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==== Front Lancet Infect Dis Lancet Infect Dis The Lancet. Infectious Diseases 1473-3099 1474-4457 Elsevier Ltd. S1473-3099(22)00741-1 10.1016/S1473-3099(22)00741-1 Grand Round Monkeypox encephalitis with transverse myelitis in a female patient Cole Joby PhD ae* Choudry Saher MBChB a Kular Saminderjit MBBS b Payne Thomas MBChB c Akili Suha MD ad Callaby Helen BMBS g Gordon N Claire DPhil g Ankcorn Michael PhD ad Martin Andrew MBChB b Hobson Esther PhD cf Tunbridge Anne J MBChB a a Department of Infectious Diseases and Tropical Medicine, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK b Department of Neuroradiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK c Academic Department of Neurology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK d Department of Virology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK e Department of Infection, Immunity, and Cardiovascular Diseases, University of Sheffield, Sheffield, UK f Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, UK g Rare and Imported Pathogens Laboratory, UK Health Security Agency, Porton Down, UK * Correspondence to: Dr Joby Cole, Department of Infectious Diseases and Tropical Medicine, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield S10 2JF, UK 2 12 2022 2 12 2022 © 2022 Elsevier Ltd. All rights reserved. 2022 Elsevier Ltd Elsevier has created a Monkeypox Information Center (https://www.elsevier.com/connect/monkeypox-information-center) in response to the declared public health emergency of international concern, with free information in English on the monkeypox virus. The Monkeypox Information Center is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its monkeypox related research that is available on the Monkeypox Information Center - including this research content - immediately available in publicly funded repositories, with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the Monkeypox Information Center remains active. The 2022 monkeypox outbreak has affected 110 countries worldwide, outside of classic endemic areas (ie, west Africa and central Africa). On July 23, 2022, the outbreak was classified by WHO as a public health emergency of international concern. Clinical presentation varies from mild to life-changing symptoms; neurological complications are relatively uncommon and there are few therapeutic interventions for monkeypox disease. In this Grand Round, we present a case of monkeypox with encephalitis complicated by transverse myelitis in a previously healthy woman aged 35 years who made an almost complete recovery from her neurological symptoms after treatment with tecovirimat, cidofovir, steroids, and plasma exchange. We describe neurological complications associated with orthopoxvirus infections and laboratory diagnosis, the radiological features in this case, and discuss treatment options. ==== Body pmcIntroduction The increase in the number of people with monkeypox outside of classic endemic regions (ie, west Africa and central Africa) was first noted to WHO by the UK Health Security Agency (UKHSA) on May 7, 2022.1 Infections were noted in all WHO regions, with 88% of laboratory-confirmed cases being reported from the European region in the first 2 months of the outbreak.2 Previously, only a small number of cases of monkeypox had been observed outside of Africa.3 Monkeypox infections were first recognised in humans in the 1970s.4 Monkeypox is a zoonotic disease originally described in primates; to date the definitive animal reservoir (ie, the animal species that can host the virus without major evidence of disease so that virus transmission is possible) remains unknown. The disease is caused by an orthopox DNA virus. Currently, whole-genome sequencing data divide monkeypox species into clade 1 (previously known as the central African or Congo Basin clade), responsible for endemic infections in central Africa, and clade 2 (previously known as the west African clade), responsible for endemic infections in west Africa. The 2022 outbreak of monkeypox has spread extensively in areas outside of endemic countries due to subclade 2b,5 and has been shown to disproportionally affect gay and bisexual men who have sex with men.6 Monkeypox virus infection has an incubation period of 3–21 days and is characterised by a prodrome of fever, myalgia, and lethargy, then a characteristic maculopapular rash; it is often a self-limiting illness.6 However, it has been associated with severe disease, and before the 2022 outbreak had a 3–6% mortality rate.2 Clinicians worldwide should be familiar with both the common presentation of genital, skin, and pharyngeal lesions6 and the rarer, life-threatening complications of monkeypox, such as encephalitis. Neurological complications have previously been documented in relation to many viral infections of epidemic potential, including SARS-CoV-2,7 MERS-CoV, Zika virus,8 Ebola virus, smallpox, and monkeypox.9 Encephalopathy, seizures, stroke, and Guillain-Barré syndrome are all recognised as substantial but rare complications which increase both morbidity and mortality.9 Three documented cases of encephalitis during the 2022 monkeypox outbreak have been fatal.10 In this Grand Round, we discuss the case of a female patient with monkeypox infection exacerbated by both encephalitis and transverse myelitis. We highlight the diagnostic tests that were done, the radiological features that were seen, and our rationale for the treatment options chosen. Case report A White woman aged 35 years and born in the UK developed abdominal pain and groin swelling, and the next day developed painful vesicular vulval lesions. She reported unprotected sex with a regular male partner 5 days before these symptoms. Her only medical history was mild gastro-oesophageal reflux, and she had no history of underlying immune deficiency. She had not been vaccinated against orthopoxviruses. On the fourth day of her symptoms, she presented to her local emergency department complaining of a headache. She was assessed the next day in her local sexual health clinic, where review was advised. Monkeypox was considered as a possible diagnosis; swabs of the lesions were tested for herpes simplex virus (HSV) and varicella zoster virus (VZV), but both were negative. A monkeypox virus PCR was positive on the genital lesion swab (table ). Her blood-borne virus screen was negative for HIV, hepatitis B, and hepatitis C. She also tested negative for syphilis. Later that day, she was informed by her partner that he had a confirmed monkeypox infection. Due to severe genital pain and problems with passing urine, she was admitted to the local Infectious Disease Unit on the sixth day of her illness. She was noted to have monkeypox lesions at varying stages of evolution on her limbs, hands, and torso. She had extensive, painful lesions on the vulvovaginal area, with local oedema and groin lymphadenopathy. She was managed conservatively with analgesia and oral antibiotics for secondary vulval cellulitis. In addition to her genital and skin swabs, her throat swab was also positive for monkeypox virus on admission.Table Diagnostic investigations on various days of illness Day 4 Day 9 Day 17 Day 20 Day 25 Cerebrospinal fluid White cell count, cells per μL (lymphocyte %) .. 16* .. 92 (100%) .. Red blood cells, cells per μL .. <1 .. <1 .. Protein, g/L .. 0·4 .. 0·8 .. Glucose .. .. .. .. Cerebrospinal fluid, mmol/L .. 3·4 .. 3·1 .. Plasma glucose, mmol/L .. 4·7 .. 5·5 .. Orthopoxvirus PCR (Ct value) .. Positive (36·8) .. Negative .. Monkeypox virus PCR (Ct value) .. Positive (34·0) .. .. .. HSV, VZV, and enterovirus PCR .. Negative .. Negative .. Cytomegalovirus PCR .. .. .. Negative .. Lesions Orthopoxvirus PCR (Ct value) Positive (18·7) .. .. .. .. Monkeypox virus (Ct value) Positive (16·3) .. .. .. .. Throat swab Orthopoxvirus PCR (Ct value) .. .. Positive (30·4) .. Negative Urine Orthopoxvirus PCR (Ct value) .. .. .. .. Negative Ct=cycle threshold. HSV=herpes simplex virus. VZV=varicella zoster virus. * Differential white cell count not done. On the ninth day of symptoms, she continued to have fever (≥38·1°C) and became drowsy (ie, Glasgow coma scale [GCS] score of 14), needing encouragement to have any food or fluids. She continued to have severe genital pain despite escalating analgesia, with new monkeypox lesions developing on her limbs. After urgent discussion with members of the national monkeypox multidisciplinary team, oral tecovirimat (600 mg) twice per day was started. On the tenth day, she became drowsier and more confused (GCS score of 11) than she had previously been. Encephalitis was suspected, and so a CT scan of the head and a lumbar puncture were done. The lumbar puncture revealed a mild leukocytosis of 16 white blood cells per μL with normal protein and glucose levels (table). Aciclovir and ceftriaxone treatment were started pending further results. Bacterial culture and PCR tests on cerebrospinal fluid samples for HSV, VZV, and enterovirus were negative. Subsequently, an initial orthopoxvirus PCR done by the UKHSA High Containment Microbiology Laboratory was positive with a cycle threshold (Ct) value of 36·8. Confirmatory testing was then performed on two extracts from the cerebrospinal fluid sample with a monkeypox-virus-specific PCR assay; both were positive with Ct values of 34·0 and 36·0. An MRI head scan (figure 1 ) showed multifocal areas of atypical cortical, thalamic, cerebral, and cerebellar white matter T2 hyperintensities. The distribution of the signal change and cortical thickening was radiologically suggestive of encephalitis. Aciclovir was discontinued and tecovirimat was administered via a nasogastric tube (the intravenous formulation was not available in Europe at the time) until the patient was alert enough to swallow. Antibiotics were continued to treat possible secondary skin infection.Figure 1 Initial MRI scan (A–B) Axial proton density images showing a supratentorial white matter atypicality (arrow, A) and cortical swelling (dotted arrow, A). Swelling of both thalami was noted (arrows, B). (C–D) T2-weighted images showed further hyperintensities within the supratentorial white matter (arrow, C), middle cerebellar peduncle (arrow, D), and brainstem (dotted arrow, D). After 10 days of treatment, the patient remained confused, although her GCS score had steadily improved from 8 to 14. She then developed painless urinary retention, and on the next day (day 19 of illness) was noted to have decreased power (power 2 of 5 on a scale) in both legs throughout all muscle groups. After 24 h, this weakness developed into flaccid paralysis of both lower limbs with areflexia and absent sensation to all modalities up to the level of the tenth thoracic vertebrae. Upper limb neurology, including reflexes, was normal. A repeat MRI head scan on day 20 of illness revealed diffuse T2 and Fluid Attenuated Inversion Recovery (FLAIR) hyperintensities in the cerebral periventricular white matter bilaterally, indicative of diffuse encephalitis (figure 2 ). A whole-spine MRI showed multiple central and peripheral intramedullary high T2 signal lesions of varying lengths, with regions of enhancement and associated enhancement of the cauda equina nerve roots (figure 3 ). The appearances of the whole-spine MRI were considered likely to represent extensive myelitis, with features of cauda equina enhancement. A repeat lumbar puncture showed ongoing lymphocytosis, a mild elevation in protein levels, and negative viral (including orthopoxvirus) PCRs (table).Figure 2 Encephalitis changes on MRI of the head (A–E) Axial 3D-FLAIR images showing atypical hyperintensity within the posterior limb of the left internal capsule (arrow, A), bilateral thalami (B), splenium of the corpus callosum (arrow, C), middle cerebellar peduncle (arrow, D), and left side of the medulla (arrow, E). (F–G) Axial T2 sequence highlighting extensive cortical swelling (arrows, F) and resulting early uncal herniation with brainstem mass effect (arrow, G). (H–K) DWI (H, J) and ADC maps (I, K) show patchy, low ADC signal, suggesting reduced diffusivity within the cerebral cortex (arrows, H–I) and the left brachium pontis lesion (arrows, J–K). ADC=apparent diffusion coefficient. DWI=diffusion-weighted imaging. FLAIR=Fluid Attenuated Inversion Recovery. Figure 3 Transverse myelitis on MRI of the spine (A–B) Sagittal short-tau inversion recovery sequence showing extensive transverse myelitis of the spinal cord with long segments of T2 hyperintensity and cord swelling in the cervicothoracic (arrow, A) and lumbar (arrow, B) cord. (C–D) Pre-contrast and post-contrast sagittal T1 sequences showing avid enhancement of the cauda equina nerve roots. (E–F) Axial T2 through the upper and mid cervical spine showing signal hyperintensity involving central grey and peripheral white matter (arrows). (G–H) Post-contrast axial T1 imaging through the upper and mid cervical spine showing patchy enhancement within the cervical spine (arrows). (I–J) Axial, post-contrast T1 imaging through the cauda equina nerve roots showing enhancement of the cauda equina nerve roots. After discussion with the national monkeypox multidisciplinary team and the national encephalitis multidisciplinary team, the neurological complications of longitudinally extensive transverse myelitis (LETM) were considered to be secondary to a post-infectious, immune-mediated event. The patient had no history of CNS demyelination or optic neuritis. Because of the presence of ongoing skin lesions and the risks associated with immunosuppression in the context of ongoing infection, she was treated with methylprednisolone on day 22 of illness and a single dose of cidofovir on day 24 of illness. Her cognitive function improved during the next 5 days; mini-Addenbrooke's Cognitive Examination score was 17 of 30 initially on day 22 of illness, improving to 21 of 30 on day 26 of illness. The power in her lower limbs started to recover to power 2 of 5 in both legs, with sensation to light touch returning. The course of tecovirimat was extended to 19 days, at which point treatment was discontinued due to abdominal pain (day 28 of illness). After further discussion with the neurology team, the patient was switched to high-dose prednisolone (60 mg once per day), and a 14-day course of plasmapheresis was started on day 35 of illness. After seven plasma exchanges as part of the plasmapheresis, a reducing dose of prednisolone, and an extended period of rehabilitation, the patient was able to walk independently after her discharge from hospital. She remains healthy and had recovered from almost all her neurological deficits 3 months after her initial infection. Neurological complications of epidemic viral infections To our knowledge, this is the first reported case of PCR-confirmed monkeypox encephalitis complicated by post-infectious LETM in a woman. On Sept 23, 2022, two cases of monkeypox-associated encephalitis were reported in healthy gay and bisexual men who have sex with men.11 Encephalitis has been reported as part of the current outbreak and remains a serious and sometimes fatal complication.2, 12 Two cases of encephalitis in patients with confirmed monkeypox virus from skin lesions and a report of three cases in suspected monkeypox exist in the literature; these cases have predominantly been in children.12 Altered mental state (eg, confusion and central nervous deficits) was a recognised feature of smallpox with encephalomyelitis documented after both variola virus (ie, smallpox) and vaccinia virus (ie, post-smallpox vaccination).9, 10, 13, 14, 15 Histological findings in people who have died of smallpox and had neurological complications showed acute perivenular demyelination.14 Encephalitis from smallpox occurred in approximately 1 in 500 patients with variola major and 1 in 2000 patients with variola minor presenting at 6–10 days of illness.9, 14 Post-infectious isolated transverse myelitis is around 27% of post-infectious CNS neurological syndromes, as observed in a previous prospective cohort study.16 44% of patients with post-infectious isolated transverse myelitis did not have detectable aquaporin-4 antibodies. Most cases were responsive to steroids. Predictors of poor outcomes in post-infectious transverse myelitis are increased spinal cord involvement, increased disability at onset, and sphincter involvement.16, 17 A small number of cases of both transverse myelitis and acute disseminated encephalomyelitis have been reported after smallpox vaccination; however, post-infectious isolated transverse myelitis after active monkeypox infection is a newly described occurrence.18, 19 Laboratory diagnostics Due to the advances in molecular testing, there is increased diagnostic ability to identify the underlying pathogen of monkeypox, as well to monitor treatment. All monkeypox patients are recommended to have swabs from throat and skin lesions sent for molecular testing.20 If there are concerns of CNS involvement, then cerebrospinal fluid testing could be informative. A previous case of imported monkeypox, acquired from close contact with prairie dogs, had detectable IgM for orthopoxvirus in cerebrospinal fluid.21 The patient in this Grand Round had two lumbar punctures that showed an evolving lymphocytosis and raised protein levels. The initial sample was positive for monkeypox virus by PCR (table). Therefore, we hypothesise that this is the first reported case in the current outbreak of monkeypox to show the presence of monkeypox virus in cerebrospinal fluid with encephalitis, suggesting that there is direct viral invasion of the cerebrospinal fluid. The second lumbar puncture was negative for monkeypox virus, suggesting that LETM might be secondary to post-infectious autoimmunity rather than direct viral invasion. Other common infectious pathogens known to cause encephalitis should also be considered and tested for in the cerebrospinal fluid, if clinically relevant. These pathogens include HSV-1; HSV-2; VZV; cytomegalovirus; Epstein-Barr virus; West Nile virus; Borrelia burgdorferi (ie, lyme disease); syphilis; and cultures for bacterial, mycobacterial, and fungal pathogens.22 The analysis of cerebrospinal fluid can provide insight into the causes of neurological symptoms. Infectious causes are usually associated with increased opening pressures, increased leucocyte counts, reduced glucose, and increased protein levels, whereas non-infectious causes are often associated with normal opening pressures, normal glucose levels, increased protein levels, and increased leukocyte counts.22 Other markers for other non-infectious causes of myelitis should also be considered. Aquaporin-4, myelin oligodendrocyte glycoprotein antibodies, and oligoclonal bands on cerebrospinal fluid were tested and were negative in our case, as were serum antinuclear antibodies, extractable nuclear antigens, cerebrospinal fluid and serum for paraneoplastic autoantibodies, and autoantibodies to glial fibrillary acidic protein.22 Radiological features The imaging features seen in this female patient with monkeypox infection differ from features seen in other common viral encephalitis. For example, the most common cause of viral encephalitis is HSV, which typically has involvement of the mesolimbic system, insular cortex, and cingulate gyrus, affecting one or both cerebral hemispheres.23 In 2020, there were several documented cases of COVID-19 encephalitis, in which commonly described findings include venous sinus thrombosis, grey matter signal changes, and microhaemorrhages.24 Other non-infectious differential diagnoses to consider in our case would include Creutzfeldt-Jakob disease, autoimmune encephalitis, and hypoxic–ischaemic injury. However, the clinical picture and radiology seen in this case are not typical for these diagnoses. In this female patient, the initial MRI examination showed diffuse T2 hyperintensities throughout the cerebral white matter, with further hyperintensities in both thalami, the left middle cerebellar peduncle, and the brainstem. There was also diffuse T2 hyperintensity of the cerebral cortices. These appearances were considered suspicious for an encephalitic process. 10 days after treatment, new signs of areflexia and reduced lower limb power prompted repeat MRI examination. This showed increased T2 and FLAIR hyperintense signal change in the cerebral white matter. In addition to the established signal change in the thalami, new hyperintensities in the posterior limb of the left internal capsule and splenium of the corpus callosum were also identified. There was increased diffuse cerebral and new cerebellar cortical swelling with some patchy areas of low apparent diffusion coefficient signal, implying restricted diffusion (figure 1). These findings were suggestive of an acute phase of encephalitis. Previously noted lesions in the left middle cerebellar peduncle and brainstem were again evident. However, they had increased in size and now also showed regions of isointense and low apparent diffusion coefficient signals consistent with reduced diffusivity. There was no evidence of any intracranial, pathological contrast enhancement. Spinal imaging was also done (figure 3), which displayed long segments of T2 and short-tau inversion recovery hyperintense signal and cord swelling along the whole length of the spinal cord, involving both grey and white matter tracts. Post-contrast imaging showed patchy foci of enhancement in the cervical spine and avid enhancement of the cauda equina nerve roots. These imaging features were consistent with LETM and are likely to correspond with the acute deterioration of the patient. Follow-up MRI imaging done 9 days later showed reduced cortical swelling with some modest reduction in the volume of intracranial T2 and FLAIR signal change. There was also a reduction in signal change and swelling of the spinal cord, with some improvement in cord enhancement. Enhancement of the cauda equina nerve roots remained unchanged. Treatment options The optimal antiviral treatment for monkeypox disease and associated complications is not known, but options include tecovirimat,25, 26 cidofovir, brincidofovir, and vaccinia immunoglobulin. The current recommendations for treatment of monkeypox disease in the UK were published on Sept 20, 2022.27 In this patient, oral or nasogastric tecovirimat were initiated (intravenous tecovirimat was not available in Europe at the time) when encephalitis was suspected as tecovirimat has been shown to cross the blood–brain barrier in animal studies. However, no human data for cerebrospinal fluid penetration of the drug has been published.25 Because of the new neurological symptoms in our patient, a second antiviral cidofovir was administered. Although cidofovir does not show good penetration of the blood–brain barrier, there might be synergy between antivirals.26 Brincidofovir, an oral lipid prodrug of cidofovir, has been shown to be synergistic with tecovirimat in both cell culture and mouse orthopoxvirus models,28 but brincidofovir is not readily available in the UK. These murine studies have shown encouraging findings for the use of tecovirimat, and future trials will seek to confirm them in humans. Furthermore, many patients who have been admitted to or treated in hospitals have been recruited to observational studies that will provide detailed outcome data. Because of the LETM and substantial neurological symptoms our patient had, which were thought to be secondary to a post-infectious, autoimmune event, and because of the poor prognostic markers, we proceeded to treat her with methylprednisolone and plasmapheresis, as they have previously been shown to be beneficial in acute CNS inflammatory demyelinating disease.29 At her 3-month follow-up, plasmapheresis did not appear to have reactivated monkeypox infection, and could therefore be a safe approach in similar patients. Conclusion Because of negative outcomes of the cases of encephalitis in Spain,2 we would like to highlight the positive outcome in our case with an unusual presentation of neurological sequelae of monkeypox infection. We believe the care of our patient was helped by fast involvement of appropriate national multidisciplinary teams and early initiation of antiviral therapy, alongside active management of transverse myelitis. Future research There is a clear need for ongoing epidemiological surveillance to establish whether the current monkeypox outbreak will lead to transmission into novel animal reservoirs, allowing it to become endemic in countries outside of Africa. Current treatment options have not been evaluated in human clinical trials and ongoing efforts to evaluate the use of these drugs are currently happening. Vaccination with smallpox vaccines has formed an important part of the public health response to date, but how efficacious this will be against monkeypox virus remains unclear. Declaration of interests We declare no competing interests. Acknowledgments We thank the High Consequence Infectious Diseases airborne network for invaluable discussions and advice. We also thank the national encephalitis multidisciplinary team for input. We thank Marian Killip (High Containment Microbiology Laboratory, Colindale) who did thorough PCR work on the cerebrospinal fluid sample and Mehmet Yavuz (Sheffield Virology Laboratory, Sheffield) who did and developed the local PCR assay. We would like to thank the patient, who has provided written informed consent. Contributors JC is the primary author of the article. JC and SC drafted the initial manuscript and did the literature searches. AJT supervised manuscript planning. JC, SC, and AJT were part of the infectious diseases team that provided direct clinical care to the patient. TP and EH were part of the neurology team that provided consultation and direct clinical care to the patient. SK and AM reported the radiological examinations of the patient. SA and MA provided local virology input. HC and NCG provided virology input from the UK Health Security Agency reference laboratory. All authors participated in manuscript revision, agreed to submit the manuscript, and approved the final version of the manuscript. All clinical authors had full access to the clinical data. ==== Refs References 1 WHO Monkeypox—United Kingdom of Great Britain and Northern Ireland. 2022 https://www.who.int/emergencies/disease-outbreak-news/item/2022-DON381 2 WHO Emergency situation reports. 2022 https://www.who.int/emergencies/situation-reports 3 Adler H Gould S Hine P Clinical features and management of human monkeypox: a retrospective observational study in the UK Lancet Infect Dis 22 2022 1153 1162 35623380 4 Breman JG Kalisa-Ruti K Steniowski MV Zanotto E Gromyko AI Arita I Human monkeypox, 1970–79 Bull World Health Organ 58 1980 165 182 6249508 5 Gigante CM Korber B Seabolt MH Multiple lineages of monkeypox virus detected in the United States, 2021–2022 Science 2022 published online Oct 20. 10.1126/science.add4153 6 Patel A Bilinska J Tam JCH Clinical features and novel presentations of human monkeypox in a central London centre during the 2022 outbreak: descriptive case series BMJ 378 2022 e072410 35902115 7 Sharifian-Dorche M Huot P Osherov M Neurological complications of coronavirus infection; a comparative review and lessons learned during the COVID-19 pandemic J Neurol Sci 417 2020 117085 32871412 8 Muñoz LS Parra B Pardo CA Neurological implications of Zika virus infection in adults J Infect Dis 216 suppl 10 2017 S897 S905 29267923 9 Billioux BJ Mbaya OT Sejvar J Nath A Neurologic complications of smallpox and monkeypox: a review JAMA Neurol 2022 published online Sept 20. 10.1001/jamaneurol.2022.3491 10 Kozlov M How deadly is monkeypox? What scientists know Nature 609 2022 663 36100744 11 Pastula DM Copeland MJ Hannan MC Two cases of monkeypox-associated encephalomyelitis—Colorado and the District of Columbia, July-August 2022 MMWR Morb Mortal Wkly Rep 71 2022 1212 1215 36136957 12 Pastula DM Tyler KL An overview of monkeypox virus and its neuroinvasive potential Ann Neurol 92 2022 527 531 35932225 13 Barton LL Friedman NR The neurological manifestations of pediatric infectious diseases and immunodeficiency syndromes 2008 Humana Press New Jersey 14 McEntire CRS Song KW McInnis RP Neurologic manifestations of the World Health Organization's list of pandemic and epidemic diseases Front Neurol 12 2021 634827 33692745 15 Shafaati M Zandi M Monkeypox virus neurological manifestations in comparison to other orthopoxviruses Travel Med Infect Dis 49 2022 102414 35926767 16 Marchioni E Ravaglia S Montomoli C Postinfectious neurologic syndromes: a prospective cohort study Neurology 80 2013 882 889 23325908 17 Gastaldi M Marchioni E Banfi P Predictors of outcome in a large retrospective cohort of patients with transverse myelitis Mult Scler 24 2018 1743 1752 28967297 18 Shyamalan NC Singh SS Bisht DB Transverse myelitis after vaccination BMJ 1 1964 434 435 14085976 19 Sejvar JJ Labutta RJ Chapman LE Grabenstein JD Iskander J Lane JM Neurologic adverse events associated with smallpox vaccination in the United States, 2002–2004 JAMA 294 2005 2744 2750 16333010 20 UK Health Security Agency Monkeypox: diagnostic testing. 2022 https://www.gov.uk/guidance/monkeypox-diagnostic-testing 21 Sejvar JJ Chowdary Y Schomogyi M Human monkeypox infection: a family cluster in the midwestern United States J Infect Dis 190 2004 1833 1840 15499541 22 Hrishi AP Sethuraman M Cerebrospinal fluid (CSF) analysis and interpretation in neurocritical care for acute neurological conditions Indian J Crit Care Med 23 suppl 2 2019 S115 S119 31485118 23 Schroth G Gawehn J Thron A Vallbracht A Voigt K Early diagnosis of herpes simplex encephalitis by MRI Neurology 37 1987 179 183 3808297 24 Kandemirli SG Dogan L Sarikaya ZT Brain MRI findings in patients in the intensive care unit with COVID-19 infection Radiology 297 2020 E232 E235 32384020 25 European Medicines Agency Tecovirimat SIGA. 2021 https://www.ema.europa.eu/en/medicines/human/EPAR/tecovirimat-siga 26 Siegrist EA Sassine J Antivirals with activity against monkeypox: a clinically oriented review Clin Infect Dis 2022 ciac622 27 National Health Service Central alerting system. 2022 https://www.cas.mhra.gov.uk/ViewandAcknowledgment/ViewAlert.aspx?AlertID=103213 28 Quenelle DC Prichard MN Keith KA Synergistic efficacy of the combination of ST-246 with CMX001 against orthopoxviruses Antimicrob Agents Chemother 51 2007 4118 4124 17724153 29 Weinshenker BG O'Brien PC Petterson TM A randomized trial of plasma exchange in acute central nervous system inflammatory demyelinating disease Ann Neurol 46 1999 878 886 10589540	36470282	PMC9718539	NO-CC CODE	2022-12-08 23:18:11	no	Lancet Infect Dis. 2022 Dec 2; doi: 10.1016/S1473-3099(22)00741-1	utf-8	Lancet Infect Dis	2,022	10.1016/S1473-3099(22)00741-1	oa_other
==== Front Artif Intell Rev Artif Intell Rev Artificial Intelligence Review 0269-2821 1573-7462 Springer Netherlands Dordrecht 10346 10.1007/s10462-022-10346-7 Article Rough sets models inspired by supra-topology structures http://orcid.org/0000-0002-8074-1102 Al-shami Tareq M. [email protected] 1 Alshammari Ibtesam [email protected] 2 1 grid.412413.1 0000 0001 2299 4112 Department of Mathematics, Sana’a University, Sana’a, Yemen 2 grid.494617.9 0000 0004 4907 8298 Department of Mathematics, University of Hafr Al Batin, Hafr Al Batin, Saudi Arabia 2 12 2022 129 © The Author(s), under exclusive licence to Springer Nature B.V. 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. Our aim of writing this manuscript is to found novel rough-approximation operators inspired by an abstract structure called “supra-topology”. This approach is more relaxed than topological ones and extends the scope of applications because an intersection condition of topology is dispensed. Firstly, we generate eight types of supra-topologies using Nk-neighborhood systems induced from any arbitrary relation. We elucidate the relationships between them and investigate the conditions under which some of them are identical. Then, we create new rough sets models from these supra-topologies and present the main characterizations of their lower and upper approximations. We apply these approximations to classify the regions of the subset and compute its accuracy measures. The master merits of the current approach are to produce the highest accuracy values compared with all approaches given in the published literature under a reflexive relation as well as preserve the monotonicity property of accuracy and roughness measures. Moreover, we demonstrate the good performance of the followed technique through analysis of some data of dengue fever disease. Ultimately, we debate the advantages and disadvantages of the followed approach and make a plan for some upcoming work. Keywords Nk-neighborhood Supra-topology Supra upper and supra lower approximations Accuracy and roughness measures Dengue fever ==== Body pmcIntroduction In recent years, rough set theory and its extended models have raised more and more scholars attention in various fields; especially, those who work in computer science and artificial intelligence. This theory was introduced by Pawlak (1982), as an effective and robust tool to cope with imperfect knowledge problems. It starts from an equivalence relation to classifying the objects and capture to what extent the information obtained from a set is complete. Two core principles in this theory are approximation operators and accuracy measures which supply the decision-makers with the required data regarding the structure and size of boundary region. A strict stipulation of an equivalence relation limits the applications of conventional rough set theory, so several generalizations of rough set theory have been introduced under an arbitrary relation or a specific relation. Yao (1996, 1998) launched this line of research, in 1996, by defining two types of neighborhoods with respect to an arbitrary relation called “right neighborhood” and “left neighborhood”. Then, some researchers assumed a specific relation to present various types of generalized rough set theory generated from right and left neighborhoods such as similarity (Abo-Tabl 2013; Slowinski and Vanderpooten 2000), tolerance Skowron and Stepaniuk (1996), quasiorder (Qin et al. 2008; Zhang et al. 2009) and dominance Zhang et al. (2016). In the light of this research trend, many authors and scholars made use of some operations between Nk-neighborhoods to explore new types of neighborhood systems; the recent ones of them are Ck-neighborhoods defined using superset relation Al-shami (2021a), Sk-neighborhoods defined using subset relation Al-shami and Ciucci (2022), Ek-neighborhoods defined using intersection relation Al-shami et al. (2021), maximal neighborhoods defined using union relation Dai et al. (2018), core neighborhoods defined using equality relation Mareay (2016), and remote neighborhood Sun et al. (2019). Admittedly, they were created with the goal of improving approximation operators, increasing accuracy measures, and handling some practical problems. In line with this trend, Abu-Donia (2008) displayed new generalized rough set models induced from a finite family of arbitrary relations. Syau et al. (2021) studied the characterization of incomplete decision tables using a variable precision generalized rough set approach. Campagner et al. (2022) reviewed the most relevant contributions studying the links between belief functions and rough sets. Another interesting orientation of study rough sets is a topology. Skowron (1988) and Wiweger (1989) noted the similarity behaviours of topological and rough-set concepts, which implies the possibility of replacement of the rough-set concepts by their topological counterparts. This motivated many researchers to establish some topological structures and study rough-set notions and properties via them. Lashin et al. (2005) proposed a technique to initiate a topology from Nk-neighborhood systems. This technique is based on considering Nk-neighborhoods a subbase utilized to build a topology. Investigation of multi knowledge bases using rough approximations and topology was done by Abu-Donia (2012). Salama (2010) explored the solution of the missing attribute values problem from a topological view. Al-shami (2021b, 2022) benefited from two near open subsets of topological spaces called somewhere dense and somewhat open sets to introduce different types of lower and upper approximations and illustrated their merits compared to the past methods. A lot of published contributions were done to reformulate the rough set notions using topological ideas such as (Abo-Tabl 2014; Hosny 2018; Jin et al. 2021; Kondo and Dudek 2006; Li et al. 2012; Singh and Tiwari 2020; Zhu 2007). Topological structures were used in many applications such as those presented in El-Bably and Abo-Tabl (2021), El-Bably and El-Sayed (2022). In recent years, it has been exploited some topological generalizations such as infra topology Al-shami and Mhemdi (2022), minimal structure (Azzam et al. 2020; El-Sharkasy 2021) and bitopology Salama (2020) to deal with rough set concepts and address some medical problems. Following this line, we suggest novel kinds of rough set models inspired by another abstract structure called “supra-topology”. This concept was introduced by Mashhour (1983), in 1983, as an extension of topology. Afterward, many authors discussed the topological concepts and examined the validity of their characterizations via supra-topological structures. We draw attention to that some topological properties such as Int(X∩Z)=Int(X)∩Int(Z) and Cl(X∪Z)=Cl(X)∪Cl(Z) are evaporated via supra-topology. However, a supra topology frame offers a convenient environment to cope with some real-life problems as illustrated in Kozae et al. (2016). The major inducements to debate rough set models using a “supra-topology” standpoint are, first, to relieve some conditions imposed on topological rough set models, which make us in a position to dispense with some conditions that limit applications. Second, the followed approach preserves most of Pawlak properties of approximation operators, which are evaporated in some previous approaches induced from topological structures such as (Abd El-Monsef et al. 2014; Abu-Donia 2008; Dai et al. 2018; Yao 1996, 1998). Thirdly, the values of accuracy and roughness given herein satisfy the monotonic property. Fourthly, the best approximations and accuracy values produced by our approach are obtained in cases of union and minimal union, whereas they are obtained in cases of intersection and minimal intersection in the previous approaches. This implies our approach is more suitable to analyze and describe the large samples. Finally, the approximation operators and accuracy measures obtained from our approach under a reflexive relation are better than all preceding methods defined by topological structures (Abd El-Monsef et al. 2014; Abo-Tabl 2013; Allam et al. 2006; Al-shami 2021b; Amer et al. 2017; Hosny 2018; Kondo and Dudek 2006; Kozae et al. 2007) and their generalizations (Azzam et al. 2020; El-Sharkasy 2021; Salama 2020) and all the preceding methods directly defined by neighborhood systems (Abu-Donia 2008; Al-shami 2021a, 2022; Dai et al. 2018; Lashin et al. 2005). The rest of this paper is designed as follows. Section 2 mentions the basic principles and results of rough sets and supra-topology required to understand this context as well as elaborates the motivations that led to these developments. In Sect. 3, we show how to construct supra-topology spaces utilizing Nk-neighborhood systems induced from any arbitrary relation. Then, we make use of these spaces to establish new rough set models and scrutinize their fundamental characterizations in Sect. 4. Also, we build an algorithm to illustrate how supra k-exact sets are determined. In Sect. 5, we investigate the effectiveness and robustness of the followed approach to analyze the data of dengue fever disease. In Sect. 6, we present the pros and cons of the followed approach compared to the past ones. In the end, in Sect. 7, we summarize the main contributions and give some thoughts that can be applied to expand the scope of this manuscript. Basic concepts and results We recall, in this section, the principles and results regarding rough sets and supra-topological structures that are required to understand the manuscript context. Also, we tackle the historical development of these concepts as well as the motivations of their study. Moreover, we give proof for equality of accuracy measures induced from Nk-neighborhoods and their counterpart topologies under a quasiorder relation. Rough approximation operators and neighborhood systems Through this manuscript, an approximation space is the ordered pair (E,δ) such that E is a nonempty finite set and δ is an arbitrary relation on E. (E,δ) is called Pawlak approximation space if δ is an equivalent relation, i.e. reflexive, symmetric and transitive. The following definition, introduced by Pawlak (1982), is the cornerstone of this research scope. Definition 1 We associate each subset X of Pawlak approximation space (E,δ) with two sets defined with respect to the equivalences classes E/δ by the next formulas.δ_(X)=∪{U∈E/δ:U⊆X},andδ¯(X)=∪{U∈E/δ:U∩X≠∅} The sets δ_(X) and δ¯(X) are respectively recognized as lower and upper approximations of X. The core properties of these approximations are listed in the next proposition which is the key point of rough set theory. Proposition 1 (Pawlak 1982) Let X and Z be subsets of Pawlak approximation space (E,δ). The next properties are satisfied.(L1)δ_(X)⊆X(U1)X⊆δ¯(X)(L2)δ_(∅)=∅(U2)δ¯(∅)=∅(L3)δ_(E)=E(U3)δ¯(E)=E(L4)IfX⊆Z,thenδ_(X)⊆δ_(Z)(U4)IfX⊆Z,thenδ¯(X)⊆δ¯(Z)(L5)δ_(X∩Z)=δ_(X)∩δ_(Z)(U5)δ¯(X∩Z)⊆δ¯(X)∩δ¯(Z)(L6)δ_(X)∪δ_(Z)⊆δ_(X∪Z)(U6)δ¯(X∪Z)=δ¯(X)∪δ¯(Z)(L7)δ_(Xc)=(δ¯(X))c(U7)δ¯(Xc)=(δ_(X))c(L8)δ_(δ_(X))=δ_(X)(U8)δ¯(δ¯(X))=δ¯(X)(L9)δ_((δ_(X))c)=(δ¯(X))c(U9)δ¯((δ¯(X))c)=(δ¯(X))c(L10)IfX∈E/δ,thenδ_(X)=X(U10)IfX∈E/δthenδ¯(X)=X The approximation operators are exploited to divide every subset into three regions helping us to specify the knowledge induced from a subset and discover its structure. Definition 2 (Pawlak 1982) Every subset X of Pawlak approximation space (E,δ) is associated with three regions called positive, boundary, and negative. They are respectively given by the following formulas.P+(X)=δ_(X),B(X)=δ¯(X)\δ_(X),P-(X)=E\δ¯(X) To capture the degree of completeness and incompleteness of knowledge obtained from a subset, the next measures were familiarized. Definition 3 (Pawlak 1982) Every subset X of Pawlak approximation space (E,δ) is associated with two measures (or values) called accuracy and roughness measures. They are respectively defined as follows.M(X)=∣δ_(X)∣∣δ¯(X)∣,whereXis nonempty.R(X)=1-M(X). As it is well known the equivalence relation limits the application scope of rough sets, this motivated Yao (1996, 1998) to come up with a brilliant idea called “right and left neighborhoods” which are formulated under any arbitrary relation as follows. Definition 4 (Yao 1996, 1998) Let (E,δ) be an approximation space (herein, δ is an arbitrary relation need not be an equivalence relation). ThenThe right neighborhood ofw∈E,denoted byNr(w),is defined byNr(w)={x∈E:(w,x)∈δ},andThe left neighborhood ofw∈E,denoted byNl(w),is defined byNl(w)={x∈E:(x,w)∈δ}. Then, Yao formulated the approximation operators with respect to right and left neighborhoods as follows. Definition 5 (Yao 1996, 1998) For k∈{r,l}, the k-lower and k-upper approximations induced from an approximation space (E,δ) are defined as follows.δ_k(X)={w∈E:Nk(w)⊆X},andδ¯k(X)={w∈E:Nk(w)∩X≠∅} Remark 1 It should be noted that some features of Pawlak approximation space, displayed in Proposition 1, are lost, for instance, the properties report that “the k-lower approximation of the empty set is empty” and “the k-upper approximation of the universal set is the universal set” are generally false. Also, the distributive properties of intersection and union under k-lower and k-upper approximations, respectively, are evaporated. Posteriorly, the researchers investigated different types of generalized rough sets with aim of increasing the accuracy measures and improving the approximations of rough subsets. These efforts produced several types of neighborhood systems listed in the following. Definition 6 (Abd El-Monsef et al. 2014; Allam et al. 2005, 2006) For k∈{⟨r⟩,⟨l⟩,i,u,⟨i⟩,⟨u⟩}, the k-neighborhoods of each w∈E, denoted by Nk(w), induced from an approximation space (E,δ) are formulated as follows. (i) N⟨r⟩(w)=⋂w∈Nr(x)Nr(x)thereexistsNr(x)includingw∅Otherwise (ii) N⟨l⟩(w)=⋂w∈Nl(x)Nl(x)thereexistsNl(x)includingw∅Otherwise (iii) Ni(w)=Nr(w)∩Nl(w). (iv) Nu(w)=Nr(w)∪Nl(w). (v) N⟨i⟩(w)=N⟨r⟩(w)∩N⟨l⟩(w). (vi) N⟨u⟩(w)=N⟨r⟩(w)∪N⟨l⟩(w). The neighborhoods above were applied to introduce novel kinds of approximation operators following similar technique given in Definition 5. We draw attention to the shortcoming caused by using Pawlak accuracy measures when δ is not a reflexive relation, that is, we sometimes obtain accuracy measures greater than one or undefined as illustrated in the next example. Example 1 Consider δ={(w,w),(w,x),(x,y)} is a relation on E={w,x,y,z}. It is clear that Nr(w)={w,x}, Nr(x)={y} and Nr(y)=Nr(z)=∅. It follows from Definition 5 that δ_k({x,y})={x,y,z} and δ¯k({x,y})={w,x}. According to Definition 3 we find M({x,y})=32>1 which is a contradiction. Also, note that δ_k({z})={y,z} and δ¯k({z})=∅, which means that M({z}) is undefined. To get rid of these failures, it was proposed a slight modification for accuracy measure definition as given below. Definition 7 (Abd El-Monsef et al. 2014; Allam et al. 2005, 2006; Yao 1996, 1998) For each k, the accuracy measures of a set X in an approximation space (E,δ) is given byMk(X)=∣δ_k(X)∩X∣∣δ¯k(X)∪X∣,whereXis nonempty. Remark 2 (i) If δ is a quasiorder (reflexive and transitive), then Nk=N⟨k⟩ for each k∈{r,l,i,u} (ii) If δ is reflexive, then the formula given in Definition 7 is written as follows Mk(X)=∣δ_k(X)∣∣δ¯k(X)∣. To investigate the monotonicity property of our accuracy and roughness measures, the next result will be helpful. Proposition 2 (Al-shami 2022) Let (E,δ1) and (E,δ2) be approximation spaces such that δ1⊆δ2. Then N1k(w)⊆N2k(w) for each w∈E and k∈{r,l,i,u}. Definition 8 (see, Al-shami 2022) We call the approximations spaces (E,δ1) and (E,δ2) have the property of monotonicity accuracy (resp. monotonicity roughness) if Mδ1(X)≥Mδ2(X) (resp., Mδ1(X)≤Mδ2(X)) whenever δ1⊆δ2. Rough set concepts via topological structures A subcollection τ of the power set of a nonempty set E is called a topology on E provided that it is closed under finite intersection and arbitrary union as well as ∅ and E are members of τ. Pawlak (1982) noted that the equivalences classes form a base for a specified type of topology (known as a quasi-discrete topology), which means there is a similarity between the behaviors of some topological and rough set concepts, for example, interior topological operator and lower approximation, and closure topological operator and upper approximation. Then, Skowron (1988) and Wiweger (1989) studied topological structures of rough sets. These pioneering works paved to conducting deep investigations concerning rough set concepts from a topological standpoint. Later on, Nk-neighborhood systems were used to establish new sorts of rough approximations inspired by topological structures. One of the suggested manners to do that is demonstrated by the next interesting result. Theorem 1 Abd El-Monsef et al. (2014) The topology on E generated from an approximation space (E,δ) given by τk={U⊆E:Nk(w)⊆U for each w∈U} for each k. The approximation rough operators were familiarized topologically depending on Theorem 1 as follows. Definition 9 (Abd El-Monsef et al. 2014) The k-lower and k-upper approximations and accuracy measure of a set X induced from a topological space (E,τk) are respectively given byO_k(X)=∪{U∈τk:U⊆X},O¯k(X)=∩{F:Fc∈τkandX⊆F},andTk(X)=∣O_k(X)∣∣O¯k(X)∣,whereXis nonempty. The regions of a subset were formulated using O_k(X) and O¯k(X) following a similar manner to their counterparts given in Definition 2. Now, we prove that accuracy measures produced by Nk-neighborhood systems are better than accuracy measures produced by topological approaches under an arbitrary relation, then we demonstrate that they are identical under a quasiorder relation. Proposition 3 For every subset X of an approximation space (E,δ), we have Tk(X)≤Mk(X). Proof Suppose that w∈O_k(X). Then, there exists U∈τk such that w∈U⊆X and Nk(U)⊆U. This implies that Nk(w)⊆Nk(U)⊆X, so w∈δ_k(X). This means that1 ∣O_k(X)∣≤∣δ_k(X)∩X∣ Now, let w∉O¯k(X). Then, there exists U∈τk such that w∈U and U∩X=∅, so U⊆Xc. Therefore, Nk(w)⊆Nk(U)⊆Xc. This means that Nk(w)∩X=∅, thus w∉δ¯k(X). Hence, we get2 ∣δ¯k(X)∪X∣≤∣O¯k(X)∣ It follows from (1) and (2) that ∣O_k(X)∣∣O¯k(X)∣≤∣δ_k(X)∩X∣∣δ¯k(X)∪X∣. This completes the proof that Tk(X)≤Mk(X). □ To illustrate that the converse need not be true, consider a subset {y,z} of an approximation space (E,δ) given in Example 1. Now, τr={∅,E,{y},{z},{y,z},{x,y},{x,y,z},{w,x,y}}. By calculation, we find that Mr({y,z})=23 whereas Tr({y,z})=12. Proposition 4 Let (E,δ) be an approximation space such that δ is quasiorder. Then Tk(X)=Mk(X) for every X⊆E. Proof By proposition 3, we have Tk(X)≤Mk(X). To prove that Mk(X)≤Tk(X), it suffices to show that Nk(w)∈τk for each w∈E. In other words, Nk(w)=Nk(Nk(w)). It follows from the reflexivity of δ that Nk(w)⊆Nk(Nk(w)). Conversely, without lose of generality, we consider k=r. let x∈Nr(Nr(w)). Then there is z∈Nr(w) such that (z,x)∈δ. Now, we have (w,z)∈δ. By transitivity of δ we obtain (w,x)∈δ, which means that x∈Nr(w). Thus, Nr(Nr(w))⊆Nr(w). Hence, Tk(X)=Mk(X), as required. □ Remark 3 There are different methods to form a topological structure from Nk-neighborhood systems such as studied by Lashin et al. (2005). Its methodology depends on considering the collection {Nk(w):w∈E} as a subbase for a topology on E. In 1983, Mashhour (1983) extended the concept of topology to “supra-topology” by neglecting the intersection condition. That is, a supra-topology is defined on a nonempty set E as a subcollection U of the power set of E satisfying two axioms 1)∅,E∈U, and 2) U is closed under arbitrary union. Definition 10 (Mashhour 1983) Let U be a supra-topology on E and X⊆E. We call X a supra-open (resp. supra-closed) set if it is a member of U (resp., its complement belongs to U). We define the supra-interior points of a set X, denoted by Int(X), as a union of all supra-open subsets of this set, and we define the supra-closure points of a set, denoted by Cl(X), as the intersection of all supra-closed supersets of this set. Generating supra-topologies from Nk-neighborhoods induced by an arbitrary relation We dedicate this part to introducing new techniques for initiating supra-topologies from Nk-neighborhoods under any arbitrary relation. With the help of an illustrative example, we make some comparisons between these supra-topologies and determine under which relations we get equivalences between some of them. As we note from the techniques in the published literature that the largest structures are obtained in cases of intersection and minimal intersection, whereas our techniques produce the largest structures in cases of union and minimal union, which is convenient to model some phenomena. Let us start with the next lemma which assists us to prove that Nk-neighborhood systems are closed under a union operator. Lemma 1 If Nk-neighborhood systems are induced from an approximation space (E,δ), then Nk(X∪Z)=Nk(X)∪Nk(Z) for each X,Z⊆E. Proof It is apparent that Nk(X)⊆Nk(X∪Z) and Nk(Z)⊆Nk(X∪Z), so Nk(X)∪Nk(Z)⊆Nk(X∪Z). Conversely, let w∈Nk(X∪Z). Then there exists x∈X∪Z such that w∈Nk(x). This implies that Nk(x)⊆Nk(X) or Nk(x)⊆Nk(Z). Accordingly, w∈Nk(X)∪Nk(Z), which means that Nk(X∪Z)⊆Nk(X)∪Nk(Z). Hence, the proof is complete. □ The next result presents a method of generating supra-topology structures from Nk-neighborhood systems. Theorem 2 Assume (E,δ) is an approximation space. Then, the collection U={E}∪{U⊆E:U⊆Nk(U)} forms a k-supra topology on E. Proof According to the collection definition, E∈U, also, Nk(∅)=∅ which means that ∅∈U. To prove that U is closed under union operator, let U1,U2∈U. Then U1⊆Nk(U1) and U2⊆Nk(U2). We automatically obtain U1∪U2⊆Nk(U1)∪Nk(U2). By Lemma 1, we get U1∪U2⊆Nk(U1∪U2). Thus, U1∪U2∈U. Hence, U is a supra-topology on E, as required. □ Definition 11 The triple system (E,δ,Uk) is said to be a k-supra topological space (briefly, kSTS), where Uk is a k-supra topology on E generated by Theorem 2. We call a subset of E a k-supra open set if it is a member of Uk, and we call a subset of E a k-supra closed set if its complement is a member of Uk. The family of all k-supra closed subsets of E will be denoted by Ukc. It is very important to note that the collection given in Theorem 2 need not be a topology, which makes this method is completely different than a method given in Theorem 1. To validate this note, consider {w,x} and {w,y} which are members of Uu given in Example 2; obviously, their intersection {w} is not a member of Uu. To investigate these structures topologically, we need to put forward the counterparts of interior and closure topological operators. Definition 12 The k-supra interior and k-supra closure points of a subset X of a kSTS (E,δ,Uk) are defined respectively byIntk(X)=∪{G∈Uk:G⊆X},andClk(X)=∩{Y∈Ukc:X⊆Y} The next example demonstrates how to produce k-supra topologies from an approximation space. Example 2 Consider δ={(y,y),(w,x),(w,y),(z,x)} is a binary relation on E={w,x,y,z}. Then, we first compute a neighborhood of each point in E in the Table 1. Table 1 Nk-neighborhoods w x y z Nr {x,y} ∅ {y} {x} Nl ∅ {w,z} {w,y} ∅ Ni ∅ ∅ {y} ∅ Nu {x,y} {w,z} {w,y} {x} N⟨r⟩ ∅ {x} {y} ∅ N⟨l⟩ {w} ∅ {w,y} {w,z} N⟨i⟩ ∅ ∅ {y} ∅ N⟨u⟩ {w} {x} {w,y} {w,z} According to Definition 11, the k-supra topologies Uk generated from these neighborhoods are the following.3 Ur={∅,E,{y}};Ul={∅,E,{y},{w,y}};Ui={∅,E,{y}};Uu={∅,E,{y},{w,x},{w,y},{x,z},{w,x,y},{w,x,z},{x,y,z}};U⟨r⟩={∅,E,{x},{y},{x,y}};U⟨l⟩={∅,E,{w},{y},{z},{w,y},{w,z},{y,z},{w,y,z}};U⟨i⟩={∅,E,{y}};U⟨u⟩=P(E). Now, we reveal the relationships between these structures and study the conditions under which some of these structures are identical. Proposition 5 Let (E,δ,Uk) be a kSTS. Then (i) Ui⊆Ur⊆Uu. (ii) Ui⊆Ul⊆Uu. (iii) U⟨i⟩⊆U⟨r⟩⊆U⟨u⟩. (iv) U⟨i⟩⊆U⟨l⟩⊆U⟨u⟩. Proof To prove (i) and (ii), let X be a set in Ui. Then X⊆Ni(X). It is well recognized that Ni(X)⊆Nr(X) and Ni(X)⊆Nl(X), so X⊆Nr(X) and X⊆Nr(X). This means that X∈Ur and X∈Ul. Thus, Ui⊆Ur and Ui⊆Ul. Similarly, we prove that Ur⊆Uu and Ui⊆Uu. Following similar arguments, (iii) and (iv) are proved. □ Corollary 1 Let (E,δ,Uk) be a kSTS and X⊆E. Then (i) Inti(X)⊆Intr(X)⊆Intu(X) and Clu(X)⊆Clr(X)⊆Cli(X). (ii) Inti(X)⊆Intl(X)⊆Intu(X) and Clu(X)⊆Cll(X)⊆Cli(X). (iii) Int⟨i⟩(X)⊆Int⟨r⟩(X)⊆Int⟨u⟩(X) and Cl⟨u⟩(X)⊆Cl⟨r⟩(X)⊆Cl⟨i⟩(X). (iv) Int⟨i⟩(X)⊆Int⟨l⟩(X)⊆Int⟨u⟩(X) and Cl⟨u⟩(X)⊆Cl⟨l⟩(X)⊆Cl⟨i⟩(X). It follows from Example 2 and Example 3 that relations given in the four items of Proposition 5 are proper. Also, these examples show that Ur and Ul (U⟨r⟩ and U⟨l⟩) are independent of each other. Moreover, these examples demonstrate the converses of the four items of Corollary 1 are false in general. Proposition 6 Let (E,δ,Uk) be a kSTS such that δ is symmetric. Then (i) Ur=Ul=Ui=Uu. (ii) U⟨r⟩=U⟨l⟩=U⟨i⟩=U⟨u⟩. Proof Follows from the fact that Nr(w)=Nl(w) and N⟨r⟩(w)=N⟨l⟩(w) under a symmetric relation. □ Corollary 2 Let X be a subset of a kSTS (E,δ,Uk). If δ is symmetric, then (i) Intu(X)=Intr(X)=Intl(X)=Inti(X) and Clu(X)=Clr(X)=Cll(X)=Cli(X). (ii) Int⟨u⟩(X)=Int⟨r⟩(X)=Int⟨l⟩(X)=Int⟨i⟩(X) and Cl⟨u⟩(X)=Cl⟨r⟩(X)=Cl⟨l⟩(X)=Cl⟨i⟩(X). Example 2 emphasizes that the symmetry condition of Proposition 6 and Corollary 2 is indispensable. Recall that a relation δ is called serial if every element has a nonempty Nr-neighborhood. And it is called inverse serial (or surjective) if every element has a nonempty Nl-neighborhood. Proposition 7 If δ is an inverse serial relation on E, then U⟨r⟩ and U⟨u⟩ are identical; moreover, they are discrete topologies. Proof Since δ is an inverse serial relation, we have ⋃w∈ENr(w)=E. This means that N⟨r⟩(w)≠∅. In this case we have w∈N⟨r⟩(w) for each w∈E. This implies that any singleton subset of E is an r-supra open. Hence, U⟨r⟩ is a discrete topology. Since U⟨r⟩⊆U⟨u⟩, we obtain U⟨u⟩ is also a discrete topology. □ Proposition 8 If δ is a serial relation on E, then U⟨l⟩ and U⟨u⟩ are identical; moreover, they are discrete topologies. Proof Similar to the proof of Proposition 7. □ Corollary 3 If a relation δ is serial and inverse serial on E, then all U⟨k⟩ are identical; moreover, they are discrete topologies. In Example 2, note that Nr(x)=Nl(z)=∅, which means that a relation δ is neither serial nor inverse serial. On the other hand, U⟨u⟩ is a discrete topology. So that, the converses of Proposition 7, Proposition 8 and Corollary 3 are false in general. The significance of the following result is that we will rely on it to prove that the method introduced in the next section is better than all previous ones to produce approximations and accuracy values under a reflexive relation. Proposition 9 If δ is a reflexive relation on E, then all Uk are discrete topologies. Proof Let U be an arbitrary subset of E. Since δ is a reflexive relation, we obtain U⊆Nk(U) for each k. Therefore, U∈Uk, which means that every subset of E belongs to Uk. Hence, Uk is the discrete topology on E, as required. □ Again, Example 2 serves as a fantastic tool to illustrate the invalidity of some results. It illuminates that the converse of Proposition 9 is generally not true. Note that U⟨u⟩ is a discrete topology in spite of δ is not reflexive. Proposition 10 A relation δ on E is reflexive iff Uk is a discrete topology, where k∈{r,l,i}. Proof The condition of necessity comes from Proposition 9. We prove the sufficient part for k=r and the other two cases are proved similarly. Since Ur is discrete, we have {w}∈Ur for each w∈E. This means that {w}⊆Nr(w), i.e. (w,w)∈δ for each w∈E. Hence, δ is reflexive. □ The following result will help us to prove the monotonicity and roughness properties of rough sets models presented in the next section. Proposition 11 Let (E,δ1,U1k) and (E,δ2,U2k) be kSTSs such that δ1⊆δ2. Then U1k⊆U2k for each k∈{r,l,i,u}. Proof Let X be a set in U1k, where k∈{r,l,i,u}. Then X⊆N1k(X). Since δ1⊆δ2 we get N1k(X)⊆N2k(X). So that, X⊆N2k(X), which means that X∈U2k. Hence, we obtain the desired result. □ New rough models generated by supra-topology In this section, we will establish novel rough models depending on k-supra topologies induced from Nk-neighborhood systems. We investigate their main properties and give an algorithm to illustrate how the supra accuracy values are calculated. To show the importance of these models, we elucidate that they improve the approximations and produce accuracy values which are better than all previous ones if the relation is reflexive. Supra k-lower and supra k-upper approximations Definition 13 We define supra k-lower approximation λ_k and supra k-upper approximation λ¯k of a subset X of a kSTS (E,δ,Uk) as follows.λ_k(X)=∪{U∈Uk:U⊆X},andλ¯k(X)=∩{F∈Ukc:X⊆F}. Note that λ_k(X) and λ¯k(X) represent the supra-interior and supra-closure points of X, respectively. Accordingly, we obtain w∈λ¯k(X) iff U∩X≠∅ for each U∈Uk containing w. As a special case, if δ is an equivalence relation, then λ_k(X) and λ¯k(X) represent the lower and upper approximations in sense of Pawlak. Foremost, we prove the first advantage of the current approximations which is to preserve most of properties of Pawlak approximations. Proposition 12 Let X and Z be subsets of a kSTS (E,δ,Uk). Then the next properties are satisfied. (i) λ_k(X)⊆X. (ii) λ_k(∅)=∅. (iii) λ_k(E)=E. (iv) If X⊆Z, then λ_k(X)⊆λ_k(Z). (v) λ_k(Xc)=(λ¯k(X))c. (vi) λ_k(λ_k(X))=λ_k(X). Proof The proofs of (i) and (ii) come from Definition 13. The proof of (iii) comes from the fact that E is the largest supra-open subset of a kSTS (E,δ,Uk). (iv): Let X⊆Z. Then ∪{U∈Uk:U⊆X}⊆∪{U∈Uk:U⊆Z} and so λ_k(X)⊆λ_k(Z). (v): Let w∈λ_k(Xc). Then there is a supra-open set U satisfying w∈U⊆Xc, so U∩X=∅, which means that w∉λ¯k(X). Thus, w∈(λ¯k(X))c. On the other hand, let w∈(λ¯k(X))c. Then w∉λ¯k(X), which means there is a supra-open set U satisfying w∈U and U∩X=∅. So w∈U⊆Xc. Hence w∈λ_k(Xc). (vi): From (i) we get λ_k(λ_k(X))⊆λ_k(X). Conversely, let w∈λ_k(X). Then there is a supra-open set U such that w∈U⊆X. It follows from (iv) that λ_k(U)⊆λ_k(X). According to Definition 13 we have U=λ_k(U), so w∈λ_k(U)⊆λ_k(λ_k(X)). Thus, λ_k(X)⊆λ_k(λ_k(X)). Hence, we get the wished result. □ Corollary 4 Let X and Z be subsets of a kSTS (E,δ,Uk). Then λ_k(X∩Z)⊆λ_k(X)∩λ_k(Z) and λ_k(X)∪λ_k(Z)⊆λ_k(X∪Z). Proof Directly follows from (iv) of Proposition 12. □ In a uSTS (E,δ,Uu) given in eq. (3) consider X={w,x}, Y={x,y} and Z={w,y,z}. Then λ_u(Z)={w,y}⊂Z. λ_u(Y)={y}⊂λ_l(Z)={w,y} whereas Y⊈Z. λ_u(X∩Z)=∅⊂λ_u(X)∩λ_u(Z)={w}. λ_u(X)∪λ_u(Z)={w,x,y}⊂λ_u(X∪Z)=E. It follows from these computations that the inclusion relations of (i) and (iv) of Proposition 12 as well as Corollary 4 are proper. Proposition 13 Let X and Z be subsets of a kSTS (E,δ,Uk). Then the next properties are satisfied. (i) X⊆λ¯k(X). (ii) λ¯k(∅)=∅. (iii) λ¯k(E)=E. (iv) If X⊆Z, then λ¯k(X)⊆λ¯k(Z). (v) λ¯k(Xc)=(λ_k(X))c. (vi) λ¯k(λ¯k(X))=λ¯k(X). Proof Similar to the proof of Proposition 12. □ Corollary 5 Let X and Z be subsets of a kSTS (E,δ,Uk). Then λ¯k(X∩Z)⊆λ¯k(X)∩λ¯k(Z) and λ¯k(X)∪λ¯k(Z)⊆λ¯k(X∪Z). Proof Directly follows from (iv) of Proposition 13. □ In a uSTS (E,δ,Uu) given in equation (3) consider X={w,x}, Y={x,z} and Z={w,z}. Then X⊂λ¯u(X)={w,x,z}. λ¯u(Y)=Y⊂λ¯u(X) whereas Y⊈X. λ¯u(X∩Z)={w}⊂λ¯u(X)∩λ¯u(Z)={w,x,z}. λ¯u({w})∪λ¯u({z})={w,z}⊂λ¯u({w,z})={w,x,z}. It follows from these computations that the relations of inclusion given in (i) and (iv) of Proposition 13 as well as Corollary 5 are proper. Definition 14 The supra k-accuracy and supra k-roughness measures (or values) of a set X in a kSTS (E,δ,Uk) are defined respectively byAk(X)=∣λ_k(X)∣∣λ¯k(X)∣,whereX≠∅.Rk(X)=1-Ak(X). Note that for every set X⊆E the two values Ak(X) and Rk(X) lie in the closed interval [0, 1]. Definition 15 We call the kSTSs (E,δ1,U1k) and (E,δ2,U2k) have the property of monotonicity accuracy (resp. monotonicity roughness) if A1k(X)≤A2k(X) (resp., R1k(X)≥R2k(X)) whenever δ1 is a subset of δ2. The next two result illustrate that the supra k-accuracy and supra k-roughness measures satisfy the monotonicity property. Proposition 14 Let (E,δ1,U1k) and (E,δ2,U2k) be two kSTSs such that δ1 is a subset of δ2. Then for k∈{r,l,i,u} and any set X⊆E we have A1k(X)≤A2k(X). Proof Since δ1 is a subset of δ2, it follows from Proposition 11 that U1k is a subset of U2k for each k∈{r,l,i,u}. This automatically means that ∣λ_1k(X)∣≤∣λ_2k(X)∣ and 1∣λ¯1k(X)∣≤1∣λ¯2k(X)∣. Therefore, ∣λ_1k(X)∣∣λ¯1k(X)∣≤∣λ_2k(X)∣∣λ¯2k(X)∣. Hence, A1k(X)≤A2k(X), as required. □ Corollary 6 Let (E,δ1,U1k) and (E,δ2,U2k) be two kSTSs such that δ1 is a subset of δ2. Then for k∈{r,l,i,u} and any set X⊆E we have R1k(X)≥R2k(X). Definition 16 The supra k-positive, supra k-boundary, and supra k-negative regions of a set X in a kSTS (E,δ,Uk) are defined respectively byδk+(X)=λ_k(X),Bk(X)=λ¯k(X)\λ_k(X),andδk-(X)=E\λ¯k(X). Proposition 15 Let (E,δ1,U1k) and (E,δ2,U2k) be kSTSs such that δ1 is a subset of δ2. Then for k∈{r,l,i,u} and any set X⊆E the following results hold true. (i) B2k(X)⊆B1k(X). (ii) δ1k-(X)⊆δ2k-(X). Proof Follows from Proposition 11 and Proposition 14. □ Definition 17 A subset X of a kSTS (E,δ,Uk) is called supra k-exact if λ_k(X)=λ¯k(X)=X. Otherwise, it is called a supra k-rough set. Proposition 16 A subset X of a kSTS (E,δ,Uk) is supra k-exact iff Bk(X)=∅. Proof Assume that X is a supra k-exact set. Then Bk(X)=λ¯k(X)\λ_k(X)=λ¯k(X)\λ¯k(X)=∅. Conversely, Bk(X)=∅ implies that λ¯k(X)\λ_k(X)=∅; therefore, λ¯k(X)⊆λ_k(X). But it is well recognized that λ_k(X)⊆λ¯k(X). Thus, λ¯k(X)=λ_k(X). Hence, X is supra k-exact. □ In Algorithm 1 and Flow chart (in Fig. 1), we elaborate how we can determine whether a subset of a k-supra topology is supra k-exact or supra k-rough.Fig. 1 Flow chart of determining supra k-exact and supra k-rough subsets of k-supra topologies Comparison of our approach with the previous ones In the following results, we explain some unique characteristics of our approximations and accuracy measures. As we will see they produce best approximations and highest accuracy measures in cases of union neighbourhood Nu and minimal union neighbourhood N⟨u⟩, which is more different than topological approaches. In fact, this behaviour is attributed to the way of creating our neighbourhood systems given in Theorem 2. Then, we prove that the current method produces higher accuracy and better approximations than all approaches defined in the published literatures under a reflexive relation such as those given in Abd El-Monsef et al. (2014); Abo-Tabl (2013); Abu-Donia (2008); Allam et al. (2006), Al-shami (2021a, 2021b, 2022), Amer et al. (2017), Azzam et al. (2020), El-Sharkasy (2021), Dai et al. (2018), Kondo and Dudek (2006), Kozae et al. (2007), Lashin et al. (2005), Salama (2020). Proposition 17 Let X be a subset of a kSTS (E,δ,Uk). Then (i) λ_i(X)⊆λ_r(X)⊆λ_u(X). (ii) λ_i(X)⊆λ_l(X)⊆λ_u(X). (iii) λ_⟨i⟩(X)⊆λ_⟨r⟩(X)⊆λ_⟨u⟩(X). (iv) λ_⟨i⟩(X)⊆λ_⟨l⟩(X)⊆λ_⟨u⟩(X). (v) λ¯u(X)⊆λ¯r(X)⊆λ¯i(X). (vi) λ¯u(X)⊆λ¯l(X)⊆λ¯i(X). (vii) λ¯⟨u⟩(X)⊆λ¯⟨r⟩(X)⊆λ¯⟨i⟩(X). (viii) λ¯⟨u⟩(X)⊆λ¯⟨l⟩(X)⊆λ¯⟨i⟩(X). Proof To prove (i) and (ii), let w∈λ_i(X). Then there is U∈Ui such that w∈U⊆X. By Proposition 5, Ui is a subfamily of Ur and Ul, so U∈Ur and U∈Ul. Thus, w∈Intr(X)=λ_r(X) and w∈Intl(X)=λ_l(X). Hence, λ_i(X)⊆λ_r(X) and λ_i(X)⊆λ_l(X). Similarly, the relations λ_r(X)⊆λ_u(X) and λ_l(X)⊆λ_u(X) are proved. Following similar arguments, one can prove the other cases. □ Corollary 7 Let X be a subset of a kSTS (E,δ,Uk). Then (i) Ai(X)≤Ar(X)≤Au(X). (ii) Ai(X)≤Al(X)≤Au(X). (iii) A⟨i⟩(X)≤A⟨r⟩(X)≤A⟨u⟩(X). (iv) A⟨i⟩(X)≤A⟨l⟩(X)≤A⟨u⟩(X). Proof (i): It follows from Proposition 17 that λ_i(X)⊆λ_r(X)⊆λ_u(X) and λ¯u(X)⊆λ¯r(X)⊆λ¯i(X), so we get4 ∣λ_i(X)∣≤∣λ_r(X)∣≤∣λ_u(X)∣ and5 1∣λ¯i(X)∣≤1∣λ¯r(X)∣≤1∣λ¯u(X)(X)∣ By (4) and (5) we get∣λ_i(X)∣∣λ¯i(X)∣≤∣λ_r(X)∣∣λ¯r(X)∣≤∣λ_u(X)∣∣λ¯u(X)∣which is equivalent toAi(X)≤Ar(X)≤Au(X). In a similar way, we prove the other cases. □ The data given in Tables 2 and 3 are computed for a different kinds of a kSTSs (E,δ,Uk) displayed in Example 2. These computations emphasize the validity of the results presented in Proposition 17 and corollary 7.Table 2 The operators of approximations and values of accuracy obtained from k-supra topology, where k∈{i,r,l,u} X λ_i(X) λ¯i(X) Ai(X) λ_r(X) λ¯r(X) Ar(X) λ_l(X) λ¯l(X) Al(X) λ_u(X) λ¯u(X) Au(X) {w} ∅ {w,x,z} 0 ∅ {w,x,z} 0 ∅ {w,x,z} 0 ∅ {w} 0 {x} ∅ {w,x,z} 0 ∅ {w,x,z} 0 ∅ {x,z} 0 ∅ {x,z} 0 {y} {y} E 14 {y} E 14 {y} E 14 {y} {y} 1 {z} ∅ {w,x,z} 0 ∅ {w,x,z} 0 ∅ {x,z} 0 ∅ {z} 0 {w,x} ∅ {w,x,z} 0 ∅ {w,x,z} 0 ∅ {w,x,z} 0 {w,x} {w,x,z} 23 {w,y} {y} E 14 {y} E 14 {w,y} E 12 {w,y} {w,y} 1 {w,z} ∅ {w,x,z} 0 ∅ {w,x,z} 0 ∅ {w,x,z} 0 ∅ {w,x,z} 0 {x,y} {y} E 14 {y} E 14 {y} E 14 {y} E 14 {x,z} ∅ {w,x,z} 0 ∅ {w,x,z} 0 ∅ {x,z} 0 {x,z} {x,z} 1 {y,z} {y} E 14 {y} E 14 {y} E 14 {y} {y,z} 12 {w,x,y} {y} E 14 {y} E 14 {w,y} E 12 {w,x,y} E 34 {w,x,z} ∅ {w,x,z} 0 ∅ {w,x,z} 0 ∅ {w,x,z} 0 {w,x,z} {w,y,z} 1 {w,y,z} {y} E 14 {y} E 14 {w,y} E 12 {w,y} E 12 {x,y,z} {y} E 14 {y} E 14 {y} E 14 {x,y,z} E 34 Table 3 The operators of approximations and values of accuracy obtained from k-supra topology, where k∈{⟨i⟩,⟨r⟩,⟨l⟩,⟨u⟩} X λ_⟨i⟩(X) λ¯⟨i⟩(X) A⟨i⟩(X) λ_⟨r⟩(X) λ¯⟨r⟩(X) A⟨r⟩(X) λ_⟨l⟩(X) λ¯⟨l⟩(X) A⟨l⟩(X) λ_⟨u⟩(X) λ¯⟨u⟩(X) A⟨u⟩(X) {w} ∅ {w,x,z} 0 ∅ {w,z} 0 {w} {w,x} 12 {w} {w} 1 {x} ∅ {w,x,z} 0 {x} {w,x,z} 13 ∅ {w,x} 0 {x} {x} 1 {y} {y} E 14 {y} {w,y,z} 13 {y} {x,y} 12 {y} {y} 1 {z} ∅ {w,x,z} 0 ∅ {w,z} 0 {z} {x,z} 12 {z} {z} 1 {w,x} ∅ {w,x,z} 0 {x} {w,x,z} 13 {w} {w,x} 12 {w,x} {w,x} 1 {w,y} {y} E 14 {y} {w,y,z} 13 {w,y} {w,x,y} 23 {w,y} {w,y} 1 {w,z} ∅ {w,x,z} 0 ∅ {w,z} 0 {w,z} {w,x,z} 23 {w,z} {w,z} 1 {x,y} {y} E 14 {x,y} E 12 {y} {x,y} 12 {x,y} {x,y} 1 {x,z} ∅ {w,x,z} 0 {x} {w,x,z} 13 {z} {x,z} 12 {x,z} {x,z} 1 {y,z} {y} E 14 {y} {w,y,z} 13 {y,z} {x,y,z} 23 {y,z} {y,z} 1 {w,x,y} {y} E 14 {x,y} E 12 {w,y} {w,x,y} 23 {w,x,y} {w,x,y} 1 {w,x,z} ∅ {w,x,z} 0 {x} {w,x,z} 13 {w,z} {w,x,z} 23 {w,x,z} {w,x,z} 1 {w,y,z} {y} E 14 {y} {w,y,z} 13 {w,y,z} E 34 {w,y,z} {w,y,z} 1 {x,y,z} {y} E 14 {x,y} E 12 {y,z} {x,y,z} 23 {x,y,z} {x,y,z} 1 The following two results demonstrate the conditions under which we obtain some equivalences. Proposition 18 Let X be a subset of a kSTS (E,δ,Uk) such that δ is symmetric. Then (i) λ_u(X)=λ_r(X)=λ_l(X)=λ_i(X) and λ¯u(X)=λ¯r(X)=λ¯l(X)=λ¯i(X). (ii) λ_⟨u⟩(X)=λ_⟨r⟩(X)=λ_⟨l⟩(X)=λ_⟨i⟩(X) and λ¯⟨u⟩(X)=λ¯⟨r⟩(X)=λ¯⟨l⟩(X)=λ¯⟨i⟩(X). Proof From the equalities λ_k(X)=Intk(X) and λ¯k(X)=Clk(X) as well as Corollary 2, the proof follows. □ Corollary 8 Let X be a subset of a kSTS (E,δ,Uk) such that δ is symmetric. Then (i) Au(X)=Ar(X)=Al(X)=Ai(X). (ii) A⟨u⟩(X)=A⟨r⟩(X)=A⟨l⟩(X)=A⟨i⟩(X). Proposition 19 Let (E,δ) be an approximation space such that δ is reflexive. Then λ_k(X)=λ¯k(X)=X for each X⊆E. Proof Since δ is a reflexive relation, it follows from Proposition 9 that Uk is a discrete topology for each k. This implies that λ_k(X)=Intk(X)=X and λ¯k(X)=Clk(X)=X. Hence, we obtain the desired result. □ Corollary 9 Let (E,δ) be an approximation space such that δ is reflexive. Then Mk(X)=1 for each nonempty subset X of E. Proposition 19 and Corollary 9 give an important characteristic of our method under a reflexive relation is that it is better than all previous methods defined by topological structures (Abd El-Monsef et al. 2014; Abo-Tabl 2013; Allam et al. 2006; Al-shami 2021b; Amer et al. 2017; Hosny 2018; Kondo and Dudek 2006; Kozae et al. 2007) and their generalizations such as minimal structures (Azzam et al. 2020; El-Sharkasy 2021) and bitopological spaces (Salama 2020). Also, it is better than all previous methods which were directly defined by some neighborhood systems such as (Abu-Donia 2008; Allam et al. 2005, 2006; Yao 1996, 1998) and those methods introduced depending on neighborhood systems and ideal structures (Hosny 2020; Hosny et al. 2022, 2021; Kandil et al. 2020; Nawar et al. 2022). Proposition 20 Let (E,δ) be an approximation space and X⊆E. If δ is reflexive, then (i) δ_k(X)⊆λ_k(X). (ii) λ¯k(X)⊆δ¯k(X). Proof Let w∈δ_k(X). According to Definition 5, Nk(w)⊆X. Since δ is reflexive, w∈Nk(w)⊆X, and Nk(w) is a supra-open set in Uk. This means that w∈Intk(X)=λ_k(X). Hence, the proof is complete. Following similar arguments, (ii) is proved. □ Corollary 10 Let (E,δ) be an approximation space such that δ is reflexive. Then Mk(X)≤Ak(X) for each X⊆E. To validate that the current method produces higher accuracy and better approximations than those given (Abd El-Monsef et al. 2014; Allam et al. 2005, 2006; Yao 1996, 1998), we provide the following example. Example 3 Consider δ={(w,w),(x,x),(y,y),(w,y),(y,x)} is a binary relation on E={w,x,y}. Then, we suffice by computing Nr-neighborhood of each point in E as follows: Nr(w)={w,y}, Nr(x)={x} and Nr(y)={x,y}. According to Theorem 1, the r-topology τk generated from Nr-neighborhoods is τr={∅,E,{x},{x,y}}. Since δ is reflexive, the generated supra-topology Uk is the discrete topology for each k. Now, we calculate, in Table 4, the approximations and accuracy values of each subset induced from Nr-neighborhood, rTS (E,δ,τr), and rSTS (E,δ,Ur). Table 4 The approximations and accuracy values induced from Nr-neighborhood, r-topology and r-supra topology X δ_r(X) δ¯r(X) Mr(X) O_r(X) O¯r(X) Tr(X) λ_r(X) λ¯r(X) Ar(X) {w} ∅ {w} 0 ∅ {w} 0 {w} {w} 1 {x} {x} {x,y} 12 {x} E 13 {x} {x} 1 {y} ∅ {w,y} 0 ∅ {w,y} 0 {y} {y} 1 {w,x} {x} E 13 {x} E 13 {w,x} {w,x} 1 {w,y} {w} {w,y} 12 ∅ {w,y} 0 {w,y} {w,y} 1 {x,y} {x,y} E 23 {x,y} E 23 {x,y} {x,y} 1 Analysis of dengue fever using the supra-topology approach In this section, we examine the performance of our method to analyze the data of dengue fever disease and prove it is better than the previous ones given in Abd El-Monsef et al. (2014), Allam et al. (2005, 2006), Yao (1996, 1998). Dengue fever disease which is a global problem. It is transmitting to humans by virus-carrying Dengue mosquitoes Prabhat (2019). The symptoms of this disease mostly start from the third day of infection. The period of recovery takes a few days; usually, 2-7 days Prabhat (2019). According to the statistics of the World Health Organization (EHO), it spreads in more than 120 nations and causes a huge number of deaths around the world; in particular, Asia and South America World Health Organization (2016). Accordingly, this disease occupies an important place worldwide, which motivates us to analyze it by the approach introduced in this manuscript. The data displayed in Table 5 decide this disease such that the columns give the symptoms of dengue fever as follows joint and muscle aches O1, headache with puke O2, skin rashes O3, a temperature O4 with three levels (normal (n), high (h), very high (vh)), and finally the decision D of infected or not. In contrast, the rows represents the patients under study E={w1,w2,w3,w4,w5,w6,w7,w8}. The mark ‘✓’ (resp., ‘✗’) denotes the patient has a symptom (resp., the patient has no symptom).Table 5 Information system of dengue fever E O1 O2 O3 O4 Dengue fever w1 ✓ ✓ ✓ vh ✓ w2 ✓ ✗ ✗ h ✗ w3 ✓ ✗ ✗ h ✓ w4 ✗ ✗ ✗ vh ✗ w5 ✗ ✓ ✓ h ✗ w6 ✓ ✓ ✗ vh ✓ w7 ✓ ✓ ✗ n ✓ w8 ✓ ✓ ✗ vh ✓ Now, the descriptions of attributes {Oi:i=1,2,3,4} will be transmitted into quantity values showing the degree of similarities among the patients’ symptoms; see, Table 6. We calculate similarity degree function between the patients a, b, denoted by s(a, b), with respect to m conditions attributes by the next formula.6 s(w,x)=∑j=1m(Xk(w)=Ak(x))m Table 6 Similarity degrees between patients’ symptoms w1 w2 w3 w4 w5 w6 w7 w8 w1 1 0.25 0.25 0.25 0.5 0.75 0.5 0.75 w2 0.25 1 1 0.5 0.25 0.5 0.5 0.5 w3 0.25 1 1 0.5 0.25 0.5 0.5 0.5 w4 0.25 0.5 0.5 1 0.25 0.5 0.25 0.5 w5 0.5 0.25 0.25 0.25 1 0.25 0.25 0.25 w6 0.75 0.5 0.5 0.5 0.25 1 0.75 1 w7 0.5 0.5 0.5 0.25 0.25 0.75 1 0.75 w8 0.75 0.5 0.5 0.5 0.25 1 0.75 1 The next procedure is proposing a relation, it is given according to the requirements of the standpoint of system’s experts. In this example, we propose the following relation(w,x)∈δ⟺s(w,x)≥0.5. Note that the given relation ≥ and number 0.5 can be replaced according to the conceptions of system’s experts. It is clear that the suggested relation δ is reflexive and symmetric, so it produces two types of Nk-neighborhood systems. But δ is not transitive. This means that the Pawlak approximations space fails to describe this system. In Table 7, we compute the two types of Nk and N⟨k⟩ neighborhoods for each patient wi.Table 7 Nk and N⟨k⟩ of each wi∈W Nk N⟨k⟩ w1 {w1,w6,w8} {w1} w2 {w2,w3} {w2,w3} w3 {w2,w3} {w2,w3} w4 {w4} {w4} w5 {w1,w5} {w1,w5} w6 {w1,w6,w7,w8} {w6,w8} w7 {w6,w7,w8} {w6,w7,w8} w8 {w1,w6,w7,w8} {w6,w8} All supra-topologies produced from Table 7 are the discrete topologies because δ is a reflexive relation. To confirm the performance of our approach, we consider two subsets U={w1,w2,w3,w4,w7} and V={w5,w6,w8}. Then, the approximations and accuracy measures of these two sets are computed with respect to Nk-neighborhoods and k-supra topology.Table 8 Accuracy measures induced from Nk-neighborhoods and k-supra topology of the sets U and V X Nk-neighborhoods N⟨k⟩-neighborhoods k-supra topology δ_k δ¯k Mk δ_⟨k⟩ δ¯⟨k⟩ M⟨k⟩ λ_k λ¯k Ak U {w2,w3,w4} E\{w5} 37 {w1,w2,w3,w4} E\{w6,w8} 23 {w1,w2,w3,w4,w7} {w1,w2,w3,w4,w7} 1 V ∅ E\{w2,w3,w4} 0 {w6,w8} {w5,w6,w7,w8} 12 {w5,w6,w8} {w5,w6,w8} 1 According to Table 8, the approximations and accuracy measures induced from k-supra topology are better than those induced from Nk-neighborhoods and N⟨k⟩-neighborhoods. Discussions: strengths and limitations This section demonstrates the main advantages of technique followed herein as well as shows its limitations.Strengths The approach of supra-topological structures that we rely on to initiate new models of rough set theory in this manuscript is more relaxed than topological structures. This gives us a large scope for describing many phenomena because we get rid of an intersection condition that is unsuitable to them. This approach also enables us to deal with some practical problems under any arbitrary relation, where as Pawlak approach stipulates an equivalent relation to model the problems under study. It can be compared the different types of approximations and accuracy values generated from our approach as it is proved in the obtained results; see Proposition 17 and Corollary 7. But this characteristic does not hold for some previous approaches such as those produce approximations and accuracy values from near open subsets of topological spaces like α-open, pre-open, semi-open, b-open, and β-open sets. The accuracy and roughness measures induced from the current approach are monotonic; whereas, this property is lost in some preceding topological approaches like those investigated in Abd El-Monsef et al. (2014), Abo-Tabl (2013), Al-shami (2021b). The current method preserves all Pawlak properties of lower approximation operator except for the distributive property of intersection; see Proposition 12. Also, it preserves all Pawlak properties of upper approximation operator except for the distributive property of union; see Proposition 13. In contrast, some previous approaches such as Yao (1996, 1998) lose most of these properties. The current approach is more suitable to handle the large samples because the best approximations and accuracy measures are obtained in cases of k=u,⟨u⟩ which represent the largest Nk-neighbourhoods as we elaborated in Proposition 17 and Corollary 7. The importance of this matter is that we obtain a more accurate decision for the problems in which these cases are the appropriate frame to describe them; for instance, infectious diseases like COVID-19, flu, etc., in which the infection is proportional to the sample size. That is, the decision made in these two cases is more accurate. On the other hand, the performance of these cases via rough set models induced from topology is the weakest in terms of approximation operators and accuracy measures; hence, lack of confidence in the made decision. The present method is more accurate than all foregoing methods with respect to the approximations and accuracy values obtained under a reflexive relation. More precisely, it represents an ideal case under reflexivity, so it is better than all previous methods defined by topological structures (Abd El-Monsef et al. 2014; Abo-Tabl 2013; Allam et al. 2006; Al-shami 2021b; Amer et al. 2017; Hosny 2018; Kondo and Dudek 2006; Kozae et al. 2007) and their generalizations such as minimal structures (Azzam et al. 2020; El-Sharkasy 2021) and bitopological spaces Salama (2020). Also, it is better than all previous methods which were directly defined by some neighborhood systems such as Abu-Donia (2008), Allam et al. (2005, 2006), Yao (1996), Yao (1998) and those methods introduced depending on neighborhood systems and ideal structures (Hosny 2020; Hosny et al. 2022, 2021; Kandil et al. 2020; Nawar et al. 2022). limitations The present approach is generally incomparable with the topological approach introduced in Abd El-Monsef et al. (2014) when the relation is not reflexive. To illustrate this point, consider the neighborhoods system displayed in Example 1. It is clear that τr={∅,E,{y},{z}, {y,z},{x,y},{x,y,z},{w,x,y}} is an r-topology on E induced by Theorem 1, and Ur={∅,E,{w},{w,x},{w,x,y}} is a supra r-topology on E induced by Theorem 2. By calculations, we obtain Tr({w})=0<Ar({w})=14 whereas Tr({y})=13>Ar({y})=0. The distributive property of intersection and union operators are respectively lost by the supra k-lower and supra k-upper approximations introduced herein, whereas this property holds under rough models induced from topological or infra-topological structures. Conclusion Rough approximation operators and values of accuracy are the most significant characteristic of rough set theory. In practice, they provide a conception of the data contained in a subset and determine to what extent this subset is complete. Improvement of these operators and increase their values of accuracy lead to an accurate prediction. There are two main techniques to do that, one is to define new types of neighborhood systems such as those introduced in Abu-Donia (2008), Allam et al. (2005, 2006), Al-shami (2021a, 2022), Al-shami and Ciucci (2022), Dai et al. (2018), Hosny (2018), and the second is obtained by studying rough-sets concepts using their counterparts via topological spaces and their related structures as those given in Abd El-Monsef et al. (2014), Abo-Tabl (2013), Amer et al. (2017), Azzam et al. (2020), El-Sharkasy (2021), Hosny (2020). Through this manuscript, we have followed the second technique to generate rough sets models. We have applied the concept of “supra-topology” to create new models which are more relaxed than topological models because a finite intersection stipulation is removed. We have begun our work by forming supra-topology spaces from an approximation space. Then, we have established novel rough set models by these spaces and investigated their master properties. We have explained their main advantages to improve approximation operators and accuracy values better than all previous models existing in the literature under a reflexivity condition. As an application, we have discussed the followed technique to describe dengue fever disease. Finally, we have demonstrated the merits of our approach and its failures compared with the foregoing ones. In the future, we are going to study the next themes. (i) Discuss the rough models introduced herein with respect to the recent neighborhood systems such as Ck-neighborhoods Al-shami (2021a) and Sk-neighborhoods Al-shami and Ciucci (2022). (ii) Form a new frame consisting of ideal structure and supra-topology to improve the approximation operators and accuracy values given herein similarly to the combination of classical topology and ideal (Hosny 2020; Kandil et al. 2020; Nawar et al. 2022). (iii) Investigate the manuscript thoughts with respect to some celebrated extensions of supra-open sets. (iv) Reformulate the concepts studied herein in some frames such as soft rough set and fuzzy rough set. Acknowledgements The authors are extremely grateful to the editor and the five anonymous reviewers for their valuable comments and helpful suggestions which helped to improve the presentation of this paper. Funding This research received no external funding. Declarations Conflict of interest The authors declare that there is no conflict of interests regarding the publication of this article. 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==== Front International Economics 2110-7017 2110-7017 CEPII (Centre d'Etudes Prospectives et d'Informations Internationales), a center for research and expertise on the world economy. Published by Elsevier B.V. S2110-7017(22)00029-4 10.1016/j.inteco.2022.04.001 Article Measuring exchange rate risks during periods of uncertainty☆ Ferrara Laurent a∗ Yapi Joseph b a Skema Business School, University Cote d’Azur, France b Central Bank of Luxembourg and EconomiX, University Paris Nanterre, France ∗ Corresponding author. 23 4 2022 8 2022 23 4 2022 170 202212 4 1 2022 8 3 2022 8 4 2022 © 2022 CEPII (Centre d'Etudes Prospectives et d'Informations Internationales), a center for research and expertise on the world economy. Published by Elsevier B.V. All rights reserved. 2022 CEPII (Centre d'Etudes Prospectives et d'Informations Internationales), a center for research and expertise on the world economy Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. In this paper, we empirically look at the effects of uncertainty on risk measures for exchange rates, by focusing on two recent specific periods: the Brexit and the outbreak of the Covid-19. Based on a Fama regression extended with uncertainty measures, we forecast exchange rates in the short run through a quantile regression approach. By fitting a Skewed-Student distribution to the quantile forecasts, we put forward measures of risks for appreciation and depreciation of the expected exchange rates. We point out two interesting results. First, we show that the increase in Brexit-related uncertainty is strongly associated with higher future depreciation risks of the British Pound vs. the Euro, as a mistrust towards the British economy. Second, we find that the Covid-related uncertainty is perceived as a global risk, leading to a flight-to-safety move toward the US Dollar and associated high depreciation risks for emerging currencies. Keywords Exchange rate Risk measures Fama regression Uncertainty Covid-19 crisis Brexit ==== Body pmc1 Introduction On 23 June 2016, the outcome of the British referendum came as a surprise as prediction markets and polls suggested the vote would be in favor of remaining in the European Union. Until 31 January 2020, no decisive action was taken, leaving the U.K. economy, and the rest of the world connected to the U.K., with a large uncertainty. Many research papers have shown that this political uncertainty generated various adverse macroeconomic effects on the British economy, in line with the theory on uncertainty initiated by Knight (1921) and recently popularized by Bloom (2009). Negative domestic effects of the Brexit have been shown on U.K firms (Bloom et al., 2019), on trade (Born et al., 2019) or on household income (Dhingra et al., 2016). The main channel of transmission is the wait-and-see attitude of agents facing higher uncertainty, as well as increasing financial frictions. In principle, as soon as uncertainty fades, the economy experiences a bounce-back accompanying the business cycle. A major issue related to the Brexit case is that the uncertainty period lasted for several months in a row, generating more complex transmission mechanisms to economic and financial sectors. Although the euro area is likely to be one of the most affected economic zone by the Brexit uncertainty, worldwide effects are also non-negligible in some exposed countries (see Hassan et al., 2019). However, immediately after the Brexit shock, the first macroeconomic adjustment took place through the exchange rate. Indeed, between May 2015 (the date of the Conservatives’ victory in the general elections, whose program included the holding of the referendum) and July 2017, the sterling depreciated in real terms by about 15%. As pointed out by Gourinchas and Hale (2017) this drop reflects beliefs from market participants that the Brexit is expected to permanently affect the UK economic growth through reversal from trade gains between European partners and reduced cross-border labor flows, both factors weighing on potential growth. More recently the Covid-19 pandemic, that started in China end of2019,1 generated a worldwide recession during the first months of 2020, the U.S. and Western Europe being the most impacted as regards both public health and economic consequences. Indeed, facing the immediate absence of vaccines, tests and masks, governments decided, more or less rapidly, to implement lockdown measures with the objective of minimizing health issues at the cost of economic consequences. This unexpected and unusual sequence of supply and demand shocks led to a drop in GDP growth and, among other events, to a rise in financial volatility as measured by the VIX, in global economic policy uncertainty and in pandemic-related uncertainty (see Baker et al., 2020). Yet, at the same time, we observed that the U.S. nominal broad effective exchange rate appreciated by 5% between January and March 2020, that can be understood as a flight-to-quality move in the presence of increasing global risks. Relationships between uncertainty measures and exchange rates have been only recently considered in the literature. For example, when taking financial volatility as uncertainty measure, Bussière et al. (2022) show only mixed evidence about the role of the VIX in the determination of exchange rates for a bunch of advanced country currencies. Ismailov and Rossi (2018) show some evidence that the relationship between uncertainty and exchange rates follows a non-linear pattern. Indeed they point out that the Uncovered Interest rate Parity (UIP) condition, a core relationship in international finance relating exchange rate evolution and differential of interest rates, is more likely to hold when uncertainty is low but breaks down during periods of high uncertainty. The argument they put forward is that arbitrage opportunity gains become more uncertain during high uncertainty times, blurring thus the link between exchange rates and interest rate differentials. Ramirez-Rondan and Terrones (2019) also highlight the non-linear dependence between differentials of interest rates and exchange rate depreciation, uncertainty being the transition variable of a multivariate threshold model. As in Ismailov and Rossi (2018), they empirically show that the UIP condition holds when uncertainty is low, while it is not verified in a high uncertainty regime, but they do not provide any clear explanation for this stylized fact. Recently, Eguren-Martin and Sokol (2022) provide a characterization of exchange rate tail risks by only conditioning by global financial conditions, without accounting for interest rate differentials. In this paper, we reconsider the role of uncertainty in exchange rate determination by focusing on two specific periods of time, namely the Brexit episode, starting just after the June 2016 referendum outcome, and the more recent Covid-19 crisis starting in January 2020. Especially, our aim is to provide some new measures of risk assessment for two specific currencies: (i) the GBP/EUR exchange rate, one of the bilateral currency that reacted the most due to the close integration between the euro area and the U.K, and (ii) the MXN/USD exchange rate, an emerging country currency (Mexican Peso) vs the currency hegemon (US Dollar). We focus on the Mexican Peso as we wanted to have a look at an emerging country with tight links with the U.S. and a floating currency. This case is also interesting to contrast results from the first analysis on the GBP/EUR as it has been shown that currencies of advanced and emerging countries behave differently (Bansal and Dahlquist, 2000). Our objective is twofold. First we would like to account for uncertainty in the relationship between exchange rate and interest rate differentials, possibly by allowing for non-linear effect. Second, we would like to compute risk measures around expected exchange rates, for both appreciation and depreciation risks. In this respect, our econometric methodology relies on the extension of the standard Fama regression, that explains exchange rate depreciation by interest rate differentials, by integrating various uncertainty measures as explanatory variables. In order to account for possible asymmetries, we estimate a quantile regression, allowing for a differentiated impact of interest rate differentials and uncertainty, depending on the evolution of the exchange rate. We use this quantile regression to forecast one-month-ahead expectations, as well as the conditional moments of the distribution around the forecasts of exchange rates. Then, in order to quantify risk measures, we fit a generalized Skewed-Student distribution to the one-step-ahead quantiles, in the spirit of Adrian et al. (2019), in order to get the whole conditional density. We see this quantile regression approach as an efficient way to fill our twofold objective. Empirical results show that this conditional density provides a good way to anticipate appreciation and depreciation risks. Especially, we show that the evolution of risks around the British Pound during the Brexit-related uncertainty period shows a clear shift towards depreciation. In addition, we show that there is a sharp risk of US Dollar appreciation during the Covid-19 crisis as a flight-to-quality move. Our tool can thus be used to monitor in real-time expected appreciation/depreciation risks for any currency. The rest of the paper is organized in three sections. Section 2 presents the econometric methodology and Section 3 contains our main results for both the GDP/EUR and MXN/USD currencies. Last, some conclusions are discussed in Section 4. 2 Econometric methodology 2.1 Fama equation: A quantile regression approach There have been many attempts in the econometric literature in order to explain the evolution of exchange rates, and possibly to forecast out-of-sample. A nice review has been done by Rossi (2013), especially focusing on forecasting ability of models. However, one of the most considered relationship in international finance is the one between bilateral exchange rate changes of two countries and the differential of interest between those countries. This relationship, referred to as Uncovered Interest Parity (UIP) condition, is a no-arbitrage condition that can be tested, jointly with rational expectation hypothesis, through a Fama regression (Fama, 1984). In its general form, the Fama regression at a monthly frequency can be written as:(1) st+h−st=α+β×(ih,tD−ih,tF)+ɛt+h, where s t the logarithm of exchange rate measured as the number of units of domestic currency per one foreign currency, h is a given horizon and ih,tD and ih,tF are respectively the domestic and foreign interest rates for a given maturity h. In such a background, s t + h − s t > 0 refers to an appreciation of the foreign currency (or a depreciation of the domestic currency) over the horizon h. Against this background, under the null hypothesis of uncovered interest rate parity and rational expectations we should have β = 1 and the error term ɛ t is supposed to be white noise and orthogonal to interest rate differentials. It turns out that empirical evidence that UIP conditions holds is quite scarce, leading to the well known Fama puzzle. This puzzle relies on the empirical observation that estimated parameters β are much lower than the expected value of one, generally very negative. In other words, data tend to show that the high-interest rate currency is likely to appreciate, at odds with theoretical hypothesis. Various explanations have been put forward in the literature pointing out the instability overtime of this relationship (Bussière et al., 2022), the sensitivity to the horizon (Chinn and Meredith, 2004) or the non-linearity of the relationship (Baillie and Kilic, 2006). More recently, some papers have shown that the uncertainty is likely to have a non-linear impact on the relationship between changes in exchange rate and interest rate differentials (Ismailov and Rossi, 2018), as high uncertainty is blurring this relationship. In this paper, we extend this line of research by (i) extending a standard Fama regression through the integration of uncertainty measures and (ii) allowing non-linearities into this relationship through quantile regression. First, we extend the previous Fama equation (1) by integrating the uncertainty generated by the Brexit in the following way:(2) st+h−st=α+β×(ih,tD−ih,tF)+γ×unct+ɛt+h, where unc t is a measure of uncertainty. There exist various available measures of uncertainty in the literature, such as economic policy uncertainty, forecasts dispersion, financial volatility … (for a review see Ferrara et al., 2017). However, we think that the Brexit uncertainty is a typical case of uncertainty generated by economic policy and thus we use the Economic Policy Uncertainty (EPU) index for the U.K. developed by Baker et al. (2016). This EPU index is computed by counting some specific keywords in media articles dealing simultaneously with economy, policy and uncertainty (see graph in Annex). As regards the Covid-19 crisis, the choice of an adequate uncertainty measure is not obvious. Baker et al. (2020) have put forward a new measure of Covid-induced uncertainty but this index is like a dummy jumping suddenly in January 2020 to 50, while being close to zero over the remaining of the sample. Our preferred choice is the financial volatility, as measured by the VIX (see graph in Annex), as a proxy for uncertainty. We agree that financial volatility is only a proxy of this pandemic-related uncertainty as other factors are likely to drive the VIX. However, to our view, it makes sense to consider that due to the nature of this unexpected and unusual pandemic shock, the VIX reflects to a large extent the current global uncertainty. Second, we allow for non-linearities in Equation (2) by estimating parameters within a quantile regression framework, as put forward by Koenker and Bassett (1978). In opposition to the ordinary least squares (OLS) estimation procedure, that minimizes the sum of squared errors, quantile estimation is based on the asymmetric minimization of the weighted absolute errors. This methodology presents several advantages. One of them is its capacity to depict, in a better way than OLS, the relationship between two random variables. Instead of estimating only the conditional mean like the OLS, quantile regression enables to estimate all the conditional quantile functions. Furthermore, Koenker and Bassett (1978) argue that in non-Gaussian settings, estimators from a quantile regression are more reliable than the OLS ones. Let's denote y t + h the h-month-ahead domestic currency depreciation (s t + h − s t), x t the vector of conditioning variables, namely the differential of interest rates and the uncertainty measure and θ the parameter that relates the two previous variables. The quantile regression estimator for a given quantile τ is given as follows:(3) θˆτ=argminθτ∑t=1T−1τ.1(yt+h≥xtβ)∣yt+h−xtθτ∣+(1−τ).1(yt+h<xtθ)∣yt+h−xtθτ∣, where 1(.) the indicator function. Koenker and Bassett (1978) demonstrate that the predicted value Qˆyt+h\|xt(τ\|xt)=xtθˆτ is a consistent linear estimator of the conditional quantile function of y t + h. 2.2 Risk measurement Based on previous estimates of the quantiles of the distribution of expected values yˆt+h, we are now able to estimate some risk measures of future evolution of the bilateral exchange rate. However, estimated quantiles do not ensure a continuous distribution function, it may be that some quantiles are badly estimated, if for example only few data points belong to this specific quantile. In this respect, we use the methodology pointed out in a recent paper by Adrian et al. (2019), in which they try to assess risks around future GDP growth, and we fit a generalized Skewed-Student distribution based on the estimated quantiles. Finally, we will use the estimated conditional distributions to measure the associated risks, within the expected shortfall/longrise framework. The conditional quantiles Qˆyt+h\|xt(τ\|xt) obtained from the quantile regressions can be used to fit distributions. In a key paper in the field, Koenker (2005) provides a formula to transform the estimated conditional quantile functions into conditional densities. Gaglianone and Lima (2012) build on this framework and fit an Epanechnikov Kernel to smooth the inverse cdf obtained from the quantile regressions. Similarly, Korobilis (2017) uses this methodology in a panel framework. More recently, Adrian et al. (2019) fit a Skewed-Student distribution to estimate the conditional densities of the US economic growth. We follow Adrian et al. (2019) and fit a Skewed-Student distribution to estimate the conditional densities of the expected currency. The probability density function of the generalized Skewed-Student distribution is given by:(4) f(y;μ,σ,α,ν)=2σty−μσ;νTαy−μσν+1ν+y−μσ2;ν+1, where μ is a location parameter, σ a scale parameter, ν a fatness parameter and α a shape parameter. t(.) and T(.) are respectively the probability density function (pdf) and the cumulative density function (cdf) of the Student t-distribution. The quantile matching approach consists in estimating the four parameters of the generalized Skewed-Student distribution by minimizing the squared distance between the estimated conditional quantile functions and the inverse cdf of the generalized Skewed-Student distribution. Since we estimate four parameters, we run the quantile matching for four different percent quantiles: 5%, 25%, 75% and 95%. In other words, we choose the four parameters to match the four different percentiles mentioned above. The quantile matching approach boils down to solve the following minimization:(5) {μˆt+h,σˆt+h,αˆt+h,νˆt+h}=argmin(μ,σ,α,ν)∑τQˆyt+h\|xt(τ\|xt)−F−1(τ;μ,σ,α,ν)2, where F −1(.) is the inverse cumulative skewed t-distribution. As a result, we get the four estimated parameters of the Skewed-Student distribution and we denote fˆ(.) the estimated Skewed-Student density and Fˆ−1(.) its inverted estimated cdf. Starting from this estimated density function fˆ(.), we can now compute some risk measures to assess depreciation/appreciation risk h-step-ahead of the British Pound versus the Euro. A standard risk measure in empirical finance is the Value-at-Risk (VaR). It measures the maximum loss of an asset over a given time horizon and at a defined confidence level. However, it is now well known that the VaR presents some drawbacks among them its inability to account for extreme losses. In this respect, the Expected Shortfall (ES hereafter) is presented now as a better risk measure than the VaR and is defined as the total probability mass that the conditional density assigns to the left tail. In other words, it measures the average loss beyond the VaR. The ES is given by:(6) ESt+h=1π∫0πFˆyt+h\|xt−1(τ\|xt)dτ, where π is the risk level and Fˆyt+h\|xt−1(τ\|xt), the estimated conditional cumulative Skewed-Student distribution. As we are also interested in the appreciation risk of the British Pound, we consider the symmetric of the ES, that is the Expected Longrise (EL hereafter) which is the upper tail counterpart of the ES. The EL can be expressed as follows:(7) ELt+h=1π∫1−π1Fˆyt+h\|xt−1(τ\|xt)dτ In the subsequent empirical part of this paper, we will consider a risk level of 5% for both ES and EL. 3 Empirical results In this section, we present empirical results of the previously introduced approach to measure and forecast in the short term appreciation and depreciation risks. The first application deals with the Brexit-related uncertainty period and focuses on the risks around the British Pound vs the Euro, while the second application assesses risks around the US Dollar vs the Mexican Peso during the Covid-19 crisis. In this paper we focus on the short-term horizon of h = 1 month as the literature is pointing out that the main deviation to UIP condition is for short-term horizons (Chinn and Meredith, 2004).2 3.1 GBP/EUR during the Brexit In this application, we collect monthly data for the GBP/EUR exchange rate (i.e. the value of one British Pound expressed in Euros, see monthly log-changes in Fig. 6), interest rate differentials between the euro area and the United Kingdom (see Fig. 7) and the Economic Policy Uncertainty index (see Fig. 8) as computed by Baker et al. (2016). All figures are presented in the Annex. The data spans from January 2000 to December 2018. Let's start by presenting the results of the quantile regression stemming from the estimation of Equation (2), by lagging the uncertainty measure by one month. Fig. 1 presents the estimated parameters stemming from the quantile regression, for various quantiles, as well as confidence intervals estimated by Bootstrap with a 95% confidence level.3 The procedure considers 20 different quantiles τ, from τ = 0.05, …0.95. The estimated parameter of the Fama coefficient β switches from negative (for the left tail) to positive (for the right tail). In other words, in periods of strong depreciation of the British Pound (or strong appreciation of the Euro), the Fama coefficient tends to be negative (about −10 for quantiles below 0.2). Against this background, a rise of the interest rate in the euro area, everything else equal, induces an appreciation (a depreciation) of the Euro (the British Pound). This result reflects what has been called the Fama puzzle in the empirical literature. In opposition, in times of high appreciation of the British Pound (or high depreciation of the Euro), the Fama coefficient tends to be positive. It turns out that starting from the quantile τ = 0.6, the coefficient is significantly positive, close to +10. This means that in times of strong depreciation of the Euro, a monetary policy tightening from the ECB leads to a further depreciation of the Euro (in opposition to common wisdom about the impact of monetary policy on exchange rate). Those results related to the right tail of the distribution reflect the new Fama puzzle of Bussière et al. (2022).Fig. 1 Quantile regression estimators. Fig. 1 Compared to the OLS estimator (horizontal red line in Fig. 1), we see that the Fama coefficient tends to show significant evidence of non-linearity depending on the values of the exchange rate. The OLS estimator is equal to βˆOLS=3.73, that is much higher than its expected value of one, according to the UIP and rational expectations condition. Thus it means that an increase in the euro area interest rate tends to depreciate the Euro the month after. The estimated parameter γˆ for the British uncertainty remains negative for any quantile, close to the OLS estimate γˆOLS=−0.00006 that is significantly negative. It means that a rise in UK policy uncertainty always leads to a depreciation of the British Pound (or an appreciation of the Euro). In the left tail of the distribution (when the British Pound is rapidly depreciating), the negative impact of the uncertainty is a bit stronger. Overall, this parameter doesn't show a strong non-linear pattern, namely uncertainty seems to always play negatively, whatever the exchange rate values. Let's now focus on the conditional distribution fˆ(.) of the 1-month-ahead forecasts of the GBP/EUR exchange rate. Using estimated quantile regression estimators we draw the Skewed-Student conditional densities for any date t from Equation (4). Time-varying conditional distributions are presented in Fig. 2 . We observe that the conditional densities are somewhat symmetric before 2016. Then at time of the referendum result, we note a clear shift to the left of the distribution, indicating an increase in the forecast depreciation risk of the sterling, in line with the jump in the economic policy uncertainty. Just after the surprising outcome of the referendum, the distribution became less skewed to the left, but depreciation risks stayed much higher and the variance larger.Fig. 2 Conditional distribution of EUR/GBP exchange rate. Fig. 2 The last part of our methodology is the computation of the risk measures: the expected shortfall/longrise (ES and EL) as given by Equation (6) and Equation (7). Fig. 3 highlights an asymmetric pattern between depreciation and appreciation risks. Indeed, the depreciation risk of the British Pound is more volatile and larger than the appreciation risk. If we focus on the Brexit period, we notice a sharp drop in the depreciation risk. It decreases from −0.08 in May 2016 to −0.15 in June 2016 and reaches −0.17 in July 2016, just after the Brexit vote. To sum up, the uncertainty around Brexit leads to a significant increase of the 1-month-ahead depreciation risk of the British Pound.Fig. 3 Depreciation and appreciation risks for the British Pound. Fig. 3 3.2 MXN/USD during the Covid-19 crisis In this application, we collect monthly data for the MXN/USD exchange rate (see monthly log-changes in Fig. 9), 1-month interest rate differentials between Mexico and the U.S. (see Fig. 10) and the VIX index (see Fig. 11). All figures are presented in the Annex. The data spans from January 2003 to April 2020, covering thus the beginning of the Covid-19 crisis. Let's start by presenting the results of the quantile regression stemming from the estimation of Equation (2). Fig. 4 presents the estimated parameters stemming from the quantile regression, for various quantiles, as well as confidence intervals estimated by Bootstrap, with a 95% confidence level. The procedure considers 20 different quantiles τ, from τ = 0.05, …0.95. The OLS estimated parameter of the Fama coefficient β is negative, βˆOLS=−2.2 in line with the standard Fama puzzle. However when considering the quantile estimated parameters, they are positive for lowest quantiles and are slightly negatively significant for higher quantiles. In other words, in periods of strong depreciation of the Peso (or strong appreciation of the US Dollar), the Fama coefficient tends to be highly positive, though non-significant. Against this background, a cut in US interest rates, everything else equal, induces a depreciation (an appreciation) of the Peso (the US Dollar) one month after. This stylized fact tends to reflect the perception of the US Dollar as a safe haven currency. Even when the US economy lies in the low phase of the cycle, the US Dollar tends to appreciate vs the Mexican Peso.Fig. 4 Quantile regression estimators for MXN/USD. Fig. 4 An interesting result is about the role of uncertainty. Overall, the OLS estimate is slightly significantly negative, γˆOLS=−0.00054, meaning that on average a positive shock to the VIX translates to into a USD appreciation, in a flight-to-quality move. However, the estimated uncertainty coefficient γ shows a high degree of non-linearity and tends to increase with the quantiles. It turns out that for lowest quantiles, i.e. when the Peso depreciates (or the US Dollar appreciates), the VIX is playing negatively even though the confidence interval is large. It means that during US Dollar appreciation periods, a spike in financial volatility tends to appreciate even more the US Dollar. In opposition, for highest quantiles the coefficient γ is slightly significantly positive, meaning that during Peso appreciation phases, a jump in the financial volatility leads to a continuation of this appreciation phase. Let's focus now on the conditional distribution fˆ(.) of the 1-step-ahead forecasts of the MXN/USD exchange rate. Using estimated quantiles, we estimate the Skewed-Student conditional densities for any date t from equation (4). Time-varying conditional 1-step-ahead distributions are presented in Fig. 12 in the Annex. First, we observe a recent shift to the left in the conditional distribution over the last points for beginning of 2020. From those results we derive the ES and EL risk measures as defined by equations (6), (7). Fig. 5 presents the ES and EL risk measures, highlighting a clear asymmetry between depreciation and appreciation risks. Indeed, each time the VIX rises we have a contemporaneous large drop in the ES, meaning higher expected Mexican Peso depreciation risks. The observed drops in the ES are mirrored by much smaller spikes in the EL reflecting the asymmetric behaviour of the conditional distribution. In the wake of the Covid-19 crisis, the ES falls to levels close to what we observed during the Global Financial Crisis. This reflects the overall uncertainty on financial markets during this crisis, leading as usual to a U.S. Dollar appreciation, specially versus emerging economies. This expected depreciation risk on the Peso is likely to put additional macroeconomic risks on Mexico in upcoming months.Fig. 5 Depreciation and appreciation risks for the Mexican Peso vs the US Dollar. Fig. 5 4 Conclusion In this paper, we put forward an innovative approach based on quantile regression in order to assess 1-month-ahead appreciation and depreciation risks for a given currency, conditionally to uncertainty. First, we apply this methodology to anticipate the Sterling depreciation risk with respect to the Euro, during the Brexit-related uncertainty period, by augmenting the standard Fama regression with an economic policy uncertainty measure. We point out that depreciation risks for the British Pound largely increased by comparison with appreciation risks, reflecting low expectations of future UK potential growth. In a second application, we focus on the Covid-19 crisis to underline that this crisis generated an increase in global uncertainty as measured by the VIX, leading in turn to a flight-to-quality phenomenon that is visible in the increasing appreciation risk of the US Dollar vs the Mexican Peso. Overall, we show that our approach enables to anticipate short-term risks around exchange rates during periods of high uncertainty. APPENDIX Fig. 6 Monthly log-changes in GBP/EUR exchange rate Fig. 6 Fig. 7 Interest rates differential between EUR and GBP Fig. 7 Fig. 8 Economic policy uncertainty for the UK Fig. 8 Fig. 9 Monthly log-changes of the MXN/USD exchange rate Fig. 9 Fig. 10 Interest rate differential between Mexico and the U.S. Fig. 10 Fig. 11 VIX index Fig. 11 Fig. 12 Conditional distribution of MXN/USD exchange rate Fig. 12 ☆ We would like to thank Menzie Chinn and Valérie Mignon for helpful comments on previous versions of the paper. Laurent Ferrara acknowledges financial support from the French 10.13039/501100001665 ANR (DEMUR research project ANR-20-CE26-0013). The views expressed in this paper are those of the authors and do not necessarily reflect the views of the Banque Centrale du Luxembourg or the Eurosystem. 1 The first reported death from the Covid-19 is a 61 year-old man who died in China on 9 January 2020, see Baldwin and Weder di Mauro (2020) 2 Results for h = 3 are qualitatively similar and are available upon request. 3 We use the R package quantreg for computations. ==== Refs References Adrian T. Boyarchenko N. Giannone D. Vulnerable growth Am. Econ. Rev. 109 4 2019 1263 1289 Baillie R. Kilic R. Do asymmetric and nonlinear adjustments explain the forward premium anomaly? J. Int. Money Finance 25 1 2006 22 47 Baker S.R. Bloom N. Davis S.J. Measuring economic policy uncertainty Q. J. Econ. 131 4 2016 1593 1636 Baker S.R. Bloom N. Davis S. Terry S. Covid-induced Economic Uncertainty 2020 NBER Working Paper No. 26983 Baldwin R. Weder di Mauro B. Mitigating the COVID Economic Crisis: Act Fast and Do Whatever it Takes 2020 VoxEU.org Book, CEPR Bansal F. Dahlquist M. The forward premium puzzle: different tales from developed and emerging economies J. Int. Econ. 51 2000 115 144 Bloom N. The impact of uncertainty shocks Econometrica 77 3 2009 623 685 Bloom N. Bunn P. Chen S. Mizen P. Smietanka P. Thwaites G. The Impact of Brexit on UK Firms 2019 NBER Working Paper 26218 Born B. Mueller G. Schularick M. Sedlacek P. The costs of economic nationalism: evidence from the Brexit experiment Econ. J. 129 623 2019 2722 2744 Bussière M. Chinn M. Ferrara L. Heipertz J. The New Fama Puzzle 2022 (IMF Economic Review, forthcoming) Chinn M. Meredith G. Monetary policy and long horizon uncovered interest parity IMF Staff Pap. 51 3 2004 409 430 Dhingra S. Ottaviano G.I.P. Sampson T. Reenen J.V. The Consequences of Brexit for UK Trade and Living Standards 2016 LSE Research Online Documents on Economics 66144 Eguren-Martin F. Sokol A. Attention to the Tail(s): Global Financial Conditions and Exchange Rate Risks 2022 IMF Economic Review forthcoming Fama E. Forward and spot exchange rates J. Monetary Econ. 14 3 1984 319 338 Ferrara L. L'Huissier S. Tripier F. Uncertainty Fluctuations: Measures, Effects and Macroeconomic Policy Challenges 2017 CEPII Policy Brief No. 20, December 2017 Gaglianone W.P. Lima L. Constructing density forecasts from quantile regressions J. Money Credit Bank. 44 8 2012 1589 1607 Gourinchas P.-0. Hale G. Whiter the Pound”. Economic Letter 2017 Federal Reserve Bank of San Francisco, No. 2017-11 Hassan T.A. Hollander S. van Lent L. Tahoun A. The Global Impact of Brexit Uncertainty 2019 NBER Working Paper 26609 Ismailov A. Rossi B. Uncertainty and deviations from uncovered interest rate parity J. Int. Money Finance 88 2018 242 259 Koenker R. Bassett G. Regression quantiles Econometrica 46 1 1978 33 50 Koenker R. Quantile Regression 2005 Cambridge University Press Cambridge Korobilis D. Quantile regression forecasts of inflation under model uncertainty Int. J. Forecast. 33 1 2017 11 20 Knight F. Risk, Uncertainty and Profits 1921 Hart, Schaffner and Marx; Houghton Mifflin Company Boston, MA Ramirez-Rondan N.R. Terrones M.E. Uncertainty and the Uncovered Interest Parity Condition: How Are They Related?” 2019 MPRA Paper 97524 Rossi B. Exchange rate predictability J. Econ. Lit. 51 4 2013 1063 1119	0	PMC9718643	NO-CC CODE	2022-12-06 23:23:41	no	2022 Aug 23; 170:202-212	utf-8	null	null	null	oa_other
==== Front Cell Host Microbe Cell Host Microbe Cell Host & Microbe 1931-3128 1934-6069 Elsevier Inc. S1931-3128(22)00559-5 10.1016/j.chom.2022.11.003 Short Article Defining antigen targets to dissect vaccinia virus and monkeypox virus-specific T cell responses in humans Grifoni Alba 19 Zhang Yun 29 Tarke Alison 13 Sidney John 1 Rubiro Paul 1 Reina-Campos Maria 1 Filaci Gilberto 45 Dan Jennifer M. 16 Scheuermann Richard H. 12781011 Sette Alessandro 16101112∗ 1 Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology (LJI), La Jolla, CA 92037, USA 2 Department of Informatics, J. Craig Venter Institute, La Jolla, CA 92037, USA 3 Center of Excellence for Biomedical Research, Department of Experimental Medicine, University of Genoa, Genoa 16132, Italy 4 Center of Excellence for Biomedical Research, Department of Internal Medicine, University of Genoa, Genoa 16132, Italy 5 Biotherapy Unit, IRCCS Ospedale Policlinico San Martino, Genoa 16132, Italy 6 Department of Medicine, Division of Infectious Diseases and Global Public Health, University of California, San Diego, La Jolla, CA 92037, USA 7 Department of Pathology, University of California, San Diego, La Jolla, CA 92093, USA 8 Global Virus Network, Baltimore, MD 21201, USA ∗ Corresponding author 9 These authors contributed equally 10 These authors contributed equally 11 Senior author 12 Lead contact 3 12 2022 14 12 2022 3 12 2022 30 12 16621670.e4 4 9 2022 17 10 2022 7 11 2022 © 2022 Elsevier Inc. 2022 Elsevier Inc. Elsevier has created a Monkeypox Information Center (https://www.elsevier.com/connect/monkeypox-information-center) in response to the declared public health emergency of international concern, with free information in English on the monkeypox virus. The Monkeypox Information Center is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its monkeypox related research that is available on the Monkeypox Information Center - including this research content - immediately available in publicly funded repositories, with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the Monkeypox Information Center remains active. The monkeypox virus (MPXV) outbreak confirmed in May 2022 in non-endemic countries is raising concern about the pandemic potential of novel orthopoxviruses. Little is known regarding MPXV immunity in the context of MPXV infection or vaccination with vaccinia-based vaccines (VACV). As with vaccinia, T cells are likely to provide an important contribution to overall immunity to MPXV. Here, we leveraged the epitope information available in the Immune Epitope Database (IEDB) on VACV to predict potential MPXV targets recognized by CD4+ and CD8+ T cell responses. We found a high degree of conservation between VACV epitopes and MPXV and defined T cell immunodominant targets. These analyses enabled the design of peptide pools able to experimentally detect VACV-specific T cell responses and MPXV cross-reactive T cells in a cohort of vaccinated individuals. Our findings will facilitate the monitoring of cellular immunity following MPXV infection and vaccination. Graphical abstract Grifoni et al. developed orthopox- and monkeypox-specific epitope pools to measure monkeypox T cell responses in natural infection and vaccination. The pools were validated by detection of memory T cell responses in PBMCs from Dryvax vaccinees. A majority of the Dryvax-vaccinee CD4 responses were cytotoxic and produced granzyme B. Keywords MPXV VACV monkeypox mpox vaccinia virus orthopoxvirus T cell epitope infectious disease sequence conservation Published: December 14, 2022 ==== Body pmcIntroduction On August 24th, 2022, the World Health Organization (WHO) reported 41,664 confirmed cases of monkeypox virus (MPXV) infection and five deaths in non-endemic regions.1 Although MPXV infections and outbreaks have been reported on the African continent in the past three decades, this current outbreak is unprecedented in size and scope; having spread globally to almost 100 countries, the vast majority of these countries have not historically reported MPXV cases, including European countries and the US.2 With the current outbreak, it is important to understand immunity against MPXV, but only a few studies have addressed immune responses to MPXV infections in humans.3 , 4 , 5 First, little information is available on the quality and duration of immune responses to MPXV infection in humans. Second, little data on efficacy in humans are available for the MPXV vaccines based on the vaccinia virus (VACV). Third, it is unknown to what extent human cellular immune responses induced by VACV vaccination are cross-reactive with MPXV. These knowledge gaps should be addressed in MPXV and also in the strain associated with the current outbreak.6 Information is available on immune responses and correlates of protection from VACV infection,7 , 8 , 9 the virus utilized as a vaccine to protect from smallpox disease caused by variola virus (VARV) infection.10 Several VACV studies demonstrate that antibody responses are crucial for disease prevention,8 , 9 whereas T cell responses are important to control and terminate pox-virus infections.11 , 12 Many studies describe T cell epitopes for Orthopoxviruses (OPXVs), and VACV in particular;12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 however, only two studies have investigated T cell responses against MPXV in humans.20 , 21 VACV was utilized under the brand name of Dryvax to eradicate smallpox in the 1980s,22 but Dryvax-vaccinated individuals with impaired cellular immunity were deficient in VACV control23 , 24 and risked severe reactions and safety issues.25 Dryvax and Acambis 2000 (the related attenuated vaccine) vaccination was largely discontinued after 2001 and replaced by the modified vaccinia Ankara (MVA) virus (under the brand name JYNNEOS), which has a superior safety profile26 and induces similar levels of antibody responses.27 Studies in non-human primate models using VACV vaccination to prevent MPXV infection show efficacy in preventing infection and/or attenuating disease severity.5 , 28 , 29 , 30 Data from human studies are limited to an observational study demonstrating 85% efficacy in preventing MPXV disease in subjects vaccinated with Dryvax.31 The JYNNEOS vaccine is approved for use to prevent MPXV infection/disease based on serological responses, but no data are available addressing clinical efficacy in humans. An additional knowledge gap is the degree of T cell epitope conservation elicited by VACV vaccination for MPXV infection. MPXV shares 90% overall sequence homology with VACV,32 suggesting that VACV-induced T cell responses might be largely cross-reactive with MPXV epitopes. VACV epitopes are largely conserved in VARV,33 suggesting a similar strategy can be applied to other OPXVs. Here, we used the Immune Epitope Database and Analysis Resource (IEDB)34 and Virus Pathogen Resource (ViPR)35 to compile known OPXV T cell epitopes to determine protein conservation and assess immunodominance. We previously showed that large pools of peptides (megapools [MPs])36 can be used to measure CD4+ and CD8+ T cell responses against a number of allergens, as well as bacterial and viral targets.37 , 38 , 39 , 40 , 41 Here, we develop and validate pools of previously defined epitopes to assess T cell responses to VACV and MPXV. Results Orthopox T cell epitopes curated in IEDB and conservation of Orthopox epitopes within MPX Pox viruses have relatively large genomes, encoding ∼200 different open reading frames (ORFs),32 with studies reporting broad immune responses to many ORFs.19 To define MPs to measure T cell responses in MPXV vaccination and infection, we inventoried experimentally defined epitopes described in the literature and curated by IEDB, as of May 2022 (step 1, Figure 1 A). The analysis identified 318 CD4+ and 659 CD8+ T cell epitopes derived from OPXVs (Table S1). The vast majority of epitopes (88%) have been described in the context of VACV, of which 78% of CD4+ epitopes and 36% of CD8+ epitopes were associated with responses in humans (Table S1).Figure 1 Schematic representation of peptide pools design Based on these data, we developed two MPs: OPX-CD8-E and OPX-CD4-E. For OPX-CD8-E, we selected the 238 CD8+ epitopes recognized in humans, whereas for OPX-CD4-E, we included all 318 CD4+ epitopes recognized in any species, based on the high degree of overlap between binding repertoires of MHC class II.42 For the CD4+ epitopes, we performed a clustering analysis to create epitope regions of up to 22 residues that encompass nested or overlapping epitopes. Accordingly, a set of 300 CD4+ epitopes was generated and is listed in Table S1 along with the CD8+ epitopes. The MPXV and VACV viruses have been reported32 to share a high degree of sequence homology and conservation, suggesting that Orthopox-specific T cell epitopes may be conserved in MPX.6 We ascertained whether each epitope was conserved in the MPXV isolate MA001 (step 2, Figure 1A). The results indicate that both CD4+ and CD8+ epitopes are highly conserved with 94% and 82%, respectively, being 100% conserved in MPXV (Table S1), with high conservation (range 74%–96%) irrespective of the viral species in which the epitopes were originally defined. The results indicated that most previously defined Orthopox epitopes were highly conserved and could be used to generate MPs as a potential reagent (step 3, Figure 1A). T cell immune responses after Dryvax vaccination are detected by Orthopox MPs We evaluated T cell responses for their capacity to recognize the Orthopox MPs, using peripheral blood mononuclear cells (PBMCs) in cohorts of Dryvax-vaccinated and non-vaccinated subjects (step 4, Figure 1A). Cohort characteristics are provided in Table S2 and STAR Methods. To measure the T cell responses to OPX-CD8-E and OPX-CD4-E, we combined activation-induced marker (AIM) assays with cytokine intracellular staining (ICS) (Figure S1A). For CD4+ T cells, the highest responses were observed in recently vaccinated subjects. The magnitude of T cell reactivity was comparable pre-vaccination and 5–7 months post-vaccination (geometric mean [GM] ± geometric standard deviation [GSD]; pre = 0.09 ± 2.90, 2 weeks = 0.26 ± 2.53, and 5–7 months = 0.11 ± 2.86; p = 0.004 Kruskal-Wallis test; Figure 2 A). The frequency of CD4+ T cell responders increased from 52% to 95% 2 weeks post-vaccination with a decline to 61% 5–7 months post-vaccination (χ2 p = 0.01).Figure 2 T cell responses against Orthopox MPs after vaccinia vaccination; refers to Figures S1 and S2 and Table S1. List of orthopox IEDB epitopes, related to Figures 1 and 3, Table S4. List of predicted MPX CD4 and CD8 epitopes, related to Figures 1 and 4 PBMCs from each time point were tested in AIM/ICS assays with experimentally defined OPXV MPs, OP-CD4-E (blue), and OP-CD8-E (red). (A and B) OP-CD4-E-specific CD4 + T cell reactivity is shown for (A) AIM and (B) ICS. (C and D) OP-CD8-E specific CD8 + T cell reactivity is shown for (C) AIM and (D) ICS. The y axis of each bar graph starts at the limit of detection (LOD), and the limit of sensitivity (LOS) is indicated with a dotted line. Columns represent the geometric mean and error bars indicate the geometric standard deviation. Pie charts below each bar indicate the frequency of positive responders. Mann-Whitney t test was applied to each graph, and p values symbols are shown when significant. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, and ∗∗∗∗p < 0.0001. X2 test was applied to the frequencies of positivity, and p values are listed on the right of each graph. Similar findings were observed by ICS, although CD4+ T cell responses showed a more modest and insignificant decline 5–7 months post-vaccination; responses post-vaccination were significantly higher in magnitude and frequency than in pre-vaccination (GM ± GSD; pre = 0.01 ± 3.80, 2 weeks = 0.15 ± 3.42, and 5–7 months = 0.07 ± 3.64; p < 0.0001 Kruskal-Wallis test, χ2 p = 0.0128; Figure 2B). Unexposed subjects did not yield appreciable responses (Figures S1B and S1C). The ICS assays demonstrated that CD40L+CD4+ T cell responses were Th1 or cytotoxic responses, encompassing mostly granzyme B (GZMB) and IFNγ, followed by TNF-α and IL-2 (Figure S2). Significant increases in TNF-α and IL-2 and a decrease in GZMB production were observed at 5–7 months post-vaccination, as compared with 2 weeks (TNF-α: 2 weeks = 18% and 5–7 months = 33%, p = 0.0088; IL-2: 2 weeks = 3% and 5–7 months = 13%, p = 0.0059; GZMB: 2 weeks = 53% and 5–7 months = 35%, p = 0.0292). A prevalence of CD40L+IFNγ+GZMB+ population followed by CD40L+GZMB+, CD40L+IFNγ+, and CD40L+TNF-α+ populations were observed (Figures S2A and S2E). In CD8+ T cells in AIM assays, the highest responses were observed in recently vaccinated subjects, with the 5–7 months post-vaccination samples reverting to a magnitude similar to pre-vaccination (GM ± GSD; pre = 0.04 ± 1.04, 2 weeks = 0.19 ± 3.24, and 5–7 months = 0.06 ± 2.15; p < 0.0001 Kruskal-Wallis test; in Figure 2C). The frequency of positive CD8+ T cell responses increased to 79% 2 weeks post-vaccination and decreased to 22% at the 5–7 months’ time point (χ2 p < 0.0001). Similar results were observed in the ICS assay, where IFNγ, GZMB, TNF-α, and IL-2 secreting CD69+CD8+ T cells were quantified (GM ± GSD; pre = 0.01 ± 3.39, 2 weeks = 0.04 ± 7.22, and 5–7 months = 0.01 ± 3.06; p = 0.01 Kruskal-Wallis test, χ2 p = 0.025; Figure 2D). CD8+ T cell functionality was also assessed based on IFNγ, TNF-α, IL-2, and/or GZMB expression and showed a prevalence of IFNγ followed by IL-2 and GZMB (Figures S2C–S2F). Definitionof immunodominant ORFs and prediction of MPX T cell epitopes We next wanted to define MPs based on MPXV sequences that would be more suited to characterize responses in MPXV infection. We started with the same list of Orthopox epitopes (Table S1) and identified corresponding conserved sequences in MPXV (step 1, Figure 1B) with sequence identities of ≥70% for class II and ≥80% for class I epitopes. By mapping those epitopes to the corresponding MPXV ortholog proteins (step 2, Figure 1B), we derived a set of MPXV proteins that is potentially immunodominant (see Table S3). Accordingly, we identified 19 antigens for CD4+ and 40 for CD8+ (step 3, Figure 1B). The 19 MPXV CD4+ ORFs accounted for 67% of the homologous IEDB Orthopox CD4+ epitopes, and the 40 CD8+ ORFs accounted for 61% of the corresponding IEDB CD8+ epitopes (Figure 3 ). Overall, 48 proteins were immunodominant, corresponding to 25% of the MPXV proteome. T cell immunodominant targets identified were mostly early transcribed proteins.43 Several targets, such as A10L, A3L, D5R, A26L/A30L, H3L, J6R, A27L, D13L, A24R, A4L, and F8L, are of particular interest, since they were dominant for both CD4+ and CD8+ T cells. The study of immunodominant ORFs is also of particular relevance, since JYNNEOS vaccine is based on MVA, an attenuated VACV that lost 14% of the original genome,44 retaining only 157 ORFs (GenBank: U94848.1).45 Importantly, all the ORFs considered as dominant for CD4+, CD8+, or both were conserved in the MVA/JYNNEOS sequences. This confirms the large breadth of immunogenic ORFs in OPXVs.15 We predicted potential T cell epitopes from the MPXV orthologs of the 19 CD4+ and 40 CD8+ immunodominant antigens, using IEDB tools46 (step 4, Figure 1B). For CD4+, we predicted 276 promiscuous HLA class II binders (Table S3).47 In parallel, we predicted 1,647 potential CD8+ epitopes binding to a panel of common HLA class I alleles, as previously utilized in other viral systems39 , 48 (step 5, Figure 1B; Document S1. Figures S1 and S2 and Tables S2 and S3, Table S4. List of predicted MPX CD4 and CD8 epitopes, related to Figures 1 and 4).Figure 3 Orthopox protein immunodominance; refers to Table S1 The IEDB was mined for experimentally defined epitopes derived from Orthopoxviruses. A total of 47 different antigens were identified as the protein source for defined epitopes, corresponding to 19 and 39 antigens associated with CD4 + or CD8+ epitopes, respectively. The pie charts represent the total proteins recognized by CD4+ and CD8+ as defined by the IEDB and listed in Table S1. The most immunodominant antigens are listed in the figure legend, and the remaining antigens (“others”) are colored in gray. Assessment of CD4+ and CD8+ T cell cross-reactive responses able to recognize MPXV in smallpox-vaccinated individuals We then evaluated whether VACV-induced T cell responses could cross-recognize the MPXV-derived, predicted epitope pools (Figures 4 and S2). By the AIM assay, the magnitude of CD4+ T cell cross-reactivity peaked at 2 weeks post-vaccination and declined at 6–7 months, reaching comparable reactivity to pre-vaccination (GM ± GSD; pre = 0.06 ± 2.51, 2 weeks = 0.23 ± 2.91, and 5–7 months = 0.08 ± 2.63; p = 0.0005 Kruskal-Wallis test; Figure 4A). The frequency of CD4+ T cell responders significantly increased from 43% to 84% 2 weeks post-vaccination, with a decline to 67% 5–7 months post-vaccination (χ2 p = 0.024). Similar results were observed by ICS, although the post-vaccination decline was less pronounced and responses were still significantly higher than those observed pre-vaccination (GM ± GSD; pre = 0.011 ± 3.58, 2 weeks = 0.12 ± 4.67, and 5–7 months = 0.04 ± 4.13; p < 0.0001 Kruskal-Wallis test; Figure 4B). Finally, the functionality of CD4+ T cells was comparable to what was observed for the OPXV-specific T cell responses (Figures S2B and S2E).Figure 4 T cell responses able to cross-recognize MPXV MPs after vaccinia vaccination; refers to Figures S1 and S2 and Table S1. List of orthopox IEDB epitopes, related to Figures 1 and 3, Table S4. List of predicted MPX CD4 and CD8 epitopes, related to Figures 1 and 4 PBMCs from each time point were tested in AIM/ICS assays with predicted MPXV MPs, MPXV-CD4-P (purple), and 5 pools of MPXV-CD8-P1-P5 summed to get the overall MPXV-CD8-P reactivity (gray). (A and B) MPXV-CD4-P-specific CD4 + T cell reactivity is shown for (A) AIM and (B) ICS. (C and D) MPXV-CD8-P-specific CD8 + T cell reactivity is shown for (C) AIM and (D) ICS. The y axis of each bar graph starts at the LOD, and the LOS is indicated with a dotted line. Columns represent the geometric mean and error bars indicate the geometric standard deviation. Pie charts below each bar indicate the frequency of positive responders. Mann-Whitney t test was applied to each graph, and p values symbols are shown when significant. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, and ∗∗∗∗p < 0.0001. X2 test was applied to the frequencies of positivity, and p values are listed on the right of each graph. CD8+ T cell responses were also able to cross-recognize the MPXV pools. Reactivity peaked at 2 weeks and further declined 5–7 months post-vaccination, with comparable reactivity with the pre-vaccination by AIM (GM ± GSD; pre = 0.05 ± 1.44, 2 weeks = 0.17 ± 4.93, and 5–7 months = 0.06 ± 2.23; p = 0.0014 Kruskal-Wallis test, χ2 p = 0.0212; Figure 4C) and a less pronounced decline by ICS (GM ± GSD; pre = 0.02 ± 4.42, 2 weeks = 0.03 ± 7.97, and 5–7 months = 0.02 ± 3.96; p = 0.8653 Kruskal-Wallis test, χ2 p = 0.9961; Figure 4D). The quality of CD8+ T cells was comparable to and driven by IFNγ production, although an increase in IL-2 was also observed (Figures S2D and S2F). Little reactivity was again observed in non-vaccinated subjects for both CD4+ and CD8+ (Figures S1D and S1E). Discussion There is an urgent need to understand adaptive immune responses to MPXV in both natural immunity and vaccination. This study is focused on CD4+ and CD8+ T cell responses and peptide MPs to detect these responses. In the context of SARS-CoV-2, we have used three different approaches to design MPs. The first one uses experimentally defined epitopes of highly homologous viral species and typically generates pools with higher activity, but it is limited to described epitopes and may underrepresent less frequent HLAs. A second approach utilizes T cell epitope predictions targeting common HLA alleles.49 It is limited by the accuracy of bioinformatic predictions, but it can be valuable when little information is available on the actual epitopes recognized in a population and/or when the target pathogen is particularly large so use of complete peptide sets is unfeasible. The third approach utilizes overlapping peptides spanning the entire antigen sequence37 , 50 and is the most comprehensive agnostic approach. This approach requires synthesis and testing of many peptides and becomes unfeasible with larger genomes, such as the Orthopoxviridae, which contain ≥200 ORFs. Accordingly, we defined dominant cross-reactive ORFs to generate peptide MPs to measure T cell responses, which we plan to make available to the research community. These reagents were validated by assessing T cell responses induced by Dryvax vaccination in PBMC samples. Because all current vaccinations against MPXV are based on VACV/MVA, it was important to determine the sequence conservation frequency of previously defined VACV T cell epitopes curated in the IEDB with the current outbreak MA001 strain reference. Overall, the median degree of amino acid sequence conservation was 61%–67%, implying that responses induced by VACV vaccination should recognize ortholog protein sequences in the MPXV genome. McKay and collaborators reported a similar sequence conservation frequency of 70% between MPXV-2022 and the MPXV-CB (Congo Basin) strains.6 VACV-reactive T cells recognizing MPXV-derived epitopes are not surprising because they cross-react to ectromelia virus epitopes as shown on a mousepox infection model.51 This observation enabled the definition of Orthopox-specific MP reagents. The IEDB epitope data were utilized to define dominant ORFs recognized by Orthopox-specific T cell responses. This analysis revealed the remarkable breadth of CD4+ and CD8+ T cell immune responses, consistent with earlier reports.18 , 52 Nineteen ORFs were required to cover 67% of CD4+ T cell responses, and 40 ORFs were required to cover 61% of CD8+ T cell responses. This significant finding suggests that focusing on 10%–25% of the 200+ ORFs typically encoded in pox genomes will still capture the majority of T cell responses. This observation enabled the definition of MPXV-specific MP reagents. The definition of dominant ORFs provided insights into the mechanisms underlying the development of these responses, showing that dominant antigens are predominantly early ORFs, confirming earlier studies.52 In vaccination, we note that all dominant ORFs were conserved in MVA/JYNNEOS and had orthologs in MPXV. This suggest that the response directed to MPXV induced by MVA should mirror the response induced by previous VACV vaccines, with known clinical efficacy against MPXV in humans. We developed peptide pools based on experimentally defined Orthopox T cell epitopes or predicted T cell epitopes derived from the most immunodominant ortholog proteins of MPXV, similar to the approach we used for SARS-CoV-2.37 , 48 The use of epitope pools has the important potential advantage of obviating the use of infected cells to quantify T cell responses, which is prone to interference by pox-virus expression of immune antagonizing genes53 and is associated with biosafety concerns. These Orthopox MPs were validated using PBMC from subjects who received the Dryvax vaccine. Early time points (2 weeks from vaccination) were associated with positive responses in 100% and 63% of the subjects for CD4+ and CD8+ T cells, respectively, and decreased 5–7 months after vaccination. CD4+ and CD8+ T cell responses to the pools of predicted MPXV epitopes were similarly detected in 84% and 52% of vaccinees 2 weeks post-vaccination and also decreased 5–7 months after vaccination. Our results are consistent with the report of Amara et al. that shows a better persistence of CD4+ T cells, with a 2-fold contraction between effector and memory phase, in contrast with the CD8+ T cell response that shows a 7-fold contraction.54 Hammarlund and Slifka reported that the humoral and cellular T cell responses are long-lasting, with a half-life of 8–15 years.55 This apparent discrepancy can be reconciled considering that in the Hammarlund-Slifka report, the immune response is measured 1+ years after vaccination. Here, we compare pre-vaccination and 6-month post-vaccination samples. Considering cytokine+ CD4+ T cell responses, which mirror the readout of Hammarlund and Slifka, positive responses observed are 94% 6 months post-vaccination and 71% for pre-vaccinated with a previous history of vaccination in the 5–20 years range. Thus, the % of positivity is comparable. In the context of CD8+, we observed a frequency of response of 30%, whereas Hammarlund, noting several negative donors, reports an overall higher % of responses. Additionally, we observed that human CD4+ T cell responses are associated with a large fraction of GZMB-secreting, antigen-specific cells. It was previously reported that the role of CD4+ T cells in protection from VACV and MPXV infections outweighs the contribution provided by CD8+ T cells in macaques.29 , 56 The current data suggest that in addition to their role supporting the development of antibody responses, their longevity and a cytotoxic component could also contribute to a sustained antiviral function of CD4+ T cells. In conclusion, the use of available information related to VACV epitopes in conjunction with bioinformatic predictions points to specific regions that are conserved across several OPXV species, including MPXV, making them suitable for vaccine evaluation. To the best of our knowledge, this is the first demonstration of the use of epitope MPs suitable to characterize vaccine-specific responses and also likely to detect immune responses in the context of MPXV infection and disease. Limitations and future directions The cohort selected has the following limitations: cross-sectional sample collection decreases the accuracy of kinetic analysis of responses, a lack of information on childhood vaccination status, and inclusion of healthcare workers previously exposed to occupational vaccination before the Dryvax vaccination administered in relation to this specific study. Antigen selection for MPXV was based on studies performed considering other OPXVs (mainly VACV). Future studies might use overlapping peptide pools spanning various ORFs to define in more detail the ORFs specifically recognized following MPX vaccination or infection. Most previously defined epitopes are focused on more common HLAs, and this might impact detecting responses from individuals with dominant responses restricted by rare HLAs, although this is partially addressed by the prediction of a set of alleles with over 95% coverage in the general population worldwide. The use of peptide pools representing immunodominant viral protein targets may not fully reflect physiological targets. The current analysis did not address antibody responses, which are the dominant correlate of vaccine-induced protection. A strong limitation of the current study is the lack of validation of the MPXV pools in MPXV-infected individuals. This is important because while OPXVs are highly related, their pathogenesis—including processing and presentation of T cell epitopes and being able to evade innate and adaptive immunity—within different hosts may vary. Future directions will include the following: (1) using the MPs to characterize immune responses in acute and convalescent MPXV natural infection, (2) addressing the Th and memory phenotypes of responding T cells, (3) comparing responses induced by different vaccines, (4) widely disseminating the MPs to the scientific community, and (5) further optimizing these reagents. STAR★Methods Key resources table REAGENT or RESOURCE SOURCE IDENTIFIER Antibodies Mouse anti-human CD8 BUV496 (clone RPA-T8) BD Biosciences Cat# 612942; RRID:AB_2870223 Mouse anti-human CD3 BUV805 (clone UCHT1) BD Biosciences Cat# 612895; RRID:AB_2870183 Mouse anti-human TNF alpha eFluor450 (clone MAb11) Life Tech Cat# 48-7349-42; RRID:AB_2043889 Mouse anti-human CD14 V500 (clone M5E2) BD Biosciences Cat# 561391; RRID:AB_10611856 Mouse anti-human CD19 V500 (clone HIB19) BD Biosciences Cat# 561121; RRID:AB_10562391 Mouse anti-human CD4 BV605 (clone RPA-T4) BD Biosciences Cat# 562658; RRID:AB_2744420 Mouse anti-human IFN gamma FITC (clone 4S.B3) Invitrogen (Thermo Fisher Scientific) Cat# 11-7319-82; RRID:AB_465415 Rat anti-human IL-2 BB700 (clone MQ1-17H12) BD Biosciences Cat# 566405; RRID:AB_2744488 Mouse anti-human CD69 PE (clone FN50) BD Biosciences Cat# 555531; RRID:AB_395916 Mouse anti-human CD134 (OX40) PE-Cy7 (clone Ber-ACT35) BioLegend Cat# 350012; RRID:AB_10901161 Mouse anti-human CD137 APC (clone 4B4-1) BioLegend Cat# 309810; RRID:AB_830672 Mouse anti-human Granzyme B AF700 (clone GB11) BD Biosciences Cat# 560213; RRID:AB_1645453 Mouse anti-human CD154 (CD40 Ligand) APC-ef780 (clone 24-31) eBioscience (Thermo Fisher Scientific) Cat# 47-1548-42; RRID:AB_1603203 Biological samples Pre-vaccinated donor PBMCs samples LJI Clinical Core N/A Two weeks post vaccinated donor PBMCs samples LJI Clinical Core N/A 5-7 months post vaccinated donor PBMCs samples LJI Clinical Core N/A Unexposed donors PBMCs samples LJI Clinical Core N/A Chemicals, peptides, and recombinant proteins Brilliant Staining Buffer Plus BD Biosciences Cat# 566385; RRID:AB_2869761 Live/Dead Viability Dye eFluor506 Invitrogen (Thermo Fisher Scientific) Cat# 65-0866-14 Synthetic peptides TC Peptide Lab https://www.tcpeptide.com Deposited data OPXV and MPXV peptides This study Table S1 OPXV and MPXV megapools (MP) This study Table S4 Software and algorithms GraphPad Prism 9 GraphPad https://www.graphpad.com/; RRID:SCR_002798 FlowJo 10.8.1 FlowJo https://www.flowjo.com/; RRID:SCR_008520 IEDB Immune Epitope DataBase https://www.iedb.org; RRID:SCR_006604 ViPR Virus Pathogen Resource http://www.viprbrc.org; RRID:SCR_010685 Resource availability Lead contact Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Alessandro Sette ([email protected]). Materials availability Aliquots of synthesized sets of peptides identified in this study are available from the lead contact. There are restrictions to the availability of the peptide reagents due to cost and limited quantity. Experimental model and subject details Cohort of VACV vaccinees to assess T cell responses The characteristics of the study population that donated the banked PBMC utilized for the present study was described previously.15 Healthy male and female donors between 7 and 62 years of age that had received a vaccinia virus (Dryvax) vaccination as a prophylactic measure, either because of their potential exposure to vaccinia in a laboratory or hospital setting, or because of their enrollment into military and health worker vaccination programs, within one year of providing the blood donation. PBMCs were collected at 2 weeks (n=19) or 5-7 months after vaccination (n=18). In addition, we also utilized two additional control cohorts. For the first control cohort, Pre-vaccination (“pre”) PBMC samples (n=21) were collected from a similar cohort of healthcare donors prior to vaccination. Since some of the pre-vaccinated donors had received childhood Smallpox vaccination, a second control cohort of truly unexposed donors (n=15) was enrolled; this cohort consisted of individuals born after 1980, the official year of worldwide eradication of Smallpox and eight years after the United States stopped childhood Smallpox vaccinations. Further, this unexposed cohort had no history of occupational vaccination with Smallpox. Characteristics of the donor cohorts are also summarized in Table S2. Institutional Review Board approval and appropriate consent were obtained for this study. PBMCs For all donors in this study, PBMCs were isolated from heparinized blood by density gradient centrifugation with a Histopaque-1077 and cryopreserved in 10% DMSO in FBS prior to long term storage in liquid nitrogen freezers. The PBMC isolation of these specific donors has been described in greater detail by Oseroff et al.57 Method details IEDB analysis of Orthopoxvirus-derived T cell epitopes Known OPXV-derived T cell epitopes reported in the published literature, or through direct database submission, were identified by searching the IEDB at the end of May, 2022. Queries were performed broadly for the Orthopox genus, using NCBI taxonomy ID 10242, and specifying positive T cell assays. This retrieved 1076 records, from which 31 were removed because responses had not been defined in the context of either MHC class I or class II. For epitopes with responses in the context of class II, the set was further filtered to select epitopes of 12-25 residues, comporting with the canonical size of class II ligands associated with CD4+ T cell responses. Epitopes with class I responses were filtered to select those of 8-11 residues, canonical for class I ligands associated with CD8+ T cell responses. As a result, a final set of 977 epitopes, including 318 associated with class II responses, and 659 with class I responses, was identified for subsequent analyses. About 70% of the epitope data in this set is derived from the peer-reviewed literature. Identification of Orthopox T cell epitope homologs in MPXV The MPXV_USA_2022_MA001 (MA001) isolate (GenBank accession ON563414) was selected as the representative strain of the 2022 MPXV outbreak because MA001 was the first sequence from the 2022 outbreak deposited in GenBank58 and had been annotated by BV-BRC (www.bv-brc.org). The protein sequences of MA001 were retrieved from the BV-BRC website (=https://www.bv-brc.org/view/Genome/10244.322#view_tab=overview) on May 25, 2022. To identify the MPXV homologs of VACV antigens, a BLAST based homology search was used. All the 26 VACV antigens related to IEDB T cell epitopes except for VP8 and A47 had high identity (>83%) and good length coverage in MA001. With regard to the VP8 antigen we removed 1 nt insertion which caused a frameshift and re-annotated with GATU tool in ViPR (www.viprbrc.org). For the A47 antigen, neither MA001 nor the RefSeq Zaire strain had a good match and it is unlikely to have an MPXV homolog. To assess the sequence conservation of the OPX-CD8-E and OPX-CD4-E epitope pools in MPXV, a k-mismatch string search program was developed to find all matched sequences of an input epitope. The matched sequences meet the criteria of having the same length as the input epitope, and having at most k mismatched residues in comparison to the input epitope. In case of multiple matches for the same input epitope, the program also picks the optimal match. In order to identify the MPXV protein region homologous to the OPXV T cell epitopes, a k-mismatch string search method was used. Conceptually, the k-mismatch string search program searches through a protein sequence file using a fixed-size sliding window and identifies all windows with a maximum of k-mismatches compared to the input epitope sequence. In identifying OPXV epitope homologs in MPXV, the search pool used included all MA001 protein sequences, while the maximum number of k mismatches was set to be the larger of 20% of the input epitope length and 1, i.e., k = max (epitope length ∗ 0.2, 1). We additionally set up a maximum of 2 and 3 mismatches for class I and class II epitopes, respectively. Besides finding all epitope homologs in proteins, the epitope search program also picks the best match if multiple matches were found. The best match was defined as the one with the smallest number of mismatches, and in case of ties, the one(s) with the least shift in the start coordinate compared with the input epitope. In validating the epitope search result, two metrics were used: (1) whether the epitope hit resides in a protein homologous to the input epitope's parent protein identified from the pairwise analysis, and (2) whether the start coordinate of the epitope hit was near the start coordinate of the input epitope. In case of a start coordinate shift of 10 or more residues, the sequences of the parent proteins were aligned and then manually examined to see if the match was a false positive. For all epitopes evaluated using these criteria, only one was found to be a false positive following manual curation and was excluded from the downstream analysis. T cell epitope predictions Epitope prediction was carried out using the various dominant MPXV ORFs described above (Table S3). For CD4+ T cell epitope prediction, we applied a previously described algorithm that was developed to predict dominant HLA class II epitopes, using a median consensus percentile of prediction cutoff ≤ 20 percentile as recommended.47 For CD8+ T cell epitope prediction, we selected the 12 most frequent HLA class I alleles in the worldwide population,59 , 60 using a phenotypic frequency cutoff ≥ 6%. The specific alleles included were: HLA-A∗01:01, HLA-A∗02:01, HLA-A∗03:01, HLA-A∗11:01, HLA-A∗23:01, HLA-A∗24:02, HLA-B∗07:02, HLA-B∗08:01, HLA-B∗35:01, HLA-B∗40:01, HLA-B∗44:02, HLA-B∗44:03. HLA class I binding predictions were performed using the IEDB recommended class I prediction algorithm (as recommended in June, 2022) and selecting for each allele the top 1 percentile of peptides based on the total amino acid sequences of the 40 MPXV antigens selected. This initial list of epitopes was then filtered to eliminate redundancies and nested peptides by clustering61 to a single occurrence, and nested peptides were included within longer sequences, up to 12 residues in length, before assigning the multiple corresponding HLA restrictions for each region. Peptide synthesis and Megapool preparation OPXV and MPXV peptides were synthesized as crude material (TC Peptide Lab, San Diego, CA), and then individually resuspended in dimethyl sulfoxide (DMSO) at a concentration of 10–20 mg/mL. Aliquots of all peptides were pooled into megapools (MP) designated as OP-CD4-E, OP-CD8-E, MPX-CD4-P, MPX-CD8-P1, MPX-CD8-P2, MPX-CD8-P3, MPX-CD8-P4, and MPX-CD8-P5. These MPs underwent a sequential lyophilization. The resulting lyocake was resuspended in DMSO to yield a stock solution in which each individual peptide was present at a concentration of 1 mg/mL, as previously described,37 resulting in a final test concentration of 1ug/mL in the assay after dilution. All peptides and MPs are listed in Tables S1 and S4. AIM/ICS assay We performed the combined AIM/ICS assay as previously described.62 In brief, after thawing, 1–2x106 PBMCs per well were cultured with the OPXV- or MXPV- specific peptide MPs (1 μg/mL of each individual peptide contained in the peptide pool). An equimolar amount of DMSO was added to the cells in triplicate wells as a negative control. Phytohemagglutinin (PHA, Roche, 1 μg/mL) was used to stimulate cells as a positive control. Treated cells were incubated at 37°C in 5% CO2 for 22 hours before the addition of Golgi-Plug containing brefeldin A, Golgi-Stop containing monensin (BD Biosciences, San Diego, CA), and the CD137 APC antibody (2:100, Biolegend, Cat# 309810) for an additional 4-hour incubation. Then the cells underwent membrane surface staining for 30 minutes at 4°C protected from light with Fixable Viability Dye eFluor506 (1:1000, eBiosience, Cat# 65-0866-14) and the following antibodies: CD3 BUV805 (1:50, BD, Cat# 612895), CD8 BUV496 (1:50, BD, Cat# 612942), CD4 BV605 (1:100, BD, Cat# 562658), CD14 V500 (1:50, BD, Cat# 561391), CD19 V500 (1:50, BD, Cat# 561121), CD69 PE (1:10, BD, Cat# 555531), CD137 APC (1:50, Biolegend, Cat# 309810), and OX40 PE-Cy7 (1:50, Biolegend, Cat# 350012). After staining, the cells were fixed with 4% paraformaldehyde (Sigma-Aldrich, St. Louis, MO), permeabilized with saponin buffer (0.5% saponin [Sigma-Aldrich, St. Louis, MO], 1% bovine serum albumin, and 0.1% sodium azide), and blocked for 15 minutes with 10% human serum (Gemini Bio-Products, Sacramento, CA) in saponin buffer. After blocking, the cells were stained intracellularly for 30 minutes at room temperature with the following antibodies: TNFα ef450(3:100, Life Tech, Cat# 48-7349-42), IFNγ FTIC (1:100, Invitrogen, Cat# 11-7319-82), IL-2 BB700 (1:25, BD, 566405), IFNγ (1:100, Invitrogen, Cat# 11-7319-82), Granzyme B Alexa700 (1:100, BD, 560213), and CD40L APC-eFluor780 (3:100, eBioscience, Cat# 47-1548-42). All samples were acquired on a ZE5 5-laser cell analyzer (Bio-Rad laboratories) and were analyzed with FlowJo software (Tree Star Inc.). The data was analyzed to establish the Limit of Detection (LOD) and Limit of Sensitivity (LOS) based on all the DMSO-only conditions for AIM and ICS. For ICS these calculations were done on the IFNγ data. The LOD was calculated as twice the upper 95% confidence interval of the geometric mean and the LOS was calculated as two times the standard deviation from the median. Only responses with Stimulation Index (SI) > 2 were considered significant for AIM (CD4: LOS = 0.06%, SI > 2; CD8: LOS = 0.07%, SI > 2). For ICS, responses with SI>2 were considered significant for CD4+ (LOS = 0.006%) and responses with SI>3 for CD8+ (LOS = 0.017%). For AIM, the CD8+ T cell response to MPXV-CD8-P was calculated by summing the background subtracted, SI>2 and >LOS AIM data. For ICS, the same calculation was performed for the ICS data but considering an SI>3. Quantification and statistical analysis All the statistical analyses are described separately in each section of the STAR Methods, results and figure legends. Supplemental information Document S1. Figures S1 and S2 and Tables S2 and S3 Table S1. List of orthopox IEDB epitopes, related to Figures 1 and 3 Table S4. List of predicted MPX CD4 and CD8 epitopes, related to Figures 1 and 4 Figure360. Design peptide pools able to detect T cell responses, related to Figure 1 Document S2. Article plus supplemental information Data and code availability All data presented and analyzed in the present study was retrieved from the IEDB (www.IEDB.org) and ViPR (www.viprbrc.org), as described below. The published article includes all data generated or analyzed during this study, and summarized in the accompanying tables, figures and supplemental information. This study uses publicly available algorithms and does not report original code. Any additional information required to reanalyze the data reported in this work paper is available from the lead contact upon request. Acknowledgments This project has been funded in whole or in part with federal funds from the National Institute of Allergy and Infectious Diseases, National Institutes of Health, and Department of Health and Human Services under contract nos. 75N93019C00001 and 75N9301900065 to A.S. and HHS75N93019C00076 to R.H.S. A.T. was supported by a PhD student fellowship through the Clinical and Experimental Immunology Course at the University of Genoa, Italy. Author contributions Conceptualization, A.G., Y.Z., R.H.S., and A.S.; data curation and bioinformatic analysis, A.G., J.S., and Y.Z.; formal analysis, Y.Z., A.T., and A.G.; funding acquisition, R.H.S. and A.S.; investigation, A.T., M.R.-C., and A.G.; resources, J.M.D. and P.R.; supervision, G.F., A.G., R.H.S., and A.S.; writing, A.G., Y.Z., R.H.S., and A.S. Declaration of interests L.J.I. has filed for patent protection for various aspects of T cell epitope and vaccine design work. Inclusion and diversity We worked to ensure sex balance in the selection of non-human subjects. While citing references scientifically relevant for this work, we also actively worked to promote gender balance in out reference list. Supplemental information can be found online at https://doi.org/10.1016/j.chom.2022.11.003. ==== Refs References 1 WHO Multi-country outbreak of monkeypox, External Situation Report #10–16 November 2022 https://www.who.int/emergencies/situation-reports 2022 2 Bunge E.M. Hoet B. Chen L. Lienert F. Weidenthaler H. Baer L.R. Steffen R. The changing epidemiology of human monkeypox-A potential threat? A systematic review PLoS Negl. Trop. Dis. 16 2022 e0010141 10.1371/journal.pntd.0010141 3 Hammarlund E. Dasgupta A. Pinilla C. Norori P. Früh K. Slifka M.K. 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==== Front J Pediatr Nurs J Pediatr Nurs Journal of Pediatric Nursing 0882-5963 1532-8449 Elsevier Inc. S0882-5963(22)00169-5 10.1016/j.pedn.2022.07.006 Article The emotional neglect potentials of nurses working in the COVID-19 service towards their children: A qualitative study Apaydin Cirik Vildan a⁎ Bulut Elif b Kahriman İlknur b a Karamanoğlu Mehmetbey University, Faculty of Health Sciences, Department of Midwifery, Child Health and Disease Nursing, Karaman, Turkey b Karadeniz Technical University, Faculty of Health Sciences, Child Health and Disease Nursing Department, Trabzon, Turkey ⁎ Corresponding author. 22 7 2022 November-December 2022 22 7 2022 67 e224e233 9 3 2022 5 7 2022 6 7 2022 © 2022 Elsevier Inc. All rights reserved. 2022 Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Purpose This study aims to examine in depth the potential child emotional neglect behaviors of nurses working in the COVID-19 service, and their feelings, thoughts, and experiences regarding the causes and effects on their children. Design and methods The study was designed as a qualitative study based on a descriptive phenomenological approach. A purposeful sample of service providers (N = 22) in the COVID-19 clinics of the region's largest hospital in northeast Turkey in terms of education and patient care were recruited for the study. The data were collected through semi-structured interviews using the individual in-depth face-to-face interview method. The interviews were audio-recorded, transcribed verbatim, and analyzed with Braun and Clarke's thematic analysis method. The research was reported by following Consolidated criteria for reporting qualitative research-COREQ. Results The findings enabled the identification of four unique themes expressed by the participants: parent-child interaction, social impact, physiological impact, and psychological impact. The first theme consists of adversely affected time nurses spent with their children, decreased physical contact, and communication problems; the second theme includes nurses' and their children's social isolation and social stigma; the third theme includes a change in eating habits and daily activities; the fourth theme includes fear of losing parents and emotional change. Conclusions and practice implications To prevent the increased emotional neglect potential due to the COVID-19 pandemic, it is necessary to regulate the working conditions of parents who are nursing professionals and support the parent/child emotionally and psychologically. Keywords COVID-19 Emotional neglect Child maltreatment Children Qualitative ==== Body pmcIntroduction Children witnessing domestic violence, not receiving adequate care and affection from their parents, and no parental intervention in inappropriate behaviors, etc. are defined as childhood emotional neglect (Lavi et al., 2019; Stoltenborgh et al., 2013). Open to interpretation by nature, emotional neglect is hard to assess, unlike more easily detectable physical neglect (Fung et al., 2020). Neglect continues to receive less attention in research than other forms of maltreatment (Lavi et al., 2019; Logan-Greene & Semanchin Jones, 2018). Emotional neglect is often questioned along with other types of abuse and neglect, but it is estimated that 184 out of every 1000 people are exposed to emotional neglect (Stoltenborgh et al., 2013). The prevalence of emotional neglect is reported to vary between 45 and 74%, and 16.8% of children aged 13–18 are exposed to moderate and severe emotional neglect (Musetti et al., 2021; Tingberg & Nilsson, 2020). Çalgı and Saydam (2020) evaluated the potential neglect behaviors of mothers with children aged 0–11 and stated that 60% of mothers allowed their children to watch TV for more than two hours a day, and 42% allowed their children to spend time outside without an adult (Çalgı & Saydam, 2020). Child and parent-related factors that cause emotional neglect need to be adequately evaluated (Debowska et al., 2017). Risk factors associated with a child that may cause emotional neglect include being an unwanted child, chronic illness, disability, dangerous behavior problems, difficult temperament, constant crying, and being a stepchild (T.R. Ministry of Health, 2021; WHO, 2020). Parent-related risk factors include being young, living alone, not having knowledge and awareness about child development, using alcohol and/or drugs, having a physical or psychiatric illness, having anger control problems, and increased stress and anxiety levels (T. R. Ministry of Health, 2021; WHO, 2020). These factors can be considered risk factors for the parent to exhibit emotional neglect behavior (Debowska et al., 2017; T. R. Ministry of Health, 2021; WHO, 2020). Therefore, it is pivotal to evaluate the impacts of the COVID-19 pandemic as a factor that triggers the risk factors for the emergence of emotional neglect behavior (Adams et al., 2021; Brown et al., 2020; Lee et al., 2021; Marchetti et al., 2020). The measures taken during the COVID-19 pandemic have caused children/parents to stay at home longer, increased childcare burden, inadequate social support systems for working parents, job loss of parents, the need for technological devices in the distance education process, and economic problems in the family, resulting in increased anxiety and stress in the parents (Brown et al., 2020; Griffith, 2020; Lee et al., 2021). It is argued that the loss of a job and the symptoms of depression in the parents during the COVID-19 pandemic increase the possibility of psychological maltreatment towards their children, and the social isolation perceived by the parents and the change in employment are associated with the possibility of increased emotional neglect (Lawson et al., 2020; Lee et al., 2021). Parents' job loss in this period causes an increase in anxiety and stress levels, inadequacy in parenting roles, and disruptions in the execution of family processes (Griffith, 2020; Kovler et al., 2021). Literature has citations that nurses who are healthcare professionals are more exposed to these risk factors during the COVID-19 pandemic (Kisely et al., 2020; Lai et al., 2020; Özdemir & Kerse, 2020; Söğütlü & Göktaş, 2021; Tengilimoğlu et al., 2021). A study examining the optimism, job stress, and emotional exhaustion of healthcare professionals during the COVID-19 period revealed that healthcare professionals experience stress and emotional exhaustion but try to remain optimistic (Özdemir & Kerse, 2020). Various studies have shown that health workers experience depression (50.4%), anxiety (44.6%), insomnia (34%), stress (71.5%), and fear of transmitting the virus to their immediate circle of people (Lai et al., 2020), increased state anxiety (50.5%), trait anger (34.8%), insomnia (35.4%) and difficulty in emotion regulation (36.1%) (Söğütlü & Göktaş, 2021), and increased workload and emotional load (Kisely et al., 2020; Lai et al., 2020; Özdemir & Kerse, 2020; Söğütlü & Göktaş, 2021) during the COVID-19 pandemic. The people affected by the COVID-19 period are not only the nurses themselves, but their family members and especially their children (Chen et al., 2020; Gray et al., 2021; Riguzzi & Gashi, 2021). Health workers isolated themselves from their families because of the fear and anxiety of transmitting the virus to the family, as they were more concerned about the health of the individuals in their families than their health (Chen et al., 2020; Gray et al., 2021; Maraqa et al., 2020; Tengilimoğlu et al., 2021). During COVID-19, decreased time nurses spent with their children (Chen et al., 2020; Gray et al., 2021), increased childcare burdens (Adams et al., 2021; Brown et al., 2020; Griffith, 2020; Marchetti et al., 2020), and inadequate social support systems in the care of their children (Gray et al., 2021; Zheng et al., 2021) may lead them to potential emotional neglect towards their children. In addition, due to the burden of care for the child and work stress/burden (Maraqa et al., 2020; Riguzzi & Gashi, 2021; Tengilimoğlu et al., 2021), nurses' spending productive time with their children may be adversely affected. The psychological and behavioral effects of emotional neglect on the child can last a lifetime (Cohen & Thakur, 2021; Yang et al., 2021). Based on these considerations, it is important to identify and prevent the behaviors and causes of potential emotional neglect that nurses unconsciously exhibit towards their children during the COVID-19 period. Contribution of the present study Despite the current scientific studies on emotional neglect in children, there is a lack of data to gain insights into nurses' potential emotional neglect behaviors during COVID-19, their causes, and their effects on their children. To the best of our knowledge, there is no study in the literature on this subject, which constitutes the strength of this study and will provide basic data for further relevant studies. The study aims to examine in depth the potential childhood emotional neglect behaviors of nurses working in the COVID-19 service, and their feelings, thoughts, and experiences regarding the causes and effects on their children. We addressed the following research questions: What are the potential emotional neglect behaviors of nurses working in the COVID-19 service towards their children?, What are the causes of childhood emotional neglect during the COVID-19 pandemic?, What are the effects of nurses' working in the COVID-19 service on childhood emotional neglect? This study will provide an empirical and qualitative reflection of emotional neglect in children of nurses during the COVID-19 period. Material and methods Design This research has a qualitative research design. Qualitative research is the process of revealing the perspectives and experiences of individuals or groups and making sense of the contexts in which these perspectives and experiences are acquired in a multidimensional way (Creswell & Poth, 2017). In this study, which aims to reveal the views and thoughts of nurses working in COVID-19 clinics on childhood emotional neglect during the pandemic, a descriptive phenomenological approach, one of the qualitative research designs, was used. The descriptive phenomenological pattern is recommended for researchers who aim to investigate situation-specific factors and comprehensively evaluate the impact of factors on nurses (Creswell & Poth, 2017). The main purpose of the phenomenological approach, which focuses on the life experiences of individuals and the meaning of these experiences, is to define and group personal experiences and reveal conceptual perceptions. In this type of research, the source of data is embedded in in-depth interviews between the researcher and the participant (Creswell & Poth, 2017; Willis et al., 2016). Consolidated criteria for reporting qualitative research-COREQ checklist standards were considered in the reporting of the study data to increase the reliability and quality of the study (supplementary material). Participants The research was carried out in a hospital located in northeast Turkey. This hospital was chosen because it is the largest hospital in the region in terms of education and patient care, it is a center where patients diagnosed with COVID-19 are treated, and it serves as a pandemic hospital at regular intervals. In the hospital, 138 nurses provide care to patients diagnosed with COVID-19 in 11 clinics, five of which are in the intensive care unit. Therefore, the population of the study consisted of 138 nurses working in these COVID-19 clinics. The sample of the study was determined by using the criterion sampling method, one of the purposive sampling methods. The criteria of the study were set by the researchers using the literature data (Griffith, 2020; Kovler et al., 2021). Inclusion criteria were (a) having been working at the COVID-19 clinic for at least 6 months, (b) having at least one child, (c) being a volunteer to participate in the study, and (d) being over 18. The exclusion criteria were the termination of the interview due to nurses' getting sick during data collection and their preferences to quit the study. Four nurses refused to participate in the study because the interviews were audio-recorded. In qualitative research, sample size depends on what researchers want to know, what they want to do, their purpose, what will be useful and reliable, and their time and resources. There is no specific rule about sample size. One of the suggested approaches in deciding the sample size in qualitative studies is the “saturation point” (Polit & Beck, 2018). In this study, data saturation was reached after interviews with 22 nurses. Data collection tools Face-to-face interviews, the most frequently used method in research with a phenomenological approach, were conducted with the participants (Creswell & Poth, 2017). In the interviews, the “Nurse Identification Form” and the “Semi-structured Interview Form” prepared by the researchers in line with the literature were used (Griffith, 2020; Kovler et al., 2021). The Nurse Identification Form consists of main questions and subsidiary questions, including information about nurses' sociodemographic characteristics, profession, and children. The Semi-Structured Interview Form, created within the framework of the research topic, includes open-ended main questions and subsidiary questions asked when the participants could not recall their experiences (Table 1 ). Two experts in the field of nursing were consulted to evaluate the questions in the form in terms of purpose, meaning, and scope. In addition, a pilot study was conducted with three nurses to evaluate the intelligibility of the form, and then the form was finalized. Those involved in the pilot study were excluded from the sample.Table 1 Semi-structured interview form. Table 11. What do you think about the term and scope of childhood emotional neglect? Can you please explain your thoughts? 2. What does working in the COVID-19 service mean to you in terms of emotional neglect? Could you please give an example? What are the factors that influence your thoughts? 3. Do you think there has been any change in your and your child(ren)’s life after working in the COVID-19 service?a) If your answer is yes, how was it affected, can you please share the changes? b) How do you think it will be affected in the future, could you please share? c) Can you give an example? Can you explain in more detail? How did you think/feel? 4. Do you think that your behavior towards your child(ren) has changed during working in the COVID-19 service?a) If your answer is yes, how was your behavior towards your child/children affected? b) If your answer is yes, please explain why, what are the influencing factors? c) Can you explain in more detail? How did you think/feel? 5. What did you do to protect your child/children from emotional neglect during this period? Could you please give an example?, Could you please explain in more detail? 6. Finally, is there anything you would like to add? Data collection Individual face-to-face interviews were conducted with each participant by the researcher (EB) between December 2021 and January 2022. Due to some busy clinics, lack of suitable environment, and the COVID-19 pandemic, interviews were conducted in an interview room. The interview room was arranged to be quiet, calm, suitably lit, ventilated, and not distracting before the interview. Since the interviews were face-to-face, extra precautions were taken due to COVID-19 (using an N95 mask, visor, overalls, and social distance). For the participants to express themselves comfortably, behave sincerely, and give accurate information, they were given detailed information about the content of the interview and time to read the interview form. Before the interviews, they were informed that the interviews would be recorded on an electronic voice recorder to be used only for this research purpose and that they could withdraw from the research at any time. Then, semi-structured in-depth individual interviews were conducted. All the interviews were recorded on the electronic recorder by obtaining written consent from the nurses to avoid data loss, make quality data analysis, and use time effectively. Nikon brand voice recorder with good sound quality was used for recording and no video was recorded. In addition, the researcher took notes on body language such as tone of voice, gesture, and posture to better understand the participants' experiences of the interviews with the note-taking method. The length of the interviews varied between 20 and 60 min but lasted an average of 40 min. The interviews continued until the researchers were sure that no new data could be added to any data code related to the questions. After the interview, a summary of the information obtained was presented to the nurses for comment and revision. All nurses confirmed that the summary matched their statements. Data analysis In the analysis of the data, the thematic analysis approach was used to obtain a rich and unique description of the interview content (Clarke & Braun, 2014). Thematic analysis is one of the types of qualitative data analysis that allows the data to be examined and explained in more depth (Vaismoradi et al., 2013). Based on this approach, the data were analyzed in six stages (Fig. 1 ) (Clarke & Braun, 2014). Before proceeding to the six stages, the audio recordings obtained from the interviews and the observation notes kept were written down by the researcher, and 188 pages of written text were obtained. In the first stage, familiarization with the data set was ensured by repeated reading and taking notes. In the second stage, codes related to the data obtained from the interviews were created by following a systematic way. One of the most common ways of coding is that researchers prepare a set of code lists (suggested by current theories) and organize the entire coding process according to the ready-made codes in the list. This provides great convenience to the researcher, but it may also cause the researcher to move away from the basic philosophy of qualitative research, that is, not to see the data-specific codes coming from the data itself (Creswell & Poth, 2017). Therefore, a codebook with a ready-made code list was not used in order not to deviate from the philosophy of the research. In the third stage, as a result of a detailed examination of the obtained initial codes and data set, comprehensive potential themes were created. In the fourth stage, the themes created by the researchers and the codes embedded in the themes were reviewed to ensure that the data corresponding to the research questions. In the fifth stage, the names, scope, and explanations of the themes were checked, and a detailed analysis was made. In the last stage, the findings were reported by the researchers. The COREQ followed throughout the reporting was reviewed at the end of the research.Fig. 1 Stages of thematic analysis. Fig. 1 Rigor and trustworthiness To ensure the validity and reliability of the research, credibility, transferability, dependability, and confirmability criteria should be considered (Marshall & Rossman, 2015). A researcher diary was used, and the COREQ guideline was followed to improve reflexivity and reduce bias in the study. More than one data collection method (individual interview, observation notes) was used to increase the construct validity of the study. To increase the internal validity (credibility) of the research, an interview form was developed, and a conceptual framework was created by scanning the relevant literature. The interview form was finalized in line with the opinions of the experts. For transferability, inclusion criteria of criterion sampling were specified in sample selection, the purposive sampling method was used, and homogeneity was taken into account. To increase credibility, professional communication was established with the nurses and the interviews were held in a quiet room away from external factors. Before the interview, the nurses were given the necessary information about the research, they were told that they could withdraw from the study whenever they wanted, and their verbal and written consent was obtained. The researcher was not guiding during the interview and observation. To increase dependability, all data obtained from the recording and observation were directly transcribed by the researcher. The relationships between all created themes and sub-themes were checked for integrity. After the final versions of the codes and themes were created, two experts, independent of the research, were consulted for the intercoder consistency ratio (Kappa) analysis. Cohen's Kappa analysis and independent expert opinion ensured dependability for the study. Kappa value was calculated based on expert opinion and found to be 0.869. A Kappa value between 0.81 and 1.00 is interpreted as a perfect fit. For confirmability, the form created for the in-depth interview and the final version of the data, made into themes, were evaluated by the expert. To increase the external validity (transferability) of the research, an external expert was consulted about the data collection tools, raw data, coding and observation notes, writings, and inferences that form the basis of the report. The researcher and other researchers interviewing patients are trained in pediatric nursing and qualitative research. Ethical considerations Institutional permission was obtained from the Ministry of Health (Date: 03.05.2021), the General Directorate of Health Services (Date: 01.06.2021), and the hospital (Date: 04.06.2021). Ethics committee approval (Number: 2021/124, Date: 01.12.2021) was obtained from the scientific research ethics committee of the hospital where the study was conducted. Before the data collection, the participants were informed about the purpose of the study, how it would be conducted, that the interviews would be recorded on an electronic voice recorder, that they could withdraw from the study at any time, and their verbal and written consent was obtained. The principles of the Declaration of Helsinki were followed in the study. Results Characteristics of nurses 22 nurses caring for hospitalized COVID-19 patients were involved in the study. The mean age of the nurses was 34 (SD 6.80), 3 nurses were male, 19 were female, and 21 were married. 7 were high school graduates, 5 had an associate degree, 9 had an undergraduate degree, and 1 had a postgraduate degree. The work experience of nurses was 12.82 (SD 6.07) years, and the work experience in the COVID-19 service was 13.32 (SD 4.35) months. 4 of them had 3 children, 8 had 2 children, and 10 had 1 child. During the COVID-19 pandemic, 23 of these children received hybrid education (online + face-to-face), 5 received face-to-face education, and 10 did not go to any educational institution. The age range of nurses' children is between 1 and 18 years old. 10 children are younger than 3 years old and did not attend any educational institution. Themes Child potential emotional neglect behaviors of nurses working in the COVID-19 service, their causes, and their impacts on their children are explained under four themes and nine sub-themes. The main themes are: (first theme) parent-child interaction, (second theme) social impact, (third theme) physiological impact, and (fourth theme) psychological impact (Table 2 ).Table 2 Data analysis samples of themes and subthemes. Table 2Main themes Sub-themes Codes Meaning unit analysis Parent-child interaction Adversely affected time nurses spent with their children Working hard N 1. ‘I think the reason for the emotional neglect in children may be that we spend less time at home due to our working conditions. If there is a problem at home, it will reflect on the child. Ummm…’ N 7. ‘I haven't been able to spare time for my daughter for a year and a half because of the workload (she sighed).’ N 9. ‘While we were working in the COVID-19 service, the number of shifts increased, annual leaves were canceled, and the time I allocated to my children automatically decreased. We worked a lot of overtime…’ Living in separate houses N 2. ‘My daughter is not with me, she is a high school student, she needed me a lot, but I sent her to her grandmother. Because her school is important… If she were with me now, if she got sick, she would not be able to study, so we are not even in the same house anymore.’ N 3. ‘At first, they opened the state dormitory across this hospital for us, I don't know if you know or not, we barely stayed there for two weeks during that time, so we had a two-week break.’ N 17. ‘You know, people don't believe when they see it on TV, we lived in separate places, there was the talk of going and seeing through the door, for example, I did it. I was going through the door, I knew my daughter's favorite things, and I bought them for her. Here is the neglect…’ Feeling tired N 9. ‘Physically, my son was 2 years old at the beginning of the epidemic, I was feeling very tired even though I was at home, so I couldn't be very alert.’ N 13. ‘Yes, yes, this period definitely affected it. Well, you go home more worn out, scattered, tired, finished… I don't even see the child…’ Decreased physical contact Not hugging and kissing N 2. ‘I have not hugged my children since I started working in the COVID service, of course, I am afraid that something will happen to them.’ N 4. ‘As a father who loves to hug, love, and kiss my children, of course, I was extremely influenced during this period. So, no matter how much we brush our teeth or take a shower all the time, I abstain from kissing, (sigh) hugging.’ N 8. ‘This period affected me touching the child… (stopped for a few seconds)’ N 3. ‘I was scared… it is not clear how it affects people, no study on the child or us for now. I mean, as a mother, it would be a great shame if I infected her/him and caused a bad effect. You know, because as I said, no one knew at that time what it does, you know, there is news about the child in the hospital that the 13-year-old child was intubated from COVID and so on. When we saw such examples, as I said, I was avoiding my child, I couldn't hug, I couldn't kiss… I mean, God forbids. if she gets infected or something (sighing)’. N 19. ‘(sighing) my daughter's birthday was in May, she told me something that day, ‘Mommy, today is my birthday, aren't you going to hug me today? It's really one of the saddest things you can hear as a mother.’ N 7. ‘I used to kiss her cheeks a lot, I stopped it, I just smell her, I never kiss anywhere on her face.’ Not sleeping together N 3. ‘Um, for example, I never put him/her to sleep next to me, his father always put him/her to sleep. So, in this way, there was emotional neglect, we experienced it …’ Communication problems Communicating on the phone N 10. ‘I talked to my child from a distance or on the phone all the time. Of course, it was very difficult, but I had to.’ N 6. ‘My communication is only on the phone and something like that, from a distance …’ Decreased communication N 4. ‘There was really no communication with our children. It didn't even happen during our busy periods.’ Yelling N 7. ‘Especially when I was trying to establish a new order again, I shouted and yelled. That is not your house, this is your house, because she wanted to go for a while, she never listened to me, I couldn't accept it, I shouted.’ Social impact Social isolation Staying away from people outside the family N 3. ‘As the simplest example, now I have neighbors on the site where I live, those neighbors and my children are the same age as those of my neighbors, let me tell you…. that we haven't met for a year, for example.’ Not going to social areas N 5. ‘They couldn't go out, they couldn't participate in social activities, we haven't even been to the park for months.’ Social stigma Having a mother working in the COVID service N2. ‘Families don't want it either, mmmm how can I say? Her/his mother is a nurse, stigma happens, so it definitely does.’ N 17. “Something happened because she was a nurse's child. I mean to my daughter… I was freer, when she went out, they said that her mother is a nurse and they moved away from my daughter, there were those who said, ‘Oops, be careful, please. Your mother a nurse?’ The child did not understand what was happening, so I pulled her away from there, my daughter did not understand.” Being a COVID nurse N 3. ‘Because I work in COVID, you know, people's perspectives towards you change, of course. This situation is frustrating.’ Physiological impact Changes in eating habits Consuming ready-made food N 4. ‘Well, when we are tired at home, we always have ready meals, sometimes you can't cook, you order ready-made food, either you can't actually trust ready meals or you don't know what the food is like’ Changing mealtimes N 12. ‘Regarding their diet, while I regularly fed them at a certain time, mealtimes shifted towards later hours in this period. This situation has changed the children's diet a lot..’ Changes in daily activities Increased use of TV/phone/tablet N 5. ‘Children have become much more addicted to television, they watch the cartoons they want all day long. I think they will wear glasses in the future.’ N 16. ‘They play for a long time with the tablet, the computer. Sometimes when we go home, we look them. Well obviously, they looked at the computer until the evening.’ Decreased movement N 2. ‘Nothing has happened to my two children at home right now, they are fine. Um… maybe I can say (pause) that they always sit, it's okay for now, but they will be weak in the future because they are too inactive, maybe they will gain weight...’ Psychological impact Fear Fear of losing parents N 8. “The child, for example, knew, for example, that I was in contact with those patients. For example, when we got on the elevator, ‘Mom, Don't be afraid, COVID will not see us here.’ for example, he was trying to hug me …” Fear of being COVID-19 positive N 13. ‘For example, when I was working on COVID, my daughter was very afraid of COVID.’ Emotional change Crying N 12. ‘When we were apart, they cried a lot when we broke up with them at first, of course, they got used to it over time. My little girl was crying on the phone all the time, asking when I would go.’ Being unhappy N 19. ‘My child was already unhappy because her routine was broken. I am afraid of the future.’ Exhibiting aggressive behavior N 8. “During this period, my child naturally became aggressive, became aggressive all the time, started hitting here and there, picked a fight with friends.’ Theme 1. Parent-child interaction Parent-child interaction is the first main theme and includes the sub-themes of adversely affected time nurses spent with their children, decreased physical contact, and communication problems. Subtheme 1. Adversely affected time nurses spent with their children. Most of the nurses stated that the time they spent with their children was adversely affected and decreased due to their intense work in the COVID-19 services. Nurses stated that before the COVID-19 pandemic, they worked normal monthly working hours, they did not have to work overtime, and the overtime was not more than 8 or 16 h. However, with the onset of the COVID-19 epidemic, the working hours of nurses increased due to the interruption of the work of nurses who have chronic diseases, who are pregnant, and who use immunosuppressive drugs, and the nurses stated that they worked an average of 56–80 h per month. They could not spend time with their children, which could cause emotional neglect in their children. Many nurses stated that since they lived in separate homes from their children during this COVID period, the time they spent together decreased, and thus neglect occurred. In addition, some nurses expressed that they spent less time with their children due to the fatigue caused by working in the COVID wards. N 3. ‘I mean, when I think about it, even just being a nurse's child is already very difficult. I think now, for example, my child is away from me ten full days a month, it's really sad (sigh), I don't like 24 hours shift, but unfortunately, we have to work... overtime...’. N 4. ‘In the early days of COVID-19, we sent the children to my mother's village, and we were separated for a month. I mean, the child was calling us from there, asking when we would go. At that time, there was neglect (with a worried expression) …’. N 7. ‘Our children have always had to live in a house separate from us, we have deprived them of their mother's love, their home, and their food to protect them. I get so angry...’ N 4. ‘There is really extreme tiredness, and no one can come and spend time with their child when they are tired. In our case, this is very possible. When you are really tired, you come and just take a shower and go to bed…’. Subtheme 2. Decreased physical contact Almost all the nurses reported that they could not hug or kiss their children due to working in the COVID services, which they thought decreased physical contact and lead to a potential behavior of emotional neglect. Nurses stated that they could not hug their children because they were afraid. In addition, some nurses stated that they could not sleep with their children during this period. N 1. ‘We have already been alert since the emergence of COVID-19, and when I started working in service, we hugged each other less and touched our children less. Or when I came home from work, they didn't hug me directly, they waited for me to wash my hands first, or be completely clean. They said, ‘Can I hug you now? which is something my children like a lot when I lay them on my lap and caress their hair. Don't you think this is a kind of neglect? (she swallowed and waited for a few seconds), I think so (with a sad expression)’. Statement by a male nurse about not being able to hug his baby: N 6. ‘For example, my baby was born. I haven't seen him for a very long time, you think about his well-being, but, his father's voice, we were rarely together. Well, he hardly heard my voice, I couldn't touch my baby (he took a deep breath). Do I want to neglect my child? Sometimes there are obligations. My baby may not understand right now and doesn't know me yet, hardly held my hand. Now, how does he know about his father, how can he be connected to me? Oh, this is my personal preference, (in a bittersweet tone) …’. N 12. ‘Of course, my children want to sleep with me at night. Especially my little girl was calling me a lot during the day, well… at bedtime.’ Subtheme 3. Communication problems Nurses expressed that they were able to communicate with their children via telephone during the COVID-19 period, and their communication decreased. In this section, five nurses stated that they or their spouses shouted at their children while communicating with them, as an emotional neglect behavior. N 12. ‘When I was away, I always video chatted so that my child would not be affected. I called, and we talked for hours. Our communication was adversely affected.’ N 7. ‘Our communication with my daughter was affected a lot and even shortened (she got upset and stopped for a few seconds).’ N 9. ‘I am not normally a mother who yells a lot, I became a mother a little late. Once my daughter cried a lot at the door, I couldn't stand it because I was going out, whatever I said didn't work. As a rule, I told her to stay in her room so that she should be a little scared, I told her to cry in her room, I shouted to her to stay there (pausing for a few seconds).’ N 13. ‘So, I yell even more at my children in this period (with a sad expression). When this happens, I feel sad, then, for example, after shouting at children, it becomes very sad and painful.’ Theme 2. Social impact Social impact is the second main theme and includes sub-themes of social isolation and the social stigma of nurses and their children. Subtheme 1. Social isolation Some nurses stated that both they and their children distanced themselves from people outside the family during this period. Most nurses could not go to social areas with their children during this period, and they banned their children from doing it. N 1. ‘Children have already distanced themselves from people outside the family. Because they were watching many things during the day, then we warned them to be careful. They never talked to anyone without asking me.’ N 7. ‘I used to meet with two of my neighbors on the site, they both have children close to my daughter's age, but we haven't seen each other since COVID started, maybe we can infect each other, and it was not clear what kind of effect it has on children.’ N 11. ‘Both of my sons loved to play outside and ride bikes, but of course, I couldn't allow them during this period. They were locked in the house.’ N 9. ‘There were already a lot of bans. At first, the children could not go out at all, we generally withdrew into our shells.’ N 22. ‘Um…I always had a thought in my mind, I wonder if I got this disease as a carrier, but it didn't affect me and I always thought about whether I could infect someone else or if I had it, so we didn't go out in any social area.’ Subtheme 2. Social stigma Nurses reported that their children were exposed to a stigma in the society or their close circle of friends since their mother was a nurse in the COVID service, and they were excluded. Some of the nurses emphasized that they were also exposed to a stigma in society due to their work in the COVID-19 service. N 8. ‘My house is somewhere in the middle of the street like this, so the children are walking out of the door, and people say ‘his mother is working in COVID…. My child is excluded!’ N 12. ‘Since the mother of the child is a nurse, that is me, families kept their children away.’ A nurse stated that her caregiver stopped taking care of her child because she was working in the COVID-19 service. N 15. ‘When I returned from unpaid leave, I worked in a service for about a month. Then they appointed me to the COVID-19 service. Earlier, we had found a caregiver, but since I am a nurse, her husband didn't approve of it. Then we convinced her that I was not working in COVID. But then, when I switched to the COVID-19 service, she quit. In that period, I had to look for a caregiver again.’ A nurse could not even use the elevator in the apartment she lived in. N 12. ‘It was even written on the elevator that there was a health worker in the apartment, and it was written that they should use a certain elevator and that they should stay away from people in the building.’ Theme 3. Physiological impact The physiological impact is the third main theme and includes discussions on sub-themes of changes in eating habits and daily activities. Subtheme 1. Changes in eating habits Many nurses stated that their children's consumption of ready-to-eat food increased because they were left alone at home in this period, and their children's meal times changed. N 16. ‘Children were left alone then. I mean, when they are on their own, they either don't eat even if there is food at home, or they always order ready-made burgers, wraps, pizza... Well, they eat unhealthy foods.’ Two nurses expressed that their children's eating times changed. N 2. ‘Since I was not at home, everyone ate whenever they wanted (laughs). They may become obese in the future’. Subtheme 2. Changes in daily activities All nurses explained that children's activities of daily living changed during this period, and they spent most of their days using TV/phone/tablet. In addition, nurses emphasized that their children moved less during this period, which could result in health problems. N 13. ‘Using a tablet has become an addiction in children. Well, there is already COVID, we were already in the mode of letting them play, for example, we could not take the tablet from them because we had no other alternative. Well, this time the tablet became an addiction for them, something else… (she breathed out deeply) something else… They were addicted to cartoons, so they embraced technology during the day.’ A nurse said that her child did not use to have a phone, but she had to get a phone call during this period so that she could talk to him more while at work but then regretted it: N 8. ‘Okay, I bought the phone myself for the child (in a tone like saying sorry, how can I do it)… For example, firstly, I am a very anti-phone person … But… right now, the habit is extremely common, and I can't get over it, I mean, I can't stop.’ Some nurses' children's daily activities decreased or even disappeared. N 4. ‘Well, there is no activity in the house anymore except going from one room to the other. No movement at all.’ Theme 4. Psychological impact The psychological impact is the fourth main theme and consists of the fear of losing a parent and emotional change sub-themes. Subtheme 1. Fear of losing parents Many nurses reported that their children were afraid of losing their parents during this period, and they expressed this fear. In addition, some children were afraid of being COVID in this period. N 13. ‘My child seems to have more fear of losing, he wants to hug me all the time, he doesn't leave my side, he watches me all the time, I don't know if he is afraid of losing but this is the case.’ N 22. “We say it doesn't affect my son much, but once he lined up our shoes in front of the door, then put a barrier in front of them and said, ‘Mom! no, no going’. I think he thought that if I went to the hospital, I would never come back… because that was something he had never done before.” N 20. ‘My son, that is, 11 years old, since I work in the COVID service, asked us such questions as ‘Mom, will the disease come home in this way, what will it be like if we get sick, what will they do to us?’ Subtheme 2. Emotional change Most of the nurses stated that their children experienced emotional changes for different reasons during this period, and therefore they cried and felt unhappy. In addition, some nurses expressed regret that their children exhibited aggressive behavior because of this mood change. N 14. ‘I could not transfer my prenatal leave, I started working when my baby was two and a half months old. He cried a lot on the first day I started work... (Her eyes filled with tears and stopped for a few seconds)’. N 21. ‘Sometimes in our games, she played a mother who went to the hospital, then the child was always alone, unhappy or something…’. N 17. ‘There was that period when everywhere was closed at first, then when I was talking to my daughter on video chatting, I saw that my daughter's face fell, she was getting upset.’ N 21. ‘He started to like violent games more, he punched us sometimes during the game, I think he got angry about this situation, so we are always at home (with a sad expression). He hurt himself once or twice with his toys, but I can't say that something else happened.’ Discussion The current study contributes to the field of child maltreatment and emotional neglect by gaining insight into the potential emotional neglect behaviors of nurses working in the COVID-19 service towards their children, its causes, and how it affects their children. The fact that no studies have been found in the existing literature on the qualitative research of emotional neglect towards children in the COVID-19 period is the innovative and robust aspect of this current research and provides the literature with a new perspective. Childhood emotional neglect in nurses working in the COVID-19 service was explained with four unique themes: (a) parent-child interaction, (b) social impact, (c) physiological impact, and (d) psychological impact. In the first main theme, many nurses stated that the time they spent with their children was adversely affected and decreased due to their busy work in the COVID-19 wards or living in separate homes. In addition, some nurses could not interact with their children because of their fatigue. Shortly before the COVID-19 pandemic emerged, a serious patient density started in hospitals and continued throughout the period (Riguzzi & Gashi, 2021). Due to the pandemic, the workload of nurses, the number of patients they care for, and their working hours have increased. For this reason, the working conditions of nurses became even more difficult than before the pandemic (Maraqa et al., 2020; Tan et al., 2020; Fernández-Castillo et al., 2021; Malinowska‐Lipień et al., 2022; Riguzzi & Gashi, 2021; Tengilimoğlu et al., 2021). These challenges caused nurses to become overtired and burnt out, which increased employee tension, anxiety, stress disorders, and sleep problems during the COVID-19 pandemic (Maraqa et al., 2020; Lipien et al., 2021; Lai et al., 2020; Zheng et al., 2021). Nursing is a professional occupation with a predominantly female gender group of workers (World Health Organization, 2020). In this study, only 3 of 21 nurses were male. Therefore, it can be concluded that the majority of those who continue to bear the burden of the nursing profession in the COVID-19 period are women. On the other hand, the roles imposed on women by society, such as childcare and housework, caused the second shift of female nurses to continue at home. (Llop-Gironés et al., 2021; Maraqa et al., 2020). Considering all these conditions and the increasing workload and difficult living conditions of nurses, fatigue, burnout, high stress, and anxiety levels are inevitable. All these burdens are considered an obstacle to the desired level of childcare and a risk factor for exposure to emotional neglect (Debowska et al., 2017; Stoltenborgh et al., 2013; WHO, 2020). Nurses isolated themselves, especially in the early stages of the pandemic due to the fear of infecting family members with viruses (Chen et al., 2020; Gray et al., 2021; Maraqa et al., 2020). Similarly, in this study, some nurses were afraid of infecting others and therefore isolated themselves. These conditions, which negatively affect the parent-child relationship, paved the way for emotional neglect. To reduce these risk factors, new adjustments to the number of nurses, daytime hours, overtime, and inadequate equipment are required. Touching, which is one of the ways of showing love for a healthy neurodevelopmental process in children, contributes to the child's cognitive and emotional development (Orben et al., 2020). In this study, all the nurses reported that they could not have physical contact with their children during this period. Parents have a significant role in their children's emotion regulation, meeting their emotional needs, and personality development (Spinelli et al., 2021; Yang et al., 2021). Fear of transmitting the COVID-19 virus to others has led to the isolation of healthcare workers and a reduction in their physical interactions (Chen et al., 2020; Maraqa et al., 2020; Orben et al., 2020; Gray et al., 2021). In a study conducted in Turkey, it was reported that 20.8% of health workers could not see their families in the current situation (Tengilimoğlu et al., 2021). Nurses with children may be inadequate in meeting their emotional needs due to difficult working conditions (Maraqa et al., 2020; Tan et al., 2020; Fernández-Castillo et al., 2021; Lipien et al., 2021; Riguzzi & Gashi, 2021; Tengilimoğlu et al., 2021) and mandatory isolation (Chen et al., 2020; Gray et al., 2021; Maraqa et al., 2020; Tengilimoğlu et al., 2021; Zheng et al., 2021). In this respect, a vicious circle may occur between inadequate family support, nurses' psychological problems, and potential emotional neglect towards the child. Nurses emphasized that they had communication problems with their children in this period, and the interruption of communication may cause the child to experience mental, emotional, and social problems (Orben et al., 2020). In addition, some nurses reported yelling at their children during this period. Increasing childcare burdens of parents due to COVID-19 and changing family routines cause burnout in parents (Adams et al., 2021; Brown et al., 2020; Griffith, 2020; Marchetti et al., 2020). Regardless of the reason, yelling at a child is emotional abuse and this experience in childhood can lead to behaviors such as affective disorders and aggression in adulthood (Schwarzer et al., 2021). Considering that potential emotional neglect and emotional abuse behaviors are due to changeable causes, social and psychological support for nurses working under difficult conditions and whose lives have changed with COVID-19 will be a significant step. Since the beginning of the COVID-19 pandemic, nurses have been declared heroes in Turkey and many other countries (Einboden, 2020; Karasu & Çopur, 2020; Boulton et al., 2022; Mohammed et al., 2021; Halberg et al., 2021). This outstanding performance of nurses and health workers was evaluated as self-sacrificing behavior as well as heroism (Boulton et al., 2022). The term “heroic nurse”, emphasized by both politicians and the public, especially on social media, has positively affected the image of nursing (Boulton et al., 2022; Mohammed et al., 2021). However, even this attitude did not prevent nurses from being stigmatized due to the high risk of COVID-19 (Halberg et al., 2021; Kisely et al., 2020; Maraqa et al., 2020). Another remarkable finding of this study was that nurses stated that children with mothers working in these services were excluded by other people and were exposed to social stigma and labeled by society. Various studies have shown that when healthcare professionals declare that they work in the COVID-19 clinic, other people automatically move away from them (Halberg et al., 2021; Maraqa et al., 2020). Inhibition of social interaction can have lasting negative consequences on a child's physical and mental health (Orben et al., 2020). It is essential to reduce this potential emotional neglect behavior of the society that influences children as well. By making use of the widespread influence of the media, awareness training should be organized (Halberg et al., 2021). In this study, nurses stated that their children's nutritional habits and daily activities have changed physiologically because they work in the COVID-19 services. Studies have emphasized that with the onset of COVID-19, family meal routines have changed, they eat more junk food (Carroll et al., 2020), and the consumption of potato chips, red meat, fried foods, and sugary drinks in children increased significantly (Pietrobelli et al., 2020). Studies evaluating daily activities highlight that 52% of children's physical activities (Carroll et al., 2020) and movement in all physical areas except housework (Moore et al., 2020) decreased. The nurses in the study stated that their children's consumption of ready-made food increased, and their daily movements decreased. The weight gain that occurred during the quarantine period is reported not to be easily reversed and causes excessive body fat in adulthood (Pietrobelli et al., 2020). In addition, most nurses in this study stated that their children spent most of their days using TV/phones/tablets. The time spent by children looking at the screen has increased due to the measures taken during the pandemic and the switching of schools to distance education (Carroll et al., 2020; McCormack et al., 2020). Ness et al. (2021) suggested that children's homeschooling and physical activity routines have changed (Ness et al., 2021). As part of COVID-19, restrictions on social interaction and the use of playgrounds and parks have been imposed (McCormack et al., 2020; Moore et al., 2020). In this study, some nurses stated that the use of TV/tablets increased because their children could not go to social areas. These changes, directly influencing the health of children, can also be considered negligence. Therefore, parent and child-based training and activities can be planned to reduce such habits. Many nurses in our study expressed that their children were afraid of losing their parents, and some expressed their fear of their children getting infected with COVID-19. Children's direct exposure to negative information about COVID-19 from their teachers, friends, and the media has had a direct impact on children's fear levels (Radanović et al., 2021). Children need to see that their parents can manage the fear and cope with stress during crisis periods such as the COVID-19 pandemic (Duan & Zhu, 2020). As parents' levels of fear of COVID-19 increase, the children's levels of fear increase (Radanović et al., 2021), and the high-stress levels of parents struggling with challenging conditions during the quarantine period increase the stress levels of their children (Spinelli et al., 2021). It is also expected that a child whose parent is afraid of COVID-19 will focus more on negative news (Radanović et al., 2021). Family dynamics and intra-family communication are effective in preventing all these negativities (Prime et al., 2020). Nurses should inform their children about the difficulties of their working conditions, their requirements to go to work, and the ways to protect themselves from the virus without frightening them. Within the scope of emotional changes, nurses in our study stated that their children were crying, unhappy, and exhibiting aggressive behavior. The sudden change in school, social life, and daily routines caused by COVID-19 caused aggression in some children (Pavone et al., 2020). To identify parents' maltreatment behaviors early, it is important to have knowledge of their emotional roles and risk factors for emotional maltreatment (Lavi et al., 2019). Therefore, following up and psychologically supporting parent nurses and children at risk and understanding these emotional processes of nurse parents will be beneficial in determining psychological interventions for them and their children. Practice implications The emergence of the COVID-19 pandemic has led to an increase in the working hours and workload of nurses, negatively affecting them psychologically and disrupting their family processes. The measures taken to prevent the spread of the virus and social restrictions have also caused some changes in parenting roles. To reduce the negative effects, it is critical to increase the number of nurses per patient and make new adjustments to ensure nurse recruitment. The adverse effects of emotional neglect in childhood can last a lifetime. Therefore, it is necessary to determine the risk factors and plan the necessary interventions. It is recommended to evaluate the nurses and their children emotionally and psychologically and follow and support those in risk groups. Limitations This study has some identified limitations. Using a single clinic for recruitment could limit the transferability of the findings, so the experiences and perspectives of the nurse sample in this study cannot be superimposed on nurses from different backgrounds and experiences. That most of the samples were female may also be a limitation. Conducting the interviews in an interview room determined by the researchers can be considered a research limitation. In addition, another limitation of the external validity of the study may be that the participants did not review their transcripts. Nevertheless, the results of this study provide new and powerful information to the literature on child emotional neglect behaviors in nurses working in COVID-19 wards, their causes, and how they affect their children. Conclusions No study has been found on emotional neglect in children of nurses during the COVID-19, and in this study, conducted with a qualitative approach, the feelings, thoughts, and experiences of nurses regarding potential emotional neglect behaviors, causative factors, and their effects on their children during the COVID-19 were explained under four themes. Main themes are parent-child interaction, social impact, physiological impact, and psychological impact and include sub-themes of adversely affected time nurses spent with their children, decreased physical contact, and communication problems, nurses' and their children's social isolation and social stigma, a change in eating habits and daily activities, fear of losing parents and emotional change. Ethical approval Institutional permission was obtained from the Ministry of Health (Date:03.05.2021), the General Directorate of Health Services (Date:01.06.2021), and the hospital (Date:04.06.2021). In order to conduct the study, ethics committee approval was obtained from T.R. Ministry of Health Health Sciences University Trabzon Kanuni Training and Research Hospital Clinical Research Ethics Committee (Number:2021/124, Date:01.12.2021). This study was performed according to the Helsinki Declaration. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Credit authorship contribution statement Vildan Apaydin Cirik: Conceptualization, Data curation, Investigation, Formal analysis, Methodology, Supervision, Visualization, Writing – original draft, Writing – review & editing. Elif Bulut: Investigation, Data curation, Writing – original draft. İlknur Kahriman: Conceptualization, Methodology, Supervision, Writing – review & editing. Declaration of Competing Interest The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Appendix A Supplementary data Supplementary material Image 1 Acknowledgments The research team would like to thank the nurses who participated in the study for spending time and sharing their experiences. 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==== Front J Clean Prod J Clean Prod Journal of Cleaner Production 0959-6526 1879-1786 Elsevier Ltd. S0959-6526(22)04990-3 10.1016/j.jclepro.2022.135416 135416 Article Insight into core -shell microporous zinc silicate adsorbent to eliminate antibiotics in aquatic environment under the COVID-19 pandemic Hu Xueli a Zhou Yuanhang a Zhou Yingying a Bai Yun a Chang Ruiting ac Lu Peng b∗∗ Zhang Zhi a∗ a Key Laboratory of the Three Gorges Reservoir Region's Eco-environment of Ministry of Education, College of Environment and Ecology, Chongqing University, Chongqing, 400045, PR China b Chongqing Key Laboratory of Catalysis and New Environmental Materials, College of Environment and Resources, Chongqing Technology and Business University, Chongqing, 400067, PR China c Chongqing Academy of Ecology and Environmental Sciences, Chongqing, 401147, PR China ∗ Corresponding author. ∗∗ Corresponding author. 3 12 2022 3 12 2022 13541626 8 2022 11 11 2022 26 11 2022 © 2022 Elsevier Ltd. All rights reserved. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Under the new crown pneumonia (COVID-19) epidemic, the intensive use of therapeutic drugs has caused certain hidden danger to the safety of the water environment. Therefore, the core-shell microporous zinc silicate (SiO2@ZSO) was successfully prepared and used for the adsorption of chloroquine phosphate (CQ), tetracycline (TC) and ciprofloxacin (CIP) for eliminating the threat of COVID-19. The adsorption efficiencies of 20 mg·L−1 of CQ, TC and CIP by SiO2@ZSO were all up to 60% after 5 min. The adsorption capacity of SiO2@ZSO for CQ, TC and CIP can reach 49.01 mg·g−1, 56.06 mg·g−1 and 104.77 mg·g−1, respectively. The adsorption process is primarily physical adsorption, which is heterogeneous, spontaneous and preferential. Moreover, the effects of temperature, pH, salinity, and reusability on the adsorption of CQ, TC, and CIP on SiO2@ZSO were investigated. The adsorption mechanism mainly involves electrostatic attraction, partitioning and hydrogen bonding, which is insightful through the changes of the elements and functional groups before and after adsorption. This work provides a solution to the problems faced by the treatment of pharmaceuticals wastewater under the COVID-19 epidemic. Graphical abstract Image 1 Keywords Core-shell zinc silicate Chloroquine phosphate Tetracycline Ciprofloxacin Adsorption mechanisms Handling Editor: Zhen Leng ==== Body pmc1 Introduction Since 2019, the new crown pneumonia (COVID-19) epidemic has swept the world. So far, it has not only deprived millions of lives, but also has a huge impact on the development of human society (Morales-Paredes et al., 2022; Peng et al., 2022). Massive amounts of anti-COVID-19 drugs (chloroquine phosphate (CQ, quinolines)) and antibiotics (tetracycline (TC, tetracyclines) and ciprofloxacin (CIP, quinolones)) are being poured into treatment to eliminate the threat of COVID-19 (Adebisi et al., 2021; Yacouba et al., 2021). However, only a small percentage of drugs will be absorbed into the human body to treat diseases, and most will be excreted into the environment through human excrement (Chen et al., 2021; Rajiv et al., 2021). Large quantities of CQ, TC and CIP residues in the ecological environment will affect the physiological functions of plants and animals by cumulative effect, promoting the generation of drugresistant bacteria and causing potential harm for ecosystem (Al-Musawi et al., 2021b; Yi et al., 2021). Therefore, the simple, rapid and efficient simultaneous removal of anti-COVID-19 and antibiotic drugs from water has become the focus of water treatment. Multifarious strategies have been reported previously to effectively remove drugs from wastewater, such as biodegradation (J. J. Li et al., 2022), chemical oxidation (Balarak et al., 2021; Kyzas et al., 2022), electrochemical treatment (Ji et al., 2021), chemical coagulation (Gan et al., 2021; He et al., 2021), and adsorption (Xu et al., 2022b; Yilmaz et al., 2022). Among these methods, adsorption is the most competitive potential technology for the removal of drugs due to its simplicity, high efficiency, economic advantages and environmental friendliness (Z. Wei et al., 2022, Wei et al., 2022; Zhu et al., 2021). Although many nanomaterials such as metal–organic frameworks (MOFs) (Xu et al., 2022a), carbon nanotubes (Sajid et al., 2022), etc. are used to effectively remove drugs from water, traditional adsorption materials such as bentonite (Guan et al., 2022), kaolinite (Gao et al., 2022), diatomaceous earth (Kodali et al., 2022), activated carbon (Balarak and McKay, 2021) and clay minerals (Zhao et al., 2022) are still the mainstream adsorbents in the market. Silicate minerals are the most abundant minerals in the earth's crust, and are composed of anionic groups of silicon-oxygen tetrahedra and metal ions located between layers or chains. Its economical, high chemical and thermal stability, and abundant hydroxyl groups on the surface make it an ideal candidate for adsorption of organic pollutants. For example, Zhu et al. used an one-pot hydrothermal method to prepare hierarchically porous sea urchin-like Cu2−xSi2O5(OH)3·xH2O hollow microspheres for the adsorption and removal of the cationic dye methylene blue and achieved good adsorption activity (Zhu et al., 2021). Li et al. constructed a flower-like mesoporous magnesium silicate composites (MMSCs) by subjecting sepiolite to acid bleaching process and hydrothermal method to efficiently adsorb aflatoxin B1 in water (Y. Y. Li et al., 2022). Tian et al. prepared hybrid silicate adsorbents via a simple one-step hydrothermal process for efficiently adsorb chlortetracycline and oxytetracycline (Tian et al., 2016). Deng et al. synthesized cost-effective hydrochar composite (MgSi-HC) by a simple one-step process using waste sawdust, inexpensive silicate and magnesium salts as raw materials. The results show that it has high adsorption activity for tetracycline (TC) in aqueous solution (Deng et al., 2020). However, the non-specific adsorption of organic pollutants by silicate adsorbents is rarely investigated. Notably, zinc silicate has attracted extensive attention due to its unique structure such as variable morphology and crystal diversity (Dong et al., 2020; Park and Kim, 2021). Although the use of wastes as raw materials to prepare zinc silicate can realize waste utilization, the processes such as washing and sorting will increase the preparation cost, and the obtained adsorbents often have large impurities and low adsorption activity (Wang et al., 2016; Zhang et al., 2017). In addition, few studies have explored the non-selective adsorption of drugs by zinc silicate, especially chloroquine phosphate for the treatment of COVID-19. The development of low-cost, high-adsorption activity zinc silicate for emergency treatment of pharmaceutical wastewater is an interesting and promising research. To address these challenges, a core-shell microporous zinc silicate (SiO2@ZSO) with no selective adsorption of drugs was successfully prepared by a facile hydrothermal method. Broad-spectrum drugs such as chloroquine phosphate (CQ), tetracycline (TC) and ciprofloxacin (CIP) for eliminating the threat of COVID-19 are extracted from wastewater by synthetic SiO2@ZSO. The control steps and energy conversion of the adsorption process of CQ, TC and CIP by SiO2@ZSO were analyzed kinetically and thermodynamically, respectively. Different factors affecting the adsorption activity such as temperature, pH and dosage, salinity, etc. were investigated. In addition, the mechanism of SiO2@ZSO adsorption of CQ, TC and CIP was discussed in depth by means of characterization methods such as FT-IR, XPS, TEM, Zeta potential, etc. This study provides a feasible strategy for the elimination of pharmaceuticals in the water environment through a rich source of silicate-based sorbents. 2 Experimental 2.1 Materials The chemical reagents including Zn(NO3)2.6H2O, NH4Cl, ammonia, ethanol, tetraethylorthosilicate (TEOS), NaNO3, NaCl, Na2SO4, NaHCO3, Na2CO3, Na3PO3 and NaH2PO3, hydrochloric acid, and sodium hydroxide were purchased from Macklin Biochemical Co. Ltd. (Shanghai, China). Chloroquine phosphate (CQ, quinolines), tetracycline (TC) and ciprofloxacin (CIP) were obtained from Aladdin Reagent Co., Ltd. (Shanghai, China). All chemicals were analytical grade and were employed without further purification. 2.2 Synthesis 2.2.1 Preparation of SiO2 pellets Monodisperse, uniform particle size (about 500 nm) SiO2 pellets were prepared by Stöber method. The preparation method is as follows: 50 mL of ethanol, 20 mL of deionized water, and 7 mL of ammonia water were separately measured and mixed in a beaker, and 6 mL of TEOS was added dropwise to the mixture under vigorous stirring. Then the mixture was stired at a constant speed (about 600 r/min) for 8h, until the white precipitate at the bottom of the beaker observed. Washed it with ethanol and deionized water for 3 times each, then dried at 60 °C for 12 h to obtain white SiO2 pellet powder. 2.2.2 Preparation of core-shell structure ZnSiO3 1.0 g SiO2 balls were placed in 50 mL deionized water, ultrasonicated for 1h to make them evenly dispersed, and then 1mL NH3·H2O and 10 mmol NH4Cl were added to the above dispersion to make solution A. Under the action of magnetic stirring, 16 mmol of Zn(NO3)2.6H2O was dispersed in 20 mL of deionized water and added to solution A to form solution B. The mixed solution B was continuously stirred for 30 min and then transferred to a 100 mL reaction kettle, placed in a blast heating drying oven, and reacted at 100 °C for 12 h. After the reaction, the precipitate was collected, centrifuged and washed three times with deionized water and absolute ethanol, and dried in a drying oven at 60 °C for 12 h. The obtained white powder samples are zinc silicate core-shell microspheres, marked as SiO2@ZSO. 2.3 Characterization The physical properties and chemical structure of the as-prepared adsorbent were obtained by SEM (ZEISS MERLIN Compact, U.K.), N2-adsorption-desorption curve (BET, ASAP 2020, USA), XRD (XRD-6100, Shimadzu, Japan), FT-IR (IRPrestige-21, Shimadzu, Japan), HRTEM (JEOL F200, Japan), EDS-mapping, respectively. The changes of the adsorbent chemical composition before and after adsorption was analyzed by XPS (Thermo Scientific, ESCALAB 250xi, USA). Total organic carbon (TOC-2000, METASH, China) analysis of the purification effect of the adsorbent. 2.4 Adsorption experiment The adsorption performance of SiO2@ZSO was investigated by adsorbing pollutants in wastewater, including three typical broad-spectrum drugs (see Fig. S1 for structural formulas), namely chloroquine phosphate (CQ), tetracycline (TC) and ciprofloxacin (CIP). A certain mass of adsorbent was placed in a 100 mL conical flask containing target solution, and then placed in a rotary water bath shaker (rotational speed 200 r·min−1) for adsorption experiments. Periodically, 5 mL of the supernatant was taken, filtered with a 0.22 μm water filter, and the concentration changes of pollutants were measured with a UV spectrophotometer (DR 6000, HACH, USA) or HPLC (5120, Hitachi, Japan). For details on the detection methods and conditions of antibiotics, see Text S1. All experiments were performed 3 times in parallel. Batch experiments were carried out to investigate the effects of adsorption reaction time, initial concentration, adsorbent dosage, initial pH and temperature on the adsorption process. The pH value was controlled by 0.1 M HCl and 0.1 M NaOH using the equipment (HQ11d, HACH, USA). Due to the high salt content in drugs wastewater, seven common salts were placed in the adsorption system to investigate their effects on the adsorption of TC by SiO2@ZSO, including NaNO3, NaCl, Na2SO4, NaHCO3, Na2CO3, Na3PO3 and NaH2PO3. The adsorption removal rate (η) and adsorption capacity (Q or q, mg·g−1) of drug are shown in Equation (1) and Equation (2).(1) η=1−CtC0 (2) Q=(C0−Ct)Vm where C0 (mg·L−1) and Ct are the drug concentrations at time 0 and t, respectively; V (mL) is the drug solution volume; m (mg) is the adsorbent dose. 2.5 Adsorption models and thermodynamics Adsorption kinetics were performed at an initial pollutant concentration ranging from 20 mg·L−1 to 100 mg·L−1 at a temperature of 20 °C, an adsorbent dosage of 1 g·L−1, and unadjusted pH. Pseudo-first-order kinetics and pseudo-second-order kinetics were used for nonlinear fitting of the data according to Equations (3), (4),(3) qt=qe(1−e−k1t) (4) qt=k2qe2t1+k2qet where k1 (min−1) and k2 (g·mg−1·min−1) are pseudo-first-order kinetic constants and pseudo-second-order kinetic constants, respectively; qt and qe are the adsorption capacity (mg·g−1) at time t and equilibrium, respectively. In addition to the coefficient (R2), the normalized standard deviation (Δq) was calculated to describe the adsorption process by Equation (5):(5) Δq(%)=∑i=1N[(qexp−qcat)/qexp]2N−1×100% where qexp and qcal are the experimental and calculated adsorption capacities (mg·g−1), respectively, and N is the experimental data point. The diffusion mechanism was revealed by intra-particle diffusion and liquid film diffusion models based on Equations (6), (7),(6) qt=kidt1/2+C (7) ln(1−qtqexp)=−kft where kid (min−0.5) and kf (min−1) are the intra-particle diffusion and liquid film diffusion constants, respectively, and C is the intercept related to the boundary layer. Adsorption isotherms were investigated in batches at 20 °C, 25 °C and 30 °C with an adsorbent dosage of 1 g·L−1. Langmuir, Freundlich, Tempkin and D-R models were used to analyze the adsorption behavior from Equations (8), (9), (10), (11),(8) Qe=qmKlCe1+KlCe (9) Qe=KfCe1/n (10) Qe=RTbln(KtCe) (11) Qe=qmeβE2 where Kl, Kf, Kt and β are the adsorption equilibrium constants of the corresponding models, respectively; qm (mg·g−1) is the theoretical maximum equilibrium adsorption capacity; n is the adsorption intensity; b is a constant; R is the ideal gas constant of 8.314 J·mol−1·K−1; T (K) is the reaction temperature; β is the parameter related to the adsorption energy; and E=RTln(1+1/Ce) represents the adsorption potential which is related to the concentration of the adsorbate in the solution. Thermodynamic parameters such as Gibbs free energy ΔG°, enthalpy change ΔH ° and entropy change ΔS ° can be calculated by Equations (12), (13),(12) ΔG°=−RTlnKC (13) lnKC=ΔS°R−ΔH°RT where Kc=QeCe is the adsorption equilibrium constant (L·mg−1) when the solution is in equilibrium. 3 Results and discussion 3.1 Characterization The phase structure of the adsorbent was obtained by XRD in Fig. S2. A hill peak is observed around 23.6°, which is typical for SiO2. In addition, a weaker characteristic peak of ZnSiO3 (JCPDS No. 34–0575) was observed in the SiO2@ZSO structure. The XRD patterns show that all the prepared SiO2@ZSO adsorbents exhibit poor crystallization. The morphologies and microstructures of the as-prepared SiO2 spheres and SiO2@ZSO were investigated by SEM and TEM in Fig. 1 . SiO2 spheres exhibited smooth spherical shapes with a diameter of about 500 nm. In Fig. 1b, SiO2@ZSO exhibit a core-shell structure composed of Zn2+ precipitated on the surface of SiO2 spheres and reacted with it to form a zinc silicate shell and an inner core of SiO2 spheres. To confirm that zinc silicate is supported on the surface of SiO2 spheres, the element content and dispersion of the adsorbent were preliminarily obtained by SEM mapping in Fig. 1c. Zn, Si and O were observed and distributed in the SiO2@ZSO structure, suggesting that the SiO2@ZSO adsorbent was successfully synthesized. Solid spheres of SiO2 with smooth surfaces were observed in Fig. 1d–e by HRTEM images. Clearly, zinc silicate appears on the surface of SiO2 spheres, forming a core-shell structure in Fig. 1f–g. The insets in Fig. 1e and g do not observe a clearly bright ring, suggesting that SiO2 and SiO2@ZSO are present in an amorphous form, a conclusion that corresponds to XRD.Fig. 1 SEM images: SiO2 (a) and SiO2@ZSO (b); SEM-mapping image of SiO2@ZSO (c); HRTEM images: SiO2 (d-e) and SiO2@ZSO (f-g) (Insets in e and g are the corresponding SAED images). Fig. 1 FT-IR and XPS provide the surface groups and element valence states of the adsorbents in Fig. 2 . In Fig. 2a, the broad bands at 1640 cm−1 and 3443 cm−1 are attributed to the bending vibration of O–H and the stretching vibration of hydroxyl groups, respectively (Zhu et al., 2022a). This suggests that the large number of functional groups on the surface of the adsorbent may be the active sites for reaction with organic molecules. The three peaks at 1101 cm−1, 955cm−1 and 467 cm−1 belong to the asymmetric stretching, symmetric stretching and asymmetric deformation vibrations of Si–O–Si, respectively (Huang et al., 2017; Wang et al., 2022). The narrow peak at 798 cm−1 is usually attributed to the Si–OH stretching vibration in the unique silanol nests (Naini et al., 2022). In SiO2@ZSO, the peak at 663 cm−1 is attributed to the asymmetric stretching vibration model of Zn–O (Saberi Rise et al., 2022). Notably, the successful synthesis of zinc silicate may be due to Zn occupying the site of Si element in Si–OH. The full XPS spectrum of SiO2@ZSO (in Fig. S3) contains Zn, Si and O elements, and their atomic ratios are about 1:2:6 (in Table S1). The Zn 2p narrow spectrum has two peaks corresponding to Zn 2p3/2 and Zn 2p1/2 (Zhu et al., 2022b). In Fig. 2c, the binding energy belonging to the Si–O bond is shifted by about 0.5 eV to lower binding energy in SiO2@ZSO, which may be due to the influence of Zn2+. In the enlarged O 1s spectrum in Fig. 2d, it is observed that O–Si and adsorbed oxygen molecules emerge at 532.50 eV and 533.40 eV at the SiO2 surface (Z. Wei et al., 2022). Unlike SiO2, SiO2@ZSO were additionally found a binding energy at 531.66 eV, which was attributed to the Zn–O bond (Khan et al., 2021). Through the above analysis, it can be seen that the hydroxyl-rich SiO2@ZSO core-shell adsorbent was successfully prepared.Fig. 2 FT-IR spectrum of SiO2 and SiO2@ZSO (a) and XPS narrow spectra of Zn 2p (b), Si 2p (c) and O 1s (d). Fig. 2 The specific surface area and pore size distribution of SiO2@ZSO were calculated by BET and BJH equations based on N2 adsorption and desorption curves (in Fig. 3 ). The SiO2 spheres and SiO2@ZSO show the IUPAC type 4 curve with H3-type hysteresis loop. In Table S1, the specific surface area of SiO2 is only 11.85 m2·g−1, the total pore volume is 0.028 cm3··g−1, and the largest proportion of pore size is 4.67 nm. Moreover, the specific surface area of SiO2@ZSO is 73.55 m2·g−1, which is 6.20 times that of SiO2, and its largest proportion of pore size is 0.17 nm. The above conclusions indicate that SiO2@ZSO has better pore structure, more adsorption sites and larger adsorption capacity compared with SiO2.Fig. 3 N2 adsorption-desorption isotherms and pore-size distribution curves (inset) of SiO2 and SiO2@ZSO. Fig. 3 3.2 Adsorption activity 3.2.1 Adsorption activity of SiO2@ZSO The adsorption activities of SiO2 pellets and SiO2@ZSO for CQ, TC and CIP were investigated in Fig. 4 a. At the onset of adsorption, the concentration of comtaminants dropped significantly due to the abundant active sites, and then slowly reached the adsorption-desorption equilibrium on the adsorbent surface over time. The SiO2 spheres showed poor adsorption activities for CQ, TC and CIP, which were embodied in the adsorption activities of only 11.04%, 1.67% and 7.77% respectively after adsorbing the target pollutants for 2 h. Obviously, SiO2@ZSO shows excellent adsorption activity for CQ, TC and CIP. The corresponding adsorption efficiencies are as high as 71.45%, 69.60% and 61.74% after adsorption for 5 min. Moreover, the adsorption capacities of CQ, TC and CIP are 17.37 mg·g−1, 18.15 mg·g−1 and 19.25 mg·g−1 for SiO2@ZSO adsorption (Fig. 4b), which are 6.72, 49.65 and 9.62 times higher than those of SiO2 pellets, respectively.Fig. 4 Adsorption activity (a) and adsorption capacity (b) of various adsorbents for CQ, TC and CIP. (Reaction conditions: dosage is 1 g/L; contaminant concentration is 20 mg/L; temperature is 20 °C; pH is not adjusted.) Fig. 4 3.2.2 Kinetics analysis Pseudo-first-order and pseudo-second-order kinetic models were used to analyze the adsorption rate law and adsorption behavior of TC, CQ and CIP on SiO2@ZSO, and the fitting results are shown in Fig. 5 and Table 1 . By comparing the coefficients (R2), we found that the adsorption process of TC, CQ and CIP by SiO2@ZSO could well fit the pseudo-second-order equation, which is reflected in their R2 is greater than 0.90 at different concentrations. Moreover, compared with the adsorption amount calculated by the pseudo-first-order, the value obtained by the pseudo-second order is more in line with the actual adsorption amount. Kinetic analysis showed that the adsorption processes were mainly related to the adsorption sites (Xia et al., 2022).Fig. 5 Pseudo-first-order (a-c) and pseudo-second-order (d-f) kinetics of adsorption of CQ, TC and CIP on SiO2@ZSO. (Reaction conditions: contaminant concentration is 0–100 mg·L−1, dosage is 1 g·L−1, temperature is 20 °C, pH is not adjusted). Fig. 5 Table 1 Kinetic parameters of adsorption of CQ, TC and CIP on SiO2@ZSO. Table 1 Conc. (mg·L‒1) qexp Pseudo-first-order model Pseudo-second-order model Liquid film diffusion k1 (min‒1) qcat (mg·g‒1) R2 Δq k2 (g··mg‒1·min‒1) qcat (mg·g‒1) R2 Δq kf (min‒1) R2 CQ 20 17.372 0.688 16.963 0.9908 2.35% 0.083 17.492 0.9989 0.69% 0.060 0.8415 40 32.140 0.413 28.286 0.9087 11.99% 0.020 30.104 0.9660 6.33% 0.026 0.9320 80 38.759 0.556 33.352 0.8747 13.95% 0.022 35.445 0.9405 8.55% 0.024 0.9214 100 46.406 0.608 40.744 0.8913 12.20% 0.021 43.110 0.9456 7.10% 0.030 0.9410 TC 20 18.164 0.457 16.978 0.9560 6.53% 0.042 17.859 0.9924 1.68% 0.042 0.9491 40 35.069 0.329 31.590 0.9206 9.92% 0.015 33.661 0.9769 4.01% 0.033 0.9684 80 56.784 0.108 50.516 0.9120 11.04% 0.003 56.127 0.9682 1.16% 0.044 0.9899 100 62.463 0.151 55.975 0.9526 10.39% 0.003 61.633 0.9850 1.33% 0.028 0.9672 CIP 20 19.247 0.476 18.062 0.9596 6.16% 0.042 18.948 0.9938 1.55% 0.041 0.9272 40 29.593 0.087 27.418 0.9180 7.35% 0.004 30.565 0.9655 3.28% 0.036 0.8033 80 57.548 1.015 49.191 0.9050 14.52% 0.036 51.049 0.9321 11.29% 0.023 0.6111 100 68.677 1.838 67.385 0.9960 1.88% 0.196 67.736 0.9966 1.37% 0.022 0.3733 To gain insight into the rate-limiting steps of adsorption of TC, CQ and CIP by SiO2@ZSO, intra-particle diffusion and liquid film diffusion models were used for the analysis. In Figs. S4a–c, the adsorption process of organic pollutants by SiO2@ZSO shows a typical multi-linear fitting, indicating that the adsorption mechanism is composed of different stages. The initial stage of adsorption is external diffusion adsorption, the second stage is internal diffusion of particles, and the third stage is equilibrium stage. Generally, the stage with the lower slope k id is considered to be the rate control stage, but the third stage is not included because it occurs faster and cannot represent the rate determination step. The multi-stage results indicate that the intraparticle diffusion process is the main rate-determining step, but not the only ratecontrolling mechanism, in SiO2@ZSO adsorption of antibiotics. In Figs. S4d–f, the liquid film diffusion model shows that all points behave linearly and do not pass through the origin, implying that intra-particle diffusion is not the only decision step again. Within a given adsorption time, the liquid film diffusion at the initial stage of adsorption is a rate-controlling step; once the pollutants cross the liquid film, intra-particle diffusion becomes a rate-controlling step until the adsorption equilibrium is reached. 3.2.3 Isotherms and thermodynamics Adsorption isotherms at different reaction temperatures were studied in order to elucidate the mechanisms and interactions between adsorbent and adsorbate. The fitting results of the Langmuir, Fredunlich, Temkin and D-R models are shown in Fig. 6 and Table 2 . The correlation coefficients of the four isotherm fittings are all greater than 0.95, indicating that they can fit the data well. From the perspective of R2, the Langmuir model can better explain the adsorption process of CQ, while TC and CIP are more suitable for the Temkin model. The maximum adsorption amounts calculated according to Langmuir were 49.01 mg·g−1, 56.06 mg·g−1 and 104.77 mg·g −1 for SiO2@ZSO adsorption of CQ, TC and CIP, respectively. Comparing the adsorption activities and capacities of the reported adsorbents for CQ, TC and CIP, SiO2@ZSO shows outstanding advantages in Table S2. In the Fredunlich model, 0 < 1/n < 1, which implies that the adsorption process of SiO2@ZSO for CQ, TC and CIP is preferential adsorption, and the adsorption process for CQ and TC is easy adsorption (0.1 < 1/n < 0.5). Temkin model also achieved good fitting results, which indicates there is a strong electrostatic attraction force during the adsorption process (Z. Wei et al., 2022). In D-R, the fitted heat of adsorption is < 1 kJ·mol−1, which is defined as the physical adsorption category (E < 8 kJ·mol−1). In general, it is not appropriate to consider only one model when R2 is not much different. Therefore, we speculate that the adsorption process of SiO2@ZSO for CQ, TC and CIP might be heterogeneous adsorption, and the adsorption process is closely related to electrostatic attraction.Fig. 6 Isotherm study of Langmuir (a), Fredunlich (b), Temkin (c) and Redlich342 Peterson (d) models for adsorption of CQ, TC and CIP on SiO2@ZSO. Fig. 6 Table 2 Isotherm parameters of adsorption of CQ, TC and CIP on SiO2@ZSO. Table 2Poll. Temp. Langmuir Freundlich Tempkin D-R Kl (L·mg−1) qm (mg·g−1) R2 Δq Kf (L·mg−1) 1/n R2 Kt (L·mg−1) b (kJ·mol−1) R2 qm (mg·g−1) E (kJ·mol−1) R2 Δq CQ 20 °C 0.15 49.01 0.9582 5.61% 14.00 0.29 0.9399 1.84 0.251 0.9512 42.55 0.36 0.9568 8.31% 25 °C 0.15 46.78 0.9884 8.90% 13.48 0.29 0.9653 1.84 0.271 0.9792 39.78 0.38 0.9567 7.39% 30 °C 0.15 46.52 0.9904 10.76% 13.44 0.28 0.9625 1.82 0.276 0.9786 39.61 0.38 0.9678 8.52% TC 20 °C 0.19 53.68 0.9971 8.83% 15.76 0.29 0.9888 2.50 0.239 0.9988 43.67 0.61 0.8249 11.46% 25 °C 0.20 55.41 0.9761 6.55% 15.67 0.31 0.9997 2.86 0.238 0.9941 43.96 0.78 0.7338 15.47% 30 °C 0.23 56.06 0.9862 7.55% 17.19 0.30 0.9967 3.39 0.243 0.9984 45.21 0.85 0.7633 13.27% CIP 20 °C 0.06 104.77 0.9906 16.53% 9.84 0.57 0.9880 0.55 0.102 0.9931 66.44 0.32 0.9815 26.10% 25 °C 0.06 95.32 0.9841 8.55% 9.69 0.55 0.9875 0.57 0.113 0.9870 61.96 0.32 0.9598 29.44% 30 °C 0.06 100.11 0.9890 13.55% 9.29 0.56 0.9919 0.53 0.110 0.9911 62.68 0.32 0.9621 28.90% Thermodynamic behavior is determined by the thermodynamic parameters Gibbs free energy (ΔG°, kJ·mol−1), enthalpy change (ΔH °, kJ·mol−1) and entropy change (ΔS °, J·mol−1·K−1). In Table 3 , the ΔG° values of different pollutants at different temperatures are all negative, indicating that the adsorption process is spontaneous and favorable. The ΔH ° of SiO2@ZSO adsorbing CQ, TC and CIP are −11.03 kJ·mol−1, −12.22 kJ·mol−1 and −2.46 kJ·mol−1, respectively. These negative values indicate that the adsorption is an exothermic process and increasing of the temperature is not favorable for the adsorption to proceed, and the absolute value of ΔH ° <20 kJ·mol−1 indicates physical adsorption. Further, the negative ΔS ° indicates that the adsorption is entropy-decreasing and irreversible during this adsorption experiment.Table 3 Thermodynamic parameters of adsorption of CQ, TC and CIP on SiO2@ZSO. Table 3Poll. Temp. Thermodynamic ΔG0 (kJ·mol−1) ΔH0 (kJ·mol−1) ΔS0 (J·mol−1·K−1) CQ 20 °C −0.99 −11.03 −34.20 25 °C −0.87 30 °C −0.65 TC 20 °C −2.13 −12.22 −34.50 25 °C −1.89 30 °C −1.79 CIP 20 °C −1.27 −2.46 −4.00 25 °C −1.26 30 °C −1.23 3.2.4 Influence of external environment A comparison of the adsorption activities of SiO2@ZSO at different initial concentrations of different pollutants is given in Fig. 7 a. It was pointed out that SiO2@ZSO had better adsorption activities for TC, CQ and CIP at low concentrations such as 20 mg·L−1, which are 91.56%, 79.21% and 83.52%, respectively. The adsorption capacities of SiO2@ZSO at different initial concentrations of different pollutants are shown in Fig. 7b. The amount of adsorbent and the reaction temperature are important factors affecting the adsorption activity of the adsorbent. In Fig. 7c, the adsorption capacities of SiO2@ZSO for TC, CQ and CIP increased with the increase of the dosage, but the growth rate of the adsorption capacity decreased significantly when the dosage is greater than 0.5 g·L−1. In Fig. S5, we can clearly see that the removal rate of TC, CQ and CIP can reach 50% after adsorption for 2 min by SiO2@ZSO (contaminant concentration is 20 mg·L−1 and adsorbent dosage is 0.5 g·L−1), indicating that SiO2@ZSO has a good application prospect. In Fig. 7d, we observed that the adsorption capacity of SiO2@ZSO for TC, CQ and CIP is very weakly dependent on temperature, which indicated that temperature is not a factor limiting the adsorption of TC, CQ and CIP by [email protected]. 7 The effect of initial concentration of organic pollutants (a-b), adsorbent dosage (c) and reaction temperature (d) on the adsorption activity of TC, CQ and CIP on 16- SiO2@ZSO. (Reaction conditions: contaminant concentration is 20–100 mg·L−1, dosage is 0.25–1.25 g·L−1, temperature is 20–30 °C, pH is not adjusted). Fig. 7 3.2.5 Effect of pH The structural stability of antibiotics is closely related to the pH in the solution, so it is necessary to investigate the effect of the pH change of the initial solution on the adsorption process. The pKa value of CQ is 8.40, it is a cation when pH < 8.40, and an anion otherwise (Yi et al., 2021). TC has three pKa values of 3.3, 7.7 and 9.7, corresponding to the existence of cation, zwitterion and anion, respectively (Álvarez-torrellas et al., 2016; Qiao et al., 2020). The pKa values of CIP are 6.10 and 8.70 corresponding to the presence of cations, zwitterions and anions, respectively (Al-Musawi et al., 2021a). As depicted in Fig. 8 a, the effect of initial pH range of 3–11 for CQ, TC and CIP solutions on adsorption capacity is presented. For pollutants with an initial concentration of 20 mg·L−1, the dosage of SiO2@ZSO is 1 g·L−1, and the adsorption temperature is 20 °C, the adsorption capacities of SiO2@ZSO for CQ, TC and CIP decreased with increasing pH. In addition, the Zeta potential (Zetasizer Nano ZS90, Malvern, U.K.) is given out to explore the pHzpc and the surface charge properties of SiO2@ZSO in Fig. 8b. With the increase of pH, the zeta potential value of SiO2@ZSO becomes more and more negative, which implies that the pHzpc of SiO2@ZSO is less than 3.0 and has a negative surface charge in the pH range of 3–11. When the pH is alkaline, the three pollutants exist in the form of anions, which lead to electrostatic repulsion with the negatively charged SiO2@ZSO, resulting in a decrease in the adsorption capacity. The above conclusions point out that the electrostatic force is a significant adsorption force during the adsorption of CQ, TC and CIP by [email protected]. 8 Effects of pH on the adsorption activities (a) of CQ, TC and CIP on SiO2@ZSO and zeta potential values (b) of SiO2@ZSO. Fig. 8 3.2.6 Effect of salt environment Drugs wastewater is one of the high salinity wastewaters, where the presence of salt can corrode equipment and affect the removal of antibiotics. Therefore, it is necessary to study the effect of salt on the adsorption process of antibiotics. Using seven kinds of salts widely present in water, the effect of SiO2@ZSO on the adsorption activity of CQ, TC and CIP wastewater containing salt was analyzed. The effect of salt presence on the activity of SiO2@ZSO to adsorb organic contaminants at concentrations of 5 mM, 10 mM, 20 mM and 50 mM is examined in Fig. 9 . Compared with the environment without adding any salt, the adsorption effect of SiO2@ZSO on CQ remained at about 80% (the adsorption capacity was maintained at 17 mg·g−1). This indicated that the presence of the seven salts hardly changed the adsorption capacity of SiO2@ZSO for CQ. Obviously, the presence of Na3PO4, Na2CO3, NaH2PO4 and NaHCO3 inhibited the adsorption of TC and CIP by SiO2@ZSO, and the adsorption capacity became weaker with the increase of their concentrations. This may be due to the differences in the ability of different pollutants to compete with anions for the adsorption sites of SiO2@ZSO (Q. Wei et al., 2022).Fig. 9 Effect of salt on the adsorption activity of SiO2@ZSO for CQ (a,d), TC (b,e) and CIP (c,f). (Reaction conditions: contaminant concentration is 20 mg·L−1, dosage is 1 g·L−1, temperature is 20 °C, pH is not adjusted). Fig. 9 3.3 Adsorption mechanism According to the previous kinetic and thermodynamic analysis, there is a physical and chemical interactive adsorption mechanism in the process of SiO2@ZSO adsorption of CQ, TC and CIP. Further, for in-depth analysis of the mechanism of action of the adsorption process, HPLC and UV scan spectra over time, FT-IR and XPS spectra before and after adsorption are provided. In Fig. 10 , it can be judged whether there are other adsorption intermediates in the adsorption process by the time-varying spectra of HPLC and UV. The conclusion shows that no new absorption peaks were observed during the adsorption of organic pollutants by SiO2@ZSO, and the peak intensities of all absorption peaks gradually decreased with time. Further, the TOC data showed that most of the pollutants were adsorbed on the SiO2@ZSO surface after 2 h of adsorption, resulting in water purification (in Fig. 10d).Fig. 10 Changes in the detection spectra of SiO2@ZSO adsorption process of CQ (a), TC (b) and CIP (c) with time; and the removal efficiency of TOC after adsorption for 2 h (d). Fig. 10 The FT-IR and XPS spectra before and after adsorption are provided in Fig. 11 to further analyze the interaction process of SiO2@ZSO and pollutant molecules. In Fig. 11a, the FT-IR spectra before and after adsorption are almost unchanged. But obviously, the adsorption of CQ, TC and CIP on SiO2@ZSO can be observed through the stretching vibration of the benzene ring in the range of 1600-1400 cm−1. The Si–OH vibration at 798 cm−1 and the adsorbed –OH at 3443 cm−1 were found to become weaker after adsorption of organic pollutants, indicating that the abundant –OH groups on the surface of SiO2@ZSO were involved in the adsorption process. In addition, through the XPS full spectra (Fig. S6) before and after adsorption and the proportion of related elements (Table 4 ), the proportion of C and N elements increased after the adsorption of pollutants. In addition, Cl element was also observed after SiO2@ZSO adsorption of CQ, which again confirmed the adsorption of pollutant molecules on the SiO2@ZSO surface. The fine spectra of Zn 2p, Si 2p and O 1s show that the binding energies at the typical outgoing peaks after adsorption are shifted towards low in Fig. 11b, which indicates that the electron cloud of pollutants is shifted towards the SiO2@ZSO structure.Fig. 11 Comparison of FT-IR spectra (a) and XPS narrow spectra of Zn 2p (b), Si 2p (c) and O 1s (d) of SiO2@ZSO and SiO2@ZSO after adsorption of CQ, TC and CIP. Fig. 11 Table 4 Comparison of element content of SiO2@ZSO and after adsorption of CQ, TC and CIP. Table 4Sample Atomic (%) C N O Zn Si Cl SiO2@ZSO 13.52 1.39 56.57 9.76 18.76 / SiO2@ZSO -CQ 16.82 1.55 57.98 6.43 16.34 0.88 SiO2@ZSO -TC 16.47 1.65 57.07 6.55 18.27 / SiO2@ZSO-CIP 16.82 1.64 57.04 5.74 18.33 0.43 The above conclusions show that the adsorption of pollutants by SiO2@ZSO is mainly involves physical adsorption, involving three forces of electrostatic attraction, hydrophobic interaction and hydrogen bonding, as shown in Fig. 12 . The electrostatic attraction of negatively charged SiO2@ZSO in water filled with cationic antibiotics is one of the main adsorption driving forces. This conclusion is confirmed by Fig. 8. When CQ, TC and CIP exist as cations or zwitterions, there is electrostatic attraction between them and the adsorbent. Conversely, electrostatic repulsion exists when both the pollutant molecules and the SiO2@ZSO surface are negatively charged (under alkaline conditions), resulting in a decrease in adsorption capacity. According to the principle of similar compatibility, the hydrophobic interaction is an important adsorption mechanism when both the organic pollutant and the adsorbent surface are hydrophobic (Xu et al., 2021). The solubility (at 20 °C) of CQ, TC and CIP was 50 mg·mL−1, 50 mg·mL−1 and 35 mg·mL−1, respectively. Compared with the adsorption of CQ and TC by SiO2@ZSO, the maximum adsorption capacity was obtained when SiO2@ZSO adsorbed the more insoluble CIP under the same conditions (in Fig. 5), implying that the hydrophobic interaction is one of the adsorption forces. The abundant hydroxyl groups on the surface of SiO2@ZSO are the prerequisites for hydrogen bond adsorption with pollutant molecules. From the molecular structures of CQ, TC and CIP, their hydrogen bond acceptor and donor numbers are shown in Table S3. In Fig. 11, the abundant –OH and Si–OH bonds on the surface of SiO2@ZSO easily form hydrogen bonds such as –OH⋯O/−OH⋯N with the –OH bonds on CQ, TC and CIP molecules. It was confirmed from the FT-IR and XPS spectra of SiO2@ZSO after adsorption of pollutants (in Fig. 11). In Fig. 12, after the core-shell adsorbent enters the polluted water body (left), the pollutants are adsorbed on the surface of SiO2@ZSO under the action of intermolecular van der Waals forces such as electrostatic attraction, hydrophobic distribution and hydrogen bonding, so as to achieve the purpose of purifying the water body (right).Fig. 12 Possible mechanism of adsorption of pollutant molecules by SiO2@ZSO. (Gray balls are C atoms, red balls are O atoms, blue balls are N atoms, green balls are Cl atoms, and rose-red balls are F atoms; the core-shell structure is SiO2@ZSO). Fig. 12 3.4 Repetition and application potential of adsorbent The repeated adsorption experiments are described as follows: First, after the adsorbent was saturation, the adsorbent was collected by centrifugation, washed with water for several times, and then dried for the next experiment. In addition, actual wastewater tends to be complex in composition, so it is important to consider the activity of the adsorbent when multiple drugs coexist. The results of repeated experiments and the activity of the adsorbents in complex waters are shown in Fig. 13 . The adsorption capacities of SiO2@ZSO for CQ, TC and CIP decreased to 10.58 mg·g−1, 8.32 mg·g−1 and 13.65 mg·g−1, respectively, after four repeated experiments (Fig. 13a). This conclusion indicates that the reproducibility of SiO2@ZSO is weak, possibly because the antibiotics adsorbed on the surface of SiO2@ZSO are not completely desorbed, and they occupy part of the adsorption sites, thereby weakening the adsorption performance. This indicates that SiO2@ZSO has great application prospects in dealing with emergencies, but further research is needed in terms of reusability. In Fig. 13b–c, SiO2@ZSO exhibited excellent adsorption activity against complex wastewater (where OTC and CTC refer to oxytetracycline and chlortetracycline, respectively). The content of organic matter in the solution decreased by 80.24% after adsorption for 2 h. The conclusion points out that SiO2@ZSO has the potential for practical application.Fig. 13 Reproducibility experiment of adsorption of CQ, TC and CIP by SiO2@ZSO (a), UV–vis adsorption curves of complex wastewater adsorbed by SiO2@ZSO (b) and the removal efficiency of TOC after adsorption for 2 h (c). Fig. 13 4 Conclusion In conclusion, this work provides a new adsorption material as one of the solutions to the newly faced antibiotic contamination problem in the COVID-19 environment. Core-shell microporous SiO2@ZSO was prepared using SiO2 as a template, and SiO2@ZSO exhibited nonspecific and excellent adsorption activity for CQ, TC and CIP. The adsorption capacity of SiO2@ZSO for CQ, TC and CIP is 6.72, 49.65 and 9.62 times higher than that of SiO2. Batch experiments indicated that the optimum adsorption condition for SiO2@ZSO adsorption of CQ, TC and CIP is 0.5 g/L adsorbent dose, 20 mg/L initial concentration of contaminant and acidic conditions. Based on kinetics, the process of SiO2@ZSO adsorption of pollutant molecules consists of intra-particle diffusion and liquid film diffusion as rate-controlling steps. Thermodynamic analysis indicated that the adsorption was heterogeneous, spontaneously and preferentially. The adsorption mechanisms mainly include: 1) electrostatic attraction, obtained from the effect of pH and zeta potential analysis; 2) partition effect, derived from pollutant molecules and structural properties of the adsorbent; 3) hydrogen bonding, based on functional group changes before and after adsorption. In addition, the salinity and repeated experiments show that SiO2@ZSO is less affected by ions and has better reuse value, indicating that SiO2@ZSO has excellent stability. Therefore, SiO2@ZSO is an ideal and excellent adsorbent for mixed antibiotic wastewater, especially the complex organic wastewater brought by the COVID-19 epidemic. CRediT authorship contribution statement Xueli Hu: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing – original draft. Yuanhang Zhou: Investigation, Validation, Visualization. Yingying Zhou: Conceptualization, Investigation, Methodology. Yun Bai: Investigation, Validation. Ruiting Chang: Conceptualization, Investigation. Peng Lu: Supervision, Conceptualization, Formal analysis, Investigation. Zhi Zhang: Supervision, Project administration, Conceptualization, Investigation, Resources. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix A Supplementary data The following is the Supplementary data to this article:Multimedia component 1 The support material includes: methods to detect changes in CQ, TC and CIP concentrations, XRD patterns, element content, physical properties of SiO2@ZSO and SiO2, fitting results of intraparticle diffusion kinetic model for adsorption of CQ, TC and CIP by SiO2@ZSO, and comparison of the full XPS spectra of SiO2@ZSO before and after adsorption. Multimedia component 1 Data availability Data will be made available on request. Acknowledgment This work was supported by the Science and Technology Program of the 10.13039/501100005316 Ministry of Housing and Urban-Rural Development of China (2021-k-112), the Chongqing Postgraduate Research and Innovation Project (CYB22038) and the Construction Science and Technology Project of Chongqing, China (2020, No.5–2). Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.jclepro.2022.135416. ==== Refs References Adebisi Y.A. Jimoh N.D. Ogunkola I.O. Uwizeyimana T. Olayemi A.H. Ukor N.A. Lucero-Prisno D.E. The use of antibiotics in COVID-19 management: a rapid review of national treatment guidelines in 10 African countries Trop. Med. Health 49 2021 10.1186/s41182-021-00344-w Al-Musawi T.J. Mahvi A.H. Khatibi A.D. Balarak D. Effective adsorption of ciprofloxacin antibiotic using powdered activated carbon magnetized by iron (III) oxide magnetic nanoparticles J. Porous Mater. 28 2021 835 852 10.1007/s10934-021-01039-7 Al-Musawi T.J. 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==== Front Am J Emerg Med Am J Emerg Med The American Journal of Emergency Medicine 0735-6757 1532-8171 Elsevier Inc. S0735-6757(22)00635-0 10.1016/j.ajem.2022.10.005 Article The authors respond: monkeypox presentations Brady William J. a⁎ Long Brit b Koyfman Alex c Gottlieb Michael d Liang Stephen Y. e Carius Brandon M. f Chavez Summer g a Departments of Emergency Medicine and Medicine (Cardiovascular), University of Virginia School of Medicine, Charlottesville, VA, United States of America b SAUSHEC Emergency Medicine, Brooke Army Medical Center, Fort Sam Houston, San Antonio, TX, United States of America c Department of Emergency Medicine, University of Texas Southwestern Medical Center, Dallas, TX, United States of America d Department of Emergency Medicine, Rush University Medical Center, Chicago, IL, United States of America e Divisions of Emergency Medicine and Infectious Diseases, Washington University School of Medicine, St. Louis, MO, United States of America f 121 Field Hospital, Camp Humphreys, US Army, Republic of Korea g Department of Health Systems and Population Sciences, University of Houston, Houston, TX, United States of America ⁎ Corresponding author. 12 10 2022 1 2023 12 10 2022 63 166166 27 9 2022 4 10 2022 © 2022 Elsevier Inc. All rights reserved. 2022 Elsevier Inc. Elsevier has created a Monkeypox Information Center (https://www.elsevier.com/connect/monkeypox-information-center) in response to the declared public health emergency of international concern, with free information in English on the monkeypox virus. The Monkeypox Information Center is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its monkeypox related research that is available on the Monkeypox Information Center - including this research content - immediately available in publicly funded repositories, with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the Monkeypox Information Center remains active. ==== Body pmcWe appreciate the response to our article “Monkeypox: A focused narrative review for emergency medicine clinicians.” [1] In the response, they note that they believe that “…the key to effectively containing the epidemic of monkeypox is an early diagnosis and fast treatment.” We agree entirely; in fact, the primary objective of our review is to assist the emergency clinician in the timely diagnosis of monkeypox infection. The authors also point out the importance of recognizing atypical presentations. Once again, we agree with these comments and have addressed this important concept as well in our review article. For instance, we discuss the varying signs and symptoms of the current outbreak and compare these to prior outbreaks. One particular area of noted difference seen in the current outbreak is the predominance of painful perineal/genitourinary lesions. We thank the authors for emphasizing two important messages from our review article. CRediT authorship contribution statement William J. Brady: Conceptualization, Writing - original draft, Writing - review, Writing - review & editing. Brit Long: Conceptualization, Writing – original draft, Writing – review & editing. Alex Koyfman: Conceptualization, Writing – review & editing. Michael Gottlieb: Conceptualization, Writing – review & editing. Stephen Y. Liang: Conceptualization, Writing - review, Writing - review & editing. Brandon M. Carius: Conceptualization, Writing – review & editing. Summer Chavez: Conceptualization, Writing – original draft, Writing – review & editing. ==== Refs Reference 1 Long B. Koyfman A. Gottlieb M. Liang S.Y. Carius B.M. Chavez S. Monkeypox: a focused narrative review for emergency medicine clinicians Am J Emerg Med 20 61 2022 Aug 34 43 10.1016/j.ajem.2022.08.026 [Online ahead of print]	36253213	PMC9719105	NO-CC CODE	2022-12-08 23:18:11	no	Am J Emerg Med. 2023 Jan 12; 63:166	utf-8	Am J Emerg Med	2,022	10.1016/j.ajem.2022.10.005	oa_other
