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==== Front
Public Health
Public Health
Public Health
0033-3506
1476-5616
The Author(s). Published by Elsevier Ltd on behalf of The Royal Society for Public Health.
S0033-3506(22)00350-X
10.1016/j.puhe.2022.12.003
Original Research
Evolution of social mood in Spain throughout the COVID-19 vaccination process. A machine learning approach to tweets analysis
Turón Alberto 1
Altuzarra Alfredo 1
Moreno-Jiménez José María 1
Navarro Jorge 1∗
1 Grupo Decisión Multicriterio Zaragoza (GDMZ), Dpt. Economía Aplicada, Facultad de Economía y Empresa, Universidad de Zaragoza, Gran Vía 2, 50003, Zaragoza, Spain
∗ Corresponding author.
14 12 2022
14 12 2022
18 7 2022
5 12 2022
8 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.
Objectives
This paper presents a new approach based on the combination of machine learning techniques, in particular sentiment analysis using lexicons, and multivariate statistical methods to assess the evolution of social mood through the COVID-19 vaccination process in Spain.
Methods
Analysing 41,669 Spanish tweets posted between 27-02-2020 and 31-12-2021, different sentiments were assessed using a list of Spanish words and their associations with eight basic emotions (anger, fear, anticipation, trust, surprise, sadness, joy and disgust) and three valences (neutral, negative and positive). How the different subjective emotions were distributed across the tweets was determined by using several descriptive statistics; a trajectory plot representing the emotional valence versus narrative time was also included.
Results
The results achieved are highly illustrative of the social mood of citizens, registering the different emerging opinion clusters, gauging public states of mind via the collective valence, and detecting the prevalence of different emotions in the successive phases of the vaccination process.
Conclusions
The present combination in formal models of objective and subjective information would therefore provide a more accurate vision of social reality, in this case regarding the COVID-19 vaccination process in Spain, which will enable a more effective resolution of problems.
Keywords
COVID-19 vaccination process
Sentiment Analysis
Machine learning
Multivariate Statistics
Tweets
Social mood
==== Body
pmc
| 0 | PMC9747693 | NO-CC CODE | 2022-12-15 23:22:03 | no | Public Health. 2022 Dec 14; doi: 10.1016/j.puhe.2022.12.003 | utf-8 | Public Health | 2,022 | 10.1016/j.puhe.2022.12.003 | oa_other |
==== Front
Cell
Cell
Cell
0092-8674
1097-4172
The Author(s). Published by Elsevier Inc.
S0092-8674(22)01531-8
10.1016/j.cell.2022.12.018
Article
Alarming antibody evasion properties of rising SARS-CoV-2 BQ and XBB subvariants
Wang Qian 18
Iketani Sho 18
Li Zhiteng 18
Liu Liyuan 28
Guo Yicheng 18
Huang Yiming 2
Bowen Anthony D. 13
Liu Michael 1
Wang Maple 1
Yu Jian 1
Valdez Riccardo 4
Lauring Adam S. 5
Sheng Zizhang 1
Wang Harris H. 2
Gordon Aubree 4
Liu Lihong 1∗
Ho David D. 1367∗
1 Aaron Diamond AIDS Research Center, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
2 Department of Systems Biology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
3 Division of Infectious Diseases, Department of Medicine, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
4 Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
5 Division of Infectious Diseases, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
6 Department of Microbiology and Immunology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
∗ Corresponding author (Lihong. L.), (D.D.H.)
7 Lead contact
8 These authors contributed equally
14 12 2022
14 12 2022
21 11 2022
5 12 2022
8 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.
The BQ and XBB subvariants of SARS-CoV-2 Omicron are now rapidly expanding, possibly due to altered antibody evasion properties deriving from their additional spike mutations. Here, we report that neutralization of BQ.1, BQ.1.1, XBB, and XBB.1 by sera from vaccinees and infected persons was markedly impaired, including sera from individuals boosted with a WA1/BA.5 bivalent mRNA vaccine. Titers against BQ and XBB subvariants were lower by 13-81-fold and 66-155-fold, respectively, far beyond what had been observed to date. Monoclonal antibodies capable of neutralizing the original Omicron variant were largely inactive against these new subvariants, and the responsible individual spike mutations were identified. These subvariants were found to have similar ACE2-binding affinities as their predecessors. Together, our findings indicate that BQ and XBB subvariants present serious threats to current COVID-19 vaccines, render inactive all authorized antibodies, and may have gained dominance in the population because of their advantage in evading antibodies.
Graphical abstract
Recent BQ and XBB subvariants of SARS-CoV-2 demonstrate dramatically increased ability to evade neutralizing antibodies, even those from people who received the bivalent mRNA booster or who are immunized and had previous breakthrough Omicron infection. Additionally, both BQ and XBB are completely resistant to bebtelovimab, meaning there are now no clinically authorized therapeutic antibodies effective against these circulating variants.
Keywords
SARS-CoV-2
BQ.1
BQ.1.1
XBB
XBB.1
COVID-19
neutralizing monoclonal antibody
mRNA vaccine
receptor binding affinity
antibody evasion
==== Body
pmc
| 0 | PMC9747694 | NO-CC CODE | 2022-12-15 23:22:03 | no | Cell. 2022 Dec 14; doi: 10.1016/j.cell.2022.12.018 | utf-8 | Cell | 2,022 | 10.1016/j.cell.2022.12.018 | oa_other |
==== Front
J Am Med Dir Assoc
J Am Med Dir Assoc
Journal of the American Medical Directors Association
1525-8610
1538-9375
Published by Elsevier Inc. on behalf of AMDA -- The Society for Post-Acute and Long-Term Care Medicine.
S1525-8610(22)00970-7
10.1016/j.jamda.2022.12.010
Original Studies
Risk Factors Surrounding an Increase in Burnout and Depression Amongst Healthcare Professionals in Taiwan During the COVID-19 Pandemic
Chu Wei-Min 123459
Ho Hsin-En 678
Lin Yu-Li 39
Li Jhih-Yan 10
Lin Cheng-Fu 9
Chen Cing-Hua 1112
Shieh Gow-Jen 1213
Tsan Wei-Cheng 9
Tsan Yu-Tse 49∗
1 Department of Family Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
2 School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
3 Department of Post‐Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan
4 School of Medicine, Chung Shan Medical University, Taichung, Taiwan
5 Department of Geriatric Medicine, National Center for Geriatrics and Gerontology, Aichi, Japan
6 Department of Family Medicine, Taichung Armed Force General Hospital, Taichung, Taiwan
7 Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan
8 National Defense Medical Center, Taipei, Taiwan
9 Division of Occupational Medicine, Department of Emergency Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
10 Department of Public Health, China Medical University, Taichung, Taiwan
11 Department of Nursing, Taichung Veterans General Hospital, Taichung, Taiwan
12 Department of Occupational Safety and Health Office, Taichung Veterans General Hospital, Taichung, Taiwan
13 Department of Top Hospital Administration, Taichung Veterans General Hospital, Taichung, Taiwan
∗ Corresponding author: Yu-Tse Tsan, M.D, Ph.D, Division of Occupational Medicine, Department of Emergency Medicine, Taichung Veterans General Hospital, Taichung, Taiwan1650 Taiwan Boulevard Sect. 4, Taichung, Taiwan 40705. Phone: 886-4-2395-2525 #3667.
14 12 2022
14 12 2022
2 4 2022
18 11 2022
6 12 2022
© 2022 Published by Elsevier Inc. on behalf of AMDA -- The Society for Post-Acute and Long-Term Care 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.
Objectives
This study aimed to investigate the risk factors surrounding an increase in both burnout levels and depression amongst healthcare professionals in Taiwan through use of a longitudinal study design.
Design
This is a 2-year observational study which took place from January 2019 to December 2020.
Setting and Participants
Data amongst healthcare professionals was extracted from the Overload Health Control System of a tertiary medical center in central Taiwan.
Methods
Burnout was measured through use of the Chinese version of the Copenhagen Burnout Inventory (C-CBI), while depression was ascertained by the Taiwanese Depression Questionnaire. Each participant provided both burnout and depression measurements during a non-pandemic period (2019), as well as during the COVID pandemic era (2020). Risk factors surrounding an increase in burnout levels and depression were analyzed through a multivariate logistic regression model with adjusting confounding factors.
Results
Two thousand nineteen (2,019) participants completed the questionnaire over two consecutive years, including 132 visiting doctors, 105 resident doctors, 1,371 nurses and 411 medical technicians. After adjustments, sleeplessness, daily working hours >8, and stress due to one’s workload were all found to be risk factors for an increase in depression levels, while sleeplessness, lack of exercise, and stress due to one’s workload were all found to be risk factors for an increase in personal burnout level. Being a member of the nursing staff, at a younger age, sleeplessness, and lack of exercise were all risk factors for an increase in work-related burnout levels.
Conclusions and Implications
Poor sleep, lack of exercise, long working hours and being a member of the nursing staff were risk factors regarding an increase in person burnout level, work-related burnout and depression amongst healthcare professionals. Leaders within the hospital should investigate the working conditions and personal habits of all medical staff regularly and systematically during the COVID-19 pandemic and take any necessary preventive measures, such as improving resilience for nursing staff, in order to best care for their employees.
Key words
burnout
depression
healthcare professionals
COVID-19
==== Body
pmcFundingsource: This manuscript received no funding
Brief summary
Increasing in burnout and depression within each healthcare professional were associated with multiple modifiable risk factors based on data from a two-year observational study in Taiwan.
Ethics approval and consent to participate
This study was approved by Institutional Review Boards I and II of Taichung Veterans General Hospital (Case number: CE20343B-1).
Consent for publication
Not applicable.
Availability of data and materials
The datasets used and analyzed during the current study are not publicly available, but are available from the corresponding author on reasonable request with the permission of Taichung Veterans General Hospital, Taiwan.
Competing interests
The authors declare no conflicts of interests.
Author Contributions
Conceptualization, W.-M.C.; methodology, W.-M.C.; software, J.-Y.L.; validation, W.-M.C. and Y.-L.L.; formal analysis, J.-Y.L. and W.-C.T.; investigation, C.-H.C. and W.-M.C.; resources, C.-F.L. and Y.-L.L.; data curation, C.-H.C.; writing—original draft preparation, W.-M.C. and H.-E.H.; writing—review and editing, W.-M.C. and H.-E.H.; supervision, Y.-T.T.; project ad-ministration, G.-J.S.. All authors have read and agreed to the published version of the manuscript.
| 0 | PMC9747695 | NO-CC CODE | 2022-12-15 23:22:03 | no | J Am Med Dir Assoc. 2022 Dec 14; doi: 10.1016/j.jamda.2022.12.010 | utf-8 | J Am Med Dir Assoc | 2,022 | 10.1016/j.jamda.2022.12.010 | oa_other |
==== Front
Farm Hosp
Farm Hosp
Farmacia Hospitalaria
1130-6343
2171-8695
Sociedad Española de Farmacia Hospitalaria (S.E.F.H). Published by Elsevier España, S.L.U.
S1130-6343(22)00005-8
10.1016/j.farma.2022.11.003
Original
Impact of systemic corticosteroids on hospital length of stay among patients with COVID-19
Impacto de los corticoides sistémicos en el tiempo de hospitalización en pacientes con COVID-19Ester Zamarrón 11
Carlos Carpio 1⁎
Elena Villamañán 2
Rodolfo Álvarez-Sala 1
Borobia Alberto M. 3
Gómez-Carrera L. 1
Antonio Buño 4
Concepción Prados M. 11
on behalf of the COVID@HULP Working Group
2
POSTCOVID@HULP Working Group3
1 Pneumology Department, La Paz University Hospital-IdiPAZ, Autonomous University of Madrid, Madrid, Spain
2 Pharmacy Department, La Paz University Hospital-IdiPAZ, Autonomous University of Madrid, Madrid, Spain
3 Clinical Pharmacology Department, La Paz University Hospital-IdiPAZ, Autonomous University of Madrid, Madrid, Spain
4 Clinical Analytics Department, La Paz University Hospital-IdiPAZ, Autonomous University of Madrid, Madrid, Spain
⁎ Corresponding author at: Carlos Javier Carpio Segura, Address: Paseo de la Castellana, 261. 28046. Madrid.
1 The two authors contributed equally to this work
2 A complete list of the members of the COVID@HULP Working Group is provided in the Supplementary Materials
3 A complete list of the members of the POSTCOVID@HULP Working Group is provided in the Supplementary Materials
14 12 2022
14 12 2022
7 4 2022
© 2022 Sociedad Española de Farmacia Hospitalaria (S.E.F.H). Published by Elsevier España, S.L.U.
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.
Background and Objective: The COVID-19 pandemic has posed a threat to hospital capacity due to the high number of admissions, which has led to the development of various strategies to release and create new hospital beds. Due to the importance of systemic corticosteroids in this disease, we assessed their efficacy in reducing the length of stay (LOS) in hospitals and compared the effect of 3 different corticosteroids on this outcome.
Methods: We conducted a real-world, controlled, retrospective cohort study that analysed data from a hospital database that included 3934 hospitalised patients diagnosed with COVID-19 in a tertiary hospital from April to May 2020. Hospitalised patients who received systemic corticosteroids (CG) were compared with a propensity score control group matched by age, sex and severity of disease who did not receive systemic corticosteroids (NCG). The decision to prescribe CG was at the discretion of the primary medical team.
Results: A total of 199 hospitalized patients in the CG were compared with 199 in the NCG. The LOS was shorter for the CG than for the NCG (median = 3 [interquartile range = 0–10] vs. 5 [2–8.5]; p = 0.005, respectively), showing a 43% greater probability of being hospitalised ≤ 4 days than > 4 days when corticosteroids were used. Moreover, this difference was only noticed in those treated with dexamethasone (76.3% hospitalised ≤ 4 days vs. 23.7% hospitalised > 4 days [p < 0.001]). Serum ferritin levels, white blood cells and platelet counts were higher in the CG. No differences in mortality or intensive care unit admission were observed.
Conclusions: Treatment with systemic corticosteroids is associated with reduced LOS in hospitalised patients diagnosed with COVID-19. This association is significant in those treated with dexamethasone, but no for methylprednisolone and prednisone.
Introducción y objetivo: El COVID-19 supuso una amenaza para la capacidad hospitalaria por el elevado número de ingresos, lo que llevó al desarrollo de diversas estrategias para liberar y crear nuevas camas hospitalarias. Dada la importancia de los corticoides sistémicos en esta enfermedad, se evaluó la eficacia de estos en la reducción de la duración de la estancia hospitalaria (LOS) y se comparó el efecto de tres corticosteroides diferentes sobre este resultado.
Métodos: Se realizó un estudio en vida real de cohorte retrospectivo, controlado que analizó una base de datos hospitalaria que incluyó 3.934 pacientes hospitalizados diagnosticados con COVID-19 en un hospital terciario de abril a mayo de 2020. Se comparó un grupo de enfermos que recibieron corticosteroides sistémicos (CG) frente a un grupo de control que no recibió corticosteroides sistémicos (NCG) emparejado por edad, sexo y gravedad de la enfermedad mediante una puntuación de propensión. La decisión de prescribir CG dependía principalmente del criterio del médico responsable.
Resultados: Se compararon un total de 199 pacientes hospitalizados en el GC con 199 en el GNC. La LOS fue más corta para el GC que para el NCG (mediana = 3 [rango intercuartílico = 0-10] vs. 5 [2-8,5]; p = 0,005, respectivamente), mostrando un 43% más de probabilidad de ser hospitalizado ≤ 4 días que > 4 días cuando se usaron corticosteroides. Además, esta diferencia solo la mostraron aquellos tratados con dexametasona (76,3% hospitalizados ≤ 4 días vs. 23,7% hospitalizados > 4 días [p < 0,001]). Los niveles de ferritina sérica, glóbulos blancos y plaquetas fueron más elevados en el GC. No se observaron diferencias en la mortalidad ni en el ingreso a la unidad de cuidados intensivos.
Conclusiones: El tratamiento con corticosteroides sistémicos se asocia con una disminución de la estancia hospitalaria en pacientes hospitalizados con diagnóstico de COVID-19. Esta asociación es significativa en aquellos tratados con dexametasona, no así en metilprednisolona o prednisona.
Keywords
COVID-19
Corticosteroids
Dexamethasone
hospitalization
Palabras clave
COVID-19
corticoides
dexametasona
hospitalización
==== Body
pmcA complete list of the members of the POSTCOVID@HULP Working Group is provided in the Supplementary Materials
INTRODUCTION
The coronavirus disease 2019 (COVID-19) continues to be responsible for a high number of hospitalizations. 12%–20% of patients with COVID-19 need hospitalisation due to a severe illness causing acute respiratory failure that can develop even just a few hours after the beginning of the dyspnoea1 , 2. Mortality is extremely high in this subgroup of patients, with a reported rate of 20%-52%3 , 4.
These alarming statistics have posed an enormous threat to the capacity of hospitals, which have had to reduce the use of hospital beds for non-COVID-19 illnesses and expand the number and availability of ICU hospital beds as well as providing other resources and amenities. In fact, the demand for available beds was so high in Madrid during the first pandemic surge that it was necessary to convert hotels to hospital-hotels5 and to adapt an exhibition space into a provisional hospital. In fact, a new pandemic hospital has been constructed specifically for this difficult situation, and throughout the Spanish territory numerous field hospitals have been built.
To improve the data on treatments and outcomes, several therapies for hospitalised patients have been evaluated. Thus far, corticosteroids3, together with anticoagulation, the antiviral remdesivir, or immunomodulators such as tocilizumab or the Janus kinase inhibitor baricitinib have shown some efficacy in randomised clinical trials, but many others are under investigation6.
Regarding systemic corticosteroids, experience in other viral acute respiratory distress syndromes (ARDS), such as Middle East respiratory syndrome, severe acute respiratory syndrome and influenza, had shown delayed viral clearance, no benefit and even potential injury7., 8., 9.. Therefore, although corticosteroids were not recommended for COVID-19 treatment in the early phases of the pandemic10, we now know that in the inflammatory phase of severe COVID-19 they can reduce proinflammatory and augment anti-inflammatory cytokines, as well as improve lung barrier integrity and microcirculation11., 12., 13.. Fortunately, the evidence is growing, and in the RECOVERY randomised trial, dexamethasone demonstrated a reduction in mortality in patients with respiratory failure3. In addition, in several observational studies, the benefits of corticosteroids in regard to delaying intensive care unit (ICU) admission, shortening mechanical ventilator support14, and even reduced mortality have been observed14 , 15.
Dexamethasone is a well-known drug with more than 60 years of clinical use. Its therapeutic potential comes from several actions. First, it binds to glucocorticoid receptors present in the cell cytoplasm, which are responsible for the initiation of immune cells responses that lead to proinflammatory suppression of several cytokines, some of which are related to COVID-19 progression. It also increases the gene expression of interleukin (IL)-10, which is an anti-inflammatory cytokine mediator. Second, it inhibits neutrophil adhesion to endothelial cells, preventing the release of lysosomal enzymes and chemotaxis at the site of inflammation, as well as inhibiting macrophage activation, one of the main authors of cytokine storms in COVID-19, which in turn is the landmark of severe COVID-19. Additionally, dexamethasone has other important benefits, such as its low-cost, easy availability and its long-lasting effect that allows a once-a-day regimen11 , 16.
Given the positive results of previously mentioned studies on corticosteroids, we suspected that corticosteroids also could shorten the hospital length of stay (LOS), thus reducing the consumption of resources and increasing available beds for other patients who need them. However, no study has focused on this outcome. Furthermore, while the evidence has been accumulating on dexamethasone, other groups of corticosteroids have not yet been evaluated.
Thus, we focused on the first wave of the pandemic, when corticosteroids were beginning to be used, and we compared patients who received corticosteroids with patients who did not. We conducted a real-world study in which we aimed to determine the efficacy of corticosteroids in shortening the LOS in patients with COVID-19 compared with patients who did not receive corticosteroids. In addition, we evaluated which group of corticosteroids was the most effective in reducing the LOS.
MATERIALS AND METHODS
1. Study design and objectives
This was a real-world, controlled, retrospective cohort study. Our main objective was to determine the impact of systemic corticosteroids on the LOS in hospitalised patients with COVID-19. We also evaluated whether the use of corticosteroids was associated with the occurrence of severe complications of COVID-19, such as death and admission to the ICU. Finally, we aimed to assess which specific subgroup of corticosteroids acts most effectively on theses outcomes.2. Patient population and COVID-19 database
We included all individuals, 18 years or older, who were hospitalised in a 1286-bed hospital in Madrid (La Paz University Hospital) with a diagnosis of COVID-19 from April to May 2020, who received systemic corticosteroids (corticosteroid therapy group [CG]). Due to the limited evidence on the use of systemic corticosteroids in this disease until this time, their prescription mainly depended on the physicians’ previous experience in their use.
Patients not hospitalised or discharged from the emergency department after a stay of less than 24 h were not included. A control group of patients who did not require systemic corticosteroid treatment (non-corticosteroid therapy group [NCG]) was recruited from a hospital database that comprised all patients hospitalised with a COVID-19 diagnosis during the same period. The characteristics of this database have been previously published17 and included 3934 patients consecutively treated in the Emergency Department of an University Hospital between February 25, 2021 and June 16, 2021, and who were later hospitalised. The database (called COVID@HULP) includes 372 variables, grouped into demographics, medical history, infection exposure history, symptoms, complications, treatments (excluding clinical trials) and disease progression during hospitalisation. For this study, we extracted age, sex, smoking status, transmission, comorbidities, symptoms on admission, severity of disease, complications, ICU admission and death during hospitalisation. The severity of disease was evaluated according to the Spanish Official Document on the management of COVID-19. It considered mild pneumonia as oxygen saturation higher than 90%, with no signs of severity and a CURB-65 pneumonia severity score lower than 2; and severe COVID-19 pneumonia as organ failure, oxygen saturation lower than 90% or respiratory rate higher than 3018.
Patients (with or without systemic corticosteroid treatment) were matched 1:1 by age, sex and severity of disease. Matching was performed by statisticians of the Central Clinical Research Unit who were blinded to completion of the data.
Laboratory results (haematology, biochemistry, microbiology) were extracted from various hospital data management systems, and information regarding the drugs used during hospitalisation was extracted from the electronic prescription system.
Patients with corticosteroids were identified using the computerised physician order entry (CPOE) program to make prescriptions. The task of identifying patients treated with corticosteroids was performed by a pharmacist with high experience using the CPOE program.
The study was approved by the Research Ethics Committee of La Paz University Hospital (PI-4455).3. Outcomes
The main outcomes were LOS in hospital, death and admission to the ICU. We also evaluated differences between the CG and NCG as well as the development of complications during hospitalisation.
Statistical analysis
In the first part of the analysis, baseline characteristic data on both groups (CG and NCG) were evaluated. In the second part, analyses were focused on the subgroups of corticosteroids used. Patients in both groups were propensity score matched 1:1, accounting for age, sex and severity of disease. Quantitative variables were expressed as medians with interquartile range (IQR). For categorical variables, frequencies and proportions were used. Prior to the analyses, a normality analysis was performed with the Shapiro–Wilk test. For the parametric analysis, Student’s t-test was used, and the Mann–Whitney U test was used for non-parametric analyses. For correlations between quantitative variables, Spearman’s correlation was employed. For the associations between qualitative variables, the chi-squared test (or Fisher's test when necessary) was used. Finally, to investigate the association between corticosteroids and the LOS, we employed a logistic regression analysis. For this purpose, the hospital LOS was dichotomised into ≤ 4 and > 4 days, given it corresponded to the median of the included population. Statistical significance was set at a p-value ≤ 0.05. Statistical analyses were performed using R version 4.0.4.
RESULTS
1. Baseline characteristics of the included patients
A total of 288 hospitalised patients diagnosed with COVID-19 were identified as treated with corticosteroids during the study period. Of these, 89 were not included because of the inability to find a control participant in the hospital’s database after applying the propensity score matching. Ultimately, 199 patients allocated to the CG and 199 patients in the NCG were included in the analysis (Figure 1 ).Figure 1 Flowchart of the study
Figure 1
The distributions of comorbidities were not different when comparing the CG with the NCG. Regarding the systemic inflammatory response to COVID-19, only serum ferritin levels (620.5 [IQR 216.5–1191.8] vs 312.5 [IQR 105.5–594.5]; p < 0.001), white blood cell count (6.5 [IQR 5–9.4] vs 5.9 [IQR 4.4–8.5]; p = 0.041) and platelets (256 [IQR 192–342] vs 225.5 [IQR 179–301.5]; p = 0.016) were significantly higher in the CG compared with the NCG. Comparisons between both groups are detailed in Table 1 .Table 1 Baseline characteristics of hospitalised patients diagnosed with COVID-19 treated or not with systemic corticosteroids
Table 1 CG
(n = 199) NCG
(n = 199) p
Men, n (%) 115 (57.8) 115 (57.8) 1
Age, years 68 [56–78] 68 [56–78] 1
Current smoker, n (%) 16 ( 8.4) 13 ( 6.8) 0.688
Comorbidities
Obesity, n (%) 33 ( 16.8) 27 ( 13.8) 0.510
Cardiac disease, n (%) 49 ( 24.6) 46 ( 23.1) 0.814
Hypertension, n (%) 97 ( 49) 101 ( 50.8) 0.802
COPD, n (%) 17 ( 8.6) 20 ( 10.1) 0.730
Asthma, n (%) 15 ( 7.6) 8 ( 4.0) 0.197
Diabetes mellitus, n (%) 46 ( 23.2) 52 ( 26.1) 0.580
Dyslipidaemia, n (%) 84 ( 42.9) 84 ( 42.2) 0.978
Liver disease, n (%) 11 ( 5.5) 9 ( 4.5) 0.243
Neurological disease, n (%) 37 ( 18.9) 24 ( 12.1) 0.086
Neoplastic disease, n (%) 36 ( 18.4) 29 ( 14.6) 0.390
Kidney disease, n (%) 28 ( 14.1) 18 ( 9.0) 0.153
Patient’s functional status 0.454
Totally dependent 16 ( 8.5) 10 ( 5.3)
Partially dependent 12 ( 6.4) 11 ( 5.9)
Independent 160 (85.1) 167 (88.8)
Long-term oxygen therapy 2 ( 1) 1 ( 0.5) 0.868
Pregnancy 1 ( 0.5) 4 ( 2.0) 0.374
Cohabitation/familial infection 33 (18.2) 30 (16.2) 0.710
Severe COVID-19 105 (52.8) 105 (52.8) 1
Laboratory results
RCP, mg/L 48.3 [10.9–126.5] 64.40 [17.9–147.6] 0.120
Fibrinogen, mg/dL 562.5 [357.3–808.5] 625 [445–777] 0.078
Ferritin, ng/mL 620.5 [216.5–1191.8] 312.5 [105.5–594.5] < 0.001
WBC count, x103/μL 6.5 [5–9.4] 5.9 [4.4–8.5] 0.041
AL count, x103/μL 0.9 [0.6–1.3] 1 [0.7–1.5] 0.214
Platelet count, x103/μL 256 [192–342] 225.5 [179–301.5] 0.016
Total systemic corticosteroid dose
Dexamethasone, mg 60 [22–98] -
Methylprednisolone, mg
(Median dose [CI 95%])
(Median of equivalent dose of dexamethasone [CI 95%])
492.5 [145–1000]
98.5 [29–200]
-
Prednisone, mg
(Median dose [CI 95%])
(Median of equivalent dose of dexamethasone [CI 95%])
60 [28.8–152.5]
9.6 [4.61–24.4]
-
Data expressed as median [interquartile range] or number (percentage).
Comparisons between groups by unpaired samples using Student’s t-test, Mann–Whitney U test and chi-squared test. Abbreviations: AL = absolute lymphocyte; CG = corticosteroid group; COPD = chronic obstructive pulmonary disease; NCG = non-corticosteroid group; RCP = C-reactive protein; WBC = white blood cell.
In the group treated with corticosteroids, the median age was 68 (IQR 56–78) and 57.8% were men. The total systemic corticosteroid dose classified according to the group of corticosteroids were 60 mg (IQR 22–98) for dexamethasone, 492.5 mg (IQR 145–1000) for methylprednisolone and 60 mg (IQR 28.8–152.5) for prednisone (Table 1). The amounts of corticosteroids employed were converted to an equivalent dose of dexamethasone, resulting in a total median dexamethasone dose of 12 mg (IQR 22–98) (Table 1).2. Outcomes associated with the prescription of corticosteroids
The hospital LOS was statistically shorter in the CG than in the NCG (3 [IQR 0–10] vs. 5 [IQR 2.0–8.5] days; p = 0.005). This difference might not be associated with higher mortality, given the mortality rate was not different between the groups (31% vs. 29.6%; p = 0.861); or with a higher severity of the disease at the time of hospital admission, because severity was considered in the matching process of the NCG with the CG. In fact, the CG had a higher rate of ARDS complications during hospitalisation than the NCG (p = 0.006). No differences were observed in the rate of admission to the ICU or in the development of other complications during hospitalisation (Table 2 ). In addition, when converting the doses of the different types of corticosteroids into equivalent doses of dexamethasone, this dose was well correlated with LOS. (r = 0.31; p = 0.058).Table 2 Outcomes among hospitalised patients diagnosed with COVID-19 treated or not with systemic corticosteroids
Table 2 CG
(n = 199) NCG
(n = 199) p
Length of stay in hospital 3 [0–10] 5 [2.0–8.5] 0.005
Admission to the ICU, n (%) 21 ( 10.7) 16 ( 8.1) 0.470
Death, n (%) 61 ( 31.0) 59 ( 29.6) 0.861
Invasive mechanical ventilation, n(%) 11 (6.6) 15 (9.2) 0.508
Concomitant infections during hospitalisation, n (%) 31 ( 15.8) 19 ( 9.5) 0.085
ARDS, n (%) 31 ( 15.8) 13 ( 6.5) 0.006
Concomitant bacterial pneumonia, n (%) 20 ( 10.3) 11 ( 5.5) 0.120
Heart failure, n (%) 10 ( 5.1) 7 ( 3.5) 0.598
Cardiac arrest, n (%) 5 ( 2.6) 5 ( 2.5) 1.000
Renal insufficiency, n (%) 23 ( 11.8) 22 ( 11.1) 0.942
Acute confusional syndrome, n (%) 26 ( 13.3) 26 ( 13.1) 1.000
Psychiatric complications 7 ( 3.6) 6 ( 3.0) 0.985
Data expressed as median [interquartile range] or number (percentage).Comparisons between groups by unpaired samples Student’s t-test, Mann–Whitney U test and chi-squared test. Abbreviations: ARDS = acute respiratory distress syndrome; GC = corticosteroid group; ICU = intensive care unit; NCG = non-corticosteroid group.
The LOS was dichotomised into ≤ 4 and > 4 days, which corresponded to the median of the included population. The logistic regression model revealed that the prescription of corticosteroids was associated with a 43% greater probability of being hospitalised ≤ 4 days compared with the NCG (OR 0.57 [0.37-0.87; p = 0.009]).3. Analysis of the impact of the type of corticosteroid on the length of hospital stay
For this purpose, we only included patients treated with a single group of corticosteroids throughout their hospitalisation. Differences were only noticed in those treated with dexamethasone, in which 76.3% were hospitalised ≤ 4 days and 23.7% stayed > 4 days (p < 0.001). In the other groups, no differences in LOS were observed (Figure 2 ).Figure 2 Distribution of length of stay in hospital according to the group of corticosteroids used
Figure 2
DISCUSSION
The COVID-19 pandemic has meant, especially during the first wave, the near paralysis of hospitalisations for non-COVID-19 health problems as well as for non-urgent surgeries, in order to deal with all the patients with serious COVID-19 who required hospital admission. In addition, although the number of ICU beds has been significantly increased, in some time periods it was still insufficient19. Therefore, reducing the hospital LOS was (and still is) profoundly beneficial in helping cope with new patients who need hospitalisation.
In the first wave of the COVID-19 pandemic, we had a period in which corticosteroids were not routinely recommended and were even contraindicated, after which the first evidence supporting their use was reported18. This real-world controlled retrospective cohort study suggests that corticosteroids, specifically dexamethasone, reduced the LOS in patients with higher inflammation markers compared with the control group. As we have seen, patients in the CG expressed higher levels of platelets and white blood cells, and they had two times higher ferritin levels than those in the NCG. Severe COVID-19 is caused by an excessive systemic increase of cytokines and chemokines in the patient, also called a “cytokine storm”, which leads to immunopathological lung damage and diffuse alveolar injury, with the development of ARDS and death20. In this subgroup of patients, a hyperinflammatory phenotype has been described in which the serum concentrations of inflammatory and coagulation markers (including ferritin, D-dimer, and C-reactive protein), as well as pro-inflammatory cytokines (such as IL-2R, IL-6, IL-10 and tumour necrosis factor-α) are increased, accompanied by reduced lymphocytes and neutrophils with immunometabolic reprogramming13 , 21 , 22. Given corticosteroids are potent immunomodulatory drugs that can break the inflammatory feedforward loop in some individuals 11, as we have seen in the CG group, those with higher inflammation might obtain a greater benefit in terms of LOS11., 12., 13. , 21.
This investigation occurred during a time period in which the first evidence on the benefit of corticosteroids in COVID-19 was being published. At the time of this study, given the data were heterogeneous and we did not know which corticosteroid type was the most appropriate, our hospital protocol allowed us to choose between the 3 corticosteroids described based on the criteria of the attending physicians. We have shown that, while dexamethasone reduces the LOS, methylprednisolone and prednisone did not achieve this outcome.
Most of the evidence accumulated to date on COVID-19 is on dexamethasone. Indeed, the largest randomised study with corticosteroids in severe COVID-19 was the RECOVERY trial, in which it was observed that dexamethasone administration led to a reduction in mortality in patients with respiratory failure3. This outcome has been further supported in 2 meta-analyses that included a high number of critically ill patients with heterogeneous data23 , 24. Methylprednisolone has also been shown better clinical outcomes, to increase ventilator-free days, and a lower mortality rate in moderate to severe COVID-1914 , 25 , 26. In fact, there have been published two randomized trials with hospitalized COVID-19 patients in which methylprednisolone demonstrated a lower ventilator use and shorter length of hospital stay compared to dexamethasone27 , 28.
It is important to note that, when assessed both clinical trials, the applied dose of methylprednisolone was much higher than that of dexamethasone, which makes difficult to draw conclusions regarding whether methylprednisolone is better option than methylprednisolone, or if the higher dose of corticosteroid is the reason for the improvement in this group of patients. In the other hand, when comparing the results of our study with other series, we have several observations. First, although this cohort exhibited a higher mortality rate than that of the RECOVERY trial3, it is within the range reported in other series2., 3., 4.. We must consider the selection bias of randomised clinical trials, in which the most severe patients could be excluded. Fortunately, mortality might be decreasing as the pandemic progresses. Second, there was also a lower proportion of patients who were admitted to the ICU compared with other cohorts3 , 4 , 29. This difference is probably due to the participation of the Intermediate Respiratory Care Units within the Pulmonary Department in our hospital during the pandemic19 , 30. Noninvasive ventilation and other noninvasive respiratory support, such as high-flow nasal cannula oxygen therapy, have played an important role here1 , 29 , 31. These therapies could be applied together with close cardiorespiratory monitoring in these units to try to reduce or delay ICU admissions among patients who require noninvasive respiratory support in a crisis situation, as well as to manage early discharges from the ICU and for those patients who were ineligible for admission to the ICU due to comorbidities.
The main strength of our study is that it is a real-world cohort at a time when corticosteroid treatment had started; therefore, corticosteroid treatment groups could be compared in the same clinical setting (one hospital’s treatment protocols, during the same COVID-19 surge). Additionally, we included a control group, matched for sex, age and severity of disease, and representative of a large proportion of hospitalised patients with COVID-19 in Spain.
This study has several potential concerns and limitations. First, it is a single-centre study with a limited sample size, which reduces the external validity of our results and is insufficient to analyse the effect on mortality. However, it is larger than most of the observational studies evaluating corticoid effects14 , 26 , 27. Second, although we have explored several baseline characteristics of the patients, due to the design of the study and its retrospective nature, it is possible that confounders have not been evaluated. Nevertheless, the data have been extracted from a complex database that includes a multitude of possible confounders as described previously. Third, the cross-sectional design only permits assessing potential associations or relationships. To evaluate causality, it would be necessary to conduct a longitudinal study with long-term patient follow-up. Additionally, we have no information about the need for oxygen supplementation or noninvasive mechanical ventilation. A final limitation is that, at the time of the compilation of these results, we did not have data on long-term outcomes and mortality, which would further enrich the results. However, these patients are in a post-COVID follow-up consultation, which could resolve this limitation in the future.
In conclusion, corticosteroids, especially dexamethasone, might reduce the length of stay in hospitalised patients, which would have a positive impact on hospital capacity during the COVID-19 pandemic.
Supplementary data
Supplementary material 1
Image 1
Supplementary material 2
Image 2
Acknowledgements
We would like to thank María Jiménez González from the Central Clinical Research Unit at La Paz University Hospital for her collaboration in the statistical analysis.
Funding: The authors declare that they have not received funding to perform this article
Conflict of interest
The authors declare that they do not have conflict of interest.
Author Contributions- Ester Zamarrón: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Resources; Software; Supervision; Validation; Visualization; Roles/Writing - original draft; Writing - review & editing.
- Carpio Carlos: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Resources; Software; Supervision; Validation; Visualization; Roles/Writing - original draft; Writing - review & editing
- Villamañán Elena: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Resources; Supervision; Validation; Visualization; Roles/Writing - review & editing
- Álvarez-Sala Rodolfo: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Resources; Software; Supervision; Validation; Visualization; Roles/Writing - original draft; Writing - review & editing
- Borobia Alberto M: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Resources; Software; Supervision; Validation; Visualization; Writing - review & editing
- Gómez-Carrera Luis:Conceptualization; Supervision; Validation; Visualization; Roles/Writing Writing - review & editing
- Buño Antonio: Data curation; Formal analysis; Supervision; Validation; Visualization; Roles/Writing Writing - review & editing
- Prados Concepción: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Resources; Software; Supervision; Validation; Visualization; Roles/Writing - original draft; Writing - review & editing
Supplementary data to this article can be found online at https://doi.org/10.1016/j.farma.2022.11.003.
==== Refs
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| 0 | PMC9747696 | NO-CC CODE | 2022-12-15 23:22:03 | no | Farm Hosp. 2022 Dec 14; doi: 10.1016/j.farma.2022.11.003 | utf-8 | Farm Hosp | 2,022 | 10.1016/j.farma.2022.11.003 | oa_other |
==== Front
Resusc Plus
Resusc Plus
Resuscitation Plus
2666-5204
The Author(s). Published by Elsevier B.V.
S2666-5204(22)00141-2
10.1016/j.resplu.2022.100341
100341
Rapid Response Systems
No fear: willingness of smartphone activated first responders to assist with cardiac arrest during the COVID-19 pandemic
Ball Jocasta ab⁎
Mahony Emily c
Ray Michael c
Nehme Ziad acd
Stub Dion ace
Smith Karen ad
a School of Public Health and Preventive Medicine, Monash University, 553 St Kilda Road, Melbourne, Victoria, Australia 3004
b Baker Heart and Diabetes Institute, 75 Commercial Road, Melbourne, Victoria, Australia 3004
c Ambulance Victoria, 31 Joseph Street, Blackburn North, Victoria, Australia 3130
d Department of Paramedicine, Monash University, Moorooduc Highway, Frankston, Victoria, Australia 3199
e Department of Cardiology, Alfred Health, 55 Commercial Road, Melbourne, Victoria, Australia 3004
⁎ Corresponding author at: Centre of Cardiovascular Research and Education in Therapeutics (CCRET) School of Public Health and Preventive Medicine Monash University 553 St Kilda Road Melbourne, Victoria, Australia 3004
14 12 2022
14 12 2022
10034129 9 2022
21 11 2022
1 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.
Aim
To understand the fear and willingness to respond of smartphone activated first responders during the COVID-19 pandemic.
Methods
We invited smartphone activated first responders registered with the GoodSAM application in Victoria, Australia to take part in an online survey in November 2020. We assessed willingness to respond to an alert and provide CPR during the pandemic and administered the Fear of COVID-19 Scale questionnaire. Regression analysis was conducted to investigate associations between occupation, clinical training, and years of clinical experience with willingness to respond and fear of COVID-19.
Results
The survey response rate was 5.1%. Responders (n=348) had a median age (interquartile range) of 46 years (33-55). Most (67%) were aged 30-59 years and 43% were female. Responders spanned several occupations including paramedics (12.6%), registered nurses (14.7%), and non-clinical individuals (21.8%). Most (92%) reported they would feel comfortable responding to a GoodSAM alert during the pandemic. Almost all (>95%) reported they would provide CPR. About 20% reported being afraid of COVID-19 but only 3.2% reported they had a high-level of fear of COVID-19. The odds of paramedics being willing to respond to an alert was reduced by 73% during the pandemic (OR 0.27, 95% CI 0.11 to 0.69). No other associations were found with willingness or fear of COVID-19.
Conclusion
Although willingness was high and fear of COVID-19 was low, some smartphone activated first responders were less willing to respond to an alert during the pandemic. These findings may inform future pandemic planning and decision-making around pausing first-responder programs.
Keywords
Out-of-hospital cardiac arrest
OHCA
COVID-19
smartphone activated first responders
cardiopulmonary resuscitation
CPR
GoodSAM
==== Body
pmcIntroduction
Providing early intervention in out-of-hospital cardiac arrest (OHCA) is critical to maximising survival and favourable neurological outcomes. Bystanders are an essential link in the chain of survival and can improve patient outcomes by providing cardiopulmonary resuscitation (CPR) and defibrillation using an automated external defibrillator (AED) prior to paramedic arrival.1 In multiple jurisdictions worldwide, crowdsourcing smartphone applications are also used as an important element of the OHCA system-of-care, alerting registered members of the public to nearby OHCA2. Responders contribute to resuscitation efforts by arriving before emergency services and accelerating the initiation of CPR and AED use.
The COVID-19 pandemic has disrupted early links in the chain of survival, leading to poorer patient outcomes worldwide.[3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14] During a critical time in the COVID-19 pandemic in Victoria, Australia, prior to vaccination development and when case numbers were high (>6,000) and strict social restrictions were in place, the smartphone application employed (“GoodSAM”) was deactivated due to the perceived potential risk of COVID-19 transmission to responders or other bystanders present. The deactivation period continued for 208 days. Other regions also changed their management of first responder programmes in response to the pandemic.15 Multiple regions also reported decreased rates of bystander CPR and AED application during the pandemic.[4], [5], [8], [16], [11], [12] It is unknown, however, whether smartphone activated first responders would have been willing to act on alerts during the time of deactivation or whether the fear of COVID-19 was too great. Similarly, it is unknown if the decrease in bystander CPR and AED usage rates seen internationally were due to decreased willingness and increased fear of COVID-19.
We sought to examine the willingness of GoodSAM smartphone activated first responders to respond to alerts for suspected OHCA during the COVID-19 pandemic in Victoria, Australia, in addition to responder attitudes and fears of COVID-19.
Methods
Study design and setting
A cross-sectional, self-administered online survey with closed-ended questions was conducted in Victoria, Australia which has a population of 6.5 million. Ambulance Victoria is the sole provider of Emergency Medical Services (EMS) in Victoria. Suspected OHCA identified during an emergency call receives a three-tiered response including dispatch of community emergency response teams (where available) and fire services (where available), advanced life support paramedics and mobile intensive care paramedics. In addition, up to 3 registered GoodSAM responders are alerted via the smartphone app to nearby arrests occurring within a 500m radius in metropolitan areas and a 5km radius in regional areas at the time of the emergency call.
Study population
All registered GoodSAM responders in Victoria (n=6,854) were invited to take part in this survey. An email invitation was sent out with a link to the study Explanatory Statement and questionnaires on 12 November 2020, immediately following the “second wave” of COVID-19 in Victoria, a 112-day lockdown from 9 July 2020, and a GoodSAM deactivation period (23 March 2020 to 16 October 2020). The survey link remained active for 10 weeks. All GoodSAM responders were alerted via email and text message that the system had been reactivated in October 2020 following the end of lockdown and when it was considered safe to do so.
Study tools
The structured survey was anonymous and delivered in English via the online Qualtrics platform. The questionnaire took participants a mean of 5.4 minutes to complete. We asked seven binary or multiple-choice socio-demographic questions (age, gender, current occupation, number of years of clinical experience [if applicable], living with or without family members) and questions related specifically to willingness to respond to OHCA during the COVID-19 pandemic and the provision of CPR (one binary and four multiple choice questions).
Fear of COVID-19 was assessed using the Fear of COVID-19 scale (FCV-19S)18 which has seven items, the responses of each were measured using a 5-point Likert scale (strongly disagree, disagree, neither agree nor disagree, agree, strongly agree). Scores were categorised into low (scores 7-21) and high (scores 22-35) fear of COVID-19 as defined by Rahman et al (2020) who administered the FCV-19S in a general Australian population which included frontline workers.19
Data analyses
Data was downloaded from Qualtrics and analysed using STATA version 16. Descriptive statistics are presented as frequencies and proportions for categorical data and median and interquartile range (IQR) for continuous variables. Age and gender-adjusted logistic regression analysis was conducted to identify associations between willingness to respond and low fear of COVID-19 with occupation, clinical training versus none, and number of years of clinical experience. Odds Ratios (ORs) and 95% Confidence Intervals (CIs) were calculated. Age and gender-adjusted linear regression analysis was conducted to investigate correlations between total FCV-19S score, occupation, clinical experience versus none, and number of years of clinical experience. A p-value of <0.05 (two-sided) was considered statistically significant.
Ethics
Ethics approval was obtained from Monash University Human Research Ethics Committee (#24377). Information on support services to contact was included at the end of the survey for any participant feeling distressed while completing the study questionnaire.
Results
A total of 348 GoodSAM community first responders (a 5.1% response rate) participated in this study, all from Victoria, Australia. One respondent did not answer all questions about responding to a GoodSAM alert and three respondents did not complete the FCV-19S. Most survey responses were received in November 2020 with six responses received in December and one response received in January 2021.
Participant characteristics
Table 1 demonstrates the characteristics of participating GoodSAM responders. With an overall median (IQR) age of 46 (33, 55) years, most participants (67%) were aged 30-59 years. Almost 43% of participants were female. Participants were from a wide range of occupations; almost one third identified as frontline workers (paramedics, nurses, or medical physicians), 36% were in other first responder occupations (State Emergency Service workers, first aiders, basic life support trained individuals employed by a private provider, lifesavers, EMS volunteers, fire fighters), and 22% were not in a medical or first responder occupation. Those with a clinical background had a median (IQR) of 12 (5, 23) years of experience. Seventy percent of participants were either married or living with a partner and resided with their family during the COVID-19 pandemic period.Table 1 Characteristics of GoodSAM smartphone activated first responder survey participants
Characteristics n=348
Age in years, median (IQR)<30 years, n (%)30-59 years, n (%)60+ years, n (%) 46 (33, 55)67 (19.2%)233 (67.0%)48 (13.8%)
Female gender, n (%) 148 (42.5%)
Current occupation, n (%)ParamedicRegistered NurseMedical physicianStudent (medical/paramedicine/nursing)State Emergency ServicesFirst aider/BLS paramedic/lifesaverEMS volunteerFire fighter (inc. volunteers)Non-medical occupationRetiredUnspecified 44 (12.6%)51 (14.7%)9 (2.6%)18 (5.2%)9 (2.6%)41 (11.8%)34 (9.8%)41 (11.8%)76 (21.8%)14 (4.0%)11 (3.2%)
Years of clinical experience, median (IQR) 12 (5, 23)
Married or living with partner 243 (69.8%)
With children 217 (62.4%)
Resided with family during COVID-19 pandemic 244 (70.1%)
IQR interquartile range; BLS Basic Life Support; EMS Emergency Medical Services
Responding to a GoodSAM alert during COVID-19
Most study participants (92%) reported they would feel comfortable responding to a GoodSAM alert for suspected OHCA during the COVID-19 pandemic (Table 2 ). The fear of both contracting and spreading the virus meant that the remaining 8% would not feel comfortable responding to a GoodSAM alert. Almost all (>95%) indicated they would provide CPR to anyone who required treatment. Forty percent of participants reported they would provide CPR with ventilations. Upon being hypothetically offered provision of personal protective equipment, 95% of participants said they would feel more comfortable to respond to a GoodSAM alert. In the age and gender-adjusted logistic regression analysis, we found that the odds of paramedics being willing to respond to a GoodSAM alert was reduced by 73% during COVID-19 (OR 0.27, 95% CI 0.11 to 0.69, p=0.006; data not shown) (Supplementary Table 1). No further associations were found between willingness to respond to an alert and any other occupation, clinical training versus none, or number of years of clinical experience (Supplementary Table 2).Table 2 Willingness of GoodSAM smartphone activated first responder survey participants to respond to OHCA during the COVID-19 pandemic period
Characteristics n=348
Would you feel comfortable responding to a GoodSAM alert during the COVID-19 pandemic?YesNoDue to fear of contracting the virusDue to fear of spreading the virusDue to contracting and spreading the virusOther reason 320 (92.0%)28 (8.0%)9 (32.1%)0 (0.0%)16 (57.2%)3 (10.7%)
Would you provide CPR during the COVID-19 pandemic?*Yes – to anyone who requires treatmentYes – but only if I knew the person or they were a childYes – but only if I knew the personYes – but only if they were a childNo – I would not provide CPR to anyone during the COVID-19 pandemic 331 (95.4%)10 (2.9%)3 (0.9%)0 (0.0%)3 (0.9%)
What type of CPR would you feel comfortable providing?*Compression-only CPRVentilation-only CPRCompressions and ventilations 208 (59.9%)1 (0.3%)138 (39.8%)
If you were provided with personal protective equipment (PPE), would you feel more comfortable to respond to a GoodSAM alert?*YesMaybeNo 285 (82.1%)44 (12.7%)18 (5.2%)
*One participant did not complete this question.
Fcv-19s
Of the 345 individuals who completed this questionnaire, 19.7% agreed with the statement “I feel most afraid of COVID-19” , with 47.5% reporting they disagreed or strongly disagreed with this statement (Figure 1 ). Less than 2% of participants reported experiencing a physiological response when thinking about COVID-19; 0.6% reported having clammy hands, 1.8% reported insomnia due to worrying about getting COVID-19, and 1.5% reported experiencing a racing heart or palpitations. When asked if they felt uncomfortable when thinking about COVID-19, 11.3% reported they agreed or strongly agreed with the statement. Just over 5% reported they felt they were afraid of losing their life because of COVID-19, and 7.5% agreed that when watching the news and stories about COVID-19 on social media that they became nervous or anxious. The median (IQR) overall FCV-19S score was 12 (9, 14) (Figure 2 ). Eleven of the 345 respondents (3.2%) met the criteria for a high fear of COVID-19 (scores 7-21) compared to 96.8% who met the criteria indicating a low fear of the virus (scores 22-35).Figure 1 Responses from GoodSAM smartphone activated first responders to individual items of the FCV-19S
Figure 2 FCV-19S total score for GoodSAM smartphone activated first responders
In age and gender-adjusted linear regression analysis, no significant associations were found between the FCV-19S score and occupation, clinical training versus none, or number of years of clinical experience (Supplementary Table 3).
Discussion
This study examined the willingness of GoodSAM smartphone activated first responders to continue acting in their role as essential contributors in the OHCA chain of survival during the COVID-19 pandemic. Our cohort included off-duty frontline workers and non-medical workers which is encouraging. We demonstrated that GoodSAM responders are still willing to assist during OHCA despite the pandemic and the potential for infection. Ninety-two percent of responders reported that they would still respond to a GoodSAM alert and more than 95% stated that they would still provide CPR (40% with ventilations). Our work complements data from other regions where the willingness of bystanders to provide CPR did not waver during the COVID-19 pandemic.[3], [20], [21], [22]
Although almost 20% of responders reported being afraid of COVID-19, almost 97% reported that their fear of COVID-19 was low. The reported 73% reduced odds of paramedics (12.6% of the cohort) responding to a GoodSAM alert is likely due to various reasons such as psychological distress caused by seeing first-hand the impact of COVID-19 on patients and colleagues prompting every effort to avoid infection for themselves and their families, already feeling overly exposed to COVID-19 through their work or feeling the need to switch off outside the workplace given the significant increase in workload seen during the pandemic period. A systematic review conducted by Muller et al (2020) involving 59 studies and 54,707 participants demonstrated that 20-25% of healthcare professionals reported a significant increase in mental health issues, psychological distress and sleep disorders during the pandemic which was associated with increased workload.23
In the study conducted in June 2020 by Rahman and colleagues, 587 individuals in the general Australian population were recruited to take part in an online survey to understand psychological distress, fear, and coping strategies during the COVID-19 pandemic.19 Participants had an average age of 41 years and were recruited through general practice, allied health practice and community groups across Australia. Unlike our findings, those who identified themselves as frontline workers (42.3%) were more likely to have lower levels of fear of COVID-19 than other study participants. However, it was unclear what proportion of those who identified as frontline workers were paramedics. In addition, >88% of the cohort resided in the state of Victoria and the dates of administration were prior to the 112-day lockdown imposed in Victoria, one of the longest worldwide. Furthermore, females have consistently demonstrated higher levels of fear and distress than their male counterparts, and almost 62% of the cohort in the study by Rahman et al were female unlike our study in which 43% were female.[24], [25], [26]
Our study has some limitations that require comment. Firstly, although the aim of our survey was to understand the attitudes of GoodSAM first responders with hypothetical questions, it is possible that responders may have been alerted to and attended a nearby OHCA prior to undertaking the survey. Secondly, questions relating to responding to a GoodSAM alert were not pre-tested or validated due to the rapidity we required to administer the survey at this significant point in time. Third, given the low survey response rate, validity cannot be ensured, and non-response bias may have been introduced. In addition, no reminder was sent to registered GoodSAM first responders to complete the survey which may have limited our final sample size. There is also the possibility that responses may have differed given a different pandemic trajectory.
Conclusion
This study confirmed that most smartphone activated first responders, with the exception of paramedics, remain willing to assist in the response to OHCA despite the COVID-19 pandemic. Timely assistance for OHCA and appropriate intervention provided by responders remains available in this context. Our data may inform future pandemic planning and decision making, with smartphone activated first responders likely to remain willing to assist in resuscitation attempts within a pandemic context.
Sources of funding
ZN is supported by a National Heart Foundation of Australia Future Leader Fellowship (105690). DS is supported by a National Heart Foundation of Australia Future Leader Fellowship (101908).
Uncited references
[17].
CRediT authorship contribution statement
Jocasta Ball: Conceptualization, Methodology, Formal analysis, Writing – original draft, Writing – review & editing, Visualization, Project administration. Emily Mahony: Methodology, Data curation, Writing – review & editing, Project administration. Michael Ray: Writing – review & editing, Project administration. Ziad Nehme: Methodology, Writing – original draft, Writing – review & editing. Dion Stub: Writing – review & editing. Karen Smith: Conceptualization, Resources, Writing – review & editing, Supervision.
Conflicts of interest
The authors have no conflicts of interest to declare.
==== Refs
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==== Front
Cell Rep
Cell Rep
Cell Reports
2211-1247
The Author(s).
S2211-1247(22)01802-2
10.1016/j.celrep.2022.111903
111903
Article
A delicate balance between antibody evasion and ACE2 affinity for Omicron BA.2.75
Huo Jiandong 123#∗
Dijokaite-Guraliuc Aiste 4#
Liu Chang 45#
Das Raksha 4
Supasa Piyada 4
Selvaraj Muneeswaran 4
Nutalai Rungtiwa 4
Zhou Daming 25
Mentzer Alexander J. 47
Skelly Donal 789
Ritter Thomas G. 7
Amini Ali 7810
Bibi Sagida 11
Adele Sandra 7
Johnson Sile Ann 7
Paterson Neil G. 6
Williams Mark A. 6
Hall David R. 6
Plowright Megan 1213
Newman Thomas A.H. 1213
Hornsby Hailey 12
de Silva Thushan I. 1213
Temperton Nigel 14
Klenerman Paul 781015
Barnes Eleanor 781015
Dunachie Susanna J. 781617
Pollard Andrew J. 1115
Lambe Teresa 511
Goulder Philip 818
OPTIC consortium&
ISARIC4C consortium$
Fry Elizabeth E. 2∗
Mongkolsapaya Juthathip 45∗
Ren Jingshan 2∗∗
Stuart David I. 256ˆ∗∗
Screaton Gavin R. 45∗∗
1 State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
2 Division of Structural Biology, Nuffield Department of Medicine, University of Oxford, The Wellcome Centre for Human Genetics, Oxford, UK
3 Guangzhou Laboratory, Bio-island, Guangzhou 510320, China
4 Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
5 Chinese Academy of Medical Science (CAMS) Oxford Institute (COI), University of Oxford, Oxford, UK
6 Diamond Light Source Ltd, Harwell Science & Innovation Campus, Didcot, UK
7 Oxford University Hospitals NHS Foundation Trust, Oxford, UK
8 Peter Medawar Building for Pathogen Research, Oxford, UK
9 Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
10 Translational Gastroenterology Unit, University of Oxford, Oxford, UK
11 Oxford Vaccine Group, Department of Paediatrics, University of Oxford, Oxford, UK
12 Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
13 Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
14 Viral Pseudotype Unit, Medway School of Pharmacy, University of Kent and Greenwich Chatham Maritime, Kent ME4 4TB, UK
15 NIHR Oxford Biomedical Research Centre, Oxford, UK
16 Centre For Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
17 Mahidol-Oxford Tropical Medicine Research Unit, Bangkok, Thailand, Department of Medicine, University of Oxford, Oxford, UK
18 Department of Paediatrics, University of Oxford, Oxford, UK
∗ Corresponding authors: , , ,
∗∗ Corresponding authors: , ,
# These authors contributed equally to this work.
& See acknowledgements
$ See acknowledgements
ˆ Lead contact
14 12 2022
14 12 2022
11190314 9 2022
5 11 2022
8 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.
Variants of SARS CoV-2 have caused successive global waves of infection. These variants, with multiple mutations in the spike protein are thought to facilitate escape from natural and vaccine-induced immunity and often increase in the affinity for ACE2. The latest variant to cause concern is BA.2.75, identified in India where it is now the dominant strain, with evidence of wider dissemination. BA.2.75 is derived from BA.2 and contains four additional mutations in the receptor binding domain (RBD). Here we perform an antigenic and biophysical characterization of BA.2.75, revealing an interesting balance between humoral evasion and ACE2 receptor affinity. ACE2 affinity for BA.2.75 is increased 9-fold compared to BA.2; there is also evidence of escape of BA.2.75 from immune serum, particularly that induced by Delta infection which may explain the rapid spread in India, where BA.2.75 is now the dominant variant. ACE2 affinity appears to be prioritised over greater escape.
Graphical abstract
Huo et al. characterize the SARS-CoV-2 variant BA.2.75 (originally identified in India). Its affinity for ACE2 is increased 9-fold over BA.2 and there is evidence of escape of BA.2.75 from immune serum, particularly from Delta infection. ACE2 affinity appears to be prioritized over greater escape via the R493Q reversion mutation.
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pmc
| 0 | PMC9747698 | NO-CC CODE | 2022-12-15 23:22:04 | no | Cell Rep. 2022 Dec 14;:111903 | utf-8 | Cell Rep | 2,022 | 10.1016/j.celrep.2022.111903 | oa_other |
==== Front
Cytokine
Cytokine
Cytokine
1043-4666
1096-0023
The Author(s). Published by Elsevier Ltd.
S1043-4666(22)00320-9
10.1016/j.cyto.2022.156111
156111
Article
Interferon gamma-induced protein 10 (IP-10) for the early prognosis of the risk for severe respiratory failure and death in COVID-19 pneumonia
Samaras Charilaos a
Kyriazopoulou Evdoxia bc
Poulakou Garyfallia d
Reiner Eran e
Kosmidou Maria f
Karanika Ioanna g
Petrakis Vasileios h
Adamis George i
Gatselis Nikolaos K. j
Fagkou Archontoula k
Rapti Aggeliki l
Taddei Eleonora m
Kalomenidis Ioannis n
Chrysos George o
Bertoli Giulia p
Kainis Ilias q
Alexiou Zoi r
Castelli Francesco s
Saverio Serino Francesco t
Bakakos Petros u
Nicastri Emanuele v
Tzavara Vassiliki w
Kostis Evangelos x
Dagna Lorenzo y
Koukidou Sofia z
Tzatzagou Glykeria aa
Chini Maria ab
Bassetti Matteo ac
Trakatelli Christina ad
Tsoukalas George ae
Selmi Carlo af
Samarkos Michael ag
Pyrpasopoulou Athina ah
Masgala Aikaterini ai
Antonakis Emmanouil aj
Argyraki Aikaterini ak
Akinosoglou Karolina al
Sympardi Styliani am
Panagopoulos Periklis h
Milionis Haralampos f
Metallidis Simeon g
Syrigos Konstantinos N. d
Angel Alon e
Dalekos George N. j
Netea Mihai G. anao
Giamarellos-Bourboulis Evangelos J. bc⁎
a 1st Department of Internal Medicine, Asklepieio General Hospital of Voula, Greece
b 4th Department of Internal Medicine, National and Kapodistrian University of Athens, Medical School, Athens, Greece
c Hellenic Institute for the Study of Sepsis, Athens, Greece
d 3rd Department of Internal Medicine, National and Kapodistrian University of Athens, Medical School, Athens, Greece
e MeMed Diagnostics, Tirat Carmel, Israel
f 1st Department of Internal Medicine, University of Ioannina, Medical School, Ioannina, Greece
g 1st Department of Internal Medicine, Aristotle University of Thessaloniki, Medical School, Thessaloniki, Greece
h 2nd Department of Internal Medicine, Democritus University of Thrace, Medical School, 681 00, Alexandroupolis, Greece
i 1st Department of Internal Medicine, G. Gennimatas General Hospital of Athens, Athens, Greece
j Department of Medicine and Research Laboratory of Internal Medicine, National Expertise Center of Greece in Autoimmune Liver Diseases, Full Member of the European Reference Network on Hepatological Disases (ERN RARE-LIVER), General University Hospital of Larissa, 41110 Larissa, Greece
k Department of Internal Medicine, Elpis General Hospital, Athens, Greece
l 2nd Department of Pulmonary Medicine, Sotiria General Hospital of Chest Diseases, Athens, Greece
m Dipartimento Scienze di Laboratorio e Infettivologiche - Fondazione Policlinico Universitario Agostino Gemelli IRCCS - Roma, Italy
n 1st Department of Critical Care and Pulmonary Medicine, Medical School, National and Kapodistrian University of Athens, Evangelismos General Hospital, Athens, Greece
o 2nd Department of Internal Medicine, Tzaneio General Hospital of Piraeus, Athens, Greece
p Department of Infectious – Tropical Diseases and Microbiology, IRCSS Sacro Cuore Hospital, Negrar, Verona, Italy
q 10th Department of Pulmonary Medicine, Sotiria General Hospital of Chest Diseases of Athens, Greece
r 2nd Department of Internal Medicine, Thriasio General Hospital of Eleusis, Athens, Greece
s Spedali Civili, Brescia ASST Spedali Civili Hospital, University of Brescia, Italy
t Department of Internal Medicine, Hospital of Jesolo, Italy
u 1st Department of Chest Medicine, National and Kapodistrian University of Athens, Medical School, Athens, Greece
v Department of Internal Medicine, Spallanzani Institute of Rome, Italy
w 1st Department of Internal Medicine, Korgialeneion-Benakeion General Hospital, Athens, Greece
x Department of Therapeutics, National and Kapodistrian University of Athens, Medical School, Athens, Greece
y Unit of Immunology, Rheumatology, Allergy and Rare Diseases (UnIRAR), IRCCS Ospedale San Raffaele & Vita-Salute San Raffaele University, Milan, Italy
z 5th Department of Pulmonary Medicine, Sotiria General Hospital of Chest Diseases, Athens, Greece
aa 1st Department of Internal Medicine, Papageorgiou General Hospital of Thessaloniki, Thessaloniki, Greece
ab 3rd Dpt of Internal Medicine and Infectious Diseases Unit, Korgialeneion-Benakeion General Hospital, Athens, Greece
ac Infectious Diseases Clinic, Ospedale Policlinico San Martino IRCCS and Department of Health Sciences, University of Genova, Genova, Italy
ad 3rd Department of Internal Medicine, Aristotle University of Thessaloniki, Medical School, Thessaloniki, Greece
ae 4th Department of Pulmonary Medicine, Sotiria General Hospital of Chest Diseases, Athens, Greece
af Department of Biomedical Sciences, Humanitas University, 20072 Pieve Emanuele and IRCCS Humanitas Research Hospital, via Manzoni, 56, 20089 Rozzano, Milan, Italy
ag 1st Department of Internal Medicine, National and Kapodistrian University of Athens, Medical School, Athens, Greece
ah 2nd Department of Propedeutic Medicine, Aristotle University of Thessaloniki, Medical School, Thessaloniki, Greece
ai 2nd Department of Internal Medicine, Konstantopouleio General Hospital, Athens, Greece
aj Department of Pulmonary Medicine, General Hospital of Kerkyra, Greece
ak Department of Internal Medicine, Sotiria General Hospital of Chest Diseases, Greece
al Department of Internal Medicine, University of Patras, Rion, Greece
am 1st Department of Internal Medicine, Thriasio General Hospital of Eleusis, Athens, Greece
an Department of Internal Medicine and Center for Infectious Diseases, Radboud University, Nijmegen, The Netherlands
ao Department of Immunology and Metabolism, Life and Medical Sciences Institute, University of Bonn, Germany
⁎ Corresponding author at: 4th Department of Internal Medicine, ATTIKON University Hospital 1, Rimini Street, 12462, Athens, Greece
14 12 2022
14 12 2022
1561119 10 2022
18 11 2022
8 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.
Objectives
Elevated concentrations of soluble urokinase plasminogen activator receptor (suPAR) predict progression to severe respiratory failure (SRF) or death among patients with COVID-19 pneumonia and guide early anakinra treatment. As suPAR testing may not be routinely available in every health-care setting, alternative biomarkers are needed. We investigated the performance of C-reactive protein (CRP), interferon gamma-induced protein-10 (IP-10) and TNF-related apoptosis-inducing ligand (TRAIL) for predicting SRF or death in COVID-19.
Methods
Two cohorts were studied; one discovery cohort with 534 patients from the SAVE-MORE clinical trial; and one validation cohort with 364 patients from the SAVE trial including also 145 comparators. CRP, IP-10 and TRAIL were measured by the MeMed Key® platform in order to select the biomarker with the best prognostic performance for the early prediction of progression into SRF or death.
Results
IP-10 had the best prognostic performance: baseline concentrations 2000 pg/ml or higher predicted equally well to suPAR (sensitivity 85.0%; negative predictive value 96.6%). Odds ratio for poor outcome among anakinra-treated participants of the SAVE-MORE trial was 0.35 compared to placebo when IP-10 was 2,000 pg/ml or more. IP-10 could divide different strata of severity for SRF/death by day 14 in the validation cohort. Anakinra treatment decreased this risk irrespective the IP-10 concentrations.
Conclusions
IP-10 concentrations of 2,000 pg/ml or higher are a valid alternative to suPAR for the early prediction of progression into SRF or death the first 14 days from hospital admission for COVID-19 and they may guide anakinra treatment.
Trial registration
ClinicalTrials.gov, NCT04680949 and NCT04357366
Keywords
suPAR
IP-10
TRAIL
prognosis
COVID-19
respiratory failure
anakinra
Abbreviations
CI, confidence interval
CRP, C-reactive protein
HR, hazard ratio
IFN, interferon
IL, interleukin
IP-10, interferon gamma-induced protein-10
LPS, lipopolysaccharide
NPV, negative predictive value
PaO2/FiO2, Fraction of partial pressure of oxygen divided by inspired oxygen air mix
PPV, positive predictive value
ROC, receiver operating characteristics curve
SD, standard deviation
SRF, severe respiratory failure
suPAR, soluble urokinase plasminogen activator receptor
TNF, tumor-necrosis factor
TRAIL, TNF-related apoptosis-inducing ligand
WHO-CPS, World Health Organization Clinical Progression Scale
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pmc1 Introduction
Since the beginning of the COVID-19 pandemic, more than 6 million affected individuals have died [1]. Deaths are due to progression into severe respiratory failure (SRF) requiring invasive or non-invasive mechanical ventilation [2]. Therefore, early recognition of patients at risk and early start of treatment is an important strategy to prevent progression into SRF. In order to achieve this, early biomarkers of SRF are crucial. Soluble urokinase plasminogen activator receptor (suPAR) is the only biomarker so far proven to guide an appropriate immunomodulatory treatment [3], [4]. In the SAVE-MORE trial, patients with elevated concentrations of plasma suPAR, an indicator of endothelial activation by the IL-1 inflammatory pathway, were allocated to treatment with anakinra or placebo in addition to Standard-of-Care (SoC) therapy [5]. Using the World Health Organization Clinical Progression Scale (WHO-CPS) as a measure of clinical efficacy, anakinra treatment had 0.36 odds ratio for poor outcome compared to placebo by day 28. Clinical improvement was associated with significant decrease of progression into SRF. Results of the SAVE-MORE trial led to the approval of anakinra guided by suPAR for the treatment of COVID-19 pneumonia in adults by the European Medicines Agency [6]. The Food and Drug Administration has also recently provided Emergency use Authorization to anakinra treatment in the United States [7].
As suPAR testing may not be routinely available in every health-care setting, alternative biomarkers easy to perform by non-invasive tests in clinical laboratories, are needed to identify patients with COVID-19 pneumonia who are at risk of SRF. In this context, previous studies have shown calprotectin, soluble interleukin-2 receptors and SCOPE score as potential predictors for the development of SRF and/or death in COVID-19 patients [8], [9], [10]. In order to explore this topic, we used the novel platform (MeMed Key®) which measures the circulating concentrations of three endogenous inflammatory mediators: C-reactive protein (CRP), interferon gamma- induced protein-10 (IP-10), and TNF (tumor necrosis factor)-related apoptosis-inducing ligand (TRAIL) [11], [12], [13]. The platform was originally designed to distinguish between bacterial and viral infection, but recent studies have shown that these biomarkers may be also helpful to predict COVID-19 severity and potentially guide immunomodulatory treatment [14], [15], [16]. We aimed to investigate whether any of these three inflammatory mediators could be used to early predict the risk of progression into SRF and predict response to anakinra treatment. The prediction of risk was performed using samples coming from the phase 3 SAVE-MORE study. Response to anakinra was done using one independent discovery cohort (phase 3 SAVE-MORE study) and one validation cohort (phase 2 SAVE study).
2 Methods
2.1 Patients
The discovery cohort included patients screened for eligibility for the SAVE-MORE study (NCT04680949) [5] and the validation cohort included patients enrolled in the SAVE study (NCT04357366) [4]. The SAVE-MORE trial was approved by the National Ethics Committee of Greece (approval 161/20) and by the Ethics Committee of the National Institute for Infectious Diseases Lazzaro Spallanzani, IRCCS, in Rome (1 February 2021). The SAVE trial was approved by the National Ethics Committee of Greece (approval 38/20). Written informed consent was provided by all patients prior to enrolment in either study.
Both studies had similar inclusion and exclusion criteria. The two studies differed in the design of the intervention. SAVE-MORE was a double-blind randomized clinical trial and study participants were 1:2 randomly allocated to once daily subcutaneous treatment with either placebo or anakinra for 10 days in addition to SoC. The daily dose of anakinra was 100mg. SAVE was an open-label single-arm non-randomized trial and all study participants were treated with active drug, i.e. anakinra. Daily anakinra dose (100mg) and duration of anakinra treatment (10 days) were the same as in the anakinra arm of the randomized SAVE-MORE trial. An interim analysis of the first 130 patients of the SAVE trial has been published [4]. Since then, the SAVE trial has been completed with the enrolment of 1,000 patients.
Study participants of both trials were adults of either gender, hospitalized with radiological findings of pneumonia by SARS-CoV-2 and plasma suPAR 6 ng/ml or more. Infection was confirmed by PCR testing. Similar exclusion criteria applied in both studies: non-invasive or mechanical ventilation, stage IV malignancy, any do-not-resuscitate decision, ratio of partial oxygen pressure to fraction of inspired oxygen less than 150, severe hepatic failure, any primary immunodeficiency, neutrophils less than 1500/mm3, oral or intravenous corticosteroids more than 0.4 mg/kg/day of equivalent prednisone the last 15 days, any anti-cytokine biologic treatment the last month, hemodialysis, and pregnancy or lactation.
For the purposes of the current study, only available stored samples of patients of the two cohorts, collected at screening were used for measurement of biomarkers. Available data of patients screened for both studies were demographics, treatment with dexamethasone, severity according to WHO and development of SRF or death by day 14.
2.2 Biomarker measurements
suPAR concentrations were measured in plasma samples using the suPARnostic Quick Triage kit (Virogates) and a point-of-care reader. Serum screening samples were kept refrigerated at -80°C in the central study lab, which was the Laboratory of Immunology of Infectious Diseases at the 4th Department of Internal Medicine, Attikon University General Hospital. Levels of CRP, IP-10 and TRAIL were measured on a MeMed Key® (MeMed Diagnostics, Tirat Carmel, Israel) according to the manufacturer’s instructions. Lower limits of quantitation for CRP, IP-10 and TRAIL were 1 mg/ml, 100 pg/ml and 15 pg/ml respectively.
2.3 Endpoints
The primary endpoint was to identify which of the three studied biomarkers, CRP, IP-10 or TRAIL, is the best predictor of progression of COVID-19 pneumonia to SRF or death by day 14. SRF was defined as PaO2/FiO2 <150 necessitating high-flow oxygen or non-invasive ventilation or mechanical ventilation. This analysis was conducted among participants of the SAVE-MORE cohort. Patients allocated to the anakinra and SoC treatment arm were excluded from this analysis to avoid confounding coming from anakinra treatment benefit on progression into SRF/death.
The secondary endpoints were a) the clinical efficacy of anakinra treatment according to the distribution of the 11-point WHO-CPS for patients with biomarker concentrations above the defined cut-offs (this analysis included participants in the SAVE-MORE trial); and b) the impact of anakinra treatment on the progression to SRF/death by day 14 among patients stratified by the concentrations of the biomarker (this analysis included participants in the SAVE trial) (Figure 1 ).Figure 1 Study flow chart The discovery cohort was composed of available samples coming from patients who were screened for eligibility for the SAVE-MORE trial. The primary study endpoint aimed to the development of a cut-off concentration of one of the studied biomarkers CRP, IP-10 and TRAIL to early discriminate the risk for progression into SRF/death by day 14. To make this comparison, patients who failed screening because of suPAR less than 6 ng/ml and patients who were enrolled in the SAVE-MORE trial and were allocated to treatment with placebo and SoC (standard-of-care) were compared. The secondary endpoint was the comparison of the allocation of the 11-point WHO-CPS by day 28 between placebo-treated and anakinra treated patients. For this comparison, only patients with biomarker concentration above the developed cut-off were encountered. The validation cohort was composed by patients who participated in the SAVE trial. The progression into SRF/death by day 14 was compared between patients treated with SoC and anakinra and comparators.Abbreviations CPS: Clinical Progression Scale; CRP: C-reactive protein; IP-10: interferon gamma- induced protein-10; suPAR: soluble urokinase plasminogen activator receptor; TRAIL: TNF- related apoptosis-inducing ligand; WHO: World Health Organization
2.4 Statistical analysis
Categorical data were presented as frequencies and confidence intervals (CI); continuous variables with normal distribution as mean with standard deviation (SD). Fisher’s exact test was used for comparison of categorical data whereas Student’s t-test or non-parametric Mann Whitney test were used for the comparison of continuous data, as appropriate. The prognostic capacity of studied biomarkers was evaluated by the area under the receiver operating characteristics (ROC) curve and 95%CI. The optimal cut-offs were calculated by the Youden’s index. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated by a 2 × 2 table. The areas under the ROC curve were compared by the method of Hanley and McNeil [17]. For the analysis of the impact of anakinra treatment in the distribution of the 11-point WHO-CPS by day 28 among patients with increased biomarkers in the SAVE-MORE trial, multivariate ordinal regression analysis was run. COVID-19 severity, treatment with dexamethasone and body mass index entered as co-variates according to the original statistical analysis plan [5]. For the analysis of the impact of anakinra treatment for progression into SRF/death by day 14 in the SAVE trial, a multivariate Cox forward conditional regression model was used. Biomarker concentrations, COVID-19 severity and dexamethasone treatment entered as co-variates. Adjusted hazard ratio (HR) and 95%CI were calculated. Any two-sided p value < 0.05 was considered statistically significant. Statistical analysis was performed using the software SPSS version 26.0.
3 Results
3.1 Patient population and analysis of the primary endpoint
In order to develop prognostic cut-offs for CRP, IP-10 and TRAIL, screening samples from patients who were not enrolled in the SAVE-MORE trial because of suPAR less than 6 ng/ml and from patients who were enrolled in the SAVE-MORE trial and who were allocated to placebo treatment were analyzed. In total, patients of this cohort had a mean age of 58 (± 13.6) years, 63.3% were male and 75.3% had severe infection (Supplementary Table 1). The concentrations of CRP and IP-10 at screening were higher among the patients who progressed into SRF or died the first 14 days (Figures 2 A and 2B). In contrast, TRAIL concentrations did not differ between patients with a poor or favorable outcome (Figure 2C). ROC curve analysis showed that among the three biomarkers, IP-10 concentrations had the best performance for the early progression into SRF or death (Supplementary Figure 1). IP-10 had equal performance to suPAR (Figure 2D) and a concentration of 2,000 pg/ml was the best cut-off with sensitivity of 85.0% and specificity of 67.2% to predict a poor outcome (Figure 2E and Supplementary Table 2).Figure 2 Development of IP-10 (interferon gamma- induced protein-10) for the early detection of risk of progression to severe respiratory failure (SRF) or death the first 14 days The analysis includes 293 patients who were screened for eligibility of participation at the SAVE-MORE trial; 177 patients failed screening because they had circulatory concentrations of suPAR (soluble urokinase plasminogen activator receptor) less than 6ng/ml; 116 patients had suPAR 6 ng/ml or more, were enrolled in the SAVE-MORE trial and were allocated to treatment with placebo and standard-of-care.A)Comparison of circulating concentrations of C-reactive protein (CRP) at screening between patients who progressed into SRF/death and patients who did not progress into SRF/death by day 14.B)Comparison of circulating concentrations of IP-10 at screening between patients who progressed into SRF/death and patients who did not progress into SRF/death by day 14.C)Comparison of circulating concentrations of TRAIL (TNF- related apoptosis-inducing ligand) at screening between patients who progressed into SRF/death and patients who did not progress into SRF/death by day 14.D)Receiver operating characteristic (ROC) curves of IP-10 and suPAR for the early prediction of the risk for progression into SRF or death the first 14 days.E) Prognostic performance of concentrations of IP-10 greater than 2,000 pg/ml for the early prediction of the risk for progression into SRF or death the first 14 days.Abbreviations AUC: area under the curve; CI: confidence intervals; OR: odds ratio NPV: negative predictive value; PPV: positive predictive value
In the discovery cohort, 34 patients (29.1%) with IP-10 concentrations ≥ 2,000 pg/ml developed SRF or died after 14 days, compared to only 6 patients (3.4%) with IP-10 concentrations <2,000 pg/ml (Figure 3 A). Multivariate Cox regression analysis among all variables associated with unfavorable prognosis revealed that IP-10 concentrations ≥ 2,000 pg/ml was an independent predictor of progression to SRF or death with adjusted HR 6.8 (Figure 3B).Figure 3 IP-10 (interferon gamma- induced protein-10) as an independent variable for the early detection of risk of progression to severe respiratory failure (SRF) or death in the first 14 days The analysis includes 293 patients who were screened for eligibility of participation at the SAVE-MORE trial; 177 patients failed screening because they had circulatory concentrations of suPAR (soluble urokinase plasminogen activator receptor) less than 6ng/ml; 116 patients had suPAR 6 ng/ml or more, were enrolled in the SAVE-MORE trial and were allocated to treatment with placebo and standard-of-care.A)Time to progression to SRF or death the first 14 days B) Univariate and multivariate (Cox Forward Conditional) models for the association of IP-10 with early progression to SRF or death the first 14 days *HR cannot be calculated because one value is zero **excluded after two steps of forward analysis Abbreviations CI: confidence intervals; HR: hazard ratio; n: number of patients, suPAR: soluble urokinase plasminogen activator receptor
Concentrations of IP-10 ad CRP were positively correlated with the concentrations of suPAR (rs: +0.25; p<0.001 and rs: +0.313; p<0.0001, respectively); no significant correlation was found between TRAIL and suPAR (rs: -0.074; p: 0.084) (Supplementary Figure 2).
3.2 Discovery cohort: efficacy of anakinra compared to placebo in patients with IP-10 concentrations higher than 2,000 pg/ml
The primary endpoint of the SAVE-MORE trial was the distribution of the patients to the 11-point WHO-CPS by day 28. Comparisons among the subgroup of patients with IP-10 concentrations ≥ 2.000 pg/ml revealed that randomization to anakinra treatment was associated with 0.35 odds for poor outcome compared to placebo treatment (Figure 4 ). These patients were enrolled in the SAVE-MORE trial so as per inclusion requirements, all had suPAR levels of ≥ 6 ng/ml.Figure 4 Efficacy of anakinra treatment among SAVE-MORE participants with IP-10 (interferon gamma- induced protein-10) 2,000 ng/ml or more This analysis involves 160 participants of the randomized SAVE-MORE trial with circulating concentrations of IP-10 2000 pg/ml or more. These patients were enrolled in the SAVE-MORE trial so as per inclusion requirements, all had suPAR levels of ≥ 6 ng/ml.A)Allocation of patients allocated to treatment with placebo and standard-of-care (SoC) and to treatment with anakinra and SoC in the 11-points of the WHO clinical progression scale (WHO-CPS) by day 28.B)Univariate and multivariate analysis of the WHO-CPS by day 28 Abbreviations CI: confidence interval; n: number of patients; OR: odds ratio
3.3 Validation in SAVE cohort
SAVE trial was open-label non-randomized, and all participants were treated with anakinra and SoC. Studied comparators had suPAR concentrations 6 ng/ml or more and received SoC treatment (Supplementary Table 1). Both comparators and trial participants could be divided into different strata of risk for progression into SRF or death the first 14 days according to the circulating IP-10 concentrations (less than 2,000 pg/ml; 2,000 pg/ml or more) (Figure 5 A). Anakinra treatment was an independent variable with hazard ratio of 0.25 for risk after multivariate analysis in which concentrations of IP-10, severity and dexamethasone treatment were used as co-variates (Figure 5B).Figure 5 Validation of the role of IP-10 (interferon gamma- induced protein-10) as an independent variable for the early detection of risk of progression to severe respiratory failure (SRF) or death the first 14 days The analysis includes 364 patients who participated in the SAVE trial and 145 comparators treated with standard-of-care (SoC) therapy. A)Time to progression to SRF or death the first 14 days. Patients are stratified according to the circulating concentration of IP-10. Comparisons by the log-rank test and respective P-values are indicated by the arrows B) Univariate and multivariate (Cox Forward Conditional) models for the association of IP-10 with early progression to SRF or death the first 14 days **excluded after two steps of forward analysis Abbreviations CI: confidence intervals; HR: hazard ratio; n: number of patients
4 Discussion
In the current study, we showed that IP-10 is a reliable biomarker, with similar performance to suPAR, to predict progression to SRF or death among patients hospitalized with COVID-19 pneumonia. suPAR concentrations ≥ 6 ng/ml have been shown to detect patients with COVID-19 pneumonia at great risk for progression to SRF or death and adequately guide immunomodulatory treatment with anakinra, a recombinant human anti-IL-1 receptor antagonist [3], [4], [5]. In health-care settings in which rapid suPAR concentration measurements may not be easily available, measurement of IP-10 concentrations on a point-of-care platform, such as MeMed Key®, may represent a good alternative. Concentrations of IP-10 ≥ 2,000 pg/ml have a sensitivity 85.0%, and negative predictive value of 96.6% for the early detection of progression risk in COVID-19 pneumonia.
IP-10 (also known as CXCL10) is a chemokine secreted from cells stimulated with type I and II interferons (IFNs) and lipopolysaccharide (LPS), behaving as a chemoattractant for activated T cells and monocytes/macrophages. IP-10 is secreted by several cell types, including monocytes, endothelial cells and fibroblasts. Expression of IP-10 is described for many Th1-type inflammatory diseases, where it is thought to play an important role in recruiting activated T cells into the site of tissue inflammation [18]. TRAIL is a transmembrane protein belonging to the TNF superfamily and is expressed on the surface of natural killer (NK) and T cells, macrophages, and dendritic cells. TRAIL can be anchored in the membrane or can be released as a soluble protein. Both forms function as trimers and can induce apoptosis, making TRAIL important for the regulation of innate immunity and the homeostasis of memory T cells [19]. It becomes obvious that both IP-10 and TRAIL are viral-induced markers and recent studies have shown that like other viral illnesses, COVID-19 is mediated through interferon responses [20]. In this context, we hypothesized that both IP-10 and TRAIL may be involved in COVID-19 pathogenesis and progression to severe disease.
Our results are in alignment with previous studies reporting that low TRAIL concentrations are associated with severe disease [21]. Nonetheless due to the high baseline severity of this cohort, the biomarker was nonspecific, and therefore, was not associated with progression to SRF or death compared to recovered patients [22]. In contrast, we and others have shown that higher IP-10 concentrations are measured in patients with COVID-19 pneumonia who progress to severe disease or death [23], [24]. Tegethoff et al, supported the notion that concentrations of IP-10 more than 3,000 pg/ml was an independent predictor of ICU mortality. In a study of 132 patients in Germany, baseline values of IP-10 were able to predict unfavorable disease evolution [20]. High and persistent levels of IP-10 indicate uncontrolled viral replication in many viral infections such as influenza, SARS, MERS and possibly COVID-19 [25], [26]. Although such an investigation was not within the scope of the current study, our results suggest that markers of uncontrolled viral replication including but not limited to IP-10, could also serve as predictive enrichment tools to guide anakinra or other immunomodulatory treatment. This probably implies that patients who benefit most from anakinra treatment are those with the highest degree of viral replication.
To the best of our knowledge, this is the largest study so far evaluating IP-10 performance in patients with COVID-19. The performance of IP-10 was tested in a discovery and validation cohort, both enrolling prospectively recruited patients. IP-10 serum concentrations may stratify patients into two strata of risk of unfavorable outcome. Even in the validation cohort of our study, in which all patients had suPAR levels of 6 ng/ml or more indicating that a certain degree of immunological dysregulation was already in place, IP-10 still remained a strong predictor of unfavorable outcome. Anakinra treatment could significantly reduce this risk in both strata. Apart from suPAR, all other prognostic scores developed so far require measurement of a large number of biomarkers and a combination with clinical and radiological parameters [7], [27], [28], approach which is difficult to implement in clinical practice. After high vaccination rates, epidemiology might have changed and other comorbidities than those already described may increase the risk for unfavorable outcome. Combination of biomarkers or radiological tests may rise the cost; in this context measurement of a low cost, easy to perform sole biomarker such as IP-10, with good predictive performance, which can be measured rapidly in a point-of-care setting may be cost-effective, but this remains to be shown in future trials. The main limitation of this study is the retrospective analysis of anakinra treatment efficacy when IP-10 is 2,000 pg/ml. This is subject to selection bias. Definite answers on the role of IP-10 as a tool to select patients who receive most of benefit from anakinra treatment may come only from a future prospective study.
In conclusion, when suPAR measurements are not available to predict progression, IP-10 concentrations of 2000 pg/ml or higher may be conceived as an alternative to predict the risk of progression to SRF or death and possibly guide anakinra treatment in COVID-19.
Funding
The SAVE study was funded in part by a kind donation by Technomar shipping company, in part by the Hellenic Institute for the Study of Sepsis (HISS) and in part by Sobi AB (publ). The SAVE-MORE trial was funded in part by the Hellenic Institute for the Study of Sepsis (HISS) and in part by Sobi AB (publ). Measurements of CRP, IP-10 and TRAIL for the needs of this manuscript were supported by MeMed Diagnostics, Tirat Carmel, Israel.
CRediT authorship contribution statement
Charilaos Samaras: Formal analysis, Writing – original draft, Visualization. Evdoxia Kyriazopoulou: Formal analysis, Writing – original draft, Visualization. Garyfallia Poulakou: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. Eran Reiner: Software, Methodology, Resources, Writing – review & editing. Maria Kosmidou: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. Ioanna Karanika: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. Vasileios Petrakis: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. George Adamis: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. Nikolaos K. Gatselis: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. Archontoula Fagkou: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. Aggeliki Rapti: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. Eleonora Taddei: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. Ioannis Kalomenidis: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. George Chrysos: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. Giulia Bertoli: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. Ilias Kainis: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. Zoi Alexiou: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. Francesco Castelli: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. Francesco Saverio Serino: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. Petros Bakakos: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. Emanuele Nicastri: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. Vassiliki Tzavara: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. Evangelos Kostis: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. Lorenzo Dagna: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. Sofia Koukidou: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. Glykeria Tzatzagou: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. Maria Chini: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. Matteo Bassetti: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. Christina Trakatelli: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. George Tsoukalas: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. Carlo Selmi: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. Michael Samarkos: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. Athina Pyrpasopoulou: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. Aikaterini Masgala: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. Emmanouil Antonakis: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. Aikaterini Argyraki: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. Karolina Akinosoglou: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. Styliani Sympardi: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. Periklis Panagopoulos: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. Haralampos Milionis: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. Simeon Metallidis: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. Konstantinos N. Syrigos: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. Alon Angel: Software, Methodology, Resources, Writing – review & editing. George N. Dalekos: Investigation, Validation, Methodology, Resources, Data curation, Writing – review & editing. Mihai G. Netea: Conceptualization, Supervision. Evangelos J. Giamarellos-Bourboulis: Conceptualization, Supervision, Project administration, Funding acquisition.
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.
Acknowledgements
None
Access to data
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.
==== Refs
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25 Chan R.W. Leung C.Y. Nicholls J.M. Peiris J.S. Chan M.C. Proinflammatory cytokine response and viral replication in mouse bone marrow derived macrophages infected with influenza H1N1 and H5N1 viruses PLoS One 7 2012 e51057 23226456
26 Zhou J. Chu H. Li C. Wong B.H. Cheng Z.S. Poon V.K. Active replication of Middle East respiratory syndrome coronavirus and aberrant induction of inflammatory cytokines and chemokines in human macrophages: implications for pathogenesis J Infect Dis 209 2014 1331 1342 24065148
27 Liang W. Liang H. Ou L. Chen B. Chen A. Li C. Development and validation of a clinical risk score to predict the occurrence of critical illness in hospitalized patients with COVID-19 JAMA Intern Med 180 2020 1081 1089 32396163
28 Wynants L. Van Calster B. Collins G.S. Riley R.D. Heinze G. Schuit E. Prediction models for diagnosis and prognosis of Covid-19: systematic review and critical appraisal BMJ 369 2021 m1328
| 0 | PMC9747699 | NO-CC CODE | 2022-12-16 23:21:34 | no | Cytokine. 2023 Feb 14; 162:156111 | utf-8 | Cytokine | 2,022 | 10.1016/j.cyto.2022.156111 | 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)00292-6
10.1016/j.abd.2022.08.006
Original Article
Impact of COVID-19 pandemic on the course of refractory chronic spontaneous urticaria under omalizumab treatment⋆
Olgaç Müge a⁎
Yeğit Osman Ozan b
Beyaz Şengül b
Öztop Nida b
Tüzer Can b
Eyice Deniz b
Karadağ Pelin b
Coşkun Raif c
Demir Semra b
Çolakoğlu Bahaauddin b
Büyüköztürk Suna b
Gelincik Aslı b
a Division of Immunology and Allergic Diseases, Şişli Hamidiye Etfal Research and Education Hospital, Istanbul, Turkey
b Division of Immunology and Allergic Diseases, Department of Internal Medicine, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
c Division of Immunology and Allergic Diseases, Okmeydanı Research and Education Hospital, Istanbul, Turkey
⁎ Corresponding author.
14 12 2022
14 12 2022
13 5 2022
11 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 course of chronic spontaneous urticaria (CSU) can be influenced by infections, depression, and stress.
Objective
Our aim was to investigate the impact of the COVID-19 pandemic on the course of refractory CSU together with patient adherence to omalizumab and treatment adjustments.
Methods
Urticaria Activity Score (UAS7) was used to assess disease activity. Fear of COVID-19 Scale (FC-19s), and Depression Anxiety Stress Scale (DASS-21s) were performed to assess mental health status. All scales were performed during the Quarantine Period (QP) and Return to the Normal Period (RTNP). UAS7 Before Pandemic (BP) was recorded from the patients medical records.
Results
The authors evaluated 104 omalizumab-receiving CSU patients. UAS7 scores during QP were significantly higher than those in RTNP and BP (p < 0.01). DASS-21 and FC-19 scores were significantly higher during QP compared to RTNP (p < 0.01). 19 (18.2%) patients ceased omalizumab, 9 patients prolonged the intervals between subsequent doses during the pandemic. UAS7 scores in QP were significantly higher in patients who ceased omalizumab than in those who continued (p < 0.001). Among patients who continued omalizumab, 22.4% had an increase in urticaria activity and higher FC-19 scores in comparison with those with stable disease activity (p = 0.008).
Study limitations
The small sample size of patients with prolonged intervals of omalizumab and the lack of mental health evaluation with the same tools prior to the study.
Conclusion
Fear induced by COVID-19 can determine an increase in disease activity. Therefore, patients on omalizumab should continue their treatment and prolonged interval without omalizumab can be considered in patients with good urticaria control.
Keywords
Anxiety
COVID-19
Omalizumab
Pandemics
Urticaria
==== Body
pmcIntroduction
Chronic Spontaneous Urticaria (CSU) is a debilitating condition presenting with wheals, angioedema, or both lasting for 6 weeks or more.1 The international EAACI/GA2 LEN/EDF/WAO urticaria guideline recommends second-generation H1-antihistamines as the first-line therapy. Up-dosing to fourfold as second-line therapy can improve response; however, some cases do not respond to antihistamines. Omalizumab is the only approved biological for patients with CSU who remain symptomatic despite a high dose of H1-antihistamine.1, 2, 3, 4
The unpredictability of wheals or angioedema and the overwhelming nature of itching can lead to persistent stress and reduced quality of life. Studies have reported an association between stress, anxiety, depression and CSU.5, 6, 7 It is important to assess these factors with validated tools.
Since The World Health Organization declared Coronavirus Disease 2019 (COVID-19) a public health emergency of international concern, a pandemic,8 diverse clinical consequences have come to the scene. This unpredictable, fast-spreading infectious disease has been causing universal awareness, but also anxiety and distress.9 Since prior studies elucidated that mental well-being had been affected during outbreaks, adverse psychosomatic outcomes are expected to increase.10, 11, 12 The psychological aspect of COVID-19, in terms of fear, stress, anxiety, and depression has yet to be considered thoroughly, especially in patients with chronic diseases.
After the first COVID-19 case was detected in Turkey on March 9, 2020, several containment measures such as social distancing, travel restrictions, and closure of schools and workplaces were implemented. This “Quarantine Period (QP)” lasted for three months. As the number of cases declined by the end of May 2020, restrictions decreased gradually in an effort to maintain routine daily life. This “Return to Normal Period (RTNP)” lasted for three months till the end of August 2020. Behaviors of patients with chronic diseases have been heavily affected during the pandemic considering their adherence to medications, and attendance to follow-up visits however there are no documented data showing the consequences of restrictions on behaviors of patients having chronic diseases. CSU patients can also be expected to be affected similarly by the pandemic and it is important to know the influence of the pandemic on these patients especially when the close relationship between CSU activation and the psychological status of the patients is considered. Therefore, our aim was to investigate the impact of the COVID-19 pandemic on the course of refractory CSU together with patients’ adherence to omalizumab and treatment adjustments.
Methods
Study population and the study design
A total of 104 CSU patients who were on omalizumab treatment were recruited at adult outpatient allergy clinics of three tertiary hospitals in Istanbul. Patients who did not attend follow-up visits, who skipped omalizumab injections for various reasons prior to the COVID-19 pandemic, and who were diagnosed as having concomitant psychiatric diseases and malignancies were not included in the study.
Patients were allocated into three groups according to their patterns of omalizumab application. Accordingly, patients who ceased omalizumab (Group 1), patients who extended the time interval between subsequent omalizumab injections (Group 2), and patients who continued regular omalizumab injections (Group 3) during the study period were determined. All subjects were assessed with the study instruments in QP and RTNP. Additionally, urticaria activity data assessed by Urticaria Activity Score (UAS7) Before the Pandemic Period (BP) was obtained from patient's medical records.
Assessment of urticarial activity and mental status
Disease activity was assessed by UAS7, a simple patient-reported scoring system. Patients assess urticaria by scoring the severity of itch and the number of hives from 0 to 3 over 7 consecutive days.13
UAS7 scores were stratified into 5 score bands describing the disease activity from urticaria free to severe urticarial.14 Change into a higher-activity disease score band was defined as “increased urticaria activity”.
The mental status of the participants was determined by the Turkish version of the Depression-Anxiety-Stress Scale (DASS-21) and Fear of COVID-19 Scale (FC-19S).15, 16
The DASS-21 is a 21-item self-reported questionnaire. Respondents indicate how much the statements apply to them over the past week, using a Likert scale ranging from 0 (never) to 3 (most of the time, always).
Fear of COVID-19 was assessed with FCV-19S. The responses of FCV-19S were recorded on a five-point Likert scale ranging from ‘strongly disagree’ (1) to ‘strongly agree’ (5). A higher score represented greater fear of COVID-19.
This study was approved by the Turkish Ministry of Health (TMoH) (2020-09-09T12_40_50) and the institutional review board and the Ethics Committee of the coordinating center (2021‒96399). Informed consent was obtained from all study participants.
Statistical analysis
Statistical analysis was performed by SPSS.25 version (SPSS Inc., Armonk, NY, USA). Categorical variables were summarized as frequencies and percentages. Continuous variables were given as mean and standard deviations or median (IQR 25‒75). The Wilcoxon test was used for the comparison of data that were not normally distributed. Mann-Whitney U test and Kruskal-Wallis test were conducted to evaluate the different groups. The relationship between scores of UAS7, DASS-21, and FC-19 was analyzed by Spearmen’s correlation test. The two-sided p-value <0.05 determined the statistical significance. GraphPad Prism software was used for graphical analysis.
Results
Clinical and demographic features of the CSU patients
104 patients were included in the study. The mean age was 42.74 ± 12 years and the majority (74%) of the patients were female. The median duration of CSU was 4 years (IQR 25‒75: 3‒8.75) and the median duration of omalizumab treatment was 24 months (IQR 25‒75: 14‒36.75). 50 (48.1%) patients had concomitant chronic inducible urticaria as shown in detail in Table 1 . Stress was the most common triggering factor (51.9%) whereas infections were triggers in 7.7% of patients. The demographic and clinical features of 104 CSU patients are shown in Table 1.Table 1 Demographic and clinical features of 104 patients.
Table 1Age, mean years ± SD 42.74 ± 12
Sex, n (%)
Female 77 (74)
Male 27 (26)
Concomitant disease, n(%) 46 (44.2)
Cardiovascular Diseases 4 (3.8)
Respiratory Diseases 10 (9.6)
Thyroid diseases 10 (9.6)
Hypertension 10 (9.6)
Arrhythmia 2 (1.9)
Diabetes Mellitus 13 (12.5)
Hyperlipidemia 6 (5.8)
Others 8 (7.7)
Duration of urticaria, years, median (IQR 25‒75) 4 (3‒8.75)
Duration of omalizumab, months, median (IQR 25‒75) 24 (14‒36.75)
Presence of angioedema, n (%) 66 (63.5)
Presence of inducible urticaria, n (%) 50 (48.1)
Cold urticaria 13 (12.5)
Solar urticaria 31 (29.8)
Aquagenic urticaria 3 (2.9)
Late pressure urticaria 18(17.3)
Vibratory urticaria 1 (1)
Cholinergic urticaria 13 (12.5)
Contact urticaria 3 (2.9)
Dermographism 23 (22.1)
Presence of triggering factors, n (%) 68 (65.4)
Drugs 22 (21.2)
Foods 31 (29.8)
Stress 54 (51.9)
Infection 8 (7.7)
Others 7 (6.7)
Comparison of the study instruments in all subjects in between pandemic periods
UAS7 scores in all patients during QP (UAS7QP) were significantly higher than those determined in RTNP (UAS7RTNP) and before pandemics (UAS7BP) (p < 0.01) In post hoc analysis, median UAS7QP was higher than those determined before pandemics (UAS7BP) (p = 0.004), while median UAS7 scores were similar among QP and RTNP (UAS7RTNP) (Fig. 1 ).Figure 1 Comparison of median UAS-7 scores in BP, QP, RTNP. UAS-7, Seven Days Urticaria Activity Score; IQR, interquartile range; BP, before pandemic; QP, quarantine period; RTNP, return to normal period; NS, not significant.
Figure 1
DASS-21 and FC-19 scores were significantly higher during QP compared to RTNP (p < 0.01) (Figure 2, Figure 3 ).Figure 2 Comparison of median DASS-21 scores in QP and RTNP. DASS-21, Depression Anxiety, and Stress Scale; IQR, interquartile range; QP, quarantine period; RTNP, return to normal period.
Figure 2
Figure 3 Comparison of mean FC-19 scores in QP and RTNP. FC-19, Fear of COVID-19 scale; QP, quarantine period; RTNP, return to normal period; SD, standart deviation.
Figure 3
Females had significantly higher scores of FC-19 (p = 0.024) during QP, while UAS7 and DASS-21 didn’t differ between genders. UAS7, DASS-21, and FC-19 scores didn’t differ in terms of age, education status, or presence of angioedema or chronic disease during the two periods (Table 2 ).Table 2 UAS7, DASS-21s, FC-19s of patients according to demographic and clinical features.
Table 2 UAS7 median (IQR: 25‒75) FC-19 (mean ±SD) DASS-21t median (IQR: 25‒75)
BP QP RTNP QP RTNP QP RTNP
Sex
Female 2 (0‒5.5) 5 (0‒18) 3 (0‒12) 19.25 ± 7.93 17.31 ± 7.19 7 (2.5‒14.5) 3 (1‒9)
Male 3 (0‒8) 6 (0‒12) 4 (0‒9) 15.04 ± 8.07 14 ± 8.15 6 (0‒14) 1 (0‒9)
p NS NS NS 0.024 NS NS NS
Age
18‒40 2 (0‒5.25) 4.5 (0‒14.25) 3 (0‒7.5) 18.45 ± 7.66 16.64 ± 6.49 8 (1‒14) 3 (0‒10.5)
40‒65 4 (0‒8.5) 7 (0‒19.5) 4 (0‒12.5) 18.34 ± 8.52 16.7 ± 8.29 7 (1.5‒13.5) 3 (1‒9)
> 65 0 (0‒0) 0 (0‒21) 0 (0‒15) 14 ± 5.09 11.5 ± 3.11 3.5 (0‒9.25) 3 (0‒9)
p NS NS NS NS NS NS NS
Presence of angioedem 2 (0‒6) 5 (0‒15) 3 (0‒7.5) 18.94 ± 8.13 17.09 ± 7.66 9 (3‒14.25) 3.5 (1‒10)
p NS NS NS NS NS NS NS
Presence of chronic diseases 1 (0‒7) 10.5 (0‒21.75) 4.5 (0‒14) 18.67 ± 8.44 16.67 ± 8.02 6 (1‒11) 3 (0‒8.25)
p NS NS NS NS NS NS NS
UAS-7, Seven Days Urticaria Activity Score; DASS-21, Depression Anxiety and Stress Scale; FC-19, Fear of COVID-19 scale; IQR, Interquartile Range; SD, Standart Deviation; BP, Before Pandemic; QP, Quarantine Period; RTNP, Return to Normal Period; NS, Not Significant.
UAS7 scores and FC-19 scores were correlated to each other during QP and RTNP (p = 0.02, R = 0.228, p = 0.02, R = 0.227 respectively) (Fig. 4 A‒B), while UAS7 scores were not correlated to DASS-21 scores. (Fig. 5 A‒B).Figure 4 (A) Correlation of scores of UAS7 and FC-19s in QP. (B) Correlation of scores of UAS7 and FC-19s in RTNP. UAS-7, Seven days Urticaria Activity Score; FC-19, Fear of COVID-19 scale; QP, quarantine period; RTNP, return to normal period.
Figure 4
Figure 5 (A) Correlation of scores of DASS-21 and UAS7 in QP. (B) Correlation of scores of DASS-21 and UAS7 in RTNP. DASS-21, Depression anxiety and stress scale; UAS-7, Seven days urticaria activity score; QP, quarantine period; RTNP; return to normal period.
Figure 5
DASS-21 and FC-19 were correlated to each other during QP and RTNP (p < 0.001, R = 0.607, p = 0.02, R = 0.465 respectively) (Fig. 6 A‒B).Figure 6 (A) Correlation of scores of DASS-21 and FC-19s in QP. (B) Correlation of scores of DASS-21 and FC-19s in RTNP. DASS-21: Depression anxiety and stress scale, FC-19: Fear of Covid-19 scale, QP: quarantine period, RTNP: return to normal period.
Figure 6
Determination of study groups according to omalizumab application during the pandemic
76 patients had omalizumab injections with the same dose intervals (Group 3). Injection intervals were prolonged in 9 patients (Group 2) and 19 (18.3%) patients ceased omalizumab (Group 1). The reason for prolongation of omalizumab intervals was fear of COVID-19, while the most common reasons for cessation of omalizumab were fear of COVID-19 and difficulty to attend to hospitals. Some patients had more than one reason for cessation of omalizumab during QP (Table 3 ). 20% of patients in Group 3, had their injections at another center different from their initial center, 2.4% had home therapy and the rest continued at their initial center during the study period. In Group 2, the median duration of intervals between omalizumab doses was 8 weeks (IQR 25‒75: 6‒8). In Group 1, the median duration of omalizumab cessation was 16 weeks (IQR 25‒75: 14‒19).Table 3 The replies regarding the reasons for the ceasation of omalizumab during the quarantine period of the patients.
Table 3 Number of patients (%)
I didn’t attend a healthcare institution due to fear of COVİD-19. 12 (63.1)
I had difficulty finding appointments and/or transportation to hospitals. 6 (31.5)
I didn’t know facilitation of access to prescribed medication during the pandemic. 5 (26.3)
Comparison of urticarial activity among patient groups before the pandemic
Patients in Group 3 had higher mean UAS7BP than those in Group 1 or the ones in Group 2 (p = 0.021). Post hoc analysis revealed that the median UAS7BP was higher in Group 3 than those in Group 2 (p = 0.015), while the median UAS7BP was similar between patients in Group 3 and those in Group 1 (Table 4 ).Table 4 UAS7, DASS-21s, FC-19s scores of 3 patient groups in BP, QP and RTNP.
Table 4 Group 1 (19 patients) Group 2 (9 patients) Group 3 (76 patients) p
UAS-7 median (IQR: 25‒75) BP 0 (0‒6) 0 (0‒1) 3 (0‒7) 0.021
QP 28 (12‒39) 0 (0‒6,5) 4 (0‒14) <0.001
RTNP 0 (0‒26) 1 (0‒21) 4 (0‒9) NS
FC-19s (mean ± SD) QP 19.42 ± 6.69 16.89 ± 9.29 17.99 ± 8.4 NS
RTNP 16.47 ± 6.12 14.78 ± 7.44 16.64 ± 7 NS
DASS-21s median (IQR: 25‒75) QP 9 (0‒15) 6 (0.5‒12.5) 5.5(1‒14) NS
RTNP 4 (0‒12) 2 (0‒4.5) 3 (0‒9) NS
UAS-7, Seven Days Urticaria Activity Score; FC-19, Fear of COVID-19 scale; DASS-21, Depression Anxiety and Stress Scale; IQR, Interquartile Range; SD, Standart Deviation; BP, Before Pandemic; QP, Quarantine Period; RTNP, Return to Normal Period; NS, Not Significant.
Comparison of study instrument results among patient groups in two pandemic periods
In QP, the median UAS7 was different among the three groups (p < 0.001). In post-hoc analysis, the median UAS7QP was the highest in patients in Group 1 (p < 0.001), while the median UAS7QP was similar between patients in Group 2 and those in Group 3 (p > 0.05) (Table 4).
In all three groups, the median UAS7QP was higher than the median UAS7BP, and this increase was statistically significant in patients in Group 1 (p < 0.001) and in those in Group 3 (p = 0.008).
FC-19 scores and DASS-21 t scores didn’t differ among the three groups in QP (Table 4).
In RTNP, it was observed that all Group 1 patients restarted omalizumab since their symptoms deteriorated. The median UAS7, FC-19, and DASS-21 scores were not different among the three groups in RTNP (Table 4).
FC-19 scores in patients with increased urticaria activity during QP
34 (32.7%) patients had increased urticaria activity during QP. Among these patients, FC-19 scores during QP and having comorbidities that are associated with more severe COVID-19 were statistically higher than those with stable urticarial activity (p = 0.013, p = 0.03, respectively). 22.4% of patients in Group 3 had an increase in urticaria activity during QP and these patients had higher FC-19 scores in comparison with those with stable disease activity (p = 0.008).
Discussion
This is the first study investigating the impact of the COVID-19 pandemic on refractory CSU patients treated with omalizumab. Our findings revealed that the COVID-19 pandemic can impair the course of refractory CSU and interventions to support ongoing treatments are therefore crucial. Furthermore, it provides an additional contribution to the recommendations of the recent EAACI statement on biological treatment in CSU during the pandemic17 by showing that up to 8 weeks extension in subsequent omalizumab injections can be tolerated in refractory CSU during the pandemic.
CSU patients are shown to have higher levels of stress and disease activity is correlated with emotional stress levels.18, 19 In a recent study evaluating the impact of the pandemic on 509 mild/moderate CSU patients, revealed increased urticaria activity and higher stress levels during QP compared to BP and RTNP.20 In this present study, the authors were able to determine the increase in disease activity in refractory CSU patients receiving omalizumab during the pandemic. Since emotional disturbances and stressful life events influence CSU courses, it might be speculated that the COVID-19 pandemic would have detrimental effects on CSU patients.
Fear is one of the characteristic features of infectious diseases.21 With the high infection rate and relatively high mortality, individuals can worry about COVID-19. In this respect, the authors used the FC-19 scale which is a valid tool for assessing fear of COVID-19 and significantly correlated with depression and anxiety.16 Shin et al.22 also stated that the fear/anxiety neurocircuitry has overlap and interacts with the neurocircuitry that orchestrates the stress response. In our study, FC-19 scores were significantly higher during QP compared to RTNP, and FC-19 scores were correlated to DASS-21 scores. Furthermore, among patients who continued omalizumab treatment, those showing an increase in urticaria activity had higher FC-19 scores in QP, indicating the impact of fear on the course of CSU.
Response to a psychosocial stressor is different between genders. Reduced fear conditioning is seen in stressed men whereas stressed women can show enhanced fear learning.23, 24 This might be the probable explanation for our female patients displaying higher FC-19 scores while DASS-21 scores weren’t different than men.
The containment measurements such as reducing visits to hospitals and isolation at home implemented to avoid the spread of this highly contagious disease became an obstacle for follow-up and treatment of chronic diseases. Similarly, Kocatürk et al. stated that the weekly number of CU patients treated at UCARE centers decreased by more than 50% during the COVID-19 pandemic.25
A study evaluating the effects of the pandemic on CU patients revealed 44% of omalizumab cessation.26 In our study, 81.7% of the patients continued omalizumab. This relatively higher rate of adherence can be attributed to the regulations in our clinics for the continuation of ongoing omalizumab treatments during the pandemic.
Omalizumab can only be injected in hospitals via prescription in Turkey. During QP, TMoH facilitated access to prescribed medication for those who had valid prescription consent reports. By the courtesy of access to prescribed medication, 20% of our patients continued their injections at different centers other than their initial healthcare center, and 2.4% of the patients had home therapy. At this point, the authors should emphasize the importance of specific attention to tailored interventions in medications for chronic diseases during special periods such as outbreaks in order to maintain effective follow-up and treatment. A recent study by Öztürk et al. stated that 59% of the adult/pediatric allergists in Turkey used telemedicine for the management of urticaria/angioedema patients.27
The recent EAACI statement on the usage of biologicals during COVID-19 advised not to change therapy in non-infected individuals based on expert opinion.17 Nevertheless, successful omalizumab treatment adjustments have been shown in patients with refractory CSU before the pandemic.28, 29, 30 In our study, the authors for the first time showed that dose interval adjustments can be tolerated in refractory CSU patients during the pandemic, but cessation can worsen the course of the disease. Patients those prolonged omalizumab intervals had higher UAS7BP scores. Therefore, interval prolongation can be recommended for patients with good urticaria control in order to reduce hospital visits during the pandemic.
On the other hand, 78.9% of patients who ceased omalizumab had increased urticaria activity. Since our patients were refractory CSU patients, loss of disease control when omalizumab was halted was expected.
COVID-19 has been a risk for all human beings and the presence of comorbidities such as diabetes mellitus, hypertension, cardiovascular disease, and chronic respiratory disease were identified as risk factors for severe COVID-19. In our study, patients having these comorbidities had higher UAS7, DASS-21t, and FC-19s, but weren’t statistically significant. Nevertheless, 47.1% of them had an increase in urticaria activity during QP and this is statistically significant when compared to patients without comorbidities. It can be speculated that knowing to have comorbidities that are associated with severe COVID-19 infection resulted in the loss of disease control, which might be another indicator of the association of fear and stress with urticaria activity.
There were two limitations in this study. The first is the small sample size of patients with prolonged intervals of omalizumab which may have affected the accuracy of statistical results. The authors recommended our patients not change therapy according to the EAACI statement.17 Patients in Group 2 prolonged the intervals independent of our suggestions.
As the second limitation, the authors could not evaluate the mental health of our patients with the same tools prior to the study.
Conclusion
The authors found that both the fear and stress induced by COVID-19 and cessation of omalizumab yielded an increase in urticaria activity. Therefore, patients on omalizumab should continue their treatment either with the same dose intervals or prolonged intervals and tailored interventions such as telemedicine and home therapy should be considered for patients with chronic diseases during periods of outbreaks in order to maintain effective follow-up and reduce unnecessary visits to hospitals.
Financial support
None declared.
Authors' contributions
Müge Olgaç: Has made substantial contributions to conception and design, acquisition of data, analysis, and interpretation of data; has been involved in drafting the manuscript, revising it critically for important intellectual content.
Osman Ozan Yeğit: Has made substantial contributions to or acquisition of data, analysis and interpretation of data; has been involved in drafting the manuscript, revising it critically for important intellectual content.
Sengul Beyaz: Has made substantial contributions to the acquisition of data, analysis and interpretation of data; has been involved in drafting the manuscript.
Özdemir Can Tüzer: Has made substantial contributions to the acquisition of data, analysis and interpretation of data; has been involved in drafting the manuscript.
Deniz Eyice Karabacak: Has made substantial contributions to conception and design, acquisition of data, and has been involved in drafting the manuscript.
Nida Oztop: Has made substantial contributions to the acquisition of data, analysis and interpretation of data; has been involved in drafting the manuscript.
Pelin Karadağ: Has made substantial contributions to the acquisition of data, and interpretation of data; has been involved in drafting the manuscript.
Raif Coşkun: Has made substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data; has been involved in drafting the manuscript.
Semra Demir: Has made substantial contributions to conception and design, acquisition of data, analysis and interpretation of data; has been involved in drafting the manuscript and revising it critically for important intellectual content.
Bahauddin Colakoglu: Has made substantial contributions to conception and design, acquisition of data, analysis and interpretation of data; has been involved in drafting the manuscript or revising it critically for important intellectual content.
Suna Buyukozturk: Has made substantial contributions to conception and design, acquisition of data, analysis and interpretation of data; has been involved in revising the manuscript critically for important intellectual content.
Aslı Gelincik: Has made substantial contributions to conception and design, acquisition of data, analysis and interpretation of data; has been involved in revising the manuscript critically for important intellectual content.
Conflicts of interest
None declared.
⋆ Study conducted at the Division of Immunology and Allergic Diseases, Şişli Hamidiye Etfal Research and Education Hospital; Division of Immunology and Allergic Diseases, Department of Internal Medicine, Istanbul Faculty of Medicine, Istanbul University, and Division of Immunology and Allergic Diseases, Okmeydanı Research and Education Hospital, Turkey.
==== Refs
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8 Organization. WH. WHO characterizes COVID-19 as a pandemic 2020 [Available from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/events-as-they-happen.
9 who.int [Internet]. World Health Organisation. Physical and mental health key to resilience during COVID-19 pandemic 2020, March 26. Available from: http://www.euro.who.int/en/health-topics/health-emergencies/coronavirus-covid-19/statements/statement-physical-and-mental-health-key-to-resilience-during-covid-19-pandemic.
10 Sim K Chua HC. The psychological impact of SARS: a matter of heart and mind Cmaj. 170 2004 811 812 14993176
11 Wu P. Fang Y. Guan Z. Fan B. Kong J. Yao Z. The psychological impact of the SARS epidemic on hospital employees in China: exposure, risk perception, and altruistic acceptance of risk The Canadian Journal of Psychiatry. 54 2009 302 311 19497162
12 Depoux A. Martin S. Karafillakis E. Preet R. Wilder-Smith A. Larson H. The pandemic of social media panic travels faster than the COVID-19 outbreak J Travel Med. 27 2020 taaa031
13 Młynek A. Zalewska‐Janowska A. Martus P. Staubach P. Zuberbier T. Maurer M. How to assess disease activity in patients with chronic urticarial? Allergy. 63 2008 777 780 18445192
14 Stull D. McBride D. Tian H. Arnau A.G. Maurer M. Marsland A. Analysis of disease activity categories in chronic spontaneous/idiopathic urticaria British Journal of Dermatology. 177 2017 1093 1101 28295198
15 Yilmaz O. Boz H. Arslan A. The validity and reliability of depression stress and anxiety scale (DASS 21) Turkish short form Research of Financial Economic and Social Studies. 2 2017 78 91
16 Satici B. Gocet-Tekin E. Deniz M. Satici S.A. Adaptation of the Fear of COVID-19 Scale: Its association with psychological distress and life satisfaction in Turkey International journal of mental health and addiction. 19 2021 1980 1988 32395095
17 Vultaggio A. Agache I. Akdis C.A. Akdis M. Bavbek S. Bossios A. Considerations on biologicals for patients with allergic disease in times of the COVID‐19 pandemic: an EAACI statement Allergy. 75 2020 2764 2774 32500526
18 Maurer M. Weller K. Bindslev‐Jensen C. Giménez‐Arnau A. Bousquet P. Bousquet J. Unmet clinical needs in chronic spontaneous urticaria. A GA2LEN task force report 1 Allergy. 66 2011 317 330 21083565
19 Varghese R. Rajappa M. Chandrashekar L. Kattimani S. Archana M. Munisamy M. Association among stress, hypocortisolism, systemic inflammation, and disease severity in chronic urticaria Annals of Allergy, Asthma & Immunology. 116 2016 344 348 e1
20 Psychological burden of COVID-19 on mild and moderate chronic spontaneous urticaria Beyaz S. Demir S. Oztop N. Karadag P. Coskun R. Colakoglu B. Allergy & Asthma Proceedings 2021
21 Pappas G. Kiriaze I. Giannakis P. Falagas M. Psychosocial consequences of infectious diseases Clinical microbiology and infection. 15 2009 743 747 19754730
22 Shin L.M. Liberzon I. The neurocircuitry of fear, stress, and anxiety disorders Neuropsychopharmacology. 35 2010 169 191 19625997
23 Merz C.J. Tabbert K. Schweckendiek J. Klucken T. Vaitl D. Stark R. Investigating the impact of sex and cortisol on implicit fear conditioning with fMRI Psychoneuroendocrinology. 35 2010 33 46 19683399
24 Merz C.J. Wolf O.T. Schweckendiek J. Klucken T. Vaitl D. Stark R. Stress differentially affects fear conditioning in men and women Psychoneuroendocrinology. 38 2013 2529 2541 23790683
25 Kocatürk E. Salman A. Cherrez‐Ojeda I. Criado P.R. Peter J. Comert‐Ozer E. The global impact of the COVID‐19 pandemic on the management and course of chronic urticaria Allergy. 76 2021 816 830 33284457
26 Erdem Y. Polat Ekinci A. Altunay I.K. Sivaz O. Inal S. Gokalp M.O. The impact of COVID‐19 pandemic on the management of patients with chronic urticaria: An observational two‐center study from Turkey Dermatologic Therapy. 34 2021 e14652
27 Ozturk A.B. Baççıoğlu A. Soyer O. Civelek E. Şekerel B.E. Bavbek S. Change in allergy practice during the COVID-19 pandemic International Archives of Allergy and Immunology. 182 2021 49 52 33059353
28 Türk M. Maurer M. Yılmaz İ. How to discontinue omalizumab in chronic spontaneous urticarial? Allergy. 74 2019 821 824 30478912
29 de Montjoye L. Herman A. Dumoutier L. Lambert M. Tromme I. Baeck M. Omalizumab in chronic spontaneous urticaria: a real-life experience of dose and intervals adjustments in Belgium Annals of Allergy, Asthma & Immunology. 121 2018 620 622
30 Aghdam M.A. Pieterse R.H. Kentie P.A. Rijken F. Knulst A.C. Röckmann H. Effective omalizumab interval prolongation in the treatment of chronic urticaria The Journal of Allergy and Clinical Immunology: In Practice. 8 2020 3667 3668 e1 32679351
| 0 | PMC9747700 | NO-CC CODE | 2022-12-15 23:22:04 | no | An Bras Dermatol. 2022 Dec 14; doi: 10.1016/j.abd.2022.08.006 | utf-8 | An Bras Dermatol | 2,022 | 10.1016/j.abd.2022.08.006 | oa_other |
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Nephron Clin Pract
Nephron Clin Pract
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Clinical Practice: Case Report
ANCA-Associated Vasculitis following the First Dose of Pfizer-BioNTech COVID-19 Vaccine
El Hasbani Georges a
Uthman Imad b *
aDepartment of Internal Medicine, St. Vincent's Medical Center, Bridgeport, Connecticut, USA
bDepartment of Internal Medicine, American University of Beirut Medical Center, Beirut, Lebanon
*Imad Uthman, [email protected]
18 7 2022
18 7 2022
15
30 1 2022
3 6 2022
Copyright © 2022 by S. Karger AG, Basel
2022
https://www.karger.com/Services/SiteLicenses Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher.
Coronavirus disease (COVID-19) vaccine can alter the body's immunological balance leading to autoimmune disease in rare cases. Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis is one of the autoimmune diseases which have been rarely reported to appear post-COVID-19 vaccine. Herein, we report the case of a 47-year-old woman who developed acute renal failure few days after receiving the first dose of the Pfizer-BioNTech COVID-19 vaccine. Corticosteroids along with azathioprine were used for the management.
Key Words
Coronavirus disease
Autoimmunity
ANCA-associated vasculitis
Acute renal failure
The authors have received no funds to publish this report.
==== Body
pmcIntroduction
The humongous medical, financial, and social impact of coronavirus disease (COVID-19) necessitated the rapid implementation of vaccination. Several types of vaccines were found using novel pathways such as mRNA delivered via lipid nanoparticles, viral vectors, inactivated virus, and protein subunits [1]. These vaccines were significantly effective in reducing COVID-19-related mortality [1].
The induction of an autoimmune/autoinflammatory response, such as vaccine-induced immune thrombotic thrombocytopenia and immune-mediated myocarditis, are some of the rare side effects reported with different types of vaccines [2, 3]. Interestingly, different types of vasculitides, including anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis, were reported in the context of the Pfizer-BioNTech COVID-19 vaccine [4]. As a consequence, multiple ongoing clinical trials are currently studying the safety profile of COVID-19 vaccines [5].
In this case report, we illustrate the case of a healthy woman who developed acute renal failure few days following the Pfizer-BioNTech COVID-19 vaccine. This patient was successfully treated with oral corticosteroids and azathioprine.
Case Report/Case Presentation
A previously healthy 47-year-old woman presented to the primary care clinic for bilateral flank pain, generalized weakness, and bilateral lower extremity swelling that started 3 days following the first dose of Pfizer-BioNTech COVID-19 vaccine. No shortness of breath or hemoptysis was reported. Vital signs were within normal limits, including a blood pressure of 129/76 mm Hg. Physical examination was significant for bilateral lower extremity edema reaching the knees. No sacral edema was noted. Costovertebral angle tenderness was negative. Otherwise, the examination was unremarkable, including a regular rate rhythm, with normal S1 and S2, no murmurs, and palpable peripheral pulses.
Urinalysis was positive for proteins, red blood cells of 40–50/high power field, and white blood cells of 8–10/high power field. Urine protein/creatinine ratio was significantly elevated at 1,777 mg/g. Laboratory investigations were compatible with acute kidney injury as the serum creatinine was 2.91 mg/dL (baseline 0.8 mg/dL measured 7 months prior to presentation), urea was 208 mg/dL, and eGFR was 18 mL/min/1.73 m2. In addition, bicarbonate was decreased at 17 mEq/L. The complete blood count was significant for leukocytosis (white blood count 14.99 × 103/µL) with neutrophil predominance of 82.8%. Inflammatory markers were elevated with C-reactive protein of 2.34 mg/dL (normal range: 0.8–1.0 mg/dL). The extractable nuclear antigen profile was positive for anti-neutrophil cytoplasmic antibody (ANCA) IgG myeloperoxidase (MPO) at 2.8 units/mL (normal <1 units/mL). The IgG class ANCA directed to proteinase 3 (PR3) titers were within normal limits (normal <1 units/mL). Complement levels and other serologic tests were unremarkable. A CT scan of the chest was negative for any lung involvement. A kidney biopsy revealed changes of fibrous crescents with interstitial fibrosis and tubular atrophy with negative immunofluorescence compatible with a systemic disease mediated by ANCA (Fig. 1a, b). The patient was started on intravenous methylprednisolone for 3 days and prednisone 50 mg daily thereafter along with azathioprine 50 mg twice daily.
Serum creatinine trended down to 2.01 mg/dL 2 weeks after treatment initiation. Urine protein/creatinine ratio also trended down to 862 mg/g. In a follow-up visit to the clinic 3 months after initial presentation, the patient reported a better functional status. The lower extremity edema completely resolved. Serum creatinine was 1.23 mg/dL in the 3- month follow-up visit.
Discussion/Conclusion
Despite being rare events, vaccines have been long thought to induce autoimmune diseases, such as swine flu vaccine inducing Guillain-Barré syndrome [6]. ANCA-associated vasculitis (AAV) has been also reported in the context of vaccination. For example, multiple reports have illustrated a temporal relationship between AAV and influenza vaccination [7].
Besides the appearance of autoinflammatory/autoimmune phenomena in COVID-19 patients [8], different types of COVID-19 vaccines have been very rarely linked with several autoimmune diseases, such as rheumatoid arthritis [9] and lupus nephritis [10]. Vasculitis induction has been also reported in the context of COVID-19 vaccine. Both induction of vasculitis and a flare of a pre-existing vasculitis have been described post-COVID-19 vaccine [11, 12, 13]. One type of vasculitides, the AAV, has been also rarely illustrated to be induced secondary to different types of COVID-19 vaccines, including the Pfizer-BioNTech vaccine (Table 1). Furthermore, a case series of 29 patients who developed glomerular disease post-severe acute respiratory syndrome coronavirus 2 immunization was reported in the literature [14]. Only two of these cases had a complete recovery. Out of all 29 cases, six had a crescentic glomerulonephritis. Four out of 10 ANCA-positive glomerulonephritis cases had the disease occurring after the Pfizer-BioNTech COVID-19 vaccine, none of which had a complete recovery, although the treatment is unclear. There was no overall increase in the incidence of biopsy-proven glomerular disease when compared to the era prior to the COVID-19 pandemic [14]. The glomerular disease secondary to COVID-19 vaccination was deemed to be rare, although it should be monitored as a potential adverse event [14].
Our case is unique in the rapid onset of symptoms and the onset post the first dose in particular [15, 16, 17]. It is important to note that the presence of fibrous crescents and interstitial fibrosis in the kidney biopsy might point to a chronic process. The AAV might have been silent in our patient and exacerbated after COVID-19 vaccination. A recent consensus statement on COVID-19 vaccination in patients with immune-mediated kidney disease highlighted the rarity of induction/flare of immune-mediated kidney disease post-COVID-19 vaccination [21]. The consensus statement indicated that these rare cases respond to immunosuppression and mainly occur post-second vaccination dose.
Acute kidney injury has been rarely associated with severe acute respiratory syndrome coronavirus 2 infection [17] and COVID-19 vaccine [22, 23]. Although there is no recommendation to randomly check serum creatinine in COVID-19 patients or those recently vaccinated, an elevated serum creatinine or abnormal urinalysis in this population warrants further investigation keeping in mind a possible autoimmune process.
In conclusion, COVID-19 vaccines have substantial benefits. Rarely, autoimmune processes have been described post-vaccination. AAV is an example of an autoimmune disease that can be induced or flared up from a silent state by COVID-19 vaccines. A high index of suspicion regarding the presence of an autoimmune renal process is needed whenever a recently COVID-19-vaccinated individual presents for acute kidney injury.
Statement of Ethics
An ethics statement is not applicable because this study is based exclusively on published literature. The patient has given her written informed consent to publish this case (including publication of images).
Conflict of Interest Statement
The authors have no conflicts of interest to declare.
Funding Sources
The authors have received no funds to publish this report.
Author Contributions
Georges El Hasbani took care of reviewing the literature and writing this case. Imad Uthman generated the idea and provided the images.
Data Availability Statement
All data generated or analyzed during this study are included in this article. Further inquiries can be directed to the corresponding author.
Fig. 1 a Two glomeruli are globally obsolescent (sclerosed) with capillary tuft of glomeruli with cellular crescents showing few or no inflammatory cell elements. b On periodic acid-Schiff stain, the glomerular basement membranes of unaffected segments show wrinkling and thickening of the glomerular basement membrane. Tubules reveal focal atrophy and interstitium is minimally infiltrated by chronic inflammatory cells.
Table 1 Positive ANCA and AAV cases following Pfizer-BioNTech COVID-19 vaccine
Case Clinical presentation CBC and chemistry Serology Biopsy Treatment Outcome
Shakoor et al. [15] Nausea, vomiting, diarrhea, and lethargy Two weeks Second dose Creatinine 3.54 mg/dL Urinary albumin-creatinine ratio 205 µg/mg Anti-MPO 1.1 IU/mL Pauci-immune crescentic necrotizing glomerulonephritis Steroids and rituximab Creatinine
1.71 mg/dL
One month
Dube et al. [16] Asymptomatic Seven weeks Second dose Creatinine of 1.91 mg/dL Urine albumin-creatinine ratio 633 µg/mg Anti-MPO 71 IU/mL Pauci-immune crescentic glomerulonephritis Steroids, rituximab, and cyclophosphamide Creatinine
1.01 mg/dL
Ten weeks
Hakroush and Tampe [17] Generalized weakness and thigh pain Two weeks Second dose WBC 22,900/µL Creatinine kinase levels
14,243 U/L
Myoglobin >12,000 µg/L Creatinine 1.38 mg/dL Nephrotic range proteinuria Anti-MPO
>134 lU/mL Pauci-immune crescentic glomerulonephritis Steroids and cyclophosphamide Proteinuria 1,603 µg/mg No specified period
Okuda et al. [18] Pain, redness, and swelling in the left auricle Three weeks First dose CRP 10.16 mg/dL Creatinine 0.62 mg/dL Anti-MPO 494 IU/mL
Anti-PR3 28.3 IU/mL Not performed Steroids Negative CRP Nine days
Obata et al. [19] Fever spikes, malaise, and cough Two weeks Second dose WBC 12,600/uL
CRP of 18.4 mg/dL Creatinine 1.22 mg/dL Anti-MPO 112.8 IU/mL Focal necrotizing glomerulonephritis with cellular crescents Masson's trichrome stain revealed fibrinoid necrosis with marked endothelial swelling Steroids Creatinine
1.35 mg/dL
Anti-MPO 10.0 lU/mL Eight weeks
Shirai et al. [20] Severe vertigo and hearing loss Three weeks First dose WBC 12,800/µL
CRP 14.32 mg/dL Negative anti-–MPO
Anti-PR3 259 IU/mL Fibrin deposition in the small vessels and granulation tissue with intensive infiltration of inflammatory cells, predominantly neutrophils Steroids and cyclophosphamide NA
Current case Bilateral flank pain, generalized weakness, and bilateral lower extremity edema Three days First dose WBC 14,990/µL Creatinine 2.91 mg/dL Urine protein/creatinine 1,777 µg/mg Anti-MPO 2.8 IU/mL Pauci-immune crescentic glomerulonephritis Steroids and azathioprine Creatinine 2.01 mg/dL Negative CRP Negative ESR Two weeks
CBC, complete blood count; BUN, blood urea nitrogen; MPO, myeloperoxidase; WBC, white blood count; CRP, C-reactive protein.
==== Refs
References
1 Speiser DE Bachmann MF COVID-19: mechanisms of vaccination and immunity Vaccines 2020 8 (3) 404 32707833
2 Arepally GM Ortel TL Vaccine-induced immune thrombotic thrombocytopenia: what we know and do not know Blood 2021 138 (4) 293 298 34323940
3 Kim HW Jenista ER Wendell DC Azevedo CF Campbell MJ Darty SN Patients with acute myocarditis following mRNA COVID-19 Vaccination JAMA Cardiol 2021 6 (10) 1196 34185046
4 Bomback AS Kudose S D'Agati VD De novo and relapsing glomerular diseases after COVID-19 vaccination: what do we know so far? Am J kidney Dis 2021 78 (4) 477 480 34182049
5 Chaudhary JK Yadav R Chaudhary PK Maurya A Kant N Rugaie OA Insights into COVID-19 vaccine development based on immunogenic structural proteins of SARS-CoV-2, host immune responses, and herd immunity Cells 2021 10 (11) 2949 34831172
6 Toussirot E Bereau M Vaccination and induction of autoimmune diseases Inflamm Allergy Drug Targets 2016 14 (2) 94 98
7 Watanabe T Vasculitis following influenza vaccination: a review of the literature Curr Rheumatol Rev 2017 13 (3) 188 196 28521688
8 Rodriguez Y Novelli L Rojas M De Santis M Acosta-Ampudia Y Monsalve DM Autoinflammatory and autoimmune conditions at the crossroad of COVID-19 J Autoimmun 2020 114 102506 32563547
9 Baimukhamedov C Makhmudov S Botabekova A Seropositive rheumatoid arthritis after vaccination against SARS-CoV-2 infection Int J Rheum Dis 2021 24 (11) 1440 1441 34585843
10 Tuschen K Brasen JH Schmitz J Vischedyk M Weidemann A Relapse of class V lupus nephritis after vaccination with COVID-19 mRNA vaccine Kidney Int 2021 100 (4) 941 944 34352310
11 Bostan E Gulseren D Gokoz O New-onset leukocytoclastic vasculitis after COVID-19 vaccine Int J Dermatol 2021 60 (10) 1305 1306 34241833
12 Cohen SR Prussick L Kahn JS Gao DX Radfar A Rosmarin D Leukocytoclastic vasculitis flare following the COVID-19 vaccine Int J Dermatol 2021 60 (8) 1032 1033 33928638
13 Tagini F Carrel L Fallet B Gachoud D Ribi C Monti M Behçet's-like adverse event or inaugural Behçet's disease after SARS-CoV-2 mRNA-1273 vaccination? Rheumatology 2021 61 (5) e112 3
14 Caza TN Cassol CA Messias N Hannoudi A Haun RS Walker PD Glomerular disease in temporal association with SARS-CoV-2 vaccination: a series of 29 cases Kidney360 2021 2 (11) 1770 1780 35372991
15 Shakoor MT Birkenbach MP Lynch M ANCA-associated vasculitis following pfizer-BioNTech COVID-19 vaccine Am J kidney Dis 2021 78 (4) 611 613 34280507
16 Dube GK Benvenuto LJ Batal I Antineutrophil cytoplasmic autoantibody-associated glomerulonephritis following the Pfizer-BioNTech COVID-19 vaccine Kidney Int Rep 2021 6 (12) 3087 3089 34423176
17 Hakroush S Tampe B Case report: ANCA-associated vasculitis presenting with rhabdomyolysis and pauci-immune crescentic glomerulonephritis after Pfizer-BioNTech COVID-19 mRNA vaccination Front Immunol 2021 12 762006 34659268
18 Okuda S Hirooka Y Sugiyama M Propylthiouracil-induced antineutrophil cytoplasmic antibody-associated vasculitis after COVID-19 vaccination Vaccines 2021 9 (8) 842 34451967
19 Obata S Hidaka S Yamano M Yanai M Ishioka K Kobayashi S MPO-ANCA-associated vasculitis after the Pfizer/BioNTech SARS-CoV-2 vaccination Clin Kidney J 2021 15 (2) 357 359 35140936
20 Shirai T Suzuki J Kuniyoshi S Tanno Y Fujii H Granulomatosis with polyangiitis following Pfizer-BioNTech COVID-19 vaccination Mod Rheumatol Case Rep 2022 rxac016 Online ahead of print 35246689
21 Stevens KI Frangou E Shin JI Anders HJ Bruchfeld A Schonermarck U Perspective on COVID-19 vaccination in patients with immune-mediated kidney diseases: consensus statements from ERA-IWG and EUVAS Nephrol Dial Transplant 2022 gfac052 Online ahead of print
22 Liakopoulos V Roumeliotis S Papachristou S Papanas N COVID-19 and the kidney: time to take a closer look Int Urol Nephrol 2021 54 (5) 1053 1057 34383205
23 D'Agati VD Kudose S Bomback AS Adamidis A Tartini A Minimal change disease and acute kidney injury following the Pfizer-BioNTech COVID-19 vaccine Kidney Int 2021 100 (2) 461 463 34000278
| 35850104 | PMC9747720 | NO-CC CODE | 2022-12-15 23:22:04 | no | Nephron Clin Pract. 2022 Jul 18;:1-5 | utf-8 | Nephron | 2,022 | 10.1159/000525562 | oa_other |
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Nephron Clin Pract
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S. Karger AG Allschwilerstrasse 10, P.O. Box · Postfach · Case postale, CH–4009, Basel, Switzerland · Schweiz · Suisse, Phone: +41 61 306 11 11, Fax: +41 61 306 12 34, [email protected]
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Clinical Practice: Case Report
A Fabry Disease Patient Who Developed Hypersensitivity Reaction against Agalsidase Beta following COVID-19 Infection
Sonmez Ozge a
Ozcan Seyda Gul a
Trabulus Sinan b
Seyahi Nurhan b *
aDepartment of Internal Medicine, Cerrahpasa Medical Faculty, Istanbul University-Cerrahpasa, Istanbul, Turkey
bDivision of Nephrology, Department of Internal Medicine, Cerrahpasa Medical Faculty, Istanbul University-Cerrahpasa, Istanbul, Turkey
*Nurhan Seyahi, [email protected]
29 9 2022
29 9 2022
14
30 5 2022
13 7 2022
Copyright © 2022 by S. Karger AG, Basel
2022
https://www.karger.com/Services/SiteLicenses Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher.
Fabry disease (FD) is a rare, X-linked inherited lysosomal storage disorder, characterized by the accumulation of globotriaosylceramide (Gb3) due to the deficiency or absence of alpha-galactosidase A. Due to the accumulation of Gb3, cardiac, renal, neurological, and skin manifestations can be observed. Enzyme replacement therapy (ERT) with agalsidase alfa or agalsidase beta is the cornerstone in the management of FD. Both enzymes are clinically effective and widely used. In this study, we present a 19-year-old male patient with FD who had received ERT for almost two and half years without any complications. In January 2021, he was diagnosed with COVID-19 infection. Later, he developed an infusion reaction during his first ERT infusion following the resolution of COVID-19 infection. The patient experienced shortness of breath, shivering, and rash. Despite decreased infusion rate and premedication in repetitive infusion, his symptoms were not resolved. Subsequently, he developed an IgE antibody against agalsidase beta, and his skin prick test was positive. Since IgG positivity against agalsidase beta was also detected, agalsidase beta was replaced with agalsidase alfa. The patient did not experience any allergic reaction with agalsidase alfa. Moderate to severe allergic reactions during ERT infusion should be alarming for IgE development. Furthermore, COVID-19 should be considered a trigger for allergic reaction against ERT in patients with FD.
Key Words
Fabry disease
Hypersensitivity
COVID-19
The present work was not funded.
==== Body
pmcIntroduction
Fabry disease (FD) is an X-linked lysosomal glycosphingolipid storage disease and is caused by the absence or deficiency of alpha-galactosidase A (AGAL) enzyme due to the mutation in the galactosidase alpha gene [1]. AGAL deficiency results in progressive accumulation of globotriaosylceramide (Gb3) and globotriaosylsphingosine (lyso-Gb3) in tissues [1]. Glycosphingolipid depositions induce cellular dysfunction, endothelial damage, inflammation, and apoptosis [2]. Cardiac, renal, neurological, and skin manifestations including acroparesthesias, hypohidrosis, angiokeratoma, proteinuria, kidney failure, abdominal pain, cardiomyopathy, arrhythmia, hearing loss, corneal changes (cornea verticillata), transient ischemic attacks, and strokes can be seen in FD [3].
Disease-specific treatment is primarily focused on replacing the missing or deficient enzyme with the recombinant human AGAL alfa or beta enzyme. Enzyme replacement therapy (ERT) includes the administration of agalsidase alfa (0.2 mg/kg every 2 weeks) produced from human cell lines and agalsidase beta (1 mg/kg every 2 weeks) produced from Chinese hamster ovary cell lines, which are the two available pharmaceutical preparations on the market. Both of them have been shown to be clinically effective. In some amenable mutations, migalastat, a pharmacological chaperone that corrects folding errors and restores lysosomal trafficking of the enzyme, might also be used [4, 5].
In this case report, we describe a patient with FD who developed hypersensitivity reaction to agalsidase beta following COVID-19 infection. Informed consent was obtained from the patient for publication of this case report and accompanying images.
Case Presentation
A 19-year-old male patient with an unremarkable medical history was admitted for family screening following his elder brother's diagnosis with FD. His AGAL level was 0.2 nmol/mL/h (reference level >2.5 nmol/mL/h), and lyso-Gb3 level was 45.9 ng/mL (reference level <1.30 ng/mL). Genetic examination revealed that the patient had the p. R227X mutation of the galactosidase alpha gene. On physical examination, angiokeratomas on his buttocks, elbows, and back were noticed. Hearing and vision screening did not reveal any pathology. Neurological examination was normal. On laboratory examination, his creatinine level was 0.83 mg/dL, e-GFR was 129 mL/min/1.73 m2, and proteinuria was 107 mg/day. On cardiac MRI, septal T1 was decreased, which was compatible with FD. Thus, the patient was diagnosed with FD, and agalsidase beta 1 mg/kg every other week was initiated in August 2017. He has received ERT with full compliance and without any complications. In January 2021, he had a COVID-19 infection, confirmed by the SARS-CoV-2 PCR test. Although he was unvaccinated, he had only mild symptoms including dry cough and runny nose and did not require hospitalization. During his first infusion after he had COVID-19 infection, he experienced shortness of breath, shivering, and an urticarial rash. Two weeks later, even though premedication (pheniramine 45.5 mg and prednisolone 40 mg) was administered, and the infusion rate was lowered from 30 mg/h to 15 mg/h, he had similar symptoms during the enzyme infusion. A workup was performed to reveal the cause of the newly onset allergic reactions. Agalsidase beta IgE antibody was found positive (its titer was 0.52 kUA/L [references: 0.35–0.54 kUA/L low positive, 0.55–1.39 kUA/L moderate positive, 1.40–3.89 kUA/L high positive, and >3.90 kUA/L very high positive]). Complement C3a and tryptase were negative. Due to an allergic reaction, the patient did not receive ERT for almost a year. After this period, he was admitted to our center, and we made an additional workup before the reinitiation of ERT. Total IgE titer was 125.4 IU/mL (reference value <100 IU/mL), and the skin prick test was positive (Fig. 1). Eosinophilia was not present. Agalsidase beta IgG was also found positive (800 IU/mL [reference value: 100–204.8 IU/mL]). Due to the presence of a high IgE titer, positive prick test, and antienzyme antibodies against agalsidase beta, we considered switching to agalsidase alfa treatment. We also performed a skin test against agalsidase alfa, and following a negative reaction, we reinitiated the ERT with agalsidase alfa with a dose of 0.2 mg/kg every other week. The infusion was administered over a period of 2 h without any premedication. We did not observe any adverse reaction to agalsidase alfa.
Discussion
The temporal association between COVID-19 infection and the development of allergic reactions against ERT might be causal in nature. SARS-CoV-2 might cause symptoms of autoimmune/autoinflammatory disease by immune system hyperactivation in susceptible adults. Even though this symptom is generally experienced temporarily, some patients can suffer from it permanently [6, 7]. Furthermore, hypersensitivity reactions were observed during COVID-19 infection [8]. Therefore, the allergic reaction in our patient could have been triggered by the COVID-19 infection. It could also be suggested that the present case rather demonstrates a nonimmunologic anaphylaxis (also previously known as anaphylactoid response), which indeed might be triggered by an activated immune system due to COVID-19.
IgE development against agalsidase beta has been reported in the literature. Tanaka et al. [9] noted that one of their patients with FD, who had severe atopic dermatitis history, experienced purulent eczema with hyperthermia and IgE development against agalsidase beta following his first agalsidase beta infusion. After the fourth reinfusion, his symptoms disappeared, but eosinophilia became prominent. Therefore, the medication was replaced by agalsidase alfa. No cross-reactivity of the IgE antibody against agalsidase alfa was observed and the treatment was continued without a major side effect [9]. On the other hand, in Bodensteiner et al.'s study [10], patients withdrawn from several previous clinical studies due to the detection of IgE or skin prick test positivity against agalsidase beta were gathered, and a rechallenge protocol was administered. In this cohort, one patient had IgE positivity, and five had a positive skin prick test. During reinstatement, no anaphylactic shock was observed, and all patients were able to switch to treatment with commercial drugs. However, information about the patients' medical history was not available. Even though a successful reinstatement protocol was previously reported [10], we decided to switch to ERT, based on the result of the skin prick test performed with both drugs and the presence of agalsidase beta-specific IgE reactivity.
The emergence of IgG against agalsidase alfa and agalsidase beta is common in repetitive ERT. In earlier studies, although a partially impaired Gb3 clearance has been observed in patients with circulating anti-agalsidase IgG antibodies, a direct correlation between IgG production and clinical outcome has not been found [11, 12]. However, subsequent studies emphasized that patients who had anti-agalsidase antibodies had a lower glomerular filtration rate and a greater left ventricular mass. Additionally, those patients suffered more frequently from neuropathic pain, diarrhea, and fatigue [13, 14]. Therefore, the presence of IgG antibodies might be associated with reduced ERT efficacy.
Conclusion
Patients who develop moderate to severe allergic reactions during ERT should be carefully monitored and screened for IgE development. A recent COVID-19 infection might facilitate those allergic reactions in these patients, where ERT might be considered.
Statement of Ethics
The present work was conducted in accordance with the Declaration of Helsinki. Informed consent was obtained from the patient for publication of this case report and accompanying images.
Conflict of Interest Statement
The authors have no conflicts of interest to declare.
Funding Sources
The present work was not funded.
Author Contributions
Ozge Sonmez, Seyda Gul Ozcan, and Nurhan Seyahi: conception or design, or analysis and interpretation of data, or both; drafting the article or revising it; providing intellectual content of critical importance to the work described; and final approval of the version to be published. Sinan Trabulus: drafting the article or revising it and final approval of the version to be published.
Data Availability Statement
All data generated or analyzed during this study are included in this article.
Fig. 1 Skin prick test with agalsidase beta. Different dilutions were marked with arrows; boundaries of the corresponding indurations are marked with pen over the skin.
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References
1 Svarstad E Marti HP The changing landscape of fabry disease Clin J Am Soc Nephrol 2020 Apr 7 15 (4) 569 576 32132142
2 Tuttolomondo A Simonetta I Riolo R Todaro F Di Chiara T Miceli S Pathogenesis and molecular mechanisms of anderson-fabry disease and possible new molecular addressed therapeutic strategies Int J Mol Sci 2021 Sep 18 22 (18) 10088 34576250
3 Germain DP Fabry disease Orphanet J Rare Dis 2010 5 (1) 30 21092187
4 Miller JJ Kanack AJ Dahms NM Progress in the understanding and treatment of Fabry disease Biochim Biophys Acta Gen Subj 2020 1864 (1) 129437 31526868
5 Lidove O Joly D Barbey F Bekri S Alexandra JF Peigne V Clinical results of enzyme replacement therapy in Fabry disease: a comprehensive review of literature Int J Clin Pract 2007 Feb 61 (2) 293 302 17263716
6 Maggi E Azzarone BG Canonica GW Moretta L What we know and still ignore on COVID-19 immune pathogenesis and a proposal based on the experience of allergic disorders Allergy 2022 Apr 77 (4) 1114 1128 34582050
7 Talotta R Robertson E Autoimmunity as the comet tail of COVID-19 pandemic World J Clin Cases 2020 Sep 6 8 (17) 3621 3644 32953841
8 Tan C Zheng X Sun F He J Shi H Chen M Hypersensitivity may be involved in severe COVID-19 Clin Exp Allergy 2022 Feb 52 (2) 324 333 34570395
9 Tanaka A Takeda T Hoshina T Fukai K Yamano T Enzyme replacement therapy in a patient with Fabry disease and the development of IgE antibodies against agalsidase beta but not agalsidase alpha J Inherit Metab Dis 2010 Dec 33 (S3) S249 52
10 Bodensteiner D Scott CR Sims KB Shepherd GM Cintron RD Germain DP Successful reinstitution of agalsidase beta therapy in Fabry disease patients with previous IgE-antibody or skin-test reactivity to the recombinant enzyme Genet Med 2008 May 10 (5) 353 358 18496035
11 Bénichou B Goyal S Sung C Norfleet AM O'Brien F A retrospective analysis of the potential impact of IgG antibodies to agalsidase beta on efficacy during enzyme replacement therapy for Fabry disease Mol Genet Metab 2009 Jan 96 (1) 4 12 19022694
12 Linthorst GE Hollak CEM Donker-Koopman WE Strijland A Aerts JMFG Enzyme therapy for Fabry disease: neutralizing antibodies toward agalsidase alpha and beta Kidney Int 2004 Oct 66 (4) 1589 1595 15458455
13 Lenders M Stypmann J Duning T Schmitz B Brand SM Brand E Serum-mediated inhibition of enzyme replacement therapy in fabry disease J Am Soc Nephrol 2016 Jan 27 (1) 256 264 25933799
14 Lenders M Neußer LP Rudnicki M Nordbeck P Canaan-Kühl S Nowak A Dose-dependent effect of enzyme replacement therapy on neutralizing antidrug antibody titers and clinical outcome in patients with Fabry disease J Am Soc Nephrol 2018 29 (12) 2879 2889 30385651
| 36174537 | PMC9747721 | NO-CC CODE | 2022-12-15 23:22:04 | no | Nephron Clin Pract. 2022 Sep 29;:1-4 | utf-8 | Nephron | 2,022 | 10.1159/000526235 | oa_other |
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ORL J Otorhinolaryngol Relat Spec
ORL J Otorhinolaryngol Relat Spec
ORL
ORL; journal for oto-rhino-laryngology and its related specialties
0301-1569
1423-0275
S. Karger AG Allschwilerstrasse 10, P.O. Box · Postfach · Case postale, CH–4009, Basel, Switzerland · Schweiz · Suisse, Phone: +41 61 306 11 11, Fax: +41 61 306 12 34, [email protected]
36318894
10.1159/000527141
orl-0001
Smell and Taste Corner
Olfactory Cleft Length: A Possible Risk Factor for Persistent Post-COVID-19 Olfactory Dysfunction
Alves de Sousa Francisco a *
Tarrio João b c
Sousa Machado André a
Costa Joana Raquel a
Pinto Catarina b
Nóbrega Pinto Ana a
Moreira Bruno b
Meireles Luís a
aOtorhinolaryngology and Head & Neck Surgery Department, Centro Hospitalar Universitário do Porto, Porto, Portugal
bNeurorradiology Department, Centro Hospitalar Universitário do Porto, Porto, Portugal
cNeurorradiology Department, Hospital Central do Funchal Dr. Nélio Mendonça, Funchal, Portugal
*Francisco Alves de Sousa, [email protected]
1 11 2022
1 11 2022
19
8 6 2022
14 9 2022
Copyright © 2022 by S. Karger AG, Basel
2022
https://www.karger.com/Services/SiteLicenses Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher.
Introduction
To date, little is known about predisposing factors for persistent COVID-19-induced olfactory dysfunction (pCIOD). The objective was to determine whether olfactory cleft (OC) measurements associate with pCIOD risk.
Material and Methods
Three subgroups were recruited: group A included patients with pCIOD, group B included patients without olfactory dysfunction following SARS-CoV-2 infection (ntCIOD), and group C consisted in controls without past history of SARS-CoV-2 infection (noCOVID-19). Olfactory perception threshold (OPT) and visual analog scale for olfactory impairment (VAS-olf) were obtained. OC measurements were obtained through computed tomography scans. Results were subsequently compared.
Results
A total of 55 patients with a mean age of 39 ± 10 years were included. OPT was significantly lower in pCIOD patients (group A: 4.2 ± 2.1 vs. group B: 12.3 ± 1.8 and group C: 12.2 ± 1.5, p < 0.001). VAS-olf was significantly higher in pCIOD (group A: 6 ± 2.6 vs. group B: 1.7 ± 1.6 and group C: 1.6 ± 1.5, p < 0.001). OC length was significantly higher in group A (42.8 ± 4.6) compared to group B (39.7 ± 3.4, p = 0.047) and C (39.8 ± 4, p = 0.037). The odd of pCIOD occurring after COVID-19 infection increased by 21% (95% CI [0.981, 1.495]) for a one unit (mm) increase in OC length. The odd of pCIOD occurring was 6.9 times higher when OC length >40 mm.
Conclusion
Longer OC may be a predisposing factor for pCIOD. This study is expected to encourage further research on OC morphology and its impact on olfactory disorders.
Key Words
COVID-19
Olfactory dysfunction
Olfactory cleft
Length
Hyposmia
This is an independent study. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
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pmcIntroduction
Olfactory dysfunction (OD) is one of the most commonly recognized symptoms of coronavirus disease 2019 (COVID-19) [1, 2, 3]. Underlying mechanisms for COVID-19-induced OD (CIOD) are likely caused by SARS-CoV-2 colonization of the nasal mucosa with interference in olfactory sustentacular cells' function, ultimately affecting olfactory neuroepithelium homeostasis [3, 4]. Despite the affection, CIOD is generally transitory [3, 5, 6, 7]. To date, it remains obscure why some COVID-19 patients suffer from long-lasting OD, while most patients regain functionality after 7–15 days [3, 4, 8, 9, 10, 11, 12, 13].
From the existing knowledge of postviral or postinfectious olfactory loss (PIOL), recent studies already made the effort to demark predisposing factors contributing to persistent CIOD (pCIOD). These came to include various factors such as immunological determinants [14], higher age, female gender, allergy, smoking, or diabetes [15, 16, 17]. Recently, appealing new evidence raised the idea that innate anatomical factors may also associate with PIOL and pCIOD probability after infection [18, 19]. Based on computed tomography (CT) and magnetic resonance imaging (MRI) measurements, olfactory cleft (OC) morphology (namely, width, area, and volume) has recently been implied in persistent OD [18, 19, 20]. Although some suggest that OC configuration could help explain pCIOD predisposition, to date no original study simultaneously compared OC measurements between pCIOD patients, patients with past history of COVID-19 without resulting OD or with only transient CIOD (ntCIOD) and controls (noCOVID-19). This could allow pCIOD risk estimations based on individual OC anatomy.
Given the current knowledge, we hypothesized that OC morphology could at least partially explain individual susceptibility to OD persistence after COVID-19. Thus, in this study the primary objective was to perform comprehensive OC measurements in case and control groups, in order to compare and investigate whether OC morphology associates with pCIOD incidence.
Materials and Methods
Sample Enrollment and Evaluation
All participants were recruited from a tertiary referral center in Portugal. The study included three different groups: group A − composed of patients with subjective persistence of OD ≥30 days following SARS-CoV-2 infection (pCIOD), group B − patients with past history of SARS-CoV-2 infection but no CIOD or only transient CIOD ≤30 days (ntCIOD), and group C − control patients without history of SARS-CoV-2 infection and without any reported OD (noCOVID-19). General inclusion criteria for all groups were age ≥18 years, cognitive ability of signing informed consent, and available paranasal sinus CT scan.
Group A formed the case group and included patients followed at a specialized post-COVID-19 smell loss consultation. Specific inclusion criteria were sudden onset of OD concomitant with SARS-CoV-2 infection documented by nasal swab and PCR method, subjective persistence of OD ≥30 days, olfactory perception threshold (OPT) <7 at the assessment, and ability to tolerate rhinoendoscopy (if concomitant nasal polyposis the patient was excluded).
Group B consisted of patients followed by otorhinolaryngology for reasons other than OD, who have had a past SARS-CoV-2 infection proved by PCR, who reported no OD or only transient OD ≤30 days after COVID-19, without present subjective OD and with OPT >7 at the formal assessment. Exclusion criteria for group A and B were as follows: chronic rhinosinusitis, severe septal deviation with lateralization/asymmetry in OPT values, pregnancy, past head trauma with loss of consciousness, documented pre-existent OD, former neurosurgery or endonasal surgery, known olfactory bulb lesions on imaging, known neurologic disease (Parkinson, dementia, epilepsy), and major psychiatric disease.
Group C was composed of patients followed by otorhinolaryngology for reasons other than OD, who underwent paranasal sinus CT before the COVID-19 era (prior to December 2019), without past history of COVID-19 infection, without history of OD complaints, and with OPT >7 at the formal assessment. All groups answered both a subjective visual analog scale of olfactory impairment adapted from Langstaff et al. [21] with permission (VAS-olf), and objective assessment with OPT using Burghart Sniffin' Sticks threshold test with n-butanol with 16 levels (48 pens), a validated method [22, 23]. The later was performed by birhinal testing in all patients (both nostrils tested together) and additionally monorhinally if there was a significant septal deviation in the rhinoendoscopy, in order to exclude OPT asymmetries.
OC Measurements
The available CT scans were mostly of the paranasal sinuses, but also cranioencephalic, neck and ear, with slice thicknesses varying between 0.6 and 3 mm. The reasons for CT imaging in this sample were as follows: in group A − complaint of long-lasting olfactory impairment itself − pCIOD; in group A, B, and C − former available CT studies due to nasal obstruction, headache, otologic complaints, neck adenopathy's. The exams were obtained by GE® or SIEMENS® tomographs, and measurements were performed by SECTRA® software using multiplanar reconstructions. Multiplanar reconstructions initially allowed orientation of the axial slices parallel to the cribriform plate in the sagittal plane (Fig. 1). From these axial slices, the anteroposterior length was measured, considering the anterior insertion of the middle turbinate as the anterior limit of the OC (since the vertical lamella of the middle turbinate is generally not deformed and has a vertical path without significant deviation) and, as a posterior limit, the anterior wall of the sphenoid sinus at the level of the sphenoethmoidal recess. This measurement was performed bilaterally (Fig. 2), and the length value consisted of the mean between right and left sides. The width of the OC was measured bilaterally in the coronal plane, at the point of intersection between the anterior third and the posterior two thirds of the OC, 5 mm inferior to the cribriform plate, considering the nasal septum as the medial limit and the middle or superior turbinate as the lateral limit (Fig. 3). The 5 mm depth was used to measure OC width since a greater depth could result in alteration of configuration due to angulation of middle concha laminae and a shallower depth could have excluded part of OC surface lined with olfactory epithelium [20] (nasal respiratory epithelium thickness varies from 0.3 to 5 mm [24]). We determined the cut-off depth as 5 mm after looking at all cases prior to measurements and similar literature in postinfectious OD [19]. The depth of the olfactory fossa was also evaluated, taking into account the measurement of the height of the lateral lamella of the cribriform plate and application of the Keros classification. All measurements were performed by a single member of the Neuroradiology Department (to avoid inter-observer variability), who was blinded to demographic data, clinical information, and study's subgroup to which patients belonged. All the CT measurements were made using the same aforementioned technique.
Statistical Analysis
Statistical analysis was performed using SPSS (IBM SPSS Statistics 26). In the descriptive analysis, categorical variables are presented as percentages, and continuous variables as means and standard deviations (SDs), or medians and interquartile range for variables with skewed distributions. Normal distribution was checked using both skewness and kurtosis and Kolmogorov-Smirnov tests. The bivariate associations were analyzed using either independent t test (parametric analysis) or Mann-Whitney test (nonparametric analysis) depending on the tests for normality, or Spearman's test for continuous variables. ANOVA was performed to increase statistical validity when needed. All reported p values are two-tailed, with a p value ≤ 0.05 indicating statistical significance.
Results
Study Population
A total of 55 patients were included, 18 patients formed the group A (pCIOD), 16 patients formed the group B, and 21 patients formed the group C. Fifty-three percent of patients were male, and 47% were female. Mean age at inclusion across groups was 39 ± 10 years. Regarding comorbidities: 7% of patients had diabetes mellitus, 9% dyslipidemia, 7% arterial hypertension, 2% autoimmune disease, 7% pulmonary disease, 5% cardiac disease, 2% previous chemotherapy, 2% obstructive sleep apnea, and 2% used immunosuppression drugs. The mean time from COVID-19 diagnosis to first evaluation was 249 ± 148 days for group A and 239 ± 145 days for group B, p = 0.437. Hospital admission rates due to COVID-19 were 14.3% in the group A against 10% in the group B (p = 0.375). Relevant comparisons among study subgroups are displayed in Table 1. Note that no significant differences in age, gender, or comorbidities were found between subgroups: age mean in group A − 39 ± 12 years versus group B − 36 ± 10 years versus group C − 41 ± 9 years, p = 0.865; Gender (male) group A: 9 versus group B: 9 versus group C: 11, p = 0.971; check Table 1 for comorbidities within groups.
Olfactory Examination
Considering olfactory thresholds, subgroup comparisons are displayed in Table 1. Group A showed an OPT mean of 4.2 ± 2.1 with a minimum threshold of 0 and a maximum of 6. Group B showed an OPT mean of 12.3 ± 1.8 with a minimum threshold of 9 and a maximum of 15. Group C showed an OPT mean of 12.2 ± 1.5 with a minimum threshold of 10 and a maximum of 15. The analysis by one-way ANOVA with post hoc Dunnett's test showed a statistically relevant difference concerning OPT means with significant lower values in group A (p < 0.001). Considering VAS of olfactory impairment, in group A the mean VAS-olf was 6 ± 2.6 with a minimum registered of 1 and a maximum of 10, in group B the mean VAS-olf was 1.7 ± 1.6 with a minimum registered of 0 and a maximum of 5, and in group C the mean VAS-olf was 1.6 ± 1.5 with a minimum of 0 and a maximum of 4. The analysis by one-way ANOVA with post hoc Dunnett's test showed a statistically relevant difference concerning VAS-olf means with significant higher values in group A (p < 0.001). Thus, objective and subjective measurements confirmed significant olfactory impairment in all the patients from group A.
OC Measurements
Imaging slice thicknesses mean did not differ between groups (1.9 ± 1.1 in group A vs. 2 ± 1.2 in group B and 1.8 ± 1.1 in group C, p = 0.759). The mean OC length in the sample was 40.9 ± 4.3, mean OC width was 4.3 ± 1, mean OC area was 176.9 ± 50.7, median Keros score was 2. In the bivariate analysis, OC length was significantly higher in group A (OC length 42.8 ± 4.6 mm) compared to group B (OC length 39.7 ± 3.4 mm, p = 0.047) and C (OC length 39.8 ± 4 mm, p = 0.037). The same was confirmed by using an ANOVA model with post hoc Dunnett's test with a p value of 0.027 showing significantly higher OC length in group A compared with other groups (Table 2; Fig. 4). No statistically significant differences were seen regarding any other OC measurement (Table 2): OC width was 4.3 ± 0.8 in group A versus 4.3 ± 1.2 in group B versus 4.2 ± 1 in group C, with a p value of 0.580 in the ANOVA model with post hoc Dunnett's test. The mean OC area, also dependent on OC length, tended to vary among subgroups (Table 2; Fig. 5), although not achieving statistical significance: OC area mean of 187 ± 50.5 in group A versus 174.8 ± 57.6 in group B versus 169.7 ± 48.4 in group C, with p value of 0.246 in the ANOVA model with post hoc Dunnett's test. Keros score did not differ between groups (Table 2), since the median value was 2 in all subgroups with a p = 0.540 in the analysis of variance by Kruskal-Wallis test for nonparametric variables.
Within group A, there was no significant association between OPT score and OC width (p = 0.642), OC length (p = 0.218), and OC area (p = 0.945) in the Spearman test. For analysis purposes, a cut-off of 40 mm for OC length was created for further data description, since it was close to the general mean of OC length in this cohort. In group A, 72% of patients had OC length >40 mm, whereas in group B = 27% had OC length >40 mm and group C = 43% had OC length >40 mm. The difference between groups was significant when using a χ2 test categorical comparison (p = 0.044). In order to estimate the risk conferred by a longer OC in pCIOD incidence, further analysis was restricted to patients afflicted by COVID-19 (group A and group B). By using a binary logistic regression to analyze the relationship between OC length and pCIOD, it was found that the odds of pCIOD occurring after COVID-19 infection increased by 21% (95% CI [0.981, 1.495]) for a one unit (mm) increase in OC length. When a categorical cut-off for OC length is used (>40 mm) instead of the continuous OC length, the odds of pCIOD occurring are 6.9 times higher when OC length >40 mm than when OC length <40 mm.
Discussion
Several pathological mechanisms have been described for COVID-19-related OD, including nasal cytokine storms and neurological tropism [24]. One of the most accepted relates to SARS-CoV-2 entry, infection, and death of sustentacular cells in the olfactory neuroepithelium, with arrest of the normal neural processing [3, 8, 9, 10, 11, 12]. Nevertheless, it remains obscure why some patients suffer from long-lasting post-COVID-19 OD while others recover rapidly or do not even get to complaint about OD [13, 14]. There is indeed an ongoing debate about determinants of persistent postviral OD [3, 4, 8, 9, 10, 11, 12, 13]. While some comorbid factors have already been described [15, 16, 17], to date very few studies explored the relationship between constitutive anatomical factors and the incidence of PIOL [19] and pCIOD [18].
The primary objective of comparing OC anatomy in pCIOD, ntCIOD, and noCOVID-19 patients was met. The major finding of the study was that the mean anteroposterior length of the OC is higher among pCIOD, comparing to ntCIOD and noCOVID-19 subgroups. Our data suggest that the odd of pCIOD occurring after COVID-19 infection increases by 21% for a one unit increase (mm) in OC length. Likewise, the odd of pCIOD occurring is 6.9 times higher when OC length >40 mm than when OC length <40 mm. An overall resume of the study findings may be found in Figure 6.
Only recently the possibility of innate structural anatomy contributing to PIOL and pCIOD risk was raised [18, 19, 20]. In the pre-COVID-19 era, from CT and MRI findings, Altundag et al. [19] concluded that patients with PIOL had increased OC width and volume comparing to healthy controls, stating that an extra-wide OC could be a predisposing factor in the pathogenesis of PIOL. After the COVID-19 outbreak, the same research group published another interesting work focusing on pCIOD, concluding that patients with COVID-19 anosmia had higher OC widths and volumes compared to control subjects [18]. There was also a significant negative correlation between these morphological findings and threshold discrimination and identification scores [18]. In that same study, the OC area measured by MRI T2 signal intensity sequences was also significantly higher in PIOL and pCIOD compared to controls [18]. Thus, this study has shown increased OC width, area, and volume in pCIOD [18] patients. Our study results may be in part comparable to the ones aforementioned since in our sample OC morphology also correlated with the incidence of pCIOD. Nevertheless, our findings were distinct in many ways: (1) we found significant differences in OC length in pCIOD, while former studies do not present direct OC length measurements, instead reporting area and volume; (2) we did not find significant differences in OC width among subgroups; (3) we did not find significant differences in OC area (although this may have been caused by a shorter sample size, since there were important variations in means); (4) in some of the former works, no subgroup afflicted by COVID-19 without pCIOD (ntCIOD) was recruited, which could have allowed risk estimations for pCIOD after SARS-CoV-2 infection.
