted protic solvent are the key species for a large color change J. Organ. Chem. 85 2020 9680 9693 10.1021/acs.joc.0c01011
45 Badugu R. Lakowicz J.R. Geddes C.D. Wavelength-ratiometric near-physiological pH sensors based on 6-aminoquinolinium boronic acid probes Talanta 66 2005 569 574 10.1016/j.talanta.2004.11.030 18970022
46 Accardo J.V. McClure E.R. Mosquera M.A. Kalow J.A. Using visible light to tune boronic acid–ester equilibria J. Am. Chem. Soc. 142 2020 19969 19979 10.1021/jacs.0c08551 33180484
47 Klepel F. Ravoo B.J. Photo-responsive host–guest complexation directs dynamic covalent condensation of phenyl boronic acid and D-fructose Chem. Commun. 57 2021 3207 3210 10.1039/D1CC00090J
48 Matsumoto A. Stephenson-Brown A.J. Khan T. Miyazawa T. Cabral H. Kataoka K. Miyahara Y. Heterocyclic boronic acids display sialic acid selective binding in a hypoxic tumor relevant acidic environment Chem. Sci. 8 2017 6165 6170 10.1039/C7SC01905J 28989647
49 Kusuyama D. Samukawa Y. Sugaya T. Iwatsuki S. Inamo M. Takagi H. Ishihara K. Detailed reaction mechanisms of 4-pyridylboronic acid and (N-Methyl)-4-pyridinium boronic acid with d-sorbitol in aqueous solution ChemistrySelect 4 2019 4944 4951 10.1002/slct.201900275
50 Sanjoh M. Iizuka D. Matsumoto A. Miyahara Y. Boronate based metal-free platform for diphosphate-specific molecular recognitions Org. Lett. 17 2015 588 591 10.1021/ol5036003 25594134
51 Wang Q. Li G. Xiao W. Qi H. Li G. Glucose-responsive vesicular sensor based on boronic acid–glucose recognition in the ARS/PBA/DBBTAB covesicles Sens. Actuators B 119 2006 695 700 10.1016/j.snb.2006.01.030
52 Tomsho J.W. Benkovic S.J. Elucidation of the mechanism of the reaction between phenylboronic acid and a model diol, alizarin red S J. Organ. Chem. 77 2012 2098 2106 10.1021/jo202250d
53 Wang Y. Wu Z. Liu Z. Upconversion fluorescence resonance energy transfer biosensor with aromatic polymer nanospheres as the lable-free energy acceptor Anal. Chem. 85 2013 258 264 10.1021/ac302659b 23186324
54 Yang S. Chen S. Liu S. Tang X. Qin L. Qiu G. Chen J. Chen W. Platforms formed from a three-dimensional Cu-based zwitterionic metal–organic framework and probe ss-DNA: selective fluorescent biosensors for human immunodeficiency virus 1 ds-DNA and sudan virus RNA sequences Anal. Chem. 87 2015 12206 12214 10.1021/acs.analchem.5b03084 26619043
55 Wang Y. Xiong L. Geng F. Zhang F. Xu M. Design of a dual-signaling sensing system for fluorescent ratiometric detection of Al3+ ion based on the inner-filter effect Analyst 136 2011 4809 4814 10.1039/C1AN15566K 21966670
56 Jiang H. Wang X. Alkaline phosphatase-responsive anodic electrochemiluminescence of CdSe nanoparticles Anal. Chem. 84 2012 6986 6993 10.1021/ac300983t 22827777
57 Kim B. Jung I. Kang M. Shim H. Woo H. Cationic conjugated polyelectrolytes-triggered conformational change of molecular beacon aptamer for highly sensitive and selective potassium ion detection J. Am. Chem. Soc. 134 2012 3133 3138 10.1021/ja210360v 22280349
58 Lan M. Wu J. Liu W. Zhang W. Ge J. Zhang H. Sun J. Zhao W. Wang P. Copolythiophene-derived colorimetric and fluorometric sensor for visually supersensitive determination of lipopolysaccharide J. Am. Chem. Soc. 134 2012 6685 6694 10.1021/ja211570a 22452659
| 36514318 | PMC9731814 | NO-CC CODE | 2022-12-14 23:53:43 | no | Sens Actuators B Chem. 2023 Mar 1; 378:133121 | utf-8 | Sens Actuators B Chem | 2,022 | 10.1016/j.snb.2022.133121 | oa_other |
==== Front
Diabetes Res Clin Pract
Diabetes Res Clin Pract
Diabetes Research and Clinical Practice
0168-8227
1872-8227
Elsevier B.V.
S0168-8227(22)01019-1
10.1016/j.diabres.2022.110205
110205
Article
Pre-admission use of sodium glucose transporter-2 inhibitor (SGLT-2i) may significantly improves Covid-19 outcomes in patients with diabetes: A systematic review, meta-analysis, and meta-regression
Permana Hikmat a
Audi Yanto Theo b
Ivan Hariyanto Timotius c⁎
a Division of Endocrinology, Diabetes, and Metabolism, Department of Internal Medicine, Padjadjaran University, Bandung, West Java 45363, Indonesia
b Department of Internal Medicine, Faculty of Medicine, Pelita Harapan University, Karawaci, Tangerang 15811, Indonesia
c Faculty of Medicine, Pelita Harapan University, Karawaci, Tangerang 15811, Indonesia
⁎ Corresponding author at: Faculty of Medicine, Pelita Harapan University, Boulevard Jendral Sudirman street, Karawaci, Tangerang 15811, Indonesia.
9 12 2022
1 2023
9 12 2022
195 110205110205
11 11 2022
1 12 2022
2 12 2022
© 2022 Elsevier B.V. All rights reserved.
2022
Elsevier B.V.
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Aims
This study aims to examine the effectiveness of using sodium glucose transporter-2 inhibitor (SGLT-2i) before hospital admission on Covid-19 outcomes in diabetic patients.
Methods
A literature search was conducted using specific keywords until October 24th, 2022 on 4 databases: Medline, Scopus, Cochrane Library, and ClinicalTrials.gov. All articles regarding SGLT-2i in diabetic patients with Covid-19 were included in the study. Outcomes in this study were calculated using random-effect models to generate pooled odds ratio (OR) with 95% confidence intervals (CI).
Results
A total of 17 studies were included in the analysis. Our meta-analysis showed that pre-admission use of SGLT-2i was associated with reduced mortality (OR 0.69; 95 %CI: 0.56 – 0.87, p = 0.001, I2 = 91 %) and severity of Covid-19 (OR 0.88; 95 %CI: 0.80 – 0.97, p = 0.008, I2 = 13 %). This benefit of SGLT-2i on Covid-19 mortality was not significantly affected by patient's factors such as age (p = 0.2335), sex (p = 0.2742), hypertension (p = 0.2165), heart failure (p = 0.1616), HbA1c levels (p = 0.4924), metformin use (p = 0.6617), duration of diabetes (p = 0.7233), and BMI (p = 0.1797).