==== Front Protein Expr Purif Protein Expr Purif Protein Expression and Purification 1046-5928 1096-0279 Elsevier Inc. S1046-5928(22)00167-X 10.1016/j.pep.2022.106210 106210 Article Expression, purification, and study on the efficiency of a new potent recombinant scFv antibody against the SARS-CoV-2 spike RBD in E. coli BL21 Yaghoobizadeh Fatemeh a∗ Ardakani Mohammad Roayaei a Ranjbar Mohammad Mehdi b Galehdari Hamid a Khosravi Mohammad c a Department of Biology, Shahid Chamran University of Ahvaz, Ahvaz, Khouzestan, Iran b Razi Vaccine and Serum Research Institute, Karaj, Alborz, Iran c Department of Pathobiology, Shahid Chamran University of Ahvaz, Ahvaz, Khouzestan, Iran ∗ Corresponding author. Golestan highway, Ahvaz, Khouzestan, 6135744337, Iran. 4 12 2022 4 12 2022 1062107 9 2022 19 11 2022 27 11 2022 © 2022 Elsevier Inc. All rights reserved. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Many efforts have been made around the world to combat SARS-CoV-2. Among these are recombinant antibodies considered to be suitable as an alternative for some diagnostics/therapeutics. Based on their importance, this study aimed to investigate the expression, purification, and efficiency of a new potent recombinant scFv in the E. coli BL21 (DE3) system. The expression studies were performed after confirming the scFv cloning into the pET28a vector using specific PCRs. After comprehensive expression studies, a suitable strategy was adopted to extract and purify periplasmic proteins using Ni2+-NTA resin. Besides the purified scFv, the crude bacterial lysate was also used to develop a sandwich ELISA (S-ELISA) for the detection of SARS-CoV-2. The use of PCR, E. coli expression system, western blotting (WB), and S-ELISA confirmed the functionality of this potent scFv. Moreover, the crude bacterial lysate also showed good potential for detecting SARS-CoV-2. This could be decreasing the costs and ease its utilization for large-scale applications. The production of high-quality recombinant proteins is essential for humankind. Moreover, with attention to the more aggressive nature of SARS-CoV-2 than other coronaviruses, the development of an effective detection method is urgent. Based on our knowledge, this study is one of the limited investigations in two fields: (1) The production of anti-SARS-CoV-2 scFv using E. coli [as a cheap heterologous host] in relatively high amounts and with good stability, and (2) Designing a sensitive S-ELISA for its detection. It may also be utilized as potent therapeutics after further investigations. Keywords Recombinant scFv SARS-CoV-2 Sandwich ELISA Escherichia coli Expression Purification ==== Body pmc1 Introduction The coronavirus disease-2019 (COVID-19) emerged in late December 2019 from Wuhan, China, spread rapidly around the globe, and became a pandemic. The causative agent of the COVID-19 pandemic was nominated as SARS-CoV-2 by World Health Organization (WHO). This virus is an enveloped, positive-sense single-strand RNA virus belonging to the Coronaviridae family. The dominant feature of this disease is the high mortality and morbidity rate in comparison with MERS (Middle East Respiratory Syndrome) and SARS (severe acute respiratory syndrome). So, various complications have been reported including acute respiratory distress syndrome (ARDS), vascular thrombosis, coagulopathy, etc. [1,2]. To combat this virus, many diagnostic and therapeutic agents have been introduced. These agents may be small chemical compounds and peptides’ therapeutic proteins (such as antibodies or nanobodies), etc. As the important components of the immune system, the capability of antibodies for highly specific binding with various ligands (including viral proteins, tumor-associated markers, etc.) and their great applications turn them into an interesting field of research. So, as a fast-growing field in the pharmaceutical industry, about 131 antibody therapeutics are currently approved by FDA for clinical use in the United States or European Union by November 2021, since their first introduction in the 1980s. Among these, there are 20 antibodies for COVID-19 disease that are considered for emergency use or approval by 15 November 2021. Moreover, it is worth nothing to say that the global antibody market size is approximately $145.7 billion in 2022, and it is estimated to grow to $248.9 billion by the end of 2027 (https://www.marketdataforecast.com/). In this regard, researchers are focused on maximizing their production and efficiency [[3], [4], [5], [6]]. The neutralizing antibodies could have various formats i.e., Fab (antigen-binding fragments), diabody, minibody, scFv (single-chain fragment variable), scFv-zipper, single-domain antibody (dAb), or nanobodies. Among these, the scFv format is a good choice based on the following reasons: (1) the low molecular weight (27–30kDa), (2) improvement of thermostability and solubility, (3) the low immunogenicity, (4) high specificity, (5) no requirement to post-translational modifications (PTMs), (6) more rapid clearance, etc. This antibody has been frequently used for the treatment or detection of various diseases, e.g., Infectious Bursal Disease (IBD), human H5N1 influenza virus, Newcastle virus disease [7], and tumor imaging [3,5,[8], [9], [10]]. After the selection of a suitable antibody format, the selection of a good and robust heterologous host is an important issue for its rapid production and purification, and the following commercial and academic purposes [11]. Among the great vast of expression hosts, the bacterium Escherichia coli stands for the reduction of downstream bioprocesses according to some reasons including (1) the low-cost and low growth requirements, (2) its well-characterized physiology and genetics for the production of proteins, (3) its capability for mass-production of the recombinant proteins in a relatively short time, (4) ease of its manipulation. Therefore, as a valuable cell factory for the production of recombinant proteins, designing the expression systems based on this attractive host gets the most attention in recent years [[11], [12], [13]]. In contrast to the relatively reductive nature of the cytoplasm, the oxidizing environment of E. coli periplasmic space enables the proper disulfide bond formation in recombinant proteins using the disulfide bond formation (Dsb)-system. For this, various studies have targeted the proteins (especially disulfide bond-harboring ones) to this space to ensure their proper native conformation and facilitate the translocation across the cytoplasmic membrane by fusion of a peptide signal sequence (e.g., PelB, PhoA, OmpA, MalE) to their N-terminal sequence, too. Every of these signal sequences is translocated by discrete secretion pathways, e.g., the Sec-pathway is used for the translocation of OmpA, PhoA, and PelB signal peptides. Moreover, some strains of E. coli have a superior advantage. For example, E. coli BL21 (DE3) is one suitable host due to the lack of cellular proteases (opmT and Lon) and decrease in protease activity [3,[12], [13], [14], [15]]. Accordingly, we investigated the ability of E. coli BL21 (DE3) for successful expression of a potent recombinant anti-RBD scFv, in the current study. This scFv has been recently designed by in-depth and comprehensive bioinformatics studies. Following, the potential of the purified scFv in the detection of whole viral particles was studied based on the designed sandwich ELISA (S-ELISA). 2 Materials and methods 2.1 Transformation After cloning the gene cassette of the scFv construct into the pET28a vector, this vector was transformed into E. coli BL21 (DE3) competent cells (for expression studies) by the heat shock method according to Sambrook and Russel [16]. The inoculated plates with transformants of E. coli BL21 and control negative samples were incubated at 37 °C for 18–24hrs. 2.2 Plasmid extraction One transformant colony was cultured in 10ml LB broth medium at 37 °C, 220 rpm, overnight. The plasmid extraction was done using the Yekta-Tajhiz plasmid extraction kit according to the manufacturer's instructions. The extraction product was analyzed by 0.8% agarose in 0.5X TAE (Tris-Acetate-EDTA) buffer at 85V for 90min. 2.3 Polymerase chain reaction (PCR) At this stage, PCR was done for amplification of ∼280bp fragment and confirmation of the proper cloning of scFv construct into pET28a vector. For this, forward and reverse primers (Table 1 ) were designed using OligoAnalyzer v7.0 and analyzed by Primer-BLAST and oligoanalyzer servers. These primers were synthesized by the metaBion company.Table 1 The properties of forward and reverse primers. Table 1Name Sequence (5′-3′) Length Tm GC Forward TATCTCTACCCCGATGGACG 20 67.8 55 Reverse AGAGAAACGGTCCGGAACAC 20 69.6 55 The PCR was carried out in 20μl volume containing: 2μl DNA plasmid as a template, 10μl of PCR Master Mix (Amplicon, 1.5mM MgCl2), and 0.5μl of each primer (10pM). The gene of interest was amplified using the following thermal cycling program: initial denaturation at 95 °C for 4min, followed by 30 cycles at 94 °C for 40sec, 55,60 °C for 30sec, 72 °C for 1min, with the final extension at 72 °C for 10min. The final products were analyzed with 1% agarose in 0.5X TAE buffer at 85V for 75min. 2.4 Expression studies Expression of the pET28a-scFv construct was performed in E. coli BL21 (DE3). Briefly, the transformed bacterium was cultured in Luria-Bertani (LB) broth (30μg/ml kanamycin) and incubation was done at 37 °C, 220 rpm, overnight. Sub-culture was done at 1:50 (v/v) ratio in 100ml of fresh LB broth medium (30μg/ml kanamycin) and incubation was done under the above-mentioned conditions. When optical density at 600nm (OD600) reached ∼0.8–0.9, 2ml of culture was withdrawn as expression-control negative. Thereafter, the expression of the target protein was induced by the addition of isopropyl-β-d-Galactopyranoside (IPTG) (as an analog of allo-lactose for induction of lac promoter) at a final concentration of 0.5 and 1.0mM [17]. The samples were incubated at 30 °C, 200 rpm. The expression time-course studies were performed in 2, 4, 6, 8, 15, and 24hrs. after the promoter induction. Finally, pellets were harvested by centrifugation of each sample at 7000 rpm, 10min at 22 °C, and were processed for further studies [18,19]. 2.4.1 Total protein extraction Following the centrifugation of all the above-mentioned samples, the gathered pellets were resuspended in protein sample buffer (5X) plus 2-mercapto ethanol according to the Laemmli (1970) protocol [16]. Based on the predicted molecular weight of scFv (∼30kDa), the resolving and stacking SDS-PAGE gel concentration was selected as 14% and 4%, respectively. Electrophoresis was done in running buffer (25mM Tris-base, 192mM glycine, 1% SDS, pH 8.3; [CinnaGen Co., Tehran, Iran]) at 85V for 2–3hrs. The gel was stained by staining solution (1% Coomassie blue R-250, [Merck, Darmstadt city, Germany]) and de-stained by 7% acetic acid (Merck, Darmstadt city, Germany); 5% methanol (Merck, Darmstadt city, Germany); 88% water solution. The molecular mass standard (CinnaGen Co., Tehran, Iran) was run in parallel with other samples in order to calculate the molecular weights of the proteins [20,21]. 2.4.2 Periplasmic proteins extraction At this stage, two methods were adopted and compared for the preparation of the periplasmic proteins: (1) the osmotic shock method, and (2) the sonication method.1) Generation of spheroplasts was done according to Abdolrasouli [22]. However, it should be noted that the presence of EDTA (as a chelating agent) in one buffer could interfere with the function of Ni2+-NTA resin in the purification stage. For this, as described later, the preferred method for the extraction of periplasmic proteins at the following stages was sonication. 2) All samples were treated by lysis buffer (50% v/v) [20mM Tris-Cl (pH 7.4), triton X-100 1% (w/v), 137mM NaCl, 50μM EDTA] for 30min on ice. Thereafter, pulse-sonication was performed for 5 × 1min (1min working and 1min resting) on the ice at 16–20% amplitude [23]. For inhibition of the possible proteases in extracted samples, 1.0 mM PMSF (phenyl methyl sulfonyl fluoride) was added to each sample as a protease inhibitor. The suspensions were incubated at room temperature for 1hrs. After centrifugation at 10000 rpm, 20min, and 4 °C, the soluble and insoluble fractions were analyzed using reducing SDS-PAGE as described before [17,24]. 2.4.3 Determination of the protein solubility for downstream stages It was so important to determine the best bacterial fraction for utilization in the purification stage. For this, in order to determine the distribution of the recombinant scFv between the soluble and insoluble fractions, the time-course studies were performed under the best IPTG concentration at 30 °C, 200 rpm, as described before. Both harvested fractions (pellet and supernatant) from each sample at the predefined intervals were analyzed using reducing SDS-PAGE [25]. 2.5 Western blotting (WB) The expression of the scFv construct was confirmed by the western blotting (immunoblotting) technique. In the current study, the nitrocellulose membrane was used for blotting and the wet blotter was applied for western blotting. Briefly, SDS-PAGE was done as described above without protein staining of the gel. Thereafter, the sandwich was assembled as follows in cathode→ anode direction: support pad → watman no. 1 filter paper→ SDS-PAGE gel → nitrocellulose membrane → watman no. 1 filter paper→ support pad. The blotting procedure was performed at 20V for 2–3hrs. in presence of transfer buffer [25mM Tris, 192mM glycine, 20%(v/v) methanol, up to 1L double-distilled water, pH 8.3]. Following, the remaining protein-binding sites on the nitrocellulose membrane were blocked by 5% (w/v) skimmed milk powder in PBST buffer i.e., 1X phosphate-buffered saline [PBS: 6.4g/L NaCl, 0.16g/L KCl, 1.152g/L Na2HPO4, 0.192g/L KH2PO4, pH 7.2] plus 0.05% (v/v) tween 20. Blocking was done at 4 °C, overnight. Thereafter, washing was performed three times with PBST. In the following stage, the membrane was incubated by anti-poly histidine monoclonal antibody at 1:1000 in 1% (w/v) Bovine Serum Albumin (BSA)/PBST, for 2hrs. at room temperature. The washing was repeated and the 4-chloro-1-naphthol (4CN) (Merck, Darmstadt city, Germany) solution was added as the enzyme chromogen substrate. After incubation at room temperature in dark and appearing a purple precipitate of protein band, the reaction was stopped by tap water [26,27]. 2.6 Protein purification In the current study, Ni-NTA (Nitrilozceticacid) (QIAGEN, USA) resin was used for protein purification based on the presence of C-terminal poly His tag in the target scFv. This resin is cross-linked by Ni2+ which could selectively interact with poly-histidine residues on the recombinant proteins [23,28]. Briefly, the purification was performed in the native condition with equilibration, washing (plus 20mM imidazole), and elution (plus 250mM imidazole) buffers according to the manufacturer's instructions. It should be noted that a pH 7.5 was adopted as the best one for all buffers. All collected samples from various purification stages were analyzed using SDS-PAGE as described before. Moreover, protein concentration was measured by the Bradford method, and BSA (SIGMA-ALDRICH, St. Louis, USA) was used in 0–2.5mg/ml as standard (Bradford reactant) [29]. 2.7 Study on the efficiency of the scFv using sandwich ELISA As a critical stage for the possible application of the synthesized recombinant scFv in diagnostic assays, the proper folding, function, and specificity of the purified scFv were determined using sandwich ELISA (S-ELISA). Moreover, for comparison and determining the necessity of strict purification strategies for large-scale industrial applications, the functionality of two other formats was also studied, i.e., the whole bacterial sample after 15hrs. of induction (as crude bacterial lysate) and the gathered supernatant from the treatment of the expression pellet with urea. After the Bradford assay, the ELISA microtiter plate (SPL life sciences, Gyeonggi-do, Korea, Republic) was coated with the 1–2 μg/well of these samples (as a capture antibody) in coating buffer (1.50g/L Na2CO3, 2.93g/L Na2HCO3 in 1000ml distilled water, pH 8.0–8.2), and incubated at 4 °C, overnight. Thereafter, washing was performed three times with PBST buffer. The blocking was performed for 1 hrs. with two blocking agents to select the best blocking agent for future applications: (1) 4% (w/v) skimmed milk powder (Merck, Darmstadt city, Germany) in PBS buffer at room temperature, and (2) 0.3% (v/v) tween 20 in PBS buffer at 37 °C. After three times washing with PBST, 20μg/well of the concentrated viral antigen with PEG:NaCl (30%:6.4% w/v) method was added to each well. Following the 1hrs. incubation at room temperature and three times washing with PBST, 100μl of the secondary antibody (i.e., the COVID-19-positive human serum) was added to each well in 1:50 and 1:10 dilution, separately. It should be noted that for each coated sample, the two-control negatives were also applied i.e., COVID-19-negative human serum and PBS buffer. Then, the plate was incubated at room temperature for 2hrs. and washed with PBST. Horseradish peroxidase (HRP)-conjugated A+G antibody (100μl) was added to each well in 1:2000 dilution in PBS buffer. After 2hrs. incubation at room temperature, reactions were developed by adding 60μl of TMB (3, 3′, 5, 5′- tetramethyl benzidine) (IDvet, Grabels, France) as a substrate. Finally, the reactions were stopped by 2M sulfuric acid (Merck, Darmstadt city, Germany). The absorbance at 450nm was determined by an ELISA microtitre plate reader (AccuReader, Metertech Inc., Taipei, Taiwan) [19,28]. 2.8 Statistical analysis The gathered results of the ELISA stage were analyzed using SPSS software version 26.0 and their significance level was evaluated. 2.9 Ethics approval This work was performed at the Shahid Chamran University of Ahvaz and the Ethical Committee of this institution has approved the work (Ethical code: EE/1400.3.02.25165/Scu.ac.ir). 3 Results 3.1 Transformation After 18–24hrs. incubation at 37 °C, the results indicated the growth of transformed bacteria on the LB-agar medium (30μg/ml kanamycin), whereas there was not any growth on the control negative medium (Data not shown). This indicates the successful ligation of the scFv construct to the pET28a vector and the successful transformation. 3.2 Plasmid extraction Plasmid extraction results showed the typical distinct bands of circular plasmids on the agarose gel. It was expected that a band with ∼6190bp size was seen on the agarose but it should be noted that the observed difference is based on the fact that circular DNA (such as plasmid) has about 30% lower movement on agarose in comparison to a linear DNA (such as DNA ladder). So, it places in a higher position than anticipation (Fig. 1 ). Moreover, the investigation of its quality by nanodrop revealed its concentration as 83.2ng/ml.Fig. 1 Plasmid extraction results. Lane 1: 1kb DNA ladder; Lane 2: The extracted plasmid. Fig. 1 3.3 Polymerase chain reaction (PCR) The insertion of the recombinant scFv gene into the expression plasmid was analyzed using specific PCR for ∼280bp fragment (Fig. 2 ).Fig. 2 PCR results. Lane 1: DNA ladder (1Kb); Lane 2: Amplification at 55 °C; Lane 3: Amplification at 60 °C; Lane 4: Control negative. Fig. 2 3.4 Expression studies The expression vector, pET28a, with the scFv gene positioned between the Nco I and Bam HI RE site, was used for the generation of the transformants of E. coli BL21 (DE3). To obtain the target protein, the promoter was induced by the addition of 0.5mM and 1mM of IPTG, separately. All samples were run on 14% SDS-PAGE under reducing conditions to confirm the expression of scFv. The following figures indicate the total protein and periplasmic protein expression of E. coli BL21 (DE3) in a final concentration of 0.5mM and 1mM of IPTG. As shown in Fig. 3, Fig. 4, Fig. 5 and judging from the presence of ∼30kDa band in the two experiments, the expression was successful for both IPTG concentrations. However, the protein solubility was determined as follows to select the suitable fraction for the following stages.Fig. 3 Expression of total protein in (A) 0.5mM, (B) 1mM of IPTG. Lane 1: Protein marker (10–250kDa), Lane 2: Expression after 0hrs. Lane 3: Expression after 2hrs. Lane 4: Expression after 4hrs. Lane 5: Expression after 6hrs. Lane 6: Expression after 8hrs. Lane 7: Expression after 15hrs. Lane 8: Expression after 24hrs. of induction. In each section, the right arrow shows the corresponding ∼30kDa band of recombinant scFv. Fig. 3 Fig. 4 Expression of periplasmic proteins in (A) 0.5mM of IPTG: Lane 1: Expression after 0hrs. Lane 2: Protein marker (10–250kDa), Lane 3: Expression after 2hrs. Lane 4: Expression after 4hrs. Lane 5: Expression after 6hrs. Lane 6: Expression after 8hrs. Lane 7: Expression after 15hrs. Lane 8: Expression after 24hrs. of induction (B) 1mM of IPTG. Lane 1: Protein marker (10–250kDa), Lane 2: Expression after 0hrs. Lane 3: Expression after 2hrs. Lane 4: Expression after 4hrs. Lane 5: Expression after 6hrs. Lane 6: Expression after 8hrs. Lane 7: Expression after 15hrs. Lane 8: Expression after 24hrs. of induction by osmotic shock protocol. In each section, the right arrow shows the corresponding ∼30kDa band of recombinant scFv. Fig. 4 Fig. 5 Expression of periplasmic proteins in (A) 0.5mM of IPTG: Lane 1: Protein marker (10–250kDa), Lane 2: expression after 24hrs. Lane 3: Expression after 15hrs. Lane 4: Expression after 8hrs. Lane 5: Expression after 6hrs. Lane 6: Expression after 4hrs. Lane 7: Expression after 2hrs. Lane 8: Expression after 0hrs. of induction (B) 1mM of IPTG: Lane 1: Protein marker (10–250kDa), Lane 2: Expression after 0hrs. Lane 3: Expression after 2hrs. Lane 4: Expression after 4hrs. Lane 5: Expression after 6hrs. Lane 6: Expression after 8hrs. Lane 7: Expression after 15hrs. Lane 8: Expression after 24hrs. of induction by sonication. In each section, the right arrow shows the corresponding ∼30kDa band of recombinant scFv. Fig. 5 3.4.1 Determination of the protein solubility for downstream stages Comparing the gathered results, transformants of E. coli BL21 expressed the recombinant scFv in both the soluble and insoluble fractions. However, it was observed that the most amount of scFv was present in the insoluble fraction after 15hrs. induction in presence of 1.0mM IPTG (Fig. 6 ). With this regard, this fraction was selected for the following studies.Fig. 6 Determination the recombinant scFv solubility. (A) Lane 1: Protein marker (10–250kDa), Lane 2: Supernatant, and Lane 3: Pellet after 0hrs. of induction, Lane 4: Supernatant, and Lane 5: Pellet after 2hrs. of induction, Lane 6: Supernatant, and Lane 7: Pellet after 4hrs. of induction, Lane 8: Supernatant, and Lane 9: Pellet after 6hrs. of induction, (B) Lane 1: Protein marker (10–250kDa), Lane 2: Supernatant, and Lane 3: Pellet after 8hrs. of induction, Lane 4: Supernatant, and Lane 5: Pellet after 15hrs. of induction, Lane 6: Supernatant, and Lane 7: Pellet after 24hrs. of induction. In each section, the right arrow shows the corresponding ∼30kDa band of recombinant scFv. Fig. 6 3.5 Western blotting The purity and functionality of the target protein were detected by western blotting (Fig. 7 ). As it was shown, it was observed that conjugated anti-His-tag antibody specifically reacts with the recombinant scFv containing the poly-His tag and there are not any non-specific bands.Fig. 7 Western blotting of recombinant scFv. Lane 1: Protein marker (10–250kDa), Lane 2: Pellet after 15hrs. of induction. The left arrow shows the corresponding ∼30kDa band of recombinant scFv. Fig. 7 3.6 Protein purification As it was said, the most amount of the recombinant scFv presented in the insoluble fraction. Accordingly, this fraction was solubilized in 7.0 M urea solution and utilized for further investigations. By utilization of various pHs (6.0, 7.0, 7.5, and 8.0) for the purification buffers, the procedure was followed by the best pH (7.5) (Data not shown). Finally, the purification using Ni2+-NTA resin resulted in a ∼30kDa band (corresponding to the target scFv) on the 14% SDS-PAGE gel with a relatively high purity (Fig. 8 ). This purity was suitable for the following studies.Fig. 8 Purification of the recombinant scFv. (A) Lane 1: Protein marker (10–250kDa), Lane 2: Flow through sample, Lane 3: Washing 1 sample, Lane 4: Washing 2 sample, Lane 5: Elution 1 sample, Lane 6: Elution 2 sample, Lane 7: Wash-final sample. The right arrow shows the corresponding ∼30kDa band of recombinant scFv. Fig. 8 Moreover, the protein concentration was calculated based on the Bradford assay as in the previous stages. Accordingly, the amount of the purified scFv was calculated as 3.40g/L. It should be noted that the cost for production of this scFv was evaluated as 1.00 mg/ml/0.145$ (including the costs of the recombinant construct synthesis for its use alongside all stages of the present work and future studies). On the other hand, the lower costs (e.g., culture media ingredients, growth requirements, inexpensive and accessible laboratory instruments, etc.), and the required time for the recombinant protein expression by E. coli, are also comparable with the other expressing platforms (especially hybridoma technology and the mammalian expression systems). 3.7 Study on the efficiency of the purified scFv via S-ELISA To provide some evidence about the correct folding and functionality of the purified scFv, the S-ELISA assay was performed as described before. The specific binding capacity and high affinity of the crude and purified scFv were confirmed by the ELISA assay for both blocking agents (i.e., skimmed milk and tween 20). However, as it could be observed, the results showed a slightly better reaction with skimmed milk than with the tween 20 blocking agent (Fig. 9 ). On the other hand, both negative controls did not show any positive reaction. These results indicated the good sensitivity of synthetic recombinant scFv in the detection of SARS-CoV-2 whole particles.Fig. 9 Graphical representation of ELISA results. Reactivity of recombinant scFv in various forms to COVID positive serums in presence of two blocking agents: (A) Tween 20 blocker, (B) Skimmed milk blocker. Fig. 9 However, the statistical analysis did not show a significant difference between the purified and crude protein formats (P > 0.05). 4 Discussion Based on the International Committee on Taxonomy of Viruses (ICTV), the SARS-CoV-2 belongs to the beta genus of coronaviruses. The coronaviruses belong to the Othocoronavirinae sub-family, Coronaviridae family, Coronavirinae sub-order, and Nidovirales order. This family infects the respiratory tract of humans, other mammalians, and birds, and thus they are not only important for public health but also may cause economical and veterinary issues [[30], [31], [32], [33]]. Like other coronaviruses, the genome of SARS-CoV-2 has at least 10 open-reading frames (ORFs) which encode 16 non-structural proteins (nsp) and accessory proteins (ORF3a, ORF6, ORF7a,b, ORF8, ORF10). Among these, some ORFs encode the four main structural proteins: The spike (S), The envelope (E), The nucleocapsid (N), and the membrane (M). These structural proteins are necessary for virion assembly and CoV infection [30,[34], [35], [36], [37], [38], [39]]. The spike glycoprotein is a key mediator for the infection of target cells by SARS-CoV-2. It is the main determinant of virus neutralization. This protein consists of S1 and S2 functional subunits which could be proteolytically cleaved. The RBD region of the spike is located within the S1 subunit. This domain interacts with the angiotensin-converting enzyme-2 (ACE-2) receptor located on the target host cells to enter them. It is a mediator of membrane fusion and receptor binding; therefore, it determines the pathogenicity and tropism to the host cells. This domain could be targeted by neutralizing antibodies (nAbs) for the prevention of virus particles entering [1,38,40]. With this regard, the development of diagnostics and therapeutics tools has rapidly grown with unprecedented race as the greatest combat against the SARS-CoV-2 outbreak. For example, there are 40 approved vaccines and 217 vaccine candidates by July 6, 2020 in the prophylactic field (http://covid19.trackvaccines.org). Previous studies reported many recombinant antibodies against the different proteins of SARS-CoV-2. Among this, much evidence showed the high efficiency of vaccines targeting the spike protein because the mutations in the spike gene highly impress the virus virulence and pathogenesis. On the other hand, the RBD could generate a similar immune response and protection in comparison to the whole spike. Accordingly, various prophylactic and therapeutic agents have been developed based on this domain. Among these, the nAbs have attracted the most attention, recently. This is due to the fact that nAbs address the high concerns about the possible effects of antibody-dependent enhancement (ADE) of disease or infection, which could be raised by the non-neutralizing antibodies [31,37,[40], [41], [42]]. Recently, advancements in recombinant antibody production based on bioinformatics studies, have attracted the most attention. Moreover, in contrast to several limitations of whole antibody production in E. coli (e.g., inability to glycosylation, lower production yields, proper folding problems, etc.), other fragments such as Fab and scFv produce faster and easier in bacterial expression platforms [3,13]. In this regard, we used a cross-reactive anti-RBD scFv which was produced based on the comprehensive in-silico and bioinformatics studies. This scFv was successfully expressed in both the soluble and insoluble forms and its efficiency against the SARS-CoV-2 particles was evaluated. Many factors affect the successful expression of recombinant proteins including a suitable host with a compatible expressing system, an expression vector, precise codon optimization, etc. Several traditional platforms have been used for the production of recombinant proteins (e.g., antibodies) such as hybridoma technology [33,43,44], mammalian expression systems [9,45,46], transgenic animal and plant cells [9,[47], [48], [49], [50]], insect cells [51,52], phage display technology [53,54]. These systems have their potential advantages and limitations. As a frequently used platform in the generation of monoclonal antibodies (mAbs), hybridoma cell cultures have various disadvantages including the high costs for recombinant protein production, time-consuming processes, etc. In this regard, many types of research have been coordinated for the utilization of cheap and efficient bacterial expression systems, recently. In the current study, we successfully expressed the recombinant scFv with a relatively high yield and efficiency in E. coli BL21 (DE3). Many types of research are in accordance with our study [3,13,14,[55], [56], [57]]. As it was noted previously, the evaluated cost for the production of 1 mg scFv/ml was 0.145$ in this study. In some studies, the relatively high production costs in different expression systems (i.e., yeast, and mammalian cells) have been reported in comparison to the E. coli expression platform. For example, Lebozec et al. (2018) conducted studies in fed-batch conditions using these three platforms for the production of a humanized Fab fragment ACT017. They reported the following costs for each system: 49,616$ for 7.0g/L protein production using E. coli, 54,989$ for 1.8g/L protein production using Pichia pastoris, and 144,534$ for 1.0g/L protein production using CHO cell line, during 42 h shrs., 108 h shrs., and 10 days, respectively [58]. These results are comparable with the present study in terms of production costs, cultivation time, and volumetric productivity. As another important factor in expression studies, the pET system family consists of advanced and powerful vectors that have been developed in the field of cloning and expression in E. coli. In this system, the protein-coding sequence is located downstream of a T7 promoter (a strong bacteriophage transcription signal) [59]. Moreover, the 6x-HisTag sequence is inserted at the N-terminal of the recombinant protein coding sequence for the following purification stages [15]. According to these advantages, these vectors have been used in the current study and various studies with different purposes including Rostami et al. (2013) [56], Rouhani Nejad et al. (2017) [60], Zhang et al. (2018) [13], Khobbakht et al., 2018 [15], Roghanian et al., 2019 [25], Alizadeh et al. (2019) [61]. As the important disease management measures, emerging detection tests have high importance. Moreover, it is important to develop accurate, rapid, and sensitive diagnostics for efficient control of any infectious agent. Accordingly, there are two approaches to the diagnosis of COVID-19 infections including clinical approaches and in-vitro diagnostic platforms (i.e., nucleic acid amplification tests (NAAT) and antibody/or antigen-based serological assays [e.g., ELISA]). While RT-PCR has been introduced as a gold standard for the diagnosis of SARS-CoV-2, NAAT has its own drawbacks as a detective tool for SARS-CoV-2 infection. Accordingly, it is so important to develop rapid, accurate, and sensitive immune-based diagnostics to combat emerging infectious agents [8,17,[62], [63], [64]]. In contrast to NAAT approaches in the field of detection and tracking the evolution of SARS-CoV-2, the immune and antibody-based ones could be used for epidemiological investigations [63]. Among these, ELISA could detect the whole virus/its sub-particles or detect antiviral antibodies in serum samples. As an antibody-based diagnostic tool, the sandwich ELISA (S-ELISA) attract high attention in recent years [17]. This platform recommends high specificity and sensitivity for the detection of antigens and reduces the rate of false-positive results [64]. In this regard, this technique has been widely used for the detection of various infections including Foot-and-Mouth Disease (FMD) [62], and SARS-CoV [65]. For its importance, the S-ELISA was designed in the present study and the results showed the good reactivity of the purified and unpurified recombinant scFv. Since it was confirmed elsewhere (data is under publication) that this recombinant immunoinformatics-based scFv is a nAb rather than a binding antibody (bAbs), these results could point out its importance. This is in contrast to the results by Chan et al. [17] which reported more efficiency of natural Fab libraries over the synthetic scFv for utilization as a capture antibody in the capture ELISA kit [17]. This technique has also been used to confirm the potency of recombinant antibodies such as Alibeiki et al. [23]. Besides the diagnostic value of recombinant/isolated antibodies, these products could also be used for therapeutic purposes. This ability was reported with the comparison of the efficiency of two different combination products in treatments of mild-to-moderate COVID-19 patients, i.e., casirivimab plus imdevimab (antibodies targeting non-overlapping epitopes of the RBD), and bamlaniviman plus etesevimab (two mAbs which react with overlapping epitopes in the spike RBD region) [66]. 5 Conclusion Herein, we reported the successful expression of a potent recombinant scFv which was designed by third-generation technology (in-silico methods). Moreover, the expression of recombinant scFv was studied using pET28a (as an expressing vector harboring the efficient T7 promoter) and E. coli BL21 (DE3) (as a heterologous host). The SDS-PAGE and western blotting analysis showed the presence and its successful expression in both soluble and insoluble fractions. According to the good expression and the purification yield on one hand, and the potent in-vitro reaction, on the other hand, it is possible that this scFv could introduce to the diagnostics and may be utilized as a potent nAb. Moreover, it could be utilized as a complementary tool for the RT-PCR detection approach which is being used as a gold standard. Author statement Fatemeh Yaghoobizadeh: The conceptualization, Methodology, Data curation, Formal analysis, Validation, Investigation, Resources, Writing-original draft/ Writing- review & editing, Project administration Mohammad Roayaei Ardakani: The conceptualization, Methodology, Data curation, Formal analysis, Validation, Resources, Supervision, Writing-original draft/ Writing- review & editing, Funding acquisition Mohammad Mehdi Ranjbar: The conceptualization, Data curation, Methodology, Formal analysis, Validation, Resources, Supervision, Writing-original draft/ Writing- review & editing Hamid Galehdari: The conceptualization, Data curation, Formal analysis, Resources Mohammad Khosravi: The conceptualization, Data curation, Formal analysis, Resources Funding This work was supported by the Center for International Scientific Studies & Collaboration (CISSC), 10.13039/501100008798 Ministry of Science Research and Technology, Iran . Declaration of competing interest The authors declare there are no competing interests. Data availability Data will be made available on request. Acknowledgments We thank Dr. Majid Esmaeilizad (Research and Development Department, Razi Vaccine and Serum Research Institute, Iran) for his valuable guidance. ==== Refs References 1 McCallum M. Bassai J. De Marco A. Chen A. Walls A.C. Di Iulio J. Tortorici M.A. Navarro M.J. Silacci-Fregni C. Saliba C. Sprouse K.R. Agostini M. Pinto D. Culap K. Bianchi S. Janoni S. Cameroni E. Bowen J.E. Tilles S.W. Pizzuto M.S. Guastalla S.B. Bona G. Pellanda A.F. Garzoni C. Voorhis W.C.V. Rosen L.E. Snell G. Telenti A. Virgin H.W. Piccoli L. Corti D. Veesler D. SARS-CoV-2 immune evasion by the B.1.427/B.1.429 variant of concern Science 373 6555 2021 648 654 10.1126/science.abi7994 34210893 2 Park Y.S. Thrombosis and severe acute respiratory syndrome coronavirus 2 vaccines: vaccine-induced immune thrombotic thrombocytopenia Clin. Exp. 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==== Front Nurse Educ Today Nurse Educ Today Nurse Education Today 0260-6917 1532-2793 The Author. Published by Elsevier Ltd. S0260-6917(22)00003-X 10.1016/j.nedt.2022.105267 105267 Research Article Academic self-efficacy, resilience and social support among first-year Israeli nursing students learning in online environments during COVID-19 pandemic Warshawski Sigalit ⁎ Nursing Department, School of Health Professions, Sackler Faculty of Medicine, Tel-Aviv University, Israel ⁎ Corresponding author at: The Stanley Steyer School of Health Professions, Sackler Faculty of Medicine, Tel Aviv University, 6997801, Israel. 14 1 2022 3 2022 14 1 2022 110 105267105267 25 9 2021 24 12 2021 8 1 2022 © 2022 The Author 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 Academic self-efficacy (ASE) has been found to be an important motivator for academic success among nursing students. The associations between ASE, resilience and social support have not been fully explored among nursing students, especially those in their first year who are learning online. Objectives To explore a) the associations between ASE, resilience and social support among first-year nursing students learning in an online learning environment; and b) students' views regarding the difficulties they have encountered and the available assistance. Design and methods A cross-sectional survey design on a sample of 222 undergraduate first-year Israeli nursing students. Questions were uploaded in the format of a commercial internet survey provider (Qualtrics.com) and distributed through the university's online learning platform. Results Positive correlations were found between ASE and resilience and social support. Significant differences were found in the research variables according to the students' gender, cultural group and their perceived difficulty in studies. Resilience, social support, perceived difficulty in studies and being a female explained 31% of the students' variance in ASE. Conclusions Nurse educators should develop and promote strategies to enhance students' resilience and perceived social support. These have the potential to significantly improve students' ASE also in online environments. In addition, faculty should promote the preparation of online learning environments in accordance with students' needs and proficiencies. Keywords Academic self-efficacy Resilience Social support Undergraduate nursing students Online learning ==== Body pmc1 Introduction Nursing students typically experience more academic challenges and stress than other students (Labrague et al., 2017; He et al., 2018). First year nursing students cope with the additional stress and adjustment difficulties of transitioning to higher education which may negatively impact their academic achievement (McDonald et al., 2018). Academic achievement has been found in previous studies to be associated to students' ASE; the latter is considered fundamental for academic performance (Hodges, 2008). Due to the global spread of COVID-19, the Israeli government implemented large-scale social restrictions, including closing schools and universities. This meant that from May 2020, university studies were fully transferred to an online format. This enabled the continuity of studies while maintaining the social distance restrictions. However, currently, there is little evidence to suggest that nursing students who were then challenged with new technological, educational and social difficulties may have had their ASE impaired (Rohmani and Andriani, 2021; Bdair, 2021). ASE and perceived social support have been identified in the literature as attributes to the development of resilience among nursing students, mostly in the clinical context (Stephens, 2013; Walsh et al., 2020). Yet, there is a paucity of studies exploring the associations between social support and resilience, and ASE among first-year nursing students. Understanding students' perceived ASE in their first year of studies while learning in an online environment is important to promote their academic success. 2 Background 2.1 Perceived ASE and online learning environments ASE is defined as students' self-perceived confidence in their ability to accomplish their planned educational goals (Bandura, 2001). It is grounded in Bandura's self-efficacy theory, which assumes that human achievements depend on the interactions between one's behaviors, beliefs, and environmental conditions. Previous studies have indicated that ASE significantly predicts academic performance among nursing students and functions as an internal motivator for dealing with academic challenges and the achievement of goals (McLaughlin et al., 2008; Silvestri, 2010). Students with high ASE tend to accept difficult and challenging tasks, they demonstrate greater levels of motivation and persevere in the face of difficulties, compared to students with low ASE who tend to be unconfident in their educational capabilities and have difficulty meeting their tasks (Bandura, 2001; McLaughlin et al., 2008; Satici and Can, 2016). Self-efficacy perceptions can change due to daily environmental, cognitive, or behavioral effects (Bandura, 2001). The sudden transition to an online environment may be an example of such a change. The findings of recent studies conducted among first-year university students indicate that students believe that the changes in their learning experiences following the COVID-19 pandemic will negatively impact their academic performance (Aguilera-Hermida, 2020; Talsma et al., 2021). Among nursing students, and specifically first-year students, there is a paucity of studies exploring this subject. The little evidence found demonstrates a negative association between ASE and online learning (Rohmani and Andriani, 2021; Ko and Han, 2021). 2.2 Perceived ASE and resilience Resilience is defined as “an interactive concept that refers to a relative resistance to environmental risk experiences or the overcoming of stress or adversity” (Rutter, 2006). Moreover, it is considered as a personal trait that can be developed or enhanced through life by using specific strategies (Stephens, 2013). Resilience has been widely explored within the caring professions and found to be a contributing factor in individuals' ability to adapt to stressful workplace environments, develop effective coping strategies and improve wellbeing (Cleary et al., 2018). It is therefore considered important for practicing nurses and nursing students and recommended as an integral part of nursing education programs (Walsh et al., 2020). Among nursing students, a higher sense of resilience was found to significantly influence academic success, perseverance and the dropout rate from studies (Van Hoek et al., 2019; Hwang and Shin, 2018). A recent literature review aimed at exploring the concept of resilience and the promotion of resilient practices among nursing students, emphasized the role of self-efficacy as one of the main characteristics of resilient behavior (Walsh et al., 2020). Indeed, high levels of resilience have been indicated as increasing the levels of self-efficacy (Cuartero and Tur, 2021). Accordingly, by promoting and strengthening students' sense of resilience, their perceived ASE may improve. 2.3 Perceived ASE and social support Perceived social support is defined as the individual's perception of others as an available source for effective help when needed (Bagci, 2018). These sources usually include significant relationships. For nursing students, social support can be provided by their families, friends, peers and faculty members, and is a central external factor in influencing their academic success and retention (Laack, 2013). Studies conducted recently indicate a statistically significant positive association between ASE and perceived social support. Consequently, promoting and strengthening social support can improve students' ASE (Park and Jeong, 2020; El-Sayed et al., 2021). Additionally, perceived faculty support has been found to be related to persistence in nursing studies and academic success (Shelton, 2012). Perceived social support (mainly from peers and faculty members) has also been found to contribute to the development of resiliency in nursing students (Sweeney, 2021; Caton, 2021). Based on the above, the aims of the current study were to i) explore the associations between ASE, resilience and social support among first-year nursing students learning in an online environment; and ii) explore their views regarding the difficulties they face and what could have helped them succeed in their studies. 3 Methods 3.1 Design and setting The current study utilized a descriptive, cross-sectional design. The online survey included close questions and two open-ended questions. This approach has enabled the discovery of additional and complementary explanations regarding students' perceived ASE and difficulties with online learning. 3.2 Sample All first-year undergraduate nursing students at a major Israeli university (222 students) were invited to participate in the study. Of these, 186 returned completed questionnaires (response rate of 83.7%). First-year students were selected since they first experienced online academic studies. All participating students answered an online survey that included close questions and tow open-ended questions. The required sample size was obtained by means of the WINPEPI COMPARE2 program. The sample size required for achieving a power of 0.90 and α of 0.05 was 175 participants. 3.3 Instrument A structured questionnaire written in Hebrew including five sections:A) The participants' sociodemographic data and educational profile. Sociodemographic data included age, gender, country of birth, religion, marital status and mother tongue. The educational profile consisted of four additional items: 1) rating their proficiency in English (writing + reading) on a 3-point scale: 1 = very good, 2 = medium, and 3 = low; 2) Past experience in distance learning (yes / no); 3) Effective Internet access during the semester (yes / no); and 4) Rating their perceived level of difficulty in studies to date (on a 10-point scale: 1 = very easy to 10 = very difficult). B) Personal resilience: Resilience was measured by the shortened version of the 10-item Resilience Scale (Campbell-Sills and Stein, 2007) and translated into Hebrew by Fridenzon (Fridenzon, 2011). Respondents were asked to relate to their feelings as students over the last month on a 5-point scale from 0 (not true at all) to 4 (true most of the time). An overall score was calculated according to the mean score of all items, where a higher mean indicated higher resilience. The Cronbach's alpha score in a previous study (Sigalit et al., 2017) was 0.88. The Cronbach's alpha score in the present study was 0.81. C) Social support: This section was developed and validated by the author in an earlier study (Warshawski et al., 2018). The questionnaire consists of five items describing five different possible sources of social support: immediate family members, extended family members, friends prior to nursing studies, present classmates, and social networks. Respondents were asked to rank to what extent they perceive receiving social support from each source, on a 5-point scale from 0 (never) to 5 (always). For example, “To what extent do you receive social support from present classmates?”. Cronbach's alpha in an earlier study (Zimmerman et al., 1992) was 0.74. The Cronbach's alpha score in the present study was 0.75. D) Academic self-efficacy: This section was based on the Hebrew adaptation of the Zimmerman self-efficacy scale for learning (Zimmerman et al., 1992). The Hebrew version was tested among Israeli college students (Ozeri-Roitberg and Harpez, 2013). The questionnaire includes 11 items describing the perceived ability of students to use self-regulation strategies in tasks that need to be tackled in order to succeed in their studies. For example, “How confident are you that you will be able to summarize and write down the important material in the lessons?”. Respondents were asked to grade their self-efficacy perception regarding the execution of each task on a 7-point Likert scale ranging from 1 = “not at all sure” to 7 = “very sure”. Scores ranged between 11 and 77. All scores were averaged. A higher score indicates a higher ASE. The Cronbach's alpha in previous studies (Ozeri-Roitberg and Harpez, 2013) was 0.88. The Cronbach's alpha in the present study was 0.87. E) Student's views: This section included two open-ended questions: “What are the main difficulties you have encountered during your studies this semester?” and “What do you think could have helped you succeed in your studies this semester?” 3.4 Procedure All first-year undergraduate nursing students were approached six weeks before the end of their first semester during December 2020–January 2021. The study was conducted using the format of a commercial internet survey provider (Qualtrics.com). The link to the online questionnaire appeared on a short explanatory page that explained the research purposes. The page was posted in the forum group of first-year students through the university's online learning platform. Participants were assured that the questionnaires were anonymous and that their confidentiality would be maintained. Consent was assumed by submission of the questionnaire. 3.5 Data analysis Data were analyzed using the SPSS-25 statistical package (SPSS Inc., Chicago, Ill., USA). Statistical significance was considered at p < .05. Means and frequencies were used as descriptive statistics for personal characteristics and for the main research variables. Pearson correlation coefficients were calculated to measure the associations between ASE, resilience, social support and personal and educational characteristics. A t-test analysis for the independent variables was used to analyze differences between groups of research variables. Stepwise linear regressions were performed to measure the relationship between socio-demographic and educational characteristics, ASE, resilience and social support. Constant comparative analysis was applied to the two open-ended questions, in which recurring data is identified, marked, and coded. Similar codes are categorized using constant comparisons (Glaser and Strauss, 1967). First, the author read all the answers, compared the data through open coding, identified recurring content, and developed initial categories. Validation of the findings was carried out using the peer debriefing procedure (Lincoln and Guba, 1985). This procedure, which also ensured trustworthiness and credibility, was carried out by presenting the findings and the literature explaining the findings, to two nurse educators familiar with the subject. Findings were discussed and questioned, until agreement regarding analysis was reached in a group discussion (Lincoln and Guba, 1985). Representative quotations were identified to highlight the categories. 3.6 Ethical considerations The study received the approval of the university's ethics committee. 4 Results The sample consisted of 186 first-year nursing students, with a mean age of 23.6 ± 4.52 years. Most were women (82.8%) and Israeli born (92.5%). The majority (76.3%) were Israeli-Jewish while the remainder identified as Muslim or other religions. About two thirds of respondents (65.1%) reported their mother tongue as Hebrew, most (68.3%) perceived themselves as proficient in English, and (69.4%) had no prior experiences of distance learning. Regarding students' perceived difficulty in studies, this sample rated the studies relatively difficult (M = 6.36, SD = 1.72, range-1-10). Table 1 presents the socio-demographics and educational profile of the sample.Table 1 Students' socio-demographics and educational profile (N = 186). Table 1Variable M (SD) Age (years) 23.6 (4.52) N (%) Gender Male 32 (17.2) Female 154 (82.8) Place of Birth Israel 172 (92.5) Not Israel 14 (7.5) Religion Jewish 142 (76.3) Muslim 39 (21.0) Other 5 (2.7) Relationship status In a relationship 73 (39.2) Not in a relationship 113 (60.8) Mother tongue Hebrew 121 (65.1) Arabic 42 (22.6) Other 23 (12.3) Proficiency in English Very good 127 (68.3) Moderate 55 (29.6) Low 4 (2.2) Distance learning in the past Yes 57 (30.6) No 129 (69.4) Effective Internet access Yes 167 (89.8) No 19 (10.2) The associations between ASE, resilience, social support and perceived difficulty in studies. Table 2 shows significant moderate positive correlations between ASE and resilience (r = 0.44, P < .01), ASE and social support (r = 0.36, P < .01), and resilience and social support (r = 0.31, P < .01). The more students felt resilient and that they received social support from family and friends, the higher they reported their ASE. Furthermore, students' feelings of resilience increased if they felt they were receiving social support from family and friends.Table 2 Pearson's correlation of study variables (N = 186). Table 2 Variable M (SD) Range 1 2 3 4 1 Academic self-efficacy 5.0 (0.98) 1–7 1 2 Resilience 3.71 (0.62) 0–4 0.44⁎⁎ 1 3 Social support 3.81 (1.06) 0–5 0.36⁎⁎ 0.31⁎⁎ 1 4 Perceived difficulty in studies 6.36 (1.72) 1–10 −0.35⁎⁎ −0.27⁎⁎ −0.17⁎ 1 ⁎ p < .05. ⁎⁎ p < .01. Negative significant correlations were found between ASE and students' perceived difficulty in studies (r = −0.35, P < .01) and resilience to students' perceived difficulty in studies (r = −0.27, P < .01). Meaning, students who had low ASE and were less resilient perceived their studies as more difficult. Differences in the main research variables according to gender, cultural group and past experience with distance learning. As shown in Table 3 , significant differences were found in the research variables according to gender, cultural group and past experience with online learning. Women reported a higher ASE than men (t = −2.9, p < .01), Israeli-Jewish students reported a higher ASE than Israeli non-Jewish students (t = 2.54, p < .05), and students that had prior experience with online learning reported a higher ASE than those who had no experience at all (t = 2.08, p < .05). Regarding resilience and social support, Israeli-Jewish students reported a higher sense of resilience than Israeli non-Jewish students (t = 3.43, p < .01) and women felt more socially supported by family and friends than men (t = −2.06, p < .01).Table 3 Independent t-tests for the differences between study variables according to gender, cultural group and past experience with distance learning. Table 3Variable Gender t Cultural group t Past distance learning t Male Female Jewish Non-Jewish Yes No Academic efficacy 4.55 ± 1.13 5.09 ± 0.93 −2.9⁎⁎ 5.12 ± 0.88 4.62 ± 1.19 2.54⁎ 5.22 ± 1.11 4.90 ± 1.01 2.08⁎ Resilience 3.80 ± 0.55 3.39 ± 0.73 3.43⁎⁎ Social support 3.46 ± 1.04 3.89 ± 1.05 −2.06⁎ Data are shown as mean ± standard deviation. ⁎ p < .05. ⁎⁎ p < .01. The relationship between the main research variables and students' socio-demographic and educational characteristics. A stepwise multiple linear regression was conducted with ASE as the dependent variable. The independent variables entered were gender, age, cultural group, resilience, social support and perceived difficulty in studies. The results showed that resilience, social support, perceived difficulty in studies and gender were all related to ASE (R2 = 0.31 and adjusted R = 0.30). Accordingly, higher resilience, greater social support and being a female were related to higher ASE, whereas an increased perception of difficulties in studies was related to lower ASE. The results are presented in Table 4 .Table 4 Stepwise linear regression for students' ASE. Table 4Variable B SE β t P Resilience 0.49 0.10 0.31 4.70 0.0001 Social support 0.18 0.06 0.20 3.07 0.002 Perceived difficulty in studies −0.12 0.03 −0.21 −3.36 0.001 Gendera 0.34 0.16 0.13 2.10 0.03 R2 = 0.31; Adjusted R = 0.30. a Male = 1, Female = 2. 4.1 Qualitative results As part of the survey, students were asked regarding their views on online learning and available assistances. Students' answers identified two main themes: “Difficulties encountered during the semester” and “What could have helped me in my studies”. “Difficulties encountered during the semester” This theme included four codes describing issues students found to be difficult for them during their first semester in nursing studies: Studies overload: Students described feeling that there was too much material to learn, and not enough time and high pressure to accomplish their assignments: “I study for very long hours…trying to fill the gaps, yet...there is a lot of study material, a lot of assignments and too little time and it just grows with time, it's difficult...I cannot get to everything”. (Student 10). Difficulties in online learning: Students stated they encounter various difficulties while studying in an online environment. These included unreliable internet, difficulties concentrating and understanding learning materials through the Zoom platform and organizing their study materials and schedules. A student wrote: “I had a hard time learning through Zoom, I wasn't used to it, it's different…I couldn't concentrate and understand…its tiring” (Student 11). Another student added: “I couldn't order all the recorded lectures and organize the learning materials so I could learn from them…it's my first semester, I'm not used to it…beyond that, I constantly had disconnections from the internet”. (Student 32). Lack of academic support: Students described an expectation that the academic staff would initiate a personal approach to students in order to examine their academic status and the need for assistance, not just when the student requests: “It feels like the personal touch of one of the faculty is missing…someone with experience that would guide us through our studies…regular conversations or meetings with one of the faculty members…to check out what is happening with our progress...not everyone feels comfortable or knows who to turn to… and it's harder when you're new and not on campus…” (Student 34). Lack of social interactions between students: The need for social interaction arose from most students' responses. Interaction was needed for shared studying but also for social support, stress reduction and emotional support: “Meetings with other students are very much lacking. All day at home you study alone…I feel the lack of friendships, studying together, supporting each other” (Student 12). Another student added: “Friends help you adapt, especially in the beginning, you see more people like you going through the same thing...there is the opportunity to help each other” (Student 30). “What could have helped me in my studies”. This theme included three codes describing students' recommendations for their preferred assistance. “Faculty academic support”: As described above, students felt there was inadequate academic support. A recurring proposal included regular meetings (preferably frontal) with an academic advisor, accessibility of lecturers, and additional individual lessons to reinforce the ones given as part of the program: “I think individual lessons or maybe in small groups with faculty could help us; learning online is difficult” (Student 5). “Promoting social interactions between students”: Students recommended the academic staff initiate the development of social interactions between students. For example, by providing a list of contacts to the students, encourage joint work classes and organize meetings through Zoom: “We need to get to know the students even in the class Zoom sessions...We are new and unfamiliar...This is how new friendships are formed; it will help us to acclimatize...consult, help each other, at least in the beginning” (Student 7). “Financial aid”: Financial difficulties also arose from most of the students. Students recommended providing additional information on what financial assistance exists including scholarships and help with finding suitable jobs to integrate with students' studies: “It would be very helpful if the university could help us financially, provide more scholarships, and maybe help us find suitable jobs during this period” (Student 16). 5 Discussion The majority of students in the current study reported perceiving their studies as difficult and had no prior experience of online learning. These findings illustrate the challenges students in this sample faced. Similar findings were found among first-year nursing students, but not in the context of online learning (McDonald et al., 2018). A complementary explanation is found in students' answers to the open-ended questions: students clearly stated feeling that they had an overload of study material and difficulties organizing and managing their studies through the online environment. This is probably due to their lack of prior experience with this educational method or maybe as a result of a lack of academic support. Additionally, students reported a lack of social interaction with other students which may have impaired their ability to receive help from their classmates and further increased their sense of finding their studies difficult. Positive moderate correlations were found between ASE and resilience, ASE and social support, and between resilience and social support. These findings are consistent with earlier findings among nursing students (Stephens, 2013; Walsh et al., 2020; Van Hoek et al., 2019; Hwang and Shin, 2018; Park and Jeong, 2020; El-Sayed et al., 2021; Sweeney, 2021; Caton, 2021). and emphasize the importance of these factors in both traditional and online learning. Negative correlations were found between ASE and perceived difficulty in studies and between resilience to students' perceived difficulty in studies. As mentioned earlier, students with low ASE tend to be unconfident of their educational capabilities and have difficulty meeting their tasks (Bandura, 2001; McLaughlin et al., 2008; Satici and Can, 2016). Accordingly, these respondents will likely perceive their studies as more difficult. Resilient students tend to be more able to resist environmental risk experiences or adversity (Rutter, 2006). Students experiencing less resilient behavior will therefore likely have more difficulty coping with challenges and consequently perceive their studies as more difficult. Women in the current study reported higher ASE and experienced more social support than men. These findings are consistent with previous reports showing that female students score consistently higher ASE (Sachitra and Bandara, 2017) and perceived more social support than men (Cuartero and Tur, 2021). Jewish students reported higher ASE compared to non-Jewish students. A possible explanation may be the technological and educational gaps recently reported among the general population of Israeli students during the transition to online learning. Israeli non-Jewish students reported facing more Internet infrastructure, difficulties with digital capabilities, language gaps (the mother tongue is not Hebrew) and fears that they would have to drop out of studies (Tehawkho et al., n.d.). That may also be the case in the present study sample, leading to decreased ASE. Moreover, Jewish students in the current study reported a higher sense of resilience than non-Jewish students. This finding is consistent with an earlier study among Israeli students (Kimhi et al., 2017). but may be also connected to the sample's composition with the majority being Israeli-Jewish students. Students that had prior experience with online learning reported a higher ASE than those who had no experience at all. This finding is consistent with previous studies (Peechapol et al., 2018). Furthermore, Bandura's source of efficacy (Bandura, 2001) states that prior experience is one of the factors influencing an individual's perceived self-efficacy. The current study found resilience, social support, perceived difficulty in studies and gender (female) to explain 31% of the ASE variance among first-year nursing students. These findings contribute specifically to the importance of resilience and social support to ASE among nursing students in online environments. To date, these associations have been poorly explored and these new findings now provide evidence for increasing students' ASE through strengthening resilience and increasing social support. Possible solutions may be found in student's recommendations for their preferred assistance. These add and complement the quantitative findings. Interestingly, students asked for regular frontal meetings with an academic advisor, additional individual lessons to reinforce the ones given and social interactions to improve their learning. These may represent an interpretation of the recommendations suggested by Walsh et al., (Walsh et al., 2020) in their integrated review. The latter indicated on: peer activities, reflective practice, directed study, problem based learning and experiential learning as strategies strengthening resilience. Most of these strategies can serve students' requests, strengthen their resilience and social support and lead to improved ASE in online environments. This study has two limitations. It employed a convenience sampling drawn from one university, based on self-reports. This might limit the generalizability of the findings to the entire population of first-year nursing students in Israel. In future, this study could be improved by drawing participants from several universities in Israel. The second is related to the questionnaire which included only two open-ended questions. Adding more open questions and interviews with students and educators would have provided more information and a deeper understanding of students' perceptions and understanding of the situation. 6 Conclusions Online learning provides convenience and availability for the user. Nevertheless, for nursing students in their first year, it appears that this learning environment has created a perceived high workload, difficulty in understanding and organizing learning materials, as well as a loss of learning interactions with classmates and faculty. All of these together contributed to the students' general perception of their studies being difficult and in turn, likely decreasing their ASE. It is recommended to strengthen and develop regular social interactions between students and faculty members face-to-face and when needed, by online means. Social support could also enhance resilience as well as the use of learning through peer activities initiated and developed by faculty and nurse educators. Resilience and social support, as found in the current study, contribute to ASE. Variance in students' ASE, resilience and social support according to personal characteristics highlight the need of faculty to consistently assess students' needs and identify at an early stage struggling students, especially during times when distance learning is required. Finally, effective online learning requires that students are given sufficient preparation to get the most out of this new approach to learning. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. CRediT authorship contribution statement Sigalit Warshawsk: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Supervision, Project administration. Declaration of competing interest The authors report no conflict of interest. Appendix A Supplementary data Supplementary material Image 1 Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.nedt.2022.105267. ==== Refs References Aguilera-Hermida P. College students’ use and acceptance of emergency online learning due to COVID-19 International Journal of Educational Research Open. 1 2020 100011 10.1016/j.ijedro.2020.100011 Bagci S.C. Does everyone benefit equally from self-efficacy beliefs? The moderating role of perceived social support on motivation J. Early Adolesc. 38 2 2018 204 219 10.1177/0272431616665213 Bandura A. Social cognitive theory: an agentic perspective Annu. Rev. 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==== Front Nurse Educ Today Nurse Educ Today Nurse Education Today 0260-6917 1532-2793 The Author. Published by Elsevier Ltd. S0260-6917(22)00003-X 10.1016/j.nedt.2022.105267 105267 Research Article Academic self-efficacy, resilience and social support among first-year Israeli nursing students learning in online environments during COVID-19 pandemic Warshawski Sigalit ⁎ Nursing Department, School of Health Professions, Sackler Faculty of Medicine, Tel-Aviv University, Israel ⁎ Corresponding author at: The Stanley Steyer School of Health Professions, Sackler Faculty of Medicine, Tel Aviv University, 6997801, Israel. 14 1 2022 3 2022 14 1 2022 110 105267105267 25 9 2021 24 12 2021 8 1 2022 © 2022 The Author 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 Academic self-efficacy (ASE) has been found to be an important motivator for academic success among nursing students. The associations between ASE, resilience and social support have not been fully explored among nursing students, especially those in their first year who are learning online. Objectives To explore a) the associations between ASE, resilience and social support among first-year nursing students learning in an online learning environment; and b) students' views regarding the difficulties they have encountered and the available assistance. Design and methods A cross-sectional survey design on a sample of 222 undergraduate first-year Israeli nursing students. Questions were uploaded in the format of a commercial internet survey provider (Qualtrics.com) and distributed through the university's online learning platform. Results Positive correlations were found between ASE and resilience and social support. Significant differences were found in the research variables according to the students' gender, cultural group and their perceived difficulty in studies. Resilience, social support, perceived difficulty in studies and being a female explained 31% of the students' variance in ASE. Conclusions Nurse educators should develop and promote strategies to enhance students' resilience and perceived social support. These have the potential to significantly improve students' ASE also in online environments. In addition, faculty should promote the preparation of online learning environments in accordance with students' needs and proficiencies. Keywords Academic self-efficacy Resilience Social support Undergraduate nursing students Online learning ==== Body pmc1 Introduction Nursing students typically experience more academic challenges and stress than other students (Labrague et al., 2017; He et al., 2018). First year nursing students cope with the additional stress and adjustment difficulties of transitioning to higher education which may negatively impact their academic achievement (McDonald et al., 2018). Academic achievement has been found in previous studies to be associated to students' ASE; the latter is considered fundamental for academic performance (Hodges, 2008). Due to the global spread of COVID-19, the Israeli government implemented large-scale social restrictions, including closing schools and universities. This meant that from May 2020, university studies were fully transferred to an online format. This enabled the continuity of studies while maintaining the social distance restrictions. However, currently, there is little evidence to suggest that nursing students who were then challenged with new technological, educational and social difficulties may have had their ASE impaired (Rohmani and Andriani, 2021; Bdair, 2021). ASE and perceived social support have been identified in the literature as attributes to the development of resilience among nursing students, mostly in the clinical context (Stephens, 2013; Walsh et al., 2020). Yet, there is a paucity of studies exploring the associations between social support and resilience, and ASE among first-year nursing students. Understanding students' perceived ASE in their first year of studies while learning in an online environment is important to promote their academic success. 2 Background 2.1 Perceived ASE and online learning environments ASE is defined as students' self-perceived confidence in their ability to accomplish their planned educational goals (Bandura, 2001). It is grounded in Bandura's self-efficacy theory, which assumes that human achievements depend on the interactions between one's behaviors, beliefs, and environmental conditions. Previous studies have indicated that ASE significantly predicts academic performance among nursing students and functions as an internal motivator for dealing with academic challenges and the achievement of goals (McLaughlin et al., 2008; Silvestri, 2010). Students with high ASE tend to accept difficult and challenging tasks, they demonstrate greater levels of motivation and persevere in the face of difficulties, compared to students with low ASE who tend to be unconfident in their educational capabilities and have difficulty meeting their tasks (Bandura, 2001; McLaughlin et al., 2008; Satici and Can, 2016). Self-efficacy perceptions can change due to daily environmental, cognitive, or behavioral effects (Bandura, 2001). The sudden transition to an online environment may be an example of such a change. The findings of recent studies conducted among first-year university students indicate that students believe that the changes in their learning experiences following the COVID-19 pandemic will negatively impact their academic performance (Aguilera-Hermida, 2020; Talsma et al., 2021). Among nursing students, and specifically first-year students, there is a paucity of studies exploring this subject. The little evidence found demonstrates a negative association between ASE and online learning (Rohmani and Andriani, 2021; Ko and Han, 2021). 2.2 Perceived ASE and resilience Resilience is defined as “an interactive concept that refers to a relative resistance to environmental risk experiences or the overcoming of stress or adversity” (Rutter, 2006). Moreover, it is considered as a personal trait that can be developed or enhanced through life by using specific strategies (Stephens, 2013). Resilience has been widely explored within the caring professions and found to be a contributing factor in individuals' ability to adapt to stressful workplace environments, develop effective coping strategies and improve wellbeing (Cleary et al., 2018). It is therefore considered important for practicing nurses and nursing students and recommended as an integral part of nursing education programs (Walsh et al., 2020). Among nursing students, a higher sense of resilience was found to significantly influence academic success, perseverance and the dropout rate from studies (Van Hoek et al., 2019; Hwang and Shin, 2018). A recent literature review aimed at exploring the concept of resilience and the promotion of resilient practices among nursing students, emphasized the role of self-efficacy as one of the main characteristics of resilient behavior (Walsh et al., 2020). Indeed, high levels of resilience have been indicated as increasing the levels of self-efficacy (Cuartero and Tur, 2021). Accordingly, by promoting and strengthening students' sense of resilience, their perceived ASE may improve. 2.3 Perceived ASE and social support Perceived social support is defined as the individual's perception of others as an available source for effective help when needed (Bagci, 2018). These sources usually include significant relationships. For nursing students, social support can be provided by their families, friends, peers and faculty members, and is a central external factor in influencing their academic success and retention (Laack, 2013). Studies conducted recently indicate a statistically significant positive association between ASE and perceived social support. Consequently, promoting and strengthening social support can improve students' ASE (Park and Jeong, 2020; El-Sayed et al., 2021). Additionally, perceived faculty support has been found to be related to persistence in nursing studies and academic success (Shelton, 2012). Perceived social support (mainly from peers and faculty members) has also been found to contribute to the development of resiliency in nursing students (Sweeney, 2021; Caton, 2021). Based on the above, the aims of the current study were to i) explore the associations between ASE, resilience and social support among first-year nursing students learning in an online environment; and ii) explore their views regarding the difficulties they face and what could have helped them succeed in their studies. 3 Methods 3.1 Design and setting The current study utilized a descriptive, cross-sectional design. The online survey included close questions and two open-ended questions. This approach has enabled the discovery of additional and complementary explanations regarding students' perceived ASE and difficulties with online learning. 3.2 Sample All first-year undergraduate nursing students at a major Israeli university (222 students) were invited to participate in the study. Of these, 186 returned completed questionnaires (response rate of 83.7%). First-year students were selected since they first experienced online academic studies. All participating students answered an online survey that included close questions and tow open-ended questions. The required sample size was obtained by means of the WINPEPI COMPARE2 program. The sample size required for achieving a power of 0.90 and α of 0.05 was 175 participants. 3.3 Instrument A structured questionnaire written in Hebrew including five sections:A) The participants' sociodemographic data and educational profile. Sociodemographic data included age, gender, country of birth, religion, marital status and mother tongue. The educational profile consisted of four additional items: 1) rating their proficiency in English (writing + reading) on a 3-point scale: 1 = very good, 2 = medium, and 3 = low; 2) Past experience in distance learning (yes / no); 3) Effective Internet access during the semester (yes / no); and 4) Rating their perceived level of difficulty in studies to date (on a 10-point scale: 1 = very easy to 10 = very difficult). B) Personal resilience: Resilience was measured by the shortened version of the 10-item Resilience Scale (Campbell-Sills and Stein, 2007) and translated into Hebrew by Fridenzon (Fridenzon, 2011). Respondents were asked to relate to their feelings as students over the last month on a 5-point scale from 0 (not true at all) to 4 (true most of the time). An overall score was calculated according to the mean score of all items, where a higher mean indicated higher resilience. The Cronbach's alpha score in a previous study (Sigalit et al., 2017) was 0.88. The Cronbach's alpha score in the present study was 0.81. C) Social support: This section was developed and validated by the author in an earlier study (Warshawski et al., 2018). The questionnaire consists of five items describing five different possible sources of social support: immediate family members, extended family members, friends prior to nursing studies, present classmates, and social networks. Respondents were asked to rank to what extent they perceive receiving social support from each source, on a 5-point scale from 0 (never) to 5 (always). For example, “To what extent do you receive social support from present classmates?”. Cronbach's alpha in an earlier study (Zimmerman et al., 1992) was 0.74. The Cronbach's alpha score in the present study was 0.75. D) Academic self-efficacy: This section was based on the Hebrew adaptation of the Zimmerman self-efficacy scale for learning (Zimmerman et al., 1992). The Hebrew version was tested among Israeli college students (Ozeri-Roitberg and Harpez, 2013). The questionnaire includes 11 items describing the perceived ability of students to use self-regulation strategies in tasks that need to be tackled in order to succeed in their studies. For example, “How confident are you that you will be able to summarize and write down the important material in the lessons?”. Respondents were asked to grade their self-efficacy perception regarding the execution of each task on a 7-point Likert scale ranging from 1 = “not at all sure” to 7 = “very sure”. Scores ranged between 11 and 77. All scores were averaged. A higher score indicates a higher ASE. The Cronbach's alpha in previous studies (Ozeri-Roitberg and Harpez, 2013) was 0.88. The Cronbach's alpha in the present study was 0.87. E) Student's views: This section included two open-ended questions: “What are the main difficulties you have encountered during your studies this semester?” and “What do you think could have helped you succeed in your studies this semester?” 3.4 Procedure All first-year undergraduate nursing students were approached six weeks before the end of their first semester during December 2020–January 2021. The study was conducted using the format of a commercial internet survey provider (Qualtrics.com). The link to the online questionnaire appeared on a short explanatory page that explained the research purposes. The page was posted in the forum group of first-year students through the university's online learning platform. Participants were assured that the questionnaires were anonymous and that their confidentiality would be maintained. Consent was assumed by submission of the questionnaire. 3.5 Data analysis Data were analyzed using the SPSS-25 statistical package (SPSS Inc., Chicago, Ill., USA). Statistical significance was considered at p < .05. Means and frequencies were used as descriptive statistics for personal characteristics and for the main research variables. Pearson correlation coefficients were calculated to measure the associations between ASE, resilience, social support and personal and educational characteristics. A t-test analysis for the independent variables was used to analyze differences between groups of research variables. Stepwise linear regressions were performed to measure the relationship between socio-demographic and educational characteristics, ASE, resilience and social support. Constant comparative analysis was applied to the two open-ended questions, in which recurring data is identified, marked, and coded. Similar codes are categorized using constant comparisons (Glaser and Strauss, 1967). First, the author read all the answers, compared the data through open coding, identified recurring content, and developed initial categories. Validation of the findings was carried out using the peer debriefing procedure (Lincoln and Guba, 1985). This procedure, which also ensured trustworthiness and credibility, was carried out by presenting the findings and the literature explaining the findings, to two nurse educators familiar with the subject. Findings were discussed and questioned, until agreement regarding analysis was reached in a group discussion (Lincoln and Guba, 1985). Representative quotations were identified to highlight the categories. 3.6 Ethical considerations The study received the approval of the university's ethics committee. 4 Results The sample consisted of 186 first-year nursing students, with a mean age of 23.6 ± 4.52 years. Most were women (82.8%) and Israeli born (92.5%). The majority (76.3%) were Israeli-Jewish while the remainder identified as Muslim or other religions. About two thirds of respondents (65.1%) reported their mother tongue as Hebrew, most (68.3%) perceived themselves as proficient in English, and (69.4%) had no prior experiences of distance learning. Regarding students' perceived difficulty in studies, this sample rated the studies relatively difficult (M = 6.36, SD = 1.72, range-1-10). Table 1 presents the socio-demographics and educational profile of the sample.Table 1 Students' socio-demographics and educational profile (N = 186). Table 1Variable M (SD) Age (years) 23.6 (4.52) N (%) Gender Male 32 (17.2) Female 154 (82.8) Place of Birth Israel 172 (92.5) Not Israel 14 (7.5) Religion Jewish 142 (76.3) Muslim 39 (21.0) Other 5 (2.7) Relationship status In a relationship 73 (39.2) Not in a relationship 113 (60.8) Mother tongue Hebrew 121 (65.1) Arabic 42 (22.6) Other 23 (12.3) Proficiency in English Very good 127 (68.3) Moderate 55 (29.6) Low 4 (2.2) Distance learning in the past Yes 57 (30.6) No 129 (69.4) Effective Internet access Yes 167 (89.8) No 19 (10.2) The associations between ASE, resilience, social support and perceived difficulty in studies. Table 2 shows significant moderate positive correlations between ASE and resilience (r = 0.44, P < .01), ASE and social support (r = 0.36, P < .01), and resilience and social support (r = 0.31, P < .01). The more students felt resilient and that they received social support from family and friends, the higher they reported their ASE. Furthermore, students' feelings of resilience increased if they felt they were receiving social support from family and friends.Table 2 Pearson's correlation of study variables (N = 186). Table 2 Variable M (SD) Range 1 2 3 4 1 Academic self-efficacy 5.0 (0.98) 1–7 1 2 Resilience 3.71 (0.62) 0–4 0.44⁎⁎ 1 3 Social support 3.81 (1.06) 0–5 0.36⁎⁎ 0.31⁎⁎ 1 4 Perceived difficulty in studies 6.36 (1.72) 1–10 −0.35⁎⁎ −0.27⁎⁎ −0.17⁎ 1 ⁎ p < .05. ⁎⁎ p < .01. Negative significant correlations were found between ASE and students' perceived difficulty in studies (r = −0.35, P < .01) and resilience to students' perceived difficulty in studies (r = −0.27, P < .01). Meaning, students who had low ASE and were less resilient perceived their studies as more difficult. Differences in the main research variables according to gender, cultural group and past experience with distance learning. As shown in Table 3 , significant differences were found in the research variables according to gender, cultural group and past experience with online learning. Women reported a higher ASE than men (t = −2.9, p < .01), Israeli-Jewish students reported a higher ASE than Israeli non-Jewish students (t = 2.54, p < .05), and students that had prior experience with online learning reported a higher ASE than those who had no experience at all (t = 2.08, p < .05). Regarding resilience and social support, Israeli-Jewish students reported a higher sense of resilience than Israeli non-Jewish students (t = 3.43, p < .01) and women felt more socially supported by family and friends than men (t = −2.06, p < .01).Table 3 Independent t-tests for the differences between study variables according to gender, cultural group and past experience with distance learning. Table 3Variable Gender t Cultural group t Past distance learning t Male Female Jewish Non-Jewish Yes No Academic efficacy 4.55 ± 1.13 5.09 ± 0.93 −2.9⁎⁎ 5.12 ± 0.88 4.62 ± 1.19 2.54⁎ 5.22 ± 1.11 4.90 ± 1.01 2.08⁎ Resilience 3.80 ± 0.55 3.39 ± 0.73 3.43⁎⁎ Social support 3.46 ± 1.04 3.89 ± 1.05 −2.06⁎ Data are shown as mean ± standard deviation. ⁎ p < .05. ⁎⁎ p < .01. The relationship between the main research variables and students' socio-demographic and educational characteristics. A stepwise multiple linear regression was conducted with ASE as the dependent variable. The independent variables entered were gender, age, cultural group, resilience, social support and perceived difficulty in studies. The results showed that resilience, social support, perceived difficulty in studies and gender were all related to ASE (R2 = 0.31 and adjusted R = 0.30). Accordingly, higher resilience, greater social support and being a female were related to higher ASE, whereas an increased perception of difficulties in studies was related to lower ASE. The results are presented in Table 4 .Table 4 Stepwise linear regression for students' ASE. Table 4Variable B SE β t P Resilience 0.49 0.10 0.31 4.70 0.0001 Social support 0.18 0.06 0.20 3.07 0.002 Perceived difficulty in studies −0.12 0.03 −0.21 −3.36 0.001 Gendera 0.34 0.16 0.13 2.10 0.03 R2 = 0.31; Adjusted R = 0.30. a Male = 1, Female = 2. 4.1 Qualitative results As part of the survey, students were asked regarding their views on online learning and available assistances. Students' answers identified two main themes: “Difficulties encountered during the semester” and “What could have helped me in my studies”. “Difficulties encountered during the semester” This theme included four codes describing issues students found to be difficult for them during their first semester in nursing studies: Studies overload: Students described feeling that there was too much material to learn, and not enough time and high pressure to accomplish their assignments: “I study for very long hours…trying to fill the gaps, yet...there is a lot of study material, a lot of assignments and too little time and it just grows with time, it's difficult...I cannot get to everything”. (Student 10). Difficulties in online learning: Students stated they encounter various difficulties while studying in an online environment. These included unreliable internet, difficulties concentrating and understanding learning materials through the Zoom platform and organizing their study materials and schedules. A student wrote: “I had a hard time learning through Zoom, I wasn't used to it, it's different…I couldn't concentrate and understand…its tiring” (Student 11). Another student added: “I couldn't order all the recorded lectures and organize the learning materials so I could learn from them…it's my first semester, I'm not used to it…beyond that, I constantly had disconnections from the internet”. (Student 32). Lack of academic support: Students described an expectation that the academic staff would initiate a personal approach to students in order to examine their academic status and the need for assistance, not just when the student requests: “It feels like the personal touch of one of the faculty is missing…someone with experience that would guide us through our studies…regular conversations or meetings with one of the faculty members…to check out what is happening with our progress...not everyone feels comfortable or knows who to turn to… and it's harder when you're new and not on campus…” (Student 34). Lack of social interactions between students: The need for social interaction arose from most students' responses. Interaction was needed for shared studying but also for social support, stress reduction and emotional support: “Meetings with other students are very much lacking. All day at home you study alone…I feel the lack of friendships, studying together, supporting each other” (Student 12). Another student added: “Friends help you adapt, especially in the beginning, you see more people like you going through the same thing...there is the opportunity to help each other” (Student 30). “What could have helped me in my studies”. This theme included three codes describing students' recommendations for their preferred assistance. “Faculty academic support”: As described above, students felt there was inadequate academic support. A recurring proposal included regular meetings (preferably frontal) with an academic advisor, accessibility of lecturers, and additional individual lessons to reinforce the ones given as part of the program: “I think individual lessons or maybe in small groups with faculty could help us; learning online is difficult” (Student 5). “Promoting social interactions between students”: Students recommended the academic staff initiate the development of social interactions between students. For example, by providing a list of contacts to the students, encourage joint work classes and organize meetings through Zoom: “We need to get to know the students even in the class Zoom sessions...We are new and unfamiliar...This is how new friendships are formed; it will help us to acclimatize...consult, help each other, at least in the beginning” (Student 7). “Financial aid”: Financial difficulties also arose from most of the students. Students recommended providing additional information on what financial assistance exists including scholarships and help with finding suitable jobs to integrate with students' studies: “It would be very helpful if the university could help us financially, provide more scholarships, and maybe help us find suitable jobs during this period” (Student 16). 5 Discussion The majority of students in the current study reported perceiving their studies as difficult and had no prior experience of online learning. These findings illustrate the challenges students in this sample faced. Similar findings were found among first-year nursing students, but not in the context of online learning (McDonald et al., 2018). A complementary explanation is found in students' answers to the open-ended questions: students clearly stated feeling that they had an overload of study material and difficulties organizing and managing their studies through the online environment. This is probably due to their lack of prior experience with this educational method or maybe as a result of a lack of academic support. Additionally, students reported a lack of social interaction with other students which may have impaired their ability to receive help from their classmates and further increased their sense of finding their studies difficult. Positive moderate correlations were found between ASE and resilience, ASE and social support, and between resilience and social support. These findings are consistent with earlier findings among nursing students (Stephens, 2013; Walsh et al., 2020; Van Hoek et al., 2019; Hwang and Shin, 2018; Park and Jeong, 2020; El-Sayed et al., 2021; Sweeney, 2021; Caton, 2021). and emphasize the importance of these factors in both traditional and online learning. Negative correlations were found between ASE and perceived difficulty in studies and between resilience to students' perceived difficulty in studies. As mentioned earlier, students with low ASE tend to be unconfident of their educational capabilities and have difficulty meeting their tasks (Bandura, 2001; McLaughlin et al., 2008; Satici and Can, 2016). Accordingly, these respondents will likely perceive their studies as more difficult. Resilient students tend to be more able to resist environmental risk experiences or adversity (Rutter, 2006). Students experiencing less resilient behavior will therefore likely have more difficulty coping with challenges and consequently perceive their studies as more difficult. Women in the current study reported higher ASE and experienced more social support than men. These findings are consistent with previous reports showing that female students score consistently higher ASE (Sachitra and Bandara, 2017) and perceived more social support than men (Cuartero and Tur, 2021). Jewish students reported higher ASE compared to non-Jewish students. A possible explanation may be the technological and educational gaps recently reported among the general population of Israeli students during the transition to online learning. Israeli non-Jewish students reported facing more Internet infrastructure, difficulties with digital capabilities, language gaps (the mother tongue is not Hebrew) and fears that they would have to drop out of studies (Tehawkho et al., n.d.). That may also be the case in the present study sample, leading to decreased ASE. Moreover, Jewish students in the current study reported a higher sense of resilience than non-Jewish students. This finding is consistent with an earlier study among Israeli students (Kimhi et al., 2017). but may be also connected to the sample's composition with the majority being Israeli-Jewish students. Students that had prior experience with online learning reported a higher ASE than those who had no experience at all. This finding is consistent with previous studies (Peechapol et al., 2018). Furthermore, Bandura's source of efficacy (Bandura, 2001) states that prior experience is one of the factors influencing an individual's perceived self-efficacy. The current study found resilience, social support, perceived difficulty in studies and gender (female) to explain 31% of the ASE variance among first-year nursing students. These findings contribute specifically to the importance of resilience and social support to ASE among nursing students in online environments. To date, these associations have been poorly explored and these new findings now provide evidence for increasing students' ASE through strengthening resilience and increasing social support. Possible solutions may be found in student's recommendations for their preferred assistance. These add and complement the quantitative findings. Interestingly, students asked for regular frontal meetings with an academic advisor, additional individual lessons to reinforce the ones given and social interactions to improve their learning. These may represent an interpretation of the recommendations suggested by Walsh et al., (Walsh et al., 2020) in their integrated review. The latter indicated on: peer activities, reflective practice, directed study, problem based learning and experiential learning as strategies strengthening resilience. Most of these strategies can serve students' requests, strengthen their resilience and social support and lead to improved ASE in online environments. This study has two limitations. It employed a convenience sampling drawn from one university, based on self-reports. This might limit the generalizability of the findings to the entire population of first-year nursing students in Israel. In future, this study could be improved by drawing participants from several universities in Israel. The second is related to the questionnaire which included only two open-ended questions. Adding more open questions and interviews with students and educators would have provided more information and a deeper understanding of students' perceptions and understanding of the situation. 6 Conclusions Online learning provides convenience and availability for the user. Nevertheless, for nursing students in their first year, it appears that this learning environment has created a perceived high workload, difficulty in understanding and organizing learning materials, as well as a loss of learning interactions with classmates and faculty. All of these together contributed to the students' general perception of their studies being difficult and in turn, likely decreasing their ASE. It is recommended to strengthen and develop regular social interactions between students and faculty members face-to-face and when needed, by online means. Social support could also enhance resilience as well as the use of learning through peer activities initiated and developed by faculty and nurse educators. Resilience and social support, as found in the current study, contribute to ASE. Variance in students' ASE, resilience and social support according to personal characteristics highlight the need of faculty to consistently assess students' needs and identify at an early stage struggling students, especially during times when distance learning is required. Finally, effective online learning requires that students are given sufficient preparation to get the most out of this new approach to learning. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. CRediT authorship contribution statement Sigalit Warshawsk: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Supervision, Project administration. Declaration of competing interest The authors report no conflict of interest. Appendix A Supplementary data Supplementary material Image 1 Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.nedt.2022.105267. ==== Refs References Aguilera-Hermida P. College students’ use and acceptance of emergency online learning due to COVID-19 International Journal of Educational Research Open. 1 2020 100011 10.1016/j.ijedro.2020.100011 Bagci S.C. Does everyone benefit equally from self-efficacy beliefs? The moderating role of perceived social support on motivation J. Early Adolesc. 38 2 2018 204 219 10.1177/0272431616665213 Bandura A. Social cognitive theory: an agentic perspective Annu. Rev. 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Nursing Students’ College-related stress, self-efficacy, social support and interpersonal skills Journal of The Korean Society of Integrative Medicine. 8 3 2020 153 162 10.15268/ksim.2020.8.3.153 Peechapol C. Na-Songkhla J. Sujiva S. Luangsodsai A. An exploration of factors influencing self-efficacy in online learning: a systematic review Int. J. Emerg. Technol. Learn. 13 09 2018 64 10.3991/ijet.v13i09.8351 Rohmani N. Andriani R. Correlation between academic self-efficacy and burnout originating from distance learning among nursing students in Indonesia during the coronavirus disease 2019 pandemic J Educ Eval Health Prof. 18 2021 9 10.3352/jeehp.2021.18.9 33971088 Rutter M. Implications of resilience concepts for scientific understanding Ann. N. Y. Acad. Sci. 1094 1 2006 1 12 Sachitra, V, Bandara, U. Measuring the Academic Self-Efficacy of Undergraduates: The Role of Gender and Academic Year Experience. 2017;11(11):7. Satici S.A. Can G. Investigating academic self-efficacy of university students in terms of socio-demographic variables Univ. J. Educ. Res. 4 8 2016 1874 1880 Shelton E.N. A model of nursing student retention Int. J. Nurs. Educ. Scholarsh. 9 1 2012 10.1515/1548-923X.2334 Sigalit W. Sivia B. Michal I. Factors associated with nursing Students’ resilience: communication skills course, use of social media and satisfaction with clinical placement J. Prof. Nurs. 33 2 2017 153 161 10.1016/j.profnurs.2016.08.006 28363390 Silvestri L.A. Self-Efficacy and the Predictors for NCLEX-RN® Success for Baccalaureate Nursing Students Available at: https://digitalscholarship.unlv.edu/cgi/viewcontent.cgi?article=1245&context=thesesdissertations 2010 Stephens T.M. Nursing student resilience: a concept clarification Nurs. Forum 48 2 2013 125 133 10.1111/nuf.12015 23600641 Sweeney A.B. Transitional challenges in a two-plus-two nursing program: phenomenology of student experiences Nurse Educ. Pract. 54 2021 103139 10.1016/j.nepr.2021.103139 Talsma K. Robertson K. Thomas C. Norris K. COVID-19 beliefs, self-efficacy and academic performance in first-year university students: cohort comparison and mediation analysis Front. Psychol. 12 2021 2289 10.3389/fpsyg.2021.643408 Tehawkho M, Haddad Hajj-Yahya, N, Matar, H. The gaps between Arab and Jewish students in the Corona period - a special policy paper. Available at: https://www.idi.org.il/articles/33499 (Hebrew). Accessed July 10, 2021. Van Hoek G. Portzky M. Franck E. The influence of socio-demographic factors, resilience and stress reducing activities on academic outcomes of undergraduate nursing students: a cross-sectional research study Nurse Educ. Today 72 2019 90 96 10.1016/j.nedt.2018.10.013 30463034 Walsh P. Owen P.A. Mustafa N. Beech R. Learning and teaching approaches promoting resilience in student nurses: an integrated review of the literature Nurse Educ. Pract. 45 2020 102748 10.1016/j.nepr.2020.102748 Warshawski S. Itzhaki M. Barnoy S. The associations between peer caring behaviors and social support to nurse students’ caring perceptions Nurse Educ. Pract. 31 2018 88 94 10.1016/j.nepr.2018.05.009 29800765 Zimmerman B.J. Bandura A. Martinez-Pons M. Self-motivation for academic attainment: the role of self-efficacy beliefs and personal goal setting Am. Educ. Res. J. 29 3 1992 663 676	0	PMC9719670	NO-CC CODE	2022-12-06 23:23:43	no	Ann Oncol. 2022 Nov 4; 33:S1615	latin-1	Ann Oncol	2,022	10.1016/j.annonc.2022.10.483	oa_other
==== Front Ann Oncol Ann Oncol Annals of Oncology 0923-7534 1569-8041 Published by Elsevier Ltd. S0923-7534(22)04678-6 10.1016/j.annonc.2022.10.487 Article 457P The effect of Interleukin-6 (IL-6) on malignancy in COVID-19 patients in Dr. Moewardi General Hospital Surakarta Ardianti M. Jawa Tengah, Medical Faculty of Sebelas Maret University, Surakarta, Indonesia 4 12 2022 11 2022 4 12 2022 33 S1616S1616 Copyright © 2022 Published by Elsevier Ltd. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcBackground Interleukin-6 (IL-6) is a cytokine with multifaceted effects playing a remarkable role in the initiation of the immune response. IL-6 also represents one of the main signals in communication between cancer cells and their non-malignant neighbors within the tumor niche. IL-6 also participates in the development of a premetastatic niche and in the adjustment of the metabolism in terminal-stage patients suffering from a malignant disease. IL-6 is a fundamental factor of the cytokine storm in patients with severe COVID-19, where it is responsible for the fatal outcome of the disease. This study aims to determine the effect of IL-6 in patients associated with cancer and COVID-19 infection. Methods Case control studies were conducted in Moewardi hospital, Surakarta, Central Java, from February to June 2022. Samples were taken from medical records. All patients with cancer and COVID-19 infection were included. Incomplete data is excluded. Therapy was categorized as hormonal therapy, chemotherapy, and evaluation. Mann Whitney was performed to investigate the average difference. The P-value of <0.05 is significant. Results We included 130 patients with cancer and SARS-CoV-2 infection, and 23 patients for the control. We included the total sample of 153. The median age was 50 ± 13 years. The most frequent kind of cancer was breast cancer (n=59, 38.6%) followed by gastrointestinal cancer (n=25, 16.3%), non hodgkin lymphoma (n=21, 13.7%) and other cancers such as hematological malignancy, thyroid cancer, squamous cell carcinoma, and parotid cancer (n=23,15%). A total of 95 patients received active treatment, with hormonal therapy (n = 8, 0.08%) and chemotherapy (n = 87, 91.57%) of them. The median of IL-6 was 6.80 ± 23.66. There are significant differences of the IL-6 between COVID-19 patients with cancer compared with the control (p=0.001). Conclusions The high level of IL-6 in a patient’s body are influenced by cancer progression and serious viral infections such as COVID-19. Interleukin-6 may be responsible for the failure of therapy and, eventually, fatal complications in patients with cancer and COVID-19. Legal entity responsible for the study The author. Funding Has not received any funding. Disclosure The author has declared no conflicts of interest.	0	PMC9719671	NO-CC CODE	2022-12-06 23:23:43	no	Ann Oncol. 2022 Nov 4; 33:S1616	utf-8	Ann Oncol	2,022	10.1016/j.annonc.2022.10.487	oa_other
==== Front Ann Oncol Ann Oncol Annals of Oncology 0923-7534 1569-8041 Published by Elsevier Ltd. S0923-7534(22)04678-6 10.1016/j.annonc.2022.10.487 Article 457P The effect of Interleukin-6 (IL-6) on malignancy in COVID-19 patients in Dr. Moewardi General Hospital Surakarta Ardianti M. Jawa Tengah, Medical Faculty of Sebelas Maret University, Surakarta, Indonesia 4 12 2022 11 2022 4 12 2022 33 S1616S1616 Copyright © 2022 Published by Elsevier Ltd. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcBackground Interleukin-6 (IL-6) is a cytokine with multifaceted effects playing a remarkable role in the initiation of the immune response. IL-6 also represents one of the main signals in communication between cancer cells and their non-malignant neighbors within the tumor niche. IL-6 also participates in the development of a premetastatic niche and in the adjustment of the metabolism in terminal-stage patients suffering from a malignant disease. IL-6 is a fundamental factor of the cytokine storm in patients with severe COVID-19, where it is responsible for the fatal outcome of the disease. This study aims to determine the effect of IL-6 in patients associated with cancer and COVID-19 infection. Methods Case control studies were conducted in Moewardi hospital, Surakarta, Central Java, from February to June 2022. Samples were taken from medical records. All patients with cancer and COVID-19 infection were included. Incomplete data is excluded. Therapy was categorized as hormonal therapy, chemotherapy, and evaluation. Mann Whitney was performed to investigate the average difference. The P-value of <0.05 is significant. Results We included 130 patients with cancer and SARS-CoV-2 infection, and 23 patients for the control. We included the total sample of 153. The median age was 50 ± 13 years. The most frequent kind of cancer was breast cancer (n=59, 38.6%) followed by gastrointestinal cancer (n=25, 16.3%), non hodgkin lymphoma (n=21, 13.7%) and other cancers such as hematological malignancy, thyroid cancer, squamous cell carcinoma, and parotid cancer (n=23,15%). A total of 95 patients received active treatment, with hormonal therapy (n = 8, 0.08%) and chemotherapy (n = 87, 91.57%) of them. The median of IL-6 was 6.80 ± 23.66. There are significant differences of the IL-6 between COVID-19 patients with cancer compared with the control (p=0.001). Conclusions The high level of IL-6 in a patient’s body are influenced by cancer progression and serious viral infections such as COVID-19. Interleukin-6 may be responsible for the failure of therapy and, eventually, fatal complications in patients with cancer and COVID-19. Legal entity responsible for the study The author. Funding Has not received any funding. Disclosure The author has declared no conflicts of interest.	0	PMC9719672	NO-CC CODE	2022-12-06 23:23:43	no	Ann Oncol. 2022 Nov 4; 33:S1612	latin-1	Ann Oncol	2,022	10.1016/j.annonc.2022.10.474	oa_other
==== Front Ann Oncol Ann Oncol Annals of Oncology 0923-7534 1569-8041 Published by Elsevier Ltd. S0923-7534(22)04678-6 10.1016/j.annonc.2022.10.487 Article 457P The effect of Interleukin-6 (IL-6) on malignancy in COVID-19 patients in Dr. Moewardi General Hospital Surakarta Ardianti M. Jawa Tengah, Medical Faculty of Sebelas Maret University, Surakarta, Indonesia 4 12 2022 11 2022 4 12 2022 33 S1616S1616 Copyright © 2022 Published by Elsevier Ltd. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcBackground Interleukin-6 (IL-6) is a cytokine with multifaceted effects playing a remarkable role in the initiation of the immune response. IL-6 also represents one of the main signals in communication between cancer cells and their non-malignant neighbors within the tumor niche. IL-6 also participates in the development of a premetastatic niche and in the adjustment of the metabolism in terminal-stage patients suffering from a malignant disease. IL-6 is a fundamental factor of the cytokine storm in patients with severe COVID-19, where it is responsible for the fatal outcome of the disease. This study aims to determine the effect of IL-6 in patients associated with cancer and COVID-19 infection. Methods Case control studies were conducted in Moewardi hospital, Surakarta, Central Java, from February to June 2022. Samples were taken from medical records. All patients with cancer and COVID-19 infection were included. Incomplete data is excluded. Therapy was categorized as hormonal therapy, chemotherapy, and evaluation. Mann Whitney was performed to investigate the average difference. The P-value of <0.05 is significant. Results We included 130 patients with cancer and SARS-CoV-2 infection, and 23 patients for the control. We included the total sample of 153. The median age was 50 ± 13 years. The most frequent kind of cancer was breast cancer (n=59, 38.6%) followed by gastrointestinal cancer (n=25, 16.3%), non hodgkin lymphoma (n=21, 13.7%) and other cancers such as hematological malignancy, thyroid cancer, squamous cell carcinoma, and parotid cancer (n=23,15%). A total of 95 patients received active treatment, with hormonal therapy (n = 8, 0.08%) and chemotherapy (n = 87, 91.57%) of them. The median of IL-6 was 6.80 ± 23.66. There are significant differences of the IL-6 between COVID-19 patients with cancer compared with the control (p=0.001). Conclusions The high level of IL-6 in a patient’s body are influenced by cancer progression and serious viral infections such as COVID-19. Interleukin-6 may be responsible for the failure of therapy and, eventually, fatal complications in patients with cancer and COVID-19. Legal entity responsible for the study The author. Funding Has not received any funding. Disclosure The author has declared no conflicts of interest.	0	PMC9719673	NO-CC CODE	2022-12-06 23:23:43	no	Ann Oncol. 2022 Nov 4; 33:S1615-S1616	latin-1	Ann Oncol	2,022	10.1016/j.annonc.2022.10.486	oa_other
==== Front Ann Oncol Ann Oncol Annals of Oncology 0923-7534 1569-8041 Published by Elsevier Ltd. S0923-7534(22)04470-2 10.1016/j.annonc.2022.10.279 Article 244P Alternate-day hypofractionated radiotherapy for radical treatment of head & neck cancer during the COVID-19 pandemic: A single institute experience Singh P. Joseph D.M. Krishnan A.S. Ahuja R. Gupta S. Gupta M. Radiation Oncology Department, All India Institute of Medical Sciences - Rishikesh, Rishikesh, India 4 12 2022 11 2022 4 12 2022 33 S1528S1529 Copyright © 2022 Published by Elsevier Ltd. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcBackground Managing head and neck squamous cell carcinoma (HNSCC) requires a prolonged course of concurrent chemoradiotherapy and other supportive care measures. Such a multidisciplinary approach was significantly hampered during the emergence of the COVID-19 pandemic which necessitated divergence of health resources towards treating the infected patients thereby compromising cancer care. Therefore, we adopted an alternate-day hypofractionated radiotherapy (ADH RT) schedule, aiming to decrease patients’ hospital visits daily without compromising the oncological outcomes. This study assesses the response and toxicity of the ADH RT schedule in HNSCC patients. Methods Retrospective analysis of all histopathologically proven HNSCC patients treated with ADH RT regimen during April – October 2020 in our institute. Hypofractionation dose schedule: 63Gy/21 fractions, 3Gy/fraction was delivered on alternate days without concurrent chemotherapy after patients' consent. Weekly radiation toxicity assessment and post-radiotherapy response assessment by CECT face and neck at 3 and 6 months. Results A total of 26 patients were planned for ADH RT. Most (96%) of them were males with a median age of 60 years and ECOG PS ≤2. The most common tumor site was the oropharynx (58%), the stage was IVA (54%) followed by stage IVB (27%). 93% of patients had a history of tobacco smoking. Only 23 patients completed the treatment and were included in the final assessment. Mucositis and dermatitis grade 1, 2 and 3 was observed in 44%, 52%, 4% and 78%, 18%, and 4% patients, respectively. At three months of follow-up, 5 patients were lost to follow-up and 4 patients expired due to COVID/disease-related complications. Complete response (CR) was observed in 10 patients (71.4%) and partial response in 4 patients. At 6 months, CR was observed in 7 (64.5%) patients. Conclusions Most of the patients were able to tolerate and complete treatment. At analysis, around half of the patients either expired or were lost to follow-up which is the major limitation of this study. Among the available patients, a good response was observed. The practical applicability of this regimen needs to be tested further with a larger sample size and longer follow-up. Legal entity responsible for the study The authors. Funding Has not received any funding. Disclosure All authors have declared no conflicts of interest.	0	PMC9719674	NO-CC CODE	2022-12-06 23:23:43	no	Ann Oncol. 2022 Nov 4; 33:S1528-S1529	utf-8	Ann Oncol	2,022	10.1016/j.annonc.2022.10.279	oa_other
==== Front Ann Oncol Ann Oncol Annals of Oncology 0923-7534 1569-8041 Published by Elsevier Ltd. S0923-7534(22)04470-2 10.1016/j.annonc.2022.10.279 Article 244P Alternate-day hypofractionated radiotherapy for radical treatment of head & neck cancer during the COVID-19 pandemic: A single institute experience Singh P. Joseph D.M. Krishnan A.S. Ahuja R. Gupta S. Gupta M. Radiation Oncology Department, All India Institute of Medical Sciences - Rishikesh, Rishikesh, India 4 12 2022 11 2022 4 12 2022 33 S1528S1529 Copyright © 2022 Published by Elsevier Ltd. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcBackground Managing head and neck squamous cell carcinoma (HNSCC) requires a prolonged course of concurrent chemoradiotherapy and other supportive care measures. Such a multidisciplinary approach was significantly hampered during the emergence of the COVID-19 pandemic which necessitated divergence of health resources towards treating the infected patients thereby compromising cancer care. Therefore, we adopted an alternate-day hypofractionated radiotherapy (ADH RT) schedule, aiming to decrease patients’ hospital visits daily without compromising the oncological outcomes. This study assesses the response and toxicity of the ADH RT schedule in HNSCC patients. Methods Retrospective analysis of all histopathologically proven HNSCC patients treated with ADH RT regimen during April – October 2020 in our institute. Hypofractionation dose schedule: 63Gy/21 fractions, 3Gy/fraction was delivered on alternate days without concurrent chemotherapy after patients' consent. Weekly radiation toxicity assessment and post-radiotherapy response assessment by CECT face and neck at 3 and 6 months. Results A total of 26 patients were planned for ADH RT. Most (96%) of them were males with a median age of 60 years and ECOG PS ≤2. The most common tumor site was the oropharynx (58%), the stage was IVA (54%) followed by stage IVB (27%). 93% of patients had a history of tobacco smoking. Only 23 patients completed the treatment and were included in the final assessment. Mucositis and dermatitis grade 1, 2 and 3 was observed in 44%, 52%, 4% and 78%, 18%, and 4% patients, respectively. At three months of follow-up, 5 patients were lost to follow-up and 4 patients expired due to COVID/disease-related complications. Complete response (CR) was observed in 10 patients (71.4%) and partial response in 4 patients. At 6 months, CR was observed in 7 (64.5%) patients. Conclusions Most of the patients were able to tolerate and complete treatment. At analysis, around half of the patients either expired or were lost to follow-up which is the major limitation of this study. Among the available patients, a good response was observed. The practical applicability of this regimen needs to be tested further with a larger sample size and longer follow-up. Legal entity responsible for the study The authors. Funding Has not received any funding. Disclosure All authors have declared no conflicts of interest.	0	PMC9719675	NO-CC CODE	2022-12-06 23:23:43	no	Ann Oncol. 2022 Nov 4; 33:S1608	latin-1	Ann Oncol	2,022	10.1016/j.annonc.2022.10.460	oa_other
==== Front Ann Oncol Ann Oncol Annals of Oncology 0923-7534 1569-8041 Published by Elsevier Ltd. S0923-7534(22)04503-3 10.1016/j.annonc.2022.10.312 Article 286P Breakthrough COVID-19 infections in patients with cancer from a prospective study of COVID-19 vaccine response Body A. 1 Ahern E.S. 2 Lal L. 1 Abdulla H. 1 Opat S. 1 Downie P. 1 Leahy M. 3 Fuentes-Bolanos N. 4 Padhye B. 5 Hamad N. 6 Milch V. 7 Segelov E. 8 1 Medical Oncology Department, Monash Health - Monash Medical Centre, Clayton, VIC, Australia 2 Medical Oncology Department, Monash Health - Monash Medical Centre, Clayton, VIC, Australia 3 Haematology, Royal Perth Hospital, Perth, WA, Australia 4 Oncology, SCH - Sydney Children's Hospital, Randwick, NSW, Australia 5 Oncology, The Children's Hospital, Westmead, NSW, Australia 6 Haematology, St Vincent's Hospital Sydney, Darlinghurst, NSW, Australia 7 Medical Director, Cancer Australia, Surry Hills, NSW, Australia 8 Medical Oncology Department, Monash Health - Monash Medical Centre, Clayton, VIC, Australia 4 12 2022 11 2022 4 12 2022 33 S1544S1544 Copyright © 2022 Published by Elsevier Ltd. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcBackground COVID-19 disease is more severe in unvaccinated cancer patients compared with the general population. There is limited data regarding clinical efficacy of vaccination in these patients. Methods SerOzNET (ACTRN12621001004853) is a prospective observational cohort study of adults and children with cancer receiving COVID-19 vaccination. The primary endpoint is serological response. An important secondary endpoint is outcome of COVID-19 infection after vaccination. We report self- and clinician reported COVID-19 infections. Results Of 395 adults (20 years +), 74 (19%) reported COVID-19 infection over mean duration on study of 259 days. 71 (97%) had received 2 vaccine doses, 51 (69%) received 3+ doses. 21 (28%) had antiviral treatment. 62 (84%) had symptoms, 7 (9%) required hospitalisation, 0 required ICU admission or died due to COVID-19. Of 113 children/adolescents (5-19 years), 31 (27%) reported COVID-19 infection over mean duration on study of 215 days. 28 (90%) had received 2 vaccination doses, and 12 (39%) received 3+ doses. 23 (74%) had symptoms, 8 (25%) required hospitalisation, 2 (6%) had antiviral therapy, 0 required ICU admission or died due to COVID-19. Pediatric pts with COVID-19 infection had increased risk of hospitalisation compared with adults (p=0.03). Hematological cancer pts had non-significant but numerically higher rates of hospitalisation (Table).Table: 286P Adults (20 years+)* Hematological cancer Solid cancer P value (Fisher’s exact test) Study participants- all 136/393 (35%) 257/393 (65%) Infected participants 34/74 (46%) 40/74 (54%) 0.24 Hospitalised infected participants 5/7 (71%) 2/7 (29%) 0.24 Children, adolescents and young adults (5-19 years)* Study participants - all 67/103 (65%) 36/103 (35%) Infected participants 17/31 (55%) 14/31 (45%) 0.18 Hospitalised infected participants 5/8 (62%) 3/8 (38%) 0.70 *Diagnosis data unavailable for 2 adults and 10 children Conclusions Pts with cancer are likely to be exposed to COVID-19, with infection rates similar to the wider population. Vaccination appears to protect against ICU admission in cancer patients. However, 9% of adults and 25% of children with cancer required hospitalisation for COVID-19, demonstrating increased severity of symptoms compared to the general population. Higher rates of infection and hospitalisation in pediatric pts may be partly attributable to the lower proportion of children who had received a 3rd vaccination dose at the time of infection. Clinical trial identification ACTRN12621001004853. Legal entity responsible for the study Monash Health. Funding Cancer Australia (Australian Federal Government) Victorian Cancer Agency (Victorian State Government, Australia) Leukaemia Foundation (Foundation, Australia). Disclosure All authors have declared no conflicts of interest.	0	PMC9719676	NO-CC CODE	2022-12-06 23:23:43	no	Ann Oncol. 2022 Nov 4; 33:S1544	utf-8	Ann Oncol	2,022	10.1016/j.annonc.2022.10.312	oa_other
==== Front Ann Oncol Ann Oncol Annals of Oncology 0923-7534 1569-8041 Published by Elsevier Ltd. S0923-7534(22)04491-X 10.1016/j.annonc.2022.10.300 Article 273MO A supportive and expanding nurse led model of care, symptom urgent review clinic (SURC) Taylor L. Poole W. Wong Z.W. Oncology Department, Peninsula Health, Frankston, VIC, Australia 4 12 2022 11 2022 4 12 2022 33 S1540S1540 Copyright © 2022 Published by Elsevier Ltd. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcBackground Cancer patients undergoing systemic anti-cancer therapies (SACT) invariably experience toxicities precipitating presentations to Emergency Departments (ED). With the ongoing COVID-19 pandemic, it is imperative to continue to keep vulnerable immunocompromised patients out of hospital and encourage patients to contact SURC when symptoms develop. Peninsula Health (PH), SURC service was initiated post completion of a 12-month funded grant through the Victorian Government and has grown rapidly since its commencement. This nurse-led SURC model of care has been reported to achieve an investment return of $1.73 for every dollar invested. Methods ED presentations of Peninsula Health Oncology/Haematology patients pre- and post-SURC commencement were examined if potentially avoidable presentations have reduced. Ongoing SURC Episodes of care (Educations, phone, and physical attendances) between January 2022 to September 2022 captured in the SURC Access Database. Patient experience surveys were conducted post SURC phone contact and physical attendance if unwell. Patients and clinicians' surveys are ongoing. Results Intermediate statistical data (COSA2021) collated June 2021 to December 2022 post-grant, we observed 43.30% reduction in ED presentations within SURC operation hours by patients considered SURC eligible when compared to pre-SURC figures. The SURC from January 2022 to September 2022 has recorded, 2567 episodes of care, provided to 601 individuals; educations (12.43%), incoming phone triage (45.77%), outgoing phone triage (31.40%), and attendances (10.40%). Most frequent SURC contacts were for care-coordination (28.43%), gastrointestinal symptoms (17.97%), diagnostics (8.81%), pain management (7.56%),)and medication advice (6.23%). Notably, more than one-third indicated they would have done nothing (36.93%) with 7.13% indicating they would have presented to ED without SURC. Closely aligning with the local cancer prevalence rates, the commonest tumour streams are breast (22.63%), lung (17.14%), and colorectal (15.64%). Conclusions The SURC model of care continues to be an invaluable resource at PH to support cancer patients undergoing SACT which allows prompt access to specialist care while avoiding emergency presentations in the ambulatory setting. The model continues to expand post an additional government grant “SURC-additional support during COVID-19 and recovery” to increase additional support to vulnerable populations. Legal entity responsible for the study The authors. Funding Has not received any funding. Disclosure All authors have declared no conflicts of interest.	0	PMC9719677	NO-CC CODE	2022-12-06 23:23:43	no	Ann Oncol. 2022 Nov 4; 33:S1540	utf-8	Ann Oncol	2,022	10.1016/j.annonc.2022.10.300	oa_other
==== Front Ann Oncol Ann Oncol Annals of Oncology 0923-7534 1569-8041 Published by Elsevier Ltd. S0923-7534(22)04491-X 10.1016/j.annonc.2022.10.300 Article 273MO A supportive and expanding nurse led model of care, symptom urgent review clinic (SURC) Taylor L. Poole W. Wong Z.W. Oncology Department, Peninsula Health, Frankston, VIC, Australia 4 12 2022 11 2022 4 12 2022 33 S1540S1540 Copyright © 2022 Published by Elsevier Ltd. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcBackground Cancer patients undergoing systemic anti-cancer therapies (SACT) invariably experience toxicities precipitating presentations to Emergency Departments (ED). With the ongoing COVID-19 pandemic, it is imperative to continue to keep vulnerable immunocompromised patients out of hospital and encourage patients to contact SURC when symptoms develop. Peninsula Health (PH), SURC service was initiated post completion of a 12-month funded grant through the Victorian Government and has grown rapidly since its commencement. This nurse-led SURC model of care has been reported to achieve an investment return of $1.73 for every dollar invested. Methods ED presentations of Peninsula Health Oncology/Haematology patients pre- and post-SURC commencement were examined if potentially avoidable presentations have reduced. Ongoing SURC Episodes of care (Educations, phone, and physical attendances) between January 2022 to September 2022 captured in the SURC Access Database. Patient experience surveys were conducted post SURC phone contact and physical attendance if unwell. Patients and clinicians' surveys are ongoing. Results Intermediate statistical data (COSA2021) collated June 2021 to December 2022 post-grant, we observed 43.30% reduction in ED presentations within SURC operation hours by patients considered SURC eligible when compared to pre-SURC figures. The SURC from January 2022 to September 2022 has recorded, 2567 episodes of care, provided to 601 individuals; educations (12.43%), incoming phone triage (45.77%), outgoing phone triage (31.40%), and attendances (10.40%). Most frequent SURC contacts were for care-coordination (28.43%), gastrointestinal symptoms (17.97%), diagnostics (8.81%), pain management (7.56%),)and medication advice (6.23%). Notably, more than one-third indicated they would have done nothing (36.93%) with 7.13% indicating they would have presented to ED without SURC. Closely aligning with the local cancer prevalence rates, the commonest tumour streams are breast (22.63%), lung (17.14%), and colorectal (15.64%). Conclusions The SURC model of care continues to be an invaluable resource at PH to support cancer patients undergoing SACT which allows prompt access to specialist care while avoiding emergency presentations in the ambulatory setting. The model continues to expand post an additional government grant “SURC-additional support during COVID-19 and recovery” to increase additional support to vulnerable populations. Legal entity responsible for the study The authors. Funding Has not received any funding. Disclosure All authors have declared no conflicts of interest.	0	PMC9719678	NO-CC CODE	2022-12-06 23:23:43	no	Ann Oncol. 2022 Nov 4; 33:S1607-S1608	latin-1	Ann Oncol	2,022	10.1016/j.annonc.2022.10.459	oa_other
==== Front Ann Oncol Ann Oncol Annals of Oncology 0923-7534 1569-8041 Published by Elsevier Ltd. S0923-7534(22)04648-8 10.1016/j.annonc.2022.10.457 Article 426P Safety of Sputnik V COVID-19 vaccine in cancer patients receiving chemotherapy: An observational study Rumyantsev A. 1 Glazkova E. 2 Mariam M. 3 Darenskaya A. 1 Lud H. 1 Tryakin A. 3 Tyulyandina A. 1 1 Department of Chemotherapy No4, N.N. Blokhin National Medical Research Center of Oncology, Moscow, Russian Federation 2 Department of Chemotherapy No1, N.N. Blokhin National Medical Research Center of Oncology, Moscow, Russian Federation 3 Department of Chemotherapy No2, N.N. Blokhin National Medical Research Center of Oncology, Moscow, Russian Federation 4 12 2022 11 2022 4 12 2022 33 S1607S1607 Copyright © 2022 Published by Elsevier Ltd. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcBackground COVID-2019 had a dramatic impact on cancer care worldwide. There are numerous of vaccines developed or being developed in order to prevent the spread of the disease. A recombinant adenovirus-based vaccine, Gam-COVID-Vac (Sputnik V), has shown a favorable safety profile and efficacy in Phase 3 trial. Nowadays it is a main SARS-CoV-2 vaccine in Russia, but there is lack of information on its safety in cancer patients. We conducted a retrospective trial to assess safety of Sputnik V in adult patients with cancer. Methods we screened N.N. Blokhin NMRCO records for 01.2021-05.2022 timeframe and identified adult cancer patients vaccinated against SARS-CoV-2 with Sputnik V vaccine and contacted them to assess the tolerability and safety of the above mentioned vaccine. The patients were asked to report any new adverse events they experienced up to 28 days after the last dose of the vaccine. All the adverse events were recorded in the database and graded according to CTCAE criteria. Patients were specifically asked to report the following: pyrexia, asthenia, nausea, vomiting, local reactions, abdominal pain, muscle or joint pain and to report any other concerning symptoms. Symptoms were graded according to CTCAE4.03 criteria. Results we identified 145 patients who received at least 1 dose of vaccine, safety data were available for 141 of them. Median age was 55 years (21-83), 70 (48.9%), 27 (19.2%), 21 (14.9%) and 19 (13.5%) patients had gynecologic, breast, genitourinary, gastrointestinal tumors, respectively; 5 (3.5%) of patients had other types of tumors. Overall, 70 (49.6%) of patients experienced AE of any grade. Most common AEs were injection reactions (40.4%), pyrexia (24.1%), asthenia (22.0%) and arthralgia (13.5%), results are summarized in the table below. Few patients experienced grade 3-4 AEs, however 1 patient developed grade 4 cerebellar ataxia probably related to vaccination. Cancer type and active treatment were not predictors of AEs.Table: 426P AE Grade 1-2 Grade 3-4 Injection reactions 55 (39.0%) 2 (1.4%) Pyrexia 32 (22.7%) 2 (1.4%) Asthenia 30 (21.3%) 1 (0.7%) Arthralgia 19 (13.5%) 0 (0%) Other 5 (3.5%) 1 (0.7%) . Conclusions Sputnik V vaccination appears to be safe and tolerable in patients with cancer, however additional studies should be conducted to assess efficacy and safety of the vaccine in cancer setting. Legal entity responsible for the study The authors. Funding Has not received any funding. Disclosure A. Rumyantsev: Financial Interests, Personal, Invited Speaker: BIOCAD, AstraZeneca, Eisai, Pfizer, Merck, MSD, R-Pharm; Financial Interests, Personal, Stocks/Shares: AstraZeneca, Pfizer, Novartis, MSD. E. Glazkova: Financial Interests, Personal, Invited Speaker: Pfizer, AstraZeneca, MSD, Merck, Novartis, R-Pharm. A. Tryakin: Financial Interests, Personal, Invited Speaker: Bristol Myers Squibb, MSD, Eli Lilly, Merck, Amgen, Biocad; Financial Interests, Personal, Advisory Board: Bristol Myers Squibb, Astra Zeneca, Biocad; Financial Interests, Personal, Expert Testimony: R-pharm; Financial Interests, Institutional, Invited Speaker: MSD, BMS; Financial Interests, Personal and Institutional, Invited Speaker: Eli Lilly. A. Tyulyandina: Financial Interests, Personal, Funding: AstraZeneca, Roche, MSD, RUSSCO; Financial Interests, Personal, Speaker’s Bureau: AstraZeneca, Eisai; Financial Interests, Personal, Invited Speaker: Roche, MSD, Pfizer, Tesaro, BIOCAD. All other authors have declared no conflicts of interest.	0	PMC9719679	NO-CC CODE	2022-12-06 23:23:43	no	Ann Oncol. 2022 Nov 4; 33:S1607	utf-8	Ann Oncol	2,022	10.1016/j.annonc.2022.10.457	oa_other
==== Front Lancet Reg Health Eur Lancet Reg Health Eur The Lancet Regional Health - Europe 2666-7762 The Author(s). Published by Elsevier Ltd. S2666-7762(22)00250-2 10.1016/j.lanepe.2022.100554 100554 Articles Natural course of health and well-being in non-hospitalised children and young people after testing for SARS-CoV-2: A prospective follow-up study over 12 months Pinto Pereira Snehal M. a∗ Shafran Roz b Nugawela Manjula D. b Panagi Laura c Hargreaves Dougal d Ladhani Shamez N. ef Bennett Sophie D. b Chalder Trudie g Dalrymple Emma b Ford Tamsin c Heyman Isobel b McOwat Kelsey e Rojas Natalia K. b Sharma Kishan i Simmons Ruth e White Simon R. ch Stephenson Terence b a Division of Surgery & Interventional Science, Faculty of Medical Sciences, University College London, WC1E 6BT, UK b UCL Great Ormond Street Institute of Child Health, 30 Guilford Street, London, WC1N 1EH, UK c Department of Psychiatry, University of Cambridge, Hershel Smith Building Cambridge Biomedical Campus, CB2 0SZ, UK d Mohn Centre for Children's Health & Wellbeing, School of Public Health, Imperial College London, UK e Immunisation Department, Public Health England, 61 Colindale Avenue, London, NW9 5EQ, UK f Paediatric Infectious Diseases Research Group, St. George's University of London, Cranmer Terrace, London, SW17 0RE, UK g Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De’Crespigny Park, London, SE5 8AF, UK h Medical Research Council Biostatistics Unit, University of Cambridge, East Forvie Building, Cambridge Biomedical Campus, CB2 0SR, UK i Division of Neuroscience & Experimental Psychology, University of Manchester, UK ∗ Corresponding author. 5 12 2022 5 12 2022 1005544 11 2022 8 11 2022 14 11 2022 © 2022 The Author(s) 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Background Despite high numbers of children and young people (CYP) having acute COVID, there has been no prospective follow-up of CYP to establish the pattern of health and well-being over a year following infection. Methods A non-hospitalised, national sample of 5086 (2909 SARS-COV-2 Positive; 2177 SARS-COV-2 Negative at baseline) CYP aged 11–17 completed questionnaires 6- and 12-months after PCR-tests between October 2020 and March 2021 confirming SARS-CoV-2 infection (excluding CYP with subsequent (re)infections). SARS-COV-2 Positive CYP was compared to age, sex and geographically-matched test-negative CYP. Findings Ten of 21 symptoms had a prevalence less than 10% at baseline, 6- and 12-months post-test in both test-positives and test-negatives. Of the other 11 symptoms, in test-positives who had these at baseline, the prevalence of all symptoms declined greatly by 12-months. For CYP first describing one of these at 6-months, there was a decline in prevalence by 12-months. The overall prevalence of 9 of 11 symptoms declined by 12-months. As many CYP first described shortness of breath and tiredness at either 6- or 12-months, the overall prevalence of these two symptoms in test-positives appeared to increase by 6-months and increase further by 12-months. However, within-individual examination demonstrated that the prevalence of shortness of breath and tiredness actually declined in those first describing these two symptoms at either baseline or 6-months. This pattern was also evident for these two symptoms in test-negatives. Similar patterns were observed for validated measures of poor quality of life, emotional and behavioural difficulties, poor well-being and fatigue. Moreover, broadly similar patterns and results were noted for the sub-sample (N = 1808) that had data at baseline, 3-, 6- and 12-months post-test. Interpretation In CYP, the prevalence of adverse symptoms reported at the time of a positive PCR-test declined over 12-months. Some test-positives and test-negatives reported adverse symptoms for the first time at six- and 12-months post-test, particularly tiredness, shortness of breath, poor quality of life, poor well-being and fatigue suggesting they are likely to be caused by multiple factors. Funding 10.13039/100012411 NIHR /10.13039/100014013 UKRI (ref: COVLT0022). Keywords Long COVID Symptoms Well-being Children and young people Longitudinal Abbreviations CYP, Children and young people UKHSA, United Kingdom Health Security Agency IQR, Interquartile range ==== Body pmc Research in context Evidence before this study We previously published health and well-being profiles of children and young people (CYP) three months after a positive or negative PCR test for SARS-COV-2. There are now a number of cross-sectional surveys from several countries but we are unaware of any published studies (including from those identified in our systematic review) on individual-level prospective follow-ups of CYP with confirmed SARS-CoV-2 infection and matched SARS-CoV-2 test-negatives to assess the natural course of post-COVID-19 health and well-being in individuals. Here, we describe the self-reported health and well-being profiles on a matched cohort of individuals at both six and twelve months after a positive or negative SARS-CoV-2 PCR test. Added value of this study This is a unique population-based cohort study of CYP with PCR-confirmed SARS-CoV-2 infection status where health and well-being are reported by CYP themselves. Importantly, there is a matched test-negative group of CYP who have lived through the ‘long pandemic’ and who have never tested positive for SARS-CoV-2 (determined by PCR test and self-report). Participants were recruited nationally. We evaluated the prevalence of health and well-being in both test negative and positive groups. We tracked the adverse symptoms in this cohort longitudinally over a 12-month period and show that the prevalence of adverse symptoms reported at the time of a positive PCR-test declined over 12-months. However, new adverse symptoms were reported six- and 12-months post-test by both test-positives and test-negatives, particularly tiredness, shortness of breath, poor quality of life, poor well-being and fatigue. Our study demonstrates the added value of longitudinal, individual-level follow-up studies. Implications of all the available evidence This unique study finds that in most CYP, specific adverse symptoms reported at testing and 6-months later had resolved by 12-months, although in a minority they were persistent, and that new-onset had emerged. If we had simply looked at cross-sectional prevalence of adverse symptoms at testing, 6-months and 12-months, as is commonly done in other studies, it would have appeared as if the prevalence of specific common post-COVID-symptoms stayed largely stable, or increased, over time. However, we show that this is not the case. The new-onset adverse symptoms arising 6- or 12-months after initial viral infection should not exclusively be viewed as new long COVID symptoms as a consequence of the initial SARS-COV-2 infection. Rather, these adverse symptoms should be seen in the wider context of health and well-being in the general adolescent population. Recent reviews of Long COVID in CYP indicate that higher quality studies are needed and that a consistent definition of Long COVID is required; our research goes one step further and indicates that studies with repeat measurement on the same CYP are needed to track individual trajectories and not simply report repeat cross-sectional prevalence's of symptoms over time. Introduction For most children and young people (CYP), SARS-CoV-2 infection has been asymptomatic or a mild illness1 compared to adults.2 However, as the cumulative incidence of infection in CYP increases, the incidence of post-COVID sequelae has become a growing concern. Long COVID (post-COVID-19 condition), has a debilitating impact on some CYP but little is known about the frequency, distribution or duration of poor health and well-being after SARS-CoV-2 infection in CYP.3 In our systematic review4 of 22 studies, the most common symptoms in CYP at 3 months were fatigue, insomnia, loss of smell, and headaches; additional reported symptoms included anxiety, low mood and ‘brain fog’. Only five studies identified in the review had a negative test control group to disentangle the effects of infection from living through a pandemic. There is considerable variation in the published literature on the natural history of long-term poor health and well-being associated with SARS-CoV-2 infection and even less data on the associated burden beyond 3 months in CYP.1 , 5, 6, 7, 8, 9, 10 The CLoCk study is the largest national, matched longitudinal cohort study of CYP in England,11 whereby non-hospitalised teenagers self-report on post-COVID-19 health and well-being after PCR-confirmed SARS-CoV-2 infection compared to SARS-CoV-2 PCR-negative CYP.11 , 12 At 3-months post-test, among a subsample of 6084 participants,12 66.5% of test-positives and 53.3% of test-negatives had any symptoms. In contrast, at testing, 35.4% test-positives and 8.3% test-negatives reported any symptoms. This paradoxical increase in symptoms from time of testing to 3 months post-test, potentially due to self-selection, made it essential to follow the cohort longitudinally for 12 months after PCR-testing to understand the within-individual trajectory of health and well-being over time. We therefore collected longitudinal information on a larger group of CYP at 6- and 12-months post-test and here we describe the within-individual variation in health and well-being 6- and 12-months after testing. Methods The CLoCk study, described in detail elsewhere,11 is a cohort study of SARS-CoV-2 PCR-positive CYP aged 11–17 years, matched by month of test, age, sex, and geographical area to SARS-CoV-2 test-negative CYP using the national SARS-CoV-2 testing dataset held by United Kingdom Health Security Agency (UKHSA). The study has recruited >30,000 CYP in total with a goal of collecting data for 24-months after a SARS-CoV-2 PCR test taken between September 2020 and March 2021. Depending on the month of test, for some participants we collect data at 3-, 6-, 12- and 24-months post-test; for others 6-, 12- and 24-months post-test; and for some 12- and 24-months post-test.11 Here we report on data acquired on the same CYP at 6-months and 12-months after PCR-testing (we also do a sensitivity analysis on the sub-sample of CYP with data at 3-, 6- and 12-months after PCR-testing, see below). Following informed consent, at first contact included CYP completed an online questionnaire about their health at the time of their PCR test (i.e. baseline at 0-months) and at the time of completing the questionnaire (approximately 3- or 6-months after their PCR test). CYP completed subsequent questionnaires at 6-months (for the sub-sample first contacted at 3-months) and 12-months, that asked about their health and well-being at the time of the questionnaire. Questionnaires were filled in by the CYP themselves, however, a carer could assist younger CYP and those with special educational needs or disability. After excluding test-positives who were reinfected and test-negatives who were infected after baseline testing (determined by PCR test results held by UKHSA and self-report of whether (or not) the CYP ever had a positive COVID-19 test, including Lateral Flow Tests), 12,949 participants who responded at 6 months post-test were included (Fig. 1 ). This group was approached again at 12 months post-test, and after additional exclusions, the final analytical sample comprised 5086 CYP (2909 test-positives, 2177 test-negatives, see Fig. 1).Fig. 1 Participant flow diagram. In this analytical sample, 1934 of 2909 (66.5%) test-positive and 1445 of 2177 (66.4%) test-negative CYP had received a COVID-19 vaccine between 6- and 12-months follow-up. Sixty-two of 2909 (2.1%) SARS-CoV-2 PCR-positive CYP attended hospital during the 12-month follow-up period, including 16 who were hospitalised overnight. Measures The measures included demographics, elements of the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC) Paediatric COVID-19 questionnaire,13 and the recent Mental Health of Children and Young people in England surveys.14 Based on the ISARIC Paediatric Working Group, we included 21 symptoms12 and validated instruments for loneliness (the adapted 3-item UCLA Loneliness Scale),15 , 16 mental health and wellbeing (Strengths and Difficulties Questionnaire,17 Short Warwick Edinburgh Mental Wellbeing Scale18 , 19), the Chalder Fatigue Scale20 and the EQ-5D-Y21 as a measure of quality of life and functioning (see details in Supplementary Table S1). The questionnaires were largely unchanged between the 6- and 12-month follow-up (see Supplementary text A for details). We operationalised the established Delphi research definition of long COVID22 as having at least one of the 21 reported symptoms and experiencing more than minimal problems on any one of the five EQ-5D-Y questions (see Supplementary Table S1). The Delphi research definition requires laboratory confirmation of SARS-COV-2 infection but of course that was not required when assessing how many test-negatives would also have met this definition. Statistical methods We first assessed the representativeness of our analytic sample by comparing their demographic characteristics (sex, age at testing, region of residence, and Index of Multiple Deprivation) to the target population invited 6-months post-test. Second, we described the prevalence of each of the health and well-being measures in two ways: (a) we tabulated the prevalence in CYP who had an adverse symptom never, once, twice or thrice and assessed whether the prevalence differed by SARS-CoV-2 PCR status; (b) taking into consideration the temporal nature of the data and the repeated measures on the same CYP over time, we generate stacked bar charts that show the distribution of health and well-being across the three time-points and indicate when the adverse symptom was first reported. Both analyses were stratified by SARS-CoV-2 status. Sensitivity and exploratory analyses We did one sensitivity and one exploratory analysis. Sensitivity analysis: as indicated above, information was collected on a sub-sample 3-months post-test; the above-described analysis was therefore repeated on the smaller sample with data at 0-, 3-, 6-, and 12-months post-test. Exploratory analysis: although not designed to answer questions regarding school attendance after COVID-19 infection, this information is needed to guide education support strategies. Thus, we explored self-reported school absence data in CLoCk participants 6-months after initial PCR-testing. Role of funding source The 10.13039/501100000276 Department of Health and Social Care , as the National Institute for Health Research (NIHR), and 10.13039/100014013 UK Research & Innovation (UKRI) awarded grant COVLT0022 but were not involved in study design, data collection, analysis, interpretation or writing. Results The 6- and 12-month follow-up questionnaires were returned at a median of 27.7 [IQR: 26.1, 29.6] and 52.1 [IQR: 50.7, 54.1] weeks after testing, respectively. In total, 2909 of 6407 (45.4%) SARS-COV-2 positive and 2177 of 6542 (33.3%) SARS-COV-2 negative CYP who responded at 6-months also responded at 12-months. Both test-positives and test-negatives in the analytical sample were broadly similar to the target population responding at 6 months, albeit test-negatives were slightly older than test-positives (Table 1 ).Table 1 Comparison of target population to analytic sample; and characteristics of children and young people (CYP) in analytic sample by baseline PCR-test result: N (%). Characteristic Target population of CYP who responded at 6 months post-test CYP in analytic sample (responding at 6- and 12-months post-test) SARS-CoV-2 Negative SARS-CoV-2 Positive N 12,949 5086 2177 2909 SARS-CoV-2 Negative 6542 (50.5) 2177 (42.8) 2177 (100.0) 0 (0.0) Positive 6407 (49.5) 2909 (57.2) 0 (0.0) 2909 (100.0) Age at testing (years) 11–14 5573 (43.0) 2047 (40.2) 806 (37.0) 1241 (42.7) 15–17 7376 (56.9) 3039 (59.8) 1371 (63.0) 1668 (57.3) Sex Male 4845 (37.4) 1785 (35.1) 765 (35.1) 1020 (35.1) Female 8104 (62.6) 3301 (64.9) 1412 (64.9) 1889 (64.9) Ethnicity White 10,004 (77.3) 3958 (77.8) 1680 (77.2) 2278 (78.3) Asian or Asian British 1774 (13.7) 694 (13.7) 304 (14.0) 390 (13.4) Mixed 570 (4.4) 228 (4.5) 109 (5.0) 119 (4.1) Black, African, or Caribbean 325 (2.5) 126 (2.5) 56 (2.6) 70 (2.4) Other 205 (1.6) 57 (1.1) 20 (0.9) 37 (1.3) Prefer not to say 71 (0.5) 23 (0.4) 8 (0.4) 15 (0.5) IMDa 1 (most deprived) 2554 (19.7) 894 (17.6) 390 (17.9) 504 (17.3) 2 2344 (18.1) 903 (17.7) 384 (17.6) 519 (17.8) 3 2340 (18.1) 953 (18.7) 425 (19.5) 528 (18.2) 4 2710 (20.9) 1104 (21.7) 474 (21.8) 630 (21.7) 5 (least deprived) 3001 (23.2) 1232 (24.2) 504 (23.2) 728 (25.0) Region East Midlands 1353 (10.4) 531 (10.4) 239 (11.0) 292 (10.1) East of England 1391 (10.7) 599 (11.8) 269 (12.4) 330 (11.3) London 1549 (12.0) 613 (12.1) 295 (13.6) 318 (10.9) North East 786 (6.1) 290 (5.7) 112 (5.1) 178 (6.1) North West 1901 (14.7) 713 (14.0) 282 (13.0) 431 (14.8) South East 1775 (13.7) 751 (14.8) 321 (14.7) 430 (14.8) South West 987 (7.6) 402 (7.9) 168 (7.7) 234 (8.1) West Midlands 1724 (13.3) 672 (13.2) 294 (13.5) 378 (13.0) Yorkshire and The Humber 1483 (11.5) 515 (10.1) 197 (9.0) 318 (10.9) a Index of Multiple Deprivation (IMD), derived from the CYP's lower super output area (a small local area level based geographic hierarchy), was used as a proxy for socio-economic status. We used IMD quintiles from most (quintile 1) to least (quintile 5) deprived. Symptom profiles at baseline, 6- and 12-months post-test The prevalence of CYP reporting the same symptom never, once, twice or at all three time points is shown in Supplementary Table S2. Among the test-positives, 10.9% reported fatigue, 4.4% reported shortness of breath, 3.3% loss of smell or taste, 1.7% dizziness or light-headedness, and 1.1% described skipping meals at all three time points. The other 16 symptoms affected less than 1% of test-positives at all three time points. Among test-negatives, 1.2% reported fatigue at all three time points. The other 20 symptoms were reported by less than 1% of test-negatives at all three time points. Thus, the distribution of symptom prevalence differed by SARS-CoV-2 PCR status (p ≤ 0.004) except for experiencing sores or blisters on feet (p = 0.064). When assessing overall prevalence at the three time points in more detail, we categorised symptom patterns into three broad groups: (i) Ten symptoms with low overall prevalence (less than 10%) at all three time points in both test-negatives and test-positives (Supplementary Fig. S1), (ii) Nine symptoms where the overall prevalence declined from baseline to 12 months post-test in test-positives (Supplementary Fig. S2) and fluctuated variably but at low prevalence in test-negatives; and (iii) Two symptoms with overall prevalence increasing from baseline to 12-months and remaining high in both test-negatives and test-positives (Fig. 2 ).Fig. 2 Symptoms with overall prevalence increasing from baseline to 12 months and remaining high. When examining within-individual change in symptom profiles, the prevalence of the 11 more common symptoms at baseline (i.e., baseline prevalence >10%) declined greatly by 12-months, in the test-positives (Supplementary Fig. S2 and Fig. 2). For CYP who first describe one of these symptoms at 6-months, again there is a decline in prevalence by 12 months (Supplementary Fig. S2 and Fig. 2). In keeping with this, the overall prevalence (i.e., total height of bar charts) for 9 out of 11 symptoms declined by 12-months (p ≤ 0.2 for difference between proportion of CYP with symptoms at baseline and 12-months post-test in test-positives; Supplementary Fig. S2). However, for two symptoms, shortness of breath and tiredness, the overall prevalence in test-positives increased by 6-months and increased further by 12-months, because large numbers of CYP first describe these symptoms at either 6-months or 12-months; this pattern was also observed for these two symptoms among test-negatives (Fig. 2). At 12-months, the difference in prevalence between test-positives and test-negatives for these two symptoms, varied by when the symptom was first reported. For example, for test-positives and test-negatives who reported shortness of breath for the first time at baseline (time of PCR test), the difference in prevalence of shortness of breath at 12 months between the test-positives and test-negatives was 5.43% (95% CI:4.49%, 6.36%); the difference in prevalence among those reporting shortness of breath for the first time at 12 months was 0.44% (95% CI:-1.10%,1.98%), Table 2 .Table 2 Difference in prevalence of selecteda health and well-being measures between test-positives and test-negatives at 12 months, by time symptom first reported. Prevalence difference at 12 months in test-positives and test-negatives (95% CI) Shortness of breath First reported at: 0 months 5.43 (4.49, 6.36) 6 months 4.30 (2.89, 5.71) 12 months 0.44 (−1.10, 1.98) Tiredness First reported at: 0 months 13.75 (12.33, 15.16) 6 months 3.21 (1.12, 5.28) 12 months −4.50 (−6.44, −2.56) Having pain or discomfortb First reported at: 0 months −1.79 (−3.19, −0.38) 6 months 1.71 (0.67, 2.75) 12 months 0.21 (−1.37, 1.80) Difficulty doing usual activitiesb First reported at: 0 months −1.52 (−2.64, −0.39) 6 months 2.23 (1.26, 3.20) 12 months 1.08 (- 0.28, 2.44) Mental healthc High/very high total difficulties First reported at: 6 months −1.98 (−3.79, −0.16) 12 months 0.17 (−1.31, 1.64) High/very high emotional difficulties First reported at: 6 months −1.61 (−3.77, 0.55) 12 months −0.39 (−2.13, 1.34) High/very high hyperactivity First reported at: 6 months −0.40 (−2.19, 1.38) 12 months −0.07 (−1.52, 1.38) High/very high peer difficulties First reported at: 6 months −3.02 (−4.95, −1.10) 12 months −1.44 (−3.09, 0.20) High/very high impact First reported at: 6 months −2.51 (−4.41, −0.60) 12 months −0.18 (−1.84, 1.48) Poor well-beingc First reported at: 6 months −3.30 (−5.68, −0.92) 12 months 1.61 (−0.39, 3.60) Severe fatiguec First reported at: 6 months 4.30 (1.94, 6.65) 12 months −1.49 (−3.43, 0.46) Long COVIDc First reported at: 6 months 6.25 (4.42, 8.07) 12 months −0.30 (−2.05, 1.45) Calculated as: % with symptom at 12 months in test-positives - % with symptom at 12 months in test-negatives. a Selected based on (i) overall prevalence increasing from baseline to 12 months and (ii) prevalence in test-positives >10% at least twice. b From EQ-5D-Y. c Using the Strengths and Difficulties Questionnaire, Short Warwick Edinburgh Mental Wellbeing Scale, Chalder Fatigue Scale and operationalisation of the Delphi definition of Long COVID respectively (see Supplementary Table S1 for details). Quality of life/functioning and Loneliness profiles at baseline, 6- and 12-months post-test The overall prevalence of problems with mobility, self-care, feeling sad (EQ-5D-Y) or lonely (adapted 3-item UCLA Loneliness scale) was low (less than 10%) at all three time points in both test-negatives and test-positives (Fig. 3, Fig. 4 ). Problems with doing usual activities and having pain followed similar patterns to those observed for shortness of breath and tiredness (i.e., overall prevalence in test-positives increased by 6-months and generally increased further by 12-months, because large numbers of CYP first report these conditions at either 6-months or 12-months; Fig. 3). However, there was little difference in the prevalence of having pain or difficulty doing usual activities between test-positives and test-negatives reporting these for the first time at 12 months (Table 2).Fig. 3 Prevalence of poor quality of life and functioning∗ over a 12-month period in test-positives and test-negatives. Fig. 4 Prevalence of loneliness∗ over a 12-month period in test-positives and test-negatives. Mental health, well-being, fatigue and long COVID at 6- and 12-months post-test The overall prevalence of conduct difficulties was low at 6- and 12-months post-test and for low prosocial skills, decreased slightly (Fig. 5 ). For the other five adverse outcomes from the Strengths and Difficulties Questionnaire, between 6- and 12-months the overall prevalence increased slightly (Fig. 5) and there was little difference in the prevalence of these measures between test-positives and test-negatives reporting them for the first time at 12-months (Table 2). The overall and within-individual prevalence patterns of poor well-being (Fig. 6 ), fatigue (Fig. 7 ) and Long COVID (Fig. 8 ) were broadly similar, and again there was little difference in the prevalence of these measures between test-positives and test-negatives reporting them for the first time at 12-months (Table 2).Fig. 5 Prevalence of emotional and behavioural difficulties∗ over a 12-month period in test-positives and test-negatives. Fig. 6 Prevalence of poor well-being∗ over a 12-month period in test-positives and test-negatives. Fig. 7 Prevalence of severe fatigue∗ over a 12-month period in test-positives and test-negatives. Fig. 8 Prevalence of long COVID∗ over a 12-month period in test-positives and test-negatives. Sensitivity and exploratory analysis In the sub-sample with data collected at 3-months post-test (N = 1808, Supplementary Fig. S3), broadly similar patterns and results were observed to those reported above (Supplementary Figs. S4–S12; Supplementary Tables S3 and S4). In exploratory analysis, we found that among symptomatic CPY, school absence (≥1 day) 6-months post-test was less common in SARS-CoV-2 PCR-positive participants than PCR-negative participants, but a higher proportion reported extended school absence of >10 days (p < 0.001). In contrast, CYP who were asymptomatic reported lower absence rates 6-months post-test (Supplementary text B; Supplementary Fig. S13; Supplementary Table S5). Discussion We report here the prevalence of health and well-being at 6- and 12-months after laboratory-confirmed SARS-COV-2 infection, which we believe to be the only longitudinal follow-up in CYP in a matched cohort. The results show that aggregating across all three time points, adverse symptoms were generally more common in test-positive compared to test-negative CYP (the ‘Never had adverse symptoms’ columns in Supplementary Table S2). The majority of test-positive CYP who had a particular adverse symptom at testing were free from that symptom at both 6- and 12-months post-test, demonstrating that these symptoms generally improved over time. Additionally, most CYP who first developed a particular symptom 6-months after their positive (or negative) PCR-test did not report that symptom at 12-months. We also found in the sub-sample with data collected at 3-, 6- and 12-months post-test, broadly similar patterns and results. The symptom prevalence during acute SARS-CoV-2 infection among test-positive CYP was similar in our cohort when compared to those reported in other adolescent cohorts,23 , 24 indicating that our analytical cohort is representative of CYP in general. The very low prevalence of loss of smell/taste among test-negatives – both at testing and over the 12-month follow-up period, also provides some reassurance of a low rate of unconfirmed SARS-CoV-2 infections in the test-negative group, although we acknowledge (re)infections may have gone undetected. For two symptoms, (shortness of breath and tiredness) as well as measures of poor quality of life (in particular having pain and problems doing usual activities), poor well-being and fatigue, the overall prevalence in test-positives increased over time. Importantly, our within-individual exploration demonstrates that the prevalence actually declined in those who first described these adverse symptoms at either baseline or 6-months. Differences in the prevalence of these adverse symptoms between test-positives and test-negatives remained at 12-months but varied depending on when the symptom was first reported. For example, there was no difference in the prevalence of shortness of breath between test-positives and test-negatives if it was first reported at 12-months post-test. The prevalence of tiredness was (surprisingly) less common in the test-positives, if first reported at 12-months post-test. However, if either symptom was first reported at time of testing, the prevalence at 12-months was higher among test-positives by 5.4% (shortness of breath) and 13.8% (tiredness) compared to test-negatives. The broadly similar pattern of adverse health and well-being reported as new-onset at 6- and 12-months among test-positives and test-negatives highlights the non-specific nature of these symptoms and suggests that multiple aetiologies may be responsible for CYP experiencing these symptoms over time. Further studies are therefore needed to understand the cause of persistent adverse health and well-being in test-positive CYP and how they differ from test-negatives reporting the same adverse symptoms. Our consistent and robust findings across a diverse range of health and well-being measures emphasises (i) the close relationship between physical and mental health and (ii) the value of repeated measures over time in the same individuals. Taking all the data in consideration, we found that if we had simply looked at cross-sectional prevalence's at baseline, 3- (in the sub-sample), 6- and 12-months, it would have appeared as if the prevalence of several adverse post-COVID-symptoms remained largely stable, or even increased, over time. In fact, most (but not all) CYP recovered from the adverse symptoms which they experienced at baseline and 6-months post-infection. However, the reporting of new onset of these same symptoms at 6- and 12-months follow-up by both test-positive and test-negative CYP suggests that these symptoms may be causally related to multiple factors and not just the original SARS-COV-2 infection. For example, the development of new symptoms 6- or 12-months after their SARS-COV-2 PCR-test in both test-positives and test-negatives could represent background levels of symptomatology in CYP in England. This highlights the need for appropriate control groups in long COVID studies and normative population studies of common symptoms among CYP outside of the context of a pandemic. Similar to our within-individual findings, in adults persisting post-COVID-19 symptoms have also been shown to decline with time.5 Pooled prevalence data from 27 eligible research publications in adults showed the 5 most prevalent reported symptoms were fatigue, shortness of breath, muscle pain, cough and headache, overlapping with the commonest symptoms we describe in CYP in our cohort.25 Furthermore, in a recent review of nine UK longitudinal studies in adults, totalling over 42,000 participants, the symptoms characteristic of long COVID were similar to the commonest symptoms we describe in CYP, including fatigue, shortness of breath and muscle pain or aches, but also difficulty concentrating and chest tightness.26 Long-term follow-up data in CYP is sparse. A single-centre, hospital-based Australian study followed 171 CYP for 1-1.5 years after SARS-COV-2 infection and showed resolution of all symptoms.6 A national cohort study of 37,522 CYP with laboratory-confirmed SARS-CoV-2 infection in Denmark and a control group of 78,037 randomly selected uninfected children9 also reported that in most children, ‘long COVID’ symptoms resolved by 5 months. However, a large population study using nationwide registry data from 706,855 Norwegian CYP found an increase in primary care use after SARS-COV-2 infection which persisted for up to six months among 1–5-year-olds.8 Our study is unique, examining within-individual longitudinal data after laboratory testing for SARS-CoV-2 in test-positive and test-negative CYP, and provides added value over repeated cross-sectional prevalence surveys. Indeed, the two follow-up time points is a major study strength, although more follow-up and continuous time-points would further strengthen the study. This is in-part why we present the sensitivity analysis on the sub-sample with an additional follow-up time point (at 3-months). Notably, we were specifically funded to study non-hospitalised CYP, the milder end of the acute COVID-19 spectrum, which is likely to be relevant to many COVID-19 cases in CYP. However, anecdotal reports from carers and clinical colleagues suggest that there are undoubtedly some CYP severely affected by chronic debilitating long-term symptoms. The CLoCk study has limitations which have been discussed at length11 , 12 and here we detail main limitations relevant to the current manuscript. Symptoms at baseline are subject to recall bias as they were reported at time of first contact with the CLoCk study (at either 3-months or 6-months post-test); however, 6-month and 12-month symptoms were reported prospectively. The dominant UK virus was the original wild-type SARS-COV-2 between September and December 2020 and the Alpha (B.1.1.7) variant from January to March 2021; our cohort was drawn from these two periods. From June 2021 the Delta variant dominated and from January 2022, Omicron. In relation to symptoms at the time of the acute infection, evidence suggests that the seven most prevalent symptoms were common to both Alpha and Delta variants.24 However, given we excluded test-positives who were reinfected and test-negatives who were infected after baseline testing (PCR testing remained widely available in the UK throughout the 12-month follow-up period and we also took into consideration self-report of Lateral flow tests), our study did not include CYP infected with Delta or Omicron variants and cannot therefore be definitive about post-COVID-19 condition in CYP infected with Delta or Omicron variants. Moreover, it is possible that some CYP might have been misdiagnosed as SARS-CoV-2 negative and vice-versa: false negatives might be attributable to the timing of the PCR, swab technique, and assay sensitivity, but false-positive PCR results are rare. The response rate for the 6-month follow-up questionnaire was 11.2% (14,384 of 127,896; Fig. 1) and at 12 months 48.7% (6307 of 12,949; Fig. 1), but there was little difference in demographic characteristics between respondents and the target population, nor between test-positive and test-negative participants (with the exception of age; Table 1), reflecting the matched-cohort study design. However, we note that the study design may induce selection biases, for example, by favouring those with internet access, and CYP may be more likely to take participate if they had symptoms to report. We acknowledge the limitations of examining self-reported data, compared to in-person medical interviews which were not practical or feasible to conduct. However, we also note that self-report is an appropriate data collection technique for large scale epidemiological studies such as CLoCk. Our unique study emphasises the importance of longitudinal follow-up in the same individuals over time alongside matched test-negatives to avoid the pitfalls of repeated cross-sectional prevalence studies. Whilst we have examined adverse health and well-being at 6- and 12-months post-test (and in a subsample at 3-months post-test), we cannot infer whether these adverse symptoms waxed and waned in the intervening time-periods. While the research definition of ‘Long Covid’ in CYP22 rightly requires that the experienced symptoms have an impact on everyday functioning, it is our view that understanding the impact of individual symptoms as well as their collective impact is required to fully understand the impairment resulting from SARS-COV-2 infection. Therefore, in this paper we report the prevalence of symptoms which were assessed by single items as well as reporting validated scales and our operationalisation of the research definition of Long COVID. Nonetheless, we acknowledge that some symptoms (e.g., shortness of breath) might be better assessed by additional validated measures and acknowledge the issue of floor/ceiling effects (i.e., if the question/validated scale is relatively easy or difficult such that substantial proportions of CYP obtain either maximum or minimum scores and the true extent of their abilities cannot be determined). In relation to the data collected, researchers want to ask about as much as possible to allow extensive/varied analysis addressing as many specific research questions as possible and our initial draft questionnaire took over an hour to complete. However, in our pilot study CYP said they would only be willing to spend 20 minutes maximum completing the survey. Therefore, compromises were made and while our data is wide ranging and unique, adding value to the literature, it also has limitations in terms of depth of information available. Finally, much remains unknown in relation to the long-term implications of SARS-COV-2 infection in CYP and as the background epidemiological situation in relation to SARS-COV-2 infection prevalence changes, as well as the rate of vaccination up-take in CYP, more questions need answering, such as, how does vaccination status influences subsequent outcomes after SARS-COV-2 infection? Conclusions In CYP, the prevalence of adverse health and well-being reported at the time of a positive PCR-test declined over 12 months. New adverse symptoms were reported 6- and 12-months post-test for both test-positives and test-negatives, particularly tiredness, shortness of breath, poor quality of life, having emotional and behavioural difficulties, poor well-being, fatigue and Long COVID (according to the Delphi definition).22 Such common symptoms may be caused by multiple factors including SARS-COV-2 infection in CYP. Contributors Terence Stephenson [email protected] conceived the idea for the study, submitted the successful grant application and drafted the manuscript. Snehal M Pinto Pereira [email protected] designed and conducted the statistical analyses for the manuscript, accessed and verified the data and drafted the manuscript. Roz Shafran [email protected] contributed to the design of the study, submitted the ethics and R&D applications and drafted the manuscript. Manjula D Nugawela [email protected] conducted the statistical analysis for the manuscript, accessed and verified the data. Kelsey McOwat [email protected] adapted the questionnaire for the online SNAP survey platform. Ruth Simmons [email protected] accessed and verified the data, designed the participant sampling and dataflow. Trudie Chalder [email protected] contributed to the design of the study and reviewed the manuscript. Tamsin Ford [email protected] contributed to the design of the study and reviewed the manuscript. Isobel Heyman [email protected] contributed to the design of the study reviewed the manuscript. Shamez Ladhani [email protected] developed the study methodology, operationalised the regulatory and recruitment ideas for the study and revised the manuscript. Emma Dalrymple [email protected] contributed to the design of the study and reviewed the manuscript. Dougal Hargreaves [email protected] contributed to the design of the study, the drafting and the analysis of school attendance data, and reviewed the manuscript. Simon R White [email protected] contributed to the analysis and reviewed the manuscript. Laura Panagi [email protected] contributed to the drafting, analysis and reviewed the manuscript. Kishan Sharma [email protected] contributed to the design of the study. Natalia K Rojas [email protected] contributed to the analysis and reviewed the manuscript. Sophie D Bennett [email protected] reviewed the manuscript. All members of the CLoCk Consortium (listed below) made contributions to the conception or design of the study; and were involved in drafting the original funding application. All authors of this manuscript; approved the version to be published; and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Additional Co-Applicants on the grant application and CLoCk Consortium members (alphabetical) Marta Buszewicz, University College London, [email protected]. Esther Crawley, University of Bristol, [email protected]. Bianca De Stavola, University College London, [email protected]. Shruti Garg, University of Manchester, [email protected]. Anthony Harnden, Oxford University, [email protected]. Michael Levin, Imperial College London, [email protected]. Vanessa Poustie, University of Liverpool, [email protected]. Terry Segal, University College London Hospitals NHS Foundation Trust, [email protected]. Malcolm Semple, University of Liverpool, [email protected]. Olivia Swann, Edinburgh University, [email protected]. Elizabeth Whittaker, Imperial College London, [email protected]. Data sharing statement Data is not publicly available. All requests for data will be reviewed by the Children & young people with Long Covid (CLoCk) study team, to verify whether the request is subject to any intellectual property or confidentiality obligations. Requests for access to the participant-level data from this study can be submitted via email to [email protected] with detailed proposals for approval. A signed data access agreement with the CLoCk team is required before accessing shared data. Code is not made available as we have not used custom code or algorithms central to our conclusions. Declaration of interests Terence Stephenson is Chair of the 10.13039/100005622 Health Research Authority and therefore recused himself from the Research Ethics Application. Trudie Chalder is a member of the National Institute for Health and Care Excellence committee for long COVID. She has written self-help books on chronic fatigue and has done workshops on chronic fatigue and post infectious syndromes. Dougal Hargreaves had a part-time secondment as Deputy Chief Scientific Adviser from September 2020 to September 2021, whereby his salary for 2 days per week was paid by the Department for Education (England) to Imperial College London. Sophie Bennett and Roz Shafran are both part of Great Ormond Street Hospital 10.13039/100015819 NHS Foundation Trust and UCL 10.13039/501100001282 Great Ormond Street Institute of Child Health , where their research is made possible by the National Institute of Health Research (NIHR) 10.13039/501100019256 Great Ormond Street Hospital Biomedical Research Centre . Sophie Bennett and Roz Shafran are co-authors on a book published in August 2020, titled Oxford Guide to Brief and Low Intensity Interventions for Children and Young People. All remaining authors have no conflicts of interest. Appendix A Supplementary data Supplementary Material Revision 1 LANCET STROBE Acknowledgements Michael Lattimore, UKHSA, as Project Officer for the CLoCk study. Olivia Swann and Elizabeth Whittaker designed the elements of the ISARIC Paediatric COVID-19 follow-up questionnaire which were incorporated into the online questionnaire used in this study to which all the CLoCk Consortium members contributed. Lana Fox-Smith and Jake Dudley supported the formatting of the manuscript and references. This work is independent research jointly funded by the 10.13039/100012411 National Institute for Health and Care Research (NIHR) and 10.13039/100014013 UK Research & Innovation (UKRI) who have awarded funding grant number COVLT0022. All research at 10.13039/501100001279 Great Ormond Street Hospital Charity NHS Foundation Trust and UCL Great Ormond Street Institute of Child Health is made possible by the 10.13039/501100019256 NIHR Great Ormond Street Hospital Biomedical Research Centre . The views expressed in this publication are those of the authors and not necessarily those of the NHS, the NIHR, UKRI or the Department of Health and Social Care. SMPP is supported by a 10.13039/501100000265 UK Medical Research Council Career Development Award (ref: MR/P020372/1). DH is supported by the NIHR through the 10.13039/501100019219 Applied Research Collaboration (ARC) North-West London and the School of Public Health Research. SRW is supported by the 10.13039/100014013 UKRI 10.13039/501100000265 Medical Research Council (MC_UU_00002/2) and the 10.13039/501100018956 NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). Ethics Approval: Yorkshire & The Humber - South Yorkshire Research Ethics Committee (REC reference: 21/YH/0060; IRAS project ID: 293495). Appendix A Supplementary data related to this article can be found at https://doi.org/10.1016/j.lanepe.2022.100554. ==== Refs References 1 Molteni E. Sudre C.H. Canas L.S. Illness duration and symptom profile in symptomatic UK school-aged children tested for SARS-CoV-2 Lancet Child Adolesc Health 5 10 2021 708 718 34358472 2 Sudre C.H. Murray B. Varsavsky T. 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Dunn ISARIC Global COVID-19 paediatric follow-up https://isaric.org/research/covid-19-clinical-research-resources/paediatric-follow-up/ 2021 14 NHS Digital Mental health of children and young people in England, 2020: Wave 1 follow up to the 2017 survey https://tinyurl.com/NHSWave1FU 2020 15 Office for National Statistics Measuring loneliness: guidance for use of the national indicators on surveys https://www.ons.gov.uk/peoplepopulationandcommunity/wellbeing/methodologies/measuringlonelinessguidanceforuseofthenationalindicatorsonsurveys#recommended-measures-for-children 2018 16 Klein E.M. Zenger M. Tibubos A.N. Loneliness and its relation to mental health in the general population: Validation and norm values of a brief measure J Affect Disord Rep 4 2021 100120 17 Goodman R. Psychometric properties of the strengths and difficulties questionnaire J Am Acad Child Adolesc Psychiatry 40 11 2001 1337 1345 11699809 18 Tennant R. Hiller L. Fishwick R. The warwick-edinburgh mental well-being scale (WEMWBS): development and UK validation Health Qual Life Outcome 5 2007 63 19 Child Outcomes Research Consortium Short warwick-edinburgh mental wellbeing scale (SWEMWS) https://www.corc.uk.net/outcome-experience-measures/short-warwick-edinburgh-mental-wellbeing-scale-swemws/ 2022 20 Chalder T. Berelowitz G. Pawlikowska T. Development of a fatigue scale J Psychosom Res 37 2 1993 147 153 8463991 21 Wille N. Badia X. Bonsel G. Development of the EQ-5D-Y: A child-friendly version of the EQ-5D Qual Life Res 19 6 2010 875 886 20405245 22 Stephenson T. Allin B. Nugawela M.D. Long COVID (post-COVID-19 condition) in children: a modified Delphi process Arch Dis Child 107 7 2022 674 35365499 23 Berg S.K. Palm P. Nielsen S.D. Acute symptoms in SARS-CoV-2 positive adolescents aged 15-18 years - results from a Danish national cross-sectional survey study Lancet Reg Health Eur 16 2022 100354 24 Molteni E. Sudre C.H. Canas L.D.S. Illness characteristics of COVID-19 in children infected with the SARS-CoV-2 Delta variant Children 9 5 2022 25 Aiyegbusi O.L. Hughes S.E. Turner G. Symptoms, complications and management of long COVID: a review J R Soc Med 114 9 2021 428 442 34265229 26 Bowyer R.C.E. Huggins C. Toms R. Characterising patterns of COVID-19 and long COVID symptoms: Evidence from nine UK longitudinal studies medRxiv 2022 2022.06.20.22275994	36504922	PMC9719829	NO-CC CODE	2022-12-06 23:23:44	no	Lancet Reg Health Eur. 2022 Dec 5;:100554	utf-8	Lancet Reg Health Eur	2,022	10.1016/j.lanepe.2022.100554	oa_other
==== Front J Infect Chemother J Infect Chemother Journal of Infection and Chemotherapy 1341-321X 1437-7780 Japanese Society of Chemotherapy and The Japanese Association for Infectious Diseases. Published by Elsevier Ltd. S1341-321X(22)00320-8 10.1016/j.jiac.2022.11.013 Original Article Nosocomial transmission of SARS-CoV-2 from infected children to uninfected caregivers: A retrospective cohort study in a Japanese tertiary children's hospital☆ Shimizu Akihiko a∗ Kitazume Sachiko b a Department of Infectious Diseases, Gunma Children's Medical Center, Shibukawa, Japan b Department of Nursing, Gunma Children's Medical Center, Shibukawa, Japan ∗ Corresponding author. Department of Infectious Diseases, Gunma Children's Medical Center, 779 Shimohakoda, Hokkitsu-machi, Shibukawa, Gunma, 3778577, Japan. 5 12 2022 5 12 2022 5 10 2022 24 11 2022 29 11 2022 © 2022 Japanese Society of Chemotherapy and The Japanese Association for Infectious Diseases. Published by Elsevier Ltd. All rights reserved. 2022 Japanese Society of Chemotherapy and The Japanese Association for Infectious Diseases 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 transmission rate of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is unclear when caregivers accompany pediatric COVID-19 patients in the same isolation room in a hospital setting. Aim We investigated SARS-CoV-2 transmission from infected children to caregivers at our hospital. Methods This retrospective cohort study included 34 discordant pairs of patients admitted between September 2020 and April 2022. Findings The median ages of the children and caregivers were 3.7 years (interquartile range [IQR]: 1.6–8.1) and 33.1 years (IQR: 28.3–43.4), respectively. Of the 34 caregivers, 31 were mothers, two were fathers, and one was a relative. Sixteen caregivers received at least two doses of the mRNA vaccine. The mean duration of the hospital stays was 7.7 ± 4.1 days (range: 3–19). Two unvaccinated caregivers developed COVID-19 after admission; the onset was within 48 h after admission. It is likely that they had been infected in their household prior to admission, since the incubation period for COVID-19 is usually >2 days. Conclusions Nosocomial SARS-CoV-2 transmission from infected children to caregivers was not confirmed in this study. The combination of negative-pressure rooms, vaccinations, and infection-control bundles appears to be effective at preventing SARS-CoV-2 transmission. It is acceptable to allow caregivers to accompany pediatric COVID-19 patients in a hospital ward if they can comply with basic infection control measures. Keywords COVID-19 SARS-CoV-2 Nosocomial transmission Pediatric Caregivers ==== Body pmc1 Introduction Although COVID-19 is relatively less severe in children than in adults, children affected by COVID-19 occasionally require hospitalization. When pediatric patients with COVID-19 are hospitalized, their caregivers may need to stay in the same room for providing care to them. Consequently, caregivers may need to have close contact with the infants and younger children for feeding and personal hygiene. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can spread from infected children to adult caregivers in the ward room unless they are already infected with SARS-CoV-2 at admission. However, few reports have evaluated the risk of transmission of SARS-CoV-2 within discordant pairs (a child positive for SARS-CoV-2 with a negative caregiver) [1]. Therefore, we investigated SARS-CoV-2 transmission from infected children to adult caregivers in our hospital. 2 Patients and methods This retrospective cohort study evaluated the SARS-CoV-2 transmission rate among discordant pairs of patients who were admitted to our hospital. All discordant pairs during the study period (September 2020 to April 2022) were eligible for the study. The ward had five isolation rooms dedicated to COVID-19 patients. Each room is 15.2–19.0 m2 in area and has an attached toilet and a shower room. Each room is equipped with a negative-pressure device, and air was ventilated at least 12 times per hour. The children and caregivers were in close contact during their hospital stay. The caregivers provided for, the children's feeding, diaper and clothing changes, oral medication, and personal hygiene needs. The caregivers were informed about the basic infection-control measures on admission, including wearing masks, hand hygiene, routine cleaning of surfaces with alcohol cloths, and not facing each other at the same time while eating meals (Table 1 ). Most of the caregivers underwent nucleic acid amplification testing prior to admission and had negative results. If they were not tested, nucleic acid amplification testing was performed upon admission. We performed nucleic acid amplification testing again if they developed fever or respiratory symptoms compatible with COVID-19 during hospitalization. After hospital discharge, we followed-up the patients by phone to confirm whether they had developed COVID-19.Table 1 Guidelines for patients and caregivers to patients and caregivers to prevent transmission of COVID-19 in our hospital. Table 1Wear surgical masks whenever possible, except with patients younger than three years old. Perform hand hygiene when necessary. Clean high-touch surfaces with an alcohol-containing cloth at least once a day. Do not face each other at the same time while eating meals. Spend the entire time from admission to discharge in a negative-pressure room. 3 Results A total of 140 children (mean age: 4.8 ± 4.5 years, median age: 3.1 years, interquartile range [IQR]: 1.2–7.9) with COVID-19 were admitted to our hospital during the study period; 36/140 (25.7%) children stayed with 34 asymptomatic PCR-negative caregivers. Two of the 34 discordant pairs were two children and their respective mothers who stayed in the same room. The mean age of the children of the discordant pairs was 4.8 ± 3.7 years; the median age was 3.7 years (IQR: 1.6–8.1). Sixteen (44%) children were <3 years old and could not comply with the request to wear masks. Ten (28%) children had underlying chronic conditions (four with congenital heart disease, three with chromosomal abnormalities, two with bronchial asthma, and one hematologic malignancy). Only one child received at least two doses of the mRNA vaccine. Most children showed mild symptoms, and only two children developed respiratory distress requiring oxygen therapy (Table 2 ).Table 2 Clinical characteristics of the patients and caregivers. Table 2 Patients (N = 36) Caregivers (N = 34) Females (%) 21 (58) 31 (91) Age (years), median (IQR) 3.7 (1.6–8.1) 33.1 (28.3–43.4) Nationality, n (%) Japanese 30 (83) 30 (88) Others 6 (17) 4 (12) Underlying diseases, n (%) 10 (28) 0 Vaccination history, n (%) 1 (3) 16 (47) Symptomatic before or on admission, n (%) 35 (97) 0 Developed symptoms after admission, n (%) 0 2 (6) Severity, n (%) Asymptomatic to mild 34 (94) 34 (100) Moderate to severe 2 (6) 0 IQR: interquartile range. Of the 34 caregivers, 31 (91%) were mothers, two (6%) were fathers, and one (3%) was a relative. The mean age of the caregivers was 35.1 ± 9.1 years; the median age was 33.1 years (IQR: 28.3–43.4). Sixteen (47%) caregivers received at least two doses of mRNA vaccines. Most caregivers adhered well to the infection control measures. The mean duration of the hospital stays was 7.7 ± 4.1 days (range: 3–19). Two caregivers (6%), neither of whom were vaccinated, developed signs and symptoms within 48 h after admission (Table 3 ). One developed fever and headache on the day after admission (April 2021), and the SARS-CoV-2 test result was positive on the same day. She was breastfeeding her 6-month-old daughter. The other caregiver developed a sore throat and rhinorrhea on the third day of hospitalization (October 2021), but two consecutive SAR-CoV-2 PCR tests were negative. She had a 1-month-old daughter who was breastfeeding.Table 3 Clinical characteristics of the pairs in which a caregiver developed COVID-19 after admission. Table 3Pair Age Sex Vaccinated Season Onset Symptoms and signs Exposure history 1 6 months F No April 2021 (Alpha variant predominant period) 2 days before admission Fever, cough 31 years F No 1 day after admission Fever, headache Husband developed COVID-19 1 week before admission. 2 1 month F No October 2021 (Delta variant predominant period) 1 day before admission Fever, rhinorrhea 21 years F No 2 days after admission Sore throat, rhinorrhea Mother-in-law developed COVID-19 3 days before admission. 4 Discussion Most pediatric cases of COVID-19 are mild, but occasionally hospitalization is required [2,3]. Caregivers play an essential role in providing care for ill children, and it is better for the children to have their caregivers present to offer appropriate care and emotional stability, especially if the child is very young [4]. However, many hospitals have implemented policies that restrict or limit caregivers since the start of the pandemic [4]. Household exposure is the most common route of transmission of SARS-CoV-2 in children, and adult family members are the typical primary source of infection [5]. In a systematic review, the overall secondary household transmission rate was found to be 18.9% (95% confidence interval [CI]: 16.2–22) [6]. However, household transmission can also occur from infected children to adults [7]. Caregivers can be infected with SARS-CoV-2 by staying in a ward room with infected children; therefore, many hospitals do not allow caregivers to accompany COVID-19 pediatric patients. To the best of our knowledge, there is only one report that analyzed the transmission of SARS-CoV-2 from infected children to caregivers in a strictly controlled ward room [1]; in this study 12 discordant pairs stayed in the same ward room and no caregivers developed COVID-19 or had a positive PCR test during hospitalization. The Japan Pediatric Society issued a statement in April 2020 advising that it is acceptable for a caregiver to stay in the same room of a hospital with a pediatric patient with COVID-19 [8]. Since then, our hospital has followed the policy of allowing pediatric patients and their caregiver to stay in the same room. We made requests based on the recommendations of the Japanese Ministry of Health, Labour and Welfare (Table 1), and the available evidence at the time that this study was conducted. Several studies have shown that wearing surgical masks and hand hygiene reduce the risk of SARS-CoV-2 transmission [9,10]. In our opinion, hand hygiene and wearing masks are reasonable protective measures. There is no clear evidence that cleaning high-touch surfaces reduces the risk of transmission. However, it has been reported that no viable viruses were isolated from environmental surfaces in medical facilities that performed similar environmental cleaning [11]. We thought that cleaning high-touch surfaces would be feasible without imposing a significant burden. Because several reports of COVID-19 outbreaks have been associated with restaurants [12], we instructed the patients and caregivers not to eat meals at the same time face-to-face. Although we believe that patients with COVID-19 do not necessarily need to be isolated in negative-pressure rooms, in our hospital negative-pressure rooms are the only private rooms with adequate ventilation. It has been reported that sufficient ventilation reduces the number of virus particles in the air and can lower the probability of infection [13]. In this study, two unvaccinated caregivers developed symptoms consistent with COVID-19 within 48 h after admission. Their admission was in April and October 2021, which was before the Omicron variant predominant period. Both of the associated children were breastfed infants with whom the caregivers could not avoid close contact. Given that the median incubation period for COVID-19 was 5.1 days before the appearance of Omicron variant, and less than 2.5% of infected persons displayed symptoms within 2.2 days [14], it is highly likely that the two caregivers were infected by household contact prior to hospital admission. Therefore, caregivers were unlikely to have been infected with SARS-CoV-2 during hospitalization.. Telephone health follow-ups after discharge revealed that no caregivers became sick, and no COVID-19 cases were reported in the family. The combination of these infection-control measures seems to be effective in preventing nosocomial transmission to caregivers. Household contact is reported to be responsible for about 70% of SARS-CoV-2 transmission and some experts emphasize that isolation of index cases from household members can reduce secondary infection [15]. However, it is almost impossible to isolate young children with COVID-19 from caregivers. Our prevention measures may be also useful for adults caring for children with COVID-19 at home. This study had several limitations. First, we did not perform PCR tests unless the patients were symptomatic after admission. Although we verified that the caregivers were PCR-negative at or just prior to admission, we could not detect asymptomatic infections in the caregivers. The second limitation was the small sample size. If we were to follow a greater number of discordant pairs, we might identify nosocomial transmission cases and assess the precise transmission rate or identify risk factors for transmission. Third, we experienced outbreaks of several variants during the study period. Omicron variants have been reported to have an increased risk of household transmission [16]. Our strategy might not be sufficient to prevent transmission if patients were infected with an emerging variant more contagious than the omicron variant. In conclusion, nosocomial SARS-CoV-2 transmission from infected children to their adult caregivers during hospitalization was not confirmed among 34 discordant pairs. The combination of negative-pressure rooms, vaccination, and basic infection-prevention bundles such as wearing masks and hand hygiene appears to be effective at preventing SARS-CoV-2 transmission. This study showed that the transmission risk is sufficiently low to allow caregivers to accompany their wards with COVID-19 in a controlled hospital setting. From a psychological perspective, it would be beneficial to allow caregivers to accompany children with COVID-19. Funding statement This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authorship statement All authors meet the ICMJE authorship criteria. AS contributed to the study conception and design; AS and SK collected the data; AS performed data analysis; AS wrote the draft of the manuscript; and AS and SK interpreted the data and assisted in the review of the final manuscript. All authors critically reviewed the draft of the manuscript and approved the final manuscript. Declaration of competing interest None. Acknowledgements We acknowledge the exemplary service rendered by the physicians, nurses, and all staff at Gunma Children's Medical Center. Specifically, we would like to thank Mariko Shimizu, MD; Shigeru Nomura, MD, PhD; and Yoshiyuki Yamada, MD, PhD. ☆ This study was approved by the ethics committee of the Gunma Children's Medical Center, Japan (GCMC2021-29). ==== Refs References 1 Nunziata F. Poeta M. Vassallo E. Continisio G.I. Lo Vecchio A.L. Guarino A. No spread of SARS-CoV-2 from infected symptomatic children to parents: a prospective cohort study in a controlled hospital setting Front Pediatr 9 2021 720084 10.3389/fped.2021.720084 2 Liguoro I. Pilotto C. Bonanni M. Ferrari M.E. Pusiol A. Nocerino A. SARS-CoV-2 infection in children and newborns: a systematic review Eur J Pediatr 179 2020 1029 1046 10.1007/s00431-020-03684-7 32424745 3 Katsuta T. Shimizu N. Okada K. Tanaka-Taya K. Nakano T. Kamiya H. The clinical characteristics of pediatric coronavirus disease 2019 in 2020 in Japan Pediatr Int 64 2022 e14912 10.1111/ped.14912 4 Kaye E.C. COVID-19 caregiver restrictions in pediatrics Hosp Pediatr 11 2021 10.1542/hpeds.2020-004291 e12–4 5 Zachariah P. Johnson C.L. Halabi K.C. Ahn D. Sen A.I. Fischer A. Epidemiology, clinical features, and disease severity in patients with coronavirus disease 2019 (COVID-19) in a Children's Hospital in New York City, New York JAMA Pediatr 174 2020 e202430 10.1001/jamapediatrics.2020.2430 6 Madewell Z.J. Yang Y. Longini I.M. Jr. Halloran M.E. Dean N.E. Factors associated with household transmission of SARS-CoV-2: an updated systematic review and meta-analysis JAMA Netw Open 4 2021 e2122240 10.1001/jamanetworkopen.2021.22240 7 Chu V.T. Yousaf A.R. Chang K. Schwartz N.G. McDaniel C.J. Lee S.H. Household transmission of SARS-CoV-2 from children and adolescents N Engl J Med 385 2021 954 956 10.1056/NEJMc2031915 34289272 8 Japan Pediatric Society Statement on the medical care system for pediatric patients with COVID-19 http://www.jpeds.or.jp/modules/guidelines/index.php?content_id=114 9 Hirose R. Ikegaya H. Naito Y. Watanabe N. Yoshida T. Bandou R. 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COVID-19 outbreak associated with air conditioning inrestaurant, Guangzhou, China, 2020 Emerg Infect Dis 26 2020 1628 1631 10.3201/eid2607.200764 32240078 13 Azuma K. Yanagi U. Kagi N. Kim H. Ogata M. Hayashi M. Environmental factors involved in SARS-CoV-2 transmission: effect and role of indoor environmental quality in the strategy for COVID-19 infection control Environ Health Prev Med 25 2020 66 10.1186/s12199-020-00904-2 33143660 14 Lauer S.A. Grantz K.H. Bi Q. Jones F.K. Zheng Q. Meredith H.R. The incubation period of coronavirus disease 2019 (COVID-19) from publicly reported confirmed cases: estimation and application Ann Intern Med 172 2020 577 582 10.7326/M20-0504 32150748 15 Haroon S. Chandan J.S. Middleton J. Cheng K.K. Covid-19: breaking the chain of household transmission BMJ 370 2020 10.1136/bmj.m3181 m3181 16 Madewell Z.J. Yang Y. Longini I.M. Jr. Halloran M.E. Dean N.E. Household secondary attack rates of SARS-CoV-2 by variant and vaccination status: an updated systematic review and meta-analysis JAMA Netw Open 5 2022 e229317 10.1001/jamanetworkopen.2022.9317	36470374	PMC9719842	NO-CC CODE	2022-12-07 23:15:50	no	J Infect Chemother. 2022 Dec 5; doi: 10.1016/j.jiac.2022.11.013	utf-8	J Infect Chemother	2,022	10.1016/j.jiac.2022.11.013	oa_other
==== Front Soc Sci Med Soc Sci Med Social Science & Medicine (1982) 0277-9536 1873-5347 Elsevier Ltd. S0277-9536(22)00891-7 10.1016/j.socscimed.2022.115585 115585 Article Anxiety in the face of the first wave of the spread of COVID-19 in Israel: Psychosocial determinants of a “Panic-to-complacency-continuum”☆ Shahar Golan a∗ Ahronson-Daniel Limor bc Greenberg David d Shalev Hadar e Tendler Avichai f Grotto Itamar b Malone Patrick g Davidovitch Nadav b a Department of Psychology, Ben-Gurion University of the Negev, Beer-Sheva, Israel b School of Public Health, Faculty of Health Sciences, Ben-Gurion University of the Negev, Israel c PREPARED Center for Emergency Response Research, Ben-Gurion University of the Negev, Beer-Sheva, Israel d Pediatrics Infectious Disease Unit, Soroka University Medical Center, Beer-Sheva, Israel e Department of Psychiatry, Soroka University Medical Center, Beer-Sheva, Israel f Google Health, Israel g Malone Quantitative, Durham, NC, USA ∗ Corresponding author. Department of Psychology, Ben-Gurion University of the Negev, Beer-Sheva, Israel. 5 12 2022 5 12 2022 1155854 6 2022 16 11 2022 26 11 2022 © 2022 Elsevier Ltd. All rights reserved. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Background and Methods: Based on an established ongoing prospective-longitudinal study examining anxiety in response to COVID-19, a representative sample of 1018 Jewish-Israeli adults were recruited online. A baseline assessment was employed two days prior to the first spread of COVID-19, followed by six weekly assessments. Three classes of general anxiety and virus-specific anxiety were identified: (1) “Panic” (a very high and stable anxiety throughout the spread), (2) “Complacency” (a very low and stable anxiety throughout the spread), and (3) “Threat-Sensitivity” (a linear increase, plateauing at the 5th wave). For general-anxiety only, a fourth, “Balanced,” class was identified, exhibiting a stable, middle-level of anxiety. We tested theory-based, baseline, social-cognitive predictors of these classes: self-criticism, perceived social support, and perceptions/attitudes towards the Israeli Ministry of Health. We also controlled for trait anxiety. Multinomial regression analyses in the context of General Mixture Modeling were utilized. Results Baseline virus-specific anxiety linearly predicted emerging virus-specific anxiety classes. Virus-specific panic has higher trait anxiety than the other two classes. The general anxiety panic class was over-represented by women and exhibited higher baseline general anxiety and self-criticism than all other classes, and higher baseline virus-specific anxiety along with lower perceived support and less positive perceptions of the ministry of health than two of the three other classes. Conclusions Preexisting anxiety shapes subsequent anxious responses to the spread of COVID-19. The general-anxiety panic class may be markedly demoralized, requiring targeted public-health interventions. Keywords COVID-19 Anxiety Social-cognition Israel ==== Body pmc1 Introduction Since its eruption in December 2019, the coronavirus (COVID-19) pandemic has delivered devastating blows to societies worldwide. This predicament is still operative despite the development of effective vaccines and medications. The ever-oscillating course of the pandemic yields one stable insight: Human behavior is paramount, both in term of spreading the virus and of containing it. Specifically, hygiene behavior, physical distancing, vaccine compliance, and use/misuse of social media are key behaviors impacting the spread of COVID-19. To understand human behavior in this crisis, we designed and implemented the COVID-19-Israeli Public Behavior Project (COVID-19-IPBP; Shahar et al., 2022), an interdisciplinary, multi-wave investigation of a large sample of Israelis, targeting participants' emotional distress, social relations, and compliance with guidelines during the COVID-19 crisis. This report is the second from the COVID-19-IPBP. Like the first report, it focuses on the first wave of the spread and on participants' anxiety. Still, the second report extends the first one on theoretical and empirical counts. Below we account for our focus on anxiety in mass medical crisis, and this also necessitates reviewing findings of the first report. Next, we present the rationale for the present investigation. 1.1 Anxiety during mass medical crises While in virtually all epidemics and pandemics, at least some level of anxiety is expected, no answer exists to the question of how much anxiety is warranted. Most studies focused on two extreme scenarios: a very high anxiety, also titled “panic,” and a very low anxiety, also labeled “complacency.” Evidence as to the presence of panic in the face of epidemics and pandemics is mixed, with some studies documenting its presence (e.g., Schultz et al., 2016), others documenting its absence (e.g., Sherlaw and Raude, 2013). Conversely, there is evidence for the presence of complacency (Jones and Salathé, 2009; Wang and Kapucu, 2006). Complacency might hinder preparedness (Wang and Kapucu, 2006) and compliance with governments’ instructions, such as those concerning social distancing (Jones and Salathé, 2009). Notably, however, most extant research on population anxiety in the face of pandemics is cross-sectional, rendering it uninformative with respect to trajectories of anxiety over time. Moreover, this research is predicated upon the assumption that the population in question is characterized by a unified reaction to the crisis. This stands in contrast to other strands of trauma research that identify heterogeneous responses to traumatic events (Donoho et al., 2017). Both limitations are addressed in the first report from COVID-19-IPBP, targeting the first wave of the spread of COVID-19 in Israel (Shahar et al., 2022). Israeli residents have been occasionally exposed to military threats; hence they are experienced in managing life-threatening situations. Indeed, owing to ongoing threats, the government and army as well as municipal and law-enforcement agencies have considerable power over civilians, particularly at times of crisis. Similar to other (but not all) countries, Israel hermetically closed its borders, thereby controlling the level of the spread. We examined two types of anxiety: general anxiety and virus-specific anxiety. The data used for modeling trajectories of both outcomes were six weekly assessments post-exposure, with baseline, pre-exposure data used to predict the trajectories. We hypothesized both panic and complacency trajectories, as well as a third class titled Threat Sensitivity: a moderate linear increase in both general anxiety and virus-specific anxiety throughout the spread. The rationale for proposing threat-sensitivity was that an increasing threat should result in an increasing anxious response. Findings from General Mixture Modeling were largely consistent with our hypotheses. For both general anxiety and virus-specific anxiety, the hypothesized panic, complacency, and TS trajectories/classes were identified. Panic was represented via elevated and stable levels of throughout the study period (12% and 25% for each outcome, respectively), whereas complacency was manifested through very low and stable levels (29% and 9%). Threat sensitivity was expressed via a linear increase in anxiety from Wave 1 (post-exposure) to Wave 4. Unexpectedly, a plateau—arguably manifesting habituation—was observed at the fourth wave, continuing until the sixth (29% and 66% for general anxiety and virus-specific anxiety). In addition, for general anxiety only, an unexpected, fourth class was evinced, exhibiting stable mid-levels (30%). Our interpretation of this class, again, post hoc, was that it reflects a balanced response, escaping the extreme high and low responses exhibited by panic and complacency. We therefore titled this class “Balanced." Another important objective of our analyses was to predict membership in the identified classes via baseline, pre-exposure variables. As for demographics, gender, and age were revealed as important, but only for general anxiety: Women were more likely to belong to the Panic group than to each of the other three groups. Additionally, older respondents were more likely to be in the general anxiety complacency class than the balanced class. More importantly, we sought to establish directionality in the link between general anxiety – representing a psychopathological outcome – and virus-specific anxiety – deemed tantamount to “perceived stress”, a known predictor of psychopathology. Indeed, a clear directionality was evinced. When general anxiety classes were considered as outcomes, baseline levels of both general anxiety and virus-specific anxiety predicted these classes in expected ways. Baseline general anxiety was also associated with higher likelihood of membership in the panic class than any of the other three and lower likelihood of membership in the complacency class than any other class, although not discriminating between the balanced and threat-sensitive classes. Baseline virus-specific anxiety predicted a lower likelihood of membership in the complacency class compared with the panic or threat-sensitive classes and greater likelihood of membership in the panic class than the balanced class. In contrast, when virus-specific anxiety classes were considered as outcomes, baseline virus-specific anxiety – but not baseline general anxiety, predicted the three classes in an expected, linear fashion: Panic > threat sensitivity > Complacency. Thus, the directionality appears to emanate from virus-specific anxiety to general anxiety, rather than vice versa. The first report thus identified a continuum of anxiety responses for both general anxiety and virus-specific anxiety. The continuum commences with panic as the most extremely anxious response, culminates with complacency as the least extreme anxious response, with threat sensitivity—and, for general anxiety, the balanced class —in the middle. Furthermore, findings of this first report suggest that baseline virus-specific anxiety may lead to the development of subsequent, post-exposure, general anxiety classes. 1.2 The present investigation: a social-cognitive (self-in-relations) theoretical perspective The underlying theoretical umbrella for this investigation is a group of psychological theories focusing on representations of self-in-relations, or, theories that capture the Subjective-Agentic Personality Sectors (SAPS), as articulated by Shahar (2020). Chief among these theories are social-cognitive theory (e.g., Bandura, 1997; Mischel and Shoda, 1995) and empirically supported versions of object relations theory (Blatt et al., 1997; Westen, 1991). The common denominator of these theories is the assumption whereby mental representations of the self, primarily in an interpersonal context, constitute the cornerstones of subjective processes, regulating behavior and response to stress (Shahar, 2020). From the wide array of SAPS variables, we focused on three concepts: self-criticism, perceived social support, and positive and negative perceptions of the Israeli ministry of health. Self-criticism, defined as the tendency to set increasingly high self-standards and to adopt a punitive stance toward the self once these standards are not met. This dimension is considered one of the most robust dimensions of personality vulnerability to both depression and anxiety (Shahar, 2015). Mounting evidence indicate that self-criticism is distinct from low self-esteem, as the former dimension pertains to an overarching stance taken by the self toward the self, whereas the latter dimension refers to a primarily cognitive process of taking a stock of self-strength (Shahar, 2015). In fact, self-esteem appears to be an outcome of self-criticism and a moderator of self-criticism's effect on emotional distress (Abela et al., 2006). Self-criticism is shown to be associated with maladaptive coping strategies, including avoidant coping (Dunkley et al., 2006), a maladaptive strategy that is shown to be linked with COVID-19 related emotional distress (Carnahan et al., 2022). As such, self-criticism could be expected to predict membership in the more extreme anxiety classes concerning both general anxiety and virus-specific anxiety. Yet, because anxiety is strongly tied to temperamental, trait-based differences (Fox and Pine, 2012; Spielberger, 1989), it was necessary to control for temperamental, trait-based anxiety in evaluating the impact on self-criticism on symptomatic anxiety. Self-criticism is a risk-related factor focused on the self-concept. In contrast, perceived social support is a resilience-related factor focused on other people. The literature distinguishes between received and perceived support, with the former type of support often exhibiting both protective and adverse effects whereby the later type results in mostly protective ones. In particular, the stress-buffering hypothesis of social support, according to which support ameliorates the effect of life stress on distress (e.g., Bowen et al., 2014; Cohen, 2004), is shown almost exclusively for perceived support (Cohen, 2004; e.g., (e.g., Shahar et al., 2009). Another important social-cognitive variable is perception/attitude toward the governmental agency that oversees the COVID-19 crisis, which, in Israel, is the ministry of health. Attitudes toward agencies overseeing population-level crises have been shown to be predictive of populations' emotional responses in expected ways: positive perceptions/attitudes such as trust and respect are associated with positive emotional responses whereas negative perceptions/attitudes such as mistrust and hostility are associated with negative emotional responses (e.g., Taylor-Clark et al., 2005). Epidemics/pandemics are no exception in this respect. A strong illustration is our own program of research examining perceptions/attitudes toward the IMOH during the mass-vaccination campaign launched in Israel in 2013, targeting newly-erupted poliovirus. Participants who reported experiencing the ministry of health as “caring” (a positive attitude) prior to the beginning of the campaign were particularly likely to get vaccinated, (Shahar et al., 2017), whereas participants who reported experiencing the IMOH as “hysteric” (negative attitude) showed higher levels of vaccine hesitancy (Veksler-Noyman et al., 2021). From a theoretical point of view, a focus on attitudes toward the Israeli ministry of health complements the above focus on self-and-other representation: Attitudes toward the ministry of health can be seen as an indication of individuals' perception of their higher-order social ecology, in other words, perception of their societal leadership. According to Bronfenbrenner's (1977) social-ecological theory, governmental actions represent the higher-order “exosystem”, pertaining to features of the social structure and/or large-scale societal processes. Effects of the exosystem trickle down on the micro-system (i.e., those systems directly impacting the individual) and the mesosystem (interactions between the systems directly influencing the individuals without the latter's active involvement), ultimately impacting the individual. While there are myriad ways to examine exosystemic effects, one important way may be the assessment of individuals' subjective perception of exosystemic agencies (e.g., Rizzo et al., 2021). In the context of the COVID-19 crisis in Israel, the pertinent exosystemic agency is the Israeli ministry of health. Therefore, we hypothesized H1: Even after controlling for baseline levels of general anxiety and virus-specific anxiety, demographics, and baseline trait anxiety—baseline levels of self-criticism, perceived social support, and perceptions/attitudes toward the Israeli ministry of health will predict the various classes in the panic-complacency continuum. In terms of specific patterns, it was easiest to make predictions concerning the Panic group, because our assumption was that its members are the most impaired. Thus, we hypothesized H2: Over and above baseline general anxiety and virus-specific anxiety and the aforementioned role of female gender (Shahar et al., 2022), members of the general anxiety panic group will be characterized by the highest levels of trait anxiety, self-criticism, and negative perceptions/attitudes toward the Israeli ministry of health, as well as by the lowest levels of perceived social support and positive attitudes toward the Israeli ministry of health. Similar predictions were made for virus-specific anxiety panic as compared to threat sensitivity and complacency. It was more difficult to predict specific patterns for differences between the remaining general anxiety and virus-specific anxiety classes, although it made sense to expect that the complacency group for both outcomes will differ from the other classes in terms of low trait anxiety. Other, more nuanced class differences were expected, although they were examined on an exploratory basis. We also hypothesized H3: The two protective factors – perceived social support and positive perceptions/attitudes toward the Israeli ministry of health – would buffer against the adverse effect of baseline virus-specific anxiety on the pain-complacency class continuum. Recalling that (Shahar et al., 2022) found that bassline virus-specific anxiety predicts subsequent general anxiety classes, but not vice versa, we see that this directional relationship is consistent with the depiction of virus-specific anxiety as the (perceived) stress leading to a distress response (general anxiety A). From a risk/resilience perspective, it would be natural to expect that pre-exposure levels the two social-cognitive protective factors assessed in this study may ameliorate this adverse effect of baseline virus-specific anxiety. 2 Methods 2.1 Procedure and participants This study received approval from the Ethics Committee of the Department of Psychology of Ben-Gurion University of the Negev in Beer-Sheva, Israel. Extensive details as to the sampling and design, participants' recruitment, and measurement are provided in (Shahar et al., 2022) and are also publicly available online (https://osf.io/gu4st/). For clarity's sake, we highlight the project's most pertinent features. We already noted that our analyses are based on seven measurement occasions: a baseline assessment and six weekly waves. In Table 1 we outline each wave's dates, related COVID-19 effects (i.e., number of cases, deceased), and related government restrictions (See also the flowchart in Fig. 1 of Shahar et al., 2022).Table 1 Dates of assessment waves and related COVID-19 status and restrictions. Table 1Assessment Wave Date COVID-19 Status Restrictions Wave 0 February 19th, 2020 No known carrier None Wave 1 February 25th, 2020 Three adults infected Warning against traveling to Hubei, China. Wave 2 March 4th, 2020 16 adults infected Mandatory quarantine to Israelis incoming from Italy, France, Spain, Austria, Germany, and Switzerland. Wave 3 March 11th, 2020 99 adults infected; Two adults hospitalized. Gatherings of > 100 people were prohibited, and people younger than 65 were instructed to refrain from visiting the elderly. Wave 4 March 18th, 2020 524 adults infected All Israelis arriving from other countries were instructed to self-quarantine. The education system was inoperative. Gathering of > 10 people was prohibited. Lockdowns were placed on targeted areas were spread was high. Wave 5 March 25th, 2020 2436 adults infected. One hospitalized adult died from the virus. People were instructed to Minimize outings to strictly crucial activities, and not to travel more than 100 m from home otherwise. Group praying (“minyan”) was prohibited. Wave 6 April 1st, 2020 6168 adults infected. Prohibitions and guidelines were dramatically increased and were enforced thereafter (e.g., fines for not wearing masks). Fig. 1 Implied means of the continuous study variables as a function of the General Anxiety classes. Notes: GA = General Anxiety; W0 = Wave 0; VSA = Virus-Specific Aniety; Trait Anx = Trait Anxisty; Self-Crit = Self-criticism; Perceived Sup = Perceived Social Support; IMOH Anx = Israeli Ministry of Health, perception as anxious; IMOH Ben = Israeli ministry of health, benevolent perceptions. Importantly, there are different scales for different variables. GA, IMOH-Anx and IMOH-Ben are on a 1-5-point scale, whereas VSA, Trait Anxiety, Self-Criticism, and Perceived Support are on a 1-7-point scale. Fig. 1 Data were collected online via a commercial web panel specializing in research. Because this panel only sampled Israeli-Jews, we did not have access to ethnic minorities (see limitations at the end of this article). Nonetheless, the sample was representative of the adult Israeli-Jewish population in terms of gender and age, and very closely representative in terms of residence, religiousness, education and social-economic status. The original N was 1018. Using Week 0 (the “strong baseline”) as an anchor, attrition during subsequent weeks was 14%, 19%, 20%, 23%, 23%, and 26%, respectively, with an average of 21%. Stringent data cleaning (Shahar et al., 2022) reduced N to 991, although specific analyses had to rely on listwise deletion of missing values, yielding an N of 958. 2.2 Measures Our assessment strategy was based on numerous, very brief, self-report measures. This strategy was developed because of the rapidly changing nature of the COVID-19 crisis, frequent quarantine, and curfew, coupled with our interest in a wide array of psychosocial constructs. General anxiety was assessed by averaging two items from the State Anxiety Inventory (SAI; Spielberger, 1989): “I am tense” and “I am anxious” (a 5-point Likert scale; Cronbach's αs > 0.87 for all waves). As indicated by (Shahar et al., 2022), mean levels of general anxiety in this sample were nearly identical to those reported by Israeli et al. (2018) following a military escalation involving missile attacks on Israeli civilians, thus further corroborating the measure's validity. Virus-specific anxiety was assessed via a single item worded as, “To what extent are you worried/stressed from the spread of the coronavirus?” with responses on a 7-point scale. Single-item measures were shown to be successful in tapping perceived stress during mass traumas (e.g., Shahar et al., 2009). Self-criticism was assessed via a single item taken from the Depressive Experiences Questionnaire (DEQ; Blatt et al., 1976), arguably the most extensive used measure of personality vulnerability to depression. Its 66 items tap self-criticism, dependency, and personal efficacy (construed as an index of resilience). Over the years, briefer versions of the DEQ were tested, particularly those tapping self-criticism. The briefest DEQ-self-criticism measure, developed by Shahar and colleagues (see Shahar, 2015), includes six items with a clear content validity. The item with the clearest content validity is worded: “I have a tendency to be very critical toward myself.” This item was used herein. Per the DEQ's format, a 7-point scale was used, anchored at 1 = “strongly agree” up to 7 = “strongly disagree". Trait anxiety was assessed via a straightforward, single item, worded as follows: “I am an anxious type of person.” Because this construct was measured as a temperamental counterpart of self-criticism, the same 7-point scale used for self-criticism was applied to trait anxiety. Not only does this item appear to tap the gist of the trait anxiety construct, it is also likely to assess past anxiety disorders, albeit indirectly. The rationale here is that, while many persons who are “of the anxious type” will not develop anxiety disorders, it is hard to imagine people with anxiety disorders who will not endorse this item. Perceived social support was measured via a single item: “To what extent to you feel that you have available emotional support from close people?” A 7-point scale was used, anchored at 1 = “Not at all” to 7 = “Very much.” Single-item measures of perceived social support have been successfully used in previous research (Knapstad et al., 2014). Finally, we assessed negative and positive attitudes toward the Israeli Ministry of Health using a modified measure utilized by Shahar et al. (2017) and Noyman-Veksler et al. (2021). Both reports were based on a two-item measure inquiring about the extent to which respondents believe that the ministry of health is “caring” (first item; positive attitudes) and whether the ministry of health is “hysteric” (second item; negative attitudes). Although both items were shown to be predictive of compliance and hesitancy concerning the mass vaccination launched by the IMOH against poliovirus, we sought to improve the sensitivity and accuracy of this measure in the following ways. First, realizing that the word “hysteric” is colloquial, we replaced it with “anxious.” Second, we added four additions to the caring item that taps positive attitudes. Thus, we inquired about the extent to which respondents feel that the ministry of health (1) is handling the COVID-19 crisis well; (2) is caring; and (3) conducts itself confidently, and the extent to which the respondent (4) trusts the ministry of health. All five items (i.e., manages well, caring, anxiety, confidence, trust) were based on a 5-point scale, whereby 1 = “Not at all,” 2 = “A little,” 3 = “Don't know,” 4 = “To some extent,” and 5 = “Very much." Next, we subjected these four items to a principal component analysis with varimax rotations using the STATISTICA software (StatSoft Inc, 2012). A single component was extracted with an eigenvalue of 3.00, explaining 60.10% of the variance of the items. Very high loadings of the positive attitudes items on the extracted component appeared: −0.84, -0.87, −0.84, and −0.87 for manages well, caring, confidence, and trust, respectively. The loading of the “anxiety” item on this component was small: 0.19. Thus, we created a composite score of positive attitudes by averaging the four pertinent variables (α = 0.88) and used the “anxiety” item as a single-item measure of negative attitudes. 2.3 Data analysis Analyses proceeded in four stages. 2.3.1 Stage 1: descriptive statistics and cross-sectional regressions Shahar et al. (2022) presented means and standard deviations of the study variables, with the exception of trait anxiety, self-criticism, perceived social support, and positive/negative perceptions of the IMOH. Hence, we calculated means and standard deviations of these additional variables, and the correlations among them. Note that these are correlations pertaining to the baseline (Week 0) assessment. Next, we examined associations between these study variables and the demographic variables: gender, religiousness binarized, education binarized, and age groups. For the first, binary variables, independent-sample t-tests were calculated, and statistically significant t-values were also accompanied by calculating effect size using Cohen's d. For the age groups, 5-level variable, we conducted an Analysis of Variance (ANOVA) separately for each of the study variables. Statistically significant F-tests were followed by post-hoc analyses identifying group differences, using Tukey's Honest Significant Difference (HSD) with unequal Ns. Finally, we computed correlations between these variables and the variables used for Shahar et al. (2022). 2.3.2 Stage 2: cross-sectional multiple regression analyses To examine unique associations with general and virus-specific anxiety, we regressed each of these outcomes onto demographics, trait anxiety, self-criticism, social support, and positive and negative (anxious) perceptions of the ministry of health. When general anxiety was considered as an outcome, virus-specific anxiety was added to the list of predictors, and vice versa. 2.3.3 Stage 3: tests of hypothesized main effects As reported in Shahar et al., 2022, the N of 991 was subjected to Latent Class Growth Models and General Mixture Models using the software Mplus version 8.5 (Muthen & Muthen, Los Angeles, California). The classes/trajectories identified for general anxiety and virus-specific anxiety were then predicted using multinomial regression conducted in the context of the General Mixture Modeling analyses. Separate analyses were conducted for general anxiety and virus-specific anxiety classes. The demographic predictors were: gender, age group (see above), binarized education (academic degree completed), and binarized religiousness (non-religious [secular and traditional] vs. religious [national religious and ultraorthodox]). Employment was not entered into the analysis because 92% were employed. Similarly, income was not entered into the analysis because 109 (>10%) did not respond to the income item. In addition to gender, age group, and binarized education and religious identification, the list of predictors also included Wave 0 levels of general anxiety and virus-specific anxiety. For the present investigation, these multinomial regressions were conducted again, except that the focal predictors were added: trait anxiety, self-criticism, perceived social support, perception of the ministry of health as anxious (negative attitudes), and perception of the ministry of health as positive/benevolent). Consistent with Shahar et al. (2022), in order to account for familywise statistical error (which may be increased by the semi-exploratory nature of some of our group comparisons), we applied Holm's (1979) correction for each predictor separately. 2.3.4 Stage 4: tests of hypothesized stress-buffering effects To test the hypothesized stress-buffering effects, we added two 2-way multiplicative interaction terms to the models described in Stage 3: one involving baseline virus-specific anxiety by baseline perceived social support, and the other involving baseline virus-specific anxiety by the positive attitudes towards the ministry of health index. As in Stage 3, analyses were run separately for the general anxiety and virus-specific anxiety classes. 3 Results 3.1 Stage 1: descriptive statistics Means, standard deviations, and intercorrelations among this study's variables are presented in Online Supplementary Material #1. The correlations were largely low-to-moderate. As expected, self-criticism was moderately correlated with trait anxiety. Online Supplementary Material #2 contains t-tests examining differences in gender, religious identification, and education in terms of the study variables. Women had lower levels of positive attitudes toward the ministry of health and higher self-criticism and trait anxiety than men. Religious participants had lower levels of anxious attitudes toward the ministry of health, lower self-criticism and trait anxiety, and higher positive attitudes toward the ministry of health than non-religious participants. Participants with an academic education had lower levels of positive attitudes toward the ministry of health, but also lower trait anxiety and higher perceived support, than less educated participants. Note, however, that for almost all statistically significant t-tests, the effect sizes were small (<0.20). The single exception was a moderate effect size for religiousness and positive attitudes toward the ministry of health: Religious participants had higher values than non-religious ones. Online Supplementary Material #2 also describes an Analysis of Variance (ANOVA) with the 5-level age group variable as the predictor and the five study variables as the outcomes. Numbers italicized pertain to effect surviving the Holm correction. For these effect sizes that are negatively directed (i.e., O.R.s < 1), we performed the 1/OR transformation (see Chen, P. Cohen & S. Cohen, 2010), to be able to compare them with positively directed effects (O.R.s > 1). Finally, we computed correlations among the study variables and the anxiety variables used for Shahar et al. (2022). These appear in Online Supplementary Material #3. Because of the large sample size, even small correlations may reach statistical significance. Hence, correlations above 0.10 are highlighted using bold letters, and those higher than 0.20 are bolded and underlined. The clear pattern emerging from Online Supplementary Material # is that only trait anxiety exhibits consistent correlations greater than 0.20 magnitude with the anxiety variables across the assessment waves. 3.2 Stage 2: cross-sectional multiple regression analyses 3.2.1 General anxiety The model accounted for 47% of the variance of general anxiety (Adjusted R 2; F [10,944] = 87.82, p < .001; N = 955). Statistically significant predictors were: gender (women are more anxious at baseline; b = 0.05, SE = 0.02, t = 2.39, β = 0.12, p < .05), age group (the younger the participant, the less anxious he was at baseline; b = −.04, SE = 0.01, t = −2.36, β = −0.05, p < .05), religious identification (non-religious participants were more anxious at baseline; b = −0.14, SE = 0.01, t = −2.31, β = −0.05, p < .05), baseline VSA (b = 0.32, SE = 0.01, t = 22.88, β = 0.57, p < .001), trait anxiety (b = 0.09, SE = 0.01, t = 5.92, β = 0.15, p < .001), and perceived social support (b = −0.05, SE = 0.01, t = −4.18, β = −0.10, p < .05). 3.2.2 Virus-specific anxiety The model accounted for 43% of the variance of this outcome (Adjusted R 2; F [10,944] = 73.62, p < .001; N = 955). Statistically significant predictors were: religious identification (non-religious participants were more anxious at baseline; b = −0.32, SE = 0.11, t = −2.82, β = −0.07, p < .01), education (less educated were more anxious at baseline; b = −0.29, SE = 0.09, t = −3.13, β = −0.07, p < .001), and general anxiety (b = 1.08, SE = 0.04, t = 22.88, β = 0.62, p < .001). 3.3 Stage 3: tests of hypothesized main effects 3.3.1 General anxiety In Online Supplementary Material #4, we present the means and standard deviations of the continuous predictors (i.e., Wave 0 levels of general anxiety, virus-specific anxiety, trait anxiety, self-criticism, perceived social support, perception of the ministry of health as anxious, and positive/benevolent perceptions of the ministry of health), which are implied by the multinomial regression analyses targeting the general anxiety classes. These implied means and standard deviations were derived from the “BCH” Command of MPlus. In Fig. 1 we visualize mean differences in these predictors as a function of the four general anxiety classes. In Table 2 a and Table 2b we present results of the multinomial regression analyses targeting general anxiety classes. The columns in the tables pertain to the six comparisons emanating from the four general anxiety classes. The first column presents the putative predictors: demographics (gender, age groups, binary religiosity and education), state anxiety (Wave 0 general anxiety and virus-specific anxiety), social variables (ministry of health as anxious, positive attitudes toward the ministry, and perceived social support), and personality/self variables (trait anxiety and self-criticism). The next columns are split in half, one pertaining to the multinomial regression's estimates (b) and related standard error, and the other pertaining to odds ratios and related a 95% confidence interval. Effects surviving the Holm (1979) correction method are bolded and highlighted. Effects meeting conventional statistical significance (p < .05), but not the Holm correction method, are bolded but not highlighted. Although we treat only the first type of effects as meaningful, we also present the second for the benefit of future meta-analyses and replications. Accordingly, p values are presented for all statistically significant effects.Table 2 Results of the multinomial regression analyses predicting the general anxiety classes. Table 22a. Panic vs. TS Complacency vs. TS Balanced vs. TS b/SE O.R.(C.I.) b/SE O.R.(C.I.) b/SE O.R.(C.I.) DEMOGRAPHICS Gender 1.13 (p < .01) 3.10 (1.41/6.80) −.34/.24 .70 (.43/1.14) −.17/.28 .83 (.48/1.45) Age Groups −.13/.13 .87 (.67/1.13) .13/.08 1.14 (.96/1.36) −.08/.10 .91(.74/1.21) Religiosity Binary −.44/.51 .64 (.23/1.76) .21/.28 1.23 (.70/1.66) −.04/.34 .95 (.48/1.88) Education Binary .04/.37 1.05 (.50/2.16) .25/.25 1.28 (.78/2.11) .59/.28 (p < .05) 1.80 (1.04/3.12) STATE ANXEITY GA Wave 0 .98/.18 (p < = .001) 2.68 (1.86/3.85) −.65/.19 (p < .01) .52 (.35/.77) 1/OR = 1.92 −.04/.17 .95 (.67/1.36) VSA Wave 0 .23/.14 1.26 (.95/1.66) −.42/.08 (p < .01) .65 (.55/.78) 1/OR = 1.53 −.31/.09 (p < .01) .73 (.60/.88) 1/OR = 1.36 SOCIAL VARIABLES IMOH-ANX .29/.13 1.22 .94/1.59 −.04/.10 .96 (.78/1.17) .05/.10 1.05(.85/1.29) IMOH-POS −.35/.16 (p < .05) .70 .51/.97) .21/.11 1.23 (.99/1.53) .10/.12 1.11 (.87/1.42) Perceived Support −.28/.09 (p < .01) .75 (.62/.90) 1/OR = 1.33 .02/.07 1.02 (.88/1.18) −.08/.08 .91 (.77/1.08) PERSONALITY AND SELF Trait Anxiety .08/.10 1.08 (.88/1.34) −.29/.07 (p = .000) .74 (.64/.87) I/OR = 1.35 −.10/.08 .89 (.76/1.05) Self-criticism .34/.12 (p < .01) 1.41 (1.11/1.79l) .02/.08 1.02 (.87/1.20) −.05/.10 .94 (.77/1.14) 2 b. Balanced vs. Panic Balanced vs. Complacency Complacency vs. Panic b/SE O.R.(C.I.) b/SE O.R.(C.I.) b/SE O.R.(C.I.) DEMOGRAPHICS Gender −1.30/.41 (p < .01) .27 (.12/.60)\| 1/OR = 3.70 .17/.24 1.18 (.74/1.90) −1.48/.41 (p < .01) .22 (.10/.51) 1/OR = 4.54 Age Groups .04/.13 1.04 (.80/1.36) −.22/.08 (p < .01) .79 (.67/.94) 1/OR = 1.26 .27/.13 (p < .05) 1.31 (1.00/1.71) Religiosity Binary .39/.51 1.48 (.54/4.06) −.26/.29 .77 (.43/1.36) .65/.51 1.92 (.70/5.31) Education Binary .54/.37 1.71 (.83/3.55) .33/.24 1.40 (.87/2.25) .20/.38 1.22 (.57/2.61) STATE ANXEITY GA Wave 0 −1.02/.20 (p < .001) .35 (.24/.53) 1/OR = 2.85 .60/.21 (p < .01) 1.83 (1.20/2.81) −1.63/.24 (p < .001) .19 (.12/.31) 1/OR = 5.26 VSA Wave 0 −.54/.14 (p = .000) .58 (.43/.77) 1/OR = 1.72 .11/.08 1.11 (.95/1.31) −.65/.15 (p = .000) .51 (.38/.69) 1/OR = 1.96 SOCIAL VARIABLES IMOH-ANX −.15/.14 .85 (.65/1.12) .09/.09 1.09 (.90/1.32) −.24/.14 .78 (.58/1.04) IMOH-POS .45/.17 (p < .01\|) 1.51 (1.13/2.21) −.10/.11 .90 (.72/1.12) 1.24/ 1.75 (1.24/2.47) Perceived Support .19/.10 (p < .06) 1.21 (.99/1.48) −.11/.06 .89 (.78/1.02) .30/.10 (p < .01) 1.36 (1.11/1.66) PERSONALITY AND SELF Trait Anxiety −.19/.11 .82 (.66/1.02) .18/.08 (p < .05) 1.20 (1.02/1.41) −.37/.11 (p < .01) .68 (.54/.86) 1/OR = 1.47 Self-criticism .40/.13 (p < .01) .66 (.51/.85) 1/OR = 1.51 −.08/.07 .91 (.79/1.07) −.32/.12 (p < .05) .72 (.56/.93) 1/OR = 1.38 Notes. SE = Standard Errors; OR = Odd Ratios; TS = Threat Sensitivity Class; CI = Confidence Interval; GA = General Anxiety; VSA = Virus Specific Anxiety; IMOH-ANX = Israeli Ministry of Health – Anxiety Item; IMOH-PO = Israeli Ministry of Health, Positive Attitudes Index. Numbers bolded but not highlighted pertain to effects that are conventionally statistically significant, but which did not “survive” the Holm correlation. Of those effects surviving the Holm correction method, some are positively directed (bs > 0 and OR > 1). For these, evaluation of effect size is straightforward. Some of the effects, however, are negatively directed (bs < 0 and OR < 1). For these effects, evaluation of effect size is not straightforward, because the lower bound of the OR is 0. Hence, we consulted Chen et al. (2010), and transformed OR into 1/OR. Chen et al. (2010) recommend that ORs = 1.68, 3.47, and 6.71 are equivalent to Cohen's d 0.2 (small), 0.5 (medium), and 0.8 (large). As shown in Tables 2a and 2b, the general anxiety panic class was characterized by a higher prevalence of women and higher levels of Wave 0 general anxiety and self-criticism, compared to each of the other classes. Compared to general anxiety threat sensitivity and complacency, but not Balanced, the general anxiety panic class had lower levels of perceived social support. General anxiety panic also had higher levels of Wave 0 virus-specific anxiety and lower levels of positive attitudes toward the ministry of health than the balanced and complacency classes (but not threat sensitivity). general anxiety complacency differed from each of the other classes in terms of lowest levels of Wave 0 general anxiety. Additionally, general anxiety complacency had lower levels of Wave 0 VSA and trait anxiety compared to general anxiety panic and threat sensitivity (but not balanced), and older age compared to Balanced. Finally, threat sensitivity differed from balanced only in terms of Wave 0 virus-specific anxiety (higher in the former class). Most of the effects surviving the Holm correction were small in size. Three exceptions were evinced: a moderate effect size for the higher prevalence of women in general anxiety panic compared to balanced; the large effect size for the higher prevalence of women in general anxiety panic compared to complacency; and the large effect size for the higher level of Wave 0 general anxiety in general anxiety panic compared to complacency. 3.3.2 Virus-specific anxiety In Online Supplementary Material #5, we present results of the multinomial regression analyses targeting virus-specific anxiety classes. Here, the pattern is much simpler than the one evinced for general anxiety. As expected, the three classes were ordered linearly in terms of Wave 0 virus-specific anxiety: Panic > threat sensitivity > complacency. The only additional effect surviving Holm's correction pertained to trait anxiety differentiating between panic and threat sensitivity (higher levels in the former class). The size of the effect was mostly small, with the single exception of a large effect size evinced for Wave 0 virus-specific anxiety in differentiating panic and complacency. 3.4 Stage 4: tests of hypothesized stress-buffering effects No statistically significant interactions were found. 4 Discussion Building on Shahar et al. (2022), we examined theory-based psychosocial predictors of anxiety trajectories during the first wave of the spread of COVID-19 in Israel. Using a group of theories that focus on mental representations of self and others (see integration in Shahar, 2020), as well as Bronfenbrenner's (1977) social-ecological theory, we focused on four social-cognitive predictors: self-criticism, perceived social support, and negative and positive attitudes towards the IMOH. The predictive effect of these variables was examined while controlling for the potentially confounding effect of trait anxiety. Multinomial regression analyses were run with these novel predictors, over and above those examined by Shahar et al. (2022). Our findings reaffirm the strong predictive effect of baseline levels of virus-specific anxiety. This variable was the only state-anxiety predictor differentiating all virus-specific anxiety classes. Moreover, baseline virus-specific anxiety consistently predicted general anxiety classes, on top of the expected predictive effect of baseline general anxiety. Also noteworthy is the failure of perceived social support and positive attitudes toward the ministry of health to buffer against this predictor's effect on both general anxiety and virus-specific anxiety classes. While this finding is highly consistent with global reports about the devastating mental health effects of COVID-19, it should be mentioned that two studies in Israel reported an actual reduction of the incidence of attempted suicide (Travis-Lumer et al., 2021a) and incident schizophrenia (Travis-Lumer et al., 2022) despite an increase in the incident rate of antidepressant use (Frangou et al., 2022). It should be mentioned, however, that authors of the two studies acknowledge the possibility of an increased risk for attempted suicide in the more distant future, and they also forecasted – mathematically – an increased risk for incident schizophrenia once restrictions are lifted. Social-cognitive predictors were only relevant to general anxiety classes. Members of the general anxiety panic class were more self-critical than all other counterparts, had lower levels of perceived social support than threat sensitivity and complacency, and had lower levels of positive attitudes toward the ministry of health than balanced and complacency. Thus, in addition to panic members' being more anxious pre-exposure, they also appear to be highly demoralized, having little faith in themselves, close others, and social institutions overseeing the crisis. That members of this class were also over-represented by females is consistent with long-standing findings as to the higher vulnerability of women – compared with men – to emotional disorders, both prior to and during COVID-19 (Liu et al., 2021). Our results are consistent with the existence of a complex, panic-to-complacency continuum concerning the population's anxious response to the COVID-19 crisis. Thus, while at the fifth assessment wave (Wave 4), panic and threat sensitivity evinced similar levels of general anxiety, we found that the two classes differed markedly in terms of demographics and social cognition: The general anxiety panic class consisted of more women, and exhibited higher of self-criticism and lower perceived support. Similarly, complacency and balanced—the two classes with the lowest levels of general anxiety— differed in terms of age and baseline general anxiety. Additionally, general anxiety and balanced, constituting the two mid-level general anxiety classes, nevertheless differed in terms of baseline virus-specific anxiety. Even in the case of virus-specific anxiety classes, where the pattern was much more parsimonious, baseline virus-specific anxiety differentiated the middle group, threat sensitivity, from both the highest (panic) and the lowest (complacency). Finally, threat sensitivity had lower levels of trait anxiety than panic. These effects encourage a nuanced appraisal of the unfolding of anxiety during medical crises. 4.1 Limitations and strengths While the representativeness of our sample in terms of Israel's Jewish adult population appears to be very high, ethnic minorities (e.g., Arab, Bedouin) were left out because of the online sampling procedure (see Shahar et al., 2022). It is thus incumbent on us to reach out to all Israeli ethnic minorities. Another limitation pertains to our reliance on very brief, solely self-report measures. Although the results attained constitute evidence for the psychometric quality of our abbreviated measures, long measures are psychometrically superior and might have yielded additional findings. In particular, the single item assessing negative attitudes toward the Israeli Ministry of Health – “anxious” – did not yield statistically significant findings, and is thus flagged for future replication and upgrades. Finally, generalizability to countries outside of Israel should be made with great caution. The study's strengths include the utilization of a strong, pre-exposure baseline, the employment of high-resolution assessments, high retention of participants across assessment waves, and the implementation of General Mixture Modeling analyses and multinomial regression analyses with Holm correction. These strengths yield a fine-grained understanding of the unfolding of anxiety in the Jewish-Israeli population, while also enabling causal inference (within the confines of non-experimental studies). 4.2 Public health implications That people who are generally anxious before the crisis (whether because of general state, crisis-specific, or trait anxiety) become even more anxious throughout the crisis underscores the utility of mass screening of anxiety by government officials and the employment of brief, digital interventions to address the fears of anxious individuals (Dorison et al., 2020). Members of the general anxiety panic class were highly demoralized. Demoralization is identified as a barrier to evidence-based psychological treatment (e.g., Tarescavage et al., 2015), and may also derail compliance with official guidelines. Identifying and intervening with demoralized individuals may thus increase compliance and improve the management of the medical crisis. The third implication concerns leadership. Attitudes toward the acting agency (e.g., the ministry of health) may be expressed in terms of personification, that is, by individuals experiencing the agency as a protagonist in their lives (Shahar et al., 2017; Noyman-Veksler et al., 2021). Leaders of the acting agency are likely to symbolize this protagonist, and thus have the power to shape the nature of the aforementioned personification. Calm, coherent, and realistically optimistic leadership has the potential to tilt the personification in a positive direction, thus increasing individuals' alliance with the agency and compliance with its guidelines (Kahn, 2009). 5 Conclusions As of the time of writing of this paper, the entire world has been hit by six waves of the pandemic since its declaration as a pandemic at the beginning of 2020. Despite the presence of effective vaccinations, the world still braces itself to collaborate globally in the fight against COVID-19, and the public's behavior is still considered the paramount factor dictating the level of the spreads. Emerging public health threats from monkeypox, re-emergence of polio in several countries and the effects of climate change show the importance of developing broad perspectives on public health responses. Understanding public anxiety and its unfolding during the crisis is a crucial element in promoting effective public health interventions, demanding multi-disciplinary collaborations. Preexisting anxiety shapes subsequent anxious responses to the spread of COVID-19. The general anxiety panic group may be markedly demoralized, requiring public-health attention. Because self-criticism has been shown, over almost five decades of research, to serve as a formidable marker of vulnerability to psychopathology and/distress, we believe that the panic group could be identified – both in the general population and in clinics – as evincing elevated levels of general anxiety coupled with high levels of self-criticism. The COVID-19-Israeli Public Behavior Project can serve as an example for an interdisciplinary collaboration. Nevertheless, it remains to be seen whether these kinds of projects are embedded within existing public health structures sustainably. As mentioned above, leadership is crucial during emergencies, but not less important during “normal” preparation and capacity-building times. \|Collaboration between Israeli and European universities and public health institutes are currently underway, focusing on capacity building among future public health leaders (Bashkin et al., 2021). We hope that our findings would be useful to policymakers and public health experts – inside and beyond Israel – not just in stirring public behavior toward responsible routes, but on broader terms how to create the structures to study and engage in understanding better how best to engage with different parts of society. Credit author statement Golan Shahar,Ph.D: Conceptualization; Data curation; Formal analysis; Funding acquisition; Investigation; Methodology; Project administration; Roles/Writing - original draft.; Limor Ahronson-Daniel,Ph.D: Conceptualization; Funding acquisition; Investigation; Methodology; Roles/Writing – Review & Editing.; David Greenberg, MD: Conceptualization; Funding acquisition; Investigation; Methodology; Roles/Writing – Review & Editing.; Hadar Shalev, MD: Conceptualization; Funding acquisition; Investigation; Methodology; Roles/Writing – Review & Editing.; Avichai Tendler, Ph.D: Conceptualization; Formal analysis; Investigation; Methodology; Roles/Writing – Review & Editing.; Itamar Grotto, M.D., Ph.D: Conceptualization; Investigation; Methodology; Roles/Writing – Review & Editing.; Patrick Malone, Ph.D.: Formal analysis; Methodology; Roles/Writing - original draft.; Nadav Davidovitch, MD, Ph.D.: Conceptualization; Funding acquisition; Investigation; Methodology; Roles/Writing – Review & Editing. Appendix A Supplementary data The following is the Supplementary data to this article:Multimedia component 1 Multimedia component 1 Data availability Data will be made available on request. ☆ Financial Support Was Received from the BGU President Task Force on COVID-19 Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.socscimed.2022.115585. ==== Refs References Abela J.R. Webb C.A. Wagner C. Ho M.H.R. Adams P. The role of self- criticism, dependency, and hassles in the course of depressive illness: a multiwave longitudinal study Pers. Soc. Psychol. Bull. 32 3 2006 328 338 16455860 Bandura A. Self-efficacy: the Exercise of Control 1997 Freeman New York, NY Blatt S.J. Auerbach J.S. Levy K.N. Mental representations in personality development, psychopathology, and the therapeutic process General Review of Psychology 1 1997 351 374 10.1037/1089-2680.1.4.351 Blatt S.J. D'Afflitti J.P. Quinlan D.M. 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==== Front iScience iScience iScience 2589-0042 The Author(s). S2589-0042(22)01990-3 10.1016/j.isci.2022.105717 105717 Article A multi-omics based anti-inflammatory immune signature characterizes long COVID-19 syndrome Kovarik Johannes J. 18 Bileck Andrea 238 Hagn Gerhard 38 Meier-Menches Samuel M. 23 Frey Tobias 4 Kaempf Anna 4 Hollenstein Marlene 4 Shoumariyeh Tarik 1 Skos Lukas 3 Reiter Birgit 4 Gerner Marlene C. 5 Spannbauer Andreas 6 Hasimbegovic Ena 6 Schmidl Doreen 7 Garhöfer Gerhard 7 Gyöngyösi Mariann 6∗ Schmetterer Klaus G. 4∗∗ Gerner Christopher 239∗∗∗ 1 Department of Internal Medicine III, Clinical Division of Nephrology and Dialysis, Medical University of Vienna, Waehringer Gürtel 18-20, Vienna 1090, Austria 2 Joint Metabolome Facility, Faculty of Chemistry, University of Vienna, Waehringer Straße 38, 1090 Vienna, Austria 3 Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Waehringer Straße 38, 1090 Vienna, Austria 4 Department of Laboratory Medicine, Medical University of Vienna, Waehringer Gürtel 18-20, Vienna, Austria 5 Division of Biomedical Science, University of Applied Sciences FH Campus Wien, Vienna, Austria 6 Department of Medicine II, Division of Cardiology, Medical University of Vienna, Waehringer Gürtel 18-20, Vienna, Austria 7 Department of Clinical Pharmacology, Medical University of Vienna, Vienna, Austria ∗ Corresponding author ∗∗ Corresponding author ∗∗∗ Corresponding author 8 These authors contributed equally 9 Lead contact 5 12 2022 20 1 2023 5 12 2022 26 1 105717105717 17 8 2022 13 10 2022 29 11 2022 © 2022 The Author(s) 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. To investigate long COVID-19 syndrome (LCS) pathophysiology, we performed an exploratory study with blood plasma derived from three groups: 1) healthy vaccinated individuals without SARS-CoV-2 exposure; 2) asymptomatic recovered patients at least three months after SARS-CoV-2 infection and; 3) symptomatic patients at least 3 months after SARS-CoV-2 infection with chronic fatigue syndrome or similar symptoms, here designated as patients with long COVID-19 syndrome (LCS). Multiplex cytokine profiling indicated slightly elevated pro-inflammatory cytokine levels in recovered individuals in contrast to patients with LCS. Plasma proteomics demonstrated low levels of acute phase proteins and macrophage-derived secreted proteins in LCS. High levels of anti-inflammatory oxylipins including omega-3 fatty acids in LCS were detected by eicosadomics, whereas targeted metabolic profiling indicated high levels of anti-inflammatory osmolytes taurine and hypaphorine, but low amino acid and triglyceride levels and deregulated acylcarnitines. A model considering alternatively polarized macrophages as a major contributor to these molecular alterations is presented. Graphical abstract Immunology; Immune response; Omics Subject areas Immunology immune response omics Published: January 20, 2023 ==== Body pmcIntroduction The outbreak of the COVID-19 pandemic in late 2019 has led to an unprecedented worldwide health crisis with the rapid spread of a novel pathogenic member of the coronavirus family (termed SARS-CoV-2) infecting more than 500 million people worldwide. Acute SARS-CoV-2 infection may induce an inappropriate and unique inflammatory response causing pathognomonic severe respiratory symptoms which can be further accompanied by damage to multiple organs such as brain, heart, and kidneys.1 , 2 , 3 Accordingly, acute COVID-19 infection may cause high lethality claiming more than 6 million deaths worldwide so far (www.who.int/emergencies/diseases/). Since the start of the pandemic, it has also become evident, that not all patients fully recover following SARS-CoV-2 infection. At first, the observed symptoms were mainly attributed to psychological conditions such as anxiety and stress in the affected individuals.4 However, it is now recognized that chronic persistence of COVID-19 symptoms after acute infection constitutes a novel somatic disease entity termed post-acute COVID-19 syndrome (PACS) or long COVID-19 syndrome (LCS).5 Typically, patients with LCS suffer from general fatigue, lack of concentration (self-described as “brain fog”) and physical fitness, dyspnea, postural tachycardia as well as a broad range of other clinical symptoms throughout the whole organism, which severely impedes the quality of life.6 , 7 These clinical presentations mirror the situations found in chronic fatigue syndrome (CFS) which can be secondary to infection with different viruses and was also during previous coronavirus epidemics including Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). Especially for the latter symptom persistence for up to two years has been observed.8 Strikingly, the development of LCS is not associated with disease severity and so far, potential risk factors and associated comorbidities are poorly understood.6 , 9 , 10 Similarly, the pathogenesis and pathophysiology of this disease remain rather elusive to date. The limited set of available studies on LCS have indicated tissue damage accompanied by chronic inflammation following recovery from acute COVID-19 infection exacerbation of pre-existing (auto)immune-pathologies or persistence of SARS-CoV-2 at distinct sites in the body.11 , 12 Yet, neither general consent about the predisposition leading to LCS nor consent about the optimal therapeutic management of the disease has emerged so far. This may also be owed to the rather heterogeneous presentation of symptoms and the lack of awareness of this condition, which allows the speculation that most cases so far remain unrecognized. In light of the high infection rate worldwide, it can be expected that the prevalence of LCS will massively increase in the future, leading to a further COVID-19-associated long-term challenge and burden for the healthcare system. Accordingly, a definition of biomarkers for the diagnosis of LCS as well as in-depth studies for the characterization of pathophysiological processes are clearly warranted. Thus, we here set up an exploratory study to perform broad scale mass spectrometry-based multi-omics analysis of blood specimens from healthy donors after vaccination (termed healthy; H), patients with COVID-19 who had fully recovered (termed recovered; R) and compared them to plasma samples of patients with LCS characterized by lack of concentration, fatigue and associated symptoms (termed long COVID-19; LC) The combination of proteomics and metabolomics focusing on oxylipin analyses has already been demonstrated to strongly support the investigation of pathomechanisms in various diseases.13 , 14 , 15 Using this versatile analysis approach, we were able to identify anti-inflammatory and hypo-metabolic signatures in the proteome, lipidome, and metabolome of LCS patients, thereby providing insights into the molecular regulation and pathophysiology of LCS. Results Patient characteristics and study design The first study group consisting of 13 healthy individuals with no history of SARS-CoV-2 infection was recruited three months after full anti-SARS-CoV-2 immunization which was also confirmed by anti-N (−)/anti-S (+) status (termed healthy; H). The second recruited group consisted of 13 age- and gender-matched individuals who had a SARS-CoV-2 infection history at least three months prior to inclusion into this study, but were symptom free in anamnesis at the time of blood draw (termed recovered; R). Infection status was confirmed by positive anti-N and anti-S antibody levels. The third group of 13 study patients had similarly succumbed to PCR-positive SARS-CoV-2 infection at least three months before presentation. All patients in this group had a positive anamnesis of chronic fatigue and/or severe chronic lack of concentrations following SARS-CoV-2 infection combined with at least one more chronic symptom including dyspnea, coughing, and loss of smell among others at the time of presentation, qualifying them for the diagnosis LCS (whole patient characteristics are displayed in Tables 1A and 1B). Again, post-infection status was confirmed by positive antibody testing for anti-N and anti-S antibodies. For the here described exploratory study, specimens from 13 individuals from each group were selected for multi-omics analysis including a cytokine array, untargeted shotgun proteomics, an untargeted eicosanoid/docosanoid analysis, and a targeted metabolomics assay. Routine laboratory testing of basic protein, lipid, and lipoprotein parameters showed no differences between the three groups. Similarly, CRP levels in all three groups were below or only minimally above the threshold (Table 2 ). Furthermore, none of the analyzed individuals presented with fever or displayed any clinical signs of systemic infection at the time of blood withdrawal, ruling out major systemic metabolic or acute inflammatory processes in the tested individuals at this time point.Table 1 Study group characteristics Table 1A healthy (n = 13) recovered (n = 13) long COVID (n = 13) General data Gender (f:m) 7:6 7:6 9:4 Age [years] 30 (25–43) 32 (24–49) 33 (21–53) Time after exposure [months] 6 (3–8) 10 (3–12) 7 (3–10) Chronic disease (total number (percentage)) Asthma bronchiale 0 1 (0.07) 4 (0.31) Multiple sclerosis 0 0 1 (0.07) Autoimmune thyreoiditis 0 1 (0.07) 1 (0.07) Atopic dermatitis 0 1 (0.07) 0 Psoriasis arthritis 1 (0.07) 0 0 Table 1B healthy (n=13) recovered (n=13) long COVID (n=13) Chronic Symptom (total number (percentage)) Lack of concentration – – 10 (0.77) Fatigue – – 9 (0.69) Dyspnoea – – 9 (0.69) Chronic cough – – 2 (0.15) Muscular aching – – 3 (0.23) Amnesic aphasia – – 5 (0.38) Sleep disorder – – 4 (0.31) Urinary incontinence – – 2 (0.15) Nausea – – 3 (0.23) Tinnitus – – 4 (0.30) Other symptoms – – 9 (0.69) Table 2 Routine serum parameters (mean values; ranges) healthy (n = 13) recovered (n = 13) long COVID (n = 13) total protein [g/L] 72.7 [65.2–77.5] 73.10 [67.5–78.7] 75.3 [63.5–84.3] albumin [g/L] 49.7 [46.3–54.4] 48.5 [41–53.4] 50.7 [43.7–55.6] triglyceride [mg/dL] 58 [36-166] 115 [49-353] 77 [48-159] cholesterol [mg/dL] 171 [108-198] 207 [146-281] 176 [100-192] HDL [mg/dL] 60 [36-94] 61 [49-93] 54 [37-74] LDL [mg/dL] 85.2 [53.6–129.2] 102.6 [76.2–201.2] 98.2 [38.6–122.8] LP(a) [nmol/L] 18 [0-235] 13 [0-421] 15 [0-135] CRP [mg/L] 0.4 [0–5.7] 1 [0-3] 0.6 [0–6.2] ferritin [μg/L] 51 [15.1–175.1] 91 [22.4–565] 52.6 [18.7–207.6] To confirm our hypothesis regarding the role of fatty acids in LCS (see later in discussion) and to exclude an effect from oral supplementation, we analyzed samples from the fourth group including 10 healthy subjects who had not been exposed to SARS-CoV-2. For this purpose, these subjects received tablets containing 870 mg Omega-3 (including 420 mg EPA and 330 mg DHA) twice daily in a prospective study design for one week and plasma samples were obtained before the start of intake and after intake of the last dose. Immune activation marker profiling displays a lack of systemic inflammation in patients with long COVID-19 syndrome In order to investigate whether LCS may result from still unresolved inflammatory processes after the viral infection, a panel of 65 cytokines, chemokines, and soluble receptors associated with immune activation was assayed. Remarkably, neither pro- nor anti-inflammatory cytokines were found significantly up-regulated in patients with LCS compared to the other two groups. However, remarkable was the down-regulation of IL-18 in patients with LCS, as this T cell and macrophage-derived pro-inflammatory cytokine is orchestrating migration and antiviral response of macrophages (Figure 1A) Furthermore, the monocyte/macrophage-derived factors MCP-1/CCL-2 and sTNF-RII, were found significantly down-regulated in patients with LCS compared to fully recovered patients (Figure 1A). Overall, cytokine levels were rather low in patients with LCS, indicating a lack of pro-inflammatory activities.Figure 1 Cytokine and proteome profiles demonstrate a lack of pro-inflammatory activities in patients with long COVID-19 (A) Significant differences of IL-18 as well as the macrophage-derived cytokines MCP-1 and TNF-RII between long COVID-19 and fully recovered patients are depicted. (B) A Principal Component Analysis based on proteome profiling of plasma samples from patients with long COVID-19(blue square), fully recovered patients (red triangle) and vaccinated healthy controls (gray circle) is shown. (C) Significant differences in plasma protein abundance between long COVID-19 and fully recovered patients are visualized by a volcano plot. (D) Label-free quantification (LFQ) intensities derived from untargeted plasma proteomics of SERPINA5, Biotinidase (BTD) and Vitamin D-binding protein (GC) are depicted for all study groups. ∗p ≤ 0.05, ∗∗p ≤ 0.01 according to one-way ANOVA tests. Proteome profiling displays an anti-inflammatory pattern in patients with long COVID-19 syndrome To further elucidate the inflammatory status in patients with LCS, we performed shotgun proteomics of plasma samples of the three study groups following an established protocol.16 Principal component analysis (PCA) distributed the proteome profiles of healthy vaccinated individuals mainly between the almost completely separated groups of LCS and recovered patients (Figures 1B, S1 and Table S1). Thus, significant (FDR <0.05) proteome alterations were mainly observed comparing the latter two groups (Figure 1C). Apart from several cell leakage products typically derived from uncontrolled cell death, including actin (ACT), tubulin (TUBA1B), myosin-9 (MYH9), and profilin (PFN1), only alpha-1-anti trypsin (SERPINA1) and vitamin D binding protein (GC; Figure 1D) were found significantly up-regulated in the LCS group. A larger number of proteins was found down-regulated, including the protease inhibitors SERPINA5 and SERPINF2, Biotinidase (BTD; Figure 1D), and the macrophage-associated proteins soluble CD14, ANG, and Proteoglycan 4 (PRG4). The acute phase protein CRP was only detected (near threshold levels) in a few samples and thus removed upon filtering. Other acute phase proteins including serum amyloid A and serum amyloid P component, fibrinogens, orosomucoid, and alpha-2-macroglobulin were readily detectable but slightly down-regulated in patients with LCS. These findings further demonstrate the absence of systemic inflammation in patients with LCS and potentially indicate shifts in the activation of inflammatory immune subsets. Fatty acid and oxylipin analysis indicated increased phospholipase A2 activities Oxidized products of polyunsaturated fatty acids such as arachidonic acid (AA) represent highly bioactive but short-lived signaling molecules and lipid mediators, often termed eicosanoids. Phospholipase A2 catalyzes the first step of biosynthesis, the release of polyunsaturated fatty acids from cell membrane phospholipids. The bioactive oxylipins are subsequently formed by the action of cyclooxygenases, lipoxygenases, or cytochrome P450 monooxygenases and modulate numerous physiological functions including bronchial and vascular tonus, thrombocyte function, inflammation, and other immune responses.17 Thus, the assessment of the lipidome allows insights into diverse physiological and pathophysiological processes. Following the analysis of plasma eicosanoids from the three study groups, PCA of fatty acids and their oxidation products separated healthy controls from patients with LCS, with the group of recovered patients dispersed in between (Figure 2A and Table S2). Thus, here most of the significant events were observed comparing patients with LCS to healthy controls (Figure 2B). Evidently, generally higher levels of all kinds of polyunsaturated fatty acids were characteristic of LCS, pointing to higher phospholipase A2 activities. However, increased plasma levels of the pro-inflammatory AA were mainly observed in the recovered group (Figure 2C). In contrast, LCS was marked by the predominant release of the anti-inflammatory molecules EPA and DHA into the blood of affected patients (Figures 2B and 2C). Plasma levels of these molecules may derive from intracellular sources but can also be affected by confounders such as nutrition. Two independent observations indicate that this was not the case in the studied group of patients with LCS. First, the DHA oxidation products 17- and 22-HDoHE were also found significantly increased in patients with LCS (Figure 2D). Second, polyunsaturated fatty acids may occur together with their trans-isoforms, which typically stem from nitric oxide signaling.18 In an independent group of 10 healthy volunteers, we observed that the nutritional supplementation of DHA was not associated with increased levels of trans-DHA (Figure 2E). In contrast, the levels of trans-DHA were consistently increased in patients with LCS, suggesting that DHA was preferentially released from intracellular sources. Accordingly, the overall ratio of Ω3-fatty acids to Ω6-fatty acids was found significantly increased in patients with LCS (Figure 2F). A general anti-inflammatory lipid mediator pattern in patients with LCS was also corroborated by increased levels of the 15-LOX product from linoleic acid, 13-OxoODE,19 when compared to recovered patients (Figure 2E). Thus, here we provide evidence that increased plasma levels of anti-inflammatory oxylipins may be a characteristic feature of patients with LCS.Figure 2 Increased levels of fatty acids with a prevalence for Ω3-fatty acids and anti-inflammatory docosanoids is characteristic for long COVID-19 (A) A Principal Component Analysis based on eicosanoid analysis of plasma samples from patients with long COVID-19 (blue square), fully recovered patients (red triangle) and vaccinated healthy controls (gray circle) is shown. (B) Significant differences in plasma eicosanoids between patients with long COVID-19 and vaccinated healthy controls are visualized by a volcano plot. (C and D) Normalized area under the curve (nAUC) values of arachidonic acid (AA), eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA) and the eicosanoids 17-HDoHE, 13-OxoODE as well as 22-HDoHE are depicted for each study group (H, vaccinated healthy controls; R, fully recovered patients; LC, patients with long COVID-19). (E) Increased levels of trans-DHA in patients with long COVID-19 are shown in an extracted ion chromatogram (m/z = 327.2330 at a retention time (Time) of 12.5 min). No increase in trans-DHA levels after nutritional supplementation of omega-3 (Ω-3) capsules was observed in healthy controls. Percentage (%) of trans-DHA to DHA is depicted in gray for each study group. (F) A significant increase in the omega-3 to omega-6 ratio (Ω-3/Ω-6) was observed in patients with long COVID-19 compared to vaccinated healthy controls as well as fully recovered patients. ∗p ≤ 0.05 according to two-sided t-tests. Metabolomic aberrations relate to chronic fatigue syndrome and display an osmolyte-mediated anti-inflammatory signature In case of metabolites, PCA analysis indicated maximal contrast between recovered and patients with LCS (Figure 3A) similar to the plasma protein analyses described above. Indeed, out of 474 metabolites and lipids analyzed, 107 were found significantly regulated between these two groups (Figure 3B and Table S3). Most apparently, a large number of metabolic alterations were related to a disturbance in energy metabolism, as evidenced by the down-regulation in the LCS group of branched chain amino acids Val, Leu, and Ile, the aromatic amino acid Tyr and amino acids Trp and Arg which also act as inflammation mediators (Figures 3C and S2). Furthermore, increased levels of C18:1-acylcarnitine accompanied by a down-regulation of C3-acylcarnitine were observed in patients with LCS (Figure 3C). This pattern may indicate reduced levels of beta-oxidation accompanied by increased amino acid catabolism. A further indication of disturbed energy metabolism in patients with LCS was the significant increase in lactate, pointing to increased anaerobic glycolysis (Figure S2 and Table S3). Furthermore, the majority of lipids including various triacylglycerols, glycosylceramides, glycerophospholipids, and several ceramides were found down-regulated in LCS (Figure 3D and Table S4), which bears resemblance to a pattern characteristic for chronic fatigue syndrome (CFS).20 Remarkably, an LCS-associated lack of inflammatory processes was also evidenced by the present metabolomics results. The metabolite most significantly up-regulated in patients with LCS was hypaphorine (TrpBetaine), an anti-inflammatory alkaloid21 described to induce sleep in mice.22 Hypaphorine may act as osmolyte similar to the anti-inflammatory molecule taurine (Figure 3C), which was also significantly up-regulated in LCS. In contrast, hypoxanthine levels were strongly increased in the recovered group while patients with LCS showed comparable levels to the healthy vaccinated group.Figure 3 Plasma metabolomics reveals disturbances in the energy metabolism of patients with long COVID-19 (A) A Principal Component Analysis based on metabolomic analysis of plasma samples from patients with long COVID-19 (blue square), fully recovered patients (red triangle) and vaccinated healthy controls (gray circle) is shown. (B) Significant higher levels of TrpBetaine as well as significant lower levels of triacylglycerols (TGs, red star) in plasma samples of patients with long COVID-19 are visualized by a volcano plot. (C) Plasma levels of the amino acids tyrosine (Tyr), tryptophan (Trp), valine (Val), leucine (Leu) and isoleucine (Ile) as well as of carnitines, hypoxanthine, TrpBetaine and taurine are depicted for each study group (H, vaccinated healthy controls; R, fully recovered patients; LC, patients with long COVID-19). (D) A heatmap of all identified triacylglycerols (TGs) displays higher levels of TGs in fully recovered patients compared to patients with long COVID-19 and vaccinated healthy controls. ∗p ≤ 0.05, ∗∗p ≤ 0.01, ∗∗∗p ≤ 0.001 according to two-sided t-tests. Alternatively polarized macrophage-like cells display features of the long COVID-19 syndrome-associated anti-inflammatory signature A systemic inhibition of inflammatory processes as presently observed in the LCS group may result from a predominance of alternatively polarized macrophages.23 To address this issue, we investigated a U937 cell-based macrophage in vitro model system again applying proteome and eicosanoid/docosanoid analysis. U937 cells were differentiated into macrophages using PMA and polarized into either M1-like cells by LPS or M2-like cells with GMCF and IL-4 (Figure 4A). As expected, the M1-like cells were found to robustly produce pro-inflammatory cytokines such as IL-1beta, CCL1, CXCL2, CXCL5, and other inflammation marker such as MMP9 (Figure 4B). Indeed, two of the cytokines down-regulated in LCS, MCP-1 (CCL2) and TNFRSF1B (see Figure 1A), were found mainly secreted by M1-like cells and rather down-regulated in M2-like cells (Figure 4B). M1-like macrophages also secreted the protease furin (Figure 4B). Furin was described to cleave the SARS-CoV-2 spike protein in order to allow SARS-CoV-2 virus particles to enter human cells. The main furin inhibitor, SERPINA5, was found significantly down-regulated in patients with LCS (Figure 1D), pointing to a relevant pathomechanism. The M2-like cells displayed a tolerogenic phenotype illustrated by the expression of the immune suppressor CCL1824 and the angiogenesis-promoting factor ANGPT4. In addition, the M2-like cells expressed several proteins promoting lipolysis (PLA2G4A), altered lipid metabolism (PLIN2 and FABP4), and lipid peroxidation (HMOX1) (Figure 4C). HMOX1 is actually both an essential enzyme for iron-dependent lipid peroxidation during ferroptotic cell death25 and an antiviral protein.26 Polarized macrophages also released fatty acids and their oxidation products. Both arachidonic acid and DHA were released by M2-like cells more than by M1-like cells (Figure 4D). In line with the specific expression of COX2 (PTGS2) in M1-like cells, the COX-products PGE2, PGF2alpha and TXB2 were only detected in M1-like cells (Figure 4D). On the other hand, the lipid peroxidation products HpODE and the cytochrome P450 product 12,13-DiHOME were found at higher levels released from M2-like cells. Thus, the molecular patterns observed in patients with LCS are effectively mimicked by M2 macrophages in vitro.Figure 4 Multi-omics of in vitro polarized macrophages reveal molecular features similar to the LCS (A–C) (A) Scheme of in vitro polarization of U-937 cells to either M1-like or M2-like macrophages (d, days). Log2-transformed label-free quantification (LFQ) intensities derived from untargeted proteomics of (B) the secretome and (C) the cell lysates of M1-like macrophages (M1) and M2-like macrophages (M2) are depicted using profile plots of the three replicates per condition. (D) Normalized area under the curve (nAUC) values of arachidonic acid (AA), docosahexaenoic acid (DHA) and the eicosanoids PGE2, PGF2a, TXB2, HpODE as well as 12,13-DiHOME in the secretome of in vitro polarized M1-like and M2-like macrophages are shown. Discussion Post-acute sequelae of SARS-CoV-2 infection (termed long COVID-19 syndrome) can be found in about 10% of affected patients and thus pose an ever-increasing burden. So far, the pathophysiology of LCS is unknown and subject to speculative hypotheses such as persisting chronic inflammation after infection.12 Given this lack of information we chose to perform a broad-scale exploratory study assessing the proteome, lipidome and metabolome in patients with LCS. As control groups, we recruited individuals who had fully recovered after acute COVID-19 infection and healthy individuals after COVID-19 vaccination. Blood plasma of these groups were obtained around three months after vaccination or PCR confirmed SARS-CoV-2 infection to specifically assess the resolution of inflammation. These analyses allowed us to identify LCS specific molecular patterns which strongly point at several unexpected processes of LCS pathophysiology. Numerous studies have clearly established that acute COVID-19 infection is associated with hyperinflammation. Thus, a failure to clear such inflammatory activities has been commonly assumed to account for LCS symptoms.12 A recent study employing multi-omics and single cell transcriptomics with regard to LCS suggests several risk factors and a specific role of T cells in patients with gastrointestinal complications.11 Here, we focused on patients with LCS reporting chronic fatigue syndrome or similar symptoms. We present evidence suggesting systemic anti-inflammatory conditions in patients with LCS after the acute infection with SARS-CoV-2. A lack of pro-inflammatory activities and the predominance of anti-inflammatory mediators in blood plasma was independently confirmed at the levels of cytokines, acute phase proteins, oxylipins and metabolites. Furthermore, metabolomics analyses indicated a sustained catabolic metabolism in patients with LCS, which may account for the characteristic chronic fatigue symptoms. Of the detected cytokines, chemokines and soluble receptors, the three markers IL-18, soluble TNF-RII and MCP-1/CCL2 were significantly down-regulated in the LCS group. All three factors have pro-inflammatory functions and reflect the activation and communication of T-lymphocytes and monocytes/macrophages. In the proteome, down-regulation of acute phase proteins was observed, which was most pronounced between the recovered and the LCS group. Of note, SERPINA5 levels were significantly decreased in the LCS group compared to both the healthy as well as the recovered group. SERPINA5 serves as antagonist of the protease furin, which is essential for viral entry into human cells.27 Thus, it is intriguing to speculate that SERPINA5 may represent a predictive biomarker indicating an increased risk for the development of LCS. Furthermore, the observed proteome patterns indicate differential monocyte/macrophage polarization and activity between the LCS and the recovered group since the most significantly down-regulated proteins in patients with LCS were found to be derived from macrophages or to directly affect macrophage function (Figure 5 ). CD14 is a macrophage-specific membrane protein eventually secreted into plasma upon inflammatory activation.28 Down-regulation of CD14 may thus indicate less M1-like macrophage responses in patients with LCS. Angiogenin (ANG) is an angiogenic protein also described as anti-bacterial protein secreted by macrophages.29 Proteoglycan-4 (PRG4) is mainly expressed by fibroblast-like cells but serves as an important regulator of inflammation30 and has been described to act as an essential regulator of synovial macrophage polarization and inflammatory macrophage joint infiltration.31 Biotinidase has been described to be essential for basic macrophage functions. Intriguingly, biotinidase deficiency may account for hypotonia, lethargy, cognitive retardation, and seizures32 thus potentially linking our observations to the symptom complex of patients with LCS. Finally, Vitamin D binding protein (GC), actually up-regulated in plasma of patients with LCS, may also be involved in macrophage functions, as it is a precursor for the so-called macrophage-activating signal factor.33 Furthermore, among the up-regulated proteins in the LCS group, SERPINA1 has been described as characteristic marker of M2-polarized macrophages,34 thus additionally giving evidence about alternative macrophage polarization in LCS.Figure 5 Outline of the suggested pathomechanisms. Monocytes may be specifically affected in patients with LCS In the course of chronic viral infection, there is evidence for a decrease in the occurrence and activities of M1 macrophages accompanied with an increase in M2-like macrophage activities. The indicated M1-derived molecules were found downregulated in patients with LCS, whereas the indicated M2-derived molecules were found up-regulated, indicating an anti-inflammatory signature. Apart from the proteome analyses, also the observed patterns in the lipidome of patients with LCS provided independent evidence for an anti-inflammatory status. Along those lines, we identified increased levels of DHA, its metabolites and other docosanoids in patients with LCS, which are mainly regarded as anti-inflammatory and may promote tolerogenic macrophage polarization.35 Of note, DHA does not only act as anti-inflammatory mediator but high DHA levels are relevant for normal brain function36 as well as mitochondrial functions in different cell types.37 DHA and other Ω3-fatty acids are preferentially released by calcium-independent phospholipase iPLA238 which is broadly expressed throughout the body and induced upon oxidative stress and mitochondrial damage.38 Thus, we hypothesized that the observed increase in Ω3-fatty acids in LCS was a long-term response to a sustained catabolism combined with oxidative stress generated during the COVID-19 infection. In this respect, longitudinal studies addressing these points with more patient are clearly warranted. An LCS-associated lack of inflammatory processes was also evidenced by the present metabolomics results. The osmolyte taurine, which has been attributed with anti-inflammatory and anti-oxidative properties,39 was significantly increased in patients with LCS compared to the other two groups. The metabolite most significantly up-regulated in patients with LCS was hypaphorine (TrpBetaine), an anti-inflammatory alkaloid21 described to induce sleep in mice,22 which might thus also relate to the chronic fatigue symptoms in LCS. Inversely, hypoxanthine levels, which are among others indicative of tissue hypoxia as found during acute inflammation, were strongly increased in the recovered group, while patients with LCS showed similar levels to the healthy group. Apart from this overall pattern of anti-inflammation, the metabolome analyses also provided first indications regarding an aberrant amino acid catabolism in LCS. Along those lines, levels of branched chain amino acids were significantly decreased in the LCS group, including both glucogenic as well as ketogenic amino acids. This observation gives evidence for increased energy consumption from protein breakdown, which is in line with reports of increased muscle weakness and sarcopenia due to metabolic alterations after SARS-CoV-2 infection.40 Furthermore, tryptophan levels were found down-regulated in the LCS group potentially resulting from prolonged IDO activity, which has also been observed during acute SARS-CoV-2 infection.40 Metabolic alterations were also found at the level of triacylglycerols and other complex lipids which were slightly but consistently reduced in the LCS group. Increased levels of fatty acids accompanied by decreased levels of triacylglycerols point to increased activities of endothelial lipase,41 which was associated with cognitive impairment.42 Intriguingly, this pattern is highly similar to the observations described for CFS,43 providing a possible explanation for the fatigue and brain fog symptoms observed in LCS. These findings also underline the potential to introduce novel dietary approaches into tailored rehabilitation regimen. In this regard also strict control of metabolic comorbidities like diabetes, could help in reducing and managing LCS.44 From the above observed molecular patterns an overall contribution of alternatively polarized M2 macrophages may be deduced. During acute infection peripheral blood monocytes are strongly affected45 and are major contributors to the inflammatory reaction.46 While not contributing to viral replication, macrophages may eventually get infected themselves by SARS-CoV-247 , 48 and peripheral blood monocytes were described to be significantly altered during COVID-19 infection.45 In vitro polarized M1-like macrophages express the protease furin (see Figure 4B) which is essential for viral entry into human cells27 and might therefore contribute to the course, severity and long-term sequelae of the infection. However, the pro-inflammatory M1 state may be switched, e.g. by oxidized phospholipids accumulated during acute infection, to a tolerogenic M2-like phenotype,49 , 50 thus becoming long-lived cells coordinating tissue regeneration.51 Lipid peroxidation may occur consequent to inflammatory responses also during sterile inflammation such as atherosclerosis52 and thus represents a plausible mechanism inducing tolerogenic macrophage polarization after SARS-CoV-2 infection. Polarized tolerogenic macrophages suppress pro-inflammatory cytokines53 and induce an anti-inflammatory state,54 and are promoted by anti-inflammatory docosanoids,35 highly reminiscent to the presently observed molecular pattern characteristic for patients with LCS. As a consequence, here we suggest system-wide alternative macrophage polarization as key cell mechanism accounting for LCS symptoms. The present data also provide insights into the processes of successful recovery after acute SARS-CoV-2 infection. It is noteworthy that the symptom-free recovered individuals showed alterations compared to the healthy control group in many assessed parameters throughout the different biomolecular compartments. Taken together these findings provide evidence that, even months after acute infection, systemic processes are still active in these individuals. Thus, SARS-CoV-2 infection might leave molecular remnants such as infected macrophages long after symptomatic recovery. Three potential mechanisms have been suggested recently to account for LCS: immune dysregulation, autoimmunity or viral persistence.55 Our data are fully compatible with immune dysregulation and viral persistence, but hardly support a general role of autoimmunity, as this should be expected to be associated with a pro-inflammatory pattern. Thus, the obtained molecular patterns do not only provide first insights into the pathophysiology of COVID-19 sequelae as depicted in Figure 5, but may also provide a first basis for the definition of LCS specific biomarkers. Our broad scale analyses could not detect a unique and specific marker for the disease. However, many significant molecular alterations can be clearly associated with characteristic symptoms of the disease. It is possible to hypothesize regarding the biological causes of these alterations. In addition, it can be envisioned that a combination of presently described proteins (e.g. low SERPINA5), docosanoids (e.g. high DHA) and small metabolites (e.g. high hypaphorine) in patients with characteristic anamnesis and symptoms could help to identify and better define LCS. In this regard, large scale studies to assess the potential sensitivity and specificity of such scores, including the consideration of different SARS-CoV-2 strains, are clearly warranted. In summary, here we present a distinct multi-omics signature demonstrating a prevalence of anti-inflammatory effector molecules combined with molecular patterns of characteristically altered metabolism detectable in plasma of patients with LCS, offering a unique chance for diagnosis with selected molecular biomarkers and providing novel hypotheses about the pathophysiology of the disease, thus potentially aiding the development of urgently required treatment options. Limitations of the study The disease state “Long Covid Syndrome” is mainly characterized by clinical symptoms and may not only be related to a single causative molecular mechanism. It is to be expected that there will be main aberrations from physiologic pathways associated with the viral infection, that may eventually result in fatigue syndromes including LCS. Molecular profiling provides rich information regarding ongoing processes in human individuals, but may also be distorted by medications or other lifestyle parameters. While this report presents a conclusive pathomechanism potentially accounting for disease symptoms and a potential contributor, i.e. alternatively polarized macrophages, it requires further clinical verification. The study group was of limited size, patients had been infected with only one virus variant, and mainly patients suffering from fatigue syndrome associated with LCS were considered. Thus, the present study cannot claim generality. However, the present data rule out pro-inflammatory mechanisms as general feature of LCS. The focus on a single cell type, here macrophages, is only one important aspect. Other immune cells as well as epithelial cells and endothelial cells will also contribute to the molecular aberrations described in this report and to the characteristic symptoms of LCS. Only ongoing clinical research will show whether the consideration of the specific role of macrophages may help to establish rational therapeutic strategies for patients with LCS. STAR★Methods Key resources table REAGENT or RESOURCE SOURCE IDENTIFIER Chemicals, peptides, and recombinant proteins ProtiFi S-trap™ micro columns PROTIFI N/A StrataX 33 μm SPE columns Phenomenex 8B-S100-TAL Phorbol 12-myristate 13-acetate (PMA) Sigma-Aldrich P1585-1MG Lipopolysaccharides from Escherichia coli 055:B5, γ-irradiated, BioXtra, suitable for cell culture Sigma-Aldrich L6529-1MG 12S-HETE-d8 Cayman 334570 15S-HETE-d8 Cayman 334230 5-Oxo-ETE-d7 Cayman 334250 11,12-DiHETrE-d11 Cayman 10007975 PGE2-d4 Cayman 314010 20-HETE-d6 Cayman 390030 Critical commercial assays MxP® Quant 500 Kit (96) - SCIEX Biocrates life sciences 21094 ProcartaPlex™ Multiplex Immunoassay Thermo Fisher Scientific MAN0017980 Elecsys Anti-SARS-CoV-2 S immunoassay Roche 09 289 267 190 Elecsys Anti-SARS-CoV-2 immunoassay Roche 09 345 272 190 Deposited data Plasma proteomics data ProteomeXchange PXD036969 Cell lysates of M1 and M2-like macrophages ProteomeXchange PXD036972 Supernatants of M1 and M2-like macrophages ProteomeXchange PXD036970 Experimental models: Cell lines U937 cells ATCC CLO:0009465 Software and algorithms MaxQuant 1.6.17.0 max planck institute of biochemistry www.maxquant.org Perseus 1.6.14.0 max planck institute of biochemistry Maxquant.net/perseus/ SwissProt human proteome database version 141219 UniPro consortium www.uniprot.org GraphPad Prism 6.07 PraphPad Software www.graphpad.com MetIDQ-Oxygen-DB110-3005 Biocrates N/A Other Aurora Series Emitter column 25 cm × 75 μm, 1.6um FSC C18, CSI ionoptics AUR2-25075C18A-CSI Kinetex® 2.6 μm XB-C18 100 Å, LC Column 100 × 2.1 mm, Ea Phenomenex 00D-4496-AN MxP Quant 500 kit column system Biocrates life sciences 21117.1 Resource availability Lead contact Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Christopher Gerner at [email protected]. Materials availability This study did not generate new unique reagents. Experimental model and subject details Ethics statement This exploratory study is in compliance with the Helsinki Declaration (Ethical Principles for Medical Research Involving Human Subjects) and was conducted in accordance with the guidelines of research boards at the study site after written informed consent. Recruitment of Long COVID Syndrome patients was approved by the local ethics committee of the Medical University of Vienna under the number EC#1280/2020, while the recruitment of the COVID-19 recovered and the healthy/vaccinated control groups was approved by the local ethics committee of the Medical University of Vienna under the number EC#2262/2020. The recruitment of an independent group including 10 healthy subjects which had not been exposed to SARS-CoV-2 receiving tablets containing 870 mg Omega-3 (420 mg EPA and 330 mg DHA) twice daily in a prospective study design for one week was approved by the local ethics committee of the Medical University of Vienna under the number EC#2250/2020. Patient recruitment and sample acquisition All study participants were recruited in the timeframe between May and July 2021. LCS patients were recruited at the outpatient ward of the Division of Cardiology of the Department for Internal Medicine II of the Medical University of Vienna, Austria. The age and gender matched recovered/healthy and the healthy/vaccinated study groups were recruited among volunteers after calls at the Vienna General Hospital/Medical University of Vienna and the University of Applied Sciences, Vienna, Austria. All vaccinated participants had received two doses of either the vector-based vaccine Vaxzevria (Astra Zeneca, Oxford, UK) or the mRNA-based vaccine Comirnaty (Pfizer, New York City, NY, USA). Serum and EDTA-anticoagulated plasma were obtained by peripheral venous blood draw. Serum samples were left for 15 min to allow for clotting before centrifugation for 15 min at 1500 g at 4 °C while EDTA plasma samples were immediately centrifuged after blood collection. Following these steps, all samples were immediately frozen at −80 °C until analyses. Regarding the fourth group of 10 healthy subjects which had not been exposed to SARS-CoV-2, tablets containing 870 mg Omega-3 (420 mg EPA and 330 mg DHA) were administered twice daily in a prospective study design for one week. EDTA-anticoagulated plasma was obtained before start of intake and after intake of the last dose. EDTA plasma samples were immediately centrifuged after blood collection and frozen at −80 °C until analyses. Determination of anti SARS-CoV-2 antibody status Antibody levels against SARS-CoV-2 Spike protein (anti-S) and Nucleocapsid Protein (anti-N) were determined from sera of the study participants using the Elecsys Anti-SARS-CoV-2 S immunoassay as well as the qualitative Elecsys Anti-SARS-CoV-2 immunoassay (detecting SARS-CoV-2 N protein) on a cobas e801 analyzer (Roche Diagnostics, Rotkreuz, Switzerland). Analyses were performed at the diagnostic laboratory at the Department of Laboratory Medicine, Medical University of Vienna (certified acc. to ISO 9001:2015 and accredited acc. to ISO 15189:2012). Determination of routine laboratory parameters Total protein, albumin, ferritin, CRP, HDL, LDL, LP(a) were determined using standard routine diagnostic tests on a cobas e801 analyzer (Roche) at the diagnostic laboratory at the Department of Laboratory Medicine, Medical University of Vienna. Determination of serum cytokines Undiluted blood serum samples were analyzed using the ProcartaPlex™ Multiplex Immunoassay (Human immune monitoring 65 Plex, Thermo Fisher Scientific, Reference Number MAN0017980) according to manufacturer’s instruction. Of the 65 analytes, 51 were below the lower limit of quantification in >95% of all samples (irrespective of group) and were therefore excluded from further analysis. Measurements and analysis of all Human ProcartaPlex Immunoassays were performed on a Luminex 200 instrument (Luminex Corp., Austin, Tx, USA) as described in detail before.2 Plasma proteomics For untargeted plasma proteomics analyses, EDTA-anticoagulated plasma samples were diluted 1:20 in lysis buffer (8 M urea, 50 mM TEAB, 5% SDS), heated at 95 °C for 5 min prior the protein concentration was determined using a BCA assay. For enzymatic protein digestion, 20 μg of protein was used and the ProtiFi S-trap technology56 applied. Briefly, solubilized protein was reduced and carbamidomethylated by adding 64 mM dithiothreitol (DTT) and 48 mM iodoacetamide (IAA), respectively. Prior to sample loading onto the S-trap mini cartridges, trapping buffer (90% v/v methanol, 0.1M triethylammonium bicarbonate) was added. Thereafter, samples were thoroughly washed and subsequently digested using Trypsin/Lys-C Mix at 37 °C for two hours. Finally, peptides were eluted, dried and stored at −20 °C until LC-MS analyses. Reconstitution of dried peptide samples was achieved by adding 5 μL of 30% formic acid (FA) containing 4 synthetic standard peptides and subsequent dilution with 40 μL of loading solvent (97.9% H2O, 2% ACN, 0.05% trifluoroacetic acid). Thereof, 1 μL were injected into the Dionex Ultimate 3000 nano high performance liquid chromatography (HPLC)-system (Thermo Fisher Scientific), one injection per sample. In order to pre-concentrate peptides prior to chromatographic separation, a pre-column (2 cm × 75 μm C18 Pepmap100; Thermo Fisher Scientific) run at a flow rate of 10 μL/min using mobile phase A (99.9% H2O, 0.1% FA) was used. The subsequent peptide separation was achieved on an analytical column (25 cm × 75 μm 1.6 μm C18 Aurora Series emitter column (Ionopticks)) by applying a flow rate of 300 nL/min and using a gradient of 7% to 40% mobile phase B (79.9% ACN, 20% H2O, 0.1% FA) over 43 min, resulting in a total LC run time of 85 min including washing and equilibration steps. Mass spectrometric analyses were performed using the timsTOF Pro mass spectrometer (Bruker) equipped with a captive spray ion source run at 1650 V. Further, the timsTOF Pro mass spectrometer was operated in the Parallel Accumulation-Serial Fragmentation (PASEF) mode and a moderate MS data reduction was applied. A scan range (m/z) from 100 to 1700 to record MS and MS/MS spectra and a 1/k0 scan range from 0.60 to 1.60 V.s/cm2 resulting in a ramp time of 100 ms to achieve trapped ion mobility separation were set as further parameters. All experiments were performed with 10 PASEF MS/MS scans per cycle leading to a total cycle time of 1.16 s. Furthermore, the collision energy was ramped as a function of increasing ion mobility from 20 to 59 eV and the quadrupole isolation width was set to 2 Th for m/z < 700 and 3 Th for m/z > 700. Subsequent LC-MS data analysis was performed using the publicly available software package MaxQuant 1.6.17.0 running the Andromeda search engine.57 Protein identification as well as label-free quantification (LFQ) was achieved by searching the raw data against the SwissProt database “homo sapiens” (version 141219 with 20380 entries). General search parameter included an allowed peptide tolerance of 20 ppm, a maximum of 2 missed cleavages, carbamidomethylation on cysteines as fixed modification as well as methionine oxidation and N-terminal protein acetylation as variable modification. A minimum of one unique peptide per protein was set as search criterium for positive identifications. In addition, the “match between runs” option was applied, using a 0.7 min match time window and a match ion mobility window of 0.05 as well as a 20 min alignment time window and an alignment ion mobility of 1. FDR calculation was based on the use of a reversed decoy database, an FDR≤0.01 was set for all peptide and protein identifications. Further LC-MS data processing and evaluation was accomplished using the Perseus software (version 1.6.14.0).58 First, identified proteins were filtered for reversed sequences as well as common contaminants and annotated according to the different study groups. Prior to statistical analysis, LFQ intensity values were transformed (log2(x)), and proteins were additionally filtered for their number of independent identifications (a minimum of 5 identifications in at least one group). Afterwards, missing values were replaced from a normal distribution (width: 0,3; down shift: 1,8). Plasma lipidomics Frozen EDTA-anticoagulated plasma was freshly thawed on ice. For precipitation of proteins, plasma (300 μL) was mixed with cold EtOH (1.2 mL, abs. 99%, −20°C; AustroAlco) including an internal standard mixture of 12S-HETE-d8, 15S-HETE-d8, 5-Oxo-ETE-d7, 11,12-DiHETrE-d11, PGE2-d4 and 20-HETE-d6 (each 100 nM; Cayman Europe, Tallinn, Estonia). The samples were stored over-night at −20°C. After centrifugation (30 min, 4536 g, 4°C), the supernatant was transferred into a new 15 mL FalconTM tube. EtOH was evaporated via vacuum centrifugation at 37°C until the original sample volume (300 μL) was restored. For solid phase extraction (SPE) samples were loaded onto preconditioned StrataX SPE columns (30 mg mL−1; Phenomenex, Torrance, CA, USA) using Pasteur pipettes. After sample loading, the SPE columns were washed with 5 mL of MS grade water and eluted with ice-cold MeOH (500 μL; MeOH abs.; VWR International, Vienna, Austria) containing 2% formic acid (FA; Sigma-Aldrich). MeOH was evaporated using a gentle nitrogen stream at room temperature and the dried samples were reconstituted in 150 μL reconstitution buffer (H2O:ACN:MeOH + 0.2% FA–vol% 65:31.5:3.5). The samples were then transferred into an autosampler held at stored at 4°C and subsequently measured via LC-MS/MS. For LC-MS analyses, analytes were separated using a Thermo ScientificTM VanquishTM (UHPLC) system equipped with a Kinetex® C18-column (2.6 μm C18 100 Å, LC Column 150 × 2.1 mm; Phenomenex®) applying a gradient flow profile (mobile phase A: H2O + 0.2% FA, mobile phase B: ACN:MeOH (vol% 90:10) + 0.2% FA) starting at 35% B and increasing to 90% B (1–10 min), further increasing to 99% B within 0.5 min and held for 5 min. Solvent B was then decreased to the initial level of 35% within 0.5 min and the column was equilibrated for 4 min, resulting in a total run time of 20 min. The flow rate was kept at 200 μL min−1 and the column oven temperature at 40°C. The injection volume was 20 μL and all samples were analysed in technical duplicates. The Vanquish UHPLC system was coupled to a Q ExactiveTM HF Quadrupole-OrbitrapTM high-resolution mass spectrometer (Thermo Fisher Scientific, Austria), equipped with a HESI source for negative ionization to perform the mass spectrometric analysis. The MS scan range was 250-700 m/z with a resolution of 60,000 (at m/z 200) on the MS1 level. A Top 2 method was applied for fragmentation (HCD 24 normalized collision energy), preferable 33 m/z values specific for well-known eicosanoids and precursor molecules from an inclusion list. The resulting fragments were analysed on the MS2 level at a resolution of 15,000 (at m/z 200). Operating in negative ionization mode, a spray voltage of 3.5 kV and a capillary temperature of 253 °C were applied. Sheath gas was set to 46 and the auxiliary gas to 10 (arbitrary units). For subsequent data analysis, raw files generated by the Q ExactiveTM HF Quadrupole-OrbitrapTM high-resolution mass spectrometer were checked manually via Thermo XcaliburTM 4.1.31.9 (Qual browser) and compared with reference spectra from the Lipid Maps depository library from July 2018.59 Peak integration was performed using the TraceFinderTM software package (version 4.1—Thermo Scientific, Vienna, Austria). Principal Component Analysis and volcano plots were generated using the Perseus software (version 1.6.14.0) applying an FDR of 0.05 58. Therefore, data were normalised to the internal standards and missing values were replaced by a constant which corresponds to the half of the lowest normalized area under the curve (nAUC) value of each individual eicosanoid. The ratio of Ω-3/Ω-6 fatty acids depicted in Figure 2 reflects the ratio of the omega-3 polyunsaturated fatty acids eicosapentaenoic acid (EPA) and docosahexaenoic (DHA) to the omega-6 polyunsaturated fatty acids arachidonic acid (AA) and dihomo-gamma-linolenic acid (DGLA). Plasma metabolomics EDTA plasma samples (10 μL) of study participants were analyzed by a targeted metabolomic assay. Targeted metabolomics experiments were conducted using the MxP® Quant 500 Kit (Biocrates Life Sciences AG, Innsbruck, Austria). We detected 630 analytes, including 40 acylcarnithines, 1 alkaloid, 1 amine oxide, 50 amino acid related metabolite, 14 bile acids, 9 biogenic amines, 7 carboxylic acids, 28 ceramides, 22 cholesteryl esters, 1 cresol, 44 diacylglycerols, 8 dihydroceramides, 12 fatty acids, 90 glycerophospholipids, 34 glycosylceramides, 4 hormones, 4 indole derivatives, 2 nucleobase related metabolites, 15 sphingolipids, 242 triacylglycerols, the sum of hexoses and 1 vitamin/cofactor. A total of 474 metabolites showed signal intensities within the quantification window and were further evaluated. Measurements were carried out using LC-MS and flow injection (FIA)-MS analyses on a Sciex 6500+ series mass spectrometer coupled to an ExionLC AD chromatography system (AB Sciex, Framingham, MA, USA), using the Biocrates MxP Quant 500 kit column system and utilizing the Analyst 1.7.1 software with hotfix 1 (also AB SCIEX). All required standards, quality controls and eluents were included in the kit, as well as the chromatographic column for the LC-MS/MS analysis part. Phenyl isothiocyanate (Sigma-Aldrich, St. Louis, USA) was purchased separately and was used for derivatization of amino acids and biogenic amines according to the kit manual. Preparation of the measurement worklist as well as data validation and evaluation were performed with the software supplied with the kit (MetIDQ-Oxygen-DB110-3005, Biocrates Life Sciences). The heatmap including 126 triacylglycerols (Table S4) was generated by dividing the concentration of each lipid through the average concentration of this lipid over all samples using Excel. Cell culture and differentiation of U937 cells U937 cell line was cultured in RPMI medium (1X with L-Glutamine; Gibco, Thermo Fischer Scientific, Austria) supplemented with 1% Penicillin/Streptomycin (Sigma-Aldrich, Austria) and 10% Fetal Calf Serum (FCS, Sigma-Aldrich, Austria) in T25 polystyrene cell culture flasks for suspension cells (Sarstedt, Austria) at 37 °C and 5% CO2. Cells were counted using a MOXI Z Mini Automated Cell Counter (ORFLO Technologies, USA) using Moxi Z Type M Cassettes (ORFLO Technologies, USA). Based on this, 2 × 106 cells were used for each differentiation approach and seeded in T25 polystyrene cell culture flasks with cell growth surface for adherent cells (Sarstedt, Austria). Differentiation of U937 cells into M1-like macrophages was induced by adding 100 ng/mL Phorbol 12-myristate 13-acetate (PMA, ≥99%, Sigma-Aldrich, Austria) (d0). After 48 h (d2), the cell culture medium was exchanged and fresh full medium supplemented with 100 ng/mL LPS (Lipopolysaccharides from Escherichia coli 055:B5, γ-irradiated, BioXtra, Sigma-Aldrich, Austria) was added. Again, after 48 h (d4), M1-like macrophages were washed twice with PBS and further incubated with 3 mL of serum free RPMI for 4 h. Thereafter, supernatants were harvested, precipitated using 12 mL cold EtOH (abs. 99%, −20°C; AustroAlco) including an internal standard mixture of 12S-HETE-d8, 15S-HETE-d8, 5-Oxo-ETE-d7, 11,12-DiHETrE-d11, PGE2-d4 and 20-HETE-d6 (each 100 nM; Cayman Europe, Tallinn, Estonia) and stored at −20 °C until further processing. M1-like macrophages were lysed in 200 μL of a 4% SDC buffer containing 100 mM Tris-HCl (pH 8.5), immediately heat-treated at 95°C for 5 minutes, ultra-sonicated and stored at −20 °C until further processing. M2-like macrophage differentiation of U937 cells was achieved by first adding 100 ng/mL PMA (d0) for 24 h to the full medium. Afterwards, 50 ng/mL M-CSF (ImmunoTools, Friesoythe, Germany) were directly added to the culture medium (d1) for a total of 72 h before the medium was exchanged and cells cultivated in fresh full medium supplemented again with 50 ng/mL M-CSF (d4) for another 72 h. After this (d6), cells were incubated in fresh medium containing 20 ng/mL IL-4 (ImmunoTools) for 24 h to induce the M2-like phenotype. At day 7 (d7) of the differentiation process, M2-like macrophages were washed twice with PBS and further incubated with 3 mL of serum free RPMI for 4 h. Sample harvesting was performed as described above. M1-like macrophage differentiation as well as M2-like macrophage differentiation of U937 cells were carried out in triplicates. Sample preparation and LC-MS analyses of M1-like and M2-like macrophages Precipitated FCS-free cell supernatants were centrifuged (30 min, 4536 g, 4°C) and the supernatant was transferred into new 15 mL FalconTM tubes for lipid extraction as described above. The protein pellet corresponding to the secreted proteins was dissolved in 4% SDC buffer containing 100 mM Tris-HCl (pH 8.5), ultra-sonicated and heat-treated at 95°C for 5 minutes. Protein concentration of supernatants as well as cell lysates was determined using a BCA assay. For proteomic analyses, an adapted version of the EasyPhos workflow was applied.60 In short, 20 μg of protein was reduced and alkylated in one step using 100 mM TCEP and 400 mM 2-CAM, respectively. Subsequent enzymatic digestion was performed with a Trypsin/Lys-C mixture (1:100 Enzyme to Substrate ratio) at 37 °C for 18 h. For desalting, peptide solution was first dried to approximately 20 μL, mixed with loading buffer containing 1% TFA in isopropanol and loaded on SDB-RPS StageTips. After washing twice, peptides were eluted with 60% ACN and 0.005% ammonium hydroxide solution, dried and stored at −20 °C until LC-MS analyses. LC-MS analyses of the supernatants of M1-like and M2-like macrophages were carried out as describe above (plasma proteomics and plasma lipidomics) with slight adaptions regarding the proteomics analyses. All samples were measured in technical duplicates and biological triplicates, resulting in 6 measurements per sample type. In case of secretome analysis 5 μL of resuspended sample were injected into the Dionex Ultimate 3000 nano high performance liquid chromatography (HPLC)-system (Thermo Fisher Scientific) but using the same gradient as for plasma proteomics. Regarding the proteomic analyses of cell lysates, again 5 μL of resuspended sample were injected but an adapted LC-gradient from 8% to 40% mobile phase B over 90 min, resulting in a total LC run time of 135 min including washing and equilibration steps, was applied. Quantification and statistical analysis With regard to serum cytokine analysis, values from the three groups (pg/mL) were compared by one-way ANOVA followed by Tukey’s multiple comparisons test. With regard to proteome, lipidome and metabolome profiling experiments, Principal Component Analyses, two-sided t-tests as well as statistics for volcano plots were performed using the Perseus software (version 1.6.14.0) and applying an FDR (permutation-based with 250 permutations) of 0.05 and a S0 of 0.1, whereby S0 controls the relative importance of t-test p-value and difference between the means. Histograms were generated using GraphPad Prism Version 6.07 (2015). Supplemental information Document S1. Figures S1 and S2 Table S1. Student's t-test statistics between long COVID-19 and recovered, long COVID-19 and healthy as well as recovered and healthy are shown, related to Figure 1 For each identified protein, log2 label-free quantification (LFQ) intensities, numbers of identified peptides, numbers of identified unique peptides as well as the sequence coverage are listed. Table S2. Student's t-test statistics between long COVID-19 and recovered, long COVID-19 and healthy as well as recovered and healthy are shown, related to Figure 2 For each identified eicosanoid normalized area under the curve (nAUC) values are listed. Table S3. Student's t-test statistics between long COVID-19 and recovered, long COVID-19 and healthy as well as recovered and healthy are shown, related to Figure 3 For each identified metabolite log2 concentrations in μM are listed. Table S4. Heatmap data matrix, related to Figure 3 Data and code availability The proteome analysis datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: http://www.proteomexchange.org/, TBA, ProteomeXchange, identifier PXD036969 (plasma proteins), PXD036972 (macrophage cell lysates) and PXD036970 (macrophage supernatants). This paper does not report original code. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request. Acknowledgments This study was supported by the Faculty of Chemistry, University of Vienna, by grants from the Austrian Science Funds (FWF project #P-34728B) and the Medical Scientific Funds of the Mayor of the City of Vienna (project #COVID010). This work was further supported by the 10.13039/501100003065 University of Vienna (intramural funding) and the Joint Metabolome Facility (University of Vienna, Medical University of Vienna), member of the VLSI (Vienna Life Science Instruments). Author contributions Conceptualization: JK, MG, KS, and CG, methodology: KS and CG, investigation: AB, GH, SMM, TF, AK, LS, BR, DS, and GG, visualization: AB, GH, and SMM, funding acquisition: JK and KS, patient recruitment and clinical assessment: MH, TS, AS, EH, and MG, supervision: AB and CG, writing – original draft: CG, writing – review & editing: JK, AB, KS, and CG. Declaration of interests The authors CG, KS, SMM, JK, AB and MG have filed a patent application to the European Patent Office with the application number EP22176741 (date June 1st. 2022) and the title:” METHODS AND MEANS FOR MOLECULAR CHARACTERIZATION OF POST-VIRAL FATIGUE SYNDROME.” The other authors declare no competing interests. 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Med. 144 2019 266 278 10.1016/j.freeradbiomed.2019.03.036 30946962 53 Shrivastava R. Shukla N. Attributes of alternatively activated (M2) macrophages Life Sci. 224 2019 222 231 10.1016/j.lfs.2019.03.062 30928403 54 Naito Y. Takagi T. Higashimura Y. Heme oxygenase-1 and anti-inflammatory M2 macrophages Arch. Biochem. Biophys. 564 2014 83 88 10.1016/j.abb.2014.09.005 25241054 55 Brodin P. Casari G. Townsend L. O'Farrelly C. Tancevski I. Löffler-Ragg J. Mogensen T.H. Casanova J.L. COVID Human Genetic Effort Studying severe long COVID to understand post-infectious disorders beyond COVID-19 Nat. Med. 28 2022 879 882 10.1038/s41591-022-01766-7 35383311 56 Zougman A. Selby P.J. Banks R.E. Suspension trapping (STrap) sample preparation method for bottom-up proteomics analysis Proteomics 14 2014 1006 10.1002/pmic.201300553 24678027 57 Cox J. Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification Nat. Biotechnol. 26 2008 1367 1372 10.1038/nbt.1511 19029910 58 Cox J. Mann M. 1D and 2D annotation enrichment: a statistical method integrating quantitative proteomics with complementary high-throughput data BMC Bioinf. 13 2012 S12 10.1186/1471-2105-13-S16-S12 59 Fahy E. Sud M. Cotter D. Subramaniam S. LIPID MAPS online tools for lipid research Nucleic Acids Res. 35 2007 W606 W612 10.1093/nar/gkm324 17584797 60 Humphrey S.J. Karayel O. James D.E. Mann M. High-throughput and high-sensitivity phosphoproteomics with the EasyPhos platform Nat. Protoc. 13 2018 1897 1916 10.1038/s41596-018-0014-9 30190555	36507225	PMC9719844	NO-CC CODE	2022-12-15 23:17:52	no	iScience. 2023 Jan 20; 26(1):105717	utf-8	iScience	2,022	10.1016/j.isci.2022.105717	oa_other