To date, the importance of increased OC in rhinologic diseases remains unclear [25, 26]. In the specific case of SARS-CoV OD, we hypothesize that higher OC length renders a higher surface of exposure to SARS-CoV-2 entry and infection of sustentacular cells through angiotensin-converting enzyme 2 receptor, thus culminating in higher immune response in the olfactory neuroepithelium [3, 27]. Could this be true, it could help to explain why other determinants of total OC surface such as OC width, area, and volume have also been implicated in pCIOD in other studies [18, 19].
To our knowledge, this is the first study that simultaneously compares pCIOD individuals (group A) with ntCIOD ones (group B) and controls without past history of COVID-19 or OD (group C). Also, a direct relationship between pCIOD and OC length is described for the first time. We also attempted to bring a tangible calculation of risk estimates for pCIOD depending on OC anatomy (namely, anteroposterior OC length). This study has limitations, starting with the relatively limited number of patients in each study group. The limited sample size is partially explained by the fact that many potential group B patients did not have previously available CTs so they had to be excluded. No previous sample size calculations were possible due to the lack of similar studies in post-COVID-19 olfactory pathology. Also, group B patients had imaging done prior to COVID-19 infection, since performing paranasal sinus imaging after COVID-19 and solely for research purposes could incur in ethical issues. Another limitation to point out is the fact that the thickness of the imaging slices varied depending on the type of CT scan available. For the OC surface area calculation, we used estimates based on the separate measurement of width and length, which may have been affected by operator-dependent errors.
Conclusion
In this study, patients afflicted by COVID-19 who developed persistent OD confirmed by psychophysical tests have shown higher OC length. Longer OC length may be a predisposing factor for pCIOD, raising the risk by 21% with each 1 mm increment in OC length. Contrarily to other studies' findings, OC width did not show any relevant associations with pCIOD. It is possible that nonacquired nasal anthropometry fundamentally contributes to the olfactory outcome in CIOD. Nevertheless, new studies using larger samples and diverse imaging techniques are needed. This study is expected to encourage further research in this topic.
Statement of Ethics
This study protocol was reviewed and approved by the Investigation Department (Departamento de Ensino, Formação e Investigação [DEFI]) and the Ethics Committee (Comissão de ética [CE]) of Centro Hospitalar Universitário do Porto (approval number: 2021.93 [075-DEFI/078-CE]), and the design complies with the Declaration of Helsinki ethical standards. Written informed consent was obtained from all the enrolled patients.
Conflict of Interest Statement
The authors have no conflicts of interest to declare.
Funding Sources
This is an independent study. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Author Contributions
Francisco Alves de Sousa: conceptualization, methodology, formal analysis, investigation, resources, data curation, and writing − original draft. João Tarrio: investigation, data curation, resources, and writing − review and editing. André Sousa Machado and Joana Raquel Costa: investigation, resources, and data curation. Catarina Pinto: writing − review and editing. Bruno Moreira and Ana Nóbrega Pinto: writing − review and editing, supervision, and project administration. Luís Meireles: supervision and project administration.
Data Availability Statement
There are no publicly available data related to this work. All data generated or analyzed during this study are included in this article. Further inquiries can be directed to the corresponding author.
Acknowledgment
We would like to express deep gratitude to the clinical body of the Otorhinolaryngology Department of Centro Hospitalar Universitário do Porto for contributing for this work.
Fig. 1 CT evaluation of the olfactory cleft: initial orientation of the axial slices parallel to the cribriform plate in the sagittal plane.
Fig. 2 Measurement of the anteroposterior length of the olfactory cleft in the axial plane parallel oriented to the cribriform plate, between the anterior insertion of the middle turbinate and the anterior wall of the sphenoid sinus.
Fig. 3 Measurement of the olfactory cleft width bilaterally, in the coronal plane, at the intersection between the anterior and posterior two-thirds of the olfactory cleft, 5 mm inferior to the cribriform plate.
Fig. 4 Mean olfactory cleft (OC) length across subgroups. Note: Results from ANOVA model.
Fig. 5 Mean olfactory cleft (OC) area across subgroups. Note: Results from ANOVA model.
Fig. 6 Flowchart of the main findings.
Table 1 Demographic features and olfactory test results of the study groups
Characteristic Group A (pCIOD) Group B (ntCIOD) Group C (noCOVID) p value
Age 39±12 36±10 41±9 0.865a
Gender 9/9 9/7 11/10 0.971b
Comorbidities, n (%)
Diabetes mellitus 2/18 (11) 1/16 (6) 1/21 (5) 0.363b
Dyslipidemia 2/18 (11) 2/16 (13) 2/21 (10) 0.945b
Hypertension 2/18 (11) 1/16 (6) 2/21 (10) 0.637b
Autoimmune disease 0/18 (0) 0/16 (0) 1/21 (5) 0.571b
Immunosuppressive drugs 1/18 (6) 0/16 (0) 1/21 (5) 0.723b
Pulmonary disease 0/18 (0) 2/16 (13) 1/21 (5) 0.104b
Cardiac disease 2/18 (11) 0/16 (0) 0/21 (0) 0.106b
Past chemotherapy 1/18 (6) 0/16 (0) 0/21 (0) 0.334b
Obstructive sleep apnea 0/18 (0) 1/16 (6) 0/21 (0) 0.137b
OPT 4.2±2.1 12.3±1.8 12.2±1.5 <0.001a
VAS of olfactory impairment 6±2.6 1.7±1.6 1.6±1.5 <0.001a
a Analysis of variance by one-way ANOVA with post hoc Dunnett's test.
b χ2 test.
Table 2 OC measurements according to the study groups
Characteristic Group A (pCIOD) Group B (ntCIOD) Group C (noCOVID) p value
Right OC width, mm 2.2±0.4 1.9±0.7 2.2±0.6 0.258a
Left OC width (mean ± SD, mm) 2.1±0.5 2.4±0.5 2±0.4 0.612a
Total OC width (mean ± SD, mm) 4.3±0.8 4.3±1.2 4.2±1 0.580a
OC length (mean ± SD, mm) 42.8±4.6 39.7±3.4 39.8±4 0.027a
Estimated OC area (width × length, mean ± SD, mm2) 187±50.5 174.8±57.6 169.7±48.4 0.256a
Keros score (median, Q1-Q3) 2 (1–3) 2 (1–3) 2 (1.5–2.5) 0.540b
Q1-Q3: These values are quartile 1 (Q1) and quartile 3 (Q3). The interquartile range is the difference between Q3 and Q1. SD, standard deviation.
a Analysis by one-way ANOVA with post hoc Dunnett's test.
b Analysis by Kruskal-Wallis test for nonparametric variables.
==== Refs
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1 da Silva Júnior PR Gomes ALOR Coelho LEA Morais MA de Almeida PVFC Neri WJR Anosmia and COVID-19: perspectives on its association and the pathophysiological mechanisms involved Egypt J Neurol Psychiatry Neurosurg 2021 57 (1) 8
2 Walker A Pottinger G Scott A Hopkins C Anosmia and loss of smell in the era of covid-19 BMJ 2020 370 m2808 32694187
3 Butowt R von Bartheld CS Anosmia in COVID-19: underlying mechanisms and assessment of an olfactory route to brain infection Neuroscientist 2021 27 (6) 582 603 32914699
4 Othman BA Maulud SQ Jalal PJ Abdulkareem SM Ahmed JQ Dhawan M Olfactory dysfunction as a post-infectious symptom of SARS-CoV-2 infection Ann Med Surg 2022 75 103352
5 Lechien JR Chiesa-Estomba CM De Siati DR Horoi M Le Bon SD Rodriguez A Olfactory and gustatory dysfunctions as a clinical presentation of mild-to-moderate forms of the coronavirus disease (COVID-19): a multicenter European study Eur Arch Otorhinolaryngol 2020 277 (8) 2251 2261 32253535
6 Printza A Katotomichelakis M Metallidis S Panagopoulos P Sarafidou A Petrakis V The clinical course of smell and taste loss in COVID-19 hospitalized patients Hippokratia 2020 24 (2) 66 71 33488054
7 Lechien JR Chiesa-Estomba CM Beckers E Mustin V Ducarme M Journe F Prevalence and 6-month recovery of olfactory dysfunction: a multicentre study of 1363 COVID-19 patients J Intern Med 2021 290 (2) 451 461 33403772
8 Torabi A Mohammadbagheri E Akbari Dilmaghani N Bayat A-H Fathi M Vakili K Proinflammatory cytokines in the olfactory mucosa result in COVID-19 induced anosmia ACS Chem Neurosci 2020 11 1909 1913 32525657
9 Bryche B St Albin A Murri S Lacôte S Pulido C Ar Gouilh M Massive transient damage of the olfactory epithelium associated with infection of sustentacular cells by SARS-CoV-2 in golden Syrian hamsters Brain Behav Immun 2020 89 579 586 32629042
10 Jia C Roman C Hegg CC Nickel sulfate induces location-dependent atrophy of mouse olfactory epithelium: protective and proliferative role of purinergic receptor activation Toxicol Sci 2010 115 (2) 547 556 20200219
11 Bilinska K Butowt R Anosmia in COVID-19: a bumpy road to establishing a cellular mechanism ACS Chem Neurosci 2020 11 (15) 2152 2155 32673476
12 Vaira LA Salzano G Fois AG Piombino P De Riu G Potential pathogenesis of ageusia and anosmia in COVID-19 patients Int Forum Allergy Rhinol 2020 10 (9) 1103 1104 32342636
13 Neta FI Fernandes ACL Vale AJM Pinheiro FI Cobucci RN Azevedo EP Pathophysiology and possible treatments for olfactory-gustatory disorders in patients affected by COVID-19 Curr Res Pharmacol Drug Discov 2021 2 100035 34870148
14 Saussez S Sharma S Thiriad A Olislagers V Vu Duc I Le Bon SD Predictive factors of smell recovery in a clinical series of 288 coronavirus disease 2019 patients with olfactory dysfunction Eur J Neurol 2021 28 (11) 3702 3711 34157187
15 Zhao Y Liu Y Yi F Zhang J Xu Z Liu Y Type 2 diabetes mellitus impaired nasal immunity and increased the risk of hyposmia in COVID-19 mild pneumonia patients Int Immunopharmacol 2021 93 107406 33601246
16 Makaronidis J Firman C Magee CG Mok J Balogun N Lechner M Distorted chemosensory perception and female sex associate with persistent smell and/or taste loss in people with SARS-CoV-2 antibodies: a community based cohort study investigating clinical course and resolution of acute smell and/or taste loss in people with and without SARS-CoV-2 antibodies in London, UK BMC Infect Dis 2021 21 (1) 221 33632171
17 Galluzzi F Rossi V Bosetti C Garavello W Risk factors for olfactory and gustatory dysfunctions in patients with SARS-CoV-2 infection Neuroepidemiology 2021 55 (2) 154 161 33794531
18 Altundag A Yıldırım D Tekcan Sanli DE Cayonu M Kandemirli SG Sanli AN Olfactory cleft measurements and COVID-19: related anosmia Otolaryngol Head Neck Surg 2021 164 (6) 1337 1344 33045908
19 Altundag A Temirbekov D Haci C Yildirim D Cayonu M Olfactory cleft width and volume: possible risk factors for postinfectious olfactory dysfunction Laryngoscope 2021 131 (1) 5 9 32027030
20 Tekcan Sanli DE Altundag A Yıldırım D Kandemirli SG Sanli AN Comparison of olfactory cleft width and volumes in patients with COVID-19 anosmia and COVID-19 cases without anosmia ORL J Otorhinolaryngol Relat Spec 2022 84 1 9 34569549
21 Langstaff L Pradhan N Clark A Boak D Salam M Hummel T Validation of the olfactory disorders questionnaire for english-speaking patients with olfactory disorders Clin Otolaryngol 2019 44 (5) 715 728 31038840
22 Denzer MY Gailer S Kern DW Schumm LP Thuerauf N Kornhuber J Quantitative validation of the n-butanol Sniffin' Sticks threshold pens Chemosens Percept 2014 7 (2) 91 101 24883171
23 Hummel T Sekinger B Wolf SR Pauli E Kobal G “Sniffin” sticks': olfactory performance assessed by the combined testing of odor identification, odor discrimination and olfactory threshold Chem Senses 1997 22 (1) 39 52 9056084
24 Beule AG Physiology and pathophysiology of respiratory mucosa of the nose and the paranasal sinuses Laryngorhinootologie 2010 89 S15 34 20352568
25 Huart C Philpott C Konstantinidis I Altundag A Whitcroft KL Trecca EMC Comparison of COVID-19 and common cold chemosensory dysfunction Rhinology 2020 58 (6) 623 625 32812014
26 Worley ML Schlosser RJ Soler ZM Dubno JR Eckert MA Age-related differences in olfactory cleft volume in adults: a computational volumetric study Laryngoscope 2019 129 (2) E55 60 30329151
27 Butowt R Bilinska K SARS-CoV-2: olfaction, brain infection, and the urgent need for clinical samples allowing earlier virus detection ACS Chem Neurosci 2020 11 (9) 1200 1203 32283006
| 36318894 | PMC9747724 | NO-CC CODE | 2022-12-15 23:22:04 | no | ORL J Otorhinolaryngol Relat Spec. 2022 Nov 1;:1-9 | utf-8 | ORL J Otorhinolaryngol Relat Spec | 2,022 | 10.1159/000527141 | oa_other |
==== Front
ORL J Otorhinolaryngol Relat Spec
ORL J Otorhinolaryngol Relat Spec
ORL
ORL; journal for oto-rhino-laryngology and its related specialties
0301-1569
1423-0275
S. Karger AG Allschwilerstrasse 10, P.O. Box · Postfach · Case postale, CH–4009, Basel, Switzerland · Schweiz · Suisse, Phone: +41 61 306 11 11, Fax: +41 61 306 12 34, [email protected]
36063810
10.1159/000525861
orl-0001
Smell and Taste Corner
COVID-19-Related Quantitative and Qualitative Olfactory and Gustatory Dysfunction: Long-Term Prevalence and Recovery Rate
Boscolo-Rizzo Paolo *
Tofanelli Margherita
Zanelli Enrico
Gardenal Nicoletta
Tirelli Giancarlo
Section of Otolaryngology, Department of Medical, Surgical and Health Sciences, University of Trieste, Trieste, Italy
*Paolo Boscolo-Rizzo, [email protected]
5 9 2022
5 9 2022
15
28 3 2022
26 6 2022
Copyright © 2022 by S. Karger AG, Basel
2022
https://www.karger.com/Services/SiteLicenses Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher.
Introduction
No studies have reported data on 2-year prevalence and recovery rates of self-reported COVID-19-related quantitative and qualitative olfactory and gustatory dysfunction. The aim of the present study was to estimate the 2-year prevalence and recovery rate of self-reported COVID-19-related olfactory and gustatory dysfunction in a cohort of patients with antecedent mild-to-moderate disease.
Methods
This is a prospective observational study, measuring the prevalence of altered sense of smell or taste at follow-up and their variation from baseline, on adult patients consecutively assessed at Trieste University Hospital, who tested positive for SARS-CoV-2 RNA by polymerase chain reaction during March 2020.
Results
Overall, 174 (68.8%), 53 (20.9%), and 36 (14.2%) of 253 responders reported an altered sense of smell or taste (SNOT-22 >0) at baseline, 12 months, and 24 months, respectively. Among the 174 patients who have complained a COVID-19-associated olfactory or gustatory dysfunction at baseline, 138 (79.3%) reported complete resolution of smell or taste impairment with 17 subjects (9.8%) recovering after more than 1 year after the initial infection, 33 (19.0%) reported a decrease in the severity, and only 3 (1.7%) reported that the symptom was unchanged at the 24-month interview. Twenty subjects (7.9%) complained of at least one qualitative long-term symptom.
Conclusion
Two years after the infection, most patients experience a favourable evolution of COVID-19-related olfactory or gustatory dysfunction. A late recovery was observed in 10% of subjects.
Key Words
COVID-19
Gustatory dysfunction
Olfactory dysfunction
Parosmia
Prognosis
SARS-CoV-2
This manuscript did not receive any funding.
==== Body
pmcIntroduction
Two years after the outbreak of the COVID-19 pandemic in Europe, olfactory and gustatory dysfunction has appeared as a highly prevalent and persistent symptom of the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection at least until the advent of the Omicron variant [1, 2, 3]. Since in other forms of post-viral anosmia a recovery has been described even years after the onset [4], it is essential to reassess the persistence and recovery rate of these COVID-19-related disorders with long-term interviews.
Olfactory and gustatory dysfunctions are categorized not only into quantitative but also into qualitative disorders: parosmia (perception of qualitatively altered smells in the presence of an odour source), phantosmia (perceived odour when no odorant is present), parageusia (perception of qualitatively altered taste in the presence of a gustatory stimulus), and phantogeusia (spontaneous abnormal taste with no gustatory stimulus) can accompany or follow a reduction in the perception of smell and taste [5]. While several papers have reported on the persistence and recovery rates of quantitative disturbances, few have focused on qualitative alterations [6, 7]. Nevertheless, qualitative olfactory and gustatory dysfunctions may significantly impact the quality of life more than quantitative alterations [8, 9].
We previously reported the prevalence of quantitative olfactory and gustatory dysfunction 12 months after COVID-19 [10]. The aim of the present study was to both estimate the 24-month prevalence and recovery rate of self-reported quantitative olfactory and gustatory dysfunction and the rate of long-term qualitative impairment in the same series of subjects with previous mild-to-moderate symptomatic COVID-19.
Materials and Methods
We conducted a prospective observational study on adult patients consecutively assessed at Trieste University Hospital, who tested positive for SARS-CoV-2 RNA by polymerase chain reaction on nasopharyngeal and throat swabs performed according to World Health Organization recommendation [11] during the first wave of the pandemic in Italy (March 1–22, 2020). All patients were initially home-isolated with mild-to-moderate symptoms. Patients were considered mildly symptomatic if they had less severe clinical symptoms with no evidence of pneumonia, not requiring hospitalization, and therefore considered suitable for being treated at home. Patients with a history of previous craniofacial trauma, surgery, or radiotherapy in the oral and sinonasal area and those reporting a pre-existing olfactory dysfunction were excluded from the study. Demographic and clinical data were collected through ad hoc questions administered during the baseline interview and included gender, age, self-reported height and weight, smoking and alcohol habits, and the following comorbidities: immunosuppression, diabetes, cardiovascular diseases, active cancer, chronic respiratory disease, kidney disease, liver disease. Obesity was defined as having a body mass index of 30 or more. The sense of smell and taste was assessed by the Sino-Nasal Outcome Test 22 (SNOT-22) [12], item “sense of smell or taste,” both at baseline and during the follow-up interviews at 12 and 24 months to evaluate their persistence and the recovery rate. The SNOT-22 grades symptom severity as none (0), very mild (1), mild or slight (2), moderate (3), severe (4), or as bad as it can be (5) and refers to the presence of self-reported alterations in the sense of smell alone, in the sense of taste alone, or both. Patients with SNOT-22 >0 were asked whether the alteration involved the sense of smell, taste, or both. During the last interview, patients were questioned about the presence at 24 months of qualitative olfactory or gustatory symptoms including parosmia (“Do you smell odours differently compared to previous experiences?”), phantosmia (“Do you smell odours in absence of an apparent source?”), parageusia (“Do you perceive tastes differently compared to previous experiences?”), and phantogeusia (“Do you have taste sensations in the absence of an apparent gustatory source?”), based on a binary outcome of yes and no. Symptoms prevalence was expressed as a percentage of total patients, and the 95% confidence interval (CI) was calculated using the Clopper-Pearson method; differences in prevalence were evaluated through Fisher's exact test. Analyses were performed using R 3.6., and statistical significance was claimed for p < 0.05 (two-tailed).
Results
Of 315 patients completing the survey at baseline, 47 and other 15 did not respond or decline to take part at the 12 months and at 24 months follow-up interviews, respectively, leaving 253 responders with complete follow-up (80.3%; median [IQR] age, 48 [38–56] years; 158 [62.5%] women). Patients' characteristics are reported in Table 1. Associated comorbidities were reported by 84 participants (33.2%) with the most common being obesity reported by 30 patients (11.9%) followed by cardiovascular diseases (n = 23, 9.1%). Overall, 174 (68.8%, 95% CI: 62.7–74.4), 53 (20.9%, 95% CI: 16.1–26.5), and 36 (14.2%, 95% CI: 10.2–19.2) of 253 responders reported an altered sense of smell or taste (SNOT-22 >0) at baseline, 12 months, and 24 months, respectively. Thus, 121 subjects (69.5%, 95% CI: 62.1–76.3) completely recovered within 1 year, and 17 (9.8%, 95% CI: 5.8–15.2) fully recovered between one and 2 years. After 24 months, 12 participants (4.7%, 95% CI: 2.5–8.1) reported both taste and smell complaints, 21 (8.3%, 95% CI: 5.2–12.4) had only smell complaints, and 3 (1.2%, 95% CI: 0.25–3.4) had only taste complaints. At 24 months, 20 subjects (7.9%, 95% CI: 4.9–11.9%), also reporting a quantitative smell or taste impairment, complained at least one qualitative symptom with parosmia being the most frequent (n = 13; 5.1%, 95% CI: 2.8–8.6%) following by phantosmia (n = 12; 4.7%, 95% CI: 2.5–8.1%), phantogeusia (n = 8; 3.2%, 95% CI: 1.4–6.1%), and parageusia (n = 2; 0.8%, 95% CI: 0.1–2.8%). Among the 174 patients who have complained a COVID-19-associated olfactory or gustatory dysfunction at baseline, 138 (79.3%, 95% CI: 72.5–85.1%) reported complete resolution of smell or taste impairment, 33 (19.0%, 95% CI: 13.4–25.6%) reported a decrease in the severity, only 3 (1.7%, 95% CI: 0.4–5.0%) reported the symptom was unchanged, while none reported a worsening of the olfactory or gustatory alteration at the 24-month interview (Table 2).
Discussion
Two years after COVID-19, 14% of patients with previous mild-to-moderate symptomatic disease still self-reported a quantitative olfactory or gustatory dysfunction. The complete and partial recovery rates were 79% and 19%, respectively. Consequently, most of the patients reported a complete resolution of the olfactory and gustatory symptoms or, in any case, an improvement with only a marginal proportion reporting the symptoms had remained unchanged. No subject reported a persistent and total loss of the sense of smell or taste 2 years after the infection.
Thus, although this may take a long time, patients should be reassured that most of them experience a favourable evolution of the loss of smell and taste following COVID-19 and that late recovery is possible. Despite this, given the enormous spread of the disease, it should be considered that, one in 7 patients still complaining of persistence of olfactory or gustatory dysfunction 2 years after COVID-19, may represent an unprecedented burden on health systems and a challenge for the specialists who have to treat these patients. Unfortunately, with the exception of olfactory training [13, 14] which should be recommended in all cases of persistent olfactory disorders after the acute event that caused it, other therapeutic strategies are mainly based on weak evidence [15]. Further efforts are therefore needed to design clinical trials aimed at testing the efficacy of new therapeutic opportunities as well as intense research activity, especially in areas likely to lead to therapies as recently pointed out by a panel of international experts [16].
During the COVID-19 pandemic, the attention of researchers has mainly focused on persistent quantitative olfactory disorders, while only few studies have investigated the impact of qualitative dysfunction [6, 7, 17, 18]. In the present investigation, 8% of subjects complained of at least one symptom of qualitative impairment 2 years after SARS-CoV-2 infection with parosmia/phantosmia being the most prevalent. Previous studies observed that many patients report the onset of parosmia on average 3 months after the initial infection with the distortion being typically unpleasant and triggered by specific foods [19]. Consequently, these symptoms can be very distressing causing depression, anxiety, loss of appetite, significant weight loss, and malnourishment [20]. Nonetheless, many patients claim that their symptoms are underestimated by healthcare professionals. It is therefore of paramount importance to capture qualitative olfactory dysfunction and to be aware of its possible psychological and physical implications to offer support to the patients. While there are no evidence-based treatments for parosmia, some precautions such as avoiding trigger foods or consuming room temperature or cold foods may be helpful [20].
The data from the present study should be taken cautiously. Particularly, symptoms were self-reported, based on cross-sectional surveys, and may therefore contain suboptimal sensitivity, leading to underestimation of the prevalence of olfactory dysfunction compared to psychophysical tests [21].
In conclusion, only a marginal proportion of patients with previous mild-to-moderate symptomatic COVID-19 characterized by new onset of olfactory or gustatory dysfunction still complained of unchanged altered sense of smell or taste 2 years after the onset. Further assessments will be needed to establish whether in these cases smell and taste dysfunction will be permanent. Importantly, a late recovery was observed in 10% of subjects.
Statement of Ethics
The study was conducted in accordance with the World Medical Association Declaration of Helsinki and was approved Ethics Committee of the Friuli Venezia Giulia Region (Application No. CEUR-2020-Os-156). All participants gave their verbal informed consent for a telephone interview. As the researchers did not engage in any medical treatment or direct patient-researcher interaction, verbal informed consent was approved by the Ethics Committee of the Friuli Venezia Giulia Region.
Conflict of Interest Statement
The authors have no conflicts of interest to declare.
Funding Sources
This manuscript did not receive any funding.
Author Contributions
Paolo Boscolo-Rizzo contributed to the study conception, data analysis, and interpretation, as well as drafting and revising the article. Margherita Tofanelli, Enrico Zanelli, and Nicoletta Gardenal contributed to data collection and interpretation, as well as critical revision of the article. Giancarlo Tirelli contributed to the study conception, data interpretation, and critical revision of the article.
Data Availability Statement
All data generated or analysed during this study are included in this article. Further enquiries can be directed to the corresponding author.
Table 1 Characteristics of 253 COVID-19 patients included in the study
Characteristics n % (95% CIa)
Sex
Man 95 37.6 (31.6–43.8)
Woman 158 62.5 (56.2–68.4)
Tobacco smoking
Never 149 58.9 (52.6–65.0)
Ever 104 41.1 (35.0–47.4)
Comorbidityb
No 169 66.8 (60.6–72.6)
Yes 84 33.2 (27.4–39.4)
Smell or taste impairment SNOT-22 at baseline
0 = none 79 31.2 (25.6–37.3)
1 = very mild 2 0.8 (0.1–2.8)
2 = mild or slight 17 6.7 (4.0–10.6)
3 = moderate 25 9.9 (6.5–14.2)
4 = severe 37 14.6 (10.5–19.6)
5 = as bad as it can be 93 36.8 (30.8–43.0)
Type of chemosensory impairment
Smell 159 62.8 (56.6–68.8)
Taste 157 62.1 (55.8–68.1)
Smell or taste 174 68.8 (62.7–74.4)
COVID-19, coronavirus disease 2019; SNOT-22, Sino-Nasal Outcome Test 22.
a 95% CIs were calculated using the Clopper-Pearson method.
b Comorbidity includes obesity (body mass index ≥30), diabetes, hypertension, cardiovascular disease, chronic respiratory disease, active cancer, renal disease, and liver disease.
Table 2 Evolution of alteration of sense of smell or taste in 174 patients positive for SARS-CoV-2 infection
Alteration of sense of smell or taste at baselinea N (%) Alteration of sense of smell or taste after 24 monthsa
total 0: no 1: very mild 2: mild or slight 3: moderate 4: severe 5: as bad as it can be
1: very mild 2 (1.1) 2 0 0 0 0 0
2: mild or slight 17 (9.8) 17 0 0 0 0 0
3: moderate 25 (14.4) 20 3 0 2 0 0
4: severe 37 (21.3) 30 1 0 5 1 0
5: as bad as it can be 93 (53.4) 69 5 2 12 5 0
Total 174 138 (79.3) 9 (5.2) 2 (1.1) 19 (10.9) 6 (3.4) 0 (0.0)
SARS-CoV-2, severe acute respiratory syndrome coronavirus.
a According to SNOT-22 item “sense of smell or taste.”
==== Refs
References
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2 Boscolo-Rizzo P Tirelli G Meloni P Hopkins C Madeddu G De Vito A COVID-19-related smell and taste impairment with widespread diffusion of SARS-CoV-2 Omicron variant Int Forum Allergy Rhinol 2022 Mar
3 Spinato G Fabbris C Polesel J Cazzador D Borsetto D Hopkins C Alterations in smell or taste in mildly symptomatic outpatients with SARS-CoV-2 infection JAMA 2020 May 323 (20) 2089 2090 32320008
4 Lee DY Lee WH Wee JH Kim JW Prognosis of postviral olfactory loss: follow-up study for longer than one year Am J Rhinol Allergy 2014 Sep–Oct 28 (5) 419 422 25198029
5 Frasnelli J Landis BN Heilmann S Hauswald B Hüttenbrink KB Lacroix JS Clinical presentation of qualitative olfactory dysfunction Eur Arch Otorhinolaryngol 2004 Aug 261 (7) 411 415 14610680
6 Cook E Kelly CE Burges Watson DL Hopkins C Parosmia is prevalent and persistent amongst those with COVID-19 olfactory dysfunction Rhinology 2021 Apr 59 (2) 222 224 33377890
7 Lerner DK Garvey KL Arrighi-Allisan AE Filimonov A Filip P Shah J Clinical features of parosmia associated with COVID-19 infection Laryngoscope 2022 Mar 132 (3) 633 639 34870334
8 Vaira LA Gessa C Deiana G Salzano G Maglitto F Lechien JR The effects of persistent olfactory and gustatory dysfunctions on quality of life in long-COVID-19 patients Life 2022 Feb 12 (2) 141 35207429
9 Croy I Nordin S Hummel T Olfactory disorders and quality of life: an updated review Chem Senses 2014 Mar 39 (3) 185 194 24429163
10 Boscolo-Rizzo P Guida F Polesel J Marcuzzo AV Antonucci P Capriotti V Self-reported smell and taste recovery in coronavirus disease 2019 patients: a one-year prospective study Eur Arch Otorhinolaryngol 2022 Jan 279 (1) 515 520 33963433
11 Technical guidance [Internet] [cited 2020 Apr 27]. Available from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/technical-guidance
12 Hopkins C Gillett S Slack R Lund VJ Browne JP Psychometric validity of the 22-item sinonasal outcome test Clin Otolaryngol 2009 Oct 34 (5) 447 454 19793277
13 Oleszkiewicz A Bottesi L Pieniak M Fujita S Krasteva N Nelles G Olfactory training with Aromastics: olfactory and cognitive effects Eur Arch Otorhinolaryngol 2022 Jan 279 (1) 225 232 33864109
14 Genetzaki S Tsakiropoulou E Nikolaidis V Markou K Konstantinidis I Postinfectious olfactory dysfunction: oral steroids and olfactory training versus olfactory training alone: is there any benefit from steroids? ORL J Otorhinolaryngol Relat Spec 2021 83 (6) 387 394 34107478
15 Addison AB Wong B Ahmed T Macchi A Konstantinidis I Huart C Clinical Olfactory Working Group consensus statement on the treatment of postinfectious olfactory dysfunction J Allergy Clin Immunol 2021 May 147 (5) 1704 1719 33453291
16 Mainland JD Barlow LA Munger SD Millar SE Vergara MN Jiang P Identifying treatments for taste and smell disorders: gaps and opportunities Chem Senses 2020 Oct 45 (7) 493 502 32556127
17 Raad N Ghorbani J Safavi Naeini A Tajik N Karimi-Galougahi M Parosmia in patients with COVID-19 and olfactory dysfunction Int Forum Allergy Rhinol 2021 11 (10) 1497 1495 34109762
18 Rashid RA Alaqeedy AA Al-Ani RM Parosmia due to COVID-19 disease: a 268 case series Indian J Otolaryngol Head Neck Surg 2021 May 1 8
19 Duyan M Ozturan IU Altas M Delayed parosmia following SARS-CoV-2 infection: a rare late complication of COVID-19 SN Compr Clin Med 2021 3 (5) 1200 1202 33817555
20 Walker A Kelly C Pottinger G Hopkins C Parosmia-a common consequence of covid-19 BMJ 2022 Apr 377 e069860 35477684
21 Boscolo-Rizzo P Menegaldo A Fabbris C Spinato G Borsetto D Vaira LA Six-month psychophysical evaluation of olfactory dysfunction in patients with COVID-19 Chem Senses 2021 Jan 46 bjab006 33575808
| 36063810 | PMC9747726 | NO-CC CODE | 2022-12-15 23:22:04 | no | ORL J Otorhinolaryngol Relat Spec. 2022 Sep 5;:1-5 | utf-8 | ORL J Otorhinolaryngol Relat Spec | 2,022 | 10.1159/000525861 | oa_other |
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Phys Med
Phys Med
Physica Medica
1120-1797
1724-191X
Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd.
S1120-1797(22)02318-3
10.1016/S1120-1797(22)02318-3
Poster Abstracts–Dir
ATLAS VALIDATION FOR AUTOMATIC LUNGS SEGMENTATION AND FUNCTIONAL SUBREGIONS IDENTIFICATION IN COVID-19 PATIENTS: AN APPROACH COMBINED WITH QUANTITATIVE DENSITOMETRY
Mori Martina Dr
Alborghetti Lisa Dr
Palumbo Diego Dr
Broggi Sara Dr
Raspanti Davide Dr
Querini Patrizia Rovere Prof
Del Vecchio Antonella Dr
De Cobelli Francesco Prof
Fiorino Claudio Dr
14 12 2022
12 2022
14 12 2022
104 S87S88
Copyright © 2022 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.
2022
Associazione Italiana di Fisica Medica e Sanitaria
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
pmc
| 0 | PMC9747727 | NO-CC CODE | 2022-12-15 23:22:04 | no | Phys Med. 2022 Dec 14; 104:S87-S88 | utf-8 | Phys Med | 2,022 | 10.1016/S1120-1797(22)02318-3 | oa_other |
==== Front
Phys Med
Phys Med
Physica Medica
1120-1797
1724-191X
Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd.
S1120-1797(22)02429-2
10.1016/S1120-1797(22)02429-2
Eposter Abstracts–Biomedical Engineering
CLINICAL EVALUATION OF PREVENTION OF INDOOR COVID-19 TRANSMISSION WITH COLD PLASMA TECHNOLOGY
Kniazeva Volha Dr
Loizou Mr. Konastantinos
Apostolou Mr. Theofylaktos
Kornev Alexander Dr.
Kostevich Mr Serhei
Constantinou Costas Dr.
Hadjihannas Mr. Linos
14 12 2022
12 2022
14 12 2022
104 S131S132
Copyright © 2022 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.
2022
Associazione Italiana di Fisica Medica e Sanitaria
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
pmc
| 0 | PMC9747728 | NO-CC CODE | 2022-12-15 23:22:04 | no | Phys Med. 2022 Dec 14; 104:S131-S132 | utf-8 | Phys Med | 2,022 | 10.1016/S1120-1797(22)02429-2 | oa_other |
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Eur Addict Res
Eur Addict Res
EAR
European Addiction Research
1022-6877
1421-9891
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36195067
10.1159/000526584
ear-0028-0471
Research Article
Adolescents' Alcohol Use and Related Expectancies before and during the Early COVID-19 Pandemic: Evidence from the Nationwide MyLife Study
Burdzovic Andreas Jasmina a b *
Brunborg Geir Scott a
aNorwegian Institute of Public Health, Oslo, Norway
bDepartment of Psychology, University of Oslo, Oslo, Norway
*Jasmina Burdzovic Andreas, [email protected]
4 10 2022
4 10 2022
28 6 471480
24 2 2022
22 7 2022
2022
Copyright © 2022 by S. Karger AG, Basel
2022
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Introduction
We examined a range of alcohol use indicators among Norwegian adolescents before and during the early COVID-19 pandemic.
Methods
We examined two cohorts of Norwegian 16-year-olds from the nationwide MyLife study who entered high school in fall 2020 (i.e., COVID-19 pandemic cohort; n = 915) and fall 2019/18 (i.e., prepandemic cohort; n = 1,621). Through e-surveys, adolescents reported their past year drinking frequencies and quantities (generating the Alcohol Use Disorders Identification Test-Concise; AUDIT-C scores) and completed Social Facilitation (SF) and Tension Reduction (TR) subscales of the Alcohol Outcome Expectancies Scale. Cohort differences across these outcomes were examined with linear and modified Poisson regression models.
Results
There were no cohort differences in the proportion of adolescents who reported drinking in the past year or in drinking/binge drinking frequencies. However, alcohol quantities consumed on a typical drinking day were 1/3 of a drink greater in the COVID-19 cohort than in the prepandemic cohort; aIRR (95% CI) = 1.13 (1.02–1.25). These consumption differences compounded into significantly greater AUDIT-C scores (aIRR [95% CI] = 1.16 [1.02–1.32]) and positive AUDIT-C screens (31.2% vs. 26.4%; aRR [95% CI] = 1.21 [1.07–1.36]) in the COVID-19 cohort. In terms of alcohol-related expectancies, there were no SF differences, but the COVID-19 cohort reported significantly greater TR expectancies; b (95% CI) = 0.11 (0.02–0.20).
Conclusion
Despite the 2020 lockdown, Norwegian adolescents who started high school during the first pandemic year did not differ from their prepandemic peers in terms of how many of them drank, how often they drank, or in prosocial expectations they had of alcohol use. However, they consumed greater alcohol quantities per drinking day, had greater AUDIT-C scores, and reported greater tension reduction expectations of alcohol use. These results underscore the value of examining adolescents' alcohol-related behaviors during the COVID-19 pandemic above and beyond the basic drinking prevalence and frequencies.
Key Words
Adolescence
Hazardous alcohol use
Alcohol expectancies
COVID-19 pandemic
The study received no external funding.
==== Body
pmcIntroduction
The COVID-19 pandemic and the accompanying measures profoundly altered the lives and affected multiple domains of adolescents' health and adjustment, including their alcohol use [1, 2, 3, 4, 5, 6, 7, 8, 9]. Indeed, the emerging research paints a complex picture of adolescent alcohol use during the early course of the pandemic [8, 10], including evidence of no substantial changes, but also of both the declines and increases across diverse alcohol-related outcomes. Similar complexities of alcohol-related behaviors were observed among young adults and college students following pandemic-specific disruptions and campus closures [11, 12, 13, 14]. Thus, understanding how adolescents' alcohol-related behaviors may have changed during the pandemic remains a public health priority [15, 16], especially as such an exogenous shock during sensitive developmental periods may precipitate critical transitions and shape subsequent alcohol use trajectories [17].
Consequently, this study aimed to examine a range of alcohol-related outcomes in two cohorts of Norwegian adolescents: one assessed before and one assessed during the pandemic. First, while there is some evidence that the pandemic conditions were associated with changing drinking patterns of Norwegian adults, it remains less clear whether this was the case, and if so, how, for Norwegian youth as well. For example, while most national studies observed minimal or no population-level changes in alcohol use in Norway during the initial pandemic [18, 19, 20], more (hazardous) drinking was nevertheless reported by adults experiencing greater worries, quarantine, or work from home orders [21], and by those characterized by greater prepandemic alcohol consumption, including heavy drinking [19, 20, 22]. Still, despite the clear developmental relevance [23, 24, 25], only a handful of non-Norwegian studies focused specifically on adolescents [5] or examined their hazardous alcohol use during the COVID-19 period [4, 6]. Second, recent research also suggests that at least some of the observed changes in substance use during the pandemic may be accompanied by changes in expectations and motivations for use both among adults and youth [9, 11, 13, 22], but research on adolescents is still somewhat lacking. Adolescents' positive alcohol-related cognitions − such as the expectations that alcohol will help improve social experiences or negative moods, for example − are especially developmentally pertinent as they relate to various aspects of alcohol use over time [26, 27, 28, 29]. Detecting pandemic-related differences, if any, in such salient precursors of (problem) alcohol use may thus be relevant to understanding alcohol-related behaviors in youth during, but possibly also after, the pandemic period. Consequently, in addition to basic epidemiological indicators of adolescent alcohol use (i.e., prevalence and frequency), we also examined their more complex alcohol use patterns (i.e., hazardous drinking) and their alcohol use expectancies in relation to early pandemic conditions in Norway.
Specifically, we examined if there were pandemic-related differences across multiple indicators of alcohol use and alcohol expectations among adolescents in Norway − the first country to impose a strict nationwide lockdown on March 12, 2020 [30, 31]. In addition to physical closures of all educational institutions and all businesses/services save for grocery stores, pharmacies, and gas stations; cancellations and/or strict limitations on social, cultural, sports, and religious gatherings; and restrictions of both domestic and international travel throughout first half of 2020 [30, 31], this lockdown also entailed regulations on serving of alcohol [18]. Specifically, alcohol-serving venues such as bars and restaurants were closed during early 2020 under the umbrella shutdown of all nonessential services. Although the nationwide lockdown was eased and replaced with local control strategies as of May–June 2020 [30, 31], limitations on serving of alcohol − including serving at dining tables only and no later than 11:00 p.m. − largely remained in place as part of national guidelines well into spring 2022. High school graduation ceremonies traditionally accompanied by an extensive partying period were principally canceled or held “digitally” at the end of spring 2020 semester. Both the activities and the amount of time spent with peers were also likely substantively transformed due to the rules regulating both the nature and size of all gatherings.
In sum, the initial pandemic year marked by school closures, work from home orders for working parents, and strict restrictions on nightlife, entertainment, and get-togethers fundamentally re-shaped the lives and experiences of Norwegian youth during this period. How these conditions may have shaped various aspects of their alcohol use was the main question investigated in this study. To answer this question, we examined how alcohol use, hazardous alcohol use, and alcohol expectations may have differed across two temporally adjacent but otherwise sociodemographically comparable adolescent cohorts; i.e., between the prepandemic (2018/2019) and COVID-19 (2020) cohorts of grade 11 students from Norway.
Methods
Study Design and Procedures
We analyzed data from the ongoing MyLife longitudinal study of adolescent development and substance use [32]. MyLife was initiated in 2017 (T1 baseline), and it aimed to enroll all students in grades 8, 9, and 10 from 33 middle schools throughout Norway. The study was approved by the Norwegian Data Protection Authority (reference No.: 15/01495) following ethical evaluation by The National Committee for Research Ethics in the Social Sciences and Humanities (reference No.: 2016/137). Because at the time of enrollment all potential participants were minors, parental consent was sought prior to data collection; students eventually provided their assent through study participation.
All eligible students − that is, those for whom parental consent was obtained − were invited for annual participation involving e-surveys completion during school hours while in middle school (i.e., grades 8–10) and individually once in high school (i.e., grades 11–13). Baseline T1 assessment was completed in fall 2017, when 2,975 of the eligible 3,512 students participated. Follow-up longitudinal assessments were completed during the fall semesters of 2018 (T2; n = 2,875), 2019 (T3; n = 2,651), and 2020 (T4; n = 2,323). Adolescents were reimbursed for their time through a 1,000 NOK (EUR 100) contribution to their class savings account while in middle school and through individual gift cards valued 200–250 NOK (EUR 20–25) once in high school. Participation remains entirely voluntary, and all participants are free to withdraw from the study at any time. Study protocol and core cohort were described in detail elsewhere [32, 33].
Because the MyLife study utilized accelerated longitudinal design [34] and assessed three adolescent cohorts/school grades over time, the study assessment waves, adolescent cohorts/grades, and secular period of interest (i.e., before- vs. during the pandemic) could be examined separately. Grade 11 was selected for all analyses because the youngest baseline cohort entered high school (i.e., grade 11) in August, while the two older cohorts entered high school in the two preceding years. Identical analytical approaches were used in previous reports from these data to examine adolescent mental and physical health in relation to the COVID-19 pandemic [33].
Sample
We analyzed two sociodemographically comparable cohorts of Norwegian adolescents who started grade 11 in high school either during fall 2020 (i.e., the COVID-19 pandemic cohort, n = 915) or during fall 2019 and 2018 (combined into the single prepandemic cohort for ease of analyses, n = 1,621). Because school enrollment in Norway is largely determined by the birth year, most students start high school (11th grade) during the fall of the year of their 16th birthday. Consequently, our sample included two cohorts of 16-year-old adolescents and the developmental period traditionally associated with both the onset and escalation of alcohol use in Norway [35, 36].
Measures
Adolescents completed comprehensive e-surveys during the fall semester of their first year in high school.
Alcohol Use
Students reported frequency of drinking and binge drinking (i.e., consuming 5 or more alcoholic drinks in 1 day). The original response categories ranging from “none”; “1–2 days”; to “every day or almost every day” were dichotomized to obtain estimates of any alcohol use during previous year; that is, of past year drinking prevalence. They were also recoded into the actual number of drinking and binge drinking days for those who did drink using the mid-point method. For example, those reporting using alcohol 2–3 days/month were assigned the value of 30 drinking days (i.e., 2.5 days × 12 months) in the past year. Finally, drinkers reported typical alcohol quantities; that is, the usual number of drinks consumed on a drinking day.
Hazardous Alcohol Use
The three items above also constitute the slightly adjusted AUDIT-C screener for hazardous alcohol use, which is a concise version of the Alcohol Use Disorders Identification Test (AUDIT) [37]. AUDIT-C has been successfully used to identify problem drinking among various youth populations, including those from the Nordic region [38, 39, 40]. The response options of the above frequency and quantity items were recoded to align with the AUDIT-C scoring, and the resulting sum scores ranged from 0 to 12. Internal consistency of the short scale was α ≥ 0.77 at all study assessments. We also dichotomized AUDIT-C scores into negative (AUDIT-C scores <3) and positive screeners (AUDIT-C scores ≥3) following the established international and Nordic cut-off values for identification of problem and binge drinkers in adolescent samples [38, 39, 40].
Alcohol Expectancies
All students completed the 6-item Social Facilitation and the 3-item Tension Reduction subscales from the Alcohol Outcome Expectancies Scale (AOES) [41], indicating what they think happens when/if they drink. Both the subscales tap into the positive expectations resulting from alcohol use, such as “I am more sociable” (Social Facilitation subscale, SF) and “It takes away my negative moods and feelings” (Tension Reduction subscale, TR). The original response options were slightly modified to reflect a 5-point scale, ranging from 1 = “definitely not” to 5 = “definitely.” The corresponding SF and TR scores were computed as averages of the respective subscale items; both had strong internal consistency (Cronbach's α ≥ 0.88 for SF and Cronbach's α = 0.90 for TR) at all study assessments. Identical or comparable screeners have been successfully utilized in adolescent samples [42, 43], including those from the Nordic region [26, 29].
Covariates
Because our cohorts were recruited from the same middle schools, they were by design sociodemographically comparable except for the initial COVID-19 pandemic period. Nevertheless, we accounted for possible sociodemographic differences.
Sociodemographic Characteristics
At study baseline, participants reported their gender, whether their parents live together (proxy for family structure), and the language spoken at home (proxy for immigrant background if not Norwegian only). In addition, participants completed the adolescent version of the MacArthur Scale of Subjective Social Status [44], comparing their families' social status to other families in their neighborhoods on a 1–10 scale. T1 and T2 reports were averaged and then categorized to reflect three levels of adolescents' subjective social status: low (scores up to 4.5), average (4.5–7.5), and high (8–10).
Analyses
Missing values on all covariates were low and classified into the dummy “unknown” category which was included as such in all models to prevent data loss. The cohort differences test the risk associated with the COVID-19 pandemic conditions. The outcomes were examined using either linear (for continuous variables) or modified Poisson (for count and binary variables) regression models, as this approach is suitable for both dichotomous and count outcomes [45] while providing easily interpretable estimates in the form of relative risks (RR). Specifically, we fit a set of nested models for each outcome where we first estimated a crude model (model 0 = unadjusted) and then a model adjusted for demographics (model 1 = model 0 + adolescent gender, family structure, subjective social status, and immigrant background). Identical models were estimated for the entire sample and then for drinkers only to assess if these estimates may be driven by (a) the differences in drinking prevalence, if any, between the two cohorts or (b) differences in alcohol expectancies between drinkers and nondrinkers.
All analyses were conducted in Stata v.15 [46]. The school-level nesting was accounted for by cluster-robust standard errors and the vce (cluster) option in Stata [47]. Predicted probabilities, means, and counts were obtained using the -margins command in Stata and were estimated at the average values of the remaining covariates. The hypotheses were not preregistered, and the results should be considered exploratory.
Results
Sample Characteristics
Characteristics of both cohorts are shown in Table 1.
Adolescent Drinking Frequencies and Quantities before and during the First Pandemic Year
Table 2 shows the regression estimates from both crude and adjusted models for all drinking outcomes. Shown first are the estimates for the entire sample, followed by drinkers only. We observed no significant cohort differences in the proportion of adolescents who engaged in any drinking or in the frequency (i.e., number of days) of drinking or binge drinking previous year.
The only significant difference was observed in the usual quantity of alcohol consumed on a drinking day. On average, the COVID-19 cohort consumed an additional 1/3 of a drink on a typical drinking day compared with the prepandemic cohort (fully adjusted model 1; MCOVID-19 = 2.76 [0.20] vs. MPRE-COVID-19 = 2.42 [0.12]; incidence RR [95% CI] = 1.13 [1.02–1.25], p = 0.02; absolute difference = 0.34). Identical patterns were observed among drinkers-only (bottom of Table 2), such that the COVID-19 cohort drinkers consumed an additional 1/2 of a drink per drinking day than drinkers from the prepandemic cohort.
Adolescent Hazardous Drinking before and during the First Pandemic Year
Table 3 shows the regression estimates from both crude and adjusted models for all AUDIT-C outcomes. Unlike the drinking frequency estimates, hazardous drinking was elevated in the COVID-19 cohort as evidenced both by the significantly greater AUDIT-C average scores (fully adjusted model 1; MCOVID-19 = 2.31 [0.19] vs. MPRE-COVID-19 = 1.99 [0.11]; incidence RR [95% CI] = 1.16 [1.02–1.32], p = 0.02; absolute difference = 0.32) and the proportion of adolescents scoring positive on the AUDIT-C screener (fully adjusted model 1; 31.2% vs. 26.4%; RR [95% CI] = 1.21 [1.07–1.36], p = 0.002; absolute difference = 4.8%). Identical pattern of results was observed among drinkers-only (bottom of Table 3), such that the greater proportion of adolescents from the COVID-19 cohort scored positive on the AUDIT-C screener compared with the pre-COVID-19 cohort (i.e., 59.8% vs. 50.3%).
Adolescent Drinking Expectancies before and during the First Pandemic Year
Table 4 shows the regression estimates from both crude and fully adjusted models for SF and TR drinking expectancy outcomes. We observed no significant SF differences between cohorts. However, TR expectancies were significantly greater in the COVID-19 cohort than in the pre-COVID-19 cohort in the entire sample (MCOVID-19 = 2.96 [0.04] vs. MPRE-COVID-19 = 2.85 [0.04]; b [95% CI] = 0.11 [0.018–0.20], p = 0.02) but not among drinkers-only (Table 4 bottom).
Discussion
This is the first study to examine multiple aspects of alcohol use and associated expectancies among Norwegian adolescents during and before the COVID-19 pandemic conditions. Using the up-to-date information from an accelerated longitudinal design study, we were able to examine drinking prevalence, drinking frequencies and quantities, hazardous drinking, and alcohol-related expectations in two cohorts of 16-year-olds differentiated only by the pandemic (non)-exposure; i.e., before and during a global “experiment by nature” [48].
Our results show that the Norwegian adolescents who started high school during the first pandemic year did not differ from their prepandemic peers in terms of how many of them drank, how often they drank, or in their prosocial expectations of alcohol use. This was the case despite the restrictions imposed on social gatherings and alcohol-serving venues, suggesting that the adolescents' access to alcohol − but not necessarily their drinking patterns, contexts, or motivations − likely remained unchanged during the initial pandemic year. Indeed, the COVID-19 cohort consumed significantly greater alcohol quantities per drinking day, had significantly greater AUDIT-C scores indicative of hazardous drinking, and reported significantly greater tension reduction expectancies of alcohol use when compared with their demographically identical peers from one and 2 years prior. In short, we observed complex shifts in the nature of Norwegian adolescents' relation with drinking and alcohol during the initial COVID-19 period. These findings are largely consistent with drinking patterns observed among Norwegian adults during the same period, characterized by minimal population-level changes in overall alcohol consumption but also by meaningful shifts in hazardous drinking and drinking motivations in certain groups [19, 20, 21, 22].
Our results further suggest that some basic aspects of alcohol use among high school-aged adolescents from Norway were not necessarily affected by the 2020 pandemic conditions. Despite the limitations imposed on social gatherings and alcohol venues, as many 16-year-olds from our COVID-19 cohort managed to obtain alcohol and to (binge) drink as frequently during 2020 as did the 16-year-olds from our prepandemic cohort. It appears that the newly imposed regulations of alcohol sales in bars and restaurants did not necessarily disrupt adolescents' access to alcohol, which was likely reliant on informal sources even before the pandemic. Indeed, the regulation of alcohol sales in grocery stores (i.e., beer) and state monopolies (i.e., beer, vine, and liquor) remained largely intact [18]. In fact, alcohol sales through these outlets precipitously increased and reached all-time records during the first pandemic year [49, 50].
Our overall results underscore the value of examining adolescents' alcohol-related behaviors during the pandemic above and beyond the basic estimates of drinking prevalence and frequencies, as evident in the pandemic cohort's elevated hazardous drinking and alcohol expectancies. Specifically, increases in hazardous drinking in this cohort were not generated by their more frequent drinking but by their consumption of greater alcohol quantities on the days when they did drink. That is, drinkers from the COVID-19 cohort reported consuming an additional half a drink per drinking day, such that the average typical alcohol intake exceeded 5 drinks per drinking day in that group. This change placed these drinking patterns within the definition heavy episodic drinking [36] and 6 out of 10 drinkers from the COVID-19 cohort within the AUDIT-C diagnostic criteria for adolescent hazardous drinking [38, 39].
Finally, the nature of adolescents' relation with alcohol also appeared to be fundamentally altered during the early pandemic period, such that they increasingly expected alcohol to help alleviate stress and negative emotions. This shift was not evident in the prosocial expectations and was not generated by drinkers alone as in other studies examining pandemic-related changes in drinking motivations among young drinkers [13]. In fact, the greater tension-reduction expectations in our COVID-19 cohort appear to be driven primarily by nondrinkers. This suggests that adolescent nondrinkers from our samples increasingly expected alcohol to aid in the management of stress and negative moods − be it their own or others' − during the initial pandemic year. Why this may be the case is not yet clear, but these cohort differences could reflect a number of possibilities, including the adolescent nondrinkers' observations of altered drinking patterns, contexts, and motivations among drinkers (including possibly their own parents) during the early pandemic [1, 3, 9, 11, 14, 22].
How these cohort differences in hazardous drinking and alcohol-related expectancies may shape both the current and future alcohol use among all adolescents − and not drinkers alone − cannot be inferred from these results and should be investigated further. Nevertheless, it is conceivable that Norwegian adolescents who entered high school during the pandemic period may be at greater risk for future alcohol problems, as hazardous patterns of alcohol consumption during this sensitive developmental period are associated with multiple adverse outcomes over time [23, 24, 25, 40]. Similarly, positive drinking expectations in general and positive social/relaxation expectancies in particular are both early emerging and strongly associated with (problem) alcohol use in later years [27, 28]. For example, a long-term longitudinal investigation reported that positive alcohol expectancies at age of 16 years − the same ages as in our adolescent sample − predicted multiple negative alcohol-related outcomes 20 years later even after accounting for gender, family SES, age of drinking onset, early delinquency, and school test scores [27]. Even though we did not have longitudinal data, these findings may be relevant in the context of adolescents' prolonged pandemic-related stress, the emerging evidence for the use of substances as a means of coping with such stress [9, 11], and the long-term sequelae of positive alcohol expectancies [27, 28].
Methodological Considerations and Study Limitations
These results should be interpreted in relation to the specific social and political context of the COVID-19 pandemic in Norway − a sparsely populated Nordic country characterized by a generous welfare system and currently ranking #1 in the Human Development Index. Even though the initial pandemic period was marked by a strict lockdown, its effects were to a certain degree buffered by accompanying economic packages. Ultimately, these measures were evaluated as successful [30], with Norway recording relatively low COVID-19 incidence and no excess mortality in 2020 [51, 52].
Further, the results should be interpreted in the context of our study design, including a relatively short-term pandemic exposure (spring 2020–fall 2020) and exclusion of schools from the capital city (Oslo) in the original MyLife sampling strategy. Even though the pandemic conditions in Norway varied both in terms of geography and timing [53], how such variations may have affected adolescents' alcohol outcomes was beyond the scope of this report. While other metropolitan areas were represented in our nationwide sample, it should be noted that the capital was disproportionally affected by the pandemic and had perhaps been subjected to the strictest control measures. Further, self-reports are vulnerable to measurement error because of socially desirable responding, misunderstanding, imperfect memory, and biased recall − a general set of concerns in all studies utilizing self-reports. However, presence of such biases would have the impact primarily on the overall over- or under-reporting of drinking behaviors, not necessarily on the cohort differences which were of substantive interest in this study. Finally, some of our key measures (such as the Alcohol Outcome Expectancies Scale [AOES] [41]) have not been extensively used in Norwegian samples. That is, even though we did observe shifts in alcohol expectancies in our study, it is not known how representative or normative they may be of Norwegian youth.
Conclusions
Norwegian adolescents who started high school during the first pandemic year did not differ from their prepandemic peers in terms of how many of them drank, how often they drank, or in prosocial expectations they had of alcohol use during previous year. However, they consumed more alcohol per drinking day, had greater hazardous drinking as evidenced in greater AUDIT-C scores, and reported greater tension reduction expectations of alcohol use. These results underscore the value of nuanced examinations of adolescents' alcohol-related behaviors and cognitions during the COVID-19 pandemic above and beyond the simple drinking prevalence and frequencies, as well as the need for future studies addressing these questions using prospective longitudinal designs. Future research should examine the putative mechanisms through which various pandemic conditions may have altered adolescents' drinking expectations, drinking patterns, and − possibly − subsequent alcohol use trajectories.
Statement of Ethics
The original study protocol was evaluated by The National Committee for Research Ethics in the Social Sciences and the Humanities (reference No.: 2016/137) and approved by the Norwegian Data Protection Authority (DPA) (reference No.: 15/01495). Written parental consent was obtained for all adolescent participants before baseline data collection, while adolescents provided their assent through survey participation.
Conflict of Interest Statement
The authors have no conflicts of interest to declare.
Funding Sources
The study received no external funding.
Author Contributions
Dr. J. Burdzovic Andreas: concept and design; acquisition, analysis, and interpretation of data; and drafting of the manuscript. Dr. G.S. Brunborg: concept and design; acquisition, analysis, and interpretation of data; and revision of the manuscript.
Data Availability Statement
Study participants did not agree for their data to be shared publicly, so supporting data are not available. Further inquiries can be directed to the corresponding author.
Acknowledgments
We extend our gratitude to all adolescents and their families who participated in the MyLife study.
Table 1 Sample characteristics, N = 2,536
Prepandemic cohort n = 1,621 n (%) COVID-19 cohort n = 915 n (%)
Gender (girl) 952 (58.7) 553 (60.4)
Immigrant background
No 1,210 (74.7) 716 (78.2)
Yes 164 (10.2) 103 (11.2)
Unknown 247 (15.2) 96 (10.5)
Family structure
Parents live together 988 (60.9) 609 (66.5)
Parents do not live together 384 (23.7) 212 (23.2)
Unknown 249 (15.4) 94 (10.3)
Subjective social status
Low 59 (3.6) 24 (2.6)
Average 857 (52.8) 492 (53.8)
High 646 (39.9) 354 (38.7)
Unknown 59 (3.6) 45 (4.9)
Shown are the proportions (%) of participants in each group of Grade 11 students. All covariates were assessed at study T1 baseline in 2017, save for the subjective social status which was an average of adolescents’ T1 (2017) and T2 (2018) reports. Grade 11 is the first year of high school in Norway. Because school entry and enrollments in Norway are determined by the birth year, the variation in age is low, and students usually start high school in the fall semester of the year of their 16th birthday.
Table 2 Alcohol use in pre- and COVID-19 adolescent cohorts from Norway
Alcohol use Prepandemic cohort COVID-19 cohort
Entire sample (N = 2,536) n = 1,621 n = 915 Estimate (95% CI)
Any alcohol use % % RR p value
Model 0: unadjusted 53.5 53.7 1.005 (0.93–1.09) 0.91
Model 1: demographics 53.0 53.4 1.01 (0.93–1.09) 0.82
Entire sample (N = 2,536) n = 1,621 n = 915 Estimate (95% CI)
Drinking days,a M (SE) M (SE) IRR p value
Model 0: unadjusted 10.61 (0.75) 10.97 (0.96) 1.03 (0.85–1.26) 0.74
Model 1: demographics 10.29 (0.81) 10.92 (0.98) 1.06 (0.85–1.31) 0.58
Typical number of drinksb M (SE) M (SE) IRR p value
Model 0: unadjusted 2.47 (0.11) 2.76 (0.20) 1.12 (1.02–1.25) 0.03
Model 1: demographics 2.42 (0.12) 2.76 (0.20) 1.13 (1.02–1.25) 0.02
Binge days,c M (SE) M (SE) IRR p value
Model 0: unadjusted 5.57 (0.61) 6.21 (0.87) 1.11 (0.89–1.39) 0.33
Model 1: demographics 5.38 (0.61) 6.12 (0.88) 1.14 (0.90–1.42) 0.27
Drinkers only (n = 1,347) n = 860 n = 487 Estimate (95% CI)
Drinking days,a M (SE) M (SE) IRR p value
Model 0: unadjusted 19.83 (1.11) 20.41 (1.17) 1.03 (0.88–1.20) 0.71
Model 1: demographics 19.42 (1.19) 20.43 (1.17) 1.05 (0.85–1.31) 0.55
Typical number of drinksb M (SE) M (SE) IRR p value
Model 0: unadjusted 4.68 (0.09) 5.17 (0.12) 1.10 (1.05–1.16) <0.001
Model 1: demographics 4.62 (0.09) 5.12 (0.12) 1.11 (1.06–1.18) <0.001
Binge days,c M (SE) M (SE) IRR p value
Model 0: unadjusted 10.45 (0.94) 11.59 (1.07) 1.11 (0.93–1.32) 0.24
Model 1: demographics 10.07 (0.90) 11.37 (1.07) 1.13 (0.94–1.35) 0.19
Shown are the unstandardized risk ratio (RR) estimates for dichotomous outcomes and incidence risk ratios (IRR) for count outcomes; all with corresponding 95% CI and estimated marginal probabilities/means/counts. All tests reflected comparisons between the pre-COVID-19 versus COVID-19 cohort of Grade 11 students. Sociodemographic covariates included gender, immigrant background, parental cohabitation, and subjective social status evaluation; all were assessed at T1 2017 baseline, save for the subjective social status (T1/2017-T2/2018 average). All models accounted for nesting by schools of origin.
a Number of drinking days in past 12 months.
b Usual number of alcoholic drinks consumed on a drinking day.
c Number of binge drinking days (i.e., 5 or more alcoholic drinks) in past 12 months.
Table 3 Hazardous alcohol use in pre- and COVID-19 adolescent cohorts from Norway
AUDIT-C Prepandemic cohort COVID-19 cohort
Entire sample (N = 2,536) n = 1,621 n = 915 Estimate (95% CI)
AUDIT-C (full scale) M (SE) M (SE) IRR p value
Model 0: unadjusted 2.02 (0.11) 2.32 (0.19) 1.14 (1.004–1.29) 0.04
Model 1: demographics 1.99 (0.11) 2.31 (0.19) 1.16 (1.02–1.32) 0.02
AUDIT-C (cut-off ≥3) % % RR p value
Model 0: unadjusted 27.05 32.2 1.19 (1.05–1.35) 0.007
Model 1: demographics 26.4 31.2 1.21 (1.07–1.36) 0.002
Drinkers only (n = 1,347) n = 860 n = 487 Estimate (95% CI)
AUDIT-C (full scale) M (SE) M (SE) IRR p value
Model 0: unadjusted 3.79 (0.11) 4.31 (0.14) 1.14 (1.05–1.23) 0.001
Model 1: demographics 3.76 (0.11) 4.32 (0.14) 1.15 (1.06–1.25) 0.001
AUDIT-C (cut-off ≥3) % % RR p value
Model 0: unadjusted 50.6 59.9 1.18 (1.08–1.29) <0.001
Model 1: demographics 50.3 59.8 1.19 (1.09–1.30) <0.001
Shown are the unstandardized risk ratio (RR) estimates for dichotomous outcomes and incidence risk ratios (IRR) for count outcomes; all with corresponding 95% CI and estimated marginal probabilities and means/counts. All tests reflected comparisons between the pre-COVID-19 versus COVID-19 cohort of Grade 11 students. Sociodemographic covariates included gender, immigrant background, parental cohabitation, and subjective social status evaluation; all were assessed at 2017 T1 baseline, save for the subjective social status (T1/2017–T2/2018 average). All models accounted for nesting by schools of origin.
Table 4 Alcohol expectancies in pre- and COVID-19 adolescent cohorts from Norway
Alcohol expectancies Prepandemic cohort COVID-19 cohort
Entire sample (N = 2,536) n = 1,621 n = 915 Estimate (95% CI)
Social Facilitation (SF) M (SE) M (SE) b p value
Model 0: unadjusted 2.58 (0.04) 2.63 (0.06) 0.05 (–0.05 to 0.14) 0.33
Model 1: demographics 2.58 (0.04) 2.63 (0.06) 0.05 (–0.04 to 0.15) 0.28
Tension Reduction (TR) M (SE) M (SE) b p value
Model 0: unadjusted 2.85 (0.04) 2.96 (0.04) 0.11 (0.012–0.20) 0.028
Model 1: demographics 2.85 (0.04) 2.96 (0.04) 0.11 (0.018–0.20) 0.02
Drinkers only (n = 1,347) n = 860 n = 487 Estimate (95% CI)
Social Facilitation (SF) M (SE) M (SE) b p value
Model 0: unadjusted 2.95 (0.03) 3.03 (0.05) 0.09 (–0.03 to 0.21) 0.14
Model 1: demographics 2.95 (0.03) 3.04 (0.05) 0.10 (–0.03 to 0.23) 0.18
Tension Reduction (TR) M (SE) M (SE) b p value
Model 0: unadjusted 3.22 (0.04) 3.33 (0.05) 0.11 (–0.04 to 0.26) 0.16
Model 1: demographics 3.21 (0.04) 3.33 (0.05) 0.11 (0.018–0.20) 0.12
Shown are the unstandardized regression coefficient estimates (b) with corresponding 95% CI and estimated marginal means. All tests reflected comparisons between the pre-COVID-19 versus COVID-19 cohort of Grade 11 students. Sociodemographic covariates included gender, immigrant background, parental cohabitation, and subjective social status evaluation; all were assessed at 2017 T1 baseline, save for the subjective social status (T1/2017-T2/2018 average). All models accounted for nesting by schools of origin.
==== Refs
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| 36195067 | PMC9747729 | NO-CC CODE | 2022-12-15 23:22:04 | no | Eur Addict Res. 2022 Oct 4; 28(6):471-480 | utf-8 | Eur Addict Res | 2,022 | 10.1159/000526584 | oa_other |
==== Front
Blood Purif
Blood Purif
BPU
Blood Purification
0253-5068
1421-9735
S. Karger AG Allschwilerstrasse 10, P.O. Box · Postfach · Case postale, CH–4009, Basel, Switzerland · Schweiz · Suisse, Phone: +41 61 306 11 11, Fax: +41 61 306 12 34, [email protected]
36075200
10.1159/000526446
bpu-0001
Critical Care Nephrology − Research Article
Effect of Hemadsorption Therapy in Critically Ill Patients with COVID-19 (CYTOCOV-19): A Prospective Randomized Controlled Pilot Trial
Jarczak Dominik
Roedl Kevin
Fischer Marlene
de Heer Geraldine
Burdelski Christoph
Frings Daniel Peter
Sensen Barbara
Boenisch Olaf
Tariparast Pischtaz Adel
Kluge Stefan
Nierhaus Axel *
Department of Intensive Care Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
*Axel Nierhaus, [email protected]
8 9 2022
8 9 2022
110
30 12 2021
27 6 2022
Copyright © 2022 by S. Karger AG, Basel
2022
https://www.karger.com/Services/SiteLicenses Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher.
Introduction
Immunomodulatory therapies have shown beneficial effects in patients with severe COVID-19. Patients with hypercytokinemia might benefit from the removal of inflammatory mediators via hemadsorption.
Methods
Single-center prospective randomized trial at the University Medical Center Hamburg-Eppendorf (Germany). Patients with confirmed COVID-19, refractory shock (norepinephrine ≥0.2 µg/kg/min to maintain a mean arterial pressure ≥65 mm Hg), interleukin-6 (IL-6) ≥500 ng/L, and an indication for renal replacement therapy or extracorporeal membrane oxygenation were included. Patients received either hemadsorption therapy (HT) or standard medical therapy (SMT). For HT, a CytoSorb® adsorber was used for up to 5 days and was replaced every 18–24 h. The primary endpoint was sustained hemodynamic improvement (norepinephrine ≤0.05 µg/kg/min ≥24 h).
Results
Of 242 screened patients, 24 were randomized and assigned to either HT (N = 12) or SMT (N = 12). Both groups had similar severity as assessed by SAPS II (median 75 points HT group vs. 79 SMT group, p = 0.590) and SOFA (17 vs. 16, p = 0.551). Median IL-6 levels were 2,269 (IQR 948–3,679) and 3,747 (1,301–5,415) ng/L in the HT and SMT groups at baseline, respectively (p = 0.378). Shock resolution (primary endpoint) was reached in 33% (4/12) versus 17% (2/12) in the HT and SMT groups, respectively (p = 0.640). Twenty-eight-day mortality was 58% (7/12) in the HT compared to 67% (8/12) in the SMT group (p = 1.0). During the treatment period of 5 days, 6/12 (50%) of the SMT patients died, in contrast to 1/12 (8%) in the HT group.
Conclusion
HT was associated with a non-significant trend toward clinical improvement within the intervention period. In selected patients, HT might be an option for stabilization before transfer and further therapeutic decisions. This finding warrants further investigation in larger trials.
Key Words
Hyperinflammation
Cytokines
Coronavirus disease 2019
Hemadsorption
Hemoperfusion
This research received no external funding.
==== Body
pmcIntroduction
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in 2019 and caused a global healthcare emergency [1, 2, 3]. Up to 20% of patients with coronavirus disease 2019 (COVID-19) were hospitalized and about 5% required intensive care treatment including mechanical ventilation due to severe acute respiratory failure [4, 5, 6]. Mortality rates in critically ill patients with COVID-19 remain unacceptably high [6, 7, 8, 9].
In severe COVID-19, a dysregulated systemic immune overactivation causes the elevation of inflammatory cytokines [10, 11]. High interleukin-6 (IL-6) levels were associated with multiorgan failure and mortality [12, 13, 14]. Similar to septic shock caused by bacteria, SARS-CoV-2-associated hyperinflammation can also initiate a proinflammatory feedback loop, triggering hypercytokinemia and leading to hemodynamic instability or even shock [15]. Immunomodulatory therapies, including corticosteroids and IL-6 antagonists, have recently shown beneficial effects [16, 17, 18]. Removal of circulating inflammatory mediators by cytokine adsorption might represent a biologically plausible method to achieve a less proinflammatory cytokine milieu, thus conferring significant clinical improvement in severe COVID-19. Hemadsorption using CytoSorb® (CytoSorbents Corporation, Monmouth Junction, NJ, USA) is approved in Europe and has previously been shown to attenuate an excessive systemic inflammatory response [19]. By eliminating various mediators (e.g., IL-1/6/8/10), bacterial toxins, and danger-associated molecular patterns (DAMPS), the treatment may contribute to the hemodynamic stabilization of patients with septic shock [20]. The adsorber consists of porous polystyrene with an effective surface area of >40,000 m2, thus allowing permanent binding of molecules in the range of 5–60 kDa in a concentration-dependent manner [21]. The device can be inserted into a renal replacement therapy (RRT) circuit or an extracorporeal membrane oxygenation (ECMO) system [20, 22, 23]. Because of its potentially beneficial effect on critically ill patients with COVID-19, CytoSorb® received emergency use authorization in the US by the FDA [24]. The purpose of this randomized controlled trial was to evaluate the effect of cytokine elimination by hemadsorption on hemodynamics and disease severity in critically ill patients with COVID-19 with proven hypercytokinemia.
Materials and Methods
Trial Design
The CYTOCOV-19 trial was an investigator-initiated, open-label, prospective, randomized, controlled study in critically ill patients with COVID-19 admitted to the ICUs of the Department of Intensive Care Medicine at the University Medical Center Hamburg-Eppendorf (Germany). The study protocol was approved by the Ethics Committee of the Hamburg Chamber of Physicians (No.: PV7314) and complies with the Declaration of Helsinki.
Patients, Inclusion and Exclusion Criteria
All critically ill patients with confirmed COVID-19 were screened for eligibility. Patients were included when they presented with confirmed COVID-19 and refractory shock with the need for norepinephrine ≥0.2 μg/kg/min to maintain a mean arterial pressure (MAP) ≥65 mm Hg, IL-6 ≥500 ng/L and a need for RRT and/or ECMO. Exclusion criteria were diagnosis of advanced liver cirrhosis (Child-Pugh C), do-not-resuscitate order, moribund condition, expected survival of less than 14 days due to comorbidities, pregnancy or breastfeeding, or participation in another interventional trial.
Randomization
Eligible patients were randomly assigned in a 1:1 ratio to standard medical therapy (SMT) plus hemadsorption therapy (HT) or SMT alone. The randomization sequence was generated using permuted blocks with a size of 4 and was not stratified. Medical staff involved in patient care was aware of group assignment since use of a hemadsorption device in addition to standard therapy could not be blinded with reasonable effort.
Trial Intervention
In the intervention group, a hemadsorption device was incorporated into either the RRT or the ECMO system, respectively. For HT, a CytoSorb® adsorber (total volume 300 mL, priming volume 120 mL, filled with sterile normal saline) was used and placed in a pre-filter position within the RRT circuit. The device was replaced every 18–24 h. Treatment duration was five consecutive days, and treatment was stopped early when shock reversal was observed for at least 24 h (primary endpoint). Flow rates through the hemadsorption device were above 150 mL/min. Early replacement was indicated when blood flow decreased below 100 mL/min or complications like line clotting were observed. For RRT, the multiFiltratePRO Ci-Ca system was used throughout for pre-dilution CVVHD with the Ultraflux AV 600 polysulfone capillary hemofilter (both Fresenius Medical Care, Bad Homburg, Germany).
Blood samples were taken routinely before the initiation of HT and on each subsequent day until day 10. Clinical laboratory parameters included differential blood count, serum electrolytes, kidney and liver function parameters, coagulation, IL-6, mid-regional pro-adrenomedullin (MR-pro-ADM), and procalcitonin (PCT). The reference timepoint was the time of randomization. Patient follow-up was performed for at least 28 days after randomization.
Primary and Secondary Endpoints
The primary endpoint was shock reversal defined as hemodynamic stabilization with a significant reduction of norepinephrine to a dose of 0.05 µg/kg/min or lower while maintaining MAP ≥65 mm Hg for at least 24 h [20]. Secondary endpoints included improvement of organ dysfunction measured by sequential organ failure assessment (SOFA) score, lactate clearance, time on RRT, time on ECMO, duration of mechanical ventilation, time to shock reversal, length of ICU stay, total vasopressor dose, and ICU and hospital mortality within 28 days. Further secondary endpoints included reduction (≥20%) of IL-6, PCT and MR-pro-ADM within 10 days after randomization.
Study Definitions and Patient Management
Confirmed COVID-19 was defined as at least one positive result of reverse transcriptase-polymerase chain reaction (rt-PCR) for SARS-CoV-2 obtained from naso-pharyngeal swabs and/or bronchial secretions or blood. Acute respiratory distress syndrome (ARDS) was defined according to the Berlin definition, using the PaO2/FiO2 ratio (Horowitz index) [25]. Severity of illness was evaluated by SOFA and simplified acute physiology (SAPS II) scores [26, 27]. A Charlson Comorbidity Index (CCI) was calculated for all patients [28]. Medical treatment was performed following national and international recommendations. Norepinephrine was infused to obtain a MAP above 65 mm Hg [29, 30, 31]. ECMO was evaluated in patients with severe refractory hypoxemia (PaO2/FiO2 ratio <80) not responding to conservative ARDS management. RRT was started in patients with severe metabolic acidosis, anuria unresponsive to fluids, hyperkalaemia, and/or uremic complications, according to the most recent Austrian/German recommendations [32, 33]. IL-6 was measured by an electrochemiluminescence assay (Atellica IM Analyzer; Siemens Healthcare GmbH, Erlangen, Germany).
Statistical Analysis
Data are presented as absolute and relative frequency for categorical variables and as median and interquartile range for continuous variables. Categorical variables were compared with χ2-tests or Fisher's exact tests. Continuous variables were compared using the Mann-Whitney U test. Within-group and between-group comparisons of IL-6 levels were Bonferroni corrected for multiple comparisons. Survival function estimates were calculated using the Kaplan-Meier method and were compared using the log-rank test. Statistical tests were two-sided with a 5% significance level and with nominal p values reported for description outside the primary analysis. Statistical analyses were performed using IBM SPSS Statistics Version 24.0 (IBM Corp., Armonk, NY, USA) and GraphPad Prism 9 (GraphPad Software, San Diego, CA, USA). The study was prepared in accordance with the Consolidated Standards of Reporting Trials recommendations.
Results
A total of 242 patients were assessed for eligibility, and 24 patients underwent randomization. Of these, 12 patients were assigned to either the HT group or SMT. The last day of follow-up was May 1, 2021. The flow diagram displaying screening, randomization, and outcomes is depicted in Figure 1.
Characteristics of the Study Population
The characteristics of the study population are shown in Table 1. Thirteen (54%) patients were referred from other hospitals for further intensive care management. Before randomization, patients had been treated in the ICU for a median time of 6.3 (2.5–10.7) days (HT: 5.9 [2.4–10.7] vs. SMT: 6.3 [3.3–9.5], p = 0.799). At time of inclusion, 22 (92%) and 11 (46%) patients of the whole cohort were on RRT and vvECMO, respectively. The initial RRT mode was continuous veno-venous hemodialysis (CVVHD) in 22 (92%) and continuous veno-venous hemofiltration (CVVH) in 2 (8%) patients. Indications for RRT were metabolic acidosis in 16 (67%) patients, fluid overload in 11 (46%), and hyperkalemia irresponsive to conservative management in 3 (13%); 7 (29%) patients had more than one indication for RRT. Two patients (8%) were on chronic dialysis. The median Horowitz index was 102 (73–181) in the HT and 105 (88–126) in the SMT group at the time of study inclusion. Patients in the HT group received a median dose of 0.399 (0.252–0.791) and in the SMT group of 0.792 (0.457–1.195) μg/kg/min norepinephrine (p = 0.128). Median IL-6 levels were 2,269 (948–3,679) and 3,747 (1,301–5,415) ng/L in the HT and SMT groups at baseline, respectively (p = 0.378).
Hemadsorption
Details regarding hemadsorption treatment are shown in Table 2. Time from randomization to the start of hemadsorption was 0.9 (0.5–2.3) h. The hemadsorption device was added to the RRT circuit in 11 (92%) and to the ECMO system in 1 (8%) patient, respectively. All patients were on continuous RRT; anticoagulation was performed using systemic heparin in 1 (8%) patient and regional anticoagulation with citrate-calcium in 11 (92%) patients assigned to the HT group. Overall, 74 hemadsorption devices were used and patients received 6 (5.8–6.3) hemadsorption treatments during the intervention period. Duration of treatment was 22.9 (17.4–24.7) h per adsorber. Overall, 9 (12%) hemadsorption treatments had to be terminated early because of circuit clotting. In addition, treatment duration below 18 h was observed in 5 (7%) treatment sessions which was due to logistical problems. Due to technical difficulties when exchanging the hemadsorption device within the ECMO circuit, one treatment was prolonged to 46.6 h. No other device-related complications were observed during the intervention period. Two (16%) patients reached the primary trial endpoint before day 5, as predefined in the study protocol, and HT was discontinued at the next planned hemadsorption device exchange. We did not observe device-related adverse or serious adverse events.
Laboratory Changes
During the first 5 days of treatment, IL-6 levels decreased. In particular, IL-6 concentration fell to 478 (240–841) ng/L (HT, p = 0.012) and 597 (488–2,436) ng/L (SMT, p = 0.657) after 24 h, 254 (73–1,381) ng/L (HT, p = 0.012) and 390 (163–599) ng/L (SMT, p = 0.086) after 48 h, 116 (60–755) ng/L (HT, p = 0.002) and 293 (145–1,786) ng/L (SMT, p = 0.093) after 72 h, 147 (23–1,457) ng/L (HT, p = 0.006) and 189 (125–972) ng/L (SMT, p = 0.028) after 4 days and to 287 (22–1,457) ng/L (HT, p = 0.012) and 211 (101–376) ng/L (SMT, p = 0.028) after 5 days in the HT and SMT group respectively (p indicates comparison for each timepoint with baseline values of each group; a p = 0.05/5 = 0.01 was considered statistically significant). Serum IL-6 reduction in the first 24 h of treatment compared between HT and SMT groups was 79% versus 85% (p = 0.335). Serum PCT values were similar at baseline (HT: 4.69 [1.67–8.75] µg/L, SMT 4.21 [1.80–17.61] µg/L) and showed a persistent increase in the SMT group (>3 µg/L) throughout the intervention period (online suppl. Fig. 1; for all online suppl. material, see www.karger.com/doi/10.1159/000526446), whereas PCT levels decreased and remained lower in the HT group.
Analysis of Endpoints and Outcomes
The primary endpoint of shock reversal within 10 days of randomization was reached by 4 patients (33%) in the HT group and 2 patients (17%) in the SMT group (p = 0.640). The time to shock reversal was 6.3 (3.7–10.0) days in the HT and 9.2 (5.1–15.9) days (p = 0.110) in the SMT group. We observed a 28-day mortality of 58% (n = 7) in the HT group and of 67% (n = 8) in the SMT group (p = 0.382, cf. Kaplan-Meier survival estimates (Fig. 2). Primary and secondary endpoints are shown in detail in Table 3.
Discussion
This is the first randomized controlled trial investigating HT for cytokine elimination in critically ill patients with COVID-19 with proven and profound hypercytokinemia. In this study, the primary endpoint of shock reversal was not reached for the intervention group, and we could not demonstrate a significant reduction of IL-6 by HT. However, HT may potentially be accompanied by early clinical stabilization of severely ill patients when compared to SMT.
The COVID-19 pandemic resulted in high hospitalization rates with up to 5% admitted to the ICU, mainly due to respiratory failure [1, 2, 6]. The interplay between direct viral damage to alveolar epithelial cells and excessive endothelial activation results in SARS-CoV-2 related lung injury accompanied by excessive cytokine production [19]. Extensive pulmonary and multiorgan endothelial lesions are largely described as a hallmark of severe respiratory failure [34]. Among others, high IL-6 levels were observed and strongly associated with multiorgan failure and mortality in critically ill patients with COVID-19 [12, 13]. Hemadsorption techniques targeting circulating inflammatory mediators may lead to a re-balancing of the internal cytokine milieu. Several case series and small studies in patients with COVID-19 or septic shock have shown promising results using hemadsorption [20, 22, 35, 36]. Although initial reports suggested an uncontrolled cytokine response in patients with COVID-19, cytokine levels have been reported to be not as high as compared to other causes of ARDS [10, 11, 37, 38]. However, some patients exhibit uncontrolled hyperinflammatory cytokine release, which in many cases entails multiple organ dysfunction and death. Therefore, we sought to specifically target the population, which might benefit most from hemadsorption treatment by including only severely ill patients with cytokinemia defined as IL-6 ≥500 ng/L accompanied by refractory shock. Recently, a small randomized controlled trial by Supady et al. [39] using HT in COVID-19 patients with ARDS requiring ECMO therapy could not show beneficial effects. The primary endpoint was IL-6 serum concentration 72 h after randomization. However, median baseline IL-6 levels in the intervention group were low (357 ng/L), compared to 2,269 ng/L in our study. However, we observed a significant decrease in IL-6 levels both in the intervention and the control group within the first 24 h. We further observed a significant and sustained decrease in PCT (see online suppl. Fig. 1), which supports the effectiveness of HT to reduce PCT as shown earlier [40]. We hypothesize that initiation of hemadsorption should probably not be solely based on the clinical condition and acute respiratory failure, as recently shown by Supady et al. [39] As depicted in the flow diagram (Fig. 1), we had a 100% recruitment of suitable patients in the present study (based on clinical/predefined inclusion criteria). We defined a suitable target population that, in contrast to the work of Supady et al. [39], did not show any conspicuous mortality during therapy. Notably, our cohort consisted of severely ill patients, which is demonstrated by high SOFA and SAPS II scores, usually associated with a mortality rate of above 80% [26, 27].
Observational data suggest improvement of hemodynamics and a trend toward improved mortality with the use of hemadsorption in critically ill patients with septic shock. One study by Friesecke et al. [20] showed that hemoperfusion was associated with decreased vasopressor requirement and shock reversal in 65% of treated patients, and that this was accompanied by a significant reduction of IL-6 and lactate levels. In the present study, we observed a higher rate of shock reversal within 10 days after randomization in patients of the HT (33%) than in the SMT group (17%); however, this did not reach statistical significance. The survival curve in our study shows that the treatment group (HT) had a survival advantage, which was, however, limited to the intervention period (Fig. 2). If this survival advantage is attributable to the pre-randomization differences is unclear. Further larger studies have to clarify the finding. We neither observed nor expected differences in 28-day mortality between the two groups. This is in line with a previous RCT of hemadsorption in patients with septic shock which did not result in improved survival [41, 42]; but again, in this trial, initial IL-6 levels were substantially lower in both groups (median 357 vs. 289 ng/L) compared with those in our study. In a recent retrospective study with propensity score matching analysis, no difference could be demonstrated in terms of hemodynamic stabilization between cytokine adsorption and SMT. However, this was an uncontrolled short-term (<24 h) intervention in a mixed population with sepsis, septic shock and hyperinflammation due to a variety of causes [43].
Of particular interest is our observation that patients in the HT group could be stabilized during the intervention period compared to patients in the SMT group. We can only speculate if extended treatment duration or targeting only patients with sustained hyperinflammation would result in beneficial and clinically meaningful effects of hemadsorption. However, addressing this question would require a different study design. To date, specific therapies for severe COVID-19 are scarce. Early stabilization of severely affected patients with proven hypercytokinemia to allow referral to a tertiary care center or to bridge to further interventions might be a reasonable indication for the use of hemadsorption. For this reason, our findings warrant further investigation in larger trials.
This study has important limitations which should be mentioned. We are reporting a small randomized single-center open-label trial. Owing to the small sample size, we observed important differences regarding age and norepinephrine dose at baseline between both groups. Although these differences did not reach statistical significance, they could have influenced the primary outcome. Methodologically, trials involving rather complex medical devices are inherently difficult to double-blind, so that bias cannot be ruled out. Further, even though the flow chart (Fig. 1) shows that we comprehensibly enrolled all available patients after screening for eligibility, this study may still be subject to selection bias, and external validity of our results may be limited. Although statistically non-significant, there was a noticeable imbalance in noradrenalin dose, age, and arterial pH to the disadvantage of the control group. Before randomization, our patients had been treated in the ICU for a median time of 6.3 days, and more than half of the cohort were referrals from other hospitals. It is conceivable that an earlier initiation of HT might have resulted in more beneficial effects. Lastly, the duration of hemadsorption was prespecified and limited to 5 days. Whether an extended use of hemadsorption beyond 5 days would result in an improved outcome remains unclear.
This study also has some strengths. Our study is consistent with previous findings in patients with septic shock. To our knowledge, this is the first study evaluating efficacy and outcome of HT in critically ill COVID-19 patients with hypercytokinemia, severe systemic inflammation, and multiple organ dysfunction. Screening more than 200 ICU patients only yielded inclusion of 24 patients, which confirms previous findings that uncontrolled hypercytokinemia is only present in some patients, and those might require a tailored and personalized therapeutic approach based on biological plausibility.
Conclusion
Uncontrolled hypercytokinemia accompanied by severe systemic inflammation and multiple organ dysfunction occurs in a subgroup of critically ill patients with COVID-19. There were no effects on IL-6 levels or 28-day mortality. Early mitigation of organ dysfunction leading to clinical stabilization was observed in the HT group. HT in patients with severe COVID-19 was feasible and safe and might be used for stabilization before transfer to a tertiary care center or for decision of further interventions. Whether longer duration or an earlier start of HT would prove beneficial should be elucidated and warrants further clinical investigations.
Statement of Ethics
This clinical study complies with the Declaration of Helsinki and was approved by the Ethics Committee of the Hamburg Chamber of Physicians on March 30, 2020 (No.: PV7314). Written informed consent was obtained from all patients or their legal representatives. If this was not possible in time before enrollment, the ethics committee had approved a deferred consent procedure in which trial participation is initiated following the presumed will of the patient in the context of the existing emergency situation. As soon as the patient's legal representative was available, written informed consent was obtained immediately.
Conflict of Interest Statement
Dominik Jarczak has received lecture honoraria and travel reimbursement from ADVITOS and CytoSorbents Europe GmbH. Marlene Fischer receives research support from the External Research Program, Medtronic, Minneapolis, MA. Stefan Kluge received research support by Ambu, E.T. View Ltd., Fisher & Paykel, Pfizer, and Xenios, lecture honoraria from ArjoHuntleigh, Astellas, Astra, Basilea, Bard, Baxter, Biotest, CSL Behring, CytoSorbents, Fresenius, Gilead, MSD, Orion, Pfizer, Philips, Sedana, Sorin, Xenios, and Zoll, and consultant honorarium from AMOMED, Astellas, Baxter, Bayer, Fresenius, Gilead, MSD, Pfizer, and Xenios. Axel Nierhaus has received lecture honoraria and travel reimbursement from ThermoFisher Scientific GmbH, Fresenius AG, CytoSorbents Europe GmbH and Biotest AG, Germany over the past 5 years. Daniel Peter Frings reports lecture honoraria within the last 5 years from Xenios AG. Kevin Roedl, Geraldine de Heer, Christoph Burdelski, Barbara Sensen, Olaf Boenisch and Pischtaz Adel Tariparast do not report any conflicts of interest. No other potential conflict of interest relevant to this article was reported.
Funding Sources
This research received no external funding.
Author Contributions
Dominik Jarczak, Kevin Roedl, and Axel Nierhaus conceived and designed the study. Kevin Roedl, Dominik Jarczak, Geraldine de Heer, Christoph Burdelski, Daniel Peter Frings, Barbara Sensen, Olaf Boenisch, Pishtaz Tariparast, and Axel Nierhaus were involved in data acquisition. Marlene Fischer, Kevin Roedl, Dominik Jarczak and Axel Nierhaus analyzed and interpreted the data. Kevin Roedl drafted the manuscript. Dominik Jarczak, Marlene Fischer, Stefan Kluge and Axel Nierhaus critically revised the manuscript for important intellectual content. All the authors read and approved the final manuscript.
Data Availability Statement
Due to local ethical and federal data privacy rules the complete dataset is available upon written request directed to the corresponding author.
Acknowledgments
We sincerely thank the study nurses of the Department of Intensive Care Medicine involved in data acquisition and management − Grit Ringeis, Melanie Kerinn, Lisa Krebs, and Andrea Conrad.
Dominik Jarczak and Kevin Roedl: shared first authorship.
Trial registration: ClinicalTrials.gov − registration number: NCT04344080.
A preprint version of this article is available on Research Square (DOI: 10.21203/rs.3.rs-704552/v1) [44]. DOI: https://doi.org/10.21203/rs.3.rs-704552/v1. Object type: preprint. Version: Version 1; posted 23 July, 2021. Web: https://www.researchsquare.com/article/rs-704552/v1; last accessed July 14, 2022.
Fig. 1 Flow diagram of the study − screening, randomization and outcome.
Fig. 2 Kaplan-Meier survival estimates: shown are Kaplan-Meier estimates of the probability of survival for patients assigned to HT or SMT. The shaded area indicates the intervention period. Log-rank:p= 0.382.
Table 1 Characteristics of randomized patients at time of inclusion who were assigned to HT or SMT group
Parameters HT (n = 12) SMT (n = 12) p value
Demographics
Age, years 60 (56–63) 69 (58–76) 0.114
Male gender 9 (75) 9 (75) 1.0
Weight, kg 93 (70–95) 103 (84–123) 0.178
Height, cm 178 (174–180) 176 (172–183) 0.887
Comorbidities
Charlson comorbidity index (pts.) 2 (1–3) 2 (1–3) 0.843
Any comorbidity 10 (83) 11 (92) 1.0
Arterial hypertension 6 (50) 10 (83) 0.193
Diabetes mellitus 5 (42) 3 (25) 0.667
Coronary heart disease 2 (17) 4 (33) 0.640
Congestive heart disease 1 (8) 0 (0) 1.0
Chronic kidney disease 2 (17) 3 (25) 1.0
Chronic respiratory disease 3 (25) 3 (25) 1.0
Liver disease 0 (0) 0 (0) 1.0
Malignant condition 1 (8) 1 (8) 1.0
Lymphoma 0 (0) 0 (0) 1.0
Solid organ tumor 3 (25) 3 (25) 1.0
History of smoking 1 (8) 3 (25) 0.590
Disease severity (baseline)
SAPS II (pts.) 75 (72–83) 79 (74–84) 0.590
SOFA (pts.) 17 (15–18) 16 (15.75–18) 0.551
APACHE II (pts.) 33 (29–41) 38 (36–41) 0.198
ICU characteristics (baseline)
Mean arterial pressure, mm Hg 74 (66–80) 67 (64–72) 0.178
Norepinephrine, dose, µg/kg/min 0.399 (0.252–0.791) 0.792 (0.457–1.195) 0.128
RRT 11 (92) 11 (92) 1.0
PaO2/FiO2 – ratio 102 (73–181) 105 (88–126) 0.178
vvECMO 6 (50) 5 (42) 1.0
Blood gas analysis
paO2, mm Hg 77 (70–81) 74 (68–81) 0.932
paCO2, mm Hg 46 (40–61) 54 (46–64) 0.378
pH, level 7.31 (7.27–7.38) 7.25 (7.21–7.30) 0.033
HCO3, mmol/L 24.2 (20.7–27.8) 21.9 (19.4–23.4) 0.178
Lactate, mmol/L 2.5 (1.4–3.1) 2.8 (2.2–3.5) 0.478
Laboratory values
Leukocytes, G/L 11.1 (5.6–18.9) 12.9 (11.0–21.7) 0.319
Thrombocytes, G/L 157 (97–246) 272 (163–312) 0.378
D-dimers, mg/L 8.64 (4.01–10.70) 7.05 (2.37–14.29) 0.630
IL-6, ng/L 2,269 (948–3,679) 3,747 (1,301–5,415) 0.378
pro-ADM, nmol/L 6.25 (4.03–7.26) 10.01 (4.74–12.24) 0.089
PCT, µg/L 4.69 (1.67–8.75) 4.21 (1.80–17.61) 0.932
CRP, mg/L 290 (208–319) 179 (208–298) 0.887
Data are expressed as n (%) or median (interquartile range). ARDS, acute respiratory distress syndrome; SOFA, sequential organ failure assessment; SAPS II, simplified acute physiology score II; pts., points; vvECMO, veno-venous extracorporeal membrane oxygenation; IL, interleukin; PCT, procalcitonin; CRP, C-reactive protein; pro-ADM, pro-adrenomedullin.
Table 2 Intensive care unit characteristics of patients who were assigned to the HT group or SMT
Parameters HT (n = 12) SMT (n = 12) p value
HT details – –
Treatment mode
RRT 11 (92) – –
ECMO 1 (8) – –
Randomization to start of hemadsorption, h 0.9 (0.5–2.3) – –
Total duration of hemadsorption, h 131.7 (121.3–143.8) – –
Duration of hemadsorption per session 22.9 (17.4–24.7) – –
Number of adsorbers per patient 6 (5.8–6.3) – –
ICU – characteristics & management
Acute respiratory distress syndrome
No ARDS 0 (0) 1 (8) 1.0
Mild 1 (8) 0 (0)
Moderate 1 (8) 1 (8)
Severe 10 (83) 10 (83)
ARDS – management
Prone positioning 9 (75) 10 (83) 1.0
Neuromuscular blockade 7 (58) 4 (33) 0.414
Inhaled NO 9 (75) 3 (25) 0.039
Glucocorticoid therapy 11 (92) 11 (92) 1.0
vvECMO 7 (58) 5 (42) 0.684
RRT 12 (100) 12 (100) –
Tracheostomy 5 (42) 6 (50) 1.0
Dexamethasone 8 (67) 5 (42) 0.414
Remdesivir 3 (25) 3 (25) 1.0
Tocilizumab 1 (8) 0 (0) 1.0
Data are expressed as n (%) or median (interquartile range). ARDS, acute respiratory distress syndrome; RRT, renal replacement therapy; vvECMO, veno-venous extracorporeal membrane oxygenation; h, hours.
Table 3 Primary and secondary endpoints and outcomes of patients who were assigned to HT or SMT
Parameters HT (n = 12) SMT (n = 12) p value
Primary endpoint
Shock reversal within 10 days 4 (33) 2 (17) 0.640
Secondary endpoints
Change in SOFA Score (points) 1 (0.8–1.5) 1 (0–2.3) 0.843
Lactate clearance <2 mmol/L 6 (50) 6 (50) 1.0
Length of RRT, days 14.4 (7.2–24.8) 7.93 (1.3–23.3) 0.242
Length of vvECMO, days 25.5 (12.6–33) 18.4 (2.4–20.5) 0.149
Time to shock reversal, days 6.3 (3.7–10) 9.2 (5.1–15.9) 0.110
Length of mechanical ventilation, days 15.3 (7.5–25.6) 11.9 (2.0–35.5) 0.378
Reduction (≥20%) of
IL-6 11 (92) 8 (67) 0.317
PCT 9 (75) 6 (50) 0.400
D-Dimers 0 (0) 1 (8) 0.478
Outcome
28-day mortality 7 (58) 8 (67) 1.0
Data are expressed as n (%) or median (interquartile range). SOFA, sequential organ failure assessment; RRT, renal replacement therapy; vvECMO, veno-venous extracorporeal membrane oxygenation; PCT, procalcitonin; IL-6, interleukin-6.
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Research Article
Oncologic Rehabilitation in the COVID-19 Pandemic: The Situation in Clinics
Rick Oliver a *
Hoffmann Wilfried b
Steimann Monika Anna c
aCinic Reinhardshöhe, Bad Wildungen, Germany
bHamm Clinic Park Therme, Badenweiler, Germany
cStrandklinik Boltenhagen, Boltenhagen, Germany
*Oliver Rick, [email protected]
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Background
Oncological rehabilitation is an important pillar in the treatment of cancer patients. Due to the COVID-19 pandemic, this form of therapy is particularly challenged, as it relies heavily on group therapies. The aim of the study was to find out what impact the pandemic has had on oncological rehabilitation so far and how the rehabilitation clinics have dealt with it.
Methods
A web-based survey was used to collect data from 14 oncological rehabilitation clinics on the impact of the COVID-19 pandemic on occupancy, staffing trends, and hygiene measures for the observation period from March 1, 2020, to February 28, 2021. The data were compared with the same period 1 year earlier. In addition, the compensatory measures taken with regard to therapy were recorded.
Results
While only 15,272 patients were rehabilitated in the period under review, 21,257 patients were rehabilitated in the same period 1 year earlier. This corresponds to a decrease in occupancy of 28%. Three clinics were affected by temporary closures due to the pandemic. In 39% of the clinics, screening tests for patients had already been started for more than 8 months, while this was also offered to staff in only 23% of the clinics. With regard to changes in the therapeutic offer, more physiotherapeutic small groups with a reduced number of participants were used. This was also used in the area of sports therapy and education offers by 73% and 60% of the clinics, respectively. Overall, 92% of the participants assumed an economic recovery at the time of the survey.
Conclusion
Despite a considerable decrease in occupancy in the oncological rehabilitation clinics, the therapies could be changed and carried out in a hygiene-compliant manner. Screening tests were offered at an early stage for patients as well as somewhat delayed for staff. The data show that pandemic-consistently changes in oncological rehabilitation are possible and that supply chains can be maintained.
Key Words
COVID-19 pandemic
Oncological rehabilitation
Rehabilitation therapies
Hygiene rules in rehabilitation
This research did not receive grants from any funding agency in public, commercial, or not-for-profit sectors
==== Body
pmcIntroduction
Today, oncological rehabilitation represents the third pillar of medical care for patients with cancer. It is a component of our health care system and is firmly anchored in the Social Insurance Code in Germany [1]. Oncological rehabilitation is mainly financed by the German Pension Insurance and to a lesser extent by the statutory and private health insurance funds. In 2019, more than 160,000 patients were treated by means of oncological rehabilitation [2]. The basis of medical rehabilitation is the alleviation or elimination of health disorders resulting from cancer. This should enable or at least facilitate participation in social life and, if necessary, in working life. Medical rehabilitation follows a holistic approach that goes beyond the recognition, treatment, and healing of a disease and also takes into account contextual factors such as the social and professional environment [3]. Oncological rehabilitation therefore regularly includes a complex, multimodal therapy package from various medical fields such as sports science, physiotherapy, occupational therapy, and speech therapy as well as nutritional medicine and psycho-oncology [1]. Under the supervision of an interdisciplinary and thus multiprofessional rehabilitation team, the patients are mainly treated within the framework of group therapies on the premises of the clinic but also outside. This active form of therapy is supplemented by training and counselling with regard to their health problems. Because of this constellation, oncological rehabilitation is particularly affected by the COVID-19 pandemic [4, 5, 6]. In addition, patients in oncological rehabilitation represent a particularly vulnerable group, as their immune function is especially weakened by the tumour disease and therapy. With the onset of the COVID-19 pandemic, all rehabilitation clinics revised their hygiene concept and adapted the forms of therapy accordingly. Not only were the hygiene regulations stringently implemented but many clinics also implemented corona screening at an early stage [7].
This led to the fact that oncological rehabilitation was and continues to be possible even during the peak phases of the first three pandemic waves, in order to ensure continuous care for patients and to avoid disruption of participation. In a recent publication, it was shown that the timely implementation of pandemic rules in rehabilitation clinics enables the continuation of high-quality oncological rehabilitation, preserves the supply chains in the treatment of cancer patients, and at the same time, does not endanger patients [8]. However, it is not yet clear how these changes have taken place in the clinics, what effort had to be made for this, and how the clinics have dealt with this enormous problem for our health system. Therefore, the Working Group on Oncological Rehabilitation of the German Society for Haematology and Oncology (DGHO), in cooperation with the Working Group on Oncological Rehabilitation and Social Medicine of the German Cancer Society, conducted a survey among the senior physicians of member hospitals to generate data to answer these questions.
Material and Methods
In the survey period from May 07 to May 25, 2021, 84 oncological rehabilitation clinics and specialist departments were contacted by e-mail and asked to participate in the online survey. The participants consented to participation and data analysis at the beginning of the survey. The name of the clinic or the participant was not collected so that the survey was anonymous.
The survey was web-based using SurveyMonkey and comprised 37 questions on the topics of clinic occupancy, structural and personnel changes as well as hygiene measures and changes in the range of therapeutic services. The respondents were asked to evaluate the observation period from March 01, 2020, to February 28, 2021, and to compare it with the same period 1 year earlier.
A response of 22/84 (26%) of the questionnaires was received. Of these, however, only 14/22 (63%) were analysable for the survey. The remaining 8 questionnaires were only created but not completed, so they were not available for data analysis.
The data was analysed descriptively using SPSS. Due to the low response rate with evaluable questionnaires, all clinics were combined, and no subgroups were formed. Significance analyses were also not possible due to the low participation in the survey.
Results
Clinic Occupancy and Impact
Thirteen participants provided information on the number of hospital beds they had for oncology patients. The median was 130 beds (range: 10–200 beds). In the period under review, 15,272 patients were rehabilitated in 11 surveyed clinics (median: 1,580 patients, range: 224–2,644 patients). In the comparable period before, 21,257 patients were treated (median: 2,100 patients, range: 163–3,167 patients) (Fig. 1). This corresponds to a cumulative decrease of 5,598 in patients served (82%). Of the 11 participants in the surveyed clinics, 10 (91%) stated that they had suffered a decline in occupancy. 9/13 of the clinics (70%) reported a decline in occupancy of between 20 and 50% and 2/13 (15%) reported a decline in occupancy of up to 75% (Table 1).
For the period under review, 3/11 participants (27%) reported having conducted a pandemic clinic closure. The period of the clinic closure was 1–9 weeks. Furthermore, 4/10 respondents (40%) stated that they had carried out an admission freeze in the period under review of 1–9 weeks. In addition, a pandemic-related loss of specialist staff had to be recorded at 4/13 clinics (33%) (Table 1).
Hygiene Measures
Nine of the participants commented on the number of staff and patients who tested positive for COVID-19. In total, a median of 5 staff/patients tested positive during the period under review (range: 1–20). In 5/13 clinics (39%), screening tests for COVID-19 (PCR or rapid test) had already been carried out on patients for 9–12 months at the time of the survey. While in 6/13 clinics (46%) this had been done for 3–8 months, 2/13 clinics (15%) were not yet testing patients. With regard to screening tests for staff, the picture was somewhat different. In all clinics, screening tests had already been carried out at the time of the survey, although in only 3/13 clinics (23%) had this been done for 9–12 months and in 10/13 clinics (77%) only for 3–8 months (Table 1).
Changes in the Therapeutic Offer
In order to pay attention to the distance between patients in rehabilitative therapies, changes were made in the number of participants in group therapies and training offers. All 11 participants interviewed stated that they offered more physiotherapy small groups in their clinics. In addition, 3/11 participants (27%) resorted to more individual therapies in the clinic or chose alternative services. Changes also occurred in the area of sports therapy, so that 8/13 clinics (73%) offered more small groups, while only one clinic used more individual therapies and two other clinics implemented alternative offers. With regard to the training offers, 6/13 clinics (60%) also reacted with more small groups, while 2/10 clinics (20%) each used more individual counselling or also resorted to alternative offers. In addition, in 7/14 clinics (50%), there was the possibility to offer training virtually (Table 2).
Perspective
At the time of the survey, the participants of 5/12 clinics (42%) assumed a recovery in occupancy development for 2021. In 6/12 other clinics (50%), an unchanged occupancy rate was expected for the current year, and only in one clinic, a further decrease in occupancy was assumed. Whether the clinic was economically threatened could not be assessed by 5/11 of the participants (38%), while 31% (4/11 of the participants) saw an economic threat or did not assume this. None of the participants expected a definite closure of the clinic.
Discussion
While more and more data can be found regarding rehabilitation of patients after COVID-19 infection, the impact of the pandemic on inpatient oncological rehabilitation in Germany is still unclear [9]. In order to record these effects of the pandemic, the Working Group on Oncological Rehabilitation of the DGHO, in collaboration with the Working Group on Oncological Rehabilitation and Social Medicine of the German Cancer Society, conducted a survey within its members in May 2021.
However, the response rate of 26% of questionnaires was low, of which only just under one-third could ultimately be evaluated for the survey. In the authors' view, the data obtained nevertheless allow the conclusion that oncological rehabilitation reacted quickly to the pandemic by changing its structures. More than half of the clinics carried out PCR tests on patients at an early stage and then replaced them with rapid antigen tests as they became available. Somewhat delayed, screening tests were also offered to staff, mostly after the introduction of rapid antigen tests.
In order to comply with the hygiene regulations, the group sizes were reduced in all clinics surveyed and mostly compensated by more small groups. To a lesser extent, more individual therapies or alternative offers were carried out. Half of the clinics stated that they were also able to conduct training and counselling virtually, which partly speaks for a good digital equipment of the rehabilitation clinics, but also reveals a potential for improvement.
Nevertheless, despite these measures, a clear decline in occupancy rates in the oncological rehabilitation clinics had to be recorded. The reason for this is most likely to be found in the postponement of acute medical treatments such as operations, in the provision of rehabilitation beds for the acute care of COVID-19 patients, but also in the restrained use of oncological rehabilitation by the patients themselves [10]. More than two-thirds of the clinics experienced occupancy declines of up to 50%, while only a smaller proportion of clinics experienced even higher occupancy declines. Compared to 2019, the very high occupancy decline of 28% was not insignificant. This is mainly due to the COVID-19 pandemic, as such a pronounced drop in occupancy has not been recorded in the last 20 years. In particular, this should be seen against the background that the majority of oncological rehabilitation clinics can only operate profitably if they have an occupancy rate of more than 85%. Certainly, it is most likely due to the use of the short-time working regulation as well as the financial compensation within the framework of the Social Service Providers Act that there has not been a relevant proportion of clinic closures so far. It was also possible to prevent a pandemic-related loss of specialist staff at more than two-thirds of the clinics. Nevertheless, this was recorded at one-third of the clinics, which may have further exacerbated the precarious staff situation in oncological rehabilitation.
In a presentation during the annual meeting of the DGHO 2020, Reuss-Borst et al. [7] were able to demonstrate the data from over 5,000 rehabilitation patients who had received a PCR test for COVID-19 from April to September 2020. Only a vanishingly small proportion of less than 0.1% of the patients tested COVID-19 positive. Then, in a second series of tests from October 2020 to February 2021, only 0.9% of nearly 7,000 patients tested positive for COVID-19. The authors interpreted their data from over 12,000 patients to mean that they saw rehabilitation as a safe and defensible medical tool even in times of a pandemic [7].
In a previously published paper, Leibbrand and Seifart [8] were able to show that the timely application of pandemic rules in rehabilitation clinics makes it possible to continue oncological rehabilitation at a high level and thus maintains the supply chain for cancer patients. The acceptance of staff and patients was high in the oncological rehabilitation clinics, so that although a significant drop in occupancy was also recorded in the two clinics involved, specialists could largely be retained [8].
Our data show the impact of the COVID-19 pandemic on oncological rehabilitation and how clinics dealt with it in terms of implementing hygiene measures. To the best of our knowledge, there have been no comparable studies to date. Thus, our data complement the published studies already mentioned and show that oncological rehabilitation can be carried out safely for patients and staff even in times of a severe pandemic.
Our data are limited, in particular, by the low response rate to the questionnaires, so that we were unable to collect representative data on oncological rehabilitation in Germany. The low response rate is not so much due to the way the survey was conducted but rather to the participants' reluctance to provide information. In particular, we were not able to collect data with regard to the financial effects on the clinics. Nevertheless, our results offer a first insight into oncological rehabilitation during the COVID-19 pandemic and show that rehabilitative processes can be quickly adapted to acute health policy changes.
Conclusion
Based on the data we collected on oncological rehabilitation during the COVID-19 pandemic, we conclude that this therapeutic measure can be carried out hygienically and safely. Despite the considerable drop in occupancy and the financial burdens for the clinics that this probably entailed, it was possible to prevent worse things from happening in this medical sector by means of legal regulations.
Statement of Ethics
After review by the Ethics Committee of the Philipps University Marburg, Department of Medicine, it is not necessary to obtain an ethics vote for a purely anonymized data collection (EK_MR_8_3_2022).
Conflict of Interest Statement
All authors declare that they have no conflicts of interest.
Funding Sources
This research did not receive grants from any funding agency in public, commercial, or not-for-profit sectors
Author Contributions
Oliver Rick, Wilfried Hoffmann, and Monika Anna Steimann acknowledge that they made significant contributions to the conception; design of the work; and the acquisition, analysis, and interpretation of the data. In addition, all authors critically revised the paper for important intellectual content. Finally, all authors have given final approval of the version to be published and declare responsibility for all aspects of the work and for ensuring that issues regarding the accuracy or integrity of any part of the work have been adequately investigated and resolved.
Data Availability Statement
All data analysed during this study are included in this article. Further enquiries can be directed to the corresponding author.
Acknowledgement
The authors would like to thank Christoph Mussmann, project leader of the DGHO, for preparing the survey and the analysis.
Fig. 1 Number of oncological patients in 2019 versus 2020 per respondent (n= 11).
Table 1 Changes in clinic occupancy, staff, and corona screening (n = 13)
Question N %
How high was the decrease in clinic occupancy?
20–50% 9 70
51–75% 2 15
n.a. 2 15
How long have you been carrying out corona screening tests (PCR/ST) on patients?
3 months 3 23
6 months 3 23
9 months 4 31
12 months 1 8
No tests so far 2 15
How long have you been carrying out corona screening tests (PCR/ST) on staff?
3 months 7 54
6 months 3 23
9 months 2 15
12 months 1 8
No tests so far 0 0
Do you have a pandemic-related loss of specialist staff?
Yes 4 30
No 9 70
n.a., not answered; PCR, polymerase chain reaction; ST, rapid antigen test.
Table 2 Changes in the therapeutic offer
Question N
How has the number of individual therapies changed (n = 14)? Become less 2 14
Become more 3 21
Remains the same 9 65
How was the reduction of patients in group therapies compensated (n = 11)?
More small groups 11 100
More individual therapies 3 27
Through alternative offers 3 27
How was the reduction of patients compensated in sports groups (n = 11)?
More small groups 8 73
More individual therapies 1 9
Through alternative offers 2 18
How was the reduction of patients compensated in training offers (n = 10)?
More small groups 6 60
More individual counselling 2 20
Through alternative offers 2 20
Do you have the possibility to conduct trainings virtually (n = 14)?
Yes 7 50
No 7 50
==== Refs
References
1 Rick O Dauelsberg T Kalusche-Bontemps EM Oncological rehabilitation Oncol Res Treat 2017 40 772 777 29183040
2 Rehaatlas Die Teilhabeleistungen der Deutschen Rentenversicherung in Zahlen Fakten und Trends 2020 Available from. https://www.deutsche-rentenversicherung.de/SharedDocs/Downloads/DE/Statistiken-und-Berichte/Rehaatlas/2020/rehaatlas_2020_download.html
3 Bundesarbeitsgemeinschaft für Rehabilitation. Rehabilitation und Teilhabe Wegweiser für Ärzte und andere Fachkräfte der Rehabilitation Herausgegeben von der Bundesarbeitsgemeinschaft für Rehabilitation: Deutscher Ärzte Verlag 2005
4 Amatya B Khan F. Rehabilitation response in pandemics Am J Phys Med Rehabil 2020 99 (8) 663 668 32452879
5 Carda S Invernizzi M Bavikatte G Bensmaïl D Bianchi F Deltombe T Covid-19 pandemic. What should physical abd rehabilitation medicine specialists do? A clinican's perspective Eur J Phys Rehabil Med 2020 56 515 524 32434314
6 Choon-Huat Koh G Hoenig H How should the rehabilitation community prepare for 2019-nCoV? Arch Phys Med Rehab 2020 101 1068 1071
7 Reuss-Borst M König V Sallmann D Rick O. Evaluation of the test strategy in oncological rehabilitation in the CORONA pandemic Oncol Res Treat 2021 44 (Suppl 4) S276
8 Leibbrand B Seifart U. Oncological rehabilitation and development of strategies in crisis situations using the example of the Covid 19 pandemic in 2020 by using a patient and staff survey results of pandemic care Rehabilitation 2021 60 (2) 142 151 33858023
9 AWMF S2k-LL SARS-CoV-2 COVID-19 und (Früh-) Rehabilitation https://www.awmf.org/leitlinien/detail/ll/080-008.html
10 Rick O. Rehabilitation in Zeiten von COVID-19 InFo Hämatologie und Onkologie 2020 23 (9) 18 22
| 35850098 | PMC9747733 | NO-CC CODE | 2022-12-15 23:22:04 | no | Oncol Res Treat. 2022 Sep 18; 45(10):568-575 | utf-8 | Oncol Res Treat | 2,022 | 10.1159/000525804 | oa_other |
==== Front
Oncol Res Treat
Oncol Res Treat
ORT
Oncology Research and Treatment
2296-5270
2296-5262
S. Karger AG Allschwilerstrasse 10, P.O. Box · Postfach · Case postale, CH–4009, Basel, Switzerland · Schweiz · Suisse, Phone: +41 61 306 11 11, Fax: +41 61 306 12 34, [email protected]
35850098
10.1159/000525804
ort-0045-0568
Research Article
Oncologic Rehabilitation in the COVID-19 Pandemic: The Situation in Clinics
Rick Oliver a *
Hoffmann Wilfried b
Steimann Monika Anna c
aCinic Reinhardshöhe, Bad Wildungen, Germany
bHamm Clinic Park Therme, Badenweiler, Germany
cStrandklinik Boltenhagen, Boltenhagen, Germany
*Oliver Rick, [email protected]
9 2022
18 7 2022
18 7 2022
45 10 568575
14 3 2022
15 6 2022
2022
Copyright © 2022 by S. Karger AG, Basel
2022
https://www.karger.com/Services/SiteLicenses Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher.
Background
Oncological rehabilitation is an important pillar in the treatment of cancer patients. Due to the COVID-19 pandemic, this form of therapy is particularly challenged, as it relies heavily on group therapies. The aim of the study was to find out what impact the pandemic has had on oncological rehabilitation so far and how the rehabilitation clinics have dealt with it.
Methods
A web-based survey was used to collect data from 14 oncological rehabilitation clinics on the impact of the COVID-19 pandemic on occupancy, staffing trends, and hygiene measures for the observation period from March 1, 2020, to February 28, 2021. The data were compared with the same period 1 year earlier. In addition, the compensatory measures taken with regard to therapy were recorded.
Results
While only 15,272 patients were rehabilitated in the period under review, 21,257 patients were rehabilitated in the same period 1 year earlier. This corresponds to a decrease in occupancy of 28%. Three clinics were affected by temporary closures due to the pandemic. In 39% of the clinics, screening tests for patients had already been started for more than 8 months, while this was also offered to staff in only 23% of the clinics. With regard to changes in the therapeutic offer, more physiotherapeutic small groups with a reduced number of participants were used. This was also used in the area of sports therapy and education offers by 73% and 60% of the clinics, respectively. Overall, 92% of the participants assumed an economic recovery at the time of the survey.
Conclusion
Despite a considerable decrease in occupancy in the oncological rehabilitation clinics, the therapies could be changed and carried out in a hygiene-compliant manner. Screening tests were offered at an early stage for patients as well as somewhat delayed for staff. The data show that pandemic-consistently changes in oncological rehabilitation are possible and that supply chains can be maintained.
Key Words
COVID-19 pandemic
Oncological rehabilitation
Rehabilitation therapies
Hygiene rules in rehabilitation
This research did not receive grants from any funding agency in public, commercial, or not-for-profit sectors
==== Body
pmcIntroduction
Today, oncological rehabilitation represents the third pillar of medical care for patients with cancer. It is a component of our health care system and is firmly anchored in the Social Insurance Code in Germany [1]. Oncological rehabilitation is mainly financed by the German Pension Insurance and to a lesser extent by the statutory and private health insurance funds. In 2019, more than 160,000 patients were treated by means of oncological rehabilitation [2]. The basis of medical rehabilitation is the alleviation or elimination of health disorders resulting from cancer. This should enable or at least facilitate participation in social life and, if necessary, in working life. Medical rehabilitation follows a holistic approach that goes beyond the recognition, treatment, and healing of a disease and also takes into account contextual factors such as the social and professional environment [3]. Oncological rehabilitation therefore regularly includes a complex, multimodal therapy package from various medical fields such as sports science, physiotherapy, occupational therapy, and speech therapy as well as nutritional medicine and psycho-oncology [1]. Under the supervision of an interdisciplinary and thus multiprofessional rehabilitation team, the patients are mainly treated within the framework of group therapies on the premises of the clinic but also outside. This active form of therapy is supplemented by training and counselling with regard to their health problems. Because of this constellation, oncological rehabilitation is particularly affected by the COVID-19 pandemic [4, 5, 6]. In addition, patients in oncological rehabilitation represent a particularly vulnerable group, as their immune function is especially weakened by the tumour disease and therapy. With the onset of the COVID-19 pandemic, all rehabilitation clinics revised their hygiene concept and adapted the forms of therapy accordingly. Not only were the hygiene regulations stringently implemented but many clinics also implemented corona screening at an early stage [7].
This led to the fact that oncological rehabilitation was and continues to be possible even during the peak phases of the first three pandemic waves, in order to ensure continuous care for patients and to avoid disruption of participation. In a recent publication, it was shown that the timely implementation of pandemic rules in rehabilitation clinics enables the continuation of high-quality oncological rehabilitation, preserves the supply chains in the treatment of cancer patients, and at the same time, does not endanger patients [8]. However, it is not yet clear how these changes have taken place in the clinics, what effort had to be made for this, and how the clinics have dealt with this enormous problem for our health system. Therefore, the Working Group on Oncological Rehabilitation of the German Society for Haematology and Oncology (DGHO), in cooperation with the Working Group on Oncological Rehabilitation and Social Medicine of the German Cancer Society, conducted a survey among the senior physicians of member hospitals to generate data to answer these questions.
Material and Methods
In the survey period from May 07 to May 25, 2021, 84 oncological rehabilitation clinics and specialist departments were contacted by e-mail and asked to participate in the online survey. The participants consented to participation and data analysis at the beginning of the survey. The name of the clinic or the participant was not collected so that the survey was anonymous.
The survey was web-based using SurveyMonkey and comprised 37 questions on the topics of clinic occupancy, structural and personnel changes as well as hygiene measures and changes in the range of therapeutic services. The respondents were asked to evaluate the observation period from March 01, 2020, to February 28, 2021, and to compare it with the same period 1 year earlier.
A response of 22/84 (26%) of the questionnaires was received. Of these, however, only 14/22 (63%) were analysable for the survey. The remaining 8 questionnaires were only created but not completed, so they were not available for data analysis.
The data was analysed descriptively using SPSS. Due to the low response rate with evaluable questionnaires, all clinics were combined, and no subgroups were formed. Significance analyses were also not possible due to the low participation in the survey.
Results
Clinic Occupancy and Impact
Thirteen participants provided information on the number of hospital beds they had for oncology patients. The median was 130 beds (range: 10–200 beds). In the period under review, 15,272 patients were rehabilitated in 11 surveyed clinics (median: 1,580 patients, range: 224–2,644 patients). In the comparable period before, 21,257 patients were treated (median: 2,100 patients, range: 163–3,167 patients) (Fig. 1). This corresponds to a cumulative decrease of 5,598 in patients served (82%). Of the 11 participants in the surveyed clinics, 10 (91%) stated that they had suffered a decline in occupancy. 9/13 of the clinics (70%) reported a decline in occupancy of between 20 and 50% and 2/13 (15%) reported a decline in occupancy of up to 75% (Table 1).
For the period under review, 3/11 participants (27%) reported having conducted a pandemic clinic closure. The period of the clinic closure was 1–9 weeks. Furthermore, 4/10 respondents (40%) stated that they had carried out an admission freeze in the period under review of 1–9 weeks. In addition, a pandemic-related loss of specialist staff had to be recorded at 4/13 clinics (33%) (Table 1).
Hygiene Measures
Nine of the participants commented on the number of staff and patients who tested positive for COVID-19. In total, a median of 5 staff/patients tested positive during the period under review (range: 1–20). In 5/13 clinics (39%), screening tests for COVID-19 (PCR or rapid test) had already been carried out on patients for 9–12 months at the time of the survey. While in 6/13 clinics (46%) this had been done for 3–8 months, 2/13 clinics (15%) were not yet testing patients. With regard to screening tests for staff, the picture was somewhat different. In all clinics, screening tests had already been carried out at the time of the survey, although in only 3/13 clinics (23%) had this been done for 9–12 months and in 10/13 clinics (77%) only for 3–8 months (Table 1).
Changes in the Therapeutic Offer
In order to pay attention to the distance between patients in rehabilitative therapies, changes were made in the number of participants in group therapies and training offers. All 11 participants interviewed stated that they offered more physiotherapy small groups in their clinics. In addition, 3/11 participants (27%) resorted to more individual therapies in the clinic or chose alternative services. Changes also occurred in the area of sports therapy, so that 8/13 clinics (73%) offered more small groups, while only one clinic used more individual therapies and two other clinics implemented alternative offers. With regard to the training offers, 6/13 clinics (60%) also reacted with more small groups, while 2/10 clinics (20%) each used more individual counselling or also resorted to alternative offers. In addition, in 7/14 clinics (50%), there was the possibility to offer training virtually (Table 2).
Perspective
At the time of the survey, the participants of 5/12 clinics (42%) assumed a recovery in occupancy development for 2021. In 6/12 other clinics (50%), an unchanged occupancy rate was expected for the current year, and only in one clinic, a further decrease in occupancy was assumed. Whether the clinic was economically threatened could not be assessed by 5/11 of the participants (38%), while 31% (4/11 of the participants) saw an economic threat or did not assume this. None of the participants expected a definite closure of the clinic.
Discussion
While more and more data can be found regarding rehabilitation of patients after COVID-19 infection, the impact of the pandemic on inpatient oncological rehabilitation in Germany is still unclear [9]. In order to record these effects of the pandemic, the Working Group on Oncological Rehabilitation of the DGHO, in collaboration with the Working Group on Oncological Rehabilitation and Social Medicine of the German Cancer Society, conducted a survey within its members in May 2021.
However, the response rate of 26% of questionnaires was low, of which only just under one-third could ultimately be evaluated for the survey. In the authors' view, the data obtained nevertheless allow the conclusion that oncological rehabilitation reacted quickly to the pandemic by changing its structures. More than half of the clinics carried out PCR tests on patients at an early stage and then replaced them with rapid antigen tests as they became available. Somewhat delayed, screening tests were also offered to staff, mostly after the introduction of rapid antigen tests.
In order to comply with the hygiene regulations, the group sizes were reduced in all clinics surveyed and mostly compensated by more small groups. To a lesser extent, more individual therapies or alternative offers were carried out. Half of the clinics stated that they were also able to conduct training and counselling virtually, which partly speaks for a good digital equipment of the rehabilitation clinics, but also reveals a potential for improvement.
Nevertheless, despite these measures, a clear decline in occupancy rates in the oncological rehabilitation clinics had to be recorded. The reason for this is most likely to be found in the postponement of acute medical treatments such as operations, in the provision of rehabilitation beds for the acute care of COVID-19 patients, but also in the restrained use of oncological rehabilitation by the patients themselves [10]. More than two-thirds of the clinics experienced occupancy declines of up to 50%, while only a smaller proportion of clinics experienced even higher occupancy declines. Compared to 2019, the very high occupancy decline of 28% was not insignificant. This is mainly due to the COVID-19 pandemic, as such a pronounced drop in occupancy has not been recorded in the last 20 years. In particular, this should be seen against the background that the majority of oncological rehabilitation clinics can only operate profitably if they have an occupancy rate of more than 85%. Certainly, it is most likely due to the use of the short-time working regulation as well as the financial compensation within the framework of the Social Service Providers Act that there has not been a relevant proportion of clinic closures so far. It was also possible to prevent a pandemic-related loss of specialist staff at more than two-thirds of the clinics. Nevertheless, this was recorded at one-third of the clinics, which may have further exacerbated the precarious staff situation in oncological rehabilitation.
In a presentation during the annual meeting of the DGHO 2020, Reuss-Borst et al. [7] were able to demonstrate the data from over 5,000 rehabilitation patients who had received a PCR test for COVID-19 from April to September 2020. Only a vanishingly small proportion of less than 0.1% of the patients tested COVID-19 positive. Then, in a second series of tests from October 2020 to February 2021, only 0.9% of nearly 7,000 patients tested positive for COVID-19. The authors interpreted their data from over 12,000 patients to mean that they saw rehabilitation as a safe and defensible medical tool even in times of a pandemic [7].
In a previously published paper, Leibbrand and Seifart [8] were able to show that the timely application of pandemic rules in rehabilitation clinics makes it possible to continue oncological rehabilitation at a high level and thus maintains the supply chain for cancer patients. The acceptance of staff and patients was high in the oncological rehabilitation clinics, so that although a significant drop in occupancy was also recorded in the two clinics involved, specialists could largely be retained [8].
Our data show the impact of the COVID-19 pandemic on oncological rehabilitation and how clinics dealt with it in terms of implementing hygiene measures. To the best of our knowledge, there have been no comparable studies to date. Thus, our data complement the published studies already mentioned and show that oncological rehabilitation can be carried out safely for patients and staff even in times of a severe pandemic.
Our data are limited, in particular, by the low response rate to the questionnaires, so that we were unable to collect representative data on oncological rehabilitation in Germany. The low response rate is not so much due to the way the survey was conducted but rather to the participants' reluctance to provide information. In particular, we were not able to collect data with regard to the financial effects on the clinics. Nevertheless, our results offer a first insight into oncological rehabilitation during the COVID-19 pandemic and show that rehabilitative processes can be quickly adapted to acute health policy changes.
Conclusion
Based on the data we collected on oncological rehabilitation during the COVID-19 pandemic, we conclude that this therapeutic measure can be carried out hygienically and safely. Despite the considerable drop in occupancy and the financial burdens for the clinics that this probably entailed, it was possible to prevent worse things from happening in this medical sector by means of legal regulations.
Statement of Ethics
After review by the Ethics Committee of the Philipps University Marburg, Department of Medicine, it is not necessary to obtain an ethics vote for a purely anonymized data collection (EK_MR_8_3_2022).
Conflict of Interest Statement
All authors declare that they have no conflicts of interest.
Funding Sources
This research did not receive grants from any funding agency in public, commercial, or not-for-profit sectors
Author Contributions
Oliver Rick, Wilfried Hoffmann, and Monika Anna Steimann acknowledge that they made significant contributions to the conception; design of the work; and the acquisition, analysis, and interpretation of the data. In addition, all authors critically revised the paper for important intellectual content. Finally, all authors have given final approval of the version to be published and declare responsibility for all aspects of the work and for ensuring that issues regarding the accuracy or integrity of any part of the work have been adequately investigated and resolved.
Data Availability Statement
All data analysed during this study are included in this article. Further enquiries can be directed to the corresponding author.
Acknowledgement
The authors would like to thank Christoph Mussmann, project leader of the DGHO, for preparing the survey and the analysis.
Fig. 1 Number of oncological patients in 2019 versus 2020 per respondent (n= 11).
Table 1 Changes in clinic occupancy, staff, and corona screening (n = 13)
Question N %
How high was the decrease in clinic occupancy?
20–50% 9 70
51–75% 2 15
n.a. 2 15
How long have you been carrying out corona screening tests (PCR/ST) on patients?
3 months 3 23
6 months 3 23
9 months 4 31
12 months 1 8
No tests so far 2 15
How long have you been carrying out corona screening tests (PCR/ST) on staff?
3 months 7 54
6 months 3 23
9 months 2 15
12 months 1 8
No tests so far 0 0
Do you have a pandemic-related loss of specialist staff?
Yes 4 30
No 9 70
n.a., not answered; PCR, polymerase chain reaction; ST, rapid antigen test.
Table 2 Changes in the therapeutic offer
Question N
How has the number of individual therapies changed (n = 14)? Become less 2 14
Become more 3 21
Remains the same 9 65
How was the reduction of patients in group therapies compensated (n = 11)?
More small groups 11 100
More individual therapies 3 27
Through alternative offers 3 27
How was the reduction of patients compensated in sports groups (n = 11)?
More small groups 8 73
More individual therapies 1 9
Through alternative offers 2 18
How was the reduction of patients compensated in training offers (n = 10)?
More small groups 6 60
More individual counselling 2 20
Through alternative offers 2 20
Do you have the possibility to conduct trainings virtually (n = 14)?
Yes 7 50
No 7 50
==== Refs
References
1 Rick O Dauelsberg T Kalusche-Bontemps EM Oncological rehabilitation Oncol Res Treat 2017 40 772 777 29183040
2 Rehaatlas Die Teilhabeleistungen der Deutschen Rentenversicherung in Zahlen Fakten und Trends 2020 Available from. https://www.deutsche-rentenversicherung.de/SharedDocs/Downloads/DE/Statistiken-und-Berichte/Rehaatlas/2020/rehaatlas_2020_download.html
3 Bundesarbeitsgemeinschaft für Rehabilitation. Rehabilitation und Teilhabe Wegweiser für Ärzte und andere Fachkräfte der Rehabilitation Herausgegeben von der Bundesarbeitsgemeinschaft für Rehabilitation: Deutscher Ärzte Verlag 2005
4 Amatya B Khan F. Rehabilitation response in pandemics Am J Phys Med Rehabil 2020 99 (8) 663 668 32452879
5 Carda S Invernizzi M Bavikatte G Bensmaïl D Bianchi F Deltombe T Covid-19 pandemic. What should physical abd rehabilitation medicine specialists do? A clinican's perspective Eur J Phys Rehabil Med 2020 56 515 524 32434314
6 Choon-Huat Koh G Hoenig H How should the rehabilitation community prepare for 2019-nCoV? Arch Phys Med Rehab 2020 101 1068 1071
7 Reuss-Borst M König V Sallmann D Rick O. Evaluation of the test strategy in oncological rehabilitation in the CORONA pandemic Oncol Res Treat 2021 44 (Suppl 4) S276
8 Leibbrand B Seifart U. Oncological rehabilitation and development of strategies in crisis situations using the example of the Covid 19 pandemic in 2020 by using a patient and staff survey results of pandemic care Rehabilitation 2021 60 (2) 142 151 33858023
9 AWMF S2k-LL SARS-CoV-2 COVID-19 und (Früh-) Rehabilitation https://www.awmf.org/leitlinien/detail/ll/080-008.html
10 Rick O. Rehabilitation in Zeiten von COVID-19 InFo Hämatologie und Onkologie 2020 23 (9) 18 22
| 0 | PMC9747734 | NO-CC CODE | 2022-12-15 23:22:04 | no | Phys Med. 2022 Dec 14; 104:S43 | latin-1 | Phys Med | 2,022 | 10.1016/S1120-1797(22)02215-3 | oa_other |
==== Front
Int Arch Allergy Immunol
Int Arch Allergy Immunol
IAA
International Archives of Allergy and Immunology
1018-2438
1423-0097
S. Karger AG Allschwilerstrasse 10, P.O. Box · Postfach · Case postale, CH–4009, Basel, Switzerland · Schweiz · Suisse, Phone: +41 61 306 11 11, Fax: +41 61 306 12 34, [email protected]
36265449
10.1159/000526764
iaa-0001
Clinical Allergy − Research Article
Allergy Workup in the Diagnosis of COVID-19 Vaccines-Induced Hypersensitivity Reactions and Its Impact on Vaccination
Petrelli Fiorella a
Giannini Daiana a
Pucci Celestino a
Del Corso Isabella a
Rocchi Valeria a
Dolcher Maria Pia a
Pieve Giulio b
Pratesi Federico a
Migliorini Paola a
Puxeddu Ilaria a *
aImmunoallergology Unit, Department of Clinical and Experimental Medicine, Pisa University, Pisa, Italy
bUO Direzione Medica di Presidio, Azienda Ospedaliero Universitaria Pisana, Pisa, Italy
*Ilaria Puxeddu, [email protected]
20 10 2022
20 10 2022
19
20 6 2022
17 8 2022
Copyright © 2022 by S. Karger AG, Basel
2022
https://www.karger.com/Services/SiteLicenses Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher.
Introduction
Immediate and delayed hypersensitivity reactions (HSR) to COVID-19 vaccines are rare adverse events that need to be prevented, diagnosed, and managed in order to guarantee adherence to the vaccination campaign. The aims of our study were to stratify the risk of HSR to COVID-19 vaccines and propose alternative strategies to complete the vaccination.
Methods
1,640 subjects were screened for vaccinal eligibility, according to national and international recommendations. Among them, we enrolled for allergy workup 152 subjects, 43 with HSR to COVID-19 vaccines and 109 at high risk of HSR to the first dose. In vivo skin tests with drugs and/or vaccines containing PEG/polysorbates were performed in all of them, using skin prick test and, when negative, intradermal tests. In a subgroup of patients resulted negative to the in vivo skin tests, the programmed dose of COVID-19 vaccine (Pfizer/BioNTech) was administered in graded doses regimen, and detection of neutralizing anti-spike antibodies was performed in these patients after 4 weeks from the vaccination, using the SPIA method.
Results
Skin tests for PEG/polysorbates resulted positive in only 3% (5/152) of patients, including 2 with previous HSR to COVID-19 vaccines and 3 at high risk of HSR to the first dose. Among the 147 patients with negative skin tests, 97% (143/147) were eligible for vaccination and 87% (124/143) of them received safely the programmed COVID-19 vaccine dose. Administration of graded doses of Pfizer/BioNTech vaccine were well tolerated in 17 out of 18 patients evaluated; only 1 developed an HSR during the vaccination, less severe than the previous one, and all developed neutralizing anti-spike antibodies after 4 weeks with values comparable to those subjects who received the vaccine in unfractionated dose.
Conclusion
On the whole, the usefulness of the skin tests for PEG/polysorbates seems limited in the diagnosis of HSR to COVID-19 vaccines. Graded doses regimen (Pfizer/BioNTech) is a safe and effective alternative strategy to complete the vaccinal course.
Key Words
COVID-19
Vaccine
Allergy
Skin test
Polyethylene glycol
Polysorbates
This work was partially funded (in vitro tests) by the Italian Ministry of Health grant COVID-200-12371849.
==== Body
pmcIntroduction
The COVID-19 is a devastating global pandemic with limited therapeutic options. In this scenario, vaccination represents the most effective global strategy to achieve optimal population herd immunity [1, 2, 3, 4]. Since the beginning of the COVID-19 vaccination campaign, immediate and delayed vaccine-induced hypersensitivity reactions (HSRs) have been reported [5, 6, 7, 8]. Although these reactions are rare adverse events, they need to be prevented, diagnosed, and managed in order to guarantee adherence to the vaccination campaign.
A careful allergy workup may be relevant for the stratification of patients at high risk of COVID-19 vaccine-induced HSR as well as for the diagnosis of HSR to the first dose. The estimate rate of mRNA COVID-19 vaccine-induced anaphylaxis was initially of 11.1 cases per million doses of the Pfizer/BioNTech, but subsequent reports revealed a lower rate of 4.7 and 2.5 cases with Pfizer/BioNTech and Moderna vaccines, respectively [9, 10]. Moreover, a recent meta-analysis showed case rate of anaphylaxis of 7.9 cases of all available COVID-19 vaccines, and no anaphylaxis-related fatalities were reported [11]. These data strongly suggest that even if severe reactions may occur after vaccination, these are very rare events and so far not fatal.
According to the literature, various components present in the currently available COVID-19 vaccines, including active and/or inactive ones, might be responsible for the development of HSR [12]. In this regard, polyethylene glycol (PEG) and polysorbates, excipients used to improve water solubility, were identified as potential allergens in COVID-19 vaccines, responsible for IgE-mediated reactions [1, 2, 3, 4]. Since several drugs and nonmedical products (e.g., cosmetics) contain PEG and/or polysorbates, it has been postulated that a previous sensitization to these excipients might cause anaphylaxis during vaccination, even if the evidences supporting their role in HSR are still lacking [13, 14, 15, 16, 17]. Following first reports of COVID-19 vaccine-induced anaphylaxis [5, 6, 7, 8], international and national scientific societies [18, 19] provided an algorithm for helping allergists to stratify patients at high risk of reactions and to perform in vivo skin tests for identifying PEG/polysorbates sensitization [20, 21, 22]. Assuming that patients who developed HSR were sensitized to vaccine components other than PEG/polysorbates, some medical centers tested the entire vaccine, getting however conflicting results. In fact, some [23, 24] but not all [25] clinical reports showed sensitization to components of the entire vaccine solution instead of drugs containing PEG/polysorbates.
Although HSR to the COVID-19 vaccines are rare and not life-threatening, it is still necessary to adopt all the diagnostic tools for the prevention and diagnosis of these reactions, useful to improve adherence to the vaccination campaign. Therefore, based on the clinical history and in vivo skin tests results, administration of the vaccine in graded doses regimen has been proposed as an alternative strategy to complete the vaccinal course [26]. In some cases, this approach has been successfully adopted [23, 27] using a protocol already proposed for other vaccines [28]. However, the immunological efficacy of COVID-19 vaccination using graded doses regimen has not been deeply investigated yet, and this issue is crucial for completing an efficient vaccinal course. Therefore, the aims of our study were to stratify in a large population the risk of developing HSR to currently available COVID-19 vaccines, to assess the role of in vivo skin tests for PEG/polysorbates in the diagnosis and prevention of COVID-19-induced HSR, and to identify safe and effective alternative strategies to complete the vaccinal course.
Methods
Patients
From February 2021 to September 2021, 1,640 subjects were screened for vaccinal eligibility, according to Banerji et al. [13] (Fig. 1). The study was part of a screening protocol for vaccine eligibility for COVID-19, organized by the health service of our region (Tuscany) to comply with published recommendations from AAIITO/SIAAIC societies [18]. Among them, 152 were enrolled at the Azienda Ospedaliera Universitaria Pisana, Pisa, Italy. Demographic and clinical characteristics of these patients were registered, including history of atopy, asthma, allergy to food and/or latex and/or venom of hymenoptera, and previous HSR to drugs and/or vaccines containing PEG/polysorbates. In those patients who experienced HSR to the first dose of COVID-19 vaccine, the reaction was classified on the basis of the time of onset (immediate or delayed) and severity (grades 1–5) [29].
In vivo Skin Test to PEG/Polysorbates
In all our cohorts, the in vivo skin tests (skin prick tests [SPT] and intradermal tests [IDT]) were performed using drugs and vaccines containing PEG/polysorbates, according to previous published protocols [13, 18, 22] with some modifications as reported in Table 1. Macrogol 3350 (pediatric Movicol without flavorings) and methylprednisolone acetate (Depo-Medrol) 40 mg/mL were used to evaluate sensitization to PEG 3350, while triamcinolone acetonide (Kenacort) 40 mg/mL, Optive lubricant eye drops, and Prevenar 13 were used to test sensitization to polysorbate 80. The hepatitis A vaccine Havrix was additionally tested as a source of polysorbate 20. Furthermore, methylprednisolone sodium succinate (Urbason) 40 mg/mL, containing neither PEG nor polysorbate 80, was used as additional control. SPT and IDT were evaluated after 15 min for immediate HSR and 24, 48, and 72 h or more for the delayed ones. The results of both SPT and IDT were compared with the results obtained with positive (histamine, Lofarma Allergeni, Milan, Italy) and negative (0.9% saline solution) controls, and an arbitrary score was assigned based on the size of the wheal (>3 mm). Informed written consent was obtained from each patient before performing the in vivo skin tests, according to the hospital internal protocol (Azienda Ospedaliera Universitaria Pisana, Pisa, Italy).
Different Strategies for COVID-19 Vaccination after in vivo Skin Tests
Based on the clinical history and the outcome of the in vivo skin tests, the patients were invited to be vaccinated according to the AAIITO/SIAAIC recommendations [18]. In order to know if they had successfully completed their vaccinal course, after the allergy workup a telephone interview was conducted. In the case of HSR, these patients were reevaluated in the allergy setting.
To improve the adherence to the COVID-19 vaccination campaign, we selected 18 consecutive subjects: 17 who developed an immediate HSR to the first dose (grade 1–3) with negative in vivo skin tests for PEG/polysorbates, and 1 at high risk of vaccine-induced HSR. The programmed dose of Pfizer/BioNTech vaccine was administered using graded doses regimen as reported in Table 2. In order to reduce the risk of HSR, oral cetirizine (10 mg) was administered prior the vaccination, and the patients were observed 60 min after receiving the last dose of the vaccine.
Detection of Neutralizing Anti-Spike Antibodies
The neutralizing capacity of anti-spike antibodies was measured in 18 patients who received COVID-19 vaccines (Pfizer/BioNTech) in graded doses regimen. The serological analysis was performed 4 weeks after administration of the second vaccine dose, by using the kit SPIA (Spike Protein Inhibition Assay; DiaMetra, Perugia, Italy), according to the manufacturer's instructions. This assay is based on the competition between patient's antibodies and the peroxidase-conjugated ACE2 for the binding to viral receptor binding domain (RBD) coated on the solid phase. Sera samples were tested in duplicate at 1:15 dilution, and the inhibition value was calculated using the following formula: % inhibition = (1 − [absorbance sample]/[absorbance calibrator]) × 100.
The results were compared to those obtained in the sera of 17 healthy subjects who received Pfizer/BioNTech COVID-19 vaccine in unfractionated dose. Neutralizing antibodies in both groups were then compared to those measured in 17 healthy subjects prior vaccination.
Statistical Analysis
Ages of the patients were expressed as mean of years (range) and compared using the Student t test. All the other variables were expressed as absolute number and percentage. The frequencies of atopy, asthma, and other clinical characteristics were compared using the χ2 test for nonbinary comparisons. The percentage of neutralizing anti-spike antibodies in different groups were compared by the Kruskal-Wallis test. Data were analyzed and plotted using GraphPad Prism software (version 5.01; GraphPad Software Inc., San Diego, CA, USA). A p value <0.05 was considered statistically significant.
Results
Study Population
After screening for vaccinal eligibility in a large population, 152 subjects underwent allergy workup: 109 (72%) were at high risk of developing HSR to COVID-19 vaccines, according to the first published recommendation from AAIITO/SIAAIC societies [18] and 43 (28%) experienced a previous HSR to the first dose. Most of the subjects evaluated were females (87.5%) and to a lesser extent males (12.5%) with a mean age of 55 years (range 20–93); 77 (51%) were atopic, 42 (28%) asthmatics, and 74% reported a previous HSR to drugs or vaccines containing PEG/polysorbates. As reported in Table 3, analysis of the two subgroups evaluated revealed that less than 30% of patients in the group of subjects with a previous HSR to COVID-19 vaccines had a history of HSR to drugs or vaccine containing PEG/polysorbates. However, when we restricted this evaluation to only anaphylactic reactions (grade 3–5), this percentage was almost comparable to that of the subgroup at high risk of developing HSR to the COVID-19 vaccine. No differences in clinical and demographic parameters were detected between the two subgroups.
Characteristics of HSR to COVID-19 Vaccines
As reported in Figure 2, the most frequent HSR to the first dose of COVID-19 vaccines were observed after administration of Pfizer/BioNTech (56%), followed by AstraZeneca (30%) and Moderna (14%). Based on the time of onset, 60.5% were immediate and 39.5% were delayed reactions. Most of the reactions to Pfizer/BioNTech and Moderna were immediate (71% and 67%, respectively), while those to AstraZeneca were delayed (61.5%). Among the immediate ones, only 19% were severe (grade 4–5) and 38.5% classified as grade 3 (Table 4). A rescue medication with epinephrine was required in 4 out of the 5 patients who experienced a severe immediate HSR (grade 4–5) with resolution of the anaphylaxis within 1 h. No fatal events were reported.
Role of Skin Tests in the Diagnosis and Prevention of HSR to COVID-19 Vaccines
As reported in Figure 3, only 3% (5/152) of all the tested patients were positive to the in vivo skin tests, 3 at high risk of developing HSR to the first vaccine dose and 2 with a previous COVID-19 vaccine-induced HSR. Among the 3 patients at high risk of HSR, 2 successfully received the first dose of Pfizer/BioNTech vaccine.
The 2 patients with a previous HSR to the first dose, one to Moderna (immediate HSR) and the other one to AstraZeneca (delayed-HSR), resulted positive to Kenacort (polysorbates containing drug), but no sensitization to Macrogol (PEG 3350) has been detected. Therefore, who developed an immediate HSR to Moderna vaccine was not eligible for the second dose of COVID-19 vaccine containing PEG/polysorbates, while who developed the HSR to AstraZeneca was eligible for vaccination and successfully received the second dose of Pfizer/BioNTech vaccine.
In most of the patients tested (97%; 147/152), no sensitization to PEG/polysorbates has been detected by in vivo skin tests, and they were advised to receive the COVID-19 vaccine, according to the recommendations [18]. At the reevaluation, 84% (124/143) completed the vaccination course, 3% (4/124) with a mild-moderate immediate HSR (grades 1–3), and 1.6% (2/124) with a severe immediate HSR (grade 4), none requiring epinephrine injection.
In order to improve the adherence to the vaccinal campaign, we selected 18 out of the 147 patients, resulted negative to the skin tests for receiving the programmed dose using graded doses of Pfizer/BioNTech vaccine, 17 with a previous immediate HSR to the first dose, and one at high risk of reaction. The graded doses were well tolerated in most of the patients; only one developed an immediate HSR (grade 2), less severe than the previous one (grade 3), resolved after medical intervention with injective corticosteroids.
Immunological Response in Graded Doses of COVID-19 Vaccine
In order to evaluate the immunological efficacy of the COVID-19 vaccination with Pfizer/BioNTech vaccine using graded doses regimen, we evaluated neutralizing RBD/ACE2-binding antibodies in the sera of these patients 4 weeks after completing the vaccinal course. As reported in Figure 4, neutralizing antibodies were produced in this group at the same level as those produced in the group that received the unfractionated dose, indicating that graded doses regimen of Pfizer/BioNTech COVID-19 vaccine does not impair the production of protective antibodies. The neutralizing antibodies in both groups (unfractionated and graded doses) were significantly higher than those detected in the healthy subjects prior vaccination (p < 0.0001) (Fig. 4).
Discussion
Since the beginning of COVID-19 vaccination campaign, the main goal of allergists was to prevent COVID-19 vaccine-induced HSR and, in the case of reactions, to adopt an allergy workup for the diagnosis and the management of them. In line with other allergy centers [17, 26, 30, 31, 32], and according to the national and international recommendations [18, 19], we performed, when indicated, risk stratification of HSR to COVID-19 vaccines and allergy workup, including in vivo skin tests for identifying sensitization to PEG/polysorbates [13, 18, 22].
Our cohort included 72% of patients at high risk of HSR to COVID-19 vaccine and 28% with previous HSR to the first dose. As reported in other centers [8, 32], the majority of subjects requiring an allergy workup were females, even though no other differences in clinical characteristics were identified between males and females (data not shown). The atopic status and a previous diagnosis of asthma were not able to discriminate between patients at high risk of HSR versus those who developed HSR to the first dose. Although a previous HSR to drugs or vaccines containing PEG/polysorbates was identified as a high risk factor for developing a COVID-19 vaccine-induced HSR, in our cohort this parameter was not predictive of reaction and did not affect either the detection of sensitization of PEG/polysorbates using skin tests or the subsequent tolerance of COVID-19 vaccine in subjects with previous HSR. In fact, most of the patients with HSR to the first dose and/or previous HSR to drugs or vaccines containing PEG/polysorbates were able to receive safely the COVID-19 vaccine. These data were confirmed by the most recent AAIITO/SIAAIC recommendations (available from April 2022) [33] that include in the group of subjects at “high risk” of HSR to COVID-19 vaccine only those with previous HSR to injectable drugs containing PEG, to echocardiographic contrast agents, or to laxatives, or bowel preps for colonoscopy, both containing PEG as active components. On the contrary, the subjects reporting a previous HSR to oral drugs containing PEG/polysorbates are now considered at “low risk” to develop HSR to COVID-19 vaccine [33].
In our experience, most of the HSR to the first dose of COVID-19 vaccines were immediate and not anaphylactic (grade 1–2), with a prevalence of erythema and/or urticaria as clinical manifestations. In line with previous reports [17, 26, 30, 31, 32], only 5% of the patients with HSR to the first dose resulted positive to skin tests to drugs and/or vaccine containing PEG/polysorbates. However, even if it was not possible to identify a sensitization to PEG/polysorbates, these excipients might still be involved in COVID-19 vaccine-induced HSR through other mechanisms such as non-IgE-mediated or nonimmunological ones. Moreover, we can suggest that the active components of the vaccines, rather than excipients, may be responsible for vaccine-induced HSR, and the in vivo skin tests used in the allergy workup are not able to identify it.
In our cohort, the in vivo skin tests for identifying sensitization to PEG/polysorbates revealed an unexpected low rate of positive results despite a previous HSR to drugs and/or vaccines containing PEG/polysorbates. Under this respect, it should be taken into account that the molecular weight (MW) of PEG contained in COVID-19 vaccines (Pfizer/BioNTech and Moderna) is 2,000, not the same MW of PEG commonly used in other drugs. Therefore, these differences might in part explain the negative skin test results obtained in our and in other studies. Thus, the level of cross-reactivity among PEG polymers with different MW needs to be further investigated. Moreover, when skin tests to drugs and vaccines containing PEG/polysorbates are negative, we should perform skin tests using PEGylated liposomes [24]. In fact, skin testing to native PEG may provide negative results, while their sensitivity may be improved by testing PEGylated liposomes, which more closely resemble the form of PEG contained in COVID-19 vaccine [24]. According to our results, we can assume that skin tests using drugs and vaccines containing PEG/polysorbates are not optimal for the diagnosis of HSR to COVID-19 vaccines and their usefulness and validity need to be reconsidered. The use of culprit vaccine for in vivo skin tests may overcome some of these limitations, but positive results by this approach were less frequent than expected as described in some reports [25]. Thus, other diagnostic tools for identifying PEG/polysorbates sensitization [26, 32] are required, including detection of specific IgE and/or basophil activation test.
Despite the limits of the currently available diagnostic tools, the main goal for allergists remains the identification of safe and effective alternative strategies useful to complete the vaccinal course. According to national and international recommendations [18], a desensitization protocol should be considered in selected patients with a previous immediate HSR to COVID-19 vaccines, with no evidence of sensitization to PEG/polysorbates. However, the stability, the safety, and the immunogenicity of the vaccine diluted, as required by desensitization protocols [18], have not been deeply investigated yet [13]. Therefore, this vaccinal strategy has some limitations that need to be overcome. Thus, we decided to administer Pfizer/BioNTech COVID-19 vaccine not diluted by using graded doses regimen in order not to impair the stability of the nanoparticles. According to our results, this approach was useful to complete the vaccinal course in subjects at high risk of HSR to the first dose and/or after a previous HSR. In fact, most of them did not develop any HSR during the vaccine administration, and in only one case, the HSR observed was less severe than the previous one. Our goal was also to complete the vaccinal course by means of an alternative strategy effective in inducing a specific immunological response. Our data confirmed that after 4 weeks from Pfizer/BioNTech COVID-19 vaccine in graded doses regimen, these patients developed protective neutralizing anti-spike antibodies. This is the first report demonstrating that graded doses regimen of COVID-19 vaccination is able to induce an efficient immunological response comparable to unfractionated doses. In line with our experience, few other Allergy centers administered safely COVID-19 vaccines by using the same approach [23, 27, 34]. In only few clinical reports, the anti-spike antibodies were measured after receiving the COVID-19 vaccine in graded doses regimen, but their neutralizing capacity was not evaluated [27, 34].
In conclusion, although COVID-19-induced HSRs are very rare adverse events, the fearfulness of developing these reactions can cause loss of adherence to the vaccinal campaign. Therefore, the allergy workup is useful to stratify the risk of developing HSR to COVID-19 vaccines and to identify safe and effective alternative strategies to complete the vaccinal course. However, the usefulness of the skin tests for PEG/polysorbates seems limited in the diagnosis of HSR to COVID-19 vaccines and other diagnostic tools are required. Graded doses of Pfizer/BioNTech COVID-19 vaccine has resulted a safe and effective alternative strategy to complete successfully the vaccinal course.
Statement of Ethics
This research was conducted ethically in accordance with the World Medical Association Declaration of Helsinki. The clinical protocol was approved by the Ethical Committee of the Pisa University Hospital (approval No. 17522). Informed written consent was obtained from all the participants.
Conflict of Interest Statement
The authors have no conflicts of interest to declare.
Funding Sources
This work was partially funded (in vitro tests) by the Italian Ministry of Health grant COVID-200-12371849.
Author Contributions
Fiorella Petrelli, Daiana Giannini, Celestino Pucci, Isabella Del Corso, Valeria Rocchi, Maria Pia Dolcher, Giulio Pieve, Federico Pratesi, Paola Migliorini, and Ilaria Puxeddu made substantial contributions to conception and design, acquisition of data, and interpretation of data; reviewed it critically for important intellectual content; gave final approval of the version to be published; and agree to be accountable for all aspects of the work related to its accuracy or integrity; Fiorella Petrelli, Daiana Giannini, and Ilaria Puxeddu drafted the article.
Data Availability Statement
All data generated or analyzed during this study are included in this article. Further inquiries can be directed to the corresponding author.
Edited by: H.-U. Simon, Bern.
Fig. 1 Screening for vaccinal eligibility. GP, general practitioner; PEG, polyethylene glycol.
Fig. 2 Type of HSR to different COVID-19 vaccines according to the timing. COVID-19, coronavirus disease 2019; HSR, hypersensitivity reactions.
Fig. 3 In vivo skin test results and its impact on vaccinal eligibility. PEG, polyethylene glycol; HSR, hypersensitivity reaction. *Vaccine avoidance: the vaccination was not recommended by an allergist, according to the results of in vivo skin tests [18]. #No vaccination: the vaccination was refused by the patient even if he was eligible for the vaccination.
Fig. 4 Neutralizing anti-spike antibodies following graded doses of Pfizer/BioNTech COVID-19 vaccine. Apvalue <0.05 was considered statistically significant. RBD, receptor binding domain; ACE2, angiotensin-converting enzyme 2; n.s., not significant.
Table 1 Skin tests with drugs containing PEG 3350 and polysorbates
PEG 3350 Control Polysorbate 20 Polysorbate 80
Movicol (Macrogol), mg/mL Depo-Medrol (MP acetate), mg/mL Urbason (MP sodium succinate), mg/mL Havrix (Hepatitis A vaccine) Kenacort (TA), mg/mL Optive Prevenar 13 (pneumococcal vaccine)
Step 1 SPT 1.7 40 40 1:1 40 1:1 1:10
Step 2 SPT 17
Step 3 SPT 170
Step 4 IDT 0.04 0.04 1:100 0.4 1:100
Step 5 IDT 0.4 0.4 1:10 4
Step 6 IDT 4 4 40
IDT, intradermal tests; MP, methylprednisolone; SPT, skin prick tests; TA, triancinolone acetonide.
Table 2 Graded doses regimen of Pfizer/BioNTech COVID-19 vaccine
Undiluted doses Volume, mL Time following previous dose, min
Protocol 1 for patients with HSR to the first dose of COVID-19 vaccines
1 0.05 0
2 0.10 30
3 0.15 30
Protocol 2 for patients at high risk of developing HSR to COVID-19 vaccine
1 0.05 0
2 0.25 30
COVID-19, coronavirus disease 2019; HSR, hypersensitivity reaction.
Table 3 Demographic and clinical characteristics of patients
Total, n = 152 First dose HSR, n = 43 High risk of HSR, n = 109 p value
Age, years, mean (range) 55 (20–93) 53.5 (23–89) 55.5 (20–93) 0.4611
Sex, M/F, n 19/133 4/39 15/94 0.4540
Atopy, n (%) 77 (51) 17 (39.5) 60 (55) 0.0849
Asthma, n (%) 42 (28) 12 (28) 30 (27.5) 0.9619
Food allergy, n (%) 27 (18) 7 (16) 20 (18) 0.7636
Hymenoptera venom allergy, n (%) 11 (7) 2 (5) 9 (8) 0.4396
Latex allergy, n (%) 15 (8) 5 (12) 10 (9) 0.6477
Urticaria/angioedema, n (%) 18 (12) 7 (16) 11 (10) 0.2876
AD and/or ACD, n (%) 27 (18) 8 (19) 19 (17) 0.8646
HSR to drugs and/or vaccines with PEG/polysorbates, n (%) 113 (74) 12 (28) 101 (93) <0.00001
Anaphylaxis (grade 3–5) 65 (58) 5 (42) 60 (59) 0.2398
HSR to other vaccines, n (%) 20 (13) 2 (5) 18 (16.5) 0.0513
Mastocytosis or MCAS, n (%) 1 (0.6) 0 (0) 1 (1) NA
ACD, allergic contact dermatitis; AD, atopic dermatitis; HSR, hypersensitivity reaction; n, number; MCAS, mast cells activation syndrome; NA, not applicable; PEG, polyethylene glycol.
Table 4 Grade of immediate COVID-19 vaccine-induced HSR
Grade COVID-19 vaccine (n = 26)
Pfizer/BioNTech
(n = 17), n (%) Moderna
(n = 4), n (%) AstraZeneca (n = 5), n (%)
1 2 (12) 1 (25) 0 (0)
2 5 (29) 2 (50) 1 (20)
3 7 (41) 1 (25) 2 (40)
4 1 (6) 0 (0) 2 (40)
5 2 (12) 0 (0) 0 (0)
COVID-19, coronavirus disease 2019; HSR, hypersensitivity reaction; n, number.
==== Refs
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| 36265449 | PMC9747735 | NO-CC CODE | 2022-12-15 23:22:04 | no | Int Arch Allergy Immunol. 2022 Oct 20;:1-9 | utf-8 | Int Arch Allergy Immunol | 2,022 | 10.1159/000526764 | oa_other |
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Clinical Practice: Research Article
Humoral Response to the Third Dose of BNT162b2 COVID-19 Vaccine among Hemodialysis Patients
Agur Timna a b *
Zingerman Boris a b
Ben-Dor Naomi a b
Alkeesh Weaam b c
Steinmetz Tali a b
Rachamimov Ruth a b
Korzets Asher a b
Rozen-Zvi Benaya a b
Herman-Edelstein Michal a b
aDepartment of Nephrology and Hypertension, Rabin Medical Center, Petah-Tikva, Israel
bSackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
cInternal Medicine B, Rabin Medical Center, Hasharon Hospital, Petah-Tikva, Israel
*Timna Agur, [email protected]
27 7 2022
27 7 2022
18
23 2 2022
3 6 2022
Copyright © 2022 by S. Karger AG, Basel
2022
https://www.karger.com/Services/SiteLicenses Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher.
Background
Hemodialysis patients are at high risk for severe COVID-19 disease. Despite a high early seropositivity rate, dialysis patients mount a dampened immune response following two doses of an mRNA vaccine. This study aimed to evaluate the serologic response to a booster dose of BNT162b2 vaccine, 6 months after the second dose, among hemodialysis patients.
Methods
This prospective study included 80 hemodialysis patients and 56 healthcare workers serving as controls. Serologic samples were evaluated before and ∼3 weeks after the third vaccine dose. The primary outcomes were the seropositivity rate and the log-transformed anti-SARS-COV-2 S1 (RBD) IgG as a continuous variable after the third dose. Secondary outcomes were the proportion of participants with “high response,” defined as antibody levels >1,000 AU/mL, and “robust response,” defined as antibody levels >4,160 AU/mL, according to prespecified cutoff values associated with neutralizing antibodies. Univariate and multivariate analyses were conducted to identify predictors of antibody response.
Results
Among 80 hemodialysis patients, seropositivity rates improved from 78% (62/80) before the third dose, up to 96% (77/80) after the booster dose. The S1-RBD log-transformed antibody level increased significantly following the third dose from 2.15 ± 0.75 to 3.99 ± 0.83 compared with 2.65 ± 0.4 to 4.31 ± 0.42 in the control group. Among the hemodialysis patients, 88% (70/80) became “high responders” (>1,000 AU/mL), and of these, 79% (63/80) mounted a “robust response” (>4,160 AU/mL). Baseline antibody level, dialysis therapy, and hypoalbuminemia were independent predictors of impaired antibody response.
Conclusions
A third dose of BNT162b2 COVID-19 vaccine, 6 months after the standard two-dose vaccination regimen, substantially improved humoral response in hemodialysis patients.
Key Words
Hemodialysis patients
COVID-19 vaccine
Third dose
Antibody response
No external funding.
==== Body
pmcIntroduction
Hemodialysis patients (HDP) are at high risk for severe COVID-19, with a case fatality rate above 20% [1]. Most incenter hemodialysis units cannot adequately maintain strict isolation procedures, and this coupled with an impaired immune response and high prevalence of comorbidities makes HDP a particularly susceptible population to COVID-19 infection [2]. Despite innovative therapeutics and vaccines, the COVID-19 pandemic remains a worldwide problem and is evolving constantly with recurrent surges. Hence, it is essential to try and establish an efficient vaccination strategy with sustainable protection against SARS-COV-2, especially in vulnerable populations such as HDP [3].
Vaccine efficacy is known to decline with decreasing kidney function. For example, HDP have a dampened immune response and weaker maintenance of protective antibodies in response to the common hepatitis B virus vaccine, and this led to changes in the internationally accepted hepatitis B immunization schedule for HDP [4]. In line with the above, the protection level achieved by HDP, in response to the standard anti mRNA-COVID-19 vaccine regimen of two doses, is impaired. Although a high seroconversion rate following two doses of the anti mRNA-COVID-19 vaccination has been seen in HDP, the antibody response was delayed, and the seroconversion rate and antibodies titers were substantially lower when compared with age-matched controls [5, 6, 7, 8, 9]. Several studies that assessed vaccine-induced neutralizing antibodies against SARS-COV-2 in HDP found a late induction and a decreased final neutralizing antibody capacity [3, 7, 10]. Furthermore, Espi et al. [3] recently showed that although vaccination reduced the number of COVID-19 infection events, the incidence of overt infection among two-dose vaccinated HDP is higher than what has been reported in the general population. And moreover, despite an increased proportion of mild and moderate forms of COVID-19 in vaccinated patients, more than 10% of HDP that received two doses of the mRNA vaccine subsequently died from COVID-19 infection [3]. Therefore, the known evidence of impaired immune response among HDP coupled with the high mortality rates from COVID-19 infection supports the need to study an intensification of the standard two-dose scheme with BNT162b2 vaccine in these patients.
In July 2021, the administration of a third (booster) dose of BNT162b2 vaccine was approved in Israel for very high-risk population such as severely immunocompromized and solid organ transplant recipients. One month later, the third dose was offered to the whole population, at least 5 months after the second dose [11].
In this current study, we aimed to evaluate humoral response among HDP after a late third dose of the BNT162b2 vaccine dose, given to patients at least 6 months following the second vaccine dose. We also tried to identify predictors of a positive antibody response.
Materials and Methods
This is a prospective comparative study conducted in continuation with our previous study, evaluating humoral response following a two-dose schedule of BNT162b2 vaccine among dialysis patients [12]. In the current HDP cohort, we included hemodialysis adult patients (age >18 years) who received all vaccine doses after chronic dialysis treatment initiation and participated in the previous study. The 80 HDP who were included in the current final cohort were compared with 56 healthcare workers (HCW) controls. Patients who had documented infection with COVID-19 at any time were excluded from the study. All the participants received a third BNT162b2 vaccine dose (V3) from Pfizer-BioNTech of 30 μg (0.3 mL) according to the Pfizer and the Israeli Ministry of Health recommendation for the entire adult population, at least 6 months after the second dose (V2).
Patients and control groups were followed for 8 weeks following vaccine administration, for breakthrough SARS-CoV-2 infection events. Clinical, demographic, and laboratory data were obtained by questioning and electronic medical records of both HDP and control groups. Blood samples for anti-SARS-COV-2 IgG levels were taken during the routine hemodialysis treatments, approximately 3 weeks before and 3 weeks after V3. Blood samples for anti-SARS-COV-2 IgG were collected before the commencement of a hemodialysis session. Blood samples from the control group were collected in the outpatient clinic during the same time period, approximately 3 weeks before and 3 weeks after V3, SARS-CoV-2 IgG II Quant (Abbott©) assay was used for quantitative measurement of IgG antibodies against the receptor-binding domain of the spike protein (anti-S1-RBD IgG). The cutoff for positivity is 50 arbitrary units per ml (AU/mL). An anti-S1-RBD IgG concentration of 1,050 AU/mL, 3,550 AU/mL, 4,160 AU/mL, and 6,950 AU/mL corresponds to a 95% probability of being at or above a plaque reduction neutralization test with 50% inhibition of infection of cultured cells (PRNT50) dilution of 1:80, 1:160, 1:250, and 1:640, respectively [13]. On the basis of the results of the World Health Organization International Standard study [14], the mathematical relationship of the Abbott unit (AU/mL) to the World Health Organization binding antibody units follows the equation: binding antibody units/mL = 0.142× AU/mL [13].
The primary outcomes were the seropositivity rate and the log-transformed anti-S1-RBD antibodies as a continuous variable following the third vaccine dose. Secondary outcomes were the proportion of “high response” above >1,000 AU/mL and “robust response” >4,160 AU/mL.
Statistical Analyses
Categorical variables are presented as numbers (percentages) and continuous variables as median (interquartile range, IQR) or mean (SD), according to their distribution. The former is compared using the χ2 test or Fisher's exact test and the latter using t test or Mann Whitney U test, as appropriate.
Univariate and multivariate logistic regression models were used for evaluation of predictors for response. All variables were introduced into multivariate analysis after testing for collinearity, using forward regression model with p value below 0.05 used for inclusion. Linear regression analyses were performed to explore factors associated with higher log-transformed antibody titer among HDP.
General linear model was used for comparison of the log-transformed antibodies level between the HDP and HCW groups. Age, gender, body mass index (BMI), and diabetes status were introduced into the fixed-effect model as covariates. Estimated marginal mean adjusted to the above variables was calculated to evaluate the adjusted difference of the log antibodies level with 95% confidence interval (95% CI). Analyses were performed using IBM SPSS statistics, version 27.