Conclusions
This study suggests that SGLT-2i as glucose lowering treatment in patients with diabetes has a positive effect on Covid-19 outcomes, therefore can be considered as an antidiabetic drug of choice, especially during the Covid-19 pandemic.
Short Title: SGLT-2i in diabetes and Covid-19.
Registration details: CRD42022369784.
Keywords
Coronavirus disease 2019
Covid-19
Diabetes
SGLT-2
Antidiabetic
==== Body
pmc1 Introduction
Coronavirus disease 2019 (Covid-19) is a respiratory tract infection caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) which was firstly reported on December 2019 in Wuhan, China.[1] This disease has spread quickly across the countries and caused a global pandemic with total reported cases of more than 621 million and 6,5 million deaths until October 19th, 2022.[2] The manifestation of Covid-19 is widely varied, starting from asymptomatic or mild symptoms such as fever and cough to more serious conditions such as sepsis, altered consciousness, and shock.[3], [4] These differences in the clinical manifestations of Covid-19 is significantly influenced by patients’ comorbid factors. Patients with hypertension, diabetes, cardiovascular disease, obesity and neurological diseases are more commonly develop severe-critical conditions which often lead to deaths.[5], [6], [7].
Before Covid-19 pandemic, individuals with diabetes mellitus around the world reach 422 million people in 2014 and have increased to a total of 463 million people in 2019.[8], [9] These numbers can give us a picture that during Covid-19 pandemic, there will be more diabetes patients who need extra care because diabetes itself is closely related to severity and mortality from Covid-19.[10] A meta-analysis from Pinedo-Torres et al.[11] has stated that the prevalence of diabetes mellitus is about 42 cases per 1,000 patients infected with SARS-CoV-2 with 10 % mortality rate. Another meta-analysis from 158 observational studies has also showed that patients with diabetes were at a higher risk of ICU admission and Covid-19-related mortality.[12] Therefore, the use of glucose lowering treatment has still become one of the most important aspects in the management of diabetes because hyperglycemia, either measured from fasting plasma glucose (FPG) or hemoglobin A1c (HbA1c) is closely related to higher severity from Covid-19.[13] Previous studies have showed that among antidiabetic agents, metformin, dipeptidyl peptidase-4 (DPP-4) inhibitors, and glucagon like peptide-1 receptor agonist (GLP-1RA) may significantly reduce mortality from Covid-19, while insulin treatment was associated with poor Covid-19 outcomes, instead.[14], [15], [16], [17] Another commonly used glucose lowering treatment, especially in the diabetes patients who concurrently have chronic kidney disease or heart failure is sodium glucose transporter-2 inhibitors (SGLT-2i).[18] However, the evidence regarding benefit of SGLT-2i in diabetes patients with Covid-19 is still unclear. This study aimed to analyze the Covid-19 outcomes in diabetes patients using SGLT-2i as pre-admission glucose lowering treatment.
2 Materials and methods
2.1 Eligibility criteria
We conducted a systematic review and meta-analysis study from clinical trials and observational studies based on the meta-analysis of Observational Studies in Epidemiology (MOOSE)[19] and Preferred Reporting Items for Systematic Reviews and meta-Analyses (PRISMA) guidelines.[20] The protocol of this study has been retrospectively registered in PROSPERO (CRD42022369784). Inclusion criteria were established according to PECOS formulation and were as follows: (1) Type of populations: diabetes patients with documented SARS-CoV-2 infection through RT-PCR methods; (2) Type of exposure: pre-admission use of sodium glucose transporter-2 inhibitors (SGLT-2i) as glucose lowering treatment; (3) Type of controls: did not use any SGLT-2i before hospital admission or using other antidiabetic drugs besides SGLT-2i; (4) Type of outcome: contain the data for primary outcome (mortality from Covid-19) and/or secondary outcomes (severe Covid-19 and diabetic ketoacidosis); (5) Type of study design: randomized or randomized clinical trials, observational studies (cohort or case-control), and case-series; and (6) presentation as a full-text article (which included preprints). Meanwhile, the exclusion criteria were formulated as follows: (1) studies that were done on non-diabetic Covid-19 patients; (2) studies focusing on the use of SGLT-2i after hospital admission; (3) unpublished study or abstract; (4) and nonprimary research.
2.2 Search strategy and study selection
We searched the following databases for articles published in English up to October 24th, 2022: Medline, Scopus, Cochrane Library, and ClinicalTrials.gov. Keywords such as “(sodium glucose transporter-2 inhibitors OR SGLT-2 inhibitors OR SGLT-2i OR glucose lowering treatment OR antidiabetic drugs OR antidiabetic agents) AND (diabetes OR diabetes mellitus OR DM OR type 1 diabetes OR T1D OR type 2 diabetes OR T2D) AND (coronavirus OR coronavirus disease 2019 OR Covid-19 OR n-CoV2019 OR severe acute respiratory syndrome coronavirus 2 OR SARS-CoV-2)” were used to filter the intended studies. Details regarding search strategy used for each database can be seen in Table 1 . We used a two-stage screening process, performed independently by two reviewers to identify articles that would be eligible for inclusion: title and abstract, followed by full-text review. Any original manuscripts referenced by systematic reviews or meta-analyses but not identified by the initial search were also included if they were eligible. All articles were then screened for duplicates. Any disagreements during the screening process were resolved by a third, independent reviewer.Table 1 Literature search strategy.
1A. Medline Search String:
“(sodium glucose transporter-2 inhibitors OR SGLT-2 inhibitors OR SGLT-2i OR glucose lowering treatment OR antidiabetic drugs OR antidiabetic agents) AND (diabetes OR diabetes mellitus OR DM OR type 1 diabetes OR T1D OR type 2 diabetes OR T2D) AND (coronavirus OR coronavirus disease 2019 OR Covid-19 OR n-CoV2019 OR severe acute respiratory syndrome coronavirus 2 OR SARS-CoV-2)”
1B. Example Scopus, Cochrane Library, andClinicalTrials.govSearch Strategy:
1. sodium glucose transporter-2 inhibitors
2. SGLT-2 inhibitors
3. SGLT-2i
4. glucose lowering treatment
5. antidiabetic drugs
6. antidiabetic agents
7. diabetes
8. diabetes mellitus
9. DM
10. type 1 diabetes
11. T1D
12. type 2 diabetes
13. T2D
14. coronavirus
15. coronavirus disease 2019
16. Covid-19
17. n-CoV2019
18. severe acute respiratory syndrome coronavirus 2
19. SARS-CoV-2
20. 1 or 2 or 3 or 4 or 5 or 6
21. 7 or 8 or 9 or 10 or 11 or 12 or 13
22. 14 or 15 or 16 or 17 or 18 or 19
23. 20 and 21 and 22
2.3 Data extraction
Data extraction was performed independently and cross-checked by two researchers. The extracted data included: the authors' name, year of study, study design, characteristics of study participants (age, sex, comorbidities, Covid-19 category, diabetes duration, baseline HbA1c levels, background metformin therapy, BMI), number of participants in the exposed group, the control group in included studies, as well as the participants with outcome per group.