==== Front J Shoulder Elbow Surg J Shoulder Elbow Surg Journal of Shoulder and Elbow Surgery 1058-2746 1532-6500 Published by Elsevier Inc. on behalf of Journal of Shoulder and Elbow Surgery Board of Trustees. S1058-2746(22)00863-1 10.1016/j.jse.2022.10.034 Original Article Outpatient Shoulder Arthroplasty in the COVID-19 Era: 90-day Complications and Risk Factors Reddy Rajiv P. BS Sabzevari Soheil MD Charles Shaquille MSc Singh-Varma Anya BS Como Matthew BS Lin Albert MD ∗ Department of Orthopaedic Surgery, UPMC Freddie Fu Sports Medicine Center, Pittsburgh, PA, USA ∗ Corresponding Author: Albert Lin MD, UPMC Freddie Fu Sports Medicine Center, University of Pittsburgh, 3200 S. Water St, Pittsburgh, PA, 15203, USA, Tel: +1-412-432-3648, Fax: +1-412-432-3690. 5 12 2022 5 12 2022 1 9 2022 13 10 2022 24 10 2022 © 2022 Published by Elsevier Inc. on behalf of Journal of Shoulder and Elbow Surgery Board of Trustees. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Introduction With the COVID-19 pandemic placing an increased burden on healthcare systems, shoulder arthroplasties are more commonly being performed as outpatient procedures. The purpose of this study was to characterize the 90-day episode-of-care complications of consecutive shoulder arthroplasties defaulted for outpatient surgery without using a prior algorithm for patient selection and to assess for their risk factors. We hypothesized that outpatient shoulder arthroplasty would be a safe procedure for all patients, regardless of patient demographics and comorbidities. Methods A retrospective review of consecutive patients who underwent planned outpatient anatomic or reverse total shoulder arthroplasty between March 2020 and January 2022 with 3-month follow-up was performed. All patients were scheduled for outpatient surgery regardless of medical comorbidities. Patient demographics; pre/postoperative patient-reported outcomes (PROs) including visual analog scale (VAS), subjective shoulder value (SSV), and American Shoulder and Elbow Surgeons score (ASES); pre/postoperative range of motion (ROM); and complications were collected from medical chart review. Multivariate logistic regression was used to identify predictors of the following outcomes: 1. Unplanned overnight hospital stay, 2. 90-day unplanned ED/clinic visit, 3. 90-day hospital readmission, 4. 90-day complications requiring revision. Results 127 patients (47% male, 17% tobacco users, 18% diabetics) with a mean age 69±9 years were identified, of whom 92 underwent reverse total shoulder arthroplasty (rTSA) and 35 underwent anatomic total shoulder arthroplasty (aTSA). All PROs and ROM were significantly improved at 3 months. There were 15 unplanned overnight hospital stays (11.8%) after the procedure. Within 90 days postoperatively, there were 17 unplanned ED/clinic visits (13.4%), 7 hospital readmissions (5.5%), and 4 complications requiring revision (3.1%). Factors predictive of unplanned overnight stay included age above 70 years (OR,36.80 [95% CI, 2.20-615.49]; p=0.012), tobacco use (OR,12.90 [95% CI, 1.23-135.31]; p=0.033), and American Society of Anesthesiologists (ASA) status of 3 (OR,13.84 [95% CI, 1.22-156.57]; p= 0.034). The only factor predictive of unplanned ED/clinic visit was age over 70 years old (OR,7.52 [95% CI, 1.26-45.45]; p=0.027). No factors were predictive of 90-day hospital readmission or revision. Conclusion Outpatient shoulder arthroplasty is a safe procedure with excellent outcomes and low rates of readmissions and can be considered as the default plan for all patient undergoing shoulder arthroplasty. Patients who are above 70 years of age, use tobacco, and have ASA score of 3, however, may be less suitable for outpatient arthroplasty and should be counseled regarding the higher risk of unplanned overnight hospitalization. Keywords outpatient arthroplasty outpatient arthroplasty RSA TSA outcomes complications COVID-19 ==== Body pmcA waiver of consent was granted by the Institutional Review Board at the University of Pittsburgh for STUDY20030061. Retrospective chart review - waiver of consent was granted. No formal informed consent was obtained for patient inclusion in this study as no patient identifiers are included in the study. Disclaimers: Funding: No funding was used for this study. Conflicts of interest: Albert Lin is a paid consultant for Arthrex and Wright Medical. The other authors, their immediate families, and any research foundation with which they are affiliated have not received any financial payments or other benefits from any commercial entity related to the subject of this article. Level of Evidence: Level IV; Case Series; Treatment Study	36470518	PMC9719845	NO-CC CODE	2022-12-08 23:16:15	no	J Shoulder Elbow Surg. 2022 Dec 5; doi: 10.1016/j.jse.2022.10.034	utf-8	J Shoulder Elbow Surg	2,022	10.1016/j.jse.2022.10.034	oa_other
==== Front J Heart Lung Transplant J Heart Lung Transplant The Journal of Heart and Lung Transplantation 1053-2498 1557-3117 International Society for Heart and Lung Transplantation. S1053-2498(22)02222-7 10.1016/j.healun.2022.11.006 Original Clinical Science COVID-19 in pediatric lung transplant recipients: Clinical course and outcome Schütz Katharina MD a Davids Jeanne a Petrik Britta a Scharff Anna Zychlinsky MD b Carlens Julia MD a Heim Albert MD c Salman Jawad MD d Ius Fabio MD d Bobylev Dmitry MD d Hansen Gesine MD aef Müller Carsten MD a Schwerk Nicolaus MD ae⁎ a Department of Pediatric Pneumology, Allergology and Neonatology, Hannover Medical School, Hannover, Germany b Department of Pediatric Haematology and Oncology, Hannover Medical School, Hannover, Germany c Department of Virology, Hannover Medical School, Hannover, Germany d Department of Cardiothoracic, Transplant and Vascular Surgery, Hannover Medical School, Hannover, Germany e BREATH (Biomedical Research in End-stage and obstructive Lung Disease Hannover), German Center for Lung Research (DZL), Hannover, Germany f Excellence Cluster RESIST (EXC 2155), Hanover Medical School, Hannover, Germany ⁎ Reprint requests: Nicolaus Schwerk, MD, Department of Pediatric Pneumology, Allergology and Neonatology, Hannover Medical School, Hannover, Germany. Telephone: +49 511-532-3220. Fax: +49-511-532-9474. 5 12 2022 5 12 2022 © 2022 International Society for Heart and Lung Transplantation. All rights reserved. 2022 International Society for Heart and Lung Transplantation 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 COVID-19 causes high morbidity and mortality in adult lung transplant (LTX) recipients. Data on COVID-19 in children after LTX is limited. We report the clinical presentation and outcome of SARS-CoV-2 infection in 19 pediatric LTX recipients. Methods Between March 2020 and June 2022, SARS-CoV-2 testing was performed on all pediatric LTX patients with COVID-19 symptoms or contact with a SARS-CoV-2 infected person. Positive patients were prospectively evaluated for symptoms, treatment and outcome. Vaccination status and immune response were recorded. Results Nineteen out of 51 pediatric LTX recipients had a SARS-CoV-2 infection. Mean age was 12.3 years (IQR 9-17), 68% were female, 84% had preexisting comorbidities. Mean time between LTX and SARS-CoV-2 infection was 4.8 years (IQR 2-6). No patients experienced severe COVID-19: 11% were asymptomatic, and 89% had mild symptoms, primarily rhinitis (74%), fever (47%), and cough (37%). One SARS-CoV-2 positive patient was hospitalized due to combined fungal and bacterial infection. Mean duration of symptoms was 10.5 days (IQR 3-16), whereas mean period of positivity by antigen test was 21 days (IQR 9-27, p = 0.013). Preventive antiviral therapy was initiated in 3 patients. After a mean follow-up of 2.5 months (IQR 1.1-2.4), no patient reported persistent complaints related to COVID-19. Lung function tests remained stable. Conclusions Unlike adult LTX recipients, children and adolescents are at low risk for severe COVID-19, even with risk factors beyond immunosuppression. Our findings cast doubt on the necessity of excessive isolation for these patients and should reassure clinicians and caregivers of LTX patients. Keywords COVID-19 pediatric lung transplants lung transplantation immunosuppression SARS-CoV-2 antibody response ==== Body pmcSince the discovery of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in December 2019 in Wuhan, China, the virus has caused a pandemic of Coronavirus Disease 19 (COVID-19). In contrast to its devastating effects on elderly populations and adults with preexisting conditions such as diabetes mellitus, obesity, and cardiovascular comorbidities,1 , 2 data suggest that children overwhelmingly experience asymptomatic or mild disease.3 , 4 However, children with various underlying illnesses, including chronic lung disease, are reportedly at higher risk of developing severe COVID-19.5 Currently, little is known about the risks of SARS-CoV-2 infection in children with solid organ transplants. Some studies on children with liver or kidney transplants suggest that severe courses of infection are rare.6, 7, 8 However, to the best of our knowledge, no study has yet examined the clinical course of SARS-CoV-2 infection in children after lung transplantation. This is especially interesting since adult solid organ recipients, especially lung transplant recipients who contract SARS-CoV-2 frequently experience severe disease, with intensive care admission in more than 50% of cases and a mortality rate as high as 10% to 49%.9, 10, 11, 12, 13, 14 Material and methods Study cohort We conducted a single-center study from March 2020 to June 2022 at Hannover Medical School Children's Hospital in Germany, one of the largest LTX centers in Europe. All pediatric post-LTX patients in regular follow-up care (n = 51) were eligible for this study. All patients are routinely instructed to immediately report all symptoms of infection, deterioration of daily pulmonary function, reduction in general wellbeing or contact with a SARS-CoV-2 infected person to the pediatric LTX care team. All patients reporting any of these complaints were tested by rapid antigen test. If rapid antigen testing revealed a positive result, PCR testing was conducted to confirm the infection. Additionally, schoolchildren regularly performed rapid antigen tests up to 3 times per week and all patients were tested during their regularly scheduled visits in our outpatient clinic, which occur every 3 months. During this period, the administration of SARS-CoV-2-specific drugs or modification of immunosuppressive therapy during COVID-19 was not yet standardized. These decisions were based on individual assessment by the medical team or consultant on call after a careful risk-benefit analysis. Data collection Electronic charts were reviewed to collect data on demographics, date of transplantation, SARS-CoV-2 vaccination status, immunoglobulin levels, SARS-CoV-2 specific antibodies pre-and postvaccination, comorbidities, chronic lung allograft dysfunction (CLAD), immunosuppressive regimen, clinical signs and symptoms of SARS-CoV-2 infection, transient or persistent decline in lung function testing, COVID-19 treatments, clinical course and outcome. Time of follow-up was variable, depending on the timing of infection. Data from the last available visit prior to SARS-CoV-2 infection were used for baseline parameters. Depending on the individual clinical course, data were recorded during clinical presentation in our outpatient clinic, hospitalization if indicated or by regularly conducted telemedicine visits during the acute infection. Follow-up visits after infection occurred in the context of routine presentation to our outpatient clinic or by telephone visit. SARS-CoV-2 infection was defined as a positive result on nasopharyngeal swab by rapid antigen test confirmed by polymerase chain reaction (PCR) in any medical laboratory available to outpatients. In 1 adolescent, repetitive antigen tests were positive and the family refused PCR testing. Period of positivity was determined as the time period between the first positive testing result (by rapid antigen test or PCR) to first negative testing result by rapid antigen test. One adolescent hospitalized due to a superinfection received serial PCR tests in the context of inpatient treatment, and did not undergo antigen testing. This patient was therefore excluded from the calculation of the period of positivity. SARS-CoV-2 specific IgG antibodies were determined by CE marked ELISAs for nucleocapsid protein (NCP) and the Spike domain (Euroimmun, Lübeck Germany) pre- and postinfection in a single laboratory at our medical center. Positive antibody response was defined according to manufacturer instructions and to a calibrator provided with the assay (extinction ratio sample/calibrator ≥ 1.1). All pediatric LTX patients in our center perform daily lung function testing using standardized portable pulmonary function test devices, starting at age 4. Three children under the age of 4 did not perform daily lung function testing. Reported results were used for lung function evaluation before, during and after SARS-CoV-2 infection. CLAD was defined according to the current recommendations of the International Society of Heart and Lung Transplantation.15 Disease severity of SARS-CoV-2 infection was classified according to criteria of German Society for Pediatric Infections which are in line with the National Health Institution recommendations.16 , 17 Statistical analysis Statistical analysis was performed by Excel or GraphPad Prism V9 (San Diego, CA). Descriptive data were calculated as mean with interquartile range (IQR), categorical data as frequencies and percentages. Differences between groups were analyzed by t-test unless data were not normally distributed. Here, Mann-Whitney-U test was used. A p-value <0.05 was considered statistically significant. Ethical approval Written informed consent was provided by all patients or their legal guardians regarding anonymized use of personal clinical data for research purposes. Study approval by the institutional ethical review board was waived given its retrospective observational design. Results Study group During the study period, 19 out of 51 (37.3%) pediatric LTX recipients tested positive for SARS-CoV-2 infection and were included in our analysis (Figure 1 ). A total of 13 (68%) patients were female, mean age was 12.3 years (IQR 9.1-17.0) and 3 children were younger than 5 years of age. A total of 16 (84%) patients had at least 1 cardiovascular comorbidity and 6 (32%) were diagnosed with diabetes mellitus prior to infection. Most of COVID-19 in our cohort occurred in the later phases of the pandemic. No patient contracted the disease in 2020 and 2 of the 19 cases occurred in 2021 (1 in February and 1 in December). The remaining 17 cases (89.5%) occurred in 2022. During the study period we did not observe any cases of patients with COVID-19 typical symptoms and suggestive radiological or blood signs who were tested negative for the virus.Figure 1 Study group. Abbreviations: LTX, lung transplantation. Figure 1 Detailed characteristics of the study group including comorbidities and immunosuppressive regimens are given in Table 1 .Table 1 Patients Characteristics at Time of SARS-CoV-2 Infection Table 1Demographic data Female 13 (68) Mean age at SARS-CoV2 infection, years (IQR) 12.3 (9.1-17.0) Mean time after LTX until SARS-CoV-2 infection, years. (IQR) 4.8 (2.4-6.3) Diagnosis leading to LTX PHT 10 (52) child 3 (16) CF 6 (32) Comorbidities 16 (84) CRI 9 (47) CLAD 0 AH 14 (74) DM 6 (32) Immunosuppression before infection Tac/MMF/Pred 17 (89) Tac/Eve/MMF/Pred 1 (5) Tac/Eve/Predni 1 (5) High-dose Steroids within 6 month 1 (5) Rituximab > 12 month before SARS-CoV2 infection 9 (47) Rituximab < 12 month prior SARS-CoV2 infection 1 (5) Hypogammaglobulinemia requiring Immunoglobuline substitution 3 (16) Abbreviations Table 1 AH, arterial hypertension; CF, cystic fibrosis; chILD, children's interstitial lung disease; CLAD, chronic lung allograft dysfunction; class., classification; CRI, chronic renal insufficiency; Eve, everolimus; DM, diabetes mellitus; FU, follow up; IQR, interquartile range; LTX, lung transplantation; MMF, mycofenolat-mofetil; Pred, prednisolon; Tac, tacrolimus. Clinical course of SARS-CoV-2 infection Data on symptoms, clinical course and treatment were available in all patients. Two children (11%) remained asymptomatic. All other patients experienced mild symptoms. Rhinitis (74%), fever (47%), cough (37%), and cephalgia (32%) were the most common complaints (Table 2 A). Mean duration of symptoms was 10.5 days (IQR 3-16). Mean period of positivity by rapid antigen test was 21.0 days (IQR 9-27, p = 0.013).Table 2 COVID-19 Related Symptoms (A) and Patient Outcome (B) Table 2(A) Symptoms n (%) Rhinitis 14 (74) Fever 9 (47) Cough 7 (37) Cephalgia 6 (32) GI symptoms 4 (21) Decrease FEV1 ≥ 10% from baseline 4 (25) Anosmia 2 (11) Fatique 2 (11) Myalgia 1 (5) Hypoxemia 0 Dyspnea 0 Tachypnea 0 (B) Mean time of symptoms, days (IQR) 10.5 (3-16) Mean time of SARS-CoV-2 positivity, days (IQR) 21.0 (9-27) Mean time of FU after SARS-CoV2 infection, month (IQR) 2.5 (1.1-2.4) Hospitalizationa 3 Graft related complications 0 Other organ dysfunction 0 Death 0 Data on symptoms were available in all patients (n = 19). Daily lung function testing was performed in all patients over the age of 5 years (n = 16). a n = 2 for Sotrovimab application; n = 1 for bacterial and fungal superinfection. Abbreviations: FU, follow up; IQR, inter quartile range. Transient FEV1 decline during infection was noted in 4 patients (maximum decrease: -18%) with complete recovery at the latest 3 days after symptoms began. No patient reported dyspnea. COVID19-specific antiviral treatment was given to 3 patients. One 3-year-old (Patient 14, Table 3 ) received Sotrovimab due to her age. The patient was too young to be vaccinated against SARS-CoV-2 at the time. An adolescent (Patient 16, Table 3) received Remdesivir on 3 consecutive days due to severe immunosuppression with recent high-dose steroid treatment and 6 rituximab courses (375 mg/m2) following relapse of post-transplant lymphoproliferative disease 4 months prior to SARS-CoV-2 infection. A 16-year-old girl (Patient 1, Table 3) with arterial hypertension, diabetes mellitus and terminal renal failure necessitating daily peritoneal dialysis 1.6 years after transplantation was treated with Sotrovimab directly after her positive test result, 1 day after onset of mild symptoms. The symptoms persisted for 3 days. Transient FEV1 decline of -18% was recorded during this period. After further 6 days without any complaints, she developed severe invasive aspergillosis and bacterial pneumonia. The patient remained hospitalized and under intensive antibiotic treatment for 56 days; the period of SARS-CoV-2 positivity was 43 days. Lung function returned to baseline 44 days after symptoms of invasive aspergillosis and bacterial superinfection began. The patient recovered fully and was asymptomatic with a normalized lung function at a follow-up visit 101 days after the first positive PCR test.Table 3 Vaccination Status and Antibody Detection Table 3Patient ID Amount of SARS-CoV2 vaccinations Anti-Spike IgG titer prior infection Anti-NCP IgG detection post infection Anti-Spike IgG detection post infection 1a 4 - - + 2 3 - n.a. n.a. 3 2 - n.a. n.a. 4 0 n.a. + + 5 0 n.a. - - 6 3 n.a. - + 7 3 + - + 8 3 - n.a. n.a. 9 3 - - - 10 3 - - - 11 1 - - + 12 0 n.a. + + 13 0 n.a. - + 14a 0 n.a. - + 15 3 - - + 16 4 - - + 17 2 - n.a. n.a. 18 4 - - - 19 3 - n.a. n.a. Abbreviations: n.a., not analyzed; -, negative antibody response; +, positive antibody response. a Patient 1 and 14 received Sotrovimab, which may have resulted in detectable levels of anti-Spike IgG in these children. Except for patient 1, we did not prescribe mucolytics, anticoagulants, antibiotics or other antiviral treatment due to the satisfactory clinical conditions of our patients (Table S1). In cases of fever, some families gave paracetamol or metamizole to their children. Mean time of follow up was 2.5 months (IQR 1.1-2.4). All patients were asymptomatic at the end of the observation period (Table 2B). We did not observe any graft related complications, new appearance of other organ dysfunctions, or post/long-COVID complications. SARS-CoV-2 specific antibody response SARS-CoV-2 specific postvaccination antibodies were available for 13 of the 14 patients who received at least 1 dose of vaccine. However, of these 13, only 1 patient produced measurable amounts of anti-Spike-IgG in response to vaccination. In 14 children (74%) who experienced SARS-CoV-2 infection, specific antibody response was determined after a mean time of 79 days (IQR 34-72) after the first day of COVID-19 symptoms. Six of previously vaccinated children developed a positive anti-Spike-IgG-antibody response after infection. None of these children developed detectable levels of anti-NCP-IgG-antibodies. Four of 5 unvaccinated children developed anti-Spike-IgG-antibodies. One child (Patient 14, Table 3) without SARS-CoV-2 vaccination and 1 adolescent (Patient 1, Table 3) with previous vaccination received Sotrovimab which may have resulted in detectable levels of anti-Spike-IgG in these children. Only 2 children (both previously unvaccinated) developed anti-NCP-IgG-antibodies (Table 3). Discussion To the best of our knowledge, this is the first report on the clinical course and outcomes of pediatric lung transplant recipients with confirmed SARS-CoV-2 infection. In contrast to previous studies on adult lung transplant recipients, which show a high rate of hospitalization and death,9, 10, 11, 12, 13, 14 we did not observe any severe disease or deaths related to SARS-CoV-2 infection. Furthermore, no long-term effects or graft related complications occurred within the observation period, although preexisting comorbidities and immunosuppressive regimens in our cohort are comparable to previously described adult lung transplant populations.4 , 5 This observation supports previous findings that age is 1 of the main risk factors for severe COVID-19.1 , 2 There are several explanations for the less severe courses observed in our study cohort. First, the young age of our patients likely impacted their clinical outcome, reflecting overall less severe COVID-19 in children compared to adults.3 , 4 This is in line with previous studies, showing that children with kidney or liver transplants had less severe disease compared to adults with solid organ transplants.6 , 7 Second, no child with preexisting CLAD contracted a SARS-CoV-2 infection. CLAD patients are known to carry elevated risk of mortality (50%) from COVID-19 in adult lung transplant recipients.9 Third, the majority of SARS-CoV-2 infections in our cohort occurred in 2022, when the Omicron variant (B.1.1.529) was prevalent in Germany.18 Previous studies included mainly adults infected by the British (B.1.1.7) or Delta variant (B.1.617.2), which may cause more severe disease. Fourth, our cohort comprised a high rate of vaccinated children (74%). Of note, only 1 patient had a positive anti-spike-antibody titer after vaccination and before infection, which is in line with previous reports showing extremely low response rates following vaccination in lung transplant recipients due to their intensive immunosuppressive regimens compared to other solid organ recipients.19 It is possible that SARS-CoV-2-specific T-cells may have played a role in protecting our cohort from severe disease. Unfortunately, this question cannot be definitively answered as data on SARS-CoV-2 specific T-cells in these patients are not available. Although none of our patients had severe COVID-19, 1 patient developed invasive pulmonary aspergillosis in association with a confirmed SARS-CoV-2 infection. This complication has been previously described in the context of COVID-19.20 , 21 It is important to consider invasive aspergillosis as a diagnosis in immunocompromised patients with COVID-19 and secondary clinical deterioration. Our study has several limitations. First, it is a single center study with a small number of patients. However, lung transplantation in children is extremely rare and few centers routinely provide pediatric lung transplantation care. To our knowledge, this is the first report in this rare patient population. Secondly, we did not perform chest-X-ray imaging, computer tomography or blood tests during SARS-CoV-2 infection except in the patient with aspergillosis and bacterial superinfection, because all other patients did not experience severe disease courses and therefore further diagnostic work-up was not indicated. In conclusion, our data demonstrate that in contrast to adult LTX recipients, children and adolescents are at considerably lower risk for severe COVID-19 even with pre-existing risk factors beyond immunosuppression. Only 1 adolescent developed severe disease due to superinfection. This finding casts doubt on the value of preventive antiviral treatment in this population, in the context of significant side effects related to treatment. Further studies are needed to evaluate the benefit of COVID-19 specific treatment to prevent severe disease in this patient subset. Importantly, many families of pediatric LTX recipients have been hesitant to reengage in social interaction with peers, regular school attendance and other higher-risk activities due to their childrens’ immunosuppressed state and failure to produce measurable antibody titers post-vaccination. Data suggest that social isolation, particularly absence from in-person schooling, may negatively impact psycho-social health, normal development and academic success.22, 23, 24, 25, 26, 27 The results of our study suggest that the risk of severe COVID-19 in children and adolescents post-LTX without CLAD is low, and this should be considered when creating recommendations for these patients and in conversations with parents and caregivers. Appendix Supplementary materials Image, application 1 Disclosure statement The authors have no competing interests to declare. The authors would like to thank all the patients and families for their contribution. This manuscript contains no individual person's data in any form. Supplementary material associated with this article can be found in the online version at https://doi.org/10.1016/j.healun.2022.11.006. ==== Refs References 1 Wolff D Nee S Hickey NS Marschollek M. Risk factors for covid-19 severity and fatality: a structured literature review Infection 49 2021 15 28 32860214 2 Ou M Zhu J Ji P Risk factors of severe cases with COVID-19: a meta-analysis Epidemiol Infect 148 2020 e175 32782035 3 Parri N Magista AM Marchetti F Characteristic of COVID-19 infection in pediatric patients: early findings from two italian pediatric research networks Eur J Pediatr 179 2020 1315 1323 32495147 4 DGPI. DGPI- statement: symptoms, diagnostics and therapy. Available at: https://dgpi.de/wp-content/uploads/2022/02/COVID-19-Klinik-Diagnostik-Therapie-Stellungnahme-2022-02_Final2.pdf. Updated 2022 Feb. Accessed July 15th, 2022. 5 Ankermann T Brinkmann F. 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==== Front Photodiagnosis Photodyn Ther Photodiagnosis Photodyn Ther Photodiagnosis and Photodynamic Therapy 1572-1000 1873-1597 Elsevier B.V. S1572-1000(22)00502-6 10.1016/j.pdpdt.2022.103216 103216 Article Evaluation of the Effect of İnfection and İmmunity on the Tear Film by Scheimpflug-Placido Disc Topography- A Case Control Study DAĞ Yaşar 1⁎ ACET Yakup 2 1 Department of Ophthalmology, Başakşehir çam ve sakura city hospital. Istanbul, Turkey, Mobile: +0905330188247 2 Department of Ophthalmology, Mardin Training and Research Hospital. Mardin, Turkey, Mobile: +0905307849019 ⁎ Corresponding author. 5 12 2022 3 2023 5 12 2022 41 103216103216 14 10 2022 19 11 2022 29 11 2022 © 2022 Elsevier B.V. All rights reserved. 2022 Elsevier B.V. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Purpose We aimed to compare the tear film stability of individuals who had recovered from coronavirus disease (COVID-19), that of individuals vaccinated against COVID-19 and that of healthy individuals in a control group. Methods This study included 61 eyes of 61 post-COVID-19 patients, 63 eyes of 63 participants who had received at least two doses of the SARS-CoV-2 mRNA BNT162b2 (Pfizer–BioNTech) vaccine, and 57 eyes of healthy individuals in a control group. We compared the groups’ tear film stability. Results The mean non-invasive first tear break-up time (NIF-BUT) value was 4.1±2.7 seconds in the post-COVID-19 group, 4.7±2.9 seconds in the vaccinated group, and 5.8±2.8 seconds in the control group. This value was statistically significantly lower in the post-COVID-19 and vaccinated groups than in the control group (p= 0.007). The rate of superotemporal (ST) quadrant breakup, statistically significantly higher in the vaccinated group than in the other two groups (p=0.001). According to a qualitative examination of the results, at least one breakup occurred in 47 (77%) of the post-COVID-19 participants’ eyes, 50 (79.4%) of the vaccinated group's eyes, and 33 (57.9%) of the control group's eyes. In terms of this qualitative value, the post-COVID-19 and vaccinated groups had significantly higher breakup rates than the control group (p=0.018). Conclusions Destabilization in the tear film was more common in both the post covid group and the vaccinated group. In addition to individuals who have post-Covid, we think that post-vaccination individuals should be followed closely in terms of ocular surface diseases. Keywords Coronavirus disease Coronavirus vaccine Noninvasive breakup time test Tear film impairment Ocular surface in coronavirus Editor: Ron R. Allison ==== Body pmc1 INTRODUCTION The World Health Organization (WHO) declared coronavirus disease (COVID-19) to be a pandemic approximately 3 months after the first severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) case was diagnosed and recorded in December 2019 in China [1]. Although respiratory symptoms are prominent in this disease, COVID-19 infects ocular tissues and may produce symptoms associated with them [2], [3], [4], [5], [6], [7]. In a study by Kelvin et al. it was found that meibomian gland dysfunction was higher and the tear film was more unstable in post-COVID-19 patients. They also showed that these ocular surface problems increased with increasing viral load as found by the cycle threshold in PCR at diagnosis of the disease [8]. In a study by Kase et al. it was found that Inflammatory cell infiltration and glandular damage in the lacrimal gland was observed after Covid-19. Expression of angiotensin converting enzyme 2 was measured in the lacrimal gland indicating that this gland could be the target for Covid-19 infection [9]. According to these two studies, the eye may be the site of primary viral infection, which can affect two important structures that contribute to the tear film, the lacrimal glands and the Meibomian glands [8,9]. COVID-19’s ocular symptoms and signs have been revealed in many retrospective and prospective studies [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13]. Hong et al. found that the Ocular Surface Disease Index (OSDI) score was higher in post-COVID-19 patients [5]. A study by Gambini et al. revealed that the rate of dry eye disease increased in a post-COVID-19 group [4]. Acet et al. evaluated tear film in a non-invasive way and found that tear film demonstrated more instability in post-COVID-19 patients than in healthy individuals [7]. The high transmission of COVID-19 has led to an increased predisposition in vaccine studies at the national and international levels [14,15]. At the end of 2020, many national and international organizations have approved the emergency use of vaccines developed against COVID-19 [16], [17], [18], [19], [20], [21]. With the introduction of vaccines in 2021, studies on vaccines’ effects and side effects have been conducted [22], [23], [24], [25]. Tear film stability and patterns vary depending on infectious and inflammatory factors [26], [27], [28]. This variability is explained by various mechanisms: (a) immune system reaction, (b) opportunistic infections, and (c) the destruction of the ocular surface due to direct microbial infection effect [26], [27], [28], [29]. In this study, we used the non-invasive tear film break-up time (NI-BUT) to compare the tear film patterns of participants who had been infected with COVID-19 disease, participants who had received COVID-19 vaccines, and healthy participants who did not have COVID-19 disease. To the best of our knowledge, this is the first prospective study to compare post vaccination ocular surface parameters. 2 MATERIALS AND METHODS We conducted this prospective case–control study in accordance with the principles outlined in the Declaration of Helsinki. We obtained the required ethical approval. Additionally, we obtained approval from the Provincial Health Directorate as well as written informed consent forms from all participants. We conducted the study at Mardin Training and Research Hospital (Mardin, Turkey) between October 2021 and January 2022. To eliminate the effects of long-term mask use on tear film, we examined the three groups of participants included in this study during the same period of the pandemic, including the conditions for mask use. To eliminate the effects of short-term mask use, we isolated all participants in the test room and ensured that their masks were removed 15 minutes before the procedure. All participants took the NI-BUT test between 8:30 a.m. and 10:00 a.m. Thus, we minimized issues that could affect tear film stability [30,31]. We selected all participants from among patients who visited the ophthalmologist for routine eye examinations. The post-COVID-19 group included patients who had positive PCR test results, were diagnosed with COVID-19 and treated and had completed their quarantine processes at least a month before the start of the study. The vaccinated group included patients who had not previously detected PCR positivity, had no COVID-19 symptoms, had no positive COVID-19 tests (which we determined by examining their medical records), and had received their second dose of the SARS-CoV-2 mRNA BNT162b2 (Pfizer–BioNTech) vaccine over 15 days before the start of the study. The control group included patients who had not demonstrated positive PCR test results, had no COVID-19 symptoms and had no positive COVID-19 tests in their medical records, had not received any SARS-CoV-2 vaccines, and had not volunteered in any vaccine studies. We did not take into account whether patients had previous diagnoses of dry eye in any of the three groups. However, we excluded patients who had used cyclosporine, steroids, or artificial tears for the treatment of dry eye in the past month. We also excluded patients with previous diagnoses of glaucoma and/or history of using glaucoma medication. We included volunteers with a history of cataracts and/or refractive surgery, those with a history of contact lens use, those with no autoimmune diseases causing dry eye, and those with no apparent ptosis in the study. Tear film stability was evaluated by non- invasive break-up time test (NI-BUT). For each of the three groups, we conducted the NI-BUT test on only one of each participant's eyes and planned to record the NI-BUT values of only one eye in the study results. We informed the participants regarding the test's application and process immediately before the test. We asked them to close and open their eyes twice and then to keep their eyes open for as long as they could. We conducted the NI-BUT test using the Sirius™ (Costruzione Strumenti Oftalmici [CSO] S.R.L., Italy) corneal topography device. This device uses a combination of Scheimpflug and Placido disks, analyses 25 film frames per second for a total of approximately 400 film frames, and provides quantitative values, such as the first break-up time(NIF-BUT) and the average value of all breakup (NIAvg-BUT) for each participants . We compared these values between the groups. The device software also provides qualitative values, such as each breakup's localization [7,32,33]. After taking the participants’ NI-BUT measurements, we performed detailed slit-lamp and funduscopic examinations of all participants. These examinations included best-corrected visual acuity and intraocular pressure measurements. After eliminating eyes with blepharitis, conjunctivitis, keratopathy, eyelid deformity, and concretion from all three groups, we included 61 eyes of 61 participants in the post-COVID-19 group, 63 eyes of 63 participants in the post-vaccine group, and 57 eyes of 57 participants in the control group. We compared the results obtained from these three groups. 2.1 Values and Abbreviations NIF-BUT: The time, in seconds, at which the first breakup occur Avg-BUT: This is the average value of all breakups (NIAvg-BUT) for each volunteer. Damaged (DMGD): This qualitative value indicates a situation in which at least one breakup occurred in the tear film during the measurement. F5:This qualitative value indicates a case in which the first break-uptime (NIF-BUT) value was less than or equal to 5 seconds. F5–7:This qualitative value indicates a case in which the first breakup value (NIF-BUT) was 5.1 to 7 seconds. F7–10:This qualitative value indicates a case in which the first breakup value (NIF-BUT) was 7.1 to 10 seconds. F10:This qualitative value indicates a case in which the first breakup value (NIF-BUT) was greater than 10seconds. A5:This qualitative value indicates a case in which the mean breakup value (Avg-BUT) was less than or equal to 5 seconds. A5–7:This qualitative value indicates a case in which the mean breakup value (Avg-BUT) was 5.1 to 7 seconds. A7–10:This qualitative value indicates a case in which the mean breakup value (Avg-BUT) was 7.1 to 10 seconds (Fig. 1).Fig. 1 NI-BUT output of a participant in the vaccinated group; the first breakup (NIF-BUT) occurred after 3.6 seconds. The mean average value of all breakup (NIAvg-BUT) for each volunteer was 7.6 seconds. The first two breakup occurred in the inferotemporal region, and common breakup occurred in all quadrants and across the surface. Fig. 1: A10:This qualitative value indicates a case in which the mean breakup value (Avg-BUT) was greater than 10seconds. S:This qualitative value indicates that a breakup occurred in the ocular surface's superior quadrant. I:This qualitative value indicates that a breakup occurred in the ocular surface's inferior quadrant. T:This qualitative value indicates that a breakup occurred in the ocular surface's temporal quadrant. N:This qualitative value indicates that a breakup occurred in the ocular surface's nasal quadrant. ST:This qualitative value indicates that a breakup occurred in the ocular surface's superotemporal quadrant. SN:This qualitative value indicates that a breakup occurred in the ocular surface's superonasal quadrant. IT:This qualitative value indicates that a breakup occurred in the ocular surface's inferotemporal quadrant. IN:This qualitative value indicates that a breakup occurred in the ocular surface's inferonasal quadrant. 2.2 Statistical method We used averages, standard deviations, medians, minimum and maximum values, frequencies, and ratios in the data's descriptive statistics. We measured the variables’ distributions using the Kolmogorov–Smirnov test. We used analysis of variance (ANOVA;Tukey's test), the Kruskal–Wallis test, and the Mann–Whitney U test in the analysis of the quantitative data. We used a chi-squared test to analyse the qualitative data, and we used Fisher's exact test when chi-squared test conditions were not met. We used SPSS 22.0 software for the analysis. Table 1 Table 1 Demographic, Qualitative, and Quantitative NI-BUT Values for All Participants. Table 1: Min–Max Median Mean ± SD/n-% Age(years) 18.0–66.0 42.0 40.9 ± 11.5 Gender Female 95 52.5% Male 86 47.5% NIF-BUT(sec) 1.2–14.6 4.0 4.8 ± 2.9 Avg-BUT(sec) 1.2–14.8 7.4 7.4 ± 3.1 DMGD (−) 51 28.2% (+) 130 71.8% F5 84 64.6% F5–7 24 18.5% F7–10 14 10.8% F10–17 10 7.7% A5 31 23.8% A5–7 32 24.6% A7–10 37 28.5% A10–17 30 23.1% S 51 39.2% I 76 58.5% T 63 48.5% N 73 56.2% SN 61 46.9% ST 67 51.5% IT 85 65.4% IN 92 70.8% NIF-BUT: The time, in seconds, at which the first breakup occurs. Avg-BUT: average value of all breakup (NIAvg-BUT) for each volunteer during the measurement. Damaged (DMGD): This qualitative value indicates a case in which at least one breakup occurred in the tear film during the measurement. F5: This qualitative value indicates a case in which the first breakup value (NIF-BUT) was less than or equal to 5 seconds. F5–7: This qualitative value indicates a case in which the first breakup value (NIF-BUT) was 5.1 to 7 seconds. F7–10: This qualitative value indicates a case in which the first breakup value (NIF-BUT) was 7.1 to 10 seconds. F10: This qualitative value indicates a case in which the first breakup value (NIF-BUT) was greater than 10 seconds. A5: This qualitative value indicates a case in which the mean breakup value (Avg-BUT) was less than or equal to 5 seconds. A5–7: This qualitative value indicates a case in which the mean breakup value (Avg-BUT) was 5.1 to 7 seconds. A7–10: This qualitative value indicates a case in which the mean breakup value (Avg-BUT) was 7.1 to 10 seconds. A10: This qualitative value indicates a case in which the mean breakup value (Avg-BUT) was greater than 10 seconds. S: This qualitative value indicates that a breakup occurred in the ocular surface's superior quadrant. I: This qualitative value indicates that a breakup occurred in the ocular surface's inferior quadrant. T: This qualitative value indicates that a breakup occurred in the ocular surface's temporal quadrant. N: This qualitative value indicates that a breakup occurred in the ocular surface's nasal quadrant. ST: This qualitative value indicates that a breakup occurred in the ocular surface's superotemporal quadrant. SN: This qualitative value indicates that a breakup occurred in the ocular surface's superonasal quadrant. IT: This qualitative value indicates that a breakup occurred in the ocular surface's inferotemporal quadrant. IN: This qualitative value indicates that a breakup occurred in the ocular surface's inferonasal quadrant. 3 RESULTS The youngest participant included in the study was 18 years old, and the oldest participant was 66 years old. The participants’ mean age was 40.9 ±11.5 years. Of the participants, 95 were women, and 86 were men. Among all participants, the shortest NIF-BUT value was 1.2 seconds, the longest NIF-BUT value was 14.6 seconds, and the mean NIF-BUT value was 4.8 ±2.9 seconds. The lowest Avg-BUT value was 1.2 seconds, and the highest Avg-BUT value was 14.8 seconds. The mean Avg-BUT value was 7.4±3.1 seconds. The percentage of eyes with at least one breakup during the test, which are referred to as damaged (DMGD) by the device, was 71.8%. Among all participants, the inferonasal quadrant was the quadrant with the most breakups (70.8%), and the superior quadrant was the quadrant with the least breakups(39.2%). In 84 (64.6%) of the study participants, the NIF-BUT value was 5 seconds or less (F5), making this interval the most frequently detected interval of NIF-BUT values. In contrast, the NIF-BUT interval was 10.1–17 seconds (F10–17) in only 10 (7.7%) of the participants. In the vast majority of the patients, breakups occurred in the inferior hemisphere. 3.1 Subgroup Analysis We found no significant differences among the post-COVID-19, vaccinated and control groups in terms of demographic characteristics such as the patients’ age and gender distributions (p> 005; Table 2 ).Table 2 Comparison of Groups’ Demographic Data, NIF-BUT, Avg-BUT, and Damaged Values. Table 2: Post-COVID-19 group Post-vaccine group Control group p Age(years) Mean ± SD 42.1 ± 11.7 38.3 ± 10.4 42.4 ± 12.1 0.108 K Median 42.0 42.0 45.0 Gender Female n-% 30 49.2% 34 54.0% 31 54.4% 0.817 X² Male n-% 31 50.8% 29 46.0% 26 45.6% NIF-BUT(sec) Mean ± SD 4.1 ± 2.7 4.7 ± 2.9 5.8 ± 2.8 0.007 K Median 3.6 4.2 4.8 Avg-BUT(sec) Mean ± SD 6.7 ± 3.2 7.6 ± 3.1 8.3 ± 2.8 0.063 A Median 6.3 7.8 8.4 DMGD (−) n-% 14 23.0% 13 20.6% 24 42.1% 0.018 X² (+) n-% 47 77.0% 50 79.4% 33 57.9% A ANOVA / K Kruskal–Wallis (Mann–Whitney U test) / X² chi-squared test NIF-BUT: The time, in seconds, at which the first breakup occurs. Avg-BUT: average value of all breakup (NIAvg-BUT) for each volunteer during the measurement. Damaged (DMGD): This qualitative value refers to a situation in which at least one breakup occurred in the tear film during the measurement. The mean NIF-BUT value was 4.1±2.7 seconds in the post-COVID-19 group, 4.7±2.9 seconds in the vaccinated group, and 5.8±2.8 seconds in the control group. The mean NIF-BUT values was statistically significantly lower in the post-COVID-19 and vaccinated groups than in the control group (p= 0.007). There was no statistically significant difference between the post-COVID-19 and vaccinated groups in terms of mean NIF-BUT value (p> 0.05). The mean NIAvg-BUT values were 6.7±3.2 seconds in the post-COVID-19 group, 7.6±3.1 seconds in the vaccinated group, and 8.3±2.8 seconds in the control group. This difference among the groups was statistically insignificant (p> 0.05), and there was no significant difference in binary group comparisons in terms of NIAvg-BUT value (p> 0.05). Examining the results qualitatively, at least one breakup occurred in 47 (77%) of the post-COVID-19group's eyes, 50 (79.4%) of the post-vaccine group's eyes, and 33 (57.9%) of the control group's eyes. In terms of this qualitative value, the post-COVID-19 and post-vaccine groups had significantly higher incidences of breakup than the control group (p=0.018). In contrast, there was no significant difference between the post-COVID-19 group and the vaccinated group in terms of the rate of eyes with at least one breakup (p > 0.05),which the device referred to as damaged (DMGD;Table 2). There was no significant difference among the three groups in terms of rates of NIF-BUT values of less than or equal to 5 seconds (F5), NIF-BUT values of 5.1–7 seconds (F5–7), or NIF-BUT values of 7.1–10 seconds(F7–10; p> 005; Table 3 ).Table 3 Comparisons of the Groups in Terms of Breakup Values. Table 3: Post-COVID group Post-vaccine group Control group p n % n % n % F5 33 70.2% 33 66.0% 18 54.5% 0.341 X² F5-7 9 19.1% 8 16.0% 7 21.2% 0.826 X² F7-10 5 10.6% 4 8.0% 5 15.2% 0.589 X² F10-17 3 6.4% 5 10.0% 2 6.1% 0.736 X² A5 15 31.9% 13 26.0% 3 9.1% 0.056 X² A5-7 13 27.7% 9 18.0% 10 30.3% 0.370 X² A7-10 11 23.4% 13 26.0% 13 39.4% 0.262 X² A10-17 8 17.0% 15 30.0% 7 21.2% 0.303 X² X² Chi-Square test F5: This qualitative value indicates a case in which the first breakup value (NIF-BUT) was less than or equal to 5 seconds. F5–7: This qualitative value indicates a case in which the first breakup value (NIF-BUT) was 5.1 to 7 seconds. F7–10: This qualitative value indicates a case in which the first breakup value (NIF-BUT) was 7.1 to 10 seconds. F10: This qualitative value indicates a case in which the first breakup value (NIF-BUT) was greater than 10 seconds. A5: This qualitative value indicates a case in which the mean breakup value (Avg-BUT) was less than or equal to 5 seconds. A5–7: This qualitative value indicates a case in which the mean breakup value (Avg-BUT) was 5.1 to 7 seconds. A7–10: This qualitative value indicates a case in which the mean breakup value (Avg-BUT) was 7.1 to 10 seconds. A10: This qualitative value indicates a case in which the mean breakup value (Avg-BUT) was greater than 10 seconds. Considering the distribution rates of the breakup on the ocular surface, the rate of breakup formation in the nasal quadrant (N) was significantly lower in the post-vaccine group than in the post-COVID-19 group (p=0.011). However, there was no significant difference between the post-vaccine group and the control group in terms of nasal quadrant involvement (p>005). The rates of breakup in the superotemporal quadrant (ST) were 72% in the post-vaccine group, 40.4% in the post-COVID-19 group, and 36.4% in the control group. The incidence of ST quadrant breakup after vaccination was statistically significantly higher in the post-vaccine group than in the other two groups (p=0.001). There was no significant difference between the post-COVID-19 group and the control group in terms of ST quadrant involvement. (p> 005). Additionally, the rates of breakup in the inferotemporal quadrant were 80% in the post-vaccine group, 63.8% in the post-COVID-19 group, and 45.5% in the control group. Although the rate of breakup formation in the inferotemporal quadrant indicated a significant difference between the post-vaccine group and the control group (p=0.005), there was no significant difference between the post-vaccine group and the post-COVID-19 group (p>005). We found no significant difference among the groups or between pairs of groups in terms of the other quadrant distributions (p> 005; Table 4 ).Table 4 Comparisons of the Groups in Terms of Breakup Values. Table 4: Post-COVID-19 group Post-vaccine group Control group p n % n % n % S 19 40.4% 19 38.0% 13 39.4% 0.970 X² I 31 66.0% 28 56.0% 17 51.5% 0.393 X² T 26 55.3% 26 52.0% 11 33.3% 0.125 X² N 34 72.3% 21 42.0% 18 54.5% 0.011 X² SN 19 40.4% 25 50.0% 17 51.5% 0.531 X² ST 19 40.4% 36 72.0% 12 36.4% 0.001 X² IT 30 63.8% 40 80.0% 15 45.5% 0.005 X² IN 35 74.5% 35 70.0% 22 66.7% 0.743 X² X² Chi-Square test S: This qualitative value indicates that a breakup occurred in the ocular surface's superior quadrant. I: This qualitative value indicates that a breakup occurred in the ocular surface's inferior quadrant. T: This qualitative value indicates that a breakup occurred in the ocular surface's temporal quadrant. N: This qualitative value indicatesthat a breakup occurred in the ocular surface's nasal quadrant. ST: This qualitative value indicatesthat a breakup occurred in the ocular surface's superotemporal quadrant. SN: This qualitative value indicatesthat a breakup occurred in the ocular surface's superonasal quadrant. IT: This qualitative value indicatesthat a breakup occurred in the ocular surface's inferotemporal quadrant. IN: This qualitative value indicatesthat a breakup occurred in the ocular surface's inferonasal quadrant. 4 DISCUSSION In this study, we detected increased tear film destabilization in both the post-COVID-19 group and the vaccinated group compared to the control group. In a different study, we detected increased tear film destabilization in post-COVID-19 patients compared to the control group [7]. Because COVID-19 vaccines had not yet been administered at the time of previous study, we compared only post-COVID-19 patients and a control group in that study [7]. At that time, it was thought that the increased destabilization in post-COVID-19 patients could be a direct effect of the virus or result of an immune reaction [8,9,[26], [27], [28]]. In the present study, we found the NIF-BUT values to be 4.1 seconds in the post-COVID-19 group, 4.7 seconds in the vaccinated group, and 5.8 seconds in the control group. Although there was no significant difference between the post-COVID-19 group and the vaccinated group in terms of NIF-BUT value, we found that the values of both the post-COVID-19 group and the vaccinated group were significantly lower than those of the control group, and the post-COVID-19 and vaccinated groups had more unstable tear film. Additionally, when we examined the rates of eyes in which at least one breakup occurred, these rates were 77.0% in the post-COVID-19 group, 79.4% in the vaccinated group, and 57.9% in the control group. We found that tear film stability was lower in both the post-COVID-19 and vaccinated groups than in the control group, and we did not detect a significant difference between the post-COVID-19 and vaccinated groups in terms of this qualitative value. Based on these results, we found that the post-COVID-19 group had the worst tear film health of the three groups, but the tear film stability values of the vaccinated group were similar to those of the post-COVID-19 group. Although the study's design has prevented us from determining causality our observational results may indicate that COVID-19 could cause tear film destabilization because of an immune effect rather than an infectious effect. However, this result must be further investigated and supported by clinical trials that include inflammation parameters. Ocular surface inflammation causes tear film instability, and tear film instability and loss of homeostasis are accepted as two multifactorial etiological factors for dry eye [34]. Baudouin et al. suggested that a “vicious cycle of inflammation” is the leading risk factor for dry eye [35]. According to this “vicious cycle of inflammation”, autoimmunity in intrinsic factors and conjunctivitis in extrinsic factors [[33], [36]] were evaluated as etiological factors. These etiological factors can be adapted as immunity (an intrinsic factor), which is the only effect caused by the vaccine in our study, and conditions that arise from the virus's binary effects on the ocular surface. These effects include (a) an extrinsic factor with a direct effect of conjunctivitis and (b) an intrinsic factor (immunity) caused by an immune reaction caused by the virus's immune-system-stimulating effect. This provides us with information regarding why the post-COVID-19 group had the most unstable tear film. In a study conducted by telephone in the form of questionnaire questions in the early stages of the pandemic, dry eye symptoms were detected in approximately 20% of patients who had contracted and overcome COVID-19 [6]. In the pandemic's later stages, further studies involving additional dry eye tests revealed that dry eye symptoms were significantly more prevalent in post-COVID-19 patients than in control groups [3,4,7]. Unlike those proving the increased occurrence of ocular surface defects in post-COVID-19 patients [2], [3], [4], [5], [6], [7], [8], [9], there is no study to the best of our knowledge on ocular surface defects after vaccination or on COVID-19 vaccines’ side effects on ocular tissues. Case reports of eyelid lesions and superficial mild dermatitis post-vaccination have been reported [37,38]. Cases of corneal graft rejection (which is the most frequently reported anterior segment finding after vaccination) also reported [39], [40], [41], [42], [43], [44]. The possible mechanism of graft rejection is thought to be increased vascular permeability and induction of a strong antibody response by COVID-19 vaccines with CD4+ and Th1 cells [41,45]. Some case reports are available on disorders such as Vogt–Koyanagi–Harada syndrome [46], uveitis [47], [48], [49], central serous chorioretinopathy [50], retinal vascular occlusions [51,52], arteritis anterior ischemic optic neuropathy [53] and abducens nerve palsy [54]. The US Vaccine Adverse Events Reporting System (VAERS) has received reports of similar adverse effects. However, it has been concluded that some of these adverse effects may be real effects related to the vaccine, or they may be considered random events unrelated to the vaccine [55]. Adverse ocular effects are associated with not only COVID-19 vaccines but also some vaccines that have been applied for a long time; the reported cases include uveitis and choroid after yellow fever vaccines [56,57]; multiple evanescent white dot syndrome after influenza vaccination [58]; unilateral acute idiopathic maculopathy [59], acute macular neuroretinopathy [60] and Vogt–Koyanagi–Harada disease [[61], [62]] after hepatitis B vaccination. This study reveals deteriorations in the tear film in both the post-COVID-19 and vaccinated groups. In our study, we did not examine the antibody titers showing the immune response in the vaccinated and post-COVID-19 groups; this could be evaluated as a limitation of this study. 5 Conclusion We detected impaired tear film in the post-COVID-19 and vaccinated groups. We found that the post-COVID-19 and post-vaccine groups were similar in terms of tear film impairment. We think that both post-COVID-19 patients and vaccinated individuals should be followed closely in terms of dry eye. CRediT authorship contribution statement Yaşar DAĞ: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. Yakup ACET: Writing – review & editing, Software, Resources, Methodology, Formal analysis. Declaration of Competing Interest No Conflicts of Interest Ethics approval No funding was received for this research 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. The study was approved by the Bioethics Committee of the Medical University of Harran (HRU/21.05.27). Informed consent Informed consent was obtained from all individual participants included in the study. Data and materials are Available upon reguest :[email protected] ==== Refs References 1 World Health Organization Novel Coronavirus (2019-nCoV) Situation Report 22 2020 World Health Organization Geneva 2 Wu P Duan F Luo C Characteristics of ocular findings of patients with coronavirus disease 2019 (COVID-19) in Hubei Province China. JAMA Ophthalmol. 138 2020 575 578 10.1001/jamaophthalmol.2020.1291 32232433 3 Zulvikar SU Gita VS. 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Ophthalmic complications of COVID19 vaccination Indian J Ophthalmol 69 2021 2900 2902 10.4103/ijo.IJO_2122_21 34571680 57 Marinho PM Nascimento H Romano A Muccioli C Belfort R Jr. Diffuse uveitis and chorioretinal changes after yellow fever vaccination: a re-emerging epidemic Int J Retina Vitreous 5 2019 30 10.1186/s40942-019-0180-0 31608161 58 Biancardi AL Moraes HV Jr. Anterior and intermediate uveitis following yellow fever vaccination with fractional dose: case reports Ocul Immunol Inflamm 27 4 2019 521 523 10.1080/09273948.2018.1510529 30153765 59 Ng CC Jumper JM Cunningham ET Jr. Multiple evanescent white dot syndrome following influenza immunization - A multimodal imaging study Am J Ophthalmol Case Rep 19 Sep, 2020 100845 10.1016/j.ajoc.2020.100845 60 Jorge LF Queiroz RP Gasparin F Vasconcelos-Santos DV. Presumed unilateral acute idiopathic maculopathy following H1N1 vaccination Ocul Immunol Inflamm Mar 11, 2020 1 3 10.1080/09273948.2020.1734213 61 Shah P Zaveri JS Haddock LJ. 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==== Front Am J Infect Control Am J Infect Control American Journal of Infection Control 0196-6553 1527-3296 Published by Elsevier Inc. on behalf of Association for Professionals in Infection Control and Epidemiology, Inc. S0196-6553(22)00843-4 10.1016/j.ajic.2022.11.020 Major Article Impact of an inclusive COVID-19 visitation policy on patient satisfaction and visitor safety Nguyen Chau MS, CIC, FAPIC a⁎ Lampen Russell DO b Grooms Austen b Polega James MD b Donkin Joshua MD b Bhugra Mudita MD b a Spectrum Health System, Department of Infection Control and Prevention, Grand Rapids, MI b Spectrum Health System, Department of Infectious Diseases, Grand Rapids, MI ⁎ Address correspondence to Chau Nguyen, MS, CIC, FAPIC. Department of Infection Control and Prevention, Spectrum Health System, 100 Michigan St NE, MC 175, Grand Rapids, MI 49503. 5 12 2022 5 12 2022 © 2022 Published by Elsevier Inc. on behalf of Association for Professionals in Infection Control and Epidemiology, Inc. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Background The COVID-19 pandemic presented unique and unprecedented challenges due to limited knowledge regarding the virus's transmissibility. With guidance from the Center for Disease Control (CDC), healthcare systems instituted widespread visitor restrictions. Hospitalization is a stressful time for patients. Visitor support can help minimize this during and after discharge. Methods A telephone interview was conducted among hospitalized COVID-19 positive patients discharged between March 1st and August 31st, 2021 to explore the patients and visitors’ experiences and the impact of the visitor policy during their hospitalization. Results A total of 238 patients were interviewed. For patients with visitors, 98% felt that the presence of visitors improved their overall wellbeing and satisfaction. Additionally, 86% reported that visitors were involved in helping with their care upon discharge. For patients with no visitors, 59% felt that having a visitor would have improved their hospital stay. Nearly 50% reported that the absence of visitors made it difficult for family members to remain updated and informed of their hospital care. Conclusion This study demonstrates that visitation for COVID-19 patients can be done safely and that there is a positive impact on patient wellbeing with increased visitor access. As we move towards COVID-19 endemicity, implementing evidence-based visitation policies that maximize patient wellbeing will be essential. Key Words Coronavirus disease Visitor restrictions Telephone interviews Quality improvement ==== Body pmcIntroduction Hospitalization can be a stressful and emotionally taxing event for patients. Support provided to patients by visitors is recognized as an integral component in addressing the complex needs of hospitalized patients. Patient and family centered care has been associated with improved outcomes and satisfaction for both patients and their families.1 Liberal visitation policies have resulted in decreases in rates of delirium and even shorter intensive care unit (ICU) stays in critically ill patients.2, 3, 4 A key aspect of patient and family centered care is the ability of the patient to have visitors be present during their hospitalization to actively participate in their care.5 , 6 While visitation restrictions have been employed by healthcare systems in the past during outbreaks of viral respiratory illnesses as a measure to minimize the spread of infectious agents to protect patients, visitors and staff, the COVID-19 pandemic presented an unprecedented challenge to healthcare organizations. COVID-19 has proven to be particularly challenging especially early in the pandemic due to a lack of information related to disease transmission, the limitation of available resources to treat hospitalized patients, and duration of the pandemic which has far exceeded any seasonal respiratory illness timeframe. Healthcare systems across the country, in response to the Centers for Disease Control (CDC) guidance, instituted policies restricting visitor access to most COVID-19 patients and in many cases eliminated visitation entirely. These policies were inconsistent between various healthcare agencies and were continually changing during the pandemic resulting in confusion on the part of patients and their families. Several small studies have looked at the impact of these restrictive visitation polices across differing patient populations with results ranging from minimal impact in adult oncology patients to feelings of higher stress in women experiencing labor and childbirth and even worsened clinical outcomes as evidenced by poor nutritional intake in individuals residing in long term care facilities.7 These studies have had small sample sizes and narrowly defined patient populations.8, 9, 10, 11 As the pandemic unfolded, it has become increasingly apparent that in-hospital transmission rates of COVID-19 are lower than initially feared. Data collected in England during the first half of 2020 suggests that approximately 1% of total infections in the population are hospital associated. Our organization initially adopted a policy consistent with the CDC guidance of only allowing visitors during a patient's hospitalization for: end of life decisions, labor and delivering persons, and visitors essential for helping to provide patient care and/or caring for pediatric patients.12 After more than a year of allowing visitors to selective COVID-19 patients without any apparent transmission events and recognizing the importance of visitors in patient wellbeing, the health system transitioned to a more inclusive visitor policy in August of 2021. This approach balanced the potential risk of COVID-19 exposures for patients, staff, and visitors against the potential benefit to patients. Following the changes in visitation policy, COVID-19 patients were allowed a single visitor per day. The visitor was instructed on proper donning and doffing of PPE and was provided with PPE during the visit. We present the results from a telephone survey of discharged COVID-19 patients and their visitors, aiming to help capture their reported experiences toward visitor restrictions, establish the role that visitors played in the patients’ healthcare during and post hospitalization, and compare the differences in patient satisfaction and readmissions among those reported having visitors to those without. Methods This project was deemed as a Quality Improvement project and was exempt from the health system's Institution Review Board (IRB). Spectrum Health is a not-for-profit system serving patients across the state of Michigan. The Spectrum Health West Michigan division is comprised of seven acute care hospitals combined to have over 1300 beds. A line listing of hospitalized COVID-19 positive patients discharged from Spectrum Health West Michigan between March 1st, 2021 and August 31st, 2021, this duration of time included both the initial restrictive and the more inclusive visitation policies. Patient demographic and hospitalization elements were obtained, password protected and made accessible only to study investigators. Patient exclusions were individuals under 18 years of age and patients with discharged dispositions not equal to home or self-care. A telephone interview was used to explore our hospitalized patients and their visitors’ experiences and the impact of the visitor policy at the time of the patient's hospitalization. A prepared standardized questionnaire with closed-ended questions was developed for data collection. Telephone calls were made to discharged patients from the line list. Consent for information was obtained from patients verbally prior to proceeding with the interviews. Individuals with verbal consent for participation were asked if they had a visitor during their hospitalization. This was then followed by questions related to the patient's experience with the presence or absence of visitors, the impact and involvement of such, their overall hospitalization satisfaction and readmission for COVID-19 treatment. For patients who reported having visitors, we also asked if their visitor contracted COVID-19 during their visit in the hospital. Additionally, we seek for permission from the patient to contact their visitor(s) and made calls to those whose’ info was provided. For visitors with successful interviews, questions related to their relationship with the patient, the number of visits, the availability of personal protective equipment (PPE), team member's assistance in PPE use and COVID-19 status post visit was obtained. Data analyses were performed using IBM SPSS Statistics Data and Microsoft Excel for descriptive statistics and for comparing the responses of patients with visitors and those without respectively. Patient satisfaction was rated on a scale of 1 – 5 and the duration of hospitalization in comparison to patient's satisfaction score was assessed. Results Between March 1st, 2021 and August 31st, 2021, a total of 2,456 COVID-19 patients were discharged from the health system. Excluding patients less than 18 years of age and those who were not discharged to home or self-care, 2,004 patients remained for study inclusion of which 1069 patients were telephoned, and 238 patients were successfully interviewed. Among those interviewed, 61 (26%) reported having a visitor during their course of hospitalization (see Fig 1 ). After their initial hospitalization, 95% of patients interviewed were not readmitted for additional COVID-19 treatment. For patients who reported having visitors, 8% were readmitted for additional COVID-19 treatment compared to 7% for patients without. The average age of study participants was 59 years (range: 21-91). Patients with visitors were on average 3 years younger than those without visitors (56 vs 59). Among interviewed patients, 83% were white or Caucasian (83%).Fig 1 Study population. Fig 1 Interviewed patients with visitors Among interviewed patients who reported having visitors during their course of hospitalization, 1-2 visits were the most common (49%). A quarter of patients had visitors with seven or more visits. A higher percentage of females reported having visitors than males (59% vs 41%). When asked if having a visitor improved their overall wellbeing and satisfaction during their hospital stay, 98% said yes. Additionally, 87% of the patients' visitors were involved in helping with their care upon discharge as reported in Table 1 . Ninety-seven percent of patients reported that their visitors did not contract COVID-19 during their visit in the hospital with the patient.Table 1 Questions and answers for patients with visitors (N = 61) during their course of hospitalization Table 1Question Yes (n, %) No (n, %) No difference (n, %) Did you feel that having a visitor improved your overall wellbeing and satisfaction during your hospital stay? 60 (98%) 0 (0%) 1 (2%) Was your visitor involved in helping with your care once you left the hospital? 53 (87%) 8 (16%) Did your visitor contract COVID-19 during their visit in the hospital? 2 (3%) 59 (97%) Interviewed patients without visitors Among interviewed patients who reported not having visitors during their course of hospitalization, 40% stated that not having a visitor during their hospital stay decreased their overall wellbeing and satisfaction and 59% felt that having a visitor would have improved their hospital stay. Close to 50% of patients felt that the absence of visitors made it harder for their family members or caregivers to remain updated and informed of their hospital care (see Table 2 ).Table 2 Questions and answers for patients with no visitors (N = 177) during their course of hospitalization Table 2Question Yes (n, %) No (n, %) No difference (n, %) Did you feel that not having a visitor decreased your overall wellbeing and satisfaction during your hospital stay? 70 (40%) 94 (53%) 13 (7%) Would your hospital stay have been improved if visitors were allowed? 104 (59%) 58 (33%) Do you feel that not having visitors made it harder for your family members/caregivers to remain informed regarding your hospital care? 83 (50%) 88 (47%) Patient satisfaction The presence of visitors during one's course of hospitalization is advantageous for the patient. Those with visitors reported an average hospital stay satisfaction score of 4.6 (very satisfied) compared to 4.3 (satisfied) for those with no visitors. Moreover, 72% of patients with visitors reported being very satisfied with their hospital stay in comparison to 59% for patients with no visitors (see Table 3 ). Despite their absence of visitors, 83% of patients were satisfied/very satisfied with their hospital stay.Table 3 Patient satisfaction Table 3Question 1= Very Unsatisfied (n, %) 2 = Unsatisfied (n, %) 3 = Neutral (n, %) 4 = Satisfied (n, %) 5 = Very Satisfied (n, %) Overall, how satisfied were you with your hospital stay on a scale of 1-5? 8 (3%) 7 (3%) 20 (8%) 54 (23%) 149 (63%) Patients reported having visitors (N = 61) 0 (0%) 1 (2%) 4 (7%) 12 (20%) 44 (72%) Patient reported having no visitors (N = 177) 8 (5%) 6 (3%) 16 (9%) 42 (24%) 105 (59%) Length of stay and hospitalization satisfaction The average length of stay (LOS) was 4.6 days (range: 1-28.7 days; see Fig 2 ). Patients with visitors had an average of 1 day longer in duration of hospitalization compared to patients without visitors (5.4 vs 4.4 days; see Fig 3 ). In assessing the length of hospitalization and patient's reported average satisfaction during their hospital stay, the overall average satisfaction score reported was 4.4 (satisfied). For patients with length of hospitalizations between 4 and 9 days, they reported being very satisfied with their hospital stay. However, this score dropped to 4 (satisfied) for patients with 10 days of hospitalization or greater (see Fig 4 ). Patients with visitors reported on average being very satisfied with their hospital care whereas this average score is slightly lower for patients without visitors. The largest differences in satisfaction scores are observed among those with 10 days of hospitalization or greater.Fig 2 Length of hospitalization of COVID-19 discharged patients who were interviewed. Fig 2 Fig 3 Length of hospitalization of COVID-19 discharged patients who were interviewed and reported having visitors or no visitors. Fig 3 Fig 4 Length of hospitalization and patient's reported average satisfaction during their hospital stay. Fig 4 Visitor interviews Thirteen visitors were successfully contacted and interviewed with majority (62%) reported as being spouses or significant others of the patient. Their number of visits ranged between 1 and 2 (46%) to greater than 7 (31%). Not only did these individuals play the role of visitors, 85% were involved in the care of the patient after their discharge. Despite their visits to COVID-19 hospitalized patients, all visitors (100%) reported that they did not develop any symptoms of COVID-19 in the 2 weeks post visits. Regarding questions related to PPE, 69% reported that PPE were readily available during their visits, however, only 38% reported receiving instructions for PPE use. When asked about the availability of team members and the assistance they provided with PPE usage during their visit, 77% reported team members were available for questions when needed, but only 30% reported team members aided with PPE usage. Discussion Visitor restrictions seek to balance the risks and benefits of select patient experiences where visitors are suspected to have the greatest impact on a patient's care. Initially during the early phases of the COVID-19 such restrictions were reasonable due to limited understanding of disease transmission, inadequate PPE supplies, lack of effective treatment and no available vaccines. Guidance for healthcare facilities from the CDC has evolved since the early phases of the pandemic, but they remain restrictive.13 Overly restrictive visitor polices can contribute to feelings of isolation among patients, anxiety of family members, and moral distress among health care workers.14 This study demonstrates that not only can visitation for COVID-19 patients be done safely throughout the hospital, but that there is a positive impact on patient wellbeing with increased visitor access. Of those who were able to have visitors during their hospitalization, 98% felt they improved their wellbeing. Even more impactful is that 87% of visitors were involved in the patient's care after discharge. While visitors played a role in post-discharge care, there was not a difference in length of stay or readmission rates between the two groups. Despite best efforts to update families by healthcare workers and to assist in phone and video visits during times of visitor restrictions, there was a clear benefit in patient satisfaction between those who were allowed visitors and those who could not have in person visitation (72% vs 59% very satisfied). The physical presence of a valued visitor is difficult to emulate with virtual visits. Understandably, the gap in patient satisfaction grew wider as the hospital length of stay increased (see Fig 4). Although the study found that visitors had a positive impact on patient wellbeing during their course of hospitalization, however, the positive impact of permissive visitation policies may not universally apply to hospital staff. While the negative impact of patient isolation can cause moral distress among healthcare workers, according to a 2011 survey, even though nurses believe that visitors are beneficial to patients, they also report that flexible visitation policies increase their workloads and hinders their ability to practice.15 Visitors can overcrowd the hospital rooms, which limits staff member's ability to perform procedures. Furthermore, under the watchful gaze of family members, greater accountability is expected from health care providers, which may cause them to feel more burdened and stressed.15 While these concerns are legitimate, allowing visitors access should be balanced with patient safety and wellbeing as family members play a crucial role throughout the patient's course of hospitalization. While not a measured outcome, anecdotally during phone interviews, patients expressed reluctance to seek care during times of more strict visitor policies as when compared to a more inclusive visitor policy. Raphael et al.16 in their analysis of unintended consequences of visitation restrictions for pediatric hospitalized patients also express concern that parents will elect to delay care due to COVID visitor restrictions. Reports related to delay or avoidance of care found that 41% of Americans delayed seeking healthcare during the first year of the COVID pandemic.17 These delays in seeking care potentially influenced by the impact of restrictive visitor policies could result in poor patient outcomes. The other goal of this quality improvement project was to assure that allowing visitors to see COVID-19 patients could be done safely. None of the visitors interviewed reported contracting COVID-19 in the two weeks post their hospital visits. Only two patients (3% of those with visitors) were reported to have had visitors who developed COVID-19. Since patients were able to have more than one unique visitor during their hospitalization, the overall percentage of visitors who developed COVID-19 may be lower than 3%. These self-reported cases of visitor associated COVID are certainly limited by patient recall but recognizing that these visitors often had close contact with the admitted COVID-19 patient prior to admission, the rates of transmission to visitors are substantially lower than published reports on rated of household transmission.18 , 19 Visitor acquisition of COVID-19 outside of the healthcare setting is certainly possible due to the lack of masking and removal of capacity limits on businesses in the community. Based upon household transmission studies, patients with COVID-19 are the most contagious during pre-symptomatic and early phases of their illness with up to 75% of household transmission occurring by day 5.18 Studies have shown that the median delay in seeking care for individuals aged 20-80 years is nearly 4 days.20 By the time most patients have sought care at a hospital, it is likely their viral load is already declining. The CDC on Aug 1, 2022, updated isolation guidance for individuals in the community and allowed for ending isolation on day 5 of illness if the individual will wear a mask and has improvement in their symptoms.21 These changes recognized the ongoing understanding of when individuals infected with SARS-CoV2 are the most contagious. This evolving understanding of COVID, has not been reflected in the CDC guidance surrounding in hospital visitation as currently the CDC continues to recommend limiting visitors for COVID-19 positive inpatients. Weaknesses of this study include the potential for retrospective recall bias on the part of patients and visitors. Not surprisingly, the vast majority (98%) of patients who were able to have visitors felt the impact improved their wellbeing and satisfaction, however, only 40% of those without visitors felt the lack of visitation had a negative impact. Patients who recover successfully and return home without complications, may retrospectively minimize the impact that visitors may have had during their hospitalization. Despite the potential inflation or minimization of the impact of visitors on patient wellbeing, there was a difference in patient satisfaction scores for their hospitalization which is likely attributed to the presence of visitors. Despite a large patient population eligible to participate in this survey, a limited sample responded to requests for interview. Interviews were primarily conducted directly with patients; we were able to interview only a small number of in-person visitors to patients in our study. Additionally, limiting interviews to only patients who were discharged to home failed to capture the benefits of family visitation for those who required ongoing assistance after their illness at a nursing facility as well as those who may have passed away due to COVID-19. Conclusion Without proven benefits of decreasing COVID-19 transmission, visitor restrictions have the unintended consequence of negatively impacting our patients, creating anxiety and hopelessness among families. This study not only furthers the growing body of literature showing the negative impact on visitor restrictions during the COVID-19 pandemic, it also demonstrates that permissive visitation for COVID-19 patients can be done safely without significant risk to visitors. While restrictive visitation policies may have been valuable during the early phases of this pandemic, as we move towards COVID-19 endemicity, implementing evidence-based visitation policies that maximize patient wellbeing will be essential. Primary funding source None. IRB approval This project was deemed as a Quality Improvement project and was exempt from the health system's Institution Review Board (IRB). Acknowledgments The authors are grateful to J. Moses, J. Bonner and D. Berriel-Cass for their support of this work and to S. Sahini from the Integrated Data Analytics team for her kind assistance. All authors certify that there is no conflict of interest with any financial/research/academic organization, with regards to the content/research work discussed in the manuscript. ==== Refs References 1 Davidson JE Aslakson RA Long AC Guidelines for family-centered care in the neonatal, pediatric, and adult ICU Crit Care Med 45 2017 103 128 27984278 2 Rodriguez-Ruiz E Campelo-Izquierdo M Estany-Gestal A Rodríguez-Núñez A Latour JM. Impact of different visiting policies on family satisfaction in two Spanish ICUs before and during COVID-19 Intensive Care Med 47 2021 1165 1166 34363501 3 Fumagalli S Boncinelli L Lo Nostro A Reduced cardiocirculatory complications with unrestrictive visiting policy in an intensive care unit: results from a pilot, randomized trial Circulation 113 2006 946 952 16490836 4 Zeh RD Santry HP Monsour C Impact of visitor restriction rules on the postoperative experience of COVID-19 negative patients undergoing surgery Surgery 168 2020 770 776 32943203 5 Kandori K Okada Y Ishii W Narumiya H Maebayashi Y Iizuka R. Association between visitation restriction during the COVID-19 pandemic and delirium incidence among emergency admission patients: a single-center retrospective observational cohort study in Japan J Intensive Care 8 2020 90 6 Rosa RG Tonietto TF da Silva DB Effectiveness and safety of an extended ICU visitation model for delirium prevention: a before and after study Crit Care Med 45 2017 1660 1667 28671901 7 Jaswaney R Davis A Cadigan RJ Hospital policies during COVID-19: an analysis of visitor restrictions J Public Health Manag Pract 28 2022 E299 E306 33729198 8 Hyczko AV Fu C Graf Z Evaluating pediatric families’ understanding of and reactions to COVID-19 visitor restrictions J Patient Exp 9 2022 23743735221077548 9 Mayopoulos GA Ein-Dor T Li KG Chan SJ Dekel S COVID-19 positivity associated with traumatic stress response to childbirth and no visitors and infant separation in the hospital Sci Rep 11 2021 13535 10.1038/s41598-021-92985-4 10 Bolton KC Lawler M Hauptman J Patient experience on a hospital oncology service before and after implementation of a no-visitor policy during COVID-19 J Patient Exp 8 2021 23743735211034620 11 Shum CK Shea YF Tang M Wan WH Chan MMK. Poor feeding due to visitor restrictions in long-term care facilities during the coronavirus disease 2019 pandemic Psychogeriatrics 20 2020 929 930 33067899 12 Knight GM Pham TM Stimson J The contribution of hospital-acquired infections to the COVID-19 epidemic in England in the first half of 2020 BMC InfectDis 22 2022 556 13 https://www.cdc.gov/coronavirus/2019-ncov/hcp/infection-control-recommendations.html. Updated Feb 2, 2022. https://www.cdc.gov/coronavirus/2019-ncov/hcp/non-us-settings/hcf-visitors.html. Updated Sept 15, 2020 14 Honarmand K Mehta S. Consequences of visitor restriction policies in the intensive care unit during the COVID-19 pandemic Can J Anaesth 68 2021 1465 1470 10.1007/s12630-021-02048-5 Epub 2021 Jul 1. PMID: 34212307; PMCID: PMC8247615 34212307 15 Ciufo D Hader R Holly C. A comprehensive systematic review of visitation models in adult critical care units within the context of patient-and family-centered care Int J Evid Based Healthc 9 2011 362 387 https://www.ncbi.nlm.nih.gov/pubmed/22093387 22093387 16 Raphael JL Kessel W Patel M. Unintended consequences of restrictive visitation policies during the COVID-19 pandemic: implications for hospitalized children Pediatr Res 89 2021 1333 1335 33654277 17 Czeisler MÉ Marynak K Clarke KE Delay or avoidance of medical care because of COVID-19–related concerns — United States, June 2020 MMWR Morb Mortal Wkly Rep 69 2020 1250 1257 32915166 18 Grijalva CG Rolfes MA Zhu Y Transmission of SARS-COV-2 infections in households - Tennessee and Wisconsin, April-September 2020 MMWR Morb Mortal Wkly Rep 69 2020 1631 1634 PMID: 33151916; PMCID: PMC7643897 33151916 19 Madewell ZJ Yang Y Longini IM Jr Halloran ME Dean NE. Household transmission of SARS-CoV-2: a systematic review and meta-analysis JAMA Netw Open 3 2020 e2031756 PMID: 33315116; PMCID: PMC7737089 20 Faes C Abrams S Van Beckhoven D Meyfroidt G Vlieghe E Hens N Belgian Collaborative Group on COVID-19 hospital surveillance. Time between symptom onset, hospitalisation and recovery or death: statistical analysis of Belgian COVID-19 patients Int J Environ Res Public Health 17 2020 7560 PMID: 33080869; PMCID: PMC7589278 33080869 21 https://www.cdc.gov/coronavirus/2019-ncov/hcp/duration-isolation.html#:∼:text=For%20these%20people%2C%20CDC%20recommends,duration%20of%20isolation%20and%20precautions. Updated Aug 31, 2022.	36470450	PMC9719848	NO-CC CODE	2022-12-15 23:15:08	no	Am J Infect Control. 2022 Dec 5; doi: 10.1016/j.ajic.2022.11.020	utf-8	Am J Infect Control	2,022	10.1016/j.ajic.2022.11.020	oa_other
==== Front Spat Spatiotemporal Epidemiol Spat Spatiotemporal Epidemiol Spatial and Spatio-Temporal Epidemiology 1877-5845 1877-5853 The Authors. Published by Elsevier Ltd. S1877-5845(22)00082-X 10.1016/j.sste.2022.100559 100559 Original Research National lockdowns in England: The same restrictions for all, but do the impacts on COVID-19 mortality risks vary geographically? Muegge Robin ⁎ Dean Nema Jack Eilidh Lee Duncan School of Mathematics and Statistics, University of Glasgow, United Kingdom ⁎ Correspondence to: School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8SQ, United Kingdom. 5 12 2022 2 2023 5 12 2022 44 100559100559 22 4 2022 22 9 2022 1 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. Quantifying the impact of lockdowns on COVID-19 mortality risks is an important priority in the public health fight against the virus, but almost all of the existing research has only conducted macro country-wide assessments or limited multi-country comparisons. In contrast, the extent of within-country variation in the impacts of a nation-wide lockdown is yet to be thoroughly investigated, which is the gap in the knowledge base that this paper fills. Our study focuses on England, which was subject to 3 national lockdowns between March 2020 and March 2021. We model weekly COVID-19 mortality counts for the 312 Local Authority Districts in mainland England, and our aim is to understand the impact that lockdowns had at both a national and a regional level. Specifically, we aim to quantify how long after the implementation of a lockdown do mortality risks reduce at a national level, the extent to which these impacts vary regionally within a country, and which parts of England exhibit similar impacts. As the spatially aggregated weekly COVID-19 mortality counts are small in size we estimate the spatio-temporal trends in mortality risks with a Poisson log-linear smoothing model that borrows strength in the estimation between neighbouring data points. Inference is based in a Bayesian paradigm, using Markov chain Monte Carlo simulation. Our main findings are that mortality risks typically begin to reduce between 3 and 4 weeks after lockdown, and that there appears to be an urban–rural divide in lockdown impacts. Keywords Bayesian inference COVID-19 mortality Lockdowns Spatio-temporal modelling ==== Body pmc1 Introduction The COVID-19 pandemic is the deadliest respiratory disease pandemic since the “Spanish” influenza in 1918, and it is one of at least four newly detected coronaviruses that have emerged since the year 2000 (Morens et al., 2020). The disease is believed to have originated in the Hunan seafood market (Shereen et al., 2020) in Wuhan, China. Without an effective early response strategy, the newly formed coronavirus grew from local chains of infection to a worldwide pandemic, as declared by the World Health Organisation (WHO) on 11th March 2020. As of 9th September 2022, there have been over 6.5 million recorded deaths that were linked to COVID-19, from over 607 million recorded infections worldwide ( https://coronavirus.jhu.edu/map.html). The rapid and dramatic development of the pandemic resulted in widespread scientific research. Various statistical analyses modelled and predicted the spread of COVID-19 infections (Dong et al., 2020, Lee et al., 2022), identified the factors that were associated with a higher risk of displaying severe symptoms (Rashedi et al., 2020, Williamson et al., 2020, Wolff et al., 2021), or identified impacts on healthcare (Remuzzi and Remuzzi, 2020). The new insights were particularly vital in the early stages of the pandemic, as they provided governments with the scientific knowledge necessary for developing strategies to contain the virus. In the first months of the pandemic, there was no effective medicine or vaccine to contain the virus and hence, governments were forced to implement non-pharmaceutical interventions. Mendez-Brito et al. (2021) provided a systematic review of empirical studies comparing the effectiveness of non-pharmaceutical interventions in reducing the number of confirmed COVID-19 cases. Their review, consisting of 34 studies, suggested that the most effective measures included the closing of schools, workplaces, businesses, and venues, as well as banning events. Whilst not ranked amongst the most effective measures, other effective interventions included lockdowns, travel restrictions (national and international), social gathering bans, social distancing, public information campaigns, and mask wearing. Examples of spatio-temporal analyses of non-pharmaceutical interventions include Aravindakshan et al. (2020), Zhang et al. (2021), and Ge et al. (2022). Enforcing local or national lockdowns was amongst the most common responses of governments who had to react to the increasing infection rates and death tolls. While lockdowns might differ by country or region, a general definition of lockdown is “a temporary condition imposed by governmental authorities in which people are required to stay in their homes and refrain from or limit activities outside the home involving public contact” (Merriam-Webster Online Dictionary, 2022). The overriding goal of this study is to investigate spatio-temporal trends in COVID-19 mortality risks following the implementation of three national lockdowns in England to identify geographical differences in the impact of lockdown. While this study adds valuable insights to the observed spatio-temporal changes in mortality risks, it should be noted that the analysis of these trends cannot be used to draw conclusions about causality between lockdowns and mortality risks, as we do not know how the risks might have changed had the lockdowns not been implemented. Several studies have previously analysed the impact of lockdown on the number of deaths or mortality risks over a particular time and area. Palladino et al. (2021) provided national results on the number of deaths in Italy, France, Spain, and the UK and showed a decreasing trend in the number of deaths upon the implementation of national lockdowns. However, they did not highlight similarities or differences in the temporal trends of COVID-19 death numbers between these countries. Conyon et al. (2020) did provide comparisons between countries, showing a connection between stricter lockdowns and lower numbers of deaths. Gerli et al. (2020) linked the timing of when lockdowns were introduced to the number of deaths, although the exact regulations varied amongst the countries they considered. Coccia (2021) showed an association between the duration of lockdown and fatality rates, but some apparent shortcomings are inherent in the analysis. For example, the fatality rates were computed as the number of deaths divided by the number of infected individuals, suggesting that higher testing capacities will lead to lower fatality rates. While some of these studies showed an impact of lockdown on the number of deaths, they all analysed national data without looking into spatial patterns within the countries under study. Some studies looked at regions within countries rather than nations as a whole, such as Silva et al. (2020) who modelled the numbers of deaths for four state capitals in Brazil, and Siqueira et al. (2020) who estimated mortality risks in autonomous communities in Spain. However, neither of these studies assumed underlying spatio-temporal autocorrelation structures to inform the trends in the number of deaths or mortality risks across the respective areas and time periods. In England, our study region, there have been three national lockdowns between the start of 2020 and the end of 2021. We aim to explore the spatio-temporal trends in mortality risks after the introduction of these lockdowns, using weekly counts of deaths on a local authority district level in England. Since weekly death counts have been relatively low at the local authority level, they are subject to substantial random variability. Hence, we apply a spatio-temporal smoothing model to obtain more stable risk estimates by borrowing strength across neighbouring data points in space and time. To our knowledge, our study is the first comprehensive investigation of the spatio-temporal trends in COVID-19 mortality risks following the implementation of national lockdowns in England, and it is the first to consider all three national lockdowns that have occurred thus far. The main questions that we will answer are (i) How long after the implementation of lockdown did mortality risks reduce at a national level, and did this vary by lockdown? (ii) How did the temporal trends in mortality risks differ by region in England in the weeks following the implementation of lockdown? (iii) Which local authorities shared similar temporal trends in mortality risks? 2 Materials and methods 2.1 Data 2.1.1 Study duration and region The time frame of our study is from 1st February 2020 (the week of the first registered death due to COVID-19 in England) to 14th May 2021 (seven weeks after the third lockdown was lifted), and the study region is mainland England which is partitioned into 312 local authority districts (LADs). The average population size for a single LAD in our study is 179,945, and sizes range from 9721 to 1,141,816 people. 2.1.2 England’s national lockdowns During the observed time frame, there have been three national lockdowns in England, which ranged from 26th March to 12th May 2020 (48 days), 5th November to 2nd December 2020 (28 days), and 5th January to 28th March 2021 (83 days). It should be noted that the weeks spent in lockdown do not align with the weeks in our data, where each week is defined to range from Saturday to Friday. Therefore, each week in our data with at least four days of lockdown is considered as a week during lockdown, while any week with three or fewer days of lockdown is considered as a week outside of lockdown. In our study the lockdowns are therefore defined from 28th March to 15th May 2020 (49 days), 7th November to 4th December 2020 (28 days), and 2nd January to 26th March 2021 (84 days). 2.1.3 Data sources The Office of National Statistics (ONS) has provided occurrences of deaths as weekly accumulations by local authority districts in England (ONS, 2021b) over the time frame specified in Section 2.1.1. From this data, we extracted the weekly number of deaths with COVID-19 mentioned on the death certificate, which we denote Ykt for LAD k (k=1,…,312) and week t (t=1,…,67). It should be noted that the data include only the deaths of registered people in England, and a person’s death is counted towards the LAD in which they were registered, disregarding the actual place where the person had died, should these locations differ. We obtained age–sex specific population data for mid-2020 from ONS (2021c) but used 2019 population data for seven LADs due to a lack of data for 2020. Age–sex specific COVID-19 mortality rates were taken from https://coronavirus.data.gov.uk/details/deaths?areaType=nation&areaName=England on 30th August 2021. 2.1.4 Exploratory analysis using the Standardised Mortality Ratio (SMR) Risks of COVID-19 mortalities differ by age and sex. For example, many more deaths occur amongst the older population than in younger age groups, and males tend to be at higher risk than females (Biswas et al., 2021). Hence, when comparing the risks of COVID-19 mortality for different areas, the underlying age and sex demographics should be considered, which we do via indirect standardisation by computing the expected weekly number of deaths ek for LAD k, for which we include a mathematical definition in Section S1 of the supplementary materials. Note that the expected counts do not differ by week as population sizes and mortality rates are not available at that frequency. The standardised mortality ratio (SMR) is an exploratory measure of disease risk that accounts for the underlying age and sex demographics, and is given by SMRkt=Ykt/ek, for LAD k=1,2,…,K(=312) and week t=1,2,…,N(=67). For example, SMRkt=1.2 suggests that area k has a 20% elevated risk in week t compared to the national average risk over the study duration. Fig. 1(a) presents the weekly SMR for all LADs with the weeks of lockdown highlighted in beige, where the black line shows the average SMR over all LADs. The plot shows two distinct waves in SMR values, with peaks in April 2020 and January 2021. The values appear to decline sometime after the introduction of lockdowns 1 and 3. For lockdown 2, the average SMR value did not decrease much, likely because the lockdown was lifted after only four weeks. Fig. 1(b) displays the spatial pattern in the average SMRs across England, measured for each LAD over all weeks in the time frame of our study. The map shows higher average SMRs for urban areas, such as London, Birmingham, Manchester, Liverpool, and Sheffield, while the average SMRs appear lower for more rural areas. The average SMRs appear to change relatively smoothly across the map, suggesting spatial autocorrelation is present in these data.Fig. 1 (a) SMR by week for all LADs in England; weeks of lockdown are highlighted in beige; the black line shows the average SMR per week; the dashed red line indicates an SMR of 1. (b) Average SMR over the entire study by LAD, for England. To verify the assumption of spatial autocorrelation in the SMR values, we perform Moran’s I tests (Moran, 1950) for the 65 weeks in our study that have seen at least one death. The Moran’s I statistics take on values between −0.02142 and 0.66014, with a mean value of 0.2572. A permutation test based on 10,000 random permutations of the data evaluated at a significance threshold of 0.05/65≈0.00077 (Bonferroni correction, Haynes, 2013) results in rejecting the null hypothesis of no spatial autocorrelation for 48 of the 65 weeks (73.95%). Hence, spatial autocorrelation is present for the majority of weeks in our study. Additional explanations of the methods applied and how to implement them in R can be found in Lee (2020). We check for temporal autocorrelation in the SMR values by computing temporal autocorrelation coefficients (Chatfield, 2003) for each LAD over the weeks in the study. We apply a Ljung–Box test (Ljung and Box, 1978) to evaluate temporal autocorrelation for lags up to 10 weeks simultaneously. At a significance threshold of 0.05/312≈0.00016 (Bonferroni correction), the temporal autocorrelation test results in rejecting the null-hypothesis of no temporal autocorrelation for 310 of the 312 LADs (99.36%). Hence, temporal autocorrelation is present in the data. Having identified spatial and temporal autocorrelation in the SMR values of our data, we now fit a spatio-temporal model to obtain estimates of mortality risks that account for the clear trends and autocorrelations. 2.2 Methods The observed number of COVID-19 deaths in area k and week t is denoted Ykt, while the expected weekly number of deaths is denoted ek and does not vary by week. Since the observed number of COVID-19 deaths are counts of rare events, the natural choice is to fit a Poisson log-linear model to the data. Specifically, we fit the model in a Bayesian setting, where the data likelihood is of the form (1) Ykt∼Poisson(ekθkt), (2) ln(θkt)=β0+ϕkt. Here, our goal is to estimate the relative mortality risk θkt for area k at time t, and the estimated risk can be interpreted as a spatio-temporally smoothed version of the noisy SMR. The natural log of the relative risk {θkt} is modelled by an intercept term β0 and a spatio-temporal trend that is modelled by random effects {ϕkt}. When working with areal-type data, a common method to capture its spatial structure is to incorporate a neighbourhood matrix W in the model. Here, we define W to be a K×K binary adjacency matrix, with elements wkj=1 if LADs k and j share a border and wkj=0, otherwise. The spatio-temporal random effects model we implement was developed by Rushworth et al. (2014). This particular model is a good choice for our data since it can capture the spatio-temporal autocorrelations we have identified in Section 2.1.4 without restricting the smoothing over space and time to follow a rigid parametric form or making assumptions about the shape of the temporal trends. It allows the mean of the random effects from one week to depend on the effects from previous weeks, and the (co)variance of the random effects’ multivariate Normal distribution depends on the spatial structure of the data captured by the neighbourhood matrix W. Note, Rushworth et al. (2014) only considered an autoregressive process of order 1 (AR(1)) but here, we present both AR(1) and AR(2) models. Thus, depending on the version of the model the joint prior distribution of the random effect vectors in ϕ=(ϕ1,…,ϕN) is decomposed as either (3) AR(1):f(ϕ1,…,ϕN)=f(ϕ1)∏t=2Nf(ϕt\|ϕt−1) =N0,τ2Q(ρ,W)−1×∏t=2NNαϕt−1,τ2Q(ρ,W)−1, (4) AR(2):f(ϕ1,…,ϕN)=f(ϕ1)f(ϕ2)∏t=3Nf(ϕt\|ϕt−1,ϕt−2) =N0,τ2Q(ρ,W)−1N0,τ2Q(ρ,W)−1×∏t=3NNα1ϕt−1+α2ϕt−2,τ2Q(ρ,W)−1, where ϕt=(ϕ1t,…,ϕKt) denotes the vector of random effects for areas 1,…,K in week t, and the precision matrix is defined as Q(ρ,W)=ρ(diag(W1)−W)+(1−ρ)I (Leroux et al., 2000), where W is the binary adjacency matrix from above, I is a K×K identity matrix, and 1 is a K×1 vector of ones. The random effects in ϕ1 (AR(1)) or in ϕ1 and ϕ2 (AR(2)) are assigned solely spatial Leroux CAR prior distributions. The full conditional prior distribution of the random effect ϕkt for area k and time period t, given the random effects of all other areas in that time period can then be expressed as (5) ϕkt\|ϕ−kt∼Nρ∑j=1Kwjkϕjtρ∑j=1Kwjk+1−ρ,τ2ρ∑j=1Kwjk+1−ρ, where ϕ−kt is the vector of random effects for all areas except for area k in time period t, the parameter ρ measures the autocorrelation imposed by the spatial structure captured in W, and τ2 is a variance parameter. The mean from the prior distribution of ϕkt\|ϕ−kt is a weighted average of random effects in time period t from areas adjacent (neighbours) to area k, where the weight is determined by the dependence parameter ρ, and the variance term is smaller when there are more areas adjacent to area k. For the later time periods (t=2,…,N in the AR(1) model, or t=3,…,N in the AR(2) model), the mean of the prior joint distribution of ϕt depends on the effects from the preceding time period (Eq. (3)) or the last two preceding time periods (Eq. (4)), thus temporally smoothing the risks. Spatial smoothness is induced by the covariance matrix, which is specified by the Leroux CAR prior. In the AR(1) model, α is a temporal dependence parameter that takes on a value in the interval [−1,1]. Thus, α=0 indicates temporal independence, and α=1 indicates strong temporal autocorrelation and makes the distribution a first order random walk. For the AR(2) model, α1 and α2 are again temporal dependence parameters that determine the relationship between spatio-temporal random effects that are temporal neighbours of order 1 and 2, respectively. Note that α1=2 and α2=−1 define a second-order random walk. The mean of the log-transformed mortality risk β0 from Eq. (2) is assigned a Normal prior distribution with mean zero and large variance, i.e. β0∼N(μ0=0,σ02=10,000). This weakly informative prior distribution reflects that we have no prior knowledge for the underlying mortality risk of COVID-19 in England in the given time frame. In both the AR(1) and AR(2) models, the spatial autocorrelation parameter ρ is assigned a flat Uniform(0,1) prior, and the variance parameter τ2 is assigned an Inverse-Gamma(a=1,b=0.01) prior distribution. The temporal autocorrelation parameter α in the AR(1) model is assigned a flat Uniform(0,1) prior while the temporal dependence parameters α1 and α2 in the AR(2) model are assigned a flat improper joint prior distribution, i.e. f(α1,α2)∝1. These are the default prior and hyper-prior distributions specified in the function ST.CARar from the package CARBayesST (Lee et al., 2018), which we used to fit the Bayesian hierarchical model in R, via MCMC simulation. 3 Results 3.1 Model fitting We fitted both the AR(1) and AR(2) versions of the model outlined above to the COVID-19 mortality data and obtained estimated risks from the posterior mean of the fitted values divided by the expected counts. The MCMC algorithm produced 2,200,000 samples for each parameter in these models, and we discarded the first 200,000 simulations as the burn-in period. We thinned the simulations by saving only every 1,000th simulation to reduce the autocorrelation in the Markov chains, which resulted in 2,000 simulated values for each parameter. Geweke diagnostics (Geweke, 1992) between (−2,2) and an examination of the corresponding trace plots (those for the AR(2) model are included in Section S2 of the supplementary materials) suggested no evidence of a lack of convergence in the algorithm for either the AR(1) or AR(2) model. For example, for the AR(2) model the Geweke diagnostics took on the values 1.4 for β0, −0.8 for τ2, −0.5 for ρ, 0.0 for α1, and −0.2 for α2. Since both models seem to have converged, we use the Deviance Information Criterion (DIC) (Spiegelhalter et al., 2002) to compare the two models. The AR(1) model has a DIC value of 69,067, while the AR(2) model has a DIC value of 68,772. Hence, the model with second-order temporal autocorrelation fitted the data slightly better, so we use its estimated relative risks in the following analysis. Note that we have confirmed that the AR(2) model fits the data appropriately, via posterior predictive checks, which can be found in Section S3 of the supplementary materials. Additionally, we assessed the sensitivity of our results to prior choice, which is displayed in Section S4 of the supplementary materials. 3.2 How long after the implementation of lockdown did mortality risks reduce? The first question in this study is the most important from an epidemiological perspective because it quantifies how long lockdowns have to be in place before mortality risks reduce. We answer the question by analysing the panels in Fig. 2, which show boxplots of the distributions of mortality risks across all LADs in the weeks preceding, during and after each of the three lockdowns (left), and plots with 95% credible intervals around the median average estimated risk across England (right). Panels (a), (b), and (c) show these results for the first, second, and third lockdown, respectively, and all the risks presented are relative to the average risk across England for the entire study period. The weeks after each lockdown was lifted are included to see if the lockdowns had a lasting impact on mortality risk. To analyse risk over similar periods of time, we set the panels for the first and third lockdowns to contain 13 weeks from the start of lockdown (weeks 0,…,12). However, the panel for the second lockdown ranges over fewer weeks, as week 7 is already the week before the third lockdown was introduced. The plots on the right were included to visualise the variability in the accumulated risks by week across the entire country. Since the 95% credible intervals are very narrow, there does not appear to be much variation in the averages of the simulated estimated risks across all LADs. The first striking feature apparent from Fig. 2 is that the second lockdown (panel (b)) exhibits a very different trend in risk over time compared with the first and third lockdowns. The second lockdown shows a constant or slightly increasing trend in risk throughout, while the overall risks in the first and third lockdowns show an increasing trend in the first three weeks of lockdown, followed by a decreasing trend thereafter. The second lockdown of only four weeks was much shorter than the first lockdown of seven weeks and the third lockdown of twelve weeks, and the short duration of lockdown 2 may have prevented it from having a sizeable impact on reducing risk. Additionally, the lack of an increasing trend in the first few weeks after the introduction of lockdown 2 may suggest that the pandemic was not yet approaching a severe new wave in terms of mortality. A possible reason for the lack of an increasing trend could be that England had implemented a three-tier system of mobility restrictions that started on 14th October before lockdown 2, with varying degrees of restrictions for the LADs in the three tiers, which (Davies et al., 2021) suggested has had a sizeable effect in reducing the number of COVID-19 deaths. Altogether, since lockdown 2 is markedly different from the other lockdowns in that it did not show any reductions in risks, the remainder of this section will focus on lockdowns 1 and 3.Fig. 2 Left: Boxplots of the posterior median estimated risks across LADs, by week after lockdown. Right: Median line with 95% credible intervals for the average estimated risk across LADs, by week after lockdown. In each panel, week −1 is the week preceding the lockdown, week 0 is the onset week of lockdown, week 1 is the first week after the onset of lockdown, etc. The weeks coloured in beige comprise the lockdowns. The y-axes measuring estimated risk and average estimated risk are on the same scale for the three panels to allow comparison across the lockdowns. The blue dashed line shows the median estimated risk across England from the last week before lockdown, the red dashed line represents a risk of 1. Note that week 7 in panel (b) is the same as week −1 in panel (c). The second key feature from Fig. 2 is that for lockdowns 1 and 3, the risk of mortality increased for the first three weeks after the introduction of the lockdown, before it started to reduce from the fourth week onward. The reason for this delayed reduction in mortality risk is the lag between a COVID-19 infection and mortality, which the ONS estimate is between 21 and 25 days (3–4 weeks) on average (ONS, 2021a). Thus the high numbers of infections in the last few weeks before the introduction of lockdown would transfer to the high mortality risks observed three to four weeks into lockdown. The final important finding from Fig. 2 is the times it took for the risks to reduce to baseline levels. For a baseline level of a risk of 1, it took nine weeks after the introduction of both lockdowns 1 and 3 for the median risk across England to reduce to that level. As an alternative comparison, the figure also reveals that it took ten (lockdown 1) and six (lockdown 3) weeks after the introduction of the lockdowns for the median risks across England to reduce back to their respective pre-lockdown levels. However, the median risks across England were 0.56 and 2.05 in the week preceding lockdowns 1 and 3, respectively, so it might have taken longer for the median risk in lockdown 1 to reduce to its pre-lockdown level simply because this initial level was much lower than that preceding lockdown 3. In conclusion for question (i), lockdowns appear to reduce mortality risk after approximately four to five weeks. However, based on the observations for lockdown 2, the timing and duration of lockdown are likely to be decisive factors for lockdowns to impact mortality risks. 3.3 How did the temporal trends in mortality risks differ by region in England? Differences in the temporal trends of mortality risks can be explored at a larger geographical scale by analysing regional data. The 312 LADs from the English mainland are nested exactly within the following nine regions: East of England, East Midlands, London, North East, North West, South East, South West, West Midlands, Yorkshire and The Humber. Note that a map of the regions of England with outlines of the LADs can be found in Section S5 of the supplementary materials. We compute weighted averages of risks for region r and week t as θˆrt=1Pr∑k∈rPk×θˆkt, where θˆkt denotes the estimated risk from area k and week t, and k∈r indicates that LAD k falls into region r, while Pk and Pr denote the population sizes of LAD k and region r, respectively. The reason for taking the weighted averages with regard to population size is that the risk in a more populous LAD will have a more substantial impact on the region’s risk than the risk in a less populous LAD. We have created plots showing 95% credible intervals on the population-based weighted averages of estimated risks by region, which we present in Section S6 of the supplementary material. These credible intervals are again quite narrow, suggesting that there is little variability in the regional average estimated risks. As an alternative measure, we divide the weekly risk from each LAD by its risk from the week before the respective lockdown was implemented to obtain scaled risks. Thus, each area starts at a scaled risk of 1 in the week preceding the introduction of lockdown and the scaled risks in the succeeding weeks are relative to the initial risk levels, allowing for a more fair comparison of temporal trends in mortality risks in different areas. The scaled estimated risk is computed as sˆkl=θˆkl/θˆk(−1), for l=−1,0,1,2,…, where θˆkl denotes the estimated risk from LAD k and week l after the introduction of lockdown. So l=−1 denotes the week before the introduction of lockdown, l=0 denotes the week during which the lockdown was introduced, l=1 denotes the first week after the introduction of lockdown, and so forth. Thus, the scaled risk should be interpreted as the risk from a specific week during or after lockdown relative to the risk in the week preceding lockdown. For example, if the scaled risk of a particular week is 2.5, then this suggests that the risk was 2.5 times as large in that week relative to the week preceding lockdown. From the scaled estimated risks, we compute weighted averages of scaled risk for region r and week l of lockdown as sˆrl=1Pr∑k∈rPk×sˆkl, with notation analogous to that of the weighted average risks above. Fig. 3 shows line plots for the population-based weighted averages of estimated risk and scaled estimated risk by week and region. For lockdown 2 the average mortality risks stayed relatively constant for all regions when the lockdown was in place. However, the risks increased rapidly in London, South East, and East of England after the lockdown was lifted. Note that the Alpha variant of COVID-19 was detected in the Kent area (South East) in September 2020, and this variant was estimated to be 1.5 times more transmissible, while it featured mortality risks that were 1.6 times higher than that of the earlier variants of COVID-19 (Page and McNamara, 2021). Hence, it is likely that the Alpha variant drove the distinct temporal trends in mortality risks in southeast England after lockdown 2.Fig. 3 Average estimated risk (left) and average scaled estimated risk (right) in weeks following the implementation of national lockdown, by region. In each panel, week −1 is the week preceding the lockdown, week 0 is the onset week of lockdown, week 1 is the first week after the onset of lockdown, etc. The weeks coloured in beige comprise the lockdowns. The red dashed line represents a risk of 1, and the blue dashed line represents a scaled risk of 1. Note that week 7 in panel (b) is the same as week −1 in panel (c). The most striking feature of lockdowns 1 and 3 is that the average risk was highest in London during their first three weeks of lockdown. The average risk in London reduced quickly after the implementation of each lockdown. In the average risk plot (left) for lockdown 1, this is particularly noticeable as London had an exceptionally high peak in the second week of lockdown but was the second region to reach an average risk of 1 in the seventh week of lockdown. According to Batty et al. (2021), the proportion of essential workers in the UK is approximately 23.6%, while that of London is only 16%. Hence, the smaller proportion of essential workers might explain why mortality risks reduced so quickly in London after the first weeks of lockdowns 1 and 3, while they reduced more slowly for some of the other regions. The scaled risk plots provide some additional insights into the temporal trends of risks. In lockdown 1, they reveal that the average risk in London did not increase as drastically when put in relation to the high level where it started, and that the average risk in London was quickest to return to its initial level within five weeks. Relative to their lower initial risk levels, the regions Yorkshire and The Humber, North East, and North West saw the most drastic increases in average risks. It also took the longest time for the average risks to return to their initial levels in these regions. In lockdown 3 the only region that stands out on the scaled risk plot is the South West, but this can be explained by its particularly low initial risk level. The scaled risk plot emphasises that the temporal trends in risks were very similar during the third lockdown for the remaining eight regions. In conclusion to question (ii), our analysis suggests regional differences in the temporal trends in mortality risks for lockdowns 1 and 2 and no substantial differences for lockdown 3. In lockdown 1, it took longer for risks to reduce in the northern regions of England (North East, North West, Yorkshire and The Humber) compared to the rest of the country. For lockdown 2, we observed sharp increases in average risks in southeast England (London, East of England, South East) after the lockdown was lifted, while the average risks remained relatively constant for the other regions. Since none of the regions showed a substantial reduction in mortality risks in the weeks during or after lockdown 2, the remainder of this study will consider only lockdowns 1 and 3. 3.4 Which local authorities shared similar temporal trends in mortality risks? Question (iii) requires us to check if there are groups of LADs with similar temporal risk trends in the weeks after lockdowns 1 and 3 (note that a sequence of maps showing the estimated risks by LAD in those weeks can be found in Section S7 of the supplementary materials). Clustering methods can be used to identify these groups. Here, we apply k-means clustering (Hartigan and Wong, 1979) for lockdowns 1 and 3, assigning the LADs to between one and ten clusters according to similarities in their estimated risks. We consider the same number of weeks after the introduction of each lockdown to allow a comparison of their temporal trends. The weeks we consider range from the week before lockdown to the twelfth week after the introduction of lockdown to include all weeks from the longer lockdown 3. Note that it is common to standardise the observations from each variable before applying k-means clustering. However, since we are interested in distinguishing LADs both by peak risk and time period of when the peak risk occurred, we do not standardise the estimated risks for each week so that the k-means algorithm can pick up these differences. We performed a sensitivity check of the k-means method for our data by running the algorithm on reduced datasets that excluded potential outliers identified with agglomerative hierarchical clustering using the single linkage method (Gower and Ross, 1969). The resulting clusterings were very similar for the full and reduced datasets, suggesting that the clustering we obtained for the whole dataset is not affected by extreme values. Thus, the following analysis uses the results for the whole dataset. We analysed the within-cluster sum of squares and the average silhouette width (Rousseeuw, 1987) for the clusters obtained from the k-means algorithm, for different numbers of clusters k (the corresponding plots can be found in Section S8 of the supplementary materials). The within-cluster sum of squares plots show a substantial drop when moving from one cluster to two clusters, which is consistent for both lockdowns. We choose the optimal number of clusters by checking the average silhouette plots, which show that the average silhouette width is the largest for 2 clusters in lockdowns 1 and 3. For the week before each lockdown, the week when the lockdown was introduced, and the 12 weeks thereafter, we compute weighted averages of estimated risk by cluster c and week t as θˆct=1Pc∑k∈cPk×θˆkt, where Pc and Pk are the population sizes for cluster c and LAD k respectively, and k∈c denotes that LAD k is in cluster c. We accumulate the risks by cluster since it is not practical to look at the risks of all 312 LADs simultaneously. We take population-size based weighted averages since the risks from the LADs with larger populations contribute stronger to the risk of the entire population in that cluster than the risks from LADs with smaller populations. Fig. 4 presents plots that show the median average estimated risks with 95% credible intervals by cluster and week, over the 2,000 simulations obtained from the fitted model. The 95% credible intervals are barely visible, suggesting that there is very little variation in the simulated average risks by cluster. In the first three to four weeks of each lockdown, the LADs in cluster 1 were on average at a substantially higher risk than those in cluster 2. The peak average estimated risk for cluster 1 of lockdown 3 was not as high (approximately 6.59) as for cluster 1 of lockdown 1 (approximately 8.34). The peak average estimated risk for cluster 2 was similar for lockdowns 1 (approximately 3.31) and 3 (approximately 3.07). Lastly, the average estimated risks from the two clusters were very similar towards the last weeks of each lockdown.Fig. 4 Median average estimated risks with 95% credible intervals in the weeks following the implementation of national lockdown, by cluster. The red dashed line indicates an estimated risk of 1. Further investigation of the clustering assignments shows that 45 of the 65 LADs in cluster 1 of the first lockdown were also in cluster 1 of the third lockdown, while 81 LADs switched from cluster 2 of lockdown 1 to cluster 1 of lockdown 3. The much larger number of LADs in the higher risk cluster for lockdown 3 is likely to have caused a relatively low Rand index (Rand, 1971) of 0.56, which suggests that the two cluster assignments are not very similar. Our ultimate goal for this part of the study is to see any geographical patterns for LADs with similar temporal risk trends. Therefore, Fig. 5 shows maps for lockdowns 1 and 3 that display the cluster memberships of the LADs in the two lockdowns.Fig. 5 Maps showing clusters that were formed according to weekly estimated risk, by lockdown. The LADs in cluster 1 had a higher peak estimated risk, on average. In lockdown 1, most of the separation into the two clusters appears to be explained by an urban/rural divide. The peak estimated risk was higher in LADs close to London (9.3 million people), Manchester (2.73 million people), Birmingham (2.6 million people), Liverpool (902,000 people), and Sunderland (341,000 people), while lower in most rural areas. In lockdown 3 some urban/rural divide might still be possible, but the most prominent feature is that most LADs at higher risk levels are in the southeast of England in London (82% of LADs in cluster 1), South East (62% of LADs in cluster 1), and East of England (71% of LADs in cluster 1) which might be explained by the more accelerated spreading of the Alpha variant of COVID-19 which originated in Kent in the southeast. Additionally, Grint et al. (2021) suggested that many of the early Alpha variant cases of COVID-19 were observed in the North West, which could explain the higher risks in the adjacent LADs of Eden, Allerdale, and Carlisle. Note that COVID-19 mortality risks had previously been linked to deprivation, such that more deprived areas tend to be exposed to greater mortality risks (Tinson, 2021). The government website (Ministry of Housing, 2019a) provides a 2019 ranking of lower layer super output areas (LSOAs) with regard to their index of multiple deprivation (IMD), which combines deprivation scores from several domains (Ministry of Housing, 2019c provides an infographic for the IMD). They also provide population-based weighted averages of LSOA ranks for each LAD (Ministry of Housing, 2019b), from which we created a deprivation ranking for the LADs in our data, from 1 (most deprived) to 312 (least deprived). Fig. 6 shows the distribution of IMD rankings in each cluster for lockdowns 1 and 3. Cluster 1 of lockdown 1 has a median IMD rank of 89, while that of cluster 2 is 176. In comparison to the median ranks from lockdown 1, the median IMD rank in cluster 1 of lockdown 3 is substantially higher at 130, while that of cluster 2 is the same at 176. Hence, there might be an association between higher deprivation and increased risks during lockdown 1, while in lockdown 3 the difference is less pronounced.Fig. 6 Boxplots for the IMD rankings of LADs in the two clusters of lockdowns 1 and 3, where a lower IMD score implies higher deprivation. 4 Discussion Our motivation for this study was to explore the temporal trends in COVID-19 mortality risks after the introduction of national lockdowns in England, with the main quantity of interest being how long it took for them to be effective at reducing mortality risks below the levels from before the lockdowns were introduced. At a national level (Section 3.2), it took around three weeks after the introduction of lockdowns 1 and 3 for mortality risks to stop increasing, likely due to a 21 to 25 day lag time between disease onset and mortality (ONS, 2021a). The risks reduced to pre-lockdown levels after ten (lockdown 1) and six weeks (lockdown 3) of lockdown, respectively. Lockdown 2 did not lead to a meaningful reduction in mortality risk. However, in the first weeks of lockdown 2 the risks did not increase like they did in lockdowns 1 and 3. Hence, it is not possible to quantify the efficacy of lockdown 2 based on the observed changes in mortality risks. Additionally, it should be noted that lockdown 2 was lifted after only four weeks. Al-Zoughool et al. (2022) have studied the association between lockdown duration and timing with hospital cases and infection rates rather than mortality risks. Nevertheless, their suggestion that a lockdown of fewer than 30 days might not lead to substantial reductions in hospital cases and infection rates was not conflicted by our results. The cluster analysis of temporal trends at a local authority level (Section 3.4) revealed an urban/rural divide in risks in lockdown 1, with urban areas tending to have a higher peak risk. Hamidi et al. (2020) partially corroborate our findings by showing higher risks for larger metropolitan areas in the USA but lower risks for more densely populated counties. Hence, the urban/rural divide might be driven by multiple factors such as population density, accessibility to medical aid, hospital occupancy rates, or mobility within and between districts. Further, our analysis of lockdown 1 suggests a possible association between higher deprivation and increased mortality risks, which has previously been shown in several studies, including Williamson et al. (2020) or Sartorius et al. (2021). For lockdown 3, the cluster analysis indicated a higher value at the peak of the risk in LADs from southeast England, which might be explained by the Alpha variant detected in the Kent area (South East) in September 2020. Grint et al. (2021) adds weight to this hypothesis by finding both an increased early spreading of the Alpha variant in southeast England and higher mortality risks associated with the Alpha variant when compared to earlier versions of COVID-19. Lastly, although mortality risks were at similar levels in weeks 7 through 12 after the implementation of lockdowns 1 and 3, the first lockdown was lifted in its seventh week, while the third lockdown was only lifted in its twelfth week. The third lockdown continued because the number of patients in hospitals was high (Prime Minister’s Office, 2021). Hence, the relationship between mortality risks and the number of patients in hospitals might have changed from lockdown 1 to lockdown 3, and this assumption is supported by Gray et al. (2021). When communicating these results, it is important to state that this is an exploratory study and thus it could not assess an ‘effect’ of lockdown on mortality risk, as the counterfactual event of what would have happened without lockdown in place could not be observed. Hence, the study comments on trends alone. Although only lockdowns 1 and 3 showed a reduction in mortality risks, the temporal trends at a national level were quite similar for these lockdowns, which suggests that lockdowns might have a real effect on reducing mortality risks. Section 2.1.3 mentioned that in the ONS dataset, a person’s death was counted towards the LAD where they were registered, rather than the actual place where they died, should these locations differ. A limitation of this study is that we do not know to which extent the results might be affected by such mismatches. For example, especially for the most vulnerable, such as older or severely ill people, it might be possible that they are treated in a specialised hospital in a LAD other than the one where they were registered, for some disease unrelated to COVID-19, before getting infected and dying of COVID-19. Analysing the impact of such mismatches could form an extension to this study if data on the place of death rather than registration were available. Another exciting extension to this study could be to perform a multivariate analysis that also considers the temporal trends in the number of hospitalisations when looking at mortality risks in the weeks following lockdown. Explanatory variables such as vaccination status or the particular variant of COVID-19 that led to death could be included in the analysis, depending on data availability. Funding The first author was funded by a University of Glasgow Maclaurin PhD Scholarship . Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix A Supplementary data The following is the Supplementary material related to this article. MMC S1 The supplementary materials contain additional notations, maps, credible interval plots, posterior predictive checks, and a sensitivity analysis on the prior choice of the variance parameter. Data availability Data will be made available on request. Appendix A Supplementary material related to this article can be found online at https://doi.org/10.1016/j.sste.2022.100559. ==== Refs References Al-Zoughool M. Oraby T. Vainio H. Gasana J. Longenecker J. Ali W.A. AlSeaidan M. Elsaadany S. Tyshenko M.G. 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==== Front Int J Infect Dis Int J Infect Dis International Journal of Infectious Diseases 1201-9712 1878-3511 The Author(s). Published by Elsevier Ltd on behalf of International Society for Infectious Diseases. S1201-9712(22)00630-0 10.1016/j.ijid.2022.11.040 Article The Use of Antivirals in the Treatment of Human Monkeypox Outbreaks: A Systematic Review Antivirals in treatment of monkeypox: systematic review Shamim Muhammad Aaqib 1# Padhi Bijaya Kumar 2# Satapathy Prakasini 3 Veeramachaneni Sai D 4 Chatterjee Chandrima 5 Tripathy Snehasish 6 Akhtar Naushaba 7 Pradhan Anindita 1 Dwivedi Pradeep 18 Mohanty Aroop 9 Rodriguez-Morales Alfonso J. 101112 Sah Ranjit 1314⁎ Ala'a Al Tammemi 15 Al-Tawfiq Jaffar 161718 Behdin-Nowrouzi-Kia 19 Chattu Vijay Kumar 192021⁎ 1 Department of Pharmacology, All India Institute of Medical Sciences, Jodhpur, India 2 Department of Community Medicine and School of Public Health, Postgraduate Institute of Medical Education and Research, Chandigarh, India 3 Department of Virology, Postgraduate Institute of Medical Education and Research, Chandigarh, India 4 PSG College of Pharmacy, Coimbatore, India 5 Parexel International Services, Chandigarh, India 6 Department of Preventive Oncology, Homi Bhabha Cancer Hospital and Research Centre, Muzaffarpur, India 7 Indian Council of Medical Research, Regional Medical Research Centre, Bhubaneswar, INDIA 8 Co-Lead, Centre of Excellence for Tribal Health, All India Institute of Medical Sciences, Jodhpur, INDIA 9 All India Institute of Medical Sciences, Gorakhpur, India 10 Grupo de Investigación Biomedicina, Faculty of Medicine, Fundación Universitaria Autónoma de las Américas, 660003, Pereira, Risaralda, Colombia 11 Institución Universitaria Visión de las Américas, Pereira, Risaralda, Colombia. 12 Clinical Epidemiology and Biostatistics, Universidad Cientifica del Sur, Lima, Peru. 13 Tribhuvan University Teaching Hospital, Institute of Medicine, Kathmandu, Nepal. 14 Research Scholar, Harvard Medical School, Boston, MA, USA 15 Migration Health Division, International Organization for Migration (IOM), Amman, Jordan 16 Infectious Diseases Division, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA 17 Infectious Disease Division, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21218, USA 18 Prince Abdullah bin Khaled Coeliac Disease Research Chair, King Saud University, Riyadh, 11362, Saudi Arabia 19 ReSTORE Lab, Department of Occupational Science & Occupational Therapy, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada 20 Center for Transdisciplinary Research, Saveetha Institute of Medical and Technological Sciences, Saveetha University, Chennai, India 21 Department of Community Medicine, Faculty of Medicine, Datta Meghe Institute of Medical Sciences, Wardha 442107, India ⁎ Corresponding author # Contributed Equally 5 12 2022 5 12 2022 14 10 2022 27 11 2022 28 11 2022 © 2022 The Author(s). Published by Elsevier Ltd on behalf of International Society for Infectious Diseases. 2022 Elsevier has created a Monkeypox Information Center (https://www.elsevier.com/connect/monkeypox-information-center) in response to the declared public health emergency of international concern, with free information in English on the monkeypox virus. The Monkeypox Information Center is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its monkeypox related research that is available on the Monkeypox Information Center - including this research content - immediately available in publicly funded repositories, with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the Monkeypox Information Center remains active. Objectives Human monkeypox virus infection is the recently declared public health emergency of international concern (PHEIC) by the World Health Organization. Besides, there is scanty literature available on the use of antivirals in monkeypox virus infection. This systematic review compiles all evidence of various antivirals used on their efficacy, safety and summarizes their mechanisms of action. Methods A review was done for all original studies mentioning individual patient data on the use of antivirals in patients with monkeypox virus infection. Results Of the total 487 non-duplicate studies, 18 studies with 71 individuals were included. Tecovirimat was used in 61 individuals, followed by Cidofovir (CDV) in 7 and Brincidofovir (BCV) in 3 individuals. Topical Trifluridine was used in 4 ophthalmic cases in addition to Tecovirimat. Of the total, 59 (83.1%) were reported to have complete resolution of symptoms, 1 was experiencing waxing and waning of symptoms, only 1 (1.8%) had died, and the others were having resolution of symptoms. The death was thought unrelated to Tecovirimat. Elevated hepatic panels were reported among all individuals treated with BCV (leading to treatment discontinuation) and 5 treated with Tecovirimat. Conclusions Tecovirimat is the most used and has proven beneficial in several aggravating cases. No major safety concerns were detected upon its use. Topical trifluridine was used as an adjuvant treatment option along with Tecovirimat. BCV and CDV were seldom used, with the latter often being used due to the unavailability of Tecovirimat. BCV was associated with treatment discontinuation due to adverse events. Keywords Antiviral Monkeypox Tecovirimat Brincidofovir Cidofovir treatment ==== Body pmcIntroduction The Global health emergency of the COVID-19 pandemic had worsened the situation of public health. Furthermore, as the adverse effects of this pandemic subsided, another public health threat in the form of the Monkeypox virus (MPXV) outbreak stirred nations across the globe. MPXV belongs to the same family as the smallpox virus eradicated in 1980. First reported in 1970, it has historically been largely confined to endemic regions in Western and Central Africa. However, it has spread rapidly throughout the globe in 2022. Moreover, it has become a public health emergency of international concern, the seventh such declaration ever by WHO (World Health Organization, 2022). As of 6th November 2022, the CDC reports 78,229 confirmed cases and 41 deaths. These cases are spread across 109 countries, with most (102) countries reporting MPXV cases for the first time ever (Centers for Disease Control and Prevention, 2022). MPXV infection could lead to severe disease in certain groups, especially children, immunocompromised and pregnant(Huang et al., 2022). MPXV belongs to the Orthopoxvirus family. Studies have revealed that poxviruses are generally large, double-stranded DNA-structured viruses and their genome size lies between 130 to 360kbp. Due to their large size, they are slow at replicating and surviving in the host body. The orthopoxviruses are surrounded by virulent genes acting as modulators against the host immune system(Okyay et al., 2022). Some in-vitro studies suggest these modulators enable the MPXV to invade the host's immune system. On entering the human host cells, the MPXV replicates in the nasopharyngeal and oropharyngeal mucosa. Then, the viral load spreads through the lymph nodes and various organs(Tolonen et al., 2001). The common clinical manifestations of MPXV infection can be categorised based on the rapid spread from the site of inoculation to other lymph nodes in different stages of infection. The incubation period lasts for 7-17 days. The prodromal period includes fever, headache, and lymphadenopathy, lasting about 1-4 days. Rashes initially appear over the face and then spread centrifugally to cover other body parts like the palms, soles, and oral cavity. They stay for about 14-28 days(Kumar et al., 2022). In various studies, supportive treatments and antivirals were used. This review will explore the use of antivirals and their mechanisms against the MPXV in clinical settings. Though antivirals and vaccines have been recommended for treatment and prevention, numerous protocols for treating poxvirus are restricted to various at-risk populations, children, pregnant women, or other immunocompromised individuals. Drugs have been selected based on the target range involved in viral replication(Baker et al., 2003). Tecovirimat is an antiviral used against poxviruses, and it is the first antiviral drug approved in the United States against orthopoxvirus (Kaler et al., 2022). In-vitro studies also indicate the effective use of cidofovir and Brincidofovir against pox viruses(Andrei & Snoeck, 2010). Depending on how closely related the various orthopoxvirus (including MPXV) are to one another, immune responses to one orthopoxvirus can recognize other orthopoxvirus and produce different degrees of protection. This cross-reactivity is because of shared immune epitopes and a broad extensive response covering at least two dozen structural and membrane proteins. Vaccinations against smallpox and MPXV infection can be administered pre-exposure and post-exposure (Poland et al., 2022). Prior smallpox vaccines coincidentally protected against MPV. Two live-attenuated vaccines for preventing MPXV infection have received US FDA approval: ACAM2000 and JYNNEOS. The former is of replicating type while the latter is not. Fewer complications have been reported in JYNNEOS, as compared to ACAM2000 (Abdelaal et al., 2022). Specifically for MPXV, a vaccine is currently being developed (Pruc et al., 2022). Insufficient evidence on a particular treatment regimen hinders decision-making in the clinical setting. This ambiguity could be resolved by developing clinical guidelines enabling standardisation of care across different sites. Therefore, this study aims to summarise and give a detailed view of the use of antivirals for treating MPXV infection, including the resolution of disease and complications during treatment. It also summarises their mechanisms of action and clinical pharmacology. This study can throw light on a scenario of uncertainty regarding antiviral therapy in MPXV infection. Methods The trial protocol was submitted to the International Prospective Register of Systematic Reviews PROSPERO [CRD42022355596](Shamim et al., 2022). The preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Statement was complied with. The search strategy, criteria for eligibility of studies, and variables of interest were pre-specified in the protocol. The variables of interest were chosen to keep in mind the lack of guidelines for managing human MPXV disease and the subsequently anticipated heterogeneity in managing and reporting the same. The variables of interest are the resolution of the disease and complications during treatment among individuals with MPXV disease receiving antiviral therapy. Search Strategy and Selection Criteria Following the protocol, three reviewers (M.A.S., S.D.V, C.C.) independently conducted an extensive literature search. ‘Monkeypox’ was searched along with terms like ‘management’, ‘treatment’, ‘antiviral’, ‘Tecovirimat’, ‘Cidofovir’, and ‘Brincidofovir’ in the title or abstract. A comprehensive search was carried out in Ovid-MEDLINE, Scopus, Cochrane Library, Google Scholar, and preprint servers for all the articles from the inception of each database through September 9, 2022. A search was repeated on November 1, 2022, to add new articles. For uniformity, the search was limited to studies on humans with MPXV disease. Reference lists were manually reviewed for additional relevant studies. Other details of the search strategy can be referred to in supplementary appendix 1. Given the lack of data on this topic, all original studies on the usage of antivirals in human MPXV infections were included. Clinical trials, cohort studies, case-control studies, cross-sectional studies, case series, and case studies were eligible. Pooled fraction of recovered individuals will be calculated. Studies performed on animals, in-vitro studies, and any study not involving human patients of MPXV infection were excluded. Studies involving related diseases like smallpox were also excluded. If a case is detected to be published more than once, it was reported only once. Studies were excluded where some individuals received antivirals while others did not, and individual patient data was unavailable in the article or the accompanying supplementary. This was done to ensure homogeneity in the review by not combining the data of those who received antivirals with those who did not. The deduplication of studies was verified by a reviewer (M.A.S.). This was followed by three reviewers (M.A.S., S.D.V, C.C.) independently considering the eligibility of each study based on the title and abstract obtained from the literature search. The full text was screened for the eligible studies, and ineligible studies were further excluded. Disagreements were resolved by mutual consensus. In the absence of consensus, the opinion of a fourth independent reviewer (S.T.) was considered binding. Study Quality Since no validated tools are available for assessing the risk of bias in uncontrolled cohort studies or case reports and case series, a previously used and adapted iteration of the Newcastle Ottawa scale was implemented(Bazerbachi et al., 2017, 2018; Haffar et al., 2017). This tool has a good inter-rater agreement(Murad et al., 2018). For uncontrolled studies, items assessing comparability and adjustment were excluded, while those assessing selection, representativeness and ascertainment were retained. Thus, five questions were finalised, as shown in Table 1 . Studies were qualitatively assessed as good when none of the items gave a negative response, moderate when one was negative, and poor when more than one was negative.Table 1 Assessment of risk of bias of the included studies Table 1: Is the case definition adequate? Representativeness of the cases Exclusion of other important diagnoses Presence of all important data Ascertainment of outcome Risk of bias Adler et al. Yes Yes Yes Yes Yes Low Rao et al. Yes Yes Yes No No High* Matias et al. Yes Yes Yes Yes Yes Low Ajmera et al. Yes No Yes Yes Yes Moderate Desai et al. Yes Yes Yes Yes Yes Low Moschese et al. Yes Yes Yes Yes Yes Low Lucar et al. Yes No Yes Yes Yes Moderate Mailhe et al. Yes Yes Yes Yes Yes Low Peters et al. Yes No Yes Yes Yes Moderate Shaw et al. Yes No Yes Yes Yes Moderate Mbrenga et al. Yes Yes Yes Yes Yes Low Hernandez et al. Yes No Yes Yes Yes Moderate Cash-Goldwasser et al. Yes Yes Yes Yes Yes Low Raccagni et al. Yes Yes Yes Yes Yes Low Pastula et al. Yes Yes Yes Yes Yes Low Viguier at al. Yes No Yes Yes Yes Moderate Hermanussen et al. Yes No Yes Yes Yes Moderate Scandale et al. Yes No Yes Yes Yes Moderate * This study focused more on the public health response, rather than the management of the individual patient. Data Extraction Using a standard form, the following data were extracted: antivirals used, dosage, number of individuals in the study, clinical features, resolution of disease, complications during antiviral therapy, and duration of hospitalisation after initiation of antiviral therapy. Data were extracted by three independent reviewers (M.A.S., S.D.V, C.C.). Disagreements were solved by consensus. In the absence of consensus, the opinion of a fourth independent reviewer (S.T.) was considered binding. Results A summary of the complete search process followed by the subsequent selection of studies is shown in Figure 1 . Following a comprehensive literature search, a total of twenty-two studies that reported the use of antivirals in human individuals with MPXV infection were included. However, four studies were excluded in the final phase as not all the participants had received antivirals, and the authors presented a summarised patient data without individual data (Català et al., 2022; O'Laughlin et al., 2022; Patel et al., 2022; Thornhill et al., 2022). Efforts were made, by sending an email to them requesting individual patient data for individuals who received antivirals. We have not received the requested information to date and therefore we couldn't include these studies in our review. Table 2 depicts the findings of 18 eligible studies, all of which were uncontrolled studies (Adler et al., 2022; Ajmera et al., 2022; Cash-Goldwasser et al., 2022; Desai et al., 2022; Hermanussen et al., 2022; Hernandez et al., 2022; Lucar et al., 2022; Mailhe et al., 2022; Matias et al., 2022; Mbrenga F., Nakouné E., Malaka C., Bourner J., Dunning J., Vernet G., Horby P., 2022; Moschese et al., 2022; Pastula et al., 2022; Peters et al., 2022; Raccagni et al., 2022; Rao et al., 2022; Scandale et al., 2022; Shaw et al., 2022; Viguier et al., 2022). Thus, a total of seventy-one (71) individuals have been considered. No published randomised or controlled studies were found on antiviral use for humans with MPXV infection. The heterogeneity of studies led to not all the data being included in all the studies.Figure 1 Summary of the search process, screening, and selection of studies Figure 1: Table 2 Table 2Study Country Study design Antiviral Dosage Patients Clinical features (apart from MPXV lesions) MPXV lesions Resolution Complications during treatment Important details Duration of hospitalisation $ Age Sex Comorbidities & coinfections Concomitant medications Adler et al., (2022) UK R BCV 200 mg once weekly orally 3 fever, coryzal illness, lypmhadenopathy Face, scalp, trunk, limbs, palms, glans penis, soles, hands(incuding nail bed), labia majora, penile shaft, legs and scrotum Resolved all had elevated transmainase, and course could not be completed; 2 each had conjuctivitis and lower limb abscess; one had neuropsychiatric issues In one patient, all skin lesions and viraemia resolved except ulcerative genital lesions that were PCR positive for monkeypox virus and took longer to resolve 20, 21, 28 30-40 2 M, 1 F bacterial conjunctivitis antibiotics, opioid analgesics, neuropathic analgesics TPOXX 600 mg twice daily orally 1 malaise, headache, pharyngitis Face, trunk, arms, and hands Resolved - No new lesions developed after 24 hours; and URT swab PCR came negative 48 hours later 7 30-40 F - - Rao et al., (2022) USA C/R TPOXX - 1 fever, GI upset, cough, fatigue Face Resolved - - 32 days (date of starting antiviral not known) Middle age M - - Matias et al., (2022) USA C/S TPOXX 600 mg twice daily orally 3 fever, malaise, chills, tonsillar pain with odynophagia Face, oropharynx, hands, feet (including the soles), arm, chest, foreskin of penis and lower eyelid. Resolved in 1; almost completely resolved in the other 2 till the last date of followup mentioned (day 7, 14) ALT rose to <2 time UNL and resolved without discontinuation of drug in one; loose stool after each dose in another - 2, 5, not mentioned for third 20-30 2, 40-50 1 M 1 had gonococcal urethritis, 1 had HIV Prophylactic HIV treatment Ajmera et al., (2022) USA C/R TPOXX 200 mg twice daily orally post discharge 1 sore throat, tongue swelling, burning sensation in mouth, odynophagia, lypmhadenopathy Mouth, tongue, and face Resolving at discharge - Lesions were aggravating during hospitalization. TPOXX was started on day 3 of hospitalizations, and patient started improving two days later 2 26 M syphillis tenofovir/emtricitabine for HIV pre-exposure prophylaxis (PrEP) Desai et al., (2022) USA u/C TPOXX Weight-based, twice or thrice daily orally 25 fever, lymphadenopathy, headache, fatigue, sore throat, chills, backache, myalgia, nausea, and diarrhea Perianal, genital, chest, eyelid, face, neck, arm, buttocks, back, thigh, wrist, shin, throat, abdomen and forearm. 1 had all over the body Resolved in 23 by day 21; only one of the other two developed new lesions fatigue, headache, nausea, itching, and diarrhea - Outpatients were evaluated 26 - 76 (median of 40.7) M 9 had HIV - Moschese et al., (2022) Italy C/S CDV 5 mg/kg day 1 and 7 1 fever, chills, sweat, lymphadenopathy Nose and limb Resolved Patient was administered CDV because of unavailability of TPOXX 7 26 M Possible bacterial superinfection in nasal lesions Amoxicillin, Clavulanic acid Lucar et al., (2022) USA C/S TPOXX 600 mg twice daily orally 2 fever, proctitis, lymphadenopathy, ocular pain and redness, fatigue, rectal bleeding mouth, face, limbs, trunk and perianal area Resolved mild transient fatigue in one Both patients were developing new lesions, and pain required opioids. TPOXX was started on day 9 and 10. Pain improved within 2 days and no new lesions developed. One patient was clearly not hospitalized during antiviral therapy, while hospitalisation is not mentioned for the other 26, 37 M - HIV preexposure prophylaxis Mailhe et al., (2022) France u/C CDV 5mg/kg 1 fever, paronychia, lymphangitis, blepharitis, conjunctivitis, keratitis Eye Evolving till the last date of follow up mentioned - - 7 day(date of starting antiviral not known) 30 M - Acetaminophen Peters et al., (2022) USA C/S TPOXX - 1 lesions on tongue, fever, fatigue Tongue Resolving - After testing positive for monkeypox, the patient developed several new lesions involving his arms, legs, and torso. He is still symptomatic but improving with TPOXX. - 38 M - emtricitabine-tenofovir for the prevention of HIV Shaw et al., (2022) USA C/R TPOXX * 600 mg twice daily orally 1 fever, chills, myalgia, fatigue Face, back, pubic region and left foot Resolving - Patient had multiple vesicular lesions, and were positive for both Herpes Simplex Virus - 1, and monkeypox Lesions continued to aggravate though fever subsided within 72 hours, and the patient was discharged from the hospital 3 30-40 M hypertension otosyphilis pre-exposure (HIV) prophylaxis Mbrenga et al., (2022) Central African Republic u/C TPOXX 600 mg twice daily orally in adults; weight-based 14 Muscle pain, headache, lymphadenopathy, fever, back pain, and upper respiratory symptoms hands, feet, face, back, thighs, legs, arms, forearms, abdomen and chest Resolved in 13; 1 died Death in one, and anaemia in one; both considered unrelated to TPOXX An unusually high 85% comorbidity with malaria was seen - 23 (median) 4 M, 10 F few patients had malaria infection Hernandez et al., (2022) USA C/R TPOXX 600 mg twice daily orally 1 fever, chills, headaches, sore throat, generalised malaise, and rectal pain and discomfort pustules on the trunk, upper and lower extremities, groin, and peri-anal region Resolved - - - 37 M metastatic Kaposi sarcoma and hypertension. HIV and secondary syphilis. hypertension. emtricitabine-tenofovir, doravarine, darunavir-cobicistat, and hydrochlorothiazide. Cash-Goldwasser et al., (2022) USA C/S TPOXX - 5 eye pain, itching, redness, swelling, discharge, foreign body sensation, photosensitivity, vision changes, rectal pain Eye, facial skin, scalp, chest, abdomen, wrist, rectum, penis, vagina Resolved in 4, one is experiencing waxing and waning of symptoms - Four patients were hospitalized, and one experienced marked vision impairment. 10,5,3 One patient was not admitted One patient is still admitted (day 14) 20-29 1, 30-39 4 4 M, 1 F 2 had HIV disease topical povidone iodine, anti retroviral therapy, antibiotics FTD - 4# eye pain, itching, redness, swelling, discharge, foreign body sensation, photosensitivity, vision changes Eye, facial skin, scalp, chest, abdomen, wrist, penis, vagina Resolved in 3, one is experiencing waxing and waning of symptoms - All four patients were hospitalized, and one experienced marked vision impairment. 10,5,3 One patient is still admitted (day 14) 20-29 1, 30-39 3 3 M, 1 F 2 had HIV disease topical povidone iodine, anti retroviral therapy, antibiotics Raccagni et al., (2022) Italy C/S CDV 5 mg/kg day single dose 4 dyspnea, dysphonia, dysphagia, perianal pain, intrarenal pain, ocular pain, photophobia Pharyngo laryngeal, cutaneous, genital, rectal, ocular Resolved - The second administration of CDV was not required as a positive clinical evolution of MPX lesions was observed among all individuals following the first dose. 8, 7, 4, 3 36, 36, 37, 53 M 2 of 4 had HIV disease Crohn's disease in 1, Chronic gastritis in 1 antirertoviral therapy, sulfasalazine, testosterone, probenecid Pastula et al., (2022) USA C/S TPOXX - 2 fever, chills, malaise, hemiparesis, paraparesis, urinary retention, bladder and bowel incontinence, priapism, myalgia face Resolved - Both were cases of mpnkeypox virus-associated encephalitis in presumedly immunocompetent gay man - 30-39 M Several tests including those for chlamydia, HIV, gonococci, syphillis were negative methylprednisolone, IV immunoglobulin, penicillin, plasma exchange, rituximab Viguier at al., (2022) France C/R TPOXX 600 mg twice daily orally 1 fever, asthenia, shivers, watery diarrhea face, scalp, trunk, limbs, and anal margins Resolved transient rise in ALT (acme 97 IU/l) and AST activities (acme 86 IU/l) His clinical condition deteriorated for 37 days, with fever, skin lesions and diarrhea before going to the infectious diseases department, where his severe, protracted infection was treated with TPOXX for 14 day 14 28 M HIV, latent syphillis, scalp superinfections with Klebsiella aerogenes and Staphylococcus lugdunensis Cotrimoxazole, benzathine benzylpenicillin, antirertoviral therapy Hermanussen et al., (2022) Germany C/S TPOXX 1200 mg daily 3 lymphadenitis, fever, malaise, fatigue penis, anus, face Resolved in 2, Resolving in 1 transient increase of the γ-glutamyltransferase in 1 Overall, the antiviral treatment with tecovirima was well tolerated with no significant side effects. 7 31, 44, 54 M ulcerative colitis, syphillis, HIV positive in 1 vedolizumab, HIV pre-exposure prophylaxis, penicillin Scandale et al., (2022) Italy C/R CDV 5 mg/kg day single dose 1 ocular pain, photophobia, generalised lymphadenopathy, fever conjunctiva, oropharynx, skin, rectum Resolved - unilateral ocular involvement with multiple papules at conjunctiva, the fornix, and at the temporal limbus - 35 M - - A majority (58, 81.7%) of 71 participants were male. Most individuals (47, 66.2 %) were from the United States of America. Around 14 (19.7%) individuals were from the Central African Republic, while 10 (14.1%) were from Europe – Italy, the United Kingdom, and France. Of the total included individuals, 59 (83.1%) were reported to have complete resolution of symptoms, one is experiencing waxing and waning of symptoms, only 1 (1.8%) had died, and the others were having a resolution of symptoms at the study report (table 2). The death was thought not related to Tecovirimat. Elevated hepatic panels were reported among 3 of 3 individuals treated with BCV and 5 of 61 treated with Tecovirimat. Drugs for MPXV infection are still under research. To date, only five drugs have been considered for treatment: Tecovirimat, Cidofovir (CDV), Brincidofovir (BCV), Trifluridine, and Vaccinia Immune globulin Intravenous (VIG). VIG has been used in only one individual in a single study (Thornhill et al., 2022). However, individual patient data could not be retrieved, so we have not included this study in our review. Tecovirimat was the most used drug in these studies. An individual had oral and facial lesions, a burning sensation in the mouth and dysphagia(Ajmera et al., 2022). The lesions were aggravating while he was initially on vancomycin, piperacillin/tazobactam, dexamethasone, acyclovir, and fluconazole. Later, penicillin was also added. Two days after Tecovirimat was started, the individual improved and was subsequently discharged. Two individuals presented with severe proctitis (Lucar et al., 2022), and both had rectal pain, and new lesions were cropping up in the trunk and limbs requiring opioids. On days 9 and 10, respectively, Tecovirimat was started for both individuals. In both cases, there was an improvement in pain within 48 hours. An individual had lesions in the tongue, later spreading to the limbs and torso(Peters et al., 2022). Tecovirimat was given, and the individual started recovering. An individual had vesicular lesions positive for both Herpes Simplex Virus - 1 and MPXV (Shaw et al., 2022). Tecovirimat was started, and lesions continued to aggravate, but the fever subsided within 72 hours, and the individual was discharged. Two individuals with encephalomyelitis started improving within days of starting Tecovirimat and other supportive therapies and were subsequently discharged (Pastula et al., 2022). The clinical condition of an immunocompromised individual living with HIV deteriorated for over a month before starting Tecovirimat, and symptoms subsided in 2 days (Viguier et al., 2022). A case series from Germany describes rapid improvement in 2 individuals. However, the third individual is slowly recovering (Hermanussen et al., 2022). Overall, Tecovirimat does indeed seem to help individuals with progressive disease. Tecovirimat was associated with a few complications, like fatigue, headache, and nausea during treatment. However, one individual developed loose stools a few hours after each dose, while three developed transiently elevated hepatic enzymes that resolved itself(Hermanussen et al., 2022; Matias et al., 2022; Viguier et al., 2022). Treatment did not have to be paused or stopped in any case. The death and anaemia seen in one case each was deemed unrelated to Tecovirimat(Mbrenga F., Nakouné E., Malaka C., Bourner J., Dunning J., Vernet G., Horby P., 2022). Brincidofovir and Cidofovir have been used in one and four studies, respectively. In three studies, individuals receiving CDV recovered(Moschese et al., 2022; Raccagni et al., 2022; Scandale et al., 2022). Another individual presented with severe ocular involvement(Mailhe et al., 2022). Here, two doses of CDV have been administered, and the lesions are evolving as of the last follow-up. None of the four studies reported any adverse events. As for BCV, all three individuals developed elevated hepatic enzymes (peak alanine transaminase of 127, 331, and 550 U/L, respectively), and the course of medication had to be stopped prematurely. Conjunctivitis, lower limb abscess, and neuropsychiatric symptoms were the other problems encountered (Adler et al., 2022). Four individuals with ocular MPXV disease received Trifluridine eye drops in addition to Tecovirimat therapy. Three of them have recovered, while one has suffered marked vision impairment, and his symptoms were fluctuating. No individual reported any adverse event (Cash-Goldwasser et al., 2022). Curating their mechanisms of action, Tecovirimat decreases the production of extracellular forms of the MPXV by inhibiting the p37 viral proteins needed for cellular localisation and formation of the viral envelopment. By inhibiting the envelope on the virus, tecovirimat prevents the systemic spread of the virus by not letting out the virus from the infected cell, thereby preventing subsequent damage to the host cell. For children weighing less than 13 kg, the CDC-held Emergency Access Investigational New Protocol allows opening the capsule to mix the medicine with liquid or soft food. The Strategic National Stockpile offers Tecovirimat as an oral capsule formulation (600 mg twice daily for 14 days) or an intravenous injection. Side effects associated with tecovirimat are usually minimal such as headache, nausea, abdominal pain, and vomiting. Injections-site reactions may happen with intravenous administration. Although there are currently no known contraindications, it should be prescribed or administered with caution in individuals with renal or hepatic impairment (DrugBank, 2022; National Center for Biotechnology Information, 2022; Rizk et al., 2022). Cidofovir (CDV) is mainly considered in cytomegalovirus retinitis, a condition commonly seen among the immunosuppressed, including people living with HIV. Cellular enzymes are required to activate CDV once it has entered the cells. CDV is converted to its monophosphoryl form (CDVp), which is then further phosphorylated to CDV-diphosphoryl (CDVpp), the active form. These reactions are catalysed by Pyrimidine nucleoside monophosphate kinase and Nucleoside 5′-diphosphate kinase, respectively. CDVpp interacts with the viral DNA polymerase, finally getting incorporated into the DNA. CDVpp can behave as a competitive inhibitor. Alternatively, it can substitute the substrate and get incorporated, leading to chain termination (Andrei & Snoeck, 2010). This has been explained diagrammatically in Figure 2 . Brincidofovir (BCV) is a pro-drug and a lipid conjugate of CDV that resembles a natural lipid. Thus, it enters infected cells by taking on the natural lipid absorption mechanisms(Chimerix, 2021). Following absorption, the lipid molecule is broken down, thereby releasing CDV for additional intracellular kinase phosphorylation to form cidofovir diphosphate, the active form of the drug used. Comparing these two drugs, in contrast to CDV, BCV does not act as a substrate for Organic Anion Transporter 1, which makes BCV less harmful to the kidneys. Therefore, compared to CDV, BCV is safer for the kidneys. CDV is less well tolerated than BCV. BCV is available in oral or suspension form, whereas CDV is in intravenous form. A dosage of 200 mg weekly for two weeks of BCV is recommended in adults weighing ≥ 48 kg, for adults and children weighing from 10 to 48 kg: 4 mg/kg weekly for two weeks are recommended and for children with body weight below 10 kg, 6 mg/kg is recommended weekly for two weeks. The recommended dosage of CDV is 5 mg/kg once weekly for 14 days, followed by 5 mg/kg IV once every other week. Those on BCV may experience minor adverse events such as diarrhoea, nausea, vomiting, and abdominal pain. In contrast, CDV presents side effects such as decreased serum bicarbonate, proteinuria, neutropenia, infection, hypotony of the eye, iritis, uveitis, nephrotoxicity, and fever. BCV is contraindicated in pregnant and lactating women. It may elevate hepatic transaminases and serum bilirubin; therefore, the individual must be assessed for liver function before and after the therapy. On the other hand, CDV may harm kidneys; dose adjustment must be made in case of renal impairment (Das & Hong, 2019; Rizk et al., 2022).Fig 2 Mechanism of action of Cidofovir CDV: Cidofovir; CDV: Cidofovir monophosphoryl; CDVpp: Cidofovir diphosphoryl; BCV (CDV + L): Brincidofovir (as Cidofovir conjugated to a lipid molecule) Fig 2: Coming to Trifluridine, there is limited data on managing ocular manifestations of MPXV disease per se (Kaufman et al., 2022). Topical trifluridine eye drops have been approved for other ophthalmological conditions like herpes simplex keratitis and vaccinia. It has also been beneficial against other viruses of the same family in-vitro (Kern, 2003; Yu & Mahendra Raj, 2019). It is being used in MPXV infection also. It is used as a 1% ophthalmic solution. It is administered as a single drop every two hours till re-epithelialisation occurs. Then, it is administered four-hourly. Long-term administration exceeding three weeks is avoided due to fear of toxicity. It is generally well-tolerated. Transient burning and oedema of eyelids are common adverse events(Milligan et al., 2022) (Carmine et al., 1982). Vaccinia immune globulin (VIG) is prepared from the pooled blood of the recipients of the vaccine for smallpox. The main component is immune globulin G (IgG), which is involved in the human body's physiological response to infection. VIG may prevent extracellular orthopoxvirus from infecting its target cells, limiting the ability of viruses to spread from an extracellular to an intracellular location. It is administered intravenously at 6000 U/kg after symptoms appear. Another dose may be given depending on the clinical profile and treatment response. The dose may be increased to 9000 U/kg in case of lack of response. Headache, nausea, rigors, and dizziness may occur following the administration. It is contraindicated in isolated vaccinia keratitis, a history of anaphylactic response to human globulins, IgA deficiency with antibodies against IgA and a history of IgA hypersensitivity. No contraindications have been reported for this drug; however, it should be used carefully in individuals with renal insufficiency (DRUG MONOGRAPH: VACCINIA IMMUNE GLOBULIN, VIG, 2022; Rizk et al., 2022). Discussion In this first systematic review, we report the antiviral treatment of individuals infected with MPXV. A previous study reviewed the quality of the available guidelines(Webb et al., 2022). Many in-vitro and animal-model studies are present on this topic, and we included only reported cases/studies of therapy in humans. However, this review aimed to gather the evidence and compile the available information on the usage of antivirals in human individuals with MPXV infection. Tecovirimat has shown promising results and has been tested earlier on non-human primates(Berhanu et al., 2015; Russo et al., 2020). It has been conditionally approved for MPXV infection by the Food and Drug Administration (FDA) and the European Medicines Agency (CDC, 2022). It has been beneficial in several cases of progressive MPXV infection among the reported cases. It also did not generate any major signal for adverse events. Hepatic dysfunction was transient, unlike in the case of Brincidofovir, where all three individuals had to discontinue treatment (Adler et al., 2022). The solitary case of death amongst those receiving Tecovirimat was also considered unrelated to the drug. Cidofovir is the next most used drug. Of the four studies, it was only used in two because of the unavailability of Tecovirimat (Mailhe et al., 2022; Moschese et al., 2022). Cidofovir has been well-tolerated in these studies which is in line with findings from other studies where intravenous Cidofovir showed a good tolerability profile when used in different indications (Caruso Brown et al., 2015; Cesaro et al., 2005; Held et al., 2000). The major concern with its use is nephrotoxicity. However, this was fortunately not seen in any of the individuals. Topical trifluridine has been used as an adjuvant to Tecovirimat in some cases with MPXV–associated ocular lesions (Cash-Goldwasser et al., 2022). Most recovered, and no complications were reported. However, readers should be aware that even in these ocular cases, topical trifluridine has not yet been used as monotherapy. Given the limited number of individuals in published studies who have received antivirals, there is a need for better-designed studies on the efficacy and safety of antivirals and other drugs in human MPXV disease. The promising results seen with Tecovirimat should be investigated further in well-designed research. An exploration of ClinicalTrials.gov and Cochrane Central Register of Controlled Trials shows a few results. The National Institute of Allergy and Infectious Diseases has sponsored two blinded randomised controlled trials comparing Tecovirimat to a placebo. These studies are underway (NCT05534984, NCT05559099). PLATINUM-CAN seeks to assess Tecovirimat in MPXV infection in Canada and is expected to start recruiting soon (NCT05534165). The strength of our study is that this is the first systematic review on management, specifically the pharmacological treatment of MPXV infection in humans. All the sparsely available information was compiled. However, the study has a few limitations. Only eighteen studies could be included and were summarised in this review according to the inclusion criteria. Moreover, all were uncontrolled studies. However, no more eligible studies on the usage of antivirals in individuals with MPXV infection are present. Thus, we could not draw enough conclusions to draft guidelines or recommendations. We faced another limitation in not having individual patient data from a few studies. Important data on the use of Tecovirimat and Cidofovir could not be incorporated. We tried our best to use this data, including sending a mail to the concerned authors, but after failing to retrieve this data, we could not include them in our systematic review. Additionally, as anticipated, the data were not amenable to performing a quantitative synthesis and meta-analysis. This first systematic review on the usage of antivirals in humans with MPXV infection shows that antivirals - Tecovirimat, Cidofovir, Brincidofovir, Trifluridine, and Vaccinia immune globulin - have been used so far. Among these, Tecovirimat was used most often. It demonstrated promising results in individuals with progressive disease and a better safety profile than some other drugs. The data available is limited, and randomised controlled trials can add valuable evidence. Contributors BKP, MAS, and SDV conceptualised and designed the study. MAS, SDV, CC, ST, PS, CC and ST were involved in the screening of articles, assessment of the risk of bias, and data extraction. MAS, BKP, VKC, JAT contributed to the methodology. Software, data analysis and interpretation were done by AP, PD, AM, AJR, RS, ATA, and JAT. Project administration was done by BKP, RS and VKC. Resources were provided by BNK, VKC and RS. The original draft of various sections in the initial phase was contributed by MAS, NA, AP, and BKP. BNK, VKC and JAT provided critical comments and edited the final draft. Each author had access to all the data in the study and provided inputs to the preparation of the final manuscript. All the authors accepted the final version of the manuscript. Data sharing All data used in this review were obtained from studies available online. The protocol has been made publicly available at PROSPERO, CRD42022355596. Funding No funding was sought or received to conduct this study. Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. None of the authors have any competing interests. Ethical approval Ethical approval is not required for this study. Acknowledgements We wish to thank Dr. Shailesh Advani (Terasaki Institute of Biomedical Innovation) and Dr. Gitismita Naik (All India Institute of Medical Sciences, Kalyani) for their assistance during the development of the protocol of this systematic review. We also thank Dr. Rabbanie Tariq Wani (Directorate of Health Services, Kashmir) for suggesting few edits in the text. 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==== Front Sci Afr Sci Afr Scientific African 2468-2276 The Author(s). Published by Elsevier B.V. on behalf of African Institute of Mathematical Sciences / Next Einstein Initiative. S2468-2276(22)00377-5 10.1016/j.sciaf.2022.e01472 e01472 Article Psychological first aid intervention: rescue from psychological distress and improving the pre-licensure nursing students’ resilience amidst COVID-19 crisis and beyond Eweida Rasha Salah ab⁎ Rashwan Zohour Ibrahim cd Khonji Leena Mohammad e Shalhoub Abdullah Abdulrahman Bin f Ibrahim Nashwa g a Psychiatric and Mental Health Nursing Department, Faculty of Nursing, Alexandria University, Egypt b Psychiatric and Mental Health Nursing Department, College of Health and Sport Sciences, University of Bahrain, Bahrain c Pediatric Nursing Department, Faculty of Nursing, Alexandria University, Egypt d Pediatric Nursing specialty, Nursing Department, College of Health and Sport Sciences, University of Bahrain, Bahrain e Midwifery Speciality, Nursing Department, College of Health and Sport Sciences, University of Bahrain, Bahrain f KSU Fellowship in Clinical Pathology, Ministry of Health, Saudi Arabia g Psychiatric and Mental Health Nursing Department, Faculty of Nursing, Mansoura University, Egypt ⁎ Corresponding author at: Psychiatric and Mental Health Nursing Speciality, Nursing Department, College of Health and Sport Sciences, University of Bahrain, Bahrain. 28 11 2022 3 2023 28 11 2022 19 e01472e01472 21 6 2022 19 11 2022 26 11 2022 © 2022 The Author(s) 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Background The public health emergencies such as the COVID-19 pandemic resulted in mental and psychological ramifications on the healthcare professionals. The pre-licensure nursing students found themselves not only fighting against the baneful virus but also weak ego resilience. At this point, enriching the pre-licensure nursing students with psychological first aid (PFA) could help them to recover from the feeling of psychological distress and improve their resilience capacity to encounter any upcoming outbreaks. Methods A quasi-experimental two groups, a pre-post-test study was used in which sixty-four pre-licensure nursing students completed a baseline survey which revealed high levels of psychological distress and low resilience capacity due to the COVID-19 crisis. The study group engaged in the Psychological First- aid Intervention (PFA) at the end of the clinical practicum course period, while the comparison group received routine psychological support. Results A significant reduction in the psychological distress levels among students in the PFA group (FET=7.83, P = 0. 001). Likewise, significant improvements in the students' resilience capacity level immediately after the intervention (FET=3.34, P = 0.019) and during the two-month follow-up (FET=12.94, P = 0. 001). The implementation of PFA enhanced the pre-licensure nursing students' psychological health status and resilience capacity levels after their clinical training amid the ambiance of the COVID-19 crisis. Conclusion The PFA effectively fostered the pre-licensure nursing students’ recovery from the COVID-19 related- psychological distress and improve their resilience capacity. The application of RAPID model is recommended to reduce stress and prevent burnout among novice and future nurses who show signs of psychological exhaustion. Keywords Psychological first- aid Psychological distress Pre-licensure nursing students Abbreviations Psychological first- aid Intervention, (PFA) General Health Questionnaire, (GHQ) Connor-Davidson Resilience Capacity Scale, (CD-RISC) National Institute of Mental Health, (NIMH) Editor: Edited by DR B Gyampoh ==== Body pmcIntroduction The nursing clinical practicum course period is the main transition from academic education to a professional nursing career, and it is essential to enable new nursing professionals to apply academic knowledge in clinical practice, bridging the theory-practice gap among newly qualified professionals [1]. During this period, nursing students are exposed to various clinical settings and caring for patients with different diagnoses, and they cooperate with the health team members and carry out nursing procedures in line with the relevant ethical principles, to ensure their proper orientation to the professional life [33]. In the shadow of the COVID-19 pandemic and associated public health policy has fundamentally challenged health systems in all aspects, including changing the context of traditional clinical training. Pre-licensure nursing students found themselves in a tough and unpredictable academic as well as practice environment during their transition to practice during this period, in addition to being called upon for extraordinary service in caring for patients with COVID-19 and administering vaccination programs [38]. The COVID-19 pandemic itself as well as lockdowns and other intermitted restrictions in most countries worldwide caused severe psychological distress [28], particularly among healthcare professionals [17]. Many nursing students will carry this traumatic experience with them for an extended period, and some might develop post-traumatic stress disorder because of what they have experienced during these fateful moments (National Center for [30]). Pre-licensure nursing students have been exposed to bouts of severe stress in dealing with confirmed and suspected cases of COVID-19, along with poor health system readiness, such as woefully depleted personal protective equipment (PPE) in most health systems [36]. After prolonged periods of Pre-licensure nursing students’ exposure to extreme stress and fatigue, they are primed for mental strain, physical exhaustion, depression, burnout, and other negative psycho-social outcomes [3,14]. They may also face moral dilemmas regarding maintaining their own safety and providing care for these patients with a highly contagious infection despite the limited resources [32,38]. Beside, nurses or health care professionals might have concerns and fear-related to the transmission of COVID-19 infection to their families, having witnessed large numbers of their peers being infected, quarantined, or dying [14]. The psychological trauma of the pandemic renders nurses particularly vulnerable to developing depression, suicidal ideation, and difficulty recovering from feelings of psychological distress [24]. Profound psychological reactions reflect that future nurses face severe challenges to their psychological resilience [4,15], defined as "the individual's ability to bounce back from an adverse event and have a relatively positive outcome may help in protecting the individual from negative perceptions of stress" [40]. A lack of resilience leaves an indelible impact on pre-licensure nursing students’ psychological wellness and the nursing care provided to patients. Manzano and Ayala [26] reported that nurses’ resilience capacity is a crucial protective factor against psychological exhaustion and supports them in coping with the profession's mental and psychological hardship. Scientific evidence endorses that resilience-focused intervention, based on the principles of cognitive-behavioral therapy (CBT), targets individuals’ internal protective factors at critical times, enabling them to adopt adaptive coping strategies that are conducive to supporting their resilience and positive mental health [43]. A meta-analysis showed the positive impact of the resilience-focused intervention on improving depressive symptoms immediately after the intervention, and at follow-up intervals of 6, 8, and 12 months [5]. Moreover, Dray et al. [9] demonstrated the benefits of resilience-focused interventions in reducing psychological distress and depressive and anxiety symptoms among adolescents. The study included a total of 57 trials from 5984 records, with 49 contributing to meta-analysis. The authors reported that resilience-focused interventions in all trials were more beneficial in reducing internalizing problems, externalizing problems, depressive symptoms, and general psychological distress compared to the control group. Again, the public health emergencies such as the COVID-19 pandemic resulted in mental and psychological ramifications for healthcare professionals. They might find themselves working in the chaotic environment of disasters with a woeful depletion of resources. Even experienced nurses occasionally faced the perilous challenges of performing a simple task in these critical times [37]. At this point, enriching the healthcare professionals with psychological first aid (PFA) information could lessen their anxiety and stress levels while improving their durability [42]. Furthermore, Eweida et al. [14] recommended that psychological first aid intervention be provided for vulnerable pre-licensure nursing students to recover from the adverse psychological impact of the pandemic and enable them to excel in the nursing profession. Psychological first- aid intervention (PFA) is essential for those future nurses during this particularly vulnerable professional phase [6,25]. In the mid-twentieth century, 1-3 in the post-9/11 era, the concept of PFA was introduced. It has evolved to become the flagship early intervention programme for disaster survivors to mend their recovery from the immediate or short-term aftermath of trauma. The PFA frameworks are proliferating and are underpinned on five essential principles: (1) safety, (2) calming, (3) hope, (4) self- and community efficacy, and (5) social connectedness [19]. Given the considerable developmental adversities, the PFA approach has arisen as a mainstay for the early psychological intervention of survivors of disasters and or those with posttraumatic stress disorder (PTSD) [35]. On the other side, the resilience-focused intervention targets building resilience or strengthening multiple protective factors that are conducive to supporting the development of resilience and positive mental health, of individuals at risk for developing mental health problems [15]. The PFA is a humanitarian and supportive intervention offered to individuals who may suffer from a wide range of psychosocial impacts immediately post-disaster period [35,41]. Pre-licensure nursing students who attend PFA sessions can benefit by mitigating the cruel impacts of psychological distress experienced during traumatic events and improving their resilience capacity. It is particularly crucial to promote healthcare professionals’ resilience capacity during the COVID-19 pandemic to recover from the experienced psychological crises and developed resilience in readiness for future outbreaks [2]. Purpose This study aimed to investigate the effect of the PFA on psychological distress and resilience capacity levels among pre-licensure nursing students amidst the COVID-19 crisis. Research hypothesis Pre-licensure nursing students who attend PFA exhibit lower COVID-19-related psychological distress levels and higher resilience capacity than those who receive routine psychological support. Methodology Design A quasi-experimental, pre-posttest, two-group research design. Setting The study was conducted at Critical, Medical-Surgical, Obstetric, and Pediatric Care Units of Alexanderia University Hospitals (27 units) during the period of the COVID-19 pandemic. Data collection was started from the end of January 2020 to the middle of October 2020. Participants A list of pre-licensure nursing students who were posted to the previously mentioned settings was obtained from the Internship Affairs Office (n = 450). Initially, the researchers conducted a preliminary survey and sent the relevant link to the whole patch of the pre-licensure nursing students via their academic emails. The participants were invited to voluntary filled out the electronic form of the preliminary survey and rated the psychological distress and resilience capacity levels immediately after their clinical experience amid the ambiance of COVID-19 pandemic. The pre-licensure nursing students who demonstrated both high level of psychological distress and low resilience capacity as a result of their exposure to patients with COVID-19 during their internship period were included in the study. However, students who did not exposed to patients with COVID-19 during their internship period were excluded. Epi Info Program version 10 was used to estimate the sample size using the following parameters; population size of 86, confidence coefficient of 97%, expected frequency of 50%, and acceptable error of 10%. A convenience sampling of 64 pre-licensure nursing students was recruited. They were divided into two groups, a study and a control group, as illustrated in Fig. 1 .Fig. 1 Flow chart of participants’ recruitment process. Fig 1 Instruments of data collection The General Health Questionnaire (GHQ-12) The General Health Questionnaire (GHQ-12) [18] comprises 12 self-reported items measuring the severity of psychological distress. The presence of symptoms is rated on a four-point Likert-type scale (0 = not at all present; 1 = same as the usual present; 2 = rather more than the usual present; 3 = much more than the usual present). The tool demonstrated high reliability, with a Cronbach's alpha coefficient of 0.87. The total score ranges from 0-to 36, with higher scores representing higher levels of psychological distress. The students’ socio-demographic characteristics such as age, sex, and clinical practicum course duration were attached to this tool. Abridged Connor-Davidson Resilience Capacity Scale-10 (CD-RISC-10) The original CD-RISC scale [7] consists of 25 items to assess the resilience capacity; the CD-RISC-10 is an abridged 10-item version that reflects individuals’ ability to tolerate painful experiences. The ability to adapt to change tends to bounce back after hardship, whereby people can stay focused under pressure. The scale had high internal consistency (Cronbach's alpha = 0.88) and showed adequate test-retest reliability, and convergent and divergent validity [7]. Responses were rated on a five-point Likert scale ranging from (0) not true at all to (4) true nearly all the time. The total score ranges from 0 to 40, with higher scores indicating higher resilience capacity. A pilot study A pilot study was carried out on 10% (9 participants) who were selected randomly from total participants to test the tool's applicability, feasibility, and clarity. Those students who engaged in the pilot study were excluded from the actual study sample. The reliability of the GHQ-12 and CD-RISC-10 were tested by measuring the internal consistency of its items using the Cronbach alpha coefficient test. The two tools were reliable as α = 0.91 for GHQ-12 and α = 0.89 for CD-RISC-10. Study procedure Initial assessment An approval from the Ethical Research Review Board of Faculty of Nursing, Alexandria University was obtained. After the permission to conduct the study, the researchers started with a preliminary survey. The researchers scheduled an online meeting for the eligible students who met the inclusion criteria and explained the aim of the study. Students who expressed their voluntary willingness to participate in the study were randomly assigned to PFA and comparison groups. RAPID psychological first aid model The PFA followed the path of the RAPID Psychological First Aid model of Johns Hopkins University [12]. The PFA content represents a simple structure that revolves around five core phases represented in the acronym RAPID: R, establishing rapport and reflective listening; A, assessment; P, prioritization; I, intervention; and D, disposition and follow-up. The program was translated into the Arabic language to be congruent with the Egyptian culture. The research team conducted the intervention at the end of the clinical practicum course period for the academic year 2020/2021. Students of the PFA group were divided into five subgroups, with approximately six students each. The program was delivered in 10 sessions, two times per week, with each session taking around one hour. Session one started with establishing rapport and using reflective listening (R). This was done within an empathetic atmosphere that conveys caring, tolerance, and using genuine communication techniques, accompanied by verbal reassurance and a compassionate attitude. The assessment phase of session two implies a deep assessment of the participants’ reactions to traumatic experiences of caring for patients with COVID-19 infection in relation to the following dimensions: psychological (e.g., feelings of anxiety and fear of impending doom); cognitive (e.g., perception and judgment concerning the past, the present, and the future); behavioral (e.g., the experience of fatigue and lethargy, changing in eating habits and sleep patterns); social (e.g., interpersonal relationships with surroundings); and spiritual (e.g., the meaning of life). In session three, the participants were asked to sort and prioritize their major psychological problems to determine the urgency of the supportive care. The appropriate psychological interventions were tailored based on the assessment and prioritization of the pre-licensure -nursing students’ addressed needs. The intervention phase was covered through six sessions of psychological first-aid tactics. The interventions included empowering the studied participants by enriching them with information, practicing cognitive reframing, stress management, installing future orientation (hope), enlisting family and friends’ support, and delaying any life-altering decisions/changes. The cognitive reframing implies subthemes, including correcting the stress-inducing errors regarding the COVID-19 pandemic's facts; disputing the illogical and catastrophic thinking; and finding the positive aspects, the hidden benefits, and lessons learned from the COVID-19 crisis. Stress management involved practicing relaxation techniques, such as meditation and diaphragmatic deep breathing exercises. With regard to involving the supportive system (like family and friends), the PFA stresses that mobilizing interpersonal resources, and ensuring that they were available and accessible, is beneficial for individuals in crisis. Finally, the researchers advocated a delay before making any important or life-changing decisions. The evaluation phase incorporated an immediate post-test after the PFA and a two-months follow-up assessment. As for the comparison group, the researchers provided them with routine psychological support that mainly revolved around enhancing their self-compassion, practicing mindfulness exercises, and keeping them connected with their family and peers. Moreover, adopting a healthy lifestyle was encouraged, such as engaging in physical activity, eating a well-balanced diet, and sleeping well.Phases of the training program Gerneral Objective Session Actions or tactics Phase one: R: Rapport and Reflective Listening. (covered through one session) Establishing rapport and using reflective listening. - Being visible and available. - Maintain confidentiality. - Pay attention to students’ own emotional and physical reactions. - The researchers listen and focus on hearing what it is the students’ wants the authors to understand Phase Two: Assessment phase (covered through one session) Assessment of the participants’ reactions to traumatic experiences of caring for patients with COVID-19 infection. 1. Emotional dimension The feelings of: ■ Worthlessness. ■ Helplessness. ■ Hopelessness. ■ Anger and irritability. 2. Cognitive dimension Thought process: ■ In relation to the past, the present t and the future. ■ Judgment, Memory and concentration are poor. 3.Behavioral dimension Physical behaviors ■ Fatigue and lethargy. Psychomotor retardation. ■ Withdrawn and seek social isolation. 4- Spiritual dimension and meaning of life. Phase Four: Prioritization and Intervention Phase (covered through six sessions) Remain Students’ Psychologically Safe A Stabilizing patients’ Emotions. B Grounding technique. C Fostering Helpful Spirituality. D Practicing self- compassion exercises. E Self-soothing activities. e.g: Phone a friend, Make a nice meal or snack for yourself. F Practicing Mindfulness. G Instillation of empowerment and hope feelings. Phase Five: Disposition and Follow up Phase (covered through two sessions) Evaluate students’ functioning after the received intervention The researchers assess the participants’ abilities to function effectively without the researchers’ help, or without further intervention and conduct immediate post-test after the PFA and a two-months follow-up assessment. Ethical considerations The Ethical Research Review Board approved the study of the Faculty. Online informed consent was obtained from each student who participated in the study. The participants gave their consent by filling out the questionnaire. Data confidentiality was assured. The participants were informed about their rights to decline or withdraw from the research study at any time. Research No: (NCT04822285). Data analysis SPSS version 23 was utilized for data analysis. Descriptive statistics included the number, percentage, mean, and standard deviation, used to describe the demographic characteristics, psychological distress, and resilience capacity. Kolmogorov-Smirnov test was used to check the normality of study variables. Analytical statistics included Fisher's Exact, used to test the significance of psychological distress and resilience capacity levels before the intervention, immediately after it (baseline), and at two-months follow-up. The comparison between the two groups regarding the mean score of psychological distress and resilience capacity before, during, and two months after the PFA was done using the Mann–Whitney (Z) test. All of the statistical analyses were considered significant at P < 0.05. Results The results of the preliminary survey are illustrated in Fig. 2 . It is clear that 97 out 207 intern-nursing students had high level of psychological distress during COVID-19 pandemic and 89 of them experience low resilience capacity.Fig. 2 Intern-nursing students’ psychological distress and resilience capacity initial assessment. Fig 2 Fig. 3 reveals that 80.0% of students in the intervention group and 87.9% of the comparison group were aged 24-26 years. Female students constituted 64.5% in the intervention group compared to 57.6% in the comparison group. Regarding the duration of the clinical practicum course, 67.7% of pre-licensure nursing students in the intervention group had five months of experience compared to 57.6% of the students in comparison one. No significant statistical differences between the two groups in relation to their age, gender and duration of internship (FET= 1.575, P = 0.758; FET= 2.055, P = 0.633 and FET= 1.873, P = 0.631, respectively).Fig. 3 Intern-nursing students’ socio-demographic characteristics FET: fisher exact test significant at P ≤ 0.05. Fig 3 The effect of the PFA on COVID-19-related psychological symptoms is shown in Table 1 . Significant reductions in self-reported COVID-19 related feelings are evident comparing the immediately after and two-months follow-up as P-values for the intervention group in comparison to the control group for: depression (immediately after: 0.003, follow-up: 0.046); unhappiness (immediately after: 0.005, follow-up: <0.001); lack of ability to concentrate (immediately after: 0.009, follow-up: 0.008); enjoyment of day-to-day activities (immediately after: 0.021, follow-up: 0.001); and overcoming difficulties (immediately after: <0.001, follow-up:0.046).Table 1 Comparison between Intern-Nursing Students' COVID-19-related Psychological Distress Symptoms before, during, and after Psychological First- aid Intervention (PFA). Table 1 Baseline Sig. Immediately after Sig. Follow-up Sig. Statement Intervention group Comparison group Intervention group Comparison group Intervention group Comparison group Felt under strain 1.68 ± 0.95 1.97 ± 0.95 0.216 1.65 ± 0.88 1.79 ± 0.96 0.493 1.35 ± 0.71 1.67 ± 0.96 0.139 Felt depressed 2.23 ± 1.12 1.94 ± 1.12 0.258 1.26 ± 0.77 2.12 ± 1.22 0.003* 1.45 ± 0.85 1.94 ± 1.09 0.047* Felt worthless 2.26 ± 1.13 2.67 ± 0.65 0.188 1.74 ± 0.97 2.15 ± 1.09 0.049* 1.39 ± 0.67 2.21 ± 1.08 0.001 Not feeling happy 2.26 ± 1.06 2.64 ± 0.70 0.149 1.48 ± 0.93 2.21 ± 1.02 0.005 1.29 ± 0.74 2.21 ± 0.96 <0.001*** Not enjoying day-to-day activities 2.52 ± 0.85 2.76 ± 0.50 0.343 1.71 ± 0.94 2.27 ± 0.94 0.021* 1.52 ± 0.81 2.24 ± 0.87 0.001* Lost confidence 2.45 ± 0.93 2.42 ± 0.83 0.703 1.68 ± 0.91 2.06 ± 1.12 0.135 1.87 ± 0.85 2.09 ± 1.13 0.282 Could not overcome difficulties 2.71 ± 0.74 2.33 ± 0.96 0.048* 1.29 ± 0.78 2.27 ± 1.10 <0.001*** 1.35 ± 0.84 1.91 ± 1.21 0.046* Lost sleep 2.58 ± 0.85 2.15 ± 1.09 0.064 1.48 ± 0.85 2.18 ± 1.01 0.004 1.52 ± 1.03 1.67 ± 1.22 0.592 Not playing a useful role 1.97 ± 1.33 2.82 ± 0.47 0.006 1.58 ± 0.92 2.15 ± 1.15 0.032* 1.68 ± 0.91 1.97 ± 1.21 0.262 Could not make decisions 1.97 ± 1.25 2.30 ± 1.08 0.324 1.87 ± 0.99 2.12 ± 1.17 0.293 1.71 ± 0.86 1.94 ± 1.25 0.320 Could not concentrate 2.97 ± 0.18 2.39 ± 1.00 0.054 1.71 ± 1.13 2.42 ± 0.90 0.009* 1.35 ± 1.17 2.18 ± 1.13 0.008 Could not face problems 2.90 ± 0.54 2.06 ± 1.30 0.031* 1.65 ± 1.23 2.42 ± 1.00 0.009* 1.29 ± 1.27 2.24 ± 1.15 0.004 ZMW: Mann Whitney Test Significant at P≤0.05 P<0.01 P<0.001 Fig. 4 demonstrates the total mean scores of students for psychological distress. There was a significant reduction in the overall mean score of psychological distress after the implementation of the intervention among the PFA group (28.45 ± 0.40, 17.77 ± 0.96, and 19.0 ± 0.79, respectively) compared to the comparison one (28.48 ± 0.35, 24.27 ± 0.45, and 26.18 ± 0.61, respectively). The p-values were ZMW=-0.094, p = 0.903, ZMW=-2.69, p = 0.007, and ZMW=-3.26, p < 001 for the three measurement periods.Fig. 4 Total mean scores of students' psychological distress The total score ranged from 0-to 36. A higher score represents a higher level of psychological distress, Z: Mann Whitney Test Significant at P < 0.01 P < 0.001. Fig 4 Table 2 displays significant improvements in the students' resilience capacity in terms of their ability to deal with whatever comes their way, and coping with stress can make them stronger (P = 0.049, and 0.034 immediately after the intervention and in the follow-up PFA; P = 0.034, and 0.045). Moreover, students believe in achieving goals even if there are obstacles, and their abilities to deal with life's challenges and difficulties showed a significant improvement (p < 0.001) immediately after the intervention and in the follow-up PFA; P = 0.040 and 0.009 for both groups).Table 2 Comparison between Intern-Nursing Students' Resilience Capacity Profile before, during, and after Psychological First- aid Intervention (PFA). Table 2Statement Baseline Sig. Immediately After Sig. Follow-up Sig. Intervention group Comparison group Intervention group Comparison group Intervention group Comparison group I am able to adapt when changes occur 0.84 ± 0.86 0.67 ± 0.74 0.466 1.65 ± 0.88 0.79 ± 0.96 0.015 1.45 ± 0.71 1.06 ± 1.09 0.022* I can deal with whatever comes my way 0.65 ± 0.88 0.88 ± 0.74 0.116 1.26 ± 0.77 0.73 ± 0.98 0.049* 1.45 ± 0.85 1.15 ± 1.12 0.034* I try to see the humorous side of things when I am faced with problems 0.52 ± 1.06 0.45 ± 0.71 0.748 1.74 ± 0.97 0.73 ± 1.04 0.032* 1.59 ± 0.67 0.88 ± 1.29 0.04* Having to cope with stress can make me stronger 0.84 ± 0.86 0.67 ± 0.74 0.466 1.48 ± 0.93 0.85 ± 1.00 0. 034* 1.39 ± 0.74 0.88 ± 0.99 0.045* I tend to bounce back after illness, injury, or other hardships 0.94 ± 1.12 0.97 ± 1.10 0.796 1.71 ± 0.94 0.91 ± 1.10 0.091 1.52 ± 0.81 1.52 ± 1.39 0.110 I believe I can achieve my goals, even if there are obstacles 0.29 ± 0.46 0.61 ± 0.79 0.108 1.68 ± 0.91 0.73 ± 1.04 <0.001*** 1.87 ± 0.85 1.06 ± 1.37 0.040* Under pressure, I stay focused and think clearly 0.71 ± 0.69 0.67 ± 0.89 0.526 1.29 ± 0.78 0.91 ± 1.01 0.003** 1.55 ± 0.84 1.15 ± 1.25 0.035* I am not easily discouraged by failure 1.06 ± 1.03 1.03 ± ± 0.95 0.977 1.48 ± 0.85 1.15 ± 1.09 0.002 1.52 ± 1.03 1.36 ± 1.25 0.521 I think of myself as a strong person when dealing with life's challenges and difficulties 0.32 ± 0.65 0.67 ± 1.14 0.213 1.58 ± 0.92 0.73 ± 1.15 <0.001* 1.68 ± 0.91 1.15 ± 1.44 0.009* I am able to handle unpleasant or painful feelings like sadness, fear, and anger 0.42 ± 0.56 0.52 ± 0.62 0.546 1.87 ± 0.99 0.82 ± ± 0.88 <0.001*** 1.71 ± 0.86 1.03 ± 1.10 0.120 ZMW: Mann Whitney Test Significant at P≤0.05 P<0.01 P<0.001 Fig. 5 demonstrates that on the baseline measurement, the overall mean scores were 7.12 ± 0.42 for the PFA group and 6.58 ± 0.58 for the comparison one, with no significant differences between both groups (P = 0.787). However, the intervention group's mean scores were significantly raised immediately after the PFA compared to the comparison group (12.94 ± 1.13 and 8.33 ± 0.77), (P < 0.001). At two-months follow-up, a slight decline in the resilience capacity among the students in the PFA group was observed among the intervention group at 20.58 ± 0.64, compared to 11.24 ± 0.96 for the comparison one, and the p-value was 0.003.Fig. 5 Total mean scores of students' resilience capacity before, during, and after the PFA The total score ranged from 0 to 40, as the higher score indicates the higher resilience capacity, ZMW: Mann Whitney Test Significant at P < 0.01 *P < 0.001. Fig 5 Discussion The rapid proliferation of COVID-19 infection has resulted in an extraordinary global psychological health emergency, particularly among healthcare workers [3]. Therefore, this study demonstrated the promising effect of attending the PFA to mitigate the devastating impact of the COVID-19 crisis on the pre-licensure nursing students' psychological health status and improve their resilience capacity. Globally, these objectives were achieved by demonstrating an empathetic and compassionate attitude and adopting a package of maneuvers such as stress reduction techniques, cognitive reframing, hope installation, and the mobilization of the participants' support system. Our hypothesis related to reducing the psychological distress level among pre-licensure nursing students who attended the PFA was accepted. This finding could be partly attributed to the empathetic and compassionate attitudes paired with using reflective listening. This eventually would encourage the cathartic ventilation of the psychologically distressed pre-licensure nursing students throughout the entire PFA, as the participants would feel quite visible and their voices would be heard. Hence, they freely described their emotional suffering during the clinical practicum course period amidst the COVID-19 crisis. These positive ramifications can be considered in the same line with previous studies’ results, which asserted that respecting individuality, showing understanding, valuing concerns, and acknowledging the feelings of distress are essential for preserving the nurses’ psychological health during the pandemic [20,21]. In this sense, the research team accurately perceives and understands the pre-licensure student's feelings on a deeper level and communicates that understanding back to the pre-licensure students. The researchers have committed to being visible and available and stand as an emotional mirror or as a reflection of the pre-licensure student's negative facial expression. The student then explains why he/she does not appear to be happy. In the ensuing talk, the researchers leaned forward, maintain eye contact, and react to the pre-licensure student's emotional concerns with both verbal and nonverbal ways of communication, exhibiting that they followed their talk and conveyed their emotional support toward the pre-licensure student's suffering. The researchers then suggest alternative ways to pin down the student's ongoing overwhelmed feeling and replace 'cannot feel happy with 'feel down'. In this extract, the researchers attempt to provide a more precise description of the student's feelings of unhappiness related to the COVID-19 pandemic and thus seem to have more accurate knowledge about the contents of the student's ongoing feelings. It was hypothesized that practicing stress reduction techniques could cultivate the pre-licensure -nursing students’ control over their own emotions. In this case, the PFA allows them to respond more rationally, rather than being hijacked by their automatic psychological responses. [8] (2020) reported that practicing breathing exercises can help create a space between rising psychological pressure and one's reaction to it. This, in turn, detaches individuals from the stressful situations they find themselves in and relieves their psychological burden. Yu Liu et al. [23] claimed that deep breathing exercises could effectively de-escalate the body in response to a traumatic event. Such exercises stimulate the parasympathetic response and release endorphins that induce emotional calm and foster relaxation and positive feelings [39]. The World Health Organization [44] also reported that practicing stress reduction techniques during the pandemic was highly valuable in recovering the healthcare providers from sense of psychological trauma, and protecting their psychological safety. The PFA also showed promise in improving the total mean scores of the resilience capacity of the pre-licensure nursing students. These findings could stem from PFA tactics providing them with mental filtration opportunities and enabling them to reframe their thoughts. This consequently enabled them to view the harsh reality of the COVID-19 pandemic from another perspective. For instance, the acceptance that the entire world is mutually fighting against a common enemy of COVID-19 and exploring its related pathological changes helped contextualize the problems they were facing. Developing the relevant therapeutic management protocols and pursuing the manufacturing of a vaccine could nurture an optimistic view toward the upcoming future [31]. The PFA tactics empowered the pre-licensure nursing students to be cognitively flexible and try to see any positive aspect in this adversity rather than focusing only on the negative ones. Moreover, acknowledging nurses’ sacrifices amidst the increased risk of contagion while being committed to allaying the sufferings of patients with COVID-19 infection and saving lives has proven positive impacts on nurses’ psychological recovery [16]. Courses of ten online PFA sessions strengthen participants’ resilience capacity through hope installation and positive outlooks. Thus, the PFA participants might have hopeful expectations that COVID-19 will eventually fade away and pass [22,29]. The acquired skills and competencies developed during the PFA implementation were even retained, as shown by the two-month follow-up results, despite the expected slight declines. This finding reflects the effectiveness of the content and maneuvers of the PFA [34]. From another perspective, the PFA is devoted to enlisting the availability of the participants’ support system. This considerably enhances the mutual openness and allows the psychologically distressed students to recover from the lived COVID-19 crisis during the clinical practicum course period. Everly et al. [13] asserted that an interpersonal support system's availability would be the single and most potent factor in cherishing the resilience of those affected by adversity and trauma. Practice implications Acknowledging the optimistic impact of the PFA on enhancing the psychological health status and resilience capacity among the pre-licensure nursing students, it is feasible to implement the PFA for other groups of healthcare providers especially those who are working in the frontline. Undoubtedly, such a vulnerable sector encounters unforeseen stressful situations every day [10]. The adequate psychological intervention requires well-trained PFA providers who possess advanced survival skills. Beside, delivering the appropriate, timely, actionable counsel that guarantee the optimal mental health and tailored psychosocial support required acknowledging the interconnected physical and psychological ramifications of the exceptional events [27]. Therefore, rescuing the health care providers with PFA intervention can help in alleviating the unfavorable psychological impact of health-related disasters, foster their psychological recovery, rebuild and affirm their resilience following the disaster period [11]. Nurses are indispensable health care providers who are actively involved in health-related disaster management [16]. As undertaken for a variety of catastrophic situations around the world in recent years, the Egyptian health care discipline should embed the prompt PFA intervention within the hospital policy and guidelines. Clearly, these policies must allocate responsibilities for core PFA actions to the health care providers at all levels. In accordance with the National Institute of Mental Health (NIMH) recommendations, reintegrating PFA into a multi-faceted disaster mental health response is urged. These includes validated assessments of disaster survivors, identification of those at high risk of developing psychopathology, and referrals for mental health care specialists [42]. Limitations and strengths The study had limitations in considering a small sample size of pre-licensure nursing students, which is overcome with random assignments of cases. Providing the comparison group with the routine psychological support such as practicing self-compassion, and mindfulness exercises might be considered as a confounding factor. Beside, the short timeframe for follow-up outcome measures is only two months limits our ability to determine the longer-term impacts of the intervention. Despite this limitation, the present study magnets strength from the finding that displays the positive effect of the PFA on mitigating distress and enhancing resilience. Conclusion The Psychological First- aid Intervention effectively fostered the pre-licensure nursing students’ recovery from the COVID-19 related psychological distress and improves their resilience capacity. The RAPID model application is recommended to reduce stress and prevent burnout among novice and future nurses who show signs of psychological exhaustion. Declarations Ethical approval and consent to participate Faculty of Nursing, Alexanderia University Ethics committee provided ethical approval for this study. Participants in the current study consented to participate and to their data being used for research purposes only. Consent for publication Not Applicable. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Funding Not Applicable. Ethical approval The Research Ethical Committee of the Faculty of Nursing approved the study. CRediT authorship contribution statement Rasha Salah Eweida: Investigation, Conceptualization, Methodology, Data curation, Writing – original draft, Writing – review & editing, Supervision. Zohour Ibrahim Rashwan: Conceptualization, Formal analysis, Data curation, Writing – review & editing. Leena Mohammad Khonji: Writing – review & editing. Abdullah Abdulrahman Bin Shalhoub: Writing – review & editing. Nashwa Ibrahim: Data curation, Writing – original draft, Writing – review & editing, Supervision. Declaration of Competing Interest On behalf of all authors, the corresponding author states that there is no conflict of interest. Acknowledgments The authors are grateful to all pre-licensure nursing students who were appreciated to participate in this study. ==== Refs References 1 Abdulghani H. Irshad M. Al Zunitan M. AlSulihem A. Al Dehaim M. Al Esiafir W. 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==== Front J Clin Anesth J Clin Anesth Journal of Clinical Anesthesia 0952-8180 1873-4529 Elsevier Inc. S0952-8180(22)00392-0 10.1016/j.jclinane.2022.111034 111034 Editorial Burnout and depression in anesthesiology trainees: A timely assessment to guide a roadmap for change Mohamed Basma MBChB Fahy Brenda G. MD, MCCM ⁎ Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL 32610, USA ⁎ Corresponding author at: Department of Anesthesiology, University of Florida College of Medicine, PO Box 100254, Gainesville, FL 32610-0254, USA. 5 12 2022 5 2023 5 12 2022 85 111034111034 23 11 2022 26 11 2022 © 2022 Elsevier Inc. All rights reserved. 2022 Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcThe COVID-19 pandemic has shed light on the need for systems-based interventions to reduce burnout in physicians, an occupational health phenomenon resulting from excessive work-related stress [1]. Anesthesiologists and critical care physicians are at increased risk of burnout due to the high acuity of the level of patient care in their professions: making quick clinical decisions when managing critically ill patients and leading crisis management responses while providing clinical coverage that requires frequent night shifts and long work hours. At least 50% of anesthesiologists and critical care physicians experience one major dimension of burnout [2,3]. Physician trainees in anesthesiology must learn a large volume of knowledge in addition to developing cognitive and technical skills. In addition, anesthesiology trainees must efficiently integrate themselves into the perioperative team while maintaining adequate communication skills and resilience while working with a variety of personalities. The common pressures of residency led to burnout prior to COVID-19, but the pandemic resulted in unprecedented stress on healthcare professionals, including physician trainees, related to work-life balance, unpredictable schedules, and professional acuity [4]. In a national survey of all anesthesiology trainees, Bui et al. [5] reevaluated the prevalence of burnout and depression in anesthesiology trainees in the United States nearly a decade after their first evaluation in 2013. In addition, the authors desired to investigate the risk factors contributing to burnout and depression among anesthesiology residents utilizing an abbreviated version of the validated Maslach Burnout Inventory to assess job-related burnout. The two questions selected from the assessment previously showed consistency in evaluating the emotional exhaustion and depersonalization dimensions of burnout. Furthermore, the authors included questionnaires to assess for depression, medical errors, family support, work characteristics (eg, work hours), and questions related to the COVID-19 pandemic. The overall rate of high burnout risk for anesthesiology trainees was 24%, lower than the rate of 41% in 2013 despite the survey occurring in February 2021 during the COVID-19 pandemic. In addition, the screening rate for depression was lower: 15% compared to 22% in 2013. The authors postulated the decrease in rates of high-risk burnout and depression prevalence was related to the efforts of various residency programs to institute the 2014 Accreditation Council for Graduate Medical Education (ACGME) recommendations that included a focus on professionalism with specific attention to maintaining personal, emotional, physical, and mental health. Furthermore, the ACGME recommendations emphasized the importance of trainee well-being as essential to developing a competent and resilient physician [6] and encouraged residency programs to evaluate residents for their well-being, implement interventions as required, and assess the impact of these interventions on the residents' burnout levels. To remain in compliance with these recommendations, multiple interventions were implemented in training programs, including anesthesiology residency programs, to improve rates of burnout among physician trainees [7]. These interventions ranged from organizational initiatives that targeted duty hours and analyzed workflows to individual-focused strategies like mindfulness-based curricula and cognitive and behavioral interventions [7]. Bui et al. found working more than 70 h a week and overnight calls were independent risk factors for increased odds of burnout and independently associated with depression [5], findings similar to the results shown in 2013. Working longer hours continues to be a common risk factor for burnout and depression. In a systematic review by Chong et al., 5 out of 12 included studies showed prolonged working hours as the precipitating factor for burnout [3]. These findings were similar to a recent survey of members of the American Society of Anesthesiology that found longer working hours were a driver for burnout among anesthesiologists, among other factors [2]. Additionally, Sun et al. found that longer work hours were associated with higher risks of distress and depression [8]. These findings indicate well-being interventions addressing organizational and systems-based factors, such as rescheduling shifts and reducing duty hours and workload, may impact a trainee's well-being more effectively than individual-based interventions such as mindfulness courses and stress-coping strategies [3,7]. Despite the reported reduction in the Bui et al. survey [5], burnout rates in anesthesiology trainees continue to be high and comparable to other specialties. The high burnout rate is concerning due to the association with higher rates of suicide and substance use in the field of anesthesiology. Residency training poses daily challenges that can be distressing. Anesthesiology residency is particularly stressful given the diverse clinical assignments, intense clinical experiences, and need to adapt to high-acuity environments based on the level of training. Although the clinical anesthesiology first year (CA1) initially poses a steep learning curve, many CA1 residents achieve their learning goals during the second half of the training year. However, the clinical anesthesiology second year (CA2) training is challenging given the different subspecialty training during that year [7]. Understanding the stressors during residency training at different levels may be required to guide future well-being interventions. Lisann-Goldman et al. [7] evaluated individual-based well-being interventions focusing on cognitive behavioral therapy and a mindfulness stress reduction strategy. The authors found many barriers to implementing individual-based interventions that required additional time on the part of the residents in addition to their clinical demands. In addition, the authors noticed the lack of interest in participating in mindfulness-based stress reduction sessions and cognitive behavioral therapy, thus making these interventions unfeasible to implement. The issues of non-feasibility and lack of effectiveness with individual-based interventions have prompted the shift to alternative strategies identifying burnout risk factors and tailoring interventions to address burnout based on data derived from survey results. As a result, organization-based interventions have been proposed that would focus on eliminating the 24-h call, creating long shifts followed by a day free of duties, and expanding evening shifts for certified registered nurse anesthetists and/or certified anesthesiology assistants. However, despite the need for organization-based interventions, many programs face barriers to implementing these changes due to production pressure and increased surgical volume at different institutions. Therefore, advocating for additional staffing, including expanding the residency program, may be of value to many anesthesiology residencies. In addition to the prevalence of burnout, Bui et al. reported the relationship between residents' burnout and the poor quality of patient care [5]. In a meta-analysis reviewing 82 studies, provider burnout was negatively associated with quality of care, including medical errors, patient satisfaction, quality indicators, and perceptions of safety [9]. These findings emphasize the importance of prioritized well-being interventions at the institutional level for all healthcare providers, including physician trainees, as many healthcare institutions aim for improved quality and safety of patient care. As institutions create strategies to improve patient care, stakeholders should focus on clinician well-being incorporated into the overall mission of enhanced quality and safety. These strategies may allow program directors and executive leaders to invest in organization-based interventions that will address the economic costs of physician burnout for their healthcare organizations [10]. Reevaluation of the prevalence of burnout and depression among anesthesiology trainees during the COVID-19 pandemic reflected lower rates compared to the 2013 survey as well as the current burnout burden after two years of the pandemic. However, highlighting this issue to address residents' well-being may serve as a starting point for many more anesthesiology residency programs to formally address their trainees' burnout levels and use the survey results as a guide to reevaluate the levels of burnout longitudinally after implementing interventions. Individual-based interventions in anesthesiology may not be feasible given the additional barriers of time constraints and perioperative scheduling. Focusing efforts on organization-based interventions that reduce long work hours and optimize staffing patterns with regards to well-being may be more impactful on anesthesiology trainees' burnout as well as on practicing anesthesiologists' quality of patient care. However, individual-based interventions that improve physicians' resilience while not requiring additional time in residency might be the focus of future studies. Some potential avenues for these individual-based interventions could focus on mitigating anesthesiology trainees' fatigue and chronic sleep deprivation, training to improve work efficiency, and developing clinical learning environments that minimize the time needed to study. CRediT authorship contribution statement Basma Mohamed: Conceptualization, Writing – original draft, Writing – review & editing. Brenda G. Fahy: Conceptualization, Data curation, Methodology, Writing – original draft, Writing – review & editing. Declaration of Competing Interest The authors declare no conflicts of interest. ==== Refs References 1 World Health Organization Burn-out an “occupational phenomenon”: International Classification of Diseases https://www.who.int/news/item/28-05-2019-burn-out-an-occupational-phenomenon-international-classification-of-diseases 2019 [accessed 20 November 2022] 2 Afonso A.M. Cadwell J.B. Staffa S.J. Zurakowski D. Vinson A.E. Burnout rate and risk factors among anesthesiologists in the United States Anesthesiology. 134 2021 683 696 10.1097/ALN.0000000000003722 33667293 3 Chong M.Y.F. Lin S.H.X. Lim W.Y. Ong J. Kam P.C.A. Ong S.G.K. Burnout in anaesthesiology residents: a systematic review of its prevalence and stressors Eur J Anaesthesiol 39 2022 368 377 10.1097/EJA.0000000000001585 34397509 4 Gupta N. Dhamija S. Patil J. Chaudhari B. Impact of COVID-19 pandemic on healthcare workers Ind Psychiatry J 30 2021 S282 S284 10.4103/0972-6748.328830 34908710 5 Bui D. Winegarner A. Kendall M.C. Almeida M. Apruzzese P. De Oliveria G. Burnout and depression among anesthesiology trainees in the United States: an updated national survey J Clin Anesth 84 2022 110990 10.1016/j.jclinane.2022.110990 6 Accreditation Council for Graduate Medical Education Common program requirements https://www.acgme.org/what-we-do/accreditation/common-program-requirements/ 2022 [accessed 20 November 2022] 7 Lisann-Goldman L. Cowart C. Lin H.M. Orlando B. Mahoney B. Well-being in anesthesiology graduate medical education: reconciling the ideal with reality Anesthesiol Clin 40 2022 383 397 10.1016/j.anclin.2022.01.011 35659409 8 Sun H. Warner D.O. Macario A. Zhou Y. Culley D.J. Keegan M.T. Repeated cross-sectional surveys of burnout, distress, and depression among anesthesiology residents and first-year graduates Anesthesiology. 131 2019 668 677 10.1097/ALN. 0000000000002777 31166235 9 Salyers M.P. Bonfils K.A. Luther L. Firmin R.L. White D.A. Adams E.L. The relationship between professional burnout and quality and safety in healthcare: a meta-analysis J Gen Intern Med 32 2017 475 482 10.1007/s11606-016-3886-9 27785668 10 Shanafelt T.D. Physician well-being 2.0: where are we and where are we going? Mayo Clin Proc 96 2021 2682 2693 10.1016/j.mayocp.2021.06.005 34607637	36470000	PMC9719879	NO-CC CODE	2022-12-06 23:23:45	no	J Clin Anesth. 2023 May 5; 85:111034	utf-8	J Clin Anesth	2,022	10.1016/j.jclinane.2022.111034	oa_other