Results
Participants' Characteristics
Of the 122 HDP in the original cohort (12), 80 (66%) had a baseline anti-S1-RBD-IgG test collected before the third vaccine dose (V3) and together with 56 HCW controls were included in this current study (Fig. 1). The mean age in the HDP group was 72.6 years (SD 11.7), as compared to 69.3 years (SD 5.32) in the HCW group. Diabetes mellitus (58.8%, 47/80) and male sex (70%, 56/80) were more common among HDP than HCW (12.5%, 7/56; 52%, 29/56, respectively). BMI was comparable between the groups (26.92 ± 4.98 vs. 26.63 ± 3.43) (Table 1). Six HDP, under chronic immunosuppression treatment, were included in the current study (Fig. 1).
Seropositivity Rate and S1-RBD Antibody Response to a Third Dose of BNT162b2 Vaccine
The seropositivity rate (>50 AU/mL) in the HDP group substantially improved after V3 from 77.5% (62/80) to 96.3% (77/80). Only three HDP (3/80) remained seronegative following V3. The anti-S1-RBD-IgG titer increased significantly following V3 from a median level of 153 [IQR 56–409] AU/mL to 15,529 [IQR 5,634–39,314] AU/mL. The log-transformed antibody level increased from 2.15 ± 0.75 log AU/mL to 3.99 ± 0.82 log AU/mL, while the age-adjusted log-transformed antibody level increased from 2.16 (95% CI 2.01–2.36) to 4.01 (95% CI 3.85–4.16).
In the HCW group, the baseline seropositive rate before V3 was high: 98.2% (55/56). The only patient who was seronegative seroconverted following V3. The baseline anti-S1-RBD-IgG level in the HCW group was significantly higher than in the HDP group (median 514 [IQR 259–857] AU/mL) (p < 0.001). After V3, a substantial increase in the antibody titer to 23,800 [IQR 13,343–41,511] AU/mL was documented. The log-transformed antibody level increased from 2.65 ± 0.4 log AU/mL to 4.31 ± 0.41 log AU/mL, and the age-adjusted log-transformed antibody increased from 2.63 (95% CI 2.43–2.83) to 4.29 (95% CI 4.1–4.48).
Despite a substantial improvement in the anti-S1-RBD-IgG level in both HDP and HCW, the mean difference between the two groups decreased following V3 (mean difference 0.5, [95% CI 0.3–0.7] vs. 0.3 [95% CI 0.1–0.5], before and after V3, respectively, p = 0.045). Among the 6 HDP patients under chronic immunosuppression, only 3 (50%) patients were seropositive pre-V3. Following the third dose, 1 patient became seropositive, while the 2 other patients remained seronegative. The median anti-S1-RBD-IgG titer among the HDP under chronic immunosuppression treatment before V3 was 84.56 AU/mL (range: 0 to 724 AU/mL) and increased to 10,686 AU/mL (range: 1.6 to 18,360 AU/mL) post-V3.
Using two reported prespecified cutoff values, >1,000 AU/mL and >4,160 AU/mL, which were previously shown to be correlated with neutralizing antibodies (3, 13, 15–17), a vigorous response was seen following V3. Before V3, only 7.5% (6/80) in the HDP group and 17.9% (10/56) in the HCW group had a “high” anti-S1-RBD-IgG level above 1,000 AU/mL. After the third dose, in the HDP group, 87.5% (70/80) became “high responders” (>1,000 AU/mL) while 78.8% (63/80) mounted a “robust response” (>4,160 AU/mL). In the HCW group, 96.4% (54/56) became “high responders” (>1,000 AU/mL) and 92.9% (52/56) became “robust responders” (>4,160 AU) (Table 1; Fig. 2).
Predictors of S1-RBD Antibody Response to the Third Dose of BNT162b2 Vaccine in HDP
To evaluate general predictors of antibody response among all study participants (HDP and HCW), we used linear regression analysis and evaluated the log-transformed antibody level as a continuous variable by univariate analysis. The only factors that were significantly associated with the log-transformed antibody level after V3 were the need for dialysis therapy (B; −0.16, 95% CI −0.04 to −0.28, p = 0.009) and baseline antibody level before V3 (B; 0.75, 95% CI 0.63–0.87, p < 0.001). We did not find any significant relation between age, gender, BMI or diabetes status, and antibody response (online suppl. Table S1; see www.karger.com/doi/10.1159/000525519 for all online suppl. material).
Univariate and multivariate analysis for specific predictors among the HDP group alone demonstrated that the variables associated with the log-transformed antibody level after V3 included hypoalbuminemia (B; −0.5, 95% CI −0.95 to −0.05, p = 0.03) and log-transformed antibody level pre-V3 (B; 0.71, 95% CI 0.53–0.89, p < 0.001). Chronic immunosuppression was associated with poor response to V3, in the univariate analysis only (B; −1.01 per year, 95% CI −1.63 to −0.4, p = 0.002). However, this association was not significant in the multivariate analysis (B; −0.38 per year, 95% CI −0.84 to 0.08, p = 0.105) (Table 2). Notably, antibody response following V3 in the HDP group was well correlated not only with the baseline antibody level before V3 (6 months following V2) but also with the antibody level at 1 month following V2 (Pearson correlation 0.718, p < 0.001 and 0.828, p < 0.001, respectively).
Tolerance and Outcome
Overall tolerance to the third dose of BNT162b2 mRNA vaccine was excellent among all the study participants in both groups. No participant developed severe side effects requiring hospitalization. Only three cases of COVID-19 infection among HDP and no case among the HCW group were diagnosed during a follow-up period of up to 8 weeks following V3. Two cases were mild with favorable outcomes. The third case was defined as mod-severe, with complete recovery.
Discussion
In this study including 80 dialysis patients, an excellent antibody response to the third dose of BNT162b2 vaccine was found. We documented a high seropositivity rate of 96%, which increased from 77.5% before V3. The S1-RBD antibody level following V3 increased significantly in HDP and age-matched control groups, while the mean difference between the two groups decreased. Furthermore, according to prespecified cutoff values which were previously reported to be associated with neutralizing capacity of the serum, 87.5% (70/80) in the HDP group became “high responders” (anti-S1-RBD-IgG >1,000 AU/mL), and 78.8% (63/80) became “robust responders” (anti-S1-RBD-IgG >4,160 AU/mL). Baseline antibody level, the need for dialysis therapy, and hypoalbuminemia were independent predictors of inferior antibody response to a late third dose of BNT162b2 vaccine.
A large 6-month prospective study involving vaccinated HCW demonstrated a significant waning of anti-spike IgG and neutralizing antibodies over a 6-month period following two doses of BNT162b2 vaccine [15]. Our study has also documented decreased anti-S1-RBD-IgG titer 6 months following the standard two-dose vaccine regimen in both groups of HCW and HDP. However, the pre-V3 median antibody titer of the HDP group was substantially lower as compared with the median antibody titer in the HCW group.
In an aim to extend the protection achieved by the vaccine among high-risk populations, intensification of vaccine schedule has been suggested. Several studies that evaluated the efficacy of an early third dose (1–2 months after the second dose) among HDP reported an increase in the antibody level [16, 17]. Notably, the elevation in the antibody response following V3 was more substantial in patients with low antibody levels after the second dose [3, 18, 19] and with a longer interval between V2 and V3 [19].
Nevertheless, Payne et al. found that SARS-COV-2-neutralizing antibody titers were higher after an extended dosing interval (6–14 weeks), compared to the conventional 3–4-week regimens for the second vaccine dose of BNT162b2 [20]. This finding rationalizes extending the interval between the second and third doses to maximize booster benefit. Indeed, despite the considerable waning of anti-S1-RBD-IgG 6 months following V2, this study documented a vigorous response with high median antibody titers following V3. Although yet to be established, a high correlation between the increased level of anti-S1-RBD-IgG and neutralizing capacity of the serum has been demonstrated [21]. Using two different incremental prespecified cutoff values, previously shown to correlate with virus neutralization [3, 13, 22, 23, 24], this study found that 80% of HDP became “high responders” (>1,000 AU/mL), while 76% became “robust responders” (>4,160 AU/mL) following the late third dose. Furthermore, in contrast to the efficacy of the early third dose that seems to be limited to specific HDP subgroups, we documented favorable results in the entire HDP cohort, while the best predictor of antibody response was an elevated baseline antibody titer. Either antibody response 1 month or 6 months following V2 was highly positively correlated to antibody response following the late booster. Therefore, to take full advantage of the booster, our findings support postponing the third dose and providing it after antibody waning and protection diminution to all HDP, without the need for unnecessary screening tests.
The need for dialysis treatment and hypoalbuminemia (serum albumin <3.5 g/dL) were independent predictors associated with inferior antibody response following V3 in HDP. The uremic state and hypoalbuminemia are established as being associated not only with poor nutritional status but also with hypercatabolic state and chronic inflammation. Both poor nutritional status and chronic inflammatory state can negatively influence adequate humoral and cellular immunity. Accumulation of uremic toxins and chronic inflammation might contribute to impaired humoral response in the uremic state. Nonetheless, the malnutrition inflammation complex syndrome may also cause, impaired immunity and high mortality among dialysis patients [2, 4, 25]. This finding is consistent with our first report about the association between hypoalbuminemia below 3.5 g/dL and inferior antibody response to two doses of BNT162b2 among HDP [12]. In agreement, several other studies identified hypoalbuminemia in HDP as a major predictor of impaired response to the COVID-19 vaccine that may be modified before the vaccination protocol [23, 26, 27].
Chronic immunosuppression was associated with a reduced response to V3 in the univariate analysis but not in the multivariate analysis. These unequivocal results are probably due to the small size of the particular group that was analyzed in this study. Immunosuppression is a well-known predictor for weak immunogenicity of mRNA vaccine, in either solid organ transplant recipients or immune-mediated inflammatory diseases [23, 28]. A strategy of transient diminution of the immunosuppressive protocol prior to and shortly after the vaccine has been suggested [29].
Both age and diabetes are considered risk factors for poor response to vaccination. However, these correlations were inconsistent in previous studies [9]. Although in our previous report, age was found to be an independent predictor of antibody response post-V2, in this study, no inverse linear association was detected between age or diabetes and immunogenicity rates following V3 [12]. Intriguingly, Van Praet et al. [23] reported that increased age was an independent predictor of the early immune response but not for the long-term immune response to SARS-CoV-2 mRNA vaccine.
Patient tolerance to the third vaccine dose was excellent, without any major adverse reactions. Only three cases of COVID-19 infection among HDP and no case among HCW group were diagnosed during a follow-up period of up to 8 weeks following V3. Two cases were defined as mild or asymptomatic with favorable outcomes. The third patient had a mod-severe disease, with complete recovery following treatment. Of note, this patient got ill before the antibodies sampling post-V3 was taken, and the case was not included in this study. However, this patient was negative for S1-RBD antibodies post-V2 as well as pre-V3.
This study has several limitations. It was conducted as a single-center study with a small-sized group, which limited statistical analyses and could induce bias. We assessed for anti-S1-RBD-IgG response only. We did not assess for anti-N antibodies to recognize undocumented infections. However, routine PCR surveys have been frequently conducted in our dialysis units during the study period. Therefore, most of the infections including the asymptomatic events were documented. Furthermore, neither cellular response nor neutralizing antibodies were directly assessed. Although the neutralizing antibodies level is a more precise predictor of immune protection, their assessment is not practical in clinical practice. Therefore, we used two incremental prespecified cutoff values of 1,000 AU/mL and 4,160 AU/mL of S1-RBD antibodies as a surrogate, as previously described. Both Espi et al. [3] and Robert et al. [16] found a substantial increase of neutralizing antibodies following the third dose with a high correlation to the S1-RBD-IgG response among HDP. Nevertheless, the third dose of vaccine did not result in a significant increase in the cellular response [3].
In summary, given the increased risk for severe COVID-19 disease, together with the impaired immunogenicity of the vaccine in these high-risk patients, there is an urgent need to extend the duration of vaccine efficacy among patients on hemodialysis. A late third dose of BNT162b2, 6 months after the second vaccine dose, improved S1-RBD antibody response over two doses in HDP. Despite a significant antibody waning, the seropositivity rate in the HDP group substantially increased, and the antibody titers robustly boosted close to the control group. These findings strongly support the importance of standard vaccine regimen intensification with repetitive booster doses in HDP.
Statement of Ethics
This study protocol was reviewed and approved by the local Ethics Committee of the Rabin Medical Center, Israel, approval number 0096-21-RMC. A written informed consent was obtained from all participants to participate in the study.
Conflict of Interest Statement
The authors of this manuscript have no conflicts of interest to disclose. The results presented in this article have not been published previously in whole or part.
Funding Sources
No external funding.
Author Contributions
All of the authors contributed significantly to this work and approve this manuscript submission. Timna Agur, Michal Herman-Edelstein, Benaya Rozen-Zvi, and Boris Zingerman − research design, data collection and analysis, and writing the paper. Naomi Ben-Dor, Weaam Alkeesh, Tali Steinmetz, and Ruth Rachamimov − data collection and analysis. Asher Korzets − critical review of the paper.
Data Availability Statement
The original data from this study may be available upon reasonable request to the corresponding author.
Acknowledgments
The authors wish to thank Eti Gibly, Michal Nachtomy-Disatnik, and Ehud Ben-Dor for their invaluable help in the collection of the patient data and samples.
Fig. 1 Flowchart of the study.
Fig. 2 Antibody response before and after a third dose of BNT162b2 vaccine. The anti-S1-RBD IgG level before and after a third vaccine dose in HDP and healthy control. Results refer to the HDP group (n= 80) and HCW group (n= 56).
Table 1 Baseline characteristic and antibody response to a third dose of BNT162b2 vaccine in HD patients compared with controls
All participants (136) HDP (80) HCW (56) p value
Age (year) ±SD 71.25±9.77 72.61±11.78 69.3±5.32 0.052
Female (%) 51 (37.5%) 24 (30%) 27 (48.2%) <0.001
Diabetes mellitus (%) 54 (39.7%) 47 (58.8%) 7 (12.5%) <0.001
BMI (per kg/m2) ±SD 26.8±4.42 26.92±4.98 26.63±3.43 0.712
Baseline Ab level before V3 (AU/mL, median [IQR]) 310 [95–694] 153 [56–409] 514 [259–857] <0.001
Baseline log Ab level (log AU/mL, mean ± SD) 2.35±0.68 2.15±0.75 2.65±0.4 <0.001
Baseline age-adjusted log Ab level (95% CI) – 2.16 (2.01–2.36) 2.63 (2.43–2.83) 0.001
Baseline seropositive – Ab>50 AU/mL 117 (86%) 62 (77.5%) 55 (98.2%) <0.001
Baseline high responders – Ab >1,000 AU/mL 16 (11.8%) 6 (7.5%) 10 (17.9%) 0.102
Baseline robust responders – Ab >4,160 AU/mL 2 (1.5%) 2 (2.5%) 0.00 0.512
Ab level after V3 (AU/mL, median [IQR]) 18,245 (9,091–39,592) 15,529 (5,634–39,314) 23,800 (13,343–41,511) 0.037
Log Ab level±SD (log AU/mL, mean ± SD) 4.12±0.7 3.99±0.83 4.31±0.42 0.009
Age-adjusted log Ab level (95% CI) – 4.01 (3.85–4.16) 4.29 (4.1–4.48) 0.024
Seropositive – Ab >50 AU/mL 133 (97.8%) 77 (96.3%) 56 (100%) 0.143
High responders – Ab >1,000 AU/mL 124 (91.1%) 70 (87.5%) 54 (96.4%) 0.071
Robust responders – Ab >4,160 AU/mL 115 (84.6%) 63 (78.8%) 52 (92.9%) 0.025
Baseline characteristic and antibody response to a third dose of BNT162b2 vaccine in HDP compared with healthy controls. Antibody response – anti-SI-RBD IgG levels in AU/mL. V3, third dose of BNT162b2 vaccine; HDP, hemodialysis patients; HCW, healthcare workers.
Table 2 Multivariate analysis for predictors of S1-RBD antibody levels in response to a third dose of BNT162b2 in HD patients
Variable HD patients, N = 80 Univariate Multivariate
B (95% CI) p value B (95% CI) p value
Age (per year) 72.61±11.8 –0.01 (–0.03 to 0.0) 0.156 – –
Female sex 24 (30%) 0.11 (–0.29 to 0.52) 0.574 – –
Dialysis vintage (per month) 41.27±34 –0.003 (0.002–0.008) 0.470 – –
Diabetes mellitus 47 (59%) –0.03 (–0.41 to 0.35) 0.876 – –
IHD 40 (50%) –0.053 (–0.42 to 0.32) 0.777 – –
Malignancy Hx 19 (24%) –0.23 (–0.67 to 0.2) 0.281 – –
Chronic IS 7 (9%) –1.01 (–1.63 to –0.4) 0.002 –0.38 (–0.84 to 0.08) 0.105
Dialysis access (catheter) 34 (43%) 0.12 (–0.25 to 0.5) 0.512 – –
KT/V 1.44±0.26 0.069 (–0.64 to 0.78) 0.848 – –
nPCR 1.14±0.29 0.11 (–0.35 to 0.57) 0.630 – –
Residual renal function 36 (45%) –0.07 (–0.44 to 0.31) 0.724 – –
BMI (per kg/m2) 26.9±5 0.02 (–0.02 to 0.06) 0.271 – –
Log Ab before V3 2.15±0.75 0.8 (0.63–0.97) <0.001 0.71 (0.53–0.89) <0.001
Hemoglobin (per g/dL) 10.68±1.18 –0.02 (–0.17 to 0.14) 0.842 – –
Albumin <3.5 g/dL) 7 (9%) –0.95 (–1.57 to –0.33) 0.003 –0.5 (–0.95 to –0.05) 0.030
Multivariate analysis for predictors of S1-IgG antibody levels in response to a third dose of BNT162b2 vaccine in HDP. Multivariate analysis of factors associated with the serologic response (log-transformed anti-SI-RBD IgG levels in AU/mL) in HDP vaccinated with a third dose (V3) of BNT162b2 vaccine. B >0 indicates a positive correlation with the log antibody titer. For linear regression, all variables with p < 0.05 in univariate association were inserted. IHD, ischemic heart disease; IS, immunosuppression; BMI, body mass index; nPCR, normalized protein catabolic rate; ESA, erythropoietin stimulating agents.
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References
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| 35896080 | PMC9747736 | NO-CC CODE | 2022-12-15 23:22:04 | no | Nephron Clin Pract. 2022 Jul 27;:1-8 | utf-8 | Nephron | 2,022 | 10.1159/000525519 | oa_other |
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Research Article
Evaluation of Outcomes of Peritoneal Dialysis Patients in the Post-COVID-19 Period: A National Multicenter Case-Control Study from Turkey
Ozturk Savas a
Gursu Meltem b
Arici Mustafa c *
Sahin Idris d **
Eren Necmi e ***
Yilmaz Murvet f
Koyuncu Sumeyra g
Karahisar Sirali Semahat h
Ural Zeynep i
Dursun Belda j
Yuksel Enver k
Uzun Sami l
Sipahi Savaş m
Ahbap Elbis n
Yazici Halil a
Altunoren Orcun o
Tunca Onur p
Ayar Yavuz q
Gok Oguz Ebru r
Yilmaz Zulfukar s
Kahvecioglu Serdar t
Asicioglu Ebru u
Oruc Aysegul v
Ataman Rezzan w
Aydin Zeki x
Huddam Bulent y
Dolarslan Murside Esra z
Azak Alper A
Bakırdogen Serkan B
Yalcin Ahmet Uğur C
Karadag Serhat l
Ulu Memnune Sena D
Gungor Ozkan o
Ari Bakir Elif E
Odabas Ali Rıza F
Seyahi Nurhan w
Yildiz Alaattin a
Ates Kenan G
aDivision of Nephrology, Department of Internal Medicine, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
ADivision of Nephrology, Department of Internal Medicine, Balikesir Atatürk Education and Research Hospital, Balikesir, Turkey
bDivision of Nephrology, Department of Internal Medicine, Faculty of Medicine, Bezmialem Vakıf University, Istanbul, Turkey
BDivision of Nephrology, Department of Internal Medicine, Faculty of Medicine, Canakkale Onsekiz Mart University, Canakkale, Turkey
cDivision of Nephrology, Department of Internal Medicine, Faculty of Medicine, Hacettepe University, Istanbul, Turkey
CDivision of Nephrology, Department of Internal Medicine, Faculty of Medicine, Eskisehir Osmangazi University, Eskisehir, Turkey
dDivision of Nephrology, Department of Internal Medicine, Faculty of Medicine, Malatya Inonu University, Malatya, Turkey
DDivision of Nephrology, Department of Internal Medicine, Faculty of Medicine, Bahçeşehir University, Istanbul, Turkey
eDivision of Nephrology, Department of Internal Medicine, Faculty of Medicine, Kocaeli University, Kocaeli, Turkey
EDivision of Nephrology, Department of Internal Medicine, Dr. Lutfi Kirdar City Hospital, University of Health Sciences, Istanbul, Turkey
fDivision of Nephrology, Department of Internal Medicine, Bakırkoy Dr. Sadi Konuk Training and Research Hospital, University of Health Sciences, Istanbul, Turkey
FDivision of Nephrology, Department of Internal Medicine, Goztepe Prof. Dr. Suleyman Yalcin City Hospital, Istanbul Medeniyet University, Istanbul, Turkey
gDivision of Nephrology, Department of Internal Medicine, Faculty of Medicine, Erciyes University, Kayseri, Turkey
GDivision of Nephrology, Department of Internal Medicine, Faculty of Medicine, Ankara University, Ankara, Turkey
hDivision of Nephrology, Department of Internal Medicine, Ankara Training and Research Hospital, University of Health Sciences, Istanbul, Turkey
iDivision of Nephrology, Department of Internal Medicine, Ankara Faculty of Medicine, Gazi University, Ankara, Turkey
jDivision of Nephrology, Department of Internal Medicine, Faculty of Medicine, Pamukkale University, Denizli, Turkey
kDivision of Nephrology, Department of Internal Medicine, Diyarbakir Gazi Yasargil Training and Research Hospital, University of Health Sciences, Diyarbakir, Turkey
lDivision of Nephrology, Department of Internal Medicine, Haseki Training and Research Hospital, University of Health Sciences, Istanbul, Turkey
mDivision of Nephrology, Department of Internal Medicine, Sakarya University Medical Faculty Education and Research Hospital, Sakarya, Turkey
nDivision of Nephrology, Department of Internal Medicine, Sisli Hamidiye Etfal Training and Research Hospital, University of Health Sciences, Istanbul, Turkey
oDivision of Nephrology, Department of Internal Medicine, Faculty of Medicine, Kahramanmaras Sutcu Imam University, Kahramanmaras, Turkey
pDivision of Nephrology, Department of Internal Medicine, Faculty of Medicine, Afyonkarahisar Health Sciences University, Afyonkarahisar, Turkey
qDivision of Nephrology, Department of Internal Medicine, Bursa City Hospital, Bursa Faculty of Medicine, University of Health Sciences, Bursa, Turkey
rDivision of Nephrology, Department of Internal Medicine, Diskapi Yildirim Beyazit Education and Research Hospital, University of Health Sciences, Ankara, Turkey
sDivision of Nephrology, Department of Internal Medicine, Faculty of Medicine, Diyarbakir Dicle University, Diyarbakir, Turkey
tDivision of Nephrology, Department of Internal Medicine, Bursa Yuksek Ihtisas Training and Research Hospital, University of Health Sciences, Bursa, Turkey
uDivision of Nephrology, Department of Internal Medicine, Pendik Training and Research Hospital, Faculty of Medicine, Marmara University, Istanbul, Turkey
vDivision of Nephrology, Department of Internal Medicine, Faculty of Medicine, Bursa Uludag University, Bursa, Turkey
wDivision of Nephrology, Department of Internal Medicine, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey
xDivision of Nephrology, Department of Internal Medicine, Darica Training and Research Hospital, University of Health Sciences, Kocaeli, Turkey
yDivision of Nephrology, Department of Internal Medicine, Faculty of Medicine, Mugla Sitki Kocman University, Mugla, Turkey
zDivision of Nephrology, Department of Internal Medicine, Trabzon Kanuni Training and Research Hospital, University of Health Sciences, Trabzon, Turkey
*Mustafa Arici, [email protected]
**Idris Sahin, [email protected]
***Necmi Eren, [email protected]
30 9 2022
30 9 2022
19
11 1 2022
14 7 2022
Copyright © 2022 by S. Karger AG, Basel
2022
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Introduction
There are not enough data on the post-COVID-19 period for peritoneal dialysis (PD) patients affected from COVID-19. We aimed to compare the clinical and laboratory data of PD patients after COVID-19 with a control PD group.
Methods
This study, supported by the Turkish Society of Nephrology, is a national, multicenter retrospective case-control study involving adult PD patients with confirmed COVID-19, using data collected from April 21, 2021, to June 11, 2021. A control PD group was also formed from each PD unit, from patients with similar characteristics but without COVID-19. Patients in the active period of COVID-19 were not included. Data at the end of the first month and within the first 90 days, as well as other outcomes, including mortality, were investigated.
Results
A total of 223 patients (COVID-19 group: 113, control group: 110) from 27 centers were included. The duration of PD in both groups was similar (median [IQR]: 3.0 [1.88–6.0] years and 3.0 [2.0–5.6]), but the patient age in the COVID-19 group was lower than that in the control group (50 [IQR: 40–57] years and 56 [IQR: 46–64] years, p < 0.001). PD characteristics and baseline laboratory data were similar in both groups, except serum albumin and hemoglobin levels on day 28, which were significantly lower in the COVID-19 group. In the COVID-19 group, respiratory symptoms, rehospitalization, lower respiratory tract infection, change in PD modality, UF failure, and hypervolemia were significantly higher on the 28th day. There was no significant difference in laboratory parameters at day 90. Only 1 (0.9%) patient in the COVID-19 group died within 90 days. There was no death in the control group. Respiratory symptoms, malnutrition, and hypervolemia were significantly higher at day 90 in the COVID-19 group.
Conclusion
Mortality in the first 90 days after COVID-19 in PD patients with COVID-19 was not different from the control PD group. However, some patients continued to experience significant problems, especially respiratory system symptoms, malnutrition, and hypervolemia.
Key Words
COVID-19
Peritoneal dialysis
Outcome
Complication
There were no funding sources for this work.
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pmcIntroduction
The presence of comorbidities, including chronic kidney disease, has been reported to be a risk factor for short-term adverse outcomes, such as hospitalization, need for intensive care support, and mortality during the COVID-19 [1, 2, 3, 4]. Among chronic kidney disease patient groups, these outcomes are more pronounced in patients with end-stage kidney disease. However, almost all studies on this subject are related to hemodialysis (HD) patients and included early results (the active period of COVID-19). The European Renal Association COVID-19 Database (ERACODA) showed that the 28-day probability of death in dialysis patients was 25.0% (95% CI: 20.2–30.0%) [5]. In this study, 125 of 4,298 patients were on peritoneal dialysis (PD), and the 28-day probability of death was also 25.0%. Data from New York, USA, showed 28% in-hospital mortality among HD patients [5]. Our group showed that maintenance HD is an independent risk factor for intensive-care-unit (ICU) admission and in-hospital mortality [2, 6]. However, PD had become a priority since it is an individual treatment at home during the pandemic period and the risk of COVID-19 transmission was lower [7, 8]. In a study that includes historical trends from Medicare and Medicaid data, the initialization rate of PD (against HD) was 24% higher during the pandemic [9]. In a study comparing outcomes of hospitalized PD, HD, and non-uremic control COVID-19 patients, we showed that in-hospital mortality of PD and HD was not significantly different [10].
Nevertheless, there are limited studies on PD patients regarding the COVID-19 outcomes. In a multicenter study by Jiang et al. [11] from Wuhan, China, 8 of 818 patients on PD reported being diagnosed with COVID-19 from January 1, 2020, to April 12, 2020. The incidence rate of symptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in this study was 2.44 per 1,000 person-months. In another study from New York, United States, only 2 of 59 patients were on PD [12]. On the other hand, there are not enough published data on the outcomes at the follow-up of PD patients in the post-COVID-19 period. Herein, we aimed to present the outcomes data, including symptoms, rehospitalization, and mortality obtained in the follow-up of PD patients in the immediate post-COVID-19 period for 90 days, and compare them with a control PD group.
Materials and Methods
This retrospective study followed the report Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) [13]. Ethics Committee of Health Sciences University Haseki Training and Research Hospital approved this study (#2020-256).
Population and Setting
We conducted a national multicentric retrospective case-control study including PD patients aged 18 years or older who survived after a confirmed COVID-19. The study was unconditionally supported by the Turkish Society of Nephrology. In addition, a control PD group was selected among patients who did not have COVID-19 in the same PD unit. While forming the control group, we tried to include, as far as possible, the next patient who was started PD treatment at the same unit at similar times and did not have COVID-19. We collected data through a Web-based database specifically designed for PD patients. This study was prepared from the data recorded in this database between April 21, 2021, and June 11, 2021. The same project is also collecting data regarding the active COVID-19 phase (the first month of COVID-19) among the PD patients, and another paper prepared from that data related to outcomes of the active COVID-19 patients was also submitted.
This study included patients who had confirmed SARS-CoV-2 infection based on positive reverse transcriptase-polymerase chain reaction (RT-PCR) testing of a nasopharyngeal swab from the main database. Patients in the active period of COVID-19 (SARS-CoV-2 RT-PCR still positive and/or still receiving antiviral treatment for COVID-19). SARS-CoV-2 RT-PCR-negative COVID-19 patients and the patients without 3rd-month outcome data were excluded.
Measurements and Definitions
Demographic data, comorbidities and medications, primary kidney diseases that cause end-stage kidney disease, duration of PD, type of PD modality, residual urine amount, weight, systolic and diastolic blood pressures, Kt/V, and daily ultrafiltration volumes were recorded. Data regarding basic laboratory tests (hemogram, serum creatinine, albumin, CRP, ferritin) at the last monthly routine check before the development of COVID-19 at that PD center were also collected. The same laboratory tests were obtained in the same monthly check for the control group. In addition, we obtained data regarding the presence of pneumonia in computerized chest tomography (CT) for the COVID-19 group and COVID-19 treatment, and if hospitalized, hospitalization in ICU, mechanical ventilation, and major treatments in ICU. We classified COVID-19 patients according to the clinical severity of the disease at presentation, in accordance with the Ministry of Health guidelines [14]: Patients with no symptoms and/or detected on screening were classified as asymptomatic disease. If the patient has complaints such as fever and cough but does not have concomitant shortness of breath (there may be an abnormal finding on CT), it was classified as mild disease. If shortness of breath (perhaps with other symptoms) required oxygen delivery presented, it was termed moderate to severe illness. The disease was defined as those with blood arterial oxygen saturation <90% or hemodynamic disorders requiring ICU follow-up despite oxygen support at admission.
Outcomes
We investigated mortality, rehospitalization, the persistence of respiratory symptoms associated with COVID-19, and the development of lower respiratory system infection or peritonitis within the first 30 days and 90 days after diagnosis of COVID-19. For the control group patients, the same endpoints were also questioned during the same period (30 days and 90 days) and compared with the COVID-19 group.
Statistical Analyses
Categorical variables were presented as numbers and percentages, and numeric variables were presented as median and interquartile ranges (25–75%) in descriptive statistics. We determined the variables‧ normality using visual methods (histograms and probability plots) and Kolmogorov-Smirnov tests. The χ2 test was used for two or multiple group comparisons of categorical variables, and the independent t test or Mann-Whitney U test was used as appropriate in comparing numerical variables. In the multiple group comparisons, we used the variance (ANOVA) test for numerical variables with normal distribution and the Kruskal-Wallis test for numerical variables that were not normally distributed. We used IBM SPSS Statistics for Windows, Version 26.0 (IBM Corp., Armonk, NY, USA) for statistical analyses. p < 0.05 was accepted as the level of significance.
Results
Participants, Demographics, and Baseline Characteristics
The main database had 316 patients from 27 centers in Turkey. We excluded RT-PCR-negatives (20 patients), the patients who died during the active phase of COVID-19 (26 patients), the patients that had no control group from the same center (8 patients), resubmissions (8 patients), the patients with missing main outcome data (14 patients), and the patients with active COVID-19 (17 patients). The remaining 223 patients (113 patients in the COVID-19 group, 110 patients in the control group) were included in this study. Table 1 shows the patients' baseline demographics, comorbidities, PD-related data, and laboratory tests. A comparative presentation of some additional demographic baseline laboratory and PD data and the results of the patients at the first and third months are presented in online supplementary Table 1 (for all online suppl. material, see www.karger.com/doi/10.1159/000526234). The median age of the COVID-19 group was lower than the control group (median age [IQR]: 50 [40–57] years and 56 [46–64], respectively, p < 0.001).
PD-Related Data of the Groups
The laboratory data of the COVID-19 group in the month before the COVID-19 development and the control group in the same month as the COVID-19 patient were almost similar (Table 1). The PD duration, weight, dialysate volume, UF volume, daily exchange number, weekly Kt/V, and daily residual urine volume of both groups were not significantly different. Only D/P creatinine at the 4th hour in the peritoneal equilibrium test was significantly higher in the COVID-19 group. None of the other baseline laboratory values were different between the groups.
The Data regarding COVID-19
Comparing demographics, comorbidities, laboratory and COVID-19 outcome data according to whether COVID-19 PD patients are outpatient or inpatient, age, albumin, ferritin, CRP, leukocyte, the rate of multiple bilateral lesions on CT, and the rate of clinically severe disease at admission were significantly higher in hospitalized patients than in outpatients (online suppl. Table 2). We compared the patients' outcomes according to the presence or absence of shortness of breath at the 3rd month; fibrinogen level, the presence of shortness of breath, rehospitalization for any reason, hypervolemia, and UF failure in the 1st month and the presence of hypervolemia and peritonitis the 3rd month were significantly higher in the shortness of breath positive group (online suppl. Table 3).
Outcomes at the 28th Day and between the 28th Day and 90th Day
Significant differences were observed among the groups in terms of the outcomes on the 28th day of the diagnosis of COVID-19 (Table 2; shown in Fig. 1). Among laboratory tests on the 28th day, serum albumin and hemoglobin levels were significantly lower in the COVID-19 group than in the control group. Respiratory symptoms, readmission to hospital for any reason, development of lower respiratory tract infection, change in the PD modality, UF failure, and hypervolemia were significantly higher in the COVID-19 group on the 28th day. Venous or arterial thromboembolic events were not diagnosed in any COVID-19 and control patients. There was no difference in peritonitis rates between the groups.
There was no significant difference in the PD and laboratory parameters assessed on the 90th day. Only 1 (0.9%) patient died from the COVID-19 group between the 28th and 90th days of the diagnosis of COVID-19, but there was no dead in the control group. Respiratory symptoms, malnutrition, and hypervolemia were significantly higher in the COVID-19 group than the control group at the 90th day.
When we compared the baseline laboratory and PD outcome data at the first and third months of the COVID-19 patients according to the presence of shortness of breath at the third month (online suppl. Table 3), shortness of breath, rehospitalization, hypervolemia, and UF failure within the first month and hypervolemia and peritonitis rate at the third month were significantly higher in patients with shortness of breath at the third month than patients without shortness of breath.
Discussion
In this multicenter retrospective study involving PD patients recovering from COVID-19 and a control group, we have found no significant 90th-day mortality difference between the groups. Although the mortality of PD patients with COVID-19 was significantly higher than the COVID-19 patients from the general population [15], as far as we know, no study has shown similar mortality after the acute phase of COVID-19 (post-COVID-19 period) than the control counterparts among PD cohort. Hence, our data clearly show no continued increased risk of mortality after COVID-19 in PD.
There were no published studies comparing ongoing COVID-19 symptoms in PD patients in the post-COVID period. We observed that complications such as ongoing respiratory symptoms, readmission to hospital for any reason, development of lower respiratory tract infection, change in the PD modality, UF failure, and hypervolemia were significantly higher in the COVID-19 group in the first month. Recovery time from COVID-19 is variable but usually takes 2 weeks, while those with severe illness can take months [16]. Although most of our patients recovered before the 28th day, respiratory symptoms persisted in 18.6% of patients in the first month and 10.6% in the third month. In our study, hypervolemia and UF failure were also reported more in the patients with persisting shortness of breath. It was not possible to distinguish whether the patients' dyspnea was from post-COVID-19 sequelae or hypervolemia due to UF failure; modality change rate was significantly higher in the first month and nonsignificantly higher in the third month. These may be due to changes in peritoneal transport function after COVID-19. There is no study investigating peritoneal transport changes after COVID-19, but there were data regarding the transfer of PD patients to HD during COVID-19. In their study, Jiang et al. [11], in which 8 PD patients were diagnosed with COVID-19, 2 patients died. Median Kt/V, amount of UF, and residual urine volume were found to be lower in these COVID-19 patients. Our data showed that all patients who still had shortness of breath at the 3rd month were in the COVID-19 group also shows the fact that these patients continue to have the risk of long-term respiratory failure symptoms. On the other hand, PD patients can sometimes switch to HD during the active period of COVID-19 [11]. However, according to our study, PD patients have not been transferred to HD after COVID-19 follow-ups. This indicates that the persisting respiratory symptoms or hypervolemia in the post-COVID period are not sufficient to cause a patient's renal replacement type change.
Rehospitalization rates within 28 days and between day 28 and day 90 were significantly higher in the COVID-19 group than in the control group. Rehospitalization rates of PD patients in the post-COVID-19 period have not been previously reported. However, similarly increased readmission or hospitalization rates were published in non-uremic populations [17, 18].
COVID-19 usually causes a hypercoagulable state [19, 20, 21]. In our study, there were no patients with venous or arterial thromboembolic events found in either the COVID-19 group or the control group. We were unable to identify any published studies of thrombotic events in PD patients who survived COVID-19. In a study including a median follow-up of 7 months in 185 HD patients, an increase in late thrombotic events in COVID-19 survivors was shown compared to the uninfected cohort (18.5% vs. 1.9%, p = 0.002) [22]. However, it may not be appropriate to compare PD patients with HD patients with continuous extracorporeal circulation and rapid fluid withdrawal.
This study has some limitations as it was retrospective, and the groups were not fully randomized. According to 2020 Turkish registry reports, there are 3,387 PD patients [23], and 223 patients registered in this study constitute only 6.6% of the PD patients. However, we collected patient data from different regions and included a control group from each center. Therefore, our results may be considered close to those encountered in real life. We also designed our study with simple randomization to avoid selection bias, making the results more valuable. But some patients who had this condition but discontinued PD before the study due to technical failure or transplantation may not have been included in the study. This may have influenced the selection of control patients. During the pandemic, some PD patients with COVID-19 came consecutively and were enrolled in the COVID-19 group, while sometimes, a candidate patient in the control group had exclusion criteria. For this reason, the numbers of the COVID-19 and control groups could not be precisely equal. In addition, we were unable to use multivariate analyzes of risk factors associated with deaths as mortality data from subjects enrolled in this study were very scarce.
In conclusion, the mortality rate in the first and third months after COVID-19 in PD patients with COVID-19 was not different from the control PD group. However, some of these patients continue to experience significant problems, especially respiratory system symptoms. Therefore, there is a need for studies with a more extended follow-up period and detailed investigations of the findings in PD patients recovering from COVID-19.
Statement of Ethics
Ethics Committee of Health Sciences University Haseki Training and Research Hospital approved this study (#2020-256).
Conflict of Interest Statement
The authors have no conflicts of interest to declare.
Funding Sources
There were no funding sources for this work.
Author Contributions
Conception/design: Savas Ozturk, Meltem Gursu, Mustafa Arici, and Kenan Ates. Data collection: Idris Sahin, Necmi Eren, Murvet Yilmaz, Sumeyra Koyuncu, Semahat Karahisar Sirali, Zeynep Ural, Belda Dursun, Enver Yuksel, Sami Uzun, Savaş Sipahi, Elbis Ahbap, Halil Yazici, Orcun Altunoren, Onur Tunca, Yavuz Ayar, Ebru Gok Oguz, Zulfukar Yilmaz, Serdar Kahvecioglu, Ebru Asicioglu, Aysegul Oruc, Rezzan Ataman, Zeki Aydin, Bulent Huddam, Murside Esra Dolarslan, Alper Azak, Serkan Bakırdogen, Ahmet Uğur Yalcin, Serhat Karadag, Memnune Sena Ulu, Ozkan Gungor, Elif Ari Bakir, Ali Rıza Odabas, Nurhan Seyahi, and Alaattin Yildiz. Analysis and interpretation of data: Savas Ozturk, Meltem Gursu, and Mustafa Arici. Drafting the article or revising it: Savas Ozturk, Meltem Gursu, and Mustafa Arici. Final approval of the version to be published: Savas Ozturk and Mustafa Arici.
Data Availability Statement
All data generated or analyzed during this study are included in this article and its online supplementary material. Further inquiries can be directed to the corresponding author.
Acknowledgments
We thank the Turkish Society of Nephrology for the organization.
Fig. 1 The clinical outcomes obtained at the first and third months compared by groups (*p< 0.05).
Table 1 Demographic and baseline laboratory data of patients, comorbidities, and primary kidney disease
COVID-19 group, N = 113 Control group, N = 110 p value
Demographic data
Age,* years, median (IQR) 50 (40–57) 56 (46–64) 0.039
Gender, female, n (%) 68/113 (60.2) 62/110 (56.4) 0.564
PD duration, years, median (IQR) 3.0 (1.88–6.0) 3.0 (2.0–5.6) 0.993
BMI, kg/m2, median (IQR) 25.1 (23.4–28.7) 25.7 (23.3–29.6) 0.375
Primary kidney disease, n (%)
Primary glomerulonephritis 15/113 (13.3) 18/110 (16.4) 0.842
Diabetic nephropathy 24/113 (21.2) 19/110 (17.3)
Hypertensive nephrosclerosis 29/113 (25.7) 31/110 (28.2)
ADPCKD 10/113 (8.8) 7/110 (6.4)
Other 35/113 (31.0) 35/110 (31.8)
Comorbidities, n (%)
Diabetes mellitus 25/113 (22.1) 23/110 (20.9) 0.825
Hypertension 88/113 (77.9) 93/110 (84.5) 0.203
COPD 3/113 (2.7) 1/110 (0.9) 0.326
Ischemic heart disease 16/113 (14.2) 16/110 (14.5) 0.934
Heart failure 10/113 (8.8) 10/110 (9.1) 0.950
Cerebrovascular disease 6/113 (5.3) 1/110 (0.9) 0.060
Medications, n (%)
ACE inhibitor* 20/113 (17.7) 34/110 (30.9) 0.021
Angiotensin receptor blocker 27/113 (23.9) 17/110 (15.5) 0.113
Calcium channel blocker 66/113 (58.4) 56/110 (50.9) 0.261
Beta-blocker 54/113 (47.8) 49/110 (44.5) 0.627
Other antihypertensives 25/113 (22.1) 28/110 (25.5) 0.559
Insulin 20/113 (17.7) 18/110 (16.4) 0.791
Oral antidiabetic agents 6/113 (5.3) 5/110 (4.5) 0.792
Statin 10/113 (8.8) 16/110 (14.5) 0.185
Antiaggregant* 40/113 (35.4) 16/110 (14.5) <0.001
Anticoagulant 8/113 (7.1) 12/110 (10.9) 0.317
Pre-COVID-19 data,1 median (IQR)
Systolic blood pressure,* mm Hg 130 (120–150) 140 (130–150) 0.151
Diastolic blood pressure, mm Hg 80 (74–90) 80 (80–90) 0.242
Average dialysate volume, L/day 8 (7.3–9.5) 8 (8–10) 0.314
Average UF, mL/day 1,100 (700–1,500) 1,000 (775–1,500) 0.604
Exchanges, n (pcs/day) 4 (4–4) 4 (4–5) 0.651
Weekly Kt/V (total) 2.12 (1.89–2.58) 2.09 (1.89–2.56) 0.748
Weekly Kt/V (dialysate) 1.8 (1.4–2) 1.79 (1.5–2) 0.560
Daily residual urine, mL/day 775 (200–1,150) 800 (200–1,275) 0.970
Creatinine, mg/dL 7.9 (6.3–10.1) 8.105 (6.32–9.78) 0.325
Parathormone, pg/mL 332 (212–637) 330 (192–523) 0.263
Albumin, g/dL 3.69 (3.3–4) 3.7 (3.4–3.9) 0.205
Ferritin, ng/mL 269 (150–505) 248 (123–500) 0.972
CRP, mg/L 4.5 (2–12.36) 4.0 (2–8.1) 0.404
Hemoglobin, g/dL 10.9 (9.7–12) 11.0 (9.9–12.1) 0.450
Leukocytes, /mm3 7,400 (5,780–8,800) 7,500 (5,800–9,040) 0.444
IQR, interquartile range; ADPCKD, autosomal dominant polycystic kidney disease; COPD, chronic obstructive pulmonary disease; ACE, angiotensin-converting enzyme; D/P, dialysate/plasma; AST, aspartate transaminase; ALT, alanine transaminase; LDH, lactate dehydrogenase; CRP, C-reactive protein.
* p < 0.05 1 All data were based on the month before the development of COVID-19, in the COVID-19 group, and the same month with the COVID-19 patient in the control group.
Table 2 Comparative presentation of patients’ baseline laboratory and PD data at the first and the third months and outcomes
COVID-19 group, N = 113 Control group, N = 110 p value
First-month data, median (IQR) Weight, kg 71 (61–76) 71 (60–83) 0.398
Systolic blood pressure, mm Hg 130 (120–145) 135 (120–150) 0.350
Diastolic blood pressure, mm Hg 80 (80–95) 80 (76–90) 0.675
Average dialysate volume, L/day 8 (8–9.5) 8 (8–10) 0.231
Average UF, mL/day 1,200 (700–1,500) 1,100 (700–1,600) 0.879
Number of exchanges, pcs/day 4 (4–5) 4 (4–5) 0.451
Daily residual urine, mL/day 750 (200–1,000) 800 (250–1,200) 0.975
Creatinine, mg/dL 7.5 (6.1–10.5) 8,2 (6.2–9,7) 0.762
Albumin,* g/dL 3.5 (3.2–3.9) 3.7 (3.5–3.9) 0.004
Ferritin, ng/mL 329 (161–638) 266 (133–478) 0.194
CRP, mg/L 6.0 (2.35–11.7) 4.4 (2.0–9.1) 0.300
Hemoglobin,* g/dL 10.1 (9.4–11.6) 11.0 (10.1–12.1) <0.001
Leukocytes, /mm3 7,040 (5,770–9,070) 7,580 (5,700–9,060) 0.264
First-month outcomes, n/N (%)
Respiratory symptoms* 21/113 (18.6) 0/110 (0) <0.001
Hospitalization for any reason* 18/113 (15.9) 1/110 (0.9) <0.001
Lower respiratory tract infection* 6/113 (5.3) 0/110 (0) 0.014
Venous or arterial thromboembolic event 0/113 (0) 0/110 (0) –
Malnutrition 4/113 (3.5) 1/110 (0.9) 0.185
Hypervolemia* 10/113 (8.8) 1/110 (0.9) 0.006
Peritonitis 3/113 (2.7) 2/110 (1.8) 0.673
UF problems* 5/113 (4.4) 0/110 (0) 0.026
Modality change* 5/113 (4.4) 0/110 (0) 0.026
Third-month data, median (IQR)
Weight, kg 71 (62–76) 72 (60–82,4) 0.721
Systolic blood pressure, mm Hg 135 (127–140) 130 (115–140) 0.765
Diastolic blood pressure, mm Hg 80 (80–95) 80 (76–90) 0.082
Average UF, mL/day 1,100 (700–1,500) 1,025 (700–1,500) 0.853
Number of exchanges, pcs/day 4 (4–5) 4 (4–5) 0.617
Daily residual urine, mL/day 750 (200–1,200) 800 (200–1,200) 0.693
Creatinine, mg/dL 8 (6–10.4) 7.9 (6.0–9.8) 0.615
Albumin, g/dL 3.6 (3.2–3.9) 3.7 (3.5–4.0) 0.064
Ferritin, ng/mL 240 (144–456) 246 (121–469) 0.876
CRP, mg/L 3.3 (2.0–9.7) 4.7 (2.7–13.0) 0.081
Hemoglobin, g/dL 10.8 (9.6–11.7) 11.0 (9.8–12.3) 0.144
Leukocytes, /mm3 7,240 (5,940–9,170) 7,390 (5,910–8,890) 0.296
Third-month outcomes, n/N (%)
Death 1/113 (0.9) 0/110 (0) 0.323
Respiratory symptoms* 12/113 (10.6) 0/110 (0) <0.001
Hospitalization for any reason 6/113 (5.3) 3/110 (2.7) 0.327
Lower respiratory tract infection 1/113 (0.9) 0/110 (0) 0.323
Venous or arterial thromboembolic event 0/113 (0) 0/110 (0) –
Malnutrition* 4/113 (3.5) 0/110 (0) 0.046
Hypervolemia* 9/113 (8.0) 2/110 (1.8) 0.034
Peritonitis 5/113 (4.4) 3/110 (2.7) 0.496
UF failure 7/113 (6.2) 2/110 (1.8) 0.097
Modality change 6/113 (5.3) 2/110 (1.8) 0.161
All data were obtained according to time the diagnosis of COVID-19 in the COVID-19 group and the same month with the COVID-19 patient in the control group. IQR, interquartile range; UF, ultrafiltration; CRP, C-reactive protein.
* p < 0.05.
==== Refs
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| 36183694 | PMC9747737 | NO-CC CODE | 2022-12-15 23:22:04 | no | Nephron Clin Pract. 2022 Sep 30;:1-9 | utf-8 | Nephron | 2,022 | 10.1159/000526234 | oa_other |
==== Front
Oncology
Oncology
OCL
Oncology
0030-2414
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S. Karger AG Allschwilerstrasse 10, P.O. Box · Postfach · Case postale, CH–4009, Basel, Switzerland · Schweiz · Suisse, Phone: +41 61 306 11 11, Fax: +41 61 306 12 34, [email protected]
36063800
10.1159/000525802
ocl-0001
Clinical Study
SARS-CoV-2 Virus in Cancer Patients: A New Unknown in an Unsolved Equation
Martín Margarita a *
Vallejo Carmen a
López-Campos Fernando a
Quereda Carmen b
Muñoz Teresa a
Sánchez-Conde Matilde b
Dominguez Jose Antonio a
Soriano Cruz c
Martín Mercedes a
Suárez-Carantoña Cecilia d
Muriel Alfonso e
Garrido Pilar f
Acero Julio g
Alvarez-Diaz Ana h
de la Pinta Carolina a
Martínez-García Laura i
Hernánz Raúl a
Fernández Eva a
Alarza Marina a
Hervás Asunción a
Sancho Sonsoles a
aRadiation Oncology Department, Ramón y Cajal University Hospital, IRYCIS, Madrid, Spain
bInfectious Diseases Department, Ramón y Cajal University Hospital, IRYCIS, Madrid, Spain
cIntensive Medicine Department, Ramón y Cajal University Hospital, IRYCIS, Madrid, Spain
dInternal Medicine Department, Ramón y Cajal University Hospital, IRYCIS, Madrid, Spain
eBiostatistics Clinic Unit, Ramón y Cajal University Hospital, IRYCIS, Madrid, Spain
fMédical Oncology Department, Ramón y Cajal University Hospital, IRYCIS, Madrid, Spain
gOral and Maxillofacial Surgery Department, Ramón y Cajal University Hospital, IRYCIS, Madrid, Spain
hPharmacy Department, Ramón y Cajal University Hospital, IRYCIS, Madrid, Spain
iMicrobiology Department, Ramón y Cajal University Hospital, IRYCIS, Madrid, Spain
*Margarita Martín, [email protected]
5 9 2022
5 9 2022
111
13 4 2022
7 5 2022
Copyright © 2022 by S. Karger AG, Basel
2022
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Introduction
Cancer patients are more susceptible to infections, and infection can be more severe than in patients without cancer diagnosis. We conducted this retrospective study in patients admitted for SARS-CoV-2 infection in order to find differences in inflammatory markers and mortality in cancer patients compared to others.
Methods
We reviewed the electronic records of patients admitted for SARS-CoV-2 infection confirmed by PCR from March to September 2020. Data on socio-demographics, comorbidities, inflammatory makers, and cancer-related features were analyzed.
Results
2,772 patients were admitted for SARS-CoV-2, to the Hospital Universitario Ramón y Cajal in Madrid during this period. Of these, 2,527 (91%) had no history of neoplastic disease, 164 (5.9%) patients had a prior history of cancer but were not undergoing oncological treatment at the time of infection, and 81 (2.9%) were in active treatment. Mortality in patients without a history of cancer was 19.5%, 28.6% for patients with a prior history of cancer, and 34% in patients with active cancer treatment. Patients in active oncology treatment with the highest mortality rate were those diagnosed with lung cancer (OR 5.6 95% CI: 2.2–14.1). In the multivariate study, active oncological treatment (OR 2.259 95% CI: 1.35–3.77) and chemotherapy treatment (OR 3.624 95% CI: 1.17–11.17), were statistically significant factors for the risk of death for the whole group and for the group with active oncological treatment, respectively.
Conclusion
Cancer patients on active systemic treatment have an increased risk of mortality after SARS-CoV-2 infection, especially with lung cancer or chemotherapy treatment.
Key Words
SARS-CoV-2
Cancer
Oncologic treatments
There was no funding support for this study.
==== Body
pmcIntroduction
The emergence of a new betacoronavirus called SARS-CoV-2 since December 2019 and its rapid spread, causing the COVID-19 pandemic, has resulted in more than 700,000 deaths in Europe [1]. In some individuals, the virus triggers an exaggerated immune response through the expression of proinflammatory factors, such as increased synthesis of interferon I and stimulation of signaling pathways that activate phagocytosis, dendritic cell maturation, and immune cell chemotaxis, which contribute to virus control but also to tissue damage especially at the pulmonary level, leading to severe respiratory distress [2]. Given the damage that the uncontrolled immune reaction causes in patients, immunosuppressive agents such as tocilizumab have been added to the therapeutic arsenal against SARS-CoV-2 infection. In cancer patients, the immunosuppressed state caused by the disease itself and cancer treatments makes them more susceptible to infection and the prognosis is worse. Paradoxically, cancer patients in active treatment may be protected from the uncontrolled immune reaction caused by SARS-CoV-2, which causes severe respiratory distress. Thus, although several retrospective studies have shown a worse prognosis in patients diagnosed with cancer under active cancer treatment [3, 4, 5, 6, 7, 8, 9], other studies find a similar mortality rate [10], or even a lower percentage of hospital admissions in patients treated with targeted therapies [11].
In order to minimize the risk of infection and its severity, during the first months of the pandemic when vaccines were not yet available, different strategies were proposed in cancer patients. These strategies included modifications of cancer treatments that could affect their effectiveness, such as delaying adjuvant treatments. Elucidating which cancer patients have the highest morbidity and mortality from SARS-CoV-2 infection is important because these measures could be applied more selectively in case of a return to situations like the first months of the pandemic with no vaccine available due to new variants or new viruses. In this study, we analyzed the inflammatory profile and evolution of SARS-CoV-2 infection in patients admitted while undergoing active cancer treatment or prior history of cancer without oncologic treatment at the time of infection, compared to patients without a pre-existing history of neoplastic disease.
Material and Methods
We retrospectively analyzed the cohort of patients admitted for SARS-CoV-2 infection at the Hospital Universitario Ramón y Cajal in Madrid, Spain, between March 2020 and September 2020. No vaccines were available at that time. Study data were collected and managed using Research Electronic Data Capture (REDCap) electronic data capture tools hosted at IRYCIS [12]. REDCap is a secure, web-based application designed to support data capture for research studies, providing (1) an intuitive interface for validated data entry; (2) audit trails for tracking data manipulation and export procedures; (3) automated export procedures for seamless data downloads to common statistical packages; and (4) procedures for importing data from external sources. We reviewed the electronic health records of patients with a history of solid neoplastic or hematological neoplastic disease. Only patients with a diagnosis of SARS-CoV-2 infection confirmed by PCR in nasopharyngeal exudate and with available data for status, current oncological treatment, and follow-up of the oncological disease were included. The cohort of patients was divided into three groups: no history of cancer disease, history of prior cancer without active treatment at the time of infection, and patients with active cancer treatment for neoplastic disease, in any setting, curative, adjuvant, or neoadjuvant and including systemic, surgical, or radiotherapy treatment. The values of lymphocytes, neutrophils, D-dimer, procalcitonin, and C-reactive protein are those obtained in the analysis of the day of admission or the following day when they were not requested in the emergency department. For the analysis of interleukins (ILs), ferritin, and lactate dehydrogenase (LDH), the first available determination during admission was used.
Statistical Study
Categorical variables are presented as absolute and relative frequencies and percentages. Continuous data are presented as mean and standard deviation (±) or as median with interquartile range (IQR). Pearson's χ2 test was used to compare categorical variables. The one-factor ANOVA test was used to compare continuous data from three groups, and Student's t test was used to compare continuous values of two variables. When the criteria of normality and homogeneity of variances were not met, the nonparametric Kruskal-Wallis test was used. Binary logistic regression was used for the multivariate study. The significance level was set at 0.05.
Results
General Characteristics
Between 1 March and 4 September 2020, 2,772 patients were admitted to the Hospital Universitario Ramón y Cajal for SARS-CoV-2 infection. Of these, 2,527 (91.1%) had no history of cancer, 164 (5.9%) had history of prior cancer, and 81 (2.9%) had received active cancer treatment in the 12 weeks prior to admission. Among the patients without active treatment, cancer diagnosis had been made between 44 years, and shortly before infection, this group also included 9 patients who were pending treatment or who were diagnosed during SARS-CoV-2 infection, 8 patients who were decided not to receive any cancer treatment and were on surveillance and 1 patient on supportive care. Of the 81 patients on active cancer treatment, 41 (50.6%) were in stage IV cancer disease. The general characteristics of the 3 groups of patients can be seen in Table 1. The median age of patients with no cancer history was 69 years (0–104), 80 years (19–97) for cancer patients without treatment and 71 (38–89) for patients in treatment. The median age of patients who died relative to the overall group was higher for patients with no history of cancer and patients without treatment (82 and 84 years, respectively), but was the same for patients in active cancer treatment, 71 years. Oncologic patients without active treatment had higher comorbidity with statistically significant differences for diabetes, hypertension, chronic kidney disease, cardiovascular disease, and chronic lung disease than nononcologic patients.
The most frequent site of neoplasia in patients with history of prior cancer was colorectal (20.7%) followed by prostate (19.5%) and breast (9.8%). Among patients on active cancer treatment, the most frequent location was breast (23.5%), prostate (19.8%), lung (12.3%), and colorectal (11.1%) (Table 2). Twenty-five patients were receiving hormonal treatment when they contracted SARS-CoV-2 infection, 27 chemotherapy treatment (2 of them also received hormone therapy, and 2 others received chemotherapy treatment combined with bevacizumab), 11 immunotherapy, 7 targeted therapies, 2 bladder instillations with BCG, 6 treated with major surgery in the month prior to infection. Only 3 patients were receiving radiotherapy, one of them palliative radiotherapy for spinal cord compression secondary to bone metastases from lung cancer, another one radiotherapy concomitant with chemotherapy for tongue cancer, and the third one RT on surgical site of subcutaneous leiomyosarcoma at scapular level.
Laboratory Findings
Anemia, hemoglobin (HGB) below 12 g/dL, was present in 18% of patients without a history of cancer, while in patients on cancer treatment the rate of anemia was 50% (mean HGB for patients on active cancer treatment 11.4 and for patients without a history of cancer 13; p < 0.001). The rate of lymphopenia (less than 1,000 cells per mm3) was 69% and 55%, respectively. Albumin levels <3 g/dL were found in 57% of patients without a history of cancer and 83.9% in patients on active cancer treatment (mean albumin in patients on active cancer treatment 2.5 and in patients without a history of cancer 2.8; p < 0.001). There were no differences in inflammatory parameters between the 3 groups of patients (Table 3), except for LDH which was higher in patients on active oncology treatment and a trend toward lower IL-6 values, although not statistically significant, probably due to the low number of patients on active oncology treatment in whom IL-6 levels were available.
There were also no statistically significant differences compared to the population without a history of cancer. Although IL-6 levels were lower in patients treated with chemotherapy than with other types of cancer treatment, these differences were not statistically significant (Table 4).
Survival
SARS-CoV-2 mortality in noncancer patients was 19.5%, for patents with prior history of cancer without treatment 26.8% and for patients on active cancer treatment 34% (p 0.005). Among patients on active cancer treatment, the highest mortality was found in patients with lung cancer 70%, and odds ratio (OR) with respect to nononcologic patients was 9.6 (95% CI: 2.4; 37.3) followed by patients with colorectal carcinoma, 44.4% OR 3.3 (95% CI: 0.8; 12.3), prostate cancer, 37.5% OR 2.4 (95% CI: 0.8; 6.8), hematological tumors 37.5% OR 2.4 (95% CI: 0.5; 10.3), and breast cancer 15.7% OR 0.7 (95% CI: 0.2; 2.6). In terms of mortality by type of treatment, 43% of patients treated with chemotherapy died (OR 3.15 95% CI: 1.52–6.53), 32% of those treated with hormone therapy (OR 1.9 95% CI: 0.83–4.52), 36.4% treated with immunotherapy (2.35 95% CI: 0.6–8.08), and there were no deaths in patients being treated with targeted therapies.
In the multivariate study, adjusted for age, sex, obesity, hypertension, and diabetes, active cancer treatment was statistically significant risk factor for mortality (OR 2.25 95% CI: 1.35–3.77) (Table 5). For patients on active oncologic treatment, chemotherapy versus other treatments (OR 3.6 95% CI: 1.17–11.17) was statistically significantly associated with the risk of death, adjusted by age, sex, hypertension, and diabetes (Table 6).
Discussion
In this study, we analyzed the inflammatory profile and mortality of patients with SARS-CoV-2 infection, on active cancer treatment and with a history of cancer compared to infected patients without a history of cancer. This is a patient population series selected from a register of patients admitted to a tertiary hospital, covering a health area with an aging population and a medium-low socioeconomic level. No vaccines were available in the study timeframe. All patients were admitted due to the need for oxygen support and therefore with severe SARS-CoV-2 infection. We found a higher mortality rate in cancer patients under active treatment, especially in patients with lung cancer and undergoing chemotherapy. Oncology patients who did not require admission due to infection were not represented.
The incidence of a history of neoplastic disease in patients admitted for SARS-CoV-2 infection was 8.8% (246 patients out of 2,772). Of these, 81 patients were receiving some form of treatment for oncological disease, representing 2.9% of admissions, which is very similar to the 2.6% (1.7% in China and 5.6% for Western countries) described in the meta-analysis by Zarifkar et al. [13]. Since we only analyzed patients who required admission, we cannot state that the incidence of this infection is higher in cancer patients, although Liang et al. found a percentage of patients with a history of cancer among those infected with SARS-CoV-2 of 1%, while in the general population it is 0.29% [3] any case, our data do show a higher mortality in hospitalized patients than in the general population.
The sociodemographic characteristics are similar to other studies, with a higher percentage of men among cancer patients and a lower percentage of hypertension and obesity, but the mean age of our cancer patients is slightly higher than in other studies [5, 8, 10, 14], similar to that reported by Lee et al. [15] who found a greater association between COVID-19 and cancer in male patients and those over 65 years of age. Regarding the analytical profile, in our study oncology patients have lower HGB and albumin levels, as in the series of Zhang et al. [6], probably related to the nutritional deterioration of oncology patients that may contribute to the morbidity and mortality associated with the infection.
In relation to the immune system, most cancer treatments are immunosuppressive. High levels of IL-6 have been linked to an increased risk of death from SARS-CoV-2 infection due to cytokine storming [16, 17]. IL-6 inhibitors such as tocilizumab have been added to the therapeutic arsenal for SARS-CoV-2 patients. Retrospective studies have found a decrease in mortality in patients with severe pneumonia treated with tocilizumab, especially if they have high levels of C-reactive protein [18, 19]. In the two randomized studies published to date, no survival benefit is found with tocilizumab treatment, but while the COVACTA study found no improvement in clinical status, the EMPACTA study found a reduced likelihood of worsening pneumonia and need for mechanical ventilation when tocilizumab was used [20, 21]. Neither study selects patients according to inflammatory markers such as IL-6 or C-reactive protein. In oncology patients, IL-6 hyperactivation has also been reported, so it is unknown whether immunosuppressive oncology treatments could lead to a decreased risk of cytokine storm and thus death from SARS-CoV-2. Except for neutrophil levels, which were significantly lower in patients treated with chemotherapy, we found no statistically significant differences in the levels of IL-6 or other cytokines involved in cytokine release syndrome [22] between patients who did and did not die, between oncology and nononcology patients, or between the different oncology treatments, probably due to the small number of patients in whom these factors were available.
Our data suggest that the state of immunosuppression prevents a correct reaction against the virus, given the excess mortality from SARS-CoV-2 in oncology patients under active treatment (34%) compared to patients without a history of cancer (19.5%). Other retrospective studies [4, 8, 23, 24, 25] and two meta-analyses [6, 26] also find higher mortality in these patients, although there is no clear consensus in the literature, as different studies find similar mortality rates when adjusted for patient morbidities [10, 27, 28, 29]. In general, patients with cancer under active treatment who are admitted for SARS-CoV-2 infection have an advanced tumor stage, in our case 50.6% stage IV, older age, and a higher percentage of males. We did not find a higher rate of associated morbidities in cancer patients on active treatment. However, patients with history of prior cancer without active treatment had higher comorbidity rates, not only hypertension, diabetes, and obesity, but also other chronic pathologies such as heart, lung, and kidney disease, which justifies the higher mortality in this group compared to patients without a history of cancer. In the multivariate study, for patients undergoing active cancer treatment, age was not statistically significantly related to mortality in our cohort. Other studies find higher mortality in cancer patients with COVID infection in relation to age [30, 31], but these studies also include outpatients. In our work, with cancer patients with severe COVID infection, the stage of neoplasia, location, and treatments received had a greater influence on the risk of mortality than age. However, in the case of prostate carcinoma, the second site of mortality after lung cancer, patients had a median age of 81 years (IQR 74–81) higher than patients with breast cancer, patients had a median age of 68 years (IQR 55–83), lung 71 (IQR 67–76), or colorectal cancer 69 (IQR 58–72), and age was probably a determining factor in these prostate cancer patients. The 6 patients who died were on hormone treatment. It has been described in the Montopoli study that patients on androgen deprivation treatment may have a lower risk of infection [32] although there are no data on the influence of these treatments on mortality caused by SARS-CoV-2. By location, the lung cancer represents the tumor with the highest mortality risk with an OR of 6.4 (95% CI 1.38–29.98), findings consistent with those reported in other studies such as the meta-analysis by Zhang et al. [6], the TERAVOLT thoracic tumor registry [11], and the studies by Dai et al. [8] and Calvo et al. [10] which found a higher risk of death in patients with lung carcinoma. A greater severity of pneumonia has not been described at the time of admission, but lung damage due to previous pulmonary pathologies or the tumor may trigger a rapid worsening and hinder recovery [3]. In our study, only 1 patient with lung cancer was admitted to the ICU. In addition, 2 deceased patients diagnosed with lung cancer had received at least two therapeutic modalities, radiotherapy, and chemotherapy in one case, chemotherapy and surgery in another, and two others had previously required pleural drainage and draining for pleural effusion, so that although the number of patients is very small, the use of several therapeutic modalities and locally advanced disease could have influenced the evolution of the infection. Regarding the type of cancer treatment, chemotherapy is statistically significantly associated with the risk of death independently of the primary tumor. Two meta-analyses and retrospective studies also find an increased risk of death in patients treated with chemotherapy, when adjusted for risk factors [3, 33, 34]. In general, this is attributed to possible complications inherent to chemotherapy treatment or advanced stage disease. Treatment with immunotherapy also poses an increased risk of mortality in our work. In Liu's meta-analysis, they describe immunotherapy as the systemic treatment with the highest risk [34] and attribute this to the activation of T cells by the immune therapy, which can lead to an aberrant and uncontrolled inflammatory response [35]. However, it has been described as a safe treatment in studies with larger numbers of patients and including outpatients [36]. In this study, we found no differences in IL-6 levels between patients treated with immunotherapy and those without [36]. Other systemic treatments, such as targeted therapies, do not appear to increase the risk of death in patients with SARS-CoV-2 infection in our series, in agreement with the works of Garassino and Liu [11, 34].
Our study has limitations inherent to retrospective studies. The number of cancer patients with inflammatory parameters measured is small, which, together with the severity of the patients at the time of hospital admission, probably prevented us from finding statistically significant differences in IL-6 levels between the different subgroups of patients analyzed. The population of patients with cancer under active treatment is low, we were not able to study the influence of treatments such as surgery or radiotherapy, and only 3 patients were receiving radiotherapy treatment. The systemic treatments received are diverse, which prevents us from drawing accurate conclusions about the influence of each of them on the evolution of the infection. Likewise, we have not been able to analyze the type of complications of infection with or without death in oncology patients.
Conclusions
Our data confirm a high mortality of SARS-CoV-2 infection in cancer patients under active treatment, especially if they have been diagnosed with lung carcinoma or were receiving chemotherapy treatment. It is therefore necessary to make an effort to protect these patients from infection, to give them priority access to vaccination and in case of infection to closely monitor the clinical situation in order not to delay effective treatment against the virus if necessary. Given the impact of infection in these patients and the recommendation to administer suboptimal oncology treatments, in some cases, to reduce the risk of infection, prospective studies or joint efforts to collect data from multiple centers are needed to clarify questions about the management of oncology patients during a pandemic such as the current one.
Statements of Ethics
This study protocol was reviewed and approved by Comité de Ética para la Investigación con Medicamentos del Hospital Universitario Ramón y Cajal, approval number 098/20. This is a retrospective study with no clinical implications for the patients, and always we work with coded data files, so informed consent was not necessary. The consent protocol was reviewed, and the need for written informed consent was waived by Comité de Ética para la Investigación con Medicamentos del Hospital Universitario Ramón y Cajal.
Conflict of Interest Statement
The authors have no competing interests to declare that are relevant to the content of this article.
Funding Sources
There was no funding support for this study.
Author Contributions
Margarita Martín, Carmen Vallejo, Fernando López-Campos, and Sonsoles Sancho conceived, designed the study, and wrote the paper. Alfonso Muriel carried out the statistical analysis. Teresa Muñoz, Jose Antonio Dominguez, Pilar Garrido, Mercedes Martín, Carolina de la Pinta, Raúl Hernánz, Eva Fernandez, Marina Alarza, and Asunción Hervás contributed to manuscript revisions. Carmen Quereda, Matilde Sánchez-Conde, Cruz Soriano, Cecilia Suárez-Carantoña, Julio Acero, Ana Alvarez-Diaz, and Laura Martínez-García contributed to the design and development of the database, as well as to the collection and review of data.
Data Availability Statement
All data generated or analyzed during this study are included in this article. Further inquiries can be directed to the corresponding author.
Acknowledgments
The IT and admissions staff of the hospital as well as the Bioinformatics Unit of the IRYCIS are gratefully acknowledged for their collaboration in the compilation of the registry. This work has been carried out thanks to the COVID-19 IRYCIS Team composed by Val Fernández Lanza, Gerardo Gómez Montero, Almudena Ortiz Fernández, Nieves Vaquero Pinto, Otilia Navarro Carrión, Susana Valenciano Rozalén, Marta Navarro Carmena, Zulema Plaza Almuedo, María Urrutia Sánchez, Sandra Merino Ávila, Santos del Campo Terrón, Javier Moreno García, Mario Pons Abad, Alejandro Gómez Alonso, Mercedes Peña Rodríguez, Isabel García Sánchez, Marta Bajo González, Alberto Pérez Nieva, María Pilar Iranzo Alcolea, Jorge Fernández Sedeño, Cristina Sánchez Díaz, Elisa Riera González, Cecilia Suárez Carantoña, Jimena Rey García, Borja Merino Ortiz, Beatriz del Hoyo Cuenca, Yasmina Sarhane, Oscar Alberto López Cisneros, María Alejandra Gumboa Osorno, Adrian Viteri, Alejandra Restrepo Ochoa, Francois Croset, Martín Fabregate Fuente, Santiago Moreno Guillén, Jesús Fortún Abete, Luisa María Villar Guimerans, Cruz Soriano Cuesta, María Laura García Bermejo, Luis Manzano Espinosa, Raúl de Pablo Sánchez, Julio Acero Sanz, Ana María Álvarez Díaz, Rafael Cantón Moreno, José Palacios Calvo, María Ángeles Gálvez Múgica, and Javier Zamora Romero.
Table 1 Characteristic of patients
Nonhistory of cancer (n = 2,527) Cancer survival (n = 164) Active oncologic treatment (n = 81) p value
Sex, n (%)
Male 1,487 (58.8) 115 (70) 49 (60.5) 0.01
Female 1,040 (41.2) 49 (30) 32 (39.5)
Age, median (IQR), years 69 (55; 81) 80 (70; 85) 71 (62; 79) ns
Age, median for death patients (IQR), years 82 (73; 87) 84 (79; 88) 71 (67; 83) ns
Comorbidities
Obesity, n (%)
No 2,148 (85) 136 (83) 74 (91.4) ns
Yes 379 (15) 28 (17) 7 (8.6)
Hypertension, n (%)
No 1,434 (55.3) 56 (34.1) 42 (51.9) 0.01
Yes 1,093 (43.3) 108 (65.9) 39 (48.1)
Diabetes, n (%)
No 2,061 (81.5) 115 (70.1) 60 (74.1) 0.04
Yes 466 (18.5) 49 (29.9) 21 (25.9)
Chronic kidney disease, n (%)
No 2,330 (92.2) 137 (83.5) 77 (95.1) 0.01
Yes 197 (7.8) 27 (16.5) 4 (4.9)
Chronic cardiovascular disease, n (%)
No 2,069 (81.9) 100 (61) 67 (82.7) 0.01
Yes 458 (18.1) 64 (39) 14 (17.3)
Chronic pulmonary disease, n (%)
No 2,245 (88.8) 130 (79.3) 71 (87.7) 0.01
Yes 282 (11.2) 34 (20.7) 10 (12.3)
Clinical outcomes
Discharge from hospital 2,034 120 54 0.01
Death, n (%) 493 (19.5) 44 (26.8) 27 (33.3)
Table 2 Site of neoplasms for oncologic patients
Site Patients in active treatment, N (%) Cancer survival, N (%)
Breast 19 (23) 16 (9.7)
Stage IV 8
Pegylated liposomal doxorubicin 2 (5.8)
Capecitabine 2 (5.8)
Carboplatin; gemcitabine 1 (2.9)
Trastuzumab 1 (2.9)
Exemestan palbociclib 1 (2.9)
Letrozole 8 (22.9)
Tamoxifen 2 (5.7)
Exemestan 1 (2.9)
Surgery 1 (2.9)
Death 3 (15.7) OR 0.7 (95% CI: 0.2; 2.6) 2 (12.5)
Prostate 16 (19.3) 32 (19.5)
Stage IV 8
LHRH 10 (42)
LHRH + enzalutamide 2 (4.2)
LHRH; docetaxel 1 (2.1)
LH; RH; cabazitaxel 1 (2.1)
Darolutamide 1 (2.1)
Surgery 1 (2.1)
Death 6 (37.5) OR 2.4 (95% CI: 0.8; 6.8) 10 (31.2)
Lung 10 (13.4) 10 (6)
Stage IV 6
Carboplatin pemetrexed 2
Carboplatin paclitaxel 1
Durvalumab 2
Nivolumab 1
Pembrolizumab 2
Alectinib 1
Surgery 1
Death 7 (70) OR 9.6 (95% CI: 2.4; 37.3) 4 (40)
Colorectal 9 (10.8) 35 (20.9)
Stage IV 6
FOLFOX 2
FOLFOX; bevacizumab 4
Capecitabine 1
Regorafenib 1
Surgery 1
Death 4 (44.4) OR 3.3 (95% CI: 0.8; 12.3) 7 (20.6)
Hematologic 8 (9.6) 14 (8.6)
Lenalidomide 3
Cyclophosphamide 1
Cytarabine, idarubicin, IT 1
Pomalidomide bortezomib 1
Brentuximab 1
Cytarabine 1
Death 3 (37.5) OR 2.4 (95% CI: 0.5; 10.3) 4 (28.5)
Head and neck 4 (4.8) 5 (3.1)
Stage IV 2
CDDP radiotherapy 1
Nivolumab 2
Surgery 1
Death 0 3 (60)
Kidney 3 (3.6) 12 (7.4)
Stage IV 3
Nivolumab 1
Axitinib 1
Sunitinib 1
Death 0 4 (33.3)
Bladder 3 (3.6) 15 (9.2)
Stage IV 1
BCG instillation 2
Durvalumab 1
Death 0 3 (20)
Pancreatic 2 (2.4)
Stage IV 1
FOLFIRI 1
Gemcitabine; paclitaxel 1
Death 1 (50)
Endometrial 1 (1.2) 2 (1.2)
Stage IV 1
Surgery 1
Death 0 0
Ovarian 1 (1.2) 1 (0.6)
Stage IV 1
Rituximab 1
Death 0 0
Sarcoma 2 (1.2) 3 (1.8)
Stage IV 1
Doxorubicin 1
Radiotherapy 1
Death 1 1 (33.3)
CNS 1 (1.2)
Temozolamide 1
Death 0
Melanoma 1 (1.2) 3 (1.8)
Stage IV 1
Pembrolizumab 1
Death 1 (100) 1 (33.3)
Thyroid 1 (1.2)
Stage IV 1
Tremelimumab 1
Death 0
Seminoma 2 (1.2)
Death 0
Skin 8 (4.3)
Death 3 (42)
Stomach 3 (1.8)
Death 1 (33)
Hepatocarcinoma 3 (1.8)
Death 0
CDDP, cisplatin; BCG, bacillus Calmette-Guerin.
Table 3 Inflammatory markers
Marker (units) No history of cancer (n) mean Cancer survival (n) mean Active oncologic treatment (n) mean p value
Ferritin, ng/mL (780) 1,337 (53) 986 (25) 2,002 ns
LDH, U/L (2,107) 350 (136) 337 (68) 498 0.01
Procalcitonin, ng/mL (885) 0.99 (48) 1.2 (35) 1.2 ns
C-reactive protein, mg/L (2,168) 105.9 (140) 97.49 (68) 110.46 ns
D-dimer, ng/mL (1,250) 2,531.8 (79) 2,746.7 (40) 1,680.1 ns
Lymphocytes, 103/µL (2,255) 1,235 (149) 1,101 (71) 1,000 ns
Neutrophils, 103/µL (2,256) 6,181 (149) 5,692 (71) 5,097 ns
IL-6, pg/mL (754) 133.2 (39) 117.2 (14) 45.6 ns
IL-12, pg/mL (728) 0.48 (38) 0.73 (14) 0.53 ns
IL-10, pg/mL (744) 7.2 (39) 5.1 (14) 10.2 ns
IL-17, pg/mL (479) 1.3 (25) 0.82 (9) 0.69 ns
IL-1B, pg/mL (416) 0.45 (15) 0.26 (12) 0.53 ns
IL-8, pg/mL (745) 37.6 (36) 37.74 (14) 25 ns
TNF, pg/mL (610) 6.8 (30) 7 (14) 8.3 ns
INFg, pg/mL (478) 4.1 (25) 1.7 (9) 8.1 ns
LDH, lactate dehydrogenase; IL, interleukin; TNF, tumor necrosis factor; INFg, interferon gamma.
Table 4 Clinical characteristics and outcomes by cancer treatment type
Chemotherapy (n = 30) Hormonotherapy (n = 25) Targeted therapies (n = 7) Immunotherapy (n = 11) Surgery (n = 6) Nononcologic patients (n = 2,527)
Age, years (IQR) 69 (56; 74) 79 (70; 85)* (p 0.004) 63 (57; 76) 67 (61; 71) 71 (57; 85) 69 (58; 81)
Sex, n (%)
Male 22 (68.8) 14 (56) 2 (28.6) 9 (81.8) 2 (40)
Female 10 (31.2) 11 (44) 5 (71.4) 2 (18.2) 3 (60)
Ferritin, ng/mL (mean) 2,663* (p 0.4) 548 1,227 1,493 4,691 1,327
LDH, U/L (mean) 384 338 391 507 295 349
Procalcitonin, ng/mL (mean) 1.3 0.76 0.12 2.59 0.04 0.9
C-reactive protein, mg/L (mean) 126.96 93 95.9 114.99 106.8 105.4
D-dimer, ng/mL (mean) 2,583 1,457 761 700 561 2,544
Lymphocytes, 103/µL (mean) 1,092 1,032 999 797 1,133 1,220
Neutrophils, 103/µL (mean) 4,428* (p 0.49) 5,488 6,620 5,420 4,652 6,151
IL-6, pg/mL (mean) 19 126 51.7 44 6.9 132
IL-12, pg/mL (mean) 0.13 0 1.8 1.41 0.24 0.49
IL-10, pg/mL (mean) 3.6 3.09 56.7 16.9 0.97 7.13
IL-17, pg/mL (mean) 0.28 0 2.99 0.43 0.26 1.32
IL-1B, pg/mL (mean) 0.73 0.09 0 0.37 0.21 0.45
IL-8, pg/mL (mean) 16.9 41.17 79.3 36 0.2 36.87
TNF, pg/mL (mean) 4.6 0.4 62.66 1.53 5.7 6.8
INFg, pg/mL (mean) 1.33 0 59.77 0.744 0.99 4.05
Discharge from hospital, mean days 18 10 3 7 16 11
Death, n (%) 14 (45.2) 8 (32) 0 4 (36.4) 1 (16.7) 493 (19.5)
* Statistically significant differences from nononcologic patients.
Table 5 Uni- and multivariate analysis of risk of death in patients under active oncological treatment
Univariate Multivariate
OR (95% CI) p value OR (95% CI) p value
Oncologic active treatment 2.06 0.003 2.259 (1.35; 3.77)* 0.002
* Adjusted for age, sex, obesity, hypertension, and diabetes.
Table 6 Uni- and multivariate analysis of risk of death in patients under active oncological treatment by treatment type
Univariate Multivariate
OR (95% CI) p value OR (95% CI) p value
Chemotherapy treatment versus other treatments 3.15 (1.52; 6.53) 0.002 3.624 (1.17; 11.17)* 0.025
* Adjusted for age, sex, obesity, hypertension, diabetes, and site of the neoplasm.
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| 36063800 | PMC9747739 | NO-CC CODE | 2022-12-15 23:22:04 | no | Oncology. 2022 Sep 5;:1-11 | utf-8 | Oncology | 2,022 | 10.1159/000525802 | oa_other |
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Vis Inform
Vis Inform
Visual Informatics
2543-2656
2468-502X
The Author(s). Published by Elsevier B.V. on behalf of Zhejiang University and Zhejiang University Press.
S2468-502X(22)00126-7
10.1016/j.visinf.2022.12.001
Article
Corrigendum to “The Use of Facial Expressions in Measuring Students’ Interaction with Distance Learning Environments During the COVID-19 Crisis” Visual Informatics, Volume 7, Issue 1, March 2023, Pages 1–17
Maqableh Waleed a⁎
Alzyoud Faisal Y. b
Zraqou Jamal c
a Luminus Technical University College, SAE Amman Institute, Amman, Jordan
b Isra University, Amman, Jordan
c University of Petra, Amman, Jordan
⁎ Corresponding author.
14 12 2022
14 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.
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pmcThe authors regret that they missed to add the fourth author Abdallah Altahan Alnuaimi who contributed to this paper in formalizing the research methodology. His affiliation details and email address are as below:
Abdallah Altahan Alnuaimi a,b
a Luminus Technical University College, SAE Amman Institute, Amman, Jordan
b On leave without payment, Isra University, Amman, Jordan.
E-mail address: [email protected]
The authors would like to apologise for any inconvenience caused.
| 0 | PMC9747742 | NO-CC CODE | 2022-12-15 23:22:04 | no | Vis Inform. 2022 Dec 14; doi: 10.1016/j.visinf.2022.12.001 | utf-8 | Vis Inform | 2,022 | 10.1016/j.visinf.2022.12.001 | oa_other |
==== Front
Phys Med
Phys Med
Physica Medica
1120-1797
1724-191X
Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd.
S1120-1797(22)02358-4
10.1016/S1120-1797(22)02358-4
Poster Abstracts–Rp
CHEST CT PRACTICES EVOLUTION DURING COVID-19 PANDEMIA IN FRANCE
Moreno Ramiro Dr
Devic Clément Dr
Meyrignac Olivier Dr
14 12 2022
12 2022
14 12 2022
104 S103S103
Copyright © 2022 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.
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Associazione Italiana di Fisica Medica e Sanitaria
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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pmc
| 0 | PMC9747743 | NO-CC CODE | 2022-12-15 23:22:04 | no | Phys Med. 2022 Dec 14; 104:S103 | utf-8 | Phys Med | 2,022 | 10.1016/S1120-1797(22)02358-4 | oa_other |
==== Front
Phys Med
Phys Med
Physica Medica
1120-1797
1724-191X
Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd.
S1120-1797(22)02304-3
10.1016/S1120-1797(22)02304-3
Poster Abstracts–Biomedical Engineering
COVID-19 TECHNOLOGY ASSESSMENT SERVICE
Soraghan Chris Dr
Finucane Ciaran Dr
Mc Garrell Mr Seamus
Foran Tim Dr
Boyle Gerard Dr
O’Reilly Geraldine Dr
14 12 2022
12 2022
14 12 2022
104 S82S82
Copyright © 2022 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.
2022
Associazione Italiana di Fisica Medica e Sanitaria
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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pmc
| 0 | PMC9747744 | NO-CC CODE | 2022-12-15 23:22:04 | no | Phys Med. 2022 Dec 14; 104:S82 | utf-8 | Phys Med | 2,022 | 10.1016/S1120-1797(22)02304-3 | oa_other |
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Neuroimmunomodulation
Neuroimmunomodulation
NIM
Neuroimmunomodulation
1021-7401
1423-0216
S. Karger AG Allschwilerstrasse 10, P.O. Box · Postfach · Case postale, CH–4009, Basel, Switzerland · Schweiz · Suisse, Phone: +41 61 306 11 11, Fax: +41 61 306 12 34, [email protected]
36323239
10.1159/000526653
nim-0029-0269
Review Article
The Infected Lungs and Brain Interface in COVID-19: The Impact on Cognitive Function
Joaquim Larissa a
Della Giustina Amanda b
Machado Richard Simon a
Metzker Kiuanne Lino Lobo a
Bonfante Sandra a
Danielski Lucineia Gainski c
Goldim Mariana Pereira de Souza a
Petronilho Fabricia c *
aHealth Sciences Unit, Laboratory of Neurobiology of Inflammatory and Metabolic Processes, Graduate Program in Health Sciences, University of South Santa Catarina, Tubarao, Brazil
bSchool of Nutrition Sciences, Faculty of Health Sciences, University of Ottawa, Ottawa, Ontario, Canada
cLaboratory of Experimental Neurology, Graduate Program in Health Sciences, University of Southern Santa Catarina, Criciuma, Brazil
*Fabricia Petronilho, [email protected]
2 11 2022
2 11 2022
29 4 269281
18 1 2022
4 7 2022
2022
Copyright © 2022 by S. Karger AG, Basel
2022
https://www.karger.com/Services/SiteLicenses Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher.
Many coronavirus disease 2019 (COVID-19)-recovered patients report signs and symptoms and are experiencing neurological, psychiatric, and cognitive problems. However, the exact prevalence and outcome of cognitive sequelae is unclear. Even though the severe acute respiratory syndrome coronavirus 2 has target brain cells through binding to angiotensin-converting enzyme 2 (ACE2) receptor in acute infection, several studies indicate the absence of the virus in the brain of many COVID-19 patients who developed neurological disorders. Thus, the COVID-19 mechanisms for stimulating cognitive dysfunction may include neuroinflammation, which is mediated by a sustained systemic inflammation, a disrupted brain barrier, and severe glial reactiveness, especially within the limbic system. This review explores the interplay of infected lungs and brain in COVID-19 and its impact on the cognitive function.
Key Words
COVID-19
SARS-CoV-2
Neuroinflammation
Cognitive dysfunction
Fabricia Petronilho is a National Council for Scientific and Technological Development (CNPq) research fellow. The funding sources were not involved in the conduction of the research, preparation of the article, or the decision to submit the article for publication.
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pmcIntroduction
In December 2019, the world began to face a pandemic caused by the new coronavirus, discovered in the city of Wuhan, China [1, 2], called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), being the etiologic agent of the coronavirus disease 2019 (COVID-19) [2]. Since its discovery, the virus has had a rapid evolution reaching almost all countries in the world [3]. In March 2020, the World Health Organization (WHO) announced the SARS-CoV-2 virus as a pandemic [2, 4, 5].
Currently, COVID-19 represents a major challenge to public health and economy, as the host's immune response is not completely understood [6]. SARS-CoV-2 infection can also affect the CNS. The number of patients with respiratory infection and neurological damage is increasing [7, 8]. Thus, there is a great need for understanding the immune responses to this virus and how it can compromise the brain [9].
During an innate immune response to a viral infection, pattern recognition receptors, such as Toll-like receptors (TLR) and NOD-like receptors, recognize different molecular structures that are characteristic of the invading virus [10, 11]. These molecular structures are known as pathogen-associated molecular patterns (PAMPs). The interaction between PAMPs and pattern recognition receptors triggers the onset of the inflammatory response against the invading virus leading to a signal transduction that involves the activation of the nuclear factor kappa B (NF-kB) [12, 13]. As a consequence, pro-inflammatory mediators are produced like tumor necrosis factor-alpha (TNF-α), interleukin-1β, and interleukin-6 (IL-6), which favor an intense cellular response with the release of secondary mediators [14, 15, 16]. This results in the influx of several immune cells, such as macrophages, neutrophils, and T cells from the circulation to the site of infection [10].
Therefore, when the attempt to limit the infection is augmented and sustained, nonspecific oxidative and inflammatory effects will result in cellular damage [10]. SARS-CoV-2 infection leads to low levels of oxygen saturation, being one of the main causes of mortality; although these mechanisms are not fully understood, the excessive synthesis of pro-inflammatory cytokines is considered one of the main contributing factors [17, 18], and a study points the association of a cytokine profile with the severity of COVID-19 disease [19]. Fatality predictors from a recent retrospective multicenter study of 150 confirmed COVID-19 cases in Wuhan, China, included elevated ferritin [20], suggesting that the mortality may be due to hyperinflammation [21].
Patients with severe disease are more likely to develop neurological symptoms, including loss of taste and smell, as well as encephalitis and cerebrovascular disorders [22]. However, whether neurological complications are actually due to direct viral infection of the nervous system or arise as a result of the immune reaction against the virus in patients who had preexisting deficits or had a certain harmful immune response is still a question to be properly addressed [22]. Thus, this review aimed to provide an overview of the current neurological symptoms associated with COVID-19 as well as to present a perspective between infected lungs and the brain in COVID-19, including the impact on cognitive function.
Neurological Manifestations after COVID-19 Infection
The potential of SARS-CoV-2 in causing neuroinflammatory and neurodegenerative manifestations in short- and long term have become a target of great interest in scientific research [23, 24, 25]. Given that neurological dysfunction due to inflammation increases the burden of cognitive impairment [25, 26, 27] and the mortality rate [28], our search strategy focused on listing studies concerning the manifestations of cognitive impairment in COVID-19 patients, and the articles are shown in the Table 1.
Studies suggest that patients infected by SARS-CoV-2 may present neurological damage and impaired cognitive functions during different phases of recovery. Alemanno et al. [29] evaluated 87 patients at 5–20 days after COVID-19 symptoms onset, and they detected cognitive dysfunctions that included deficits in memory, executive functions, language, orientation, and abstraction; also, patients who received invasive ventilation and sedation presented better cognitive functions, especially the younger individuals.
Jaywant et al. [30] analyzed 57 individuals undergoing inpatient rehabilitation after hospitalization for 43.2 days (±19.2) due to COVID-19. They observed that 81% presented some cognitive deficits (47% had mild impairment while 25% showed moderate impairment), and the main alterations were seeing in the domains of attention and executive functions, e.g., rapid visual attention, immediate recall, and information processing speed.
The medium-term effects of SARS-CoV-2 infection on cognition were assessed by Raman et al. [31]. The evaluations performed at 2–3 months post COVID-19 in 58 patients and 30 healthy controls indicated that 28% of the patients presented global cognitive impairment, but the most pronounced deficit was found in the domain of executive/visuospatial. Diminished executive functions (dysexecutive syndrome) were also found by Versace et al. [32] in 12 patients at 9–13 weeks post COVID-19 onset.
Similar results were demonstrated by Miskowiak et al. [33] at 3–4 months after disease onset, denoting global cognitive impairment in 38% of the COVID-19-positive individuals (n = 29), while 24% of the patients presented selective impairment, and the main deficits were found in the domains of verbal learning and working memory. Blazhenets et al. [34] evaluated eight COVID-19 patients at the subacute stage of the disease (37 ± 19 days) and at approximately 6 months post disease onset, demonstrating that although there was an improvement in global cognitive function, 5 patients remained below the cut-off value for detection of cognitive impairment.
These cognitive impairments are not limited to symptomatic patients, as found by Amalakanti et al. [35] in a case-control study involving 93 COVID-19 patients and 102 controls, showing that asymptomatic individuals presented an impaired function in the visuoperception, naming, and fluency domains of cognition, and the impairment was worse in older participants.
Confusion and delirium were also observed in several COVID-19 patients. Helms et al. [36] found that 26 of 40 participants (65%) presented confusion during the course of the disease and hospitalization in the ICU. D'Ardes et al. [37] noted that 14 patients (25%) presented delirium and had test results indicative of cognitive impairment. In addition, Bowles et al. [38] demonstrated that in their retrospective cohort confusion was observed in different domains, e.g., in new and complex situations only (575; 41%), and on awakening or at night, during the day/evening, or constantly (85; 6%); cognitive impairments were also detected. In a case report, Payne et al. [39] pointed a case of an 85-year-old patient who experienced confusion and functional decline in the acute phase of the disease, but after more than 3 months from diagnose, the cognitive status and delirium did not return to the baseline.
Cross Talk between Infected Lungs and the Brain in COVID-19
Lung Infection by SARS-CoV-2
A summary of a report of over 70,000 cases of SARS-CoV-2 infection pointed that most cases (81%) were classified as mild (without pneumonia or mild pneumonia), whereas 14% of the cases were considered severe: presence of dyspnea, respiratory frequency ≥30/min, blood oxygen saturation ≤93%, partial pressure of arterial oxygen to a fraction of inspired oxygen ratio <300, and lung infiltrates >50% within 24–48 h, and 5% were critical (with respiratory failure, septic shock, and multiple organ dysfunction or failure). The overall case fatality rate was 2.3%; however, patients with comorbidities or those over 70 years old were more susceptible to complications and death [40].
The airborne and persistence of the virus on surfaces explain the rapid spread of COVID-19 infection [41]. The acute clinical features of COVID-19 are fever, cough, myalgia, headache, and sore throat. The following clinical stage was characterized by high fever, shortness of breath, hypoxemia, and atypical pneumonia [42].
SARS-CoV-2 spreads through droplets and secretions from the respiratory tract of an infected person [43]. The SARS-CoV-2 is an enveloped positive-stranded RNA virus. This virus replicates in the cytoplasm of host cells and the viral RNA genome merges with the plasma membrane, releasing viral replicates into the extracellular space [44].
It was recently predicted that SARS-CoV-2 directly attacks type 2 pneumocytes by binding to the human angiotensin-converting enzyme 2 (ACE2) receptor [45], a membrane carboxypeptidase enzyme present in distal airways and alveoli, especially type 2 pneumocytes which have the highest expression of ACE2, along with alveolar macrophages and dendritic cells. For this reason, the surface area of the lung serves as a reservoir for viral binding and replication [46]. ACE2 is also expressed on the vascular endothelium, nasal, oral, nasopharyngeal, and oropharyngeal epithelia, gut epithelia, cardiac pericytes, renal proximal tubular cells and in the skin, reticuloendothelial, and the CNS [47].
Dendritic cells and alveolar macrophages phagocytose the virus-infected epithelial cells and induce alveolar injury and interstitial inflammation [48]. In addition, recruited macrophages release chemokines, increasing capillary permeability, and allowing neutrophils to migrate into the space alveolar. The migration of neutrophils results in the rupture of the alveolar-capillary barrier and the formation of edema due to the migration of blood proteins [49]. In the alveolar space, monocytes are recruited and secrete pro-inflammatory cytokines that induce pneumocyte apoptosis [44]. Also, interstitial edema contributes to alveolar dysfunction [50] and severe impairment of alveolar gas exchange and oxygenation [51]. The massive production of cytokines is involved in this process and is called “cytokine storm.” Cytokine storm results from an inflammatory overreaction that ultimately leads to endothelial cell dysfunction, damage of the vascular barrier, capillary leak, and diffuse alveolar damage [10].
Cytokine Storm after SARS-CoV-2 Infection
Cytokine storm is an umbrella term encompassing several disorders of immune dysregulation characterized by constitutional symptoms, systemic inflammation, and multiorgan dysfunction that can lead to multiorgan failure if inadequately treated [52]. The mortality of hospitalized individuals with pneumonia due to COVID-19 has been attributed mainly to the cytokine storm syndrome [10].
The spike surface glycoprotein S on the SARS-CoV-2 binds to ACE2, and cell entry requires priming of the spike protein by the cellular serine protease TMPRSS2 or other proteases. The alveolar epithelial cells, lymphocytes, and vascular endothelial cells are the primary targets of the virions (Fig. 1). The virus inhibits the production of interferons that are part of cellular defense mechanisms. The viral replication releases a large number of virions, leading to infection of neighboring target cells and viremia, which then cause an exaggerated pulmonary and systemic inflammatory response, respectively [53, 54]. This explains the clinical presentation of severe COVID-19 that is predominated by acute respiratory distress syndrome, shock, and coagulopathy [53].
Renin cleaves angiotensinogen to produce angiotensin I, which is further cleaved by ACE to produce angiotensin II, having a dual role. By acting through angiotensin II type 1 receptor, it facilitates vasoconstriction, fibrotic remodeling, and inflammation, whereas through angiotensin II type 2 receptor, it leads to vasodilation and growth inhibition. Angiotensin II is cleaved by ACE2 to Ang 1–7, which counteracts the harmful effects of the ACE/Ang II/AT1 axis. Thus, ACE2 primarily plays a key role to physiologically counterbalance ACE and regulate angiotensin II. The internalization of ACE2 after viral interaction leads to its downregulation and consequent upregulation of angiotensin II. The latter, by acting through angiotensin II type 1 receptor, activates the downstream inflammatory pathways, leading to the cytokine storm that adversely affects multiple organs [55].
Specifically, cytokine storm evolves through several pathways, like the NF-κB, janus kinase/signal transducers and activators of transcription (JAK/STAT), and the macrophage activation pathway, triggering the release of interleukin-1β, IL-6, C-X-C motif chemokine ligand 10 (CXCL10), TNF-α, interferon-γ (IFN-γ), macrophage inflammatory protein-1α and -1β, and the vascular endothelial cell growth factor [56]. IL-6 is a key player in the cytokine storm, stimulating several cell types and forming a positive feedback loop [19, 57], and higher IL-6 levels are strongly associated with shorter survival [58]. The large-scale unregulated production of interleukins, particularly IL-6, further stimulates several downstream pathways, increasing the production of acute-phase reactants, like C-reactive protein [59].
Hematological alterations are also related with the cytokine storm. Peripheral blood leukocyte and lymphocyte counts are normal or slightly reduced in early disease, when symptoms tend to be nonspecific [60]. Approximately 7–14 days from the onset of symptoms, the appearance of significant lymphopenia coincides with a decline in clinical status, enhanced levels of inflammatory mediators, and cytokine storm [61].
TNF-α can promote T-cell apoptosis and IL-6 may suppress normal T-cell activation [62]. Additionally, reduced lymphocyte turnover due to the cytokine storm induces atrophy of lymphoid organs. Thus, the SARS-CoV-2 infection may cause lymphopenia resulting in reduced CD4+, CD8+ T-cell counts, and suppressed IFN-γ production [63]. Type 1 IFNs are important in inhibiting the early stage of COVID-19 infection, so a failure in the immune response of type 1 IFNs excessively enhances the activity of the immune system, increasing pro-inflammatory cytokine production [64]. In the CNS, the expression of ACE2 in neurons and glial cells makes the brain vulnerable to COVID-19 infection [65], but the peripheral cytokine storm can be an important factor to the brain alterations.
Brain Barriers and SARS-CoV-2 Infection
The CNS has long been described as immunologically privileged due to its natural barriers that separate it from peripheral organs, but this dogma has undergone modifications [66]. Macroscopic examples are the brain meninges [67], but there are also microscopic brain barriers formed by different cells that assist in controlling the influx of substances from the blood into the brain parenchyma [68]. The blood-brain barrier (BBB) and the blood-cerebrospinal fluid barrier form these brain barriers [69]. The BBB is located at the level of the endothelial cells within CNS microvessels, while the blood-cerebrospinal fluid barrier is established by the choroid plexus epithelial cells [70].
The cells that compose brain barriers can be stimulated by microorganisms, such as bacteria, or by PAMPs, e.g., gram-negative lipopolysaccharide [71], and by immune or toxic molecules present in peripheral blood, such as interleukins [72] or reactive oxygen species [73]. However, viruses can also activate brain barrier cells [74], as emerging data indicate that neurological complications occur as a consequence of SARS-CoV-2 infection [75] and it could be caused by BBB activation [76]. The activation of endothelial cells from the BBB occurs due to the high expression of ACE2 receptors [77], but it is also known that BBB cells can be infected with the SARS-CoV-2 virus, and after viral replication, virions can be released into the brain parenchyma [78].
Recent studies show different routes of entry for the COVID-19 virus into the CNS [79, 80] (Fig. 2). The first route of entry would be through the olfactory epithelium, crossing the cribriform plate of the ethmoid bone and reaching the olfactory bulb from which it could spread to different areas of the brain [81]. The second route would be through the activation of brain barriers, as already mentioned, which allows the action of a third route. In this third route, infected leukocytes could enter through dysfunctional brain barriers, acting as a vehicle for dissemination within the CNS [82, 83]. And the fourth mechanism would be through the neuronal pathway, in the lower respiratory tract through the vagus nerve, where viruses are transported by endocytosis and exocytosis through neuronal cell bodies [84]. Evidently, viruses can also damage neurons directly or indirectly by stimulating the reaction of microglial cells and astrocytes [85].
However, we intend to show the neuronal damage due to neuroinflammation occurring as a response to peripheral inflammation, independently of brain infection with SARS-CoV-2. A previous study identified that COVID-19 patients presented enhanced levels of inflammatory markers in the blood and cerebrospinal fluid, being the encephalopathy the neurological condition mainly influenced by peripheral inflammation[86]. The SARS-CoV-2 infection can be classified as acute, that is, there is no persistence of the virus in the human body for long periods, indicating that only a section of the neurological changes seen in COVID-19 patients is caused directly by the presence of the virus [87, 88]. In fact, postmortem studies show that a significant rate of individuals diagnosed with COVID-19 who presented neurological disorders tested negative for the presence of the virus in the CNS [89, 90, 91, 92, 93, 94].
For a long time, many peripheral infectious diseases were not associated to the changes in the CNS [68], but this concept is rapidly changing due to the COVID-19 pandemic, where many surviving patients show cognitive and functional changes [89]. We support the idea that the cytokine storm may be intrinsically involved in early and long-term neurological damage, during SARS-CoV-2 infection and after COVID-19 recovery [95, 96]. In addition, the activation of brain barriers is possibly the most accurate mechanism for enhancing neurological damage in COVID-19 individuals [75, 97] since in response to stressful events, such as infections or release of inflammatory mediators, the characteristics of these barriers can be altered, leading to edema and recruitment of inflammatory cells and the release of toxics metabolites into the brain parenchyma [70].
Abdominal sepsis is an example that illustrates what is been proposed to happen in COVID-19: peripheral pro-inflammatory cytokines, along with oxidative stress, stimulate the expression of extracellular matrix metalloproteinases that degrade the tight junctions of the BBB and impair its functioning [98, 99]. Increased expression of the type 1 adhesion molecule, responsible for the scrolling and infiltration of leukocytes in the cerebral microvasculature, was verified in experimental sepsis [100]. In addition, an increasing permeability of BBB can lead to the activation of glial cells and the production of cytotoxic mediators which in turn act on the brain barriers, propagating the damage. Thus, brain barriers permeability is not only a cause but a consequence of brain injury in sepsis [68]. In summary, neurological damage can arise regardless of the presence of the virus, considering that peripheral mediators can access glial cells and induce their reactiveness, and these in turn initiate and maintain neuroinflammation, which in excess causes more cellular damage (Fig. 3).
Glial Involvement in Neurodegeneration Caused by COVID-19
Microglia and astrocytes constitute two important glial cells. Microglia are the CNS-resident immune cells, responsible for the immune surveillance of CNS, regulation of neuronal activity, synaptic maintenance, and plasticity [101]. Microglia are dynamic cells that continuously promote self-remodeling and can attack stressed neurons [102]. Along with microglia, the astrocytes are involved in physiological and pathological activities within the CNS [103]. These activities include not only the formation and maturation of synapses [104] but also the maintenance, pruning and remodeling of synaptic transmission, and plasticity [105]. Astrocytes are capable of releasing several neurotrophic factors that assist in neuron differentiation and survival [106].
Microglia and astrocytes become reactive to inflammatory processes mainly because of vascular alterations in the brain that may lead to hypoxia and cytokine storm, and these alterations can arise from a direct CNS viral infection or a systemic inflammation due to a peripheral organ dysfunction (Fig. 3) [7]. As seen in sepsis [107], COVID-19 patients can experience cognitive impairment, since the systemic inflammation and the acute respiratory distress syndrome together produce a wide range of insults that favors glial cells activation, BBB disruption, and neurodegeneration, being the hippocampus highly susceptible [108].
In the brain, ACE2 receptors are present on neurons and glial cells [109], but it has been also discovered in substantia nigra, ventricles, middle temporal gyrus, the posterior cingulate cortex, and the olfactory bulb. Such widespread expression in the brain has reinforced that SARS-CoV-2 can infect neurons and glial cells in the CNS [109, 110].
A case report of a SARS-CoV-2 patient with cerebellar hemorrhage showed microglial nodules and neuronophagia bilaterally and in the cerebellar dentate nuclei, as highlighted by CD68 immunostains, and also CD8+ cells in microglial nodules [111]. COVID-19 infection induces severe hypoxia conditions, which potentiate and exacerbate microglia responsiveness and delays its shift to a surveillant state important for tissue repair. This is a possible pathway for the pathogenicity of COVID-19 and the complications in tissues sensible of oxygenation variation, such as the brain, and tissue damage as observed in severe patients of COVID-19 [112].
Moreover, pathogens are able to directly induce astrocytes to become reactive, such as herpes simplex virus type 2 [113], Japanese encephalitis virus [74], and more recently, SARS-CoV-2 [89], since astrocytes express ACE2 receptors, though in a lower concentration than in neurons [114]. In August 2020, the first preclinical evidence emerged indicating that coronaviruses can stimulate the release of pro-inflammatory cytokines in type I astrocytes. Evidences of astrocyte activation have been identified in the plasma, brain, and cerebrospinal fluid of patients with COVID-19 [115, 116, 117].
Another important evidence emerged when a postmortem study found similar histopathological alterations among COVID-19 patients and sepsis patients [118], supporting the hypothesis that not only the presence of the virus [119] but also the cytokine storm contributes to neurological damage after COVID-19, in the same way that occurs during sepsis [68]: peripheral COVID-19 infection induces an expressive release of cytokines, which can subsequently compromise the BBB and cause the reactiveness of microglia- and astrocyte-borne TLR, stimulating these cells to release toxic substances and inflammatory mediators, thus leading to neuronal tissue damage without the presence of the virus in situ [77]. In fact, some works show that most of the patients with neurological alterations due to COVID-19 did not present the virus in the CNS [89, 118, 120, 121].
Independently of the reason, COVID-19 survivors are at a high risk of developing long-term neurological alterations either because of an aggravated pre-existing disorder or by triggering a new one [36]. Researchers pointed that SARS-CoV-2 infection, by causing several cellular imbalances, oxidative stress, and mitochondrial and lysosomal dysfunctions, would prone the cells to be less infection-resistant, thus in the long-term it may accelerate aging of the immune system and disturbed tissues [122], and this would explain the reason COVID-19 survivors are susceptible to the development or worsening of Parkinson's disease [123], especially because the cortex and substantia nigra are the two brain regions with higher SARS-CoV-2 penetration and the most frequently associated with neurodegenerative diseases [124].
As seen in other infections, like sepsis [68], reactive microglia and astrocytes are a part of the neuroinflammation cascade, a process that can be beneficial in eliminating pathogens; however, when exacerbated or sustained, it tends to be detrimental, which generates imbalances in neurotransmitters and causes short- and long-term clinical manifestations, and these pathophysiological mechanisms may also be involved in the neurological manifestations of COVID-19 survivors.
Conclusion
Overall, the findings discussed in this review indicate that the systemic inflammatory response induced by SARS-CoV-2 seems enough to set off the alarms on its potential association with neuroinflammation, regardless of the presence of the virus in the brain. This interplay between periphery and brain is controlled by brain barriers, and the exposure to SARS-CoV-2 virus can disrupt this control, damaging this important protection against the deleterious consequences of systemic inflammation and favoring the entry of pro-inflammatory cytokines and peripheral immune cells into the CNS. Therefore, glial responsiveness displays an exaggerated release of pro-inflammatory mediators that induces synaptic loss and demyelination, reinforcing the evidence that neuroinflammation associated with COVID-19 is involved in subsequent neurodegeneration and cognitive decline.
Conflict of Interest Statement
The authors have no conflicts of interest to declare.
Funding Sources
Fabricia Petronilho is a National Council for Scientific and Technological Development (CNPq) research fellow. The funding sources were not involved in the conduction of the research, preparation of the article, or the decision to submit the article for publication.
Author Contributions
Amanda Della Giustina and Fabricia Petronilho conceived and coordinated the manuscript preparation. Kiuanne Lobo Metzker and Sandra Bonfante prepared the table. Richard Simon Machado and Mariana Pereira de Souza Goldim revised the table. Larissa Joaquim and Lucineia Gaisnki Danielski wrote the manuscript. Fabricia Petronilho revised and edited the manuscript.
Acknowledgments
Santa Catarina State Foundation for Research Support (FAPESC) and National Council for Scientific and Technological Development (Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPq).
Fig. 1 Schematic of the SARS-CoV-2 infection. (1) The S protein binds to the receptor ACE2. (2) Cleavage of SARS-COV-2 S protein. (3) Activation of S2 domain. (4) The virus-cell fusion process.
Fig. 2 Routes of entry for the COVID-19 virus in the CNS. (1) The first route of entry would be through the olfactory epithelium, crossing the cribriform plate of the ethmoid bone and reaching the olfactory bulb from which it could spread to different areas of the brain. (2) The second route would be through the activation and permeability of BBB. The virus in the bloodstream may infect the peripheral immune cells and cause the cytokine storm. Cytokines can signal and alter the structure of BBB, and infected leukocytes could enter the CNS through dysfunctional brain barriers, acting as a vehicle for dissemination within the CNS or aggravate cytokine production within the CNS. (3) Another mechanism would be through the neuronal pathway in the lower respiratory tract through thevagus nerve, where viruses are transported by endocytosis and exocytosis through neuronal cell bodies.
Fig. 3 SARS-CoV-2-mediated cognitive impairment. After entry into the epithelial cells, the virus activates NF-κB and JAK/STAT pathway by TLR binding and leads to the production of TNF-α, IL-1β, IL-6, IFN-γ, CXCL10, MIP-1α and -1β, and VEGF. The cytokines stimulate the production of C-reactive protein and promote T-cell apoptosis, suppress T-cell activation, and IFN-γ production. The cytokines and chemokines attract immune cells from the circulation, like the monocytes, macrophages, T cells, and neutrophils, to the site of infection. Additionally, TNF-α, IL-4, IL-6, IL-12, and IL-23 further recruit the immune cells, establishing a pro-inflammatory feedback loop that leads to BBB permeability and stimulates microglia and astrocyte responsiveness. Consequently, these cells produce more inflammatory mediators that exert neurotoxic effects, promoting neuronal dysfunction and cognitive impairment.
Table 1 Cognitive impairment manifestation of COVID-19 patients
Study type Location n Manifestation Ref.
Retrospective cohort France 58 Dysexecutive syndrome: 14/39 (36%)
Agitation: 40/58 (69%)
Confusiona: 26/40 (65%) [35]
Case-control India 93 COVID-19 versus 102 controls COVID-19 patients presented lower scores in the domainsb of visuoperception (2.4±0.7 vs. 2.8±0.7); naming (3.6±0.5 vs. 3.9±0.2); fluency (0.9±0.6 vs. 1.6±0.7). Correlated with age [34]
Case-control China 29 COVID-19 versus 29 controls COVID-19 patients exhibited deficits in attention domainc [125]
Cross-sectional USA 57 Cognitive deficitsd varied in severity: mild (27; 47%), moderate (14; 25%), severe (5; 9%); delirium during acute hospitalization (37; 66%) [30]
Prospective cohort Italy 87 Patients divided into four groups according to the respiratory assistance. MoCAb/MMSEe: group 1: 74.2% deficits/12.9% mild to severe deficit; group 2: 94.4% deficits/55.6% mild to moderate deficits; group 3: 89.6% deficits/48.3% mild to severe deficits; group 4: 77.8% deficits/44.4% moderate deficits. Correlated with age [29]
Case series Italy 9 Low scorese in the domains of attention, calculation, short-term memory, constructional praxia, and written language (3; 33.3%) [126]
Case-control Italy 12 COVID-19 versus 12 controls COVID-19 patients showed a significantly poorer cognitive performanceb and smaller scores in different tests (p < 0.001) [127]
Retrospective and prospective cohort Italy 185 Evaluations performed at 23 [20–29] days post discharge. Cognitive impaired patientsb: 47 (25.4%). Of these: required hospitalization: 36; were discharged from emergency department: 11 [128]
Retrospective cohort USA 1,409 Cognitive dysfunctiong in different domains: requires prompting (327; 23%), requires assistance, and direction (92; 7%). Confusion: in new and complex situations only (575; 41%), on awakening or at night, during the day/evening, or constantly (85; 6%) [37]
Prospective cohort Germany 29 Cognitive impairmentb in executive abilities, visuoconstruction, memory, and attention: 29 patients. Severity: mild to moderate (14; 54%); severe (4; 15%). Alterations in different domainsh: memory (7/14); executive functions (6/15) [129]
Prospective case series Germany 8 Subacute stage: 37±19 days post COVID-19 onset; chronic stages: 6 months post COVID-19 onset. Global cognitive functionb improved over time (from subacute to chronic stages), but the mean score was still indicative of cognitive impairment. Persistent deficits cognitive: 5 patients (visuoconstruction, executive functions, memory) [130]
Cross-sectional Italy 56 Patients with delirium (14; 25%): higher scores in different testsa,i (p < 0.001) and were older than patients without delirium (p = 0.002) [36]
Prospective cohort Denmark 29 COVID-19 versus 100 controls Global cognitive impairments (11; 38%) or selective impairment (7; 24%). Deficits in domains of verbal learning and working memoryj,k [33]
Prospective cohort UK 58 COVID-19 versus 30 controls Global cognitive impairmentb (16; 28%) and deficits in the domain of executive/visuospatial (40% vs. 16% in controls) [31]
Case report USA 1 Delirium during acute phase of infection. Delirium and cognitive status did not improve at more than 3 months after diagnosing [38]
Prospective cohort Italy 266 (130 had cognitive assessment) Poor performance in different functions of a cognitive testl: at least one function (21; 16%), two (22; 17%), three (18; 14%), four (14; 11%), five (7; 5%), and 2 patients (1.5%) showed no good performance at all. Patients with psychopathology at 1-month after discharge performed worse on verbal fluency, information processing, and executive functions at the 3 months assessment, whereas psychopathology at 3 months associated with worse information processing [131]
Prospective cohort Germany 53 (13 had cognitive assessment) Cognitive deficitsb in executive function, attention, language, and delayed recall (8; 61.5%) [132]
Case-control Italy 12 COVID-19 versus 10 controls Evaluations performed at 9–13 weeks post COVID-19. Diminished executive functions (dysexecutive syndrome)f [32]
Randomized clinical trial Germany 1,030 (1,026 had cognitive assessment) Cognitive impairmentb: 622 (60.6%) [133]
Prospective cohort USA 50 COVID-19 versus 50 controls Cognitive deficitsm in short-term memory (15; 30%) and attention (12; 24%) [134]
Prospective cohort Austria 23 (14 had cognitive assessment) Cognitive deficitsn in concentration, memory, and/or executive functions (4; 29%) [135]
a The Confusion Assessment Method for the ICU [intensive care unit] (CAM-ICU).
b Montreal Cognitive Assessment (MoCA) test.
c Continuous Performance Test (CPT).
d Brief Memory and Executive Test (BMET).
e Mini Mental State Evaluation (MMSE).
f Frontal Assessment Battery (FAB).
g Outcome and Assessment Information Set version D-1 (OASIS D-1).
h Neuropsychological Test Battery (NTB).
i The 4 ‘A's Test (4AT).
j Psychiatry Danish Version (SCIP-D).
k Trail Making Test-Part B (TMT-B).
l Brief Assessment of Cognition in Schizophrenia (BACS).
m NIH Toolbox for the Assessment of Neurological and Behavioral Function.
n Tests of Attentional Performance (TAP).
==== Refs
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106 Escartin C Galea E Lakatos A O'Callaghan JP Petzold GC Serrano-Pozo A Reactive astrocyte nomenclature, definitions, and future directions Nat Neurosci 2021 24 (3) 312 325 33589835
107 Sonneville R Verdonk F Rauturier C Klein IF Wolff M Annane D Understanding brain dysfunction in sepsis Ann Intensive Care 2013 3 (1) 15 11 23718252
108 Sasannejad C Ely EW Lahiri S Long-term cognitive impairment after acute respiratory distress syndrome: a review of clinical impact and pathophysiological mechanisms Crit Care 2019 23 (1) 352 31718695
109 Baig AM Khaleeq A Ali U Syeda H Evidence of the COVID-19 virus targeting the CNS: tissue distribution, host-virus interaction, and proposed neurotropic mechanisms ACS Chem Neurosci 2020 11 (7) 995 998 32167747
110 Divani AA Andalib S Biller J Di Napoli M Moghimi N Rubinos CA Central nervous system manifestations associated with COVID-19 Curr Neurol Neurosci Rep 2020 20 (12) 60 33128130
111 Al-Dalahmah O Thakur KT Nordvig AS Prust ML Roth W Lignelli A Neuronophagia and microglial nodules in a SARS-CoV-2 patient with cerebellar hemorrhage Acta Neuropathol Commun 2020 8 (1) 147 147 32847628
112 Lang M Buch K Li MD Mehan WA Lang AL Leslie-Mazwi TM Leukoencephalopathy associated with severe COVID-19 Infection: sequela of hypoxemia? Am J Neuroradiol 2020 Jun 41 (9) 1641 1645 32586959
113 Słońska A Cymerys J Chodkowski M Bąska P Krzyżowska M Bańbura MW Human herpesvirus type 2 infection of primary murine astrocytes causes disruption of the mitochondrial network and remodeling of the actin cytoskeleton: an in vitro morphological study Arch Virol 2021 166 (5) 1371 1383 33715038
114 Chen R Wang K Yu J Howard D French L Chen Z The spatial and cell-type distribution of SARS-CoV-2 receptor ACE2 in the human and mouse brains Front Neurol 2020 Jan 11 573095 33551947
115 Matschke J Lütgehetmann M Hagel C Sperhake JP Schröder AS Edler C Neuropathology of patients with COVID-19 in Germany: a post-mortem case series Lancet Neurol 2020 Nov 19 (11) 919 929 33031735
116 Kanberg N Ashton NJ Andersson LM Yilmaz A Lindh M Nilsson S Neurochemical evidence of astrocytic and neuronal injury commonly found in COVID-19 Neurology 2020 Sep 95 (12) e1754 9 32546655
117 Virhammar J Kumlien E Fällmar D Frithiof R Jackmann S Sköld MK Acute necrotizing encephalopathy with SARS-CoV-2 RNA confirmed in cerebrospinal fluid Neurology 2020 Sep 95 (10) 445 449 32586897
118 Deigendesch N Sironi L Kutza M Wischnewski S Fuchs V Hench J Correlates of critical illness-related encephalopathy predominate postmortem COVID-19 neuropathology Acta Neuropathol 2020 Oct 140 (4) 583 586 32851506
119 Franke C Ferse C Kreye J Reincke SM Sanchez-Sendin E Rocco A High frequency of cerebrospinal fluid autoantibodies in COVID-19 patients with neurological symptoms Brain Behav Immun 2021 93 415 419 33359380
120 El-Sayed A Aleya L Kamel M COVID-19: a new emerging respiratory disease from the neurological perspective Environ Sci Pollut Res 2021 28 (30) 40445 59
121 Paniz-Mondolfi A Bryce C Grimes Z Gordon RE Reidy J Lednicky J Central nervous system involvement by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) J Med Virol 2020 Jul 92 (7) 699 702 32314810
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123 Ferini-Strambi L Salsone M COVID-19 and neurological disorders: are neurodegenerative or neuroimmunological diseases more vulnerable? J Neurol 2021 268 (2) 409 419 32696341
124 Gomez-Pinedo U Matias-Guiu J Sanclemente-Alaman I Moreno-Jimenez L Montero-Escribano P Matias-Guiu JA Is the brain a reservoir organ for SARS-CoV2? J Med Virol 2020 Nov 92 (11) 2354 2355 32437002
| 36323239 | PMC9747745 | NO-CC CODE | 2022-12-15 23:22:04 | no | Neuroimmunomodulation. 2022 Nov 2; 29(4):269-281 | utf-8 | Neuroimmunomodulation | 2,022 | 10.1159/000526653 | oa_other |
==== Front
Annales de Dermatologie et de Vénéréologie - FMC
2667-0623
2667-0623
Published by Elsevier Masson SAS
S2667-0623(22)00425-1
10.1016/j.fander.2022.09.151
Co15-129
Étude de la tolérance de la vaccination anti-SARS-CoV-2 chez les patients atteints de maladies bulleuses auto-immunes
Ou S. 1⁎
Tancrede E. 2
Alexandre M. 3
Oro S. 4
Jelti L. 4
Castel M. 5
Debarbieux S. 6
Calugareanu A. 6
Duvert Lehembre S. 7
Konstantinou M.P. 8
Veron M. 9
Abasq C. 10
Berthin C. 11
Couzan C. 12
Caux F. 3
Joly P. 5
Viguier M. 1
pour le groupe Bulles de la SFD
1 Dermatologie, hôpital Robert-Debré (CHU de Reims), Reims, France
2 Dermatologie, hôpital Saint-Louis, AP–HP, Paris, France
3 Dermatologie, hôpital Avicenne, AP–HP, Bobigny, France
4 Dermatologie, hôpital Henri-Mondor, AP–HP, Créteil, France
5 Dermatologie, CHU de Rouen, Rouen, France
6 Dermatologie, hôpital Lyon Sud – HCL, Pierre-Bénite, France
7 Dermatologie, CHU de Lille, Lille, France
8 Dermatologie, hôpital Larrey, Toulouse, France
9 Dermatologie, CH de Dunkerque, Dunkerque, France
10 Dermatologie, CH régional universitaire Morvan de Brest, Brest, France
11 Dermatologie, CHU Angers, Angers, France
12 Dermatologie, CHU Nord Saint-Étienne, Saint-Priest-en-Jarez, France
⁎ Auteur correspondant.
14 12 2022
11 2022
14 12 2022
2 8 A118A119
Copyright © 2022 Published by Elsevier Masson SAS.
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
La pandémie à SARS-CoV-2 (COVID) a été marquée par le développement rapide de vaccins à ARNm et à vecteur viral. Leur profil de sécurité a été étudié dans la population générale, mais est peu connu chez les patients à terrain dysimmunitaire. Nous avons évalué la tolérance de la vaccination anti-COVID chez les patients atteints de maladies bulleuses auto-immunes (MBAI).
Matériel et méthodes
Une étude multicentrique rétrospective a été menée de mai à septembre 2021. L’objectif a été de déterminer si des poussées évolutives de MBAI pouvaient être induites par la vaccination. Tout patient atteint d’une MBAI déjà connue (exclusion des MBAI de novo) et ayant reçu au moins 1 dose de vaccin anti-COVID avec un recul ≥ 8 semaines après celle-ci était inclus. Les données étaient extraites des dossiers médicaux et collectées de manière standardisée dans chaque centre. L’imputabilité du vaccin dans la poussée n’était pas retenue si un autre facteur causal était objectivé (inobservance thérapeutique…).
Résultats
Au total, 325 patients ont été inclus, atteints essentiellement de pemphigoïde bulleuse (PB, 35 %), de pemphigoïde des muqueuses (PM, 33 %), et de pemphigus vulgaire (PV, 20 %), ayant en majorité reçu un schéma vaccinal à 2 doses (83 %) et le vaccin Cominarty® (82 %). Après un suivi moyen de 3,5 mois après la 1re dose de vaccin, une poussée était observée chez 30 patients (9,2 %), i.e., 15/115 PB (13 %), 7/107 PM (6,5 %), 4/66 PV (6 %), 3/19 pemphigus superficiels (15,8 %) et 1/11 épidermolyses bulleuses acquises (9 %). La poussée survenait après la 1re dose dans 13 cas (délai médian : 15 j), après la 2e dans 14 cas (délai médian : 9 j) et après la 3e dans 3 cas (délai médian : 4 j). Chez les patients avec poussée à la suite de la 1re dose, 2 cas ont eu une récurrence après une nouvelle vaccination. Les poussées de PB étaient modérées (BPDAI moyen augmentant de 2,4 à 12,7, p = 0,02 ; IGA moyen de 0,1 à 2,6, p = 0,004). La corticothérapie locale contrôlait la maladie dans 73 % des cas. Les poussées de PM étaient modérées (MMPDAI moyen augmentant de 0,3 à 7,3), sans atteinte de muqueuse jusque-là épargnée. Une corticothérapie systémique était introduite/majorée chez 2 patients, et du rituximab débuté chez 1. En analyse univariée, les facteurs associés au risque de rechute de MBAI étaient un traitement de base par cyclines (p = 0,015) ou par omalizumab (p = 0,05). Hormis un cas de purpura vasculaire, aucun autre effet indésirable grave post-vaccinal n’était rapporté.
Discussion
Notre étude suggère un faible taux de poussée de MBAI dans les 3 mois suivants la vaccination anti-COVID. Pour la PB, ce taux n’est pas plus élevé que ceux de rechute spontanée, en dehors de contexte vaccinal, issus de la littérature (de 18,6 % à 23,5 % vs 13 % ici). Notre étude appuie ainsi les recommandations internationales vis-à-vis de la vaccination anti-COVID, qui doit être fortement encouragée dans cette population, souvent âgée et immunodéprimée.
==== Body
pmcDéclaration de liens d’intérêts
Les auteurs déclarent ne pas avoir de liens d’intérêts.
| 0 | PMC9748131 | NO-CC CODE | 2022-12-15 23:22:39 | no | 2022 Nov 14; 2(8):A118-A119 | utf-8 | null | null | null | oa_other |
==== Front
Annales de Dermatologie et de Vénéréologie - FMC
2667-0623
2667-0623
Published by Elsevier Masson SAS
S2667-0623(22)00421-4
10.1016/j.fander.2022.09.147
Co14-125
COVID-19 et psoriasis de l’enfant : facteurs associés à une évolution défavorable de la COVID-19 et impact de l’infection sur le psoriasis. Registre Chi-PsoCov
Zitouni J. 1⁎
Bursztejn A.C. 2
Belloni Fortina A. 3
Beauchet A. 4
Di Lernia V. 5
Lesiak A. 6
Thomas J. 7
Topkarci Z. 8
Murashkin N. 9
Brzezinski P. 10
Torres T. 11
Chiriac A. 12
Luca C. 13
Mcpherson T. 14
Akinde M. 15
Maruani A. 16
Epishev R. 17
Vidaurri De La Cruz H. 18
Luna P. 19
Amy De La Breteque M. 20
Lasek A. 21
Bourrat E. 22
Bachelerie M. 23
Mallet S. 24
Steff M. 25
Bellissen A. 26
Neri I. 27
Zafiriou E. 28
Van Den Reek J. 29
Sonkoly E. 30
Kupfer-Bessaguet I. 31
Leducq S. 32
Mahil S. 33
Smith C. 33
Flohr C. 34
Bachelez H. 35
Mahé E. 36
Groupe de recherche sur le psoriasis de la SFD
Groupe de recherche de la Société française de dermatologie pédiatrique
PsoProtect study group
British Society of Paediatric Dermatology
Società Italiana di Dermatologia Pediatrica
1 Dermatologie, hôpital Necker, AP–HP, Paris, France
2 Dermatologie, CHU, Nancy, France
3 Pediatric dermatology unit, Department of Medicine DIMED, University of Padova, Padoue, Italie
4 Santé publique, hôpital Ambroise-Paré, AP–HP, Boulogne-Billancourt, France
5 Dermatologie, Arcispedale Santa Maria Nuova, Reggio Emilia, Italie
6 Dermatology, paediatric dermatology and oncology, Medical Univeristy of Lodz, Łódź, Pologne
7 Dermatologie, J.T. Skin Care Centre, Chennai, Inde
8 Dermatologie, Dr. Sadi Konuk Training and Research Hospital, Istanbul, Turquie
9 Dermatologie, Scientific Center of Children's Health of the Ministry of Health, Moscou, Fédération de Russie
10 Dermatologie, Voivodship Specialist Hospital in Slupsk, Ustka, Pologne
11 Dermatologie, Centro Hospitalar Universitário do Porto, Porto, Portugal
12 Dermatologie, Nicolina Medical Center, P. Poni Institute of Macromolecular Chemistry, Romanian Academy, Iași, Roumanie
13 Infectious disease, Sf. Parascheva “Clinical Hospital, “Gr. T. Popa” University of Medicine, Iași, Roumanie
14 Dermatologie, Oxford University Hospitals NHS Trust, Oxford, Royaume-Uni
15 Paediatric dermatology, St John's Institute of Dermatology, Guy's and St Thomas’ NHS Foundation Trust, Londres, Royaume-Uni
16 Dermatologie, CHRU de Tours, hôpital pédiatrique Clocheville, Tours, France
17 Dermatologie, Scientific Center of Children's Health of the Ministry of Health of the Russian Federation, Moscou, Fédération de Russie
18 Paediatric dermatology, Hospital General de México Dr. Eduardo Liceaga, Secretaría de Salud, Ciudad de Mexico, Mexique
19 Dermatologie, Hospital Alemán, Buenos Aires, Argentine
20 Dermatologie, C.H. Victor Dupouy, Argenteuil, France
21 Dermatologie, hôpital Saint-Vincent-de-Paul, Lille, France
22 Dermatologie, AP–HP, Paris, France
23 Dermatologie, CHU Clermont-Ferrand Site Estaing, Clermont-Ferrand, France
24 Dermatologie, hôpitaux universitaires de Marseille – AP–HM, Marseille, France
25 Dermatologie, CH Intercommunal Robert Ballanger, Aulnay-sous-Bois, France
26 Dermatologie, centre hospitalier Edmond Garcin, Aubagne, France
27 Dermatologie, IRCSS Azienda Ospedaliero Universitaria di Bologna, Bologne, Italie
28 Dermatologie, Faculty of Medicine, School of Health Sciences, University of Thessaly, Biopolis, Larissa, Grèce
29 Dermatologie, Radboud University Medical Center, Nimègue, Pays-Bas
30 Dermatologie, Department of Medicine Solna, Karolinska Institutet, Stockholm, Suède
31 Dermatologie, CH de Niort, Niort, France
32 Dermatologie, université de Tours, CHU de Tours, Tours, France
33 Dermatologie, St John's Institute of Dermatology, Londres, Royaume-Uni
34 Paediatric dermatology, St John's Institute of Dermatology, Londres, Royaume-Uni
35 Dermatologie, hôpital Saint-Louis AP–HP, Paris, France
36 Dermatologie, CH d’Argenteuil, Argenteuil, France
⁎ Auteur correspondant.
14 12 2022
11 2022
14 12 2022
2 8 A115A116
Copyright © 2022 Published by Elsevier Masson SAS.
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
La pandémie à SARS-CoV-2 a soulevé de nombreuses questions sur la prise en charge des maladies chroniques et leurs traitements. Les données concernant l’utilisation des biothérapies chez les adultes atteints de psoriasis sont rassurantes, mais manquent chez l’enfant. Par ailleurs, l’infection à SARS-CoV-2 pourrait influencer l’évolution du psoriasis chez l’enfant. L’objectif de cette étude était d’évaluer l’impact de l’infection à SARS-CoV-2 sur le psoriasis, et la sévérité de l’infection selon le traitement systémique reçu.
Matériel et méthodes
Les données ont été collectées de février 2021 à février 2022 en provenance de 14 pays. Les enfants étaient inclus s’ils avaient moins de 18 ans, un antécédent de psoriasis ou apparu dans le moins suivant l’infection COVID-19, et avaient été infectés par le SARS-CoV-2 avec ou sans symptômes.
Résultats
Au total, 117 enfants ont été inclus (filles : 49,6 %, âge moyen : 12,4 ans) avec 120 infections) SARS-CoV-2. La principale forme de psoriasis était le psoriasis en plaques (69,4 %) ; le psoriasis était actif avant l’infection dans 70,1 % des cas. La majorité des enfants ne prenaient pas de traitement systémique au moment de l’infection (54,2 %), 33 enfants (28,3 %) étaient sous biothérapie (principalement anti-TNF alpha et ustékinumab), et 24 (20,0 %) sous traitement systémique conventionnel (principalement méthotrexate). L’infection était confirmée chez 106 enfants (88,3 %) et 3 ont eu la maladie 2 fois (1 enfant asymptomatique sous ustékinumab et 2 enfants symptomatiques sans traitement systémique). L’infection était symptomatique chez 75 enfants (62,5 %) avec une durée moyenne des symptômes de 6,5 jours, significativement plus longue chez les enfants sous traitement systémique conventionnel (p = 0,002) ou sans traitement systémique (p = 0,006) par rapport aux enfants traités par biothérapies. Six enfants ont nécessité une hospitalisation, dont un enfant en réanimation ; ils étaient plus fréquemment sous traitements systémiques conventionnels par rapport aux autres enfants (p = 0,01), et principalement sous méthotrexate (p = 0,03). Aucun enfant sous biothérapie n’a été hospitalisé, et aucun décès n’a été rapporté.
Après l’infection, le psoriasis s’est aggravé dans 17 cas (15,2 %), sans modification du phénotype sauf pour un enfant avec un psoriasis initialement en plaques qui a eu suite à l’infection une poussée de psoriasis en gouttes. Neuf enfants (8,0 %) ont développé un psoriasis de novo dans le mois qui a suivi l’infection, plus souvent un psoriasis en gouttes (p = 0,01) par rapport aux enfants ayant un antécédent connu de psoriasis. Ces enfants avaient un antécédent familial de psoriasis dans 75,0 % des cas.
Discussion
L’utilisation des biothérapies semble rassurante sans augmentation du risque de forme sévère de COVID-19 chez les enfants atteints de psoriasis. L’infection à SARS-CoV-2 était responsable du développement de psoriasis de novo ou de l’aggravation d’un psoriasis connu chez certains enfants.
==== Body
pmcDéclaration de liens d’intérêts
E. Mahé : AbbVie, Amgen, Celgene, Janssen Cilag, Leo Pharma, Lilly, Novartis, Sanofi.H. Bachelez has undertaken activities as a paid consultant, advisor or speaker for AbbVie, Almirall, Anaptysbio, Boehringer Ingelheim, Bristol Myers Squibb, Celgene, Kyowa Kirin, Janssen Cilag, Leo Pharma, Lilly, Novartis, UCB, and received research funding support from Boehringer Ingelheim, Bristol Myers Squibb, Leo Pharma, Novartis, PfizerC. Flohr is Chief Investigator of the UK National Institute for Health Research-funded TREAT (ISRCTN15837754) and SOFTER (Clinicaltrials.gov : NCT03270566) trials as well as the UK-Irish Atopic eczema Systemic Therapy Register (A-STAR ; ISRCTN11210918) and a Principle Investigator in the European Union (EU) Horizon 2020-funded BIOMAP Consortium (http ://www.biomap-imi.eu/). He also leads the EU Trans-Foods consortium. His department has received funding from Sanofi-Genzyme for skin microbiome work.C. Smith has received departmental research funding from AbbVie, Novartis, Pfizer, and Sanofi and has served as an investigator on Medical Research Council–and Horizon 2020–funded consortia with industry partners (see psort.org.uk and biomap — imi.eu).S.K. Mahil has received departmental funding from AbbVie, Celgene, Eli Lilly, Janssen-Cilag, Novartis, Sanofi, and UCB.E. Sonkoly has undertaken activities as a paid consultant, advisor or speaker for Eli Lilly, AbbVie, Janssen Cilag, Leo Pharma, Bristol Myers Squibb, Novartis, and UCB.J M.P.A. van den Reek carried out clinical trials for AbbVie, Celgene and Janssen and has received speaking fees/attended advisory boards from AbbVie, Janssen, BMS, Almirall, Leo Pharma, Novartis, UCB, and Eli Lilly and reimbursement for attending a symposium from Janssen, Pfizer, Celgene and AbbVie. All funding is not personal but goes to the independent research fund of the department of dermatology of Radboudumc Nijmegen, the Netherlands.I. Neri : Janssen Cilag, Sanofi, LillyR. Epishev : Eli Lilly, Novartis, AbbVie, Amryt Pharma, Janssen, Pfizer, Celgene, Mölnlycke Health Care AB.T. McPherson AbbVie, Leo pharma, SanofiT. Torres : AbbVie, Almirall, Amgen, Arena Pharmaceuticals, Biocad, Boehringer Ingelheim, Bristol-Myers Squibb, Celgene, Eli Lilly, Janssen, Leo Pharma, MSD, Novartis, Pfizer, Samsung-Bioepis, Sandoz, SanofiN. Murashkin Jansen Cilag, Eli Lilly, Novartis, AbbVie, Amryt Pharma, Pfizer, Celgene, Mölnlycke Health Care AB, Zeldis Pharma, Galderma, BayerA. Lesiak : AbbVie, Almirall, Janssen Cilag, Leo Pharma, Lilly, Novartis, Pfizer, Sandoz, Pierre-FabreVito di Lernia : AbbVie, Janssen Cilag, Novartis, Sanofi.
| 0 | PMC9748132 | NO-CC CODE | 2022-12-15 23:22:39 | no | 2022 Nov 14; 2(8):A115-A116 | utf-8 | null | null | null | oa_other |
==== Front
Annales de Dermatologie et de Vénéréologie - FMC
2667-0623
2667-0623
Published by Elsevier Masson SAS
S2667-0623(22)00554-2
10.1016/j.fander.2022.09.278
Po099
Récurrence d’une maladie de Kaposi après un épisode de COVID-19 : quelle relation ?
Manaa L. 1⁎
Aounallah A. 1
Salah N. Ben 1
Gaied M. Lahoual Ep 1
Fetoui N. Ghariani 1
Mokni S. 1
Ghariani N. 1
Sriha B. 2
Belajouza C. 1
Denguezli M. 1
1 Dermatologie, hôpital Farhat-Hached, Sousse, Tunisie
2 Anatomo-pathologie, hôpital Farhat-Hached, Sousse, Tunisie
⁎ Auteur correspondant.
14 12 2022
11 2022
14 12 2022
2 8 A184A185
Copyright © 2022 Published by Elsevier Masson SAS.
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
L’infection par le SRAS-CoV-2 est associée à plusieurs manifestations cutanées qui peuvent être directement liées à l’infection virale ou aux traitements administrés. Un rôle potentiel du SRAS-CoV-2 dans la réactivation d’autres virus, tels que l’herpès virus humain (HHV) 6, le HHV-7, le HHV-8 et le virus d’Epstein-Barr a été discuté. Nous rapportons le cas d’un patient qui a eu une récurrence de sa maladie de Kaposi après une hospitalisation pour une infection COVID-19.
Observations
Un patient âgé de 71 ans était suivi depuis 2004 pour une maladie de Kaposi dans sa forme classique confirmée histologiquement et traitée par radiothérapie et chimiothérapie avec une rémission complète. Il était hospitalisé en pneumologie en juillet 2021 pour une infection sévère COVID-19. Vingt jours après, il a présenté des lésions violacées s’étendant progressivement au niveau du tronc et des membres. À l’examen, il avait des plaques violacées angiomateuses non douloureuses, étendues au niveau du tronc et des membres sans atteinte muqueuse et sans adénopathies palpables.
Résultats
Une biopsie a été faite confirmant le diagnostic de maladie de Kaposi avec un marquage positif avec l’anticorps anti-HHV8 à l’immunohistochimie. La sérologie VIH était négative. Le patient était traité par des cures de bléomycine avec bonne évolution.
Discussion
Le sarcome de Kaposi est une tumeur angioproliférative d’origine endothéliale strictement liée à l’infection par le HHV-8, qui a été documentée dans plus de 95 % des lésions. Le HHV-8 fait partie de la famille des Herpesvirus humains et a un cycle viral divisé en deux phases, latente et lytique. Il a été démontré que divers facteurs font passer le HHV-8 de la latence à la réactivation lytique, tels que l’immunosuppression, les cytokines pro-inflammatoires et les co-infections virales. En effet, nous supposons qu’il existe une interaction entre le SRAS-CoV-2 et le HHV8, où le premier contribue à un état hyperinflammatoire notamment par la production de l’IL-6 qui joue un rôle important dans la réactivation lytique et la prolifération du HHV8. Une étude récente a argumenté cette hypothèse en détectant la coexistence des particules virales du SRAS-CoV-2 et du HHV8 en microscopie électronique chez une patiente qui a présenté une réactivation de sa maladie de Kaposi après une infection COVID-19. Une autre étude a récemment rapporté que les protéines du SRAS-CoV-2 et les médicaments anti-COVID-19 sont capables d’induire la réactivation lytique du HHV8 par la modification des voies de signalisation intracellulaire. En conclusion, l’infection par le SRAS-CoV-2 peut entraîner la réactivation de plusieurs infections latentes, notamment le HHV-8 et, par conséquent, une récidive de la maladie de Kaposi.
==== Body
pmcDéclaration de liens d’intérêts
Les auteurs déclarent ne pas avoir de liens d’intérêts.
| 0 | PMC9748136 | NO-CC CODE | 2022-12-15 23:22:39 | no | 2022 Nov 14; 2(8):A184-A185 | utf-8 | null | null | null | oa_other |
==== Front
Annales de Dermatologie et de Vénéréologie - FMC
2667-0623
2667-0623
Published by Elsevier Masson SAS
S2667-0623(22)00424-X
10.1016/j.fander.2022.09.150
Co15-128
Pemphigoïde muco-cutanée sévère post-vaccination anti-SARS-Cov2
Macaudiere P. 1⁎
Delaumenie S. 2
Landais C. 3
Juzot C. 1
Jeudy G. 1
Bedane C. 14
1 Dermatologie, centre hospitalier universitaire F. Mitterrand Dijon-Bourgogne, Dijon, France
2 Dermatologie, CH de Brive, Brive-la-Gaillarde, France
3 Dermatologie, CH Chalon sur Saône William Morey, Chalon-sur-Saône, France
4 Dermatologie, CNR Malibul, Limoges, France
⁎ Auteur correspondant.
14 12 2022
11 2022
14 12 2022
2 8 A118A118
Copyright © 2022 Published by Elsevier Masson SAS.
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
La pemphigoïde bulleuse (PB) est la dermatose bulleuse auto-immune la plus fréquente. Elle est liée à des anticorps anti-membrane basale dermo-épidermique dirigés contre les antigènes BP180 et BP230. La réactivation d’une PB par une vaccination et particulièrement la vaccination antigrippale est un phénomène bien connu. Elle concerne des sujets de plus de 75 ans. Nous rapportons 6 observations de pemphigoïdes particulièrement sévères avec atteinte muqueuse chez des sujets plus jeunes sans antécédent dermatologique survenues dans les suites très proches d’une vaccination anti-SARS-Cov2 à ARN messager.
Observations
Nous rapportons l’observation de 6 patients (4 femmes et 2 hommes), âgés de 48 à 76 ans (moyenne de 54 ans) atteints de pemphigoïde bulleuse dans 3 centres différents. Aucun de ces patients n’avait de maladies neurologiques, neuropsychiques ou de médicaments imputables classiques après enquête médicamenteuse. Une vaccination COVID-19 (2 SPIKEVAX, 4 CORMINATY) avait eu lieu peu de temps avant l’apparition des symptômes qui débutaient chez 67 % des patients après la 1re dose. La 2e dose était suivie d’une aggravation du tableau clinique initial. Chez les 33 % restant, les symptômes débutaient après la 2e dose. Le délai d’apparition était de quelques jours à 2 semaines (1re et 2e dose confondues). L’atteinte était multi-bulleuse et souvent profuse (> 200 bulles par jour). Il existait une atteinte des muqueuses dans 83 % des cas (50 % d’atteinte génitale, 83 % d’atteinte buccale). On remarquait une atteinte du visage et une atteinte acrale prédominante. L’immunofluorescence directe positive en IgG et/ou C3 à la jonction dermo-épidermique permettait de confirmer le diagnostic. Les anticorps anti-BP180 étaient fortement positifs (> 200U/ml) chez l’ensemble des patients, seul un patient avait des anticorps anti-BP230 positifs (50U/ml). L’immunofluorescence indirecte était positive sur peau clivée au niveau du toit de la bulle chez 50 % des patients. Tous les patients ont été traités par dermocorticoïdes d’activité très forte mais ce traitement n’a jamais suffi à lui seul pour contrôler la maladie (résistance et récidive). 50 % d’entre eux ont été traités par une corticothérapie orale, 33 % par des cyclines et 83 % par du méthotrexate (en association aux corticoïdes topiques, oraux ou aux cyclines). Une patiente a finalement été traité par rituximab. Chez l’ensemble des patients l’atteinte cicatricielle était sévère avec de nombreux grains de milium.
Discussion
La survenue de ces pemphigoïdes bulleuses faisait suite à une vaccination à vaccin à ARN messager contre la COVID-19 fortement imputable (délai compatible, pas d’autres facteurs de risques ou médicaments imputables). La présentation était caractérisée par un âge de survenue jeune (moyenne d’âge de 54 ans versus 80 ans), une atteinte cutanée bruyante, une atteinte des muqueuses (83 % versus 10 %), une rançon cicatricielle importante, une forte immunisation anti-BP180 et pour la plupart des patients un caractère récidivant avec nécessité de recours aux immunosuppresseurs.
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| 0 | PMC9748161 | NO-CC CODE | 2022-12-15 23:22:40 | no | 2022 Nov 14; 2(8):A118 | utf-8 | null | null | null | oa_other |
==== Front
Annales de Dermatologie et de Vénéréologie - FMC
2667-0623
2667-0623
Published by Elsevier Masson SAS
S2667-0623(22)00773-5
10.1016/j.fander.2022.09.497
Po311
Facteurs prédictifs d’une poussée de psoriasis après une infection par le SARS-CoV-2 chez l’enfant
Mahé E. 1⁎
Zitouni J. 2
Di Lernia V. 3
Belloni Fortina A. 4
Lesiak A. 5
Brzezinski P. 6
Topkarci Z. 7
Murashkin N. 8
Torres T. 9
Vidaurri De La Cruz H. 10
Maruani A. 11
Chiriac A. 12
Mallet S. 13
Kupfer-Bessaguet I. 14
Sonkoly E. 15
Bursztejn A.C. 16
Van Den Reek J. 17
Epishev R. 18
Severino Freire M. 19
Akinde M. 20
Beauchet A. 21
Luca C. 22
Thomas J. 23
Mcpherson T. 24
Bachelerie M. 25
Bourrat E. 26
Bellissen A. 27
Zafiriou E. 28
Leducq S. 29
Neri I. 30
Luna P. 31
Steff M. 32
Sergeant M. 33
Mahil S. 34
Smith C. 34
Flohr C. 35
Bachelez H. 36
Groupe de Recherche sur le Psoriasis de la Socité Française de Dermatologie
Groupe de Recherche de la Société Française de Dermatologie Pédiatrique, PsoProtect study group
British Society of Paediatric Dermatology
Società Italiana di Dermatologia Pediatrica
1 Dermatologie, CH d’Argenteuil, Argenteuil, France
2 Dermatologie, hôpital Necker, AP–HP, Paris, France
3 Dermatologie, Arcispedale Santa Maria Nuova, Reggio Emilia, Italie
4 Pediatric dermatology unit, Department of Medicine DIMED, University of Padova, Padoue, Italie
5 Dermatology, paediatric dermatology and oncology, Medical Univeristy of Lodz, Łódź, Pologne
6 Dermatologie, Voivodship Specialist Hospital in Slupsk, Ustka, Pologne
7 Dermatologie, Dr. Sadi Konuk Training and Research Hospital, Istanbul, Turquie
8 Dermatologie, Scientific Center of Children's Health of the Ministry of Health, Moscou, Fédération de Russie
9 Dermatologie, Centro Hospitalar Universitário do Porto, Porto, Portugal
10 Paediatric dermatology, Hospital General de México Dr. Eduardo Liceaga, Secretaría de Salud, Ciudad de Mexico, Mexique
11 Dermatologie, CHRU de Tours, hôpital pédiatrique Clocheville, Tours, France
12 Dermatologie, Nicolina Medical Center, P. Poni Institute of Macromolecular Chemistry, Romanian Academy, Iaşi, Roumanie
13 Dermatologie, hôpitaux universitaires de Marseille, AP–HM, Marseille, France
14 Dermatologie, CH de Niort, Niort, France
15 Dermatologie, Department of Medicine Solna, Karolinska Institutet, Stockholm, Suède
16 Dermatologie, CHU, Nancy, France
17 Dermatologie, Radboud University Medical Center, Nimègue, Pays-Bas
18 Dermatologie, Scientific Center of Children's Health of the Ministry of Health of the Russian Federation, Moscou, Fédération de Russie
19 Dermatologie, hôpital Larrey, Toulouse, France
20 Paediatric dermatology, St John's Institute of Dermatology, Guy's and St Thomas’ NHS Foundation Trust, Londres, Royaume-Uni
21 Santé publique, hôpital Ambroise-Paré, AP–HP, Boulogne-Billancourt, France
22 Infectious disease, Sf. Parascheva Clinical Hospital, “Gr. T. Popa” University of Medicine, Iași, Roumanie
23 Dermatologie, J. T. Skin Care Centre, Chennai, Inde
24 Dermatologie, Oxford University Hospitals NHS Trust, Oxford, Royaume-Uni
25 Dermatologie, CHU Clermont-Ferrand Site Estaing, Clermont-Ferrand, France
26 Dermatologie, AP–HP, Paris, France
27 Dermatologie, centre hospitalier Edmond Garcin, Aubagne, France
28 Dermatologie, Faculty of Medicine, School of Health Sciences, University of Thessaly, Biopolis, Larissa, Grèce
29 Dermatologie, université de Tours, CHU de Tours, Tours, France
30 Dermatologie, IRCSS Azienda Ospedaliero Universitaria di Bologna, Bologne, Italie
31 Dermatologie, Hospital Alemán, Buenos Aires, Argentine
32 Dermatologie, C.H. Intercommunal Robert Ballanger, Aulnay-sous-Bois, France
33 Dermatologie, CHRU de Nancy, hôpitaux de Brabois, Vandœuvre-lès-Nancy, France
34 Dermatologie, St John's Institute of Dermatology, Londres, Royaume-Uni
35 Paediatric dermatology, St John's Institute of Dermatology, Londres, Royaume-Uni
36 Dermatologie, hôpital Saint-Louis, AP–HP, Paris, France
⁎ Auteur correspondant.
14 12 2022
11 2022
14 12 2022
2 8 A294A295
Copyright © 2022 Published by Elsevier Masson SAS.
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
La pandémie à SARS-CoV-2 a été source de nombreuses questions quant à l’impact de l’infection sur les dermatoses inflammatoires chroniques, et de l’impact des traitements de ces dermatoses sur la sévérité de l’infection. Le registre international Chi-PsoCov (enfants psoriasique souffrant de psoriasis et ayant développé une infection à SARS-CoV-2) a permis de montrer que les biothérapies n’augmentaient pas le risque de formes sévères de COVID-19 chez les enfants atteints de psoriasis. Par ailleurs, il était montré que le COVID-19 était responsable du développement de psoriasis de novo ou de l’aggravation d’un psoriasis connu chez certains enfants.
Dans cette partie du travail nous nous sommes concentrés sur les enfants ayant développé une poussée de psoriasis après l’infection : aspects phénotypiques des poussées, et recherche de facteurs de risque liés à la maladie, au psoriasis, ou aux traitements, associés à l’aggravation du psoriasis après l’infection.
Matériel et méthodes
Les données ont été collectées de février 2021 à mai 2022 en provenance de 14 pays. Les enfants étaient inclus s’ils avaient moins de 18 ans, un antécédent de psoriasis ou psoriasis apparu dans le moins suivant l’infection COVID-19, et avaient été infectés par le SARS-CoV-2 avec ou sans symptômes. Les enfants ayant développé un psoriasis de novo étaient exclus de cette étude.
Résultats
Sur les 152 inclusions du registre Chi-PsoCov, dix enfants ont développé un psoriasis dans le mois suivant l’infection et n’ont pas été retenus dans ce travail. L’analyse a porté sur 135 enfants ayant développé 142 COVID-19. Le psoriasis était stable dans 120 cas (84,5 %) et s’aggravait dans le mois suivant l’infection dans 22 cas (15,5 %).
Dans 20 cas, lors de la poussée, le phénotype était inchangé, et dans 2 cas, il y avait un changement de phénotype : psoriasis en plaques en psoriasis en gouttes (n = 1) ou inversé (n = 1).
Ni les caractéristiques démographiques, ni les aspects du psoriasis (notamment psoriasis actif vs en rémission), ni la sévérité de l’infection à SARS-CoV-2 n’étaient associés à des poussées de psoriasis. Seule l’utilisation de traitements systémiques du psoriasis, conventionnels ou biothérapies, lors de l’infection semblait protectrice de la survenue de poussées (50,0 % dans le groupe stable vs 27,3 % dans le groupe poussées, p = 0,049).
Discussion
L’infection à SARS-CoV-2 est responsable dans environ 15 % des cas de poussées de psoriasis. Dans la grande majorité des cas, le phénotype précédent l’infection est conservé. Ces poussées ne sont pas associées à la sévérité du psoriasis, de l’infection ou autres paramètre cliniques. Seuls les traitements systémiques semblent réduire ce risque, probablement en « contrôlant » la poussée. Il est possible qu’une susceptibilité d’ordre génétique, non explorée ici, explique aussi cette susceptibilité à l’infection.
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| 0 | PMC9748162 | NO-CC CODE | 2022-12-15 23:22:40 | no | 2022 Nov 14; 2(8):A294-A295 | utf-8 | null | null | null | oa_other |
==== Front
Annales de Dermatologie et de Vénéréologie - FMC
2667-0623
2667-0623
Published by Elsevier Masson SAS
S2667-0623(22)00384-1
10.1016/j.fander.2022.09.110
Co10-088
Réactions cutanées retardées après vaccination contre le SARS-CoV-2 : étude CoVacskin
Darrigade A.S. 12⁎
Oules B. 34
Sohier P. 54
Jullie M.L. 6
Moguelet P. 7
Barbaud A. 8
Soria A. 9
Vignier N. 10
Lebrun-Vignes B. 11
Sanchez-Pena P. 12
Chosidow O. 1314
Beylot-Barry M. 1516
Milpied B. 15
Dupin N. 317
1 Service de dermatologie, CHU de Bordeaux, Bordeaux, France
2 Fisard, French Investigators for skin Adverse Reactions to Drugs of the French Society of Dermatology, Paris, France
3 Dermatologie, hôpital Cochin, Paris, France
4 Inserm U1016, université Paris Cité, Paris, France
5 Pathologie, hôpital Cochin, Paris, France
6 Anatomopathologie, CHU Haut Leveque, Pessac, France
7 Anatomopathologie, hôpital Saint-Antoine, AP–HP, Paris, France
8 Dermatologie, hôpital Tenon, AP–HP, Paris, France
9 Dermatologie et allergologie, hôpital Tenon, AP–HP, Paris, France
10 Infectiologie cayenne, CHU de Cayenne, Cayenne, France
11 Pharmacovigilance, hôpitaux universitaires Pitié Salpêtrière–Charles-Foix, Paris, France
12 Pharmacovigilance, hôpital Saint-André, Bordeaux, France
13 Dermatologie, hôpital Henri-Mondor, AP–HP, Créteil, France
14 Gridist, groupe infectiologie dermatologique - infections sexuellement transmissibles, Société française de dermatologie, Paris, France
15 Dermatologie, hôpital Saint-André, Bordeaux, France
16 Inserm U 1312 Bordeaux Cancer Institute of Oncology, université de Bordeaux, Bordeaux, France
17 Inserm U1016-CNRS UMR8104, équipe biologie cutané, institut Cochin - CNRS - Inserm - université Paris Cité, Paris, France
⁎ Auteur correspondant.
14 12 2022
11 2022
14 12 2022
2 8 A93A94
Copyright © 2022 Published by Elsevier Masson SAS.
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
Depuis 2020, environ 12 milliards de doses de vaccins contre le SARS-CoV-2 ont été administrées. Des réactions cutanées retardées localisées ou généralisées ont été rapportées avec une fréquence de près de 2 % après la 1re dose et un risque de récidive de 20 %. Le but de cette étude était de colliger l’expérience française.
Matériel et méthodes
De mai à septembre 2021, la SFD a lancé un appel à cas afin de recueillir les réactions cutanées localisées retardées (> 4 jours (j) après l’injection) et généralisées (> 4 h après l’injection) (étude CoVacskin, No APP-2021-17). Un questionnaire standardisé permettait de recueillir antécédents, type de vaccin, numéro de l’injection, type de réaction (liste de diagnostics prédéfinis), traitement et délai de rémission de la réaction. Les photographies et comptes-rendus histologiques étaient analysés par le comité scientifique. Les biopsies étaient relues par 3 dermatopathologistes.
Résultats
Au total, 194 cas ont été recueillis pour 192 patients dont 121 femmes, âge médian 54 ans. Ces réactions survenaient dans 88 % après un vaccin de type ARN. Pour 135 cas la réaction cutanée survenait après la 1re dose. Le délai médian injection-réaction était de 2,6 j. Quarante-huit réactions localisées retardées (24,7 %) étaient rapportées et 146 réactions généralisées sans réaction localisée (75,3 %), incluant urticaires ou angioedèmes, eczémas, exanthèmes maculopapuleux, purpura, réactions au produit de comblement, livedos, pseudo-engelures et 66 cas classés « autres » La relecture des photographies confirmait le diagnostic proposé par le clinicien pour 49/66 « autres ». Parmi les 17 cas restants, 2 tableaux anatomocliniques spécifiques ont été identifiés (13 avec biopsies cutanées disponibles): chez 5 patients, le tableau « Syndrome de Sweet-like » associait des lésions cliniques évocatrices de syndrome de Sweet (SS), un bilan étiologique négatif et histologiquement un SS « classique » ou histiocytoïde. Le deuxième tableau, chez 7 patients, jamais décrit antérieurement, nommé « Covid-arm multiples » était caractérisé par de multiples plaques inflammatoires et une histologie identique aux lésions cutanées localisées type « Covid-arm » (infiltrat péri-vasculaire et interstitiel de lymphocytes et éosinophiles, spongiose). Le délai de guérison moyen était de 21,4 j, spontanément dans 24,2 %. Une nouvelle injection de vaccin était administrée dans 117 cas, le plus souvent sans récidive (67,5 %).
Discussion
Cette étude confirme les données de la littérature avec des réactions cutanées retardées prédominant chez les femmes, surtout après la 1ère dose, en majorité généralisées, hétérogènes, mais le plus souvent bénignes, incluant deux tableaux anatomocliniques originaux. Ces réactions étaient résolutives souvent en moins d’un mois, récidivaient peu, et ne contre-indiquaient donc pas la poursuite du schéma vaccinal.
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==== Front
Annales de Dermatologie et de Vénéréologie - FMC
2667-0623
2667-0623
Published by Elsevier Masson SAS
S2667-0623(22)00474-3
10.1016/j.fander.2022.09.200
Po022
Érythème pigmenté fixe bulleux généralisé post-vaccination anti-SARS-CoV-2
Sueur P. 1⁎
Castelain F. 1
Charollais R. 2
Aubin F. 2
Pelletier F. 1
1 Dermatologie-allergologie, CHU de Besançon, Besançon, France
2 Dermatologie, CHU de Besançon, Besançon, France
⁎ Auteur correspondant.
14 12 2022
11 2022
14 12 2022
2 8 A145A145
Copyright © 2022 Published by Elsevier Masson SAS.
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
Les réactions cutanéomuqueuses post-vaccinales ont fréquemment été rapportées depuis le début de la vaccination anti-SARS-CoV-2 mais sont rarement liées à un mécanisme hypersensibilité allergique retardée. Nous rapportons le cas d’une patiente de 92 ans adressée pour une éruption cutanéomuqueuse bulleuse récidivante post-vaccination.
Observations
Il s’agissait d’une patiente sans antécédent notable traitée uniquement par clorazépate dipotassique. Elle reçut une première dose du vaccin anti-SARS-CoV-2 Pfizer BioNtech Comirnaty® en avril 2021 bien tolérée, suivie de la deuxième injection en juin 2021. Le lendemain, elle présenta des lésions érythémateuses arrondies isolées du dos des mains et sur une jambe, partiellement résolutives sous dermocorticoïdes.
La troisième dose de rappel du vaccin fut effectuée six mois plus tard : elle présenta alors le lendemain le même type de lésions, cette fois plus diffuses et associées à une dysphonie. Le 22/04/2022, elle reçut la 4e dose de rappel, préférant cette fois-ci le vaccin du laboratoire Moderna compte tenu des réactions précédentes. Le lendemain, elle rapporte une récidive des lésions mais de façon plus importante et plus diffuse que lors des vaccinations précédentes, associée à une sensation de brûlure avec un angiœdème des lèvres, une atteinte conjonctivale ainsi qu’une dysphonie.
Lors de la consultation, la patiente avait en effet des lésions ovalaires maculopapuleuses, érythémato-violacées et parfois bulleuses avec un décollement cutané médio-dorsal. Les lésions atteignaient l’ensemble du corps (visage, tronc, membres supérieurs et inférieurs). Il existait une atteinte de la muqueuse buccale avec des érosions de la face intérieure de la joue droite, des lésions aphtoïdes sublinguales et érosives des lèvres. La patiente avait également une dysphonie sans dysphagie.
La biologie sanguine était sans anomalie en dehors d’un syndrome inflammatoire biologique et il n’y avait pas d’altération de l’état général. La biopsie cutanée d’une lésion était en faveur du diagnostic d’érythème pigmenté fixe évoqué cliniquement.
Discussion
Il s’agit d’un cas rarement décrit d’érythème pigmenté fixe bulleux (EPFB) d’aggravation progressive vers une forme généralisée avec atteinte muqueuse malgré le changement de marque de vaccin à ARNm. Des cas d’EPFB ont été décrits avec le vaccin contre la grippe, l’HPV et la fièvre jaune. La majeure partie des réactions cutanées liées au vaccin anti-SARS-CoV-2 sont non sévères. Les dermatologues et allergologues doivent connaître la possibilité de ces réactions d’hypersensibilités sévères.
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| 0 | PMC9748164 | NO-CC CODE | 2022-12-15 23:22:40 | no | 2022 Nov 14; 2(8):A145 | utf-8 | null | null | null | oa_other |
==== Front
Annales de Dermatologie et de Vénéréologie - FMC
2667-0623
2667-0623
Published by Elsevier Masson SAS
S2667-0623(22)00974-6
10.1016/j.fander.2022.10.079
Po236
Maladie de Kawasaki de l’adulte satellite d’une infection à SARS-CoV-2
Beytout Q. 1⁎
Rossi G. 2
Lefort A. 2
Descamps V. 3
Le Bozec P. 1
1 Uf de dermatologie, hôpital Beaujon, AP–HP, Clichy, France
2 Médecin interne, hôpital Beaujon, AP–HP, Clichy, France
3 Dermatologie, hôpital Bichat – Claude-Bernard, Paris, France
⁎ Auteur correspondant.
14 12 2022
11 2022
14 12 2022
2 8 A252A252
Copyright © 2022 Published by Elsevier Masson SAS.
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
L’infection par le SARS-CoV-2 est responsable de nombreuses atteintes cutanées. Des syndromes proches de la maladie de Kawasaki ont été décrits dans la littérature, notamment chez les enfants, avec des tableaux abdominaux et systémiques parfois mortels.
Nous présentons un cas de maladie de Kawasaki satellite d’une infection à SARS-CoV-2 avec retard diagnostique.
Observations
Un homme de 24 ans sans antécédent notable consultait pour des douleurs abdominales fébriles associées à des diarrhées. Une échographie trouvait un épaississement des parois vésiculaires. Un traitement symptomatique était prescrit par son médecin traitant qui signalait la présence initiale d’adénopathies cervicales bilatérales.
Il consultait aux urgences après 5 jours d’évolution devant l’absence d’amélioration clinique. Il était fébrile avec un syndrome inflammatoire important (CRP 125 mg/L, hyperleucocytose à 10 000/mm3) associé à une cytolyse hépatique et un ictère cholestatique. Une PCR COVID systématique aux urgences était positive. La prise en charge initiale aux urgences était celle d’une angiocholite, avec début d’une antibiothérapie probabiliste par ceftriaxone et métronidazole. Les explorations radiologiques ne montraient pas de dilatation des voies biliaires ni de calcul. Au troisième jour d’hospitalisation, il présentait une desquamation des mains et des pieds en doigts de gants et du scrotum, une fièvre persistante, une conjonctivite, une chéilite avec langue framboisée. Biologiquement on notait une hypoalbuminémie et une leucocyturie aseptique.
Le diagnostic retenu était alors celui d’une maladie de Kawasaki satellite d’une infection COVID. La prise en charge thérapeutique adaptée au diagnostic associait une corticothérapie générale à 1 mg/kg et une cure d’immunoglobuline intraveineuse (IgIV) à 1 g/kg/j sur 2 jours. Un traitement par aspirine 100 mg par jour était également débuté. Au lendemain des IgIV, la fièvre cessait, et le patient se rétablissait. Le bilan cardiologique (ECG, ETT et IRM cardiaque) était normal et le patient n’avait pas eu de défaillance hémodynamique.
Un coroscanner est programmé pour s’assurer de l’absence d’atteinte coronarienne au décours.
Discussion
Il s’agit d’un cas de maladie de Kawasaki au décours d’une infection par la COVID-19 avec retard de diagnostic. En effet, la prise en charge initiale avait été celle d’une angiocholite. La maladie de Kawasaki associée à une infection par le SARS-CoV-2 de l’adulte est moins fréquente que chez l’enfant. Néanmoins, les complications peuvent être sévères et le diagnostic sémiologique précoce est nécessaire pour adapter rapidement la prise en charge.