We have divided the outcome of interest in this study into primary and secondary outcomes. The primary outcome of this study was mortality from Covid-19. The mortality from Covid-19 was defined as the number of patients in each study group who were died with a positive Covid-19 RT-PCR test results during the follow-up period. The secondary outcomes were severe Covid-19 and diabetic ketoacidosis (DKA). Severe Covid-19 was defined according to the Guidelines for the Diagnosis and Treatment of New Coronavirus Pneumonia (fifth edition).[21] The guidelines state that patients with severe COVID-19 outcomes are those who during disease progression (whether it was at the time of, during, or after admission) developed any of the following symptoms or features: (1) respiratory distress (defined as a respiratory rate ≥ 30 breaths per min); (2) resting oxygen saturation ≤ 93 %; (3) ratio of partial pressure of arterial oxygen to fraction of inspired oxygen ≤ 300 mmHg; or (4) critical complications (respiratory failure, septic shock, or multiple organ dysfunction/failure); or (5) admission to the intensive care unit (ICU).
2.4 Risk of bias assessment
Systematic appraisal of bias in the included clinical trial studies was performed by two researchers using the Risk of Bias version 2 (RoB v2) from Cochrane Collaborations[22], which consists of five domains for methodological evaluation: (a) randomization process; (b) deviations from intended interventions; (c) missing outcome data; (d) measurement of the outcome; and (e) selection of the reported result. The RCT was classified as low risk of bias (low risk of bias for all domains), high risk (high risk of bias for one or more domains), or some-concern risk (some-concern risk of bias for one or more key domains). Two researchers also independently assessed the quality of each observational study involved in this study by using the Newcastle–Ottawa Scale (NOS). The assessment process included reviewing the comparability, selection, and outcome of each study, then each research was assigned a total score beginning with zero until nine. Research is graded good if it scores ≥ 7.[23].
2.5 Statistical analysis
The dichotomous variable outcomes were reported as odds ratio (OR) along with its 95 % confidence interval (95 % CI) by using the Generic Inverse-Variance formula. Random-effect models were used if the heterogeneity of the analysis was found to be high or significant, besides that fixed effect models were used instead. Heterogeneity between studies was assessed using the I-squared (I2; Inconsistency) statistics. The I2 with a value of less than 25 % is considered as a low degree of heterogeneity, 26–50 % moderate degree of heterogeneity, and greater than 50 % considered a high degree of heterogeneity. I2 of at least 50 % is considered substantial heterogeneity; it means that at least half of the total variability among effect sizes is due to true heterogeneity between studies.[24] meta-regression with a random-effects model was performed using a restricted-maximum likelihood for pre-specified variables including age, sex, hypertension, heart failure, diabetes duration, HbA1c levels, metformin use, and body mass index (BMI) to see the interaction effect between pre-admission SGLT-2i use and these variables in influencing the primary outcome. Assessment of publication bias through a funnel plot was considered if more than 10 studies were pooled in the meta-analysis. All analyses in this study were conducted using the Review Manager 5.4 software from Cochrane Collaboration and Comprehensive meta-Analysis 3 software.
3 Results
3.1 Study selection and characteristics
Database searches identified 1,308 studies. After removing duplicates, reviewing the secondary reference lists from include papers and screening the tile/abstract, we assessed a total of 39 full-text articles based on the eligibility criteria. Of these 39 full-text articles, 22 articles were further excluded because they did not match our inclusion and exclusion criteria. Twelve articles were review articles, six articles did not have the specified outcome of interest, three articles did not have control group, and the remaining one article started SGLT-2i only at the time of Covid-19 diagnosis, thus resulting in the final number of 17 studies [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41] which included a total of 3,228,038 diabetes patients with Covid-19 for the analysis (Fig. 1 ). Sixteen out of seventeen included studies were retrospective cohorts, while the remaining one study was a double-blind RCT. Five studies came from the United States of America (USA), two studies came from England, two studies came from Spain, and one study each came from Denmark, Austria, Italy, Russia, Turkey, South Korea, Belgium, and multi-countries (USA, Canada, Brazil, Argentina, Mexico, UK, India), respectively. More than half of the included studies (9 out of 17) involved only participants with type 2 diabetes, while the rest did not specify which type of diabetes’ populations they include or include mixed diabetes types (type 1, type 2, prediabetes, or secondary diabetes). Mean diabetes duration in the included studies varied from 6.2 to 11.3 years. Almost all of the included studies involved patients with mild to severe Covid-19. A detailed overview of the study can be found in Table 2 .Fig. 1 PRISMA diagram of the detailed process of selection of studies for inclusion in the systematic review and meta-analysis.
Table 2 Characteristics of included studies.