==== Front iScience iScience iScience 2589-0042 The Authors. S2589-0042(22)01993-9 10.1016/j.isci.2022.105720 105720 Article The SARS-CoV-2 spike S375F mutation characterizes the Omicron BA.1 variant Kimura Izumi 120 Yamasoba Daichi 1220 Nasser Hesham 3420 Zahradnik Jiri 520 Kosugi Yusuke 1620 Wu Jiaqi 7820 Nagata Kayoko 9 Uriu Keiya 16 Tanaka Yuri L. 10 Ito Jumpei 1 Shimizu Ryo 3 Tan Toong Seng 11 Butlertanaka Erika P. 10 Asakura Hiroyuki 12 Sadamasu Kenji 12 Yoshimura Kazuhisa 12 Ueno Takamasa 11 Takaori-Kondo Akifumi 9 Schreiber Gideon 5 the Genotype to Phenotype Japan (G2P-Japan) Consortium Toyoda Mako 11 Shirakawa Kotaro 9 Irie Takashi 13 Saito Akatsuki 101415∗ Nakagawa So 78∗∗ Ikeda Terumasa 3∗∗∗ Sato Kei 168161718192122∗∗∗∗ 1 Division of Systems Virology, Department of Microbiology and Immunology, the Institute of Medical Science, the University of Tokyo, Tokyo 1088639, Japan 2 Faculty of Medicine, Kobe University, Kobe 6500017, Japan 3 Division of Molecular Virology and Genetics, Joint Research Center for Human Retrovirus Infection, Kumamoto University, Kumamoto 8600811, Japan 4 Department of Clinical Pathology, Faculty of Medicine, Suez Canal University, Ismailia 41511, Egypt 5 Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 76100, Israel 6 Graduate School of Medicine, the University of Tokyo, Tokyo 1130033, Japan 7 Department of Molecular Life Science, Tokai University School of Medicine, Isehara 2591193, Japan 8 CREST, Japan Science and Technology Agency, Kawaguchi 3220012, Japan 9 Department of Hematology and Oncology, Graduate School of Medicine, Kyoto University, Kyoto 6068507, Japan 10 Department of Veterinary Science, Faculty of Agriculture, University of Miyazaki, Miyazaki 8892192, Japan 11 Division of Infection and Immunity, Joint Research Center for Human Retrovirus Infection, Kumamoto University, Kumamoto 8600811, Japan 12 Tokyo Metropolitan Institute of Public Health, Tokyo 1690073, Japan 13 Institute of Biomedical and Health Sciences, Hiroshima University, Hiroshima 7398511, Japan 14 Center for Animal Disease Control, University of Miyazaki, Miyazaki 8892192, Japan 15 Graduate School of Medicine and Veterinary Medicine, University of Miyazaki, Miyazaki 8891692, Japan 16 International Research Center for Infectious Diseases, the Institute of Medical Science, the University of Tokyo, Tokyo 1088639, Japan 17 International Vaccine Design Center, the Institute of Medical Science, the University of Tokyo, Tokyo 1088639, Japan 18 Graduate School of Frontier Sciences, the University of Tokyo, Kashiwa 2778561, Japan 19 Collaboration Unit for Infection, Joint Research Center for Human Retrovirus Infection, Kumamoto University, Kumamoto 8600811, Japan ∗ Corresponding author ∗∗ Corresponding author ∗∗∗ Corresponding author ∗∗∗∗ Corresponding author 20 These authors contributed equally 21 Twitter: @SystemsVirology 22 Lead contact 5 12 2022 22 12 2022 5 12 2022 25 12 105720105720 25 8 2022 15 10 2022 30 11 2022 © 2022 The Authors 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Recent studies have revealed the unique virological characteristics of Omicron, particularly those of its spike protein, such as less cleavage efficacy in cells, reduced ACE2 binding affinity, and poor fusogenicity. However, it remains unclear which mutation(s) determine these three virological characteristics of Omicron spike. Here, we show that these characteristics of the Omicron spike protein are determined by its receptor-binding domain. Of interest, molecular phylogenetic analysis revealed that acquisition of the spike S375F mutation was closely associated with the explosive spread of Omicron in the human population. We further elucidated that the F375 residue forms an interprotomer pi-pi interaction with the H505 residue of another protomer in the spike trimer, conferring the attenuated cleavage efficiency and fusogenicity of Omicron spike. Our data shed light on the evolutionary events underlying the emergence of Omicron at the molecular level. Graphical abstract Molecular biology; Virology Subject areas Molecular biology Virology Published: December 22, 2022 ==== Body pmcIntroduction Since the emergence of SARS-CoV-2 at the end of 2019, this virus has become spectacularly diverse. In April 2022, the WHO defined two variants of concern, Delta (B.1.617.2 and AY lineages) and Omicron (originally the B.1.1.529 lineage, then reclassified into BA lineages)1; currently, Omicron is the predominant variant spreading worldwide. Even before detection of the Omicron B.1.1.529 lineage at the end of November 2021 in South Africa,2 SARS-CoV-2 had become highly diversified from the original lineage, the B lineage, which was isolated in Wuhan, China, on December 24, 2019 (strain Wuhan-Hu-1, GISAID ID: EPI_ISL_402123).3 Regarding the evolutionary scenario leading to the emergence of Omicron, the B.1 lineage, which had acquired the D614G mutation in the spike (S) protein,4 , 5 , 6 , 7 , 8 was first reported on January 24, 2020 (GISAID ID: EPI_ISL_451345). Thereafter, the B.1.1 lineage was first reported in England on February 16, 2020 (GISAID ID: EPI_ISL_466615). The B.1.1 lineage is the common ancestor of both Alpha (B.1.1.7 lineage), a prior variant of concern by March 2022, and Omicron (B.1.1.529 lineage), and the Alpha variant caused a large surge of infection worldwide beginning in the fall of 2020.9 Omicron was first reported in South Africa on September 30, 2021 (GISAID ID: EPI_ISL_7971523).2 Soon after the press briefing on Omicron emergence on November 25, 2021,2 the virological characteristics of Omicron, currently designated BA.1 (i.e., B.1.1.529.1 lineage, hereafter referred to as Omicron in this study), were intensively investigated. For example, Omicron exhibits profound resistance to the humoral immunity induced by vaccination and natural SARS-CoV-2 infection.10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 In addition, we demonstrated that the Omicron spike (S) protein is less prone to cleavage by furin, a cellular protease, and exhibits poor fusogenicity.18 , 22 Moreover, we showed that the binding affinity of the receptor-binding domain (RBD) of Omicron S to human ACE2 is significantly lower than that of ancestral B.1 S.14 , 23 However, it remains unclear why Omicron has spread so rapidly worldwide. In particular, although the explosive infectious spread of Omicron in the human population can be mainly characterized by the virological properties of Omicron S, the mutation(s) in Omicron S that are responsible for its virological characteristics, such as inefficient S cleavage, lower fusogenicity, reduced ACE2 binding affinity and profound immune resistance, have not been well elucidated. In this study, we first demonstrate that the representative characteristics of Omicron S, such as immune resistance, poor S cleavage efficiency and poor fusogenicity, are determined by its RBD. Based on molecular phylogenetic analysis, we show that acquisition of the S375F mutation in the Omicron RBD is closely associated with its explosive spread. Moreover, we experimentally demonstrate that the S375F mutation is critical for the virological properties of Omicron S, namely, attenuation of S cleavage efficiency and fusogenicity as well as the decrease in ACE2 binding affinity. Furthermore, we determined how attenuated S cleavage and fusogenicity are conferred by the S375F mutation. Results The Omicron RBD determines the major virological features of the Omicron variant To determine the mutation(s) responsible for the virological features of Omicron, we prepared a series of expression plasmids for Omicron S-based chimeric mutants with swapping of the N-terminal domain (NTD) and/or RBD of B.1 (D614G-bearing strain) S (Figure 1A). Experiments showed that pseudoviruses with B.1 RBD-bearing Omicron S [Omicron S/B.1 S_RBD (spike 4 in Figure 1A) and Omicron S/B.1 S_NTD+RBD (spike 5)] exhibited increased infectivity compared to pseudovirus with Omicron S (spike 2) in HOS-ACE2/TMPRSS2 cells (Figure 1B) and A549-ACE2 cells (Figure S1A). Western blot analysis (Figure 1C) showed that the S cleavage efficacy in cells (Figure 1D, left) correlated with the level in virion-incorporated S2 protein (Figure 1D, right) and pseudovirus infectivity (Figure 1B). In particular, the cleavage efficacy of Omicron S was lower than that of B.1 S, which is consistent with our recent studies (spikes 1 and 2 in Figures 1C and 1D).18 , 22 , 23 On the other hand, chimeric Omicron S proteins bearing the B.1 RBD (spikes 4 and 5) displayed increased cleavage efficacy (Figures 1C and 1D). Although the surface expression levels of a series of Omicron S chimeras bearing the B.1 domains (spikes 3–5) were lower than those of Omicron S chimeras (Figure 1E), a cell-based fusion assay18 , 22 , 23 , 24 revealed that the fusogenicity of B.1 RBD-bearing Omicron S was significantly higher than parental Omicron S (Figure 1F). To verify the importance of the RBD for the phenotype of Omicron S, we performed reversal experiments based on B.1 S [B.1 S/Omicron S_RBD (spike 6) in Figure 1A]. Corresponding to the results for Omicron S, the pseudovirus infectivity (Figure 1B), S cleavage efficacy (Figures 1C and 1D), and fusogenicity (Figure 1F) of Omicron RBD-harboring S [B.1 S/Omicron S_RBD (spike 6)] were attenuated compared to those of parental B.1 S. These results suggest that the RBD of Omicron S mainly determines the attenuated cleavage efficacy and decreased fusogenicity of Omicron S.Figure 1 Virological properties conferred by the Omicron RBD (A) Scheme of S chimeras used in this study. The numbers in parentheses are identical to those in Figures 1B–1E and 2. NTD, N-terminal domain; RBD, receptor-binding domain; TMD, transmembrane domain. (B) Pseudovirus assay. HIV-1-based reporter viruses pseudotyped with SARS-CoV-2 S chimeras (summarized in Figure 1A) were prepared. The pseudoviruses were inoculated into HOS-ACE2/TMPRSS2 cells at 1 ng HIV-1 p24 antigen, and the percentages of infectivity compared to that of the virus pseudotyped with B.1 S (spike 1) are shown. (C and D) Western blot. Representative blots of S-expressing cells and supernatants (C) and quantified band intensity (the ratio of S2 to the full-length S plus S2 proteins for “cell”; the ratio of S2 to HIV-1 p24 for “supernatant”) (D) are shown. M, mock (empty vector-transfected). Uncropped blots are shown in Figure S4. (E) Flow cytometry. The summarized results of the surface S expression are shown. MFI, mean fluorescent intensity; M, mock (empty vector-transfected). (F) SARS-CoV-2 S-based fusion assay. The fusion activity was measured as described in the STAR Methods, and fusion activity (arbitrary units) is shown. For the target cells, HEK293 cells expressing ACE2 and TMPRSS2 (filled) and HEK293 cells expressing ACE2 (open) were used. The results for B.1 S or Omicron S are shown in other panels as black and green lines, respectively. The results in HEK293-ACE2/TMPRSS2 cells and HEK293-ACE2 cells are shown as normal or broken lines, respectively. (G) Scheme of the S-chimeric recombinant SARS-CoV-2 used in this study. FCS, furin cleavage site. The backbone is SARS-CoV-2 strain WK-521 (GISAID ID: EPI_ISL_408667, A lineage).25 Note that the ORF7a gene is swapped with the sfGFP gene. The numbers in parentheses are identical to those in Figures 1H–1K. (H–J) SARS-CoV-2 infection. VeroE6/TMPRSS2 cells were infected with a series of chimeric recombinant SARS-CoV-2 (shown in G) at MOI (m.o.i.) 0.01. Viral RNA in the supernatant (H) and GFP intensity (I) were measured using routine techniques. Note that the yaxes of the graphs shown in H are log scales. The result for Omicron (virus II) is shown in other panels as a broken green line. (J) Syncytium formation. Left, GFP-positive area at 48 h.p.i. Scale bar, 500 μm. Right, summarized results. I, n = 6,483 cells; II, n = 5,393 cells; III, n = 8,704 cells; IV, n = 13,188 cells; and V, n = 12,749 cells. Representative images are shown in Figure S1. (K) Plaque assay. Left, representative figures.Right, summary of the plaque diameters (20 plaques per virus). Data are expressed as the mean with SD (B, D-F, and H–K) or the median with 95% confidence interval (CI) (J). Assays were performed in quadruplicate (B, H, and I) or triplicate (D–F). Each dot indicates the result of an individual replicate (B, D and E) or an individual plaque (K). Statistically significant differences (∗p <0.05) versus Omicron S (pseudovirus 2 for B, D and E, virus II for J and K) were determined by two-sided Student’s t test (B and E), two-sided paired t test (D), or two-sided Mann–Whitney U test (J and K). In F, H and I, statistically significant differences versus Omicron (spike 2 or virus II) [∗familywise error rates (FWERs)<0.05] (except for the rightmost panel in F) or B.1 (spike 1 or virus I) [#familywise error rates (FWERs)<0.05] (rightmost panel in F) through timepoints were determined by multiple regression. FWERs were calculated using the Holm method. See also Figures S1 and S4. To further investigate the impact of the Omicron S RBD on multicycle viral replication, we generated a series of recombinant chimeric SARS-CoV-2 strains by reverse genetics (Figure 1G).25 As shown in Figure 1H, the growth of rOmicron S-GFP (virus II) and rOmicron S/B.1 S_NTD (virus III) was lower than that of rB.1 S-GFP (virus I). In sharp contrast, recombinant viruses bearing the B.1 RBD [rOmicron S/B.1_RBD-GFP (virus IV) and rOmicron S/B.1 S_NTD+RBD-GFP (virus V)] replicated more efficiently than rOmicron S-GFP (virus II) in VeroE6/TMPRSS2 cells (Figure 1H). In addition, to monitor the spread of these recombinant viruses, we measured GFP intensity in infected cell cultures. We found that the GFP intensity of cells infected with recombinant viruses bearing the B.1 RBD was significantly higher than that of cells infected with rOmicron S-GFP (virus II) (Figures 1I and S1B). These data suggest that the RBD of Omicron S attenuates viral growth capacity in cell cultures. We then measured the number of GFP-positive cells to evaluate the fusogenicity of the chimeric viruses. As shown in Figure 1J, the GFP-positive area of cells infected with the recombinant viruses at 48 h post infection (h.p.i.) was significantly larger for viruses bearing the B.1 RBD [rOmicron S/B.1_RBD-GFP (virus IV) and rOmicron S/B.1 S_NTD+RBD-GFP (virus V)] than for rOmicron-GFP (virus II). Consistent with the results in cells transfected with S expression plasmids (Figure 1F), these findings suggest that the Omicron RBD attenuates viral fusogenicity. Moreover, the plaques formed by infection with rOmicron S/B.1 S_RBD-GFP (virus IV) and rOmicron S/B.1 S_NTD+RBD-GFP (virus V) were significantly larger than those formed by rOmicron S-GFP virus (virus II), though the plaques formed by rOmicron S-GFP (virus II) and rOmicron S/B.1 S_NTD-GFP (virus III) were comparable (Figure 1K). Altogether, these results suggest that the Omicron RBD determines the virological features of this viral lineage, such as the observed attenuation of S1/S2 cleavage efficacy and fusogenicity. The Omicron RBD mainly determines the immune resistance of Omicron We next assessed the domains of Omicron S that are associated with the profound immune resistance of Omicron.10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 Because swapping of Omicron S with the B.1 S NTD (Omicron S/B.1 S_NTD, spike 3) severely decreased pseudovirus infectivity (Figure 1B), we performed neutralization assays using pseudoviruses with Omicron RBD-bearing B.1 S [Omicron S/B.1 S_RBD (spike 4)] and Omicron S/B.1 S_NTD+RBD (spike 5) as well as the S proteins of Omicron (spike 2), Delta and B.1 (spike 1) (the list of sera used is shown in Table S1). Consistent with recent studies,10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 Omicron S (spike 2) was highly resistant to the vaccine sera [BNT162b2 (Figure 2A) and mRNA-1273 (Figure 2B)] as well as convalescent sera from individuals infected with early-pandemic virus (collected before May 2020) (Figure 2C) or the Delta variant (Figure 2D). Pseudoviruses with the Omicron S/B.1 S_RBD (spike 4) and Omicron S/B.1 S_NTD+RBD (spike 5) were significantly more sensitive to vaccine sera (Figures 2A and 2B) and convalescent sera obtained from early-pandemic virus-infected patients than was Omicron S (spike 2) (Figure 2C). These results suggest that the RBD of Omicron S is closely associated with its pronounced resistance to the antiviral humoral immunity elicited by vaccination or previous SARS-CoV-2 infection. Moreover, we used convalescent sera from hamsters infected with B.1.1 (note that the S gene sequences of B.1 and B.1.1 are identical) and Omicron, as collected in our previous study,22 for the assay. As shown in Figure 2E, Omicron S (spike 2) was completely resistant to the B.1.1 convalescent sera, whereas it was sensitive to the Omicron convalescent sera. Notably, chimeric Omicron S bearing the B.1 RBD [Omicron S/B.1 S_RBD (spike 4) and Omicron S/B.1 S_NTD+RBD (spike 5)] exhibited the opposite results: these chimeric pseudoviruses were sensitive to the B.1.1 convalescent sera (Figure 2E) but completely resistant to the Omicron convalescent sera (Figure 2F). These results further suggest that the Omicron RBD determines its immune resistance and is an immunodominant epitope for inducing humoral immunity. However, we found that Omicron S/B.1 S_NTD+RBD (spike 5) is significantly more sensitive to antisera than is Omicron S/B.1 S_RBD (spike 4) (Figures 2A–2C and 2E). These findings suggest that mutations in the NTD of Omicron S are also partly associated with the immune resistance of Omicron S.Figure 2 Immune resistance conferred by the Omicron RBD Neutralization assays were performed with pseudoviruses harboring a series of S protein sequences (summarized in Figure 1A). The numbers are identical to those in Figure 1A. D, Delta variant. Vaccinated sera [BNT162b2 (A, 11 donors); or mRNA-1273 (B, 16 donors)], convalescent sera of individuals infected with an early pandemic virus (before May 2020) (C, 12 donors), or Delta (D, 10 donors) and convalescent sera of hamsters infected with B.1.1 (E, 6 hamsters)22 or Omicron (F, 6 hamsters)22 were used. The list of sera used in this experiment is shown in Table S1. Each serum sample was analyzed in triplicate to determine the 50% neutralization titer (NT50). Each dot represents one NT50 value, and the geometric mean and 95% CI are shown. The numbers indicate the fold changes of resistance versus each antigenic variant. Horizontal gray lines indicate the detection limit of each assay (120 for A and C–F; 40 for B). Statistically significant differences between spikes 4 and 5 were determined by a two-sided Wilcoxon signed-rank test. See also Table S1. The S S375F mutation increases binding affinity to human ACE2 Twelve substitutions are uniquely present in the RBD (residues 319–541) of Omicron S; another 3 substitutions (K417N, T478K and N501Y) are common among the other variants (Figure 3A).18 To determine the residue(s) responsible for the virological phenotype of Omicron, particularly the reduced binding affinity of the Omicron S RBD to human ACE2,14 , 23 we prepared a series of B.1 S RBD point mutants that bear the respective mutations of Omicron and conducted screening experiments based on a yeast surface display assay.14 , 23 , 24 , 26 , 27 As shown in Figure 3B (left panel), compared to the RBD of parental (i.e., B lineage-based) S, the KD values of the G339D, N440K and S477N mutants were significantly decreased, whereas those of the S375F, S371L/S373P/S375F, G496S and Y505H mutants were significantly increased. Altogether, these data suggest that the S375F, G496S and Y505H substitutions are closely associated with the reduced binding affinity of the Omicron S RBD to human ACE2.Figure 3 Mutations in the Omicron RBD and the evolution of Omicron (A) Structural insights into the mutations in the Omicron RBD. Left, overlaid cryo-EM structures of SARS-CoV-2 B.1 S (PDB: 7KRQ)28 (white) and Omicron S (PDB: 7T9J)29 (green) are shown. The NTD and RBD are indicated in blue. The region in the RBD indicated by a square is enlarged in the top right panel. Right, mutated residues in the RBD. The residues in B.1 S and Omicron S are shown in black and red, and the mutations in Omicron S are indicated. (B) ACE2 binding affinity of a series of SARS-CoV-2 S RBD (residues 336–528) mutants tested by yeast surface display. The KD values of the binding of the SARS-CoV-2 S RBD expressed on yeast to soluble ACE2 are shown. (C and D) Evolution of Omicron. (C) Top, a time tree of 44 Omicron variants and two outgroups (B and B.1 lineages). The same tree annotated with the GISAID ID, PANGO lineage and sampling date at each terminal node is shown in Figure S2. Green, Omicron variants containing the S371L, S373P and S375F mutations; blue, Omicron variants containing the S371L and S373P mutations; black, Omicron variants without the S371L/S373P/S375F mutations; and gray, the two outgroups (B and B.1 lineages). The bars on each internal node indicate the 95% highest posterior density (HPD) interval of the estimated time. The size of the circle on each internal node is proportional to the value of posterior probability. Note that “Node 1” corresponds to the time to before the emergence of the S371L and S373P mutations; “Node 2” corresponds to the time after the acquisition of the S371L and S373P mutations and before the emergence of the S375F mutations; and “Node 3” corresponds to the fixation time of the S371L/S373P/S375F mutations in the Omicron variants. The estimated time of each node is as follows: Node 1, September 23, 2021 (95% HPD August 26, 2021 to October 10, 2021); Node 2, October 8, 2021 (95% HPD September 24, 2021 to October 21, 2021); and Node 3, October 16, 2021 (95% HPD October 5, 2021 to October 23, 2021). Bottom, distribution of the posterior probability of the time to the tMRCA of Node 1 (black), Node 2 (blue), and Node 3 (green). (D) Bayesian skyline plot showing the history of the effective population size of 44 Omicron variants. The 95% HPD is shaded in green. The dot (in gray) indicates the estimated tMRCA of the 44 variants (October 5, 2021), and the error bar (in gray) indicates the lower (August 13, 2021) and upper (October 23, 2021) boundaries of the 95% HPD tMRCA. In B, the data are expressed as the mean with SD. The assay was performed in triplicate, and each dot indicates the result of an individual replicate. The horizontal broken lines indicate the value of B.1 S (left) and Omicron S (right), respectively. Statistically significant differences (∗p <0.05) versus B.1 S (left) or Omicron S (right) were determined by two-sided Student’s t tests, and FWERs were calculated using the Holm method. In C and D, the estimated time of S375F emergence [i.e., between “Node 2” and “Node 3” (October 8–16, 2021) in C] is shaded in dark red. The lower and upper boundaries of the 95% HPD tMRCA of “Node 2" and “Node 3”, respectively (i.e., September 24 to October 23, 2021) is shaded in light red. See also Figure S2. Omicron emergence is closely associated with acquisition of the S S375F mutation The S375F, G496S and Y505H mutations in the S protein are almost exclusive to Omicron variants (Table S2). To infer the evolutionary sequence of the emergence of these mutations in the Omicron lineage, we generated a time tree of 44 Omicron genomes detected in 2021 (for more detail, see STAR Methods) (Figures 3C and S2). The G496S and Y505H mutations were detected in all sequences used in this analysis, suggesting that these two mutations were present in the common ancestor of all Omicron variants reported thus far. In contrast, the S371L, S373P and S375F mutations are not present in the older Omicron sequences (shown in black in Figures 3C and S2). Although the emergence times of S371L and S373P cannot be estimated independently, our analysis assumed that the S371L and S373P mutations were first acquired between Node 1 [95% highest posterior density (HPD): August 26, 2021 to October 10, 2021] and Node 2 (95% HPD: September 24, 2021 to October 21, 2021) in Figure 3C, as based on the estimated time to the most recent common ancestor (tMRCA). The S375F mutation emerged thereafter, between Node 2 and Node 3 (95% HPD: October 5, 2021 to October 23, 2021) (Figure 3C). Of interest, the Bayesian skyline plot of the 44 Omicron genomes suggested that the effective population size of Omicron increased around the time of S375F substitution acquisition (Figure 3D). These data suggest that the emergence of the S375F mutation might have been a crucial event triggering the massive spread of Omicron variants in the human population. To verify the possibility that the S375F mutation is crucial for the phenotype of Omicron, we performed yeast binding assays using the RBD of Omicron S. As depicted in Figure 3B (right panel), the F375S and L371S/P373S/F375S mutations in the RBD of Omicron S significantly increased binding affinity to human ACE2. Overall, these observations suggest that the three substitutions at positions 371, 373 and 375, particularly the S375F substitution, determine the reduced binding affinity of the Omicron S RBD to human ACE2. The S S375F mutation determines the S cleavage efficacy, fusogenicity, and ACE2 binding affinity of the Omicron variant To investigate the impact of the S375F mutation, we prepared pseudoviruses with a series of Omicron S-based mutations (Figure 4A). In the yeast surface display assay (Figure 3B), the assay based on Omicron S showed that pseudovirus infectivity was clearly increased by the Omicron S F375S mutation (spikes 9 and 11–13 in Figure 4A) (Figure 4B, top). Western blot analysis showed that the S1/S2 cleavage efficacy and level of S2 in virions were rescued by the F375S mutation (Figures 4C and 4D, top). Similar to the results illustrated in Figures 1C and 1D, the mutated S proteins that were efficiently cleaved in cells (e.g., spikes 9 and 11–13) were also efficiently incorporated into the viral particles released (Figures 4C and 4D). These results indicate that the level of virion-incorporated S2 is modulated by the S cleavage efficacy in producer cells and that pseudovirus infectivity can be an indicator of the level of S protein cleavage in producer cells. Although the surface S expression level was decreased by the F375S mutation (Figure 4E, top), a cell-based fusion assay demonstrated that the mutation significantly increased the efficacy of SARS-CoV-2 S-mediated cell–cell fusion (Figure 4F, top). Conversely, the assay based on B.1 S showed that the S375F mutation (spikes 16 and 18–20) decreased pseudovirus infectivity (Figure 4B, bottom), S cleavage efficacy (Figures 4C and 4D, bottom) and fusion activity (Figure 4F, bottom). These results suggest that the S375F mutation in Omicron S is responsible for the decreased S cleavage efficacy in producer cells and the attenuated fusogenicity observed. However, the S371L/S373P/S375F mutations did not affect sensitivity to the antiviral humoral immunity elicited by vaccination and infection (Figure S3), suggesting that the S375F mutation is not associated with the immune resistant phenotype of Omicron.Figure 4 Virological features conferred by the S S375F mutation (A) Scheme of the S mutants used in this study. The numbers in parentheses are identical to those in Figures 4B–4F and S3. (B) Pseudovirus assay. HIV-1-based reporter viruses pseudotyped with SARS-CoV-2 S mutants (summarized in A) were prepared. The pseudoviruses were inoculated into HOS-ACE2/TMPRSS2 cells at 1 ng HIV-1 p24 antigen, and the percent infectivity compared to that of the virus pseudotyped with Omicron S (spike 2, top) or B.1 S (spike 1, bottom) are shown. (C and D) Western blot. Representative blots of S-expressing cells and supernatants (C) and quantified band intensity (the ratio of S2 to the full-length S plus S2 proteins for “cell”; the ratio of S2 to HIV-1 p24 for “supernatant”) (D) are shown. M, mock (empty vector-transfected). Uncropped blots are shown in Figure S4. (E) Flow cytometry. The summarized results of the surface S expression are shown. (F) SARS-CoV-2 S-based fusion assay. The fusion activity was measured as described in STAR Methods, and fusion activity (arbitrary units) is shown. For the target cells, HEK293 cells expressing ACE2 and TMPRSS2 (filled) and HEK293 cells expressing ACE2 (open) were used. The results for Omicron S (top) or B.1 S (bottom) are shown in other panels as green and black lines, respectively. The results in HEK293-ACE2/TMPRSS2 cells and HEK293-ACE2 cells are shown as normal and broken lines, respectively. Data are expressed as the mean with SD. Assays were performed in quadruplicate (B) or triplicate (D–F). In B, D and E, each dot indicates the result of an individual replicate. Statistically significant differences (∗p <0.05) versus the respective parental S [Omicron S (pseudovirus 2, top panels) or B.1 S (spike 1, bottom panels)] were determined by two-sided Student’s t test (B and E) or two-sided paired t test (D). In F, statistically significant differences (∗FWERs<0.05) versus the respective parental S [Omicron S (spike 2, top panels) or B.1 S (spike 1, bottom panels)] through timepoints were determined by multiple regression. FWERs were calculated using the Holm method. See also Figures S3 and S4. To further assess the impact of the S375F mutation, we generated two additional recombinant chimeric SARS-CoV-2 strains, B.1 S S375F-GFP (virus VI) and Omicron S F375S-GFP (virus VII) (Figure 5A). Although the mutation at position 375 of the S protein did not affect the viral RNA load in the culture supernatant of infected VeroE6/TMPRSS2 cells (Figure 5B), the GFP intensity in infected VeroE6/TMPRSS2 cells was significantly altered by this mutation: the S375F mutation in the B.1 S backbone decreased the GFP intensity, whereas the F375S mutation in the Omicron S backbone increased the intensity (Figures 5C and S1). In addition, quantitative fluorescence microscopy showed that the GFP-positive area of B.1 S S375F-GFP (virus VI) was significantly lower than that of parental B.1 S-GFP (virus I); however, that of Omicron S F375S-GFP (virus VII) was significantly higher than that of parental Omicron S-GFP (virus II) (Figure 5D). Moreover, plaque assays showed that the plaques formed by infection with B.1 S S375F-GFP (virus VI) were significantly smaller than those formed by B.1 S-GFP (virus I); conversely, plaque size was increased by the presence of the F375S mutation in Omicron S (Figure 5E). Altogether, these results suggest that the S375F mutation in the Omicron S protein determines the major virological characteristics (i.e., decreased S1/S2 cleavage efficacy, decreased fusogenicity, and decreased ACE2 binding affinity) of Omicron.Figure 5 Effect of the S S375F mutation on viral growth dynamics (A) Scheme of the S-chimeric recombinant SARS-CoV-2 used in this study. The numbers in parentheses are identical to those in Figures 5B–5E. (B–D) SARS-CoV-2 infection. VeroE6/TMPRSS2 cells were infected with a series of S-chimeric recombinant SARS-CoV-2 (summarized in A) at an m.o.i. 0.01. The viral RNA in the supernatant (B) and GFP intensity (C) were measured routinely. Note that the yaxes of the graphs shown in B are log scales. The results for the respective parental S are shown in other panels as broken green lines. Assays were performed in quadruplicate (B and C). (D) Syncytium formation. Left, GFP-positive area at 48 h.p.i. Scale bar, 500 μm. Right, summarized results. I, n = 6,483 cells; VI, n = 2,780 cells; II, n = 5,393 cells; and VII, 12,857 cells. The results for B.1-GFP (virus I) and Omicron-GFP (virus II) in C and D (right) are identical to those shown in Figures 1I and 1J (right). Representative images are shown in Figure S1. (E) Plaque assay. Left, representative figures.Right, summary of the plaque diameters (20 plaques per virus). Each dot indicates the result of an individual plaque. Data are expressed as the mean with SD (B, C, and E) or the median with 95% CI (D). In B and C, statistically significant differences (∗FWERs<0.05) versus Omicron-GFP (virus II) through timepoints were determined by multiple regression. FWERs were calculated using the Holm method. In D and E, statistically significant differences (∗p <0.05) versus Omicron-GFP (virus II) were determined by a two-sided Mann–Whitney U test. See also Figure S1. F375-H505 pi-pi interaction contributes to the decreased cleavage efficacy and fusogenicity of Omicron S Here, we experimentally demonstrate that the S375F mutation attenuates the cleavage efficacy and fusogenicity of Omicron S (Figures 4 and 5). In addition, molecular phylogenetic analysis suggested that the emergence of this mutation was closely associated with the explosive growth of Omicron in the human population (Figures 3C and 3D). Nevertheless, it remains unclear how the S375F mutation contributes to the decrease in cleavage efficacy and fusogenicity of Omicron S at the molecular level. We addressed this question using a structural biology approach. As shown in Figure 6A (top), we predicted that the F375 residue in a fully closed Omicron S trimer could form a pi-pi interaction, a sort of dispersion via van der Waals forces between aromatic residues,30 with the H505 residue in another S protein of the same trimer. Importantly, the cryo-EM structure of the Omicron BA.1 S protein has been determined.31 The result demonstrated that the interprotomer interaction mediated by the F375 and H505 residues of Omicron S causes the S trimer conformation to be more rigid and leads to less cleavage efficacy, supporting our prediction. Because residue 375 in the B.1.1 S protein is a serine, the pi-pi interaction cannot be formed (Figure 6A, bottom). To address the hypothesis that the F375-H505-mediated interprotomer pi-pi interaction contributes to the decreased cleavage efficacy and fusogenicity of Omicron S, we prepared the Omicron S H505A mutant, in which an aromatic side chain at position 505 is disrupted. Western blot analysis showed that the cleavage efficacy of Omicron S was increased by the insertion of the H505A mutation (Figure 6B). To further test this possibility, the residues at position 375 of B.1 S were substituted with amino acids bearing aromatic side chains (i.e., F, Y and H). Similar to the S375F mutant, the B.1 S mutants bearing the S375Y or S375H mutation showed decreased S protein cleavage efficacy (Figure 6C). These results further suggest that the interprotomer pi-pi interaction is formed between Y505 and S375F/Y/H. Moreover, insertion of the Y505A mutation in B.1 S bearing the S375F/Y/H mutation (i.e., disruption of the aromatic residue at position 505) rescued the S cleavage efficacy (Figure 6C).Figure 6 Effect of the pi-pi interaction between 375F and 505H (A) Structural insights into the SARS-CoV-2 S trimer. Top, the structure of the Omicron S trimer (PDB: 7T9J)29 reconstructed as described in the STAR Methods. Bottom, cryo-EM structure of the B.1 S trimer (PDB: 7KRQ).28 The regions indicated in squared are enlarged in the bottom right panels. In the enlarged panels, the residues at position 375 [F in an Omicron S monomer indicated in green (top); S in a B.1 S monomer indicated in black (bottom)] and 505 [H in an Omicron S monomer indicated in white (top); Y in a B.1 S monomer indicated in white (bottom)] are shown. The putative pi-pi interaction between F375 and H505 in the Omicron S trimer is indicated in red (3.9 Å) (B and C) Western blot. Representative blots of S-expressing cells (top) and quantified band intensity (the ratio of S2 to the full-length S plus S2 proteins) (bottom) are shown. In the bottom panels, the residues at positions 375 and 505 are indicated, and aromatic residues (F, H or Y) are indicated in red. Uncropped blots are shown in Figure S4. (D and E) Flow cytometry. The summarized results of the surface S expression are shown. (F and G) SARS-CoV-2 S-based fusion assay. The fusion activity was measured as described in the STAR Methods, and fusion activity (arbitrary units) is shown. For the target cells, HEK293 cells expressing ACE2 and TMPRSS2 (filled) and HEK293 cells expressing ACE2 (open) were used. In F, normal lines, Omicron S with HEK293-ACE2/TMPRSS2 cells; broken lines, Omicron S with HEK293-ACE2 cells. In the panels of S375F, S375Y and S375H in G, normal black lines, B.1 S using HEK293-ACE2/TMPRSS2 cells; broken black lines, B.1 S using HEK293-ACE2 cells; normal red lines. In the panels for S375F/Y505A, S375Y/Y505A and S375H/Y505A in G, normal red line, the result for the respective mutant without the Y505A mutation using HEK293-ACE2/TMPRSS2 cells; broken red line, the result for the respective mutant without the Y505A mutation using HEK293-ACE2 cells. Data are expressed as the mean with SD (B–G). Assays were performed in triplicate (B, D–G) or sextuplicate (C). In B–E, each dot indicates the result of an individual replicate. Statistically significant differences versus Omicron S (∗p <0.05) and between the mutant with and without the Y505A mutation (#p <0.05) were determined by two-sided paired t test (B and C) or two-sided Student’s t test (D and E). In F and G, statistically significant differences versus Omicron S (∗FWERs<0.05) or the mutant without the Y505A mutation (#FWERs<0.05) through timepoints were determined by multiple regression. FWERs were calculated using the Holm method. See also Figure S4. Finally, we verified the impact of the interprotein pi-pi interaction on S-mediated fusogenicity. The Omicron S F375S mutant exhibited decreased surface expression, but the H505A mutation did not (Figure 6D). In the case of the B.1 S-based mutants, the Y505A mutation decreased surface expression levels when the S375F/Y mutations were also present (Figure 6E). Corresponding to western blot results (Figure 6B), disruption of the pi-pi interaction by F375S and H505A in Omicron S significantly increased fusion activity (Figure 6F). Moreover, in the case of the B.1 S-based mutant, substitution of residue 375 with an aromatic residue (F, Y or H) significantly reduced fusion activity (Figure 6G). However, when the Y505A substitution was present in the S375F/Y/H mutants, disrupting the aromatic residue at position 505, fusion activity was significantly increased (Figure 6G). Altogether, our results suggest that the interprotomer pi-pi interaction mediated by the aromatic residues at positions 375 and 505 of the S protein contributes to the decreased cleavage efficacy and fusogenicity of Omicron S. Discussion In the present study, we performed multiscale investigations to unveil the virological characteristics of the S protein of the SARS-CoV-2 Omicron variant, including (1) profound immune resistance, (2) decreased cleavage efficacy in cells, (3) poor fusogenicity, and (4) reduced ACE2 binding affinity. By using pseudoviruses, a yeast surface display system and the chimeric recombinant SARS-CoV-2 generated by reverse genetics, we showed that the RBD of Omicron S is responsible for these four virological features of this variant. In particular, the S375F mutation in the RBD of Omicron S is one of the most critical mutations that determine three of the four major virological properties of Omicron: decreased affinity to ACE2, attenuated efficacy of S cleavage, and reduced fusogenicity. Moreover, molecular phylogenetic analysis provided evidence suggesting that the acquisition of the S375F mutation was closely related to the onset of the explosive spread of Omicron in the human population (Figure 3C). Furthermore, experiments based on structural biology revealed that the pi-pi interaction mediated by residues F375 and H505 is responsible for the observed decreased cleavage efficacy in cells and fusogenicity. We and others demonstrate that the Omicron S RBD shows reduced binding affinity to human ACE2.14 , 23 In this study, our mutagenesis experiment revealed that the S375F, G496S and Y505H substitutions are responsible for this reduced binding affinity of the Omicron S RBD to human ACE2 (Figure 3B). Considering the importance of ACE2 binding in viral replication, it is intriguing how the Omicron variant acquired high transmissibility with decreased ACE2 binding. It may be reasonable to speculate that evasion from the preexisting immunity induced by previous infection or vaccination was the priority for the evolution of Omicron. We revealed that the nascent pi-pi interaction of the Omicron S trimer is established by the F375 and H505 residues and characterizes Omicron S. After the initial submission of this study, structural analysis by cryo-electron microscopy (cryo-EM) showed that the interprotomer interaction mediated by the F375 and H505 residues of Omicron S firms the S trimer conformation and leads to reduced cleavage efficacy,31supporting our experimental results in this study. Because the Y505H mutation was already present in the ancestral Omicron sequences, our results suggest that acquisition of the S375F mutation during the evolution of Omicron resulted in attenuated S cleavage efficacy and fusogenicity in SARS-CoV-2 S protein, which led to the explosive spread of Omicron in the human population. The S375F mutation is highly conserved in the Omicron lineage and has not been detected in the other SARS-CoV-2 variants. However, our data suggest that substitution of residues possessing an aromatic ring, such as phenylalanine, tyrosine and histidine, at residue 375 may confer Omicron-like properties. Therefore, the emergence of SARS-CoV-2 variants bearing such substitutions at residue 375 should be considered a potential risk for health of the global population. Our previous studies suggested a close association between viral fusogenicity and pathogenicity.22 , 23 , 32 For example, Omicron S is less susceptible to cleavage than parental B.1.1 S harboring the D614G mutation.18 , 22 This decreased S1/S2 cleavage is associated with a reduction in the fusogenicity of Omicron S and attenuates the pathogenicity of Omicron variant.22 Here, we demonstrate that S cleavage efficacy and fusogenicity are determined by S375F mutation in the RBD of Omicron S (Figures 1C, 1F, 4C and 4F). Therefore, it is likely that acquisition of the S375F mutation in the S protein may, at least partially, contribute to the attenuated pathogenicity of the Omicron variant. Further investigation will be required to determine whether the S375F mutation is critical for viral pathogenicity because this mutation is present among more than 30 changes. Here, we show the importance of the S375F mutation to the major virological properties of Omicron S, particularly its decreased cleavage efficacy, poor fusogenicity, and reduced ACE2 binding affinity. However, the following issues remain to be fully elucidated. First, although we showed that the S375F mutation determines a part of the virological features of Omicron S, it remains unclear which mutations in Omicron S determine its pronounced immune resistance. We showed that the RBD of Omicron S is closely associated with its resistance to the humoral immunity induced by vaccination and natural SARS-CoV-2 infection (Figure 2), yet there are dozen substitutions in the Omicron S RBD (Figure 3B). Therefore, it would be reasonable to assume that multiple substitutions in the RBD cooperatively contribute to the profound immune resistance of Omicron S. Second, in addition to the Omicron BA.1 variant that we focused on this study, a variety of Omicron subvariants, such as BA.2 and BA.5, have emerged, and these subvariants also bear the S375F mutation. However, we have recently shown that the fusogenicity of BA.2 S is significantly higher than that of BA.1 S.23 Together with the results of this study, these observations suggest that BA.2 S has acquired certain compensatory mutation(s) that increase fusion efficacy. Further investigations will be needed to unveil the full evolutionary history of the Omicron lineage. Furthermore, the question of why acquisition of the S375F mutation caused explosive spread despite reduced infectivity in tissue culture, S cleavage efficacy and fusogenicity also needs to be elucidated in detail by further studies. In summary, our multiscale investigations reveal that the major virological characteristics of Omicron S, namely, attenuated S cleavage efficacy, attenuated fusogenicity, and reduced ACE2 binding affinity, are determined by one specific mutation, S375F, in the RBD. Assays based on structural biology revealed that the pi-pi interaction mediated by residues F375 and H505 is responsible for the observed attenuated S cleavage efficacy and fusogenicity. Furthermore, the molecular phylogenetic analysis suggested that acquisition of the S375F mutation was closely associated with the massive spread of Omicron in the human population. Altogether, our results suggest that acquisition of the S375F mutation was a crucial event for the emergence of a highly transmissible SARS-CoV-2 variant, Omicron. Limitations of the study In this study, we used lentivirus-based pseudovirus to examine impact mutations on S packaging; thus, the phenotype observed in this study may differ from the S2 incorporation occurring in authentic SARS-CoV-2 virions. However, our recent finding with authentic SARS-CoV-2 virions demonstrated that the S2 incorporation pattern of BA.1 was significantly lower than that of the Delta variant,18 reproducing the low S2 incorporation of BA.1 S observed in this study. Therefore, these data suggest that lentivirus-based pseudovirus can be used to examine the S2 incorporation pattern of authentic SARS-CoV-2 virions. Further investigations, such as the identification of S protein incorporation in lentivirus-based pseudoviruses, are required to solve this issue. In addition, it is reported that the Omicron variants deposited early contain artifactual reversions possibly derived from contamination of non-Omicron (mainly Delta) variants due to the low affinity of primers for sequencing.33 , 34 Although such low-quality Omicron genomes were removed in this analysis (see STAR Methods), we cannot exclude the possibility that some genomes may contain artifactual reversions, which would affect the phylogenetic results. Consortia The Genotype-to-Phenotype Japan (G2P-Japan) Consortium, Keita Matsuno, Naganori Nao, Hirofumi Sawa, Mai Kishimoto, Shinya Tanaka, Masumi Tsuda, Lei Wang, Yoshikata Oda, Marie Kato, Zannatul Ferdous, Hiromi Mouri, Kenji Shishido, Takasuke Fukuhara, Tomokazu Tamura, Rigel Suzuki, Hayato Ito, Naoko Misawa, Shigeru Fujita, Mai Suganami, Mika Chiba, Ryo Yoshimura, Yasuhiro Kazuma, Ryosuke Nomura, Yoshihito Horisawa, Yusuke Tashiro, Yugo Kawai, Ryoko Kawabata, MST Monira Begum, Otowa Takahashi, Kimiko Ichihara, Chihiro Motozono, and Maya Shofa. STAR★Methods Key resources table REAGENT or RESOURCE SOURCE IDENTIFIER Antibodies Rabbit anti-SARS-CoV-2 S S1/S2 polyclonal antibody (1:100 for FACS) Thermo Fisher Scientific Cat# PA5-112048; RRID: AB_2866784 Normal rabbit IgG (1:100 for FACS) SouthernBiotech Cat# 0111-01; RRID: AB_2732899 APC-conjugated goat anti-rabbit IgG polyclonal antibody (1:50 for FACS) Jackson ImmunoResearch Cat# 111-136-144; RRID: AB_2337987 Mouse anti-SARS-CoV-2 S monoclonal antibody (clone 1A9) (1:10,000 for immunoblotting) GeneTex Cat# GTX632604; RRID: AB_2864418 Mouse anti-HIV-1 p24 monoclonal antibody (clone 183-H12-5C) (1:2,000 for immunoblotting) NIH HIV Reagent Program Cat# ARP-3537; RRID: AB_2819250 Rabbit anti-beta actin (ACTB) monoclonal antibody (clone 13E5) (1:5,000 for immunoblotting) Cell Signaling Cat# 4970; RRID: AB_2223172 Mouse anti-tubulin (TUBA) monoclonal antibody (clone DM1A) (1:10,000 for immunoblotting) Sigma-Aldrich Cat# T9026; RRID: AB_477593 HRP-conjugated horse anti-mouse IgG antibody (1:2,000 for immunoblotting) Cell Signaling Cat# 7076S; RRID: AB_330924 HRP-conjugated donkey anti-rabbit IgG polyclonal antibody (1:10,000 for immunoblotting) Jackson ImmunoResearch Cat# 711-035-152; RRID: AB_10015282 HRP-conjugated donkey anti-mouse IgG polyclonal antibody (1:10,000 for immunoblotting) Jackson ImmunoResearch Cat# 715-035-150; RRID: AB_2340770 Bacterial and virus strains Recombinant SARS-CoV-2, rB.1 S-GFP (Saito et al.32; Yamasoba et al.23) N/A Recombinant SARS-CoV-2, rOmicron S-GFP (Yamasoba et al.23) N/A Recombinant SARS-CoV-2, rOmicron S/B.1 S_NTD-GFP This study N/A Recombinant SARS-CoV-2, rOmicron S/B.1 S_RBD-GFP This study N/A Recombinant SARS-CoV-2, rOmicron S/B.1 S_NTD+RBD-GFP This study N/A Recombinant SARS-CoV-2, rB.1 S S375F-GFP This study N/A Recombinant SARS-CoV-2, rOmicron S F375S-GFP This study N/A Biological samples Human sera (see details in Table S1) This study N/A Chemicals, peptides, and recombinant proteins TransIT-LT1 Transfection Reagent Takara Cat# MIR2300 TransIT-293 Transfection Reagent Takara Cat# MIR2700 Recombinant RNase inhibitor Takara Cat# 2313B Carboxymethyl cellulose Wako Cat# 039-01335 4% paraformaldehyde phosphate buffer solution Nacalai Tesque Cat# 09154-85 Methylene blue Nacalai Tesque Cat# 22412-14 Fetal bovine serum Sigma-Aldrich Cat# 172012-500ML Penicillin-streptomycin Sigma-Aldrich Cat# P4333-100ML DMEM (high glucose) Sigma-Aldrich Cat# 6429-500ML DMEM (low glucose) Wako Cat# 041-29775 Expi293 expression medium Thermo Fisher Scientific Cat# A1435101 Ham’s F-12K Wako Cat# 080-08565 Puromycin Sigma-Aldrich Cat# P9620-10ML Blasticidin InvivoGen Cat# ant-bl-1 G418 Nacalai Tesque Cat# G8168-10ML KpnI New England Biolab Cat# R0142S NotI New England Biolab Cat# R1089S PEI Max Polysciences Cat# 24765-1 Nonidet P40 substitute Nacalai Tesque Cat# 18558-54 Protease inhibitor cocktail Nacalai Tesque Cat# 03969-21 Protein assay dye Bio-Rad Cat# 5000006 4 × NuPAGE LDS sample buffer Thermo Fisher Scientific Cat# NP0007 Doxycycline Takara Cat# 1311N TURBO DNase Thermo Fisher Scientific Cat# AM2238 Triton X-100 Nacalai Tesque Cat# 35501-15 EnduRen live cell substrate Promega Cat# E6481 Glycerol Nacalai Tesque Cat# 17018-25 Soluble human ACE2 (residues 18–740) (Yamasoba et al.23) N/A SARS-CoV-2 B.1 S RBD (Kimura et al.27; Motozono et al.24; Yamasoba et al.23) N/A SARS-CoV-2 B.1 S RBD G339D This study N/A SARS-CoV-2 B.1 S RBD S371L This study N/A SARS-CoV-2 B.1 S RBD S373P This study N/A SARS-CoV-2 B.1 S RBD S375F This study N/A SARS-CoV-2 B.1 S RBD S371L/S373P/S375F This study N/A SARS-CoV-2 B.1 S RBD N440K This study N/A SARS-CoV-2 B.1 S RBD G446S This study N/A SARS-CoV-2 B.1 S RBD S477N This study N/A SARS-CoV-2 B.1 S RBD E484A This study N/A SARS-CoV-2 B.1 S RBD Q493R This study N/A SARS-CoV-2 B.1 S RBD G496S This study N/A SARS-CoV-2 B.1 S RBD Q498R This study N/A SARS-CoV-2 B.1 S RBD Y505H This study N/A SARS-CoV-2 Omicron S RBD (Dejnirattisai et al.14; Yamasoba et al.23) N/A SARS-CoV-2 Omicron S RBD L371S This study N/A SARS-CoV-2 Omicron S RBD P373S This study N/A SARS-CoV-2 Omicron S RBD F375S This study N/A SARS-CoV-2 Omicron S RBD L371S/P373S/F375S This study N/A Bilirubin Sigma-Aldrich Cat# 14370-1G CF®640R succinimidyl ester Biotium Cat# 92108 Critical commercial assays QIAamp viral RNA mini kit Qiagen Cat# 52906 NEB next ultra RNA library prep kit for Illumina New England Biolabs Cat# E7530 MiSeq reagent kit v3 Illumina Cat# MS-102-3001 OneStep TB Green PrimeScript PLUS RT-PCR kit Takara Cat# RR096A SARS-CoV-2 direct detection RT-qPCR kit Takara Cat# RC300A Nano Glo HiBiT lytic detection system Promega Cat# N3040 KAPA HiFi HotStart ReadyMix kit Roche Cat# KK2601 PrimeSTAR GXL DNA polymerase Takara Cat# R050A Bright-Glo luciferase assay system Promega Cat# E2620 One-Glo luciferase assay system Promega Cat# E6130 SuperSignal West Femto Maximum Sensitivity Substrate Thermo Fisher Scientific Cat# 34095 SuperSignal West Atto Ultimate Sensitivity Substrate Thermo Fisher Scientific Cat# A38554 Western BLoT UltraSensitive HRP Substrate Takara Cat# T7104A GENEART site-directed mutagenesis system Thermo Fisher Scientific Cat# A13312 ACE2 activity assay kit SensoLyte Cat# AS-72086 Deposited data Viral genome sequencingdata of working viral stocks (see Table S4) DDBJ Sequence Read Archive Accession number: PRJDB13805 Experimental models: Cell lines Human: HEK293T cells ATCC CRL-3216 Human: HEK293 cells ATCC CRL-1573 Human: HEK293-C34 cells (Torii et al.25) N/A Human: Expi293F cells Thermo Fisher Scientific Cat# A14527 Human: HOS-ACE2/TMPRSS2 cells (Ferreira et al.35; Ozono et al.36) N/A Human: A549-ACE2 cells (Motozono et al.24) N/A African green monkey (Chlorocebus sabaeus): VeroE6/TMPRSS2 cells JCRB Cell Bank (Matsuyama et al.37) JCRB1819 Yeast (Saccharomyces cerevisiae): strain EBY100 ATCC MYA-4941 Oligonucleotides Primers for the construction of plasmids expressing the codon-optimized S proteins of a series of SAR-CoV-2 S mutants and chimeras, see Table S3 This study N/A Primers for SARS-CoV-2 reverse genetics, see Table S3 This study N/A RT-qPCR, forward: AGC CTC TTC TCG TTC CTC ATC AC (Meng et al.18; Motozono et al.24; Saito et al.32; Suzuki et al.22; Yamasoba et al.23) N/A RT-qPCR, reverse: CCG CCA TTG CCA GCC ATT C (Meng et al.18; Motozono et al.24; Saito et al.32; Suzuki et al.22; Yamasoba et al.23) N/A Primers for the construction of yeast-optimized SARS-CoV-2 B.1 and Omicron S RBD expression plasmid, see Table S3 This study N/A Recombinant DNA Plasmid: pCAGGS (Niwa et al.38) N/A Plasmid: psPAX2-IN/HiBiT (Ozono et al.39) N/A Plasmid: pWPI-Luc2 (Ozono et al.39) N/A Plasmid: pC-ACE2 (Ozono et al.36) N/A Plasmid: pC-TMPRSS2 (Ozono et al.36) N/A Plasmid: pJYDC1 Addgene Cat# 162458 Plasmid: pDSP1-7 (Kondo et al., 2011) N/A Plasmid: pDSP8-11 (Kondo et al., 2011) N/A Plasmid: pC-B.1 S (Motozono et al.24; Ozono et al., 202136) N/A Plasmid: pC-Delta S (B.1.617.2 S) (Kimura et al.27; Saito et al.32) N/A Plasmid: pC-Omicron S (BA.1 S) (Meng et al.18; Suzuki et al.22) N/A Plasmid: pC-Omicron S/B.1 S_NTD This study N/A Plasmid: pC-Omicron S/B.1 S_RBD This study N/A Plasmid: pC-Omicron S/B.1 S_RBD This study N/A Plasmid: pC-Omicron S/B.1 S_NTD+RBD This study N/A Plasmid: pC-B.1 S/Omicron S_RBD This study N/A Plasmid: pC-Omicron S L371S This study N/A Plasmid: pC-Omicron S P373S This study N/A Plasmid: pC-Omicron S F375S This study N/A Plasmid: pC-Omicron S L371S/P373S This study N/A Plasmid: pC-Omicron S P373S/F375S This study N/A Plasmid: pC-Omicron S L371S/F375S This study N/A Plasmid: pC-Omicron S L371S/P373S/F375S This study N/A Plasmid: pC-B.1 S S371L This study N/A Plasmid: pC-B.1 S S373P This study N/A Plasmid: pC-B.1 S S375F This study N/A Plasmid: pC-B.1 S S375F/Y505A This study N/A Plasmid: pC-B.1 S S371L/S373P This study N/A Plasmid: pC-B.1 S S373P/S375F This study N/A Plasmid: pC-B.1 S S371L/S375F This study N/A Plasmid: pC-B.1 S S371L/S373P/S375F This study N/A Plasmid: pC-B.1 S S375Y This study N/A Plasmid: pC-B.1 S S375Y/Y505A This study N/A Plasmid: pC-B.1 S S375H This study N/A Plasmid: pC-B.1 S S375H/Y505A This study N/A Plasmid: pC-B.1 S S375F/Y505A This study N/A Plasmid: pC-B.1 S S375Y/Y505A This study N/A Plasmid: pC-B.1 S S375H/Y505A This study N/A Plasmid: pC-Omicron S H505A This study N/A Software and algorithms fastp v0.21.0 (Chen et al.40) https://github.com/OpenGene/fastp BWA-MEM v0.7.17 (Li and Durbin,41) http://bio-bwa.sourceforge.net SAMtools v1.9 (Li et al.42) http://www.htslib.org snpEff v5.0e (Cingolani et al.43) http://pcingola.github.io/SnpEff RDP4 v4.101 (Martin et al.44) http://web.cbio.uct.ac.za/∼darren/rdp.html MAFFT suite v7.407 (Katoh and Standley,45) https://mafft.cbrc.jp/alignment/software BEAST v1.10.4 (Suchard et al.46) https://beast.community FigTree v1.4.4 http://tree.bio.ed.ac.uk/software/figtree/ http://tree.bio.ed.ac.uk/software/figtree/ Tracer v1.7.1 (Rambaut et al.47) https://beast.community/tracer.html R v4.1.3 The R Foundation https://www.r-project.org/ Sequencher v5.1 software Gene Codes Corporation N/A Prism 9 software v9.1.1 GraphPad Software https://www.graphpad.com/scientific-software/prism/ Fiji software v2.2.0 ImageJ https://fiji.sc Image Studio Lite v5.2 LI-COR Biosciences https://www.licor.com/bio/image-studio/ FlowJo software v10.7.1 BD Biosciences https://www.flowjo.com/solutions/flowjo Python v3.7 Python Software Foundation https://www.python.org PyMOL molecular graphics system v2.5.0 Schrödinger https://pymol.org/2/ BZ-X800 analyzer software Keyence N/A Photoshop 2021 v22.4.1 Adobe N/A Other Centro XS3 LB960 Berthhold Technologies N/A GloMax explorer multimode microplate reader 3500 Promega N/A 96-well black plate PerkinElmer Cat# 6005225 FACS Canto II BD Biosciences N/A GISAID database (Khare et al., 2021) https://doi.org/10.55876/gis8.221004su QuantStudio 3 Real-Time PCR system Thermo Fisher Scientific N/A Thermal Cycler Dice Real Time System III Takara N/A CFX Connect Real-Time PCR Detection system Bio-Rad N/A Eco Real-Time PCR System Illumina N/A qTOWER3 G Real-Time System Analytik Jena N/A 7500 Real-Time PCR System Thermo Fisher Scientific N/A Amersham Imager 600 GE Healthcare N/A iBright FL1500 Imaging System Thermo Fisher Scientific N/A All-in-One Fluorescence Microscope BZ-X800 Keyence N/A HisTrap Fast Flow column Cytiva Cat# 17-5255-01 Superdex 200 16/600 Cytiva Cat# 28-9893-35 ÄKTA pure chromatography system Cytiva N/A Tycho NT.6 system NanoTemper N/A FACS S3e Cell Sorter device Bio-Rad N/A Resource availability Lead contact Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Kei Sato ([email protected]). Materials availability All unique reagents generated in this study are listed in the key resources table and available from the lead contact with a completed Materials Transfer Agreement. Experimental model and subject details Ethics statement All protocols involving specimens from human subjects recruited at Kyoto University and Kuramochi Clinic Interpark were reviewed and approved by the Institutional Review Boards of Kyoto University (approval ID: G1309) and Kuramochi Clinic Interpark (approval ID: G2021-004). All human subjects provided written informed consent. All protocols for the use of human specimens were reviewed and approved by the Institutional Review Boards of The Institute of Medical Science, The University of Tokyo (approval IDs: 2021-1-0416 and 2021-18-0617), Kyoto University (approval ID: G0697), Kumamoto University (approval IDs: 2066 and 2074), and University of Miyazaki (approval ID: O-1021). Human serum collection Vaccine sera were collected from eleven vaccinees four weeks after their second vaccination with the BNT162b2 (Pfizer/BioNTech) vaccine (average age: 35, range: 29–56, 18% male) and sixteen vaccinees four weeks after their second mRNA-1273 (Moderna) vaccine (average age: 27, range: 20–47, 38% male). Convalescent sera were collected from vaccine-naïve individuals who had been infected with the Delta variant (n = 10; average age: 47, range: 22–63, 70% male). To identify the SARS-CoV-2 variants infecting patients, saliva was collected from COVID-19 patients during infection onset, and RNA was extracted using a QIAamp viral RNA mini kit (Qiagen, Cat# 52906) according to the manufacturer’s protocol. To identify the Delta variants, viral genome sequencing was performed as previously described.18 For details, see the "viral genome sequencing" section below. Sera collected from twelve convalescents during the early pandemic (until May 2020) (average age: 71, range: 52–92, 8% male) were purchased from RayBiotech. Sera were inactivated at 56°C for 30 min and stored at −80°C until use. The details of the sera used in this study are summarized in Table S1. Cell culture HEK293T cells (a human embryonic kidney cell line; ATCC, CRL-3216), HEK293 cells (a human embryonic kidney cell line; ATCC CRL-1573), and HOS-ACE2/TMPRSS2 cells, HOS cells (a human osteosarcoma cell line; ATCC CRL- 1543) stably expressing human ACE2 and TMPRSS235 , 36 were maintained in Dulbecco’s modified Eagle’s medium (DMEM) (high glucose) (Sigma-Aldrich, Cat# 6429-500ML) containing 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin (PS) (Sigma-Aldrich, Cat# P4333-100ML). HEK293-C34 cells, IFNAR1 KO HEK293 cells expressing human ACE2 and TMPRSS2 by doxycycline treatment,25 were maintained in DMEM (high glucose) containing 10% FBS, 10 μg/mL blasticidin (InvivoGen, Cat# ant-bl-1) and 1% PS. VeroE6/TMPRSS2 cells (VeroE6 cells stably expressing human TMPRSS2; JCRB1819)37 were maintained in DMEM (low glucose) (Wako, Cat# 041-29775) containing 10% FBS, G418 (1 mg/mL; Nacalai Tesque, Cat# G8168-10ML) and 1% PS. Expi293F cells (Thermo Fisher Scientific, Cat# A14527) were maintained in Expi293 expression medium (Thermo Fisher Scientific, Cat# A1435101). A549-ACE2 cells, A549 cells (a human lung epithelial cell line; ATCC CCL-185) stably expressing human ACE224 were maintained in Ham’s F-12K (Wako, Cat# 080-08565) containing 10% FBS, puromycin (1 μg/mL; Sigma-Aldrich, Cat# P9620-10ML) and 1% PS. Method details Viral genome sequencing The virus sequences were verified by viral RNA-sequencing analysis. Viral RNA was extracted using a QIAamp viral RNA mini kit (Qiagen, Cat# 52906). The sequencing library employed for total RNA sequencing was prepared using the NEB Next Ultra RNA Library Prep Kit for Illumina (New England Biolabs, Cat# E7530). Paired-end 76-bp sequencing was performed using a MiSeq system (Illumina) with MiSeq reagent kit v3 (Illumina, Cat# MS-102-3001). Sequencing reads were trimmed using fastp v0.21.040 and subsequently mapped to the viral genome sequences of a lineage A isolate (strain WK-521; GISAID ID: EPI_ISL_408667)37 using BWA-MEM v0.7.17.41 Variant calling, filtering, and annotation were performed using SAMtools v1.942 and snpEff v5.0e.43 Molecular phylogenetic analyses The SARS-CoV-2 genomes and annotation information used in this study were downloaded from the GISAID EpiCoV database (https://www.gisaid.org/) on January 8, 2022 (6,780,682 sequences). A total of 204,375 Omicron BA.1 variants were obtained, which included 1,074 B.1.1.529 variants because the B.1.1.529 lineage was recategorized as BA.1 as of February 24, 2022 (https://cov-lineages.org/lineage_list.html). For each sequence, we counted the number of undetermined nucleotides (such as N, Y, W) for whole genomes as well as S genes and obtained 40,739 sequences with fewer than 1,000 undetermined nucleotides in the genome and fewer than 10 undetermined nucleotides in the S-coding region. We then obtained BA.1 variant genomes that met the following criteria: 1) genomes were isolated from humans; 2) genomes did not contain any undetermined nucleotides in genomic regions corresponding to amino acid positions 371–375 in the S protein; 3) genomes were sampled from September 2021 to November 2021; and 4) genomes did not contain any of the 3 amino acid replacements in the S protein. We then selected 12 genomes and randomly selected 100 genomes that met criteria 1 and 2. We then removed Omicron genomes containing recombination sites using RDP4 v4.10144 because such genomes may contain artifactual reversions possibly derived from contamination of non-Omicron (mainly Delta) variants due to the low affinity of primers.33 , 34 We also checked the sequences manually, and 44 Omicron genomes were obtained. The 44 Omicron genomes with two outgroup genomes EPI_ISL_402125 (strain Wuhan-Hu-1, B lineage) and EPI_ISL_406862 (B.1 lineage; one of the earliest sequences carrying the S D614G mutation) were aligned using FFT-NS-1 in MAFFT suite v7.407.45 We then deleted gapped regions in the 5' and -3′ regions. BEAST v1.10.446 was used to construct a timetree under an exponential growth coalescent model using a strict molecular clock. The GTR model with the four categories of discrete gamma rate variation was used as a nucleotide substitution model.48 , 49 We ran Markov Chain Monte Carlo (MCMC) procedures with a 1 × 108 chain length for all calculations, discarding the first 10% as burn-in and sampling every 10,000 replicates. The effective sample size for all run was confirmed to be larger than 200. FigTree v1.4.4 (http://tree.bio.ed.ac.uk/software/figtree/) was used to show the tree. To further determine the population history of the Omicron genomes, we generated a Bayesian skyline plot using the same model (2 × 108 chain length for MCMC) and summarized the results using Tracer v1.7.1.47 Plasmid construction Plasmids expressing the codon-optimized SARS-CoV-2 S proteins of B.1 (the parental D614G-bearing variant), Delta (B.1.617.2) and Omicron (BA.1 lineage) variants were prepared in our previous studies.22 , 23 , 24 , 27 , 32 , 35 , 50 , 51 Plasmids expressing a series of SAR-CoV-2 S mutants were generated by site-directed overlap extension PCR using the primers listed in Table S3. The resulting PCR fragment was digested with KpnI (New England Biolabs, Cat# R0142S) and NotI (New England Biolabs, Cat# R1089S) and inserted into the corresponding site of the pCAGGS vector.38 Nucleotide sequences were determined by DNA sequencing services (Eurofins), and the sequence data were analyzed by Sequencher v5.1 software (Gene Codes Corporation). Pseudovirus assay Lentivirus (HIV-1)-based, luciferase-expressing reporter viruses were pseudotyped with the SARS-CoV-2 spikes. HEK293T cells (500,000 cells) were cotransfected with 800 ng psPAX2-IN/HiBiT,39 800 ng pWPI-Luc2,39 and 400 ng plasmids expressing parental S or its derivatives using TransIT-293 Transfection Reagent (Takara, Cat# MIR2700) or PEI Max (Polysciences, Cat# 24765-1) according to the manufacturer’s protocol. Two days posttransfection, the culture supernatants were harvested, and the pseudoviruses were stored at −80°C until use. The same amount of pseudoviruses [normalized to the HiBiT value measured by Nano Glo HiBiT lytic detection system (Promega, Cat# N3040)], which indicates the amount of p24 HIV-1 antigen) was inoculated into HOS-ACE2/TMPRSS2 cells and A549-ACE2 cells. At two days postinfection, the infected cells were lysed with a Bright-Glo Luciferase Assay System (Promega, cat# E2620) or a One-Glo luciferase assay system (Promega, cat# E6130) and the luminescent signal was measured using a GloMax Explorer Multimode Microplate Reader (Promega) or a CentroXS3 plate reader (Berthhold Technologies). Western blot For the blot, the HEK293 cells cotransfected with the S expression plasmids and HIV-1-based pseudovirus producing plasmids (see “pseudovirus assay” section above) or the HEK293 cells transfected with the S expression plasmids were used. To quantify the level of the cleaved S2 protein in the cells, the harvested cells were washed and lysed in lysis buffer [25 mM HEPES (pH 7.2), 10% glycerol, 125 mM NaCl, 1% Nonidet P40 substitute (Nacalai Tesque, Cat# 18558-54), protease inhibitor cocktail (Nacalai Tesque, Cat# 03969-21)]. After quantification of total protein by protein assay dye (Bio-Rad, Cat# 5000006), lysates were diluted with 2 × sample buffer [100 mM Tris-HCl (pH 6.8), 4% SDS, 12% β-mercaptoethanol, 20% glycerol, 0.05% bromophenol blue] and boiled for 10 m. Then, 10 μL samples (50 μg of total protein) were subjected to Western blot. To quantify the level of the S2 protein in the virions, 900 μL culture medium containing the pseudoviruses was layered onto 500 μL 20% sucrose in PBS and centrifuged at 20,000 g for 2 hours at 4°C. Pelleted virions were resuspended in 1× NuPAGE LDS sample buffer (Thermo Fisher Scientific, Cat# NP0007) containing 2% β-mercaptoethanol and incubated at 70°C for 10 m. For protein detection, the following antibodies were used: mouse anti-SARS-CoV-2 S monoclonal antibody (clone 1A9, GeneTex, Cat# GTX632604, 1:10,000), mouse anti-HIV-1 p24 monoclonal antibody (183-H12-5C, obtained from the HIV Reagent Program, NIH, Cat# ARP-3537, 1:2,000), rabbit anti-beta actin (ACTB) monoclonal antibody (clone 13E5, Cell Signalling, Cat# 4970, 1:5,000), mouse anti-tubulin (TUBA) monoclonal antibody (clone DM1A, Sigma-Aldrich, Cat# T9026, 1:10,000), horseradish peroxidase (HRP)-conjugated horse anti-mouse IgG antibody (Cell Signaling, Cat# 7076S, 1:2,000), HRP-conjugated donkey anti-rabbit IgG polyclonal antibody (Jackson ImmunoResearch, Cat# 711-035-152, 1:10,000) and HRP-conjugated donkey anti-mouse IgG polyclonal antibody (Jackson ImmunoResearch, Cat# 715-035-150, 1:10,000). Chemiluminescence was detected using SuperSignal West Femto Maximum Sensitivity Substrate (Thermo Fisher Scientific, Cat# 34095), SuperSignal West Atto Ultimate Sensitivity Substrate (Thermo Fisher Scientific, Cat# A38554) or Western BLoT UltraSensitive HRP Substrate (Takara, Cat# T7104A) according to the manufacturer’s instruction. Bands were visualized using an Amersham Imager 600 (GE Healthcare) or iBright FL1500 Imaging System (Thermo Fisher Scientific), and the band intensity was quantified using Image Studio Lite v5.2 (LI-COR Biosciences) or Fiji software v2.2.0 (ImageJ). Uncropped blots are shown in Figure S4. SARS-CoV-2 S-based fusion assay The SARS-CoV-2 S-based fusion assay24 , 52 utilizes a dual split protein (DSP) encoding Renilla luciferase and GFP genes; the respective split proteins, DSP8-11 and DSP1-7, are expressed in effector and target cells by transfection. Briefly, on day 1, effector cells (i.e., S-expressing cells) and target cells (see below) were prepared at a density of 0.6–0.8 × 106 cells in a 6-well plate. To prepare effector cells, HEK293 cells were cotransfected with the S expression plasmids (400 ng) and pDSP8-11 (400 ng) using TransIT-LT1 (Takara, Cat# MIR2300). To prepare target cells, HEK293 cells were cotransfected with pC-ACE2 (200 ng) and pDSP1-7 (400 ng). Target HEK293 cells in selected wells were cotransfected with pC-TMPRSS2 (40 ng) in addition to the plasmids above. HEK293-ACE2 cells and HEK293-ACE2/TMPRSS2 cells were transfected with pDSP1-7 (400 ng). On day 3 (24 h posttransfection), 16,000 effector cells were detached and reseeded into 96-well black plates (PerkinElmer, Cat# 6005225), and target HEK293 cells were reseeded at a density of 1,000,000 cells/2 mL/well in 6-well plates. On day 4 (48 h posttransfection), target cells were incubated with EnduRen live cell substrate (Promega, Cat# E6481) at 37°C for 3 h and then detached, and 32,000 target cells were added to a 96-well plate with effector cells. Renilla luciferase activity was measured at the indicated time points using Centro XS3 LB960 (Berthhold Technologies). To measure the surface expression level of S protein, effector cells were stained with rabbit anti-SARS-CoV-2 S S1/S2 polyclonal antibody (Thermo Fisher Scientific, Cat# PA5-112048, 1:100). Normal rabbit IgG (SouthernBiotech, Cat# 0111-01, 1:100) was used as negative controls, and APC-conjugated goat anti-rabbit IgG polyclonal antibody (Jackson ImmunoResearch, Cat# 111-136-144, 1:50) was used as a secondary antibody. Surface expression level of S protein was measured using FACS Canto II (BD Biosciences) and the data were analyzed using FlowJo software v10.7.1 (BD Biosciences). To calculate fusion activity, Renilla luciferase activity was normalized to the MFI of surface S proteins. The normalized value (i.e., Renilla luciferase activity per the surface S MFI) is shown as fusion activity. SARS-CoV-2 reverse genetics To generate recombinant SARS-CoV-2 by circular polymerase extension reaction (CPER),25 , 24 9 DNA fragments encoding the partial genome of SARS-CoV-2 (strain WK-521, PANGO lineage A; GISAID ID: EPI_ISL_408667)37 were prepared by PCR using PrimeSTAR GXL DNA polymerase (Takara, Cat# R050A). A linker fragment encoding hepatitis delta virus ribozyme, bovine growth hormone poly A signal and cytomegalovirus promoter was also prepared by PCR. The corresponding SARS-CoV-2 genomic region and the PCR templates and primers used for this procedure are summarized in Table S3. The 10 obtained DNA fragments were mixed and used for CPER.25 To prepare GFP-expressing replication-competent recombinant SARS-CoV-2, we used fragment 9, in which the GFP gene was inserted in the ORF7a frame, instead of the authentic F9 fragment (Table S3).25 To generate chimeric recombinant SARS-CoV-2 (Figures 1G and 5A), mutations were inserted in fragment 8 by site-directed overlap extension PCR or the GENEART site-directed mutagenesis system (Thermo Fisher Scientific, Cat# A13312) according to the manufacturer’s protocol with the primers listed in Table S3. Recombinant SARS-CoV-2 that bears B.1 S [rB.1 S-GFP (virus I)] or Omicron S [rOmicron S-GFP (virus II)] was prepared in our previous studies.23 , 32 Nucleotide sequences were determined by a DNA sequencing service (Fasmac), and the sequence data were analyzed by Sequencher v5.1 software (Gene Codes Corporation). To produce chimeric recombinant SARS-CoV-2, the CPER products were transfected into HEK293-C34 cells using TransIT-LT1 (Takara, Cat# MIR2300) according to the manufacturer’s protocol. At 1 d posttransfection, the culture medium was replaced with Dulbecco’s modified Eagle’s medium (high glucose) containing 2% FCS, 1% PS and doxycycline (1 μg/mL; Takara, Cat# 1311N). At 7 d posttransfection, the culture medium was harvested and centrifuged, and the supernatants were collected as the seed virus. To remove the CPER products (i.e., SARS-CoV-2-related DNA), 1 mL of the seed virus was treated with 2 μL TURBO DNase (Thermo Fisher Scientific, Cat# AM2238) and incubated at 37°C for 1 h. Complete removal of the CPER products (i.e., SARS-CoV-2-related DNA) from the seed virus was verified by PCR. The working virus stock was prepared from the seed virus as described below (see “SARS-CoV-2 preparation and titration” section). SARS-CoV-2 preparation and titration To prepare the working virus stocks of chimeric recombinant SARS-CoV-2,25 , 24 20 μL of the seed virus was inoculated into VeroE6/TMPRSS2 cells (5,000,000 cells in a T-75 flask). One hour post infection (h.p.i.), the culture medium was replaced with DMEM (low glucose) (Wako, Cat# 041-29775) containing 2% FBS and 1% PS. At 3 d.p.i., the culture medium was harvested and centrifuged, and the supernatants were collected as the working virus stock. The titer of the prepared working virus was measured as the 50% tissue culture infectious dose (TCID50). Briefly, one day before infection, VeroE6/TMPRSS2 cells (10,000 cells) were seeded into a 96-well plate. Serially diluted virus stocks were inoculated into the cells and incubated at 37°C for 4 days. The cells were observed under microscopy to judge the CPE appearance. The value of TCID50/mL was calculated with the Reed–Muench method.53 To verify the sequence of chimeric recombinant SARS-CoV-2, viral RNA was extracted from the working viruses using a QIAamp viral RNA mini kit (Qiagen, Cat# 52906) and viral genome sequence was analyzed as described above (see "viral genome sequencing" section above). In brief, the viral sequences of GFP-encoding recombinant SARS-CoV-2 (strain WK-521; GISIAD ID: EPI_ISL_408667)25 , 37 that harbor the S genes of respective variants were used for the reference. Information on the unexpected mutations detected is summarized in Table S4, and the raw data are deposited in DDBJ Sequence Read Archive (accession number: PRJDB13805). SARS-CoV-2 infection One day before infection, VeroE6/TMPRSS2 cells (10,000 cells) were seeded into a 96-well plate. SARS-CoV-2 (100 TCID50, m.o.i. 0.01) was inoculated and incubated at 37°C for 1 hour. The infected cells were washed, and 180 μL of culture medium was added. The culture supernatant (10 μL) was harvested at the indicated timepoints and used for RT–qPCR to quantify the viral RNA copy number (see “RT–qPCR” section below). RT–qPCR Five microliters of culture supernatant was mixed with 5 μL of 2 × RNA lysis buffer [2% Triton X-100 (Nacalai Tesque, Cat# 35501-15), 50 mM KCl, 100 mM Tris-HCl (pH 7.4), 40% glycerol, 0.8 U/μL recombinant RNase inhibitor (Takara, Cat# 2313B)] and incubated at room temperature for 10 minutes. RNase-free water (90 μL) was added, and the diluted sample (2.5 μL) was used as the template for real-time RT-PCR performed according to the manufacturer’s protocol using the OneStep TB Green PrimeScript PLUS RT-PCR kit (Takara, Cat# RR096A) and the following primers: Forward N, 5′-AGC CTC TTC TCG TTC CTC ATC AC-3′; and Reverse N, 5′-CCG CCA TTG CCA GCC ATT C-3′. The viral RNA copy number was standardized with a SARS-CoV-2 direct detection RT-qPCR kit (Takara, Cat# RC300A). Fluorescent signals were acquired using QuantStudio 3 Real-Time PCR system (Thermo Fisher Scientific), Thermal Cycler Dice Real Time System III (Takara), CFX Connect Real-Time PCR Detection system (Bio-Rad), Eco Real-Time PCR System (Illumina), qTOWER3 G Real-Time System (Analytik Jena) or 7500 Real-Time PCR System (Thermo Fisher Scientific). Fluorescence microscopy One day before infection, VeroE6/TMPRSS2 cells (10,000 cells) were seeded into 96-well, glass bottom, black plates and infected with SARS-CoV-2 (100 TCID50, m.o.i. 0.01). At 24, 48, and 72 h.p.i., GFP fluorescence was observed under an All-in-One Fluorescence Microscope BZ-X800 (Keyence) in living cells, and a 13-square-millimeter area of each sample was scanned. under the same parameters. Images were reconstructed using an BZ-X800 analyzer software (Keyence), and the area and the fluorescent intensity of the GFP-positive cells was measured using this software. Plaque assay One day before infection, VeroE6/TMPRSS2 cells (100,000 cells) were seeded into a 24-well plate and infected with SARS-CoV-2 (1, 10, 100 and 1,000 TCID50) at 37°C for 2 hours. Mounting solution containing 3% FBS and 1.5% carboxymethyl cellulose (Wako, Cat# 039-01335) was overlaid, followed by incubation at 37°C. At 3 d.p.i., the culture medium was removed, and the cells were washed with PBS three times and fixed with 4% paraformaldehyde phosphate buffer solution (Nacalai Tesque, Cat# 09154-85). The fixed cells were washed with tap water, dried, and stained with staining solution [0.1% methylene blue (Nacalai Tesque, Cat# 22412-14) in water] for 30 minutes. The stained cells were washed with tap water and dried, and the size of plaques was measured using Adobe Photoshop 2021 v22.4.1 (Adobe). Neutralization assay For neutralization assay,35 pseudoviruses were prepared as described above (see “pseudovirus assay” section). For the neutralization assay, the SARS-CoV-2 S pseudoviruses (counting ∼20,000 relative light units) were incubated with serially diluted (40-fold or 120-fold to 29,160-fold dilution at the final concentration) heat-inactivated sera at 37°C for 1 hour. Pseudoviruses without sera were included as controls. Then, an 80 μL mixture of pseudovirus and serum/antibody was added to HOS-ACE2/TMPRSS2 cells (10,000 cells/50 μL) in a 96-well white plate. At 2 d.p.i., pseudovirus infectivity was measured as described above (see “pseudovirus assay” section). The assay of each serum was performed in triplicate, and the 50% neutralization titer (NT50) was calculated using Prism 9 (GraphPad Software). Protein structure All protein structural analyses were performed using the PyMOL molecular graphics system v2.5.0 (Schrödinger). The cryo-EM structures of SARS-CoV-2 D614G (B.1 lineage) S (PDB: 7KRQ)28 and Omicron S (PDB: 7T9J)29 were used. To predict inter-subunit interaction of the Omicron S trimer, each subunit of the D614G S trimer was replaced with the Omicron S monomer.29 The distance between F375 and H505 was measured using the PyMOL molecular graphics system v2.5.0 (Schrödinger). Yeast surface display For yeast surface display,26 the carboxypeptidase domain of human ACE2 (residues 18–740) was expressed in Expi293F cells and purified by a 5-mL HisTrap Fast Flow column (Cytiva, Cat# 17-5255-01) and Superdex 200 16/600 (Cytiva, Cat# 28-9893-35) using an ÄKTA pure chromatography system (Cytiva), and the purified soluble ACE2 was labelled with CF640R (Biotium, Cat# 92108). Protein quality was verified using a Tycho NT.6 system (NanoTemper) and ACE2 activity assay kit (SensoLyte, Cat# AS-72086). An enhanced yeast display platform for SARS-CoV-2 S RBD [wild-type (B.1.1), residues 336–528] yeast surface expression was established using Saccharomyces cerevisiae EBY100 strain and pJYDC1 plasmid (Addgene, Cat# 162458) as previously described.14 , 23 , 24 , 26 , 27 To prepare a series of SARS-CoV-2 S RBD mutants, the site-directed mutagenesis was performed using the KAPA HiFi HotStart ReadyMix kit (Roche, Cat# KK2601) by restriction enzyme-free cloning procedure.54 Primers for mutagenesis are listed in Table S3. The binding affinities of SARS-CoV-2 S RBDs to human ACE2 were determined by flow cytometry titration experiments. The CF640R-labelled ACE2 at 12–14 different concentrations (200 nM to 13 pM in PBS supplemented with bovine serum albumin at 1 mg/mL) per measurement were incubated with expressed yeast aliquots and 10 nM bilirubin (Sigma-Aldrich, Cat# 14370-1G) and analyzed by using FACS S3e Cell Sorter device (Bio-Rad). The background binding subtracted fluorescent signal was fitted to a standard noncooperative Hill equation by nonlinear least-squares regression using Python v3.7 (https://www.python.org) as previously described.26 Quantification and statistical analysis In the single timepoint experiments, statistical significance was tested using a two-sided Student’s t test (Figures 1B, 1E, 3B, 4B, 4E, 6D, 6E and S1A), a two-sided paired t test (Figures 1D, 4D, 6B and 6C), a two-sided Mann–Whitney U test (Figures 1J, 1K, 5D, 5E), or a two-sided Wilcoxon signed-rank test (Figure 2). The tests above were performed using Prism 9 software v9.1.1 (GraphPad Software). In the time-course experiments (Figures 1F, 1H, 1I, 4F, 5B, 5C, 6F, and 6G), a multiple regression analysis including experimental conditions (i.e., the types of infected viruses) as explanatory variables and timepoints as qualitative control variables was performed to evaluate the difference between experimental conditions thorough all timepoints. p value was calculated by a two-sided Wald test. Subsequently, familywise error rates (FWERs) were calculated by the Holm method. These analyses were performed in R v4.1.2 (https://www.r-project.org/). In Figures 1J, 1I, 5C, 5D and S1B, assays were performed in triplicate. Photographs shown are the representatives of >18 fields of view taken for each sample. Supplemental information Document S1. Figures S1–S4 Table S1. Human sera used in this study, related to Figure 2 Table S2. Mutations detected in the Omicron RBD, related to Figure 3 Table S3. Primers used in this study, related to Figures 1 and 3–6 Table S4. Summary of the mutations detected in the working virus stocks compared to the original sequences, related to Figures 1 and 5 Data and code availability RNA-seq data generated in this study were deposited on the DDBJ Sequence Read Archive (https://www.ddbj.nig.ac.jp/dra/) with the accession numbers DRR385305 - DRR385309 with BioProject ID PRJDB13805. GISAID IDs used in this study was available at the following https://doi.org/10.55876/gis8.221004su. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request. Acknowledgments We would like to thank all members belonging to The Geno-type-to-Phenotype Japan (G2P-Japan) Consortium. We thank Dr. Kenzo Tokunaga (National Institute for Infectious Diseases, Japan) and Dr. Jin Gohda (The University of Tokyo, Japan) for providing reagents. The super-computing resource was provided by Human Genome Center at The University of Tokyo. We also would like to acknowledge sequencing laboratories participating in GISAID, whose data used in this article was summarized at the following website: https://doi.org/10.55876/gis8.221004su. This study was supported in part by 10.13039/100009619 AMED Program on R&D of new generation vaccine including new modality application (JP223fa727002, to K.Sato); AMED Research Program on Emerging and Re-emerging Infectious Diseases (JP20fk0108268, to Akifumi Takaori-Kondo; JP20fk0108517, to Akifumi Takaori-Kondo; JP20fk0108146, to Kei Sato; JP20fk0108270, to Kei Sato; JP20fk0108413, to So Nakagawa, Terumasa Ikeda and Kei Sato; JP20fk0108451, to Takamasa Ueno, Akifumi Takaori-Kondo, G2P-Japan Consortium, Takashi Irie, Akatsuki Saito, So Nakagawa, Terumasa Ikeda and Kei Sato; JP21fk0108494, to G2P-Japan Consortium, Kotaro Shirakawa, Takashi Irie, Terumasa Ikeda, Kei Sato; JP21fk0108574, to Hesham Nasser); 10.13039/100009619 AMED Research Program on HIV/AIDS (JP21fk0410034, to Akifumi Takaori-Kondo; JP21fk0410033, to Akatsuki Saito; and JP21fk0410039, to Kei Sato; JP22fk0410055, to Terumasa Ikeda); AMEDCRDF Global Grant (JP21jk0210039 to Akatsuki Saito); 10.13039/100009619 AMED Japan Program for Infectious Diseases Research and Infrastructure (JP21wm0325009, to Akatsuki Saito); JSTA-STEP (JPMJTM20SL, to Terumasa Ikeda); JSTSICORP (e-ASIA) (JPMJSC20U1, to Kei Sato); JSTSICORP (JPMJSC21U5, to Kei Sato), JSTCREST (JPMJCR20H6, to So Nakagawa; JPMJCR20H4, to Kei Sato); 10.13039/501100001691 JSPS KAKENHI Grant-in-Aid for Scientific Research C (19K06382, to Akatsuki Saito; 22K07103, to Terumasa Ikeda); 10.13039/501100001691 JSPS KAKENHI Grant-in-Aid for Scientific Research B (18H02662, to Kei Sato; and 21H02737, to Kei Sato); 10.13039/501100001691 JSPS KAKENHI Grant-in-Aid for Early-Career Scientists (22K16375, to Hesham Nasser); 10.13039/501100001691 JSPS Fund for the Promotion of Joint International Research (Fostering Joint International Research) (18KK0447, to Kei Sato); 10.13039/501100001691 JSPS Core-to-Core Program (A. Advanced Research Networks) (JPJSCCA20190008, to Kei Sato); 10.13039/501100001691 JSPS Research Fellow DC1 (19J20488, to Izumi Kimura) and DC2 (22J11578, to Keiya Uriu); JSPS Leading Initiative for Excellent Young Researchers (LEADER) (to Terumasa Ikeda); The Tokyo Biochemical Research Foundation (to Kei Sato); Mitsubishi Foundation (to Terumasa Ikeda); Takeda Science Foundation (to Terumasa Ikeda); Shin-Nihon Foundation of Advanced Medical Research (to Mako Toyoda and Terumasa Ikeda); Waksman Foundation of Japan (to Terumasa Ikeda); Tsuchiya Foundation (to Takashi Irie); a Grant for Joint Research Projects of the Research Institute for Microbial Diseases, 10.13039/501100004206 Osaka University (to Akatsuki Saito); an intramural grant from 10.13039/501100004091 Kumamoto University COVID-19 Research Projects (AMABIE) (to Terumasa Ikeda); Intercontinental Research and Educational Platform Aiming for Eradication of HIV/AIDS (to Terumasa Ikeda); and Joint Usage/Research Center program of Institute for Frontier Life and Medical Sciences, Kyoto University (to Kei Sato). Author contributions I.K., D.Y., H.N., Y.K., K.N., K.U., Y.L.T., R.S., T.S.T., E.P.B., M.T., T.I., A.S., and T.I. performed cell culture experiments. J.Z. and G.S. performed a yeast surface display assay. J.I., H.A., K.S., and K.Y. performed viral genome sequencing analysis. T.U., A.T-K., and K.S. contributed clinical sample collection. J.I. performed statistical analyses. J.Z. and Y.K. performed structural analyses. J.W. and S.N. performed molecular phylogenetic analyses. M.T., K.S., T.I., A.S., S.N., T.I. and K.S. designed the experiments and interpreted the results. K.S. wrote the original manuscript. All authors reviewed and proofread the manuscript. The Genotype-to-Phenotype Japan (G2P-Japan) Consortium contributed to the project administration. Declaration of interest The authors declare that no competing interests exist. 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==== Front Am J Infect Control Am J Infect Control American Journal of Infection Control 0196-6553 1527-3296 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. S0196-6553(22)00841-0 10.1016/j.ajic.2022.11.021 Major Article An unheard voice: infection prevention professionals reflect on their experiences during the covid-19 pandemic Pintar Paula A. MSN, RN, ACNS-BC, CIC, FAPIC c⁎ McAndrew Natalie S. PhD, RN, ACNS-BC, CCRN-K ab a College of Nursing, University of Wisconsin-Milwaukee, Milwaukee, WI b Froedtert & the Medical College, Froedtert Hospital, Milwaukee, WI c Infection Prevention & Control Froedtert Health, Froedtert & the Medical College of Wisconsin, Milwaukee, WI ⁎ Address correspondence to Paula A. Pintar, MSN, RN, ACNS-BC, CIC, FAPIC, Infection Prevention & Control, Froedtert Health, Froedtert & Medical College of Wisconsin, Milwaukee, WI 53226. 5 12 2022 5 12 2022 © 2022 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved. 2022 Association for Professionals in Infection Control and Epidemiology, 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 The COVID-19 pandemic required a shift away from the evidence-based practices known to infection prevention professionals’ (IPP). Relaying these guidelines to beleaguered front line staff contributed to the experience of moral distress and burnout among IPPs. Methods A mixed methods design was used to explore the experiences of IPPs during the COVID-19 pandemic. An electronic survey was sent to a convenience sample from the Wisconsin APIC membership. A subset of this sample completed additional semi-structured interviews. Results A total of 61 IPPs responded to the survey, 18 agreed to interviews with 11 completions. Most respondents identified as female (n=58, 95.0%) and White (n=55, 90.1%). More than half of the respondents (n=39, 63.9 %) reported they experienced moral distress (MD). Themes from one-on-one interviews included: Feeling depleted, challenges to IPP role, validation of IPP expertise, value of peer support. Conclusions We found that IPPs endured significant distress and exhaustion during the COVID-19 pandemic regardless of their practice setting. The long-term effects on the IPP profession must be examined. IPPs are susceptible to high levels of stress and anxiety similar to other frontline healthcare workers. IPPs deserve recognition for their service during the pandemic and should have access to resources that can support their well-being. Key Words Infection prevention professional Moral distress Mixed methods COVID-19 pandemic ==== Body pmcIntroduction The initial phases of the SARS-CoV-2 pandemic was a time of clinical uncertainty and shortages of personal protective equipment (PPE) which required a shift away from the evidence-based practices foundational to the work of infection prevention professionals (IPP). IPPs were challenged to deliver rapidly changing guidance across their institutions about PPE use that contradicted best practice. IPPs were overwhelmed by the content, frequency and volume of their communication needed to ensure the safety of frontline staff.1 Infection prevention and control departments were positioned front and center of the pandemic and IPPs had to work around the clock to write practice protocols that reflected emerging guidance from the Centers for Disease Control and Prevention (CDC). The pandemic created interruptions to the global supply chain that filtered down to create critical PPE shortages and led to purchasing substandard substitutions in healthcare facilities. These shortages caught most healthcare facilities off guard. Staff delivering patient care did not have adequate protection. To address this issue, guidelines were created to preserve the limited supply of PPE.2 Frequently, IPPs were confronted with frontline staff who were frustrated with the rapidly changing guidelines and questioned their validity. Relaying these guidelines to beleaguered front line staff and auditing compliance contributed to the experience of moral distress and burnout among IPPs. Moral distress is defined as knowing what one considers the right ethical course of action but being in a situation in which it is nearly impossible to take that action.3 , 4 IPP's were in a situation of having the knowledge of how to act but were prevented from acting accordingly due to pandemic related practice constraints. While the current IPP workforce experienced other pandemics, such as SARS in 2003 and H1N1in 2009, they had never been confronted with such a rapid and pervasive spread of disease, as well as high mortality rate. Inaccuracies spread through social media encouraged non-adherence to infection prevention measures and fueled controversies in the hospital and the community at large, further burdening IPPs. There are numerous research studies highlighting the stress and anxiety of front-line health care workers during the height of the pandemic.5 , 6 To date there are few studies reporting the experiences of IPPs and the implications of delivering guidance that was contrary to their education and training. The purpose of this study was to explore the experiences of IPPs during the height of the COVID-19 pandemic. Methods Study design/sample We used a mixed methods design to explore the experiences of IPPs during the COVID-19 pandemic. We administered an electronic survey (Survey Monkey®) to a convenience sample of the membership of the Wisconsin Association of Professionals in Infection Prevention (APIC). The survey was distributed through the four Wisconsin APIC chapters whose members work in healthcare across multiple settings, such as acute care, long-term care, and public health. To further explore their survey responses we conducted semi-structured interviews with IPP's who volunteered their contact information on the survey. Instruments The principal investigator (PI) developed a survey to determine IPP levels of moral distress and professional respect with responses on a Likert-type scale. Survey content was peer-reviewed by two certified IPP and members of the National APIC Research committee. A semi-structured interview guide was used to explore IPP experiences in depth. Procedure The survey was distributed to all active members of the Wisconsin-based APIC chapters (277 active members) through their chapter leaders from July 12 to August 9, 2021. An informational page about the study preceded the survey. Completion of the survey signified voluntary consent to participate in the study. Those providing their contact information were contacted to schedule interviews. The PI conducted the interviews remotely through secure Zoom from July 29, 2021 – August 19, 2021. A semi-structured interview guide was used to provide general structure to the interview process. The guide included questions to elicit personal responses, feelings, and experiences of how the COVID-19 pandemic affected the IPP from the point in which the pandemic started in March 2020. The principal investigator contacted each respondent by email or telephone to schedule the interview. A separate verbal informed consent was obtained with each participant along with demographic questions. Each interview was audio/video recorded and transcribed within 24 hours of completion. Ethics All participants engaged in an informed consent process. The study was approved through the Medical College of Wisconsin and Froedtert Hospital Institutional Review Board (PRO0039934). Data analysis An inductive approach to qualitative content analysis7 , 8 was used to analyze the interview data. The PI reviewed all video/audio recordings within 8 hours following the interview sessions, taking notes and documenting observations. The written and audio transcripts were re-reviewed by the PI on three separate occasions, and each time the PI took notes and highlighted information in order to extract nuances of the participants’ experiences. Coding was completed by the PI and reviewed/audited by the second author. These researchers then collaboratively developed themes and subthemes. An audit trail was created documenting the analytic process and decisions made. Results A total of 61 IPPs responded to the survey out of the 4 Wisconsin based APIC chapters reflecting a 22% response rate. IPP characteristics and responses to the survey questions are summarized in Table 1 . Most respondents identified as female (n=58, 95.0%) and White (n=55, 90.1%). Of those who reported their age (n=59), most respondents were in the age bracket of 31 to 40 years (n = 17, 27.8%) and 41 to 50 years (n = 21, 34.4%). Almost half (n = 30, 49.1%) were board certified in infection control (CBIC) and half had been working in the IPP field 5 years or less (n=32, 53%). More than half of the respondents (n=39, 63.9 %,) reported they experienced moral distress (MD) during their work as an IPP during the peak of the pandemic. Many continued to experience MD as an IPP after the peak (n=34, 56%). More than half felt they were looked upon as a trusted partner of the front line patient care team during the pandemic (n=48, 78%).Table 1 IPP demographic results and responses by survey questions. Items rated on a 7-point Likert scale (1 = strongly agree and 7 = strongly disagree). All respondents N=61. Table 1Survey question N = 61 % Age 18-22 0 0 23-30 1 1.60% 31-40 17 27.80% 41-50 21 34.40% 51-60 10 16.30% 61 or older 10 16.30% Prefer not to answer 2 3.20% Ethnicity Hispanic or Latino or Spanish 2 3.20% American Indian or Alaskan native 1 1.60% Asian 1 1.60% Native Hawaiian or Other Pacific Islander 0 0 Black or African American 1 1.60% White 55 90.10% Two or more races 0 0 Not listed 0 0 Prefer not to answer 1 1.60% Prefer to describe 0 0 Gender Male 3 4.90% Female 58 95.00% Education ADN or Associate Degree 5 8.20% Diploma 0 0 BSN, BA, BS 33 54.10% MSN, MN, MS, MA 16 26.20% DNP 0 0 PhD 1 1.60% Did not respond 6 9.80% Certification in Infection Control Yes 30 49.10% No 21 34.40% Planning to certify within the year 13 21.30% No plans to certify 2 3.20% Responded to more than one option 5 8.20% Year working in Infection control <1 year 3 5.00% 1-2 yrs. 9 15.00% 3-5 yrs. 20 33.30% 6-10 yrs. 12 20.00% 11-16 yrs. 6 10.00% 17-20 yrs. 4 6.60% 21 yrs. or more 6 10.00% Missing 1 1% Are you a Fellow of the Association for Professionals in Infection Control & Epidemiology (FAPIC) Yes 4 6.60% No 56 93.30% Missing 1 1% I understand the term “Moral distress.” Strongly agree 18 30.00% Agree 31 50.00% Neither agree nor disagree 1 1.60% Disagree 2 3.20% Strongly disagree 3 5.00% Missing 6 10.00% I have experienced moral distress in my role as an IPP during the COVID-19 pandemic. Strongly agree 15 24.00% Agree 12 20.00% Somewhat agree 12 20.00% Neither agree nor disagree 1 1.60% Somewhat disagree 4 7.00% Disagree 6 10.00% Strongly disagree 5 8.00% Missing 6 10.00% I have experienced moral distress often (more than 2 times/ month) during the COVID-19 pandemic in my role as an Infection Preventionist since the pandemic start date in March 2020. Strongly agree 22 36.00% Agree 15 24.50% Somewhat agree 5 8.00% Neither agree nor disagree 2 3.20% Somewhat disagree 0 0 Disagree 7 11.00% Strongly disagree 4 6.50% Missing 6 9.80% I continue to feel moral distress in my role as an IPP even though the peak of the pandemic has passed. Strongly agree 8 13.00% Agree 15 25.00% Somewhat agree 11 18.00% Neither agree nor disagree 1 1.60% Somewhat disagree 2 3.20% Disagree 8 13.00% Strongly disagree 9 15.00% Missing 7 11.00% During the peak of the COVID-19 pandemic (March 1, 2020 to May 1, 2021) in Wisconsin) my moral distress was related to PPE supplies. Strongly agree 11 18.00% Agree 6 9.80% Somewhat agree 15 25.00% Neither agree nor disagree 3 5.00% Somewhat disagree 3 5.00% Disagree 10 16.00% Strongly disagree 6 10.00% Missing 7 11.00% During the peak of the COVID-19 pandemic, I felt I was a trusted partner of the front-line patient care team? Strongly agree 20 33.00% Agree 13 21.00% Somewhat agree 8 13.00% Neither agree nor disagree 2 3.20% Somewhat disagree 7 11.00% Disagree 4 6.50% Strongly disagree 1 1.60% Missing 6 10.00% I feel respected by the front-line patient care team in my role as an IPP. Strongly agree 16 26.00% Agree 27 44.00% Neither agree nor disagree 5 8.00% Disagree 5 8.00% Strongly disagree 2 3.20% Missing 6 10.00% N % During the peak of the COVID-19 pandemic, I was recognized as the expert by my organization's leaders? Strongly agree 12 20.00% Agree 26 42.00% Neither agree nor disagree 7 11.00% Disagree 6 10.00% Strongly disagree 4 6.50% Missing 6 10.00% One-on-one interviews Eighteen survey respondents volunteered to participate in an interview and eleven completed the interview session. Interview sessions ranged from 45-60 minutes. Four themes and 13 subthemes were identified (Table 2 ). Themes included: Feeling depleted, challenges to IPP role, validation of IPP expertise, value of peer support. The themes uncovered in the one-on-one interviews and open-ended comments from the survey confirmed and expanded upon the closed-ended survey question findings.Table 2 Themes, subthemes, and supportive quotations. Table 2Themes Subthemes Feeling depleted Overwhelming workload and stress Working in a whirlwind Loss of joy in work Feeling helpless Lack of drive Challenges to IPP role Guilt not being direct provider of care Not being heard Changing Guidelines Lack of recognition and support Public disbelief in science Validation of IPP expertise Staff, leaders and public recognition of the value of IPP Personal recognition of professional growth Value of peer support Building peer relationships virtually Theme 1. Feeling Depleted IPP professionals felt depleted and “burned out” by their responsibilities. Participants expressed fear for front line staff members who had to reuse and extend PPE use. They also reflected on the stress of ensuring that the frequently updated and sometimes conflicting PPE guidelines were implemented correctly. “ I didn't realize how burnt out I was until things kind of started to settle down and I actually could step back and really think about it and look at my life and realize that “Oh my God! (H).” 1a. Overwhelming workload and stress Participants described the stress of shouldering a dramatically increasing workload. An IPP shared, “It was overwhelming most days, at best. At first, you think that it's not even real and it's not going to hit us. It's not going to be that bad. And then it just kind of blew up and went crazy” (Q). One participant described feeling “like I had a little PTSD, that is, just sometimes things kind of triggered emotions that I had during that exhausting time” (H). Those who held leadership positions avoided venting to their IPP staff so as not to contribute to their burden. 1b. Working in a whirlwind The pace of new infections and deaths was relentless. “It's like, you got on a carnival ride and you expected to do one round and then it never stopped and you couldn't get off. I'm sure many people felt this way, but it was a whirlwind (N). 1c. Loss of joy in work Participants discussed how navigating the pandemic changed how they felt about their job. “Initially, I loved it (being an IPP). I loved the job, I loved learning it. But now, I'm over it (P).” 1d. Feeling helpless Participants struggled to feel good about their performance as an IPP as they were “pulled in so many different directions, it was just kind of flying by the seat of your pants” (N). Another shared “…Not feeling like I ever made the right decision at the right time, fast enough, good enough. That was hard” (H). 1e. Lack of drive As the pandemic dragged on and IPPs endured relentless demands, some respondents experienced a lack of professional drive. “There was a while there where I stopped caring. I wanted to just be left alone. I didn't want to hear about it, I didn't want to talk about it. I'm not doing this anymore (K).” Theme 2. Challenges to IPP Role IPPs described unprecedented personal and professional challenges while navigating their role during the pandemic. 2a. Guilt not being direct provider of care IPPs, many of whom had formerly been direct care providers, felt conflicted about seeing their peers delivering care in uncertain situations when they were removed from that frontline risk. “It hit me, like I feel so bad for the people on the front lines.” (P) 2b. Not being heard One IPP explained her frustration with feeling invisible while trying to communicate important information. (J) “I felt like I was screaming at the top of the mountain, and no one was listening.” 2c. Changing guidelines Due to the complexity of the pandemic, IPPs managed frequent updates and often conflicting guidelines published by the CDC. In turn, staff questioned their leadership. “Well are you sure you are following the proper guidelines?” (G) 2d. Lack of recognition and support IPPs remembered that initially there was a lack of support from institutional leadership for their unique role in the institution. One participant explained that there was a lag time “for some hospital leadership to defer things to us as specialists …and subject matter experts.” (M). 3e. Public disbelief in science IPPs shared that as they struggled to address a public health crisis they also had to deal with disruptive public responses to the pandemic that stemmed from divisive politics.”(G). Some disbelief in science even came from healthcare professionals, “I remember the first week where I started getting the slew of conspiracy theories from healthcare workers, and that became very difficult to manage” (J). Theme 3. Validation of the IPP Expertise A positive occurrence shared by the participants was the frequently reported support system of relying on family members, colleagues, or peer connections through their APIC chapter. IPP's that participated in these peer connections relayed that these connections eased stress and formed stronger relationships with peers. 3a. Staff, leaders, and public recognition of the value of IPP Greater awareness of IPP knowledge and expertise was also identified during the interviews. As the pandemic continued there was realization of the value of the IPP at multiple levels in organizations. “I think it put a highlight on our department and the role that we can play within the hospital.” (M) 3b. Personal recognition of professional growth IPP's experienced a sense of personal accomplishment that arose from their ability to connect with the front line staff and dialogue with them on how it was affecting them. “I still feel happy every time I can change something to make it better.” (J) Theme 4. Value of Peer Support IPPs developed a deeper bond with each other through peer support. “During the height of the pandemic, we were meeting daily. It was very therapeutic. It was kind of like group therapy sessions, where we literally just vented about everything and all of our experiences and what happened and a lot of sharing between ourselves” (N). 4a. Building peer relationships virtually Face-to-face meetings within teams and with colleagues in other systems were replaced with virtual meetings. Many IPPs had older technology that didn't include access to computer cameras which decreased personal connection. Over time, reconnecting virtually with peers in our professional organization, APIC, was another source of support that sustained IPPs. Discussion We found that IPPs experienced moral distress, stress, anxiety, and burnout consistent with findings from other studies about the experiences of front line healthcare workers.9 In contrast, IPPs developed strong teams over the course of the pandemic and felt that their expertise was recognized and received validation of their role publicly and throughout their institutions. To our knowledge, our study was the first to explore the personal and professional IPP experience in depth during the early phase of the pandemic using one-on-one interviews and survey methods. Our findings build on a study conducted by the APIC COVID-19 Task Force which elicited information about PPE availability and management using conservation and reuse strategies.1 Our results were similar to the APIC Task Force findings that IPPs were challenged by PPE management and providing guidance to the front line healthcare workers. Interestingly the APIC study identified that when there was direct IPP involvement in the development of the PPE use protocols or crisis standards of care (CSC) there was a sense of increased assurance in the safety efficacy of the standards.1 Similarly, we noted that over time, the organization and direct care providers turned to the IPPs for their expertise and guidance in navigating the pandemic. Our study not only presents complementary information to that of the APIC Task Force study; it provides a unique perspective by focusing on the psychosocial, holistic view of what it was like to be an IPP during the COVID-19 pandemic. IPPs experienced feeling depleted, overwhelmed, and helpless and experienced a loss of joy in their work. Interviewees explicitly identified anxiety related to PPE access and use. IPPs were unique in that they had to deviate from their training to make recommendations that would best protect healthcare professionals given the shortage of PPE supplies. The pandemic required the implementation of crisis standards of care and the interviewees thought that these management practices were not evidence-based. There was a constant barrage of requests for the IPPs to provide data to back up the CDC's recommendations. Not only were these requests overwhelming and exhausting for the IPPs, these situations contributed to moral distress because IPP did not have data to support these recommendations.10 The pace that was required to quickly transition, educate and disseminate CSC protocols was out of the norm for all involved in healthcare, and this indeed triggered stress in the IPP. The COVID-19 pandemic was a fertile environment to develop “infodemic: the global spread of misinformation that poses a serious problem for public health”11 (p. 2). The study participant's frequently cited their concerns about the inaccurate and contrary information spread on social media platforms and the negative effect it had in combating the spread of the virus. Social media was often tied to strong political opinions that further caused distrust in the work the IPP was doing.12 There seemed to be no end to the task of refuting misinformation cited by frontline workers, neighbors and family members from social media posts. Concerningly, individuals were more inclined to believe disreputable sources of information rather than the IPP.11 IPPs struggled with long work hours trying to balance work/life responsibilities. Many IPPs were working independently and did not have peer support at their facility. IPPs were expected to quickly digest, communicate, and update hospital policies based on evolving CDC guidelines. This overwhelming workload in a stressful environment negatively affected IPPs driving some to leave the profession and healthcare all together. The IPPs were seldom recognized by leadership for working overtime without additional compensation and without administrative or data support services to lessen the burden. Initially, hospital leaders were unaware of the negative and angry comments IPPs endured from stressed front line workers. At the point in which we surveyed IPPs our data revealed that 78% felt they were respected by the front-line patient care team. This may represent a turning point in greater appreciation for the IPP role. One of the positive outcomes of this study identified was the development of strong and lasting bonds between team members. This contributed to team resiliency fostered by relationship building among their peers in coworker groups and APIC. Delgado and colleagues13 describe a community of practice (CoP) as a group of people with a shared practice who come together to contribute knowledge based on their experiences and to learn from each other. A CoP is a platform for ongoing dialogue and mutual engagement. The pandemic drove the need for IPPs to develop a safe forum for supporting each other as they faced similar challenges. After face-to-face APIC meetings were suspended during the initial phase of COVID-19, some IPPs in our study organically developed their own CoPs as they shared their experiences and tactics to manage work. For some IPPs, CoPs motivated them to keep moving forward despite daily work stresses. The importance of teams and relationship building in the context of a pandemic cannot be underestimated. Implications for future research and practice An important direction for future research is understanding how IPP CoPs within healthcare systems can improve hospital preparedness for future pandemics and additional healthcare crises.13 The IPP has been somewhat forgotten in healthcare system efforts to address the trauma experienced by frontline healthcare professionals practicing during the pandemic. It is important to develop more comprehensive organizational support for IPPs who continue to face political interference and misinformation on social media platforms related to infection control, as well as heavy workloads related to spikes in infectious disease transmission and newly emerging pathogens. The COVID-19 pandemic has raised awareness of the importance of the IPP professional in health systems. Further studies are needed to identify strategies to rebuild and expand the IPP workforce. Limitations and strengths As this study was only open to APIC members within a single state in the Midwest, it may only capture a small subset of IPP's experiences during the COVID-19 pandemic. The participants were mostly female and largely working in inpatient acute care facilities and therefore the results cannot be generalized to IPPs working in other practice areas. Participant self-selection may have resulted in a sample of IPPs that did not fully represent the range of experiences of IPPs practicing during the pandemic. A strength of our study was the focus on the experiences of IPP's who were practicing in multiple settings including long term care, public health, and acute care. Conclusion We found that IPPs endured significant distress and exhaustion during the COVID-19 pandemic regardless of their practice setting. Although this was not the first respiratory pandemic experienced by this generation of IPPs, it was the most deadly pandemic and was complicated by PPE shortages, as well as the growing mistrust in science due to the influence of misinformation on social media platforms. The long-term effects on the IPP profession must be examined. This study highlights the need to create a better infrastructure of support for IPP's who bring specialized knowledge and expertise critical to current and future pandemics. IPPs are susceptible to high levels of stress and anxiety similar to other frontline healthcare workers. These professionals deserve recognition for their service during the pandemic and need access to resources that can support their well-being. Acknowledgments We express our deep appreciation to the infection control professionals who shared their stories. We would also like to thank Maryann Moon, MSN, APRN, ACNS-BC, APNP, Associate Chief Nursing Officer for supporting access to research resources and ongoing guidance through this project. Funding: We have no funding for this study. Conflict of interest: We have no conflict of interest to disclose. ==== Refs References 1 Rebmann T Alvino RT Holdsworth JE. Availability and crisis standards of care for personal protective equipment during fall 2020 of the COVID-19 pandemic: a national study by the APIC COVID-19 task force Am J Infect Control 49 2021 657 662 10.1016/j.ajic.2021.03.015 33775741 2 Centers for Disease Control & Prevention. Optimizing the supply of PPE and other equipment during shortages. COVID-19. Published September 23, 2022. https://www.cdc.gov/coronavirus/2019-ncov/hcp/ppe-strategy/index.html 3 Jameton A. Nursing Practice: The Ethical Issues 1984 Prentice-Hall 4 American Association of Critical-Care Nurses. Moral distress in times of crisis. 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==== Front Nurse Educ Today Nurse Educ Today Nurse Education Today 0260-6917 1532-2793 Elsevier Ltd. S0260-6917(22)00412-9 10.1016/j.nedt.2022.105675 105675 Research Article The impact of changes in nursing practicum caused by COVID-19 pandemic on new graduate nurses Kang Younhee Hwang Hyeyoung ⁎ College of Nursing, Ewha Womans University, Seoul, Republic of Korea ⁎ Corresponding author at: College of Nursing, Ewha Womans University, 52, Ewhayeodae-gil, Seodaemun-gu, Seoul 03760, Republic of Korea. 5 12 2022 5 12 2022 1056754 7 2022 28 10 2022 27 11 2022 © 2022 Elsevier Ltd. All rights reserved. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Background The impact of the COVID-19 pandemic has brought about major changes throughout nursing education. Most clinical practicum has been substituted by skills laboratories, simulation laboratories, virtual simulation or written assignments. Nursing students who have experienced this change in practicum have fears about their future role as new graduate nurses. However, to date, no studies have been conducted exploring how their fears work when they become new graduate nurses. Objectives To investigate the status of nursing practicum at nursing universities before and during the COVID-19 pandemic, and to explore the relationship between difficulties in nursing tasks, work readiness, reality shock, and organizational socialization among new graduate nurses with nursing practicum experience during this pandemic. Design Descriptive comparative research design. Participants 178 new graduate nurses with a clinical experience from 1 month to less than 12 months and graduating from nursing universities in 2021, the 3rd grade in 2020 and the 4th grade in 2021. Methods Cross-sectional study via a self-administered online questionnaire measuring difficulties in nursing tasks, work readiness, reality shock, and organizational socialization. Data were analyzed by descriptive statistics, independent t-tests, Pearson correlation. Results New graduate nurses were divided into 72 in the Clinical/Clinical group and 106 in the Clinical/Substitute group. There was a significant difference in the difficulties in nursing tasks (t = −2.342, p = .020), but there were no significant differences in work readiness, reality shock, and organizational socialization between the two groups. Conclusions Efforts in the clinical field to increase the adaptation of new graduate nurses could prevent problems that may arise due to the restrictions and absence of clinical practicum. Discussions should be continued to develop and implement efficient nursing practicum education that not only can reduce the gap between nursing education and nursing practice, but also respond appropriately to any pandemic situation. Keywords COVID-19 Nursing education Nursing students Socialization ==== Body pmc1 Introduction On March 11, 2020, the World Health Organization (WHO) declared a global pandemic of Coronavirus disease-2019 (COVID-19) (World Health Organization, 2020). Within Korea, the Government announced a national action plan to prevent the spread of COVID-19 and implemented intensive social distancing policies (Ministry of Health and Welfare, 2020). In accordance with the Ministry of Education's academic management guidelines, all educational institutions postponed the start of the 2020 academic year and conducted the semester with all sessions delivered in online mode (Ministry of Education, 2020). The response to this pandemic was not limited to Korea, but was a global trend. Nursing schools have transitioned to distance learning (Weston and Zauche, 2021). The shift to distance learning posed many challenges for teachers and students, with these challenges being more prominent in nursing education, which requires more than a certain number of hours of nursing practicum but also skills based workshops (Wallace et al., 2021). Nursing is a practical discipline based on theoretical knowledge. Nursing students must develop the ability to deal with potential and actual problems through nursing practicum experiences (Song and Kim, 2013). Within most nursing universities in Korea, theory classes are held in the first and second years, and nursing practicum is added from the third year. Therefore, third and four-grade nursing students must complete both theory classes and nursing practicum during the semester. One of the nursing practicums takes place in different nursing fields outside of university, within various healthcare institutions including hospitals and public health clinics, which are referred to as clinical practicum. However, due to the spread of COVID-19, most institutions were unable to provide clinical practicum sites for safety reasons, and many of the clinical practicums were substituted by non-face-to-face online learning (Bang et al., 2021; Kang, 2020). The International Nursing Association of Clinical Simulation and Learning (INACSL) and the Society for Simulation in Healthcare (SSH) officially stated their support for the use of virtual simulation as a replacement for clinical hours for nursing students during the COVID-19 crisis (Foronda and Armstrong, 2020). These cause a sharp decrease in the opportunities for nursing students to experience clinical field prior to graduation, which may be related to maladjustment of new graduate nurses. Substitute practicum included ‘skills laboratories’, ‘simulation laboratories’ using models or mannequins, ‘virtual simulation’ in an online environment, and ‘written assignments’ (Lim, 2021; Sim, 2021). Among substitute practicum, virtual simulation enables students to experience patient care similar to the real world (Kang, 2020). Virtual simulation is a type of simulation that injects humans in a central role by exercising motor control skills, decisions skills, or communication skills in a virtual reality similar to reality (Hancock et al., 2008). It has positive effects on critical thinking (Lim and Yeom, 2020), problem-solving ability (Ha and Lee, 2021), clinical performance (Yoon et al., 2021). The results from a meta-narrative review showed comparable student engagement, educational outcomes, and student proficiency with both simulation and clinical practicum, even when a percentage of clinical hours is replaced with simulation (Roberts et al., 2019). Therefore, virtual simulation has been suggested as a teaching-learning strategy that can substitute for clinical practicum. There is a long-running debate about the value of simulation versus practical experience (Badowski et al., 2021; Ma and Nickerson, 2006). Despite the advantages of simulation, one reason for the ongoing debate is that nursing students who have experienced simulation instead of practical experience fear their future role as nurses (Lim, 2021; Suliman et al., 2021). However, to date, no studies have been conducted exploring how these fears translate to the practice environment when they become new graduate nurses. 1.1 Background The significant nursing shortage is an ongoing global issue and a threat to the delivery of quality healthcare (Kaihlanen et al., 2020). In 2030, it is expected that there will be a shortage of at least 160,000 nurses in Korea (Korea Institute for Health and Social Affairs, 2015). As a way to address this issue, the enrollment quota for nursing universities has been steadily increasing, and as a result, the number of graduate nurses increased by about 100,000, from 13,065 in 2013 to 23,362 in 2022 (Korea Health Personnel Licensing Examination Institute, 2022). Nevertheless, the shortage of nurses in Korea remains unresolved. The main reason for this problem is the resignation of nurses. In 2020, the average resignation rate of Korean nurses is 14.5 %, and that of new graduate nurses with less than one year of working experience is 34.1 %, which is remarkably higher than the average one. Of the 23,064 newly employed nurses in 2020, 10,999 nurses (47.7 %) resigned within one year of employment, and the main reason for resignation was maladaptation (Korean Hospital Nurses Association, 2020). It can be confirmed that the core problem of the Korean nursing workforce is not the absolute shortage of nurses, but due to nurses leaving the profession. Resignation of new graduate nurses not only provides the individual nurse with an experience of fatal frustration, but also causes a major setback in the demand and supply of registered nurses (Yoon, 2002). Specific measures to increase the retention of nurses should begin with resolving the ‘maladaptation’ identified as the main reason for the resignation of new graduate nurses. In the process of adapting to clinical settings, most new graduate nurses experience reality shock due to the gap between the knowledge or skills learned at university and the expected roles in hospitals (Caliskan and Ergun, 2012). At this time, new graduate nurses with low work readiness have difficulties in applying what they learned in school to practice (Leong and Crossman, 2015), and experience pressure and interpersonal problems due to poor work performance (Gardiner and Sheen, 2016; Sin and Kim, 2017). These difficulties lead to reality shock accompanied by excessive anxiety, fear of interpersonal relationships, rejection and degradation, hostility, anger, and fatigue (Kramer, 1974). Reality shock lengthens the clinical adaptation process (Duchscher, 2009). Clinical adaptation refers to the learning process of values, performance capabilities and expected behaviors required to perform roles as an organizational member, and social knowledge within the organization (Van Maanen and Schein, 1979). In other words, the organizational socialization of nurses means adaptation to the clinical field. It is difficult for new graduate nurses to adapt to the new organizational environment due to inexperienced knowledge and skills, role conflicts on job performance, and complex interpersonal relationships. Therefore, a systematic curriculum that can alleviate reality shock and promote organizational socialization is required from nursing universities by increasing the work readiness of nursing students. However, due to the COVID-19 pandemic, most of the theory classes were changed to online lectures, and clinical practicum, which is a mandatory course for nursing students, has been mostly substituted by skills laboratories, simulation laboratories, virtual simulation, or written assignments (Ha and Lee, 2021; You and Cho, 2021). This means that nursing students lose important opportunities such as reducing fear of clinical field and gaining confidence as future nurses by providing nursing process to actual patients. Changes in nursing education imply that there is a possibility of negatively affecting the clinical adaptation of new graduate nurses. Accordingly, previous studies have been conducted with nursing students who experienced a sudden transition to online practicum (Kang, 2020; Lim, 2021; Wallace et al., 2021) or to confirm the effectiveness of online practicum (Bang, 2021; Kang, 2020). All of these were conducted for nursing students who had just experienced substitute practicum during the pandemic. Now that nursing students who participated in substitute practicum at the time of the COVID-19 pandemic have become new graduate nurses, it is necessary to identify their degree of clinical field adaptation. 1.2 Research aims This study aimed to investigate the status of nursing practicum at Korean nursing universities before and during the COVID-19 pandemic, and to explore the relationship between difficulties in nursing tasks, work readiness, reality shock, and organizational socialization among new graduate nurses with nursing practicum experience during this pandemic. 2 Methods 2.1 Research design We used a descriptive comparative research design to explore the work readiness, reality shock, and organizational socialization of new graduate nurses according to the type of nursing practicum. 2.2 Research participants Participants were new graduate nurses with a clinical experience from 1 month to less than 12 months and independently provide direct nursing care at hospitals. Among new graduate nurses graduating from nursing universities in 2021, only nurses who were in the 3rd grade in 2020 (before the COVID-19 pandemic) and the 4th grade in 2021 (during the COVID-19 pandemic) were included using the convenience sampling. A sample size of 140 participants (70 participants per condition) was calculated through G*Power 3.1 by applying differences between two independent means with an effect size of 0.5, power of 0.9, and significance level of 0.05. 2.3 Instruments We used a self-reported questionnaire consisting of two sections. The first section asked about demographic characteristics and nursing practicum related characteristics. Demographic characteristics included sex, age, hospital type and location, working department, university location, and the clinical experience. The characteristics related to nursing practicum included questions about the practicum types, satisfaction, usefulness in performing nursing tasks, and the proportion of clinical practicum according to grade. The second section consisted of four tools that measure difficulties in nursing tasks, work readiness, reality shock, and organizational socialization. Permission to use each of the instruments was obtained from its developers (Lee et al., 2021; Sin et al., 2014; Sohn et al., 2008). 2.3.1 Difficulties in nursing tasks The question, “Please indicate the degree of difficulties in nursing tasks that you felt as a new graduate nurse due to the lack of clinical practicum” was answered using the Visual Analog Scale (VAS) developed by researchers. The VAS consists of a 10 cm line with two end points representing 0 (no difficult) and 10 (extremely difficult). 2.3.2 Work readiness Work readiness was defined as “the degree to which graduates possess the characteristics and attributes that prepare them for success in the workplace” (Walker et al., 2015). This was measured using the Korean version (Lee et al., 2021) of the Work Readiness Scale for Graduate Nurses (Walker et al., 2015), which consists of 46 items in four subdomains: work competence, social intelligence, organizational acumen, and personal work characteristics. Each item was measured on a 10-point Likert scale (1 = completely disagree to 10 = completely agree). A higher score indicated a higher level of work readiness. Cronbach's alpha was equally 0.95 in both the Korean version scale and this study. 2.3.3 Reality shock Reality shock was defined as “the total social, physical, and emotional response of a person to the unexpected, unwanted, or undesired, and in the most severe degree, to the intolerable. It is the startling discovery and reaction to the discovery that school-bred values conflict with work-world values” (Kramer, 1974). This was measured by using the Korean version (Sin et al., 2014) of the Environmental Reality Shock Instrument Issues and Concerns (Kramer et al., 2013). The scale consists of 22 items and each item was answered on a 4-point Likert scale (1 = no worry at all to 4 = extremely worried). A higher score indicated a higher level of reality shock. Cronbach's alpha was equally 0.89 in both the Korean version scale and this study. 2.3.4 Organizational socialization Organizational socialization was defined as “the process by which an individual comes to appreciate the values, abilities, expected behaviors, and social knowledge essential for assuming an organizational role and for participating as an organizational member” (Louis, 1980). This was measured using the instrument developed by Sohn et al. (2008). The scale consists of 39 items and the items were scored on a 5-point Likert scale (1 = strongly agree to 5 = strongly disagree). A higher score indicated a higher level of organizational socialization. Cronbach's alpha in the original scale and this study were 0.97 and 0.88, respectively. 2.4 Data collection Data collection was conducted from October 21, 2021 to April 5, 2022. Recruitment documents were posted in nurses' rest areas including a direct link to the survey website. We also posted it in three online communities for nurses. Only participants who voluntarily agreed to participate signed the online consent form, and completed the online questionnaire. A total of 186 new graduate nurses participated, and 178 participants were finally included in the analysis, excluding 8 participants who did not meet the inclusion criteria. In order to categorize nursing practicum experience during the COVID-19 pandemic, 72 nurses were assigned to the Clinical/Clinical group (CC group) and 106 nurses were assigned to the Clinical/Substitute group (CS group) based on the 40 % of clinical practicum rate for each year. 2.5 Statistical analysis Data were analyzed by descriptive statistics, independent t-tests, Pearson correlation, and Cronbach's alpha coefficient using SPSS version 20.0. 2.6 Ethical considerations The study was approved by the Institutional Review Board of the researcher's university (No. OOOO-202111-0031-02). 3 Results 3.1 Descriptive characteristics As shown in Table 1 , 92.1 % of participants were female and 98.9 % of those in their 20s, with an average age of 24.7 years. 78.1 % were working in tertiary hospitals, and all participants belonged to various departments in the following proportions: medical ward (26.4 %), surgical ward (23.0 %), intensive care unit (16.9 %), and integrated nursing care service ward (12.4 %). While most of them were working in hospitals located in Seoul (78.1 %) and Gyeonggi-do (20.8 %), the nursing universities they graduated from were distributed in all 13 cities and provinces. More than half the participants had 4 to 9 months of clinical experience (68.6 %) and the average tenure was confirmed to be 7.4 months.Table 1 Descriptive characteristics (N = 178). Table 1Characteristics Categories n (%) M ± SD Sex Male 14 (7.9) Female 164 (92.1) Age (years) 23–29 179 (98.9) 24.74 ± 1.66 30–39 2 (1.1) Type of hospital Tertiary hospital 139 (78.1) Secondary hospital 39 (21.9) Working department Integrated nursing care service warda 22 (12.4) Medical ward 47 (26.4) Surgical ward 41 (23.0) Mother-child ward 4 (2.2) Emergency room 10 (5.6) Intensive care unit 30 (16.9) Operation room 17 (9.6) Othersb 7 (3.9) Location of hospital Seoul 139 (78.1) Incheon 1 (0.6) Gyeonggi-do (province) 37 (20.8) Gyeongsang-do (province) 1 (0.6) Location of university Seoul 55 (30.9) Incheon 7 (3.9) Daejeon 11 (6.2) Busan 5 (2.8) Ulsan 6 (3.4) Daegu 6 (3.4) Gwangju 7 (3.9) Gyeonggi-do (province) 21 (11.8) Gangwon-do (province) 3 (1.7) Chungcheong-do (province) 14 (7.9) Jeolla-do (province) 20 (11.2) Gyeongsang-do (province) 17 (9.5) Jeju-do (province) 6 (3.4) Clinical experience (months) 1–3 12 (6.7) 7.35 ± 2.68 4–6 56 (31.5) 7–9 66 (37.1) 10–12 44 (24.7) a Department that provides comprehensive 24-hr inpatient care systems operated by professionally trained nurses. b Psychiatric ward (2), oriental medicine ward (3), nonresponse (2). 3.2 Nursing practicum related characteristics Nursing practicum conducted in third-grade (before COVID-19 pandemic) was followed by clinical practicum (35.0 %), skills laboratories (25.9 %), simulation laboratories (23.8 %), virtual simulation (9.4 %), and written assignments (5.9 %). In the case of fourth-grade (after COVID-19 pandemic), skills laboratories (23.4 %), clinical practicum (21.1 %), simulation laboratories (20.4 %), virtual simulation (19.6 %), and written assignments (15.5 %) were ranked in the order. When comparing the clinical practicum of fourth-grade nursing students with third-grade, the substitute practicum rate increased as the clinical practicum decreased. When duplicate responses were allowed, the total number of responses to third and fourth-grade practicum was 509 and 607, respectively, indicating that fourth-grade practicum curriculum consisted of more diverse types of practical education. As for the satisfaction by types of substitute practicum, the average satisfaction in simulation laboratories was the highest with 2.86 ± 0.64, and that of written assignments was the lowest with 2.24 ± 0.75 (Table 2 ).Table 2 Type of nursing practicum education (N = 178). Table 2Practicum 3rd grade (before pandemic) 4th grade (after pandemic) Satisfaction n (%) Case % n (%) Case % n Min Max M ± SD Clinical Clinical practicum 178 (35.0) 100.0 128 (21.1) 71.9 Substitute Skills laboratories 132 (25.9) 74.2 142 (23.4) 79.8 104 1 4 2.74 ± 0.64 Simulation laboratories 121 (23.8) 68.0 124 (20.4) 69.7 92 1 4 2.86 ± 0.64 Virtual simulation 48 (9.4) 27.0 119 (19.6) 66.9 111 1 4 2.59 ± 0.59 Written assignments 30 (5.9) 16.9 94 (15.5) 52.8 90 1 4 2.24 ± 0.75 Totala 509 (100.0) 286.0 607 (100.0) 341.0 397 a Allow duplicate responses. As a result of ranking of usefulness in performing nursing tasks in clinical practice, the priorities were shown in the order of clinical practicum, simulation laboratories, skills laboratories, and written assignments. The fact that 84.2 % of participants chose clinical practicum as their top priority indicates that clinical practicum is considerably useful for clinical nursing practice (Table 3 ).Table 3 Usefulness in performing nursing tasks in clinical practice (N = 178). Table 3Practicum 1st 2nd 3rd 4th 5th Total n (%) n (%) n (%) n (%) n (%) n (%) Clinical Clinical practicum 144 (84.2) 12 (7.0) 11 (6.4) 4 (2.3) 0 (0.0) 171 (100.0) Substitute Skills laboratories 9 (5.4) 65 (38.9) 72 (43.1) 19 (11.4) 2 (1.2) 167 (100.0) Simulation laboratories 19 (11.2) 82 (48.5) 56 (33.1) 10 (5.9) 2 (1.2) 169 (100.0) Virtual simulation 4 (2.6) 12 (7.9) 20 (13.2) 97 (63.8) 19 (12.5) 152 (100.0) Written assignments 2 (1.4) 0 (3.4) 5 (13.8) 20 (13.8) 118 (81.4) 145 (100.0) Table 4 shows the percentage of clinical practicum by grade, namely the rate of clinical practicum before and after COVID-19 pandemic. Third-grade clinical practicum conducted before the pandemic accounted for 80–89 % of the total nursing practicum in most cases (27 %), and the lowest clinical practicum rate was at least 40 %. However, the clinical practicum rate decreased after the COVID-19 pandemic. In the case of fourth-grade, the number of participants with 0 % of clinical practicum was 40, accounting for the largest proportion (22.5 %) of the total.Table 4 Percentage of clinical practicum in nursing practicum education (N = 178). Table 4Clinical practicum (%)a 3rd grade 4th grade n (%) n (%) 0 0 (0.0) 40 (22.5) 1–9 0 (0.0) 4 (2.2) 10–19 0 (0.0) 11 (6.2) 20–29 0 (0.0) 27 (15.2) 30–39 0 (0.0) 24 (13.5) 40–49 13 (7.3) 11 (6.2) 50–59 30 (16.8) 31 (17.4) 60–69 32 (18.0) 9 (5.1) 70–79 33 (18.5) 10 (5.6) 80–89 48 (27.0) 4 (2.2) 90–99 14 (7.9) 5 (2.8) 100 8 (4.5) 2 (1.1) Total 178 (100.0) 178 (100.0) Min/max 40/100 0/100 a Clinical Practicum / (Clinical Practicum + Skills Laboratories + Simulation Laboratories  + Virtual Simulation + Written Assignments) ∗ 100. As shown in Table 4, 40 % was the lowest rate of clinical practicum before COVID-19 pandemic. Therefore, Table 5 shows the prominent type of nursing practicum education by grade based on the 40 % clinical practicum rate. In third and fourth-grade practicum curriculum, 72 participants (40.4 %) had a clinical practicum rate of 40 % or more. On the other hand, as the rate of substitute practicum increased due to the pandemic, 106 participants (59.6 %) were found to have less than 40 % of the fourth-grade clinical practicum rate after COVID-19 pandemic.Table 5 Labeled nursing practicum education according to the rate of clinical practicum (N = 178). Table 53rd grade 4th grade n (%) Clinical practicuma Clinical practicum 72 (40.4) Clinical practicum Substitute practicumb 106 (59.6) Total 178 (100.0) a When the proportion of clinical practicum is 40 %, or more than 40 %. b When the proportion of clinical practicum is less than 40 %. 3.3 Comparison by group according to the type of nursing practicum There was a significant difference in the degree of difficulties in nursing tasks between the two groups classified by Table 5. The average score of the CC group was 4.64, which was significantly lower than 5.37 of the CS group (t = −2.342, p = .020), indicating that the CC group had lower difficulties in performing nursing tasks. On the other hand, there were no statistically significant differences in work readiness, reality shock, and organizational socialization between the two groups. In the CC group, the higher work readiness, the lower reality shock (r = −0.369, p < .01) and the higher organizational socialization (r = 0.606, p < .01). The higher reality shock, the lower organizational socialization (r = −0.615, p < .01). In the CS group, the higher work readiness, the lower reality shock (r = −0.519, p < .01) and the higher organizational socialization (r = 0.628, p < .01). The higher reality shock, the lower organizational socialization (r = −0.655, p < .01). Therefore, the direction of the correlation between the variables that were significant in both groups were the same, but the degree of correlation was confirmed that the CS group was stronger than the CC group. There was no significant difference in the correlation between difficulties in nursing tasks and other variables in both groups (Table 6 ).Table 6 Differences in difficulties in nursing tasks, work readiness, reality shock, and organizational socialization according to labeled nursing practicum education (N = 178). Table 6 Type n M ± SD t p Difficulties in nursing tasks Clinical/Clinical 72 4.64 ± 2.09 −2.342 .020⁎ Clinical/Substitute 106 5.37 ± 2.00 Total 178 5.07 ± 2.01 Work readiness Clinical/Clinical 72 6.01 ± 1.04 0.294 .769⁎⁎ Clinical/Substitute 106 5.96 ± 0.97 Total 178 5.98 ± 1.00 Reality shock Clinical/Clinical 72 2.32 ± 0.48 −0.377 .707⁎⁎⁎ Clinical/Substitute 106 2.35 ± 0.49 Total 178 2.34 ± 0.49 Organizational socialization Clinical/Clinical 72 2.86 ± 0.38 0.214 .831 Clinical/Substitute 106 2.85 ± 0.43 Total 178 2.85 ± 0.41 ⁎ p < .05. ⁎⁎ p < .01. ⁎⁎⁎ p < .001. 4 Discussion Before and after the COVID-19 pandemic, the types of nursing practicum can be divided into clinical practicum and substitute practicum. As opportunities for clinical practicum decreased due to the pandemic, various types of substitute practicum, among them, the increase in virtual simulation and written assignments was found to be remarkable (Table 7 ).Table 7 Correlations between main variables according to labeled nursing practicum education (N = 178). Table 7Type Variables Difficulties in nursing tasks Work readiness Reality shock Clinical/Clinical (N1 = 72) Work readiness 0.067 Reality shock 0.165⁎ −0.369⁎⁎ Organizational socialization −0.078⁎⁎⁎ 0.606⁎⁎ −0.615⁎⁎ Clinical/Substitute (N2 = 106) Work readiness 0.015 Reality shock 0.185 −0.519⁎⁎ Organizational socialization −0.061 0.628⁎⁎ −0.655⁎⁎ ⁎ p < .05. ⁎⁎ p < .01. ⁎⁎⁎ p < .001. Virtual simulation is in the spotlight as an alternative practicum method for clinical practicum as it can compensate for the shortcomings of the existing simulation methods, that is, it requires a separate space for simulator installation, time and cost for management, and the limitation of the number of students available at a time (Frick et al., 2014). However, in the results of this study, satisfaction with virtual simulation and usefulness in performing nursing tasks in actual clinical practice were relatively lower than those of other types of nursing practicum. This suggests that virtual simulation is not yet ready to substitute for clinical practicum in the COVID-19 pandemic situation. Written assignments were given the lowest score in satisfaction and usefulness. Based on the experience of new graduate nurses who received substitute practicum in the context of the COVID-19 pandemic, further research is needed to analyze shortcomings, and explore development plans of virtual simulation and written assignments. From the results obtained, in 2020, after the pandemic, the case where the clinical practicum rate was less than 40 % was about 60 %, accounting for more than half of the cases. In addition, about a quarter of the respondents said they had never experienced clinical practicum, indicating that substitute practicum prevailed in Korean nursing universities after the pandemic. These results serve as empirical evidence to show that the following backgrounds had a great influence on nursing education, especially practical education: significant increase in non-face-to-face teaching, requests from hospitals to suspend clinical practicum for reasons of infectious disease prevention and safety of patients and students, and students' fear of being infected with COVID-19 during clinical practicum (Kang, 2020; Lim, 2021). Bang et al. (2021) investigated the percentage of clinical practicum substituted by online or school practicum in 2020. As a result, 43.1 % of the respondents answered that the substitute practicum rate was 41–60 %, and 10.2 % of the respondents answered that it was 81 % or more. Comparing this with the substitute practicum rate identified in this study, our result was higher than that of the Bang et al. (2021). A study by Bang et al. (2021) was conducted at four national nursing universities in Korea. Most national universities have their own university hospitals, and in this case, it is relatively easy to secure places for clinical practicum. Therefore, it can be inferred that the substitute practicum rate presented in the study of Bang et al. (2021) was lower than that of this study due to the presence of their own hospitals. As a result of comparing difficulties in nursing tasks, work readiness, reality shock, and organizational socialization between the CC and CS group, the difficulties in nursing tasks was significantly lower in the group with a high rate of clinical practicum. This means that the difficulties in performing nursing tasks felt by new graduate nurses differed according to the rate of clinical practicum. Nursing universities should carefully review the current curriculum for substitute practicum and specific discussion should be introduced as soon as possible for better development and operation of the curriculum. In Korea, practicum hours are stipulated by an accrediting body, Korean Accreditation Board of Nursing Education. Prior to COVID-19 pandemic, the institution has increased the accreditation rate for simulation practicum, which can substitute for clinical practicum, from 10 % to 12 % of the total clinical practicum hours (Korean Accreditation Board of Nursing Education, 2017). After this pandemic, as a large proportion of clinical practicum is being substituted with other types of practicum including simulation, there is an urgent need to discuss the ratio of clinical practicum in nursing curriculum. It is noteworthy that there were no significant differences in work readiness, reality shock, and organizational socialization between the two groups. These results could be due to the effect of education provided to new graduate nurses in the clinical field. As the resignation rate of new graduate nurses continues to increase, the Government announced ‘Improvement Plan of Nursing Work Environment and Labor Conditions’ including the placement of nurses in charge of training for new graduate nurses and guarantee of a three-month training period (Korean Ministry of Health and Welfare, 2019). The Korea Nurses Association (2019) has distributed ‘New Graduate Nurses’ Education Management System Guidelines' to each hospital. Based on this guideline, Song et al. (2020) found that a project to support nurses in charge of training had a positive effect on new graduate nurses' adaptation to the new organizational environment. Practical adaptation support programs (Kwon et al., 2021) and mentoring programs (Park and Lee, 2018) have been developed and are being used in clinical field. Various efforts to enhance the field adaptation of new graduate nurses are thought to have compensated for possible problems caused by the substitution of clinical practicum. Regarding the limitations of this study, most of the new graduate nurses who participated in this study worked at tertiary general hospitals in Seoul and Gyeonggi-do. Therefore, it is difficult to generalize the analysis results to all new graduate nurses, and careful interpretation and caution are required. Despite these limitations, this study has great significance in that, for the first time in Korea, a new graduate nurse, who received practical training during the COVID-19 pandemic and is currently working in a clinical setting, was selected as the research participant. Through this study, it can be used as a useful data for developing substitute practicum education to increase field adaptation of new graduate nurses by confirming how the effect of substitute practicum was manifested when working as a new graduate nurse in the clinical setting. 5 Conclusion The impact of the COVID-19 pandemic has brought about major changes throughout nursing education. There were many changes in practical education due to restrictions on clinical practicum. The new graduate nurses who participated in this study were divided into the CC group and CS group according to the rate of clinical practicum before and during the pandemic. As a result of this study, there were no significant differences in work readiness, reality shock, and organizational socialization between the two groups. These results show that efforts, especially in clinical fields, to increase the field adaptation of new graduate nurses. However, only the difficulties in nursing tasks showed a significant difference. Difficulties in nursing tasks reported in the CS group, which had a clinical practicum rate of less than 40 %, was significantly higher than that of the CC group with more than 40 %. This suggests that discussions should be continued to develop and implement efficient nursing practicum education that not only can reduce the gap between nursing education and nursing practice, but also respond appropriately to any pandemic situation. Funding This work was supported by the 10.13039/501100003725 National Research Foundation of Korea (NRF) grant funded by the Korea government (10.13039/501100014188 MSIT ) (No. 2021R1A2C2006359). CRediT authorship contribution statement Younhee Kang: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Writing – review & editing, Supervision, Project administration, Funding acquisition. Hyeyoung Hwang: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Project administration. Declaration of competing interest The authors declare that they have no conflict of interest. ==== Refs References Badowski D. Rossler K.L. Reiland N. Exploring student perceptions of virtual simulation versus traditional clinical and manikin-based simulation J. Prof. Nurs. 37 4 2021 683 689 10.1016/j.profnurs.2021.05.005 34187664 Bang K.S. Kang J.H. Nam E.S. Hyun M.Y. Suh E. Chae S.M. Choi H. Shin D. Nursing students' confidence in clinical competency and job readiness during the COVID-19 pandemic era J. Korean Acad. Soc. Nurs. Educ. 27 4 2021 402 411 10.5977/jkasne.2021.27.4.402 Caliskan A. Ergun Y.A. Examining job satisfaction burnout and reality shock amongst newly graduated nurses Procedia Soc. Behav. Sci. 47 2012 1392 1397 10.1016/j.sbspro.2012.06.831 Duchscher J.E.B. 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==== Front Explor Res Clin Soc Pharm Explor Res Clin Soc Pharm Exploratory Research in Clinical and Social Pharmacy 2667-2766 Published by Elsevier Inc. S2667-2766(22)00104-4 10.1016/j.rcsop.2022.100205 100205 Article Coping mechanisms used by pharmacists to deal with stress, what is helpful and what is harmful? Shahin Wejdan a⁎ Issa Sara a Jadooe Marwah a Shmoae Massara a Yelegin Muhammed a Selvarajah Sharmitha a Dunkley Kay b Thrimawithana Thilini a Stupans Ieva a a Discipline of Pharmacy, School of Health and Biomedical Sciences, RMIT University, Bundoora, VIC 3083, Australia b Pharmacists' Support Services, 381, Royal Parade, Parkville, VIC 3052, Australia ⁎ Corresponding author. 5 12 2022 5 12 2022 1002053 1 2022 5 11 2022 29 11 2022 © 2022 Published by Elsevier Inc. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Background Australian pharmacists encountered increased stressors during the COVID-19 pandemic. This has raised questions regarding the effectiveness of the coping mechanisms used to manage this high work-related stress. Identifying useful and harmful coping mechanisms is critical for providing advice regarding addressing pharmacists' future work-related stress. Objectives This study aimed to explore the impact of pharmacy work on stress experienced by Australian pharmacists and the coping mechanisms used during the COVID-19 pandemic. This study also aimed to evaluate the pharmacists' perceptions of the impact of these coping mechanisms on their stress. Methods A cross-sectional study was conducted. Practising pharmacists and interns were recruited to complete an online survey that included the Perceived Stress Scale (PSS), which was used to measure pharmacists' work-related stress, and the Brief-COPE scale, used to assess the coping mechanisms used during the COVID-19 pandemic. The key outcome measure was the PSS score. A multiple regression analysis was used to evaluate the relationship between coping mechanisms and stress levels in a sample of Australian pharmacists. Results A total of 173 pharmacists and interns were recruited. The mean PSS was 18.02 (SD = 6.7). Avoidant coping mechanisms such as social withdrawal (β = 0.31; p = 0.0001) were significantly positively associated with work-related stress. In contrast, exercise was significantly negatively associated with work-related stress (β = −0.21; p = 0.009). The most frequently reported perceived barrier to seeking help was feeling burnt out and underappreciated. Conclusions This study highlights the association of coping mechanisms used by pharmacists during the COVID-19 pandemic with work-related stress. The study results demonstrate the importance of physical activity and spending time with pets in reducing work-related stress levels. Avoiding harmful coping mechanisms such as social withdrawal and drinking alcohol is recommended. This study also highlights the need for interventional studies to reduce work-related stress levels among pharmacists by addressing useful coping mechanisms. Keywords Perceived stress scale Coping mechanisms Pharmacists Covid19 pandemic ==== Body pmc1 Introduction The Australian Psychological Society describes stress as feeling overloaded, wound-up, tense, and worried, and occurs when an individual faces a situation that they feel unable to cope with.1 Different causes of stress have been identified; one of these is an individual's workplace. The World Health Organization (WHO) defines work-related stress as “the response people may have when presented with work demands and pressures that are not matched to their knowledge and abilities and which challenge their ability to cope”.2 Although the terms “stress” and “burnout” are sometimes used interchangeably, they are not the same, and there are key differences between these two terms. Burnout is a response to extended, excessive stress that leaves an individual mentally and physically drained, cynical, detached, and less effective. The WHO defines burnout as “a syndrome conceptualized as resulting from chronic workplace stress that has not been successfully managed.3” Therefore, it is important to manage stress before it leads to burnout. It has been recognized that healthcare professionals, including pharmacists, suffer from high levels of stress,4 with almost 60% of all Australian pharmacists' stressors being related to workplace issues.5 In comparison, workplace issues accounted for approximately 32% of all stressors for the entire Australian population.6 In addition, the scope of practice for a pharmacist has expanded in the past two decades with increasingly autonomous, direct patient care roles across all settings.7 These additional roles include the provision of vaccination services, medication management reviews, and chronic disease screening services, which have led to an increase in work-related stress experienced by pharmacists due to inefficient work environments, the burden of non-clinical duties, excessive workloads and lack of resources necessary to achieve desired clinical outcomes.7 More recently, the COVID-19 pandemic has further increased stress for pharmacists.8 According to a recent study involving more than 600 pharmacists in Australia, 35.9% of pharmacists reported an increased workload during the pandemic, and more than 50% of the pharmacists were obliged to work overtime.9 Moreover, an increase in harassment and verbal abuse experienced by pharmacists during the pandemic has been reported.10 Consequently, stress among pharmacists working under such conditions has precipitated serious health issues, including insomnia, depression, and anxiety, and has negatively impacted mental health and well-being.4 , 11 , 12 Therefore, it is important to evaluate pharmacists' work-related stress levels and identify useful coping strategies to support pharmacists' adjustments to stressful work conditions. Coping is broadly defined as the conscious or unconscious cognitive and behavioural strategies an individual employs to manage stress.13 The coping process consists of two parts: the primary appraisal of the event as being harmful or threatening; and the secondary appraisal of one's coping options or mechanisms that can be used to deal with the potentially stressful event.14 The American Psychological Association has proposed coping strategies to minimise work-related stress, and these include tracking (e.g., identifying workplace stress situations), developing healthy responses (e.g., good-quality sleep, exercise), establishing boundaries between work and home life (e.g., not checking work email from home), taking time to recharge, learning how to relax (e.g., meditation, deep breathing exercises), talking to supervisors, and/or getting professional support.15 Coping strategies are often categorised into three different groups: avoidant, emotional, and problem-focused coping strategies. Avoidant strategies seek to avoid the stressor and one's reaction to it, such as withdrawing from others, substance use, and denying the reality of the stressor.16 Emotion-focused strategies aim to manage emotional distress (e.g., seeking out social support), while problem-focused strategies intend to modify the problem at hand, e.g., informational support and active coping.17 There is much debate about strategies most beneficial in managing work-related stressors. For example, avoidance strategies may help reduce short-term stress but are generally considered harmful from the perspective of physical well-being as no direct actions are taken to reduce the stressor, leaving the individual feeling helpless or self-blaming.16 Previous studies have reported on the coping mechanisms used by healthcare providers,18 including pharmacists.5 , 8 , 19 The Australian National Stress and Wellbeing Survey involved more than 700 pharmacists, interns, and pharmacy students. This survey addressed several coping strategies used by the participants to manage work-related stress. These included turning to family, friends, and colleagues, exercise, mindfulness, praying, drinking alcohol, self-medication, turning to support groups or insurance companies, holidays, or changing jobs.5 However, limited studies have evaluated coping mechanisms used by pharmacists to manage stress during the COVID-19 pandemic or determined the association of coping mechanisms used by pharmacists with their stress levels. Consequently, this study aimed to explore pharmacists' work-related stress during the COVID-19 pandemic and to identify the coping strategies used during the pandemic. The results of this study may assist in developing support strategies for pharmacists to improve work satisfaction and reduce work-related stress. 2 Materials and methods 2.1 Study design and setting This study was conducted as a cross-sectional study in Australia after obtaining ethics approval from the RMIT University Human Ethics Committee (24747). The study was designed to assess the relationship between stress experienced by Australian pharmacists during the COVID-19 pandemic and their coping strategies. After being piloted by 6 pharmacists, an online survey displayed in Qualtrics software® was launched from September 6th to September 29th, 2021. Data were collected using a convenience sampling process. Participants were recruited from the Australian pharmacy workforce. Participants were given access to the participant information sheet, which explained the study. The completion of the anonymous survey implied consent. 2.2 Study participants Both hospital and community pharmacists were recruited by contacting them through their workplace email. The survey was a convenience sample, email addresses of Australian pharmacies were sourced in three ways: 1) researchers' professional networks (emailing pharmacies known to researchers); 2) using Google as a search engine and searching for pharmacies; 3) calling the pharmacies and requesting their email addresses if their email addresses were not shown online. In addition, the survey was promoted on the Society of Hospital Pharmacists of Australia Newsletter (SHPA), the Australian Journal of Pharmacy (AJP), and on popular social media, such as Facebook pharmacy groups and LinkedIn. A reminder email was sent at the end of the first week of data collection to encourage participants to complete the survey. Those who met the following inclusion criteria were invited to participate in this study: registered pharmacist, intern pharmacist, community pharmacist, hospital pharmacist, over 20 years of age, and had worked in Australia in 2020–2021. Regarding registration status, participants were asked to select one of the following responses: “community pharmacist,” “intern pharmacist,” “hospital pharmacist,” and “others.” Participants who selected “others” were excluded from the study as the study focused on pharmacists with a direct patient care role. 2.3 Questionnaire development The self-report questionnaire consisted of 4 sections and 19 questions. The first section was comprised of socio-demographic information, including age, gender, location of pharmacy workplace, work status (full-time, part-time, casual, or other), number of working hours, and years of experience. The second section of the questionnaire assessed perceived barriers to seeking help for work-related stress. Respondents were asked to select one or more of the following barriers that they believe prevented them from seeking help when they were stressed in their workplace: not aware of adequate stress coping mechanisms, cannot rely on friends, family, or colleagues, work-life balance is compromised, feeling burnout or underappreciated, understaffed, work environment is not a safe place, do not feel fit in at work, too shy to seek help, time and resources are limited, self-reliant to seek help, job security, stigmas or/and others. The third and fourth sections used validated and reliable tools to assess stress levels20 , 21 and to explore coping mechanisms used by pharmacists.22 Content validity of the questionnaire was reviewed by two academic researchers and one social worker to ensure that the questions covered all aspects of the study's aims. 2.3.1 Assessment of stress level Stress experienced by pharmacists' was measured using Perceived Stress Scale (PSS), a validated and widely used instrument.20 , 21 , 23 , 24 The PSS normally consists of 10 or 14 items. In this study, the 10-item version (PSS-10) of the survey was used due to the reported benefits in measuring psychometric properties compared to the 14 items tool.25 The PSS-10 has previously been used in a similar research study and with large probability samples.5 All items are measured on a 5-point Likert scale from 0 to 4, and four items in the PSS-10 tool are reverse-scored. An aggregate stress score was calculated for each participant by taking the sum of scores across the 10 items on the PSS, where the lowest possible score is 0, and the highest possible score is 40. The higher scores indicate higher stress levels, as stated by Cohen et al..26 In this study, the original questions were revised by replacing the words “in the last month” with “during the COVID-19 pandemic” to ensure the study captured the effects of the COVID-19 pandemic on stress levels (Appendix 1). In addition, participants were asked to indicate the percentage of stress experienced during the COVID-19 pandemic that they perceived was related to the pharmacy-specific workplace. 2.3.2 Coping mechanisms used by pharmacists The fourth section of the questionnaire utilized the Brief-COPE, a validated and widely used instrument that measures coping strategies in the health context.22 , 27 This questionnaire has been used in previous studies for measuring coping mechanisms used by pharmacists.19 In addition, it is reported to provide valid results for measuring coping strategies used by different populations, such as patients with different types of illnesses,28 healthcare providers, such as nurses and doctors,29 and pharmacy students.23 The Brief-COPE questionnaire has a 3-factor structure that measures coping across 14 dimensions30: acceptance, active coping, behavioural disengagement, denial, emotional support, humour, instrumental support, planning, positive reframing, religion, self-blame, self-distraction, substance use, and venting. However, dimensions may be omitted or replaced, or more dimensions may be added to the scale depending on the studied population.22 Participants were asked to report how often they used each mechanism to cope with their stressors, ranging from 1 (I have not been doing this at all) to 4 (I have been doing this a lot). Higher scores indicated higher levels of using these coping approaches. Elements of the original questionnaire were reframed into real-life coping mechanisms to make it easier for participants to understand the concepts of the three factors of the Brief-COPE questionnaire. These included: 1) visiting healthcare providers, doctors, psychologists, and therapists; 2) spending time with pets; 3) spending time with family and friends; 4) sport; 5) spirituality (meditation, yoga, religion, mindfulness, and other); 6) taking leave; 7) leaving the current workplace or changing the job; 8) using pharmacy organisations such as Pharmacist's Support service (PSS), Professional Pharmacists Australia (PPA), or other support services; 9) drinking alcohol; 10) substance misuse; 11) social withdrawal; 12) binge eating. Many of these strategies were reported to be used by Australian pharmacists in a national survey that recruited more than 1000 participants.31 In line with previous research and to enhance the practicality of the Brief-COPE scale, we used a previously-derived 3-factor model for our analyses32., 33., 34.: problem-focused coping (active coping, planning), emotion-focused coping (positive reframing, acceptance, humour, religion), and avoidant coping (behavioural disengagement, denial, substance and alcohol use, binge eating, and venting). Prior to implementation of the survey, a pilot study was conducted to examine the questions and statements. This pilot study evaluated how long it took to complete the survey and identified concerns that might lead to biased answers. The process involved administering the survey to 6 practicing pharmacists and to volunteer pharmacists working at the Pharmacists' Support Service. In addition, the intraclass correlation was used to determine the degree to which the researchers in this study agreed with one another on the extent to which each selected coping mechanism belonged to one of the three main categories (i.e., emotional, problem-focused, or avoidant). 2.4 Data analysis Data were analysed using the IBM Statistical Package for the Social Sciences software® (Version 26) for Windows. Qualtrics' inbuilt data analysis and report system were also used as a supplementary data analysis tool for basic statistics and data. Descriptive statistics examined participants' socio-demographics characteristics and all other variables, including frequencies, percentages, means, and standard deviations. One-way analysis of variance (ANOVA) was used to assess for significant differences in perceived stress based on pharmacists' workplace (community vs. hospital), work experience, age group, and gender. A stepwise regression analysis was used to identify possible predictors of stress level of the following variables: all coping mechanisms listed in Table 1 and demographics listed in Table 2 . Variables were tested and added according to their contribution to the model. The variable with the smallest p-value was selected first, and then additional independent variables were added in a stepwise method to determine whether one would make a significant contribution to the current model. If so, it was kept; if not, this variable was eliminated. At each step, variables were chosen based on their contribution to the model's R2 and were excluded if the p-value was insignificant (>0.05).35 , 36 Table 1 General information and demographic data of study participants (n = 173). Table 1Respondent type N (%) Community pharmacist 120(69.4) Hospital pharmacist 38 (22.0) Intern pharmacist 6 (3.9) Missing 9 (4.7) State or Territory Victoria 89 (51.4) New South Wales 34 (19.7) Canberra 3 (1.7) Queensland 12 (6.9) North Territory 5 (2.9) South Australia 7 (4) Western Australia 6 (3.4) Tasmania 17 (9.8) Location Metropolitan area -Major city 100 (57.8) Regional city 47 (27.2) Rural town 21 (12.1) Remote region 2 (1.2) Other 2 (1.2) Missing 1 (0.57) Age (range) 20–30 48 (27.7) 31–40 76 (43.9) 41–50 25 (14.5) 51–60 15 (10.4) 61+ 5 (2.9) Missing 4 (2.3) Gender Male 52 (30.1) Female 119 (68.8) Do not wish to specify 1 (0.6) Missing 1 (0.57) Work experience <1 year 6 (3.5) 1–5 33 (19.1) 5–10 50 (28.9) 10–20 50 (28.9) >20 years 30 (17.3) Missing 3 (1.7) Current employment type Full time 106 (61.3) Part-time 44 (25.4) Casual 8 (4.6) Missing 15 (8.7) Table 2 Correlations between pharmacists' work-related stress and coping mechanisms. Table 2 1 2 3 4 5 6 7 8 9 10 11 12 13 1. Stress level 1 2. Binge eating 0.36 1 3. Social withdrawal 0.50 0.38 1 4. Alcohol 0.41 0.38 0.34 1 5. Substance misuse 0.04 0.05 0.13 0.26 1 6. Friends, colleagues, family −0.35 0.12 −0.36 −0.17 0.13 1 7. Spending time with pets −0.3 0.09 −0.21 −0.14 0.04 0.24 1 8. Exercise −0.42 −0.25 −0.45 −0.15 0.06 0.44 0.32 1 9. Spirituality, meditation, religion −0.08 0.02 −0.14 −0.14 −0.02 0.30 0.07 0.33 1 10. Taking leave 0.08 0.01 0.19 0.05 −0.13 0.02 0.08 −0.02 0.1 1 11. Visiting GPs, psychologist, counsellor, etc 0.06 0.06 0.01 0.03 −0.12 0.01 −0.03 −0.04 0.17 0.27 1 12. Leaving current job 0.15 0.09 0.14 −0.03 −0.06 0.13 0.06 −0.01 0.06 0.57 0.41 1 13. Pharmacy organisations 0.07 0.1 0.01 0.02 −0.05 0.08 0.05 −0.07 0.02 0.46 0.39 0.64 1 All significant correlations (p < 0.05) are bolded. 3 Results 3.1 Survey tools The internal reliability of PSS and Brief-COPE mechanisms were assessed using Cronbach's alpha (0.87 and 0.7, respectively) and Intra Class Correlation (ICC) estimates, and their 95% confidence intervals were calculated based on absolute-agreement and 2-way mixed-effects model (ICC = 0.93, CI 0.84–0.97, p = 0.0001). 3.2 Demographics A total of 173 participants were recruited. Participants' demographic characteristics are described in Table 1. The highest response rate was from community pharmacists (69.4%), followed by hospital pharmacists (22.0%). More than half of the participants were from the state of Victoria (51.4%). Most participants were working full-time (61.3%), between 31 and 40 years of age (43.9%), and working in metropolitan areas (57.8%) during the study period. The sample included more females (68.8%) than males (30.1%), reflecting the current gender balance of the pharmacy profession. According to the Pharmacy Board of Australia registrant statistics, 63% of pharmacists are female, and 37% are male.37 The study sample reflected more experienced pharmacists with 5–10 years of experience (28.9%) and 10–20 years experience (28.9%). Only 3.5% (n = 6) of the participants had less than one year of experience. In addition, the highest number of participants were in the 30–34 year-old age bracket. 3.3 Assessment of stress level 3.3.1 Perceived stress scale There were no significant differences in stress levels between male and female pharmacists (p = 0.86), between age groups (p = 0.37), nor between part-time and full-time employment status (p = 0.96). In addition, there was no significant difference in stress perceived by hospital and community pharmacists (p = 0.6). Respondents were asked to indicate the proportion of stress related to a pharmacy workplace, non-pharmacy workplace, and other sources. The pharmacy workplace accounted for 56.3% of the total stress they encounter in their life. 3.3.2 Perceived barriers to seeking help Most participants reported burnout and being underappreciated as perceived barriers to seeking help (60.7%). This was closely followed by pharmacists being understaffed in their workplaces (55.3%). In addition, pharmacists reported that time and resources were barriers to seeking help (45.7%) (Fig. 1 ).Fig. 1 Perceived barriers to seeking help to manage work-related stress by Australian pharmacists. Fig. 1 3.3.3 Coping mechanisms used by pharmacists The most frequently reported coping mechanism used by pharmacists in this study was taking leave (45.9% of participants), followed by spending time with friends/colleagues/family (26.0%) and spending time with pets (23.8%). The least frequently used coping mechanisms were substance misuse and pharmacy organisations or support services (1.4%). Pearson's correlations (r) were used to examine the bivariate associations for dependent variables. Emotional coping mechanisms (spending time with friends, colleagues, and family; spending time with pets and exercise) were significantly negatively correlated with pharmacists' stress, while avoidant coping mechanisms (binge eating, social withdrawal, and alcohol) were significantly positively associated with pharmacists' stress. Problem-focused coping strategies were positively but non-significantly associated with pharmacists' stress. Stepwise multiple regression analysis was conducted to determine the predictors that accounted for the most variance in pharmacists' stress levels. Six independent variables that the researchers determined to be significant underwent stepwise regression analysis (Table 2). Stepwise regression revealed that the significant predictors of stress levels were the use of alcohol, social withdrawal, spending time with pets, and exercise. (R2 = 0.38; p = 0.001) (Table 3 ).Table 3 Beta coefficients of significant coping mechanisms. Table 3Coping mechanisms Beta Coefficient t value P value Alcohol 0.27 3.9 0.0001 Binge eating 0.11 1.7 0.1 Exercise −0.21 −2.7 0.009 Friends/colleagues −0.10 −1.4 0.16 Social withdrawal 0.31 4.2 0.0001 Spending time with pets −0.14 −2.1 0.03 Emotional coping strategies, particularly exercise (β = −0.21, p = 0.009) and spending time with pets (β = −0.14, p = 0.03), were negatively associated with pharmacists' stress levels. This indicates that those strategies are useful in managing pharmacists' work-related stress. In contrast, alcohol (β =0.27, p = 0.0001) and social withdrawal (β =0.31, p = 0.01) were associated positively with stress levels (Table 3). 4 Discussion This study evaluated Australian pharmacists' stress levels during the COVID-19 pandemic and the coping mechanisms used to manage work-related stress. In addition, this study found a positive relationship between exercise and spending time with pets to manage work-related stress among Australian pharmacists and a negative relationship between drinking alcohol and social withdrawal as coping mechanisms. The results from this study are consistent with existing studies on stress levels and coping mechanisms used by healthcare providers.37 , 38 In previous studies, stress level was negatively associated with emotional coping and positively with maladaptive or denial coping mechanisms. However, none of these studies addressed coping mechanisms used by pharmacists. Additionally, most studies address burnout rather than stress.39 , 40 Burnout makes it challenging for people to cope with stress.41 Thus it is important to understand factors that can help to mitigate stress in pharmacy settings, as stress has been significantly associated with medication errors.42 Interestingly, participants in this study had lower stress levels than in a similar study in Australia that was completed prior to the pandemic (PSS score of 18.0 in this study vs. 19.6 in the previous study). The large number of participants (586 registered pharmacists) in the earlier study may explain the differences in work-related stress.5 In addition, in the current study, most participants had more than 5 years of experience working as a pharmacist. Increasing proficiency could explain why stress decreases as experience increases.43 According to the current study, pharmacists frequently use avoidant coping mechanisms such as drinking alcohol, social withdrawal, and binge eating. Avoidant coping mechanisms are considered harmful because they exacerbate stress and do not help a person deal with the stressors.44 Unexpectedly, social withdrawal was found to be the most significant coping mechanism that increased stress levels of pharmacists. Due to COVID-19 restrictions, some pharmacists were separated physically from their families and friends, leading to unintended social withdrawal. This may also involve shutting themselves off from social support and blaming themselves for the stressors leading to ineffective management of emotions.45 It is reported that social withdrawal immediately following exposure to a stressor may alleviate some of the negative effects of the stressful encounter by returning the individual's mood, arousal, and energy to baseline levels.46 However, a lack of social connection predisposes individuals to maladaptive stress responses like smoking and alcohol abuse with negative implications for physical and mental health.47 Therefore, there is a need to address social withdrawal by implementing regular workplace meetings and facilitating training activities and professional peer support networks.48 For example, managers' personal life coaching and constructive feedback may increase pharmacists' self-efficacy and reduce the belief of incapability and the tendency to isolate.49 Alcohol consumption was another significantly harmful coping mechanism. Alcohol consumption significantly increased emotional exhaustion as alcohol can change mood and modify awareness of emotions.50 Considering this, it is crucial to prevent an increase in alcohol intake. It may be prudent to increase pharmacists' awareness about the negative consequences of alcohol use, defuse the perception of the instant relief of alcohol in stressful situations, and promote safe consumption. Pharmacists also should be advised to use emotional focus coping strategies such as exercise to reduce alcohol intake. Spending time with pets is an important coping mechanism for pharmacists to lower stress levels. Attachment to a pet can increase self-efficacy and self-esteem and encourage owners to feel positive emotions, which can positively affect their coping strategies for managing stress.51 According to a recent meta-analysis, animal-assisted interventions help reduce healthcare workers' stress levels and enhance their overall well-being by affecting their moods and perceptions of feelings such as happiness, relaxation, and calmness. Also, implementing animal interventions in healthcare settings is feasible and acceptable by healthcare providers because of its significant positive impact on mental health, social contact, and communication.52 Therefore, pharmacy workplaces should consider adopting strategies such as pet therapy to provide pharmacists with a mode to reduce work-related stress; this may be achieved by allocating time for a trained animal handler to provide the service in pharmacy settings. Expectedly, the impact of physical activities and exercise on stress levels in this study appears in alignment with the findings of several previous studies.53., 54., 55. Regular physical activity should be considered for inclusion into the daily schedule of a pharmacist to manage work-related stress levels. The employment of casual staff members may achieve this by providing a short physical activity break for regular pharmacists. Additionally, employers may enroll pharmacists in career coaching programs that may decrease stress among pharmacists and lead to an increase in workplace satisfaction.56 The COVID-19 pandemic may have introduced barriers to help-seeking, such as increased pressures on time and being understaffed. The concept of work-life balance has long been a method of sustaining well-being and mitigating these barriers. However, high work demands, fear of contracting the COVID virus, and various social restrictions during the pandemic have impeded common ways of maintaining work-life balance.57 This study recommends further research to understand the nature of pharmacists' perceived barriers to help-seeking behaviours. Additionally, qualitative research is needed to explore how coping mechanisms such as social withdrawal, alcohol, spending time with pets, and exercise are associated with stress to enable the implementation of effective interventions in pharmacy work settings. This study also highlights the importance of conducting future studies to reduce the use of avoidant coping mechanisms by using interventions such as coaching, education, and peer support. 5 Limitations A self-reported measure was used to determine pharmacists' coping mechanisms and stress levels; hence, there might be over- or under-estimation of stress and the use of coping mechanisms. Also, due to the nature of the questionnaire outlining a pre-determined list of coping mechanisms, stressors, and barriers, this may limit the participants from selecting other options that were not listed. Additionally, participant responses were measured at only one-time point. In practice, there are likely to be complex inter-relationship among stressors at home and work, lockdown restrictions, and coping strategies that cannot be untangled with a cross-sectional survey alone. Moreover, over 50% of recruited participants were from the state of Victoria, which experienced extended COVID-19-related lockdowns. Another limitation of this study was the pandemic restrictions that might have influenced coping strategies such as exercise and social withdrawal. Therefore, a longitudinal study is warranted to understand better the relationship between coping mechanisms and pharmacists' work-related stress. Additionally, the role of pets in managing stress can not be generalised because people who do not like spending time with pets or those with pet allergies are less likely to benefit from this coping mechanism. 6 Conclusion This study highlights the concerning moderate work-related stress levels of pharmacists across Australia during the COVID-19 pandemic. Additionally, it highlights the importance of addressing work-related stress through emotion-based coping strategies such as exercise and pet therapy. In addition, the results demonstrate the need to avoid harmful coping mechanisms such as the consumption of alcohol and social withdrawal. Declaration of Competing Interest None. Appendix A Supplementary data Supplementary material Image 1 Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.rcsop.2022.100205. ==== Refs References 1. 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The psychiatric impact of COVID-19 on healthcare workers Aust J Gen Pract 49 2020 791 795 33254202	36506648	PMC9719933	NO-CC CODE	2022-12-09 23:15:18	no	Explor Res Clin Soc Pharm. 2023 Mar 5; 9:100205	utf-8	Explor Res Clin Soc Pharm	2,022	10.1016/j.rcsop.2022.100205	oa_other
==== Front Innovation (Camb) Innovation (Camb) The Innovation 2666-6758 The Authors. S2666-6758(22)00155-2 10.1016/j.xinn.2022.100359 100359 Report Single-cell transcriptome sequencing reveals the immune response and homeostasis mechanism following administration of BBIBP-CorV SARS-CoV-2 inactivated vaccine Yin Jianhua 123 Zhao Yingze 23423 Huang Fubaoqian 1523 Yang Yunkai 623 Huang Yaling 123 Zhuang Zhenkun 1523 Wang Yanxia 723 Wang Zhifeng 18 Lin Xiumei 19 Zheng Yuhui 19 Zhou Wenwen 110 Wang Shuo 1 Xu Ziqian 2 Ye Beiwei 2 Guo Yaxin 2 Lei Wenwen 2 Li Lei 211 Tian Jinmin 212 Gan Jinxian 213 Wang Hui 14 Wang Wei 14 Ma Peiyao 15 Liu Chang 1 Wei Xiaoyu 115 Shi Xuyang 115 Wang Zifei 1 Wang Yang 1 Liu Ying 19 Yang Mingming 16 Yuan Yue 115 Song Yumo 1 Ma Wen 1 Huang Zhuoli 19 Liu Ya 1 Huang Yunting 17 Lu Haorong 17 Liu Peipei 2 Liang Hao 13 Hou Yong 189 Xu Xun 118 Liu Longqi 115 Zhang Yuntao 6∗∗ Wu Guizhen 24∗∗∗ Gao George F. 23419∗ Jin Xin 12021∗∗∗∗ Liu Chuanyu 115∗∗∗∗∗ Yang Xiaoming 622∗∗∗∗∗∗ Liu William J. 234∗∗∗∗∗∗∗ 1 BGI-Shenzhen, Shenzhen 518103, China 2 NHC Key Laboratory of Biosafety, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention (China CDC), Beijing 100052, China 3 Research Unit of Adaptive Evolution and Control of Emerging Viruses (2018RU009), Chinese Academy of Medical Sciences, Beijing 102206, China 4 Center for Biosafety Mega-Science, Chinese Academy of Sciences, Wuhan 430071, China 5 School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China 6 China National Biotec Group Company Limited, Beijing 100029, China 7 Henan Provincial Center for Disease Control and Prevention, Zhengzhou 450018, China 8 Shenzhen Key Laboratory of Single-Cell Omics, BGI-Shenzhen, Shenzhen 518120, China 9 College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China 10 South China Agricultural University, Guangzhou 510642, China 11 School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou 325035, China 12 School of Optometry and Ophthalmology and Eye Hospital, Wenzhou Medical University, Wenzhou 325035, China 13 Biosafety Level-3 Laboratory, Life Sciences Institute & Collaborative Innovation Centre of Regenerative Medicine and Medical BioResource Development and Application, Guangxi Medical University, Nanning 530021, China 14 Beijing Institute of Biological Products, Beijing 100176, China 15 BGI-Hangzhou, Hangzhou 310012, China 16 BGI-Qingdao, BGI-Shenzhen, Qingdao 266555, China 17 China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China 18 Guangdong Provincial Key Laboratory of Genome Read and Write, BGI-Shenzhen, Shenzhen 518120, China 19 CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences (CAS), Beijing 100101, China 20 School of Medicine, South China University of Technology, Guangzhou 510006, China 21 Guangdong Provincial Key Laboratory of Human Disease Genomics, Shenzhen Key Laboratory of Genomics, BGI-Shenzhen, Shenzhen 518083, China 22 National Engineering Technology Research Center for Combined Vaccines, Wuhan Institute of Biological Products Co Ltd, Wuhan 430207, China ∗ Corresponding author (G.F.G.) ∗∗ Corresponding author (Y.Z.) ∗∗∗ Corresponding author (G.W) ∗∗∗∗ Corresponding author (X.J.) ∗∗∗∗∗ Corresponding author (C.L.) ∗∗∗∗∗∗ Corresponding author (X.Y.) ∗∗∗∗∗∗∗ Corresponding author (W.J.L.) 23 These authors contributed equally 5 12 2022 5 12 2022 10035912 3 2022 1 12 2022 1 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. The BBIBP-CorV severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) inactivated vaccine has been authorized for emergency use and widely distributed. We used single-cell transcriptome sequencing to characterize the dynamics of immune responses to the BBIBP-CorV inactivated vaccine. In addition to the expected induction of humoral immunity, we found that the inactivated vaccine induced multiple, comprehensive immune responses, including significantly increased proportions of CD16+ monocytes and activation of monocyte antigen presentation pathways; T-cell activation pathway upregulation in CD8+ T cells, along with increased activation of CD4+ T cells; significant enhancement of cell–cell communications between innate and adaptive immunity; and the induction of regulatory CD4+ T cells and co-inhibitory interactions to maintain immune homeostasis after vaccination. Additionally, comparative analysis revealed higher neutralizing antibody levels, distinct expansion of naïve T cells, a shared increased proportion of regulatory CD4+ T cells, and upregulated expression of functional genes in booster dose recipients with a longer interval after the second vaccination. Our research will support a comprehensive understanding of the systemic immune responses elicited by the BBIBP-CorV inactivated vaccine, which will facilitate the formulation of better vaccination strategies and the design of new vaccines. Graphical abstract ==== Body pmc	36506806	PMC9719934	NO-CC CODE	2022-12-06 23:26:02	no	Innovation (Camb). 2022 Dec 5;:100359	utf-8	Innovation (Camb)	2,022	10.1016/j.xinn.2022.100359	oa_other
==== Front Pregnancy Hypertens Pregnancy Hypertens Pregnancy Hypertension 2210-7789 2210-7797 International Society for the Study of Hypertension in Pregnancy. Published by Elsevier B.V. S2210-7789(22)00128-3 10.1016/j.preghy.2022.11.008 Article Role of biomarkers (sFlt-1/PlGF) in cases of COVID-19 for distinguishing preeclampsia and guiding clinical management Nobrega Guilherme M. a Guida Jose P. a Novaes Juliana M. a Solda Larissa M. a Pietro Luciana ab Luz Adriana G. a Lajos Giuliane J. a Ribeiro-do-Valle Carolina C. a Souza Renato T a Cecatti Jose G. a Mysorekar Indira U. cd Dias Tabata Z. a Laura Costa Maria a⁎ a Department of Obstetrics and Gynecology, School of Medical Sciences, University of Campinas, Campinas, SP, Brazil b Institute of Health Sciences, Paulista University, Brazil c Department of Medicine, Section of Infectious Diseases, Baylor College of Medicine, Houston, TX 77030, USA d Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX 77030, USA ⁎ Corresponding author. 5 12 2022 3 2023 5 12 2022 31 3237 23 5 2022 11 10 2022 28 11 2022 © 2022 International Society for the Study of Hypertension in Pregnancy. Published by Elsevier B.V. All rights reserved. 2022 International Society for the Study of Hypertension in Pregnancy Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Objectives To analyze soluble fms-like tyrosine kinase 1 (sFlt-1) and placental growth factors (PlGF) concentrations and their ratio in pregnant and postpartum women with suspected COVID-19, and further investigate conditions associated with an increased ratio (sFlt-1/PlGF > 38), including preeclampsia (PE) and severe acute respiratory syndrome (SARS). Study Design The present study is a secondary analysis of a prospective cohort. Blood samples were collected at time of COVID-19 investigation and the serum measurements of sFlt-1 and PlGF were performed. Clinical background, SARS-CoV-2 infection characteristics, maternal and perinatal outcomes were further analyzed. Main outcome measures Serum measurements of sFlt-1 and PlGF; obstetrics and clinical outcomes. Results A total of 97 SARS-CoV-2 unvaccinated women with suspected infection were considered, 76 were COVID-19 positive cases and 21 COVID-19 negative. Among COVID-19 positive cases, 09 presented with SARS and 11 were diagnosed with PE, of which 6 had SARS-CoV-2 infection in first and second trimester (04 with sFlt-1/PlGF ≥ 38) and 05 with PE and COVID-19 diagnosed at the same time, during third trimester (03 with sFlt-1/PlGF ≥ 38). Five presented with PE with severe features. sFlt-1/PlGF ratio was significantly higher in the COVID-19 positive/PE positive group compared to COVID-19 positive/PE negative group (p-value = 0.005), with no increase in cases complicated by SARS. Conclusions sFlt-1/PlGF ratio could be a useful tool for differential diagnosis and adequate counseling among cases of COVID-19 and PE, especially if severe disease. COVID-19 early in pregnancy could potentially be a risk factor for PE later during gestation. Keywords Maternal health COVID-19 Hypertensive disorders of pregnancy Renin-angiotensin system ==== Body pmc1 Introduction Hypertensive disorders of pregnancy, especially preeclampsia (PE), represent main contributors to maternal mortality and morbidity worldwide and most significantly in low and middle-income populations [1], [2]. During the COVID-19 pandemic, these populations have also been disproportionately affected by severe infections due to delays in healthcare, difficulties in testing and managing the disease, restricted availability of intensive care units, and inadequate management of complications, all aggravated by low access to vaccination. Outcomes are particularly striking for COVID-19 infections during pregnancy and postpartum, with increased numbers reported for maternal morbidity and mortality not only due to the challenges with healthcare, but also because of pregnancy itself as a risk factor for severe disease [3], [4], [5]. The association between COVID-19 and PE has been recently highlighted by different studies, showing increased frequencies of PE among cases of COVID-19 [6]. The rationale to explain such findings is based on the similar pathophysiology and clinical presentation of both conditions [7]. PE is a syndrome with multisystemic organ involvement associated with inflammation and endothelial damage. Plasma concentrations of pro-angiogenic/anti-angiogenic factors released by the placental syncytiotrophoblast have been identified as markers of disease progression. In PE, soluble fms-like tyrosine kinase 1 (sFlt-1, an anti-angiogenic protein) increases, and placental growth factor (PlGF, a pro-angiogenic protein) decreases. Therefore, sFlt-1 and PlGF directly and inversely correlate, respectively, with disease onset [8], [9], [10]. COVID-19 has a few similarities as it also presents with multi-organ involvement and extensive inflammation and endothelial damage, therefore the differential diagnosis can be challenging especially in severe acute respiratory syndrome (SARS) cases, leading to what has been referred to as “PE-like syndrome” [11], [12]. Adequate diagnosis is key in ascertaining appropriate clinical management, especially in cases of preterm gestation since the decision on the timing of delivery has a significant impact on perinatal outcomes. Because clinical parameters of hypertension, proteinuria, and target organ damage can be similar, biomarkers could be a useful tool in distinguishing and guiding such decision-making for cases that involve COVID-19 and preeclampsia [13]. The sFlt-1/PlGF ratio at a threshold of below 38 can provide reassurance for the absence of PE at that given time, as well as indicate a low likelihood of onset in the following week. However, there are still uncertainties about the levels of biomarkers in COVID-19 cases given previous reports of high values of sFlt-1 in patients with COVID-19 pneumonia vs COVID-19 without pneumonia [8]. Therefore, the aim of this study was to analyze sFlt-1 and PlGF concentrations and their ratio in pregnant/postpartum women with suspected COVID-19, and further investigate conditions associated with the increased ratio (sFlt-1/PlGF > 38), including preeclampsia and severe COVID-19 cases. 2 Methods 2.1 Participants The present research is part of a large prospective cohort study included in REBRACO – Brazilian Network of COVID-19 in Obstetrics – a multicenter study of 15 Brazilian referral centers from different regions across the country aiming to understand the burden of disease on maternal and perinatal outcomes related to COVID‐19 infection, with the University of Campinas (Unicamp) as coordinating center [14], [15]. At Unicamp, SARS-CoV-2 quantitative reverse transcriptase polymerase-chain-reaction (RT-qPCR) assay testing for suspected cases started in April 2020 and routine universal screening was implemented in June 2020. COVID-19 suspected women had a diversity of biological samples collected at Unicamp, including peripheral blood – specifically serum. A total of 135 cases of unvaccinated pregnant or postpartum women who were admitted to the institution from June 2020 to July 2021 were included and considered for testing for SARS-CoV-2 infection by RT-qPCR assay in upper respiratory secretion samples. Of the 135 COVID-19 suspected women considered, 28 who had mild flu-like syndrome did not have their peripheral blood sampled. Of the remaining 107 cases, 5 delivered elsewhere and have no available data for analysis of outcomes – therefore these cases were further excluded. Thus, the total cases eligible for the current analysis were 102 women. The blood sampling occurred at different times in relation to SARS-CoV-2 infection – mostly during active infection or near recovery. Clinical background, maternal SARS-CoV-2 infection characteristics, socio-demographic information and maternal and perinatal outcomes of the included cases were analyzed. The cases were grouped by PE diagnosis, COVID-19 testing, and clinical severity (acute respiratory syndrome (SARS). Pharmacological or immunobiological methods were not administered to any study participants. 2.2 Blood serum samples The blood serum samples were obtained by maternal peripheral blood collection in a dry vacuum tube with clot separator gel. Peripheral blood samples were processed within 1 h after sampling for sample integrity. The blood tubes were centrifuged for 10 min, at 1,200xG at room temperature (18 °C). After centrifugation, the blood serum samples were aliquoted in sterile cryotubes (around 600 μL per cryotube) inside a class II biological safety cabinet in a NB-2 laboratory. The samples in cryotubes were immediately stored in an Ultrafreezer at −80 °C temperature. The entire sample collection and processing team wore appropriate personal protective equipment (PPE) recommended for such pathogens, including disposable N95 masks, disposable apron, disposable gloves, disposable cap, eye protection (glasses or face shield) and closed shoes. 2.3 Electrochemiluminescence for determination of sFlt-1 and PlGF concentration The measurements of angiogenesis-related factors sFlt-1 and PlGF in peripherical blood were performed in the Roche Cobas e411 device (ROCHE®), with automated Elecsys® sFlt-1/PlGF kits (ROCHE®), using 50 μL of maternal serum, through immunoassays for the quantitative determination of such biomarkers based on electrochemiluminescence technology. The protocol was implemented according to the manufacturer instructions. The concentration results obtained by the assay were expressed in pg of the analyte per mL of serum - pg/mL. The values obtained were also used for the calculation of sFlt-1/PlGF ratio. The sFlt-1/PlGF ratio had a threshold implemented of 38 for statistical analysis. 2.4 Clinical data Medical charts were reviewed to retrieve information on sociodemographic characteristics including age; obstetric and clinical background – parity; nulliparous; multiparous; mean height (cm); mean weight (kg); mean BMI (kg/m2); obesity (IMC > 30); hypertension and diabetes variables – and maternal/perinatal outcomes including, PE; PE with severe features; eclampsia; HELLP syndrome; confirmed COVID-19; heart rate (bpm); respiratory rate; systolic blood pressure; diastolic blood pressure; temperature; ICU admission; gestational age at delivery (<37 or ≥37 weeks); route of delivery (vaginal or cesarean section); mean birth weight at delivery (g); 5th minute APGAR score < 7; stillbirth; neonatal death. For confirmed COVID-19 cases, data on gestational age at diagnosis, symptoms and severity of disease were considered (use of supplementary oxygen, intubation, ICU admission). For cases with the diagnosis of PE, detailed information on gestational age at diagnosis, severe features, and outcomes were also retrieved. Suspected COVID-19 was based on the presence of fever and/or at least one respiratory symptom or sign of flu-like syndrome: sore throat, runny nose, cough, sputum production, shortness of breath, nasal or conjunctival congestion, pain swallowing, and O2 saturation < 95 %, signs of cyanosis, and/or flapping of the nose and dyspnea were considered signs of SARS. Other symptoms such as diarrhea, anosmia and dysgeusia were also considered [15]. PE was considered based on the presence of hypertension associated with proteinuria (proteinuria/creatinine > 0.3 or 24 h proteinuria over 300 mg) after 20 weeks of gestation, in a previously normotensive pregnant woman. PE was also considered in the absence of proteinuria if there was target organ damage [16], [17]. 2.5 Data analysis Initially, all cases of suspected and/or confirmed COVID-19 with available blood sample collection and clinical data (including childbirth outcomes) were selected. The biomarker level was considered using a threshold of 38 for the sFlt-1/PlGF ratio. Women were grouped according to such results and clinical data on sociodemographic characteristics, clinical background, and maternal and perinatal outcomes were compared among groups of sFlt-1/PlGF ≥ 38 and sFlt-1/PlGF < 38. Cases of confirmed COVID-19 during pregnancy that also presented with the diagnosis of PE were detailed to describe the timing of infection, the severity of disease and further association to PE. A comparison between different groups was performed that considered sFlt-1 and PlGF serum concentrations and the sFlt-1/PlGF ratios, as well as diagnosis of PE and SARS associated with COVID-19-positive cases. The groups analyzed were classified as: COVID-19-positive cases with diagnosis of PE (COVID-19 + PE + ) versus COVID-19-positive cases without diagnosis of PE (COVID-19 + PE -); and COVID-19-positive cases with diagnosis of SARS (COVID-19 + SARS + ) versus COVID-19-positive cases without diagnosis of SARS (COVID-19 + SARS -). Finally, biomarkers: PlGF ratios, sFlt-1 and sFlt-1/PlGF ratios were described among cases of suspected COVID, comparing those with positive and negative testing and no PE. Comparisons between groups were performed using the Odds Ratio with 95 % confidence interval (CI) and Chi-square test for categorical variables, and Mann-Whitney test (U test) for continuous variables. Outliers were considered by the ROUT method. Statistical analysis tests were done using GraphPad Prism version 7 for Mac (GraphPad Software, San Diego, CA, United States) and Epi.Info 7.0 for Windows. A p-value ≤ 0.05 was considered statistically significant. 2.6 Ethical considerations The research project followed all recommended rules for the use of human biological samples, with approval by the Research Ethics Committee of the University of Campinas, IRB #31591720.5.0000.5404. All women signed an informed consent form authorizing the collection, storage, and use of clinical samples and data. 3 Results There were 135 COVID-19 suspected cases of unvaccinated pregnant and postpartum women included at Unicamp between June 2020 and July 2021. Of these, 97 cases were eligible for electrochemiluminescence assay for determination of sFlt-1 and PlGF concentration in blood serum. After laboratory analysis, 37 cases presented sFlt-1/PlGF ratio ≥ 38 and 60 sFlt-1/PlGF ratio < 38. And among these, 76 were COVID-19 positive cases and 21 COVID-19 negative. In the COVID-19 positive cases, 11 were diagnosed with PE and 9 with SARS. And among the COVID-19 negative cases, 7 had only PE and 1 had PE and SARS (Fig. 1 ).Fig. 1 Flowchart of the study included cases and cases grouping. When grouping women according to a threshold ratio of biomarkers (sFlt-1/PlGF ≥ 38 or sFlt-1/PlGF < 38) to investigate whether clinical diagnosis of PE and COVID-19 were associated with biomarker levels, there were no significant differences (Table 1 ). The presence of PE was more frequent in the sFlt-1/PlGF ≥ 38 (29.73 % vs 11.67; p = 0.051), but with no significance most likely due to the small number of cases and moment of sample collection (gestational age of COVID-19 investigation and not of PE diagnosis). For COVID-19 diagnosis, the frequency was similar in both groups (p = 0.72).Table 1 Sociodemographic, clinical, obstetric characteristics and maternal and perinatal outcomes comparing cases of sFlt-1/PlGF ≥ 38 (increased ratio) and sFlt-1/PlGF < 38. Variable sFlt-1/PlGF ratio ≥ 38 sFlt-1/PlGF ratio < 38 p-value N 37 60 Age (Years) 30.49 ± 5.97 29.08 ± 6.29 0.279 Parity 0.401 Nulliparous 13 (35.14) 15 (25.00) Multiparous 24 (64.86) 45 (75.00) Mean height (cm) 1.62 ± 0.06 1.62 ± 0.05 0.785 Mean weight (kg) 85.25 ± 25.98 83.12 ± 21.26 0.698 Mean BMI (kg/m2) 32.43 ± 9.60 31.87 ± 7.49 0.777 Obesity (IMC > 30) 16 (59.26) 28 (56.00) 0.972 Hypertension 12 (32.43) 10 (16.67) 0.121 Diabetes 14 (37.84) 20 (33.33) 0.816 Preeclampsia 11 (29.73) 7 (11.67) 0.051 Confirmed COVID-19 27 (72.97) 47 (78.33) 0.721 Preeclampsia with severe features 6 (16.22) 4 (6.67) 0.133 Eclampsia 0 (0.00) 0 (0.00) – HELLP Syndrome 2 (5.41) 0 (0.00) 0.068 Heart rate (bpm) 89.86 ± 13.02 92.94 ± 15.09 0.359 Respiratory rate 22.84 ± 11.49 19.32 ± 6.82 0.689 Systolic blood pressure 153.41 ± 183.08 134.15 ± 131.94 0.579 Diastolic blood pressure 75.27 ± 13.56 74.09 ± 11.46 0.672 Temperature 36.75 ± 0.82 36.53 ± 0.85 0.149 ICU admission 4 (10.81) 8 (13.33) 0.961 Gestational age at delivery 0.705 <37 weeks 12 (32.43) 16 (26.67) ≥37 weeks 25 (67.57) 44 (73.33) Route of delivery 0.954 Vaginal delivery 20 (54.05) 31 (53.45) Cesarean section 17 (45.95) 27 (46.55) Mean weight at delivery (g) 2778 ± 828 2917 ± 813 0.420 5th minute APGAR < 7 2 (5.41) 2 (3.57) 0.669 The detailed description of cases of COVID-19 and PE diagnosis are presented in Table 2 . Among the 11 cases of PE and COVID-19, 5 presented PE with severe features and received magnesium sulfate due to severe hypertension and/or imminent eclampsia, such as headache, visual changes, nausea, and vomiting. Almost all considered cases were obese (10 cases), only two were previously diagnosed with chronic arterial hypertension, and over half of them developed gestational diabetes (6 cases). There was only one patient who needed supplementary oxygen use due to COVID-19 infection; she had asthma as a previous condition and did not need invasive ventilatory support. This patient developed secondary bacterial pneumonia and was treated with antibiotics.Table 2 Detailed description of cases of confirmed COVID-19 and preeclampsia. Cases Maternal age Parity Type of pregnancy Previous conditions GA at COVID-19 Diagnosis (weeks) COVID-19 severity* GA at PE diagnosis (weeks) GA At Birth Weight At Birth (g) 5 Min Apgar PE Severe Features** Route Of Delivery sFlt-1/ PlGF ratio Prot/Crea 1 35 Multiparous Single Obesity, GDM 33 Mild 33 36 4595 10 No C-Section 65.12 0.60 2 39 Multiparous Single Obesity, GDM 9 Mild Puerperium 36 2175 9 Yes C-Section 33.13 0.17 3 29 Multiparous Single Obesity 16 Mild 39 40 3645 9 No C-Section 48.84 --- 4 28 Primiparous Twin No 23 Mild 33 33 1120/ 1462 1–4 No C-section 46.95 0.15 5 32 Multiparous Single Obesity, GDM 10 Mild Puerperium 38 2750 9 No C-Section 26.52 0.84 6 27 Primiparous Single Obesity, GDM 24 Asymptomatic 33 33 2085 10 Yes C-Section 222.4 0.70 7 40 Multiparous Single Obesity, GDM, Hep B 35 Mild 37 37 3350 4 Yes C-Section 12.82 0.15 8 34 Multiparous Single Obesity, GDM 19 Asymptomatic 36 36 3100 10 No Vaginal 51.82 0.20 9 28 Primiparous Single Obesity, Asthma, CH 38 Severe 38 38 2880 9 Yes C-Section 32.78 1.03 10 34 Multiparous Twin Obesity 34 Mild 33 36 2430/ 2535 9–8 No C-Section 81.66 0.38 11 35 Multiparous Single Obesity Postpartum Asymptomatic 34 37 2465 10 Yes Vaginal 212.26 2.65 Severity of COVID-19: Severe – hospitalization/oxygen supplementary use; Mild - no hospitalization, mild symptoms (cough, coryza, sore throat, anosmia, mild dyspnea, mild fever). * PE Severe Features: Use of Magnesium Sulfate (imminence of Eclampsia), Eclampsia, HELLP Syndrome. GDM: gestational diabetes melittus; CH: chronic hypertension; HEP B: chronic hepatitis B; GA: gestational age; PE: pre-eclampsia; PROT/CREA: Protein/Creatinine ratio. Based on the timing of COVID-19 infection, only 3 cases presented a concomitant diagnosis of COVID-19 and PE, which was during the third trimester. The majority presented COVID-19 prior to the diagnosis of PE, during the first/second trimester. The only reported case of fetal malformation was a case with COVID-19 at 9 weeks gestation, with Down syndrome (presented with fetal growth restriction and cardiac involvement). One case was diagnosed with COVID-19 postpartum, during the routine screening to visit the neonatal ICU (after a preterm delivery). Our data only included 2 cases with sFlt-1/PlGF ratio over 85, both ratios actually over 200 (cases 6 and 11); and both cases presented PE with severe features and short interval between PE diagnosis and delivery, with asymptomatic COVID-19 infection. The results for sFlt-1 and PlGF serum concentrations and sFlt-1/PlGF ratios were further compared between groups categorized for PE and SARS diagnosis related to COVID-19-positive cases. The groups analyzed were classified as: COVID-19-positive cases with diagnosis of PE [COVID-19 + PE + (n = 11)] versus COVID-19-positive cases without diagnosis of PE [COVID-19 + PE-(n = 65)]; and COVID-19-positive cases with diagnosis of SARS [COVID-19 + SARS + (n = 09)] versus COVID-19-positive cases without diagnosis of SARS [COVID-19 + SARS-(n = 67)]. The findings revealed that the sFlt-1/PlGF ratio was significantly higher in the COVID-19 + PE + group compared to COVID-19 + PE-group (p-value = 0.0047). No significant difference was found in comparison of these groups related to sFlt-1 and PlGF concentrations when isolated. Considering COVID-19 cases grouped by SARS incidence, the analysis of COVID-19 + SARS + versus COVID-19 + SARS - revealed no significant difference in sFlt-1 and PlGF serum concentration, or in the sFlt-1/PlGF ratio (Fig. 2 ).Fig. 2 Boxplots for values of sFlt-1, PlGF and their ratios (sFlt-1/PlGF) according to the presence of preeclampsia (PE) and severe acute respiratory syndrome (SARS). Boxes encompass a range from the 1st to the 3rd quartile, whiskers represent 10–90 percentile and points represent outliers. Mann-Whitney test (test U) was used to compare groups, with p-value ≤ 0.05 as significant. Regarding COVID-19 positive with COVID-19 negative cases, excluding cases with PE, no differences were observed in sFlt-1 levels only (p-value = 0.0861), PlGF levels only (p-value = 0.6473) and in sFlt-1/PlGF ratio (p-value = 0.5337) (Supplemental Fig. 1). 4 Discussion This study presented the sFlt-1/PlGF ratio evaluated in women during their suspected and/or confirmed COVID-19 infection during pregnancy. Considering the threshold of 38 as abnormal, the findings support that this value, in association with clinical findings, can be used to identify cases of PE. COVID-19 alone did not impact these biomarker levels. The rationale for the presented analysis was the reported increased frequency of PE among cases of COVID-19 and possible difficulty in the differential diagnosis of severe cases since both conditions can evolve to multiorgan damage, with inflammation and endothelial impairment6. Classical hypertension and proteinuria as diagnostic criteria might not be enough for such cases, nor the severity of laboratory findings such as altered liver enzymes, kidney, or coagulation. Therefore, biomarker evaluation could be a feasible approach [11]. There is still a lack of consistent data on the possible impact of COVID-19 alone on levels of sFlt-1 and/or PlGF [13]. COVID-19 and PE could possibly present common pathophysiological mechanisms [18], [19], [20]. One of the most considered is that SARS-CoV-2 binds ACE2-receptors in the placenta, leading to alteration of the placental renin-angiotensin system (RAS). As RAS regulates blood pressure, the virus may therefore increase adverse hemodynamic outcomes, such as PE [3], [21]. Another possible pathway in common is NLRP3 activation. SARS‐CoV‐2 leads to direct activation of NLRP3 by a viral protein, and this activation has been strongly correlated to the inflammatory response in COVID‐19 patients. This pathway can also be activated in PE, mainly in cases with severe features [22]. The timing of diagnosis and follow-up presents additional challenges. Previous studies have shown an increased risk of future PE among women that presented with COVID-19 during first/second trimesters [23]. This information can be valuable for counseling during pregnancy and adequate follow-up. Among the few cases of PE and confirmed COVID-19 that we reported here, nearly half presented with SARS-CoV-2 infection during first/second trimesters and further developed PE, weeks or months later. And even with the serum collected at the infection moment, most showed an altered sFlt-1/PlGF ratio. Increased sFlt-1/PlGF ratio or decreased PlGF values have been associated to adverse maternal and perinatal outcomes [3], [13], [24], [25], [26] Studies indicate that COVID-19 in pregnancy can induce higher incidence of adverse perinatal outcomes, as well as PE-like syndrome [24], [25], [26]. The same studies suggest that the biomarkers testing (such as PlGF and sFlt-1/PlGF ratio) could be used to differentiate PE from severe COVID-19 and improve clinical management [24], [25]. Our data, when comparing outcomes among cases with altered sFlt-1/PlGF ratio (using 38 as the threshold) did not present significant differences. Nevertheless, we had limited number of cases and only very few with sFlt-1/PlGF ratio over 85 (the most used ratio for PE diagnosis) [27]. Regardless, biological samples were collected at the time of SARS-CoV-2 infection and not at the time of PE diagnosis, which may have experimental implications. Other inflammatory markers can be used adjunctively for the management of COVID-19 cases, mainly the severe ones. COVID-19 cases commonly indicate elevated rates of C-reactive protein (CRP), erythrocyte sedimentation rate (ESR) and d-dimer [28], [29]. Other inflammatory markers that are altered are interleukins and cytokines, mostly decreasing the expression of IL-1β, which may indicate the state of clinical evolution depending on their high expression, especially in the cytokine storm state [30], [31]. In our study, we did not evaluate these inflammatory markers. This study included all case identification and sample collection prior to vaccination. Another similar cohort study is being conducted in which most women are already vaccinated, which may allow investigation into the question of whether vaccination plays any role in these associations. As we acknowledge the advances of such intervention among pregnant women, we also should consider the potential impact of the rise of new variants of concern (VOCs) [32], [33]. Although there appears to be a recent reduction of the number of severe COVID-19 cases, the number of infected pregnant women are nevertheless still high and the effects on rates of hypertensive disease/PE remains a topic of relevant interest. The relatively small number of cases included in this study is a limitation, especially considering PE and COVID-19 and also the moment of sample collection (with no biomarker levels at PE diagnosis). However, the present results provide detailed clinical outcomes tied to biomarker evaluations that add relevant evidence for the association of COVID-19 and PE. This should raise increased awareness about the risk of PE after COVID-19 diagnosis. More adequate pregnancy counseling considering patient information on the history of infection should be a key part of interventions moving forward. Funding This study was supported by Fundo de Apoio ao Ensino, à Pesquisa e à Extensão (FAEPEX-Unicamp) [Grant No 2300/20] and by the Washington University at Saint Louis, USA, McDonnell Academy seed grant for research on infectious diseases and the impact of COVID-19 (to IUM and MLC). MLC was also supported by Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) [grant number 2021/09937-1] and by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) [grant number 408407/2021-2]. GMN was supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) [grant number 88882.329828/2019]. Roche Diagnostics (ROCHE®) supported the measurements of angiogenesis-related factors sFlt-1 and PlGF, providing automated Elecsys® sFlt-1/PlGF kits. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 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==== Front Ann Tour Res Ann Tour Res Annals of Tourism Research 0160-7383 1873-7722 Elsevier Ltd. S0160-7383(22)00150-5 10.1016/j.annals.2022.103499 103499 Article Cruising through a pandemic: Or not? Walters Gabby ⁎1 Magor Thomas Kelly Sarah Wallin Ann UQ Business School, University of Queensland, Australia ⁎ Corresponding author. 1 Lead author. 31 10 2022 11 2022 31 10 2022 97 103499103499 18 1 2022 7 10 2022 11 10 2022 © 2022 Elsevier Ltd. All rights reserved. 2022 Elsevier Ltd Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. The features of the cruise value offering that once appealed to the cruising market have changed as a result of COVID 19. This paper employs a choice experiment to reveal how COVID-19 has influenced consumer preferences for and trade-offs between specific aspects of the cruise experience across four different COVID-19 scenarios. Such insight is highly valuable for cruise organisations seeking to better understand the evaluative criteria by which their consumer segments are now making decisions. Theoretically, this study employs Protection Motivation Theory to determine how ones self-rated ability to protect themselves against the virus while cruising may in turn influence choice behaviour. Our research is the first to report actual choice behaviours of cruise consumers adopting a choice modelling method. Keywords Cruising COVID 19 Protection Motivation Theory Choice modelling Tourist choice behaviour Associate editor: Astrid Kemperman ==== Body pmcIntroduction Prior to COVID-19 the cruising industry experienced significant growth with approximately 28.5 million people globally choosing to cruise in 2018 and an estimated 32 million in 2020 (Giese, 2020). This was a promising outlook for the global economy, especially for developing nations with economic dependence on the industry (da Silva, 2021). By February 2020, however, the cruising industry became front and centre of the COVID-19 pandemic, as a well-known cruise liner played host to a large outbreak. This resulted in significant reputational damage for not only the cruise line but the entire industry, which came to a standstill in the wake of the pandemic (Giese, 2020). This suspension in cruising activity came at an estimated cost of $77 billion dollars to the global economy (Cruise Lines International Association Australasia, 2021). While we expect the industry to rebound, the underlying research question we address is whether or not varying levels of ongoing COVID-19 risk influence travellers' preferences for the features of cruise packages. This is important to provide much-needed evidence to inform the industry in COVID-19 and future pandemic crisis response and recovery. There remains some debate regarding the socio-economic benefits of cruising for developing nations (e.g., Cheer, 2017; MacNeill & Wozniak, 2018), but there is no doubt that COVID-19 has had a significant impact on those who rely on this form of tourism. The pandemic-induced damage to the cruise sector has left a $150 billion USD gap in the global economy (Giese, 2020). The Australasian arm of the Cruise Lines International Association (CLIA) has lobbied the Australian government to resume cruising with a promise of rigid COVID-19 safety measures on board (Cruise Lines International Association Australasia, 2021). Government decision makers are not the only stakeholders that the industry will need to reassure. Research by Holland (2021) indicates that consumer confidence towards cruising has been severely impacted by the industry's perceived failures in the handling of the COVID-19 outbreak. We test how travellers' scores on the dimensions of Protection Motivation Theory affects their preferences under different risk scenarios. Protection Motivation Theory has been demonstrated in the tourism literature as being a highly valid and relevant approach to understanding the impact of COVID-19 and health related risk generally on travel behaviour, see for example Wang et al. (2019), Zheng et al. (2021), Hsieh et al. (2021) and Bhati et al. (2021). COVID-19 will continue to generate uncertainty in the cruise industry well after the widespread rollout of vaccines and relaxation of restrictions on movement. We explore how varying the level of COVID-19 risk may affect travellers' preferences across four different scenarios ranging from no/low COVID-19 risk to moderate/high risk. This scenario testing facilitates understanding of how a sudden resurgence of COVID-19 may alter the preferences of those considering a cruise, which in turn will enable the industry to position offerings optimally. Literature review Since the onset of the COVID-19 pandemic, notable changes to travel planning and decision making have been observed. For example, Kim et al. (2021) in their consumer choice study show tourists are now likely to seek holiday options that cater to their safety needs which subsequently leads to preferences for intermediate and non-extreme options when travel planning. In pre-COVID-19 times, the quality of tourist accommodation was typically defined by the level of luxury and facilities (Pappas & Glyptou, 2021) while sanitation and hygiene are now more likely to have an increased salience. As restrictions on mobility continue to occur across the world, the level of trust tourists have in such health-related policies will have some relevance to traveller decision making. Shin et al. (2021) demonstrated that political trust will impact travel willingness and frequency both during and beyond the pandemic era. Shin et al. (2021) further suggest that social norms are also important when considering travel during the pandemic. As COVID-19 has evolved, so has the holiday preferences of tourists. For example, Abdullah et al. (2020) reveal that since the pandemic, more tourists are using private modes of transport. Walters (2020) suggests that tourists feel more confident traveling in their own vehicles as opposed to flying or taking modes of transport where they would be sharing with others outside their family or social circles. Regional and coastal destinations have become highly popular at the expense of capital cities and urban centres due to the new-found preference for open spaces where tourists are able to safely distance themselves from others and avoid crowded spaces (Canina & McQuiddy-Davis, 2020; Kock et al., 2020; Rogerson & Rogerson, 2021; Walters, 2020; Sohn et al., 2021). Tourists are also choosing to travel closer to home regardless of whether government restrictions are in place (Li et al., 2021; Yilmazkuday, 2020). Kock et al. (2020) argue that the psychological concept of ethnocentrism offers a potential explanation for this observed change in tourist behaviour, suggesting that tourists may choose to travel closer to home to support their own local tourist economy, while other research suggests local preferences are simply a travel risk reduction strategy (Matiza, 2020; Wolff & Larsen, 2016). In China, a population that is accustomed to mass travel in large tour groups, more tourists are now preferring to travel independently or in small groups while at the same time substituting iconic tourist attractions with less well-known or ‘off the beaten track’ locations (Wen et al., 2020). In contrast Williams, Chen, Li & Balaz (2022) concluded that tourists' preferences to travel close to home can be explained by their finding that long haul destinations were more associated with uncertainties, ambiguities and, consequently, a higher degree of risk. When it comes to travel frequency, most studies in this context demonstrate that COVID-19 has and will continue to result in a decrease in travel activity (e.g., Chua et al., 2021; see for example, de Haas et al., 2020; Neuburger & Egger, 2021). The evidence is clear that COVID-19 has raised the importance of health and safety risk among most travellers. While the extent to which COVID-19 related health concerns impact travel behaviour may differ according to demographic and cultural background (Chua et al., 2021; Golets et al., 2021; Kim et al., 2021, Williams et al., 2022; Pappas, 2021), research indicates that health and safety still feature strongly in evaluation of travel-related risk (Pappas & Glyptou, 2021). According to Chua et al. (2021) tourists' concerns relating to health risk have created uncertainty, particularly when traveling abroad. The ‘home is safer than abroad’ bias implies that people tend to rate their home country as less risky than other foreign countries, regardless of where their home is located (Wolff et al., 2019). This makes sense in the COVID-19 context as tourists are likely to have familiarity with their own country's medical systems, number of cases, vaccination rates and government travel restrictions and regulations. COVID-19 and cruising A cruise holiday typically involves overseas travel, within a contained environment that on the one hand poses a heightened risk of infection due to the high density of tourists in the one space, yet on the other hand provides a COVID-19 safe ‘travel bubble’ assuming passengers and crew are fully vaccinated and COVID-19 free. Research by Holland, Mazzarol, et al. (2021a) suggests that the latter scenario is far from the perceptions of prospective cruise passengers. Looking at both the UK and Australian markets, Holland and colleagues explored the perceptions of cruisers and non-cruisers towards cruising. It was apparent that Australians have less faith in the cruise industry to keep them safe than those from the UK, with the authors claiming that this is partially due to the Australian media's negative portrayal of the cruising industry in the early days of COVID-19 when a ship docking in Sydney was responsible for a major community outbreak. Interestingly, Holland et al.'s (2021a) research revealed no difference in risk perceptions between cruisers and non-cruisers. Those who had cruised previously had more confidence in cruise operators to keep them safe and well. This contrasts with Holland, Mazzarol, et al.'s (2021a) study on Australian cruisers that found previous cruisers were more concerned about health risk than non-cruisers. Consumer trust is another construct to be explored in a cruise related context, yet the literature implies that the pandemic has not only changed the way in which trust influences the consumers' risk perception towards cruising, but also who they are more likely to trust. For example, according to research undertaken before COVID-19, consumer trust was shown to reduce perceived risk, anxiety, and concern towards cruising (Forgas-Coll et al., 2014). Quintal et al. (2021) on the other hand explored the role of trust in risk-reducing behaviours and concern towards cruise travel in Australia during the pandemic. Interestingly the authors revealed that consumers are more likely to trust the voice of the Australian government as opposed to the cruise lines themselves and consequently, consumers who placed their trust in the government were more concerned about cruise travel. This finding supports the notion that governments can be quite influential when it comes to travel decision making during a pandemic. The banning of cruising during the time of this study has clearly sent a strong message to consumers in relation to the risks associated with this form of travel. During the pandemic there have been several published works that explore how COVID-19 has impacted tourists' risk perceptions in relation to cruising, as discussed. Collectively this research presents valuable managerial implications concerning how to manage and respond to public health concerns towards this form of travel. A recent overview of COVID-19 related tourism research by Yang et al. (2021) revealed the need for post COVID-19 research which reflects representative samples and the effects of COVID-19 risk on travellers' preferences for the features of cruise packages. Our review found extensive application of relevant risk perception theoretical frameworks and concluded that much of the research is cross sectional and associated with the early stages of the COVID-19 pandemic. Our research responds to this gap in the literature by analysing the effects of constantly changing levels of COVID-19 risk on cruise travellers' preferences using a rigorous choice-based conjoint model that embeds the concepts of Protection Motivation Theory. Theoretical framework Perceived risk has been operationalised by several tourism scholars over the years to better understand the specific risks that apply to tourism and tourism destinations. Roehl and Fesenmaier (1992) for example present seven risk factors perceived to affect tourism: equipment, financial, physical, health, satisfaction, social and time. Reisinger and Mavondo (2005) explored perceptions of risk relating to health, finance, terrorism, and socio-cultural differences. Most studies agree that perceived risk is a significant determinant of destination choice. While health related risk is a common factor when assessing tourism related risk, these risk perception frameworks do not incorporate insights into how travellers' evaluate and respond to the dynamic nature of risk. Therefore, to better understand the interplay between choice preferences and risk perceptions in the COVID-19 context, we employ Protection Motivation Theory. Our contribution provides further evidence for the growing body of knowledge on how Protection Motivation Theory affects travel preferences using a choice-based method of analysis not previously used to study this theory. Protection Motivation Theory suggests that individuals go through two cognitive mediating processes when evaluating risk. These processes comprise four dimensions. The first two dimensions, perceived severity, and perceived vulnerability, involve the appraisal of the threat in terms of its severity and likelihood of occurrence. The third and fourth dimensions, self-efficacy, and response efficacy, together represent the individual's assessment of their own ability to protect themselves against the threat by performing a recommended behaviour or action and the extent to which they believe the action or behaviour will be effective (Rogers & Prentice-Dunn, 1997). We do not present any specific hypotheses but expect to find differences across individuals with respect to how their profiles captured by these dimensions affect cruise travel package preferences. We do not make specific formal hypotheses as our aim is to examine the effects of varying levels of COVID-19 risk on preferences. Protection Motivation Theory has featured in tourism studies investigating COVID-19 and travel risk extensively (e.g., Bhati et al., 2021; Hsieh et al., 2021; Quintal et al., 2021; Yuen et al., 2021; Zheng et al., 2021). While earlier tourism studies employing Protection Motivation Theory tended to focus on single dimensions such as perceived vulnerability (Schroeder et al., 2013; Sönmez & Graefe, 1998) or perceived severity (Law, 2006), the majority of COVID-19 and tourism related studies have applied this framework in its entirety confirming a positive relationship between the tourists cognitive evaluation of risk, their ability to cope and their motivation to protect themselves against the risk. A key reason for what one might conclude as an overabundance of tourism research that applies Protection Motivation Theory to the COVID-19 and travel context is the fact that this framework was created for the assessment of people's perceptions of health threats and their uptake of preventative recommendations (Rogers & Prentice-Dunn, 1997). According to Wang et al. (2019) Protection Motivation Theory is one of the most used models in public health to evaluate individual health-related behaviours. It is no surprise that the Protection Motivation Theory framework has been applied to a recent study that explored tourists' willingness to engage in health-related self-protection behaviour when cruising (see Fisher et al., 2018). Method Sample This study recruited Australian residents who had an interest in cruising. Respondents were invited to participate in the study via the panel provider PureProfile and were told that the purpose of the study was to understand preferences for cruise travel packages in Australia. Respondents who spent <5 min completing the questionnaire were not included in the analysis. Research design Respondents completed a mix of rating scale items to measure travellers' scores on the dimensions of Protection Motivation Theory and a discrete choice experiment (DCE) to measure travellers' preferences for the features of cruise packages under four levels of COVID-19 risk. We use a form of choice-based conjoint analysis that provides a model of hypothetical behaviour in response to hypothetical scenarios. Compared to methods which might rely on rating scales of intentions or perceptions, our approach has the advantage of being able to examine potential behavioural changes in a tractable manner. Given the context and the timing of our research it was not possible to model actual behaviour by way of a field study in this market. Eight attributes described the hypothetical cruise packages in the choice tasks, five related to traditional cruise considerations (ship size, length of trip, bonus, cabin type and price) and three specific to COVID-19 (precautions, medical and cancellation policies). The traditional cruise attributes were developed through a literature review of papers investigating preferences for cruise attributes. From the review a list of attributes that (a) had evidence that they impacted preferences, and (b) were physical attributes that would be suitable to code in the experiment. This list was then compared to cruise company websites, with the attributes being used as attributes for cruise packages in-market being selected for the final design. The levels of the attributes reflected the high and low range of levels occurring in the Australian cruise marketplace. The three COVID-19 attributes were developed from desktop research discussing the common consumer concerns about travel at the time: precautions being taken at travel sites, access to appropriate medical care and cancellation policies. The final stimuli adopted were also shown to cruise industry experts and compared to recent cruise advertising campaigns. The discrete choice experiment (DCE) approach was used to leverage the stimuli manipulation structure of a classic experiment and the trade-off response structure of a choice experiment. Procedures Respondents were asked about their general and cruise-specific travel experience, willingness to cruise in the near (within 12 months) and distant future (within 3 years). Burton et al.'s (1998) risk aversion scale and items measuring the five dimensions of Protection Motivation Theory, adapted from Witte et al. (1998), were also included in the survey. The items specific to self-efficacy, response efficacy and intentions to engage in risk reduction behaviours were modified to represent the COVID-19 context and risk-taking propensity, threat and coping appraisal captured respondents' level of agreement. All items were measured using a 5-point Likert scale. Respondents then completed the DCE. The choice experiment uses systematically manipulated alternatives (Crouch et al., 2007) that allow us to capture respondents' trade-offs. The experimental conditions we designed using an orthogonal main effects plan (OMEP) generated using SPSS software following the procedures outlined in Street et al. (2005). Compared to other quantitative methods, particularly structural equations modelling (SEM), choice-based conjoint and choice modelling more generally is under-utilised in tourism. Viglia and Dolnicar (2020) review the use of experiments in tourism and Kemperman (2021) specifically discuss the use of choice modelling in tourism. In our study, respondents completed nine choice sets with each containing three cruise packages described by eight attributes. Fig. 1 depicts an example choice set from our study. (See Table 1, Table 2.) Fig. 1 Example of choice scenario. Fig. 1 Table 1 Sample Characteristics (N = 808). Table 1Sample dimension n % Sample dimension n % Gender Have you ever been on a cruise holiday? Female 443 55 % Yes 381 47 % Male 365 45 % No 427 53 % Prefer not to respond 0 0 % Age How many times have you been on a cruise? 18–25 84 10 % 1–3 times 273 72 % 26–35 145 18 % 4–5 times 46 12 % 36–45 129 16 % >5 times 62 16 % 46–55 120 15 % 56–65 131 16 % How often would you travel within Australia for leisure related purposes? Above 65 199 25 % 1–3 times per year 520 64 % Prefer not to respond 0 0 % 4–5 times per year 126 16 % Education level >5 times per year 59 7 % Less than High School 22 3 % Never 103 13 % High School 179 22 % TAFE/Trade 218 27 % How often would you travel overseas for leisure related purposes? University Degree 241 30 % 1–3 times per year 465 58 % Post-Graduate University Degree 146 18 % 4–5 times per year 31 4 % Prefer not to respond 2 0 % >5 times per year 11 1 % Income Never 301 37 % Less than $50,000 212 26 % $51,000–$80,000 149 18 % Have you had COVID-19, or have you been considered as a “close contact” of someone who has had COVID-19 $81,000–$120,000 188 23 % $121,000–$150,000 89 11 % Yes 45 6 % $151,000 and above 108 13 % No 763 94 % Prefer not to respond 62 8 % Willingness and probability of cruising (7-point scale, low to high) M SD Willingness to take a cruise in the next 12 months 2.79 2.17 Willingness to take a cruise in the next 3 years 3.43 2.28 Probability of taking a cruise in the next 12 months 2.40 1.91 Probability of taking a cruise in the next 3 years 3.31 2.22 Table 2 Tendency to cruise across experimental conditions. Table 2Experimental condition Total choices No-choice option Alternatives not acceptable (ASC1) Would not travel (ASC2) Scenario One: (baseline) – There is a Vaccine 1953 207 (10.6 %) 650 (33.3 %) Scenario 2: COVID-19 is still around but under control 1710 149 (8.7 %) 462 (27.0 %) Scenario 3: There has been a resurgence of COVID-19 in Australia 1791 143 (8.0 %) 796 (44.4 %) Scenario 4: Outbreaks continue in Australia and there has been reported cases on cruise ships 1818 173 (9.5 %) 781 (43.0 %) Econometric specification We used the conditional logit (McFadden, 1974) model to find travellers' preferences for the attributes of cruise packages. A random parameters model (Revelt & Train, 1998; Walters et al., 2019) was also estimated, but was only estimable for the main effects of each of the cruise package attributes, hence is excluded from our results. A fully parametrized random parameters model was almost attempted but due to numerical complexities could not be estimated (see Walker, 2002; Chiou & Walker, 2007 for a full overview of these estimation issues). The conditional logit model presented includes interactions between each of the attributes of the hypothetical cruise packages and respondents' scores on the Protection Motivation Theory scales, interactions with each of the COVID-19 risk scenarios to determine how the theory of Protection Motivation Theory manifests under different COVID-19 risk scenarios and interactions between individual level attributes (age, gender, income, and cruise experience) and the no-choice options. All choice models were estimated using the choice modelling suite available in Stata 16. Results Respondent characteristics and previous travel experience The data were collected during April of 2021. A total of 808 usable responses were collected that generate 36,360 observations from 7272 choices. Incomplete and responses which spent <5 min in total to complete the questionnaire were not included giving a usable response rate of 74 % from a total of 1087 responses. The final sample contains 55 % females, the most common age bracket is those above 65 years and most have completed a tertiary education. About half have income exceeding AUD$80,000 per annum and about half had been on a cruise holiday at least once, and of those the most had cruised 1–3 times. Most respondents travel 1–3 times per year domestically, and over half travel internationally 1–3 times per year. Willingness to cruise within the next 12 months is significantly lower compared to within the next 3 years (M<12month = 2.79 vs M1–3 years = 3.43, t = −13.341, df = 807, p < .05). Likewise, respondents' self-reported likelihood of cruising in the short term is significantly less than in the long term (M<12month = 2.40 vs M1–3 years = 3.31, t = −18.783, df = 807, p < .05). The socio-demographic characteristics of respondents who have cruised are representative of industry trends, which is that they are from an older demographic, have a higher income, and higher perceived risks. Specifically, the 65+ age bracket accounts for about one third of those who have cruised (n = 115) whilst all other brackets each accounted for <20 % of cruisers. Among non-cruisers, all age brackets are equally represented. Just over half of those who had cruised had incomes above $81,000 (n = 199), whilst non-cruisers this income bracket proportion was about 43 % (n = 186). There is a balanced gender split within cruisers (females = 50 %), but within non-cruisers females represent 60 % of respondents. To account for differences between these groups, an interaction term is specified in the model that models travellers preferences for cruise packages across this different subsamples. Frequency analysis and scale reliability testing In the lowest risk scenarios, most responses were for cruise packages whereas in scenarios three and four over half of all responses were for a no-choice option. A confirmatory factor analysis was performed using AMOS 27 to assess the scales adopted from previous studies. All of our adapted scales were measure on 5-point scales and all achieved composite reliabilities being above 0.7, except for risk taking propensity, and all average variance extracted being above 0.5. Risk taking propensity had one item removed from the original scale due to a low beta value. Table 3 lists the Cronbach's α, CR and AVE for each measurement scale. Table 3 Confirmatory factor analysis results. Table 3Measurement Scale Beta Cronbach's α CR AVE Willingness to Cruise 0.95 0.80 0.80 My willingness to take a cruise in the next 12 months 0.86 The probability that I would take a cruise in the next 12 months is… 0.83 My willingness to take a cruise in the next 3 years 0.96 The probability that I would take a cruise in the next 3 years 0.96 Risk Taking Propensity 0.69 0.70 0.50 I don't like to take risks compared to most people I know 0.68 I have no desire to take unnecessary chances on things 0.79 Perceived Severity 0.87 0.90 0.70 If I were to contract COVID 19 while taking a cruise it would have serious negative consequences on my travel experience 0.79 Contracting COVID 19 would have a severe impact on my trip 0.87 If I were to contract COVID 19 during the cruise, it would be harmful to my well-being 0.84 Perceived Vulnerability 0.89 0.90 0.70 My chances of contracting COVID 19 on a cruise ship are extremely high 0.85 I am at risk of being exposed to COVID 19 when taking a cruise 0.88 It is likely that I will be exposed to COVID 19 whilst taking a cruise 0.82 Self-Efficacy 0.89 0.90 0.60 How confident are you in your ability to perform the following behaviours to protect yourself from COVID-19 should there be an outbreak on your cruise Self-isolating in my cabin as much as possible 0.69 Physically distancing myself from others 0.70 Washing hands and using hand sanitiser consistently 0.80 Wearing a mask 0.82 Following instructions, abiding by rules 0.85 Avoiding public spaces and crowded places (swimming pools, gym, cinemas, restaurants) 0.79 Limit my time indoors 0.60 Response Efficacy 0.92 0.90 0.60 Please indicate how effect you believe these actions would be in protecting yourself against COVID-19 Self-isolating in my cabin as much as possible 0.75 Physically distancing myself from others 0.83 Washing hands and using hand sanitiser consistently 0.83 Wearing a mask 0.81 Following instructions, abiding by rules 0.85 Avoiding public spaces and crowded places (swimming pools, gym, cinemas, restaurants) 0.83 Limit my time indoors 0.64 Protection Motivation 0.91 0.90 0.60 Please indicate how likely you would be to engage in these behaviours to protect yourself from COVID-19 Self-isolating in my cabin as much as possible 0.77 Physically distancing myself from others 0.85 Washing hands and using hand sanitiser consistently 0.76 Wearing a mask 0.81 Following instructions, abiding by rules 0.80 Avoiding public spaces and crowded places (swimming pools, gym, cinemas, restaurants) 0.83 Limit my time indoors 0.59 Table 4 Model catalogue and fit statistics. Table 4Model Parameters LL AIC BIC M1 Conditional Logit 16 −10,628.17 21,288.34 21,424.36 M2 Conditional Logit – Risk Level interactions included 64 −10,514.27 21,156.55 21,700.62 M3 Conditional Logit – Risk Level, Protection Motivation Theory and Other interactions included 149 −9152.26 18,576.51 19,732.68 Table 5 Model 3 results. Table 5Cruise Package Attribute Preferences Aggregate Means, including Scenario 1: There is a Vaccine (baseline) n = 217 Scenario 2: COVID-19 is still around but under control n = 190 Scenario 3: There has been a resurgence of COVID-19 in Australia n = 199 Scenario 4: Outbreaks continue in Australia and there has been reported cases on cruise ships n = 202 Ship Size <1000 passengers (baseline) – – – – 1000–2000 passengers −0.234⁎⁎ (0.082) −0.209 (0.117) 0.060 (0.122) 0.009 (0.122) 2000 + passengers −0.774⁎⁎⁎ (0.098) 0.028 (0.137) 0.012 (0.145) −0.017 (0.147) Length of Trip 0.007 (0.009) −0.003 (0.013) −0.006 (0.013) 0.014 (0.013) Bonuses $150 onboard credit (baseline) – – – – Free room upgrade 0.099 (0.092) 0.017 (0.130) 0.192 (0.136) −0.041 (0.136) Kids cruise free 0.074 (0.099) −0.111 (0.142) 0.060 (0.147) −0.315⁎ (0.148) Cabin Type Interior (baseline) – – – – Exterior 0.389⁎⁎⁎ (0.106) −0.224 (0.151) −0.181 (0.158) 0.001 (0.157) Ocean View Room with Balcony 0.688⁎⁎⁎ (0.090) −0.132 (0.127) −0.004 (0.134) −0.153 (0.135) COVID-19 Precautions Tracing App (baseline) – – – – Security onboard for compliance 0.086 (0.095) −0.015 (0.135) 0.169 (0.141) −0.290⁎ (0.139) Increased cleaning of rooms and common areas 0.126 (0.095) 0.096 (0.133) −0.030 (0.142) −0.244 (0.140) COVID-19 Medical Free COVID-19 Medical Treatment (baseline) – – – – Ventilators on board −0.369⁎⁎⁎ (0.092) −0.042 (0.131) 0.118 (0.136) 0.095 (0.138) Rapid COVID-19 testing −0.142 (0.087) −0.020 (0.124) −0.100 (0.131) 0.206 (0.130) Cancellation Policies 100 % credit (baseline) – – – – 50 % credit +50 % refund −0.246⁎ (0.101) 0.188 (0.143) 0.064 (0.149) −0.014 (0.151) 100 % refund 0.218⁎ (0.090) 0.045 (0.129) −0.216 (0.134) 0.033 (0.135) Price −0.003⁎ (0.001) 0.000 (0.001) 0.001 (0.001) 0.000 (0.001) ASC1: Would travel, but none of the options suitable 0.895 (0.724) −4.063⁎⁎⁎ (1.077) −1.259 (0.945) −0.017 (0.888) ASC2: Would not travel −4.828⁎⁎⁎ (0.668) 0.646 (0.843) −0.449 (0.743) 0.968 (0.733) Interactions between Not Cruising and Protection Motivation Theory Aggregate Means, including Scenario 1: There is a Vaccine (baseline) n = 217 Scenario 2: COVID-19 is still around but under control n = 190 Scenario 3: There has been a resurgence of COVID-19 in Australia n = 199 Scenario 4: Outbreaks continue in Australia and there has been reported cases on cruise ships n = 202 Risk Taking Propensity × ASC1 0.156 (0.093) −0.703⁎⁎⁎ (0.161) −0.099 (0.150) 0.023 (0.142) Risk Taking Propensity × ASC2 0.086 (0.071) −0.157 (0.115) −0.856⁎⁎⁎ (0.105) −0.534⁎⁎⁎ (0.107) Perceived Vulnerability × ASC1 0.358⁎⁎⁎ (0.106) −0.509⁎⁎⁎ (0.148) −0.256 (0.158) −0.070 (0.158) Perceived Vulnerability × ASC2 0.933⁎⁎⁎ (0.085) −0.375⁎⁎ (0.123) −0.584⁎⁎⁎ (0.120) −0.375⁎⁎ (0.119) Perceived Severity × ASC1 −0.089 (0.134) 0.074 (0.204) −0.003 (0.191) −0.327 (0.190) Perceived Severity × ASC2 −0.083 (0.108) 0.115 (0.169) 0.183 (0.148) 0.075 (0.153) Response Efficacy × ASC1 −0.925⁎⁎⁎ (0.128) 1.019⁎⁎⁎ (0.210) 1.435⁎⁎⁎ (0.241) 0.629⁎⁎⁎ (0.186) Response Efficacy × ASC2 −0.452⁎⁎⁎ (0.097) −0.156 (0.142) −0.362⁎ (0.147) −0.017 (0.135) Self-Efficacy × ASC1 0.155 (0.134) 0.143 (0.231) −1.010⁎⁎⁎ (0.225) −0.508⁎ (0.197) Self-Efficacy × ASC2 −0.164 (0.102) −0.167 (0.144) 0.115 (0.156) −0.473⁎⁎⁎ (0.144) Protection Motivation × ASC1 0.055 (0.132) −0.455 (0.249) 0.104 (0.234) 0.341 (0.206) Protection Motivation × ASC2 0.167 (0.103) 0.193 (0.172) 0.157 (0.158) 0.185 (0.158) Note: Standard errors in parentheses, scenario coefficients are interpreted as differences relative to aggregate means ⁎p < .05, ⁎⁎p < .01, ⁎⁎⁎p < .001 Interactions between not cruising and individual level covariates Aggregate means, includes all scenarios N = 808 Age × ASC2 (18–25 year old - baseline) – Age × ASC2 (26–35 years old) 0.612⁎⁎ (0.201) Age × ASC2 (36–45 years old) 0.697⁎⁎ (0.213) Age × ASC2 (46–55 years old) 0.842⁎⁎⁎ (0.202) Age × ASC2 (56–64 years old) 1.147⁎⁎⁎ (0.199) Age × ASC2 (65+ years old) 1.100⁎⁎⁎ (0.191) Age × Price (18–25 years old - baseline) $100 per day – $200 per day – $300 per day – Age × Price (26–35 years old) $100 per day 0.175 (0.195) $200 per day 0.016 (0.198) $300 per day 0.231 (0.192) Age × Price (36–45 years old) $100 per day 0.545⁎⁎ (0.204) $200 per day 0.344 (0.208) $300 per day 0.243 (0.203) Age × Price (46–55 years old) $100 per day −0.356 (0.202) $200 per day −0.601⁎⁎ (0.206) $300 per day −0.758⁎⁎⁎ (0.204) Age × Price (56–64 years old) $100 per day −0.820⁎⁎⁎ (0.204) $200 per day −0.829⁎⁎⁎ (0.209) $300 per day −1.029⁎⁎⁎ (0.206) Age × Price (65+ years old) $100 per day −0.912⁎⁎⁎ (0.189) $200 per day −1.110⁎⁎⁎ (0.194) $300 per day −1.107⁎⁎⁎ (0.189) Gender × ASC2 −0.091 (0.062) Income × ASC2 0.015 (0.020) Cruise Experience × ASC2 −1.562⁎⁎⁎ (0.065) Note: Standard errors are in parentheses, individual level coefficients are computed on aggregate sample. ⁎p < 0.05, ⁎⁎p < 0.01, ⁎⁎⁎p < .001. Choice models Three choice-based conjoint models were estimated and compared for their relative model fit, including a conditional logit model without any interactions, a model with risk scenario interactions but without the Protection Motivation Theory scales, and a third model that includes interactions with the No-Choice option and the risk scenarios, the dimensions of Protection Motivation Theory, demographics, and previous cruise experience. The third model (M3 reported in Table 4 below) fits best to the data on both Akaike information criterion (AIC) and Bayesian information criterion (BIC) measures compared to the first baseline models and offers the most behaviourally rich interpretations, hence we present the coefficients from this model. The practice of using comparative model fit statistics in choice modelling is supported by Hess et al. (2020). Aggregate Cruise Package Attribute Preferences across all four conditions The traditional cruise package features which travellers have significant preferences for are smaller ship sizes (b 2000+passengers = −0.774, SE = 0.098, p < .001), exterior cabins (b Ocean View Room with Balcony = 0.688, SE = 0.090, p < .001), and lower prices (b Price = −0.003, SE = 0.001, p < .05). Travellers were indifferent towards different trip lengths or different types of bonus inclusions. From among the COVID-19 specific attributes, free COVID-19 medical treatment and/or having rapid testing available were preferred over having ventilators onboard (b Ventilators on board = −0.369, SE = 0.092, p < .001), and a fully refundable cancellation policy (b 100% refund = 0.218, SE = 0.090, p < .05). Travellers were indifferent to what mix of COVID-19 precautions were used. Cruising preferences when exposed to varying levels of COVID-19 risk The interpretation of the coefficients in Table 5 under each of the scenarios are in comparison with the aggregate (baseline) data. For Scenarios 2 and 3, there were no significant differences in pattern of preferences relative to the baseline, except for some difference in consumers' likelihood to choose a no-choice option in scenario 2 (b ASC1: Would travel, but none of the options suitable = −4.063, SE = 1.077, p < .001). In Scenario 4, which is the highest risk scenario, the provision of on-board credit or free room upgrades increase consumers likelihood of choosing a cruise package (b Kids Cruise Free = −0.315, SE = 0.148, p < .05). Further, those willing to travel in the highest risk scenario prefer having either a tracing app and/or increased cleaning in common areas over having security onboard to enforce COVID-19 safety precautions (b Security onboard for compliance = −0.290, SE = 0.139, p < .05). Protection motivation and demographic impacts on opt-out behaviour The five dimensions of protection motivation were included in the model as interaction terms to assess respondents' risk-taking propensity, perceived vulnerability, perceived threat severity, response efficacy and self-efficacy impacted their likelihood to select a no-choice option. Risk taking propensity This dimension is important in Scenario 2. Those with a higher propensity to take risks were more likely travel, but not select one of the options presented in the choice tasks (b Risk Taking Propensity × ASC1 = −0.703, SE = 0.161, p < .001). As the level of risk increases in Scenarios 3 and 4, travellers with low risk taking propensity are more likely to opting out of travel completely (b Risk Taking Propensity × ASC2 = −0.856, SE = 0.105, p < .001; b Risk Taking Propensity × ASC2 = −0.534, SE = 0.107, p < .001). Perceived vulnerability Overall, travellers with higher levels of perceived vulnerability were more likely to select a no-choice option (b Perceived Vulnerability × ASC1 = 0.358, SE = 0.106, p < .001; b Perceived Vulnerability × ASC2 = 0.933, SE = 0.085, p < .001) but as the risk levels increase, we see a sign reversal on this dimension, indicating those with higher perceived vulnerability are more likely to cruise. Perceived severity This dimension had no impact on preferences to cruise. This might explain why those with higher perception of vulnerability are willing to cruise in the higher risk settings (i.e., they anticipate a risk, but do not anticipate significantly high levels of severity). Response efficacy Overall, travellers who trust in the efficacy of COVID-19 safety strategies implemented by the cruise operator are more likely to travel (b Response Efficacy × ASC1 = −0.925, SE = 0.128, p < .001; b Response Efficacy × ASC2 = −0.452, SE = 0.097, p < .001), but we again see a sign reversal across the scenarios that suggests the response of cruise operators is an important predictor of whether or not travellers will decide to cruise. Self-efficacy Travellers with higher levels of confidence in their own ability to protect themselves are more likely to cruise in higher risk scenarios. This effect is particularly strong in Scenario 4, highest risk scenario, in which the parameters for both the ASC1 and ASC2 are significant and negative (b Self Efficacy×ASC1 = −0.508, SE = 0.197, p < .05; b Self Efficacy×ASC2 = −0.473, SE = 0.144, p < .001). This may in part account for travellers' disutility for security onboard to enforce safety measures. Protection motivation This dimension captured respondents' likelihood to follow COVID-19 safety protocols, but it shows no significant impact on travellers' preferences to cruise in any scenario. Individual level covariates Interaction effects for age, gender, income, and cruise experience were only estimated on the aggregate data due to limits on the number of identifiable parameters. For age, the two oldest cohorts are about twice as likely not to travel relative to younger demographics (b Age×ASC2 (65+ years old) = 1.100, SE = 0.191) and are also the most price sensitive (b Age×Price ($300 per day) = −1.107, SE = 0.189). Gender and income had no significant effects, but past cruising experience had a strong negative interaction with the no-choice option indicating that those with prior cruising experience were more likely to select a cruise package (b Cruise Experience×ASC2 = −1.562, SE = 0.065). Discussion Our findings suggest that for Australian travellers are still willing to cruise irrespective of the pandemic. Aligning with previous studies that explore the influence of past travel experience on sentiment towards travel during or following a crisis (e.g., Holland, Mazzarol, et al., 2021a; Walters et al., 2015), our study found that regular cruisers are a dependable market during COVID-19. For example, while those who had cruised previously are likely to delay cruising for at least 3 years, regular cruisers reported a stronger propensity to cruise within a 12-month period. Consistent with the recent works of Holland, Mazzarol, et al. (2021b), Ivanova et al. (2021) and Pan et al. (2021), we find that the provision of COVID-19 medical options and safety on board is paramount to the market regardless of the risk status of the pandemic. Overall, specific attributes selected to reduce the risks associated with cruising during a pandemic included smaller ship size (i.e., <2000 passengers), exterior rooms with a balcony, free medical treatment, and availability of Rapid Antigen Testing. Travellers were indifferent towards bonuses and trip duration. In terms of financial risk, prospective cruisers opted for a full refund policy over a credit or a 50 % credit, 50 % refund option. Our findings also indicate a demographic shift among traditional cruisers with older cohorts being less likely to cruise. This finding resonates with that of Holland, Weeden, et al. (2021) and Pappas (2021) whose studies both revealed that older segments felt more at risk and more vulnerable to COVID-19 compared with younger cohorts. Older segments in our study also proved to be more price sensitive. We found some differences in preferences among those that indicated a willingness to cruise. Responsiveness to bonuses and financial incentives are consistent with Pan et al.' (2021) research revealing that safety and pricing are top of mind for those considering cruising during the pandemic. As COVID-19 increases consumers' core underlying preferences for the features of a cruise package do not differ much from those chosen in response to a scenario where COVID-19 is under control. However, the decision to opt in or out of cruising does differ significantly across the four conditions. We explored this further by integrating the dimension of Protection Motivation Theory to better understand the risk related reasoning behind the decision. Our findings align with recent studies (e.g., Bhati et al., 2021; Hsieh et al., 2021; Quintal et al., 2021; Yuen et al., 2021; Zheng et al., 2021) that establish the relevance of Protection Motivation Theory in examining tourist behaviour in the COVID-19 era and more broadly self-protection from health-related risk. When looking into the psychology of risk, those with a higher propensity to take risks were less likely to opt out of cruising, irrespective of how severe the risk became. However, with increased risk in Scenarios 3 and 4, travellers with low risk taking propensity were more likely to opt out of travel completely. With reference to the Protection Motivation Theory framework, our study revealed that those who felt they were more vulnerable to contracting the virus while cruising were less likely to opt out as the risk intensified. Possible explanation for this counter intuitive result lies in the work of Wolff et al. (2019) that suggests worry is a more reliable predictor of risk -taking behaviour. Wolff et al.'s proposition would imply that just because tourists felt vulnerable to the risk of contracting COVID doesn't mean they were worried by it – hence their willingness to still cruise as the risk intensified. Those with a higher level of confidence in their ability to follow and comply with protection strategies to prevent the spread of COVID-19 may not be deterred as much in higher risk scenarios. In Scenario 4 our results show strong aversion towards Security on board for compliance, further suggesting travellers prefer and have confidence in managing their own levels of risk. Those who felt the severity of the risk was high and those who were more likely to engage in self-protection did not show any differences in their likelihood of travel across the four scenarios, again demonstrating some level of risk tolerance among prospective cruisers. This is contrary to findings revealed by Holland, Mazzarol, et al. (2021b) that indicated a reluctance to cruise during the COVID-19 era, however the authors did suggest that the introduction of health and safety measures, such as those proposed in this study could entice crusie travel. Our findings are supported by recent research into travel and COVID-19 by Zheng et al. (2021) who revealed that people who have protection motivation are more willing to choose cautious travel rather than travel avoidance. Baker and Stockton (2013) also found that while cruisers perceive there to be significant health risks with this type of holiday, they do tend to take more precautions to help mitigate the risk. This study revealed that travellers' trust in the cruise provider's efficacy in providing COVID-19 safety measures was an important predictor of cruising across all the COVID-19 risk scenarios, suggesting that safety response was critical in attracting cruisers. In terms of demographics, age and previous cruise experience predicted cruise choice, with younger travellers twice as likely to cruise than older travellers, and experienced cruisers more likely to choose a cruise option than inexperienced cruisers. Income did not influence respondents' decision to cruise. This finding contrasts with Quintal et al.'s (2022) research that suggested government policy may potentially overshadow any risk mitigation attempts by cruise lines to instil trust among consumers during the pandemic. Contribution to theory Theoretically, our research confirms and extends prior research adopting Protection Motivation Theory in tourism research and in particular travel during COVID-19 (e.g., Wang et al., 2019; Zheng et al., 2021) to a cruise context. Our research departs from the plethora of COVID-19-focussed tourism research by examining how prospective cruisers are likely to respond to and manage the risks that cruising presents across four different COVID-19 scenarios. By using a choice modelling method that directly assesses consumer preferences for specific cruise features and risk mitigation strategies, our research suggests there is an element of crisis resistance in the Australian cruise market. The application of choice modelling has enabled the researchers to assess travellers' preferences for the features of cruise packages. Such insight not only deepens our theoretical understanding of how the current pandemic, or future pandemics will influence travel preferences at different levels of severity, but also informs relevant marketing and risk mitigation approaches for the cruise industry under evolving conditions typically manifested by pandemic. This methodological contribution addresses a previously identified gap related to the majority of prior research adopting cross-sectional or qualitative studies and is one of the first studies to test Protection Motivation Theory relevance in a cruise context. Practical implications Our research also provides much needed evidence of cruise consumer preferences to inform the cruise industry and cruise destinations for their current recovery strategies. By eliciting preferences, the industry can be practically and reliably guided in relation to optimal marketing communications and product offerings to limit perceived risk and incentivise cruise travel. For example, advertising campaigns which integrate images and promotions of smaller cruise ship options, younger travellers, and outside rooms with COVID-19 safety and cancellation refund guarantees are recommended to appeal to a younger, less risk averse segment. Loyalty programs should be mined to leverage experienced travellers who appear to exhibit less reluctance associated with self-protection and perceived pandemic risk, as the most cost-effective target segment during COVID-19 recovery in the short term. Incentives through promotion and more elaboration in relation to COVID-19 safety measures should also be targeted towards inexperienced cruise travellers, and an agile marketing and promotional strategy is recommended to effectively respond to the market as pandemic risk changes over time, warranting differing strategies to meet perceived risk and protection needs of the market and particular segments. With these identified features included in the packages, there is potential for cruise operators to increase price and yield to meet these new norms for consumer preference. Our research reveals that cruising remains an option for specific segments, such as younger consumers and previous cruisers, who are more confident of their ability to manage their own levels of risk independently. Hence, we recommend that pricing strategies consider safety and quality over low-cost deals aiming for high yield over high occupancy. This strategy will enable ships to sail with less passengers on board that in turn will allow for social distancing. While the older generation of traveller may have been a popular and responsive segment for the cruising industry in pre-COVID-19 times, it is suggested that Cruise companies diversify both their product and market focus in the short term. We recommend that Cruising companies target younger cohorts who are well travelled and are perhaps looking to substitute their overseas travel adventures with a local cruise itinerary. Our data also suggests a need for cruise operators to ensure offerings exhibit and deliver lower crowd density, COVID-19 cleaning in common areas and on-board contact tracing. In terms of the most feasible target segments, if COVID-19 persists, younger and experienced cruisers who are less risk averse and show lower price sensitivities are likely to be more responsive than older segments. Features for cruise product bundling include prioritisation of fully flexible cancellation and effective health compliance policies on board to enable cruisers to engage in protective behaviour and keep themselves safe from the virus. With society becoming more confident with travel more broadly in parallel to virus becoming more endemic, the cruise industry can consider reducing some measures commensurate with our findings relating to different COVID-19 risk levels. Longer term, with inevitable risk of further pandemics, our research provides practical guidance to the industry on the likelihood of consumer response to cruise packages and incentives to reduce barriers to travel associated with perceived risk. With the cruise market increasingly concerned about its impacts upon environmental sustainability and social licence to operate (e.g., de Almeida Ramoa et al., 2019), our findings relating to health-related pandemic risk may align with future sustainability strategy to reduce cruise size and footprint. Limitations and future research Some limitations to our research are noted, including the Australian sample and the 2021 timing of our data collection, during the early stages of pandemic recovery, as Australia was experiencing international border lockdown. Further replication and extension of our findings adopting choice modelling methodology is warranted through testing of additional cruise and health features implemented since our study, including vaccine passports, mandatory testing pre and during cruise and perceived destination safety. Additional modelling of brand dimensions such as trust, prominence and sustainability would also be of interest in predicting consumer choice, and their interaction with the patterns we have found in this initial study. While our research provides useful causal data to predict likely cruise consumer preferences in different risk scenarios, longitudinal replication and studies are needed as industry recovery evolves. Qualitative research is also a worthwhile research direction, to gain richer insights into different segments' behaviours and motivations to travel on a more granular level. For example, pre- and post- cruise interviews, observational data during cruise in collaboration with the cruise industry and field experiments would all be useful future research investigations. CRediT authorship contribution statement Gabby Walters: Team lead, conceptualisation of the study, literature review and discussion, conclusion, proof reading and editing. Ann Wallin: Co- Design of the choice experiment, proof reading and editing, methodology write up Thomas Magor: Co-design of the choice experiment, data analysis, results write up, editing Sarah Kelly: Contribution to the conceptualisation of the research, industry liaison and consultation, editing and proof reading, contributed to discussion and conclusion. Gabby Walters is an Associate Professor in Tourism at the University of Queensland. Her research interests lie in crisis and disaster recovery, namely marketing communications and image management. She also holds significant expertise in experimental research that focuses on neurological and psychophysiological measurement. Thomas Magor is a lecturer in Marketing with the UQ Business School. He is highly experienced in Choice Modelling and his research interests include marketing, tourism and transport. Thomas has published in Journals such as the Journal of Choice Modelling and Frontiers in Psychology. Sarah Kelly is an Associate Professor in Law and Marketing at the University of Queensland. She is globally known for her research, speaking and consulting in the sports field and is also co-leading a research hub at UQ in Trust, Ethics and Governance. Sarah is a visiting fellow at Loughborough University Institute of Sport, London and OP Jindal University, Delhi. Her current research projects are focused upon esports, women's sport, sports integrity and mega-event legacy. Ann Wallin is a Lecturer in Marketing UQ Business School and researchers a variety of topics: consumer decision making; brand signalling and portfolio strategy; and marketing education. Her interdisciplinary research has been published in leading journals such as Decision and Journal of Travel Research, and presented globally at ISMS Marketing Science and International Choice Modelling conferences. 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Afraid to travel after COVID-19? Self-protection, coping and resilience against pandemic ‘travel fear’ Tourism Management 83 2021 104261	36506848	PMC9719968	NO-CC CODE	2022-12-06 23:23:49	no	Ann Tour Res. 2022 Nov 31; 97:103499	utf-8	Ann Tour Res	2,022	10.1016/j.annals.2022.103499	oa_other
==== Front Qual Soc Work Qual Soc Work spqsw QSW Qualitative Social Work 1473-3250 1741-3117 SAGE Publications Sage UK: London, England 10.1177_14733250221143766 10.1177/14733250221143766 Main Paper “We’ll dance harder and love deeper”: LGBTQIA+ resilience and resistance during the COVID-19 pandemic https://orcid.org/0000-0002-4064-2927 Seelman Kristie L School of Social Work, 1373 Georgia State University , Atlanta, GA, USA https://orcid.org/0000-0001-5126-9606 Holloway Brendon T Graduate School of Social Work, 2927 University of Denver , Denver, CO, USA MacIntyre Grace 7397 Mount Holyoke College , South Hadley, MA, USA Mynatt Elizabeth Khoury College of Computer Sciences, 1848 Northeastern University , Boston, MA, USA Kristie L Seelman, School of Social Work, Georgia State University, PO Box 3992, Atlanta, GA 30303-3992, USA. Email: [email protected] 1 12 2022 1 12 2022 14733250221143766© 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. In March 2020, the World Health Organization declared a global pandemic due to the rapid spread of COVID-19. Two years into the pandemic, there have been over one million COVID-19 deaths in the United States alone. While the pandemic has impacted everyone, the most extreme impacts have been experienced by marginalized communities, including those who identify as LGBTQIA+. Although LGBTQIA+ people have faced the negative impacts of the pandemic, the LGBTQIA+ community may be well equipped to navigate the COVID-19 pandemic due to the historic and current societal oppression this community has endured. Using both a resilience and resistance framework, the present study explores the resilience and resistance strategies employed by LGBTQIA+ adults in the Southeast U.S. during the COVID-19 pandemic through the collection and analysis of monthly diary entries and video interviews. Findings show that resilience and resistance build on the knowledge base and histories of LGBTQIA+ people, and resilience and resistance have been re-imagined for this community during the COVID-19 pandemic. As a result of the pandemic, many LGBTQIA+ people are dreaming of and re-imagining a better future, a future that social work educators and practitioners can help co-create. Resistance resilience LGBTQIA+ COVID-19 mutual aid Public Interest Technology Universities Network N/A edited-statecorrected-proof typesetterts10 ==== Body pmc“We’ll dance harder and love deeper”: LGBTQIA+ resilience and resistance during the COVID-19 pandemic In March 2020, COVID-19 began to rapidly spread, resulting in the declaration of a global pandemic (World Health Organization, 2022). As of September 2022, there have been nearly 94 million positive COVID-19 cases and over one million deaths in the U.S. (Centers for Disease Control and Prevention (CDC, 2021). While the pandemic has impacted the population at large, marginalized communities – including those who identify as lesbian, gay, bisexual, transgender, queer, intersex, or asexual (LGBTQIA+) – have experienced the most extreme impacts (Konnoth, 2020). However, the LGBTQIA+ community may be uniquely equipped and taking action to navigate the COVID-19 pandemic given the historical violence this community has experienced and the need to build resilience and resistance for survival. Through the collection of diary entries and video interviews, the present study explores how LGBTQIA+ individuals living in the Southeast U.S. have navigated and built resilience and resistance during the COVID-19 pandemic. Such knowledge can be of help to social workers who want to advocate for conditions, policies, and practices that support the thriving of LGBTQIA+ people, particularly in a region like the Southeast U.S. where there continue to be many systemic and political barriers to promoting LGBTQIA+ well-being. Literature review Resilience and resistance framework Resilience is “the quality of being able to survive and thrive in the face of adversity” (Meyer, 2015, p. 210) and recovering quickly from difficulties (Robinson and Schmitz, 2021). Resilience has been conceptualized as a positive outcome and a process for managing challenges and can include individual and contextual factors in the larger socio-ecological environment (Asakura, 2015; DiFulvio, 2015; Singh et al., 2014). Although resilience is a useful framework that shifts the narrative to focus on strengths, it has been critiqued for inadvertently placing the burden on the individual or community to build resilience and navigate harmful systems. In this way, resilience does not account for “how to change, challenge, and dismantle oppressive structures” (Robinson and Schmitz, 2021, p. 4). As an alternative, some scholars are shifting to a resistance framework to explore how LGBTQIA+ communities resist oppression and dominant narratives. Stemming from a critical consciousness framework, resistance highlights the individual and collective acts to resist oppressive systems, stigma, and violence (Ward, 2007). Shifting to a resistance framework over a resilience framework centers the work required to address and dismantle the systems that create hardships (Robinson and Schmitz, 2021). However, resistance and resilience are often used interchangeably in the literature, and “resistance may be a form of resilience” (Paceley et al., 2021, p. 30). For example, trans and nonbinary youth may build resilience to resist prejudice (Singh et al., 2014). Within this example, resilience and resistance are not mutually exclusive and may potentially influence one another. For this study, we view resilience and resistance as interconnected and explore resistance strategies used by LGBTQIA+ individuals during the COVID-19 pandemic as a form of resilience. Our study is built on the foundation of past knowledge suggesting that resilience and resistance are fruitful and critical to the survival of LGBTQIA+ people (e.g. Paceley et al., 2021). We begin by reviewing the literature on historical trauma and resilience and resistance within LGBTQIA+ communities. Then, we shift to the impacts of the COVID-19 pandemic generally and for LGBTQIA+ communities specifically. Violence and trauma affecting LGBTQIA+ communities Over time, the LGBTQIA+ community has endured oppression and violence due to systemic discrimination and harm based on their identities, visibility, and expression, and demonstrated resilience and resistance as survival mechanisms. LGBTQIA+ community members have experienced historical trauma ranging from, but not limited to, the police raid on an LGBTQIA+ night club that prompted the Stonewall Riots in 1969 (Jenkins, 2019), the HIV epidemic that began in 1981 (Department of Health and Human Services, 2019) and involved the villainization of the LGBTQIA+ community by public leaders, and laws making it illegal to engage in same-gender sexual activity. Presently, the LGBTQIA+ community continues to face hostile legislation in the U.S. (American Civil Liberties Union, 2021). As a result of this systemic violence, LGBQIA+ individuals are at higher risk for poorer mental health (Di Giacomo et al., 2018; James et al., 2016) and poorer general health than their heterosexual counterparts (Downing and Przedworski, 2018). Transgender and nonbinary people have higher rates of depression, anxiety, and psychological distress than cisgender individuals (Bockting et al., 2013) and experience higher rates of discrimination in employment and healthcare (James et al., 2016). The systemic violence LGBTQIA+ communities have faced and still endure results in serious health disparities and inequities. Resilience and resistance among LGBTQIA+ communities In response to police brutality, the Stonewall Riots, and employment discrimination, transgender women—namely Marsha P. Johnson and Sylvia Rivera—created an organization (STAR) in 1970 to support unhoused LGBTQIA+ youth and sex workers (Jenkins, 2019). A facet of STAR turned into a house, where LGBTQIA+ youth could live in a supportive, safe environment (Shepard, 2013). Still today, houses provide a supportive space for LGBTQIA+ youth in major U.S. cities, and there have been additional community efforts to provide housing (Spade, 2020a). This community support, also known as mutual aid, is where people are caring for each other through social networks that are more survivable than those provided through formal supports (Spade, 2020a). Other examples of mutual aid in LGBTQIA+ circles include the development of “families of choice” (Gabrielson and Holston, 2014), the use of websites like Tumblr to connect online and share information within the community (Hawkins and Haimson, 2018), and the creation of peer support networks for healthcare needs (Johnson and Rogers, 2020). COVID-19 context The arrival of a pandemic in the past 2 years has created additional challenge. The COrona VIrus Disease 19 (COVID-19) pandemic began as an outbreak of a novel respiratory virus that was first detected in Wuhan, China in December 2019. The virus quickly spread and was declared a pandemic in March 2020 (Ciotti et al., 2020). The COVID-19 pandemic has had significant global impact. As of September 2022, Johns Hopkins’ COVID-19 dashboard (Johns Hopkins University of Medicine, 2022) reports nearly 600 million cases worldwide, including over 94 million cases in the U.S., and more than 6,480,000 global deaths, with over one million in the U.S. During the pandemic, most people faced significant changes in their daily lives. Common stressors include economic uncertainty, significant changes in daily routines, increased isolation, and changes in sleep patterns and nutrition (Salari et al., 2020). A Pew Research Center survey found that 25% of adults reported that they or someone in their household had lost a job due to the pandemic, and about one-third had experienced a cut in pay (Parker et al., 2020). With the closures of schools and daycare centers during lockdowns and implementation of quarantines, those responsible for children were faced with increased caregiving demands and were often more isolated from social contacts than usual. Recent studies have documented elevated rates of anxiety, depression, and/or stress among adults during the pandemic (Salari et al., 2020; Shevlin et al., 2020). Others have documented elevated mental health concerns and/or disrupted sleep among caregivers of children or of adults with dementia (Brown et al., 2020; Mazzi et al., 2020). Correlates of poorer mental health and increased stress during the pandemic include being a young adult, having children in one’s home, having a loss of income or low income, and having existing health conditions (Nwachukwu et al., 2020; Salari et al., 2020; Shevlin et al., 2020). COVID-19 and LGBTQIA+ communities Generally, the pandemic creates greater risk for infection and mortality among marginalized communities, including LGBTQIA+ people. For example, transgender and nonbinary populations may face both increased risk for COVID-19 mortality due to greater likelihood of having chronic health conditions and increased stress when shelter-in-place policies isolate them with family members who are not supportive of their identities (Zubizarreta et al., 2021). The pandemic has also disrupted LGBTQIA+ people’s access to services and community, including cancellations of Pride events and closing of LGBTQIA+ spaces (Riggle et al., 2021). Data from LGBTQ youth ages 13-19 suggest that the pandemic—especially when social distancing orders were the norm—led to youth being isolated at home with unsupportive family members and experiencing a loss of access to LGBTQ safe spaces (Fish et al., 2020). A longitudinal study among transgender and nonbinary individuals in three U.S. cities found that participants reported reduced access to LGBTQ supports and gender-affirming care during COVID-19 compared to earlier timepoints, and those with reduced support and access to gender-affirming care had significantly greater psychological distress (Kidd et al., 2021). Qualitative research has highlighted the intersectional impact of the pandemic across subgroups in the LGBTQIA+ community, with African American/Black and Latinx sexual minority women discussing increased sense of stigmatization, intersections of racism and health equity in relation to COVID-19, and amplified vulnerability and risk with White supremacist violence (Riggle et al., 2021). With such challenges in mind, scholars have been documenting the role of resilience for marginalized communities during COVID-19. A study by Goldbach et al. (2021) among LGBTQ+ adults indicated that greater resilience lessens the impact of pandemic-related concerns on anxiety. In an online survey, 129 LGBTQ adults in the U.S. detailed themes of resilience such as previous experiences with challenging times (e.g. AIDS crisis), practicing radical acceptance, and providing support to others and building community (Gonzalez et al., 2021). Additionally, Hafford-Letchfield et al. (2022) found that LGBT+ older adults described significant practices of caregiving and explicit demonstration of empathy, reciprocity, and active community outreach during the pandemic. Gaps and research question While there have been some emerging studies of the impact of COVID-19 on LGBTQIA+ communities as well as resilience during this pandemic, there is a need for research that takes a more in-depth look at patterns not just of resilience but resistance in times of great struggle. As the scholar Alexander McClelland has said, “Resistance is what enables us to actualize our very means to our survival as queers...Queer resistance helps us in moving beyond merely existing as queer, to flourishing as queer” (Community-Based Research Centre, 2020, para. 3). Additionally, few studies focus on the unique situations of LGBTQIA+ adults in the Southeast U.S. who often face a particularly hostile community-level climate. This study was designed to address such gaps through the research question: How are LGBTQIA+ adults in the Southeast U.S. demonstrating resilience and resistance during the COVID-19 pandemic? Methods This year-long project was initiated through a fellowship with the Public Interest Technology Universities Network, which promotes research collaborations across disciplines to use technology for the public good in the Southeast U.S. As part of this initiative, the co-PIs (Kristie Seelman and Beth Mynatt) planned and initiated an online, multimedia, mixed methods study that would document stories of resilience and coping among LGBTQIA+ adults. The study was designed to collect multimedia data because of the funder’s emphasis on technology for the public good: we wanted to document examples of resilience and resistance using technology that could later be shared in public ways that allowed for deeper engagement in multimedia stories of the pandemic. The project’s design and data collection were informed by a community advisory board consisting of six LGBTQIA+ individuals, with attention to the involvement of groups that are underrepresented in research to help strengthen the study design and findings in relation to needs of intersectional LGBTQIA+ communities in the South. The community advisory board met virtually with the PIs several times over the course of 1 year and helped influence components such as what questions participants would be asked, the topics for the monthly diaries, and ways to share participant stories with the general public. The project was approved as exempt by the IRBs of the two universities partnering for this project (Georgia Institute of Technology and Georgia State University). Sampling and recruitment To be eligible to participate in the study, individuals had to: (a) be 18 or older; (b) live in one of nine states in the Southeast U.S.; and (c) identify as LGBTQIA+. Participants were recruited via LGBTQIA+ community leaders and organizations, social media (including paid advertisements), online groups, and email lists. Interested individuals completed a series of questions via an online screening instrument, and a member of the research team confirmed eligibility before sending an email invitation and a unique participant code. The final sample included 30 LGBTQIA+ adults, ages 18 to 73 (mean = 36). 60 percent (n = 18) of the sample were White, 23.3% (n = 7) were Black/African American, 13.3% (n = 4) were another race or ethnicity, and 3.3% (n = 1) preferred not to answer. 40 percent (n = 12) of the sample were cisgender men, 30% (n = 9) were transgender/nonbinary/gender diverse, 26.7% (n = 8) were cisgender women, and 3.3% (n = 1) were questioning their gender. Data collection Once an individual was deemed eligible, they received an invitation to complete an online consent form and an online pre-survey that contained questions about demographics, mental health, COVID-19 exposure, social distancing, and other topics. Individuals who completed the pre-survey were invited via email to respond to monthly diary prompts on topics linked to resilience and resistance; the thought was that online diary prompts that participants could access themselves would be an efficient and easy way to gather data at different timepoints to reflect the evolving nature of the pandemic, and would prompt a level of reflection, personal disclosure, and creativity that might not occur as easily through focus groups or individual interviews. Diary topics were accessed via a secure, password-protected Web site that required participants to enter their unique participant code; administrative access was restricted to the research team and infrastructure administrators. Example diary topics included Hope, Health, Social and Emotional Connections, and Adaptability. An example diary prompt (for the Health topic) was: “Have you faced any significant health challenges or disability/ies during the COVID-19 pandemic? Describe. If you faced challenges, what strategies are you using to cope with these challenges on a daily basis? How do you promote health for yourself?” Participants indicated whether they wanted each diary entry to be (a) available for public sharing, including possible donation to a library archive, with the name (real or pseudonym) and age they listed; (b) available for public sharing, but kept anonymous (name, age, and identifying information removed, and video/audio transcribed into text); or (c) kept confidential – used for research purposes only, without a name attached. Participants could upload text, audio, video, and/or photograph files as part of their diary entries. Diary entries generally ranged in length from a few short phrases to several paragraphs; audio entries were usually a few minutes in length, and if a participant uploaded photos, they generally uploaded between 1-4 photos in one entry. Most participants tended to upload text-based diaries, sometimes supplemented with a few photos; there were two audio-only diary entries, one photograph-only diary entry, and one video-only diary entry. Diary data were securely stored on a Web site hosted by one of the universities involved with this project. To help ensure rich multimedia data collection, a subgroup of eight participants were also invited to video interviews over the course of the study that asked follow-up questions related to the diary topics. These participants were selected to ensure diverse representation in multimedia data collection in terms of age/generation, gender, and race and ethnicity. Interviews were between 10 to 45 min in length. In some cases, participants were asked the diary prompts in these interviews if they had not responded to that particular recent diary. These were supplemented with additional interview questions such as: Do you think there are unique ways that your generation might be able to adapt to the pandemic that is not as common among other generations of LGBTQIA+ adults? At the end of the year, all participants were invited to complete a post-survey that included similar topics as the pre-survey. Participants were offered $10 for each diary entry and $20 for each video interview. At least two researchers took detailed notes on video interviews and audio diary entries, and these notes were used along with text-based diary entries for data analysis. Positionality This work brings the praxis of desire-based research (Tuck, 2009) into LGBTQIA+ scholarship that often utilizes a damage-centered lens. While damage-centered research documents a community’s pain and suffering, desire-based research strives to understand “complexity, contradiction, and the self-determination of lived lives” (Tuck, 2009, p. 12). Researching for desire accounts for the hope, visions, and wisdom of communities. As (Tuck, 2009) states, “Desire is about longing, about a present that is enriched by both the past and the future” (p. 417). We (white, queer, lesbian, nonbinary, cisgender, and disabled), as authors, approach this work with the intention of uplifting how LGBTQIA+ community members have built resilience and resistance during one of the grimmest times in U.S. history. Our lived experiences, with most of us identifying as LGBTQIA+, shaped how we approached this work. Data analysis Guided by the research question, we used content analysis to review our data (Bengtsson, 2016). We included both text-based diary entries and detailed notes summarizing audio diary entries and video interviews. Because we were most interested in participants’ words for this particular paper, we did not analyze visual data, such as photographs or the visuals of the video interviews or diaries. Although the larger research project was mixed methods, we only focus on analysis of qualitative data for the present study because the diaries and video interviews were where data about resilience and resistance were captured. Four members of the research team were provided access to the diaries and notes and engaged in reviewing these data and identifying meaning units (decontextualization; Bengtsson, 2016). Each team member applied an open coding process to these meaning units. We met virtually multiple times to discuss our coding process and used the virtual white board tool Miro to visually display our codes to one another as digital “sticky notes” and finalize a list of codes. We began clustering “like” codes together on Miro and identifying themes and more narrow categories (categorization). In this inductive thematic analysis, all authors first worked independently to generate initial codes, generally identifying multiple codes per each diary instance. We then worked collaboratively to harmonize our codes, identify gaps, and resolve differences. We then identified overarching themes through continued collaborative discussions and independent reviews of the remaining diary data. Two members of the team (Kristie and Brendon) then began creating a narrative for the story of our themes (compilation), while also reviewing the original meaning units for larger context (recontextualization) to help ensure enough of the original data were included to give detail to any exemplar quotes. Findings We captured two primary themes related to resilience and resistance among LGBTQIA+ adults: (1) resilience and resistance building on the histories of LGBTQIA+ people, and (2) resilience and resistance re-imagined during the pandemic. For the first theme, data reflect the historical trauma and common experiences for LGBTQIA+ people and build upon the knowledge base with details related to resilience and resistance during the COVID-19 pandemic. Within this theme, we identified three categories: (a) activism and political change, (b) mutual aid, and (c) having a mindset for health promotion and optimism. The second theme highlights what the research team identified as unique and less explored dimensions of resilience and resistance, some of which emerge strongly in the present context. The six categories captured were (a) adapting one’s mindset, (b) drawing on hardiness, (c) using technology to stay connected and engage in mutual aid, (d) deepening relationships, (e) resisting capitalism’s brutality, and (f) envisioning a better world. Resilience and resistance building on the histories of LGBTQIA+ people The first theme that emerged, resilience and resistance building on the histories of LGBTQIA+ people, is captured by Charles:Since the beginning of the pandemic, I’ve been contemplating the role of crisis and catastrophe in my life and the lives of people who experience significant structural violence, marginalization, and oppression. The moments when the rug is pulled out from under you and you have to cope and survive… For some of us, personal and collective trauma is not new. For some of us, confronting crisis, sickness, and death, is not completely unfamiliar. And the ability to have joy, to find pleasure, to remain connected to one’s humanity even in the most horrific moments, is also not new. Activism and political change. LGBTQIA+ adults in the Southeast are engaging in activism and hoping for political change as part of their resilience and resistance to oppression and violence. Participants are actively contributing to and supporting social justice movements, such as the Black Lives Matter Movement. Such efforts build upon histories of engagement among LGBTQIA+ activists and leaders. One participant (Publius) noted in their diary entry that there will always be more activism to do: “We LGBT/Q persons must fight for survival, what this looks like in 2021, with assaults on Oberfell versus Hodges continuing or in 2070 with whatever wars and crises come, LGBT/Q persons must fight in all ways we can.” The need for LGBTQIA+ people to fight oppressive systems is a display of resistance, as these formal systems and governments continue to fail and harm LGBTQIA+ people. Mutual aid. LGBTQIA+ people are both giving and receiving support from their community, neighborhoods, and families of origin and chosen families. During COVID-19, relationships with families of choice continue to be tapped into and deepened as sources of social support and meaning. One participant (Leyousef) wrote about how important community support and collective care has been during the pandemic: “Carrying people through this pandemic is much more important than having your needs met on your own - carrying your community leads to your community carrying you.” Another participant (Taylor) echoed similar sentiments: “I think the most important thing to understand about these difficult times is that we rarely go through them alone. There is peace and relief in solidarity, particularly among other LGBTQIA+ folk.” Other participants shared examples of how they are engaging in mutual aid, ranging from providing support on social media to offering financial help. One participant highlighted the importance of having older, more experienced community members to lean on: “My chosen family is made of a lot of older people. They have been through a lot and [that feels like a common thread]. They are able to identify what I need, and I owe a lot to them.” Another participant (Sunny) summed up the importance of mutual support and care:This pandemic has shown how crucial it is for us to move in solidarity with other people, to care for one another and to demand and create the conditions that allow everyone to thrive. Companies were not motivated to provide work-from-home solutions to employees with disabilities until working-from-home was prioritized for people who do not have disabilities due to the pandemic. That is systemic ableism. So many people and communities and movements have already envisioned pathways towards equity and liberation and community care. We have to trust each other and do those things. Having a mindset for health promotion and optimism. Previous research has documented how health promoting behaviors and an optimistic mindset contribute to resilience, both among LGBTQIA+ people and other populations. Participants shared stories of how they further these trends. Health promotion included participants finding ways to physically move their bodies, being aware of mental health needs, intuitive eating, engaging in self-care, connecting with community, and following scientific guidelines around reducing risk for COVID-19 transmission. One participant (Ike) highlighted how they have been able to cope during the pandemic:What has helped me cope – I “stay strong,” I read, I take breaks from social media when it is too much, and I was doing smaller exercises instead of biking. Usually I use a stationary bike – good for relaxing, get good bang for your buck. I used to run but that was not good for my knees. I also tend to do cardio dance – used to dance on the weekends out with people, and now really miss connecting on the dance floor. Twitch and Discord are ways I still stay in touch with community now. Additionally, one participant discussed health promotion in terms of being involved in a queer mentorship program and viewing queer content on TikTok. They described how these avenues helped them connect with other queer people: “It is so affirming and comforting and exciting that there are other people being queer out here, existing in the world.” Optimism was more about how participants found and maintained hope for the future. Once more, Ike shared that the COVID-19 pandemic had been hard for them, but their resilience through the pandemic gave them optimism for the future:I’m just an optimistic person and the hope that this will get better and things will change ‘cause it always does change. Things always do change, and things do always get better or it’s a more manageable situation. So, I always have hope. Resilience and resistance Re-imagined during the COVID-19 pandemic The second theme identified in the data reflects dimensions of resilience and resistance that tend to be less frequently covered in previous scholarship and that represent a re-imagining of methods for surviving, thriving, and pushing back against systematic oppression. Some of the categories in this theme represent dimensions that are particularly relevant to the COVID-19 pandemic era. Adapting one’s mindset. Participants spoke of adjusting their mindset to persist during COVID-19, including focusing on the present, enforcing boundaries (related to safety, social distancing, news consumption), focusing on things other than COVID, and recognizing that pain and suffering are temporary. An example of an adapting mindset was shared in a diary entry (Taner):It has truly been one of the hardest years of my life with this pandemic piggy-backing on a time of mental health instability where I haven't had unemployment and therefore limited access to healthcare options. And so what gives me hope in the midst of all this?.. I've learned that hope can be small and fragile at times, and doesn't always need to be defined in grandiose terms. There's been days hope has meant surviving a day of self-harm and suicidal ideation. Hope has meant my partner staying up with me into the early hours of the morning, both of us trying to find some comfort in Adult Swim cartoons and bearing witness to each other in our darkest moments. Some days hope has simply been the belief that the pain will pass… even when my trauma loops feel like endless excruciating torture. I've started to see hope in more delicate, ephemeral things, moments, conversations, my friends finding ways to reach out and hold space for each other in whatever ways we can. Another participant (Charles) similarly voiced that coping is “day by day, moment by moment.” This participant also shared an example of enforcing new boundaries:For a while, I was a bit of a mad scientist, tracking charts and graphs and data points [about COVID-19 cases, hospitalizations, and deaths]. This became a part of my daily routine as much as brushing my teeth. But at a certain point I stopped. And it helped. Oh, it helped...And I found other things to do again. I wrote. I went for walks. I went for long drives...I found other things...I even started baking. And before long I looked up and it was late summer. I was about to turn 40, and the world had not come to [an] end. Hope I learned was not something that just happens. It's something you have to commit to. Something you have to will into existence. Hope requires discipline. Drawing on hardiness. Participants spoke to how part of their resilience and resistance was related to having survived rough times before and drawing on this hardiness to get through the pandemic. For example, multiple participants spoke about linkages between the HIV/AIDS epidemic for the LGBTQIA+ community and the experiences of surviving through COVID-19. Katherine, a Gen X participant, shared in a diary entry:...for those people affected by HIV and/or those who work in the field, like me, we are well aware that making concessions like using condoms or wearing masks can save lives and is well worth the inconvenience… The comparison is clunky but I believe that these experiences may help some queer people manage their own exposure to the pandemic and [its] mental health impact. For others, however, I worry that it becomes a double helping of isolation, especially for those in unsupportive living situations, including young people. Those people who are not independent and/or who did not have a queer support network before the pandemic may have had a more difficult time finding one during the last few months. Katherine’s reflections highlight how the preventative health behaviors and experiences going through the HIV/AIDS pandemic may actually help the LGBTQIA+ community be better able to adjust during COVID-19. In contrast, a Baby Boomer participant, Bubba, spoke in an interview about COVID-19’s differences from the 1980s HIV/AIDS epidemic based on direct experience:Living through the AIDS Crisis in the 80’s, I was able to be by my friends’ side. Was able to serve community by providing comfort, a hand to hold or a meal. COVID has been difficult because you cannot provide comfort through direct contact. This lack of being able to give back has been extremely difficult. Bubba’s experience highlights the difficulty of often not being able to be physically present with loved ones during the early phases of the COVID-19 pandemic. Another participant shared other reflections that highlight the role of hardiness during COVID-19: “I was born in [a country in the Middle East] during the [Gulf] War and there were constants threats of invasions and other dangerous events. Those events have contributed to [my] resiliency." Using technology to stay connected and engage in mutual aid. At the onset of the COVID-19 pandemic, most youth and adults in the U.S. shifted to the virtual world (e.g. Discord, Zoom, FaceTime), notably to engage in online education or work but also informally as scientists and public health experts encouraged people to stay home unless necessary. These experiences, some involuntary and potentially combined with voluntary experimentation, led participants to use technology in new ways. Powerful examples for the LGBTQIA+ community include building community on Twitch or Discord and using video chats to engage in more frequent communication with social supports outside of one’s “bubble.” One participant (Ike) shared their experience of using technology to connect with community:I started streaming, where people play live video games. Through that I got a community where people talk to me regularly...I also used the application Discord. With Discord, it’s a bunch of private chat rooms where people can go, who, for example, love dogs...It was a way to connect with other queer people, which was already hard. Now I’ve made a lot of friends streaming and through the chat rooms. It’s kept me grounded because I was able to connect with new people. Taner noted the shift to being online for community engagement and how mutual aid efforts are happening: “Drag queens are performing on Twitch streams and creating amazing visuals and videos to entertain, artists are performing and making masks and sharing skills and resources with each other.” Other participants highlighted how they started volunteering with nonprofits to help meet community members’ needs, how in-person LGBTQIA+ dinners shifted to virtual, and how they built community online to combat isolation. Deepening relationships. Social relationships are frequently a contributing factor to resilience. In this study, participants spoke not just of the value of relationships, but of how the pandemic (with lockdowns and social distancing) prompted them to be both more intentional and more selective about how they connected and deepened relationships. For example, Kayla spoke of purposefully reaching out to friends more often than usual:The strategy I've [been] using to maintain social and emotional connections during this time is by reaching out to my friends more often than I used to by texting, calling and using FaceTime and Zoom… My relationships have strengthened by this. Another participant (Sunny) spoke of how relationships have strengthened and become more stable due to having "more self-disclosure...becoming more transparent about my curiosities about other people.” A third participant (Taner) expressed:[It] feels like our bullshit meters have all run out a bit, and our dialogue feels more sincere and open. I find friends leaning into difficult conversations about mental health more instead of just exchanging pleasantries… We can't have get togethers but we can still look out for one another and ourselves, and acknowledge that self-care is damn hard during a crisis like this. Most of all, I think we've started finding out what's really worth fighting for in our lives, and what we're willing to do to hold on to it. Resisting capitalism’s brutality. The pandemic often prompted participants to reflect on life purpose, how they use their energy to make a wage, and their place in the capitalist system. Others experienced job insecurity because of the pandemic. As a result of these experiences and reflections, some participants questioned whether they wanted to continue in their current jobs; others made changes in their work, schedules, and when/how they took breaks to reflect a shift in priorities. All of these dimensions connect to the concept of resistance. Taner shared:As I continue working in therapy and at home on my PTSD symptoms, I’ve also been having broader conversations with my family and mother specifically. My views on capitalism and its brutality, as well as the disproportionate effects on at-risk groups have come up in talks where before I used to just stick to safe topics. Taner’s words share a sense that the pandemic changed his perspective about the importance of speaking up about the violence inflicted by capitalism. Ike’s diary included the entry:I lost my job. I couldn’t see people… I spiraled at the beginning, I think everyone did. And then I had to find my groove because there’s only so much someone can spiral. I picked myself up by my boots. I got a break from this fast-paced life, go go go. There was a lot of time open. I was like ‘I’m going to learn some stuff; I’m going to push my mind.’ I started taking online courses, some stuff for user experience, coding. I started steaming, where people play live video games. Losing one’s job prompted Ike to reflect on the pace of his life and make changes about priorities. A third participant (Sunny) reflected:About two years ago, a friend told me…’be gentle on yourself.’ I commonly say this phrase to myself and others now, and I personally take it to mean: do not expect less from yourself and do not expect more. I am learning to be patient with myself...The pace that I take towards my goals or purpose in life may seem meandering or stagnant at times, but that pace is also what feels most appropriate and fitting for me in that moment. Sometimes, I do not have a lot of energy, and I feel drained, but I just let myself feel that. I try not to let myself down by creating unrealistic standards for myself without caring for what I am feeling. I think this saying has helped me establish boundaries. Envisioning a better world. Participants noted that the pandemic era is a “cultural awakening”—a key moment for seizing possibilities of who we will be in the future. Many expressed that to get through this time, they focused on possibilities, future generations, and a belief that some cultural changes made during this time will be for the better. In a diary entry, Katherine reflected on hopes for future generations:I hope that the children growing up during this time will see the mistakes that we, their elders, have made and make better choices in their politics and their use of science and public health services. BT spoke of hoping for recalibration towards what makes people happy, and recognition of connectedness:One of my hopes is that this moment in our history will result in a significant shift in how many people want to operate in the world. That we will see how connected and important we are all to each other. That all of this time with ourselves will result in some introspection, and that people will seek to find what truly makes them happy… instead of just being consumer cogs in the machine. Discussion The present study explored resilience and resistance strategies among LGBTQIA+ individuals living in the Southeast U.S. during the COVID-19 pandemic. The first theme indicated that LGBTQIA+ people are showing some strategies of resilience and resistance that build on LGBTQIA+ knowledge and history: (a) activism and political change, (b) mutual aid, and (c) having a mindset for health promotion and optimism. These are all strategies identified within past research in relation to other contexts of struggle, oppression, and violence for LGBTQIA+ people outside of the pandemic (Baker-Pitts and Martin, 2021; Johnson and Rogers, 2020; Seelman et al., 2017; Singh and McKleroy, 2011). Although participants rarely spoke about activism or politics specifically about the pandemic, they felt a sense of agency in contributing to various movements, and the engagement, energy, community, and hope produced was what mattered most. Mutual aid, in contrast, was frequently discussed as addressing pandemic-specific needs, such as strengthening relationships and addressing financial needs. Social work educators and practitioners can elevate mutual aid efforts by sharing these efforts with clients, especially with those who hold a marginalized identity, as mutual aid is often relied on most by those who are marginalized (Berne, 2015). During the pandemic, mutual aid practices have included meal sharing, helping with transportation needs, and redistributing wealth to communities who are in need of support (Arani, 2021). For individuals/communities who have been harmed by formal systems of support, mutual aid efforts are an alternative to these harmful systems, making these efforts important for social workers to acknowledge and share with clients. Findings also shed light on some strategies of resilience and resistance that were re-imagined and unique to this point in time. Adapting one’s mindset was a strategy for surviving day-to-day, finding distractions, and also drawing boundaries around social media and news engagement; recent research has highlighted the challenges of news and social media consumption for adult mental health during the pandemic (Liu et al., 2021; Nguyen et al., 2021). The need to adapt daily activities and to forge connections online led participants to use technologies in new ways, both to carve out individual connections and to re-imagine community activities (Haesler et al., 2021). Social work practitioners who work with LGBTQIA+ communities can highlight the community building aspects of online platforms, such as gaming with other LGBTQIA+ people on Discord or finding online social and/or support groups that are specific to LGBTQIA+ communities. The pandemic, and the halt of usual daily routines and employment, prompted a reflection for many participants about life purpose and the ways capitalism can take advantage of and even destroy human well-being. Larger patterns of such reflection have appeared in news stories about voluntary job resignations across the U.S. and individuals reflecting on what type of day-to-day life they want to maintain (Kanell, 2021). These actions of our participants demonstrate an active resistance to the oppressive aspects of capitalism during the pandemic. This represents the importance of incorporating macro-level social work content into social work classrooms that explores larger patterns of oppression linked to capitalism, employer policies, and the overlap of ableism with other forms of marginalization in employment. Future social workers, including those in micro/clinical concentrations, should be learning about the U.S. systems people are forced to navigate and how these systems can further oppress marginalized groups. One word that continuously came up throughout the diary entries was community. Whether participants relied on community support, created their own chosen families, or broadly shared about LGBTQIA+ people, there was a strong sense of community present. Many participants either have, need, or desire counterspaces that are specifically for LGBTQIA+ people. Counterspaces are “sites where deficit notions of people of color can be challenged and where a positive climate can be established and maintained” (Solorzano et al., 2000, p. 70). These sites are meant to exhibit radical positivity to ensure that patterns of societal oppression are not reproduced within the setting (hooks, 1990). However, the authors identified only one study that has examined counterspaces in the context of LGBTQIA+ populations, specifically related to school climate for LGBTQIA+ youth (Cerezo and Bergfeld, 2013). Future studies and social work researchers and educators may want to use the framework of counterspaces to understand support and collective care among LGBTQIA+ communities. By doing so, this cultivates a space that centers “radical positivity” (hooks, 1990) rather than solely focusing on the pain and suffering experienced by LGBTQIA+ people. Lastly, many participants noted that they are envisioning a better future. This could be due to the impacts of the COVID-19 pandemic or it could be due to the historical trauma and the current harmful legislation and political climate impacting LGBTQIA+ people. Given that systems were not designed with LGBTQIA+ people in mind, it is no surprise that LGBTQIA+ individuals are dreaming of and re-imagining a better future. Future studies—as well as ongoing social work practice—may want to incorporate aspects of radical imagination to offer individuals the opportunity to imagine the world and social institutions not as they are but how they could be (Haiven and Khasnabish, 2014) and to incorporate futures thinking (Nissen, 2021). This re-imaginative process is an act of resistance to oppressive systems that have attempted to strip LGBTQIA+ people of their creativity and ability to dream of a better future. Limitations There are several limitations with the present study. First, the data were based on diary entries and video interviews driven by certain a priori topics that were first identified by the research team and community advisory board as being central to resilience among LGBTQIA+ people. Some critical topics may not have been discussed by participants simply because they were not identified among these a priori topics. Second, while efforts were made to conduct outreach to recruit participants of diverse backgrounds and positionalities, the sample had a larger proportion of White respondents (60%) than would be expected for adults in the Southeast overall. The majority of participants were of the Millennial and Gen X generations, and it was more difficult to recruit individuals who identified as either Gen Z or Baby Boomers and older generations. More research is needed on resilience and resistance strategies among LGBTQIA+ adults of color and the youngest and oldest adults in the Southeast. Our study sample did, however, have a sizeable portion of transgender and gender diverse participants (30%), which helps shed greater light on the experiences of this understudied subgroup. Our analysis only examined text-based diary entries and notes from video/audio diaries or interviews. This meant visual data, such as analyzing the arrangement of information in photos, were not included in this analysis. There was one instance in which a participant uploaded a diary entry composed only of photos, and one instance of video-only diary submissions (all on the Natural World theme), thus excluding the visual aspects of these entries from the present analysis. The photographs and the visuals of video data could possibly be analyzed in future research with these data. Our project is already actively sharing visual diary entries (photographs) and video clips that had participant permission for public sharing on our social media channels as part of distributing findings with the general public. Additionally, we have been planning other ways of making text, audio, photographic, and video data available in public-facing formats and dissemination, such as a library archive about this research project. Finally, although this study collected data over multiple timepoints across 12 months, not all participants engaged consistently, and much of the data came from a highly engaged subgroup of participants. Additionally, there is value in studying resilience and resistance related to the COVID-19 pandemic over a longer period of time or in retrospect. Conclusion This study relied on diary entries and video interviews from 30 LGBTQIA+ individuals to examine resilience and resistance strategies used during the COVID-19 pandemic. The findings show that resilience and resistance are historically embedded in the LGBTQIA+ community due to past systemic trauma, and that resilience and resistance have also been re-imagined in response to the COVID-19 pandemic. This re-imaginative process is allowing LGBTQIA+ people to envision what a better world looks like, individually and collectively. To co-create a better world with LGBTQIA+ individuals, social work educators and researchers can collaborate with LGBTQIA+ people to create counterspaces in the classroom and in research settings. Social work practitioners can engage with LGBTQIA+ community members to learn what they need to feel safe, supported, and to thrive in their communities. As we conclude this paper, we want to leave you with a final quote from one of our participants (Taner): On the other side of this, we’ll dance harder and love deeper, and take less of those beautiful moments for granted. That maybe this becomes the point we started waking up a bit more, and seeing each other, and taking time to see ourselves as well. ORCID iDs Kristie L Seelman https://orcid.org/0000-0002-4064-2927 Brendon T Holloway https://orcid.org/0000-0001-5126-9606 The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. 