==== Body
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| 0 | PMC9748165 | NO-CC CODE | 2022-12-15 23:22:40 | no | 2022 Nov 14; 2(8):A252 | utf-8 | null | null | null | oa_other |
==== Front
Annales de Dermatologie et de Vénéréologie - FMC
2667-0623
2667-0623
Published by Elsevier Masson SAS
S2667-0623(22)00447-0
10.1016/j.fander.2022.09.173
Co17-151
La pandémie COVID-19 est associée à des mélanomes diagnostiqués à un stade plus avancé
Skowron F. 1⁎
Mouret S. 2
Seigneurin A. 3
Montaudié H. 4
Maubec E. 5
Grange F. 6
Quéreux G. 7
Celerier P. 8
Adle A. 9
Dalac S. 10
De Quatrebarbes J. 11
Zehou O. 12
Safia A. 13
Muller P. 14
Modiano P. 15
Misery L. 16
Litrowski N. 17
Brunet Possenti F. 18
Mortier L. 19
Bens G. 20
Hervieu A. 21
Leduc N. 22
Jouary T. 23
Lesage C. 24
Beneton N. 25
Le Corre Y. 26
Geoffrois L. 27
Thomas-Beaulieu D. 28
Khammari A. 29
Wierzbicka-Hainaut E. 30
Leccia M.T. 31
1 Dermatologie, hôpitaux Drôme Nord, Romans-sur-Isère, France
2 Dermatologie, CHU de Grenoble-Alpes, La Tronche, France
3 Dermatologie, service des urgences, CHU du Nord de Grenoble, hôpital Albert-Michallon, La Tronche, France
4 Service de dermatologie, hôpital l’Archet, Nice, France
5 Service de dermatologie, centre de compétence maladies génétiques à expression cutanée, hôpital Avicenne AP–HP, Bobigny, France
6 Dermatologie, centre hospitalier de Valence, Valence, France
7 Dermatologie, Hôtel-Dieu, CHU de Nantes, Nantes, France
8 Dermatologie, centre hospitalier, La Rochelle, France
9 Dermatologie, centre Léon-Bérard, Lyon, France
10 Dermatologie, CHU de Dijon-Bourgogne, Dijon, France
11 Dermatologie, centre hospitalier Annecy-Genevois, Epagny Metz-Tessy, France
12 Dermatologie, CHU de Henri-Mondor, Créteil, France
13 Dermatologie, hôpital d’instruction des Armées Sainte-Anne, Toulon, France
14 Dermatologie, CHR Metz-Thionville, hôpital de Mercy, Metz, France
15 Dermatologie, hôpital Saint-Vincent-de-Paul, Lille, France
16 Service de dermatologie, CHRU de Brest, Brest, France
17 Dermatologie, centre hospitalier, Le Havre, France
18 Dermatologie, hôpital Bichat – Claude-Bernard, Paris, France
19 Dermatologie, CHU de Lille, Lille, France
20 Dermatologie, centre hospitalier régional D’orléans, hôpital de La Source, Orléans, France
21 Dermatologie, CHU de Dijon, Dijon, France
22 Dermatologie, centre catalan d’oncologie, Perpignan, France
23 Dermatologie, centre hospitalier de Pau, Pau, France
24 Soins externes, institut du cancer de Montpellier – cancer du sein – ICM/Val d’Aurelle, Montpellier, France
25 Dermatologie, centre hospitalier, Le Mans, France
26 Dermatologie, CHU d’Angers, Angers, France
27 Oncologie, institut de cancérologie de Lorraine – Alexis-Vautrin, Vandœuvre-lès-Nancy, France
28 Dermatologie, centre hospitalier de Versailles, Le Chesnay-Rocquencourt, France
29 Inserm UMR 1302/EMR6001 INCIT, dermatologie, CIC 1413, CHU de Nantes, université de Nantes, Nantes, France
30 Dermatologie, CHU de Poitiers site de la Milétrie, Poitiers, France
31 Dermatologie, services des urgences, CHU de Grenoble, hôpital Sud, Échirolles, France
⁎ Auteur correspondant.
14 12 2022
11 2022
14 12 2022
2 8 A131A131
Copyright © 2022 Published by Elsevier Masson SAS.
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
Les effets de la pandémie COVID-19 sur la prise en charge du mélanome sont diversement appréciés selon les études; certaines montrent des formes plus agressives après le début du confinement, d’autres montrent un impact minime. Nous avons analysé l’effet de la 1re vague de la pandémie de COVID-19 sur les nouveaux cas de mélanomes diagnostiqués en France au sein du consortium RIC-Mel (Réseau pour la recherche et l’investigation clinique sur le mélanome).
Matériel et méthodes
Tous les nouveaux cas de mélanomes diagnostiqués un an avant le début du 1er confinement (17/03/2019–16/03/2020) ont été comparés à ceux diagnostiqués après (11/05/2020–10/05/2021). Les critères analysés étaient l’âge au diagnostic, le sexe, l’épaisseur du mélanome (mm), la présence d’ulcération, le type de mélanome, la présence de métastase répartie en 4 stades (8e classification AJCC).
Résultats
Au total, 2137 nouveaux cas de mélanomes étaient inclus sur 28 centres : 1119 avant le confinement et 1018 après. L’âge moyen (64 avant, 63 après), la répartition en tranche d’âge, le sexe-ratio (1,14 avant, 0,98 après) étaient similaires dans ces 2 périodes. En post-confinement, les mélanomes primitifs tendaient à être plus épais, étaient plus souvent ulcérés (22,3 % pré vs 25,4 % post). Les patients présentaient une maladie à un stade plus avancé après le confinement : moins de stade 0, plus de stade 2 et 3.
Discussion
Notre étude montre un effet du confinement sur le stade des mélanomes au moment de leur prise en charge initiale. Comme plusieurs études l’ont montré, les stades 0 étaient moins fréquents. L’épaisseur des mélanomes invasifs n’était pas significativement augmentée contrairement aux résultats d’études de plus faibles effectifs ou monocentriques. Deux grandes études multicentriques retrouvent des résultats contradictoires : l’une européenne (4033 patients) montre un Breslow plus épais tandis que l’autre nationale (20 434 patients) ne révèle pas d’effet significatif. Contrairement à ces 2 études basées sur des registres histologiques, notre analyse est clinique, incluant l’ensemble des patients tous stades confondus. Ainsi, les stade 2 et 3 après le confinement étaient plus fréquents en accord avec la plupart des publications. En conclusion, notre étude nationale montre que la pandémie de COVID-19, à partir du 1er confinement, a eu un impact péjoratif conduisant au diagnostic de mélanome à un stade plus avancé.
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| 0 | PMC9748166 | NO-CC CODE | 2022-12-15 23:22:40 | no | 2022 Nov 14; 2(8):A131 | utf-8 | null | null | null | oa_other |
==== Front
Annales de Dermatologie et de Vénéréologie - FMC
2667-0623
2667-0623
Published by Elsevier Masson SAS
S2667-0623(22)00578-5
10.1016/j.fander.2022.09.302
Po123
Dermatite atopique et psoriasis chez l’enfant et infection à SARS-CoV-2. Impact différent de l’infection sur ces dermatoses, et de ces dermatoses sur l’infection ?
Mahé E. 1⁎
Zitouni J. 2
Amy De La Breteque M. 3
1 Dermatologie, CH d’Argenteuil, Argenteuil, France
2 Dermatologie, hôpital Necker, AP–HP, Paris, France
3 Dermatologie, CH Victor-Dupouy, Argenteuil, France
⁎ Auteur correspondant.
14 12 2022
11 2022
14 12 2022
2 8 A196A197
Copyright © 2022 Published by Elsevier Masson SAS.
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
La pandémie à SARS-CoV-2 a soulevé de nombreuses questions concernant les dermatoses inflammatoires chroniques et leur traitement : impact des traitements sur la sévérité de l’infection, et impact de l’infection sur les dermatoses.
La dermatite atopique (DA) et le psoriasis sont deux dermatoses inflammatoires chroniques à profils immunologiques très différents. Leur sensibilité aux infections diffère aussi: lien étroit entre poussées de DA et infection à staphylocoque, infections herpétiques sévères et molluscums plus fréquents dans la DA, alors que le psoriasis est surtout associé à des poussées inflammatoires post-infectieuses, essentiellement streptococciques.
Nous nous sommes intéressés au lien entre infection à SARS-CoV-2 et ces deux dermatoses chroniques chez l’enfant : impact différent de l’infection sur la DA et le psoriasis (poussées de la maladie), et de ces deux dermatoses sur la sévérité de cette infection (sévérité).
Matériel et méthodes
Étude monocentrique par inclusions consécutives de février 2021 à mai 2022 des enfants (< 18 ans) souffrant de DA ou psoriasis ou ayant développé ces dermatoses dans le mois qui suivait l’infection (dermatose de novo), et ayant développé une infection à SARS-CoV-2.
Résultats
Au total, 32 enfants souffrant de DA et 66 de psoriasis ont été inclus. L’âge, le genre, la fréquence de l’excès pondéral étaient comparables dans les 2 groupes. L’âge de début était plus précoce dans le groupe DA (2,3 ans vs 6,0 ans, p < 0,0001). Au moment de l’infection à SARS-CoV-2, la DA était considérée comme active chez 78,1 % des enfants et le psoriasis chez 87,3 % (p = 0,25) ; 25 % des enfants souffrant de DA recevaient un traitement général (systémique ou biothérapie) et 34,8 % des enfants souffrant de psoriasis (p = 0,33).
La fréquence des formes symptomatiques, et la durée des symptômes n’étaient pas statistiquement différentes entre les 2 groupes.
Dans 15,6 % des DA et 12,7 % des psoriasis (p = 0,69) on notait une aggravation le mois suivant le début de l’infection. On a noté des psoriasis de novo, mais pas de DA.
Discussion
Dans cette étude portant sur près de 100 enfants souffrant de DA ou de psoriasis, on ne notait pas d’impact différent de l’infection à SARS-CoV-2 sur l’évolutivité de ces dermatoses, à l’exception peut-être de l’apparition de psoriasis post-infection ce qui n’a pas été observé avec la DA. De même ces deux dermatoses ne semblaient pas avoir un impact différent sur la sévérité de l’infection à SARS-CoV-2.
Ces résultats mériteraient d’être confirmés en s’appuyant sur les cohortes de plus grande envergure comme Secure-AD et Chi-PsoCov.
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| 0 | PMC9748167 | NO-CC CODE | 2022-12-15 23:22:40 | no | 2022 Nov 14; 2(8):A196-A197 | utf-8 | null | null | null | oa_other |
==== Front
Annales de Dermatologie et de Vénéréologie - FMC
2667-0623
2667-0623
Published by Elsevier Masson SAS
S2667-0623(22)00776-0
10.1016/j.fander.2022.09.500
Po314
Impact de l’infection au SARS-CoV-2 et de la vaccination anti-COVID-19 sur le psoriasis cutané
Jabri H. ⁎
Hali F.
Rachadi H.
Chiheb S.
Dermatologie, CHU Ibn Rochd, Casablanca, Maroc
⁎ Auteur correspondant.
14 12 2022
11 2022
14 12 2022
2 8 A297A297
Copyright © 2022 Published by Elsevier Masson SAS.
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
La COVID-19 causée par le SARS-CoV-2 a affecté le système de santé à l’échelle mondiale. Cette pandémie a entraîné des répercussions chez les patients psoriasiques sur le plan clinique ainsi que thérapeutique. L’objectif de cette étude était d’évaluer son impact.
Matériel et méthodes
Cent-vingt patients psoriasiques ont été inclus dans cette étude. Tous ont été contactés par téléphone ou interrogés lors de leur consultation ou hospitalisation. Un questionnaire a été établi incluant les données démographiques des patients, les caractéristiques de la maladie, notion d’infection au SARS-CoV-2, de la vaccination anti-COVID19 ainsi que le caractère évolutif de la maladie après infection ou vaccin.
Résultats
Parmi les 120 patients, 68 (56,7 %) étaient des femmes. L’âge moyen des patients était de 41,6 ans avec des extrêmes allant de 6 à 73 ans ; 22 patients (19,3 %) étaient diabétiques, 8 (7 %) suivis pour dyslipidémie et 10 pour obésité. Soixante dix (59,3 %) étaient suivis pour psoriasis vulgaire, 12 pour psoriasis en gouttes, 18 psoriasis pustuleux, 10 suivis pour rhumatisme psoriasique et 10 érythrodermies. Parmi les patients, 66 (79,6 %) avaient eu une infection au SARS-CoV-2 asymptomatique, 18,4 % avaient des symptômes légers et seulement 1 patiente avait été hospitalisée dans un service de soins intensifs pour infection sévère ; 52 patients (38,2 %) étaient sous traitement systémique, 22 (42,3 %) ont dû arrêter leurs traitements au cours de l’infection par inquiétude. L’évolution clinique était marquée par une exacerbation des lésions érythémato-squameuses chez 18 patients (16,7 %) avec un intervalle moyen de 5 jours. Concernant la vaccination anti-COVID-19, 92 patients (76,7 %) étaient vaccinés ; une exacerbation des lésions était notée chez 12 patients (13 %) constatée après la 2e dose chez 8 parmi eux.
Discussion
Les patients infectés par le SARS-CoV-2 présentent des concentrations plasmatiques accrues de cytokines inflammatoires notamment les interleukines 2, 7 et 10. L’augmentation de ces cytokines est également impliquée dans la pathogénie du psoriasis, ce qui pourrait suggérer que le SARS-CoV-2 est pourvoyeur d’exacerbation de cette dermatose. Les vaccins anti-COVID 19 s’avèrent indispensables pour faire face à cette pandémie. Dans la littérature, l’aggravation de psoriasis préexistant après vaccination était rapportée dans plusieurs études et a été retrouvée chez 13 % des patients inclus dans notre étude. La crainte de subir des conséquences graves à la suite d’une infection COVID-19 a pu inciter quelques patients à interrompre leurs traitements sans consulter leurs dermatologues.
Les limites de cette étude incluent l’incapacité d’établir la causalité. De ce fait, il serait indispensable pour les professionnels de santé de surveiller de près les patients infectés par le SARS-CoV-2 ainsi que les vaccinés et à rester au courant des directives nationales et internationales.
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| 0 | PMC9748168 | NO-CC CODE | 2022-12-15 23:22:40 | no | 2022 Nov 14; 2(8):A297 | utf-8 | null | null | null | oa_other |
==== Front
Annales de Dermatologie et de Vénéréologie - FMC
2667-0623
2667-0623
Published by Elsevier Masson SAS
S2667-0623(22)00887-X
10.1016/j.fander.2022.09.611
Po144
L’érythème en lunettes, un signe commun au syndrome inflammatoire multisystémique pédiatrique (PIMS) et à la maladie de Kawasaki
Piroth M. 1⁎
Bourrat E. 2
Barbarot S. 1
1 Dermatologie, Hôtel-Dieu, CHU de Nantes, Nantes, France
2 Dermatologie, AP–HP, Paris, France
⁎ Auteur correspondant.
14 12 2022
11 2022
14 12 2022
2 8 A206A207
Copyright © 2022 Published by Elsevier Masson SAS.
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
Le syndrome inflammatoire multisystémique pédiatrique (PIMS), nouvelle entité apparue durant la pandémie à SARS-CoV-2, a des caractéristiques proches de la maladie de Kawasaki (MK). Parmi ses signes cutanéomuqueux, l’atteinte périorbitaire a été suggérée comme signe spécifique. Nous rapportons 2 cas d’atteinte périorbitaire chez des enfants atteints de PIMS et MK.
Observations
Cas 1 : une enfant de 6 ans était vue en février 2022 pour un PIMS apparu 3 semaines après une infection SARS-CoV-2 asymptomatique. Elle présentait des céphalées, une altération de l’état général, de la fièvre, des signes digestifs (vomissements, diarrhées, douleurs) et cutanés associés à un syndrome inflammatoire marqué (CRP = 279 g/L) et une lymphopénie. L’atteinte cutanée était marquée par une chéilite, une conjonctivite bilatérale, un érythème maculopapuleux en cocarde diffus avec notamment une localisation en lunettes périorbitaire œdématiée.
Cas 2 : une enfant de 7 ans était vue en 2016, avant toute pandémie COVID-19, pour une MK. Elle présentait une fièvre associée à des symptômes digestifs (diarrhées, vomissements), des douleurs articulaires, une conjonctivite bilatérale, un érythème cutané maculeux des membres et une atteinte périorbitaire marquée. L’évolution chez ces 2 enfants était favorable après injection d’immunoglobulines et corticothérapie générale.
Discussion
Le PIMS est une maladie rare et sévère, encore mal comprise, décrite au cours de la pandémie COVID-19. Il se manifeste par l’association de plusieurs signes : fièvre élevée, altération de l’état général, parfois un choc, une atteinte digestive, neurologique, respiratoire mais également des signes cutanéomuqueux polymorphes (hyperhémie conjonctivale, éruption maculopapuleuse, prurit, œdème et rougeur des extrémités, chéilite, glossite). Le traitement actuel du PIMS est empirique et comprend des mesures symptomatiques et des traitements immunomodulateurs. Bien que cliniquement proches, de nombreux PIMS ne répondent pas aux critères de MK et ces deux atteintes semblent distinctes. Le PIMS se définit comme une inflammation systémique plus étendue que la MK avec un plus haut taux de complications myocardiques, touchant des enfants plus âgés et présentant des caractéristiques biologiques différentes (inflammation notamment plus importante). Cependant, peu de données sont actuellement disponibles pour l’identification et la prise en charge de ces patients, et l’intérêt est porté sur la recherche de signes cliniques spécifiques. Parmi les signes cutanéomuqueux, l’atteinte périorbitaire a été suggérée comme un potentiel signe spécifique de PIMS versus MK. Cette hypothèse est infirmée par la présentation de nos 2 cas.
Conclusion
PIMS et MK sont des maladies inflammatoires très proches mais possiblement distinctes. L’érythème périorbitaire, rapporté comme signe spécifique potentiel du PIMS, existe également dans la MK. La recherche de nouveaux signes clinicobiologiques spécifiques apparaît indispensable.
==== Body
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| 0 | PMC9748169 | NO-CC CODE | 2022-12-15 23:22:40 | no | 2022 Nov 14; 2(8):A206-A207 | utf-8 | null | null | null | oa_other |
==== Front
Annales de Dermatologie et de Vénéréologie - FMC
2667-0623
2667-0623
Published by Elsevier Masson SAS
S2667-0623(22)00423-8
10.1016/j.fander.2022.09.149
Co15-127
Étude de l’efficacité clinique et humorale de la vaccination anti-SARS-CoV-2 chez les patients atteints de maladies bulleuses auto-immunes
Ou S. 1⁎
Tancrede E. 2
Alexandre M. 3
Oro S. 4
Jelti L. 4
Bouteiller J. 5
Debarbieux S. 6
Calugareanu A. 6
Duvert Lehembre S. 7
Berthin C. 8
Caux F. 3
Joly P. 5
Viguier M. 1
pour le groupe Bulles de la SFD
1 Dermatologie, hôpital Robert Debré (CHU de Reims), Reims, France
2 Dermatologie, hôpital Saint-Louis, AP–HP, Paris, France
3 Dermatologie, hôpital Avicenne, AP–HP, Bobigny, France
4 Dermatologie, hôpital Henri-Mondor, AP–HP, Créteil, France
5 Dermatologie, CHU de Rouen, Rouen, France
6 Dermatologie, hôpital Lyon Sud – HCL, Pierre-Bénite, France
7 Dermatologie, CHU de Lille, Lille, France
8 Dermatologie, CHU Angers, Angers, France
⁎ Auteur correspondant.
14 12 2022
11 2022
14 12 2022
2 8 A117A118
Copyright © 2022 Published by Elsevier Masson SAS.
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
L’enjeu vaccinal durant la pandémie à SARS-CoV-2 (COVID) est majeur : obtenir et maintenir une immunité collective, protéger les sujets à risque de forme grave. L’émergence de nouveaux variants nécessite une évaluation régulière des stratégies vaccinales, notamment chez les immunodéprimés. Nous avons cherché à évaluer l’efficacité de la vaccination anti-COVID chez les patients atteints de maladies bulleuses auto-immunes (MBAI).
Matériel et méthodes
Une étude multicentrique et rétrospective a été menée d’octobre 2021 à mars 2022. L’objectif a été de déterminer si la vaccination anti-COVID chez les patients ayant une MBAI permettait d’éviter les formes graves d’infection COVID. Tout patient atteint d’une MBAI ayant reçu au moins 1 dose de vaccin avec un recul ≥ 6 mois après celle-ci était inclus. Les données étaient extraites des dossiers médicaux des patients et collectées de manière standardisée dans chaque centre. L’infection COVID post-vaccinale était documentée par un test antigénique ou une PCR positive. Le taux d’anticorps anti-Spike était étudié chez les patients ayant reçu au moins 2 doses vaccinales (taux protecteur si ≥ 264 BAU/mL).
Résultats
Au total, 245 patients ont été inclus, atteints essentiellement de pemphigoïde bulleuse (34 %), de pemphigoïde des muqueuses (33 %), et de pemphigus vulgaire (23 %). Avec un recul moyen de 10 mois après la 1ère dose de vaccin, une infection COVID était notée chez 19 patients (7,7 % de la population étudiée, variant non identifié n = 13, Omicron n = 4, Delta n = 2). L’infection était symptomatique dans 17/19 cas (89 %, symptômes pseudo-grippaux et ORL), traitée en ambulatoire (n = 15) ou en hospitalisation (n = 4 ; 1,6 % de la population étudiée), avec Optiflow (n = 2), oxygène au masque à haute concentration (n = 1), corticothérapie (n = 3) et anticorps monoclonaux (n = 3) ; 1 décès en lien avec Delta était noté (0,4 % de la population étudiée). Au cours des 3 mois post-vaccination, 81 % des patients immunocompétents obtenaient une réponse humorale à des taux protecteurs, contre 56 % des patients avec immunosuppression (IS) médicamenteuse (rituximab, corticoïdes ≥ 10 mg/j, MMF, cyclophosphamide, azathioprine). Après 4 à 8 mois, ce taux diminuait dans les 2 groupes (59 % et 38 %, respectivement). En analyse univariée, les facteurs associés à un risque de COVID post-vaccination étaient un antécédent d’infection COVID avant vaccination (p = 0,0007), une IS médicamenteuse (p = 0,0007) et un taux d’anticorps anti-Spike non protecteur (p = 0,008).
Discussion
Notre étude suggère un faible taux d’infections COVID, notamment sévères, après vaccination chez les patients atteints de MBAI, en particulier durant la période d’exposition à Delta. La comparaison avec la population générale est en cours. Il est possible que certaines infections asymptomatiques, liées à Omicron, n’aient pas été détectées. Chez les patients avec IS, la réponse humorale est moindre, nécessitant le maintien des mesures-barrières et le recours aux mesures prophylactiques pré et post exposition.
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| 0 | PMC9748170 | NO-CC CODE | 2022-12-15 23:22:40 | no | 2022 Nov 14; 2(8):A117-A118 | utf-8 | null | null | null | oa_other |
==== Front
Annales de Dermatologie et de Vénéréologie - FMC
2667-0623
2667-0623
Published by Elsevier Masson SAS
S2667-0623(22)00862-5
10.1016/j.fander.2022.09.586
Po396
Impact de la pandémie COVID-19 sur la prise en charge des patients adultes atteints de dermatite atopique modérée à sévère : analyse complémentaire de l’étude MOVE
Lacour J.P. 1⁎
Fougerousse A.C. 2
Becherel P.A. 3
Droitcourt C. 4
Dupuy A. 4
Falissard B. 5
Gackiere-Katsogiannou M. 6
Aubert N. 7
Thenie C. 8
Helman N. 8
1 Service de dermatologie, CHU de Nice, Nice, France
2 Dermatologie, Hôpital d’Instruction des Armées Bégin, Saint-Mandé, France
3 Dermatologie, Hôpital privé d’Antony - Ramsay Santé, Antony, France
4 Dermatologie, CHU Rennes - Hôpital Pontchaillou, Rennes, France
5 Cesp (centre de recherche en épidémiologie et santé des populations), Inserm, Paris, France
6 Mw, ICTA PM, Fontaine-lès-Dijon, France
7 Icta pm, ICTA PM, Fontaine-lès-Dijon, France
8 Direction médicale, Sanofi Aventis France, Gentilly, France
⁎ Auteur correspondant.
14 12 2022
11 2022
14 12 2022
2 8 A348A348
Copyright © 2022 Published by Elsevier Masson SAS.
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
La pandémie COVID-19 a débuté quelque mois après le début de l’étude MOVE, étude visant à décrire les modalités d’utilisation en vie réelle du dupilumab chez les patients adultes atteints de dermatite atopique (DA) modérée à sévère. En France, des mesures de confinement strictes ont été adoptées pour limiter l’expansion de l’infection, puis différentes périodes ont suivi: déconfinement, restrictions sanitaires… Au cours de ces différentes périodes, après une phase initiale de sidération pendant laquelle l’initiation de traitements systémiques a été réduite, les experts et sociétés savantes ont été unanimes sur l’absence de risque d’immunosuppression du dupilumab du fait de son mécanisme d’action. Son utilisation a donc été poursuivie. L’objectif de cette analyse complémentaire était de décrire l’impact de la pandémie sur la prise en charge thérapeutique des patients adultes atteints de dermatite atopique (DA) modérée à sévère.
Matériel et méthodes
Une comparaison a été effectuée parmi les 594 patients de l’étude MOVE entre ceux dont le traitement par dupilumab a été initié avant le premier confinement (17 mars 2020) et ceux dont le traitement a été initié après, afin de comparer leurs caractéristiques et leurs traitements systémiques antérieurs.
Résultats
Cent cinquante-trois patients (25,8 %) avaient débuté le traitement par dupilumab avant le 17 mars 2020 : 100 (65,4 %) avaient eu une prescription antérieure de ciclosporine A (CsA) ; 441 patients (74,2 %) avaient débuté le traitement par dupilumab après le début du confinement : 191 (43,3 %) avaient eu une prescription antérieure de CsA. À partir du confinement, la CsA n’a pas été prescrite pour des raisons liées à la pandémie pour 67 patients sur 233 (30,0 %), et la CsA a été arrêtée chez 8 patients sur 15 (53,3 %) pour les mêmes raisons. Au total, parmi les 594 patients éligibles, la contre-indication circonstancielle (risque d’infection COVID-19) a été le motif rapporté spontanément par les médecins observateurs pour 77 patients (12,7 %) pour expliquer soit la non-prescription : 8 patients (1,3 %), soit l’arrêt de CsA : 69 patients (11,6 %).
Discussion
La pandémie COVID-19 a eu un impact sur la prescription de CsA chez les patients adultes atteints de DA modérée à sévère à partir de mars 2020. La contre-indication circonstancielle a été déclarée spontanément par les médecins observateurs, et est probablement sous-estimée dans ces résultats. Cette étude montre que, malgré ces circonstances exceptionnelles, les modalités de prescription du dupilumab étaient, pour la majorité des patients, conformes à son périmètre de remboursement.
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pmcDéclaration de liens d’intérêts
N. Helman : SanofiC Thenie : SanofiB Fallissard : Abbvie, Actelion, Allergan, Almirall, Alnylam, Amgen, Astellas, Astrazeneca, Bayer, Biogen, Biopecs, Bioproject, Biotronik, BMS, Boehringer, Celgène, Daiichi-Sankyio, Ethypharm, Forestlab, Genevrier, Genzyme, Gilead, Grünenthal, GSK, Guerbet, HRA, IDM Pharma, Idorsia, IMS, Indivior, IQVIA, J.N.J., Kephren, Lafon, Léo, Lilly, Lundbeck, Menarini, MSD, Novartis, Novonordisk, Otsuka, Pfizer, Pierre-Frabre, Recordati, Roche, SANOFI, Servier, Stallergene, Takeda, UCB, ViiV, Wellmera. A Dupuy : UCB Pharma. C. Droicourt : Sanofi, Lilly, UCB, Janssen–Cilag. P.A. Bécherel : Abbvie, Almirall, Amgen, Boehringer-Ingelheim, Celgène, Janssen–Cilag, Leo Pharma, Lilly, MSD, Novartis, Pfizer, Sanofi, UCB. A.C. Fougerousse : Sanofi, Lilly, Abbvie, Leo Pharma. J.P. Lacour : AbbVie, Amgen, Avene, Boehringer-Ingelheim, Celgene, Galderma, GSK, Lilly, Leo-Pharma, MSD, Novartis, Pfizer, Regeneron, Roche-Posay, Sanofi, SVR, Uriage.
| 0 | PMC9748171 | NO-CC CODE | 2022-12-15 23:22:40 | no | 2022 Nov 14; 2(8):A348 | utf-8 | null | null | null | oa_other |
==== Front
Annales de Dermatologie et de Vénéréologie - FMC
2667-0623
2667-0623
Published by Elsevier Masson SAS
S2667-0623(22)00329-4
10.1016/j.fander.2022.09.055
Co04-033
Efficacité de l’induction de tolérance au vaccin ARNm COVID-19 Comirnaty Pfizer dans une série de 7 cas d’anaphylaxie prouvée au PEG ou polysorbate
Dupire G. 1⁎
Pijpen N. 2
Elleni V. 1
Michel O. 1
Ben Said B. 3
1 Dermatologie, clinique immunoallergologie, CHU Brugmann – site Reine Astrid, Bruxelles, Belgique
2 Pharmacie centrale, CHU Brugmann – site Reine Astrid, Bruxelles, Belgique
3 Dermatologie, centre de reference sur les dermatoses bulleuses toxiques, hospices civils de Lyon – HCL, Lyon, France
⁎ Auteur correspondant.
14 12 2022
11 2022
14 12 2022
2 8 A60A60
Copyright © 2022 Published by Elsevier Masson SAS.
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
La vaccination COVID lancée en 2020 reste un enjeu de santé majeur car elle évite a minima les formes graves de la maladie, diminue la mortalité et évite la saturation des hôpitaux. Néanmoins des cas d’anaphylaxie ont été rapportés (évaluée à 1/100 000) notamment liées à une sensibilisation aux excipients de type PEG ou polysorbate, rendant les vaccinations à risque. Nous rapportons une série de 7 cas avec efficacité d’un protocole d’induction de tolérance (8 étapes. ITS) élaboré pour respecter la stabilité du produit sur la tolérance du vaccin. Nous confirmerons aussi son efficacité vaccinale avec un contrôle des anticorps neutralisant à un mois après la vaccination par ITS.
Observations
Une induction de tolérance selon le protocole suivant a été déterminée en fonction de la stabilité du produit avec le vaccin Comirnaty à ARN messager réputé le plus efficace dans la période de variant delta ou omicron. Nous savons suivi un protocole de 8 étapes.
Résultats
La tolérance a été bonne dans tous les cas et l’efficacité vaccinale déterminée par la présence d’anticorps associé à la neutralisation du virus SARS-CoV-2 était excellent dans tous les cas témoignant de la pertinence et de l’efficacité du protocole.
Discussion
Nous démontrons dans cette série et dans ce contexte ou la vaccination COVID reste un enjeu majeur que la présence d’une anaphylaxie aux excipients n’est pas une CI formelle et qu’un protocole de désensibilisation sous surveillance est efficacité du point de vue de la tolérance et de l’efficacité en termes de réponse vaccinale. Cette induction de tolérance a été réalisée sans prémédication par corticoïdes ce qui a évité une perte d’efficacité vaccinale.
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| 0 | PMC9748172 | NO-CC CODE | 2022-12-15 23:22:40 | no | 2022 Nov 14; 2(8):A60 | utf-8 | null | null | null | oa_other |
==== Front
Annales de Dermatologie et de Vénéréologie - FMC
2667-0623
2667-0623
Published by Elsevier Masson SAS
S2667-0623(22)00603-1
10.1016/j.fander.2022.09.327
Po148
Nécrolyse épidermique toxique (NET) : manifestation cutanée de COVID-19
Er-rachdy N. ⁎
Fliti A.
Elomari Alaoui M.
Ismaili N.
Meziane M.
Benzekri L.
Senouci K.
Service de dermatologie et vénérologie, université Mohamed V de Rabat, hôpital universitaire Ibn Sina, Rabat, Maroc
⁎ Auteur correspondant.
14 12 2022
11 2022
14 12 2022
2 8 A209A209
Copyright © 2022 Published by Elsevier Masson SAS.
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
La nécrolyse épidermique toxique (NET) est classée parmi les toxidermies graves, mais elle peut être également induite par des infections, des tumeurs malignes ou être idiopathique. Nous rapportons un cas d’un syndrome de Lyell induit par l’infection COVID-19.
Observations
Il s’agit d’un patient âgé de 69 ans, sans antécédents pathologiques notable, notamment pas de notion de prise médicamenteuse, qui s’est présenté avec un tableau de fièvre, toux, syndrome pseudo-grippal évoluant depuis 5 jours, associé à une érythrodermie humide avec décollement cutané > 30 % et atteinte de la muqueuse génitale. Le patient avait un test COVID-19 positif, avec cultures et sérologies négatives pour toutes les autres infections dépistées. La biopsie a montré un clivage sous-épidermique focale, une nécrose épidermique, une vacuolisation des cellules basales, une dyskératose et un léger infiltrat lymphocytaire périvasculaire superficiel. L’immunofluorescence directe était négative. Le diagnostic de syndrome de Lyell a été retenu avec une bonne évolution sous traitement symptomatique.
Discussion
Bien que le COVID-19 provoque surtout des symptômes respiratoires et des séquelles thromboemboliques, il a également été signalé comme étant associé à des manifestations dermatologiques.
De nombreuses manifestations cutanées de COVID-19 ont été rapportées dans la littérature suggérant la possibilité d’une NET induite par l’hydroxychloroquine ou autres médicaments chez des patients atteints du COVID-19, contrairement à notre cas où le syndrome de Lyell était secondaire au COVID-19.
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| 0 | PMC9748173 | NO-CC CODE | 2022-12-15 23:22:40 | no | 2022 Nov 14; 2(8):A209 | utf-8 | null | null | null | oa_other |
==== Front
Annales de Dermatologie et de Vénéréologie - FMC
2667-0623
2667-0623
Published by Elsevier Masson SAS
S2667-0623(22)00472-X
10.1016/j.fander.2022.09.198
Po020
Dermatomyosite amyopathique induite par le vaccin anti-SARS-CoV-2 : une présentation rare
Walid N. 1⁎
Baline K. 2
Hali F. 2
Meftah A. 3
Filali H. 4
Chiheb S. 2
1 Dermatologie-vénérologie, CHU Ibn Rochd, Casablanca, Maroc
2 Dermatologie, CHU Ibn Rochd, Casablanca, Maroc
3 Service de pharmacologie-toxicologie, CHU Ibn Rochd, Casablanca, Maroc
4 Pharmacologie, CHU Ibn Rochd, Casablanca, Maroc
⁎ Auteur correspondant.
14 12 2022
11 2022
14 12 2022
2 8 A144A144
Copyright © 2022 Published by Elsevier Masson SAS.
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
La vaccination est utilisée pour contrôler la pandémie de SARS-CoV-2, cependant on observe des réactions cutanées secondaires multiples et hétérogènes, dont des dermatoses auto-immunes. Nous rapportons le premier cas, à notre connaissance, de dermatomyosite amyopathique secondaire au vaccin COVID-19.
Observations
Une femme de 60 ans, sans antécédents, a été admise dans notre structure pour un rash photodisposé prurigineux apparu 5 jours après la deuxième dose du vaccin anti-COVID-19 AstraZeneca. L’éruption affectait les mains et le cou avec extension secondaire au visage et aux avant-bras, associée à une photosensibilité et au phénomène de Raynaud, le tout évoluant dans un contexte de sensations fébriles.
L’examen dermatologique révélait un œdème facial, un érythème flagellé du décolleté, des papules de Gottron en regard des espaces interphalangiens et des plaques érythémateuses infiltrées des faces d’extension des avant-bras. L’examen des ongles montrait un érythème péri-unguéal, une trachyonychie et un épaississement cuticulaire.
La dermoscopie unguéale a objectivé des images de méga-capillaires, des hémorragies en flammèche, une trachyonychie et des zones avasculaires sans structures indiquant une hypoperfusion unguéale. L’examen neuromusculaire était normal avec signe du tabouret et du peigne négatifs.
Les enzymes musculaires étaient élevées en particulier la créatine phosohokinase [CPK], l’aldolase, la lactate déshydrogénase [LDH] et l’aspartate aminotransférase [ASAT]. Le bilan immunologique révélait des anticorps anti-Mi-2 et antinucléaires. L’électroneuromyographie était en faveur d’un syndrome myogène. La tomodensitométrie n’a pas révélé d’atteinte pulmonaire interstitielle ni de signes de malignité. Une biopsie cutanée montrait des nécroses kératinocytaires. L’enquête pharmacologique a conclu à l’imputabilité du vaccin.
Le diagnostic de dermatomyosite amyopathique induite par le vaccin anti-COVID-19 a été retenu. La patiente a été mise sous hydroxychloroquine 400 mg/j et prednisone 1 mg/kg/j avec une bonne amélioration clinique et biologique.
Discussion
La dermatomyosite induite est rapportée dans la littérature pour différents médicaments et vaccins chez des patients prédisposés comme les formes développées après vaccin H1N1, vaccin antigrippal trivalent et vaccination contre le VHB.
Le vaccin anti-COVID-19 trouve également sa place dans l’induction de cette maladie auto-immune, mais les cas documentés étaient des formes classiques de dermatomyosite. À notre connaissance, il s’agit du premier cas de dermatomyosite amyopathique induite par le vaccin AstraZeneca.
La survenue de DM suite à la vaccination peut s’expliquer par l’homologie existant entre les composants du vaccin et les antigènes musculaires, responsable d’un dérèglement immunologique et du déclenchement d’une réponse auto-immune.
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| 0 | PMC9748174 | NO-CC CODE | 2022-12-15 23:22:40 | no | 2022 Nov 14; 2(8):A144 | utf-8 | null | null | null | oa_other |
==== Front
Annales de Dermatologie et de Vénéréologie - FMC
2667-0623
2667-0623
Published by Elsevier Masson SAS
S2667-0623(22)00965-5
10.1016/j.fander.2022.10.070
Po227
Morphée généralisée post-vaccin anti-COVID-19
El Kissouni A. 1⁎
Hali F. 1
Rachadi H. 1
Meftah A. 2
Filali H. 2
Chiheb S. 1
1 Dermatologie, CHU Ibn Rochd, Casablanca, Maroc
2 Pharmacologie, CHU Ibn Rochd, Casablanca, Maroc
⁎ Auteur correspondant.
14 12 2022
11 2022
14 12 2022
2 8 A248A248
Copyright © 2022 Published by Elsevier Masson SAS.
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
Les vaccins contre le SARS-CoV-2 ont été approuvés en un temps record. Ce qui n’a pas permis de déceler tous leurs effets indésirables (EI), et ce n’est qu’après la vaccination de masse que la majorité des EI y compris ceux cutanés ont été observés, nous rapportons ici 2 cas de morphée généralisée après vaccin anti-COVID-19.
Observations
Observation 1 : un patient de 62 ans, sans antécédents pathologiques particuliers a eu 2 semaines après la 2e dose du vaccin (Sinopharm) un prurit féroce associé à une induration cutanée initialement localisée au niveau de l’abdomen s’étendant progressivement au tronc et aux membres, sans atteinte du visage ni des extrémités sans sclérodactylie ni phénomène de Raynaud, avec un aspect luisant de la peau et un signe de la prière positif.
Observation 2 : une patiente de 59 ans, sans antécédents notables a eu 3 semaines après la 2e dose du vaccin (Sinopharm) une induration cutanée indolore au niveau du bras (à côté du site d’injection), avec une extension progressive à l’abdomen et aux cuisses, sans signes extracutanées.
Chez ces deux malades, e bilan immunologique notamment Les AAN et l’anti-Scl70 et était négatif. La biopsie avait conclu à une morphée profonde.
Discussion
La morphée est une maladie inflammatoire fibrosante caractérisée par une sclérose limitée à la peau ; la physiopathologie reste mal élucidée, or des facteurs déclenchants médicamenteux ou traumatiques ont été décrits. À notre connaissance, un seul cas post-vaccin anti-COVID-19 est rapporté dans la littérature. L’effet profibrotique de certains médicaments et vaccins pourrait expliquer leur possible effet inducteur de morphée. Nos patients ont été mis sous corticostéroïdes oraux avec une bonne évolution clinique.
==== Body
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| 0 | PMC9748175 | NO-CC CODE | 2022-12-15 23:22:40 | no | 2022 Nov 14; 2(8):A248 | utf-8 | null | null | null | oa_other |
==== Front
Annales de Dermatologie et de Vénéréologie - FMC
2667-0623
2667-0623
Published by Elsevier Masson SAS
S2667-0623(22)00972-2
10.1016/j.fander.2022.10.077
Po234
Engelures au cours de la première vague année de la pandémie de COVID-19 : étude observationnelle de 63 patients au centre hospitalier universitaire Grenoble-Alpes
Dubus M. 1⁎
Trabelsi S. 1
Mouret S. 1
Enquebecq M. 1
Templier I. 1
Charles J. 12
Leccia M.T. 12
Mathilde T. 1
1 Dermatologie, CHU Grenoble-Alpes, La Tronche, France
2 Faculté de médecine, université Grenoble-Alpes, Grenoble, France
⁎ Auteur correspondant.
14 12 2022
11 2022
14 12 2022
2 8 A251A251
Copyright © 2022 Published by Elsevier Masson SAS.
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
Lors de la première vague de COVID-19, un nombre inhabituel d’engelures a été rapporté, suggérant un lien avec l’infection émergente. Les objectifs étaient de décrire les caractéristiques des patients ayant eu des engelures pendant la première année de la pandémie, d’estimer la prévalence du COVID-19 dans cette population par sérologie ou RT-PCR et d’identifier les spécificités des patients ayant présenté le COVID-19 et des engelures.
Matériel et méthodes
Étude observationnelle rétrospective des patients ayant présentés des engelures au sein du bassin de population du CHU Grenoble-Alpes entre janvier 2020 et février 2021. Les patients ayant eu un COVID-19 et des engelures (RT-PCR ou sérologie positive) étaient comparés aux patients sans infection connue.
Résultats
Les 63 patients avaient âge médian 25 ans [9–79 ans], avec 61 % de femmes. Une exposition au froid, un antécédent d’engelure ou de syndrome de Raynaud étaient observés dans 12, 16 et 11 % des cas respectivement. Au moins un symptôme évocateur de COVID-19 était présent chez 43 % des patients. La prévalence du COVID-19 dans la population testée était de 11 %. Il n’existait pas de différence significative entre les patients avec ou sans infection par le SARS-CoV-2.
Discussion
Les engelures observées avaient une présentation distincte des engelures décrites avant la pandémie (patients plus jeunes, prédominance féminine moins importante, exposition au froid, antécédents d’engelures et de syndrome Raynaud moins fréquents). Cependant, la faible prévalence du COVID-19 dans la population (confirmée par tests biologiques) ne permettait pas de préciser le rôle du SARS-CoV-2 dans la survenue de ces engelures.
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==== Front
Annales de Dermatologie et de Vénéréologie - FMC
2667-0623
2667-0623
Published by Elsevier Masson SAS
S2667-0623(22)00439-1
10.1016/j.fander.2022.09.165
Co16-143
Engelures associées au COVID : plus de lien de causalité avec le virus SARS-CoV-2 et moins d’évidence pour une réponse systémique augmentée de l’interféron de type 1
Bessis D. 1⁎
Trouillet-Assant S. 2
Secco L.P. 3
Blatière V. 4
Girard C. 4
Molinari N. 5
Pallure V. 6
Peyron N. Raison 4
Schwob E. 7
Pescarmona R. 8
Samaran Q. 9
Vincent T. 10
Sofonea M.T. 11
Belot A. 12
Tuaillon É. 13
1 Dermatologie, CHU, hôpital Saint-Éloi, Montpellier, France
2 Inserm U1111, institut des agents infectieux, centre international de recherche en infectiologie, université Lyon, Lyon, France
3 Anatomie pathologique, cliniques universitaires Saint-Luc (UCLouvain), Bruxelles, Belgique
4 Dermatologie, CHU de Montpellier, Montpellier, France
5 Département d’information médicale, CHU de Montpellier, Montpellier, France
6 Dermatologie, centre hospitalier de Perpignan, Perpignan, France
7 Service de dermatologie, CHU, hôpital Saint-Éloi, 80, avenue A-Fliche, Montpellier, France
8 Departement immunologie, hôpital Lyon Sud – HCL, Pierre-Bénite, France
9 Service de dermatologie, CHU de Montpellier, Montpellier, France
10 Immunologie, CHU, hôpital Saint-Éloi, Montpellier, France
11 Mivegec, université Montpellier, Montpellier, France
12 Pédiatrie, hospices civils de Lyon – HCL, Lyon, France
13 Virologie, CHU de Lapeyronie, Montpellier, France
⁎ Auteur correspondant.
14 12 2022
11 2022
14 12 2022
2 8 A126A126
Copyright © 2022 Published by Elsevier Masson SAS.
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
Le rôle pathogénique direct du virus SARS-CoV-2 et l’implication d’une surexpression de la voie de l’interféron de type 1 dans la genèse des engelures-COVID restent controversés.
Matériel et méthodes
Étude prospective monocentrique entre avril 2020 et janvier 2022 des enfants et adultes avec engelures-COVID suivis dans les services de dermatologie, Centre de tri COVID, maladies infectieuses, médecine interne et réanimation médicale.
Résultats
Cinquante patients atteints d’engelures-COVID ont été inclus : ratio F/H de 1,5, âge moyen 21 ans, IMC moyen de 19,8. Un phénomène de Raynaud acquis, un syndrome BASCULE et des hémorragies en flammèches sous-unguéales étaient notés, respectivement, dans 18 %, 13 % et 8 % des cas. Après un suivi moyen de 12 mois, la rémission complète, la persistance et la récidive des engelures étaient notées, respectivement, dans 93 %, 7 % et 13 % des cas. Des symptômes extra-cutanés, suggérant une origine infectieuse, étaient rapportés dans 63 % des cas. La PCR nasopharyngée SARS-CoV-2 et la recherche d’anticorps (Ac) anti-SARS-CoV-2 de type IgG et/ou IgM et/ou IgA étaient positives, respectivement, dans 10 % et 23 % des cas. La détection antigénique nucléocapsidique (N) sérique SARS-CoV-2 était positive dans 13 % des cas et la recherche d’Ac neutralisant IgA isolée positive chez 1 patient. Considérant l’ensemble des tests virologiques, une infection SARS-CoV-2 était prouvée dans 22 % des cas, dont 32 % chez l’enfant et 18 % chez l’adulte. Après analyse multivariée, aucune différence significative clinique, histologique ou immunobiologique n’était mise en évidence entre les groupes engelures associées ou non à une infection SARS-CoV-2 prouvée. La mesure sanguine du score de l’IFN (SC-IFN), à partir du début des signes infectieux, était comparée entre le groupe engelures-COVID avec signes infectieux (n = 19) et deux groupes COVID sans engelures ayant été hospitalisés en réanimation (n = 72) et considérés comme bénins, sans hospitalisation (n = 8). À la différence de ces deux derniers groupes, aucun pic significatif de SC-IFN n’était observé dans le groupe engelures-COVID. Dans ce dernier groupe, une augmentation modérée et stable du SC-IFN était notée.
Discussion
Notre étude confirme les principales caractéristiques cliniques précédemment rapportées au cours des engelures-COVID. La forte fréquence du phénomène de Raynaud, du syndrome BASCULE et des hémorragies unguéales pourrait être expliquée par le caractère prospectif de notre étude et l’absence de détails cliniques dans les larges séries. L’utilisation de l’antigénémie sérique N SARS-CoV-2 et la recherche d’anticorps neutralisants augmentent la sensibilité de détection d’une infection virale SARS-CoV-2 associée aux engelures, présente dans près d’un tiers des cas chez l’enfant. Ce dernier résultat est en accord avec la fréquence moyenne de séroconversion observée chez les enfants ayant développé une infection bénigne ou asymptomatique SARS-CoV-2, estimée entre 30 à 37 %. Il conforte le lien de causalité potentiel entre l’infection virale et les engelures-COVID. L’absence d’hypersécrétion systémique d’IFN au cours des engelures-COVID, par comparaison avec les COVID sévères ou bénins sans engelures associées, laisse supposer que le rôle éventuel de l’IFN dans la genèse des engelures serait plus en rapport avec une susceptibilité tissulaire virale qu’à une hypersécrétion systémique d’IFN de type 1.
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| 0 | PMC9748177 | NO-CC CODE | 2022-12-15 23:22:40 | no | 2022 Nov 14; 2(8):A126 | utf-8 | null | null | null | oa_other |
==== Front
Annales de Dermatologie et de Vénéréologie - FMC
2667-0623
2667-0623
Published by Elsevier Masson SAS
S2667-0623(22)00609-2
10.1016/j.fander.2022.09.333
Po153
Un cas de pemphigoïde p200 après une vaccination contre la COVID-19
Bailly-Caille B. 1⁎
Dompmartin A. 1
Morice C. 2
1 Dermatologie, CHU de Caen Normandie, Caen, France
2 Dermatologie, CHU Caen, Caen, France
⁎ Auteur correspondant.
14 12 2022
11 2022
14 12 2022
2 8 A212A212
Copyright © 2022 Published by Elsevier Masson SAS.
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
De nombreux effets indésirables cutanés dans les suites de la vaccination contre la COVID-19 ont été rapportés dans la littérature. Parmi ces derniers, quelques cas de maladies bulleuses auto-immunes ont été décrits, principalement des cas de pemphigoïdes bulleuses.
Observations
Un patient de 74 ans a développé une éruption vésiculeuse sur les poignets survenant 10 jours après la première dose du vaccin à ARN messager contre la COVID-19 Moderna ; puis 48 heures après la seconde dose, une éruption bulleuse des extrémités, sans atteinte muqueuse associée. L’analyse histologique montrait un décollement de la jonction dermo-épidermique avec un dépôt d’IgG et de C3 le long de la membrane basale épidermique à l’immunofluorescence directe ; mais la recherche des anticorps anti-BPAG1 et 2 était négative. L’immunofluorescence indirecte trouvait un marquage sur le versant dermique. L’analyse de l’immunoblot sur extrait dermique a révélé la présence d’anticorps reconnaissant la protéine de 200 kDa permettant de retenir le diagnostic de pemphigoïde p200. Le patient a été traité par clobétasol propionate crème 0,05 % avec une rechute lors de la décroissance des dermocorticoïdes motivant l’introduction d’un traitement systémique par colchicine ; permettant une rémission persistante depuis 6 mois.
Discussion
La pemphigoïde p200 est une maladie bulleuse auto-immune sous-épidermique rare caractérisée par des anticorps reconnaissant la laminine γ1 (p200). Un cas de pemphigoïde P200 dans les suites d’une vaccination a été décrit, après un vaccin contre le pneumocoque. Nous rapportons ici un cas de pemphigoïde p200 après une vaccination par Moderna, contre la COVID-19.
La chronologie et l’évolution de la maladie, avec une réaction plus rapide et intense lors de la deuxième dose, ainsi qu’une rémission à 1 an renforce le potentiel rôle déclencheur de la vaccination par ARN messager contre la COVID-19 dans le déclenchement de maladies bulleuses sous-épidermiques auto-immunes, possiblement latentes, par une stimulation importante du système immunitaire. À ce jour, craignant une rechute, le patient n’a pas souhaité réaliser de rappel vaccinal anti-SRAS-CoV-2, y compris avec les alternatives vaccinales non ARN messager.
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| 0 | PMC9748178 | NO-CC CODE | 2022-12-15 23:22:40 | no | 2022 Nov 14; 2(8):A212 | utf-8 | null | null | null | oa_other |
==== Front
Annales de Dermatologie et de Vénéréologie - FMC
2667-0623
2667-0623
Published by Elsevier Masson SAS
S2667-0623(22)00959-X
10.1016/j.fander.2022.10.064
Po221
Dermatomyosite post-vaccination COVID-19 : lien de causalité ou association fortuite ?
Ouni N.E.I. 1⁎
Lahoual Ep Gaied M. 1
Ben Salah N. 1
Sriha B. 2
Mokni S. 3
Ghariani Fetoui N. 1
Ben K.M. 1
Aounallah A. 1
Ghariani N. 1
Belajouza C. 1
Denguezli M. 1
1 Dermatologie, hôpital Farhat Hached, Sousse, Tunisie
2 Anatomopathologie, hôpital Farhat Hached, Sousse, Tunisie
3 Service de dermatologie, CHU Farhat Hached, faculté de médecine de Sousse, Sousse, Tunisie
⁎ Auteur correspondant.
14 12 2022
11 2022
14 12 2022
2 8 A245A246
Copyright © 2022 Published by Elsevier Masson SAS.
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
Des manifestations cutanées variées survenant après la vaccination contre le coronavirus-19 (COVID-19) ont été rapportées dans la littérature. La plupart d’entre elles sont bénignes, principalement à type de réactions locales retardées, d’urticaire et d’éruptions morbilliformes. De rares cas de dermatomyosites post-vaccination COVID (DM) ont été décrits. Nous présentons un nouveau cas de DM apparue dans les suites de la vaccination ARNm Pfizer-BioNTech contre le COVID-19.
Observations
Il s’agissait d’une patiente âgée de 19 ans sans antécédents pathologiques notables qui était adressée pour une éruption du visage associée à une photosensibilité apparue 2 semaines après avoir reçu sa 1re dose du vaccin de Pfizer-BioNTech (BNT162b2). Il y avait un érythème héliotrope des paupières supérieures, du front et des joues, un érythème en bande du dos des mains avec des papules érythémato-violines kératosiques en regard des faces d’extension des articulations métacarpophalangiennes et interphalangiennes. La patiente avait par ailleurs une faiblesse musculaire prédominant au niveau des ceintures scapulaires et pelviennes. Les examens complémentaires montraient une élévation des enzymes musculaires. Les anticorps antinucléaires et les auto-anticorps spécifiques des myosites étaient négatifs. L’électromyogramme montrait une atteinte myogène. La biopsie montrait une dermite d’interface avec infiltrat inflammatoire périvasculaire et un œdème du derme ; l’immunofluorescence directe était négative. À l’interrogatoire, il n’y avait pas de terrain d’auto-immunité familiale, de prise médicamenteuse ou d’infection récente. Le diagnostic de dermatomyosite déclenchée par la vaccination COVID-19 était retenu et la patiente était mise sous corticothérapie orale avec une bonne évolution au bout de 3 semaines.
Discussion
La DM est une myopathie inflammatoire primitive d’étiologie inconnue. Sa pathogénie est encore mal comprise. Elle a été rapportée sporadiquement après la vaccination contre le virus de l’hépatite B, la tuberculose, le tétanos, la grippe, la variole, la polio et la diphtérie. Chez notre patiente la relation causale probable était établie sur la base de : (1) la relation chronologique observée (2) l’absence d’autres facteurs déclenchants et (3) le rapport de cas similaires. Le mécanisme étiopathogénique de cette association n’a pas été clairement élucidé. La réponse immunitaire à médiation cellulaire T induite par les vaccins à ARNm pourrait jouer un rôle. En effet, les patients atteints de DM ont une augmentation des gènes inductibles d’interféron de type I dans les fibres musculaires, les cellules endothéliales, la peau, et le sang et les signes cliniques peuvent se développer directement en réponse à la signalisation d’interféron de type 1. Dans ce contexte, il a été démontré que le vaccin contre le COVID-19 Pfizer-BioNTech induit la production d’interféron de type I, ce qui pourrait expliquer en partie cette association. En conclusion, la très faible incidence de cette affection et l’efficacité des vaccins contre la COVID-19 ne devraient pas changer la pratique vaccinale. Néanmoins, il est essentiel de reconnaître la vaccination comme un facteur déclenchant potentiel des maladies auto-immunes.
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| 0 | PMC9748179 | NO-CC CODE | 2022-12-15 23:22:40 | no | 2022 Nov 14; 2(8):A245-A246 | utf-8 | null | null | null | oa_other |
==== Front
Annales de Dermatologie et de Vénéréologie - FMC
2667-0623
2667-0623
Published by Elsevier Masson SAS
S2667-0623(22)00967-9
10.1016/j.fander.2022.10.072
Po229
Morphée monomélique au cours d’une infection par le virus SARS-CoV-2
El Sayed F. 1⁎
Ezzedine K. 2
Ortonne N. 3
1 Université libanaise, Beyrouth, Liban
2 Dermatologie, hôpital Henri-Mondor, AP–HP, Créteil, France
3 Laboratoire de pathologie, hôpital Henri-Mondor, AP–HP, Créteil, France
⁎ Auteur correspondant.
14 12 2022
11 2022
14 12 2022
2 8 A249A249
Copyright © 2022 Published by Elsevier Masson SAS.
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
Les manifestations dermatologiques associées à l’infection COVID-19 comportent soit une exacerbation de dermatoses préexistantes, soit des nouvelles manifestations cutanées parfois propres à cette infection faisant l’originalité de cette pathologie. Nous rapportons un cas original d’une sclérodermie cutanée monomélique apparue au cours d’une infection par le SARS-CoV-2.
Observations
Une patiente âgée de 22 ans contractait l’infection par la COVID-19 début janvier 2021 avec les symptômes habituels comprenant une fatigue, fièvre, toux et diarrhée ne nécessitant ni hospitalisation ni oxygénothérapie à domicile, avec une PCR positive au 3e jour de la symptomatologie. Dix jours plus tard, elle développait un érythrœdème du dos de la main droite avec une extension à l’avant-bras, devenu écarlate, luisant et infiltré. Une biopsie montrait un aspect typique de morphée. La biologie de routine et le bilan immunologique étayé étaient normaux ou négatifs. Des examens radiologiques comprenant une radio standard et une IRM de cette zone montraient un hâle nuageux et une infiltration liquidienne minime du tissu mou sous-cutanée. La PCR COVID-19 était négative à 3 semaines d’évolution. Un traitement symptomatique par corticothérapie générale faible dose s’était rapidement accompagnée d’une régression marquée des symptômes en 3 semaines. Un suivi régulier était alors adopté. Une rechute isolée de la symptomatologie cutanée était observée suite à une première vaccination anti-COVID-19 pratiquée 5 mois à distance de l’infection avec une régression spontanée en 10 jours. L’examen cutané au dixième mois montrait une régression complète de la morphée avec une pigmentation linéaire séquellaire.
Discussion
Notre patiente a eu une morphée monomélique apparue au cours d’une infection par le SARS-CoV-2 avec une réactivation lors de la première vaccination anti-COVID-19. Aucune atteinte systémique de sclérodermie n’était relevée. La morphée est associée au lupus érythémateux, vitiligo, pelade, polyarthrite rhumatoïde et thyroïdite auto-immune. Dix cas récents rapportaient cette association morphée–infection par la COVID-19, la moitié après vaccination et l’autre moitié après l’infection. L’atteinte était plutôt diffuse et non monomélique comme dans notre cas. Quant au bilan immunologique, il était aussi négatif. Enfin, la pathogénie exacte de l’association COVID-19 et morphée n’est pas bien élucidée. Cependant, les infections virales représentent un facteur de risque pour développer une morphée. L’infection par le SARS-CoV-2 induit des lésions vasculaires cutanées avec une libération massive de cytokines, activation des molécules d’adhésion et des cellules T pouvant être à l’origine de connectivite.
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| 0 | PMC9748180 | NO-CC CODE | 2022-12-15 23:22:40 | no | 2022 Nov 14; 2(8):A249 | utf-8 | null | null | null | oa_other |
==== Front
Annales de Dermatologie et de Vénéréologie - FMC
2667-0623
2667-0623
Published by Elsevier Masson SAS
S2667-0623(22)00373-7
10.1016/j.fander.2022.09.099
Co09-077
Nécrolyse épidermique après vaccination contre le COVID-19 : que savons-nous ?
Ahouach B. 1⁎
Diaz E. 1
Bertin B. 2
Ben Said B. 3
Combret S. 4
Grandvuillemin A. 5
Petitpain N. 6
Rabier M.B. 4
Thomas L. 7
Trenque T. 8
Oro S. 1
Lebrun-Vignes B. 9
1 Dermatologie, hôpital Henri-Mondor, AP–HP, Créteil, France
2 Pharmacovigilance, hospices civils de Lyon – HCL, Lyon, France
3 Dermatologie, hôpital Lyon Sud – HCL, Pierre-Bénite, France
4 Pharmacovigilance, centre régional de pharmacovigilance de Franche-Comté, Besançon, France
5 Pharmacovigilance, CHU Dijon, Dijon, France
6 Pharmacovigilance, CHRU de Nancy, hôpitaux de Brabois, Vandœuvre-lès-Nancy, France
7 Pharmacovigilance, hôpital Henri-Mondor, AP–HP, Créteil, France
8 Pharmacovigilance, hôpital Robert-Debré, CHU de Reims, Reims, France
9 Pharmacovigilance, hôpitaux universitaires Pitié-Salpêtrière–Charles-Foix, Paris, France
⁎ Auteur correspondant.
14 12 2022
11 2022
14 12 2022
2 8 A87A88
Copyright © 2022 Published by Elsevier Masson SAS.
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
À ce jour, près de 2 milliards de doses vaccinales anti-COVID-19 ont été administrées. Divers effets indésirables dermatologiques ont été décrits (réactions locales au point d’injection, urticaire, éruptions morbilliformes, etc.). Douze cas de nécrolyse épidermique (NE, incluant les syndromes de Stevens–Johnson et de Lyell) ont été publiés. Notre objectif était d’analyser les cas de NE post-vaccin anti-COVID-19 notifiés en pharmacovigilance (PV) et de faire une revue de la littérature.
Matériel et méthodes
Nous avons extrait de la base de données mondiale de PV (VigiBase), en requêtant avec le « Prefered terms » NE et les vaccins anti-COVID-19 comme médicaments « suspect », les cas de NE déclarés jusqu’au 03/03/2022. Nous avons analysé les caractéristiques de la NE, le déclarant, le délai d’apparition des symptômes, le type de vaccin et la dose (1re : D1, ou 2e : D2), la présence éventuelle d’un autre médicament suspect. Nous avons décrit plus précisément les cas rapportés dans la base de données de PV française et calculé le score ALDEN pour chaque molécule suspectée. Enfin, nous avons analysé les cas de la littérature en revoyant les photos et les données cliniques publiées.
Résultats
Nous avons identifié dans VigiBase 240 cas de NE pour lesquels les vaccins anti-COVID-19 étaient considérés comme « suspects », dans 64 % des cas en provenance des États-Unis, et pour 60,5 % survenus chez des femmes, majoritairement entre 45–64 ans. Il s’agissait de syndrome de Stevens–Johnson (décollement < 10 %) dans 80 % des cas, 7 % sont décédés. Le vaccin le plus souvent suspect était à ARNm (82 %). Ces cas étaient difficiles à interpréter en raison d’un manque de données (biopsies, symptomatologie, nature du déclarant).
La base française de PV comporte 8 cas de NE. Un cas survenu chez une patiente asiatique avec HLA favorisant était plutôt lié à la lamotrigine d’après le calcul du score ALDEN (6 pour lamotrigine et 2 pour le vaccin), 4 cas étaient probablement des erreurs diagnostiques ou non médicamenteux (1 érythème polymorphe, 1 NE post-mycoplasme, 1 éruption morbiliforme et 1 surdosage en méthotrexate) et pour 1 cas, les données descriptives étaient manquantes. Nous avons donc retenu 2 cas de NE potentiellement en lien avec le vaccin : un homme de 40 ans dont la NE a débuté dans les 24 heures suivant la D1 du vaccin Pfizer®, et un homme de 81 ans ayant débuté 3 jours après la D2 du vaccin Pfizer® une NE d’issue fatale.
Après analyse critique des 12 cas de la littérature, nous n’en avons retenu que 3 correspondant sémiologiquement à une NE possiblement en lien avec le vaccin.
Discussion
Notre étude de PV, conjuguée à l’analyse critique de la littérature, ne retient au total que 5 cas de NE possiblement induite par le vaccin anti-COVID-19, sans toutefois pouvoir affirmer le lien de causalité. La majorité des autres cas ne sont pas des NE ou ont d’autres médicaments suspects. Comme avec les autres vaccins, l’éventualité d’une NE post-vaccinale anti-COVID-19 semble donc exceptionnelle et ne remet pas en question le bénéfice attendu de cette vaccination au regard de la morbi-mortalité du SARS-CoV-2.
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==== Front
Le Pharmacien Clinicien
2772-9540
2772-9532
Published by Elsevier Masson SAS
S2772-9532(22)00608-6
10.1016/j.phacli.2022.10.494
000350
Évaluation des pratiques de prescription dans le syndrome inflammatoire multisystémique pédiatrique (PIMS)
Malagouen I. ⁎
Beuzit K.
Pharmacie, CHU de Poitiers, Poitiers
⁎ Auteur correspondant.
14 12 2022
12 2022
14 12 2022
57 4 e73e73
Copyright © 2022 Published by Elsevier Masson SAS.
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.
Contexte
Une nouvelle entité de maladie inflammatoire systémique a vu le jour chez l’enfant dans le contexte épidémique de l’infection à SARS-CoV-2 : le syndrome inflammatoire multisystémique pédiatrique (PIMS). La Société française de pédiatrie (SFP) ainsi que l’Organisation mondiale de la santé ont mis au point des recommandations sur la prise en charge de cette nouvelle entité évoluant au fil des mois.
Objectifs
Évaluer les pratiques de prise en charge du PIMS dans un centre hospitalier universitaire.
Patients et méthodes
Une étude observationnelle rétrospective des prescriptions du 01/01/2020 au 30/09/2021 a été réalisée. Les données ont été collectées à partir du logiciel de prescription Logipren et des dossiers patients. L’adéquation avec les recommandations des sociétés savantes (algorithme PIMS COPIL rédigé par le COPIL COVID inflammation pédiatrique) a été évaluée.
Résultats
La cohorte étudiée est composée de 4 patients, 75 % de garçons, âgés en moyenne de 10,25 ans [4–15], originaire du Maghreb pour 50 % d’entre eux et ayant contracté un PIMS en moyenne 13 jours [6–30] après les premiers signes d’infection à la COVID-19. En 2021, la SFP scinde la prise en charge (PEC) du PIMS en deux groupes, se définissant par la présence d’au moins 1 des 3 critères suivants : instabilité hémodynamique, lactatémie augmentée et/ou défaillance myocardiaque à l’échographie. Ces critères permettent d’orienter l’hospitalisation, en réanimation (70 % des patients) ou en unité de soins continus USC (30 % des patients), ainsi que l’attitude thérapeutique. En réanimation, 66 % des patients ont reçu de l’enoxaparine à la posologie de 100 UI/kg une fois par jour. Contrairement à l’algorithme, l’aspirine a été administrée en relais de l’héparine dans 75 % des cas, secondaire à une augmentation des D-dimères, supérieure à 5 fois la normale, et du fibrinogène, supérieur à 6 g/L, dans 25 % des cas. Tous les patients ont reçu une corticothérapie par méthylprednisolone à la dose de 2 mg/kg/jour pendant 8 semaines, ainsi qu’une immunoglobuline polyvalente (IgPv), Privigen®, à la posologie de 1 g/kg/j pendant 2 jours.
Discussion/Conclusion
La PEC du PIMS s’est inspirée de la forme sévère de la maladie de Kawasaki, recommandant l’utilisation d’IgPv hors AMM, médicaments remboursés en sus du groupe homogène de séjour. Cette utilisation nécessite, lors de l’analyse pharmaceutique, la demande d’une fiche de justification. Par ailleurs, la discussion avec les médecins a permis : de privilégier l’IgPv la plus concentrée, permettant l’injection de petits volumes chez l’enfant corrélé à leurs petits poids et à un moindre risque de surcharge hydrique, de préciser les molécules utilisées et leurs posologies, de comprendre les divergences de pratiques observées par rapport à l’algorithme PIMS COPIL, la référence utilisée par les équipes étant celle de l’étude APHP validée par le récent congrès de cardiopédiatrie.
Mots clés
Pédiatrie
Prescriptions hors indications
Revue des pratiques de prescriptions des médicaments
==== Body
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| 0 | PMC9748193 | NO-CC CODE | 2022-12-15 23:22:40 | no | 2022 Dec 14; 57(4):e73 | utf-8 | null | null | null | oa_other |
==== Front
Le Pharmacien Clinicien
2772-9540
2772-9532
Published by Elsevier Masson SAS
S2772-9532(22)00582-2
10.1016/j.phacli.2022.10.468
000462
Peut-on encore utiliser les immunoglobulines IV dans une indication émergente ? Le cas des PIMS
Ruiz C.
Delpech L.
Rascle P.
Bréant V.
Chamouard V. ⁎
Pharmacie, groupement hospitalier Est, HCL, Bron
⁎ Auteur correspondant.
14 12 2022
12 2022
14 12 2022
57 4 e59e59
Copyright © 2022 Published by Elsevier Masson SAS.
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.
Contexte
Au cours de la pandémie de SARS-CoV-2, au niveau national et international a été observée chez l’enfant, l’émergence d’une nouvelle forme d’inflammation systémique à tropisme cardiaque dénommée « PIMS » (paediatric inflammatory multisystem syndrome), présentant des signes cliniques comparables à ceux de la maladie de Kawasaki. La prise en charge médicamenteuse repose sur un traitement immunomodulateur composé d’immunoglobulines polyvalentes et de corticoïdes. Notre hôpital a été confronté à des pics d’afflux de patients atteints de PIMS, souvent en décalage de quelques semaines par rapport aux pics épidémiques.
Objectifs
Comparer les caractéristiques et les traitements des populations pédiatriques prises en charge pour une maladie de Kawasaki ou PIMS afin de déterminer les principaux facteurs qui les différencient et mesurer l’impact des consommations en période de tension sur les approvisionnements.
Patients et méthodes
Il s’agit d’une étude rétrospective sur la période de mars 2020 à octobre 2021. Les données ont été relevées à l’aide du dossier médical des patients afin de recueillir les critères suivants : caractéristiques anthropométriques, posologie des immunoglobulines (Ig), paramètres biologiques, examens complémentaires réalisés.
Résultats
Le nombre de cas recensés de PIMS sur la période étudiée est de 58, ce qui représente 7,5 % de la cohorte nationale. Leur âge moyen était de 8,7 ans et les filles représentait 41,4 % des patients. Les patients atteints de Kawasaki sont plus jeunes (3,2 ans en moyenne). Quatre-vingt-six pour cent des patients PIMS ont eu un contact avec le SARS-CoV-2. Les patients atteints de maladie de Kawasaki ne présentaient pas de myocardite à l’échographie trans-thoracique contre 37,9 % chez les PIMS. La posologie moyenne était de 1,9 g/kg généralement répartie sur 2 jours, concernant les patients Kawasaki la dose était de 2 g/kg en 1 jour. La consommation d’immunoglobulines sur la période pour l’indication de PIMS était de 3,9 kg ce qui représente 3,8 % des consommations totales d’immunoglobulines de notre hôpital sur la période étudiée. On remarque aussi que la variation du nombre de diagnostics de PIMS est corrélée aux vagues épidémiques de SARS-CoV-2.
Discussion/Conclusion
Ce travail nous permet de décrire la population de notre centre par rapport au données nationales désormais existantes et d’analyser l’utilisation des immunoglobulines polyvalentes dans une indication émergente et ce dans le contexte de fortes tensions d’approvisionnement. Les recommandations nationales voire internationales permettent d’encadrer la prise en charge de la population pédiatrique exposée à la pandémie. Ces données permettent d’alerter les tutelles afin de prendre en compte ces nouveaux besoins d’approvisionnement en immunoglobulines polyvalentes dans la priorisation des indications. Enfin, ces notions doivent être intégrées à la formation continue des pharmaciens y compris des internes en pharmacie intervenant notamment dans le contexte de la garde dans le cadre des validations de ces prescriptions puisque désormais les immunoglobulines IV font partie de l’arsenal thérapeutique des PIMS.
Mots clés
Pédiatrie
Immunoglobulines IV
SARS-CoV-2
==== Body
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| 0 | PMC9748194 | NO-CC CODE | 2022-12-15 23:22:40 | no | 2022 Dec 14; 57(4):e59 | utf-8 | null | null | null | oa_other |
==== Front
Le Pharmacien Clinicien
2772-9540
2772-9532
Published by Elsevier Masson SAS
S2772-9532(22)00807-3
10.1016/j.phacli.2022.10.693
000125
Le syndrome inflammatoire multisystémique pédiatrique, une nouvelle alerte dans le contexte du SARS-CoV-2 : retour sur les 11 premiers cas d’un Centre hospitalier universitaire (CHU)
Le Guen C. 1⁎
Launay E. 2
Leroy E. 1
Prot-Labarthe S. 1
1 Pharmacie hospitalière, Hôtel-Dieu – CHU de Nantes, Nantes
2 Pédiatrie générale, Hôtel-Dieu – CHU de Nantes, Nantes
⁎ Auteur correspondant.
14 12 2022
12 2022
14 12 2022
57 4 e177e178
Copyright © 2022 Published by Elsevier Masson SAS.
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.
Contexte
En avril 2020, une alerte a été lancée sur une recrudescence de syndromes apparentés au Kawasaki, survenant dans les suites d’une infection au SARS-CoV-2. Ils ont été secondairement définis comme des syndromes inflammatoires multisystémiques pédiatriques (PIMS). Le PIMS a été évoqué dans les médias dans le contexte de cette pandémie et commence à être de plus en plus décrit dans la littérature.
Objectifs
Nous avons mis en regard une analyse rétrospective d’une cohorte d’enfants pris en charge dans un CHU de l’Ouest de la France pour un PIMS avec une veille bibliographique PubMed® jusqu’en février 2021.
Patients et méthodes
Les patients inclus dans notre analyse rétrospective sont les patients hospitalisés pour un PIMS entre novembre 2020 et avril 2021 et les données cliniques collectées concernent les critères démographiques, signes cliniques, prise en charge médicamenteuse et suivi durant l’hospitalisation. La bibliographie a été réalisée en fonction de la diversité géographique, des premiers cas publiés et du nombre de patients inclus.
Résultats
Au total, 11 patients âgés de 4 à 15 ans ont été inclus dans notre analyse et 6 articles regroupant les cas de 664 patients de 3 continents différents. Ce travail nous a permis de comparer la présentation clinique du PIMS à celle de la maladie de Kawasaki avec certaines atypies retrouvées dans le PIMS telles que l’âge (âge médian autour de 10 ans), la fièvre élevée et persistante, les frissons, l’asthénie, le syndrome inflammatoire biologique d’avantage marqué, le rash cutané, les douleurs abdominales, l’atteinte multiviscérale et l’absence de vascularite des artères coronaires. La prise en charge thérapeutique associant des immunoglobulines polyvalentes (IVIG) par analogie avec la maladie de Kawasaki (avec un débit modifié lors d’une dysfonction cardiaque échographique), les corticoïdes (avec davantage d’évidences dans leur bénéfice) et l’aspirine en cas d’atteinte coronarienne sera présentée à l’aide des recommandations actuelles. Sauf pour les deux premiers patients, la dose d’IVIG a été fractionnée sur deux jours. Des réponses parfois partielles en IVIG ont fait recommander secondairement une nouvelle cure pour 2/11 patients de notre population comme pour 41/664 patients présentés dans la littérature. Les nouvelles recommandations du comité de pilotage « COVID inflammation pédiatrique » seront présentées avec une synthèse des indications, posologies et modalités d’administration de ces médicaments.
Discussion/conclusion
La prise en charge des enfants hospitalisés a évolué au fur et à mesure de l’augmentation des connaissances sur ce nouveau syndrome inflammatoire multisystémique. Ce travail permet de mettre en regard de l’actualité l’évolution des connaissances de la prise en charge du PIMS et offre une synthèse du bon usage des médicaments dans cette pathologie.
Mots clés
COVID-19
Kawasaki disease
Pediatric multisystem inflammatory disease
==== Body
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| 0 | PMC9748195 | NO-CC CODE | 2022-12-15 23:22:40 | no | 2022 Dec 14; 57(4):e177-e178 | utf-8 | null | null | null | oa_other |
==== Front
Le Pharmacien Clinicien
2772-9540
2772-9532
Published by Elsevier Masson SAS
S2772-9532(22)00540-8
10.1016/j.phacli.2022.10.426
000432
Pandémie COVID-19 : YouTubeFR, un outil de communication efficace pour répondre aux besoins d’informations des professionnels de santé concernant la campagne vaccinale
Vitale E. 1
Tanty A. 2⁎
Allenet B. 1
Chanoine S. 1
Bedouch P. 1
1 Pharmacie, CHU Grenoble-Alpes, université Grenoble-Alpes, CNRS, TIMC-IMAG UMR5525, La Tronche
2 Pharmacie, CHU Grenoble-Alpes, université Grenoble-Alpes, La Tronche
⁎ Auteur correspondant.
14 12 2022
12 2022
14 12 2022
57 4 e35e35
Copyright © 2022 Published by Elsevier Masson SAS.
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.
Contexte
L’arrivée des vaccins contre la COVID-19 a généré de nombreux espoirs mais aussi beaucoup d’interrogations, aussi bien chez les professionnels de santé qu’auprès du grand public. Devant la complexité de la thématique scientifique que représentent les nouvelles technologies vaccinales employées, est apparu l’enjeu de pouvoir diffuser une information validée et synthétique. Au début de la crise, peu de pharmaciens hospitaliers communiquaient de façon indépendante sur les réseaux sociaux pour participer à l’information scientifique pendant cette période particulière.
Objectifs
Proposer aux professionnels de santé une synthèse claire et indépendante des données scientifiques en rapport avec la campagne vaccinale.
Patients et méthodes
Un groupe de 3 pharmaciens s’est spontanément constitué. Le choix s’est porté vers un format de vidéo d’informations de courte durée diffusée sans restriction sur internet. Chaque vidéo a été construite selon le schéma suivant : (1) Recensement des thématiques d’intérêt à aborder, (2) Choix de la thématique à traiter, (3) Collecte des données récentes, (4) Rédaction d’un script, (5) Validation du script, (6) Tournage de la vidéo, (7) Montage, (8) Validation collégiale, (9) Mise en ligne.
Résultats
Neuf capsules vidéo nommées « Les Bolus » ont été créées en fonction de l’actualité, avec un rythme moyen d’une vidéo tous les 15 jours. Les « Bolus » traitaient principalement 3 thématiques : technologie et développement des vaccins, bases scientifiques expliquant les recommandations et la stratégie vaccinale, aspects techniques et manipulation des vaccins. Ceux concernant la surveillance post-vaccinale et la pharmacovigilance ont été réalisés en collaboration avec 2 experts (un médecin urgentiste et un médecin pharmacovigilant). La durée moyenne des « Bolus » était de 7,2 min (4,8 à 10,6 min). Le processus de création d’un bolus a nécessité en moyenne 30 heures de travail. Les « Bolus » ont été mis en ligne sur YouTubeFR, après création d’une chaîne de diffusion, du 3 février au 18 juin 2021. Au 19 novembre 2021, la chaîne comptabilisait 27 149 vues et 387 abonnés. La promotion de chaque nouveau « Bolus » a été faite via les réseaux sociaux Twitter, Facebook et Instagram. Ce projet a été soutenu par plusieurs sociétés savantes nationales : l’Association nationale des enseignants de pharmacie clinique, la Société française de pharmacie clinique, le Centre national hospitalier d’information sur le médicament, la Société française de pharmacologie et de thérapeutique, Euro-Pharmat et la Société française de gériatrie & gérontologie.
Discussion/Conclusion
La création par des pharmaciens de vidéos synthétisant en temps réel les informations concernant la campagne vaccinale a semblé répondre aux besoins des professionnels de santé. La promotion de ces vidéos sur des réseaux sociaux a été une opportunité de les diffuser auprès du grand public.
Mots clés
Webcasts
Vaccination
Health communication
==== Body
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| 0 | PMC9748196 | NO-CC CODE | 2022-12-15 23:22:40 | no | 2022 Dec 14; 57(4):e35 | utf-8 | null | null | null | oa_other |
==== Front
Le Pharmacien Clinicien
2772-9540
2772-9532
Published by Elsevier Masson SAS
S2772-9532(22)00727-4
10.1016/j.phacli.2022.10.613
000487
Promouvoir la vaccination au sein des populations à risque : l’équipe pharmaceutique hospitalière s’implique !
Laloi L. 1
Delmas M. 1
Morfin F. 2
Pivot C. 1
Paillet C. 1
Janoly Dumenil A. 3⁎
1 Pharmacie, hôpital Édouard-Herriot – HCL, Lyon
2 Service de virologie, hôpital de la Croix Rousse – HCL, Lyon
3 Pharmacie et université Lyon 1 ispb et ea 4129 parcours santé systémique, hôpital Édouard-Herriot – HCL, Lyon
⁎ Auteur correspondant.
14 12 2022
12 2022
14 12 2022
57 4 e135e135
Copyright © 2022 Published by Elsevier Masson SAS.
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.
Contexte
La promotion à la vaccination représente un enjeu de santé public majeur. La couverture vaccinale de façon globale (hors vaccination contre la COVID-19), n’est aujourd’hui pas optimale en France, comme le montre notamment la recrudescence des épidémies de rougeoles depuis 2018. Les lieux de dispensation de médicaments au patient, comme la rétrocession en milieu hospitalier, sont des endroits particulièrement propices à la sensibilisation et à l’information du grand public sur le sujet de la vaccination.
Objectifs
Les objectifs de l’étude étaient 1/d’interroger les connaissances des patients et besoins d’informations sur la vaccination, 2/de promouvoir/informer sur la vaccination.
Patients et méthodes
Les patients ciblés par l’enquête présentaient les critères suivants : âge ≥ 65 ans (quel que soit le traitement) ou âge ≥ 18 ans et au moins 1 des traitements suivants (prescrits pour une pathologie ciblée par les recommandations vaccinales : antirétroviraux (ARV), anticancéreux oraux (ATCO), médicaments de l’hypertension artérielle pulmonaire (HTAP), des déficits immunitaires (DI) ou de la drépanocytose/thalassémie). Un questionnaire a été élaboré pour l’objectif 1/et remis au patient au moment de la dispensation. Une plaquette d’information sur la vaccination a été élaborée pour l’objectif 2/et proposée au patient suite au remplissage du questionnaire.
Résultats
Entre le 27/01/21 et le 05/03/21, 100 patients ont participé à l’enquête. Parmi eux, 74 ont été inclus sur le critère du traitement dispensé (68 % ARV, 19 % HTAP, 8 % ATCO, 4 % DI et 1 % thalassémie) et 26 sur le critère de l’âge. Les patients connaissaient pour 28 % la date de leur prochaine vaccination, 24 % seulement connaissaient les nouvelles recommandations vaccinales (depuis 2017). Concernant la vaccination antigrippale : 50 % indiquaient se faire vacciner chaque année et 20 % certaines années ; 28 % se sentaient vulnérables face à l’infection à pneumocoque, 43 % pour la grippe et 45 % pour la COVID-19. De façon globale, 71 % des patients étaient très favorables à la vaccination, 19 % plutôt favorable, 8 % sans avis et 2 % contre. Les principales hésitations à la vaccination étaient : la peur des effets indésirables (34 %), des adjuvants (7 %) ou le manque de confiance envers les laboratoires (8 %). Si 36 % des patients n’avaient pas d’inquiétude, 52 % souhaitaient avoir plus d’information sur la vaccination : 33 % par un échange avec un professionnel de santé et 30 % des affiches en salle d’attente.
Discussion/conclusion
Au vu des résultats, les professionnels de santé ont un rôle central reconnu par les patients pour apporter des réponses à leurs interrogations/craintes pouvant être un frein à la vaccination. Les secteurs de dispensation de médicaments peuvent être un lieu propice pour cette promotion de la vaccination. Ainsi, il serait intéressant d’aborder la question de la vaccination avec les patients au décours de la dispensation en rétrocession hospitalière, après formation de l’ensemble de l’équipe pharmaceutique.
Mots clés
Information
Rétrocession hospitalière
Vaccination
==== Body
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| 0 | PMC9748197 | NO-CC CODE | 2022-12-15 23:22:40 | no | 2022 Dec 14; 57(4):e135 | utf-8 | null | null | null | oa_other |
==== Front
Le Pharmacien Clinicien
2772-9540
2772-9532
Published by Elsevier Masson SAS
S2772-9532(22)00836-X
10.1016/j.phacli.2022.10.722
000132
Adaptation des séances d’Éducation thérapeutique du patient (ETP) à la crise sanitaire : retour sur la mise en place de l’e-ETP dans un centre hospitalier
Labbe E. 1
Grasmuck C. 2⁎
Deberles E. 3
Loison V. 3
Gendera S. 3
Perdriel A. 1
Benoist H. 1
1 Pharmacie, centre hospitalier Général Falaise, Falaise
2 Pharmacie, rue Jean-Schlumberger, Guebwiller
3 Diabétologie, centre hospitalier Général Falaise, Falaise
⁎ Auteur correspondant.
14 12 2022
12 2022
14 12 2022
57 4 e192e193
Copyright © 2022 Published by Elsevier Masson SAS.
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.
Contexte
Depuis 2018, la e-santé est en plein essor, notamment avec le plan « Ma santé 2022 ». En 2020, la crise sanitaire de la COronaro VIrus Disease-19 (COVID-19) a accéléré son développement. En effet, le suivi des patients atteints de maladies chroniques est devenu complexe et les professionnels de santé ont dû s’adapter en proposant notamment de nouvelles prises en charge numériques du patient afin d’éviter toute rupture de suivi. L’e-Éducation thérapeutique du patient (ETP) constitue, dès lors, une alternative aux séances d’ETP en présentiel et permet de garantir un maintien de la prise en charge de ces patients.
Objectifs
Retour d’expérience à 18 mois de la mise en place de séances d’e-ETP consécutive à la crise sanitaire due à la COVID-19.
Patients et méthodes
Mise en place de séances d’e-ETP en diabétologie réalisées par une infirmière diplômée d’État (IDE) et une diététicienne formées à l’ETP au sein d’un établissement hospitalier de 600 lits en mars 2020. Deux outils ont été utilisés : le téléphone et/ou la visioconférence à l’aide de l’application « app’e-santé » mise à disposition par l’Agence régionale de santé. Le contenu des e-séances d’une durée théorique d’une heure était identique aux séances en présentiel du programme ETP labélisé. Retour d’expérience sur la période de mars 2020 à septembre 2021.
Résultats
En 2020, 63 séances d’e-ETP ont été réalisées et 14 en 2021, soit 77 séances au total. Il s’agissait, dans 89 % des cas, de séances de renforcement, dans 8 % des cas de séances initiales et dans les 3 % restants de séances de reprise. Ces séances d’e-ETP ont représenté 11 % de l’ensemble des séances réalisées sur cette période (n = 77/670). Les principaux inconvénients soulevés par les professionnels de santé interrogés étaient les problèmes de connexion au réseau internet (zones d’habitation blanches), l’énergie dépensée à déployer et mener ce type d’activité, les plages horaires plus rigides et la difficulté à donner des explications techniques à distance (expliquer la technique d’injection de l’insuline, par exemple). D’autre part, les avantages relevés étaient le maintien d’un lien avec le patient et la possibilité de prise en charge de nouveaux patients diabétiques. Les professionnelles de l’ETP se sont déclarées pleinement satisfaites de la réalisation des séances d’e-ETP.
Discussion/conclusion
La mise en place de séances d’e-ETP au sein de notre établissement a permis de maintenir un suivi des patients diabétiques au moment des différents confinements. Le rythme des séances a ralenti en 2021 dû au retour des séances en présentiel. Néanmoins, l’e-ETP est un outil numérique à maintenir, car il permet d’étendre l’offre de soins aux patients en ambulatoire et d’intégrer d’autres professionnels à ce parcours tels que le pharmacien clinicien dans le cadre de la e-pharmacie.
Mots clés
Équipe soignante
Continuité des soins
Éducation pour la santé
==== Body
pmcDéclaration de liens d’intérêts
Les auteurs déclarent ne pas avoir de liens d’intérêts.
| 0 | PMC9748198 | NO-CC CODE | 2022-12-15 23:22:40 | no | 2022 Dec 14; 57(4):e192-e193 | utf-8 | null | null | null | oa_other |
==== Front
Le Pharmacien Clinicien
2772-9540
2772-9532
Published by Elsevier Masson SAS
S2772-9532(22)00488-9
10.1016/j.phacli.2022.10.374
000489
Effets du confinement lié à la COVID-19 chez les patients atteints de maladies chroniques
Boulin M. 1⁎
Cransac A. 1
Adam H. 1
Vadot L. 2
Pistre P. 1
Gilbert K. 3
1 Pharmacie, CH régional universitaire de Dijon-Bourgogne, Dijon
2 Pôle pharmaceutique, centre hospitalier universitaire F.-Mitterrand Dijon-Bourgogne, Dijon
3 Pharmacie, CH universitaire de Bordeaux, Bordeaux
⁎ Auteur correspondant.
14 12 2022
12 2022
14 12 2022
57 4 e7e7
Copyright © 2022 Published by Elsevier Masson SAS.
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.
Contexte
Alors que l’attention était focalisée sur la prise en charge des patients atteints de COVID-19, les conséquences potentiellement négatives des confinements successifs chez les patients atteints de maladies chroniques ont été peu explorées.
Objectifs
L’objectif de notre étude est d’évaluer l’impact du premier confinement lié à la COVID-19 chez les patients atteints de maladies chroniques en termes d’adhésion médicamenteuse, accès aux soins, règles hygiénodiététiques et santé mentale.
Patients et méthodes
Une étude de cohorte prospective téléphonique a été conduite entre le 14 avril 2020 et le 2 juin 2020 auprès de patients ambulatoires tirés au sort aléatoirement dans l’une des 8 cohortes (ou registres) régionales suivantes : artérite à cellules géantes, insuffisance cardiaque, dégénérescence maculaire liée à l’âge, fibrose pulmonaire idiopathique/hypertension artérielle pulmonaire, hémopathie maligne, hémophilie, sclérose en plaques, syndrome coronarien chronique. La non-adhésion médicamenteuse était définie comme une modification délibérée de la prise d’un ou plusieurs médicaments par le patient en dehors de tout avis médical, pharmaceutique ou infirmier. La détresse psychologique d’un patient était définie par un score ≥ 5 sur l’échelle Kessler Psychological Distress Scale K61. Un consentement oral était obtenu pour chaque participant à l’étude COVID-19 Lockdown Effects On Chronic Diseases (CLEO-CD ; NCT04390126).
Résultats
La population finale de l’étude comprenait 1274 patients d’âge moyen 66 ± 17 ans dont 55 % d’hommes. L’adhésion médicamenteuse était de 98 %. Les principales classes/molécules concernées par une non-adhésion des patients étaient : agents anticancéreux/immunosuppresseurs, anti-inflammatoires non stéroïdiens, antiangiogéniques, aspirine antiagrégante, inhibiteurs de l’enzyme de conversion. Parmi les 738 patients ayant au moins un rendez-vous médical programmé pendant la période, 305 (41 %) ont déclaré que le rendez-vous avait été annulé pour une raison qui n’était pas de leur ressort. Les principales modifications hygiénodiététiques étaient une détérioration du sommeil (qualité et/ou quantité ; 71 %), une augmentation > 25 % du temps passé devant un écran (46 %), une diminution de plus de 25 % de l’activité physique (46 %). Une détresse psychologique était présente chez 19 % des patients. En analyse multivariée, un habitat urbain (OR 1,76 [vs rural, < 2000 habitants] ; IC95 % 1,3–2,3 ; p = 10−4) et une détresse psychologique (OR 1,52 [vs score K6 < 5] ; IC95 % 1,1–2,2 ; p = 0,03) étaient des facteurs indépendants associés à la présence d’au moins un facteur délétère pour la santé des patients.
Discussion/Conclusion
Nos résultats sont rassurants en termes d’adhésion médicamenteuse. Chez les patients atteints de maladie chronique, une attention particulière est requise pour leur suivi s’ils habitent en milieu urbain et/ou s’ils présentent une détresse psychologique.
Mots clés
Coronavirus
Maladie chronique
Adhésion et observance thérapeutiques
==== Body
pmcDéclaration de liens d’intérêts
Les auteurs déclarent ne pas avoir de liens d’intérêts.
| 0 | PMC9748199 | NO-CC CODE | 2022-12-15 23:22:40 | no | 2022 Dec 14; 57(4):e7 | utf-8 | null | null | null | oa_other |
==== Front
Le Pharmacien Clinicien
2772-9540
2772-9532
Published by Elsevier Masson SAS
S2772-9532(22)00706-7
10.1016/j.phacli.2022.10.592
000372
Évaluation de la prise en charge thérapeutique des patients atteints du SARS-Cov2 dans un hôpital tunisien
Mhiri A. 1⁎
Dridi B. 2
Kalboussi N. 2
Golli R. 2
Kacem B. 2
1 Pharmacie, CHU Sahloul de Sousse, Sousse
2 Service de pharmacie, CHU Sahloul, Sousse, Tunisie
⁎ Auteur correspondant.
14 12 2022
12 2022
14 12 2022
57 4 e123e124
Copyright © 2022 Published by Elsevier Masson SAS.
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.
Contexte
Les recommandations de prise en charge thérapeutique des patients atteints du SARS-CoV2 sont en perpétuelle évolution.
Objectifs
L’objectif de cette étude est d’analyser les prescriptions des patients COVID-19 hospitalisés dans un hôpital tunisien afin d’optimiser leur prise en charge.
Patients et méthodes
Il s’agit d’une étude prospective des prescriptions des patients COVID-19 hospitalisés entre le 15 décembre 2020 et le 28 février 2021, dans un hôpital tunisien. Les prescriptions ont été analysées par deux pharmaciens et les interventions pharmaceutiques (IP) réalisées ont été tracées sur des fiches de suivi des patients. La classification des problèmes identifiés et des IP a été effectuée selon la fiche de la Société française de pharmacie clinique (SFPC).
Résultats
Durant la période de l’étude, 155 patients ont été hospitalisés et 860 lignes de prescriptions ont été analysées. Tous les patients ont reçu une antibiothérapie. Le nombre d’IP réalisées était de 169. Ces IP concernaient 58 femmes et 47 hommes. L’âge moyen était de 64 ans (min : 16, max : 87). Les médicaments impliqués étaient essentiellement les antibiotiques (44 %), les corticoïdes (25 %) et les anticoagulants (20 %). Les principaux problèmes rencontrés étaient les erreurs de posologie (41 %), les erreurs de prescription informatisée (33 %), les non-conformités aux référentiels (18 %) et les interactions médicamenteuses (4,7 %). Parmi les non-conformités aux consensus, on retrouvait principalement le non-respect de la durée de prescription. Les IP réalisées se répartissaient en 69 adaptations posologiques, 45 arrêts, 26 substitutions, 20 ajouts et 9 suivis thérapeutiques. Le taux d’acceptation de ces interventions était de 98 %. Trois grands problèmes récurrents ont été identifiés : le problème d’antibiothérapie systématique, d’anticoagulation curative systématique et le recours à des posologies élevées de dexaméthasone. Un protocole de prise en charge thérapeutique a été rédigé afin de standardiser les pratiques de prescription. L’application de ce protocole a permis une diminution de la prescription des antibiotiques de 60 % et une diminution de 73 % de la prescription d’anticoagulation curative.
Discussion/conclusion
En raison de l’évolution constante des recommandations de prise en charge thérapeutique des patients atteints de la COVID-19, le suivi du traitement des patients atteints du SARS-Cov2 et la mise en place de protocoles standardisés sont essentiels afin de rationaliser et d’optimiser leur prise en charge thérapeutique.
Mots clés
Patients hospitalisés
Prescription inappropriée
COVID-19
==== Body
pmcDéclaration de liens d’intérêts
Les auteurs déclarent ne pas avoir de liens d’intérêts.
| 0 | PMC9748200 | NO-CC CODE | 2022-12-15 23:22:40 | no | 2022 Dec 14; 57(4):e123-e124 | utf-8 | null | null | null | oa_other |
==== Front
Le Pharmacien Clinicien
2772-9540
2772-9532
Published by Elsevier Masson SAS
S2772-9532(22)00721-3
10.1016/j.phacli.2022.10.607
000552
Démarche qualité à l’officine en période de crise sanitaire : enquête auprès de pharmaciens en Auvergne Rhône-Alpes
Vallet M.-A. 1
Janoly Dumenil A. 2⁎
1 Faculté de pharmacie ISPB, université Lyon 1, Lyon
2 Pôle management de la qualité – santé publique – ea 4129 parcours santé systémique, faculté de pharmacie – ISPB – université Lyon 1, Lyon
⁎ Auteur correspondant.
14 12 2022
12 2022
14 12 2022
57 4 e132e132
Copyright © 2022 Published by Elsevier Masson SAS.
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.
Contexte
L’année 2020 restera marquée par une situation exceptionnelle : la crise sanitaire de la COVID-19 (Coronavirus disease-19) qui a eu des conséquences multiples sur l’organisation de l’activité officinale et sur la relation pharmacien patient.
Objectifs
L’objectif de notre enquête était de dresser un état des lieux des mesures et aménagements mis en place pendant la pandémie et de déterminer l’impact d’une démarche qualité à l’officine sur la gestion de la crise sanitaire de la COVID-19.
Patients et méthodes
Un questionnaire de 21 questions a été diffusé par mail principalement par le canal des maîtres de stage agréés des départements suivants : Loire, Rhône et Ain. Il est resté accessible du 2 avril au 2 mai 2021.
Résultats
Au total, 100 pharmaciens titulaires ont répondu au questionnaire. Les pharmaciens ont dû faire face à de nouvelles responsabilités : vaccination contre la COVID-19 (99 % des interrogés), dispensation exceptionnelle (92 %), dépistage (67 %), portage à domicile (71 %), fabrication de solution hydroalcoolique (49 %), alerte contre les violences intrafamiliales (44 %), mise à disposition de médicaments rétrocédables (11 %), télésoin (4 %), conseils et éducation des patients inquiets. La multitude de décrets et arrêtés ont compliqué l’application des bonnes pratiques de dispensation à l’officine et sont la source d’un manque de cohérence (67 % des pharmaciens répondants) et d’un dysfonctionnement dans la communication ou un manque de concertation avec les tutelles (94 %). Le confinement a entraîné une recrudescence de situations conflictuelles (60 % des répondants) en France et à l’étranger, ainsi qu’une augmentation des détournements d’ordonnance suite à la dématérialisation (57 %), et a donc mis en lumière des fragilités, dont une disparité entre officines urbaines et rurales, avec une diminution du chiffre d’affaire pour 42 % des interrogés et à l’inverse un impact positif sur le chiffre d’affaire observé chez 31 % des titulaires. La fonction managériale est ressentie comme essentielle par 91 % des répondants. La démarche qualité est un soutien en période de crise sanitaire pour 73 % des pharmaciens engagés dans une démarche qualité et 63 % des pharmaciens voient une utilité à la démarche même s’ils ne sont pas encore engagés dans cette démarche.
Discussion/conclusion
Une crise sanitaire d’une telle ampleur engendrera inévitablement des évolutions pérennisables au sein du milieu officinal. Le rôle du pharmacien, comme professionnels de santé de premier recours, devra être développé, dans le cadre d’une vision plus collective de la santé de demain, pour assurer aux patients un parcours de soins plus efficient. En ce sens, l’ordre national des pharmaciens engage les pharmaciens dans la démarche qualité en proposant des outils pour soutenir les pratiques. La démarche qualité et la culture d’un climat de confiance sont un cadre nécessaire dans l’amélioration des pratiques et le développement de nouvelles missions à l’officine.
Mots clés
Management qualité
Pratiques officinales
Pandémie COVID-19
==== Body
pmcDéclaration de liens d’intérêts
Les auteurs déclarent ne pas avoir de liens d’intérêts.
| 0 | PMC9748201 | NO-CC CODE | 2022-12-15 23:22:40 | no | 2022 Dec 14; 57(4):e132 | utf-8 | null | null | null | oa_other |
==== Front
Le Pharmacien Clinicien
2772-9540
2772-9532
Published by Elsevier Masson SAS
S2772-9532(22)00622-0
10.1016/j.phacli.2022.10.508
000383
Articulation des pharmaciens (cliniciens dans les services et à la pharmacie à usage intérieur) au service du patient de réanimation en période de pandémie à SARS-CoV-2
Lemtiri J. 1⁎
Matusik E. 2
Lambiotte F. 1
Elbeki N. 3
Lehmann L. 4
Dautel D. 4
Cléry M.-D. 1
Cordier S. 4
Cousein E. 4
Georgel C. 4
Drancourt P. 4
Pruvost A. 4
1 Réanimation, CH de Valenciennes, avenue Desandrouin, Valenciennes, France
2 Réanimation-pharmacie, CH de Valenciennes, avenue Desandrouin, Valenciennes, France
3 Anesthésie, CH de Valenciennes, avenue Desandrouin, Valenciennes, France
4 Pharmacie, CH de Valenciennes, avenue Desandrouin, Valenciennes, France
⁎ Auteur correspondant.
14 12 2022
12 2022
14 12 2022
57 4 e80e81
Copyright © 2022 Published by Elsevier Masson SAS.
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.
Contexte
La réanimation accueille son tout premier patient atteint d’une infection à SARS-CoV-2 (patient COVID-19) en mars 2020.
Objectifs
Une organisation pharmaceutique efficiente a été rapidement requise, particulièrement en réanimation et est présentée dans ce travail.
Patients et méthodes
Nous disposions de 1,5 ETP pharmaciens cliniciens dans les secteurs de soins critiques et une équipe pharmaceutique basée à la pharmacie à usage intérieur (PUI). En période COVID, la création d’un comité de pilotage (COPIL) au sein du pôle de soins critiques a permis des prises de décision rapides sur la base d’une adéquation flux malades et ressources humaines/matérielles disponibles, avec une présence du pharmacien clinicien (PC) et du pharmacien de la PUI pour leur expertise en pratiques thérapeutiques de soins critiques, en produits de santé et en logistique.
Résultats
Initialement, une antenne pharmaceutique délocalisée a été créée pour un fonctionnement autonome de l’unité COVID. Rapidement, devant l’ampleur de la crise et le déploiement de lits (jusqu’à 42 pour une base à 23), une réorganisation pharmaceutique complète est réalisée avec une révision des dotations de médicaments (hausse des anti-infectieux, des agents sédatifs, des morphiniques et des curares ; ajout de l’albumine) et de dispositifs médicaux (spécifiques tels que des filtres hydrophobes avec formation de l’équipe paramédicale et audits et des systèmes clos d’aspiration). Des outils informatiques ont été créés, côté PUI et services de soins, afin de pouvoir interroger en instantané les stocks en lien avec les consommations des thérapeutiques « prioritaires » identifiées (agents sédatifs, curares, épuration extra-rénale). Les stratégies thérapeutiques, validées en COPIL, ont été adaptées afin de préserver l’usage du propofol et de limiter les entrées itératives en chambre des infirmiers diplômés d’État (mise en place de la sédation inhalée par gaz halogénés, pousse-seringue électrique de midazolam/sufentanil double concentration) et d’offrir des alternatives de choix de matériel pour les administrations actives d’anti-infectieux. Des seringues prêtes à l’emploi de cisatracurium et de propofol ont été réalisées par la PUI avec des petits dosages, après adaptation des stratégies au bloc opératoire, évitant toute rupture de traitement sur ces molécules précieuses. Cette proximité avec le service de soins via la collaboration PC/pharmacien PUI a également pu s’exprimer avec efficience dans le cadre de la recherche clinique.
Discussion/Conclusion
Les produits de santé ont représenté un enjeu majeur dans la gestion de cette crise COVID. Une organisation pharmaceutique basée sur la coopération avec une adaptation précoce de l’offre pharmaceutique globale, en lien avec les besoins du service de soins exprimés par le PC, a constitué la ligne d’attaque contre toute rupture de traitement des patients. Ces pratiques d’anticipation, en maximisant et optimisant les échanges PC/pharmacien PUI se sont déclinées sur les « vagues » suivantes et ont montré leur efficience. Hors COVID, un projet CIVAS (Centralized IntraVenous Additive Service) est en cours de réflexion.
Mots clés
Infections à coronavirus
Réanimation
Services pharmaceutiques
==== Body
pmcDéclaration de liens d’intérêts
Les auteurs déclarent ne pas avoir de liens d’intérêts.
| 0 | PMC9748202 | NO-CC CODE | 2022-12-15 23:22:40 | no | 2022 Dec 14; 57(4):e80-e81 | utf-8 | null | null | null | oa_other |
==== Front
Le Pharmacien Clinicien
2772-9540
2772-9532
Published by Elsevier Masson SAS
S2772-9532(22)00739-0
10.1016/j.phacli.2022.10.625
000316
La veille d’information COVID-19, où comment la SFPC a permis de recenser les informations pertinentes pour les professionnels
Chaumais M.-C. 1⁎
Chenailler C. 2
Matusik E. 3
Cambon A. 4
Tanty A. 5
Cabelguenne D. 6
Hache G. 7
Humbert C. 8
Pourrat X. 9
Renaudin P. 10
Allenet B. 11
Prot-Labarthe S. 12
1 Pharmacie, hôpital Bicêtre, AP–HP, Le Kremlin-Bicêtre
2 Pharmacie, C.H.U.-Hôpitaux de Rouen, Rouen
3 Pharmacie, anesthésie-réanimation, C.H. de Valenciennes, Valenciennes
4 Pharmacie, C.H.U. Toulouse – Casselardit Ancely, Toulouse
5 Pharmacie, C.H.U. Grenoble Alpes, La Tronche
6 Pharmacie, groupement hospitalier Sud – hospices civils de Lyon, Pierre-Bénite
7 Pharmacie à usage intérieur, Assistance publique–Hôpitaux de Marseille, Marseille
8 Pharmacie, C.H.U. Bicêtre, Le Kremlin-Bicêtre
9 Pharmacie, hôpital Trousseau – C.H.R.U. Hôpitaux de Tours, Chambray-lès-Tours
10 Pharmacie, C.H.U. Lapeyronie, Montpellier
11 Pharmacie, université Grenoble Alpes/CNRS/TIMC-IMAG UMR5525/Themas, La Tronche
12 Pharmacie, hôpital Robert-Debré, AP–HP, Paris
⁎ Auteur correspondant.
14 12 2022
12 2022
14 12 2022
57 4 e142e142
Copyright © 2022 Published by Elsevier Masson SAS.
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.
Contexte
La pandémie de la COVID-19 a bouleversé les pratiques liées à la prise en charge des patients. Afin d’accompagner au mieux les pharmaciens dans leurs missions, la Société française de pharmacie clinique (SFPC) a créé un groupe de travail « veille COVID SFPC » pour diffuser en temps réel les nouvelles recommandations.
Objectifs
L’objectif de cette communication est de présenter la méthodologie et le résultat de cette collaboration.
Patients et méthodes
La première étape a été de constituer le groupe de travail (GT) avec des pharmaciens directement impliqués et exerçant en ville et à l’hôpital. Un appel a été fait auprès des membres du conseil d’administration et de la commission junior de la SFPC pour identifier ces pharmaciens motivés. Ce GT devait pouvoir se réunir facilement, réunir les informations utiles pour tous les pharmaciens de terrain et les mettre à disposition sur le site internet de la SFPC. Ces informations devaient être issues de recommandations officielles, de sociétés savantes ou émanant d’établissements de santé, faciles à mettre à jour et ne pas faire doublon avec les autres sites de spécialités ou d’instances existantes. Le travail à fournir devait être compatible avec l’activité quotidienne de chacun pour permettre un résultat pérenne, pertinent et garder les équipes motivées.
Résultats
Le GT final était constitué de 12 pharmaciens exerçant dans 7 villes. Ce groupe, largement aidé par l’expérience de nos confrères de l’Est, s’est réuni quotidiennement en visioconférence. Le rythme des points s’est ensuite allégé de manière hebdomadaire voire mensuelle entre les deux vagues de pandémie et a finalement pris fin début mai 2021. Au total, sur 13 mois, 35 réunions ont eu lieu (temps moyen de 45 minutes). Il a été choisi de ne pas relayer les publications évaluant la balance bénéfice/risque des médicaments, mais seulement les synthèses des évidences. Au final, la page spécifique du site SFPC comprenait un tableau synthétique des outils de prise en charge thérapeutique classé par spécialité médicale (40 liens dans les spécialités de cardiologie, infectiologie, psychiatrie, soins intensifs, etc.) et plus de 70 liens disponibles dans 12 rubriques (veille bibliographique, essais thérapeutiques, ordre des pharmaciens, informations relatives à la dispensation, bon usage des produits de santé, mesures barrières et tests diagnostiques, informations à destination des patients, partenaires, liens institutionnels, épidémiologie, webinars et formations, travaux collaboratifs menés par la SFPC). Dès sa mise à disposition (23 mars 2020), la page a été visitée 4350 fois en 3 mois, avec un temps moyen passé de 4 minutes. À ce jour, la page a été visitée 8182 fois.
Discussion/conclusion
La COVID-19 a imposé une situation sanitaire, économique et sociale sans précédent. Le pharmacien, dans tous ses modes d’exercice et au sein de la SFPC, a contribué à aider les professionnels à la prise en charge des patients souffrant de COVID dans un contexte d’ajustement permanent des recommandations.
Mots clés
Internet
Active learning
« COVID-19 » [Supplementary Concept]
==== Body
pmcDéclaration de liens d’intérêts
Certains auteurs sont membres du bureau ou Conseil d’Administration de la SFPC.
| 0 | PMC9748203 | NO-CC CODE | 2022-12-15 23:22:40 | no | 2022 Dec 14; 57(4):e142 | utf-8 | null | null | null | oa_other |
==== Front
Le Pharmacien Clinicien
2772-9540
2772-9532
Published by Elsevier Masson SAS
S2772-9532(22)00734-1
10.1016/j.phacli.2022.10.620
000344
Renfort en réanimation pendant l’épidémie à Covid-19 : formation flash des infirmiers par les pharmaciens
Yailian A.-L. 1⁎
Rerbal D. 2
Charniguet D. 3
Ferreira A. 4
Dumes J. 4
Debord-Peguet S. 4
Paillet C. 1
1 Pharmacie, hôpital Édouard-Herriot – H.C.L., Lyon
2 Urgences, hôpital Édouard-Herriot – H.C.L., Lyon
3 C.E.S.U., hôpital Édouard-Herriot – H.C.L., Lyon
4 Réanimation chirurgicale, hôpital Édouard-Herriot – H.C.L., Lyon
⁎ Auteur correspondant.
14 12 2022
12 2022
14 12 2022
57 4 e139e139
Copyright © 2022 Published by Elsevier Masson SAS.
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.
Contexte
Dès le début de l’épidémie Covid-19, des professionnels ont dû être mobilisés en réanimation pour répondre à l’afflux massif de patients. Des étudiants infirmiers et des infirmiers diplômés d’état (IDE) n’exerçant habituellement pas dans des services de réanimation ou de soins continus ont ainsi rejoint les équipes de réanimations dans un contexte sanitaire inédit. Dans notre établissement, les équipes de réanimations, en lien avec le Centre d’enseignement aux soins d’urgence (C.E.S.U.)ont organisé des formations pour ces professionnels. Les sessions de 2 jours devaient permettre aux soignants d’acquérir les compétences minimales leur permettant de prendre en charge des patients Covid-19+ en réanimation.
Objectifs
L’objectif de notre travail était d’intégrer dans cette formation une partie dédiée à la pharmacologie des médicaments de réanimation.
Patients et méthodes
Le contenu de la formation a été défini de façon pluridisciplinaire entre médecins, pharmaciens et cadres de santé impliqués dans la formation. Il a été décidé d’aborder : les médicaments les plus fréquemment utilisés dans la prise en charge des patients Covid-19+ et les spécificités du circuit du médicament en réanimation. Pour chaque médicament, les notions essentielles de pharmacologie, les posologies usuelles, ainsi que les modalités de préparation, d’administration et de surveillance/traçabilité ont été précisées. Le support de formation a été élaboré par 2 pharmaciens et validé par 1 réanimateur.
Résultats
Au total, ce sont 55 étudiants et infirmiers qui ont participé à la formation de 2 heures proposée par les pharmaciens. Les classes médicamenteuses abordées étaient les suivantes : sédatifs, analgésiques, curares, catécholamines, antibiotiques (dont bêtalactamines et aminosides) et anticoagulants injectables. Des prescriptions extraites directement du logiciel de prescriptions ont été discutées (modalités de dilution et d’administration, monitorage). Un point spécifique était réalisé sur le bon usage des médicaments hautement contingentés et leur approvisionnement multiple. Une utilisation raisonnée, ainsi qu’une vigilance accrue lors de la lecture des étiquetages étaient recommandées. Le principe et l’intérêt des armoires sécurisées, utilisées spécifiquement en réanimation dans notre établissement, ont été présentés. La formation se voulait participative pour favoriser les échanges entre les professionnels venant de milieux différents. La présence d’une IDE de réanimation détachée des soins a favorisé les échanges entre pharmaciens et infirmiers. Au terme de la formation, le support de formation a été envoyé à tous les professionnels afin de favoriser l’acquisition des compétences dans un délai court. L’intérêt de la formation a été souligné par les professionnels mobilisés.
Discussion/conclusion
L’accompagnement des professionnels via une formation spécifique et un partage des compétences étaient nécessaires pour maintenir le niveau d’efficience habituel. Une formation structurée en pharmacologie était primordiale au vu de la diversité des professionnels impliqués. Elle était complémentaire des autres formations plus techniques. La collaboration déjà existante entre médecins, pharmaciens et équipe soignante a permis une mise en place rapide de la formation qui a été facilement reconduite par la suite.
Mots clés
Formation en santé
Pharmacologie
Réanimation
==== Body
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| 0 | PMC9748204 | NO-CC CODE | 2022-12-15 23:22:40 | no | 2022 Dec 14; 57(4):e139 | utf-8 | null | null | null | oa_other |
==== Front
Le Pharmacien Clinicien
2772-9540
2772-9532
Published by Elsevier Masson SAS
S2772-9532(22)00710-9
10.1016/j.phacli.2022.10.596
000502
Immunoglobulines et Covid-19 : j’en ai ma dose !
Maes A. 1⁎
Mouton Sclaunich H. 2
Chamoux T. 1
Lesueur G. 3
Bardin M. 3
Tiret I. 3
1 Pharmacie, C.H.U de Rouen, Rouen
2 Gériatrie St Julien, CHU-Hôpitaux de Rouen, Rouen
3 Pharmacie, CHU-hôpitaux de Rouen, Rouen
⁎ Auteur correspondant.
14 12 2022
12 2022
14 12 2022
57 4 e126e126
Copyright © 2022 Published by Elsevier Masson SAS.
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.
Contexte
Parmi les impacts de la pandémie de Covid-19, on peut citer la diminution drastique des dons du sang au niveau mondial. Cette diminution de dons du sang a eu des répercussions sur la production des médicaments dérivés du sang. C’est notamment le cas des immunoglobulines (Ig) polyvalentes qui se sont retrouvées en forte tension d’approvisionnement.
Objectifs
Réaliser une analyse rétrospective des prescriptions d’Ig au sein du CHU de Rouen en période de tension d’approvisionnement et des actions mises en place pour palier au défaut d’approvisionnement.
Patients et méthodes
Cette analyse rétrospective couvre les prescriptions d’Ig polyvalentes intraveineuses réalisées sur une période de six mois de janvier à juin 2021. Toutes ces prescriptions ont été tracées dans un tableau qui comportait comme information la spécialité et la dose souhaitée, la spécialité et la dose dispensée (switch nécessaire ou non vers une autre Ig) ainsi que le caractère ponctuel ou chronique de la cure.
Résultats
Au CHU de Rouen, 3 spécialités d’Ig polyvalentes sont référencées : CLAIRYG®, PRIVIGEN® et TEGELINE®. En anticipation de la pénurie, plusieurs mesures ont été prises : une note d’information a été envoyée aux services pour les avertir des fortes tensions d’approvisionnement en Ig ; une commande exceptionnelle de 10 kg de GAMUNEX® a également été passée et un suivi quotidien des stocks était réalisé par la pharmacie. Sur cette période de 6 mois, il y a eu 522 prescriptions d’Ig polyvalentes pour des patients hospitalisés. Sur 522 prescriptions, 61 ont eu une modification de dose entre la dose prescrite et la dose dispensée : en moyenne, la dose dispensée était inférieure de 5 g à la dose prescrite. Ces modifications de dose étaient toujours faites en accord avec un prescripteur (interne ou sénior) du service concerné. Sur ce même effectif de 522 prescriptions, 60 ont bénéficié d’un switch pour une autre Ig polyvalente. Contrairement aux réductions de dose, il avait été convenu que les switch devaient obligatoirement être validés par un sénior du service. Les cures considérées comme ponctuelles représentaient 103 prescriptions sur les 522 et les switch d’Ig représentaient 35,0 % de ces prescriptions (soit 36 prescriptions sur 103). Pour les cures dites chroniques (n = 419), seules 5,7 % (n = 24) d’entre elles ont nécessité un switch.
Discussion/conclusion
Cette analyse rétrospective a mis en évidence une diminution moyenne des doses de 5 g par cure. On observe également que le pourcentage de switch était plus important pour les cures ponctuelles que pour les cures chroniques, dans le but d’éviter aux patients ayant des cures chroniques d’avoir plusieurs switch. Cette analyse a été réalisée en début de pénurie en Ig. D’autres enjeux tels que la priorisation des indications ou le renforcement de l’optimisation des cures (diminution de doses et/ou augmentation de l’espacement des cures) seront intéressants à évaluer dans les prochains mois.
Mots clés
Immunoglobuline
Substitution de médicament
Systèmes hospitaliers de dispensation et de distribution de médicaments
==== Body
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| 0 | PMC9748205 | NO-CC CODE | 2022-12-15 23:22:40 | no | 2022 Dec 14; 57(4):e126 | utf-8 | null | null | null | oa_other |
==== Front
Le Pharmacien Clinicien
2772-9540
2772-9532
Published by Elsevier Masson SAS
S2772-9532(22)00586-X
10.1016/j.phacli.2022.10.472
000456
FAQ Vaccins COVID-19 : la mobilisation des sociétés savantes pharmaceutiques pour répondre aux enjeux de la vaccination
Tanty A. 1⁎
Vitale E. 1
Chanoine S. 1
Jost J. 2
Mille F. 3
Gourieux B. 4
Collomp R. 5
Allenet B. 6
Thiveaud D. 7
Dode X. 8
Honoré S. 9
Bedouch P. 1
1 Pharmacie clinique, ANEPC, SFPC, Grenoble
2 Pharmacie clinique, ANEPC, SFPC, Limoges
3 Pharmacie, centre national hospitalier d’information sur le médicament (CNHIM), Wissembourg
4 Pharmacie clinique, SFPC, Strasbourg
5 Pharmacie clinique, SFPC, Nice
6 Pharmacie clinique, SFPC, ANEPC, Grenoble
7 Pharmacie, Euro-Pharmat, Toulouse
8 Pharmacie, centre national hospitalier d’information sur le médicament (CNHIM), Bron
9 Pharmacie clinique, SFPC, ANEPC, Marseille
⁎ Auteur correspondant.
14 12 2022
12 2022
14 12 2022
57 4 e61e61
Copyright © 2022 Published by Elsevier Masson SAS.
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.
Contexte
L’année 2021 a été marquée par la mise à disposition des premiers vaccins contre la COVID-19. Deux vaccins à vecteur viral et deux vaccins à ARNm ont rapidement reçu une AMM conditionnelle en Europe. Pour répondre aux problématiques quotidiennes liées à la mise en œuvre de la campagne vaccinale et face à l’évolution constante des connaissances scientifiques, il a été rapidement identifié le besoin de pouvoir mettre à disposition des pharmaciens des informations synthétiques, claires, validées et à jour.
Objectifs
Mettre à disposition des pharmaciens et autres professionnels de santé une information technique et scientifique sur les vaccins contre la COVID-19.
Patients et méthodes
À partir du 4 janvier 2021, l’Association nationale des enseignants de pharmacie clinique (ANEPC), la Société française de pharmacie clinique (SFPC), le Centre national hospitalier d’information sur le médicament (CNHIM) et Euro-Pharmat ont décidé de mettre en œuvre un groupe de travail (GT) organisé autour de 12 pharmaciens hospitaliers, exerçant dans différentes régions de France pour construire une Foire Aux Questions (FAQ) mise en ligne sur leurs sites internet. Le GT s’organisait selon la méthode suivante : (1) proposition de questions au GT ; (2) rédaction des réponses par des volontaires du GT ; (3) validation collégiale par le GT des questions/réponses ; (4) intégration et mise en ligne des questions/réponses validées à la FAQ ; et (5) mise à jour de la FAQ suivant une méthodologie similaire.
Résultats
La première version de la FAQ a été mise en ligne le 25 janvier 2021. Elle comprenait 21 questions réparties en 5 rubriques : Questions « approvisionnement/dispensation », Questions « administration », Questions « conservation/stockage/transport », Questions « bon usage du vaccin » et Questions diverses. À mesure de la mise sur le marché et de l’évolution des connaissances sur les vaccins, 3 mises à jour ont été mises en ligne (février 2021, mars 2021 et août 2021). La dernière version mise en ligne comprenait 49 questions dont 19 questions ont été mises à jour lors des deux précédentes versions.
Discussion/Conclusion
La coordination rapide des différentes sociétés savantes pharmaceutiques et la collaboration efficace du GT a permis de mettre à disposition des professionnels de santé une ressource documentaire opérationnelle, pratique et à jour en période de crise afin de faciliter la mise en œuvre de la campagne vaccinale contre la COVID-19.
Mots clés
Health information management
Pharmaceutical care
COVID-19 vaccine
==== Body
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| 0 | PMC9748206 | NO-CC CODE | 2022-12-15 23:22:40 | no | 2022 Dec 14; 57(4):e61 | utf-8 | null | null | null | oa_other |
==== Front
Le Pharmacien Clinicien
2772-9540
2772-9532
Published by Elsevier Masson SAS
S2772-9532(22)00633-5
10.1016/j.phacli.2022.10.519
000460
Retour d’expérience sur la prise en charge de myocardite aiguë dite « Kawasaki-like » associée à la COVID-19 : respect du bon usage et de la juste prescription des immunoglobulines intraveineuses
Scherer L. 1⁎
Videau M. 1
Corvo C. 1
Lecefel C. 1
Genest E. 1
Quenehen K. 1
Hekimian G. 2
Luyt C.-E. 2
Liou A. 1
1 Pharmacie, hôpital Pitié-Salpêtrière, AP–HP, Sorbonne université, Paris
2 Médecine intensive réanimation, Institut de cardiologie, hôpital Pitié-Salpêtrière, AP–HP, Sorbonne université, Paris
⁎ Auteur correspondant.
14 12 2022
12 2022
14 12 2022
57 4 e86e87
Copyright © 2022 Published by Elsevier Masson SAS.
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.
Contexte
La maladie de Kawasaki (MK) est une vascularite fébrile aiguë multisystémique infantile nécessitant une prise en charge urgente. Le traitement recommandé est l’administration précoce d’immunoglobulines intraveineuses (IgIV), renouvelée à 72 heures si besoin. Depuis la pandémie de COVID-19, une émergence de patients atteints de myocardites aiguës dites « Kawasaki-like » (mKL), ne présentant pas tous les critères de MK, a été constatée par les services de cardiologie et réanimation médicale cardiaque adulte. En l’absence de recommandations officielles, il a été statué de traiter ces patients comme des patients atteints de MK.
Objectifs
L’objectif de ce travail a été d’évaluer l’utilisation d’IgIV dans cette indication urgente, en accord avec le comité des médicaments dérivés du sang (MDS), dans un contexte de forte tension d’approvisionnement des IgIV.
Patients et méthodes
Une étude rétrospective monocentrique observationnelle a été réalisée d’avril 2020 à avril 2021. Tous les patients ayant reçu sur la période au moins une cure d’IgIV pour une myocardite aiguë associée à la COVID-19 ont été inclus. Le statut de mKL associée à la COVID-19 était définie par une myocardite aiguë avec sérologie COVID positive et/ou un antécédent de RT-PCR positive. Les données clinico-biologiques et d’administration des IgIV ont été analysées à partir du logiciel de gestion et traçabilité des MDS et du dossier patient informatisé.
Résultats
Vingt-huit patients ont été inclus dans l’étude. L’âge médian était de 29 ans, 15 patients (58 %) présentaient un IMC supérieur à 25 kg/m2 et 10 des comorbidités (hypertension artérielle, diabète). Le statut COVID a été déterminé par sérologie pour 21 patients (81 %) et par antécédent de RT-PCR pour 5 patients (19 %). Le délai moyen d’apparition de myocardite après une infection COVID était de 21 jours. Des symptômes cardiaques (FEVG < 30 %, douleur thoracique) et extracardiaques (syndrome pseudo-grippal, signes cutanéomuqueux) étaient observés. Le temps d’hospitalisation moyen en soins intensifs était de 15 jours avec un support hémodynamique pour 50 % des patients. Dans 68 % des cas (n = 19), l’initiation d’IgIV a été faite en période de garde. La réévaluation des indications a permis l’arrêt des IgIV pour 2 patients (myocardite non virale ; absence d’infection COVID). Finalement, 93 % des patients (n = 25) ont reçu une administration unique de 2 g/kg d’IgIV.
Discussion/conclusion
Les patients présentant une mKL sont jeunes avec peu de comorbidités. Le délai d’apparition est variable et les symptômes peu spécifiques (COVID, vascularite). La définition stricte de critères d’administration des IgIV et la réévaluation des indications ont permis une allocation juste et pertinente des IgIV, malgré le caractère urgent de prise en charge dans un contexte de pénurie. La mKL étant encore mal connue, une étude comparative sur l’efficacité et la place des IgIV pour les patients atteints de myocardite aiguë associée au COVID-19 complètera cette première analyse.
Mots clés
Covid-19
IgIV
Myocardite
==== Body
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| 0 | PMC9748207 | NO-CC CODE | 2022-12-15 23:22:40 | no | 2022 Dec 14; 57(4):e86-e87 | utf-8 | null | null | null | oa_other |
==== Front
Le Pharmacien Clinicien
2772-9540
2772-9532
Published by Elsevier Masson SAS
S2772-9532(22)00818-8
10.1016/j.phacli.2022.10.704
000077
Utilisation du Remdésivir et impact potentiel sur divers paramètres biologiques
Loche N. ⁎
Chatron C.
Hassan A.
Bouraima F.
Bernard L.
Sautou V.
Pharmacie, CHU de Clermont-Ferrand : site Gabriel-Montpied, Clermont-Ferrand
⁎ Auteur correspondant.
14 12 2022
12 2022
14 12 2022
57 4 e183e183
Copyright © 2022 Published by Elsevier Masson SAS.
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.
Contexte
Le Remdésivir (R), antiviral inhibant l’ARN polymérase du SARS-CoV-2, a été utilisé dans le cadre d’une Autorisation temporaire d’utilisation (ATU), puis de l’Autorisation de mise sur le marché (AMM) pour les pneumopathies à COVID-19 modérées à sévères.
Objectifs
Les objectifs de ce travail sont de décrire la population ayant reçu le R et les variations biologiques possiblement associées à son administration.
Patients et méthodes
Une étude rétrospective monocentrique incluant les patients ayant reçu du R entre le 15/09 et le 15/12/2020 a été menée. Les caractéristiques des patients, les modalités de leur Prise en charge (PEC), leur devenir et les paramètres biologiques incluant créatinine, ASAT, ALAT, CRP et D-dimères ont été recueillis dans les 48 heures avant et après l’administration de R via Crossway®, ICCA® et Cyberlab®. Les données étaient exploitables (DE) quand les deux valeurs avant et après R étaient disponibles pour chaque paramètre.
Résultats
Au total, 88 patients ont été inclus (âge médian = 70 ans, sexe-ratio = 2/1, moy indice masse corporelle = 30,19 ± 6,38 kg/m2). Une comorbidité était présente chez 94,3 % des patients : antécédent cardiovasculaire (76,1 %), obésité (48,9 %), diabète (30,7 %), antécédent pulmonaire (13,6 %) et immunodépression (6,8 %). Le R était initié dans 62,5 % des cas en réanimation et 37,5 % en médecine, en moyenne 7,9 ± 3,7 jours (j) [1 ; 19] (médiane = 8 jours) après le début des symptômes ; la durée moyenne de traitement était de 6,2 ± 2,2 jours [1 ; 10] (médiane = 5 j). R a été arrêté prématurément pour 7 patients : 2 bilans biologiques altérés, 2 transferts vers d’autres hôpitaux, 2 améliorations et un décès. La durée moyenne de séjour des patients sous R était de 12,5 ± 8,3 jours [4 ; 55] (médiane = 10 jours). Après sortie d’hospitalisation, 47,7 % sont rentrés à domicile, 27,3 % sont sortis en soins de suite et de réadaptation, 14,8 % sont décédés et 10,2 % des patients ont été transférés vers d’autres hôpitaux. Une diminution du débit de filtration glomérulaire (77,3 % de DE) était relevée chez 54,4 % des patients. Le passage d’un stade d’insuffisance rénale légère à sévère a été noté chez 3 patients (dont 2 dialysés à la fin du R). Après R, 18,8 % avaient des ALAT > normale (N) (54,5 % DE) et 17,0 % ASAT supérieure à la normale (N) (53,4 % DE) pour une concentration initialement N. Les valeurs maximales d’ASAT ont atteint 4 fois la N. La moyenne des CRP avant R était de 115,4 ± 82,5 mg/L (66 patients) versus 31,9 ± 45,0 mg/L après (39 patients). Les D-dimères avant R étaient de 1728,5 ± 2101 ng/mL pour 79 patients. Aucun effet indésirable n’a été rapporté.
Discussion/conclusion
L’échantillon présente des caractéristiques statistiquement comparables aux données de la littérature (p > 0,05). Les bilans biologiques ont été moins réalisés après que le R ait obtenu l’AMM. Aussi, le manque de données exploitables n’a pas permis d’établir de corrélation statistique de l’utilisation du R à la variation des paramètres étudiés. La pathologie, les comorbidités et la PEC pourraient être des facteurs confondants. Il serait intéressant d’effectuer une étude multicentrique sur un large échantillon et d’inclure des données supplémentaires.
Mots clés
Antiviraux
Coronavirus
Infectiologie
==== Body
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| 0 | PMC9748208 | NO-CC CODE | 2022-12-15 23:22:40 | no | 2022 Dec 14; 57(4):e183 | utf-8 | null | null | null | oa_other |
==== Front
Le Pharmacien Clinicien
2772-9540
2772-9532
Published by Elsevier Masson SAS
S2772-9532(22)00715-8
10.1016/j.phacli.2022.10.601
000281
Évaluation de la satisfaction des patients et des pharmaciens d’officine suite à la pérennisation du service de portage des traitements rétrocédés au sein d’un centre de lutte contre le cancer
Libossart V. ⁎
Behague P.
Benabderrahmane N.
Leroy R.
Boulanger R.
Stala T.
Strobbe G.
Feutry F.
Marliot G.
Service de pharmacie, Centre Oscar Lambret, Lille
⁎ Auteur correspondant.
14 12 2022
12 2022
14 12 2022
57 4 e129e129
Copyright © 2022 Published by Elsevier Masson SAS.
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.
Contexte
L’arrêt du service de portage des traitements rétrocédés aux officines, initié à titre gratuit lors la crise sanitaire à Sars-Cov-2, a encouragé notre centre à souscrire de façon temporaire à un service payant du même type proposé par notre grossiste-répartiteur (COLIPASS® ; OCP). Ce dispositif a été proposé à l’ensemble de nos patients bénéficiant, pour la plupart, d’une rétrocession d’anticancéreux oral. La principale motivation est de limiter les déplacements des patients parfois éloignés, tout en garantissant l’accès sécurisé et la continuité des traitements rétrocédés. Afin de préserver la transmission des conseils lors de la première dispensation, ce service concernait uniquement les renouvellements.
Objectifs
Évaluer la satisfaction des pharmaciens d’officine et des patients en regard des nouveaux frais engendrés et conclure sur la poursuite ou non du dispositif de portage.
Patients et méthodes
Deux questionnaires destinés spécifiquement aux patients et pharmaciens d’officine ont été rédigés puis validés par l’équipe de pharmacie. Ils comprenaient des questions ouvertes et abordaient plusieurs items : bénéfices et inconvénients du service, évaluation de la satisfaction et avis sur la poursuite ou non du service. Après avoir établi la liste des patients concernés, les questionnaires ont été soumis par appel téléphonique.
Résultats
Trente-trois des 41 patients ayant bénéficié du service ont été interrogés. 97 % des patients (n = 32) sont satisfaits. Pour 76 % d’entre eux (n = 25), le principal avantage évoqué est la proximité de l’officine. Dans 88 % des cas (n = 29), le traitement a été dispensé dans la pharmacie habituelle. Pour 4 patients, la pharmacie habituelle ne participant pas au service, la livraison a été faite dans une autre pharmacie de proximité sans gêne engendrée pour 3 d’entre eux. Concernant les pharmaciens d’officine, 3 d’entre eux ont apprécié la sûreté du transport et la facilité de la procédure à suivre. Cependant, 6 % (n = 2) souhaitent être prévenus par appel téléphonique en plus du courrier personnalisé rédigé par les pharmaciens hospitaliers, et ceci en amont de l’envoi du traitement. Au sujet de la pérennisation du portage, 97 % (n = 32) y sont favorables.
Discussion/conclusion
Compte tenu du taux de satisfaction (97 %) et des faibles frais engagés (4,90 € par envoi déduit de la marge de rétrocession de 22 €), notre centre a décidé de poursuivre COLIPASS® pour l’ensemble des patients. Si besoin, une priorisation des patients éligibles à ce service pourra être envisagée en fonction de leur éloignement et de leur fréquence de venue dans notre centre. Des axes d’amélioration sont à prévoir comme l’optimisation de la communication ville-hôpital en appelant systématiquement les officines avant l’envoi du traitement et en leur transmettant des fiches de bon usage des anticancéreux oraux. En proposant au patient d’avertir notre centre avant tout renouvellement, ce dispositif a l’avantage de le responsabiliser et de le rendre acteur de sa prise en charge.
Mots clés
Coûts et analyse des coûts
Satisfaction des patients
Systèmes hospitaliers de dispensation et de distribution de médicaments
==== Body
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| 0 | PMC9748209 | NO-CC CODE | 2022-12-15 23:22:41 | no | 2022 Dec 14; 57(4):e129 | utf-8 | null | null | null | oa_other |
==== Front
Le Pharmacien Clinicien
2772-9540
2772-9532
Published by Elsevier Masson SAS
S2772-9532(22)00491-9
10.1016/j.phacli.2022.10.377
000453
Pandémie COVID-19 : impact des soins pharmaceutiques dans la prise en charge des patients atteints de COVID-19 en période de crise sanitaire
Tanty A. 1⁎
Vitale E. 1
Lombardo D. 1
Grévy A. 1
Gibert P. 1
Chapuis C. 1
Chevallier Brilloit C. 1
Allenet B. 1
Bedouch P. 2
Chanoine S. 1
1 Pharmacie, CHU Grenoble Alpes, La Tronche
2 Pharmacie Chuga, université Grenoble Alpes, CNRS, TIMC-IMAG UMR5525, MESP, La Tronche
⁎ Auteur correspondant.
14 12 2022
12 2022
14 12 2022
57 4 e8e9
Copyright © 2022 Published by Elsevier Masson SAS.
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.
Contexte
Lors de la première vague de la pandémie de COVID-19, de nombreuses stratégies thérapeutiques ont été explorées, exposant les patients à un risque iatrogène médicamenteux important. Notre hôpital a rapidement décidé de renforcer ou mettre en place des activités de soins pharmaceutiques (SP) dédiés aux patients atteints de COVID-19 pour répondre aux besoins des équipes soignantes et garantir une prise en charge sécurisée.
Objectifs
Évaluer la pertinence des activités de SP lors de la prise en charge médicamenteuse des patients hospitalisés atteints de COVID-19 en période de crise sanitaire.
Patients et méthodes
Une analyse observationnelle rétrospective a été menée lors de la première vague (du 17 mars au 31 avril 2020), dans toutes les unités de soins recevant des patients atteints de COVID-19. Les activités de SP étaient réalisées au sein des unités de soins par des pharmaciens, internes et étudiants en pharmacie. Elles comprenaient la conciliation des traitements médicamenteux à l’admission et à la sortie, l’analyse pluriprofessionnelle des prescriptions, l’information sur le bon usage des produits de santé auprès des soignants et des patients, une veille documentaire systématique. La pertinence des activités de SP a été évaluée à partir des interventions pharmaceutiques (IP), selon l’échelle CLEO© de la Société française de pharmacie clinique.
Résultats
Au total, 749 IP ont été réalisées dans les 13 unités de soins concernées (dont 2 unités ayant des activités de SP avant la pandémie). Le taux d’acceptation des IP était de 80,6 % (n = 604). Parmi les IP acceptées, 17,2 % (n = 249) des IP étaient liées à des médicaments définis comme spécifiques de la COVID-19 selon les recommandations en vigueur lors de l’étude (hydroxychloroquine, lopinavir/ritonavir, remdesivir, énoxaparine, tinzaparine, héparine calcique, dexaméthasone, méthylprednisolone, prednisolone, oxygène). L’impact clinique des IP était plus important pour les médicaments spécifiques de la COVID-19 que pour les autres (IP ayant un impact majeur ou vital : 27,9 % vs 9,6 %, p < 0,0001). L’impact clinique des IP était plus important dans les unités de soins n’ayant pas de SP avant la pandémie que dans les autres (IP ayant un impact majeur ou vital : 23,0 % vs 9,3 %, p < 0,0001). En considérant l’ensemble des IP réalisées, les impacts économique et organisationnel étaient majoritairement positifs ou nuls (respectivement 64,8 % et 73,2 %).
Discussion/Conclusion
Dans une crise sanitaire mondiale impliquant un virus émergent et des stratégies thérapeutiques encore incertaines, les SP ont permis d’améliorer la prise en charge thérapeutique des patients, tant au niveau des stratégies spécifiques que de la prise en charge globale. De manière significative, la mise en œuvre rapide de ces activités en période de crise n’a pas affecté sa pertinence, alors qu’elle nécessite habituellement plusieurs mois.
Mots clés
Iatrogenic prevention
Pharmaceutical care
COVID-19
==== Body
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| 0 | PMC9748210 | NO-CC CODE | 2022-12-15 23:22:41 | no | 2022 Dec 14; 57(4):e8-e9 | utf-8 | null | null | null | oa_other |
==== Front
Le Pharmacien Clinicien
2772-9540
2772-9532
Published by Elsevier Masson SAS
S2772-9532(22)00695-5
10.1016/j.phacli.2022.10.581
000427
Aspects cliniques et traitements du syndrome inflammatoire multisystémique pédiatrique en réanimation pédiatrique
Hanafia O. 1⁎
Ghandour A. 2
Honoré S. 2
Bertault-Peres P. 2
1 Pharmacie, hôpitaux universitaires de Marseille Timone, rue Saint-Pierre, Marseille
2 Pharmacie, hôpitaux universitaires de Marseille Timone, Marseille
⁎ Auteur correspondant.
14 12 2022
12 2022
14 12 2022
57 4 e118e118
Copyright © 2022 Published by Elsevier Masson SAS.
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.
Contexte
Depuis la pandémie de la maladie à Coronavirus (COVID-19), on constate un nombre élevé d’enfants hospitalisés en réanimation pédiatrique pour syndrome inflammatoire multisystémique pédiatrique (PIMS) ressemblant à la maladie de Kawasaki (KD)
Objectifs
Cette étude avait pour objectifs de décrire les aspects cliniques et les pratiques de prescription pour traiter ce syndrome, comparativement à ceux utilisés pour KD, d’étudier l’efficacité des traitements et d’observer l’évolution clinique des patients.
Patients et méthodes
Il s’agit d’une étude observationnelle rétrospective au sein du service de réanimation pédiatrique, sur une période de 9 mois d’avril à décembre 2020. Le recueil des données cliniques et biologiques a été réalisé via les logiciels Axigate et Visual Patient. Les ordonnances ont été extraites des logiciels Logipren et Pharma.
Résultats
Nous avons inclus 12 enfants, d’âge médian 8 ans [2–16 ans] et de sex-ratio = 2, diagnostiqués PIMS et admis en réanimation. La faible incidence de tests PCR positifs à l’admission et la présence d’anticorps anti-SRAS-CoV-2 chez tous les patients ont indiqué que le PIMS constituerait une maladie post-COVID cliniquement et chronologiquement distincte de la COVID-19. Tous ont présenté de la fièvre durant leurs séjours, avec une durée moyenne de 5 jours. 5 patients ont présenté au moins 2 critères cliniques caractéristiques d’une KD mais non suffisants pour diagnostiquer une KD complète. Des symptômes gastro-intestinaux (10 patients), rarement constatées dans KD. Tous avaient des marqueurs inflammatoires et cardiaques très élevés et supérieures à ceux dans KD, témoignant d’un état hyper inflammatoire et d’une insuffisance cardiaque aiguë. Des atteintes cardiaques ont été observées chez 10 patients : 50 % ont présenté une hypotension systémique persistante et 5 ont présenté des anomalies à l’ECG. L’objectif du traitement médicamenteux était de réduire l’inflammation. Neuf patients ont reçu des immunoglobulines intraveineuses (IGIV). Devant une fièvre persistante et/ou une aggravation des marqueurs inflammatoires, 5 patients ont reçu une 2e dose d’IGIV et 2 une 3e dose. Une corticothérapie d’une durée de 4 jours a été administrée à 10 patients et 9 ont nécessité un traitement anti-inflammatoire supplémentaire par acide acétylsalicylique. Ces traitements, associés à un soutien vasopresseur ou diurétique et anticoagulant ont été nécessaires. Il n’y a eu aucun décès dans notre cohorte et le temps de pris en charge moyen dans le service était de 6 jours [2–13 jours].
Discussion/conclusion
Les enfants atteints de PIMS présentaient un état hyper inflammatoire associé à des défaillances cardiaque, digestive et respiratoire. Bien que nos patients présentaient une clinique évoquant une KD, nous avons observé des caractéristiques distinctes : un spectre d’expression plus large, tant dans la symptomatologie que dans sa sévérité, des marqueurs inflammatoires et cardiaques beaucoup plus marqués, une fièvre plus courte une numération plaquettaire plus faible, des atteintes gastro-intestinales plus fréquentes. L’âge médian de notre cohorte était supérieur à celui des enfants atteints de KD. Enfin, la stratégie thérapeutique, IGIV et corticothérapie, semble efficace.
Mots clés
Maladie de Kawasaki
Coronavirus
Pharmacie
==== Body
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| 0 | PMC9748211 | NO-CC CODE | 2022-12-15 23:22:41 | no | 2022 Dec 14; 57(4):e118 | utf-8 | null | null | null | oa_other |
==== Front
Le Pharmacien Clinicien
2772-9540
2772-9532
Published by Elsevier Masson SAS
S2772-9532(22)00538-X
10.1016/j.phacli.2022.10.424
000042
Gestion de la pénurie des immunoglobulines polyvalentes humaines pendant la crise sanitaire liée au coronavirus
Delemer F. 1⁎
Carpentier T. 1
Gossart A. 1
Libessart M. 1
Vacher H. 2
Pelloquin N. 1
1 Pharmacie, CHU Amiens-Picardie (site sud), Amiens
2 Pharmacie, CHU Amiens-Picardie (site nord), Amiens
⁎ Auteur correspondant.
14 12 2022
12 2022
14 12 2022
57 4 e34e34
Copyright © 2022 Published by Elsevier Masson SAS.
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.
Contexte
Les immunoglobulines polyvalentes humaines (IgPH) sont indiquées dans les déficits immunitaires et les maladies dysimmunitaires. L’augmentation permanente de la consommation des IgPH et la diminution des dons de plasma entraînent des tensions d’approvisionnement récurrentes et imprévisibles. Cela constitue un élément de préoccupation pour les professionnels de santé.
Objectifs
Mettre en place et évaluer les mesures pour maîtriser les prescriptions des IgPH.
Patients et méthodes
Le traitement de chaque patient a été réévalué au cours d’une rencontre médecin–pharmacien en tenant compte du référentiel de hiérarchisation de l’Agence nationale de sécurité du médicament et des produits de santé version avril 2019. Les prescriptions ont été systématiquement validées par un pharmacien senior. Une étude observationnelle rétrospective a été réalisée pour mesurer l’impact de cette réévaluation sur les consommations d’IgPH de septembre 2020 à mai 2021. Les données recueillies à partir du dossier patient informatisé sont l’indication, la spécialité, la posologie et la modification éventuelle du traitement. En parallèle, les données de consommation ont été recueillies à partir de l’outil de gestion économique et financière et du logiciel de traçabilité des médicaments dérivés du plasma. Une comparaison des consommations globales des IgPH entre les périodes hivernales 2019–2020 et 2020–2021 a été réalisée.
Résultats
L’étude a inclus 261 patients. Parmi les indications, 84,3 % étaient dans le cadre de l’autorisation de mise sur le marché (AMM), 13,4 % hors AMM mais conformes au référentiel et 2,3 % étaient hors référentiel. Sur l’ensemble des patients, 33 % (n = 85) ont eu une modification de leur traitement : 29 % d’arrêt ou pause thérapeutique (n = 25), 26 % de diminution de posologie (n = 22), 25 % de changement de spécialité (n = 21), 6 % de changement de voie d’administration (n = 5), 5% d’espacement de cure (n = 4), 5 % de report d’initiation du traitement (n = 4). Entre les deux périodes hivernales, une diminution de 9,5 % des consommations a été observée.
Discussion/Conclusion
La concertation médecin-pharmacien a permis de rationaliser les prescriptions et de diminuer sensiblement les consommations des IgPH entre les deux périodes hivernales. Cette diminution est vraisemblablement le reflet de l’efficacité des mesures mises en place. Ces mesures prises en concertation avec les prescripteurs ont permis de maîtriser les consommations des IgPH et donc d’assurer une gestion optimale durant cette pénurie. La tension d’approvisionnement reste très préoccupante et imprévisible et nécessite une vigilance constante en relation avec les médecins. Le respect du référentiel est essentiel pour permettre de réserver les traitements aux indications prioritaires sans alternative thérapeutique.
Mots clés
Consommation
Indication
Immunoglobuline
==== Body
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| 0 | PMC9748212 | NO-CC CODE | 2022-12-15 23:22:41 | no | 2022 Dec 14; 57(4):e34 | utf-8 | null | null | null | oa_other |
==== Front
Annales de Dermatologie et de Vénéréologie - FMC
2667-0623
2667-0623
Published by Elsevier Masson SAS
S2667-0623(22)00553-0
10.1016/j.fander.2022.09.277
Po098
Psoriasis et lymphome cutané B après vaccin anti-COVID-19 : association ou coïncidence ?
Manaa L. 1⁎
Aounallah A. 1
Salah N. Ben 1
Gaied M. Lahoual Ep 1
Mokni S. 1
Fetoui N. Ghariani 1
Sriha B. 2
Ghariani N. 1
Belajouza C. 1
Denguezli M. 1
1 Dermatologie, hôpital Farhat-Hached, Sousse, Tunisie
2 Anatomo-pathologie, hôpital Farhat-Hached, Sousse, Tunisie
⁎ Auteur correspondant.
14 12 2022
11 2022
14 12 2022
2 8 A184A184
Copyright © 2022 Published by Elsevier Masson SAS.
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
L’association du psoriasis et du lymphome cutané est rarement décrite dans la littérature. Nous présentons un cas atypique associant un psoriasis et un lymphome cutané B survenu 10 jours après vaccination anti-COVID-19 (Astrazeneca).
Observations
Un patient âgé de 82 ans, sans antécédents, était hospitalisé pour une éruption psoriasiforme étendue associée à des nodules de la jambe gauche évoluant depuis 2 mois, apparu 10 jours après le vaccin Astrazeneca. À l’examen, il avait des plaques érythémato-squameuses, par endroit croûteuses, généralisées, atteignant 80 % de la surface corporelle. Ces plaques étaient associées à des nodules violacés faisant 8 cm au niveau de la jambe gauche et 2 nodules sous-cutanés indurés à surface violacée au niveau de l’avant-bras gauche. Il n’avait pas d’adénopathies palpables.
Résultats
Deux biopsies ont été faites. L’aspect histologique était en faveur d’un psoriasis pour les plaques érythémato-squameuses et en faveur d’un lymphome B à grandes cellules type jambe au niveau de la tumeur de la jambe avec en immunohistochimie : CD20+, CD3−, CD30−, Mum1+, Bcl2−. Le bilan sanguin objectivait une thrombopénie, une anémie macrocytaire, un taux de LDH élevé, un bilan hépatique et rénal correct. Le scanner thoracique et l’échographie abdominale étaient sans anomalies. Le patient a été mis sous dermocorticoïdes associés à une chimiothérapie R-mini CHOP (rituximab, cyclophosphamide, doxorubicine, vincristine et prednisone).
Discussion
En se référant aux données de la littérature, le psoriasis est associé à un risque plus élevé de lymphome surtout de lymphomes cutanés T. Cette association est liée soit à une erreur diagnostique pour les stades précoces de lymphome T, soit à la stimulation lymphocytaire chronique du psoriasis, qui conduit finalement à un clone dominant et à l’évolution vers le lymphome cutané T, soit à l’immunodépression induite par les traitements du psoriasis. En effet, la prévalence du psoriasis chez les patients atteints de lymphome cutané T est supérieure à celle estimée dans la population générale (19,8 % vs 3 %), alors qu’elle était de 7,1 % pour les lymphomes B. Cependant, aucun cas associant un psoriasis et un lymphome B à grandes cellules type jambe n’a été rapporté. Le rôle du vaccin anti-COVID-19 dans l’induction ou l’aggravation d’un psoriasis pourrait être lié à la production d’interféron 1 et d’interleukine 6 et au recrutement des cellules Th17 qui sont impliquées dans la physiopathologie du psoriasis. À notre connaissance, seulement 3 cas de lymphomes cutanés ont été rapportés après le vaccin anti-COVID-19, tous étaient des lymphomes T. Dans notre cas, les protéines virales introduites par le vaccin peuvent induire une dysrégulation immunitaire et une stimulation antigénique chronique qui pourrait expliquer, chez un sujet prédisposé, la genèse du lymphome. D’autres études sont nécessaires pour expliquer ces mécanismes.
==== Body
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| 0 | PMC9748246 | NO-CC CODE | 2022-12-15 23:22:41 | no | 2022 Nov 14; 2(8):A184 | utf-8 | null | null | null | oa_other |
==== Front
Int J Hyg Environ Health
Int J Hyg Environ Health
International Journal of Hygiene and Environmental Health
1438-4639
1618-131X
The Authors. Published by Elsevier GmbH.
S1438-4639(22)00186-9
10.1016/j.ijheh.2022.114103
114103
Article
Face mask performance related to potentially infectious aerosol particles, breathing mode and facial leakage
Berger Simon ∗
Mattern Marvin
Niessner Jennifer
Institute of Flow in Additively Manufactured Porous Media (ISAPS), Heilbronn University of Applied Sciences, Max-Planck-Str. 39, 74081, Heilbronn, Germany
∗ Corresponding author.
14 12 2022
3 2023
14 12 2022
248 114103114103
27 9 2022
24 11 2022
8 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.
During the COVID 19 pandemic, wearing certified Respiratory Protective Devices (RPDs) provided important means of protection against direct and indirect infections caused by virus-laden aerosols. Assessing the RPD performance associated with infection prevention in standardised certification tests, however, faces drawbacks, such as the representativeness of the test aerosols used, the protection of third parties during exhalation or the effect of facial leaks. To address these drawbacks, we designed a novel test bench to measure RPD performance, namely the number based total efficiency, size-segregated fractional filtration efficiency and net pressure loss, for 11 types of certified surgical masks and Filtering Face Pieces dependent on breathing mode and facial fit. To be representative for the context of potentially infectious particles, we use a test aerosol based on artificial saliva that is in its size distribution similar to exhaled aerosols. In inhalation mode excluding facial leaks, all investigated samples deposit by count more than 85% of artificial saliva particles, which suggests a high efficiency of certified RPD filter media related to these particles. In exhalation mode most RPDs tend to have similar efficiencies but lower pressure losses. This deviation tends to be significant primarily for the RPDs with thin filter layers like surgical masks or Filtering Face Pieces containing nanofibers and may depend on the RPDs shape. Both the filtration efficiency and pressure loss are strongly inter-dependent and significantly lower when RPDs are naturally fitted including facial leaks, leading to a wide efficiency range of approximately 30–85%. The results indicate a much greater influence of the facial fit than the filter material itself. Furthermore, RPDs tend be more effective in self-protection than in third-party protection, which is inversely correlated to pressure loss. Comparing different types of RPDs, the pressure loss partially differs at similar filtration efficiencies, which points out the influence of the material and the filter area on pressure loss.
Keywords
Respiratory protective device
Face mask
Performance measurement
Filtration efficiency
Pressure loss
Respiratory aerosol
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pmc1 Introduction
Pathogen dissemination through aerosol particles emitted by the respiratory system best explains several super-spreading events during the COVID-19 pandemic (Katelaris et al., 2021; Kutter et al., 2021; Lu et al., 2020; Zhang et al., 2020) and is therefore in the focus of SARS-CoV-2 transmission. Particles that are formed and expelled through the respiratory system, for example when talking, coughing or breathing, may differ in size and number based on several factors such as the individual's physiology, health condition or activity (Archer et al., 2022; Morawska et al., 2009; Schwarz et al., 2010). In SARS-CoV-2 infected persons, these particles may act as vehicles for pathogens (Gutmann et al., 2022; Ma et al., 2021) and thus are determinant for the definition of protective measures. Present studies suggest that the mode of the exhaled particle size distribution most likely is in the order of 0.1–0.5 μm (Scheuch, 2020) allowing for the particles to stay airborne over several hours in indoor environments. Contrary to breathing, talking or coughing produces larger particles from the submicronic and small micrometre range to particles larger than 50 μm (Alsved et al., 2020; Asadi et al., 2019). Exposure to these respiratory-emitted particles leads to two possible routes of infection. On the one hand, particles may be transported directly from an infected person to a susceptible host (direct route of infection), whereby the probability of larger particles reaching a susceptible host decreases with the distance due to the particles' settling velocity. Airborne transmission, on the other hand, only occurs indoors, where the smaller fraction of respiratory-emitted particles may accumulate in the indoor air with increasing durations of stay, numbers of persons present and their activity. Since particle transport is still possible after an infected person has left the room, airborne transmission may also be referred to as an indirect infection route (Brlek et al., 2020; Cai et al., 2020).
To reduce the risk of both direct and indirect infections, infection control measures such as ventilation, air purifying technologies or the wearing of Respiratory Protective Devices (RPDs) were discussed and introduced during the COVID-19 pandemic in many areas of public life, such as schools, kindergartens, offices, public buildings, hospitals or the transportation sector. While ventilation and air purifying technologies may affect mostly the indirect route of infection (Nardell, 2021), the wearing of RPDs counteracts both direct and indirect infections by reducing the number of inhaled as well as exhaled particles and thus potentially provides an effective means of protecting oneself (self-protection) and others (third-party protection) (Asadi et al., 2020). Since certified RPDs, in particular surgical masks (DIN EN 14683:2019-10) and filtering face piece respirators such as FFP (DIN EN 149:2009-08), N95 (NIOSH approved 42 CFR 84) or KN95 (GB 2626–2006) are subjected to standardised test procedures, requirements for the separation performance are defined. Filtering Face Pieces according to DIN EN 149:2009-08 are categorized into three classes, with class FFP2 requiring a mass-based total efficiency of at least 94% and the total inward leakage not exceeding 11%. Test procedures use aerosols containing submicronic solid-phase sodium chloride or liquid-phase paraffin oil particles with a broad range allowed for the geometric standard deviation (Zoller et al., 2021) that partly overlap the size range of potentially infectious aerosols (Penner et al., 2022). As the test procedure originates from occupational health and safety, the focus is on self-protection against occupational pollutants, with third-party protection not being considered. Surgical masks according to DIN EN 14683:2019-10, on the other hand, are designated to protect others from infectious droplets during medical procedures. In certification, the number-based total filtration efficiency of the filter medium is determined by the use of infectious particles from a bacterial suspension with a median diameter of 3 μm that are one order of magnitude larger than exhaled virus-laden aerosol particles from the respiratory tract. As with all filtration processes, however, the efficiency of the separation mechanisms is highly dependent on the particle size and particle characteristics (Hinds and Zhu, 2022; Lee and Liu, 1982). To effectively remove respiratory particles from both the inhaled and exhaled air, RPD filter media need to be highly efficient with respect to particles in the relevant size range and with similar properties to infectious particles such as shape, charge and density. Furthermore, the overall efficiency is dependent on the face-to-mask seal, whereby leakage flows can cause unfiltered breathing air to be inhaled or exhaled that bypasses the filter medium (Koh et al., 2021; Pan et al., 2021). As a result, the performance related to potentially infectious particles considering the nature and size of exhaled aerosol particles and also the filtration performance associated with facial leakages in both self-protection and third-party protection may be a drawback of certification procedures for evaluating the RPD performance in the COVID-19 pandemic context.
Several studies with a focus on RPD performance related to infection protection have already been conducted. Studies involving submicronic particle collectives to determine total filtration efficiencies of certified RPDs show that the certified filter media are highly effective even when considered on a number basis. Rengasamy et al. (2014) reported penetration rates of less than 1% for sealed respirators and less than 10% for surgical masks at a flow rate of 40 l/min using a NaCl aerosol. Bagheri et al. (2021) suggested similar penetration rates for FFP2 masks with dolomite dust, which are all below 6%, but have found a higher variance in the penetration rates of different surgical masks. This includes several masks with penetration rates below 12%, as well as up to 75%. Other work (Bałazy et al., 2006; Grinshpun et al., 2009; Zangmeister et al., 2020) similarly shows that penetrations are distributed over a wider area in surgical masks than in respirators. When looking at fractional efficiencies, the most penetrating particle size (MPPS) varies for certified RPDs and is typically in the order of 30–300 nm, with the upper bound being more relevant for surgical masks (Bagheri et al., 2021; Bałazy et al., 2006; Grinshpun et al., 2009; Zangmeister et al., 2020). RPDs that have not been sealed in the test procedure show that facial leakage sharply decreases the overall efficiency. Grinshpun et al. (2009) found that the total inward leakage is particle size dependent from 7 to 20 times greater than the penetration through the filter medium for respirators and size independent from 4.8 to 5.8 times greater for surgical masks. Various studies point to facial leakages lead to similar filtration efficiencies for both respirators and surgical masks, independent of the initial efficiency of the filter medium (Grinshpun et al., 2009; Li et al., 2006; Rengasamy et al., 2014). When looking at the total outward leakage, which is relevant for the effectivity in third-party protection, however, only a few studies were conducted. Koh et al. (2021) and Pan et al. (2021) indicate that both inward and outward leakages are similar in respirators. For surgical masks, however, they indicate that the outward leakage exceeds the inward leakage.
Despite the clear evidence that the filter media used in certified RPDs is efficient for submicronic particles, to date little is known about how the filtration performance is modulated by facial leaks on both the self-protection and third-party protection. Questions on how the filtration performance is influenced by the real use case in the context of infection prevention remain unanswered. For example, how is the filtration efficiency affected when using a test aerosol representative for exhaled aerosols? Does the certification or characteristics of the RPD influence the filtration performance in the real use case considering facial leaks? How is the pressure loss, as a measure for the breathing resistance, effected by facial leaks or the flow direction? To answer these questions, the aim of this work is to determine performance parameters, namely the fractional filtration efficiency, number based total efficiency and net pressure loss, for different RPD classes and characteristics under conditions representative for infection prevention in the COVID-19 pandemic context. This includes the− set-up of a test bench to determine the performance under representative test conditions.
− selection of a suitable fluid for particle generation related to respiratory-emitted aerosols and the evaluation of the test aerosol by comparison to human exhaled particles.
− determination of RPD performance as a result of fractional filtration efficiency, representative number based total efficiency and net pressure loss.
− comparison of performance parameters dependent on flow direction in terms of self- and third-party protection, as well as dependent on facial fit considering facial leakages.
First, in Sec. 2, the basic transport mechanisms of filtration and the equations of the performance parameters are presented. Then, in Sec. 3, the experimental setup, the test procedure and the materials used are described. In Sec. 4, the results are presented and discussed. This includes the validation of the test aerosol with human exhaled particles, as well as the screening of different RPDs. Finally, in Sec. 5, a conclusion of this work is drawn and an outlook on future work is given.
2 Theory
Aerosol particles may be removed from the gas phase by porous media when they reach the inner surface of a filter through various transport mechanisms, namely Brownian diffusion, direct interception, inertial impaction and electrostatic attraction (see Fig. 1 ).Fig. 1 Schematic illustration of the transport mechanisms in depth filtration.
Fig. 1
Transport mechanisms are strongly dependent on particle size and flow velocity. Brownian diffusion is the dominating mechanism for small particle sizes and low flow velocities, whereby the particle motion is governed by a superordinated chaotic movement. Thus, particles do not follow the streamlines exactly and may randomly hit filter fibres. Brownian diffusion may be significant for the separation of the smaller particle fraction in the size of the infectious SARS-CoV-2. Particles that follow exactly the streamlines may be removed by direct interception, if the streamline passes within the particle radius on a filter fibre. Inertial impaction is mainly dominant on larger particles that, due to their inertia, are deflected of their streamline by its redirection around a fibre. With mask leakage, also the total leakage flow can be accelerated and redirected at the mask-to-face seal, potentially allowing inertial impaction to effect the deposition of larger particles in the case of unsealed RPDs (Hinds and Kraske, 1987). The interaction of the three transport mechanisms typically results in a most penetrating particle size (MPPS), which represents the least effectively separated particle size and is thus a characteristic of the respective filter medium. In air filtration, the MPPS is empirically around 0.3 μm and therefore in the size range of exhaled aerosol particles. In order to increase the removal probability of small particles, filter media, such as media based on nanofibres, aim to increase the efficiency at the MPPS through small pore sizes. However, materials based on synthetic melt blown fibres are most commonly used in RPDs. Meltblown fibres are electrostatically charged due to their manufacturing process and therefore able to attract particles of the opposite charge by electrostatic attraction. This may be advantageous in increasing the efficiency at the MPPS without reducing permeability and thus, increasing pressure loss.
To evaluate filtration performance dependent on particle size as well as to determine the MPPS, the fractional filtration efficiency is an elementary parameter. The fractional filtration efficiency is defined according to Eq. 1 (1) ECn(xi)=dCn,upstream(xi)−dCn,downstream(xi)dCn,upstream(xi)
and represents the measurable difference in particle concentration of discretised particle size intervals in the raw gas (upstream of the filter) and clean gas (downstream of the filter) related to the raw gas particle concentration.
When only considering total particle concentrations, the total filtration efficiency is obtained according to Eq. (2).(2) ECn=Cn,upstream−Cn,downstreamCn,upstream
Compared to the fractional filtration efficiency, the total filtration efficiency depends on the particle size distribution and the metric with which particle concentrations are measured (Zoller et al., 2021). Therefore, test aerosols with a size distribution similar to that of potentially infectious aerosol particles are a pre-requisite to evaluate the protective effect of RPDs. With mass concentrations, larger particles have a higher relative importance for the overall efficiency than with number concentrations. However, since particularly the small particles are relevant in the context of disease transmission, we only use efficiencies based on number concentrations.
To evaluate the wearing comfort of RPDs, the second performance parameter is the pressure loss, which is given in porous media by the Darcy equation if the flow is creeping, i.e. if the Reynolds number is smaller than 1:(3) ΔpH=η∙v‾fB
The pressure loss Δp related to the layer thickness H depends on the dynamic viscosity η, the filter velocity v‾f and the permeabilityB, which is a material constant depending on the fiber diameter and porosity. The net differential pressure Δpnet of RPDs is determined similar to the procedure described in DIN EN 13274-3:2002-03 (4) Δpnet=ΔpF−ΔpH
where ΔpF is the measured differential pressure with the RPD mounted to a test head. ΔpH takes into account pipe friction losses, changes in cross-section and diversions due to the measuring apparatus, which is determined from a second measurement.
3 Material and methods
In this section, the materials and methods used for testing RPDs are described. First, the mask test bench is presented in detail with focus on both the experimental set-up and the test procedure. Subsequently, the materials used and the considered RPDs are described.
3.1 Mask test bench
Fig. 2 first illustrates the experimental test set-up to determine filtration-specific performance parameters of RPDs. Essential components are an aerosol generator (1), a test head (2), a volume flow-controlled fan (3) and measuring devices for the fractional particle number concentration (4a) and the differential pressure (4 b).Fig. 2 Experimental test set-up for the determination of mask performance parameters (fractional filtration efficiency and net pressure loss).
Fig. 2
The primary function of the aerosol generator (AGK 2000, Palas GmbH) (1) is to produce test aerosol particles from a feeding liquid by the use of a two-substance nozzle and compressed air in order to mimic infectious particles from the respiratory system. Test aerosol particles are injected at the beginning of the test bench tubing and thereby diluted with ambient air to obtain a dry aerosol in measurable concentration. The total volume flow is controlled and generated by a radial fan mounted on the suction side. For flow control, an ultrasonic flowmeter is used to contactless measure the pressure- and temperature-compensated volumetric flow without influencing the flow profile and thus interfering particle sampling. The different RPDs under consideration are mounted to an additively manufactured head within a measuring cell. The measuring cell allows the test head to be mounted in such a way that it can be either flowed through from the outside to the inside (third-party protection) or vice versa from the inside to the outside (self-protection). In order to be representative towards facial leaks the dimensions of this test head are similar to ISO/TS 16976-2:2015-04 and represent an average Central European head size. Differential pressure is measured by the use of static ring pressure taps upstream and downstream the measuring cell with two differential pressure sensors in the range of 250 Pa and 1250 Pa, respectively. Particle concentrations are measured in both the raw and clean gas using an optical particle counter (Promo 3000, Palas GmbH). Therefore, an intrument-specific sampling volume flow of 5 l/min is taken isokinetically upstream and downstream the measuring cell. The particle concentration is determined by scattered light using an optical particle sensor with a measuring range of 106 P/cm³ (WELAS 2070).
3.2 Test procedure
A total of four different test scenarios are considered. First, RPDs are attached to the test head by their existing head or ear loops. This intends to mimic the natural fit with leakage flows through the mask-to-face seal may influence the RPD performance. Second, to exclude facial leakage, RPDs are firmly attached to the test head by the use of a sealing compound. This provides the performance of RPDs if they would perfectly fit to a wearer's face, which partly is a comparable configuration to standardised certification procedures. Both modes of attachment are looked at separately for two flow directions, inhalation and exhalation, by varying the position of the test head in the measuring cell. As a result, the mask performance for perfect and imperfect fitting RPDs can be ascertained distinctive for the flow directions of inhalation and exhalation in self-protection and third-party protection. Here, third-party protection only represents the efficiency of particle removal in the expiratory volume flow, while self-protection represents particle reduction in the inspiratory volume flow.
To prepare a measurement, first the test head is fitted with an RPD and then installed in the measuring cell according to the considered test scenario. A constant volume flow of 95 l/min is then applied to represent most unfavourable conditions. Thus, 95 l/min is an unrealistically high flow rate for breathing, it is also used in DIN EN 149:2009-08 DIN EN 149 as an inspiratory flow rate and intends to mimic the peak condition during sinusoidal breathing at 30 l/min according to DIN EN 13274–3. As a result, the determined mask performance is representative only for the peak condition of the breathing cycle. After preparation, tests are carried out under room air conditions (p = 105 Pa, T = 25 °C, φ = 30–45%). Absolute pressure and temperature are measured online and used for volume flow compensation with regard to small fluctuations in ambient conditions. After equilibration, the pressure loss of the unloaded RPD is measured over a time interval of 30 s. The net pressure loss is then determined according to Eq. (4), subtracting the reference pressure loss of the test head and measuring cell in this configuration. After the pressure loss measurement is finished, test aerosol is injected into the tesdslbt tubing. In the first 300 s, the raw gas concentration is determined. Here, the loading time of 300 s is necessary to equilibrate the particle concentration in the measuring cell. The total number concentration in the raw gas is approx. 50,000 P/l, thus background particle concentration of 20 P/l is three orders of magnitude lower and is therefore neglected. After a steady-state particle concentration has been established, the clean gas concentration is determined during the next 300 s. To determine the fractional efficiency according to Eq. (1), the last 60 s of the raw gas measurement and the first 60 s of the clean gas measurement are used.
3.3 Artificial saliva
Aiming on a representative test aerosol to respiratory-emitted particles, a saliva substitute solution (apomix® Speichelersatzlösung SR) is used as a feeding liquid for aerosol generation. Saliva substitutes are often used to moisten the oral mucosa in patients with xerostomia and therefore intended to imitate certain properties of human saliva, such as viscosity (Łysik et al., 2019). In saliva substitutes, the viscosity is mainly influenced by either the additive carboxymethylcellulose (CMC) or mucin (Foglio-Bonda et al., 2022). Other components include electrolytes such as sodium chloride, potassium chloride, calcium chloride and magnesium chloride. Moreover, water, sorbitol and substances that serve as pH buffers and for preservation are contained.
3.4 RPDs
Certified surgical masks and filtering face pieces were selected for a screening in four different test scenarios, as described. The selected RPDs are listed in Fig. 3 and are categorized into five groups based on in their shape and characteristics. Four fish-shaped masks were considered, with two based on meltblown filter media (FFP2_3; FFP3_1) and two based on nanofibres (FFP2_1*; FFP2_2*). Further, duckbill-shaped (FFP2_4; FFP2_5) and classical axe-shaped filtering face pieces (FFP2_6; FFP2_7) all based on meltblown filter media were selected. In addition, two medical masks (SM_1; SM_2) as well as a reusable fabric mask with a nanofilter insert (FFP2_8* (R)) were screened.Fig. 3 Selected meltblown based and nanofibre* based RPDs for performance screening.
Fig. 3
4 Results and discussion
Prior to the actual measurements, the particle size distribution of the test aerosol was investigated and compared to exhaled aerosols (Sec. 4.1) in order to evaluate its representativeness for respiratory-emitted aerosols. Thereafter, the actual RPD screening was done on five new mask samples in each configuration with the test bench and test procedure described in Section 3. Performance parameters, namely the net pressure loss, the number based total efficiency and the fractional filtration efficiency were determined, aiming at a differentiated distinction between flow direction (third-party/self-protection) and fitting (including/excluding facial leakage) (Sec. 4.2).