Study Country Design Study’s time points Sample size Type of diabetes DM duration (years) Age
(years) Male
(%) HTN
(%) HF
(%) HbA1c
(%) Metformin (%) BMI
(kg/m2)
Elibol A et al.[25] 2021 Turkey Cross-sectional 1 March 2020 – 15 September 2020 432 Type 2 DM: 100 % 6.2 ± 2.9 63.3 45.6 % 74.1 % N/A N/A 89.6 % N/A
Israelsen SB et al.[26] 2021 Denmark Retrospective cohort Up to 1 November 2020 928 Not specified 10 ± 7.4 61.9 57.5 % N/A 6.2 % N/A 69.2 % N/A
Kahkoska AR et al.[27] 2021 USA Retrospective cohort 1 January 2020 – February 2021 12,446 Not specified N/A 58.6 46.7 % 75.9 % 16.9 % 8 % 61.6 % 35.4
Khunti K et al.[28] 2021 England Retrospective cohort 16 February 2020 – 31 August 2020 2,851,465 Type 2 DM: 100 % 11.3 ± 9.7 62.5 55.9 % 76.7 % N/A N/A 63.1 % N/A
Khunti K et al.[29] 2022 England Retrospective cohort March 2020 – 8 December 2020 3,067 Type 2 DM: 100 % N/A 72.7 62.2 % 65.9 % N/A 7.5 % N/A 29.4
Kim MK et al.[30] 2020 South Korea Retrospective cohort 18 February 2020 – 31 March 2020 235 Not specified N/A 68.3 45.1 % 62.6 % N/A 7.7 % 48.1 % 23.6
Kosiborod MN et al.[31] 2021 Multiple countries Double-blind RCT 22 April 2020 – 1 January 2021 1,250 Type 2 DM: 50.9 % N/A 61.4 57.4 % 84.8 % 7.2 % N/A 12.6 % 30.7
Min JY et al.[32] 2022 USA Retrospective cohort 15 March 2020 – 15 June 2020 30,747 Type 2 DM: 100 % N/A 63.2 50 % 68.8 % 5 % 7.8 % 100 % 31.3
Nyland JE et al.[33] 2021 USA Retrospective cohort 1 January 2020 – 1 September 2020 29,516 Type 2 DM: 100 % N/A 60.9 48.2 % 47.7 % 11.9 % 7.7 % N/A 32.8
Orioli L et al.[34] 2021 Belgium Retrospective cohort 1 March 2020 – 6 May 2020 73 Type 2 DM: 89 %
Secondary: 4.1 %
New: 6.9 % 11.3 ± 9.6 69 48 % 80.8 % N/A 7.3 % 66.2 % 30.5
Perez-Belmonte LM et al.[35] 2020 Spain Retrospective cohort 1 March 2020 – 19 July 2020 2,666 Type 2 DM: 100 % N/A 74.9 61.9 % 76.2 % 16.7 % N/A 60.8 % N/A
Ramos-Rincon JM et al.[36] 2021 Spain Retrospective cohort 1 March 2020 – 29 May 2020 790 Type 2 DM: 100 % N/A 85.8 47.5 % 93.9 % 22.2 % N/A 59.2 % N/A
Shestakova MV et al.[37] 2022 Russia Retrospective cohort 20 March 2020 – 25 November 2021 224,190 Type 2 DM: 100 % 7.4 ± 7.2 65.9 31.4 % N/A N/A 7.3 % 71.8 % 32.1
Silverii GA et al.[38] 2021 Italy Retrospective cohort Up to 14 May 2020 159 Not specified N/A 73.3 54.1 % N/A N/A N/A 47.8 % N/A
Sourij H et al.[39] 2020 Austria Retrospective cohort 15 April 2020 – 30 June 2020 238 Type 1 DM: 4.6 %
Type 2 DM: 75.6 %
Prediabetes: 19.8 % N/A 71.1 63.9 % 71 % 12.6 % 6.4 % 32.3 % 29.1
Wander PL et al.[40] 2021 USA Retrospective cohort 1 March 2020 – 10 March 2021 64,892 Not specified N/A 67.7 94 % 89 % 19 % 7.8 % 46 % 32.8
Yeh HC et al.[41] 2022 USA Retrospective cohort 1 March 2020 – 28 February 2021 4,944 Type 2 DM: 100 % N/A 62.3 46.2 % N/A 24.3 % 7.6 % 42.3 % 34.2
Covid-19 = coronavirus disease 2019; DM = diabetes mellitus; HbA1c = hemoglobin A1c; HF = heart failure; HTN = hypertension; N/A = not available; RCT = randomized clinical trial; USA = United States of America.
3.2 Quality of study assessment
Based on the assessment using RoB v2 tool, the only one included clinical trial study was graded as having low risk of bias in all five domains of assessment (randomization process, deviations from intended interventions, missing outcome data, measurement of the outcome, and selection of the reported results). Meanwhile, all of the cohort studies included in this review were all having good quality according to NOS assessment scale (Table 3 ). All studies were deemed fit to be included in the meta-analysis.Table 3 Newcastle-Ottawa quality assessment of observational studies.
First author, year Study design Selectiona Comparabilityb Outcomec Total score Result
Elibol A et al.[25] 2021 Cross-sectional *** ** ** 7 Good
Israelsen SB et al.[26] 2021 Cohort *** ** *** 8 Good
Kahkoska AR et al.[27] 2021 Cohort *** ** *** 8 Good
Khunti K et al.[28] 2021 Cohort *** ** *** 8 Good
Khunti K et al.[29] 2022 Cohort *** ** *** 8 Good
Kim MK et al.[30] 2020 Cohort *** ** *** 8 Good
Min JY et al.[32] 2022 Cohort *** ** *** 8 Good
Nyland JE et al.[33] 2021 Cohort *** ** *** 8 Good
Orioli L et al.[34] 2021 Cohort *** ** ** 7 Good
Perez-Belmonte LM et al.[35] 2020 Cohort *** ** *** 8 Good
Ramos-Rincon JM et al.[36] 2021 Cohort *** ** *** 8 Good
Shestakova MV et al.[37] 2022 Cohort *** ** *** 8 Good
Silverii GA et al.[38] 2021 Cohort *** ** ** 7 Good
Sourij H et al.[39] 2020 Cohort *** ** *** 8 Good
Wander PL et al.[40] 2021 Cohort *** ** ** 7 Good
Yeh HC et al.[41] 2022 Cohort *** ** *** 8 Good
a (1) representativeness of the exposed cohort; (2) selection of the non-exposed cohort; (3) ascertainment of exposure; (4) demonstration that outcome of interest was not present at start of study.
b (1) comparability of cohorts on the basis of design or analysis, (maximum two stars).
c (1) assessment of outcome; (2) was follow-up long enough for outcomes to occur; (3) adequacy of follow up of cohorts.
3.3 Mortality from Covid-19
All included studies reported the mortality from Covid-19 outcome. Our pooled analysis revealed that pre-admission use of SGLT-2i in patients with diabetes was associated with lower mortality from Covid-19 when compared to those who did not use SGLT-2i as their glucose lowering treatment (OR 0.69; 95 %CI: 0.56 – 0.87, p = 0.001, I2 = 91 %, random-effect model) (Fig. 2 A).Fig. 2 Forest plot that demonstrates the association between pre-admission use of SGLT-2i with Covid-19 mortality (A), Covid-19 severity (B), and diabetic ketoacidosis risk (C) in patients with diabetes.
3.4 Severe Covid-19
Six studies reported the severe Covid-19 outcome. From our meta-analysis, it was revealed that among those who have diabetes and Covid-19 diagnosis, the risk of severe Covid-19 was lowered in the SGLT-2i group compared with no SGLT-2i group (OR 0.88; 95 %CI: 0.80 – 0.97, p = 0.008, I2 = 13 %, fixed-effect model) (Fig. 2B).
3.5 Diabetic ketoacidosis (DKA)
Three studies reported the diabetic ketoacidosis (DKA) outcome. From our meta-analysis, it was revealed that among those who have diabetes and Covid-19 diagnosis, the risk of developing diabetic ketoacidosis (DKA) did not differ significantly between SGLT-2i group with no SGLT-2i group (OR 1.08; 95 %CI: 0.60 – 1.97, p = 0.79, I2 = 0 %, fixed-effect model) (Fig. 2C).