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==== Front Work Occup Work Occup WOX spwox Work and Occupations 0730-8884 1552-8464 SAGE Publications Sage CA: Los Angeles, CA 10.1177/07308884221128481 10.1177_07308884221128481 Original Research Article Do Workers Speak Up When Feeling Job Insecure? Examining Workers’ Response to Precarity During the COVID-19 Pandemic https://orcid.org/0000-0001-8651-566X Rho Hye Jin 1* https://orcid.org/0000-0002-1257-4496 Riordan Christine 2 Ibsen Christian Lyhne 3 https://orcid.org/0000-0003-4935-2341 Lamare J. Ryan 2 https://orcid.org/0000-0003-0180-9891 Tapia Maite 1 1 School of Human Resources and Labor Relations, 3078 Michigan State University , East Lansing, MI, USA 2 School of Labor and Employment Relations, 14589 University of Illinois at Urbana-Champaign , Champaign, Urbana, IL, USA 3 FAOS/Department of Sociology, 4321 University of Copenhagen , Copenhagen, Denmark * Authors are listed in alphabetical order, with the first two as leading authors. Hye Jin Rho, School of Human Resource and Labor Relations, Michigan State University, 368 Farm Lane, East Lansing, Michigan 48824-1312, USA. Email: [email protected] 30 11 2022 30 11 2022 07308884221128481© The Author(s) 2022 2022 SAGE Publications This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. The COVID-19 pandemic inflicted unprecedented precarity upon workers, including concerns about job insecurity. We examine whether workers respond to job insecurity with voice, and assess the role of unions, managers, and employment arrangements in this relationship. Analyses of an original 2020 survey representative of Illinois and Michigan workers show that job insecurity is not significantly associated with voice. Further, while we find that union membership and confidence in organized labor are positively associated with voice, insecure workers are less likely to speak up than secure workers as confidence in organized labor increases. Last, we find that insecure nonstandard workers are less likely to use voice than their secure counterparts. job security employment precarity voice unions nonstandard work COVID-19 edited-statecorrected-proof typesetterts19 ==== Body pmcIn March 2020, the pandemic inflicted unprecedented economic uncertainty on U.S. workers. Mandated shutdowns, skyrocketing COVID-19 caseloads, and a lack of coordinated political response caused millions to lose their jobs and instilled fear among others that their employment could be at risk. These challenges unfolded rapidly and acutely: by April 2020, the unemployment rate reached nearly 15% and by June, nearly 33 million workers were receiving unemployment insurance (Bureau of Labor Statistics, 2021a; USDOL ETA, 2021). Almost a year later, over 10 million workers remained out of work, and an additional six million were working part-time involuntarily (Bureau of Labor Statistics, 2021a). Those workers whose jobs remained intact—frontline and essential workers, for example—were not spared the anxiety that accompanied heightened and widespread job insecurity, defined as concerns about the threat to continued employment. For these workers, worries about catching the virus and the potential consequences for their employment status became inescapable realities of work (Hertel-Fernandez et al., 2020). Combined, these concerns contributed to pandemic precarity, or “material deprivation and economic anxiety resulting from the COVID-19 pandemic” (Perry et al., 2021, p.1). At the same time, the media was replete with accounts of workers responding to pandemic precarity with voice—defined as meaningful input into decisions (Budd, 2004)—to make their work safer and more secure. Workers have spoken up directly to managers, supervisors, and colleagues about health and safety measures needed in the workplace (e.g., Andrews & Kaiser Health News, 2020). Others have engaged in collective voice, for example by writing to organizational leadership (e.g., Reyes, 2021) or organizing walkouts with colleagues (e.g., Bellafante, 2020). Essential and gig workers, the latter of whom lack protections under labor law and at workplaces, collectively held protests, sickouts, and strikes (e.g., Payday Report, 2021; Scheiber & Conger, 2020). Still others have turned to unions to represent their concerns. Even though union density in the U.S. has decreased over the past four decades to about 10% in 2021 (Bureau of Labor Statistics, 2021b), unions have played a significant role in addressing challenges associated with COVID-19. Essential workers who belong to unions have reported better workplace practices—such as receiving personal protective equipment (PPE) and disinfecting resources—than those without representation (Hertel-Fernandez et al., 2020). Unions have won hazard pay and paid sick leave for members (e.g., Communication Workers of America, 2020), and have supported workers in nonstandard arrangements who lack access to voice via unionization, such as gig workers shopping for Instacart (Jones, 2021). At the same time, one in eight workers reported having perceived employer retaliation against them or their coworkers for raising COVID-19 related concerns (National Employment Law Project, 2020). Such outcomes underscored the importance of the extent to which workers perceive managers to be receptive to workers’ input as a determinant of voice. This leads to the question: To what extent have workers responded to pandemic precarity with voice? We investigate this by focusing on the relationship between voice and one dimension of pandemic precarity: job insecurity. How does perceived receptiveness of managers and effectiveness of unions—which we refer to as the contextual determinants of voice—factor into these decisions? And, given that workers in different arrangements, such as gig and temporary workers, have varying access to some of these voice mechanisms, how do employment arrangements influence voice responses? We assess these questions using an original survey representative of workers in Illinois and Michigan, the 2020 Civic Engagement and Voice Survey, conducted in July 2020. Our study contributes to our understanding of employment precarity during the pandemic in several ways. First, we show how contextual elements of voice—in particular, how workers perceive their managers and unions—influence voice decisions under conditions of job insecurity during the pandemic. Second, we analyze nonstandard work in our assessment, accounting for variation in job insecurity among workers in nonstandard employment arrangements and how it influences voice actions. Finally, our study offers a snapshot of how the macro-level consequences of the pandemic intersect with concerns over job insecurity, and response decisions workers make. Literature The Meaning and Consequences of job Insecurity We define job insecurity as concerns about the threat to continued employment. Job insecurity captures an individual's perception of the threat of job loss. Notably, the subjective nature of job insecurity makes it conceptually distinct from job loss or reduced work hours, the occurrence of which brings certainty to an individual's employment status (De Witte, 2005; Lee et al., 2018; Sverke et al., 2002). Job insecurity is also a multidimensional construct, encompassing collective perceptions of insecurity in a workplace (Sora et al., 2009) as well concerns that extend beyond job loss to include declining job quality or employment standards (Låstad et al., 2015). Here, however, we focus on the individual, subjective definition of job insecurity in our analysis of pandemic precarity. Job insecurity is rooted in a variety of sources, from a specific organizational change (e.g., organizational restructuring) to broader, macro-level social and economic changes (e.g., an economic recession). Regardless of the source, however, individuals’ perceptions of insecurity are situated in their immediate context. This means that job insecurity may fluctuate as change unfolds, underscoring its unpredictability (Breevaart et al., 2020). Likewise, its dependence on context means that more than individual-level factors shape job insecurity. Relationships with coworkers, interactions with supervisors, communication from management or unions, and work settings and arrangements are all workplace-specific factors that influence how individuals perceive job insecurity (Lee et al., 2018). Of particular concern during the COVID-19 pandemic are the adverse consequences of job insecurity. Job insecurity adversely impacts workers’ mental and physical health, compromising their well-being, inducing stress and exhaustion, and negatively affecting their sense of personal control and autonomy (Glavin, 2013; Sverke et al., 2002). Job insecurity likewise can adversely affect workers’ attitudes towards work and their employing organization (Sverke et al., 2002) and motivate a response. Voice as a Response to Job Insecurity Given the adverse consequences of job insecurity, how do workers respond? What actions do they take to mitigate or cope with such uncertainty during the pandemic? Prior research establishes that individuals facing job insecurity are more likely to exit an organization (Berntson et al., 2010), or reduce job and task involvement (Cheng & Chan, 2008; Lee et al., 2018). Many of these outcomes, however, emerge from studies that evaluate insecurity in the context of a specific organizational change, such as restructuring, and not an economy-wide shock that renders exit less feasible, such as the pandemic. In the U.S., workers’ various efforts to seek safety and security at work during the pandemic, ranging from informal, individual conversations with supervisors to well-organized collective protest, illustrate a different response: voice. Voice is defined as meaningful input into decisions (Budd, 2004). The aim of voice is to induce change—a departure from the status quo—within an organization (Dundon et al., 2004; Marchington & Wilkinson, 2000). As observed during the pandemic, voice can take on a variety of forms. On one hand, workers who speak directly to managers and supervisors about COVID-19 concerns are exercising direct voice. This occurs when workers make demands to employers on their own, without the support of a representative body such as a union. Direct voice can occur through informal means, such as casual conversations with supervisors, or through formal practices, such as an employee survey conducted by management (Marchington & Wilkinson, 2000; Mowbray et al., 2015). Suggestions about how to reconfigure workspaces to account for social distancing as well as expressions of dissatisfaction made directly to managers or supervisors (e.g., the lack of paid sick leave) during the pandemic thus comprise direct voice. On the other hand, workers engaging in collective action through their unions, or protests and wildcat strikes organized outside of union settings, are exercising indirect voice (Dundon et al., 2004). Likewise, channels of voice outside of workplaces—including social media, online communities, and electronic petitions (Klaas et al., 2012; Kochan et al., 2019), all widely used during the pandemic—are used for indirect voice, both collectively and individually. Indirect voice is typically conceptualized as a countervailing force in the employment relationship, addressing an imbalance of power or conflict of interests through union representation (Dundon et al., 2004). In this sense, indirect voice frequently reflects conflictual or justice-oriented voice claims (Klaas et al., 2012), as observed in essential and gig workers’ protests and strikes. As with direct voice, though, indirect voice is not limited to this orientation: representative voice mechanisms can also serve to further organizational interests, such as improving work processes (Mowbray et al., 2015). Scholarship on voice often treats these different dimensions of the concept as distinct, isolating, for example, direct versus indirect voice, or collective versus individual voice. Yet increasingly scholars note that different forms of voice often appear as bundles and are not as rigidly isolated from one another as is sometimes assumed (Wilkinson & Fay, 2011; Wilkinson et al., 2004). Recent empirical work on voice follows a similar logic, examining these different dimensions of voice concurrently (e.g., Kochan et al., 2019). Given the varied nature of voice exercised by workers during the pandemic, we adopt this broad, inclusive view of the concept. A different stream of scholarship supports the idea that these dimensions of voice are important as a response to job insecurity. Some research suggests that a positive relationship exists between the two, finding that job insecurity motivates voice and similar participatory behaviors (e.g., Freeman & Medoff, 1984; Sverke & Hellgren, 2001). In other words, workers view job insecurity as a challenge to overcome: employees’ proactive engagement is a strategy to preserve one's job or attempt to exert control over uncertainty (Morrison, 2011; Shoss, 2017). This idea is consistent with other research that shows that, when confronted with job insecurity, workers proactively engage with strategies such as impression management to secure their employment status (De Cuyper et al., 2014). A significant number of studies, however, find a negative relationship between job insecurity and voice (e.g., Berntson et al., 2010; Schreurs et al., 2015) and some a nonsignificant relationship (e.g., Breevaart et al., 2010; Sverke & Goslinga, 2003). These findings build on a theory of job insecurity as a stressor, motivating responses such as withdrawal from work as a coping strategy. Concerns about the costs of exercising voice—the further depletion of one's own resources, for instance, through voice efforts, or retaliation that may result if voice claims are made to unreceptive supervisor—also account for why individuals exercise less voice when faced with job insecurity (Schreurs et al., 2015; Shoss, 2017). In this sense, job insecurity is thus not a challenge to overcome but a stressor that workers seek to alleviate (Schreurs et al., 2015). Meta-analyses of job insecurity are generally consistent with this idea, showing that job insecurity is negatively associated with outcomes that align with the participatory nature of voice, such as job involvement (Cheng & Chan, 2008; Lee et al., 2018; Sverke et al., 2002). Thus, these mostly negative findings suggest that voice as a response to job insecurity during the pandemic may be less likely than initially expected. Hypothesis 1: Job insecurity will be negatively associated with voice. Voice and its Contextual Determinants Both job insecurity and voice are rooted in the context in which they unfold, tied to organizational climates and practices, social relationships among coworkers and managers, and shared beliefs and customs from within those groups (see, e.g., Morrison, 2011 for a discussion of voice climate). Whether individuals choose to respond to job insecurity with voice is thus situated against the backdrop of relationships with and perceptions of workplace actors like unions and managers. The Role of Unions and Managers on Voice Unions express collective voice or capture an indirect, representative voice mechanism (Budd, 2004; Dundon, et al., 2004). They not only articulate and promote workers’ interests to employers but also serve as an important source of support for workers, especially in periods of employment precarity (Freeman & Medoff, 1984). Unions are also a voice mechanism independent from management; as such, workers may be less concerned about the potential costs of exercising voice via union representation, and more apt to address the kinds of threats posed to their security by the pandemic. For instance, recent analysis of Occupational Health and Safety Administration (OHSA) enforcement activity finds that unionized workers are more likely to voice concerns (i.e., file a complaint) regarding health and safety violations relative to their non-union peers. This is because unions educate their members on health and safety measures and protect them from retaliation (Sojourner & Yang, 2021). Thus, we expect that union membership will be positively associated with voice. Moreover, because decisions to exercise voice are also subjective—in this case, meaning that they are influenced by how supportive workers perceive unions to be—we also expect that workers’ assessments of unions will factor into this relationship. Therefore, we expect a positive relationship between confidence in organized labor and voice.1 Managers, on the other hand, are significant because they are most often the target of voice claims; a recent national survey on worker voice showed that just under three-quarters of respondents directed voice claims to supervisors, while less than 20% used most other mechanisms, such as unions or grievance procedures (Kochan et al., 2019). Whether managers are perceived to be receptive and responsive to voice are key determinants of such decisions to exercise voice (Bryson et al., 2006; Morrison, 2011). Managers shape workers’ perceptions of voice utility—i.e., whether workers perceive voice will produce desired effects—based on how active managers are in responding to voice claims. Similarly, managers also shape whether voice claims are perceived to be legitimate within an organization, thus motivating voice behaviors when workers perceive their input to be valued and justified (Klaas et al., 2012). Workers are also more apt to exercise voice when they perceive that doing so poses minimal costs (such as to their reputation) or risks (such as retaliation), communicated in part by cues from organizational leaders such as managers (Klaas et al., 2012). Fear of these risks can motivate silence, or the decision to withhold voice (Morrison, 2014). Embedded within both sets of considerations—workers’ perceptions of the usefulness and legitimacy of voice, and their safety in making their input known—are features of manager-worker relationships such as trust, which positively affect engagement in voice (Mowbray et al., 2015). Thus, workers are more apt to exercise voice when they perceive that the supervisor is receptive and responsive to input, and is unlikely to impose costs. We therefore expect a positive relationship between these potential sources of social support—union membership, confidence in organized labor, and receptive managers—and worker voice. Hypothesis 2a: Union membership and confidence in organized labor will each be positively associated with voice. Hypothesis 2b: Perceived managerial receptiveness will be positively associated with worker voice. The Role of Unions and Managers on Voice Responses to Job Insecurity Both unions and managers are potential sources of social support for workers facing job insecurity. Unions may do so by offering protection, reducing feelings of powerlessness in the face of uncertainty, and providing collective support in times of stress. Managers and supervisors who are receptive and responsive to workers’ concerns may provide information that alleviates concerns and reduces negative attitudes toward the employer (Sverke et al., 2006). This scholarship builds on aforementioned theories of job insecurity as a stressor, which motivates behaviors like withdrawal. Sources of social support, in other words, provide coping strategies through which the negative effects of job insecurity can be mitigated, thus buffering against adverse outcomes like reduced job satisfaction or negative attitudes. Other work questions the extent to which buffering takes place. In their study of a large Australian employer, Dekker & Schaufeli (1995), for instance, find that social supports, captured through workers’ perceptions of confidence in managers and protection from unions, do not mitigate negative outcomes of job insecurity such as mental health complaints and withdrawal from the organization. This is because such supports are unable to reduce the concerns of insecurity directly. Workers’ underlying concerns regarding threats to their employment status remain intact even with support from these actors. In their analysis of multiple European contexts, Hellgren & Chirumbolo (2003) find similar results regarding unions; unions do not moderate the relationship between job insecurity and mental health complaints. Additionally, De Witte et al. (2008), also using European data, theorized that workers’ perceptions of union support would be negatively correlated with insecurity because of a perceived violation of the social contract between employees and unions. This perceived violation comes from the fundamental role workers expect unions to play in protecting their job security; lacking said security, workers’ support for their unions may drop. Evidence regarding how social supports may affect the relationship between job insecurity and voice specifically is somewhat limited. Most direct tests of this relationship have been done in unionized contexts and suggest that union support does not increase the likelihood of workers responding to job insecurity with voice. Underlying explanations for this finding vary. For instance, union workers facing job insecurity are found to be less likely to raise individual direct voice (e.g., speaking up to a supervisor) than non-union workers. This is argued to be because of union workers’ increased organizational loyalty in the face of insecurity, perhaps reflecting a solidaristic sense of “being in it together” (Sverke & Hellgren, 2001). Other research suggests that exercising voice via active participation with a union—for example, by bringing concerns to a shop steward, a form of indirect voice—is an uncommon response to insecurity. This may be because unionized workers, especially those who feel insecure about their jobs, rely on the union to address concerns and do not feel the need to act themselves (Sverke & Goslinga, 2003). Taken together, these pieces of evidence and stress theories of job insecurity suggest that while union membership, confidence in organized labor, and receptive managers are positively associated with voice, they do not buffer the negative effects of job insecurity that make voice responses less likely. Hypothesis 3a: Even as confidence in organized labor increases, insecure workers will remain less likely to exercise voice than secure workers. Hypothesis 3b: Even as perception of managerial receptiveness increases, insecure workers will remain less likely to exercise voice than secure workers. Precarity in Employment Arrangements A notable feature of voice during the pandemic is the extent to which it has involved workers in nonstandard employment arrangements, such as strikes organized by gig workers performing work for Instacart (e.g., Scheiber & Conger, 2020). Many labored in frontline, essential roles during mandated lockdowns but were forced to contend with rising COVID-19 caseloads, fewer protections to ensure their safety, and low pay. The pandemic illustrated, in sharp relief, that nonstandard workers—who include temporary help agency workers, contract firm workers, gig workers, and independent contractors or freelancers—must grapple with both employment precarity and voice in very different organizational contexts than workers in standard arrangements. However, existing evidence suggests that nonstandard workers are less likely to engage in voice than workers in standard arrangements (Doellgast et al., 2018). In nonstandard arrangements, relationships between workers and managers are unclear, targets of voice are multiple, and access to organizational practices and rules is uncertain. Therefore, scholars argue that such arrangements stymie voice: they render unions inaccessible due to ambiguity in employment status; introduce divisions and tensions among workers; and make organizational and managerial practices meant to foster voice less effective (Marchington et al., 2004). Because nonstandard workers find themselves in a different voice context than workers in standard arrangements, they are more likely to hold different beliefs about the utility and legitimacy of voice. Temporary and contract workers, for example, are more likely than permanent, full-time workers to report not taking action—deciding on silence—when encountering a workplace problem because of the belief that doing so would make no difference (MIT Survey on Worker Voice, 2018, cited in Riordan & Kowalski, 2021). Thus, we expect that nonstandard workers are less likely than standard workers to engage in voice during the pandemic. We also expect that job insecurity will factor into nonstandard workers’ likelihood of engaging in voice. Existing evidence suggests that job insecurity is particularly pronounced among workers in these arrangements, despite some researchers’ contention that such workers have different expectations of security because of their self-selection into nonstandard work (e.g., De Witte, 2005). Nonstandard workers are more likely to experience unpredictability than workers in permanent arrangements (Kalleberg, 2009) and report feeling more job insecure, across different types of work and position (Lee et al., 2018). Further, evidence suggests that nonstandard workers’ job insecurity is linked to the objective nature of uncertainty inherent in their different arrangements. For instance, workers with little objective security, such as temporary help agency workers, report a higher perceived probability of job loss than nonstandard workers with relatively secure positions, such as some types of independent contractors. Both, however, evaluate their odds of losing employment to be higher than workers in permanent positions (Klandermans et al., 2010). Further evidence regarding the nature of insecurity among nonstandard arrangements supports the assertion that they will be less likely to engage in voice in response. Although nonstandard workers perceive a greater likelihood of job loss relative to permanent workers, they also anticipate that the effects of loss will not be as severe. In part, this is argued to derive from different expectations regarding mutually understood obligations that tie permanent workers more closely to their employers, making outcomes such as decreased job satisfaction or organizational commitment much more pronounced for this group (Klandermans et al., 2010). In other words, because nonstandard workers may be less invested in their employing organizations, we expect that those who feel insecure will be even less likely to engage in voice than nonstandard workers who feel secure. Hypothesis 4a: Workers in nonstandard work arrangements will be less likely to engage in voice than workers in standard arrangements. Hypothesis 4b: Insecure workers in nonstandard arrangements will be less likely to engage in voice than secure workers in nonstandard arrangements. Data and Methods Data Our study draws on original data collected as part of the 2020 Civic Engagement and Voice Survey (CEVS), a representative survey of non-institutionalized adults ages 18 to 65 in Illinois and Michigan. The survey was administered by Ipsos in July 2020, who sent out online questionnaires to the Ipsos KnowledgePanel, the largest probability-based web panel in the U.S. Ipsos recruits its panel members by using address-based sampling methods. Panel members are notified of their recruitment and assignment to a study sample by email. We first pre-tested and validated our survey instrument with approximately 200 panel members in the week prior to the main survey being distributed. To increase the response rate, the invitations for the survey were followed by two subsequent email reminders on days three and five of the 11-day field period. This led to a 57% survey completion rate. In all of our analyses, we use post-stratified weights produced by Ipsos, which incorporate geodemographic benchmarks, such as gender, age, race and ethnicity, education, and household income, from the March supplement of the U.S. Census Bureau's Current Population Survey (CPS), American Community Survey (ACS), or, in certain instances, the weighted KnowledgePanel profile data. We collected a total of 1,285 responses, including 649 from Illinois and 636 from Michigan. Our final sample consists of 797 adults in Illinois and Michigan who have worked in any capacity for pay from March to July of 2020. We exclude 43 workers who identified themselves as self-employed business owners in their most recent job, and a small number of individuals who had missing values for our main job insecurity variable. Given our focus on capturing work-related voice throughout the pandemic, survey items repeatedly prompted respondents to think about their work situation from the beginning of the pandemic (March 2020) to when the survey was administered (July 2020), even if they had experienced a change in their work situation such as a layoff, furlough, or involuntary reduction of hours.2 Measures Outcome variable Worker voice We capture COVID-19-specific voice actions with questions regarding common employer response measures aimed at addressing risks of COVID-19. We ask respondents to consider voice raised to encourage these specific employer actions prior to the time our survey was administered, as well as voice raised to encourage employer actions that were perceived as needed but had not yet occurred. To illustrate: first, we ask respondents if their employers implemented any of the following COVID-19 response measures: remote work options, PPE, safety guidelines, paid sick leave, reassurance of job security, additional pay, and clear guidelines for communications. If yes, respondents are asked about specific voice actions taken to provide input regarding these already-implemented measures; many of these voice actions are drawn from Kochan et al.'s MIT Survey on Worker Voice (see, Kochan et al., 2019). Then, we ask whether respondents thought their employers should do a better job in implementing the same set of response measures, thus identifying a perceived need for some change. If yes, we ask once again about specific voice actions taken to encourage employer action. Taken together, these questions allow us to identify voice claims about COVID-19 responses with or without any employer action. These voice actions include: 1 = Had a conversation with a supervisor or manager; 2 = Sought advice from coworkers or others like me; 3 = Used internal formal process at my workplace; 4 = Reported to union shop steward or tried to organize/join a union; 5 = Turned to a non-union worker organization or community organization; 6 = Joined a political protest, rally or strike; 7 = Used social media or online community, electronic petition. We combine these two voice questions to generate our COVID-specific worker voice variable. The variable receives a value of 1 if a respondent took any of the aforementioned actions (worker voice = 1) in relation to any COVID-19 response measure, and 0 if they did not. Together, both questions are structured such that they capture voice action responding to a specific need (i.e., an objective voice need); we exclude workers who reported that they did not perceive a need for their employer to implement or do a better job implementing any response measure. Further, consistent with the literature, we created two additional dichotomous variables to distinguish direct and indirect voice actions, assigning direct voice as 1 if respondents indicated that they had a conversation with a supervisor or manager; sought advice from coworkers; or used internal formal processes at the workplace, and indirect voice as 1 if respondents indicated that they reported to a union shop steward or tried to organize; turned to a non-union worker organization; joined a political protest, rally or strike; or used social media or online community, or an electronic petition. Key predictors Job insecurity The key independent variable in our analysis is job insecurity. To capture individual perceptions of job insecurity, we ask respondents to answer the question adopted from the Quality of Working Life module of the General Social Survey: “Overall, I feel my job is secure—1 = Very true, 2 = Somewhat true, 3 = Not too true, and 4 = Not at all true.” For the purposes of our analyses, we re-coded the job insecure measure as a dummy variable that equals 1 if the respondent responded, “Not too true” and “Not at all true” and 0 if “Very true” and “Somewhat true.” Contextual determinants of voice In our main analyses, we investigate the extent to which contextual determinants of voice influence the relationship between job insecurity and worker voice. We first use union membership, assigning a value of 1 if respondents were members of a union or an employee association similar to a union since March 1, and 0 otherwise. We also ask about confidence in organized labor (or other worker organizations) in order to capture respondents’ perceptions of unions. We amended a Gallup Poll question that, since the 1970s, has tracked confidence in organized labor, including non-traditional worker organizations (e.g., worker associations) that have become increasingly prevalent (Kochan et al., 2019). Specifically, we ask: “In general, how much confidence do you have in organized labor or other worker organizations?—1 = Complete confidence, 2 = A great deal of confidence, 3 = Some confidence, 4 = Very little confidence, 5 = No confidence at all.” The responses were reverse-coded and had a mean of 2.77. For the second measure of voice context, we use the managerial receptiveness construct based on a three-item scale adopted from the 2011 UK Workplace Employment Relations Survey (WERS). Respondents were asked, in relation to their employer's response to COVID-19, how good their managers are at: (1) seeking the views of employees or employee representatives; (2) responding to suggestions from employees or employee representatives; and (3) allowing employees or employee representatives to influence final decisions (1 = Excellent to 5 = Poor). The responses were reverse-coded and combined into a single continuous variable, with a mean of 3.15 and Cronbach's Alpha of 0.938. Nonstandard work To measure nonstandard work arrangements, we ask the following question to all respondents who have worked in any capacity for pay since March 1: “Which of the following best describes the type of employment arrangement at your current or most recent job since March 1?—1 = Employed directly by my employer, with either full-time or part-time hours; 2 = Temporary help agency worker (for instance, you are paid by a temporary help agency, whether or not the job is temporary); 3 = Contract firm worker (for instance, you are paid by a company that assigns you to work at their client company's worksite or for a single customer instead); 4 = Gig worker (you perform tasks through platforms like Uber, Instacart, Lyft, or TaskRabbit); 5 = Independent consultant or freelance worker (whether you are self-employed or receive a wage or salary; your work is typically on a task, project or client basis); 6 = Business owner (self-employed and run your own business); 7 = On-call worker (for instance, you are called into work only when needed, although you may work several days in a row). The question is adopted from the Worker Organization Study (Hertel-Fernandez et al., 2022), with clarifications intended to address misreporting of self-employment activity that plagues other survey instruments (Abraham et al., 2019) and a specific response item to separate out gig work. We construct a measure of nonstandard work by assigning 1 if the respondents reported being a temporary help agency worker, contract firm worker, gig worker, independent consultant or freelance worker, or on-call worker (nonstandard work = 1) and 0 (nonstandard work = 0) if the respondents reported being employed directly by their employer. Controls All models in this paper control for a set of demographic, employment, and financial characteristics of our respondents. The literature largely suggests that these factors have mixed or inconsistent effects on measures of voice comparable to those used here (Morrison, 2011).3 Findings from more recent studies (e.g., Kochan et al., 2019), however, show that the likelihood of using specific types of voice mechanisms varies by gender, race/ethnicity, and education, although overall patterns remain mixed; thus, we include such controls here. Female is coded as 1 if the respondent is female and 0 if male. Race is categorized into non-Hispanic White, non-Hispanic Black, non-Hispanic other, non-Hispanic 2 or more races, and Hispanic. Age is divided into four categories (18–32, 35–45, 45–54, and 55–65 years). Socioeconomic status is measured through three levels of educational attainment—less than high school and high school, some college, and bachelor's or advanced degree—as well as level of employment—entry level, experienced (non-manager), manager/supervisor or staff/director, and executive (SVP, VP, Department Head, President, CFO, etc). We use metropolitan area and Michigan (as opposed to Illinois) dummies and political ideology (liberal, moderate, and conservative) to account for regional and political differences across the two states during the pandemic. The analysis also includes a series of dummies for occupation (24 major group occupations closely following the BLS Standard Occupational Classification) and industry (25 sectors closely following the NAICS classification system). To account for financial characteristics, we use household income categorized into six levels (<$25,000, $25,000-$49,999, $50,000-$74,999, $75,000-$99,999, $100,000–149,999, $150,000+) and two measures that capture household financial instability. We adopt a question from the Household Pulse Survey conducted by the U.S. Census Bureau during the pandemic and ask the following questions to all respondents: “Have you, or has anyone in your household experienced a loss of employment income (e.g., due to lay-off or pay-cut)”; and “Do you expect that you or anyone in your household will experience a loss of employment income (e.g., due to lay-off or pay-cut) in the next 30 days?” For each item, we assigned a value of 1 if respondents answered yes; 0 otherwise. A summary of demographic and financial characteristics of the sample by job insecurity is presented in Table 1. Insecure workers in our sample are slightly older, non-white, and have fewer years of formal education than secure workers. A much larger share of insecure workers is likely to report both actual and expected loss of employment income in their household (58.8% and 30.8%, respectively) than secure workers (33.4% and 9.7%, respectively). Table 1. Demographic and Financial Characteristics of Workers in the 2020 CEVS, by Job Insecurity. Overall Secure Insecure Overall Secure Insecure N = 797 (N = 685) (N = 112) N = 797 (N = 685) (N = 112) Variable (%) (%) (%) Variable (%) (%) (%) Women 47.8 48.5 43.8 Age (years) 37.3 33.4 58.8 18–34 32.8 33.6 28.4 Expected loss of Employment Income in the HH 12.9 9.7 30.8 35–44 22.5 22.6 21.8 Income Quartiles 45–54 25.1 24.0 31.6 <$25,000 5.6 5.1 8.4 55–65 19.6 19.9 18.3 $25,000–$49,999 15.0 13.9 21.3 Race $50,000–$74,999 18.0 18.4 18.8 White 71.1 72.3 64.5 $75,000–$99,999 17.1 18.0 12.3 Black 11.8 11.6 12.8 $100,000–149,999 22.8 23.1 21.5 Hispanic 10.5 5.1 2.1 $150,000+ 21.0 21.6 17.7 2+ Races 2.0 8.9 19.6 Top 7 Occupations Other 4.7 2.1 1.1 Other 14.1 13.4 18.5 Education Office and Administrative Support 9.1 8.9 10.4 LHS/HS 27.7 24.6 45.2 Sales 8.1 8.4 6.3 Some College 32.1 32.6 29.1 Management 6.7 6.5 8.2 Bachelor's degree or higher 40.3 42.9 25.7 Education, Training, and Library 6.6 6.9 4.8 Level of Employment Top 5 Occupations 6.2 5.3 11.2 Entry Level 15.6 14.3 23.2 6.1 6.6 3.2 Experienced 57.4 57.4 57.5 Top 7 Industries Manager 24.7 25.7 19.3 Factory, Manufacturing, and Woodworking 12.6 11.5 19.2 Executive 2.2 2.6 — Health Care 11.3 12.2 6.5 Political Ideology Retail/Stores/Shopping 10.0 10.0 10.0 Liberal 28.8 28.5 30.2 Professional, Scientific, Technical, and Business Services 9.5 10.8 2.6 Moderate 32.2 31.8 34.8 Education and Tutoring 8.7 8.7 8.5 Conservative 39.1 39.8 35.1 Top 5 Industries 7.8 7.1 12.0 Metropolitan Area 87.7 87.1 91.0 Top 5 Industries 6.8 7.7 1.5 State Michigan 42.8 42.2 46.5 Illinois 57.2 57.8 53.5 Household (HH) Income Insecurity Age (years) Loss of Employment Income in the HH Notes. Weighted by sample weights. The sample excludes business owners and respondents who had missing values for job insecurity variable. + p < .10, p < .05, p < .01, p < .001. Results We begin by presenting means and standard deviations for the key variables used in our analysis. Table 2 shows that 31.1% of workers exercised any voice, whether it was to encourage employers to implement any COVID-19 response measures in the past, or to do a better job going forward, as opposed to 68.9% of workers who did not exercise voice. Most voice claims were made in the form of direct voice (96.2%) rather than indirect voice (19.1%). For each voice type, 29.9% of workers used direct voice, 5.9% used indirect voice, and 4.8% used both direct and indirect voice. In terms of job insecurity, 84.9% of workers indicated that they perceived their job to be very or somewhat secure. Managerial receptiveness measure had a mean of 3.15 overall, with secure workers reporting higher managerial receptiveness than insecure workers (3.31 and 2.24; p < .001). While a slightly higher share of insecure workers reported belonging to a union (19% for insecure workers and 17.7% for secure workers), insecure workers reported slightly less confidence in organized labor or other worker organizations. Further, about 5.2% of workers worked in nonstandard arrangements. Consistent with literature, a higher share of workers reporting job insecurity were in nonstandard work arrangements than those reporting job security (11.1% and 4.2%; p < .10). Table 2. Descriptive Statistics of Key Variables for Respondents Who Worked in Any Capacity for Pay, March–July 2020, Overall and by Job Insecurity. Overall Secure Insecure (N = 797) (N = 685) (N = 112) Variable Mean Mean Mean COVID-Specific Worker Voice Voice 31.1% 31.4% 29.3% COVID-Specific Worker Voice, by Type Direct 29.9% 30.3% 27.7% Indirect 5.9% 5.7% 7.1% Both Direct and Indirect 4.8% 4.7% 5.5% Job Insecurity (Overall, I feel my job is secure) 1-Very true 41.7% 2-Somewhat true 43.2% 3-Not too true 9.5% 4-Not at all true 5.6% Managerial Receptiveness (alpha = 0.9382) (1-Poor to 5-Excellent; Reverse-coded) 3.15 (1.17) 3.31 (1.12) 2.25 (1.07)* Union Membership (1-Union member) 17.9 17.7 19.0 Confidence in Organized Labor or Other Worker Organizations (1-No confidence at all to 5-Complete confidence; Reverse-coded) 2.77 (0.84) 2.79 (0.85) 2.64 (0.80) Nonstandard Work Arrangement Nonstandard Worka 5.2% 4.2% 11.1%+ Notes. Weighted by sample weights. The sample excludes business owners and respondents who had missing values for job insecurity variable. Standard deviations in parenthesis. Last column denote results from t-tests showing statistical significance of the difference in means between job security and insecurity. a Excludes business owners for the purposes of analysis. + p < .10, p < .05, p < .01, p < .001. Who is More Likely to Speak Up during COVID-19? We first look at the likelihood of reporting COVID-specific voice—that is, speaking up to encourage employers to implement COVID-19 response measures—by perceptions of job insecurity. Table 2, columns (2) and (3) show the proportion of insecure versus secure workers who reported voice. While a smaller share of insecure workers (29.3%) than secure workers (31.4%) exercised voice, this difference is not statistically significant. Thus, we do not find statistically significant support for Hypothesis 1, which predicted that job insecurity will be negatively associated with voice. Likewise, we find no significant differences by job insecurity, for direct, indirect, and both direct and indirect voice. Interestingly, we see a higher share of insecure workers reporting indirect voice than secure workers (7.1% and 5.7%), though the results are insignificant and are derived from too small a sample to draw a meaningful conclusion. Table 3 confirms the descriptive results using logistic regression models and including control variables. The odds that job insecure workers exercise voice are about 0.8 times as large as the odds were for their secure counterparts (smaller by a factor of about 0.2), and the results are not statistically significant. Table 3. Logit Regression Predicting the Likelihood of Exercising Voice (Odds Ratios). (1) (2) (3) (4) (5) (6) Job Insecurity 0.797 0.806 0.877 0.632 0.826 0.727 (0.263) (0.269) (0.290) (0.208) (0.270) (0.245) Union Membership 2.604 2.369* (0.844) (0.796) Confidence in Organized Labor 1.301+ 1.316+ (0.177) (0.191) Managerial Receptiveness 0.771 0.765 (0.076) (0.076) Nonstandard Work Arrangement 0.472 0.455 (0.268) (0.254) Female 1.110 1.121 1.023 1.133 1.109 1.036 (0.262) (0.271) (0.246) (0.268) (0.264) (0.254) Race (White as reference) Black 0.927 0.858 0.860 0.850 0.925 0.720 (0.323) (0.303) (0.322) (0.307) (0.324) (0.283) Hispanic 1.423 1.368 1.468 1.476 1.486 1.576 (0.590) (0.556) (0.612) (0.608) (0.609) (0.641) Other 2.638+ 2.391 2.770* 3.090* 2.572+ 2.911+ (1.360) (1.325) (1.431) (1.613) (1.320) (1.635) 2+ Races 0.254+ 0.229* 0.316 0.245+ 0.281+ 0.283 (0.183) (0.168) (0.235) (0.179) (0.203) (0.234) Age (18–34 as reference) 35–44 0.857 0.879 0.911 0.906 0.860 0.994 (0.247) (0.258) (0.265) (0.270) (0.248) (0.307) 45–54 0.471* 0.470* 0.485* 0.476* 0.491* 0.509+ (0.150) (0.155) (0.154) (0.158) (0.156) (0.176) 55–65 0.764 0.791 0.790 0.800 0.794 0.897 (0.229) (0.242) (0.239) (0.247) (0.240) (0.286) Education (LHS/HS as reference) Some College 1.905+ 1.895+ 1.976* 1.813+ 1.940* 1.919+ (0.642) (0.639) (0.658) (0.621) (0.653) (0.645) Bachelor or Higher 2.256* 2.435* 2.428* 2.213* 2.345* 2.669** (0.853) (0.892) (0.902) (0.868) (0.896) (0.994) Level of Employment (Entry level) Experienced 2.422* 2.350* 2.431* 2.415* 2.419* 2.325* (0.972) (0.945) (0.977) (1.000) (0.971) (0.963) Manager 2.568* 2.792* 2.473* 2.552* 2.522* 2.549* (1.152) (1.269) (1.121) (1.186) (1.139) (1.214) Executive 2.932 3.876+ 3.126 2.887 2.906 3.866+ (2.201) (2.856) (2.346) (2.275) (2.188) (3.000) HH Income Loss (1 = Yes) 1.548+ 1.695* 1.503 1.587+ 1.568+ 1.731* (0.388) (0.424) (0.381) (0.399) (0.392) (0.440) Constant 0.000* 0.000* 0.000* 0.000* 0.000* 0.000* (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) N 773 771 764 772 773 762 Notes. Standard errors in parentheses. All models control for a series of demographic and financial characteristics. Due to space limitations, undisclosed in this table are the coefficients for dummies concerning metropolitan area, state, political ideology, household income, expected household income loss, occupation, and industry. All models use sample weights. + p < .10, p < .05, p < .01, p < .001. Table 3 also presents findings regarding the relationship between contextual determinants of voice (union membership, confidence in organized labor, and managerial receptiveness) with decisions to exercise voice. Consistent with Hypothesis 2a, Model 1 shows that the odds of voice among union members are 2.6 times greater than the odds for nonunion members. In addition, the odds of exercising voice are 1.3 times greater with each additional unit increase in confidence in organized labor. Turning to managerial receptiveness, Table 3 Model (2) shows that the odds of exercising voice decreased by a factor of about 0.23 with each additional increase of perceived managerial receptiveness, which does not support Hypothesis 2b.4 Relationship between Job Insecurity, Voice Context, and Worker Voice Next, we look at how these contextual determinants intersect with the relationship between job insecurity and worker voice in order to evaluate Hypotheses 3a and 3b. We theorized that, even when insecure respondents report union membership, increasing confidence in organized labor, and increasing managerial receptiveness, they will be less likely to engage in voice than secure respondents. We present the estimates of the marginal effects of job insecurity at representative values of these contextual determinants of voice in Table 4, as it is difficult to draw substantive conclusions from interaction terms in nonlinear models. We first estimate a series of logistic regression models with interaction terms, controlling for demographic and financial characteristics. Then, we show how insecurity intersects with voice context by comparing group differences in marginal effects of voice contexts and job insecurity (following, e.g., Long and Mustillo, 2021).5 As can be seen in Table 4, columns (1) and (2) present marginal effects across low to high levels of voice context (e.g., low to high confidence in organized labor), separately for secure workers and insecure workers. The last column shows the difference between insecure workers and secure workers at each unit of union membership status, confidence in organized labor, and perceived receptiveness of management. The results in Panel (A) show that union membership is associated with a higher probability of voice for both insecure and secure workers, and that there is no statistically significant difference between the two. Turning to confidence in organized labor, however, we find in Panel (B) column (3) that the voice-insecurity gap—i.e., the difference in probability of exercising voice between job insecure and secure workers— increases. In fact, insecure workers have a higher probability of reporting voice at lower levels of confidence in organized labor and an increasingly lower probability of reporting voice at higher levels of confidence (p < .05 at confidence in organized labor = 5). The opposite is true for secure workers: the probability of reporting voice increases with higher levels of confidence in organized labor. This shows that overall, insecure workers remain less likely to exercise voice, even as confidence in organized labor increases, providing partial support for Hypothesis 3a. Table 4. Estimates of Marginal Effects of Job Insecurity from Logit Regression Models Predicting Worker Voice by Voice Context. Voice Context Job Secure Job Insecure Difference (Insecurity-Security) A. Union Membership (N=771) Nonunion member 0.289* 0.263* −0.026 (0.025) (0.054) (0.059) Union member 0.485* 0.404 −0.081 (0.062) (0.144) (0.152) B. Confidence in Organized Labor (N=764) 1-Low 0.221* 0.384 0.163 (0.042) (0.131) (0.138) 2 0.276* 0.323* 0.047 (0.026) (0.070) (0.077) 3 0.339* 0.267* −0.072 (0.019) (0.048) (0.055) 4 0.407* 0.217** −0.189* (0.039) (0.076) (0.086) 5-High 0.478*** 0.174+ −0.304* (0.067) (0.105) (0.124) C. Managerial Receptiveness (N=772) 1-Low 0.411* 0.453* 0.042 (0.051) (0.088) (0.104) 2 0.373* 0.314* −0.059 (0.032) (0.054) (0.064) 3 0.337* 0.200* −0.138* (0.022) (0.051) (0.057) 4 0.302*** 0.117* −0.185 (0.026) (0.054) (0.061) 5-High 0.269* 0.064 −0.205*** (0.038) (0.046) (0.061) Notes. Standard errors in parentheses. All models control for a series of demographic and financial characteristics. Columns (1) and (2) of this table presents predictive margins of secure and insecure workers in exercising voice at representative values of voice context. Column (3) of this table presents marginal effects at representative values (MER) of union membership, confidence in organized labor, and managerial receptiveness, with job security as the reference. + p < .10, p < .05, p < .01, p < .001. For perceived managerial receptiveness, recall that we find a negative relationship with worker voice, which is inconsistent with Hypothesis 2b. Interestingly, Panel (C) of Table 4 shows that both insecure and secure workers have a decreasing probability of exercising voice even as their perceived managerial receptiveness increases, and the probability of voice for insecure workers decreases at a much faster rate. Likewise, the difference in the probability of exercising voice between job insecure and secure workers is much larger at higher levels of perceived managerial receptiveness (column 3 of Panel C). This lends support for Hypothesis 3b, which predicted that insecure workers remain less likely to exercise voice than secure workers even as perception of managerial receptiveness increases. Figure 1 plots the predicted probability of worker voice by job insecurity across the range of values for managerial receptiveness and confidence in organized labor. The plot shows clearly that the voice-insecurity gap increases as confidence in organized labor and perceived managerial receptiveness each increase. Figure 1. Predicted probability of worker voice by job insecurity and voice context. Explaining the Lack of Voice in the Presence of Receptive Managers Contrary to our expectation, we do not find that perceived managerial receptiveness is positively associated with worker voice. In order to understand why, we turn to a discussion of silence. Silence is the decision to not engage in voice (Morrison, 2011, 2014). Notably, silence is not merely an absence of voice, which may simply result when individuals do not perceive the need to make a voice claim. Silence, rather, is an active decision to refrain from offering input, motivated by a sense that speaking up will not make any difference, or that managers will not value voice claims. Silence is also motivated by concerns regarding the risks, or costs, associated with voice. These can range from risks that are subjective in nature—how coworkers and supervisors might perceive those who speak up, or potential effects on reputation—to repercussions such as lost promotions or even termination. Like voice, silence is therefore contextual, dependent upon shared beliefs, social relationships, and organizational climate (Morrison, 2011, 2014). In our survey, we asked a subset of our respondents who indicated, “I did not take any action” to encourage employers to implement COVID-19 response measures about their decision to choose silence. Specifically, we asked: “Please tell us more about why you did not take any action to encourage your employer to implement the COVID-19 response measures.” Table 5 details three key reasons for choosing silence (respondents could select as many as were applicable): (a) “My employer/management initiated the measure(s) in response to COVID-19 proactively”; (b) “I was afraid that taking an action would hurt my career or future in this job”; and (c) “I didn't think any action I could take would make a difference.” Table 5. Reasons for Silence, by Managerial Receptiveness and Job Insecurity. Overall (N = 619) Secure (N = 534) Insecure (N = 85) All (a) “My employer/management initiated the measure(s) in response to COVID-19 proactively” 60.9 65.5 34.8* (b) “I was afraid that taking an action would hurt my career or future in this job.” 5.5 4.2 13.3* (c) “I didn't think any action I could take would make a difference.” 18.3 16.7 27.6 Managerial Receptiveness < = 2.5 (Low) (a) “My employer/management initiated the measure(s) in response to COVID-19 proactively” 40.5 51.4 18.0*** (b) “I was afraid that taking an action would hurt my career or future in this job.” 15.2 13.1 19.5 (c) “I didn't think any action I could take would make a difference.” 32.5 31.0 35.6 Managerial Receptiveness >2.5 (High) (a) “My employer/management initiated the measure(s) in response to COVID-19 proactively” 69.2 69.6 64.0 (b) “I was afraid that taking an action would hurt my career or future in this job.” 1.6 1.5 2.5 (c) “I didn't think any action I could take would make a difference.” 12.6 12.5 13.7 Notes. Weighted by sample weights. Managerial receptiveness is measured in a scale of 1 to 5. Last column denote results from t-tests showing statistical significance of the difference in means between job security and insecurity. + p < .10, p < .05, p < 0.01, **p < .001. Overall, 18.3% of workers reported that they didn't think their action could make a difference, and only 5.5% of workers reported fears of potential retaliation for the reason for silence. A much higher share of workers—60.9%—reported that they chose not to engage in voice due to having employers/management who proactively initiated COVID-19 response measures. Then, we look at whether these reasons for silence vary by perceived managerial receptiveness. We find that respondents who perceived high managerial receptiveness (>2.5) are more likely to report that their employers were proactive in implementing COVID-19 response measures relative to respondents who perceived low managerial receptiveness (≤2.5) (69.2% as opposed to 40.5%, respectively). To a lesser extent, those who perceived high managerial receptiveness reported concerns about voice utility (12.6%) or retaliation (1.6%); these reasons for silence were more common among those with low perceived managerial receptiveness (32.5% and 15.2%, respectively). These results suggest that the unexpected negative relationship we found between perceived managerial receptiveness and voice is partially due to employers who proactively pursued COVID-19 response measures. The reason for silence also varies by perceived job insecurity. Overall, a smaller portion of insecure workers (34.8%) gave proactive employers as their reason for silence, compared to 65.5% of secure workers (p < .001). Proactive employers were even less likely to be a reason for silence among insecure workers with low levels of perceived managerial receptiveness than their secure counterparts (18.0% and 51.4%, respectively (p < .001)). However, a larger share of insecure workers than secure workers reported fears of potential retaliation (13.3% and 4.2%, respectively; p < .05). Although this difference loses significance at high levels of managerial receptiveness, it is nonetheless consistent with the underlying idea that, even with receptive managers, the stressor of job insecurity may induce silence. The Relationship between Job Insecurity, Employment Arrangement, and Worker Voice Finally, we investigate the relationship between job insecurity, voice, and employment arrangement. As discussed previously, our descriptive results on Table 2 show that a higher share of nonstandard workers perceived their jobs to be insecure than standard workers, which is consistent with previous evidence that job insecurity is more pronounced among workers in nonstandard employment arrangements (e.g., Klandermans et al., 2010). Turning to voice, Table 3 shows that the odds of workers in nonstandard work arrangements engaging in voice are smaller by a factor of about 0.54, consistent with Hypothesis 4a, but the results are not statistically significant. Essentially, this means that, during the COVID-19 pandemic, we observe no statistically significant difference in the likelihood of speaking up between nonstandard and standard workers. Next, we look at whether job insecurity among nonstandard workers intersects with this relationship to voice—whether job insecurity, in other words, influences voice decisions differently for standard and nonstandard workers. We find that, among nonstandard workers, insecure workers had a much lower probability of exercising voice than secure workers, supporting Hypothesis 4b.6 To present the substantive effect, Figure 2 plots predicted probabilities of job insecurity on worker voice by employment arrangement. The predicted probabilities plot shows that for standard workers, there is basically no difference in the probability of exercising voice by job insecurity. Figure 2. Probability of worker voice by employment arrangement and job insecurity. Discussion In this study, we sought to understand the relationship between job insecurity and voice, given that both are notable features of the COVID-19 pandemic. We find that job insecurity is negatively but not significantly related to voice. Although we do not find support for our hypothesis, our findings are in line with some studies that found nonsignificant relationship between insecurity and voice. Because both job insecurity and voice are context-dependent, we probed the effect of workers’ beliefs about the receptiveness of their managers to voice and their confidence in organized labor, in addition to their union membership status, all of which are theorized to support voice. We also assessed how workers’ specific employment arrangements factored into the relationship between job insecurity and voice, as nonstandard arrangements are elsewhere found to be associated with both greater job insecurity and less voice than standard arrangements. We discuss each of these in turn. We find that union members are more likely to speak up than nonunion workers, consistent with our expectations and findings from other studies of union voice during the pandemic (e.g., Sojourner & Yang, 2021). Similarly, workers are increasingly likely to speak up as their confidence in labor increases. Surprisingly, however, we find an overall negative relationship between managerial receptiveness and voice, contrary to expectations based on the literature, which posits that managers who are receptive to input can positively influence workers’ perceptions of voice utility, legitimacy, and safety, thus encouraging voice (Klaas et al., 2012). Factoring in the reasons for silence helps explain this finding: during the pandemic, many employers proactively initiated COVID-19 response measures. This was even more likely among workers who reported higher levels of perceived managerial receptiveness, thus helping explain our observed negative finding. Further, we look at how the contextual determinants of voice—captured through measures concerning perceptions of unions and managers—intersect with the relationship between job insecurity and voice. We find that insecure workers remain less likely to exercise voice than secure workers, even as confidence in organized labor and perceived managerial receptiveness increases, consistent with our expectations derived from stress theories of job insecurity (e.g., Dekker & Schaufeli, 1995). Finally, our analysis of employment arrangements shows that nonstandard workers report higher levels of insecurity than workers in standard arrangements, but they are no more or less likely to engage in voice, contrary to our expectations about their voice behavior. Prior literature also suggests, however, that perceptions of job insecurity vary among workers in different nonstandard arrangements, leading us to assess whether this variation affects the relationship between nonstandard work and voice. We find that insecure nonstandard workers are less likely to exercise voice than nonstandard workers who are secure, consistent with our expectations. Limitations and Contributions One potential limitation of our study is its cross-sectional design, which constrains our ability to assess potential issues with reverse causality, i.e., that perceptions of insecurity are not the result of voice behaviors. Based on existing research, however, a cross-sectional survey is appropriate for assessing the question of voice as a response to job insecurity during the pandemic. First, the effects of job insecurity are assumed to be more or less immediate, because its context dependence means that experienced job insecurity can change from one day to the next (Berntson et al., 2010; Breevaart et al., 2020; Sverke et al., 2006). Second, longitudinal studies of job insecurity and voice did not find evidence of reverse causality, offering further evidence of voice as a response behavior to job insecurity as opposed to the other way around (Breevaart et al., 2020). Finally, like other analyses of responses to job insecurity in the context of organizational change, our study captures workers’ voice behaviors soon after a very severe economic shock, making it reasonable to assume that voice emerged as a response (Schreurs et al., 2015). Another limitation is the extent to which our findings are generalizable to the broader sample of the U.S. population or the conditions that differ from the COVID-19 pandemic. While we find that our sample is comparable to the full U.S. population in the CPS, workers in Illinois in Michigan were more likely to be white and educated. We also did not survey our respondents prior to the onset of the pandemic, and so are not able to assess the extent to which our findings hold under different conditions. This could theoretically change the calculus of workers deciding whether or not to engage in voice; parsing out these effects and the extent to which they affect generalizability of this study and others is a topic for future research. In conclusion, the COVID-19 pandemic has resulted in an unprecedented labor market experience for millions of workers. Our study adds to an emerging body of research unpacking the relationship between macro-level sources of job insecurity, such as the Great Recession, and various outcomes, including worker responses (e.g., Kalleberg, 2012; Lowe, 2018). Lowe persuasively argues that these broader considerations are necessary for understanding workers’ perceptions of insecurity, given that workers generally are incurring increasing risk in the labor market. We offer a critical snapshot of how the macro-level consequences of the pandemic intersect with concerns over job security, and decisions workers make regarding how to respond. Author Biographies Hye Jin Rho is an Assistant Professor at Michigan State University at the School of Human Resources and Labor Relations. Her research focuses on the changing nature of work and organizations and its implications for employment processes and outcomes dictating the future of work and workers. Her recent work is to appear in Management Science. Christine Riordan is an Assistant Professor in the School of Employment Relations, UIUC. Her research centers on voice and the changing organization of work. She recently co-authored “From Bread and Roses to #MeToo: Multiplicity, distance, and the changing dynamics of conflict in IR theory,” with A.M. Kowalski in ILR Review. Christian Lyhne Ibsen is Associate Professor at FAOS/University of Copenhagen. His research focuses on collective bargaining, vocational education and training, and the future of work and employment relations. Recently, his work has been published in journals such as ILR Review, World Politics, Socio-Economic Review, and European Sociological Review. J. Ryan Lamare is the Reuben G. Soderstrom professor, School of Labor and Employment Relations, University of Illinois at Urbana-Champaign. His research interests include employee voice, industrial relations and politics, and workplace dispute resolution. Recent publications include articles in ILR Review, Industrial Relations, and British Journal of Industrial Relations. Maite Tapia is Associate Professor at Michigan State University at the School of Human Resources and Labor Relations. Her research focuses on worker voice. Her recent article is on the lack of Critical Race Theory and Intersectionality within Industrial Relations (in the ILR Review co-authored with Tamara L. Lee). The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding: The author(s) disclosed receipt of the following financial supportfor the research, authorship, and/or publication of this article: This research was supported by funding from the School of Human Resources and Labor Relations at Michigan State University. ORCID iDs: Hye Jin Rho https://orcid.org/0000-0001-8651-566X Christine Riordan https://orcid.org/0000-0002-1257-4496 J. Ryan Lamare https://orcid.org/0000-0003-4935-2341 Maite Tapia https://orcid.org/0000-0003-0180-9891 1. The subjective nature of confidence in organized labor is also more consistent than union membership with the subjective nature of job insecurity. In this study, we capture the role of unions through both dimensions for these reasons. 2. We compared the demographic and financial characteristics of our survey respondents to those of the Current Population Survey (CPS) Basic Monthly, pooled from March to July of 2020. Despite the differences in how CPS and CEVS ask about work, the estimates for each state are very similar across the two surveys. The Illinois and Michigan sample is also broadly similar to the sample of the entire U.S. population, although they are more white and slightly more educated. 3. In similar fashion, the literature also suggests mixed or inconsistent relationships between demographic factors and comparable measures of job insecurity, although some meta-analyses and reviews point to exceptions (e.g., age (Cheng & Chan, 2008), gender (Shoss, 2017)). 4. We ran the same set of models in Table 3 without controlling for demographic and financial characteristics and found consistent results. 5. To ensure that our findings are robust, we estimated a set of count models, where we used worker voice intensity—number of voice claims used (min = 0; max = 9)—as the dependent variable. Looking at the results from negative binomial binomial regression models in the presence of overdispersion, we found consistent results in the interaction terms between job insecurity and confidence in organized labor (interaction b = −0.455; p < .10), as well as job insecurity and employer receptiveness (interaction b = −0.342; p < .10). 6. We repeated the analyses using negative binomial regression models and found consistent results looking at the interaction term between job insecurity and nonstandard work arrangement (interaction b = −2.595; p < .01). ==== Refs References Abraham K. G. Hershbein B. Houseman S. (2019). Independent contract, informal, and online intermediary work: Preliminary evidence on developing better measures in household surveys. IZA working paper. https://conference.iza.org/conference_files/Statistic_2019/abraham_k16798.pdf Andrews M. , & Kaiser Health News (2020). Workers are getting fired and penalized for reporting COVID safety violations. 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Human Resource Management, 50 (1 ), 65–74. 10.1002/hrm.v50.1	0	PMC9720047	NO-CC CODE	2022-12-06 23:23:52	no	Work Occup. 2022 Nov 30;:07308884221128481	utf-8	Work Occup	2,022	10.1177/07308884221128481	oa_other
==== Front J Health Psychol J Health Psychol HPQ sphpq Journal of Health Psychology 1359-1053 1461-7277 SAGE Publications Sage UK: London, England 35297292 10.1177/13591053221083819 10.1177_13591053221083819 Articles Predicting health behaviors across Belgium and France during the first wave of COVID-19 pandemic https://orcid.org/0000-0001-9272-5874 Schmitz Mathias Wollast Robin Bigot Alix Luminet Olivier * Université catholique de Louvain, Belgium Mathias Schmitz, Institute for Research in the Psychological Sciences, Université catholique de Louvain, Place Cardinal Mercier 10 bte L3.05.01, Louvain-la-Neuve, 1348, Belgium. Emails: [email protected], [email protected] * This Author is now affiliated to Fund for Scientific Research – Belgium (FRS-FNRS). 12 2022 12 2022 12 2022 27 14 30973105 © The Author(s) 2022 2022 SAGE Publications This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. The objective of the current research was to investigate how a series of psychological factors may underlie two COVID-19 health behaviors, and how a contextual factor (country of residence) could shape their influence. Cross-sectional results from the first pandemic wave (NBelgium = 4878, NFrance = 1071) showed that handwashing and social contacts limitation are predicted by a unique set of psychological variables that holds across Belgium and France, despite their distinct lockdown-policies strictness. In practice, policy-makers could leverage on these unique predictors and fine-tune their strategies accordingly to promote adherence to each measure while generalizing it across similar nations. COVID-19 handwashing health behavior health behavior models social contact Foundation Louvain https://doi.org/10.13039/100007355 typesetterts1 ==== Body pmcIntroduction Since the beginning of 2020, the world has been faced with a pandemic caused by the COVID-19 outbreak, with a toll of more than 5 million deaths (as of 7 February 2021; Worldometers, 2021) and psychological sequelae such as depression and anxiety (Rajkumar, 2020). To overcome the spread of the virus, the World Health Organization (WHO, 2021) prompted countries to implement a series of health guidelines, notably handwashing and the social contacts limitation. However, their enforcement has been challenging, especially at the beginning of the pandemic. It is therefore crucial not only to identify the factors that may foster or hamper their application, but also how they could vary across different contexts. In this light, the current research aimed to investigate some of these psychological factors and examine if their influence on health behaviors could be shaped by a key contextual factor, namely country of residence. Although both preventive measures are highly effective against the spread of COVID-19, they are in fact quite different in terms of their application. Handwashing is simple to enact, engrained in the social norms, and a substantial proportion of the population already practice it daily. Conversely, and because of humans’ social nature, social contacts limitation is an avoidant behavior that requires a consciously-driven and effortful change from everyday life that can even be detrimental to mental health (Hagger et al., 2020). These two health behaviors also differ in terms of their underlying predictors. Of particular interest, Bigot et al.’s (2021) findings within the Belgian context revealed that both health behaviors increased with higher intentions and more positive attitudes (outcome efficacy), but only handwashing was enhanced by the ability to have control over it, whereas the social contact limitation increased with the subjective norms (the perception that others value the behavior) (see the Theory of Planned Behavior—TPB; Ajzen, 1991). At an emotional level, feeling attentive/determined or frightened/anxious boosted the former, whereas feeling enthusiastic/happy curtailed the latter. In sum, many psychological and socio-demographic variables allow to independently predict the compliance with health-related measures. Contextual variables can also influence health behaviors. For instance, so-called “tight” cultures or countries (e.g. China) which have stricter norms and punishment for deviance tend to best cope with the spread of the virus than “loose” ones (e.g. Italy) (Gelfand et al., 2021). Moreover, the influence of behavioral health predictors can also be shaped by the context. Indeed, the norms-intention relationship was stronger for tighter than looser countries, whereas it was the opposite for the attitudes-behavior and control-behavior relations (Fischer and Karl, 2022). It becomes thus interesting to contrast health behaviors and their predictors between two neighbors’ countries, namely France and Belgium, that share similarities but also differences in terms of how they handled the outbreak. At the time of data collection (March-April 19, 2020), both countries were facing the first wave of the pandemic. Although they share similar cultural and political backgrounds (e.g. in terms of the Hofstede’s cultural dimensions1) and adhered to the WHO prevention guidelines, the two differed in terms of what we could refer to as tight vs. loose management of the pandemic (Gelfand et al., 2021). Indeed, France’s lockdown measures were more stringent,2 for example, longer curfews and travel times bans, and harsher fines. Despite these variations, Wollast et al. (2021) showed by means of Structural Equations Modeling that the two countries were aligned in terms of how the TPB components related to both behaviors. Notwithstanding the importance of these findings, their results exclusively focused on TPB predictors, a limitation that we addressed in the present research. A crucial question thus remains whether countries that share similar cultural backgrounds yet distinct political lockdown policies (tight vs loose) differ or converge on a more comprehensive array of health behaviors predictors. Considering this dearth, the present research offers two main contributions. First, it builds and expands on Bigot et al.’s (2021) work by investigating how a series of psychological factors may underlie the health behaviors and may generalize to another country (France). The factors include emotional, cognitive, and socio-demographic predictors that were initially chosen by these authors based on the most used and relevant models of behavioral change (e.g. Ajzen, 1991), including the ones assessing psycho-behavioral responses during other health crises (e.g. Bish and Michie, 2010). Second, it assesses how a key contextual factor, country of residence,3 could shape their effect. To the best of our knowledge, no research has compared such a large array of preventive health predictors between two states that differ in terms of their lockdown policies strictness while sharing similar cultures. Method Participants This study is part of a broad international research program about COVID-19. Specifically, this research builds on data from Bigot et al. (2021) which focused on Belgian residents and expands the previous dataset by incorporating a new sample of participants residing in France. The current cross-sectional sample, recruited through convenience sampling, comprised 4878 participants from which 3807 (78%) resided in Belgium and 1071 (22%) in France, which demonstrated similar characteristics. The mean age in the Belgian subsample was 42.25 (SD = 16.85), 74% were women, 75% had at least completed secondary, and 73% did not work/study in the (para)medical field. The mean age in the French subsample was 37.03 (SD = 13.91), 76% were women, 81% had at least completed secondary, and 70% did not work/study in the (para)medical field. Measures The scales and sub-dimensions presented hereafter were almost identical to Bigot et al.’s (2021) research (please refer to the article for more information about the measures). Health behaviors Using single items, participants indicated to what extent they limited their social contacts on a scale from 1 (completely disagree) to 5 (completely agree), and how often they washed their hands on a scale from 1 (never) to 5 (more than 15 times a day). Intentions, attitudes, social norms, and perceived control The four components of the TPB were assessed with one item each on a scale from 1 (totally disagree) to 5 (totally agree), for each of the two health behaviors. The items were the following (with the X indicating the target behavior, that is, handwashing or social contacts limitation): “I am ready to do X” (intentions), “I believe that doing X will limit the spreading of the coronavirus” (attitudes), and “My relatives expect from me to do X” for social norms, and “For me, doing X is easy” (perceived control). Emotions Current emotional states were assessed based on the French Positive and Negative Affect Scale—State version (Gaudreau et al., 2006). Bigot et al. (2021) grouped these states into four categories based on an exploratory factor analysis: Attentive/determined (αBelgium = 0.75, αFrance = 0.74), enthusiastic/happy (αBelgium = 0.67, αFrance = 0.65), angry/agitated (αBelgium = 0.77, αFrance = 0.77), fearful/anxious (αBelgium = 0.77, αFrance = 0.76). Health anxiety Five items were selected from the Whiteley Index (Pilowsky, 1967) to assess how anxious people are about their health (αBelgium = 0.69, αFrance = 0.72). An item sample is “I am worried about my health.” Impulsivity This personality trait was based on the items from the French version of the UPPS impulsive behavior scale (Van der Linden et al., 2006; Whiteside et al., 2005). Premeditation (αBelgium = 0.65, αT2 = 0.64) and urgency (αBelgium = 0.74, αFrance = 0.77) were built on two items each, whereas sensation seeking, and perseverance were based on single items. Social connection This construct was divided in three dimensions from various scales (e.g. Davis, 1983; Hughes et al., 2004), namely social relationships (αBelgium = 0.59, αFrance = 0.51), empathy, (αBelgium = 0.73, αFrance = 0.68), and loneliness (αBelgium = 0.83, αFrance = 0.83). The social relationships dimension was excluded from the current analyses because of its low reliability score.4 Interoception This scale was based on the Three-domains Interoceptive Sensations Questionnaire (Vlemincx et al., 2020). Both the cardio-respiratory activation (αBelgium = 0.80, αFrance = 0.80) and deactivation (αBelgium = 0.82, αFrance = 0.83) dimensions had good levels of reliability. Demographic information Participants provided their age, gender, level of education, country of residence, and whether their work/studies were related to the (para)medical field. Procedure Data collection took place from 18th of March to the 19th of April 2020 during the first pandemic wave in Belgium and France. Participants were contacted through universities mailing lists, social platforms, or news outlets and were asked to fill an online survey. All participants provided consent. This research was approved by the ethics committee from the Research Institute for Psychological Sciences at Université catholique de Louvain (Project 2021-13). Statistical analyses The handwashing and social contacts limitation outcome variables were dichotomized to indicate whether the behavior was applied or not.5 Multiple logistic regression models were used to assess the impact of the predictors on the behaviors. Results Regarding handwashing, 74.14% of participants residing in France and 70.92% in Belgium reported washing their hands on a regular basis. The difference between the two was significant (χ2(1, 4876) = 4.25, p = 0.039). As for social contacts limitation, no differences were observed across countries, with 91.69% of participants residing in France and 92.51% in Belgium reported limiting their social contacts (χ2(1, 4876) = 0.80, p = 0.371). Table 1 presents the multiple logistic regressions of the study predictors and their interaction with participant’s residency on the two health behaviors. On the one hand, and as shown in the first part of Table 1, main effects indicate that demographic variables such as being a woman (vs a men), being older (vs younger), being part of the (para)medical field (vs not) significantly increased the likelihood of handwashing. Likewise, some components of the TPB, that is, intentions, perceived control, and emotional states of feeling attentive/determined or frightened/anxious contributed to higher chances of performing the health behavior. A similar contribution was observed for the perseverance sub-dimension of the impulsivity construct. On the other hand, sensation seeking, and loneliness had a detrimental effect on handwashing. As shown in the second part of Table 1, an interaction effect revealed that premeditation significantly increased handwashing in France (OR = 1.32, p = 0.016), whereas it had no significant impact in Belgium (OR = 0.96, p = 0.555). In other words, the impact of the predictors on handwashing does not significantly differ between France and Belgium, except for premeditation. Table 1. Multiple logistic regression analyses associated with the application of handwashing and social contacts limitation. Handwashing Social contacts limitation OR 95% CI Wald p OR 95% CI Wald p Main effects Socio-demographics Residency 1.02 0.08, 11.90 0.00 .986 0.04 0.00, 1.27 3.27 .071 Sex 1.42 1.13, 1.77 9.16 .002 1.35 0.97, 1.86 3.29 .070 Age 1.02 1.01, 1.03 24.01 <.001 0.98 0.97, 0.99 9.33 .002 Education level 1.00 0.80, 1.24 0.00 .988 1.90 1.41, 2.55 17.95 <.001 (Para)medical field 1.34 1.11, 1.64 8.75 .003 1.13 0.82, 1.57 0.55 .460 Theory of Planned Behavior Intentions 1.75 1.50, 2.05 49.63 <.001 1.23 1.07, 1.41 9.05 .003 Attitudes 1.10 0.98, 1.23 2.82 .093 1.50 1.28, 1.77 24.48 <.001 Social norms 1.09 1.00, 1.19 3.86 .050 1.12 0.96, 1.29 2.24 .134 Perceived control 1.56 1.41, 1.74 68.59 <.001 0.95 0.85, 1.07 0.61 .433 Emotions Attentive/Determined 1.30 1.13, 1.50 12.95 <.001 1.12 0.89, 1.39 0.91 .340 Enthusiastic/Happy 0.99 0.87, 1.14 0.01 .929 0.74 0.60, 0.90 8.87 .003 Angry/Agitated 1.07 0.95, 1.22 1.25 .263 1.25 1.03, 1.52 4.88 .027 Frightened/Anxious 1.20 1.04, 1.39 6.16 .013 0.92 0.73, 1.15 0.51 .474 Physiological aspects Health anxiety 1.12 0.99, 1.27 3.12 .077 0.94 0.77, 1.15 0.34 .558 Intero-active 0.97 0.83, 1.13 0.16 .692 1.26 1.01, 1.55 4.33 .038 Intero-relax 0.98 0.87, 1.11 0.07 .786 1.12 0.92, 1.35 1.29 .256 Impulsivity Premeditation 1.13 0.99, 1.28 3.44 .064 1.19 0.97, 1.45 2.81 .094 Urgency 1.02 0.93, 1.11 0.11 .739 0.90 0.78, 1.04 2.14 .144 Sensation seeking 0.90 0.83, 0.98 5.72 .017 1.03 0.90, 1.18 0.16 .694 Perseverance 1.11 1.02, 1.22 5.24 .022 1.03 0.89, 1.19 0.15 .702 Social connection Empathy 0.97 0.84, 1.12 0.16 .692 0.95 0.75, 1.19 0.22 .638 Loneliness 0.89 0.82, 0.97 7.75 .005 1.01 0.88, 1.16 0.03 .865 Interaction effects Socio-demographics Sex × Residency 0.86 0.54, 1.34 0.46 .499 1.09 0.56, 2.07 0.07 .790 Age × Residency 1.01 1.00, 1.03 2.68 .102 1.02 1.00, 1.04 3.48 .062 Education level × Residency 0.85 0.55, 1.31 0.53 .467 1.65 0.91, 2.98 2.77 .096 (Para)medical field × Residency 0.97 0.66, 1.44 0.02 .884 1.32 0.70, 2.55 0.74 .391 Theory of Planned Behavior Intentions × Residency 0.98 0.72, 1.35 0.01 .919 1.17 0.89, 1.53 1.21 .272 Attitudes × Residency 0.94 0.75, 1.17 0.30 .583 1.27 0.91, 1.75 2.05 .152 Social norms × Residency 1.01 0.85, 1.19 0.01 .934 0.82 0.61, 1.09 1.80 .180 Perceived control × Residency 1.05 0.85, 1.30 0.20 .656 0.83 0.66, 1.05 2.52 .113 Emotions Attentive/Determined × Residency 1.07 0.81, 1.42 0.22 .641 0.94 0.60, 1.47 0.06 .801 Enthusiastic/Happy × Residency 1.07 0.82, 1.39 0.22 .636 0.97 0.65, 1.44 0.03 .869 Angry/Agitated × Residency 0.90 0.70, 1.16 0.64 .424 1.99 1.35, 2.95 11.92 <.001 Frightened/Anxious × Residency 1.05 0.78, 1.41 0.10 .746 0.59 0.37, 0.93 5.16 .023 Physiological aspects Health anxiety × Residency 1.04 0.81, 1.34 0.08 .777 1.19 0.80, 1.76 0.72 .397 Intero-active × Residency 0.81 0.60, 1.09 2.01 .157 1.84 1.20, 2.82 7.75 .005 Intero-relax × Residency 0.98 0.76, 1.26 0.03 .869 1.07 0.73, 1.56 0.12 .726 Impulsivity Premeditation × Residency 1.37 1.06, 1.77 5.80 .016 1.26 0.85, 1.88 1.32 .250 Urgency × Residency 1.10 0.92, 1.32 1.07 .300 0.77 0.58, 1.02 3.29 .069 Sensation seeking × Residency 0.97 0.81, 1.15 0.15 .698 1.06 0.81, 1.40 0.18 .673 Perseverance × Residency 1.16 0.97, 1.39 2.51 .113 0.76 0.56, 1.02 3.38 .066 Social connection Empathy × Residency 0.78 0.58, 1.05 2.63 .105 0.80 0.50, 1.28 0.83 .363 Loneliness × Residency 0.87 0.73, 1.03 2.73 .098 0.93 0.71, 1.23 0.24 .624 Model’s Nagelkerke’s R2 0.24* 0.14* Binary variables were coded as follow: Handwashing (Not applied = 0, Applied = 1); Social contacts limitation (Not applied = 0, Applied = 1); Residency (Belgium = −1/2, France = +1/2); Sex (Male = −1/2, Female = +1/2); Education level (At least primary = −1/2, At least secondary = +1/2); (Para)medical field (No = −1/2, Yes = +1/2). OR = Odds-ratios; 95% CI = 95% confidence interval of the OR. Significant effects (p < 0.05) are in bold. Regarding social contacts limitation, main effects indicate that higher education level, intentions, and attitudes significantly increased the likelihood of this behavior. Similarly, feeling angry/agitated, and higher levels of activation (i.e. the sub-dimension of interoception) increased the probability of social contacts limitation, whereas being older and feeling enthusiastic/happy had the opposite effect. Interaction effects shown in the second part of Table 1 show that the activation sub-dimension of interoception significantly increased social contacts limitation in France (OR = 1.70, p = 0.006), whereas it had no significant consequences in Belgium (OR = 0.93, p = 0.456). This pattern was mirrored for feeling angry/agitated (France: OR = 1.76, p < 0.001; Belgium: OR = 0.88, p = 0.204). Finally, although there was a significant feeling frightened/anxious × residency interaction effect, none of the simple effects reached significance (France: OR = 0.71, p = 0.086; Belgium: OR = 1.20, p = 0.116). Otherwise stated, the effect of the predictors on social contacts limitation was globally the same across the two countries, except for the activation sub-dimension of interoception, and for feeling angry/agitated. Discussion The identification of underlying psychological and contextual factors that may foster or hinder preventive health behaviors is vital to manage the pandemic (Van Bavel et al., 2020). The present work examined how a series of psychological factors may underlie application of health behaviors and how they could be shaped by the context, namely across France and Belgium. A first finding indicates that during the first lockdown, handwashing, and even more so social contacts limitation, were highly followed by participants from both nations, possibly because the former was encouraged whereas the latter was mandatory. Additionally, the fear and risk perception of being infected may have boosted their motivation to stick to the measures ( Schmitz et al., 2022). Interestingly, self-reported compliance with these measures barely differed across the two countries, despite the more stringent restrictions deployed in France, suggesting that the tightening of policies may have a limited impact on rule-abidance (Fischer and Karl, 2022). Secondly, results show that the discrepancy between the general pattern of predictors underlying the health behaviors identified by Bigot et al. (2021) is minimal between the two nations and could thus potentially be generalized across other similar countries (as in Wollast et al., 2021) that may or not vary in their degree of lockdown-policies strictness. For instance, our findings may apply to other European countries with similar cultural backgrounds (e.g. in terms of Hofstede’s cultural dimensions) and political systems (i.e. democracy), irrespectively of whether they had a strict (e.g. Italy) or soft/no lockdown (e.g. Sweden). Remarkably, there were no differences on the socio-demographics, the TPB and social connection predictors across countries. The only few exceptions being that premeditation increased handwashing, and that active interoception and feeling angry/agitated enhanced social contacts limitation in France but had no significant effect in Belgium. Evidence suggests that appealing to negative emotions leads people to adjust their behavior when presented with an accessible solution (Witte and Allen, 2000). It may thus be that, in France, health guidelines related to social contacts limitation more clearly appeared to participants as a solution to cope with this threatening situation and their emotions. Moreover, excessive negative emotions conveyed by the French media or authorities may have led to paralyzing inaction (Schimmenti et al., 2020). Both these rationales remain highly speculative and further research is needed on the matter. If this is the case, clearly explaining the need to follow the measures in government communication and moderating the threat level could strengthen the adaptative impact of emotions on behavioral change. Finally, and in accordance with Bigot et al. (2021), a distinct set of predictors is associated with each behavior which can be accounted by their specific nature (see the authors’ article for an in-depth discussion). Particularly, and across the two nations, education, attitudes, feeling enthusiastic/happy, and active interoception were uniquely associated with social contacts limitation. Conversely, practicing in the (para)medical field, control over the behavior, feeling attentive/determined or frightened/anxious, sensation seeking, perseverance, and loneliness where exclusively linked to handwashing. In practice, policy-makers could take advantage of these unique predictors and fine-tune their strategies accordingly to promote adherence. For instance, communication campaigns could boost positive attitudes toward social contacts limitation by disseminating information that highlights the efficacy of this health behavior to stop the spread of the virus. Likewise, handwashing might be bolstered by easing the application of the behavior and thus increasing the perceived behavioral control. An effective way to do so is via nudging techniques, for example, providing resources that facilitate the behavior such as soap or hand sanitizer at the entrance of public spaces. A limitation is that the sample is not representative of either the Belgian or French population and the correlational design of the present research prevents us from making inferences of causality. Future studies should seek to further generalize our findings by relying on representative and longitudinal samples (see e.g. Wollast et al., 2022). In conclusion, our findings reveal high levels of adherence to the preventive measures across both European countries. Furthermore, the predictive pattern that underlies these health behaviors holds for both countries, despite contrasted lockdown strictness. Data sharing statement: The current article includes the complete raw dataset collected in the study including the participants’ data set, syntax file and log files for analysis. These files are all available in the Figshare repository and as Supplemental Material on the SAGE Journals platform. https://osf.io/gm68w/?view_only=c86e2cc2fc7440f4872e9f0b45657467 The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study has been supported by a grant from the Louvain Foundation. ORCID iD: Mathias Schmitz https://orcid.org/0000-0001-9272-5874 1. See https://www.hofstede-insights.com/country-comparison/belgium,france/ 2. The Government Response Stringency Index (0–100, 100 = strictest) was, on average, 77 for Belgium and 88 for France during the data collection period (https://ourworldindata.org/grapher/covid-stringency-index; see also Hale et al., 2021). This index encompasses nine response indicators including school closures, workplace closures, and travel bans. 3. For the ease of the reader, we often refer to “country” instead of “country of residence” in the results section. 4. Only scales and dimensions with αs > 0.60 were included in this study. 5. As in Bigot et al. (2021), for a response to qualify as applied behavior (versus not), participants had to report washing their hands more than six times a day for the handwashing behavior, and (totally) agreeing to limiting their social contact for the social contact limitation behavior. The handwashing threshold was based on the guidelines from sanitary institutions (e.g. Centers for Disease Control and Prevention, 2021; World Health Organization, 2021), that advise people to wash their hands at the very least before and after eating and after using the bathroom. ==== Refs References Ajzen I (1991) The theory of planned behavior. 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(2022) The dynamics of health behaviors during the COVID-19 pandemic: A longitudinal analysis of handwashing, mask wearing, social contacts limitation, and physical distancing, and their mental health consequences. [Manuscript submitted for publication]. World Health Organization (2021) Advice for the public: Coronavirus disease (COVID-19). Available at: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/advice-for-public (accessed 4 October 2021). Worldometers (2021) Covid-19 coronavirus pandemic. Available at: https://www.worldometers.info/coronavirus/ (accessed 4 October 2021).	35297292	PMC9720059	NO-CC CODE	2022-12-06 23:25:50	no	J Health Psychol. 2022 Dec; 27(14):3097-3105	utf-8	J Health Psychol	2,022	10.1177/13591053221083819	oa_other
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Revue Canadienne de Psychiatrie 0706-7437 1497-0015 SAGE Publications Sage CA: Los Angeles, CA 36453004 10.1177/07067437221140384 10.1177_07067437221140384 Original Research Disruptions in Primary Care among People with Schizophrenia in Ontario, Canada, During the COVID-19 Pandemic Perturbations des soins de première ligne chez les personnes souffrant de schizophrénie en Ontario, Canada durant la pandémie de la COVID-19Stephenson Ellen PhD 1 https://orcid.org/0000-0001-6189-6857 Yusuf Abban BSc 2 Gronsbell Jessica PhD 3 Tu Karen MD, MSc 145 https://orcid.org/0000-0002-9663-2226 Melamed Osnat MD, MSc, MCFP 16 Mitiku Tezeta MD 7 https://orcid.org/0000-0001-5401-2996 Selby Peter MBBS, CCFP, FCFP, MHSc, dipABAM, DFASAM 16 https://orcid.org/0000-0003-2164-8263 O’Neill Braden MD, DPhil, CCFP 128 1 Department of Family and Community Medicine, Temerty Faculty of Medicine, 7938 University of Toronto , Toronto, Ontario, Canada 2 MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada 3 Department of Statistical Sciences, 7938 University of Toronto , Toronto, Ontario, Canada 4 Department of Family and Community Medicine, 8613 North York General Hospital , Toronto, Ontario, Canada 5 Department of Family and Community Medicine, 26625 Toronto Western Hospital , Toronto, Ontario, Canada 6 Centre for Mental Health and Addiction (CAMH), Toronto, Ontario, Canada 7 Department of Psychiatry, 6363 University of Ottawa , Ottawa, Ontario, Canada 8 Department of Family and Community Medicine, St. Michael's Hospital, 508783 Unity Health Toronto , Toronto, Ontario, Canada Braden O’Neill, MD, DPhil, CCFP, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, St. Michael's Hospital, 209 Victoria Street, Toronto, Ontario, Canada M5B 1T8. Email: [email protected] 30 11 2022 30 11 2022 07067437221140384© The Author(s) 2022 2022 Canadian Psychiatric Association This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. Objective To investigate how primary care access, intensity and quality of care changed among patients living with schizophrenia before and after the onset of the COVID-19 pandemic in Ontario, Canada. Methods This cohort study was performed using primary care electronic medical record data from the University of Toronto Practice-Based Research Network (UTOPIAN), a network of > 500 family physicians in Ontario, Canada. Data were collected during primary care visits from 2643 patients living with schizophrenia. Rates of primary care health service use (in-person and virtual visits with family physicians) and key preventive health indices indicated in antipsychotic monitoring (blood pressure readings, hemoglobin A1c, cholesterol and complete blood cell count [CBC] tests) were measured and compared in the 12 months before and after onset of the COVID-19 pandemic. Results Access to in-person care dropped with the onset of the COVID-19 pandemic. During the first year of the pandemic only 39.5% of patients with schizophrenia had at least one in-person visit compared to 81.0% the year prior. There was a corresponding increase in virtual visits such that 78.0% of patients had a primary care appointment virtually during the pandemic period. Patients prescribed injectable antipsychotics were more likely to continue having more frequent in-person appointments during the pandemic than patients prescribed only oral or no antipsychotic medications. The proportion of patients who did not have recommended tests increased from 41.0% to 72.4% for blood pressure readings, from 48.9% to 60.2% for hemoglobin A1c, from 57.0% to 67.8% for LDL cholesterol and 45.0% to 56.0% for CBC tests during the pandemic. Conclusions There were substantial decreases in preventive care after the onset of the pandemic, although primary care access was largely maintained through virtual care. Addressing these deficiencies will be essential to promoting health equity and reducing the risk of poor health outcomes. Objectif Investiguer comment l’accès, l’intensité et la qualité des soins de première ligne ont changé chez les patients vivant avec la schizophrénie avant et après le début de la pandémie de la COVID-19 en Ontario, Canada. Méthodes La présente étude de cohorte a été menée à l’aide des données des dossiers médicaux électroniques des soins de première ligne du réseau de recherche basé sur la pratique de l'Université de Toronto (UTOPIAN), un réseau de > 500 médecins de famille de l’Ontario, Canada. Les données ont été recueillies durant les visites aux soins de première ligne des 2,643 patients vivant avec la schizophrénie. Les taux d’utilisation des services de santé de première ligne (en personne et les visites virtuelles avec les médecins de famille) et les principaux indicateurs de santé préventive révélés dans la surveillance antipsychotique [lectures de la pression artérielle, l’hémoglobine glyquée, le cholestérol et la numération sanguine complète (CBC)] ont été mesurés et comparés dans les 12 mois avant et après le début de la pandémie de la COVID-19. Résultats L’accès à des soins en personne a chuté avec l’arrivée de la pandémie de la COVID-19. Durant la première année de la pandémie, seulement 39.5% des patients souffrant de schizophrénie avaient au moins une visite en personne comparé à 81.0% l’année précédente. Il y a eu une augmentation correspondante des visites virtuelles de sorte que 78.0% des patients avaient virtuellement un rendez-vous dans les soins de première ligne durant la période pandémique. Les patients à qui étaient prescrits des antipsychotiques injectables étaient plus susceptibles de continuer d’avoir des rendez-vous en personne plus fréquents durant la pandémie que les patients à qui n’étaient prescrits que des antipsychotiques par voie orale ou pas du tout. La proportion des patients qui n’avaient pas de tests recommandés a augmenté de 41.0% à 72.4% pour les lectures de la pression artérielle, de 48.9% à 60.2% pour l’hémoglobine glyquée, de 57.0% à 67.8% pour le cholestérol LDL et de 45.0% à 56.0% pour la numération sanguine complète durant la pandémie. Conclusions Il y a u des diminutions substantielles des soins préventifs après le début de la pandémie, bien que l’accès aux soins de première ligne ait été largement maintenu grâce aux soins virtuels. Il sera essentiel de tenir compte de ces déficiences pour promouvoir l’équité en santé et réduire le risque de mauvais résultats de santé. schizophrenia COVID-19 primary health care health care quality access and evaluation Ontario Canada electronic health record Canadian Institutes of Health Research https://doi.org/10.13039/501100000024 45030 edited-statecorrected-proof typesetterts19 ==== Body pmcIntroduction People with serious mental illnesses such as schizophrenia have markedly worse health status than the general population and die 10–25 years sooner than those without these conditions.1,2 Those with schizophrenia have more comorbidities such as diabetes and dyslipidemia than people without this condition,3,4 while often having lower access to healthcare services.5–9 The primary cause of premature mortality among this population is cardiovascular disease, for which primary care plays a key role in prevention and treatment.10 There were substantial gaps in the availability of primary care services for those living with serious mental illnesses pre-pandemic, including a much higher use of emergency services among this demographic.11,12 The COVID-19 pandemic upended healthcare systems around the world. One of the key changes has been a rapid and now sustained increased use of telemedicine and virtual care, particularly in primary care settings.13–17 Telemedicine during the COVID-19 pandemic has been defined as ‘the use of electronic information and telecommunication technology to get the health care [needed] while practicing social distancing’.18 Examples include ‘virtual care’ appointments with primary care physicians delivered via phone call or video chat.13 On March 11, 2020, the WHO declared COVID-19 a pandemic19 and then the Government of Ontario, Canada declared a state of emergency on March 17, 2020, which included orders for hospitals and outpatient providers in primary and secondary care to cancel or postpone all health services deemed ‘non-urgent’.20 In response to these health policy changes, Ontario experienced a rapid decrease in in-person primary care visits and a concomitant increase in virtual visits.17,21 Prior to the pandemic, there were 110 in-person office visits for every 1 virtual visit in primary care in Ontario; after the pandemic onset, virtual visits outnumbered in-person office visits at a rate of 2.5:1.15 This switch from in-person to virtual visits was a global phenomenon.22 This rapid expansion of telemedicine has been critiqued for the additional complexities it places on providers and patients.23 These challenges may be even more concerning for those with complex medical conditions such as schizophrenia. Changes in healthcare delivery may be stressful for patients with schizophrenia; some of the challenges include being unable to see providers’ faces due to masks, longer wait times or having to meet with new providers due to staffing shortages.10 Impaired cognition experienced by people with this condition24 compounded by multiple negative social determinants of health may cause difficulties adhering to COVID-19 guidelines and adjusting to changes in the structure of the healthcare system resulting from the pandemic.24 Communication barriers, such as access to and use of new technologies,25 may hinder provision and receipt of adequate care.24,25 A recent study from the United States showed decreases in mental health outpatient visits, emergency department use and medication dispensing among people with serious mental illness (schizophrenia, schizophrenia-related disorders and bipolar I); this study did not describe changes in the quality of care provided and was not focused on primary care.26 To our knowledge, no research has been published illustrating how the onset of the COVID-19 pandemic is associated with changes in access, intensity and quality of primary care among people with schizophrenia. Given the marked poorer health experienced by people with schizophrenia, and the possibility that health outcomes and care may have worsened among these patients, we aimed to assess the effect of the COVID-19 pandemic on their care. This paper describes how primary care health service use and key preventive health indices indicated in antipsychotic monitoring have changed after the onset of the COVID-19 pandemic among people with schizophrenia accessing primary care in Ontario, Canada. Describing these changes may lead to identifying intervention targets, ensuring that deficiencies in important aspects of primary care for people with schizophrenia are addressed in the future. Methods Study Design We conducted a longitudinal cohort study of family medicine patients with schizophrenia in the University of Toronto Practice-Based Research Network (UTOPIAN). Data from electronic medical records (EMRs) were used to identify patients with schizophrenia and assess primary care health service use during the first year of the COVID-19 pandemic (March 14, 2020–March 13, 2021) and a pre-pandemic period covering the same dates the year before the pandemic (March 14, 2019–March 13, 2020). The start of the pandemic period was defined based on the introduction of new billing codes for the provision of virtual care.27 Prior to this policy change, the use of virtual care was extremely limited.28 The study is reported in accordance with The Reporting of Studies Conducted Using Observational Routinely Collected Health Data (RECORD) guidelines.29 The completed RECORD checklist is included as Supplementary Information (see eMethods 1 and eTable 1 in Supplementary Materials). Data Source and Setting We used data from the UTOPIAN Data Safe Haven (dfcm.utoronto.ca/utopian-data-safe-haven), a primary care EMR database with records collected from primary care practices in Ontario, Canada. Data extracted as of August 31, 2021, were used for this project. Records that meet minimum data quality criteria are available for use in research studies30; the criteria used to assess data quality can be found in eMethods 2 of the Supplementary Material. Eligibility Criteria Eligibility was assessed separately for the pre-pandemic and pandemic periods such that patients could be eligible for inclusion in one or both periods. This design allowed for the inclusion of new patients who joined the practice over time or who were newly diagnosed with schizophrenia and recognized that some patients will leave the practice over time. Patients were enrolled in the cohort based on the following criteria: (1) their first family physician visit occurred before March 14 of 2019 or 2020 (the start of the observation period), (2) they had at least two family physician visits within three years before March 13 of 2019 or 2020 (the end of the observation period), and (3) they were 18 years or older and had evidence of a schizophrenia diagnosis documented in the EMR before March 14 of 2019 or 2020 (the start of the observation period). Patients with schizophrenia were identified based on current or past medical history in the cumulative patient profile, billing codes and medications (see eMethods 3 for description of case detection rules and eTable 2 for a list of antipsychotic medications used to help identify patients with schizophrenia). This approach ensures that people are not classified as having schizophrenia as a result of only being on antipsychotic medication, given that antipsychotics are routinely used in many other conditions. Similar rule-based definitions have demonstrated good clinical face validity and have been used previously with UTOPIAN EMR data.30 Outcome Measures Billing codes were used to assess both access to care (i.e., no visit vs at least one visit) and intensity of service use (i.e., number of visits). As in previous studies using UTOPIAN EMR data,16,22 visits were further classified based on format of care delivery (in-person or virtual). Four indices for quality of care were created to capture distinct services provided: (a) at least one test for hemoglobin A1c, (b) at least one test for LDL cholesterol, (c) at least one test for white blood cell count (as a measure of CBC tests), and (d) at least one blood pressure measurement. Guidelines for the management of patients prescribed antipsychotic medication (including those with schizophrenia) recommend yearly assessment of these diabetes and cardiovascular risk factors (CBC, A1c, lipid measurement and blood pressure measurement) at least once annually31 but are not recommended to be done yearly in the general population. Measures of Demographics and Clinical Characteristics The UTOPIAN database includes measures of demographic characteristics (sex, age, neighborhood income quintile), chronic conditions (e.g., diabetes, hypertension) and medication history. Neighborhood-level income quintiles were derived based on the patient's residential postal codes using Statistics Canada's Postal Code Conversion Files.32 Patients with a current or past medical history of diabetes or hypertension were identified based on all information available within the EMR at the time of data extraction using existing case definitions for UTOPIAN EMR data.30 Patients were classified based on their history with antipsychotic medications as having been prescribed at least one injectable antipsychotic medication, only oral antipsychotic medications or no antipsychotic medications. Injectable and oral antipsychotics were distinguished based on the name, frequency and form of the medication as captured in the EMR (see eMethods 4 and eTables 3–4). Statistical Analysis For the pre-pandemic and pandemic periods, we calculated the proportion of patients who had at least one visit of any format, at least one visit in-person, the mean number of visits per patient for all visits and for in-person visits and the proportion of patients who had a blood pressure reading, a hemoglobin A1c test, an LDL cholesterol test and a CBC test. We fit generalized linear models to assess the association between demographic and clinical characteristics and the access, intensity and quality of care measures and to assess the effects of the pandemic on the strength of these associations. Logistic regression was used for binary outcomes (access and quality of care measures) and negative binominal regression was used to model the counts of visits per patient (intensity measures). For each outcome measure, we considered the effects of sociodemographic (age, sex and income quintile) and clinical characteristics (diabetes, hypertension and antipsychotic medication history), pandemic period (vs pre-pandemic period) and the multiplicative interaction between these characteristics and pandemic period. This allowed us to assess whether the level of health service use varied across patient characteristics and whether the patient characteristics associated with health service use change during the pandemic. Generalized estimating equations (GEEs) were used to estimate the model parameters. We adopted a working exchangeable correlation structure to account for the clustering of observations within providers and calculated standard errors and 95% confidence intervals using the robust sandwich estimator. Analyses were performed in R version 4.1.1 and SAS version 9.4. Ethics and Funding This project received research ethics board (REB) approval from the University of Toronto (#40129) and North York General Hospital (#20-0044). This study was funded by a Canadian Institutes of Health Research (CIHR) Operating Grant: COVID-19 Mental Health & Substance Use – Matching Access to Service with Needs (Grant #45030). Results Sample Characteristics A total of 2643 patients with schizophrenia met criteria for inclusion (Table 1) across the pre-pandemic and pandemic periods; 82.8% of patients (n = 2188) were included in both time periods, while 7.5% (n = 197) were only eligible in the pre-pandemic period and 9.8% (n = 258) were only eligible in the pandemic period. Consistent with the clinical presentation of schizophrenia, there were more males than females, more patients from the lowest income quintile, and high rates of co-morbid diabetes and hypertension.33–35 Table 1. Demographic and Clinical Characteristics of the Population. Total (N = 2643) Both periods (N = 2188) Pre-pandemic period only (N = 197) Pandemic period only (N = 258) Sex Female 1210 (45.78%) 1012 (46.25%) 77 (39.09%) 121 (46.90%) Male 1433 (54.22%) 1176 (53.75%) 120 (60.91%) 137 (53.10%) Mean age as of March 14, 2019 (SD) 49.70 (16.01) 50.49 (15.72) 46.22 (16.42) 45.66 (17.21) Age group (as of March 14, 2019) 18–34 years 563 (21.30%) 420 (19.20%) 62 (31.47%) 81 (31.40%) 35–49 years 718 (27.17%) 588 (26.87%) 59 (29.95%) 71 (27.52%) 50–64 years 870 (32.92%) 758 (34.64%) 43 (21.83%) 69 (26.74%) 65 years and older 492 (18.62%) 422 (19.29%) 33 (16.75%) 37 (14.34%) Neighborhood income quintile Lowest 1060 (40.11%) 904 (41.32%) 66 (33.50%) 90 (34.88%) Low-mid 499 (18.88%) 407 (18.60%) 40 (20.30%) 52 (20.16%) Middle 370 (14.00%) 315 (14.40%) 22 (11.17%) 33 (12.79%) Mid-high 296 (11.20%) 243 (11.11%) 23 (11.68%) 30 (11.63%) Highest 307 (11.62%) 246 (11.24%) 21 (10.66%) 40 (15.50%) Missing 111 (4.20%) 73 (3.33%) 25 (12.70%) 13 (5.04%) Diabetes comorbidity Diabetes 601 (22.74%) 534 (24.41%) 29 (14.72%) 38 (14.73%) No diabetes 2042 (77.26%) 1654 (75.59%) 168 (85.28%) 220 (85.27%) Hypertension comorbidity Hypertension 685 (25.92%) 615 (28.11%) 27 (13.71%) 43 (16.67%) No hypertension 1958 (74.08%) 1573 (71.89%) 170 (86.29%) 215 (83.33%) Antipsychotic medications Injectable medication 384 (14.53%) 326 (14.90%) 25 (12.69%) 33 (12.79%) Non-injectable medications only 1833 (69.35%) 1517 (69.33%) 126 (63.96%) 190 (73.64%) No medications 426 (16.12%) 345 (15.77%) 46 (23.35%) 35 (13.57%) Access to Care Access to in-person care dropped substantially in the first year of the pandemic with only 39.5% of patients having at least one in-person visit with their family physician compared to 81.0% the year before (Figure 1A; Table 2). The increased use of virtual care meant that the proportion of patients with at least one visit of any format was only slightly reduced from 81.0% to 78.0% in the pandemic compared to the pre-pandemic period. This means that 38.5% of patients had only virtual visits during the first year of the pandemic. Patients who were older, female and who had comorbid diabetes or hypertension were more likely to access care relative to other patients (Figure 2A), and the magnitude of these effects was similar in the pre-pandemic and pandemic periods. Figure 1. Access, intensity and quality of primary care services during the pre-pandemic and pandemic periods. (A) Changes in patient accessing of primary care pre-pandemic compared to pandemic period. (B) Changes in intensity of primary care visits pre-pandemic compared to pandemic period. (C) Changes in quality of care indices pre-pandemic compared to pandemic period. Figure 2. Factors associated with primary care health services use among people with schizophrenia. (A) Factors associated with patient access to primary care in pandemic versus pre-pandemic period. (B) Factors associated with intensity of primary care visits in pandemic versus pre-pandemic period. (C) Factors associated with quality of care measures in pandemic versus pre-pandemic period. Table 2. Primary Care Health Service use among People with Schizophrenia Before and After the COVID-19 Pandemic Onset. Period Pre-pandemic (N = 2385) Pandemic (N = 2446) Access measures Visits, any format, n (%) No visit 453 (18.99%) 538 (22.00%) 1 or more visits 1932 (81.01%) 1908 (78.00%) Visits, in-person, n (%) No in-person visit 453 (18.99%) 1479 (60.47%) 1 or more in-person visits 1932 (81.01%) 967 (39.53%) Intensity measures Number of visits per patient, mean (SD) 3.89 (4.32) 4.55 (6.40) Number of in-person visits per patient, mean (SD) 3.89 (4.32) 0.97 (2.22) Quality of care measures Blood pressure measurement, n (%) No measurement 979 (41.05%) 1770 (72.36%) 1 or more measurements 1406 (58.95%) 676 (27.64%) LDL cholesterol, n (%) No test 1360 (57.02%) 1658 (67.78%) 1 or more tests 1025 (42.98%) 788 (32.22%) Hemoglobin A1c, n (%) No test 1165 (48.85%) 1472 (60.18%) 1 or more tests 1220 (51.15%) 974 (39.82%) White blood cell count, n (%) No test 1072 (44.95%) 1370 (56.01%) 1 or more tests 1313 (55.05%) 1076 (43.99%) Intensity of Care On average, patients with schizophrenia visited their family physician four times per year before the pandemic, and with the introduction of virtual visits, this was maintained during the pandemic (Table 2; Figure 1B). However, the number of in-person visits per patient dropped substantially during the pandemic to a mean of 1 per year (Table 2; Figure 1B). Having been prescribed an antipsychotic medication became a stronger predictor of the number of in-person visits a patient had during the pandemic than it was pre-pandemic (RR = 1.52, 95% CI = 1.20, 1.1.92; Figure 2B). During the pandemic, patients prescribed injectable antipsychotics visited in-person more often (M = 1.45 visits per patient, 95% CI = 1.14, 1.87) than patients prescribed only oral medications (M = 0.89 visits per patient, 95% CI = 0.75, 1.06) or no antipsychotic medications (M = 1.02 visits per patient, 95% CI = 0.71, 1.47), whereas there was no difference in frequency of visits based on medication status before the pandemic (M = 4.05, 95% CI = 3.57, 4.60 for patients prescribed injectable medications, M = 3.75, 95% CI = 3.49, 4.05 for patients prescribed oral medications and M = 3.51, 95% CI = 3.04, 4.06 for patients on no medications). Quality of Care In the year prior to the pandemic, more than 40% of patients were missing tests for important health indices. This worsened during the pandemic (Table 2; Figure 1C). Before the pandemic, 41.1% of patients with schizophrenia had no records of blood pressure measurements; however, during the pandemic, the proportion of patients with schizophrenia having no blood pressure measurements rose to 72.4%. Missing A1c tests rose from 48.9% pre-pandemic to 60.2% during the pandemic. Similarly, there was an increase in missing cholesterol tests, from 57.0% to 67.8% during the pandemic period. Missing CBC tests increased from 45.0% pre-pandemic to 55.0% during the pandemic period. People who were older, female, diagnosed with diabetes or hypertension or prescribed antipsychotic medication were more likely to receive these tests relative to other patients (Figure 2C), and the magnitude of these effects was similar in the pre-pandemic and pandemic periods. Discussion This study is the first to use detailed clinical information to evaluate primary care utilization and quality among people living with schizophrenia as a result of the COVID-19 pandemic. Most patients in this study had regular access to primary care, visiting their family physician at least once per year. However, annual testing for preventive care measures such as blood pressure assessment and recommended lab tests was lower. Consistent with other research on primary care utilization, we found that older adults, women and patients with comorbid health conditions were more likely to receive primary care services. We did not find that neighborhood income was associated with access, intensity or quality of primary care among people living with schizophrenia, which is encouraging from a health equity perspective. Pandemic Effects on Access to Care Once the Government of Ontario started to discourage ‘non-urgent’ primary and secondary healthcare services, lower rates of in-person attendance to primary care appointments began to be observed across Ontario.15,16,20 We also observed a decrease in the number of patients visiting their family physician in-person. The proportion of patients who had at least one in-person primary care visit during the first year of the pandemic was half of what it was in the year before the pandemic began. However, the rapid increase in the use of virtual care meant that some access to care was maintained. Pandemic Effects on Intensity of Care In conjunction with the increased use of virtual care, we observed an increase in the frequency of primary care visits. Similar increases in the intensity of care have been observed for other patient groups and different types of primary care visits.16,17 Although the number of in-person visits per patient decreased for most patients, there was some evidence that in-person visits were appropriately prioritized based on patient needs. Patients prescribed injectable antipsychotics had more frequent in-person visits during the pandemic compared to patients prescribed oral medications. This is likely because some medications, like injectable antipsychotics, must be administered in-person.24 This is likely a welcome finding from an equity perspective. In addition, patients on injectable antipsychotics are particularly vulnerable as they often have a history of treatment non-compliance, more frequent relapse and impaired insight.36 Although there are no Canadian studies about emergency department visits among people with schizophrenia during the COVID-19 pandemic, an overview of studies from other countries showed either no change in emergency department visits or a decline, suggesting that there was no substantial decompensation in mental status necessitating emergency care a result of changes in outpatient care provision.37 Pandemic Effects on Quality of Care Most primary prevention of cardiovascular disease occurs in the primary care setting. Routine (at least yearly) screening of blood pressure, glycosylated hemoglobin (Hb1Ac) and cholesterol levels are recommended in Canada and are particularly warranted among individuals taking antipsychotic medications; however, these are conducted in less than a third of patients.38 Reduction in frequency of completion of HbA1c tests in the general population is associated with poorer glycemic control and increased progression to chronic kidney disease.39 Several studies across multiple contexts have demonstrated that patients with schizophrenia and other serious mental illnesses are less likely to receive metabolic screening, compared to the general population.5,8 The proportion of patients who had completed Hb1Ac, cholesterol levels and white blood cell tests during the study period dropped by 10–30 percentage points. These gaps in testing for physical comorbidities may have been influenced by the rise of virtual primary care visits, which make the logistics of ordering laboratory testing more complicated, including having to get the lab requisition to either the patient (through mail or email) or to the laboratory (through email or fax).10 Given the increased risk of cardiovascular disease among people with schizophrenia, it is concerning that the completion of blood pressure readings after the onset of the pandemic dropped by >50%. Reductions in appropriate hypertension screening and monitoring may lead to poorer outcomes4,40–42; a Canadian study of older adults found that increased cardiovascular screening and management was associated with reductions in heart attacks and strokes.41 Limitations Data analysed in this study were from primary care EMRs in Ontario, Canada. EMRs contain detailed clinical information not available in other health data sources (e.g., records of blood pressure assessments); however, there are limitations. Primary care EMRs contain incomplete data on patients’ health conditions such that diagnostic status can sometimes be difficult to determine43; for example, there is incomplete documentation of smoking status and cessation counselling in the UTOPIAN database, and therefore, despite this being an important risk factor for cardiovascular disease, we did not include this variable in this analysis. The data available in the UTOPIAN database might not wholly describe all patients living with schizophrenia in Ontario. Our study was limited to patients who had recent contact with a family physician. Patients who are not attached to a family medicine practice or do not have access to a family physician are under captured in these data. Some of the ‘encouraging’ findings related to frequency of in-person visits and equitable use of primary care services among people living in neighborhoods at different income quintiles may reflect the fact that practices in UTOPIAN are more likely to be academic practices providing team-based care than the Ontario average.44 Patients who were female, older age and higher income are overrepresented in the UTOPIAN database relative to the Ontario population, but this is consistent with characteristics of health care users in general.17 The more concerning findings related to decreased lab test and blood pressure measurement completion therefore may reflect a ‘best case’ scenario, and it is possible that people with schizophrenia receiving care outside this network may have worse access and quality of care. Primary care providers in UTOPIAN receive ‘push’ notifications from the Ontario Laboratories Information System when any blood tests are ordered by other providers (such as psychiatrists). Although there is the possibility of tests occasionally not being ‘pushed’ to providers, this would only occur when the system malfunctions, and therefore, ‘missing’ tests can be interpreted to have not been completed at any point. Data in this study were excluded from family physicians practicing in ‘team-based capitation’ models, and therefore, it was not possible to compare results between those models and those who accessed care from non-team-based capitation or fee-for-service type settings.45 Conclusion This study of over 2500 patients with schizophrenia in Ontario, Canada, demonstrated substantial reductions in the completion of recommended laboratory testing after the onset of the COVID-19 pandemic. Although there were some encouraging findings related to access and intensity of care, overall this demonstrates the detrimental effects of the COVID-19 pandemic. Further work to address these identified deficits is urgently required to support people with schizophrenia. These results demonstrate the importance of developing and evaluating proactive approaches to primary care management of people with schizophrenia, to optimize the primary prevention of cardiovascular disease. Further work assessing the effect of these changes in care on outcomes including incident cardiovascular disease and mortality will be essential to determine the extent to which patients may have been harmed by these changes in care. Although overall primary care access was maintained for a substantial proportion of people with schizophrenia in this study, these results do not describe what happened to people who did not attend primary care after the onset of the COVID-19 pandemic; further research including qualitative exploration of primary care non-attenders will be essential to improving primary care for people with schizophrenia. Data Access Data were obtained from the UTOPIAN Data Safe Haven (dfcm.utoronto.ca/utopian-data-safe-haven), a primary care EMR database with records collected from primary care practices in Ontario, Canada. We used data extracted as of August 31, 2021, for this study. The datasets generated and/or analysed during the current study are not publicly available due to limitations of ethical approval involving patient data and anonymity but are available from the corresponding author on reasonable request. Supplemental Material sj-docx-1-cpa-10.1177_07067437221140384 - Supplemental material for Disruptions in Primary Care among People with Schizophrenia in Ontario, Canada, During the COVID-19 Pandemic Click here for additional data file. Supplemental material, sj-docx-1-cpa-10.1177_07067437221140384 for Disruptions in Primary Care among People with Schizophrenia in Ontario, Canada, During the COVID-19 Pandemic by Ellen Stephenson, Abban Yusuf and Jessica Gronsbell, Karen Tu, Osnat Melamed, Tezeta Mitiku, Peter Selby, Braden O’Neill in The Canadian Journal of Psychiatry Acknowledgements The authors thank Jemisha Apajee for assistance with statistical analysis. The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Braden O'Neill is a member of the Ontario Health Primary Care Expert Panel on Guidelines for Clinically Appropriate Use of Virtual Care. Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Canadian Institutes of Health Research (grant number 45030). Braden O'Neill and Karen Tu receive salary support as Clinician-Investigators from the Department of Family and Community Medicine, University of Toronto. ORCID iDs: Abban Yusuf https://orcid.org/0000-0001-6189-6857 Osnat Melamed https://orcid.org/0000-0002-9663-2226 Peter Selby https://orcid.org/0000-0001-5401-2996 Braden O’Neill https://orcid.org/0000-0003-2164-8263 Supplemental Material: Supplemental material for this article is available online. ==== Refs References 1 Gatov E Rosella L Chiu M Kurdyak PA . Trends in standardized mortality among individuals with schizophrenia, 1993–2012: a population-based, repeated cross-sectional study. CMAJ. 2017;189 (37 ):E1177–E1187.28923795 2 Livingston JD . 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==== Front Eval Rev Eval Rev sperx ERX Evaluation Review 0193-841X 1552-3926 SAGE Publications Sage CA: Los Angeles, CA 36453754 10.1177_0193841X221141812 10.1177/0193841X221141812 Original Research Article Evaluating the Safe-Haven Abilities of Bitcoin and Gold for Crude Oil Market: Evidence During the COVID-19 Pandemic https://orcid.org/0000-0001-9582-0818 Wang Qian 1 Wei Yu 1 https://orcid.org/0000-0002-6942-2848 Zhang Yifeng 1 Liu Yuntong 1 1 School of Finance, 66569 Yunnan University of Finance and Economics , Kunming, China Yifeng Zhang, School of Finance, Yunnan University of Finance and Economics, 237 Longquan Road, Kunming 650221, China. Email: [email protected] 1 12 2022 1 12 2022 0193841X221141812© The Author(s) 2022 2022 SAGE Publications This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. The COVID-19 pandemic poses a serious threat to investors in the crude oil market. Furthermore, investors have an increasing need to find a safe haven in their investment portfolios when facing unprecedented risks in crude oil markets during the COVID-19 pandemic. According to a review of the literature, there are contradictory findings on which investment is the safer haven for the oil market. Therefore, this paper aims to evaluate whether bitcoin is a safer haven for the crude oil market than the commonly used gold during the COVID-19 pandemic. Three spillover measurements based on the time, and frequency domains, and a network framework are employed to quantify the return spillover effects among bitcoin, gold and three major crude oil futures markets. We divide the sample into two periods, pre-COVID-19 and post-COVID-19. The results show that bitcoin has a weak safe-haven effect on the crude oil market only over a short period, while gold maintains a good safe-haven ability for crude oil futures across various time horizons (frequencies), both before and after the outbreak of the COVID-19 pandemic. The findings of this study have important implications for policy-makers, crude oil producers and global investors. In particularly, investors cannot ignore the importance of bitcoin and gold in selecting more profitable portfolio policies when searching for safe-haven assets. bitcoin gold safe haven crude oil COVID-19 network analysis National Natural Science Foundation of China https://doi.org/10.13039/501100001809 71971191, 72261034 Science and Technology Innovation Team of Yunnan Provincial Universities 2019014 Yunnan Fundamental Research Projects 202001AS070018 Yunnan Education Department Scientific Research Fund Project 2022Y478 edited-statecorrected-proof typesetterts10 ==== Body pmcJEL classification: C32, G13, G15, Q43 Introduction On March 11, 2020, COVID-19 was declared a pandemic by the World Health Organization (WHO). The COVID-19 pandemic delivered an enormous shock to the global economy and led to the deepest global recession since the Second World War, by far surpassing the recession in 2009 triggered by the global financial crisis (World Bank 2020).1 The pandemic affected financial markets as well, but its impact has varied in magnitude for different types of commodities. As shown in Figure 1, the price returns of crude oil decreased by nearly 27% between January and June 2020. The West Texas Intermediate (WTI) on the New York Mercantile Exchange (NYMEX) prices tumbled to $19.5 per barrel, falling the bedrock price since 2002. The single most striking observation to emerge from the data comparison was Brent. The per barrel price of the Brent crude plummeted to $−37.63 on April 20, 2020. China’s first oil futures contract, whose trading symbol is SC, began trading with the Shanghai International Energy Exchange (INE) on March 26, 2018; its price reached less than $0.3 per barrel, dropping to the lowest level.Figure 1. Time evolutions for the time series of oil markets returns. Three considered oil futures contracts are Brent, SC and WTI. The sample period for prices (returns) runs from March 26 (27), 2018, to April 26, 2021. As the most important source of energy worldwide, crude oil has played a prominent role in the global economy (Wei et al., 2017; Li & Wei, 2018; Bai et al., 2019; Chen et al., 2020; Liu et al., 2020; Li et al., 2022). With the financialization of oil commodities, the structure of oil markets has been elevated highly as oil markets are becoming increasingly capitalized (Li et al., 2020a; Liang et al., 2019; Wei et al., 2020b). Therefore, investors have an increasing need to find a safe haven on their investment portfolio when facing unprecedented risks in crude oil markets during the COVID-19 pandemic (Sharma, 2017; Wei et al., 2019; Jefferson, 2020; Li & Dong, 2020; Bai et al., 2021; Liu et al., 2022; Wei et al., 2022a). Traditionally, gold is considered a safe-haven investment (Ji et al., 2020; Liang et al., 2020b; Mokni et al., 2020; Morema & Bonga-Bonga, 2020; Salisu & Adediran, 2020). The results of Huynh et al., (2020) show that gold can be a good safe-haven instrument due to its independence. Although a vast body of literature has testified to ‘Gone with the Gold’ (Jin et al., 2019; Wei et al., 2020a; Salisu et al., 2020), the safe-haven potential of gold is still time-varying, regime dependent and nonlinear, implying that it varies across different regimes Adekoya et al., (2020). In contrast, some scholars debate that gold and oil markets have become increasingly inefficient in safe-haven assets during the outbreak, which means that gold’s safe-haven properties in the outbreak period have become useless (Mensi et al., 2020). However, whether bitcoin can replace gold as a safe-haven is also under discussion when investors are sceptical of gold’s safe-haven attributes (Conlon et al., 2020; Corbet et al., 2020; Liang et al., 2020a). Some researchers propose bitcoin as a safe haven for traditional assets for many reasons, including independence from monetary policy, storage of value and limited correlation with traditional assets (Bouri et al., 2016; Stensås et al., 2019; Kliber et al., 2019; Shahzad et al., 2020; Mariana et al., 2021). In contrast, others considering bitcoin as a safe haven are unlikely to be worthwhile (Smales, 2019; Chaim & Laurini, 2019; Geuder et al., 2019; Conlon and McGee, 2020). More recently, literature that offers contradictory findings on which bitcoin or gold is a safer haven has emerged. The economic impact of the COVID-19 pandemic on the dependence structures between oil and gold prices has not yet been analysed (Bedoui et al., 2019; Wei et al., 2021). Bouri et al., (2020) find that bitcoin’s safe-haven properties are better than those of gold and commodities. In contrast, Dutta et al., 2020), using the DCC-GARCH model, suggest that gold is a safer haven asset than bitcoin for global crude oil markets. Moreover, Thampanya et al., (2020) findings imply that adding gold or cryptocurrency to a stock portfolio does not enhance its risk-adjusted return. Specifically, it is significant for investors to identify the effective safe-haven assets on crude oil markets in view of the returns on the investment portfolio when the ‘black swan’ events outbreak. Therefore, the main purpose of this study is to evaluate whether bitcoin is a safer haven for the crude oil market than the commonly used gold during the COVID-19 pandemic. To identify the safe-haven abilities of gold and bitcoin, the concept of a safe-haven asset must first be defined. An asset that is negatively correlated with a main asset during an economic downturn is called a safe-haven asset. The presence of such assets in a portfolio allows possible losses to be overcome under standard market conditions, as well as in times of turbulence (Akhtaruzzaman et al., 2021; Baur & Lucey, 2010; Kliber et al., 2019; Li et al., 2020b). With regard to methods of measuring safe-haven assets, current research can mainly include the following categories. (1) Static models. For instance, Hasbrouck, (1995) suggests an econometric approach based on an implicit unobservable efficient price common to all markets in measuring price discovery for equities traded on the NYSE and regional exchanges. Anand and Madhogaria, (2012) show the safe-haven ability of gold in situations of dire economic distress by the Granger causality test. However, the static approach takes the form of a single-equation model that fails to describe the variable ordering and dynamic characteristics in the markets. (2) Dynamic models. For example, Joy, (2011) concludes that gold has acted as an effective safe haven against currency risk associated with the US dollar based on a multivariate GARCH model of dynamic conditional correlations. Mariana et al., (2021) also utilize the DCC-GARCH methodology to examine bitcoin and Ethereum as safe-havens for stocks. However, their approach includes dynamic regression and neglects the directionality of information transmission. (3) Other model analysis. Bredin et al., (2015) find that gold acts as a safe-haven asset for a variety of international equity and debt markets for horizons of up to 1 year utilizing wavelet analysis. However, the model does not take into account the investment frequency domain and network analysis. In terms of safe-haven assets, we find that most of the current studies ignore information transmission, the time–frequency relationship and the network framework relationship between assets and markets. It is crucial to consider these aspects. First, information transmission between assets and the financial market can provide empirical evidence when studying safe-haven assets (Yang et al., 2022; Wang et al., 2022). In addition, considering the frequency can provide strong support for the impact of short-term adjustments, median-term adaptations and long-term policy supervision, which is beneficial for investors to make different choices when connectedness and information transmission vary across time horizons. More importantly, the directional spillover network analysis directly and visually shows that the assets and the financial market act as net spillover recipients or transmitters, which can help investors build suitable investment portfolios with different investment maturities. Based on the above analysis, this paper employs the connectedness approaches from Diebold and Yilmaz, (2012), Baruník and Křehlík, (2018), and network analysis to evaluate whether bitcoin is a safer haven for the crude oil market than the commonly used gold during the COVID-19 pandemic. The advantages of combining these methods are as follows. (1) The method by Diebold and Yilmaz, (2012) is a well-established approach that is particularly useful in studying information transmission, which appeals to measuring the safe haven of the asset. Moreover, this method solves the problem of researching the spillovers of no more than two markets, such as Granger causality. (2) The Baruník and Křehlík, (2018) method can gain insights into describing the frequency bands (or long-, medium- and short-term horizons) of spillovers, providing a means of estimating the safe haven on the frequency domain. It also avoids the loss of effective information, which is similar to the DCC-GARCH. (3) In view of the network spillover analysis, one advantage of the framework is that it intuitively describes the direction and strength of five markets. Another advantage of using the network is that it is particularly useful in distinguishing the net transmitters and net receivers of a system. (4) The dynamic spillover used in this paper has an important advantage in the evolution of time-varying returns (mainly from four aspects: ‘Total spillover’, ‘Net spillover’, ‘From spillover’ and ‘To spillover’), which allows us to capture gradual and unexpected changes in bitcoin, gold and three crude oil futures and investigate the dynamic spillover effects among markets. Generally, this study contributes to the research on safe havens during the COVID-19 pandemic. In particular, this study is the first to compare the safe-haven abilities of bitcoin and gold for crude oil markets during the COVID-19 pandemic based on static, dynamic and network perspectives. The empirical results obtained in this work not only demonstrate a return spillover effect in the time and frequency domains but also can help people recognize which market is the main risk transmitter or recipient. The remainder of this paper is organized as follows. The Evaluation Methodology section is concerned with the methodology. The Data section describes the data. The Empirical Results section presents the findings, focussing on two key themes, namely, static and dynamic analyses. The Robustness Examinations section is concerned with the robustness examinations. The Conclusions section presents the conclusions of this study. Evaluation Methodology Spillover Analysis in the Time Domain Using the Diebold–Yilmaz (DY) Method The process of N-dimensional vector autoregression (VAR) of order p is as follows:(1) Xt=∑i=1pΦiXt−i+εt,t=1,…,T, where Xt=(x1t,x2t,…,xNt) is the N-dimensional column vector, which represents the log-return of N markets; Φi is the coefficient matrix of N×N; and εt is the N-dimensional perturbed column vector. No sequence correlation exists, but an autocorrelation does; that is, εt∼iid(0,∑), and its variance covariance matrix is ∑. VAR (p) can be transformed into an infinite-order vector moving average (VMA) process (∞) when it satisfies the stability condition:(2) Xt=∑i=0∞Ψiεt−i, where Ψi is the coefficient matrix of VMA, which is submitted to Ψi=Φ1Ψi−1+Φ2Ψi−2+⋯+ΦpΨi−p, and Ψ0 is the N-order unit matrix when i<0, Ψi=0. The variance decomposition method measures the proportion of prediction error variance in any endogenous variable affected by different information shocks in the VAR system (Diebold & Yilmaz, 2009, 2011, 2012). It reveals to what extent the trajectory of a variable is due to the impact of itself or other variables in the system. The proportion explained by the variable k in the H-step prediction error variance of variable j is (ΘH)j,k:(3) (ΘH)j,k=σkk−1∑h=0H−1((ΨhΣ)j,k)2∑h=0H−1(ΨhΣΨh′)j,j,H=1,2,3,⋯, where σkk=(Σ)k,k, and Ψh is the VMA coefficient matrix in Eq. (2). Given ∑k=1N(ΘH)j,k≠1 in the generalized variance decomposition, (ΘH)j,k can be standardized by row summation as follows:(4) (ΘH′)j,k=(ΘH)j,k/∑k(ΘH)j,k,H=1,2,3,⋯, where ∑k=1N(ΘH′)j,k=1, and ∑k,j=1N(ΘH′)j,k=N, (ΘH′)j,k. The spillover level of variable k to variable j in the time domain is measured. The total spillover index CH measures the overall spillover level of bitcoin, gold and crude oil market systems in the time domain. It reveals the influence proportion of the information spillover contribution to system change.(5) CH=100∑j≠kΘH′j,k∑ΘH′j,k=1001−TrΘH′N. Directional spillover indexes CH,j←• and CH,•←j measure the spillover level of a single market receiving peripheral markets and external information spillovers. They reflect the overall information spillover scale of a single market.(6) CH,j←•=100∑k,k≠jΘH′j,k∑ΘH′j,k=100∑k,k≠jΘH′j,kN. (7) CH,•←j=100∑k,k≠jΘH′k,j∑ΘH′k,j=100∑k,k≠jΘH′k,jN. Traditionally, CH,j←• measures the information spillover of other markets to market j, whereas CH,•←j measures market j ’s information spillover to other markets. The difference between CH,j←• and CH,•←j represents the overall net spillover CH,j and the pairwise net spillover Cj,k of market j. It is the pairwise net spillover that plays the cornerstone role in network analysis.(8) CH,j=CH,•←j−CH,j←•, (9) Cj,k=(ΘH′)j,k−(ΘH′)k,j, where CH,j helps identify the asset as to whether it is a safe haven. Specifically, in the case when CH,j≤0, market j is viewed as a safe-haven asset. Spillover Analysis in the Frequency Domain Using the Baruník–Křehlík (BK) Method To describe the frequency dynamics (long, medium and short terms) of spillover, Baruník and Křehlík, (2018) consider a spectral representation of variance decompositions based on frequency responses (instead of impulse responses) to shocks. On the basis of the frequency response function Ψ(e−iω)=∑he−iωhΨh, the spectral density SX(ω) of frequency Xt is(10) Sxω=∑h=−∞∞EXtXt−h′e−iωh=Ψe−iωΣΨ′e+iω. Recent advances in this method facilitate investigation into frequency dynamics. Ψ(e−iω) is obtained by Fourier transform from Ψh, i=−1. SX(ω) describes how the variance of Xt is distributed among frequencies ω; it is a key parameter for understanding frequency dynamics. The generalized causation spectrum can be defined as(11) (f(ω))j,k=σkk−1\|(Ψ(e−iω)Σ)j,k\|2(Ψ(e−iω)ΣΨ′(e+iω))j,j, where (f(ω))j,k is the part of the spectrum of variable j caused by the impact on variable k at a given frequency ω. Given that the denominator of Eq. (11) is the spectrum of variable j at a given frequency ω, (f(ω))j,k can be interpreted as with-frequency causation. Furthermore, the frequency share of variance between variable j is introduced as the weight function, as shown as follows:(12) Γj(ω)=(Ψ(e−iω)ΣΨ′(e+iω))j,j12π∫−ππ(Ψ(e−iλ)ΣΨ′(e+iλ))j,jdλ, where Γj(ω) is the work of variable j at a given frequency. In this manner, the generalized variance decomposition in frequency band d is(13) (Θd)j,k=12π∫dΓj(ω)(f(ω))j,kdω. The additional condition is that d=(a,b), (a,b)∈(−π,π), and a<b. Moreover,(14) (Θ∞)j,k=12π∫−ππΓj(ω)(f(ω))j,kdω, where (Θ∞)j,k is equal to (ΘH)j,k when H→∞ in the time domain. (Θd)j,k can be further standardized as(15) (Θd′)j,k=(Θd)j,k/∑k(Θ∞)j,k , where (Θd′)j,k measures the spillover level of variable k to variable j in frequency band d. The index of total spillover CdF and directional spillover index Cd,j←• and Cd,•←j in frequency band d can be expressed as follows:(16) CdF=100∑k,j=1;k≠jNΘd′j,kN. (17) Cd,j←•F=100∑k,k≠jΘd′j,kN. (18) Cd,•←jF=100∑k,k≠jΘd′k,jN. Similar to Eqs. (6)–(9), Cd,j←•F measures the information spillover of other markets to market j in frequency band d, whereas Cd,•←jF measures the information spillover of market j to other markets in frequency band d. The difference between Cd,j←•F and Cd,•←jF represents the overall net spillover Cd,jF of market j.(19) Cd,jF=Cd,•←jF−Cd,j←• .F Data The sample data used in this study consist of three categories, namely, bitcoin, gold and crude oil. Bitcoin is the most popular digital currency, and its daily prices are recorded from the website https://coinmarketcap.com/. The gold price used in this study is the Gold Fixing Price at 10:30 AM (London time) in the London Bullion Market, based on U.S. dollars. Three types of oil markets are selected, namely, the NYMEX-listed West Texas Intermediate (WTI) crude oil market, the Brent crude oil (Brent) market on the London Intercontinental Exchange (ICE) and the Shanghai INE-listed and traded Shanghai crude oil futures (SC). The markets are selected for the following reasons. On the one hand, current international oil trade is based on three major crude oil quotations. WTI and Brent crude oil futures play the role of benchmark crude oil contracts in North America and Europe, respectively. Since its launch, the daily trading volume of Chinese crude oil futures (SC) has exceeded that of Dubai crude oil futures, becoming the largest crude oil futures contract in Asia and the third largest international oil market (Li et al., 2021; Song & Li, 2015). On the other hand, it is worth noting that SC differs from WTI and Brent in terms of trading hours, product quality, trading geography and currency (Wei et al., 2022b; Zhang et al., 2021). Specifically, SC futures contracts are traded in Chinese yuan (CNY), while WTI and Brent are both traded in US dollars. Trading in Chinese yuan (CNY) not only weakens the pricing control over WTI and Brent to some extent but also facilitates the use of the Chinese currency. Overall, the choice of WTI, Brent and SC as representatives of the crude oil market is useful to study. Additionally, WTI and Brent crude oil daily prices are collected from the Data Stream, whereas the price of SC is obtained from the Wind Financial Terminal. To enhance the comparability of the data, we initially convert Chinese crude oil futures prices into US dollar-denominated prices through the exchange rate.2 In addition, the daily returns are computed as ln(Pt/Pt−1). The period selected for analysis in this study is from March 26, 2018, to April 26, 2021. The starting point of the sample period is constrained by the availability of SC data. For improved results, data with inconsistent trading times due to holidays or other factors are excluded, and a total of 735 sample data are obtained. The sample data are further divided into two periods: Period Ⅰ is pre-COVID-19, from March 26, 2018, to January 12, 2020, and period Ⅱ is post-COVID-19, from January 13, 2020, to April 26, 2021.3 Figures 1 and 2 depict the time series of the returns of bitcoin, gold and crude oil futures over the sample period. Before the outbreak of the epidemic, the yield of the three major crude oil futures changed relatively smoothly. After the epidemic, the volatility was relatively large, particularly from January 2020 to May 2020, after which the volatility was more moderate until August 2020, when it began to fluctuate more volatile again. Before the COVID-19 pandemic, we observed a close dependence on the changes in the returns of bitcoin, gold and the three crude oil futures. In particular, after the COVID-19 outbreak, bitcoin and gold are more closely related.Figure 2. Time evolutions for the time series of Bitcoin and gold returns. The sample period for prices (returns) runs from March 26 (27), 2018, to April 26, 2021. Tables 1 and 2 show the descriptive statistics before and after the COVID-19 subsample, respectively. The table results show that all the variables are non-normally distributed, as indicated by the skewness, excess kurtosis and Jarque–Bera statistics. Broadly, the fat-tail is more prominent after the epidemic. In terms of skewness, a change is observed from a right skewness (positive skewness) before the epidemic to a left skewness (negative skewness). When the data on the left side of the average are less than the data on the right side, intuitively, the left tail is longer than the tail relative to the right. The reason for this result is that the values of a few variables are extremely small, which makes the left side of the curve drag extremely long. For excess kurtosis, the post-epidemic peaks are considerably larger than the pre-epidemic ones. Ljung–Box statistics indicate no significant autocorrelation for WTI, Brent and SC returns, either before or after the epidemic. The autocorrelation between bitcoin and gold is significantly stronger after the epidemic. To avoid the potential pseudoregression problem, we use ADF and P-P statistics to test the unit root of the stationary attribute of each series. The test results show that before and after the epidemic, the null hypothesis of the unit root of the 1% significance level is rejected, indicating that the entire time series is smooth and can be used directly without further transformation.Table 1. Descriptive Statistics of all Return Series Before the COVID-19 Outbreak. Brent WTI SC Bitcoin Gold Observations 428 428 428 428 428 Standard deviation 0.02 0.02 0.02 0.04 0.01 Minimum −0.07 −0.09 −0.08 −0.16 −0.02 Maximum 0.13 0.11 0.11 0.20 0.03 Skewness −0.01 0.60 0.29 0.13 0.25 Excess kurtosis 8.57 16.17 7.60 5.65 5.12 Jarque–Bera 552.82* 3119.73* 383.64* 126.51* 84.59*** Q(5) 10.68* 33.34*** 1.72 2.39 6.21 Q(10) 17.25* 34.91*** 5.42 11.07 9.37 Q(20) 29.67* 43.07* 10.56 26.89 28.19 ADF −23.37* −27.00* −20.17* −20.16* −21.08* P-P −23.39* −27.58* −20.14* −20.19* −21.07* Notes: The Jarque–Bera statistic tests for the null hypothesis of normality in sample return distribution. Q (n) is the Ljung–Box statistics of the return series for up to nth order serial correlation. ADF and P-P are statistics of augmented Dickey–Fuller and Phillips–Perron unit root tests, respectively, based on the lowest AIC. , * and indicate rejection at the 1%, 5% and 10% significance levels, respectively. The whole sample period spans from March 26, 2018, to January 13, 2020. Table 2. Descriptive Statistics of all Return Series After the COVID-19 Outbreak. Brent WTI SC Bitcoin Gold Observations 307 307 307 307 307 Standard deviation 0.04 0.03 0.03 0.05 0.01 Minimum −0.28 −0.14 −0.12 −0.50 −0.05 Maximum 0.19 0.13 0.09 0.19 0.05 Skewness −1.44 −1.37 −0.23 −2.50 −0.74 Excess kurtosis 16.55 12.89 4.22 28.26 6.64 Jarque–Bera 2455.05 1345.75* 21.84* 8479.51* 197.98* Q(5) 7.89 18.07 7.03 14.34** 7.97 Q(10) 18.50 19.02* 10.78 17.54* 14.81 Q(20) 30.47 26.85 17.52 32.75 24.06 ADF −15.41* −22.09* −16.09* −19.66* −16.52* P-P −15.48* −22.29* −16.27* −19.65* −16.56* Notes: The Jarque–Bera statistic tests for the null hypothesis of normality in sample return distribution. Q (n) is the Ljung–Box statistics of the return series for up to nth order serial correlation. ADF and P-P are statistics of augmented Dickey–Fuller and Phillips–Perron unit root tests, respectively, based on the lowest AIC. , * and *indicate rejection at the 1%, 5% and 10% significance levels, respectively. The whole sample period spans from January 13, 2020, to April 26, 2021. Empirical Results Static Analysis Using Full-Sample Spillover Analysis In the static analysis, we first construct VAR models using the returns of bitcoin, gold, and three crude oil indices in period Ⅰ and period Ⅱ. The lag order of both models is 2 according to the Akaike Information Criterion (AIC). The spillover indexes for the full-sample period are estimated based on 10-day ahead forecast error variance (FEV) decomposition referring to Diebold and Yilmaz, (2012). Then, we mainly analyse the correlation (defined in Eq. (5)) among bitcoin, gold and the three crude oil markets from the time and frequency domains. From the perspective of the time domain, we mainly study the correlation among bitcoin, gold and the three selected crude oil futures markets using the method proposed by Diebold and Yilmaz, (2011). Table 3 reports the correlation between each market in the time domain before and after the epidemic. The ‘From’ column provides the directional spillover or spillover from all other markets into a special single market. The ‘TO’ row provides the directional spillover into all others markets from market j. ‘To’ (defined in Eq. (7)) and ‘From’ (defined in Eq. (6)) in Table 3 represent the spillover contribution and income of a single market in the whole economic system. The spillover effect in period Ⅰ represents the spillover effect among markets before the outbreak, and that in period II represents the post-epidemic spillover effect.Table 3. Return Spillover Results in the Time Domain. Brent WTI SC Bitcoin Gold From Period Ⅰ: March 26, 2018–January 12, 2020 Brent 75.13 21.66 0.68 1.86 0.67 4.97 WTI 20.87 75.59 0.93 2.27 0.34 4.88 SC 3.77 1.59 93.10 0.40 1.15 1.38 Bitcoin 0.28 0.21 0.20 98.34 0.97 0.33 gold 1.80 0.30 0.95 0.77 96.19 0.76 To 5.34 4.75 0.55 1.06 0.62 12.33 Period Ⅱ: January 13, 2020–April 26, 2021 Brent 75.63 17.02 0.23 6.78 0.35 4.87 WTI 17.37 74.54 1.51 4.61 1.97 5.09 SC 0.92 0.69 93.89 1.26 3.24 1.22 Bitcoin 4.72 1.15 0.91 84.52 8.71 3.10 gold 0.45 0.86 0.72 8.80 89.16 2.17 To 4.69 3.94 0.68 4.29 2.85 16.46 This table reports the return spillover for period Ⅰ and period Ⅱ for three international crude oil futures (i.e. Brent, WTI and SC), Bitcoin and gold. The spillover measures are calculated based on the method of Diebold and Yilmaz, (2012). The jk-th entry of the upper-left 5 × 5 market submatrix provides the jk-th pairwise connectedness calculated by Eq. (4) (i.e. the percent of forecast error variance of crude oil futures market j due to shocks from market k). The rightmost (FROM) column provides directional connectedness from all others to j calculated by Eq. (6) (i.e. row means except for diagonal elements). The bottom (TO) row provides directional connectedness to all others from k calculated by Eq. (7) (i.e. column means except for diagonal elements). The bottom-right element is the total connectedness calculated by Eq. (5) (i.e. the sum of directional connectedness). The full-sample period spans from March 26, 2018, to April 26, 2021. Overall, the total spillover effect increased from 12.33% before the epidemic to 16.46% after the epidemic. During the epidemic period, the correlation among bitcoin, gold and three crude oil futures markets is enhanced. That is, in this system, the risk of one market is likely to transfer to other markets. Moreover, the correlation between bitcoin and the three crude oil futures markets increased after the outbreak. The spillovers of bitcoin to Brent, WTI and SC increased from 1.86%, 2.27% and 0.40% before the outbreak to 6.78%, 4.61% and 1.26% after the outbreak, respectively. The spillovers of gold to WTI and SC increased from 0.34% and 1.15% before the outbreak to 1.97% and 3.24% after the outbreak, respectively. Thus, bitcoin and gold markets are more closely related to the crude oil market during the outbreak, indicating that investors have the strong choice of liquidity in bitcoin and gold. The time-domain spillover method proposed by Diebold and Yilmaz, (2009) fails to describe the correlation among different frequencies (time scales). Therefore, based on this methodology, we further study the correlation in the frequency domain using the method proposed by Baruník and Křehlík, (2018). Table 4 clearly shows the correlation measurements (defined in Eq. (15)) of three time periods (short-term period: 1–5 days, medium-term period: 5–22 days, and long-term period: more than 22 days). The values in Table 3 have the same meaning as those in Table 2. We obtain the following results.Table 4. Return Spillover Results in the Frequency Domain. Brent WTI SC Bitcoin Gold From Period Ⅰ: March 26, 2018–January 12, 2020 Panel A: Short-term frequency, 1–5 days Brent 65.05 18.78 0.43 1.72 0.61 4.31 WTI 18.79 68.59 0.76 1.96 0.33 4.37 SC 2.68 1.13 77.70 0.27 1.13 1.04 Bitcoin 0.27 0.20 0.20 78.51 0.52 0.24 gold 1.18 0.22 0.59 0.68 77.11 0.53 To 4.58 4.07 0.40 0.93 0.52 10.49 Panel B: Median-term frequency, 5–22 days Brent 7.51 2.14 0.18 0.10 0.04 0.49 WTI 1.56 5.24 0.13 0.23 0.01 0.38 SC 0.79 0.32 11.40 0.09 0.01 0.24 Bitcoin 0.01 0.01 0.00 14.56 0.32 0.07 gold 0.46 0.06 0.26 0.07 13.97 0.17 To 0.56 0.51 0.11 0.10 0.08 1.35 Panel C: Long-term frequency, longer than 22 days Brent 2.57 0.75 0.07 0.03 0.01 0.17 WTI 0.52 1.77 0.05 0.08 0.00 0.13 SC 0.30 0.13 4.00 0.04 0.00 0.09 Bitcoin 0.00 0.00 0.00 5.28 0.13 0.03 gold 0.16 0.02 0.10 0.02 5.11 0.06 To 0.20 0.18 0.04 0.03 0.03 0.48 Period Ⅱ: January 13, 2020–April 26, 2021 Panel A: Short-term frequency, 1–5 days Brent 56.39 13.64 0.18 4.00 0.34 3.63 WTI 13.76 66.04 1.44 3.00 1.52 3.94 SC 0.28 0.57 70.14 0.81 2.59 0.85 Bitcoin 3.97 1.06 0.60 70.97 7.97 2.72 gold 0.29 0.76 0.65 5.43 69.70 1.43 To 3.66 3.21 0.57 2.65 2.48 12.57 Panel B: Median-term frequency, 5–22 days Brent 14.06 2.49 0.05 1.98 0.00 0.90 WTI 2.59 6.28 0.06 1.14 0.31 0.82 SC 0.44 0.09 17.03 0.33 0.50 0.27 Bitcoin 0.59 0.07 0.22 10.07 0.56 0.29 gold 0.12 0.08 0.06 2.42 14.23 0.54 To 0.75 0.54 0.08 1.17 0.28 2.82 Panel C: Long-term frequency, longer than 22 days Brent 5.18 0.89 0.01 0.80 0.00 0.34 WTI 1.02 2.21 0.02 0.47 0.14 0.33 SC 0.20 0.03 6.71 0.13 0.16 0.10 Bitcoin 0.16 0.02 0.09 3.48 0.18 0.09 gold 0.04 0.02 0.02 0.95 5.23 0.21 To 0.29 0.19 0.03 0.47 0.10 1.07 This table reports the return spillover for period Ⅰ and period Ⅱ for three international crude oil futures (i.e. Brent, WTI and SC), Bitcoin and gold in different frequency domains. Panels A, B and C report the connectedness measures for the short-term frequency (1–5 days), medium-term frequency (5–22 days) and long-term frequency (longer than 22 days), respectively. The connectedness measures are calculated based on the method of Baruník and Křehlík, (2018). In each panel, the jk-th entry of the upper-left 5 × 5 market submatrix provides the jk-th pairwise connectedness calculated by Eq. (15) (i.e. the percent of forecast error variance of crude oil futures market j due to shocks from market k). The rightmost (FROM) column provides directional connectedness from all others to j calculated by Eq. (17) (i.e. row means except for diagonal elements). The bottom (TO) row provides directional connectedness to all others from k calculated by Eq. (18) (i.e. column means except for diagonal elements). The full-sample period spans from March 26, 2018, to April 26, 2021. Before the epidemic, the total spillover effect of the system is 10.49%, 1.35% and 0.48% for the short, medium and long terms, respectively, whereas after the epidemic, the total spillover effect becomes 12.57% (short-term), 2.82% (short-term) and 1.07% (long-term). On the one hand, the spillover after the outbreak is greater than that before the epidemic at each frequency, which is consistent with the results of the DY method. On the other hand, the total spillover decreases from the short term to the long term, whether before or after the epidemic, suggesting that the spillover effect mainly spreads in the short term, and only a few spillover effects spread in the medium and long terms. Thus, in the short term, the relationship among systems is closer, and the return spillover effect is more obvious. To investigate which market is the net disseminator (or net receiver) during the outbreak, we also calculate the net spillover of each market before and after the outbreak. Table 5 shows that in period Ⅰ, WTI and SC act as the spillover net recipients, whereas Brent is the spillover net transmitter. Furthermore, the change in Brent from a pre-epidemic spillover-contributing market to a receiving market indicates that different markets may change from one role to another during different periods of an epidemic. What is surprising is that bitcoin has a weak safe-haven effect on the crude oil market only for a short period (−0.01%). In addition, gold maintains a good safe-haven ability for crude oil futures whether in period Ⅰ (−0.14%) or the medium term (−0.26%) and long term (−0.11%) of period Ⅱ. According to these results, investors follow the principle of risk minimization in the investment process and can select a reasonable risk portfolio.Table 5. The Results of Net Directional Return Spillover Measures. Net return Brent WTI SC Bitcoin Gold Period Ⅰ: March 26, 2018–January 12, 2020 Overall 0.37 −0.13 −0.83 0.73 −0.14 Short-term 0.28 −0.30 −0.65 0.69 −0.01 Medium-term 0.07 0.12 −0.13 0.03 −0.09 Long-term 0.02 0.05 −0.05 0.01 −0.03 Period Ⅱ: January 13, 2020–April 26, 2021 Overall −0.18 −1.15 −0.55 1.19 0.67 Short-term 0.03 −0.74 −0.28 −0.07 1.06 Medium-term −0.16 −0.28 −0.20 0.89 −0.26 Long-term −0.05 −0.14 −0.08 0.38 −0.11 This table reports the net return spillover for three international crude oil futures ((i.e. Brent, WTI and SC), Bitcoin and gold in both the time and frequency domains, which is based on the methods of Diebold and Yilmaz, (2012) and Baruník and Křehlík, (2018), respectively. The top (overall) row provides the net spillover measures of Diebold and Yilmaz, (2012), which are calculated by Eq. (8), that is, the difference in the total directional spillover to others and from others. The five bottom rows give the net connectedness measures of Baruník and Křehlík, (2018) on the short-term frequency (1–5 days), medium-term frequency (5–22 days) and long-term frequency (longer than 22 days), which are calculated by Eq. (19) with the five corresponding frequencies. The full-sample period spans from March 26, 2018, to April 26, 2021. Network Analysis Using Network Modelling To better visualize the structure of spillover, we plot the spillover network that specifies the direction and strength of spillover among the five markets. The important parameters are the same as those in the static analysis. Figures 3 and 4 provide the network of pairwise return spillover (defined in Eq. (9)) during press and post-COVID-19. Several important conclusions can be obtained from the network spillover.Figure 3. Directional return spillover network over the pro-COVID-19. Notes: This figure shows the net directional spillover among the Bitcoin, gold and oil markets’ returns. The size of each node indicates the overall magnitude of spillover transmission for each sample, which is measured by pairwise spillover. The thickness of the arrow reflects the strength of the spillover between a pair of variables, with thicker arrows indicating stronger net directional pairwise spillover. Figure 4. Directional-returns spillover network post-COVID-19. Notes: This figure shows the net directional spillover among the Bitcoin, gold and oil markets' returns. The size of each node indicates the overall magnitude of spillover transmission for each sample, which is measured by pairwise spillover. The thickness of the arrows reflects the strength of the spillover between a pair of variables, with thicker arrows indicating stronger net directional pairwise spillover. Generally, the intersystem spillover effects after the outbreak are greater than those before the outbreak. First, the spillover during the post-COVID-19 period is stronger than that during the pre-COVID-19 period according to a tighter network. Second, after the outbreak, the spillover effect for bitcoin and gold with the three crude oil markets increased significantly. Bitcoin and gold are strongly connected with Brent, WTI and SC. In particular, SC is significantly affected by bitcoin and gold. Third, in view of the three crude oil markets, SC firmly plays the same role. What has changed is that the connection between WTI and Brent weakened. Finally, a weaker spillover network is expected to emerge from bitcoin and gold; however, the relationship between the two has increased after the outbreak (from 0.01% to 0.04%). Dynamic Analysis Using Rolling-Sample Spillover Analysis It is widely accepted that spillover effects can vary at any time, and correlations among different markets may increase or decrease under conditions of uncertainty (Sun et al., 2020). However, the full-sample effects are static and represent the average among the five markets, which may overlook any time variation in the spillover effect. Given this, it seems unlikely that any single fixed-parameter model would apply to the full sample. As a result, we study the dynamic spillover effect of markets mainly from four aspects, namely, ‘total spillover’, ‘net spillover’, ‘from spillover’ and ‘to spillover’, based on a 22-day period (contains 1-month observations) with a 10-day forecast horizon referring to Diebold and Yilmaz, (2012). As shown in Figure 5, at the beginning of the COVID-19 outbreak, the level of short-term risk spillover rose rapidly. Thus, the impact on risk events in the early stage mainly has a short-term influence on each market and aggravates the short-term risk spillover among markets. In March, with the rapid spread of the epidemic, the risk spillover among markets reached the maximum. After May, the spillover effect gradually decreased. The reason may be due to the government’s control of the epidemic situation and people’s understanding and attention to the epidemic situation (e.g. isolation; decreased outdoor activities; and drug research, development and treatment).Figure 5. Dynamic return total spillover. The return ‘Total’ spillover plotted here is computed by the Diebold and Yilmaz, (2011) method of Eq. (5), which is computed based on a 1-month moving window. The sample period for returns is from May 2, 2018, to April 26, 2021. The time-varying characteristic of net directional return spillover from each market to all other markets is shown in Figure 6. In most cases, the net spillover effects switch to negative and positive territories, suggesting that each market can act as a net or receiver at given points in time. Specifically, in the crude oil market, WTI and Brent show almost the same trend. SC was the opposite; it was negative before the epidemic but became positive after the epidemic.Figure 6. Dynamic return net spillover for sample. The ‘Net’ spillover of returns plotted here is computed by the Diebold and Yilmaz, (2012) method of Eq. (8), which is defined as the difference in the total directional connectedness to others and from others. The dynamic net spillover is computed based on a 1-month moving window. The sample period for returns is from May 2, 2018, to April 26, 2021. The following conclusions are obtained from Figures 7 and 8. First, the spillover of ‘From’ and ‘To’ have the opposite relationship. The directional spillover almost changes before and after the outbreak. Second, crude oil was greatly affected in April 2020. Finally, at the beginning of the COVID outbreak, especially from January to March 2020, bitcoin and gold show the same trend, but gold is more affected by the outbreak. Thus, gold still maintains a good risk aversion attribute at times of risk. In the short term of risk events, bitcoin also has a certain degree of risk aversion, but compared with gold, it is small. Bitcoin has a little safe-haven effect because it has won the favour of investors due to its convenient trading and high return rates.Figure 7. Dynamic return from spillover for sample. The return ‘From’ spillover is computed based on a 1-month moving window. The sample period for returns is from May 5, 2018, to April 26, 2021. Figure 8. Dynamic return to spillover for sample. The dynamic ‘To’ spillover is computed based on a 1-month moving window. The sample period for returns is from May 2, 2018, to April 26, 2021. Robustness Examinations Robustness Analysis by Different Rolling Windows To establish the robustness of the dynamic results, we employ alternative the three dynamic rolling window sizes of 65-day (one-quarter), 100-day (half-year) and 250-day (1-year) with 10-day forecast horizons, respectively (instead of the 1-month estimates in the Dynamic Analysis Using Rolling-Sample Spillover Analysis section). Then, we also calculate the ‘Total spillover’, ‘Net spillover’, ‘From spillover’ and ‘To spillover’ spanning from the three rolling windows. The following conclusions are obtained. First of all, as can be seen from Figure 9, it is noticeable that the five markets’ total spillover effects based on three different rolling window sizes (i.e. 65, 100 and 250) increase significantly during the outbreak of COVID-19. More specifically, starting with a lower value of approximately 10%, the total spillover index spike in January 2020 during the pandemic followed a continued decline until April 2021 because of the government’s control of the epidemic situation. Consequently, all these results verify that the spillover indexes follow a similar pattern regardless of the choice of the size of the rolling window, which means that our results are robust and in accordance with the Dynamic Analysis Using Rolling-Sample Spillover Analysis section.Figure 9. Dynamic return total spillover based on different windows. The return ‘Total’ spillover plotted here is computed by the Diebold and Yilmaz, (2011) method of Eq. (5), which is computed based on one-quarter and 1-year moving windows. The sample periods are from July 9, 2018, to August 27, 2018, and April 17, 2019, to April 26, 2021. Secondly, Figure 10 reports the net directional spillover measure based on three different rolling window sizes (i.e. 65, 100 and 250). Table 6 is organized by the average value of net directional spillover on the basis of Figure 10. The values of the net spillover for gold are always negative both in periods Ⅰ (−0.62%, 0.54% and −0.53%, respectively) and Ⅱ (−0.48%, −0.29% and −0.21%, respectively) with the three different rolling windows, which provides strong evidence that gold remains a safe-haven asset. However, bitcoin has a weak safe-haven effect on the crude oil market when using a 250-day rolling window (−0.11%).Figure 10. Dynamic return net spillover based on different windows. The ‘Net’ spillover of returns plotted here is computed by the Diebold and Yilmaz, (2012) method of Eq. (8), which is defined as the difference in the total directional connectedness to others and from others. The dynamic net spillover is computed based on one-quarter, half-year and 1-year moving windows. The sample periods are from July 9, 2018, August 27, 2018, and April 17, 2019, to April 26, 2021. Table 6. Dynamic Net Spillover With Different Rolling Windows. Window = 65 Window = 100 Window = 250 Period Ⅰ: July 9, 2018–January 13, 2020 August 27, 2018–January 13, 2020 April 17, 2019–January 13, 2020 Brent 0.49 0.29 −0.15 WTI 0.33 0.19 0.06 SC −0.76 −0.57 −0.44 Bitcoin 0.55 0.63 1.06 Gold −0.62 −0.54 −0.53 Period Ⅱ: January 13, 2020–April 26, 2021 Brent 0.23 −0.01 0.07 WTI 0.88 0.52 0.25 SC −0.66 −0.28 −0.00 Bitcoin 0.03 0.06 −0.11 Gold −0.48 −0.29 −0.21 Note: The values are produced by the average value of net directional spillover on the basis of Figure 10. Last but not least, Tables 7 and 8 provide, respectively, the mean dynamic ‘From’ and ‘To’ spillovers with different rolling windows in line with Figures 11 and 12. These results show that SC oil acts as the net spillover recipient, while Brent is the net transmitter. What calls for special attention is that the role of WTI seems to be inconsistent with former results. This difference can be explained by the loss of valid sample dates by rolling window lengths. Moreover, Brent and WTI are the larger receivers and transmitters of return information with average ‘From’ and ‘To’ spillover regardless of the window sizes.Table 7. Dynamic From Spillover With Different Rolling Windows. Window = 65 Window = 100 Window = 250 Period Ⅰ: July 9, 2018–January 13, 2020 August 27, 2018–January 13, 2020 April 17, 2019–January 13, 2020 Brent 6.89 6.57 5.97 WTI 6.65 6.42 5.82 SC 3.17 2.28 0.89 Bitcoin 2.67 1.95 0.59 Gold 2.70 1.96 1.05 Period Ⅱ: January 13, 2020–April 26, 2021 Brent 6.21 5.47 4.04 WTI 5.61 5.05 3.69 SC 2.67 1.74 0.40 Bitcoin 4.18 3.74 3.03 Gold 4.87 3.90 2.54 Note: The values are produced by the average value of net directional spillover on the basis of Figure 11. Table 8. Dynamic to Spillover With Different Rolling Windows. Window = 65 Window = 100 Window = 250 Period Ⅰ: July 9, 2018–January 13, 2020 August 27, 2018–January 13, 2020 April 17, 2019–January 13, 2020 Brent 7.38 6.86 5.82 WTI 6.98 6.61 5.87 SC 2.42 1.71 0.46 Bitcoin 3.22 2.58 1.65 Gold 2.08 1.42 0.52 Period Ⅱ: January 13, 2020–April 26, 2021 Brent 6.43 5.46 4.12 WTI 6.49 5.60 3.93 SC 2.01 1.47 0.40 Bitcoin 4.20 3.79 2.92 Gold 4.40 3.62 2.33 Note: The values are produced by the average value of net directional spillover on the basis of Figure 12. Figure 11. Dynamic return from spillover based on different windows. The dynamic ‘From’ spillover is computed based on one-quarter and 1-year moving windows. The sample periods are from July 9, 2018, to August 27, 2018, and April 17, 2019, to April 26, 2021. Figure 12. Dynamic return to spillover based on different windows. The dynamic ‘To’ spillover is computed based on one-quarter and 1-year moving windows. The sample periods are from July 9, 2018, to August 27, 2018, and April 17, 2019, to April 26, 2021. In general, terms, by setting different rolling window sizes, we further confirm the robustness of the conclusions obtained in the Empirical Results section. Robustness Analysis Based on the TVP-VAR Method It is instructive at this point to note that the Diebold and Yilmaz, (2012) and Baruník and Křehlík, (2018) VAR-based spillover approaches facilitate the measurement based on the notion of the forecast error variance decomposition from the rolling window. However, the traditional Diebold and Yilmaz, (2012) and Baruník and Křehlík, (2018) methods have not been able to consider small-sample estimation. In the present study, Antonakakis et al., (2020) provide an extension to the Diebold and Yilmaz, (2012) connectedness approach by applying a time-varying parameter vector autoregressive model (TVP-VAR) with a time-varying covariance structure, as opposed to the constant-parameter rolling window VAR approach. This methodology improves the seminal approach in several ways, such as the fact that no observations are lost as no rolling window is employed, there is no need for choosing an arbitrarily sized rolling window and it overcomes outlier sensitivity. Furthermore, Chatziantoniou et al., (2021) introduce the novel TVP-VAR frequency approach, which is predicated upon previous work by Baruník and Křehlík, (2018) and Antonakakis et al., (2020). Henceforth, we write down the two methods as TVP-VAR-DY and TVP-VAR-BK, respectively. Consequently, we choose the TVP-VAR-DY and TVP-VAR-BK methods to re-estimate the empirical results, which is one of our alternative robustness checks. Dynamic analysis using the TVP-VAR-DY method First, Table 9 shows the average (time-mean) return spillover in the time domain among bitcoin, gold and the three crude oil futures markets, which are similar to the results in Table 3. More explicitly, we note that the average total spillover index increased from 25.73% in period Ⅰ to 26.52% in period Ⅱ. In the meanwhile, Figure 13 displays the dynamic total spillover effects based on the TVP-VAR-DY method. It is worth noting that after the COVID-19 crisis, the time-varying total return spillover rapidly rises. During the first quarter of 2020, the value of total spillover reaches over 35%, which is the higher level for the entire sample period.Table 9. The Results of Average Return Spillover Based on the TVP-VAR-DY Method. Brent WTI SC Bitcoin Gold From Period Ⅰ: March 26, 2018–January 12, 2020 Brent 66.10 25.50 2.00 3.00 3.50 35.46 WTI 25.80 66.30 3.30 3.10 1.50 36.75 SC 21.00 12.60 60.40 2.90 3.10 38.87 Bitcoin 2.90 2.00 3.10 88.80 3.30 13.24 gold 4.40 2.30 3.80 3.20 86.30 15.03 To 61.49 48.43 11.08 10.53 7.81 25.73 Period Ⅱ: January 13, 2020–April 26, 2021 Brent 69.50 20.10 1.40 7.00 1.90 22.86 WTI 20.70 69.90 2.50 3.70 3.20 22.65 SC 3.10 2.10 85.50 4.40 4.90 8.22 Bitcoin 4.60 1.70 2.70 81.60 9.30 14.10 gold 3.30 3.00 2.40 10.00 81.30 11.94 To 24.25 20.81 4.05 16.97 13.67 26.52 This table reports the period Ⅰ and period Ⅱ average (time-mean) spillover for three international crude oil futures (i.e. Brent, WTI and SC), bitcoin and gold. The spillover measures are calculated based on the method of Antonakakis et al., (2020). The full-sample period spans from March 26, 2018, to April 26, 2021. Figure 13. Total spillover of sample returns based on the TVP-VAR-DY method. The sample period for returns is from March 26, 2018, to April 26, 2021. Moreover, Figure 14 depicts the time-varying evolution of net directional spillovers among the five markets. We can identify the transmitter or the recipient of the net directional spillovers even though the net directional connectedness oscillates in either a negative or positive direction, while their magnitudes often change over time. Consistent with the results in Figure 6, the net spillover results show that WTI and SC oil act as net spillover recipients, while Brent is the net transmitter.Figure 14. Net spillover of sample returns based on the TVP-VAR-DY method. The sample period for returns is from March 26, 2018, to April 26, 2021. In addition, Figures 15 and 16 present the time-varying ‘From’ and ‘To’ spillover estimated by the TVP-VAR-DY method of Antonakakis et al., (2020). Note that in Figures 7 and 8, the values of spillover are leptokurtic and incomplete. In contrast, the results shown in Figures 15 and 16 are not only smooth but also persistent. It reveals the advantage of a TVP-VAR-based connectedness over the traditional VAR-based approach by its insensitivity, and no observations are lost in the data sample.Figure 15. From the spillover of sample returns based on the TVP-VAR-DY method. The sample period for returns is from March 26, 2018, to April 26, 2021. Figure 16. To spillover of sample returns based on the TVP-VAR-DY method. Dynamic Analysis Using the TVP-VAR-BK Method In the first place, Figure 17 describes the total spillover in the short term (i.e. 1–5 days), the median term (i.e. 5–22 days) and the long run (i.e. longer than 22 days) based on the TVP-VAR-BK method. It should also be noted that the value total spillover is rising extremely fast when COVID-19 occurred. More particularly, the dynamic total average spillover is approximately 10% during phase Ⅰ in the short term, while it is 30% during the COVID-19 outbreak, demonstrating a relatively stronger information spillover in the short term. These findings practically confirm the analysis in the Empirical Results section and provide a more granular picture of the evolution of spillover over time.Figure 17. Total spillover of sample returns based on the TVP-VAR-BK method. Additionally, Table 10 is organized by the average value of net directional spillover across various time frequencies by the TVP-VAR-BK method on the basis of Figures 14 and 18. Apparently, it is obvious that the net total directional spillover effects of gold are almost always negative during all sample phases. By comparison, the role of bitcoin as a safe-haven asset is in short term with values of −1.36% (Period Ⅰ) and −0.16% (Period Ⅱ). Therefore, evidence suggests that bitcoin has a weak safe-haven effect on the crude oil market only in a short period, while gold maintains a good safe-haven ability for crude oil futures. These results are in accordance with the Empirical Results section.Table 10. The Results of Average Net Directional Return Spillover Based on the TVP-VAR-DY and TVP-VAR-BK Methods. Net Return Brent WTI SC Bitcoin Gold Period Ⅰ: March 26, 2018–January 12, 2020 Overall 26.03 11.68 −27.79 −2.71 −7.22 Short-term frequency 20.40 7.08 −19.60 −1.36 −6.51 Medium-term frequency 3.49 2.75 −4.96 −0.70 −0.59 Long-term frequency 2.15 1.85 −3.24 −0.65 −0.12 Period Ⅱ: January 13, 2020–April 26, 2021 Overall 1.40 −1.84 −4.17 2.88 1.73 Short-term frequency 1.56 −1.36 −2.59 −0.16 2.56 Medium-term frequency −0.10 −0.25 −0.97 1.87 −0.55 Long-term frequency −0.06 −0.23 −0.62 1.17 −0.27 This table reports the ‘Net’ average (time-mean) directional spillover for three international crude oil futures (i.e. Brent, WTI and SC), bitcoin and gold in both the dime and frequency domains. The overall row provides the net spillover measures of the TVP-VAR-DY method. The three bottom rows give the net spillover measures of the TVP-VAR-BK method on the short-term frequency (1–5 days), medium-term frequency (5–22 days) and long-term frequency (longer than 22 days). The full-sample period spans from March 26, 2018, to April 26, 2021. Figure 18. Net spillover of sample returns based on the TVP-VAR-BK method. Finally, Figures 19 and 20 further indicate the ‘From’ and ‘to’ spillovers using TVP-VAR-BK, and Tables 11 and 12 are ordered by the mean value of the figures, respectively. Interestingly, the dynamic ‘From’ spillover and ‘To’ spillover measures are ranked as follows: short-term>median-term>long-term. In comparison with Table 4, Figures 7 and 8, it becomes clearer that the dynamic ‘From’ spillover and ‘To’ spillover can be seen in different time frequencies spillover among bitcoin, gold and three crude oil futures markets, again confirming the major evidence found in the Empirical Results section.Figure 19. From the spillover of sample returns based on the TVP-VAR-BK method. Figure 20. To spillover of sample returns based on the TVP-VAR-BK method. Table 11. The Results of the Average Directional Return Spillover Based on the TVP-VAR-BK Method. Short-term Medium-term Long-term Period Ⅰ: March 26, 2018–January 12, 2020 Brent 29.79 3.48 2.19 WTI 31.64 3.16 1.95 SC 29.53 5.69 3.65 Bitcoin 10.70 1.48 1.05 Gold 12.81 1.50 0.72 Period Ⅱ: January 13, 2020–April 26, 2021 Brent 18.00 3.15 1.71 WTI 18.25 2.81 1.59 SC 5.76 1.53 0.93 Bitcoin 12.22 1.27 0.60 Gold 8.92 1.97 1.05 Note: The values are produced by the average value of ‘From’ directional spillover on the basis of Figure 19. The values give the ‘From’ spillover measures of the TVP-VAR-BK method on the short-term frequency (1–5 days), medium-term frequency (5–22 days) and long-term frequency (longer than 22 days). The full-sample period spans from March 26, 2018, to April 26, 2021. Table 12. The Results of Average to Directional Return Spillover Based on the TVP-VAR-BK Method. Short-term Medium-term Long-term Period Ⅰ: March 26, 2018–January 12, 2020 Brent 50.19 6.97 4.33 WTI 38.72 5.91 3.80 SC 9.93 0.73 0.42 Bitcoin 9.34 0.78 0.41 Gold 6.30 0.91 0.61 Period Ⅱ: January 13, 2020–April 26, 2021 Brent 19.56 3.04 1.66 WTI 16.89 2.56 1.36 SC 3.17 0.57 0.32 Bitcoin 12.06 3.14 1.77 Gold 11.47 1.42 0.78 Note: The values are produced by the average value of ‘To’ directional spillover on the basis of Figure 20. The values give the ‘To’ spillover measures of the TVP-VAR-BK method on the short-term frequency (1–5 days), medium-term frequency (5–22 days) and long-term frequency (longer than 22 days). The full-sample period spans from March 26, 2018, to April 26, 2021. Conclusions The COVID-19 outbreak has brought strong shocks to a range of crude oil markets. The main goal of this study was to determine whether bitcoin or gold provides investors with an effective safe-haven instrument for the oil market during the COVID-19 pandemic. Our empirical results obtained in this paper demonstrate that, first, the static connectedness measurements reveal that the five markets’ total spillover effect increases significantly after the COVID-19 outbreak, demonstrating a relatively strong information spillover among the bitcoin, gold and three major crude oil futures markets. Specifically, in the time domain, the total spillover effect increased from 12.33% before the epidemic to 16.46% after. In the frequency domain, the total spillover is much larger in the short term (Period Ⅰ: 10.49%; Period Ⅱ: 12.57%) than in the medium (Period Ⅰ: 1.35%; Period Ⅱ: 2.82%) and long terms (Period Ⅰ: 0.48%; Period Ⅱ: 1.07%), implying that a range of shock spillovers for crude oil markets from the outbreak of COVID-19 are mainly transmitted in the short term and just a few spillovers are transmitted in the medium and long term. Then, the directional spillover network analysis directly and visually shows that WTI and SC act as spillover net recipients, whereas Brent is the spillover net transmitter. Finally, all the static and dynamic analysis measurements demonstrate that bitcoin has a weak safe-haven effect on the crude oil market in the short term, while gold always maintains a good safe-haven ability for crude oil markets across various time horizons (frequencies), either before or after the outbreak of the COVID-19 pandemic. The findings of this study have important implications for policy-makers, crude oil producers and global investors. Given the strong spillover effects among bitcoin, gold and crude oil markets, policy-makers, crude oil producers and global investors need to keep an eye on the spillovers transmitted from other markets when making decisions. In addition, according to the variation rule of the spillover in the frequency domain, investors can adopt the strategy of ‘buying short’ or ‘selling long’ positions to gain benefits. Moreover, the significant effect of the pandemic on the dynamic spillover among the bitcoin, gold and three major crude oil futures markets implies that policy-makers and global investors should consider the factor of ‘Black Swan’ factor in analysing the oil-bitcoin and oil-gold relationships. Last but not least, investors cannot ignore the importance of bitcoin and gold in selecting more profitable portfolio policies when searching for safe-haven assets. ORCID iDs Qian Wang https://orcid.org/0000-0001-9582-0818 Yifeng Zhang https://orcid.org/0000-0002-6942-2848 Notes The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors are grateful for the financial support from the National Natural Science Foundation of China (71971191, 72261034), Science and Technology Innovation Team of Yunnan Provincial Universities (2019014) and Yunnan Fundamental Research Projects (202001AS070018), Yunnan Education Department Scientific Research Fund Project (2022Y478). 1. https://www.worldbank.org/commodities. 2. The exchange rate makes uses of the midpoint of the RMB exchange rate. (http://www.chinamoney.com.cn/chinese/index.html). 3. On January 13, 2020, the WHO officially named the virus ‘ COVID-19’ (https://www.who.int/). ==== Refs References Adekoya O. B. Oliyide J. A. Oduyemi G. O. (2020). How COVID-19 upturns the hedging potentials of gold against oil and stock markets risks: Nonlinear evidences through threshold regression and Markov-regime switching models. 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==== Front Comput Ind Eng Comput Ind Eng Computers & Industrial Engineering 0360-8352 1879-0550 Elsevier Ltd. S0360-8352(22)00809-9 10.1016/j.cie.2022.108821 108821 Article Designing humanitarian logistics network for managing epidemic outbreaks in disasters using Internet-of-Things. A case study: An earthquake in Salas-e-Babajani city Ehsani Behdad a Karimi Hamed a Bakhshi Alireza a Aghsami Amir ab Rabbani Masoud a⁎ a School of Industrial & Systems Engineering, College of Engineering, University of Tehran, Tehran, Iran b School of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran ⁎ Corresponding author. 22 11 2022 1 2023 22 11 2022 175 108821108821 © 2022 Elsevier Ltd. All rights reserved. 2022 Elsevier Ltd Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Along with the destructive effects of catastrophes throughout the world, the COVID-19 outbreak has intensified the severity of disasters. Although the global aid organizations and philanthropists aim to alleviate the adverse impacts, many employed actions are not impactful in dealing with the epidemic outbreak in disasters. However, there is a gap in controlling the epidemic outbreak in the aftermath of disasters. Therefore, this paper proposes a novel humanitarian location-allocation-inventory model by focusing on preventing COVID-19 outbreaks with IoT-based technology in the response phase of disasters. In this study, IoT-based systems enable aid and health-related organizations to monitor people remotely, suspect detection, surveillance, disinfection, and transportation of relief items. The presented model consists of two stages; the first is defining infected cases, transferring patients to temporary hospitals promptly, and accommodating people in evacuation centers. Next, distribution centers are located in the second stage, and relief items are transferred to temporary hospitals and evacuation centers equally regarding shortage minimization. The model is solved by the LP-metric method and applied in a real case study in Salas-e-Babajani city, Kermanshah province. Then, sensitivity analysis on significant model parameters pertaining to the virus, relief items, and capacity has been conducted. Using an IoT-based system in affected areas and evacuation centers reduces the number of infected cases and relief item's shortages. Finally, several managerial insights are obtained from sensitivity analyses provided for healthcare managers. Keywords Humanitarian logistics Supply Chain Design COVID-19 management Internet-of-Things Location-Allocation problem Inventory management ==== Body pmc1 Introduction Annually, cataclysms, e.g., earthquakes, hurricanes, volcanic eruptions, and floods, claim people's lives worldwide. Based on information released from the international disaster database (https://www.emdat.be/emdat_atlas), Japan's deadliest earthquake had more than 20,033 fatalities. The last decade has experienced significant earthquakes triggering more than 400,000 deaths and 500,000 injuries. Two of the most destructive earthquakes, the Sichuan earthquake in China and earthquakes in Haiti, affected more than 56 million people in both countries (Ahmadi et al., 2020). Regarding the statistics published recently, Iran is seventh of the world's top ten countries with the most dangerous earthquake (Zolfaghari and Peyghaleh, 2016, Heydari et al., 2021). There is evidence that the most frequent natural catastrophe in Iran is the earthquake. Managing the disaster in the situation of occurrence of an earthquake is one of the most critical issues. Aside from a massive proportion of casualties, the economy of Affected Areas (AAs) may deteriorate after disasters, along with the healthcare system (Bakhshi et al., 2022). While an unprecedented SARS-CoV-2 outbreak emerged in Wuhan on 31st December 2019, World Health Organization (WHO) disseminated information about the fatal disease to ensure people's safety (Sohrabi et al., 2020). Until now, many patients who are infected have mild symptoms, e.g., a dry cough, a sore throat, and a mild fever, which improve over a few days. However, others are faced with more severe symptoms such as organ failure, septic shock, and severe pneumonia (Chen et al., 2020). As noted by the online statistics platform (https://www.worldometers.info/coronavirus ), the number of confirmed cases worldwide is now more than 145 million, with 3.1 million deaths. As far as COVID-19 is concerned, the global economy has declined due to preventative measures such as social distancing and lockdowns to eradicate coronavirus (Heydari & Bakhshi, 2022). To summarize, industries, international trade, and the global supply chain all suffer during the given timeframe (Ibn-Mohammed et al., 2021). Along with the crippling effect of COVID-19 on the supply chain network, relief logistics, encompassing vaccine, medicine, and ventilators distribution, have been experiencing some disruptions throughout the period (Sharma et al., 2020). Regarding the destructive impacts of COVID-19, the spread of epidemic outbreaks amid the disaster can pose a potential menace to people's lives and increase the number of casualties noticeably. The outbreak of acute gastroenteritis in Hurricane Katrina in August 2005 and cholera followed by the Haiti Earthquake in January 2010 are the cases in point (Barzilay et al., 2013). Unambiguously, the lack of alcohol-based disinfectants, masks, paper towels, medical supplies, and drinkable water brings about unhygienic circumstances within the disaster-affected zones (Sakamoto et al., 2020). Therefore, planning the evacuation procedure that will apply during the COVID-19 pandemic has the utmost importance. Prevention, preparedness, response, and recovery constitute the four phases of managing a disaster (Goretti et al., 2017). Disaster prevention has become a global problem to lessen the impact of disasters. One strategic aspect of the pre-disaster phase is the facilities' location and the quantity of relief items (RIs) to be processed, as they are closely linked to timely service and cost of response within the Humanitarian Relief Logistics (HRL) (Duhamel et al., 2016). Aim in HRL is the reducing the response time and the rate of casualty (Momeni et al., 2020). The response phase involves the evacuation measures and transferring of affected people from AAs. During the epidemic outbreak, the response phase should be combined with continuous surveillance and assessment. Additionally, in the preparedness stage, locating facilities and Evacuation Centers (ECs) and distributing RIs should be based on virus features. To tackle the COVID-19 outbreak in AAs, Sakamoto et al. (2020) suggested a recommendation obtained from previous experiences mentioned in the following: (1) The total area required per person is six square meters. (2) Thermometers and sensors can be deployed to find symptomatic patients. (3) Public places, i.e., schools, can be utilized to accommodate unoccupied people along with ECs. (4) About one-third of ECs' capacity should be accepted to occupy. (5) Tankers replete with drinkable water should be allocated to ECs. (6) The mask and alcohol-based disinfectants should be distributed to evacuees immediately. (7) A system is necessary to ensure that information and guidelines reach evacuees. (8) Continuous surveillance and tests would be implemented in ECs to find infected people. (9) The isolation space is crucial for symptomatic patients. As noted above, mitigating the shambolic situation amid the disaster outbreak is one of the main challenges severely impacted by the COVID-19 outbreak (Dehghan-Bonari et al., 2021). Planning and executing guidelines, accommodating symptomatic and asymptomatic patients simultaneously, utilizing the IoT-based technology, using cargo drones for distribution, and distributing RIs fairly and quickly can control the epidemic outbreak. The proposed IoT framework in this study collects the data of the symptoms from individuals and patients to define the infected cases and calculate the infection rate. A decision support system is incorporated into the IoT framework to make critical decisions based on the infection rate in the aftermath of a disaster. Also, the framework is utilized for virtual communication between patients and physicians and for informing people of the latest guidelines. The discrepancies between the management of disasters simultaneous with a pandemic and without it are as follows: (1) Relief items should contain sanitization and personal protective equipment, along with excess hygienic water; (2) allocation of patients and symptomatic people should be based on guidelines; (3) the medical IoT framework, along with the PCR test, should be used to detect suspects in a timely manner; (4) quarantine places should be considered to separate infected cases from the crowds; (5) novel vehicles and transportation methods (i.e., autonomous drones) should be hired to distribute RIs without human intervention. Therefore, these differences make management complicated. Questions that need to be answered in this study are as follows:• Where and how many Temporary Hospitals (THs) and Distribution Centers (DCs) should be instituted to cope with COVID-19 in the aftermath of disasters? • Are IoT-based technologies impactful for controlling COVID-19 in disasters? • What is the optimum inventory level for DCs? • How many drones and trucks are needed? • How many RIs are required? Therefore, the multi-objective, multi-period, multi-fleet location-allocation-inventory mixed-integer IoT-based mathematical programming model with uncertain parameters is developed for the response phase based on Japan's experiences proposed by Sakamoto et al. (2020) to answer the questions mentioned above. Additionally, an IoT framework for finding suspected and infected cases, enriched with a novel decision support system for logistics management, is proposed in our study. Due to the paramount importance of disaster management amid the COVID-19 outbreak, affected people are allocated to ECs and THs regarding the allocation policy and time consideration. Then, RIs are distributed from DCs to ECs and THs swiftly. The main goals of our RI logistics problem are to minimize the delivery time of RIs, the shortage and surplus of RIs in demand zones, and total cost. As a main result of this study, reducing the infection rate can be impactful on cost reduction. Also, the infection rate in AAs has more impact on cost, shortage, and the number of the infected cases in THs than the rate in ECs. In terms of item distribution, drones have more impact on the shortage reduction than trucks. Furthermore, increasing the capacity of distribution centers has less effect on a shortage than increasing the fleet capacity or the number of fleets. This paper is composed of six main sections. The introduction is considered the first one. The literature of previous studies is analysed in Section 2. In Section 3, the problem description and suggested mathematical model are presented, along with tackling uncertainty. Section 4 points out the solution methodology. Next, in Section 5, the proposed model is evaluated by the actual case study, and comprehensive sensitivity analyses are conducted concerning model parameters. Finally, the managerial insight, as well as the conclusion, are reported in Section 6. 2 Literature review 2.1 The location-allocation problems in humanitarian relief logistics (HRL) The location problem has numerous supply chain network applications, capturing the scholar's attention in recent years. Some studies in humanitarian logistics are highlighted below. In the aftermath of floods, the telecommunication network may be disrupted as well as accommodation may be demolished. Mohammadi et al. (2016) presented the stochastic mathematical model to locate DCs, shelters, and telecommunication towers to enhance service efficiency and communication in disasters. Paul and Hariharan (2012) conducted the research to mitigate disaster-impacted zones, taking into account the reduction of delays in allocating stockpiles and evacuation. Survivability time and severity of injuries have a significant role in the mentioned study. While AAs encounter severe devastations, donations from non-governmental organizations (NGOs) and international contributions are requisite to evacuate people. Sarma et al. (2019) introduced the mathematical model, including minimizing total costs and operational time for inventory and allocation of daily consumed RIs, and supplying machinery equipment delivered by NGOs. In the recovery phase of disasters, Ahmadi et al. (2020) presented a two-stage mathematical model to allocate Save and Response (SAR) teams to AAs as soon as possible. The model aims to maximize demand coverage and minimize operational time to identify the casualties in the shortest time. Tofighi et al. (2016) proposed the two-stage inventory-location-allocation model to define the location of warehouses and DCs in the first phase and distribute the RIs to DCs in the second phase. Considering demands and items' priority, the model aims to minimize total cost and transportation time. Furthermore, Aslan and Çelik (2019) designed the location-routing-inventory model consisting of two stages; the first aims to locate DCs and warehouses during the preparedness phase, and the latter tries to find the indefectible roads at the post-disaster time. The road's vulnerability amid the disaster and restoration time of affected roads is incorporated in the mentioned model. Since RIs have various lifetimes, ordering policy plays a crucial role in HRL. Rezaei-Malek et al. (2016) proposed the model considering selling surplus perishable items and buying them periodically to decrease the surplus amount of perishable RI. Amid the catastrophe, Road disruptions and people's foreboding will bring about massive traffic congestion in the aftermath of disasters. To cope with the rampant problem, Wang and Nie (2019) proposed the location-allocation single-objective model involving traffic function incorporated into transportation costs. The model aims to find the road with the lowest traffic congestion. Vahdani et al. (2018) developed the comprehensive two-stage multi-period multi-commodity multi-vehicle mathematical model that encompasses locating DCs and warehouses in the first stage and routing and distributing RIs in the second one. To fulfil people's demand and deliver RIs promptly, the priority of damage AAs, split delivery, and hard time window are incorporated into the model. A novel transportation method has emerged regarding the road disruptions during calamities, which name is aeromedical logistics. Abazari et al. (2021) tackled the problem of distribution of perishable and imperishable RIs by minimizing total traveling time and distance and considering the time window. Jenkins et al. (2020) addressed mobile aeromedical facility location and allocation of helicopters to stages. The model aims to maximize the demand coverage in AAs and facilitate distributions. Bozorgi-Amiri and Khorsi (2016) considered people's satisfaction in AAs by minimizing the maximum amount of shortage, total travel time, and total cost. The proposed model regards multi-modal transportation encompassing a heterogeneous fleet of vehicles. In the response phase, patient hospitalization and provision of medical supplies decrease the disaster's fatality rate. Habibi-Kouchaksaraei et al. (2018) considered the problem of temporary blood facility location and blood distribution to the temporary and existing hospitals. The bi-objective multi-echelon model aims to minimize the total costs and blood deficiency. Additionally, Salehi et al. (2017) developed the previous work and presented the multi-objective multi-period, multi-product model considering all types of blood types, their derivations, and the possible blood substitution. Moreover, to transfer casualty aftermath earthquakes, Haghi et al. (2017) considered THs in HRL. The mathematical model entails minimizing costs and maximum demand shortage, and neglected casualties. Ghasemi et al. (2019) proposed the bi-objective model regarding the distribution of RIs and injured people's hospitalization simultaneously. Due to the severity of injuries, the patients are divided into two groups; the first is outpatients transferred to temporary medical centers, and the latter is seriously injured patients transferred to hospitals. 2.2 Uncertainty in humanitarian logistics Due to the unpredictable nature of disasters, embracing the uncertain environment can make the model more real-world and efficacious. Based on the literature review conducted by Peidro et al. (2009), in most logistics models, the inventory parameters (i.e., inventory cost and capacity of the storage), supplier-side parameters (i.e., establishment cost, production cost, and quality parameters), distribution parameters (i.e., transportation cost and the capacity of vehicles), and demand parameters (i.e., demand quantity) were taken as uncertain parameters. As shown in numerous studies in HRL, uncertainty stems from supply, demand, inventory, and network connectivity. Specifically, for the demand side, Mohamadi et al. (2016) used Fuzzy Mathematical Programming (FMP) for demand uncertainties. The uncertain population in each region (demand level) for maximizing demand coverage in the problem of selecting telecommunication towers Also, the hired possibilistic method does not control the level of uncertainty. Additionally, Jenkins et al. (2020) considered volatile demands in the aeromedical location-allocation problem. To predict disasters' unspecified behaviours, some researchers have considered uncertain demand and supply simultaneously whether many scholars have presented all the facets of uncertainty sources. It is worth noticing that disaster time walks hand in hand with demand levels at specific locations. For instance, in working hours, the total population can increase in the business district. Simultaneously, considering location and demand level are found in Rezaei-Malek et al., 2016, Salehi et al., 2017, and Habibi-Kouchaksaraei et al. (2018). Aside from fluctuating demand, Abazari et al. (2021) focused on uncertainty corresponding to distribution parameters, including travel, loading, unloading time, transportation, and inventory cost. This work did not consider the uncertainty in either demand or supply. However, the study developed by Tofighi et al. (2016) considered demand sides with the accompaniment of parameters mentioned in the previous work. Note that in this study, the uncertain capacity of distribution centers is added into model to concentrate more on the uncertainty for the distribution side. Similarly, Sarma et al. (2019) embraced the FMP method to convert transportation and inventory costs and demand levels into the crisp model. However, this study did not consider time and capacity as fuzzy numbers. In addition, in the problem of pre-positioning and procurement planning, Torabi et al. (2018) utilized both FMP and scenario-based methods for uncertain parameters in all fields, involving uncertain capacity, production and procurement costs, transportation costs, and demand level. This study focused on the supplier side and prepositioning planning, which model is not appropriate for the disaster response phase. Similar to Mohamadi et al. (2016), the utilized fuzzy method cannot control the degree of uncertainty. Danesh Alagheh Band et al. (2020) presented a multi-objective problem to maximize the gain from the assessment of roads and areas with uncertain parameters. 2.3 Impact of COVID-19 on supply chain management Regarding the business closure, lack of workforce, and massive lockdowns, the logistics network's efficiency decreases considerably amid the coronavirus (Wu et al., 2021, Spieske and Birkel, 2021). Some studies are conducted pertaining to the COVID-19 outbreak to tackle the lack of products and emergency items mentioned below. Regarding the distribution of critical items to tackle the COVID-19 outbreak, Tirkolaee et al. (2022) proposed the location-allocation closed-loop green network for distributing and collecting face masks, considering all components wrestling with COVID-19 (i.e., quarantine, distribution, and recycling centers). In order to eradicate the Hazardous Medical Wastes (HMW), including masks, among the COVID-19, Kargar et al. (2020) designed the network enriched by Temporary Treatment Centers (TTCs) and all potential waste generators (hospitals, treatment centers, and quarantine places). The multi-period model aims to locate the TTCs and allocate the influx volume of HMW to TTCs in terms of minimizing the maximum quantity of uncollected waste, which is similar to minimizing the unmet demand in Goodarzian et al. (2022). Similarly, in Goodarzian et al. (2021), the sustainable production–distribution–inventory–allocation–location model was developed for perishable medicine amid the coronavirus, taking into consideration of minimizing maximum shortage. Aside from masks and their waste management, Mondal and Roy (2021) designed a production–distribution model to distribute required items for COVID-19 to hospitals concerning the minimizing time and backlogged works to increase people’s satisfaction. In the HRL network, distributing foods, along with relief items, plays a pivotal role, which is considered in Azani et al. (2022). In this study, food is allocated to reduce the virus transmission and people’s communication. In order to utilize the state-of-the-art technologies for eradicating COVID-19, Zahedi et al. (2021) proposed the application of the medical internet of things (m-IoT) for ambulance allocation for COVID-19 patients with consideration of their priorities that are efficacious in reducing COVID-19 patients noticeably. The role of IoT in this paper is to define suspected cases and allocate the ambulance to them. In addition, Goodarzian et al. (2022) designed the COVID-19 vaccine supply chain network with the goal of minimizing the maximum unmet demand, total cost, and delivery time. Similar to Zahedi et al. (2021), the IoT framework plays a critical role in gathering people’s information and prioritizing sensitive tiers. Hence, the impact of IoT on supply chain management became stark after the emerging of the virus that the proposed Methods and IoT equipment are discussed in Yousif et al. (2021). 2.4 Research gaps To sum up the literature, a meticulous analysis of Table 1 provides research gaps of the HRL. Some specifications of research gaps are mentioned below.• None of the research considers the management of epidemic and disease outbreaks among the disasters (i.e., earthquake, tsunami, flood, storm). There are several guidelines in essays, but this problem lacks mathematical modelling. Also, none of the aforementioned essays consider time, cost, distance, and demand coverage as objective functions simultaneously. • In a few recent articles, fleet management and multi-modal transportation play a crucial role in distribution and delivery. It should be mentioned that none of the previous research does not consider various types of transportation, including road, air, rail, and sea methods, along with the heterogeneous types of fleets. • Practically speaking, evacuating people and distributing RIs happen concurrently, which is considered in numerous essays separately. There is an stark gap to make a mathematical model that involves both of them. • In the aftermath of a disaster, the level of uncertainty surges in all aspects. Most papers concentrated on demand fluctuations at the time of the disaster. Based on the systematic review conducted in Section 2.2, none of the research considered all types of uncertainty for the response phase of a disaster. • Based on two recent articles (Zahedi et al., 2021, Goodarzian et al., 2022), they combined IoT-based technology into supply chain management to tackle the impact of COVID-19 on the logistics network. However, there is a gap in incorporating the medical IoT into the humanitarian logistics network. Table 1 A comparison of relevant literature on the topic of HRL network design. Author Objective function Type of problem Period Commodity Uncertainty Fleet IoT Solution method H D DC T C L A R I S M S M SB F S R N Paul and Hariharan. (2012) * * * * * * * commercial solver Bozorgi-Amiri and Khorsi (2016) * * * * * * * * * ε-constraint method Mohamadi et al. (2016) * * * * * * * * * commercial solver Rezaei-Malek et al. (2016) * * * * * * * * tchebycheff method Tofighi et al. (2016) * * * * * * * * * metaheuristic Vahdani et al. (2018) * * * * * * * * * * * metaheuristic Haghi et al. (2017) * * * * * * * * metaheuristic + ε-constraint method Habibi-Kouchaksaraei et al. (2018) * * * * * * * * commercial solver Aslan and Çelik (2019) * * * * * * * * heuristic Salehi et al. (2017) * * * * * * * * * branch and cut method Sarma et al. (2019) * * * * * * * commercial solver Ghasemi et al. (2019) * * * * * * * * * metaheuristic Wang and Nie (2019) * * * * * * general benders decomposition Ahmadi et al. (2020) * * * * * * * commercial solver Abazari et al. (2021) * * * * * * * * * * metaheuristic Jenkins et al. (2020) * * * * * * ε-constraint method This Study * * * * * * * * * * * * * commercial solver Cue: [Objective function: H = humanitarian, D = distance, DC = demand coverage, T = time, C = cost], [Type of problem: L = location, A = allocation, R = routing, I = inventory], [Period and Commodity: S = single, M = multi], [Uncertainty: SB = scenario-based, F = fuzzy, S = stochastic, R = robust, N = none]. After a meticulous analysis of research gaps, the novelties of our paper are listed below:• The study herein seeks to cope with natural cataclysms amid the epidemic outbreak. In this study, the mathematical model is presented to address the location of THs and DCs, determination of required RI's quantity, e.g., sanitizers, water, Personal Protective Equipment (PPE), transferring people and patients, and allocation of them to THs and ECs. The proposed IoT-based multi-echelon multi-commodity multi-period model aims to make a trade-off between unmet demand, costs, distances, and travel time simultaneously to increase people's satisfaction and fairness in distribution. Also, some practical experiments, proposed by Sakamoto et al. (2020), were added to our model; these can be seen in model assumptions. • The heterogeneous fleet is utilized in the study to deliver RIs swiftly. In our article, two models of transportation are deployed, including air and road. Drones are utilized for air transportation, and trucks are considered for road distribution. • The transferring of people along with the distribution of RIs to individuals is considered concurrently in our model. • The novel IoT framework is incorporated into our humanitarian logistics network for calculating and reducing the infection rate of COVID-19. Note that this framework is based on Otoom et al. (2020), that we make it specific for the disaster situation by adding some components (e.g., quarantine centers). • To cope with uncertainty, all types of uncertainty, involving supply (institutional cost), demand (number of people living in city zones), inventory (capacity of storage and inventory cost), and distribution (capacity of fleet and distribution cost), are covered in our study. Aside from parameters, fuzzy mathematical programming and scenario-based optimization cope with uncertainty in all aspects of this study. Finally, the proposed model is evaluated in three different scenarios by a real-world case study in Salas-e-Babajani city, located in Iran. 3 Problem description Our problem is composed of two interconnected sections, including a proposed IoT framework and a decision support system. Note that this IoT framework is the developed version of the study proposed by Otoom et al. (2020), specifically for monitoring and detecting infected cases in disasters. Regarding the review article written by Asadzadeh et al. (2020), one of the DSS applications that was made to deal with the COVID-19 epidemic is supply chain management. As a significant novelty of this work, a novel decision support system as a part of an IoT system is proposed to make critical decisions in the aftermath of disasters. 3.1 The proposed IoT framework The Internet of Things (IoT) is a system of interconnected computer, electronic, and mechanical equipment capable of transmitting data across a specified network without human intervention. What makes IoT capable of the COVID-19 Pandemic is its significant benefits, including a lower probability of error, lower costs, superior treatment, improved diagnosis, proper monitoring system during the quarantine, reduction in medical staff’s workload, and effective control (Singh et al., 2020). The lack of this framework can increase the infection rate noticeably because doing a COVID-19 PCR test on all individuals and then allocating people based on the test result takes a great deal of time and also, a huge number of tests are not available at that time. Practically speaking, the medical IoT system is utilized in numerous countries to cope with the COVID-19 issue. Some practical examples of IoT are presented in the following. For the first time, China hired the IoT to build the questionnaire-based application used for COVID-19 treatment and diagnosis (Bai et al., 2020). The Health Beats application was developed along with the phone application for monitoring vital signs and diagnosing suspected COVID-19 cases (https://www.healthbeats.co/covid-19/). Also, the Mhero application is utilized for physician-patient communications by using text messages and SMS (https://www.mhero.org/). Aside from a mobile phone-bed infected detector, Shanghai Public Health Clinical Center (SPHCC) deployed a continuous body temperature monitoring system with wearable sensors, which are based on Bluetooth (https://www.mobihealthnews.com/news/asia/sphcc-employs-iot-tech-and-wearable-sensors-monitor-covid-19-patients). Additionally, to illustrate the accuracy of the ML-driven infected detector models, the study proposed by Otoom et al. (2020) showed that five out of eight supervised classification models have more than 90 % accuracy in detecting COVID-19 suspects. This section illustrates our planned IoT-based infrastructure for monitoring coronavirus infections in real-time and making critical decisions automatically. The framework of our suggested IoT is depicted in Fig. 1 , comprising eight major components specifically for disaster management amid the outbreak.1. Data collection section in THs and ECs: Fig. 1 The proposed IoT-driven framework. This section seeks to collect real-time symptom data from individuals’ bodies using a set of sensors. Based on the study proposed by Alzubaidi et al. (2021), these symptoms were recognized as Fever, Cough, Fatigue, Sore Throat, and Breathlessness. In our study, biosensors, involving thermal and infrared sensors for monitoring people's body temperature and detecting fever, heart-rate sensors located on wearable rings for measuring oxygen level and detecting breathlessness, and a web-based application for assessing the general wellbeing of patients based on the daily questionnaire, notifying people to comply with the regulations, and sending symptoms data to the cloud, are utilized in ECs and THs. Noteworthy, in AAs, body temperature data is based on thermal sensors located on the drones and medical infrared thermometer guns, and then other symptoms are measured after allocating people to ECs and THs.2. Cloud and data center: With cloud infrastructure, it is possible to gather real-time symptom data from each person in AAs, THs, and DCs and store personal health records. It should be mentioned that the data collection is based on the internet, Bluetooth, and Wi-Fi systems. The use of each way is regarded as the destruction of network connection in AAs. In our study, the data of the symptoms from THs, ECs, and AAs, the data of RIs inventory level from DCs, and data from the PCR team are transferred to the data center.3. Data analysis and decision support system: The most significant part of the IoT infrastructure is data analysis. The symptom data stored in the data centers is analyzed by Machine Learning (ML) algorithms to detect suspected COVID-19 cases based on the ML approaches proposed by Otoom et al. (2020). Note that the utilization of IoT for the detection of suspected cases is not enough in the aftermath of a disaster because suspected cases should take the PCR test. After analyzing the result of PCR test team, infection rate in AAs and ECs by dividing the total number of individuals in AAs and ECs by the number of infected cases in these centers, respectively. The related parameters used in our mathematical model were P1 and P2. Moreover, our study proposes a novel decision-making system based on the infection rate determined by the mentioned ML approaches and data analysis. This decision system aims to (1) allocate individuals from AAs to THs and ECs in a timely manner; (2) define the best location for DCs; (3) define the number of required vehicles and drones for DCs; and (4) calculate the demand for RIs and distribute them to demand points regarding time and satisfaction. The decision-making system is based on mathematical modeling, which is solved by optimization software, e.g., GAMS and LINDO.4. Testing team Using a machine learning-based identification algorithm, the testing team performs a PCR test on suspected cases with abnormal symptom data, which is defined by the data analysis section. After defining the results, those results are transmitted to the cloud, and then the data analysis section determines the exact number of infected cases and the infection rate of COVID-19 in ECs and AAs.5. Temporary Hospitals or Quarantine places This section shoulders the responsibility of gathering vital signs, e.g., body temperature, pulse rate, respiration rate, and blood pressure, from patients who are isolated in THs. Additionally, other data, including gender, age, and incurable diseases, is transmitted with vital signs with tablets allocated to each patient to the cloud and data analysis section. Aside from data transmission, the tablets provide mutually virtual physician-patient communication.6. Medical center After allocating the infected cases to THs, physicians will monitor the real-time symptom data in THs. Therefore, this integrated system allows physicians to communicate with patients remotely.7. Distribution section The indispensable decision after transferring people is to distribute the RIs to the used THs and ECs. The proposed decision-making system calculates the exact quantity of required RIs for each demand point in each period. Additionally, DCs will monitor the inventory level of RIs based on scanning the barcode of RIs with the barcode reader and sending the real-time inventory data to the cloud and decision system.8. Operational team Operational teams will receive the final decision pertaining to the location of DCs, THs, and ECs from the decision support system, and they will institute and equip these points properly with regards to the number of individuals allocated to each center. Note that data transfer is based on the team’s phone and internet. 3.2 The proposed decision support system In the aftermath of a disaster, the casualties must be allocated to hospitals, RIs should be distributed to ECs, and affected people should be transferred to ECs as soon as possible. However, the COVID-19 outbreak affects the distribution of RIs and the evacuation of people. Detecting infected people in crowds is complicated, and disasters worsen the situation. Until now, no effective medications have been discovered for COVID-19. Thus, isolation and reducing the infection rate of COVID-19 is the only solution in this period. This rate, playing a critical role in our decision system, will be calculated by dividing the total number of individuals by the number of infects cases determined by the proposed IoT framework. Similar to the situation before the COVID-19 pandemic, the severely wounded patients are transferred to hospitals. Due to the transmission of the virus, people, along with mildly wounded patients, are allocated to ECs and THs according to the allocation guideline shown in Fig. 2 . Unambiguously, testing all people sensitively with a PCR diagnostic kit in the aftermath of a disaster is robustly impossible because it is time-consuming. PCR tests with IoT-based infected detector systems can be taken as a viable solution throughout the period. Among rapid tests, symptomatic individuals should take the PCR test to transfer COVID-19 patients to THs. In contrast, asymptomatic people are transferred to ECs with the accompaniment of symptomatic people who have negative PCR tests. The TH is a place where symptomatic patients are quarantined to prevent the spread of the COVID-19 pandemic amid the earthquake. After transferring people to ECs, they will undergo continuous monitoring based on the proposed IoT-driven system. Aside from patients' allocation, RIs encompassing face masks, sanitizers, hygienic products, and drinkable water should be provided in Relief Collection Warehouses (RCW). Governmental Organizations (GO) and NGOs contribute towards RCWs (Bakhshi et al., 2022). RIs are distributed to DCs, and then they are allocated to ECs and THs according to their needs. Therefore, it is necessary to locate temporary DCs and THs based on cost, distance, time minimization, the increase in people's satisfaction, and fair distribution. Moreover, a heterogeneous fleet of trucks and IoT-based cargo drones are hired to deliver RIs expeditiously. Note that drones and trucks can simultaneously operate in all distributional sections. Therefore, the location-allocation-inventory multi-period, multi-commodity, multi-fleet, and multi-objective IoT-based models are presented in this study. The structure of this network has been depicted in Fig. 3 . The proposed model is the two-phase model illustrated in Fig. 4 . In the first phase, the critical decisions correspond to COVID-19 patients and people transferring, defining their numbers, and determining which ECs are used and which THs are instituted are made. In the second phase, the strategic decisions on the location of DCs and the distribution of RIs equally between selected THs and ECs are made.Fig. 2 People allocation guidelines amid the COVID-19 outbreak in AAs and DCs. Fig. 3 The considered HRL network. Fig. 4 The relevance between two phases of the mathematical model in each period. 3.3 Assumption The principal assumptions are made for the HRL model:• Each period is considered 24 h in this study. • The model involves three periods after an earthquake occurred. • The capacity and potential location of temporary DCs are defined. • The heterogeneous fleets, including cargo drones and trucks, are considered for RIs transportation. • RIs are provided by NGOs and GOs, which are collected in RCWs. • The number and location of RCWs, AAs, and ECs are known. • The heterogeneous fleet can work simultaneously at each period. • The distances between RCWs, DCs, ECs, THs, and AAs are known. • DCs and THs may be instituted in potential locations. • Uncertainties of the model encompass capacities, demands, transportation cost, and inventory holding costs. • Six square meters are provided for each individual in ECs • The RIs, including drinkable water, sanitizers, and essential items, are delivered to ECs and THs. • THs are taken as the quarantine place for COVID-19 patients. • The allocation of people in AAs is based on the mentioned guideline. • The model is only designed for non-severely wounded patients and impeccable people. 3.4 Proposed mathematical model The mathematical model is formulated on two levels, and the necessary notations are explained in this regard. The first stage specifies the number of individuals in ECs and THs. Then the demand for RIs in each center is calculated by Eqs (14), (15). Afterward, the second level locates the suitable DCs and distributes RIs.Set of indices j Index of DCjj=1,⋯,J i Index of RCWsi=1,⋯,I p Index of relief items p=1,⋯,P h Index of THsh=1,⋯,H k Index of ECsk=1,⋯,K m Index of AAsm=1,⋯,M t Index of periods t=1,⋯,T x Index of periods x=1,⋯,X s Index of scenarios s=1,⋯,S f Index of fleet types f=1,⋯,F Parameters IHCpj Inventory holding cost at DCj for product p ICDj Institution cost of DCj ICHh Institution cost of THh TCpfts Transportation cost for product p using fleet type f at period t(tomans/(km. kg))in scenario s PCSpk Penalty cost of shortage of product p in ECk PCSupk Penalty cost of surplus of product p in ECk dijfs Distance between RCWi and DCj with fleet type f in scenario s djkfs Distance between DCj and ECk with fleet type f in scenario s djhfs Distance between DCj and THh with fleet type f in scenario s PCSÂ´ph Penalty cost of shortage of product p in THh PCSuph Penalty cost of surplus of product p in THh imppk Importance of product p at ECk impÂ´ph Importance of product p at THh CapFf Capacity of fleet type f CapHÂ´hs Capacity of THh in scenario s CapDjs Capacity DCj in scenario s CapECks Capacity of ECk in scenario s M Big M Volp Volume of product p Asf Average speed of fleet type f αp Consumption coefficient of product p PSs Probability of occurrence of scenario s tÂ´mhs Transferring time of patients from AAm to THh in scenario s tmks Transferring time of patients from AAm to ECk in scenario s MDCs Maximum number of distribution centers that can be instituted under scenario s MNFf Maximum number of fleet type f that can be used P1 infection rate of COVID-19 in AAs P2 infection rate of COVID-19 in ECs Opmts Total population in AAm that should be transferred to EC s at period t in scenario s PCOmts Total population in AAm that should be transferred to THh at period t in scenario s PAmts Population should be evacuated in AAm at period t in scenario s Decision variables Qpjhfts Quantity of delivered product p from DCj to THh using fleet f at period t in scenario s Qpijfts Quantity of delivered product p from RCLi to DCj using fleet f at period t in scenario s Qpjkfts Quantity of delivered product p from DCj to ECk using fleet f at period t in scenario s numFfijts Number of fleet type f from RCLi to DCj at period t in scenario s numFÂ´fijts Number of fleet type f from DCj to ECk at period t in scenario s numFÂ´Â´fijts Number of fleet type f from DCj to THh at period t in scenario s Ipjts Inventory level of product p in DCj at period t in scenario s THhs (Binary variable) = 1, if THh is opened in scenario s; ow = 0 ECks (Binary variable) = 1, if ECk is used in scenario s; ow = 0 TDCjs (Binary variable) = 1, if DCj is opened in scenario s;ow = 0 Zphts2 Surplus amount of product p in THh at period t in scenario s Zphts3 Shortage amount of product p in THh at period t in scenario s ZÂ´pkts2 Surplus amount of product p in ECk at period t in scenario s ZÂ´pkts3 Shortage amount of product p in ECk at period t in scenario s PÂ´mhts Number of symptomatic patients transferred from AAm to THh at period t in scenario s Pmkts Number of affected people transferred from AAm to ECk at period t in scenario s pÂ´Â´khts Number of infected cases in ECk detected by IoT system and transferred to THh at period t in scenario s PkÂ´Â´kxs Number of people entering in ECk at period x in scenario s from AA s PChxs Number of people entering in THh at period x in scenario s from AA s RQCpkts Required demand of product p by ECk at period t in scenario s RQHphts Required demand of product p by THh at period t in scenario s 3.4.1 The first level (1) minZ1=∑h,sPSs×ICHh×THh,s (2) minZ2=∑sPSs×∑m,htÂ´mhs×THhs+∑m,ktmks×ECks St.(3) ∑m,tpÂ´mhts+∑k,tpÂ´khts≤CapHhs∼×THhs∀h,s (4) ∑m,tPmkts-∑t,hpÂ´khts≤CapECks∼×ECks∀k,s (5) pÂ´mhts+pÂ´khts≤M×THhs∀m,h,t,k,s (6) pmkts≤M×ECks∀m,k,t,s (7) PCOmts∼=PA∼mts×P1∀t,m,s (8) Opmts∼=PA∼mts×1-P1∀t,m,s (9) Opmts∼=∑kpmkts∀t,m,s (10) PCOmts∼=∑hpÂ´mhts∀t,m,s (11) ∑x≤tPkÂ´Â´kxs-∑hpÂ´kht-1,s×P2=∑hpÂ´Â´khts∀t,k,s (12) PkÂ´Â´kxs=∑m,t≤xpmkts-∑m,t≤xpmkt-1,s∀x,k,s (13) PChxs=∑m,t≤xpÂ´mhts-pÂ´mht-1,s∀x,h (14) ∑x≤tPkÂ´Â´kxs-∑hpkhts×αp=RQHpht∀p,h,t (15) ∑x≤tPChxs+∑kpÂ´khts×αp=RQCpkt∀p,h,t (16) pÂ´mhts,pmkts,RQHpht,RQCpkt,PkÂ´Â´kxs,PChxs,pÂ´mhts,pÂ´Â´khts≥0∀m,k,x,t,h,s (17) THhs,ECks∈0,1∀k,h,s The first objective function aims to minimize the institutional cost of temporary hospitals. The second objective function concentrates on the transferring time of patients and people to THs and ECs, respectively. Constraints (3), (4) imply that the number of patients and people allocated to THs and DCs, respectively, must be less than their capacities. Constraint (5) represents that patients in an AA and EC can be transferred to a TH if the TH is instituted. Likewise, people in an AA are allocated to an EC if used in the constraint (6). Eq (8) calculates the number of infected cases in AAs, and other people in AA are assigned to ECs, which is calculated in Eq (7) based on the infection rate defined by the IoT framework. Constraints (9), (10) illustrate the total number of individuals, including patients and people, transferred to each TH and EC. Eqs (11), (12) calculate the number of positive cases detected by IoT and PCR tests in an EC. The number of patients transferred from AAs and DCs to a TH is multiplied by the RI consumption coefficient to calculate the patient's demand in a TH for each RI. Likewise, the people's demand in an EC is computed by multiplying the number of people accommodated in an EC by the consumption coefficient. Eqs. (13), (14), (15) imply the amount of demand in each demand zone. Note that the demand for each TH and EC accumulates by period t. Eventually, constraints (16), (17) are related to the problem's decision variables. 3.4.2 The second level (18) minZ1Â´=∑sPSs×∑j,p,tIHCpj∼×Ipjts+∑f,t,pTCfpts∼×∑i,jdijfs×Qpijfts+∑j,kdjkfs×Qpjkfts+∑j,hdjhfs×Qpjhfts+∑jICDj×TDCjs+∑p,kimppk×PCSpk×∑tZpkts3+∑p,himpÂ´ph×PCSÂ´ph×∑tZÂ´phts3+∑p,kPCSupk×∑tZpkts2+∑p,hPCSuÂ´ph×∑tZÂ´phts2 (19) minZ2Â´=maxp,k,t,simppk×(RQCpkts-∑j,fQpjkfts)+maxp,h,t,simpph×(RQHphts-∑j,fQpjhfts) (20) minZ3Â´=∑sPSs×∑i,j,f,tdijfsAsf×numFfijts+∑j,k,f,tdjkfsAsf×numFÂ´fjkts+∑j,hf,tdjhfsAsf×numFÂ´Â´fjhts (27)(26)(29)St.(21) ∑pIpjts×volp≤CapDjs∼×TDCjs∀j,t,s (22) ∑pQpijfts×volp≤CapFf∼×numFfijts∀f,i,j,t,s (23) ∑pQpjkfts×volp≤CapFf∼×numFÂ´fjkts∀f,j,k,t,s (24) ∑pQpjhfts×volp≤CapFf∼×numFÂ´Â´fjhts∀f,j,h,t,s (25) Ipjt-1,s+∑i,fQpijfts-∑h,fQpjhfts-∑h,fQpjkfts=Ipjts∀p,t,j,s (26) ∑f,pQpjhfts≤M×THhs∀j,h,t,s (27) ∑f,pQpijfts≤M×TDCjs∀i,j,t,s (28) ∑p,fQpjkfts≤M×TDCjs∀k,j,t,s (29) ∑f,jQpjkfts-RQCpkts=Zpkts2-Zpkts3∀p,k,t,s (30) ∑f,jQpjhfts-RQHphts=ZÂ´phts2-ZÂ´phts3∀p,h,t,s (31) Zpkts3≤RQCpkts-∑f,jQpjkfts∀p,k,t,s (32) ZÂ´phts3≤RQHphts-∑f,jQpjhfts∀p,h,t,s (33) Zpkts2≥∑f,jQpjkfts-RQCpkts∀p,k,t,s (34) ZÂ´phts2≥∑f,jQpjhfts-RQHphts∀p,h,t,s (35) ∑i,jnumFfijts+∑j,knumFÂ´fjkts+∑j,hnumFÂ´Â´fjhts≤MNFf∀t,s,f (36) ∑jTDCj,s≤MDCs∀s (37) Qpijfts,Qpjkfts,Qpjhfts,numFfijts,numFÂ´fjhts,numFÂ´Â´fjhts,Ipjts,Zphts2,Zphts3,ZÂ´pkts2,ZÂ´pkts3≥0∀p,k,t,i,h,j,f,s (38) TDCj,s∈0,1∀j,s The objective function (18) consists of five main sections. The first term aims to minimize the inventory holding cost of RIs stored in temporary DCs. The transportation costs of RIs between RCWs, DCs, ECs, and THs are considered in the second term, taking into account distances and RIs quantities. The third part of this objective function is related to institution costs of THs and temporary DCs. The fourth and fifth terms aim to penalize the shortage and surplus RIs in THs and ECs, respectively. To increase the affected people's satisfaction and equality in RI distribution, the first term of the objective function (19) reduces the maximum difference between people's demand in each ECs and the amount of RIs shipped from DCs to ECs. Likewise, the other term does a similar way for patients in THs. It is worth noticing that the importance level of RIs is incorporated to distribute RIs being in dire need. The objective function (20) minimizes the delivery time of RIs carried by fleet type f. Time is obtained by dividing distances by the average speed of vehicle type f. That is to say, Eq (20) aims to minimize the number of fleets with higher delivery times. Constraint (21) guarantees that the volume of RIs stored in the DC is less than the DC's capacity. Constraints (22), (23), (24) state the capacity of RIs transportation from RCW to DC, DC to TH, and DC to EC using fleet type f, respectively. Constraint (25) represents the inventory balance at the DC. Constraint (26) implies that RIs can be distributed to the TH on the condition that the TH is instituted in the location h. Constraint (27) states that RIs are delivered from RCWs to the DC when the DC is opened in location j. Constraint (28) represents that the RIs transfer from DCs to the EC when the DC in location j is opened. Constraints (29), (30) express the balancing equations for the amount of shortage and surplus of RIs in ECs and THs, respectively. Constraints (31), (32) illustrate the upper bound of RI's shortage in demand zones, and whether the lower bound of RI's surplus is represented in constraints (33), (34). Constraint (35) implies that the total number of fleets at each period in the supply chain network does not exceed the maximum number. Constraint (36) implies that the instituted DCs must be lower than the maximum number. Eventually, constraints (37), (38) are related to the problem's decision variables. 3.5 Linearization In order to linearize the objective function (19), it can be converted to the objective function (39) and two constraints (40), (41) by two free variables proposed in the following:(39) minZ2Â´=y1+y2 s.t.(40) y1≥(imppk×(RQCpkts-∑j,fQpjkfts))∀p,k,t,s (41) y2≥(impph×(RQHphts-∑j,fQpjhfts))∀p,h,t,s 3.6 The proposed credibility-based fuzzy chance-constrained programming model Regarding the discrepancies between Fuzzy Mathematical Programming and the stochastic approach, stochastic modeling uses probabilistic modeling and theory to cope with uncertainty. Hence, a known probability distribution for the uncertain parameters is required in this approach. In contrast, FMP deploys the Fuzzy Set Theory to tackle uncertain data, which is not stochastic. In other words, the uncertain parameters do not have a specific probability distribution (Zadeh, 1996). Furthermore, the major benefit of FMP is that it models a problem with linguistic variables instead of exact numerical variables to illustrate the imprecise data (a soft approach to tackle uncertainty), and it considers these parameters as fuzzy numbers (Ross, 2005). Similar to FMP, the probability distribution of uncertain parameters is not defined in robust optimization. However, these variables belong to an uncertainty set and they do not have an exact central value (Ghaffarinasab, 2022). In our case study, the distribution of uncertain parameters is not well-defined, and we encounter imprecise data with no distributional information. Many parameters are defined by experts and officials of humanitarian organizations in the aftermath of a disaster, which are based on linguistics and their perception. For instance, to calculate the imprecise transportation cost after an earthquake, experts assess the roads by aerial pictures and define the price based on the road demolition linguistically (e.g., if the roads are destroyed badly, the transportation cost is around 1$, otherwise it is 0.75$). Regarding the uncertain ambiance prevailing in the aftermath of disasters, some parameters corresponding to the demand, supply, distribution, and network connection are considered uncertain in our study. Therefore, the input parameters involving inventory holding cost, transportation cost, the capacity of DCs, and the number of individuals residing in each AA that affects the demand of RIs quantity are considered fuzzy numbers. Regarding the severity of disasters, it is possible that some distribution centers or parts of them cannot be utilized. Hence, uncertainty in capacity of DCs and inventory holding cost make the model real-life. Also, Transportation costs may be volatile by the destruction of roads and fluctuation in the gasoline price because many fuel suppliers may fall into disuse. Additional information regarding the ambiguities of many of these parameters can be seen in Sarma et al., 2019, Torabi et al., 2018. The reasons for hiring FCCP approach are presented in the following: (Pishvaee et al., 2012).• In general, this method is a computationally efficient FMP depending on mathematical concepts, e.g., credibility measure and expected value. • It can support all kinds of fuzzy numbers, including triangular, trapezoidal, and pentagonal fuzzy numbers. • At least defined confidence levels, FCCP allows the decision-maker to meet those chance constraints. • FCCP uses the credibility measure instead of possibility and necessity measures, which allow decision-makers to consider both optimistic and pessimistic views concurrently. In other words, a fuzzy event may fail even if its possibility hits 1 and happen even if its necessity equals 0. The fuzzy event must happen if its credibility is 1, and fail to happen if its credibility equals 0, though.” Assume ϑ∼ is a fuzzy number defined by three prominent value as ϑ∼=ϑ(1),ϑ(2),ϑ(3), μx is the membership function, and r is a real number. The credibility measure can be defined as below like Liu & Liu (2002).(42) Crϑ∼≤r=12supx≤rμx+1-supx>rμx=12Posϑ∼≤r+Necϑ∼≤r Noteworthy, the FCCP utilized possibility and necessity measures simultaneously called the average of both measures. Additionally, the expected value of ϑ∼ can be calculated as follows using credibility measure.(43) Eϑ∼=∫0∞Crϑ∼≥rdr-∫-∞0Crϑ∼≤rdr Based on Eq. (43) and credibility measure functions (44), (45) the expected value of ϑ∼ is ϑ(1)+2×ϑ(2)+ϑ(3)/4.(44) Crϑ∼≤r0,r∈-∞,ϑ1r-ϑ12ϑ2-ϑ1,r∈ϑ1,ϑ2r-2ϑ2+ϑ32ϑ3-ϑ(2),r∈ϑ2,ϑ31,r∈ϑ3,+∞ (45) Crξ∼≥r1,r∈(-∞,ϑ(1))2ϑ2-ϑ1-r2ϑ2-ϑ1,r∈(ϑ(1),ϑ2)ϑ(3)-r2ϑ3-ϑ(2),r∈(ϑ2,ϑ(3))0,r∈(ϑ(3),+∞) Also, it can be calculated if ϑ∼ is a triangular fuzzy number and ω>0.5 then:(46) Crϑ∼≤r≥ω⇔r≥2-2ωϑ2+2ω+1ϑ3 (47) Crϑ∼≥r≥ω⇔r≥2-2ωϑ2+2ω+1ϑ1 Eqs. (46), (47) can be deployed to convert the fuzzy constraint to crisp ones (Zhu & Zhang, 2009). Based on Pishvaee et al. (2012), the hybrid approach of FCCP is embraced because using only expected values to make a crisp model makes the optimization simpler, but it does not consider control on the confidence level. However, considering credibility-based measures for objectives and constraints may increase the number of constraints and complexity of the model, it needs the ideal solution for each objective. Hence, hybrid models are hired to convert uncertain objective functions with expected values and chance constraints with credibility measures into a crisp model. It does not increase the number of constraints and does not require additional information, e.g., confidence level and ideal solution. According to the descriptions mentioned above, the proposed FCCP can be formulated for this HRL problem. 3.6.1 The first level of the fuzzy model minZ1,Z2 St. Constraints (5), (6), (7), (8), (11), (12), (13), (14), (15), (16), (17) andCr∑m,tpÂ´mhts+∑k,tpÂ´Â´khts≤CapHhs∼×THhs≥αh∀h,s Cr∑m,tPmKts-∑h,tpÂ´Â´khts≤CapECks∼×ECks≥αhÂ´∀k,s CrOpmts∼=∑kpmkts≥γm∀t,m,s CrPCOmts∼=∑hpÂ´mhts≥μm∀t,m,s 3.6.2 The second level of the fuzzy model minZ1Â´=∑sPSs×∑j,p,tIHCpjt×Ipjts+∑f,tETCfts×∑i,jdijfs×Qpijfts+∑j,kdjkfs×Qpjkfts+∑j,hdjhfs×Qpjhfts+∑jEICDj×TDCjs+∑p,kimppk×PCSpk×∑tZpkts3+∑p,himpÂ´ph×PCSÂ´ph×∑tZÂ´phts3+∑p,kPCSupk×∑tZpkts2+∑p,hPCSuÂ´ph×∑tZÂ´phts2 minZ2Â´,Z3Â´ St. Constraints (25), (26), (27), (28), (29), (30), (31), (32), (33), (34), (35), (36), (37), (38) andCr∑pIpjts×volp≤CapDjs∼×TDCjs≥α¨j∀j,t,s Cr∑pQpijfts×volp≤CapFf∼×numFfijts≥βf∀f,i,j,t,sCr∑pQpjkfts×volp≤CapFf∼×numFÂ´fjkts≥βfÂ´∀f,j,k,t,sCr∑pQpjhfts×volp≤CapFf∼×numFÂ´Â´fjhts≥βfÂ´Â´∀f,j,h,t,s Based on Eqs. (46), (47) and expected value formula, the credibility-based model can be converted to the crisp model. 3.6.3 The first level of the crisp model minZ1,Z2 St. Constraints (5), (6), (7), (8), (11), (12), (13), (14), (15), (16), (17) and∑m,tpÂ´mhts+∑k,tpÂ´khts≤2αh-1CapHh1s+2-2αhCapHh2s×THhs∀h,s ∑m,tPmkts-∑h,tpÂ´khts≤2αhÂ´-1CapECk(1)s+2-2αhÂ´CapECk(2)s×ECks∀k,s 2γm-1Opm(1)ts+2-2γmOpm(2)ts≤∑kpmkts∀t,m 2γm-1Opm(3)ts+2-2γmOpm(2)ts≥∑kpmkts∀t,m,s 2μm-1PCOm(1)ts+2-2μmPCOm(2)ts≤∑hpÂ´mht∀t,m,s 2μm-1PCOm(3)ts+2-2μmPCOm(2)ts≥∑hpÂ´mht∀t,m,s 3.6.4 The second level of the crisp model minZ1Â´=∑sPSs×∑j,p,tIHCpj×Ipjts+∑p,f,tTCpf(1)ts+2∙TCpf(2)ts+TCpf(3)ts4×∑i,jdijfs×Qpijfts+∑j,kdjkfs×Qpjkfts+∑j,hdjhfs×Qpjhfts+∑jICDj(1)+2∙ICDj(2)+ICDj(3)4×TDCjs+∑p,kimppk×PCSpk×∑tZpkts3+∑p,himpÂ´ph×PCSÂ´ph×∑tZÂ´phts3+∑p,kPCSupk×∑tZpkts2+∑p,hPCSuÂ´ph×∑tZÂ´phts2 minZ2Â´,Z3Â´ St. Constraints (25), (26), (27), (28), (29), (30), (31), (32), (33), (34), (35), (36), (37), (38) and∑pIpjts×volp≤2αj-1CapDj(1)s+2-2αjCapDj(2)s×TDCjs∀j,t,s ∑pQpijfts×volp≤2βf-1CapFf1+2-2βfCapFf2×numFfijt∀f,i,j,t,s∑pQpjkfts×volp≤2βfÂ´-1CapFf1+2-2βfÂ´CapFf2×numFÂ´fjkts∀f,j,k,t,s∑pQpjhfts×volp≤2βfÂ´Â´-1CapFf1+2-2βfÂ´Â´CapFf2×numFÂ´Â´fjhts∀f,j,h,t,s It should be assumed in the model mentioned above, the confidence level in chance constraints should be met by more than 0.5. 4 Solution method The proposed two-phase model is a multi-objective linear model. There are several methods in the literature to convert multi-objective functions to single-objective functions. To deal with the multi-objective function, we use the LP-metric method in this study for both phases. The LP-metric technique aims to minimize the deviation (distance) of objective functions from the ideal solution. This method was elaborated clearly in the study proposed by Bagheri and Bashiri (2013).(48) LP={∑y=1Ywj×[fi(x∗j)-fi(x)fi(x∗j)-fi(x¯j)]p}1/p First, the optimal value or ideal solution, considered as fi(x∗j), (in this study, the minimum values are calculated) for each objective function must be calculated separately. Afterward, the anti-ideal solution, considered as fi(x¯j), (the maximum values are calculated) must be calculated. Also, the obtained values should be placed in Eq (48). In order to minimize derivations from the ideal solution, Eq (48) should be minimized. Note that, wj represents the importance (weight) of each objective function. Moreover, p emphasizes the deviation. It should be noted that all calculations are conducted by GAMS 28.2 with a LINDO solver. Additionally, a laptop with a 2.71 GHz processor and 4 GB of internal memory is utilized. 5 Case study 5.1 Case description As mentioned above, the proposed model has two interconnected phases, evacuating people from AAs and distributing RIs equally regarding the COVID-19 outbreak. In order to model and solve the problem, the data is collected from Salas-e-Babajani city, located in Kermanshah province, in the east of Iran. The exact location of this city in Iran is depicted in Fig. 5 . This city is selected because of the occurrence of numerous severe earthquakes in recent years, explained in the following. Also, this city is surrounded by two faults, increasing the earthquake dangers, as shown in Fig. 6 .Fig. 5 Location of Salas Babajani city. Fig. 6 Iran fault lines. Based on statistics (Jamalreyhani et al., 2017), in the most severe earthquake with Mw 7.3 that occurred on 12 November 2017, more than 600 people were killed, and over 8000 people were injured in Salas-Babajani and Sarpol-zahab cities. Moreover, in August and November 2018, two severe earthquakes with Mw 6.0 and Mw 6.4 occurred in the proximity of both cities, respectively. It is worth noticing that this region experienced more than 133 aftershocks continuously, exceeding Mw 4.0 until 30 December 2019. The epicentre and intensity of the most severe earthquake are illustrated in Fig. 7 and Fig. 8 , respectively. What makes this city more sensitive amid the COVID-19 outbreak is the lack of appropriate healthcare sectors because of the earthquake that occurred in 2017 crippled this section. According to the COVID-19 outbreak throughout the world, evacuation of people and transferring RIs are conducted difficultly. Akin to the world situation, this city has been experiencing a dangerous situation more than five times, shown as the red area on the Iranian COVID-19 interactive map (https://app.mask.ir/map).Fig. 7 Epicentre of Mw 7.3 earthquake. Fig. 8 The intensity of Mw 7.3 earthquake. In order to handle the disaster effectively, RCWs, DCs, THs, and ECs are utilized in this real case. The location of the above-mentioned centers is illustrated in Fig. 9 . The RCW is located in Kermanshah city, the capital of Kermanshah province. Regarding severe earthquakes mentioned above, three scenarios are considered to involve all types of intensive earthquakes. Each scenario relates to the Richter scale. The first scenario is considered for the fifth Richter scale, and the second and third one is taken for the sixth and seventh Richter scales, respectively. The planning horizon of this model is 72 h, which is made up of three days because most aftershocks happened in the 3-day period in this region.Fig. 9 Location of centers in Salas Babajani city. According to the Iranian Seismological Center (http://irsc.ut.ac.ir/index.php?lang=ea?lang=fa), most earthquakes that occurred in Kermanshah range from 4.5 to 5.5 Richter scale. The probability of earthquake occurrence with five Richter is 50 percent, and for 6 and 7 scale are 30 % and 20 %, respectively. The infection rate determined by IoT systems and diagnostic tests is considered 3 % in AAs and ECs. Also, the sensitivity analysis on the infection rate is conducted to illustrate different situations before an earthquake occurs. RIs in this study include sanitizing package, 1.5L hygienic drinkable water, PPE package, and prepared foods. The consumption rate for the first and second items is one. Also, two PPE packages and three cans of conserved food are considered for people in ECs and THs for daily consumption. The volume of RIs packages are 0.0015, 0.003, 0.005, and 0.0015 m3 respectively. In the proposed model, trucks and cargo drones transport RIs simultaneously. The maximum volume of products transferred by a truck and a drone is 6 and 1 m3. The capacity of THs, DCs, and ECs has been presented in Table 2 . Data relating to costs are obtained by interviews with officers of the Red Crescent Society of Kermanshah province. The ECs capacity is calculated by areaofEC/6m2 because based on Sakamoto et al. (2020), each person needs 6 m in ECs. It is noteworthy that the uncertain parameters in the proposed model are considered a triangular fuzzy number with a 10 % deviation from the center.Table 2 Capacity of ECs, THs, and DCs. Capacity s = 1 s = 2 s = 3 EC TH DC EC TH DC EC TH DC 1 1000 150 10 950 143 9.5 900 135 9 2 950 120 8 903 114 7.6 855 108 7.2 3 1000 160 10 950 152 9.5 900 144 9 4 850 150 8 808 143 7.6 765 135 7.2 5 650 100 8 618 95 7.6 585 90 7.2 6 950 100 – 903 95 – 855 90 – 7 550 150 – 523 143 – 495 135 – In previous articles, most of them considered Tehran (capital of Iran) or other metropolitans in Iran as a case study. In these cities, the level of uncertainty is somewhat controllable, but in our case, this level may be difficult to control because this location has experienced a lot of harsh earthquakes and it has become underdeveloped in recent years. All uncertain parameters with the accompaniment of FMP and scenario-based optimization are considered to tackle the ambiguous environment. In addition, because of the lack of an airport in this city, airplanes and helicopters cannot have high efficiency. Thus, we added drones to our logistics networks, along with trucks to distribute RIs. As mentioned before, the distributional infrastructure of the city is weak in comparison to other giant cities. To control the outbreak, the distribution of RIs is based on minimizing costs, transportation time, distance, and demand coverage. Furthermore, the IoT framework is widely used in COVID-19 management worldwide (some practical examples are discussed in 3.1). In our study, we developed the framework that a basic version of this was utterly practical in finding COVID-19 suspects. Based on the centers located in Fig. 9, the distances and travel time between centers are acquired by Google map (https://www.google.com/maps) listed in Table S1 – S3, S12, and S13, respectively. All data about costs, including inventory holding costs, institution costs, penalty costs, and transportation costs, have been presented in Tables S4 – S11. The number of individuals in each AA who needs accommodation is presented in Table S14, changed in each scenario. This number is estimated by officers of the Red Crescent Society based on their recent experiences with earthquakes. 5.2 Results Regarding Fig. 4, our solution comprises two phases, including selection of the location of ECs and THs and calculation of the individuals number living in them in the first phase and selection of DCs locations and allocation RIs to them in the second phase, along with an intermediate stage, called the calculation of demand for RIs. It is worth mentioning that demand quantity is obtained by the multiplication of the number of individuals by consumption coefficients. 5.2.1 Results of phase 1 In order to distribute RIs among people in THs and ECs, the number of individuals in both centers should be defined first. This result is obtained by solving the first phase of the proposed model. In the first phase, the best locations for instituting THs and ECs are selected from potential ones. Then, the individuals are allocated to ECs based on Fig. 2. In our case, the results are depicted in Table 3 and Table 4 .Table 3 Number of people living in ECs. People in EC s = 1 s = 2 s = 3 t = 1 t = 2 t = 3 t = 1 t = 2 t = 3 t = 1 t = 2 t = 3 k = 1 k = 2 423 576 626 592 685 731 892 892 892 k = 3 437 898 972 198 669 909 k = 4 798 798 798 k = 5 610 611 611 k = 6 712 919 981 873 883 938 2 702 860 k = 7 379 380 379 Table 4 Number of people living in THs. People in TH s = 1 s = 2 s = 3 t = 1 t = 2 t = 3 t = 1 t = 2 t = 3 t = 1 t = 2 t = 3 h = 1 1 1 7 15 100 58 50 93 80 h = 2 5 49 71 h = 3 72 96 96 107 56 109 99 76 52 h = 4 27 36 76 After defining the number of individuals in both centers, the demand for RIs can be computed simply by multiplying the consumption rate with data in Table 3 and Table 4. 5.2.2 Result of phase 2 In the second phase, first, the location of DCs should be selected from the potential ones, and afterwards the RIs should be distributed from DCs to ECs and THs. Flows of RIs between the instituted and used centers are presented in Fig. 10 . The total quantity of RIs transferred from RCWs to DCs is illustrated in Table 5 . Also, the RIs quantity transferred from DCs to ECs and THs is represented in Table 6 and Table 7 . It is worth noticing that the objective function weights in the second phase are considered 0.3, 0.5, and 0.2, respectively.Fig. 10 The flow of RIs in three different scenarios (green arrows illustrate the flow between DCs and THs and yellow arrows define the flow for ECs). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) Table 5 Quantity of RIs transferred from RCW to DCs. Q s = 1 s = 2 s = 3 i→j t = 1 t = 2 t = 3 t = 1 t = 2 t = 3 t = 1 t = 2 t = 3 →j = 1 1226 3716 3328 2309 4819 3472 3399 4450 4859 i = 1 →j = 2 3926 3685 4636 →j = 3 5474 4085 4832 4666 5764 4739 →j = 5 4690 4416 4583 3614 4418 5412 3713 5279 5227 Table 6 Quantity of RIs transferred from DCs to THs. Q s = 1 s = 2 s = 3 j→h t = 1 t = 2 t = 3 t = 1 t = 2 t = 3 t = 1 t = 2 t = 3 j = 1 →h = 1 3 292 262 →h = 2 321 344 →h = 3 278 →h = 4 149 j = 2 →h = 1 465 →h = 3 544 533 308 404 →h = 4 115 273 j = 5 →h = 1 35 104 498 642 →h = 3 363 482 643 Table 7 Quantity of RIs transferred from DCs to ECs. Q s = 1 s = 2 s = 3 j→k t = 1 t = 2 t = 3 t = 1 t = 2 t = 3 t = 1 t = 2 t = 3 j = 1 → k=2 196 784 3328 1198 3757 2004 656 →k = 3 980 1274 653 →k = 4 2236 2538 →k = 5 →k = 6 638 3321 817 2504 1502 →k = 7 22 1522 j = 2 →k = 2 653 1590 →k = 3 601 521 →k = 4 1825 →k = 6 1826 653 2613 →k = 7 1499 j = 3 →k = 2 1924 392 2613 →k = 3 2613 2613 2570 →k = 4 1702 →k = 5 2317 2461 →k = 6 3549 1081 1675 →k = 7 1875 j = 5 →k = 2 2086 2371 1021 644 2432 3713 1960 →k = 3 1749 2613 →k = 4 2116 653 →k = 5 2521 →k = 6 2241 1223 4889 3336 3018 One of the essential objectives of the study is to minimize the amount of shortage and surplus of RIs. It is clear that the shortage in the first scenario is lower than in other scenarios because of the lower Richter scale. The surplus amount, in this case, is zero in all scenarios due to RIs shortage. The amount of shortage in THs and DCs is represented in Table 8, Table 9 . The result of the shortage demonstrates that the area of the city with a higher population density has more shortages. The RIs stored in the inventory are illustrated in Table 10 .Table 8 RIs shortage in THs. Shortage in TH s = 1 s = 2 s = 3 t = 1 t = 2 t = 3 t = 1 t = 2 t = 3 t = 1 t = 2 t = 3 h = 1 1 2 14 3 52 185 157 h = 2 65 h = 3 145 193 192 214 111 148 209 57 162 h = 4 46 109 112 Table 9 RIs shortage in ECs. Shortage in EC s = 1 s = 2 s = 3 t = 1 t = 2 t = 3 t = 1 t = 2 t = 3 t = 1 t = 2 t = 3 k = 2 751 651 1058 428 789 1647 3208 3593 k = 3 1201 2695 2916 435 2206 3103 k = 4 1575 2116 2283 k = 5 1219 1730 1822 k = 6 1246 1818 1956 1746 1767 2005 731 1524 1821 k = 7 608 609 941 Table 10 Quantity of RIs stored in DCs. Inventory s = 1 s = 2 s = 3 t = 1 t = 2 t = 3 t = 1 t = 2 t = 3 t = 1 t = 2 t = 3 j = 1 262 425 98 98 j = 3 98 327 1624 292 j = 5 223 98 327 Aside from RIs quantity, the number of trucks and drones used in this case is represented in Table 11 . As shown in Table 11, most drones are allocated to centers with lower distances between them.Table 11 number of required trucks and drones in mentioned routes. Fleet s = 1 s = 2 s = 3 t = 1 t = 2 t = 3 t = 1 t = 2 t = 3 t = 1 t = 2 t = 3 f = 1 i→j 3 4 4 6 6 6 7 8 8 j→k 3 5 5 7 7 7 8 7 7 j→h 1 1 2 1 f = 2 i→j 2 3 4 4 2 j→k 2 5 5 2 3 5 j→h 1 2 1 1 2 1 3 5 5 5.3 Sensitivity analyses and discussion Sensitivity analysis is conducted on critical parameters of this study. In order to reduce the redundancy, all analyses are executed on the third scenario (the most severe earthquake) presented in the following. 5.3.1 Sensitivity analysis of virus parameters Parameters of virus transmission directly impact the number of infected patients transferred to hospitals. Since the most significant objective is to minimize the shortage and surplus of RIs, the effect of the infection rate of the virus on RIs shortage, RIs quantity, number of people living in THs and ECs, and total cost are analyzed. According to Fig. 11, Fig. 12 and Tables S15 and S16, the infection rate defined by IoT system and PCR test in AAs directly impacts the number of people living in THs and affects the number of people living in ECs reversely. Because of that, RIs shortage and RIs quantity in ECs decreased. Adversely, the trend of shortage and quantity in THs is increased because more people are transferred to THs. Moreover, this rate incurs an extra cost in this supply chain which is noticeable in the aftermath of disasters. Notably, the more deployment of IoT-based equipment can reduce this probability which is impactful in reducing the infection rate of the virus in ECs.Fig. 11 The effects of infection rate in AAs on shortage, quantity, and cost. Fig. 12 The effects of infection rate in AAs on the number of people living in THs and ECs. Cue: (NoPEC: number of people living in ECs) & (NoPTH: number of people living in THs). As shown in Fig. 13, Fig. 14 and Tables S17 and S18, the infection rate calculated by IoT systems and PCR tests in interiors and ECs has similar behaviour to AAs' rate. Based on the meticulous analysis of Fig. 12, Fig. 14, the utilization of the system mentioned above in AAs can detect more infected cases than in ECs. However, faster detection of infected cases increases the supply chain costs.Fig. 13 The effects of infection rate in ECs on shortage, quantity, and cost. Fig. 14 The effects of infection rate in ECs on number of people living in THs and ECs. Cue: (NoPEC: number of people living in ECs) & (NoPTH: number of people living in THs). 5.3.2 Sensitivity analysis of capacity parameters The second sensitivity analysis is based on the capacity of DCs and transportation capacity. The available number of vehicles represents the transportation capacity in this model. The SA results are shown in Fig. 15, Fig. 16, Fig. 17 . As shown in Fig. 15 and Table S19, the increase in ECs capacity can decrease RIs shortage and increase RIs quantity transferred from DCs to THs and ECs. Furthermore, a significant increase in the capacity of distribution centers alone would not alleviate the shortage but would only increase storage capacity because the transportation capacity is limited.Fig. 15 The effect of DCs capacity on RIs shortage, quantity, and inventory. Fig. 16 The impact of number of available trucks on RIs quantity and shortage (number of trucks represents MNFf for f=1). Fig. 17 The impact of number of available drones on RIs quantity and shortage (number of drones represents MNFf for f=2). According to Fig. 16, Fig. 17 and Tables S20 and S21, the transportation capacity plays a significant role in decreasing shortage along with DCs capacity. The utilization of more trucks and cargo drones, interconnected with IoT systems, can deliver more quantity to demand points. Therefore, it is concluded that RIs shortage can be depleted on the condition transportation and DCs capacities increase simultaneously. Regarding the comparison between drones and trucks, the increase in drones is more impactful for transferring RIs and reduction of shortages because drones can carry items to remote areas in a shorter time. 5.3.3 Sensitivity analysis of RIs parameters In this section, SA is performed on the RIs specifications involving the importance and consumption coefficient of RIs. The decision-maker determines both coefficients according to the criteria defined by the IoT surveillance and monitoring system. As shown in Fig. 18 and Table S22, the importance of RIs may differ in enhancing the hygienic condition and reducing virus transmission. To illustrate, if the importance of sanitizer package in ECs increases, the shortage of sanitizer package decrease drastically and reaches zero in the highest importance, and the delivered quantity of this package increases and reaches the peak. The quantity of other RIs transferred to demand points decreases because an item with higher priority should be transferred at first. Note that less important RI requires less storage because the warehouse should be allocated to items with high priority.Fig. 18 The effect of importance of an RI (type 1) on shortage, quantity, and inventory. Moreover, the changes in RIs importance may engender changes in RIs consumption coefficient. The more people consume RIs in demand points, the more shortage of RIs we have. According to Fig. 19 and Table S23, the amount of sanitizer package transferred to demand increases noticeably because of the increase in sanitizer demand. Therefore, the total costs experience tremendous growth.Fig. 19 The effect of consumption coefficient of an RI (type 1) on shortage, quantity, and total cost. 5.4 Managerial insights and discussion The main goal of the research is to detect and transfer people and infected patients along with planning a fair and prompt distribution of RIs in catastrophes among the COVID-19 outbreak. The main contribution of this article is the extensive use of IoT-based systems in the humanitarian supply chain, which distinguishes it from the article written by Ghasemi et al. (2019), who considered only patient transfer and RI allocation with shortage reduction. Notably, IoT frameworks proposed by Zahedi et al., 2021, Goodarzian et al., 2022 cannot be utilized for disaster management. In our work, a novel framework is presented, connected with our mathematical model. Also, fleet management is considered in the article by Abazari et al. (2021). However, they did not consider the shortage reduction of RIs. In addition, Torabi et al. (2018) considered all aspects of uncertainty in humanitarian logistics networks, but the model is not commensurate with the response phase of disaster. Regarding the specification of this model, several implications are presented to managers of health-related and Red Cross organizations. Some of the managerial insights obtained by the results and sensitivity analysis are mentioned in the following:1. According to Fig. 12, Fig. 14, one way to manage disasters in a COVID-19 epidemic is to reduce the infection rate because an outbreak can spread quickly. The presence of domestic and foreign humanitarian workers and aid organizations may increase the risk of transmitting the virus. There are some practical solutions to tackle this inevitable problem mentioned below: A. Informing people about the instructions and keeping them posted about how to get aid can significantly reduce the rate of virus transmission. It is recommended to managers that IoT-based systems are an appropriate way to send information quickly and securely, consisting of Web-based applications and announcement drones shown in Fig. 20 .Fig. 20 Announcement drone. B. In addition to suitable announcements, surveillance of people for paying obedience to guidelines and social distancing plays a pivotal role. The utilization of two types of IoT surveillance systems can be taken as a panacea. Surveillance drones, shown in Fig. 21 , and monitoring cameras can detect people's neglect.Fig. 21 Surveillance drone. C. Prompt and timely detection of infected people and their transfer to quarantine centers can reduce the spread of the COVID-19 virus (infection rate). It is also impossible to test all people with PCR-kit in the affected areas because it imposes a high cost and time. Due to emerging new mutations of the coronavirus, e.g., Indian and Brazilian, which have higher transmission rates, patients should be diagnosed and transferred immediately. Therefore, the use of the IoT monitoring system can quickly identify suspects. This interconnected system comprises web-based applications, thermal sensors and cameras, and monitoring drones and powered by Artificial Intelligence, as shown in Fig. 22, Fig. 23, Fig. 24 . Suspects detected by this system are shown on monitors. So, taking PCR tests from suspects to ensure the virus infects them is not complicated via this real-time data. Note that all people should answer a questionnaire in the web-based application and report their general well-being continuously.Fig. 22 IoT thermal sensor. Fig. 23 IoT monitoring system for infected people detection. Fig. 24 Monitoring drone. D. Replacing clinical care robots and IoT-based systems with humans in hospitals and quarantine places to treat and care for infected patients can prevent the healthcare section from being crippled significantly in the aftermath of disasters. That is to say, China has been utilizing disinfection and clinical care robots in a field hospital located in Wuhan,1 which city designed for 20,000 patients. The mentioned systems are illustrated in Fig. 25, Fig. 26 .Fig. 25 Disinfection drone. Fig. 26 Autonomous robot. 2. Along with controlling the virus transmission rate in disasters, the fair and rapid distribution of RIs plays a crucial role in controlling the virus. Also, either the lack or surplus of items or unfair distribution causes dissatisfaction among people in ECs and THs. Therefore, several recommendations have been made for the distribution of RIs: A. Regarding Fig. 15, one of the critical recommendations for managers is to increase the capacity of distribution centers to the extent determined by the model. It should be noted that an excessive increase in the centers' capacity cannot help improve the situation noticeably. Also, more RIs with lower priority can be stored in DCs, and RIs with higher importance can be transferred to THs and ECs if the capacity of DCs increases, but the total cost increases drastically. It is worth mentioning that it is better to extend storage in densely populated areas because most of the shortage happens in these zones. B. Based on Fig. 16, Fig. 17, one way to increase the amount of transported RIs is to improve transportation capacity. Managers are advised that instead of spending much money to increase distribution centers, increasing the number of trucks is better. In addition, cargo drones can be used with trucks to quickly send RIs to remote and inaccessible areas, albeit with lower capacity to expand transportation capacity. Thus, Investing in IoT-based cargo drones can also improve shortages and equitable distribution. For example, JD Company2 in china has used cargo drones to transfer commercial items and rapid covid-19 test kits to remote locations illustrated in Fig. 27, Fig. 28 .Fig. 27 Cargo drone introduced by JD Company. Fig. 28 Cargo drone. C. According to Fig. 18, due to new COVID-19 virus mutations, the importance of RIs can be changed by the medical decision-maker. These changes lead to increase demands and shortages of RIs. Paying more attention to the above-mentioned recommendations for reducing virus transmission is a way to control the changes in importance. D. Regarding Fig. 19, daily RIs should include the necessary items for people and patients. Consuming RIs without a predefined plan can increase shortage and unfair distribution. Determining the proper consumption pattern by managers for people in THs and DCs, along with timely announcement and consumption surveillance with IoT-based systems, can modify the situation. 3. In terms of results obtained from this case study, infected cases detected by IoT-based technology and PCR test should be transferred to quarantine swiftly. Location of temporary quarantine places plays a pivotal role in controlling outbreaks because the more accessibility centers have, the less time it takes to transfer infected, and the more patients are transferred 6 Conclusion and further research In this paper, an uncertain, scenario-based, two-stage, multi-objective, multi-products, multi-fleet, multi-period, IoT-based, location-allocation-inventory, mixed-integer mathematical programming model was proposed for the response phase of disasters in the epidemic outbreak. The proposed model has five echelons involving Affected Areas (AAs), Relief Collection Warehouse (RCW), Distribution Centers (DCs), Temporary Hospitals (THs), and Evacuation Centers (ECs). Fuzzy mathematical programming is hired to cope with uncertainty in this problem. Also, due to the uncertain behaviour of the COVID-19 outbreak, the infection rate of the virus is obtained by the proposed IoT framework. Two objective functions, including minimizing the establishment cost of ECs, and minimizing transferring time of patients to THs, are considered for patients and people transferring. In the next phase, three objective functions involve minimizing the total cost, shortage of RIs, and the number of used fleets. The preliminary decision of the proposed model was locating ECs and THs, finding infected cases in AAs and ECs using an IoT system, transferring them to THs, locating DCs based on people's demand, allocating RIs fairly between people in THs and ECs, and finding an optimum number of drones and trucks. The proposed two-level model was solved using the LP-metric method with GAMS software. Then, this model was evaluated by a real case study in Salas-e-Babajani city, Kermanshah province. Finally, sensitivity analyses were conducted on obtained data that might differ in three scenarios of earthquakes and presence of a new mutation of COVID-19. The results of sensitivity analyses indicate that the increase the infection rate of COVID-19 in AAs and ECs leads to increase patients’ numbers, demand in THs, and RIs shortage in THs. We find that the utilization of IoT-based systems in monitoring and informing can alleviate the situation in ECs and THs. Notably, using this system in AAs meticulously can be more effective than detecting infected cases in ECs, but it incurs an extra cost on the supply chain. We also find that the increase in the number of fleets and DCs capacity simultaneously reduce the shortage effectively, which is not very impactful if one increases solely. One of the study's findings is the use of cargo drones instead of trucks. Although they have less capacity than trucks, but have a more significant impact on reducing shortage. It should be noted that the use of drones also increases supply chain costs. The last finding is the effect of the importance and consumption coefficient of RIs on the shortage, inventory, and quantity of RIs transferred to demand points. If the importance and consumption rate increase, the RIs shortage will increase in ECs. Also, we find that the inventory is allocated to RIs with higher priority. Our model has some limitations but can be developed in many ways. For further research, it is recommended that the routing problems of fleets be considered, along with traffic congestion and road disruption. Second, waste is an inevitable problem that should be treated. Locating temporary treatment centers for this problem can be taken as future work. Third, incorporating the location problem of portable blood distribution centers and the allocation problem of portable ventilators can modify decision-making. Researchers can incorporate the injured patients into this model and consider the problem of location-allocation-routing of ambulances for this supply chain network amid the COVID-19 outbreak. Another significant novelty that can enrich this study is to model a location-allocation problem for IoT equipment. For instance, because of the internet's interruption after a severe disaster, the location and number of the internet antennas to provide the internet for IoT devices can be an intriguing issue for future research. Furthermore, in some logistics networks, in order to reduce the shortage, we can consider the min–max type objective function on the transportation objective function (minimizing transportation time), along with minimizing shortage quantity and the penalty costs. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix A Supplementary material The following are the Supplementary data to this article:Supplementary data 1 Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.cie.2022.108821. 1 https://www.cnbc.com/2020/03/18/how-china-is-using-robots-and-telemedicine-to-combat-the-coronavirus.html. 2 https://www.weforum.org/agenda/2020/03/three-ways-china-is-using-drones-to-fight-coronavirus. ==== Refs References Abazari S.R. Aghsami A. Rabbani M. 