4.1 Test aerosol
Test aerosols for determining the filtration performance of RPDs can be generated from various liquids such as those used in certification, for example sodium chloride solutions and liquid paraffin oil (DIN EN 13274-7:2019-09,, DIN EN 149:2009-08,), or biogenic solutions containing viable bacteria (DIN EN 14683:2019-10). Unlike the norms, the focus of the RPD screening is to determine the mask performance based on a representative test aerosol that mimics respiratory emitted particles. Representative in this context means that the characteristic of droplets and the size distribution are similar between exhaled and technically generated particles. Therefore, we use a saliva substitute solution as a feeding liquid for technical aerosol generation (see Sec. 3.3). To evaluate representativeness, the particle size distribution of the technically generated aerosol from artificial saliva is compared to an exhaled aerosol optically measured in Penner et al. (2022) and compared in Fig. 4 . Since the total number of exhaled particles is several orders of magnitude lower than of technically generated aerosols, a different optical sensor with a lower measuring range was used for the exhalation measurement. To allow for comparison, the particle number concentration of each particle size interval (dCn) is normalized to the total particle number concentration (Cn) as well as the logarithmic bin size (Δlog (xi)) of the optical particle counter.Fig. 4 Comparison of the particle size distributions of the technically generated aerosol from saliva substitute solution and human exhaled aerosols of 13 subjects normalized to the total particle concentration and logarithmic bin size (Penner et al., 2022).
Fig. 4
The size distribution of exhalation measurements is presented as the mean of 21 measurements of 13 test persons and compared to a single measurement of the saliva substitute solution that is technically dispersed with the aerosol generator. The results show that both aerosols contain particles in a similar size range that are mainly smaller than 2 μm. However, in the technically generated aerosol, the mode of the size distribution is close to the metrological boundary of the optical particle counter in the range of 0.2 μm. Here, counting errors may occur, which suggests that the actual concentration at the mode may be even higher. The mode for exhaled particles, on the other hand, is in the range of 0.4 μm and thus the exhaled size distribution contains relatively larger particles. Thus, the technical generation principle is based on a two-substance nozzle with larger particles partly being removed by a cyclone, the used aerosol generator does not mimic the generation mechanism of particles in the human lungs, which may explain the slight differences in both distributions. Another reason may be the test conditions for both set-ups, with the exhaled particle size distribution determined undiluted at an air humidity of approx. 90% due to the low particle concentration. Although the time required for exhaled particles to evaporate is very short due to their small size and the associated high surface tension (Gregson et al., 2022; Walker et al., 2021), incomplete evaporation cannot be ruled out in this set-up due to the high humidity. Technical aerosol, on the other hand, is diluted to a total flow of 95 l/min, which may result in a faster evaporation of the water content and thus to a smaller particle size. On the whole, the overall differences are minor; moreover, saliva substitute solution represents a more comparable fluid in terms of its composition and properties in the context of infection protection and is therefore used for the RPD screening.
4.2 Screening of RPDs in new condition
RPDs act as particle sinks for the inhaled and exhaled air and thus potentially provide an effective means of protecting oneself and others from direct and indirect infections. The filtration performance, however, may differ in self-protection and third-party protection for both perfect and natural fitted RPDs that partly allow for unfiltered breathing air to pass at the mask-to-face seal. The RPD screening aims to provide the performance related to this dependency on flow direction and facial leakage by the use of a representative test aerosol (Sec. 4.1) and a newly conceived test bench (Sec. 3.1). Therefore, 11 surgical masks and Filtering Face Pieces (Sec. 3.3) are tested at a steady-state volume flow of 95 l/min, which aims to represent the peak volume flow occurring during sinusoidal breathing at 30 l/min. For each type of RPD, the fractional filtration efficiency, number based total efficiency and the net pressure loss (Sec. 2) are determined using five new RPD samples. Fig. 5 illustrates the averaged fractional filtration efficiencies.Fig. 5 Fractional separation efficiencies of selected RPDs at 95 l/min using artificial saliva in sealed installation excluding leakage, as well as in natural installation including leakage, differentiated in self-protection and third-party protection. Each fractional filtration efficiency curve is the mean of five measurements on five new RPD samples.
Fig. 5
The diagrams aligned vertically differ in whether RPDs were sealed or naturally fitted to the test head. When the RPDs were sealed, thus were “perfectly fitted”, 7 out of 11 masks exceed an efficiency of 95% at each particle size, which indicates a good filtration performance related to aerosol particles from saliva substitute. Surgical mask SM_1 is similar efficient compared to meltblown based Filtering Face Pieces, while the second surgical mask (SM_2) shows a lower efficiency that is still above 85% at the MPPS. RPDs containing nanofibres (FFP2_1*, FFP2_2*, FFP2_8*(R)) appear to have lower efficiencies of approx. 75% (disposable) and 85% (reusable) at the MPPS. For sealed nanofibre-based RPDs, however, the fractional filtration efficiency curves deviate significantly in self-protection and third-party protection, thus the differences cannot be explained solely by a lower efficiency of nanofibre-based filter media but may also be the result of a more complicated sealing of these materials to the test head with the sealing compound used.
When RPDs were naturally fitted and facial leakage is expected to occur, the fractional efficiency curve of each RPD type is significantly lower than in its sealed installation variant. This confirms the expectation in general. Moreover, it can be observed that the efficiency curves deviate over a wider range of approx. 20%–90%, which suggests a significant influence of facial leakage on filtration performance dependent on the RPD fit. To take a closer look, diagrams aligned horizontally differ only in inhalation and exhalation mode, which is intended to represent self-protection and third-party protection. Naturally fitted RPDs in self-protection, generally, tend to have a higher efficiency than their equivalent in third-party protection. This difference is most pronounced in the case of surgical masks, nanofibre-based RPDs and two of the meltblown-based FFP masks, with these masks depositing partly twice as the amount in self-protection as in third-party protection. The RPD models FFP2_3, FFP2_7 and FFP3_1, however, deviate in their filtration performance in both modes only slightly. As a result, this indicates that the efficiency of an RPD may strongly differ between inhalation and exhalation dependent on its properties to minimize facial leakage, which is discussed in the context of Fig. 7 in more detail.
With the sealed installation, there are fewer differences between the two flow directions compared to naturally attached RPDs. Deviating filtration efficiency curves can be seen in the surgical masks and nanofibre-based RPDs, which, in addition to the directionality of the facial leakage, also indicates a directionality of the filter material on filtration performance. Since these mask materials are generally thinner and less rigid, they may be more easily drawn to the test head in the inhalation mode, thus reducing the effective filter area and increasing the specific load. As described in Section 2, the transport mechanisms of particles to the inner surface of the filter material are dependent on the flow velocity, which would well explain the observed differences here. Assuming further that the distance between an RPD and a wearers face is very small, so that the time required for complete evaporation of the water content of the particles during exhalation is insufficient, larger particle sizes could be relevant for third-party protection. In this case, RPDs with increasing efficiency over particle size, such as the SM_1 and SM_2 surgical masks and the FFP2_1* and FFP2_2* nanofibre-based masks, would be more efficient in a real application.
Pressure loss, as the second key performance parameter, is an indicator of breathing resistance and thus crucial for the wearing comfort. RPDs with a low pressure loss impair breathing less and are thus desirable especially for vulnerable individuals with pre-existing conditions of the respiratory system or low tidal volumes. As with the filtration efficiency, also the pressure loss may depend on facial leakage and the direction of flow for different mask characteristics. In order to view both performance parameters side-by-side, the number-based total filtration efficiencies determined from the fractional efficiencies are illustrated above the net pressure loss in Fig. 6 . Each point is the mean of five measurements on five new RPD samples, with the error bars representing the standard deviation. The total filtration efficiency in sealed installation shows for most RPDs again that the requirement of DIN EN 149 for a lower penetration than 6% is fulfilled, if the total efficiency is determined on a number basis and with a representative test aerosol of saliva substitute solution, both in third-party and self-protection. Naturally fitted masks, on the other hand, vary in the range of 30% and 85%, thus the efficiency is significantly decreased due to facial leakage.Fig. 6 Comparison of the number-based total separation efficiency with the net pressure loss @ 95 l/min. Open symbols represent measuring points in self-protection, filled symbols represent the third-party protection. Each point is the mean of five measurements on five new mask samples, with the error bars representing the standard deviation of the fivefold determination.
Fig. 6
Fig. 7 Illustration of the relative change of the performance parameters in self-protection in relation to third-party protection. A relative value of 100% would mean that twice the value was measured in self-protection as in third-party protection.
Fig. 7
A comparison of the different RPDs in sealed installation shows that the pressure loss varies over a wide range, with the surgical masks at the lower bound of approximately 30 Pa–90 Pa. FFP masks, for example FFP2_5 and FFP2_7, tend to highly differ in pressure loss although the filtration efficiency is similar. In general, these observed differences can simply be explained by different effective filter areas, material thicknesses and permeabilities. When comparing naturally fitted RPDs, on the contrary, the pressure loss is strongly reduced, resulting from the effect of facial leakage. RPDs with a sharp decrease in pressure loss, compared to its sealed fit, also show a sharp decrease in total filtration efficiency, suggesting that both performance parameters are affected in a mutually dependent manner. Nevertheless, when comparing different RPD types in the natural fit, such as FFP2_3 and FFP2_7, for example, then similar efficiencies but different pressure losses can be observed. Despite the strong influence of facial leakage, this demonstrates the still existing dependency on filter area and filter material. By looking at the dependency of pressure loss on flow direction in Fig. 6, with open symbols representing self-protection and filled symbols representing third-party protection, RPDs in the inhalation mode exhibit the highest pressure losses in both sealed and non-sealed installation. This suggests a greater resistance when inhaling than when exhaling. Comparing the surgical mask SM_2 and the Filtering Face Piece FFP3_1 in both breathing modes, it is also evident that this difference in pressure loss but also filtration efficiency between inhalation and exhalation is significantly greater with the surgical mask.
In order to take a closer look at how breathing mode-based differences result for different mask types, Fig. 7 aims at a relative comparison. Here, the performance parameters of self-protection are related to those of third-party protection, subdivided into the mask groups described in Section 3.
As already seen on the basis of the fractional filtration efficiencies in sealed installation, the filtration efficiency is similar in both breathing modes when avoiding facial leakage. However, axe-shaped FFP and surgical masks have a higher pressure loss during inhalation, which may be due to a deformation of the mask caused by the direction of flow. In axe-shaped masks, both halves of the mask may contact each other due to the negative pressure during inhalation, while medical masks may touch the test head due to their flexible material. Both would lead to a reduction in filter area, increasing the flow velocity at constant volume flow, which in turn leads to a higher pressure loss based on Equation (3). As discussed above, relatively higher filtration efficiencies but also pressure losses are determined in self-protection in the natural fit including facial leaks. Subdivided into the different RPD shapes, this difference is found to be most pronounced for surgical masks. Fish-shaped FFP masks show the smallest differences, while duckbill-shaped masks and axe-shaped masks are in between. A closer look at the measured pressure loss of fish-shaped RPDs shows that the pressure loss increase in self-protection related to third-party protection is also differently pronounced within this subgroup. FFP2_1 and FFP2_2 with nanofibre materials show a higher relative pressure loss increase on inhalation than FFP2_3 and FFP3_1 based on meltblown filter media, highlighting the still existing influence of the mask material. Mask shape and filter material most likely affect the extent to which RPDs are being drawn to the test head on inhalation due to the negative pressure. The reduction of leakage areas may be advantageous for self-protection, but the minimization of leakage areas also increases the pressure loss.
5 Conclusions
This work focused on a screening of certified surgical and FFP masks used in the COVID-19 pandemic context with respect to respiratory-emitted particles. To this end, we presented a novel experimental set-up that allows the determination of mask performance parameters, namely the fractional filtration efficiency and the net pressure loss, as a function of the flow direction (self and third-party protection) and of the facial fit (sealed and natural fit) using a test aerosol based on artificial saliva. The particle size distributions of exhaled breath and the test aerosol were compared in exhalation mode. Measurements show that they are in a similar size range up to 0.4 μm with most particles smaller than 2 μm. The results of the mask screening in sealed fitting show that both the FFP and surgical masks examined feature a high filtration efficiency with regard to artificial saliva and, with a few exceptions, would meet total number-based efficiencies of 94% related to the requirements in DIN EN 149 using artificial saliva. The filtration efficiencies of the sealed fit are similar in both flow directions, but higher pressure losses are found in self-protection. One reason for this might be a reduction in filter area during inhalation, which presumably results from the drawing of masks with less stiff and thinner materials against the test head or, especially in the case of axe-shaped masks, might be the result of two mask surfaces being merged. In natural fitting, facial leakage significantly decreases both the filtration efficiency and pressure loss for each mask model tested. Here, the total efficiencies between different masks are in the order of 30%–85%, whereas the pressure loss appears to decrease in an inter-dependent manner with filtration efficiency. As a result, we conclude that the mask performance is more influenced by the mask fit and sealing material qualities than the filtration-specific properties of the filter media. As far as the flow direction is concerned, the filtration efficiency and pressure loss tend to be lower in third-party protection than in self-protection. This can similarly be caused by a drawing to the test head during inhalation, which might reduce the size of leakage areas between test face and RPD. Here, the relative change of the performance parameters may be influenced by thin and less stiff materials that favour such drawing to the test head, but may also be influenced by different RPD shapes (fish-, duckbill-, axe-shape).
One can conclude from our study that, considering naturally fitted masks, in addition to filtration-specific material properties, the flow direction, the dimensional stability, the mask shape as well as the sealing material properties influence the RPD performance significantly. However, these properties may be influenced also through humid and particle-laden breath, which is why future work needs to focus on the influence of wearing time on RPD performance. In addition, the influence of RPD shape indicates potential for optimisation, especially for the development of well-separating masks with reduced pressure losses that are suitable for infection prevention even for high-risk patients with restricted tidal volume.
Data availability statement
Data are available from the corresponding author upon reasonable request.
Declaration of competing interest
The authors declare no conflict of interest.
Acknowledgements
This work was funded by the German 10.13039/501100002347 Federal Ministry of Education and Research (BMBF), grant no. 01KI20241B. The authors are thankful to National Instruments for providing a data acquisition device and to Oliver Wachno for its set-up and commissioning.
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| 0 | PMC9748312 | NO-CC CODE | 2022-12-15 23:22:42 | no | Int J Hyg Environ Health. 2023 Mar 14; 248:114103 | utf-8 | Int J Hyg Environ Health | 2,022 | 10.1016/j.ijheh.2022.114103 | oa_other |
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J. Comput. Educ.
Journal of Computers in Education
2197-9987
2197-9995
Springer Berlin Heidelberg Berlin/Heidelberg
250
10.1007/s40692-022-00250-y
Article
An improved adaptive learning path recommendation model driven by real-time learning analytics
http://orcid.org/0000-0003-0200-3312
Raj Nisha S. [email protected]
Nisha S. Raj
is a Ph.D. Scholar in the Division of Information Technology, School of Engineering, Cochin University of Science and Technology, India. Her research interests include applications of EDM/LA in MOOCs, designing Personalized Learning Environments, and Recommender Systems. She is an activist in the field of novel practices in e-learning. Towards the same, she has delivered workshops and talks in Innovative Tools and Practises in elearning ecologies.
Renumol V. G. V. G. Renumol
is a Professor in Information Technology, School of Engineering, Cochin University of Science and Technology, India. She secured her doctoral degree from IIT, Madras, India, and Post-Doctoral degree from IIT, Bombay, India. Her research interests include Computing Education, Cognitive Psychology, Personalised Learning, Educational Technology, Special Education. She possesses several national and international publications as conference proceedings and journals.
grid.411771.5 0000 0001 2189 9308 Division of Information Technology, School of Engineering, Cochin University of Science and Technology, Kochi, Kerala India
14 12 2022
128
3 2 2022
22 9 2022
20 10 2022
© Beijing Normal University 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis 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.
The advancements in the education sector made e-learning more popular in recent years. The velocity of learning content creation and its availability is also growing exponentially day after day. It is challenging for a learner to find a learning path for a course with a vast content repository. So, recommending a learning path helps the learners streamline the learning materials systematically and achieve their goals. This article proposes a learning path recommendation approach focused on knowledge building and learning performance analysis. The model considers both static and dynamic learner parameters for learning path generation. The difficulty level of the learning resources is tuned based on the real-time performance analysis of the students. The learning resources are recommended based on learning preferences and the ability of a learner to learn the specific learning resource. The model also predicts the learning time and the expected score for each learner. Root Mean Square Deviation and Coefficient of Determination (R-Squared error) measures are used to find the correctness of the prediction. The model is also checked for its adaptivity to the learners’ changing behavior and diversity of the LOs recommended for different learners. Ninety-six undergraduate learners participated in the study. The experimentations are conducted with 530 LOs from selected courses. The comparison results with three existing models show a better performance from the proposed approach with an average accuracy rise of 30% in learning path prediction based on the expected duration of learning 27.8% in expected score prediction with the second-best performing model. It is observed that the real-time learning analytics using the implicit learner log data benefits the recommendation process. LO rating strongly indicated the enhancement of learner satisfaction and experience with a rise of 25.5% when comparing the rating share with the second-best model.
Keywords
Recommender systems
Personalized learning
Learning paths
Learning environments
E-learning
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pmcIntroduction
Technological and pedagogical advances are redefining education. E-learning is at the center of this conjunction. Along with technology advancements, scalability and reduced costs also made e-learning attractive. Many learning materials, text resources such as basic web pages, and multimedia resources as videos have been uploaded in recent years due to this fast growth of information and communication technology usage in education. The substantial amount of information in the learning systems creates cognitive overload and disorientation. Also, the learner population is highly dynamic and heterogeneous, with differences in their learning preferences, basic knowledge, learning style, and interaction with the learning environment (Chen et al., 2014, Ciloglugil & Inceoglu, 2018, Christudas et al., 2018). Hence, the knowledge delivered in fixed sequences or patterns will create dissatisfaction and disinterest in learners. The “one-size-fits-all” approach may not be satisfactory for the learners (Essalmi et al., 2010). Learners demand personalized knowledge delivery which adapts to their changing needs (Deng et al., 2017; Hwang et al., 2020a). In general, Learning Management Systems, LMS, do not meet the requirements of individual learners depending on their profile. However, taking learners’ profiles into account can improve the learning experiences and course success of students (Imran et al., 2016). In facilitating personalization in LMS, recommender systems can be used to suggest appropriate learning objects to learners in order to enhance their learning. Thus, generating adaptive, customized learning paths is an important research topic in the design of learning environments (George & Lal, 2019; Hwang et al., 2020b; Raj & Renumol, 2021). In education technology, it is advantageous to extract new hidden patterns in learner data for online learning systems. Personalized learning full-path recommendation research is particularly significant for the advancement of E-learning systems (Zhou et al., 2018). Online learners develop massive data with big-data features about their learning habits, which helps discover individual learning patterns (Chen & Zhang, 2014). The data generated from the learning environments can be fed back to the system, contributing to learning evaluation and monitoring (Sachan & Saroha, 2022). Thus, the learning material recommenders can be improved to monitor the student performance and adapt to the changes in the performance and their learning preferences.
According to Chen et al. (2014), the critical challenges to be focused on in the design of the learning resource recommenders are to provide convenient and effective access to the learning resources and boost the learner’s learning experience and satisfaction. So, the recommender system needs to adapt to the learner’s changing performance and preferences. The recommender system should map adaptive and personalized resources reducing the knowledge gap with the learner (Shi et al., 2020). This research work aims to generate personalized learning paths adapted to the changes in learning preferences and performance in real-time. Here, the learner log is constantly monitored, and getting feedback from this log, adjust the values of dynamic factors contributing to personalized content recommendations. A learning object (LO) is needed to learn a topic, and the sequences of the topics form the learning path. Learning objects are internet deliverable and reusable, instructional components that support learning (Wiely, 2002). We plan to find suitable learning objects for each topic and order them based on the knowledge dependencies between the topics.
To reduce the knowledge gap between the learner and the learning resources, the proposed model dynamically calculates the difficulty level of an LO and selects the LO with high similarity with the learner’s ability. The system consists of the learner model, the LO model, learner logs, and the recommender engine (Raj & Renumol, 2018). The learner model represents the learner’s behavioral information (learning preference, learning style), status information (learned LO, basic knowledge), and dynamic information (time for LO completion, score of LOs, number of attempts). The current study uses the Felder-Silverman learning style to analyze the learner’s learning style (Felder & Silverman, 1988). The LO is modeled using IEEE LOM (Risk, 2002), and the fields are used as described in Raj and Renumol (2019). The IEEE LOM fields are Structure, Format, Learning Resource Type, Interactivity Type, Interactivity Level, and Difficulty. An additional field is used to hold the average rating for each LO. To model the learner, learning material, and learner log this study uses the ontology-based method, and the design of the model is explained in Sect. 4. The earlier works show that the ontology-based LO recommender system can perform well in personalized learning environments. The significance of ontology-based models is that they better handle the cold-start issues and data sparsity problems in recommendations. The SPARQL queries extract similar learning objects based on similar learner grouping (Joy et al., 2021). Thus, the proposal works in the following steps:Find all possible sequences of topics forming paths between the starting and final topics.
For each topico Find the Top N LOs based on the learning log of similar learners
o Refine the Top N LO list so that the ability of the learner suitably maps with the difficulty of the LO.
Form the LO sequences based on the topic sequences
Suggest the sequence with the best time duration suitable for the learner’s available time
Log the interactions of learner such as score, time taken to learn, number of attempts, the rating is given for each learned material
Update the difficulty level of the learning material and ability of the learner according to the logged information
For every course, the instructor annotates the learning resources and develops the knowledge link between the topics. The relationship between the topics is stored as a graph structure called a concept graph or knowledge graph. This work uses a two-layered knowledge graph, with lessons forming the upper layer and LOs in the lower layer. The knowledge graph is traversed to find the concept sequence. The instructor assigns an initial difficulty level for each LOs. Also, the instructor gives an approximate time expected to learn the LO. These values are used to cold start the model. Each time a learner interacts with the LO, the time and score associated with LO are updated. The suggested learning paths are optimized for learner attributes and aim to maximize learner satisfaction and performance.
The major implications of our work are:Design of a learner model and LO model with static and dynamic parameters, where the length of the parameters is comparatively larger than most of the existing works. Most of the recommendation system studies focus on learning object ratings as feedback from the learner. The model in this study considers the dynamic parameters as the time taken for learning, score obtained from learning, number of attempts taken for learning a concept along with the ratings provided by the learners. The model adjusts the difficulty of the learning objects and the ability of a learner to learn that object in real-time so that the adaptivity of the recommendations is enhanced.
Real-time learner data analytics is incorporated to improve the accuracy of the next recommendations. Evaluation of the model using real learner data.
Implementation of an adaptive learning path recommendation model which works in two steps. Initially, a concept path is constructed by arranging the concept in order following a knowledge graph. Secondly, personalized learning objects are selected for each of the concepts in the path, forming a learning path.
The model addresses data sparsity and cold-start issues of recommender systems.
A summary of recent studies on the learning path adaptation.
The rest of the paper is organized as follows. The problem statement and research questions are introduced in Sect. 2. Section 3 briefly explains the related works on the current domain from 2018 to 2021. Section 4 elaborates on the design of the knowledge and domain models. Section 5 describes the learning path recommendation model that is proposed in this paper. The subsections of Sect. 5 elaborate on the algorithm used for learning path generation. The experimentation procedure and results are presented in Sect. 6, and Sect. 7 discusses the developments in correlation with the research questions and states the limitations of the work. Section 8 concludes the current work with a discussion on future work.
Problem statement and research questions
The adaptive learning path recommendation model aims to provide learners with the most suitable personalized sequences of learning activities to follow (de Marcos et al., 2008). So, the objective of this work is to find a flexible model for personalized and adaptive recommendations than the “one-size-fits-all” approach. It was stated by Chen et al. (2014) about the importance of providing better access to learning resources to enhance the learner’s performance and satisfaction. Also, suppose the learning model is generating the learning path by optimizing learner performance in terms of learning time and scores obtained. In that case, the paths are more effective (Chen, 2011). So, we decided to focus more on the learning duration, expected score, adaptivity, and acceptance of recommended learning resources. So, the research questions addressed are stated as follows:RQ1: Can we accurately predict learning duration and expected score during the learning path recommendation process?
RQ2: What learner-centric personalization parameters generate adaptive and diverse learning paths in an e-learning environment?
Related works
Many studies address the problem of learning path recommendations. From the literature, we can observe that this area’s works fall under domain-based and heuristic models (Raj & Renumol, 2021). Both studies use a knowledge structure for recommending, considering the similarity between the learning objects. The researchers use various algorithms/methods/tools based on the method chosen for guiding the learning path. They have evaluated the models in online or offline mode, considering how much the students learn through the system. Some studies adopted the system performance evaluation methods, and few studies conducted a user satisfaction study.
Table 1 summarizes the related works study done as a preamble for the current research.Table 1 Summary of related works from 2018 to 2021
Citation Recommendation Approach Method/Algorithm/Tools Evaluation approaches Personalization parameters
Nabizadeh et al. (2018) Considering the time limit of students to learn suggests learning path Mean, Median, Item Response Theory (IRT), DFS, Probability of Error for learning time and score Offline/system performance Learning style/knowledge level/time taken
Segal et al. (2019) Endorsing a set of questions to users in ascending order of difficulty based on their responses EduRank, Voting method, Collaborative filtering methods System performance/online Performance based on score
Cun-Ling et al. (2019) They suggest a path that improves a student's learning results while considering their learning style, learning need, and prior knowledge Graph Theory, Improved Immune Algorithm, Felder-Silverman Learning Style Index System Performance/Online/UserStudy Learning style/knowledge level
Vanitha et al. (2019) Recommending a path based on a user’s emotion and cognitive capacity Ant Colony Optimization, GeneticAlgorithm System performance/online Performance based on score
Li and Zhang (2019) Repeatedly recommending the unattempted courses with the best score to a user based on user similarities and the learning effect of prior users Network embedding, Learning effects, Breadth first search, Depth-first search, Random traverse Offline Learning style
Cai et al. (2019) During the learning process, suggesting the best path specific to the needs of each knowledge unit Knowledge tracing model, reinforcement learning, neural network, Markov decision process Offline/system performance Performance based on score
Nabizadeh et al. (2020) Recommending a path that improves a user's score in the least amount of Time Item Response Theory (IRT), Two-Layer course graph, Probability of error for learning Time and score, Depth First Search Offline/system performance/online/user Time taken/performance based on score/knowledge level
Niknam and Thulasiraman (2020) Learners are divided into different groups, and a path is chosen for them dependent on their prior knowledge Fuzzy C-Mean, Clustering methods, Ant colony optimization algorithm Online Learning style/knowledge level
Shi et al. (2020) Creating all possible paths while considering the students’ learning objectives and needs, and recommending the one with the greatest score Graph traversal algorithm, Knowledge graph, Cohen kappa coefficient for finding the quality of learning materials Online/UserStudy Time Taken/Performance based on score
Benmesbah et al. (2021a) Generate sequences of LO considering the learner preference, course relations and LO features Modified Genetic Algorithm Offline/simulated data Performance
Benmesbah et al. (2021b) Learning Path Adaptation using concept graphs Modified Genetic Algorithm Offline/simulated data Performance
Ramos et al. (2021) Generating visual representation of learning path and suggestion are made to group of collaborative learners Clustering algorithms K-means and clustering done based on learning path Online/UserStudy Performance based on score
Son et al. (2021) Generate learning paths suitable for specific learning skills based on MOOCs Metaheuristic algorithms, Genetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO) Offline Time taken/Performance based on score/Learning Style
Wang et al. (2021) Recommending a learning path and evaluating learner satisfaction. Based on that selecting an alternate path Differential evolution (DE) algorithm and knowledge graph Offline Performance based on score
The studies above show that the learning paths are sequences of LOs using different machine learning methods or algorithms. The major personalization parameters used are the learning style, learning time, the score obtained, prior knowledge. The LOs with more similarity with the learner characteristics are recommended. But none of the studies explore the likelihoods that the ‘difficulty’ parameter of LO can be different for different students. Also, the difficulty of an LO differs for the learner to varying points in the timeline as learning progresses. This gap is addressed in the current research paper. It is observed that most studies concentrate on selecting LOs according to learner needs or preferences, only very few works give attention to sequencing the topics. So, here we are using a two-layer model for sequencing the selected LOs based on knowledge relations.
A learning path is the linear list of LOs, organized based on their knowledge relation. So, the problem of recommending a learning path can be reduced to two issues, (1) To know the knowledge relations between topics (2) To suggest appropriate LO for a topic. The knowledge relations are obtained from the knowledge graph of the course. Following the relationships, the knowledge units are organized. And the LOs are selected based on the mapping between the LO and learner characteristics. The model predicts the learning time and score for every LO recommendation. The learner’s performance can be calculated in terms of learning time, scores obtained and the number of attempts needed, and satisfaction can be calculated based on the ratings given for each LO (Nabizadeh et al., 2017; Tarus et al., 2017).
Context: learning material and learner modeling
The learner model represents the learner’s behavioral information (learning preference, learning style), status information (learned LO, basic knowledge), and achievement (time for LO completion, score of LOs, number of attempts).
The learning path adaptation models deal with extracting relationships between the learning materials and the learner to select an appropriate learning path. As it is observed from the earlier studies that modeling the learning materials and learner are very significant (Tarus et al., 2017, Dorça et al., 2016). This section elaborates on the learner and learning material modeling used in this work. Ontologies are used as storage units for saving the learner-related parameters, learning object metadata, and learner activity log (Joy et al., 2021).
Learner model
To achieve learning adaptation, the personalized preferences and differences between the learners should be considered. In this study, we are focused on static and dynamic parameters for modeling the learners. The ontology’s main notion is a learner class, which is represented by various object properties or parameters for each student.
The learner is modeled using static and dynamic information, which are listed as follows:
Static Parameters: Learner identifier (LID), age, gender, stream of study, basic qualification, basic knowledge and learning style < active/reflective, visual/verbal, intuitive/sensitive, global/sequential > . These factors are explicitly fed into the system.
Dynamic parameters which are extracted from the learner activity log: ID of learning materials visited, learning time in minutes, score, rating, count of repeated attempts of the same material. These parameters act as implicit feedback to the recommendation process.
Also, the work considers additional information that is read from the learners as search topic information and the time availability to learn the topic. These values are not stored in the ontology but are stored in variables. The parameters are represented as the type values of the learner ontology (static) and learning log (dynamic) ontology classes.
Learning material model
The learning material metadata forms a significant part in the content recommendation. The learning content forms a hierarchical order (Nabizadeh et al., 2017). The four levels are course, lesson, concept, and learning object. The courses, represented as concept maps/graphs, form the topmost level and are often called subjects. A course can be covered using more than one lesson. The next level of abstraction is concept/topic. They are units of knowledge and learned by a learning object. One concept is mapped to one or more learning objects. These LO selection and recommendations are the major task in an adaptive learning environment (Belacel et al, 2014; Dharani and Geetha, 2013). A sequence LOs forms a learning path. Sequencing the LOs according to the order of the related concepts makes the sequencing more meaningful (Shi et al., 2020). Figure 1 represents the level of learning content abstraction assumed in this work.Fig. 1 Levels of abstraction of the learning content with example
The learning paths form two-layered topics/concepts as the outer layer and the associated LOs in the second layer. The cognitive linkage between the concepts is visualized using a directed graph named a concept graph or knowledge graph. The graph’s vertices form the concepts/topics, and the edges form their relationship (Benmesbah et al., 2021a; Zhu et al., 2018). Sequencing the concepts to develop a concept graph forms one part of the learning path adaptation problem.
The second part of the problem is selecting the appropriate LO for each topic. In Fig. 1, we can see that the Pop() topic is associated with two LOs. So, more than one LOs associated with any topic, and the adaptation of LO selection is significant in personalized learning path recommendation. As stated in the introduction, we have used the IEEE LOM schema for representing the LO metadata.
Out of the nine categories of metadata description, the study adopts the general field and education field as both have significantly contributed to the personalized LO recommendations in our previous works (Joy et al., 2021; Raj & Renumol, 2019). The title is a unique name given to identify the LO. Duration in minutes, the organization of the structure, type or format of the LO, the level of its interactivity and the type (active, expositive, mixed) and learning resource type of the LO forms the static information about the LO. The difficulty level is considered as an integer value in this work, which is dynamically computed for each LO depending upon the learning log. The values of all static metadata and the initial difficulty level of the LO are provided by the instructor. The LearningObject class of the ontology stores LO metadata. This study considers two types of LO: Expository LO, LOex, and Evaluative LOs, LOev (Nabizadeh et al., 2017). For each concept, the model tries to recommend one LOex and one LOev. LOex helps the learner learn the concept, and LOev evaluates the knowledge.
The graphical representation the ontology classes and their relationships are depicted in the Fig. 2. The dotted lines show the common data object shared by two types. The ontology is created completely in Java using a set of JENA APIs. The data is defined using RDF tools. Jena is a Java framework for creating Semantic Web apps. It includes rich Java libraries for writing code that works with different versions of RDF and SPARQL following W3C standards. Jena has a rule-based inference feature, an ontology reasoning engine based on OWL and RDFS ontologies.Fig. 2 Learner and learning object model
Learning path recommendation model
This section discusses the procedure to generate the learning paths as sequences of LO. This study uses a two-layered context/knowledge graph, KG, to represent the relation between the topics/KUs and the LOs, as shown in Fig. 3. We maintain a separate KG with specific starting and ending units for each lesson. The learner is asked to enter both the starting (SU) and target (TU) topics in a particular lesson they want to learn. The model creates a learning path connecting different KUs based on the first layer of KG. Also, from the second layer of the KG, an appropriate LO is chosen. The LOs selected are connected as a graph and are suggested to the students as a learning path (Algorithm 1, Fig. 4). Based on this observation, the learner’s interaction is recorded and later recommends LOs.Fig. 3 An abstract model representing the workflow
Fig. 4 Comparison of predicted time for completing LOs and actual time taken
The Depth First Search (DFS) is used to find the sequence of KUs (Algorithm 2) and algorithm 3 proposes the method to rank the LOs associated with each KUs.
Path generation
To generate the learning path, learners choose the starting and target knowledge units/topics SU and TU, respectively. From the graph, the sequence of topics is obtained and stored as a DAG, S = (LT, RE), a subset of KG. S’s starting and ending topics are explicitly obtained from learners’ input. We assume that the learners are familiar with all of the predecessor KUs of the SU. Initially, SU is set as the first and only node of the learning path. A suitable LO is selected for SU, estimate the expected time and score, and attach the LO with SU. Algorithm 3 will help to choose apt LO for the learner. Algorithm 2 is invoked recursively to get all the possible paths from SU to TU and stores the result in the variable PathList[]. The model suggests the shortest path from this set of possible LO paths.
Get all possible paths
The recursive function AllPathsSelection() will generate all possible paths from SU to TU (Algorithm 2). To nodes in the paths are LOs, and appropriate LOs are selected using Algorithm 3.
Selecting suitable LOs
A set of LOs represents a topic or knowledge unit. Each LO is a self-sufficient module for learning a particular topic. In this study, we considered two types of LOs: explanatory LO (LOex) and evaluative LO (LOev). Explanatory LOs form the set of descriptive LOs in text, video, audio with different difficulty levels and interactivity. The Evaluative LOs forms the group of LOs that facilitates assessments of each knowledge unit. LOev is also of varying difficulty levels. The initial step in selecting a suitable LO for the required topic is to generate the top N recommendation list of LOs based on the learners’ preferences.
In the case of a new learner, natural learner groups are generated by running SPARQL queries against Learner ontology. The learning history of the existing learners included in the learner group is extracted by running the SPARQL query from the LearnerLog and Learning Material sub-ontologies. Learner similarity with the multivariate clustering method is used in this step, as explained in our previous works (Joy et al., 2019, 2021). For each LO in the top N list, the LO with a difficulty level compatible with the learner’s ability to learn is selected. The LO’s difficulty and the learner’s ability to understand the LO are computed dynamically using the Eqs. (1) and (2) respectively.
In an educational environment, the implicit feedbacks logged by the learners are the test scores, time taken to learn the LO, and the number of attempts made by the learner (Raj & Renumol, 2021). The ability of the learner to learn an LO and the difficulty of an LO is dynamically computed based on these logged parameters (Table 2).Table 2 Parameters and description
Parameters Description
S Maximum Score assigned for the LO
T Minimum Time assigned for the LO
Sij The score obtained by learner i for the LOj
Tij Time taken by learner i for the LOj
s^j Mean score obtained by learners who studied LOj; ratio of sum of scores and count of learners who attempted the LO
T^j Mean time taken by learners who studied LOj; ratio of sum of Time taken and count of learners who attempted the LO
Rij Number of attempts made by learner i to study LOj
1 Aij=α×SijS+β×T-TijT+γ×1-Rij,
Aij in Eq. 1 is the ability of the ith learner to learn the jth LO. Here the weighted sum of the score obtained, the time taken and the number of attempts made by the learner is used for updating the ability of the learner. S and T represent the maximum score obtained and the maximum time taken for the LO. Rij is the number of attempts taken by the ith learner to learn the jth LO. We have assumed that when learner attempts an LO multiple times, they find it more difficult. Hence the value is considered to have an inverse effect in finding the ability of the learners. Hence the Rij is subtracted from the desired maximum number of attempts for any LO, i.e.,1. Similarly, the higher the time taken for learning the LO, the learner is considered to be less able for learning the LO. Here also the maximum time taken recorded for that LO is subtracted from the Tij and weight is applied. The higher the score, the better is the ability, so Sij is taken as a positively correlated parameter. α, β, γ (Table 3) are the weights applied for the mean score, time and number of attempts respectively. The values are obtained by repeated trials for better prediction accuracy, using simulated and previously logged data.Table 3 Optimal parameter values obtained through trials using simulated and historical data
Parameters α β γ δ ζ θ
OptimalValue 0.4 0.3 0.3 0.5 0.3 0.2
Suppose the learner is not opting for the provided LO or is not completing the learning process. In that case, the score is adjusted to zero, and the time taken is admitted to the maximum assigned.2 Di=δ×Dj+ξ×1-s^jS+θ×T^jT.
The Eq. 2 represents the computation of difficulty of the jth learning object, Dj. The previous value of Dj is one parameter in computation. Also, the score obtained and time taken by the learners who have learnt the LOj is also considered for recomputing Dj. We have assumed that as the score obtained by learning the LO increases the difficulty decreases and when the time taken for learning the LO increases the difficulty also increases. The weights applied, δ, ζ, θ are obtained by conducting trials in the simulated and previously logged data.
Each LO is a combination of LOev and LOex. When the LO is initially stored in the LO repository, for each new LO, the instructor assigns a maximum score, maximum learning time, and difficulty level associated with the LO. When the jth LO is processed, each ith learner produces a new set of values as score Sij, time Tij and number of attempts Rij committed to learning the concept. The expectation about efficient learning is a better score in a shorter and fewer attempt (Nabizadeh et al., 2020; Raj & Renumol, 2021). So, we took the weighted sum of the three parameters to quantify the learner’s ability. A lower score and longer learning time make the LO more challenging to learn. Thus, the difficulty of LO is computed as the weighted sum of three parameters, the current difficulty, mean score obtained by all learners who learned the LO and mean of time taken by all learners who learned the LO.
The parameters named as α, β, γ, δ, ζ, θ are weights applied to the factors in the above equation where α + β + γ = 1 and δ + ζ + θ = 1. The optimal values that are observed using simulated data are shown in Table 3.
Consider the score obtained by ith learner for the jth LO is 5 where the maximum score is 10 in their initial attempt, by spending 2 min where the expected time is 3 min, then the ability is calculated as: Aij = 0.4 × (5/10) + 0.3 × (1- (2/3)) + 0.3 × (1–1) = 0.3 If the same happens in three attempts then the ability score will be − 0.3. Similar effects for score and time parameters too.
Suppose, the mean score obtained for the previously considered LO by all of its learners is 8, by spending an average of 3 min, and the LOs previously calculated difficulty is 0.6, then the new value will be: Dj = 0.5 × 0.6 + 0.3 × (1 − (8/10)) + 0.2 × (3/3) = 0.56, since the score decreased the difficulty increased.
The highlight of Algorithm 3 for Selecting LO suitable for a learner is the dynamic computation of ability and difficulty factors. The similarity of ith learner and jth LO, Sij is calculated using Euclidean distance, Eq. (3). The more similar LOs are selected from LOex and LOev lists for a KU, as explained in algorithm 3.3 Sij=Aij-Dj2.
So, if Aij is 0.3 and Dj is 0.56 the Sij = 0.26, which is the difference between the values; square is applied to make the values positive. Higher the Sij more the jth LO compatible for the ith learner, assuming a match between the ability of the learner and difficulty of the LO.
Experimentation and results
This section explains how the learning path recommendation model is evaluated. Also, it includes details of the experimentation process conducted and the results of the experimentations. We have created concept/knowledge graphs based on a C Programming and Data Structure course comprising 468 learning items and 1065 relationships. Another KG is made for Data Mining Course with 155 LOs and 254 connections. The distribution of LOs that are used in this study is shown in Table 4. Each LO is characterized by IEEE LOM parameters and the table shows the generic nature and count of the LOs used in the study. Both the KGs are fed into the system. The learning objects are crawled from various educational websites and extracted (Joy et al., 2019, 2021). The concepts are mapped based on the CUSAT syllabus for the courses CS201B Computer Programming, CS405 Data Structures and Algorithms, and CS604 Data Mining.Table 4 Distribution of LOs
LO used Count
Study materials in the form of PDFs 120
Study materials in the form of PPTs 71
Online Quizzes 69
Tutorials with description and practice question (interactive and non-interactive) 99
Study materials in the form of Videos 149
Others (Exercise, audio, diagrams, simulations etc.) 115
The relationships among the LOs are manually established by three instructors based on the course syllabuses and are verified by an expert. The LOs are fed into the system, and the initial fields are annotated by a group of ten instructors who teach the undergraduate courses in Computer Science and Engineering. Each learning object is annotated by at least three instructors. The majority decision is taken into consideration for fixing the initial values for discrete parameters of each LOs. The mean of three values is considered for fixing the initial values for continuous parameters. If there is no common agreement, the LO is passed to the subject expert for final decision. The initial values given to every field describing the characteristics of the LOs are anticipatory values given by the instructors. These values helped in the initiation of recommendation. As elaborated in the previous sections, when the recommendation process progresses, the values for each LO field is updated according to the learner feedback.
According to the learner’s ability to learn and preferences, the current study suggests adaptive and personalized learning paths. The learners should analyze the quality and usability of the output. A total of 96 learners evaluated the model. The participants are enrolled for undergraduate Computer Science and Engineering programs in the two Indian state universities: APJ Abdul Kalam Technological University (KTU) and Cochin University of Science and Technology (CUSAT), Kerala, India. The experiments are conducted between February 2021 and September 2021. The experimentation was not a continuous process for all days. Each batch of experimentation was conducted for a batch of 10 students. Every participant is asked to join the learning process’s three phases: (1) pre-test, (2) learning, and (3) practice. The pre-test and learning style identification is made at the entry into the procedure. The pre-test identifies the knowledge of the learner. In the learning phase they searched for a term, say “stack push”, and the learning path is recommended to them. In the practice phase, they are asked to answer a maximum of three questions based on their study, which evaluates their learning performance. Three instructors monitored the student’s activities and guided them throughout the procedure. All tests are conducted with questions of different difficulty levels based on Bloom’s Taxonomy (Sosniak, 1994; Amstrong, 2016). In the experiment, it is verified that (i) the learning path generation algorithm proposed is better (minimizing the difference of actual and predicted score/time/rating values) than the baseline models (ii) more diverse paths are generated by the proposed approach than the LO-Learner mapping-based algorithm (iii) the learner’s satisfaction is progressing with the recommended learning path. The proposed model is tested against three other models. We have named the existing models considered as SeqSt, SeqDyn, PathSt for better comprehension while discussing the experiments. PathDyn is the name that is given for the proposed model. The models are explained as:SeqSt: The model suggested a sequence of learning materials based on the static values of the parameters used (Tarus et al., 2017). Here, the ontology-based domain modeling method is explored. A personalized sequence of LOs is recommended based on historical library data. Learners previously rate the annotated LOs. This rating is used as the parameter to select LO and user preferences.
SeqDyn: This model explores the dynamicity of the learner parameter. The changing values of learner satisfaction are considered here. But the sequencing of LOs is not done considering the knowledge relationship between the learning materials. We have implemented the SeqDyn based on previous works (Joy et al, 2021; Raj & Renumol, 2019). The change in student performance measures is incorporated with the basic model to evaluate the influence of dynamic parameters.
PathSt: This model uses a knowledge graph as the structure used to sequence the LOs, thus providing a cognitive linkage between the learning objects (Nabizadeh et al., 2020). The base model tries to optimize suggestions based on the historical or available data of the learner. The difficulty parameter of the LO is considered static here.
PathDyn: This proposed model considers a curated knowledge graph connecting the concepts/topics. The LOs are associated with these topics. LOs are selected based on the historical/available data and filtered further optimized by the ability of a learner to learn the LO of a particular difficulty level. Unlike other models here, the learner’s ability and the LO’s difficulty are adjusted based on their academic achievement, time invested to learn, the number of attempts they made, and ratings.
The primary aim of the experimentation is to compare the effectiveness of content sequence recommenders and path recommenders. The sequence recommenders SeqSt and SeqDyn recommend learning object sequences by analyzing the learner log. The learning path recommenders, PathSt and PathDyn suggest a learning path considering the learner log and a pre-designed knowledge graph. Here an inherited cognitive linkage is established between the suggested LO lists. The SeqSt and PathSt uses parameters with static values whereas the dynamicity of the parameters are used by the SeqDyn and PathDyn models. So two different aspects are experimented here, 1. The effectiveness of learning sequence and learning path 2. The effectiveness of static and dynamic parameters. The rest of the section elaborates on the experiment conducted and the result obtained.
A control experiment is conducted to find the model’s accuracy to predict the learning time and score of the learners. The participants are divided into four groups, balancing their prior knowledge and learning style for control experiments. They were asked to conduct multiple topic searches on the available lists. For each iteration in the learning phase, the model predicted the time required to complete the learning path by the learner. Also, the actual time spent by the learner on each learning path is recorded. In the practice phase, both time and score are predicted and observed. Root Mean Square Error (RMSE) as in Eq. (4) and R-Squared (R2) Error as in Eq. (5) are used to derive interpretations from the predicted score and time against the observed values in the practice phase. The number of topic searches by the learners varies from 8 to 11 in the experimentation phase, so the first eight searches are considered here. The results are observed to be stabilizing by the first 8 iterations.4 RMSE=1m∑i=1mxi-yi2,
5 R2=1-∑i-1mxi-yi2∑i=1my^-yi2,
where m is the number of observations in each iteration, xi is the predicted value, yi is the observed value and y^ is the mean of the observations.
RMSE gives the standard deviation of the residuals (prediction error) between the observed, xi and predicted values, yi. Here we have squared the residuals, took the mean and obtained the root of the value.
The results of experimentation are given below in Figs. 4 and 5. Each experimentation are considered for the first 8 iterations and x-axes of the figures represent the times of iteration.Fig. 5 Comparison of the predicted score on completing LOs and actual score obtained
Figures 4 and 5 shows that the proposed model is better in maintaining an accurate prediction of learning duration and expected score. The second-best model is observed as the sequence generating model which is modeled with dynamic parameters. The proposed path recommender shows an average of 30% more accuracy in predicting duration and 27.8% more accuracy in predicting the expected score. The time prediction is crucial as the learner wishes to complete the learning process in an available time depending on their learning goal (Zhu et al., 2018). The predicted score helps select the LO that can help the struggling learners perform better (Jdidou et al., 2021).
The Adaptivity, ADP, measures how much the recommendations suit a learner’s preferences. In the current study, Euclidean distance is used for finding the similarity between a learner and recommended LO is used to measure the adaptivity (Meng et al., 2021). The lesser the value, the more chances for the learner to learn the topic (Plass & Pawar, 2020). The adaptivity is calculated as the mean of similarity measures of all recommendations in that iteration (Eq. 6).6 ADPt=∑i,j=1NSimijN,
where N is the total number of learners, LO combinations in the iteration t. Figure 6 shows the adaptivity measure obtained for each model.Fig. 6 Similarity between the recommended LO and learner characteristics
Diversity, DIV of the learning path: Ensures that the learning path suits the learner’s needs is least likely to repeat (Liu et al., 2018, Meng et al., 2021). More diverse paths need to be generated by the model, and different classes of learners should get different learning paths also.7 DIV=∑i≠jN1-pi∩pjLN-1,
where DIV represents the diversity of the learning paths recommended and P represents each path. L is the length of a path, and the model suggests n number of total paths. Figure 7 shows the path diversity obtained for each model.Fig. 7 Diversity in recommended LO for different learners
Participants are asked to rank the recommendations on a scale of 1 to 5 (1—poor, 2—average, 3—good, 4—very good, 5—excellent). This feedback from the learners is used to calculate the learner’s satisfaction. The total recommendations made in the entire learning process comprises to 4285 LO ratings. From Fig. 8, the graph’s X-axis denotes the rating share in percentage and the Y axis represents the rating level 1–5. We can observe the rating share of each model. For example, the 27% of LOs rated 1 are recommended by SeqSt, 29.8% by SeqDyn, 27% by PathSt, and 16.2% by PathDyn. Similarly, considering the 5 rated LOs, the rating share is observed as SeqSt -17.39% SeqDyn-24.63% PathSt—26.08% PathDyn-31.88%.Fig. 8 Rating share of each model
Again, suppose the rating levels are greater than two are considered. In that case, the total ratings obtained by each model are shown in Fig. 9. The values are normalized on a scale of 0–100.Fig. 9 Number of Ratings obtained for the satisfactory level > 2, normalized to 0–100 scale
Discussion
This section discusses the results obtained from systematic experimentation to answer RQ1 and RQ2. The RQ1 is a decision problem: Can we accurately predict learning duration and expected score during the learning path recommendation process? From the controlled experimentation performed, we are getting a positive answer for the RQ1. The learners are asked to search and learn various topics available in the repository in the process. RMSE and R2 measures are used to evaluate the regression performance of the model. Figure 4 shows the comparison between the learner’s predicted and actual time taken through 8 different iterations.
Here we can observe that the proposed model, PathDyn, steadily improves prediction accuracy. The average RMSE values are almost decreasing in subsequent iterations for the proposed model. But the baseline models where the static characteristics are alone used for predicting LOs (SeqSt and PathSt) have comparatively higher error rates in predicting the duration of the learning process. The heterogeneous set of learners cannot follow the predictions by the system that uses static learner parameters (Tseng et al., 2008). In contrast, the proposed model uses the time taken log of the learners as one parameter to compute their ability. The time factor is also considered while updating the difficulty of each LO (Meng et al., 2021). So, the model can learn better with an increasing number of iterations and interactions, reducing the error and improving the predictions.
Similarly, a score log is also used while computing the learner’s ability. The score is also considered as a parameter in the computation of the difficulty of each LOs. From Fig. 5, it is evident that the difference in the predicted and observed values of the score is decreasing with an increase in iterations. Also, the R2 measure is showing promising results with the proposed model. The baseline model does not offer any pattern in the relation between the learners’ observed and predicted score measures. We have compared the two models which function on dynamic parameters (SeqDyn and PathDyn), analyzing the real-time implicit learner data. From the result analysis, PathDyn better predicts the score and learning duration. Here, the advantage of the knowledge-based sequencing of LOs forming path helped reduce the error rates (Tarus et al., 2018; Wu et al., 2020).
The RQ2 tries to evaluate the adaptive and dynamic nature of the proposed model, and the question is: What are the factors contributing towards generating adaptive and diverse learning paths in an e-learning environment? The answer to this question is the dynamic parameters. From Fig. 6, it is understood that the adaptivity of the model increases as the interactions increase. With more iterations, the system can log more information about the learners, and based on that, more adaptive LOs can be recommended. The diversity factor is also used to measure recommendations’ adaptive and dynamic behavior. From Figs. 6 and 7, we can observe that the adaptivity and diversity of the recommended learning materials are better with the models designed with the addition of dynamic parameters too. The static parameters help find the learning object that suits the learner’s preferences. But, naturally, the learner’s performance varies as the learning process progresses. The dynamic parameters are needed to be analyzed to recommend LO adaptively to this change in performance. The dynamic parameters considered in this study are the real-time learning duration, score obtained, number of attempts taken to study an LO, and the list of learned materials. These parameters are implicitly logged and analyzed in real-time scenarios. The implicitly collected learner log helps in adapting to the learner’s changing needs, and the integration of implicit and explicit parameters makes the recommendations more adaptive (Gomede et al., 2021; Xie et al., 2019). Thus, we can see that from the results of the experimentations, RQ1 and RQ2 are answered.
The learner satisfaction analysis is also done as discussed in Sect. 6. From the results, it is evident that the learners show more inclination toward the PathSyn generated LOs. The count of LOs recommended by the PathSyn model, which is rated with 3,4 and 5 levels, is more than all other models. Since the ontology model is used for representing the domain knowledge and involved in selecting LOs the cold-start issue is handled by the model (Joy et al., 2019, 2021).
Limitations
Even though the model generates a learning path suitable for the learner’s style and ability, there are few limitations also. The limitations observed in the proposed model for learning path generation are as follows:The linkage between the LOs is made based on the proficiency of the subject experts only. The initial difficulty level and time expected to learn an LO are marked by the experts, so in the cold-start phase, these values are not personalized.
The performance of learning path models cannot be compared as the behavior of learners changes dynamically. The data cannot be reused either.
We have used simulated data for early training of the system to find the constraint parameters.
The number of topics and LOs are limited in this study.
The participants are assumed to have learned all the previous topics.
Few learners made errors in data entry, and we needed to discard their records completely
Conclusion and future work
This research article presents a model for generating learning paths suitable for learners’ preferences and abilities. The present study tries to solve the learning path adaptation problem by exploring the knowledge relationship between the learning resources. The learner, learning materials, and learner log data are represented as classes in the ontology model. The data objects of these classes are comprised of static and dynamic parameters defining each class. The study focuses on analyzing the dynamic parameters such as the time taken for learning, the obtained score, the number of repeated attempts, and the learning resource rating. Based on the analysis, the ability of the learner to learn a particular learning material is computed in real-time for every recommendation. Also, the difficulty level of the learning material is adjusted based on the learner’s performance and LO rating. The LO is selected in two steps 1. Generate a list of LO using Collaborative filtering exploiting the similarity of learners 2. From this list, select the LOs that best match the learner’s current ability. The LOs are sequenced using a concept graph for the generating the learning path. As the model trust more on the implicit feedback represented as the dynamic parameters, the ability to predict learning duration and expected score is progressing as with the learning process. Thus, recommending more adaptive and diverse learning paths. The comparison results with three existing models show a better performance from the proposed approach with an average accuracy rise of 30% in learning path prediction based on the expected duration of learning 27.8% in expected score prediction with the second-best performing model. The rating levels indicated the enhancement of learner satisfaction and experience with a rise of 25.5% when comparing the rating share with the second-best model. Ninety-six undergraduate Computer Science and Engineering students participated in the study which involved 623 learning materials from C Programming, Data Structures, and Data Mining courses.
Even the proposed model shows progressive results, the progression is slower. More experiments are planned with parameters such as students’ cognitive ability, engagement, and procrastination to recommend learning paths to learners (Agnihotri et al., 2020; Farrell et al, 2019; Raj et al., 2021; Shimada et al., 2018). Also, we have plans to incorporate the model with the existing learning management system for a better e-learning experience. In the current situation of sudden shifting between on-campus and online modes of education caused by COVID-19 pandemic, a more personalized LMS will benefit toward the student engagement, performance and satisfaction (Clark et al., 2021; Patil & Naqvi, 2020).
Data availability
The datasets generated during and/or analysed during the current study are not publicly available due to privacy reasons but are available from the corresponding author on reasonable request.
Declarations
Ethical statements
I hereby declare that this manuscript is the result of my independent creation under the reviewers' comments. There is no conflict of interests. Except for the quoted contents, this manuscript does not contain any research achievements that have been published or written by other individuals or groups. The legal responsibility of this statement shall be borne by the authors of this manuscript.
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==== Front
J Child Fam Stud
J Child Fam Stud
Journal of Child and Family Studies
1062-1024
1573-2843
Springer US New York
2504
10.1007/s10826-022-02504-w
Original Paper
Assessing the Feasibility of Peer Coach Training for Disruptive Middle School Youth: A Mixed Methods Pilot Study
Galbraith Katharine [email protected]
Tarbox Jonathan
Huey Stanley J. Jr.
grid.42505.36 0000 0001 2156 6853 University of Southern California, Department of Psychology, 3620 McClintock Avenue, Suite 501, Los Angeles, CA 90089 US
14 12 2022
112
17 11 2022
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis 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.
In U.S. schools, disruptive behavior is by far the primary reason for disciplinary referrals, including suspensions and expulsions. School-based interventions targeting disruptive behavior usually position struggling youth as treatment recipients and neglect the psychosocial benefits of helping others. In this mixed methods pilot study, we evaluate the preliminary feasibility and acceptability of Peer Coach Training (PCT), a novel, school-based intervention for youth referred for disruptive behavior that deemphasizes the youth’s existing problems and focuses instead on training youth to help their peers. We used quantitative and qualitative methods to evaluate the feasibility and acceptability of PCT on two cohorts of disruptive youth (N = 9) in an urban middle school in Southern California. Youth and teachers completed assessments at baseline, post-treatment, and three-month follow-up. At posttreatment and follow-up, youth reported significant reductions in externalizing problems, as well as reductions in conduct problems, attention problems, and aggressive behavior; in contrast, teacher ratings yielded null findings. Qualitative interviews revealed that youth and teachers observed positive changes in peer interactions, self-confidence, and classroom participation efforts. Youth satisfaction data indicated that youth enjoyed participating in PCT and would highly recommend it to their friends. Results from this pilot evaluation suggest that training youth to help their peers is an appealing, feasible, and promising strategy for reducing disruptive behavior, however, controlled trials are needed to provide evidence for treatment efficacy.
Highlights
Disruptive behavior problems are a significant risk factor for negative life outcomes among school-age youth.
Existing studies have not tested whether positioning youth as helpers can reduce disruptive behavior.
This study tests a novel, strengths-based program for disruptive youth that position them as helpers.
Study findings indicate that positioning disruptive youth as helpers shows promise in reducing externalizing behavior.
Keywords
School-based interventions
Disruptive behavior
Externalizing behavior
Strengths-based interventions
Pilot study
==== Body
pmcIn U.S. public schools, disruptive behavior (e.g., noncompliance, fighting) is the primary reason for disciplinary referrals, including suspension and expulsion (Gregory & Weinstein, 2008; McClay, 2019; Mendez & Knopf, 2003). Disruptive behavior problems in childhood are associated with a range of adverse outcomes through adolescence and beyond (Barnert et al., 2021; Dishion & Patterson, 2006; Magnusson & Laftman, 2019). Childhood disruptive behavior problems are linked to poor academic achievement (e.g., heightened risk of school failure or dropout), association with deviant peers, and disciplinary referrals at school during adolescence (Deighton et al., 2018; McEvoy & Welker, 2000; Reinke et al., 2008; Vitaro et al., 2018), as well as higher rates of unemployment and criminal involvement in adulthood (Barnert et al., 2021; Border et al., 2018; Colman et al., 2009; Magnusson & Laftman, 2019; Mohr-Jensen & Steinhausen, 2016). Given the negative trajectory of childhood disruptive behavior, effective intervention efforts have broader implications beyond the present; intervention programs could help shape more favorable life outcomes for these youth in the long term.
Schools are optimal settings for delivering interventions for disruptive behavior because they eliminate key barriers (e.g., lack of transportation, limited financial resources) that often prevent youth from receiving the services they need (Atkins et al., 2017). However, school-based interventions for disruptive behavior are limited in several ways. First, they are often time and resource intensive, and on average, show small effects for externalizing behaviors (Barnes et al., 2014; Durlak et al., 2011; Eiraldi et al., 2016; Waschbusch et al., 2019; Wilson & Lipsey, 2007). Second, like most psychosocial treatments for problem behaviors, school-based interventions typically position youth as treatment recipients – i.e., none of these interventions explicitly position youth as leaders or otherwise competent, knowledgeable individuals that have the potential to create positive change in others.
Experimental work with non-clinical samples shows the motivational benefits of helping others (Dunn et al., 2008; Eskreis-Winkler et al., 2018; Eskreis-Winkler et al., 2019). For example, a recent study by Eskreis-Winkler et al. (2018) found that individuals randomized to be advice “givers” reported increased motivation, prosocial behavior, and overall well-being compared with individuals assigned to be advice “receivers.” These findings were consistent across different advice categories (e.g., financial, interpersonal, health, work) and age groups (middle school students and adults). The authors hypothesized that these results were in part due to larger gains in self-confidence among advice givers compared with advice receivers post-intervention.
Additional work shows similar effects for youth who take on formalized helping roles in “real-world” contexts. For example, youth who engage as peer mentors and tutors often show significant improvements in self-confidence, personal growth, social skills, and interpersonal relationships (e.g., Beltman & Schaeben, 2012; Coyne-Foresi & Nowicki, 2021). Furthermore, research on “peer support” interventions indicates that peer leaders (compared to support group members) show higher levels of problem-solving and school connectedness at postintervention – which suggests that giving help can yield greater benefits than receiving help (Ellis et al., 2009).
The benefits of helping appear to extend to criminal offending populations as well. For example, Hanniball et al. (2019) found that both delinquent youth and adult ex-offenders who were randomized to a prosocial helping condition reported greater positive affect compared with those assigned to a personal benefit condition. Another qualitative study found that incarcerated adults who were assigned to act as caretakers for inmates with mental or physical impairments showed improved relationships with prison staff and reduced levels of self-reported violent or aggressive behaviors. Participants cited the “sense of purpose” and “meaning” they gained from being caretakers as motivation to engage in fewer antisocial behaviors while in prison (Einat, 2017). Relatedly, a study of previously incarcerated adults found that those with stronger “helper” orientations had higher levels of psychological well-being, lower levels of pro-criminal attitudes, and lower expectations of recidivism (LeBel, 2007).
In this study, we sought to extend this work to disruptive youth in middle-school contexts by developing an intervention that addresses some limitations of many existing school-based interventions. We piloted Peer Coach Training (PCT; Huey & Galbraith, 2020), a brief, strengths-based intervention to remediate disruptive behavior in middle school contexts. PCT deemphasizes the youth’s existing problems and focuses instead on training youth to act as “coaches” to help their peers engage in prosocial behaviors. PCT is modeled on Ross and McKay’s (1976) Peer Therapist Program, which encouraged females in the juvenile justice system to help their peers by serving as informal therapists to those struggling with disruptive behavior. Ross and McKay found that the Peer Therapist Program was more effective than alternative interventions at reducing recidivism (1976). They argued that by labeling these girls as “therapist” and persuading them to act as such, the girls came to view themselves as prosocial change agents rather than as troublesome youth. Our PCT intervention adopted Ross and McKay’s (1976) general approach, while integrating evidence-based strategies from the peer mentoring (e.g., DeMarco, 1993; Raposa et al., 2019), social skills training (e.g., Dryburgh et al., 2020; Jackson et al., 1983), and behavior modification (e.g., Axelrod & Hall, 1991; Azrin & Besalel-Azrin, 1999; Kazdin, 2001; Martin & Pear, 2019) literatures.
Mixed methods were used to test the feasibility and acceptability of PCT. We used a pre-post-follow-up design with assessments at baseline, posttreatment, and three-month follow-up. Intervention acceptability was assessed with surveys and qualitative interviews with participating students and teachers. We predicted that youth who received PCT would show decreases in externalizing behaviors at posttreatment, and that these gains would be largely maintained at three months post intervention. In accordance with previous findings showing high satisfaction ratings for strengths-based interventions (e.g., Craig & Furman, 2018; Yuen et al., 2020), we anticipated that the strengths-based orientation of PCT would appeal to students and teacher informants. As such, we predicted that quantitative and qualitative intervention acceptability data would show that youth and teachers have positive impressions of PCT – i.e., they find it to be a satisfying and acceptable intervention.
Method
Participants and Recruitment
Participants were nine 7th and 8th grade students from a public middle school located in a low-income, urban setting in Los Angeles County. Of the nine participating youth, 77.8% were male, and the average age was 12.4 years (SD = 0.5). 77.8% identified as Latinx, and 22.2% as Black/African American. Over half (55.5%) spoke English as a second language. A third (33.3%) of the youth endorsed lifetime gang involvement, and 22.2% of the sample reported that they were currently gang-involved.
PCT participants were selected by the Assistant Principal, who was asked to refer students who exhibited the most extreme (either in frequency or type) disruptive behavior in classroom contexts. For eligibility, youth must have received at least one disciplinary referral for disruptive behavior between the first day of school and recruitment, which began approximately one month into the semester. However, youth who were unable to speak or understand English proficiently were ineligible, as we only had the capacity to lead the intervention in English.
Procedures
Ten eligible youth received a take-home consent form that explained the study and expectations for participation, and informed consent was completed by their caregivers. After the Assistant Principal received signed consent forms, undergraduate research assistants (RAs) met with the youth during the last class period of school to complete the assent and baseline assessment. During the assent process, youth provided names of each of their academic teachers and gave permission for investigators to contact them for assessment data. We successfully engaged nine of the original ten youth referred to our study.
From each student’s list of classroom teachers, one was randomly selected and contacted to complete youth assessments. Each teacher was first sent an email with an information sheet describing the nature of the PCT program and was asked to participate. If the teacher did not respond after 24 hours, the first author visited the teacher to explain the study and request their involvement. If no contact was made after 48 hours (via e-mail, phone call, and school visit) or the teacher declined before that time, another teacher was randomly selected from the remaining teachers on the student’s list (and contacted in the same fashion) until a teacher agreed to participate. After agreeing, each teacher completed a consent form and baseline assessment.
Two groups received PCT, with 4–5 participants in each group. Youth were assigned to each group by grade level (i.e., 7th graders in one group, 8th graders in another group) to maximize attendance, as 7th and 8th grade students at this school often had scheduling conflicts that were specific to grade level (e.g., state-wide exams). Group sessions for the first PCT cohort were co-facilitated by the first and senior authors, whereas sessions for the second cohort were co-facilitated by the first author and another graduate student. PCT sessions were held weekly after school over five weeks. The first session was a lengthier orientation session lasting approximately three hours. The subsequent four sessions were approximately one hour each. All sessions were guided by an intervention manual that evolved over the course of the evaluation (Huey & Galbraith, 2020). On average, participants attended 80% of PCT sessions.
Youth and teachers were asked to complete a post-treatment assessment approximately one week after PCT ended. During the final PCT session, youth were instructed to practice the peer coaching skills they had learned over the previous weeks on their friends, write about their experience on a worksheet, and turn in this worksheet to one of the group facilitators approximately one week post intervention. As such, we assessed students exactly one week following the intervention, rather than immediately after the intervention ended, to capture any changes in behavior that may have occurred from completing this final assignment. Approximately three months post-intervention, teachers and youth completed follow-up assessments. Participants received $10 for each assessment they completed.
After completing follow-up assessments, youth and teachers were asked to participate in a qualitative interview to provide their overall impressions of PCT as well as specific feedback about the program. Due to COVID-19 shelter-in-place orders implemented shortly after follow-up data were collected, interviews were conducted on an encrypted, HIPAA compliant, university IRB-approved video conferencing application. Without the school as a hub for connecting with students and teachers, we were only able to contact a subset of the enrolled youth (n = 6) and teachers (n = 6) to complete interviews. All study procedures were approved by the University of Southern California Institutional Review Board (IRB).
PCT curriculum
PCT is modeled on Ross and McKay’s (1976) Peer Therapist program, an intervention offered to adolescent females in a juvenile detention setting. PCT retains three core features of the Peer Therapist curriculum. First, instead of directly “treating” youth, PCT trains youth in behavior change strategies, which youth subsequently use to affect change in their peers. Second, youth serve as “coaches” by using their newly acquired skills to influence their close peers in a prosocial direction. In PCT, youth are not asked to formally mentor or “coach” one specific peer but are instead encouraged to use their skills as much as they can with any of their peers who might benefit. Finally, our PCT model adopts a strengths-based, de-pathologizing approach that eschews the use of punishment, criticism, or confrontation. Instead, the focus is on reinforcing youth competencies, prosocial skills, and personal strengths.
The first three sessions were each dedicated to teaching the youth a new skill: the introductory session focused on positive reinforcement, the second focused on critical feedback, and the third focused on active listening. In the final two sessions, youth integrated the skills they learned in the first three sessions. They were also asked to practice using these skills in and outside of session to solidify their roles as peer coaches. All sessions were designed to maximize youth engagement throughout the intervention. Didactics were kept to a minimum, and sessions were structured to be as interactive as possible. For example, rather than asking youth to simply describe examples of the concepts they were taught, group facilitators would ask youth to act out examples of each concept to assess their comprehension (e.g., youth role-played examples of positive reinforcement).
Youth were assigned brief exercises to complete between sessions to encourage practice and retention of their peer coaching skills. These assignments were typically worksheets that instructed youth to practice what was taught during the previous session and report on the outcomes of this practice. See Table 1 for a brief overview of PCT session themes and homework assignments. Sessions occurred over the course of five weeks (one session per week) with a final, brief check-in regarding the last homework assignment one week after the fifth and final session. Additionally, one of the intervention facilitators (first author) checked in with each youth once monthly via phone call or text message to remind youth to use their peer coaching skills; these check-ins ended at the final follow-up assessment.Table 1 Summary of Peer Coach Training (PCT) Session Structure and Content
Session 1 Session 2 Session 3 Session 4 Session 5
Session Description Orientation & Positive Reinforcement Constructive Feedback Focused Listening Coaching Orientation Coaching Wrap-up
Primary Themes PCT overview; reinforcement & positive practice skills Giving & receiving critical feedback Active listening & reflecting skills Being a peer coach; integrate skills learned in sessions 1–3 Final review of PCT skills; post-PCT coaching
Length 150 min 50 min 50 min 50 min 50 min
Homework Themes Practice positive reinforcement with peers & family Practice constructive feedback with peers & family Practice focused listening skills with peers & family Practice using peer coach skills to help a peer Practice peer coach skills with two other peers
Intervention adaptations
Although the session goals and content were identical for both cohorts, we made various adjustments to program structure throughout the intervention to optimize youth engagement, most of which were based on experiences the facilitators had conducting the cohort one sessions. Throughout session one, especially during the didactic portions, youth in the first cohort were relatively disengaged and easily distracted (e.g., playing on their phones during the session, making inappropriate comments about other group members, getting in and out of their seats, climbing on furniture). Thus, after that initial session, we instituted a set of “group rules” for subsequent sessions to which the facilitators and participants could refer to curb future disruptions. We also minimized the didactic portion of the curriculum and increased the amount of interactive role play. Additionally, during the first session, several youth wanted to help videotape the skits and asked if they could share clips of the skits with their friends. In response, we added an extensive videotaping component to the curriculum, which involved having youth record each other perform the skits in each session. At the end of the program, each participant was given a short video that compiled the skits their group acted out throughout the first four sessions. Each participant was given a digital copy of the video to have and share with family and friends as they wished. The purpose of these two adjustments was to increase youth participation during session and to address the youth’s requests to potentially share what they were doing with others outside of the program (rather than “sharing” via social media). Finally, it was decided that to maximize homework completion (which served as prompts to “practice” as peer coaches outside of session), group-level contingencies (e.g., movie tickets, soda) were awarded if the majority of group members returned their completed homework. In sum, although the content of session one (and subsequent sessions) was similar for both cohorts, the intervention developers adjusted the structure of each session after cohort one’s first session to better facilitate youth engagement.
Assessment Measures
Disruptive behavior
Youth rated their own behavior problems using the Youth Self-Report form (YSR; Achenbach, 1991) at baseline, posttreatment, and three-month follow-up. The YSR has 102 items for which youth provide ratings of “not true” (0), “somewhat true” (1) or “always or often true” (2) about their own problem behaviors.
Teachers rated youth behavior problems using the Teacher’s Report Form of the Child Behavior Checklist (TRF; Achenbach, 1991) at baseline, posttreatment, and three-month follow-up. The TRF consists of 118 items. Teachers provide ratings of each item with either “not true” (0), “somewhat true” (1) or “always or often true” (2).
The TRF and YSR are well-validated assessment tools that have high reliability, criterion validity, discriminant validity, and convergent validity across a diverse range of populations (Achenbach, 2019; Raines & Crumpton, 2017). Each measure produces syndrome scales, DSM-oriented scales, broadband scales, and a total problems scale, with internal consistencies ranging from α = 0.67–0.95 (Achenbach, 2014). Given the target of our intervention, we reported only the YSR and TRF scales relevant to disruptive behavior. The syndrome scales used in this study include attention problems, rule-breaking behavior, and aggressive behavior. The DSM-5 oriented scales used include attention-deficit/hyperactivity problems, oppositional defiant problems, and conduct problems. The broadband externalizing problems scale was also used, which includes the three syndrome scales noted above. All scale scores are normed based on nationally representative samples. T scores below 65 are within the normal range, T scores between 65–69 fall within the borderline range, and scores of 70 or higher fall in the clinical range (Achenbach, 1991).
At each assessment period, teachers also completed the Disruptive Behavior Disorders Rating Scale (DBDRS; Pelham et al., 1992), a 45-item survey that assesses for disruptive behavior disorder symptoms. Respondents are asked to rate each item based on what best describes the youth’s behavior on a four-point likert scale (from 0 “Not at All” to 3 “Very Much”). It is a well-validated and reliable assessment tool used across diverse youth populations (Erford, 1997; Hambly et al., 2017; Pelletier et al., 2006). Internal consistency for the three DBDRS scales – Oppositional Defiant and Conduct Disorder (OD/CD), Inattention, and Impulsivity – ranges from 0.75 to 0.96 (Hambly et al., 2017; Pelham et al., 1992).
Youth satisfaction
A brief satisfaction survey was developed based on Attkinson’s Client Satisfaction Questionnaire, a well-validated measure used to assess client satisfaction in health and human services settings. The eight items on this scale were adapted to fit the intended goals of the PCT intervention, i.e., to provide youth with skills to effectively help their peers. A sample item includes “Have the services you received improved your ability to help your friends in need?” Youth provided ratings for each item on a scale of 1–4, with higher scores indicating higher levels of satisfaction.
Statistical Analyses
The primary goal of the study was to assess the feasibility and acceptability of this novel intervention. Thus, despite our small sample, we felt that nonparametric statistical tests were appropriate given our study goals. A priori power analyses using G*Power (Franz et al., 2009) suggested that with three time points and nine participants, we would have 80% power to detect medium or larger effect sizes. We used Friedman’s one-way analysis of variance (ANOVA; Friedman, 1937) by ranks to examine changes in disruptive behavior across the three assessment time points. Friedman’s ANOVA is a nonparametric statistical test used to assess changes in single samples across three or more time points; it is an extension of the sign test and involves ranking each row of data. It is often used with small samples, as the data is less likely to be normally distributed (Zimmerman & Zumbo, 1993). Effect sizes for Friedman’s ANOVA can be calculated using Kendall’s W tests (Friedman, 1937). Kendall’s W values range from 0 to 1, and the effect size categorizations are as follows: small effect (0.1), moderate effect (0.3), and large effect (0.5 and above; Tomczak & Tomczak, 2014). We conducted post-hoc Dunn-Bonferroni tests to reduce likelihood of Type I error (Dinno, 2015).
Youth and Teacher Interviews
The interview scripts were developed by study authors to assess youth and teacher impressions of PCT. The qualitative interviews were conducted by a trained research assistant, under the supervision of the study investigators. The interviews lasted approximately 15–30 min, and all were audio or video recorded. Participants (youth and teachers) were compensated $25 for completing both the interview and satisfaction survey (youth only).
The interview guides included questions about the following: 1) impressions of PCT, 2) overall impressions of PCT facilitators, 3) most and least helpful parts of the PCT program (youth only), 4) changes in behavior, 5) changes in academic performance, 6) student-teacher relationships, and 7) whether respondents would recommend PCT to peers/other schools.
Interview Data Analyses
Interview recordings were reviewed by the first author and a research assistant for completeness and accuracy. The first author extracted central themes using thematic analysis (Braun & Clarke, 2006; Clarke & Braun, 2014; Terry et al., 2017) for both teacher and student interviews. Thematic analysis is a flexible qualitative analytic strategy for identifying, analyzing, and reporting patterns within data. We used inductive thematic analysis (i.e., data driven rather than theory driven) to generate data on the youth’s experiences with and teachers’ perspective on PCT. Thematic analysis, as outlined by Braun and Clarke (2006), includes the six following phases: 1) familiarizing oneself with the data, 2) generating initial codes, 3) searching for themes, 4) reviewing themes, 5) defining and naming themes, and 6) producing the report. In phase 1, a research assistant transcribed the data and the first author read and reread all interviews. In phase 2, the first author manually and systematically coded the data and used an inductive approach to generate codes (i.e., developed codes based on interview data rather than interview questions). In phase 3, after all data were coded and collated, codes were sorted into potential themes. In phase 4, these codes and themes were reviewed and refined (e.g., themes that were originally too broad, such as “improved classroom behavior” were expanded into more specific themes including “increased collaboration with peers” and “improved class performance”) based on discussions with an expert in qualitative data coding and analysis. In phase 5, these themes were then formally defined and named. The final themes that emerged from the data were generated by the first author and are reported in the results section (i.e., phase 6).
Results
Assessment Data
Disruptive behavior
Youth
Table 2 summarizes outcome results for the YSR from baseline to follow-up among youth enrolled in the PCT program. Significant effects were found over time for externalizing problems and for the following syndrome scales: attention problems, rule-breaking behavior, and aggressive behavior. A Dunn-Bonferroni post hoc test indicated significant reductions in externalizing behavior from baseline to posttreatment (p = 0.003), and from baseline to follow-up (p = 0.018). Similarly, post hoc tests indicated significant reductions in attention problems from baseline to posttreatment (p = 0.025), and from baseline to follow-up (p = 0.045). Dunn-Bonferroni post hoc tests indicated significant reductions in rule-breaking behavior (p = 0.013) and aggressive behavior (p = 0.034) from baseline to posttreatment, but no significant effects for either from baseline to follow-up.Table 2 Youth Self-Report Scores
Scale Name Mean (SD) Baseline Mean (SD) Posttreatment Mean (SD) 3 Month Follow-Up χ2 p-value Kendall’s W
Broadband Scale
Externalizing problems 63.00 (13.91) 52.44 (15.79) 55.67 (18.65) 10.00 0.007 0.556
Syndrome Based Scales
Attention problems 63.56 (9.54) 56.44 (8.38) 56.00 (5.66) 6.23 0.044 0.346
Rule-breaking behavior 65.33 (10.34) 58.11 (10.58) 61.33 (10.73) 9.36 0.009 0.520
Aggressive behavior 62.89 (11.66) 57.44 (8.52) 60.56 (10.62) 6.48 0.039 0.360
DSM-5 Based Scales
Attention-deficit/hyperactivity problems 61.33 (9.82) 56.44 (7.88) 55.11 (4.81) 6.000 0.050 0.333
Oppositional defiant problems 59.22 (9.69) 55.22 (8.32) 58.22 (8.50) 3.58 0.167 0.199
Conduct problems 66.44 (11.27) 58.89 (10.48) 63.44 (11.40) 6.467 0.039 0.359
There were also significant effects over time for two of the DSM based scales: attention-deficit/hyperactivity problems and conduct problems. Post-hoc tests showed significant reductions in attention-deficit/hyperactivity problems from baseline to follow-up (p = 0.034), and in conduct problems from baseline to posttreatment (p = 0.025) among PCT participants. All other pairwise comparisons were nonsignificant.
Effect sizes for each YSR scale ranged from small (0.1) to large (>0.5). Of the YSR scales with significant results, externalizing behavior and rule-breaking behavior yielded large effect sizes. Attention problems, aggressive behavior, attention-deficit/hyperactivity problems, and conduct problems showed moderate effect sizes.
Teacher
Table 3 summarizes outcome results for the TRF from baseline to follow-up. All TRF scales on PCT youth’s behavior yielded null findings.Table 3 Teacher Report Form Scores
Scale Name Mean (SD) Baseline Mean (SD) Posttreatment Mean (SD) 3 Month Follow Up χ2 p-value Kendall’s W
Broadband Scale
Externalizing problems 56.43 (14.33) 56.86 (14.39) 58.14 (14.85) 2.111 0.348 0.151
Syndrome based scales
Attention problems 59.57 (10.26) 60.29 (12.47) 61.14 (14.86) 0.091 0.956 0.006
Rule-breaking behavior 59.00 (9.75) 59.29 (10.03) 59.86 (9.96) 2.842 0.241 0.203
Aggressive behavior 59.43 (15.73) 59.14 (15.99) 61.00 (16.77) 2.714 0.257 0.360
DSM-5 Based Scales
Attention-deficit/hyperactivity problems 59.57 (11.21) 59.71 (11.16) 59.71 (12.58) 1.529 0.465 0.109
Oppositional defiant problems 59.00 (9.64) 56.57 (9.34) 58.57 (9.76) 1.400 0.497 0.100
Conduct problems 61.43 (14.99) 60.57 (14.23) 62.00 (14.55) 1.778 0.411 0.127
Table 4 summarizes outcome results for the DBDRS from baseline to follow-up. All DBDRS scales assessing changes in the PCT participant’s behavior yielded null findings.Table 4 Teacher Disruptive Behavior Disorder Rating Scores
Scale Name Mean (SD) Baseline Mean (SD) Posttreatment Mean (SD) Three Month Follow Up χ2 p-value Kendall’s W
OD/CD symptoms 4.56 (7.60) 4.22 (7.79) 5.50 (8.28) 2.286 0.319 0.143
Inattention 8.78 (9.48) 8.89 (8.75) 9.75 (10.35) 0.692 0.707 0.043
Impulsivity symptoms 6.22 (8.90) 5.22 (8.70) 7.12 (10.96) 3.00 0.223 0.188
Youth satisfaction
Table 5 summarizes acceptability outcomes from the Youth Satisfaction Survey. Results show that on average, youth participants were satisfied with PCT. Only one item “Have the services improved your ability to help your friends in need?” yielded an average below 3. Means, standard deviations, and median scores for each PCT Satisfaction item are included in Table 3 below, with higher scores indicating higher satisfaction ratings on a scale of 1–4.Table 5 Peer Coach Training Youth Satisfaction Ratings
Item Mean (SD) Median
How would you rate the quality of PCT sessions attended? 3.50 (0.54) 3.50
Did you get the kind of experience you wanted? 3.5 (0.84) 4.00
To what extent did PCT meet your expectations? 3.17 (0.75) 3.00
Would you recommend PCT to a friend? 3.83 (0.41) 4.00
How satisfied are you with the skills you learned in PCT? 3.67 (0.52) 4.00
Have the services you received improved your ability to help your friends in need? 2.83 (0.98) 3.00
In an overall general sense how satisfied are you with PCT? 3.17 (0.75) 3.00
If you were offered PCT again, would you do the program? 4.00 (0.00) 4.00
Interview Data
Youth Interview Data
Four central themes emerged from thematic analysis of youth interviews with respect to youth behavior change. Youth participants stated that they had 1) improvements in their self-confidence, 2) increases in their helping behaviors to their friends, 3) improvements in their own and their peers’ behavior as a result of their coaching, and 4) high satisfaction with their experience in PCT.
Theme 1: Improvements in Self-Confidence. Half of the youth interviewed endorsed that their participation in PCT led to improvements in their self-confidence with respect to social interactions. For example, one youth stated:
I’m shy but PCT made me feel confident enough to open up in class.
Another student noted:
PCT gave me the skills to use words in conflict. It made me feel better about myself to be able to use words instead of getting physical.
Other youth participants said that PCT helped them feel more confident in handling conflict as well. For example, one student indicated:
PCT made me feel more confident in handling situations that got out of control.
Theme 2: Increases in Helping Behavior. Every PCT participant interviewed indicated that PCT motivated them to increase their helping behavior toward their friends. Specifically, youth participants stated that PCT motivated them to use their peer coaching skills to help their friends act in more prosocial ways. For example, one youth noted:
PCT made me want to do what I could to keep my friends on the right track.
PCT students also increased their efforts to help their peers act in fewer disruptive ways in class. One youth stated:
I wanted to make sure I used what I learned from PCT to help my friends stay out of trouble.
Finally, another PCT youth mentioned:
After PCT I used my leadership to de-escalate situations a lot more than I did before.
Theme 3: Improvements in Own and Peers’ Behavior. The majority of youth reported changes in their own and their peers’ behavior from learning peer coaching skills. With respect to behavior changes in their peers, one PCT youth said:
Using my coaching skills made my friends act more nicely to me and to others.
Another PCT participant reported a similar experience:
My friends followed the rules more when I used my peer coaching skills to encourage them.
And with respect to changes in participants’ own behavior, one youth in the PCT program reported:
I have a lot of anger, but PCT helped me learn to control my anger and use my words when I have to give someone constructive feedback.
Finally, one youth highlighted how PCT produced improvements in his own and his friends’ behaviors simultaneously:
PCT helped me use my words and helped me get my friends to use their words more instead of getting physical like we normally do.
Theme 4: Satisfaction with PCT. Aside from the three prior interview themes that emerged regarding self-reported behavioral changes, interviews from participating youth indicated high acceptability of the PCT program, which is congruent with findings from the PCT Satisfaction Survey. All youth stated that they would participate in the intervention again if it was offered. Most youth highlighted that their favorite part of the program was the role play and videotaping aspects of the sessions. For example, one youth stated:
I liked when we acted everything out and got to videotape what we did.
And another mentioned:
I really liked when we could record each other and direct the scenes that were “real-life” scenarios that happened in school to help kids act better.
Youth also indicated that they found the coaching skills they learned in PCT to be quite useful. Every youth participant stated that they would recommend the program to their friends. In general, interviewed youth struggled to come up with negative qualities about the program when asked, although two youth did note that they wished the counselors were “stricter” with one of the more disruptive students in their cohort.
Teacher interview data
Four central themes emerged from teacher interviews. Teachers primarily endorsed 1) improved student effort on classroom assignments and class participation more broadly, 2) increased collaboration with peers, 3) increased self-confidence among PCT youth, and 4) high satisfaction with PCT.
Theme 1: Increased Self-Confidence Among PCT Youth. Almost every teacher commented that PCT helped boost the PCT participant’s confidence in the classroom. For example, one teacher stated:
I saw a positive change in [the PCT student’s] demeanor and how he carried himself.
A different teacher stated that they saw their student “brighten up.” A third teacher indicated:
I noticed she [PCT student] was smiling a lot more. She seemed to be more confident to speak up in class.
Theme 2: Improved Class Performance. Four of the six teachers interviewed indicated that they had witnessed improvement in the PCT student’s academic performance, especially with respect to assignment efforts and class participation. Specifically, teachers stated that they saw an increase in the number of homework assignments turned in, and that their PCT student spoke up more in class. For example, one teacher reported:
[PCT student] showed a bump in his homework completion.
Another teacher stated about their PCT student:
I saw a big improvement in classroom engagement.
And a third teacher indicated about their PCT student:
I saw a big change in effort. She put in her best effort much more consistently.
Finally, a fourth teacher indicated that her PCT student’s grades improved from failing to a B + over the course of PCT, which she attributed to the student’s participation in the program.
Theme 3: Increased Collaboration with Peers. The majority of teachers witnessed increases in their PCT student’s positive collaborative efforts in the classroom. For example, one teacher stated that:
I saw [the PCT student] collaborate more with other students in the class.
Another teacher noted:
[The PCT student] seemed to make more effort to help his peers in class when they needed it.
Furthermore, one teacher indicated that:
[The PCT student] was often antagonistic and standoffish, but then that started to level off and she began to collaborate much more with her classmates.
Theme 4: Satisfaction with PCT. In general, teacher informants reported that they liked PCT’s strengths-based approach. Every teacher noted that they would like to see PCT implemented at their school the following year, and that they would recommend the program to other schools with similar populations (e.g., low-income). Only two teachers reported that they did not witness any positive behavioral changes in their students participating in PCT, but both teachers noted that they still had positive impressions of the program.
Discussion
This paper evaluated the acceptability and feasibility of PCT, a brief strengths-based intervention adapted for disruptive youth in an urban, middle school setting. Throughout the program, youth were encouraged to view themselves as coaches with the capacity to influence their peers to act in more prosocial ways. During the implementation process, several key adjustments were made to improve youth engagement and enthusiasm for the program, which included adding: (1) group rules to clarify and cue acceptable in-session behaviors, (2) a protocol for performing and videotaping behavioral practice skits, and (3) group contingencies to encourage the youth to practice their peer coach training skills outside of group sessions. Quantitative analyses showed promising improvements for youth report of externalizing behavior, rule-breaking behavior, aggressive behavior, attention-deficit problems, and conduct problems, whereas teacher report indicated no changes in behavior over time.
According to qualitative interview data, youth unanimously reported that they enjoyed participating in PCT, and they would highly recommend it to their friends. Youth also noted that the intervention led them to increase their helping behavior toward their friends (e.g., helping their friends use “words” instead of physical force), which ultimately led to improved behavior among their peers. Teachers appreciated the strengths-based orientation of PCT and witnessed improvements in the participating youths’ classroom participation and self-confidence. All youth and teachers indicated that they would like to see PCT implemented again the following school year. Overall, we found that PCT was a promising and feasible intervention for disruptive, predominantly Latinx, middle-school youth.
We believe that youth adoption of a “peer coach” identity could be one potential mechanism explaining post-PCT reductions in problem behaviors. PCT youth were encouraged to support prosocial behaviors in their peers by using positive reinforcement, constructive feedback, and focused listening skills. In doing so, youth may have internalized new identities as “coaches” to their peers, which may have caused a shift in their behavior to better reflect their new identities (Ross & McKay, 1976). Indeed, studies show that having a positive self-identity is linked to higher levels of prosocial behavior (e.g., Crocetti et al., 2012; Crocetti et al., 2014; Patrick et al., 2018), and lower levels of antisocial behavior (e.g., Bruner et al., 2014; Kavussanu & Al-Yaaribi, 2021; Shields et al., 2018). However, changes in youth behavior over the course of the program were either not noticed by teachers or did not occur in the presence of their teachers.
There are several possible explanations for this discrepancy between teacher and youth reports of problem behavior. First, teachers were reporting on student behavior in their classes and youth were likely reporting on their behavior in general (i.e., in all classes and outside of school). Supporting this hypothesis, although youth reported reductions in their disruptive behaviors more broadly in qualitative interviews, they did not specify that these reductions occurred in classroom contexts per se – e.g., these reductions may have been more salient in recreational contexts, but not necessarily in the classroom. Thus, it is possible that overall, youth did reduce their disruptive behavior, but that the skills they learned were perhaps more salient in settings outside of the classroom.
A second explanation for the discrepancy in perceptions of disruptive behavior could be that teachers and students often disagree on reports of problem behavior. ASEBA cross-informant research shows low correlations between youth and teacher ratings of youth externalizing behavior (De Los Reyes et al., 2019; Youngstrom et al., 2000), which could indicate that youth behaviors are different across contexts (e.g., home versus school; Achenbach, 2014, De Los Reyes et al., 2015; Rescorla et al., 2017; Santos et al., 2020). Observations of youth behavior during the intervention lends credence to this explanation. Although many within-session role plays reflected school themes, when youth were asked to generate their own peer coaching scenarios, they often offered examples that occurred outside of the classroom (e.g., with siblings at home, at the park with friends). Furthermore, when youth were asked to report on instances in which they practiced their peer coaching skills, they often wrote about coaching behavior outside of school. It is important to note, however, that we do not have collateral data from other sources to confirm this hypothesis, as we were unable to gather data from caregivers or peers that could provide insight on the youth’s behavior outside of school (e.g., Dodge et al., 2015; Rescorla et al., 2017).
There are other possible explanations for the discrepancy between youth and teacher assessment data. For example, it is possible teacher assessment measures were less sensitive in detecting youth behavior change relative to qualitative interview questions. This could explain why youth and teacher qualitative findings converged much more than quantitative data from youth and teacher assessments. Moreover, it is possible that differences in the salience of the “peer coach” identity between youth and teachers may have contributed to discrepancies. PCT youth were actively encouraged to self-identify as “helpers” throughout the five-week intervention, and thus may have been more likely than teachers to rate themselves as less disruptive because of a prosocial identity shift (Ross & McKay, 1976).
Limitations and future directions
There are important limitations to consider. Given the feasibility focus of this study, our sample size was small, no comparison group was used, and the intervention was carried out at a single middle school in South Los Angeles. Although we did find significant improvement for some youth-reported problem behaviors, results may not generalize beyond Latinx youth, and we cannot say that our intervention necessarily caused these changes. Moreover, increased variability in the data due to small sample size may have contributed to discrepancies between self and teacher reports of youth behavior; it is possible that a larger sample may have yielded results for which youth and teacher data converged. A randomized trial with a larger and more diverse sample is needed to demonstrate that PCT is effective in reducing disruptive behavior in middle school youth from different backgrounds.
Despite these limitations, this small pilot trial has important implications for future research. First, this brief intervention is feasible to implement in a low-income middle school. In addition, these preliminary results suggest that PCT may be a promising approach to facilitating prosocial behavior, although additional refinements might be needed given the disappointing teacher assessment findings. Future trials of PCT should consider collecting data on the behavior of the enrolled youth’s peers as well, as it would allow us to examine whether this intervention has impacts beyond the individuals participating in PCT, as youth qualitative interview data suggested. In short, it appears that PCT is a promising intervention that should be tested on a larger scale with additional resources.
Funding
This study was funded by the University of Southern California’s James H. Zumberge Faculty Research and Innovation Fund.
Compliance with Ethical Standards
Conflict of Interest
The authors declare no competing interests.
Ethical Approval
The Institutional Review Board at the University of Southern California granted approval for the study.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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| 0 | PMC9748382 | NO-CC CODE | 2022-12-15 23:22:42 | no | J Child Fam Stud. 2022 Dec 14;:1-12 | utf-8 | J Child Fam Stud | 2,022 | 10.1007/s10826-022-02504-w | oa_other |
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10.1007/s12103-022-09707-3
Article
Religion and Rehabilitation as Moral Reform: Conceptualization and Preliminary Evidence
http://orcid.org/0000-0003-2228-158X
Jang Sung Joon [email protected]
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We examine how religion contributes to rehabilitation, which we conceptualize as moral reform and operationalize in terms of self-identity, existential belief, and character. We hypothesize that religion contributes to identity transformation, a sense of meaning and purpose in life, and virtue development. We also hypothesize that faith-based rehabilitation reduces negative emotions and the risk of interpersonal aggression. We conducted a quasi-experiment on a faith-based program in a state jail and a maximum-security prison in Texas, using a convenience sample of male inmates. To test our hypotheses, we compare inmates who graduated the program with those who did not and applied manifest-variable structural equation modeling to analyze data from pretest and posttest surveys. Program participation was linked to an increase in religiosity, which contributed to identity transformation (cognitive and emotional transformations and crystallization of discontent), the perceived presence of meaning and purpose in life, and virtues (including self-control, compassion, and forgiveness). Faith-based rehabilitation in turn reduced state depression and anxiety and the probability of engaging in aggression toward another inmate. This study provides preliminary evidence of religion’s rehabilitative effect on offenders; findings which hold promise for prison administrators looking for creative ways to support evidence-based and cost-effective approaches to rehabilitation within the correctional system.
Supplementary Information
The online version contains supplementary material available at 10.1007/s12103-022-09707-3.
Keywords
Prison ⋅ Rehabilitation ⋅ Religion ⋅ Moral Reform
Restoration Outreach of DallasN.A.
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pmcIntroduction
About two-thirds of state prisoners released are rearrested for a new crime within three years (Alper et al., 2018; Durose et al., 2014), and the 3-year rearrest rate has not changed much for several decades (Beck & Shipley, 1989; Langan & Levin, 2002). High recidivism among ex-prisoners seem to confirm the limited rehabilitative impact of incarceration. This is consistent with an assessment of prison chaplains in a national survey, where less than half (45.0%) of them reported a positive view on how their state correctional system was doing to prepare inmates for reintegration into the community (Boddie & Funk, 2012). Almost three-quarters (73.0%) said that access to religious programs in prison was “absolutely critical” to successful rehabilitation of inmates (see also Sundt & Cullen, 2002). This opinion is worthy of attention in a time of ever-tightening budgets, especially since volunteer-led and externally funded faith-based programs tend to be one of the few remaining resources available for promoting rehabilitation in prison.
Prior research shows that inmate involvement in religion or religiosity and participation in faith-based programs tend to be positively related to subjective well-being and inversely to prison misconduct (Clear et al., 2000; Dammer, 2002; Johnson, 2011; Kerley, Matthews, & Schulz, 2005; Kerley et al., 2011a; O’Connor & Perreyclear, 2002). These relationships—based mostly on cross-sectional data—imply the salutary effects of religion: cognitive, affective, and behavioral consequences of faith-based rehabilitation or reform that seek to change character traits, motivations, or disposition. These effects, however, are not the indicators or measures of rehabilitation itself, so the concept needs to be explicitly defined and observed separately from its outcomes. In this paper, we conceptualize rehabilitation as moral reform or “moral improvement” (Forsberg & Douglas, 2020) and operationalize the concept in terms of self-identity, existential belief, and character.
To empirically examine how religion contributes to reforming offenders, we conducted a quasi-experiment on a faith-based program, using a sample of male inmates housed at two correctional facilities in Texas: a state jail and a maximum-security prison. We hypothesized that program-increased religiosity contributes to inmate rehabilitation: that is, identity transformation, a new sense of meaning and purpose in life, and virtue development. We also hypothesized about key affective and behavioral outcomes of rehabilitation: religion-based reform reduces negative emotional states and the risk (i.e., probability) of interpersonal aggression among program graduates. To test these hypotheses, we applied manifest-variable structural equation modeling to analyze data from pretest and posttest surveys. Before describing our methodology and presenting findings, we begin with a conceptual discussion of the key concept, rehabilitation.
The Concept of Rehabilitation
Noting that offender rehabilitation (henceforth, rehabilitation) has not been adequately defined in the criminological as well as philosophical literature, McNeill (2012; 2014) offered a typology that consists of four forms of rehabilitation. Psychological or personal rehabilitation seeks to promote positive individual-level change in an offender by developing new skills or abilities and addressing deficits or problems, whereas legal or judicial rehabilitation concerns addressing the collateral consequences of the offender’s conviction by setting aside a criminal record and removing the stigma so they can requalify as a citizen (Maruna, 2011). Barriers to restoring social position as a citizen are moral as well as legal in that crime is not simply a legal but moral offense. Thus, the offender has to both improve moral capacities and seek redress for a wrongdoing as redemption is to be earned, which is the main concern of moral rehabilitation. The last form, social rehabilitation, is perhaps the most challenging to achieve because it involves not only “the restoration of the citizen’s formal social status and the availability of the personal and social means to do so” but also “the informal social recognition and acceptance of the reformed ex-offender” (McNeill, 2012:15; emphasis added).
More recently, Forsberg and Douglas (2020) developed an alternative taxonomy that distinguished five conceptions of rehabilitation based on the aims of rehabilitative measure and the means to achieve the intended end: rehabilitation as (1) anti-recidivism, (2) harm-reduction, (3) therapy, (4) moral improvement, and (5) restoration. The first two conceptions aim to reduce the likelihood of reoffending or engaging in conduct harmful to the well-being of others and an offender, using other means than reducing the offender’s capacity to reoffend or engage in such conduct (e.g., incapacitation), disincentivizing the offender’s reoffending or harmful conduct (e.g., deterrence), or incentivizing non-offending or less harmful conduct by the offender. The next conception intends “to cure or ameliorate a mental deficit … [whether] a mental illness or disorder, or … some defect in the capacities relevant for criminal responsibility” that caused an offender’s past offense and predisposes the offender to further offending.
The fourth and fifth conceptions correspond to McNeill’s last two forms of rehabilitation, though not exactly the same. McNeill’s “moral rehabilitation” includes an offender offering moral redress to the victim or the community, but, for Forsberg and Douglas, reparation is a part of rehabilitation as restoration that overlaps with McNeill’s “social rehabilitation.” On the other hand, their conception of rehabilitation as moral improvement focuses on making an offender morally better, while the nature and scope of moral improvement vary among the proponents of rehabilitation intended to have offenders become morally better. For example, Morris (1981:265) favors measures that help an offender develop an “identity as a morally autonomous person attached to … a moral good … that one feel contrite, that one feel the guilt that is appropriate to one’s wrongdoing, that one be repentant, that one be self-forgiving and that one have reinforced one’s conception of oneself as a responsible being.” Other scholars suggest that the scope should be narrower, like fortifying the moral capacities of offenders to reduce the likelihood of reoffending or targeting moral improvements relevant to crime that has been committed (Duff, 2001; Hampton, 1984; Howard, 2017).
In this paper, we conceptualize rehabilitation as moral reform—consistent with Forsberg and Douglas’ (2020) conception of rehabilitation as moral improvement—and suggest religion as a source of moral reform is well-positioned to have a wide-ranging rehabilitative impact on various domains of an offender’s life, including physical health and social relationships. We thus focus on three life domains: self-identity, existential belief, and character.
Religion and Rehabilitation
In our conceptualization of rehabilitation as moral reform, the term moral refers to “an orientation toward understandings about what is right and wrong, good and bad, worthy and unworthy, just and unjust, that are not established by our own actual desires, decisions, or preferences but instead believed to exist apart from them” (Smith, 2003:8). Since the matter of rightness, goodness, worthiness, and justice is determined by something outside of self, moral reform needs to be based on a system of self-transcendence rather than expediency or self-interest. Religion provides one such system and thus becomes a potential source of moral reform.
Self-Identity: Identity Transformation
Our conception of rehabilitation as moral reform assumes that offenders as humans are moral beings in that they are moral agents, one of whose “central and fundamental motivations for human action is to act out and sustain moral order” (Smith, 2003:8).1 Of course, people do not always act morally or consistently live up to their own or others’ moral standards. Offenders are those who have failed to demonstrate reasonable firmness in response to criminogenic pressures they faced (Howard, 2017). This failure, especially when repeated, is likely to distort their understanding of who they are (i.e., moral beings) and lead them to adopt a criminal identity, while struggling to rationalize or make some sense of their own action by blaming others and society instead of owning moral responsibility. As a result, offenders may end up accepting that they are an automaton at the mercy of external forces rather than a morally autonomous person. Thus, rehabilitation as moral reform should aim at helping offenders discover their “true self” or “real me” (Maruna, 2001:88) in place of a criminal identity.
Religion offers an opportunity to replace an “old self” with a “new self” (James, 2007), helping offenders write a “redemption script,” a narrative that allows a new start built on the new self (Hallett & McCoy, 2015). Identity transformation via religion is a cognitive process that involves self-reflection and a change in self-concept, based on a new “living narrative” religion provides (Smith, 2003). It is also an affective process, which includes introspection and dealing with feelings of guilt over their wrongdoing and negative emotions (e.g., depression and anxiety) associated with criminal punishment (e.g., imprisonment) and the losses it caused (Clear et al., 2000). Identity transformation is the focus of identity theories of desistance from crime.
Giordano et al.’s (2002) symbolic interactionist theory posits that four types of “cognitive transformations” are necessary for desistance: (1) one’s openness to change (a general cognitive readiness for change), (2) one’s exposure to a particular hook (or set of hooks) for change, (3) one’s construction of a conventional “replacement self” or new identity, and (4) one’s perception of crime to be negative, unviable, or personally irrelevant. Identity transformation also involves “emotional transformations” that lead to “an increased ability to regulate their emotions in socially acceptable ways,” thereby reducing the likelihood to identify oneself with negative emotions (Giordano et al., 2007:1610). For Giordano et al. (2002), religion is a major hook for change among offenders, as it functions as a catalyst that provides a conventional replacement self and positive emotions (Giordano et al., 2008).
Paternoster and Bushway’s (2009) rational choice theory of desistance posits that offenders are committed to a criminal identity until they determine the cost of this commitment is greater than the benefit and perceive what they fear may become (“the feared self”) to be more likely than what they hope to become (“the positive possible self”). This perception is assisted by the “crystallization of discontent” (Baumeister, 1994), in which offenders see “failures or dissatisfactions across many aspects of [their] life [being] linked together and attributed to the criminal identity itself” (Paternoster & Bushway, 2009:1123). This cognitive process provides the initial motivation to change the self, and religion contributes to the process by helping offenders attribute their failures to their old self and offering a new self for a new start.
In a rare quantitative test of identity theories of desistance, using survey data from 2,249 inmates at America’s largest maximum-security prison, the Louisiana State Penitentiary (a.k.a., “Angola”), Jang et al. (2018b) found that religion played a role in contributing to identity transformation (see also Hallett et al., 2017). Specifically, they found religious conversion was positively related to cognitive transformation and crystallization of discontent, whereas inmate involvement in religion was positively related to emotional transformation. More recently, inmate participation in a faith-based program was found to increase religiosity, which in turn contributed to crystallization of discontent among prisoners in Colombia and South Africa (Jang et al., 2022a; Jang et al., 2022b; Johnson et al., 2021). In addition, in their qualitative study of 63 male inmates who had undergone a religious conversion, Kerley and Copes (2009) found that religion helped those inmates maintain their new identity through support networks (i.e., friendships with other religious individuals, whether inmates or local volunteers), formal and informal group activities (e.g., chapel services and Bible study or prayer meetings), “sharing” (whether evangelistic or altruistic), and personal reflection (e.g., “quiet time”).
Existential Belief: A Sense of Meaning and Purpose in Life
Offenders as moral beings have an orientation toward understanding what is worthy (Smith, 2003) or significant because humans are existential beings that have an innate need for a meaningful life, which largely derives from having purpose (a goal or goals) in life. A life of crime, particularly, common-law crime is hard to justify as meaningful given its destructiveness to others and the self, no matter how it is neutralized or rationalized (Sykes & Matza, 1957). As a result, offenders tend to lack a sense of meaning and purpose in life, which contributes to their criminal continuity. Thus, rehabilitation as moral reform should aim to help offenders how they can find a meaning and purpose in life for a change. According to Frankl (1984), the “true meaning of life” should be self-transcendent. While religion is a source of such meaning, self-transcendent meaning can also come from outside of religion, like close relationships with others (Costin & Vignoles, 2020) or a commitment to a cause, like environmental care or patriotism. In correctional institutions, however, religion is readily available to offer a time-honored system of meaning to offenders, helping them develop a new sense of meaning and purpose in life.
Prior research shows a positive association between religiosity and a sense of meaning and purpose in life among offenders. For example, in a study of male inmates at three maximum-security prisons in Texas, Jang et al. (2018a) found that inmate religiosity was positively related to perceived meaning in life (see also Jang et al., 2018b). Using data collected in a non-Western country, Jang et al. (2021) replicated the positive relationship (see also Jang et al., 2022a; Jang et al., 2022b). Specifically, analyzing data from a survey with male and female inmates housed in four South African prisons, they found that more religious inmates were more likely to report a sense of meaning and purpose in life than their less or non-religious peers. This positive relationship was found among both male and female inmates, showing that the relationship was gender neutral as well as cross-cultural.
Virtue Development
To the extent that crime is a result of limited moral capacities (Howard, 2017), rehabilitation as moral reform needs to aim at developing virtues among offenders. Since most religious traditions promote virtues like forgiveness, gratitude, accountability, and self-control (Emmons & McCullough, 2004; Evans, 2019; Rye et al., 2000), religious involvement is expected to increase virtues. First, religion not only emphasizes but also reveres virtues, teaching adherents to adopt and practice divine-like qualities (Rye et al., 2000). In Judaism, Christianity, and Islam, for example, forgiveness is a way to imitate God who forgives, carry out God’s plan beyond self-pity and resentment, and enhance one’s relationship with God. In Hinduism and Buddhism, forgiveness is a way to attain divinity or reach nirvana. Second, religion provides adherents with a spiritual or self-transcendent narrative, whereby virtue (e.g., self-sacrifice or forgiveness) has meaning even when it goes against human instincts (e.g., self-preservation) or counteracts a natural tendency (e.g., vengefulness). Finally, religious communities strive to stimulate virtue development as they collectively engage in practices (e.g., worship) that promote the connection between a transcendental narrative and virtuous behavior (Schnitker et al., 2019).
Prior research provides evidence that religion fosters virtues among individuals in the general population (Batson et al., 1999; Emmons & Paloutzian, 2003; Krause, 2018; McCullough et al., 2000; Rye et al., 2000). While research on religion and virtues among offenders is scant, Jang et al. (2018a) found that more religious inmates reported higher levels of forgiveness, compassion, and gratitude than their less or non-religious counterparts. Similarly, religiosity was found to be positively related to forgiveness, accountability, gratitude, and self-control among prisoners in Colombia and South Africa, both males and females (Jang et al., 2021; Jang et al., 2022a; Jang et al., 2022b).
Consequences of Rehabilitation
Faith-based rehabilitation as moral reform is likely to have affective and behavioral outcomes. First, identity transformation is expected to reduce negative emotions and deviant act among offenders, as it enables offenders to disassociate themselves from negative emotions that they used to identify with and to behave, consistent with the new self (Giordano et al., 2002; Giordano et al., 2007; Paternoster & Bushway, 2009). Second, a new sense of meaning and purpose in life is likely to decrease an offender’s negative emotions and misconduct as the new existential belief leads them to strive for conventional life goals and to manage their behaviors accordingly (Jang, 2016; McKnight & Kashdan, 2009; Steger & Frazier, 2005; Vanhooren et al., 2017). Finally, fostering virtues among offenders is expected to not only decrease deviance but also enhance emotional well-being (Emmons & McCullough, 2003; McCullough, 2000), since moral character is a central component of “eudaemonic” happiness (Ward & Maruna, 2007).
Research on rehabilitation as moral reform and its affective and behavioral consequences is limited, but three recent studies provide supportive evidence. First, Jang et al. (2018a) found crystallization of discontent and emotional transformation were inversely related to disciplinary convictions among prison inmates. They also found that inmates’ perceived presence of meaning in life and virtues (forgiveness, compassion, and gratitude) were inversely related to negative emotional states (depression and anxiety) and the likelihood of aggression toward another inmate. Second, the virtue of self-control was also inversely related to negative emotional states and the risk of aggressive misconduct (Jang et al., 2021; Jang et al., 2022b).
The Present Study: Hypotheses
To examine whether religion contributes to rehabilitation as moral reform, we conducted a quasi-experiment on a part of faith-based program, which is operating in seven units of Texas Department of Criminal Justice. The program, sponsored by a non-profit organization, called “Restoration Outreach of Dallas (ROD) Ministries,” consists of four in-prison Bible study classes (ROD I to IV) and aftercare following release from prison (ROD Ministries, 2015). The classes—each of which meets weekly for three months and cover 12 to 13 sessions—are facilitated not only by volunteers from local churches but also inmates who have both completed the classes and been trained to lead it. To complete each ROD class, inmates must attend at least nine sessions and are required to do homework. Following successful completion of a class and requirements, inmates participate in gradation and receive a certificate, becoming eligible for enrolling in the next class (e.g., ROD II after ROD I). The present study focuses on the first class (ROD I, henceforth, ROD program or, in short, ROD), comparing between inmates who completed the class (graduates) and those who did not complete (incompletes).2
First, the ROD program was a Bible study and thus expected to increase inmate involvement in religion or religiosity, so we hypothesize the following.
• Hypothesis 1
• ROD graduates are more likely to report an increase in religiosity than the incompletes.
Next, based on the literature reviewed above, we expect ROD-increased religiosity to contribute to rehabilitation. Since rehabilitation is a process of moral reform in terms of self-identity, existential belief, and character, it can be observed in terms of degree. Thus, inmates ahead of others in their progress in moral reform are likely to show signs of positive change compared to those who are making less progress. Thus, we hypothesize as follows:
• Hypothesis 2
• A change in religiosity is positively related to a change in (a) identity transformation, (b) a sense of meaning and purpose in life, and (c) virtues.
Finally, we hypothesize about affective and behavioral consequences of religion-based rehabilitation as moral reform as follows:
• Hypothesis 3
• A change in identity transformation, a sense of meaning and purpose in life, and virtues are inversely related to a change in negative emotional states and the risk (i.e., probability) of interpersonal aggression.
Methods
Sample
We conducted a quasi-experimental study based on one-group pretest-posttest design to assess the effectiveness of ROD between July 2019 and March 2020. Two male-only units of the Texas Department of Criminal Justice (TDCJ) were selected for the study because the program had been operating there longer than other units. One was a state jail near the city of Dallas, and the other was a maximum-security prison, southeast of Dallas about 100 miles from the city.
Recruitment for the first ROD class was based on the facility-wide distribution of promotional flyers, which introduced inmates to the faith-based program and explained prerequisites for the class, including having at least six months left on their sentence and planning to reside in Dallas area upon release given that ROD aftercare is available only in that area. We visited the research sites to invite inmates screened and enrolled in the class to participate in our study. If they agreed, they signed a consent form and then completed a pretest survey.3 After completing the class, graduates were asked to participate in a posttest survey, while those who failed to complete ROD were also asked to do the second survey. We obtained information about inmate’s sociodemographic and justice-related backgrounds from TDCJ.
A convenience sample of 231 inmates participated in the pretest survey, and 132 (57.1%) of them graduated with 99 not graduating.4 Nearly half of them (109, 47.2%, 81 graduates and 28 incompletes) did the posttest.5 Thus, the sample size for hypothesis testing was 109.
Measurement
The key exogenous variable, the ROD program completion, is dichotomous (0 = incomplete, 1 = graduate). Other exogenous variables were inmate’s backgrounds, including sociodemographic variables: age, race (dummy variables of Black, Hispanic, and Asian with the reference category of White), education (1 = 8th grade or less, 2 = 9th grade, 3 = 10th grade, 4 = 11th grade, 5 = 12th grade or GED, 6 = some college, 7 = college degree), intelligence (IQ score), marital status (dummy variables of being married including common-law marriage and divorced/separated/widowed [D/S/W] with the omitted category of being single), and religion (dummy variables of being Protestant, Catholic, Muslim, Jewish, an adherent of Eastern religion, and a follower of other religion with the omitted category of having no religion). Also included were justice-related backgrounds: a total number of prior incarcerations in prison (prior prison) and jail (prior jail) and current sentence length in year. In addition, a dummy variable, state jail, was created to control for any differences between the two research sites not only because one is a state jail and the other is a prison but also because ROD classes in the state jail were facilitated by local volunteers, whereas the program in the prison was led by inmates.
A first endogenous variable is inmate’s religious involvement or religiosity, which was measured by creating a scale summing standardized scores of five items (see Appendix A): two items of religious beliefs (perceived closeness to God and importance of religion) and three items of religious behaviors (frequency of religious service attendance, praying outside of religious services, and reading the Bible or other sacred text in private). Exploratory factor analysis generated a single-factor solution with moderate-to-high loadings, ranging from 0.490 to 0.779 at the pretest and from 0.545 to 0.759 at the posttest, and good inter-item reliability with Cronbach’s α being 0.827 and 0.780 at the pretest and posttest, respectively.
The next endogenous variables involve three domains of rehabilitation as moral reform: identity transformation, a sense of meaning and purpose in life, and virtues. First, identity transformation was operationalized by cognitive transformation, emotional transformation, and crystallization of discontent. The first and the last were measured by three items, whereas four items were used to measure the second (see Appendix A). Items of cognitive and emotional transformations loaded on a single factor with moderate-to-high factor loadings and acceptable-to-high internal reliability at both tests. On the other hand, the crystallization of discontent items had a poor internal reliability at both tests perhaps due to one item that had a low factor loading.
Second, to measure an inmate’s sense of meaning and purpose in life, we used Steger et al.’s (2006) four items of presence of meaning, which had a single-factor solution with high loadings and high-to-excellent internal reliability (see Appendix A).
Third, we created measures of seven virtues. To measure self-control, we used reverse-coded four items of Grasmick et al.’s (1993) Low Self-Control Scale, which had moderate-to-high loadings on a single factor and acceptable internal reliability at both pretest (from 0.472 to 0.709, α = 0.695) and posttest (from 0.498 to 0.651, α = 0.621). Compassion was measured by five items (Krause et al., 2016), which had a single-factor solution with moderate-to-high loadings and good inter-item reliability at both pretest (from 0.550 to 0.780, α = 0.795) and posttest (from 0.528 to 0.794, α = 0.777). Emmons et al.’s (2003) three items were used to measure gratitude based on the items that loaded on a single factor with moderate-to-high loadings and had acceptable-to-high internal reliability at both pretest (from 0.681 to 0.883, α = 0.835) and posttest (from 0.543 to 0.966, α = 0.692), whereas gratitude to God was measured by Krause’s (2006) two items that had an excellent inter-item reliability at both tests (α = 0.904 and 0.933).
The virtue of accountability (Evans, 2019) was measured separately for other people (human accountability) and God or a higher power (transcendent accountability), using 11- and 10-item scales, respectively (Witvlietet al., 2022a; Witvliet et al., 2022b). Items of human accountability loaded on a single factor with loadings higher than 0.600 with one exception (see Appendix A) and an excellent internal reliability at both tests (α = 0.890 and 0.923), whereas those of transcendent accountability had a single-factor solution with loadings higher than 0.800 with inter-item reliability being 0.964 and 0.961. To measure forgiveness of others, we used a single item asking inmates whether they had forgiven a person who recently hurt them. Regarding the person, we also measured their vengefulness, using five items of McCullough et al.’s (1998) Transgression-Related Interpersonal Motivations (TRIM) Inventory (see Appendix A), which loaded on a single factor with moderate-to-high loadings and high-to-excellent internal reliability at both pretest (from 0.594 to 0.842, α = 0.844) and posttest (from 0.775 to 0.921, α = 0.925).
Lastly, the ultimate endogenous variables, affective and behavioral outcomes of religion-based rehabilitation were measured in terms of two negative emotional states and behavioral intention. State depression was the average of six items from the CES-Depression Scale (Radloff, 1977), which loaded on a single factor with moderate-to-high loadings and had high internal reliability at both pretest (from 0.531 to 0.827, α = 0.849) and posttest (from 0.548 to 0.856, α = 0.862). Spitzer et al.’s (2006) 7-item generalized anxiety disorder scale (GAD-7) was used to measure state anxiety, and the items had a single-factor solution with moderate-to-high loadings and excellent inter-item reliability at both pretest (from 0.731 to 0.900, α = 0.929) and posttest (from 0.536 to 0.901, α = 0.909).
Behavioral intention was measured by an inmate’s self-reported probability of engaging in interpersonal aggression or, in short, intended aggression. To measure this construct, we used the vignette method, in which inmates were first asked to read the following scenario.It’s Sunday afternoon. Mike is watching an NFL football game in the prison dayroom with other inmates. During a halftime break, Mike goes to the restroom. To reserve his seat, he asks a friend to “hold it down” for him. When Mike comes back, Joe is in his seat. Mike asks Joe to leave because it is his seat. Joe says he can sit anywhere he wants. Mike asks Joe to leave one more time. This time Joe ignores Mike. Meanwhile, everyone is watching what’s going on. Feeling not only dissed but also that he is right, Mike gets into an argument with Joe.
Then inmates were asked to indicate how likely it was that they would do the same as Mike (1 = not likely at all [0%], 2 = very unlikely, 3 = unlikely, 4 = likely, 5 = very likely, 6 = certainly [100%]).6
Analytic Strategy
To test our hypotheses, we applied a manifest-variable structural equation modeling (SEM) approach to analyze data from the pretest and posttest. The modeling approach enabled us to not only simultaneously estimate for 16 endogenous variables (i.e., 12 mediating and three ultimate endogenous variables as well as religiosity), but also directly test the statistical significance of mediation, which path analysis would have not allowed us to. For model estimation, we employed Mplus 8 that incorporates Muthén’s (1983) “general structural equation model” and full information maximum likelihood (FIML) estimation. As concepts were measured by ordered categorical and continuous variables, we used the estimation option of MLR, which generates maximum likelihood estimates with standard errors that are robust to non-normality and non-independence of observations.
Next, to treat missing data, we used FIML, which tends to produce unbiased estimates similar to multiple imputation (Baraldi & Enders, 2010; Graham, 2009). Because of this missing data treatment method, the total number of observations Mplus used for model estimation was 321, who participated in the pretest survey, although 109 was the number of inmates who also participated in posttest survey. While SEM is a “large sample” method, either number indicated that our sample size was appropriate given that 100 to 150 is usually considered a minimum sample size for conducting SEM (Anderson & Gerbing, 1988; Ding et al., 1995; Tinsley & Tinsley, 1987). Finally, statistical significance (α = 0.05) was generally assessed using two-tailed tests, but we also applied one-tailed tests for the hypothesized relationships since their directions were a priori predicted.
Results
Table 1 presents descriptive statistics of variables measured at the pretest. The total sample (n = 109) included more ROD graduates (81, 74.3%) than incompletes (28, 25.7%). They were, on average, about 44 (44.06) years of age, with the youngest and oldest being 21 and 65, respectively (not shown in the table), and their racial backgrounds were White (37.6%), Black (40.4%), Hispanic (21.1%), and Asian (0.9%). The average education (5.69) fell between “12th grade or GED” and “some college,” and the inmates had, on average, a score (92.60) close to the lower end of “average intelligence” range (90–109) according to Wechsler Adult Intelligence Scale (Wechsler, 2008). While most (45.4%) of them were single, about a quarter (26.0%) were married or in common law marriage, whereas the remainder (28.6%) had post-marital status. Nine out of ten (89.6%) had Christian (77.3%) or other religion (3.8% Islam, 2.8% Judaism, 0.9% Eastern religion, and 4.7% “other religion”) with 10.4% reporting no religion.
Table 1 Descriptive Statistics of Variables Measured at the Pretest
Variable Total sample (n = 109) Graduates (n = 81) Incompletes (n = 28)
n/f Mean/% S.D. n/f Mean/% S.D. n/f Mean/% S.D. p
Program completion 109 0.74 0.44 81 1.00 0.00 28 0.00 0.00
Age 109 44.06 10.13 81 44.37 9.93 28 43.14 10.82 0.58
Education 102 5.69 1.36 74 5.70 1.40 28 5.64 1.28 0.84
IQ score 109 92.60 13.95 81 92.21 13.00 28 93.71 16.62 0.63
State jail 109 0.55 0.50 81 0.62 0.49 28 0.36 0.49 0.02
Prior prison 109 1.93 1.21 81 1.99 1.08 28 1.75 1.53 0.37
Prior jail 109 0.47 0.97 81 0.53 1.07 28 0.29 0.54 0.12
Sentence length 109 24.09 27.15 81 19.98 22.21 28 36.00 35.91 0.03
Religiosity 108 0.05 0.73 80 0.08 0.68 28 − 0.03 0.88 0.48
Cognitive transformation 109 3.57 0.51 81 3.53 0.52 28 3.68 0.47 0.17
Emotional transformation 108 2.90 0.78 80 2.89 0.76 28 2.92 0.84 0.87
Crystallization of discontent 109 3.61 0.44 81 3.57 0.45 28 3.73 0.39 0.07
Presence of meaning 109 5.46 1.30 81 5.37 1.32 28 5.72 1.20 0.21
Self-control 109 3.69 0.57 81 3.70 0.59 28 3.64 0.52 0.61
Compassion 109 2.85 0.49 81 2.82 0.48 28 2.92 0.52 0.38
Gratitude 109 5.79 1.41 81 5.72 1.44 28 5.99 1.31 0.39
Gratitude to God 108 4.64 0.68 80 4.66 0.68 28 4.59 0.71 0.63
Human accountability 109 4.20 0.53 81 4.16 0.52 28 4.32 0.54 0.16
Transcendent accountability 108 4.24 0.62 80 4.25 0.60 28 4.23 0.70 0.88
Forgiveness 107 3.79 1.08 81 3.70 1.10 26 4.08 0.98 0.13
Vengefulness 108 2.02 1.01 80 2.06 0.98 28 1.89 1.10 0.43
State depression 109 2.57 0.82 81 2.55 0.78 28 2.63 0.94 0.66
State anxiety 109 2.69 0.98 81 2.61 0.94 28 2.93 1.08 0.14
Intended aggression 109 3.05 1.69 81 3.07 1.65 28 2.96 1.84 0.77
Race 0.36
White 41 37.6% 31 38.3% 10 35.7%
Black 44 40.4% 35 43.2% 9 32.1%
Hispanic 23 21.1% 14 17.3% 9 32.1%
Asian 1 0.9% 1 1.2% 0 0.0%
Total 109 100.0% 81 100.0% 28 100.0%
Marital status 0.74
Single 49 45.4% 35 43.2% 14 51.9%
Married 22 20.4% 19 23.5% 3 11.1%
Common law marriage 6 5.6% 4 4.9% 2 7.4%
Divorced 21 19.4% 16 19.8% 5 18.5%
Separated 9 8.3% 6 7.4% 3 11.1%
Widowed 1 0.9% 1 1.2% 0 0.0%
Total 108 100.0% 81 100.0% 27 100.0%
Religion 0.11
Protestant 65 61.3% 51 65.4% 14 50.0%
Catholic 17 16.0% 13 16.7% 4 14.3%
Islam 4 3.8% 2 2.6% 2 7.1%
Judaism 3 2.8% 3 3.8% 0 0.0%
Eastern religion 1 0.9% 0 0.0% 1 3.6%
Other religion 5 4.7% 4 5.1% 1 3.6%
No religion 11 10.4% 5 6.4% 6 21.4%
Total 106 100.0% 78 100.0% 28 100.0%
Note. n = number of observations, f = frequency, S.D = standard deviation. Using Bonferroni correction (i.e., α = 0.00185185…), no mean or group difference was statistically significant.
* p < .05
In addition, 55% of the sample were housed at the state jail at the time of pretest, and the study participants had been in prison, on average, about twice (1.93) prior to the current incarceration. Their average length of sentence was 24 (24.09) years. Results from t-test showed that graduates were serving shorter sentence than incompletes (19.98 vs. 36.00) because they were more likely to be inmates housed at the state jail (61.7%) than maximum-security prison (38.3%, not shown in the table) as we found earlier in the sample of pretest participants (n = 321; see footnote 4). However, these differences were found to be not significant, using the Bonferroni correction (α = 0.00185185…). Thus, the two groups were statistically equivalent at the pretest, that is, before they participated in the program.
Table 2 shows our model estimated for hypothesis testing (standardized coefficients are presented).7 We found completion of ROD was positively related to religiosity at the posttest or Time 2 (0.206). Since religiosity’s Time 1 or previous (pretest) measure (religiosity T1) was controlled for, the positive relationship can be interpreted in terms of change: that is, the program completion increased inmate involvement in religion between the pretest and posttest. In other words, graduates were more likely to report an increase in religiosity than incompletes. Thus, Hypothesis 1 received empirical support. Next, the increased religiosity (religiosity T2) was positively related to a change in all three indicators of identity transformation—cognitive (0.275) and emotional transformations (0.479) and crystallization of discontent (0.349), perceived presence of meaning (0.554), and all seven virtues: self-control (0.502), compassion (0.335), gratitude (0.497), gratitude to God (0.481), human accountability (0.295), transcendent accountability (0.368), and forgiveness (0.382). In addition, the ROD-associated increase in religiosity was inversely related to vengefulness (‒0.495). That is, as hypothesized (Hypothesis 2), we found that the program had rehabilitative effects on inmates by increasing their religiosity, which in turn contributed to identity transformation, a sense of meaning and purpose in life, and virtue development.
Table 2 Estimated Structural Equation Model of Program Completion, Indicators of Rehabilitation, Negative Emotional States, and the Risk of Interpersonal Aggression (n = 109)
Variable Religiosity
T2 Cognitive transformation T2 Emotional transformation T2 Crystallization of discontent T2 Presence of meaning
T2 Self-control T2 Compassion
T2 Gratitude
T2
Program completion 0.206* − 0.218* − 0.146 − 0.150 0.052 − 0.160* − 0.196* − 0.141
Religiosity T1 0.513* − 0.120 − 0.072 − 0.228 0.083 − 0.243 0.027 0.147
Cognitive transformation T1 − 0.012 0.248* 0.141* − 0.067 0.085 0.051 0.124+ 0.137+
Emotional transformation T1 − 0.112 0.350* 0.402* − 0.218 0.221* 0.166 0.235* 0.226*
Crystallization of discontent T1 0.026 − 0.052 0.077 0.342* 0.106 0.153+ − 0.061 0.119
Presence of meaning T1 − 0.012 − 0.127 0.144 − 0.155 0.338* − 0.203* − 0.141 − 0.126
Self-control T1 − 0.114 − 0.173 0.009 0.022 0.029 0.483* 0.256* − 0.177
Compassion T1 − 0.062 − 0.040 − 0.051 0.065 − 0.002 0.063 0.639* 0.204*
Gratitude T1 − 0.112 − 0.204* − 0.243* 0.372* 0.026 − 0.013 − 0.074 0.139
Gratitude to God T1 0.222* 0.196* 0.192+ − 0.257* − 0.061 − 0.090 0.197+ 0.122
Human accountability T1 0.129 − 0.069 − 0.073 0.001 0.105 − 0.080 0.040 − 0.052
Transcendent accountability T1 0.041 − 0.019 − 0.452* 0.052 − 0.340* − 0.066 − 0.389* − 0.228
Forgiveness T1 0.061 0.013 − 0.042 − 0.260* − 0.145 0.178* − 0.197* − 0.247*
Vengefulness T1 − 0.009 − 0.175+ − 0.127 0.038 − 0.082 0.066 − 0.036 − 0.003
State depression T1 − 0.007 0.118 0.046 0.018 0.047 0.096 0.357* 0.162
State anxiety T1 − 0.010 − 0.072 − 0.309+ − 0.158 − 0.005 − 0.260+ 0.005 − 0.133
Intended aggression T1 − 0.116+ 0.063 0.237* 0.030 0.241* 0.121 0.015 − 0.152+
Religiosity T2 0.275* 0.479* 0.349* 0.554* 0.502* 0.335* 0.497*
Cognitive transformation T2 1.000
Emotional transformation T2 − 0.072 1.000
Crystallization of discontent T2 0.271* 0.118 1.000
Presence of meaning T2 0.291* 0.108 0.086 1.000
Self-control T2 − 0.123 0.259* 0.030 0.223* 1.000
Compassion T2 0.079 0.078 − 0.003 0.237* 0.005 1.000
Gratitude T2 − 0.033 0.111 0.087 0.265+ 0.034 0.293* 1.000
Gratitude to God T2 0.090 − 0.031 − 0.046 0.454* 0.202* 0.092 0.173
Human accountability T2 0.486* 0.090 0.188 0.361* 0.056 0.350* 0.020
Transcendent accountability T2 0.271* 0.159+ 0.183 0.423* 0.146 0.337* 0.099
Forgiveness T2 − 0.114 0.178+ 0.063 0.172* 0.104 0.204* 0.151*
Vengefulness T2 0.040 − 0.198* − 0.181 − 0.073 − 0.241* − 0.118 − 0.248*
R 2 0.689 0.523 0.652 0.538 0.573 0.651 0.610 0.488
Variable Gratitude to God
T2
Human accountability T2 Transcendent accountability T2 Forgiveness T2 Vengefulness T2 State depression
T2
State anxiety T2 Intended aggression
T2
Program completion 0.169* − 0.308* − 0.040 0.021 − 0.020 − 0.013 − 0.114 − 0.246*
Religiosity T1 0.075 0.189 0.116 − 0.189 0.182 − 0.019 0.028 0.045
Cognitive transformation T1 − 0.049 − 0.008 − 0.008 0.162+ − 0.091 − 0.006 − 0.118 0.084
Emotional transformation T1 0.196* 0.238 0.110 − 0.028 0.025 − 0.099 0.096 0.500*
Crystallization of discontent T1 0.209* 0.062 0.114 0.004 − 0.191* 0.049 0.015 − 0.080
Presence of meaning T1 0.113 − 0.309* 0.030 − 0.138 0.132 − 0.063 − 0.006 − 0.188
Self-control T1 − 0.013 − 0.011 − 0.060 − 0.132 0.104 − 0.202 0.088 0.195*
Compassion T1 0.027 0.097 0.049 0.110 − 0.133 0.196* 0.052 0.217*
Gratitude T1 0.162* 0.110 − 0.125 0.053 0.232* − 0.022 0.177 0.101
Gratitude to God T1 0.204 0.062 0.323* 0.071 − 0.059 0.034 0.079 − 0.090
Human accountability T1 − 0.242* 0.379* − 0.179* − 0.207 0.041 − 0.051 − 0.033 0.116
Transcendent accountability T1 − 0.132 − 0.413* 0.285* 0.004 0.055 0.215 0.021 − 0.231+
Forgiveness T1 − 0.026 − 0.221* − 0.111 0.178+ − 0.352* 0.222* − 0.036 − 0.119
Vengefulness T1 − 0.018 − 0.127 − 0.169* − 0.226* 0.229* 0.029 0.114* − 0.052
State depression T1 − 0.083 − 0.025 0.184 0.018 0.016 0.166 − 0.023 0.267*
State anxiety T1 0.234* 0.155 − 0.041 − 0.192 0.134 − 0.085 0.446* 0.019
Intended aggression T1 0.017 0.011 0.070 − 0.163+ − 0.048 − 0.085 − 0.038 0.472*
Religiosity T2 0.481* 0.295* 0.368* 0.382* − 0.495* − 0.009 0.010 0.194
Cognitive transformation T2 − 0.141 − 0.093 − 0.020
Emotional transformation T2 − 0.233* − 0.369* − 0.339*
Crystallization of discontent T2 0.078 0.072 − 0.015
Presence of meaning T2 − 0.207* − 0.009 0.360*
Self-control T2 − 0.183+ − 0.177* − 0.139
Compassion T2 − 0.057 − 0.086 − 0.136+
Gratitude T2 − 0.180 0.023 − 0.110+
Gratitude to God T2 1.000 0.042 − 0.164 − 0.304*
Human accountability T2 − 0.054 1.000 0.048 − 0.086 − 0.368*
Transcendent accountability T2 0.247* 0.324* 1.000 0.086 0.153 0.070
Forgiveness T2 0.057 0.188* 0.198* 1.000 − 0.006 0.056 0.065
Vengefulness T2 − 0.165+ − 0.109 − 0.197* − 0.520* 1.000 0.068 0.142 0.107
R 2 0.741 0.428 0.756 0.549 0.648 0.719 0.771 0.780
Note. Standardized coefficients are presented (those in italics are residual correlations among the secondary endogenous variables), and sociodemographic and criminal justice background variables were controlled for but not shown in the table (see Supplemental Table 3 for the coefficients of control variables); T1 = Time 1 (pretest), T2 = Time 2 (posttest).
+ p < .05 (one-tailed test), * p < .05 (two-tailed test).
The last three columns show that the religion-based rehabilitation significantly reduced negative emotional states and the probability of interpersonal aggression. Specifically, state depression was decreased by emotional transformation (‒0.233), presence of meaning (‒0.207), and self-control (‒0.183), and state anxiety was reduced by emotional transformation (‒0.369) and self-control (‒0.177). Next, the likelihood of engaging in aggression toward another inmate was lowered by an increase in emotional transformation (‒0.339) and the virtues of compassion (‒0.136), gratitude (‒0.110), gratitude to God (‒0.304), and human accountability (‒0.368).8 In sum, Hypothesis 3 received partial support. It is worth noting that the program completion was inversely related to the probability of aggression (‒0.246), which indicates that ROD had rehabilitative effects that remained to be explained by other than what we included in the model.
A supplemental analysis was conducted to test the significance of indirect effects of the program completion and the program-increased religiosity on the secondary mediating and/or ultimate endogenous variables. We found that ROD significantly contributed to identity transformation, a sense of meaning and purpose in life, and the development of all seven virtues, while reducing vengefulness toward a person who caused pain in the past, by increasing inmate involvement in religion (see the first panel of Supplemental Table 4). Next, the increased religiosity significantly decreased negative emotional states and the risk of interpersonal aggression: specifically, state depression via religiosity-increased emotional transformation and perceived presence of meaning, state anxiety via emotional transformation and self-control, and intended aggression via emotional transformation, gratitude, gratitude to God, and human accountability (see the second panel). Taken together, the program was found to significantly decrease the negative emotional states and the risk of aggression by increasing religiosity, which in turn contributed to rehabilitation as moral reform: emotional transformation, presence of meaning, gratitude to God, and human accountability (see the bottom panel).
Another supplemental analysis was conducted to examine potential selection bias. Since ROD is a faith-based program, more religious inmates might have been drawn to and benefitted by the program than those less or not religious. To explore this issue, we used the medium of religiosity T1 (0.335) to divide the pretest sample (n = 229; two missing cases on religiosity T1) into low (n = 114) and high religiosity groups (n = 115), which included 53 and 55 inmates of the total sample (n = 108; one missing case), respectively. First, we found that the two groups had the same number of graduates (40 each) and did not significantly differ in graduation rate, while the low religiosity group’s rate (75.5%) was slightly higher than the high religiosity group’s (72.7%). Next, results from multi-group analysis revealed that the program completion increased religiosity among inmates who were not very religious at the pretest but had no significant effect among relatively religious, and religiosity was more likely to have rehabilitative effects on self-identity and character in the low than high religiosity group (see Supplemental Table 5). In sum, the faith-based program tended to contribute to rehabilitation by increasing religiosity among inmates who were not religious before the program compared to those who were already religious.
Discussion
Both rehabilitation and religion have long been linked to the original purpose of American penal system (Cullen et al., 2014). In 1790, Quakers pressured the Pennsylvania legislature to call for a renovation of local county jails, which eventually resulted in the creation of a separate wing of Philadelphia’s Walnut Street Jail to house felons in solitary cells, called “the penitentiary house.” This was a forerunner of the Pennsylvania state prisons—the Western and Eastern Penitentiaries, built in the early 19th century, and the penitentiary was a place for penance as inmates were meant to reflect on their wrongdoings and seek reform. Since the Pennsylvania system ended by the 1870s, however, prison reform efforts have not included religion because of the secularization of American society and the development of scientific disciplines concerned with human behavior, such as psychiatry, psychology, and sociology.
Nevertheless, religion remains an invaluable resource for American corrections, as religiously motivated volunteers continue to provide prisoners with non-religious (e.g., adult basic education, anger management, and entrepreneurship) as well as religious programs when prison administrators find it increasingly difficult to fund educational, vocational, and rehabilitative programs due to constricting budgets. Furthermore, correctional research empirically demonstrates the benefits of faith-based programs (Johnson, 2011), and an emerging body of evidence shows that inmate involvement in religion is related positively with emotional well-being and inversely with prison misconduct (Clear & Sumter, 2002; Jang et al., 2021; Kerley et al., 2011b; Kerley, Matthews, & Blanchard, 2005).
Despite the increasing evidence of rehabilitative effects of religion, prior research has been limited to examining the consequences of religion-based rehabilitation (e.g., a reduction in negative emotions and misconduct) rather than the rehabilitation per se, that is, how religion rehabilitates prison inmates and what changes happen to them. To address this gap in research, we conceptualized rehabilitation as moral reform and operationalized it in terms of positive changes in self-identity, existential belief, and character. To test hypotheses about religion-based rehabilitation and its consequences, we analyzed data from pretest and posttest surveys with male inmates who participated in a faith-based program operating in two correctional facilities in Texas. Results generally supported these hypotheses.
First, as expected, program graduates tended to report an increased involvement in religion between the two tests compared to inmates who participated but did not complete the program. Next, consistent with a second hypothesis, the program-increased religiosity was found to contribute to rehabilitation, enhancing identity transformation (cognitive and emotional transformations and a motivation for self-change à la crystallization of discontent), a sense of meaning and purpose in life, and virtues (self-control, compassion, gratitude, gratitude to God, human accountability, transcendent accountability, and forgiveness), while decreasing the vice of vengefulness. Finally, some indicators of faith-based rehabilitation were found to reduce state depression and anxiety and the probability of aggression toward another inmate, which provided partial support for the last hypothesis. A supplemental analysis revealed that the program’s indirect effects on rehabilitation via religiosity and on the emotional states and the risk of aggression via religiosity and its associated rehabilitation were statistically significant.
The notion that offenders need rehabilitation presumes that there is something wrong with them or they lack something, which led them to commit an offense. Consequently, rehabilitative measures aim to address the issue one way or the other. For example, McNeill’s (2012; 2014) “psychological or personal rehabilitation” and Forsberg and Douglas’ (2020) “rehabilitation as therapy” are concerned with fixing the problem (e.g., a mental illness or deficit) and having offenders develop new skills or abilities through job training or education. Our conception of rehabilitation as moral reform overlaps with their “moral rehabilitation” or “rehabilitation as moral improvement,” which is intended to morally improve offender who did not have “moral power” (Rawls, 2005). Specifically, it intends to address the two components of moral failure, epistemic (mistaken conclusions about whether certain conduct is permissible or wrongful) and motivational (non-compliance with moral duties).
Religion is a viable option to morally reform offenders because it provides them with philosophical reasons for not reoffending as well as teaching justice-related moral duties. For example, Christianity— from which the ROD program is based—teaches inmates that they are redeemable by the grace of God, and the redemption comes with a new identity (e.g., a child of God) that enables them to start a life anew. Inmates are also told that God has a specific plan for their lives, and that God’s purpose for their life will provide meaning and replace the desire for reoffending. In addition, the religion contributes to developing virtues among inmates because the religion teaches inmates to imitate God’s character, being compassionate and forgiving toward those who hurt them rather than taking revenge upon them, but the purpose-driven life is also likely to motivate inmates to practice self-control and be grateful for the gift of second chance and willing to be held accountable for their life by others as well as God. The present findings provide preliminary evidence for the rehabilitative effects of religion on prison inmates.
Our conception of rehabilitation as moral reform is based on two key premises: one concerns crime, and the other human nature. First, crime is not simply a legal but moral offense in that it is a violation of collective morality as well as a criminal law. However, the relationship between morality and criminal law is anything but straightforward because, while no behavior can become criminal without any moral basis or justification, behaviors deemed immoral cannot be criminalized without a certain level of moral consensus (Meier et al., 2006). Depending on the degree of agreement about immorality or seriousness of an act, crimes are distinguished between mala in se and mala prohibita or “consensus crimes” (e.g., murder or burglary) and “conflict crimes” (e.g., drug use or prostitution) (Hagan, 1985). Since the immorality of conflict crimes is contested (e.g., a nationwide debate over the legalization of marijuana), we acknowledge that our premise about crime being a “moral” offense is not politically neutral when it comes to conflict crimes. A relevant question here is whether rehabilitation as moral reform is more applicable to offenders incarcerated for consensus than conflict crimes given that the former offenders violated moral codes largely accepted in society, whereas the latter committed an act whose immorality is questioned by many (including the offenders themselves), thereby seeing no need to morally improve themselves. Although this is an empirical question for future research, we expect our concept of rehabilitation as moral reform to benefit both types of offenders because it focuses on positive changes in self-identity, existential belief, and character rather than targeting moral improvements relevant to reducing the likelihood of repeating crime that has been committed (Duff, 2001; Hampton, 1984; Howard, 2017), unlike “rehabilitation as anti-recidivism” (Forsberg & Douglas, 2020).9
Next, we assumed that offenders as humans are morally autonomous beings, although they made a morally wrong choice by yielding to criminogenic pressures: thus, “contemptuous” punishment that fails to respect offenders as “moral persons,” who are capable of self-reform, undermines the prospect of their rehabilitation (Hoskins, 2013). Based on the same assumption, decrying the contemporary amoral penology, Cullen et al., (2014:74) proposed “the virtuous prison” to restore the moral purpose of American corrections—restorative rehabilitation—by using “offenders’ time of incarceration to cultivate moral awareness and the capacity to act virtuously.” For example, productive activities with a moral purpose that provide opportunities to be virtuous (e.g., using inmate wages to compensate victims and making toys for poor children) would help inmates redefine who they are, believe in meaning and purpose in life, and build character. Although a virtuous prison does not require religion, Cullen et al. illustrated the prospect of creating one with a faith-based prison (Johnson, 2014; see also Johnson et al., 2021). We agree that a virtuous prison can be based on a secular entity but cannot think of any better system ready to fill that space than religion.
While this study provides empirical evidence of how religion is likely to contribute to rehabilitation, it is necessary to acknowledge key limitations. First, we had no control group as our research was prematurely ended by the COVID-19 lockdown. Thus, we could not examine the rehabilitative effect of the ROD class based on observed differences between inmates who participated in the program and those who did not. Instead, we compared inmates who graduated the class with those who did not complete it. However, given that the graduates and incompletes were likely to have been similar in their motivation to participate in the program since they all voluntarily applied and that they were statistically equivalent at the pretest, the present findings provide at least preliminary evidence of the rehabilitative effect of religion.
A second limitation is nontrivial attrition: that is, about a half of pretest participants were not available for the posttest. While it was not surprising that posttest participants tended to be the program graduates and higher on self-control at the pretest compared to the non-participants, ROD’s impact on rehabilitation might have been overestimated to the extent that the former were more motivated to change themselves than the latter. Third, while we explored the possibility of rehabilitation as moral reform being more applicable to offenders who committed consensus than conflict crimes, we could not formally test whether the rehabilitative effect varies among offenders who committed different types of crimes because of our small sample size, which is a worthy topic for future research. Finally, while studying gender differences in the rehabilitative effect of religion is an important topic given that women tend to be more religious than men (Sherkat & Ellison, 1999), we could study only male inmates because the ROD program has not been extended yet to female facilities.
Despite these limitations, we believe our study contributes to the literature on offender rehabilitation by providing empirical evidence, though preliminary, of the rehabilitative effects of religion on inmate’s self-identity, existential belief, and character. The present study suggests that it would be prudent for correctional policy makers and prison administrators to be open to religious programs like the ROD class to not only protect an inmate’s constitutional right to practice religion but also help them achieve reform before returning to society.
Electronic Supplementary Material
Below is the link to the electronic supplementary material.
Supplementary Material 1
Supplementary Material 2
Appendix A. Items Used for Analysis
Item (Response categories) Factor loading (α)
Pretest Posttest
Religiosity (0.827) (0.780)
In general, how important is religion (or relationship with God) to you?
(1 = not at all, 2 = somewhat, 3 = fairly, 4 = very, 5 = extremely)
0.729 0.565
How close do you feel to God most of time?
(1 = not close at all, 2 = not very close, 3 = somewhat close, 4 = pretty close, 5 = extremely close)
0.762 0.585
How often do you currently attend religious services at a place of worship?
(1 = never, 2 = less than once a year, 3 = once or twice a year, 4 = several times a year, 5 = once a month, 6 = 2–3 times a month, 7 = about weekly, 8 = several times a week)
0.490 0.545
About how often do you currently pray outside of religious services?
(1 = never, 2 = only on certain occasions, 3 = once a week or less, 4 = a few times a week, 5 = once a day, 6 = several times a day)
0.779 0.745
Outside of attending religious services, about how often do you currently spend private time reading the Bible, Koran, Torah, or other sacred book?
(1 = never, 2 = less than once a year, 3 = once to several times a year, 4 = once a month, 5 = 2–3 times a month, 6 = about weekly, 7 = several times a week, 8 = everyday)
0.746 0.759
Cognitive transformation (0.680) (0.697)
How strongly do you agree or disagree with the following statements?
(1 = strongly disagree, 2 = disagree, 3 = agree, 4 = strongly agree)
1. I am open for change. 0.764 0.875
2. I have a good new self that replaced my old bad self. 0.556 0.657
3. I am willing to have myself changed completely. 0.650 0.549
Emotional transformation (0.829) (0.862)
How likely is it you would use each of the following words to describe yourself in general (e.g., “Angry John” or “Depressed Bob”), regardless of how you feel at this moment?
(1 = very unlikely, 2 = unlikely, 3 = likely, 4 = very likely)
1. Depressed 0.773 0.796
2. Angry 0.655 0.708
3. Nervous 0.765 0.837
4. Frustrated 0.770 0.782
Crystallization of discontent (0.590) (0.542)
How strongly do you agree or disagree with the following statements?
(1 = strongly disagree, 2 = disagree, 3 = agree, 4 = strongly agree)
1. I would face a miserable future if I do not change. 0.451 0.349
2. A life of offending will do more harm than good to me. 0.801 0.520
3. I have made a conscious decision to improve myself. 0.507 0.921
Presence of meaning (0.900) (0.826)
We would like you to take a moment to think about what makes your life feel important to you. Please respond to the following statements as truthfully and accurately as you can.
(1 = strongly disagree, 2 = disagree, 3 = agree, 4 = strongly agree)
1. I understand my life’s meaning. 0.770 0.626
2. My life has a clear sense of purpose. 0.855 0.601
3. I have a good sense of what makes my life meaningful. 0.890 0.835
4. I have discovered a satisfying life purpose. 0.819 0.895
Self-control (0.695) (0.621)
How often would you say you do each of the following?
(1 = never, 2 = rarely, 3 = sometimes, 4 = often, 5 = always)
1. Act on the spur of the moment without stopping to think* 0.709 0.498
2. Test myself by doing something a little risky* 0.472 0.513
3. Try to get what I want even if it causes problems for others* 0.663 0.651
4. Lose my temper* 0.599 0.562
Compassion (0.795) (0.777)
How strongly do you agree or disagree with the following statements?
(1 = strongly disagree, 2 = disagree, 3 = agree, 4 = strongly agree)
1. When I see someone in a difficult situation, I try to imagine how they feel. 0.550 0.591
2. I feel compelled to help someone even when doing so requires me to go out of my way. 0.598 0.711
3. It’s not enough to feel sorry for someone who is in trouble. Whenever it is possible, I must also do something to help them. 0.780 0.794
4. I feel sorry for someone who is in trouble even when they caused the problem that faces them. 0.748 0.599
5. I feel sorry for someone even when they’ve done something that hurts me. 0.661 0.528
Gratitude (0.835) (0.692)
Please indicate how much you agree with each of the statements, using the scale below.
(1 = strongly disagree, 2 = disagree, 3 = slightly disagree, 4 = neutral, 5 = slightly agree, 6 = agree, 7 = strongly agree)
1. If had to list everything that I felt grateful for, it would be a very long list. 0.681 0.553
2. I am grateful to a wide variety of people. 0.883 0.966
3. As I get older, I find myself more able to appreciate the people, events, and situations that have been part of my life history. 0.834 0.543
Gratitude to God (0.904) (0.933)
Please indicate how much you agree with each of the statements, using the scale below.
(1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, 5 = strongly agree)
1. I am grateful to God for all He has done for me.
2. I am grateful to God for all He has done for my family members and close friends.
Human accountability (0.890) (0.923)
Think about how you usually respond to people who hold you accountable. Think about people to whom you owe a response for your actions or lack of action. Please select a response to indicate how much you honestly disagree or agree with each statement based on how you typically are in real life.
(1 = disagree strongly, 2 = disagree, 3 = neither agree nor disagree, 4 = agree, 5 = agree strongly)
1. I try to understand the perspectives of people who evaluate me. 0.625 0.603
2. I am comfortable showing the details of my work. 0.612 0.747
3. Being accountable helps me do my best. 0.681 0.777
4. I welcome corrective feedback from people who evaluate me. 0.694 0.713
5. I willingly explain my work on a project to people I am responsible to. 0.697 0.777
6. I usually welcome being accountable to others. 0.695 0.691
7. I take responsibility for my actions even if it costs me. 0.654 0.793
8. I care about the people affected by what I do. 0.666 0.784
9. I am willing to be held responsible for my contributions on tasks. 0.743 0.823
10. I feel responsible for my work with others. 0.725 0.855
11. I care a lot about whether the people I am accountable to are fair. 0.416 0.486
Transcendent accountability (0.964) (0.961)
Think about how you usually respond to God (or the Divine, the Sacred, a higher power, etc.) for living your life. Please select a response to indicate how much you honestly disagree or agree with each statement based on how you typically are in real life.
(1 = disagree strongly, 2 = disagree, 3 = neither agree nor disagree, 4 = agree, 5 = agree strongly)
1. I value being accountable to God in living my life. 0.838 0.829
2. I seek God’s guidance for my life (e.g., through prayer, meditation, study, or counsel). 0.877 0.876
3. I willingly live with accountability to God. 0.876 0.853
4. I try to be honest about my actions in light of God’s standards. 0.824 0.909
5. I consider whether advice is consistent with God’s standards before going along with it. 0.819 0.747
6. I am motivated to live according to God’s ideals. 0.845 0.828
7. I care about God’s perspective on my actions. 0.859 0.923
8. I welcome correction that helps me live according to God’s standards. 0.847 0.809
9. When I mess up, I want to make things right by following God’s values. 0.891 0.850
10. I grow as a person by being accountable to God. 0.880 0.876
Vengefulness (0.844) (0.925)
Please indicate your current thoughts and feelings about the person who recently hurt you? Use the following scale to indicate your agreement with each of the statement.
(1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, 5 = strongly agree)
1. I’ll make him/her pay. 0.842 0.845
2. I wish that something bad would happen to him/her. 0.594 0.921
3. I want him/her to get what he/she deserves. 0.790 0.775
4. I’m going to get even with him/her. 0.781 0.911
5. I want to see him/her hurt and miserable. 0.811 0.826
State depression (0.849) (0.862)
During the past week, how often have you felt or experienced the following?
(1 = never, 2 = rarely, 3 = sometimes, 4 = often, 5 = very often)
1. I felt I could not shake off the blues, even with the help of others 0.675 0.548
2. I felt depressed. 0.827 0.856
3. I did not feel like eating, and my appetite was poor. 0.531 0.707
4. My sleep was restless. 0.627 0.696
5. I could not get going. 0.683 0.763
6. I felt sad. 0.816 0.727
State anxiety (0.929) (0.909)
Over the last 2 weeks, how often have you been bothered by any of the following problems?
(1 = never, 2 = rarely, 3 = sometimes, 4 = often, 5 = very often)
1. Feeling nervous, anxious 0.755 0.536
2. Not being able to stop or control worrying 0.900 0.859
3. Trouble relaxing 0.818 0.901
4. Being so restless that it is hard to sit still 0.777 0.791
5. Worrying too much about different things 0.875 0.895
6. Becoming easily annoyed or irritable 0.731 0.700
7. Feeling afraid as if something awful might happen 0.789 0.752
*Reverse-coded item
Acknowledgements
The authors are grateful to Restoration Outreach of Dallas (ROD) Ministries and its administration (including Dr. Jeffery Parker, Mr. Dick LeBlanc, Mr. Ken Sandstad, Mr. Butch McCaslin, and Ms. Salena Williams), the two participating units of the Texas Department of Criminal Justice and their wardens and staff, as well as all participants in this study.
Statements and Declarations
This study was funded by Restoration Outreach of Dallas (ROD) Ministries. The research contained in this document was coordinated in part by the Texas Department of Criminal Justice (TDCJ) (793-AR18). The contents of this document reflect the views of the authors and do not necessarily reflect the views or policies of the TDCJ or ROD Ministries.
1 Moral order refers to “intersubjectively and institutionally shared social structurings of moral system that are derived from … larger narratives and belief systems” (Smith, 2003:10).
2 We originally planned to study all four classes (ROD I to IV) and create control group, but the Texas Department of Criminal Justice locked down all units after the outbreak of COVID-19, while we were collecting data from experimental group inmates after ROD I ended. So, we had to end our project early without knowing how long the lockdown would last.
3 Survey was prepared in Spanish as well as English because the class was offered in both languages.
4 To see whether inmates who graduated and those who did not complete the program were different at the pretest, we conducted t-tests and crosstabulation analysis. The results showed that graduates tended to be the state jail inmates, thereby serving a shorter sentence, and report higher levels of religiosity and self-control than incompletes (see Supplemental Table 1). However, none of these differences was significant using the Bonferroni correction (α = 0.00192308…). Thus, graduates and incompletes were statistically equivalent before participating in the program.
5 To compare the posttest participants and non-participants, we conducted t-tests (see Supplemental Table 2). While the participants tended to be ROD graduates, inmates housed at the prison, higher on self-control, and serving a longer sentence compared to the non-participants at the conventional significance level (α = 0.05), the Bonferroni correction revealed that they were not significantly different except that the graduates were more likely to participate in the second survey than the incompletes. This difference needs to be kept in mind when interpreting our results.
6 We acknowledge that intended aggression was not the same as actual aggression since it might have been a biased, specifically, socially desirable response. The vignette method, however, has been used in criminological research, and previous studies found a strong correlation between intended and actual behaviors when a scenario was created to reflect locally relevant details (Mazerolle et al., 2003; Nagin & Paternoster, 1993). We created a vignette of a specific situation likely to happen in prison and found reported probability was distributed across the six response options, though somewhat positively skewed—not likely at all (22.1%), very unlikely (14.7%), unlikely (24.2%), likely (20.3%), very likely (8.2%), and certainly (10.4%), implying that their responses were not completely biased.
7 Sociodemographic and criminal justice-related background variables were controlled for but are not presented in the table (see Supplemental Table 3 for the coefficients of control variables).
8 Presence of meaning was also significantly related to intended aggression but in the opposite direction (0.360). They were significantly correlated at the pretest in the expected direction (r = ‒.183), but their zero-order correlation at the posttest was not significant (r = ‒.081, p = .402). Their partial correlation, controlling for their pretest measures, was not significant, either (r = ‒.114, p = .243). So, while it is difficult to explain this counter-intuitive finding without additional data, it might be a methodological artifact.
9 A supplemental, crosstabulation analysis showed that the graduates and incompletes were not significantly different in the type of offense they were incarcerated for, including the mala prohibita or conflict crime of drug offense, in both pretest (χ2 = 0.729, d.f. = 5, p = .981) and posttest samples (χ2 = 1.835, d.f. = 4, p = .766). This finding implied that offense type was unlikely to have affected motivation (or lack thereof) for completing the program. We also conducted paired-samples t-tests to explore whether a reduction in the risk of interpersonal aggression between the two tests differed across types of offense. A significant reduction was observed among inmates incarcerated for conflict (drug offense) as well as consensus crimes (violent and property offenses), while no significant reduction was found among sex offenders (see Supplemental Table 6).
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Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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| 0 | PMC9748388 | NO-CC CODE | 2022-12-15 23:23:24 | no | Am J Crim Justice. 2022 Dec 14;:1-27 | utf-8 | Am J Crim Justice | 2,022 | 10.1007/s12103-022-09707-3 | oa_other |
==== Front
J Child Fam Stud
J Child Fam Stud
Journal of Child and Family Studies
1062-1024
1573-2843
Springer US New York
2502
10.1007/s10826-022-02502-y
Original Paper
Mindfulness and Imagery Enhanced Behavioral Parenting: Effectiveness Pilot of the Confident Carers Cooperative Kids Program
http://orcid.org/0000-0001-6606-5518
Donovan Mark O. [email protected]
1
Briscoe-Hough Kathryn 1
Barkus Emma 2
Herbert Jane S. 1
Miller Leonie 1
Konza Greg 3
Pickard Judy A. 1
1 grid.1007.6 0000 0004 0486 528X School of Psychology, University of Wollongong, Keiraville, NSW 2522 Australia
2 grid.42629.3b 0000000121965555 Department of Psychology, Northumbria University, Newcastle-upon-Tyne, NE7 7YT UK
3 Private Practice, Figtree, NSW 2525 Australia
14 12 2022
115
23 11 2022
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis 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.
Mindfulness is increasingly offered to parents of children presenting with behavioral problems, either as a stand-alone intervention, or integrated within existing behavioral parenting interventions. There is relatively modest support for mindful parenting, with small to medium effect size improvements demonstrated across child and parent outcome measures. Here we introduce a mindfulness and imagery enhanced behavioral parenting program. We argue blending mindfulness, imagery and behavioral skills could produce improved parenting engagement and perseverance, leading to stronger outcomes. Pilot data is presented from two contrasting real world clinical settings. Parents attending the 8-week Confident Carers Cooperative Kids (CCCK) group program in a university clinic setting were invited to be included in the study (n = 20). Permission was also gained to use archival data from a community organisation offering CCCK groups to parents who were at risk of child welfare involvement (n = 14). Pre- and post-intervention measures were completed across both groups on parent-reported child behavior, parent wellbeing, adaptive parenting, and mindful parenting. Parents from both groups achieved significant pre- to post-intervention improvements in child behavior problems, parent wellbeing, adaptive parenting, and mindful parenting, with large effect sizes. Larger improvements in child behavior problems were reported by parents from the community group compared with the university group. The CCCK intervention appears beneficial across child and parent outcomes, including for families most in need. A larger sample is required to replicate and extend these promising findings.
Highlights
Mindfulness and imagery have potential to improve behavioral parent training outcomes.
Mindfulness and imagery enhanced behavioral parenting group found large effect size pre-post intervention improvements.
Improvements in child behavior greater for higher need community participants compared with university clinic participants.
Improvements in parent wellbeing, adaptive and mindful parenting similar across community and university participants.
Keywords
Parenting
Mindfulness
Imagery
Child behavior
Real-world effectiveness
==== Body
pmcUntreated child behavioral problems are associated with long term adverse social and economic consequences (Fergusson 2005; Romeo et al., 2006; Simonoff et al., 2004). Behavioral problems also account for over half of the referrals to child mental health services, highlighting the scope of the issue, and underlining the need to find effective interventions (Sawyer et al., 2001; Scott et al., 2001). Behavioral parent training (BPT) emerged amidst the 1950s paradigm shift away from using individual psychodynamic approaches to treat child behavioral difficulties and is widely regarded as the “gold standard” intervention for such problems (Buchanan-Pascall et al., 2018; Kaminski & Claussen, 2017; Turner et al., 2020). BPT is based on social learning theory, helping parents to adjust social contingencies in response to children’s behaviors. Undesirable behaviors are discouraged through use of planned-ignoring and consequences, and desired behaviors are positively reinforced through attention, praise, and rewards (Shaffer et al., 2001). BPT also focuses on strengthening the parent-child relationship through play and theorises that problem behaviors tend to escalate through coercive parent-child transactions (Patterson 1982; Webster-Stratton & Reid, 2018).
A plethora of BPT programs have generated substantial evidence, with medium to large effect sizes shown for BPT over control groups for sustained improvements in child behavior, adaptive parenting, and parental wellbeing (Buchanan-Pascall et al., 2018; Kaminski & Claussen, 2017; Kazdin 2008; Sanders et al., 2014; van Aar et al., 2017). Generalisability of benefits beyond tightly controlled efficacy trials has also been demonstrated through real-world effectiveness studies, leading to wide dissemination (Gardner et al., 2010). Balanced against this support, a recent systematic review of 20 BPT studies (N = 2097) found considerable variability in outcomes, with between-group effect sizes ranging from d = 0 to 1.26 (Buchanan-Pascall et al., 2018). Similarly, a recent evidentiary review of 64 BPT studies (N = 6537) found effect sizes ranged from d = 0.02 to 1.41 for pre- to post-intervention (Kaminski & Claussen, 2017). In practical terms, this indicates some programs produce very large effects while others have no measurable benefit for troubled families.
Poorer effects from traditional BPT programs have often been linked to parents’ difficulties in engaging and persevering with behavioral strategies, or difficulties implementing these strategies within a challenging environment (Chacko et al., 2016; Lundahl et al., 2006). A review of over 250 BPT studies found that at least twenty-five percent of families dropped-out prior to starting the program, and a further twenty-five percent failed to complete a minimum number of sessions, with attrition rates being higher for families of lower socio-economic status (Chacko et al., 2016). Reviews of BPT programs have identified medium effect size impacts on treatment outcome for families with low-income status, low education level, low occupation status, maternal depression, more severe child behavior problems, harsh discipline, and negative parental attributions towards the child (Orrell-Valente et al., 1999; Reid et al., 2003; Reyno & McGrath, 2006). It appears that there is capacity for BPT programs to evolve to meet the needs of vulnerable families.
Many BPT programs have developed additional modules to ameliorate known parent-related factors which reduce effectiveness. For example, Triple P offers “enhanced” and “pathways” programs which include modules such as partner relationships and communication, personal coping strategies, problem-solving, and anger management (Sanders et al., 2014; Sanders et al., 2004). The Incredible Years program has similar modules that extend the program from 14 to 30 sessions (Webster-Stratton & Reid, 2018). However, there is mixed evidence on the effectiveness of such additional modules on parent and child outcomes (Reid et al., 2003; Sanders et al., 2000). Furthermore, concerns have been raised about lengthy treatments creating burden, limited depth and breadth from one-off modules, and the effectiveness of implementing additional modules after the core behavioral components have been covered (Kazdin 2008). Taken together, it appears that additional modules may not represent the most parsimonious solution for improving BPT outcomes.
Alongside the development of enhanced BPT programs, mindful parenting has emerged over the past 20 years to help parents manage their emotional and attributional processes while parenting their child (Townshend et al., 2016). Mindful parenting programs (MPPs) encourage the use of non-judgemental, conscious, and fully accepting presence in parenting (Kabat-Zinn & Kabat-Zinn, 1997). Some MPPs rely solely on delivering mindfulness training, whereas others adapt mindfulness concepts to parenting, or integrate mindfulness with behavioral skills components. Empirical research has demonstrated support for all three types of MPPs in reducing child behavior problems and improving adaptive parenting, parent mindfulness, and parent wellbeing (Bögels et al., 2014; Burgdorf et al., 2019). That said, recent systematic reviews on MPPs have found only small to medium effect size improvements across parent and child outcomes, compared with medium to large effect size improvements for BPT programs (Burgdorf et al., 2019; Kaminski & Claussen, 2017). Similar to BPT, demographic factors such as low parent education level and younger child age have been shown to moderate attendance and outcomes in MPPs (e.g., Potharst et al., 2021). Factors such as parental stress, over-reactivity, experiential avoidance, psychological flexibility, and mindfulness have been found to inconsistently mediate improvements in adaptive parenting or child behavior or both (Brassell et al., 2016; Emerson et al., 2021; Ferraioli & Harris, 2013). Thus, the mechanisms of change in MPPs remain unclear. There are also questions about which children benefit most from MPP and BPT interventions, with the bulk of evidence supporting greater improvement among children presenting with more severe behavioral problems (Gardner et al., 2010; Leijten et al., 2013).
Some studies have compared MPP against BPT to help understand the key effective and active ingredients in both types of programs. For example, an 8-week randomised pilot study by Ferraioli & Harris (2013) found significant pre- to post-intervention improvements in parent wellbeing and mindfulness for parents of children with autism spectrum disorder (ASD) within their MPP condition, but not their BPT condition. Benefits of MPP over BPT have been demonstrated in other studies with children with ASD and developmental disabilities (Whittingham et al., 2019). However such findings need to be considered in the context of underlying aetiological mechanisms. Parents of children with disabilities may need to find flexible ways of responding to their child’s life-long difficulties, well-suited to mindfulness intervention. Conversely, parents of children with oppositional or conduct presentations may need to step back from coercive cycles that inadvertently reinforce child behavior problems. In keeping with this suggestion, Ferraioli & Harris (2013) found that parents from their BPT condition showed significant improvements on an applied behavioral analysis measure, and no significant changes on a dispositional mindfulness measure. Similarly, only parents from the mindfulness condition showed significant improvements on the dispositional mindfulness measure. The importance of face validity was also highlighted by this pilot study, with a parent randomised to the mindfulness group withdrawing because they wanted to “actually learn something” (p.97). The authors concluded that both conditions demonstrated medium to large effect size improvements from pre- to post-intervention, suggesting they each had useful active ingredients, and proposed an integration of components as a way forward. Many researchers have likewise called for mindfulness to be routinely integrated into BPT programs, rather than pitting BPT and MPP against each other (Brassell et al., 2016; Coatsworth et al., 2015; Dumas 2005; Emerson et al., 2021; Harnett & Dawe, 2012; Maliken & Katz, 2013).
In a study aligned with the aims of the current paper, Lengua et al., (2021) integrated mindfulness with parenting skills in their 6-week SEACAP program for 50 parents from socially disadvantaged backgrounds with children aged 2 to 6 years. Pre-to post-intervention improvements were found on parent-reported measures of consistent limit-setting (d = 1.28), rejection (d = 0.35), self-regulation (d = 0.36), and small improvements in observed parental scaffolding (d = 0.20) and negativity (d = 0.16). There was no change in mindfulness, however the measure used captures dispositional rather than interpersonal mindfulness and may therefore be less sensitive to changes in mindful parenting (Meppelink et al., 2016). No improvements were found for observational measures of parental warmth, responsiveness, and consistent limit-setting. Satisfaction ratings by parents were generally high, and additional parent feedback alluded to the benefits of self-regulation, increased attention towards their children, and feeling more effective as a parent. While conclusions about effectiveness are limited by the absence of a control group and the small sample size, the authors emphasised the importance of the results in terms of benefits demonstrated by a brief real-world program with disadvantaged families.