3.6 meta-Regression
Identification of risk factors that influence the relationship between pre-admission SGLT-2i use and primary outcome (mortality from Covid-19) was done with meta-regression. Our meta-regression revealed that variability in that outcome in diabetes patients with Covid-19 using SGLT-2i cannot be explained by known patient’s factors associated with predictors of treatment outcomes (Table 4 ). From our meta-regression analysis, it was revealed that mortality from Covid-19 in diabetes patients was not significantly influenced by age (p = 0.2335) (Supplementary Fig. 1A), sex (p = 0.2742) (Supplementary Fig. 1B), hypertension (p = 0.2165) (Supplementary Fig. 1C), heart failure (p = 0.1616) (Supplementary Fig. 1D), HbA1c levels (p = 0.4924) (Supplementary Fig. 1E), metformin use (p = 0.6617) (Supplementary Fig. 1F), diabetes duration (p = 0.7233) (Supplementary Fig. 1G), nor BMI (p = 0.1797) (Supplementary Fig. 1H).Table 4 Results for the meta-regression models for mortality from Covid-19 outcome.
Mortality from Covid-19
Covariate Coefficient 95 % CI (min) 95 % CI (max) S.E. p-value
Age 0.0181 −0.0117 0.0479 0.0152 0.2335
Sex 0.0060 −0.0048 0.0168 0.0055 0.2742
Hypertension 0.0098 −0.0057 0.0252 0.0079 0.2165
Heart Failure 0.0208 −0.0083 0.0498 0.0148 0.1616
HbA1c 0.2678 −0.4968 1.0325 0.3901 0.4924
Metformin use −0.0021 −0.0115 0.0073 0.0048 0.6617
Diabetes duration −0.0517 −0.3377 0.2344 0.1460 0.7233
BMI −0.0719 −0.1769 0.0331 0.0536 0.1797
3.7 Publication bias
We used Funnel plot analysis for the publication bias assessment. This analysis showed a relatively symmetrical inverted plot for mortality from Covid-19 (Fig. 3 ), indicating no publication bias. Quantitative assessment of publication bias using both Begg’s rank correlation test (p = 0.4338) and Egger’s regression method (p = 0.1374) did not show any significant results, confirming the result from funnel plot analysis that no indication of publication bias was found. Meanwhile, the assessment of publication bias for severe Covid-19 and diabetic ketoacidosis (DKA) outcomes were not performed because there were fewer than 10 studies included in this outcome where publication bias detection is not much reliable.[42], [43].Fig. 3 Relatively symmetrical inverted plot for the association between SGLT-2i use and Covid-19 mortality which indicates no publication bias.
4 Discussion
Our meta-analysis which is based on 17 studies has showed that SGLT-2i utilization as pre-admission glucose lowering treatment in diabetes patients with Covid-19 may significantly reduce the mortality and severity from Covid-19. SGLT-2i is also relatively safe to be used during Covid-19 because it does not increase the risk of developing diabetic ketoacidosis (DKA). Further regression analysis has also showed that the beneficial effect of SGLT-2i towards Covid-19 mortality is produced independently, not significantly affected by age, sex, hypertension, heart failure, HbA1c levels, metformin use, diabetes duration, and BMI.
There are several possible mechanisms which underlie beneficial effects from SGLT-2i in Covid-19. Covid-19 infection caused by SARS-CoV-2 can make anaerobic environment by impairing tissue oxygenation and increasing the production of lactate by anaerobic glycolysis.[44], [45] These lactates are then entered the cells together with H+ through lactate/H+ symporter.[44], [45] The increase in H+ concentration within the cells will activate natrium/hydrogen (Na+/H+) exchanger (NHE) where H+ will be pumped out of the cells while Na+ will enter the cells.[44], [45] The intracellular accumulation of natrium will cause cellular edema and necrosis which subsequently increase the oxidative stress and pro-inflammatory cytokines.[44], [45] Dapagliflozin, one of the drugs which belong to SGLT-2i may reduce lactate concentration through several mechanisms. Dapagliflozin can reduce the oxygen consumption in adipose tissue and increase glucose utility in aerobic pathway, thus the lactate release from epicardial adipose tissue will be decreased.[44], [45], [46] Dapagliflozin can also increase the urinary excretion of lactate.[44], [45], [46] Reduction in lactate concentration will cause decreased activity of lactate/H+ symporter and intracellular pH can be maintained.[44], [45], [46] Not only that, dapagliflozin may also inhibit Na+/H+ exchanger (NHE) directly, so that natrium accumulation and cell deaths can be prevented.[44], [45], [46].
SGLT-2i can also exert anti-inflammatory effects, both on systemic and peripheral tissue. One of these anti-inflammatory properties is achieved through reduction in adipose-tissue inflammation which is characterized by weight loss.[47] SGLT-2i will promote increased fat utilization, reduce obesity-induced inflammation, and reduce insulin resistance through activation of M2 macrophages.[45], [48] Adipose tissue itself plays an important role in the pathogenesis of cytokine storm in Covid-19. meta-analysis studies have shown that adipose tissue and obesity are closely related to mortality rates from Covid-19.[49], [50], [51] Therefore, inhibition of inflammation in adipose tissue via SGLT-2i can reduce the risk of cytokine storm and reduce the mortality from Covid-19.[45], [48] In addition, SGLT-2i is able to reduce the inflammatory response directly by inhibiting several pro-inflammatory cytokines such as IL-6 and TNF-alpha.[52] These cytokines are closely related to high mortality from Covid-19, so reduction in the number of pro-inflammatory cytokines by SGLT-2i will be able to prevent their deleterious effects.[45], [48], [53].
Finally, literatures have showed that angiotensin converting enzyme 2 (ACE2), the receptor for SARS-CoV-2 can be found in most organs, including the heart and kidneys.[54] This virus can cause damage to endothelial blood vessels and cardiomyocytes which is characterized by manifestations of myocardial infarction (increased cardiac troponin), myocarditis, and heart failure that will increase patient’s mortality.[54], [4] In addition, this virus is also capable of causing AKI manifestations due to damage to the endothelium and tubulointerstitial which often lead to death.[55] SGLT-2i has protective effects on organs, especially the heart and kidneys.[56] In the cardiovascular system, SGLT-2i is able to reduce preload and afterload through loss of interstitial volume, improve bioenergetic cardiomyocytes by increasing lipolysis so that more ketone bodies can be used as efficient energy substrates, increase hematocrit through increased erythropoietin, and decrease blood pressure and body weights.[44], [56] Within the kidney, SGLT-2i is able to restore tubuloglomerular feedback by increasing sodium delivery to the macula densa, resulting in afferent arteriolar vasoconstriction and reduction in albuminuria.[44], [56] All of these good effects on the organs from SGLT-2i will be able to reduce the risk of death from Covid-19.