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==== Front Econ Ind Democr Econ Ind Democr EID speid Economic and Industrial Democracy 0143-831X 1461-7099 SAGE Publications Sage UK: London, England 10.1177/0143831X221138882 10.1177_0143831X221138882 Article Covid-19 and health and safety at work: Trade union dilemmas in Germany, France and Luxembourg (March 2020–December 2021) https://orcid.org/0000-0001-7943-2049 Thomas Adrien Luxembourg Institute of Socio-Economic Research, Luxembourg Dörflinger Nadja Federal Institute for Occupational Safety and Health BAuA, Germany Yon Karel IDHES, University Paris-Nanterre, National Centre for Scientific Research, France Pletschette Michel Department of Tropical and Infectious Diseases, Medical Centre of the University of Munich, Germany Adrien Thomas, Luxembourg Institute of Socio-Economic Research, 11 Porte des Sciences, L-4366 Esch-sur-Alzette, Luxembourg. Email: [email protected] 2 12 2022 2 12 2022 0143831X221138882© The Author(s) 2022 2022 Department of Economic History, Uppsala University, Sweden 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. Given the traditional commitment of trade unions to occupational health and safety standards, unions might have been expected to be strongly involved in containing the Covid-19 pandemic. This article focuses on their policy positions at national and sectoral level towards the occupational health and safety measures taken to limit the spread of Covid-19 in Germany, France and Luxembourg from the beginning of the pandemic in March 2020 until the surging fourth wave of infections in November–December 2021. The authors’ data show that unions have found it increasingly difficult over the course of the pandemic to develop policy positions in the domain of occupational health and safety that address the variegated situations and needs of their different member groups and that achieve a balance between membership logics and public health considerations. Covid-19 France Germany Luxembourg occupational safety and health (OSH) risk assessment trade unions edited-statecorrected-proof typesetterts1 ==== Body pmcIntroduction The protection of the health and safety of workers has always been a core concern of trade unions. The Covid-19 pandemic has posed manifold challenges in this respect for workers and their representatives around the world (Purkayastha et al., 2021), with workplace transmission of the SARS-CoV-2 virus a centre of attention since the beginning of the outbreak. Numerous clusters were recorded early in the pandemic, leading to the assumption that workplaces as well as commuting settings were ideal places of transmission (WHO, 2021). This was one of the reasons why many countries imposed lockdowns in various industries. These lockdowns reduced the likelihood of a further spread of the virus, thereby also protecting workers’ health. Trade unions have been involved in numerous countries in the policy decisions on how to mitigate the social and health impacts of the Covid-19 pandemic on workers, either through tripartite social dialogue or collective bargaining (ILO, 2021). In Europe, unions have been involved at all levels with the consequences of the Covid-19 pandemic, calling for stricter policies to protect workers from the risk of workplace infections and to maintain the economic security of workers negatively affected by the pandemic through income support and short-time work schemes (Eurofound, 2021). Given the traditional commitment of trade unions to occupational safety and health (OSH) standards, this article investigates union positions on OSH policies adopted to limit the spread of Covid-19 in Germany, France and Luxembourg between the beginning of the pandemic in March 2020 and the surging fourth wave of Covid-19 infections in November–December 2021. It focuses on the positions taken by union confederations at national level and on union positions in the healthcare sector. In addition to its specific role throughout the pandemic, healthcare is a sector characterised by high levels of interactive work. Social interactions with patients make social distancing almost impossible, leading to high risks of contagion (Doerflinger, 2022). The purpose of the article is to discuss how unions have engaged with Covid-19-related OSH policies and, more generally, how they positioned themselves vis-à-vis the challenging situation caused by the pandemic, and what factors shaped their positions, rather than to evaluate whether trade unions have reduced the number of Covid-19 infections. Considering their traditionally strong engagement in the protection of the health and safety of workers and the fact that they are backed up by comprehensive European and national OSH regulations, one could assume that unions generally supported government measures to fight the Covid-19 outbreak, and that they were proactive in proposing measures to protect workers’ health and safety. Yet, our empirical material indicates that engaging with Covid-19-related OSH policies confronted unions with various dilemmas. The article is structured as follows. We begin by sketching the engagement of trade unions in the field of OSH. After setting forth our research design and methodology, we turn to the empirical evidence, examining trade union positions in the three investigated countries at the national level and in the healthcare sector. The discussion and conclusion section explains and reflects on the dilemmas involved in trade unions’ engagement with the Covid-19 pandemic. Trade unions, occupational safety and health and the Covid-19 pandemic Trade unions and OSH Unions have been involved in developing OSH policies since the 19th century (Dembe, 1996; McIvor, 2020; Quinlan et al., 2010), with the prevention of work-related accidents and the minimisation of risks resulting from industrial pollution on workers a key concern (Elling, 1986). Unions have traditionally operated at various levels to establish and enforce health and safety norms. At international and national level, they have lobbied policymakers to establish binding norms, while at industry level they have pursued health and safety objectives in collective bargaining. Similarly, at company level union representatives have contributed to the enforcement of standards with a view to reducing workers’ health risks (Elling, 1986; ILO, 2002; Rosental, 2017). At workplace level, unions may exhort employers to directly reduce occupational hazards, influence the intensity of regulatory oversight and educate workers about on-the-job hazards (Morantz, 2009; Ollé-Espluga et al., 2015). Following the deindustrialisation of many developed countries, attention has shifted from workplace fatalities and injuries to psychosocial issues, such as work-related stress and burn-out (McIvor, 2020). Despite this shift in focus, unions have continued to play an active role in protecting workers from potential occupational hazards. At present, the legal basis for the regulation of OSH in the European Union (EU) is the so-called ‘Framework Directive’ 89/391/EEC. The directive defines the main principles for healthy and safe workplaces throughout the EU, thereby guaranteeing minimum standards across member states (Gagliardi et al., 2012; Niskanen et al., 2012). Complementing the Framework Directive, various other EU directives regulate specific OSH aspects. So-called ‘risk assessments’ constitute a key element in identifying potential work-related hazards. Moreover, Principle 10 of the European Pillar of Social Rights states that ‘workers have the right to a high level of protection of their health and safety at work’ and ‘to have their personal data protected in the employment context’ (European Commission, 2021). Highlighting prevention and protection, this bundle of regulations stresses the obligation of employers to take action to ensure safe and healthy workplaces; as a consequence, OSH has become an important aspect of general management processes. Importantly, trade unions have been involved in shaping EU OSH legislation as members of the Advisory Committee of Safety and Health at Work, a tripartite body including government representatives and the social partners. At member state level, trade unions are involved in the management of OSH through participation in various bodies. In sum, OSH has always been a core topic for trade unions. In the EU, their role is supported by a comprehensive regulatory framework which foresees an active role for unions and worker representation bodies at different levels. Hence, considering unions’ traditionally strong engagement with OSH and the fact that they are backed up by a comprehensive regulatory framework, one could expect a strong union involvement with regard to the protection of workers’ health during the Covid-19 pandemic. This potentially concerns two aspects: first, the support for protective measures taken by governments; second, own initiatives and demands to increase the protection of workers. Trade unions and OSH in the context of the pandemic The Covid-19 pandemic has raised numerous OSH issues. Workers in critical services such as (health-)care, retail or public transport where social distancing is often difficult have been confronted with high risks of infection and increased work demands (Amossé et al., 2021; Mutambudzi et al., 2021; Purkayastha et al., 2021). Workers have faced job losses or income cuts in branches of the service sector reliant on mobility and close social interactions such as hospitality, tourism or arts and entertainment (Eurofound, 2021). Working conditions in other industries have changed through the massive recourse to remote and hybrid work, in many cases heightening existing labour market inequalities and bringing to the fore new occupational risks (Holst et al., 2021; Moulac et al., 2022). Trade unions have been involved in tripartite social dialogue with governments and employers over the measures to be taken to counter the health and socio-economic effects of the pandemic, for instance with regard to short-time work schemes, income protection and support for businesses (Brandl, 2021; Eurofound, 2021; ILO, 2021). In numerous instances, unions have asked for stricter health and safety policies at workplaces in Europe (Purkayastha et al., 2021). Furthermore, they have taken legal action against employers not adequately fulfilling their legal obligation to carry out risk assessments in the context of the Covid-19 pandemic (Pereira, 2021; Tonneau, 2021). More specific aspects of the impact of the pandemic on industrial relations have also been analysed, such as the negative effect of the pandemic on company-level collective bargaining (Dupuy and Simha, 2021) and union demands targeting groups of vulnerable workers such as non-standard and platform workers (Spasova et al., 2021). In the context of the Covid-19 pandemic, there were no binding OSH policies at European level. Instead, a number of recommendations were issued to Member States by the European Agency for Safety and Health at Work (EU-OSHA), with a twofold aim: to reduce the risk of workplace contagion, and to ensure the protection of (home-based) workers. These were complemented by special recommendations on updating risk assessments (EU-OSHA, 2021). Given that health policies are to a large degree the prerogative of Member States, a certain heterogeneity can be expected in national policies, but also in trade union responses. Research design and methods This contribution is based on a cross-national comparative qualitative methodology. It combines the analysis of selected international and national documents (particularly trade union statements, media and legal sources) and a small number of expert interviews with trade union officials at both national and sectoral level (Germany: 3; France: 6; Luxembourg: 3) to reconstruct the positioning of trade unions vis-à-vis Covid-19-related OSH policies in the three investigated countries. The number of experts on the topic – also within trade unions – is still relatively limited because Covid-19 is a new disease and the situation the pandemic caused is unprecedented. This explains the relatively limited number of interviews. The combination of primary and secondary data also serves the purpose of triangulation, as interviews are useful to cross-validate the contents of the documents and vice versa. The selection of countries follows the rationale of comparing similar systems in a contextualised way (Locke and Thelen, 1995), as illustrated in Table 1. Hence, we select countries with similar ‘starting points’ (Locke and Thelen, 1995: 359) to investigate trade union positions regarding Covid-19-related OSH policies to reveal and explain cross-national variation. The three neighbouring countries, Germany, France and Luxembourg, belong to the cluster of coordinated market economies in continental Europe. Traditionally and despite differences in the concrete institutionalisation of specific rights, worker participation through workplace representation structures and the social protection of workers have been important in these countries, and trade unions as representatives of the workforce have had a strong position in this respect. Despite changes in the last two decades across countries and differing unionisation levels, trade unions continue to be relevant actors when it comes to policymaking and exerting workplace influence (Gumbrell-McCormick and Hyman, 2013). The political systems of Germany and Luxembourg share neo-corporatist traits allowing unions to exert influence on political decisions (Thomas et al., 2019), whereas French unions, despite membership losses, have demonstrated their continued ability for collective mobilisation (Giraud et al., 2018). Moreover, health and safety representation at workplace level is ensured across the three countries. In Germany, works councils do not only have codetermination rights on certain OSH-related aspects, they also send representatives to the joint health and safety committee. The employee delegation in Luxembourg covers all aspects related to OSH. Similarly, in France, the social and economic committee deals with everything related to OSH. In the same vein, important EU directives – like the aforementioned Framework Directive on OSH (89/391/EEC) – ensure the same minimum level of protection in all three countries. Table 1. Country selection. Germany France Luxembourg Trade union structure DGB as the main union confederation CGT, CFDT, FO, CFTC and CFE-CGC OGBL and LCGB Dominant level of collective bargaining Sector (and company) Sector and company Sector and company Workplace representation Works council (many representatives are union members) Social and economic committee and trade unions Employee delegation (many representatives are union members) Health and safety representation Health and safety committee (also includes works council members) Through the social and economic committee (includes in some instances a commission for health, security and working conditions) Through the employee delegation and the dedicated delegate for safety and health Own table (main source: www.worker-participation.eu/National-Industrial-Relations/Map-of-European-Industrial-Relations). Alongside these socio-economic, political and institutional similarities, Germany, France and Luxembourg have one more relevant aspect in common: relatively low full vaccination rates (two doses) of 70.8%, 73% and 68.8% of the total population respectively at the end of 2021 (ECDC, 2021a). The collected data were analysed by country based on predefined common themes (links between unions and politics, test strategies, mandatory vaccinations, etc.). On the one hand, this ensured a certain degree of comparability across cases; on the other hand, it left ample space for considering national specificities. Once the three country narratives had been constructed, the authors discussed them to spotlight particularly compelling aspects – also in the light of what could have been expected based on the reviewed literature – and identify areas to be elaborated in the article’s discussion. Unions and Covid-19 occupational health and safety measures at national level Trade unions in Germany, France and Luxembourg found themselves confronted with both the socio-economic and health-related consequences of the Covid-19 pandemic. During the first phase of the pandemic, workplace OSH measures were implemented in all three countries (protective equipment, social distancing, mandatory mask-wearing, adaptation of workplaces), non-essential shops and venues closed, and teleworking arrangements promoted wherever possible. Businesses received public subsidies to keep afloat, while short-time work schemes were used to avoid lay-offs. Unions criticised the lack of health and safety equipment in some sectors and called for stronger measures to safeguard workers’ incomes. Over the course of 2021, in a context of insufficient vaccination rates, the issues of mandatory vaccinations and test requirements have moved to the foreground. Germany: a revival of crisis corporatism? The pandemic hit Germany in mid-March 2020, leading to a first lockdown. On 28 March 2020, the ‘epidemic state of crisis’ (Epidemische Lage von nationaler Tragweite) was declared, allowing the government to take decisions without the involvement of parliament. Since then, infection numbers have varied, reflecting lockdown periods and periods with less stringent rules. Specific work-related rules were first implemented in April 2020, when the Federal Ministry of Labour and Social Affairs introduced the so-called SARS-CoV-2 Arbeitsschutzstandard (BMAS, 2020) containing a specific set of workplace OSH rules to reduce the risk of infections at work. Regularly updated, these rules have been concretised in the so-called SARS-CoV-2 Arbeitsschutzregel (BAuA, 2021), clearly defining an employer’s responsibility for infection protection: its main pillars are social distancing, face masks and hygiene at work. Risk assessments are a key instrument and are to be extended to cover pandemic-related hazards. The results of the assessments need to be translated into concrete actions and should be implemented in consultation with employee representatives. Furthermore, a specific Covid OSH committee was established on which the Confederation of German Trade Unions (DGB, Deutscher Gewerkschaftsbund) and the German Employers’ Association (Bundesvereinigung der Deutschen Arbeitge-berverbände) each have two seats. According to the Ministry of Labour and Social Affairs, the German social partnership model is essential in times of pandemic, as it contributes to implementing protective measures at work and to getting workers to accept them (BMAS, 2021). German unions mainly relied on lobbying at the national level and on their institutional clout to influence OSH policies. As the social partners were involved in defining the set of OSH rules applying to companies during the pandemic, it is hardly surprising that the DGB unions generally agree with them. In April 2020, the DGB published a position paper entitled ‘OSH in times of pandemic requires collaboration to protect people and strengthen the economy’ (DGB, 2020). The paper stresses that effective protection is not only important for workers and their families but also ensures the functioning of organisations. The pandemic has, however, revealed a lack of protection in those sectors and occupations most exposed to potential hazards. As a result, the paper calls for OSH-related actions during and after the pandemic. These include functioning OSH policies enabling quick reactions to crisis situations, the involvement of employee representatives and an expansion of their codetermination rights in this respect. Risk assessments are considered the key instrument for identifying and mitigating potential hazards. The paper also highlights working time issues, as some occupational groups (particularly those in essential services) could be exposed to long working hours and high work intensity. It thus calls for sufficient rest times for workers. Turning to the post-pandemic period, the DGB sees a need for action to be taken by several actors. Employers should negotiate collective agreements with employee representatives on mobile work and tackling situations like a pandemic. The state should compel organisations to carry out risk assessments covering both physical and mental hazards. Legislation on mobile work and telework should follow. The DGB also calls for changes to be made to the mandatory accident insurance scheme. Those working from home need to have the same level of (accident) protection as in the workplace. In the same vein, Covid-19 should be recognised as an occupational disease in particular sectors. Throughout the pandemic, more specific policy positions on such topics as vaccinations were developed. Although the DGB considers vaccinations to be the most effective way of fighting the pandemic, it is not calling for mandatory vaccinations for the whole adult population or for particular occupational groups (DGB, 2021a). There are two reasons for this. First, there was a debate over the interpretation of the right to physical integrity set forth in the EU Charter of Fundamental Rights and the German constitution. This has since been settled by the 10 December 2021 decision of the German parliament to introduce mandatory Covid-19 vaccinations in the heath and care sectors, a decision upheld by the Federal constitutional court in May 2022. Before this decision, the DGB instead called on employers to offer free rapid (lateral flow) tests to workers. In April 2021, this demand was accepted as an update to the existing regulation, meaning that employers now have to offer their employees two free tests a week. Second, the DGB is a member of the specific Covid OSH committee and thus directly involved in policymaking. Given this role, it would be unlikely for the DGB to take a position fundamentally different to that of the government. According to a union officer, there is one further potential explanation. The DGB and its affiliated unions are avoiding taking a stance as there is no clear position on mandatory vaccinations within these organisations and their membership. When the government introduced the so-called ‘3G-rule’ – meaning that only those employees who are vaccinated, recovered or tested negative are allowed to access their workplaces – in late November 2021, the DGB solely issued a comment instead of a clear evaluation (DGB, 2021b), stating that the 3G-rule could contribute to avoiding infections at work but only if combined with the existing OSH rules. Moreover, the collection of personal data on employees was to be kept to a minimum, with clear rules defining when data were to be deleted. Despite the large overlap between the positions of the DGB and the state, the two disagreed on one topic: the government decision to discontinue the payment of wages during quarantine or isolation for unvaccinated workers from November 2021 onwards. In the DGB view, it would be better to promote voluntary vaccinations instead of putting more pressure on the unvaccinated, especially as the discontinuation of wages ‘is basically synonymous with mandatory vaccinations’ (RND, 2021). France: mistrust and sidelining The pandemic struck France in a troubled context for the labour movement: the almost uninterrupted four-year protest against neoliberal reforms of the ‘French social model’, and more widely against the deterioration of the living conditions of large sections of the working population (Yon, 2019, 2020). As a first consequence, the health crisis put an end to the confrontation between the unions and the government over a reform of the pension systems, forcing the government to suspend the reform and concentrate on the urgent public health issues. In his televised address announcing the first lockdown on 17 March 2020, President Emmanuel Macron declared ‘war’ on the virus, resulting in the adoption of the law of 23 March 2020. Introducing a ‘state of health emergency’, it concentrated management of the health crisis in the hands of the President, sidelining not only parliament and the social partners (Turlan, 2020), but also the government itself: the main decisions are taken in a ‘National Defence and Security Council’ gathered around the head of state. While trade unions and employers’ organisations were only informally consulted at the national level, ‘social dialogue’ continued to be encouraged closer to the ground, in line with previous trends fostering company micro-corporatism (Baccaro and Howell, 2017; Howell, 2009). Since then, there have been four phases during which trade unions have taken action on different issues. During the first lockdown, the labour administration, in conjunction with medical advisers, public health experts and government officials, drew up practical recommendations for preventive measures to be implemented by employers, such as the use of teleworking, hygiene and physical distancing rules, the provision of masks and ventilation measures in enclosed spaces.1 Drawing on their organisational power resources (Gumbrell-McCormick and Hyman, 2013), trade unions put forward proposals in defence of workers’ health, calling for suspension of work in industries deemed non-essential and for the effective implementation of appropriate health protocols in the other industries. This union mobilisation, backed by work stoppages and recourse to the courts, was most visible in large companies such as La Poste, Amazon or Renault (Tonneau, 2021). Depending on the balance of power and the level of trade union presence, OSH protocols were negotiated in a number of instances at industry level (for example in construction or the audiovisual sector) or at company level (as in banking). Other protocols were implemented under the control of ad hoc local health committees (as in car plants), or defined by the employer alone (as in fast-food restaurants) (DIASOCOV, 2021). At the end of the first lockdown, in May–June 2020, trade unions focused on the ‘world after’, understood as a world having learned the lessons of the health crisis and of the social and ecological crises that it brought to light. The trade unions worked with other civil society organisations (associations, NGOs and foundations) on drawing up demands for a social and ecological transition, with the aim of influencing the political agenda. Two rival coalitions were formed, one around the French Democratic Confederation of Labour (CFDT, Confédération Française Démocratique du Travail) and the other around the General Confederation of Labour (CGT, Confédération Générale du Travail), though without really succeeding in making themselves heard in the political field. In autumn 2020, the successive implementation of a curfew (from mid-October) and a second lockdown (from 30 October to 15 December) was a reminder that the ‘world after’ was not yet within reach. In this context, the social partners were strongly encouraged by the government to conclude a collective agreement on telework, a measure which had covered almost 30% of the workforce in the spring and was once again a pressing need. Due to employer hostility to greater regulation of telework, the national agreement signed at the end of November was nothing more than a recapitulation of the existing rules (Binet et al., 2021). Finally, as ‘living with the virus’ became the new norm in the wake of the third lockdown (from 3 April to 3 May 2021), two issues preoccupied the unions: jobs and mandatory vaccinations. Even though the health crisis had caused numerous job losses (Ghrairi, 2021), the unions were unable to change the state’s doctrine of non-interference in employer decisions (Béroud and Gourgues, 2021). The policy of massive recourse to short-time work thus proved to be a parenthesis intended to ‘dampen the fire’. The rejection of any policy to prohibit redundancies was further justified by situations of job shortages in certain sectors. But the main issue dividing the unions is that of mandatory vaccinations. In the summer of 2021, the government pushed through a health crisis management law introducing two controversial schemes: making a large number of public places only accessible to people carrying a ‘health pass’ attesting that they are fully vaccinated against Covid-19 or have tested negative in the past 24 hours with either a PCR or antigen test; and an obligation for employees in the medical and medico-social sector to be vaccinated against Covid-19. While the country’s leading trade union confederation, the CFDT, backed the health pass and mandatory vaccinations without much fanfare, the CGT, the second largest confederation, along with other trade unions such as Solidaires and the Unitary Union Federation (Fédération Syndicale Unitaire), strongly opposed the government measures. While declaring themselves in principle in favour of vaccination, they criticised the coercive nature of the government text, deeming it to be liberticidal and repressive, and rejecting any strengthening of the disciplinary power of employers. Indeed, in a first version of the bill, a refusal to be vaccinated or to present a health pass would have allowed an employer to terminate the employment contract. Following the joint intervention of the trade unions and the constitutional council, eventually only a suspension of the employment contract (and consequently of pay) was authorised. Whatever they were, the trade union positions gave rise to internal controversy, in a context marked both by the resurgence of the pandemic and a surge of major demonstrations against the health pass throughout the summer of 2021, in which many trade unionists participated, even though the demonstrations were often characterised by the presence of the far right. Luxembourg: between consensus and conflict In Luxembourg, many of the decisions taken to contain the pandemic were decided by the government under the ‘state of crisis’ in effect from 18 March to 24 June 2020 and allowing the government to rule by decree, without the assent of parliament. This limited the scope for consulting trade unions, which are usually routinely consulted on many public policy issues. Unions nevertheless generally supported the first wave of government measures to contain the spread of Covid-19, though with some debate over their extent. In the early phase of the pandemic, for instance, the Luxembourg Confederation of Christian Trade Unions (LCGB, Lëtzebuerger Chrëschtleche Gewerkschaftsbond) requested that all non-essential companies be shut down, including those in the manufacturing sector, in order to protect the health and safety of workers. A regulation on Covid-19-related OSH rules dated 17 April 2020 stressed the need for employers to carry out risk assessments with the goal of avoiding risks in relation to the Covid-19 pandemic and to minimise unavoidable risks. Given the strong service orientation of Luxembourg’s economy, focused on financial services, most employees were able to work remotely, making Luxembourg the EU country with the largest share of employees working from home in 2020, according to the EU Labour Force Survey. With the end of the first lockdown in May 2020 and the imminent return to the workplace of a large share of the workforce, union attitudes became more critical over the speed and scope of the lifting of restrictions. In April 2020, the General Public Sector Confederation (CGFP, Confédération Générale de la Fonction Publique) called for stricter workplace health measures and for vulnerable employees to be released from work, while the LCGB asked for mandatory testing before employees returned to work after the first lockdown and for mandatory Covid-19-related health and safety training for staff representatives in the construction industry. To limit transmissions, the government implemented a ‘large-scale testing’ strategy, where the resident population and cross-border workers were grouped into representative categories and invited on a regular but random basis to take voluntary PCR tests (Wilmes et al., 2021). Subsequently focusing more on the social and economic impact of the Covid-19 crisis and less on its health and safety implications, the unions demanded to be consulted more by the government. When, in November 2020, a second lockdown became necessary in the face of soaring infection rates, the Chamber of Employees (Chambre des Salariés), in which all unions are represented, questioned the necessity of many of the measures taken to contain the pandemic, without however putting forward any alternative proposals (CSL, 2020a). With the start of the vaccination campaign in late 2020, the prioritisation of vaccinations and the issue of testing requirements became key issues of debate. With medically vulnerable citizens and health workers the only groups prioritised during the vaccination campaign, the public sector union CGFP called for the priority vaccination of its strongest member groups, teachers and police. By contrast, the Luxembourg Independent Trade Union Confederation (OGBL, Onofhängege Gewerkschaftsbond Lëtzebuerg) declared that, lacking expertise, it was unable to state which occupational groups should be prioritised. With the onset of the fourth wave of Covid-19 infections, discussions started over how to increase the vaccination rate that had remained stagnant since the summer months (at around 60% of the general population in October 2021). When in October 2021 the government gave employers the possibility to only give vaccinated, recovered or negatively tested workers access to their workplaces under the ‘Covid-Check’ health pass system similar to the German 3G arrangements, it met with strong union opposition. Regretting that they had not been sufficiently consulted, unions in particular criticised that non-vaccinated workers would have to pay for the regular tests out of their own pockets and denounced possible sanctions for workers refusing to comply. As larger companies were required to negotiate with staff delegations over the implementation of the Covid-Check, the negative attitude of unions represented an obstacle to its implementation. Luxembourg’s national airline Luxair was for instance unable to introduce it due to staff delegation opposition. Amid mounting worries over the surge of infections during the fourth wave of the pandemic, the government held tripartite negotiations (government, employers, trade unions) in December 2021 over making the Covid-Check system mandatory at workplaces. This time, together with the umbrella employer organisation, Union of Luxembourg Enterprises (Union des Entreprises Luxembourgeoises), trade unions endorsed, albeit without much conviction, the Covid-Check pass. The only concession achieved by unions was that the government removed dismissal as a sanction for workers refusing to be tested, though created the possibility of putting them on leave without pay. Union responses in the healthcare sector Covid-19-related OSH issues were particularly salient in the healthcare sector. As frontline fighters against Covid-19, healthcare workers even received daily applause from the public during the first lockdown in Germany, France and Luxembourg. The sector is nevertheless confronted with longer-standing issues of understaffing, difficult working conditions and ward closures. From the start of the pandemic, concerns over Covid-19 transmissions in hospitals and other care facilities were high. Numerous early studies were quick to demonstrate that healthcare workers were disproportionally exposed to patient to health worker transmission of the SARS-CoV-2 virus, in particular due to the significant shortage of protective gear and other equipment in the first weeks of the pandemic. Subsequent studies of the genome sequences of the virus circulating among healthcare workers soon pointed to transmissions between health workers being an equally important issue (Ellingford et al., 2021; Paltansing et al., 2021; Schneider et al., 2020). Mandatory vaccinations in Germany: unions divided There are two unions active in the German healthcare sector: the United Services Union (Ver.di, Vereinte Dienstleistungsgewerkschaft) represents all kinds of occupational groups working in the sector, while the Marburger Bund (MB) is a professional association and union of medical doctors with the right to bargain collectively. In contrast to Ver.di, MB is not affiliated to the German union confederation, the DGB. In its 6 November 2021 general meeting, the MB voted for mandatory vaccinations for certain occupational groups – particularly those working in medical facilities, nursing homes, childcare and schools (Marburger Bund, 2021). In its opinion, care staff needed to be vaccinated to effectively protect vulnerable groups like children or old people. Hence, the rationale behind demanding mandatory vaccinations was not to protect healthcare professionals (and MB members) but to protect vulnerable groups. MB was thus the first union in Germany to call for mandatory vaccinations despite legal concerns over their practical implementation. By contrast, Ver.di has not called for any mandatory vaccinations. In accordance with the DGB policy position, it encourages vaccinations but stresses the personal freedom of every individual to decide whether or not to be vaccinated. According to a union official, Ver.di will not take a stance for or against mandatory vaccinations as there is no consensus internally or among its members on this topic. Consensus, however, is the precondition for any clear position publicly taken by Ver.di. In addition, there are internal concerns about the impact of any clear positioning, with Ver.di chair Frank Werneke fearing that even more care staff would leave the sector, should vaccinations become mandatory (Ver.di, 2021). This would in turn not only increase pressure on remaining staff but might also – according to an interviewed union officer – lead to membership losses. Due to these concerns and the internal disagreement on mandatory vaccinations, Ver.di prefers to refer to the DGB position. On 10 December 2021, the German parliament decided to implement mandatory vaccinations in the care sector as of 15 March 2022. French unions between support of and opposition to mandatory vaccinations In France, the doctors’ and healthcare workers’ unions used the contrast between the deterioration of the public health service and public recognition of the sector’s efforts during the first lockdown to force the government to negotiate a general pay rise for staff in public healthcare institutions in July 2020. In July 2021, a second round of negotiations was held with the employers of private health facilities to pass on this increase. Notwithstanding these initial advances on the pay front, unions continued to denounce the lack of resources for public hospitals, where the fourth wave of the pandemic, in November 2021, quickly saw certain hospital departments reaching full capacity. From summer 2021 onwards, the main challenge for health sector unions has been to adopt positions on the health pass and mandatory vaccinations. Together with firefighters, healthcare workers were the first to be affected by these rules, while police officers, another group in close contact with the public, managed to escape the obligation. Trade union pluralism in the healthcare sector has led to a range of positions being expressed. The CFDT’s health federation spoke out in favour of mandatory Covid-19 vaccinations for health professionals, stressing that certain other vaccinations were already mandatory for them. However, it regretted that they were thus stigmatised. For its part, the CGT’s health federation opposed the government measures, pointing out that healthcare workers, just recently celebrated for their dedicated fight against the pandemic, were now being singled out. Like the Force Ouvrière’s health federation, it opposed the suspension of recalcitrant staff, considering that ‘far from protecting hospitals, mandatory vaccinations are killing them’ (CGT, 2021). By mid-September 2021, around 3000 people had been suspended for not being vaccinated. While denouncing sanctions and the new ‘work permit’, the CGT nevertheless defended the need for vaccination ‘which has proved its worth’ and warned employees about ‘biased information’ fostering vaccine distrust (CGT, 2021). By contrast, the health union federation Solidaires, Unitaires, Démocratiques (SUD-Santé Sociaux) took a much more complaisant stance on vaccination refusals, solely declaring that it was ‘not hostile to vaccination’ and that it ‘respected the right of every individual to freedom of choice’ (SUD-Santé Sociaux, 2021a). However, in mid-summer 2021, it had called for people to join the anti-health-pass demonstrations, which it described as ‘defending freedoms’ (SUD-Santé Sociaux, 2021b), despite the controversy surrounding them and the clearly ‘anti-vax’ nature of many of them. In its view, the health crisis management law was part of an ‘authoritarian continuum’ (SUD-Santé Sociaux, 2021c). Luxembourg: rejection of mandatory vaccinations Luxembourg’s health and care sector has a strong union presence. The mostly self-employed medical doctors are organised in the Association of Medical Doctors and Dentists (AMMD, Association de Médecins et Médecins Dentistes), an organisation focused on upholding the economic interests of medical doctors and with no tradition of broader societal involvement. Most of the unionised staff of hospitals and care facilities are members of the OGBL and 75% of workers in the health and social work sector were covered by a collective agreement in 2018. During the Covid crisis, staff delegations were part of the ‘crisis units’ set up in the various hospitals. Working hours in the healthcare and care sector were extended by law to up to 60 hours per week during crisis peaks to cope with the rise of hospitalisations and ensure service continuity. While the OGBL’s health federation drew attention to the risks involved for care workers in extending working hours (work-related stress, medical errors), the Chamber of Employees, in which union representatives from all industries are represented, supported the temporary extension when consulted by the government on the proposed legislation (CSL, 2020b). While health and care workers were prioritised during the vaccination campaign, personnel working for subcontractors in hospitals and care homes were not vaccinated along with regular staff, a situation criticised by the unions. Despite low vaccination rates among workers in care homes for the elderly (estimated at approximately 60% in July 2021) and numerous reported deaths among care home residents, the OGBL – as the main union in the healthcare sector – did not support the mandatory regular testing requirements introduced in June 2021 for non-vaccinated personnel in the health and care sector. The union demanded that these tests be strictly voluntary and that personnel refusing to take them should not be sanctioned. Stressing data protection issues (despite the fact that Article 9 of the EU General Data Protection Regulation specifically sets exemptions for the processing of health data), unions also insisted that staff should not be placed under any obligation to disclose their vaccination status to the employer. Representing medical doctors, the AMMD did not call for mandatory vaccinations in the health and care sector, pleading in November 2021 for a cautious approach in order to avoid divisions. Discussion and conclusion Our initial assumption that trade unions would be strongly involved in containing the Covid-19 pandemic through supporting protective measures taken by governments as well as own initiatives and demands to increase the protection of workers is not entirely confirmed by the collected empirical data. Our data show that union positions on Covid-19-related OSH policies in Germany, France and Luxembourg have varied, and that unions have often found it difficult to develop proactive policy positions on Covid-19. At the time of the first lockdowns, unions across countries generally emphasised the precautionary principle and called for suspensions of business activities. They raised demands for better protective equipment and supported government recommendations on social distancing, mandatory mask-wearing and adaptation of workplaces. Unions also supported the closure of non-essential businesses and called for measures to uphold the economic security of workers affected by the pandemic. However, after the end of the first lockdowns, unions’ positions became increasingly differentiated. Two issues were particularly challenging for unions – finding clear positions towards workplace test strategies and mandatory vaccinations. The growing variety of union positions over time raises the question of the factors that may have shaped their positions and that led to different responses than assumed. In the following sections, we will identify and discuss the dilemmas involved for unions in articulating membership logics and public health considerations in a context of political polarisation, the influence of varying relationships between unions and the state, and the limits of risk anticipation mechanisms. Membership logics and public health considerations Public policies aiming to heighten vaccination rates confronted unions with the need to choose between the preferences of part of their membership and public health imperatives. In a general climate of uncertainty and lack of knowledge about the virus, this choice has challenged unions. From a public health point of view, vaccinations contribute to limiting infections and prevent severe illness, in turn helping to keep the economy and society running. Despite vaccine breakthrough infections during the fourth Covid-19 wave in late 2021, studies show that vaccinated individuals still have high levels of protection (Shah et al., 2021). In the health sector, the issue of vaccinations is especially important because of the high risk of transmissions between health workers and between health workers and patients. The combination of vaccinations, specific health and safety policies (such as protective equipment) and so-called barrier nursing techniques is key to cutting down transmissions in the health sector (ECDC, 2021b). Despite such evidence, unions in Germany, France and Luxembourg were struggling to define a clear position regarding vaccination policies. This became even more difficult since public opinion was also influenced by vocal and sometimes radicalised minorities criticising Covid-19 policies and propagating vaccine scepticism. In this context of political polarisation, unions felt pressure from their unvaccinated members and feared membership losses. Consequently, they have been reluctant to support concrete policies aiming to increase vaccination rates, such as testing requirements for non-vaccinated workers or mandatory vaccinations. With a few exceptions such as the CFDT in France or Marburger Bund in Germany, unions representing healthcare workers have generally opposed mandatory vaccinations. They put forward arguments ranging from not requiring workers to become vaccinated after being exposed to extraordinary pressure during the pandemic to the value of the individual freedom not to be vaccinated. Data protection issues were also strongly emphasised by unions. Generally, when unions address issues that have implications beyond the workplace, they face the challenge of balancing the points of views of segments of their membership and common goods (Flanders, 1970; Hyman, 2015; Thomas, 2017). In the case of Covid-19, the internal heterogeneity of members’ interests and preferences regarding Covid-19 policies has represented a further obstacle to reaching an internal consensus over the definition of members’ interests. Depending on their perception of infection risks, many workers supported workplace tests and mandatory vaccinations while others did not. The reluctance to openly take a more active pro-vaccination position may also be due to the fact that unions, after having frequently seen their representativeness questioned by governments and employers, tend to conceive their representational function in terms of a simple reflection of member attitudes rather than a political mandate built through dialogic democracy (Offe and Wiesenthal, 1980). The sharp inter-union competition in France (Giraud et al., 2018), and to a lesser extent in Luxembourg (Thomas et al., 2019), has been an additional obstacle to trade unions’ acquiescence with government policies targeting high levels of population immunity. Such competition can restrain unions from entering into agreements with the government that risk not being supported by sections of their membership and subsequently leading them to join another union. Union power resources and union–state relations Positions towards Covid-19 policies have also been shaped by unions’ power resources and previous trends and patterns of interaction between unions and the state. Unions in Germany and Luxembourg mainly relied on institutional power resources and on lobbying at national level. In contrast, French unions combined the use of organisational and institutional power resources (Gumbrell-McCormick and Hyman, 2013). In Germany, the active involvement of unions in managing the pandemic in the tradition of corporatist decision-making under a coalition government of Christian and Social Democrats – two parties attached to corporatist forms of governance (Streeck, 2006) – has probably fostered their general agreement with the policy measures adopted. While unions in Luxembourg were critical of many measures adopted over the course of the pandemic, they were involved in the negotiations over the introduction of the Covid-Check pass at workplaces, which they eventually supported. In contrast, in France, the centralised management of the pandemic (Bandelow et al., 2021) left little space for social concertation with trade unions or employer organisations. In addition, relations between French unions and the state were tense after numerous conflicts over government policies in the years preceding the Covid-19 pandemic (Yon, 2020). Nonetheless, at the onset of the pandemic, unions in France succeeded through collective and legal action in imposing better health protection at some workplaces where they have a strong presence (Pereira, 2021; Tonneau, 2021). This mobilisation of organisational power resources is remarkable as Covid-19 containment has often implied limitations to the basic rights upon which trade unions rely to defend their members’ interests, such as the right to assemble and to protest. In Germany, Luxembourg and France, unions’ reluctance or lack of outright support towards mandatory testing policies for unvaccinated workers may also be explained by the fact that these policies transfer responsibility for monitoring and sanctioning recalcitrant employees to employers. This turns employers into the only legitimate actor in the domain of workplace health and safety policies, instead of using workers’ representatives or imposing mandatory vaccinations in the name of public health. But union scepticism towards mandatory workplace tests and vaccinations gives testimony to a lack of strategic foresight as unions risk jeopardising a key principle of OSH: the legal obligation of employers to protect workers once the necessary means to do so are available (Alli, 2008). Indeed, if workers are free to choose to remain unprotected or to deliberately expose themselves to hazards, the employer obligation regarding OSH is in danger of being nullified. Risk assessments and workplace OSH bodies Unions in Germany, France and Luxembourg have not been able to take advantage of the pandemic to push through stronger OSH policies. Unions have rather stressed the usefulness and importance of existing European and national regulations on risk assessments, under which such assessments have to be carried out in workplaces to minimise risks to workers’ physical and mental health (Gagliardi et al., 2012; Niskanen et al., 2012). However, even before the pandemic, the ESENER survey (EU-OSHA, 2019) underlined the unequal implementation of risk assessments among EU Member States (Germany, Luxembourg and France scored relatively low), dependent on sector and company size. Workplace representatives – often unionised in the three investigated countries – play an important role in monitoring risk assessments and in checking whether they are really carried out. The pandemic has demonstrated that the risks involved in many jobs and tasks have never been properly assessed. In France, the 2017 abolition of the health, safety and working conditions committees reduced the clout of workplace representatives in health and safety matters, further undermining a workplace ‘culture of prevention’ already extremely fragile in smaller companies (Darbus and Legrand, 2021). Indeed, in 2016, only 45% of French private-sector employers said they had an updated risk assessment document (Document unique d’évaluation des risques); in establishments with fewer than 10 employees, the figure was just 38% (DARES, 2021). Moreover, successive reforms of occupational health services and labour inspections have resulted in lower numbers of professionals and state representatives responsible for prevention and control – precisely those able to support employee representatives in the field (Mauroux, 2021). In Luxembourg too, the representative bodies specifically in charge of OSH in larger companies were abolished as part of the 2015 reform of social dialogue. The effects of this measure remain unclear. In 2013, however, OSH was the issue most discussed by workplace representatives according to a survey carried out by LISER, ahead of working conditions and training. In Germany, the number of works councils as well as the number of employees represented by a works council has shrunk in recent years (Ellguth and Kohaut, 2019). Consequently, workplace monitoring and enforcement of risk assessments have become even more patchy. This development has led to a situation in which numerous workers’ jobs and tasks have never been assessed with regard to potential risks. Against this background, unions in Germany, France and Luxembourg could demand (legal) sanctioning mechanisms for those establishments not (regularly) carrying out risk assessments. The 2019 ESENER survey reveals indeed that risk assessments are more likely to be carried out in countries where sanctioning mechanisms apply; for example in Denmark, a prison sentence of up to one year is possible (EU-OSHA, 2019). In addition, concrete regulations on the protection of teleworkers remain lacking in all countries, meaning that teleworkers continue to perform their work in a legal grey zone (Moulac et al., 2022). Although OSH has always been an important topic in the world of work, it was sidelined in the years preceding the pandemic, as standards were already considered to be high and because other topics, such as digitalisation, became dominant in the public discourses on work and employment. The current situation, however, stresses the need to re-prioritise the topic of safe working conditions and its practical shopfloor implementation. Risk assessments are a key tool, insofar as they are carried out regularly and cover a range of potential physical and mental hazards. As the necessary legislation already exists, unions could be(come) key actors in implementing the related rules in practice – provided they make this a strategic priority in the years to come. Risk assessments could also be crucial in the context of another major development likely to change the world of work and heighten the importance of OSH: climate change. The International Labour Organization is forecasting that, by 2030, 2.2% of all working hours will be lost due to heat stress (ILO, 2019). Extended heatwaves will thus confront many industries with unprecedented difficulties, requiring sustained and coherent strategies for risk anticipation and mitigation. Author biographies Adrien Thomas is a researcher at the Luxembourg Institute of Socio-Economic Research (LISER). His research focuses on employment relations, cross-border labour markets and trade unions’ engagement with environmental policies. Nadja Dörflinger is a senior researcher in the ‘Changing World of Work’ department at the Federal Institute for Occupational Safety and Health (BAuA), Germany. Her research has focused on employment relations, labour markets and trade unions in a comparative European perspective as well as on service work. Karel Yon is a CNRS research fellow in sociology at IDHES, University of Paris-Nanterre. His main research interests concern trade unionism, social movements and the politics of work and labour. Michel Pletschette is a physician with the Department of Infectious and Tropical Diseases of the University of Munich LMU. His research interests span the control of socially relevant diseases, social determinants of health and the functioning of global health. The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding: The authors received no financial support for the research, authorship, and/or publication of this article. ORCID iD: Adrien Thomas https://orcid.org/0000-0001-7943-2049 1. 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In: Schmalz S Sommer B (eds) Confronting Crisis and Precariousness: Organized Labour and Social Unrest in the European Union. Lanham, MD: Rowman & Littlefield. Yon K (2020) The French labour movement on the hot seat, Rosa-Luxemburg-Foundation. Available at: www.rosalux.eu/en/article/1654.the-french-labour-movement-on-the-hot-seat.html	0	PMC9720418	NO-CC CODE	2022-12-06 23:26:07	no	Econ Ind Democr. 2022 Dec 2;:0143831X221138882	utf-8	Econ Ind Democr	2,022	10.1177/0143831X221138882	oa_other
==== Front Eval Rev Eval Rev sperx ERX Evaluation Review 0193-841X 1552-3926 SAGE Publications Sage CA: Los Angeles, CA 36460484 10.1177_0193841X221143680 10.1177/0193841X221143680 Original Research Article Government Bonds and COVID-19. An International Evaluation Under Different Market States https://orcid.org/0000-0001-9778-7345 Jareño Francisco 1 https://orcid.org/0000-0003-2422-3383 Martínez-Serna María-Isabel 2 Chicharro María 3 1 Faculty of Economic and Business Sciences, 16733 University of Castilla-La Mancha , Albacete, Spain 2 Faculty of Economics and Business, 73082 University of Murcia , Murcia, Spain 3 Faculty of Economic and Business Sciences, 73073 University of Castilla-La Mancha , Albacete, Spain Francisco Jareño, Facultad de CC Económicas y Empresariales, Universidad de Castilla-La Mancha, Plaza de la Universidad, 1, Albacete 02071, Spain. Email: [email protected] 2 12 2022 2 12 2022 0193841X221143680© 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. This study evaluates the sensitivity of government bond yields from the countries most affected by the COVID-19 pandemic to variations in some international risk factors during the period between January 2020 and April 2021. This sample period allows us to focus the study on the first, and the subsequent waves of the COVID-19 pandemic. Specifically, we propose an extended risk factor model estimated using the quantile regression approach. In addition, this study compares the COVID-19 pandemic period with a pre-pandemic and a post-vaccination period. Interesting differences among them are observed, remarking that gold is the key risk factor during the pandemic, whereas VIX and crude oil play that role in the pre-pandemic and the post-vaccination periods, respectively, mainly for bearish states. As expected, the explanatory power of the model is better at extreme quantiles, showing relevant differences between sensitivities, because the found effects are quantile-, country- and risk factor-dependent. The results during the pandemic are robust to the inclusion of a country-specific factor and a factor accounting for the mutual influence of the government bonds. JEL classification C22, C51, F21, G12, G32, H12 risk factors bond returns quantile regression COVID-19 pandemic Junta de Comunidades de Castilla-La Mancha https://doi.org/10.13039/501100011698 CLM21-PIC-068 Ministerio de Ciencia e Innovacion https://doi.org/10.13039/501100004837 PID2021-128829NB-I00 Universidad de Castilla-La Mancha https://doi.org/10.13039/501100007480 2020-GRIN-28832 edited-statecorrected-proof typesetterts10 ==== Body pmcIntroduction and Theoretical Framework There is extensive literature about evaluating the effect that external shocks on different financial and economic variables have on the returns of financial assets (Cong et al., 2008; Czaja et al., 2010; Fama & French, 1992; Jareño, 2005, 2008; Stone, 1974; among many others). Nevertheless, few studies analyse how international risk factors influence the returns of fixed income securities, despite the undeniable importance of bond markets, not only in terms of trading volume but also because of their impact on modern economies. Thus, fixed income securities are integrated into the portfolios of banks, governments, institutional and individual investors. Moreover, debt instruments are the main item in the portfolios of pension funds and insurance companies. The arrival of the COVID-19 global pandemic (on March 11, 2020, according to the World Health Organization-WHO) has caused a widespread fall in virtually all financial markets and economies. In particular, Goodell (2020) defines the COVID-19 pandemic as an unprecedented episode of global crisis with exceptionally destructive economic damage. This period of economic and social turbulence, comparable to the global financial crisis caused by subprime mortgages in 2007–2008, has emerged as a time period of great relevance for the analysis of the impact of risk factors on financial assets. Several previous studies have focused on studying the effect of the COVID-19 pandemic on various financial markets (Gonzalez et al., 2021; Umar et al., 2021a, 2021b) but Cepoi (2020), among others, highlight the interest of deepening the study of stock and bond market returns, focusing attention on the countries most affected by COVID-19. While the role played by the pandemic on stock market reaction has been widely scrutinised (Ashraf, 2020, Goodell & Huynh, 2020, Scherf et al., 2022, among others), the response of fixed income markets to this crisis has received much less attention. The purpose of this paper is to evaluate the impact that the COVID-19 pandemic has had on the degree of sensitivity to economic and financial factors on public fixed income assets, more specifically, on sovereign bond returns. This analysis is of relevance, not only for the above-mentioned importance of the evaluation of debt securities in economy, but also in terms of bond risk management. Bondholders are investors particularly worried by their income security and, although fixed income is an investment focused on the preservation of capital and income, it is not exempt of risk. Moreover, our study is carried out at the international level and provides the comparison of results between the pandemic period and a pre-pandemic and a post-vaccination period. Thus, this paper contributes to the literature in three ways: (1) we add new research to the limited literature about the influence of economic and financial factors on fixed income assets; (2) we contribute to the limited existing empirical evidence on the influence of the recent pandemic on bond markets, more specifically on sovereign bonds, by analysing the degree of sensitivity of this type of asset. To the best of our knowledge, the only papers which analyse the effect of the pandemic on sovereign bonds are Arellano et al. (2020) and Sène et al. (2021), who focus on emerging markets, He et al. (2022), who investigate US Treasury bonds and Zaremba et al. (2021 and 2022), who examine the effect of the COVID-19 on the term structure of interest rates and on the volatility of international sovereign bonds, respectively. (3) To our best knowledge, this is the first study that compares the pandemic period with a post-vaccination period, shedding light to the role played by the vaccination on bond market reaction. The selected countries for this study are among those most affected by the pandemic in terms of the number of deaths.1 From this ranking, we have chosen those for which we have data available on the Investing website.2 In particular, these countries are Spain, the United States, the United Kingdom, Italy, France, Germany and South Africa. This way, we have, as well as the US and the UK, a representation of the major economies of the euro area and an emerging market. All of them are among the 15 most affected countries according to Salgotra et al. (2020) and there is availability of homogeneous data on the yields of their sovereign bonds. The selection would leave countries such as China out of the study, which, a priori, could be a first candidate for the study, but which, subsequently, it is verified that it does not have data of sufficient quality and reliability to be included in the study and, furthermore, has not been so severely affected by the pandemic (in terms of contagion and deaths), at least from the point of view of Chinese official data. Based on previous literature (González et al., 2021; Jammazi et al., 2017; Jareño et al., 2021; Sevillano & Jareño, 2018, among many others), the factors selected in this paper are the prices of gold, oil and Bitcoin, which are exploitable assets with prices that vary greatly and influence the world economy. Important indices with great relevance at the international level have also been incorporated. They include VIX (Chicago Board Options Exchange Market Volatility Index), which shows the returns in the volatility index of the North American asset market; EURO STOXX50, which shows the performance of the 50 largest companies among the 19 supersectors in terms of market capitalization in 11 Eurozone countries; S&P 500, which measures the performance of the 500 largest companies with shares listed on the US stock exchange; and the FSI or Financial Stress Index, which measures the degree of stress in the financial system of each country, so the higher the financial stress, the higher the probability of sudden variations in stock markets, according to Sevillano and Jareño (2018). The relationship between the price of gold and bond returns is based on the fact that gold is often identified as a safe haven that competes with stock market equities and serves as compensation in the case of losses in the bond market. There are different opinions about the behaviour of gold among the authors who have studied the existing correlation. Traditionally, gold has played an important role as a safe haven during times of political and economic crisis based on the assumption that gold is negatively correlated with other assets (Ciner et al., 2013). On the one hand, Baur and Lucey (2010) and Baur and McDermott (2010) indicated, for example, that while gold can and does act as a hedge against falling stock returns, it does not appear to act as a hedge against bonds. On the other hand, different studies concluded that the price of gold can be used mainly as a predictor of stock and bond prices because there is no substantial relationship between this precious metal and different financial assets (Summer et al., 2011). The price of crude oil has been included as a relevant variable since it is a highly consumed fuel by companies and economies, which generates large movements of money worldwide. Several studies on the relationship between the price of oil and variables such as Gross Domestic Product (GDP), investment, exports and imports can be highlighted (Cong et al., 2008; Sevillano & Jareño, 2018; Umar et al., 2021b, among many others). The relevance of crude oil in the economy is due to its capacity to influence the price of bonds through future cash flows, or through inflation, since an increase in the oil price raises the general level of prices, which leads to a rise in interest rates, and the consequent decrease in bond prices (Moya-Martínez et al. 2013).3 Finally, some recent literature has analysed the cryptocurrency market, as well as the dominant role played by the virtual currency Bitcoin in this market. All of the papers emphasize the importance that cryptocurrencies are acquiring in the global financial arena, which has led us to include this international risk factor in our study. Among these papers, we highlight González et al., 2021; Jareño et al., 2020a, 2020b. As for the equity indices, many papers have explored the co-movement between equity and government bonds. Whereas in normal economic conditions, correlation between equity and bond returns is usually positive, in times of economic crisis there can be a flight-to-quality effect with investors moving from equity to bonds, which reduces the correlation between the two classes of assets or even makes it negative (Baur & Lucey, 2009; Papadamou et al., 2021). Regarding volatility indices, authors who have studied the relationship of bond yields with different variables have jointly analysed the Chicago Board Options Exchange Market Volatility Index (VIX) and the change in the Financial Stress Index (FSI). These factors are considered key in the correlation between bond and equity returns in the US market (Aslanidis & Christiansen, 2012; Ohmi & Okimoto, 2015). On the one hand, when the VIX increases, a negative impact is observed because there is a decrease in the correlation between the stock and bond markets (Ohmi & Okimoto, 2015; Varela & Sánchez, 2020). On the other hand, the FSI causes an increase in the volatility of bond and stock markets and, moreover, is able to influence the causal relationships between the two markets. It is therefore concluded, due to the various studies mentioned above, that the negative correlation produced between financial stress and security returns increases, especially with the onset of economic crises (Bianconi et al., 2013). Based on previous studies that we have referenced throughout this work, such as González et al., 2021; Jareño et al., 2020a, 2020b, 2020c; Sevillano & Jareño, 2018; Jareño et al., 2022, among others, the quantile regression method is used to develop our model, and it offers more robust results compared to other methods, such as estimation by ordinary least squares (OLS). Thus, in this context, this work sets out to test the following hypotheses: Hypothesis 1 (H1): the first hypothesis assumes that the bond yields of the selected countries are expected to be more sensitive to variations in international risk factors in extreme quantiles (both high and low), which are associated with extreme bond market conditions. Hypothesis 2 (H2): the second hypothesis assumes that the bond yields of the countries most affected by COVID-19 are expected to behave differently due to the different intrinsic characteristics of each country’s economy, such as the level of economic growth and the economic area to which it belongs. Hypothesis 3 (H3): the third hypothesis assumes that the sensitivity of bond yields of the analysed countries to variations in international risk factors presents a different pattern in the COVID-19 pandemic period compared with a prior and a posterior period. According to the results obtained, we can confirm the three hypotheses. The bond yields of the analysed countries show a greater sensitivity to changes in risk factors at low moments in the market, as well as in stages of economic euphoria, which confirms the first hypothesis H1. In addition, we observe that the behaviour of the returns is considerably different depending on the country analysed, which confirms the second hypothesis (H2). Finally, comparing between periods, we find singularities in the sensitivity to international factors during this period, compared with a previous and a posterior period, what confirms the third hypothesis (H3). The structure of the paper is composed of four main sections. Data and Methodology Section describes the data and methodology. Results of the Extended Risk Factor Model During the COVID-19 Pandemic Section estimates the model, showing the resulting coefficients for each of the quantiles analysed using the QR method. Evaluation of Bearish, Normal and Bullish States of International Government Bond Yields Section focuses on exploring the analysis of the international government bond yields in bearish, normal and bullish market states. Evaluation of the Comparison of the COVID-19 Period With Pre-Pandemic and Post-Vaccination Periods Section shows the results for the pre-pandemic and post-vaccination periods. Evaluation of the Mutual Influence Between Government Bonds Considering Country-Level Macroeconomic Risk Factors Section evaluates an additional extension of the model as a robustness check and Concluding Remarks and Implications Section presents the main conclusions of the research. Data and Methodology Data This section shows the data on which this study is based. The sample period chosen as pandemic period extends from January 2, 2020 (two and a half months prior to the declaration of the COVID-19 pandemic by the World Health Organization, WHO),4 to April 30, 2021. This information is intended to analyse the impact of the global economic crisis generated by the arrival of COVID-19 on the bond yields of the countries that have been most affected by the pandemic (according to the coronavirus disease (COVID-19) situation reports (WHO, 2020)), including in the study some relevant risk factors at the international level. These data are analysed on a daily basis, although it should be noted that the data have been homogenized to obtain the same number of observations (332 days) for all variables. Although the daily periodicity offers greater volatility than the weekly and monthly periodicity, it allows us to see in detail the evolution of the variables (Fernández, 1994). For most of the series obtained, the information was expressed in indices, so a transformation prior to analysis was necessary to work with the same unit, in this case with logarithmic returns (rn)(1) rn=Ln (PnPn−1) where Pn is the value of the variable in the day in question (n) and Pn-1 is the value in the immediately preceding day (n-1) for which information is available. Only in the case of the FSI index has the transformation consisted of applying first differences, that is, changes in the variable between the day in question (n) and the previous day (n-1). These data were obtained from the Investing website, one of the most important financial platforms in the world, which offers information on the main economic indices and is updated daily. For the preliminary analysis of the variables, some descriptive statistics were obtained. In addition, a stationarity analysis of all the series was carried out, with the aim of verifying that the mean and median do not vary over the sample period. For this purpose, three tests have been used to determine the existence of a unit root: augmented Dickey-Fuller (ADF), based on the Dickey-Fuller test but incorporating lags; Phillips-Perron (PP) and stationarity: Kwiatkowsky-Phillips-Schmidt-Shin (KPSS). Authors such as Granger and Newbold (1974) emphasize the importance of analysing the presence of stationarity in the series. In these tests, the null hypothesis or hypothesis to be tested is that of ‘nonstationarity’ of the random disturbance. In the KPSS test, the hypothesis to be rejected, on the contrary, is that of stationarity. In those cases, in which we find a unit root or nonstationarity with any of the tests carried out, the data will be transformed using the first differences technique, with the aim of making all the variables stationary. Finally, the correlation matrix between the variables was also studied. Specifically, the bond yields of the countries selected in this study have been calculated through the daily quotes found on the Investing website, obtaining the following basic descriptive statistics which are analysed below (Panel A, Table 1). As seen in Table 1, the average of all the bond yields is positive, which indicates that there is an upward trend in the fixed income market of the various countries in the period studied. The bonds with the highest average yield are those of South Africa, while those with the lowest average are those of Germany and UK. All markets maintain a very similar and stable mean because daily data has been used. The standard deviation allows us to assess the volatility of bond yields, which are positive and with data close to zero. This shows us the variation of the prices of these fixed income assets with respect to their average. We can see that the least volatile bonds are those of France, Germany and the United Kingdom and, therefore, those that incur the least risk for potential investors who decide to invest in them. The most volatile bonds, on the other hand, would be those of South Africa, Italy and the United States, which implies that they have undergone large variations and have been less stable over time.Table 1. Main Descriptive Statistics for the International Government Bond Yield During the COVID-19 Pandemic. Panel A: For the International Government Bond Returns Variable Mean Median Max. Min. Std. Dev. Skewness Kurtosis JB Stat. ADF Stat. PP Stat. KPSS Stat. Spain 0.00006 0.00027 0.03221 −0.02102 0.00474 0.69802 17.11435 2782.8* −13.24022* −14.4018* 0.0748 US 0.00021 0.00018 0.02089 −0.02468 0.00503 −0.29333 9.30479 554.64* −7.3812* −16.8356* 0.9048* UK 0.00004 0.00008 0.02718 −0.01989 0.00378 0.56608 12.59337 1290.85* −15.8316* −15.8155* 0.6562 Italy 0.00022 0.00066 0.05194 −0.05800 0.00705 −1.00237 27.93949 8659.6* −17.4780* −17.4677* 0.0620 France 0.00006 0.00030 0.01471 −0.01667 0.00316 −0.48785 8.017044 361.36* −15.3046* −15.2257* 0.1411 Germany 0.00005 0.00008 0.01707 −0.01998 0.00355 −0.49636 8.787418 476.97* −15.6771* −15.5555* 0.14595 S. Africa 0.00030 0.00042 0.04110 −0.04694 0.00825 −0.37451 12.32282 1210.09* −15.0236* −15.4365* 0.04952 Panel B: For the international risk factors’ returns Variable Mean Median Max. Min. Std. Dev. Skewness Kurtosis JB stat. ADF stat. PP stat. KPSS stat. Gold 0.00090 0.00105 0.05627 −0.05073 0.01324 −0.21562 6.24015 147.8* −17.6795* −17.8869* 0.4143* Crude oil 0.00214 0.00275 0.31963 −0.28221 0.05249 0.13571 16.71930 2589.03* −17.4566* −17.5318* 0.1511 Bitcoin 0.00657 0.00482 0.17742 −0.49728 0.04772 −3.22712 40.02367 19538.33* −20.8517* −20.7988* 0.0965 VIX 0.00076 −0.01209 0.48021 −0.26623 0.09001 1.61830 8.71389 596.55* −21.6965* −21.5041* 0.1717 Euro stocks 0.00005 0.00029 0.08834 −0.13241 0.01830 −1.29217 14.32530 1866.69* −11.0557* −18.5067* 0.1947 US Stocks 0.00066 0.00182 0.08968 −0.12765 0.01954 −0.94756 13.72379 1640.51* −6.2490* −24.6844* 0.1879 Financial stress 0.00028 −0.03450 3.45400 −1.58000 0.42824 3.23085 25.33047 7475.58* −7.6149* −319.6331* 0.14953 Notes: This table shows the descriptive statistics of daily government bond yields (Panel A) and the risk factors (Panel B) included in the model. The sample period ranges between January 2, 2020 and April 30, 2021. They include mean, median, minimum (Min.) and maximum (Max.) values, standard deviation (Std. Dev.) and Skewness and Kurtosis measures. JB denotes the statistic of the Jarque-Bera test for normality. The results of the augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root tests and the Kwiatkowski et al. (KPSS) stationarity test are also collected in the last three columns. As usual, , , ** indicate statistical significance at the 10%, 5% and 1% levels, respectively. With the skewness measure, we can obtain information on the shape of the data and determine symmetry. Specifically, the skewness measure shows which way the ‘tail’ of the distribution points. The closer this data is to zero, the more symmetric it is. Positive asymmetries are observed for the bonds yields of Spain and the U.K. (implying that the distribution points to the right), while the rest of the countries follow negative asymmetric distributions (to the left). The measure of kurtosis will allow us to assess how these ‘tails’ of the distribution differ from the normal distribution and what type of distribution the data follow. If we assume that a normal distribution has a kurtosis equal to 3, we observe excess kurtosis for all variables, which indicates that the tails are heavier than a normal distribution, being more pointed and with wider tails than normal. Regarding the stationarity of the variables, the standard unit root (augmented Dickey-Fuller (ADF, 1979) and Phillips-Perron (PP, 1988)) and stationarity (Kwiatkowski-Phillips-Schmidt-Shin (KPSS, 1992)) tests confirm that all bond yields are stationary, although the U.S. and U.K. bond yields show some hesitation in the KPSS test. The variation rates of the relevant international factors have been obtained in the same way as the bond yields, through information extracted from the Investing website. As seen in Panel B of Table 1, the mean of all the variables is positive, which indicates that they are all growing in the period studied. The variables with the highest positive mean are Bitcoin, S&P 500 and VIX. The standard deviation lets us know the volatility of these international factors, which are positive and with data close to zero, with the exception of the FSI. The least volatile factors are gold, EURO STOXX50 and the S&P 500 and, therefore, are those factors that incur the least risk for economic agents who decide to invest in them. These results make sense since assets such as gold have historically been considered a ‘safe haven’ because of their stability and low risk for investors. The most volatile and, therefore, least stable factor has been the FSI because it is a variable that studies the impact of news related to COVID-19 and, consequently, is the variable that has the most important variation in the chosen period due to its direct relationship with the pandemic. If we analyse the measure of asymmetry, we observe positive asymmetries for the crude oil, VIX and FSI (which implies that the distribution points to the right), while the rest of the international factors follow negative asymmetric distributions (to the left). Regarding kurtosis, all the variables show excess kurtosis, which indicates that the tails are heavier than a normal distribution, are more pointed and have wider tails than normal. In addition, the Jarque-Bera statistic shows that the null hypothesis of normality is rejected for all the relevant international factors at a 1% significance level. Finally, all of the risk factors are stationarity series according to the unit root and stationarity tests. Regarding the correlation between the international risk factors included in this research, Table 2 shows that the level of correlation between the variables has in most cases a positive value (except for FSI and VIX), which generally fluctuates between reasonable levels. Thus, the correlation between the FSI and all other factors is negative except for VIX (66%), reaching high values (about 80%) for the EURO STOXX50 and the S&P 500. It is interesting to highlight two expected results that show a high degree of correlation between the variables. We observe an approximately 70% correlation (positive and statistically significant) between the U.S. and European equity market returns. In addition, we also find that there is a high correlation (approximately 70%) with a negative and statistically significant sign between the U.S. equity market and the Chicago Board Options Exchange Market Volatility Index (VIX). These values are those expected to be found a priori and which, moreover, have been widely reported in the financial literature. To avoid unsteady estimates, this paper conducts a robustness check, proposing additional estimates that involve alternatively eliminating the variables with the highest level of pairwise correlation (Evaluation of the Mutual Influence Between Government Bonds Considering Country-Level Macroeconomic Risk Factors Section).Table 2. Correlation Matrix Between the Risk Factors Included in the Study During the COVID-19 Pandemic. Gold Crude oil Bitcoin VIX Euro Stocks US Stocks Financial Stress Gold 1 Crude oil 0.10018* 1 Bitcoin 0.19814* 0.15280* 1 VIX −0.04370 −0.22847* −0.37369* 1 Euro stocks 0.11454 0.27629* 0.37114* −0.48353* 1 US stocks 0.16020* 0.30303* 0.39168* −0.70413* 0.67356* 1 Financial stress −0.14549* −0.34723* −0.36351* 0.66272* −0.78761* −0.81272*** 1 Notes: The sample period ranges between January 2, 2020 and April 30, 2021. , , indicate statistical significance at the 10%, 5% and 1% levels, respectively. Last, this section contains a preliminary analysis of the evolution of the variables included in this work. In broad terms, we can highlight that all of them show great variability and were significantly impacted by the declaration of the COVID-19 pandemic around March 2020 and the first wave. Figure 1 collects the graphs for each of the variables of the model for the period from January 2, 2020, to April 30, 2021, showing the evolution of the performance of these variables and highlighting the first wave of COVID-19 in red. As seen, all variables follow the same trend and suffer from the impact of COVID-19 from March 2020 until May-June 2020. After the first wave of the pandemic, there is a more linear trend. The variables even suffer from the second and third waves of the pandemic, but there was a lesser impact on the economy in general due to the measures taken by the governments.Figure 1. Time evolution of the variables included in the study. (a) International government bond returns. (b) International risk factors’ variation rates. Notes: The first wave of the COVID-19 pandemic is remarked in red. Subsequently, this paper will carry out a comparative analysis of the COVID-19 period with respect to a period before the pandemic and a period after mass vaccination, so that different behaviour of bond yields for the selected countries can be detected in the face of changes in the risk factors analysed. Thus, the pre-pandemic period considered covers from January 3, 2018, and December 31, 2019. The post-vaccination sample period ranges from May 3, 2021, to February 17, 2022. Methodology This section focuses on presenting the methodology used in this study, which consists of analysing the sensitivity of bond yields to changes in different international risk factors, focusing the analysis on the period of pandemic caused by the COVID-19. The methodology used for this study is quantile regression (QR). This methodology was first used and explained by Koenker and Bassett (1978) and was an alternative to the classical ordinary least squares (OLS) estimation. The OLS technique has been the most widely used throughout history in the statistical field in the different investigations for the analysis of variables. This indicates that the model is consistent as long as there is no perfect multicollinearity, that is, when there is strong collinearity between explanatory variables. It also considers that the model will be optimal when the errors are homoscedastic; that is, the value of the explanatory variables does not affect the variance of the error and is constant in all observations. If this is not the case, it would be a case of heteroscedasticity. Therefore, this method indicates a minimum mean-unbiased variance when there are finite variances in the errors, provided that the variances are normally distributed. If the distribution of the dependent variables, in this case the bond returns of the countries most affected by COVID-19, show skewness or kurtosis (nonnormality features), erroneous estimates could be obtained, and therefore, this method would not be efficient in those assumptions. In contrast, with the QR technique, more efficient estimates and results are obtained when the errors do not follow a normal distribution and, in addition, it allows the calculation of growth curves and reference values. Thus, this method has been chosen because it focuses on estimating specific percentiles of a specific sample, and better explains the sensitivity of countries’ bonds to changes in international factors (Koenker & Hallock, 2001). Numerous recent studies, including Escribano et al., 2022; González & Jareño, 2019; Jareño et al., 2016, 2020, 2022; Sevillano & Jareño, 2018, among others, have conducted their analyses through the application of the QR method to estimate the models proposed in their research. This method offers multiple advantages, such as obtaining changes produced in parameters in many quantiles, evaluating linear programming, using monotonic transformations and showing more reliable results of the variable in the presence of outliers. Unlike classical regression methods that seek to minimize the sum of squared residuals (and use the median as an estimator), the QR methodology seeks to minimize the sum of absolute errors weighted with asymmetric weights (using the quantiles as estimators). Thus, this method assigns weights to each distribution span depending on whether the point cloud would be above or below the line showing the regression and, therefore, varies only the quantile θ. Koenker and Bassett (1978) applied a minimum absolute deviation to quantile regressions, where given an explained variable (yb) and several explanatory variables (xb) for each quantile θ, the sum of the square of the absolute values of the errors is minimized with respect to β according to the sign of these errors or residuals and is obtained as follows(2) [∑b:yb≥xb′βbθ\|yb−xb′β\|+∑yb<xb′βb(1−θ)\|yb−xb′β\|] where p(θ) is called the verification function with values in period (0,1) and from which we find the approximation of β, which is a vector. Assuming that yb - xb’ β = ub, and Ub\|xb = 0, that is, that the conditional expected value of the error ub with respect to the observations is zero, the conditional mean of yb with respect to xb depends linearly on the vector(3) (yb\|xb)=xb′β The solution to the optimization problem is the inverse of the conditional quantiles(4) F−1=(yb\|xb) According to Buchinsky (1998), if we assume that(5) yb=xb′βθ+ubθ the conditional expected value need not be zero, but the θth quantile of the error with respect to the explanatory variables is(6) (Qθ(ub\|xb))=0 The θ-th quantile of yb with respect to the explanatory variables is(7) (yb\|xb)=xb′β Therefore, the linear model of QR is specified as follows(8) yb=xb′βθ+ubθ where ub is the unknown conditional error, yb is the dependent variable (i.e., the bonds of the countries most affected by the COVID-19 pandemic), βθ: k × 1 is the vector of unknown parameters associated with the quantile θ, and xb′: k x 1 vector of independent variables that may include a constant term. This research is based on the two-factor linear regression model introduced by Stone (1974), which is extended to separately analyse the effect of changes in the price of gold, oil and Bitcoin, as well as changes in the VIX, S&P 500, EURO STOXX50 and the FSI. The formulation used to analyse the sensitivity of bond yields of the selected countries to changes in these risk factors is the two-index model introduced by Stone (1974) but extended to include seven risk factors, whose expression adapted to our model is as follows(9) yjt=αj+β1jRAUt+β2jROILt+β3jRBTCt+β4jRVIXt+β5jRES50t+β6jRSP500t+β7jΔFSIt+sjt where Yjt is a country’s bond yield (j) at t, RAUt is the gold returns, ROILt is the oil returns, RBTCt is the Bitcoin returns, RVIXt is the Chicago Board Options Exchange Market Volatility Index return (VIX), RES50t is the EURO STOXX50 return, RSP500t captures the performance of the S&P 500, ∆FSIt is the change in the Financial Stress Index (FSI) and sjt represents the random disturbance of bond yields. In line with Ferrando et al. (2017), among others, our extended risk factor model can be expressed as follows within the QR methodologyQθ(Yjt\|RAUt,ROILt,RBTCt, RVIXt,RES50t, RSP500t,∆FSIt)= β0jθ+β1jθRAUt+β2jθROILt+β3jθRBTCt+β4jθRVIXt+ (10) …+β5jθRES50t+β6jθRSP500t+β7jθR∆FSIt+εjt where Qθ shows the θth conditional quantile of the return of each government bond market, 0<θ<1, and the parameters (β0jθ, … β7jθ ) may measure the sensitivity of each international bond market at the θth quantile to fluctuations in the selected risk factors. The quantiles would indicate different bond market states. Therefore, parameters estimated for higher theta values may show sensitivity estimates at the upper end of the distribution (large increases of government bond returns). On the contrary, parameters estimated for lower theta values would exhibit exposure estimates at the lower tail of the government boned return distribution (great decreases of government bond returns). The estimation of the model was carried out by means of a system of seven equations due to the dependent variables in the analysis, which correspond to the bonds of the countries we analysed, namely, Spain, the United States, the United Kingdom, Italy, France, Germany and South Africa.5 Thus, considering that the methodology used allows us to analyse bullish (related to high values of theta) and bearish (linked to low values of theta) moments of the bond market of the different countries selected in this work and which have been the most affected by the COVID-19 pandemic, this research aims to test the following hypotheses. H1 assumes that the bond yields of the analysed countries should show a higher sensitivity to changes in international risk factors for high and low values of theta, which are associated with extreme bond market conditions. In addition, H2 assumes that the bond yields of the countries most affected by COVID-19 are expected to behave differently due to the particular characteristics of each country’s economy, such as the level of economic growth, growth opportunities and the economic area to which it belongs, among others. To test the robustness of the methodology used, H3 suggests that the sensitivity of bond yields for the selected countries to changes in international risk factors behaves differently during the pandemic period with respect to a prior and a posterior period. Results of the Extended Risk Factor Model During the COVID-19 Pandemic This section shows the results obtained through the estimations made with the QR method during the pandemic period. As mentioned above, the aim of this model is to know the sensitivity of the bond returns of some of the countries most affected by COVID-19 to variations in relevant international factors, such as the yield of gold, oil, Bitcoin and globally known and influential indices (VIX, EURO STOXX50, S&P 500, and FSI). The period ranges from January 2, 2020 to April 30, 2021 (330 observations). This study will allow us to understand the changes that occur in the sensitivities shown by bond returns to changes in the selected risk factors over the different quantiles. In our case, it was decided to analyse the sensitivity shown by bond yields in three specific quantiles: 0.1 (lower end of the distribution), 0.5 (median of the distribution) and 0.9 (upper end of the yield distribution) to observe what happens beyond the median, that is, at more extreme values, as this is one of the advantages of using the QR method. The selected theta values are in line with recent studies such as Jareño et al. (2022) and Escribano et al. (2022), among others. It also allows for the evaluation by linear programming and the use of monotonic transformations in the returns of the countries’ bonds, in addition to offering more robust results, which enriches the interpretation of the results obtained. Previous works, such as Ferrando et al. (2017), Sevillano and Jareño (2018), Jareño et al. (2020, 2021) and Escribano et al. (2022) among many others, have been used as a reference for the estimation and interpretation of this model by means of QR. Estimates of the Extended Risk Factor Model: Theta 0.1 This section shows the coefficients obtained for a theta-quantile = 0.1 (see Table 3, Panel A), which reveals information on the behaviour of the bonds on the lower end of the return distribution function, together with the significance level of each of them.Table 3. Estimates of the Extended Risk Factor Model During the COVID-19 Pandemic. Panel A: For Theta-Quantile 0.1 Gold Crude oil Bitcoin VIX Euro Stocks US Stocks Financial Stress R2 Spain 0.10049* −0.00079 0.01208* −0.00647 0.07234* −0.01944 0.00132 0.08410 US 0.10887* 0.00448 −0.00169 −0.00883 −0.03505 −0.05810 0.00190 0.15823 UK 0.08056* 0.00154 −0.00186 −0.00194 −0.02164 −0.01767 0.00027 0.09172 Italy 0.11491* −0.00571 0.03201* 0.00087 −0.01004 −0.04968 −0.00958* 0.16880 France 0.06325 −0.00954* 0.01648 −0.00477 0.01521 −0.02019 0.00112 0.07449 Germany 0.08700* −0.01019 0.00015 −0.00981 −0.04913 0.02217 0.00133 0.10911 S. Africa −0.03827 −0.03501 −0.01046 0.01472 0.02528 −0.01136 −0.01408* 0.17602 Panel B: For theta-quantile 0.5 Gold Crude oil Bitcoin VIX Euro stocks US stocks Financial stress R2 Spain 0.03665 −0.01169 −0.00191 −0.00008 0.04231 −0.00352 0.00014 0.02243 US 0.07889* −0.00541 0.00232 −0.00308 −0.02390 −0.06024 0.00334* 0.17905 UK 0.06817* 0.00120 0.00303 0.00190 −0.05567 −0.01919 −0.00082 0.07869 Italy 0.04384 −0.01443* 0.01129 −0.00312 0.01179 −0.01126 −0.00236 0.04563 France 0.04753 −0.00369 0.00003 −0.00276 −0.00266 −0.02405 0.00042 0.03206 Germany 0.05522* −0.01138 −0.00032 −0.00024 −0.06695*** 0.00027 0.00054 0.10868 S. Africa 0.04338 −0.01701 0.00225 0.01525* 0.05038 −0.00702 −0.00952 0.07516 Panel C: For theta-quantile 0.9 Gold Crude oil Bitcoin VIX Euro stocks US stocks Financial stress R2 Spain 0.05645 −0.00496 0.02227* −0.01018* 0.06388 −0.09091*** 0.00218 0.0554 US 0.06464* −0.01427 0.02332 −0.01213* 0.01453 −0.02749 0.00752* 0.2550 UK 0.07771 −0.00519 0.00282 −0.00506 −0.04298 −0.03738 −0.00009 0.0613 Italy 0.03391 0.00351 0.03248** −0.00381 0.04139 −0.04830 −0.00140 0.0590 France 0.03662* 0.00406 0.00778 −0.00168 −0.01991 0.00159 0.00126 0.0459 Germany 0.02537* −0.00475 0.00414 −0.00402 −0.06931** −0.01584 0.00014 0.1262 S. Africa 0.04410 0.02836* −0.02605 0.00954 0.14526 −0.08199 −0.00867 0.1153 Notes: The sample period ranges between January 2, 2020, and April 30, 2021. , , indicate statistical significance at the 10%, 5% and 1% levels, respectively. First, the gold yield is statistically significant for all bond returns except South Africa. Thus, for the developed countries, we find significant positive values mostly at a level of 1% (only France shows a significance level of 5%). These positive coefficients signal a direct relationship between gold yields and bond returns which implies, in line with Baur and Lucey (2009), that gold acts as a diversifier instead of as a safe haven with government bonds. For oil returns, statistically significant coefficients are found for the bond yields of France, Germany and South Africa, which are all at a significance level of 5% except for France, whose level is 1%. As expected, a negative coefficient, which indicates an inverse relationship, is observed for all three countries. In terms of the number of countries concerned, a similar result is observed for Bitcoin yields, as we find positive and statistically significant coefficients for the bond yields of Spain, Italy and France. A 1% significance level is detected for Italy. Regarding the VIX yields, we observe that there is only a statistically significant coefficient for the German bond returns. Moreover, the negative sign indicates an inverse relationship, that is, an increase in the VIX yield produces a decrease in the German bond returns. Statistically significant coefficients are observed for the bonds returns of Spain and Germany, for the EURO STOXX50 yield, with positive and negative coefficients, respectively. Here, we can observe the classic capital transfer that usually occurs between fixed income (bonds) and equity markets, according to much of the financial literature. To continue, we analyse the performance of the S&P 500, which does not show any statistically significant relationship. Finally, if we analyse the FSI, we observe that positive variations in this index imply decreases in the returns of Italy and South African bonds, at a level of 1%. Last, it is interesting to comment on the explanatory power of the model, which fluctuates between 7.5% for the French market and 17.6% for the South African market. Estimates of the Extended Risk Factor Model: Theta 0.5 This section collects the coefficients obtained for a theta-quantile = 0.5 (median), together with information on the significance level of each coefficient (Table 3, Panel B). For this quantile, the gold yield is also significant for the bond returns of all the developed countries (in this case, for Spain and Italy the significance level is 5%). All the coefficients are positive although somewhat lower that in the previous quantile. For Bitcoin and S&P 500 yields, we observe that there are no coefficients that show statistical significance; therefore, there is no influence of any kind of these explanatory variables on the government bonds of the countries that we have included in the analysis of the median of the distribution. This result differs from that extracted in the study of the lowest quantile (0.1), in which we did observe coefficients with statistical significance in the case of Bitcoin for the bonds of different countries (Spain, Italy and France). This result shows the suitability of the methodology chosen and applied in this work, as it confirms that a traditional methodology such as ordinary least squares (OLS), which focuses only on the median of the distribution, would lose information from the extremes of the distribution of returns (for example, quantiles 0.1 and 0.9), in which we observe statistically significant relationships between the variables analysed in this work. For the EURO STOXX50 return, we observe statistically significant coefficients for the United Kingdom and Germany, with a negative coefficient (which shows an inverse relationship). Finally, if we analyse oil yields, VIX yields and variations in the FSI, we observe that there are only some relationships with some specific bond returns. For example, crude oil negatively impacts on Spanish and Italian bond returns (other markets than those found for a lower quantile). Finally, variations in the FSI negatively affect South African bonds, whereas its impact is positive for U.S. bond returns. The explanatory power of the model moves between the 2.2% for the Spanish market and 17.9% for the U.S. market. We note that the explanatory power (analysed through the R2 coefficient) has been generally reduced with respect to the study of the 0.1 quantile, which involves the analysis of the lower end of the distribution of yields, except for the U.S. Estimates of the Extended Risk Factor Model: Theta 0.9 This section illustrates the coefficients obtained for a quantile theta = 0.9, which shows information about the upper end of the distribution function of the yields, together with the significance level of each one of them (Table 3, Panel C). First, the gold yield is statistically significant for all bond returns, except for Italy and South Africa, with positive values. Furthermore, the significance level is lower than in the case of the lower quantiles. For the S&P 500 yield and financial stress variations, statistically significant coefficients are found only for Spain and the U.S. at significance level of 1% and 5%. Moreover, the first coefficient shows a negative sign, which indicates an inverse relationship: when oil or the S&P 500 yields rise, bond returns in this country fall. Nevertheless, the second coefficient show a positive sign, indicating a direct effect of FSI shocks on U.S. bond returns. A similar observation occurs for the rest of risk factors, in the sense that all of them have a statistically significant effect on the bond returns of two out of the seven countries analysed. In particular, the performance of crude oil negatively impacts on U.S. bond returns, but positively on South African bond returns; Bitcoin returns positively affect the Spanish and Italian markets; VIX yields negatively impact on bond returns in Spain and the US and, finally, the EURO STOXX50 performance influence on the Spanish (positively) and German (negatively) bond returns. Thus, most of the risk factors (except the VIX) show a different impact depending on the bond market analysed, which could be evidence of the relevance of the peculiarities of each country. In the case of the EURO STOXX50 yield, as previously said, we observe statistically significant coefficients for Spain and Germany in both extreme quantiles, but with opposite sign in each country. The negative sign in the case of Germany can indicate that the German government bonds act as a safe haven to investors in a period of turbulence in the European equity market. Last, the explanatory power of the model at the extreme quantile 0.9 ranges from 4.6% for the French market to 25.5% for the U.S. market, reaching the maximum explanatory power at this quantile and for the U.S. market. The result is to be expected, since some of the variables included as international risk factors refer to the U.S. market, although their influence extends worldwide. Evaluation of Alternative Estimates During the Pandemic Period In order to verify the stability of the results obtained, as a robustness test, we propose additional estimations that have been implemented during the COVID-19 pandemic period in which we alternatively eliminate risk factors that present high levels of correlation (both positive and negative) with other explanatory variables proposed in this work. The aim of these first tests is to check whether the results achieved are stable and whether or not they depend on the inclusion in the estimation of some risk factors or others. Table 4 reports the estimates made for the 0.1 quantile, which is the extreme of the distribution that showed the greatest variety of statistically significant risk factors in the previous tests. First, in the case of the Spanish bond market, we observe how the estimates remain stable (in terms of sign and significance level) as the different variables that showed strong correlation with other risk factors enter and exit the tests. In the respective tests, the S&P500 and EURO STOXX50 yields (which show a high degree of positive correlation), the VIX (with which the previous variables show a strong negative correlation), as well as the FSI (which is highly and negatively correlated with the European and US market indices, as well as positively correlated with the VIX) have been alternatively eliminated. Similar tests have been conducted in the other bond markets, and the results have been found to remain virtually unchanged, being highly stable in countries such as South Africa, Germany and Italy.Table 4. Alternative Estimates of the Extended Risk Factor Model During the COVID-19 Pandemic: For theta-Quantile 0.1 Original Estimation Spain US UK Italy France Germany S. Africa Gold 0.10049* 0.10887* 0.08056* 0.11491* 0.06325 0.08700* −0.03827 Crude oil −0.00079 0.00448 0.00154 −0.00571 −0.00954* −0.01019 −0.03501 Bitcoin 0.01208* −0.00169 −0.00186 0.03201* 0.01648 0.00015 −0.01046 VIX −0.00647 −0.00883 −0.00194 0.00087 −0.00477 −0.00981 0.01472 Euro stocks 0.07234* −0.03505 −0.02164 −0.01004 0.01521 −0.04913 0.02528 US stocks −0.01944 −0.05810 −0.01767 −0.04968 −0.02019 0.02217 −0.01136 Financial stress 0.00132 0.00190 0.00027 −0.00958* 0.00112 0.00133 −0.01408* Alternative 1 Spain US UK Italy France Germany S. Africa Gold 0.09725* 0.11805* 0.06936* 0.11576* 0.06152* 0.07977* −0.03226 Crude oil −0.00063 −0.00366 0.00195 0.00172 −0.00602 −0.00942* −0.02966* Bitcoin 0.01143 0.00031 −0.00408 0.03474* 0.01279* 0.00045 −0.00610 VIX −0.05647 −0.01158* −0.00388 0.00569 −0.00467 −0.01075* 0.01551 Euro stocks 0.07814* — — −0.02219 0.00779 −0.04871*** — US stocks — −0.08788* −0.02616 — — — −0.03388 Financial stress 0.00186 0.00315 0.00066 −0.00847* 0.001653 0.00068 −0.01528* Alternative 2 Spain US UK Italy France Germany S. Africa Gold 0.07949* 0.10410* 0.07892* 0.11631* 0.06934* 0.06825 0.02127 Crude oil 0.00125 0.00547 0.00195 −0.00165 −0.00892 −0.01004* −0.02927 Bitcoin 0.01336* −0.00096 −0.00044 0.03256*** 0.01250* 0.00406 −0.00427 VIX — — — — — — — Euro stocks 0.08579* −0.06186 −0.02523* −0.01017 0.01341 −0.05290 0.01949 US stocks 0.00417 −0.05893 −0.01535 −0.05410 −0.01681 0.02610 −0.05128 Financial stress 0.00141 −0.00042 0.00019 −0.00927* 0.00082 0.00060 −0.01153* Alternative 3 Spain US UK Italy France Germany S. Africa Gold 0.08708* 0.12636* 0.07128* 0.11752* 0.05682* 0.07049*** 0.00608 Crude oil −0.00110 −0.00101 0.00217 0.00522 −0.00671* −0.00889* −0.02262* Bitcoin 0.01528* −0.00533 −0.00484 0.02933* 0.01629* 0.00401 0.01563 VIX — −0.01133 −0.00424 — — — 0.02016 Euro stocks 0.07602* — — −0.02009 0.00130 −0.04611* — US stocks — −0.11304* −0.03853* — — — 0.17061* Financial stress 0.00118 — — −0.00775* 0.00112** −0.00001 — Note: The sample period ranges between January 2, 2020, and April 30, 2021. , , ** indicate statistical significance at the 10%, 5% and 1% levels, respectively. So, we can draw the conclusion that the correlations detected at the beginning of the study between some risk factors do not affect the stability of the results obtained in the pandemic period. Evaluation of Bearish, Normal and Bullish States of International Government Bond Yields Next, an analysis of the evolution of the sensitivity of the bond yields of the countries studied to changes in the explanatory variables will be carried out along the different quantiles of the yield distribution (Figure 2). This analysis allows us to find the differences that may exist in the influence of the factors on the different bonds depending on the bearish, normal or bullish state of the yields of the bonds studied.Figure 2. Impact of changes in the explanatory factors on government bond returns across quantiles during the whole sample period. (a) Spain. (b) US. (c) UK. (d) Italy. (e) France. (f) Germany. (g) South Africa. Notes: C (1): constant; C (2): Gold returns, C (3). Oil returns, C (4): Bitcoin returns, C (5): VIX returns, C (6): EuroStoXX50 returns, C (7): S&P500 returns, C (8): FSI changes. The following figures show the graphs of the evolution of the sensitivities studied with different quantiles, which show the magnitude of the impact of a change in the explanatory variables on the bond yields of the countries included in the study, which have been the hardest hit by the COVID-19 pandemic. Specifically, Figure 2 shows the evolution of the sensitivity of bond yields to changes in the risk factors (in blue) for the different quantiles. Thus, the extreme quantiles are associated with the bullish and bearish situations of the bond market, as described in the works of Sevillano and Jareño (2018), Jareño et al. (2020, 2021) and Escribano et al. (2022), among others. In all cases, the sensitivity of bond yields shows an increasing trend for changes in the constant, with the highest values being those we obtained when analysing the 0.9 quantile (González & Jareño, 2019). According to the aforementioned papers, the higher the value of the constant is, the lower the explanatory power of the model on the dependent variable. Therefore, we would expect to find, in line with previous literature, a higher explanatory power of the model for low values of theta, that is, at bearish moments in bond yields. Regarding the evolution of the sensitivity of bond yields to changes in the other risk factors included in this paper, we find the following evidence. In line with some previous works, such as Sevillano and Jareño (2018) and Jareño et al. (2020), we confirm the hypothesis that assumes that the analysed bond yields are more sensitive to changes in the risk factors in the extreme quantiles, that is, in extreme conditions of the studied bond markets (H1). Moreover, we also confirm that the analysed bond yields behave differently in many scenarios, depending on the specific characteristics of each country’s economy (H2). Thus, we corroborate the heterogeneity in the sensitivity of bond yields of the countries that were most severely impacted by the COVID-19 pandemic to fluctuations in risk factors. Therefore, these results support the relevance of using the QR methodology. Moreover, in this section, we analyse in detail the evolution of the sensitivity of bond yields to changes in each international risk factor to detect which factors change the sensitivity more over time, and if there are similar patterns of sensitivity behaviour for some of the bonds of the different countries studied. The international gold price factor, identified in the graphs as C (2), remains constant or with a downwards trend for most of the bonds of different countries, which means that the importance of the gold price seems to be greater in bearish market situations. The price of oil, identified as C (3), is a factor that does not follow a homogeneous behaviour for all countries, but rather its evolution varies. For Spain, there are rises and falls from low to high quantiles. For the U.S., there is a decreasing trend, and for the rest of the countries, there is generally an increasing trend, although it should be noted that between the 0.6 and 0.8 quantiles, there are decreases in the sensitivity values. For the price of Bitcoin and the VIX, identified as C (4) and C (5), respectively, constant increasing values are maintained for all countries, except for the United Kingdom, which shows a notably decreasing trend. If we analyse the yields of the European (EURO STOXX50) and American (S&P 500) equity market indices, which have been identified as C (6) and C (7), we observe very disparate trends, especially in the case of the S&P 500, which does not follow a clear trend for the different countries and which is the factor that varies most from one country to another. It is worth highlighting the case of Spanish bonds, where the highest values are found at median values and decreases are observed at the extremes, so the importance of this factor is greater in stable market situations (contrary to what normally occurs with the rest of the factors). Finally, for the FSI, identified as C (8), a constant trend is followed for most of the countries analysed, except for German bonds, which decrease. U.S. bonds behave similarly to Spanish bonds in comparison to the EURO STOXX50, where we find higher values in the middle quantiles and lower values at the extremes. In summary, we cannot make a generalized description for all the countries’ bonds and all the factors, since, as we have seen when analysing the coefficients and exploring the evolution in the graphs shown, the trends are very disparate, and depend on the conditions of each country, its particular economy and its economic culture, which will cause some factors to have more or less influence in extreme situations, both bullish and bearish, in the market. Therefore, the results allow us to accept the initial hypotheses of this research (H1 and H2). Evaluation of the Comparison of the COVID-19 Period With Pre-Pandemic and Post-Vaccination Periods In order to check the appropriateness of using the QR methodology, this paper proposes to test the hypothesis H3, which assumes that the sensitivity of the international government bond yields selected in this research to variations in international risk factors behaves differently during the pandemic period compared to the pre-pandemic and post-vaccination periods. Pandemic versus Pre-Pandemic Periods This sub-section compares the results obtained in the pandemic period with estimates during the pre-pandemic period, to detect some peculiarities observed during stress times such as the COVID-19. As in Table 3, which provides the estimates made for the pandemic period, Table 5 shows the estimates made at the extreme quantiles (theta = 0.1 and 0.9), as well as for the median quantile, during the pre-pandemic period (between January 3, 2018, to December 31, 2019). First, for the lowest quantile we find some interesting differences with respect to the results found in the COVID-19 pandemic period. Thus, some risk factors such as the price of crude oil and the performance of Bitcoin do not have a statistically significant impact in any of the countries analysed. Moreover, in a pre-pandemic situation and low bond market states, gold continues to play an important role as an explanatory variable, although its position is less relevant in some countries such as Spain, the UK and Italy. However, for this lowest quantile during the pre-pandemic period, risk factors related to uncertainty emerge as key explanatory variables in this analysis. On one hand, the VIX shows negative and statistically significant sensitivities, and, on the other hand, the financial stress index also would exhibit some statistically significant but positive coefficients, showing the period of relative financial calm prior to the pandemic (in contrast to the results found in Bianconi et al., 2013). Moreover, it is interesting to note that, in all market states, the sign of the sensitivity shown by the different countries is negative for the VIX, which may herald the imminent COVID-19 pandemic that would mark the beginning of a period of great uncertainty at a global level. Thus, in line with the financial literature, such as recent papers like Zhang and Giouvris (2022), the volatility of the US stock market (VIX) would emerge as a good predictor of the imminent COVID-19 pandemic crisis. Nevertheless, another uncertainty index, the Financial Stress Index (FSI), which is a broader measure of uncertainty, of all financial markets, would not show that predictive power that we observe in the VIX. Moreover, once the crisis stage is anticipated during the pre-pandemic period, the VIX no longer shows these statistically significant results in the COVID-19 period.Table 5. Estimates of the Extended Risk Factor Model During the Pre-COVID-19 Pandemic. Panel A: For Theta-Quantile 0.1 Gold Crude oil Bitcoin VIX Euro Stocks US Stocks Financial Stress R2 Spain 0.01512 −0.00829 −0.00983 −0.00990* 0.06061* −0.07770 0.00153 0.03141 US 0.04532* −0.01845 −0.00270 −0.01161* 0.04903 −0.00166 0.01324*** 0.14858 UK 0.03470 −0.01254 −0.00138 −0.00906* −0.03545 −0.03819 0.00394 0.05368 Italy 0.01933 −0.01361 0.00840 −0.01092 0.25682* −0.21307* −0.00348 0.08645 France 0.05200* −0.00915 0.00059 −0.01563* 0.07453*** −0.04238* 0.00820* 0.09860 Germany 0.05196* −0.01472 0.00192 −0.01536* 0.03449 −0.04492 0.00777* 0.10230 S. Africa 0.04348* −0.00586 0.00484 −0.00894* −0.00390 −0.06634 −0.00318 0.11290 Panel B: For theta-quantile 0.5 Gold Crude oil Bitcoin VIX Euro stocks US stocks Financial stress R2 Spain 0.03698* 0.00129 0.00085 −0.00507 0.06876 0.00191 0.00408 0.01523 US 0.08060* −0.00721 −0.00149 −0.00771 0.04610* −0.01738 0.01285* 0.20247 UK 0.03395* 0.00052 −0.00028 −0.00691* 0.01193 −0.00746 0.00823* 0.09105 Italy 0.02043 0.00008 −0.00485 −0.00891* 0.18926*** −0.09632* 0.00195 0.03319 France 0.03130* −0.00508 −0.00206 −0.00913* 0.00941 −0.01738 0.00565* 0.07110 Germany 0.03974* −0.00292 −0.00102 −0.00666* −0.01480 0.00743 0.00700* 0.12260 S. Africa 0.05829*** 0.00933 0.00453 −0.00264 0.03492 −0.03687 −0.00392* 0.06135 Panel C: For theta-quantile 0.9 Gold Crude oil Bitcoin VIX Euro stocks US stocks Financial stress R2 Spain 0.05846 −0.00240 −0.00720* −0.00427 0.09933 −0.00574 0.00488 0.02934 US 0.08516* −0.01503 −0.00812* −0.00672 0.08796* −0.01296 0.01285* 0.27713 UK 0.04134 0.01324 −0.00565 −0.01226 −0.03096 −0.02796 0.00702* 0.08769 Italy 0.12921 0.00391 −0.00426 −0.01011 0.28780* −0.02129 0.00734 0.05246 France 0.04133* −0.00366 0.00858* −0.01166 0.05738 −0.03440 0.00974* 0.08836 Germany 0.04467 0.01139 −0.00316 −0.01391 −0.01957 −0.05018 0.01039* 0.14492 S. Africa 0.10316 −0.01031 0.00359 −0.00080 0.04020 0.00333 −0.00478* 0.05080 Notes: The sample period ranges between January 3, 2018, and December 31, 2019. , , indicate statistical significance at the 10%, 5% and 1% levels, respectively. Finally, bond markets such as Italy and France show statistically significant sensitivities to both European (with a positive sign) and US (with a negative sign) equity market returns. Therefore, if we compare these results with the pandemic period, the US stock market maintains the negative sign, while the European stock market does not. Thus, these results would support the hypothesis that the European market would not have anticipated the pandemic situation in the run-up to the pandemic. However, the US stock market would have anticipated the crisis (Zhang & Giouvris, 2022). Finally, the results show that portfolio managers should use German bonds to diversify risk, particularly in the pre-crisis period, as well as during market downturns. For the median quantile this behaviour peculiar to the pre-pandemic period seems to be maintained, highlighting the significance of the VIX as the most important factor affecting all the bond markets studied. However, when examining the results for the highest quantile (theta = 0.9), we observe that the VIX loses relevance, which is gained by the financial stress index, which shows a positive and statistically significant effect on all bond markets, except for the South African market, whose impact is negative, as well as the Italian market, on which there is no impact. This result would be in accordance with the traditional link between higher quantiles and bull market states. Pandemic versus Post-Vaccination Periods This sub-section compares the results obtained in the most intense waves of the pandemic with estimates made in the period after mass vaccination (in particular, from May 2021 to February 2022). According to recent studies, such as Demir et al. (2021) and Rouatbi et al. (2021) among others, bond market behaviour is expected to be significantly different in this vaccination period compared to the earlier and more severe period of the COVID-19 pandemic. To verify this fact, we look at the results reported in Table 6, which shows the estimates made for the different theta-quantiles 0.1, 0.5 and 0.9, distributed at the median and the tails of the distribution. In the lowest quantile we can observe an important fact that, although gold remains an important risk factor in all markets, with its traditional positive sign, the price of crude oil has a negative and statistically significant impact in all markets, except in South Africa. This result could be indicative of the escalation in energy prices experienced in recent months and currently exacerbated by the Russian invasion in Ukraine. This interesting effect holds at the median quantile of the distribution, but disappears at the highest quantile (theta = 0.9). Therefore, the results obtained would show that the price of crude oil would have a negative impact mainly at bearish market moments, which are associated with low quantiles, although the effect is maintained up to the median quantile of the distribution.Table 6. Estimates of the Extended Risk Factor Model During the Post-Vaccination COVID-19 Pandemic. Panel A: For Theta-Quantile 0.1 Gold Crude oil Bitcoin VIX Euro Stocks US Stocks Financial Stress R2 Spain 0.10820 −0.06993*** −0.01052* −0.00597 0.14817* 0.06887 0.00872* 0.13224 US 0.12900* −0.07243* 0.00396 0.00095 0.06685 −0.01751 0.00175 0.14523 UK 0.15686* −0.09752* 0.00501 0.01642 0.06995 0.15445 0.00487 0.14288 Italy 0.08668 −0.04045** −0.01243* 0.01267 0.13869* 0.32196 0.01028 0.13092 France 0.11673* −0.04017* −0.00486 −0.00003 0.04610 0.05756 0.00566* 0.12728 Germany 0.15434* −0.03541* −0.00590 −0.00322 0.08803** 0.03202 0.00673 0.14908 S. Africa 0.06735* −0.01747 0.01526 −0.01450 0.00022 −0.21130 −0.00404 0.09055 Panel B: For theta-quantile 0.5 Gold Crude oil Bitcoin VIX Euro stocks US stocks Financial stress R2 Spain 0.06014 −0.04078*** −0.00866 0.00719 0.06969 0.13806* 0.00348 0.05369 US 0.20814* −0.06593* 0.00233 0.00475 0.01180 0.13124 0.00638 0.18717 UK 0.14662* −0.06310* −0.00648 0.00846 0.02989 0.11886 0.00285 0.12023 Italy 0.02887 −0.06027* −0.00400 0.00611 0.10364 0.16174* 0.00467 0.05407 France 0.07029* −0.03747* −0.00230 0.00115 0.05771 0.03829 0.00420* 0.08374 Germany 0.04878 −0.03308* −0.00456 0.00627 0.04462 0.08641 0.00423* 0.09086 S. Africa 0.10519* 0.00246 0.01762 −0.00099 0.01685 −0.10949 −0.00423 0.06499 Panel C: For theta-quantile 0.9 Gold Crude oil Bitcoin VIX Euro stocks US stocks Financial stress R2 Spain 0.09873 −0.02232 −0.00814 0.01058 0.00496 0.14621 0.00207 0.12236 US 0.12414 −0.03678 0.00471 0.00256 0.04043 0.08050 0.01173* 0.27545 UK 0.16299*** −0.04753* −0.00663 0.01556 0.06485 0.15073* 0.00034 0.22260 Italy 0.09540 −0.03818 −0.01257 −0.00364 0.06141 0.08576 0.00380 0.08513 France 0.06237 −0.03997 −0.00570 0.01012 −0.05756 0.14861 −0.00187 0.11179 Germany 0.06884* −0.01970 −0.01055 0.02040 −0.04753 0.21952* 0.00003 0.16952 S. Africa 0.21548* 0.02182 0.02654* −0.01097 0.07799 −0.14903 0.00189 0.12482 Note: The sample period ranges between May 3, 2021, and February 17, 2022. , , indicate statistical significance at the 10%, 5% and 1% levels, respectively. Finally, it is worth mentioning that the coefficients of the equity indices during this period of less uncertainty are positive, indicating a back to normal correlation between equity and bond returns (Baur & Lucey, 2009; Papadamou et al., 2021). Evaluation of the Mutual Influence Between Government Bonds Considering Country-Level Macroeconomic Risk Factors In order to improve the credibility of our findings, this section includes country-level macroeconomic risk factors that presumably are highly correlated with government bond yields explored in this study. This additional analysis also takes into account potential mutual influence between the government bonds of the countries examined in this research. First, and following Zaremba et al. (2021), we reflect the influence of macroeconomic risk factors at the country level in an index that captures the response of governments to the situation generated by the COVID-19 pandemic, which affected each country differently. This index, known as the ‘Government Responses Index’ (GRI),6 provides information on the role that each government’s response may have played in the behaviour of sovereign bond markets, mainly in the sense of reducing their volatility. In concrete, among these responses are those related to social and economic life aspects, such as containment and closure (school closures, gathering restrictions …), economic measures, such as income support, and fiscal measures, among others, and, finally, health system initiatives (Zaremba et al., 2021). Second, with regard to the inclusion in the analysis of possible interdependencies between the governments bonds of the countries analysed, we have incorporated two alternative variables that include the spread of the government bond yields of each country with respect to the benchmark. Thus, the two world economic powers would be, firstly, the United States and, secondly, Germany (fundamentally in the case of the European countries analysed). Tables 7 and 8 show the results of our estimates for the COVID-19 period as well as for the post-vaccination period, respectively.7 Interestingly, this robustness check confirms the results obtained previously regarding the main risk factors in the different government bond markets analysed. Moreover, it reconfirms that the highest sensitivity of bonds is observed at the 0.1 quantile, which is related to bearish moments in that market, where any variation in risk factors can lead to a significant response in the bond yields of the countries explored in this study.Table 7. Estimates of the Extended Risk Factor Model During the COVID-19 Pandemic: Mutual Influence and Country-Level Risk Factors. Panel A: For Theta-Quantile 0.1 Gold Crude oil Bitcoin VIX Euro Stocks US Stocks Financial Stress Spread GRI R2 Spain 0.11438* −0.00229 0.01003 −0.00945** 0.02595 −0.05064* 0.00334* 0.47050* −0.00040*** 0.2837 US 0.09067* 0.00011 −0.00080 −0.00917 0.00333 −0.01225 0.00427 0.87900* −0.00088* 0.4046 UK 0.08878*** 0.00247 0.00235 −0.00002 −0.03217 −0.00571 0.00097 0.16810* 0.00008 0.1095 Italy 0.11856*** 0.00545 0.01512* −0.00758 0.02592 −0.03878 0.00258 0.66100* −0.00035 0.4501 France 0.09327* −0.00811* 0.01405* −0.00810 0.03268* −0.05886** 0.00221* 0.23085 −0.00041* 0.1603 Germany 0.09834*** −0.00582 0.00166 −0.00806 −0.00977 −0.01075 0.00285* 0.22067 0.00001 0.1398 S. Africa 0.07722 −0.00212 0.00689 −0.00270 0.01246 −0.06283 0.00073 0.77419* −0.00029 0.5375 Panel B: For theta-quantile 0.5 Gold Crude oil Bitcoin VIX Euro stocks US stocks Financial stress Spread GRI R2 Spain 0.06255* −0.00593 0.00078 −0.00004 −0.02991 −0.03002 0.00001 0.48799* −0.00001 0.1688 US 0.07477* −0.00689 0.00088 −0.00052 −0.06821*** −0.00530 0.00143* 0.74094* 0.00001 0.3710 UK 0.07319* 0.00060 0.00495 0.00104 −0.05952 −0.02285 0.00066 0.36306* 0.00022* 0.1517 Italy 0.09095* −0.00411 0.00423 0.00157 −0.01881 −0.02248 0.00031 0.67174* −0.00001 0.3472 France 0.06454 −0.00280 0.00012 −0.00153 −0.00358 −0.01562 0.00160 0.20241 −0.00012 0.0620 Germany 0.07064* −0.00573 0.00149 −0.00063 −0.06238 −0.00508 0.00168 0.26854* 0.00004 0.1392 S. Africa 0.07528* −0.00768 −0.00228 −0.00131 −0.01585 −0.03861 0.00212 0.82411* 0.00007 0.4971 Panel C: For theta-quantile 0.9 Gold Crude oil Bitcoin VIX Euro stocks US stocks Financial stress Spread GRI R2 Spain 0.06209 0.00264 0.01635 −0.01112 0.01344 −0.04243 0.00560* 0.58885*** 0.00003 0.2496 US 0.02309 −0.00981* 0.00037 −0.00367 −0.04318 0.00484 0.00216 0.75162*** 0.00047 0.4418 UK 0.03547 −0.00780 −0.00594 −0.00215 −0.03395 0.00739 0.00253* 0.39378* 0.00049 0.1510 Italy 0.08223* −0.00430 0.01747* −0.01218* 0.03585 −0.03299 0.00715* 0.67932* −0.00005 0.4117 France 0.03899 0.00081 0.00913 −0.01001** −0.02952 −0.02322 0.00237* 0.21826* 0.00021** 0.0686 Germany 0.03481 −0.01053* 0.00399 −0.00686* −0.04797 −0.01884 0.00239 0.29806 0.00007 0.1579 S. Africa 0.04329 −0.00427 0.00930 −0.00544 0.01878 −0.07911 0.00098 0.77843*** 0.00028* 0.5807 Notes: The sample period ranges between January 2, 2020, and April 30, 2021. Spread shows the spread between the yields Government bond of each country with respect to the US bond market (except for the US market, which is obtained with respect to the German market). GRI shows the ‘Government Responses Index’ of each country, by Hale et al. (2020) (it is extracted from the website of OxCGRT: https://www.bsg.ox.ac.uk/research/research-projects/covid-19-government-response-tracker). , , indicate statistical significance at the 10%, 5% and 1% levels, respectively. Table 8. Estimates of the Extended Risk Factor Model During the Post-Vaccination COVID-19 Pandemic: Mutual Influence and Country-Level Risk Factors. Panel A: For Theta-Quantile 0.1 Gold Crude oil Bitcoin VIX Euro Stocks US Stocks Financial Stress Spread GRI R2 Spain 0.14475* −0.05853* −0.00085 0.00068 0.10744 0.05838 0.00611 0.43052* 0.00079 0.2617 US 0.12727* −0.04489 0.00355 −0.00554 0.10800* −0.02341 0.00682 0.85083* −0.00074 0.3671 UK 0.16614* −0.09061* 0.00393 0.00697 0.07269* 0.04551 0.00219 0.60203* 0.00041 0.2778 Italy 0.12216* −0.06123* 0.00014 −0.00080 0.10488* 0.03072 0.00474 0.65690* 0.00029* 0.3422 France 0.12874* −0.05674* −0.00271 0.00770 0.08577** 0.15705* 0.00606 0.35513* 0.00018 0.1762 Germany 0.15429*** −0.03344 −0.00534 −0.00276 0.06846* 0.04523 0.00635* 0.10237 −0.00040 0.1936 S. Africa 0.12176 −0.04584* 0.00856 −0.00303 0.05680 −0.02976 0.00221 0.62166* 0.00002 0.3285 Panel B: For theta-quantile 0.5 Gold Crude oil Bitcoin VIX Euro stocks US stocks Financial stress Spread GRI R2 Spain 0.11386 −0.04338*** 0.00312 0.00579 0.01423 0.12552* 0.00495* 0.37569*** 0.00003 0.1484 US 0.06384* −0.03836* −0.00492 0.00181 0.02803 0.07704 0.00470 0.84362*** −0.00027* 0.4165 UK 0.17125* −0.06193* 0.00374 0.01187 0.03720 0.17491 0.00586 0.48021* −0.00029 0.2072 Italy 0.13362 −0.05100* 0.00169 0.00768 0.00802 0.17733** 0.00703* 0.61177* 0.00006 0.2732 France 0.10423 −0.04225*** −0.00080 0.00150 0.04062 0.06262 0.00467 0.19690* 0.00005 0.1059 Germany 0.06366 −0.03876* −0.00448 0.00373 0.03964 0.08960* 0.00495 0.15651 −0.00013 0.1232 S. Africa 0.14331* −0.03250 0.00973 −0.00121 0.02590 0.00897 0.00359 0.59137* −0.00006 0.3169 Panel C: For theta-quantile 0.9 Gold Crude oil Bitcoin VIX Euro stocks US stocks Financial stress Spread GRI R2 Spain 0.08962* −0.01813 0.00184 0.00782 0.05074 0.12950 0.00687 0.47204* 0.00003 0.2418 US 0.06079* −0.03994*** −0.00260 0.00703 −0.00656 0.14798* 0.00347 0.71723* 0.00019 0.4675 UK 0.14375* −0.02708 −0.00589 0.01645 0.06059 0.16369** 0.00838* 0.62205* −0.00014 0.3217 Italy 0.13009* −0.03880** −0.00007 −0.00072 0.05254 0.12226* 0.00958 0.68210* −0.00004 0.3096 France 0.07486* −0.03077* −0.00196 0.00975 0.01139 0.14816 0.00429 0.43230* −0.00027* 0.2148 Germany 0.06249* −0.04066 −0.00362 0.00616 −0.00600 0.16035 0.00416 0.28388* 0.00001 0.2127 S. Africa 0.14327* −0.03242 −0.00328 −0.00846 0.05722 0.05184 0.00948 0.56456* −0.00006 0.4233 Notes: The sample period ranges between May 3, 2021, and February 17, 2022 Spread shows the spread between the yields Government bond of each country with respect to the US bond market (except for the US market, which is obtained with respect to the German market). GRI shows the ‘Government Responses Index’ of each country, by Hale et al. (2020) [it is extracted from the website of OxCGRT: https://www.bsg.ox.ac.uk/research/research-projects/covid-19-government-response-tracker]. , , ** indicate statistical significance at the 10%, 5% and 1% levels, respectively. With regard to the variables incorporated in the study, we may validate the dependencies that exist between the government bond markets of the countries analysed, confirming, as expected, the influence that the US and German markets exert on the different countries, in the two periods analysed (COVID-19 and post-vaccination), as well as in the different quantiles (market states) examined. As for the importance of macroeconomic factors at the country level in the study of government bond markets, it should be noted that it varies depending on the country, as well as on the period studied and the state of the bond market (quantile). Specifically, the measures taken by governments, as reflected through the ‘Government Responses Index’ (GRI), show a negative impact in bond returns during the pandemic period, at bearish states in the bond market and in countries that have suffered the most from the effects of the pandemic, such as Spain, the United States and France, among others, which is in accordance with the effect of government policies on the stabilization of sovereign debt markets detected by Zaremba et al. (2021). In the post-vaccination period, this effect is diluted and no longer significant. Finally, we note, as was also predictable, that the risk factor related to the price of crude oil continues showing negative effects on most sovereign bond markets during the post-vaccination period. Moreover, this negative effect is independent of the state of the government bond market. This result would be starting to show the impact that the Ukraine war is having on global energy markets. However, a more detailed study would be necessary. Concluding Remarks and Implications Conclusions The aim of this paper is to evaluate the sensitivity of the treasury bond returns of some of the countries most affected by COVID-19 to changes in risk factors for international relevance, more specifically, to changes in the price of gold, oil and Bitcoin, as well as globally known indices such as the Chicago Board Options Exchange Market Volatility Index, the EURO STOXX50 index, the S&P 500 index and the Financial Stress Index. The explanatory factors were selected based on previous studies of this research area. To carry out the analysis, the proposed model has been estimated using the quantile regression (QR) method, which has allowed us to analyse the entire distribution of returns both at the median and at the extremes, making it a more complete method than if ordinary least squares (OLS) had been used. According to Sevillano and Jareño (2018), lower quantiles are related to what happens in times of economic crisis, such as the one occurring at the time of this work caused by the COVID-19 pandemic, while higher quantiles are related to economic situations of boom and prosperity. Thus, a priori, one would expect a greater explanatory power of the model in the study of the lowest quantile (theta = 0.1) during the pandemic period. If we analyse, therefore, the explanatory variable coefficients, we see that in low quantiles there are more numerous significant cases and they are, moreover, more frequent in the gold, oil and Bitcoin price variables, as well as in the EURO STOXX50 and the FSI. During the pandemic, the international factor with the highest statistical significance for the different quantiles studied is the price of gold, as we find significance for almost all the countries with medium-low quantiles, something that does not occur with any other factor studied. Thus, our results confirm the role of gold as a safe haven during this period of uncertainty and economic crisis. We find an average significance for the rest of the factors. It is worth mentioning that one of the explanatory variables with the lowest number of statistically significant scenarios is the VIX, with only three coefficients with significance in the case of the extreme quantiles, for German bonds (low quantile), and for Spanish and US bonds (high quantile). Comparing to the previous period, bond returns appear to be less sensitive to the equity market volatility after the outbreak of the pandemic. This result is in line with expectations, as the VIX and the U.S. stock market are good predictors of crisis periods, so once the global pandemic is declared by the WHO, the index is no longer relevant to the model (both in the pandemic and post-vaccination periods). Moreover, the results during the pandemic are robust to the inclusion of a country-specific factor and a factor accounting for the mutual influence of the government bonds. Regarding the explanatory power of the model that has been represented by the R2 coefficient, we see that for this period, we find a higher goodness of fit of the model in extreme quantiles than at the median. This shows the suitability of the methodology we have used, since, as explained throughout the paper, the QR method allowed us to have greater explanatory power not only in the central part of the distribution, as would occur in OLS, but also at the extremes. Based on the results obtained in the estimations of our model, we can affirm the good choice of using a quantile regression (QR) methodology as an estimation method, since it has been shown that the risk factors we have chosen to analyse the sensitivity of bonds explain to a greater extent the yields in low quantiles (theta = 0.1) that mainly reflect the current moment of economic crisis generated by the COVID-19, although also in high quantiles (theta = 0.9). In both cases, there were substantial differences with the results obtained at the median (theta = 0.5). Evaluation for Policy Makers Our results may show relevant implications for the evaluation of policy makers in three different aspects. Thus, we confirm the first hypothesis put forward in this paper (H1), since we find that the bond yields of the countries analysed, which have been especially affected by the current COVID-19 pandemic, are more sensitive to changes in risk factors under extreme market conditions, which are reflected in the extreme quantiles of the probability distribution of returns. This higher sensitivity entails that bondholders’ wealth will be subject to a greater risk under extreme market states. In addition, the second hypothesis included in this paper has also been confirmed (H2), as we observe that the bond yields of the countries analysed behave differently depending on the characteristics of each country. Therefore, it would be interesting to extend the study, classifying the countries according to some criterion, such as size, growth opportunities, the geographical area to which they belong, etc., so that significant differences in the observed behaviour can be found. As for the third hypothesis, we confirm that if we compare the results found during the COVID-19 pandemic period with the pre-pandemic period, we detect some interesting changes in the behaviour of the bond markets analysed. In general, in the pre-pandemic period, gold ceases to be a relevant risk factor for bond markets and the VIX emerges as a predictor of the uncertainty that was being generated in the markets as the coronavirus spread globally. Moreover, the relevance of the VIX index, which has a negative effect on the markets, is observed in bearish market moments, with this dominant role disappearing in bullish market moments. On the other hand, when we compare the results obtained during the pandemic with those observed during the period of mass vaccination, we also detect changes in the behaviour of bond markets. Thus, the price of crude oil emerges as a major factor in all bond markets, showing a negative effect on all of them. Moreover, as with the VIX index in the pre-pandemic period, this effect is found to be more pronounced in the lower tail of the distribution. Thus, all robustness tests performed confirm the relevance of using quantile regression to analyse the impact of international risk factors on bond yields in the selected countries, as they are a group of countries that have been hit hard by the COVID-19 pandemic. Thus, our findings have direct policy implications. Policymakers should be aware that, regardless of country-level macroeconomic risk factors, the sensitivity of sovereign bond returns to fluctuations in the different risk factors analysed depends on the stage of the economy, as well as on the state (bullish or bearish) of the bond market. Furthermore, it should be borne in mind that the volatility index of the US stock market (VIX) is once again a good predictor of times of crisis (in this case of COVID-19), so its evolution should be considered. As the main outcome for portfolio managers (including policymakers), gold continues to prove to be a safe haven asset or at least behaves like a good risk diversifier in crisis situations, such as the one caused by the pandemic. Lastly, as possible future extensions of this research, it would be interesting to provide valid evaluation policy recommendations by using, for instance, the Unconditional Quantile Regression proposed by Firpo et al. (2009) as baseline specification and fresh alternative to the one used in this paper. In addition, interesting extensions of this paper would be implemented by using quantile on quantile regression, Granger in quantiles and quantile coherence approaches, among others. Finally, due to the high correlation between government bond prices, this research proposes an additional analysis considering some country-level information (the ‘Government Responses Index’ and the spread return with respect to the benchmark) to enhance the credibility of the research. However, it would be recommended to extend the analysis by including more country-level macroeconomic risk factors. Such an analysis could add much value to the explanations of the sensitivities examined in this study. ORCID iDs Francisco Jareno https://orcid.org/0000-0001-9778-7345 Maria-Isabel Martinez-Serna https://orcid.org/0000-0003-2422-3383 Notes The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Spanish Ministerio de Ciencia e Innovación (PID2021-128829NB-I00), Junta de Comunidades de Castilla-La Mancha (CLM21-PIC-068), and Universidad de Castilla-La Mancha (2021-GRIN-31019). M. I. Martínez-Serna also acknowledges financial support from Fundación CajaMurcia. 1. Information extracted in April 2021. 2. https://www.investing.com/ 3. With regard to the evolution of oil prices, it can be observed that throughout history, and more specifically, in Spanish history, the variation in the price of various financial assets as a result of a change in the price of oil has been due to specific international economic and financial events. The Asian crisis is one example of such financial crises (Moya-Martínez et al., 2013). 4. Following González et al. (2021), we have selected an extended COVID-19 period to capture the potential effects of risk factors on bond markets due to the effects of the COVID-19 pandemic that are advanced in time by a few weeks. In addition, in this context, and following Gonzalez et al. (2021) and Umar et al. (2021a,b), the media coverage index (MCI) extracted from Raven Pack is used to analyse the impact of this pandemic on the financial markets, as it shows the influence of the amount of news related to the coronavirus in the world, compared to the rest of the news, which were already anticipating the problem weeks before the global declaration of pandemic by the WHO. If this index is analysed, it considers important issues related to the current situation such as the Panic Index (PI), Media Hype Index (HY) and the Fake News Index (FNI), among others. 5. We use international factors that are common to all the countries. For this reason, equation (10) estimates 7 individual equations, instead of using simultaneous equation or a panel regression model. Nevertheless, to examine the potential mutual influence across government bond markets, we have included the yield spread of each market with respect to the benchmark (US and Germany) in a robustness contrast presented below. 6. The ‘Government Responses Index’ of Hale et al. (2020) is extracted from the website of OxCGRT: https://www.bsg.ox.ac.uk/research/research-projects/covid-19-government-response-tracker 7. The tables show the spread with respect to the US bond market. Results using the German market are similar and are available upon request. ==== Refs References Arellano C. Bai Y. Mihalache G.P. (2020). Deadly debt crises: COVID-19 in emerging markets. NBER Working Paper No. 27275. https://www.nber.org/papers/w27275 Ashraf B. N. (2020). Stock markets' reaction to COVID-19: Cases or fatalities? 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==== Front Am Surg Am Surg spasu ASU The American Surgeon 0003-1348 1555-9823 SAGE Publications Sage CA: Los Angeles, CA 36459702 10.1177_00031348221144637 10.1177/00031348221144637 Article Did the COVID-19 Pandemic Impact the Caliber of Trainees Taken During Match 2021? A Survey of General Surgery Residency Program Directors Ferry Andrew M. MD 1 Asaad Malke MD 2 Rajesh Aashish MBBS 3 Grush Andrew E. BS 45 Elmorsi Rami MBBCh 6 Burns Heather R. BA 45 Mohan Vamsi C. BS 45 Bauer David F. MD, MPH 7 Maricevich Renata S. MD 45 1 Division of Plastic Surgery, Department of Surgery, 6684 Oregon Health & Science University , Portland, OR, USA 2 Department of Plastic Surgery, University of Pittsburgh Medical Center , Pittsburgh, PA, USA 3 Department of Surgery, 14742 UT Health San Antonio , San Antonio, TX, USA 4 Division of Plastic Surgery, Department of Surgery, Texas Children’s Hospital , Houston, TX, USA 5 Division of Plastic Surgery, Michael E. DeBakey Department of Surgery, 3989 Baylor College of Medicine , Houston, TX, USA 6 Faculty of Medicine, 68780 Mansoura University , Dakahlia, Egypt 7 Department of Neurosurgery, 3989 Baylor College of Medicine , Houston, TX, USA Renata S. Maricevich, MD, Division of Plastic Surgery, Baylor College of Medicine, 6701 Fannin St, Suite 610, Houston, TX 77030. Email: [email protected] 2 12 2022 2 12 2022 00031348221144637© The Author(s) 2022 2022 Southeastern Surgical Congress 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. Background The cancellation of clinical rotations (CRs) and implementation of virtual interviews (VIs) profoundly affected the residency selection process leading up to the 2021 NRMP Match. The authors investigated how these changes influenced the caliber of applicants taken by general surgery (GS) residency programs from the perspectives of program directors (PDs). Methods A 14 question, web-based electronic survey was emailed to PDs of ACGME-accredited GS residency programs. Questions sought program characteristics and PDs’ perspectives regarding potential differences in subjective characteristics and clinical skills demonstrated by their 2021 Match class relative to previous resident classes. Results A total of 75 PDs (27.2%) responded to our survey. Most respondents observed no changes in residents’ fit with their program (72.0%), communication skills (68.0%), responsiveness to clinical instruction and feedback (73.3%), work ethic (73.3%), and rotation evaluations (68.0%). Only 21.3% of PDs believed that VIs negatively impacted their ability to accurately assess applicant intangibles. Conversely, 56.0% of PDs reported that the cancellation of CRs in 2020 negatively affected residents’ clinical competency at the start of residency. At 1-year following the 2021 NRMP Match, 30.7% of PDs reported that the clinical skills exhibited by their 2021 Match class were poorer than previous resident classes. Discussion Our findings suggest that VIs limited selection committees’ ability to accurately assess applicant’s subjective characteristics to a lesser degree than previously described in the literature. Canceled CRs adversely affected the 2021 Match Class’s clinical skills at the start of residency and at 1 year following the 2021 NRMP Match. general surgery residency interviews virtual interviews education edited-statecorrected-proof typesetterts10 ==== Body pmcKey Takeaways • The majority of program directors reported no changes in their 2021 Match class’s subjective characteristics relative to previous resident classes. • Virtual interviews limited selection committees’ ability to accurately assess applicant’s subjective characteristics to a lesser degree than previously described in the literature. • The cancellation of clinical rotations in 2020 negatively affected the clinical skills exhibited by the 2021 Match class at the start of their training and at 1-year following the 2021 NRMP Match. Introduction The residency selection process is a high-stakes endeavor with lasting implications for general surgery (GS) residency programs and applicants alike. This long-standing practice, however, was greatly affected by the COVID-19 pandemic. The most significant change to the residency selection process during the 2020-2021 application cycle was the introduction of virtual interviews (VIs).1 While VIs reduced interview-related costs for both applicants and residency programs, many program directors (PDs) raised concerns regarding their ability to measure applicant intangibles during their interview.2-7 GS residency programs were also tasked with choosing from an applicant pool that demonstrated significant heterogeneity in their clinical exposure to surgery resulting from the cancellation of clinical rotations (CRs) in 2020.8 Despite these changes, 1564 out of 1569 (99.7%) of categorical GS training positions were filled at the conclusion of the 2021 NRMP Match.9 The 2021 NRMP Match was a success for GS residency programs from the standpoint of filling training positions; however, the aforementioned changes to the 2020-2021 application cycle raised several questions regarding the quality of applicants who secured these positions. To date, no studies have measured the outcomes of the 2021 NRMP Match in terms of the clinical skills and subjective characteristics (such as personality and work ethic) of the 2021 Match class. Moreover, the absence of this data has limited the analysis of the true impact that the cancellation of CRs had on medical students’ ability to develop clinical skills prior to beginning residency, and the utility of VIs for evaluating applicant intangibles during residency interviews. In this study, we seek to elucidate the impact that the cancellation of CRs and implementation of VIs had on the caliber of residents taken in the 2021 NRMP Match from the perspectives of PDs of American Council for Graduate Medical Education (ACGME)-accredited GS residency programs. Methods Data Source A web-based, electronic survey was designed using REDCap© (Vanderbilt University, Nashville, TN) and piloted prior to its distribution. The survey pilot entailed multiple cycles of sending our survey to academic surgeons across multiple subspecialties, obtaining feedback to ensure questions were being answered as intended, and modifying the survey to address areas that may confuse the respondent. Surveys were sent via email to PDs of ACGME-accredited GS residency programs during the data collection period (3/10/2022 - 5/4/2022). PDs of GS residency programs with an accreditation status other than those listed under the “continued” category (such as the “initial accreditation,” “withhold and probation,” or “withdrawal” categories) were excluded from our study. Additionally, PDs with email addresses that could not be identified and those with email addresses that blocked our recruitment email were excluded. Participation in our cross-sectional study was voluntary, and participants were able to withdraw from the study at any time. Responses were collected in a deidentified fashion and were analyzed at the conclusion of the collection period. Incomplete responses were not included in our analysis. This study was approved by the Institutional Review Board at Baylor College of Medicine. Measurements Our survey included 14 questions that were presented in multiple choice, true/false, and Likert scale formats (Supplemental file 1). Questions included in the survey sought program characteristics, the number of training positions offered during the 2021 NRMP Match, and the number of home students taken in the 2021 Match relative to prior application cycles (only programs that offered > 1 training position and are affiliated with a medical school). Program geographic location was classified using The Census Regions and Divisions of the United States Classification System.10 Several questions on our survey were designed to collect PDs’ perspectives regarding potential differences in the subjective characteristics and clinical skills demonstrated by their 2021 Match class (pandemic resident cohort) and previous resident classes (pre-pandemic resident cohort). Specific to the pandemic resident cohort, PDs were asked to provide their perspectives regarding the relationship between the 2021 Match class’s clinical competency at the start of residency and canceled CRs. Additionally, PDs were asked about their outlooks on the utility of VIs for evaluating applicant intangibles based on their experiences with their 2021 Match class. Lastly, participants were provided an optional free-response text box to add comments that they believed would be beneficial to our study. Results A total of 75 PDs of ACGME-accredited GS residency programs responded to our survey (27.2% response rate). Forty-seven (62.7%) PDs represented an academic residency program with the majority of programs residing in the South (34.7%), Northwest (28.0%), and Midwest (26.7%) geographic regions (Table 1). GS residency programs represented in our sample offered a median of 6 [Interquartile Range (IQR), 4-8] categorical training positions in the 2021 NRMP Match. Forty-four (58.7%) GS residency programs in our sample were affiliated with a medical school and offered more than 1 training position during the 2020-2021 application cycle; with 79.5% taking approximately the same number of home medical students in the 2021 NRMP Match.Table 1. Program Characteristics. Characteristic n (%/IQR) Type of program Academic 47 (62.7) Community-based 28 (37.3) Program location Midwest 20 (26.7) Northeast 21 (28.0) South 26 (34.7) West 8 (10.7) Median number of positions offered 6 (4-8) Number of home students taken during 2020-2021 cycle when compared to previous cyclesa (n = 44) More 5 (11.4) Less 4 (9.1) Approximately equal 35 (79.5) Abbreviations: IQR, interquartile range. aOnly academic programs offering >1 training position and are located at an institution with a medical school. When comparing subjective characteristics of individuals belonging to the pre-pandemic and pandemic resident cohorts, most respondents observed no differences in residents’ fit with their program (72.0%), communication skills (68.0%), responsiveness to clinical instruction and feedback (73.3%), initiative and work ethic (73.3%), and end-of-rotation evaluations (68.0%) (Figure 1). The number of residents who required disciplinary action or left their program (both voluntarily and involuntarily) were largely similar between both resident cohorts with only 8.0% and 5.3% of PDs observing increases in each respective outcome in their 2021 Match class (Table 2). Only 21.3% of PDs believed that VIs negatively impacted their selection committee’s ability to accurately assess applicant intangibles during the residency interview (Figure 2).Figure 1. Clinical and subjective characteristics exhibited by residents belonging to the 2021 match class when compared to previous resident classes. Table 2. Program Directors’ Observations Regarding Resident Conduct and Attrition. In Comparison to Previous Resident Classes, Residents Matched during the 2020-2021 Application Cycle... True (%) False (%) Received more complaints requiring disciplinary action. 6 (8.0) 69 (92.0) More residents have been dismissed or have left your residency program. 4 (5.3) 71 (94.7) Figure 2. Program directors’ responses regarding the utility of virtual interviews for evaluating applicant subjective characteristics (left) and the impact of canceled clinical rotations on the 2021 match Class’s clinical competency at the start of residency (right). Fifty-six percent of PDs reported that the cancellation of CRs (both home and away) in 2020 negatively affected their 2021 Match class’s clinical competency at the start of residency (Figure 2). When comparing the clinical and technical skills exhibited by individuals belonging to the pre-pandemic and pandemic resident cohorts 1 year after the 2021 NRMP Match, 30.7% of PDs reported that their 2021 Match class exhibited poorer clinical skills relative to previous resident classes (Figure 1). A list of all comments provided by responding PDs can be found in Supplemental file 2. Discussion In this cross-sectional study of 75 PDs representing ACGME-accredited GS residency programs, we observed that most PDs believed that their 2021 Match class exhibited similar subjective characteristics but worsened clinical skills when compared to previous resident classes. Additionally, most PDs reported that VIs did not limit the ability of their program to evaluate applicant intangibles during the interview and that the cancellation of CRs in 2020 negatively affected the 2021 Match class’s clinical competency at the start of their training. Collectively, GS residency programs primarily employ quantifiable metrics, such as research output and USMLE score reports, when selecting candidates to interview.11 After applicants are selected for the interview, subjective metrics, such as personality and communication skills, serve as the primary variables used by selection committees when drafting their rank list for the NRMP Match.11 With the cancellation of away rotations during the 2020-2021 academic calendar year, GS residency programs were limited to evaluating applicants’ subjective metrics exclusively during VIs and virtual pre-interview socials. At the conclusion of the 2020-2021 application cycle, the majority of PDs of surgical residency programs expressed concerns regarding their ability to measure applicant intangibles when hosting VIs.2-7 Specific to GS residency programs, we had previously found that PDs believed that VIs limited their program’s ability elucidate applicants’ fit with their residency program (75%), personality and communication skills (69%), and commitment to GS (57%) when compared to in-person interviews.2 When analyzing these parameters 1 year after the 2021 NRMP Match, our findings suggest that VIs are a viable alternative to in-person interviews for both accurately evaluating applicant intangibles and successfully matching applicants with desirable subjective characteristics. This is highlighted in one PD’s comment:“… we used the same criteria (when evaluating applicants), and we have always intensely involved our current residents regarding fit into our program/system. Our best process to do this was a "meet and greet" the night before hosted by the residents without any faculty. (Residents) could still discern who fit and who did not. I do not think there is any reason to return to in-person interviews given the results we have seen…” Of note, recent recommendations issued by the Association of American Medical Colleges suggest that VIs will likely remain the norm for residency interviews moving forward. Taking this into account, the authors surmise that refinement of the VI format over time, along with the removal of away rotation restrictions, will result in more programs being able to accurately gauge applicants’ intangibles in future application cycles. Leading up to the 2020-2021 residency application cycle, approximately 24% of clinical-level medical students in the United States believed that the cancellation of CRs negatively affected their ability to choose a specialty prior to submitting their residency application.12 Many residency programs were concerned about how this decreased clinical exposure would affect resident attrition rates for their 2021 Match class. Interestingly, only 5.3% of PDs observed an increase in the number of candidates who voluntarily withdrew or were dismissed from their residency program. At face value, our findings suggest that the 2021 Match class’s exposure to surgery during medical school, while limited, allowed them to make an informed decision when choosing their specialty. More likely, however, is that our findings were skewed given the high baseline attrition rate of GS residents under normal circumstances. GS residency programs have resident attrition rates up to 18% annually, and responding PDs may have failed to identify the additional resident attrition resulting from pandemic-related decreases in exposure to surgery during medical school.13,14 This rationale is supported by the anecdotal experiences of several GS PDs:“I have become aware of more openings at the PGY-2 level. I am concerned that students did not get a good CR (in 2020) and thus did not know what they were getting into. The other thing that may be at play here is that they arrived at the program, (found that) it was not what they thought it would be, and decided to leave.” “It is my opinion that applicants did not get adequate exposure surgery in many institutions due to COVID-19 restrictions/limitations. The results of this appears to be some applicants choosing the wrong profession.” Presently, discussion of the COVID-19 pandemic’s impact on attrition rates of GS residents is limited to conjecture and represent a potential area for future research efforts. At the time of CRs being canceled in March 2020, only 51%-55% of clinical-level medical students in the United States had completed a full-length, in-person surgery rotation at their institution.12,15 Instead, medical students were limited to experiencing surgery through virtual didactics and learning modules. Much of the literature investigating the utility of the aforementioned learning tools strongly suggests that they lack the educational value normally provided by in-person CRs.12,15,16 Hernandez et al. investigated the educational value of virtual surgical curriculum when compared to in-person surgery rotations by performing a cross-sectional study of medical students who participated in both a virtual surgery rotation and an abbreviated, in-person CR at a later date.15 The authors found that the in-person surgery rotation provided more educational value than the virtual rotation in terms of students learning how to manage patients in the perioperative setting, developing technical skills, and acquiring an understanding of operating room etiquette.15 More than half of PDs in our sample reported that the cancellation of CRs in 2020 negatively affected the clinical competency of their 2021 Match class at the start of their training. Our findings reinforce the notion that virtual alternatives to in-person CRs used during the COVID-19 pandemic were of limited educational value to medical students who intended to pursue surgical training. This was echoed in several comments provided by PDs:“Several residents from cities that had long-term COVID-19 shutdowns came in severely behind in clinical and technical skills.” “…Some of the new interns have more limited clinical skills which appeared to be due to less CRs during medical school…” Of note, many of the virtual learning tools used following the cancellation of CRs were hastily created by medical schools, as such, proactive development and refinement of virtual surgical curriculum would likely provide a better educational experience should similar circumstances arise in the future. While many PDs in our sample reported that the 2021 Match class arrived at their residency program with worsened clinical skills, there was significant heterogeneity in PDs’ outlooks regarding their clinical skills relative to previous resident classes 1 year following the 2021 NRMP Match. One possibility is that our findings reflect pandemic-related disparities in surgical training observed across institutions during the 2021-2022 academic calendar year. At the height of the pandemic, the quality of surgical training at healthcare facilities across the United States decreased greatly owing to profound decreases in surgical volume and the implementation of restrictive perioperative safety protocols.17-20 While burdensome safety protocols have since been lifted, surgical volume has recovered unevenly across institutions based on their ability to respond to widespread shortages of surgical staff. It is also possible that our findings are the result of residency programs’ differing approaches to “catching up” residents during their first year of residency. Presently, there are no published articles in the literature detailing how GS residency programs addressed the clinical shortcomings exhibited by their 2021 Match class. While this was outside of the scope of our study, one PD in our sample stated that their residency program extended their intern bootcamp; however, owing to the anonymous nature of our survey, we could not follow-up with them regarding their bootcamp curriculum or its observed educational value. Our study has several limitations. First and foremost, our study is susceptible to recall bias given that our survey did not objectively measure the clinical competency and subjective characteristics of individuals belonging to the pre-pandemic and pandemic resident cohorts. Second, our findings are susceptible to non-respondent bias owing to the cross-sectional design of our study. We do not believe this skewed our data given that our sample was composed of PDs belonging to programs that were proportionally represented across various characteristics. Third, while our study inquired about the number of home medical students that were taken in the 2021 NRMP Match, it did not inquire about the number of outside medical students that were taken in the 2021 NRMP Match who had previously pursued a research year or CR with the respective program. Lastly, the data published in this study reflects the perspectives of PDs 1 year following the NRMP Match, as such, future studies are necessary to account for changes in their outlooks as their 2021 Match class progresses through their training. Conclusion The COVID-19 pandemic has greatly influenced the results of the 2021 National Resident Matching Program Match for many ACGME-accredited general surgery residency programs. Most notably, the cancellation of CRs at the height of the pandemic negatively impacted the clinical competency of interns upon arriving to residency; however, deficits in residents’ clinical skills appeared to improve throughout their first year of training to a variable extent. Conversely, the majority of GS residency programs were able to match applicants with subjective characteristics comparable to previous resident classes. VIs appear to limit selection committees’ ability to accurately assess an applicant’s subjective characteristics to a lesser degree than previously described in the literature. While our study presents outcomes data for residents who were taken in the 2021 NRMP Match, future studies are needed to evaluate the professional trajectory of the 2021 Match class as they progress through their training. Supplemental Material Supplemental Material - Did the COVID-19 Pandemic Impact the Caliber of Trainees Taken During Match 2021? A Survey of General Surgery Residency Program Directors Click here for additional data file. Supplemental Material for Did the COVID-19 Pandemic Impact the Caliber of Trainees Taken During Match 2021? A Survey of General Surgery Residency Program Directors by Andrew M. Ferry, Malke Asaad, Aashish Rajesh, Andrew E. Grush, Rami Elmorsi, Heather R. Burns, Vamsi C. Mohan, David F. Bauer, and Renata S. Maricevich in The American Surgeon Supplemental Material - Did the COVID-19 Pandemic Impact the Caliber of Trainees Taken During Match 2021? A Survey of General Surgery Residency Program Directors Click here for additional data file. Supplemental Material for Did the COVID-19 Pandemic Impact the Caliber of Trainees Taken During Match 2021? A Survey of General Surgery Residency Program Directors by Andrew M. Ferry, Malke Asaad, Aashish Rajesh, Andrew E. Grush, Rami Elmorsi, Heather R. Burns, Vamsi C. Mohan, David F. Bauer, and Renata S. Maricevich in The American Surgeon 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. Supplemental Material: Supplemental material for this article is available online. ==== Refs References 1 Wolff M Burrows H . Planning for virtual interviews: Residency recruitment during a pandemic. Acad Pediatr. 2021;21 (1 ):24-31. doi:10.1016/j.acap.2020.10.006 33068812 2 Rajesh A Asaad M Elmorsi R Ferry AM Maricevich RS . The virtual interview experience for MATCH 2021: A pilot survey of general surgery residency program directors. Am Surg;13 :202131348211038555. doi:10.1177/00031348211038555 Published online August. 3 Elmorsi R Asaad M Ferry AM Rajesh A Maricevich RS . How real is a virtual interview? Perspectives of orthopaedic surgery residency directors. Eur Rev Med Pharmacol Sci. 2021;25 (24 ):7829-7832. doi:10.26355/eurrev_202112_27629 34982444 4 Asaad M Elmorsi R Ferry AM Rajesh A Maricevich RS . The experience of virtual interviews in resident selection: A survey of program directors in surgery. J Surg Res. 2021;270 :208-213. doi:10.1016/j.jss.2021.09.011 34706297 5 Ferry AM Asaad M Elmorsi R , et al. Impact of the virtual interview format on urology residency interviews: A survey of program directors. Urol Pract. 2022;9 (2 ):181-189. doi:10.1097/UPJ.0000000000000292 6 Asaad M Elmorsi R Ferry AM Rajesh A Maricevich RS . Interviewing amidst a pandemic: Perspectives of US residency program directors on the virtual format. J Eur CME. 2022;11 (1 ):2087397. doi:10.1080/21614083.2022.2087397 35711724 7 Ferry AM Asaad M Elmorsi R , et al. Impact of the virtual format on plastic surgery residency and fellowship interviews: A national cross-sectional study. Plast Reconstr Surg. 2022;150 (3 ):684e-690e. doi:10.1097/PRS.0000000000009442 8 Harries AJ Lee C Jones L , et al. Effects of the COVID-19 pandemic on medical students: A multicenter quantitative study. BMC Med Educ. 2021;21 (1 ):14. doi:10.1186/s12909-020-02462-1 33407422 9 National, Resident Matching Program. Results and Data: 2021 Main Residency Match®. Washington, DC: National Resident Matching Program; 2021. https://www.nrmp.org/wp-content/uploads/2021/08/MRM-Results_and-Data_2021.pdf. Accessed April 17, 2022. 10 United States Census Bureau. Census regions and divisions of the United States. https://www2.census.gov/geo/pdfs/maps-data/maps/reference/us_regdiv.pdf. https://www2.census.gov/geo/pdfs/maps-data/maps/reference/us_regdiv.pdf. Accessed February 12, 2022. 11 National Resident Matching Program. Data release and research committee. Results of the 2020 NRMP program director survey; 2020. https://mk0nrmp3oyqui6wqfm.kinstacdn.com/wp-content/uploads/2020/08/2020-PD-Survey.pdf. Accessed March 21, 2021. 12 Byrnes YM Civantos AM Go BC McWilliams TL Rajasekaran K . Effect of the COVID-19 pandemic on medical student career perceptions: a national survey study. Med Educ Online. 2020;25 (1 ):1798088. doi:10.1080/10872981.2020.1798088 32706306 13 Khoushhal Z Hussain MA Greco E , et al. Prevalence and causes of attrition among surgical residents: A systematic review and meta-analysis. JAMA Surg. 2017;152 (3 ):265-272. doi:10.1001/jamasurg.2016.4086 27973673 14 Shweikeh F Schwed AC Hsu CH Nfonsam VN . Status of resident attrition from surgical residency in the past, present, and future outlook. J Surg Educ. 2018;75 (2 ):254-262. doi:10.1016/j.jsurg.2017.07.015 28760500 15 Hernandez S Song S Nnamani Silva ON , et al. Third year medical student knowledge gaps after a virtual surgical rotation. Am J Surg(22 ):S000200198-S000296102. doi:10.1016/j.amjsurg.2022.03.022 Published online April 5, 2022. 16 Chakladar J Diomino A Li WT , et al. Medical student’s perception of the COVID-19 pandemic effect on their education and well-being: A cross-sectional survey in the United States. BMC Med Educ. 2022;22 (1 ):149. doi:10.1186/s12909-022-03197-x 35248030 17 Aziz H James T Remulla D , et al. Effect of COVID-19 on surgical training across the United States: A national survey of general surgery residents. J Surg Educ. 2021;78 (2 ):431-439. doi:10.1016/j.jsurg.2020.07.037 32798154 18 Ferry AM Dibbs RP Ward A , et al. Operational effect of COVID-19 on surgical care at a tertiary pediatric hospital. AORN J. 2022;115 (2 ):147-155. doi:10.1002/aorn.13604 35084769 19 Dibbs RP Ferry AM Enochs J , et al. The use of personal protective equipment during the COVID-19 pandemic in a tertiary pediatric hospital. J Healthc Risk Manag J Am Soc Healthc Risk Manag. 2021;40 (4 ):38-44. doi:10.1002/jhrm.21460 20 Ferry AM Beh HZ Dibbs RP , et al. Impact of COVID-19 on cleft surgical care. FACE. 2021;2 (1 ):6-12. doi:10.1177/2732501621996009	36459702	PMC9720422	NO-CC CODE	2022-12-06 23:26:07	no	Am Surg. 2022 Dec 2;:00031348221144637	utf-8	Am Surg	2,022	10.1177/00031348221144637	oa_other
==== Front Clin Ethics Clin Ethics CET spcet Clinical Ethics 1477-7509 1758-101X SAGE Publications Sage UK: London, England 10.1177/14777509221143016 10.1177_14777509221143016 Article Refusal of transplant organs for non-medical reasons including COVID-19 status https://orcid.org/0000-0002-7567-2835 Yeturu Sai Kaushik 1 Lerner Susan M. 2 Appel Jacob M. 1 1 Department of Psychiatry, 5925 Icahn School of Medicine at Mount Sinai , New York, USA 2 Recanati Miller Transplantation Institute, 5925 Icahn School of Medicine at Mount Sinai , New York, USA Sai Kaushik Yeturu, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, 50 E 98th St, 10A-4, New York, NY 10029, USA. Email: [email protected] 1 12 2022 1 12 2022 14777509221143016© 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. Transplant centers and physicians in the United States have limited guidance on the information which they can and cannot provide to transplant candidates regarding donors of potential organs. Patients may refuse organs for a variety of reasons ranging from pernicious requests including racism to misinformation about emerging medicine as with the COVID-19 vaccine and infection. Patient autonomy, organ stewardship, and equity are often at odds in these cases, but precedent indeed exists to help address these challenges. This work uses such cases to highlight the urgent need for uniform, national policy prohibiting informational requests unrelated to well-established risks. Transplant vaccination COVID organ refusal edited-statecorrected-proof typesetterts19 ==== Body pmcIntroduction Among the many challenges raised by the COVID-19 pandemic is the variability in policies of individual transplant centers regarding vaccination. Vaccine hesitancy among transplant candidates is an issue that predates the COVID-19 pandemic. A year-long US and Canada multicenter study conducted from 2017 to 2018 demonstrated that, among pediatric transplant recipients, only 55% were up to date on immunizations according to the National Immunization Survey benchmark.1 While the cause of pediatric under-vaccination is multifactorial, including larger issues such as access to care and incomplete vaccine tracking, parental vaccine hesitancy is one such contributor. This is evidenced by one study, conducted prior to the COVID-19 pandemic, finding a one in three rate of parental vaccine concern voiced to pediatricians, and further a one in 10 rate of requesting delayed schedules.2 Transplant center response to this issue has been both variable and unclear. For instance, only 4% of surveyed individuals at pediatric transplant programs reported that their facility had written policies regarding parental vaccine refusal.3 Adult patients as well demonstrate hesitancy in their own vaccinations, such as for the Flu vaccine where hesitancy is significantly associated with a lower likelihood of vaccination.4 Here too, center-level policy is diverse and limited.5 In order to allocate scarce resources in a just and ethical manner, standardization of such policies appears merited. The COVID-19 vaccines are among those vaccines most likely to generate vaccine hesitancy among adults in the United States. COVID-19 vaccine acceptance in the United States was reported to be at only 64.6% as of July 20216 and at the time of this writing in May 2022, only 66.3% of US residents are vaccinated.7 Some ethicists and transplant centers have begun to advocate for policy-making related to the eligibility of unvaccinated patients to be listed on donor registries and to receive transplants.8 For transplant centers, these recommendations rely on existing guidelines from organizations that publish vaccine guidance such as the Advisory Committee on Immunization Practices (ACIPs); however, the COVID-19 vaccine, which does not yet have an established track record, is not yet included in ACIP guidance.9 While a national COVID-19 vaccine mandate for transplant centers remains a hypothetical possibility, many transplant centers have unilaterally instituted their own requirements. A survey of transplant centers through 2021 revealed that 35.7% have instituted a vaccine mandate for transplant candidates and that 42% of those centers which have a mandate also required the vaccination of living donors.5 The same study found that centers most often cited efficacy and stewardship as reasons for their requirement. These centers are concerned with preventing COVID-related complications and view such preventions as a duty to organ stewardship and to the sacrifices made by donors. One ethical issue that remains largely absent from discourse in the field of transplant policy relates to potential transplant recipients who refuse organs based on the vaccination status of prospective deceased donors. Medical ethics in the United States has long placed emphasis on the protection of patient autonomy within the context of shared decision-making by physicians, patients, and caregivers.10 Existing literature lacks clear guidance for handling such requests or, for that matter, other requests based on non-medical factors related to the characteristics of the cadaveric donor. What follows are several hypothetical cases of such refusal of cadaveric organs and an analysis of how transplant centers ought to address them. This article first explores cases involving race-based and cause-of-death-based requests and then examines how they may prove valuable analogies to guide patients who wish to reject organs based on the vaccination status of donors. The hope is to highlight through this work the need for national policy to direct transplant centers on non-medical organ refusal. Case 1 Mr P is a 55-year-old patient who has been undergoing seven years of dialysis for end-stage renal disease (ESRD) with no comorbidities. Mr P's transplant has no anticipated complications, and he receives a call from his transplant center that a healthy, well-matched organ is available. He asks to know the race of the donor and states that he will only accept an organ from a white donor because he believes that only a white person's organ will function well for him. In this case, the available kidney comes from a donor who was Black. Analysis Race-based organ requests are already the subject of clear precedent and policies, so starting with this case may inform discussions of vaccine-related demands. The case presents a conflict between the autonomy of Mr P and stewardship and justice goals of the transplant system. It is clear that Mr P's request is of such a nature that if he learns the organ comes from a Black donor, he likely will refuse the organ and wait for the next matching organ. The decision which then remains in the hands of the physician or transplant center is whether or not to provide the true information that the donor was Black. In other words, is informing a patient of the race of the donor either a required or an acceptable aspect of the informed-consent process? There is no available literature which explicitly handles such a case in large part because it is a policy set forth by UNOS not to disclose the race or ethnicity of a potential donor.11 So Mr P will have to proceed without the knowledge which may influence his decision. While this policy resolves the case, it is worth discussing what a physician's ethical challenges might entail in the absence of such a policy. Assuming Mr P cannot be educated to change his prejudiced views, there would be three available options to the physician: to lie to the patient and say the organ comes from a white donor, to divulge accurately that the organ comes from a Black donor, or to refuse to share any information regarding the race of the donor despite the lack of uniform policy. Each of these approaches raises distinctive concerns. Deception in medicine has been discussed at length elsewhere and is beyond the scope of this article.12 What is worth noting is that lying in this case would have the effect of the patient accepting the available organ and saving the patient's life. Additionally, in this case, the patient's beliefs about race and organ efficacy are incontrovertibly false and defy widely accepted medical knowledge. Patient preferences and views on medical decision making vary widely both among and within cultures, with beliefs ranging from truly shared decision making to leaving decisions entirely to medical professionals.13 While such views may be varied, medical paternalism has certainly fallen out of favor.12,14 A paternalistic lie, even in the interest of a beneficent patient's outcome, is an act which violates a patient's autonomy. It would undermine Mr P's autonomy to lie and subsequently allow him to make his decision under false assumptions. While Mr P's racist views may be disturbing to physicians and violate societal norms, it is generally held that in the absence of a hostile workplace, physicians have a duty to treat such patients, though some have called for limits to accommodation of racist requests.15 Additionally, although Mr P's medical reasons for rejecting a Black donor may be unscientific and ill-informed, deception may cause him psychological damage if he does learn the truth after transplantation. Lying to a competent patient—except in the extraordinary circumstances of therapeutic privilege—is generally an ethically unacceptable option. The second option available to the physician would be to share that the organ comes from a Black donor. The anticipated outcome is that Mr P will refuse such an organ. When he refuses the organ, he will remain at the top of the transplant list as his medical needs have not changed and there are no penalties for refusing any organ offer in accordance with the Kidney Allocation System (KAS).16 However, it is possible within this system that refusing such a well-matched organ may cause the next offer of that organ to be made to a lesser-matched candidate or one in less need of an immediate transplant. It may then be argued that providing such information and allowing interruption to the KAS by non-medical refusals, though respectful of Mr P's autonomy, is in poor stewardship of the organ and therefore unjust in a systemic approach. Finally, the physician might elect to refuse to provide information regarding the donor's race, as the current rule dictates. There are important practical implications to this choice. In the absence of such a rule, the possibility exists that the patient may simply pursue transplantation from physicians or centers which would give him the information he desires. While this case remains hypothetical due to the current uniform policy, parallels to other medical requests are abundant, such as a patient who is denied certain medicine from one physician and seeks out another. Preventing such tactics requires all providers and institutions to act similarly. Refusing to provide the information is only a satisfactory solution when doing so is embraced uniformly so that it cannot be easily circumvented. Case 2 Ms Q is a 55-year-old patient, again just becoming eligible for kidney donation after seven years of dialysis without comorbidity. Ms Q historically follows her doctors’ advice on all medical matters. However, when she received the call about an available organ, she asks her doctor how the donor died and stated in the past that she would never accept an organ from a donor who had died by suicide. In this case, the donor had died by suicide but there is no evidence of injury to the organ in question. Analysis Ms Q's case resembles that of Mr P's in that she is mistaken regarding the medical quality of the organ she may receive. However, Ms Q's case does not run as clearly contrary to public policy and it would likely not strike most physicians or reasonable people as morally disturbing as Mr P's views. Suicide remains a highly stigmatized course of action in the United States, and this stigma has notable predictors. For example, gender, race, and education were found to be significant predictors of stigmatizing attitudes towards suicide.17 It is reasonable to then conclude that significant differences would exist among groups regarding who would make such a request. Ms Q is presumably motivated by misinformation that there is some biological component to the suicide which would be passed on to her through the transplanted organ. The exact cause of death and chronic conditions unrelated to the transplant are, like race, standard practice to not report to recipient candidates.11 It is unclear whether this UNOS policy recommendation exists to prevent discriminatory or otherwise pernicious requests for information. This policy may in fact only exist as a means of protecting donor and donor-family confidentiality. Regardless of the intention, the policy resolves the case similar to that of refusing to disclose race. In the absence of uniform guidance, the physician would be left with the same three options discussed in the prior case and would arrive at the same ethical conclusions, longing for a uniform embracement of withholding such non-medical information. It is therefore not the repugnance of racist requests alone that demands a uniform policy. The suicide variant offers the same options and analysis and already has similar policies in place. Such non-medical information requests, particularly the medical misinformation requests highlighted by both Mr P and Ms Q, exist on a continuum of which there is clear precedent for standardization of withholding donor information. Case 3 Ms R is a 55-year-old female who has been undergoing dialysis in New York City for seven years following a diagnosis of ESRD. She has no comorbidities and is up to date on all of her vaccinations except the COVID-19 vaccine, which she states that she is skeptical of and refuses for personal, non-religious reasons. She is evaluated by a transplant center which does not have a vaccine mandate in place for recipients and has received a call that a well-matched kidney with no high risks or foreseeable complications is available to her from a deceased donor. She asks whether the donor has received the COVID-19 vaccine and states that she will not accept a kidney from a donor who had received the vaccine antemortem because she fears that the kidney may have been damaged by the vaccine. She expresses a fear that she might suffer health consequences as a result of exposure to a vaccinated kidney. In this case, the potential donor was vaccinated. Analysis The cases of Mr P and Ms Q help shed light on that of Ms R. here. Notably, no uniform policy exists for Ms R's case; yet, the physician's potential options remain similar. He may lie, tell the truth, or withhold information. As stated earlier, the presumption is that lying is ethically unacceptable. The merits of the remaining two choices also remain largely the same. If the doctor is truthful, the patient will most likely refuse transplant, thereby leading the patient to forgo transplant and jeopardizing stewardship of the organ. However, withholding information unilaterally may cause the patient to seek care elsewhere. In this case, we face a situation in which, as previously noted, centers have taken diverging paths in their policies and physicians lack clarity. This needs to be addressed under two different frameworks: one in which the possibility that a vaccinated donor's organ might cause harm is acknowledged and another in which physicians are certain that it does not. To be clear, it is known that a variety of vaccines are efficacious and safe for COVID-19 despite the spread of misinformation.18 However, there is ethical value to considering the case from the vantage point of hypothetical, as-yet-unknown risks. If one accepts that there is no chance the vaccinated donor's organ may cause any harm to Ms R, then in some ways this resembles the first case, in which there is no chance race is a determinant of organ quality or that the donor's race can cause harm. Of course, discrimination based upon vaccine status does not have the deeply pernicious history that does discrimination based upon race, nor is vaccination-based discrimination tied to the same level of social damage. At the same time, weighed solely on the clinical merits, the best solution appears to be treating the cases similarly and adopting a uniform policy which prevents the sharing of all non-pertinent information. More challenging is the case which allows for unknown future risks from vaccinated donors. In such a scenario, if the patient is permitted to learn that the organ comes from a vaccinated individual, and therefore refuses, that organ will be passed onward to the next candidate. This second candidate may not prove as fit a match. However, even if there are identical candidates in line per the KAS, that kidney will ultimately end up being accepted by either someone who is unconcerned about the donor vaccination status, or more alarmingly, someone who is as concerned as Ms R, but does not feel he has the power to refuse. It is critical to acknowledge that healthcare remains inequitable beyond COVID-19, and as such, not all patients access the same quality of care. Race and socioeconomic status correlate to poor health care.19 In one study of early-stage breast cancer patients, Black women, when compared to white women, are less likely to report having their informational needs met and emphasized the importance of having advocates with them.20 Even closer to our subject, women and those without a college education are found to be significantly more likely to accept an increased-risk organ donation offer.21 Even if Ms R's concern for risk is acknowledged within scientific possibility, her case raises another concern: equity. Those patients who have systematically been given less control in their healthcare, such as in the aforementioned instances, will disproportionately bear the risk if any is to exist. Ms R's autonomy and her right to information on the transplanted organ comes at the expense of equity because not all patients, for systemic reasons, have the same degree of comfort in seeking out the information that she does, nor does every patient have the means to switch centers to a doctor which will address their preferences. Of course, physicians do inform patients of donor information which is medically known to produce risk, such as an HCV infection. This is fundamentally different from allowing patients to act on unknown risks because in the case of truly increased risk donations, the patient need not seek out information as it is incumbent upon the physician to inform patients of known risks, and physicians should not be in a position of subjecting patients to known risks without their approval. For Ms R, however, there is no known medical risk, and the possibility of one emerging is unwittingly borne by those least well-served by medicine. Case 4 Mr S is a 55-year-old male who has also been undergoing seven years of dialysis for ESRD with no comorbidities. Mr S is up to date with all of his vaccinations including the COVID-19 vaccine for which he has obtained every recommended dose. When he receives the call from a transplant center that a healthy, well-matched organ is available, he demands to know whether the donor has received the COVID-19 vaccine and states that he would refuse any organ from a deceased donor who failed to acquire the COVID-19 vaccine antemortem or who had a known COVID-19 infection because he fears that the kidney may have been damaged by a COVID-19 infection. In this case, the donor was not vaccinated and has had a mild case of infection in the past. Analysis As Mr S is a vaccinated patient seeking a vaccinated organ, his cause might be thought of as the inverse of Ms R's. If the same approach were to be applied, the result would ensure that policymakers are not motivated by bias against, or a desire to penalize, those who refuse vaccination. The organ available to Mr S has no known medical risk and is as healthy as a vaccinated one. One might conclude then that Mr S should be treated no differently. The same equity issues which plague the prior case are also present here. One notable difference, however, for Mr S is that there is a second piece of information he may desire in addition to vaccination status: whether or not the donor experienced a known COVID-19 infection. While the UNOS recommendations state that physicians should not share chronic illnesses unrelated to the transplant, this leaves room for interpretation in Mr S's case. It is known now that severe COVID-19 infection may cause acute kidney injury; in fact, this occurs in over a quarter of hospitalized COVID-19 patients.22 So, physicians and centers are left to discern whether relevance to the transplant exists. In the absence of evidence of damage to the organ, this would be an as-yet-unknown risk. Regardless of the validity of the concern, the issue of equity remains similar to the last case. In the absence of evidence-based risk characterized by damage to the organ or an active infection, the possible risks of the unknown will continue to be borne by those least empowered within medical systems. Discussion Whether preventing racially motivated organ transplant requests or unrelated medical requests such as suicide, there is clear ethical precedent to limiting information sharing to potential organ recipients rooted in donor confidentiality protection as well as organ stewardship. These policies and standard practices, though essential, are insufficient to cover emerging concerns such as that of COVID-19. While a physician cannot in good conscience transplant a known high-risk organ, such as one with a known HCV infection, unwittingly to the patient, other cases of concern for as-yet-unknown risk remain unresolved. Another issue of concern, but one largely beyond the scope of this paper, is that of the decisional capacity of patients making COVID-related transplant requests. Indeed, many cases may arise in which patients rejecting organs based upon transplant status lack such capacity. Our analysis applies to those cases, if and when they arise, when patients do otherwise meet capacity standards. In such cases, waiting for organizations to comfortably create requirements for new vaccines or diseases is too slow to be acceptable for emerging concerns. The only acceptable solution which balances organ stewardship, equity, and patient autonomy is one in which informational requests unrelated to the known quality and risks of an organ are prohibited uniformly by national policy. The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding: The authors received no financial support for the research, authorship, and/or publication of this article. ORCID iD: Sai Kaushik Yeturu https://orcid.org/0000-0002-7567-2835 ==== Refs References 1 Feldman AG Sundaram SS Beaty BL , et al. Immunization status at the time of liver transplant in children and adolescents. JAMA 2019; 322 : 1822–1824.31714979 2 Wheeler M Buttenheim AM . Parental vaccine concerns, information source, and choice of alternative immunization schedules. Hum Vaccines Immunother 2013; 9 : 1782–1789. 3 Ladd JM Karkazis K Magnus D . Parental refusal of vaccination and transplantation listing decisions: A nationwide survey. Pediatr Transplant 2013; 17 : 244–250.23347536 4 Quinn SC Jamison AM An J , et al. Measuring vaccine hesitancy, confidence, trust and flu vaccine uptake: results of a national survey of white and African American adults. Vaccine 2019; 37 : 1168–1173.30709722 5 Hippen BE Axelrod DA Maher K , et al. Survey of current transplant center practices regarding COVID-19 vaccine mandates in the United States. Am J Transplant 2022; 22 : 1705–1713.35143100 6 Arce JS S Warren SS Meriggi NF , et al. COVID-19 vaccine acceptance and hesitancy in low- and middle-income countries. Nat Med 2021; 27 : 1385–1394.34272499 7 U.S. COVID-19 vaccine tracker: See your state’s progress. Mayo Clinic. https://www.mayoclinic.org/coronavirus-covid-19/vaccine-tracker (accessed 15 May 2022). 8 Appel JM . Personal responsibility and transplant revisited: a case for assigning lower priority to American vaccine refusers. Bioethics 2022; 36 : 461–468.35277991 9 Kates OS Stohs EJ Pergam SA , et al. The limits of refusal: An ethical review of solid organ transplantation and vaccine hesitancy. Am J Transplant 2021; 21 : 2637–2645.33370501 10 Schneider CE . The Practice of Autonomy: Patients, Doctors, and Medical Decisions. Oup Usa, 1998. 11 United Network for Organ Sharing. Guidance-Information-Sharing-HIPAA-2012. https://unos.org/wp-content/uploads/Guidance-Information-Sharing-HIPAA-2012.pdf (2012, accessed 15 May 2022). 12 Bok S . Lying: Moral Choice in Public and Private Life. New York: Vintage Books, 1999. 13 Rodriguez-Osorio CA Dominguez-Cherit G . Medical decision making: Paternalism versus patient-centered (autonomous) care. Curr Opin Crit Care 2008; 14 : 708–713.19005314 14 McCoy M . Autonomy, consent, and medical paternalism: Legal issues in medical intervention. J Altern Complement Med 2008; 14 : 785–792.18601583 15 Paul-Emile K Smith AK Lo B , et al. Dealing with racist patients. N Engl J Med 2016; 374 : 708–711.26933847 16 Chopra B Sureshkumar KK . Changing organ allocation policy for kidney transplantation in the United States. World J Transplant 2015; 5 : 38–43.26131405 17 Kheibari A Cerel J Victor G . Comparing attitudes toward stigmatized deaths: Suicide and opioid overdose deaths. Int J Ment Health Addict Epub ahead of print 11 March 2021. DOI: 10.1007/s11469-021-00514-1 18 Olliaro P Torreele E Vaillant M . COVID-19 vaccine efficacy and effectiveness—the elephant (not) in the room. Lancet Microbe 2021; 2 : e279–e280.33899038 19 Williams DR Priest N Anderson N . Understanding associations between race, socioeconomic status and health: patterns and prospects. Health Psychol Off J Div Health Psychol Am Psychol Assoc 2016; 35 : 407–411. 20 Anderson JN Graff JC Krukowski RA , et al. “Nobody will tell you. You’ve got to ask!”: An examination of patient-provider communication needs and preferences among black and white women with early-stage breast cancer. Health Commun 2021; 36 : 1331–1342.32336140 21 Humar SS Liu J Pinzon N , et al. Attitudes of liver transplant candidates toward organs from increased-risk donors. Liver Transpl 2019; 25 : 881–888.30947392 22 Legrand M Bell S Forni L , et al. Pathophysiology of COVID-19-associated acute kidney injury. Nat Rev Nephrol 2021; 17 : 751–764.34226718	0	PMC9720467	NO-CC CODE	2022-12-06 23:26:08	no	Clin Ethics. 2022 Dec 1;:14777509221143016	utf-8	Clin Ethics	2,022	10.1177/14777509221143016	oa_other
==== Front Otolaryngol Head Neck Surg Otolaryngol Head Neck Surg OTO spoto Otolaryngology--Head and Neck Surgery 0194-5998 1097-6817 SAGE Publications Sage CA: Los Angeles, CA 35104190 10.1177/01945998221075610 10.1177_01945998221075610 General Otolaryngology COVID-19 Tracheostomy Outcomes https://orcid.org/0000-0002-9186-2954 Molin Nicole MD 1 Myers Keith MD 2 Soliman Ahmed M.S. MD 1 Schmalbach Cecelia E. MD, MSc 1 1 Department of Otolaryngology–Head and Neck Surgery, Lewis Katz School of Medicine at Temple University, Philadelphia, Pennsylvania, USA 2 Department of Surgery, Baylor College of Medicine, Houston, Texas, USA Nicole Molin, MD, Department of Otolaryngology–Head and Neck Surgery, Lewis Katz School of Medicine at Temple University, 3440 N Broad Street, Kresge West 310, Philadelphia, PA 19140, USA. Email: [email protected] 12 2022 12 2022 12 2022 167 6 923928 7 10 2021 6 1 2022 © American Academy of Otolaryngology–Head and Neck Surgery Foundation 2022 2022 Official journal of the American Academy of Otolaryngology–Head and Neck Surgery Foundation 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. Objectives (1) Assess overall COVID-19 mortality in ventilated patients with and without tracheostomy. (2) Determine the impact of tracheostomy on mechanical ventilation duration, overall length of stay (LOS), and intensive care unit (ICU) LOS for patients with COVID-19. Study Design Case series with planned chart review. Setting Single-institution tertiary care center. Methods Patients with COVID-19 who were ≥18 years old and requiring invasive positive pressure ventilation (IPPV) met inclusion criteria. Patients were stratified into 2 cohorts: IPPV with tracheostomy and IPPV with intubation only. Cohorts were analyzed for the following primary outcome measures: mortality, LOS, ICU LOS, and IPPV duration. Results An overall 258 patients with IPPV met inclusion criteria: 46 (18%) with tracheostomy and 212 (82%) without (66% male; median age, 63 years [interquartile range, 18.75]). Average LOS, time in ICU, and time receiving IPPV were longer in the tracheostomy cohort (P < .01). Ability to wean from IPPV was similar between cohorts (P > .05). The number of deaths in the nontracheostomy cohort (54%) was significantly higher than the tracheostomy cohort (29%, P < .01). Conclusions While tracheostomy placement in patients with COVID-19 did not shorten overall LOS, mechanical ventilation duration, or ICU LOS, patients with a tracheostomy experienced a significantly lower number of deaths vs those without. One goal for tracheostomy is improved pulmonary toilet with associated shortened IPPV requirements. Our study did not identify this advantage among the COVID-19 population. However, this study demonstrates that the need for tracheostomy in the COVID-19 setting does not portent a poor prognostic factor, as patients with a tracheostomy experienced a significantly higher survival rate than their nontracheostomy counterparts. COVID-19 tracheostomy mechanical ventilation typesetterts1 ==== Body pmcSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus causing coronavirus disease (COVID-19), created an unprecedented pandemic with many unknowns surrounding optimal management. While the majority of patients are asymptomatic or present with mild flu-like symptoms, approximately 20% to 25% experience severe acute respiratory distress syndrome, requiring admission to an intensive care unit (ICU) for respiratory support.1 The rapid and sudden influx of patients, potentially requiring a high level of care, created a challenge for hospitals and health care systems. In response to the influx of patients with this novel illness, guidelines for COVID-19 care necessitated continual updating by the Centers for Disease Control and Prevention, World Health Organization, hospitals, and individual societies from around the world. The potential need for intubation and respiratory support for long periods in patients with COVID-19 prompted extensive discussions surrounding the best protocol for tracheostomy. Tracheostomy has known benefits to patients requiring long-term mechanical ventilation: improved pulmonary toilet, reduced mechanical ventilation requirement, decreased laryngeal injury, and ability to improve comfort management by reducing the need for sedatives and paralytics.2 As such, multiple protocols were published regarding tracheostomy in patients with COVID-19. These protocols largely focus on the timing of tracheostomy, as well as patient and provider safety with emphasis on limiting the spread of the virus during the procedure and postoperative tracheostomy care.3 Early studies evaluated outcomes of patients with COVID-19 requiring tracheostomy; however, a direct comparison of mechanically ventilated cases with and without tracheostomy is lacking.4,5 To better understand the implications of tracheostomy for intubated patients, this study aims to investigate the impact of tracheostomy on patients with COVID-19 requiring invasive positive pressure ventilation (IPPV) with respect to mortality, mechanical ventilation duration, and hospital as well as ICU length of stay (LOS). Methods This case series with planned chart review was approved by the Temple University Institutional Review Board (protocol 27116). Data were collected via chart review at a single tertiary care center between February 2020 and December 2020. Patients were initially identified by ICD-10 code U07.1 (COVID-19 diagnosis). Patients were included if they were at least 18 years of age, had a positive objective test result for COVID-19, and required IPPV. Objective COVID-19 testing consisted of a positive nasal or throat swab or computed tomography (CT) chest consistent with COVID-19. All chest CT scans were read by a faculty member from the Department of Radiology and graded as category 1 to 3: category 1, consistent with multifocal pneumonia, including viral/atypical pneumonia (ie, COVID-19); category 2, indeterminate; category 3, consistent with other diagnosis. This grading system was institution specific and formulated by the most common CT scan findings seen in patients with COVID-19 at the time of the study period.6-8 For the purpose of this study, patients were included by CT scan alone if there was a radiologist’s interpretation of category 1.6-8 Patients were excluded if they had a preexisting tracheostomy or did not have adequate data available for primary outcome measures. Patients were divided into 2 cohorts: patients requiring IPPV who underwent tracheostomy (tracheostomy cohort) and those who required IPPV but did not undergo tracheostomy (nontracheostomy cohort). Tracheostomies were performed by various services, such as cardiothoracic surgery, general surgery, and otolaryngology. The decision for tracheostomy placement was made by the attending physician on the consulting service. The primary indications consisted of prolonged intubation and ventilator-dependent respiratory failure based on hospital practices at the time. Descriptive statistics were used to define each cohort based on race, sex, smoking status, body mass index (BMI), and comorbidities (hypertension, diabetes mellitus, coronary artery disease, asthma, and malignancy). Primary outcome variables were included mortality, overall hospital LOS, ICU LOS, and days requiring mechanical ventilation. A comparison of the 2 cohorts was conducted with Fisher’s exact test for categorical data and 2-tailed Student’s t test for continuous variables via an online calculation tool.9 A P value of .05 was required to reach statistical significance. Results An overall 258 patients met inclusion criteria. The majority of patients were treated with IPPV alone, with 46 (18%) requiring tracheostomy. Demographics of the entire sample and a cohort comparison based on tracheostomy status are summarized in Table 1. There were 89 females (34%) and 169 males (66%). The median age was 63 years, and the interquartile range (IQR) was 18.75. The majority of patients were African American (n = 129, 50%) and never smokers (n = 150, 58%). The most common comorbidity was coronary artery disease (n = 141, 55%), followed by diabetes mellitus (n = 140, 54%). The median BMI for the sample was 29.5 (IQR, 11.1). There was no significant difference between groups with regard to age, ethnicity, sex, smoking status, or BMI. There were significantly more patients with diabetes in the tracheostomy cohort (n = 34, 74%) as compared with the nontracheostomy cohort (n = 106, 50%; P = .003). The majority of patients had a CT viral screen of category 1 (n = 170, 66%), followed by category 2 (n = 39, 15%) and category 3 (n = 22, 9%). Table 1. Demographics and Clinical Characteristics. Patients, No. (%) All (N = 258) Nontracheostomy (n = 212) Tracheostomy (n = 46) P value Age, ya 63 (18.75) 64 (18.25) 62.5 (15.75) .96 Ethnicity (>1 allowed) Caucasian 43 (17) 37 (17) 6 (13) >.99 African American 129 (50) 103 (49) 26 (56) .41 Hispanic 57 (22) 47 (22) 10 (22) >.99 Asian 48 (18) 40 (19) 8 (17) >.99 Other 13 (6) 13 (6) 0 .13 Sex Female 89 (34) 71 (33) 18 (39) .50 Male 169 (66) 141 (67) 28 (61) .50 Smoking status Current 45 (17) 38 (18) 7 (15) .83 Former 63 (24) 48 (22) 15 (33) .18 Never 150 (58) 126 (60) 24 (52) .41 Comorbidity Hypertension 112 (43) 90 (42) 22 (48) .52 Diabetes 140 (54) 106 (50) 34 (74) .003b Coronary artery disease 141 (55) 118 (56) 23 (50) .52 Asthma 31 (12) 28 (13) 3 (7) .32 Malignancy 21 (8) 18 (8) 2 (4) .75 Body mass indexa 29.5 (11.1) 29.0 (10.7) 31.4 (10.3) .10 Chest viral screenc Category 1 170 (66) 134 (63) 36 (78) .06 Category 2 39 (15) 37 (17) 2 (4) .02b Category 3 22 (9) 21 (10) 1 (3) .14 None available 27 (10) 20 (10) 7 (15) .29 a Median (interquartile range). b P < .05. c Computed tomography. COVID-19–related treatments per group are summarized in Table 2. The majority of patients received corticosteroids (80%) and azithromycin (78%). Overall, 23% of patients requiring IPPV were treated on clinical trial. There were significantly more patients in the tracheostomy cohort treated with hydroxychloroquine (n = 18, 38%) vs the nontracheostomy cohort (n = 47, 22%; P < .05). Similarly, significantly more patients in the tracheostomy cohort were enrolled in a clinical trial (n = 24, 50%) when compared with the nontracheostomy cohort (n = 35, 17%; P < .05). Table 2. COVID-19 Treatments. Patients, No. (%) All (N = 258) Nontracheostomy (n = 212) Tracheostomy (n = 46) P value Azithromycin 202 (78) 160 (75) 42 (88) .02a Remdesivir 35 (14) 25 (12) 10 (21) .10 Corticosteroids 206 (80) 168 (79) 38 (79) .69 Hydroxychloroquine 35 (14) 17 (8) 18 (38) <.001a Intravenous immunoglobulin 65 (25) 47 (22) 18 (38) .02a Clinical trial 59 (23) 35 (17) 24 (50) <.001a a P < .05. Tracheostomy-specific characteristics and outcomes are summarized in Table 3. Forty-six patients (18%) underwent tracheostomy placement. To limit patient transport, most tracheostomies were placed via a percutaneous approach in the ICU bedside setting. Only 2 patients required a cricothyrotomy prior to tracheostomy placement. The median time on mechanical ventilation prior to tracheostomy was 14 days (IQR, 6). Following tracheostomy placement, the majority of patients (54%) were weaned off mechanical ventilation at an average a median of 13 days (IQR, 10). Table 3. Tracheostomy Characteristics (46 Patients). No. (%) Tracheostomy technique Open 12 (26) Percutaneous 34 (74) Tracheostomy setting Operating room 17 (37) Bedside 29 (63) Required cricothyrotomy prior to tracheostomy 2 (4) Time from intubation to tracheostomy, da 14 (6) Weaned from ventilator 25 (54) Time on ventilator following tracheostomy, da 13 (10) a Median (interquartile range). Outcome comparisons between the nontracheostomy and tracheostomy cohorts are summarized in Table 4. Median hospital LOS was 13 days (IQR, 12) and 30 days (IQR, 23), respectively (P < .001). Median ICU LOS was 6 days (IQR, 9) and 21 days (IQR, 16; P < .001). The number of deaths was 115 (54%) in the nontracheostomy cohort and 14 (29%) in the tracheostomy cohort (P < .01). Patients without a tracheostomy were intubated for a median 4 days, and 131 (62%) were weaned off the ventilator prior to discharge. Patients with a tracheostomy were intubated for a median 14 days (IQR, 6) prior to tracheostomy placement. Following tracheostomy, 25 patients (54%) were weaned off the ventilator at a median 13 days (IQR, 10) following tracheostomy placement. Total time intubated and total time on mechanical ventilation were significantly decreased in the nontracheostomy group (P < .0001). The number of patients weaned off mechanical ventilation did not differ between the cohorts (P = .329). Table 4. Comparison Nontracheostomy vs Tracheostomy Groups. Patients, No. (%) All (N = 258) Nontracheostomy (n = 212) Tracheostomy (n = 46) P value LOS, da Hospital 14 (18.75) 13 (12) 30 (23) <.001b ICU 7 (13) 6 (9) 21 (16) <.001b Disposition Home 56 (22) 54 (25) 2 (4) <.001b Skilled nursing facility 32 (12) 26 (12) 6 (13) .81 LTAC 17 (7) 0 17 (38) <.001b Death 129 (50) 115 (54) 14 (29) .0053b Hospice 8 (3) 7 (3) 1 (2) >.99 Rehabilitation 11 (4) 5 (2) 6 (15) .005b Left AMA 5 (2) 5 (2) 0 .59 Total time, da Intubated for nondeceased patients 5 (9) 4 (8) 14 (6) .0001b On ventilator 6 (14) 4 (8) 26 (13) .0001b Weaned off the ventilator 156 (60) 131 (62) 25 (54) .41 Abbreviations: AMA, against medical advice; ICU, intensive care unit; LTAC, long-term assisted care. a Median (interquartile range). b P < .05. Outcome comparisons between early and late tracheostomy placement are summarized in Table 5. Early tracheostomy was defined as tracheostomy placement on or before day 13 of intubation (n = 21); the remaining 25 patients were classified as late tracheostomy. A statistically significant difference between groups was not identified with respect to overall LOS, ICU LOS, number of deceased patients, total time on the ventilator, ability to wean off the ventilator, and time on the ventilator following tracheostomy placement. Table 5. Outcomes Early vs Late Tracheostomy Placement. Patients, No. (%) Earlya (n = 21) Late (n = 25) P value LOS, db Hospital 32 (21) 36 (30) .13 ICU 25.7 (10.8) 24 (14) .44 Deaths 5 (24) 9 (36) .52 Time on ventilator, db 27.3 (22.3) 25.5 (13.8) .84 Weaned off ventilator 13 (62) 12 (48) .39 Time on ventilator after tracheostomy, db 16 (16) 12 (9) .09 Abbreviations: ICU, intensive care unit; LOS, length of stay. a By day 13 of intubation. b Median (interquartile range). Discussion This study is one of the first to compare the outcomes of patients with COVID-19 requiring IPPV who did and did not undergo tracheostomy placement. This comparison in essential when determining the risk-benefit ratio of tracheostomy in the COVID-19 population. The benefit of tracheostomy in patients with prolonged intubation is well known, but in the setting of COVID-19, this has to be balanced with the risk of exposing health care workers, as well as the risk that the patient undergoes with this procedure, especially in the setting of low pulmonary reserve. With respect to surgical intervention, the majority of tracheostomies in this study were placed via a percutaneous approach. Recent literature suggests increased aerosolization during this approach vs an open approach due to more extensive airway manipulation.10 Chao et al, however, demonstrated the absence of health care worker transmission of COVID-19 with either approach as long as the appropriate personal protective equipment was utilized (airborne, contact, and droplet precaution level).4 The majority of tracheostomies in this study were performed at the bedside, which is supported by prior literature if done in a negative pressure room, due to avoidance of unnecessary transport of patients and repeated connection and disconnection of ventilatory circuits during transfer.10 The current study suggests that, when compared with nontracheostomy status, tracheostomy placement did not shorten the total length of hospital stay, days on positive pressure ventilation, or length of ICU stay. While tracheostomy in patients without COVID-19 has traditionally been associated with improved pulmonary toilet and shortened IPPV requirements,2 our study did not identify this advantage among the COVID-19 population. Although need for surgical tracheostomy in the setting of IPPV is often associated with patients having declining clinical status and ultimately a higher mortality, the current data suggest that patients with COVID-19 and a tracheostomy experienced significantly fewer deaths than those who did not undergo tracheostomy. Therefore, the need for tracheostomy and use of early tracheostomy intervention in the COVID-19 setting should not be deemed a poor prognostic factor, as patients with a tracheostomy experienced a significantly higher survival rate vs their nontracheostomy counterparts. Chao et al studied a cohort of 53 patients with COVID-19 who underwent a tracheostomy. They found an average time of 19 days from intubation to tracheostomy; furthermore, 56% of patients were able to be weaned from the ventilator, and the average time from tracheostomy to ventilator wean was 11.8 days.4 The current study utilized a shorter average time from intubation to tracheostomy (13 days), and it identified a similar number of patients with a tracheostomy able to wean off the ventilator (54%) although at a slightly longer time following tracheostomy (14 days). Kwak et al evaluated the outcomes of 148 patients with COVID-19 who underwent tracheostomy.5 They found an average time of 12 days from intubation to tracheostomy placement, 33 days from intubation to time weaned off the ventilator, and 51 days for total length of hospital stay, as well as a mortality rate of 20%. We report a shorter LOS in our tracheostomy group (30 days; IQR, 23), which may be due to the higher percentage of patients (50%) on clinical trial. In addition, our findings differ from Kwak et al in that we found a higher death rate at 29%. Kwak et al also evaluated the effect of timing of tracheostomy placement on outcomes. They indicated that early tracheostomy (before day 10 of intubation) was noninferior to later tracheostomy placements. Early tracheostomy placement was associated with a shorter LOS, and the late tracheostomy group was 16% less likely to wean off mechanical ventilation.5 The current study compared early tracheostomy placement (within 13 days of intubation) vs late and similarly found that early was noninferior to later placement. More so, there was no significant difference in outcomes between early and late placement. This finding may be due to the difference in definition of early and late tracheostomy placement between studies. Last, our study adds to the current literature by detailing tracheostomy characteristics, including the technique of tracheostomy placement (percutaneous vs open) and surgical setting (operating room vs bedside). This study is limited by a lack of follow-up data after hospital discharge; therefore, late complications to include laryngotracheal stenosis and overall pulmonary status were not captured. This study commenced at the onset of our COVID-19 pandemic, and set criteria for associated surgical intervention with tracheostomy were not established. The decision for tracheostomy was made by the individual attending on the consulting service, which included 3 disciplines (cardiothoracic surgery, general surgery, otolaryngology). Because this is a post hoc study based on chart review, it is subject to the inherent selection bias of a nonrandomized study. While we tried to elucidate such biases by comparing patient demographics and characteristics to include BMI and comorbidities (Table 1), we acknowledge the potential for perceived healthier patients to receive the surgical intervention. Future randomized prospective studies would be helpful in further assessing this topic. Subtyping by COVID-19 variant was not available for comparison. In addition, the electronic medical record did not allow investigation of the rate of COVID-19 conversion, if any, among the surgeons and health care providers caring for the tracheostomy cohort. In conclusion, this study is one of the first to directly compare outcomes of intubated patients with COVID-19 with and without tracheostomy placement. This study suggests that while tracheostomy placement does not appear to decrease length of hospital or ICU stay or time to wean from mechanical ventilation, placement of a tracheostomy does not portend a worse prognosis in the COVID-19 setting as compared with IPPV alone. Specifically, this subset of patients had an improved mortality rate vs their IPPV counterparts who did not undergo tracheostomy placement. Therefore, the surgical and medical team should proceed to tracheostomy placement based on individual patient needs and anticipated prognosis, similar to algorithms applied in the non–COVID-19 setting. As the pandemic continues with the introduction of new COVID-19 variants and we begin to understand the late sequela of the virus, future studies with larger sample sizes and longer follow-up will be essential to continue to understand the outcomes of patients with COVID-19 who undergo tracheostomy placement. We thank Chandra A. Dass, MBBS, DMRD, professor of Clinical Radiology, Lewis Katz School of Medicine, for his expertise in the care of our patients with COVID-19 and his guidance on the utilization of chest computed tomography in this setting. This article was presented at the AAO-HNSF Annual Meeting and OTO Experience; October 3, 2021; Los Angeles, California. Authors Contributions: Nicole Molin, design, conduct, analysis, presentation of research, writing of manuscript; Keith Myers, design, conduct, analysis, writing of manuscript; Ahmed M.S. Soliman, design, conduct, writing of manuscript; Cecelia E. Schmalbach, design, conduct, analysis, writing of manuscript. Competing interests: Cecelia E. Schmalbach—AAO-HNS/F coordinator for research and quality; Otolaryngology–Head and Neck Surgery, editorial board and editor in chief–elect; teaching honorarium, AO North America CMF (nonprofit trauma teaching consortium). Sponsorships: None. Funding source: None. ORCID iD: Nicole Molin https://orcid.org/0000-0002-9186-2954 ==== Refs References 1. Lai C-C Shih T-P Ko W-C Tang H-J Hsueh P-R . Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): the epidemic and the challenges. Int J Antimicrob Agents. 2020;55 (3 ):105924.32081636 2. Bice T Nelson JE Carson SS . To trach or not to trach: uncertainty in the care of the chronically critically ill. In: Seminars in Respiratory and Critical Care Medicine. Vol 36 . Thieme Medical Publishers; 2015:851-858.26595045 3. Heyd CP Desiato VM Nguyen SA , et al . Tracheostomy protocols during COVID-19 pandemic. Head Neck. 2020;42 (6 ):1297-1302.32329922 4. Chao TN Harbison SP Braslow BM , et al . Outcomes after tracheostomy in COVID-19 patients. Ann Surg. 2020;272 (3 ):e181.32541213 5. Kwak PE Connors JR Benedict PA , et al . Early outcomes from early tracheostomy for patients with COVID-19. JAMA Otolaryngol Neck Surg. 2021;147 (3 ):239-244. 6. Bernheim A Mei X Huang M , et al . Chest CT findings in coronavirus disease-19 (COVID-19): relationship to duration of infection. Radiology. 2020;295 (3 ):200463.32077789 7. Ding X Xu J Zhou J Long Q . Chest CT findings of COVID-19 pneumonia by duration of symptoms. Eur J Radiol. 2020;127 :109009.32325282 8. Ai T Yang Z Hou H , et al . Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology. 2020;296 (2 ):E32-E40.32101510 9. Fischer exact test calculator. Accessed September 27, 2021. https://www.socscistatistics.com/tests/fisher/default2.aspx 10. Tay JK Khoo ML, C Loh WS . Surgical considerations for tracheostomy during the COVID-19 pandemic: lessons learned from the severe acute respiratory syndrome outbreak. JAMA Otolaryngol Head Neck Surg. 2020;146 (6 ):517-518.32232426	35104190	PMC9720468	NO-CC CODE	2022-12-06 23:26:08	no	Otolaryngol Head Neck Surg. 2022 Dec; 167(6):923-928	utf-8	Otolaryngol Head Neck Surg	2,022	10.1177/01945998221075610	oa_other
==== Front J Health Psychol J Health Psychol HPQ sphpq Journal of Health Psychology 1359-1053 1461-7277 SAGE Publications Sage UK: London, England 35410516 10.1177/13591053221089722 10.1177_13591053221089722 Articles A model to understand COVID-19 preventive behaviors in young adults: Health locus of control and pandemic-related fear Bianchi Dora 1 Lonigro Antonia 2 Norcia Anna Di 1 Tata Daniele Di 1 Pompili Sara 1 Zammuto Marta 1 Cannoni Eleonora 1 Longobardi Emiddia 1 https://orcid.org/0000-0003-3969-4292 Laghi Fiorenzo 1 1 Sapienza University of Rome, Italy 2 European University of Rome, Italy Fiorenzo Laghi, Department of Developmental and Social Psychology, Sapienza University of Rome, via dei Marsi, 78, Roma, 00185, Italy. Email: [email protected] 12 2022 12 2022 12 2022 27 14 31483163 © 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. This study investigated COVID-19 preventive behaviors in young adults, exploring the predictive roles of health locus of control and pandemic fear. A sample of 188 Italian young adults (Mage = 22.76, SDage = 1.95; 85% girls) completed an anonymous online survey assessing preventive behaviors, health locus of control styles (i.e. internal, chance, powerful others), and two dimensions of pandemic fear. Fear for COVID-19 consequences—but not general fear for contagion—significantly predicted prevention behaviors, and it also moderated the relationships between each health locus of control style and preventive behaviors. Our findings have relevant implications for research and social policies. COVID-19 prevention behaviors health locus of control fear young adults typesetterts1 ==== Body pmcIntroduction During the coronavirus-19 (COVID-19) pandemic, the World Health Organization provided official guidelines for limiting the spread of contagion, encompassing individual, and environmental measures (i.e. hygiene practices; social distancing; limiting crowds of people) (WHO, 2021). These guidelines were conceived as an essential complement of pharmacological measures related to treatments and vaccinations (WHO, 2021) and they effectively reduced mortality and slowed the rate of infection in the last 2 years, preventing hospitals’ overcrowding (Bo et al., 2021). Together with the progressive spread of vaccinations, and until vaccinations will be widely available and complied in all countries, the WHO guidelines continue to have a crucial role in contrasting the pandemic. According to international research (Graupensperger et al., 2021; Luo et al., 2021), young adults show less compliance with COVID-19 preventive behaviors in comparison to older adults, a trend that has been confirmed also in Italy (Barari et al., 2020). For example, 92.5% of young adults violated lockdown restrictions (Broodryk and Robinson, 2021). Youths are less likely to suffer negative medical consequences of contagion, and perceive a low personal risk (Franzen and Wöhner, 2021). Thus, at individual level, the adoption of protective measures can be considered a limitation with more costs than benefits. However, at community level, the efficacy of COVID-19 guidelines is widely dependent by the compliance of young adults, since they have the highest probability to get infected and infect others (Abbasi, 2020), often being silent spreaders of the virus (Poletti et al., 2020). According to U.S. estimates, in 2020 young adults represented 23% of contagions, but only 1% of deaths (Centers for Disease Control and Prevention [CDC], 2020). Therefore, understanding psychological determinants of young adults’ compliance with COVID-19 preventive behaviors is of primary importance, constituting an emerging research topic (e.g. Christner et al., 2020). The protection motivation theory (PMT; Rogers, 1975) states that health behaviors are motivated by cognitions about behaviors (beliefs on costs and benefits) and by appraisals on perceived risk (concerns for severity and vulnerability). In line with this paradigm, compliance with COVID-19 preventive behaviors in young people has been studied observing its cognitive (e.g. perceived behavioral efficacy; Zhang and Kou, 2021) and affective correlates (e.g. health-related anxiety and fear; Breakwell et al., 2021). In the present study, we will refer to health locus of control (Steptoe and Wardle, 2001; Wallston, 2005; Wallston et al., 1978) as a possible cognitive predictor of preventive behaviors. This construct indeed predicts compliance with prevention and treatment in different diseases (e.g. Amit Aharon et al., 2018), and there is some initial evidence supporting its adequacy for COVID-19 behaviors (Berg and Lin, 2020). On the other hand, as emotional predictor of preventive behaviors, we will investigate the role of pandemic fear. In the Rogers’ PMT (1975), fear plays a key role in health-related behaviors. Accordingly, health psychology studies suggest that affective and cognitive components interact in driving health behaviors (review by Ferrer and Klein, 2015), with emotional variables being determinant for action (Hay et al., 2006). In pandemic research, different negative emotions have been investigated, such as anxiety and fear (e.g. Breakwell et al., 2021). However, the interaction effects of cognitive and emotional factors are still relatively understudied, despite some first evidence supporting this hypothesis (Kowalski and Black, 2021). Therefore, the present study aims to investigate the predictors of COVID-19 preventive behaviors in Italian young adults, testing a model of interaction between cognitive (i.e. health locus of control) and emotional variables (i.e. pandemic-related fears). This research has been conducted in Italy from December 2020 to April 2021, when vaccinations were not yet available for Italian young adults and preventive behaviors were the main defense against contagion. COVID-19 preventive behaviors in young adults Pandemic-related preventive behaviors are those behaviors meeting the public health rules established to stop the spread of contagion (Usher et al., 2020). Reviews of pandemic studies (Bish and Michie, 2010; Usher et al., 2020) distinguished preventive behaviors in hygiene practices and social distancing measures. Hygiene practices include: wearing face masks (reported by 85% of population in different pandemics, Kwok et al., 2020; Yeung et al., 2017), frequently washing hands (from 46% to 89%, Cowling et al., 2010; Kwok et al., 2020), maintaining good indoor ventilation and households disinfection (about 8%, Cowling et al., 2010), and visiting a doctor in presence of flu symptoms (more than 85%, Yeung et al., 2017). Social distancing includes: avoiding crowded places; avoiding public transports; canceling or postponing travels; isolation and quarantine (Cowling et al., 2010; Kwok et al., 2020; Yeung et al., 2017). Social distancing measures reached rates between 39% and 92% during COVID-19 pandemic (Kwok et al., 2020). Despite their relatively high prevalence (Berg and Lin, 2020), COVID-19 preventive behaviors were significantly less frequent among young adults in different countries (Graupensperger et al., 2021; Luo et al., 2021), including Italy (Barari et al., 2020). Pandemic research has found recurrent individual differences in young adults, with gender (i.e. women reporting more compliance than men), higher education, and better mental health predicting more adherence to COVID-19 preventive measures (Franzen and Wöhner, 2021; Imtiaz et al., 2021). Beyond individual differences, several psychological variables are associated with COVID-19 preventive behaviors in young adults, including perceived risk for oneself, for loved ones, and for society (Banker and Park, 2020; Jordan et al., 2021); perceived severity of the disease and its consequences (Berg and Lin, 2020; Imbriano et al., 2021); knowledge and information on pandemic (Rayani et al., 2021); subjective evaluation of peer group norms (Graupensperger et al., 2021). Among various models proposed in literature, an interesting line of research shed light on the role of perceived efficacy of health behaviors, suggesting that COVID-19 preventive behaviors might depend on individuals’ beliefs on determinants of health (Zhang and Kou, 2021). First findings in this direction (Berg and Lin, 2020; Zhang and Kou, 2021) indicated that individuals’ beliefs on health may significantly influence COVID-19 preventive behaviors, suggesting the need for a more in-depth investigation. The role of health locus of control styles The multidimensional health locus of control model (MHLOC; Wallston, 2005; Wallston et al., 1978) described individuals’ beliefs of control over their health, distinguishing internal health locus of control (i.e. believing that health is mainly a consequence of own behavior; IHLC) and external attribution styles (i.e. beliefs regarding external factors influencing individuals’ health). This perceived external control is divided in different dimensions, according to the specific factors supposed to influence health, such as chance, luck, or destiny (chance locus of control; CHLC), or health professionals and other people empowered on health (powerful others health locus of control, PHLC). In this model, people perceiving an internal control are assumed to enact more healthy and to limit risky behaviors, resulting in more positive health outcomes (Wallston et al., 1978). The role of health locus of control in health behaviors has been broadly studied in research (review by Cheng et al., 2016). Consistently across different studies, IHLC predicted more frequent health-promoting behaviors and reduced risk-taking, while CHLC predicted less health-promoting behaviors and more health-risks (review by Cheng et al., 2016; Mercer et al., 2018). Evidence regarding PHLC is less clear, showing positive associations with only few health behaviors (Cheng et al., 2016). Steptoe and Wardle (2001) suggested that this dimension may exclusively predict behaviors clearly supported by health professionals (e.g. adherence to medical care; review by Náfrádi et al., 2017). However, Wallston et al. (1978) indicated that the route from health beliefs to health behaviors is influenced by different interacting factors, that should be studied and interpreted in the light of specific contexts. The COVID-19 pandemic may be considered an exceptional social context of study, although research on health beliefs associated with COVID-19 prevention is still in its infancy. There is some evidence for the positive role of internality and powerful others dimensions in fostering compliance with COVID-19 prevention in the general population (Berg and Lin, 2020; Itani and Hollebeek, 2021; Tagini et al., 2021), suggesting a promising research direction. Notwithstanding, no studies so far have focused on the role of health beliefs in young adults’ COVID-19 prevention behaviors, despite the important role of youths in the spread of contagion. The role of pandemic-related fear Fear is an innate emotional response to a perceived threat, which results in physical, behavioral (e.g. flight), and cognitive (e.g. worry) activation (Adolphs, 2013). COVID-19 pandemic is a relevant source of fear, threatening physical and mental health, and impacting different areas of life. The wide prevalence of COVID-19 fear is reported in different international and Italian studies, reaching rates of 78% in personal fear and of 63% in fear for others (e.g. Sloan et al., 2021; Zammitti et al., 2021). COVID-19 fear has shown to be very heterogeneous (review by Mertens et al., 2021): The main concerns regard one’s own and others’ health (e.g. Christner et al., 2020; Schimmenti et al., 2020), but many other fears also emerged, among which the most recurrent are fears of financial losses, work failures, and resource scarcity (e.g. Mertens et al., 2020; Taylor et al., 2021). Moreover, while being mostly an adaptive response, fear may also have dysfunctional forms, with abnormal, chronic, or uncontrollable reactions, leading to clinical phobia and anxiety disorders (Brown and Lees-Haley, 1992). Accordingly, also COVID-19 fear has shown adaptive and maladaptive facets, with positive (e.g. flourishing) and negative (e.g. anxiety and depression) consequences on psychological wellbeing (Solymosi et al., 2021; Zammitti et al., 2021). In the PMT (Rogers, 1975), fear is the emotional outcome of perceived risk and vulnerability, which motivates health behaviors, therefore it may be important to understand its role in COVID-19 preventive behaviors. However, contrarily to the well-documented psychological outcomes (e.g. Fitzpatrick et al., 2020), there is still limited evidence for the behavioral outcomes of COVID-19 fear. It is confirmed that pandemic fear may lead to maladaptive behaviors, such as avoiding access to health services (Karacin et al., 2020), while there are contrasting findings for preventive behaviors, with some studies reporting fear associated with more compliance to prevention guidelines (Breakwell et al., 2021; Harper et al., 2021), and other studies disconfirming this association (Christner et al., 2020; Solymosi et al., 2021). In this regard, we should note that most international studies assessed pandemic fear with the Fear for Covid-19 scale (FCV-19S, Ahorsu et al., 2020; review by Muller et al., 2021), an instrument which investigates general symptoms of fear, but neglects its specific contents. As noted by Mertens et al. (2021), the very different areas of COVID-19 fear can be better studied using different measures. Therefore, the present study aims to assess the impact of COVID-19 fear on preventive behaviors in young adults, exploring not only general symptoms of fear, but also its most common contents as emerged in recent research (Mertens et al., 2021). The current study This study aims to investigate the predictors of COVID-19 preventive behaviors in Italian young adults. Controlling for individual differences (i.e. biological sex and age), we explored the roles of cognitive (i.e. health locus of control styles) and emotional variables (i.e. pandemic related fears), as suggested by health psychology studies (Ferrer and Klein, 2015). Since recent research has shed light on different components in pandemic fear (Mertens et al., 2021), we assessed both general fear of contagion (using the FCV-19S, Ahorsu et al., 2020), and various contents of pandemic fear (hereafter named COVID-19 fears). Moreover, in accordance with Ferrer and Klein (2015), we also investigated the presence of interaction effects between cognitive and emotional variables, hypothesizing that only specific profiles of health locus of control and fears would predict preventive behaviors. Specifically, we aim to verify the following hypotheses: H1. According to previous research on health locus of control (Cheng et al., 2016; Náfrádi et al., 2017), we expect IHLC and PHLC being positive predictors of COVID-19 prevention behaviors, whilst CHLC is supposed to be a negative predictor. H2. In line with health psychology studies (Ferrer and Klein, 2015), we hypothesize that the relationship between each health locus of control style and preventive behaviors would be moderated by pandemic fear, which is expected to be a protective factor promoting positive health behaviors (Rogers, 1975). Therefore, in presence of high pandemic fear, the positive effects of IHLC and PHLC on prevention behaviors should be enhanced, while the negative effects of CHLC should be reduced. Method Participants and procedure Data for the present study were gathered from December 2020 to April 2021, when Italy was facing the second and third waves of COVID-19 pandemic. The inclusion criteria of this research were the age range (19–26) and being currently living in Italy. Participants were contacted online with a snowball sampling method, sharing the link of the survey through the University website. Preliminary, they accepted an informed consent ensuring the complete anonymity and voluntariness of their participation. The completion of questionnaires was 15 minutes long on average. Initially, 229 youths were invited to participate in the study, but only 188 accepted the informed consent, matched the inclusion criteria for the study, and correctly completed all questionnaires, resulting in a response rate of 82.1%. Thus, the final sample was composed by 188 young adults aged 19–26 years old (Mage = 22.76, SDage = 1.95; 85.6% girls). Most of participants (78.7%) lived in central Italy, while 17.6% were from the South, and 3.7% from the North. Regarding their education level, 39.9% completed the secondary school, 54.3% had a bachelor degree, and 5.9% had a master’s degree. An a-priori power analyses was conducted using the software GPower version 3.1. Setting the power at the conventional level of 80% and alpha significance at 0.05 (Cohen, 1988), the power analysis indicated a required minimum sample size of 64 to detect small effects (Cohen’s d = 0.20). This research and its procedure were approved by the ethic committee of the Department of Developmental and Social Psychology, Sapienza University of Rome. Measures Individual information Participants reported their biological sex (0 = woman, 1 = man), age, living area (northern, central or southern Italy) and education level. COVID-19 Prevention behaviors Ten items were adapted from the official guidelines recommended by Italian Ministry of Health to avoid the risk of COVID-19 contagion (Italian Ministry of Health, 2020), as follows: (1) wearing the antivirus mask indoors; (2) wearing the antivirus mask outdoor if interpersonal distance from non-cohabiting people cannot be guaranteed; (3) maintaining an interpersonal safety distance of at least 1 m; (4) washing or sanitizing your hands often; (5) avoiding crowded places and indoor places with poor ventilation; (6) avoiding unnecessary physical contact (e.g. hugs and handshakes); (7) sneezing and coughing into a handkerchief; (8) avoiding to touch your eyes, nose or mouth with your hands; (9) do not take antiviral drugs unless prescribed by your doctor; (10) staying home and contacting a doctor in case of fever, cough, or other symptoms related to COVID-19. During fall and winter 2020, the Italian population was made aware of these health recommendations, with some of them being mandatory in public places. Therefore, following a focus group with expert researchers in developmental psychology, we decided to use the sum of these items to assess the adherence of young adults to official prevention behaviors (“Please, report how often you adopt the following behaviors to prevent COVID-19 contagion”). Participants answered on a 5 point Likert-type scale, from 1 (never or almost never) to 5 (always or almost always). The principal components analysis (PCA) detected one main factor with eigenvalue >1 (eigenvalue = 4), which explained the 43.04% of variance, suggesting the presence of one-factor structure for this measure (Gorsuch, 1983). A confirmatory factor analysis (CFA) was then run using the LISREL software version 8.80, to test the adequacy of the one-factor model. The maximum-likelihood estimates were computed from the sample correlation matrix. The goodness of fit of the model was estimated by the relative Chi-square test statistic (χ2/df), whose acceptable values range between 1 and 3 (Carmines and McIver, 1983); by the comparative fit index (CFI), the normative fit index (NFI) and the nonnormative fit index (NNFI), whose values should be >0.95 in a good model fit (Hu and Bentler, 1999); and by the root mean square error of approximation (RMSEA), and the standardized root mean square residual (SRMR), whose values <0.08 are indicative of acceptable fit (Kaplan, 2000). The CFA confirmed the adequacy of the one-factor model: χ2(26) = 47.09, p = 0.005; χ2/df = 1.84; CFI = 0.98; NFI = 0.96; NNFI = 0.97; RMSEA = 0.06; SRMR = 0.04. The instrument obtained good values of reliability (Cronbach’s alpha of 0.84), and internal consistency (average inter-item correlation of 0.36; Piedmont, 2014) in our sample. Health locus of control The Italian version of the Multidimensional Health Locus of Control Scale—Form A (MHLCS; Wallston et al., 1978; Italian validation of Gala et al., 1995) was used to assess three locus of control styles regarding own health: internal locus of control, describing a perceived control on own health which is considered depending on own behaviors (IHLC; six items, sample item: “if I take care of myself I can avoid disease”); chance locus of control, describing the beliefs that own health is determined by external uncontrollable factors, such as luck, fate and chance (CHLC; six items, sample item: “no matter what I do, if I have to get sick I’ll get sick”); powerful others locus of control, describing the beliefs that own health is under the external control of other people, such as doctors, nurses, family (PHLC; six items, sample item: “with respect to my health, I can only do what my doctor tells me to do”). Participants rated their answers on a 6 point scale from 1 (decidedly disagree) to 6 (decidedly agree). The three dimensions have shown good psychometric properties in previous studies (Wallston, 2005), which were confirmed in Italian samples (Gala et al., 1995). This instrument also obtained acceptable reliability scores in our study (Cronbach’s alpha of 0.70 for IHLC; 0.76 for CHLC; 0.68 for PHLC). Fear of COVID-19 contagion The Italian version of the Fear of Covid-19 Scale (FCV-19S; Ahorsu et al., 2020; Italian validation by Soraci et al., 2020) was administered to measure the general level of fear of COVID-19, with associated anxious symptoms (seven items; e.g. “I cannot sleep because I’m worrying about getting coronavirus-19”; “My heart races or palpitates when I think about getting coronavirus-19”). The scale has shown good psychometric properties in different international and Italian studies (review by Muller et al., 2021), and it also reached good reliability in the present study (Cronbach’s alpha of 0.87). COVID-19-related fears Five items were created ad hoc to investigate the content of fears that young adults may have about consequences of COVID-19 contagion, as follows: (1) fear of being infected and getting sick with COVID-19; (2) fear of being positive for COVID-19 and infecting other people; (3) fear of people dear to me getting sick with COVID-19; (4) fear of losing loved ones due to COVID-19; (5) fear of losing my job or having serious financial damage due to pandemic. Participants were asked to rate how much they felt described by each item, on a 5 point Likert-type scale from 1 (not at all) to 5 (very much). The items were conceived in a focus group among expert researchers in developmental psychology, after reviewing the very recent literature on pandemic fear, and covered both personal and altruistic fears (Sloan et al., 2021), and concerns for health and socio-economic risks (Mertens et al., 2021). The PCA analysis identified one single factor solution with eigenvalue higher than 1, explaining the 59.63% of variance. The subsequent CFA confirmed the adequacy of one-factor structure for the scale, χ2(2) = 3.85, p = 0.15; χ2/df = 1.92; CFI = 1.00; NFI = 0.99; NNFI = 0.98; RMSEA = 0.07; SRMR = 0.02. In our study, this measure showed good values of reliability (Cronbach’s alpha of 0.80), and concurrent validity (correlation with FCV-19S scale: Pearson r correlation coefficient of 0.61). Data analysis Data analyses were performed using the statistical software SPSS version 27.0. Descriptive statistics, biological sex differences, and bivariate correlations were computed on study variables. Two moderation regression analyses were performed, entering prevention behaviors as criterion variable. Independent predictors were preliminary centered on their grand-mean (except biological sex which was dummy coded), as suggested by Cohen et al. (2002). Then, six interaction terms were computed according with our hypotheses: IHLC COVID-19 fears; CHLC * COVID-19 fears; PHLC * COVID-19 fears; IHLC * FCV-19S; CHLC * FCV-19S; PHLC * FCV-19S. Finally, two moderation models were tested in different steps: in Step 1 of each model, biological sex and age were controlled as covariates; in Step 2, the three health locus of control styles were regressed on the criterion; in Step 3, pandemic-related fears—i.e. COVID-19 fears and FCV-19S—were added to the regression equation. Finally, Step 4 was conceived in line with our hypotheses, testing the interaction effects between health locus of control styles and COVID-19 fears in the first regression model (Model 1), and the interaction effects between health locus of control styles and FCV-19S in the second model (Model 2). Afterward, simple slope analyses were conducted in order to interpret the direction of each significant interaction. As suggested by Aiken and West (1991), the predicted values of COVID-19 prevention behaviors were plotted as a function of each predictor, for high (1 SD above the mean) versus low (1 SD below the mean) levels of the moderator. Results Preliminary analyses of skewness and kurtosis ascertained the normal distribution of variables in our study (±2; Tabachnick and Fidell, 2013). See Table 1 for descriptive statistics and biological sex differences, and Table 2 for bivariate Pearson’s correlations on study variables. Table 1. Descriptive statistics and differences by biological sex. Range Women Men F(df) Total M (SD) M (SD) M (SD) IHLC 1–6 3.86 (0.86) 4.05 (0.78) F(187) = 1.19, η2 = 0.006 3.89 (0.85) CHLC 1–6 2.42 (0.85) 2.59 (0.95) F(187) = 0.85, η2 = 0.005 2.45 (0.86) PHLC 1–6 3.47 (0.84) 3.54 (0.82) F(187) = 0.20, η2 = 0.001 3.48 (0.83) COVID-19 fears 1–5 3.72 (0.75) 3.29 (0.92) F(187) = 7.33, η2 = 0.04 3.66 (0.79) FCV-19S 1–5 2.56 (0.78) 2.33 (0.67) F(187) = 2.07, η2 = 0.01 2.53 (0.77) COVID-10 prevention behaviors 1–5 4.39 (0.51) 4.09 (0.74) F(187) = 7.16, η2 = 0.04 4.35 (0.55) Notes: *p < 0.001; p < 0.01; p <0.05. F = Fisher F values. Biological sex differences were computed by univariate analyses of variance. Table 2. Bivariate Pearson’s correlations on study variables. 1 2 3 4 5 6 7 8 1. Biological sex (0 = women; 1 = men) 1 2. Age 0.24* 1 3. IHLC 0.08 0.01 1 4. CHLC 0.07 –0.09 0.06 1 5. PHLC 0.03 –0.02 0.48* 0.10 1 6. COVID-19 fears –0.19 –0.06 0.23 –0.06 0.23 1 7. FCV-19S –0.11 –0.07 0.17* 0.11 0.25 0.61* 1 8. COVID-10 prevention behaviors –0.19 0.02 0.14 –0.25* 0.17* 0.31*** 0.16* 1 Notes: *p < 0.001; p < 0.01; p<0.05. Assumptions of multiple regression analyses were preliminarily verified, confirming the absence of multicollinearity problems (variance inflation factors between 1.02–1.72). In Model 1, the 24.1% of variance in COVID-19 prevention behaviors was explained. Step 1 accounted for the 4.2% of variance, with only biological sex emerging as significant covariate (women scored higher than men). Step 2 added a significant 10.2% to the explained variance, detecting a significant negative effect for CHLC and a significant positive effect for PHLC—while biological sex remained significant. Step 3 explained another significant 4.5% of variance. Only biological sex and CHLC remained significant and, controlling for their effects, also COVID-19 fears turned out to be significant and positive predictor of prevention behaviors. Finally, the Step 4 of Model 1 added a significant 5.3% to the explained variance, detecting significant effects for the three tested interaction terms: IHLC COVID-19 fears; CHLC * COVID-19 fears; PHLC * COVID-19 fears (see Table 3). Conversely Model 2 explained the 21.2% of variance in COVID-19 prevention behaviors. The Step 4 in this model did not contribute to the explained variance, and no significant interaction terms were found. Full statistics of the two models are reported in Table 3. Table 3. Moderation regression models predicting COVID-19 Prevention Behaviors. Predictors Model 1 Model 2 Step 1 Step 2 Step 3 Step 4 Step 1 Step 2 Step 3 Step 4 R 2 beta ΔR 2 beta ΔR 2 beta ΔR 2 beta R 2 beta ΔR 2 beta ΔR 2 beta ΔR 2 beta 0.04* 0.10* 0.05 0.05*** 0.04* 0.10* 0.05 0.02 Biological sex (0 = women; 1 = men) –0.21 –0.20 –0.15* –0.13* –0.21 –0.20 –0.15* –0.15* Age 0.07 0.05 0.05 0.08 0.07 0.05 0.05 0.07 IHLC 0.09 0.05 0.08 0.09 0.05 0.09 CHLC –0.26* –0.24* –0.30* –0.26* –0.24* –0.24* PHLC 0.16* 0.12 0.06 0.16* 0.12 0.07 COVID-19 fears 0.22* 0.19* 0.22* 0.24** FCV-19S 0.004 0.05 0.004 0.008 IHLC * COVID-19 fears 0.21** -- CHLC * COVID-19 fears 0.14* -- PHLC * COVID-19 fears –0.25** -- IHLC * FCV-19S -- 0.14 CHLC * FCV-19S -- –0.005 PHLC * FCV-19S -- 0.15 Total R2 0.24* 0.21* Notes: *p ⩽ 0.001; p ⩽ 0.01; *p ⩽0.05. Standardized regression coefficients are reported. In the first slope analysis, the relationship between IHLC and COVID-19 prevention behaviors was plotted at high versus low levels of the moderator (COVID-19 fears), controlling for all variables in the model. At low levels of fears, IHLC was not related to prevention behaviors, beta = −0.11, p = 0.28. Conversely at high levels of fears, IHLC positively and significantly predicted prevention behaviors, beta = 0.28, p = 0.01 (see Figure 1). As regards the second slope analysis, the relationship between CHLC and COVID-19 prevention behaviors was plotted at two levels of COVID-19 fears, while controlling for the other variables in the model. In this case, at low levels of fears CHLC was a negative and significant predictor of COVID-19 prevention behaviors, beta = −0.43, p < 0.001, whereas at high levels of fears, the same relationship vanished in a nonsignificant effect, beta = −0.16, p = 0.07 (see Figure 2). Finally in the third slope analysis, the relationship between PHLC and COVID-19 prevention behaviors was plotted at the two levels of the moderator, still controlling for the other variables in the model. When fears were low, PHLC positively predicted prevention behaviors, beta = 0.29, p = 0.003, conversely when fears were high the same relationship became not significant, beta = −0.16, p = 0.19 (see Figure 3). Figure 1. Moderation role of COVID-19 fears in the relationship between IHLC and COVID-19 prevention behaviors. Notes: Dashed line represents a nonsignificant relationship. Figure 2. Moderation role of COVID-19 fears in the relationship between CHLC and COVID-19 prevention behaviors. Notes: Dashed line represents a nonsignificant relationship. Figure 3. Moderation role of COVID-19 fears in the relationship between PHLC and COVID-19 prevention behaviors. Notes: Dashed line represents a nonsignificant relationship. Therefore, when COVID-19 fears were low, CHLC was a risk factor which reduced the adoption of prevention behaviors whereas PHLC was a protective factor which promoted the adoption of prevention behaviors. However, in presence of high COVID-19 fears, the negative effects of CHLC, as well as the positive effects of PHLC, became nonsignificant, and conversely the adherence to COVID-19 prevention behaviors was a function of IHLC, which became a protective factor. Discussion This study proposed a model of understanding for COVID-19 preventive behaviors in Italian young adults, taking into account cognitive (i.e. health locus of control styles) and affective variables (i.e. pandemic-related fear). Our findings detected relevant contributes of both cognitive and emotional variables and also revealed significant interaction effects, indicating that COVID-19 preventive behaviors can be predicted by specific profiles of health beliefs and pandemic fear. This study is a contribute to the emerging literature on compliance with COVID-19 preventive behaviors in young adults and provides, to the best of our knowledge, the first scientific evidence for interaction effects between health locus of control and pandemic fear. Regarding individual differences on COVID-19 preventive behaviors, only biological sex emerged as significant covariate, with women reporting more frequent prevention behaviors than men, confirming recent studies (Franzen and Wöhner, 2021; Imtiaz et al., 2021). As suggested by Mahalik et al. (2021), some prevention behaviors, such as wearing face mask, can be considered in contrast with an ideal of masculinity according to gender-related social norms, which thus discourage their use in young men. Age showed instead null associations, indicating that prevention behaviors did not vary by age across early adulthood. About pandemic related fear, our regression models detected two different effects for general fear of COVID-19 contagion and for specific concerns about possible negative consequences of pandemic (COVID-19 fears). The fear of COVID-19 contagion, measured by FCV-19S (Ahorsu et al., 2020) and including psychosomatic symptoms, was not related to preventive behaviors, while conversely the specific COVID-19 fears showed a significant and positive association. Most studies using the FCV-19S have found this dimension related to negative mental health outcomes, such as anxiety, depression, and clinical phobia (review by Muller et al., 2021), but some links also emerged with preventive behaviors (Harper et al., 2021). However our findings indicate that, when both generalized fear of contagion and realistic concerns for consequences are tested simultaneously, it is only the latter dimension that explains the variance in preventive behaviors. Therefore, in line with recent evidence on functional and dysfunctional pandemic fears (Solymosi et al., 2021), our findings suggest the need of distinguishing generalized fear for contagion (with maladaptive outcomes, see Muller et al., 2021) from realistic concerns for consequences (which conversely show positive behavioral outcomes in our model). Moreover, only COVID-19 fears—but not generalized fear for contagion—resulted to be a significant moderator in the relationships between health locus of control styles and prevention behaviors (see Model 1 vs Model 2), representing an important protective factor. As regard the effects of health locus of control styles, our findings only partially confirmed research hypotheses. IHLC, as independent predictor, was not related with COVID-19 preventing behaviors, disconfirming initial expectation. Previous studies suggested that perceiving more internal control on health would favor health-promoting behaviors (Cheng et al., 2016; Mercer et al., 2018); however, the few researches that applied this construct to COVID-19 prevention are quite contrasting (Berg and Lin, 2020; Itani and Hollebeek, 2021). In this regard, our findings indicated that the positive role of IHLC is not obvious for young adults during COVID-19 pandemic, but rather it may be observed only in individuals presenting high COVID-19 fears (interaction effect depicted in Figure 1). COVID-19 contagion has shown so far limited consequences for health in young adults, who often were asymptomatic. Therefore internal health beliefs could not be the key to motive youths in following prevention guidelines, since an eventual contagion could not endanger their health. However, for young people who are also concerned about different negative consequences of contagion in their lives (including risks of infecting beloved people and socioeconomic damages), then internal health beliefs become important to predict their preventive behaviors. Thus, COVID-19 fears appear to be a factor triggering the positive effect of IHLC on COVID-19 preventive behaviors in young adults. Conversely, CHLC was a significant and negative predictor of COVID-19 prevention behaviors, as hypothesized according with previous studies on MHLC model (Steptoe and Wardle, 2001). Young adults who believe their health being mostly determined by chance also reported lower prevention behaviors, suggesting that they undervalue the effectiveness of pandemic guidelines and neglect them, exposing themselves and others to more risk for contagion. In this perspective, CHLC should be considered a risk factor at individual and community level, contributing to the general spread of COVID-19 contagion. However, the significant interaction with COVID-19 fears (depicted in Figure 2) indicates that the negative role of CHLC can be nullified by high pandemic fear: CHLC is a risk factor for more exposure to contagion only in individuals who are unaware or unconcerned about the possible consequences of contagion in their lives. Conversely, when youths are highly concerned for consequences of COVID-19 contagion, they tend to adopt more preventive behaviors regardless of their beliefs on uncontrollability of health. Results on PHLC also confirmed our expectation, showing a positive significant association with COVID-19 preventive behaviors. In previous research (Cheng et al., 2016), this dimension has inconsistent associations with health behaviors because, according to Steptoe and Wardle (2001), PHLC can predict only behaviors clearly sponsored by healthcare authorities (Náfrádi et al., 2017). In the context of COVID-19 pandemic, it is well-understandable that young adults high in PHLC are also more compliant with prevention guidelines provided by the Ministry of Health, due to their trust in health professionals’ indications. Accordingly, the few extant research on health locus of control during pandemic has found very similar results (Berg and Lin, 2020; Tagini et al., 2021). Furthermore, the interaction effect emerged with COVID-19 fear (see Figure 3) indicates that PHLC is a positive predictor of preventive behaviors exclusively for young adults with low COVID-19 fears. Conversely their peers with high COVID-19 fears reported higher rates of compliance to prevention guidelines regardless of their PHLC beliefs. Therefore, PHLC appears to be a specific protective factor in presence of low pandemic fear, because even the youths more unconcerned about negative consequences of contagion, may be moved to adopt preventive behaviors in reason of their trust in the importance of following medical suggestions for preserving their health. Limitations and implications To the best of our knowledge, this is the first evidence of interaction effects between health beliefs and pandemic fear in predicting compliance with COVID-19 preventive behaviors in young adults. Nevertheless, some limitations should be taken into account: First, the study sample is not large, but however it is adequate for detecting significant effects as indicated by power analysis. Second, men are underrepresented in our study, so the effect of biological sex—although significant and controlled for in our models—might have been underestimated. Future studies should therefore replicate our findings in larger samples with equal prevalence of men and women. Third, the adoption of exclusively self-report instruments may have increased the risk of social desirability bias, so that undesirable behaviors (such as low compliance with official guidelines) might have been underreported. Notwithstanding, significant and interesting results emerged by our models. Fourth, results of this study exclusively describe the sociocultural context of Italy during COVID-19 pandemic, and cannot be generalized to different cultural contexts. In conclusion, this study shed a new light on the predictors of compliance with COVID-19 prevention guidelines in young adults, suggesting that a key role is played by interactions of health beliefs with a specific functional type of pandemic fear (i.e. concerns for possible negative consequences of contagion on different areas). Overall, these findings may have implications for social policies and future research. Our results can be useful during pandemic, as well as in post-pandemic period, for targeting health promoting interventions on specific risk groups, such as youths with external attributional styles on health, and/or lack of awareness for the possible negative consequences of pandemic on their lives. Moreover, our results can support implementation of public health messages targeted to young people, hopefully extendable to other health-risk conditions. These findings may also suggest different directions for future research: First, the role of health beliefs and pandemic fear should be studied in other health behaviors related to pandemic, such as compliance with vaccination in next years. Moreover, it would be desirable to test our model in other healthy and risky behaviors beyond pandemic, in order to understand its usefulness in different health conditions. Finally, future research should also deeply understand the different roles of contextualized versus generalized fear, observing their outcomes on health in a longitudinal perspective, and across different genders and sociocultural backgrounds. Supplemental Material sj-docx-1-hpq-10.1177_13591053221089722 – Supplemental material for A model to understand COVID-19 preventive behaviors in young adults: Health locus of control and pandemic-related fear Click here for additional data file. Supplemental material, sj-docx-1-hpq-10.1177_13591053221089722 for A model to understand COVID-19 preventive behaviors in young adults: Health locus of control and pandemic-related fear by Dora Bianchi, Antonia Lonigro, Anna Di Norcia, Daniele Di Tata, Sara Pompili, Marta Zammuto, Eleonora Cannoni, Emiddia Longobardi and Fiorenzo Laghi in Journal of Health Psychology Data sharing statement: The current article includes the complete raw dataset collected in the study including the participants’ data set, syntax file and log files for analysis. These files are all available in the Figshare repository and as Supplemental Material on the SAGE Journals platform. The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding: The authors received no financial support for the research, authorship, and/or publication of this article. 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==== Front Acta Radiol Acta Radiol ACR spacr Acta Radiologica (Stockholm, Sweden : 1987) 0284-1851 1600-0455 SAGE Publications Sage UK: London, England 36451533 10.1177/02841851221138557 10.1177_02841851221138557 Original Article Neurological manifestations of COVID-19: a retrospective observational study based on 1060 patients with a narrative review Negro Alberto 1* https://orcid.org/0000-0002-4745-3061 Tortora Mario 2* Gemini Laura 2 de Falco Arturo 3 Somma Francesco 1 d’Agostino Vincenzo 1 1 Department of Neuroradiology, 508856 Ospedale del Mare , Naples, Italy 2 Department of Advanced Biomedical Sciences, 9307 University “Federico II,” Naples , Italy 3 Department of Neurology, 508856 Ospedale del Mare , Naples, Italy * Equal contributors. Mario Tortora, Department of Advanced Biomedical Sciences, University “Federico II,” Via Pansini, 5, 80131, Naples, Italy. Email: [email protected] 30 11 2022 30 11 2022 0284185122113855728 7 2022 8 10 2022 © The Foundation Acta Radiologica 2022 2022 The Foundation Acta Radiologica 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. Background In the past two decades, three coronavirus epidemics have been reported. Coronavirus disease 2019 (COVID-19) is caused by a severe acute respiratory syndrome (SARS)-like coronavirus (SARS-CoV-2). In most patients, the disease is characterized by interstitial pneumonia, but features can affect other organs. Purpose To document the radiological features of the patients and to perform a narrative review of the literature. Material and Methods We conducted a retrospective, single-center study on 1060 consecutive hospitalized patients with COVID-19 at our institution. According to the inclusion criteria, we selected patients to be studied in more radiological detail. All images were obtained as per standard of care protocols. We performed a statistic analysis to describe radiological features. We then presented a systematic review of the main and conventional neuroimaging findings in COVID-19. Results Of 1060 patients hospitalized for COVID-19 disease, 15% (159) met the eligibility criteria. Of these, 16 (10%) did not undergo radiological examinations for various reasons, while 143 (90%) were examined. Of these 143 patients, 48 (33.6%) had positive neuroimaging. We found that the most frequent pathology was acute ischemic stroke (n=16, 33.3%). Much less frequent were Guillain–Barre syndrome (n=9, 18.8%), cerebral venous thrombosis (n=7, 14.6%), encephalitis or myelitis (n=6, 12.5%), intracranial hemorrhage and posterior hemorrhagic encephalopathy syndrome (n=4, 8.3%), exacerbation of multiple sclerosis (n=4, 8.3%), and Miller–Fisher syndrome (n=2, 4.2%). Conclusion Our data are coherent with the published literature. Knowledge of these patterns will make clinicians consider COVID-19 infection when unexplained neurological findings are encountered. COVID-19 neuroradiology brain spine stroke edited-statecorrected-proof typesetterts19 ==== Body pmcIntroduction Coronavirus disease 2019 (COVID-19) is caused by a severe acute respiratory syndrome (SARS)-like coronavirus (SARS-CoV-2). In most patients, the disease is characterized by fever, dry cough, dyspnea, and hypoxia, with interstitial pneumonia features on chest X-ray, computed tomography (CT), or spectral photon-counting CT scan (1–3). However, COVID-19 is not just a respiratory disease and can affect other organs, including the brain (4). The primary aim of the present study was to systematically characterize the neurological characteristics of patients admitted with COVID-19 at our institution. A secondary aim was to describe in a narrative review the current data by comparing it with previous papers (5,6). This is in order to validate the data published so far, with the large study population as our strength. Material and Methods We used a retrospective, mono-center study design. The institutional review board approved the study and waivers for informed consent were obtained at our institution. Radiological exams were performed at the request of clinicians in cases of symptoms that raised suspicion of nervous system involvement. All images were obtained as per standard of care protocols. Magnetic resonance imaging (MRI) scans of the brain and spine were obtained with 1.5-T scanners with a standardized protocol. For the brain, 3D-T1mprage, 3D-Balance, TSE-T2, and FLAIR sequences were used on the axial and coronal planes, diffusion-weighted imaging/apparent diffusion coefficient and susceptibility-weighted imaging were used on the axial planes; contrast media (Gadovist) was used only in selected cases according to clinical query. For the spinal cord, SE-T1 and STIR were used on the sagittal planes and TSE-T2 on the axial and sagittal planes; we used Gadovist and axial SE-T1 only in selected cases. The image analysis was performed at the time of the radiological exam by neuroradiologists at our institution using a structured report. Then, two neuroradiologists (AN and FS, with >20 years of experience) retrospectively performed the image analysis double-blinded. Data confidentiality was achieved using images extracted from the PACS system only after anonymization and with data reported by first and last name coding (e.g. MaRiO RoSsI = MRORSI). The inclusion criteria were as follows: (i) hospitalized patients who were positive for COVID-19 by means of real-time reverse-transcriptase polymerase chain reaction testing from March 2020 to March 2022; (ii) the presence of acute neurologic symptoms during the hospital stay; and (iii) any neurologic imaging studies. No exclusion criteria were used for this study. The reasons why some patients meeting the inclusion criteria did not undergo radiological investigations were related to internal organizational issues or extremely high-risk health conditions. We reviewed the electronic medical records, and all statistical analyses were performed using software (Stata version 15; StataCorp, College Station, TX, USA). We then conducted a systematic review of the main and conventional imaging findings in COVID-19 to help neuroradiologists in the evaluation of these patients. Results A total of 1060 consecutive hospitalized patients with COVID-19 were reviewed. Of these patients, 159 (15%) met the eligibility criteria. Of these 159 patients, 16 were not examined radiologically for various reasons, while 143 (90%) underwent radiological examinations. Among them, 96% were examined with unenhanced brain CT, 18% with head and neck CT angiography, and 20% with brain or spine MRI. Of these 143 patients, 48 (33.6%) had positive neuroimaging. Among the patients with positive neuroimaging (M/F = 29/19; mean age = 66 ± 14 years), we found that the most frequent pathology was acute ischemic stroke (n = 16, 33.3%). Much less frequent were cerebral venous thrombosis (n = 7, 14.6%) and intracranial hemorrhage (ICH) and posterior hemorrhagic encephalopathy syndrome (PRES) (n = 4, 8.3%). Guillain–Barre syndrome (GBS) (n = 9, 18.8%) was the second most frequent manifestation. Other possible manifestations were encephalitis or myelitis (n = 6, 12.5%), exacerbation of multiple sclerosis (n = 4, 8.3%), and Miller–Fisher syndrome (MFS) (n = 2, 4.2%). No significant differences were found between the prevalence of neuroradiological manifestations and the sex of patients. The most common predominant neurologic symptoms were altered mental status (n = 30, 62.5%). Other predominant symptoms were headache (n = 6, 12.5%), myalgias (n = 5, 10.4%), seizure (n = 4, 8.3%), ataxia (n = 2, 4.2%), and hyposmia (n = 1, 2.1%). There was a statistically significant association between the prevalence of altered mental status and patient age (mean age = 74 years vs. 60 years) (P = .006). About one-quarter of patients had no known past medical history while the rest had one of the following chronic disorders: hypertension (n = 25, 52.1%); diabetes mellitus (7); coronary artery disease (8); and cerebrovascular disease (9). Discussion The present study demonstrated varied imaging features without a specific pattern but dominated by acute ischemic infarction and ICH. We also demonstrated a broad neuroradiological spectrum of MRI different from stroke. The understanding of neurological symptoms in patients with COVID-19 remains poor even two years after the onset of the pandemic. Indeed, it is still debated whether they result from the critical illness or direct invasion of the central nervous system by the coronavirus. Our results showed a lower prevalence of central nervous system symptoms than the early Wuhan experiences (1) with a higher prevalence of ischemic stroke in our study (33% vs. 11%). On the other hand, our results are in agreement with the national reports (33% vs. 31%) (5). Regarding stroke, the ischemic subtype was the most common. Compared with strokes without infection, people were younger, sometimes without cardiovascular risk factors, and the stroke was more often characterized by multiplied cerebral infarcts and caused by occlusion of a large artery. An early emergency thrombectomy is useful for such patients (8–13). On the other hand, because of the difficult management of these patients, out-of-window recanalization can be assumed, even after >12 h from the onset of symptoms (14). According to our experience, it is necessary to suspect a stroke in any patient with COVID-19 with cognitive impairment and altered mental status, even in the absence of typical manifestations such as aphasia or paresis. A COVID-19 stroke may be mainly due to cardioembolism or paradoxical embolism and less often to atherosclerosis and plaque rupture (7). Ischemic stroke due to occlusion of the internal carotid artery is a potentially devastating condition that could be embolic in nature. Tandem endovascular treatment is required in such cases (15). An important question is whether stroke in COVID-19 is causally related or represents an accidental association. COVID-19 can be a trigger or risk factor for stroke. In addition, stroke could complicate the course of COVID-19. Therefore, physicians must pay attention to the signs and symptoms of cerebral involvement to ensure appropriate clinical interventions. The mechanisms of cerebrovascular manifestations could be related to conventional mechanisms of stroke, with COVID-19 acting as a factor (16,17). Alternatively, it could be directly caused by SARS-CoV-2 infection through specific pathophysiological mechanisms leading to both ischemic stroke and hemorrhagic. Activation of the coagulation pathway with elevated D-dimer and elevated fibrinogen is a feature common to many individuals with severe COVID-19 infection. This coagulopathy, called “sepsis-induced coagulopathy” (SIC), is related to the systemic inflammatory response induced by infection and may contribute to an increased risk of thrombosis and stroke (18,19). In addition, the presence of anti-phospholipid antibodies (aPL), including IgA anticardiolipin antibodies and IgA and IgG beta 2 glycoprotein I antibodies, have been reported in severely infected patients with multiple cerebral infarcts (20). COVID-19 uses the angiotensin-converting enzyme 2 (ACE-2) receptor to enter cells (21) in the lungs, heart, kidneys, and vascular endothelium. Direct viral invasion of endothelial cells causes inflammation or “endothelitis,” which has been proposed as one of the substrates for thrombotic complications of COVID-19 (22) (Fig. 1). COVID-19-related hemorrhagic strokes are much less common than ischemic strokes. Some mechanisms may also play a role in promoting intracranial bleeding (23,24). The affinity of SARS-CoV-2 for ACE-2 receptors could allow the virus to directly damage intracranial arteries, causing the vessel wall to rupture. In addition, downregulation of the renin-angiotensin system may raise blood pressure and put patients already diagnosed with hypertension at higher risk for hemorrhagic stroke (25). In addition to ICH, rupture of the blood–brain barrier (BBB) could explain cases of reversible PRES and hemorrhagic transformation of ischemic stroke that have been reported in some patients with COVID-19 (26). Furthermore, SARS-CoV-2 infection could be associated with a consumable coagulopathy related to fibrinogen depletion (from metabolic acidosis or disseminated intravascular coagulation), which may increase the risk of ICH (16). Finally, perivascular micro-hemorrhages with cerebral micro-bleeds in the corpus callosum and subcortical and deep white matter suggest a potential role of brain hypoxia in causing brain damage in severe COVID-19 (27). An atypically high incidence of venous thromboembolism (VTE) has been reported in patients hospitalized with COVID-19 (28). Fig. 1. Top, from left to right: CT axial sections without and with contrast enhancement depict a focal area of ipo-density correlated with ischemic site (white arrow) due to occlusion of right middle cerebral artery, confirmed with RAPID software – LVO detected (red circle). Bottom, from left to right: 3D CT angiography shows flow reduction with blood vessel density <45% in right M1; then, RAPID software calculates CBF and Tmax for depiction of mismatch volume and ratio. These evaluations are fundamental for our endovascular treatment planning. CBF, cerebral blood flow; CT, computed tomography; LVO, large vessel occlusion; M1, first tract of middle cerebral artery; Tmax, time-to-maximum. Studies have demonstrated that COVID-19 infection is associated with an increase in pro-thrombotic markers such as fibrinogen and D-dimer, as well as inflammatory markers such as C-reactive protein and interleukin-6, which are associated with a hypercoagulable state. Cerebral venous sinus thrombosis (cVST) is an uncommon subtype of stroke with a predilection for younger women. Interestingly, there appears to be a roughly equal incidence in men and women with COVID-19-associated cVST (29) (Fig. 2). For patients with ICH or cVST, it is necessary to not underestimate headaches, especially of the transfixed type. Encephalitis is an inflammation of the brain parenchyma, usually caused by an infection. Detection of the SARS-Cov-2 virus in the cerebrospinal fluid (CSF) on its own does not provide a diagnosis of encephalitis if there is no evidence (EEG or neuroimaging abnormalities) of brain inflammation (30) (Fig. 3). No specific treatment exists for SARS-CoV-2 encephalitis. Acute disseminated encephalomyelitis and myelitis are syndromes of multifocal demyelination, typically occurring weeks after an infection, which generally presents with focal neurological symptoms (31). Patients could have normal CSF and high signal intensities on MRI (Fig. 4a). An examination showed hyporeflexia and a sensory level. Acute disseminated encephalomyelitis and myelitis, usually considered post-infectious diseases, are treated typically with corticosteroids or other immunotherapies. In patients with COVID-19, clinicians might need to be more cautious, especially if the virus is detected in the CSF, because such treatment might diminish the patient's immune response (32). cytotoxic lesions of the corpus callosum (CLOCCs) are non-specific findings on brain MRI associated with reversible neurological signs, such as behavior changes and multiple etiologies, including viral illness, drug toxicity, seizures, malignancy, subarachnoid hemorrhage, and metabolic disturbances (33). The physiopathological hypothesis is that an inflammatory process involving cytokines such as IL-6 triggers the accumulation of glutamate in the extra-cellular space, resulting in cytotoxic edema, particularly of astrocytes. The selective vulnerability of the corpus callosum could be explained by its high density of cytokine and glutamate receptors. CLOCCs has been reported in association with SARS-CoV-2 infection. Interestingly, immune-mediated post-infectious mechanisms associated with SARS-CoV-2 are suggested. Other mechanisms are direct invasion of the brain via the olfactory bulb, carriage across the BBB after viremia or entry through infected leucocytes (34). Although intraVenous immunoglobulin and methylprednisolone are the first-choice treatments, therapeutic plasma exchange may be an option for the treatment of unresponsive cases (35) (Fig. 5). Fig. 2. (a) Computed tomography coronal section shows an area of hyper-density due to thrombotic occlusion (white arrows) of right transverse sinus, better seen in (b) the axial section. Fig. 3. (a, b) MR-FLAIR sequences show a diffuse cortical-subcortical hyper-intensity signal in the right temporo-parietal insular region (white arrows) that correlates with diffusivity restriction in (c) the ADC map (white arrow). (d) Another area of similar meaning (encephalitis) is showed at the central cortex (black arrow). (e) At the same time, hyper-intensity in FLAIR images in the right rectal and orbital gyrus, near the olfactory groove (dotted black arrow). This patient manifests anosmia so we could think about an olfactory involvement. ADC, apparent diffusion coefficient; FLAIR, fluid-attenuated inversion recovery; MR, magnetic resonance. Fig. 4. (a) MR T2-weighted sequences depict the myelitis process as an area of hyper-intensity that involved the right posterior portion of the spinal cord on the axial (white arrow) and sagittal (black arrow) planes at the D9-D10 level. (b) MR T1-weighted contrast enhancement sequence on the sagittal plane shows linear enhancement at cauda equina roots as a typical feature of Guillain–Barre syndrome. Fig. 5. (a) MR-FLAIR axial sequence shows a rounded area of hyper-intensity at the splenium of corpus callosum (white arrow). (b) This lesion is hypo-intense on T1-weighted imaging (white arrow) and involves a diffusivity restriction on (c, d) DWI/ADC maps (white arrows). This lesion is reversible and is due to an (ex)citotoxic edema mediated by COVID-19. ADC, apparent diffusion coefficient; DWI, diffusion-weighted imaging; FLAIR, fluid-attenuated inversion recovery; MR, magnetic resonance imaging. The published literature on COVID-19-related GBS commonly report a classic sensorimotor variant of GBS, often with facial palsy and a demyelinating electrophysiological subtype (36,37). The disease course is frequently severe (38). The time elapsed between infection and neurologic manifestations, and a negative PCR in spinal fluid might suggest that there is a post-infectious mechanism implicated in the etiology of COVID-19-related GBS (39,40). According to our data, men might be more prone to COVID-19-related GBS (37). Interestingly, because most of the cases of COVID-19-related GBS reported a demyelinating variant of GBS, it can be anticipated that the presence of antiganglioside antibodies would be low. Thus, the spectrum of immune cascade in COVID-19-related GBS should be expanded by studying other different antibodies (41,42). One case was reported with positive NF-155 and NF-186 antibodies, which are structural proteins in the node of Ranvier (43). Interestingly, human leukocyte antigen (HLA) analysis showed several HLA alleles that are known to be associated with GBS, such as: HLA-A33 and DQB1 * 05:01 (44). Cases of GBS are treated typically with corticosteroids or other immunotherapies. In addition, for these cases, clinicians might need to be more cautious (Fig. 4b). Last, but not least, the association between COVID-19 and MFS is largely documented. Moreover, if COVID-19 really increases the risk for MFS, it is crucial to understand the underlying mechanism. MFS is a rare neurological disorder that is considered to be a variant of GBS (45,46). Several infectious diseases have shown an epidemiological linkage. Pathologically, it is plausible that SARS- CoV-2 might directly induce neuro-pathogenic effect due to the widespread expression of ACE-2 (host receptor for SARS-CoV- 2) in the nervous system. Alternatively, deregulated immune response upon SARS-CoV-2 infection might underlie COVID-19-associated MFS. In particular, an increasing amount of evidence has illustrated that SARS-CoV-2 can induce a severe immune and inflammatory reaction that leads to tissue damage. Thus, targeting the inflammatory cascade, for example, with corticosteroids, might be effective against COVID-19-associated MFS (47). Finally, COVID-19 infection was shown to increase the risk of relapse in patients with multiple sclerosis (MS) (48–51). One of the putative mechanisms underlying the observed association between COVID-19 and MS attacks could be the expression of peripheral pro-inflammatory mediators, such as interleukin (IL)-6, IL-7, IL-10, IL- 17, granulocyte-colony stimulating factor, interferon (IFN)-γ, and tumor necrosis factor (TNF)-α in COVID-19 infection (52). High amounts of these factors can lead to BBB dysfunction and facilitate the migration of monocytes, macrophages, and CD4 + and CD8 + T cells into the central nervous system, which consequently can cause neurological worsening and exacerbation of MS. Another possible mechanism is a direct invasion of the central nervous system (CNS) by SARS-COV-2 (53). In addition, the activation and accumulation of immune cells in the perivascular spaces of the brain are highlighted as a central event, leading to the activation of glial cells resulting in neuro-axonal damage. In this regard, we sensitize neuroradiologists in the search for a “central vein sign” as a specific marker of MS (54). COVID-19 can exacerbate MS. According to these data, it would be appropriate to submit such patients to follow-up MRI protocols using intravenous contrast medium in order to identify new active lesions (55). Conclusive information on therapy modification is not yet accessible. The present study has some limitations, even with a large patient cohort (1060 people). These are due to the still poor understanding about the correlations between pathophysiology and COVID-19 clinic manifestations. In addition, stroke-related manifestations are still at only a few dozen (21/1060), so it is difficult to make generalizations. 