To date, mindful parenting is yet to deliver on the earlier promise of improving upon BPT outcomes. Reviews have shown small to medium effect size benefits in terms of child behavior, adaptive parenting, and parental wellbeing. An opportunity only implicitly addressed in existing parenting programs is the potential of visual imagery and metaphors to improve engagement with intervention components, and to amplify intervention effects. As coined by the common phrase “a picture is worth a thousand words” (Dansereau & Simpson, 2009), imagery has proven to be a more powerful change agent than verbal-linguistic activity (Baddeley 2012; Holmes et al., 2007; Holmes & Mathews, 2010). Parents are exposed to various imagery during BPT and MPP interventions, including video material and images such as the parenting triangle in the Incredible Years program. Such images have the capacity to consolidate relevant learnings as well as provide a visual prompt or metaphor for use in future high-risk parenting situations (Harvey et al., 2014). The importance of visual imagery has also been recognised within promotional material for parenting programs (Charest et al., 2019). Mindfulness programs employ guided mental imagery exercises, however opportunities to anchor these imaginal experiences via repeated use of key visual images is not routinely practiced. Multiple treatment exercises and components can be quickly forgotten by parents unless tied to personally meaningful concepts or symbols (Baddeley 2012; Harvey et al., 2014). Imagery also permits communication with parents’ right cerebral hemisphere, in contrast to left hemisphere dominant language-based communications that may trigger increased defensiveness and possible dissociation (Schore 2019). For example, providing verbal advice on how to manage a child’s behavior can inadvertently invite parents into therapy-interfering defensive justifications, rationalisations, or self-criticism such as, “it’s not my fault, my child is bad” or “I’m a bad parent, nothing will work”. The current paper explores the effectiveness of a parenting program, Confident Carers Cooperative Kids (CCCK), that combines the benefits of BPT and MPP and introduces visual imagery enhancement as a point of difference in trying to help families who are most in need.
The aim of the current study was to establish the effectiveness of CCCK for parents of children aged 3–12 years with behavioral problems, using a quasi-experimental real-world design comparing outcomes for parents from a university clinic versus a community organisation. Parents from the community organisation were experiencing a range of complex problems including domestic violence, drug and alcohol misuse, and mental health problems, and their children were at-risk of entering the child welfare system. Thus, the comparison between the University and Community groups examined whether CCCK was able to meet the needs of parents who were arguably in more need of the psychological benefits of mindfulness, and who typically have poorer outcomes from standard BPTs. We hypothesised that attendance at the CCCK program would reduce parent-reported child behavior problems and improve parental wellbeing, adaptive parenting, and mindful parenting across both groups. We also predicted that parents from the Community group would report greater improvements in child behavior than parents from the University group, due to more severe ratings for child behavior at baseline.
Methods
Participants
Twenty-seven mothers (79.4%) and seven fathers (20.6%) (all birth parents) attended CCCK groups conducted over a 15-month period in regional Australia. Inclusion criteria were: (a) parenting at least one child aged 3 to 12 years who met the diagnosis of oppositional defiant disorder, (b) commitment to attend at least six of the eight weeks of the intervention, (c) at least one day of contact with their child/ren each week, (d) absence of untreated severe mental health difficulties, and (e) ability to communicate in English. The real-world nature of the study for both groups led to a lack of data regarding parents who were offered but declined to attend CCCK, and so only data from parents who enrolled in CCCK were included in the analyses.
University Group
The University group (n = 20) had either self-referred or been referred by a health professional to a university psychology clinic for support in parenting a child with behavior problems. Following approval from the University Human Research Ethics Committee (HE12/029), parents were recruited over a 15-month period via emailed information regarding the study and one follow-up telephone call (by KBH). Attendance in the intervention was not contingent upon consent to be included in the research. From 45 parents attending CCCK across five separate groups at the university clinic, 20 parents consented in writing to have their data included in the study.
To determine if there were differences between parents who consented to be included in the study versus other parents who attended the same CCCK groups, ethical consent was gained to access de-identified archival data from all parents who had attended CCCK at the university clinic (Human Research Ethics Committee 2020/010). Independent t-tests (two-tailed) and Fisher’s exact tests revealed that parents who consented to be in the study were significantly older than non-consenting parents (M = 39.8 versus M = 35.5 years; t(43) = 2.48, p = 0.018) and attended more sessions (M = 7.5, SD = 0.7 versus M = 5.8, SD = 2.7; t(43) = 3.02, p = 0.004). All other group differences in demographic variables were non-significant, including child age, family composition, family income, parent education level, marital status, and employment status. There were also no significant differences between parents included within the study for ratings of frequency and intensity of problem behaviors, overall adaptive parenting, and parental mindfulness. However, consenting parents were found at baseline to be more stressed (M = 15.0, SD = 10.6 versus M = 8.5, SD = 10.4; t(43) = 2.07, p = 0.044), anxious (M = 4.7, SD = 5.6 versus M = 1.7, SD = 3.4; t(43) = 2.23, p = 0.031), and wordy in their parenting style (M = 29.2, SD = 6.1 versus M = 26.1, SD = 4.1; t(43) = 2.07, p = 0.044) than non-consenting parents.
Community Group
The Community group (n = 14) were clients of a local not-for-profit community organisation who had been identified as at-risk due to difficulties in parenting a child with challenging behaviors, and who voluntarily chose to attend the program. Archival de-identified data was gained following consent and permission from the University Human Research Ethics Committee (HE12/029) and the Chief Executive Officer of the community organisation. Data was included for 14 parents who had commenced one of three CCCK groups at the community organisation. The community organisation offered childcare, transport to and from the group, and assistance in completing pre- and post-intervention measures, if needed.
Procedures
CCCK is a manualised 8-week mindfulness and imagery enhanced behavioral parenting group program, with parent workbooks, therapist manuals and accompanying video materials (Donovan & Konza, 2021, unpublished treatment manuals). CCCK had been co-developed and refined through a university clinic and child mental health service partnership for seven years prior to the current community pilot. Table 1 provides a summary of CCCK weekly components. CCCK introduces key images, metaphors, and mindfulness exercises throughout the program, embedding core concepts through the power of visual imagery, memory consolidation (Baddeley 2012; Harvey et al., 2014; Holmes et al., 2007), and right-hemisphere processing (Schore 2019). CCCK thereby targets parents’ emotional and attributional factors, indicated as barriers to engagement, perseverance, and implementation within traditional BPT programs (Chacko et al., 2016; Maliken & Katz, 2013).Table 1 CCCK Weekly Behavioral Skills, Mindfulness and ACTa Components
Week Title / Goal for Week CCCK Components
Behavioral Skills Mindfulness/ACTa
1 Understanding and preventing problem behaviors Recognition of shared experiences, formulation of problem behaviors, problem list Bushfire metaphor, power struggles (defusion), mind struggles (creative hopelessness, defusion)
2 Becoming aware of your parenting values Emotion coaching Parenting compass (guided mindfulness, values-identification), doing what matters (choice point, committed action), wheel of noticing (observing self)
3 Strengthening relationships Attuned care-giving, balance between love and limits, play tips and traps Mindful play, mindful describing, doing what matters
4 Encouraging positive behaviors Learned behavior, praise & rewards, grounding exercise Mindfulness of skittle, mindful praise, ‘feeding tiger cub’ (defusion), doing what matters
5 Preventing misbehavior Setting limits, household rules, clear instructions, planned ignoring Mindfulness of breath, mindful limits, ‘drop the rope’ (defusion), doing what matters
6 Managing misbehavior Fight/flight/freeze, natural consequences, loss of privileges, time-out Breathing space, mindful consequences, doing what matters
7 Managing difficult situations Behavior action plan, consolidation Self-compassion break, doing what matters
8 Being the parent Behavior action plan, consolidation, relapse prevention Sweet-spot guided mindfulness, doing what matters
aACT Acceptance and Commitment Therapy
In terms of theoretical underpinnings, like many existing parenting programs CCCK acknowledges the importance of operant conditioning reinforcement schedules (Shaffer et al., 2001). The role of parental attention is particularly emphasised as a powerful reinforcer of children’s behavior. CCCK also incorporates the neurobiology of attachment, humans under stress, and embodies a foundation of compassion towards self and others (Davis et al., 2017; Gilbert 2013; Schore 2019; Siegel & Hartzell, 2013). CCCK is centred around parents’ deeply held values about the parent they want to be and uses further Acceptance and Commitment Therapy (ACT)-based acceptance, defusion, and mindfulness techniques in helping parents to parent more consistently with these values (Coyne & Murrell, 2009). CCCK also maps onto the mindfulness model proposed by Shapiro et al., (2006), with targeted exercises to address (a) self-regulation, (b) values-identification, (c) cognitive-behavioral flexibility, and (d) exposure tasks.
For the University group, CCCK was facilitated by provisionally registered psychologists undertaking postgraduate clinical psychology training who had been trained in program delivery. For the Community group, CCCK was delivered by child health professionals who had also undertaken program delivery training. Training for both groups was provided by the program creators (MD and GK) via a two-day workshop, including demonstration and practice of key CCCK components. To ensure program fidelity, weekly supervision was provided throughout the intervention by one of the program creators. This included review, and if necessary, role-play of each CCCK component.
Parents attended a weekly two-hour group comprising three to 11 parents and two facilitators. Pre-intervention interviews were completed prior to participation across both groups to gather relevant clinical and demographic information, to confirm a diagnosis of oppositional defiant disorder, and to ensure CCCK met the family’s needs. Pre- and post-intervention questionnaires were routinely completed immediately prior and following the intervention, regardless of involvement in the study.
Outcome Measures
Eyberg Child Behavior Inventory (Child Behavior)
The Eyberg Child Behavior Inventory (ECBI; Robinson et al., 1980) is a parent-report measure of conduct behavior problems in children aged 2 to 16 years. The ECBI describes 36 items of common behavioral problems, for example “Dawdles in getting dressed”, “Argues with parents about rules”, “Is easily distracted”. Parents respond yes or no to indicate presence of the behavioral problem for their child (ECBI-P) and the intensity at which these problems occur (ECBI-I), ranging from 1 to 7, “never” to “always”. Total scores are generated for the ECBI-P and ECBI-I. There are established cut-offs of ECBI-P (>15) and ECBI-I (>131) that indicate clinical significance. The ECBI has good internal reliability and adequate external validity (Boggs et al., 1990; Robinson et al., 1980). Results for both ECBI-P and ECBI-I are reported here.
Strengths and Difficulties Questionnaire (Child Behavior)
The Strengths and Difficulties Questionnaire (SDQ; Goodman 1997) is a parent-report measure designed to assess the extent of emotional and behavioral problems in children aged 4–17 years. The questionnaire has 25 items divided across five subscales: emotional (“Many worries or often seems worried”), conduct (“Often loses temper”), hyperactivity (“Thinks things out before acting”), peer problems (“Has at least one good friend”), and the prosocial subscale (“Considerate of other people’s feelings”). Parents rate their child’s behavior over the past six months (0 = not true, 1 = somewhat true, 2 = certainly true). The scale includes items that are reversed scored. Subscale ranges have been linked to categories for clinical use. Each of the five subscales was divided by the number of items to create average scores, ranging from zero to two. The SDQ is a commonly used measure of child mental health problems and has been shown to have adequate internal consistency (α = 0.73) and good test-retest reliability (Goodman 1997). All subscales are reported in Supplementary Table 4, with the conduct subscale (SDQ-C) included within the mixed linear analyses in consideration of our aims and sample.
Depression Anxiety and Stress Scale (Parental Wellbeing)
The Depression Anxiety and Stress Scale 21 (DASS-21; Lovibond & Lovibond, 1995) contains 21 self-report items to measure negative emotional states of depression, anxiety and stress, and was used in this study to capture parental wellbeing. Scores are generated across three subscales: depression (“I felt down-hearted and blue”), anxiety (“I felt I was close to panic”), and stress (“I tended to over-react to situations”), with higher scores indicating greater distress. The DASS-21 has demonstrated high levels of internal consistency for depression (α = 0.88), anxiety (α = 0.82), stress (α = 0.90), and total score (α = 0.93), and possesses sufficient convergent and discriminant validity (Henry & Crawford, 2005; Lovibond & Lovibond, 1995). Subscale ranges have been linked to categories for clinical use. All subscales are reported here.
Parenting Scale (Adaptive Parenting)
The Parenting Scale (PS; Arnold et al., 1993) is a self-report questionnaire consisting of 30-items of discipline styles providing a total score which comprises three subscales: over-reactivity (authoritarian discipline, irritability), laxness (permissive discipline), and verbosity (over-wordy instructions or reliance on talking). Parents are asked to rate the probability of using a specific discipline strategy along a 7-point likert scale, with higher scores indicating less adaptive parenting. For example, in response to the statement “When my child misbehaves (over-reactivity and verbosity subscale), parents rate along a 7-point scale from “I usually get into a long argument with my child” (7), to “I don’t get into an argument” (1). The scale includes items that are reversed scored. The scale has good internal consistency (α = 0.84), good test-retest reliability, and good discriminant validity (Arnold et al., 1993; Rhoades & O’Leary, 2007). Clinically significant cut-offs have been established for the subscales: laxness >4.0, over-reactivity >3.2, and verbosity >3.1 (Arnold et al., 1993; Salari et al., 2012). All three subscales were included in the current analyses, in consideration of the aims of the current study, and with awareness of concerns about the psychometric qualities of the verbosity subscale (Salari et al., 2012).
Interpersonal Mindfulness in Parenting Scale (Mindful Parenting)
The Interpersonal Mindfulness in Parenting Scale (IM-P; Duncan 2007) was used as a measure of mindful parenting. The revised version of the IM-P has been validated within Australia and uses 29 of the original 31-items, assessing mindful parenting across six dimensions: listening with full attention (LFA, five items; “I spend close attention to my child when we are spending time together”), emotional awareness of child (EAC, three items; “I can tell what my child is feeling even if he/she does not say anything”), emotional awareness of self (EAS, four items; “When I’m upset with my child, I notice how I am feeling before I take action”), emotional non-reactivity in parenting (ENRP, five items; “I often react too quickly to what my child says or does”), non-judgmental acceptance of parent functioning (NJAPF, six items; “When I do something as a parent that I regret, I try to give myself a break”), and compassion for child (CC, six items; “I am kind to my child when he/she is upset”) (Burgdorf & Szabó, 2021). Higher scores indicate greater levels of mindful parenting, with scores ranging from one to five in each of the subscales. The scale includes items that are reversed scored. The scale has good internal consistency (α = 0.89 for total, and α = 0.77 to 0.87 for subscales) and construct validity (Burgdorf & Szabó, 2021; de Bruin et al., 2014). All six subscales are reported and included in the analyses.
Data Analysis
All statistical analyses were conducted using SPSS version 25.0 (IBM Corp. 2017). Independent sample t-tests (two-tailed) and Fisher’s exact tests were used to compare baseline differences between University and Community groups for continuous and categorical variables, respectively. Fisher’s exact tests were preferred over chi-square due to cells with a minimum count of n < 10. Following inspection of the data via descriptive statistics, Mauchly’s, Box’s, and Levene’s test statistics were used to test the assumptions of normality, sphericity, and homogeneity of covariance and error covariance. The assumptions for a mixed model ANOVA were met for most variables, using commonly accepted kurtosis and skewness for small samples <|1.96| (Kim 2013). Variables outside of this range met normality assumptions following log transformation, except the pre-test Verbosity subscale of the Parenting Scale (kurtosis = 3.39). Parametric tests were preferred over non-parametric due to the lack of a repeated measures non-parametric test, as well as to maintain sensitivity of data within two real-world samples. Likewise, mixed ANOVA repeated measures analysis was preferred over MANOVA so participants could act as their own control, and thereby maintain statistical power. Separate mixed ANOVAs examined differences following intervention for dependent variables aligned to the study’s aims and hypotheses, with Time and Variable Subscale as within-subject factors, and Group as the between-subjects factor. Where possible, subscales for each outcome variable were included within the same mixed ANOVA analysis to maintain power and reduce type II error. For Behavior, scores from ECBI-I, ECBI-P and SDQ-C were analysed separately due to scale measurement differences. Bonferroni adjustments were applied to minimise possibility of type 1 error. Greenhouse-Geisser adjustments were made to the degrees of freedom as needed. Transformed variables were used within the mixed ANOVA to calculate F and p values for all measures except the IM-P. Partial eta squared effect sizes were generated from the mixed ANOVA, with accepted values of small η2 = 0.01, medium η2 = 0.06, and large η2 = 0.14. Additional t-tests (two-tailed) were used to determine magnitude of change for differences between variables, and are reported where relevant. Cohen’s d effect sizes were reported from t-tests, with accepted values of small d = 0. 2, medium d = 0. 5, and large d = 0.8. An a priori power analysis, using alpha = 0.05, power = 80% and assuming a conservative effect size d = 0.5, found a sample size of 34 was required.
Results
Baseline Demographic and Outcome Measures across Groups
Table 2 shows the demographic characteristics of the two intervention groups. As expected, there were several statistically significant demographic differences between the two groups. The parents in the Community group had lower household incomes (Fisher’s Exact Test = 18.62, p < 0.001), lower levels of education (Fisher’s Exact Test = 11.18, p = 0.035), higher unemployment (Fisher’s Exact Test = 16.17, p < 0.001), younger parental age (t (29) = 5.68, p < 0.001), and younger aged children (t (31) = 2.99, p = 0.005). Independent sample t-tests showed that University and Community groups did not differ at baseline across child and parent measures, except for higher scores in the Community group for parental depression (t (32) = −2.92, p = 0.006) and parental laxness (t (32) = −2.46, p = 0.020). Parents’ ratings for frequency and intensity of problem behaviors on the ECBI were above the clinically significant cut-off at baseline across both the University and Community groups, consistent with the oppositional behavior inclusion criteria (Boggs et al., 1990). Parents from both groups also rated themselves as above the clinically significant cut-off on measures of adaptive parenting.Table 2 Demographic Characteristics of the Intervention Groups and Session Attendance
University (n = 20) Community (n = 14) Statistical valuesd
t p
Role of Parent-Mother 16 (80%) 11 (79%) – 0.611
Sex of Child - Male 14 (70%) 10 (71%) – 0.618
Age of Child (Mean / SD) 7.55 (1.79) 5.43 (2.34) 2.99 0.005**
Age of Parent (Mean / SD) 39.63 (4.31) 29.67 (5.42) 5.68 0.000***
Identify as Australian 14 (70%) 11 (79%) – 0.016*
Aust.+Othere 6 (30%) –
Indigenous Aust. – c 3 (21%)b
Family Incomef Low 3 (18%)c 11 (92%)b - 0.000***
Middle 4 (24%) 1 (8%)
High 10 (59%) –
Education Levelg Low 1 (5%)a 6 (50%)b – 0.025*
Middle 11 (58%) 4 (33%)
High 7 (37%) 2 (17%)
Employ. Status Not employed 5 (26%)a 12 (100%)b – 0.000***
Part or full time 14 (74%) –
Family Type Two Parent 11 (58%)a 3 (25%)b – 0.065
Single Parent 5 (26%) 7 (58%)
Step/Blended 3 (16%) 2 (17%)
Sessions Attended Total = 8 sessions 7.55 (0.686) 7.07 (0.829) 1.84 0.075
*p < 0.05, **p < 0.01, ***p < 0.001
a1 missing response
b2 missing responses
c3 missing responses
dFisher’s exact test used for categorical and two-tailed t-tests for nominal data (df = 1,32)
eOther included Italy, England, Malta, Canada, New Zealand
fdefined by Australian Bureau of Statistics (2013), Low = <$800 per week, Middle = $800–1500, High = >$1500 (AUD)
gLow = primary/school certificate, Middle = high school certificate/diploma qualifications, High = university graduate
The baseline differences between the University and Community groups comprise important parent variations of interest. Given our small sample, exploratory secondary correlational analyses were preferred over covariate analyses that would diminish the effect of these differences as well as reduce power from loss of degrees of freedom. These correlations are reported in the Secondary Analyses section below.
Intervention Effects
The main aim of the study was to establish the effectiveness for CCCK by comparing the intervention across time and between the University and Community groups. All measures and subscales demonstrated adequate reliability, with many in the good to excellent ranges, despite the small number of items on many subscales (George & Mallery, 2019). Table 3 presents mixed model ANOVA findings for the main effects and interactions, across dependent variables relevant to the study aims and hypotheses. Untransformed estimated marginal means and standard errors relevant to the analyses performed are reported in Table 4 to allow comparison with other studies. Pre- and post-intervention means, standard deviations and effect sizes for all dependent variables are available in Supplementary Table 1 for consideration.Table 3 Results from Mixed ANOVA with Effect Sizesa (Time by Groupb by Variable Subscalec)
Outcome Variable Group Betw. subjects Time Within subjects Variable Subscale Within subjects Time x Variable Subscale Time x Group Variable Subscale x Group Time x Group x Variable Subscale
F(df) p (η2) F(df) p (η2) F(df) p (η2) F(df) p (η2) F(df) p (η2) F(df) p (η2) F(df) p (η2)
Child Behavior
ECBI-Intensity 0.45 (1,32) 0.509 (0.01) 50.53 (1,32) <0.001*** (0.61) – – – – 12.20 (1,32) 0.001*** (0.28) – – – –
ECBI-Problem 0.18 (1,32) 0.677 (0.01) 27.71 (1,32) <0.001*** (0.46) – – – – 7.29 (1,32) 0.010** (0.19) – – – –
SDQ-C 0.10 (1,32) 0.919 (0.00) 10.24 (1,32) 0.003** (0.24) – – – – 2.15 (1,32) 0.152 (0.06) – – – –
Parent Wellbeing 1.98 (1,32) 0.169 (0.06) 8.53 (1,32) 0.006** (0.21) 41.32 (1.48,50.66) <0.001*** (0.56) 2.66 (1.84, 58.89) 0.082 (0.08) 1.69 (1,32) 0.203 (0.05) 3.38 (1.58, 50.66) 0.053* (0.10) 2.36 (1.84, 58.89) 0.107 (0.07)
Adaptive Parenting 0.60 (1,32) 0.445 (0.02) 74.83 (1,32) <0.001*** (0.70) 13.34 (2,64) <0.001*** (0.29) 2.13 (2,64) 0.154 (0.06) 0.44 (1,32) 0.836 (0.01) 0.28 (2,64) 0.644 (0.01) 0.29 (2,64) 0.596 (0.10)
Mindful Parent 0.01 (1,32) 0.915 (0.00) 47.81 (1,32) <0.001*** (0.60) 47.27 (2,64) <0.001*** (0.60) 2.97 (4.36, 139.40) 0.019* (0.09) 2.12 (1,32) 0.155 (0.06) 0.61 (3.57, 114.16) 0.640 (0.02) 1.39 (4.36, 139.40) 0.238 (0.04)
*p < 0.05, **p < 0.01, ***p < 0.001
aPartial eta squared effect size coefficients using the commonly accepted criteria of small (η2 = 0.01), medium (η2 = 0.06) and large (η2 = 0.14)
bGroup split between university clinic and community organisation
cOutcome Variable subscales, not relevant to child behavior as analysed separately on each child behavior measure
Table 4 Estimated Marginal Means (M) and Standard Errors (SE) from Mixed ANOVA and Reliability Coefficients
Outcome Variable Group /x Variable Subscalea Time /x Variable Subscalea Time x Group /x Variable Subscalea
Variable αb Subscale
M (SE) Uni.c
M (SE) Comm.c
M (SE) Pre
M (SE) Post
M (SE) Pre M (SE) Post M (SE)
Uni.c Comm.c Uni.c Comm.c
Child Behaviourd
ECBI_I 0.93 145.50 (5.31) 147.68 (6.81) 143.32 (8.14) 161.30 (5.32) 129.69 (6.22) 155.75 (6.83) 166.86 (8.16) 139.60 (7.98) 119.79 (9.54)
ECBI_P 0.94 16.16 (1.46) 16.08 (1.87) 16.25 (2.24) 20.12 (1.49) 12.20 (1.72) 18.10 (1.90) 22.14 (2.28) 14.05 (2.21) 10.36 (2.64)
SDQ_C 0.53 0.78 (0.05) 0.78 (0.07) 0.79 (0.08) 0.89 (0.07) 0.67 (0.06) 0.84 (0.09) 0.94 (0.10) 0.71 (0.08) 0.63 (0.10)
Parent Wellbeinge 4.12 (0.69) 6.07 (0.82) 6.17 (0.63) 4.01 (0.58) 4.63 (0.81) 7.71 (0.97) 3.60 (0.75) 4.43 (0.89)
DASS_D 0.91 4.94 (0.71) 3.13 (0.91) 6.75 (1.09) 6.21 (0.86) 3.69 (0.73) 3.70 (1.10) 8.71 (1.31) 2.55 (0.94) 4.79 (1.13)
DASS_A 0.78 2.88 (0.52) 2.23 (0.66) 3.54 (0.79) 3.31 (0.69) 2.45 (0.49) 2.55 (0.882) 4.07 (1.05) 1.90 (0.62) 3.00 (0.74)
DASS_S 0.87 7.46 (0.67) 7.00 (0.86) 7.93 (1.02) 9.00 (0.83) 5.23 (0.74) 7.65 (1.06) 10.36 (1.27) 6.35 (0.94) 5.50 (1.13)
Adaptive Parentingf 3.28 (0.012) 3.42 (0.14) 3.85 (0.10) 2.84 (0.11) 3.78 (0.13) 3.92 (0.16) 2.77 (0.14) 2.91 (0.16)
Over-reactivity 0.63 3.51 (0.12) 3.61 (0.15) 3.42 (0.18) 3.98 (0.14) 3.05 (0.13) 4.06 (0.18) 3.89 (0.22) 3.15 (0.16) 2.95 (0.19)
Laxness 0.82 2.93 (0.13) 2.61 (0.16) 3.24 (0.19) 3.39 (0.16) 2.47 (0.13) 3.01 (0.20) 3.77 (0.24) 2.23 (0.17) 2.73 (0.20)
Verbosity 0.54 3.60 (0.12) 3.61 (0.16) 3.59 (0.19) 4.19 (0.15) 3.01 (0.15) 4.27 (0.20) 4.11 (0.24) 2.96 (0.19) 3.06 (0.22)
Mindful Parentingg 3.44 (0.09) 3.45 (0.10) 3.20 (0.07) 3.69 (0.08) 3.25 (0.10) 3.16 (0.11) 3.63 (0.10) 3.75 (0.12)
LFA 0.84 3.44 (0.09) 3.44 (0.11) 3.44 (0.13) 3.18 (0.11) 3.70 (0.09) 3.33 (0.15) 3.03 (0.15) 3.55 (0.11) 3.84 (0.16)
EAS 0.74 3.32 (0.10) 3.34 (0.12) 3.30 (0.15) 2.99 (0.12) 3.65 (0.11) 3.05 (0.16) 2.93 (0.17) 3.64 (0.14) 3.66 (0.16)
EAC 0.64 3.75 (0.09) 3.68 (0.12) 3.83 (0.14) 3.58 (0.11) 3.65 (0.11) 3.52 (0.12) 3.64 (0.19) 3.83 (0.12) 4.02 (0.19)
ENRP 0.73 3.29 (0.10) 3.25 (0.13) 3.33 (0.15) 2.99 (0.12) 3.59 (0.10) 3.00 (0.14) 2.97 (0.19) 3.49 (0.14) 3.69 (0.13)
NJAPF 0.80 2.76 (0.10) 2.74 (0.13) 2.78 (0.15) 2.51 (0.12) 3.01 (0.12) 2.52 (0.16) 2.51 (0.16) 2.97 (0.17) 3.05 (0.16)
CC 0.75 4.12 (0.07) 4.19 (0.09) 4.05 (0.11) 3.98 (0.07) 4.26 (0.08) 4.08 (0.09) 3.88 (0.12) 4.30 (0.10) 4.21 (0.14)
aM and SE for interactions with Variable Subscales also listed within column
bReliability coefficient for subscale, where >0.9 = excellent, 0.8–0.9 = good, 0.7–0.8 = acceptable, 0.6–0.7 = questionable, 0.5–0.6 = poor, <0.5 = unacceptable (George and Mallery 2019)
cUni. = University Group, Comm. = Community Group
dECBI_I = Eyberg Child Behavior Inventory – Intensity, ECBI_P = Eyberg Child Behavior Inventory – Problem, SDQ_C = Strengths & Difficulties Questionnaire – Conduct
eDASS_D/A/S = Depression Anxiety Stress Scale – Depression/Anxiety/Stress
fParenting Scale
gInterpersonal Mindfulness in Parenting, LFA Listening with Full Attention, EAS Emotional Awareness of Self, EAC Emotional Awareness of Child, ENRP Emotional Non-Reactivity in Parenting, NJAPF Non-Judgmental Acceptance of Parenting Function, CC Compassion for Child
Parent-reported Child Behavior
To consider the effects of the intervention for the measures of child behavior, we used separate repeated measures ANOVAs for each measure (ECBI-I, ECBI-P, SDQ-C), using a within-subject variable of Time (2 Levels: Pre and Post) and between-subjects variable of Group (2 Levels: University or Community).
The mixed ANOVA found a significant main effect of Time for all three behavior scales, with large effect sizes (see Table 3). There was however no main effect of Group.
There was a significant interaction between Time and Group for ECBI-I and for ECBI-P, however not for SDQ-C. Further analysis using independent sample t-tests (two-tailed) with change scores revealed that the Community group improved significantly more than the University group on both the ECBI-I (t(32) = 3.36, p = 0.002, d = 1.17), and the ECBI-P (t(19) = 2.88, p = 0.007, d = 1.00).
Parental Wellbeing
The repeated measures ANOVA for parental wellbeing included two within-subject variables of Time (2 Levels: Pre and Post) and DASS Subscale (3 Levels: Depression, Anxiety, and Stress), and between-subjects variable of treatment Group (2 Levels: University or Community).
The mixed ANOVA found a significant main effect of Time, with large effect size. Consideration of the estimated marginal means revealed that pre-intervention DASS scores were significantly higher than post intervention scores. There was also a significant main effect of DASS Subscale, with participants overall endorsing more stress than depression and anxiety and more depression than anxiety. Post hoc comparisons were all significant at p < 0.01. There was no main effect of Group.
There was a significant interaction between Group and DASS, and no significant interactions between Time and Group, and Time and DASS. Further analysis with independent sample t-tests (two-sided) were conducted using average of pre and post DASS Subscales, with Group as the independent variable. The Community group showed significantly higher ratings of depression than the University group (t(32) = 2.29, p = 0.029, d = 0.80). There were no significant differences between Community and University groups in ratings of anxiety (t(32) = 0.77, p = 0.449, d = 0.27), and stress (t(32) = 0.41, p = 0.686, d = 0.14). The three-way interaction of Time and DASS Subscale and Group was non-significant.
Adaptive Parenting
The repeated measures ANOVA for adaptive parenting included two within-subject variables of Time (2 Levels: Pre and Post) and Parenting Scale Subscale (PS, 3 Levels: Over-reactivity, Laxness, Verbosity), and a between-subjects variable of treatment Group (2 Levels: University or Community).
The mixed ANOVA revealed that pre-intervention PS scores were significantly higher than post-intervention scores, with large effect size. There was a main effect of PS. Consideration of the estimated marginal means revealed that Over-reactivity and Verbosity were similar, and both were higher than Laxness, with post hoc comparisons significant at p < 0.01. There was no main effect of Group.
There were no significant interactions between Group and Time, Time and PS, and PS and Group. The three-way interaction of Time and Parenting Scale and Group was also non-significant.
Mindful Parenting
Finally, the repeated measures ANOVA for mindful parenting included two within-subject variables of Time (2 Levels: Pre and Post) and IM-P Subscale (6 Levels: LFA, EAS, EAC, ENRP, NJAPF, CC), and between-subjects variable of treatment Group (2 Levels: University or Community).
The mixed ANOVA found a significant main effect of Time, with large effect size. Post-intervention IM-P scores were significantly higher than pre-intervention scores. There was also a significant main effect of IM-P. There was no main effect of Group.
There was a significant interaction between Time and IM-P, but not between Time and Group, or IM-P and Group. Independent sample t-tests (two-tailed) using changes scores found significant differences between EAS and EAC (t(33) = 2.50, p = 0.017, d = 0.43), EAS and CC (t(33) = 3.79, p < 0.001, d = 0.65), and ENRP and CC (t(33) = 3.27, p = 0.0037, d = 0.56). The three-way interaction of Time and IM-P and Group was non-significant.
Secondary Analyses
Correlations were conducted for seven factors that demonstrated baseline differences between University and Community groups (DASS-Depression, PS-Lax, child age, parent age, employment status, education level, family income), and two change scores that showed between group differences (ΔECBI-Intensity, ΔECBI-Problem). These analyses explored how these baseline factors may have influenced the outcomes, with awareness that using correlations on a small sample could lead to spurious findings and should therefore be interpreted with caution (Hung et al. 2017).
Results revealed non-significant correlations between baseline parental depression and ΔECBI-Intensity (r(34) = −0.117, p = 0.510), and ΔECBI-Problem (r(34) = −0.286, p = 0.101), baseline lax parenting and ΔECBI-Intensity (r(34) = −0.261, p = 0.136) and ΔECBI-Problem (r(34) = −0.110, p = 0.535). Correlations between Δ ECBI-Intensity/ΔECBI-Problem and employment status, child age and parent age were also non-significant (employment, r(31) = 0.299/0.301, p = 0.103/0.100; child age, r(34) = 0.122/−0.056, p = 0.493/0.754; parent age, r(34) = −0.184/0.110, p = 0.322/0.556, respectively). Therefore, baseline parent ratings of depression, lax parenting style, and child and parent age, were not associated with improvements in child behavior intensity and problems. There were significant negative correlations between ΔECBI-Intensity and family income (r(29) = −0.386, p = 0.039), and parent education level (r(31) = −0.369, p = 0.041). For our sample, parents with lower education and family income rated larger improvements in child behavior intensity. Correlations were non-significant between ΔECBI-Problem and family income (r(29) = 0.196, p = 0.309), and parent education level (r(31) = 0.237, p = 0.200).
Discussion
The current study aimed to establish the real-world effectiveness of a mindfulness and imagery enhanced behavioral parenting program, and to compare outcomes for parents from a university clinic versus parents at-risk of entering the child welfare system. We expected that attendance at the CCCK program would lead to reductions in parent-reported child behavior problems and improvements in parental wellbeing, adaptive parenting, and mindful parenting across both groups. We also predicted that parents from the Community group would experience greater improvements across child behavior measures than parents from the University group due to more severe problems at baseline. The results offered support for both hypotheses. First, following completion of the CCCK program, parents from both the University and Community groups rated their children’s behavior as less problematic, their wellbeing as improved, and their parenting approach as more adaptive and mindful. Effect size improvements were large across all measures, and were consistent with previous research (Burgdorf et al., 2019). Second, parents from the Community group showed significantly larger improvements in parent-rated child behavior than parents from the University group. Improvement was similar across groups for parent wellbeing, adaptive parenting, mindful parenting, and parent-rated child conduct problems. There were some between subscale differences in improvement on measures of parenting style and mindful parenting, however these may not be meaningful given the small sample.
Baseline differences between University and Community groups were explored using secondary correlational analyses with key outcomes. There were no associations found between baseline group differences and improvements in child behavior problems within our sample. However, lower family income and parent education were found to be associated with larger improvement in parent ratings of child behavior intensity. The ECBI distinguishes between parent ratings of problem frequency (ECBI-Intensity) and whether parents experience this as a problem (ECBI-Problem). Parents with sociodemographic disadvantage appeared to notice greater reductions in the frequency of child behavioural issues following intervention. Previous research has found greater baseline problem severity predicts larger improvements, which was consistent with our study findings (Leijten et al., 2018). In our study, parents from Community and University groups rated their children’s behavior as similarly problematic at baseline. The greater improvements in ECBI-Intensity may therefore relate to other factors that impact parents based on sociodemographic differences when undertaking a parenting intervention. These correlational findings should be interpreted with caution due to the small sample, although highlight the need for future larger MPP studies to examine sociodemographic predictors of outcome.
At baseline, parents from both groups rated their children as showing behavioral problems at a clinically significant level, and their own parenting style as problematic. These ratings had moved below the clinically significant cut-off for parents from the Community group at post-intervention, and below the cut-off for parents from the University group on three of four subscales. Parents from the Community group showed significantly higher levels of social disadvantage as well as parental depression and permissive discipline than parents in the University group. Thus, the results also support the main aim of the current study, in establishing that mindfulness and imagery enhanced parenting programs can lead to positive outcomes for families most in need.
Attendance for the CCCK intervention was high, with nearly ninety percent session attendance for parents from the Community group, and over ninety percent for the University group. The additional support offered to Community parents, such as childcare and transport, likely overcame many of the usual barriers, and thereby improved attendance and engagement (Chacko et al., 2016). The visual nature of the materials and the repetition of key images may have been an active element in increasing engagement and retention. This proposition is supported by previous literature on benefits of imagery on learning and retention (Harvey et al., 2014; Holmes et al., 2007), however this requires further empirical investigation.
In terms of mechanisms of change, parents rated improvements to both mindfulness and adaptive parenting, and so it is likely that both mechanisms contributed to improvements in child behavior and parental wellbeing. These results may have been achieved through different means: parents may have benefited from different elements of CCCK. Some parents may have responded to components that emphasised emotional awareness and listening with full attention (mindfulness); whereas others may have benefited more from being less reactive and more consistent in their parenting (behavioral skills). It is also possible that increased parental mindfulness potentiated the positive effects of the behavioral skills components, by helping parents to be more consistent and impactful. Likewise decreased reactivity and increased consistency may have brought intentionality and awareness to parenting, in a way that amplified mindful parenting.
There were several strengths of the current study. The sample was representative of families with children with significant behavioral problems, including those from disadvantaged backgrounds. Attendance was high and so parents received an adequate “dose” of the CCCK intervention. The study also checked for baseline confounding factors and ensured that parents who provided consent for the study were similar to the group of parents who attended the intervention. The measures used were found to be reliable and are widely used in other published studies. By relying on archival data for the Community group, reporting bias may have been reduced, although at a cost to overall study design. In terms of generalisability of findings, both family workers without psychology training and relatively inexperienced first year post-graduate provisional psychologists were able to deliver a parenting program that demonstrated large effect size improvements across all outcome variables.
Limitations and Future Research
There were several limitations, most associated with the real-world nature of this research. The study relied on a naturally occurring division of parents into intervention groups and was limited to pre- and post-intervention self-report measures from a small sample of parents. The lack of a control group means that reported changes could be attributable to demand effects or other factors. Social desirability may have been stronger for the Community group due to the at-risk status of their children. Additional fidelity checks for session content, homework compliance and co-interventions were not undertaken, and it was not possible to measure pre-intervention drop-out. It is also possible that group processes contributed to the positive outcomes, and that the advanced experience of facilitators in the Community group contributed to larger reductions in child behavior problems in this setting. While most measures demonstrated good internal consistency, the verbosity subscale from the Parenting Scale was rated poor. This is consistent with previous studies and supports the need to revise this subscale (Salari et al., 2012).
The current study has provided provisional support for the benefits of blending mindfulness, behavioral skills, and imagery-enhancement within a parenting intervention. A larger sample is needed to replicate these findings, to better understand mechanisms of change, and to contribute to the literature regarding differential MPP outcomes across parents from varying sociodemographic groups (e.g., low income, one-parent families, fathers). If the large effect sizes are reproduced, a randomised trial is recommended to establish efficacy under controlled conditions, and preferably across a range of treatment settings with comparison to an active control group. Qualitative interviews at 6–12 months post-intervention could provide rich information about the parents’ experience of CCCK components, and the extent to which CCCK imagery and metaphors enhanced parents’ understanding, recall, and continued use of parenting strategies, including in moments of high stress. Qualitative data could also helpfully reveal mechanisms of change. The recent shift to online provision of services in response to COVID-19 provides an opportunity to test the effectiveness of online CCCK (Cluver et al., 2020).
Troubled families need accessible, engaging, and effective interventions. CCCK represents a new parenting intervention that benefits from the potent blend of mindfulness, behavioral skills, and imagery-enhancement.
Supplementary information
Supplementary Information
Supplementary information
The online version contains supplementary material available at 10.1007/s10826-022-02502-y.
Acknowledgements
This research was supported by a University of Wollongong Australian Government Research Training Program Scholarship awarded to Mark Donovan.
Author Contributions
M.D. co-created the intervention, co-designed the study, analyzed the data, wrote the first version of the manuscript, and revised subsequent versions. K.B.H. co-designed the study, collected the data, and reviewed the final manuscript. E.B., J.H., L.M. and J.P. reviewed and revised the design, statistical analyses, and each version of the manuscript. G.K. co-created the intervention and reviewed and revised the final manuscript. All authors approved the submitted version.
Data availability
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Compliance with ethical standards
Conflict of interest
The authors declare no competing interests.
Ethical approval
This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by University of Wollongong Human Research Ethics Committee, and the Chief Executive Officer of the community organisation.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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| 0 | PMC9748389 | NO-CC CODE | 2022-12-15 23:22:42 | no | J Child Fam Stud. 2022 Dec 14;:1-15 | utf-8 | J Child Fam Stud | 2,022 | 10.1007/s10826-022-02502-y | oa_other |
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SN Soc Sci
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Sn Social Sciences
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Springer International Publishing Cham
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10.1007/s43545-022-00566-7
Original Paper
A constantly improving model for universities readiness in the application of e-learning practices during the COVID-19 pandemic: a qualitative approach
http://orcid.org/0000-0003-3900-3164
Ordoo Fatemeh [email protected]
1
http://orcid.org/0000-0002-4599-9087
Pourkarimi Javad [email protected]
2
1 grid.46072.37 0000 0004 0612 7950 Higher Education Administration, Department of Educational Administration and Planning, Faculty of Psychology and Education, University of Tehran, Tehran, Iran
2 grid.46072.37 0000 0004 0612 7950 Faculty of Psychology and Education, Department of Educational Administration and Planning, University of Tehran, Tehran, Iran
14 12 2022
2022
2 12 27624 1 2021
14 11 2022
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis 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.
The sudden outbreak of a lethal virus known as the COVID-19 pandemic spotlighted e-learning systems worldwide. That forced instructors and faculty members around the world to try the existing instructional platforms in an attempt to shift toward an effective unprecedented learning system. The present study concentrated on enhancing a specific e-learning system experienced for the first time at the University of Tehran (UT) that faced several difficulties in the development process due to the lack of required readiness in diverse aspects. As a phenomenological approach bordered with a descriptive-interpretive framework, the study targets a group of 2000 faculty members at 35 diverse departments of the UT. Data have gathered from 603 faculty members using voice calls, video calls, and emails and then analyzed and diverged into four fundamental segments: sociocultural readiness, pedagogical readiness, organizational readiness, and technological readiness (SCPOT-R). Our findings indicated some remarkable results that underline the significance and high priority of virtual and electronic learning methods since the expansion of COVID-19 and following physical restrictions.
Keywords
E-learning
Education during the COVID-19 Pandemic
The SCPOT-R model
issue-copyright-statement© Springer Nature Switzerland AG 2022
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pmcIntroduction
The sudden spread of contagious COVID-19 shocked humanity in multiple aspects of life. The subject of education was one of the most challenging aspects in which teachers, instructors, and professors had to shift toward online teaching overnight (Dhawan 2020). As the pandemic temporarily shut down universities and higher education institutions, about 1.5 billion learners on the planet lost their chance to present in classrooms. In response to these difficult circumstances, world authorities and the government of the Islamic Republic of Iran altered their view of using various electronic/virtual learning systems. But the results of this modification may not necessarily be as desirable as expected and regarded not only as an opportunity given to the fourth industrial revolution and digital transformation in all parts of societies but also as a serious threat to higher education (Petrillo et al. 2018). Although there are mixed views on how this form of instruction stands implemented in education and learning design (Torrau 2020; Dron 2018), one can acknowledge that professors who use new teaching methods play a vital role in innovation and transformation. This way of teaching motivates innovation and enables the application of new tools and technologies which assign students a more active role, promote network literacy and access to free resources, shape pathways to group learning, and provide opportunities for professional development (Paskevicius and Irvine 2019).
University e-learning system
In a technology-based education, context-based e-learning is an innovative learner-centered concept. In describing features of e-learning, Tavangarian et al. (2004) pointed out that e-learning consists of various scholarly supports based on electronic tools of teaching–learning processes. The goal is to build knowledge based on personal experience, practice, and the learner’s knowledge regardless of the acquisition of this knowledge does not always cover the same extent.
Concerning the current conditions of e-learning courses, Bloom’s revised taxonomy provides an appropriate tool for enhancing the quality of the models adopted for e-learning in the moment of COVID-19. Because of its comprehensive nature, different researchers have used Bloom’s revised taxonomy (Castleberry and Brandt 2016; Krathwohl 2002; Hubalovsky et al. 2019) for any characteristic of the learning process that needs validation and assessment of the extent to which learning objectives are designated.
Further can be inferred from reviewing the research background on e-learning environments. Several studies examined learning environments in terms of assessment: these include investigations that covered areas such as designing e-learning environments and their connection to the internet of things (Freigang et al. 2018); e-learning and traditional learning environments, dynamic and structural elements of the learning environment (Dron 2018); examining realities in the application of e-learning tools into learning and teaching in universities (Al-Hamad et al. 2020); explaining assessment approach and educational context for studying features and advantages of new tools employed in education and the outcome of using this approach (Martens et al. 2019).
Another group of studies (Wang and Wu 2008) conducted in different environments with various research methodologies suggested that students who receive effective, timely feedback in e-learning environments exhibit better and higher-quality performance in e-learning. These studies showed the importance of providing a framework to assess student learning impact by emphasizing assessment during the learning process (Wang et al. 2019). The literature on e-learning environments points to the need for further systematization requires the development of models for designing several constructs, such as user-centered design, educational diversity, blended learning spaces, and facilitating mixed or blended learning (Freigang et al. 2018).
An e-learning environment is an atmosphere based on personal characteristics consisting of e-learning (human and non-human) components. This environment enables addressing weaknesses of the learning environment through interventions while addressing problems in physical environments (Dron 2018). More satisfactory methods can be used as a reference to develop models that can agreeably display student behaviors (Wang et al. 2019). Those methods represent “instructors’ use of web-based computer-aided tools for learning” (Tatnall 2020).
Learning in bloom’s taxonomy
This section discusses several models used to develop ideas on e-learning, beginning with a discussion of Bloom’s taxonomy and its comprehensive interpretation, pursued by a review of TPACK. Then a parallel model is drawn between existing models to better explain the research findings along these lines.
The mastery of E-learning characteristics can diverge into four measurements: professors, students, information technology, and support from the university (Selim 2007). The issue is uncovering how different faculty use of the e-learning environment is influenced by their opinions and preferences when they need to understand web-based activities and computer-aided learning (Tatnall 2020). Thus, a deep knowledge of professors’ goals in adopting an e-learning environment is required to identify challenges and factors involved in the time of COVID-19 and to determine their openness to the e-learning environment and the solutions proposed in this regard. To this end, Bloom’s taxonomy (Bloom et al. 1956) presents a good candidate for assessing how the process of imparting learning is affected by the learning environment and the professor’s familiarity with teaching–learning approaches. Bloom et al. (1956) developed a classification of learning objectives to help professors assess course materials and test results, expecting that the model would classify cognitive functions in some way systematically.
Bloom et al. (1956) identified six different levels of objectives: (a) knowledge focuses on keeping, recognizing, and remembering information; (b) understanding enshrines the organization of ideas and interpretation of information; (c) application concerns problem-solving and applying details and principles; (d) analysis centers on dissecting a whole into its parts for learning; (e) synthesis or creating represents synthesizing a combination of ideas to shape a new important thing; and (f) assessment which is the highest level on Bloom’s taxonomy and focuses on a judgment about problems and resolutions. Each level is above its preceding levels and combines them into a process of reaching maturity at higher levels. The affective domain represents a method in which individuals address something with their feelings, whereas the psychomotor domain involves motor skills.
Bloom’s revised taxonomy has considerably used by many authors (Franchi 2020; Dickinson and Gronseth 2020; Sheth et al. 2020) and consists of two dimensions: (1) metacognitive dimension including remembering, understanding, applying, analyzing, and creating, and (2) knowledge dimension which was developed by Anderson and Bloom (Anderson and Krathwohl 2001; Lau et al. 2018) using a construct consisting of factual, conceptual, procedural, and metacognitive knowledge (Krathwohl 2002; Lau et al. 2018).
Factual knowledge includes features required for understanding and problem-solving, while facilitating the practical application of knowledge to experience and analyze facts. Procedural knowledge involves the implementation of processes to achieve results and functional efficiency. It also involves technical details beyond the level expected in factual and conceptual knowledge categories that require recognition of specific procedures or methods. Conceptual knowledge involves consistently linking individual concepts to each other, understanding complex processes as a result of developed factual knowledge, connection to matter-of-fact knowledge, and using the assessment, development, continuous evaluation, and planning dimensions in addition to discovery and teaching elements to mentor others in the area of factual knowledge.
And eventually, metacognitive knowledge, as the zenith of the knowledge measurement in Bloom’s research that includes fragments of factual, conceptual, and procedural knowledge, requires awareness of one’s cognition, ability to adapt to new processes and ways of thinking, plays a significant role in strategic thinking, and involves knowledge of past trends and application of cognitive dimensions such as observation, inference, surveys, and theorizing (Lau et al 2018). As a method for classifying academic goals that assess learner performance, Bloom’s original taxonomy can be used by instructors to classify the levels of learning based on the expected outcome of a program.
The revised taxonomy links the knowledge category to the cognitive process category, which supports developing learning strategies and facilitates learning assessment. Since its introduction, this classification has received considerable attention from many authors (Franchi 2020; Dickinson and Gronseth 2020; Sheth et al. 2020), showing its significance in educational domains and particularly in e-learning (Castleberry and Brandt 2016). Although instructors mostly use it to assess the extent to which learning objectives have been realized (Chyung and Stepich 2003), the classification can also be of interest during an educational crisis like the one posed by COVID-19 (Franchi 2020; Dickinson and Gronseth 2020; Sheth et al. 2020).
Education and learning during the COVID-19 based on bloom’s models and TPACK
The learning environment and the professor’s acquaintance with teaching–learning approaches are so influential that student learning can take place over a spectrum ranging from superficial to deep understanding.
In bloom’s classification, the first three levels (remembering, understanding, and applying) represent superficial learning, while the last three stations (analysis, synthesis, and assessment) represent deep learning. In physical classrooms where students and professors are physically present in the same place, it is possible to provide immediate feedback with professor–student interaction which can be viewed as the core of learning (Mirzaee 2020).
In actual classrooms, professors often experience complications regarding the individualization of students learning, and simultaneously, they should consider the average learning ability of students to complete the teaching–learning process at this moderated level. But in technology-enriched environments, including the space for the e-learning method, although instant feedbacks from the professor are rare, the professor can still individualize students’ learning to enable them to access learning outcomes (Dron 2018).
Indeed, this requires an e-learning system where the professor, the learner, and the learning environment are all prepared to bring about the expected results. Students and professors must be thoroughly ready to put much more effort into learning and teaching. Moreover, a professor in a technology-enriched environment also needs techno-pedagogical skills (Svensson and Östlund 2007; Woldab 2014).
In line with the advancement in technology, TPACK (Koehler et al. 2012) was developed by adding technology as a new layer to a model proposed earlier by Shulman (1986). Mishra and Koehler (2006) put forth technological knowledge based on the definition of information technology. From this point of view, technical knowledge is beyond the traditional opinion of computer literacy. They believed that technological knowledge provides a deep understanding of how a diverse range of things are involved in applying information technology to the development of information, communication, and problem-solving throughout one’s life (Mishra and Koehler 2006).
Components of the TPACK model
In this view, the TPACK model consists of seven components (Fig. 1) which can be matched to the six types of teaching by faculty members in the time of COVID-19 based on a combination of different factors (infrastructure, organization, input, and process).Pedagogical knowledge (PK): It consists of a profound understanding of processes, approaches, and teaching and learning methods. That encloses educational goals, such as a general understanding of how students learn, classroom management and development, and curricula being implemented and assessed. In this circumstance, the instructors act in such a way that they would in an actual classroom. They operate digital texts as notes or manuscripts to lecture through audio messages. In this kind of system, the instructors apply the instructor-centered approach to education with limited interactions with students correspondingly to what they do in physical classrooms.
Technological knowledge (TK): In its modern sense, technology includes the understanding of how to install, set up, and use computer software and hardware. That comprises skills such as system administration, using the internet, and working with programs like Word. Here, the professor uses media resources together with digital texts. In this variety of e-learning systems, the professor adds several media resources to digital textbooks.
Content knowledge (CK): This represents the educators’ knowledge of the content they are supposed to teach and what students are assumed to learn.
Technological pedagogical knowledge (TPK): This is the knowledge of various technologies available for application in teaching and learning situations in complement to the understanding of how the way of teaching may change as a consequence of using these unrestricted technologies. In the two items above, the professor attempts to employ technological tools. He or she stresses professor–student interactions and the use of academic calendars, assignments, and learning sources. For this purpose, the professor prepares a lesson plan and offers it to the students, asking them to focus on learning objectives. In addition, prepared forums are used for this purpose (Calvo et al. 2013; Abel et al. 2009). Although the tools noted above can be used for teaching in these systems, the application of these tools depends on how well prepared the students are and how skilled and experienced the professor is in using these tools.
Technological content knowledge (TCK): This represents how specific contents are mutually linked to technology. Faculty members need to know not only about the content they teach but also about how these contents may change depending on technological requirements, since technological tools today may transform the structure of course subjects. Here, the professor acts as a mentor who guides the students. As a guide, the professor tries to establish professor–student interaction in the teaching–learning process where students are directed toward interactions with educational resources and content to realize learning objectives. In addition, attempts are made to establish collaboration between students.
Pedagogical content knowledge (PCK): This proficiency determines which pedagogical approach matches each specific type of content. Here, the professor not just acts as a mentor/guide but additionally uses constructivist approaches (Anjaswari et al. 2020; Hung and Nichani 2001) and open educational resources (Mirzaee 2020; Rolfe 2012) to enable students to produce educational content and materials independently. In addition, in this system, teaching assistants work hand in hand with professors to support students.
Technological pedagogical content knowledge (TPCK): This classification of knowledge is the outcome of and goes beyond the assortment of the three types of knowledge mentioned earlier, i.e., content, pedagogical, and technological proficiency. That requires a deep understanding of the concepts stated above and takes advantage of technology to structure content. In other words, this kind of knowledge enables solving educational problems using technology. The term abbreviated later as TPACK involves the establishment of an e-learning ecosystem that is defined by several features: (1) the professor, equipped with required skills, has been prepared for teaching in digital environments; (2) the students have acquired the skills required for affective and cognitive presence in the e-learning system before the learning process begins; (3) the course is offered based on the learners’ needs, flexibility, and learning resources with a profound vision in an interactive manner; (4) the teaching–learning process takes place based on interaction and collaboration by and among the students; (5) learning analytics (Macfayden and Dawson 2012) help professors and professor assistants in assessing the learning process and providing constant feedback to students to achieve learning outcomes; and (6) quality requirements are followed not only by the professor but also by managers. In addition, to ensure quality, all structures, inputs, and system processes are constantly monitored and enhanced. In other phrases, the sixth type of e-learning system focuses on the active participation of students in the teaching–learning process in a simultaneous and non-simultaneous manner (Mirzaee 2020).
A review of these seven components of TPACK with different levels of teaching based on learning levels in Bloom’s revised taxonomy suggests that the first to the third types fall into the primary categories, and the fourth to the sixth types fall into the contextual categories of TPACK. In other words, the highest level of Bloom’s revised taxonomy (i.e., exceeding cognitive level) in higher education. And during the COVID-19 pandemic and the university shutdown, almost all classes had to be upgraded to this higher level through capacity building and follow-up efforts (Fig. 2).
Fig. 1 TPACK model
Fig. 2 A deductive model based on the theoretical foundations
Rethinking e-learning in Iran during the COVID-19 pandemic
As reported by the UT’s chair (Nili Ahmadabadi 2020), a considerable challenge that we experience today in e-learning is a consequence of lacking infrastructure, permits, and access to educational materials. Concerning how universities view these new conditions, he stated that almost all higher education institutions reported that COVID-19 impacted learning and teaching processes, and two-third of them replaced their conventional practices with distant learning. Regarding collaborations, 64% of universities conveyed that intercollegiate cooperation was affected by COVID-19.
Half of this population pointed to weakened collaboration, 18% reported that this reinforces partnerships, while 31% believed new opportunities have emerged in this area. Studies show that most negative attitudes are found in Asia since 85% of higher education institutions believe that COVID-19 will have a considerable unfavorable impact on registrations. In other words, on the one hand, enrolments in the Iranian higher education system reached 4.5 million cases in the first half of the 2010s, while on the other hand, the policies recently adopted by the Ministry of Science, Research, and Technology to enhance the quality of higher education centered on lowering the quantity, with the number of students in the Iranian higher education system dropping to 3,616,114 and the number of faculty members dropping to 85,594 in the academic year 2017–2018 (Mirabi et al. 2019). Although e-learning can be effective in enhancing the quality of higher education, it is essential to use student-centered approaches and generally novel approaches to teaching–learning processes. Therefore, during the outbreak of COVID-19, emphasis can be placed on using mixed methods that combine physical classrooms with e-learning. For this purpose, policymaking and planning for the development of e-learning in Iranian higher education must require universities to adhere to the policies set by the Ministry at a macro level while developing strategic plans for universities to encourage e-learning. This modification can gradually bring university e-learning systems from the lower levels of Bloom’s classification up to the sixth level, where it is essential to be ready socio-culturally, pedagogically, organizationally, and technologically. Various studies can complete the discussion on e-learning.
Freigang et al. (2018) used interviews to present a model for e-learning environments. Their findings showed that the literature on the e-learning environment needs further systematization and development of models for designing such constructs as user-centered approaches, educational diversity, blended learning spaces, and facilitated blended learning. When combining technology with novel learning and teaching techniques, the focus must always be on creating educational value. This study identified 30 factors classified into five categories based on their contribution: (1) collaborative culture, (2) user-centered design, (3) educational diversity, (4) blended learning environment, and (5) facilitating blended learning. (Fig. 3).Fig. 3 Thirty factors identified by Freigang et al. (2018)
Thus, arguably, further research is needed into teaching using the internet of things, and previous success factors present a good starting point for further research into e-learning environments.
Dron (2018) compared learning environments in terms of their e-learning capabilities. He noted that this capability depends on the extent of opportunities and flexibility, professor–learner adaptability, and potential changes in the characteristics of the learning environment. Continuous interactions between professors, learners, and the learning environment can enhance learning. However, realistic environments are more complicated than this. They found that an e-learning environment relies on personal characteristics and consists of (human and non-human) components for e-learning. Such an environment needs environment-adaptable segments. Any learning environment can express an e-learning environment regardless of using digital tools. Even if the most advanced tools are in place, the improper structure can turn an e-learning environment into one which cannot support e-learning. In addition, for the same logic that most of the concerns in a physical learning environment are manageable, we must be able to manage weaknesses and faults in virtual or electronic learning platforms. Thus, within traditional educational establishments, the learning environment can be regarded as an e-learning setting merely for certain people since individuals are distinct from each other. Adaptive systems and the adaptability of e-learning agents can play a vital role in the learning environment, most notably in creating and enhancing communications. The most advanced e-learning environments provide excellent opportunities for communication, interaction, support, and challenges for better learning.
Martens et al. (2019) assessed the educational context in MeinKosmos, identifying effectiveness, efficiency, scalability, the autonomy of individuals, flexibility, adaptability, and customizability as requirements for an e-learning environment. Their findings suggest minor differences between the students in the control group and those in the MeinKosmos group, probably because of the negligible advantages of this educational platform and gateway, the small number of participants, low levels of collaboration, and distributed tasks. Moreover, MeinKosmos is an effective tool that has enhanced the effectiveness of student performance compared to conventional content management practices. This cost-effective tool does not impose much higher expenses compared to traditional methods. The gateway can independently analyze the conditions of students. The system is flexible in terms of content and the number of users. Therefore, the approaches used by the new platforms can be generalizable to other learning systems and domains, while future research can provide people with brief information on meta-search techniques.
Wang et al. (2019) tried to present a framework to assess the impact of student learning. Using a framework consisting of four sections, namely data collection, data extraction, behavioral analysis, and process extraction, they found that students often complete all actions of one type first and then start the next type. They usually perform regulation actions instead of adding a new link or element. They often change the connection immediately after moving a part or an element. Some students have very irregular behaviors and some exhibit random behaviors. A smaller number of changes in the link after moving each element often results in a better understanding of chart elements and better modeling of background knowledge. Therefore, more formulated methods can be used to develop models that can better show student behaviors.
Furthermore, examining realities in the application of e-learning tools, Al-Hamad et al. (2020) found that distraction, misuse, disordered classes, ineffectiveness in achieving class objectives, oversimplification of the young generation’s efforts, lack of trust in technology, and absence of required skills are among the major obstacles. On the other hand, the possibility of interaction and higher levels of excitement are among the factors that encourage professors to incorporate technologies into their teaching. Therefore, it is essential to build a culture among instructors and professors to emphasize the importance of using technology in education. It is noteworthy to expand public awareness about e-learning by throwing public workshops, skill training courses, orientation programs for fresh instructors and professors, and graduate studies programs.
Integrating (SCPOT-R) model into universities’ e-learning systems
Although TPACK is the most widely used model in educational technology (Ottenbreit-Leftwich and Kimmons 2020), it has several limitations (Chai et al. 2011, 2013; Kimmons 2015) that prompted us to identify SCPOT-R.
Our results explain the applications of SCPOT-R during the COVID-19 pandemic. SCPOT-R consists of several subcomponents defined by the authors of this study as follows:SCR: universities’ sociocultural readiness knowledge during the COVID-19 pandemic
PR: universities’ pedagogical readiness knowledge during the COVID-19 pandemic
OR: universities’ organizational readiness knowledge during the COVID-19 pandemic
TR: universities’ technological readiness knowledge during the COVID-19 pandemic
SCPOT-R: universities’ knowledge about integrating sociocultural, pedagogical, organizational, and technological subcomponents into their e-learning system during the COVID-19 pandemic
Purpose of the present study
It is essential to understand what professors desire to expand their paradigms to an e-learning environment during COVID-19. This study aims to develop a comprehensive model applicable to e-learning environments to help the academic community during the pandemic. Findings from reviewed studies about university e-learning methods are experimented with in the constitution of a primary model.
Higher education institutions can use the model during the COVID-19 pandemic.
It is difficult to assess developments during crises. This requires research that goes beyond conventional studies by taking a functionalist approach and a new research approach based on the interpretive-symbolic paradigm. The present study is an applied one. It can help us predict expectations of institutions where e-learning processes exist, whether implemented or binding decisions in this area. The present study also contributes to the existing knowledge in this area by making it completer and more systematic. The audience of the present study includes the whole academic community in higher education institutions. Therefore, the presented theoretical and practical solutions could be helpful for higher education institutions. Thus, the main objective of this article is to adopt a model for e-learning readiness at the University of Tehran (UT) during the COVID-19 pandemic.
Methodology
In performing this study, the phenomenological approach has been employed, attempting to describe human experiences within the context where they happen (Streubert Speziale and Carpenter 2003). This research focuses on explaining the phenomenon of living studied as perceived by social actors. The case study here is the e-learning system used by the University of Tehran (UT) which experienced several problems in the initial stage of the COVID-19 outbreak.
The system ought to improve through readiness on various fronts. The readiness concept assumed as a context in which the SCPOT-R model needed to be identified. We drew on previous studies to address the existing gap by identifying two research questions. First, the faculty members asked for their opinions on what e-learning components should be given higher priority by UT during the COVID-19 outbreak. Then, we asked them to propose a final model for university readiness to confront COVID-19.
The phenomenological approach prompted a description of the major component involved in the phenomenon before we could properly understand the final model for university readiness to confront COVID-19. A selective coding system, followed by thematic analysis, was used since the questions asked here are open-ended. That conducts a more acceptable description and interpretation of the problem.
The coding process was then verified by applying the comments proposed by the second coder to ensure the elimination of biased coding in the first stage.
The interviews were completed in May 2020 by the participants who consented in advance. Since the COVID-19 outbreak had made in-person interviews impossible, we started the process by sending invitations to all 2000 UT faculty members at the following departments and colleges: Entrepreneurship, Law and Political Science, Literature and Humanities, Engineering, Economics, Foreign Language and Literature, Agriculture and Natural Resources, Graduate College of Environment, Physical Education, Theology and Islamic Knowledge, Islamic Thinking and Teachings, Social Science, Psychology and Educational Science, Geography, Modern Science and Technology, Veterinary Medicine, Management, Physics, World Studies, Fine Arts, Architecture, Institute of Biochemistry and Biophysics (IBB), Chemical Engineering, Electrical, and Computer Engineering, Mechanical Engineering, Industrial Engineering, Mine Engineering, Geology, Mathematics, Statistics, and Computer Science, Surveying Engineering, Caspian College of Engineering, Campus of Science, Fouman College of Engineering, Farabi Campus, Abu-Reyhan Campus, and Kish International Campus. After receiving consent from potential interviewees, the date-gathering process began. We contacted 603 professors through video calls, voice calls, and emails containing opinions from the members. To maintain the authenticity of the statements given by the interviewees and to avoid author-triggered bias, three main preconditions were assumed: (1) Participants freely expressed their opinions through direct speech; (2) attempts were made to make sufficiently consistent notes at all stages of data analysis; (3) the Interview scripts were emailed to faculty members of all UT departments for additional remarks and recommendations. Each interview—conducted mainly through voice and video calls—lasted about 20 min. Respondents demographic profiles are classified by gender, academic level taught, type of classes, and academic rank.
The demographic questionnaire was applied to collect information on variables such as gender, level taught (teaching degree), how classes were held (types of classes), and the academic ranks of faculty members, as indicated in Tables 1, 2, 3, and 4.Table 1 Composition of issues (respondents) based on gender
Gender Frequency Percent
1 Male 462 76/62
2 Female 136 22/55
3 Unknown 5 0/83
4 Total 603 100
Table 2 Composition of issues (respondents) based on degrees
Degrees Frequency Percent
1 Bachelor’s 71 11/77
2 Master’s 76 12/60
3 Master’s/PhD 102 16/91
4 Bachelor’s/Master’s 166 27/53
5 Bachelor’s/Master’s /PhD 154 25/54
6 Bachelor’s/PhD 12 2
7 PhD 19 3/15
8 Unknown 3 0/50
9 Total 603 100
Table 3 Composition of issues (respondents) based on types of classes
Types of classes Frequency Percent
1 Online 211 35
2 Offline 206 34/16
3 Both 167 27/69
4 Unknown 19 3/15
5 Total 603 100
Table 4 Composition of issues (respondents) based on academic ranks
Academic ranks Frequency Percent
1 Instructor 6 1
2 Assistant Professor 291 48/26
3 Associate Professor 163 27/03
4 Professor 133 22/05
5 Unknown 10 1/66
6 Total 603 100
Analysis
What components do you think have the highest significance for e-learning in UT during the COVID-19 pandemic?
Based on the findings of the study presented in Table 5 and as a finding of data analysis, four major overlapping themes emerged: (1) sociocultural readiness, (2) pedagogical readiness, (3) organizational readiness, and (4) technological readiness.Table 5 Research findings
Main components Subcomponents Source
Sociocultural Readiness Enhancing social responsibility in university Preventing the Outbreak of COVID-19
The implementation of quarantine protocols
The safeguarding psychological health of society at the time of crisis
Meeting the requirements for remote job
The setting culture for remote work
To turn national and international threats into constructive opportunities
The reduced energy consumption
Reducing air pollution and better protecting the environment
The openness to the culture of e-learning and e-teaching
Establishing educational justice To lift spatial and temporal restrictions
The possibility of equal access to educational content
Affordable access to education
Pedagogical Readiness Developing learning–teaching processes To present topics, questions, and assignments in an integrated manner over an interactive technique
The potential, diverse applications of stored knowledge
To increase the assortment of ways that can engage students in education
The question/answer processes
The improved processes used in Q/A sessions
To improve the learning experience by reviewing and replaying the videos from e-learning classes
Systematic access to educational content
Student–teacher networking over communication channels
The systematic documentation and storing of educational content
Enriching educational content The sharing of additional and diverse educational content
The simultaneous use of different sources while teaching
The ongoing process of interactive updating of educational content
The interactive reviewing and monitoring of educational content
The diversity in the methods of teaching and knowledge transfer
Continuity of learning The continuity of learning and teaching in a time of crisis
The continuous process of learning and teaching on holidays
The continuous process of learning and teaching on holidays
Maintaining fast and flexible connections with students
Organizational Readiness Continuation and development of e-learning Improving and enhancing the existing infrastructure based on the feedback
The enhanced e-learning for teachers
The enhanced e-learning skills among students
The motivation and increased belief in information and communication technologies
The reinforcement of autonomy and self-paced learning among learners
Time management Saving transportation time
The flexible timing of e-learning classes
Focused, briefed teaching
The possibility of re-accessing recorded content at a convenient time
Turning in assignments online and on time
Improved assessment and supervision over the class The continuously monitor and assess the classes by universities
Continuous monitoring and assess the classes by universities
The possibility of automatic rollcall
Enhanced assessment of teachers
The improved process of evaluating and rating students
Technological Readiness Hardware The reinforcement of the tools and resources needed for teaching in e-learning classes
Offering support to students with financial difficulty to assist them in buying a smartphone or a laptop computer
Software To develop educational multimedia content for every class
To have a platform that allows teachers to upload multimedia content
The heightened speed of delivering and conveying educational content
The diversity in multimedia content
The possibility to record
Theme 1: sociocultural readiness
First component: enhancing social responsibility in the university
The most crucial point to note is to prevent the spread of COVID-19 explicitly expressed by the faculty members. That can be done by following the WHO protocols in connection with the COVID-19 pandemic. Many participants believed that undertaking quarantine protocols by universities (as instructed by the WHO for people dealing with COVID-19) is a step forward toward the social responsibility of universities. Additionally, during the pandemic, academics face significant challenges such as fear, anxiety, isolation, obsession, limited communication, prominent presence in cyberspace, ambiguity, a disordered biological clock, physical problems caused by sedentary life, and psychological circumstances, threatening psychological health of people and can unfavorably influence their ability to learn. During this time, most academics and society in general redirect their activities toward remote working.
According to the finding from accomplished interviews, the COVID-19 pandemic delivers possibilities for e-learning that should be used to the most elevated extent possible for learning and teaching purposes by turning the threats at national and international levels into constructive opportunities.
Some of the interviewed faculty members believed that the current defeat in resources is more than ever, and that requires necessary actions to save these resources. Some faculty members also pointed to the constant disinfection of places and streets against this highly undesirable virus and called for greater attention to the environment to reduce pollution and provide enhanced environmental protection. Issues such as students’ discipline and responsibility, individual and communal identities, and educators’ role in flourishing students’ dexterities have faced substantial challenges and can be convalesced to some extent by promoting an educational culture based on e-learning methods.
Second component: establishing educational justice
Some of the interviewed faculty members pointed to e-learning as a model of acquiring knowledge, attitudes, and skills using such tools as mobile technologies that can facilitate the development of educational justice. Within the e-learning method, despite geographical locations, both local and international students have equal access to educational and academic resources without any limit on place and time.
Another point stressed by some of the interviewed educators is the possibility of equal access to educational content. Recording and keeping educational content allows learners to equally access the contents of each course, which does not happen by default in face-to-face or physical classrooms. The third issue is affordability and less expensive access to education with e-learning methods. Since attending in actual classes is occasionally costly for many students who cannot afford these classes because of financial difficulties.
Theme 2: pedagogical readiness
First component: developing learning–teaching processes
Concerning pedagogical readiness, it is essential to suggest topics, questions, and assignments in an integrated manner over an interactive system that provides e-learning platforms for visual, audio, and written feedback by students and professors. The participants stated that this creates an environment of increasing learning in classes.
The faculty members noted that diverse application of stored knowledge facilitates teaching–learning processes. On the one hand, delivering a competitive academic environment over e-learning platforms can reinforce learning and enhance creativity in teaching methods, as confirmed by the participants. And on the other hand, e-learning platforms are designed based on three forms: engagement, student–professor interaction, and sharing educational content.
As pointed out by professors, students can facilitate learning by asking questions and receiving answers, as an essential issue in classrooms.
E-learning classes incorporate different parts for improved question-answered sessions operated by professors and instructors to support students and answer their inquiries. Some faculty members acknowledged that e-learning methods allow students to learn more satisfactorily, working as a platform where professors launch their classes and supervise the education process. Students explore the course content, and professor assistants can help students and professors.
Another exclusive feature of e-learning classes is auto-archiving which suggests an unprecedented technique for archiving class content by supplying students with a chance to review/replay recorded learning materials such as videos, slide presentations, and notes.
Systematic access to educational content is another noteworthy point noted by the faculty members. In addition, student–professor networking over communication channels helps develop and facilitate learning processes. Systematic recording of course contents affects transparency in students’ learning and faculty members’ teaching methods.
Second component: enriching educational content
By sharing additional and diverse educational content, professors, professor assistants, and students can enhance the academic content. Concurrent usage of different sources and media in education can lead to considerably efficacious e-learning university courses.
In addition, some professors noted that continuous interactive reviewing and monitoring of educational content is essential in demonstrating the importance of educational goals. Furthermore, bringing diversity into teaching methods can heighten learning quality in students with different educational needs. That requires a constantly updated and interactive process of educational content, as stated by the faculty members.
Third component: continuity of learning
A point noted by the faculty members in this regard was the continuity of learning and teaching in times of crisis when some students or their families may struggle with COVID-19, which directly affects their ability to learn. That is why the respondents stated that these individuals’ learning and teaching processes should not be stopped and education should continue by providing special conditions and resources. Other points to note include the continuous process of learning and teaching on holidays, facilitating the organization of reparative classes under these conditions, and maintaining fast and flexible connections with students.
Theme 3: organizational readiness
First component: continuance and development of e-learning
It is paramount to improve and enhance the existing infrastructure based on the feedback provided. Some participants emphasized factors such as technical talents and self-paced learning skills, stating that each feature consists of particular habits, skills, attitudes, and knowledge. While enhanced teaching skills for e-learning were stressed, some professors still preferred traditional teaching methods because of insufficient e-teaching skills. Improving e-learning skills for students was pointed out following the same approach. Also, many professors pointed to the motivation and increased belief in information and communication technologies and reinforcement of autonomy and self-paced learning among learners as essential qualities contributing to successful e-learning.
Second component: time management
One advantage of e-learning noted by multiple faculty is the possibility of saving the time that had to be spent on transportation and travel to reach class locations. Unlike physical classes, which require on-time presence of students and professors, e-learning offers a much more flexible schedule for both students and professors.
The faculty members mentioned their efforts to teach briefly and to the point while involving students, up to the highest levels of learning, in different processes to help them actively feel their role in the learning–teaching process.
Eventually, the chance of re-accessing recorded content at a convenient time was the last factor noted by the faculty members in the interviews.
Some faculty described how they maintained the agenda of the course, using deadlines for assignments and exams.
Third component: improved assessment and supervision
In this regard, two components were identified: (1) the possibility of continuous observing and assessing the classes and (2) performing self-assessments by reviewing the contents and surveys.
Theme 4: technological readiness
First component: hardware
The first issue suggested by most of the interviewed faculty members, especially those with backgrounds in applied science, was the status and availability of the devices and tools needed for teaching in e-learning classes.
They asserted that tools like light pens could enhance teaching effectiveness. They also pointed out the necessity of offering aid to students with financial hardship to help them buy smartphones or laptops since many professors were concerned about students who did not have access to these tools.
Second component: software
Many professors pointed out the essentials of designing educational multimedia content for each course. They explained how creating multimedia learning environments must incorporate educational design principles and learners’ cognitive and metacognitive abilities. Representation of appropriate student-paced knowledge based on learner’s ability can reduce the cognitive load and enhance their learning discipline by integrating the presented materials and lowering the amount of information that needs to be memorized and processed.
On the other hand, the respondents mentioned their concerns about choosing high-quality content for e-learning and creating diversity in multimedia content due to the abundance of educational content. The last point to express in this respect is the possibility of capturing lessons and class information that allows absent students to catch up with the course process.
How can the model for the university e-learning system during the COVID-19 pandemic be formed?
According to the research findings, four topics: Sociocultural readiness with code 522 (21.85%), pedagogical readiness with code 960 (40.20%), organizational readiness with code 594 (24.78%), and technological readiness with code 312 (13.6%) were identified (Fig. 4).Fig. 4 Model of university readiness to tackle COVID-19
Discussion
The COVID-19 outbreak and shutdown of Iranian universities and higher education institutions on March 2, 2020, following the COVID-19 pandemic, presented an opportunity to revisit the essential role of investing in e-learning systems. The opportunity can be used to fill the digital gap more seriously and professionally.
The findings of this study led us to four overlapping themes which, in order of importance, are: (1) sociocultural readiness, (2) pedagogical readiness, (3) organizational readiness, and (4) technological readiness. As noted earlier in the discussion presented in the theoretical foundations of the study, these four themes represent the basic requirements of e-learning during the COVID-19 pandemic. The factors noted in this section can be authentic under standard conditions based on an optimistic view, while attempting to bypass the conventional structures and explain the e-learning experience in UT requires serious attention to these themes because of the context it is located.
Pedagogical readiness is the first important point to note. Development of teaching–learning processes may encompass such factors as presenting topics, questions, and assignments in an integrated way over an interactive system, applying the stored knowledge in various ways, enhancing and diversifying student participation modes, improving QA processes, reinforcing learning through reviewing and replaying the videos recorded during a class, student–professor networking through communication channels, and systematically documenting and storing educational content. The evolution of teaching–learning methods requires establishing a network-based platform in which students hold access to the latest published ideas, data mining networks, and validated articles. This helps students in building a database linked to their areas of professional interest. Something new could happen when these data are integrated into one’s database. Communication of this type among students enables a university to keep these assets at a certain level. Major studies have confirmed the positive role of social networks in developing social capital, interactive learning, academic advancements, development of professional identity, and academic adherence among students (Harris 2013; Hommes et al. 2012). Previous studies have revealed the connection between the applications of social networks and the enhanced and facilitated teaching–learning process. Freigang et al. (2018) found that intelligent learning environments need further systematization. The link between these two factors was assessed based on a general view and regardless of the unique characteristics of students and the academic environment. Furthermore, the dynamicity and elegance of learning processes in social networks can enhance several advantageous qualities. As Wang et al. (2019) discovered, modeling student behaviors are an essential factor that facilitates learning.
The second component here is optimizing educational content. Features such as sharing content with others and concurrent application of unique resources can bring significant advantages in learning and education and act as a powerful instrument in improving university productivity and survival. According to Hau et al. (2013), trust is a significant factor in sharing information. In addition, Schauer et al. (2015) showed that the qualities and views held by sharers, relationships among sharers, universities, institutions, and personal knowledge are among the primary factors contributing to knowledge sharing. Razmerita et al. (2016) classified the factors involved in knowledge sharing into individual, organizational, and technological dimensions. Inherently, through interactive, continuous updating of educational content, improved agility, and constant monitoring of educational content, these factors can better explain the importance and priority of educational goals. Variety in forms of teaching facilitates the exchange of information and inspires students to track the content. There is no standard teaching method to conform in every class and meet all students’ educational necessities. Techniques such as group presentations, question-and-answer sessions, and lectures have existed for events of various majors.
Our findings are consistent with Dron’s (2018) results, as he underlined the adaptability of intelligent agents to their environment and generally the enrichment of opportunities to create an interactive environment. In contrast, Tanak (2020) found that only pedagogical readiness had a more influential impact on TPACK, while teachers employed all three TPACK segments.
However, in further explaining the issue of pedagogical readiness, the faculty members believed that despite the university shutdowns during the pandemic, education must persist, even stronger than before. Under these conditions, potential personal, family, or organizational issues may hinder the organization of these classes in terms of quality and quantity. Therefore, facilitations are required to organize remedial classes by maintaining a sharp, flexible line of connection to students. Undoubtedly, e-learning classes experience more problems than traditional classes, including fast, on-time feedback to students. Professors’ increased flexibility in responding to students can facilitate their learning. Al-Hamad et al. (2020) pointed out the application of e-learning tools to address potential problems in this style of education (Such as the distraction of focus, misuse, disordered classes, ineffectiveness in achieving class objectives, oversimplification of the young generation’s efforts, lack of trust in technology, and absence of required skills).
Organizational readiness diverges into three components: continuation and development of e-learning, time management, and improved assessment and supervision over the class. Academic education should shift individuals toward self-discipline, self-management, and self-determination. According to the faculty members, there are three levels of assessments: “assessment of learning, assessment for learning, and assessment as learning.” The first two levels of assessments are determined to be accomplished by the professor, while the third level is needed to be conducted by the students. That indicates the students should continuously monitor themself. Accordingly, the respondents noted such factors as enhancing the existing infrastructures based on the available feedback, improving professors’ e-teaching skills, improving students’ e-learning skills, and strengthening students’ self-directing and self-determination capabilities.
We found that learning is a process that takes place in an environment beyond controlling the student and leads to an encompassing experience or interaction with other individuals. Al-Hamad et al. (2020) emphasized the necessity of a skill-learning process. In addition, Martens et al. (2019) underlined the approaches taken within new contexts and described how to generalize them into educational contexts. Moreover, our respondents pointed to increased motivation and trust in information and communication technology. In this respect, Ausubel (1968) referred to “cognitive drive” as the most significant motivational factor contributing to meaningful learning. The factors identified under time management include saving transportation time, flexible timing, focused and brief teaching, re-accessibility of recorded contents at any time, and timely and online delivery of assignments. The point indicated here was confirmed by Dhawan (2020). He demonstrated that e-learning processes and techniques are practical and properties of online learning can protect society from adverse circumstances carried by COVID-19 by presenting some appropriate student-based strategies that should offer a significant capacity for flexibility in terms of place and time.
Constant monitoring and assessment of classes by the university and professors’ self-assessment through reviewing the content facilitate improved monitoring and appraisal of courses. According to the faculty members, greater attention paid to this point can directly influence the quality of their teaching and learning. In other words, assessment of the teaching performance of faculty members through self-assessment and evaluations by students is among the most productive ways to identify strengths and weaknesses in educational performance, preparing the ground for enhanced teaching quality. The third point noted by the participants is the possibility of automatic roll calls. Unlike classes where professors must directly check for attendance by calling out names, e-learning enables automatic attendance checks. The fourth point is enhanced assessment of professors. Most participants argued that the e-learning context provides an inclusive evaluation of professors, students, and professors’ assistants. And finally, the respondents also pointed to the improved process of evaluating and rating students. The transparency offered by this type of learning enables students and learners to estimate their potential scores in any course. According to Absari et al. (2020), teachers need to have a reasonable proficiency in knowledge constituents to be qualified to realize the educational goal and enhance performance. Since administrative support positively affects technology integration by teachers (Saeed Al-Maroof et al. 2021), organizational readiness can extend similar models including those containing TCK, TPK, CK, PK, and TK. It is also important to mention that this model has skipped organizational readiness to confront sudden shifts toward e-learning.
Concerning the points mentioned above and completing our findings, Rouhani and Mirhosseini (2020) showed that having an intelligent assistant and emphasis on artificial intelligence in e-learning portals run by universities play a vital role in the effectiveness of e-learning.
Another theme noted in different studies, sociocultural readiness, identified factors that enhance the university’s social responsibility to set educational justice. The preconditions underlined in connection to those factors include preventing the spread of COVID-19, implementing quarantine protocols by universities, securing the psychological health of society at the time of crisis, and protecting the environment. That is not entirely consistent with other studies. One part of the studies concentrated on how cultural beliefs may influence misinformation about preventing COVID-19 (Adom 2020).
At the beginning of the COVID-19 pandemic, numerous efforts done, while many lacked sound scientific grounds. UT faced ambiguity regarding its social responsibility and establishing educational justice. The issue was influenced by seeking help from global, local, public, and private institutions, particularly the measures adopted by the WHO. Another part of this issue concerned remote work for academics. Scardamalia pointed out the necessity of having a scientific forum working on health issues. University established itself as the most dominant player when it recognized remote work as a competitive investment or a resource to achieve a competitive advantage, especially in times of HR-based support.
However, it is essential to promote the pedagogical culture and e-learning practices needed during the COVID-19 exposure and focus on other critical factors like providing equal access to instructional content by eliminating temporal and spatial limitations (Zhang et al. 2007). That stands in line with Scardamalia’s opinion, which examined cognitive responsibility in schools and how it contributes to and facilitates learning. The study asserted the essential role of Knowledge Forum in health, epistemological agency, and mental responsibility. Universities became more effective when they regarded remote work as a competitive asset or a source of competitive advantage, particularly one rooted in human resources.
In addition, remote work allowed university professors to deliver more flexibility in gathering the highest talents globally. That enabled attempts to turn national and international threats into constructive opportunities. However, under these conditions, greater attention to be paid to other factors involved in university openness to e-learning during the COVID-19 pandemic.
Likewise, Zhang et al. (2007) examined socio-cognitive dimensions of knowledge building through a knowledge production project intended to create a collective public space for this purpose. Their findings suggest that in an environment properly reinforced for knowledge building, students could improve their learning toward a “knowledge-building discourse” by managing the link between their existing knowledge and what they are required to know. Equal access to educational content and more affordable education indicate the importance of educational justice. Currently, educational inequalities represent a critical issue in educational planning that immensely contributes to improvements in higher education. Educational planners play an outstanding role in facilitating the route for developing all talents existing in students and providing all students with continuous and equal opportunities based on capabilities. That was also confirmed by Ma et al. (2016), who studied rotational leadership models in elementary schools and their role in social networks and discursive shifts. The method employed by their study to map collective cognitive responsibility can provide students and professors with proper analytical tools used in knowledge-building classes and in providing continuous feedback. Here, further cooperation among students in new groups helps them advance opportunistic ideas to develop their knowledge.
The last theme, namely technological readiness, emphasized software and hardware components. Confirming the role of technology, Ayebi-Arthur (2017) found that technology helps students overcome obstacles at difficult times. However, appropriate technological infrastructure is a prerequisite for online learning. Infrastructures must be strong enough to enable continuity of service during and after the crisis. That is in line with Dhawan (2020). Various studies have shown that technology integration needs systematic training to enhance teaching based on a proper understanding of learning theories (Choi and Young 2021; Tanak 2020). Most teachers use technology for motivation or in word processing or data retrieval applications (Choi and Young 2021; Tanak 2020). But during COVID-19, it is necessary to note that technological readiness is not just a motivational context, and disguised characteristics such as the emotional status of students should also be measured and taken into account accordingly.
Although direct teaching experience will improve the effectiveness of technology integration, it is essential to remember that youthful instructors with sufficient knowledge of technology can complete the work of elder professors lacking expected proficiency in working with related tools. A recurring theme in the interviews was the class duration and how various factors could affect efficient class time. Fair education and appropriate technological readiness are achievable by taking the points noted before into account. Thus, the final research model for this study can be illustrated in the design shown in Fig. 5.Fig. 5 Final research model
Limitations and future research
This study had several limitations including the process for implementing the SCPOT-R model that was designed only for UT. It is important to note that most of UT’s professors are aged professors who mainly concentrated on pedagogical and technological readiness, while the remaining relatively younger teachers emphasized the importance of sociocultural and organizational readiness. Therefore, factors such as academic rank, program and degree taught, gender, and field of study influenced the process of model identification. Although we tried to implement the study in all UT departments and faculties, the factors noted above might have shifted the focus to more or less different issues. Therefore, the analysis was duplicated by considering the opinions of university directors, graduates, staff, students, parents, and even members of the wider society. Another limitation concerned the data collection method. We focused on a phenomenological interpretive approach. Due to the lockdowns, we could not interview in person with all the 603 faculty members. So other means of communication like video calls, voice calls, emails, and phone calls have been used for data gathering in an attempt to address this problem. In addition, the second coder reviewed the extracted themes and the concepts to validate the identification of the content.
Conclusions
The case study of this research is the e-learning medium used by the University of Tehran. A platform rendered has carried several problems for users in the initial stage of the COVID-19 outbreak, while its improvement process has continued through readiness on various fronts. A proposed model is designated to enhance readiness on diverse fronts. We proposed a model formed to enhance readiness on various fronts. The level of preparation is supposed as the context for the imprint of the TPACK approach. We drew on previous studies to address the existing gap by identifying two research questions. First, the faculty members requested their opinions on what e-learning components should be given higher priority by UT during the COVID-19 outbreak. Then, we asked them to propose a final model for university readiness to confront COVID-19. The phenomenological approach prompted a description of the major component involved in the phenomenon before we could properly understand the final model for university readiness to encounter COVID-19. We assessed all the themes mentioned earlier through Bloom’s revised taxonomy and the TPACK model for e-learning in UT. A focused emphasis on all these factors can represent a strategy for learning during the COVID-19 pandemic. As mentioned in the discussion of two Bloom’s models, the highest level in Bloom’s revised taxonomy was metacognition prerequisites which are explained based on TPACK. Given our findings, the model needed further modification to apply within the UT. Thus, we attempted to identify a four-factor model, i.e., SCPOT-R, for e-learning readiness. The TPACK implementation within the UT needs to focus on SCPOT-R. That is to say, the performance of this model at UT should rely on the model identified in this study. The ambiguities involved in this model can also be clarified using these four types of readiness. Modeling is not necessarily a means of clarifying ideas. Instead, it seeks to identify what something means in connection to other things. Therefore, the robustness of elements points to a whole that defines or redefines these elements. Indeed, neglecting any of these four themes in the time of COVID-19 means a mere focus on e-learning with no consideration of context, situations, or threats involved. Thus, we can claim that our proposed method is a more interpretive approach than an optimistic view. So, the final research model (SCPOT-R) can lay the groundwork for TPACK implementation during the outbreak of COVID-19.
In addition, the four identified themes regarding preparation, i.e., sociocultural readiness, pedagogical readiness, organizational readiness, and technological readiness, may interact within an integrated framework. In this logic, we cannot ignore the interconnectedness of these factors when it comes to e-learning during the COVID-19 pandemic, and teachers and professors must have sufficient preparation in all four identified themes to fulfill academic objectives and improve university performance.
Recommendations
A set of recommendations can be listed based on the findings of the present study:∙ In line with their social responsibilities, universities should develop protocols and create conditions under which people can maintain their access to education with the least financial, safety, and social expenses.
∙ University shutdowns during the pandemic do not mean stopping the learning and teaching process. Applying specific tools and resources across the academic community can help educational institutes and universities to sustain the educative process.
∙ Within an e-learning environment, the simple presentation of content, knowledge, and information to students in a linear sequential way can return a rich set of tools and information resources that learners can use to develop their learning trajectories.
∙ Ongoing and sufficient supervision and assessment by professors and students over educational, research, and technological activities lead to further effectiveness and increasing success in turning potential threats into desirable opportunities.
∙ By enhancing capabilities, insights, mindsets, and skills for both educators and students, universities play a vital role in using e-learning platforms.
∙ Selecting educational content that fits e-learning classes is a big step toward realizing learning objectives and facilitating the learning process in the lack of other drivers. Thus, students and professors should continuously review the educational content presented in the classes.
∙ Universities should consider structural changes in the development of their academic programs to provide the required base.
∙ By determining the factors involved in time wasting and learning the techniques for efficiency in time management, the e-learning methods will be more straightforward for students and professors. That can lead to desirable modifications in personal and organizational practices when working in such contexts.
∙ Within an e-learning platform, individuals should be motivated to find new skills and bring innovation and creativity or change their habits depending on the conditions experienced by users.
∙ The e-learning classes should be formed into student-centered sessions as much as possible.
∙ To maximize efficiency, e-learning classes should benefit from the capability of teaching assistants. That will also enrich their educational experience.
∙ Providing tutorials and applied instructions for professors and students on how to use the e-learning platforms is critically essential.
∙ All e-learning materials should be appropriately in line with course materials.
∙ Blended methods (e-learning, physical classes, and self-study) should be incorporated into teaching.
∙ The university e-learning program can be augmented using the features offered by other learning platforms.
∙ Supplying the equipment and facilities can lead to effective participation of students and professors in e-learning platforms (presenting assignments, facilitating Q/A sessions, encouraging active participation in the class, providing professors with simultaneous access to the system for courses with multiple professors, providing laptops, microphones, webcams, whiteboards, light pens, practical sessions, grants, and preparing slides).
∙ E-learning platforms can be developed and optimized with the support of relevant student-oriented startups within universities.
∙ Both students and professors should have constant access to a high-speed internet connection with suitable bandwidth.
∙ Using attractive visual features could improve the quality of e-learning platforms.
∙ It would be much more satisfactory to organize the classes during off-peak hours at a time agreed upon by students and the professor.
∙ Students and professors should have reliable access to supportive experts within e-learning platforms to ask for assistance with potential problems.
∙ Incentives and promotional directions for professors and students regarding e-learning platforms can improve the popularity of such systems.
∙ Dedicated professors and instructors who prepare electronic content should be offered rewards and incentives.
Abbreviations
COVID-19 2019 Novel coronavirus
E-learning Electronic learning
SCPOT-R Sociocultural readiness, pedagogical readiness, organizational readiness, and technological readiness
UT University of Tehran
WHO World Health Organization
Acknowledgements
We owe our findings in this study to the excellent academic community at the University of Tehran, who honestly helped us. It was impossible to complete this study within such a short time frame without the support and assistance of many individuals who helped us throughout this process. We appreciate and thank this wonderful community, hoping to have taken a small step toward academic advancement by publishing the findings of this study. Any feedback or comment we receive about the shortcomings of this study will help us in our future research.
Author contributions
All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by [JP] and [FO]. The first draft of the manuscript was written by [FO] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Example: CRediT taxonomy: Conceptualization; Formal analysis and investigation; Writing—original draft preparation; Writing—review and editing; Resources: [FO]; Funding acquisition: [The authors did not receive support from any organization for the submitted work]; Methodology; Supervision: [FO] and [JP].
Funding
The authors did not receive support from any organization for the submitted work.
Data availability
All data generated or analyzed during this study are included in this article.
Declarations
Conflict of interest
The authors of the present manuscript declare that there are no conflict of interest.
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| 0 | PMC9748394 | NO-CC CODE | 2022-12-15 23:22:42 | no | SN Soc Sci. 2022 Dec 14; 2(12):276 | utf-8 | SN Soc Sci | 2,022 | 10.1007/s43545-022-00566-7 | oa_other |
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Original Article
Income inequality: a recipe for youth unemployment in Africa
Mwakalila Enock [email protected]
grid.442459.a 0000 0001 1998 2954 University of Dodoma, P.O. Box 259, Dodoma, Tanzania
14 12 2022
2023
3 1 1517 7 2021
5 12 2022
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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Youth unemployment is a problem in Africa such that young people face almost double the unemployment rate as adults. With the booming population on the rise, youth unemployment can turn into a major catastrophe in the continent if not addressed. This study presents empirical evidence on how income inequality accelerates the problem. The study uses panel data from 42 African countries spanning 29 years from 1991 to 2020. The dependent variable is youth unemployment, and the independent variable is income inequality. The control variables are gross domestic product (GDP) per capita, population growth, political stability, foreign direct investment, gross capital formation, and political stability. The study employs the Generalized Method of Moment (GMM) model for estimations. The results imply that income inequality positively impacts African youth unemployment, which varies across different income levels. Therefore, measures must be formulated to combat income inequality, such as increasing productivity among small-scale farmers, robust social protection programs, minimum wages, and better access to financial services for young people on the continent.
Keywords
Youth unemployment
Income inequality
Africa
GMM
issue-copyright-statement© Springer Nature Switzerland AG 2023
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pmcIntroduction
Youth employment is an essential element of a strong base in any country. Having a decent job is vital for young individuals and their future. Still, it also has a domino effect on local societies, governments, and the world altogether (International Labour Organization 2020). Though various factors cause high youth unemployment, income inequality undeniably exaggerates the problem. Numerous jobs, mainly the lucrative ones, are exclusively accessible only to young people from wealthy backgrounds. There are various imaginable explanations for this pattern. For beginners, the utmost prestigious educational background is necessary for high positions, and that education is expensive.
Furthermore, attractive jobs usually require entry-level internships with little or no payment. This becomes difficult for graduates with low-income family backgrounds as they do not make enough money to support themselves (Thompson 2012). In countries like Australia, income inequality can incentivize rich people to reinvest more of their wealth in their domestic countries. In contrast, rich Africans starch a large portion of their wealth abroad and cause capital flight. According to the UN Economic Development report, African countries lose an estimated US$88.6 billion each year, equal to 3.7% of their economic output, in a capital flight, mostly illicit (Fox 2020).
Growing income inequality decreases demand in Africa as it reduces the consumption share of GDP. The reduction of demand causes productive investment to diminish hence unemployment. In unequal-income countries, corrupting democracies, abuse and exploitation of workers, and a weak safety net for the vulnerable or poor are more likely to exist. All these circumstances fuel unemployment in the society. However, the public debate surrounding wealth and income inequality has given more attention to reduced growing slums, social cohesion, labor exploitation, and middle-class household pressure. But one impact has received relatively little attention: youth unemployment. The relationship between income inequality and youth unemployment in Africa is a rich mine for more research. Therefore, the contribution of this paper is to bring together evidence from empirical data that income inequality accelerates youth unemployment in the continent (United Nations 2020).
During the 19th and most of the twentieth centuries, inequality increased dramatically globally, showing widening gaps in GDP per capita between countries as developing countries grew slower compared to the advanced economies. The restoration of worldwide economic collaboration in the middle twentieth century steered a period of growth and development. Afterward, GDP per capita growth rates speeded in developing countries, mainly in Asia, causing income levels convergence across countries (Bourguignon 2015). Many households were elevated out of poverty. Thus, income inequality in the world first calmed and then quickly declined over the last thirty years. However, it should be noted that some regions did not see income convergences with developed countries. For instance, in Sub-Saharan Africa, on average, income growth were slower than in Asia. COVID-19 crisis has likely reversed some gains in the reduction of global inequality. It will likely worsen global inequality because, overall advanced economies can deal with the crisis by having more resources (Ferreira 2021).
While the reduction of global inequality over the last three decades has been significant, inequalities have increased within country, particularly in advanced economies. Within-country inequality has increased in most countries. Over the past thirty years, 90 percent of advanced economies and more than half of the countries have seen an increase in income inequality, with some countries increasing their Gini coefficients beyond two points. Some key reasons behind the surge in within-country income inequality shown in the literature include globalization, technological progress, commodity price cycles, and national economic policies.
Income inequality in Africa
Despite the remarkable progress recorded in recent years, Africa suffers from widespread uneven income distribution. Income inequalities are predominant in all the sub-regions across the continent. Nevertheless, some countries are inclined to show very high disparities, particularly in middle-income groups such as Namibia and South Africa (Africa Development Bank 2019). Africa does not give a single picture of income inequalities. The highest outstanding increase in income inequality is found in the Central African Republic and South Africa, with Gini coefficients rising from 43 to 56 and 58 to 67, respectively (Africa Development Bank 2019). The most income-unequal countries in Africa are from the Southern part of the continent, with Comoros, Namibia, South Africa, Botswana, Angola, Swaziland, and Lesotho in the top ten. Therefore, these statistics give a disturbing image and show how critical the inclusive growth plan is for the continent (Ayodele Odusola 2017).
Inequalities affect less the north sub-region than the southern sub-region countries and less in eastern sub-region countries than in western ones. The Gini index distribution in Africa shows a split effect along with concentration. Bordering countries that hold cooperative trade patterns have the same income inequalities. These income inequalities are more focused on the west and south than the east and north (World Bank 2021).
Income inequalities are prominent when measured by the income share that goes to the poorest individuals. The richest capture the largest share of income in African countries, thus aggravating income inequalities between the rich and the poor. This income inequality distribution is conveyed by inequalities between urban and rural areas, with the poor being concerted in rural areas (Africa Development Bank 2019). According to Fig. 1, the top 1% owns more than half (54%) of the overall income in the continent, while the bottom 50% own only 8% of the total income share. In Tanzania, for instance, the mean share of the lowest 50% of the population is only 5.2% of total income. In comparison, the mean share of the top 10% is about 51.5%.Fig. 1 The percentage share between income earners in Africa for the year 2020.
Source: Author's calculation
Inequality in Africa varies across different income levels. Income inequality is more severe in lower-income countries like Central Africa and DRC Congo (Abebe Shimeles and Tiguene Nabassaga 2017). From Fig. 2, the top 1% own more than the half (57%) share of the total income, followed by upper-middle/higher-income countries, with the top 1% owning 51% share of total income. The lower-middle-income countries have a 51% share of total income held by the top 1%. What leads to such a high-income inequality level in Africa compared with the rest of the world? This matter remains open and poses challenges in addressing the issue due to the limitation of factual data. Another challenge is the diversity and specificity of Africa's political and economic structures, molded by its colonial heritage and history (Fig. 3).Fig. 2 Percentage of total income owned by the top 1% according to income categories for 2020.
Source: Author's calculation
Fig. 3 Trend of youth unemployment in Africa (1991–2019).
Source: Author's calculation
Youth unemployment in Africa
Youths are Africa's most significant asset, which is rapidly increasing. Two hundred million people aged between 15 and 24 will likely double by 2050 to over 830 million (African Development Bank 2017). If correctly harnessed, this growth in the working-age population can increase inclusive economic growth and productivity across the continent. There are nearly 420 million youth aged between 15 and 35 years in Africa, but one-third of them are discouraged and unemployed, and the other third are vulnerably employed. Only one in six young people is in wage employment. Youth people face almost double the unemployment rate as adults, substantially varying by country. According to the World Bank, youths are responsible for 60% of Africa's unemployment. In North Africa, the youth unemployment rate is 25%. However, the problem is severe in other regions, such as Botswana, Senegal, the Republic of the Congo, and South Africa, among others (Ighobor 2017).
According to the African Development Bank, youth unemployment is twice as high as adults in many African countries. Only 3.1 million jobs are created, whereas 10 to 12 million youth enter the workforce each year, leaving vast numbers of unemployed youth. The costs of youth unemployment in Africa are severe: unemployment influences migration out of Africa, accelerates poorer living conditions, and encourages social unrest in the continent. In particular, youth unemployment is a failure to take advantage of the continent's most significant asset for economic growth: its enormous and increasing population of young talented people. The informal sector employs most of Africa's youth, presenting its problems. The absence of salary jobs drives young people into the informal sector, accounting for nearly 80% of total employment in some countries. Young people and women are more likely to engage in the informal sector than other groups (Ighobor 2017).
Youth unemployment in Africa varies according to the income level of the country and the region it belongs to. From Source: Author's calculation Fig. 4, youth unemployment is severe in the lower-income countries (30% youth unemployment rate) compared to lower-middle (20% youth unemployment rate) and upper-middle/higher-income countries (8.6% youth unemployment rate). Regional-wise, youth employment is severe in Southern African countries such as South Africa and Namibia, with a 42.8% of youth unemployment rate. According to regions, East African countries have the lowest unemployment rate for youth, with an 11% of the youth unemployment rate (see Source: Author's calculation Fig. 5).Fig. 4 Youth unemployment rate according to income categories in 2020.
Source: Author's calculation
Fig. 5 The youth unemployment rate in each sub-region in 2020.
Source: Author's calculation
Theoretical and empirical review
This study adopts the "political economy" approach, suggesting that inequality is detrimental to growth through different channels such as rent-seeking activities, social instability, and hence youth unemployment. The rent-seeking models highlight that inequalities encourage the disadvantaged population to become involved in rent-seeking activities such as corruption and government subsidies. These take away wealth from the economy and damage productivity and growth (Dabla-Norris 2005). The political economy approach highlights the relevance of the negative implications for growth brought about by the social and political instability, in turn, brought about by inequality. It points out that inequality can lead to social unrest. This shortens the duration of the governments in power, which, to maximize their "inter-temporal utility," reduces the time horizon of their economic plans like boosting investment to increase youth employment. More precisely, governments become more inclined to prioritize the current consumption over investment reducing the long-term youth employment (Fernando Delbianco 2014). Galor and Zeira (1993) focused on credit market imperfections. They pointed out that inequality reduces investments in human capital and assuming that credit constraints are binding, higher inequality reduces growth.
The following literatures explore the impact of income inequality on youth unemployment in Africa, which is a problem of the study. Prof. Mthuli Ncube and Anyanwu (2012) examine the impact of income inequality on unemployment across the Middle East and North Africa (MENA) countries. The study found that a one percent increase in income inequality will cause an increase in the unemployment rate by 0.78 percentage points. Yuming Sheng (2011) explored the relationship between income inequality and persisting high unemployment by empirically studying the US economy from 1941 to 2010. Using wage share in personal income (aggregate) as a measure of income inequality, he found a robust trade-off between the wage share in personal income and the unemployment rate. It means that income inequality and the unemployment rate are positively correlated. The results abide with another study by Mercedes Monfort, Javier Ordóñez, and Hector Sala (2018) that examines the convergence patterns of unemployment and income inequality. They found that there is no trade-off between inequality and unemployment to be exploited for economic policy and that the redistributive capacity of governments reduces unemployment. David Castells-Quintana and Vicente Royuela (2012) analyzed the relationship between unemployment and income inequality. The study found that income inequality harms growth hence unemployment among countries with a high level of urbanization and in countries with low levels of urbanization in which there is high and persistent unemployment. Barro (2000) and Ehrhart (2009) provide theoretical and empirical reviews on the several transmission channels through which inequality can affect long-run growth and unemployment. Barro suggested that higher inequality inclines to slow down growth in developing countries and boost growth in richer countries. The Kuznets curve, where inequality increases and later decreases in the economic development process, arises as a clear empirical constancy (Fig. 6).Fig. 6 Fitted regression line between youth unemployment and income inequality (1991–2020).
Source: Author's calculation
Ehrhart suggested that there are numerous channels through which inequality might be damaging to growth, specifically three economic explanations (the approach of endogenous fertility, the channel of the capital market imperfections, the argument relating to the domestic market size) and two politico-economic opinions (the method of endogenous fiscal policy and the political instability channel).
William Baah-Boateng (2016) empirically assessed Africa's leading causes of youth unemployment. He found that poor economic growth and population growth intensified African youth unemployment. The study also found that youth unemployment rates vary across gender and geographical location. This study includes population as a control variable that increases youth unemployment. Specifically in Ghana, William Baah-Boateng (2013) again presented the evidence that education and gender, and reservation wage increase unemployment. In Nigeria, the study by Patrick S. O. Uddin and Osemengbe Uddin (2019) examines the causes and effects of youth unemployment. The study found that youth unemployment in Nigeria is caused by population growth, corruption, education, and rural to urban migration. In Tanzania, Robert Msigwa and Erasmus Fabian Kipesha (2013) examine the determinants of youth unemployment in Tanzania. The study found that geographical location, gender, marital status, education, and skills are significant factors in explaining the difference in youth employment status in Tanzania. Anyanwu (2016) presents the features of youth employment in Africa and its determinants. The study found that economic growth, domestic investment, government consumption, inflation, and political stability influence African youth unemployment. Also found that the impact differs across sub-regions.
Mohamed Saney Dalmar, Ali Yassin Sheikh Ali, and Ali Abdulkadir Ali (2017) investigated the determining factors of unemployment in Somalia. The study found that external debt and population growth have a positive a significant impact on unemployment. In contrast, GDP growth, gross capital formation, and the exchange rate negatively and significantly influence unemployment in Somalia. Aiza Shabbir, Shazia kousar, Muhammad Zubair Alam (2020) aimed to analyze the short and long-run relationship between unemployment and macroeconomic variables in South Asian countries. They found that unemployment is negatively influenced by internet users, governance, fixed broadband subscriptions, mobile cellular subscriptions, and human capital. However, population growth and financial activity have a significant and positive relationship with the unemployment rate.
Gaber H. Abugamea (2018) analyzed the factors influencing Palestinian unemployment. The study found that inflation, GDP, external trade, and labor force are primary factors for unemployment in Palestine. Whereas GDP harms the unemployment labor force, inflation has a positive influence on unemployment significantly. The study by J D Urrutia, R L Tampis, and JB E Atienza (2017) aimed to frame a mathematical model for estimating and forecasting the unemployment rate in the Philippines. The results imply that population and the labor force rate significantly affected the unemployment rate, GDP growth, population, and GNI had a granger-causal relationship with the unemployment rate.
Athia Yumna, M. Fajar Rakhmadi, M. Firman Hidayat, Sarah E. Gultom, and Asep Suryahadi (2015) analyzed the impact of inequality on unemployment in Indonesia empirically. They found that income inequality harms growth while unemployment is severed affected by education inequality. The study also found a U-shaped relationship between inequality and unemployment. This relationship means that initially, inequality may not affect unemployment, but in the long run the impact is realized. The results abide with another study by Lin et al. (2009), who found that income inequality favors high-income countries but harms economic growth in low-income ones. The same result is attained by Shin (2012) from a theoretical standpoint. Herzer and Vollmer (2012) examined the long-run impact of income inequality on long-run growth. They found a negative effect of inequality on growth.
Similarly, Abida and Sghaier (2012) discuss the income inequality-growth relationship in Northern Africa (Algeria, Tunisia, Egypt, and Morocco). They found a negative relationship between income inequality and growth. But different results are found by José Javier Caloca Martinez (2020), who studied the relationship between income inequality and growth. The results found a positive relationship between income inequality and economic growth among low-income countries. Rohan Joshi (2017) analyzed the impact of income inequality and economic growth in Indian states. He also found that income inequality had a strong positive and significant influence on economic growth. There is a trade-off between the two macroeconomic variables. This result contradicts other studies and priori grounds. Lucas Chancel, Denis Cogneau, Amory Gethin, and Alix Myczkowski (2019) investigate that income inequality in Africa compared to other regions or countries from 1990 to 2007. The study found a very high-income inequality in Africa which equals India and Latin America. Central and Southern Africa are particularly unequal.
Fernando Delbianco (2014) explored the connection between income inequality and the economic growth of 20 the Caribbean and Latin American countries. He found that inequality is damaging to economic growth. However, higher inequality inspires economic growth for richer countries, and the relationship becomes positive.
Dr. Thieß Petersen and Dr. Ulrich Schoof (2015) argued that, on the one hand, income inequality has growth-harming effects, for example, declines in demand, social tensions, and political unrest, which lead to an increase in youth unemployment. But on the other hand, income inequality has growth-promoting effects such as investment incentives and more substantial performance incentives. Nevertheless, we should point out that despite a vast empirical literature on the link between inequality and growth, most of the studies reviewed in this section did not focus specifically on the income inequality-youth unemployment relationship in Africa. Studies on how income inequality affects youth unemployment in the continent are limited, with little evidence capturing the sample from recent years.
Data, model, and methodology
The study uses panel data from 42 African countries spanning 29 years from 1991 to 2020. The data are from secondary sources. Youth unemployment, GDP per capita, population growth, political stability, foreign direct investment, and gross capital formation (a proxy for domestic investment) data are from World Bank Indicators. The income inequality data are from the World Inequality Database (WID). Data on political stability are from World Governance Indicators (WGI). The datasets generated during and analyzed during the current study are available from the corresponding author on reasonable request. The choices of variables are supported by the following kinds of literature Abida and Sghaier (2012), David Castells-Quintana and Vicente Royuela (2012), Aiza Shabbir, Shazia kousar, Muhammad Zubair Alam (2020), Rohan Joshi (2017), William Baah-Boateng (2016), Mohamed Saney Dalmar, Ali Yassin Sheikh Ali, and Ali Abdulkadir Ali (2017), Anyanwu (2016), Dr. Thieß Petersen and Dr. Ulrich Schoof (2015), Prof. Mthuli Ncube and John C. Anyanwu (2012), Gaber H. Abugamea (2018). All variables are shown in Table 1:Table 1 Variable name, definition, and source and expectation
Variable Definition Source Priori expectations
unemplo Youth unemployment rate (% of total labor force ages 15–24) World Bank (WDI)
ineq Income inequality (top 10% share) World Inequality Database +
gdp GDP per capita growth (annual %) World Bank (WDI) −
Pop Population growth (annual %) World Bank (WDI) +
Politins Political stability and absence of violence—estimate of governance (ranges from approximately − 2.5 (weak) to 2.5 (strong) governance performance) World Governance Indicators (WGI) −
Fdi Foreign direct investment (% of GDP) World Bank (WDI) −
Gcf Gross capital formation (% of GDP) World Bank (WDI) −
The study formulates a dynamic econometric model for the regression analysis, consisting of coefficients and an error term. Therefore, the dynamic econometric model (autoregressive) is specified as follows:lnYit=ϕlnYit-1+γZit′+βXit′+dt+εit
where yit represents the dependent variable (youth unemployment), Z'it represents control variables, X'it represents an explanatory variable, dt represents the year dummy variable, and ɛit means the error term. Lastly, ϕ, β, and γ, represent the unknown parameters to be estimated. i and t represent country and time (year), respectively.
The study employs the Generalized Method of Moments (GMM) for estimation. Arellano and Bover (1995) and Blundell and Bond (1998) developed assumptions under which the study can use the GMM estimator to remove the problem of weak instruments (Bond 1991; Bover 1995).
Other models (such as pooled OLS, random and fixed effect) are weak when the lagged variables are correlated with the error term even if the study assumes that the disturbances are not to-correlated (Babajide Wintoki 2012). To reduce this problem, the study will employ the Arellano-Bond/Blundell-Bond estimator, which addresses the problem of omitted variable bias, endogeneity, and unit root effects in the choice of the instruments (Bond 1991; Bond 1998).
First, the study differentiates the variables to remove any major bias that may arise in the time-variant variable heterogeneity. Then these first differences are used as instrument variables in an equation with level variables (Roodman 2009).
The difference GMM corrects endogeneity by transforming all regressors by differencing. And therefore, the model is changed as follows:lnΔYit=ϕlnΔYit-1+γΔZit′+βΔXit′+dt+εit
Results and discussions
Table 2 represents regression results between youth unemployment and income inequality. In the first column (simple regression), the results suggest that income inequality positively and significantly impacts African youth unemployment. The positive coefficient implies that a one percent increase in income inequality increases African youth unemployment by 0.77 percent. The results correspond with the priori hypothesis of the study and other studies (Barro 2000; Mercedes Monfort 2018; Sheng 2011; Castells-Quintana 2011; Athia Yumna 2015; Anyanwu 2012).Table 2 Regression results between youth unemployment and income inequality
Model (diff.gmm) (diff.gmm) (diff.gmm) (diff.gmm) (diff.gmm) (diff.gmm)
Variables lnunempl lnunempl lnunempl lnunempl lnunempl lnunempl
L.lnunempl 0.396** 0.456** 0.455** 0.451** 0.437** 0.433**
(0.192) (0.185) (0.186) (0.186) (0.187) (0.188)
lnineq 0.771* 0.752* 0.752* 0.742* 0.749* 0.762*
(0.400) (0.385) (0.384) (0.383) (0.386) (0.393)
lngdp − 0.0144** − 0.0145** − 0.0139* − 0.0140* − 0.0137*
(0.00716) (0.00728) (0.00720) (0.00714) (0.00704)
pop − 0.00111 − 0.00167 − 0.00149 − 0.00153
(0.00457) (0.00451) (0.00462) (0.00463)
politi − 0.0219** − 0.0226** − 0.0236**
(0.00935) (0.00944) (0.00919)
fdi − 0.00259* − 0.00262*
(0.00154) (0.00154)
lngcf 0.00508
(0.00499)
Diagnostic tests
Hansen testa 0.214 0.211 0.174 0.253 0.301 0.309
AR (1) 0.217 0.178 0.178 0.183 0.190 0.192
AR (2)b 0.178 0.171 0.170 0.175 0.181 0.182
Observations 840 840 840 840 840 840
Number of id 42 42 42 42 42 42
Robust standard errors in parentheses
***p < 0.01; **p < 0.05; *p < 0.1
aTo test if the model is correctly specified, the Hansen test is performed. The result shows that the p-value is above 10% in all columns which means the study fails to reject the null hypothesis that the model is correctly specified
bAutocorrelation test results have the p-value above 10% in all columns the study fail to reject the null hypothesis that there is no autocorrelation
After including GDP per capita as a control variable in the second column, the results also imply that income inequality positively and significantly impacts African youth unemployment. The positive coefficient suggests that a one percent increase in income inequality increases African youth unemployment by 0.75 percent when other factors remain constant. This result abides with the hypothesis and other empirical literature.1 The GDP per capita as a control variable is statistically significant, implying that it affects youth unemployment in Africa. The negative coefficient means that a one percent increase in GDP per capita decreases African youth unemployment by 0.01 percent when other factors remain constant. This result abides with the hypothesis and other empirical literature (Anyanwu 2013; William Baah-Boateng 2016).
After including population growth as another control variable in the third column, the results still imply that income inequality positively and significantly impacts youth unemployment in Africa. The positive coefficient means that a one percent increase in income inequality increases youth unemployment in Africa by 0.75 percent when other factors remain constant. This result abides with the hypothesis and other empirical literature. The GDP per capita as a control variable is still statistically significant, implying that it affects youth unemployment in Africa. The negative coefficient means that a one percent increase in GDP per capita decreases African youth unemployment by 0.01 percent when other factors remain constant. This result abides with the hypothesis and other empirical literature. The population growth as a control variable is statistically insignificant, implying that it does not affect African youth unemployment. This result does not abide by the hypothesis and other empirical literature (William Baah-Boateng 2016; Uddin 2013).
In the fourth column, after adding political stability as another control variable, the results still imply that income inequality positively and significantly impacts youth unemployment in Africa. The positive coefficient means that a one percent increase in income inequality increases youth unemployment in Africa by 0.74 percent when other factors remain constant. This result abides with the hypothesis and other empirical literature. The GDP per capita as a control variable is still statistically significant, implying that it affects youth unemployment in Africa. The negative coefficient means that a one percent increase in GDP per capita decreases youth unemployment in Africa by 0.01 percent when other factors remain constant. This result abides with the hypothesis and other empirical literature. Political stability as a control variable is statistically significant, implying that it does affect youth unemployment in Africa. The negative coefficient means that the stronger the political stability the lower the youth unemployment. This result follows the hypothesis and other empirical literature (Anyanwu 2013).
In the fifth column, after adding foreign direct investment as another control variable, the results still imply that income inequality positively and significantly impacts youth unemployment in Africa. The positive coefficient means that a one percent increase in income inequality increases youth unemployment in Africa by 0.74 percent when other factors remain constant. This result abides with the hypothesis and other empirical literature. The GDP per capita as a control variable is still statistically significant, implying that it affects youth unemployment in Africa. The negative coefficient means that a one percent increase in GDP per capita decreases youth unemployment in Africa by 0.01 percent when other factors remain constant. This result abides with the hypothesis and other empirical literature. Political stability as a control variable is still statistically significant, implying that it does affect youth unemployment in Africa. The negative coefficient means that a one percent increase in political stability decreases youth unemployment by 2 percent. This result does abide with the hypothesis and other empirical literature (Anyanwu 2013). Foreign direct investment as the added control variable is statistically significant, implying that it does affect youth unemployment in Africa. The negative coefficient means that a one percent increase in foreign direct investment inflow decreases youth unemployment by 2 percent.
In the last column, after adding gross capital formation as another control variable, the results still imply that income inequality positively and significantly impacts youth unemployment in Africa. The positive coefficient means that a one percent increase in income inequality increases youth unemployment in Africa by 0.76 percent when other factors remain constant. This result abides with the hypothesis and other empirical literature. Gross capital formation as a control variable is statistically insignificant, implying that it does not affect youth unemployment in Africa. This result does not abide by the hypothesis and other empirical literature (Mohamed Saney Dalmar 2017).
Table 3 represents regression results between youth unemployment and income inequality based on the income categories. The first column suggests that income inequality positively and significantly impacts youth unemployment in Africa's lower-income countries like Burundi and Congo. The positive coefficient implies that a one percent increase in income inequality increases African youth unemployment by 0.78 percent when other factors remain constant. The results correspond with the priori hypothesis of the study and other studies (Barro 2000; Mercedes Monfort 2018; Sheng 2011; Castells-Quintana 2011; Athia Yumna 2015). All other control variables are statistically insignificant.Table 3 Regression results between youth unemployment and income inequality according to income categories
Income category (Lower) (Lower–middle) (Upper middle and higher)
Variables lnunempl lnunempl lnunempl
L.lnunempl 0.704*** 0.348*** 0.609***
(0.0948) (0.103) (0.189)
lnineq 0.871*** 0.711 0.722*
(0.287) (0.718) (0.346)
lngdp − 0.0110 − 0.00280 − 0.0210
(0.00918) (0.00592) (0.0147)
pop 0.00406 − 0.0257 − 0.00164
(0.0251) (0.0301) (0.0178)
politi − 0.0151 − 0.0589 − 0.0192
(0.0159) (0.0458) (0.0337)
fdi − 0.000588 − 0.00436* − 0.00255
(0.00196) (0.00261) (0.00332)
lncaf 0.00237 − 0.0130*** 0.0107
(0.00744) (0.00494) (0.0129)
Diagnostic tests
Hansen testa 0.431 0.171 0.997
AR (1) 0.001 0.100 0.130
AR (2)b 0.788 0.132 0.133
Observations 348 116 376
Number of id 16 6 20
Standard errors in parentheses
***p < 0.01; **p < 0.05; *p < 0.1
aTo test if the model is correctly specified, the Hansen test is performed. The result shows that the p-value is above 10% in all columns which means the study fails to reject the null hypothesis that the model is correctly specified
bAutocorrelation test results have the p-value above 10% in all columns the study fail to reject the null hypothesis that there is no autocorrelation
The results of the second column with lower-middle-income countries such as Kenya imply that income inequality has no significant impact on African youth unemployment. Foreign direct investment is statistically significant affecting youth unemployment in lower-middle-income countries. Its negative coefficient implies that a one percent increase in foreign direct investment inflow decreases youth unemployment by 0.4 percent. Gross capital formation is statistically significant affecting youth unemployment in lower-middle-income countries. The negative coefficient implies that a one percent increase in gross capital formation decreases youth unemployment by 1 percent. This result does abide by the hypothesis and other empirical literature.
In the third column, the results imply that income inequality positively and significantly impacts youth unemployment in upper-middle and higher-income countries. The positive coefficient suggests that a one percent increase in income inequality increases African youth unemployment by 0.72 percent when other factors remain constant. This result abides with the hypothesis and other empirical literature. All other control variables are statistically insignificant.
Table 4 presents the model's correlation matrix showing the relationship between variables. The results suggest that income inequality has a positive and statistically significant association with African youth unemployment. Other variables which are also positive and statistically significant with youth unemployment in Africa are political stability and gross capital formation. Economic growth, population, and foreign direct investment have a negative and statistically significant relationship with African youth unemployment.Table 4 Correlation matrix
lnunempl lnineq Pop lngdp politi fdi lncaf
lnunempl 1
lnineq 0.0928** 1
pop − 0.562*** − 0.126*** 1
lngdp − 0.189*** − 0.0139 0.126*** 1
politi 0.194*** 0.323*** − 0.204*** 0.0231 1
fdi − 0.00834 0.0882** 0.0785* 0.121*** 0.0150 1
lncaf 0.106** − 0.0895** − 0.0148 0.0525 0.0959** 0.101** 1
*p < 0.05; **p < 0.01; ***p < 0.001
Conclusion
Therefore, the study has provided empirical evidence that income inequality accelerates the youth unemployment problem in Africa. With financial status being the critical factor for employment, youths from poorer families are becoming increasingly discouraged. This situation can be the prime factor for social unrest. Unless young people have genuine prospects of improving their economic and social status, the gap between rich and poor will continue to expand, making a vicious cycle that is hard to escape. Thus, having a solid middle class and the right policies might decrease income inequalities. In this reverence, donor interventions and good government policies will reduce income inequalities and African youth unemployment levels.
Whatever the specific circumstances and history of a country, the following measures can reduce income inequalities across the region: reversing urban favoritism in economics and services, growing productivity among small-scale farmers, guaranteeing women's economic opportunities and access to land, encouraging labor-intensive industries, enhancing capacities to prevent the wealthy from evading taxes, presenting robust social protection programs, setting minimum wages and better financial services access to young people in the continent.
Data availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
Declarations
Conflict of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
1 Barro (2000), Mercedes Monfort (2018), Sheng (2011), Castells-Quintana (2011), Athia Yumna (2015) and Anyanwu (2012).
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| 0 | PMC9748399 | NO-CC CODE | 2022-12-15 23:22:42 | no | SN Bus Econ. 2023 Dec 14; 3(1):15 | utf-8 | SN Bus Econ | 2,022 | 10.1007/s43546-022-00394-0 | oa_other |
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SN Comput Sci
SN Comput Sci
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Springer Nature Singapore Singapore
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10.1007/s42979-022-01493-3
Original Research
Machine Learning and Deep Learning Based Time Series Prediction and Forecasting of Ten Nations’ COVID-19 Pandemic
http://orcid.org/0000-0002-2879-0441
Kumar Yogesh [email protected]
[email protected]
1
Koul Apeksha [email protected]
2
Kaur Sukhpreet [email protected]
3
Hu Yu-Chen [email protected]
4
1 Department of Computer Science and Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat India
2 grid.412580.a 0000 0001 2151 1270 Department of Computer Engineering, Punjabi University, Patiala, India
3 Department of Computer Science and Engineering, Chandigarh Engineering College, Landran, Mohali India
4 grid.412550.7 0000 0000 9012 9465 Department of Computer Science and Information Management, Providence University, Taichung, Taiwan, ROC
14 12 2022
2023
4 1 9129 7 2022
3 11 2022
© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis 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.
In the paper, the authors investigated and predicted the future environmental circumstances of a COVID-19 to minimize its effects using artificial intelligence techniques. The experimental investigation of COVID-19 instances has been performed in ten countries, including India, the United States, Russia, Argentina, Brazil, Colombia, Italy, Turkey, Germany, and France using machine learning, deep learning, and time series models. The confirmed, deceased, and recovered datasets from January 22, 2020, to May 29, 2021, of Novel COVID-19 cases were considered from the Kaggle COVID dataset repository. The country-wise Exploratory Data Analysis visually represents the active, recovered, closed, and death cases from March 2020 to May 2021. The data are pre-processed and scaled using a MinMax scaler to extract and normalize the features to obtain an accurate prediction rate. The proposed methodology employs Random Forest Regressor, Decision Tree Regressor, K Nearest Regressor, Lasso Regression, Linear Regression, Bayesian Regression, Theilsen Regression, Kernel Ridge Regressor, RANSAC Regressor, XG Boost, Elastic Net Regressor, Facebook Prophet Model, Holt Model, Stacked Long Short-Term Memory, and Stacked Gated Recurrent Units to predict active COVID-19 confirmed, death, and recovered cases. Out of different machine learning, deep learning, and time series models, Random Forest Regressor, Facebook Prophet, and Stacked LSTM outperformed to predict the best results for COVID-19 instances with the lowest root-mean-square and highest R2 score values.
Keywords
COVID-19
Prediction
XG Boost
Facebook Prophet
Holt model
Stacked gated recurrent units
RANSAC regressor
Random forest regressor
Stacked long short-term memory
issue-copyright-statement© Springer Nature Singapore Pte Ltd 2023
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pmcIntroduction
Throughout history, the world has confronted several major pandemic and epidemic problems. The first recorded pandemic occurred in Athens during the Peloponnesian War in 430 BC, followed by the Antonine Plague in 165 A.D., in 250 A.D.—the Cyprian Plague, in 541 A.D.—the Justinian Plague, in the eleventh century—leprosy, in 1350—The Black Death, in 1492—The Columbian Exchange, in 1665—The Great Plague of London, in 1817—The First Cholera Pandemic, in 1855—The Third Plague Pandemic, in 1875—Fiji Measles Pandemic, in 1889—Russian Flu, in 1918—Spanish Flu, in 1957—Asian Flu, in 1981—HIV/AIDS, in 2003-SARS, and 2019—COVID-19 [1]. While still a public health concern, Coronavirus 19 (also known as COVID-19) is an infectious sickness that occurred by the severe acute respiratory syndrome coronavirus 2. The first recorded case of SARS (severe acute respiratory syndrome) was identified in December of 2019 in Wuhan, China. The disease has since spread to many other nations and healthcare systems worldwide. At the same time, humans inhale contaminated air, including airborne droplets and particles that are smaller than 0.1 microns, and COVID-19 spreads [2].
Inhalation of these particles is more dangerous when people are closely packed together; nevertheless, they can be inhaled further apart, especially indoors. Infected fluids sprayed on the skin, in the eyes, nose, or mouth, or on surfaces contaminated with them may result in transmission. Someone can carry and spread the disease for up to 20 days even if they have no symptoms. During COVID-19, a first wave began in the spring, which receded significantly throughout the summer, and a second wave appeared in the fall of 2020. The initial wave of the epidemic devastated several nations, and many patients perished. The severity of this early phase was exacerbated by a lack of specialist equipment and a lack of understanding of the disease [4]. We all learned from our mistakes during the first wave of the pandemic, and as a result, our confidence in being able to handle the second wave much better was strong. Despite this, the second wave had considerably greater infection rates, more patients in ICUs, and, in certain countries, more fatalities [5].
Figure 1 depicts the death rates from March 6, 2020, to June 6, 2021, with Europe and the Americas having the most significant mortality rates compared to India and South and East Asia. Europe had 1,172,912 death cases, the Americas had 1,926,520, South and East Asia had 739,802 death cases, and India had 402,728 COVID death cases as of July 8, 2021. Europe accounted for 32% of all COVID fatality cases, followed by the Americas (55%), South and East Asia (15%), and India (11%) (approx). According to the survey, the top eight countries that have been severely affected by a novel coronavirus (in billion dollars) are the United States (3.39), India (3.09), Brazil (1.92), France (58.2), Russia (57.6), Turkey (54.9), the United Kingdom (51.9), Argentina (46.8), and Colombia (45.3) [6].Fig. 1 Ravages of the pandemic
In the beginning, no curative medication or vaccine was available for COVID-19, but 18 months later, each of the vaccines was shown to be safe and effective in treating COVID-19 symptoms and lab-confirmed cases. Though vaccinations are pretty successful, SARS-CoV-2, the virus that causes COVID-19, will emerge even in this tiny number of individuals. Many different approaches for diagnosing the illness have been developed. RT-PCR, TMA, and RT-LAMP can be used to identify the virus's nucleic acid. However, there are some situations when RT-PCR may not be an option, such as when viral RNA must be analyzed in a hurry. According to the UNICEF and World Health Organization, around 342 million vaccinations have been supplied to medical facilities, resulting in the immunization of approximately 94 million people worldwide. China had the most excellent vaccination rate, with 22.3 million.
In this study, machine learning, time series, and deep learning-based models are developed to predict future COVID's active verified, mortality, and healed cases of random 5 days, using January 22, 2020, to May 29, 2021, verified, mortality, and recovered instances of the top ten countries in the world, such as India, the United States, Russia, Argentina, Brazil, Colombia, Italy, Turkey, Germany, and France. We used random forest regressor, decision tree regressor, K-nearest neighbor regressor, kernel ridge regressor, X Boost, RANSAC regressor, linear regression, lasso regression, elastic net regressor, Bayesian regressor, and Theilsen regressor from machine learning algorithms; stacked LSTM and stacked GRU from deep learning models; and Facebook Prophet and Holt algorithms from time series model. To yet, in our investigation, we have been unable to locate any previous research case studies about the top 10 nations affected by the COVID-19 pandemic. Our participation in this research study would benefit all ten countries in rebuilding the plan and demography of COVID-19 preparation. The Root-Mean-Square Error (RMSE) and R2 Score are the evaluative metrics used to assess these models. After this section, the rest of the paper is laid out as follows: The second section tells about related work. The section “Contribution Outline” presents the article's contribution in outline form. The section “Materials and Methods” focus on the subject matter and methods and are followed by a discussion of the outcomes. The section “Result Analysis” draws the conclusion and winds down the recommended research.
Related Work
Since 2020, researchers have made significant attempts to anticipate the onset of COVID illness in people or the end of the disease around the globe. Keeping this in mind, Shastri et al. [1] suggested a deep learning-based model, such as a recurrent neural network, to forecast the future circumstances of new coronaviruses by studying instances from India and the United States. Ten different nations with the most significant number of verified cases were investigated. It was shown that the predictive accuracy of a range of six separate time series modeling approaches for coronavirus epidemic detection varied by Papastefanopoulos et al. [2]. Using an LSTM model, Chimmula et al. [3] predicted the end of the COVID-19 pandemic and worldwide epidemics due to antiviral drugs and improved access to healthcare. Indicating the date of the pandemic's demise, the writers anticipate that it will be finished by June of 2020. Using a deep learning model, Togacar et al. [4] identified coronavirus in datasets containing instances of pneumonia, as well as standard X-ray imaging data. The COVID-19 disease can be diagnosed with 99.27% accuracy with the model that the authors used. COVID-19 drug and vaccine research achievements were evaluated using artificial intelligence techniques in a recent study by Arshadi et al. [5]. In addition, the scientists gave information about the compounds, peptides, and epitopes in the CoronaDB-AI library, which were discovered both in silico and in vitro.
Categorizing chest X-rays into two groups was proposed by the researchers led by Elaziz et al. [6]. The accuracy percentage for the first and second datasets was 96.09% and 98.09%, respectively. Alimadadi et al. [7] presented a deep learning algorithm based on AlphaFold to predict the structures related to COVID-19 illness. Alazab et al. [8] used real-world datasets to detect COVID-19 patients using artificial intelligence-based approaches on a deep convolution neural network. In Australia and Jordan, their methods obtained an accuracy of 94.80% and 88.43%, respectively. Alaska et al. [9] evaluated the efficacy of deep learning models in predicting COVID-19 illness using laboratory data from 600 patients and got 91.89% accuracy. Their approach was also utilized to help medical professionals validate test data and for clinical prediction research. The Johns Hopkins dashboard data, which were the primary source of the Punn et al.’s [10] research, were utilized with machine learning and deep learning models. The team's goal was to grasp the exponential growth of the COVID-19 and then make predictions about how widespread it may become across the country. Table 1 on the left shows the researchers who worked on the forecast and detection of COVID-19.Table 1 Analysis of the existing work
Author’s name Dataset Technique Results Limitations
Wang et al. [11] 1065 CT pathogenic images Transfer Learning Model, CNN, GraphNet Accuracy: 89.5%
Specificity: 0.88
Sensitivity: 0.87
Factors such as low signal-to-noise ratio and complex data integration led to reducing the efficacy of deep learning models
Bandyopadhyay et al. [12] Data collected from WHO (Jan. 16–20,2020) Long Short-Term Memory, Gated Recurrent Unit Accuracy: 87% The model failed to represent the spatio temporal components of the LSTM network
Togacar et al. [4] Data collected from Qatar University Stacking Technique, Fuzzy Color, Deep Learning Model Classification accuracy: 99.27% Publications of COVID-19 images were limited. The system did not work with the low resolution and different size input images
Shastri et al. [1] Dataset was sourced from the Ministry of Health and Family Welfare Deep Neural Network, Long Short-Term Memory, Recurrent Neural Network, Polynomial Regression Accuracy ConvLSTM: 98% The comparative analysis had been performed only for two countries
Ghoshal et al. [13] COVID-19 chest X-ray dataset Bayesian Deep Learning Accuracy: 80% After reviewing the data, it was impossible to conclude anything regarding markers for imaging, discoveries concerning improved diagnosis and therapy for COVID-19
Punn et al. [10] Data collected from Jan 22, 2020 to Apr 1 2020 at Johns Hopkins University Support Vector Machine, Deep Neural Network, Long Short-Term Memory, Polynomial Regression RMSE confirmed: 455.92 The study needed to work on more algorithms to enhance the RMSE score
Death: 117.94
Recovered: 809.71
Alakus et al. [9] Samples collected from the Albert Einstein Israelite Hospital in Sao Paulo, Brazil Artificial Neural Network, Convolution Neural Network, Long Short-Term Memory Accuracy: 86.66%
F1 Score: 91.89%
Recall: 99.42%
AUC: 62.50%
Precision: 86.75%
The primary disadvantage of the study was the sheer amount of data. To increase the number of patients for whom the lab findings could not be assessed, the procedure was applied on 600 patients
Ismael et al. [14] 180 COVID-19 and 200 chest X-ray images CNN model, SVM, ResNet50 Accuracy: 91.6% The study needed to incorporate work on different imagistic patterns of COVID-19
Panwar et al. [15] 337 patient images from real-world data Deep learning, nCOVnet Accuracy: 97.62% The system worked on a small dataset
Elaziz et al. [6] Dataset collected from
Joseph Paul Cohen and Paul Morrison
Lan Dao
Manta Ray Foraging Optimization, Fractional
Multichannel Exponent Moments
Accuracy:
96.09%
The system dealt with resource limitations and high CPU time
Accuracy:
98.09%
Contribution Outline
The overall goal of this research is to build models that can calculate two necessary evaluative measures: RMSE and R2 Score for confirmed, death, and recovered cases from ten different nations to help future forecasts. The steps are as follows:
Step 1: Initially, data are pre-processed to capture characteristics utilizing various variables, such as active cases, recovered cases, and COVID-19 fatality cases.
Step 2: Exploratory Data Analysis of COVID-19’s active cases, closed cases, confirmed cases, recovered cases, and death cases are calculated to summarize or interpret the information that is hidden in rows or columns, and scaling techniques such as Min–Max have been applied to normalize each feature that is obtained from these attributes.
Step 3: Later, utilizing confirmed cases, recovered cases, and death cases from 10 different nations, the gathered data were used to anticipate the future conditions of a new CoronaVirus. To get the findings, several machine learning models, time series models, and deep learning models were used, and they were assessed using parameters, such as root-mean-square error and R square.
Step 4: Finally, all of the results have been ranked to choose the technique with the lowest root-mean-square error and the highest R-squared score.
As depicted in Fig. 2, the proposed approach works by collecting and preparing a dataset from the Novel Corona Virus dataset.Fig. 2 Proposed system design for COVID-19 prediction
Materials and Methods
This section provides a general description of the dataset, along with libraries and methodologies imported during implementation.
Dataset
Coronavirus (2019-nCoV) is a virus (more specifically known as a coronavirus) discovered in Wuhan, China, and responsible for an influenza-like outbreak. One of China's earliest suspected sources of the COVID-19 epidemic was an extensive seafood and animal market, which indicated possible animal-to-human transmission.
However, an increasing number of cases are claimed to have occurred in the absence of contact with animal markets, suggesting that person-to-person transmission occurs. The CDC [16] is currently unaware of how fast or sustainably this virus spreads among humans. According to a report issued in Wuhan City, Hubei Province, China, on December 31, 2019, several instances of pneumonia have been discovered in the area. The virus has no similarity to any other virus currently known. This raised concerns, as we have no idea how a novel virus may affect humans. Everyday data on individuals with a disease can lead to intriguing results when released to the broader data science community [17].
This dataset is compiled daily to offer recent news on new coronavirus infections, fatalities, and recoveries for 2019. The data will be available from January 22, 2020 to May 29, 2021. This is a time series dataset with a total of 1248 time series datasets recorded for each day, while the count of time series datasets registered for each day indicates the cumulative total.
The dataset contains a serial number, the observation date in the format MM/DD/YYYY, the province or state of observation, the country or region of compliance, and the time in UTC at which the row is updated for the given province or country, the cumulative number of confirmed cases, the cumulative number of death cases, and the cumulative number of recovered patients from January 22, 2020 to May 29, 2021. The confirmed, dead, and recovered cases from ten different countries are included in Table 2.Table 2 Analysis of COVID-19 cases among the top ten countries
Countries Confirmed cases Death cases Recovered cases
India 27,894,800 325,972 25,454,320
USA 33,251,939 594,306 –
Russia 4,995,613 118,781 46,16,422
Argentina 3,732,263 77,108 3,288,467
Brazil 16,471,600 461,057 14,496,224
Colombia 3,363,061 87,747 3,141,549
Italy 4,213,055 126,002 3,845,087
Turkey 33,251,939 47,271 5,094,279
Russia 4,995,613 118,781 4,616,422
Germany 3,684,672 88,413 3,479,700
Libraries
Several Python-based libraries, such as Pandas—a python-based software toolkit that contains data structures and strategies for working with numerical tables and time series—were imported during the prediction of COVID-19 confirmed, death, and recovered cases [18], and Numpy—a Python array manipulation library. It also contains functions for working with linear algebra, the Fourier transform, and matrices [19], among other things. Matplotlib—a cross-platform data visualization and graphical plotting program built-in Python for use with NumPy [19]; Seaborn—a python data visualization software based on Matplotlib. It uses a high-level interface to generate aesthetically beautiful and functional data visualizations [20], Plotly is a Python library that makes it easier to create professional-looking visualization by providing a flexible, open-source charting toolkit with over 40 chart types for a wide range of statistical, financial, geographic, scientific, and 3D use cases. [21], Date–time is a module that mixes date and time and characteristics like the year, month, day, hour, minute, second, microsecond, and info [22]. Sklearn—Scikit-learn is the most helpful Python machine learning package. The sklearn package contains several rapid machine learning and statistical modeling algorithms, including classification, regression, clustering, and dimension reduction [23]. Fbprophet utilizes time as a regressor and attempts to fit multiple linear/nonlinear time functions as components. FbProphet will provide the data using a linear model by default, but it may be modified to a nonlinear model (logistics growth) using its parameters [24]; XGBoost is an implementation of Gradient boosted Decision Trees (GDTs) designed for both high-performance and domination [25]; Tensor Flow—Tensor Flow is an open-source framework that processes datasets arranged as computational graph nodes. Keras is a Python-based open-source software framework that provides an artificial neural network interface. Keras is a user interface for the Tensor Flow library [26]. StatsModel is a Python package that includes classes and methods for estimating various statistical models, running statistical tests, and exploring statistical data [27]. PmdarimaMath is a statistical library created to cover a gap in Python's time series capabilities. CatBoost is an open-source package that offers a high-performance gradient boosting algorithm for decision trees. The search engine use is extensive. It is used in recommendation systems, personal assistants, self-driving vehicles, weather forecasting, and a wide variety of other applications [28].
Techniques
The pre-processing approach used to extract the characteristics is covered in the part of this work. This part also discusses the exploratory data analysis of the cleaned data, which is followed by the scaling approach. Following that, a section was presented in which many models from the COVID-19 testing dataset were described and shown.
Pre-processing
Data collected from the novel corona 19 dataset have been pre-processed using various mathematical formulas, such as active cases, percentage of recovery rate, percentage of mortality, and week of days to generate features. There is a significant likelihood that the number of active topics has increased, since some of the confirmed patients are now dead, and fewer new cases are being found. To calculate it, use Eq. (1). The recovery rate is the proportion of recovered instances, while the mortality rate is the percentage of death cases. Equations (2), (3) display the formulas. The last parameter, the week of days, is calculated by importing the library named WEEKOFYEAR [24]1 Active cases=Total number of confirmed cases-(total number of recovered cases+total number of death cases,
2 Recovery rate=Number of recovered casesnumber of confirmed cases×100,
3 Mortality rate=Number of death casesNumber of confirmed cases×100.
Exploratory Data Analysis
Exploratory Data Analysis is a vital process that entails performing preliminary analyses on data to uncover patterns, identify anomalies, test hypotheses, and verify assumptions using summary statistics and graphical representations. Some of the critical steps in exploratory data analysis are importing the data set in which we will get two data frames; one consisting of the data to be trained and the other for predicting the target value, identifying the number of features and columns, identifying the qualities or cues, identifying the data types of components, identifying the number of observations, checking if the dataset has empty cells or samples, identifying the number of empty cells by features or columns, and exploring categorical features [29].
This work employed an exploratory analysis of ten different countries after pre-processing to assess its features via statistical graphs. Figures shown below depicts the graphical analysis of active cases, death cases, closed points, and recovered cases that have been recorded from Jan 2020 to May 2021.
It was determined in Fig. 3 that 27,894,800 cases had been confirmed, 2,114,508 were still active, 325,972 had died, 25,780,292 had been closed, and 25,454,320 people had been recovered from Jan 2020 to 29 May 2021. Additionally, the numbers of confirmed cases, deaths, and recovered cases each day were, respectively, 57,397, 671, and 52,375.Fig. 3 India’s COVID-19 scenario
According to Fig. 4, it has been discovered that US has 3,325,189,940 instances with high certainty, 3,266,576,333 cases with moderate certainty, 594,306 cases with low certainty, and 0 cases with a medium certainty which were seen from January 1st, 2020 to May 29th, 2021. Additionally, the daily average of confirmed cases was reported as 673,128, while the daily average of deaths was recorded as 12,030. Finally, the daily average of recovered cases was recorded as 0.Fig. 4 US’s COVID-19 scenario
As demonstrated in Fig. 5, the numbers of confirmed, active, and death cases have been as follows: 49,956,313.0, 260,410.0, 118,781.0, 47,352,203.0, and 46,164,322.0 from January 1, 2020 to May 29, 2021. Finally, the total number of confirmed cases was 10,300. The number of death cases was 245, and the total number of recovered cases was 9518.Fig. 5 Russia’s COVID-19 scenario
In Fig. 6, it was discovered that Argentina has reported 373,263.0 total cases, with 366,688.0 currently active cases, 77,108 currently known death cases, 336,575 previously known to be closed cases, and 328,467 previously known recovered cases from January 1st, 2020 to May 31st, 2021. In addition to this, there were around 8239.0 confirmed cases of the disease each day, approximately 170.0 deaths per day, and approximately 7259.0 recovered cases per day.Fig. 6 Argentina’s COVID-19 scenario
According to Fig. 7, it was discovered that 16,471,600.0 cases had been confirmed, 133,765.0 were active, 87,747.0 had died, and 3,229,296.0 had been closed. In Brazil, from January 2020 to 29th May 2021, the total number of cases was 3,229,296.0 and 3,141,549.0 of those cases were recovered. In addition, the number of confirmed cases per day was found to be about 7,457.0, and the number of fatality cases per day was calculated to be around 195.0.Fig. 7 Brazil’s COVID-19 scenario
According to Fig. 8, it was discovered that Colombia has experienced 3,363,061.0 instances of confirmed disease, 133,765.0 cases of current cases, 87,747.0 cases of death, 3,229,296.0 cases of closed cases, and 3,141,549.0 cases of recovered disease during the first 5 months of 2020 and 2021. In addition, the number of confirmed cases per day was found to be about 7,457.0, and the number of fatality cases per day was calculated to be around 195.0.Fig. 8 Colombia’s COVID-19 scenario
The data as shown in Fig. 9 have been gathered by Italy’s Department of Public Health which shows that there were 421,305,055.0 confirmed cases, 24,19,660 active cases, 12,6020 death cases, 39,710,890.0 closed cases, and 38,450,877.0 recovered cases from January 2020 to 29th May 2021. To this, we may add the approximate total number of cases each day: 8687.0, the approximate number of deaths each day: 260.0, and the approximate total number of cases each day: 7928.0.Fig. 9 Italy’s COVID-19 scenario
In Fig. 10, it was discovered that from January 2020 to 29th May 2021, there were 523,596,780 confirmed cases, 944,281 active cases, 47,271 death cases, 514,155,50 recovered cases, and 50,942,79 newly discovered cases. In addition to this, on an average 11,766.0 confirmed cases were found every day, on an average 106.0 death cases were found every day, and on an average 11,448.0 cases were found every day.Fig. 10 Turkey’s COVID-19 scenario
Looking at Fig. 11, Germany had 368,674,702 cases throughout the time span from January 2020 to 29th May 2021, with 116,559 active cases, 884,130 death cases, and 35,684,113 open cases. The overall daily case counts were as follows: 7551.0 confirmed cases, 181.0 death cases, and 7131.0 recovered cases.Fig. 11 Germany’s COVID-19 scenario
Figure 12 shows that in France, there were 57,198,777.0 confirmed cases, 52,191,481.0 active cases, 10,953,178.0 death cases, and 500,396.0 closed cases, with 39,087,780.0 recovered cases between January 2020 and May 2021. The results of this analysis also show that there were roughly 116,626 confirmed cases, nearly 223 deaths, and about 794 recovered cases each day.Fig. 12 France’s COVID-19 scenario
Feature Scaling
Normalizing the range of independent variables or features of data using feature scaling is a feature scaling approach. Min–Max scaling technique has been used to perform normalization on the parts obtained during data pre-processing. The Min–Max Normalization or Min–Max Scaling technique creates a scale that goes from 0 to 1 or from 1 to − 1. Deciding on a range of data to aim for relies on the type of data you are working with. Min–Max for the range[0,1] can be computed using Eq. (4)4 x′=x-minxmaxx-minx.
Here, x is the original value and x′ is the normalized value [30]. To rescale a range between any arbitrary set of values [a, b], Eq. (4) becomes Eq. (5)5 x′=a+x-minxb-amaxx-minx.
After normalization, the data were split into two subsets: the training set, which would be used to assess machine learning methods, and the testing set, which would be used to evaluate deep learning techniques. It applies to issues involving classification or regression, as well as to any supervised learning technique. Following data partitioning, the first subset is utilized to fit the model; this is the training dataset. The second subset is used as an input element in the dataset supplied to the model, and predictions and comparisons to predicted values are performed. The test dataset is the second dataset. In a nutshell, the train data set is used to fit the machine learning model, while the test data set is used to verify the fit. The goal is to assess the performance of time series, machine learning, and deep learning models on new data. The most often used split percentages are as follows:
80% training, 20% testing.
67% training, 33% testing.
50% training, 50% testing.
Model Selection
Three sets of models have been used such as machine learning models (random forest regressor, decision tree regressor, K-nearest regressor, Kernel ridge regressor, XG Boost, RANSAC regressor, Linear regression, Lasso regression, Elastic Net regressor, Bayesian regressor, and Theilsen regressor), time series models (Facebook Prophet model and Holt model), and deep learning models (stacked long short-term memory and stacked gated recurrent unit) have been used to predict the confirmed cases, recovered cases. Death cases are discussed in this section.
Machine Learning Models
Random Forest Regressor
Regression using random forest regression is a supervised learning approach that employs ensemble learning techniques to develop an accurate prediction model. During the training period, a random forest is created by several decision trees, and the output is the mean of the classes. A random forest regression model is robust and accurate and works primarily on nonlinear problems [31]. It can be calculated using Eqs. (6), (7)6 RFfii=∑j∈alltreesnormfiijT,
where7 normfii=fii∑j∈allfeaturesfii,
RFfi sub(i) = the importance of feature i calculated from all trees in the random forest model. normfi sub(ij) = the normalized feature importance for i in tree j. T = total number of trees.
Decision Tree Regressor
The decision tree algorithm is an example of a supervised learning algorithm. Regression and classification challenges may be solved using a decision tree, unlike other supervised learning techniques. To forecast the class or value of a target variable, use fundamental decision rules from previous data as building blocks for a training model that incorporates decision rules outside the training dataset (training data). At the tree's root, we forecast a class label for a record. When it comes to root attributes and record attributes, the values are compared. When we find the node with that particular value, we follow the branch corresponding to that value and go to the next node [32].
K Nearest Regressor
Non-parametric regression involves averaging nearby observations to determine if one or more independent variables are associated with a continuous result. For an analysis to be effective, the size of the neighborhood should be selected by the analyst. However, in some cases, it can be randomized to reduce the mean squared error. An algorithm that considers the K-nearest neighbor numerical objective is utilized to determine the average of the K target values. KNN regression and KNN classification both utilize the same distance functions [33]. KNN regression uses the same distance functions as KNN classification. The formulae to compute K-nearest regressor are shown in Eqs. (8)–(10)8 Euclideanformula:∑i=1kxi-yi2,
9 Manhattanformula:∑i=1kxi-yi,
10 Minkowskiformula:∑i=1kxi-yiq1q.
Kernel Ridge Regressor
Using the kernel method in combination with ridge regression creates a new regression technique called Kernel Ridge Regression (KRR). It is a type of ridge regression that is non-parametric. Our goal is to learn a function in the space defined by the kernel k using an approach known as minimization with optimization, and we define a squared loss with a squared norm regularization term. The kernel ridge regression does a linear function in the data space that is also proportional to the relevant kernel [34]. The equation can be written as Eq. (11)11 α=K+τI-1y,
where K is the kernel matrix and α is the vector of weights in the space induced by the kernel.
XGBoost
A computer algorithm called XGBoost stands for "eXtreme Gradient Boosting." Supervised regression models are built using this method. XGBoost is a gradient boosted decision tree algorithm that is efficient and fast. XGBoost is a collection of software libraries with several user interfaces, such as the Command Line Interface (CLI), C++, Python, R, Java, and JVM interfaces. Three primary classes of boosting techniques are supported by XGBoost, including gradient boosting, stochastic gradient boosting, and regularized gradient boosting. The main reason to use XGBoost is to speed up model execution and gain project execution speed. Regression loss functions, such as linear and logistic, are most commonly used with XGBoost for regression issues [25]. The formula to compute it is shown in Eqs. (12, 13)12 L∅=∑ily^i,yi+∑kΩfk,
where13 Ωf=γT+12λw2,
yi is a real value (label) known from the training data set.
RANSAC Regressor
RANSAC regressor is also known as the RANdom SAmple Consensus algorithm. It is an iterative algorithm used for the robust estimation of parameters by excluding the outliers in the training dataset. RANSAC is a nondeterministic algorithm as it produces a good result only with a certain probability. This method uses machine learning and random sampling of observable data to estimate model parameters in conjunction with a voting system. The RANSAC algorithm needs to be executed to perform RANSAC analysis. The following formula is used to determine the results of the RANSAC algorithm. It involves p, the probability that the RANSAC algorithm returns valuable results, and w, the likelihood of selecting an inlier on each point. Each time a single point is selected, there is a probability of picking an inlier. The possibility of choosing an inlier on every single point is called w. The chance of picking an inlier each time a single point is set is called wn. Then, 1-wn is the probability that at least one of the n points is an outlier. Finally, k is the number of iterations [35]. The likelihood that the algorithm never selects a set of n inlier points is shown by Eqs. (14), (15)14 1-p=1-wnk,
and after taking the logarithm of both sides, Eq. (14) becomes15 k=log1-plog1-wn.
Linear Regression
A machine learning approach that uses supervised learning, known as Linear Regression Analysis (LRA), is a supervised learning algorithm. The model may be trained to predict the outcome of data using a given set of factors using a linear regression method. In quantitative sciences, linear regression is typically used to indicate a quantitative response from the predictor variable. It is intended to show how an independent variable affects the goal prediction value. In forecasting, it is used to determine how variables are related [36]. Linear regression can be written as by Eqs. (16)–(18)16 y=a+bx,
where a and b are given by the formulae17 bslope=n∑xy-(∑x)(∑y)n∑x2-(∑x)2,
18 aintercept=n∑y-b∑xn.
Here, x and y are two independent and dependent variables, respectively, on the regression line, b is slope of line, and a is an intercept of the line.
Lasso Regression
The “LASSO” stands for Least Absolute Shrinkage and Selection Operator, which is a regularization technique. It is used over regression methods for a more accurate prediction. This model uses shrinkage. The lasso technique promotes the use of sparse, basic models (i.e., models with fewer parameters). This form of regression is well suited for models with a high degree of multicollinearity or automating some aspects of model selection, such as variable selection/parameter removal, as necessary [37]. The mathematical equation of lasso regression is shown in Eq. (19)19 MinβL1=∑i=1nyi-∑jxijβj2+λ∑j=1pβj.
For simplicity, let p = 1 and βi=β. Now, Eq. (19) becomes Eq. (20)20 L1=y-xβ2+λβ=y2-2xyβ+x2β2+λβ,
where λ represents the amount of shrinkage.
Elastic Net Regressor
Elastic net linear regression regularizes regression models using both the lasso and ridge methods as shown in Eq. (21). By learning from the inadequacies of both lasso and ridge regression approaches, the methodology integrates both to enhance the regularization of statistical models. The elastic net technique overcomes the drawbacks of the lasso method, namely that it only requires a few samples for high-dimensional data. The flexible net technique allows for the addition of "n" variables until saturation is reached. When the variables are highly linked groups, lasso tends to pick one variable from each group and disregard the others completely. To overcome the constraints of a lasso, the elastic net incorporates a quadratic expression in the penalty, which becomes ridge regression when employed alone. The first step is determining the ridge regression coefficients, followed by a lasso sort of coefficient shrinkage [38]. In a nutshell21 ENR=LassoRegression+RidgeRegression,where,
22 LassoRegression=1N∑i=1Nyi-mxi+z2+λ∑i=1p(mxi+z),
23 RidgeRegression=1N∑i=1Nyi-mxi+z2+λ∑i=1pmxi+z2.
Using both Eqs. (22), (23), we get Eq. (24)24 ENR=1N∑i=1Nyi-mxi+z2+λ∑i=1pmxi+z2+λ∑i=1p(mxi+z).
Bayesian Regressor
Bayesian Regressor is a regression approach that uses Bayesian inference to do statistical analysis. This method enables a natural process to persist in the presence of limited or poorly dispersed data. It generates predictions based on the posterior probability of all feasible regression weights. With Bayesian Linear Regression, the aim is not to choose the "best" model parameter but to estimate the distribution of model parameters [39]. It is demonstrated by Eq. (25)25 Pβ|y,X=Py|β,X×P(β|X)P(y|X).
Here, P(β|y, X) is the posterior probability distribution of the model parameters given the inputs and outputs. This is equal to the likelihood of the data, P(β|y, X), multiplied by the prior probability of the parameters and divided by a normalization constant.
Theilsen Regressor
Theilsen regressor is a non-parametric statistic where a line is fitted to sampled points in the plane by selecting the median of the lines connecting pairs of points. Theilsen regression is a fast algorithm that is insensitive to outliers. Additionally, it has been referred to as the most widely used non-parametric approach for estimating a linear trend. The two-dimensional point Theilsen regression xi,yi is the median m of the slopes (yj-yi)(xj-xi) based on all pairwise sampling locations [40]. Once the slope m has been determined, we can find a line from sample points by setting the y intercept b to be the median of the values yi-mxi. A variant to Theilsen regression can be calculated using Eq. (26)26 rTSx,y=signmTSy,x·mTSy,x·mTSx,y.
Time Series Models
Facebook Prophet
A forecasting approach based on an additive model known as a prophet is used to correlate nonlinear trends with seasonal and holiday impacts as well as yearly, weekly, and daily patterns. Time series with strong seasonal influences and extensive historical data spanning many seasons do well with this approach. The Prophet works well with outliers, which makes it resistant to data and trend shifts. The time series model is built on a prophet, and it is fast, fully automated, and very exact. The trend, seasonality, and holidays form our time series model, which we break down into three key components: trend, seasonality, and holidays [24]. They are merged in Eq. (27) as follows:27 yt=gt+st+ht+∈t,
g(t): For modeling non-periodic changes in time series, a piecewise linear or logistic growth curve is used. s(t): changes on a regular basis (e.g., weekly/yearly seasonality). h(t): The impact of vacations (supplied by the user) on individuals with irregular schedules. εt: The error term is used to account for any unforeseen changes that the model does not account for.
Holt Model
The Holt model is a well-known technique for predicting data with a trend. Holt's model consists of three distinct equations that interact to create a final forecast. The first is a fundamental smoothing equation, often known as the level equation, which directly adjusts the previous smoothed value for the trend of the previous period. The trend is updated over time using the second equation, which expresses the trend as the difference between the previous two smoothed values. Finally, the final forecast is generated using the third equation. Holt's approach makes use of two parameters: one for global smoothing and another for the trend smoothing equation. Additionally, this technique is referred to as double exponential smoothing or trend-enhanced exponential smoothing [41]. It is computed using Eqs. (28)-(30)28 Level equation=lt=αyt+1-αlt-1+∅bt-1,
29 Trend equation=bt=β∗lt-lt-1+1-β∗bt-1,
30 Forecast equation=y^t+h|t=lt+hbt,
where lt represents the estimation of the series' level at time t, bt represents the estimation of the series' trend (slope) at time t, and α and β* are the smoothing parameters for the level, 0 ≤ α ≤ 1, and trend, 0 ≤ β* ≤ 1, respectively. lt is a weighted average of observation yt and the one-step-ahead training forecast for time t, denoted here by lt − 1 + bt − 1. bt is a weighted average of the estimated trend at time t based on lt − lt − 1 and bt − 1, the trend's earlier estimations, according to the trend equation. The prediction for the next h steps forward is equal to the most recent predicted level multiplied by h times the most recent estimated trend value. As a result, the predictions in terms of h are linear.
Deep Learning Models
Stacked LSTM
Deep LSTM is another name for an LSTM that has a large number of LSTM layers. The model described in Fig. 13 is called a stacked LSTM, with several hidden LSTM layers layered on top of each other.Fig. 13 Stacked LSTM architecture
Assume itl, ftl, otl, ctl and htl are the values of the input gate, forget gate, output gate, memory cell, and hidden state using Eqs. (31)–(35) at time t in the sequence and layer l, respectively. xt,k is the input of the system at time t at location k, whereas Wxj for j∈i,f,o,c are the weights that connect the input, xt=[xt,1,xt,2,…,xt,c]T to the corresponding gates and the memory cell [42]31 it=σWxixt+Whiht-1+Wcict-1+bi,
32 ft=σWxfxt+Whfht-1+Wcfct-1+bf,
33 ct=ft⊙ct-1+it⊙tanh(Wxcxt+Whcht-1+bc),
34 ot=σWxoxt+Whoht-1+Wcoct-1+bo,
35 ht=ot⊙tanhct.
Stacked GRU
The simple model, GRU, is incapable of doing advanced feature extraction. On the other hand, the deep model, stacked GRU, is formed from several simple models, with the input of the first layer being the original data, as shown in Fig. 14.Fig. 14 Stacked GRU architecture
Increased classifier performance may be realized by making use of time series data. Instead of considering whether the models are time-dependent, these individuals sidestep the trade-off between time and precision. As described in Eqs. (36)–(39) [43], the central GRU unit receives the output of the top GRU unit's hidden layer. A sigmoid layer is added to the preceding layer's hidden layer to accomplish the ultimate result in Eq. (40)36 zti=σWzi·ht-1i,hti-1,
37 rti=σ(Wri·[ht-1i,hti-1]),
38 ht∼i=tanh(Wi·[rti⊙ht-1i,hti-1]),
39 hti=zti⊙ht-1i+1-zti⊙hti-1.
Here, z represents the update gate, r is the reset gate which is used to control the direction of the data stream at time t, ht− 1 is the output of the hidden layer, and h~t is the output of candidate hidden layer at time t40 y~last=σWonhon+bon.
Here, y~last is the predicted label at the last moment, Won is the weight of the output layer, and bon is the bias of the n-th GRU unit.
Evaluative Parameters
RMSE The usual technique of quantifying the error of a model in quantitative data is the root-mean-square error. It is defined by an Eq. (41). By identifying the error, the dataset reveals how distant each data point is from a regression line, and the root-mean-square error quantifies how concentrated each data point is around the line of best fit [44]41 RMSE=∑i=1ny^i-yi2n,
y^i are predicted vales, yi are observed values, and n is the number of observations.
R2 Score The statistician's coefficient is a model's ability to predict or explain a result in a regression setting. R2 Score is a percentage used to quantify the amount of variance in the dependent variable that can be predicted using linear regression and the predictor variable (independent variable) [44]. It is shown by Eq. (42)42 R2=1-RSSTSS;
RSS is the sum of squares of residuals and TSS is the total sum of squares
Result Analysis
Different machine learning models, time series, and deep learning algorithms were used to calculate the RMSE and R2 values, features extracted in the form of confirmed cases, death cases. They recovered points of ten different countries, such as India, USA, Russia, Argentina, Brazil, Colombia, Italy, Turkey, Germany, and France.
In Fig. 15, we can see the range of different algorithms for calculating the root-mean-square error and R square values of confirmed cases, death cases, and recovered cases of ten other countries. Hence, to show the best algorithm out of these three techniques, three scenarios have been taken to elaborate the values of root-mean-square error and R2.Fig. 15 Models based analysis for confirmed, death, and recovered cases of a India, b US, c Russia, d Argentina, e Brazil, f Colombia, g Italy, h, Turkey, i Germany, and j France
Scenario 1: Predict RMSE and R Square Value Using Machine Learning Models
We have used 11 algorithms in machine learning models, such as Random Forest Regressor, Decision Tree Regressor, K neighbor Regressor, Kernel Ridge Regressor, XBoost, RANSAC Linear Regression, Lasso Regression, Elastic Net Regressor, Bayesian Regressor, and Theilsen Regressor. Out of all these algorithms, Random Forest Regressor has obtained the minor root-mean-square error value for confirmed, death, and recovered cases of India by 68,302, 813, and 64,494, Italy by 7447,256 and 8283, and France by 14,391, 243, and 763, respectively, as well as R Square achieved by it for all the three cases of these countries, is 99.9%. On the other hand, for the US, the lowest RMSE and highest R square value for confirmed cases have been achieved by XBoost by 290,098 and 97.5, respectively. For death and recovered points, random forest regressor reached the lowest RMSE and highest R square value by 1159, 53,667, and 99.9, respectively. For Russia, Brazil, and Colombia, random forest regressor achieved the highest R square value of 99.9 for all the three cases and the lowest RMSE matters by (9113, 196), (8682, 846), and (8020, 241) for confirmed and death cases, respectively, while as decision tree regressor in recovered cases by 8124,29,276 and 10,027, respectively. For Argentina, the random forest regressor achieved the highest R square value of 99.9 and the lowest RMSE value of 7976 and 7438 for confirmed and recovered cases. In contrast, the decision tree regressor scored a 182 RMSE value in terms of death cases. For Turkey, Random Forest Regressor has achieved 13,539, 100 root-mean-square error values for confirmed and instances of death, while X Boost has achieved 16,465 root-mean-square error for recovered patients with 99.9 R Square. In the end, for Germany, K Neighbor Regressor has reached 11,977 and 221 root-mean-square error values for confirmed and death cases, while the random forest regressor achieved the least RMSE for recovered instances 6753.
Scenario 2: Predict RMSE and R Square Value Using Time Series Models
We have used two algorithms in time series models, i.e., Facebook Prophet Model and Holt Model. Out of these two models, Facebook Prophet Model has played an essential role by providing the lowest root-mean-square error value for confirmed, death, and recovered cases of India by 1,112,918, 12,524, and 1,061,511, the US by 922,620,2530, and 80,401, Russia by 5262,156, and 12196, Argentina by 55,118, 1048, and 51,794, Brazil by 24,606, 2174, and 38,904, Colombia by 39,239, 1208, and 63,090, Italy by 41,057, 582, and 15,202, Turkey by 111,271, 645, and 137,165, Germany by 24,606, 191, and 31,245, and France by 85,910, 361, and 2442, respectively. Moreover, Facebook Prophet Model also achieved the highest R Square value for confirmed, death, and recovered cases of India by 97.2, 97.5, and 96.8, the US by 80.25, 99.9, Argentina, Brazil, Italy, Germany by 99.9, Colombia by 99.8, 99.7, and 99.5, Turkey by 99.5, 99.7, and 99.1, and France by 99.8 and 99.9, respectively.
Scenario 3: Predict RMSE and R Square Value Using Deep Learning Models
We have used two algorithms in deep learning models, i.e., Stacked Long Short-Term Memory and Stacked Gated Recurrent Unit. On analyzing both the algorithms, it has been seen that Stacked Long Short-Term Memory has achieved the lowest root-mean-square error value and 99.9 R2 value for confirmed cases, death cases, and recovered patients of US by 418,343, 1160, and 53,669, Italy by 8835, 328, and 11,256, and France by 14,389, 240, and 762, respectively. It has also obtained the lowest root-mean-square value for confirmed and death cases of India by 68,303, and 814, Russia by 9113 and 196, Brazil 8682 and 846, Turkey by 13,539, and 100, Colombia by 8020 and 241, respectively, while as the root-mean-square error value for the recovered cases of all these countries has been achieved by Stacked Gated Recurrent Unit by 65,760, 8124, 29,276, 17,462, and 10,027, respectively. Stacked Gated Recurrent Unit has gotten the least root-mean-square error value for all the three cases, such as confirmed, death, and recovered points of Germany by 11,977, 221, and 9533, respectively with 99.9 R2. In the case of Argentina, Stacked LSTM showed the highest R square value by 99.9 and the lowest RMSE value by 7974 and 7433 for confirmed and recovered instances, respectively, while as in the death case, Stacked GRU scored the lowest RMSE value by 182.