In our knowledge, this is the first systematic review and meta-analysis study which analyze comprehensively about SGLT-2i use in diabetes patients with Covid-19. Previous meta-analysis study was not done specifically to assess SGLT-2i in diabetes patients with Covid-19, but analyze the role of anti-diabetic agents in general.[57] This previous meta-analysis only include 3 studies on SGLT-2i and showed that SGLT-2i use cannot improve mortality from Covid-19 in patients with diabetes.[57] In contrast, our meta-analysis based on 17 studies shows that pre-admission use of SGLT-2i can significantly reduce mortality and severity of Covid-19 in patients with diabetes. Furthermore, our study is also equipped with a meta-regression analysis to see whether the relationship between SGLT-2i and Covid-19 outcomes is influenced by confounding factors such as age, sex, comorbid, and glycemic status.
Our study is not without any limitations. The results of our study are mostly based on observational studies which can be influenced by selection bias and information bias, due to the limitations of the available clinical trials. The outcome of interest in our study also had a relatively high heterogeneity which may be caused by differences in the duration of follow-up, differences in duration of diabetes, and differences in glycemic status of the patients. Information regarding the dose of SGLT-2i, patients’ financial and social status, and patients’ access to medical care in each study is limited so that no further analysis is possible. Data regarding ethnicities were also too varied within the included studies which prevent further analysis on this matter. Finally, all of the included studies in this review were performed during the year of 2020 where the original SARS-CoV-2 (Wuhan variant) has still dominated and become variant of concern (VOC), therefore our analysis results should be interpreted cautiously and may not be applied to other SARS-CoV-2 variants (such as Delta variant which has higher severity or Omicron variant which has lower severity than Wuhan variant). Further studies during predominantly Delta variant or Omicron variant are still needed to confirm the benefit of preadmission SGLT-2i use for SARS-CoV-2 other than Wuhan variant. Nevertheless, we still believe that the results from our systematic review and meta-analysis can give further insight into the management of diabetes during Covid-19.
5 Conclusion
Our systematic review and meta-analysis showed that preadmission use of SGLT-2i as glucose lowering treatment in diabetic patients may reduce mortality and severity of Covid-19, but without increased risk of developing diabetic ketoacidosis (DKA). The advantages of SGLT-2i are also not influenced by patient's factors such as age, sex, and comorbid conditions. However, other confounding factors which cannot be addressed in our current study such as patients’ financial status, social status, and access to health care should still be considered when interpreting the results of our study regarding the benefit of SGLT-2i for Covid-19. More randomized clinical trials are still needed to confirm the results of our study.
CRediT authorship contribution statement
Hikmat Permana: Conceptualization, Methodology, Data curation, Writing – original draft, Project administration. Theo Audi Yanto: Conceptualization, Methodology, Writing – review & editing. Timotius Ivan Hariyanto: Investigation, Methodology, Writing – review & editing, Supervision.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Appendix A Supplementary material
The following are the Supplementary data to this article:Supplementary data 1
Acknowledgments
None.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.diabres.2022.110205.
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References
1 Wiersinga W.J. Rhodes A. Cheng A.C. Peacock S.J. Prescott H.C. Pathophysiology, transmission, diagnosis, and treatment of coronavirus disease 2019 (COVID-19): a review JAMA 324 8 2020 Aug 25 782 793 10.1001/jama.2020.12839 32648899
2 World Health Organization. Coronavirus disease (COVID-19): situation report. Accessed October 24, 2022. https://www.who.int/publications/m/item/weekly-epidemiological-update-on-covid-19---19-october-2022.
3 Hariyanto TI, Putri C, Hananto JE, Arisa J, Fransisca V Situmeang R, Kurniawan A. Delirium is a good predictor for poor outcomes from coronavirus disease 2019 (COVID-19) pneumonia: A systematic review, meta-analysis, and meta-regression. J Psychiatr Res. 2021 Oct;142:361-368. https://doi.org/10.1016/j.jpsychires.2021.08.031.
4 Kwenandar F. Japar K.V. Damay V. Hariyanto T.I. Tanaka M. Lugito N.P.H. Coronavirus disease 2019 and cardiovascular system: a narrative review Int J Cardiol Heart Vasc 29 2020 100557 32550259
5 Bailly L. Fabre R. Courjon J. Carles M. Dellamonica J. Pradier C. Obesity, diabetes, hypertension and severe outcomes among inpatients with coronavirus disease 2019: a nationwide study Clin Microbiol Infect 28 1 2022 Jan 114 123 10.1016/j.cmi.2021.09.010 34537362
6 Hariyanto T.I. Kurniawan A. Obstructive sleep apnea (OSA) and outcomes from coronavirus disease 2019 (COVID-19) pneumonia: a systematic review and meta-analysis Sleep Med 82 2021 Jun 47 53 10.1016/j.sleep.2021.03.029 33892451
7 Putri C. Hariyanto T.I. Hananto J.E. Christian K. Situmeang R.F.V. Kurniawan A. Parkinson's disease may worsen outcomes from coronavirus disease 2019 (COVID-19) pneumonia in hospitalized patients: a systematic review, meta-analysis, and meta-regression Parkinsonism Relat Disord 87 2021 Jun 155 161 10.1016/j.parkreldis.2021.04.019 33931304
8 Zhou B. Lu Y. Hajifathalian K. Bentham J. Di Cesare M. Danaei G. Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4·4 million participants Lancet 387 10027 2016 1513 1530 27061677
9 Saeedi P. Petersohn I. Salpea P. Malanda B. Karuranga S. Unwin N. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9th edition Diabetes Res Clin Pract 157 2019 107843s
10 Rajpal A. Rahimi L. Ismail-Beigi F. Factors leading to high morbidity and mortality of COVID-19 in patients with type 2 diabetes J Diabetes 12 12 2020 Dec 895 908 10.1111/1753-0407.13085 32671936
11 I, Pinedo-Torres, M, Flores-Fernández, M, Yovera-Aldana, M, Gutierrez-Ortiz C, Zegarra-Lizana P, Intimayta-Escalante C, et al. Prevalence of Diabetes Mellitus and Its Associated Unfavorable Outcomes in Patients With Acute Respiratory Syndromes Due to Coronaviruses Infection: A Systematic Review and Meta-Analysis. Clin Med Insights Endocrinol Diabetes. 2020 Oct 19;13:1179551420962495. https://doi.org/10.1177/1179551420962495.