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B, Urban Analytics and City Science 2399-8083 2399-8091 SAGE Publications Sage UK: London, England 10.1177_23998083221143122 10.1177/23998083221143122 Manuscript Geographical detector-based assessment of multi-level explanatory powers of determinants on China’s medical-service resumption during the COVID-19 epidemic https://orcid.org/0000-0003-3875-8792 Hu Bisong Fu Sumeng Luo Jin Lin Hui School of Geography and Environment, 12642 Jiangxi Normal University , Nanchang, China Yin Qian State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, 12381 Chinese Academy of Sciences , Beijing, China Tao Vincent Wayz AI Technology Company Limited , Shanghai, China https://orcid.org/0000-0002-2337-2486 Jiang Bin Faculty of Engineering and Sustainable Development, Division of GIScience, 3485 University of Gävle , Gävle, Sweden Zuo Lijun Meng Yu Aerospace Information Research Institute, 12381 Chinese Academy of Sciences , Beijing, China Hui Lin, School of Geography and Environment, Jiangxi Normal University, No. 99, Ziyang Rd., Nanchang 330022, China. Email: [email protected] 1 12 2022 1 12 2022 23998083221143122© 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. Knowing the multi-level influences of determinants on medical-service resumptions is of great benefits to the policymaking for medical-service recovery at different levels of study units during the post-COVID-19 pandemic era. This article evaluated the hospital- and city-level resumptions of medical services in mainland China based on the data of location-based service (LBS) requests of mobile devices during the two time periods (December 2019 and from February 21 to March 18, 2020). We selected medical-service capacity, human movement, epidemic severity, and socioeconomic factors as the potential determinants on medical-service resumptions and then explicitly assessed their multi-level explanatory powers and the interactive effects of paired determinants using the geographical detector method. The results indicate that various determinants had different individual explanatory powers and interactive relationships/effects at different levels of medical-service resumptions. The current study provides a novel multi-level insight for assessing work resumption and individual/interactive influences of determinants, and considerable implications for regionalized recovery strategies of medical services. medical-service resumption geographical detector multi-level explanatory powers COVID-19 Graduate Innovation Fund of Jiangxi Normal University, China YJS2021013 Science and Technology Major Project of Jiangxi Province, China 20201BBG71010 Science and Technology Major Project of Jiangxi Provincial Office of Education, China GJJ200303 National Natural Science Foundation of China https://doi.org/10.13039/501100001809 42061075 Joint Fund of Collaborative Innovation Center of Geo-Information Technology for Smart Central Plains, Henan Province and Key Laboratory of Spatiotemporal Perception and Intelligent processing and Ministry of Natural Resources, China 212201 edited-statecorrected-proof typesetterts10 ==== Body pmcIntroduction The novel coronavirus pneumonia (COVID-19) pandemic is still a global ongoing threat and has caused enormous shocks to the world in terms of health, socioeconomics, and natural environments. Restriction measures due to the pandemic might have some positive effects on global environment (Lal et al., 2020; Muhammad et al., 2020). More attention had been shifted from understanding and mapping COVID-19 cases (Jiang and De Rijke, 2021; Shaw and Sui, 2021) to the impacts of the pandemic on air quality, pollution changes, and others (Li et al., 2020; Sicard et al., 2020). People’s workstyle and social behaviors had also been dramatically changed during the pandemic period (An and Sun, 2021; Liu et al., 2022; Mu et al., 2021; Nathan and Overman, 2020). Global economy (e.g., transportation and tourism-related industries) had been affected by the pandemic to a great extent (Abu-Rayash and Dincer, 2020; Gössling et al., 2021; Hong et al., 2020; Norouzi et al., 2020). Effective strategies and measures were subsequently required for the recovery of various industries (Dube et al., 2021; Li et al., 2021; Zhou et al., 2021). Many parts of the world have started to come out of the pandemic restrictions and prepared for the post-pandemic recovery. For example, both the United States and European Union have officially claimed the end of the pandemic. The resumption of work and production has become a key policymaking focus in many regions, which is an important guarantee for maintaining the economic and social stability during the post-pandemic phase. After fighting the epidemic for 2–3 months since December 2019, China had made a great success to control the large-scale epidemic spread (Kraemer et al., 2020; Tian et al., 2020; Xu et al., 2020a), and then the focus of strategies shifted to control international importations and recover production and life. Scholars began to focus on the assessments of regional resumptions of work, production, and social life on different geographical scales, such as the entire country (Lai et al., 2022; Tao et al., 2020; Tian et al., 2021; Xu et al., 2020b), provinces/municipalities (He et al., 2021; Zhang et al., 2021), and prefecture-level cities (Bai et al., 2021; Shao et al., 2021). Various strategies and measures for the work resumption and the corresponding risks were explicitly evaluated (Bai et al., 2021; Ge et al., 2021; Wang et al., 2020; Zhang et al., 2021). Moreover, the work resumption of a specific industry (e.g., medical services) and the influences of determinants can be assessed (Hu et al., 2022). There are multiple data sources which help assess the regional work resumption. Satellite remote sensing data, in particular nighttime light (NTL) images, are beneficial and conducive to evaluating the epidemic impact on human activities, assessing the regional work resumption, and monitoring its spatiotemporal variation in large-scale areas (Liu et al., 2020; Shao et al., 2021; Tao et al., 2020; Tian et al., 2021). Satellite observations can also be combined with other multi-source data (e.g., intracity travel intensity data) to implement the assessment of work resumption (Lai et al., 2022). Nevertheless, the above data sources cannot support the assessment of work resumption in a high-resolution space-time domain. Mobile signaling data of cellphones can be a feasible support to optimize the assessment in resolution and accuracy (Liu et al., 2022). Another better alternative is the location-based service (LBS) data of mobile devices (Huang et al., 2021; Jiang and Yao, 2006), which help indicate explicit trajectories of human movement with high-resolution spatiotemporal information. The LBS data of mobile devices had been successfully applied to explore the spatiotemporal epidemic spread associated with population flow (Hu et al., 2020, 2021a) and to assess the work resumption in hospitals during the epidemic (Hu et al., 2022). Furthermore, due to the modifiable areal unit problem (MAUP) in most geographical studies (Openshaw, 1984), the assessments of work resumption will vary depending on sampling zones/units under observation. The LBS data of mobile devices are easy to be flexibly aggregated into specific regions (e.g., administrative cities) or locations (e.g., hospitals), providing the potential to explore the work resumption for a specific industry at different levels of study units and the multi-level influences of determinants. On the other hand, multivariate statistical analysis is generally sensitive to zones/units as well (Fotheringham and Wong, 1991). The influences of consistent or similar determinants on work resumption might vary with different levels of units throughout the entire study area. The comparative analysis of the multi-level explanatory powers of determinants on work resumption can help inform more potential characteristics. In view of the above considerations, we used the data of LBS requests of mobile devices in mainland China during the two time periods (December 2019 and from February 21 to 18 March 2020) to evaluate the hospital- and city-level resumptions of medical services during the epidemic (i.e., the study units were hospitals and cities, respectively). The geographical detector method was conducive to quantifying the explanatory powers of determinants on the explained variable without the assumption of linearity and immune to the collinearity multivariable (Wang et al., 2010, 2016). Besides, it can be applied to explore the interactive relationships and effects of paired determinants of the objective (Wang et al., 2010). Thus, it was selected to further assess and comparatively analyze the individual explanatory powers of determinants on multi-level resumptions of medical services, including fundamental medical-service capacity, human movement, epidemic severity, and socioeconomic factors. Also, the interactive effects of paired determinants at different levels of medical-service resumptions were identified and assessed. This study provides a novel multi-level perspective for the assessment of work resumption and influences of determinants, and the findings may introduce helpful information for the policymaking of recovery strategies of medical services at different levels of study units during the post-pandemic phase. Data and methodology LBS data of mobile devices The data of LBS requests of mobile devices were applied to quantitatively evaluate the hospital- and city-level resumptions of medical services in mainland China during the epidemic. The LBS data used in this study were provided by Wayz Inc., Shanghai, China. The all-day LBS requests from over 80% of mobile devices supported by the three telecommunications operators in China are recorded with high-resolution locations, and the raw data collection is implemented every 2 h (Hu et al., 2020, 2021a, 2022). Note that individual mobile device with multiple LBS requests at a certain location is counted just once for a specific period during the process of data collection. Individual trajectories of mobile devices are recorded in the raw LBS data with high-resolution spatiotemporal information. The raw LBS data covered the mobile devices which activated their LBS requests in 22,098 general or specialized hospitals, during December 2019 and from February 21 to 18 March 2020 (i.e., the experimental period in this study), respectively. The former data were aggregated into hospitals based on the geofencing technology with the averages of daily medical visits during December 2019, which were expected to be representative of their fundamental medical-service capacities before the epidemic. The latter data were subsequently aggregated into hospitals with daily medical visits during the experimental period. The comparison of medical-service situations during the epidemic with the fundamental capacities before the epidemic can indicate the work resumption in hospitals to a great extent. The cleaning and aggregation of the raw LBS data were implemented by Wayz Inc., and private individual information was deleted from the raw data before data preprocessing. Calculation of hospital- and city-level resumption rates A resumption rate, defined in a space-time domain, was used to be representative of the spatiotemporal medical-service resumption situations in hospitals (Hu et al., 2022). It indicates the ratio of the number of daily visits in a specific hospital s at a given date t during the epidemic to that prior to the epidemic. Let vs,0 and vs,t be the average of daily medical visits of hospital s during December 2019 and the daily medical visits at a given date t during the epidemic, respectively, and then the corresponding medical-service resumption can be assessed by calculating the following rate(1) ys,t=vs,tvs,0 where ys,t is the medical-service resumption rate of hospital s at date t. It is worth noticing that vs,t indicates the spatiotemporal distribution of the medical-service visits in hospitals during the epidemic. Thus, the resumption rate in hospitals, ys,t, varied over space and time (Supplementary Figure S1a), and hospitals with the ys,t estimates greater than 1 at a certain date were considered to have resumed their normal activities with more visits than those prior to the epidemic. Based on the above calculation of the medical-service resumption rate in hospitals, we can further assess the medical-service resumption in China’s cities. The estimates of the resumption rates in all hospitals of a specific city at a given date can be averagely aggregated into the corresponding city. Therefore, the resumption rate in cities, which also varied over space and time, can be subsequently generated to indicate the spatiotemporal resumption situations of medical services in cities (Supplementary Figure S1b). In this study, the hospital- and city-level resumption rates of medical services were considered as two explained variables (i.e., multi-level resumption rates), and the multi-level explanatory powers of determinants were subsequently assessed. Human movement data Intercity human movement was expected to have potential influence on regional medical-service resumption. Two proxy variables were selected to indicate the human movement, including daily imported visits from Wuhan, China, which was the epidemic source of the large-scale outbreak, and daily imported visits from elsewhere excluding Wuhan, respectively. The human movement data were acquired from the LBS data of mobile devices, which activated their LBS requests in locations away from their attributions. The daily imported visits from Wuhan and from elsewhere to hospitals were generated according to hospital and date, respectively. Next, they were averagely aggregated into cities by date. Epidemic data We collected the spatiotemporal data of daily new confirmed cases from multiple official and publicly available sources and comparatively verified the epidemic data through the public platform of the 2019-nCoV-infected pneumonia epidemic (China CDC, 2021). The epidemic data of daily new confirmed cases included the spatial information of their residential districts and were associated with individual hospitals according to location information based on the geofencing technology. We selected the daily new confirmed cases within the 3 km buffer around hospitals to be a proxy variable of the epidemic severity (Hu et al., 2022). Similarly, the data of daily new cases around hospitals were averagely aggregated into cities by date. Furthermore, an extra proxy variable of epidemic severity, that is, daily cumulative confirmed cases in cities, was selected for the city-level resumption rate of medical services. Socioeconomic data Three extra socioeconomic explanatory variables, including population (POP), gross regional product (GRP), and per capita disposable income (PCDI) of cities, were further selected for the city-level resumption rate of medical services. The observations of these socioeconomic variables at the end of 2019 or in 2019 were collected from the 2020 China Statistical Yearbook. A more detailed description of the human movement, epidemic, and socioeconomic data can be found in Supplementary Material. Geographical detector Here, we introduce spatially stratified heterogeneity (SSH) to describe the variations of the hospital- and city-level resumption situations of medical services according to various stratifications. SSH refers to ubiquitous phenomena which describe that the within-strata variance is less than the between-strata variance, implies potential distinct mechanisms by stratum, and enforces the applicability of statistical inferences (Wang et al., 2016). Note that the stratification referring to the SSH of an explained variable can be either a geographical division, or determined by a categorical or numerical explanatory variable. The geographical detector q-statistic was firstly developed to quantify the SSH of an explained variable according to a stratification (Wang et al., 2010, 2016) and then was universally applied to assess the explanatory powers of determinants on the explained variable. The fundamental formula of the geographical detector q-statistic is given by(2) q=1−∑h=1LNhσh2Nσ2 where N is the number of the observations of an explained variable throughout the entire study area and σ2 denotes the variance of all observations. The explained variable is stratified into L strata, denoted by h = 1, 2, …, L, which is based on a geographical division or determined by an explanatory variable. Nh is the number of observations within stratum h, and σh2 denotes the corresponding variance. The q-statistic, ranging from 0 to 1, indicates the SSH measure of the explained variable depending on a specific stratification, or more specifically, the explanatory power of an explanatory variable on the explained variable, which can be interpreted as it explaining 100 × q% of the SSH of the explained variable. Furthermore, the geographical detector method also provides an interaction detector for two or more explanatory variables (Wang et al., 2010), which was widely applied to reveal the interactive effects of paired variables (e.g., Hu et al., 2021b; Luo et al., 2016; Xu et al., 2021; Yin et al., 2019). Let u and v be a pair of explanatory variables, and by overlaying u and v, we can calculate the q-statistic of their interactive effect, qu∩v. Note that the symbol “∩” can indicate either a spatial intersection of two geographical stratifications or a generalized overlaying operation of two variables. Next, by comparing the interactive qu∩v with qu and qv, five types of interactive relationships between variables u and v can be identified (Supplementary Table S1). More specifically, when the interactive qu∩v is greater than the sum of qu and qv, two variables u and v nonlinearly enhance each other. In order to further assess the variation of nonlinear enhancements from different pairs of variables, we defined the following two indicators to measure the absolute and relative increments, respectively(3) ∆qu∩v=qu∩v−(qu+qv) (4) ξu∩v=∆qu∩v(qu+qv)=qu∩v−(qu+qv)(qu+qv) where Δq(u∩v) denotes the absolute increment of a nonlinear enhancement between u and v, and ξ(u∩v) is the relative one. A higher value of Δq for paired variables indicates their stronger nonlinearly interactive enhancement. If a variable always receives higher ξ values of nonlinearly interactive enhancements with others, it has the potential to be a dominant interactive determinant to explain the SSH of the explained variable. Experimental setup The experimental period was set from February 21 to 18 March 2020, with a time step of 1 day. The daily medical-service resumption rates in hospitals were calculated according to equation (1) and then were aggregated into the daily resumption rates in cities. Various determinants were selected for assessing the explanatory powers on the hospital- and city-level resumption rates of medical services, respectively. As shown in Supplementary Table S2, average daily visits before the epidemic (x1), imported visits from Wuhan (x2) and from elsewhere (x3), and new confirmed cases around hospitals (x4) were considered as the consistent explanatory variables of multi-level resumption rates. In addition, two geographical divisions were selected to be the extra explanatory variables of hospital-level resumption rate, including province and city stratifications (x5 and x6). The former considered provinces, municipalities, and autonomous regions as strata, whereas the latter considered cities as strata. Regarding city-level resumption rate, we further selected daily cumulative confirmed cases (x7), POP (x8), GRP (x9), and PCDI (x10) of cities as its additional explanatory variables. In order to reduce the subjective influence of various stratifications to the calculation of q-statistics, we implemented the stratification of each numerical explanatory variable by equal-interval division after ordering data, and then equally divided the observations of resumption rates into 10 strata. According to equation (2), the daily q-statistics of explanatory variables to hospital- and city-level resumption rates were calculated, respectively. The statistical significance of q-statistics can be tested based on equations (S1) and (S2) in Supplementary Material. The geographical detector software is publicly accessible at http://geodetector.cn/, and the q-statistics in this study were performed with the use of the R software package. Moreover, the daily interactive q-statistics of paired variables were calculated and compared with two individual q-statistics, and the corresponding interactive relationships were identified based on Supplementary Table S1. While a pair of variables exhibited the interactive relationship of a nonlinear enhancement, their increment indicators, Δq and ξ, can be further calculated according to equations (3) and (4). Results Variation of the explanatory powers of determinants on multi-level resumption rates The daily hospital- and city-level resumption rates of medical services were explicitly evaluated from February 21 to 18 March 2020. Both of them exhibited an obvious ascending trend during the entire period. Areas surrounding Hubei province in central China had a relatively normal resumption of medical services until March 18 (Supplementary Figure S1). Before March 2020, there were approximately 23.3% of hospitals and 18.7% of cities which had the medical-service resumption rates higher than 1, respectively. Nevertheless, the numbers increased to 30.8% and 34.9% after the first week in March and became 41.8% and 59.2% at the experimental end date (March 18). After 2–3 months of fighting the epidemic, nearly half of hospitals had achieved relatively nice resumption of work and over half of cities had resumed their medical-service situations. We calculated the daily q-statistics of various explanatory variables to hospital- and city-level resumption rates, respectively. As shown in Table 1, imported visits from Wuhan (x2) received an average q-statistic value of 0.1000 to city-level resumption rate, with a standard deviation (SD) of 0.0539, whereas imported visits from elsewhere (x3) achieved an average value of 0.1641 with a SD of 0.0440. Note that their q-statistics to hospital-level resumption rate were extremely low (q = 0.0174 and q = 0.0267). Human movement had obviously much stronger influence on city-level resumption rate than on hospital-level resumption rate, and its two proxy variables can averagely explain 10.00% and 16.41% of the SSH of city-level resumption rate, respectively. Average daily visits before the epidemic (x1) and new cases around hospitals (x4) had no statistically significant explanatory powers on city-level resumption rate. However, they achieved significant q-statistics to hospital-level resumption rate since the beginning of March; their significant percentages with an alpha level of 0.05 were 62.96% and 66.67%, respectively. Although the explanatory powers were still weak, the medical-service capacity and epidemic severity had started to impact the work resumption in hospitals.Table 1. Descriptive statistics of daily q-statistics to multi-level resumption rates. Resumption rate Variable Mean Min Max SD Significant percentage† Hospital-level x1 0.0034 0.0003 0.0203 0.0043 62.96 x2 0.0174 0.0044 0.0347 0.0093 100 x3 0.0267 0.0106 0.0583 0.0131 100 x4 0.0016 0.0001 0.0056 0.0015 66.67 x5 0.0218 0.0027 0.0706 0.0215 100 x6 0.0709 0.0281 0.1703 0.0445 100 City-level x1 0.0264 0.0155 0.0497 0.0101 0 x2 0.1000 0.0210 0.1857 0.0539 70.37 x3 0.1641 0.0944 0.2205 0.0440 100 x4 0.0281 0.0124 0.0630 0.0141 0 x7 0.1023 0.0763 0.1276 0.0143 100 x8 0.0604 0.0250 0.1410 0.0289 51.85 x9 0.1449 0.0524 0.2965 0.0740 96.30 x10 0.1083 0.0410 0.1979 0.0542 81.48 †An alpha level of 0.05. The q-statistics of province and city stratifications (x5 and x6) to hospital-level resumption rate received low average values (q = 0.0218 and q = 0.0709) but were always significant during the entire period (Table 1). Nevertheless, both of them exhibited a consistent temporally increasing tendency, and the q-statistics of x6 were always higher than those of x5 (Supplementary Figure S2a). The maximum q-statistic of city stratification reached 0.1703. The variation of hospital-level resumptions between cities gradually increased by date. As an extra proxy variable of epidemic severity on city-level resumption rate, cumulative cases in cities (x7) achieved significant q-statistics with an average of 0.1023 and a SD of 0.0143. Its daily q-statistics exhibited no obvious increasing or decreasing tendency, and the explanatory power was relatively steady over time (Supplementary Figure S2b). Note that new cases around hospitals (x4) had low-value insignificant q-statistics to city-level resumption rate. The entire epidemic situation of cities played a great role in affecting city-level resumptions. Regarding three socioeconomic variables of city-level resumption rate, as shown in Table 1, the q-statistics of POP (x8) began significant since the first week in March (the significant percentage was 51.85%), whereas those of GRP (x9) and PCDI (x10) were nearly significant during the entire period (the percentages were 96.30% and 81.48%). GRP (x9) and PCDI (x10) can explain averagely 14.49% and 10.83% of city-level resumption rate, respectively. Both of them had a consistent increasing trend of explanatory powers, especially when entering the first week in March (Supplementary Figure S2b); their increasing trends began rather rapid and then they reached the maximum q-statistics of approximately 0.3 and 0.2, respectively. In general, various determinants had relatively weak explanatory powers on hospital-level resumption rate, whereas the explanatory powers of two geographical divisions started to increase since the beginning of March, indicating the regional disparity of hospital-level resumptions gradually increased by date. Meanwhile, human movement, epidemic severity, and socioeconomic factors played a great role in explaining the SSH of city-level resumption rate. Nevertheless, their explanatory powers exhibited different temporal variations. For instance, GRP (x9) and PCDI (x10) had the explanatory powers with an increasing tendency by date, whereas cumulative cases (x7) had a relatively steady explanatory power. Interactive effects of paired determinants on hospital-level resumption rate We further calculated the interactive q-statistics of each pair of explanatory variables and revealed their interactive relationships and effects on multi-level resumption rates. As shown in Table 2, the interactive relationships of a bivariate enhancement appeared in the majority of interactions on hospital-level resumption rate (i.e., the interactive q-statistic is greater than either of two individual ones but smaller than their sum). In particular, the interactive q-statistic of province and city stratifications (x5 and x6) was smaller than either of two individual ones, and thus, they were nonlinearly weakened by one another. Nevertheless, they always introduced a nonlinear enhancement when interacting with others (i.e., the interactive q-statistic is greater than the sum of two individual ones). More specifically, x6 played a much more important role in interactive effects than x5. It provided the dominant interactive powers on hospital-level resumption rate, especially when interacting with x1, x2, and x3. As shown in Table 2, those interactions received interactive q-statistics with the averages of 0.2130 (SD = 0.0409), 0.2710 (SD = 0.0823), and 0.2911 (SD = 0.0807), respectively, which were much greater than the sums of two corresponding individual ones.Table 2. Descriptive statistics of daily interactive q-statistics to hospital-level resumption rate.† Variable x1 x2 x3 x4 x5 x6 x1 0.0034 (0.0043) x2 0.0314 (0.0165) 0.0174 (0.0093) x3 0.1158 (0.0614) 0.046 (0.0175) 0.0267 (0.0131) x4 0.0059 (0.0053) 0.0232 (0.0126) 0.0305 (0.0148) 0.0016 (0.0015) x5 0.0381 (0.0252) 0.0585 (0.0273) 0.0608 (0.0281) 0.0309 (0.0269) 0.0218 (0.0215) x6 0.2130 (0.0409) 0.2710 (0.0823) 0.2911 (0.0807) 0.0887 (0.0512) 0.0709 (0.0445) 0.0709 (0.0445) †Standard deviations are listed in parentheses. The interactive matrix of daily q-statistics of paired determinants to hospital-level resumption rate is demonstrated in Figure 1. Note that the diagonal subplots show the boxplots of daily q-statistics for each explanatory variable, whereas the upper and lower triangular ones show the boxplots and temporal curves of daily interactive q-statistics for each pair of variables, respectively. Several lower and narrower “boxes” can be found in the upper triangular area (e.g., x1∩x4), indicating relatively weak and stable interactive enhancements. Contrarily, higher and wider “boxes” can indicate relatively strong and unstable interactive enhancements (e.g., x2∩x6). Obviously, x6 interacting with x1, x2, and x3 introduced higher and wider “boxes” in the boxplots of interactive q-statistics. Their interactions derived strongly nonlinear enhancements which were unstable during the entire period. Besides, their temporal curves in the lower triangular area show a consistent temporally increasing tendency with fluctuation, and the interactions of x2∩x6 and x3∩x6 fluctuated more intensely than that of x1∩x6 (Figure 1).Figure 1. Interactive matrix of daily q-statistics to hospital-level resumption rate. We further calculated the increment indicators, Δq and ξ, to measure the variations of nonlinear enhancements for the interactions of x1∩x6, x2∩x6, x3∩x6, and x1∩x3, respectively. As shown in Figure 2(a) and (b), ranked by Δq, it was found that Δq(x3∩x6) = 0.1935>Δq(x2∩x6) = 0.1828>Δq(x1∩x6) = 0.1387, whereas ranked by ξ, it was found that ξ(x2∩x6) = 3.4026>ξ(x3∩x6) = 3.24>ξ(x1∩x6) = 2.784. Note that their relative increments were approximately 3, indicating that the explanatory powers introduced by their nonlinear enhancements were three times more than the sum of two individual powers. City stratification (x6) received both absolute and relative strong increments when interacting with others, and was identified as a dominant interactive determinant to explain hospital-level resumption rate.Figure 2. Nonlinearly interactive enhancements of determinants to hospital-level resumption rate: (a, b) absolute and relative increments of city stratification interacting with others and (c) temporal increments of medical-service capacity interacting with human movement. Moreover, as shown in Table 2, it is worth noticing that the pair of x1 and x3 received interactive q-statistics with an average value of 0.1158 (SD = 0.0614), which was much greater than the sum of two individual ones (q = 0.0034 for x1 and q = 0.0267 for x3). Medical-service capacity and human movement nonlinearly enhanced each other to a very great extent, and their interaction can averagely explain 11.58% of the SSH of hospital-level resumption rate. Nevertheless, although the interaction of x1∩x3 derived a strongly nonlinear enhancement, this enhancement was still not steady (the “box” is wide) and exhibited a gradually increasing trend during the entire period (Figure 1). Besides, their absolute increment, Δq(x1∩x3), exhibited a decreasing trend by date, whereas their relative increment, ξ(x1∩x3), showed a gradually increasing trend (Figure 2c). Although both x1 and x3 had extremely weak individual explanatory powers, their pair introduced a nice interactive effect with a relatively large explanatory power and a gradually increasing increment; thus, the interaction of x1∩x3 can be identified to be an abnormal one. Interactive effects of paired determinants on city-level resumption rate The interactive q-statistics of paired determinants to city-level resumption rate are listed in Table 3. All of them introduced strongly nonlinear enhancements in explaining city-level resumption rate, while one interacting with another. The interactions of the selected determinants were conducive to explaining the SSH of city-level resumption rate to a great extent. The most dominant interactive effect was x3 and x10, with an average q-statistic of 0.5656 (SD = 0.1242). Their interaction of x3∩x10 can averagely explain 56.56% of the SSH of city-level resumption rate. Note that the least interactive effect still achieved an average q-statistic of 0.1899 (SD = 0.0536), which was x1 interacting with x4, with two extremely small individual ones of 0.0264 and 0.0281.Table 3. Descriptive statistics of daily interactive q-statistics to city-level resumption rate.† Variable x1 x2 x3 x4 x7 x8 x9 x10 x1 0.0264 (0.0101) x2 0.3534 (0.1026) 0.1000 (0.0539) x3 0.4004 (0.0356) 0.4392 (0.0658) 0.1641 (0.044) x4 0.1899 (0.0536) 0.2431 (0.0851) 0.3429 (0.0452) 0.0281 (0.0141) x7 0.3295 (0.0709) 0.4295 (0.0947) 0.4480 (0.0433) 0.1932 (0.0628) 0.1023 (0.0143) x8 0.3099 (0.0448) 0.3778 (0.0797) 0.3836 (0.0373) 0.2763 (0.0493) 0.2677 (0.0289) 0.0604 (0.0289) x9 0.3769 (0.0920) 0.3855 (0.1069) 0.4805 (0.0682) 0.3553 (0.0685) 0.3490 (0.0836) 0.2792 (0.1002) 0.1449 (0.0740) x10 0.4411 (0.0586) 0.3939 (0.0669) 0.5656 (0.1242) 0.2370 (0.0897) 0.4348 (0.0286) 0.3427 (0.092) 0.3985 (0.0450) 0.1083 (0.0542) †Standard deviations are listed in parentheses. Human movement, epidemic severity, and socioeconomic factors had nice individual exploratory powers on city-level resumption rate and still provided dominant interactive effects while interacting with others (Table 3). x2 and x3 received interactive exploratory powers of averagely 37.46% and 42.49%, respectively, whereas the number of x7 was 33.51%. The averages of interactive effects provided by x8, x9, and x10 were 32.76%, 37.50%, and 40.19%, respectively. They were the dominant interactive determinants to explain the SSH of city-level resumption rate. Moreover, attention should be paid to the pairs of two human movement variables (x2∩x3) and two epidemic severity ones (x4∩x7). The former pair derived an interactive q-statistic with an average of 0.4392, whereas the number of the latter pair was 0.1932. The interactive effects of paired determinants on city-level resumption rate exhibited different varying temporal trends during the entire period. As shown in Figure 3, the interactions between x7, x8, x9, and x10 derived strongly nonlinear enhancements, which were relatively stable by date or exhibited a gradually increasing tendency with less fluctuation (narrower “boxes” in the boxplots and increasing temporal curves). Nevertheless, some relatively intense fluctuations appeared in the interactions while x2 or x3 interacting with others (wider “boxes” in the boxplots and fluctuating temporal curves without obviously increasing or decreasing trends). In other words, the influence of human movement on city-level resumption rate was unsteady during the entire period, whereas the interactive influences of epidemic severity and socioeconomic factors were always steady by date or gradually getting strengthened.Figure 3. Interactive matrix of daily q-statistics to city-level resumption rate. Subsequently, we focused on the nonlinearly interactive increments of x7 interacting with other variables, and calculated the corresponding increment indicators. As shown in Figure 4, ranked by Δq, it was found that Δq(x2∩x7) = 0.2272>Δq(x7∩x10) = 0.2242>Δq(x1∩x7) = 0.2008>Δq (x3∩x7) = 0.1816 (the rest were less than 0.11), whereas ranked by ξ, it was found that ξ(x1∩x7) = 1.5650>ξ(x7∩x10) = 1.2351>ξ(x2∩x7) = 1.2054 (the rest were less than 0.72). By controlling the influence of cumulative cases in cities, city-level resumption rate was primarily affected by medical-service capacity, PCDI, and human movement. Note that Δq(x4∩x7) and ξ(x4∩x7) were both the minimums of all Δq and ξ values; the limited nonlinearly interactive increment of two epidemic severity variables might be caused by the collinearity existing between them.Figure 4. Nonlinearly interactive enhancements of cumulative cases interacting with others to city-level resumption rate: (a) absolute increments and (b) relative increments. At last, the individual explanatory powers of x1 and x4 were both extremely small, but the nonlinearly interactive increments of their interaction were large (Δq(x1∩x4) = 0.1354 and ξ(x1∩x4) = 2.7311); the enhancement was nearly three times more than the sum of individual effects, and thus, the interaction of x1∩x4 can be identified to be an abnormal one. Also, the increments caused by x1 or x4 interacting with others can be assessed, for example, Δq(x1∩x2) = 0.2270 and ξ(x1∩x2) = 2.2041, and the enhancement was over two times more than the sum of individual effects. Discussion on the implications of this study The LBS data used in this study contain substantial spatiotemporal information and cover the majority of mobile devices in China. High spatiotemporal resolution and large-scale regional coverage of the LBS data provide the potential to implement the assessments of multi-level work resumptions with different study units in a high-resolution space-time domain. This study applied the LBS data to calculate the hospital- and city-level resumption rates of medical services from February 21 to 18 March 2020, during the pandemic. Note that several “outliers” appeared in both hospital- and city-level resumptions, which had the rates greater than 1.5 or even 2 (Supplementary Figure S1). The substantial increase of medical-service visits than those prior to the epidemic may not represent the resumption of medical-service situations but had the potential to be caused by other abnormal factors. For instance, several hospitals were designated as the quarantine and isolation centers, and the increase of confirmed cases intensified the medical-service burdens of several hospitals during the epidemic. We subsequently used the geographical detector method to quantify the explanatory powers of determinants and the interactive effects of paired determinants on multi-level resumption rates. That is to say, the multi-level insight was provided in this study for assessing both medical-service resumptions and explanatory powers of determinants. It is worth noticing that the explanatory powers and the interactions of same determinants varied from level to level on medical-service resumptions, which can be explained by the MAUP effect and sensitivity of the multivariate statistical analysis depending on study units. Moreover, the findings help us inform different strategies for the recovery measures of medical services at different levels. For instance, more attention should be paid to the interaction of medical-service capacity and human movement when informing the hospital-level strategies of medical-service recovery. When dealing with city-level recovery strategies, the interactions of human movement, epidemic severity, and socioeconomic factors could be the primary criteria leading to varying degrees of recovery measures. Since there was a specific outbreak center during the early epidemic era in China, human movement played a great role in explaining the spatiotemporal epidemic spread and multi-level resumptions of medical services. Other regions which had a similar characteristic of the epidemic spread should focus on human movement and its interactions with other key factors, when informing the recovery strategies. The geographical detector method used in this study was conducive to measuring the explanatory powers of individual determinants, identifying the interactive relationships of paired determinants, and assessing the interactive effects on multi-level resumptions of medical services. It had been applied in plentiful previous studies to explore the interactive relationships and effects of paired determinants of the objective (e.g., Hu et al., 2021b; Luo et al., 2016; Wang et al., 2010; Xu et al., 2021; Yin et al., 2019; Zhang et al., 2019). In this study, however, they were identified and comparatively explored at different levels of medical-service resumptions. The interactive relationship of a bivariate enhancement was found in the majority of hospital-level interactions, whereas all city-level interactions were identified as the interactive relationship of a nonlinear enhancement. The most dominant hospital-level interaction just reached an explanatory power of 29.11%, but the majority of city-level interactions achieved an explanatory power of over 30%. More specifically, the most dominant interactive effect can explain 56.56% of the SSH of city-level resumption rate. Another potential contribution of this study is the measure of the absolute and relative increments for the interactions of a nonlinear enhancement. It can help identify the dominant interactive factors (e.g., city stratification was found to be a dominant interactive determinant to explain hospital-level resumption rate). Moreover, it provides the potential to detect abnormal interactions. For instance, x1 interacting with x3 on hospital-level resumption rate and interacting with x4 on city-level rate were identified as abnormal interactions, in which two individual explanatory powers were extremely small but the interactive increment of the nonlinear enhancement was very large. See Supplementary Material for the discussion of limitations. Conclusions This article provides a novel multi-level perspective for evaluating medical-service resumptions during the post-epidemic era based on the LBS data of mobile devices and assessing the explanatory powers of determinants and interactive effects of paired determinants on multi-level resumption rates using the geographical detector method. Various determinants had different explanatory powers on multi-level resumption rates. Human movement, epidemic severity, and socioeconomic factors played a great role in explaining city-level resumption rate, whereas city stratification had an increasing explanatory power on hospital-level resumption rate. The individual explanatory powers of same determinants varied on multi-level resumption rates. Human movement and epidemic severity can explain the SSH of city-level resumption rate to some extent, but their explanatory powers on hospital-level resumption rate were extremely weak. Different types of interactive relationships of paired determinants appeared in explaining multi-level resumption rates. The majority of interactions derived bivariate enhancement effects on hospital-level resumption rate, whereas all interactions introduced strongly nonlinear enhancements on city-level resumption rate. The interactive effects of paired determinants varied on multi-level resumption rates as well. City stratification was the most dominant interactive determinant on hospital-level resumption rate, with a maximum interactive effect of 29.11%. Human movement, epidemic severity, and socioeconomic factors provided dominant interactive effects on city-level resumption rate. Their interactive effects were always higher than 30% and even reached a maximum of 56.56%. The interactive effects on multi-level resumption rates also exhibited different temporal variations. The nonlinearly interactive enhancements of epidemic severity and socioeconomic factors on city-level resumption rate were relatively stable by date or exhibited an increasing tendency with less fluctuation. Nevertheless, the influences of human movement on city-level resumption rate and city stratification on hospital-level resumption rate were unstable or fluctuating during the entire period. Attention should be paid to several abnormal interactions of paired determinants on multi-level resumption rates, which had two extremely small individual explanatory powers but a very large interactive increment of the nonlinear enhancement. Medical-service capacity of hospitals produced abnormal interactions on both hospital- and city-level resumption rates with another determinant. Supplemental Material Supplemental Material—Geographical detector-based assessment of multi-level explanatory powers of determinants on China’s medical-service resumption during the COVID-19 epidemic Click here for additional data file. Supplemental Material for Geographical detector-based assessment of multi-level explanatory powers of determinants on China’s medical-service resumption during the COVID-19 epidemic by Bisong Hu, Sumeng Fu, and Jin Luo and Hui Lin, Qian Yin, Vincent Tao, Bin Jiang, Lijun Zuo, and Yu Meng in Environment and planning B: Urban analytics and city science Acknowledgments The authors are grateful for the support of Wayz AI for the cleaning and preprocessing of the raw LBS data. Biographies Prof. Bisong Hu is a full professor in School of Geography and Environment, Jiangxi Normal University, Nanchang, China. His research interests include spatial statistics, spatial epidemiology, epidemic spread simulation, and spatial-temporal big data analysis. Email: [email protected] Miss Sumeng Fu is currently a master candidate in Jiangxi Normal University, Nanchang, China. Her research interests include spatial epidemiology and human geography. Email: [email protected] Dr. Qian Yin is an associate professor in the Institute of Geographic Sciences and Nature Resources Research, Chinese Academy of Sciences, Beijing, China. Her research interests include spatial statistics and spatial epidemiology. Email: [email protected] Prof. Hui Lin is a full professor and the dean of School of Geography and Environment, Jiangxi Normal University, Nanchang, China. His research interests include spatial database and data mining, microwave remote sensing image processing and analysis, and virtual geographical environments. Email: [email protected] Dr. Vincent Tao works at Wayz AI Technology Company Limited, Shanghai, China, as the founder and chairman. He specializes in spatiotemporal big data analysis. Email: [email protected] Prof. Bin Jiang is a full professor of computational geography at Faculty of Engineering and Sustainable Development (Division of GIScience) of the University of Gävle, Sweden. His research interests center on geospatial analysis of urban structure and dynamics, or geospatial big data in general. Email: [email protected] Dr. Jin Luo is an associate professor and the execute dean of School of Geography and Environment, Jiangxi Normal University, Nanchang, China. He specializes in spatial database and data mining. Email: [email protected] Dr. Lijun Zuo is an associate professor in Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China. Her research interests include land-use change monitoring, and remote sensing for the sustainable use of cropland. Email: [email protected] Prof. Yu Meng is a Chair Professor in Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China. She specializes in spatial-temporal big data analysis. Email: [email protected] ORCID iDs Bisong Hu https://orcid.org/0000-0003-3875-8792 Bin Jiang https://orcid.org/0000-0002-2337-2486 The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The National Natural Science Foundation of China (No. 42061075), the Science and Technology Major Project of Jiangxi Province, China (No. 20201BBG71010), the Science and Technology Major Project of Jiangxi Provincial Office of Education, China (No. GJJ200303), the Joint Fund of Collaborative Innovation Center of Geo-Information Technology for Smart Central Plains, Henan Province and Key Laboratory of Spatiotemporal Perception and Intelligent processing, Ministry of Natural Resources, China (No. 212201), and the Graduate Innovation Fund of Jiangxi Normal University, China (No. YJS2021013). 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==== Front Journal of Retailing and Consumer Services 0969-6989 0969-6989 Elsevier Ltd. S0969-6989(22)00316-2 10.1016/j.jretconser.2022.103223 103223 Article The effect of a hotel's star-rating-based expectations of safety from the pandemic on during-stay experiences Tiwari Veenus a Mishra Abhishek b∗ a School of Management, University of Science, Penang, Malaysia b Indian Institute of Management Indore, Rau Pithampur Road, Indore, M.P, India ∗ Corresponding author. 5 12 2022 3 2023 5 12 2022 71 103223103223 26 9 2022 3 11 2022 30 11 2022 © 2022 Elsevier Ltd. All rights reserved. 2022 Elsevier Ltd Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. As a result of the COVID-19 pandemic, safety is one of the top priorities for travellers when choosing a hotel. This work examines the effect of customers’ pre-stay expectations of a hotel about its safety-focused services, shaped through its official star-rating, on the during-stay confirmation of those expectations, satisfaction, and revisit intentions. A cross-sectional research design is used spanning temporally from the pre-stay to the during-stay phases. The pre-stay phase was the peak COVID-19 period in India (June–July 2021) to stimulate the safety concerns in the travellers planning their travel, while the during-stay phase was when the planned travel was undertaken with the traveller staying at the planned hotel (October 2021–January 2022). Data were collected from 452 customers and the results supported the proposed model. Further, the star-rating, as a signal for safety-focused services, was found to have a serial effect on revisit intentions, through the pre-stay expectations of safety services, and the during-stay confirmation of expectations and satisfaction. Keywords Hotel star-rating Expectation of safety services Confirmation of safety services Revisit intention Expectation-confirmation model ==== Body pmc1 Introduction The COVID-19 pandemic had a devastating impact on several sectors, including the hospitality and tourism industry (Mehta et al., 2021). More than 60% of hotels globally have been facing challenges to survive since the onset of the pandemic (Schoening and Shapiro, 2020). Pre-pandemic, the global hospitality industry was forecasted to grow to USD 211.54 billion by 2026, with a growth rate of 4.6%1 ; however, the effects of the pandemic have resulted in a readjustment of these projections. For example, the STR and Tourism Economics estimate that by 2023, the demand for hotel rooms will fall from 57.4% to 51.2%, reflecting the continued anxiety of travellers even if the effects of the pandemic are significantly reduced (Airoldi, 2020). The criticality of studying a traveller's decision-making process during a pandemic is well-acknowledged (e.g., Zenker and Kock, 2020). Yet, there are limited insights into an individual's motivation to choose a specific hospitality venue during a travel overlapping with the pandemic (Aebli et al., 2022). It is well-known that different needs drive motivations and user decisions, consistent with the importance of those needs in a context (Gnoth, 1997; Herzberg et al., 2007). Hence, it is argued that travel-related decisions have been refocused towards safety needs evoked during a global pandemic. Interestingly, most research in the context of COVID-19 discusses customer resistance to travel, with safety from infection as a primary demotivator (e.g., Chua et al., 2020). However, the bases on which individuals who undertake travel during a pandemic, despite all risks, choose their stay venue, and experience its services remains under-researched (Aebli et al., 2022). Studies published during the pandemic suggest that projecting an image of safety by hotels is critical in uncertain times (e.g., Hoque et al., 2020; Kim et al., 2021; Rivera, 2020). Indeed, guests are concerned about their safety needs and will likely remain so long after the pandemic (Agag et al., 2022; Li et al., 2020; Villa et al., 2020). This implies that a property's image of effective COVID-19/pandemic mitigation through hygiene-focused services (e.g., sanitisation of the rooms and hotel amenities), medical services (e.g., doctor-on-call, emergency hospitalization), and deployment of modern technologies (e.g., contactless check-in) is helpful for travellers to plan their stay at the hotel. Further, the corroboration of expectations during the stay has important implications for the hotel-customer relationship, including the customer's intention to revisit the hotel (Atadil and Lu, 2021; Jiang and Wen, 2020; Rivera, 2020; Vij et al., 2021). The expectations of potential customers concerning the service quality of a hotel are often based on its official star-rating in which hotels are given a rating from 1 to 5 (with 1 being the lowest and 5 the highest) (Cser and Ohuchi, 2008; Rhee and Yang, 2015). In recent pandemic-focused research, Nunkoo et al. (2020) argue for the role of hotel star-rating in shaping customers’ expectations about the safety of a hotel as well as their stay experiences. However, the study data was captured at one point in time. Not only are expectations and confirmation two different theoretical entities, but there may also be a temporal gap when the perceptions about the safety of a hotel are formed and when the customer experiences those attributes during the stay (e.g., Gupta et al., 2020; Venkatesh et al., 2011). Thus, a research question that has not yet been addressed in the extant literature is whether hotel star-rating, as a signal of safety-related services, create expectations of safety at the hotel, and how such expectations, if met during actual stay experiences, affect the customer–hotel relationship. Based on principles of the signalling theory and the expectation–confirmation model (ECM), as well as the tenets of Herzberg's two-factor motivation theory to underpin the need for safety during travel, this study explores the influence of pre-stay expectations of safety based on hotel star-ratings on the during-stay confirmation, satisfaction and revisit intention. The model is evaluated using covariance-based structural equation modelling (CB-SEM) with data collected from 452 respondents in India. Theoretically, the study offers a unique integration of the signalling theory and the ECM to propose a temporal model spanning from the pre-stay phase to the during-stay phase of a hotel stay. For practitioners, this study examines the customer journey in hospitality service by exploring how hotel star-rating–based safety perceptions can serve as an instrument for maintaining customer loyalty. 2 Literature review 2.1 Hotel star-rating and service quality A hotel star-rating system is provided to each hotel's individual property by the competent authority based on the types of services offered and the overall service quality, with higher ratings reflecting higher service standards (Dioko et al., 2013; Nunkoo et al., 2020). Some hospitality brands consciously secure different ratings for their properties across geographies, sometimes under a different brand name, to target specific customer segments (Claver et al., 2006). This is because star-ratings not only indicate the facilities provided at the hotel, but also the prices for the same (Nilashi et al., 2022). Potential guests, with different needs, utilise various criteria to make stay-related decisions and the official star-rating may serve as a credible and trustworthy signal of the hotel's services to make that decision easier (Masiero et al., 2015). Some previous studies have shown that hotel star-ratings do create service expectations and drive experiences for hotel guests (Abrate et al., 2011; Kim et al., 2019). For example, Schuckert et al. (2015) found that customers of hotels with higher star-ratings were, in general, less satisfied compared to visitors of lower-rating hotels. This is because while customers consider reviews and electronic word-of-mouth when shaping their expectations of low-star-rating hotels, such expectations are very high for high-star-rating ones, irrespective of co-customer feedback. It is apparent that customers have different service quality expectations for hotels with different star-ratings, and such expectations can strongly influence satisfaction based on the services obtained during their stay (Lie et al., 2019). However, the existing literature provides conflicting evidence about the polarity of the relationship between the pre-stay expectations, driven by the hotel's star-rating, and the during-stay customer experiences. For example, Bulchand et al. (2011) report a consistent positive relationship, Qu et al. (2000) report the same but suggest that the relationship is stronger for high star-rating hotels, while Torres et al. (2014) argue for a weak relationship. Hence, the role of hotel star-ratings in creating service quality expectations with concomitant effects on service experiences requires greater clarity. 2.2 Travel anxiety during a pandemic Multiple works concur that stressful times are generally followed by coping behaviours by individuals (e.g., Ruvio et al., 2014). In other words, following the period of restrictions when certain activities important to an individual could not be undertaken, overconsumption of those activities may be adopted as a coping behaviour. This process helps people to release the built-up stress and is referred to as compensatory consumption (Kim and Gal, 2014). The COVID-19 pandemic was one such period where people, during the peak infection months, were constrained to their homes with restricted travel. Consequently, once the effects of a specific wave of the pandemic weakened and the travel restrictions were relaxed, the latent desire to travel led to compensatory or ‘revenge’ travel, with people travelling for longer durations and spending more money (Kim et al., 2021). However, travellers have mixed motivations during such travel (Lee et al., 2012). While one motivation is to escape the daily routine and overconsume the travel experiences, the other motivation is to stay safe and under-consume the travel occasions, since such consumption is considered a risky endeavour with implications on one's health (Kim et al., 2021). The contradictory motivations find their underpinning in Herzberg's two-factor motivation theory, with individuals facing two unique needs: hygienic needs (e.g., perceived safety) and psychological needs (e.g., achievement). The absence of the former is a demotivator/dissatisfier while the presence of the latter is a motivator/satisfier (Herzberg et al., 2007). In the context of travel safety, using this theory, it is posited that travellers seek to maximize the benefits (fruitful travel) while minimizing the costs (potential risks). Hence, increased safety and health at the destination during a pandemic may not necessarily motivate people to travel, an outcome of the socio-psychological needs; however, the absence of those safety-enabling attributes will lead to an unpleasant experience (Aebli et al., 2022). This phenomenon leads to travel anxiety. We argue that while individuals are keen to travel, driven by psychological needs, after the reduced effects of the pandemic, the anxiety during the travel, due to safety needs, will induce them to choose hotel stays which keep them safe from the infection. This argument is supported by Wen et al. (2005) and Kim et al. (2021) who confirm that pandemics such as severe acute respiratory syndrome (SARS) and COVID-19 affect travellers’ disposition towards the hotel with enhanced attention towards the hygiene at a given location or a property. For hotels, this means that the absence of hygiene factors may be strong demotivator for travellers to stay at the specific property (Herzberg et al., 2007). 2.3 Hotel safety Before the outbreak of COVID-19 pandemic, several studies examined the importance of safety for hotel customers (e.g., Alnawas and Hemsley-Brown, 2019; Kim et al., 2022, Kim et al., 2022; Nagaj and Žuromskaitė, 2020). Shin and Kang (2020) highlight that there has been a prominent rise in customer safety concerns since the 2008 terror attacks in Mumbai, India, when two prominent hotels were attacked.2 Besides physical safety, the concept of safety also applies to the health of an individual due to any major viral infections prevalent at the time of stay (Villa-Clarke, 2020). Following the SARS and the H1N1 influenza outbreaks, Henderson and Ng (2004) and Lee et al. (2012), respectively, assert that non-pharmaceutical intervention by hotels to mitigate infection risk is a key dimension to the service quality of a hotel. Thus, in the current context of health safety, we argue that when customers focus on the benefits of risk-mitigating measures taken by a hotel, they prefer a hotel with a maximum of such measures in place. Thus, to reduce the inherent uncertainty about travel and health risk, hospitality brands need to focus on improving safety perceptions and mitigating customer anxiety (Durna et al., 2015; Li and Huang, 2022; Reisinger and Mavondo, 2005; Siddiqi et al., 2022). Such best practices can range from ensuring food hygiene; offering free masks and online medical consultations; monitoring customer and employee health; closing public facilities such as laundromats, gymnasiums, and bars; inviting customers to e-observe the deep cleaning process of a hotel; leveraging technologies such as self-service check-in, cleaning with ultraviolet technology-enabled equipment, cleaning robots, voice control for room service, and facial recognition for unlocking rooms; taking temperature checks as part of the check-in process; limiting elevator rides to one customer per car; and using thermal cameras for screening guests (Elshaer and Marzouk, 2022; Hao et al., 2020). These services at a hotel are embedded into its star-rating since higher ratings allow the hotels to not only deploy the best safety technologies but also charge the customers for the same. Thus, star-ratings and the implied safety features are expected to shape the perceptions of safety at the hotel. 3 Proposed model 3.1 Integrating signalling theory and ECM Signalling theory suggests that signals of a brand/organization are the observable attributes that are/can be deployed to communicate unique values to the customers (Spence, 1973). Such signals are used by organizations for communicating the quality of their products/services (Sekar and Santhanam, 2022). Signals are used by firms because processing the information about ‘intricate’ details of a product/service is a complex task for an individual and may lead to information asymmetry (Spence, 2002). This asymmetry can be reduced by developing comprehensive trustworthy signals that provide unambiguous quality-related information to the receiver. The signalling theory has primary application in the area of brand communication, where effective messages, as signals, can enable customers to assess the brand's product/service quality (Filieri et al., 2021; Wang et al., 2021). In the context of this study, the hotel's star-rating classification is considered to be an important signal because a higher classification indicates better service attributes (Abrate et al., 2011), high-quality hospitality (Ariffin and Maghzi, 2012), and generally, at a greater cost (Martin-Fuentes, 2016). The star classification also signals a hotel's reputation (Abrate and Viglia, 2016), the reliability and integrity of its employees, and low customer risk (San-Martín et al., 2016). Hence, the hotel's star-rating can be considered a proxy for its prestige, reputation, and price, as well as the quality of customers' service consumption experiences. In the service consumption domain, Bhattacherjee (2001) proposed the ECM which aims to measure a customer's confirmation of their overall expectations, with subsequent influence on continuance intentions. The concept of confirmation of expectations as a mode for predicting behavioural intentions has gained prominence in the post-adoption literature (Oh et al., 2022). According to ECM, the desired levels of expectations from a service are based on the visible attributes of an organization that evoke those expectations (Kim and Lee, 2020). When the actual consumption experience meets/exceeds primary expectations, users confirm the expectations, are satisfied and continue their engagement with the service provider. Most applications of this model assume that satisfaction is the immediate cause of behavioural intentions; however, Churchill and Surprenant (1982), and later Hsu et al. (2006), argue that the perceived performance, shaped through signals, also needs to be considered as an antecedent to the expectations and satisfaction (Filieri et al., 2020; Jung et al., 2020). Integrating the signalling theory with the ECM, this work builds the premise that the star-rating of the hotel serves as a signal to the overall service quality that ensures the safety of its customers. Further, a higher star-rating reduces the uncertainty regarding safety-oriented hygienic services and reassures the potential patrons of their protection against any infections. The ECM helps us posit that customers' satisfaction with such safety-focused services, during their stay at the hotel, is a function of their expectations from such services, formed through the official star-rating of the hotel, and confirmation of those expectations (Bhattacherjee, 200). Such expectations are formed before the customer visits the hotel, referred to as pre-stay expectations, and enable his/her evaluation of the services during the stay to confirm their predispositions and ultimately, determine satisfaction with the stay. Based on this discussion, this work proposes that the ability of a hotel's star-rating to signal its safety, through various service features and technology, to potential visitors is referred to as star-rating-as-safety-signal (SRSS; Sekar and Santhanam, 2022). The SRSS will help customers form pre-stay safety-related dispositions for the hotel's services (Chi et al., 2022), measured in this work by the variable expectation of safety services (ESS). On arrival at the designated hotel, the visitors will validate the expectations through their during-stay experiences (Bonfanti et al., 2021), measured as confirmation of safety services (CSS). In alignment with the ECM, customer satisfaction with the stay is proposed as a during-stay variable, while the customers' intention to revisit the hotel is proposed as revisit intention (RI) and a proxy to continuance intention (Oh et al., 2022). The study's conceptual model is presented in Fig. 1 , followed by arguments for individual hypotheses.Fig. 1 Conceptual model. Fig. 1 3.2 Star-rating-as-safety-signal (pre-stay) and expectation of safety services (pre-stay) Travellers generally select appropriate service suppliers with pre-contractual information asymmetry potentially leading to adverse outcomes for them (Belver-Delgado et al., 2020). This is especially true if the information search occurs online (San-Martín and Jimenez, 2017) and the product/service quality is not directly observable. So, businesses, in this case, hotels, deploy signals to help reduce the uncertainty in guest perceptions (Chen et al., 2010; Melo et al., 2017). Star-ratings represent one such signal. However, the relation between the absolute hotel star-rating and customer perceptions of the hotel is unclear. Rhee and Yang (2015) suggest that customers of 4-star hotels are more inclined towards value, whereas customers of hotels with lower star-ratings are concerned about room attributes. This implies that customer expectations vary with star-ratings. López and Serrano (2004), on the contrary, find that there is no significant association between the star-rating of a hotel and expectations of its services by customers. Recently, Rajaguru and Hassanli (2018) argue that hotel star-ratings are more impactful than has been explained by researchers and that, beyond co-customer reviews, star-ratings can significantly affect customer expectations before arrival at the property. Hence, based on previous research and in the current context of customer safety during the pandemic, we posit that star-ratings will be positively associated with the superior expected safety measures taken by hotels to protect customers from infection. We thus hypothesise:H1 SRSS of a hotel has a positive influence on ESS. 3.3 Expectation of safety services (pre-stay) and confirmation of safety services/satisfaction (during-stay) Ajzen and Fishbein (2000) argue that customer assessment leads to an immediate and inescapable formation of beliefs about an entity. Such beliefs reflect expectations and are a prime determinant of an individual's consumption-based evaluation of the attributes. Following this argument, the actual evaluation of the safety of a hotel shaped during product/service consumption is driven by the individual's expectations regarding the benefits of consumption (Kim et al., 2021). These evaluations of products/services during consumption, as well as post-consumption reflections, curate satisfaction with the services (Bolton and Drew, 1991; Olshavsky, 1985). Hence, a primary determinant of satisfaction is the belief expressions that shape primary expectations towards the service (Hitzeroth and Megerle, 2013). Expectations represent the pre-consumption cognitive state of users, while during-consumption satisfaction indicates the cognitive and emotional evaluation of the services, determining the confirmation of those expectations (Bhattacherjee, 2001). Overall, we argue that each individual carries specific expectations about safety at a hotel, which enables them to shape their perceptions of safety, which are confirmed while they are staying at the hotel, as well as determine their satisfaction with the stay (Slattery et al., 2012). Hence, we hypothesise:H2a ESS has a positive influence on CSS. H2b ESS has a positive influence on satisfaction. 3.4 Star-rating-as-safety-signal (pre-stay) and satisfaction (during-stay) Existing frameworks fail to differentiate between the expected and the actual experiences of hotel customers (Alcántara-Alcover et al., 2013). Unlike safety expectations, which in this case is the expected experience with the safety attributes of a hotel, satisfaction is the extent to which a client feels that the facilities, amenities, staff behaviour, and deployed technologies offer real-time protection to customers during their hotel stay (Nilashi et al., 2022). Kim et al. (2016) and Gupta et al. (2019) also explain that hotel attributes such as hygiene, location, well-trained staff, hotel design, and additional amenities are key enablers for the satisfaction of customers staying at a property. However, the star-rating of a hotel may shape the evaluation of these experiences during a stay (Kim et al., 2016). For example, during a stay, a guest may feel that the hotel attributes and/or services are not up to standard for a 4- or 5-star hotel; similarly, a guest may judge the quality of a 1- or 2-star hotel to be higher than expected. In the domain of hotel safety, there is limited literature examining the relationship between hotel star-rating–based safety perceptions and evaluation of safety during the stay. We argue that perceptions of safety, which have been heightened due to the pandemic, may be driven both by the star-rating of a hotel and by the resultant practices at the hotel. Together, these drive guests’ satisfaction with the services ensuring customer safety, and we thus hypothesise:H3 SRSS of a hotel has a positive influence on CSS. 3.5 Confirmation of safety service (during-stay) and satisfaction (during-stay) For hotels to survive in the competitive tourism market in the current climate, it is essential for them to both create a safer environment and develop their image by fulfilling the customers' safety needs (Pal et al., 2019). However, little research has been conducted on what shapes customer satisfaction towards the safety facilities of a hotel (e.g., Tasci and Sönmez, 2019). A traveller's satisfaction with the stay is a mental reinforcement of the traveller's knowledge of safety, enabled through the evaluation of the safety measures to confirm their expectations of the same (Chew and Jahari, 2014). In the wake of COVID-19, travelling to new places is subject to unpredictable health-related challenges. Thus, travellers carefully evaluate safety measures at hotels in line with their expectations, and this, in turn, shapes their satisfaction towards these properties (Godovykh et al., 2021). Hence, we hypothesise:H4 CSS has a positive influence on satisfaction. 3.6 Satisfaction (during-stay) and revisit intention Various studies have measured the impact of customer satisfaction on behavioural intentions, such as repeating the visit to the destination or the leisure activities therein (Lam and Hsu, 2006). This is a post hoc assessment of products/services and is a cognitive projection of the outcomes of performing the same behaviour in the future (Ajzen, 1991). In other words, when the experiential outcomes are positive, individuals are more likely to hold a favourable disposition towards the service provider, which motivates them to repeat the behaviour (Kwon and Ahn, 2020). Negative experiences, on the other hand, lead to adverse beliefs, which cause individuals to avoid similar behaviours in the future (Jalilvand et al., 2012). We expect that, where careful evaluations of hotel safety services by guests lead to satisfactory experiences, these should encourage them to visit the same hotel again (or, in the case of a multi-property brand, to visit another property by the same hotel brand). Hence, we hypothesise:H5 Satisfaction (during-stay) has a positive influence on RI. 3.7 Star-rating-as-safety-signal and revisit intention: serial mediation The mediating role of ECM constructs, like confirmation and satisfaction, in determining continuance intention is evident in prior research. Oh (1999) argues that confirmation of expectations mediates the path between the pre-consumption perceived service quality and the satisfaction derived from the consumption. Similarly, satisfaction has also been discussed as a mediator to confirmation and behaviour. For example, Westbrook (1987) discusses satisfaction as a common link between prior product expectations, post-consumption cognitive evaluation (confirmation), and repurchase intention. Similarly, in the context of service recovery, Wirtz and Mattila (2004) and Zhu et al. (2021) suggest that post-consumption satisfaction serially mediates the relationship between the perceived and the validated service attributes and the subsequent behaviours. Based on these arguments, we argue that travellers try to make predictions about the service quality related to safety at a hotel based on signals like the hotel's star-rating. After experiencing the service at the hotel, such customers confirm those expectations, generate customer satisfaction with the stay, and develop positive intent to revisit the hotel. Thus, we propose:H6 ESS, CSS, and satisfaction serially mediate the relation between the hotel's SRSS and RI. 4 Methodology 4.1 Construct measurement To operationalise the constructs in this study, the questionnaire items were generated by aggregating items from different sources in the existing literature. The aggregated items were shortlisted and refined based on the discussion with three subject-matter experts: two academicians with significant publications in hospitality literature and one practitioner who was a top manager at a prominent hospitality chain. To measure the items of SRSS, we chose items from Zemke et al. (2015) and Rajaguru and Hassanli (2018). The items to measure ESS were adapted from Loizos and Lycourgos (2005), while items for measuring CSS were taken from Bigné et al. (2005). Finally, items to measure satisfaction and RI were adapted from Lam and Hsu (2006). This process led to a total of 31 items. Once the items were aggregated and developed, the context of the study was provided to the three experts, and they were asked to review the items related to each construct based on its operational definition. The experts were asked to select the relevant items for each construct, at first individually and then as a group to arrive at a consensus. This process led to a reduced list of 24 items. The panel also helped the authors to phrase the items more clearly. All items were measured on a 7-point Likert scale (1: strongly disagree to 7: strongly agree) (Dawes, 2008). Finally, before the main data collection, a pilot test of the questionnaire was carried out with 25 respondents who had travelled during the COVID-19 period in the months of December (2020) and January (2021) and stayed in a hotel, to check for the clarity of the questionnaire. The pilot respondents gave some suggestions for three items of the questionnaire (SRSS3, SAT4, and ESS4) which were suitably modified. The final list of items is presented in Table 2. 4.2 Sampling and data collection This study targeted respondents who have planned and executed their travel, domestic or international, during the COVID-19 pandemic in India. India was chosen as it was one of the countries most affected by the pandemic, with the second-largest caseload in the world at the end of 2021.3 It also has a very dynamic hospitality industry, which was severely affected by the pandemic (Gupta and Sahu, 2021). The tenet of the study is based on the fundamentals of travel anxiety, where travellers desire to travel but are also cautious about their stay safety at the hotel. While travellers around the world faced travel anxiety during the pandemic, it was stronger in India due to the high caseload with travellers overtly focused on the quality of the hotel stayed in.4 Close to 92% of Indians, as per the Economic Times survey, sought for visible cues of safety at a destination during their travel.5 Further, the questionnaire was divided into two parts: the first part covered the pre-stay phase, and the second part covered the during-stay phase. In the first section, the respondents were asked to reflect on their perceptions of the star-rating of a hotel and what it implies for safety when planning their travel. In the second section, the same respondents, after their travel, were asked to recall their actual stay experiences. Each section was completed at two different points in time: the first section before the travel and the second section after the travel. The choice of a COVID-19 intensive period, while the travel was being planned, was taken as extant research indicates that the information cues for infectious diseases, like the number of infections, recoveries, and deaths, enhance the general proclivity of the customer to seek safety (Kim and Lee, 2020; Kim et al., 2021). Based on such research, it can be argued that individuals planning travel at such times would be facing travel anxiety and increased sensitivity to personal safety, which affects the type of hotel chosen for the stay. To diminish the probability of common method bias (CMB) in the data collected, the a-priori method of randomising the independent variables in the pre-stay questionnaire and the dependent variables in the during-visit questionnaire was used, as suggested by Chang et al. (2020). The questionnaire was distributed through a prominent global travel agency, which had agreed to support the project. The agency is the largest in the country and serves the travel needs of over 15000 unique clients every year.6 In the absence of a population repository, the database of this firm – comprising over 100000 unique customers – was considered the population of all travellers. At the time of contact with the agency (April 2021), 8236 people were in active contact with the agency and were planning their travel once the second Delta-variant-led COVID-19 wave receded in India. The second wave officially lasted from April 2021 to October 2021, with its peak in May 2021 (Yang and Shaman, 2022). Since the objective of the study was to capture the safety expectations of a hotel when the customers' sensitivity to travel safety is highest, at the authors’ request, the agency disseminated the study questionnaire through an online survey platform to these 8236 people in April of 2021 seeking their interest to participate. Of these, a positive response was secured from 1134 people by the middle of May 2021. These people agreed to participate in the study and answer the questionnaire in both phases (before the travel and following the travel). The pre-stay data were collected in June–July of 2021, when the second COVID-19 wave was at its peak in India, while the during-stay data was collected from the same people, after they had completed their travel, in the months of October 2021 to January 2022. This was a period when the second wave had subsided, the travel restrictions were eased, and people started to travel (Yang and Shaman, 2022). All the 1134 people promptly returned the pre-stay questionnaire which was complete in all respects. However, only 610 respondents undertook the planned travel in the period mentioned above and stayed in the same hotel. These people were contacted by the agency, with the during-stay questionnaire, immediately after they returned from their travel. Of those, we obtained 452 complete and useable questionnaires across both phases. To ensure the statistical robustness of the measurement model, one-half of the data collected from June to July 2021 (N = 226) and their corresponding responses from October 2021 to January 2022, referred to as Dataset 1, were used to evaluate the psychometric properties of the measures, while the other half of the data, referred to as Dataset 2, was used to evaluate the structural model (Bagozzi and Heatherton, 1994). The sample profile is given in Table 1 .Table 1 Sample profile. Table 1Variable Category N Percentage Categorization for MGA* Gender Male 238 52.7 Male Female 214 47.3 Female Age 18–25 years 78 17.2 Low Age 26–35 years 140 30.9 Low Age 36–50 years 178 39.4 High Age 51+ 56 12.4 High Age Marital status Married 184 59.3 Married Single 268 40.7 Single Education Under Graduate 38 8.4 Low Education Graduate 218 48.2 Low Education Post Graduate 160 35.4 High Education Doctorate 36 7.9 High Education Income (Monthly) 5K-20K 30 6.6 Low Income 21K-40K 172 38 Low Income 41K-70K 198 43.8 High Income 71K-1L 38 8.4 High Income >1L 14 3.1 High Income Travel Period Oct–Jan (2020-21) 224 49.55 Not applicable Jun–Sep (2021) 228 50.45 Not applicable Type of Travel Domestic 318 70.35 Not applicable International 134 29.65 Not applicable Multi-group analysis Table 2 Psychometric properties (Dataset 1). Table 2Scale items/(Code) Mean (Std Dev) Factor loadings Reliability Convergent Validity Discriminant Validity α CR AVE MSC Star-rating as safety signal (pre-stay) (SRSS) .838 .859 .549 .534 A higher star-rating reflects the hotel's caring nature towards guests to keep them safe (SRSS1) 5.67 (1.22) .707 A higher star-rating implies error-free services to keep the guests safe (SRSS2) 5.77 (1.08) .693 A higher star-rating entails improving and improvising services to provide a safe and satisfying service to guests (SRSS3) 5.81 (1.18) .746 A hotel with high star-rating upgrades the resources to provide enhanced medical facilities to the guests (SRSS4) 5.49 (1.21) .791 A hotel with a high star-rating provides high-quality location-based services, like safe pick/drop, hospitalization, and safe regional travel (SRSS5) 5.23 (1.19) .763 Expectation of safety services (pre-stay) (ESS) .883 .876 .585 .512 . The hotel is expected to follow all hygiene protocols (ESS1) 5.11 (1.05) .796 I believe the hotel would be ready for any medical emergencies of guest (ESS2) 5.59 (1.24) .768 I believe the hotel would provide more personal protective equipment which is easily accessible (ESS3) 5.71 (1.18) .769 I believe the hotels would provide self-service technology to offer services in a contactless way (e.g., for check-in and out, kiosks, in-room services) (ESS4) 5.86 (1.19) .721 I expect the hotel to be certified for preventing and controlling infectious diseases. (ESS5) 5.68 (1.11) .768 Confirmation of safety services (during-stay) (CSS) .791 .788 .554 .481 The hotel provided safety services which were much better than what I expected (CSS1) 5.48 (1.12) .777 My stay experience at the hotel was much better than what I expected (CSS2) 5.45 (1.11) .712 Overall the safety services of the hotel mostly met my expectations (CSS3) 5.53 (1.14) .743 Satisfaction (during-stay) (SAT) .884 .843 .519 .498 The hotel provided satisfactory service for the guest rooms for new arrivals (SAT1) 5.41 (1.15) .702 The hotel employees' health condition was always satisfactorily monitored (SAT2) 5.47 (1.14) .703 The personal hygiene of front-line employees was satisfactory (SAT3) 5.65 (1.19) .724 The hotel performance in providing safety-related information to guests was satisfactory (SAT4) 5.76 (1.29) .743 The employees respiratory etiquettes were satisfactory (SAT5) 5.71 (1.23) .728 Revisit Intention (RI) .834 .865 .562 .534 I would stay at this hotel soon (RI1) 5.34 (1.09) .771 I plan to stay at this hotel on regular basis. (RI2) 5.21 (1.14) .725 I intend to book this hotel for my long term health benefits. (RI3) 5.32 (1.21) .752 I intend to book this hotel because they are more concerned about safety. (RI4) 5.66 (1.25) .721 I intend to stay at such hotels as I am concerned about my health. (RI5) 5.21 (1.14) .779 CR: Composite reliability; AVE: Average variance extracted; MSC: Maximum squared correlation 5 Data analysis and results 5.1 Control variables Spector (2021) recommended using control variables to eliminate the effect of extraneous factors on the main observed relationships. Since customer demographics, such as age, gender, education, marital status, and income (Lu and Pas, 1999) can have possible intervening effects on the outcomes of the study (see Table 1), they were modelled as control variables. 5.2 Initial checks Before evaluating the psychometric properties of the measures, the normality assumption of the variables was evaluated by examining skewness and kurtosis for the entire dataset. The skewness value of each variable was found to be between −2 and +2, and the kurtosis values were between −7 and +7, indicating sufficient univariate normality. To check for a lack of CMB in the data, the post-hoc Harman's one-factor test was run using the principal component analysis in SPSS24, where all items were forced to load onto only one factor. The largest factor explained only 23.56% of the variance of the dataset, implying a lack of CMB. 5.3 Psychometric properties (Dataset 1) Next, the psychometric properties of measures were evaluated using confirmatory factor analysis (CFA) in the CB-SEM using AMOS23 with the first dataset. The reliability values of each construct, measured through Cronbach's alpha (α) and composite reliability, exceeded the cut-off level of 0.7 (Nunnally, 1978; see Table 2). This implies that all multi-item scales used in this research were internally consistent. Following a reliability check, the constructs were evaluated for empirical validity, reflected through convergent and discriminate validities (Bagozzi and Yi, 1988). To evaluate convergent validity, besides factor loadings (>0.70), the criterion for average variance extracted (AVE; >0.50) was deployed (Hair et al., 1998) and was found to be in order. To evaluate the discriminate validity, the Fornell and Larcker (1981) criterion was used, whereby the AVE of each construct was compared with the maximum squared correlation (MSC) of that construct with the other constructs in the model. In all cases, the AVE values were found to be larger than the corresponding MSC, implying discriminant validity. Table 2 provides the results for all items, descriptive statistics, as well as the results of the convergent and discriminant validity analyses. The overall model fit was evaluated by examining the values of chi-square/degrees of freedom (χ2/df), goodness of fit index (GFI), comparative fit index (CFI), normed fit index (NFI), Tucker–Lewis index (TLI), and root mean square error of approximation (RMSEA), and was found to be satisfactory with χ2/df = 3.84, GFI = 0.91, CFI = 0.91, NFI = 0.91, TLI = 0.91, and RMSEA = 0.06. 5.4 Hypothesis testing (Dataset 2) After the measurement properties of the constructs, through CFA, were re-evaluated with the second dataset (N = 228) and were found to be satisfactory, the structural model was evaluated. The fit indices of this model were appropriate with χ2/df = 3.91, GFI = 0.90, CFI = 0.91, NFI = 0.91, TLI = 0.91, and RMSEA = 0.06,. The path from pre-stay SRSS to ESS was found to be significant (β = 0.738, p < .05); hence, H1 is supported. This means that as customers plan their trip, they expect a higher hotel star-rating to indicate enhanced safety measures. Next, pre-stay ESS was found to significantly influence during-stay CSS, and satisfaction (β = .588, p < .05; β = 0.478, p < .05). Hence, hypotheses H2a and H2b are supported. Further, the path from pre-stay SRSS and satisfaction was also significant (β = 0.801, p < .05). Hence, hypothesis H3 is also supported. It implies that higher pre-stay hotel ratings create a positive customer satisfaction towards the hotel amenities, once customers check in at the property. Also, during the stay, CSS, a validation of pre-stay expectations, is found to positively influence satisfaction (β = 0.491, p < .05). Thus, hypothesis H4 is supported. Finally, as theorised, such satisfaction has a positive influence on the customers' RI (β = 0.767, p < .05), which supports hypothesis H5. For examining the serial mediation hypothesis (H6), Model 6 in Haye's PROCESS macro in SPSS24 was deployed. The indirect effect of hotel SRSS on RI, through ESS, CSS, and satisfaction, was significant (β = 0.121, p < .05); hence, the mediation hypothesis H6 is also supported. 5.5 Multigroup analysis (control variables; Dataset 2) Since, modelling categorical variables (non-latent) in CBSEM is an extant challenge (Kupek, 2006), to evaluate the effect of control variables on the model, the multi-group analysis (MGA) protocol in AMOS23 was deployed. This is because all the control variables (traveller demographics) are categorical in nature. Each of the control variables was made binary and restricted to two categories with approximately similar respondent numbers for easy execution in the MGA, as shown in Table 1. The MGA in AMOS23 tests the difference in specific paths across the model signified by the critical ratio (CR), which is a t-value. A CR value above 1.96 suggests the paths are different across the two groups. It was found that for all the control variables, the paths in the model were not significantly different and the CR was below 1.96 for all paths (see Table 3 ). Hence, the control variables were found to have no significant effects on the model.Table 3 Control variables. Table 3Main Hypothesis Path Value PD Gender (M-F) PD Age (HA-LA) PD Education (HE-LE) PD Income (HI-LI) PD Marital Status (MA-SI) H1 SRSS → ESS .738 .022 −.013 .013 .034 .033 H2a ESS → CSS .588* .012 .035 .005 .037 −.018 H2b ESS → satisfaction .478* −.034 −.022 −.027 −.024 −.031 H3 SRSS → satisfaction .801* .031 .014 .004 .029 .012 H4 CSS → satisfaction .491* .042 −.014 −.018 .019 .011 H5 Satisfaction → RI .767* .005 −.032 .026 −.016 .015 *significant at 95% level of significance; PD: Path difference for the two categories of a variable. M: Male, F: Female, HA: High Age, LA: Low Age, HE: High Education, LE: Low Education, HI: High Income, LI: Low Income, MA: Married, SI: Single. 6 Discussion This study comprises a temporal examination of the effect of pre-consumption (here pre-stay) variables such as the expectation of safety service (ESS), a hygienic need for a traveller, shaped through the star-rating of a hotel as a signal of safety (SRSS), on during-consumption (here during-stay) variables such as confirmation of safety services (CSS), satisfaction, and revisit intention (RI). The context of the study was travellers in India undertaking travel during the COVID-19 period in India, more specifically just after the deadly second Delta wave (second half of 2021). The research design was cross-sectional with data for the study collected at two points in time, pre-stay and during-stay, with the respondents staying at the same hotel they planned during the pre-stay phase. Our results reveal that customers who had positive perceptions of a hotel based on its star-rating consider it to be safer to stay during the COVID-19 period. This implies that guests build a mental representation of hotels based on star-ratings, and that these ratings influence service expectations regarding the measures taken by the hotel to ensure customer safety (Rajaguru and Hassanli, 2018). Thus, star-ratings serve as a credible classification model that reflects the expected quality standards in the hospitality industry (Dioko et al., 2013; Serrano et al., 2014). A primary finding of this study pertains to the effect of ESS (pre-stay) on CSS and satisfaction towards the hotel. The perception towards safety is a stable mental inclination embedded in the traveller's knowledge about safety practices to be followed within a servicescape (Tasci and Sönmez, 2019). Our findings show that as expectations, shaped through hotel ratings, increase, they are not only used as a rubric to evaluate against the existing safety measures once the customer checks in, but such expectations also shape the satisfaction towards the hotel with regards to its safety provisions (Hitzeroth and Megerle, 2013). Alternately, it can be argued that the expectations themselves are manifestations of prior experiences, from the same or different service providers, and serve as a mechanism to reinforce or weaken customer satisfaction with a specific experience (Slattery et al., 2012). This study also confirms that the perceived star-rating of a hotel has a significant positive relationship with CSS. This implies that after customers arrive at the hotel, they constantly evaluate the hotel's protocols designed to provide a safer environment. Consequently, customers who are concerned about their safety prefer hotels that mitigate the risk of infection. CSS, in alignment with the star-rating of a hotel, reinforces their belief in such ratings. This also means that risk-averse customers are willing to pay a higher price for a higher-star hotel, with the hope that they will receive superior safety-oriented services through corresponding measures for infection mitigation. Next, this study finds that satisfaction with the stay has a significant positive relationship with RI. This indicates that positively constructed satisfaction leads to strong similar future behaviours, while lack of it, formed due to negative experiences, reduces the propensity of the customer to reengage (Jalilvand et al., 2012). It also means that if the hotel administration follows all necessary safety processes to meet customer safety expectations, customers will form positive dispositions towards the hotel, which will in turn foster RI. Additionally, the serial effect of pre-stay perceptions of a hotel's safety, based on its star-rating, on the during-stay experiences, satisfaction, and RI, supports similar findings in the recent literature arguing for this unique pathway connecting a hotel's star-rating as a source of customer loyalty during a pandemic (Zhu et al., 2021). Finally, this work also establishes the universal applicability of this model irrespective of the traveller characteristics, like gender, age, marital status, education, and income, as none of these control variables was found to have a significant moderating effect on the model. 7 Theoretical contributions This research makes several theoretical contributions. Recent research (e.g., Atadil and Lu, 2021) focuses on customer perceptions towards safe stays in hotels during the COVID-19 pandemic. These works have explored the dimensions underlying a safe hotel image – for example, perceptions of hygiene control, medical preparedness, use of self-service technology, and privacy – as well as the implications thereof for the visit intention of the customers. However, none of these works has moved beyond pre-travel perceptions to investigate during-travel evaluations, satisfaction and the implications of these evaluations for customer revisit intentions. Underpinned by Herzberg's dual motivation perspective, and unique integration of the signalling theory and the ECM, this study proposes that the official star-rating of a hotel serves as a signal that shapes the pre-stay ESS, and that this temporally affects the during-stay CSS, satisfaction, and RI. The finding confirms that unless the hotel's safety attributes, reflected by the star-rating, are adequate and fulfil the hygienic need of safety during a pandemic, customers will not be motivated to stay at the property (Herzberg et al., 2007). The temporal format of the research design, with data collected from the same user at two points in time, contributes to the emerging literature in the domain of hotel safety (e.g., Jung et al., 2020; Filieri et al., 2020, 2021). There is little literature on the signalling impact of the star-rating of a hotel on its safety services (Sekar and Santhanam, 2022). Ariffin and Maghzi (2012) and Setiawan et al. (2019) argue that a higher star-rating leads to higher customer expectations towards service at the hotel facility. The current research implies that the hotel star-ratings are used to first form expectations and then to judge the service quality and that these expectations and judgements are important for satisfying the psychological and physiological needs of customers (Cser and Ohuchi, 2008; Huang et al., 2018). Overall, the star-rating of a hotel appears to be a source of customer expectations that impacts perceptions of the actual service provided by the hotel (Huang et al., 2018; Israeli, 2002; Serrano et al., 2014). The present work thus establishes the importance of the official star-rating of a hotel as a signal of overall hotel service quality, and a key driver of customer satisfaction (during stay; Malik et al., 2020; Sozen and O’Neill, 2020; Tajeddini et al., 2021). Previous studies have indicated that customers dislike the risk implicit in hotel selection and feel the opacity that comes from service providers selling poor-quality services even under a high star-rating. Works by Wu and Cheng (2022) and Serrano et al. (2014) have argued that such hospitality classifications, like official star-ratings, third-party ratings (e.g., Trivago), or tier systems (e.g., Airbnb), tend to create information asymmetry, as such facilities over-promise and under-deliver. Our research establishes that beyond the absolute star-rating, it is the perception of services behind a specific rating that motivates customers to choose a property, and that reinforcement of expectations through actual experiences generates positive guest satisfaction as well as re-engagement intentions. This is especially true if the customer need in context is safety. Limited previous attention to this domain is understandable, as, besides the few studies looking at man-made disasters and terrorist attacks (e.g., Zenker and Kock, 2020), the safety of travel was not a priority research focus before the COVID-19 pandemic. Hence, this work, as a way forward, argues for more research on leveraging the visible traits of a hotel, including its star-rating, to create and deliver on the perception of having adequate safety mechanisms to limit the spread of disease during hotel stays (Contreras and Mep, 2020). 8 Practical implications Based on the findings presented here, the study offers several practical contributions. Given the importance of hotel star-ratings in setting safety expectations, perceived experiences, and revisit behaviour, hotel (chain) management could deploy niche marketing campaigns conveying the message about their safety services. Notices indicating that star-ratings are earned through exemplary safety services, or that the safety record of the hotel, in terms of infections, is reflected in its star-rating, can be disseminated through traditional, electronic, and social media. The message can also be reinforced through endorsement by celebrities and reputed doctors, or testimonials by previous customers who emphasise the safety aspects of the hotel (Sigala, 2020). Additionally, the hotel star-ratings have played an important role in attracting customers during or after the COVID-19 pandemic. The ratings reflect the importance of safety expectations and satisfaction of stay experiences, evaluated through safety-oriented amenities of the hotel (Quintal et al., 2010). Such evaluations and outcomes are universal irrespective of the traveller's demographic profile. Hence, hospitality firms must focus on raising their service standards, especially those related to safety, to upgrade their star-ratings periodically. Since star-ratings are also determined by the overall service amenities at the hotel, government bodies may also consider introducing a separate star-rating or certification based solely on the safety services of the hotel. This star-rating should be based on trustworthy information about the safety protocols followed by the hotel, such as its sanitisation processes, levels of vaccination among staff members, contactless check-in and check-out options, limitations to the number of customers in the hotel during a pandemic wave, the provision of separate services for vaccinated and non-vaccinated customers, assurances that vaccinated staff follow all safety protocols, and the presence of other technology-enabled services at the hotel. Such services can also help the hotel achieve successful containment in case of an outbreak, thus ensuring that it does not turn into an infection hotspot (Huang et al., 2020; Joo and Woosnam, 2020). Our findings depict a strong effect of safety-related expectations on the confirmation of those through actual safety-related experiences and satisfaction. Hence, hotel managers need to deliver on highlighted safety features, as such promised benefits will influence the orientation of customers towards the specific features that deliver those benefits. Further, given that safety perceptions have a positive influence on satisfaction, safety features that are highlighted to protect customers from infection act as a powerful tool to build satisfaction, which increases the likelihood that customers will revisit the hotel. In addition, promoting the safety aspects of a hotel (as reflected in its star-rating) and delivering on promises not only helps customers form positive perceptions towards the hotel but also becomes a source of customer loyalty. This is because customers attempt to converge their expectations and actual consumption experiences to shape their experiences; hence, diligent and precise delivery of safety-based promises is critical to inducing customer loyalty. 9 Limitations and future research directions Despite its theoretical and practical contributions, this research is subject to some limitations that should be addressed in future studies. First, as the focus of this study was on the impact of pre-stay expectations on during-stay experiences, ideally respondents should have been contacted for the second time during their stay at the hotel, rather than at a time after their visit, when some early memories of the stay, if long, would have faded. However, due to the limitations of online data collection that were mandated, as COVID-19 restrictions at the hotels meant physical data collection was not possible, the second contact wave could be conducted only immediately after respondents had completed their stay. Thus, they had to remember the experiences they had while staying at the hotel. Further, the study, though temporal, is cross-sectional by principle as no repeated measures are involved. Future studies should attempt to replicate the results using a pure longitudinal research design, where the contact questionnaire includes the same variables, related to the expectations and confirmation of those, measured before and during the stay, respectively. Second, this research focused solely on future hotel revisit intention due to concerns related to safety from infection. While the safety of customers at a hotel is important, customer experiences at a hotel are also driven by the other services offered by the hotel. Though in this study, satisfaction measured pertains to safety features only, it is possible that other hotel amenities and services, not directly related to pandemic safety, also contributed to customers’ satisfaction. Future research should therefore investigate the during-stay experiences of customers, as a combination of safety-related and general services, to identify the unique effects of each factor on overall satisfaction and, in turn, a more robust revisit intention measurement. Finally, the study focused on only one attribute of the hotel, the star-rating, and its implications regarding customer safety perceptions. However, safety perceptions of a hotel servicescape are made up of complex combinations of not only multiple attributes and services at the hotel itself but also other factors, such as customer reviews, social media brand engagement, prior experiences, hotel location, co-customer behaviour, and a few others. For example, even the best hotels in a city facing a large-scale outbreak will be considered unsafe. Hence, future studies should include a variety of pre-stay hotel-related information, beyond star-ratings, to gain a more comprehensive understanding of safety expectations. Declaration of competing interest None. Data availability Data will be made available on request. 1 https://www.globenewswire.com/news-release/2019/03/08/1750501/0/en/Global-Hotels-Market-Expected-To-Reach-USD-211-54-Billion-By-2025-Zion-Market-Research.html. 2 https://en.wikipedia.org/wiki/2008_Mumbai_attacks. 3 https://www.thehindu.com/news/national/coronavirus-december-20-2021-live-updates/article37994375.ece. 4 https://www.hindustantimes.com/lifestyle/travel/are-we-there-yet-tap-for-an-anxiety-free-vacation-101658235676764.html. 5 https://economictimes.indiatimes.com/magazines/panache/post-pandemic-travel-may-be-stressful-but-indians-feel-socially-distanced-spaces-sanitiser-booths-can-help-relax/articleshow/85513021.cms. 6 The identity of the agency is kept confidential at its request. ==== Refs References Abrate G. Capriello A. Fraquelli G. When quality signals talk: evidence from the Turin hotel industry Tourism Manag. 32 4 2011 912 921 Abrate G. Viglia G. 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==== Front Youth Soc Youth Soc YAS spyas Youth & Society 0044-118X 1552-8499 SAGE Publications Sage CA: Los Angeles, CA 10.1177/0044118X221138607 10.1177_0044118X221138607 Article Learning From Home: Widening Rural-Urban Educational Inequality and High School Students’ Self-Control in China During the COVID-19 Pandemic and School Closure Ma Gaoming 1 Zhang Jiayu 1 https://orcid.org/0000-0002-2250-773X Hong Liu 2 1 Zhejiang University, Hangzhou, Zhejiang, China 2 Fudan University, Shanghai, China Liu Hong, School of Social Development and Public Policy, Fudan University, 220 Handan Road, Shanghai, 200433, China. Email: [email protected] 3 12 2022 3 12 2022 0044118X221138607© 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. Worldwide school closures and remote learning have been implemented during the COVID-19 pandemic. These measures’ impact on young populations’ academic achievements is unclear. This study (N = 1,736, ages 14–20 years, 53% female, and Chinese) analyzed academic examination scores for students at a high school in Eastern China between January and July 2020. Results showed that overall, students’ academic achievements appeared to be negatively affected amid a school closure. More importantly, students’ self-control was introduced as a moderating factor that partially accounted for this difference in the context of remote learning at home. These findings extended our understanding of school closures’ unequal impact on young populations. Education and social policies should respond to these challenges during times of crisis. COVID-19 school closure self-control rural-urban disparity academic achievement educational inequality National Office for Philosophy and Social Sciences https://doi.org/10.13039/501100012325 18CSH023 edited-statecorrected-proof typesetterts1 ==== Body pmcIntroduction The closing of schools worldwide due to the COVID-19 pandemic were unprecedented. As of April 27, 2020, about 1.6 billion learners had been affected by school closures world-wide (United Nations, 2020). School measures were taken to ensure continuity of teaching and learning around the world. Nonetheless, the impact of these measures remains unclear. In a survey of 3,275 Chinese parents’ evaluation of online learning during the COVID-19 pandemic, Dong et al. (2020) found that parents generally held negative beliefs about the impact of online learning on their children’s education. Kuhfeld and colleagues estimated that most students in the United States returning from COVID-19 school closures were expected to experience a significant decline in their academic achievements (Kuhfeld et al., 2020). These exploratory studies prompted us to question the extent of the impact on children’s academic achievement and how young populations may be unequally affected. We sought to examine the impact of COVID-19 related school closures and remote learning on young people’s development in the context of China. Using data collected from a high school during its closure and subsequent reopening at the initial stage of the pandemic, we aimed: 1) to describe the changes that took place in students’ academic achievement over time; 2) to explore the extent to which rural-urban inequality in education changed during this period of time; and 3) to extend a psycho-social explanation on the unequal effects of school closure and remote learning on rural and urban students’ education. We found that academic achievements of the Chinese high school students who participated in this study had been negatively affected by the school closure. Moreover, the school closure differentially affected urban and rural students’ academic achievements, leaving the latter at a disadvantage. Instead of using the usual socio-economic explanation, we found that students’ personal self-control partially accounted for the widening rural-urban inequality in education in this context. As such, we will elaborate on these arguments through a critical literature review. Rural-Urban Disparity and Academic Achievement Rural-urban disparities posed significant challenges to students’ academic achievements in China prior to COVID-19 (Chan, 2019). These disparities were known to be associated with the hukou system, which divides Chinese citizens into agricultural (rural) and non-agricultural (urban) statuses (Cheng & Selden, 1994). Despite the hukou reform in recent years, empirical studies have shown that the system still has a great impact on education. For example, Hannum (1999) identified a dual-tiered educational system that had been formed along urban-rural lines in the 1980s. In addition, Wu (2011, p. 48) used data from a national representative survey in 2005 to show that rural populations had long-standing and consistently shorter years of schooling. In a longitudinal analysis of post-reform periods of time, Yang et al. (2014) found that hukou status was the strongest predictor of children’s educational outcomes. Chinese scholars have argued that the hukou system should be viewed as one of the most far-reaching and enduring factors for understanding educational inequality in China’s context (Chan, 2019). The pandemic may be exacerbating the enduring effects of rural-urban disparity on education. Recent educational reports by the United Nations have warned that COVID-19 may hinder the accumulation of human capital while simultaneously increasing educational inequality (United Nations, 2020). By using a projection of 5 million students in Grade 3 through Grade 7 across two school years (2017–2019), researchers estimated that all students returning to school after COVID-19-related school closures would suffer lower academic achievements (Kuhfeld et al., 2020). Furthermore, evidence from H1N1-related school closures in 2009 indicated that the educational performance of children from poor and vulnerable backgrounds was more likely to be affected (Cauchemez et al., 2009). However, scientific evidence on these effects remains scarce. To our knowledge, rarely has previous research examined the losses of learning during the COVID-19 pandemic, especially in the Chinese context. This paper aims to fill this knowledge gap by studying the impact of hukou status (a proxy for rural-urban disparity) on Chinese high school students’ academic achievement during the closures and reopening of schools. Socio-Economic and Psycho-Social Explanations Our second objective is of a theoretical nature, that is to explore explanations on the underlying mechanisms by which the rural-urban disparity affects students’ academic achievement, which shall be considered in the context of remote learning, or more specifically “learning from home” during the school closures. After the COVID-19 outbreak in late January 2020, the Chinese government suspended the opening of schools for the spring semester. At this time, national initiatives were launched to ensure that students’ education would continue amid school closures (Ministry of Education, China, 2020), including the wide adoption of online teaching and learning. China’s Ministry of Education established a National Cloud Platform in mid-February 2020 to deliver online education. By mid-May 2020, the platform had registered about 1.7 billion visits (Ministry of Education, China, 2020). However, switching to a learning-from-home method precipitated public concerns and heated debates about the implications of the new education modes on students’ learning experiences and outcomes. A literature review suggests that both environmental and personal factors may affect academic achievement for students who learn from home. On the one hand, many empirical studies have demonstrated that socio-economic factors, such as family income, parental education, and occupation, are closely associated with students’ educational outcomes (Chung, 2015; Sirin, 2005; White, 1982). Compared with adolescents growing up in rich families, studies showed that adolescents from families with economic disadvantages tend to grow up with lower paternal knowledge, expectation, monitoring, discipline, and demandingness (Shek, 2007; Conger et al., 1999). Normally, school environment can serve a buffering role for the economically disadvantaged children. For example, Tan and Bodovski’s (2020) research indicated that boarding at school compensates for family disadvantages, especially for students with little parental support and involvement. However, during the pandemic, without the school environment, family socio-economic status (SES) may directly affect students’ access to and utilization of information resources at home, which can affect students’ academic achievements in turn (Cheshmehzangi et al., 2022). Apart from environmental factors, student’s personal traits, especially self-control, may also be vital to their educational success, especially during times of school closure (Dong et al., 2020). Self-control is defined as “the self-initiated regulation of thoughts, feelings, and actions when enduringly valued goals conflict with momentarily more gratifying goals” (Duckworth et al., 2019, p. 374). Self-control has two features: it is self-motivated, and it requires a choice to be made between a valuable long-term goal and a less valuable short-term pleasure (Duckworth et al., 2019). Self-control may play critical roles in predicting academic achievement, because it is one of the most important traits to consider in relation to student’ independent and self-directed learning (Duckworth et al., 2012). For example, by designing the Brief Self-Control Scale (BSCS), Tangney et al. (2004) showed that self-control is closely related to academic achievement and psychosocial wellbeing. With a longitudinal study using the BSCS measure, Duckworth and Seligman (2005) demonstrated that self-control is more important than IQ in predicting academic performance of Grade 8 students. Duckworth et al. (2012) further explicated that high self-control helps students study and complete homework, and in conjunction with IQ, affects their academic achievement. Troll et al. (2020) found that smartphone use boosted academic achievement for students with higher self-control. Therefore, during school closures and online education, self-control can be crucial for understanding a student’s decision to study for the future rather than, for example, to play mobile phone games for immediate pleasure. Based on the literature review above, we argue for a new theoretical framework and try to explore whether SES and psycho-social traits together can explain the effects of rural-urban disparity on students’ academic achievement. Our study will contribute to the literature in the following two ways: firstly, it is, to our knowledge, the first study to explore the mechanism underlying the widening rural-urban inequality in education in the context of the COVID-19 pandemic. The model integrating both SES and psycho-social traits, as a pioneering discovery, can enrich our understanding on youth development during school closure and online education. Secondly, given the difficulties in conducting fieldwork during the outbreak of the pandemic in 2020, our data are quite unique and provide us a rare opportunity to observe how rural-urban inequality in education changed during the crisis. Methods Data and Participants For analyzing changes in academic achievements during the COVID-19 pandemic, a sample of high school students is desirable because these students take regularly scheduled examinations. By examining academic examination scores over time, we may know whether and how students’ academic achievements changed during the pandemic. For this purpose, we chose a county-level key high school (Zhongdian Gaozhong) in Eastern China and collected all attending students’ academic records during the pandemic. Like other key high schools, the institution under study receives funding from both the state and local levels of government. As a result, this school is not only equipped with more advanced facilities, but also attracts the most competitive teachers and students in the region. Despite the non-representative sampling strategy, our sample bears similarities to other key high schools since every county has a key high school in China (Liu et al., 2020). With a gross domestic product (GDP) per capita of US$10,084 in 2020, the county, where the school is located, was at a moderate level of economic development approximating the national average. The school was closed due to the pandemic from late January 2020 to late April 2020 under an order from the State Department of Education applicable to all high schools nationwide. In late April, Level 3 (or Grade 12) students returned to the school, and so did Level 1 (Grade 10) and Level 2 students (Grade 11) in early May 2020. We collected a three-point time series of students’ academic scores. The first time point was at the final examination of the Fall 2019 semester in January 2020, before the outbreak of the pandemic. The second time point was in May 2020 during the end-of-closure returning examinations for all students. The last time point was in July 2020, when the finals of the Spring 2020 semester were held. The tests were organized and administered by governmental educational bodies. The July test for the Level 3 students was the 2020 national college entrance exam. These scores were collected directly from the school. To collect students’ information and to measure key variables for the present study, we surveyed all students shortly after the Level 3 students had completed their college entrance exams. We received 1,982 responses, covering all students in the school. Following a rigorous screening to find duplicate and invalid questionnaires, a total of 1,736 questionnaires (88%) were included in the current analysis. We conducted a missing data analysis and found that the overall distribution of the sample did not significantly change after removing the missing data. The study process complied with ethical standards for research involving human subjects and cleared ethics review processes at the lead author’s university. The survey questionnaire was reviewed and approved by the high school administration before being used. Prior to completing the survey, every student was informed by their teachers that participation was voluntary. To prepare the data for analysis, we merged the survey data with the score data by matching the students’ IDs. We constructed a final balanced dataset for all students including students’ socio-demographic information, their personal and family resources related to learning from home, self-control, and the academic score data from the three time points. To protect the school’s and students’ anonymity, the dataset was completely anonymized. Measures The students’ academic achievements were measured by their academic scores in the subjects of Chinese, mathematics, English, and an integrated curriculum. The integrated curriculum represented the students’ selection of three subjects from the options of physics, chemistry, biology, history, political studies, or geography. We established three waves of students’ total academic scores by aggregating Chinese, mathematics, English, and the integrated curriculum scores. Standardized scores were used for the hierarchical linear modeling analysis. We conducted interviews with teachers at the school to gain knowledge about the difficulty levels of the three waves of exams and learned that the May exams could be relatively easier, while the January and July exams were of standard difficulties. The lower difficulty level for the May tests tends to yield higher scores and thus would not invalidate our analysis. If the students’ scores were still lower in May in comparison to January and July, we would have stronger evidence to suggest that the school closure might have had a negative impact on the students. Rural-urban disparity was captured by students’ reported hukou status. Within our sample, 1,262 students had an agricultural or rural hukou status, and 474 students had a non-agricultural or urban hukou status. We treated the rural students as the reference group. Self-control was assessed with the BSCS. Developed by Tangney et al. (2004), the BSCS includes 13 items that are rated on a 5-point scale, ranging from 1 (not at all like me) to 5 (very much like me). The BSCS has high reliability with a Cronbach’s α of 0.83. We calculated the total BSCS score by aggregating all 13 items. Students whose total BSCS scores were higher than the average were categorized in the group of high self-control. Students whose total scores were equal to or lower than the average were classified in the group of low self-control. We used low self-control as the reference. The control variables included gender, age, family structure, siblings, parental support, highest education and occupation levels of parents, family income, residential type, study room availability, access to a laptop or desktop computer, and quality of online class. For this study, we used two categories for gender, including male and female, with male acting as the reference. Age was measured by subtracting the participants’ birth years from 2020. Family structure was measured by whether students lived with one parent, both parents, or with other relatives. We used both parents as the reference. Siblings were measured by the number of brothers and sisters. Parental support was measured based on a question asking how often the student communicated with their parents during the school closure and was scored on a scale of 1 to 5, from very rarely to very often. Parents’ educational level was grouped into primary, junior middle, senior middle, university, and graduate levels. Parental occupation was divided into upper class, professional class, petty bourgeoisie, peasant class and unemployed according to the Erikson–Goldthorpe–Portocarero classification of occupation (Erikson & Goldthorpe, 2010). We controlled both the highest levels of parents’ education and highest level of parents’ occupation. Family yearly income was grouped into six categories in Chinese Yuan: below 10,000, 10,000 to 50,000, 50,000 to 100,000, 100,000 to 150,000, 150,000 to 200,000, and more than 200,000. Residential type was categorized into real estate in a city, real estate in a town, rural housing, rental apartment, factory dormitory, and others. Study room availability was assessed by asking whether a student had a dedicated study room at home. Students who had a study room acted as the reference group. Access to a laptop or desktop computer was measured by asking whether a student had such a device. Students who had a laptop or desktop computer acted as the reference group. We also controlled for the quality of online class by asking students whether the school should repeat classes to help them digest contents. Analytical Strategy We utilized Hierarchical Linear Models (HLM) and Change Score Models (CSM) to analyze the data. HLM enabled us to simultaneously explore within-person and between-person questions about changes. The within-student change of scores was regarded as the Level-1 submodel in the HLM. As shown in equation (1), Yij, which represents the value of total academic score for student i at time j, is a linear function of the months in which a student took an exam (Monthij). This model assumes that any deviation from linearity observed within the sample data is a result of random measurement error (εij). Between student variables were included in the Level-2 submodel. Equations (2) and (3) treat the intercept π0i and the slope π1i in equation (1) as the Level-2 outcomes that may be associated with the study’s predictors, hukou, SES, and self-control. For the present study, hukou status was included as the main predictor, and the other two as control variables. Each formula has its own residual term, ζ0iandζ1i, which permit one student to differ from others. The formulas for the HLM are as follows: (1) Yij=π0i+π1iMonthij+εij (2) π0i=γ00+γ01Hukoui+γ02SESi+γ03SELFi+ζ0i (3) π1i=γ10+γ11Hukoui+γ12SESi+γ03SELFi+ζ1i HLM has certain limitations. It is highly complicated to interpret results when an interaction in the Level-2 submodel is included. To illustrate, if the interaction of hukou status and SES were added to equation (3), there would be multiple interactions in equation (1). Considering these complexities, we employed a relatively simple model to analyze the panel data. As shown in equation (4), a CSM assesses predictors of change in students’ academic scores between two points in time. This simple model enabled us to investigate whether score changes were related to the fixed characteristics of students. Here, Yi2andYi1 are the total scores of student i at time points 2 and 1. In addition, Hi1 is the value of the hukou predictor, Si2 is the value of the self-control predictor, Hi1Si2 is the value of interaction between hukou status and self-control, and Ci4 is the value of various controls. ei is an error term. Equation (4) is an unconditional change score model, as it assumes that the change is independent of the score at the first time given the predictor variable. This assumption may not hold in practice. Therefore, we included the total academic score at the earlier point in time and provided the conditional change model, which is shown in equation (5). To illustrate, we included the January score as a predictor for the period of school closure and the May score as a predictor for the period of school reopening. The formulas for this aspect of the modelling are as follows: (4) Yi2−Yi1=β0+β1Hi1+β2Si2+β3Hi1Si2+β4Ci4+ei (5) Yi2−Yi1=β0+β1Hi1+β2Si2+β3Hi1Si2+β4Ci4+β5Yi1+ei Results Descriptive Statistics The total academic scores of the students at the observed high school changed during the COVID-19 pandemic. As shown in Table 1, the average total score for all high school students was about 514 in January 2020. However, the students’ average scores decreased dramatically during the school closure, falling about 44 points from January to an average 470 in May. After the reopening, when all the students had returned to school, the average score recovered to 501, falling short of the pre-closure levels recorded in January. Table 1. Descriptive Statistics (n = 1,736). Variables All children Rural Urban Mean diff p Value Average total academic score in January 513.481 (145.341) 515.458 (146.934) 508.167 (140.990) 7.291 .357 Average total academic score in May 469.893 (126.306) 468.829 (126.667) 472.756 (125.423) −3.927 .567 Average total academic score in July 500.521 (129.098) 499.576 (129.540) 503.047 (128.009) −3.470 .620 Gender (female %) 0.533 (0.499) 0.542 (0.498) 0.508 (0.500) 0.034 .212 Mean age 17.187 (1.013) 17.204 (1.040) 17.143 (0.938) 0.060 .270 Mean grade 1.950 (0.832) 1.908 (0.835) 2.063 (0.815) −0.155 .001 Self-control (high self-control %) 0.423 (0.494) 0.421 (0.494) 0.430 (0.496) −0.0100 .718 Family structure (both parents %) 0.423 (0.494) 0.465 (0.499) 0.312 (0.464) 0.153 .000* Mean of siblings 2.249 (0.783) 2.364 (0.761) 1.943 (0.760) 0.421 .000* Mean of parental support 3.507 (0.970) 3.498 (0.952) 3.530 (1.018) −0.0310 .552 Mean of parents’ highest education level 3.753 (1.509) 3.375 (1.243) 4.762 (1.683) −1.387 .000* Mean of parents’ highest occupation level 3.866 (0.966) 4.044 (0.756) 3.390 (1.258) 0.654 .000* Mean of family income 2.711 (1.122) 2.616 (1.085) 2.964 (1.180) −0.348 .000* Mean of house type 2.821 (1.250) 2.997 (1.194) 2.354 (1.278) 0.642 .000* Study room (own a room %) 0.530 (0.499) 0.572 (0.495) 0.418 (0.494) 0.154 .000* Digital device (own a laptop or desktop computer %) 0.442 (0.497) 0.494 (0.500) 0.304 (0.460) 0.191 .000*** Mean of quality of online class 2.262 (0.969) 2.253 (0.959) 2.285 (0.995) −0.0320 .540 Sample 1,736 1,262 474 + p < .10. p < .05. p < .01. p < .01 (two-tailed) The changes in total academic scores during the COVID-19 pandemic were different for rural and urban students. Table 1 shows that in January, the average total score of students with a rural hukou status was 516, which was relatively higher than the average total score of urban students at 508. During the school closure, while rural students’ average total score decreased to 469, urban students’ average total score decreased to 473. After the school reopening, the urban students maintained a minor advantage until the end of semester. t-Tests for each of the three waves showed no significant differences between the rural and urban students’ total scores. This result indicated that the hukou factor might be primarily related to the changes in academic scores over time. We then investigated this question using HLM. Hierarchical Linear Modeling Table 2 shows the analysis results using the HLM method. Growth models A1 and A2 represented the academic score change of each student during the COVID-19 pandemic. We paid closer attention to Model A2 because its slope was the most important parameter in the Level-1 submodel. Model A2 showed that students’ scores were negatively associated with time (π0i=0.100;π1i=−0.024). The slope π1i revealed that as student i moved one-point time, the total score dropped by 0.024. This result indicated that academic achievements of the high school students declined during the COVID-19 pandemic over time. Table 2. Hierarchical Linear Models (n = 1,736). Variables Level-1 model Level-2 model A1 A2 B1 B2 B3 Intercept −0.004 0.100* 0.091* 0.120* −0.202 Month −0.024* (−0.002) −0.024* (−0.002) −0.028* (−0.002) −0.027 (−0.016) Hukou 0.032 (−0.05) −0.071 (−0.055) −0.173 (−0.066) Month × Hukou 0.015 (−0.005) 0.018* (−0.005) Self-control 0.004 (−0.054) Month × Self-Control 0.017* (−0.004) Income 0.072 (−0.024) Month × Income −0.003+ (−0.002) Parents’ education 0.050* (−0.022) Month × Education −0.001 (−0.002) Parents’ occupation −0.009 (−0.033) Month × Occupation 0.001 (−0.003) Variance (month) 0.003 (0.000) 0.003 (0.000) 0.000 (0.000) 0.003 (0.000) Variance (intercept) 0.867 (−0.031) 1.106 (−0.043) 1.107 (−0.043) 0.871 (−0.031) 1.091 (−0.042) Variance (residual) 0.132 (−0.003) 0.099 (−0.003) 0.099 (−0.003) 0.123 (−0.005) 0.099 (−0.003) Note. Sample size = 1,736. Standard deviation in parentheses. + p < .10. p < .05. p < .01. *p < .01 (two-tailed). Models B1 and B2 showed the structural parts of the Level-2 submodel for between-individual differences in academic score change. Although both the intercept and slope of month in Model B1 remained significant (π0i=0.091;π1i=−0.024), the coefficient for hukou status on the total score was not significant (γ11=0.032). This result confirmed the findings from the t-tests in Table 1. However, as shown in Model B2, the interaction between month and hukou status was positive and significant (γ11=0.015). This means that while rural students had higher initial scores than urban students before the pandemic, their scores decreased more than those of their urban peers did when learning from home during the pandemic. Model B3 extended the analyses by including the variables of family income, self-control, parental education, and parental occupation. According to our theoretical model, we predicted that the widening rural-urban inequality in education during the pandemic could be attributed to both SES and psycho-social traits. However, the results seemed to reject our prediction about SES. According to Model B3, the variables of family income, parental education, and parental occupation showed no significant association with students’ academic score changes. In the meantime, self-control was significantly associated with the changes in total academic score. It indicated that the rural-urban inequality in academic score changes was not attributable to students’ SES, but self-control. The following section attempts to explore these findings in further details. Change Score Models Table 3 shows the analysis results of the CSMs. As described in the methods section, we divided the 7-month time span into three periods: January to July (the entire period), January to May (the school closure period), and May to July (the school reopening period). Model C1 and C2 showed the coefficients for the predictors between January and July. According to Model C1, the coefficient for self-control on the change score was significant and positive, indicating that students with high self-control had a 4.697-point higher increase in total academic score than students with low self-control from January to July. Table 3. Change Score Models (n = 1,736). Variables Change score from January to July Change score from January to May Change score from May to July Model C1 Model C2 Model C3 Model D1 Model D2 Model D3 Model E1 Model E2 Model E3 Hukou 3.857 (3.25) −2.548 (4.083) −2.544 (4.077) 3.857 (3.250) −2.209 (3.695) −2.210 (3.683) 0.985 (2.820) 0.740 (3.553) 0.743 (3.555) Self-control 4.697 (2.672) 0.643 (3.095) 0.055 (3.099) 4.697* (2.672) −2.102 (2.809) −1.390 (2.808) 3.640 (2.317) 3.485 (2.691) 3.475 (2.695) Hukou × Self-Control 15.087* (5.841) 15.177* (5.832) 14.009* (5.294) 13.911* (5.278) 0.576 (5.075) 0.564 (5.079) Gender −2.794 (2.664) −3.072 (2.662) −2.551 (2.666) −2.794 (2.664) 0.580 (2.413) −0.048 (2.412) −3.193 (2.313) −3.204 (2.316) −3.192 (2.322) Age −1.166 (2.253) −0.991 (2.25) −0.568 (2.252) −1.166 (2.253) −2.958 (2.036) −3.463* (2.036) 1.033 (1.948) 1.040 (1.950) 1.052 (1.957) Grade 61.386* (2.746) 61.034* (2.745) 64.894* (3.133) 61.386* (2.746) 51.519* (2.481) 46.877* (2.829) 10.794* (2.380) 10.781* (2.384) 10.832 (2.481) Family structure −1.733 (2.686) −1.938 (2.683) −1.800 (2.679) −1.733 (2.686) 0.232 (2.433) 0.078 (2.426) −2.494 (2.330) −2.503 (2.332) −2.501 (2.333) Siblings 1.671 (1.796) 1.7 (1.793) 1.721 (1.790) 1.671 (1.796) 1.458 (1.625) 1.434 (1.620) 0.454 (1.560) 0.455 (1.561) 0.454 (1.561) Parents’ education −1.451 (1.12) −1.506 (1.118) −1.709 (1.119) −1.451 (1.120) −0.354 (1.015) −0.106 (1.015) −1.258 (0.968) −1.260 (0.969) −1.264 (0.971) Parents’ occupation −1.332 (1.6) −1.168 (1.598) −1.311 (1.597) −1.332 (1.600) −0.256 (1.451) −0.066 (1.448) −0.719 (1.384) −0.712 (1.386) −0.715 (1.387) Family income −1.489 (1.24) −1.444 (1.238) −1.610 (1.237) −1.489 (1.240) −1.454 (1.124) −1.260 (1.122) −0.456 (1.078) −0.455 (1.078) −0.458 (1.080) Parental support 3.113 (1.39) 3.032 (1.388) 3.051 (1.386) 3.113 (1.390) 1.807 (1.258) 1.777 (1.254) 0.793 (1.203) 0.790 (1.204) 0.788 (1.204) House type 0.191 (1.118) 0.080 (1.117) −0.029 (1.116) 0.191 (1.118) −0.297 (1.015) −0.168 (1.012) 0.204 (0.966) 0.199 (0.967) 0.196 (0.968) Study room 1.459 (2.791) 1.383 (2.786) 1.418 (2.782) 1.459 (2.791) −3.603 (2.523) −3.666 (2.516) 4.230* (2.423) 4.227 (2.424) 4.230 (2.425) Digital device −4.627* (2.797) −4.56* (2.793) −4.151 (2.793) −4.627* (2.797) −5.407 (2.535) −5.875 (2.531) 0.926 (2.427) 0.929 (2.428) 0.944 (2.437) Quality of online class 2.945 (1.346) 3.024 (1.344) 3.001 (1.342) 2.945 (1.346) 1.861 (1.217) 1.891 (1.214) 0.699 (1.167) 0.703 (1.168) 0.701 (1.168) Total academic score in January 0.033 (0.013) −0.040* (0.012) Total academic score in May 0.001 (0.011) Constant −117.699* (36.77) −118.315* −148.487* (38.523) −117.699* (36.77) −95.208*** (33.233) −59.033* (34.816) −6.975 (31.814) −6.996 (31.824) −7.629 (32.957) R 2 .484 .486 .488 .484 .427 .431 .054 .054 .054 Note. Standard deviation in parentheses. + p < .10. p < .05. p < .01. **p < .01 (two-tailed). Model C2 tested how self-control moderated the effect of hukou status on the score change. The coefficient for the interaction between self-control and hukou status was significant and positive. Among high self-control students, those with an urban hukou status had a 12.539-point higher gain than rural students, while, among low self-control students, those from rural areas scored 2.548 points higher than their urban peers. Model C3 was a conditional change model that included the total score in January as a control variable. The result was similar to Model C2. Figure 1 illustrates Model C3. Figure 1. Interaction effect of hukou status and self-control on academic score change between January and July. We also analyzed the change score during the school closure from January to May. In Model D1, the effect of self-control was still positive and significant (β=4.697). Model D2 illustrated the interaction between hukou status and self-control. The significant coefficient meant that for high self-control students, those with an urban hukou status had a 11.800-point higher gain than rural students; for low self-control students, those from rural areas were scored 2.209 points higher than their urban peers. While Model D3 included the January score as a control variable, the result was nearly the same. Model E1 and E2 described the change score from May to July, when the school was reopened to students. In consistency with our prediction, Model E1 showed that the effect of hukou status was not significant. Model E2 indicated that the interaction between hukou status and self-control was not significant either. After the control variable of the May score was added to Model E3, the result was nearly the same. It suggested that when the students returned to school, the effect of self-control could begin to weaken or even disappear. Discussion The students in this study were spared from any COVID-19 infection due to stringent measures taken to contain the spread of the virus. Nonetheless, the school closure might negatively impact their cognitive development. In addition, these consequences might be unequally distributed among the student populations. We explored three critical research questions with the aim of uncovering the relationship between school closures and educational inequality. Based on a field study of a high school in Eastern China, we generated three important findings: first, that academic achievements of the high school students declined during the closure and reopening of the school due to the COVID-19 pandemic; second, that changes in academic achievements were not equally distributed among rural and urban students; and third, that psycho-social traits, rather than SES, explained the rural-urban inequality in education during the crisis. These findings will be interpreted and discussed below. The results revealed a decreasing trend in students’ academic test scores between January and July, especially in the period of school closure from January to May. In addition, urban students tended to be more adaptable to the pandemic’s impact on learning, especially for those with high self-control. These findings resonated with extant research in other countries. For example, Aucejo et al.’s (2020) survey of 1500 American students showed that COVID-19 negatively affected educational experiences and expectations, and that the low-income groups disproportionately bear more economic and health repercussions of the COVID-19 pandemic. Engzell et al. (2021), using about 350,000 samples in Netherland, found that students made little, or no progress when they learned from home. Moreover, they showed that the learning losses were even larger for those students from less-educated homes or weaker infrastructure. Therefore, these findings suggest that the pandemic brought about both learning losses and educational inequality. Our main question was to explore whether the widening rural-urban inequality in education during the crisis could be attributed to students’ SES and psycho-social traits. Surprisingly, we found that socio-economic factors, such as parental education, occupation and income, could not adequately explain the widening rural-urban inequality in education, whereas the students’ self-control partially accounted for the unequal changes in academic achievements between the student populations. We speculate that self-control may function like a “conversion factor” (Sen, 1992). Students with good self-control characteristics may be more likely to convert home resources into learning advantages, boosting their academic achievements in turn. By contrast, among students with relatively low self-control, the effects of school closures and online education on rural and urban children’s academic achievements may not be significantly different. Furthermore, these findings prompt us to revise our theoretical framework and embrace a more integrated model in future research. We assumed that SES and psycho-social traits were independent factors in the present study. It is possible that students’ personal traits can interact with family characteristics (Duckworth et al., 2019). In other words, self-control might play a mediating role between SES and learning losses during the crisis. We hope that revised models can be examined in future studies. While this study provided new evidence, it bears some limitations. One is that the data were collected from a single high school in Eastern China. Generalization of the findings warrants caution. Moreover, since all students were exposed to the same mode of school closure, the data did not allow us to analyze the causal effect of the COVID-19-related school closure on academic achievement. A control group or additional data points before and after the school closure would be required to make an inference about causality. Despite these limitations, the study has theoretical and policy implications. First, losses in learning during the COVID-19-related school closures suggest that education and social policies face considerable challenges. As online education was used as an important part of the response policy package in China (Lu et al., 2020), measures should be taken to guarantee the quality of online education. For example, Clark et al. (2021) showed that students earned higher scores when they received recorded online lessons from higher-quality teachers. Policymakers should also prioritize reopening schools once acceptable public health conditions have been maintained. Another implication speaks to the nature of policy measures that target the rural-urban educational inequality. Between the two options of providing monetary and material resources and extending psycho-social support to rural students, based on the present study’s results, the latter may prove more beneficial in the context of China. It is therefore a pertinent task for educators and social service providers to promote capabilities and resilience in young people and their families so that they continue to grow during times of crisis. Author Biographies Gaoming Ma is an assistant professor at the Center of Social Welfare and Governance, Department of Social Welfare and Risk Management, School of Public Affairs, Zhejiang University. His research interest is child development and social policy. Jiayu Zhang is a doctoral candidate at the Department of Social Welfare and Risk Management, School of Public Affairs, Zhejiang University. Her research interest is intergenerational care and child health. Liu Hong is an associate professor at School of Social Development and Public Policy, Fudan University. His research interests involve social policy and youth development. The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research is funded by National Social Science Fund, China (grant number 18CSH023). ORCID iD: Liu Hong https://orcid.org/0000-0002-2250-773X ==== Refs References Aucejo E. M. French J. Ugalde Araya M. P. Zafar B. (2020). The impact of COVID-19 on student experiences and expectations: Evidence from a survey. Journal of Public Economics, 191 , 104271. 10.1016/j.jpubeco.2020.104271 32873994 Cauchemez S. Ferguson N. M. Wachtel C. Tegnell A. Saour G. Duncan B. Nicoll A. (2009). 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==== Front FMC FMC Fmc 1134-2072 1134-2072 Elsevier Espana S.L S1134-2072(22)00176-1 10.1016/j.fmc.2022.02.013 Article Sobre la organización asistencial del seguimiento a la COVID persistente Vallespín Gemma Torrell a⁎ Correa Esperanza Martín b a CAP Les Indianes. Montcada i Reixac. Institut Català de la Salut. Barcelona. España b CAP Maragall. Barcelona. Institut Català de la Salut. Barcelona. España ⁎ Autor para correspondencia. 5 12 2022 12 2022 5 12 2022 29 10 532535 30 1 2022 8 2 2022 . 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. Palabras clave COVID persistente Afección pos-COVID-19 Atención primaria Manejo de casos ==== Body pmcDesde su irrupción en enero del 2020, la pandemia por SARS-CoV-2 ha incidido sobre toda la población, no solo la directamente afectada por la COVID, y ha alterado todos los ámbitos de nuestras relaciones sociales. La difusión constante de cifras e indicadores de evolución en los medios de comunicación no escondía la incertidumbre acerca de su verdadero alcance, en términos de número de contagiados y de víctimas de enfermedad grave o mortal. De hecho, a día de hoy no existe una definición internacional consensuada de caso de COVID ni de muerte por COVID; la propia definición de la OMS1 deja la puerta abierta a interpretaciones que dependen del contexto clínico, asistencial, epidemiológico y social, y a contabilizar los casos con criterios diferentes en cada país y situación. La mayoría de los pacientes con infección por SARS-CoV-2 se recuperan. La tasa de letalidad es de menos de 0,6% en los menores de 70 años y de alrededor de 5% en los de 70 o más2. No obstante, para algunas personas afectadas el desenlace parece no llegar. Viven suspendidas en el nudo de la enfermedad. Los síntomas persisten, o retornan con diferente presentación e intensidad, incluso pueden variar. Se ha estimado que la COVID persistente, recientemente (re)denominada por la Organización Mundial de la Salud (OMS) como afección pos-COVID (post-COVID condition)3 (tabla 1 ), puede afectar a uno de cada diez contagiados4.Tabla 1 Afección pos-COVID-19. Definición de consenso publicada por la OMS el 6 de octubre de 2021 Tabla 1La afección pos-COVID-19 (Post COVID-19 condition) ocurre en personas con antecedente de infección por SARS-CoV-2 probable o confirmada, generalmente en los tres meses siguientes al inicio de una COVID-19, con síntomas que duran por lo menos dos meses y no pueden explicarse por un diagnóstico alternativo. Son síntomas frecuentes la fatiga, la dificultad respiratoria, la disfunción cognitiva, pero también otros, que en general afectan el funcionamiento diario. Los síntomas pueden aparecer después de que el paciente se haya recuperado de un episodio agudo de COVID-19, o persistir desde la enfermedad inicial. También pueden fluctuar o recurrir a lo largo del tiempo. La definición podría ser diferente para la edad infantil Una proporción de pacientes con COVID persistente no padeció neumonía ni otra complicación que requiriera ingreso hospitalario durante la fase aguda de la enfermedad. A pesar de las resistencias iniciales a reconocer la posibilidad de que el SARS-CoV-2 ocasionara síntomas persistentes en pacientes que no habían sufrido enfermedad inicial grave, la iniciativa de personas afectadas en varios países determinó que a mediados de 2020 la OMS y las autoridades sanitarias reconocieran esta entidad clínica, así como la necesidad de su investigación y de atención y rehabilitación de los afectados. Estos se quejaban en particular de la fragmentación y heterogeneidad de la asistencia recibida y de su falta de integración, coordinación y seguimiento longitudinal. La propia definición de la OMS refleja la incertidumbre ligada a la COVID persistente. Se conoce mal su fisiopatología, se desconocen su previsible evolución y su impacto sobre la calidad de vida, así como sus efectos a largo plazo. Incluso algunos estudios han puesto en duda la existencia misma de la persistencia de síntomas. Se estima que el volumen de personas afectadas puede ser elevado. Cualquier propuesta sobre la organización del seguimiento asistencial a las personas con COVID persistente debe ser situada en el contexto de un sistema sanitario hospitalocéntrico y corto de financiación–sobre todo en atención primaria–, que cobija una práctica asistencial de orientación biologista y con tintes autoritarios. Contexto que invita a los profesionales a parapetarnos en la aparente certeza de “lo científico”, el solucionismo y la tecnoidolatría, a costa de prestar escasa atención a la narrativa del paciente. La aparición de la COVID persistente, con manifestaciones clínicas relativamente heterogéneas, no estaba prevista a priori en nuestras mentes5. Esto también ha contribuido a que muchos pacientes no se hayan sentido escuchados, e incluso se hayan sentido no creídos, lo que para ellos ha sido fuente adicional de angustia6. Reveladora ha sido la experiencia de profesionales sanitarios con COVID persistente, que relatan cómo han sentido la necesidad de justificar una y otra vez la mera existencia real de su enfermedad ante otros profesionales7. Dado que no disponíamos de un cuerpo de conocimiento que ofreciera un marco conceptual médico (con sus síntomas guía, su diagnóstico diferencial, pruebas de laboratorio, escalas de gravedad, tratamiento, etc.), la narrativa de las personas afectadas era poco o mal reconocida, poco o mal validada. Esto fue especialmente difícil para los afectados que no ingresaron en hospital en la infección aguda inicial, sobre todo si no se les había practicado una PCR confirmatoria, cuando no se disponía de esta prueba en los primeros meses. La organización de la atención a las personas con COVID persistente ha sido lógicamente diferente en cada país, según las características de su sistema sanitario, la difusión del problema en los medios de comunicación, y los intereses políticos y de imagen. En Inglaterra y Gales se destinaron inicialmente 20 millones de libras a la organización de unidades de atención8, y la misma cantidad a la investigación clínica sobre COVID persistente. Desde 2020 hay unas 80 unidades multidisciplinares (con profesionales de atención primaria, secundaria, trabajo social, terapia ocupacional, dietética, psicología y rehabilitación, entre otros) llamadas Long COVID Clinics, organizadas según un modelo común. Al mismo tiempo, se pusieron en marcha varios estudios sobre la entidad en personas no hospitalizadas (STIMULATE-ICP, ReDIRECT, estudio de la Universidad de Cardiff, LOCOMOTION y EXPLAIN). La red de Long COVID Clinics difunde información actualizada periódicamente sobre su actividad asistencial, tiempos de espera para los diferentes especialistas focales y tipo de visitas realizadas. Por el momento no se han dado a conocer datos sobre resultados clínicos ni económicos de este modelo organizativo. Análogamente, Escocia anunció en noviembre de 2021 la apertura de varias clínicas para la atención a personas con síndrome pos-COVID. En la Unión Europea se han abierto centros de atención a la COVID persistente en Bélgica, Italia, Alemania y Noruega. Se han descrito experiencias similares en Estados Unidos, Canadá y algunos países de América Latina, orientadas a la atención de pacientes con COVID persistente que habían sido hospitalizados o no hospitalizados con ocasión del episodio inicial. En Italia se destinaron 28 millones de euros en 2021 y 24 en 2022 al seguimiento de personas que hubieran estado ingresadas y que padecieran alteraciones respiratorias secundarias a la COVID-19. Francia optó porque la atención recayera en la atención primaria. Dejando aparte las enormes diferencias entre los sistemas sanitarios de estos países, en términos de universalidad, acceso, grado de privatización, etc., es conveniente examinar cuál ha sido la inversión económica y adónde ha sido dirigida, en términos de atención especializada o atención primaria, coordinación entre los diferentes ámbitos implicados en la atención y la investigación, inclusión o no de las personas afectadas que no ingresaron inicialmente en hospital, proporción destinada a rehabilitación, etc. No solo importa la cuantía de su financiación, sino sobre todo su integración en el sistema sanitario existente, con una atención primaria desatendida, si se nos permite la expresión. Parece evidente que los nuevos recursos que deben ser invertidos en atención primaria no deben destinarse a mejorar la atención de una sola entidad, lo que generalmente repercute en una peor atención a las demás necesidades de salud de la comunidad. Una revisión9 de lo hecho hasta ahora en el Reino Unido concluye que el cuidado de las personas con COVID persistente debe integrar la atención primaria, la rehabilitación y la atención hospitalaria, y debe acoger tanto a personas que fueron hospitalizadas, como a las no hospitalizadas en el episodio de infección aguda inicial. Para la organización de los servicios es esencial considerar la aportación de las personas afectadas. Algunos estudios cualitativos10, en su mayoría realizados con profesionales sanitarios afectados, han identificado algunas necesidades de atención de la COVID persistente: mayor acceso a la atención y a la rehabilitación multidisciplinaria, necesidad de profesionales que asuman la responsabilidad clínica de la atención y del seguimiento, y promover la investigación y el conocimiento. En España se han desarrollado diferentes modelos de atención e investigación. En algunos hospitales (la mayoría en Andalucía) se han organizado unidades multidisciplinarias llamadas pos-COVID que atienden a personas con síntomas persistentes, inicialmente las que habían sido hospitalizadas durante la enfermedad aguda y posteriormente también a las que no lo habían sido. En Cataluña, el hospital Germans Trias i Pujol constituyó una unidad de COVID persistente para personas adultas y menores, específicamente centrada desde su inicio en personas que no habían sido hospitalizadas durante la enfermedad aguda. En la mayoría de los hospitales, sin embargo, se limita la atención a las personas que habían sido ingresadas en el mismo centro, y la responsabilidad recae sobre profesionales de determinados servicios, principalmente neumología (en busca de secuelas pulmonares), reumatología o las ya existentes Unidades de Fatiga Crónica, por ser la fatiga uno de los síntomas persistentes más frecuentes. En algunas comunidades autónomas se ha optado por sistematizar el seguimiento de pacientes en unidades COVID persistente compartidas por varios centros de atención primaria, integradas por profesionales de atención primaria y con un circuito de acceso prioritario a los servicios de rehabilitación. Las unidades hospitalarias combinan la asistencia con la investigación, y no siempre trabajan de manera coordinada con la atención primaria. En ocasiones esto contribuye a crear un imaginario ficticio de lo que debería ser la atención a las personas con síntomas persistentes; a menudo, la necesidad de investigación sobre una entidad poco conocida da lugar a intervenciones diagnósticas y terapéuticas excesivas e innecesarias. En relación con el modelo de las Long COVID Clinics del Reino Unido, Margaret McCartney11 advierte que la atención multidisciplinaria no es mágicamente beneficiosa, requiere muchos recursos y puede excluir a los pacientes de la toma de decisiones. Las unidades multidisciplinares pueden ser un foco de inequidades en el acceso, de sobrediagnóstico y medicalización, y también de despilfarro de recursos al duplicar la atención. Por el momento tenemos más incógnitas que certezas sobre la COVID persistente. ¿Se aclararán su fisiopatología y etiopatogenia? ¿El riesgo de persistencia de los síntomas será el mismo con nuevas variantes del virus? ¿Hay una mayor proporción de persistentes de la primera ola que de las posteriores? ¿Cederán los síntomas con el paso del tiempo o se instalará como afección crónica, y con qué frecuencia? ¿A qué complicaciones puede dar lugar? ¿Se identificarán factores individuales que predisponen a padecerla o que afecten a su duración? ¿La vacunación modifica el riesgo o altera su curso clínico? ¿Y las reinfecciones? ¿Se encontrará un tratamiento que cure o alivie los síntomas? Nada de esto se dilucidará sin investigación apropiada. La mayoría de las personas con COVID persistente son seguidas en atención primaria, observatorio privilegiado de las condiciones de salud y de vida de las personas y para su acompañamiento. La atención primaria no puede renunciar a aportar su amplitud de mirada a la necesaria construcción de conocimiento sobre esta entidad. Si sin recursos económicos ya estamos realizando investigación, ¿os imagináis si dispusiéramos de ellos? Para concluir, el mejor modelo de organización a la atención a las personas con síntomas persistentes será aquel que permita la mejor atención, entendida esta como la más cercana y accesible, que incluya la acogida de la persona, el reconocimiento y validación de su narrativa, el conocimiento de la persona, su entorno y sus circunstancias, el acompañamiento, el seguimiento clínico sistematizado (basado en los mejores datos disponibles) y experto, la coordinación con los trabajadores sociales, rehabilitación y con otros niveles asistenciales (en aquellos casos que lo requieran), que pueda aunar la asistencia con la investigación y cuya intervención pueda ser evaluable. Y esto, lo creáis o no, se parece mucho a la atención primaria. Incluso a la no dotada de recursos aun a sabiendas de que nos exige mayor esfuerzo y que aporta menor calidad de la que quisiéramos (tabla 2 ).Tabla 2 ¿Por qué la atención primaria? Tabla 2¿Qué hace que la atención primaria sea el nivel asistencial idóneo para atender a la persona con COVID persistente? ¿Qué hace de la COVID persistente una afección que deba ser atendida en la atención primaria? Equidad territorial en el acceso Fácil accesibilidad Longitudinalidad (confianza, vínculo) Integralidad (ve a la persona en su conjunto y complejidad) Coordinación con otros niveles asistenciales y rehabilitación Acceso a pruebas complementarias Experiencia en el trato y seguimiento estrecho a personas con enfermedades de larga duración o que generan discapacidad Gran capacidad de manejo y tolerancia a la incertidumbre Multidisciplinariedad (visión de enfermería, medicina, trabajo social) Conlleva una alta carga de incertidumbre Afectación multisistémica Tiempo de evolución indeterminada (por el momento) Necesidad de recursos locales (rehabilitación, trabajo social, recursos comunitarios) Evolución fluctuante, con aparición de nuevos síntomas Diferentes grados de afectación y gravedad que pueden requerir contacto con otros niveles asistenciales Puede provocar un grado de discapacidad que requerirá de reconocimiento formal (baja laboral, incapacidad permanente) El seguimiento a las personas con COVID persistente, como el del resto de enfermedades que lo requieran y emerjan en la población, debe poder recaer en una atención primaria dotada de los recursos necesarios. ==== Refs Bibliografía 1 WHO COVID-19 Case definition. Disponible en: https://www.who.int/publications/i/item/WHO-2019-nCoV-Surveillance_Case_Definition-2020.2. 2 Axfors C, Ioannidis JPA. Infection fatality rate of COVID-19 in community-dwelling populations with emphasis on the elderly: An overview. medRxiv preprint doi: https://doi.org/10.1101/2021.07.08.21260210. https://www.medrxiv.org/content/10.1101/2021.07.08.21260210v2. 3 A clinical case definition of post COVID-19 condition by a Delphi consensus. [consultado 6 Oct 2021]. Disponible en: https://apps.who.int/iris/bitstream/handle/10665/345824/WHO-2019-nCoV-Post-COVID-19-condition-Clinical-case-definition-2021.1-eng.pdf. 4 Kuchler H. Long Covid: why do some people have symptoms months after infection? The Financial Times. [consultado 4 Ene 2022]. Disponible en: https://www.ft.com/content/ed89cad2-6f82-44f0-b01d-c4490e4a7372. 5 Fricker M. Términos de “Injusticia hermenèutica e injustícia epistémica” de Injusticia epistèmica. El poder y la ética del conocimiento. Barcelona: Ed. Herder; 2007. 6 Taylor A.K. Kingstone T. Briggs T.A. O’Donnell C.A. Atherton H. Blane D.N. ‘Reluctant pioneer’: A qualitative study of doctors’ experiences as patients with long COVID Health Expect. 24 3 2021 833 842 doi: 10.1111/hex.13223. Publicación electrónica 22 Mar 2021. Erratum en: Health Expect. 2021;24(5): 1902. PMID: 33749957; PMCID: PMC8235894. 33749957 7 Berenstain, N. Epistemic exploitation. Ergo. 2016; 3(22). Disponible en: https://doi.org/10.3998/ergo.12405314.0003.022. 8 Baraniuk C. Covid-19: How Europe is approaching long covid BMJ. 2022; 376: o158 doi:10.1136/bmj.o158 Disponible en: https://www.bmj.com/content/376/bmj.o158. 9 Decary S, Dugas M, Stefan T, Langlois L, Skidmore B, Bhéreur A, et al., Care Models for Long COVID–A Rapid Systematic Review. SPOR Evidence Alliance, COVID-END Network. 2021. Disponible en: https://www.medrxiv.org/content/10.1101/2021.11.17.21266404v1.full.pdf. 10 Ladds E. Rushforth A. Wieringa S. Taylor S. Rayner C. Husain L. Persistent symptoms after Covid-19: qualitative study of 114 “long Covid” patients and draft quality principles for services BMC health services research 20 1 2020 1 13 11 McCartney M, Byng R. Long covid clínics should be run as research hubs BMJ. 2021; 374:n1996 doi:10.1136/bmj.n1996.	36506710	PMC9720901	NO-CC CODE	2022-12-06 23:26:18	no	FMC. 2022 Dec 5; 29(10):532-535	utf-8	FMC	2,022	10.1016/j.fmc.2022.02.013	oa_other
==== Front Soc Sci Med Soc Sci Med Social Science & Medicine (1982) 0277-9536 1873-5347 Elsevier Ltd. S0277-9536(22)00897-8 10.1016/j.socscimed.2022.115591 115591 Article Futile or fertile? The effect of persuasive strategies on citizen engagement in COVID-19 vaccine-related tweets across six national health departments Wang Di a Lu Jiahui b∗ Zhong Ying a a Faculty of Humanities and Arts, Macau University of Science and Technology, R322, Avenida Wai Long, Taipa, Macau, 999078, China b School of New Media and Communication, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin, 300072, China ∗ Corresponding author. 5 12 2022 1 2023 5 12 2022 317 115591115591 16 8 2022 25 10 2022 30 11 2022 © 2022 Elsevier Ltd. All rights reserved. 2022 Elsevier Ltd Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. National health departments across the globe have utilized persuasive strategies to promote COVID-19 vaccines through Twitter. However, the effectiveness of those strategies is unclear. This study thereby examined how national health departments deployed persuasive strategies to promote citizen engagement in COVID-19 vaccine-related tweets in six countries, including the UK, the US, Germany, Japan, South Korea, and India. Guided by the heuristic-systematic model and the health belief model, we found that national health departments differed significantly in the use of systematic-heuristic cues and health belief constructs in COVID-19 vaccine-related tweets. Generally, the provision of scientific information and appeals to anecdotes and fear positively, while appeals to bandwagon negatively, predicted citizen engagement. Messages about overcoming barriers and promoting vaccine benefits and self-efficacy positively affected engagement. Emphases of COVID-19 threats and cues to vaccinate demonstrated negative impacts. Importantly, health departments across countries often used futile or detrimental strategies in tweets. A locally adapted evidence-based approach for COVID-19 vaccination persuasion was discussed. Keywords COVID-19 Vaccine Health belief model Heuristic-systematic model Persuasive strategies Citizen engagement ==== Body pmcFunding This research was funded by The Higher Education Fund of the Macao S.A.R. Government: HSS-MUST-2021-01. 1 Introduction COVID-19 vaccines have been available since 2020 but remain controversial in many societies. A study that analyzed data from 23 countries in June 2021 found that the acceptance rate of the COVID-19 vaccines was 75.2% for the general population (Lazarus et al., 2022). The most mentioned reasons for vaccine refusal are opposition to vaccines in general, safety concerns, lack of trust in general, doubts about the vaccine efficiency, the belief that one has been already immunized, and doubt about the provenience of the vaccine (Troiano and Nardi, 2021). Muric et al. (2021) pointed out that COVID-19 vaccine-related misinformation on social media such as Twitter may raise the public's vaccine hesitancy. Under this circumstance, it is pivotal for policymakers to promote COVID-19 vaccines to the public. Previous studies have shown that social media such as Twitter could set the agenda for vaccine debates for other online news media (Jang et al., 2019). Accordingly, national health departments around the world have actively used Twitter as a platform to promote COVID-19 vaccines. However, health authorities often used social media only as a one-way communication channel, overlooking citizen engagement such as the number of “likes” and “retweets” (Harris et al., 2013; Thackeray et al., 2012). Citizens' social media engagement is critical as it can help health promotion by building trusted relationships and achieving public consensus in health affairs (Agostino and Arnaboldi, 2016). By inducing positive citizen engagement on social media, national health departments could potentially change their audiences’ views on COVID-19 vaccines and increase the inoculation rate. In order to understand what persuasion strategies are effective in inducing citizen engagement on social media, this research examined the persuasion strategies used by six national health departments’ tweets on COVID-19 vaccination using two persuasion frameworks: the heuristic-systematic model (Chaiken, 1980) and the health belief model (Rosenstock, 1974). Previous studies showed that persuasion strategies may produce different effects in different contexts and cultures. Therefore, we also compared the effects of persuasion strategies across countries on citizen engagement. 2 Literature review 2.1 Citizen engagement on social media Citizen engagement on social media refers to the involvement of citizens in public affairs on social media, functioning as a way to establish trusted relationships and achieve common goods (Agostino and Arnaboldi, 2016; Chen et al., 2020). Regarding the Twitter platform, citizen engagement has often been measured by indicators such as the number of likes and retweets. According to Twitter.com, the engagement of “like” is used to show appreciation for a tweet (Twitter.com, n.d.a), while that of “retweet” further shows that users not only are interested in the tweet but also publicly share it with their followers (Twitter.com, n.d.b.). Perceived usefulness and informativeness of a tweet are the most common predictors for the engagement of “like”, while users' motivation to provide others with useful information is the most common predictor of “retweet” (Dolan et al., 2019; Khan, 2017). Engagement of “like” can thereby indicate users’ agreement, perceived usefulness, and positive attitudes toward the information addressed in tweets. This corresponds to the findings that social media engagement is positively predictive of offline attitudes and behaviors in public affairs such as voting and vaccination (Skoric et al., 2020; Pedersen et al., 2020). Even though “retweet” does not always indicate agreement and users can retweet with criticism or sarcastic comments, such retweeting can still arouse public attention to the issue and facilitate public conversations and common understandings (Marsili, 2021). Therefore, communication practitioners have tried to promote citizen engagement on social media. While accumulating studies have examined how government authorities can promote citizen engagement on social media in health affairs through the use of visuals, dialogic strategies, and emotional expressions (Chen et al., 2020; Guidry et al., 2020; Haro-de-Rosario et al., 2018), studies on the effectiveness of persuasion techniques in promoting citizen engagement in health domains are scarce. Existing studies in other domains suggest that not all persuasive strategies are effective in inducing citizen engagement on social media and some even have detrimental impacts. For example, Chen et al. (2021) found that misinformation-containing posts using emotional appeals and anecdotes had a positive effect on social media engagement, while posts using authority appeals harmed citizen engagement. Dolan et al. (2019) found that rational appeals in social media had positive effects on social media engagement, whereas emotional appeals did not demonstrate significant effects. Given that persuasion techniques have been extensively applied to guide message design for digital health communication, it is important to investigate how persuasion techniques can impact citizen engagement in health affairs like the COVID-19 vaccines on social media. 2.1 The heuristic-systematic model and persuasive strategies The heuristic-systematic model (HSM) is often used to design digital health communication (Hitt et al., 2016). The model assumes that people may engage in two different information-processing modes to judge the validity of persuasive messages: systematic and heuristic modes (Chaiken, 1980). Systematic mode refers to making a judgment based on a comprehensive analysis of a message's information while heuristic mode refers to using mental shortcuts to make judgments (Chaiken and Maheswaran, 1994). When processing information heuristically, people rely on cues such as the characteristics of the message, the expertise of the source, or the context (Chaiken et al., 1999). In previous HSM research, systematic processing has been assessed by examining the effect of the argument quality and heuristic processing has been assessed by manipulating source expertise, source likability, or consensus information (Chaiken et al., 1999). Researchers have used HSM to analyze the characteristics of online information. For example, Zhang and Watts (2008) found that both messages that promote systematic processing based on cues (argument quality) and heuristic processing element (source credibility) had a positive effect on the adoption of online reviews in online communities. Son et al. (2020) found that heuristic cues such as Twitter profile (source credibility cue) had a positive impact on the quickness of retweeting during natural disasters. While previous studies have predominantly examined the positive impacts of systematic and heuristic cues on information adoption in general domains, seldom have they considered the potential impacts of persuasion cues in health domains. With an exception, in the context of vaccine persuasion, Scannell et al. (2021) have found that both systematic and heuristic cues are frequently utilized in COVID-19 vaccine discourse. Notably, they further revealed that pro-vaccine messages were more likely to provide scientific information to systematically convince the audiences, while anti-vaccine messages were more likely to use cues such as sarcasm and anecdotal to prompt audiences’ heuristic, low-cost processing of the information. These suggest that not all persuasion cues are appropriate for promoting COVID-19 vaccines; otherwise, there should not be any difference in cue use between pro-vaccine and anti-vaccine messages. Therefore, it is essential to tell what persuasion cues can demonstrate positive impacts, while some other cues may show detrimental impacts, on vaccine promotion for health departments. However, none of the previous studies have examined the above questions. In our study, we aim to fill the research gap by examining the use and the impacts of heuristic-systematic cues for vaccine persuasion in health departments' tweets. We treat tweets that use hard science as messages that can withstand systematic processing. The use of hard science refers to messages that contain references to journal articles or scientific studies (Moran et al., 2016). These references would likely promote audiences’ cognitive elaboration in understanding the issue at hand. In addition, we consider anecdotal evidence and the bandwagon effect as heuristic cues. Anecdotal evidence means using anecdotes and stories to support a claim (Hoeken, 2001). The bandwagon effect refers to the tendency for people to adopt certain behaviors, styles, or attitudes because others are doing so (Kiss and Simonovits, 2014). Fear appeal is a persuasive tactic that intends to arouse fear to promote preventive motivation and self-protection action (Rogers and Deckner, 1975). The fear appeal could be processed either systematically or heuristically, depending on whether the message induced danger control (based on the cognitive process) or fear control (based on emotional arousal) (Leventhal et al., 2005). Therefore, it is treated as a combined cue. 2.2 Health belief model Health Belief Model (HBM) argues that people are more likely to take preventative actions to avoid a disease if they perceive that (1) they are personally susceptible to the disease, (2) it is a severe disease, (3) taking preventative actions is beneficial, (4) the preventative actions would not entail barriers, (5) they have received cues (from the mass media, advice from others such as physicians and important others) to take action, and (6) they can successfully implement the preventative behavior (Rosenstock, 1974). Although HBM was mostly used to predict individuals’ health behavior, it has also been widely applied in message design for persuasion in health promotion scenarios (Ahadzadeh et al., 2015). Consequently, in recent years, researchers began to examine the expression of HBM concepts in health promotion posts on social media platforms, such as Twitter (Guidry et al., 2020; Wang et al., 2021), Facebook (Luisi, 2020) and Pinterest (Guidry et al., 2015). Though most of the above studies examined whether the HBM constructs were presented in social media content, only a few studies examined the effect of the presence of these HBM constructs on citizen engagement (Guidry et al., 2020; Wang and Lu, 2022). These limited studies suggest a critical application challenge: while the HBM has been tested extensively in the health behavior literature, its effectiveness may vary in different practical scenarios when it is applied in social media persuasion contexts. For example, Wang and Lu (2022) found that the impacts of HBM constructs on citizens’ Twitter engagement were not stable and varied among the three big news agencies. Vaccination promotion using HBM constructs was effective for Reuters but seemed to be counterproductive for AFP. Therefore, it is imperative to explore whether the use of HBM constructs would be effective in the context of vaccine persuasion for health authorities such as national health departments. In light of the above, this study examined how persuasion techniques informed by HSM and HBM could affect social media engagement in COVID-19 vaccine-related tweets from national health departments. Although the HSM and HBM have been used to predict health attitudes and behaviors, we expect that the constructs of these two models can also impact social media engagement. According to the theories of attitude change and persuasion (Cooper and Croyle, 1984; Petty et al., 1997), persuasive elements of a message will elicit people's cognitive efforts (either high or low, as suggested in HSM) on the message. These cognitive efforts will interact with other cognitive factors (e.g., motivations, heuristics, existing knowledge) for judgment and its outcome can be manifested in behavioral engagement in the message on social media. For example, users will judge the information as useful when it can fulfill their needs for information seeking and sharing so that they will like and retweet the message (Dolan et al., 2019; Khan, 2017). Users can also perceive such information as new knowledge and incorporate it into their attitudes; they may like the tweet for appreciation and share it if they are motivated to exert attitude influence on others. Users who perceive the information as contradictive to their knowledge and value system may retweet and refute the information. In any case, the elicited cognitive efforts by persuasive elements can be manifested in social media engagement, though in different valences and levels. Therefore, we expect that the use of HSM and HBM constructs will elicit users' cognitive efforts for message processing and can thereby predict their social media engagement with the messages. We raised the first two questions:RQ1 To what extent are the different persuasion tactics (HSM and HBM) used by national health departments' tweets? RQ2 To what extent does the presence of persuasive tactics in tweets impact citizen engagement? 2.3 Differences in vaccine persuasion contexts across countries Health departments from different countries may utilize different persuasion techniques to promote COVID-19 vaccines according to their cultural preferences of persuasion, vaccine policies, and the levels of public acceptance of vaccines. First of all, persuasive styles vary from culture to culture and the effectiveness of the strategies may also vary across cultures. For example, individuals from collectivist cultures tend to put more emphasis on social acceptance and maintaining harmonious interpersonal relationships among groups (Yuki et al., 2005), and accordingly, they are likely to be persuaded by strategies that are related to social norms (Li et al., 2022). In contrast, perceived costs and benefits were considered more important by individualists (Davidson et al., 1976), and accordingly, persuasive cues that mention the costs and benefits of behavior may be more effective in persuading individualists. Therefore, we expect that persuasion effects may differ among countries with different cultures. In addition to cultural differences, countries also differ in their policies on vaccination. For example, Germany and the UK put restrictions on the unvaccinated to enter leisure facilities and South Korea only allowed people with 1st dose of vaccine to travel without masks in 2021(News wires, 2021; Gov.uk, 2021; Sajid, 2021), while other countries had less strict policies. As audiences under different vaccine policies may have different reactions to their national health departments’ vaccine promotion posts, we expect that persuasion effects may be different in countries with different vaccine policies. Furthermore, the COVID-19 vaccine acceptance rates also varied greatly, with 82.3% for South Korea, 78% for India, 81.2% for the UK, 73.7% for Germany, and 66.6% for the United States in June 2021 (Lazarus et al., 2022). Studies have suggested that while HBM constructs have a positive effect on citizen engagement for regions with high vaccine acceptance rates, they have a detrimental effect on citizen engagement for regions that possess low vaccine acceptance rates (Wang and Lu, 2022). Accordingly, we predict that persuasion effects will be different for countries with different vaccine acceptance rates. No research we can be aware of has explored whether and how differences in culture, policy, and vaccine acceptance rate across countries would correspond to differences in each country's persuasion strategies and their persuasion effects. Therefore, as the first but critical step, we will compare the persuasion strategies used by different countries and examine their differences in effects on citizen engagement in tweets. We raise our RQ3 and RQ4:RQ3 How did the use of persuasive tactics in COVID-19 vaccine-related tweets vary across countries? RQ4 How did the impact of persuasive tactics on citizen engagement vary across countries? 3 Method We conducted a content analysis with COVID-19 vaccine-related tweets from six national health departments' Twitter accounts. We selected countries with rich vaccine resources because they will be more likely to put efforts into vaccine persuasion than those who are struggling with vaccine access. Vaccine resources are linked to a country's economic strength, so we chose the countries with the highest Gross Domestic Product (GDP) in 2021 (International Monetary Fund, 2021). To diversify our data, we drew the three highest-ranking GDP countries from the East (Japan, India, and South Korea) and the three highest-ranking GDP countries from the West (the US, Germany, and the UK). China, the country with the largest GDP in the East, was not included in the sample because Twitter is not available there. 3.1 Data collection Python (Python Software Foundation) was used to retrieve the original tweets, the number of likes, and the number of retweets about the COVID-19 vaccine on Twitter using Twitter's Application Programming Interfaces in December 2021. As COVID-19 vaccines were first approved to be used in December 2020 (BBC News, 2020; European Medicines Agency, 2020; Flaherty et al., 2020; Ledford et al., 2020), the time frame of the study was from December 1, 2020, to November 30, 2021 (when the study was conducted). The search key words were “vaccination/vaccinations/vaccine/vaccines immunization/vaccinate/vaccinated/Impfung/impfen/CoronaSchutzimpfung/Impfangebot/geimpfte/접종/백신/예방접종/접종한다/ワクチン.” The keyword searches yielded a total of 330 tweets about the COVID-19 vaccine from the U.S. Department of Health and Human Services' Twitter account (@HHSGov), 567 tweets from the Germany Federal Ministry of Health's Twitter account (@BMG_Bund), 1096 tweets from the UK Department of Health and Social Care's Twitter account (@ DHSCgovuk), 302 tweets from the Japanese Ministry of Health, Labour and Welfare's Twitter account (@MHLWitter), 301 tweets from the South Korea Ministry of Health and Welfare's Twitter account (@mohwpr) and 2109 tweets from the India Ministry of Health's Twitter account (@MoHFW_INDIA). We randomly selected 300 tweets from the search results of each of the six national health departments’ Twitter accounts after eliminating unrelated tweets. The full text and images of each file were examined for coding. All the files were downloaded in English except those from Germany, Japan, and South Korea, which were translated into English by professional translators for analysis. 3.2 Operationalization of variables Citizen engagement. Previous studies have used the number of likes and retweets to measure citizen engagement on Twitter (Guidry et al., 2020; Wang and Lu, 2022). Therefore, we also used the two indicators as well. Persuasion constructs. Each article was examined for one or more of the constructs of the HSM (i.e., hard science, anecdotal evidence, bandwagon effect, and fear appeal) and the HBM (i.e., susceptibility, severity, benefit, overcoming barriers, cues to action, and ways to increase self-efficacy). If a construct was mentioned, it was coded as 1; otherwise, it was coded as 0. During the coding process of the HBM constructs, we found that few tweets mentioned barriers (n = 29), while 589 tweets were about overcoming barriers. Therefore, we coded overcoming barriers. The operationalizations of the variables are shown in Table 1 .Table 1 Operational definitions and examples. Table 1HSM & HBM Constructs Operational Definition Examples Hard science References to supporting content from journal articles, scientific research essays, or reliable sources such as the CDC and the WHO; Scientific refutation of rumors to convince people to get vaccinated against COVID-19. CDC: “Side effects are normal, and they mean that the COVID-19 vaccine is working with your child's body to build immunity. Learn more about the side effects your child may experience after getting their COVID-19 vaccine at http://CDC.gov/coronavirus." Anecdotal Evidence Tell the story of one or more ordinary people to prove a point. “I decided I was going to get vaccinated even though I was breastfeeding.” New mother Chantelle explains why she will be vaccinated against COVID-19. Bandwagon effect Showing how many people in the general population have been vaccinated to convince people to get vaccinated against COVID-19. Four out of five adults in the UK have now had their first dose. Fear appeal To persuade people to get vaccinated against COVID-19 by describing the possible or dire consequences of not getting vaccinated (focused on the COVID-19 vaccine) Amanda's a mom who's lucky to be alive after being on a ventilator and in a coma for 11 days due to COVID. Now, she's urging everyone to get vaccinated so no one else has to suffer like she and her family did. Susceptible Define population(s) at risk and risk levels. 265,000 medicare beneficiaries have been infected with COVID-19. Severity Specify the consequences of the risk and the condition (focused on the disease of COVID-19). COVID-19 is physically devastating, causing severe illness/death: 312,000 Americans lost their lives and 1.7 million people died worldwide. Ways to increase self-efficacy Provide training and guidance for the implementation of operations. Do not exercise too much or drink too much on the day you receive the vaccine; Please keep the inoculation site clean; Explain the mild side effects of the vaccine. Benefit State the benefit of the recommended action to reduce the severity of the risk or impact. These vaccines have worked so well, they've brought our country back to normal. Overcoming barriers Overcoming barriers to vaccination. The Trump administration's vaccine development campaign says it will ship Pfizer's vaccines to nearly 150 distribution centers across the United States. Cues to action Strategies to appeal, remind, and motivate audiences to get vaccinated. The Vaccination Committee provides vaccination recommendations for adolescents aged 12 to 17. Control variable. This study also assessed the six countries' health departments’ attitudes toward vaccination (1 = positive, 0 = neutral; there were no negative tweets on COVID-19 vaccines) as a control variable. Two graduate students who are fluent in English coded all the files. Inter-coder reliability of the two coders was calculated by double-coding a random subsample (n = 360 or 20%) of the data. Krippendorff's alpha ranged from 0.85 to 0.99 for the 10 main variables and 1 control variable. 4 Results 4.1 HBM constructs used in six national health department's tweets Fig. 1 showed the HBM constructs used by the six national health department's tweets. The UK national health department mentioned the most overcoming barriers (58.7%), benefits (54.0%), ways to increase self-efficacy (52.0%), and cues to action (M = 48.3%) and mentioned the least severity (12.0%). The US national health department mentioned the most cues to action (66.0%), ways to increase self-efficacy (59.7%), and mentioned the least severity (19.30%). The German national health department used the most cues to action (70.0%) and mentioned the least severity (6.3%). The Japanese national health department mentioned the most ways to increase self-efficacy (68.0%) and mentioned the least severity (4.70%). The South Korean national health department mentioned the most cues to action (72.0%), susceptibility (67.0%), and severity (62.0%), and mentioned the least overcoming barriers (9.7%). The Indian national health department deployed the most cues to action (77.7%) and did not mention severity at all (0%).Fig. 1 HBM constructs used by the six national health department's tweets. Fig. 1 In general, cues to action were mentioned frequently by five out of the six countries except for Japan. Severity was mentioned the least by five out of the six countries except for South Korea, which mentioned severity to a large extent. 4.2 Systematic-heuristic cues used in six national health department's tweets As shown in Fig. 2 , the UK and the US health department did not use much the four persuasive techniques under study. The most frequently used technique used by the UK was hard science, which was only used by 9.7% of its tweets. The most frequently used techniques used by the US were hard science (12.0%) and fear appeal (12.0%). Germany and India used the bandwagon effect (24.7% for Germany and 28.3% for India) the most. Japan used hard science the most (22.7%). South Korea stood out among the six countries as it used more persuasive techniques than other countries. Among its 300 tweets, 58.3% used bandwagon, and 61.0% used fear appeal.Fig. 2 Heuristic-systematic cues used in six national health department's tweets. Fig. 2 4.3 Differences in citizen engagement As can be seen in Table 2 , tweets from the Japanese health department received the highest likes (M = 905.65, SD = 1599.65), followed by the health department of India (M = 181.16, SD = 396.17), Germany (M = 149.47, SD = 798.02), the US (M = 97.06, SD = 229.74), the UK (M = 95.30, SD = 180.00) and South Korea (M = 31.52, SD = 73.80). Similarly, Japanese health department also induced the highest retweets (M = 563.51, SD = 1096.25), followed by the health department of India (M = 99.32, SD = 709.26), Germany (M = 79.10, SD = 433.72), the UK (M = 53.38, SD = 65.29), South Korea (M = 37.04, SD = 89.10) and the US (M = 36.74, SD = 95.26). All six countries’ data showed a positive skew, which indicates that the tail is on the right side of the distribution. The kurtosis values were all greater than 3, which meant that the data produced more outliers than the normal distribution (see Table 2).Table 2 Descriptive statistics for citizen engagement in the six national health departments’ tweets. Table 2Country Engagement variable Range Mean (SD) Skewness Kurtosis N The UK Like 0–2000 95.30 (180.00) 6.01 48.78 300 Retweet 0–552 53.38 (65.29) 3.66 18.28 300 The US Like 2–3100 97.06 (229.74) 10.24 120.86 300 Retweet 0–1200 36.74 (95.26) 9.22 98.19 300 Germany Like 8–13,200 149.47 (798.02) 15.07 242.56 300 Retweet 5–7100 79.10 (433.72) 14.76 233.64 300 Japan Like 38–14,000 905.65 (1599.65) 3.67 19.10 300 Retweet 18–10,000 563.51 (1096.25) 4.44 26.12 300 South Korea Like 8–1100 31.52 (73.80) 11.77 157.67 300 Retweet 7–1300 37.04 (89.10) 11.46 149.09 300 India Like 7–4600 181.16 (396.17) 7.31 65.96 300 Retweet 1–12,000 99.32 (709.26) 16.01 267.67 300 Total Like 0–14,000 243.36 (813.79) 8.72 105.41 1800 Retweet 0–12,000 144.85 (594.75) 11.21 165.36 1800 4.4 The effects of HBM and HSM constructs in tweets on citizen engagement As the citizen engagement variables, like and retweet, were over-dispersed count outcome variables, we used negative binomial regression for the analysis (Chen et al., 2020). Table 3 showed that when we used the HBM and the HSM constructs to predict the number of likes, controlling for the positive attitude on vaccination, the overall model was significant (Model χ2 (11) = 924.27, p < .001). Of the ten predictor variables in the model, six variables positively predicted the number of likes (hard science, B = 1.00, SE = 0.08, IRR = 2.69, p < .001; anecdote, B = 0.66, SE = 0.12, IRR = 1.94, p < .001; fear appeal, B = 0.40, SE = 0.10, IRR = 1.49, p < .001; ways to increase self-efficacy, B = 0.44, SE = 0.06, IRR = 1.55, p < .001; benefit, B = 0.20, SE = 0.06, IRR = 1.22, p < .05; and overcoming barriers, B = 0.33, SE = 0.06, IRR = 1.40, p < .001). Three variables negatively predicted the number of likes (susceptibility, B = −0.79, SE = 0.07, IRR = 0.45, p < .001; severity B = −0.63, SE = 0.09, IRR = 0.54, p < .001; and cues to action, B = −0.24, SE = 0.06, IRR = 0.79, p < .001). The control variable, positive tone on vaccines, was negatively related to the number of likes (B = −0.52, SE = 0.07, IRR = 0.59, p < .001).Table 3 HSM & HBM constructs and citizen engagement for the overall sample. Table 3 Like Retweet B SE IRR B SE IRR Hard science 1.00* .08 2.69 1.05* .08 2.85 Anecdote .66* .12 1.94 .45* .12 1.57 Bandwagon effect −.08 .08 .92 −.16* .08 .85 Fear appeal .40* .10 1.49 .70* .11 2.01 Susceptibility −.79* .07 .45 −.71* .08 .49 Severity −.63* .09 .54 −.47* .10 .63 Ways to increase self-efficacy .44* .06 1.55 .49* .06 1.62 Benefit .20* .06 1.22 .02 .06 1.02 Overcoming barriers .33* .06 1.40 .53* .06 1.69 Cues to action −.24* .06 .79 −.43* .05 .65 Positive −.52* .07 .59 −.52* .07 .60 Log-likelihood −11231.76 −10206.14 Model χ2 924.27* 112.58* df 11 11 N 1800 1800 When we used the HBM and the HSM constructs to predict the number of retweets, controlling for the positive attitude on vaccination, the overall model was also significant (Model χ2 (11) = 112.58, p < .001). Of the ten predictor variables in the model, five variables positively predicted the number of retweets (hard science, B = 1.05, SE = 0.08, IRR = 2.85, p < .001; anecdote, B = 0.45, SE = 0.12, IRR = 1.57, p < .001; fear appeal, B = 0.70, SE = 0.11, IRR = 2.01, p < .001; ways to increase self-efficacy, B = 0.49, SE = 0.06, IRR = 1.62, p < .001; and overcoming barriers, B = 0.53, SE = 0.06, IRR = 1.69, p < .001). Four variables negatively predicted the number of retweet (bandwagon effect, B = −0.16, SE = 0.08, IRR = 0.85, p < .05; susceptibility, B = −0.71, SE = 0.08, IRR = 0.49, p < .001; severity, B = −0.47, SE = 0.10, IRR = 0.63, p < .001; and cues to action, B = −0.43, SE = 0.05, IRR = 0.65, p < .001). The control variable, positive tone on vaccines, was negatively related to the number of retweets (B = −0.52, SE = 0.07, IRR = 0.60, p < .001). In general, hard science, anecdote, fear appeal, ways to increase self-efficacy, and overcoming barriers were generally effective in inducing citizen engagement (i.e., likes and retweets), while susceptibility, severity, cues to action, and positive tone had a detrimental effect on citizen engagement. Benefit had a positive effect only on the number of likes while the bandwagon effect had a detrimental effect only on the number of retweets. 4.5 Country differences in the effects of HBM and HSM constructs To examine the country differences in the effects of HBM and HSM constructs on citizen engagement (likes and retweets), we ran two negative binomial regressions by country (Table 4 and Table 5 ).Table 4 HSM & HBM constructs and the number of likes for each country. Table 4 UK US Germany Japan South Korea India B SE IRR B SE IRR B SE IRR B SE IRR B SE IRR B SE IRR Hard science −.03 .21 .97 .60 .21 1.83 −.21 .24 .81 1.09* .16 2.99 −.45 .44 .64 .05 .24 1.05 Anecdote .25 .34 1.28 −.61 .35 .54 −1.62* .29 .20 1.20 .38 3.31 −.45 .40 .64 −.35 .21 .71 Bandwagon effect −.26 .31 .77 −.23 .21 .80 −.26 .21 .77 .86 .46 2.35 −.01 .39 .99 .29 .17 1.34 Fear appeal −.11 .27 .09 −.09 .22 .92 .27 .34 1.31 1.34* .37 3.84 −.61 .46 .54 .13 .61 1.14 Susceptibility .08 .16 1.08 −.52* .14 .60 −.26 .16 .77 −.85 .29 .43 −.14 .25 .87 −.06 .22 .94 Severity .33 .20 1.39 .03 .19 1.03 −.54 .33 .58 −.22 .35 .81 .49 .40 1.64 -a -a 1.00 Ways to increase self-efficacy −.21 .13 .81 −.03 .13 .97 −.59* .16 .56 .33* .14 1.39 −.07 .22 .94 .21 .14 1.23 Benefit .10 .15 1.10 −.05 .13 .95 .17 .16 1.19 .46 .17 1.59 .03 .22 1.03 −.55 .18 .58 Overcoming barriers .44 .14 1.55 −.18 .15 .84 .50 .16 1.65 −.02 .16 .98 .58* .27 1.79 .67* .18 1.96 Cues to action .49* .14 1.63 −.20 .14 .82 1.26*** .15 3.52 .28* .14 1.33 −.16 .21 .85 −.51 .18 .60 Positive −.24 .19 .79 .56 .18 1.75 −.88* .24 .42 −.03 .14 .97 −1.30* .32 .27 1.01* .46 2.75 Model χ2 34.62* 51.41* 253.07* 87.04* 71.49* 85.20* df 11 11 11 11 11 11 Log-likelihood −1651.38 −1648.43 −1676.60 −2299.24 −1304.18 −1817.55 N 300 300 300 300 300 300 a. Note that the Indian health department did not mention severity in any of its tweets, therefore, severity could not be included in the regression analysis. b. IRR: Incident Rate Ratio; SE: Standard Error. Table 5 HSM & HBM constructs and the number of retweets for each country. Table 5 UK US Germany Japan South Korea India B SE IRR B SE IRR B SE IRR B SE IRR B SE IRR B SE IRR Hard science .13 .21 1.13 .56 .21 1.75 −.19 .24 .83 1.13* .17 3.09 −.53 .46 .59 −.49* .24 .61 Anecdote .05 .33 1.05 −1.08 .37 .34 −2.00* .29 .13 1.13 .40 3.09 −.64 .41 .52 −.60 .22 .55 Bandwagon effect −.06 .32 .94 −.52* .22 .60 −.88* .19 .41 .84 .47 2.32 .09 .41 1.10 .20 .17 1.22 Fear appeal .05 .27 1.05 −.02 .23 .98 .17 .34 1.18 1.39* .38 4.02 −.76 .53 .47 .04 .62 1.04 Susceptibility .00 .15 1.00 −.58* .13 .56 −.22 .16 .80 −.79 .30 .45 −.04 .26 .96 .02 .22 1.02 Severity .26 .20 1.29 −.11 .19 .89 −.45 .33 .64 −.12 .36 .89 .50 .45 1.65 -a -a 1.00 Ways to increase self-efficacy .09 .13 1.09 −.15 .13 .86 −.42 .16 .65 .17 .14 1.19 −.22 .23 .81 .55* .15 1.74 Benefit −.04 .15 .96 −.15 .14 .86 .00 .16 1.00 .28 .18 1.19 .35 .22 1.41 −.80*** .18 .45 Overcoming barriers .31* .14 1.36 −.46 .15 .63 .45 .15 1.57 .05 .16 1.05 .52 .28 1.68 1.15* .19 3.16 Cues to action .10 .14 1.11 −.50* .14 .61 1.15*** .15 3.17 .19 .14 1.21 −.44* .21 .64 −.91*** .20 .40 Positive −.13 .18 .88 .37 .18 1.45* −.83* .24 .44 .04 .14 1.04 −1.27* .33 .28 1.17* .47 3.22 Model χ2 7.82 72.52* 244.54* 89.84* 92.83* 333.35*** df 11 11 11 11 11 11 Log-likelihood −1492.09 −1348.95 −1490.82 −2155.60 −1341.17 −1514.34 N 300 300 300 300 300 300 aNote that the Indian health department did not mention severity in any of its tweets, therefore, severity could not be included in the regression analysis. bIRR: Incident Rate Ratio; SE: Standard Error. As can be seen in Table 4, the overall models predicting the number of likes were significant for the six countries respectively. Specifically, for the UK, overcoming barriers and cues to action were positive predictors. For the US sample, hard science and positive tone were positive predictors while susceptibility was a negative predictor. For Germany, overcoming barriers and cues to action were positive predictors; three variables, including anecdotes, ways to increase self-efficacy, and positive tone were negative predictors. For Japan, hard science, anecdote, fear appeal, ways to increase self-efficacy, benefits, and cues to action were positive predictors and susceptibility was a negative predictor. For South Korea, overcoming barriers was a positive predictor and positive tone was a negative predictor. For India, overcoming barriers and positive tone were positive predictors, and benefits and cues to action were negative predictors. As can be seen in Table 5, the overall models predicting retweets were significant for all the countries respectively except the UK. Specifically, for the UK, overcoming barriers were positively related to the number of retweets. For the US, hard science and positive tone were positive predictors; five variables, including anecdote, bandwagon, susceptibility, overcoming barriers, and cues to action were negative predictors. For Germany, overcoming barriers and cues to action were positive predictors and four variables (anecdote, bandwagon, ways to increase self-efficacy, and positive tone) were negative predictors. For Japan, hard science, anecdote, and fear appeal were positive predictors and susceptibility was a negative predictor. For South Korea, cues to action and positive tone were negative predictors. For India, three variables (ways to increase self-efficacy, overcoming barriers, positive tone) were positive predictors and four variables (hard science, anecdote, benefit, and cues to action) were negative predictors. In general, hard science had a positive effect on the US and Japan. Peripheral cues such as anecdotes and bandwagon effect had negative effects for the US and Germany. The fear appeal had a positive effect for Japan. Ways to increase self-efficacy had a positive effect for Japan and India but a negative effect for Germany. Benefits had a positive effect for Japan but a negative effect for India. Overcoming barriers had a positive effect for the UK, German, South Korea, and India and a negative effect for the US. Cues to action had a positive effect for the UK, Germany, and Japan and a negative effect for the US, South Korea, and India. A positive tone had a positive effect for the US and India and a negative effect for Germany and South Korea. 5 Discussion 5.1 Summary of findings This study aims to understand how national health departments leveraged Twitter for citizen engagement in COVID-19 vaccine-related tweets. To achieve this, we examined the frequencies and impacts of heuristic-systematic cues and health belief constructs on citizen engagement through health authorities’ social media posts based on HSM and HBM. Our research results showed that both persuasion strategies had impacts on citizen engagement on social media, and there were national differences in the impacts of these strategies. This study found both similar and different patterns in deploying persuasion strategies among countries. Regarding the HSM cues, the health department of South Korea used more persuasive techniques than other countries as it tended to appeal to fear in their COVID-19 vaccine-related tweets. In addition, health departments from South Korea, Germany, and India often mentioned normative information while that of Japan was used to provide scientific information. Regarding the HBM constructs, all six countries used a certain amount of the six constructs in the HBM, with cues to action mentioned the most and severity mentioned the least. The South Korean health department emphasized significantly more disease threats, while departments from other countries often tried to promote perceptions of self-efficacy in vaccination, vaccine benefits, and removal of vaccination barriers. Cues to action were often presented in tweets except for Japan. Our data revealed that South Korea demonstrated a very different pattern of persuasion strategies for citizen engagement in COVID-19 vaccine-related tweets, compared with those in other countries. One possible reason is that South Korea witnessed a vaccination crisis concerning severe illness, incapacitation, and death following vaccination when the vaccination program was first commenced in February 2021 (Park et al., 2021). The country also suffered vaccine shortages before July 2021. These led to high levels of vaccine hesitancy and government mistrust in South Korea (Park et al., 2021). Accordingly, the health department of South Korea might have induced public engagement by emphasizing external threats rather than relying on internal efforts to promote efficacies. This study revealed that both systematic and heuristic cues affected citizen engagement. These findings are consistent with previous research on information adoption and credibility assessment (Zhang and Watts, 2008; Xiao et al., 2018). Information about hard science such as referring to scientific studies about vaccine effectiveness tends to promote audiences' cognitive elaboration, which then can engage them in liking and retweeting tweets. Appeals to anecdotes and fear can promote audiences' affective responses to protect themselves, boosting their interest in the tweets. Interestingly, heuristic cues about social norms (i.e., bandwagon effect) demonstrated a negative impact on tweet engagement, especially for retweeting. This finding is inconsistent with that of the health behavior literature which often found perceived social norms as a positive predictor of individuals’ health behaviors (Abdallah and Lee, 2021; Rabb et al., 2022). This may be because, in the social media persuasion context, audiences perceive emphases of social norms as advertising language, behavioral manipulation, and overly pushy, and thereby resist engaging in the tweets (Weiger et al., 2018). This finding suggests that heuristic cues may not always demonstrate positive impacts in vaccine persuasion and need to be used carefully by considering persuasion contexts. This study also confirmed that health belief constructs are essential persuasive cues in promoting citizen engagement in COVID-19 vaccine-related tweets. Generally, messages that promote self-efficacy in vaccination, emphasize the benefits of vaccines, and reveal actions to overcome vaccination barriers demonstrated positive impacts on citizen engagement. These findings suggest that those cues are likely to promote audiences’ positive attitudes toward COVID-19 vaccines and vaccination. In contrast, emphases of COVID-19 threats and cues to vaccinate in COVID-19 vaccine-related tweets showed detrimental effects on citizen engagement. These findings are consistent with previous findings about vaccines against infectious diseases (Guidry et al., 2020; Wang and Lu, 2022). Wang and Lu (2022) explained that audiences might have displayed maladaptive responses to the heightened disease threat in the tweets when they did not perceive vaccination as a high-efficacy response to the disease. In addition, audiences may also perceive cues to action as advertising and public manipulation. Though we revealed the general impacts of persuasion strategies in COVID-19 vaccine-related tweets, the findings need to be further interpreted within country contexts. We found substantial differences in, and thereby a complex landscape for, the impacts of persuasion strategies among countries. For example, appeals to anecdotes demonstrated positive impacts on citizen engagement in Japan, while they showed the reverse impacts in the US, Germany, and India. Messages involving vaccine benefits could promote citizen engagement in Japan while would reduce it in India. Cues to vaccinate could induce resistance in tweet engagement in the US, South Korea, and India, while they could engage citizens in tweets for the UK, Germany, and Japan. Interestingly, positive attitudes toward vaccines in tweets demonstrated detrimental impacts in Germany and South Korea while positive impacts were found in the US and India. Though why such differences in the effectiveness of persuasive strategies exist remains largely unknown, we propose that two potential factors and their interactions may underly this phenomenon. First, cultural differences. Past research has demonstrated that cultural orientations play important roles in preference to heuristic-systematic persuasion. For example, collectivist cultures regard heuristic cues as more diagnostic than systematic cues while individualist cultures have a reverse preference (Aaker and Maheswaran, 1997). Collectivist cultures also prefer concrete examples like anecdotes to abstract information in persuasion (Liang and Cherian, 2010). This can partly explain the diverse effects of bandwagon information and anecdote across countries. Second, audiences’ attitudes toward COVID-19 vaccination. According to the emphasis framing theory, positive frames toward vaccination can be viewed as advertising and coercion, leading to resistance to the messages (Koch and Peter, 2017). This is especially the case when the audiences are hesitant and attempt to seek “factual” information to make vaccination decisions (Ashwell and Murray, 2020; Wang and Lu, 2022). This can partly explain why positive frames of vaccines demonstrated negative impacts in Germany, whose COVID-19 vaccine acceptance rate was only 68.42% in June 2020 (Lazarus et al., 2021), and in South Korea, which was suffering vaccine hesitancy and government mistrust in 2021 (Park et al., 2021). Nevertheless, whether and how the two factors interact with each other remain unknown. Future studies should examine how these two factors affect vaccination persuasion with experimental designs and mediation analyses. 5.2 Implications Our study provided basic evidence for the rationality and irrationality of using HBM and HSM structures to promote and intervene in health behaviors in different settings. It also broadened the scope of HBM applications from predicting health attitudes and behaviors in surveys to guiding the design of health promotion messages. In addition, the complex landscape for the impacts of persuasion strategies across countries highlights the mismatches between persuasion strategies that were frequently utilized by national health departments and those that were effective in promoting citizen engagement. For example, although the health department of South Korea used the most persuasion strategies such as bandwagon effect and fear appeal, these strategies were generally not effective. In addition, the Indian health department deployed cues to action to promote engagement, while this strategy revealed a negative impact. Such mismatches suggest a locally adapted evidence-based approach for vaccination persuasion for health departments' practices in engaging citizens through social media posts. Health departments should consider fully their audiences' preferences in persuasion techniques and attitudes toward vaccination, both of which should be used to inform the design of local persuasion messages. Health authorities can use intelligent algorithms to analyze and predict the effectiveness of persuasions in real time. They can also use big-data techniques to incorporate the public's sentiment toward vaccination into the algorithms. 5.3 Limitations This study has several limitations. First, this study examined only six countries' health departments’ Twitter accounts. Using big-data techniques, future research could incorporate more countries with divergent cultures, health policies, and COVID-19 vaccine acceptance rates. Second, while this study focused on Twitter, future research should examine persuasion impacts across different social media platforms, such as Facebook and Instagram. Future research should try to build a complete picture of the impacts of persuasion strategies on different platforms for vaccination persuasion. 6 Conclusion This study examined the impacts of systematic-heuristic cues and health belief constructs on citizen engagement in COVID-19 vaccine-related tweets across national health departments in six countries based on the heuristic-systematic model and the health belief model. We found that not all persuasion techniques the health departments used were effective in promoting citizen engagement in COVID-19 vaccine tweets. Notably, health departments often used persuasion tactics that demonstrated futile or detrimental impacts. Our findings revealed a complex landscape for the impacts of persuasive strategies and suggest a locally adapted evidence-based approach for vaccine persuasion across countries. Author statement Wang Di: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing; Lu Jiahui: Conceptualization, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing; Zhong Ying: Data curation, Formal analysis, Software Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability The authors do not have permission to share data. ==== Refs References Aaker J.L. Maheswaran D. 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Online 9 2 2008 73 94	36493501	PMC9721126	NO-CC CODE	2022-12-06 23:26:40	no	Soc Sci Med. 2023 Jan 5; 317:115591	utf-8	Soc Sci Med	2,022	10.1016/j.socscimed.2022.115591	oa_other
==== Front Soc Sci Med Soc Sci Med Social Science & Medicine (1982) 0277-9536 1873-5347 Elsevier Ltd. S0277-9536(22)00901-7 10.1016/j.socscimed.2022.115595 115595 Article Self-uniqueness beliefs and adherence to recommended precautions. A 5-wave longitudinal COVID-19 study De Witte Dries a Delporte Margaux a Molenberghs Geert ba Verbeke Geert ab Demarest Stefaan c Hoorens Vera d∗ a I-BioStat, KU Leuven, Kapucijnenvoer 35 blok d - box 7001, B-3000 Leuven, Belgium b I-BioStat, Universiteit Hasselt, Martelarenlaan 42, B-3500 Hasselt, Belgium c Health Interview Survey team, Sciensano, Rue Juliette Wytsmanstraat 14, B-1050, Brussels, Belgium d LESP, KU Leuven, Tiensestraat 102 bus 3727, B-3000 Leuven, Belgium ∗ Corresponding author. 5 12 2022 1 2023 5 12 2022 317 115595115595 23 5 2022 24 11 2022 1 12 2022 © 2022 Elsevier Ltd. All rights reserved. 2022 Elsevier Ltd Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Rationale Research on health-related self-uniqueness beliefs suggested that these beliefs might predict adherence to precautions against COVID-19. Objective We examined if comparative optimism (believing that one is less at less than others), self-superiority (believing that one already adheres better to precautions than others), and egocentric impact perception (believing that adverse events affect oneself more than others) predicted intended adherence to precautions. Method We measured self-reported intentions, optimism for self and others, perceived past adherence by self and others, and perceived impact of the measures and the disease on self and others in a 5-wave longitudinal study in December 2020–May 2021 (N ≈ 5000/wave). The sample was in key respects representative for the Belgian population. We used joint models to examine the relationship between self-uniqueness beliefs and intended adherence to the precautions. Results Believing that COVID-19 would affect one's own life more than average (egocentric impact perception) was associated with higher intentions to adhere to precautions, as was believing that the precautions affected one's life less than average (allocentric impact perception). Self-superiority concerning past adherence to precautions and comparative optimism concerning infection with COVID-19 were associated with higher intended adherence, regardless of whether their non-comparative counterparts (descriptive norm, i.e., perceived adherence to precautions by others, and personal optimism, respectively) were controlled for. Comparative optimism for severe disease and for good outcome were associated with lower intended adherence if personal optimism was not controlled for, but with higher intended adherence if it was controlled for. Conclusion Self-uniqueness beliefs predict intended adherence to precautions against COVID-19, but do so in different directions. Keywords Comparative optimism Descriptive norm Self-superiority Egocentric impact perception Allocentric impact perception COVID-19 Precautions Belgium ==== Body pmc1 Introduction The COVID-19 crisis has raised awareness among public health authorities that novel infectious diseases may necessitate prolonged adherence by citizens to precautionary measures (‘precautions’ for short). Despite efforts to convince citizens of the importance of the precautions against COVID-19, adherence was often lower than authorities had hoped for (e.g., Hills and Eraso, 2021; Nelson-Coffey et al., 2021). It is therefore important to understand why people are or are not willing to adhere to precautions. We investigated the role of a set of beliefs characterizing how one differs from other people (‘self-uniqueness beliefs’). More specifically, we examined how comparative optimism, self-superiority, and egocentric impact perception predicted intended adherence to precautions. Comparative optimism is the belief that desirable events are more likely, and undesirable events are less likely to happen to the self than to others (Weinstein, 1980). Self-superiority is the belief that one is and acts better than others (Alicke, 1985). Egocentric impact perception is the belief that external events, including laws and regulations, affect oneself more than others (Blanton et al., 2001). Its hypothetical opposite is allocentric impact perception, the perception that others are more affected than the self. It is difficult to determine to what extent any given individual's self-uniqueness beliefs are accurate. However, most members of a group cannot act better, risk less, or be more affected than average (unless in very skewed distributions). The general occurrence of these phenomena has therefore been labelled ‘unrealistic optimism’, ‘illusory superiority’, and egocentric (or allocentric) impact bias. Unrealistic optimism occurs concerning many health issues. For example, most people believe that they are less likely than others to get a heart attack, an addiction, or lung cancer (Weinstein, 1987). Unrealistic optimism also occurs regarding COVID-19 (e.g., Asimakopoulou et al., 2020), but it is stronger for getting infected than for falling severely ill (e.g., Delporte et al., 2022 under review). Showing illusory superiority concerning health behaviors, most people believe that they eat healthier and exercise more than their average peer (Hoorens and Harris, 1998) and that they adhere better to precautions against COVID-19-infection than average (Rose and Edmonds, 2021). Finally, egocentric impact bias has been reported concerning the impact of precautions against COVID-19-infection on some life domains, but allocentric impact bias on other ones (Hoorens et al., 2022). People generally believe that social distancing rules affect their hobbies and contacts with individuals outside their household more than average (egocentric impact perception), but also that these rules affect their income and contacts with individuals in their household less than average (allocentric impact perception). We are not aware of earlier research on egocentric impact bias concerning the disease itself. Intuitively, the belief that getting infected with or suffering from COVID-19 may have worse consequences for the self than for others should encourage people to adhere to precautions. The belief that one is less likely to get infected or to fall severely ill, and the belief that one already acts more carefully even though that is harder for the self than for others may discourage adherence. However, findings on how self-uniqueness beliefs relate to behavior are rare and inconclusive. In one study, more egocentric impact perception concerning precautions against COVID-19 was associated with seeking more information about the disease but lower trust in information sources. Greater self-superiority was correlated with both fewer information being sought and lower trust (Hoorens et al., 2022). In another study, greater self-superiority was associated with higher intended adherence to precautions (Rose and Edmonds, 2021). Some researchers found a negative association of comparative optimism with adherence intentions (McColl et al., 2022; Park et al., 2021). Other research revealed a positive association of comparative optimism with adherence to precautions (Nordfjaern et al., 2021). A similarly inconsistent pattern occurred pre-COVID-19 concerning behavioral correlates of comparative optimism for other diseases (cf. Davidson and Prkachin, 1997; Dillard et al., 2006; Ingledew and Brunning, 1999; Park et al., 2017). In one of these pre-COVID-19 studies, individuals who were more (vs. less) comparatively optimistic concerning heart attacks were happier, exercised more, and learned more from an essay about heart attacks. The authors concluded that there was little evidence for the sometimes assumed maladaptiveness of comparative optimism; in fact, they considered it “a fairly accurate belief that is associated with a variety of favorable outcomes” (Radcliffe and Klein, 2002, p. 844). One explanation for the inconclusiveness of findings is that studies considered different statistical models, from zero-order correlations to hierarchical regressions that included other aspects of risk perception and controlled for demographic variables. Among the variables most likely to be confounded with self-uniqueness beliefs are their non-comparative counterparts. For comparative optimism and egocentric impact perception that would be personal optimism and perceived impact on the self, respectively. For self-superiority, the non-comparative counterpart of greatest interest is the descriptive norm, i.e., the adherence to precautions by others. In earlier research, higher personal optimism concerning COVID-19 predicted lower intended or reported adherence to precautions (Cipolletta et al., 2022; Qin et al., 2021) and a higher descriptive norm predicted higher intentions to adhere to precautions against COVID-19 (e.g., Latkin et al., 2022). 2 The present research To examine if self-uniqueness beliefs predict intentions to adhere to precautions over and above non-comparative aspects of risk perceptions, we conducted a five-wave longitudinal study using a representative sample of the adult Belgian population. Belgium had major COVID-19 waves in the Spring and Autumn of 2020 and 2021. The first wave led to lockdown measures that were nevertheless less stringent than in some other countries, such as France, where a much more restrictive stay-in-place order was being implemented. A successful vaccination campaign began early in 2021. It coincided with the circulation of mainly the Alpha variant and, from May 2021 onwards, mainly the Delta variant. However, and as Fig. 1 shows, the non-pharmaceutical measures that were being implemented became less stringent over time. For more detail, please see Appendix 1 in the Supplemental Materials.Fig. 1 Non-pharmaceutical measures against COVID-19 in Belgium at the time of the study. Fig. 1 We measured comparative optimism, self-superiority, and perceptions of the relative impact of the disease and the precautions. Another paper (Delporte et al., 2022 under review) has reported unrealistic optimism for infection and severe disease, but not for a good outcome of an infection. As an ancillary goal of the present study, we tested the occurrence of illusory superiority and egocentric impact bias. We expected to find illusory superiority, but the scarcity and inconclusiveness of earlier research on the egocentric impact bias made us examine its occurrence exploratorily. To disentangle self-uniqueness beliefs from their non-comparative counterparts, we measured self-uniqueness beliefs by asking judgments for the self and the average peer separately. The exception was egocentric impact perception concerning COVID-19 itself, where we had two reasons to use directly comparative items. First, we anticipated ceiling effects in personal impact ratings for at least some events, such as being admitted to an intensive care unit. Second, the subjective nature of impact ratings would have made a non-comparative scale vulnerable to the phenomenon that identical labels may convey different meanings depending on whom is being judged (‘shifting standards’; cf. Biernat et al., 1997). Thus, personal impact ratings would have been uninterpretable. 3 Method 3.1 Transparency and openness This study was part of longitudinal research on beliefs concerning COVID-19, vaccination, and precautions. We report findings on the relationship between self-uniqueness beliefs and intentions to adhere to precautions. Besides the variables used here, the questionnaire included measures of various other psychological variables. The full questionnaire is in the Supplemental Materials (Appendix 2). Findings on the relationship between comparative optimism, moralization, and vaccination have been reported elsewhere (Delporte et al., 2022 under review). The data and syntaxes for the present paper are available on OSF \| Covid.Precautions paper. The full results are in the Supplemental Materials. We report all data exclusions. 3.2 Participants Participants were Belgian members (18+) of the online panel of an international market research and polling agency (iVox). We aimed at a sample (N = 5000) that was representative for Belgium on gender, age group, education (No higher education, Higher education), and region (Brussels Capital Region, Flanders, Wallonia). In each wave, we included participants who had given informed consent for the wave and who had given likelihood estimates for at least one infection-related and one outcome-related event, and ratings of their general adherence to the precautions, of the impact of precautions, and of the impact of at least one COVID-19-related event. From Wave 2 on, panel members were invited to participate a month after they had given informed consent for an earlier wave. To compensate for attrition, new participants were invited until at least 5000 had given informed consent. Table 1 shows key demographical characteristics. Our sample was highly educated as compared to the general population, and some age groups were overrepresented (45-54-years-olds) or underrepresented (65+) by more than 1–2%. However, the sample was sufficiently representative for our purpose. We provide information about participants’ experience with COVID-19 and an overview of missing values in the Supplemental Materials (Appendix 3 & Appendix 4, respectively).Table 1 Key demographic characteristics of the samples per wave. Table 1Wave 1 2 3 4 5 Start (Day/Month/Year) 13.12.2020 12.01.2021 13.02.2021 17.03.2021 17.04.2021 End (Day/Month/Year) 29.12.2020 02.02.2021 03.03.2021 12.04.2021 16.05.2021 N Informed consent 5669 5286 5071 5083 5373 N Actual participantsa 5417 5116 4946 4968 5234 Already in wave 1 5417 3175 3430 3200 2646 Already in wave 2 – 1941 1386 1069 823 Already in wave 3 – – 130 70 58 Already in wave 4 – – – 629 138 Already in wave 5 – – – – 1569 N % N % N % N % N % Gender Men 2643 48.8 2402 47.0 2392 48.4 2377 47.8 2470 47.2 Women 2767 51.1 2708 52.9 2540 51.4 2540 51.1 2742 52.4 Neither/Other/Missing 7 0.1 6 0.1 14 0.3 51 1.0 22 0.4 Age Group 18–24 years 469 8.7 367 7.2 270 5.5 358 7.2 285 5.4 25–34 years 1025 18.9 863 16.9 719 14.5 774 15.6 750 14.3 35–44 years 815 15.0 777 15.2 715 14.5 768 15.5 822 15.7 45–54 years 1251 23.1 1160 22.7 1158 23.4 1140 22.9 1247 23.8 55–64 years 829 15.3 847 16.6 869 17.6 832 16.7 885 16.9 65+ years 1028 19.0 1102 21.5 1215 24.6 1096 22.1 1245 23.8 Education No higher education 2756 50.9 2650 51.8 2609 52.7 2589 52.1 2471 47.2 Higher education 2661 49.1 2466 48.2 2337 47.3 2379 47.9 2763 52.8 Household size 1 1003 18.5 961 18.8 930 18.8 942 19.0 994 19.0 2 2124 39.2 2108 41.2 2124 42.9 2068 41.6 2249 43.0 3 1042 19.2 927 18.1 838 16.9 866 17.4 890 17.0 4 841 15.5 784 15.3 732 14.8 752 15.1 772 14.7 5+ 407 7.5 336 6.6 322 6.5 340 6.8 329 6.3 Urbanization Large city 1258 23.2 1157 22.6 1079 21.8 1168 23.5 1148 21.9 Small city 1216 22.4 1153 22.5 1088 22.0 1101 22.2 1144 21.9 Large municipality 1350 24.9 1270 24.8 1295 26.2 1270 25.6 1406 26.9 Small municipality 1593 29.4 1536 30.0 1484 30.0 1429 28.8 1536 29.3 Region Brussels Capital Region 533 9.8 476 9.3 464 9.4 574 11.6 475 9.1 Flanders 3233 59.6 3058 59.8 3027 61.2 2903 58.4 3308 63.2 Wallonia 1651 30.6 1582 30.9 1455 29.4 1491 30.0 1451 27.7 a These are individuals who after having given informed consent answered key questions (see ‘Participants’). 4 Material Participants completed the questionnaire on the online platform Qualtrics in their preferred language (Dutch, French). The questionnaire was developed in Dutch and professionally translated into French. To minimize the burden on participants we used demographical information that they had supplied while registering for the panel. Comparative and Personal Optimism. Participants estimated the likelihood that 6 COVID-19-related events would in the next 3 months happen to them and to the average person of their age and gender, by moving a slider from 0 (this will certainly not happen) to 100 (this will certainly happen). Two infection-related events (getting infected or re-infected, infecting others) appeared in a random order, followed by four events that might happen after a (re-)infection, also in a random order. Two involved severe disease (end up in hospital, end up in an intensive care unit) and two involved a good outcome (having few symptoms, fully recovering). We created optimism scores such that higher scores always denoted greater optimism. For good outcomes, self-estimates served as personal optimism scores; comparative optimism scores were self-estimates minus other-estimates. For infection and severe disease, personal optimism scores were 100 minus self-estimates; comparative optimism scores were other-estimates minus self-estimates. By averaging across events, we obtained three personal and three comparative optimism scores per participant and per wave: for infection, severe disease, and good outcome. Self-Superiority and Descriptive Norm. Participants indicated, for six precautions that were recommended or imposed at the time of the study, how often they and the average person of their age and gender had adhered to them in the last month: “Wash or disinfect one's hands extra often”, ''Wear a face mask where one is obliged to”, “Stay at home as much as possible”, “Avoid crowded places”, “Stay indoors after curfew”, and “Limit the number of close contacts”. Within targets (self vs. other), the items appeared in random order. Participants moved a slider between 0 (never) to 100 (always). We calculated a descriptive norm score by averaging responses for the average other, as the internal consistency of the scale was very high (Wave 1 Cronbach's alpha = .90). We calculated a self-superiority score by subtracting responses for the average other from those for self and averaging the differences (Wave 1 Cronbach's alpha = .85). We also measured global self-superiority directly by asking “As compared to the average person of your age and gender, how well have you adhered to the measures against the corona virus?“. Participants answered on a 5-point scale from 1 (Much worse) to 5 (Much better). Egocentric and Personal Impact Perception Concerning Precautions. Participants indicated how much the precautions had adversely affected themselves and the average person of their age and gender in the last month on three domains: work or study, leisure activities, and contacts outside the household. A 4-point scale from 1 (Not at all or to a very limited extent) to 4 (To a large extent) was used. We calculated a personal impact score by averaging self-ratings (Wave 1 Cronbach's alpha = .64) and an egocentric impact score by subtracting other-ratings from self-ratings and averaging these (Wave 1 Cronbach's alpha = .42). We measured global egocentric impact perception directly by asking: “As compared to the average person of your age and gender, how much have the measures negatively affected your life?“, to be answered on a 5-point scale from 1 (Much less) to 5 (Much more). Egocentric Impact Perception Concerning COVID-19. Participants judged how severe the consequences would be for them as compared to the average person of their age and gender should they get (re-)infected, end up in hospital, end up in an intensive care unit, and not fully recover after an infection, by moving a slider between −2 (much less severe) to +2 (much more severe). The instructions specified that they should make these judgments considering their way of life, including elements such as their job or study program, leisure activities, caring responsibilities, and personal relationships. We averaged across life domains to derive a mean egocentric impact perception score (Wave 1 Cronbach's alpha = .91). Behavioral Intentions for Adherence to Precautions. Participants indicated how often they would in the next month show the same six behaviors as in the self-superiority measure, by moving a slider from 0 (never) to 100 (always). The items were again randomly ordered, and we calculated a mean intention score (Wave 1 Cronbach's alpha = .89). 4.1 Procedure Panel members were invited through a link in their iVox account. We informed them upfront about the five-wave design but mentioned that participation in any given wave did not imply any obligation to participate in later ones. We also informed new participants that their responses were useful regardless of their non-participation in an earlier wave. Individuals who gave informed consent filled out the survey at a time and place of their convenience. The informed consent procedure and the debriefing informed participants about support lines and the necessity to call a physician should they experience symptoms. The research was ethically and legally approved by the Social and Societal Ethical Committee (SMEC) and the Privacy/Data Protection Officer of KU Leuven (application G-2020-2626/2626R4). 4.2 Statistical analysis We used IBM SPSS Version 28.0.0 to examine the occurrence of self-superiority and egocentric impact perception and to explore the descriptive statistics for intended adherence in the various waves. All other analyses were done in JAGS, using the runjags v.2.2.1 package (Denwood, 2016), written in R v.4.1.1 (R Core Team, 2021). We used joint modelling methods to understand the relationship between self-uniqueness beliefs and intended adherence to the precautions (see Appendix 6: joint model). Because we measured self-uniqueness beliefs and their non-comparative counterparts longitudinally, we used linear mixed models to summarize these measures into latent subject-specific intercepts. Although linear mixed models are intended for continuous data on the real line, they offer useful descriptions for other types of data as well, including these in our study. The longitudinal outcomes were first modelled individually. As a robustness check, we also refitted these individual models using maximum likelihood. For more details, please see Appendix 5 in the Supplemental Materials. Afterwards, we combined these models into a joint model. We were forced to use joint models because the covariates were of a time-varying longitudinal nature. This necessitated us to treat them as endogenous variables; treating them as exogenous instead could have entailed biased results (Rizopoulos, 2012). In these joint models, the latent subject-specific intercepts of the self-uniqueness beliefs and their non-comparative counterparts served as predictors in the longitudinal linear mixed model of intended adherence. Importantly, the models for longitudinal predictor measurements were jointly fitted with the model for the longitudinal outcome. One important advantage of linear mixed models is that they are robust against departures from normality (Verbeke and Lesaffre, 1997). We built two joint models of intended adherence per self-uniqueness belief: comparative optimism (separately for infection, severe disease, and good outcome), self-superiority, and egocentric impact perception (separately concerning the disease and concerning the precautions). For each self-uniqueness belief, the first model included only the random intercept of the self-uniqueness belief as a predictor. The predictors in the second model were the random intercepts of the self-uniqueness belief, its non-comparative counterpart, and their interaction. The models for optimism (separately for infection, severe disease, and good outcome) thus included comparative optimism, personal optimism, and their interaction. The model for past behavior included self-superiority, descriptive norm, and their interaction. The model for the perceived impact of the measures included egocentric impact perception, personal impact perception, and their interaction. We did not build a second model for the perceived impact of the disease, as we did not have non-comparative ratings of it. All models also included the demographic covariates gender (men vs. women), age group (6 groups), educational level (no higher education, higher education), household size (1, 2, 3, 4, 5+), urbanization (large city, small city, large town, small town), and region (Brussels Capital Region, Flanders, Wallonia). We included these variables because earlier research has suggested that they are associated with different levels of adherence to precautions and/or vaccination recommendations (e.g., Delporte et al., 2022 under review; Qeadan et al., 2020). In the fixed-effects structure, wave of data collection was a categorical predictor. As the distributions of behavioral intentions for adherence to the precautions, personal optimism (for infection, severe disease, good outcome) and descriptive norm for past behavior were skewed, we applied a logit-transformation. Model parameters were estimated using Bayesian Markov Chain Monte Carlo (MCMC) estimation. As is common practice, we used non-informative priors for all parameters (Gelman and Hill, 2006). To assess the stability of our inferences and to examine the impact of the priors, we conducted sensitivity analyses by changing the parameters of the non-informative priors and refitted the models. Because we used a Bayesian framework and because of the exploratory nature of our study, we did not apply a correction for multiple testing. For more details, please see Appendix 7 in the Supplemental Materials. We ran four parallel chains with random generated starting values for 15 000 iterations each. The first 10 000 iterations were discarded as burn-in period. To summarize the remaining 20 000 posterior samples (5000 iterations for each chain), we report the posterior means, standard deviations and 95% equal-tailed credible intervals. Convergence was assessed by examining the trace plots and Potential Scale Reduction Factors (PRSF; Gelman and Rubin, 1992). The PRSF values were below 1.01 for all parameters in all models, indicating that convergence can be assumed. Because parameters were estimated and inferences were drawn in a Bayesian framework, their validity is preserved if missing data are missing at random (MAR; Molenberghs and Kenward, 2007; Sidi and Harel, 2018), i.e., if missingness may depend on covariates and observed outcomes but, given these, not further on unobserved outcomes. This is known as ‘ignorability’ in the missing data literature. 5 Results Descriptive statistics and test information for illusory superiority and egocentric impact perception appear in Table 2 . Descriptive statistics and test information for unrealistic optimism from Delporte et al., (2022 under review) appear in the Supplemental Materials, Appendix 10. Participants reported that they had adhered better to the precautions than average (illusory superiority) and that these measures had affected them less (allocentric impact perception). However, they also reported that the precautions had generally affected their life more than average (egocentric impact perception), and that getting infected and experiencing relatively severe disease would also affect their life more (egocentric impact perception). Global self-superiority and egocentric impact ratings were correlated with self-other difference scores, but not very strongly. Because earlier researchers found that self-uniqueness scores derived from differences between self-judgments and other-judgments reflect self-uniqueness beliefs better than scores based on relative self-ratings (Aucote and Gold, 2005), we tested models using the average scores only.Table 2 Self-superiority scores, egocentric impact scores, and intended adherence per wave. Table 2 Wave 1 Wave 2 Wave 3 Wave 4 Wave 5 Self-superiority Average self-other difference across 6 precautions (0–100) Meana 15.43 14.05 13.36 12.18 14.68 SD 16.95 16.61 16.01 15.90 16.52 t 66.99 60.42 58.66 53.95 63.49 df 5411 5107 4940 4960 5103 d .91 .85 .83 .77 .89 Global rating (Relative rating, 2 to +2) Mean 0.79 0.66 0.54 0.46 0.51 SD 0.92 0.90 0.89 0.87 0.88 t 63.24 52.81 42.43 37.49 41.19 df 5416 5115 4945 4965 5106 d .86 .74 .60 .53 .58 Correlation .39 .36 .35 .37 .37 Egocentric/allocentric impact measures Average self-other difference across 3 life domains measures (−3 to +3) Mean −0.09 −0.21 −0.23 −0.24 −0.24 SD 0.66 0.63 0.64 0.64 0.66 t 9.93 23.40 25.67 26.31 25.48 df 5339 5076 4912 4933 5070 d −.14 −.33 −.37 −.38 −.36 Global rating (−2 to +2) Mean 0.09 0.10 0.04 0.05 0.02# SD 0.92 0.88 0.84 0.84 0.82 t 7.25 7.78 3.11 4.53 1.50 df 5416 5115 4945 4967 5106 d .10 .11 .04 .06 .02 Correlation .28 .33 .35 .36 .36 Impact disease: Average rating across 4 COVID-19-related events (Relative rating, −2 to +2) Mean 0.42 0.40 0.34 0.35 0.29 SD 0.84 0.86 0.85 0.84 0.86 t 36.27 33.38 27.83 29.09 24.31 df 5416 5115 4945 4967 5106 d .49 .47 .40 .41 .34 Intended adherence, averaged across 6 precautions (0–100) Mean 89.40 89.58 88.09 86.92 86.23 SD 14.86 14.50 15.51 16.78 15.58 Positive scores = self-superiority/egocentric impact perception. a Except when indicated otherwise, all means differ from 0 at p < .001. #p = .067. Table 3 shows parameters and credible intervals for the models with behavioral intentions for adherence to the precautions as the outcome and the latent subject-specific random intercept of each type of belief as a predictor, controlling for demographic variables. Fig. 2 graphically represents the effects for Model 2 per self-uniqueness belief, with values at −1, 0, and +1 standard deviation of each predictor. This figure can be interpreted as showing the effect on intended adherence of a difference in the predictor of a standardized number of units. Appendix 9 in the Supplemental Materials includes heatmaps running from −2 to +2 standard deviations. The graphs and heatmaps represent findings for the category of reference, that is, the largest category in our sample on each demographic characteristic in Wave 1 (women, 45–54 years, no higher education, household of 2, Flemish, small municipality).Table 3 Parameter estimates and credible intervals for the psychological random intercepts as predictors in the models. Table 3 Estimate SE 95% CI Optimism infection Model 1 (Comparative optimism) 0.04 0.003 [0.035, 0.044] Model 2 Comparative optimism 0.06 0.003 [0.054, 0.067] Personal optimism −0.41 0.030 [-0.464, −0.352] Interaction 0.02 0.002 [0.012, 0.018] Optimism severe disease Model 1 (Comparative optimism) −0.01 0.002 [-0.015, −0.006] Model 2 Comparative optimism 0.03 0.004 [0.019, 0.033] Personal optimism −0.34 0.023 [-0.383, −0.293] Interaction 0.01 0.001 [0.004, 0.007] Optimism good outcome Model 1 (Comparative optimism) −0.03 0.004 [-0.033, −0.018] Model 2 Comparative optimism 0.01 0.009 [-0.009, 0.028] Personal optimism −0.26 0.074 [-0.409, −0.131] Interaction 0.01 0.002 [0.001, 0.090] Past behavior Model 1 (Self-superiority) 0.06 0.002 [0.059, 0.065] Model 2 Self-superiority 0.15 0.002 [0.141, 0.148] Descriptive norm 1.83 0.021 [1.798, 1.880] Interaction 0.01 0.001 [0.010, 0.014] Impact of measures Model 1 (Egocentric impact perception) −0.23 0.055 [-0.340, −0.128] Model 2 Egocentric impact perception −0.59 0.179 [-0.929, −0.244] Perceived impact on self 0.56 0.126 [0.327, 0.797] Interaction 1.00 0.075 [0.844, 1.136] Impact of disease Model 1 (Egocentric impact perception) 0.71 0.039 [0.641, 0.788] Fig. 2 Intended adherence to precautions as a function of optimism for infection (top panel), good outcome (middle panel), or severe disease (bottom panel) in the category of reference (Wave 1, Flemish women, small municipality, 45–54 years, no higher education, household of 2). Fig. 2 As shown in Fig. 2, higher comparative optimism for infection was associated with higher intended adherence, particularly among participants who scored high on personal optimism. In Model 2, the main effect of comparative optimism and the interaction with personal optimism were significant. When comparative optimism for severe disease or comparative optimism for good outcome was the sole predictor, it was associated with lower intended adherence. However, that association disappeared if personal optimism was also a predictor. In that case, higher comparative optimism predicted higher, rather than lower intended adherence, but only among participants with high personal optimism. For severe disease, the main effect of comparative optimism and the interaction with personal optimism were significant in Model 2. For good outcome, only the interaction of comparative optimism and personal optimism was significant. Contrary to expectations, higher self-superiority was associated with higher intended adherence. As shown by the results of Model 2, this was particularly true among participants who perceived a high descriptive norm. Consistent with expectations, egocentric impact perception concerning COVID-19 was associated with higher intended adherence to the precautions (see Fig. 3 ).Fig. 3 Intended adherence to precautions in the category of reference as a function of perceived past adherence. Fig. 3 Also as expected, higher egocentric impact perception concerning the measures was associated with lower intended adherence. However, the main effect of egocentric impact perception was in Model 2 qualified by an interaction with perceived impact on the self (see Fig. 4 ). Among participants who did not perceive a high impact of the precautions on the self, more egocentric (less allocentric) impact perception was associated with lower intended adherence. Among participants who did perceive a high impact of the precautions on the self, more egocentric (less allocentric) impact perception was associated with higher intended adherence.Fig. 4 Intended adherence to precautions in the category of reference as a function of impact perception concerning the precautions. Fig. 4 The non-comparative counterparts of self-uniqueness beliefs were also associated with intended adherence. As expected, higher personal optimism for any aspect of COVID-19 was associated with lower intended adherence, particularly among participants with low comparative optimism (Fig. 2). Higher descriptive norm was associated with higher intended adherence (Fig. 3). Unexpectedly, higher perceived impact on the self was also associated with higher intended adherence. However, that effect was qualified by the interaction with egocentric impact perception. Among participants scoring relatively low on egocentric impact perception, higher perceived impact on the self was associated with lower intended adherence; among participants relatively high on egocentric impact perception, higher perceived impact on the self was associated with higher intended adherence (Fig. 4). 6 Discussion We examined how self-uniqueness beliefs were associated with intentions to adhere to precautions against COVID-19. Participants reported that they had adhered better than average to the precautions against the spread of the virus that caused COVID-19. Our sample was balanced on gender and age and was generally representative for the Belgian population, in contrast to earlier research that involved less balanced samples (e.g., Hoorens et al., 2022; Rose and Edmonds, 2021). We thus provided strong evidence for the generality of illusory superiority. Participants reported that the precautions had generally affected them more than average, and that getting COVID-19 would affect their lives more than average. These findings are novel for COVID-19, but are consistent with the egocentric impact bias found in other contexts (Blanton et al., 2001; Davidai and Gilovich, 2016). Yet, we also found evidence for an allocentric impact bias. Participants reported that specific precautions had adversely affected their life less than average. We thus replicated a result from an earlier study that used a different measurement (i.e., directly comparative ratings), a different question wording, and non-representative sample (Hoorens et al., 2022). We thus showed that the allocentric impact perception is genuine and robust. One factor that may determine whether egocentric or allocentric impact bias occurs may be the extent to which people believe that an event's impact is under one's personal control. If the impact of an event is largely beyond one's control, it does not negatively reflect on the self. People may then readily claim that the impact is greater for them than for others. If an event's impact is within one's control, it may reflect a lack of resourcefulness. People may then be motivated to claim less-than-average impact. In our study, participants may have considered both the potential impact of getting infected and developing symptoms and the potential impact of something as general as ‘precautions’ out of their hands. They may therefore have shown egocentric impact bias. In contrast, participants may have perceived well-circumscribed precautions as challenges that they should be able to cope with, and therefore have shown allocentric impact bias. Differences in self-uniqueness beliefs were associated with differences in intended adherence to precautions. However, only the association between egocentric/allocentric impact perception and intended adherence was fully consistent with intuitive assumptions. People who strongly felt that COVID-19 would affect their lives more than average reported higher intentions to adhere to precautions, as did people who strongly felt that the precautions adversely affected them less than average. Comparative optimism concerning the transmission of COVID-19 infections was associated with higher, rather than lower intended adherence. Higher comparative optimism concerning what might happen after an infection – severe disease or a good outcome – was associated with lower intended adherence if personal optimism was not in the equation. If personal optimism was also included, higher comparative optimism was associated with higher, rather than lower intended adherence. The findings for comparative optimism are interesting for several reasons. First, the results concerning infection are at odds with an often-invoked justification of comparative optimism research, that is, that comparative optimism discourages preventative and encourages risk behavior (Weinstein, 1980). Once we controlled for personal optimism, our results were consistent with the view that strong comparative optimism may not always be a dangerous erroneous belief (e.g., Shepperd et al., 2013). Instead, it may reflect rather than (or besides) encouraging adherence to precautions (cf. Radcliffe and Klein, 2002). Consistent with that suggestion, making people contemplate their self-protective behavior enhanced their comparative optimism for infection but not for severity of the disease (Vieites et al., 2021). A similar situation may hold for self-superiority. The more people felt that they had better than average adhered to the precautions, the higher their intended adherence. Thus, higher self-superiority did not seem to impede behavioral intentions to adhere to the precautions. Second, our findings concerning severe disease and good outcome reflect the inconsistent pattern that has been reported in the literature concerning the relationship between self-uniqueness beliefs (in most studies, comparative optimism) and health behaviors. Some researchers have reported negative associations (e.g., Dillard et al., 2006; McColl et al., 2022), others have reported positive associations (e.g., Vieites et al., 2021) or no association at all (e.g., Cho et al., 2013; Rudisill, 2013). We speculated that the inconsistency may be due to different statistical models being estimated. Here, including personal optimism reversed the association of comparative optimism with intended adherence. Interestingly, other researchers who partialed out variance associated with personal optimism also showed a weak association between comparative optimism and precautionary behavior at best (Rudisill, 2013; Wise et al., 2020). The most plausible interpretation of our findings is that people more strongly intend to adhere to precautions against a potentially deadly disease if they are low on personal optimism, believe that others adhere to the precautions, and perceive the impact of the disease egocentrically but the impact of the precautions allocentrically. Then, the extent to which they believe to be less at risk than average (comparative optimism) and to adhere better to the precautions (self-superiority) is driven by, rather than driving their adherence. The associations between non-comparative counterparts of self-uniqueness beliefs and intended adherence are interesting in their own right. Most were intuitive and consistent with earlier research. These included the negative association of intended adherence with personal optimism for infection, severe disease, and good outcome, and the positive association of intended adherence with descriptive norm. An exception was the positive association of intended adherence with the perceived impact of the precautions on the self. Here, too, an interpretation where some beliefs function as antecedents and others as consequences of intended adherence seems plausible. Lower personal optimism, higher descriptive norm, and greater perceived impact of the disease on the self may encourage higher intended adherence, which may in turn inspire greater perceived impact of these measures on the self. 6.1 Strengths Our study was among the few longitudinal studies including multiple waves over a period of several months on correlates of self-uniqueness beliefs concerning COVID-19 or on intended precautions against the spread of the disease. Thus, it makes an important step forward in a research line with foundations in a multitude of cross-sectional studies (e.g., Asimakopoulou et al., 2020; Dryhurst et al., 2020) and in a limited set of longitudinal studies covering brief periods from one week to one month (e.g., Rubaltelli et al., 2020; Wise et al., 2020) or consisting of a combination of cross-sectional surveys on independent samples (Schneider et al., 2021). Moreover, we chose a statistical approach that had several strengths, of which we here discuss the two main ones. First, joint models allow the inclusion of endogenous longitudinal predictors, i.e., predictors that are affected by (previous) measurements of other variables in the model, potentially also the outcome, in contrast to exogenous variables that presumably are not affected by these other variables. When exogeneity is incorrectly assumed, estimates can be biased. Thus, unlike some more mainstream statistical models that often assume exogeneity, joint models allow for predictors to be affected by previous outcome values, as well as affecting future outcome values. In our research, the joint models allowed for self-superiority beliefs and their non-comparative counterparts to be predicted by intended adherence to precautions as well as for intended adherence being predicted by self-superiority beliefs. In our research, the joint models allowed for self-superiority beliefs and their non-comparative counterparts to be endogenous. Second, joint models optimally capture the available information about the measured variables by including all sources of variability simultaneously. Thus, they do not simply use the central tendency that summarizes various longitudinal measures as a single predictor in the model of the to-be-predicted variable, but also take the systematic and nonsystematic variability between the measures of the latent predictor variable into account. Our sample was exceptionally large and more representative for the general population in terms of critical demographic variables than studies using convenience samples or snowballing recruitment techniques might be. We thus avoided the inbalance that has characterized studies using student populations or using social media and snowballing methods to recruit participants. For example, in several earlier studies on COVID-19-related comparative optimism samples included a disproportionally high number of women (e.g., Asimakopoulou et al., 2020). We used a measure of risk perception that was conceptually unequivocal (likelihood estimates) and a measure of behavioral intentions that allowed more fine-grained responses than a binary yes/no answer. Measures where participants merely indicate whether they have adhered to precautions (with researchers counting the number of affirmative answers as a measure of adherence) are well-suited for research where the goal is to obtain a general impression of adherence to precautions (e.g., Bruine de Bruin and Bennett, 2020; Schneider et al., 2021). However, they might obscure subtle differences in respondents' perception of their own and other people's adherence levels. Our measure avoided that problem. 6.2 Limitations The associations between self-uniqueness beliefs and intended adherence were small for traditional psychological standards. However, small effects may be consequential, especially if they accumulate over time (Funder and Ozer, 2019; Götz et al., 2022). We therefore believe that our findings have practical and theoretical significance. Average intended adherence was high, and rather close to the scale maximum. This might raise questions about a potential ceiling effect. However, the sample size was large, and variation was considerable. Combined with the robustness of the linear mixed model framework against deviations from normality (Verbeke and Lesaffre, 1997), this renders it unlikely that a ceiling effect has jeopardized our results. Our non-experimental data did not allow to conclusively determine if self-uniqueness beliefs are causes or consequences of preventative behavior, a feature that our research shares with most research on comparative optimism (cf. Shepperd et al., 2013). Thus, our findings should not be construed as showing that comparative optimism, self-superiority, and egocentric impact perception concerning a disease are beneficial for taking precautions and that egocentric impact perception (low allocentric impact perception) concerning precautions is generally harmful. We merely observed that differences in self-uniqueness beliefs were associated with differences in intended adherence to precautions against COVID-19. We tried to comprehensibly capture the range of precautions that were in place at the time of the study. However, self-uniqueness beliefs may be differently associated with health behavior other than the type that we studied. Earlier studies that showed negative associations between comparative optimism and health behavior often focused on information seeking, processing, and application (e.g., Cho et al., 2013; Park et al., 2017) or participation in vaccination programs (e.g., Agarwal, 2014; Delporte et al., 2022 under review). It is possible, therefore, that some behaviors (e.g., behaviors that require discrete decisions at given points in time) are negatively associated with self-uniqueness whereas behaviors that require a sustained effort over a longer period are positively related to them. Our measures also involved limitations. First, we used self-reported measures, of which the validity has been disputed (e.g., Hansen et al., 2022). However, some studies yielded no reason to assume that self-reports of relevance to COVID-19 were substantially distorted by social desirability (e.g., Jensen, 2020; Larsen et al., 2020). Second, the conceptual clarity achieved by our operationalization of perceived risk in terms of estimated likelihoods came with the downside that our measure tapped into cognitive aspects of risk perception only. Other research has shown that affective aspects such as worry and fear also predict behavioral intentions (e.g., Harper et al., 2021). Third, we measured egocentric impact perception/bias concerning the disease comparatively only. As we already explained, this was inspired by anticipated ceiling and shifting standards effects. However, it implies that we could not distinguish between egocentric impact perception/bias and its non-comparative counterpart. That does not affect our conclusion that an egocentric impact bias exists concerning COVID-19, but caution is in order concerning the interpretation of the association between individual differences in egocentric impact perception predict and intentions to adhere to precautions against COVID-19. Fourth, our measure of egocentric impact perception concerning the precautions had a low internal consistency. We treated impact perception as a unitary construct because of the already high complexity of our dataset, but future research might usefully investigate differentiated perceptions of how precautions affect oneself more or less than others on various life domains. Finally, the impact of deviations from the MAR assumptions underlying our analysis might be explored using multiple imputation under MNAR (missing not at random, i.e., missingness depends on unobserved outcomes even after correction for covariates and observed outcomes; Molenberghs et al., 2015). However, that would take us beyond the scope of the current manuscript. 7 Implications We found no evidence that greater comparative optimism or greater self-superiority concerning past adherence to precautions was associated with lower intentions to adhere to these precautions in the future. Thus, when the aim is to enhance adherence to precautions against a contagious disease like COVID-19 (rather than, say, to encourage vaccination), there seems to be no strong need for public health communicators to pay particular attention to those subgroups or individuals who show strong comparative optimism or self-superiority. Instead, we found that the negative association between personal optimism and intended adherence was mitigated, rather than exacerbated, among individuals who showed greater comparative optimism. The feeling that precautions affect oneself more than others was associated with lower intended adherence to these precautions, particularly among participants who generally feel that the precautions did not affect them very strongly. Thus, even though public health communicators may be tempted not to worry too much about those individuals who seem to take the precautions rather lightly, the accompanying belief that one is still more affected than average should function as a warning light. The perception that one's life would be affected more than average (egocentric impact perception concerning the disease) should one get infected and get ill is associated with particularly strong intentions to adhere to precautions. That implies that the often-used rhetoric in public health messages (at least in Belgium, where the study was conducted) that people should adhere to the precautions ‘to protect others’ and ‘to save lives’ may be problematic, at least if the messages go hand in hand with an explicit acknowledgement that the target audience itself may have little to fear from a spell of COVID-19. These implications should not be taken as showing that at least two self-uniqueness beliefs – comparative optimism and self-superiority – are totally non-problematic. After all, we did find that relative self-judgments concerning COVID-19 are miscalibrated – we found illusory superiority, unrealistic optimism and impact bias. Although people who show relatively strong self-uniqueness beliefs are not always those who intent to adhere to precautions less than others, unrealistic optimism and illusory superiority may generally reduce efforts to adhere well to precautions, just like egocentric impact bias concerning the precautions may do and just like egocentric impact bias concerning the disease itself may enhance adherence. Thus, we propose that the general miscalibration that we – and, in the case of unrealistic optimism and illusory superiority other researchers as well (e.g., Asimakopoulou et al., 2020; Rose and Edmonds, 2021). – have observed may help explain why adherence has in many countries been lower than public health authorities might have desired. From a methodological point of view, arguably the most important finding of our research is that including the non-comparative counterparts of self-uniqueness beliefs affects the observed associations between them and intentions for precautions. In the case of comparative optimism concerning the outcome of infections, we found that it even reversed the association. For an unequivocal understanding of how self-uniqueness beliefs relate to health behavior, therefore, it may be good practice to test these associations once with the involved belief considered in isolation and once controlling for additional predictors. As our results show, one obvious candidate is the non-comparative counterpart of each self-uniqueness belief. In any case, one should clearly distinguish between comparative and non-comparative aspects of risk perception, as their interplay might obscure significant relationships that each of them holds with preventative behaviors, potentially even entailing null effects for risk perception (e.g., Clark et al., 2020). 8 Conclusion People show illusory superiority concerning their past adherence to precautions against a contagious disease. They also show egocentric impact bias concerning the disease and generally described precautions against it on their life, but allocentric impact bias concerning specific precautions. As expected, greater intended adherence to precautions against the disease was observed in individuals who showed more egocentric impact perception concerning the disease and greater allocentric impact perception concerning specific precautions. However, it was also observed in individuals showing stronger comparative optimism for infection and stronger self-superiority. Comparative optimism for severe disease and a good outcome predicted lower intended adherence only if personal optimism was not being controlled for. If it was being controlled for, comparative optimism predicted greater intended adherence. Thus, self-uniqueness beliefs may not always be as harmful for health and safety behavior as they are sometimes assumed to be. Credit author statement Dries De Witte: Methodology, Software, Validation, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Visualization; Margaux Delporte: Methodology, Formal analysis, Writing – original draft, Writing – review & editing; Geert Molenberghs: Conceptualization, Methodology, Resources, Writing – review & editing, Funding acquisition; Geert Verbeke: Conceptualization, Methodology, Resources, Writing – review & editing, Funding acquisition; Stefaan Demarest: Conceptualization, Methodology, Writing – review & editing, Funding acquisition; Vera Hoorens: Conceptualization, Methodology, Investigation, Project administration, Writing – original draft, Writing – review & editing, Funding acquisition. Appendix A Supplementary data The following is the Supplementary data to this article:Multimedia component 1 Multimedia component 1 Data availability The data and syntaxes for the present paper are available on osf.io/COVID_precautions. Acknowledgments The research was supported by FWO-Grant G0G6620N, awarded to the last four authors and 10.13039/501100004385 Eliane Deschrijver (UGent & UNSW ), and FWO Sabbatical Bench Fee K803121N, awarded to Vera Hoorens. We have no other conflicts of interest to disclose. The funding agency had no involvement in any stage of the research, nor in the preparation of this paper or the decision to submit it for publication. We thank Roel Vercammen, Gunther Ackermans, and Lander Van den Eynde for their help in the data collection and two anonymous reviewers for their constructive comments on an earlier draft of this paper. Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.socscimed.2022.115595. ==== Refs References Agarwal V. A/H1N1 vaccine intentions in college students: an application of the theory of planned behavior J. Am. Coll. 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S0039-9140(22)00963-8 10.1016/j.talanta.2022.124167 124167 Article Smartphone-controlled biosensor for viral respiratory infectious diseases: Screening and response Ma Yaxing a Luo Yaoyu a Feng Xinrui a Huang Chuixiu b∗∗ Shen Xiantao a∗ a State Key Laboratory of Environment Health (Incubation), Key Laboratory of Environment and Health, Ministry of Education, Key Laboratory of Environment and Health (Wuhan), Ministry of Environmental Protection, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road #13, Wuhan, Hubei, 430030, China b Department of Forensic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road #13, Wuhan, Hubei, 430030, China ∗ Corresponding author. ∗∗ Corresponding author. 5 12 2022 1 3 2023 5 12 2022 254 124167124167 4 9 2022 3 11 2022 1 12 2022 © 2022 Published by Elsevier B.V. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Outbreaks of emerging viral respiratory infectious diseases (VRIDs) including coronavirus disease 2019 (COVID-19) seriously endanger people's health. However, the traditional nucleic acid detection required professionals and larger instruments and antigen-antibody detection suffered a long window period of target generation. To facilitate the VRIDs detection in time for common populations, a smartphone-controlled biosensor, which integrated sample preparation (electromembrane extraction), biomarker detection (red-green-blue model) and remote response technology (a built-in APP), was developed in this work. With the intelligent biosensor, VRIDs could be recognized in the early stage by using endogenous hydrogen sulfide as the biomarker. Importantly, it only took 15 min to accomplish the whole process of screening and response to VRIDs. Moreover, the experimental data showed that this smartphone-controlled biosensor was suitable for ordinary residents and could successfully differentiate non-communicable respiratory diseases from VRIDs. To the best of our knowledge, this is the first time that a smartphone-controlled biosensor for screening and response to VRIDs was reported. We believe that the present biosensor will help ordinary residents jointly deal with the challenges brought by COVID-19 or other VRIDs in the future. Graphical abstract Image 1 Keywords Viral respiratory infectious diseases Biosensor Hydrogen sulfide ==== Body pmc1 Introduction Viral respiratory infectious diseases (VRIDs) are a series of diseases caused by different respiratory viruses, normally including influenza (infected with influenza virus), acute upper respiratory tract infections (infected with rhinovirus), severe acute respiratory syndrome (SARS) and novel coronavirus pneumonia (COVID-19) (infected with coronavirus), etc. VRIDs often spread rapidly, seriously endangering people's health and bringing great economic losses [1]. For example, with the emergence of highly concealed and contagious variant strains of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the coronavirus disease 2019 (COVID-19) has been threatening people's lives and the development of the social economy around the world [2]. From an epidemiological point of view, the risk of VRIDs can be reduced from three aspects: controlling the source of infection, cutting off the way of transmission, and protecting the susceptible population [3]. Timely and accurate diagnosis is the priority in the prevention and control of respiratory infectious diseases. Once a person was diagnosed or screened as positive for VRIDs, some necessary measures should be taken to stop further transmission, including quarantining at home, and seeking medical help. In this case, developing a point-of-care sensor that could screen VRIDs and instantly make responses (e.g., disinfection of the residence place and suggestions for personnel isolation) is urgently needed for common residents. So far, there have been mainly two methods for the diagnosis of VRIDs [4]. The first one is the “gold standard” for the diagnosis: Nucleic acid testing (NAT). The implementation of NAT requires professionals and instruments, and the NAT process traditionally includes sample preparation, gene extraction, and polymerase chain reaction (PCR) [5]. Another method is antigen-antibody testing. At the early stage of virus infection (generally called the window period), there may be a few antigens and nearly no antibody generated in the infectious body. Moreover, usually antigens will change with the appearance of variant strains. Thus, this method has a limit on testing time and accuracy [6,7]. In short, neither of these methods is suitable for common residents to detect the VRIDs at any time or anywhere they need and disinfect the residence place after detection in time. To address the cumbersome detection, some point-of-care approaches for antigen detection have been reported [8], which facilitate the prevention of VRIDs. However, to the best of our knowledge, a point-of-care sensor for the earlier and more accurate screening of VRIDs and the simultaneous response remains a challenge in this field. Nowadays, some paper-based or on-chip optical detection methods (colorimetry, fluorescence, etc.) have already been developed and used for the point-of-care testing of some chronic and bacterial infectious diseases. Generally, these methods have been achieved via detecting biomarkers or specific substances related to the disease [9,10]. VRIDs often cause lung injury. It has been proved in various experimental models that hydrogen sulfide has the potential to prevent acute lung injury in pulmonary inflammation [[11], [12], [13], [14]]. Recently, some studies have found that the availability of hydrogen sulfide would significantly change in COVID-19 patients [15,16]. Besides, relevant literature has proved that in the in-vitro models of respiratory syncytial virus (RSV) infection, the regulation of endogenous hydrogen sulfide significantly affected cell response and virus replication. At the same time, RSV infection affected the generation of hydrogen sulfide in airway epithelial cells [17]. As a regulatory factor in organisms, endogenous hydrogen sulfide plays an important role in the pathophysiological mechanism and has a variety of protective functions including anti-inflammatory, antioxidant stress, and antivirus [18,19]. Therefore, the concentration of hydrogen sulfide in the blood will also change in some other diseases, including cardiovascular diseases (coronary artery lesions and atherosclerosis, etc.) [20,21], chronic kidney diseases [22], and Alzheimer's disease [23]. As a potential biomarker, the preliminary diagnosis or screening of VRIDs could be achieved by detecting the level of endogenous hydrogen sulfide in plasma [15,16]. According to relevant literature, the concentration of sulfide in blood or plasma is in the range of 30–300 μmol L−1 [24]. At present, the reported detection methods of endogenous hydrogen sulfide mainly include inductively coupled plasma mass spectrometers (ICP-MS) [25], methylene blue spectrophotometry (MBSP) [26], high-performance liquid chromatography (HPLC) coupled to a UV-VIS [27], etc. However, it is difficult for common residents to use these precise instruments to accomplish the rapid detection of endogenous hydrogen sulfide. How to achieve further response towards the detection results should be considered as well. In the present age of digital information, smartphones have been inseparable from our daily life. Whether in terms of information processing (detection) or transmission (response), smartphones could finish it in the shortest time [28]. Therefore, a system based on smartphone could meet the needs of intelligent screening and response to VRIDs for common residents: i) Quantification of the endogenous hydrogen sulfide, a potential biomarker of VRIDs, can be realized by chromogenic reaction combined with the camera function. After photographing the chromogenic image, the color information is processed with the self-developed application (APP) installed on the smartphone using the red-green-blue (RGB) model [29]. By associating the values of three primary colors, the concentration of endogenous hydrogen sulfide is obtained. ii) Once the detection result shows a risk of the presence of VRIDs, the APP automatically starts the further response including suggestions for disinfection, personnel isolation, etc. Because of its features including low cost and easy operation, the paper-based chip has been used widely in environmental pollutant analysis, clinical diagnosis, and other fields [30,31]. In this work, a paper-based portable platform was designed to achieve rapid detection. Endogenous hydrogen sulfide in plasma samples was selected as an indicator of VRIDs, but the matrices in plasma (e.g., colored red blood cells) would strongly affect the chromogenic reaction and thus the detection of endogenous sulfide. To eliminate the inference effect of matrix components, a sample pretreatment procedure was integrated into the paper-based portable platform. As a sample pretreatment method, electromembrane extraction (EME) is an electrokinetic migration process, which has the advantages of short extraction time, excellent enrichment, and purification ability [32]. At present, the paper-based EME has been applied for extraction of the copper ions from complicated matrices [33]. Therefore, EME shows great potential in solving the problem of matrix interference in color detection. In short, a smartphone-controlled biosensor including a portable screening platform and a remote emergency response system was constructed to reduce the risk of VRIDs infection. The sensor consisted of a paper-based platform and a built-in response APP. After the screening of VRIDs, the APP will proceed with the risk assessment immediately according to the test result. When the biomarker level is abnormal, the response APP will suggest the subject go to a hospital for further diagnosis. At the same time, the APP can remotely switch on the disinfection equipment in advance at home or office to complete the disinfection (Fig. 1 ). We believe that the biosensor integrating sample preparation, biomarker detection and remote response technology will help ordinary residents to deal with the challenges brought by COVID-19 or other VRIDs in the future.Fig. 1 Schematic illustration of the smartphone-controlled screening and remote response system. Fig. 1 2 Experimental section 2.1 Reagents and materials Hydrochloric acid (HCl), sodium hydroxide (NaOH), sodium nitroprusside (SNP), and sodium sulfide (Na2S) were purchased from Sinopharm Chemical Reagent Co., Ltd. (Shanghai, China). 1-Heptanol, 1-octanol, 1-nonanol, 1-decanol, 1-undecanol, cetyltrimethylammonium bromide (CTAB), polyvinylpyrrolidone (PVP), and sodium dodecyl sulfate (SDS) were purchased from Aladdin Chemical Reagent Co. (Shanghai, China). Whatman 1 qualitative filter paper was purchased from Zhengcheng Experimental Instrument Co., Ltd. (Shanghai, China). The polypropylene (PP) membrane (approximately 100 μm in thickness) was purchased from Membrana (Wuppertal, Germany). The 9 V battery was purchased from Nanfu Battery Co., Ltd. (Fujian, China). All blood and plasma samples were obtained from 50 healthy volunteers and collected in tubes containing EDTA to avoid clotting. For the detection method development, 25 plasma samples were mixed, and the other samples were used for method validation. All samples were preserved at 4 °C. All volunteers signed an informed consent form and agreed to publish the experimental data publicly. This study was approved by the Ethics Committee of Tongji Medical College, Huazhong University of Science and Technology on November 25, 2020 ([2020] S284). 2.2 Construction of the paper-based platform The screening function of the sensor was based on the paper-based platform. The construction of the platform was given as follows: Construction of the platform framework: To ensure the stability of the sensor during the detection process, the platform framework was independently designed by 3D printing with resin material. As shown in Fig. S1, the framework contained two shells that formed a whole cuboid with a size of 44 mm × 24 mm × 5 mm. In the middle of each shell, a rectangle window with a size of 10 mm × 6 mm was presented. For connecting the paper chip to the power supply, two channels (4 mm × 1 mm) were reserved on both sides of the shell. Design of the paper chip: Hydrophilic paper (Whatman 1 qualitative filter paper) was used for the construction of the paper chip. Two paper layers with the shape of “±” were first cut, which contained four parts (Fig. 2 a). The head (part-I) on the paper was designed for fixing the electrode, and two horizontal bars (the part-II and part-IV) were designed to fix the chip into the framework. To confine the sample or acceptor solution within the fine area of the part-III with a size of 6 mm × 10 mm, the head, as well as two horizontal bars, were fabricated as hydrophobic areas by immersing in molten paraffin at 80 °C for 2 s. It is noted that, when constructing the hydrophobic area of the paper, a copper wire electrode with a length of 15 mm was also immersed in paraffin. The end of the wire was close to the middle of the hydrophobic head. After fabrication of the acceptor layer and donor layer, the supported liquid membrane (SLM) for EME was prepared. The size of the PP membrane was 12 mm × 14 mm (Fig. 2a), which was larger than the hydrophilic area (6 mm × 10 mm) to prevent liquid leakage. The membrane solvent in SLM was 5 μL of 1-octanol mixed with CTAB, which was cast at the center of the support membrane. The paper chip was then obtained by fixing the acceptor layer, donor layer, and SLM into the 3D-printing framework. The acceptor layer was on the top, the donor layer was on the bottom, and the SLM was in the middle of the two paper layers (Fig. 2b).Fig. 2 a) Schematic model of the paper-based platform; b) SEM image of the neat PP membrane; c) Schematic diagram of hydrogen sulfide movement; d) Linear relationships of r with hydrogen sulfide in water; e) Change of r with different concentrations of hydrogen sulfide in plasma; f) Effects of different solvents and surfactants on the target recoveries (conditions: 9 V, 12 min, pH value of the acceptor solution was 12, pH value of the donor solution was 5). Fig. 2 Connection to the power supply: The framework with the paper chip inside was placed on the power supply. The electrodes were connected to the power supply from the power supply holes. In this study, the acceptor layer was connected to the positive electrode, and the donor layer was connected to the negative electrode. In this way, the paper-based platform was successfully constructed. 2.3 Optimization of paper-based EME To solve the problem of matrix interference in plasma, EME was used for the separation of targets. Before the optimization of EME, the endogenous hydrogen sulfide in the mixed plasma sample was detected with MBSP. The standard curve was shown in Fig. S2. Using this method, the hydrogen sulfide in the mixed plasma sample was measured to be 33.2 ± 1.2 μmol L−1. In this mixed plasma sample, 30.0 μmol L−1 S2− was spiked (sodium sulfide as sulfur ion donor). This spiked plasma was then used for the optimization of paper-based EME. In the EME process (Fig. 2c), the acceptor solution was 20 μL of sodium hydroxide solution (pH = 12). The donor solution was a mixture of the 10 μL plasma sample and 10 μL hydrochloric acid (pH = 4.7). The pH value of the donor phase was around 5.0. After adding the sample and acceptor to the paper layer, the power supply (9 V) was switched on and the separation was conducted for 12 min. The concentration of hydrogen sulfide in the acceptor phase was detected with the spectrophotometry method. During the optimization, the method of the single-factor test was used to analyze the critical variables (EME voltage, pH of the acceptor, and extraction time). Only one parameter was changed as designed and the other parameters were kept constant. The spiked recovery was calculated for the evaluation of optimal parameters. The spiked extraction recovery (SER) of the hydrogen sulfide in plasma was calculated by equation (1), and the recovery in this paper was SER:(1) SER=C2−C1C3×100% C1 was the recovery concentration of hydrogen sulfide before standard addition. C 2 was the recovery concentration of hydrogen sulfide after standard addition. C 3 was the spiked concentration. 2.4 Rapid quantification of biomarkers 2.4.1 Optimization of color reaction To realize the rapid detection of biomarkers without relying on large-scale instruments, the quantification of the target was mainly based on the color reaction in this work. The conditions for color reaction were also optimized, including the concentration of chromogenic reagent, reaction time, and pH of the reaction system. Typically, 20 μL of 40 mmol L−1 chromogenic reagent was added directly to the acceptor solution on the hydrophilic area of the paper. After the desired reaction time (∼30 s), the color was observed visually and recorded by a smartphone. 2.4.2 Image capture and processing In this study, the smartphone of Honor Magic 3 was used to capture the digital images after the color reaction. The attributes of the image from Honor Magic 3 were 3072 × 4096 pixels, aperture value f/1.9, exposure time 1/100 s and ISO-320, respectively. The smartphone lens was placed horizontally about 8 cm above the color rendering area when photographing. It is noted that the edge of the platform was exactly aligned with the edge of the phone screen at this distance. The images were saved in Joint Photographic Experts Group format (.jpg). The images were processed with Adobe Photoshop CC 2018 software (PS). Briefly, the color region (6 mm × 10 mm) was selected by the tailoring tool, and the average RGB values in the region of interest (ROI) were recorded. Previous works had shown that the RGB measurements could be used for the quantitative determination of the target in samples [34]. To control the effects of the photographing conditions (e.g. the light intensity) for stable outcomes of picture processing, a quantitative RGB normalization method was developed [35]. Thus, the transformation of RGB to normalized RGB was conducted by the following equation (2) in this study:(2) r=R+BR+G+B This normalized RGB value (r) and hydrogen sulfide concentration showed a good linear relationship. 2.5 Analytical curve for construction of the biosensor Before the APP design, analytical parameters should be obtained. First, a standard curve for testing the concentrations of hydrogen sulfide in plasma was established. Firstly, the endogenous hydrogen sulfide in the mixed plasma sample was detected as 33.2 ± 1.2 μmol L−1 with the MBSP. The hydrogen sulfide concentrations in the plasma were then adjusted to10 μmol L−1, 15 μmol L−1, 37.5 μmol L−1, 75 μmol L−1, 150 μmol L−1, 200 μmol L−1, 250 μmol L−1, and 300 μmol L−1 by dilution with PBS buffer (pH = 7.4) or addition of sulfide. The hydrogen sulfide in plasma was separated by the paper-based platform, and then the color reaction was performed. r was then obtained after the capture of color reaction images with a smartphone and processing of the images with PS. The standard curve was established by plotting the r values versus the concentration of the hydrogen sulfide. In this way, the analytical parameters of the standard curve for APP design were obtained. 2.6 Design of the APP APP was developed based on the Android Studio (Google) integrated development tool. The function of APP mainly included three parts: i) acquisition of images, ii) conversion of color signal into RGB information and then conversion of RGB information into the concentration of the biomarker, and iii) the feedback of the result (e.g. connection with the disinfection equipment). The layout of the APP interface was mainly divided into two parts (the upper part was the image display area, and the lower part was the parameter display area and the function area). The photographing function was directly written into the APP program. Click the “Photograph” button to wake up the camera. In addition to taking photos, the local photos can also be imported into the APP for detection. The RGB mode was used to process the color area in this study. ROI was set as the 3 mm × 3 mm area in the middle of the rendering area (6 mm × 10 mm) according to the optimization of the detection area. To select and calculate the ROI accurately, the length-to-width ratio of the photographing area (there has a border indication on the screen when photographing) was set to 3:5.5 according to the length-to-width ratio of the platform. During photographing, the long and short sides of the platform completely coincide with the photographing border. Lock the coordinates and calculate the average value of R, G and B (by the pixel value of each channel) in the center (the detection size was converted from the actual 3 mm × 3 mm area) of the image display area. The average value of R, G and B was brought into equation (2) to obtain the r value. Then the standard curve of the r value and hydrogen sulfide concentration was used to obtain the concentration of the target. To eliminate the occurrence of abnormal values, the range of the r value and the range of detectable concentration was set at 0.66–0.76 and 0–300 μmol L−1 according to the standard curve, respectively. Once the measured value was out of this range, the screen would display that the detection value was invalid. After the target concentration was obtained, the APP would further respond to the detection results. First, 30–300 μmol L−1 was preset as the normal range in the program, and then two countermeasures were set after the comparison results appeared. If a value was within the normal range, it indicated that the infection risk would be low in this current and displayed a green prompt to end testing. On the contrary, it would prompt the warning interface and request to open the disinfection equipment. To realize the remote connection of the self-developed APP and equipment, the APP was able to connect to the “smart remote-control” software, which could jump to the control interface of the device. It should be noted that the disinfection equipment used in this study was equipped with intelligent WiFi cloud control technology, which could be directly controlled with the APP [36]. For traditional equipment requiring infrared control, it is necessary to purchase an intelligent infrared remote controller to connect with the APP to control the equipment. 