Table 3 is summed up the results by showcasing the best model out of all applied machine learning, time series, and deep learning models for ten different countries. Besides, root-mean-square error, and R square, another parameter has been added, i.e., mean square error (MSE) value to test the performance of the model for three different cases, i.e., confirmed, death, and recovered, which the author will refer to as case studies throughout this paper.Table 3 Country-wise RMSE and R square values
Countries Confirmed cases Death cases Recovered cases
RMSE MSE R2 RMSE MSE R2 RMSE MSE R2
India 68,302 261.34 98.4 813 28.51 99.9 64,494 253.96 99.7
USA 290,098 538.60 98.5 1159 34.04 98.4 53,667 231.66 98.6
Russia 9113 95.46 98.9 196 14 99.2 8124 90.13 98.9
Argentina 7974 89.29 99.3 182 13.49 98.8 7433 86.21 99.4
Brazil 8682 93.17 99.0 846 29.13 97.5 29,276 171.10 99.5
Colombia 8020 89.55 97.8 241 15.52 99.2 10,027 100.13 97.9
Italy 7447 86.29 99.2 256 16 99.5 8283 91.01 97.5
Turkey 13,539 116.35 96.4 100 10 97.7 17,462 132.14 99.9
Germany 11,977 109.43 97.7 191 13.82 99.6 6753 82.17 99.9
France 14,389 119.95 99.3 240 15.49 99.9 762 27.60 99.9
Many machine learning models, such as Facebook Prophet model and stacked long short-term memory, and the random forest regressor model from confirmed, death, and recovered cases, have been found to have achieved the lowest root-mean-square error value. In contrast, the Facebook Prophet model and stacked long short-term memory had the highest R Square value for the cases of ten countries. It has been shown that the bulk of these calculations (for confirmed, death, and recovered cases) were done using random forest regressor and stacked long short-term memory. Moreover, time series models, machine learning models, and deep learning models were also applied to predict confirmed, death, and recovered cases for ten different countries for random 5 days on separate datasets. All results are given in Tables 4, 5, and 6, respectively.Table 4 Prediction of confirmed cases
Models India US Russia Argentina Brazil Colombia Italy Turkey Germany France
Random forest regressor 27,800,239.85 33,242,792 4,990,377.92 3,679,365 3,677,138.14 3,328,153.34 4,210,680.23 5,223,509 3,681,290.1 33,242,792
27,800,239.85 33,242,792 4,990,377.92 3,679,365 3,677,138.14 3,328,153.34 4,210,680.23 5,223,509 3,681,290.1 33,242,792
27,800,239.85 33,242,792 4,990,377.92 3,679,365 3,677,138.14 3,328,153.34 4,210,680.23 5,223,509 3,681,290.1 33,242,792
27,800,239.85 33,242,792 4,990,377.92 3,679,365 3,677,138.14 3,328,153.34 4,210,680.23 5,223,509 3,681,290.1 33,242,792
27,800,239.85 33,242,792 4,990,377.92 3,679,365 3,677,138.14 3,328,153.34 4,210,680.23 5,223,509 3,681,290.1 33,242,792
Facebook Prophet model 25,489,630.74 34,368,570 5,103,636.45 3,652,250 3,336,632.71 3,336,632.71 4,595,002.18 5,869,178 3,931,678.31 34,368,570
25,647,356.8 34,429,330 5,112,575.85 3,670,628 3,349,399.23 3,349,399.23 4,611,381.59 5,912,395 3,948,639.15 34,429,330
25,809,552.6 34,491,854 5,121,715.96 3,688,887 3,361,507.91 3,361,507.91 4,628,232.7 5,943,202 3,964,065.27 34,491,854
25,971,872.92 34,548,965 5,130,710.05 3,705,250 3,373,982.73 3,373,982.73 4,644,341.77 5,973,329 3,978,063.45 34,548,965
26,133,621.05 34,601,134 5,139,831.52 3,715,388 3,383,649.47 3,383,649.47 4,662,536.7 6,007,521 3,990,134.14 34,601,134
Stacked LSTM 27,894,800 33,213,357 4,995,613 3,680,159 3,342,567 3,342,567 4,213,055 5,223,499 3,684,672 33,213,357
27,894,800 33,213,357 4,995,613 3,680,159 3,342,567 3,342,567 4,213,055 5,223,499 3,684,672 33,213,357
27,894,800 33,213,357 4,995,613 3,680,159 3,342,567 3,342,567 4,213,055 5,223,499 3,684,672 33,213,357
27,894,800 33,213,357 4,995,613 3,680,159 3,342,567 3,342,567 4,213,055 5,223,499 3,684,672 33,213,357
27,894,800 33,213,357 4,995,613 3,680,159 3,342,567 3,342,567 4,213,055 5,223,499 3,684,672 33,213,357
Table 5 Prediction of death cases
Models India US Russia Argentina Brazil Colombia Italy Turkey Germany France
Random forest
regressor
323,004.1 593,614.2 117,244.3 74,704.57 457,627.8 87,362.86 125,824.4 47,010.95 88,361.46 109,462.97
323,004.1 593,614.2 117,244.3 74,704.57 457,627.8 87,362.86 125,824.4 47,010.95 88,361.46 109,462.97
323,004.1 593,614.2 117,244.3 74,704.57 457,627.8 87,362.86 125,824.4 47,010.95 88,361.46 109,462.97
323,004.1 593,614.2 117,244.3 74,704.57 457,627.8 87,362.86 125,824.4 47,010.95 88,361.46 109,462.97
323,004.1 593,614.2 117,244.3 74,704.57 457,627.8 87,362.86 125,824.4 47,010.95 88,361.46 109,462.97
Facebook Prophet model 277,360.7 612,482.7 123,245.2 74,815.4 494,072.2 85,952.42 133,067.2 48,809.13 91,174.55 114,710.014
278,779.8 613,708.3 123,639.8 75,126.5 496,726.4 86,254.16 133,421.9 49,029.8 91,422.54 114,970.418
280,166.3 614,774.8 124,033.7 75,393.79 499,253.7 86,547.8 133,774.9 49,251.07 91,630.43 115,330.092
281,549.9 615,607.7 124,418.3 75,596.27 501,646.2 86,842.44 134,103.1 49,470.37 91,758.49 115,511.485
281,973.3 616,132.4 124,770.7 75,693.13 503,703.2 87,065.62 134,427.5 49,675.14 91,866.75 115,714.851
Stacked LSTM 325,972 593,606.5 102,855.5 75,056 457,808.8 77,694.1 125,410.2 46,721 88,413 109,304.4
325,972 593,606.5 103,112.3 75,056 457,808.8 77,900.73 125,410.2 46,721 88,413 109,304.4
325,972 593,606.5 103,369.2 75,056 457,808.8 78,107.36 125,410.2 46,721 88,413 109,304.4
325,972 593,606.5 103,626 75,056 457,808.8 78,313.98 125,410.2 46,721 88,413 109,304.4
325,972 593,606.5 103,882.9 75,056 457,808.8 78,520.61 125,410.2 46,721 88,413 109,304.4
Table 6 Prediction of recovered cases
Models India US Russia Argentina Brazil Colombia Italy Turkey Germany France
Random forest regressor 25,150,904 0 4,611,085 3,257,327 14,468,820 87,362.86 3,820,700 5,074,250 3,473,021 390,369.2
25,150,904 0 4,611,085 3,257,327 14,468,820 87,362.86 3,820,700 5,074,250 3,473,021 390,369.2
25,150,904 0 4,611,085 3,257,327 14,468,820 87,362.86 3,820,700 5,074,250 3,473,021 390,369.2
25,150,904 0 4,611,085 3,257,327 14,468,820 87,362.86 3,820,700 5,074,250 3,473,021 390,369.2
25,150,904 0 4,611,085 3,257,327 14,468,820 87,362.86 3,820,700 5,074,250 3,473,021 390,369.2
Facebook Prophet model 21,419,678 − 886,944 4,749,234 3,207,345 15,225,300 85,952.42 4,102,121 5,453,062 3,555,966 408,450.5
21,540,505 − 887,276 4,759,648 3,222,251 15,283,819 86,254.16 4,119,468 5,481,622 3,569,605 410,053.8
21,660,881 − 887,865 4,769,929 3,237,150 15,340,460 86,547.8 4,136,929 5,510,250 3,582,671 411,591.8
21,783,507 − 886,061 4,779,956 3,252,218 15,394,384 86,842.44 4,153,016 5,555,886 3,594,548 412,709.5
21,823,609 − 901,726 4,787,971 3,262,384 15,443,484 87,065.62 4,168,495 5,584,158 3,606,100 413,950.9
Stacked LSTM 25,150,904 1,064,335 4,616,422 3,288,467 14,471,076 77,694.1 2,935,741 3,696,329 3,453,918 390,369.2
25,150,904 1,064,737 4,616,422 3,288,467 14,471,076 77,900.73 2,943,479 3,706,802 3,453,918 390,369.2
25,150,904 1,065,139 4,616,422 3,288,467 14,471,076 78,107.36 2,951,218 3,717,274 3,453,918 390,369.2
25,150,904 1,065,540 4,616,422 3,288,467 14,471,076 78,313.98 2,958,956 3,727,746 3,453,918 390,369.2
25,150,904 1,065,942 4,616,422 3,288,467 14,471,076 78,520.61 2,966,694 3,738,219 3,453,918 390,369.2
The time forecasting prediction will help the COVID warriors estimate their country’s COVID affected rate. They will provide vaccinations to the respective government agencies and protect the people from this dreadful disease. Necessary steps will also be taken to ensure the mitigation of financial, mental, and physical loss done to this devastating pandemic. Based on these future assumptions, the countries mentioned above will continuously improve to defeat this unseen enemy. In addition to this, the results are also compared in Table 7 with the existing techniques on the basis of their mean R2 score for multiple dataset of confirmed COVID cases.Table 7 Comparison with the existing techniques
References Dataset Techniques Mean R2 values (%)
[45] Real time dataset Regression, cloud computing 92.2
[46] Data collected from Our World in Data Machine learning, cloud computing 98
[47] Data collected from Saudi ministry of health Non linear autoregressive artificial neural networks 98.7
[48] WHO’s official data Adaptive network based fuzzy interference system 97.63
[49] Data collected from January 23, 2020 to June 17 2020 Random forest model 95.9
Our Study Data collected from January 22, 2020 to May 29, 2021 Random forest regressor, Stacked LSTM 98.8
Conclusion and Future Scope
In this work, the active, recovered, closed, and death cases from March 2020 to May 2021 of ten different countries, which includes India, the United States of America, Russia, Argentina, Brazil, Colombia, Italy, Turkey, Germany, and France, were pre-processed and later graphically depicted to examine the pattern and find missing values. Further, the data have been scaled using a MinMax scaler to extract and normalize the features to acquire an accurate prediction rate. Various machine learning, time series, and deep learning models, such as Random Forest Regressor, Decision Tree Regressor, K Nearest Regressor, Lasso Regressor, Linear Regressor, Bayesian Regressor, Theilsen Regressor, Kernel Ridge Regressor, RANSAC Regressor, XG Boost, Elastic Net Regressor, Facebook Prophet Model, Holt Model, and Stacked Long Short-Term Memory, and Stacked Gated Recurrent Memory, had been applied to forecast the confirmed, death, and recovered COVID-19 cases. At last, all the models were evaluated and tested using root-mean-error square and R square values to predict COVID-19 cases for the aforementioned ten different countries, and during implementation, it was discovered that the random forest regressor and stacked long short-term memory produced the majority of the best values for all the three cases, i.e., confirmed, death, and recovered.
The research is entirely based on statistical data and methodology; hence, the results generated will help these countries to take all essential safeguards before becoming enslaved by the terrible COVID-19 sickness. Furthermore, an assessment of the complete economic failure in many sectors during the decrease of COVID-19 should be planned to assist countries in reviving their loss.
Funding
Not applicable.
Availability of Data and Materials
Not applicable.
Code Availability
Not applicable.
Declarations
Conflicts of interest
The authors declare no conflict of interest.
Ethical approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
Consent for participants
Informed consent was obtained from all individual participants included in the study.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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| 0 | PMC9748400 | NO-CC CODE | 2022-12-15 23:22:42 | no | SN Comput Sci. 2023 Dec 14; 4(1):91 | utf-8 | SN Comput Sci | 2,022 | 10.1007/s42979-022-01493-3 | oa_other |
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Eur Radiol
Eur Radiol
European Radiology
0938-7994
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Springer Berlin Heidelberg Berlin/Heidelberg
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10.1007/s00330-022-09337-7
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Han Wei 4
Tan Weixiong 5
Zhou Zhen 5
Wang Mengzhao 3
Mao Li 5
Li Xiuli 5
Sui Xin 1
http://orcid.org/0000-0001-6598-6964
Song Lan [email protected]
1
Xu Yan [email protected]
3
Song Wei [email protected]
1
Yu Yizhou 6
Jin Zhengyu 1
1 grid.413106.1 0000 0000 9889 6335 Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730 China
2 grid.506261.6 0000 0001 0706 7839 4+4 Medical Doctor Program, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730 China
3 grid.413106.1 0000 0000 9889 6335 Department of Respiratory and Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730 China
4 grid.506261.6 0000 0001 0706 7839 Department of Epidemiology and Biostatistics, Institute of Basic Medicine Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, Beijing, 100005 China
5 Deepwise Artificial Intelligence (AI) Lab, Beijing Deepwise & League of PhD technology Co. Ltd, Beijing, 100080 China
6 grid.194645.b 0000000121742757 Department of Computer Science, The University of Hong Kong, Hong Kong, China
14 12 2022
113
17 6 2022
8 9 2022
29 11 2022
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This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis 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.
Objectives
To develop a pre-treatment CT-based predictive model to anticipate inoperable lung cancer patients' progression-free survival (PFS) to immunotherapy.
Methods
This single-center retrospective study developed and cross-validated a radiomic model in 185 patients and tested it in 48 patients. The binary endpoint is the durable clinical benefit (DCB, PFS ≥ 6 months) and non-DCB (NDCB, PFS < 6 months). Radiomic features were extracted from multiple intrapulmonary lesions and weighted by an attention-based multiple-instance learning model. Aggregated features were then selected through L2-regularized ridge regression. Five machine-learning classifiers were conducted to build predictive models using radiomic and clinical features alone and then together. Lastly, the predictive value of the model with the best performance was validated by Kaplan-Meier survival analysis.
Results
The predictive models based on the weighted radiomic approach showed superior performance across all classifiers (AUCs: 0.75–0.82) compared with the largest lesion approach (AUCs: 0.70–0.78) and the average sum approach (AUCs: 0.64–0.80). Among them, the logistic regression model yielded the most balanced performance (AUC = 0.87 [95%CI 0.84–0.89], 0.75 [0.68–0.82], 0.80 [0.68-0.92] in the training, validation, and test cohort respectively). The addition of five clinical characteristics significantly enhanced the performance of radiomic-only model (train: AUC 0.91 [0.89–0.93], p = .042; validation: AUC 0.86 [0.80–0.91], p = .011; test: AUC 0.86 [0.76–0.96], p = .026). Kaplan-Meier analysis of the radiomic-based predictive models showed a clear stratification between classifier-predicted DCB versus NDCB for PFS (HR = 2.40–2.95, p < 0.05).
Conclusions
The adoption of weighted radiomic features from multiple intrapulmonary lesions has the potential to predict long-term PFS benefits for patients who are candidates for PD-1/PD-L1 immunotherapies.
Key Points
• Weighted radiomic-based model derived from multiple intrapulmonary lesions on pre-treatment CT images has the potential to predict durable clinical benefits of immunotherapy in lung cancer.
• Early line immunotherapy is associated with longer progression-free survival in advanced lung cancer.
Supplementary Information
The online version contains supplementary material available at 10.1007/s00330-022-09337-7.
Keywords
Immunotherapy
Immune checkpoint inhibitors
Lung neoplasms
Machine learning
Progression-free survival
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pmcIntroduction
The rapid development of immune checkpoint inhibitor (ICI) agents targeting programmed cell death protein 1 (PD-1) or programmed cell death protein ligand 1 (PD-L1) has granted immunotherapy a key role in the treatment of advanced lung cancer in the past ten years [1]. The National Comprehensive Cancer Network (NCCN) has recommended PD-1/PD-L1 blockade therapy for locally advanced and metastatic non-small-cell lung cancer (NSCLC) without targetable genetic mutations [2]. Nonetheless, the beneficial outcome only exists in 15–40% of patients based on previous literature [3, 4]. The need for identifying more efficient predictive biomarkers of immunotherapy responses is therefore crucial.
PD-L1 expression status has been clinically used to select candidates for PD-1/PD-L1 ICIs, but its efficacy as a predictive biomarker is controversial, which is partially due to the quantification nonuniformity and intratumoral heterogeneity [5–9]. In addition, it is an invasive procedure that is not suitable for all patients. Therefore, a noninvasive biomarker is still needed for the precise stratification of patients receiving immunotherapy.
Poor prognosis was associated with several clinical features, such as late lines of immunotherapy and the presence of distant metastases before the treatment, but those findings were based on relatively small cohorts [10, 11]. Computed tomography (CT) image-based radiomics have shown promising results in evaluating tumor responses to immunotherapy, but challenges remain in the aggregation of predictions made at a lesion level to predict a patient-level outcome [12]. Assigning the same patient response to all lesions is a typical lesion-wise radiomic analysis yet it fails to account for effects induced by the unique immune-related response, specifically, dissociated responses [13]. Other patient-level approaches include selecting the largest lesion or averaging by the mean, but the outcomes are far from satisfactory [11, 14]. Chang et.al used a tumor volume-based weighted radiomic features to establish the patient-level outcome in brain metastases with preliminary success [15]. However, this weighting strategy does not consider factors other than tumor size. Recent studies show that an attention-based multiple instance learning (a-MIL) technique may help build predictive models by attributing more weights to the most relevant subregions associated with a specific classification task [16–19]. Therefore, we aimed to explore whether using the adaptively weighted sum of radiomic features from multiple intrapulmonary lesions on the pre-treatment CT scans can enhance the performance of radiomic models for predicting long-term progression-free survival (PFS ≥ 6 months) benefit of immunotherapy. Furthermore, we wanted to examine the complementary ability of clinical variables to the radiomic model.
Materials and methods
Patient population and clinical data collection
This single-center study retrospectively reviewed a total of 309 patients at our hospital from June 2015 to November 2020 with pathologically confirmed advanced lung cancer treated with at least one cycle of either PD-1 or PD-L1 ICI therapies. The inclusion and exclusion criteria are detailed in Fig. 1. The final 233 patients were randomly split into a training (n = 185) and an internal test cohort (n = 48). Patient clinical data were collected through electronic medical records and are displayed in Table 1. The endpoint of our study was defined as the durable clinical benefit (DCB: PFS ≥ 6 months) or the non-DCB (NDCB: PFS < 6 months) group. PFS was defined as the time between the initiation of ICI to the progression of lung cancer or the death of the patient. The progression status was retrieved from the medical records and approved by a senior pulmonologist (M.C.) and a senior radiologist (X.S.) according to the response evaluation criteria in solid tumors (RECIST) version 1.1. Detailed methods for quantifying PD-L1 expression are illustrated in Supplementary Note 1. Fig. 1 Flow chart shows inclusion/exclusion and data split for the single-center cohort. Abbreviations: PD-1, programmed cell death protein-1; PD-L1, programmed cell death protein ligand-1; RECIST, response evaluation criteria in solid tumors
Table 1 The definitions and scoring rules for clinical characteristics
Clinical feature Definition Scoring
Age Age of patients 0, ≤ 60 years old; 1, > 60 years old
Sex Gender of patients 0, male; 1, female
Smoking history The history of smoking cigarettes 0, non-smokers; 1-smokers (ever smoker or current smoker)
Current smoker Have a smoking history within the past 6 months. 0, no; 1, yes
Clinical stage The clinical TNM stage was determined according to the 8th edition of the American Cancer Society guideline for lung cancer staging. 0, stage III; 1, stage IV
ICI treatment If multiple PD-1/PD-L1 ICI treatments are involved during the disease course, the first one is used. They are divided into three groups: pembrolizumab, nivolumab, and other PD-1/PD-L1 inhibitors. Pembrolizumab: 0, no; 1, yes
Nivolumab: 0, no;1, yes
Line of therapy A line of therapy consists of ≥ 1 complete cycle of a single agent, a regimen consisting of a combination of several drugs, or a planned sequential therapy of various regimens. 1, First line; 2, Second line; 3, Third line or more
Chemotherapy Whether or not the ICI regimen involves the use of chemotherapy. 0, no; 1, yes
Pathology Pathology subtypes are divided into three groups: adenocarcinoma, squamous cell carcinoma, and other subtypes of lung cancer (small-cell lung cancer, large-cell lung cancer, and adenosquamous carcinoma). Adenocarcinoma: 0, no;1, yes
Squamous cell carcinoma:0, no;1, yes
Driver gene mutation EGFR gene mutation 0, no; 1, yes
KRAS gene mutation 0, no; 1, yes
ROS1 gene mutation 0, no; 1, yes
Metastasis location Pulmonary 0, no; 1, yes
Pleural 0, no; 1, yes
Brain 0, no; 1, yes
Bone 0, no; 1, yes
Adrenal gland 0, no; 1, yes
Liver 0, no; 1, yes
Others (thyroid gland, prostate, distant lymph node, and etc.) 0, no; 1, yes
Note: Other PD-1/PD-L1 ICI agents include atezolizumab, durvalumab, tislelizumab, penpulimab, and sintilimab. Abbreviations: PD-1, programmed cell death protein 1; PD-L1, programmed cell death protein ligand 1; ICI, immune checkpoint inhibitor; EGFR, epidermal growth factor receptor; KRAS, Kirsten rat sarcoma 2 viral oncogene homologue; ROS1, ROS proto-oncogene 1
Image acquisition and lesion delineation
Contrast-enhanced chest CT scans were carried out from the lung apex to the lung base using various sets of CT scanners as shown in Table 2. The contrast-enhanced scans were acquired at 35 s after the injection of 80–100 mL of nonionic contrast material (Ultravist 300, Bayer Schering Pharma AG; or Ioversol 320, Hengrui Pharmaceutical Co., Ltd) intravenously at a rate of 2.5 mL/s. Table 2 Parameters of CT scanners
Parameters Peking Union Medical College Hospital
CT system information CT scanner system Dual Source CT (Siemens Healthcare) Spectral CT (Discovery CT750 HD scanner, GE Medical Systems) Spectral CT (IQon CT, PHILIPS) 64-channel CT (Aquilion 64 CT, Toshiba)
Somatom Definition Flash Somatom Force
Number of patients 101 94 22 8 8
CT scan parameters Tube voltage 120 kVp 120 kVp 120 kVp 120 kVp
Tube current Variable tube current with automatic tube-current modulation activated Variable tube current with automatic tube-current modulation activated Variable tube current with automatic tube-current modulation activated Variable tube current with automatic tube-current modulation activated
Rotation time 0.5 s 0.6 s 0.5 s 0.5 s
Detector collimation 64 × 0.6 mm 64 × 0.625 mm 64 × 0.625 mm 64 × 0.625 mm
Pitch 1.2 0.984 1.2 0.984
Arterial phase 35 s after injection 35 s after injection 35 s after injection 35 s after injection
Image matrix 512 × 512 512 × 512 512 × 512 512 × 512
Field of view 350 × 350 mm 350 × 50 mm 350 × 350 mm 350 × 350 mm
Reconstruction slice thickness/slice increment 1 mm/1 mm 0.625 mm/0.625 mm 1 mm/1 mm 5 mm/5 mm
Reconstruction algorithm standard resolution standard resolution standard resolution standard resolution
The anonymized original Digital Imaging and Communications in Medicine (DICOM) images were normalized and standardized before being imported into the Dr. Wise research platform, on which lesions were automatically delineated using algorithms described in the previous literature [20]. The results were confirmed and modified on axial images slice by slice with mediastinal (width: 450 HU, level: 50 HU) and lung (width: 1200 HU, level: −600 HU) window settings by a senior thoracic radiologist (L.S.) without knowledge of response status. The volume of interest (VOI) was drawn based on the criteria described in Supplementary Note 2 and Supplementary Fig. 1. A maximum of five lesions were retained for each patient based on their longest diameters. For the purpose of reliability analysis, a randomly selected 30 cases were drawn by a second senior radiologist (W.S.).
Feature extraction and aggregation methods
A total of 1454 radiomic features were extracted for each VOI using the PyRadiomics (version 3.0.1) package in Python (version 3.8). More details can be found in Supplementary Note 3.
All features were normalized before being aggregated to form the patient-level radiomic features in the following three ways: Largest lesion approach (LL): radiomic feature of the largest (3D diameter) lesion.
Average-multiple-lesion approach (ML): average of summed radiomic features of up to five lesions.
Weighted-multiple-lesion approach (WL): sum of adaptively weighted radiomic feature of up to five lesions. The weighted coefficients were determined by an a-MIL model that was developed in the training cohort. The weight coefficients for lesions in the test cohort were generated by the attention-based algorithm embedded in the a-MIL model. A detailed illustration of this technique is shown in Supplementary Note 4 and the codes in Python were available on GitHub at https://github.com/zhjtwx/immunity_WL for reproducibility purposes.
Feature selection and model development
The feature selection and model development were conducted in the training cohort. The patient-level radiomic features underwent three preliminary procedures in a sequential order as shown in Fig. 2d. Firstly, the features that were robust to changes in variations in contour delineation (intraclass correlation coefficient (ICC) of inter-observer variability > 0.8) were retained. Secondly, features that were significantly related to the clinical outcome (p value < 0.05 in the Mann–Whitney U test analysis) were remained. Lastly, Pearson’s correlation coefficient (PCC) of each of the two features was calculated and for the correlated pair (PCC ≥ 0.85), the one with the lower p value was remained. Both the radiomic and clinical features were then fed to the L2-regularized ridge-embedded logistic regression (ridge regression) to select the representative features (coefficient > 0.001) that were associated with the clinical outcome. Unsupervised hierarchical clustering was performed on these representative radiomic features using heatmaps to comprehend their structure (Fig. 2e). Fig. 2 Workflow of model construction. a Multiple lesions were delineated on the Deep-wise labeling system online. b Radiomic features were extracted from multiple lesions. c The three patient-level analyses were performed with (1) radiomic feature from the largest lesion (LL), (2) average radiomic features from multiple lesions per patient (ML), and (3) weighted sum of radiomic features from multiple lesions per patient (WL). See the “Materials and methods” section for details. d The robust and uncorrelated radiomic features were then selected through three preliminary steps. The L2-ridge regression was the last feature selection step to reduce redundancy. e Representative features were displayed using unsupervised hierarchical clustering. f The model was cross-validated in the training cohort and then validated in the independent test cohort. g Receiver operating characteristic (ROC) curves were drawn. *: denotes the p value of radiomic features between the durable clinical benefit (DCB) and the non-DCB group in the training cohort examined by the Mann–Whitney U test. Abbreviations: ICC, intraclass correlation coefficient; PCC, Pearson’s correlation coefficient
The radiomic model was then built based on the selected radiomic features using five machine learning classifiers: logistic regression (LR), support vector machines (SVM), extreme gradient boosting (Xgboost), multilayer perception (MLP), and linear discriminant (LD). The fivefold cross-validation technique was applied and the average performance in the 4 sub-datasets and 1 sub-dataset from five iterations was reported as the training and validation performance respectively. Similar approaches were used to build the integrated model with the additional selected clinical features. All models were validated in the test cohort. The entire workflow is depicted in Fig. 2.
Statistical analysis
Differences in all variables between the DCB and the NDCB were assessed using the Mann–Whitney U test for continuous variables and the chi-square test or Fisher’s exact test for categorical variables as appropriate. The reliability of segmentation was analyzed using the Dice similarity coefficient, and for radiomic features, the ICC and Bland-Altman plots were used. The Gradient weighted Class Activation Mapping was used to visualize the representative radiomic features.
The diagnostic performance was evaluated by classification sensitivity, specificity, accuracy, F1 score, positive predictive value, negative predictive value, and area under the curve (AUC). A two-sided 95% confidence interval for AUC was constructed following the approach of Hanley and McNeil [21]. Performance among different models was compared with the Delong test. Calibration curves and decision curve analysis were performed to evaluate the predictive accuracy and clinical utility of the models.
The Kaplan–Meier (K-M) survival curve method and Cox proportional hazards model were used to analyze PFS. To generate a binary classification, the cutoff thresholds for the prediction probabilities generated by all models were established using the maximum Youden index in the training cohort. Different curves were compared using the log-rank test. A subgroup analysis was performed to evaluate the model’s stratification ability in patients using pembrolizumab. A two-sided p value < 0.05 was used to indicate statistical significance throughout the study. All statistical analyses were performed with the R statistical package.
Results
Patient cohort
Patients with DCB account for 62.9% and 64.9% of the patients in the training and test cohort respectively. The median PFS in the entire cohort was 7.7 months (training: 7.6 months; test: 8.4 months). The data for the PD-L1 expression level were available in 89 (38% of the entire cohort) patients. The clinical and demographic characteristics of the patients in our analysis are summarized in Tables 3 and 4. There were no significant differences in the demographic and clinical characteristics between the two cohorts (p > 0.05). Early line therapy, KRAS genetic mutation, and the combination of chemotherapy were significantly associated with DCB, while the presence of bone metastasis before immunotherapy was associated with NDCB. For chemotherapy agents, compared with single-drug, the dual-drug regimen was significantly associated with better clinical outcomes in the test cohort (Supplementary Table 1). Nevertheless, no single chemotherapy regimen showed remarkable superiority over another. The elevated expression of PD-L1 was associated with epidermal growth factor receptor (EGFR)-wild type and Kirsten rat sarcoma 2 viral oncogene homologue (KRAS) mutation (Supplementary Table 2). Table 3 Demographic characteristics and the therapy regimen of patients in the analysis
Characteristics Training cohort n = 185 p value Test cohort n = 48 p value
NDCB DCB NDCB DCB
Age, median (range) 64 (34–78) 64 (40–79) .252 60 (36–72) 63 (48–77) .237
Sex Male 49 (71) 90 (78) .317 12 (71) 24 (77) .731
Female 20 (29) 26 (22) 5 (29) 7 (23)
Smoking history Non-smokers 31 (45) 38 (33) .098 8 (47) 12 (39) .760
Smokers 38 (55) 78 (67) 9 (53) 19 (61)
Current smoker No 48 (70) 69 (60) .169 13 (77) 19 (61) .350
Yes 21 (30) 47 (40) 4 (23) 12 (39)
Clinical stage III 10 (15) 29 (25) .090 0 (0) 10 (32) .009*
IV 59 (85) 87 (75) 17 (100) 21 (68)
ICI treatment Pembrolizumab 33 (48) 66 (57) .062 7 (41) 21 (68) .203
Nivolumab 20 (29) 17 (15) 6 (35) 6 (19)
Others# 16 (23) 33 (28) 4 (24) 4 (13)
Line of therapy First 22 (32) 75 (65) ≤ .001* 6 (35) 22 (71) .044*
Second 31 (31) 33 (28) 7 (41) 7 (23)
Third+ 16 (23) 8 (7) 4 (24) 2 (6)
Chemotherapy No 44 (64) 46 (40) .003* 9 (53) 12 (39) .518
Yes 25 (36) 70 (60) 8 (47) 19 (61)
Chemo. agent AC 9 (36) 31 (44) .732 1 (13) 5 (26) .017*
AP 1 (4) 2 (3) 0 (0) 2 (11)
CE 2 (8) 9 (13) 0 (0) 0 (0)
DOC 1 (4) 0 (0) 2 (25) 0 (0)
EP 0 (0) 1 (1) 0 (0) 0 (0)
GC 0 (0) 1 (1) 1 (13) 0 (0)
GP 2 (8) 4 (6) 2 (25) 1 (5)
T 1 (4) 1 (1) 1 (13) 0 (0)
TC 9 (36) 21 (30) 1 (13) 11 (58)
Note: Values are expressed as number (%), if not defined otherwise. *: p value < 0.05. #: Other ICI treatments include atezolizumab, durvalumab, tislelizumab, penpulimab, and sintilimab
Abbreviations: NDCB, non-durable clinical benefit; DCB, durable clinical benefit; ICI, immune checkpoint inhibitor; Chemo., chemotherapy; AC¸ pemetrexed and carboplatin; AP¸ pemetrexed and cisplatin; CE¸ carboplatin and etoposide; DOC¸ docetaxel; EP¸ etoposide and cisplatin; GC¸ gemcitabine and carboplatin; GP¸ gemcitabine and cisplatin; T¸ paclitaxel; TC¸ paclitaxel and carboplatin
Table 4 Immunopathologic features and metastasis statuses of tumor before the initiation of immunotherapy
Characteristics Training cohort n = 185 p value Test cohort n = 48 p value
NDCB DCB NDCB DCB
Pathology ADC& 29 (42) 48 (41) .759 10 (59) 15(52) .556
SCC 33 (48) 52 (45) 7 (41) 16(50)
Others^ 7 (10) 16 (14) 0 (0) 0(0)
EGFR mutation No 58 (84) 106 (91) .129 11 (65) 28(90) .051
Yes 11 (16) 10 (9) 6 (35) 3(10)
KRAS mutation No 67 (97) 100 (86) .019* 15 (88) 30(97) .283
Yes 2 (3) 16 (14) 2 (12) 1(3)
ROS1 mutation No 63 (91) 112 (97) .178 16 (94) 29(94) 1.000
Yes 6 (9) 4 (3) 1 (6) 2(6)
PD-L1 expression TPS < 1% 10 (14) 13 (11) .113# 2 (12) 5(16) .571#
1% ≤ TPS < 50% 11 (16) 18 (16) 2 (12) 5(16)
TPS ≥ 50% 3 (4) 17 (15) 0 (0) 3(10)
Unknown 45 (65) 68 (59) 13 (76) 18(58)
Pulmonary metastasis No 40 (58) 80 (69) .130 10 (59) 19(61) 1.000
Yes 29 (42) 36 (31) 7 (41) 12(39)
Pleural metastasis No 46 (67) 79 (68) .840 11 (65) 19(61) 1.000
Yes 23 (33) 37 (32) 6 (35) 12(39)
Brain metastasis No 59 (85) 108 (93) .092 14 (82) 27(87) .686
Yes 10 (15) 8 (6.9) 3 (18) 4(13)
Bone metastasis No 45 (65) 92 (79) .034* 11 (65) 28(90) .051
Yes 24 (35) 24 (21) 6 (35) 3(10)
Adrenal gland metastasis No 59 (85) 97 (84) .733 14 (82) 25(81) 1.000
Yes 10 (15) 19 (16) 3 (18) 6(19)
Liver metastasis No 60 (87) 109 (94) .112 13 (77) 27(87) .428
Yes 9 (13) 7 (6) 4 (3) 4(13)
Other metastases No 58 (84) 102 (88) .456 15 (88) 27(87) 1.000
Yes 11 (16) 14 (12) 2 (12) 4(13)
Note: Values are expressed as number (%), if not defined otherwise. *: p value < 0.05. #: p values are calculated using the cases with known PD-L1 statuses. &: The ADC subtype includes 1 invasive mucinous adenocarcinoma. ^: Other pathology types include small-cell lung cancer (n = 12), large-cell lung cancer (n = 7), and adenosquamous carcinomas (n = 4)
Abbreviations: NDCB, non-durable clinical benefit; DCB, durable clinical benefit; ADC, adenocarcinoma; SCC, squamous cell carcinoma; TPS, tumor proportion score; EGFR, epidermal growth factor receptor; ROS1, ROS proto-oncogene 1; KRAS, Kirsten rat sarcoma 2 viral oncogene homolog
Representative features
There was high agreement between the segmentations drawn by two radiologists (Dice coefficient of 0.89 [95%CI 0.87–0.91] for the largest lesion and 0.90 [95%CI 0.87–0.93] for multiple lesions). Five representative clinical features were identified: age (≤ 60 or > 60), clinical stage (III or IV), bone metastasis, line of therapy (first, second, or third+), and the use of pembrolizumab. Nineteen, twenty-one, and twenty-five radiomic features were selected individually using the LL, ML, and WL approaches. The number of features that remained at every selection step is shown in Supplementary Fig. 2. ICCs and the Bland-Altman plots showed excellent robustness of the selected features (Supplementary Table 3 and Supplementary Fig. 3). The unsupervised clustering analysis of all representative features resulted in three clusters. Features showed differential expression between the DCB and NDCB cases in both cohorts (Supplementary Fig. 4). A complete list of the representative features and their coefficients in the integrated models with different feature construction methods is shown in Supplementary Table 4. In the WL-based integrated model, the most contributable clinical feature was the line of therapy, and for radiomic features, GLCM_Correlation and GLDM_SDHGLE showed the largest coefficient in the negative and positive directions respectively (Supplementary Fig. 5).
Comparison of model performance
Among the three aggregation methods, the AUCs of the WL-based models were superior to those of the other two approaches in all classifiers but MLP, in which the performance of the WL-based radiomic model was not significantly better than that of the LL-based radiomic model in the test cohort (Fig. 3). Fig. 3 Comparison of the area under the curves (AUCs) of different predicative models based on three patient-level analyses. a, b The AUCs of the radiomic models for discerning DCB (PFS ≥ 6 months) from NDCB (PFS < 6 months) in the cross-validation (a) and the test cohort (b). c, d The AUCs of the integrated models discerning DCB from NDCB in the cross-validation (c) and the test cohort (d). The Y axis represents AUCs and the X axis represents different classifiers. The bar in green denotes the model’s performance based on WL-based radiomic features. P values were obtained by comparing the AUC of the integrated model with the AUCs of the other two models [LL-(blue) and ML-(orange) based radiomic models] using the Delong test. Note: * denotes p value < 0.05, ** denotes p < 0.01, *** denotes p value < 0.001. Abbreviations: LL, largest-lesion approach; ML, average-multiple-lesion approach; WL, weighted-multiple-lesion approach; DCB, durable clinical benefit; NDCB, non-durable clinical benefit; LR, logistic regression; SVM, support vector machines; Xgboost, extreme gradient boosting; MLP, multilayer perception; LD, linear discriminant
The WL-based radiomic model with logistic regression classifier yielded the most balanced performance to discern DCB from NDCB with AUCs of 0.87 [0.84–0.89], 0.75 [0.68–0.82], 0.80 [0.68–0.92] in the training, validation, and test cohort respectively (Supplementary Table 5). With the addition of five clinical characteristics, the WL-based integrated model reached a significantly better AUC than the radiomic model and the clinical model, as presented in Table 5 and Fig. 4. The calibration and decision curve analysis curves for the above models are shown in Supplementary Fig. 6 and 7. The performance of the a-MIL model for differentiating DCB from NDCB was given in Supplementary Note 5. Table 5 The performance of the logistic regression-based integrated model was compared with the clinical and the radiomic model in each of three feature construction approaches
DCB vs. NDCB Training cohort (validation fold) Test cohort
Model type AUC [95%CI] ACC F1 SPE SEN PPV NPV p value AUC [95%CI] ACC F1 SPE SEN PPV NPV p value
Clinical 0.71 [0.64, 0.78] 0.77 0.84 0.59 0.88 0.78 0.75 0.047* 0.80 [0.67, 0.92] 0.79 0.83 0.71 0.84 0.84 0.71 0.59
LL_Radiomic 0.71 [0.63, 0.78] 0.67 0.72 0.68 0.67 0.78 0.55 0.016* 0.75 [0.61, 0.89] 0.75 0.80 0.71 0.77 0.83 0.63 0.039*
LL_Integrated 0.80 [0.74, 0.86] 0.80 0.76 0.79 0.80 0.73 0.86 Ref. 0.79 [0.66, 0.92] 0.79 0.79 0.83 0.76 0.81 0.86 Ref.
Clinical 0.71 [0.64, 0.78] 0.77 0.84 0.59 0.88 0.78 0.75 0.051 0.80 [0.67, 0.92] 0.79 0.83 0.71 0.84 0.84 0.71 0.084
ML_Radiomic 0.71 [0.63, 0.78] 0.71 0.68 0.71 0.78 0.63 0.83 0.003** 0.73 [0.59, 0.87] 0.71 0.72 0.94 0.58 0.95 0.55 0.002**
ML_Integrated 0.80 [0.74, 0.86] 0.80 0.75 0.80 0.70 0.78 0.81 Ref. 0.80 [0.67, 0.92] 0.80 0.77 0.80 0.88 0.71 0.92 Ref.
Clinical 0.71 [0.64, 0.78] 0.77 0.84 0.59 0.88 0.78 0.75 0.008** 0.80 [0.67, 0.92] 0.79 0.83 0.71 0.84 0.84 0.71 0.008**
WL_Radiomic 0.75 [0.68, 0.82] 0.78 0.82 0.77 0.79 0.85 0.69 0.011* 0.80 [0.68, 0.92] 0.81 0.86 0.71 0.87 0.84 0.75 0.026*
WL_Integrated 0.86 [0.80, 0.91] 0.83 0.86 0.78 0.85 0.87 0.76 Ref. 0.86 [0.76, 0.96] 0.85 0.89 0.82 0.87 0.90 0.78 Ref.
Note: p value: the area under the curve (AUC) of the clinical model and the radiomic model were compared to the integrated models using the DeLong test. *: p value < 0.05. **: p value < 0.01. Abbreviations: NDCB, non-durable clinical benefit; DCB, durable clinical benefit; CI, confidence interval; ACC, accuracy; SPE, specificity; SEN, sensitivity; PPV, positive predictive value; NPV, negative predictive value; LL, largest lesion approach; ML, average-multiple-lesion approach; WL, weighted-multiple-lesion approach, Ref., reference
Fig. 4 Displays of the receiver operating characteristic (ROC) curves of clinical, radiomic, and integrated models. a–c ROC curves of clinical (red), radiomic (blue) and integrated (green) logistic regression model built with the LL-based radiomic features (a), ML-based radiomic features (b) and WL-based radiomic features (c) for differentiating DCB from NDCB in the cross-validation cohort. d–f ROC curves of clinical (red), radiomic (blue) and integrated (green) logistic regression model built with the LL-based radiomic features (d), ML-based radiomic features (e), and WL-based radiomic features (f) for differentiating DCB from NDCB in the test cohort
Figure 5 illustrates the discriminability of the log GLDM (SDHGLE) feature and the wavelet GLCM (correlation) feature for representative DCB and NDCB patients before ICI therapy. We observed a higher textural heterogeneity pattern on lesions of the DCB patient compared with the NDCB patient. In addition, heavier weights were attributed to the smaller lesion in most circumstances. Fig. 5 Baseline chest contrast-enhanced CT (CECT) images and visualizing heatmaps of class activation in an image of two lung cancer patients presented with durable clinical benefit (DCB) and non-DCB (NDCB). a This DCB case used penpulimab (PFS = 19.1 months). b This NDCB case used sintilimab (PFS = 5 months). L1 and L2 each represents an individual tumor lesion identified on CECT scans. Both lesions shrank significantly at follow-up 6 weeks after the first course of therapy. (i, iv) represent two original tumor lesions in the mediastinal window of CECT. (ii, v) represent GLDM_SmallDependenceHighGrayLevelEmphasis (SDHGLE) feature heatmaps with corresponding tumor lesions of CECT. (iii, vi) represent GLCM_correlation feature heatmaps with corresponding tumor lesions of CECT. (vii) shows the attributed weight coefficients of corresponding radiomic features of L1 and L2 in the LL (largest lesion), ML (average-multiple-lesion) and WL (weighted-multiple-lesion) approaches, respectively. Note: GLDM-SDHGLE measures the joint distribution of small dependence with higher gray-level values, and a greater value indicates a smaller dependence of higher gray-level values and less homogeneous textures; GLCM-Correlation measures the linear dependency of gray-level of neighbouring pixels, and a higher value indicates a less smooth gradient of the pattern in the image
Stratified pretreatment PD-L1 expression as a predictor of durable PFS
As illustrated in Supplementary Table 6, the positivity rate for PD-L1 expression was 66% (59 out of 89) if the cut-off was 1%, with an accuracy of 61.8% (55 of 89) and an AUC of 0.57 (95% CI: 0.44–0.70) in differentiating DCB from NDCB. If the cut-off was set at 50%, the positivity rate reached 26% (23 out of 89), with an accuracy of 50.6% (45 of 89) and an AUC of 0.61 (95% CI: 0.49–0.73) in differentiating DCB from NDCB. More than 46% (41 of 89) of patients with low expression of PD-L1 (tumor proportion score < 50%) experienced DCB.
Predictive ability for PFS of different predictive models
As illustrated in Fig. 6 and Supplementary Table 7, the integrated model showed better performance for predicting PFS than the other two models (HR = 2.90 [95% CI: 2.15–3.84], p = 0.014 in the test cohort). In the pembrolizumab subgroup analysis, a higher score stratified by the radiomic and integrated models was significantly associated with a longer PFS (Supplementary Fig. 8). The results of Cox regression and K-M analysis for the pembrolizumab subgroup are displayed in Supplementary Table 8. Fig. 6 Kaplan-Meier (KM) progression-free survival (PFS) curve analyses. a–c KM curves on the cross-validation cohort for model scores generated by (a) logistic regression (LR)-based clinical model, (b) LR-based radiomic model and (c) LR-based integrated model. d–f KM curves on the test cohort for scores generated by (d) LR-based clinical model, (e) LR-based radiomic model, and (f) LR-based integrated model. All radiomic and the integrated models displayed here were built with the weighted-multiple-lesion (WL)-based patient-level radiomic features. The cutoff threshold of the clinical, radiomic, and integrated model for the PFS risk stratification is 0.51, 0.43, and 0.67 respectively
Discussion
In this study, we collected a relatively large cohort of advanced lung cancer patients and constructed models to identify patients who were more likely to obtain durable clinical benefits using PD-1/PD-L1 targeted therapies. In the meantime, we explored the method of weighting the sum of radiomic features from multiple intrapulmonary lesions to construct the predictive models and found that it exhibited superior performance to discriminate DCB from NDCB compared with the conventional approaches. Furthermore, an integrated predictive model was constructed using the WL-based radiomic features and five clinical features, reaching AUCs of 0.86 in both the cross-validation dataset and test cohorts.
Considering the presence of immunotherapy-specific unconventional response patterns, the patient-level radiomic analysis that incorporates features from multiple lesions is gaining more attention [12, 14, 22]. MIL is a useful tool to aggregate features from multiple imaging patches (instances) that represent one bag-level characteristic [23]. Back in 2020, Zhang et al adopted an MIL-based supporter vector machine to identify the survival-related high-risk subregions in magnetic resonance imaging (MRI) scans for glioblastoma [16]. More recently, Li et.al. proposed an attention-based MIL framework to compute weights for each segmented patch in an abundance of chest CT images and identified regions that were most correlated with the assessment of COVID-19 severity [17]. Another study in histopathology by Lu et al aggregated patch-level features into slide-level representations and assigned scores to each patch to represent the significance to the collective slide-level representations for a specific classification (e.g., clear cell renal carcinoma) [18]. Here, we treated each delineated lesion as an instance and adaptively weighed them to represent the patient-level clinical outcome. As illustrated in Fig. 5, the difference in radiomic features between the DCB and NDCB group is distinctive in the smaller lesion, to which a higher weight was attributed. It demonstrates the strength and validity of our method.
The later line of immunotherapy was associated with poor prognosis. A similar finding was reported in Tunali et al’s study, in which they argued that multiple systematic treatments induced an “immune-desert” microenvironment that compromised the efficacy of immunotherapy [10, 24]. Currently, using pembrolizumab or atezolizumab as first-line therapy for metastatic lung cancers with high expression of PD-L1 has gained increasing acknowledgment [2, 25]. However, our study recognized that a significant proportion of patients who had low expression of PD-L1 (< 50%) could still reach DCB from PD-1/PD-L1 targeted immunotherapies. It highlights the need to identify a more precise predictive biomarker. Given our predictive model only requires pre-treatment CT images and basic clinical information, it can serve as an alternative and noninvasive biomarker to direct personalized therapeutic immunotherapy regimen, especially for those with unknown PD-L1 statuses.
GLDM_SDHGLE and GLCM_Correlation are the two textural features from our integrated model that had the largest coefficients in the positive and negative directions respectively. By visualizing them on the heatmaps, we identified a ring structure surrounding the tumor margin. Previous literature suggested that peritumoral texture features are associated with tumor infiltrating lymphocytes that can predict tumor response to immunotherapy [26–28]. The identified marginal characteristics in our study, though not being biologically validated, may be correlated with the recruitment of active lymphocytes.
The positive association between KRAS-mutated status and immunotherapy’s efficacy was identified in our study and in previous literature as well. Chen et al argued that KRAS-mutation induced an inflammatory tumor microenvironment that may result in the elevation of tumor burden [29, 30]. Other studies indicate that this environment also triggers the elevated expression of PD-L1 although no agreement has been made yet [31, 32]. Our finding suggests the prognostic value of KRAS mutation in PD-1/PD-L1 targeted therapies in lung cancer. Nonetheless, further studies in a larger KRAS-mutated cohort are needed to further warrant this statement.
We acknowledge the limitations of our study. The first is the retrospective nature of this single-center study. Although an internal independent test cohort was adopted, further external validation in a prospective cohort is warranted. Second, we used a relatively small sample size to train a deep learning–based model, although the precise annotations and radiomic features were used to reduce the network complexity. Third, the peritumoral region was not included in our radiomic analysis, which may result in the loss of useful information related to the distribution of tumor-infiltrated lymphocytes around the tumor. Fourth, PD-L1 expression data were unavailable for most patients in our cohort. Combing it with our radiomic signature may enhance the predictive performance of the models. Fifth, we aggregated the radiomic features to give more weight to the most relevant lesion but did not capture inter-lesion heterogeneity and differential patterns of response in patient. Lastly, there is a deficiency of biological validation due to the retrospective nature of our data. Further studies are warranted to help explain the biological significance of the radiomic biomarker.
Conclusion
Our noninvasive predictive model based on the weighted sum of radiomic features from multiple intrapulmonary lesions holds considerable promise as a new approach to bring substantial survival benefits to lung cancer patients who are candidates for immunotherapy.
Supplementary information
ESM 1 (DOCX 3.87 mb)
Funding
This study has received funding from the CAMS Innovation Fund for Medical Sciences (CIFMS, 2021-I2M-C&T-A-007), the AI + Health Collaborative Innovation Cultivation Project of Beijing Municipal Commission of Science and Technology (No. Z201100005620008), the Scientific and Technological Innovation 2030- New Generation Artificial Intelligence Project of the National Key Research and Development Program of China (No. 2020AAA0109503), the National Natural Science Foundation of China (NSFC No. 82171934), and the 2021 SKY Imaging Research Fund of Chinese International Medical Exchange Foundation (No. Z-2014-07-2101). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Declarations
Guarantor
The scientific guarantor of this publication is Zhengyu Jin from Peking Union Medical College Hospital, Department of Radiology.
Conflict of interest
The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.
Statistics
Dr. Wei Han (one of the authors) kindly provided statistical advice for this manuscript.
Informed consent
Written informed consent was waived by the Institutional Review Board of our institution due to the retrospective nature of the study.
Ethical approval
This study was approved by the institutional review board and the ethics committee of Peking Union Medical College Hospital (Beijing, China) (No.S-K196Z), and the requirement for informed patient consent was waived due to the retrospective nature of the study.
Methodology
• retrospective
• diagnostic or prognostic study
• performed at one institution
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Zhenchen Zhu and Minjiang Chen contributed equally to this work.
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| 36515714 | PMC9748402 | NO-CC CODE | 2022-12-15 23:22:42 | no | Eur Radiol. 2022 Dec 14;:1-13 | utf-8 | Eur Radiol | 2,022 | 10.1007/s00330-022-09337-7 | oa_other |
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Curr Rev Musculoskelet Med
Curr Rev Musculoskelet Med
Current Reviews in Musculoskeletal Medicine
1935-973X
1935-9748
Springer US New York
9811
10.1007/s12178-022-09811-1
Updates in Spine Surgery - Techniques, Biologics, and Non-Operative Management (W Hsu, Section Editor)
Impact of Social Determinants of Health in Spine Surgery
http://orcid.org/0000-0002-8073-1051
Reyes Samuel G. [email protected]
Bajaj Pranav M. [email protected]
Alvandi Bejan A. [email protected]
Kurapaty Steven S. [email protected]
Patel Alpesh A. [email protected]
Divi Srikanth N [email protected]
grid.16753.36 0000 0001 2299 3507 Department of Orthopaedic Surgery, Northwestern University Feinberg School of Medicine, 676 North St. Clair Street, Suite 1350, Chicago, IL 60611 USA
14 12 2022
19
14 11 2022
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This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis 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.
Purpose of Review
Social determinants of health (SDH) are factors that affect patient health outcomes outside the hospital. SDH are “conditions in the environments where people are born, live, learn, work, play, worship, and age that affect a wide range of health, functioning, and quality-of-life outcomes and risks.” Current literature has shown SDH affecting patient reported outcomes in various specialties; however, there is a dearth in research relating spine surgery with SDH. The aim of this review article is to identify connections between SDH and post-operative outcomes in spine surgery. These are important, yet understudied predictors that can impact health outcomes and affect health equity.
Recent Findings
Few studies have shown associations between SDH pillars (environment, race, healthcare, economic, and education) and spine surgery outcomes. The most notable relationships demonstrate increased disability, return to work time, and pain with lower income, education, environmental locations, healthcare status and/or provider. Despite these findings, there remains a significant lack of understanding between SDH and spine surgery.
Summary
Our manuscript reviews the available literature comparing SDH with various spine conditions and surgeries. We organized our findings into the following narrative themes: 1) education, 2) geography, 3) race, 4) healthcare access, and 5) economics.
Keywords
Social determinants of health
Education
Geography
Healthcare access
Race
Spine surgery
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pmcIntroduction
According to the World Health Organization, health is more than the absence or presence of disease but encompasses the complete physical, mental, and social well-being of a person [1]. To address these, physicians must understand the social determinants of health (SDH) to deliver quality healthcare. SDH can be defined as the “conditions in the environments where people are born, live, learn, work, play, worship, and age that affect a wide range of health, functioning, and quality-of-life outcomes and risks ” [2]. SDH affect patient health and well-being in multiple capacities. Education, race, geography, healthcare access, and economic factors are fundamental aspects of SDH leading to health inequalities between different patient groups (Fig. 1) [2–5]. These pillars of SDH have been documented in various medical specialties ranging from pediatrics to neurosurgery. [6–18]. Even in the present day of the SARS-CoV-2 pandemic, COVID-19 mortality has been associated with SDH [12, 18]. Within the field of orthopedic surgery, research has shown that various subspecialities including shoulder, elbow, hand, and hip post-surgical outcomes have been affected by SDH [13–17]. However, the question still remains: how does SDH affect outcomes in spine surgery? Fig. 1 The pillars of social determinants of health and subcategories
Much of the current literature regarding SDH in spine surgery has focused on lower back pain in lower education and income cohorts leading to poor post-operative outcomes [19, 20]. The interplay of individual SDH and surgical outcomes in spine surgery is not well studied in the literature. It is fundamentally important to understand and analyze relationships between spine surgery and SDH to redress health inequality amongst marginalized patient populations. The aim of this review is to highlight studies identifying the relationship between spine surgery and SDH, focusing on the aforementioned pillars.
Education
The importance of education in SDH has been widely accepted as a pivotal factor affecting patient longevity, with more years of schooling correlating to longer lifespans [21]. Each year of education attained by a patient is associated with 1.37 years gained in life expectancy [22••]. Numerous measurable metrics have demonstrated the impact that education can have on return to work (RTW), Oswestry Disability Index (ODI), and post-operative pain.
Several studies have evaluated the impact of education when investigating RTW. Using a large database, Macki et al. found that patients undergoing lumbar surgeries for degenerative disease with any college education was a strong predictor of minimal clinically important difference (MCID) for ODI (p-value = 0.003) as well as RTW at 1 year (p-value = 0.001) [23, 24]. Another study analyzing predictive factors of RTW examined 4,694 patients who underwent spine surgery for degenerative lumbar disease, and found that higher education levels (post-college degree) significantly increased the likelihood of RTW compared to patients with less than a high-school level education [25]. These findings are further supported by Zieger et al., who retrospectively evaluated 305 patients undergoing surgery for disc herniation and noted that the RTW cohort had significantly higher education (college or university) compared to non-RTW cohort [26]. Notably, these studies acknowledge a potential selection bias due to patients not responding to the questionnaires/interviews at specific follow-up time points.
When assessing pain and disability after lumbar decompression for spinal stenosis, Elsayed et al. found that patients with no college education had significantly greater back and leg pain visual analog scale (VAS) scores compared to patients with formal college education pre-operatively [27]. Despite differences in VAS, both cohorts showed improvement in functional outcomes at 3 and 12 months post-operatively. In another study, Soriano et al. analyzed 203 lumbar deformity correction cases from 2002 to 2006 and found that patients with higher education levels had more favorable post-operative VAS scores and ODI scores [28]. It should be noted Soriano’s criteria for higher education combined both high school and other higher levels of education such as college/university. Interestingly, in a 5-year prospective study on patients undergoing lumbar microdiscectomy in Greece, only patients with primary education had worse VAS, ODI, Roland Morris Disability Questionnaire (RMDQ) and Short Form 36 Health Survey (SF-36) scores compared to secondary (p-value < 0.05) and university level education (p-value < 0.05) [29]. In another study reviewing clinical outcomes in 13,406 patients undergoing decompression for lumbar spinal stenosis from 2008 to 2012 in Sweden, Iderberg et al. noted that the higher education cohort (university level or higher) had lower ODI scores at 1 year after surgery [30]. Similar findings were observed in a study performed by Kim et al. that examined 155 patients diagnosed with lumbar spinal stenosis at a Korean tertiary care center [31]. Kim et al. reported that higher education was correlated with lower leg VAS, back VAS, ODI, and less catastrophizing. This study’s findings are in accordance with the studies by Soriano, Iderberg, and Gelalis et al., which also showed that higher VAS scores correlated with lower education levels.
When considering long-term employment as an outcome measure, Furnes et al. found that a higher level of education significantly impacted employment status (p-value = 0.03) in a randomized controlled trial of 82 patients undergoing lumbar disc replacement [32]. These results support the impact of higher education on long term employment, whereas the studies by Macki et al. and Asher et al. highlight the impact of higher education on shorter term employment (2 years and 3 months, respectively).
Education levels affect disability, pain, and RTW timing of patients undergoing lumbar surgery. These findings suggest that surgeons should consider patients’ education level and anticipate perioperative support if needed. Future studies should employ a standardized cutoff to define higher education from lower education, as one of the main drawbacks from the current literature is the varying definition of higher education.
Geography
The interplay between patients and their environment is paramount to public health as location can have a significant impact on mortality rates [33–35]. The World Urbanization Prospect 2018 Revision reported that health outcomes differ among patients within rural and urban communities [36]. For example, an urban environment impacts the amount of physical activity acquired during adolescence [37]. The role of environment as a factor in SDH is multifaceted—encompassing physical, chemical, and biological factors—which make an impact at the regional and national levels [38]. The interactions between the environment and spine surgery have yet to be clearly examined.
In the context of cervical spinal surgery, Angevine et al. analyzed national and regional rates of anterior cervical discectomy and fusions (ACDF) performed from the National Hospital Discharge Survey between 1990 and 1999 [39]. They reported that the Northeast had the lowest number of ACDFs performed (19 per 100,000) while the South had the highest rates of ACDFs performed (42 per 100,000). Another study reviewed medical beneficiaries enrolled in Medicare from 1992 to 2005 [40]. Wang et al. found the highest rates of cervical fusions were performed in the Northwest and South-Central US. These differences suggest slight disparities in care and clinical decision making based upon geographic location. The authors attribute these differences to increases in the prevalence of cervical disc disease, increase in density of spine surgeons, extending candidacy for surgery, and surgical techniques [39, 40]. Regional differences for lumbar fusions have been well documented with variations based on city and county [41, 42]. When searching the PearlDiver database between 2004 and 2009, Pannell et al. found higher rates of lumbar fusion surgeries in the Midwest and South while the lowest rates were in the Northeast [41]. Pannell et al. suggest that the variability among regions is possibly linked to differences in knowledge, experience, and understanding of the current literature by spine surgeons [41].
An understudied area of research within the current literature is the differences between rural and urban environments. In a cross-sectional study for Medicare beneficiaries in 2006, Francis et al. reported rural patients were more likely to undergo lumbar fusion compared to urban beneficiaries [43]. According to Francis et al. the differences in rural vs urban populations could be attributed to cultural or behavior differences, access to healthcare, or rural communities having a higher burden of disease. In comparison, a study using the National Inpatient Sample with a cohort of 84,953 patients, Kim et al. found urban hospitals were less likely to perform lumbar decompression with or without fusion compared to rural hospitals (p-value < 0.001) [44••]. Additionally, hospitals located in suburban areas were more likely to perform decompression compared to urban locations (p-value = 0.03). [44••]. Among all of the above mentioned studies, the differences between urban, rural, and suburban hospitals have been attributed to variability of cost, reimbursement, surgeon characteristics, or resource allocation [41, 43–45].
Another important factor in spine surgery that has been demonstrated to impact outcomes is prescribing patterns in pre-operative opioids. Current literature reports a strong association between prolonged pre-operative opioid use and poor post-operative outcomes; however, regional differences exist [46–55]. In a retrospective database study reviewing 13,257 patients who underwent lumbar decompression and fusion from 2007-2016, Adogwa et al. reported that patients living in Western and Southern states had a higher likelihood of prolonged (>1 year) postoperative opioid use (West: OR 1.26, 95% CI: 1.095–1.452 South: OR 1.18, 95% CI: 1.074–1.287) [46, 56]. Of note, sampling bias may be present as there was a disproportionate number of patients in the South (63.1%) and Midwest (24.3%) cohorts compared to the West (10.5%) and Northeast cohorts (2.1%). In another study assessing 25,329 patients from 2010 to 2015, Massie et al. reported that patients who underwent a spinal procedure (anterior or posterior cervical fusion, lumbar decompression, or lumbar fusion) and lived in the Northeast were significantly less likely to refill their opioid prescription post-operatively (p-value = 0.008) [57•]. Patients living in the Midwest (p-value < 0.001) and West (p-value = 0.019) were significantly more likely to refill opioid prescriptions compared to patients in the South. Again, risk of sampling bias should be noted for the study since a higher number of patients were living in the South (39.1%) and North Central regions (26.3%) compared to the Northeast (18.3%) and West regions (15.8%). Adogwa et al. suggested that the regional variation in opioid use may not be affected solely by discrete patient characteristics (i.e., employment, insurance status, or invasiveness of procedure) but rather a combination of patient characteristics, lack of consensus for optimum post-operative opioid use, and possibly practice variations due to varying levels of policy (local, regional, or state level) [56]. In a study utilizing an insurance claims database from 2010 to 2015, Harris et al. analyzed 28,813 patients and reported data on US regional differences in opioid prescriptions in patients undergoing ACDF [58]. Similar to studies published by Adogwa et al. and Massie et al., Harris et al. found that the rates and duration of chronic opioid use were highest in the Western US (p-value < 0.001). Additionally, pre-operative opioid, drug abuse, depression, and anxiety were all risks factors of chronic opioid use [53, 59, 60].
In conclusion, the environment in which patients live affects the rate of fusion, opioid exposure, as well as other psychiatric comorbidities (anxiety/depression). It should be recognized that environmental factors within SDH cover a wide variety of elements [61–68]. Future research focused on analyzing patient outcomes should consider important environmental/geographical variables.
Race
Racial disparities in patient care have been shown to significantly impede equitable healthcare delivery. Racial minorities reportedly receive lower quality of care and face greater morbidity for different chronic diseases compared to non-minorities [69–74]. In fact, the infant mortality rate per 1000 livebirths for Black, non-Hispanic children (10.8) is more than double the rate for White, non-Hispanic children (4.9) [75]. While the cause of such disparities remain in question, the presence of such disparities is not.
Disparities in post-operative outcomes after spine surgery have also been well-documented [76–84]. Khan et al. investigated patient outcomes after surgery for degenerative spine disease and found that Black patients had a 55% higher chance of death relative to White patients [RR = 1.55, 95% CI = 1.28–1.87, I2 = 70%] [76]. Also, Black patients had a higher risk of non-home discharge (RR 1.63; 95% CI, 1.47–1.81; I2 = 89%), 30-day readmission (RR 1.45; 95% CI, 1.03–2.04; I2 = 96%), and longer average length of stay by 0.93 days (95% CI, 0.75–1.10; I2 = 73%). When examining post-operative hospital readmissions, Martin et al. found that Black patients were at greater risk of 30-day readmission as well (OR: 2.20, C.I. 95% (1.04, 4.64)) [77]. Schoenfeld et al. compiled studies that investigated complications and mortality among different racial groups following spine surgery, joint replacement, or other orthopedic procedures; approximately 64% of the studies analyzed reported disparities among racial minorities [78]. Similar trends are observed when analyzing specific spinal surgeries. Skolasky et al. found that while there were no differences in mortality or complications between Caucasian and Hispanic patients following cervical spine surgery, African American patients had a higher inpatient mortality (OR 1.59; 95% CI, 1.30–1.96) and in-hospital complications (OR 1.37; 95% CI 1.27–1.48) [79]. Furthermore, Elsamadicy et al. concluded that African American patients had lower baseline and follow-up patient-reported outcomes (PROs) after elective lumbar spine surgery—specifically ODI (p-value < 0.0001), VAS-LP (p-value = 0.0007), and VAS-BP scores (p-value = 0.0002) [80]. Additionally, in terms of patient-reported satisfaction measures, African American patients were less likely to report that the surgery met their expectations (3 months: 47.2% vs 65.5%, p = 0.01; 12 months: 35.7% vs 62.7%, p = 0.007). Reyes et al. found that when comparing procedural types, fusions were generally similar amongst racial/ethnic groups. However, for many fusion procedures, more medical complications and longer lengths of stay were observed for African American and Hispanic patients compared to White patients [81•]. Kim et al. found that Hispanic and Asian/Pacific Islander patients were less likely to receive a fusion for a similar diagnosis compared to White patients (p-value < 0.001) [82]. However, in a different study using institutional data, Elsamadicy et al. found no significant difference among Black and White patients with regards to neck disability index, VAS, or SF-12 at 3-months and 12-months after ACDF [83]. Finally, Wang et al. studied racial disparities in the setting of adult spinal deformity [84]. Using a nationwide sample, they discovered that from 2004 to 2014, adult spinal deformity surgery usage among Black patients increased from 24.0 to 50.9 per 1,000,000 people, whereas usage amongst White patients increased from 29.9 to 73.1 per 1,000,000 people, which was a greater proportional increase, indicating increased racial disparities in adult spinal deformity surgery utilization.
In conclusion, race has been shown to affect health outcomes across a multitude of spine surgeries, including degenerative cervical and lumbar spine surgery, as well as deformity cases. It should be noted that not all studies controlled for socioeconomic factors such as household income. However, most if not all studies included some type of control for various confounders. Racial minorities tend to face worse outcomes and higher mortality post-operatively. Finally, it is important to note that many of the studies acknowledge how different, complex societal factors may serve as potential confounders. Future studies must attempt to control further societal factors (e.g. access to vehicle) to clearly explore how race may affect health outcomes in spine surgery.
Healthcare Access
Different insurance plans provide patients varying types of healthcare access and quality. In 2020, according to the US Census Bureau, approximately 91.4% of individuals had health insurance for at least a portion of the year [85]. Private health insurance (66.5%) was more common than public health insurance (34.8%) [85]. The two most prevalent types of public health insurance include Medicaid and Medicare [85, 86]. Uninsured individuals, who represented about 8.6% of the population in 2020, often lack primary care providers and face financial barriers to critical health care operations and medications [85, 87]. Moreover, disparities in health outcomes between individuals of different health insurance groups—including private insurance, public insurance, and uninsured—have been shown to exist[83]
Specifically for spinal fusion operations, there is conflicting evidence over the presence of health outcome disparities between payer groups [88–95]. Tanenbaum et al. conducted a study to determine the association between insurance status and adverse quality metrics after cervical fusion procedures. Using Nationwide Inpatient Sample data from 1998–2011, they concluded that Medicaid and self-pay patients were at higher risk of hospital-acquired, post-operative conditions relative to privately insured patients [88]. In a different study in patients that underwent lumbar spinal fusions, Tanenbaum et al. found that both Medicaid and self-pay patients were at higher risk of adverse events in the postoperative period compared to privately insured patients (OR 1.16, 95% CI 1.07–1.27) [89]. Along the same lines, Rasouli et al. determined that relative to privately insured patients, Medicaid patients had longer lengths of stay (p-value = 0.004) and higher rates of 30-day (p-value = 0.0009) and 90-day (p-value = 0.0009) emergency department visits following ACDF [90•]. However, Bhandarkar et al. found that hospitals which have a higher proportion of ACDF patients billed as self-pay, Medicaid, or charity care faced greater inpatient costs, but did not have increased adverse patient events [91]. In light of the aforementioned contradicting findings, the impact of insurance on outcome disparities in ACDF patients is still unclear.
Orhurhu et al. investigated disparities in the use of spine augmentation (vertebroplasty or kyphoplasty) for patients who sustained osteoporotic fractures; they determined that patients under Medicaid (p-value < 0.001), self-pay (p-value < 0.001), and private insurance (p-value = 0.001) all were significantly less likely to receive spine augmentation procedures relative to patients under Medicare [92]. Based on these studies, the presence of healthcare disparities for patients following fracture treatment is also inconclusive.
Finally, the results of studies investigating the association between insurance plan and outcomes after decompression or fusion for lumbar spinal stenosis appear to be more consistent. Lad et al. determined that Medicaid patients had significantly lower reoperation rates at 2 years relative to commercially insured patients (7.22% vs 10.30%, p-value = 0.0002) [93]. A similar trend persisted after 2 years (13.92% vs 16.89%, p-value < 0.0001). Furthermore, Medicaid patients were much less likely to undergo reoperation with fusion (p-value < 0.0001). Elsayed et al. also found that patients with public insurance reported slightly worse outcomes and quality of life after decompression surgery for lumbar spinal stenosis compared to patients with private insurance [94]. Both these studies support the presence of disparities among different payer groups for lumbar spinal stenosis operations.
In conclusion, the presence of disparities in health outcomes among different payer groups is currently inconclusive for spinal fusions, however, there seems to be more conclusive evidence of healthcare-based disparities for lumbar decompression surgeries. It is important to note that many of the studies cite potential confounding variables, including supplemental insurance plans and different baseline levels of age and sex between insurance groups—such variables should be considered and more rigorously controlled in future studies.
Economics
Economic stability is necessary for optimizing health equity and minimizing adverse health outcomes [95]. Currently, there is scarcity of literature involving the impact of economic status on health outcomes in spine surgery. Jackson et al. demonstrated a higher prevalence of established surgical risk factors such as obesity, smoking, sedentary lifestyles, and low-quality diets in low- and middle-income adolescents [96].
To our knowledge, in one of the only studies to date examining occupation type and outcomes of elective lumbar surgery, Khan et al. investigated the relationship between type of work and return to work within 1-year post-surgery. The authors determined that patients with more physically demanding occupations, a workers’ compensation claim, or on short-term disability leave at the time of surgery all had lower RTW rates, independent of medical complications and readmissions [97]. It is important to note that the physical demand of an occupation is not a perfect proxy for income, and the results must not be interpreted as such. To date, there are few studies that have assessed socioeconomic or occupational disparities in spine surgery, necessitating further research in this field.
Conclusion
The concept of SDH is not new. Rather the incorporation of these various factors—education, race, insurance status, economics, and environment—under one umbrella term is novel to the field of research. The connection between them all is best explained by the conceptual framework outlined by the World Health Organization [98]. For example, the social, economic, and political systems within society set up socioeconomic positions. These socioeconomic positions break down populations based on social class, gender, race/ethnicity, culture, education, and income amongst many others. It’s important to note that differences in education lead to variability in occupation; therefore, leading to differences in income. All these factors lead to a wide spectrum of social support, lifestyle, living and working conditions impacting the health of patients.
To date, some evidence exists suggesting the impact of education, race, geography, healthcare, and economic factors can affect the outcomes of spine surgery. However, there are increasingly clear gaps within our current understanding. Future studies examining patient outcomes should not only include SDH elements but controls in statistical analysis. We propose the inclusion of a questionary or survey (i.e., Accountability Health Communities Screen Tool) along with propensity matching to more accurately incorporate reliable SDH qualities within spine surgery [99].
With this new knowledge, spine surgeons should consider patients SDH factors more when treating patients post-operatively. For example, the incorporation of community health workers into the care team can help reduce hospitalization while improving patient quality of care [100].
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This article does not contain any studies with human or animal subjects performed by any of the authors.
Conflict of interest
Samuel Reyes, Pranav Bajaj, Bejan Alvandi, Steven Kurapaty, Alpesh Patel, and Srikanth Divi declare that they have no conflict of interest.
This article is part of the Topical Collection on Updates in Spine Surgery - Techniques, Biologics, and Non-Operative Management
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Rev Environ Contam Toxicol
Rev Environ Contam Toxicol
Reviews of Environmental Contamination and Toxicology
0179-5953
2197-6554
Springer International Publishing Cham
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10.1007/s44169-022-00023-9
Review
An Overview of Chemical Additives on (Micro)Plastic Fibers: Occurrence, Release, and Health Risks
Chen Yuye [email protected]
1
http://orcid.org/0000-0002-3247-7861
Chen Qiqing [email protected]
12
Zhang Qun [email protected]
1
Zuo Chencheng [email protected]
1
Shi Huahong [email protected]
1
1 grid.22069.3f 0000 0004 0369 6365 State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai, 200241 China
2 grid.452927.f 0000 0000 9684 550X Yangtze Delta Estuarine Wetland Ecosystem Observation and Research Station, Ministry of Education & Shanghai Science and Technology Committee, Shanghai, China
14 12 2022
2022
260 1 2224 8 2022
2 12 2022
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis 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.
Plastic fibers are ubiquitous in daily life with additives incorporated to improve their performance. Only a few restrictions exist for a paucity of common additives, while most of the additives used in textile industry have not been clearly regulated with threshold limits. The production of synthetic fibers, which can shed fibrous microplastics easily (< 5 mm) through mechanical abrasion and weathering, is increasing annually. These fibrous microplastics have become the main composition of microplastics in the environment. This review focuses on additives on synthetic fibers; we summarized the detection methods of additives, compared concentrations of different additive types (plasticizers, flame retardants, antioxidants, and surfactants) on (micro)plastic fibers, and analyzed their release and exposure pathways to environment and human beings. Our prediction shows that the amounts of predominant additives (phthalates, organophosphate esters, bisphenols, per- and polyfluoroalkyl substances, and nonylphenol ethoxylates) released from clothing microplastic fibers (MFs) are estimated to reach 35, 10, 553, 0.4, and 568 ton/year to water worldwide, respectively; and 119, 35, 1911, 1.4, and 1965 ton/year to air, respectively. Human exposure to MF additives via inhalation is estimated to be up to 4.5–6440 µg/person annually for the above five additives, and via ingestion 0.1–204 µg/person. Notably, the release of additives from face masks is nonnegligible that annual human exposure to phthalates, organophosphate esters, per- and polyfluoroalkyl substances from masks via inhalation is approximately 491–1820 µg/person. This review helps understand the environmental fate and potential risks of released additives from (micro)plastic fibers, with a view to providing a basis for future research and policy designation of textile additives.
Graphical Abstract
http://dx.doi.org/10.13039/501100001809 National Natural Science Foundation of China 42077371 Chen Qiqing National Key Research and Development project2022YFC3105900 Chen Qiqing Research Funds of Happiness Flower of the East China2021ST2110 Chen Qiqing issue-copyright-statement© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
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pmcIntroduction
Fibers are ubiquitous polymers in daily life. Common fiber products include clothes, carpets, face masks, etc. There are mainly three types of fibers: natural fibers (cotton, wool), synthetic fibers (polyethylene terephthalate (polyester), polyamide (nylon), acrylic, polyurethane (spandex), polypropylene, polyvinyl chloride, etc.), and artificial fibers (rayon, viscose fiber, cellulose acetate, etc.). Global fiber production was about 109 million tons in 2020, of which synthetic fibers, natural fibers, and artificial fibers account for about 62%, 32%, and 6%, respectively, and the synthetic fiber production is estimated to approach 100 million t/y by 2030 (Pepper 2021). During manufacturing, different additives are incorporated into these textiles to improve their performance for different applications.
The definition of “microplastic” refers to plastic particles < 5 mm in size. The shape of microplastics includes fragments, fibers, and beads (Zhao et al. 2022). Fiber is the predominant shape of microplastics detected in both the atmospheric and aquatic environment (Lin et al. 2018; Liu et al. 2019b; Su et al. 2018). Fibrous microplastics, i.e., microplastic fibers (MFs), refer to synthetic fibers, including polyethylene terephthalate, polyamide, acrylic, polypropylene, etc. MFs are mainly released during use and wear of synthetic textile products and also become the main source of secondary microplastics in the environment. Microfibers have a broader definition than MFs, which contain both natural and synthetic fibers smaller than 5 mm. Microfibers in the air mainly originate from drying of textiles, daily wear and tear, and solid waste incineration (De Falco et al. 2020; Dris et al. 2016; Liu et al. 2019a), while those in aquatic environment mainly originate from activities such as the washing of textiles and use of fishing nets (Napper and Thompson 2016; Xue et al. 2020).
The most common additives are dyes, flame retardants, plasticizers, antibacterial agents, antistatic agents, antioxidants, etc. (Rovira and Domingo 2019). Some chemicals on textiles have been restricted or banned according to the REACH (Registration, Evaluation, Authorization and Restriction of Chemicals), including phthalates (bis (2–ethylhexyl) phthalate (DEHP), dibutyl phthalate (DBP), restricted concentration < 0.1%), bisphenol A (BPA, restricted concentration < 0.02% for thermal paper), nonylphenol ethoxylates (NPE, restricted concentration < 0.01%), flame retardants (tris (2,3-dibromopropyl) phosphate (TRIS)), and polybromobiphenyls (PBB), which should not be used in textiles contacting with the skin (Schäfer and Herter 2021). However, there is a wide variety of additives up to more than twenty thousand (https://polymer-additives.specialchem.com/). With the emergence of more and more novel additives (e.g., synthetic phenolic chemicals) (Tan et al. 2021; Wu et al. 2019), most of the additives have not been reasonably controlled and studied. In most cases, additives are physically rather than chemically bound to the plastic polymer (Hahladakis et al. 2018). Thus, MFs can release additives to the surrounding environment easily during the process of laundry, abrasion, and transport (Akhbarizadeh et al. 2021; Hahladakis et al. 2018). When MFs enter organisms, additives will be released and migrate out. In such cases, the bioaccumulation of pollutants can be altered with the presence of MFs especially in above-fugacity scenario (Li et al. 2022).
Many studies have focused on the release of MFs from synthetic textile products; however, little attention has been paid to the “trojan horse” effects of MFs for additives. The objectives of this paper are to (1) overview the extraction and quantification methods of additives on textiles and MFs; (2) summarize the types and concentrations of additives on both traditional (i.e., clothes) and emerging MFs contributors (i.e., face masks); (3) analyze the migration and release capability of these additives; (4) and finally, estimate the annual release of additives together with MFs into aquatic and atmospheric environment, and the mass of additives inhaled and ingested into human body through the carrier of MFs.
Analytical Methods for Additives on (Microplastic) Fibers
In this section, we mainly introduce the pretreatment methods (especially additive extraction methods) and analytical techniques of the predominant plastic additives, including plasticizers, antioxidants, flame retardants, and surfactants (Fig. 1).Fig. 1 Analytical methods of additives on synthetic textiles. (BFRs: brominated flame retardants; PFRs: phosphorus flame retardants; DCM: dichloromethane; THF: C; HFIP: 1,1,1,3,3,3-hexafluoro-2-propanol. ASE: accelerated solvent extraction; XRF: X-ray fluorescence; ICP-OES: inductively coupled plasma-optical emission spectrometry; GC–MS/MS: Gas chromatography-tandem mass spectrometry; TD-GC–MS: thermal desorption-gas chromatography-mass spectrometry; LC–MS: liquid chromatography-mass spectrometry; HPLC: high-performance liquid chromatography)
Sample Pretreatment and Extraction
Pretreatment of Fiber Products
For the extraction of plasticizers, antioxidants, and surfactants on synthetic textile products, solvent extraction is the most common method (Kim et al. 2016; Wang et al. 2019a). Ultrasonic extraction (USE) and microwave-assisted extraction (MAE) have the advantages of high extraction efficiency, short time consumption, low solvent amount, and extensive adaptability (Khan and Jahangir 2020; Kim et al. 2016; La Nasa et al. 2021; Llompart et al. 2019). For instance, USE can be effectively applied in the extraction of phthalates (PAEs) from polyethylene films and synthetic antioxidants from disposable face masks, with recovery rates of 83.2–116.9% and 51–113%, respectively (Kim et al. 2016; Liu and Mabury 2021). Accelerated solvent extraction (ASE) is another prominent method for organic pollutants extraction from sediment, which has not been widely used for textiles yet (Giergielewicz-Możajska et al. 2001; Hu et al. 2020). Additionally, some conventional solvent extraction methods, such as Soxhlet extraction, can also be used with recovery rates of up to 90% or more; however, it is often time-consuming (more than four hours) (Kim et al. 2016; Li et al. 2015).
In addition to the solvent extraction methods, direct qualitative determination techniques are emerging in recent years, such as X-ray fluorescence (XRF), total fluorine (F) analysis technique, and inductively coupled plasma-optical emission spectrometry (ICP-OES). For instance, bromine (Br) and phosphorus (P) contents can be screened in fiber products with XRF and ICP-OES (Negev et al. 2018; Petreas et al. 2016; Young et al. 2021). The total fluorine (F) analysis technique can be conducted before the extraction of per- and polyfluoroalkyl substances (PFAS) to screen samples containing F quickly (Muensterman et al. 2022; Schellenberger et al. 2022). After the USE step of PFAS, some researches also apply solid phase extraction (SPE) to eliminate matrix compound interference and further concentrate samples (Gremmel et al. 2016; Muensterman et al. 2022).
Pretreatment of Microplastic Fibers
Currently, there are limited methods that specifically target additives extraction from MFs. The bottleneck of additives’ extraction from MFs is mainly because of the mass of fiber samples collected from the natural environment is often too low to meet the detection limits of instruments. Recently, Sorensen et al. (2021) proved that when the collected MFs were heavier than 0.1 g, the additives on MFs can be successfully extracted by the USE and quantified.
The pretreatment methods of microplastic particles can provide referential experiences for MFs. Some pretreatment methods, i.e., Soxhlet extraction and USE methods for microplastics can be applied for MF additives extraction. For instance, Zhang et al. (2018) extracted PAEs and organophosphorus esters (OPEs) from microplastic particles (0.01–0.5 g) by the Soxhlet extraction method with dichloromethane (DCM). Besides, Rani et al. (2017) extracted the antioxidants (Irganox 1010, Irganox 1076, 2,6-di-tert-butyl-4-methylphenol (BHT)) from plastic powders by the USE method with DCM. In addition to conventional extraction methods, direct analysis in real-time high-resolution mass spectrometry (DART-MS) can be used as a rapid fingerprinting method to screen microplastic additives. The complex mixture of polymer degradation products (i.e., “chemical fingerprints” of environmental microplastics) resulted from thermal desorption and pyrolysis can reflect the composition of both the polymers and the additives, which has been successfully used to detect plasticizers and antioxidants in microplastics (Zhang et al. 2020d). Of note, this method can preliminarily identify the presence of some additives, but it cannot be used for accurate quantification. In the future, more studies should be carried out on developing sensitive novel extraction or determination methods for trace contaminants in MFs.
Extraction Solutions Selection
For extraction of plasticizers, antioxidants, and flame retardants on fibers, traditional extractants include DCM, acetone, ethyl ether, acetonitrile, n-hexane, and methanol. (Abdallah et al. 2017; Freire et al. 2019; Fu et al. 2012; Hajiouni et al. 2022; Negev et al. 2018; Wang et al. 2011). The mixture of hexane and acetone is the most common extraction solution. For example, n-hexane/acetone (1:1) was used to extract 15 PAEs from children’s clothes, resulting in high recovery rates ranging from 81.9 to 107%; and this recipe has also been successfully applied to extract 39 BFRs and 16 OPEs from children’s sleeping nap mats (made of polyurethane) (Stubbings et al. 2018; Tang et al. 2020). For extraction of surfactants like per- and polyfluoroalkyl substances (PFAS), methanol is commonly used (Muensterman et al. 2022; Schellenberger et al. 2022; Zheng and Salamova 2020). Since PFAS consists of a large number of substances, there are also different extraction solutions and analytical methods for volatile and nonvolatile PFAS, respectively (Table 2). For volatile PFAS (fluorotelomer alcohols (FTOHs)), ethyl acetate and n-hexane can be used as extraction solutions, while methanol and acetone/acetonitrile can be a choice for nonvolatile PFAS, such as perfluoroalkyl carboxylic acids (PFCAs) and perfluoroalkyl sulfonic acids (PFSAs) (Gremmel et al. 2016; Vestergren et al. 2015).
In addition to the traditional extractants mentioned above, some unconventional extraction solutions are also gradually applied. For extraction of phthalate plasticizers, tetrahydrofuran (THF) is recommended by the ISO 14389: 2014 (Wang et al. 2019b) and the Chinese national standard (Textile—Determination of the phthalate content—Tetrahydrofuran method (in Chinese), GB/T 20388–2016). Some studies prove that the THF extraction for PAEs usually exhibits better performance than other solvents, with higher recovery rate of 96.7–110.5% than that of methyl tert–butyl ether (MTBE) (38.3–58.0% recovery) or toluene (62.0–83.8%) (Al–Natsheh et al. 2015; Khan and Jahangir 2020).
Moreover, extractants that enable fibers to be “dissolved” exhibit better extraction efficiency than traditional extractants. Miyake et al. (2017) developed a novel complete dissolution extraction method, i.e., using 25% 1,1,1,3,3,3-hexafluoro-2-propanol (HFIP)/chloroform as extractant to extract 18 brominated flame retardants (BFRs) and 15 phosphorus flame retardants (PFRs) in polyester curtains. By applying the complete dissolution method, more flame retardants were extracted than that via the conventional USE method using toluene or acetone (only 0.5–10% of those measured by the complete dissolution method). Similarly, Li and Kannan (2018) compared two extraction solutions of 25% HFIP/chloroform and acetone/DCM (v/v, 1:4); the former one showed up to 286 times higher extraction efficiency than the latter one. The two examples above confirm that HFIP can well dissolve fibers, such as polyester, nylon, and spandex. Future experiments should focus more on this solvent to gain a better extraction effect.
Extraction Time Selection
The extraction time varies largely among different extraction methods. USE uses small amounts of solvents and allows batch processing of multiple samples. When the additives on fibers are extracted by ultrasonication, the extraction process is usually repeated at least twice, with extraction time ranging from 30 to 60 min (Abdallah et al. 2017; Khan and Jahangir 2020; Wang et al. 2019a). It has been found that for polybrominated diphenyl ethers (PBDEs) extraction from textiles, 30 min is the optimal extraction time, since there is no significant change in the recovery rate beyond 30 min (Abdallah et al. 2017). MAE also has a relatively shorter extraction time of about 15–30 min (Sanchez–Prado et al. 2010). In contrast, Soxhlet extraction method requires longer time, usually at least 4 h for each extraction, making it more costly (Kim et al. 2016; Li et al. 2015; Xu 2021). ASE owns the advantage of high efficiency and automation, with the extraction time of about 15–20 min per sample (Giergielewicz-Możajska et al., 2001). However, ASE cannot be used for batch extraction and may take longer time in case of large number of samples. We summarize the appropriate extraction methods for four additives (Table 1).Table 1 Recommended extraction methods for four types of additives on plastic (micro)fibers
Additives Pretreatment Extraction solution Extraction time
Conventional Novel
Plasticizers USE Tetrahydrofuran, n-hexane, acetone HFIP / chloroform (completed dissolved) 20 min × (2–3 times)
Antioxidants USE Acetone, DCM, ethyl acetate
Flame retardants USE Acetone, n-hexane, DCM
Surfactants USE Methanol
USE ultrasonic extraction
Instrumental Analysis
Gas chromatography-mass spectrometry (GC–MS) and liquid chromatography-mass spectrometry (LC–MS) are widely used for quantification of additives extracted from synthetic textiles. GC–MS is especially suitable for additives with low boiling point and good thermal stability. Bernard et al. (2017) compared eight different analytical methods for determination of plasticizers and found that GC–MS possessed higher sensitivity (LOD values ranging from 0.03–0.5 µg/ml) than other methods. LC–MS determination of pollutants is not limited by boiling point. Thus, it can be used to analyze large molecule substances with poor thermal stability and weak-volatilization ability, such as flame retardants like tri(2-ethylhexyl) phosphate (TEHP), triphenyl phosphate (TPHP), trimethylphenyl phosphate (TMPP), nonvolatile PFAS, etc. (Lorenzo et al. 2016; Muensterman et al. 2022). Tandem mass spectrometry (MS/MS) realizes selective reaction monitoring (SRM), which greatly reduces the noise level and improves selectivity in the analysis of complex sample matrices (Wang et al. 2020). Currently, high-performance liquid chromatography-tandem mass spectrometry (HPLC–MS/MS), and ultrahigh-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) have become very important techniques for the analysis of flame retardants, novel synthetic antioxidants, and surfactants, because of their good selectivity and sensitivity, high precision, and low detection limits (Abdallah 2016; Bastiaensen et al. 2018; Gremmel et al. 2016; Guo et al. 2016; Vestergren et al. 2015; Wang et al. 2019a; Wu et al. 2019). The ionization of molecules has an important influence on the final quantification. Electron ionization (EI) is very suitable for polar chemicals (Bourdeaux et al. 2016; Stubbings et al. 2019). However, EI is not suitable for high molecular weight chemicals due to the fragments’ difficulty for volatilization and poor thermal stability after ionization. Some soft ionization techniques such as electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI) interfaces can effectively solve this limitation (Wang et al. 2020). For example, Halloum et al. (2017) found that the detection limits of GC-APCI-MS/MS were 2.5–25 and 50–100 times lower than those of GC-EI-MS/MS, respectively, for the quantification of non-brominated OPEs and brominated OPEs.
In recent years, emerging quantification techniques that do not require pretreatment have become increasingly popular (Anuar et al. 2022; Jin et al. 2022; Xu et al. 2022). Thermal desorption-gas chromatography-mass spectrometry (TD-GC–MS) and pyrolysis gas chromatography-mass spectrometry (Py-GC–MS) exhibit the advantages of high sensitivity, automation, and solvent interferences-free (Humbert et al. 2022). TD-GC–MS has been found to be effective for the quantification of brominated flame retardants (especially for BDE-209) in curtains and car interiors (Shin and Baek 2012); meanwhile, Py-GC–MS has been increasingly used for the detection of PAEs, flame retardants, ultraviolet stabilizers, and bisphenols. (Akoueson et al. 2022; Deng et al. 2022).
There are also some new analytical methods, such as time of flight mass spectrometry (TOF–MS), electron probe, and environmental forensic microscopy. The principle of TOF–MS is to measure the time for ions to reach the detector from the ion source. The heavier the ion mass, the longer the time to reach the receiver; and vice versa. As a result, ions of different masses can be separated according to their specific m/z. The advantage of TOF–MS is the fast scan speed and high sensitivity. Ionas et al. (2015) have used the ambient high-resolution mass spectrometry (direct probe-TOF–MS) to qualitatively screen flame retardants in textiles (curtains and carpets). TOF–MS was capable of quickly screening BFRs and PFRs in positive and negative ion APCI modes with [M + H] and [M–Br + O]+–, respectively. Moreover, the environmental forensic microscopy is also suitable for investigation of Br distribution (originated from BFRs) on textile surface (Ionas et al. 2015). Nevertheless, environmental forensic microscopy is only recommended for the surface distribution analysis of additives with relatively high concentration. There are also techniques that can quickly screen out samples containing F. Some studies conducted the total F analysis by combustion ion chromatography (CIC) before the extraction of PFAS (Rodgers et al. 2022; Schellenberger et al. 2022). Total F concentration can also be measured by the particle-induced gamma emission (PIGE) technique (Muensterman et al. 2022; Xia et al. 2022). The advantage of CIC or PIGE technique is that total F concentration can be quickly obtained. However, these fast-screening techniques cannot avoid the interference of substances containing fluorine; therefore, they are used as preliminary screening methods. The commonly used extraction methods, solvents, time, and quantification equipment, as well as chemical recoveries for additives in fiber products are summarized (Table 2).Table 2 Analytical methods for additives on fiber products
Additives Polymer composition Fiber products Extraction Extractants Time Determination equipment Recovery Concentration References
Plasticizers
PAEs Polyester, nylon, spandex, cotton Children clothes USE n-hexane/acetone (1:1) 50 min (30 + 20) GC–MS 84.0–103% 2.92–223 μg/g Tang et al. (2020)
PAEs Polypropylene Face masks USE DCM/ethyl acetate (1:1) 30 min × 2 GC–MS 79.3–113.2% 115 –37,700 ng/g Xie et al. (2022)
PAEs Polyethylene Polymer (polyethylene films) / / / TD-GC–MS 92–103% / Kim et al. (2016)
USE Tetrahydrofuran 30 min GC–MS 83.2–116.9%
Soxhlet n-hexane 6 h 101–104%
PAEs / Childcare items, toys, textiles USE Tetrahydrofuran 60 min GC–MS 100 ± 15% 5.18–1798.14 mg/l Khan and Jahangir (2020)
PAEs / Infant fabrics, printed textiles Soxhlet n-hexane 4 h GC–MS 96.2–100.9% Infant fabrics:33.40 ± 2.29 mg/g
Printed textiles:51.60 ± 0.65 mg/g
Li et al. (2015)
Chlorinated Paraffins / Hand wipes USE n-hexane/acetone (1:1) 20 min × 3 Orbitrap-HRMS 48–103% 43–18,000 ng/ participant Yuan et al. (2020)
Flame Retardants
BFRs, OPFRs Polyester, polypropylene, PVC/glass fiber Carpet, curtain / / / direct probe-TOF–MS / / Ionas et al. (2015)
BFRs, OPFRs Polyester, acryl Curtain USE 25% HFIP/ chloroform 30 min (20 + 10) LC–MS/MS BFRs: 91–121%; PFRs: 82–122% n.d. –11,500 μg/g Miyake et al. (2017)
PAEs, BFRs, OPEs Polyester, cotton Fabrics ASE n-hexane/DCM/ acetone (2:1:1) / GC–MS 58–130% / Saini et al. (2016b)
OPEs Polyester, nylon, vinyl, cotton Infant clothing and raw textiles Solvent extraction Methanol 2 h HPLC–MS/MS 63–136% 4.85–1.18 × 106 ng/g Zhu et al. (2020)
OPEs Polypropylene Face masks USE n-hexane/acetone (1:1) 15 min × 2 LC–MS/MS 47–115% 9.71–5835 ng/g Fernandez–Arribas et al. (2021)
OPEs / Hand wipes USE n-hexane/DCM (1:1) 5 min × 3 HPLC 76.0–89.5% children’s hand wipe: 6.5–304 ng/g
adult’s hand wipe: 16.1–346 ng/g
Tan et al. (2018)
Antioxidants
Synthetic phenolic antioxidants (SPAs), Organophosphite antioxidants (OPAs) Polypropylene Face masks USE Methanol 60 min LC–MS/MS 51–113% for the 1000 ng/g spiking level ∑SPAs: 4.44 × 103–9.15 × 104 ng/g
∑OPAs: 1.55 × 104–5.13 × 105 ng/g
Liu and Mabury (2021)
Bisphenols (BPA, BPS, etc.) Polyester, nylon, cotton Infant clothes USE Acetone/DCM (1:4) 20 min × 2 HPLC 60–140% BPA:366 ng/g
BPS:15 ng/g
Xue et al. (2017)
BPA BPS Polyester, spandex, nylon, cotton Clothes USE Ethyl acetate 30 min HPLC–MS/MS BPA: 81.48 ± 19.7%
BPS:109.71 ± 6.56%
BPA: < 3.30 − 1823 ng/g
BPS: < 0.53 − 536 ng/g
Wang et al. (2019a)
Bisphenols Polyester, polyamide, polyacrylonitrile, wool Raw fibers USE DCM or Ethyl acetate 30 min GC–MS 98.7–107.4% Polyester: 17.78–243.35 ng/g
Polyamide: 67.86–128.42 ng/g
Polyacrylonitrile: 75.20–246.48 ng/g
Sait et al. (2021)
Surfactants
PFAS* Polypropylene Face masks USE Methanol 30 min LC-qTOF (nonvolatile PFAS)
GC–MS (volatile PFAS)
nonvolatile PFAS:89–90%
Volatile PFAS: 99–200%
15–46 µg/m2 Muensterman et al. (2022)
PFAS Polyester, nylon, cotton Furniture textiles, carpets USE Nonvolatile PFAS: methanol
Volatile PFAS: ethyl acetate
15 min × 2 UPLC-MS/MS (nonvolatile PFAS)
GC–MS (volatile PFAS)
nonvolatile PFAS:46–108%
Volatile PFAS: 62–143%
n.d. –374 µg/m2 Vestergren et al. (2015)
PFAS Polyester, nylon, polyamide Jackets USE Nonvolatile PFAS: acetone/acetonitrile (4:1)
Volatile PFAS: n-hexane
60 min LC–MS/MS nonvolatile PFAS:40–120%
Volatile PFAS:100–200%
0.03–719 μg/m2 Gremmel et al. (2016)
NPE Polyester, nylon, polyamide,etc. Clothes Not mentioned Acetonitrile: water (7:3) / LC–MS/MS / 1–45,000 mg/kg Brigden et al. (2012)
/: not mentioned
*Nonvolatile PFAS (i.e., ionic PFAS): Perfluorinated alkyl acids (PFAAs, represented by perfluorooctane sulfonate (PFOS)), perfluorinated alkyl sulfonic acids/perfluorinated alkyl sulfonates (PFSAs, represented by perfluorooctanoate carboxylate (PFOA)), perfluoroalkyl carboxylates (PFCAs), and perfluorohexanesulfonic acid (PFHxS); volatile PFAS: more volatile substances such as fluorotelomer alcohol, fluorotelomer acrylate, etc.)
Occurrence of Additives on Plastic Fibers
The main processes in the textile production include sizing (improving the abrasion resistance of fibers), desizing (removing sizing chemicals from textiles), scouring (removing impurities from fibers), bleaching (removing unwanted colored matters), mercerizing (improving the strength and luster of textiles), dyeing & printing (adding colors or patterns to textiles) (Athira et al. 2018). To improve the softness, flame resistance, and stability of textiles, various additives and aids are incorporated. As a consequence, some of them, such as aromatic amines, plasticizers, flame retardants, phenolic antioxidants, surfactants, antimicrobial agents, ultraviolet stabilizers (benzotriazole), anti-wrinkling resins, heavy metals, etc., may remain in the clothes (Licina et al. 2019). PAEs, bisphenols, and OPEs have been widely detected in synthetic fibers (Fig. 2a) (Tang et al. 2020; Wang et al. 2019a; Xue et al. 2017).Fig. 2 The concentration of typical additives in a clothes and b face masks (ng/g). The maximum, minimum, and median values were obtained from the literature. The upper and lower boundaries of each box represent the 75th and 25th percentiles, respectively. The horizontal line represents the median value. The small square represents the mean value. c–g: The concentration (ng/g) (mean ± SD) of additives on different fiber types. Data were collected from the literature and presented as average values (Brigden et al. 2013, 2012; Li and Kannan 2018; Sait et al. 2021; Tang et al. 2020; Wang et al. 2019a, 2022; Xie et al. 2022; Xue et al. 2017; Zheng and Salamova 2020). h The concentration (ng/g) (mean ± SD) of PAEs, OPEs, and PFAS in surgical and N95 face masks. Data were collected from the literature and presented as average values (Fernandez–Arribas et al. 2021; Muensterman et al. 2022; Wang et al. 2022). Of note, the original data of PFAS concentrations are 46 µg/m2 and 15 µg/m2. To match the unit of “ng/g,” we cut 10 cm2 of surgical and N95 masks, respectively, and weighed them to obtain mass average values, followed by a unit conversion to obtain the concentration of ng/g. Statistical analysis was performed using SPSS Statistics 26.0 software. Normality of the data was tested by the Shapiro–Wilk test. Difference between concentrations of additives in surgical and N95 masks was determined through Mann–Whitney U test (*p < 0.05). (DEHP: bis(2-ethylhexyl) phthalate; DnBP: dibutyl phthalate; DiBP: di-iso-butyl phthalate; BPA: bisphenol A; BPS: bisphenol S; BPF: bisphenol F; 2,4-DTBP: 2,4-di-tert-butyl-phenol; TPhP: triphenyl phosphate; TCEP: tris(2-chloroethyl) phosphate; TCIPP: tris(2-chloropropyl) phosphate; TEHP: tri(2-ethylhexyl) phosphate; TEP: triethyl phosphate; PFAS: per- and polyfluoroalkyl substances; NPE: nonylphenol ethoxylates)
The types of additives are closely related to textile material and functions. Plasticizers are one of the most widely used plastic additives; the addition amount can reach 10–70% (Hahladakis et al. 2018; Hermabessiere et al. 2017). Plasticizers are mainly used in polyurethane (PU) or PVC coating of textiles. In some cases, PVC can even contain 80% of plasticizers (Hahladakis et al. 2018). Clothing having abundant colors with rich prints and coats often exhibits higher concentrations of PAEs (Tang et al. 2020). Nylon (15,203 ± 10,382 ng/g) contains a higher PAEs concentration than polyester (9732 ± 6988 ng/g) (Fig. 2c). Tang et al. (2020) measured that total concentrations of 15 PAEs in children clothing (blends of polyester, nylon, and spandex) were 3.35–33.42 μg/g, indicating a moderate level of incorporated phthalates in plastics. REACH regulates that for toys or childcare articles, the individual or combined concentration of DEHP, DBP, BBP equal to or greater than 0.1% (by weight) (1 × 106 ng/g) should not be put on market (Negev et al. 2018). From the collected data (Fig. 2a), it can be seen that the concentration of major PAEs in the clothes does not exceed the standard (1 × 106 ng/g). Meanwhile, PAEs are widely detected in air particulate matter (Li and Wang 2015); in addition to additives remained during manufacturing, fiber fabrics may also adsorb and accumulate airborne plasticizers emitted by indoor furniture (Shi et al. 2018; Zhang et al. 2020b).
Flame retardants are added to reduce the flammability of objects; the addition amount is 3–25% for BFRs and 0.7–3% for PFRs in plastic materials (Hahladakis et al. 2018). Synthetic fibers should be treated with flame retardants, because the molten drops caused by combustion may burn the skin and lead to the burning of combustible materials around (Bourbigot 2008). Since the ban or restriction of some traditional BFRs according to the Stockholm Convention (Wu et al. 2020), the global consumption of organophosphate flame retardants in textile is increasing yearly (from 186,000 t in 2001 to 680,000 t in 2015) (Pantelaki and Voutsa 2019; Reemtsma et al. 2008). The content of flame retardants in synthetic fibers is often higher than that in cotton fabrics. The average concentration of ∑20OPEs (1.52 × 103 ng/g) in synthetic fibers (polyester, nylon, vinyl) was higher than that in cotton fabrics (442 ng/g), triphenyl phosphate (TPhP), accounting for the highest percentage (40.2% of the total concentration) (Zhu et al. 2020). It is noticed that although the presence of BFRs (e.g., PBDEs) has been detected in textiles, such as carpets, curtains, and seat leather (Abdallah et al. 2017; Portet-Koltalo et al. 2021; Shin and Baek 2012), there are no available reports about BFRs on clothing according to our best knowledge. It may be explained that organophosphorus flame retardants (OPFRs) are more multi-functional, which can act as both flame retardants and plasticizers. On the other hand, some chlorinated OPEs contain both halogens and phosphorus (e.g., tris(2-chloroethyl) phosphate (TCEP), tris(2-chloroiso-propyl) phosphate (TCIPP)), which are versatile in flame retardant action, with less odor and lower toxicity (Pandit et al. 2020). In addition to the elimination of some traditional BFRs, the flame retardants in clothing are therefore dominated by OPFRs. Moreover, fibers may also adsorb semi-volatile flame retardants from the air, since electronic products in offices are sources of flame retardants in air (Fig. 3) (Saini et al. 2016a; Saito et al. 2007).Fig. 3 Migration and release pathways of additives from microplastic fibers. Icons are created with BioRender.com. (SVOCs: semi-volatile organic chemicals with boiling points range between 240 °C and 400 °C (Lucattini et al. 2018))
Antioxidants are used to delay the overall oxidative degradation of plastics, the addition amount of which is 0.05–3% in plastic materials (Hahladakis et al. 2018). Antioxidants include phenolic antioxidants (e.g., BPA, BPS, BHT, Irganox 1010, Irganox 1076) and organophosphite antioxidants (tris(4-nonyl-phenyl) phosphate (TNPP), tris(2,4-di-tertbutylphenyl) phosphite (AO168), etc.) (Hahladakis et al. 2018). The concentration of bisphenols is closely related to the type of the fibers; spandex exhibits higher levels of bisphenols, especially for fibers blended of nylon and spandex (Fig. 2e). Socks (blends of spandex, nylon, polyester, and cotton) are found to contain higher levels of PAEs and BPA than other clothing (Tang et al. 2020; Xue et al. 2017). Spandex is a typical elastic fiber widely used in stretchable clothing; the addition of bisphenols improves its flexibility (Bodaghi 2020). A study found that the mean concentration of ∑7 bisphenols in pantyhose made of 21–50% spandex (535,000 ng/g) was significantly higher than that in pantyhose made of 0–20% spandex (170,000 ng/g) (Li and Kannan 2018). Studies have also shown that polyester products contain more additives than cotton ones (Xue et al. 2017). Clothes made of polyester and spandex had high concentrations of bisphenols (1823 ng/g for BPA and 536 ng/g for BPS), while mean concentration of bisphenols (BPA + BPS) was only 21 ng/g in cotton clothes (Wang et al. 2019a). The use of synthetic phenolic antioxidants has gradually increased in recent years; synthetic antioxidants have been detected in disposable face masks (Liu and Mabury 2021). However, there are only few reports on synthetic antioxidants on clothing. Thus, more attention should be paid to the content of synthetic antioxidants in clothing fibers in the future.
To improve softness, smoothness, and water resistance of clothes, especially for functional garments, surfactants (e.g., per- and polyfluoroalkyl substances (PFAS), alkylphenol polyethoxylates (APEO), NPE) are often added during production process (Gremmel et al. 2016; Heydebreck et al. 2016; Holmquist et al. 2016; Licina et al. 2019; Zhang et al. 2015). The environmental hazards of PFAS have been gradually recognized due to their environmental persistence and low degradability. Perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA) have been listed in Stockholm Convention (Groffen et al. 2021). Exposure to PFAS poses various health risks, including effects on fertility, endocrine function, obesity of children, etc. (Espartero et al., 2022). The concentration of PFAS did not show significant difference among fiber types; the mean concentration in polyester (193 ± 268 ng/g) is higher than in nylon (98 ± 100 ng/g) (Fig. 2f). PFAS has also been detected in furniture textile products (e.g., curtain, carpet, table cloth) (Vestergren et al. 2015). A study indicated that the concentrations of PFOS in two carpet samples (0.74 µg/m2 and 1.04 µg/m2) approached or even exceeded the EU regulation (1 µg/m2) (Herzke et al. 2012). PFAS function as surfactants, fabrics with fluorinated coatings may release fewer fibers after washing; however, fluorinated wastewater has a negative impact on the environment (Schellenberger et al. 2019). NPE compounds are another cheap and common surfactants. NPE and their degradation products, nonylphenol, are typical endocrine disruptors, which can affect sperm quality and lead to cancer development (Noorimotlagh et al. 2017, 2020). A survey conducted by Greenpeace International in 2012 revealed that NPE compounds were the most frequently detected substances in 20 branded textile products, with a detection rate of 63% and median concentration of 5.2–1500 mg/kg (Brigden et al. 2012). For 8 luxury brands, the detection rate of NPE was 44%, with concentrations ranging from 1.7 to 760 mg/kg (Brigden et al. 2013). It can be seen that the concentrations of NPE in some clothes exceed the REACH standard (1 × 106 ng/g) (Fig. 2a). The concentrations of NPE in polyester (84,789 ± 179,215 ng/g) are higher than that in nylon (10,000 ng/g); the presence of spandex has no effect on NPE concentration (Fig. 2g). In addition, it has been found that the mean concentration of ∑20OPEs in water-repellent fabrics made of nylon or polyester (1940 ng/g) was significantly higher than that in conventional fabrics made of cotton or polyester (313 ng/g) (Zhu et al. 2020), suggesting that functional garments may contain more additives.
With the Covid-19 pandemic, face masks made of non-woven polypropylene (PP) or polyethylene terephthalate have become emerging MFs contributors to the environment (Fadare and Okoffo 2020; Wang et al. 2022). Chemicals such as antioxidants, plasticizers, and surfactants may be added during the manufacturing of face masks (Liu and Mabury 2021; Muensterman et al. 2022; Sungur and Gulmez 2015; Xie et al. 2022). Total concentrations of PAEs and synthetic antioxidants in face masks ranged from 115 to 37,700 ng/g and from 20.0 to 575 μg/g, respectively (Liu and Mabury 2021; Xie et al. 2022). DEHP, DnBP, DiBP, 2,4-di-tert-butyl-phenol (2,4-DTBP), pentaerythritol tetrakis(3-(3,5-di-tert-butyl-4-hydroxyphenyl) propionate) (AO1010), and AO168 have been frequently detected in face masks (Liu and Mabury 2021; Xie et al. 2022). The antioxidant contents in face masks are quite high (Fig. 2b), while the types of antioxidants on face masks are different from those of clothes. Bisphenols are widely detected in clothes, while lower or undetectable levels of bisphenols are found in face masks. Only one study reported the presence of BPA in surgical masks leachates (0.8–3.2 μg/L) (Liu et al. 2022). This phenomenon may be attributed to high toxicity of bisphenols. Some other phenolic antioxidants such as BHT and butyl hydroxyanisole (BHA) may be relatively “safer,” which can even be used as food additives to extend the shelf life of fried foods (Liu and Mabury 2020; Wang et al. 2021). On the other hand, some novel antioxidants (e.g., AO168, AO1010) receive less attention and lack of effective regulatory measures.
Different types of face masks exhibit different additive concentrations (Fig. 2h). N95 masks contained more flame retardant OPEs and PAEs (OPEs:11.6 ± 10.3 µg/mask (2924.4 ± 2873.2 ng/g), PAEs: 2300 ± 150 to 5200 ± 800 ng/mask (556.0 ± 124.5 ng/g)) than surgical masks (OPEs:0.24 ± 0.27 µg/mask (93.6 ± 107.1 ng/g), PAEs: 55 ± 35–1700 ± 140 ng/mask (230.9 ± 236.6 ng/g)) (Fernandez–Arribas et al. 2021; Wang et al. 2022). However, this phenomenon has not been clearly interpreted. We speculate that this may be due to the higher filtering capacity of N95 masks for bacteria or particulate matter. The density of polypropylene in N95 masks is higher than in normal masks. Therefore, the manufacturing process is more complex, resulting in higher OPEs or PAEs levels. However, there are exceptions that not all N95 masks have higher additive levels. For instance, Muensterman et al. (2022) found that the total PFAS concentrations in surgical masks (46 µg/m2, converted to be 521.7 ng/g) were higher than that in N95 masks (15 µg/m2, converted to be 64.8 ng/g).
Compared with non-fiber plastics or microplastics, the contents of additives in plastic fibers are generally equivalent to the same order of magnitude or even higher. For example, the concentrations of 16 PAEs in PP take-out food containers were 1.62–8.62 μg/g, while the concentrations of 15 PAEs in clothing fibers were 3.35–33.42 μg/g (Han et al. 2021; Tang et al. 2020). Compared with PP fragments (Table 3), face mask fibers (made of PP) exhibit lower levels of phenolic antioxidants and higher levels of plasticizers such as PAEs.Table 3 Comparison of typical additive concentrations in plastic fibers and other shapes of plastics
Additives Concentration of additives in fibers Reference Concentration of additives in plastics Reference
PP fibers (face masks) Liu and Mabury (2021) PP plastic fragments Rani et al. (2017)
AO1010 0.0898–65.4 μg/g 17–155 μg/g
AO1076 0–49.9 μg/g 0–169 μg/g
BHT 0–2.38 μg/g 0.02–1.0 μg/g
2,4-DTBP 0–22.5 μg/g 0.64–11 μg/g
∑ phenolic antioxidants 4.44–91.5 μg/g 53.2–200.3 μg/g
PP fibers (face masks) PP flakes and fragments Zhang et al. (2018)
PAEs 115–37,700 ng/g Xie et al. (2022) 0.29–27.2 ng/g
OPEs 9.71–5835 ng/g Fernandez–Arribas et al. (2021) 6.38–2377.5 ng/g
Release of Additives from (Micro)Plastic Fibers
Release to Water
Washing of synthetic textiles is one of the most important routes for the release of additives from plastic fibers (Luongo et al. 2016; Wang et al. 2019a; Zheng and Salamova 2020). Abrasion of synthetic textiles during laundry is also an important source of microplastics released to aquatic environment (Siegfried et al. 2017). About 2.1 × 105 MFs could be released from polyester clothes during a single machine wash (Sillanpaa and Sainio 2017).
The release of MFs and additives is affected by the following factors summarized in Table 4: water volume, temperature, duration, washing program, use of detergent/softener/textile finishes, fabric types, numbers of washing, fabric weave construction, and chemical properties of additives (De Falco et al. 2018; Hernandez et al. 2017; Kelly et al. 2019; Napper and Thompson 2016; Saini et al. 2016b; Wang et al. 2019a) (Table 4). The factors affecting cotton fiber release were also included, since cotton fibers and plastic fibers may have the same release pattern (such as the use of textile finishes released more fibers, regardless of the fiber type). On the other hand, different fiber types may have different release patterns during washing.Table 4 Factors affecting the release of plastic fibers, natural fibers, and fiber additives to water
Target Factors References
MFs release Water volume High water volume wash caused more MFs release than lower water volume Kelly et al. (2019)
Temperature/time Higher temperature and longer time caused more MFs release Cotton et al. (2020); Dalla Fontana et al. (2020)
Detergent/softener The use of detergent caused more MFs release, while the use of softener reduces the MFs release De Falco et al. (2018); Hernandez et al. (2017)
Fiber type* Polyester or acrylic fabrics shed more fibers than cotton blended fabric Napper and Thompson (2016)
Polyester fabrics released fewer fibers than cotton ones Sillanpaa and Sainio (2017)
Polypropylene and polyurethane face masks released fewer microfibers than cotton ones De Felice et al. 2022)
Fabric weave construction Textile with short spun-staple yarn construction shed more MFs than those with woven construction and filamentous yarns Vassilenko et al. (2021)
Fabrics sewed with double heat-sealing released less MFs than those sewed with normal thread Dalla Fontana et al. (2021)
Mechanical treatment (brushed, sanded or sheared) Mechanically treated fabrics shed more MFs than untreated ones Vassilenko et al. (2021)
Textile finishes* Fabrics treated with finishes (dyes, durable press, and water repellent) shed more microfibers during laundering than untreated ones Zambrano et al. (2021)
Additives release Chemical properties of additives Polarity (log KOW) Polar chemicals (log KOW < 4, e.g., aliphatic OPEs: TnBP, TCEP, TCIPP) are more likely to be released to water; non-polar chemicals (log KOW > 6, e.g., DEHP, BFRs) hardly release to water Saini et al. (2016b)
Hydrophilicity Migration rate of PFAS from infant clothes reached 100% at 20 °C and 50 °C Zheng and Salamova (2020)
Salinity For polyamide MFs, 2 chemicals were identified in the 14–-day seawater leachates, but not in freshwater leachates Sait et al. (2021)
The release of MFs to water depends on the washing conditions, while the release of additives is related to their chemical properties. Additives with higher polarity or hydrophilicity are more prone to be released to aquatic environment (Table 4). Besides, additives can be released to the surrounding environment since most of them are not chemically bound to the polymer matrix (exception: TBBPA is chemically bounded) (Hermabessiere et al. 2017).
In addition to the washing of synthetic fiber products, discarding cigarette butts or face masks can also cause MFs or chemicals released to aquatic environment (Fig. 3). Discarded cigarette butts result in about 300,000 tons of cellulose acetate MFs entering the aquatic environment annually. What accompanied is the release of toxic chemicals such as nicotine, carcinogenic tar, polycyclic aromatic hydrocarbons, and heavy metals (cadmium, lead), which have been proven to pose toxic risk to marine organisms (Shen et al. 2021; Torkashvand et al. 2020; Wright et al. 2015). Micro and nano scale polymeric fibers and heavy metals such as cadmium, lead, and antimony have also been detected in face mask leachate. The presence of heavy metals may be attributed to the dyes used in production of colored masks (Sullivan et al. 2021; Sungur and Gulmez 2015). MFs released from face masks can also become carriers of additives and contaminants. It is estimated that approximately 3.4 billion disposable face masks are discarded globally every day, which cause complex environmental problems (Aragaw 2020; Benson et al. 2021). Moreover, there is also growing interest in novel environmental friendly face masks, such as polylactic acid (PLA) biodegradable masks (Soo et al. 2022). With the advantage of faster degradation rate, biodegradable fibers are also more likely to release additives.
Once MFs enter the aquatic environment, ultraviolet irradiation will accelerate fiber degradation and additives release. Ultraviolet exposure of two months resulted in surface degradation (holes appearance) of polyamide fibers and fragmentation (length reduction) of polyester fibers. In seawater leachates, the concentration of additives (TPhP, TCEP, etc.) released by MFs increased with increasing time (Sorensen et al. 2021). The leaching of additives caused by fragmentation or degradation of plastic fibers deserves further attention.
Release to Air
The release of MFs and additives to the air is also an important pathway. Via daily wear of polyester clothes and human activity, one person can release about 1.03 × 109 MFs to the air per year (De Falco et al. 2020). The drying process is another important source of MFs release (Kapp and Miller 2020). A household tumble dryer could release 433,128 (cotton) and 561,810 (polyester) microfibers in 15 min; the annual release of microfibers by a dryer may be even greater than the number of microfibers released through washing (Tao et al. 2022). Although many literatures reported the release of MFs to the air, little attention has been paid to the additives on MFs. Future study should focus more on additives release to the air together with MFs.
There are two main pathways for additives to be released to the air from MFs: (1) direct release by evaporation effect; (2) indirect release by the MFs generated by abrasion. The latter pathway is less studied. Schellenberger et al. (2022) explored the emission mechanism of PFAS from functional textiles (polyamide) under outdoor weathering conditions, revealing that in addition to the direct evaporation release, PFAS could also be released from abrasion and degradation of fibers. Moreover, some flame retardants (e.g., decabromodiphenylethane (DBDPE), PBDEs) released from electronic dryer may become indirect source of additives released to the air together with MFs (Saini et al. 2016b; Schecter et al. 2009).
MFs can account for up to 33% of the total microplastics in urban dust (Dehghani et al. 2017). These MFs can become carriers of additives during suspension, deposition, and migration in the air. PAEs, bisphenols, and flame retardants have widely been detected in airborne dust (Mitro et al. 2016). Zhang et al. (2020a) reported that the concentration of BPA in indoor dust samples was proportional to the concentration of polycarbonate (PC)-based microplastics, which also further confirmed that microplastic (fibers) is an important source of contaminants in dust.
After the outbreak of Covid-19 pandemic, face masks have become a contributor of polypropylene MFs. Additives like PAEs, OPEs, or synthetic antioxidants in them may be released to the air together with the use and abrasion of face masks. The exposure to MFs or additives through inhalation deserves attention. In regard to the humidity during breathing and higher temperature in summer, the release of some additives (e.g., OPEs) from face masks may increase (Fernandez–Arribas et al. 2021).
Release in Organisms
Plastics can act as a carrier of additives and transport over long distances. The disposal of face masks has become an emerging environmental problem in the last two years. For the first time, a PP face mask has even been found in the feces of a green sea turtle (Chelonia mydas) near the coast of Japan; the risk of exposure to additives through plastics ingestion is of concern (Chowdhury et al. 2021; Fukuoka et al. 2022). MFs are ubiquitous in the marine environment, which are easily ingested by organisms of all trophic levels due to their small sizes. Ingestion of MFs by aquatic organisms can lead to growth inhibition, impairment of the immune system, and disruption of the gut microbiota; MFs have higher acute toxicity for lower taxa aquatic organisms (Rebelein et al. 2021). However, many exposure studies of MFs fail to distinguish between the toxicity effects of MFs and their additives (Alnajar et al. 2021). Although some indoor exposure experiments point out that MFs can be excreted gradually by organisms from their bodies through digestion (Grigorakis et al. 2017; Song et al. 2019), the additives loaded on MFs may be desorbed under intestinal conditions.
Most current experiments only focus on the biological effects of MFs, ignoring the exposure risks caused by additives in MFs. Additives have been proven to be released in organism from plastics or microplastics. In addition to chemical property of additives (log KOW), unique gastric environment of certain organisms such as higher temperature (body temperature of seabirds ≈ 40℃), low pH value, and the occurrence of stomach oil may accelerate the leaching of additives (Andrade et al. 2021; Kühn et al. 2020; Sun et al. 2021; Tanaka et al. 2013). At present, indoor exposure experiments on biological effects of microplastics and chemicals mostly focus on granular microplastics, due to the ease of purchase or preparation of granular microplastics. However, fibrous microplastics rather than granular ones are the most common type of microplastics in actual aquatic environment. In view of this, there exists a vacancy in research on the release of additives to organisms from fibers or MFs.
Estimation of Additive Amounts Released by Microplastic Fibers
As estimated by De Falco et al. (2020), one person could release about 2.98 × 108 polyester MFs to water via laundry and 1.03 × 109 to air via wearing polyester clothes per year. We converted the MF number concentration to mass concentration referring to the formula of Leusch and Ziajahromi (2021), i.e., 129.2 g to water and 446.5 g to air.
Here, we took the most common plastic additives PAEs as an example. According to the collected data, the concentration of PAEs in clothes ranges about 3.35–33.42 μg/g (Chai et al. 2017; Li et al. 2019; Liu et al. 2020; Tang et al. 2020). Assuming that the concentration of PAEs on the MFs is the same as clothes, i.e., the additives in clothes can all be released with the fiber without loss. The mass of PAEs released per person per year:Towater:3.35∼33.42×129.2=0.43-4.32mg
Toair:3.35∼33.42×446.5=1.50-14.92mg
Based on a global population of 8 billion, the global mass of PAEs released to water is 3.46–34.55 t per year via washing, and to air is 11.97–119.39 t per year via wearing polyester clothes. Similarly, the global mass of OPEs, bisphenols, PFAS, and NPE released from MFs per year is 0.0050–10.09 t, 0.0060–552.98 t, 0.0046–0.39 t, and 1.24–568.48 t to water, respectively; and 0.017–34.88 t, 0.021–1911.02 t, 0.016–1.36 t, and 4.29–1964.6 t to air, respectively (Table 5).Table 5 Estimation of the mass of additives released and exposure concentrations
Additives Concentrations of additives on fibers or MFs (μg/g) Mass of additives released from polyester MFs by a person per year (mg/person)a The global mass of additives released per year (t) (based on a population of 8 billion) Exposure of additives through MFs released by clothing per year (μg/person)b Estimated daily intakes (EDIinhalation) of additives from face masks Exposure of additives through direct inhalation associated with face masks per year (μg/person)c Tolerable daily intakes (TDIs) (μg/kg BW/d) d
To water To air To water To air Inhalation Ingestion Surgical masks N95
PAEs 3.35–33.42 0.43–4.32 1.50–14.92 3.46–34.55 11.97–119.39 0–391.40 0.34–12.37 surgical masks: 0.3–4.9 ng/kg BW/d (adults)
1.9–30 ng/kg BW/d (toddler)
N95 masks: 14–27 ng/kg BW/d (adults)
85–160 ng/kg BW/d (toddler)
Wang et al. (2022)
5.46–89.18 (adults)
6.42–101.4 (toddler)
254.8–491.4 (adults)
287.3–540.8 (toddler)
/
OPEs 0.00485–9.764 0.0006–152.46 0.0021–526.87 0.0050–10.09 0.017–34.88 0–114.34 0.00049–3.61 surgical masks: 0.04–1.02 ng/kg BW/d (adults)
N95 masks: 0.46–29.2 ng/kg BW/d (adults)
Fernandez–Arribas et al. (2021)
0.73–18.56 (adults) 8.37–531.44 (adults) /
Bisphenols 0.00585–535 0.0008–69.12 0.0026–238.88 0.0060–552.98 0.021–1911.02 0–6264.85 0.00059–197.95 / / /
PFAS 0.0045–0.382 0.0005–0.05 0.002–0.17 0.0046–0.39 0.016–1.36 0–4.48 0.00045–0.14 0.04–0.10 μg/kg BW/d (adults)
0.1–0.13 μg/kg BW/d (toddler)
Muensterman et al. (2022)
728–1820 (adults)
338–439.4 (toddler)
/
NPE 1.2–550 0.15–71.06 0.54–245.58 1.24–568.48 4.29–1964.6 0–6440.5 0.12–203.5 / / /
DEHP 2.68–3.22 0.34–0.42 1.20–1.44 2.77–3.33 9.57–11.50 0–37.71 0.27–1.19 surgical masks: 0.87–1.2 ng/kg BW/d (adults)
5.3–7.5 ng/kg BW/d (toddler)
N95 masks: 4.2–14 ng/kg BW/d (adults)
25–85 ng/kg BW/d (toddler)
Wang et al. (2022)
/ 50
TCIPP 0.0038–0.23 0.0005–0.03 0.0017–0.10 0.0039–0.24 0.013–0.82 0–2.69 0.00038–0.085 Not mentioned / 10
BPA 0.0076–1.81 0.001–0.23 0.0034–0.81 0.0078–1.87 0.027–6.46 0–21.20 0.00076–0.67 Not mentioned / 50
aone person could release 129.2 g MFs to water via laundry and 446.5 g MFs to air via wearing polyester clothes per year, according to the estimation byDe Falco et al. (2020)
bone person could inhale and ingest about 0–11.71 g and 0.10–0.37 g MFs per year, according to the estimation by Zhang et al. (Zhang et al. 2022, 2020c)
cAverage body weight was assumed to be 70 kg for adults and 13 kg for toddlers (Fernandez–Arribas et al. 2021; Wang et al. 2022), assuming that the mask is worn 260 days per year (the approximate number of working days per year)
dTolerable daily intake (TDI) values were obtained from the literature (Fernandez–Arribas et al. 2021; Wang et al. 2022)
Exposure and Health Risks
Dermal Exposure
Clothes cover approximately 85% of human skin and act as a barrier to block environmental pollutants. However, clothes can also be a potential exposure source of certain chemicals (Fig. 4). For textiles (especially clothes), dermal exposure is an important exposure pathway. Dermal exposure doses of PAEs and bisphenols were 11.83–950 ng/kg BW/d (302.3–24,272.5 μg /year) and 0.21–0.26 ng/kg BW/d (5.4–6.6 μg/year), respectively (Liu et al. 2020; Tang et al. 2020; Xue et al. 2017). Socks containing BPA had great effect on infant, with a maximum BPA exposure dose of 7.28 ng/kg BW/d (Xue et al. 2017). As mentioned above, PVC prints are mostly found in children’s clothing, which contain high levels of PAEs. Children and infants are the most vulnerable groups to endocrine disruptors. Sweating can increase the risk of dermal exposure to additives such as PAEs or BPA (Liu et al. 2020; Xue et al. 2017). Bad habits such as biting and sucking fingers of infants and children may also pose exposure risk of oral ingestion. According to a survey, the mean levels of PAEs (DEHP 6.74%, DINP 1.32%) in childcare products (toys, baby mattresses and textiles, baby diaper pads) exceed the 0.1% standard of the European Union, which are likely to pose high oral or dermal exposure risks (Negev et al. 2018).Fig. 4 The additives in MFs and human exposure pathways of additives in textiles. The exposure amounts (i.e., estimated daily intake (EDI) values, expressed in the unit of ng/kg BW/d or μg/kg BW/d), were obtained from the literature (Fernandez–Arribas et al. 2021; Liu and Mabury 2021; Liu et al. 2020; Muensterman et al. 2022; Tang et al. 2020; Wang et al. 2022; Xue et al. 2017). Average body weight was assumed to be 70 kg for adult. We assumed that the mask is worn 260 days per year and clothes are worn 365 days per year. The unit of exposure amounts is expressed as μg/year in Fig. 4. Icons are created with BioRender.com. (MFs: microplastic fibers; PAEs: phthalates; BPA: bisphenol A; OPEs: organophosphorus esters; AOs: synthetic antioxidants, including synthetic phenolic antioxidants and organophosphite antioxidants; PFAS: per- and polyfluoroalkyl substances)
Inhalation and Ingestion (of Microplastic Fibers)
Human beings and other organisms are exposed to MFs mainly via three routes, including inhalation, ingestion, and dermal exposure. Only the former two exposure routes can cause actual MF intake. Inhalation of MFs can adversely affect the respiratory tracts (Lim et al. 2021; Moolgavkar et al. 2001), which has also been suggested to be associated with the formation of ground glass nodules in human lungs (Chen et al. 2022). Ingestion of MFs has been confirmed in various organisms, including aquatic organisms (fish, decapods, bivalves, zooplankton, etc.), terrestrial organisms (earthworm, snails), and even human beings (Lahive et al. 2022; Rebelein et al. 2021; Song et al. 2019; Zhang et al. 2022). MFs ingestion can cause oxidative stress and inflammation in fish (Zhao et al. 2021), MFs ingestion may be associated with inflammatory bowel disease in human beings (Yan et al. 2022), and even immune disorders and increased risk of neoplasia in the long run (Prata et al. 2020).
As a necessity under the Covid-19 pandemic, the additive inhalation risks caused by wearing face masks deserve attention. N95 masks may cause higher inhalation risk than general surgical masks (Table 5). According to the collected estimated daily intake (EDI) values, the exposure amounts of additives from face masks are 0.7–1820 μg/person/year through inhalation and 546–2912 μg/person/year through ingestion (Fig. 4, Table 5). Attentionally, although the EDI value of DEHP is at a safe level (not exceed the TDI value of 50,000 ng/kg BW/d, Table 5), wearing N95 masks for long time (occupational groups, such as doctors), taking high physical activity, and under higher temperature or humidity in summer may pose higher inhalation risk (Fernandez-Arribas et al., 2021; Muensterman et al. 2022). The chemicals in face masks may be inhaled or ingested orally under long time of wearing; thus, it is necessary to regulate the type and content of additives in face masks in the context that Covid-19 will possibly coexist with humans for a long time.
In addition to the risk caused by direct release of additives on fiber products, there are also effects posed by additive release from MFs, posing higher risks than plastic monomers (Rodrigues et al. 2019). MFs can enter organisms directly via inhalation through the respiratory tract or ingestion through the digestive tract. We calculated the exposure amounts of additives through microplastic fibers inhalation and ingestion and are listed in Table 5. Based on the data provided by Zhang et al. (Zhang et al. 2022, 2020c), i.e., one person could ingest approximately (2.3–8.5) × 104 microfibers via dining and inhale (0–3.0) × 107 microplastics per year. Since airborne microplastics are mainly fibrous, we assumed that 90% of the inhaled MPs are fibers. After conversion, the mass of microfibers inhaled and ingested per person per year is 0–11.71 g and 0.10–0.37 g, respectively (the conversion of MF mass and quantity refer to the formula of Leusch and Ziajahromi (2021)). We selected the three most common chemicals (DEHP, BPA, TCIPP) as an example. For instance, one person may inhale about 0–37.71 μg DEHP (the most typical PAE with high detection frequency and concentration) and ingest about 0.27–1.19 μg DEHP with MFs per year (Table 5). The tolerable daily intake (TDI) value of DEHP is 50 μg/kg BW/d. Assuming that average body weight to be 70 kg for adult, the TDI value for DEHP is 1.28 g/person per year (50 μg/kg BW/d *70 kg*365 d = 1.28 g), the mass of DEHP that one person may inhale or ingest per year does not exceed the TDI value. Similarly, the mass of BPA or TCIPP that one person may inhale or ingest per year does not exceed the TDI value. According to our estimation (Table 5), the maximum exposure amounts of additives through inhalation and ingestion of MFs released from clothing are 4.48–6440.5 µg/person/year and 0.14–203.5 µg/person/year, respectively. Such situation still cannot be ignored and deserves further attention.
Conclusions and Outlook
MFs are ubiquitous in our daily lives, since actions such as the washing, drying, and abrasion of clothes, human contact friction, and the use and discard of face masks all cause MFs release into the environment. However, there is insufficient understanding about additives on MFs. When MFs are abrased and released, the additives can also be released into the environment accordingly, posing potential ecological and health risks to organisms.
In this review, we first summarized analytical methods of additives in synthetic textiles, and recommended sample extraction and compounds quantification methods for typical additives. Second, we comprehensively analyzed the types and concentrations of additives in textile fibers and MFs. Typical additives in traditional fiber products (clothes) and emerging fiber product (face masks) include plasticizers (DEHP, DBP), flame retardants (TCEP, TPhP, TEHP), antioxidants (bisphenols, AO168, AO1010, DBP), and surfactants (PFAS, NPEs), at concentrations of 100–106 ng/g. Finally, we discussed the main release pathways of additives in MFs to the environment, i.e., release to water through washing and release to the air through abrasion or drying. Additives in fiber products pose health risks through inhalation (0.7–1820 μg/person/year), ingestion (546–2912 μg/person/year), and dermal exposure to MFs (4.4–24,272.5 μg/person/year).
Collectively, we reviewed the occurrence and abundance of additives in synthetic textiles, which can release MFs via various daily life processes, including laundry, drying, abrasion, etc. The wide occurrence and exposure amounts of additives from MFs/fibers were confirmed, indicating that MFs pollution in daily life and the potential health risks should not be underestimated.
Finally, several perspectives on the research of chemical additives in MFs were proposed: (1) There exists a vacancy in extraction or analysis methods targeting additives on MFs. Since the mass of environmental MFs collected is too low to meet the detection limits of instruments, future research could focus more on the development of equipment with high sensitivity, automation, and approaches without extraction pretreatment. (2) Current studies mainly focus on the release of additives from large plastic fibers, whereas little attention has been paid to the carrier role of MFs. Much more future work needs to be performed to understand the potential leaching of additives from MFs. (3) The chemical additives exposure risk is mainly obtained by estimating EDI values from large plastic fibers. However, humans are more easily to be exposed to additives released from MFs, which only received little attention yet and warrant further in-depth research.
Abbreviations
2,4-DTBP 2,4-Di-tert-butyl-phenol
A01010, Irganox 1010 Pentaerythritol tetrakis(3-(3,5-di-tert-butyl-4-hydroxyphenyl) propionate
AO168 Tris(2,4-di-tertbutylphenyl) phosphite
APEO Alkylphenol polyethoxylates
ASE Accelerated solvent extraction
BBP Benzyl butyl phthalate
BFR Brominated flame retardants
BHA Butyl hydroxyanisole
BHT 2,6-Di-tert-butyl-4-methyl phenol
BPA Bisphenol A
BPF Bisphenol F
BPS Bisphenol S
DCM Dichloromethane
DEHP Bis(2-ethylhexyl) phthalate
DIBP Di-iso-butyl phthalate
DnBP Dibutyl phthalate
FTOHs Fluorotelomer alcohols
HFIP 1,1,1,3,3,3-Hexafluoro-2-propanol
Irganox 1076 Octadecyl-3-(3,5-di-tert-buty-4-hydroxyphenyl) propionate
MAE Microwave-assisted extraction
MF Microplastic fiber
NP Nonylphenol
NPE Nonylphenol ethoxylates
OPE Organophosphorus esters
OPFR Organophosphorus flame retardants
PAE Phthalate
PBB Polybromobiphenyls
PBDE Polybrominated diphenyl ethers
PFAS Per- and polyfluoroalkyl substances
PFCA Perfluoroalkyl carboxylic acids
PFOS Perfluorooctanesulphonate
PFSA Perfluoroalkyl sulfonic acids
PP Polypropylene
TCEP Tris(2-chloroethyl) phosphate
TCIPP Tris(2-chloropropyl) phosphate
TCPP Tris(2-chloroisopropyl) phosphate
TEHP Tri(2-ethylhexyl) phosphate
TEP Triethyl phosphate
THF Tetrahydrofuran
TMPP Trimethylphenyl phosphate
TnBP Tributyl phosphate
TNPP Tris(4-nonyl-phenyl) phosphate
TPhP Triphenyl phosphate
TRIS Tris (2,3-dibromopropyl) phosphate
USE Ultrasonic extraction
XRF X-ray fluorescence
Acknowledgements
This work was financially supported by the National Natural Science Foundation of China (42077371), the National Key Research and Development project (2022YFC3105900), and the Research Funds of Happiness Flower of the East China Normal University (2021ST2110).
Declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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| 0 | PMC9748405 | NO-CC CODE | 2022-12-15 23:22:42 | no | Rev Environ Contam Toxicol. 2022 Dec 14; 260(1):22 | utf-8 | Rev Environ Contam Toxicol | 2,022 | 10.1007/s44169-022-00023-9 | oa_other |
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Crim Justice Rev
Crim Justice Rev
CJR
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Criminal Justice Review
0734-0168
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SAGE Publications Sage CA: Los Angeles, CA
10.1177/07340168221142909
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Original Article
Pandemic Policing and Community Engagement: Preparedness, Legitimacy and Public Support During the COVID-19 Crisis in Nigeria
https://orcid.org/0000-0001-7485-2351
Aborisade Richard Abayomi 1
Adeleke Oladele Adelere 1
1 Department of Sociology, 107991 Olabisi Onabanjo University , Ago-Iwoye, Nigeria
Richard Abayomi Aborisade, Department of Sociology, Olabisi Onabanjo University, Ago-Iwoye, Ogun State PMB 2002, Nigeria. Email: [email protected]
12 12 2022
12 12 2022
07340168221142909© 2022 Georgia State University
2022
Georgia State University, College of Health and Human Sciences
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.
This study brings to the fore, insights into three key factors that had been widely noted to play significant roles in driving effective pandemic policing. These are the preparedness of the police as first responders to a public health crisis, the level of public trust in the police as a legitimate power holder, and community engagement as a tool to drive public support and participation in fighting COVID-19. Using the Nigeria police as a case study, with the damning reports of abuse of power and other misconduct, this study examined how the police responded to COVID-19 mandates and community participation. Interviews with 40 police officers who enforced the COVID-19 lockdown, 16 senior police officers, and 18 community leaders within Lagos and Ogun states were conducted, and a thematic analysis of the narratives was carried out. Findings indicated that community engagement was not effectively deployed by the Nigerian police in the course of pandemic policing. This was due to a lack of police preparedness, over-reliance on the use of force for public control, public distrust in the police, and a lack of prior practice of community engagement by the police. Public distrust in the police was found to be central to peoples’ disobedience to COVID-19 rules which worsened police-community relations, culminating in protests against the police and its formations. These findings have important policy and practical implications if police legitimacy and post-COVID police-community relations are to improve.
community engagement
COVID-19
legitimacy
pandemic policing
procedural justice
edited-statecorrected-proof
typesetterts19
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pmcIntroduction
The World Health Organization (WHO) declared the coronavirus (COVID-19) outbreak a global pandemic on March 11, 2020 (WHO, 2020a). The highly contagious status of the virus culminated in an unprecedented global response with governments adopting measures to counter the pandemic and cope with the attendant increasing pressures on their public health systems. In the process, directives that have been criticised as a breach of the rule of law, and negating the role of parliament in adopting democratic decisions were taken to limit the mobility of people (Nivette et al., 2021; Stott et al., 2020). These directives widely referred to as ‘lockdown’ measures, largely bordered on the restriction of certain basic human rights, especially the rights to peaceful assembly and freedom of movement. The need to enforce the lockdown measures prompted governments across the world to engage law enforcement agents to monitor and enforce compliance. However, the processes of policing the pandemic have been trailed by widespread reports of police high-handedness, use of excessive force, and wanton abuse of human rights across the world (Aborisade & Gbahabo, 2021; Amnesty International, 2020a; Human Rights Watch, 2020). These have continued to draw concerns about the impact of the approaches adopted by the police while enforcing COVID-19 protocols on community relations and police legitimacy.
The Nigerian government announced a lockdown measure on 30th March 2020, a sequel to the first reported case of COVID-19 in the country on 27th February 2020 (Nigeria Centre for Disease Control, 2020). In declaring the total lockdown, a 24-h notice was given by the government for the people to prepare and, thereafter, obey the stay-at-home order. However, within the first month of police enforcement of the shutdown mandates, the National Human Rights Commission (2020) reported 21 extra-judicial killings of those adjudged to have flouted the lockdown orders by police officers. In addition, the commission was reported to have received 105 complaints of human rights violations against law enforcement officials within the same period (AfricLaw, 2020). The spate of reported excessive use of force and extortion by the Nigeria police culminated in the country being listed by the United Nations Human Rights among the fifteen (15) countries with the ‘most troubling’ allegations of human rights abuse and flouting of the rule of law under the guise of fighting the novel coronavirus pandemic (Aljazeera, 2020).
Amnesty International (2020b) and Transparency International (2020) acknowledged the gross abuse of human rights that characterised police enforcement of COVID-19 restriction measures in Nigeria and expressed concern that the high-handed policing may have a long-lasting impact on legitimacy and police-community relationship far beyond the pandemic period. Meanwhile, in spite of the militarised approach deployed by the Nigeria police in enforcing the COVID-19 mandates, reports indicate a gross violation of the stay-at-home order across the country (Aborisade & Ariyo, 2022; The Africa Report, 2020; The Guardian, 2020). This invariably suggests the ineffectiveness of the militarised and control-dominated approaches adopted by the Nigerian police in response to the public health crisis.
This study brings to the fore three key factors that had been widely noted to play significant roles in driving effective pandemic policing across the world. These are the preparedness of the police as first responders to the public health crisis (Stott et al., 2020), the level of public trust in the police as a legitimate power holder (Jones, 2020), and community engagement as a tool for driving public support and participation in fighting the virus (Cheng, 2020; Reicher & Stott, 2020). Gleaning from reports of unlawful use of force, extra-judicial killings, abuse of human rights, extortion, and other acts of misconduct against the officers enforcing the COVID-19 mandates, it is apparent that there is a need for a review of the approaches used by the Nigerian police while intervening in such health crisis. In doing so, this article presents an empirical review of police enforcement of the lockdown in Nigeria, drawing analysis from the accounts of police field officers, senior police officers, and community leaders in Lagos and Ogun states. Also, the study attempts to contribute to the growing body of knowledge on pandemic policing and community engagement, especially in the global south where police-community relationships have often been reported to be problematic (Amnesty International, 2020b; Amusan & Saka, 2018).
Theoretical Explanations of the Imperatives of Procedurally Just Policing in a Pandemic
Indeed, the global community found itself in a situation of uncertainty following the outbreak of the COVID-19 pandemic. The emergence and rapid spread of the virus subjected the global police system to the pressure of responding to and assisting in a public health crisis while enforcing new laws and bylaws (Laufs & Waseem, 2020; Viedma & Abdalla, 2022). In developing countries such as Nigeria, the preparedness of the police in responding to the health crisis was a substantial challenge due to the overarching problems which confronted the police in such countries like inadequate personnel, equipment, lack of regular training, and communication gadgets (Aborisade, 2021; United Nations Office on Drugs and Crime, 2020). In this study, procedural justice theory (PJT), that speaks to the idea of fair processes, and how people feel that they ought to defer to decisions and rules, following them voluntarily out of obligation rather than out of fear of punishment or anticipation of reward, is adopted (Tyler, 2001; 2006). The PTJ, a social psychological analysis of ‘why people obey the law,’ is built from the seminar work of Tyler (1990).
Procedural justice theorists have affirmed that the successful response of the police to the COVID-19 crisis is highly dependent on public trust and confidence, as well as the level at which the public perceives the police as a legitimate power holder (Jones, 2020; Stott et al., 2020). Police legitimacy, as a concept, implies that the police are seen as legitimate power holders who uphold the law and operate in the community in a procedurally just way, giving a voice to the people they serve (Tyler, 2003). Empirical studies have consistently asserted that peoples’ obedience to the law has a strong connection with the level of public trust in the police and their perception of the police as being legitimate (Bolger & Walters, 2019; Radburn & Stott, 2019; Tankebe, 2013; Terrill et al., 2016). For example, in their study, using a random sample of 1,681 residents of a metropolitan city, Nix, et al. (2015) found that procedural justice evaluations are a primary source of trust in the police. Also, Boateng (2020) examined factors affecting citizens’ perceptions of police procedural fairness. He found that citizens’ assessments of police fairness are largely driven by their experiences with the police, views about police effectiveness, levels of trust in the police, and their own individual characteristics.
In apparent contrast to the principles of procedural justice, the Nigeria police have been widely perceived as a control-dominated system characterised by a centralised police structure (Alemika, 1988; Chinwokwu, 2017; Famosaya, 2020), with crime control as its main aim, and deriving legitimacy mainly from the state (Akinlabi, 2017). This may have impacted significantly on the approach deployed by the Nigerian police to enforce the COVID-19 mandates. According to Transparency International (2020), the approach adopted at supervising the stay-at-home order and social distancing by the Nigerian police was tantamount to criminalising the lockdown rules. This was said to have contributed to the high casualty rate that trailed the enforcement of the lockdown rules in the country (AfricLaw, 2020).
Research findings have shown that the Nigeria police, as an institution, is often perceived as unjust in its operations or lacks compassion in its interactions with the public, thus leading to a state of decline in legitimacy (Aborisade, 2019; Aborisade & Fayemi, 2015a; Aborisade & Oni, 2020a; 2020b; Akinlabi, 2017; Famosaya, 2020). This may be responsible for the constraints faced by the Nigerian police in receiving public support in the course of its COVID-19 duties. Inter-governmental and non-governmental organisations that observed compliance with the COVID-19 protocols in Nigeria remarked that police enforcement of the stay-at-home orders suffered a setback due to the apparent mistrust and high-handed approach of the police in professionally engaging the public (The Africa Report, 2020; Transparency International, 2020).
In the same vein, while drawing from the PJT, it is pertinent to review the enforcement of the lockdown orders by the police as well as the level of community engagement and support in the quest to contain the spread of the virus. By so doing, the key factors that led to massive civil disobedience to the COVID-19 protocols as supervised by the police will be exposed. Eventually, the potentialities of the postulations of the PJT in enhancing positive police-community relations in Nigeria will be examined.
The Imperatives of Community Participation in Pandemic Policing
Scholars and epidemiologists have pointed out the importance of community participation in the collective response to the COVID-19 pandemic. The imperative of community participation was particularly identified as being essential in ensuring compliance with lockdown measures, guidelines for easing restrictions, and community support through volunteering (Cheng, 2020; Reicher & Stott, 2020). In several countries where community participation was sought and achieved as a response to the pandemic, considerable success was realised in curtailing the spread of the virus. For example, in the United Kingdom, over one million people volunteered to help in responding to the pandemic in various capacities which included engaging in tasks as simple as checking on people's wellbeing during the lockdown, distributing palliatives, and educating community members on how to stay safe (Butler, 2020). Also, in a qualitative study examining police officers’ use of social media in engaging the public during the COVID-19 lockdown, Ralph et al. (2022) reported how police in England utilised official, semi-official and unofficial police social media accounts to engage the public.
However, pandemic responses in Nigeria have largely involved the government giving directives and orders that must be obeyed by the people with little or no input from the community (Amnesty International, 2020b). In many instances, some of these orders kept changing as the government received information from epidemiologists and virologists on how to manage the virus. According to Marston et al. (2020), incorporating insights and ideas from diverse communities is central to the coproduction of health, which will see health professionals working together with community members in the planning, researching, delivering and evaluating the most appropriate health promotion and healthcare services. Similarly, community policing and participation in police enforcement of COVID-19 protocols have been identified as the most effective way of preventing and checking the spread of the virus (Jones, 2020; Stott et al., 2020).
Community policing has been described as capable of bringing community and police closer at the local level by encouraging police to work together with local leaders, youths and private security (Jones, 2020). It also brings to the fore inclusive problem-solving approaches wherein police and local leadership jointly identify and tackle community security challenges through existing neighbourhood councils (Anoko et al., 2020). Extant literature has posited that police-community engagement attains an additional level of importance during exceptional events or emergency situations because public outreach enables police officers to connect with those affected by dire situations (Cheng, 2020; Reicher & Stott, 2020). Such a relationship with the community helps police agencies to understand the risks that communities face, while communities are able to understand the public safety responses and how these responses can be accessed (Ejiogu, 2019; Kappeler & Gaines, 2014).
In Nigeria, the inability of the police to effectively ensure adequate security of lives and properties has particularly seen to a proliferation of vigilante organisations, ethno-militia groups and private security to fight crime and protect the public (Amusan & Saka, 2018; Ejiogu, 2019). For example, inadequate police response to armed robbery incidents in the south-eastern parts of Nigeria was attributed to the emergence and rise of the Bakassi Boys, a vigilante group (Harnischfeger, 2003). Also, the O’dua People's Congress, a political ethno-militia group, became involved in vigilantism within the southwest as a response to the growing crime rates within the region (Abdulazeez, 2013). Notably, the adoption of indigenous knowledge of administration and security structure was believed to have largely accounted for the success of OPC and Bakassi Boys in attracting millions of members, garnering public support and reducing the level of crime rate in their regions (Abdulazeez, 2013; Harnischfeger, 2003). Their successes, which were largely attributed to their involvement of local political structures and figures, like the chiefs, youths and other local residents in their vigilant operations, have further led to the proliferation of more vigilante groups (Amusan & Saka, 2018; Ejiogu, 2019). Consequently, as postulated by extant community policing literature (Felix & Hilgers, 2020; Kappeler & Gaines, 2014), and supported by the growing literature on COVID-19 and community policing (Jones, 2020; Marston et al., 2020; Stott et al., 2020), this studies examines the potentials of community participation in pandemic policing in Nigeria.
The Nigerian Police and Pre-COVID Legitimacy Policing
The establishment of police departments in Nigeria dates back to 1820 during the colonial era within the Lagos protectorate (Alemika, 1988). It was expanded in 1879 with the forming of a 1,200-member armed paramilitary Hausa Constabulary tagged the Royal Niger Company Constabulary. In 1894, a similar force, the Niger Coast Constabulary was established in Calabar under the newly proclaimed Niger Coast protectorate. More formations were subsequently established in different parts of the Northern Nigeria Protectorate, Lagos Colony and Protectorate of Southern Nigeria. Subsequently, in 1930, the British merged the northern and southern regional police forces to form the colony's first national police – the Nigeria Police Force.
In policing Nigeria, the Nigeria Police have been widely reported to rely heavily on instrumental compliance in performing its enforcement duties on crimes (Alemika, 1988; Chinwokwu, 2017). Some scholars traced the orientation of the Nigeria Police regarding its cultural reliance on instrumental compliance and use of lethal force to the British colonial rule (Agbiboa, 2015; Alemika, 2003). Meanwhile, other scholars linked the culture of police violence to the country's prolonged rule under military juntas, who used the military forces as a tool to ensure and mandate cooperation from citizens (Aborisade & Obileye, 2017; Akinlabi, 2017). Irrespectively of the source of orientation, there are considerable reports of systemic violence by officers of the Nigerian police meted out to citizens in the performance of their duties. These reports indicate that excessive and unwarranted force is often applied by officers while apprehending crime suspects (Alemika, 2003), controlling crowds and riots (Iwuoha & Anichie, 2021), enforcing movement restrictions (Famosaya, 2020), and executing stop-and-search (Adisa et al., 2018). Others include resorting to torture during investigations (Aborisade & Obileye, 2017), and committing extrajudicial killings (Amnesty International, 2020b; Babatunde, 2017). There have also been reports of officers engaging in gender-based violence against women in custody (Aborisade & Oni, 2021; Salihu & Fawole, 2021), and engaging in sundry corrupt practices (Aborisade & Fayemi, 2015b; Agbiboa, 2015).
Public encounters with the police and reports of police abuse of human rights appear to have considerably shaped public perception of the police and negatively impacted the legitimacy of policing in Nigeria (Adisa et al., 2018; Akinlabi, 2020; Famosaya, 2020). The Nigerian police have been widely perceived as a control-dominated system characterised by a centralised police structure, with crime control as the main aim, and deriving legitimacy mainly from the state (Akinlabi, 2017; Alemika, 1988). A tipping point of police illegitimacy in Nigeria was the viral video of police extrajudicial killing of a man by officers of the now disbanded Special Anti-Robbery Squad (SARS) (Etim et al., 2022). This culminated in a nationwide protest against police brutality that defied movement restrictions occasioned by the COVID-19 pandemic (Iwuoha & Anichie, 2021) and further demonstrated deep distrust between the community and the police. Prior to the #EndSARS protests, low public compliance with police directives on COVID-19 rules has been widely reported, with the public appearing to challenge police legitimacy in restricting movement (The Africa Report, 2020; The Guardian, 2020). Therefore, the pre-COVID legitimacy challenge of the Nigerian police and low public trust may not only inhibit effective pandemic policing but also stand to deepen the divides between police and the Nigerian community.
The Present Study
There is no gainsaying the fact that public health and safety emergencies have a lasting impact on the police and the communities they serve. On-going research and position papers on the COVID-19 pandemic have attested to the fact that it will have a great impact on post-COVID policing, policy-making and academic research. This study is guided by three salient questions: what was the level of preparedness of the Nigerian police as a first responder to the COVID-19 pandemic and enforcing new laws and by-laws? In addressing this question, the study aims to explore pre-COVID training, education and practice that can impact public health crisis intervention. Second, what was the level of public trust in the police as a legitimate power holder supervising the COVID-19 rules? The second question attempts to discuss the pre-COVID level of police legitimacy, and probe the willingness of the public to obey lockdown measures as enforced by the police, and their cooperativeness in complying with social distancing and safety measures after the lockdown. Finally, what was the level of police use of community engagement as a tool to driving public support and participation in fighting COVID-19? This third question aims to explore the nature of police-community relationships prior to the pandemic, during, and after the lockdown, in engendering public support and participation in COVID-19 safety measures. Considering that Nigeria was listed among fifteen countries with the ‘most troubling’ allegations of human rights abuses by law enforcement officers during the crisis lockdown, studies reflecting on police approach in engaging the public are imperative. This empirical study will serve to inform police departments and policymakers in the face of other public health emergencies.
Method
This study adopted a qualitative approach due to the unexplored nature of the topic and in order to gather detailed information from those that could positively inform this research. In addition, the aim of the research is to capture the breadth of the COVID-19 duty experiences and viewpoints of police officers and community leaders rather than the dominant discourses and commonality on police operations and conducts.
Study Locations
Lagos state, which used to be the political capital of Nigeria, is still widely described as the commercial capital of the country. According to the official website of the Lagos State Government, the state has a population of about 24.6 million and is one of the biggest cities in Africa. Ogun state is a southwestern state that borders Lagos state to the south and also shares a border with Oyo and Ondo states. According to the official website of the state, it has an estimated population of 6.1 million. It is the state with the highest number of manufacturing industries, although this has been largely attributed to its proximity to Lagos state.
Both Lagos and Ogun states are predominantly Yoruba, with the Yoruba language serving as the lingua franca of the two states. The index case of COVID-19 was discovered on February 27 2020. During the first 30 days of COVID-19 in Nigeria, Lagos state accounted for 50% of the 232 positive cases in the country (Nigeria Centre for Disease Control, 2020). Consequently, on March 30 2020, a lockdown was declared in two states, Lagos and Ogun states, as well as the Federal Capital Territory, Abuja. The inclusion of Ogun state in the lockdown was premised on its proximity to Lagos state and the risk of the disease spreading from Lagos through Ogun to other states (Nigeria Centre for Disease Control, 2020). Therefore, Lagos and Ogun states were included as study locations due to the lockdown and the full weight of other COVID-19 measures extended by the Federal Government to the two states.
Procedure
Interviews were conducted with three categories of people: the police officers that enforced lockdown and other COVID-19 protocols, senior officers that supervised police personnel on COVID-19 duties, and leaders of communities within Lagos and Ogun states. This research was approached with an interest in the type of relationship that existed between communities and police personnel during the lockdown, and the level of support that communities rendered to officers on COVID-19 duties within the two states. The Lagos and Ogun states’ police command is the Lagos and Ogun states’ arm of the Nigeria Police Force under the headship of state commissioners often appointed by the Inspector-General of the Police. The headquarters of the Lagos state police command is located at Ikeja Lagos, while that of Ogun State is located at Eleweeran, Abeokuta.
Requests and permissions were obtained to conduct the study from the Lagos and Ogun states police commands. However, in spite of the approval granted to the research team, prospective participants were approached individually and informed about the purpose of the study. This is in order to reduce officers’ likelihood of giving official responses rather than personal opinions, as well as to ensure anonymity and confidentiality assurances given to the officials at the point of their recruitment. Thirty-six police officers that enforced lockdown and other COVID-19 protocols were reached at the recruitment stage, and 16 officers that spent a minimum of four weeks on COVID-19 enforcement duties were purposively selected. Although 63 officers met this selection criterion, only 40 of them agreed to be part of the study. Senior officers that directly supervised field police officers in the enforcement of COVID-19 protocols during the lockdown period were purposively selected for the study. Out of 21 senior officers that were contacted, 16 agreed to be part of the study.
On the other hand, community leaders were approached in the two states and invited to be part of the study. In an attempt to cover three main socio-economic divides in each of the two states, categorizations were done according to– low-, middle- and upper-class neighbourhoods – and participation was sought with the use of clustered sampling. This was done in order to select communities that depict these three social class categories. Attention was then placed on residential areas within the selected areas and information about the local political structures was sought from local government agencies. In order to achieve sample diversity, a variety of community leadership consisting of landlords, local security heads, coordinators of vigilantes, local chiefs, and religious leaders was purposively selected for the study.
The confidentiality of participants was strictly safeguarded in addition to adhering to other rules and standards guiding research with human subjects. All names used in this paper to refer to participants are pseudonyms selected by the participants themselves. Interview timing ranged from 35 min to one hour and thirty minutes. While interviews were audio recorded for all community leaders, police officers declined the use of audio recording devices. Therefore, the use of two note-takers among the interviewers was adopted. In all, 74 interviews were conducted.
Participants
Forty officers that actively enforced lockdown rules and COVID-19 protocols in Lagos and Ogun states (20 from each state), 16 senior police officers that supervised lockdown operations of police officers, and 18 community leaders from the two states were interviewed in this study. The interviews were conducted between August 2020 and April 2021. The COVID-19 enforcement officers were ranked from Police Constable (PS) to Inspector of Police (IP). Only two females were part of the study out of the five approached, as an overwhelming majority of field officers for the pandemic duty were males. Their years of experience in policing ranged from three to fifteen years. The majority of them (31) had served in other states before they were transferred to Lagos and Ogun states’ police commands. Aside from specialised police courses and training, 23 of them had received post-secondary education in formal institutions, with 12 having university degrees. They formed an ethnically diverse group with all three major ethnic groups represented in the sample: Yoruba (14), Igbo (12) Hausa (8). The remaining eight identified with six minority groups in Nigeria.
The senior officers’ ranked from Assistant Superintendent of Police (ASP) to Chief Superintendent of Police (CSP), while they were between the ages of 35 to 52, and have worked in at least three police commands before their transfer to the Lagos and Ogun states commands. Only one of the participants in this category was a female, although four female officers were approached to partake in the study. All the officers in this category had post-secondary education with eight being university graduates and three having post-graduate qualifications. All three major ethnic groups were however not represented in the sample with nine Yoruba, five Igbo and the remaining two from minority ethnic groups (Ikwere and Benin).
The community leaders were between the ages of 41 and 66 with their length of leadership within their communities ranging from 5 to 14 years. Their leadership positions in their various communities ranged from the chairman – landlords and tenants association, head of community security units, head of community vigilantes, neighbourhood watch coordinator, local chiefs, opinion leaders, and spiritual/religious leaders. Twelve of them had post-secondary education, while the remaining had primary education (4) and, no formal education (2). Interviews were conducted in the English language except for the six participants with primary or no formal education, which were conducted in Pidgin English and local languages, and then translated (Table 1).
Interview Protocols
Semi-structured protocol designs were followed for the interviews in order to explore the relationship that existed between the police and the communities before, during, and after the lockdown measures occasioned by the COVID-19 pandemic. Interviewers asked participants to describe the preparations for addressing the COVID-19 pandemic and the level of support given to law enforcement agencies in carrying out the government mandates of ensuring social distancing and other preventive measures. Examples of these exploratory questions included: ‘How will you describe and evaluate the response of your community members to the health crisis occasioned by the COVID-19 pandemic?’ ‘Were communities invited and carried along by the police in their COVID-19 lockdown operations?’ ‘How will you describe the preparation of police officers for intervening in the health emergency brought about by the COVID-19 pandemic?’ ‘How will you describe the response of the people/citizens to the authority of the police in restricting movement during the lockdown and enforcement of the curfew?’
Analysis Strategy
In this present study, the nature and level of, or lack of, community engagement in police efforts at intervening in the health crisis occasioned by the COVID-19 pandemic was the key focus. Factors that could have impeded effective police-community engagement such as police preparedness and legitimacy were particularly explored. In analysing the data collected, an inductive thematic analysis approach was adopted (Braun & Clarke, 2006). The author started the analyses by selecting 20 transcripts at random and reading through them, taking note of apparently common and contrasting thematic elements among the samples, after which a preliminary coding scheme was drafted. This initial codebook tracked participants’ description of pandemic policing and the role of community members in ensuring safety during the health emergency, their use of problematising and/or normalising themes in these accounts, and information they shared about public support to the police during the lockdown, and in adhering to COVID-19 protocols. Thereafter, two scholars in the social sciences were engaged to conduct an intercoder reliability check, in order to enhance the validity of the data. Using the codebook, they both checked the same 20 transcripts that were initially checked by the author. All three coders then discussed discrepancies in their conceptualizations and made corrections to the coding schemes. Thereafter, all coders separately coded the remaining 48 transcripts. At the conclusion of this exercise, the coders met to resolve any discrepancies in order for all coders to mutually agree on all applied codes.
Results
Police Pre-COVID Preparedness for Public Health Crisis
Senior police participants of the study were requested to express their opinions on the preparedness of the Nigerian police in handling assignments related to intervening in a public health crisis before the emergence of the COVID-19 pandemic. Ten of the 16 senior officers accepted that there was no prior training for police officers engaged in COVID-19 shutdown enforcement duties and other assignments related to pandemic policing. Participants that accepted the ill-preparedness of the police premised their submission on the novel nature of the pandemic and the limited education that officers had about the disease before the lockdown duties. Also, they remarked that there had been no serious and widespread public health crisis that required police intervention in the recent past. ‘For example, even though Ebola virus disease was quite serious, it didn't take police involvement for it to be curtailed’ Salisu (Not real name) of the Ogun State police Command expressed. ASP Jegede of the Lagos State Police Command submitted that apart from officers not being trained in enforcing COVID-19 measures, they were not also trained on how to protect themselves considering that they were equally vulnerable. In the words of ASP Kazeem who also shared ASP Jegede's opinion:… we have not had this kind of crisis before, especially in my 30 years in the Nigeria police. The usual training and interventions that officers are used to are public disturbances, riots, election violence and related public disorders. But this has to do with health, especially a highly contagious and deadly disease. Several pieces of training would have been needed to get the officers prepared for this. Also, this current assignment involved the restriction of the movement of people for several weeks. How do we do that without trampling on the rights of the people, cooperating with other first responders, supporting people with health needs, and other related assignments? These are training needs that were never met before the pandemic.
(ASP Kazeem, LSPC, Lagos)
The responses of others in this category supported the comment of ASP Kazeem and they premised the ill-preparedness of the police for public health intervention on lack of specialised training, low knowledge level on the risks posed by the virus, and inadequate equipment and logistics, especially, personal protective equipment (PPE). According to CPS Layemi of the Ogun State Police Command, ‘this impacted negatively on the flow of communication from commands to field officers, other frontline workers and communities during the enforcement of the lockdown.’
The remaining six participants believed that there were routine pieces of training which police officers can extrapolate from to maintain the public order that comes with public health crises. One of the participants that are of this opinion is CSP Ogar:First of all, COVID-19 is a deadly disease and the police aim to protect life and property of the people, so the police have been more than prepared for any upcoming emergencies. Even though there were lots of challenges, officers have been trained to enforce the law fairly. This can be applied to ensure compliance with the lockdown. The police are not limited to enforcing traffic regulations and municipal by-laws.
(CSP Ogar/LSPC, Lagos)
The opinion of CSP Ogar is shared by the other five participants in this category. They mainly stated that the operational training of police officers in maintaining law and order can be extended to intervening in the prevailing health crisis. However, the comments of CPS Layemi of the Ogun State Police Command appear to contradict the submission of CSP Ogar:… it is undeniable that the COVID-19 pandemic took the world by surprise and brought with it a lot of unprecedented events. For example, police officers have never had to collaborate with health workers in the way we were forced to; this led to considerable friction between the two sectors. Also, officers had to be supportive in conveying sick people to isolation centres, stop people's movement and relate with communities in ways they were not used to successfully contain the spread of the disease. The officers were not trained for all these, and this led to the huge problems that emanated from the operations of the enforcement officers.
(CSP Layemi/OSPC, Lagos)
Despite the apparent disagreement of the participants on the level of preparedness of the police in enforcing the lockdown and COVID-19 protocols, they all agreed that the pandemic presented peculiar challenges to the police which were beyond the usual police duties, and which appeared to have strained police-community relations. These challenges included enforcing total clampdown on freedom of movement running into weeks, restrictions and regulation of freedom of association, compulsory wearing of face mask, stay-at-home and ordinance compliance enforcement Others include enlightenment of the public on COVID-19 protocols by police officers, inter-sectorial collaboration with health workers for public safety, officers’ safety from infection, handling stress and burnout occasioned by enforcement duties, unclear and conflicting rules and directives from police authorities.
Pre-COVID Public Perception of Police Legitimacy
All participants, senior police officers, COVID-19 enforcement officers and community leaders were requested to express their opinions on the public perception of police as a legitimate power holder before the emergence of the pandemic. This is to examine the role that police legitimacy may play in engendering public support for the police in the process of enforcing COVID-19 mandates given by the government. Sixteen senior police officers that participated acknowledged that the public perception of police legitimacy has been negative, but 12 of them argued that the police are not solely responsible for the low perception of the police by the citizens in the country. SP Adewale offered his opinion on this:… most of the people that negatively perceive the police have not personally had troubling experiences with men of the Force. Rather, their perception is hinged on hearsay, sensational media coverage of police misconduct, Nollywood (Nigeria film industry) exaggeration of police misconduct, and other negative utterances often by those that have not had a personal encounter with the police or had no such negative encounter.
(SP Adewale/LSPC, Lagos)
Other senior police officers supported the position of SP Adewale by stating that stories on police misconduct, brutality, and abuse of human rights are often exaggerated by the press, human rights activists, and other influencers of public opinion. On the part of the enforcement officers, although they acknowledged the low legitimate perception of the police by the public, the majority of them did not believe this was responsible for the low level of public support for police in the enforcement of COVID-19 rules. Inspector Ahmed of the OSPC posited:… the conditions of the lockdown are such that cannot be accepted by the citizens generally across the world because they were too stringent and at variance with what people were used to. So whether Nigerians have confidence in the police or not, the rules of no movement, no association, no this–no that cannot be convenient for any people to cope with irrespective of their disposition towards the police …
However, contrary to the opinions held by enforcement officers that stringent lockdown conditions, rather than negative police legitimacy, were responsible for the uncooperative attitude of the public, community leaders that participated in the study pointed at low public trust in the police as an important factor. Adamson, a Lagos-based head of a vigilante group offered details:… the police and the government at large are largely not trusted by the people and the problem of trust accounted for the large disobedience of the rules of the government as enforced by the police. If the police had been a trusted organisation devoid of large-scale corruption, as reported and witnessed by the people, it might have been easier for the people to conform to the directives of the officers.
Other community leaders engaged in the study opined that the lack of police legitimacy, before the emergence of COVID-19, created an atmosphere of conflict between the people and the police in the efforts directed at controlling the spread of the disease.
Pandemic Policing, Community Engagement and Public Support
The three categories of participants of the study, senior police officers, enforcement officers, and community leaders, were asked to describe the level of cooperation that existed between the police and community members in the fight against the spread of the COVID-19 disease. The majority of the police executives and enforcement officers stated that the public was generally uncooperative and hostile to the police and this created a great challenge that impeded efforts at maintaining social distancing and movement restrictions. ASP Nwaifor believed that officers were generally unappreciated: ‘none at all, rather, it was insult left, right, and centre; the bad news circulating about some officers abusing and extorting people even made the disrespect worse.’ DSP Ogunnusi commented that people showed gross disobedience to the restriction orders and failed to cooperate with the police:In terms of the curfew, public support was good to an extent, but it also made me realize that human beings are the worst of animals. I say this because when the curfew time was 7pm, you would see some people outside after 7pm. It was later shifted to 8pm, the same thing; you will still see people outside after 8pm giving different excuses. Now the worst was when it was shifted to 10pm, you will still see people outside around that time.
(DSP Ogunnusi, Ogun SPC, Abeokuta)
The enforcement officers agreed that the lack of public support was evident in the high level of public disobedience to the curfew and other COVID-19 protocols as well as the protests and riots against police institutions during the period of the lockdown and after. Corporal Kasali opined:… the combination of mistrust that many people have for police officers and the government played out in the manner in which they reacted to the lockdown as well as the coronavirus prevention and management guidelines. Although the enlightenment was effective in raising peoples’ awareness about the virus, people's negative perception of the police and the government made adherence very difficult to enforce. Even the danger posed by the deadly virus meant nothing to them just because of their desperate intention to disobey the police and government.