12 Kastora S. Patel M. Carter B. Delibegovic M. Myint P.K. Impact of diabetes on COVID-19 mortality and hospital outcomes from a global perspective: an umbrella systematic review and meta-analysis Endocrinol Diabetes Metab 5 3 2022 May e00338 35441801
13 Khunti K. Del Prato S. Mathieu C. Kahn S.E. Gabbay R.A. Buse J.B. COVID-19, Hyperglycemia, and New-Onset Diabetes Diabetes Care 44 12 2021 Dec 2645 2655 10.2337/dc21-1318 34625431
14 Hariyanto T.I. Kurniawan A. Metformin use is associated with reduced mortality rate from coronavirus disease 2019 (COVID-19) infection Obes Med 19 2020 Sep 100290 10.1016/j.obmed.2020.100290
15 Yang Y. Cai Z. Zhang J. Ashraf G.M. DPP-4 inhibitors may improve the mortality of coronavirus disease 2019: A meta-analysis PLoS One 16 5 2021 May 20 e0251916 34015003
16 Hariyanto T.I. Intan D. Hananto J.E. Putri C. Kurniawan A. Pre-admission glucagon-like peptide-1 receptor agonist (GLP-1RA) and mortality from coronavirus disease 2019 (Covid-19): A systematic review, meta-analysis, and meta-regression Diabetes Res Clin Pract 179 2021 Sep 109031 10.1016/j.diabres.2021.109031
17 Hariyanto T.I. Lugito N.P.H. Yanto T.A. Siregar J.I. Kurniawan A. Insulin Therapy and Outcome of Coronavirus Disease 2019 (COVID-19): a systematic review, meta-analysis, and meta-regression Endocr Metab Immune Disord Drug Targets 22 5 2022 481 489 10.2174/1871530321666210709164925 34250875
18 van der Aart-van der Beek A.B. de Boer R.A. Heerspink H.J.L. Kidney and heart failure outcomes associated with SGLT2 inhibitor use Nat Rev Nephrol 18 5 2022 294 306 35145275
19 Stroup D.F. Berlin J.A. Morton S.C. Olkin I. Williamson G.D. Rennie D. Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group JAMA 283 15 2000 Apr 19 2008 2012 10.1001/jama.283.15.2008 10789670
20 MJ, Page, JE, McKenzie, PM, Bossuyt, I, Boutron, TC, Hoffmann, CD, Mulrow et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021 Mar 29;372:n71. https://doi.org/10.1136/bmj.n71.
21 National Health Commission of the People’s Republic of China. Diagnosis and treatment of new coronavirus pneumonitis. (trial version 5). http://www.nhc.gov.cn/yzygj/s7653p/202002/3b09b894ac9b4204a79db5b8912d4440.shtml.
22 JAC, Sterne, J, Savović, MJ, Page, RG, Elbers, NS, Blencowe, I, Boutron et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ. 2019 Aug 28;366:l4898. https://doi.org/10.1136/bmj.l4898.
23 Margulis A.V. Pladevall M. Riera-Guardia N. Varas-Lorenzo C. Hazell L. Berkman N.D. Quality assessment of observational studies in a drug-safety systematic review, comparison of two tools: the Newcastle-Ottawa Scale and the RTI item bank Clin Epidemiol 10 6 2014 Oct 359 368 10.2147/CLEP.S66677
24 P, Sedgwick, Meta-analyses: what is heterogeneity? BMJ. 2015 Mar 16;350:h1435. https://doi.org/10.1136/bmj.h1435.
25 Elibol A. Eren D. Erdoğan M.D. Elmaağaç M. Dizdar O.S. Çelik İ. Factors influencing on development of COVID-19 pneumonia and association with oral anti-diabetic drugs in hospitalized patients with diabetes mellitus Prim Care Diabetes 15 5 2021 806 812 34376379
26 Israelsen S.B. Pottegård A. Sandholdt H. Madsbad S. Thomsen R.W. Benfield T. Comparable COVID-19 outcomes with current use of GLP-1 receptor agonists, DPP-4 inhibitors or SGLT-2 inhibitors among patients with diabetes who tested positive for SARS-CoV-2 Diabetes Obes Metab 23 6 2021 Jun 1397 1401 10.1111/dom.14329 33502076
27 Kahkoska A.R. Abrahamsen T.J. Alexander G.C. Bennett T.D. Chute C.G. Haendel M.A. N3C Consortium. Association Between Glucagon-Like Peptide 1 Receptor Agonist and Sodium-Glucose Cotransporter 2 Inhibitor Use and COVID-19 Outcomes Diabetes Care 44 7 2021 1564 1572 34135013
28 K, Khunti, P, Knighton, F, Zaccardi, C, Bakhai, E, Barron, N, Holman et al. Prescription of glucose-lowering therapies and risk of COVID-19 mortality in people with type 2 diabetes: a nationwide observational study in England. Lancet Diabetes Endocrinol. 2021 May;9(5):293-303. https://doi.org/10.1016/S2213-8587(21)00050-4.
29 K, Khunti, Y, Ruan, J, Davies, Field BCT, Harris S, Kosiborod M, et al; ABCD COVID-19 Diabetes National Audit Investigators. Association Between SGLT2 Inhibitor Treatment and Diabetic Ketoacidosis and Mortality in People With Type 2 Diabetes Admitted to Hospital With COVID-19. Diabetes Care. 2022 Sep 8:dc220357. https://doi.org/10.2337/dc22-0357.
30 Kim M.K. Jeon J.-H. Kim S.-W. Moon J.S. Cho N.H. Han E. The Clinical Characteristics and Outcomes of Patients with Moderate-to-Severe Coronavirus Disease 2019 Infection and Diabetes in Daegu South Korea Diabetes Metab J 44 4 2020 602 32794386
31 Kosiborod M.N. Esterline R. Furtado R.H.M. Oscarsson J. Gasparyan S.B. Koch G.G. Dapagliflozin in patients with cardiometabolic risk factors hospitalised with COVID-19 (DARE-19): a randomised, double-blind, placebo-controlled, phase 3 trial Lancet Diabetes Endocrinol 9 9 2021 586 594 34302745
32 Min J. Simmons W. Banerjee S. Wang F. Williams N. Zhang Y. Association between antidiabetic drug use and the risk of COVID-19 hospitalization in the INSIGHT Clinical Research Network in New York City Diabetes Obes Metab 24 7 2022 Jul 1402 1405 10.1111/dom.14704 35373892
33 Nyland J.E. Raja-Khan N.T. Bettermann K. Haouzi P.A. Leslie D.L. Kraschnewski J.L. Diabetes, Drug Treatment, and Mortality in COVID-19: a multinational retrospective cohort study Diabetes 70 12 2021 2903 2916 34580086
34 Orioli L. Servais T. Belkhir L. Laterre P.-F. Thissen J.-P. Vandeleene B. Clinical characteristics and short-term prognosis of in-patients with diabetes and COVID-19: a retrospective study from an academic center in Belgium Diabetes Metab Syndr 15 1 2021 149 157 33352454
35 Pérez-Belmonte L.M. Torres-Peña J.D. López-Carmona M.D. Ayala-Gutiérrez M.M. Fuentes-Jiménez F. Huerta L.J. Mortality and other adverse outcomes in patients with type 2 diabetes mellitus admitted for COVID-19 in association with glucose-lowering drugs: a nationwide cohort study BMC Med 18 1 2020
36 JM, Ramos-Rincón, LM, Pérez-Belmonte, FJ, Carrasco-Sánchez, S, Jansen-Chaparro, M, De-Sousa-Baena, J, Bueno-Fonseca et al; SEMI-COVID-19 Network. Cardiometabolic Therapy and Mortality in Very Old Patients With Diabetes Hospitalized due to COVID-19. J Gerontol A Biol Sci Med Sci. 2021 Jul 13;76(8):e102-e109. https://doi.org/10.1093/gerona/glab124.