(Corporal Kasali, Ogun SPC, Abeokuta)
The senior officers were asked to describe the methods and strategies deployed to ensure a harmonious working relationship with the community leaders and members in their various states in a bid to combat the spread of the coronavirus. The participants stated that there were no conscious community engagement strategies that involved dedicated enlightenment and involvement of community leaders and members in the fight against COVID-19 or enforcing the lockdown and other protocols. Some of them noted that only healthcare workers and institutions approached communities to seek their partnership in fighting against the virus. According to the participants, the police relied on reports from news channels, massive public enlightenment programmes, government bulletins, and health officials’ interactions with community members to create awareness of the virus and garner support from the public.
The community leaders also reiterated the opinions of the senior police officers that no concrete efforts were made to engage communities in the operations of the police in combating the pandemic. Alagba John, a chairman of landlords and tenants association within a Lagos community volunteered:… we were not contacted by the police in whatever form. It was only when troubles already started that officers came to see the local chiefs to solve the problem of insecurity that emerged with the Covid-19 lockdown.
(Alagba John/Chairman Landlord-Tenant Association, Lagos)
Consequences of Low Public Support
Senior and COVID-19 enforcement officers in the study offered various effects of low public support witnessed in the process of policing the pandemic. The participants listed physical violence and threats of violence from the citizens, verbal abuse, chronic stress, and burn-out, as some of the major consequences of negative public reactions to their COVID-19 duties. According to ASP Saheed:… hitherto, we had always grappled with inadequate personnel problems, insufficient equipment, logistics and other resources needed for our police duties. However, COVID-19 created additional strain on these insufficient resources, especially on manpower. Our field officers experienced a variety of adverse effects of the burden of enforcing COVID-19 rules and assisting the health sector workers in their duties. The non-cooperative attitude of the public made the experience more traumatic for our officers, and some of them developed negative coping behaviours. Some of them became suicidal. There was an instant when one of our officers used a strong alcoholic beverage to take drugs for his sickness while on COVID-19 duty. You can imagine, taking paracetamol with vodka!
(ASP Saheed/OSPC, Abeokuta)
Participants, senior police personnel, and other enforcement officers alike, also identified crowd control, and negative attitudes of the community members as the most significant challenges they were confronted with in their enforcement duties. ‘I have been stoned in the nights while patrolling neighbourhoods on a few occasions,’ according to Sergeant Bako.
Community Engagement as a Response to Pandemic
All the participants acknowledged the importance of community engagement in fighting the spread of the disease. However, police officers, both senior and those on enforcement duties, remarked that the public made community engagement impossible to achieve. They also identified a shortage of time due to the suddenness of the emergence of the virus, non-cordial police-community relationship before the emergence of the disease, insufficient logistics that can facilitate community engagement, and poor structure of community associations that can be used to engage the community, and limitations of the police in respect of knowledge about the virus:… many factors that hindered police partnership with communities in the policing efforts to stop the spread of the disease. There was no solid police/community structure on the ground before the onset of the virus. Communities have often been wary of the police and they will rather rely on vigilante groups and other private security or ethno-militia groups for their security …
(DSP Ogunnusi, Ogun SPC, Abeokuta)
However, the community leaders engaged in the study blamed police belief and frequent disposition towards the use of force in their assignments with members of the public, high level of corrupt practices, and other unprofessional conduct as major factors that hindered police-community partnership in the fight against COVID-19. Some of them pointed out that the police were supposed to approach the lockdown duties with some measure of empathy for community members considering the short notice (24 h) given to the public before the commencement of the shutdown was made.… they (police) will rather deploy force, and use weaponry and other crude methods rather than carry the communities along in their enforcement assignment. The only time we are consulted is when they have found it impossible to crack cases and they need information from us. At least, we are in a better position to relate with our people (community members) and we can prevail upon them to obey police directives as well as what to do to ensure their safety. But the police have never respected us as a formidable entity …
(Baba Alagbaka/ Coordinator, Vigilante group, Abeokuta)
Ideally, participants listed ways in which community engagement would have been essential in police operations against the spread of COVID-19. These would include risk communication to community members, regulating movement within communities, community support through volunteering, educating members, preventing public hostilities towards police officers on COVID-19 duties, providing information about non-compliance to the police, ensuring adherence to social distancing, wearing of facemask and other COVID-19 safety guidelines.
Discussion
Without a doubt, a global emergency and crisis like the COVID-19 pandemic affects law enforcement practices and overstretches police resources, with physical and mental health consequences on law enforcement officers (Shirzad et al., 2020; Stogner et al., 2020). However, one of the biggest challenges that faced by pandemic policing has been reported to be a lack of police legitimacy (Jones, 2020; Stott et al., 2020). Tyler (2003) posited that legal authorities gain when they receive deference and cooperation from the public. Considerable empirical evidence has suggested that the key factor that shapes public behaviour is the fairness of the processes used by legal authorities when dealing with members of the public (Bolger & Walters, 2019; Radburn & Stott, 2019; Tankebe, 2013; Terrill et al., 2016). In respect of policing the pandemic, studies have equally found that deploying procedurally just approaches have helped in effectively supervising COVID-19 rules (Aborisade & Ariyo, 2022; Farrow, 2020; Ghaemmaghami, et al., 2021). Meanwhile, in deploying procedurally just approaches for policing the pandemic, the wider context of police-public engagement has been explored (Ghaemmaghami, et al., 2021).
Community engagement has been recognised as an essential factor in reducing the burden of work on police towards ensuring compliance with lockdown protocols (Gonah, 2020; WHO, 2020a). Also, community engagement has been noted to help police connect with those affected by dire situations, and understand the risks communities face, while it also helps communities to understand the public safety response and how to access help from the police (Jones, 2020; Stott et al., 2020; WHO, 2020b). References have also been drawn from how community engagement helped in the fight against the recent Ebola outbreak, especially in Africa (Anoko et al., 2020). Furthermore, community participation was deployed in the United Kingdom to elicit volunteering services in responding to the pandemic (Butler, 2020). However, in the Nigerian case, community engagement has not been effectively deployed by the police in response to the COVID-19 pandemic, as findings from this study suggest. Therefore, pandemic policing stands to have far-reaching consequences on the well-being of officers, citizens’ human rights, police-community relations, and the effectiveness of combating the spread of COVID-19 in the country.
Transparency International (2020) describes the approach adopted by the Nigerian police in monitoring and enforcing adherence to COVID-19 mandates as tantamount to criminalising the lockdown rules. In excusing the force, police executives engaged in the study suggested that the adoption of a militarised approach to responding to the pandemic was largely based on the unpreparedness of the police in intervening in the unprecedented global health crisis. They identified limited time and material resources needed to engage the public, low level of legitimacy and public trust before the pandemic as major factors that hindered community engagement. As a result, Nigerian police officers had to extrapolate and rely on the routine police operational training they had earlier attended to perform their COVID-19 duties. Meanwhile, considering that officers of the Nigeria police have often been accused of human rights violations (Aborisade & Obileye, 2017; Aborisade & Oni, 2020a; Akinlabi, 2017; Amnesty International, 2020a), the lack of preparation of the police for pandemic policing and community engagement has been flagged as constituting high-risk condition towards efforts at fighting the virus (Transparency International, 2020).
Police participants’ narratives indicated that the enforcement of the government's COVID-19 directives by the police was characterised by a high level of public disobedience and hostilities toward officers. This position aligns with reports from news channels and monitoring organisations as regards the effectiveness of policing the pandemic in the country (The Africa Report, 2020; The Guardian, 2020). There were also reports of police engaging in human rights violations, aggression, and extra-judicial killings in the process of supervising the lockdown rules (Aborisade, 2021, 2022, Aborisade & Gbahabo, 2021; AfricLaw, 2020). Accounts of community leaders engaged in the study suggested that the low level of public trust for the police before the emergence of the pandemic was largely responsible for the uncooperative attitudes of the people.
Procedural justice theorist, Jones (2020), affirmed that the successful response of the police to the COVID-19 crisis is heavily reliant on public trust and confidence, and the level at which the public perceives the police as a legitimate power holder. Research conducted on the level at which the Nigerian police are seen by the public as a legitimate power holder which upholds the law and operates in the community in a procedurally just way has mainly reported negative perception (Akinlabi, 2017; Famosaya, 2020). Consequently, the public perception of the police and the reported high level of distrust may be responsible for the gross violation of the law governing the COVID-19 response and the low level of public support.
Suggestions from the study findings indicate that both the police executives and community leaders agree with the importance of community partnership in engendering a successful response to the pandemic. However, they both noted the challenges confronting the establishment of a police-community partnership in the country. Some of the identified problems that may impede community outreach, especially during a COVID-19 emergency include non-cordial police-community relationship, insufficient logistics, poor funding, and poor structure of community associations. Therefore, there is a need for conscious efforts to be made by the government, police agencies, and other stakeholders for the realisation of an effective community engagement in efforts aimed at combating the COVID-19 pandemic.
This study is not without limitations. We recognise that with a small-scale sample, these findings represent a pilot study; however, this exploratory study offers new insights into policing during uncertain times, which may inform further studies. We also accept the sample breadth for certain socio-demographic characteristics, ethnicity in particular. In selecting communities, the study focused more social class categorizations, while ethnic distributions were not considered. This may have led to the under-representation of minority ethnic groups in the study, despite Lagos being an ethnically heterogeneous society.
Implications for Policy, Practice and Future Studies
The findings of this study indicate that the Nigeria police were not only ill-prepared for health crisis intervention but possible adoption of community engagement in pandemic policing was also hindered by a lack of public trust in the country's police agency. All these circumstances posed considerable impediments to police-community relations for pandemic policing. Therefore, to build better community relations, the Nigerian police should purge itself of the usual militarised system of conducting its operations when dealing with the public. In doing this, police officers, especially the ones that interface with the public, should be made to undergo procedural justice training. Also, community engagement policies should be formulated to establish and build police-public relationships by opening up lines of communication. This will not only bring policing to the community level, but it will also increase the public's trust level with the police.
The Nigerian government needs to also support police efforts by facilitating police-community relations, especially in times of health crisis. Also, effective two-way communication between the police and community members should be enhanced while sharing of information on the public safety response is encouraged. To build public trust, there is a need for a drastic reduction in the corruption index of the police as well as the unwarranted use of force by the police.
Further research needs to triangulate the perspectives of other security agents (formal and non-formal) and health personnel, with that of government officials (police service commission) and a cross-section of community members. This will enable multiple viewpoints and the development of a policy framework for community engagement during health crises and other uncertain times.
Conclusion
While other research efforts have highlighted the benefits of community participation in the collective response to pandemics, this current study, inspired by the avalanche of reports of public strife against police agencies enforcing the COVID-19 protocols, examined the approaches used by the Nigeria police to intervene in the health crisis. This research has captured a rich description of the experiences of COVID-19 enforcement officers, their supervisors and community leaders during the crisis lockdown, and findings indicate that lack of legitimacy and inadequate community engagement negatively impacted police supervision of COVID-19 rules. The manifestations of legitimacy challenges in policing the COVID-19 lockdown are similar to that of routine policing, however, consequences of low public support as evinced in the study resulted in physical confrontations between the public and the police, verbal abuse, chronic stress, and burn-out. The strength of pre-COVID legitimacy of the police in pandemic policing has been highlighted. Therefore, recognition of the relevance of legitimacy in pandemic policing, consideration of procedurally just policing and development of a framework for community engagement has been suggested. This will promote an important double gain where the burden of policing emergencies will be reduced and public cooperation and safety will be gained.
Table 1. Participant Characteristics.
Variables Total N = 74 Percentage (%)
Age (years)
<30 12 16.2
31–40 16 21.6
41–50 24 32.4
51–60 20 27.1
>60 2 2.7
Gender
Male 68 91.9
Female 5 8.1
Educational qualification
No education 2 2.7
Primary education 7 9.5
Secondary 14 18.9
Post-Secondary education 51 68.9
Status of community members N = 18 Percentage (%)
Chairman – landlords and tenants association 2 11.1
Opinion leaders 2 11.1
Spiritual/Religious leader 2 11.1
Local chiefs 4 22.2
Head of community security units 4 22.2
Head of community vigilantes/Neighbourhood watch 4 22.2
Police officers’ ranks N = 56 Percentage (%)
Chief Superintendent of Police (CSP) 7 12.5
Assistant Superintendent of Police (ASP) 9 16.1
Inspector of Police 9 16.1
Sergeant 11 19.6
Corporal 14 25.0
Constable 6 10.7
Source: Field survey 2022.
Supplemental Material
sj-docx-1-cjr-10.1177_07340168221142909 - Supplemental material for Pandemic Policing and Community Engagement: Preparedness, Legitimacy and Public Support During the COVID-19 Crisis in Nigeria
Click here for additional data file.
Supplemental material, sj-docx-1-cjr-10.1177_07340168221142909 for Pandemic Policing and Community Engagement: Preparedness, Legitimacy and Public Support During the COVID-19 Crisis in Nigeria by Richard Abayomi Aborisade and Oladele Adelere Adeleke in Criminal Justice Review
Author Biographies
Richard Abayomi Aborisade, PhD, is a reader in criminology and victimology at the Department of Sociology, Faculty of Social Sciences, Olabisi Onabanjo University, Ago-Iwoye, Ogun State. He received his doctorate from the University of Ibadan, Nigeria. He also holds an MBA in Information Technology from Coventry University, UK. His research themes include violence against women and girls, policing 21st century Nigeria, security and sustainable development in Nigeria, and family-based violence. He has published in both local and international journals in these areas. He is the author of Crime and Delinquency: A Sociological Introduction; published by Ibadan University Press. Email: [email protected]
Oladele Adelere Adeleke, PhD, is a criminologist who lectures at the Department of Sociology, Faculty of Social Sciences, Olabisi Onabanjo University, Ago-Iwoye, Ogun State. He received his doctorate from Olabisi Onabanjo University, Ago-Iwoye, Nigeria. His research themes include rural criminology, policing rural Nigeria, and traditional social control. He has published in both local and international journals in these areas. He has been involved in collaborative research funded by United Nations High Commissioner for Refugees (UNHCR), Tertiary Education Trust Fund (TETFUND), United Nations Fund Population Agency (UNFPA). Email: [email protected]
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.
ORCID iD: Richard Abayomi Aborisade https://orcid.org/0000-0001-7485-2351
Supplemental Material: Supplemental material for this article is available online.
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| 0 | PMC9748523 | NO-CC CODE | 2022-12-15 23:22:44 | no | Crim Justice Rev. 2022 Dec 12;:07340168221142909 | utf-8 | Crim Justice Rev | 2,022 | 10.1177/07340168221142909 | oa_other |
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Am J Hosp Palliat Care
Am J Hosp Palliat Care
spajh
AJH
The American Journal of Hospice & Palliative Care
1049-9091
1938-2715
SAGE Publications Sage CA: Los Angeles, CA
36503251
10.1177_10499091221145202
10.1177/10499091221145202
Medical Manuscripts
Trends in Palliative Care Research During the COVID-19 Pandemic
https://orcid.org/0000-0002-4468-1974
Wang Chien-Ho 1
Chen Yu-Kai 2
Tsao Shu-Han 3
Lee Ching-Hsing 14
1 Department of Emergency Medicine, 63329 Keelung Chang Gung Memorial Hospital , Keelung, Taiwan
2 Department of Emergency Medicine, 38014 Linkou Chang Gung Memorial Hospital , Taoyuan, Taiwan
3 Department of Urology, 38014 Linkou Chang Gung Memorial Hospital , Taoyuan, Taiwan
4 Department of Emergency Medicine, 63329 Chang Gung Memorial Hospital and Chang Gung University College of Medicine , Keelung, Taiwan
Ching-Hsing Lee, Department of Emergency Medicine, Keelung Chang Gung Memorial Hospital, No. 222, Maijin Rd., Anle Dist, Keelung 204, Taiwan. Email: [email protected]
12 12 2022
12 12 2022
10499091221145202© 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.
To demonstrate the trends and variety of research on palliative care during the COVID-19 pandemic. A systematic search of the Web of Science database. Since the outbroke of the COVID-19 pandemic, the adjustment of palliative care systems is warranted to maintain a high quality of care. The COVID-19 -related palliative care studies account for approximately 4% of all publications on palliative care. However, there is a dearth of research investigating the nature of these studies. A total of 293 studies were included. Of the included studies, those related to system improvement were the most common (181/293, 61.8%), followed by those related to patient care (79/293, 27.0%), bereavement support for patients or family members (19/293, 6.5%), and the mental health of frontline practitioners (14/293, 4.8%). From these studies, 82, 137, and 74 studies were published in 2020, 2021, and 2022 (until August 1), respectively. The research trends of palliative care demonstrate the flexibility and rapid response of the global palliative care system to the COVID-19 pandemic and show how the palliative care system is evolving. While most studies are interested in system improvement, patient care, and bereavement support, the mental health of frontline practitioners has received less attention. Our findings provide palliative care practitioners with current valuable information and highlight possible future trends.
palliative care
palliative medicine
hospice
terminal care
COVID-19 pandemic
edited-statecorrected-proof
typesetterts10
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pmc Key message
This review demonstrated the trends of research on palliative care during the COVID-19 pandemic. We discovered that the most popular research topics were those pertaining to system improvement and patient care. The diversity of publications demonstrated that the palliative care system is flexible and responded rapidly to the COVID-19 pandemic.
Introduction
According to the World Health Organization (WHO), only 14% of the more than 40 million people who require palliative care every year receive such care. The COVID-19 pandemic has resulted in an even smaller proportion of people receiving the palliative care they require. Palliative care systems serve a crucial role in the fight against this devastating disease but face several COVID-19-related challenges, including the heavy burden placed on frontline caregivers, social distancing, and rapidly changing policies. The adjustment and evolution of palliative care systems are warranted to maintain a high quality of care.
Numerous studies have investigated strategies for addressing the challenges engendered by the COVID-19 pandemic.1-4 The most notable strategy entails the implementation of telemedicine to resolve challenges associated with isolation policies.5-7 Telemedicine not only supports patients but also serves as a tool for maintaining the physical and mental health of a patient’s family members and frontline practitioners and strengthens social connections between various people.6,8 Among patients with COVID-19, breathlessness, cough, fatigue, and agitation are the most common symptoms and lead to rapid deterioration of the clinical condition and potential death. Besides, spiritual suffering consists of isolation, grief, loneliness, and vulnerability also frequently caused by the pandemic. Timely pharmacologic management, including opioids, benzodiazepines, and antipsychotics combined with spiritual care is vital in palliation of the suffering. Scholars have thus investigated new strategies for relieving the symptoms of COVID-19 and shared their experiences to provide valuable guidance in caring for patients with COVID-19 infection and provide suggestions on how to manage the mental burden of all the frontline practitioners.9-16
We conducted a review of the literature to demonstrate the variety of research and the trends in research on palliative care since the start of the COVID-19 pandemic. The present study is 1 of the first to review the trends in research on palliative care during the COVID-19 pandemic. Our findings can provide palliative care practitioners with current information and highlight possible future trends.
Methods
Study Design and Setting
We conducted an observational study by reviewing publications and retrieving relevant data (eg, publication titles, publication year, first author’s nationality, Web of Science (WoS) categories, document types, languages, and number of citations) from the WoS database. This study was approved by the Chang Gung Medical Foundation Institutional Review Board, which waived the need for informed consent (IRB Number: 202201605B1).
Literature Search Methods
We conducted a search of the WoS database from 2020 (the entire year), 2021 to August 2022. All articles discussing palliative care associated with the COVID-19 pandemic were included. The key terms are as following: “COVID-19,” “SARS-CoV-2,” “2019 Novel Coronavirus Disease,” “2019-nCoV Disease,” “Coronavirus Disease-19,” and “Coronavirus Disease 2019” combined with “palliative care,” “hospice,” “terminal care,” and “palliative medicine.” The reference lists of the identified articles were also reviewed. No language restrictions were applied during the search.
Selection Criteria and Classification
All articles discussing palliative care associated with the COVID-19 pandemic were included (Figure 1). Articles that did not mention palliative care during the COVID-19 pandemic were excluded. According to the WHO, palliative care is defined as care that is aimed at improving the quality of life of patients and their families when those patients are faced with problems associated with a life-threatening illness. The articles were categorized into the following fields of palliative care research according to the main focus of the study: system improvement, patient care, bereavement support of the patient or family member, and mental health of frontline practitioners. System improvement was divided into the following subcategories: alternative delivery methods, public education, staff training, and effect on the health-care system. Two independent reviewers (CHW and YKC) independently reviewed and classified the included articles. Disagreements were resolved through discussion with a third reviewer (CHL).Figure 1. The flow diagram of the included literature.
Outcomes
The primary outcome was the distribution of palliative care research fields. The secondary outcomes were the number of citations, first author’s nationality, publication year, publication titles, WoS categories, and document types. Other outcomes included the language and the percentage of COVID-19-related palliative care articles among all articles on palliative care since the COVID-19 outbreak in February 2020.
Results
Inclusion and Exclusion Criteria
We conducted a review of published studies. A total of 661 studies were retrieved from the WoS database. Of these studies, 26 were excluded from the review because they were not related to COVID-19-related palliative care and 342 were excluded because they did not focus on palliative care. Finally, 293 studies met the inclusion criteria and were included.
Summary of Outcomes
The primary outcome (Figure 2) was the distribution of research on palliative care. We observed that studies related to system improvement constituted the highest proportion of our sample (181/293, 61.8%), followed by those related to patient care (79/293, 27.0%), those related to bereavement support for patients or their family members (19/293, 6.5%), and those related to the mental health of frontline practitioners (14/293, 4.8%).Figure 2. The percentage of each research field publications since COVID-19 pandemic. aUntil August 1st, 2022.
We noted that 82, 137, and 74 studies were published in 2020, 2021, and 2022 (until August 1), respectively (Table 1). The average number of studies related to COVID-19-related palliative care per year was approximately 110. Studies related to COVID-19-related palliative care constituted approximately 3.97% of all studies on palliative care since the start of the COVID-19 pandemic.Table 1. Analytic Statistics of the Included Publications.
Variable 2020a 2021 2022b
N = 82 N = 137 N = 74
Research field
System improvement 50 (61.0%) 83 (61.0%) 48 (64.9%)
Alternative delivery methods 19 (23.2%) 18 (13.0%) 13 (17.6%)
Public education 2 (2.4%) 2 (1.4%) 0 (0%)
Staff training 5 (6.1%) 5 (3.6%) 7 (9.5%)
Impact on the healthcare system 24 (29.3%) 58 (42.0%) 28 (37.8%)
Patient care 23 (28.0%) 40 (29.2%) 16 (21.6%)
COVID-19 patients 13 (15.9%) 20 (14.5%) 8 (10.8%)
Pediatric patients 2 (2.4%) 2 (1.4%) 2 (2.7%)
Cancer patients 2 (2.4%) 9 (6.5%) 1 (1.35%)
Others 6 (7.3%) 9 (6.5%) 5 (6.8%)
Bereavement support of the patient or family member 7 (8.5%) 9 (6.5%) 3 (4.1%)
Mental health of the frontline practitioners 2 (2.4%) 5 (3.6%) 7 (9.5%)
Nationality of first authors, n (%)
United States 31 (37.8%) 55 (40.1%) 41 (55.4%)
United Kingdom 15 (18.3%) 19 (13.9%) 9 (12.2%)
Germany 1 (1.2%) 11 (8.0%) 6 (8.1%)
Australia 4 (4.9%) 5 (3.7%) 6 (8.1%)
Italy 3 (3.7%) 5 (3.7%) 1 (1.4%)
Canada 3 (3.7%) 5 (3.7%) 1 (1.4%)
Brazil 1 (1.2%) 3 (2.2%) 2 (2.8%)
Publication titles, n (%)
Journal of pain and symptom management 37 (45.1%) 20 (14.6%) 10 (13.5%)
American journal of hospice and palliative medicine 5 (6.1%) 15 (11.0%) 14 (18.9%)
Palliative medicine 9 (11.0%) 7 (5.1%) 6 (8.1%)
BMJ supportive and palliative care 3 (3.7%) 7 (5.1%) 3 (4.1%)
Journal of hospice and palliative nursing 2 (2.4%) 6 (4.4%) 2 (2.8%)
BMJ open 2 (2.4%) 3 (2.2%) 1 (1.4%)
Type of articles, n (%)
Original article 70 (85.3%) 115 (84.0%) 61 (82.4%)
Review 8 (9.8%) 17 (12.4%) 9 (12.2%)
Editorial material 4 (4.9%) 5 (3.7%) 4 (5.4%)
Languages
English 80 (97.6%) 132 (96.4%) 71 (95.9%)
German 1 (1.2%) 4 (2.9%) 3 (4.1%)
Spanish 1 (1.2%) 1 (.7%) 0 (0%)
aEntire year.
bUntil August 1st, 2022.
The total number of citations received by all of the included articles was 2132, and the average number of citations received by each article was 9.46. Among the articles, the most-cited 1 had a total of 224 citations.14 Regarding the nationality of the first authors, American constituted the most common nationality (127/293, 43.3%), followed by British (43/293, 14.7%), German (18/293, 6.1%), and Australian (15/293, 5.1%). English publications accounted for 96.6% of all publications. Furthermore, of the included studies, 84.0% (246/293) were original articles, 4.4% (13/293) were editorial material and 11.6% (34/293) were review articles. Concerning the distribution of the articles in the various WoS categories, those belonging to the Health Care Sciences and Services category constituted the majority of our the included studies (183/293, 62.5%), followed by those belonging to the Medicine, General and Internal category (105/293, 35.8%).
Of the included studies, 22.9% (67/293), 11.6% (34/293), 7.5% (22/293), 4.4% (13/293), 3.4% (10/293), and 2.0% (6/293) were published in the Journal of Pain and Symptom Management, American Journal of Hospice Palliative Medicine, Journal of Palliative Medicine, BMJ Supportive and Palliative Care, Journal of Hospice and Palliative Nursing, and BMJ Open.
Discussion
Trends in Research on COVID-19-Related Palliative Care
The COVID-19 pandemic negatively affected the ability of global health-care systems to provide quality care. As an essential part of global health-care systems, palliative care systems faced challenges during the pandemic; nevertheless, relevant stakeholders have responded rapidly to the challenges and developed strategies for addressing them. Our review revealed that studies related to system improvement (181/293, 61.8%) and patient care (79/293, 27.0%) were the most common in the field of research on COVID-19-related palliative care. It showed the urgent need for innovative palliative care delivery methods and management of relieving distressing symptoms. On the other hand, although studies related to bereavement support for patients or their family members (19/293, 6.5%) and studies related to the mental health of frontline practitioners (14/293, 4.8%) were less common, their importance should not be underestimated. As the pandemic evolve the percentage of publications in these 2 areas didn’t increase rapidly and even inverted between 2020 and 2022 (Table 1). It may be associated with the improvement of spiritual support through generalist-level palliative care and less fear when we have more information about the disease and better treatment options. However, some other emotional stress including isolation, grief, loneliness, or anxiety may still exist in the patient, family, or frontline practitioners, thus further investigation was warranted to figure out the possible lag behind in the mental and spiritual care services throughout the pandemic and beyond. The research trends we identified reflect the hot issues and the challenges associated with palliative care, and such challenges require addressing.
Our review of the most-cited articles (Table 2) revealed that the most prominent research topics were those related to telemedicine. Four studies2,3,5,6 on system improvement were among the top 10 cited articles, and this demonstrates the urgent need for innovative methods of overcoming the challenges engendered by the COVID-19 pandemic. Notably, a study related to bereavement support received the highest number of citations in our review,14 and other studies related to bereavement support accounted for nearly half of the top 10 cited publications14,17-19; a possible explanation for this finding is the complexity of grief amid rapid changes engendered by the COVID-19 pandemic. However, articles related to the mental health of frontline practitioners and patient care received a relatively low number of citations. The majority of the most commonly cited articles were written by authors from the United States and the United Kingdom. Whether the recommendations of authors from the United States or the United Kingdom are applicable to Asia or other countries remains unclear and warrants further research.Table 2. Summary of the Top 10 Cited Publications
Article title (document type) Authors (nationality) Research field Publication titles (publication year/month) Times cited
Grief during the COVID-19-19 pandemic: Considerations for palliative care providers (Article)14 Wallace, CL, et al (United States) Bereavement support of the patient or family member Journal of pain and symptom management (2020/07) 224
Telemedicine in the time of Coronavirus (Article)5 Calton, B, et al (United States) System improvement (alternative delivery methods) Journal of pain and symptom management (2020/7) 181
The role and response of palliative care and hospice services in epidemics and pandemics: A rapid review to inform practice during the COVID-19-19 pandemic (review)2 Etkind, SN, et al (United Kingdom) System improvement (Impact on healthcare system) Journal of pain and symptom management (2020/7) 125
Family-centered care during the COVID-19-19 era (Article)17 Hart, JL, et al (United States) Bereavement support of the patient or family member Journal of pain and symptom management (2020/8) 112
Characteristics, symptom management, and outcomes of 101 patients with COVID-19-19 referred for hospital palliative care (Article)13 Lovell, N, et al (United Kingdom) Patient care (COVID-19 patients) Journal of Pain and Symptom Management (2020/7) 95
Bereavement support on the frontline of COVID-19-19: Recommendations for hospital clinicians (Article)10 Selman, LE, et al (United Kingdom) The mental health of the frontline practitioners Journal of pain and symptom management (2020/8) 90
Rapid implementation of inpatient telepalliative medicine consultations during COVID-19-19 pandemic (Article)6 Humphreys, J, et al (United States) System improvement (Alternative delivery methods) Journal of pain and symptom management (2020/7) 68
Supporting adults bereaved through COVID-19-19: A rapid review of the impact of previous pandemics on grief and bereavement (review)18 Mayland, CR, et al (United Kingdom) Bereavement support of the patient or family member Journal of pain and symptom management (2020/8) 65
The family caregiving crisis meets an actual pandemic (Article)19 Kent, EE, et al (United States) Bereavement support of the patient or family member Journal of pain and symptom management (2020/7) 62
Response and role of palliative care during the COVID-19-19 pandemic: A national telephone survey of hospices in Italy (Article)3 Costantini, M, et al (Italy) System improvement (Impact on healthcare system) Palliative medicine (2020/07) 61
Summary of Each Research Field
System Improvement
A well-designed palliative health-care system is fundamental to effective and efficient patient care. Policies for preventing disease spread, such as visitor restrictions and patient isolation, restrict the abilities of palliative care providers to provide quality care. Therefore, improvement of palliative care delivery methods, education of the public, development of health-care strategies, and implementation of staff training programs are all critical tasks.1,20 Telemedicine was the most popular research topic, accounting for approximately 17.1% (50/293) of the studies on COVID-19-related palliative care. Many of the telemedicine-related studies provided pieces of advice and information on home-based palliative care.5,7,21 Studies reported that inpatient video consultations, multidisciplinary team communication, and online family meetings were associated with improvements in the quality of care.6,8 We also noted a trend toward the increasing digitalization of palliative teaching courses.22-25 Another study discussed how palliative care systems respond to the challenges of the COVID-19 pandemic.26
Patient Care
Because of the lack of prior experience with COVID-19, providing high-quality palliative care was difficult. Some studies identified the symptoms and characteristics of COVID-19.13. Other studies developed recommendations for effectively treating symptoms.4,27 However, difficulties dealing with patient subgroups, including pediatric patients, older adult patients, patients with cancer, and critically ill patients, were noted. Further research is warranted to develop strategies for addressing these difficulties. To better support patients with cancer, research is warranted on the following issues: ensuring the accessibility of critical drugs such as opioids or targeted therapy medications28 and maintaining cancer treatment such as palliative chemotherapy or palliative radiotherapy for both outpatients and inpatients with a COVID-19 diagnosis.29,30 A global survey for the provision of pediatric palliative care revealed that changes in patient–family–provider interactions and financial considerations were common problems.12
In addition to the challenges related to the treatment of symptoms and the underlying disease, isolation policies associated with the COVID-19 pandemic and the uncertainty of disease progression and treatment were observed to cause spiritual distress. Thus, spiritual care during the COVID-19 pandemic is an urgent, nonnegligible part of palliative care,11 as emphasized in a study by a European Respiratory Society international task force.4 Although spiritual care has long been an essential component of quality palliative care, the effect of the pandemic has further highlighted the requirement for quality spiritual assessment and care.11
Bereavement Support of the Patient or Family Member
Grief is a natural human emotion while facing loss. Bereavement care is thus considered a vital component of quality palliative care. During the COVID-19 pandemic, caring for bereaved family members before and after the death of a patient was a major challenge and required much more effort than expected.9 An open observational online survey completed by bereaved relatives revealed that respondents who received emotional support under restrictive isolation policies were more likely to consider the place of their family member’s death as appropriate than did respondents who did not receive such support.31
Being unable to accompany dying relatives was associated with a higher risk of anxiety and grief disorder. Screening tools were developed to enable palliative care providers to identify those at risk of dysfunctional grief due to the loss of a relative to COVID-19 infection.32 Strategies involving the improvement of communication skills, optimization of symptom management, discussions of care plans in advance, provision of online counseling, and establishment of virtual support groups enabled palliative care providers to facilitate quality care to family members after the loss of a relative, despite the many restrictions.9,10,18,33
Mental Health of Frontline Practitioners
Repeated empathic engagement with individuals who are in grief may result in varying degrees of emotional stress. One qualitative multiple-case study revealed that having previous experience caring for dying patients does not prevent moral distress from accumulating and worsening.34 A survey in Hong Kong discovered that up to 82% and 43% of palliative care professionals reported being moderately to highly stressed and anxious, respectively, during the pandemic.35 Self-awareness and self-resilience tools are vital for coping with emotional stress. Informal debriefing and peer support facilitated by hospitals or other institutions, rather than single-session psychological courses, are recommended to support the wellbeing of frontline practitioners.10 Overall, emotional stress should not be underestimated, and early identification and appropriate response are essential for supporting frontline practitioners.
Strength and Limitations
To the best of our knowledge, our study is among the first to analyze research trends in palliative care related to the COVID-19 pandemic. Nevertheless, our study has some limitations. First, our study design prevented us from conducting comprehensive comparisons and descriptions of specific problems. Second, the generalizability of the results of worldwide studies to local populations is unclear. Additional studies in various regions are thus warranted. Third, we noted that the majority of the included studies were conducted in the United States or Europe and that relatively few studies investigated mental health. However, this may be related to publication bias. Hence, the results should be interpreted with caution.
Conclusion
As one of the first studies to review the trends in research on palliative care during the COVID-19 pandemic, the research trends demonstrate the flexibility and rapid response of the global palliative care system to the COVID-19 pandemic and show how the palliative care system is evolving. While most studies are interested in system improvement, patient care, and bereavement support, the mental health of frontline practitioners has received less attention. Our findings provide palliative care practitioners with current valuable information and highlight possible future trends.
Acknowledgments
The authors thank the entire team of the Department of Emergency Medicine of Keelung Chang Gang Memorial Hospital for their support of this study.
ORCID iD
Chien-Ho Wang https://orcid.org/0000-0002-4468-1974
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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| 36503251 | PMC9748525 | NO-CC CODE | 2022-12-15 23:22:44 | no | Am J Hosp Palliat Care. 2022 Dec 12;:10499091221145202 | utf-8 | Am J Hosp Palliat Care | 2,022 | 10.1177/10499091221145202 | oa_other |
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Contraception
Contraception
Contraception
0010-7824
1879-0518
Elsevier Inc.
S0010-7824(21)00136-0
10.1016/j.contraception.2021.04.020
Original Research Article
Federal, state, and institutional barriers to the expansion of medication and telemedicine abortion services in Ohio, Kentucky, and West Virginia during the COVID-19 pandemic
Mello Kelsey a
Smith Mikaela H. b
Hill B. Jessie c
Chakraborty Payal b
Rivlin Katherine d
Bessett Danielle e
Norris Alison H. b
McGowan Michelle L. afg⁎
a Department of Women's, Gender & Sexuality Studies, University of Cincinnati, Cincinnati, OH, United States
b Division of Epidemiology, College of Public Health, Ohio State University, Columbus, OH, United States
c School of Law, Case Western Reserve University, Cleveland, OH, United States
d Department of Obstetrics and Gynecology, The Ohio State University Wexner Medical Center, Columbus, OH, United States
e Department of Sociology, University of Cincinnati, Cincinnati, OH, United States
f Department of Pediatrics, University of Cincinnati, Cincinnati, OH, United States
g Ethics Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
⁎ Corresponding author.
27 4 2021
7 2021
27 4 2021
104 1 111116
3 2 2021
15 4 2021
19 4 2021
© 2021 Elsevier Inc. All rights reserved.
2021
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Objectives
We aimed to characterize the combined impact of federal, state, and institutional policies on barriers to expanding medication and telemedicine abortion care delivery during the COVID-19 pandemic in the abortion-restrictive states of Ohio, Kentucky, and West Virginia.
Study Design
We analyzed 4 state policies, 2 COVID-related state executive orders, and clinic-level survey data on medication abortion provision from fourteen abortion facilities in Ohio, Kentucky, and West Virginia from December 2019 to December 2020. We calculated the percent of medication abortions provided at these facilities during the study period by state, to assess changes in medication abortion use during the pandemic.
Results
We ascertained that COVID-19-executive orders in Ohio and West Virginia that limited procedural abortion in Spring 2020 coincided with an increase in the overall number and proportion of medication abortions in this region, peaking at 1613 medication abortions (70%) in April 2020. Ohio and West Virginia, which had executive orders limiting procedural abortion, saw relatively greater increases in April compared to Kentucky. Despite temporary lifting of the mifepristone REMS, prepandemic regulations banning telemedicine abortion in Kentucky and West Virginia and requiring in-person clinic visits for medication abortion distribution in Ohio limited clinics’ ability to adapt to offer medication abortion by mail.
Conclusions
Our findings illustrate how restrictive medication and telemedicine abortion policies in Ohio, Kentucky, and West Virginia created additional obstacles for patients seeking medication abortion during the pandemic. Permanently lifting federal regulations on in-clinic distribution of mifepristone would only advantage abortion seekers in states without restrictive telehealth and medication abortion policies. State policies that limit access to comprehensive abortion services should be central in larger efforts toward dismantling barriers that impinge upon reproductive autonomy.
Implication Statement
We find that abolishing the REMS on mifepristone would not be enough to expand access to patients in abortion-restrictive states with telemedicine and medication abortion laws. While the REMS is a barrier, it represents one of several hindrances to the expansion of telemedicine abortion distribution across the United States.
Keywords
Abortion
COVID-19
Mifepristone
REMS
State policy
Telemedicine
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pmc1 Introduction
Abortion through medication has been increasingly utilized in the United States (US) since mifepristone was approved by the US Food and Drug Administration (FDA) in 2000, from 29% of all abortions in 2014 to 39% of all abortions in 2017 [1]. Because medication abortion can be safely administered outside of clinic settings, it is particularly amenable to telemedicine practices, whereby patients speak with a clinician over a video or audio call but do not see them in person. For example, groups in the United States such as Aid Access [2] and the TelAbortion research study protocol1 provide virtual consultations and then distribute medication abortion pills to eligible patients by mail. Telemedicine abortion care can ameliorate transportation and financial burdens associated with in-person visit(s) for abortion counseling and medication administration [3]. Calls for the expansion of telemedicine abortion preceded the COVID-19 pandemic, with findings from a 2019 study arguing that, “in settings where abortion is legally restricted and availability of safe abortion services may be very limited, if available at all, high-quality telemedicine services undoubtedly improve access” [4].
However, use of medication abortion in the US is complicated by federal and state regulations regarding both the management of the medicine itself and its delivery by telemedicine. A federal Risk Evaluation and Mitigation Strategy (REMS) had been initiated through the FDA Amendments Act of 2007 [5], replacing an older requirement (Subpart (H)), requiring that the medication abortion drug mifepristone only be ordered, prescribed, and dispensed in a clinical setting by a certified provider [6 ]. In July 2020, in response to the pressures on the medical system created by the pandemic, a federal court ruled that the FDA could not enforce the REMS in-person dispensing requirements for the duration of the pandemic [7]. The REMS suspension allowed some abortion providers to offer medication abortion by mail, aligning with pandemic stay-at-home orders, social distancing guidelines, and the preservation of personal protective equipment for clinic personnel [7]. The Supreme Court halted this temporary loosening of the REMS in January 2021 [8], requiring certified providers to return to in-person dispensing despite the continuing public health crisis. Following this decision, the FDA's Center for Drug Evaluation and Research (CDER) reviewed the American College of Obstetricians and Gynecologists’ concerns about the mifepristone REMS in-person dispensing requirements. and concluded in April 2021 that provided the other REMS Program requirements are met, mifepristone can be distributed through the mail under the supervision of a certified provider for the duration of the COVID-19 public health emergency [9].
Abortion access in several US states was already limited before the pandemic [10], and disproportionately so for those in rural settings [11, 12]. A 2017 Guttmacher Institute study shows that restrictive state policies exacerbate persistent geographical disparities for rural abortion seekers, making “distance a significant barrier to accessing abortion care for the substantial minority who live farther away, and especially for economically disadvantaged women who make up the majority of abortion patients” [10]. Amidst the pandemic, some states – along with physicians and reproductive advocacy groups – have pushed to make telehealth for abortion more accessible [13], while abortion-restrictive states have continued to pass restrictive policies [14].
In this manuscript, we evaluate the combined impact of the REMS, state policies, and clinics’ abortion provision practices on opportunities and barriers to expand access to medication and telemedicine abortion during the COVID-19 pandemic in Ohio, Kentucky, and West Virginia, abortion-restrictive states with significant rural areas. We characterize these three abortion-restrictive state contexts to illustrate how state and federal policies and institutional limitations interlock to limit the range of reproductive health care options available to abortion seekers in these states.
2 Methods
We used a mixed-methods approach and triangulated our findings to develop a nuanced understanding of the current accessibility and feasibility of expanding telemedicine abortion services under varied complex structures [15]. We analyzed state policies, state executive orders, and clinic-level survey data on medication abortion provision in this study. We sought to understand clinic-level barriers and opportunities that result from a dynamic federal and state regulatory landscape in Ohio, Kentucky, and West Virginia prior to and during the COVID-19 pandemic (December 2019–December 2020).
To assess policy and policy change, we searched for abortion bills and policies pertaining to distribution of medication abortion in Ohio, Kentucky, and West Virginia that were in effect in 2020. We reviewed enacted abortion laws, policy tracking resources, and reports compiled by organizations such as the Guttmacher Institute and NARAL, and executive actions pertaining to abortion care provision in these states during the COVID-19 pandemic. The search yielded 4 laws (2 in Ohio, 1 in Kentucky, and 1 in West Virginia), and 2 executive actions in Ohio and West Virginia. We analyzed these laws and policies for implications for provision of medication abortion during the pandemic. Laws that were currently enjoined (such as Ohio's 6-week ban) or not yet in effect (like Ohio's telemedicine abortion ban, which was poised to go into effect in 2021 but has since been blocked by a legal challenge) are not included in this analysis.
We also describe changes in medication abortion provision at 14 abortion facilities in Ohio, Kentucky, and West Virginia, from December 2019 through December 2020. This allows us to capture service delivery before and after COVID-19-related state regulations regarding abortion that were in effect in March and April 2020. We surveyed clinics monthly via an online questionnaire in which facility staff answered questions related to abortion service delivery and availability, including the number of abortions and distribution by method of abortion (procedural and medication) [16]. Survey data collection was approved by the Ohio State University and University of Cincinnati Institutional Review Boards. Based on data completeness and availability, we report on 14 of the 16 sites that offer medication abortion care in these 3 states, capturing more than three quarters of all medication abortions in this region. Sites from which we do not have data are excluded from the current analysis.
3 Results
3.1 Policy context
In March 2020, officials in Ohio and West Virginia issued executive orders requiring all elective surgeries to cease [17, 18]. State actors used these executive orders to deem procedural abortion an “elective,” nonurgent procedure that could be delayed during the pandemic. The American College of Obstetricians and Gynecologists and other medical professional societies retorted that characterizing pregnancy termination as elective or non-urgent during the pandemic is inappropriate, as abortion is a time-sensitive procedure that generates additional risks when performed at a later gestational age [19]. Nevertheless, the Ohio and West Virginia executive orders were interpreted to require abortions before 10 weeks’ gestation to be completed by medication abortion rather than procedural abortion whenever possible and unless contraindicated. Ohio abortion clinics successfully challenged the “elective surgery” designation for procedural abortion after 10 weeks, although limitations on procedural abortions before 10 weeks remained in place until the executive order was lifted on May 1, 2020 [20]. The West Virginia executive order remained in effect until it expired on April 30, 2020 [18]. During March and April 2020, these orders significantly curtailed access to procedural abortion in these two states, making medication abortion the most readily accessible method of abortion. While the State of Kentucky also issued an executive order halting nonemergent and nonurgent health care procedures in March 2020, procedural abortion provision was not subject to the order, falling under the definition of urgent healthcare that could risk serious or irreparable harm to the patient if delayed more than 30 days [21].
In contrast to executive orders that encouraged utilization of medication abortion, existing state laws impinged upon innovative provision of medication abortion during the pandemic. In Ohio, only physicians can prescribe abortion inducing drugs [22, 23]. Since 2005, Ohio has required abortion providers to complete in-person state-mandated counseling and to provide patients with copies of materials published from the state Department of Health 24 hours prior to performing or inducing an abortion [24]. In 2011, an Ohio law went into effect prohibiting off-label use of mifepristone [25]; while the 2016 labeling changes allowed Ohio-licensed physicians to prescribe mifepristone at evidence-based dosages, Ohio law still required that mifepristone be dispensed at a clinic as required by the REMS and the labeling. These Ohio laws – the law that requires physicians to administer mifepristone in a clinical setting in line with FDA's labeling of mifepristone combined with the 24-hour waiting period law that requires at least 2 clinic visits – made it impossible for Ohio abortion providers to transition to postal delivery of medication abortion during the pandemic [26].
In Ohio, legal restrictions result in patients having to travel to a clinic twice, first for preabortion counseling and second to obtain mifepristone and a prescription for misoprostol [24], while neighboring states Kentucky and West Virginia have laws that explicitly prohibit medication abortion distribution through telehealth services. Kentucky and West Virginia both banned telemedicine abortion in 2018 [27, 28]. Despite the lifting of the elective surgery bans in Ohio and West Virginia in April 2020 and the temporary loosening of the mifepristone REMS in July 2020, state laws governing medication abortion and telemedicine abortion remained in effect in Ohio, Kentucky, and West Virginia throughout 2020, barring patients from receiving medication abortion by mail.
3.2 Clinic survey findings
Across Ohio, Kentucky, and West Virginia there are 16 abortion facilities that provide medication abortion, 15 of which are located in urban areas [29]. Fourteen of these facilities, 13 of which are in urban areas, completed monthly surveys offering data on abortion provision from December 2019 to December 2020. Among the fourteen facilities included in this analysis, nine provide both medication and procedural abortion services and one provides medication abortion only. One of these clinics began offering services in March 2020, and is included in analyses from March 2020 onward. Four additional facilities provide medication abortion via clinic-to-clinic telehealth only, wherein patients go to a health care facility for their second-day appointment to meet via videoconferencing with the physician who is located in another clinic and to obtain mifepristone. Three of these facilities began dispensing mifepristone via clinic-to-clinic telehealth before the study period began, and the fourth site began dispensing mifepristone in January 2020, and is excluded from the December 2019 analyses.
Overall, abortion facilities in these three states averaged approximately 893 medication abortions per month, ranging from 629 in December 2019 to 1613 in April 2020 (Table 1 ). Coinciding with state executive orders issued during the COVID-19 pandemic, the number of medication abortions hit a sharp peak of 1613 in April, accounting for 70% of all abortions provided at these fourteen facilities that month. This value drops to 1052 by May, returning close to prepandemic rates by June.Table 1 Number of medication abortions, total abortions, and percent of medication abortions out of total abortions provided at fourteen abortion facilities in Ohio, Kentucky, and West Virginia (December 2019–December 2020)
Table 1:Year Month All Abortions Medication Abortions
Overall Kentucky Ohio West Virginia Overall Kentucky Ohio West Virginia
2019 December 1824 285 1457 82 629 (34%) 130 (46%) 460 (32%) 39 (48%)
2020 January 2258 366 1788 104 733 (32%) 157 (43%) 534 (30%) 42 (40%)
2020 February 2048 340 1610 98 722 (35%) 167 (49%) 509 (32%) 46 (47%)
2020 March 2150 362 1700 88 788 (37%) 169 (47%) 586 (34%) 33 (38%)
2020 April 2306 359 1917 30 1613 (70%) 199 (55%) 1388 (72%) 26 (87%)
2020 May 2281 359 1826 96 1052 (46%) 185 (52%) 817 (45%) 50 (52%)
2020 June 2085 322 1677 86 856 (41%) 162 (50%) 649 (39%) 45 (52%)
2020 July 2151 343 1753 55 876 (41%) 175 (51%) 681 (39%) 20 (36%)
2020 August 1949 364 1526 59 713 (37%) 182 (50%) 500 (33%) 31 (53%)
2020 September 2113 309 1698 106 888 (42%) 165 (53%) 676 (40%) 47 (44%)
2020 October 2176 365 1736 75 941 (43%) 196 (54%) 701 (40%) 44 (59%)
2020 November 1912 284 1540 88 879 (46%) 151 (53%) 681 (44%) 47 (53%)
2020 December 2137 331 1714 92 919 (43%) 173 (52%) 692 (40%) 54 (59%)
TOTAL 27390 4389 21942 1059 11609 (42%) 2211 (50%) 8874 (40%) 524 (49%)
AVERAGE 2107 338 1688 81 893 (42%) 170 (50%) 683 (40%) 40 (49%)
Note: Percent of medication abortion in parentheses.
Absolute numbers of medication abortions varied widely by state (averaging 40 per month in West Virginia, 170 per month in Kentucky, and 683 per month in Ohio), but the relative proportion of medication abortions peaked in April for all 3 (Fig. 1 ). Notably, this peak is most stark for Ohio (72%, compared to 40% average) and West Virginia (87%, compared to 49% average); Kentucky sees only a slight increase (55%, compared to 50% average). The one rural clinic in our sample, which dispenses mifepristone via a clinic-to-clinic telehealth appointments, reported fewer medication abortion appointments after the declaration of the public health emergency, averaging 5.7 appointments per month before the pandemic (December 2019 through February 2020), and 1.6 per month from March 2020 through December 2020. In the face of facilities’ ongoing inability to take advantage of the loosening of REMS regulations, we do not see major changes in medication abortion provision at clinics in Ohio, Kentucky, and West Virginia after the July 2020 court ruling.Fig. 1 Percent of medication abortions provided at fourteen abortion facilities in Ohio, Kentucky, and West Virginia, disaggregated by state (December 2019–December 2020).
Fig. 1:
4 Discussion
Findings from our policy review illustrate how restrictive medication and telemedicine abortion policies in Ohio, Kentucky, and West Virginia created obstacles for patients seeking a medication abortion during the first 10 months of the COVID-19 pandemic. Kentucky and West Virginia's requirements that mifepristone be administered in the presence of a clinician, and Ohio regulations that limit dispensing medication abortion pills to a clinical setting, hindered medication abortion distribution by mail. Mail distribution could benefit abortion seekers throughout and after the COVID-19 pandemic and especially for those for whom a visit to a clinic is not easily attainable. Our assessment of state policies that temporarily denied and continue to limit access to comprehensive abortion services during the pandemic should inform the larger effort toward dismantling the interwound barriers and impingement upon urban and rural people's reproductive autonomy.
From survey results, we find a meaningful increase in the proportion of medication abortions provided by abortion facilities in Ohio and West Virginia in April 2020 after state executive orders were issued deeming procedural abortion elective and therefore unavailable in many circumstances. Kentucky saw a slight increase as well, but it was not as stark. As a whole, the range in the proportion of medication abortions across the study period (32%–70%) is somewhat higher than annual values seen in recent years; for example, in 2018 approximately 33% of abortions across these 3 states were medication abortions [30]. While medication abortion is increasingly utilized for a variety of reasons, the peak observed in April suggests that the increase in this month was due to state executive orders limiting procedural abortion care. The peak is particularly notable given the different policy landscapes of these three states: Ohio and West Virginia enacted state executive orders in March and April, respectively, limiting procedural abortions, resulting in a meaningful increase in medication abortion, whereas Kentucky's executive order did not halt procedural abortion and the proportion of medication abortions increased only slightly.
Furthermore, the relative increase in medication abortion during April specifically demonstrates how clinics adapted to meet their patients’ abortion care needs while state executive orders limited their ability to provide procedural abortion care. However, the public health emergency necessitated other changes in health care delivery, which may have limited clinics’ capacity to innovate in the use of telehealth and schedule appointments beyond the period of state executive orders. For instance, while the intention of clinic-to-clinic telehealth appointments to dispense mifepristone is to increase the availability of medication abortion appointment opportunities to patients and to decrease the distance patients need to travel to receive mifepristone, the only facilities that offered clinic-to-clinic telehealth appointments to dispense mifepristone during our study period were already doing so before the pandemic began. Further, the rural health center in our sample reported dispensing medication abortion to 30 patients in 2020, while the state reported that it served 50 patients in 2019 [31]. This decline in medication abortions suggests that patient volume was lower at the rural facility during the pandemic, perhaps due to constraints imposed by the executive order, but also because of pandemic-related constraints on clinic scheduling protocols (e.g., maintaining adequate distance between individuals in the clinic and conserving personal protective equipment used in family planning and sexual health care).
If federal regulations and state law had permitted, dispensing medication abortion by mail may have been a more appealing option for patients and abortion facilities for the duration of the pandemic. Indeed, state officials in Ohio encouraged telehealth otherwise wherever possible [14] except for abortion care, highlighting the continued treatment of abortion as exceptional and something out of the norm of health care provision [32]. Additionally, we see no continued elevation in medication abortion use during the period subsequent to the emergency orders, during which the REMS on mifepristone was temporarily removed from July 2020 through the end of our study period; this is not surprising, given the inability of these states to take advantage of the temporary lifting of the REMS in-person dispensing requirement due to state laws governing the distribution of medication abortion. Use of medication abortion care by mail during the study period of March–December 2020 would have been particularly beneficial for promoting contactless administration of mifepristone, given the dramatically increased use of medication abortion in these states in Spring 2020 and abortion facilities’ continued need to limit person-to-person contact and preserve personal protective equipment throughout the pandemic [16]. While the FDA modified the REMS in-person distribution requirements in April 2021 for the duration of the pandemic, this will not benefit abortion seekers who reside in states with additional telemedicine and medication abortion distribution requirements [9]. Permanently lifting the REMS on mifepristone – during and outside of a pandemic – would alleviate transportation and additional costs attributed to mandatory in-clinic consultation and administration to obtain mifepristone and may allow clinics to utilize alternative distribution methods that would benefit abortion seekers in rural settings. However, our findings demonstrate that permanently lifting the REMS would only advantage some abortion seekers, but not those who live under restrictive state telemedicine and medication abortion policies such as those seen in Ohio, Kentucky, and West Virginia.
Abortion seekers who live in both urban and rural areas in abortion-restrictive states such as Ohio, Kentucky, and West Virginia could only benefit from permanent changes to the mifepristone REMS and FDA labeling if state laws governing the distribution of medication abortion were also changed. Such regulatory changes could particularly advantage people experiencing structural oppression, such as those who are poor, low income, people of color, and geographically distant [33]. Yet even if the REMS were lifted, state laws would continue to limit abortion accessibility because of their hostility to abortion [34] and because of the limited number of facilities that even offer abortion services, most of which are not easily accessible for those who reside in rural areas [11]. Both restrictive regulations and geographical locations of clinics compound “abortion care churn,” or the clinic-level instability and uncertainty that affect the accessibility of a full range of abortion services in a particular geographic area [35].
At the outset of the COVID-19 pandemic in the United States in 2020, abortion seekers increasingly sought medication abortion by mail [2]. An uptick in medication abortion requests to organizations like Aid Access suggest that pandemic-related abortion regulations led people to seek alternatives to clinic-based abortion care, but the current study can only speak to changes in clinical care. Future research should compare utilization of medication abortion and telemedicine abortion in more and less abortion restrictive states, assess changes in family planning care provision during the pandemic, and capture rates of interstate travel of abortion seekers to states that were less restrictive than Ohio, Kentucky, and West Virginia during the COVID-19 pandemic.
The COVID-19 pandemic has highlighted the entangled obstacles federal, state, and institutional barriers continue to impose on pregnant people's ability to obtain abortions. Eliminating in-person clinic visit requirements, telemedicine bans, and FDA-labeling requirements for medication abortion would alleviate some existing barriers to abortion care, with some arguing that “remote access will be the only way during this crisis and beyond to ensure that vulnerable rural women are able to access care” [36]. While the effects the pandemic has had on people's ability to obtain an abortion will not fully be understood until vital statistics are available on abortion and birth rates for 2020 by patient state of residence to enable comparison to rates in previous years, amending telemedicine and medication abortion policies in the meantime could alleviate significant barriers towards the actualization of reproductive freedom for the duration of this pandemic and beyond.
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.
1 See for example, TelAbortion, US. https://telabortion.org/news
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| 33930382 | PMC9748601 | NO-CC CODE | 2022-12-15 23:22:44 | no | Contraception. 2021 Jul 27; 104(1):111-116 | utf-8 | Contraception | 2,021 | 10.1016/j.contraception.2021.04.020 | oa_other |
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Contraception
Contraception
Contraception
0010-7824
1879-0518
Elsevier Inc.
S0010-7824(21)00109-8
10.1016/j.contraception.2021.03.033
Article
The future of abortion is now: Mifepristone by mail and in-clinic abortion access in the United States
Mark Alice a⁎
Foster Angel M. b
Perritt Jamila c
a National Abortion Federation, Washington, DC 20005, USA
b University of Ottawa, Ottawa, Ontario, Canada
c Physicians for Reproductive Health, USA
⁎ Corresponding author.
17 4 2021
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29 3 2021
31 3 2021
© 2021 Elsevier Inc. All rights reserved.
2021
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The COVID-19 pandemic disrupted health care delivery in all aspects of medicine, including abortion care. For 6 months, the mifepristone Risk Evaluation and Mitigation Strategy (REMS) was temporarily blocked, allowing for the remote provision of medication abortion. Remote medication abortion may become a dominant model of care in the future, either through the formal health system or through self-sourced, self-managed abortion. Clinics already face pressure from falling abortion rates and excessive regulation and with a transition to remote abortion, may not be able to sustain services. Although remote medication abortion improves access for many, those who need or want in-clinic care such as people later in pregnancy, people for whom abortion at home is not safe or feasible, or people who are not eligible for medication abortion, will need comprehensive support to access safe and appropriate care. To understand how we may adapt to remote abortion without leaving people behind, we can look outside of the U.S. to become familiar with emerging and alternative models of abortion care.
Keywords
Mifepristone
Misoprostol
Abortion
Self-managed abortion
Food and Drug Administration (FDA)
Risk Evaluation and Mitigation Strategy (REMS)
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pmc1 Introduction
Since the United States Food and Drug Administration (FDA) approved mifepristone more than 20 years ago, reproductive health advocates have concentrated efforts on broadening access to medication abortion. From changes in service delivery to expanding eligibility and pregnancy dating criteria, providers and advocates have developed care models that promote patient-centered use. Abortion provision that is timely, accessible, and convenient had been a long-held goal of providers, advocates, activists, and people seeking care.
The timeline for change was accelerated in 2020 as the COVID-19 pandemic not only exacerbated existing health inequities, but dramatically disrupted the delivery of all medical care, including abortion care. As providers scrambled to modify abortion services to incorporate public health protections, such as the limitation of in-person contact, researchers worked to develop evidence-based protocols for abortion that accommodated these guidelines, including eliminating lab testing and ultrasound. These practices have since been recommended by professional organizations and deployed in clinical services [1], [2], [3]. The temporary suspension of the in-person dispensing requirement in the mifepristone Risk Evaluation and Mitigation Strategy (REMS) also permitted providers to dispense mifepristone and misoprostol through alternative means, such as the mail [4]. Online-only services supported by mail-order pharmacies started offering fully remote medication abortion care in the United States (U.S.).
Although the shifts have felt sudden to some, the pandemic simply accelerated long-term, evidence-based trends that have led to declining numbers of abortions performed in brick-and-mortar clinics. These shifts are likely to stay with us long after the pandemic has passed and may permanently alter the existing landscape of abortion care. In addition, even as the newly elected administration and Congress support reproductive rights, the prior administration leaves behind a federal judiciary that may prove dangerously hostile. We must prepare ourselves for changes in abortion access. To understand how we may adapt to change, we can look outside of the U.S. context to become familiar with emerging and alternative models of abortion care.
2 Abortion trends: Fewer abortions, with care shifting earlier in pregnancy
Over the last decade, the number of clinic-based abortions provided in the U.S. has declined. According to the Guttmacher Institute's periodic abortion provider census, from 2011 to 2017, the total number of reported abortions decreased 19% and the reported abortion rate dropped 20%, from 16.9 to 13.5 abortions per 1000 women ages 15 to 44 [5]. In addition, a higher proportion of abortions are being done earlier in pregnancy, with approximately one-third of all abortions now being provided less than 6 weeks [6]. For people under 9 weeks in pregnancy, half were medication abortion.
There are multiple factors that may be contributing to reduced abortion rates, including state-based restrictions and clinic closures and a rise in self-managed abortions that current surveillance systems do not capture. However, as abortion rates have been declining steadily, so have birth rates, indicating an overall drop in the pregnancy rate, the core driver of abortion [5]. Increased age at first intercourse, increased contraceptive use, improved access to and affordability of contraception, and increased use of more reliable contraceptive methods, including long-acting methods, have coincided with the change in pregnancy rates [7]. Notwithstanding court rulings that allow employers that assert “religious liberty” claim to deny their employees contraceptive coverage, the Affordable Care Act continues to provide contraceptive coverage for millions in the U.S. Recent evidence has also supported practices that expand access to pregnancy prevention methods, including immediate postpartum and postabortion contraception and increasing long-acting contraceptive access for adolescents and young people [8], [9], [10].
3 Accelerated change: Mailing medications from clinics and self-sourced, self-managed medication abortion
Before the COVID-19 pandemic, direct-to-patient medication abortion with mifepristone was only available in the U.S. to people enrolled in a research study [11]. The REMS specifies that mifepristone must be “dispensed to patients only in clinics, medical offices or hospitals, by or under the supervision of a certified prescriber.” [12] This language has been widely interpreted to mean that patients using mifepristone must physically enter the clinic setting to receive the medication, creating a substantial barrier to care.
Years of evidence from the U.S. and abroad shows that medication abortion provided remotely is as safe as in-person abortion [11,13,14]. During the pandemic, the British government issued emergency legal orders to allow for mifepristone use at home using a “no-test model.” A recent study of 52,142 people who were prescribed medication abortion both before and after the switch to a no-test model showed that, in comparison to the cohort who had “traditional” medication abortion with a clinic visit and ultrasound, those in the “telemedicine-hybrid” group who had a telemedicine consultation with no in-clinic visit and no ultrasound if eligible had equally effective and safe abortion outcomes with similarly low rates of major adverse events and ectopic pregnancy [15]. Of those in the “telemedicine-hybrid” cohort, 61% were fully remote with no need for ultrasound. People who were part of the telemedicine-hybrid cohort waited less time to get their appointments and could access abortion care earlier in pregnancy. Eleven people in the telemedicine-hybrid cohort who had an abortion were diagnosed with a more advanced pregnancy than the 10-week eligibility, and all of these completed the medication abortion at home with no further treatment.
In July 2020 in the U.S., a court temporarily blocked the in-person dispensing requirement in the REMS. Understanding the importance of safe, timely access to medication abortion, providers looked for opportunities to offer abortion completely remotely – using telemedicine for consultation, consent, and follow-up, and mailing medications from their offices or through a mail-order pharmacy. New online-only providers started offering services [16]. Other providers who were unable to transition completely to a telemedicine model also adopted evidence-based protocols that reduced other in-person requirements, including eliminating ultrasounds and removing unnecessary Rh testing and Rh-immune globulin for eligible patients [1,3,17].
Responding to a request for an emergency stay from the Trump administration, the Supreme Court reinstated the in-person dispensing requirement in the REMS in January 2021, despite the continued impact of COVID-19. Abortion advocates have asked the Biden administration to issue immediate nonenforcement guidance for the in-person dispensing requirement during the pandemic and direct the FDA to undertake a comprehensive reevaluation of the entire mifepristone REMS [18]. Although lifting the in-person dispensing requirement would help patients in some states, almost 20 states have laws that specifically restrict abortion by telemedicine and others have regulations that put telemedicine abortion out of reach [19]. Not surprisingly, states with telemedicine abortion bans also have the most restricted in-clinic access.
With legal paths to remote abortion unresolved, and in-clinic abortion often inaccessible, unacceptable, unavailable, or unaffordable, some people are accessing abortion outside the formal health system. Aid Access is one example of a web-based service that provides remote consultation and partners with an overseas pharmacy to ship abortion medications directly to people needing abortion care. From January 1, 2019 to April 11, 2020, Aid Access received 49,935 requests for abortion from U.S. residents [20]. From March 20 to April 11, 2020, in the early days of the pandemic, there was a 27% increase in requests. The volume of requests both before and during the pandemic shows how many people want fully remote telemedicine abortion care, regardless of whether it operates through the formal health system.
4 Anticipated effects: In-clinic abortion gives way to remote abortion
Ninety-five percent of reported abortion care in the U.S. is offered in freestanding clinics, which face increasing scrutiny and unnecessary regulation [21]. Clinics are susceptible to regulations that hostile state actors design to close doors (Targeted Regulation of Abortion Providers or TRAP laws), are subject to intense protests and violence, and operate on very slim margins [22]. As in-clinic abortion numbers and rates have decreased and state regulations have burdened abortion-providing facilities to the breaking point, clinics have closed. The number of abortion clinics declined by 7% between 2014 and 2017, with clinics disproportionately closing in the South and Midwest [21]. In addition, the pandemic caused temporary clinic closures as antiabortion politicians deemed abortion a “nonessential” service. Although court challenges to the pandemic closures were successful and clinics reopened, previous and repetitive attempts to restrict access through the legal system have led to permanent closures in the past [23].
Despite the Democratic administration and Senate, the Trump administration's appointments to the federal courts may prove fatal to legal abortion access in hostile states. That the Supreme Court lifted the injunction on the in-person dispensing requirement for mifepristone on January 12, 2021, the same day that saw 4,400 COVID-19 deaths in the U.S., suggests a readiness to support abortion restrictions that flout evidence, in a direct rebuke to human rights [24]. The change in the courts may allow many states to make abortion, which is already so difficult to access, unavailable in the years to come.
While technological innovation in clinical care delivery may provide increased access for many, it may also present challenges for those seeking in-clinic care. In 2017, the average price of a first-trimester medication abortion supplied in clinic was USD551 [25].In comparison, online-only services are generally priced at USD250 or less. Even if clinics can offer both in-clinic and remote abortion care, their remote service may need to be less expensive. People seeking abortion often pay out of pocket and are likely to go with a less costly remote option, even if an in-clinic abortion is available. In addition, some people may prefer the privacy and convenience of remote care. Clinics that only offer early abortion will encounter stiff competition from remote providers. Clinics that provide early and later care may need a higher volume of in-clinic early abortion care to support them so that they can provide later care. As remote services increase, some brick-and-mortar clinics may be unable to sustain their current business models. They will either adapt by curtailing hours and staff or considering new clinical services to offer (for example, general gynecology, gender-affirming care, remote abortion evaluation, or follow-up) or they will close.
5 Unanticipated effects: Reduced choice and disproportionate burdens
The removal of the REMS would improve access to care for some people. In states that allow full remote medication abortion, eligible people who are early in pregnancy with good access to phone, internet, and a safe and private place to take the medications and pass the pregnancy may find telemedicine abortion safer, easier, and more convenient. Remote abortion can reduce logistical barriers for people who are distant from in-person clinics and financial hurdles when in-clinic abortion is not affordable. Although abortion is legal throughout the country, in many parts of the U.S., it is not accessible. Mississippi, North Dakota, South Dakota, and West Virginia have only one operating abortion clinic. In Minot, North Dakota, a pregnant person will need to drive 232 miles to their nearest clinic in Fargo. Remote or telemedicine abortion has the potential to open access to many who could not make their way to in-clinic care.
However, some people will still want or need in-clinic care. Medication abortion may be more difficult to conceal from an unsupportive or abusive partner or parent than an in-clinic abortion. Some may not be medically eligible for medication abortion. Some may prefer an aspiration procedure to medication abortion. Some, especially people who are young and people living on low incomes, may not have access to the reliable phone, internet, and mailing address needed to coordinate remote care. Some may not have access to the necessary support to manage an abortion on their own or a safe space to pass the pregnancy.
Although most people seek abortion early in pregnancy, 9% of abortions in the U.S. are provided for people over 13 weeks [6]. Because most remote or telemedicine abortion services provide care through 10 or 11 weeks from the first day of the last menstrual period, those needing abortions later in pregnancy rely on clinic-based care. Inequities in access to later care have disparate impacts. The need for later abortion care stems from an array of individual, systemic, and structural factors including structural racism, economic injustice, and inequities in access to quality, comprehensive, reproductive health care. Compared to people seeking early abortions, those who need abortions later in pregnancy are more likely to be living on low incomes and/or living in circumstances with less access to abortion care. Financial barriers, logistical challenges, and the experience of interpersonal violence all contribute to the need to access abortion care later in pregnancy [26], [27], [28].
Clinic closures will make it even more difficult for those seeking later care to find a provider. Travel distances, expenses, and time away from home will all increase. For some, the burden will be too high, and they will be forced to remain pregnant against their will. Others may self-manage later abortions, which carries potential legal and medical risks. When abortion care is more difficult to access, those who need in-clinic care will need not just the clinical service, but wrap-around, comprehensive support just to get in the door. They will need help finding providers, negotiating childcare and time off work, raising funds, traveling to a procedure, and obtaining food and housing for the duration of the procedure. People who manage later abortion on their own need economic, social, emotional, and legal support in addition to clinical care before, during, or after the abortion itself.
6 Lessons from deregulating mifepristone: Shifting abortion care from clinics and throughout the health system
Looking to Canada may reveal one model of abortion in the U.S. in years to come. In 2015, Health Canada approved the mifepristone/misoprostol regimen under the brand name Mifegymiso for early abortion in Canada, and uptake of the method by providers and people seeking abortion care has been rapid. Prior to mifepristone's introduction, medication abortion (with methotrexate and misoprostol) accounted for 8.4% of all abortions provided by Canadian members of the National Abortion Federation (NAF) [29]. By the end of 2018, 25.6% of abortions provided by Canadian NAF members used medication and providers were offering medication abortion in all provinces and the Yukon Territory.
When initially approved by Health Canada, the mifepristone/misoprostol regimen was limited to people less than 49 days from the last menstrual period, required the use of ultrasound, and required providers to be certified and register with the distributor. Changes that took 20 years in the U.S. happened rapidly in Canada. Over the last 5 years, Canada expanded eligibility to 63 days, allowed certified prescribers, including nurse practitioners, to prescribe, removed certifications requirements, permitted pharmacy dispensing, and eliminated ultrasound requirements. Most provincial and federal health insurance programs now cover medication abortion. During the pandemic, the federal government's reaffirmation that abortion is an essential health service allowed for quick adoption of no test protocols and remote consultation at the onset of the COVID-19 pandemic [30].
The deregulation of mifepristone has expanded the pool of abortion providers by allowing primary care clinicians to offer medication abortion [31,32]. Telemedicine services combined with pharmacy dispensing of the drugs enabled early abortion care in rural areas where there had previously been limited access. These changes have certainly had implications for freestanding clinics. Many are trying to identify ways to change or expand their services to account for the shift toward community-based medication abortion providers. Unlike in the U.S. context, most provincial and federal insurance programs cover both medication and procedural abortion care. Financial assistance is available to patients who cannot find appropriate services within their own province or territory. As a result, there remains a safety net for people who need to travel if medication abortion is not a viable option.
The Canadian system is an example of one way forward for U.S. states that have supportive legislation, policies, insurance coverage, and provider networks to both promote and adapt to change, should the REMS be removed. States like California, Illinois, Massachusetts, and New York may see an increase in online-only providers or community-based providers prescribing from pharmacies, with resulting changes in in-clinic abortion, but have some of the systems in place to adapt to the needs of people seeking abortion care. These shifts may result in clinic closure, leading people who need in-clinic care to travel further. Most importantly, supportive states use state-based funds to cover abortion for people living on low incomes, a safety net if care in clinics becomes scarce [33]. This funding needs to be more inclusive of care in later pregnancy, including reimbursements to providers for costlier or hospital-based procedures and support for travel and logistics.
7 Lessons from restricted settings: Supported self-sourced, self-managed medication abortion
Suppose other states that do not support remote abortion throughout the South and Midwest also continue to have declining access to in-clinic abortion. In that case, self-sourced, self-managed medication abortion may become a dominant model. In restricted environments globally, support networks have grown around self-sourced abortion to ensure that people have the help they need to access safe, evidence-based medication abortion. Harm reduction programs [34,35], community-based distribution programs [36,37], safe abortion hotlines and telemedicine services [38], and accompaniment models [39] are some examples of abortion support outside the health system.
Hotlines and global telemedicine services have been used worldwide to promote the safe use of medication abortion in restricted settings. Hotlines have been sponsored by feminist groups, clinical providers, and nongovernmental organizations [40], [41], [42] and offer evidence-based information and support; some also provide medications. Global telemedicine services, such as Women on Web and Women Help Women, provide online consultations and send medication abortion drugs through the mail to those seeking care [38].
For those needing later care, accompaniment joins people having an abortion either in-person or virtually with an experienced supporter. In an analysis of 318 case records from people 13 to 24 weeks who used mifepristone and misoprostol outside the health system plus the accompaniment model in Argentina, Chile, and Ecuador, 76% of people successfully completed the abortion on their own. One-third of people were seen in the health system during their care [39]. Ultimately, over 95% of abortions were completed successfully. Accompaniment combines evidence-based protocols and supportive care to manage abortion successfully, including later abortion, when the health system refuses. Although accompaniment has been described where abortion is restricted, if declining access to in-clinic abortion means that people later in pregnancy cannot access in-clinic care, accompaniment may be a route for accessing supported care when in-clinic care is not obtainable.
All of these support systems also exist within the U.S. If/When/How's Repro Legal Helpline, the M&A hotline, Plan C, Aid Access, Self-managed Abortion, Safe and Supported (SASS), abortion Facebook groups and subreddits, and auntie networks and abortion funds provide pills, funding, transportation, information, emotional support, and evidence-based care to people who need an abortion. Although accessible to all in the U.S., many of these resources are explicitly aimed at those who have self-sourced abortion medications and need the social, legal, and clinical support to help them manage the abortion process.
When people manage abortion outside the health system, either in restrictive settings or with new modes of remote abortion care provided through the formal health system, some will still present to the health system before, during, or after a medication abortion. The decline of abortion clinics means that people may present to settings, like religiously affiliated hospital emergency departments, that are unaccustomed or unfriendly to people who have had an abortion and do not understand their needs. People who seek care will need accurate, respectful, evidence-based care that does not put them at risk of unnecessary procedures, reporting, arrest, and prosecution [43]. If state-based restrictions become even more punitive, ensuring their safety, along with those who care for them, is an urgent priority.
Conclusion
The COVID-19 pandemic accelerated changes in the delivery of abortion care that have already been underway for some time. Instead of these changes rolling out systematically and slowly, the urgency of ensuring access to abortion care during a public health crisis means they have occurred rapidly, over days, weeks, and months. When the pandemic ends, many of the changes will become permanent. With a change in the U.S. political landscape, re-evaluation and removal of the medically unnecessary REMS may finally occur. Also, we may see further disruption from unfavorable, restrictive legislation. The removal of the REMS will increase legal, remote abortion care in states with favorable abortion laws. It may distribute early medication abortion care away from specialized abortion clinics and into online services or community health settings where a provider can prescribe abortion medication at a pharmacy. In those states where removal of the REMS does not impact care, because they already ban remote abortion, further restrictions will push more people toward self-sourced, self-managed abortion.
No matter what happens, the current system of in-clinic abortion will likely change radically. Although the future may make abortion more accessible for many, it may further reduce access for those who currently experience the most barriers to care. As providers, advocates, activists, and people who seek abortion, we need to seek alternative models of abortion care and understand how they impact people seeking care and providers, prepare for the future, and consider the trade-offs that will come if most abortion is provided remotely.
Declaration of Competing Interest
The authors declare no conflict of interest.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
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| 33844980 | PMC9748603 | NO-CC CODE | 2022-12-15 23:22:44 | no | Contraception. 2021 Jul 17; 104(1):38-42 | utf-8 | Contraception | 2,021 | 10.1016/j.contraception.2021.03.033 | oa_other |
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Contraception
Contraception
Contraception
0010-7824
1879-0518
Elsevier Inc.
S0010-7824(21)00091-3
10.1016/j.contraception.2021.03.019
Original Research Article
Expansion of a direct-to-patient telemedicine abortion service in the United States and experience during the COVID-19 pandemic
Chong Erica a1⁎
Shochet Tara a
Raymond Elizabeth a
Platais Ingrida a
Anger Holly A. a
Raidoo Shandhini b
Soon Reni b
Grant Melissa S. c
Haskell Susan c
Tocce Kristina d
Baldwin Maureen K. e
Boraas Christy M. f
Bednarek Paula H. g
Banks Joey h
Coplon Leah i
Thompson Francine j
Priegue Esther k
Winikoff Beverly a
a Gynuity Health Projects, New York, NY, USA
b Department of Obstetrics, Gynecology, and Women's Health, University of Hawaii John A. Burns School of Medicine, Honolulu, HI, USA
c carafem, 1001 Connecticut Avenue NW, Washington, DC, USA
d Planned Parenthood of the Rocky Mountains, Denver, CO, USA
e Oregon Health and Science University, Portland, OR, USA
f Planned Parenthood MN-ND-SD, St. Paul, MN, USA
g Planned Parenthood Columbia Willamette, Portland, OR, USA
h Planned Parenthood of Montana, Missoula, MT, USA
i Maine Family Planning, Augusta, ME, USA
j Emma Goldman Clinic, Iowa City, IA, USA
k Choices Women's Medical Center, New York, NY, USA
1 Present address: Reproductive Health Education in Family Medicine, 3544 Jerome Avenue, Bronx, NY 10467.
⁎ Corresponding author.
27 3 2021
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© 2021 Elsevier Inc. All rights reserved.
2021
Elsevier Inc.
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Objective
To present updated evidence on the safety, efficacy and acceptability of a direct-to-patient telemedicine abortion service and describe how the service functioned during the COVID-19 pandemic.
Study Design
We offered the study at 10 sites that provided the service in 13 states and Washington DC. Interested individuals obtained any needed preabortion tests locally and had a videoconference with a study clinician. Sites sent study packages containing mifepristone and misoprostol by mail and had remote follow-up consultations within one month by telephone (or by online survey, if the participant could not be reached) to evaluate abortion completeness. The analysis was descriptive.
Results
We mailed 1390 packages between May 2016 and September 2020. Of the 83% (1157/1390) of abortions for which we obtained outcome information, 95% (1103/1157) were completed without a procedure. Participants made 70 unplanned visits to emergency rooms or urgent care centers for reasons related to the abortion (6%), and 10 serious adverse events occurred, including 5 transfusions (0.4%). Enrollment increased substantially with the onset of COVID-19. Although a screening ultrasound was required, sites determined in 52% (346/669) of abortions that occurred during COVID that those participants should not get the test to protect their health. Use of urine pregnancy test to confirm abortion completion increased from 67% (144/214) in the 6 months prior to COVID to 90% (602/669) in the 6 months during COVID. Nearly all satisfaction questionnaires (99%, 1013/1022) recorded that participants were satisfied with the service.
Conclusions
This direct-to-patient telemedicine service was safe, effective, and acceptable, and supports the claim that there is no medical reason for mifepristone to be dispensed in clinics as required by the Food and Drug Administration. In some cases, participants did not need to visit any facilities to obtain the service, which was critical to protecting patient safety during the COVID-19 pandemic.
Implications
Medical abortion using telemedicine and mail is effective and can be safely provided without a pretreatment ultrasound. This method of service delivery has the potential to greatly improve access to abortion care in the United States.
Keywords
COVID-19
Mail
Medical abortion
Telemedicine
United States
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pmcIntroduction
Telemedicine abortion is a broad term that describes the use of telecommunications (phone, videoconference, texting, email) to provide one or more aspects of abortion care such as counseling, eligibility assessment, medication provision, guidance through the process, and follow-up assessment. These services may be provided as part of or independent from the formal healthcare system and may involve some degree of in-person contact for parts of the process.
A substantial body of literature from around the globe provides evidence that telemedicine models of abortion provision are highly acceptable to clients and providers, and success rates and safety outcomes are similar to those reported for in-person care [1], [2], [3], [4], [5], [6]. Furthermore, a growing amount of data from the United States suggests that telemedicine allows people to obtain abortions at an earlier gestational age, improves access to care for rural patients, and may be associated with decreases in time to schedule an appointment and distance traveled [7,8].
Having the option to receive abortion care via telemedicine is critical, as accessing in-person care has become increasingly challenging in certain regions of the country. In 2017, 95% and 94% of counties in the Midwest and the South, respectively, did not have a facility that provided abortion care [9]. Individuals who can get to a clinic find an increasingly hostile environment outside; the National Abortion Federation's 2019 annual report on violence and disruption statistics documented 3387 incidents of obstructing facilities (up from 3038 in 2018), and 123,228 incidents of picketing (up from 99,409 in 2018) [10].
The COVID-19 pandemic has exacerbated barriers to accessing abortion care by hindering people's ability to pay for the service (due to loss of income) and limiting mobility because of childcare needs, stay-at-home orders, and the imperative to limit in-person interactions [11,12]. To mitigate some of these effects, many abortion providers have modified their clinical protocols and incorporated telemedicine to varying degrees [13]. Experts have advocated for adoption of “no-test medication abortion,” which, by not mandating screening ultrasound, blood tests, or follow-up tests unless clinically warranted, would allow the treatment to be provided without an in-person encounter [14].
The TelAbortion Project is a direct-to-patient service model whereby participating clinics counsel and screen patients remotely, and then send mifepristone and misoprostol by mail to those who are eligible. Because of restrictions on mifepristone imposed by the Food and Drug Administration (FDA) under the drug's Risk Evaluation and Mitigation Strategy (REMS), we implemented TelAbortion as a research study conducted under an Investigational New Drug (IND) application. Specifically, the REMS for mifepristone states that the drug must be dispensed to patients only in clinics, medical offices, and hospitals, which is commonly interpreted as prohibiting the mailing of the medication. Results from the first 32 months of the project (May 2016–December 2018) in which 248 packages were sent found that the service was safe, effective, efficient and satisfactory [6]. The objective of this analysis is to present data on safety, efficacy and acceptability collected from May 2016 through September 2020 (inclusive of the previously published data), during which time the study expanded dramatically both geographically and in sample size, and describe how the service functioned amidst the numerous challenges imposed by the COVID-19 pandemic.
Methods
In the reporting period, the study was implemented at 10 institutions (4 independent clinics, 4 Planned Parenthood affiliates, and 2 academic medical centers) that provided the TelAbortion service in 13 states (CO, GA, HI, IA, IL, MD, ME, MN, MT, NM, NY, OR, WA) and Washington, DC. Five sites provided the service in states where they were not physically located because their clinicians were licensed there. One site stopped recruitment in 2017 due to slow enrollment. Sites were added on a rolling basis, with the newest site beginning enrollment in May 2020. Before adding each state to our study, we confirmed that it had no laws that prohibited the service, although some state laws constrained or complicated the way the service was offered.
Patients interested in receiving a TelAbortion underwent a prescreening process by phone that reviewed basic eligibility requirements and explained the study procedures. Those who wished to proceed obtained any necessary tests at laboratories or radiology offices and had evaluations with a study clinician via videoconference during which the clinician confirmed eligibility, obtained consent electronically, and agreed on a plan for evaluating abortion outcome using ultrasound, serum hCG tests, or urine pregnancy test (UPT). Individuals were required to obtain a pre-abortion ultrasound or pelvic exam, and were deemed eligible for TelAbortion if the study clinician determined that the patient would be able to receive and take the mifepristone at ≤70 days of gestation and had no suspicion that the pregnancy was ectopic or nonviable (See the prior paper for a full description of procedures and eligibility requirements [6].).
Sites sent participants packages containing one tablet of mifepristone 200 mg and eight tablets of misoprostol 200 mcg (one site prescribed the misoprostol instead), an instruction sheet, and a UPT if indicated. Sites advised participants to take mifepristone followed by misoprostol 800 mcg within 48 hours either vaginally or buccally, as per standard clinic practice, and to take the other 800 mcg of misoprostol if no bleeding occurred within 24 hours after the first dose. In the event of any problems, sites instructed participants to call, recommended they seek in-person care if indicated, and followed up with them until the resolution of the problem. Following standard practice at the site, study clinicians evaluated abortion outcome using patient history, ultrasound, serum HCG tests before and after mifepristone ingestion, pelvic examination, and/or urine pregnancy testing. At sites that offered more than one method, the participant and clinician agreed on which method to use. Within a month after mailing each study package, sites conducted follow-up contacts to review any test results, assess abortion outcome, and inquire about any adverse events or unplanned visits. Once the abortion was complete, sites conducted a short, structured satisfaction questionnaire by telephone.
The study team made some modifications to study procedures over time. For the first 127 participants, the study paid for care and medications provided directly by sites. We offered participant compensation of $50 until late 2018, when we stopped to better mirror standard provision of abortion care and obtain more accurate data on acceptability and satisfaction (245 participants in our sample were offered compensation). Other changes included a switch from paper data forms to a secure browser-based electronic data capture application in early 2019. If a participant was lost to follow-up and outcome and/or satisfaction data had not been collected, the sites sent via email a link to an online survey for the participant to complete. As new practice guidelines became available, sites could make corresponding changes in care for their TelAbortion patients (as permitted within the constraints of the study protocol); these included forgoing Rh typing or prophylaxis with anti-D immunoglobulin for participants under a certain gestational age and advising participants to take a second dose of misoprostol routinely in the 9th and/or 10th week of gestation [15].
Here we present descriptive analyses of our service delivery data. For data on participant characteristics (Table 1 ) our unit of analysis was the individual so that participants who had multiple abortions were not counted more than once. For abortion outcome, unplanned encounters, and satisfaction data (Tables 2 and 3 ), we utilized the abortion as the unit of analysis as we did not want to undercount any of these outcomes. We defined an adverse event as serious if it was fatal, life-threatening or resulted in hospitalization, transfusion, or significant disability. An unplanned clinical encounter was any visit to a clinician after the study package was mailed, except visits to obtain anti-D immunoglobulin, contraception, or routine ultrasound or lab tests to evaluate abortion outcome in the absence of concerning symptoms. The study team evaluated the reason for each unplanned encounter to determine whether or not it was abortion-related. We used 3/13/20 as the start date of the COVID crisis as that was the date the federal government declared COVID-19 to be a “national emergency” [16].Table 1 Characteristics of TelAbortion study participants who were sent a medical abortion package: n (%) or median (range)a
Table 1 N = 1356
Age
15–24 years
25–34 years
35–47 years
346 (25.5)
735 (54.2)
275 (20.3)
Highest level of education completed
Less than HS
HS/GED
More than HS
65/1305 (5.0)
357/1305 (27.4)
883/1305 (67.7)
Number of previous pregnancies
0
1
≥ 2
344 (25.4)
291 (21.5)
721 (53.2)
Number of previous medical abortions
0
1
≥2
1086/1348 (80.6)
210/1348 (15.6)
52/1348 (3.9)
Gestational age at prescreenb
18–35 days
36–63 days
64–68 days
Median (range)
280 (20.6)
1053 (77.7)
23 (1.7)
42 (18-68)
Distance of residence from provider (Continental US)c
1–9.9 mi
10–49.9 mi
50–99.9 mi
100–149.9 mi
≥ 150 mi
80/1075 (7.4)
347/1075 (32.3)
169/1075 (15.7)
92/1075 (8.6)
387/1075 (36.0)
Hawaiian island of residence
Oahu
Other island
55/281 (19.6)
226/281 (80.4)
Race/ethnicity (more than 1 category allowed)c
White
Black
Hispanic
Asian/Pacific Islander
Native American
Multi-racial, not specified
750/1073 (69.9)
163/1073 (15.2)
87/1073 (8.1)
93/1073 (8.7)
42/1073 (3.9)
8/1073 (0.7)
Classification of participant's current addressc
Urban
Rural
826/1031 (80.1)
205/1031 (19.9)
Payment method for care provided by site (more than 1 method allowed)d
Private/public insurance
Self-pay
Abortion fund
470/1228 (38.3)
899/1228 (73.2)
175/1228 (14.3)
Actual/planned payment method for pre-abortion tests obtained elsewhere (more than 1 method allowed)d
Private/public insurance
Self-pay
Abortion fund
None; did not have any preabortion tests
606/919 (65.9)
332/919 (36.1)
26/919 (2.8)
327
a Does not include 32 second abortions and 2 third abortions during the study period. For participants with multiple abortions, we included only the first.
b Using clinician's estimate of gestational age at time package sent and then backdated.
c Question not asked in first version of study forms.
d Does not include 127 participants who had study site services paid for by study.
Table 2 Abortion outcomesa and unplanned encounters, n (%)
Table 2 N = 1390
Neither medication taken, or medications taken after miscarriage diagnosis
Lost to follow-up
Known abortion outcome 47 (3.4)
186 (13.4)
1157 (83.2)
Abortion outcome at last contact
Complete abortion without surgical intervention
Surgical intervention
Reason: ongoing pregnancy
Ongoing pregnancy; carrying to term or unknownb n = 1157
1103 (95.3)
47 (4.1)
14 (1.2)
7 (0.6)
Method used in outcome assessment among complete abortions with no surgical intervention
Facility-based test (ultrasound, serum HCG, and/orpelvic exam)c
No facility-based test
Urine pregnancy test (UPT)d
Patient history only n = 1103
396 (35.9)
707 (64.1)
647 (58.7)
60 (5.4)
Abortion-related unplanned encounterse
Emergency room (ER)/urgent care
Other outpatient visit
Serious Adverse Events
Hospitalization
Transfusionf n=1173
70 (6.0)
92 (7.8)
10 (0.9)
8 (0.7)
5 (0.4)
a Includes multiple abortions by same individual.
b Includes one abortion where participant threw up mifepristone after 10 minutes and then decided to continue pregnancy.
c Outcomes assessed with facility-based tests may also have utilized UPTs and/or patient history.
d In 3 cases, the UPT result(s) were positive and the diagnosis of complete abortion was made by patient history only.
e Denominator includes abortions with known outcome or any unplanned encounters that occurred after study consent was signed. Does not include encounters for lab tests, anti-D immunoglobulin, or contraception alone. Includes 1 hospitalization, 4 ER visits, and 12 other outpatient encounters that occurred prior to taking (or deciding not to take) mifepristone. Abortions may be included in more than one category.
f Two of the transfusions occurred in an ER and are not included in Hospitalization.
Table 3 Acceptability of TelAbortion to study participants at exit interview, among those with known abortion outcome: n (%)a
Table 3Satisfaction with the serviceVery satisfactorySatisfactoryUnsatisfactory/Very unsatisfactory n = 1022869 (85.0)144 (14.1)9 (0.9)
Satisfaction with speaking to provider remotely
Very satisfactory
Satisfactory
Unsatisfactory/Very unsatisfactory n = 891
763 (85.6)
123 (13.8)
5 (0.6)
Experience getting pre-abortion testsb
Easy or very easy
Difficult or very difficult n = 693
594 (85.7)
99 (14.3)
Future preference
TelAbortion
In-person abortion
No preference n = 886
754 (85.1)
56 (6.3)
76 (8.6)
Would recommend TelAbortion to a friend
Yes
No
Maybe n = 892
863 (96.7)
9 (1.0)
20 (2.2)
a Includes multiple abortions by same individual.
b Does not include abortions where no pre-abortion tests were planned.
Advarra Institutional Review Board, the University of Hawaii's Office of Research Compliance Human Studies Program, and Oregon Health and Science University's Institutional Review Board approved the protocol. We registered the study on clinicaltrials.gov (NCT02513043).
Results
Partnering sites sent 1390 packages to 1356 participants who were prescreened between May 11, 2016 and September 11, 2020. Thirty participants received 2 abortions and 2 received 3 abortions during the reporting period. Of the 1356 individuals who received a package, 26% were under 25 years of age, and 14 were minors (Table 1). Participants tended to contact the clinics early in the first trimester; 47% were less than 42 days gestation at the time of the prescreening. In the continental U.S., 60% of participants lived 50 miles or more from their study site, and 36% lived 150 miles or farther. Thirty participants had their packages mailed to a state that was not their state of residence. While only 38% used insurance to pay for care provided by the study site (e.g., counseling, abortion medications), 66% used insurance to pay for preabortion tests.
In 47 instances (3%), neither abortion medication was taken, or the medications were taken after a diagnosis of miscarriage (Table 2). The median gestational age on the day of mifepristone ingestion was 53 days gestation (range 29–76). In eleven abortions (1% of those with outcome information), the participant took mifepristone past 70 days of gestation. Of the 76 times (7%) a participant reported taking more than 800mcg of misoprostol from the study package, 20 did so due to gestational age, as advised by the site during counseling. In 42 instances, participants took another 800 mcg of misoprostol because of little or no bleeding, or due to concerns that they did not pass the pregnancy.
We obtained abortion outcome information on 83% of abortions (1157/1390) and satisfaction data after 74% of abortions (1022/1390). Fourteen percent of the satisfaction questionnaires (141/1022) were completed via the online survey. Of the 1157 abortions with outcome information, 95% were complete abortions without a procedure (Table 2). Twenty-one abortions (1.8%) resulted in ongoing pregnancy. The majority of complete abortions were confirmed using a method that did not require a visit to a facility; 59% relied on UPTs, and 5% depended on patient history alone.
There were seventy unplanned visits (6%) to emergency rooms or urgent care centers for reasons related to the abortion (Table 2). Ten serious adverse events (SAEs) occurred, including five transfusions (0.4%). We determined that none of the SAEs was attributable to the telemedicine delivery of the service (e.g., they would not have been avoided if the participants had had in-person screening or picked their pills up in person).
When the COVID-19 national emergency was declared, we worked with our study sites to adapt to new challenges. The FDA required that our protocol retain the screening ultrasound requirement, but on a case-by-case basis, and following broader FDA guidance on conduct of clinical trials during the pandemic [17], sites evaluated whether forgoing the ultrasound was necessary to protect patient and provider health and safety (e.g., if the patient's locale was under stay-at-home orders, or a patient was quarantining because of COVID exposure or infection). Overall, 52% (346/669) of abortions during COVID occurred without a screening ultrasound, though this proportion varied widely by site (0%–83%). No ectopic pregnancies were reported among those who received a package during the entire analysis period (pre- and during COVID).
Prior to COVID, we had already started encouraging sites to preferentially offer UPT follow-up to confirm abortion outcome because participants were reporting that getting testing at facilities was burdensome. Comparing the 6 months prior to COVID to the period during COVID, selection of UPT as a follow-up method increased from 67% (144/214) to 90% (602/669). While some sites were already doing UPT follow-up for nearly all participants in the pre-COVID period, four study sites that had rarely used UPT for follow-up before COVID reported substantial increases during the COVID period (Fig. 1 ).Fig. 1 Percentage of abortions with urine pregnancy test selected as follow-up method before COVID and during COVID, by site.a,ba‘Pre-COVID’ period defined as 9/13/19–3/12/20; ‘During COVID’ period defined as 3/13/20–9/11/20.bDoes not include 1 site that did not enroll patients in the defined periods, and 1 site that did not enroll patients in the pre-COVID period (and enrolled 14 participants in the during COVID period).
Fig 1
Enrollment increased dramatically in the months after March 2020; compared to January and February 2020, monthly enrollment more than tripled in April, May, and June of the same year (Fig. 2 ), Months with high enrollment were also months in which large percentages of abortions occurred without screening ultrasounds, and also in which new states were added to the project.Fig. 2 Enrollment and pre-abortion ultrasound (U/S) over time.aaEnrollment included from 10/1/19 through 9/11/20.*Arrows denote when new states added to the project.
Fig 2
Over the entire reporting period, participants were overwhelmingly satisfied with the service, and with speaking to their providers remotely (Table 3). Despite some difficulty obtaining preabortion tests reported by 14% of the sample, 85% would choose TelAbortion again, and nearly all would recommend the service to a friend.
Discussion
With a larger, more geographically diverse sample, these data confirm our earlier findings that the TelAbortion service is safe, feasible, effective, and acceptable. Mifepristone can safely be dispensed by mail, and the REMS requirement that mifepristone must be dispensed in person, instead of enhancing patient safety as it purports to do, could have the exact opposite effect, particularly during a pandemic. Our abortion success rate of 95% is comparable to rates in the literature for in-person care [18]. A substantial proportion of our participants lived significant distances from their providers (and in Hawaii, most lived on different islands), underscoring the potential of direct-to-patient services to improve access to care. Preabortion ultrasounds are usually unnecessary for safe and effective medication abortion [1,14,19,20], and we found during COVID that sites sometimes actually felt it necessary to omit the ultrasound to protect a participant's health. The finding that a higher proportion of participants used insurance to pay for preabortion tests than the abortion medications and counseling (66% vs 38%) suggests that the TelAbortion service model could lower costs for some patients. This may be true especially in states where insurance will not cover the cost of abortion but may cover the cost of tests (as there is nothing to link the tests to subsequent abortion care).
We were interested to see that only 7% of participants took a second dose of misoprostol. In light of the recent misoprostol shortage that began in September 2020 [21] (J. Price, personal communication, 2/26/21), and adhering to the general principle of conservation of resources, rather than send out a second dose to all patients as standard practice, services might consider only sending it to patients above a certain gestational age (e.g., above 64 days or 71 days depending on clinic protocols), and calling in additional doses to pharmacies as needed.
A small but noteworthy number of patients (n = 30) obtained a TelAbortion in a state other than their state of residence. As the practice of medicine occurs where the patient is physically, crossing a state border can allow a patient to access care in an environment with fewer restrictions. In these instances, participants crossed the border for their video conference and then sites mailed packages to courier or post office locations, or to friends or family members, who held the package until the participant picked it up. While this still required participants to travel, this approach may have enabled them to travel shorter distances than they would have had to in order to get to the nearest in-person appointment, and it allowed some to bypass home-state restrictions on abortions such as in-person counseling or waiting period laws. Some sites actively conducted outreach to metropolitan areas with restricted access to abortion care that were located near a border with a project state. Since 19 states prohibit the use of telemedicine for medication abortion within their borders, this may be a strategy to explore further to increase access in more restrictive states [22].
When the first surge of COVID-19 occurred in the spring, enrollment in the service soared, likely due to greater challenges in accessing in-person care, the ability for some participants to obtain the service without having to get an ultrasound (which meant they could do the entire process from home), and the addition of new states to the project. This spike in enrollment at a time when barriers to abortion care were limiting access elsewhere emphasizes the critical value of our service delivery method. Changes in practice during COVID varied widely by site, and reflected a number of factors including provider preference, institutional policies, and the provider's perception of the degree to which obtaining tests increased a patient's risk of infection or the patient's risk of infecting others.
Our data have some limitations. While we were able to improve on the follow-up rate of 77% from our first analysis (possibly due to the adoption of the online exit survey), we did not have outcome data on 13% of participants. As such, our estimates of medical abortion failure or complications may be underestimated or overestimated. As this analysis was descriptive, we are limited in the associations we can make between various aspects of the service and outcomes.
Compared to people obtaining abortions in the United States, our study population had a higher proportion of people who were older, more educated, and more likely to identify as white [23]. Telemedicine innovations need to prioritize the most disadvantaged populations so that they are not left behind. Future innovations in our project should focus on addressing this issue.
When we started the TelAbortion Project in 2016, it was the first service in the United States in which people could obtain an abortion legally without an in-person visit to an abortion provider. After a federal district court issued an injunction in July 2020 blocking the FDA from enforcing the rule that patients must pick up the abortion medication in person from their provider, several new online services launched. These promising efforts were recently threatened by a Supreme Court decision in January 2021 that reinstated the prior harmful policy. We believe our data disprove the notion that medication abortion pills must be dispensed in-person, and that direct-to-patient services that mail the pills to patients are safe, effective and feasible, even without a screening ultrasound.
Declaration of Competing Interest
None.
Funding
This work was supported by the Tara Health Foundation, the Bernard and Anne Spitzer Charitable Trust, the Lisa and Douglas Goldman Fund, and several anonymous donors. These donors had no role in the study design, in the collection, analysis and interpretation of data, in the writing of the report, or in the decision to submit the article for publication. The findings and conclusions in this article are those of the authors and do not necessarily represent the views of Planned Parenthood Federation of America, Inc.
Acknowledgments
The authors thank Fatoumata Bah and Julia Habbe for their assistance in site coordination and data cleaning.
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| 33781762 | PMC9748604 | NO-CC CODE | 2022-12-15 23:22:44 | no | Contraception. 2021 Jul 27; 104(1):43-48 | utf-8 | Contraception | 2,021 | 10.1016/j.contraception.2021.03.019 | oa_other |
==== Front
Contraception
Contraception
Contraception
0010-7824
1879-0518
Elsevier Inc.
S0010-7824(21)00189-X
10.1016/j.contraception.2021.06.006
Commentary
Self-managed abortion: Exploring synergies between institutional medical systems and autonomous health movements
Yanow Susan MSW ⁎
Pizzarossa Lucía Berro LLB, LLM (oxon)
Jelinska Kinga MA
Women Help Women, PO Box 15798, 1001 NG Amsterdam, Netherlands
⁎ Corresponding author.
15 6 2021
9 2021
15 6 2021
104 3 219221
11 8 2020
7 6 2021
8 6 2021
© 2021 Elsevier Inc. All rights reserved.
2021
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.
Keywords
Self-managed abortion
Autonomous health movements
Medication abortion
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pmcThe COVID-19 pandemic has highlighted and exacerbated the disparities and inequities within every sector of society. Women, girls, and marginalized people are disproportionately affected by this crisis. For millions of people worldwide living under lockdown, quarantine, or other measures, access to safe abortion, regardless of national laws, has become even more difficult than it was prepandemic [1].
The pandemic has shown how simple, demedicalized models of access to abortion are not only possible, but desirable. The pandemic caused a growing interest among researchers and policy makers in alternative models of abortion access, especially remote models that emulate self-care1 practices [2], [3], [4]. There is the opportunity to bring knowledge about community use of abortion medicines, advanced by feminist groups and their practices and documented in public health research, to medical practice within established institutional systems [3], [4], [5]. For the purposes of this paper, we use Braine's distinction between institutional medical systems and autonomous health movements [6]. “Institutional medical system” refers to the medical care that happens under state and institutional control. Braine defines “autonomous health movements” as a form of direct action developed by activists anchored in social justice movements and working in community contexts.
We are at a crossroads in models of medication abortion care, forced by fear of COVID-19 infection but driven by implementing long overdue innovations based on science, common sense, and feminist praxis. Protocols and counseling scripts from providers such as Women Help Women have been adopted by abortion providers within institutional medical systems. The exchange of information about models of care between autonomous community-based groups and institutional medical systems has been fruitful but must go further. It is time to recognize the critical role of self-managed abortion in expanding access to abortion care by embracing a radical paradigm transformation. This transformation calls for the permanent removal of unnecessary restrictions imposed by institutionalized systems of law, medicine, and market which impede timely access to the essential medicines mifepristone and misoprostol, both in communities and within institutional medical systems.
Since the 1980s, pregnant people have been using abortion medicines outside of institutional medical systems, which have dramatically increased access to safe abortion. The medicines are safe, effective, and easy to use. We define self-managed abortion (SMA) as the self-sourcing of abortion medicines – either mifepristone and misoprostol, or misoprostol alone - followed by self-use of the medicines and self-management of the abortion process outside of a clinical context [7]. Self-managed abortion has been identified as the probable cause of decline in severe abortion-related morbidity and mortality in some global regions [8]. There is a growing body of evidence pointing to safety and satisfaction of self-managed abortion outside of institutional medical systems [3, 16, 17]. The practice is supported in the guidelines of the World Health Organization, and since 2005, abortion medicines have been on the WHO list of essential medicines [9].
The COVID-19 pandemic spotlights the unreasonable barriers created by the traditional framework of abortion access. This framework requires a clinical setting, a licensed medical practitioner and an in-person visit, and laws that specify which practitioners are qualified to provide abortion medicines and how, where, and for whom they can provide services. Strict regulations determine who can dispense mifepristone and misoprostol. These barriers create obstacles for people with unwanted pregnancies who seek care in institutional medical systems. Those seeking to self-manage their abortions may face barriers including laws that criminalize the practice, lack of reliable and affordable sources of medicines, and accurate information.
To remove the barriers to both systems of provision of abortion medicines, basic principles can expand access in each sector and increase the synergy between them. These principles, which stem from the inherent properties of the technology and radical normative change brought by the practice of self-managed abortion, call for demystification, demedicalization, destigmatization, and decriminalization of abortion practice.
1 Demystification of abortion medicines and self-managed abortion
Understanding how to use abortion medicines should be widespread. It is critical to widely disseminate the protocols for safe use of abortion medicines, in user-friendly language. This information should be available at clinics and pharmacies, within activist and medical organizations, and through multiple internet sources. The fact that abortion medicines can be used safely with or without clinician involvement must be promulgated. The information should be easily available, in as many formats and media fora as possible, and open-sourced to encourage copying and reproduction of reliable, science-based sources.
Medical control is sometimes misused to exert pressure and limit access to the medicines, as in cases where clinicians claim “conscientious objection” and refuse to provide care or involve law enforcement when self-managed abortion is suspected [10, 11]. Additionally current product package inserts for misoprostol and mifepristone identified in 20 countries contain inadequate storage instructions and outdated gestational age limits and regimens, making it difficult for people to most effectively self-manage their abortions [12]. To counter this oppressive reality, feminist organizations including Women Help Women and its partners and networks have been translating and popularizing protocols2 , thereby putting information and power into the hands of people who need it.
The pandemic has also shown that people are routinely trusted to practice self-care in other matters of health. People are expected to detect symptoms, adjust doses of medicines, understand when medical attention is needed, and generally self-manage their health around many conditions, including COVID-19 [13]. Self-managed abortion is not qualitatively different than self-care for many conditions, especially if reliable information and remote support are provided.
2 Demedicalization of medication abortion
Abortion medicines are extremely safe and must be made available and accessible to all, free from unnecessary regulatory barriers. They should be available over the counter, including for purchase in advance of need, with clear and simple to understand user instructions that accord with the most current protocols and science. All policies that prevent wide production and distribution should be removed, to expand access and lower the production costs. As with any form of family planning, abortion medicines should be made widely available before, during, and after they are needed, and be available through a range of sites, including but not limited to community health centers, campus health centers, and pharmacies. Ideally, they could be available along with tampons and aspirin in machines in bathrooms and convenience stores.
Within institutional medical systems, unnecessary and burdensome requirements, including ultrasound and Rh testing, should not be mandatory, as reflected in recent changes in some professional guidelines [14]. Current “no touch” protocols for abortion care can be expanded, and innovative telemedicine and telehealth models should be supported nationally and internationally, in both systems of abortion access [15]. Institutional medical systems can benefit from the decades of experience and knowledge about abortion medicines provision accumulated by feminist organizations. Already existing models like the one created by the Socorristas en Red in Argentina - where acompañantes have taught doctors about the use of misoprostol and refer women to friendly providers if back up is needed, and where clinicians refer pregnant people to the network [16]—show the importance of communication and collaboration between systems, giving pregnant people the array of options of access to care and methods that they need and deserve.
We must continue to create pathways that cross institutional medical systems and autonomous health movements, including services in medical systems that provide clinical back up services to those who self-manage their abortions and are concerned about an incomplete abortion, the success of their abortion, or who may have a complication. Self-management of abortion must never mean being unwelcomed in or deprived of contact with institutional medical systems. It is important to be as public and as explicit as possible about the benefits of those pathways, synergies, and relationships in order to legitimize a model supporting those who are self-managing their abortions with medicines with providers of their choice, including nonclinical community-based providers.
3 Destigmatization of self-managed abortion
There must be recognition and respect for the reality that self-managed abortion is a common practice and may be preferred by some as their chosen method of ending a pregnancy. Positive, high quality abortion experiences happen outside institutional medical systems around the world, and demonstrate the need for a broader definition of “quality care,” and “safety” [17]. While states have the obligation to provide health care, including abortion care, self-managed abortion must be seen as a valid choice rather than compared unfavorably to an abortion obtained within institutional medical systems. Messaging about self-managed abortion must not use words like “dangerous” or “unsafe”, which create stigma and fear in those who chose this abortion method. In fact, many “good” abortions, as defined by Gerdts and colleagues [17] as being safe, effective, and supported, have been happening for decades outside of the institutional systems of medical practice. While many clinicians work hard to provide quality comprehensive reproductive health care, there are also multiple accounts of stigma, harassment, and violence within institutional systems of medical practice, which can be rigid, conservative, and slow to change [18].
A narrow biomedical conceptualization of safety and quality contributes to the stigmatization of self-managed abortion, those who access it and for those who support it. The reality of high-quality abortion is more nuanced and calls for questioning simplistic proxies of safety such as clinical setting, trained medical providers, or the statistical effectiveness of a given protocol [19]. Using principles of autonomous health movements such as autonomy, self-determination, knowledge of technologies, and control over the abortion process can contribute to building new conceptualizations of safety and quality that better reflect people's experiences and to a more nuanced understanding of those experiences.
4 Decriminalization of self-managed abortion
Under current laws in most countries, people who self-manage and those who provide information, support, and/or accompaniment risk police harassment and prosecution. Even when the threats do not yield convictions, the harms of criminalization result in further restriction of information or access to essential medicines and creating a chilling effect on these critical practices [20]. Laws and policies around the globe continue to unduly restrict abortion access, and particularly on self-managed abortion. By placing abortion medicines under unjustified regulatory restrictions or restricting legal abortion to those happening under medical supervision, laws and policies place pregnant people and those who support them under risk of criminalization or harassment.
No pregnant person should face any legal consequences for ending a pregnancy, choosing to self-manage their abortion, getting the medicines through informal markets, importing them or choosing a provider of their preference. People must be free to manage their abortion with the support of a medical professional, a trusted community member, a family member or friend, or by themselves. Self-managed abortion must be completely decriminalized; those using abortion medicines outside of institutional medical systems and those who support them and/or help them to access safe medicines should never face criminalization or harassment. Feminist organizations and skilled community providers of information and support, including those without a professional license or formal clinical training, should have explicit support from and work in collaboration and synergy with institutional health care providers. Competency and a supportive environment, not solely a government-issued license, should establish safety standards that are supported by policy and law.
5 Conclusion
The pandemic has highlighted the impact of unnecessary barriers to abortion care. This is a moment for radical transformation of how medication abortion is provided. It is time to de-medicalize abortion, inside and outside of institutional medical systems. It is also time for increased collaboration between institutional medical systems and autonomous health movements and recognition of the value of self-managed abortion as a system and a strategy for expanded access and empowerment of those who can control their own abortion choices.
Self-managed abortion is so much more than a provisional solution for the access crisis caused by the pandemic [21]. If we can build on efforts to demedicalize, demystify, destigmatize, and decriminalize abortion, the process of safely ending an unsupported pregnancy will be radically transformed and the full potential of abortion medicines to expand access to abortion will be realized.
Declaration of Competing Interest
Authors have no conflict of interest.
1 The World Health Organization defines self-care as “the ability of individuals, families and communities to promote health, prevent disease, maintain health, and cope with illness and disability with or without the support of a health- care provider.” [2, p.2].
2 Examples include user-friendly protocols for medication abortion in Swahili, Luganda, Chichewa, Igbo, French and English, at https://mamanetwork.org/resources/ and in Sign Language, Creole and Mapuzungun, at http://infoabortochile.org.
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4 Godfrey EM Thayer EK Fiastro AE Aiken ARA Gomperts R. Family medicine provision of online medication abortion in three US states during COVID-19 Contraception 2021 10.1016/j.contraception.2021.04.026 S0010-7824(21)00142-6Online ahead of print
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8 Singh S Maddow-Zimet I. Facility-based treatment for medical complications resulting from unsafe pregnancy termination in the developing world, 2012: a review of evidence from 26 countries BJOG 123 9 2016 1489 1498 26287503
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18 Uruguayan Ministry of Health. Report on Sexual and Reproductive Rights <reporthttp://ine.gub.uy/c/document_library/get_file?uuid=85e1bfd7-b3e5-4095-abf9-76be055fe3b5&groupId=10181>, 2019 [accessed 10 August 2020].
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| 34139153 | PMC9748647 | NO-CC CODE | 2022-12-15 23:22:45 | no | Contraception. 2021 Sep 15; 104(3):219-221 | utf-8 | Contraception | 2,021 | 10.1016/j.contraception.2021.06.006 | oa_other |
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Contraception
Contraception
Contraception
0010-7824
1879-0518
Elsevier Inc.
S0010-7824(21)00030-5
10.1016/j.contraception.2021.02.001
Original Research Article
The impact of the COVID-19 pandemic on economic security and pregnancy intentions among people at risk of pregnancy
Lin Tracy Kuo a⁎
Law Rachel b
Beaman Jessica c
Foster Diana Greene d
a Institute for Health & Aging, Department of Social and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
b Department of Clinical Pharmacy, University of California San Francisco, San Francisco, CA, USA
c Department of Medicine, Division of General Internal Medicine, Department of Obstetrics, Gynecology & Reproductive Sciences, University of California, San Francisco, CA, USA
d Department of Obstetrics, Gynecology & Reproductive Sciences, Advancing New Standards in Reproductive Health (ANSIRH), University of California, San Francisco, CA, USA
⁎ Corresponding author.
12 2 2021
6 2021
12 2 2021
103 6 380385
21 10 2020
1 2 2021
2 2 2021
© 2021 Elsevier Inc. All rights reserved.
2021
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 understand how the COVID-19 pandemic affected women of reproductive age, specifically their economic conditions, desire for pregnancy, and access to contraceptive services during the pandemic.
Study Designs
A total of 554 women respondents age 18 to 49 and reside in the United States were recruited using social media between May 16, 2020 and June 16, 2020. Logistic regression models assessed predictors of reporting pandemic-related changes in economic conditions, desire for pregnancy, and contraceptive access.
Results
Compared to White/Caucasian respondents, Hispanics/Latinx and Black/African Americans have 4 times the odds of experiencing inability to afford food, transportation, and/or housing (p < 0.01) during the pandemic; Hispanics/Latinx have twice the odds of experiencing food insecurity (p < 0.05). Inability to afford food, transportation, and/or housing was associated with drop in desire to be pregnant (p < 0.01). Despite the 25% of participants who reported a drop in desire for pregnancy, 1 in 6 reported difficulty accessing contraceptives, particularly those who experienced reduced income (p < 0.01).
Conclusions
In our sample, the pandemic unevenly affected people from different socioeconomic groups. Many simultaneously experienced reduced income, difficulties in accessing contraception, and a greater desire to avoid a pregnancy. This combination of factors increases the chance that people will experience unintended pregnancies.
Implications
The pandemic caused economic hardship and an increased desire to postpone or prevent pregnancy at the same time that it created new barriers to contraceptive services. This pattern may lead to a potential net effect of an increase in unintended pregnancy, particularly among people who had difficulty affording food, transportation, and/or housing during the pandemic.
Keywords
Covid-19
Pregnancy intentions
Economic hardship
Unwanted pregnancy
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pmc1 Introduction
The COVID-19 pandemic has disrupted economies and altered the lives of individuals. The negative economic impact of the pandemic disproportionally affected women – between February and April 2020 approximately 12 million women lost their employment, which accounted for 55% of job losses in the United States [1]. The pandemic's effect on economic security led many individuals to reconsider pregnancy timing, potentially generating a “baby bust” phenomenon [2], where economic shocks reduce fertility rates [3]. A report produced by the Guttmacher Institute, including 2009 cisgender women, indicated that during the COVID-19 pandemic, 36% of women wanted to delay childbearing and 27% of women wanted to have fewer children than previously planned [4]. Concerningly, given these preferences, 39% of respondents in the Guttmacher study reported they had to delay or cancel sexual and reproductive health care visits, including contraceptive care, due to the pandemic.
Worsening economic conditions can reduce the birth rate on a population level. [5] However, women of color and women in more vulnerable groups may be more likely to experience adverse economic effects during times of economic instability and also encounter barriers to reproductive health care. This study aims to assess the impact of the COVID-19 pandemic on economic conditions and reproductive health decisions related to childbearing and pregnancy; specifically, this study evaluates if, during the initial months of the COVID-19 pandemic, vulnerable populations experience different financial and reproductive health outcomes compared to the general population.
2 Methods
Recruitment occurred through social media during a one-month period between May 16, 2020 and June 16, 2020. Advertisements targeting women age 18 to 49 who reside in the United States were placed on Facebook and Instagram; the geographic location target function on the advertisement platform was used to ensure a more geographically representative sample. Interested individuals had to send an email to the study team to request a survey link. The survey was administered on the online Qualtrics platform. People of reproductive age (18–49 years old), who were female at birth and who reported having had sex with a man in the past 4 months were eligible to participate. Individuals who met inclusion criteria were sent the consent form. Research team members screened initial email and entries for potential fraud; for example, repeated entries from the same geolocation or IP address were deemed ineligible. Eligible respondents who completed the survey received a $25 gift certificate. The study and research design received an exempt status from the University of California, San Francisco Institutional Review Board.
The survey asked respondents’ age, race/ethnicity, relationship status, number of children, household size and income, employment status before and during the pandemic, income before and during the pandemic, and state of residence. For employment status and income before the onset of the pandemic, we asked respondents to report their employment status and income in the month before March 10, 2020, when US cities, counties, and states started implementing pandemic-related policies. We then calculated whether household income was below or above the federal poverty level relative to household size before the pandemic. In a separate question, we asked respondents to indicate if there was a change to their income from before the pandemic to the current time (respondents selected a response from the choice set of no change, higher, lower).
To further measure the economic impact of the pandemic we asked participants to indicate whether they were unable to afford food, transportation, and/or housing, both prepandemic (in February 2020) and during-pandemic. We also included the Food and Agriculture Organization's Food Insecurity Experience Scale (FIES), which captures respondent's reporting of any food deprivation (e.g. constraints on one's ability to obtain adequate food) both prior to and during the pandemic [6].
The survey asked if the respondent was at risk for severe illness from COVID-19 due to comorbid health conditions, specifying conditions, “such as asthma, heart conditions, lung disease, diabetes, liver disease, immunocompromised status or currently undergoing dialysis.”
Respondent reported how concerned they were about contracting COVID-19 and the current status of the shelter in place orders where they lived.
The Desire to Avoid Pregnancy (DAP) scale, a validated measure of pregnancy intention [7] was included to measure respondents’ desire to avoid pregnancy during the pandemic. High desire to avoid pregnancy is defined as above the mean DAP score in the sample. A separate question – which allowed respondents to select multiple-choice options – asked respondents how their desire to become pregnant has been affected by the pandemic (i.e., no change, want to be pregnant more, want to be pregnant less, scared to be pregnant, harder to afford a child).
We presented a set of questions about contraceptive use, including the type of contraceptive(s) used, number of times it has been used in the past 3 months, and how access to contraception changed during the pandemic. If a respondent indicated that it has been more difficult to access contraception, the survey then follow-up with a question which asked how access became more difficult (i.e., unable to get prescription, hesitate to go to the pharmacy, unable to afford usual contraceptive(s), unable to afford any contraceptives, unable to get an IUD or implant placed, unable to get an IUD or implant removed, or other reasons due to the pandemic).
We asked about frequency of sexual intercourse in the past 30 days, whether it was the respondent's choice to have sex, and reason(s) for having sex if it was not their choice. We also separately asked how desire for intercourse as changed during the pandemic as well as whether they had experienced intimate partner violence in the past month. Respondents reporting intimate partner violence were provided a national hotline number for support, and assistance.
Statistical differences in the prepandemic and during-pandemic economic outcomes were evaluated using Chi-square tests. Logistic regression models were used to evaluate determinants of loss in income, food insecurity, drop in desire for pregnancy, and difficulty in accessing contraceptive(s) due to the pandemic.
3 Results
Overall, 897 individuals initiated the survey; 52 were ineligible and 291 were suspected to be fraudulent on the basis of a completion time of less than 5 minutes and/or duplicate IP address or geolocation with an existing participant. Our final sample included 554 respondents from 43 states in the United States; 47% of the respondents reported a current shelter in place order where they lived, 42% reported no shelter in place order, and 12% were not sure of the status of a shelter in place policy. Of the 554 respondents, 41% were age 18 to 24, 37% 25 to 34, and 23% 35 to 49; 53% were White, 15% Hispanic/LatinX, 12% Asian/Pacific Islander, 11% multiracial/multiethnic/other, 7% Black/African American, and 1% American Indian or Alaskan native. The majority of respondents had a high level of education, reporting having received a bachelor's degree (38%) and/or graduate degree (16%) (See Table 1).Table 1 Sociodemographic characteristics, basic COVID-19 living condition, and desire to avoid pregnancy (DAP) among survey respondents recruited via social medial between May and June 2020
Table 1 %
Age
18–24 41
25–34 37
34–49 23
Race/Ethnicity
American Indian or Alaskan Native 1
Asian/Pacific Islander 12
Black or African-American 7
Hispanic/LatinX 15
Multiracial/Multiethnic 11
White 54
Education
Less than high school diploma 1
High school diploma or GED 10
Some college/Associates/Technical Degree 35
Bachelor's degree 38
Graduate degree 16
Higher risk of severe COVID-19 illness
Yes 20
No 80
Federal poverty level (prepandemic)
Below 26
Above 67
Don't know 7
Living in place with shelter in place orders
Yes 47
No 42
Not sure 11
Desire to avoid pregnancy scale
0–<1 8
1–<2 8
2–<3 19
3–<4 47
4 18
N = 554. Percentages do not add up to 100% due to rounding.
Nearly all respondents (99%) identified as female; 0.5% identified as gender queer/gender nonbinary and 0.2% identified as trans male. At the time of the survey, 95% were not pregnant, 3% reported that they were pregnant, and 2% were unsure if they were pregnant. Some respondents had direct experience with COVID-19: 12% reported that either they or someone in their household had symptoms or a diagnosis of COVID-19. Among respondents, 20% had a comorbid condition, such as heart condition and diabetes, which is considered higher risk for developing severe COVID-19 illness. Overall, 20% of the respondents indicated that they were very worried about contracting COVID-19, 41% were somewhat worried, 34% were a little worried, and 5% indicated that they were not worried at all. Among those with comorbid conditions, 29% reported that they were very worried about contracting COVID-19.
3.1 The COVID-19 pandemic and financial security
We documented the effect of the pandemic on economic conditions and evaluated the predictors of increased economic insecurity. Employment (p < 0.01), food insecurity (p < 0.01), and ability to afford food, transportation, and/or housing (p < 0.01) all deteriorated during the pandemic compared to the period before. The percentage who was employed full-time decreased 13% points from 48% in February 2020 to 34% in the current month or last month of the shelter in place, for those whose shelter in place order had lifted. Those who were employed part-time decreased from 27% to 17%, and those who were unemployed and looking for work more than quadrupled from 4% to 17% (Table 2 ). In assessing how their income had changed due to the pandemic, 46% reported lower income, 43% reported no change, and 10% reported higher income.Table 2 Economic and financial situations before and during the COVID-19 pandemic
Table 2Employment status* Before pandemic (%) During pandemic (%)
Employed full time 48 34
Employed part time 27 17
Unemployed and looking for work 4 17
Unemployed and not looking for work 3 9
Homemaker 7 7
Unable to work 1 7
Student 8 4
Other 3 4
Federal poverty level
Below 100% FPL 26 -
Above 100% FPL 67 -
Don't know 7 -
Inability to afford food, transportation and/or housing*
Never 70 63
Rarely 19 14
Some of the time 8 16
Most of the time 2 5
All the time 1 2
Food insecurity*
Yes 20 36
No 80 64
Total percentages do not add up to 100% due to rounding.
⁎ p < 0.05 using Chi-square tests.
During the pandemic, respondents whose household incomes was already below the federal poverty level prior to the pandemic (26% of the sample) reported 3 times the odds of experiencing a loss of income (OR = 3.2, CI 2.0–5.0) compared to those above federal poverty level. See Table 3 . Age, race/ethnicity, and inability to afford food, transportation, and/or housing prior to the pandemic did not predict decreased income during the pandemic. However, those who only had some college/associate or technical degree have twice the odds of experiencing decreased income compared to respondents who have a graduate degree (OR = 2.4, CI: 1.3–4.3).Table 3 Characteristics associated with the likelihood of lower income, subjective poverty and food insecurity during the COVID-19 pandemic: Odds ratios from logistic regression models
Table 3 (1) (2) (3)
Variables Loss of income during pandemic* Inability to afford food, transportation, housing Food insecurity during pandemic
n = 554 n = 554 n = 554
OR (95% CI) OR (95% CI) OR (95% CI)
Decreased income 2.75 2.80
(1.81–4.17) (1.88–4.17)
Age (reference: 34–49)
18–24 1.19 0.68 0.82
(0.72–1.96) (0.38–1.21) (0.47–1.41)
25–34 1.24 0.84 0.93
(0.77–2.01) (0.48–1.45) (0.54–1.59)
Race/Ethnicity (reference: White/Caucasian)
Hispanic/Latinx 1.20 4.01 1.95
(0.70–2.06) (2.25–7.15) (1.12–3.40)
Asian/Pacific Islander 1.31 1.19 0.65
(0.75–2.27) (0.62–2.27) (0.34–1.25)
Black/African American 1.11 3.92 1.41
(0.54–2.29) (1.81–8.50) (0.67–3.00)
American Indian/Alaskan Native 0.15 - 1.00
(0.02–1.54) (0.13–7.85)
Multiracial/Multiethnic/Others 1.44 2.12 1.36
(0.79–2.62) (1.10–4.07) (0.72–2.59)
Education (reference: Graduate Degree)
Less than 12th grade 1.26 7.53 13.14
(0.15–10.66) (0.46–123.81) (0.88–196.45)
High school diploma/GED 2.04 1.68 4.24
(0.93–4.51) (0.66–4.33) (1.73–10.41)
Some or technical college/associate degree 2.38 2.39 2.77
(1.31–4.34) (1.17–4.87) (1.36–5.65)
Bachelor's degree 1.57 1.81 2.24
(0.89–2.79) (0.91–3.63) (1.12–4.51)
Below Federal poverty level (reference: above poverty) 3.19 4.20 3.11
(2.05–4.98) (2.64–6.67) (1.99–4.84)
⁎ Loss of income during the pandemic is compared to 2 other categories: (1) no loss of income and (2) income gains during the pandemic.
The percentage of respondents who reported difficulty in being able to afford food, transportation, and/or housing doubled (from 8% to 16%) during the pandemic. Predictors of inability to afford food, transportation, and/or housing include education, race/ethnicity, federal poverty level, and change in income (see Table 3). Those living below the federal poverty level prior to the pandemic have 4 times the odds of experiencing inability to afford food, transportation, and/or housing, compared to those whose income were above federal poverty level before the pandemic (OR = 4.2, CI:2.6–6.7).
Not surprisingly, those who reported decreased income have nearly 3 times the odds of experiencing inability to afford food, transportation, and/or housing compared to those who did not report decreased income during the pandemic (OR = 2.7, CI:1.8–4.2). Compared to White/Caucasian respondents, Hispanics/Latinx (OR = 4.0, CI: 2.2–7.1), and Black/African Americans (OR = 4.0, CI: 1.8–8.4) have 4 times the odds of experiencing inability to afford food, transportation, and/or housing. Compared to those with a graduate degree, respondents with some college/associate or technical degree have twice the odds of experiencing inability to afford basic needs (OR = 2.4, CI: 1.2–4.9).
Reports of food insecurity using the FIES measure increased from 20% to 36% during the pandemic. The predictors for food insecurity included decreased income, education level, and prepandemic federal poverty level status (See Table 3). Respondents who experienced a loss of income due to the pandemic have nearly 3 times the odds of experiencing food insecurity compared to those who did not experience decreased income (OR = 2.8, CI: 1.9–4.2). Hispanic/Latinx respondents have twice the odds of experiencing food insecurity compared to White/Caucasian respondents (OR = 2.0, CI 1.1–3.4). There was a clear pattern of lower education attainment being associated with greater risk of food insecurity. Compared to respondents with a graduate degree, those with a bachelor's degree have twice the odds (OR = 2.2, CI: 1.2–4.5), with some college/associate/technical college degree have nearly 3 times the odds (OR = 2.8, CI 1.4–5.6), and high school diploma/GED have more than 4 times the odds (OR = 4.2, CI: 1.7–10.4) of experiencing food insecurity. Respondents who were below the federal poverty level prepandemic have 3 times the odds of experiencing food insecurity (OR = 3.1, CI: 2.0–4.8) compared to those who were above the federal poverty level.
3.2 Impact of the COVID-19 pandemic on sexual frequency and desire and intimate partner violence
The survey included only respondents who reported having had sex at least once in the past 4 months. Within this sample, most respondents (83%) reported having had sex in the past month – just over half (54%) had sex with someone they live with, and 29% had sex with someone they were not living with. In regard to desire for sex during the pandemic, 37% of respondents reported that the pandemic had not changed their desire for sex, 32% reported the pandemic made them want to have sex less, and 29% reported that the pandemic made them want to have sex more. For those who have had sex in the past month, 73% indicated they wanted to have sex every time, 25% indicated sometimes they agreed to have sex even when they did not want to, and 1% indicated they were forced to have sex. Whether or not they were currently under shelter in place orders did not affect the frequency of or desire for sex. Four percent of respondents reported intimate partner violence in the past month, 1% point higher than before the pandemic (3%).
3.3 Impact of the COVID-19 pandemic on desire for pregnancy
The pandemic affected many respondents’ desire for pregnancy. When asked “How has your desire to be pregnant been affected by the pandemic?” 41% reported wanting to be pregnant more, 25% wanting to be pregnant less, and 34% reported no change or other. More than a third (37%) reported that the pandemic made them scared to be pregnant and 1 in 7 (13%) reported that it would be more difficult to afford a child. Those who reported inability to afford food, transportation, and/or housing had twice the odds of reporting a drop in desire to be pregnant (OR = 2.1, CI: 1.2–3.2) compared to those who reported being able to afford basic needs (See Table 4 ).Table 4 Impact of COVID-19 pandemic and economic conditions on fertility intentions and contraceptive access
Table 4 (1) (2)
Variables Reports drop in desire for pregnancy due to pandemic n = 531 More difficult access to contraceptives n = 535
OR (95% CI) OR (95% CI)
High DAP 1.34 2.01
(0.84–2.12) (1.11–3.62)
Age (reference: 34–49)
18–24 0.80 1.62
(0.44–1.44) (0.76–3.47)
25–34 0.86 1.79
(0.50–1.48) (0.84–3.79)
Race/Ethnicity (reference: White/Caucasian)
Hispanic/Latinx 1.13 1.39
(0.63–2.01) (0.72–2.70)
Asian/Pacific Islander 0.80 0.64
(0.41–1.54) (0.28–1.43)
Black/African American 0.53 0.76
(0.22–1.29) (0.28–2.11)
American Indian/Alaskan Native 1.45 1.70
(0.22–9.55) (0.16–18.28)
Multiracial/Multiethnic/Others 0.54 0.50
(0.26–1.12) (0.21–1.22)
Education (reference: Graduate Degree)
Less than 12th grade 14.32
(1.53–133.99)
High school diploma/GED 0.59 3.48
(0.23–1.50) (1.02–11.85)
Some college/Associate/technical degree 0.65 2.47
(0.33–1.26) (0.86–7.09)
Bachelor's degree 1.07 2.54
(0.58- 1.99) (0.90–7.15)
Reports decreased income due to pandemic 1.11 2.18
(0.72–1.72) (1.30–3.67)
Below Federal poverty level 1.00 0.96
(0.60–1.66) (0.55–1.69)
Inability to afford food, transportation, housing 2.13 1.86
(1.32–3.43) (1.06–3.24)
Models contain only women who were not pregnant.
3.4 Impact of the COVID-19 pandemic on access to contraception
One in 6 (17%) reported that access to contraceptives had become more difficult during the pandemic (20% of those who were currently using). Only 4% reported that access had become easier. Looking at specific ways in which access had become more difficult during the pandemic: 9% reported it was harder to get to a pharmacy, 4% reported it was harder to afford contraceptives, 3% reported it was harder to get a prescription, 2% reported it was harder to have long-acting reversible contraceptives placed, and 1% reported it was harder to have long-acting reversible contraceptives removed.
Predictors of difficulty in accessing contraceptive(s) during the pandemic included high desire to avoid pregnancy (OR = 2.0, CI 1.1–3.6), decreased income (OR = 2.1, CI 1.3–3.7), and inability to afford food, transportation, and/or housing (OR = 1.9, CI: 1.1–3.2) (See Table 4).
4 Discussion
The pandemic had varying, detrimental effects on economic conditions, access to family planning, and reproductive health intentions. Especially critical to women's health, of the risk factors currently known to be associated with poor COVID-19 outcomes, vascular risk factors are also associated with increased risk of pregnancy complications. The pandemic has disproportionally affected people living in poverty and people of color and their access to contraception.
We find that almost half of the respondents (46%) reported a loss of income during the pandemic compared to prepandemic; the percentage of respondents with reduced income in this sample was higher than in the Guttmacher survey (32%) [4]. Consistent with other studies, we found that people already living below the federal poverty level were more likely to experience a loss of income during the pandemic.
The percentage of respondents indicating that at times they could not afford basic living needs nearly doubling during the pandemic from 8% to 16%; those whose household income that fell below federal poverty level have greater odds of experiencing inability to afford food, transportation, and/or housing and food insecurity during the pandemic. This finding is in line with reported food insecurity in the general population; prior to the pandemic, in 2018, 11.1% of United States’ households were considered food insecure at some point during the year [8] and data consistently indicated that women, particularly mothers with dependents, were especially vulnerable to food insecurity. [9] The findings here highlight changes in food insecurity among people of reproductive age in the US during the pandemic and how women in more vulnerable groups may be disproportionately impacted by the pandemic.
This study shows that a major factor affecting desire for pregnancy was inability to afford food, transportation, or housing during the pandemic. One in 4 respondents expressed a decreased desire to become pregnant and over 1 in 3 reported that the pandemic made them scared to be pregnant.
Given that a significant minority of respondents report a drop in desire for pregnancy, it is concerning that nearly 1 in 6 respondents expressed difficulties accessing contraceptives. Moreover, there was a statistically significant association between drop in desire for pregnancy and increased difficulty accessing contraceptives. This pattern suggests that people seeking to avoid pregnancy were also encountering difficulty in accessing the health care they needed to achieve their reproductive health goals. The increased difficulty in contraception access for those who wanted to avoid pregnancy may place many people at a higher risk of unintended pregnancy.
Our study has some limitations. This study relies on a cross-sectional survey, where respondents were asked to recall their economic conditions before the pandemic; to minimize recall errors, we included specific questions on employment, income and household size as well as self-reported change in income prior to and during the pandemic. The recruitment of survey respondents through social media resulted in a sample of people who were more educated (16% with advanced degrees) than the national average (13%) [10]. Of note, this sample of respondents with higher education level may explain why a lower proportion of respondents in this sample experienced difficulties in contraception access compared to the sample of respondents in the survey conducted by the Guttmacher Institute [4]. Similarly, the sample included a higher percentage of Asian/Pacific Islands at 11%, compared to the national average of approximately 6%. In this study, eligibility criteria included sex with a man in the past 4 months, however, sex was not specifically defined in the survey as vaginal-penile intercourse, thus the survey sample may include individuals who were engaging in other types of sex and may not be at risk of pregnancy. At last, there is likely under reporting of intimate partner violence in our sample; possibly because disclosure may have been difficult for people sheltering with abusive partners.
With online and social media recruitment, there are uncertainties regarding the source population. We sampled a population of slightly younger and more educated respondents, one with easier access to the Internet as compared to the general population. In the era of the COVID-19 pandemic, we were limited to recruitment through social media. Future studies using alternate data sources are needed to confirm these estimates.
The findings from this study add to other published studies. A study by researchers at the Guttmacher Institute found that 36% of women wanted to delay childbearing and 27% of women wanted to have fewer children than previously planned [4]; our study adds some detail to explain this finding. We find that 37% reported the pandemic made them scared to be pregnant and 25% wanted to be pregnant less. We find one area of difference from the Guttmacher study: 20% of women we sampled who were currently using contraceptives indicated it has been harder to access contraceptives while 39% of respondents in the Guttmacher study reported they had to delay or cancel general sexual and reproductive health care visits, including contraceptive care, due to the pandemic. A recent survey study on the impact of the COVID-19 pandemic on sexual and reproductive health in China [11] suggests that increased difficulty in accessing reproductive health care is not unique to the US and may be generalizable worldwide. The results from our logistic regression model provide predictors for these difficulties.
The findings from this study offer insight into how the pandemic impacted economic conditions and reproductive health and family planning intentions. The pandemic disproportionately affected people of color, resulting in significant negative economic impacts for those people who identify as Hispanic and Black, as compared to those who identify as White. During the pandemic, our study found that increased desire to avoid pregnancy and decreased income were both associated with increased difficulties in access to contraception. These findings suggest that barriers to contraceptive access and family planning services were heightened during this vulnerable time when women may have increased need for them. This difficulty in accessing contraceptive methods places women at increased risk of experiencing unintended pregnancy. In these uncertain economic times, it is of utmost importance to create policies that will ensure access to and comprehensive coverage of core sexual and reproductive health services. By doing so, we safeguard people's ability to make decisions that support their reproductive health goals.
Acknowledgments
The authors thank Tanvi Gurazada and Kaia Foster for assistance in data collection and monitoring.
Funding: The study is funded by the University of California, San Francisco National Center of Excellence in Women's Health.
Conflict of interest: The authors have no conflict of interest to declare.
==== Refs
References
1 Ewing-Nelson C. National Women's Law Center [Internet] 2020 National Women's Law Center Washington DC [updated 2020 June 01; cited 2020 Oct 19]. Available from https://nwlc.org/wp-content/uploads/2020/06/May-Jobs-FS.pdf
2 Greenwood J Seshadri A Vandenbroucke G. The baby boom and baby bust Am Econ Rev 95 1 2005 183 207 Mar
3 Dorn D Hanson G. When work disappears: manufacturing decline and the falling marriage market value of young men Am Econ Rev: Insights 1 2 2019 Sep161-78
4 Lindberg LD, VandeVusse A, Mueller J, Kirstein M. Early Impacts of the COVID-19 Pandemic: findings from the 2020 Guttmacher Survey of Reproductive Health Experiences [Internet]. Guttmacher Institute; 2020 Jun [cited 2020 Oct 20]. Available from: https://www.guttmacher.org/report/early-impacts-covid-19-pandemic-findings-2020-guttmacher-survey-reproductive-health
5 Herteliu C Richmond P Roehner BM. Deciphering the fluctuations of high frequency birth rates Phys A: Stat Mech Its Appl 509 2018 Nov1046–61
6 McGuire S. WHO, World Food Programme, and International Fund for Agricultural Development. 2012. The State of Food Insecurity in the World 2012. Economic growth is necessary but not sufficient to accelerate reduction of hunger and malnutrition. Rome, FAO.
7 Rocca CH Ralph LJ Wilson M Gould H Foster DG Psychometric evaluation of an instrument to measure prospective pregnancy preferences Med Care 57 2 2019 7
8 United States Department of Agriculture. Household food security in the United States in 2018. Washington DC: United State Department of Agriculture, Economic Research Service; 2019. 47p. ERR-279
9 Feeding America. The impact of the coronavirus on child food insecurity [Internet]. Washington DC: Feeding America; 2020 [cited 2020 Oct 07]. Available from: https://www.feedingamerica.org/research/coronavirus-hunger-research
10 The United State Bureau of Labor Statistics. A look at women's education and earnings since the 1970s [Internet]. Washington DC: United State Bureau of Labor Statistics; 2017 Dec 17 [cite 2020 Sept 21]. Available from: https://www.bls.gov/opub/ted/2017/a-look-at-womens-education-and-earnings-since-the-1970s.htm#:~:text=Bureau%20of%20Labor%20Statistics,-The%20Economics%20Daily&text=The%20educational%20attainment%20of%20women,with%2011%20percent%20in%201970/.
11 Li G Tang D Song B Wang C Qunshan S Xu C Impact of the COVID-19 pandemic on partner relationships and sexual and reproductive health: cross-sectional, online survey study J Med Internet Res 22 8 2020 Aug 6e20961
| 33587906 | PMC9748659 | NO-CC CODE | 2022-12-15 23:22:45 | no | Contraception. 2021 Jun 12; 103(6):380-385 | utf-8 | Contraception | 2,021 | 10.1016/j.contraception.2021.02.001 | oa_other |
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Contraception
Contraception
Contraception
0010-7824
1879-0518
Published by Elsevier Inc.
S0010-7824(21)00062-7
10.1016/j.contraception.2021.03.007
Article
CLINICAL ORAL ABSTRACTS
Safety and efficacy of no-test medication abortion: A retrospective multi-site study
Upadhyay U. a⁎
Raymond E. b
Koenig L. a
Coplon L. c
Ricci S. d
Kaneshiro B. e
Boraas C. f
Winikoff B. b
a University of California, San Francisco, San Francisco, CA, United States
b Gynuity Health Projects, New York, NY, United States
c Maine Family Planning, Augusta, ME, United States
d The Center for Reproductive Health Education in Family Medicine (RHEDI), Department of Family and Social Medicine, Montefiore Medical Center, New York, NY, United States
e University of Hawaii, Honolulu, HI, United States
f Planned Parenthood North Central States, St. Paul, MN, United States
⁎ Corresponding author.
3 4 2021
5 2021
3 4 2021
103 5 374374
Copyright © 2021 Published by Elsevier Inc.
2021
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 COVID-19 pandemic brought new attention to medication abortion because it does not require direct physical contact between patient and clinical staff. No-test approaches for medication abortion preserve the usual standard of care, except that they replace the in-person ultrasound or physical exam before the abortion with other evidence-based methods to assess the patient's duration of pregnancy and screen for ectopic pregnancy. Early in the pandemic, a no-test sample protocol was published to offer guidance for clinical practice. However, little has been published on the safety and efficacy outcomes of no-test approaches.
Method
Through webinars and personal contacts, we invited US-based clinics that had adopted the no-test medication abortion protocol to join the study. A no-test medication abortion was defined as not having a preabortion ultrasound or physical exam. Participating clinics abstracted data from medical records of all patients who received a no-test medication abortion and entered them into a REDCap database. We conducted descriptive analyses of the clinic protocols and the patient sample. We also developed a multilevel, multivariable model that accounted for clustering at the clinic-level to estimate the adjusted odds of medication abortion failure and adverse events.
Results
We included 11 clinics, 4 of which contributed some data from the TelAbortion Study. Clinics shared data on 791 patients served from Jan. 1 to Dec. 31, 2020. Among all patients, 58.1% received mifepristone in person and 41.9% received it by mail. At mifepristone provision, patients’ pregnancy durations ranged from 27 to 74 days; 36.4% were <=42 days, 45.3% were 43 to 56 days, 16.4% were 57 to 70 days, and 1.9% were >71 days. We received at least some follow-up data for 626 patients (79.1%) and excluded 14 patients who did not take mifepristone. Of the remaining 612 patients, 5 (0.8%) experienced serious adverse events defined as hospital admission, abdominal surgery and blood transfusion. One patient had a confirmed ectopic pregnancy and was admitted to a hospital for salpingectomy 9 days after provision of mifepristone (included as a serious adverse event). Abortion outcome data were available for 394 patients (64.4%). Overall, 94.7% (95% CI: 92.0% to 96.7%) of patients had a complete abortion with <1600 mcg of misoprostol, without an aspiration, procedure, or more mifepristone and misoprostol. 2 patients had suspected or confirmed ongoing pregnancies. Outcomes were similar for those who received medications in-person and those who received them by mail.
Conclusions
No-test medication abortion with either in-person pick up or mailing of medications is effective and safe, with outcomes similar to rates found in the published literature. Moreover, omitting tests reduce COVID-19 risk and conform with FDA REMS requirements when combined with in-person pick up. Follow up rates (64.4%) with the NTMA approach were similar to other medication abortion protocols. Combining no-test medication abortion protocols with mailing of medications to patients would support public health efforts for those who want to avoid a clinic visit.
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==== Front
Infect Dis Health
Infect Dis Health
Infection, Disease & Health
2468-0451
2468-0869
Published by Elsevier B.V.
S2468-0451(22)00079-7
10.1016/j.idh.2022.09.028
Article
58. Statewide COVID-19 hospitalised patient surveillance development and enhancement
Brett Judith 1
Bull Ann 1
Burrell Simon 1
Wang Ling 1
Kalman Tali 2
Veale Hilary 2
Hennessy Daneeta 2
Rowe Stacey 2
Lim Lyn-li 13
Worth Leon 13
1 VICNISS Coordinating Centre, Melbourne, Australia
2 Department of Health Victoria, Melbourne, Australia
3 University of Melbourne, Melbourne, Australia
14 12 2022
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14 12 2022
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Copyright © 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.
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pmcIntroduction: With onset of the first COVID-19 wave in March 2020, the Victorian Department of Health (DH) engaged the Victorian Healthcare Associated Infection Surveillance System Coordinating Centre (VICNISS) to develop a system to monitor hospital patients with COVID-19. We describe the development and evolution of this program.
Methods: A secure on-line reporting module was created using the VICNISS platform. Standardised data specifications and definitions were established to monitor hospitalised patients, including: demographics, COVID-19 status at time of admission, daily location, ICU admission and ventilation status. Hospital users were registered and provided with educational/helpline support. Hospitals and DH were able to generate real-time reports. VICNISS followed up data discrepancies, queries and/or failure to report.
Results: Weekly validation was introduced in June 2020 to confirm all hospitalized COVID cases had been reported. Upon DH request, VICNISS undertook a lookback in September 2020 to identify hospital-acquired COVID-19 infections. Historical COVID-19 episodes were reviewed and classified according to internationally-accepted definitions. In early 2021, an algorithm using these definitions was applied prospectively to inform hospitals immediately if a submitted COVID-19 case was hospital-acquired. COVID-19 vaccination status was further added to data submission in April 2021, in order to inform policy.
Conclusion: A system that successfully captures data to inform Victoria’s COVID-19 case management capacity was rapidly deployed by leveraging the existing platform used by hospitals for infection prevention and control surveillance activities. Post-implementation enhancements improved efficiencies and timeliness of reporting to support responses and risk mitigation within Victorian facilities.
| 0 | PMC9748672 | NO-CC CODE | 2022-12-15 23:22:45 | no | Infect Dis Health. 2022 Nov 14; 27:S8 | utf-8 | Infect Dis Health | 2,022 | 10.1016/j.idh.2022.09.028 | oa_other |
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Contraception
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Published by Elsevier Inc.
S0010-7824(21)00056-1
10.1016/j.contraception.2021.03.001
Commentary
The pandemic year: Research at the National Abortion Federation's 2021 Virtual Annual Meeting
Mark Alice a⁎
Foster Angel M. b
Jones Rachel K. c
Prager Sarah W. d
Reeves Matthew F. e
Ragsdale Katherine H. a
a National Abortion Federation, Washington, DC, United States
b University of Ottawa, Ottawa, Ontario, Canada
c Guttmacher Institute, New York, NY, United States
d University of Washington, Seattle, WA, United States
e DuPont Clinic, Washington, DC, United States
⁎ Corresponding author.
6 3 2021
5 2021
6 3 2021
103 5 371372
© 2021 Published by Elsevier Inc.
2021
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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pmcThe National Abortion Federation's Virtual Annual Meeting took place in May of 2021 and highlighted the work of researchers, advocates, providers, and activists who have worked tirelessly during this unprecedented year. Much of the Annual Meeting's proceedings were related to the dramatic events of the last year, both in clinical responses to the COVID-19 pandemic and in how the pandemic exposed broad inequities throughout the health care system and the effect of systemic racism and oppression on the health and lives of people seeking abortion care.
Over the last year, the COVID-19 pandemic demanded change in all aspects of health care, including abortion. As providers scrambled to respond to public health mandates to reduce the spread of the virus, they adopted evidence-based no-test abortion protocols to help people access abortion with minimal in-person contact [1]. For a brief 6 months, from July 13, 2020, to January 12, 2021, a court order blocked the in-person dispensing requirement in the mifepristone Risk Evaluation and Mitigation Strategy (REMS), allowing mifepristone by mail. Blocking the REMS made fully remote medication abortion possible in the United States. Remote medication abortion was built on a solid foundation with years of evidence, much of it presented at past NAF Annual Meetings. Although the Supreme Court reinstated the in-person dispensing requirement in the REMS in January, there is hope that with the new administration, the Food and Drug Administration will lift the REMS again during the pandemic, and then remove it for good, circumventing the need for legistlative relief and making remote provision of medication abortion care a permanent option in supportive states.
Despite the difficult year, the Annual Meeting Scientific Committee saw high-quality clinical and social science research submitted for presentation. The Committee assessed all submitted abstracts using a juried ranking process. We evaluated the abstracts for their scientific merit as well as their potential impact in the field. This issue of Contraception contains the oral abstracts presented at NAF's 2021 Annual Meeting.
1 Clinical oral abstracts
Researchers urgently documented outcomes related to no-test medication abortion. Holly Anger and colleagues conducted a prospective study comparing people who had an abortion remotely as part of Gynuity Health Project's TelAbortion Study with or without a screening ultrasound and/or pelvic exam. Ushma Upadhyay and colleagues performed a retrospective study looking at data from people provided no-test abortion at 11 sites to investigate clinical outcomes. Both studies add to the evidence that no-test abortion is safe and effective, with low rates of serious adverse events.
Alisa Goldberg and colleagues performed a retrospective cohort study of patients from Planned Parenthood League of Massachusetts to look at the abortion outcomes for people who presented with pregnancy of uncertain location who were either treated immediately or waited until pregnancy location was confirmed. As abortion takes place earlier in pregnancy, a trend supported by remote and no-test abortion, this study gives critical insight into the management of people who present before a pregnancy can be seen on ultrasound.
Finally, Matt Reeves and colleagues from DuPont Clinic presented their results from using intrafetal lidocaine injection for induced fetal demise before later abortion.
2 Social science oral abstracts
Remote abortion requires innovative patient support. Hannah Simons and colleagues from Planned Parenthood Federation of American presented the results of their study using a chat bot to support medication abortion patients. They found that patients used the chat bot widely and this subsequently reduced call volume and staff time.
Protesters are a constant source of harassment in abortion and they have been emboldened over the last year, crowding outside clinics even during the pandemic [2]. Erin Carroll and colleagues invited people seeking care at a clinic in Jackson, Mississippi, to describe the protesters’ impact on their abortion experience. Their findings support the implementation of buffer-zone ordinances carefully crafted to protect the health, humanity, and privacy of individuals accessing care.
Ushma Upadhyay and colleagues presented their investigation of abortion costs at clinics throughout the United States. They found that self-pay costs for abortion are increasing over time while the proportion of clinics that accept insurance is decreasing. Cost and coverage are major barriers to abortion access and health equity, and these barriers continue to rise.
Finally, Angel M. Foster and colleagues were able to present their abstract that was slated for last year's cancelled meeting and previously published in Contraception [3]. They interviewed former NAF Hotline workers and found that their experiences talking with people seeking abortion care transformed former Hotliners’ career trajectories and offered an opportunity to facilitate ethical abortion storytelling and advocacy.
We are grateful that we were able to gather virtually in 2021 and look forward to the day when we can see each other again in person. Thank you to all whose contributions improve the field, even in this year like no other.
==== Refs
References
1 Raymond EG Grossman D Mark A Upadhyay UD Dean G Creinin MD Commentary: No-test medication abortion: A sample protocol for increasing access during a pandemic and beyond Contraception 101 6 2020 361 366 10.1016/j.contraception.2020.04.005 32305289
2 National Abortion Federation. Violence and Disruption Statistics, 2019. https://prochoice.org/naf-releases-2019-violence-disruption-statistics/; 2020 (accessed February 26, 2021).
3 Foster AM Frappier S Crich L Messier K. Evaluating the impact of working on the NAF hotline: a qualitative study with former staff members Contraception 101 5 2020 356 357 10.1016/j.contraception.2020.03.014
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==== Front
Infect Dis Health
Infect Dis Health
Infection, Disease & Health
2468-0451
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Published by Elsevier B.V.
S2468-0451(22)00080-3
10.1016/j.idh.2022.09.029
Article
60. Transforming the way we think about infection prevention and control - It's time to challenge the biomedical rhetoric
Sparke Vanessa 123
MacLaren David 23
West Caryn 23
1 James Cook University, Cairns, Australia
2 Collaborative for the Advancement of Infection Prevention and Control, Cairns/Gold Coast/Sunshine Coast, Australia
3 Australasian College for Infection Prevention and Control, Hobart, Australia
14 12 2022
11 2022
14 12 2022
27 S8S8
Copyright © 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.
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pmcIntroduction: Infection prevention and control (IPC) came to the fore during the COVID-19 pandemic with global expectations in healthcare of compliance with recognised international guidelines. Yet despite 40 years of modern IPC practice, health services around the world struggle to maintain minimal IPC standards even without the pressures of a pandemic, many are in resource-limited settings. Atoifi Adventist Hospital (AAH) in the Solomon Islands is one such hospital.
Aim: To investigate IPC practice at AAH with the aim of creating a meaningful and sustainable program. In doing this, staff and community knowledge and beliefs about infection transmission was explored, and IPC practice and rationale determined.
Methods: This qualitative study employed a participatory action research methodology using Photovoice followed by semi-structured interviews as the primary data collection method. Participants included staff educated in biomedical principles, and staff with little or no formal education.
Results: Improving IPC practice is not straightforward. Cultural, spiritual and societal practices and beliefs influence how people view disease causation and transmission and affects healthcare worker’s practice. ‘Germ theory’ does not necessarily inform people’s beliefs, even for staff educated via the biomedical model; to implement IPC guidelines based on germ theory principles, and expect staff to practise accordingly, is not plausible.
Conclusion: IPC programs will only work if they are transformed into a context that is understood by staff and community - one that complements the biomedical model. Governments and hospital leaders need to consider this when implementing IPC programs. It's time for us to challenge the rhetoric.
| 0 | PMC9748675 | NO-CC CODE | 2022-12-15 23:22:45 | no | Infect Dis Health. 2022 Nov 14; 27:S8 | utf-8 | Infect Dis Health | 2,022 | 10.1016/j.idh.2022.09.029 | oa_other |
==== Front
Infect Dis Health
Infect Dis Health
Infection, Disease & Health
2468-0451
2468-0869
Published by Elsevier B.V.
S2468-0451(22)00078-5
10.1016/j.idh.2022.09.027
Article
53. Towards optimal quarantine: A scoping review of quarantine planning in the pandemic preparedness plans and pandemic exercises of Australia and New Zealand
Bush Matiu 1
Bouchoucha Stéphane 1
Hutchinson Ana 1
Bennett Catherine 1
1 Deakin University, Burwood, Australia
14 12 2022
11 2022
14 12 2022
27 S7S8
Copyright © 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.
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pmcIntroduction: Since 2020, the New Zealand and Australian federal, state and territory governments have used quarantine as a strategic infection control measure to contain the SRS-CoV-2 (COVID-19) virus. However, the quarantine programs of both countries were rapidly operationalised without a clear blueprint for infection prevention. This paper identifies gaps in forecasting the need, and planning, for widespread quarantine within New Zealand’s and Australia’s Pandemic Preparedness Plans and pandemic exercise reports.
Methods: This paper adhered to the Joanna Briggs Institute (JBI) methodology for scoping reviews. Parliamentary websites and databases (Parlinfo, Pandora) were searched for plans and exercise reports, that were publicly available from 2009 to May 2022. Documents were examined using directive content analysis and assessed on their alignment with the core elements of people, resources, governance, systems, and processes, as addressed in the Australian Disaster Preparedness Framework 2018.
Results: The degree to which the core elements outlined in the Australian Disaster Preparedness Framework were covered in the documents varies significantly across both New Zealand, and the Australian federal, states and territories. Of the 15 identified plans and 8 exercise reports most did not foresee the need for mandatory, large-scale quarantine of people arriving from interstate or overseas and contemplated voluntary quarantine occurring within people’s private residences.
Conclusion: This paper confirms the need to focus on widespread quarantine as an infection control measure to enhance future pandemic operational preparedness. Further development of quarantine capabilities is required in locations aside from private residences, including at Australia’s new purpose-built quarantine facilities.
| 0 | PMC9748714 | NO-CC CODE | 2022-12-15 23:22:45 | no | Infect Dis Health. 2022 Nov 14; 27:S7-S8 | utf-8 | Infect Dis Health | 2,022 | 10.1016/j.idh.2022.09.027 | oa_other |
==== Front
Infect Dis Health
Infect Dis Health
Infection, Disease & Health
2468-0451
2468-0869
Published by Elsevier B.V.
S2468-0451(22)00055-4
10.1016/j.idh.2022.09.004
Article
0. The experience of paramedics during the COVID-19 pandemic: An integrative review of the literature
Howarth Ursula 12
Zimmerman Peta-Anne 2456
van de Mortel Thea 2
Barr Nigel 3
1 Queensland Ambulance Service, Brisbane, Australia
2 School of Nursing and Midwifery, Griffith University, Gold Coast, Australia
3 School of Nursing, Midwifery and Paramedicine, University of the Sunshine Coast, Sunshine Coast, Australia
4 Collaborative for the Advancement for Infection Prevention and Control, Australia
5 Menzies Health Institute, Australia
6 Gold Coast Hospital and Health Service, Gold Coast, Australia
14 12 2022
11 2022
14 12 2022
27 S1S1
Copyright © 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.
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pmcBackground: The coronavirus pandemic (COVID-19) has focused attention on healthcare workers’ concerns about working during a pandemic, however research on the topic has primarily been conducted in hospitals, with limited research specifically on paramedic workforce. This study critically examines and synthesises what is known about paramedics’ experiences of barriers to, and enablers of, responding to suspected or known COVID-19 cases.
Methods: An integrative review of the literature was undertaken using articles found by a systematic search of four research databases. Inclusion criteria included paramedics or emergency medical technicians who had experience of barriers to, or enablers of, responding to patients during the COVID-19 pandemic. A quality assessment of included articles was conducted.
Results: Nine articles, reporting on studies conducted in eight countries, met the inclusion criteria. Barriers to caring for potential or actual COVID-19 cases included communication and poor leadership, fear of infection to self and family, frequent changes in guidelines and inconsistencies across agencies, stress/burnout, and concerns with personal protective equipment. Enablers included job security, perceived social support, solidarity with other paramedics, and use of modern technologies for communication.
Conclusions: While some of the findings parallel those reported by other healthcare workers in hospital settings during COVID-19, paramedics working in the pre-hospital environment had unique experiences of caring for suspected or known COVID-19 cases that provide learnings to improve support for paramedics in these situations. These specific unique experiences, and the support required for paramedics will be discussed.
| 0 | PMC9748715 | NO-CC CODE | 2022-12-15 23:22:45 | no | Infect Dis Health. 2022 Nov 14; 27:S1 | utf-8 | Infect Dis Health | 2,022 | 10.1016/j.idh.2022.09.004 | oa_other |
==== Front
Infect Dis Health
Infect Dis Health
Infection, Disease & Health
2468-0451
2468-0869
Published by Elsevier B.V.
S2468-0451(22)00055-4
10.1016/j.idh.2022.09.004
Article
0. The experience of paramedics during the COVID-19 pandemic: An integrative review of the literature
Howarth Ursula 12
Zimmerman Peta-Anne 2456
van de Mortel Thea 2
Barr Nigel 3
1 Queensland Ambulance Service, Brisbane, Australia
2 School of Nursing and Midwifery, Griffith University, Gold Coast, Australia
3 School of Nursing, Midwifery and Paramedicine, University of the Sunshine Coast, Sunshine Coast, Australia
4 Collaborative for the Advancement for Infection Prevention and Control, Australia
5 Menzies Health Institute, Australia
6 Gold Coast Hospital and Health Service, Gold Coast, Australia
14 12 2022
11 2022
14 12 2022
27 S1S1
Copyright © 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.
==== Body
pmcBackground: The coronavirus pandemic (COVID-19) has focused attention on healthcare workers’ concerns about working during a pandemic, however research on the topic has primarily been conducted in hospitals, with limited research specifically on paramedic workforce. This study critically examines and synthesises what is known about paramedics’ experiences of barriers to, and enablers of, responding to suspected or known COVID-19 cases.
Methods: An integrative review of the literature was undertaken using articles found by a systematic search of four research databases. Inclusion criteria included paramedics or emergency medical technicians who had experience of barriers to, or enablers of, responding to patients during the COVID-19 pandemic. A quality assessment of included articles was conducted.
Results: Nine articles, reporting on studies conducted in eight countries, met the inclusion criteria. Barriers to caring for potential or actual COVID-19 cases included communication and poor leadership, fear of infection to self and family, frequent changes in guidelines and inconsistencies across agencies, stress/burnout, and concerns with personal protective equipment. Enablers included job security, perceived social support, solidarity with other paramedics, and use of modern technologies for communication.
Conclusions: While some of the findings parallel those reported by other healthcare workers in hospital settings during COVID-19, paramedics working in the pre-hospital environment had unique experiences of caring for suspected or known COVID-19 cases that provide learnings to improve support for paramedics in these situations. These specific unique experiences, and the support required for paramedics will be discussed.
| 0 | PMC9748716 | NO-CC CODE | 2022-12-15 23:22:45 | no | Infect Dis Health. 2022 Nov 14; 27:S10 | latin-1 | Infect Dis Health | 2,022 | 10.1016/j.idh.2022.09.035 | oa_other |
==== Front
Infect Dis Health
Infect Dis Health
Infection, Disease & Health
2468-0451
2468-0869
Published by Elsevier B.V.
S2468-0451(22)00069-4
10.1016/j.idh.2022.09.018
Article
37. Australian emergency nurses’ experiences of work, using personal protective equipment during the COVID-19 pandemic. A qualitative study
Dempster Penelope
Hutchinson Anastasia (Ana)
Oldland Elizabeth
Bouchoucha Stéphane
14 12 2022
11 2022
14 12 2022
27 S5S5
Copyright © 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.
==== Body
pmcThe COVID-19 pandemic has challenged health care professionals and changed our approach to the delivery of patient care. Experience during the COVID-19 has highlighted the need to better understand and manage the challenges relating to the use of personal protective equipment (PPE) experienced by frontline health care workers.
Aims: To explore and describe nurses’ experience with the use of personal protective equipment in the Emergency Department (ED), during the COVID-19 pandemic in Australia.
Design: A qualitative explorative descriptive design was used.
Methods: Participants were 24 nurses (clinical and managerial). One focus group and 21 individual semi-structured interviews were conducted between January and April 2022. Qualitative descriptive thematic analysis was used to identify themes, elicit meaning and communicate findings using Braun and Clarke’s six steps as the guiding framework.
Results: Five main themes identified: (i) The shifting ground of the COVID pandemic response, (ii) Disconnect between the ED team and organisational leaders (iii) Working in PPE causes exhaustion, physical discomfort and injury (iv) Challenges providing safe patient care (v) Discrete event with timeless consequences.
Conclusion: This study evidenced an array of adverse effects and staff concerns arising from use of personal protective equipment during the COVID-19 pandemic response in 2020-22. Experiences during the pandemic highlighted a long standing and urgent need to bolster the nurse workforce particularly in emergency nursing. Innovation is needed in PPE design to increase both protection from novel pathogens and user comfort.
Keywords: Registered Nurse, Emergency Department, Personal Protective Equipment, COVID-19 pandemic, Australia
| 0 | PMC9748717 | NO-CC CODE | 2022-12-15 23:22:45 | no | Infect Dis Health. 2022 Nov 14; 27:S5 | utf-8 | Infect Dis Health | 2,022 | 10.1016/j.idh.2022.09.018 | oa_other |
==== Front
Infect Dis Health
Infect Dis Health
Infection, Disease & Health
2468-0451
2468-0869
Published by Elsevier B.V.
S2468-0451(22)00066-9
10.1016/j.idh.2022.09.015
Article
32. Exploring staff perspectives on caring for isolated hospitalised patients during the COVID-19 pandemic: A qualitative study
Digby Robin 12
Hopper Ingrid 2
Hughes Leanne 2
McCaskie Doug 2
Tuck Michelle 2
Fallon Kethly 2
Hunter Peter 2
Bucknall Tracey 12
1 Deakin University, Burwood, Australia
2 Alfred Health, Melbourne, Australia
14 12 2022
11 2022
14 12 2022
27 S4S4
Copyright © 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.
==== Body
pmcIntroduction: In response to the 2020 COVID-19 pandemic Australian hospitals introduced strict patient isolation and tight infection control policies including extreme visiting restrictions for families. Work practices and communication channels changed to accommodate restrictions. This study explored staff perceptions of the impact of strict isolation and infection control policies on patients, families, and staff in one Victorian acute metropolitan hospital.
Methods: A qualitative descriptive design was used to examine the opinions of frontline nurses, medical staff, allied health, and support staff. Fifty-eight staff were interviewed in eight focus groups. Interviews were audio-recorded, transcribed, and analysed using content analysis.
Results: Six main themes identified: 1) Communication challenges during COVID-19; 2) Impact of isolation on family; 3) Challenges to patients’ health and safety; 4) Impact on staff; 5) Challenging standards of care; 6) Contextual influences: policy, decision-makers, and the environment. Clear communication was pivotal to successful outcomes. Adapting to rapid change was difficult for staff. Technology including teleconferencing could be effective. Isolating patients from families caused distress for all. Some patient care was perceived as compromised. PPE was a barrier to staff/ patient communication and rapport. Staff were supported by teamwork. Existing infrastructure and equipment were frequently inadequate.
Conclusion: The hospital restrictions resulted in good pandemic management; however, it was perceived as being at considerable cost to patients, families, and staff. Preparation for future pandemics must consider workforce preparedness, adapted models of care and workflow. Further research using a co-design model with consumers and staff is recommended to construct a workable solution.
| 0 | PMC9748718 | NO-CC CODE | 2022-12-15 23:22:46 | no | Infect Dis Health. 2022 Nov 14; 27:S4 | utf-8 | Infect Dis Health | 2,022 | 10.1016/j.idh.2022.09.015 | oa_other |
==== Front
Infect Dis Health
Infect Dis Health
Infection, Disease & Health
2468-0451
2468-0869
Published by Elsevier B.V.
S2468-0451(22)00071-2
10.1016/j.idh.2022.09.020
Article
39. “Like building a plane and flying it all in one go”: Applying the hierarchy of controls in Australian general practices during the SARS-CoV-2 pandemic
Hor Su-yin 1
Burns Penelope 2
Yong Faith 3
Barratt Ruth 4
Degeling Chris 5
Williams Veazey Leah 4
Wyer Mary 4
Gilbert Lyn 4
1 University Of Technology Sydney, Ultimo, Australia
2 Australian National University, Canberra, Australia
3 The University of Queensland, St Lucia, Australia
4 The University of Sydney, Camperdown, Australia
5 University of Wollongong, Wollongong, Australia
14 12 2022
11 2022
14 12 2022
27 S5S6
Copyright © 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.
==== Body
pmcIntroduction: General practices have been at the frontline of the primary health response to the SARS-CoV-2 pandemic in Australia. Like the rest of the health system, they have had to rapidly adapt and implement a range of novel infection prevention and control (IPC) strategies. We conducted an interview study to explore not only what kinds of strategies were adopted in general practice, but also how they were adapted to diverse practice settings, and what factors facilitated and challenged their implementation.
Methods: Twenty semi-structured interviews were conducted with general practice personnel working in New South Wales, Australia, including general practitioners (GPs), nurses, practice managers and receptionists, between November 2020 and August 2021.
Results: Participants described implementing a wide-range of strategies across the hierarchy of controls to manage the demands of pandemic IPC. Strategies were creatively adapted (and reinvented) with resourcefulness and agility by participants, in ways that were sensitive to the varied contexts of general practice, and the needs and preferences of individual GPs; as well as responsive to local, State and national requirements, which changed frequently as the pandemic evolved.
Conclusion: Our findings demonstrate how the hierarchy of controls can be applied and extended to guide pandemic IPC in general practice. We show how different controls (particularly engineering and administrative) often functioned in concert within practices; as well as externally. This invites us to consider not only how strategies might be ranked for reliability, but also how healthcare professionals can combine them for greater efficacy.
| 0 | PMC9748719 | NO-CC CODE | 2022-12-15 23:22:46 | no | Infect Dis Health. 2022 Nov 14; 27:S5-S6 | utf-8 | Infect Dis Health | 2,022 | 10.1016/j.idh.2022.09.020 | oa_other |
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