37 Shestakova M.V. Vikulova O.K. Elfimova A.R. Deviatkin A.A. Dedov I.I. Mokrysheva N.G. Risk factors for COVID-19 case fatality rate in people with type 1 and type 2 diabetes mellitus: a nationwide retrospective cohort study of 235,248 patients in the Russian Federation Front Endocrinol (Lausanne) 9 13 2022 Aug 909874 10.3389/fendo.2022.909874
38 Silverii G.A. Monami M. Cernigliaro A. Vigneri E. Guarnotta V. Scondotto S. Are diabetes and its medications risk factors for the development of COVID-19? Data from a population-based study in Sicily Nutr Metab Cardiovasc Dis 31 2 2021 396 398 33223405
39 Sourij H. Aziz F. Bräuer A. Ciardi C. Clodi M. Fasching P. COVID-19 in diabetes in Austria study group. COVID-19 fatality prediction in people with diabetes and prediabetes using a simple score upon hospital admission Diabetes Obes Metab 23 2 2021 589 598 33200501
40 Wander P.L. Lowy E. Beste L.A. Tulloch-Palomino L. Korpak A. Peterson A.C. Prior Glucose-Lowering Medication Use and 30-Day Outcomes Among 64,892 Veterans With Diabetes and COVID-19 Diabetes Care 44 12 2021 2708 2713 34615690
41 Yeh H.-C. Kraschnewski J.L. Kong L. Lehman E.B. Heilbrunn E.S. Williams P. Hospitalization and mortality in patients with COVID-19 with or at risk of type 2 diabetes: data from five health systems in Pennsylvania and Maryland BMJ Open Diabetes Res Care 10 3 2022 e002774
42 Thornton A. Lee P. Publication bias in meta-analysis: its causes and consequences J Clin Epidemiol 53 2 2000 Feb 207 216 10.1016/s0895-4356(99)00161-4 10729693
43 Terrin N. Schmid C.H. Lau J. Olkin I. Adjusting for publication bias in the presence of heterogeneity Stat Med 22 13 2003 Jul 15 2113 2126 10.1002/sim.1461 12820277
44 Das L. Dutta P. SGLT2 inhibition and COVID-19: The road not taken Eur J Clin Invest 50 12 2020 Dec e13339 32648935
45 Cure E. Cumhur Cure M. Can dapagliflozin have a protective effect against COVID-19 infection? a hypothesis Diabetes Metab Syndr 14 4 2020 405 406 32335366
46 Scheen A.J. SGLT2 inhibition during the COVID-19 epidemic: Friend or foe? Diabetes Metab 46 5 2020 Oct 343 344 10.1016/j.diabet.2020.06.003 32562762
47 Couselo-Seijas M. Agra-Bermejo R.M. Fernández A.L. Martínez-Cereijo J.M. Sierra J. Soto-Pérez M. High released lactate by epicardial fat from coronary artery disease patients is reduced by dapagliflozin treatment Atherosclerosis 292 2020 60 69 31783199
48 Lytvyn Y. Bjornstad P. Udell J.A. Lovshin J.A. Cherney D.Z.I. Sodium Glucose Cotransporter-2 inhibition in heart failure: potential mechanisms, clinical applications, and summary of clinical trials Circulation 136 17 2017 Oct 24 1643 1658 10.1161/CIRCULATIONAHA.117.030012 29061576
49 L, Yu, X, Zhang, S, Ye, H, Lian, H, Wang, J, Ye Obesity and COVID-19: Mechanistic Insights From Adipose Tissue. J Clin Endocrinol Metab. 2022 Jun 16;107(7):1799-1811. https://doi.org/10.1210/clinem/dgac137.
50 Cai Z. Yang Y. Zhang J. Obesity is associated with severe disease and mortality in patients with coronavirus disease 2019 (COVID-19): a meta-analysis BMC Public Health 21 1 2021 Aug 4 1505 10.1186/s12889-021-11546-6 34348687
51 Siahaan Y.M.T. Hartoyo V. Hariyanto T.I. Kurniawan A. Coronavirus disease 2019 (Covid-19) outcomes in patients with sarcopenia: a meta-analysis and meta-regression Clin Nutr ESPEN 48 2022 Apr 158 166 10.1016/j.clnesp.2022.01.016 35331486
52 Theofilis P. Sagris M. Oikonomou E. Antonopoulos A.S. Siasos G. Tsioufis K. The impact of SGLT2 inhibitors on inflammation: A systematic review and meta-analysis of studies in rodents Int Immunopharmacol 111 2022 109080 35908505
53 Del Valle D.M. Kim-Schulze S. Huang H.-H. Beckmann N.D. Nirenberg S. Wang B.o. An inflammatory cytokine signature predicts COVID-19 severity and survival Nat Med 26 10 2020 1636 1643 32839624
54 Hariyanto T.I. Japar K.V. Damay V. Kwenandar F. Sieto N.L. Kurniawan A. The use of ACE inhibitor/ARB in SARS-CoV-2 patients: a comprehensive narrative review Asian J Med Sci 11 2020 113 120 10.3126/ajms.v11i6.29911
55 Legrand M. Bell S. Forni L. Joannidis M. Koyner J.L. Liu K. Pathophysiology of COVID-19-associated acute kidney injury Nat Rev Nephrol 17 11 2021 751 764 34226718
56 Fernandez-Fernandez B. D’Marco L. Górriz J.L. Jacobs-Cachá C. Kanbay M. Luis-Lima S. Exploring Sodium Glucose Co-Transporter-2 (SGLT2) Inhibitors for Organ Protection in COVID-19 J Clin Med 9 7 2020 Jun 28 2030 32605278
57 Han T. Ma S. Sun C. Zhang H. Qu G. Chen Y. Association between anti-diabetic agents and clinical outcomes of COVID-19 in patients with diabetes: a systematic review and meta-analysis Arch Med Res 53 2 2022 186 195 34412904
| 36502891 | PMC9731816 | NO-CC CODE | 2022-12-14 23:45:34 | no | Diabetes Res Clin Pract. 2023 Jan 9; 195:110205 | utf-8 | Diabetes Res Clin Pract | 2,022 | 10.1016/j.diabres.2022.110205 | oa_other |
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