2.7 Method validation To verify the screening accuracy of this smartphone-controlled biosensor, 11 plasma samples were detected by the MBSP and this biosensor at the same time. Further, to verify that the response system of the smartphone can operate normally, an air purifier as disinfection equipment was chosen to connect with the APP in advance, and then three samples were tested for the remote control of the equipment. To verify the practical feasibility of this biosensor for screening VRIDs, a simple verification test was designed. Four volunteers were selected for on-site detection of the endogenous hydrogen sulfide by the biosensor. At the same time, whether the volunteers would have respiratory symptoms within the next five days was observed. Moreover, five people (who had no experimental experience related to this experiment) were randomly selected to operate the biosensor to prove its serviceability for ordinary residents. 3 Results and discussion 3.1 Proof of concept The importance of fabrication of a screening platform (Fig. 1) was confirmed by the detection of the biomarker in aqueous solution (Fig. 2d) and plasma (Fig. 2e). As seen, with the color reaction, the hydrogen sulfide concentration and r showed a good linear relationship in aqueous solution (R2 ≥ 0.99), while this color reaction was strongly inhibited by the color of the plasma (resulting in a failure detection of the biomarker in plasma). These experimental data would provide the potential for biomarker detection in plasma if the matrix interference could be efficiently removed. Therefore, the fabrication of an intergraded platform including the separation and detection of the biomarker was one of the challenges in this work. 3.2 Parameters of paper-based platform The platform was designed for common residents to screen the VRIDs. Paper was a kind of promising testing material for the construction of chips in the rapid detection of various targets. Herein, a new separation technology named paper-based EME was used. The papers with pore sizes of 0.7, 1.0, 1.2, 1.6, and 2.0 μm were selected for the fabrication of the paper-based platform. It was seen in Fig. S3 that the types of papers (with different pore sizes) showed no effect on the EME efficiency. As a paper-based platform for ordinary residents to use, the sample volume required for detection should be controlled at a very low level. To find the most appropriate sample volume for the experimental effect, a sample (ferrous sulfate solution) was added in drops to the hydrophilic area (10 μL each time). It is seen in Fig. S4 that the liquid covered the whole color area and had a clear color rendering effect when the volume was 40 μL, so the total volume of acceptor solution and chromophore solution was chosen as 40 μL. When the volume ratio of the two solutions was 1:1 (each was 20 μL), it had a uniform color rendering effect. For the donor solution on the bottom of the paper layers, it was necessary to avoid liquid leakage. When the volume was less than 20 μL, the liquid only spread horizontally and without the vertical infiltrate. Therefore, to facilitate solution addition, the volume of three solutions (acceptor solution, chromophore solution, and donor solution) were all chosen at 20 μL. Besides, compared with lateral flow immunoassay to screen the VRIDs [37], the separation of samples on the paper-based platform did not need exclusive transport channels and thus avoided the residue of the samples. 3.3 Optimization of the biomarker separation In this work, the separation of the biomarker was achieved by paper-based EME. The SLM is one of the important parameters that largely determines the selectivity, efficiency, and stability of the EME. Alcohols are effective membrane solvents to separate inorganic anions in EME [38]. However, our preliminary experiment showed that the pure alcohols failed to function as SLMs to separate the hydrogen sulfide from plasma (Fig. S5a). In kinds of literature, the surfactant was reported to successfully promote the migration of anions [39]. Accordingly, cationic surfactant CTAB, anionic surfactant SDS and non-ionic polymer surfactant PVP were added to the membrane solvent of alcohols (Fig. 2f). For all the alcohols, the presence of CTAB showed much higher extraction recovery of the target than the presence of SDS and PVP. The reason is given as follows: HS− and S2− are negatively charged, which is easier to form ion pairs with cation surfactant (CTAB). Moreover, the recovery of hydrogen sulfide raised with the increase of the carbon content of alcohols. With a further increase in the carbon content of alcohols, the recovery of hydrogen sulfide decreased. This might be due to the subsequent increase in the density and viscosity of alcohols, which had a resistance to the migration of the target analyte [40]. Therefore, 1-octanol mixed with CTAB was selected as the membrane solvent for the paper-based EME. The concentration of CTAB in the 1-octanol was also optimized (Fig. S5a). The recovery of the target was almost 0 when no surfactant was used in 1-octanol. With the increase of CTAB, the recovery of the target was enhanced. When the CTAB concentration was higher than 12.5 mg mL−1, the recovery of the target reached a plateau (∼60%). For membrane solvent, the organic solvent should be used as little as possible under the guarantee of ideal recovery, so 5 μL of membrane solvent was added every time according to the experimental results (Fig. S5b). As the driving force for the transfer of charged ions, the voltage has a great influence on the extraction of the target during the EME process [41]. To meet the requirement of portable detection for ordinary residents, a low and safe voltage was preferred, which could be provided by the commercial battery. As seen in Fig. 3 a, the recovery of the target increased with the increase of the voltage and reached a plateau until the voltage was higher than 9 V. So the battery of 9 V was selected as the power supply of the platform.Fig. 3 a) Effect of EME voltage on target recovery (12 min, pH value of the acceptor solution was 12, pH value of the donor solution was 5); b) Effect of pH value of donor solutions on target recovery (9 V, 12 min, pH value of the acceptor solution was 12); c) Effect of extraction time on target recovery (9 V, pH value of the acceptor solution was 12, pH value of the donor solution was 5); d) Color changes in different pH values of chromogen and sulfur ion; e) Color changes between different concentrations of chromogen and sulfur ion; f) Relationship between the time of color reaction and r. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.) Fig. 3 For transferring the biomarker from the donor phase to the acceptor phase driven by the electric force, the biomarker should be ionized in both two phases [42]. Moreover, once the pH value of human plasma was higher than the physiological pH (7.4), sulfur-containing compounds such as cysteine and glutathione would be decomposed. The generation of hydrogen sulfide during the decomposition would affect detection results. Due to the above two criteria, the donor solution needed to be adjusted to neutral or weakly acidic [43]. The pH effect of samples on the recovery was investigated, it could be seen in Fig. 3b that the recovery of hydrogen sulfide obviously changed with altering the pH of the sample. When the pH value of the sample was near 5, the EME system showed the highest recovery of 60%. Thus, the pH of the plasma was adjusted to 5 with the hydrogen hydrochloric acid solution before the EME process. Besides, the pH of the acceptor solution was adjusted to 12 (conducive to color reaction) to ensure that the sulfur ion would not escape in the form of gas (hydrogen sulfide). The extraction duration is also an important parameter for EME. Generally, the recovery increases with the increase of the extraction duration, and then reaches a plateau period in a typical EME process [44]. For the present paper-based EME system, the recovery of hydrogen sulfide also showed a similar tendency. Briefly, the recovery gradually increased during the initial extraction duration of 12 min. After 12 min, the recovery reached the plateau (Fig. 3c). Based on this experimental result, 12 min was chosen as the extraction duration. Moreover, EME extraction time accounts for most of the detection time of the biosensor, so this biosensor can complete a round of the screening and response within 15 min (containing the time of other processes, like color reaction and smartphone information processing). 3.4 Quantification of the biomarker To obtain a sensitive and stable color reaction for biomarker detection, the conditions for color reaction should be optimized. The principle of the color reaction was the chemical reaction between sulfur ion and sodium nitroprusside which resulted in a clear purplish red. Among all the parameters that would affect the quantification of the endogenous hydrogen sulfide in this reaction, the pH value of the reaction system was the most important factor [45]. The influence of the pH value of the solution on the color reaction was shown in Fig. 3d. When the pH value was lower than 12, the reaction color was not obvious (compared to that with a pH value of 12). When the pH value was higher than 12, the reaction color displayed a dark yellow. This dark yellow was not selected for the quantitation, because it originated from the chromogen in the highly alkaline environment. Therefore, the pH value of the solution was selected to be 12 for the color reaction. Besides, the concentration of chromogen was another important factor affecting the detection. Generally, the chromogen should be excessive compared to the targets in the samples. According to relevant literature, the concentration of hydrogen sulfide in blood after infection with VRIDs was not higher than 400 μmol L−1 [15]. The concentration of chromogen was then optimized with different concentrations of hydrogen sulfide. As seen in Fig. 3e, with the same hydrogen sulfide concentration, the reaction system tended to show the color of chromogen decomposition in the highly alkaline condition when increasing the chromogen concentration. This tendency was much clear when the hydrogen sulfide concentration was ample lower. There was an obvious color change gradient when the concentration of chromogen was 40 mM, and this concentration was then selected as the optimized chromogen concentration in this study. To verify the stability of the color reaction, the reaction time was investigated here. As shown in Fig. 3f, the color reaction occurred quickly and the reaction equilibrium was achieved in a short time. Moreover, the color was relatively stable in 1 min, so it was important to capture the images within 1 min. In the present study, a reaction duration of 30 s was selected. 3.5 Design of the APP In this work, the sensor APP was developed based on the Android Studio (Google) integrated development tool. To reduce the impact of the difference in the photographing acquisition on the detection, the long-short sides of the platform completely coincided with the photographing border, and the phone lens was placed horizontally above the device, and the phone was held upright in hand (Fig. 4 ). The height between the lens and the color rendering area was selected at about 8 cm (the whole equipment was just photographed). The distance should not be too far to increase the impact of the surrounding environment, or too close to make it difficult for the lens to focus.Fig. 4 Schematic illustration of the smartphone-controlled biosensor for VRIDs screening and its response to result (combination with disinfection equipment). Fig. 4 For the rendering area of detection by APP, it should stably reflect the overall color level. The whole rendering area was 6 mm × 10 mm, it was easily affected by the edge blank area when the whole rendering area was calculated. Besides, there also was an error caused by uneven color when selecting a point for detection. So, it is necessary to optimize the area of calculation. Three different concentrations of hydrogen sulfide (low, medium, and high concentration) reacted with chromogen on the paper chip (all conditions were the same with the reaction of the acceptor and the chromogen), and then three rendering areas were obtained. After that, the four corners and centers of rendering areas were detected by selecting the point and the area of 1 mm × 1 mm, 2 mm × 2 mm, 3 mm × 3 mm, and 4 mm × 4 mm, respectively. As shown in Table S1, the standard deviation was the largest by clicking the point, and the area of 3 mm × 3 mm can stably reflect the overall color level. In short, if the selected detection area was small, it would increase the error by uneven color. On the contrary, the area at the edge of the rendering area would have an impact if the detection area was large. So, the ROI was set as 3 mm × 3 mm in the middle of the rendering area (6 mm × 10 mm). 3.6 Method validation 3.6.1 Detection method of the biomarker After the successful design of the biosensor, the screening system was utilized to measure the concentration of endogenous hydrogen sulfide in plasma. By spiking or diluting a mixed plasma (the endogenous hydrogen sulfide in mixed plasma was 33.2 ± 1.2 μmol L−1 detected with the MBSP), plasma samples with different concentrations of the hydrogen sulfide (1–300 μmol L−1) were prepared. Using these samples, the feasibility of the present biosensor was evaluated. As shown in Fig. 5 a, the biosensor showed a linear detection range of 10–300 μmol L−1 with a good linear correlation coefficient (R2 = 0.9951). In this work, the limit of detection (LOD) was calculated as LOD = 3 λb/slope, and the limit of quantification (LOQ) was calculated as LOQ = 10 λb/slope, where λb was the standard deviation of very low concentration samples. The LOD and LOQ were calculated to be 2.5 μmol L−1 and 8.3 μmol L−1, respectively. To compare the present detection method with the conventional approaches for endogenous hydrogen sulfide measurement, the MBSP method was also investigated here. As seen in Fig. S2, the LOD for the MBSP method was 1.2 μmol L−1, indicating that our system had a detection sensitivity comparable to the general MBSP method. More importantly, in the early stage of VRIDs infection, the level of the endogenous hydrogen sulfide (biomarker) in plasma would be significantly reduced (less than 30 μmol L−1), therefore the detection method by the present biosensor met the demand of VRIDs screening.Fig. 5 a) Standard curve of r and hydrogen sulfide in plasma; b) Comparation of biosensor detection and MBSP method using 11 samples; c) Application of the biosensor for plasma from a non-communicable respiratory diseases patient; d) Application of the biosensor for plasma from a VRIDs patient. Fig. 5 3.6.2 Selectivity of the detection method Some other anions also co-existed in the plasma, which would affect the detection of the biomarker (hydrogen sulfide). To confirm the high selectivity of this smartphone-controlled biosensor, the common anions (HCO3 −, SO4 2− and SCN−) were added to the plasma. As known, the HCO3 −, SO4 2−, and SCN− in normal human circulation were approximately 24 mmol L−1, 0.3 mmol L−1, and 30 μmol L−1, respectively [[46], [47], [48]]. The concentration gradient (an extra spiking) of three anions was set according to their normal content in plasma (the highest concentration was 5 folds to the normal level). After the addition of the interfering anions, the biomarker in the plasmas was detected by the smartphone-controlled biosensor. As shown in Fig. S6, compared to the detection result (35.9 ± 1.8 μmol L−1) before the extra spiking of the interferences, the concentrations of hydrogen sulfide detected by the biosensor remained constant (33.6 ± 2.3–37.4 ± 2.2 μmol L−1) even when the inferences were 5 times higher to their normal ranges. These results showed that anions in plasma did not affect the separation and detection of the hydrogen sulfide by this biosensor. The high detection selectivity of the biosensor provided the screening of biomarkers in plasma with high applicability. 3.6.3 Comparison with the conventional approach Traditional detection methods of endogenous hydrogen sulfide primarily relied on large and expensive instruments (such as ICP-MS or UV spectrophotometer), and the pretreatment of biological samples before detection was very tedious (such as the method of MBSP). To verify the accuracy and reliability of the quantitative system of the biosensor in this study, the detection results of the endogenous hydrogen sulfide by our method were compared with the detection results by MBSP. Here, a total of 11 plasma samples were evaluated. As shown in Fig. 5b, there was a strong positive correlation between our method and MBSP, with a slope of 1.04 ± 0.01 and a correlation coefficient of 0.998, suggesting that the results from the two methods matched within the experimental error. These results strongly indicated that the accuracy of our method was as good as MBSP, and the inexpensive, rapid, portable, and user-friendly detection method developed in this study was suitable and reliable. 3.6.4 Evaluation of the response system In this study, the response system mainly relied on the APP of the smartphone. To verify that the response system developed can operate normally, an air purifier as disinfection equipment was chosen to connect with the APP in advance. Three samples were prepared for testing: the first sample was provided by a health volunteer, the endogenous hydrogen sulfide in this plasma was 37.2 ± 2.5 μmol L−1 detected by MBSP; the second sample was obtained from the volunteer who had non-communicable respiratory disease with some symptoms (sneezing), and the endogenous hydrogen sulfide in this plasma was 50.7 ± 3.5 μmol L−1 detected by MBSP; the third sample was a VRIDs patient sample, in which the endogenous hydrogen sulfide was 13.5 ± 2.1 μmol L−1 detected by MBSP. The biosensor was used to detect the hydrogen sulfide in these three samples, and the detection results were then transferred to APP (instructing the air purifier to be switched on or not). As shown in Table S2, for the first sample, the concentration of the hydrogen sulfide was detected to be 39.4 ± 2.4 μmol L−1 (within the normal range), and the air purifier was therefore not switched on according to the instructions of APP. For the volunteer who had a non-communicable respiratory disease, the concentration of the hydrogen sulfide in his plasma was detected to be 52.6 ± 2.9 μmol L−1, the air purifier was therefore not switched on either (Fig. 5c). For the third sample, the concentration of the hydrogen sulfide was detected to be 14.3 ± 1.8 μmol L−1 (among the risk range of VRIDs), and the air purifier was therefore switched on (Fig. 5d). These results indicated that the response system developed could operate rationally according to the concentrations of endogenous hydrogen sulfide. In addition, the hydrogen sulfide concentration of the non-communicable respiratory diseases sample was within the normal range, so the biosensor had good specificity for the screening of VRIDs. It is noted that except for the air purifier, the APP designed in this work could also realize the remote control of the other equipment. With the era of medical practice supported by mobile phones coming [49], the detection and response systems controlled by the smartphone could continue to be upgraded to make greater contributions to disease prevention and health maintenance. 3.6.5 Practical application in the screening of VRIDs Generally, there is an incubation period in which the body has no clinical symptoms during the infection of diseases. For example, the incubation period of COVID-19 is generally 3–7 days [50]. Obviously, early diagnosis of the disease can significantly inhibit the spread of the virus. Biomarkers can be observed in the early stage of disease, which can be used for virus screening [51]. To verify that the present biosensor could be used for screening of VRIDs, four volunteers (two men and two women) were selected to test the applicability of the biosensors. First, the on-site detections of the endogenous hydrogen sulfide were accomplished by the biosensor. Within the next five days, the volunteers were continuously observed whether they had respiratory symptoms. If related symptoms were monitored, the volunteer went to the hospital for respiratory disease examinations immediately. All volunteers carried out nucleic acid testing for COVID-19 on the first and fourth days. As illustrated in Table 1 , among the four volunteers, only one volunteer showed a low hydrogen sulfide concentration (16.2 ± 3.3 μmol L−1, less than 30 μmol L−1). This volunteer developed mild respiratory infection symptoms (cough and headache) a day later and then had been diagnosed with influenza by etiological examination. This result of the hydrogen sulfide reduction was consistent with the decrease in patients with early COVID-19 [16]. The remaining three volunteers did not have any adverse respiratory symptoms during the five-day observation period, and had no respiratory infectious diseases after hospital examination. For two times nucleic acid testing of COVID-19, all results of volunteers were negative. Therefore, from the testing results of the four volunteers, the biosensor can be used to screen VRIDs, though it still needs a lot of data to obtain a more accurate reference value range.Table 1 Screening results of four volunteers using the developed biosensor. Table 1Volunteer Biomarker (μM) Risk level COVID-19 testing Other VRIDs examination Equipment connection 1 2 1 34.7 ± 2.4 Low -a - - No 2 39.8 ± 2.6 Low - - - No 3 46.5 ± 2.8 Low - - - No 4 16.2 ± 3.3 High - - Influenza Yes a “-” indicates that the result of VRIDs testing is negative. 3.6.6 Facile operation by ordinary residents Moreover, five ordinary residents (who had no experience related to this experiment) were randomly selected to prove the serviceability of the biosensor. The detection of three samples from each person was performed according to the experimental protocols. As a control, we also conducted the detection in a normal way. All detection results were summarized in Table S3. It is seen that the detection results from the ordinary residents and the professionals were matched within the experimental errors, indicating that the smartphone-controlled biosensor for screening and response to VRIDs provided great serviceability for ordinary residents. It is emphasized here that the concentration of hydrogen sulfide in blood will also change in other diseases [21,22]. Therefore, the screening method developed in this study is only suitable for ordinary residents without basic diseases, and is not applicable to people with serious cardiovascular disease, chronic kidney disease, Alzheimer's disease, etc. 4 Conclusion The spread of VRIDs makes the shortage of medical resources more prominent in a short time. One of the great difficulties is to complete the VRIDs detection in time for common populations. Therefore, it is a vital trend to develop low-cost portable screening platforms with easy operation and remote emergency response systems for reducing the risk of infection. In this work, to overcome the limitations of nucleic acid detection (requiring professionals and larger instruments) and antigen-antibody detection (low sensitivity and the window period), a smartphone-controlled biosensor was developed for screening and response to VRIDs. The biosensor developed for screening and making the response to VRIDs only needed 15 min for one test, making this smartphone-controlled biosensor suitable for ordinary residents. Using this biosensor, non-communicable respiratory diseases could be successfully differentiated from VRIDs. To the best of our knowledge, this is the first time that a smartphone-controlled biosensor integrating sample preparation, biomarker detection and remote response technology for VRIDs was reported. However, there are still some shortcomings remained in the present work: i) more experimental data are needed to upgrade the range of hydrogen sulfide for the early detection of VRIDs. This related work is undergoing in our lab with the help of Tongji Hospital affiliated with Tongji Medical College, Huazhong University of Science & Technology; ii) More positive samples (including samples from the COVID-19 patients) are needed to further test the applicability of the biosensor. This will be done with the help of our foreign collaborators. Whatever, we believe that the present biosensor will facilitate ordinary residents to better deal with the challenges brought by COVID-19 or other VRIDs in the future, and it is foreseen that this work can bring a new idea to the initial prevention and instant control of VRIDs. Credit authors statement Yaxing Ma: Conceptualization, Methodology, Formal analysis, Validation, Visualization, Writing – original draft, Writing – review & editing. Yaoyu Luo: Conceptualization, Methodology, Investigation, Formal analysis, Validation, Visualization, Writing – review & editing. Xinrui Feng: Conceptualization, Methodology, Validation, Writing – review & editing. Chuixiu Huang: Resources, Supervision, Writing – review & editing. Xiantao Shen: Supervision, Funding acquisition, Writing – review & editing. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix A Supplementary data The following is the Supplementary data to this article:Multimedia component 1 Multimedia component 1 Data availability Data will be made available on request. Acknowledgments This work was supported by 10.13039/501100012166 National Key Research and Development Project of China (Grant NO. 2019YFC1804504) and 10.13039/501100001809 National Nature Science Foundation of China (Grant NO. 21876055). 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==== Front Q Rev Econ Finance Q Rev Econ Finance The Quarterly Review of Economics and Finance 1062-9769 1062-9769 Board of Trustees of the University of Illinois. Published by Elsevier Inc. S1062-9769(22)00134-X 10.1016/j.qref.2022.11.007 Article COVID-19 related TV News and Stock Returns: Evidence from Major US TV Stations Möller Rouven a Reichmann Doron a⁎ a Ruhr-University Bochum, Universitätsstraße150, Bochum, 44801, North Rhine Westphalia, Germany * Corresponding author 5 12 2022 5 12 2022 25 10 2021 9 11 2022 22 11 2022 © 2022 Board of Trustees of the University of Illinois. Published by Elsevier Inc. All rights reserved. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. We investigate a novel dataset of more than half a million 15 second transcribed audio snippets containing COVID-19 mentions from major US TV stations throughout 2020. Using the Latent Dirichlet Allocation (LDA), an unsupervised machine learning algorithm, we identify seven COVID-19 related topics discussed in US TV news. We find that several topics identified by the LDA predict significant and economically meaningful market reactions in the next day, even after controlling for the general TV tone derived from a field-specific COVID-19 tone dictionary. Our results suggest that COVID-19 related TV content had nonnegligible effects on financial markets during the pandemic. Keywords Stock Returns COVID-19 TV News Natural Language Processing Topic Modeling ==== Body pmc1 Introduction By the end of March 2020, the outbreak of the COVID-19 pandemic caused the S&P 500 to decline by 34%, followed by an unprecedented stock market rally that drove stock prices up to pre-pandemic levels and beyond. This sharp V-shaped trajectory, however, was diametrically opposed to the general development of the real economy. Thus far, research suggests that the stock market decoupled from the economy and that investors’ information environment played a key role in explaining market movements during the pandemic (Capelle-Blancard and Desroziers, 2020, Cox et al., 2020. While media coverage has long been identified as an important determinant of investors’ information environment, this study seeks to examine the effects of COVID-19 related media coverage on US stock returns. Therefore, we make use of a palpable and highly influential media source that has barely been discussed in the extant finance literature, namely, US TV news. Survey data suggests that TV coverage is the most important mass medium. According to Nielsen (2021), adult Americans watch 4.45 hours of television per day on average, representing more than 50% of their leisure time.fn1 Due to stay home orders, the relevance of television viewing has even increased during the pandemic. In fact, recent surveys among over 15000 Americans in ten states indicate that more than 80% of respondents over the age of 18 say they watched television in 2020, and perhaps more importantly, 75% of respondents feel that TV coverage is the best source of information. In comparison, only 13% and 8% of respondents say they get the best information from social media and newspapers, respectively.fn2 Compared to 2019, ratings in 2020 have increased significantly: further analyses suggest that ratings in the 18-34 age group increased over 90% on average between March and September 2020 compared to the previous year.fn3 With this in mind, we believe that the pandemic period is an ideal setting to conduct an analysis of the relationship between TV coverage and the US stock market of 2020. Thus far, there are only few studies that have considered TV coverage in the context of financial markets. Engelberg et al. (2012) were first to document stock price reactions and subsequent reversals to stock recommendations made by Jim Cramer in his famous TV show Mad Money, indicating that TV coverage causes temporary attention shocks. More recently, Mayer (2021) uses TV-ratings from college football bowl games to measure advertising exposure and finds that the sponsoring firms’ stocks experience increases in investor attention, turnover, and temporary price pressure. Liaukonyte and Žaldokas (2022) study the real-time effects of TV advertising on financial markets. Their results suggest that TV advertising affects investors’ searches for online financial information and causes trading activities that are primarily driven by retail investors. However, to the best of our knowledge, there is no study that has examined the role of COVID-19 related TV news on stock returns during the pandemic. In order to examine the relationship between the content of COVID-19 related TV news and stock returns, we employ a novel database provided by the Global Database of Events, Language, and Tone (short GDELT). Using Google-powered AI algorithms for video and speech recognition, GDELT processes the Archive’s Television News Archive to extract 15-second snippets of spoken word containing COVID-19 related mentions. We access the database through Google BigQuery and collect more than half a million COVID-19 related TV snippets spanning ABC, Bloomberg, CBS, CNBC, CNN, Fox Business, Fox News, MSNBC, and NBC from 01 January to 31 December, 2020. Given that the COVID-19 pandemic has affected almost every aspect of people’s daily lives, we consider that COVID-19 related TV news will cover a variety of different topics concerning the pandemic. Therefore, we employ the Latent Dirichlet Allocation (LDA) proposed by Blei et al. (2003), an unsupervised machine learning algorithm that attempts to identify latent topics in a sample of documents to understand the context of each COVID-19 related mention. However, since we seek to identify topics that are potentially interesting to investors, we train our LDA model on a sample of TV snippets from networks that are specialized in business related content and apply the final model fit to the remaining sample. This approach tailors our LDA model to our research question as it reduces noise arising from topics that are unlikely to cater to the interests of investors. Our final LDA model yields seven interpretable topics that allow a more fine-grained analysis on how TV news about the pandemic affect investors’ decision-making. We find that four out of the seven COVID-19 topics predict statistically significant and economically meaningful market reactions on the next day. Three topics, namely (i) COVID-19 related news about public health issues, (ii) political tensions between President Trump and China, and (iii) news concerning the economy have a persistent impact on market returns since we find no statistically significant reversal effect that offsets their initial impact. This finding suggests that certain COVID-19 related TV content may contain value-relevant information to market participants. However, and even more interestingly, our results further show that (iv) a stock market topic identified by our LDA model causes temporary upward price pressure which almost fully reverses within the next three trading days. This pattern is consistent with models of investor sentiment (Da et al., 2014, Tetlock, 2007. Thus, stock market coverage on TV news seem to reinforce investor focus towards stock market developments and thereby impacting aggregate stock market returns. We further investigate the observed return reversals that are associated with the stock market topic. Thus far, anecdotal evidence suggests that many investors saw the historically low market levels in 2020 as an opportunity to capitalize on the COVID-19 related disruptions (Osipovich, 2020). If COVID-19 related TV news about the stock market reinforce investors focus towards market developments, we argue that the effect of stock market TV coverage on returns is closely linked to current market levels. The more the market has recovered, the less stock market coverage should be able to attract sufficient investor attention to have an effect on asset prices. Therefore, we interact changes in our stock market topic with the index level of the S&P 500. Consistent with our hypothesis, we find that with an increasing index level of the S&P 500, the positive price reaction of the S&P 500 in response to the stock market coverage as well as the reversal effects weaken. Moreover, we find that the correlations between the stock market topic and market returns increases in statistical significance and magnitude when using a split-sample that covers the market rally, starting after the COVID-19 crash in March, 2020. In this situation, TV coverage accompanying the market catch-up may have affected investors’ investment decisions during the pandemic. Moreover, we conduct several additional analyses. First, we examine the relationship between COVID-19 TV topics and portfolio returns. We find that the effect of TV coverage varies across industries. The positive relation between the stock market topic and next day’s return is more pronounced in telecommunication, wholesale/retail, health, and finance portfolios. Our results also show that the negative relationship between next day’s return and TV news concerning political tensions between President Trump and China are particularly pronounced in industries that, due to their business model, are more exposed to international trade. Second, we examine the relationship between COVID-19 TV news and trading volume, volatility, and the behavior of retail investors using SPY holding data retrieved from the Online Broker Robinhood. We find that TV news addressing the vaccine and the potential recovery from the pandemic increases next day’s trading volume. Furthermore, while we find that news about public health issues predict higher volatility which is generally consistent with the findings of prior works (Haroon and Rizvi, 2020, Huynh et al., 2021, we find no statistically significant relationship between COVID-19 related TV coverage and Robinhood stock holdings.fn4 This paper contributes to the recent literature examining the financial market impact of the COVID-19 pandemic. Consistent with prior works examining the role of news coverage in financial markets, our results indicate that COVID-19 related TV news have a statistically and economically significant impact on stock prices and that certain topics discussed in TV news contain, at least partially, value-relevant information (Ahmad et al., 2016, Calomiris and Mamaysky, 2019, Tetlock, 2010. These results also add to a growing strand of literature suggesting that the media plays an important role in shaping market activities during the pandemic (Haroon and Rizvi, 2020, Huynh et al., 2021, Sun et al., 2021. The remainder of this paper is structured as follows: Section 2 discusses related literature before Section 3 provides insights on the data and the textual analyses. Section 4 reports the results of our empirical analyses which is followed by a conclusion of the paper in Section 5. 2 Related Literature A burgeoning strand of literature examines the effects of the COVID-19 pandemic on financial markets. Several studies investigate how financial markets respond to fundamental information related to health consequences of the pandemic. For example, empirical evidence suggests that during the early stages of the pandemic, stock prices reacted negatively to the growth of confirmed COVID-19 cases and deaths (Al-Awadhi et al., 2020, Ashraf, 2020. Moreover, while Cheng (2020) show that volatility markets initially underacted to growing risks of the pandemic, several studies show that growing case numbers and deaths had an significant effect on stock volatility (Baig et al., 2021, Engelhardt et al., 2021. Moreover, Chebbi et al. (2021) document that growth in COVID-19 cases and deaths are associated with decreasing stock liquidity. However, research suggests that the stock market decoupled from the economy as market movements did not solely reflect fundamental information (Capelle-Blancard and Desroziers, 2020, Cox et al., 2020. Therefore, prior works have turned to examining the impact of investor sentiment on stock prices during the pandemic. Based on the seminal work of Da et al. (2014), several of these studies rely on Google Search Volumes (GSVs) of predefined search terms to build investor sentiment indices. For example, John and Li (2021) use GSVs to build sentiment indices that help predicting volatility jumps. Hasan (2022) develops a search based COVID-19 sentiment index that explains stock market return and subsequent return reversals that are consistent with patterns of investor sentiment, indicating that households’ pandemic sentiment negatively affects stock returns. Lyócsa et al. (2020) use search terms to gauge investors’ fears and document that their sentiment proxy explains stock price variation around the world. Moreover, Salisu and Vo (2020) use the GSV of the keyword “health news” to build an index that predicts stock returns during the pandemic. Considering that media coverage is an important determinant of investors’ information environment, another stream of literature suggests that COVID-19 related media coverage had a strong effect on financial markets during the pandemic. For example, Baek et al. (2020) document that stock volatility was more sensitive to COVID-19 news than to economic indicators. Moreover, Haroon and Rizvi (2020) suggest that COVID-19 related media coverage contributed to stock market uncertainty. Huynh et al. (2021) use COVID-19 related news coverage to build a proxy for investor sentiment that positively (negatively) predicts stock volatility (returns). Biktimirov et al. (2021) use text analysis to estimate the content of COVID-19 news in the printed edition of the Wall Street Journal. Their results show that it is not the tone but rather the content of news coverage that affects stock returns. Similarly, Mamaysky (2020) argues that the information environment played a first-order role in markets’ crisis response and documents that the content of COVID-19 related news articles had significant effects on financial markets. Collectively, the literature suggests that besides fundamental information regarding the pandemic, investors’ information environment was a key driver of stock returns during the pandemic. While prior works have mainly focused COVID-19 related internet searches and newspaper articles, we aim to advance this literature by examining a so far overlooked medium. Specifically, in this study, we examine how the content of COVID-19 related TV news relate to stock returns during the pandemic. 3 Data 3.1 TV Data In this paper we investigate a novel dataset of COVID-19 related TV snippets provided by GDELT. GDELT is arguably the most comprehensive and highest resolution open database for global news media to date. In 2017 GDELT released the GDELT 2.0 TV API that processes data from the Internet Archive’s Television News Archive.fn5 By employing Google’s Cloud Video and Natural Language API, GDELT leverages the power of artificial intelligence to covert any spoken word during TV broadcasts to raw text. In the light of the global pandemic, GDELT compiled a massive database of television mentions of COVID-19 that can be openly accessed through Google BigQuery. The database contains mentions of “coronavirus”, “covid”, “virus”, “infection”, “infected”, “infect”, and “infects” on a set of major television news stations starting on January 1, 2020. Each mention includes the URL of the matching video clip on the Archive’s website, a timestamp in UTC, the station and show it appeared on, its unique Internet Archive identifier and a 15-second snippet of spoken word transcripts that allow to understand the context of the mention and its surrounding language. By using Google BigQuery, we collect a total of 607145 TV snippets spanning ABC, Bloomberg, CBS, CNBC, CNN, Fox Business, Fox News, MSNBC, and NBC throughout the whole year of 2020.fn6 This dataset enables us to get a comprehensive view on how major US TV stations shape news about the pandemic. 3.2 Topic Modeling Since each mention includes a 15 second snippet of spoken word transcript, we seek to leverage this data in order to better understand the context in which each COVID-19 mention appears. Therefore, we employ the LDA proposed by Blei et al. (2003). The LDA is a probabilistic modeling approach that aims to extract latent “topics” from a collection of documents. The unsupervised algorithm attempts to describe a set of documents as a mixture of latent topics. By viewing each document as a bag of words, the LDA represents the topics themselves as a probability distribution over words. By using the LDA, we aim to model a certain number of latent topics that are present in our corpus of TV snippets to improve the empirical identification of a possible relationship between COVID-19 related media content and stock returns in the US. In order to relate the content of COVID-19 related TV news to market activity, it would be desirable to identify topics that may cater to the interests of investors. Having this challenge in mind, we start our modeling procedure by choosing an adequate training set that suits the purpose of our study. Specifically, we choose to train the model on TV snippets from channels that are specialized on business- and finance related topics as such broadcasts are most likely to discuss content that might be relevant for financial markets. Therefore, we employ TV snippets from Bloomberg, CNBC, and Fox Business as our LDA training set. The final model fit can then be applied to any unseen textual data such as the remainder of our sample. This approach allows us to specifically tailor our LDA model to our research question as it reduces noise arising from potentially irrelevant topics. However, since the output of the LDA model is crucially determined by its inputs, we first employ several preprocessing steps in order to prepare the data for the modeling procedure. First, we use Part-of-Speech (POS) tagging to remove all words that are neither nouns (e.g. case, market, and economy), verbs (e.g. test, rise, and say), adjectives (e.g. old, strong, and beautiful), adverbs (e.g. well, quickly, and sharply) or proper nouns (e.g. Trump, Dr. Fauci, and Pfizer). This procedure greatly reduces feature dimensionality by removing most words that are not crucial to describe a certain topic. Second, we remove stop-words that are defined as frequently appearing words with no specific meaning.fn7 Third, we replace each word with its base form using lemmatization.fn8 Since the LDA treats each document as a bag of words, it comes with the drawback that it does not account for semantic relationships between co-occurring words. To address this issue, we use the Phrases module of the gensim library in Python to implement a data-driven approach in the spirit of Mikolov et al. (2013) that attempts to identify multi-word expressions and collocations. Mikolov et al. (2013) propose using unigram and bigram counts to form phrases as follows: (1) score(wi,wj)=(count(wi,wj)−δ)×∣V∣count(wi)×count(wj), where ∣V∣ denotes the size of the preprocessed vocabulary and δ is a constant that prevents forming phrases that consist of very infrequent words.fn9 If the score(w i, w j) exceeds 10 (gensim’s default), the words w i and w j are treated as a single token by concatenating them using underscores. This process yields a total 2925 phrases, including terms such as “rising death toll”, “file (for) unemployment”, “v-shaped recovery”, and even longer phrases such as “light (at) (the) end (of) (the) tunnel”. Therefore, we replace words with significant co-occurrence in our corpus of TV snippets with their respective phrases.fn10 As a final step, we filter extreme outliers by excluding all words that appear in less then 100 TV snippets. Despite a noticeable surge of LDA applications in accounting and finance research, only few studies discuss the reasoning behind their model set up. However, besides the text preprocessing procedure, the output of the LDA is crucially determined by the choice of the following three hyperparameters that reflect certain assumptions about the data:• The number of topics K: This parameter denotes the number of topics that are expected to be found in the sample of documents. • The document-topic density α: The document-topic density determines the average frequency that each topic within a given document occurs. Therefore, higher values indicate that a document is made up of more topics. • The topic-word density β: This parameter describes the probability distribution over the total vocabulary in the corpus assigned to each topic. Put simply, a higher value of β allows for a greater word overlap between topics. We start by setting the LDA priors α and β. Since each document in our corpus represents only a short text snippet of COVID-19 related content, it seems unlikely that a given snippet will discuss a large number of topics, speaking for a low document-topic density α. Therefore, we set α equal to 0.001. Turning to the topic-word density β, it would be desirable to identify interpretable and distinct topics. Since less word overlap between different topics should help to better distinguish them, we also choose a similarly low β value of 0.001. Finally, we have to set the number of topics K. However, it is possible that the media reports on many different topics regarding COVID-19, which makes it difficult to define an expected number of topics ex ante. Consequently, we choose the optimal number of topics K by optimizing topic coherence. Topic coherence measures seek to score a single topic by measuring the semantic similarity between important words in the respective topics. Thus, topics are “coherent” when words describing the topic support each other. To measure topic coherence we follow Mamaysky (2020) and employ a novel coherence measure c_v proposed by Röder et al. (2015) that shows strong correlation to human ratings and clearly outperforms common measures of topic coherence.fn11 Figure 1 shows the coherence score of LDA models using different values for K.fn12 Since the results show that an LDA model with ten topics yields the highest coherence score, we set K equal to 10.Fig. 1 Topic Coherence. This figure presents the results of our LDA coherence pipeline. It shows the coherence measure c_v proposed by Röder et al. (2015) in respect to the number of topics K. The document-topic density α and the word-topic density β follow a symmetric prior that is set to 0.001. To assure replicability, all runs are initialized using a fixed random seed of 0. Fig. 1 Having set the LDA hyperparameters, we train an LDA model on our sample of 172885 business-related TV snippets. To assure full replicability, we initialize the model using a fixed random seed. By judging each topic in regards of their interpretability, we identify seven distinct topics that we include in our empirical analyses. Figure 2 presents word clouds of latent topics in the LDA training sample that were identified by the final model fit. Each word cloud visualizes the words of a given topic in respects of their probability weights. Since some topics will naturally bear a more positive or negative tone, we acknowledge that each topic is closely linked to its inherent tone. To ease the interpretability of the relationship between COVID-19 topics and stock returns, we color positive and negative words as either green or red. In addition, we calculate each topic’s “inherent tone” as the sum of the probability weights of tone-bearing words, where the probability weights are multiplied by + 1 and − 1 for positive and negative words, respectively.Fig. 2 Latent COVID-19 Topics of Major US TV Stations. This figure presents latent topics of the Bloomberg-, CNBC-, and FOX Business TV snippets identified by the LDA model. Each word cloud presents the words of a respective topic with larger words indicating higher topic weights. The headings are chosen manually by the authors. Positive (negative) words are colored in green (red). Each topic’s “inherent tone” is calculated using the probability weights of tone-bearing words, where the probability weights are multiplied by + 1 and − 1 for positive and negative words, respectively. Fig. 2 The first topic (a), labeled “Stock Market,” includes coverage that deals with current market activity on the stock market, as indicated by higher probability weights for words such as “stock,” “market,” “news,” “week” and is rather neutral in nature. The second topic (b), “Public Health”, refers to the coverage on the status quo of public health in the US, thus facts about the pandemic, such as the “cases”, “lockdown” measures and “death” tolls surrounding the pandemic are the defining words of the topic. This topic is obviously linked to a rather pessimistic outlook as indicated by its negative inherent tone. The third topic (c), “Relief,” maps coverage related to government actions, so that the approaching presidential election towards the end of the year 2020 and possible relief packages are the primary factors shaping this topic which is associated with more positive tone. The rather neutral topic (d) “Prevention” covers the reporting on necessary measures to protect society, including reports on e.g. vaccines and the testing of possibly infected people. The fifth topic, (e) “Politics,” focuses on reporting political tensions between President Trump’s and China (e.g. reports on Trump statements on what he called the “china virus”). Topic (f) “Economy” consists of coverage around the economic impact of the pandemic on the US economy. The last topic (g) “Recovery”, maps the coverage of hopes looking into the future in respect to vaccine development and the following recovery as indicated by the words “vaccine”, “hope”, “soon”, and “recover”.fn13 The three unreported topics are ambiguous as they mostly capture generic words with no specific reference to neither the pandemic nor economic factors and therefore, are excluded from further analyses. Applying the final model fit on the complete sample of 607145 TV snippets outputs a document-topic matrix, for which each snippet is assigned a probability distribution giving the percentages across all topics that the model estimates the snippet comprises. Formally, we estimate daily measures of topic frequency tf¯t,k as follows (Mamaysky, 2020): (2) tf¯t,k=1Nt∑j=1Nttfj,k where N t denotes the total number of snippets in day t and tf j,k denotes probability allocation of snippet j to topic k. However, since our sample only includes COVID-19 related TV coverage which certainly varies over the course of 2020, we multiply the mean probability allocation of a certain topic on a given day (tf¯t,k) with the natural logarithm of the total number of COVID-19 snippets (log(N t)) as follows: (3) Topict,k=tf¯t,k×log(Nt) Note that each snippet represents a 15 second audio snippet derived from the Television News Archive. Therefore, higher values of Topic t,k indicate more airtime of COVID-19 related coverage that is associated with topic k on day t. Finally, for our main analysis, we focus on daily changes in TV coverage: (4) ΔTopict,k=Topict,k−Topict−1,k, Prior works show that stock markets are sensitive towards the general tone (i.e., the positivity or negativity) of qualitative information sources such as annual reports (Loughran & McDonald, 2011), central bank press conferences (Möller & Reichmann, 2021), and media coverage (Tetlock, 2007). Hence, our empirical analysis poses the challenge to distinguish between topic-driven effects of TV news on stock prices and effects that are merely a reflection of changes in overall TV tone.fn14 To alleviate these concerns, we aim to construct a valid control variable that captures overall TV tone in a given day. Since we find that commonly used tone dictionaries such as the Harvard IV-4 and the Loughran and McDonald (2011) dictionaries are poorly suited to quantify the tone of COVID-19 related TV coverage that contains medical jargon and casual phrases, we make efforts to construct tone dictionaries that are specifically tailored to our research question. Therefore, we employ a neural network approach in the spirit of Li et al. (2020) and construct two tone dictionaries that consider the contextual relationships between tone-bearing words and the topics identified by our LDA model. Using our self-constructed tone dictionaries, we calculate daily measures of overall COVID-19 related TV tone. For brevity, we provide a more detailed discussion in Section A.1. Fig. 3 presents the evolution of tone and COVID-19 topics over the course of the year 2020.Fig. 3 Evolution of Tone and Topics Over Time. This figure presents the evolution of COVID-19 related TV tone and context from 01 January, 2020 to 31 December, 2020. The first plot shows standardized measures of tone derived from our field-specific dictionaries described in Section A.1. The remaining plots show standardized measures of COVID-19 related TV airtime that corresponds to either one of the seven interpretable topics identified by our LDA model. All measures are plotted using 20-day moving averages to capture a full month of trading days. Fig. 3 4 Empirical Results 4.1 TV News and Stock Returns We investigate the relationship between COVID-19 related TV content and stock returns by running the following regression (Da et al., 2014): (5) Returni,t+l=β0+∑kγkΔTopict−1k+∑mδmControli,t−1m+ϵi,t+l, where Return i,t+l denotes asset i’s return on days t + l, with l ∈ [0, 4] to cover a full trading week of returns. For our baseline analysis, we use S&P 500 index ETF returns. To avoid extreme outliers in our dependent variable, we trim the return series at the 1% level. Topict−1k denotes changes in topic frequency for each of the k = 7 topics identified by our LDA model. The set of control variables Controli,t−1m include changes in the general tone of COVID-19 related TV coverage (ΔTone) as described in Section A.1 and, similar to Da et al. (2014), lagged returns up to five lags, changes in a news-based measure of economic policy uncertainty index (ΔEPU), changes in the CBOE volatility index (ΔVIX), changes in the Aruoba-Diebold-Scotti (ΔADS) business conditions index, and the Financial and Economic Attitudes Revealed by Search (FEARS) index. In addition, since Chebbi et al. (2021) show that confirmed COVID-19 cases have a negative impact on stock liquidity, we also control for the natural logarithm of daily changes in US COVID-19 cases (log(ΔCases)) as provided by USAFacts to account for information that reflects the epidemiological situation in the US. Table 1 presents the descriptive statistics of the variables used in our empirical analysis. In addition, we report Pearson correlations in Table 2. We find that the correlations between the topics are rather moderate, suggesting that each topic is likely to capture distinct TV news content. Moreover, we find that the overall TV tone shows positive correlations with TV news regarding relief packages, prevention measures, and a potential recovery. In contrast, our TV tone measure shows negative correlations with TV news concerning public health issues and the general economy. All coefficients are rather moderate, indicating that our regression results should not suffer from multicollinearity issues. However, we also estimate Variance Inflation Factors (VIFs) for our independent variables. All values are below five, supporting the view that multicollinearity is no serious concern in our empirical analysis.Table 1 Descriptive Statistics. Table 1 N Q1 Median Mean Q3 SD Return (SPY) 221 − 0.005 0.002 0.001 0.009 0.016 ΔStock Market 221 − 0.047 − 0.000 0.001 0.040 0.095 ΔPublic Health 221 − 0.088 0.009 0.007 0.091 0.143 ΔRelief 221 − 0.029 0.001 0.001 0.041 0.145 ΔPrevention 221 − 0.082 − 0.002 0.002 0.089 0.144 ΔPolitics 221 − 0.063 0.000 0.005 0.069 0.116 ΔEconomy 221 − 0.031 0.000 0.002 0.033 0.064 ΔRecovery 221 − 0.038 0.002 0.002 0.045 0.073 ΔTone 221 − 0.316 0.006 − 0.007 0.332 0.483 ΔEPU 221 − 46.730 0.960 1.111 51.730 87.703 ΔVIX 221 − 1.220 − 0.240 − 0.046 0.790 2.466 ΔADS 221 − 0.096 − 0.037 0.050 − 0.011 0.546 FEARS 221 − 4.688 − 0.258 0.365 4.710 9.625 log(ΔCases) 221 10.023 10.622 9.129 11.558 4.149 This table presents the descriptive statistics of S&P 500 index ETF daily returns, daily changes in COVID-19 related TV content, and control variables used in our empirical analyses. To avoid extreme outliers in our dependent variable, we trim the S&P 500 index ETF series at the 1% level. Table 2 Pearson Correlations. Table 2 (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (1) Returnt (SPY) (2) ΔStock Markett−1 0.080 (3) ΔPublic Healtht−1 − 0.124* 0.231* (4) ΔRelieft−1 0.009 − 0.211* − 0.217* (5) ΔPreventiont−1 − 0.072 − 0.303* − 0.236* − 0.167 (6) ΔPoliticst−1 − 0.107 0.106 0.002 0.167 − 0.017 (7) ΔEconomyt−1 0.101 0.048 0.051 − 0.157 − 0.219* − 0.196* (8) ΔRecoveryt−1 − 0.063 − 0.135 − 0.201* 0.029 0.385* − 0.133 − 0.154 (9) ΔTonet−1 0.020 − 0.100 − 0.568* 0.123* 0.205* − 0.193* − 0.035 0.311*** (10) ΔEPUt−1 − 0.065 − 0.050 0.020 0.122* 0.026 0.012 − 0.194* − 0.162 0.001 (11) ΔVIXt−1 0.085 0.169 0.096 − 0.007 − 0.039 0.049 0.024 − 0.036 − 0.196* − 0.053 (12) ΔADSt−1 0.025 − 0.019 0.058 − 0.008 − 0.065 0.040 − 0.007 − 0.090 − 0.024 − 0.012 − 0.150 (13) FEARSt−1 0.021 0.182* 0.222*** − 0.016 − 0.111* 0.067 0.100 − 0.146 − 0.194* − 0.089 0.258*** − 0.017 (14) log(ΔCases)t−1 0.119* 0.019 − 0.034 0.022 − 0.016 − 0.043 − 0.040 − 0.022 0.018 0.008 − 0.129* 0.247*** − 0.077 This table presents Pearson correlations of S&P 500 index ETF daily returns, daily changes in COVID-19 related TV content, and control variables used in our empirical analyses. , , and denote significance at the 10%, 5%, and 1% levels, respectively. In Table 3, we relate S&P 500 index ETF returns to TV news about COVID-19. We find that, out of the seven topics identified by our LDA model, four topics predict significant market reactions in the following day. Specifically, we find that changes in TV news about the stock market as captured by ΔStock Market have a positive and statistically significant (p < 5%) effect on next day’s return. In contrast, an increase in airtime about public health issues are associated with lower returns as shown by negative coefficient of ΔPublic Health that is statistically significant below the 5% level. Further, TV coverage about political tensions between China and President Trump negatively affect next day’s return, whereas coverage about the economy predict higher returns in the following day as indicated by the statistically significant coefficients (p < 10%) of ΔPolitics and ΔEconomy, respectively. We also find that the documented effects are economically meaningful. A one-standard deviation increase in stock market coverage is associated with an increase of 24 basis points ( = 0.025 × 0.095), whereas a similar increase in coverage about public health issues corresponds to a decrease of 27 basis points ( = − 0.019 × 0.143). The economic impact of TV coverage about politics and the economy translates to a decrease of 21 basis points ( = − 0.018 × 0.116) and an increase of 13 basis points ( = 0.021 × 0.064), respectively.Table 3 TV News and Stock Returns. Table 3 (1) (2) (3) (4) (5) Returnt Returnt+1 Returnt+2 Returnt+3 Returnt+4 ΔStock Markett−1 0.025 − 0.018 0.009 − 0.022* 0.011 (2.044) ( − 1.349) (0.869) ( − 2.874) (0.948) ΔPublic Healtht−1 − 0.019 0.006 − 0.017* 0.012* − 0.005 ( − 2.545) (0.680) ( − 1.711) (1.712) ( − 0.518) ΔRelieft−1 0.010 − 0.026* 0.024 − 0.003 0.000 (0.584) ( − 1.808) (1.461) ( − 0.223) (0.004) ΔPreventiont−1 − 0.000 − 0.009 − 0.001 0.002 0.005 ( − 0.066) ( − 1.241) ( − 0.079) (0.238) (0.739) ΔPoliticst−1 − 0.018* − 0.010 − 0.003 0.009 0.008 ( − 1.806) ( − 1.088) ( − 0.426) (1.199) (1.007) ΔEconomyt−1 0.021* − 0.002 − 0.011 − 0.001 0.008 (1.917) ( − 0.140) ( − 0.671) ( − 0.097) (0.484) ΔRecoveryt−1 − 0.003 0.002 0.010 0.011 − 0.012 ( − 0.225) (0.188) (0.990) (1.048) ( − 1.044) ΔTonet−1 − 0.003 0.004 − 0.007* 0.002 0.001 ( − 1.272) (1.961) ( − 3.173) (0.873) (0.306) Returnt−1 − 0.420*** 0.212* − 0.242 − 0.072 0.018 ( − 4.383) (1.961) ( − 2.001) ( − 0.669) (0.131) Returnt−2 0.265* − 0.094 − 0.045 − 0.089 − 0.177 (3.428) ( − 0.990) ( − 0.659) ( − 1.001) ( − 2.245) Returnt−3 − 0.020 − 0.141 − 0.087 − 0.114 0.022 ( − 0.238) ( − 2.039) ( − 0.895) ( − 1.561) (0.321) Returnt−4 − 0.187** − 0.057 − 0.103 0.127* − 0.005 ( − 2.299) ( − 0.680) ( − 1.273) (1.764) ( − 0.076) Returnt−5 − 0.118 0.018 0.096 − 0.024 0.253 ( − 1.222) (0.152) (1.141) ( − 0.299) (2.379) ΔEPUt−1 − 0.000 0.000 − 0.000 0.000 − 0.000 ( − 0.064) (0.394) ( − 0.416) (1.197) ( − 1.407) ΔVIXt−1 − 0.002* − 0.000 − 0.001 0.000 0.001 ( − 2.522) ( − 0.225) ( − 2.144) (0.243) (0.818) ΔADSt−1 0.000 0.000 0.002 0.002 0.002 (0.214) (0.246) (1.318) (1.146) (1.232) FEARSt−1 0.000 − 0.000 0.000 − 0.000 − 0.000 (0.416) ( − 0.092) (0.964) ( − 1.997) ( − 0.257) log(ΔCases)t−1 0.001 0.001* 0.000 0.000 0.001* (1.575) (1.774) (1.496) (1.586) (1.937) Observations 221 220 219 218 217 Adjusted R2 0.133 0.059 0.047 0.011 0.097 This table relates S&P 500 index ETF (SPY) daily returns to changes in COVID-19 related TV content. The dependent variables are future returns in the next five days (columns (1)-(5)). The set of control variables include changes in TV tone (ΔTone), lagged returns up to five lags (Return), changes in a news-based measure of economic policy uncertainty (ΔEPU), changes in the CBOE volatility index (ΔVIX), changes in the Aruoba-Diebold-Scotti (ΔADS) business conditions index, the Financial and Economic Attitudes Revealed by Search (FEARS) index, and the natural logarithm of changes in reported COVID-19 infections in the US (log(ΔCases)). The standard errors are robust to heteroskedasticity and autocorrelation up to five lags (Newey & West, 1987). , , and ** denote significance at the 10%, 5%, and 1% levels, respectively. As our results show, the impact of coverage about politics and the economy persist throughout the trading week, since we find no statistically significant evidence of reversals. In addition to its initial effect on day t, the public health topic also shows a lagged overreaction in the following days as indicated by the negative coefficient in period t + 2 and the subsequent reversal in t + 3 that are both significant below the 10% level. However, the strong negative effect of the public health topic on day t does not fully reverse during the trading week. In line with prior works supporting the information theory of media news (Ahmad et al., 2016, Calomiris and Mamaysky, 2019, Tetlock, 2010, these findings suggest that COVID-19 related TV content may, at least partially, contain value-relevant information that helps investors price stocks. In contrast, we find that the initial positive effect of the stock market topic almost fully reverses in the following three days as indicated by its negative and statistically significant coefficient on day t + 3. The reversal corresponds to a decrease of 21 basis points ( = 0.022 × 0.095) and therefore, largely offsets the initial increase of 25 basis points on day t. This pattern is generally consistent with models of investor sentiment (Tetlock, 2007), suggesting that TV news about the stock market provide no fundamental information but rather causes temporary upward price pressure on market returns. While anecdotal evidence suggests that many investors gained interest in stock market developments after the market crash in March 2020 (Osipovich, 2020), TV news about the stock market may have reinforced investors’ focus towards current market developments, thereby impacting aggregate returns without providing fundamental information. If investors sought to capitalize on historically low prices and COVID-19 related stock market coverage amplifies this effect, we would expect that the impact of stock market coverage is closely linked to the index level of the market itself. Once the stock market has returned to high levels (i.e., the stock market already recovered), TV coverage about the stock market should cause less price pressure because it does no longer signal a historical opportunity. To test this prediction, we first examine the interaction between the natural logarithm of the S&P 500 index level (logIndex) and the stock market topic. Consistent with our conjecture, the results presented in Panel A of Table 4 suggest that higher index levels have a mitigating effect on the impact of the stock market topic as indicated by the significant interaction term which shows an opposite sign compared to the coefficient of the stock market topic in periods t, t + 2, and t + 3.Table 4 Stock Market TV News - Moderating Effects. Table 4Panel A: Stock Market News and the Index Level (1) (2) (3) (4) (5) Returnt Returnt+1 Returnt+2 Returnt+3 Returnt+4 ΔStock Markett−1 3.631* − 2.046 2.312 − 3.295** 2.368* (1.827) ( − 1.118) (1.268) ( − 2.114) (1.730) logIndext−1 − 0.013 − 0.023* − 0.021** − 0.018* − 0.017* (0.907) ( − 1.833) ( − 2.017) ( − 1.785) ( − 1.802) ΔStock Markett−1 − 0.506* 0.284 − 0.323 0.459** − 0.331* × logIndext−1 ( − 1.819) (1.110) ( − 1.266) (2.101) ( − 1.727) Controls Yes Yes Yes Yes Yes Observations 221 220 219 218 217 Adjusted R2 0.155 0.076 0.058 0.033 0.106 Panel B: Post COVID-19 Crash ( > March 20, 2020) (1) (2) (3) (4) (5) Returnt Returnt+1 Returnt+2 Returnt+3 Returnt+4 ΔStock Markett−1 0.048* − 0.051* 0.023 − 0.012 − 0.003 (4.272) ( − 3.042) (1.473) ( − 0.773) ( − 0.178) Controls Yes Yes Yes Yes Yes Observations 178 177 176 175 174 Adjusted R2 0.127 0.099 0.132 − 0.021 − 0.006 This table relates S&P 500 index ETF (SPY) daily returns to changes in COVID-19 related TV content. The dependent variables are future returns in the next five days (columns (1)-(5)). Panel A presents the moderating effect of the SPY price on changes in TV news about the stock market using interaction terms between logIndex and the stock market topic. Panel B presents a subsample analysis starting after the COVID-19 crash in March 20, 2020. The set of control variables include changes in the remaining six topics (ΔTopic), TV tone (ΔTone), lagged returns up to five lags (Return), changes in a news-based measure of economic policy uncertainty (ΔEPU), changes in the CBOE volatility index (ΔVIX), changes in the Aruoba-Diebold-Scotti (ΔADS) business conditions index, the Financial and Economic Attitudes Revealed by Search (FEARS) index, and the natural logarithm of changes in reported COVID-19 infections in the US (log(ΔCases)). All models include an unreported intercept. The standard errors are robust to heteroskedasticity and autocorrelation up to five lags (Newey & West, 1987). , , and denote significance at the 10%, 5%, and 1% levels, respectively. In addition, we also examine the impact of the stock market topic on returns by conducting a subsample analysis choosing March 23, 2020 as the date for the sample split. This date was the first Monday following the Federal Reserve’s announcement that it would purchase government bonds and mortgage-backed securities “in the quantity needed” as part of its emergency quantitative easing program with which it permanently calmed global financial markets (Rebucci et al., 2020), setting the stage for the subsequent market recovery. As documented in Panel B of Table 4, we find that the impact of the stock market topic increases in both its statistical significance and magnitude for a subsample starting after the COVID-19 crash in March, 2020. In summary, these results support our reasoning that COVID-19 related TV news about the stock market may have shifted investors’ attention to the stock market, resulting in nonnegligible effects on financial markets during the pandemic. 4.2 TV News and Portfolio Returns In additional analyses, we examine the impact of COVID-19 related TV news on portfolio returns. Since not all industries in the US economy have been equally hit by the pandemic, investors might be asymmetrically drawn to different industries. Consequently, the effect of different COVID-19 topics discussed in TV news could vary across industries. To investigate this issue, we use US return data of 12 industry portfolios from Kenneth French’s website based on four-digit Compustat or CRSP SIC codes for all companies listed on the NYSE, AMEX, and NASDAQ.fn15 fn16 For brevity, we only report the effect of the COVID-19 topics on next day’s portfolio return. The results presented in Table 5 show that the upward price pressure induced by the stock market topic is only present in very specific industries. These include telecommunications (Telcm), wholesale and retail (Shops), healthcare (Hlth), and financial institutions (Money). During the course of the pandemic, some of these industries have become increasingly important for peoples’ daily lives and thus, might be especially susceptible to raise investors’ attention.Table 5 TV News and Industry Returns. Table 5 (1) (2) (3) (4) (5) (6) NoDur Durbl Manuf Enrgy Chems BusEq ΔStock Markett−1 0.016 0.029 0.024 0.002 0.013 0.021 (1.497) (1.193) (1.469) (0.908) (1.114) (1.386) ΔPublic Healtht−1 − 0.014 − 0.037** − 0.021* − 0.018** − 0.014* − 0.023 ( − 2.202) ( − 2.336) ( − 2.309) ( − 1.877) ( − 2.088) ( − 2.359) ΔRelieft−1 0.002 − 0.017 − 0.010 − 0.002 − 0.004 0.017 (0.142) ( − 0.417) ( − 0.410) ( − 0.430) ( − 0.309) (0.855) ΔPreventiont−1 − 0.005 − 0.008 0.002 − 0.005 − 0.009 − 0.001 ( − 0.887) ( − 0.684) (0.263) ( − 0.430) ( − 1.204) ( − 0.105) ΔPoliticst−1 − 0.006 − 0.033 − 0.018* − 0.021 − 0.014 − 0.027** ( − 0.799) ( − 2.129) ( − 1.862) ( − 1.266) ( − 1.567) ( − 2.143) ΔEconomyt−1 0.009 − 0.005 0.003 − 0.003 − 0.002 0.023 (0.959) ( − 0.171) (0.211) ( − 0.103) ( − 0.194) (1.543) ΔRecoveryt−1 0.004 − 0.051* − 0.016 − 0.005 0.067 − 0.019 (0.234) ( − 1.863) ( − 0.961) ( − 0.229) (0.045) ( − 1.041) Observations 221 221 221 221 221 221 Controls Yes Yes Yes Yes Yes Yes Adjusted R2 0.077 0.005 0.034 − 0.007 0.126 0.054 (7) (8) (9) (10) (11) (12) Telcm Utils Shops Hlth Money Other ΔStock Markett−1 0.026 0.012 0.024 0.021** 0.033* 0.019 (2.379) (1.032) (2.008) (2.015) (1.893) (1.286) ΔPublic Healtht−1 − 0.016** − 0.015* − 0.001 − 0.018* − 0.030* − 0.025* ( − 2.243) ( − 1.887) ( − 1.268) ( − 2.798) ( − 3.116) ( − 3.143) ΔRelieft−1 0.003 − 0.013 0.027 0.012 − 0.001 − 0.002 (0.184) ( − 0.651) (1.393) (0.912) ( − 0.057) ( − 0.070) ΔPreventiont−1 − 0.003 − 0.014 − 0.003 − 0.000 0.006 − 0.002 ( − 0.535) ( − 1.464) ( − 0.441) ( − 0.000) (0.649) ( − 0.231) ΔPoliticst−1 − 0.019 − 0.005 − 0.026 − 0.009 − 0.018 − 0.017 ( − 2.263) ( − 0.443) ( − 2.124) ( − 0.930) ( − 1.419) ( − 1.619) ΔEconomyt−1 0.008 − 0.003 − 0.004 0.021 0.021 0.025 (0.757) ( − 0.194) ( − 0.637) (1.688) (1.573) (2.156) ΔRecoveryt−1 − 0.007 0.009 − 0.022 − 0.010 − 0.014 − 0.007 ( − 0.536) (0.547) ( − 1.505) ( − 1.012) ( − 0.870) ( − 0.457) Observations 221 221 221 221 221 221 Controls Yes Yes Yes Yes Yes Yes Adjusted R2 0.148 0.070 0.081 0.113 0.086 0.109 This table relates 12 daily industry returns based on Compustat SIC codes taken from the the website of Kenneth French (http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/det_12_ind_port.html) to changes in COVID-19 related TV content. The dependent variables are portfolio returns in the next day. The set of control variables include changes in TV tone (ΔTone), lagged returns up to five lags (Return), changes in a news-based measure of economic policy uncertainty (ΔEPU), changes in the CBOE volatility index (ΔVIX), changes in the Aruoba-Diebold-Scotti (ΔADS) business conditions index, the Financial and Economic Attitudes Revealed by Search (FEARS) index, and the natural logarithm of changes in reported COVID-19 infections in the US (log(ΔCases)). The standard errors are robust to heteroskedasticity and autocorrelation up to five lags (Newey & West, 1987). , , and denote significance at the 10%, 5%, and 1% levels, respectively. The results further show that the negative impact of TV news concerning political tensions between President Trump and China as captured by ΔPolitics has the strongest effect on production-intense industries such as consumer durables (Durbl), manufacturing (Manuf), and business equipment (BusEq), telecommunication (Telcm) as well as retailers (Shops) that rely on such products. Since these industries are likely to depend international trade, investors may associate an increasing TV coverage concerning political tensions with a greater risk of increasing trade tariffs between US and China. We also analyze the effects of COVID-19 related TV coverage on daily stock returns for univariate portfolio sorts based on the book-to-market ratio (Value), market capitalization (Size), operating profitability (Profit), and changes in total assets (Investment) using the US return data taken from the website of Kenneth French.fn17 fn18 To compare the effects of news coverage for different levels of each portfolio sort, we form two subsamples based on the bottom 20% of the distribution (Q1) and the top 20% of the distribution (Q5). The regression results for the eight resulting portfolios are shown in Table 6. While the results show similar tendencies as our baseline analysis, we find that the effects of TV news about the stock market and public heath issues are more pronounced for the small cap and the high value portfolios. Hence, retail investor activity might be a potential driver behind the effects of TV news as this group of investors is more concentrated within small cap as well as value firms (Kumar & Lee, 2006). Another interpretation of these findings might be that COVID-19 related news about the stock market attracts investors who are prone to invest in less risky firms that are less likely to be overvalued (i.e. have a higher book-to-market ratio as in portfolio Value Q5), report stronger operating performance (Profit Q5), and refrained from making huge investments prior to the pandemic (Investment Q1) which could be endangered by the fast changing economic landscape due to COVID-19 related disruptions.Table 6 TV News and Portfolio Sorts. Table 6 (1) (2) (3) (4) Value Q1 Size Q1 Profit Q1 Investment Q1 ΔStock Markett−1 0.020 0.037 0.020 0.026* (1.505) (2.038) (1.421) (1.891) ΔPublic Healtht−1 − 0.021 − 0.040* − 0.022 − 0.020 ( − 2.528) ( − 2.613) ( − 2.330) ( − 2.556) ΔRelieft−1 0.014 − 0.015 0.010 0.009 (0.805) ( − 0.595) (1.510) (0.460) ΔPreventiont−1 − 0.003 − 0.007 − 0.004 − 0.004 ( − 0.417) ( − 0.728) ( − 0.511) ( − 0.558) ΔPoliticst−1 − 0.024** − 0.018 − 0.025* − 0.023** ( − 2.165) ( − 1.478) ( − 1.965) ( − 2.015) ΔEconomyt−1 0.017 0.003 0.013 0.008 (1.289) (0.167) (1.114) (0.616) ΔRecoveryt−1 − 0.130 − 0.015 − 0.027 − 0.001 ( − 0.795) ( − 0.837) ( − 1.621) ( − 0.084) Observations 221 221 221 221 Controls Yes Yes Yes Yes Adjusted R2 0.098 0.050 0.089 0.101 (5) (6) (7) (8) Value Q5 Size Q5 Profit Q5 Investment Q5 Stock Markett−1 0.034* 0.024* 0.023* 0.023 (1.729) (1.957) (1.733) (1.595) Public Healtht−1 − 0.033* − 0.019 − 0.020 − 0.020 ( − 3.047) ( − 2.573) ( − 2.441) ( − 2.193) Relieft−1 − 0.028 0.014 0.013 0.019 ( − 0.933) (0.820) (0.735) (0.982) Preventiont−1 0.006 − 0.002 − 0.002 − 0.004 (0.519) ( − 0.263) ( − 0.216) ( − 0.478) Politicst−1 − 0.016 − 0.019 − 0.021 − 0.025** ( − 1.264) ( − 1.987) ( − 1.982) ( − 2.119) Economyt−1 − 0.001 0.030* 0.018 0.015 ( − 0.066) (1.743) (1.428) (1.154) Recoveryt−1 − 0.007 − 0.005 − 0.006 − 0.020 ( − 0.349) ( − 0.351) ( − 0.397) ( − 1.234) Observations 221 221 221 221 Controls Yes Yes Yes Yes Adjusted R2 0.054 0.141 0.102 0.069 This table relates daily returns of the lowest and highest quintile portfolios sorted on book to market (Value), size (Size), operating profitability (Profit) and investment (Investment), respectively, taken from the the website of Kenneth French (https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html) to changes in COVID-19 related TV content. The dependent variables are portfolio returns in the next day. The set of control variables include changes in TV tone (ΔTone), lagged returns up to five lags (Return), changes in a news-based measure of economic policy uncertainty (ΔEPU), changes in the CBOE volatility index (ΔVIX), changes in the Aruoba-Diebold-Scotti (ΔADS) business conditions index, the Financial and Economic Attitudes Revealed by Search (FEARS) index, and the natural logarithm of changes in reported COVID-19 infections in the US (log(ΔCases)). The standard errors are robust to heteroskedasticity and autocorrelation up to five lags (Newey & West, 1987). , , and denote significance at the 10%, 5%, and 1% levels, respectively. 4.3 Trading Volume, Volatility, and Retail Investors Thus far, we have focused on the effect of COVID-19 related TV topics on stock returns. In this section, we examine whether and how the TV content affects other aspects of the observed market activities during the pandemic, namely trading volume, volatility, and the behavior of retail investors. Therefore, we relate next day’s changes in the S&P 500 trading volume (ΔVolume), changes in the VIX (ΔVIX), and changes in SPY holdings retrieved from the Online Broker Robinhood (ΔRobinhood) to changes in COVID-19 related TV coverage as presented in Table 7.fn19 Table 7 TV News, Trading Volume, Volatility, and Retail Investors. Table 7 (1) (2) (3) ΔVolumet ΔVIXt ΔRobinhoodt ΔStock Markett−1 − 0.289 − 0.252 − 0.006 ( − 1.393) ( − 0.135) ( − 1.179) ΔPublic Healtht−1 0.201 4.253 0.005 (1.451) (2.355) (0.990) ΔRelieft−1 − 0.157 3.162 − 0.012 ( − 0.375) (0.968) ( − 1.138) ΔPreventiont−1 0.001 2.013 0.008 (0.008) (1.594) (1.483) ΔPoliticst−1 − 0.190 1.524 0.003 ( − 1.029) (1.068) (0.584) ΔEconomyt−1 − 0.145 − 1.474 − 0.007 ( − 0.427) ( − 0.512) ( − 0.995) ΔRecoveryt−1 0.716** − 2.242 − 0.005 (2.439) ( − 0.710) ( − 0.565) Observations 221 221 131 Controls Yes Yes Yes Adjusted R2 0.168 0.031 0.098 This table relates changes in trading volume, volatility, and Robinhood SPY holdings to changes in COVID-19 related TV content. The dependent variables are next day changes in S&P trading volume (column (1)), changes in the CBOE volatility index (VIX) (column (2)), and changes in Robinhood SPY holdings (column (3)). The set of control variables include up to five lags of changes in trading volume in column (1) and (3) and up to five lags of changes in VIX in column (2). All models also controls for changes in TV tone (ΔTone), changes in a news-based measure of economic policy uncertainty (ΔEPU), changes in the VIX (ΔVIX), changes in the Aruoba-Diebold-Scotti (ΔADS) business conditions index, the Financial and Economic Attitudes Revealed by Search (FEARS) index, and the natural logarithm of changes in reported COVID-19 infections in the US (log(ΔCases)). The standard errors are robust to heteroskedasticity and autocorrelation up to five lags (Newey & West, 1987). , , and ** denote significance at the 10%, 5%, and 1% levels, respectively. We find that TV coverage conveying hope about a soon recovery increase trading volume in the following day as indicated by the positive coefficient of ΔRecovery that is statistically significant below the 5% level. As documented in column (2), we find that TV news about public health issues have a positive and statistically significant (p < 5%) effect on subsequent volatility. Given that the public health topic clearly conveys a pessimistic view on the future course of the pandemic (see Fig. 2), this result adds to prior works finding that panic and fear related COVID-19 news heighten volatility in stock prices (Haroon and Rizvi, 2020, Huynh et al., 2021. Finally, we examine whether changes in the TV coverage of COVID-19 topics affect the activity of retail investors. However, as documented in column (3), we find no statistically significant relationship between changes in COVID-19 related TV content and SPY holdings on the trading platform Robinhood. While this finding suggests that the effect of TV news have negligible effects on retail investor activity, it must be taken cautiously because our analysis does not account for a large array of day traders and retail investors that focus on individual stocks not covered by SPY holdings. 4.4 Sensitivity Tests To test the sensitivity of our findings presented in our baseline analysis, we first examine the effects of COVID-19 related TV content on other domestic index funds and other asset classes retrieved from Refinitive. Unreported results show that our findings remain similar using the Standard and Poor’s Depositary Receipt Dow Jones Industrial Average ETF Trust (DOW), the SPDR Financial Select Sector Fund ETF (XLF), and the iShares Russell 2000 ETF (IWM). Finally, we relate returns of the iShares 20 + Year Treasury Bond ETF (TLT) to changes in TV coverage. Consistent with notion of flight-to-safety, we find that the significant coefficient signs switch from positive to negative and vice versa.fn20 All results are available from the authors upon request. Some readers may be concerned that we did not properly control for fundamental information related to the economic and health consequences of the pandemic within our regression analyses. First, please note that we already control for various sources of market-based, macroeconomic, as well as political uncertainty during the time of the pandemic using the EPU, ADS, VIX, and FEARS indices as well as daily US cases as provided by USAFacts. Nonetheless, as part of our sensitivity analyses, we have amended our baseline regression models to include additional variables that provide fundamental information on the consequences of the pandemic. The additional control variables include the following set of health-related variables: (1) The natural logarithm of daily changes in COVID-19 related deaths (log(ΔDeaths)) in the US as provided by USAFacts;fn21 (2) percentage change of inpatient beds occupied by COVID-19 patients in the US (ΔInpatient Beds) as reported by the US Department of Health and Human Services; (3) the percentage of total vaccine doses administered (%Vaccines) provided by Our World in Data and (4) first differences in the Containment Health Index (ΔCHI) provided by the Blavatnik School of Government of the University of Oxford (Hale et al., 2021).fn22 We further include the first differences of initial job claims (ΔIJC) as reported by the US Employment and Training Administration to account for economic consequences of the pandemic. In addition to five lagged returns we also control for prior market activity using detrended S&P 500 trading volume up to five lags. Finally, we include day-of-the-week indicators to alleviate concerns that changes of COVID-19 related TV coverage are merely a reflection of daily broadcasting schedules. Table 8 presents the regression results after including the additional control variables. All models also include our baseline controls. However, our results remain qualitatively the same.Table 8 Robustness: Additional Controls. Table 8 (1) (2) (3) (4) (5) Returnt Returnt+1 Returnt+2 Returnt+3 Returnt+4 ΔStock Markett−1 0.025* − 0.015 0.007 − 0.019* 0.017 (1.909) ( − 1.090) (0.649) ( − 1.917) (1.086) ΔPublic Healtht−1 − 0.023*** 0.003 − 0.019* 0.016** − 0.007 ( − 2.699) (0.336) ( − 1.723) (2.225) ( − 0.717) ΔRelieft−1 0.006 − 0.029* 0.030* − 0.005 − 0.007 (0.424) ( − 1.950) (1.815) ( − 0.309) ( − 0.508) ΔPreventiont−1 − 0.000 − 0.012 0.000 0.003 0.004 ( − 0.016) ( − 1.646) (0.028) (0.422) (0.549) ΔPoliticst−1 − 0.017* − 0.008 − 0.000 0.011 0.006 ( − 1.881) ( − 0.788) ( − 0.031) (1.428) (0.896) ΔEconomyt−1 0.030* − 0.005 − 0.003 − 0.010 0.000 (1.980) ( − 0.347) ( − 0.199) ( − 0.987) (0.012) ΔRecoveryt−1 − 0.004 0.006 0.009 0.004 − 0.015 ( − 0.313) (0.054) (0.859) (0.392) ( − 1.411) log(ΔDeaths)t−1 0.001 0.002* 0.002 0.003 0.002 (0.572) (1.702) (0.891) (1.585) (0.891) ΔInpatient Bedst−1 − 0.007* 0.006* − 0.005 0.009*** 0.003 ( − 1.768) (2.442) ( − 1.408) (3.349) (1.627) %Vaccinest−1 0.003 − 0.003 0.011 − 0.020 − 0.004 (0.694) ( − 0.533) (1.466) ( − 1.526) ( − 0.300) ΔCHIt−1 − 0.000 − 0.002 0.003 − 0.001 − 0.001 ( − 0.352) ( − 0.828) (1.560) ( − 1.138) ( − 0.589) ΔIJCt−1 − 0.015 0.030 − 0.020 − 0.042 0.007 ( − 0.995) (1.351) ( − 0.949) ( − 1.861) (0.409) Volumet−1:t−5 Yes Yes Yes Yes Yes Baseline Controls Yes Yes Yes Yes Yes Day FE Yes Yes Yes Yes Yes Observations 221 220 219 218 217 Adjusted R2 0.163 0.047 0.046 0.056 0.046 This table relates S&P 500 index ETF (SPY) daily returns to changes in COVID-19 related TV content. The dependent variables are future returns in the next five days (columns (1)-(5)). The set of baseline control variables include changes in TV tone (ΔTone), lagged returns up to five lags (Return), changes in a news-based measure of economic policy uncertainty (ΔEPU), changes in the CBOE volatility index (ΔVIX), changes in the Aruoba-Diebold-Scotti (ΔADS) business conditions index, and the Financial and Economic Attitudes Revealed by Search (FEARS) index. The standard errors are robust to heteroskedasticity and autocorrelation up to five lags (Newey & West, 1987). , , and ** denote significance at the 10%, 5%, and 1% levels, respectively. 5 Conclusion In this study, we shed light on the role of TV coverage for financial market outcomes during the COVID-19 pandemic. Therefore, we employ a novel dataset of more than half a million 15 second transcribed audio snippets containing COVID-19 mentions from major US TV stations. Building an LDA model, we identify seven interpretable topics that capture COVID-19 related TV news content. We document that changes in airtime corresponding to certain COVID-19 related topics can predict short-term returns. While some topics, such as news concerning public health issues and political tensions between the US and China appear to have a persistent effect on short-term returns, increasing COVID-19 related news about the stock market is associated with short-term return reversals in US stock markets. However, we acknowledge several limitations of our findings. It is important to note that our analyses focus on COVID-19 related TV news during 2020 and hence, are not transferable to a more general setting. Moreover, our sample is rather short and covers an economically challenging period, raising concerns about statistical power and omitted variable bias. Although we perform several sensitivity tests that control for fundamental information related to the pandemic, we do not make any causal claims and emphasize that our results must be taken cautiously. In general, this paper contributes to the literature by analyzing the effects of TV content on stock returns during the COVID-19 pandemic. Moreover, due to the high prevalence and usage of this medium, TV coverage could be a potent approach to explain various puzzling stock market outcomes based on limited investor attention. Thus far, our study has only focused on the content of spoken word. However, as compared to other media sources such as newspapers, TV coverage might also affect investment decisions through an auditory, or even more importantly, a visual channel. Since these dimensions have been barely discussed in the extant finance literature, TV news appear to be a promising media source to address new research questions in the field of economic behavior. Appendix A COVID-19 Tone Dictionaries A.1 Constructing Topic-Specific COVID-19 Tone Dictionaries Some topics identified by our LDA model are likely to be associated with a certain tone.fn23 For instance, content about public health issues will intuitively have a more negative tone compared to content about vaccines. Therefore, in order to better distinguish topic- and tone-driven effects of TV news on stock prices, it becomes crucial to control for the general tone conveyed through COVID-19 related TV news. Following the extant finance literature, we seek to employ a dictionary-based approach to measure the tone of each TV snippet. This approach relies on certain word lists that comprise positive and negative labeled words and approximates the tone of a given document by counting the occurrences of each word class. However, literature in the field of textual analyses strongly suggests that a profound tone proxy requires field-specific dictionaries (Loughran & McDonald, 2011,2016,2020). For instance, Loughran and McDonald (2011) show that when employing the widely-used Harvard-IV-4 TagNeg (H4N) dictionary to gauge the tone of 10-K filings, frequently occurring words that are typically not negative in a financial context such as “tax”, “cost”, “capital”, or “liability” are misclassified as being negative. The authors therefore derived word lists from 10-K files that capture the specifics of financial communication. Despite the critics of the authors themselves, these word lists have been frequently applied to various different contexts in financial research with a recent study even employing the word lists to gauge the tone of news articles on COVID-19 (Mamaysky, 2020). However, considering that 10-K files and TV news address completely different audiences and that news on COVID-19 often require medical jargon, it seems inappropriate to employ word lists derived from 10-K files to gauge the tone of COVID-19 related TV news. For instance, the Fin-Pos word list of Loughran and McDonald (2011) intuitively contains the word “positive”, which has arguably a positive tone when occurring in 10-K files. However, in the context of COVID-19 related news, the word “positive” is frequently used to tell whether a person tested positive for COVID-19 which is generally bad news. Consequently, we make efforts to construct a field-specific dictionary for COVID-19 related news coverage. We use the preprocessed TV snippets to collect all words that appear at least 60 times in our sample, which leaves us with 8887 words.fn24 We then manually label each word as either positive, negative, or neutral. At this step of the procedure, we differentiate between two different word groups. First, given that every snippet relates to news about COVID-19, we find that the majority of words clearly indicate a certain tone. For instance, words such as “fear”, “death”, and “risk” convey a negative tone, whereas words such as “great”, “safe”, and “reopen” are positive in a COVID-19 related context. These words can therefore simply be classified as being positive or negative. Second, we find that several words can have ambiguous meaning and need to be judged in regards of their contextual usage. For instance, words such as “increase”, “skyrocket”, or “surge” would be considered positive if they are associated with the stock market but would be perceived as being negative if they were associated with news coverage about infection or death rates of the virus. To better assess the tone of these words, we build on a novel approach proposed by Li et al. (2020). The authors employ word2vec, a neural network proposed by Mikolov et al. (2013), to compile field-specific word lists. The word2vec model essentially seeks to estimate word embeddings that are defined as vector representations of words that attempt to quantify semantic similarities between words. Therefore, word2vec follows an old concept in linguistics: words with similar meanings are likely to co-occur with similar neighboring words (Harris, 1954). The model thus “reads” through a sample of documents and estimates vector representations for all words in a given sample by considering their co-occurrences with neighboring words. As a result, words that are likely to appear in a similar context will share closer locations in vector space, whereas words that are used in different contexts occupy locations that are much further away. Following the spirit of Li et al. (2020) we employ the gensim library and estimate 300-dimensional vector representations for all words and phrases in our sample of TV snippets. Knowing the latent topics identified by our LDA model allows us to judge words with ambiguous meaning in respect of their co-occurrences with words that are associated with certain topics. In Fig. A.1, we employed t-distributed stochastic neighbor embedding (t-SNE) techniques to visualize our 300-dimensional word embeddings in a three-dimensional scatter plot. The plot shows that words such as “increase”, “record”, and “rise” are much more likely to co-occur with words such as “death toll” and “case”, while they are less likely to be associated with the words “stock” and “market”.Fig. A.1 Assess Ambiguous Words Using word2vec. This figure presents a visualization of our word2vec embeddings using t-SNE techniques. Each dots represent the reduced vector representations of words and phrases in our sample of COVID-19 related TV snippets with selective words being highlighted. Fig. A.1 For each latent topic identified by our LDA model, we use the top three words with the highest probability weights (e.g. those words that are most representative for a certain topic) and estimate cosine similarities to identify the top 250 words that are most likely to appear in their context. This allows us to look up whether an ambiguous word is closely associated with any one of these topics. Knowing the contextual relationships between words and their association with certain topics thus allows us to assess the tone of potentially ambiguous words and ultimately, enhances the quality of our COVID-19 dictionaries. As a result of the above process, we compile the Cov-Neg dictionary that comprises 1261 words and phrases with negative tone and the Cov-Pos dictionary that comprises 636 words and phrases that are perceived as being positive. The dictionaries can be found in the Online A.fn25 Unreported results show that the Fin-Neg (Fin-Pos) dictionaries proposed by Loughran and McDonald (2011) contain only 359 (108) words of the Cov-Neg (Cov-Pos) dictionary, indicating that many words used in COVID-19 related news coverage cannot be found in 10-K files. Figure A.2 illustrates the positive and negative words contained in the Cov-Neg and Cov-Pos dictionaries in regards to their occurrences in our sample of TV snippets. Formally, we estimate the daily net tone score Tone on day t as follows: (A.1) Tonet=1Nt∑j=1Ntposjt−negjtposjt+negjt×log(Nt), where pos j and neg j denote the positive and negative word counts in snippet j, respectively. N t is the number of snippets in a given day. Again, for our empirical analyses, we focus on daily tone changes: (A.2) ΔTonet=Tonet−Tonet−1, Fig. A.2 Occurrences of Positive and Negative Words. This figure presents a word cloud that captures the occurrences of positive (green) and negative (red) words and phrases in the sample of COVID-19 related TV Snippet. Fig. A.2 Appendix A Supplementary material Supplementary material 1 See: https://www.bls.gov/tus 2 See: https://www.tvb.org/Public/Research/COVID-19MediaUsageStudyUpdate 3 See: https://www.tvb.org/Public/Research/BroadcastTVViewershipDuringCoronavirus 4 Note that this finding must be taken cautiously because our analysis does not account for a large array of day traders and traders who focus on individual stocks. 5 The Internet Archive’s Television News Archive can be accessed using the following link: https://archive.org/details/tv. 6 We find that the database misses snippets from May 29, 2020 to June 11, 2020 and August 10, 2020. 7 We employ the stopword list of spaCy that comprises a total of 326 words. Based on the snippet search terms, we also remove the words “coronavirus”, “covid”, “virus”, “infection”, “infections”, “infected”, “infect”, “infects”, “infecting”, “corona”, “covid19”, and “covid-19”. 8 We prefer lemmatization over stemming. Since lemmatization accounts for the whole sentence structure and its syntactical dependencies, it is more reliable in keeping the semantic meaning of a word. 9 Following Li et al. (2020), we set δ equal to 50. 10 Note that we also remove single-letter words that are not part of any phrases. 11 Mamaysky (2020) proposes a method that employs a scaled topic coherence in order to find more, but less coherent topics. However, while the author’s sample only covers the first four month of the pandemic and, therefore, naturally comprises fewer topics compared to our sample spanning the whole year 2020, we choose not to scale the coherence measure. 12 Each iteration is initialized using a fixed random seed to assure replicability. However, our results are not sensitive to using different random seeds. 13 We note that the topic (c) and topic (g) assign high topic weights to the word “vaccine”. However, considering their full word clouds, these topics seem to address different contexts. 14 For instance, as shown in Fig. 2, the public health topic is associated with rather negative tone which raises concerns that any negative effect on stock prices could merely reflect overall negative TV tone. 15 The data can be accesses using the following link: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/det_12_ind_port. 16 Please note that the firms assigned to the respective portfolios based on their SIC code in June 2019 or at the fiscal year end of 2018, so that possible distortions due to the pandemic can be ruled out in the portfolio formation. 17 The data can be accesses using the following link: https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html. 18 Please note that for our sample period the portfolios for all sorting variables are formed at the end of June 2019 using NYSE breakpoints. Therefore we can rule out any distortions due to the pandemic within the portfolio sorts. 19 Robinhood holding data is openly available on https://robintrack.net/. 20 We also examine whether investors are drawn to Bitcoin as a potential safe haven property. However, consistent with the findings of Chemkha et al. (2021) and Raheem (2021) who show that Bitcoin could not provide shelter during the pandemic, we find no statistically significant effect of TV coverage on the Grayscale Bitcoin Trust (BTC). 21 Since the COVID-19 caseload is highly correlated with COVID-19 related deaths, we run a separate regression with the COVID-19 US death count instead of COVID-19 US cases. However, the results remain qualitatively the same. Hence for the purpose of brevity we decide to report the model specification with COVID-19 deaths since we already used COVID-19 cases in our baseline specification. Furthermore, in further robustness checks, we control for US COVID-19 related cases and deaths reported by the World Health Organization instead of USAFacts. The results again remain robust. 22 The COVID-19 Government Response Tracker collects publicly available information on 20 indicators of government response whereas the Containment Health Index is a sub-index consisting of 14 indicators recording information on containment and closure as well as health system policies. In addition, we validate our results using the Economic Support Index covering information on economic policies and the holistic Government Response Index covering all three policy areas. Our main findings remain unchanged. For further details on the COVID-19 Government Response Tracker, please see: Hale et al. (2021). 23 Note that we use the terms “tone” and “sentiment” interchangeably. However, in order to not confuse the terms investor sentiment and textual sentiment, we use the term “tone” to refer to textual sentiment proxies. 24 Since our sample comprises slightly more than 600000 TV snippets, the threshold denotes 0.01% of all snippets in our sample. Moreover, note that our text preprocessing procedure proposed in Section 3.2 (eliminating certain POS-Tags, stopwords, and reducing words to their base form) greatly reduces the number of unique words that have to be labelled manually when deriving the field-specific dictionary. 25 Note that all words and phrases in the dictionaries were reduced to their base forms using lemmatization. Appendix A Supplementary data associated with this article can be found in the online version at doi:10.1016/j.qref.2022.11.007. ==== Refs REFERENCES Ahmad K. Han J. Hutson E. Kearney C. Liu S. 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The Review of Financial Studies 23 2010 3520 3557	36506906	PMC9721134	NO-CC CODE	2022-12-08 23:16:01	no	Q Rev Econ Finance. 2023 Feb 5; 87:95-109	utf-8	Q Rev Econ Finance	2,022	10.1016/j.qref.2022.11.007	oa_other
==== Front Int J Disaster Risk Reduct Int J Disaster Risk Reduct International Journal of Disaster Risk Reduction 2212-4209 Elsevier Ltd. S2212-4209(22)00697-5 10.1016/j.ijdrr.2022.103478 103478 Article The non-linear and interactive effects of meteorological factors on the transmission of COVID-19: A panel smooth transition regression model for cities across the globe Zhai Guangyu a∗ Qi Jintao a Zhou Wenjuan b Wang Jiancheng b a School of Economics and Management, Lanzhou University of Technology, Lanzhou, 730050, China b Gansu Provincial Hospital, Lanzhou, 730000, China ∗ Corresponding author. Postal address: No. 287, Langongping Road, Qilihe District, Lanzhou, Gansu Province, China. 5 12 2022 1 2023 5 12 2022 84 103478103478 18 5 2022 14 10 2022 1 12 2022 © 2022 Elsevier Ltd. All rights reserved. 2022 Elsevier Ltd Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. The ongoing pandemic created by COVID-19 has co-existed with humans for some time now, thus resulting in unprecedented disease burden. Previous studies have demonstrated the non-linear and single effects of meteorological factors on viral transmission and have a question of how to exclude the influence of unrelated confounding factors on the relationship. However, the interactions involved in such relationships remain unclear under complex weather conditions. Here, we used a panel smooth transition regression (PSTR) model to investigate the non-linear interactive impact of meteorological factors on daily new cases of COVID-19 based on a panel dataset of 58 global cities observed between Jul 1, 2020 and Jan 13, 2022. This new approach offers a possibility of assessing interactive effects of meteorological factors on daily new cases and uses fixed effects to control other unrelated confounding factors in a panel of cities. Our findings revealed that an optimal temperature range (0°C–20 °C) for the spread of COVID-19. The effect of RH (relative humidity) and DTR (diurnal temperature range) on infection became less positive (coefficient: 0.0427 to −0.0142; p < 0.05) and negative (coefficient: −0.0496 to −0.0248; p < 0.05) with increasing average temperature(T). The highest risk of infection occurred when the temperature was −10 °C and RH was >80% or when the temperature was 10 °C and DTR was 1 °C. Our findings highlight useful implications for policymakers and the general public. Graphical abstract Image 1 Keywords COVID-19 Temperature Relative humidity Diurnal temperature range Panel smooth transition regression Global ==== Body pmc1 Introduction The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [1], poses unprecedented challenges to global health-care systems. Since the COVID-19 outbreak began in late 2019, over 42 million people across more than 180 countries have been infected, resulting in more than 5,890,000 deaths as of early January 2020 [2]. Meteorological factors impact coronavirus disease by their effect on host susceptibility, virus survival, aerosol transmission of the virus, and human behavior [[3], [4], [5], [6], [7]]. Previous studies have suggested that a negative significant correlation between transmission of SARS-CoV-2 and meteorological factors exists in China [8,9], the United States [10,11], Brazil [12], Europe [13,14], and other cities across the world [15,16]. In contrast, other studies reported that meteorological factors showed positive significant associations with SARS-CoV-2 transmission in China [17], Singapore [18], and Poland [19] and non-significant associations with SARS-CoV-2 in Canada [20] and other cities worldwide [21]. Most studies on the relationship between viral transmission and meteorological factors have focused on the effects of single meteorological factor on transmission rather than on their interactive effects. Coronavirus transmission is not significantly correlated with any individual weather parameter; transmission is, instead, susceptible to a certain weather pattern [22]. Owing to different ranges of temperature in analysis and the interaction of meteorological factors such as temperature and humidity, complex weather patterns lead to inconsistent conclusions from previous studies [23,24]. Therefore, the interactive effect of meteorological factors in complex weather patterns on the spread of COVID-19 must be further clarified. Some studies use a traditional infectious disease model, i.e., susceptible-exposed-infectious-removed (SEIR), to evaluate the relationship between meteorological conditions and COVID-19 [25,26], the limitation of which is that different input-parameter estimations (such as contact rate and infection rate) lead to a deviation of output results. Machine-learning methods such as random forests [19,27] and support vector machines [22] have also been used in regression to predict COVID-19 using meteorological variables as predictors, even though they cannot estimate the specific coefficients of the contribution of meteorological variables to the spread of the virus. Mainstream research uses the generalized additive model (GAM) to estimate the nonlinear relationship between temperature and humidity and transmission [9,12,15,28] and the distributed lag nonlinear model (DLNM) to estimate nonlinear-lagged effects between environmental factors and COVID-19 [8,10,29]. Several of these studies report an optimal range of temperature for transmission or nonlinear associations (U-shape) of that [8,29,30]. A limitation of GAM and DLNM is that the changing coefficient of humidity or other meteorological factors with temperature to a new crown infection cannot be estimated. The sensitivity of RH transmission of COVID-19 often is found to vary according to the average temperature [9,31]. The impact of RH on COVID-19 transmission is often found to vary according to the average temperature [9,31]. These studies usually divide temperature into two or three groups in discussing its interaction with humidity, such that the coefficients of the observed explanatory variables are identical for all observations in the same group. However, a panel smooth transition regression (PSTR) [32] model was used to evaluate RH and DTR's changing coefficients to the spread of COVID-19 with temperature. The purpose of our study is to use the PSTR model to assess the interactive effects of meteorological factors on global transmission of COVID-19. Previous studies have demonstrated that government responses and demographics (e.g., socio-economic characteristics, population density) in different counties can be even more significant impact factors for the spread of COVID-19 than meteorological factors [24,26,33,34]. In the present study, we consider human mobility, GDP, and population density in the model to control the effect of unrelated confounding factors on COVID-19 transmission. This study aimed to investigate the nonlinear interactive effects of meteorological factors on global transmission of COVID-19 by using the PSTR model with time and individual fixed effects based on a panel dataset from 58 cities across the world between July 1, 2020 and January 13, 2022. The results reveal an optimal temperature range (0°C-20 °C) for the spread of COVID-19; the effect of RH on the spread of COVID-19 becomes less positive with temperature and becomes negative when T > 10 °C, while the impact of DTR on COVID-19 spread becomes less negative with temperature. 2 Materials and methods 2.1 Study area and data Our study included 58 major cities across most of the world (22 in Europe, 17 in Asia, 13 in the Americas, and 6 in Oceania and Africa). Because the main focus of our study is the impact of meteorological factors on COVID-19, these cities were selected based on two criteria: latitude (representing climate zones) and cumulative number of confirmed cases during the study period. First, we selected cities with cumulative infections numbering greater than 100,000. Among them, in the same country, the top five cities with the largest number of infections were selected at most to avoid the sample representing a country. Second, considering the inclusion of different climate zones, we selected some cities with less than 100,000 infected people in other climate zones, such as Lima (Peru), Nigeria (Central Africa), Murmansk Oblast (north of Russian's Arctic Circle), and several Chinese cities, among others. Fig. 1 shows the 58 locations of these cities. Table S1 provides the names of these cities. The daily number of new confirmed cases of COVID-19 and meteorological data were collected in 58 cities from July 1, 2020 to January 13, 2022 to construct the panel dataset. The number of confirmed daily cases were collected from the official websites of the countries in which the cities were located. A range of meteorological data were collected from the National Oceanic and Atmospheric Administration (https://www.noaa.gov), including daily average temperature, DTR and relative humidity during the same study period for each city. Demographic density and GDP were collected from the UN World Population Prospects (https://population.un.org/wpp/) and WorldBank (https://worldbank.org/). GDP per capita is from the specific country's annual statistics. Our research period mainly covered 2020 and 2021; so, we chose a country-wise GDP average of these 2 years as the economic indicator of the corresponding city. The human mobility shows how visits and length of stay at different places change compared to a baseline that is the median value during the 5-week period Jan 3–Feb 6, 2020. This was calculated by average of changes for Groceries & pharmacy, Retail & recreation, Transit & stations, Parks, and Workplaces categories (https://www.google.com/covid19/mobility/).Fig. 1 Locations of 58 global cities included in our study. Fig. 1 2.2 Statistical analysis The impacts of relative humidity and DTR on COVID-19 are known to vary for any given temperature since the effects of temperature, humidity, and DTR on the spread of COVID-19 are interactive and complex [9,31]. Hence, coefficients can take different values, depending upon the value of average temperature. For these reasons, we employed the panel smooth transition regression (PSTR) model developed by Gonzalez et al. [32] to characterize non-linear relationships between meteorological factors and COVID-19 transmission for panel data from 58 global cities between July 1, 2020 and January 13, 2022. By using this strategy, we would be able to provide a parametric approach to cross-country heterogeneity when applied to a panel of countries and avoid the arbitrary choice of threshold values. Previous studies have shown that population density (PD), GDP and human mobility (HM) influence the transmission of COVID-19 [26,33,34]. Hence, population density, GDP and human mobility were considered in model to control their confounding effect. In additions, other unrelated confounding effect was control by fixed effect. The corresponding PSTR model with fixed effects was defined as shown in Equation (1).(1) logyit=μi+λt+β0Tit+β1RHit+β2DTRit+η0PDi+η1GDPi+η2HMit+∑j=1r(β0′Tit+β1′RHit+β2′DTRit)·g(Tit;γj,cj)+εit for countries i = 1,…, N and times t = 1,…, T, where N and T design the cross-section and time dimensions of the panel. logy it denotes the log-transformed daily new cases of COVID-19 counts in city i on day t. Here, μ i and λ t represent individual and time fixed effects, respectively. T it, RH it and DTR it are a set of 5-day lagged moving average terms of daily meteorological variables in city i on concurrent day t and the previous 4 days to account for the cumulative lag effect and control the incubation period of COVID-19 estimated to be 5.1 days [35]. PD i , GDP i and HM it show population density, GDP and human mobility respectively. ε it is an independent and identically distributed error term. g(Tit;γj,cj) stands for continuous transition function of order j bounded between 0 and 1, in which the parameter γ determines the smoothness, i.e., the speed at which the vector of coefficients goes from β to β+β’. The location parameter c j indicates the temperature at which the transition function reaches an inflexion point. The transition variable T it captures the non-linear dynamics due to the threshold effects of temperature on COVID-19 transmission and the RH and DTR-specific responses of daily cumulative cases for any given temperature. Hence, the coefficients of RH and DTR for daily cumulative cases can take different values, depending on the value of temperature. For instance, the elasticities of daily cumulative case to RH or DTR are defined by Equation (2).(2) ∂(logyit)∂RHit=β1+β1′g(Tit;γj,cj),∀i,∀t The transition function follows a logistic function as an S-shape or exponential function as a U-shape as defined by Equation (3).(3) gTit;γj,cj1+exp−γjTit−c−1,m=11−exp−γjTit−c1Tit−c2,m=2 In Equation (3), m denotes the number of threshold parameter c. The PSTR model is based on three steps. First, we started by testing the linear specification of the original model against a specification with a smooth transition threshold effect by applying the linearity test (Table S2). In the second step, we determined the number of transition functions (r in Eq (1)) required to capture all non-linearity of the original model (Table S3). Finally, we determined the type of smooth transition functions (select m = 1 or 2) according to the Akaike Information Criterion (AIC) and computational complexity (Table S4). The estimation procedure is reported in Tables S2–S4. The results showed that the linearity hypothesis was strongly rejected and there was only one transition function in the PSTR model. In addition, we selected the logistic transition function as the smooth transition function in the empirical model for a smaller value of the AIC and computational complexity. The empirical result of the PSTR model is given by Equation (4).(4) logyit=μi+λt+β0Tit+β1RHit+β2DTRit+η0PDi+η1GDPi+η2HMit+(β0′Tit+β1′RHit+β2′DTRit)[1+exp(−γ(Tit−c))]−1+εit Because the incubation period of COVID-19 ranges from 3 days to two weeks [35,36], we adjusted the transition variable temperature at 3-day,5-day,7-day and14-day lagged moving average in our sensitivity analysis to confirm that our main results were robust. Sensitivity analysis of the transition variable that was adjusted for temperature indicated the high stability of our PSTR model (Fig. S1). The PSTR model used in our analysis was implemented via the “PSTR” package (version 1.2.5) of R software (version 4.1.2). The statistical tests were two-sided, and p < 0.05 was considered statistically significant. 3 Results Table 1 summarizes the descriptive statistics for daily new cases of COVID-19 and meteorological variables. This study included 43,268 cases globally during the observation period (July 1, 2020 to January 13, 2022) and the temperature ranged from −40 °C to 40 °C (from the equator to the poles). Mean daily average temperature, relative humidity, and diurnal temperature range were 15.47 °C, 69.19% and 9.91 °C, respectively. The highest population density (245,396 persons/km2) and GDP per capita (75,419$) were Manchester in the United Kingdom and Norway respectively, while the lowest ones were Irkutsk Oblast (3 persons/km2) in Russia and Congo (545$) respectively. In the past two years, human mobility has decreased by an average of 14.21% in study locations.Table 1 Descriptive statistics of newly confirmed cases and meteorological variables across all cities and days. Table 1Variables Mean ± SD Frequency distribution Min P25 P50 P75 Max Daily confirmed cases 1075 ± 3130 0 13 190 864 73,191 ln (dailycases +1) 4.61 ± 2.80 0 2.64 5.24 6.75 11.20 Population density (persons/km2) 3795 ± 5686 3 102 811 5830 24,539 GDP per capita ($) 31,134 ± 22,432 545 9440 34,456 43,070 75,419 Human Mobility (%) −14.21 ± 16.61 −76 −22.67 −12.83 −5 58.83 T (°C) 15.47 ± 10.39 −40.56 8.73 16.72 23.59 40.88 RH (%) 69.19 ± 16.98 8.66 60.72 72.42 81.19 99.35 DTR (°C) 9.91 ± 3.97 0.99 7.00 9.43 12.62 25.32 5-day moving average T 15.50 ± 10.22 −38.18 8.79 16.70 23.47 40.27 5-day moving average RH 69.23 ± 15.7 9.23 62.00 72.39 79.88 98.24 5-day moving average DTR 9.90 ± 3.47 1.27 7.36 9.45 12.21 21.81 Table 2 shows parameter estimates for the final PSTR model. Table 2 shows that the slope coefficients γ, β and β ’ were different and significant at the 5% level, thus indicating the presence of non-linearity and the existence of one threshold in the temperature–infection nexus. Moreover, the mean Location Parameter c was 0.365, thus showing that the temperature–infection relationship was characterized by two extreme regimes (average temperature below and above 0.365 °C). The slope parameter γ was 0.108, thus illustrating a smooth transition from a regime where the temperature was below zero to above zero. The smooth transition function (m = 1) with the parameters is depicted in Fig. 2 . Estimates of the coefficients, β 0, β 1 were 0.0713 and 0.0427, were significantly positive, whereas the coefficients (β 0 ’: 0.0783 β 1 ’: 0.0569) were negative. This indicates that the correlations between COVID-19 infection and temperature or RH were positive at a low temperature. However, when the temperature increased, the links between COVID-19 transmission and temperature or RH became less positive, and the links turned negative when the temperature was higher than 20 °C and 10 °C respectively, as shown in Fig. 2(b and c).Table 2 Parameter estimates for the PSTR model. Table 2Independent variables Coefficients White SE β0× Tit 0.0713650* 0.0649645 β1× RHit 0.0427936 0.0214898 β2× DTRit −0.0496642 0.0799572 η0× PDi 0.06695327 0.0028467 η1× GDPi −0.06472412* 0.0175621 η2× HMit 0.085687529* 0.0028769 β0’× Tit× g(Tit;γ,c) −0.0783360* 0.0385418 β1’× RHit× g(Tit;γ,c) −0.0569056 0.0313088 β2’× DTRit× g(Tit;γ,c) 0.0247961 0.1043620 Location Parameter c 0.365621 2.881380 Smooth Parameter γ 0.1084030 0.0277633 Sum of Squared Residuals 1.5466 β0 is the coefficient in the linear part (first extreme regime). β0’ is the coefficient in the non-linear part. β0 + β0’ is the coefficient in the second extreme regime. (): significant at the 10% level; (): significant at the 5% level and (**): significant at the 1% level. White SE is the white heterogeneous standard error. Fig. 2 Estimated transition function and elasticities of COVID-19 daily cumulative case to the meteorological factors at different temperature. Fig. 2 Fig. 2 depicts the transition function g(T it ;γ,c) and the elasticity of daily cumulative cases to RH and DTR, β j +β j ' g(T it ;γ,c), as defined by Equation (2). While the temperature coefficient is [β 0 +β 0 ' g(T it ;γ,c)]. In other words, an increase of daily cases was associated with an increase of temperature, but the increase is more marked at a lower temperature. In contrast, the estimate of coefficient β 2 was −0.0496 whereas the coefficient β 2 ’ (0.0247) was positive, thus illustrating that the DTR-infection relationship became less negative and was always negative as temperature increased, as depicted in Fig. 2(d). The presence of the observations demonstrated in Fig. 2 implies that the variations of temperature and RH exert less interplay on COVID-19 infection when the temperature exceeded 30 °C. This means that the response of COVID-19 infection to additional increases in temperature and RH is less important when it is very hot. To further investigate the real influence of temperature on COVID-19 infection at different temperatures across the world, we generated Fig. 3 to depict the contribution that the average temperature makes to the conditional expectation of the log of daily confirmed cases through the smooth transition mechanism. Because the transition variable q it is T it, the contribution can be described as y = [β 0 +β 0 ' g(T it ;γ,c)]T it. This figure implies the real contribution of temperature to the log-transformed daily new cases of COVID-19 counts after controlling for human mobility, GDP and population density.Fig. 3 The contribution of temperature to the log daily new cases of COVID-19. Fig. 3 Fig. 3 shows that an inverted U-shaped relationship exists between the contribution of average temperature to COVID19-infection and temperature. There was an optimal temperature range from 0 °C to 20 °C (with a peak at 10 °C) with respect to COVID-19 infection. The contribution increased sharply with temperature at a subzero temperature but decreased at higher temperatures (T > 20 °C). This contribution has some negative value because of shortened survival time of the virus on extremely cold and hot conditions outside host. In addition, there were also fixed effects of different city and time contributions in the explanatory variables (Table S1 and Fig. S2). In Fig. 4 , the z-axis represents the joint contribution of temperature and DTR or RH to COVID-19 infection, the x-axis shows the RH or DTR, and the y-axis shows temperature; this allowed us to compare the impact of RH or DTR on COVID-19 infection at different temperatures. The maximum effect on infection occurred at a temperature of −10 °C and a RH of 99.35%. Fig. 4 shows that the positive relationship between RH and COVID-19 infection turns negative when T > 10 °C. In contrast, the reductions in the negative effect of DTR on infection associated with increases in temperature resulted in the greatest effect at the smallest DTR (1 °C) and the suitable temperature for coronavirus (10 °C). In additions, Fig. 4 shows that the combinations of joint contribution of temperature and RH is more effective than temperature and DTR (2.439 vs 0.092).Fig. 4 The joint contribution of temperature and RH or DTR to log daily cumulative case of COVID-19. Fig. 4 4 Discussion Meteorological factors are vital if we are to fully investigate the factors that influence the transmission of COVID-19. Some studies have reported a negative link between temperature and COVID-19 in China [8,9], the United States [10,11], Europe [13,14], African [37,38], South American [12,39,40], and global cities [15,16]. Note that the negative relationship of several studies between temperature and infection is significant over a range of temperatures in the tropical zone, such as India (29.8–36.5 °C) [22,28], Brazil (16.8–27.4 °C) [12,39,40], African (26.16 ± 0.12 °C) [37,38]. It is consistent with our conclusion that negative correlation between temperature and infection when T > 10 °C. However, other studies have reported positive correlations between temperature and COVID-19 infection in China [17], Singapore [18], Poland [19]and Nigeria [41] and non-significant associations in Canada [20] and worldwide cities [21]. Furthermore, non-linear relationships between temperature and infection have been demonstrated in the USA [42], China [8] and Korea [43]. In particular, a review [3] and studies involving China [8,44], the world [45], and England [29] found that the suitable temperature range for the highest incidence of COVID-19 was10 °C,0–17 °C,13–24 °C,8 °C and 11.9 °C, respectively. These results are similar to the temperature range identified in the present study (0–20 °C, 10 °C at peak). A previous laboratory study found that coronavirus remained stable on smooth surfaces for over 5 days when the temperature was 22°C–25 °C, and that viral viability was rapidly inactivated at higher temperatures (e.g., 38 °C) [4,46]. In aerosols, the virus is much less resistant due to ultra-violet radiation and higher temperatures [47]. A novel finding in our PSTR model was that the increase in COVID-19 transmission with temperature was slower with temperatures below 10 °C. This might be due to the immune system functioning better at higher temperatures [45]. Some epidemic studies reported that humidity is negatively related to the incidence of COVID-19 in China [9], The United States [10,11], England [12], African [37,38], South American [39], and global cities [15,48]. They are line with our result that a negative relation between RH and infection of COVID-19 when T > 10 °C. Other studies show positive associations in Italy [13], Germany [14], Nigeria [41]. Prior studies also have reported evidence of a non-linear correlation between relative humidity and the rate of COVID-19 transmission [15,26]. However, most studies above don't consider the role of temperature in the effect of humidity on transmission. In the present study, we observed the effect of RH on spread of COVID-19 becomes less positive with the temperature and turn negative when T > 10 °C. It is consistent with a previous study in China resulting that high relative humidity promotes COVID-19 transmission when temperature is low and it reverse when temperature is high [31]. In addition, a study involving the humid and continental region of Russia, also found that an increase in the average temperature led to a reduced effect of average relative humidity on the intensity of COVID-19 cases [27]. The diurnal temperature range significantly predict the infection outbreak in India and Brazil [49]. There are few previous studies on associations between DTR and COVID-19. Some reported non-significantly associations between DTR and transmission in India [22] and European [26]. Others indicated that COVID-19 transmission may be affected negatively by DTR in Russia [27] and China [50]. These findings are consistent with our findings in that DTR is negatively associated with COVID-19 transmission. This can be explained, at least in part, by the shorter life cycle of the virus outside of a host at a high DTR [49,51] and large temperature fluctuations tend to lower the immune system and consequently increase the risk of infections [52]. A novel finding in study is that the DTR has a more negative effect toward infection at lower temperatures; this is likely to be due to a reduction in population resistance to respiratory diseases in a colder environment thus allowing the virus to spread rapidly [50]. A novel finding is that the highest risk of infection occurred at subzero temperatures (−16 °C) and at the highest humidity (99.3%) in our present research. A study including 21 countries prove that low temperature combined with high relative humidity was associated with higher seasonal coronaviruses activity [53]. Aerosol transmission has become an important means of transmission of the new coronavirus because empirical data have shown that aerosolized SARS-CoV-2 particles can remain suspended in the air for hours and are subject to transport over significant distances, including outside rooms and between buildings [5,54,55]. Subzero temperatures and a higher RH facilitate greater SO4 2− formation, leading to a high fraction of aerosol to support the survival and transmission of the virus [56,57]. Furthermore, our results demonstrate that the joint contribution of temperature and RH is more effective than that of temperature and DTR (2.439 versus 0.092). There is little literature demonstrating that DTR has a direct effect on the formation of aerosols. Smaller DTR and temperature only increase the survival time of the virus outside the host [51], not its spread. While lower temperature and higher RH are due to the thermal insulation effect of aerosols, they are generally associated with smaller DTRs [58,59], which create both survival and transmission conditions for the virus. Our findings on meteorological factors and their interactions should be incorporated into the strategies adopted for the prevention of COVID-19. The optimal temperature range for COVID-19 transmission is 0°C-20 °C, suggesting that social distancing and the wearing of masks must be maintained during the next autumn-winter and winter-spring transitions to avoid another outbreak. Rising temperature attenuates the positive effect of high humidity on transmission. In colder regions, indoor temperatures can be raised to reduce the high risk of infection from high-humidity weather. A subzero temperature and high RH or low DTR were found to be related to a high risk of infection. Public health departments can issue early weather warnings for similar weather to improve the public's awareness of these weather patterns. In cold and high-humidity areas or weather, windows should be opened to prevent the virus accumulating in aerosols, and attendance at social gatherings should be reduced to avoid aerosol transmission. Moreover, the interaction of these meteorological factors suggests that policymakers should apply sophisticated machine-learning models to forecast COVID-19 transmission with complex weather parameters so as to facilitate issuing warning alerts to control the spread of COVID-19. The major advantage of our study is that we used a PSTR method to investigate the impact of meteorological factors on the transmission of COVID-19 at different temperatures since it is necessary for a complex weather condition. Moreover, we control major confounding factor, such as population density, GDP and human mobility and use fixed effects to control other unrelated confounding factors. However, several limitations should be acknowledged. First, we could not conduct subgroup analysis by gender and age to explore the sensitive population because there was a lack of patient information. Secondly, there will be a difference between the actual number of cases and the number of reported cases due to the adjustment of diagnostic criteria in different countries. 5 Conclusion We used a panel smooth transition regression (PSTR) model to investigate the non-linear interactive impact of meteorological factors on daily new cases of COVID-19 cases based on a panel dataset of 58 global cities observed between July 1, 2020 and January 13, 2022. Our study has some novel implications. First, the effect of RH on the spread of COVID-19 becomes less positive with temperature and becomes negative when T > 10 °C, while the impact of DTR on COVID-19 spread becomes less negative with temperature. Second, a low temperature and high RH or low DTR were related to a high risk of infection. The proposed PSTR model incorporates heterogeneity by allowing regression coefficients to vary as a function of an exogenous variable and fluctuate between a limited number (often two) of extreme regimes. First, this can be applied to study the interactive effect of environmental factors on public health and any other independent variable with interactions. Second, the model can be extended to handle dynamic panel data by including lagged values of the dependent variable as regressors. Third, the model allows multiple variables to enter the transition function, which might be relevant in practice. This will allow more weather factors to be incorporated into the transformation variables to jointly determine the classification of study areas into regimes with different characteristics of weather patterns in meteorological disaster research. Ethics approval and consent to participate Not Applicable. Funding This was supported by the 10.13039/501100001809 National Natural Science Foundation of China [71861026] and Gansu Provincial Natural Science Foundation [20JR10RA149]. Author contributions Z. GY. analyzed the data and prepared figures and tables. Q. JT. wrote the main manuscript text. Z. WJ. collected the data. W. JC. supervised the manuscript. All authors reviewed the manuscript. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix A Supplementary data The following is the Supplementary data to this article:Multimedia component 1 Multimedia component 1 Data availability The authors do not have permission to share data. Acknowledgements Not applicable. Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.ijdrr.2022.103478. ==== Refs References 1 Blumenthal D. Fowler E.J. Abrams M. Collins S.R. Covid-19—implications for the health care system N. Engl. J. Med. 383 2020 1483 2148 https://www.nejm.org/doi/full/10.1056/NEJMsb2021088 32706956 2 World Health Organization WHO Coronavirus (COVID-19) Dashboard 2022 https://covid19.who.int 3 McClymont H. Hu W. Weather variability and COVID-19 transmission: a review of recent research Int. J. Environ. Res. Publ. Health 18 2021 396 10.3390/ijerph18020396 4 Van Doremalen N. Bushmaker T. Munster V. 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==== Front Actas Dermosifiliogr Actas Dermosifiliogr Actas Dermo-Sifiliograficas 0001-7310 1578-2190 AEDV. Published by Elsevier España, S.L.U. S0001-7310(22)01039-0 10.1016/j.ad.2022.07.030 Cartas Científico-Clínicas Contact Dermatitis From Amplified Hand Hygiene Practices in the COVID-19 Pandemic Among Medical Students: Frequency, Knowledge, and Attitude [[Artículo traducido]]Dermatitis de contacto debido al incremento de las prácticas sobre higiene de manos durante la pandemia de COVID-19 entre los estudiantes de Medicina: frecuencia, conocimiento y actitud Batool Mutar M. ⁎ Consultant Clinical Immunology, University of Baghdad, Al-Kindy College of Medicine, Head of HLA Research Unit, Department of Microbiology, Baghdad, Iraq ⁎ Corresponding author 5 12 2022 5 12 2022 31 1 2022 2 7 2022 © 2022 AEDV. Published by Elsevier España, S.L.U. 2022 AEDV Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmc	36473521	PMC9721152	NO-CC CODE	2022-12-06 23:26:27	no	Actas Dermosifiliogr. 2022 Dec 5; doi: 10.1016/j.ad.2022.07.030	utf-8	Actas Dermosifiliogr	2,022	10.1016/j.ad.2022.07.030	oa_other
==== Front Am J Otolaryngol Am J Otolaryngol American Journal of Otolaryngology 0196-0709 1532-818X Elsevier Inc. S0196-0709(22)00350-7 10.1016/j.amjoto.2022.103723 103723 Article Vertigo/dizziness following COVID-19 vaccination Yan Hong-Yu Young Yi-Ho ⁎ Department of Otolaryngology, National Taiwan University Hospital, Taipei, Taiwan ⁎ Corresponding author at: Department of Otolaryngology, National Taiwan University Hospital, 1, Chang-Te St., Taipei, Taiwan. 5 12 2022 5 12 2022 10372324 9 2022 © 2022 Elsevier Inc. All rights reserved. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Purpose This study assessed the vertigo/dizziness in patients following COVID-19 vaccination. Patients and methods From July 2021 to June 2022, totaling 50 patients with dizzy spells following COVID-19 vaccination by AZ (AstraZeneca-Oxford University, AZD1222), BNT (BioNTech-Pfizer, BNT162b2) or Moderna (Moderna, mRNA-1273) vaccine were enrolled in this study. The interval from vaccination to the onset of vertigo/dizziness was compared with inter-episodic interval of vertigo/dizziness in the same patients, but without vaccination, during past one year (2020). Results The incidences of severe systemic complication per 106 shots were 0.86 for Moderna vaccine, 1.22 for AZ vaccine, and 1.23 for BNT vaccine. Conversely, rate of post-vaccination vertigo/dizziness was noted in the Moderna group (66 %), followed by the AZ group (20 %) and the BNT (14 %) group, meaning that type of COVID-19 vaccine may affect various organ systems. The median time to the onset of vertigo/dizziness following vaccination is 10d, which is consistent with the onset of IgG production, and significantly less than inter-episodic interval (84d) in the same patients without vaccination. Conclusion Post-vaccination vertigo/dizziness can manifest as exacerbation of previous neurotological disorder. The median time to the onset of vertigo/dizziness following COVID-19 vaccination is 10d. Since the outcome is fair after supportive treatment, the immunomodulatory effect of the vaccines does not undermine the necessity of the COVID-19 vaccination. Keywords COVID-19 infection COVID-19 vaccine Recurrent vertigo AZ vaccine BNT vaccine Moderna vaccine ==== Body pmc1 Introduction The global outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) since November 2019 led the World Health Organization (WHO) to declare a pandemic infection of “Coronavirus disease 2019 (COVID-19)”. A newly developed vaccine termed ChAdOx1 nCoV19 (AstraZeneca-Oxford University, Vaxzevria, AZD1222), briefly AZ vaccine, was approved for emergency use at the end of 2020 [1]. Thereafter, several types of vaccines against COVID-19 infection have been released for controlling the pandemic and its socioeconomic impact [2], including Elasomeran (Moderna, Spikevax, mRNA-1273), briefly Moderna vaccine [3]; Tozinameran (BioNTech-Pfizer, Comirnaty, BNT162b2), briefly BNT vaccine [4]; Ad26.COV2.S (Janssen, COVID-19 Vaccine Janssen), briefly Janssen vaccine [5], and so on. Vaccination against COVID-19 started in Taiwan in March 2021. Initially, the AZ vaccine was administered to the elderly aged >65 years, followed by the Moderna vaccine since June 2021, and the BNT vaccine since September 2021, according to a decreasing sequence of 10-year age bands among adults based on the policy of the Centers for Disease Control (CDC) in Taiwan. By the end of June 2022, overall 15,294,226, 17,926,798 and 21,860,972 shots of the AZ, BNT and Moderna vaccines were administered in Taiwan, respectively [6]. During the pandemic period of COVID-19, the annual new cases of audiovestibular disorders at our neurotological clinic of a university hospital decreased from 2068 (2019) to 1829 (2020), likely because patients with inner ear disorders did exist, yet they just never presented for medical care. Opposed to the declining numbers of a neurotological clinic, more patients with autonomic dysfunction visited our clinic. The incidence of autonomic dysfunction in 2020 (15.3 %) was significantly higher than 8.5–13.1 % during 2016–2019, probably because of increased psychological stress, panic, anxiety, or depression associated with social isolation in time of a pandemic COVID-19 period [7]. On the other hand, many patients came to the neurotological clinic due to episodic vertigo/dizziness after receiving COVID-19 vaccine, which warrants further investigation. Hence, this study assessed the episodic vertigo/dizziness following COVID-19 vaccination. 2 Patients and methods 2.1 Participants From July 2021 to June 2022, totaling 50 patients with episodic vertigo/dizziness following COVID-19 vaccination visited our neurotological clinic of the university hospital. Fifteen were males and 35 females, with a mean age of 56 ± 14 years. All patients received otoscopy first, followed by an inner ear test battery including audiometry, cervical vestibular-evoked myogenic potential (cVEMP) test, ocular VEMP (oVEMP) test, and caloric test, then diagnosis was established. Details of vaccination were obtained via face-to-face interview including types and shots of the vaccine, time from date of vaccination to the onset of vertigo/dizziness. Additionally, inter-episodic interval of vertigo/dizziness in the same patients, but without vaccination, during past one year (2020) was also recorded for comparison. Diagnosis of vestibular migraine (VM) was jointly formulated and recently updated by the Barany Society and International Headache Society [8], including a cardinal symptom of at least 5 episodes of vestibular symptoms (lasting 5 min to 72 h) with migraine features, but normal hearing and vestibular function tests between attacks. Diagnosis of Meniere disease (MD) was based on the guidelines proposed by the American Academy of Otolaryngology-Head and Neck Surgery in 1995 and the Barany Society in 2015 [9], [10]. Exclusion criteria comprised concurrent middle or inner ear anomaly/infection and head injury. Those patients with vertigo/dizziness beyond one month after vaccination, those without complete 3-month medication previously, or those without vaccination were also excluded. This study was approved by the institutional review board of the university hospital and each subject signed the informed consent to participate. 2.2 Inner ear test battery Audiometry was used for assessing the cochlear function Bithermal caloric test was performed for checking the semicircular canal function. The detailed procedures of the oVEMP and cVEMP tests were described elsewhere [11], which were utilized for assessing the utricular and saccular function, respectively. 2.3 Statistical methods The gender ratio among three types of vaccine was compared by 2 × 3 Chi-square test. Comparisons of the age between three groups were analyzed by one-way repeated measures ANOVA test, followed by Bonferroni-adjusted test. Incidence of severe systemic complication among three groups was compared by 2 × 3 Chi-square test. The interval among the three groups was compared by Kruskal-Wallis test. The interval from date of vaccination to the onset vertigo, and inter-episode interval without vaccination, was compared by Wilcoxon signed-rank test. In the time-to-event analysis, an event was defined as the presence of vertigo, while length of time was determined by either inter-episode interval without vaccination or time to vertigo onset after vaccination. Kaplan-Meier curve and Cox regression were utilized to compare the interval between two groups. The effect size is delineated with hazard ratio with 95 % confidence interval (CI). A significant difference indicates p < 0.05. 3 Results 3.1 Demographic study Totaling 50 patients with vertigo/dizziness following COVID-19 vaccination were enrolled. These patients had been vaccinated with any of three major COVID-19 vaccines, namely AZ, BNT, and Moderna vaccines. Thirty-three (66 %) patients had vertigo/dizziness after Moderna vaccination, followed by 10 (20 %) who received AZ vaccine and 7 (14 %) who received BNT vaccine. Twenty-nine patients had received two shots (58 %) of vaccines, 16 patients (32 %) had 3–4 shots, and 5 patients (10 %) had one shot only. However, people who were given multiple shots did not necessarily receive the same type of vaccine each time. The mean ages in the Moderna, AZ and BNT groups were 60 ± 10, 53 ± 16, and 38 ± 7 years, respectively, showing a significantly younger age in the BNT group (p < 0.001, one-way repeated measures ANOVA test, followed by Bonferroni-adjusted test, Table 1 ). This is likely due to the policy of vaccination in Taiwan. The COVID-19 vaccination started from the elderly first, followed by a sequence of administration via a 10-year age band from those aged 55–64 years to young adults. In addition, AZ vaccine was initially available, then Moderna and BNT vaccine. That is why most young people received BNT vaccine. However, there was no significant difference in gender ratio among the three groups (p > 0.05, 2 × 3 Chi-square test, Table 1).Table 1 Comparison of various types of COVID-19 vaccine. Table 1Types of vaccine Cases of vertigo Age (Y) Sex (M/F) Incidence of severe complication6 Moderna 33 (66 %) 60 ± 10a, c 12/21 189/21,860,972 (0.86/106) AZ 10 (20 %) 53 ± 16a, b 1/9 186/15,294,226 (1.22/106) BNT 7 (14 %) 38 ± 7b, c 2/5 221/17,926,798 (1.23/106) p value <0.001⁎ >0.05# 0.001# AZ: AstraZeneca-Oxford University, Vaxzevria, AZD1222 vaccine. BNT: BioNTech-Pfizer, Comirnaty, BNT162b2) vaccine. Moderna: Spikevax, mRNA-1273 vaccine. ⁎ One-way repeated measures ANOVA test, followed by Bonferroni-adjusted test. a p > 0.05. b p = 0.026. c p < 0.001. # 2 × 3 Chi-square test. Based on the website of vaccine adverse event reporting system (VAERS) established by the CDC in Taiwan [6], severe systemic complication of vaccination was defined as: 1) thrombosis with thrombocytopenia syndrome; 2) cerebral venous sinus thrombosis without thrombocytopenia; 3) idiopathic thrombocytopenic purpura; 4) myocarditis and/or pericarditis; 5) Guillain–Barre´ syndrome; and 6) anaphylaxis. By the end of June 2022, COVID-19 vaccination rates for the first, second and third shots have reached to 91 %, 83 % and 70 % in the general population of Taiwan, respectively. Accordingly, the incidences of severe systemic complication in relation to overall shots comprised 189/21,860,972 for the Moderna vaccine, 186/15,294,226 for the AZ vaccine, and 221/17,926,798 for the BNT vaccine, accounting for 0.86, 1.22 and 1.23 per 106 shots, respectively, exhibiting a significant difference among the three types of vaccine (p = 0.001, 2 × 3 Chi-square test, Table 1). Restated, the highest incidence of the severe systemic complication occurred in the BNT group, followed by the AZ and Moderna groups. Opposed to this declining sequence of severe systemic complication, the highest rate of post-vaccination vertigo/dizziness was observed in the Moderna group (66 %), followed by the AZ group (20 %) and BNT group (14 %), indicating that the type of COVID-19 vaccine may affect various organ systems. Hence, disease distribution in patients with post-vaccination vertigo/dizziness was subsequently analyzed. 3.2 Disease distribution All 50 patients had received an inner ear test battery coupled with a complete course of three-month medication at our clinic previously. Clinical manifestation in these patients included vertigo/dizziness in all 50 patients (100 %), followed by tinnitus (70 %), headache (66 %), nausea/vomiting (62 %), hearing loss (46 %), and fullness sensation (42 %). Disease distribution comprised Meniere's disease (MD) in 13 patients (26 %), followed by vertebrobasilar artery insufficiency (VBI) (18 %), vestibular migraine (16 %), benign paroxysmal positional vertigo (16 %), autonomic dysfunction (12 %), and others in 6 patients (12 %) including acoustic trauma 3, sudden deafness 1, downbeat nystagmus 1, and cerebellar encephalitis 1 (Table 2 ). Comparing types of vaccine at last shot in relation to each neurotological disorder revealed non-significant difference (p > 0.05, 3 × 6 Chi-square test), indicating that type of COVID-19 vaccine is unrelated to the neurotological disorders.Table 2 Disease distribution in 50 patients with post-vaccination vertigo/dizziness. Table 2Neurotological diseases Case no. Moderna AZ BNT Meniere's disease 13 (26 %) 10 1 2 Vertebrobasilar artery insufficiency 9 (18 %) 7 2 0 Vestibular migraine 8 (16 %) 4 2 2 Benign paroxysmal positional vertigo 8 (16 %) 7 1 0 Autonomic dysfunction 6 (12 %) 2 2 2 Others 6 (12 %) 3 2 1 p value (NS) AZ: AstraZeneca-Oxford University, Vaxzevria, AZD1222 vaccine. BNT: BioNTech-Pfizer, Comirnaty, BNT162b2) vaccine. Moderna: Spikevax, mRNA-1273 vaccine. NS: non-significant difference, p > 0.05, 3 × 6 Chi-square test. 3.3 Interval to the onset of vertigo/dizziness The median time to the onset of vertigo/dizziness following vaccination was 12d (range, 1-30d) for Moderna group, 6d (range, 0-16d) for AZ group, and 6d (range, 0-30d) for BNT group. Since significant difference in time to the onset of vertigo/dizziness was not shown among the three groups (p = 0.290, Kruskal-Wallis test, Table 3 ), data of 50 patients were thus pooled together, which revealed that the median time to the onset of vertigo/dizziness was 10d (range, 0-30d) following vaccination (Table 3).Table 3 Comparison of interval to the onset of vertigo/dizziness between patients with and without vaccination. Table 3Vaccine N Interval to the onset of vertigo/dizziness following vaccination (d) N Inter-episode interval without vaccination (d) Moderna 33 12 (1−30) 23 86 (28–287) AZ 10 6 (0–16) 5 96 (16–245) BNT 7 6 (0−30) 6 70 (28–210) p valuea 0.290 0.911 Total 50 10 (0–30)# 34 84 (16–287)# Data are expressed as median (range). AZ: AstraZeneca-Oxford University, Vaxzevria, AZD1222 vaccine. BNT: BioNTech-Pfizer, Comirnaty, BNT162b2) vaccine. Moderna: Spikevax, mRNA-1273 vaccine. a Kruskal-Wallis test. # p < 0.001, Wilcoxon signed-rank test. For comparison, the interval between two vertigo/dizziness episodes in the same patients during past one year (2020), but without vaccination, was served a control group. As a result, the median intervals were 86d (range, 28-287d) for the Moderna group, 96d (range, 16-245d) for the AZ group, and 70d (range, 28-210d) for the BNT group. Again, significant difference was not identified among the three groups (p = 0.911, Kruskal-Wallis test, Table 3). Thus, data of the three groups were pooled together for analysis. Accordingly, the median inter-episodic interval in patients without vaccination was 84d (range 16-287d), which was significantly longer than 10d (range 0-30d) in the same patients with vaccination (p < 0.001, Wilcoxon singed-rank test, Table 3). Additionally, in the time-to-event analysis, an event was defined as the presence of vertigo/dizziness, while length of time was determined by either time to vertigo/dizziness onset after vaccination, or inter-episodic interval without vaccination. Accordingly, patients with vaccination had significantly shorter interval for episodic vertigo/dizziness than same patients without vaccination (p < 0.001, log-rank test, Fig. 1 ). Furthermore, Cox regression demonstrated that vaccination was associated with significantly higher risk of recurrent vertigo/dizziness, with a hazard ratio of 15.4 (95 % CI, 7.0–34.2; p < 0.001).Fig. 1 The Kaplan-Meier curve demonstrates probability of recurrent vertigo/dizziness over time in patients with vs. without vaccination. The median intervals of recurrent vertigo/dizziness are 10d and 84d in patients with and without vaccination, respectively. Fig. 1 All patients underwent supportive treatment. Relief of vertigo without untoward effect was achieved after one-month medication in all patients. 4 Discussion 4.1 Types of COVID-19 vaccine Four types of COVID-19 vaccine with different mechanisms have been released, namely, whole virus vaccine (live attenuated, inactivated i.e. Sinovac vaccine), viral vector vaccine (non-replicating, replicating, i.e. AZ vaccine), nucleic acid vaccine (mRNA, DNA i.e., BNT or Moderna vaccine), and protein-based vaccine (subunit, virus-like particle i.e. Navavax vaccine). Of them, AZ, BNT and Moderna are three major types of vaccine utilized in Taiwan. The AZ vaccine comprises the replication-deficient adenovirus vector ChAdOx1, which contains genes that express spike protein of SARS-CoV-2 in human cells. Adaptive immunity produces antibody against the spike protein on SARS-CoV-2, thus reducing the incidence of symptomatic disease in vulnerable populations [1]. Both the BNT and Moderna vaccines consisted of lipid nanoparticles filled with mRNA. These nucleic acid vaccines introduced mRNA into the cells for producing antibodies against the SARS-CoV-2 spike protein. The BNT and Moderna vaccines differs on the mRNA content [12]. One dose of BNT vaccine contains 30 μg of mRNA, while that of Moderna vaccine delivers 100 μg. Hence, the Moderna vaccine triggers stronger immune response than the BNT vaccine, which may account for the higher rate of post-vaccination vertigo/dizziness in the former than the latter. 4.2 Disease distribution Although the vaccine type received by patients with post-vaccination vertigo/dizziness was in a declining sequence from the Moderna vaccine (66 %), AZ vaccine (20 %) to BNT vaccine (14 %), the incidence of severe systemic complication in general population opposed to this sequence, i.e., running from the BNT vaccine (1.23 per 106), AZ vaccine (1.22 per 106), to the Moderna vaccine (0.86 per 106), based on the VAERS by the CDC in Taiwan (Table 1). Hence, the type of vaccine that produces more adverse effects is difficult to determine, likely because the vaccine type may affect various organ systems. Thus, post-vaccination vertigo/dizziness could be noted in various neurotological disorders regardless of the type of vaccine given. Most patients with post-vaccination vertigo/dizziness were referred to MD, followed by VBI, vestibular migraine, benign paroxysmal positional vertigo, autonomic dysfunction, etc. (Table 2). This heterogenous group of post-vaccination vertigo/dizziness indicates that immunological factor may play a key role for exacerbating pre-existing neurotological disorders, originating from a spike of disease-specific IgG. More than 60 % of patients with post-vaccination vertigo/dizziness experienced tinnitus, headache, nausea/vomiting, hearing loss, and fullness sensation, meaning that these non-specific inner ear symptoms could be part of the clinical spectrum of COVID-19 vaccine side effects. 4.3 Interval to the onset of vertigo/dizziness The median time to the onset of vertigo/dizziness was 12d, 6d, and 6d after Moderna, AZ and BNT vaccination, respectively, showing a non-significant difference among the three groups (Table 3). Hence, all three groups were pooled together, revealing that a median time to the onset of vertigo/dizziness of 10d after vaccination, consistent with the onset of IgG production, because IgG antibodies to the vaccine protein antigens first appear at 10-14d after priming [13]. Since IgG binds efficiently to the antigen and aids in opsonization, it is likely to generate an aggressive systemic response, leading to exacerbation of a previous neurotological disorder manifesting as recurrent vertigo/dizziness episode. Additionally, comparing the inter-episodic interval of vertigo/dizziness in the same patients during past one year (2020), but without vaccination, median intervals were 86d, 96d and 70d for the Moderna, AZ, and BNT groups, respectively (Table 3). Since all three groups did not significantly differ in the inter-episodic interval, data of the three groups were also pooled together, which revealed a median inter-episodic interval of 84d (range, 16-287d). The latter (84d) was significantly longer than 10d of post-vaccination vertigo/dizziness (Table 3). Further, Cox regression also indicated that vaccination is associated with significantly higher risk of recurrent vertigo/dizziness, with a hazard ratio of 15.4 (Fig. 1). 4.4 Post-vaccination vertigo/dizziness in relation to immunological reaction Most patients with post-vaccination vertigo/dizziness were referred to MD (Table 2), which is a multifactorial disorder and has immunological factors that exacerbate the endolymphatic hydrops [14]. Additionally, increased osmolality in the inner ear may boost pro-inflammatory cytokines and activate the immune cells [15]. Thus, a potential systemic immune response and a spike of disease-specific IgG could intensify disease activity, as shown by vertiginous attack in a stable MD patient following vaccination [16]. Next to MD, VBI represents second commonest cause of post-vaccination vertigo/dizziness. Dysregulation of the blood flow due to altered plasma viscosity, platelet aggregation, red blood cell deformability, and endothelial function may induce vertigo in patients with VBI. Although the AZ vaccine provides effective immunization against SARS-COV-2 in the general population, increased risk of thrombotic event has led to a halt of AZ vaccination by many countries [1]. Yet, the benefits of immunization by AZ vaccine far outweigh the risk of potential side effects. Finally, an immunization anxiety related reaction cannot be neglected. This reason is easily associated with the stress and higher anxiety present during the pandemic COVID-19 period, and can also boost the pathogenic mechanism of central vestibular disorders i.e. autoimmune encephalitis [17], [18], [19], [20]. 5 Conclusion Post-vaccination vertigo/dizziness can manifest as exacerbation of previous neurotological disorder. The median time to the onset of vertigo/dizziness following COVID-19 vaccination is 10d. Since the outcome is fair after supportive treatment, the immunomodulatory effect of the vaccines does not undermine the necessity of the COVID-19 vaccination. Sponsorships None. Funding This study was supported by 10.13039/501100001868 National Science Council, Taiwan (Grant no. MOST 111-2314-B002-197) Declaration of competing interest None. ==== Refs References 1 Voysey M. Clemens S.A. Madhi S.A. Safety and efficacy of the ChAdOx1 nCoV-19 vaccine (AZD1222) against SARS-CoV-2: an interim analysis of four randomised controlled trials in Brazil, South Africa, and the UK Lancet 397 2021 99 111 33306989 2 Teo S.P. Review of covid-19 vaccines and their evidence in older adults Ann Geriatr Med Res 25 2021 4 9 33550776 3 Baden L.R. El Sahly H.M. Essink B. Efficacy and safety of the mRNA-1273 SARS-CoV-2 vaccine N Engl J Med 384 2021 403 416 33378609 4 Polack F.P. Thomas S.J. Kitchin N. Safety and efficacy of the BNT162b2 mRNA Covid-19 vaccine N Engl J Med 383 2020 2603 2615 33301246 5 Sadoff J. Gray G. Vandebosch A. Safety and efficacy of single-dose Ad26.COV2.S vaccine against Covid-19 N Engl J Med 384 2021 2187 2201 33882225 6 Official report from the Taiwan Centers of Disease Control https://covid-19.nchc.org.tw/dt_002-csse_covid_19_daily_reports_vaccine_city2.php 7 Chao C.H. Young Y.H. Evolution of incidence of audiovestibular disorders during the pandemic COVID-19 period Eur Arch Otorhinolaryngol 279 2022 3341 3345 34389915 8 Lempert K. Olesen J. Furman J. Vestibular migraine: diagnostic criteria (update) J Vestib Res 32 2022 1 6 34719447 9 AAO-HNS Committee on Hearing and Equilibrium. Committee on Hearing and Equilibrium guidelines for the diagnosis and evaluation of therapy in Meniere’s disease Otolaryngol Head Neck Surg 113 1995 181 185 7675476 10 Lopez-Escamez J.A. Carey J. Chung W.H. Diagnostic criteria for Meniere’s disease J Vestib Res 25 2015 1 7 25882471 11 Young Y.H. Potential applications of ocular and cervical vestibular-evoked myogenic potentials in Meniere’s disease: a review Laryngoscope 123 2013 484 491 23070719 12 Dickerman B.A. Gerlovin H. Madenci A.L. Comparative effectiveness of BNT162b2 and mRNA-1273 vaccines in U.S. Veterans N Engl J Med 386 2022 105 115 34942066 13 Clem A.S. Fundamentals of vaccine immunology J Glob Infect Dis 3 2011 73 78 21572612 14 Frejo L. Martin-Sanz E. Teggi R. Extended phenotype and clinical subgroups in unilateral meniere disease: a cross-sectional study with cluster analysis Clin Otolaryngol 42 2017 1172 1180 28166395 15 Kleinewietfeld M. Manzel A. Titze J. Sodium chloride drives autoimmune disease by the induction of pathogenic TH17 cells Nature 496 2013 518 522 23467095 16 Wichova H. Miller M.E. Derebery M.J. Otologic manifestations after COVID-19 vaccination: the house ear clinic experience Otol Neurotol 42 2021 e1213 e1218 34267103 17 Zuhorn F. Graf T. Klingebiel R. Schäbitz W.R. Rogalewski A. Postvaccinal encephalitis after ChAdOx1 nCov-19 Ann Neurol 90 2021 506 511 34324214 18 Marsh E.B. Kornberg M. Kessler K. Quality committee of the American Academy of Neurology. COVID-19 and vaccination in the setting of neurologic disease: an emerging issue in neurology Neurology 97 2021 720 728 34326180 19 Graus F. Titulaer M.J. Balu R. A clinical approach to diagnosis of autoimmune encephalitis Lancet Neurol 15 2016 391 404 26906964 20 Baloh R.W. Spooner J.W. Downbeat nystagmus: a type of central vestibular nystagmus Neurology 31 1981 304 310 6970904	36502671	PMC9721153	NO-CC CODE	2022-12-08 23:16:26	no	Am J Otolaryngol. 2023 Dec 5 March-April; 44(2):103723	utf-8	Am J Otolaryngol	2,022	10.1016/j.amjoto.2022.103723	oa_other
==== Front Prog Cardiovasc Dis Prog Cardiovasc Dis Progress in Cardiovascular Diseases 0033-0620 1873-1740 Elsevier Inc. S0033-0620(22)00136-0 10.1016/j.pcad.2022.11.015 Article Equitable well-being, social trust, and the economy: An integrated health system's perspectives on the long-term implications of COVID-19 Pronk Nicolaas P. abc⁎ McEvoy Charlene ade a HealthPartners Institute, Minneapolis, MN, USA b Department of Health Policy and Management, University of Minnesota, Minneapolis, MN, USA c Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA d HealthPartners Care Group, Minneapolis, MN, USA e Pulmonary, Critical Care, Allergy and Sleep Medicine Department, University of Minnesota, Minneapolis, MN, USA ⁎ Corresponding author at: HealthPartners Institute, 8170 33rd Avenue South, Minneapolis, MN 55425, USA. 5 12 2022 5 12 2022 © 2022 Elsevier Inc. All rights reserved. 2022 Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. To address organizational concerns related to the longer-term implications of coronavirus disease 2019 (COVID-19) and generate priorities for organizational focus, we facilitated an in-depth dialogue and discussion among health system leaders who collectively represented medical, public health, and business expertise. Key insights and observations were identified, prioritized, collected, discussed, and organized into overarching themes. A set of five overarching themes that are considered important themes to be addressed by the larger health system emerged. The five observed themes include: 1) Health disparities persist; 2) physical activity, healthful diet, and healthy weight reduce severe COVID-19 health outcomes; 3) an urgent need exists to rebuild social trust; 4) partnerships and collaborations among public health, business and industry, and health care are central to rebuilding social trust and implementation of equitable and sustainable solutions; and 5) health, well-being, and healing are business imperatives. Keywords COVID-19 Trust Equity Well-being Health system Abbreviations COVID-19, Coronavirus disease 2019 PA, Physical activity PI, Physical inactivity SARS-CoV-2, severe acute respiratory syndrome coronavirus 2 infection US, United States ==== Body pmcThe United States (US) has surpassed one million deaths due to the coronavirus disease 2019 (COVID-19) pandemic1; the country is now entering a phase with lessened risk for severe outcomes due to effective preventive (e.g., vaccines, masking)2 and treatment (e.g., dexamethasone),3 interventions. As the more acute ramifications of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and COVID-19 are being mitigated by improved care options, attention shifts towards the longer-term implications of the virus and the disease.4 Indeed, the term “Long COVID” has emerged referring to the more chronic sequelae of COVID-19. 4 However, such long-term implications are not limited to patients' health, disability, or quality of life considerations. Implications also affect the way care delivery functions, how public health reacts, the way business and industry cares for its workers, and how society prepares for the next pandemic or considers the syndemic interdependencies between COVID-19 and other currently ongoing pandemics, such as obesity and physical inactivity (PI). The initial response to the emerging threat of SARS-CoV-2 and COVID-19 was challenging, resulting in a devastating loss of life, continued negative impacts on disadvantaged populations, increased anxiety and mental health concerns, reduced engagement on healthy lifestyle behaviors, major shifts in employment patterns, a breakdown of trust in science, and numerous other burdens on individuals, subpopulations, organizations, and society.5 , 6 To address organizational concerns and interests to be prepared for the longer-term implications of COVID-19, we facilitated an in-depth dialogue and discussion among medical and public health leaders in the context of an integrated health system to generate priorities for organizational focus. Therefore, the purpose of this paper is to present an account of perspectives on the long-term implications of COVID-19 from a variety of organizational stakeholders along with external community representatives with the intention to generate organizational energy for multifaceted action on health system change. Methods This quality improvement activity deployed a qualitative methodology (i.e., dialogue and discussion) entirely couched within an integrated health system's setting. HealthPartners, a non-profit, consumer-governed health system that integrates care, insurance, research, and education located in the Upper Midwestern United States, played a critical role in addressing the health care needs related to the COVID-19 pandemic across the communities it serves. Our approach was to facilitate an open dialogue intentionally designed to generate a variety of perspectives among a selected panel of health system leaders with first-hand experience of the pandemic. While considering the long-term implications of the COVID-19 pandemic on medical care, health, and well-being, we asked the panel members to identify, summarize, and prioritize their main points ahead of the discussion. Those main points were shared with members of the Board of Directors of the HealthPartners Institute, a research and medical education organization embedded within the HealthPartners health system. Board members included in the dialogue session represented health system leaders as well community representatives (See Table 1 ). During the meeting, panel members presented their perspectives in brief, concise presentations that were immediately followed by a general discussion with the entire Board of Directors. The general discussion was designed to be free flowing yet guided by two questions: 1) “You have heard several perspectives. What resonates with you? Is anything missing?”; and 2) “During the pandemic, the integration of research and education into care became critical. How do we build on these gains?”Table 1 Profiles of participants engaged in the dialogue. Table 1 Area Represented Expertise/Discipline 1# Pulmonology Pulmonary and sleep medicine; vaccine efficacy 2 Hospital care Internal Medicine 3* Care delivery Infectious disease; physician executive 4* Epidemiology, maternal and child health Vaccine safety and effectiveness 5* Neuroscience Cognitive function and brain health 6* Prevention Primordial and primary prevention; health behaviors; well-being 7 Community representative Business and industry 8 Academic medicine Orthopedic surgery 9 Finance Financial performance 10 Health plan Physician executive 11 Medical Education Academic Graduate Medical Education 12 Family Medicine Social science; citizen science 13 Clinical care delivery Regional medical director; administrative 14 Community representative Social welfare 15 Practicing researcher and educator Physical Medicine and Rehabilitation 16 Care delivery administration Quality Improvement 17 Hospital administration Physician executive 18 Community representative Wellness and social determinants of health Note: * = Panel member; # = Panel moderator. Following the meeting, notes were reviewed, and an additional set of insights and observations were added to the priorities submitted by the panel members. To derive a succinct set of implications that would allow to be shared broadly, we considered all observations and identified key themes and factors that may be used to prepare for a post-pandemic future. The HealthPartners Institute Institutional Review Board determined that these activities represent internal quality improvement efforts and did not meet the definition of human subjects research under 45 CFR Part 46. Results All primary observations and implications are summarized in Table 2 . One of the observations relates to observed gaps in care and outcomes for minority populations. One of the subpopulations that showed disparities in outcomes involved the Hmong population in Minnesota.7 A significant population of Hmong residents receives care in the HealthPartners care delivery system and therefore additional attention needs to be paid to how the health system serves this subpopulation. Efforts to provide equitable care continue to be of importance.Table 2 Primary priorities, observations, and implications derived from the panel presentations and participant dialogue based on COVID-19 experiences. Table 2Perspectives Observations Hospital Care • An urgent need emerged for more attention to be paid to health and health care disparities, inequalities, and inequities that manifest in markedly different mortality rates between subpopulations • The immune system, which is activated to deal with infection, is now confirmed to be itself capable of causing disease • Our perspectives on how to view infectious disease and the immune system now requires a paradigm shift • Precision medicine will need to be considered from both a genetic and a whole person perspective, simultaneously • Infrastructure and organizational changes are urgently needed to support health care workers' health and well-being Care Delivery • The care delivery system learned and must now continue to respond quickly and be nimble. Trust in a smaller group of decision-makers on behalf of larger teams was critical to address issues during continuously evolving circumstances • Virtual care options are effective, ubiquitous, and expected by patients • Flexible schedules for work teams and individual colleagues are paramount for family-work and life balance and will be the norm going forward • The desire for care delivery to build long-term relationships with patients will be increasingly strained by the patient expectations of greater convenience and convenience-based care Vaccine Safety and Efficacy • COVID-19 vaccines are safe and effective, including for those pregnant persons and their infants • Vaccine induced immunity wanes over time necessitating the need for boosters and optimal strategies for immunity duration and the efficacy to address emerging variants • Disparities in vaccine uptake need to be addressed Cognitive Function and Brain Health • The SARS-CoV-2 virus, and its inflammatory cytokine storm, can transport along the nerves involved in smell from the nose into the brain. This may contribute to loss of smell and taste, brain fog, mood disorder, sleep disruption, encephalitis, stroke, difficulty breathing, and potentially oxygen sensing in the medulla Primordial and Primary Prevention • Certain health factors and lifestyle behaviors, such as obesity, physical inactivity, and sleep hypoxia, are associated with poorer outcomes related to COVID-19 infections (e.g., hospitalization risk or death) • The patterns across the United States for COVID-19 mortality rates and unhealthy lifestyle prevalence are consistent with patterns of health disparities, indicating a common source epidemic • The strong protective features of physical activity, healthy weight, and healthful diet should heavily weigh in addressing the unhealthy lifestyles-obesity-infectious disease syndemic that will persist for the foreseeable future. Underlying and Overarching • Social trust as a major concern for the future of our communities and key ingredients for addressing inclusivity • As a society and community, can we build capacity for social cohesion so we may be able to trust people to be responsible for the health of others? • Health considerations need to be couched in the context of economic considerations to reflect both the lives and the livelihood of people and patients • Can the health care sector morph to ensure that providers' demographics reflect the patients they care for? • Care teams are hurting and suffering from burn-out. This will not go away easily when the climate of misinformation continues to challenge the profession • What role should the business and industry sector play? Considering the primary observations and implications, we identified a succinct set of key implications reflecting themes that were observed and may be used to support systems changes. These themes are summarized as follows: 1) Health disparities persist; 2) Physical activity (PA), healthful diet, and healthy weight reduce severe COVID-19 health outcomes; 3) Need to rebuild social trust; 4) Partnerships and collaborations among public health, business and industry, and health care are central to rebuilding social trust and implementation of equitable and sustainable solutions; and 5) Health, well-being, and healing are business imperatives. Discussion The results of this facilitated dialogue session provide important insights regarding the long-term implications of COVID-19 for an integrated health system. Specific observations related to long-term implications for the health system were generated by panel members shared prior to the meeting with all Board members. In addition, a set of insights were generated as part of the open dialogue and discussion that followed the short panel presentations. Several critical factors were noted that reflect overarching considerations for the health system to address. Finally, five themes emerged that inform potential future health system actions and reinforcements. Observed COVID-19 outcomes across the care system highlight the disparities among patients—in this specific case for HealthPartners, the Hmong population was especially hard-hit.7 Such observations make it clear that to improve outcomes for the entire population, health equity must be achieved.8 In this context, health equity should be considered more than the absence of disparities. To attain equitable health outcomes, we must create an equitable society and consider health equity as advancing social justice in health.9 Community partnerships will be essential for a health system to address these issues. Health factors, notably obesity, diet, and PI/sedentary behavior, show strong associations with severe COVID-19 outcomes.10., 11, 12 To mitigate severe outcomes, efforts must be pursued to support patients in engaging in habitual PA, consumption of a healthful diet, and addressing obesity. Importantly, efforts to ensure healthful diets, habitual activity, and healthy weight status for populations and entire communities represents a critical strategy for future pandemic preparedness.13 , 14 Trust in public health agencies, health care, and other major institutions has eroded steadily.6 Health care systems, public health, and other community stakeholders need to create an ability for outreach and let patients express themselves, be heard, and feel heard.15 , 16 Genuine and authentic efforts designed to intentionally build long-term relationships with patients, members, and other key stakeholders (e.g., such as in the case of trust in vaccines) may be dependent upon a trust level that allows for change efforts to succeed.17 No single entity can be responsible for population health across an entire community. Furthermore, most of the explained variance in health outcomes does not come from health care alone. As a result, the health system needs partnerships with multiple community partners and to build alliances across multiple sectors. Such partnerships need to be designed to create shared value among all stakeholders.18 As such, achieving higher levels of health, well-being, and prosperity becomes a business imperative that should engage the business and industry sector. Strengths and limitations This project is limited to the perspectives of one single integrated health system. Furthermore, the experiences upon which these perspectives are based stem mainly from one geographic area, i.e., the midwestern US. However, the limitations should be balanced against several notable strengths. The perspectives are based on the lived experiences of health system leaders who were actively engaged in pandemic prevention and treatment efforts. In addition, the perspectives reflect considerations about prevention and treatment paradigms in addition to concerns that reflect other important issues that go beyond health care, such as social trust and the role of employers. Future research may be informed by the results of this project. Additional insights into the longer-term implications of COVID-19 may be compared against the findings of this current work. Conclusions This activity asked questions about the long-term implications of the COVID-19 pandemic experience for an integrated health system. The insights and learnings generated a succinct set of considerations that may be summarized as addressing health disparities, supporting, and improving the well-being of the health care workforce, increasing healthy living, preventing and addressing obesity, improving levels of social trust, and pursuing partnerships and collaborations with community stakeholders. Delcaration of Competing Interest None. ==== Refs References 1. CDC COVID Data Tracker https://covid.cdc.gov/covid-data-tracker/#cases_deathsper100k 2022 Accessed: 9/9/2022 2 Kharbanda E.O. Vazquez-Benitez G. COVID-19 mRNA vaccines during pregnancy: new evidence to help address vaccine hesitancy JAMA 327 15 2022 1451 1453 10.1001/jama.2022.2459 35325030 3. The RECOVERY Collaborative Group Dexamethasone in hospitalized patients with COVID-19 N Engl J Med 384 2021 693 704 10.1056/NEJMoa2021436 32678530 4. Centers for Disease Control and Prevention Long COVID or post COVID conditions 2022 https://www.cdc.gov/coronavirus/2019-ncov/long-term-effects/index.html Accessed 9/12/2022 5 Roper W.L. Science, health, and truth Science 377 6601 2022 7 35771934 6 Horton R. Offline: science and the breakdown of trust Lancet 396 2020 945 33010826 7. Star Tribune https://www.minnpost.com/community-voices/2021/05/no-excuses-covid-19-data-must-separate-out-hmong-american-cases/ 2022 Accessed 9/9/2022 8 Santana S. Brach C. Harris L. Updating health literacy for healthy people 2030: defining its importance for a new decade in public health J Public Health Manag Pract 27 6 Supp 2021 S242 S248 10.1097/PHH.0000000000001324 33278186 9 Braveman P.A. Kumanyika S. Fielding J. Health disparities and health equity: the issue is justice Am J Public Health 101 suppl 1 2011 S149 S155 21551385 10. CDC Brief Summary of Findings on the Association Between Physical Inactivity and Severe COVID-19 Outcomes https://www.cdc.gov/coronavirus/2019-ncov/downloads/clinical-care/E-Physical-Inactivity-Review.pdf 2022 Accessed: 9/9/2022 11 Lavie C.J. Sanchis-Gomar F. Henry B.M. Lippi G. COVID-19 and obesity: links and risks Expert Rev Endocrinol Metab 2020 1 2 12 Merino J. Joshi A.D. Nguyen L.H. Diet quality and risk and severity of COVID-19: a prospective cohort study Gut. 70 2021 2096 2104 34489306 13 Piernas C. Patone M. Astbury N.M. Associations of BMI with COVID-19 vaccine uptake, vaccine effectiveness, and risk of severe COVID-19 outcomes after vaccination in England: a population-based cohort study Lancet Diabetes Endocrinol 2022 10.1016/S2213-8587(22)00158-9 14 Rippe J.M. COVID-19 and health equity Am J Lifestyle Med 16 4 2022 416 419 35855782 15 Baron R.J. Khuller D. Building trust to promote a more equitable health care system Ann Intern Med 2021 10.7326/M20-6984 16 Saha Stout S. Simpson L.A. Singh P. Trust between health care and community organizations JAMA 322 2 2019 109 110 10.1001/jama.2019.1211 July 9 31180461 17 Bollyky T.J. Angelino O. Wigley S. Dieleman J.L. Trust made the difference for democracies in COVID-19 Lancet 400 2022 657 36030809 18 Pronk N.P. Public health, business, and the shared value of workforce health and wellbeing Lancet Public Health 4 7 2019 Jul e323 10.1016/S2468-2667(19)30078-7	36473506	PMC9721154	NO-CC CODE	2022-12-16 23:18:14	no	Prog Cardiovasc Dis. 2022 Dec 5; doi: 10.1016/j.pcad.2022.11.015	utf-8	Prog Cardiovasc Dis	2,022	10.1016/j.pcad.2022.11.015	oa_other
==== Front Gen Hosp Psychiatry Gen Hosp Psychiatry General Hospital Psychiatry 0163-8343 1873-7714 Elsevier Inc. S0163-8343(22)00142-6 10.1016/j.genhosppsych.2022.12.001 Letter to the Editor Long COVID and the risk of suicide Sher Leo abc⁎ a James J. Peters VA Medical Center, Bronx, NY, USA b Icahn School of Medicine at Mount Sinai, New York, NY, USA c Columbia University College of Physicians and Surgeons, New York, NY, USA ⁎ Corresponding author at: James J. Peters VA Medical Center, 130 West Kingsbridge Road, Bronx, NY 10468, USA. 5 12 2022 5 12 2022 25 10 2022 30 11 2022 1 12 2022 © 2022 Elsevier Inc. All rights reserved. 2022 Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Keywords Long COVID Suicide Depression Mental health Public health ==== Body pmcLong COVID is a major public health issue around the world [1,2]. Multiple definitions of long COVID exist but the bottom line is that long COVID is a persistent syndrome affecting a significant proportion of patients who had acute COVID-19 [[1], [2], [3]]. University of Washington's Institute for Health Metrics and Evaluation research shows that worldwide, nearly 150 million people are estimated to have developed long COVID during the first two years of the pandemic [2]. Even a mild COVID-19 infection can cause long COVID. To be considered as related to long COVID, complaints had to have appeared or worsened since acute COVID-19 infection and to persist after the acute phase of illness. The clinical spectrum of long COVID is various [1,4]. It includes respiratory, metabolic and neuropsychiatric disorders and pain syndromes. An association between neuropsychiatric complaints and somatic complaints has been observed in patients with long COVID (for example, cognitive complaints and depressed mood with respiratory symptoms) [4]. Depression, anxiety, posttraumatic symptoms, sleep disturbances, fatigue and cognitive deficits are the most frequently reported neuropsychiatric manifestations of long COVID [5]. All these conditions are associated with suicidal ideation and behavior [3,6]. For example, 60% of all individuals who die by suicide have a mood disorder at the time of death. Metabolic and other medical disorders significantly increase suicide risk [7]. For example, diabetes and cardiovascular disorders are associated with elevated risk for suicidal behavior. Also, many studies have shown that chronic pain is an independent risk factor for suicide [8]. Therefore, individuals with long COVID may be at increased risk of suicide. It has also been observed that pre-infection psychosocial distress characterized by depression, anxiety, worry, perceived stress, and loneliness was associated with a substantial increase in the suicide risk among individuals with long COVID [5]. A recent study examined possible association between long COVID, psychiatric symptoms and psychiatric disorders [9]. The authors found that the number of long COVID complaints was higher in patients with significant suicide risk. Respiratory and cognitive complaints and persistent fatigue were more frequent in patients with significant suicide risk than in patients without any psychiatric disorders. The authors also found that cognitive complaints were associated with a significant suicide risk adjusting for age, sex, and ICU stay. A recent meta-analysis showed that some post-COVID patients experience persistent suicidality [10]. Another recent study found that compared with individuals who did not have COVID, those who had COVID were 46% more likely to have suicidal ideation during the post-acute phase [11]. The presence of suicidal ideation increases suicide risk. There are very few publications regarding the relation between long COVID and suicide [3,9,10,11]. This issue does not receive sufficient attention. The goal of this note is to draw attention of the medical community to the risk of suicide in individuals with long COVID. Suicide risk in long COVID may be underappreciated by both mental health and non-mental health medical professionals. Therefore, it is very important to educate medical professionals working with long COVID patients that• individuals with long COVID may be suicidal, • persons with long COVID need to be screened for suicidality, • if necessary, suicide prevention interventions should be implemented. Families of individuals with long COVID need to be educated that psychiatric symptoms especially, suicidal ideation in long COVID patients should be taken seriously. It is necessary to advise families of long COVID patients to get immediate professional medical help if individuals with long COVID experience suicidal thoughts. It is vital to educate policy makers and public health administrators that long COVID may be associated with significant psychiatric issues including suicidal ideation and behavior. Sufficient resources need to be allocated to make sure that long COVID patients with psychiatric symptoms receive appropriate mental health care. Conflict of Interest None. Data availability No data was used for the research described in the article. ==== Refs References 1 Ceban F. Leber A. Jawad M.Y. Yu M. Lui L.M.W. Subramaniapillai M. Registered clinical trials investigating treatment of long COVID: a scoping review and recommendations for research Infect Dis (Lond) 54 7 2022 Jul 467 477 10.1080/23744235.2022.2043560 35282780 2 University of Washington's Institute for Health Metrics and Evaluation WHO: at least 17 million people in the WHO European Region experienced long COVID in the first two years of the pandemic; millions may have to live with it for years to come URL https://www.healthdata.org/news-release/who-least-17-million-people-who-european-region-experienced-long-covid-first-two-years September 13, 2022 Accessed: October 9, 2022 3 Sher L. Post-COVID syndrome and suicide risk QJM. 114 2 2021 Apr 27 95 98 10.1093/qjmed/hcab007 33486531 4 Efstathiou V. Stefanou M.I. Demetriou M. Siafakas N. Makris M. Tsivgoulis G. Long COVID and neuropsychiatric manifestations (Review) Exp Ther Med 23 5 2022 May 363 10.3892/etm.2022.11290 [Epub 2022 Apr 1] 35493431 5 Wang S. Quan L. Chavarro J.E. Slopen N. Kubzansky L.D. Koenen K.C. Associations of depression, anxiety, worry, perceived stress, and loneliness prior to infection with risk of post-COVID-19 conditions. JAMA Psychiatry. 2022 Sep 7 10.1001/jamapsychiatry.2022.2640 e222640. [Epub ahead of print] 6 Sher L. Resilience as a focus of suicide research and prevention Acta Psychiatr Scand 140 2 2019 Aug 169 180 10.1111/acps.13059 [Epub 2019 Jun 20] 31150102 7 Ahmedani B.K. Peterson E.L. Hu Y. Rossom R.C. Lynch F. Lu C.Y. Major physical health conditions and risk of suicide Am J Prev Med 53 3 2017 Sep 308 315 10.1016/j.amepre.2017.04.001 [Epub 2017 Jun 12] 28619532 8 Racine M. Chronic pain and suicide risk: a comprehensive review Prog Neuropsychopharmacol Biol Psychiatry 87 Pt B 2018 Dec 20 269 280 10.1016/j.pnpbp.2017.08.020 [Epub 2017 Aug 26] 28847525 9 Gasnier M. Choucha W. Radiguer F. Faulet T. Chappell K. Bougarel A. Comorbidity of long COVID and psychiatric disorders after a hospitalisation for COVID-19: a cross-sectional study J Neurol Neurosurg Psychiatry 2022 Aug 11 10.1136/jnnp-2021-328516 jnnp-2021-328516. [Epub ahead of print] 10 Mehta N. Patel A. Patel N. Ortiz J.F. Khurana M. Urhoghide E. Long-term neurological sequelae among severe COVID-19 patients: a systematic review and meta-analysis Cureus. 14 9 2022 Sep 28 e29694 10.7759/cureus.29694 11 Xie Y. Xu E. Al-Aly Z. Risks of mental health outcomes in people with covid-19: cohort study BMJ. 376 2022 Feb 16 e068993 10.1136/bmj-2021-068993	36494289	PMC9721155	NO-CC CODE	2022-12-07 23:15:50	no	Gen Hosp Psychiatry. 2022 Dec 5; doi: 10.1016/j.genhosppsych.2022.12.001	utf-8	Gen Hosp Psychiatry	2,022	10.1016/j.genhosppsych.2022.12.001	oa_other
==== Front Asian J Psychiatr Asian J Psychiatr Asian Journal of Psychiatry 1876-2018 1876-2026 Elsevier B.V. S1876-2018(22)00377-X 10.1016/j.ajp.2022.103379 103379 Letter to the Editor Factors associated with fewer than expected suicides in Taiwan during the COVID-19 pandemic in 2020 Lin Chien-Yu ab Hsu Chia-Yueh cde Gunnell David fg Chang Shu-Sen aeh⁎ a Institute of Health Behaviors and Community Sciences, College of Public Health, National Taiwan University, Taipei, Taiwan b Faculty of Sport Sciences, Waseda University, Tokorozawa, Japan c Department of Psychiatry, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan d Department of Psychiatry, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan e Psychiatric Research Center, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan f Centre for Academic Mental Health, Population Health Sciences, University of Bristol, Bristol, UK g National Institute of Health and care Research Biomedical Research Centre, University Hospitals Bristol and Weston National Health Service Foundation Trust, Bristol, UK h Global Health Program, College of Public Health, National Taiwan University, Taipei, Taiwan ⁎ Correspondence to: Institute of Health Behaviors and Community Sciences and Global Health Program, College of Public Health, National Taiwan University, Room 623, No.17, Xu-Zhou Road, Zhongzheng Dist., Taipei City 10055, Taiwan. 5 12 2022 5 12 2022 10337915 11 2022 22 11 2022 1 12 2022 © 2022 Elsevier B.V. All rights reserved. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Keywords COVID-19 pandemic suicide suicide methods Taiwan ==== Body pmcThe COVID-19 pandemic, outbreak control measures, and their consequences may worsen mental health and increase suicide risk. However, a study using data from 33 countries showed no increase in suicide in most countries studied during the first 9-15 months of the pandemic (Pirkis et al., 2022). Possible reasons for this counterintuitive reduction in suicides are unclear. Further investigations using robust methodologies are required (Kahil et al., 2021, Tandon, 2021). We investigated changes in suicide trends and related them to the indicators of economic and outbreak control measures in Taiwan during the COVID-19 pandemic in 2020. Suicide data by sex, age, method, and month in people aged 15+ years were extracted from national cause-of-death files. Suicide methods were classified using the International Classification of Diseases, 10th Revision (ICD-10) codes (see Supplementary files for detailed codes). We used negative binomial regression models to estimate suicide rate ratios (RRs) and their 95% confidence intervals (CIs) during the outbreak (January-May 2020) and post-outbreak (June-December 2020) periods, relative to that expected based on pre-pandemic trends (January 2015-December 2019). We controlled for the pre-pandemic suicide trends using fractional polynomials (Royston and Altman, 1994) and seasonal variations using Fourier terms (Stolwijk et al., 1999). Analyses stratified by sex, age, and suicide method were conducted as previous studies suggested sex and age differences (Okada, 2022). More details about COVID-19 case number data, economic and outbreak control measures indicators, and regression models were provided in the Supplementary files. Supplementary Figure 1 shows trends in COVID-19 cases and indicators of economy and outbreak control measures. In 2020, the overall suicide rate was 9% lower than that expected based on pre-pandemic suicide trends, with 319 fewer suicides (Supplementary Tables 1-2). Lower-than-expected suicides occurred during both the outbreak and non-outbreak periods, with the most marked reductions (12%-15%) found in March-May 2020 (Supplementary Figure 2), i.e., the peak months of the outbreak period with the greatest movement restrictions as a result of the outbreak control measures. No sex difference in changes in suicide rates was found ( Fig. 1). Fewer-than-expected suicides were found in younger people aged <65 years (RR=0.92, 95% CI 0.88, 0.97) but not in older people during the post-outbreak period (interaction p=0.013). During the outbreak period, lower-than-expected rates were found for suicide by hanging (RR=0.87, 95% CI 0.78, 0.97), charcoal burning (i.e., carbon monoxide poisoning by burning barbecue charcoal in a small space) (RR=0.85, 95% CI 0.76, 0.95), and drowning (RR = 0.82, 95% CI 0.68, 1.00) but not other methods (interaction p<0.001). During the post-outbreak period, fewer-than-expected suicides were found only for the charcoal-burning method. Similar results were found in sensitivity analyses using an alternative definition of outbreak vs post-outbreak periods and data for certified suicides only (Supplementary Tables 3-4).Fig. 1 Suicide rate ratios (RRs) and 95% confidence intervals (CIs) for Taiwan during the COVID-19 outbreak (January to May 2020) and post-outbreak (June to December 2020) periods relative to those expected based on pre-pandemic trends from January 2015 to December 2019, by sex, age, and suicide method * 95% CIs of RRs that do not include one. Fig. 1 Our data showed 9% fewer-than-expected suicides in Taiwan during the first year of the COVID-19 pandemic (2020), in keeping with findings from an earlier study (Lin et al., 2021b). This study further suggested that, during the outbreak period, the fewer-than-expected suicides were possibly associated with a reduced opportunity of implementing and accessing specific means for suicide (i.e., hanging, charcoal burning, and drowning) when the restriction on movement caused by outbreak control measures reached its peak. During the post-outbreak period, the fewer-than-expected suicides in working-age populations could be related to the strong economic recovery during this period (Supplementary Figure 1). Increased time with family at home due to restricted movement during the outbreak period may reduce the opportunity for at-risk individuals to act on suicidal thoughts by hanging or burning charcoal, which mostly occurred at home (Chang, 2017) and were associated with living alone (Chang et al., 2019, Lin et al., 2021a). Travelling to purchase barbecue charcoal (for charcoal-burning) or sites for drowning could have become more difficult during the outbreak period, too, contributing to fewer suicides by these methods. By contrast, no changes in suicides by other methods (mainly falling and poisoning) were found; many suicides using these methods were found to be impulsive, using readily accessible locations (e.g., high-rise residential buildings) or medicines/other toxic substances stored at home (Gore-Jones and O'Callaghan, 2012). This is an ecological study. The lower-than-expected suicide rates in Taiwan during the COVID-19 pandemic could be due to other factors, such as the complete ban on paraquat (a highly lethal herbicide when ingested in suicide attempts) from February 2020 (Chang et al., 2022). However, the lower-than-expected suicides in 2020 were mainly found for hanging, charcoal burning, or drowning but not poisoning. In conclusion, strategies and policies that restrict access to suicide means and mitigate the economic impact, along with other public and mental health service related policies (Suchandra et al., 2021), may contribute to suicide prevention during the pandemic. Continuous monitoring of trends in suicide and risk factors is needed as the longer-term consequences of the pandemic may unfold over time. Author Agreement Role of Funding Source The study was not funded. CYH was supported by Wan Fang Hospital (grant numbers 110-swf-07 and 111-wf-swf-04). DG is supported by the NIHR Biomedical Research Centre at University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol, England. The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health and Care Research or the Department of Health and Social Care. SSC was supported by Ministry of Science and Technology, Taiwan (grant number MOST 109-2314-B-002-144-MY3). CRediT authorship contribution statement Chien-Yu Lin: Conceptualization, Formal analysis, Writing – original draft, Writing - review & editing. Chia-Yueh Hsu: Conceptualization, Writing - review & editing. David Gunnell: Conceptualization, Writing - review & editing. Shu-Sen Chang: Conceptualization, Data curation, Funding acquisition, Writing – original draft, Writing - review & editing. Conflicts of interest The authors declare no conflict of interest. Appendix A Supplementary material Supplementary material Acknowledgements None. Funding The study was not funded. CYH was supported by Wan Fang Hospital (110-swf-07 and 111-wf-swf-04), Taipei Medical University (TMU108-AE1-B57), and Taiwan Ministry of Science and Technology (MOST 110-2628-B-038-016 and NSTC 111-2628-B-038-026). SSC was supported by Taiwan Ministry of Science and Technology (grant number MOST 109-2314-B-002-144-MY3). DG is supported by the NIHR Biomedical Research Centre at University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol, England. The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health and Care Research or the Department of Health and Social Care. Appendix A Supplementary data associated with this article can be found in the online version at doi:10.1016/j.ajp.2022.103379. ==== Refs References Chang, S.S., 2017. 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Studying seasonality by using sine and cosine functions in regression analysis Journal of Epidemiology & Community Health 53 1999 235 238 10396550 Suchandra H.H. Bhaskaran A.S. Manjunatha N. Kumar C.N. Bada Math S. Reddi V.S.K. Suicide prevention in the context of COVID-19: An Indian perspective Asian J Psychiatr 66 2021 102858 Tandon R. COVID-19 and suicide: Just the facts. Key learnings and guidance for action Asian J Psychiatr 60 2021 102695	36502779	PMC9721156	NO-CC CODE	2022-12-08 23:16:26	no	Asian J Psychiatr. 2023 Feb 5; 80:103379	utf-8	Asian J Psychiatr	2,022	10.1016/j.ajp.2022.103379	oa_other
==== Front J Photochem Photobiol B J Photochem Photobiol B Journal of Photochemistry and Photobiology. B, Biology 1011-1344 1873-2682 Published by Elsevier B.V. S1011-1344(22)00234-2 10.1016/j.jphotobiol.2022.112619 112619 Article Cardiopulmonary and hematological effects of infrared LED photobiomodulation in the treatment of SARS-COV2 Pereira Pâmela Camila ab de Lima Carlos José ac Fernandes Adriana Barrinha ac Zângaro Renato Amaro ac Villaverde Antonio Balbin ac⁎ a Anhembi Morumbi University (UAM), Institute of Biomedical Engineering, Estrada Dr. Altino Bondensan 500, Distrito de Eugênio de Melo, CEP: 12.247-016 São José dos Campos, SP, Brazil b University Center of Itajubá – (FEPI), Rua Dr. Antônio Braga Filho 687, Bairro Varginha, CEP: 37501-002 Itajubá, MG, Brazil c Center of Innovation, Technology and Education – (CITE), Estrada Dr. Altino Bondensan 500, Distrito de Eugênio de Melo, CEP: 12.247-016 São José dos Campos, SP, Brazil ⁎ Corresponding author at: Center of Innovation, Technology and Education – CITE, Estrada Dr. Altino Bondensan 500, Distrito de Eugênio de Melo, CEP: 12.247-016 São José dos Campos, SP, Brazil. 5 12 2022 1 2023 5 12 2022 238 112619112619 13 3 2022 28 11 2022 2 12 2022 © 2022 Published by Elsevier B.V. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Background COVID-19 disease is caused by SARS-CoV-2 which can trigger acute respiratory syndrome, which presents with dense alveolar and interstitial infiltrates and pulmonary edema, causing severe hypoxemia and significant alteration to pulmonary mechanics with reduced pulmonary compliance. The photobiomodulation technique alters cellular and molecular metabolism, showing promising results regarding the reduction of acute pulmonary inflammation. Objective To compare the photomodulation technique using near-infrared LED to conventional respiratory physiotherapy treatment in patients with COVID-19 in reversing acute conditions, reducing hospitalization time, and decreasing the need for oxygen therapy. Methodology The cohort was comprised of 30 patients undergoing COVID-19 treatment who were divided and allocated into two equal groups randomly: the LED group (LED), treated with infrared LED at 940 nm and conventional therapy, and the control group (CON), who received conventional treatment (antibiotic therapy for preventing superimposed bacterial infections, and physiotherapy) with LED irradiation off. Phototherapy used a vest with an array of 300 LEDs (940 nm) mounted on a 36 cm × 58 cm area and positioned in the patient's anterior thoracic and abdominal regions. The total power was 6 W, with 15 min irradiation time. Cardiopulmonary functions and blood count were monitored before and after treatment. The patients were treated daily for 7 days. Statistical analysis was conducted using a two-tailed unpaired Student's t-test at a significance level of α = 0.05. Results Post-treatment, the LED group showed a reduction in hospital discharge time and a statistically significant improvement for the following cardiopulmonary functions: Partial Oxygen Saturation, Tidal Volume, Maximum Inspiratory, and Expiratory Pressures, Respiratory Frequency, Heart Rate, and Systolic Blood Pressure (p < 0.05). Regarding blood count, it was observed that post-treatment, the LED group presented with significant differences in the count of leukocytes, neutrophils, and lymphocytes. Conclusion Photobiomodulation therapy can be used as a complement to conventional treatment of COVID-19, promoting the improvement of cardiopulmonary functions, and minimization of respiratory symptoms. Keywords SARS-COV2 Photobiomodulation Near-infrared LED Cardiopulmonary functions Hemogram ==== Body pmc1 Introduction Coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which promotes dyspnea, pulmonary edema, and pneumonia. Morbidity and mortality are associated with acute respiratory distress syndrome (ARDS) and cytokine storm. Patients hospitalized with COVID-19 are classified as severe if they require admission to the intensive care unit (ICU) [1,2]. The structure of the SARS-CoV-2 virus is represented by the envelope protein, protein E, hemagglutinin-esterase, protein M, protein S (spike), and protein N. The functions performed by the S and N proteins are crucial in the pathogenesis of COVID-19. The S protein is anchored to ACE2 receptors (angiotensin-converter enzyme 2) for subsequent entry into the respiratory epithelial cell pneumocytes. The N protein, in addition to being responsible for viral replication, is largely produced during infection, and constitutes the main cause of the virus's high immunogenicity [3]. Infected patients present different symptoms lasting on average between 5 and 8 days, depending on the severity of the disease [4]. The mean time between the onset of symptoms and hospitalization ranges from 2 to 8 days, and the need for invasive mechanical ventilation (IMV) was 11 days, with 23.7 days on average until death (88% of cases) [5]. Acute respiratory syndrome is characterized by diffuse alveolar damage and by the development of noncardiogenic pulmonary edema, due to the increased permeability of the pulmonary alveolo-capillary membrane. Its clinical expression is hypoxemic respiratory failure and bilateral pulmonary infiltrate. Dependent pulmonary areas present dense alveolar and interstitial inflammatory infiltrate, edema, cellular debris, atelectasis, and consolidation, while non-dependent areas are relatively unaffected. It causes severe hypoxemia and accented alteration of pulmonary mechanics with a significant reduction of pulmonary compliance [6]. Recent studies of the action mechanism of the virus have shown that it causes a systemic infection that significantly affects the hematopoietic system and hemostasis [7,8]. In Brazil, medication protocols are following other health institutions across the world, using hydroxychloroquine [9] with a combination of antibiotics, such asteicoplanin [10] or azithromycin [11], used for preventing secondary infections that are present in many cases (such as sepsis). Nevertheless, the side effects of such medication are still under discussion in the literature. Patients who progress to dyspnea and respiratory discomfort require hospitalization and oxygen therapy according to advice from the World Health Organization (WHO) [12]. The use of light radiation in the red/infrared region is a noninvasive therapeutic intervention for the treatment of numerous lung diseases. Several studies in animal models, as well as in humans, demonstrate the effects of photobiomodulation using low-intensity lasers and LEDs in wound therapy, reducing the infectious and inflammatory processes, decreasing edema and inflammatory cells, stimulating microcirculation, and encouraging the formation of new vessels [13,14]. The photobiomodulation technique can modify cellular and molecular metabolism, signaling, reducing inflammation and release of chemical messengers, with promising results in reducing acute pulmonary inflammation, as they present significant potential for local balancing of immune responses [15,16]. In the last few decades, photobiomodulation has been used in the treatment of community-acquired pneumonia (CAP) with promising results regarding pulmonary inflammatory response and significant effects on the recovery of patients' blood count [17]. At the appropriate dose and wavelength, light interacting with cells and tissues can induce cellular functions such as lymphocyte stimulation, mast cell activation, an increase in mitochondrial ATP production and the proliferation of various cell types, thus promoting anti-inflammatory effects, such as the cytokines Interleukin 10 (IL-10), Interferon-gamma (INF-g), interleukin 1 (IL-1) and tumor necrosis factor (TNF-a) [18,19]. Brito et al. stated that photobiomodulation may even have an antifibrotic effect by decreasing TGFβ (transforming growth factor beta) in fibroblast cells and lung tissue [20]. The reduction in inspiratory muscle strength (PIM) is observed because of transient changes in the mechanical properties of the chest wall and respiratory muscles after critical illness and it is attributed to post-intensive care syndrome, which is characterized by the presence of physical, cognitive impairment or mental illness in patients undergoing prolonged ICU stay, including those with COVID-19. Another possible explanation for respiratory weakness could be the occurrence of interstitial lung disease after COVID-19 [21]. Recently, several studies have analyzed the possibility of employing the technique of photobiomodulation in the treatment of COVID-19 based on its potential to induce local and systemic effects, significantly decreasing pro-inflammatory cytokines, inflammatory cells, and collagen fiber deposition in the pulmonary parenchyma, enabling the reduction of mortality in patients [[22], [23], [24], [25], [26], [27]]. The current scientific literature available contains few experimental studies on the effects of photobiomodulation on COVID-19. It is important to mention some of the pioneering articles that reported on the use of photobiomodulation in the treatment of COVID-19, which are: two case studies of patients with COVID-19 treated with 808 and 905 nm pulsed laser beams reported by Sigman et al., one patient 57 years old [28] and the other of 32 years old [29], and a preliminary study of 10 patients irradiated by pulsed lasers of 808 and 905 nm plus conventional medical treatment [30]. A clinical study with a larger cohort of 30 patients who were treated with pulsed lasers of 808 and 663 nm combined with a static magnetic field was investigated by de Marchi et al. [31]. In this context, aiming to improve the clinical recovery of patients, the present study proposes verifying the performance of the photobiomodulation technique with low-intensity light, using near-infrared LED, when compared to the conventional treatment of respiratory physiotherapy in patients with COVID-19. The treatment target is the reversal of the acute clinical condition of the COVID-19 patient by minimizing symptoms, reducing the need for oxygen therapy, and decreasing hospitalization time. 2 Methods 2.1 Ethical Concerns This study was performed in line with the principles of the Declaration of Helsinki and was approved by the Research Ethics Committee of the Anhembi Morumbi University (CAAE; 36,988,320.5.0000.5492) and registered with the Brazilian Registry of Clinical Trials (ReBEC) under the code U1111–1261-1981 (16/11/2020). Patients signed a free and informed consent form before the beginning of treatment. This is a prospective, descriptive, single-blinded, randomized, and longitudinal trial conducted in the Respiratory Syndrome Ward of Santa Casa de Itajubá-MG. 2.2 Cohort The cohort consisted of 30 patients admitted to the ward undergoing treatment for COVID-19. They were selected according to the following inclusion criteria: both sexes, aged between 50 and 80 years, with a clinical diagnosis of COVID-19, and in respiratory physiotherapy and medication. Exclusion criteria: patients who were from long-term institutions, overweight (Body Mass Index - BMI: above 29.9 kg/m2), aged out of the study range, presented with neoplasms, or who have a history of photosensitivity. An evaluation was performed shortly after each patient's hospitalization, which consisted of the acquisition of personal, sociodemographic, and clinical data (respiratory rate, heart rate, pulse oximetry, body temperature, diastolic and systolic pressures, and auscultation). The patient's clinical conditions were assessed daily for the seven days of treatment and the information was registered on a patient's record card. Positive reverse polymerase chain reaction (RT-PCR) tests, hospitalization stay time (in days), blood count, ventilometry (tidal volume), and respiratory muscle strength (manovacumetry) were evaluated before and after the seven days of intervention. Treatment and clinical condition evaluation were initiated soon after hospitalization. Clinical criteria evaluated by a specialist physical therapist were adopted to determine the minimization of symptoms. The patients included in the present study were not intubated, breathing spontaneously, or using oxygen therapy up to 3 L/minO2. The symptoms evaluated were cough (presence or absence), fever, risk of dyspnea on minimal exertion or at rest, respiratory rate > 22 breaths/min, and SpO2 of <90% with supplemental oxygen. 2.3 Pneumonia Severity Index – PSI and Neutrophil/Lymphocyte Count Ratio - NLCR Pneumonia severity index score is a significant scoring system for predicting the disease severity of patients with pulmonary infections, to indicate the patient's hospital admission status, and mortality risk. The PSI scoring system covers 20 variables that include demographic characteristics, associated diseases, laboratory and radiological alterations, and physical examination findings. The total score of the variables allows the stratification of severity into five classes, based on the risk of death and the need for hospitalization [32]. The neutrophil-to-lymphocyte count ratio (NLCR) is another valuable parameter used in clinical practice to evaluate the severity of pulmonary infections, such as CAP [33] and COVID-19 [34], at a patient's hospitalization. According to Hassan et al. [34], the NLCR can be recommended as a highly sensitive and specific indicator for severity prediction in Covid-19 patients. In the present study, it was used as a predictor of disease severity and treatment outcome. The higher the NLCR value, the higher the concentration of inflammatory cytokines (IL-2, IL-6 and IL-10) and the higher the IgG values and, consequently, greater severity with a worse prognosis [35]. As usual, neutrophil count increases, and lymphocyte count decreases with the advancement of any inflammatory condition. Values of NLCR below 10 indicate a low to moderate degree of disease severity [33]. 2.4 Complete Blood Count A complete blood count test (CBC) was performed, including an assessment of erythrocytes counts, hemoglobin and hematocrit concentrations, leukocytes, and platelet counts. The test was performed by the Syrius Medical Group Laboratory of Clinical Analyses using an automated XS-800i model (Sysmed, Curitiba, Brazil). Pneumonia Severity Index (PSI) and the neutrophil-to-lymphocyte count ratio (NLCR) were assessed at the time of hospital admission to determine the sickness severity risk of the patients. NLCR values were again assessed after treatment to verify the response (positive or negative) to therapy. The CBC test was conducted at the time of patient hospitalization and repeated the day after the last LED irradiation. 2.5 Ventilometry The ventilometry evaluation was performed by means of a ventilometer Mark Wright 8 ® (AAMED – Comércio de Equipamentos - Campo Belo - São Paulo, Brazil) with the placement of oral and nasal clips. The patient was requested to inhale and exhale relaxedly, and the volume of air that entered and left the lungs at each respiratory cycle was assessed (mL). 2.5.1 Maximum Inspiratory Pressure The maximum inspiratory pressure (MIP) was measured by the manovacuometer device Spire® (Murena's Produtos para a Saúde Ltda-Me- Progresso, Juiz de Fora-MG, Brazil). During the test, the patients remained seated with their nostrils occluded by a nasal clip, and the individuals firmly held the mouthpiece against their lips, avoiding air leakage. Patients were instructed to perform a maximum expiration and then a verbal command was given for the patient to perform a maximal inspiratory effort sustained for at least 2 s. MIP was measured, in cmH2O, during the exertion initiated from the residual volume (RV). At least three satisfactory measurements of each pressure were taken; that is, without air leakage through the mouth or nose and with values close to each other, being used only the highest value. 2.5.2 Maximum Expiratory Pressure The maximum expiratory pressure (MEP) was also measured using the manovacuometer Spire® (Murena's Produtos para a Saúde Ltda-Me- Progresso, Juiz de Fora-MG, Brazil). The MEP was obtained from the total lung capacity (TLC), in which the patient is requested to engage maximum inspiration before maximum expiratory effort, with minimum support of 2 s, MEP was measured in cmH2O. At least three satisfactory measurements of each pressure were performed following the same protocol as for MIP, and only the highest value was used. 2.6 Vital Signs Monitoring 2.6.1 Respiratory Rate The normal value in adults is 12 to 20 inspirations per minute. The test was performed by means of a ventilometer Mark Wright 8 ® (AAMED – Comércio de Equipamentos - Campo Belo - São Paulo, Brazil); oral and nasal clips were placed on the patient, who was requested to inhale and exhale relaxedly for 1 min. 2.6.2 Heart Rate Heart rate (HR) is the number of times that the heart beats per minute. At rest, the normal values for HR range from 60 to 100 bpm. An oximeter Model SB1000® (Medical Rossmax, Taiwan) was used to assess heart rate. 2.6.3 Blood Pressure Blood pressure (BP) is the force with which the heart pumps blood through the vessels. It is determined by the result of the product cardiac output x peripheral vascular resistance. There are two pressures: the maximal, or systolic, which is when the heart contracts, and the minimum, or diastolic, which is when the heart dilates. Blood pressure was assessed by a calibrated analog manometer of BIC® brand (Manaus, Brazil), certificated by the Instituto Nacional de Metrologia, Qualidade e Tecnologia (INMETRO) of Brazil, and a Littman Lightweight stethoscope™ (Model 2454, 3 M manufacturers, Nova Vezena – SP, Brazil). Normal values for blood pressure are systolic <120 mmHg and diastolic >90 mmHg. 2.6.4 Pulse Oximetry The oxygen level measured with an oximeter is called the oxygen saturation level (SaO2), which is the percentage of oxygen that the blood is carrying, compared to the maximum carrying capacity. Oxyhemoglobin saturation is measured by using the Rossmax® pulse oximetry (SpO2), with a normal value between 90 and 100%. 2.6.5 Body Temperature Body temperature was evaluated for one minute by means of the g-tech digital infrared thermometer G-Tech® (model FR1DZ1, Accumed Produtos Medico-Hospitalares Ltda, Duque de Caxias-RJ, Brazil). The three thermal states include euthermia, hypothermia, and hyperthermia. 2.6.6 Pulmonary Auscultation Evaluation of pulmonary sounds was conducted via the Littman Lightweight stethoscope™ (Model 2454, 3 M manufacturers, Nova Vezena – SP, Brazil), checking whether sounds are altered in frequency and intensity. It was performed in the anterior region of the thorax symmetrically in the following foci: two fingers below the clavicle, medially to the sternum bone, and laterally to the last ribs. 2.7 Treatment Protocol The cohort of 30 patients with COVID-19 was randomly divided into two equal groups. The LED group comprised patients treated with conventional therapy (medication and physiotherapy) in conjunction with infrared LED irradiation (940 nm), and the CON (placebo) group comprised patients that underwent conventional therapy alone. Patients from both groups received conventional treatment consisting of the antibiotics Meropenem (1-2 g) or Tazocin (40 mL/min), in conjunction with respiratory physiotherapy for bronchial hygiene with a 15 Hz oral oscillation device (OOAF, Shaker, NCS, São Paulo, Brazil). Respiratory physiotherapy of 30 min, consisting of three cycles of 10 repetitions each, with an interval of 1 min between cycles, was performed daily before wearing the LED vest, during the 7 days of treatment with LED irradiation or placebo. After that time, the two groups continued only with conventional treatment until discharged from the hospital. The control group undertook the same protocol as the LED group, but they used the infrared LED vest with the LEDs turned off. Blinding was obtained because infrared radiation is not perceived by the human eye and patients from both groups wore the vest for the same time (15 min). Fig. 1 depicts a flow diagram of the stages of the experimental protocol from cohort selection, distribution between the two groups, treatment description, and evaluation tests to determine the treatment progress.Fig. 1 Flow diagram showing the cohort distribution and the sequence of the stages of the experimental protocol. Fig. 1 The LED irradiation protocol is described in Pereira and coworkers' article [17]. Briefly, the LED system consisted of a set of 300 infrared LEDs (940 nm) with an optical power of 0.02 W each. The LEDs were arranged in a network-like distribution, with LEDs spaced at 2 cm (horizontal) × 4 cm (vertical) and positioned in the anterior thoracic and abdominal regions of the body via a vest with a total area of 2088 cm2, that was coated with a transparent plastic film to allow cleaning and sanitization. The patients were irradiated for 15 min, one session per day, for 7 consecutive days. The LED system parameters over the vest area were total optical power of 6 W and an average power density of 2.9 mW/cm2, corresponding to 5.4 kJ total optical energy during the 900 s of irradiation time. The vest with LEDs and the patient wearing the vest are shown in Fig. 2. Fig. 2 Photographs showing the LED-therapy protocol (Left) view of the open vest showing the array of LEDs emitting infrared radiation. The vest was mounting with 300 infrared LEDs (GaAlAs, 940 nm) of 5 mm diameter, emitting with a divergence angle of 30°, and an optical power of 0.02 W each (Model TSAL6400, Vishay Semiconductors, Vishay Intertechnology Ltd., Singapore). The LEDs were arranged spaced at 2 cm (horizontal) × 4 cm (vertical), with 10 lines and 30 columns. The entire system was constructed in our own laboratory. The LED system parameters over the vest area were total optical power 6 W, average power density 2.9 mW/cm2, SAEF (Surface average fluence) 2.6 J/cm2, and 5.4 kJ total optical energy during the 900 s of irradiation time. (Right) view of the patient wearing the vest positioned in the anterior thoracic and abdominal regions of the body, into direct contact with the skin, during the infrared irradiation process. The vest size was 36 cm × 58 cm, covering an area of 2088 cm2. Fig. 2 2.8 Evaluation of the Treatment Progress To compare the efficacies of the LED and CON therapies, two new differential variables were defined: ΔLED (variable) = parameter value in the post-treatment - value in the pre-treatment, for the LED group. Similarly, the ΔCON was defined as ΔCON (variable) = parameter value in the post-treatment - value in the pre-treatment, for the CON group. These differential variables were used to overcome statistical bias due to large data dispersion among patients in the baseline group. 2.9 Statistical Analysis The normality of the data was verified by the Kolmogorov–Smirnov normality test for both groups. A parametric two-tailed paired t-test was used for the intra-group analysis of the data before and after treatment of each group; whereas the inter-group statistical analysis of the differential variables ΔLED and ΔCON was conducted via the parametric two-tailed unpaired t-test, followed by a Welch correction when applicable. Prism 8.0 (GraphPad Software Inc., La Jolla, CA, USA) was used for the intra-group and inter-group analyses, with a significance level of α = 0.05. The number of men and women in each group and the morbidities distribution in each group were compared using Fisher's exact test and the Chi-squared test for trends (both at α = 0.05), respectively. Data are expressed as the mean ± SEM. The Cohen's d effect size parameter was used to calculate the statistical power of the two-tailed t-test of ΔLED vs. ΔCON, when a statistically significant difference was achieved. According to Sawilowsky's classification of effect size, d(0.8) = large, d(1.2) = very large, and d(2.0) = huge [36]. 3 Results A total of 30 patients participated in the study, equally allocated into LED and CON groups. Table 1 shows the values of the patients' hospital intake (mean and SEM) of BMI and age for each group, as well as the number of men and women in each group. A comparison of the distribution of the number of men and women between groups showed that both groups were homogeneous (p = 1.000). Similarly, no significant differences were found between the two groups regarding BMI (p = 0.70) and age (p = 0.10). Patients from both groups presented with different associated diseases: high blood pressure (HBP), chronic kidney disease (CKD), diabetes mellitus (DM), and heart failure (HF). Statistical analysis shows that patients' morbidities distribution between both groups was homogeneous (p = 0.542).Table 1 Data at the patients' hospital intake: body mass index (BMI), age, pneumonia severity index (PSI), sex, and comorbidities. Table 1Groups Mean SEM p Body mass index - BMI (kg/m2) LED 26.1 2.0 0.704+ CON 25.6 0.5 Age (years) LED 66.9 2.3 0.096++ CON 62.3 2.1 Pneumonia severity index (PSI) LED 97.1 (IV) 1.5 0.003+ CON 85.3 (III) 3.2 Sex (M/W) Men Women p LED 8 7 1.000+++ CON 7 8 Comorbidities (number of patients) HBP CKD DM HF pIV LED 12 1 5 0 0.542 CON 9 0 5 1 p+: parametric two-tailed unpaired t-test at α =0.05 (Welch correction). p + +: parametric two-tailed unpaired t-test at α =0.05. p+++: two-tailed Fisher's exact test at α =0.05. pIV: Chi-Squared Test for Trend at α =0.05. PSI classification: class III (71–90 points); class IV (91–130 points). HBP: High Blood Pressure; CKD: Chronic Kidney Disease; DM: Diabetes Mellitus; HF: Heart Failure. Pneumonia severity index (PSI) values were calculated for all patients at the hospital intake. Mean values depicted in Table 1 indicated that the LED group presented with higher severity risk (class IV) than the CON group (class III). PSI is a scoring system that predicts the severity of the disease, but high PSI values do not prevent a better recovery of patients after treatment. In the present study, although the PSI of the LED group is higher than in the CON group, the LED group showed better recovery than the CON group, which reinforces the beneficial effect of the photobiomodulation. Further, the hospital stay time for both groups, i.e., the time elapsed between patients' hospital intake and discharge, was measured (Mean ± SEM): LED (8 ± 0.2) days and CON (11.7 ± 1.4) days. That outcome reveals the effect of the photobiomodulation on reducing the time of hospital stay from 11.7 days to 8 days (p = 0.02). Pulmonary auscultation, the time when adventitious noises (pulmonary snoring) improved from the date of patient hospital intake was measured for both groups, obtaining the results (Mean ± SEM): LED (3.5 ± 0.2) days, CON (5.6 ± 05) days, p = 0.0006, showing a statistically significant reduction on the time of pulmonary auscultation improvement in favor of the LED group. The NLCR, index for the initial prognosis of the disease severity risk of patients with respiratory disorders, was calculated obtaining the results (Mean/SEM): group LED - 13.4 (2.8) and group CON – 16.2 (3.7), p = 0.55ns. To corroborate the beneficial effect of the photobiomodulation with infrared LED, the NLCR values were re-calculated for both groups after treatment: LED 6.3 (1.5) and CON 11.9 (1.5), p = 0.014. The results obtained showed that the control group presented a reduction of 27% in NLCR values while the LED group reduced it by 53%. Thus, comparative analysis of NLCR values before and after treatment demonstrated the beneficial effect of the photobiomodulation with infrared LED, suggesting a systemic anti-inflammatory effect. A set of different tests were carried out to conduct a thorough analysis of the progress of both treatments, either the conventional alone or in conjunction with the LED irradiation. These included the pulmonary functions: oxygen flow intake - O2 (L/min), partial oxygen saturation - SpO2 (%), Tidal volume - TV (mL), Maximum Inspiratory Pressure - MIP (cmH2O), Maximal Expiratory Pressure - MEP (cmH2O), Respiratory Frequency - RF (rpm); and the cardiological functions: Heart Rate – HR (bpm), Systolic Blood Pressure – SBP (mmHg), and Diastolic Blood Pressure – DBP (mmHg). Regarding the performance of respiratory muscles, the control group showed significant improvement in MIP (p = 0.0001), but the same was not observed in the MEP (p = 0.054). On the contrary, the LED group presented a significant improvement in MIP (p = 0.0001) and MEP (p = 0.0001), indicating an enhancement in respiratory muscle performance and functional capacity in patients with COVID-19. Hematological evaluation of the treatment was performed using the CBC test before and after treatment, assessing the following hematologic components: Erythrocytes (x106 mm−3), Hemoglobin (mg/dL), Hematocrit (%), Leukocytes (mm−3), Segmented Neutrophils (mm−3), Lymphocytes (mm−3), Monocytes (mm−3), Eosinophils (mm−3), and Platelets (x103 mm−3). The tests were completed by the body temperature assessment of the patients. The function assessments were made at baseline (patients' hospital intake) and the day after the end of the infrared LED irradiation or placebo. The data of each group passed the Kolmogorov–Smirnov normality test; hence, we used a paired two-tailed parametric t-test for the intra-group statistical analyses of both groups. Table 2 displays the data obtained from the tests applied to the two groups, before (baseline) and after treatment. Table 2 includes the p-values given by the intra-group statistical analysis of post- versus pre-treatment for each group. The intra-group analysis showed that the LED group exhibited a statistically significant improvement after treatment for all the cardiopulmonary functions (p < 0.05); however, for the CON group, no significant differences were found for heart rate and diastolic blood pressure (p > 0.05). Regarding the analysis of blood count, it was observed that the LED group exhibited significant differences after treatment for the white blood cell count: leukocytes, neutrophils, and lymphocytes. On the other hand, the CON group showed no differences in blood count. It is worth mentioning that patients belonging to the LED and CON groups did not present, on average, with anemia, since their red cell series values were within the normal range at the time of hospitalization.Table 2 Cardiopulmonary, Hematologic, and Body Temperature data, Mean (SEM), with intra-group statistical analysis of groups. Table 2 Group LED Group CON Baseline Post-treatment p† Baseline Post-treatment p‡ Cardiopulmonary analysis Oxygen Flow Intake (L/min) 3.3 (0.3) 0.3 (0.1) 0.0001* 5.4 (0.6) 1.3 (0.3) 0.0001* Partial Oxygen Saturation (%) 86.7 (0.3) 96.1 (0.3) 0.0001* 89.3 (0.6) 91.9 (0.5) 0.002 Tidal Volume -TV (mL) 320 (16) 394 (15) 0.0001* 297 (8) 319 (9) 0.0001* Maximum Inspiratory Pressure (cmH2O) −52.7 (2.3) −77.1 (1.8) 0.0001* −48.7 (1.8) −55.7 (2.2) 0.0001* Maximal Expiratory Pressure (cmH2O) 63.5 (3.6) 82.7 (2.7) 0.0001* 62.3 (1.8) 64.3 (2.0) 0.054 Respiratory Frequency (rpm) 18.1 (0.5) 12.9 (0.2) 0.0001* 16.3 (0.2) 13.5 (0.2) 0.0001* Heart Rate (bpm) 100.4 (4.4) 80.7 (2.2) 0.0001* 82.0 (1.8) 80.1 (1.8) 0.36 Systolic Blood Pressure (mmHg) 138 (2.7) 125 (2.4) 0.0001*** 137 (2.2) 132 (1.3) 0.01* Diastolic Blood Pressure (mmHg) 91.4 (1.3) 85.7 (1.2) 0.003 87.1 (1.0) 83.9 (1.6) 0.1 Hematologic analysis Erythrocytes (x 106 mm−3) 4.10 (0.2) 4.21 (0.2) 0.64 4.65 (0.15) 4.55 (0.18) 0.3 Hemoglobin (mg/dL) 12.03 (0.5) 12.75 (0.5) 0.29 12.90 (0.47) 12.75 (0.39) 0.57 Hematocrit (%) 36.30 (1.1) 37.07 (1.5) 0.69 36.75 (2.1) 37.39 (1.1) 0.74 Leukocytes (mm−3) 10,943 (1320) 7412 (730) 0.004 9086 (1100) 10,350 (1170) 0.091 Segmented Neutrophils (mm−3) 8300 (206) 7300 (253) 0.004 8390 (197) 8270 (159) 0.5 Lymphocytes (mm−3) 950 (178) 1800 (234) 0.003* 890 (163) 890 (126) 0.96 Monocytes (mm−3) 390 (45) 460 (40) 0.21 347 (42) 327 (45) 0.38 Eosinophils (mm−3) 66.7 (20) 70 (15) 0.86 53.3 (16) 100 (33) 0.44 Platelets (x 103 mm−3) 219 (32) 301 (22) 0.67 188 (22) 199 (22) 0.14 Body Temperature Temperature (°C) 39.1 (0.1) 36 (0.1) 0.0001* 39.2 (0.1) 36 (0.1) 0.0001* p†: intra-group statistical analysis for group LED; p‡ intra-group statistical analysis for group CON. Parametric two-tailed paired t-test at the significance level of α = 0.05. P-value: * p < 0.05; p < 0.01; * p < 0.001. Reference values: Partial Oxygen Saturation (%) > 90, Maximum Inspiratory Pressure (cmH2O) > −80, Maximal Expiratory Pressure (cmH20) > 60, Respiratory Frequency (rpm) 1220, Heart Rate (bpm) 60-100, Systolic Blood Pressure (mmHg) 120–129, Diastolic Blood Pressure (mmHg) 80–89, Erythrocytes (x106/mm3) 4.0–6.5, Hemoglobin (mg/dL) 12–16, Hematocrit (%) 36–45, Leukocytes (mm−3) 4000–10,000, Segmented neutrophils (mm−3) 2200–6600, Lymphocytes (mm−3) 800–3500, Monocytes (mm−3) 160–800, Eosinophils (mm−3) 40–400, Platelets (x103/mm3) 150–450, Body temperature (°C) 35–37. The inter-group statistical analysis of the differential variables ΔLED and ΔCON was conducted via the parametric two-tailed unpaired t-test, followed by a Welch correction when it was needed. Table 3 depicts the inter-group statistical analysis for ΔLED and ΔCON for all the studied tests, and the corresponding p-values of significance. It can be observed from Table 3 that treatment with LED irradiation significantly improved the effect of conventional treatment on the cardiopulmonary functions (p < 0.0001): SpO2, TV, MIP, and MEP, (p = 0.0009) for RF, (p = 0.0001) for HR, and to a lesser degree the SBP function (p < 0.007). On the contrary, the patient O2 intake was shown to be reduced more by the conventional treatment alone than when it was in conjunction with LED therapy, as seen in ΔLED versus ΔCON (Table 3). This can be explained by the fact that the initial need for O2 was much greater for patients treated with conventional therapy, as can be seen in Table 2.Table 3 Cardiopulmonary, hematologic, and body temperature outcomes, Mean (SEM), inter-group statistical analysis between groups, and Cohen's parameter. Table 3 ΔLED ΔCON p d Cardiopulmonary analysis Oxygen flow intake (L/min) −3.1 (0.2) −4.1 (0.4) 0.025* 0.9 Partial Oxygen Saturation (%) 9.4 (0.5) 2.6 (0.7) < 0 0.0001* 3.1 Tidal Volume (mL) 74.4 (9.0) 22.1 (3.3) < 0.0001* 2.0 Maximum Inspiratory Pressure (cmH2O) −24.9 (2.2) −7.0 (0.8) < 0.0001* 2.8 Maximal Expiratory Pressure (cmH2O) 19.1 (3.0) 2.0 (0.9) < 0.0001* 2.0 Respiratory Frequency (rpm) −5.1 (0.5) −2.8 (0.4) 0.0009* 1.4 Heart Rate (bpm) −19.7 (3.3) −1.9 (1.5) 0.0001* 1.7 Systolic Blood Pressure (mmHg) −13.6 (2.3) −5.3 (1.8) 0.007 1.1 Diastolic Blood Pressure (mmHg) −5.7 (1.6) −3.1 (1.8) 0.30ns – Hematologic analysis Erythrocytes (x 106 mm−3) 0.1 (0.2) −0.1 (0.1) 0.41ns – Hemoglobin (mg/dL) 0.7 (0.7) −0.15 (0.3) 0.23ns – Hematocrit (%) 0.8 (1.9) 0.6 (1.9) 0.96ns – Leukocytes (mm−3) −3531 (1030) 1270 (710) 0.0006* 1.4 Segmented Neutrophils (mm−3) −930 (270) −130 (190) 0.02* 0.9 Lymphocytes (mm−3) 850 (240) −10 (130) 0.004** 1.2 Monocytes (mm−3) 70 (53) −20 (23) 0.14ns – Eosinophils (mm−3) 3.3 (18) 46.7 (39) 0.83 ns – Platelets (x 103 mm−3) 10.4 (24) 10.4 (7) 0.99ns – Body Temperature Body Temperature (°C) −3.2 (0.15) −3.1 (0.15) 0.80ns – ΔLED = (post-treatment – baseline) LED group; ΔCON = (post-treatment - baseline) CON group. p: inter-group statistical analysis for group LED versus group CON. parametric two-tailed unpaired t-test at the significance level of α = 0.05. ns: not significant p > 0.05; * p < 0.05; p < 0.01; * p < 0.001. d: Cohen parameter; sample effect sizes d (0.8) = large, d (1.2) = very large, and d (2.0) = huge. ---- Cohen's parameter is not calculated when the difference is not statistically significant. As for the effect of each treatment on the hemogram, there was a statistically significant difference between ΔLED versus ΔCON for Leukocytes (p < 0.001), and Segmented Neutrophils and Lymphocytes (p < 0.01). No difference between treatments was observed for the red cell series. The power of the inter-group statistical analysis employing the t-test was calculated by using Cohen's d parameter. The values of this parameter are shown in Table 3 in cases where a statistically significant difference was found (p < 0.05). It can be observed from Table 3 that values of d range from 0.9 for oxygen flow intake and neutrophils up to 3.1 for partial oxygen pressure. It is worth mentioning that d ≥ 0.9 values correspond to statistical powers >80%, i.e., type II errors β < 20% [37]. 3.1 Outcomes Summary Infrared LED photobiomodulation combined with conventional therapy outcomes:➢ Enhances the effect of the conventional therapy on COVID-19 patients, presenting a statistically significant improvement in the recovery of the vital cardiopulmonary functions: Partial Oxygen Saturation, Tidal Volume, Maximum Inspiratory Pressure, Maximum Expiratory Pressure, Respiratory Rate, Heart Rate and Systolic Blood Pressure; as well as the hematological components: Leukocytes, Segmented Neutrophils and Lymphocytes. ➢ Statistically significant reduction in the time of hospitalization stays of patients. ➢ The time when adventitious noises improved from the date of the patient's hospital intake is significantly reduced ➢ Presents an improvement in the PSI and NLCR indices when compared to conventional therapy. ➢ The power of statistical analysis of the results exceeded 80% 4 Discussion This clinical trial demonstrated the beneficial effects of photobiomodulation in patients with COVID-19 symptoms, corroborating other data reported in the scientific literature on the anti-inflammatory effects of this technique on lung tissues in both animal and human models [5,17,20,[28], [29], [30], [31],38,39]. In the present study, the PSI index was used to determine the initial prognosis of the severity of risk for patients with respiratory diseases. It was observed that patients in the LED group had a worse prognosis in relation to the CON group, LED 97.1 vs CON 85.3 (p = 0.003); it is noteworthy that the distribution of patients was randomly assigned. Further, the NLCR index showed a reduction of 53% for LED against 27% for CON, after the end of the treatment, indicating the beneficial effect of the photobiomodulation. The obtained data exhibited improvement in the clinical condition of the group after irradiation with infrared LED in oxygenation (p = 0.0001), inspiratory muscle strength (p = 0.0001), tidal volume (p = 0.0001), pulse oximetry (p = 0.0001), and respiratory rate (p = 0.0001), The total clinical recovery of patients in the CON group on average took 11.7 days of hospitalization, while patients treated with LED took only 8 days (p = 0.021); the mean time for COVID-19 is usually 6–8 weeks [40]. In a study by Vetrici et al., the average number of days hospitalized in the photomodulation group was 7.6 days compared to 12.2 days for the control group (p = 0.292) [30]; these data corroborate those obtained in the present study. Improvement in respiratory function was demonstrated in patients treated with LED in relation to the control group, as evidenced by the tests assessing ventilometry, manovacumetry, and peripheral oximetry. In a study by Sigman et al., partial oxygen saturation increased significantly from 94% to 100% in the first 5 min of irradiation and then remained at the recommended levels after that period [28]. Vetrici et al. demonstrated that patients in the control group and PBM group presented with fluctuations in their pulmonary function; however, PBM patients did not require ICU admission or mechanical ventilation. In addition, all PBM patients no longer required O2 support 9 days after initiation of treatment [30]. When comparing the values obtained for MIP and MEP of both groups (Table 3), it was verified that the LED group enhanced the performance of the respiratory muscles compared to the control group (p = 0.0001). These data suggest that photomodulation preserved the main respiratory muscle, which facilitated the ventilation/perfusion process, promoting an improvement of the clinical and ventilatory status of patients. These important findings indicate that data obtained in the present study corroborate de Marchi et al. [31] results, because a significant improvement in inspiratory muscle strength (MIP) was observed. In the study by Tomazoni et al., photomodulation therapy alone or combined with a static magnetic field (PBMT-sF) was performed with irradiation in the lower chest, upper abdominal cavity, and two sites in the neck of the patients [41]. It was observed that the patients were able to leave oxygen support during treatment, increasing peripheral oxygen saturation, and showed an improvement in pulmonary severity scores and radiological findings [41]. These data agree with those obtained in the present study, showing that photobiomodulation was effective in improving the pulmonary functional capacity of the patients. Photobiomodulation is useful for cellular metabolism and to proliferate or improve lung tissue, according to a report by Nejatifarda et al. [26]. They observed significant decreases in pulmonary edema, the neutrophil influx, and the generation of pro-inflammatory cytokines such as tumor necrosis factor-α (TNF-α), interleukin 1 beta (IL-1β), interleukin 6 (IL-6), intracellular reaction molecule (ICAM), reactive oxygen species (ROS), nitric oxide synthase isoform (iNOS) and macrophage inflammatory protein 2 (MIP-2). These findings demonstrate that photomodulation can be useful in reducing pulmonary inflammation and promoting the regeneration of damaged tissue as well as minimizing the sequelae of pulmonary fibrosis caused by COVID-19 [31]. A statistically significant reduction was observed in this study for the count of leukocytes, neutrophils, and lymphocytes after therapy when infrared LED was combined with conventional treatment (p < 0.05); however, no significant reduction was observed in patients who received only medication and physiotherapy (p > 0.05). In a study by Pereira et al. [17], patients with community-acquired pneumonia (CAP) who were treated with LED therapy showed a significant reduction in the number of leukocytes, neutrophils, lymphocytes, and monocytes. The eosinophils are leukocytes tissue-resident and circulating that have potent proinflammatory effects, and antiviral and immune regulation activity. Nair et al. [42] reported in their study that the eosinophil count showed to be very variable in patients with COVID-19, with a relatively high prevalence of eosinophilia in symptomatic COVID-19 positive patients. According to Anka et al. [43], patients with severe COVID-19 present eosinopenia and lymphopenia. However, in the present study, the eosinophil counts were within the reference values (40 to 400 mm−3) for both groups. Data from this study and those reported by Pereira et al. [17] agree with the findings of de Brito et al. [20], who verified a systemic effect of LLLT (780 nm and 30 mW) on the reduction of inflammatory cell counts in the blood of animals with idiopathic pulmonary fibrosis, noting that, in relation to cytokines, LLLT reduced the release of pro-inflammatory cytokines and increased IL-10, justifying the anti-inflammatory effect presented by the photobiomodulation [20]. Several studies have recommended the use of corticosteroids for the treatment of COVID-19, and they have been included in therapeutic protocols due to their anti-inflammatory, antifibrotic, and vasoconstrictive effects, which reduce the systemic effects of the disease [44,45]. The WHO React Working group [46] reported that corticosteroids reduced mortality and ventilatory support time; dexamethasone reduced the number of deaths by approximately 36%, and hydrocortisone by 31%. The action of methylprednisolone was slightly lower, reducing mortality by 9%. The present study showed that the LED group had reduced oxygen therapy time, decreased risk of complications, and less lung damage, suggesting a promising clinical use for photomodulation using infrared LED irradiation in intensive care units and wards. This could allow the reduction or non-use of corticosteroids, since these drugs have adverse effects such as toxicity, in addition to requiring vital organs such as the liver and kidneys to metabolize or excrete drugs, which is not the case with phototherapy. No reported side effects or complications associated with LED therapy were observed during treatment, and no patients died. Due to the severity of the disease of patients with COVID-19, the use of LED therapy can improve clinical status and reduce the need for ICU beds and oxygen intake and, consequently, the use of mechanical ventilators. Other potential benefits of LED therapy include that the treatment is an easy, safe, non-invasive, non-pharmacological, painless, and low-cost modality. The results of this study are promising and will stimulate further research to evaluate the direct effect of photobiomodulation on the pulmonary condition of patients with COVID-19. It is worth mentioning that, in the present study, the pulmonary function of the groups was evaluated by objective measures, which is relevant because this approach has not been supported so far in the current literature. Among the strengths of this study, it should be noted that it is innovative because employed phototherapy using a vest with an array of 300 LEDs (940 nm) in complement to the conventional treatment of COVID-19. Moreover, the photobiomodulation reduced the average hospitalization time by four days and induced a significant improvement in the MIP (32%) and MEP (23%) pulmonary functions, these data are very promising and highlight the systemic effect of photobiomodulation. The main limitation of the present study was the size of the cohort of patients, a larger participation of patients in the study should increase the strength of the statistical analysis. Other possible items that could be investigated, such as filling out questionnaires by patients, monitoring the process of pulmonary inflammation, testing different doses of LED irradiation and wavelengths, and others, were left for future research because the study was developed during the peak of the COVID-19 pandemic when the whole effort was to find innovative therapies for the better recovery of the patients. 5 Conclusion It can be concluded that photobiomodulation with infrared LED irradiation reduces hospitalization time and eliminates the need for ICU admission or mechanical ventilation. Photobiomodulation therapy can be used as a complement to conventional treatment of COVID-19, promoting the improvement of cardiopulmonary functions and minimization of respiratory symptoms, suggesting that photobiomodulation therapy could reduce or non-use of corticosteroids. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Ethical Approval This study was performed in line with the principles of the Declaration of Helsinki. It was approved by the Research Ethics Committee of the Anhembi Morumbi University (CAAE; 36,988,320.5.0000.5492) and registered with the Brazilian Registry of Clinical Trials (ReBEC) under the code U1111–1261-1981 (16/11/2020). Informed Consent Informed consent was obtained from all individual participants included in the study. Contribution to the Work by each Author Pâmela Camila Pereira (doctoral student): conception and design of the study, data acquisition, data analysis, drafting the manuscript. Carlos José de Lima: conception and design of the study. Adriana Barrinha Fernandes: design of the study, data analysis, interpretation of data. Renato Amaro Zângaro: revising the manuscript critically for important intellectual content. Antonio Balbin Villaverde (student's advisor): conception and design of the study, statistical analysis, drafting the manuscript, final approval of the version, corresponding author. Declaration of Competing Interest The authors declare that they have no conflict of interest. Data availability Data will be made available on request. Acknowledgements PCM thanks the Coordination for the improvement of Higher Education Personnel (CAPES), the PhD scholarship. ==== Refs References 1 Huang C. Wang Y. Li X. Clinical features of patients infected with 2019 novel coronavirus in Wuhan Lancet 395 2020 497 506 10.1016/S0140-6736(20)30183-5 31986264 2 Liang T. Handbook of COVID-19 prevention and treatment Zhejiang Univ. School Med. 2020 10.1089/photob.2021.0050 3 Pascoal D.B. Carvalho A.C.S. Mata L.E.L.F.S. Acute respiratory syndrome: an exacerbated immune response to COVID19 Braz. J. Hea. Rev. 3 2020 2978 2994 10.34119/bjhrv3n2-138 4 Lauer S.A. Grantz K.H. Bi Q. 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S1551-7144(22)00370-6 10.1016/j.cct.2022.107044 107044 Article How much did it cost to develop and implement an eHealth intervention for a minority children population that overlapped with the COVID-19 pandemic Monashefsky Alexandra a Alon Dar b Baranowski Tom c Barreira Tiago V. d Chiu Kelly A. e Fleischman Amy f Green Melanie C. g Huang Shirley h Samuels Ronald C. i Sousa Caio Victor j Thompson Debbe k Lu Amy S. l⁎ a Precision Link Biobank, Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115, United States b Harvard T.H Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115, United States c Baylor College of Medicine, 1100 Bates St, Houston, TX, 77030, United States d Exercise Science Department, Syracuse University, 820 Commstock Ave, Syracuse, NY 13244, United States e Harvard Medical School, Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02130, United States f Harvard Medical School, Optimal Wellness for Life Clinic, Boston Children's Hospital, Boston, MA 02115, United States g Department of Communication, University at Buffalo, 359 Baldy Hall, Buffalo, NY 14260, United States h Tufts University School of Medicine, Tufts Medical Center, Boston, MA 02111, United States i Children's Hospital of Montefiore and Einstein Medical School, 3411 Wayne Ave, Bronx, NY, 10467, United States j Health and Human Sciences Department, Loyola Marymount University, 1 LMU Drive, MS 8888, Los Angeles, CA 90045, United States k USDA/ARS Children's Nutrition Research Center, Baylor College of Medicine, 1100 Bates Avenue, Houston, TX 77030, United States l Health Technology Lab, Department of Communication Studies, College of Arts, Media, and Design, Department of Health Sciences, Bouvé College of Health Sciences, Northeastern University, 360 Huntington Ave, Boston, MA 02115, United States ⁎ Corresponding author. 5 12 2022 2 2023 5 12 2022 125 107044107044 21 8 2022 14 11 2022 2 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. Background eHealth interventions using active video games (AVGs) offer an alternative method to help children exercise, especially during a pandemic where options are limited. There is limited data on costs associated with developing and implementing such interventions. Objectives We quantified the costs of delivering an eHealth RCT intervention among minority children during COVID-19. Methods We categorized the total trial cost into five subcategories: intervention material development, advertising and recruitment, intervention delivery, personnel salaries, and COVID-19-related equipment costs. Results The total RCT cost was $1,927,807 (Direct: $1,227,903; Indirect: $699,904) with three visits required for each participant. The average cost per participant completing the RCT (79 participants/237 visits) was $24,403 (Direct: $15,543; Indirect: $8860). Due to no-shows and cancellations (198 visits) and dropouts before study completion (61 visits; 56 participants), 496 visits had to be scheduled to ensure complete data collection on 79 participants. If all 496 visits were from participants completing the three-visit protocol, that would correspond to 165 participants, bringing the average cost per participant down to $11,684 (Direct: $7442; Indirect: $4242). Of the subcategories, intervention material development accounted for the largest portion, followed by personnel salaries. While the direct COVID-19-specific cost constituted <1% of the entire budget, the indirect effects were much larger and significantly impacted the trial. Conclusion RCTs typically involve significant resources, even more so during a pandemic. Future eHealth intervention investigators should budget and plan accordingly to prepare for unexpected costs such as recruitment challenges to increase flexibility while maximizing the intervention efficacy. Keywords Intervention cost Budget planning Active video game Exergame Physical activity Child obesity ==== Body pmc1 Introduction Physical activity (PA) plays a critical role in the battle against the worldwide epidemic of childhood obesity, which disproportionately impacts Black and Hispanic populations [16]. Successful activity-related interventions have resulted in a reduction in sedentary behavior and an increase in energy expenditure to combat obesity. [4]. An interesting alternative to traditional physical activity interventions such as sports or physical education classes is eHealth interventions using active video games (AVGs). AVGs have been increasingly employed in research as they can encourage PA behavior in a format that is accessible, engaging, and exciting [8]. Previous studies have found that with the appropriate narrative immersion into a storyline, AVGs can elicit increased minutes of moderate-to-vigorous physical activity (MVPA) in children [17]. AVGs have a promising combination of enjoyment, appropriate exercise intensity, and potential for sustainable involvement that may offer an exercise remedy for the obesity epidemic; however, more work is needed to understand their utility [2]. The aim of this paper is to calculate and categorize into distinct subcategories the direct costs of carrying out an eHealth intervention trial. Our goal is to provide insight to future investigators as they plan and budget studies that will further aid in our knowledge of AVGs or other eHealth interventions. We also share the obstacles encountered in pursuit of an eHealth intervention trial so that future research proposals are more informed by our experience. We hope to encourage research toward a better understanding of what it costs to run an eHealth study and the most cost-effective way to approach it. To study the ability of AVGs to elicit PA, our team conducted an eHealth randomized controlled trial (RCT) from late 2019 through May 2022. At this time, the study data collection has been completed and data analysis for assessing primary outcomes is ongoing. Our protocol has been registered at ClinicalTrials.gov (NCT04116515) and fully detailed in Alon et al. [1], with the updated protocol regarding COVID-19 pandemic-related changes in Monashefsky et al. [13]. In summary, we planned to test the long-term effect of narratives on players' MVPA levels as well as multiple other health outcomes, with the hypothesis that adding narratives to the AVGs would increase time spent playing AVGs and thus increase PA levels. Our study was a six-month randomized controlled single-blind trial with participants randomized into three groups attempting to specify the added contribution of a narrative: condition A [Narrative + AVG] received an Xbox with an intervention narrative animation, Ataraxia, and six AVGs during the six-month participation; condition B [AVG only] received access to the same Xbox model and games without Ataraxia; and condition C [Control] that did not receive an Xbox or games until the conclusion of their participation. The narrative animation of Ataraxia was created for this trial. Ataraxia contains elements of stories that previous studies in our lab identified as appealing to children [11] and combines those elements into a storyline that pairs with six commercial AVGs available on Xbox. The story takes place over 72 cartoon episodes in six seasons, depicting a post-apocalyptic science fiction plot further detailed in Alon et al. [1]. Each participant, regardless of condition, was required to undergo three in-person data collection visits over the course of six months. Each visit included measurements for height and weight, a fasting blood draw, a Dual-energy X-ray absorptiometry (DEXA) test for body mass and composition, a cognitive test, multiple questionnaires, and an accelerometer set up to track activity for the next seven days. The visits were spaced two-to-three months apart to track the longitudinal effects of active video gameplay. Due to the pandemic, we allowed flexibility in times of assessment. The main trial was initiated on January 11, 2020, and was suspended on March 17, 2020, due to the COVID-19 pandemic. It resumed on September 12, 2020 and continued until May 1, 2022. In the setting of severe funding constraints on public health research [15], the execution of this study was relatively expensive, especially when it overlapped with the pandemic. Thus, it was important to explore what strategies could be implemented in an RCT budget to increase spending efficiency and adjust enrollment expectations while still retaining scientific rigor, as the most successful RCTs are the ones that are robust and flexible enough to adapt to these unexpected issues, including monetary ones [3]. This is especially relevant to our RCT, as we implemented a longitudinal study consisting of three visits over six months, requiring a relatively large sample size to accurately analyze as well as account for an increasing number of dropouts as the study continued. The recent and ongoing COVID-19 pandemic has also further complicated our research progression as it not only disproportionately impacted the Black and Hispanic populations [6], who were our primary study groups, but also significantly increased their financial stress, which might have prevented them from traveling to visits [19]. Therefore, it was highly likely that these unprecedented pandemic-related factors might have led to substantially more no-shows or dropouts. RCTs are expensive. Further research on how to increase the monetary value and reduce the costs would be beneficial [9]. However, there is limited existing research on the costs of active video game RCT interventions, especially during the COVID-19 pandemic. There is not enough available data and transparency regarding the costs of different components of an RCT, such as advertising and recruitment versus intervention delivery versus personnel. There is also a lack of data on the cost of intervention material development, which we defined as the cost it takes to design and create an eHealth intervention to promote physical activity. This paper aims to address these issues by categorizing the direct cost it took to carry out an AVG intervention into multiple subcategories. We aim to provide insight for future investigators as they estimate the various financial costs of their proposed RCTs. Within our sample population, almost half of families had annual incomes lower than $40 K and all children identified as persons of color, with around 80% identifying as Black or Hispanic. In addition to costs, this paper also shares unexpected obstacles, such as the disproportional impact of COVID-19 on our sample population of primarily underprivileged children, to provide examples of the many ways in which the pursuit of a research protocol may differ from original expectations. A better understanding of what eHealth interventions actually cost may create more informed research proposals and expectations, leading to increasingly efficient research environments with potentially increased intervention efficacy. 2 Methods We have been actively monitoring costs using a master Excel workbook with over 20 sheets to track the cost of each spending category, which has been validated against the PI's internal financial report each month. The total cost can be retrieved from NIH RePORTER with the last author's name over the years. We organized the cost related to the RCT intervention into five primary subcategories: (1) intervention material development, (2) advertising and recruitment, (3) intervention delivery, (4) personnel salaries, and (5) COVID-19-related costs. Next, we reported the total amount of money allocated to each area, which was summed to calculate the overall study cost. To identify per-participant costs, the total cost of the trial was divided by 79, the number of participants who successfully completed all three required visits. While additional 56 participants completed either just one (51 participants) or two visits (5 participants), which is important for intention-to-treat analyses [7], we did not include them in the denominator since we wanted to estimate the cost per completed participant. To identify the cost per scheduled visit, we took the total study cost and divided it by the number of total visits scheduled (496 visits), including those in which participants did not show up or canceled at the last minute. We included the no-show visits in our calculations because staff time was already spent regardless of the attendance of the participant as long as they confirmed they were coming one week before and then again 48 h before their visit; thus the appropriate resources and time were blocked out for them and could not be easily recovered. We separately report the potential costs per participant should all of the study visits have been part of the three-visit intervention and generated valid data for the goals of the RCT. 3 Results 3.1 Total trial costs The terms “direct” and “indirect” refer to the NIH definitions of costs directly allocatable to the study (direct) with additional funds allocatable to institutional overhead (indirect), respectively. The total cost of the RCT was $1,927,807 (Direct: $1,227,903; Indirect: $699,904), which included the creation of an animated show that cost $544,000 (Direct only; Indirect: $310,080) to create before the start of the trial. The animated show was deployed in the intervention. Aside from the intervention material development, the total cost solely dedicated to the RCT was $683,903 (Direct only; Indirect: $389,825). 3.2 Cost per subcategory The total costs dedicated to material development totaled $546,288 (Direct only; Indirect: $311,384). This accounted for funds dedicated to scripting and creating the animated show Ataraxia (72 episodes in six seasons; around three minutes per episode) with a professional media production company ($544,000) [12] as well as additional research dedicated to exploring and qualifying Xbox active video games on the market and available for use within the trial. The intervention material development was planned at the onset of the R01 budget planning with $500,000 dedicated to the material development. This was later increased to $544 k to finetune the material for the actual intervention deployment, with $50 K being dedicated to initial story selection, $294 K for investigating the effective plot design and character presentation as well as initial animation production, and $200 K for continued work for completing the production of the six seasons. Intervention material development was completed before the pandemic and therefore its costs were not affected by the pandemic. Costs for advertising and recruitment totaled $70,937 (Direct only; Indirect: $40,434), which included funds dedicated to recruitment personnel (wages of research assistants who were exclusively working on participant recruitment), printer hardware and supplies, print order costs for information flyers and letters, and incentives such as wristbands and erasers given to potential participants. Research assistants were paid on an hourly basis to print and prepare mailings to a pre-made list of eligible participants provided by Boston Children's Hospital. Costs for intervention delivery totaled $158,436 (Direct only; Indirect: $90,309), which included funds dedicated to assessment equipment costs (DEXA machine computer, printer and maintenance, wet lab materials, privacy screen, iPad, laptops for assessment, accelerometers and accessories, cognitive testing button board, and weight scale), research supplies (snacks for participants and office supplies), technology intervention delivery (Xbox consoles, Kinect sensors, Kinect adapters, and AVGs), and participant rewards (up to $100 Amazon gift cards per participant). The DEXA and blood centrifuge machines were in the research space prior to the RCT and were not separately purchased. An annual maintenance fee was charged to the grant and factored into this category. Research personnel were trained by the DEXA's manufacturer (General Electric, Boston, MA) to be certified to operate the machine and their training costs were factored into this category. Costs for personnel totaled $449,012 (Direct only; Indirect: $255,937), which included funds to support research staff salaries over the course of the trial, excluding the principal investigator and other co-investigator salaries as they were not directly involved in data collection. Research staff included all personnel involved in the coordination, data collection, and management of the study. This total also excludes research assistants solely hired for in-person recruitment, as their wages were already included under the advertising and recruitment subcategory. COVID-19-related specific equipment costs totaled $3229 (Direct only; Indirect: $1841), which included funds dedicated to cleaning products, COVID-19 rapid tests, contactless thermometers, and Personal Protective Equipment (PPE) (disposable face masks and face shields) for both research staff and participants. Table 1 summarizes the direct cost of each of the five subcategories along with their percentages.Table 1 Break down of total direct costs into each subcategory. Table 1Category Total Cost ($) Percentage of Total (%) Intervention Material Development 546,288 44.5 Advertising and Recruitment 70,937 5.8 Intervention Delivery 158,436 12.9 Personnel Salaries 449,012 36.6 COVID-19 Related Equipment Costs 3229 0.3 Total: 1,227,902 100 3.3 Per participant costs Our study had 79 participants (out of 154 total recruited sample, 135 showed up for their first visit and completed the consent and assent forms) complete all three visits required per person in the trial. Therefore we had 237 fully completed visits. To achieve this, a total of 154 participants were scheduled to start the intervention, with 19 not showing up for their first visit, and 135, 84, and 79 completing one, two and all three study visits, respectively. The average total cost per completed participant (that completed three visits) was $24,403 (Direct: $15,543; Indirect: $8860), or $8134 per visit (Direct: $5181; Indirect: $2953). 3.4 Per scheduled visit costs Our study had 496 scheduled visits, which include the 237 fully compliant visits (those completed by the 79 participants who ended up finishing the study) in addition to completed visits of participants who dropped out of the study (56 participants, 61 visits in total), as well as visits that were scheduled and confirmed but not attended (no show or rescheduled at the last minute, 198 in total). Therefore, the cost per scheduled visit was $3887 (Direct: $2476; Indirect: $1411). However, since only 237 of 496 visits were useful for the primary outcome assessment of fully completed participants, the cost per visit from the 79 participants who concluded the entire study protocol was more than twice as high (Total: $8134). Ideally, the number of scheduled visits would equal the number of completed visits. For example, in a best-case scenario with no participant dropouts and no visit cancellations, we could have had 496 completed visits, or approximately 165 participants completing the trial (495 visits, $11,684 per participant, Direct: $7442; Indirect: $4242). Therefore, we would have collected more data with reduced per-visit costs, although we acknowledge that this is not likely for real-world interventions. To sum up, the total cost of our eHealth intervention was $1,927,807 (Direct: $1,227,903; Indirect: $699,904), with intervention material development making up the largest proportion of the subcategories, accounting for 44% of the total cost, followed by personnel (37%), intervention delivery (13%), advertising and recruitment (6%), and finally COVID-19-related equipment costs (<1%). The average cost for one participant to complete the trial was estimated to be $24,403 (Direct: $15,543; Indirect: $8860), or $8134 per visit (Direct: $5181; Indirect: $2953). 4 Discussion Previous research has outlined costs associated with similar trials. In a study reporting the financial costs of a phase III multi-site exercise intervention trial using fMRI, the cost per participant was reported to be $16,494 with a total trial cost estimated to be around $21 million [5]. Both our study and the phase III intervention study are within the range of the observed cost. For example, a meta-analysis by Speich et al. [18] systematically searched three databases for the publication of empirical data on resource use and costs of RCTs and found overall costs could run between $43 and $103,254 per participant. However, it was worth mentioning that the meta-analysis included a variety of RCTs (e.g., pharmaceutical trials) and did not specify exercise intervention trials similar to the current study. Although seemingly expensive to develop, eHealth interventions are promising methods for increasing PA among children. This is important during public health emergencies that restrict normal activities, such as the COVID-19 pandemic. The importance of identifying effective interventions is rising as the rates of PA in children are decreasing while childhood obesity is increasing, especially among Black and Hispanic children [16]. Instead of being a hindrance to physical activity, the long-lasting changes in our everyday routines, including our increasing reliance and usage of technology, can be utilized by eHealth interventions using AVGs. It is important to study the efficacy of eHealth interventions to influence the health of children at risk of childhood obesity, as changing behavior is difficult, especially in our fast-changing world. The trial cost $1,927,807. We were able to collect data from 79 children and their families. A large contributor to this steep price despite a medium-sized sample was the discrepancy between scheduled and completed visits. On average, each participant attended 1.7 out of 3 visits (Range: 0–3, SD = 1.3) and needed to reschedule 1.2 visits (Range: 0–14, SD = 1.6) for each visit over the course of this study. There were 496 scheduled visits, however, only 298 were attended (135 first visits, 84 s visits, and 79 third visits) due to participants not showing up or rescheduling at the last minute. Further, of these 298 visits, only 237 visits counted toward the overall RCT completed participant data set, i.e., when all three visits were completed by the participant. Consequently, the resources attributed to scheduled visits that ended up being no-shows or rescheduled were significant. These costs consisted of wages for research assistants and the phlebotomists scheduled for that day, along with the resources and supplies set aside for the child and their families. It is also worth mentioning that due to the pandemic-related physical distancing requirement, we were unable to double-book participants during a scheduled time slot or allow any overlap of two families in the lab. Therefore, if one family did not show up, the team would end up having an hour of unproductive time. These difficulties in recruitment and scheduling, just two of the many challenges of running an RCT, could result in clinical trials with high monetary expenses and little to no scientific benefit. When enrollment suffers, the trials may have a lesser likelihood of being accepted for publication [10]. These research assistant wages paid for non-productive hours take grant money that would preferably be spent on the trial. In addition, due to the pandemic research hiatus, our trial (and the length of time salaried workers were paid) had to be extended. We extended the trial via a no-cost extension. This was helpful for us as we had the funds remaining in the budget, but no-cost extensions do not provide additional funds to cover added months of data collection. Additionally, we also had a high dropout rate. Approximately 61.5% of pre-pandemic participants were lost to follow-up after the resumption of research post-COVID-19 suspension [13]. The pilot test of this trial saw a dropout rate of only 20–33%, much less than the dropout rate of the actual post-pandemic trial of 54%. We attributed attendance issues largely to challenges brought on by the pandemic, such as economic instability or health issues, especially among low socioeconomic status populations included in our sample [6,19]. The indirect effects of COVID-19 can be seen in each subcategory of costs in our breakdown showing the pervasive effects the pandemic had on our trial. The pandemic led to substantial changes in our protocol and a more participant-centric experience. We began to focus increasingly on how to incentivize participants to show up as well as participate fully in-between visits. We also increased recruitment efforts to combat dropouts, doubling our output of recruitment mail since October of 2021 compared to pre-pandemic levels, which led to increased resource spending. Doubling the number of recruitment mailings per week seemed to be the most effective method to combat the low participation. Another strategy that was effective in recruiting participants was doing two rounds of mailings separated one week apart to each participant (first a full mailing packet with flyers and information sheets, and second just an informative postcard). Before March 2020, our team did not anticipate a scenario in which a pandemic erupted across the world and completely disrupted the daily life of billions of people. Therefore, many issues and obstacles were not anticipated in our study. For example, our study costs were further exacerbated by having a hiatus during the lockdown, extending the study duration, and implementation of post-resumption precautionary measures directly impacting resource costs. We would like to take this opportunity to share our experience dealing with such unanticipated events. We hope to demonstrate the reality of real-world recruitment and participant retention especially with minority populations during the pandemic and the potential extra cost due to visit no-shows or incomplete visits. Because climate change can potentially exacerbate 58% of infectious diseases confronted by humanity [14] and may affect many weather and climate extremes around the world, we hope to use this work as a springboard for future researchers' pandemic and disaster outbreak preparedness. Better ideas and strategies for recruitment techniques and participant engagement should be shared and lead to more efficient budgets and thus more efficient intervention knowledge and practice. An advantage of our analysis is that we were able to obtain fully calculated costs after data collection when the trial was completed. Therefore, we have a concrete number instead of an estimated number for the entire cost. It is helpful to share the information with agencies funding eHealth clinical trials to inform their costs to successfully and efficiently execute a study. Several considerations are needed in applying our costs to other studies. Since inflation has been rapidly rising, our costs cannot be directly projected into the future. For example, in addition to direct financial impacts, the pandemic indirectly affected many aspects related to running the RCT as well, such as the rising costs of research materials due to inflation and supply/demand challenges of equipment needed. Small changes in our protocol, such as the type of video game console used, or the number of visits per participant could drastically alter the costs. Being in a university environment, some resources were available for free that other studies may not have access to, such as free laboratory space, the DEXA and centrifuge machines. On the other hand, setting aside the relatively high cost of preparing the intervention, if we imagine stripping away the research aspect and considering just the deployment of the intervention outside of a research study, the cost for ongoing maintenance and delivery would be considerably lower. Assuming (1) the narrative animation has been developed and can be deployed freely to the participants; (2) the participants do not have the game system or the games; and (3) the story delivery status does not depend on research data collection but will be done by an automated server, we would need to include the cost of the game system and the games plus the salary of one staff member whose duty is to ensure that the stories are delivered correctly to participants through remote monitoring. Given the vast number of Xbox game players in the world, we anticipate the material expense to be around $700–1000 per participant to implement this intervention out of a research context. The material expense will only include the Xbox game console and Kinect sensor, along with the AVGs. We provide a range rather than a specific price for these items because the cost may surge due to the lack of manufacturer supply. Our systematic efforts to organize and quantify the costs of an RCT into separate and meaningful subcategories revealed crucial insights. In our efforts to run an eHealth intervention during the pandemic era, one may assume the primary expenditure of money would go into PPE and precautionary measures. Interestingly, direct COVID-19-related equipment costs were not the major source of cost within our trial. Instead, what was most damaging was the impact of the pandemic on participant attendance rates. We hope our experience will help further the pursuit of efficient science. 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. Acknowledgment The authors would like to thank Drs. I-Min Lee, Sarah Lessard, Lynne L. Levitsky, and Farzad Noubary for their helpful comments on this draft. The authors would like to thank Aleksandra Baran, Rashmi Borah, Romina Cabrera-Perez, Kelly Lee, Emma McGarrity, Harshita Menon, Aika Misawa, Grace Novoa, Kyung Jin Sun, and Neha Swaminathan for their effort in data collection. This project was supported in part by a grant from the 10.13039/100000062 National Institute of Diabetes and Digestive and Kidney Diseases (R01DK109316) and Northeastern University's Interdisciplinary Research Sabbatical. 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Epidemiol. 96 2018 1 11 10.1016/j.jclinepi.2017.12.018 29288136 19 The Commonwealth Fund Beyond the Case Count: The Wide-Ranging Disparities of COVID-19 in the United States https://www.commonwealthfund.org/publications/2020/sep/beyond-case-count-disparities-covid-19-united-states 2020	36473682	PMC9721158	NO-CC CODE	2022-12-16 23:18:08	no	Contemp Clin Trials. 2023 Feb 5; 125:107044	utf-8	Contemp Clin Trials	2,022	10.1016/j.cct.2022.107044	oa_other
==== Front JSES Rev Rep Tech JSES Rev Rep Tech Jses Reviews, Reports, and Techniques 2666-6391 The Authors. Published by Elsevier Inc. on behalf of American Shoulder & Elbow Surgeons. S2666-6391(22)00111-0 10.1016/j.xrrt.2022.11.001 Article Parsonage-Turner Syndrome following COVID-19 infection: A Report of Three Cases Castaneda Diego Martinez BS 1 Chambers MaKenzie M. BS 1 Johnsen Parker H. MD 2 Fedorka Catherine J. MD 12∗ 1 Cooper Medical School of Rowan University, Camden, NJ, USA 2 Cooper Bone and Joint Institute, Cooper University Healthcare, Camden, NJ, USA ∗ Corresponding Author: Catherine J. Fedorka, MD, 3 Cooper Plaza Ste 408, Camden, NJ 08103, USA. 5 12 2022 5 12 2022 8 9 2022 1 11 2022 18 11 2022 © 2022 The Authors. Published by Elsevier Inc. on behalf of American Shoulder & Elbow Surgeons. 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 Parsonage-Turner Syndrome (PTS), previously referred to as neuralgic amyotrophy or idiopathic brachial plexopathy, is a rare neurological syndrome characterized by an abrupt onset of shoulder pain followed by neurological deficits of motor weakness, numbness, and muscular atrophy. Although the pathophysiology and specific cause of PTS remain unclear, it has been reported following trauma, vaccination, surgical procedures and viral infection. Our practice has seen an increase in the incidence of PTS with the COVID-19 pandemic. We aim to describe three cases of patients found to have PTS shortly after proven infection with COVID-19 during the Omicron surge. Case Description Three patients developed symptoms of PTS within a few weeks of having COVID-19. All three cases had normal imaging and an EMG that diagnosed PTS. The first is a 23-year-old male who developed weakness and shoulder pain two weeks after his COVID-19 diagnosis. The second was a 58-year-old male who was intubated with COVID-19 pneumonia and woke up with extreme weakness and pain in his left arm. The final case was a 76-year-old male with a remote history of a left total shoulder arthroplasty who also developed acute pain and weakness soon after testing positive for COVID-19. All three patient’s symptoms and clinical exam have been improving. Discussion In this report, we presented three patients who developed PTS soon after being diagnosed with COVID-19 during the omnicron surge. As we learn more about the COVID-19 virus and its effects on the musculoskeletal system, efficient diagnosis and multidisciplinary treatment of Parsonage-Turner Syndrome will become increasingly valuable to physicians and patients. Keywords Parsonage-Turner Syndrome Neuralgic amyotrophy Idiopathic brachial plexopathy SARS-CoV-2 COVID-19 ==== Body pmcParsonage-Turner Syndrome (PTS), previously referred to as neuralgic amyotrophy or idiopathic brachial plexopathy, is a rare neurological syndrome characterized by an abrupt onset of shoulder pain followed by neurological deficits of motor weakness, numbness, and muscular atrophy15 , 16. The distribution of peripheral nerves involved and the extent of involvement is variable however usually the upper trunk of the brachial plexus is most affected15. Although symptoms usually manifest in the upper extremity, patients can present to physicians of various specialties due to the broad range of clinical manifestations of this syndrome16. The diagnosis of PTS is primarily made through clinical history, symptoms, and physical exam findings14. Additionally, magnetic resonance imaging of the shoulder and upper extremity musculature may reveal denervation within days whereas electromyography (EMG) conducted three to four weeks after symptom onset could also aid in localizing the lesion and confirming the diagnosis13. Treatment of PTS predominantly focuses on pain management with NSAIDs or neuroleptics and physical therapy for potential muscle weakness14. However, the variability in etiology and presentation can pose challenges to physicians in both diagnosis and management. Although the pathophysiology and specific cause of PTS remain unclear, it has been reported following trauma, vaccination, surgical procedures and viral infections15. PTS has also been associated with various autoimmune disorders such as systemic lupus erythematosus and temporal arteritis8. Recent viral illness and recent immunization are the most common associated risk factors in patients who develop PTS9. Since the authorization of the COVID-19 vaccines, there have been a few reported cases of PTS following vaccination10 , 11. Additionally, though there have been various viral entities reported to precede PTS, there are few cases in the current literature that detail an association with SARS-CoV-2 (COVID-19) and PTS1 , 4, 5, 6, 7 , 17 , 18. Due to the paucity in the literature, we aim to describe three cases of patients found to have PTS shortly after proven infection with COVID-19 during the Omicron surge. Consent to report their cases was obtained from each patient. Case 1 A 23-year-old male with no significant medical history presented for evaluation of an aching, acute left shoulder pain that began three weeks prior after weightlifting without a specific injury in February 2022. He reported testing positive for COVID-19 two weeks prior to symptom onset. The patient denied previous exercise-related injury or trauma and noted that sleeping on the left side exacerbated his pain. The pain improved upon decreasing his physical activity however quickly recurred with strenuous use of his left shoulder. There was no complaint of numbness or tingling however a sensation of impending subluxation was noted when performing activities of daily living such as taking off a shirt. Physical exam revealed a full active range of motion but significant atrophy of the infraspinatus muscle and slight atrophy of the supraspinatus muscle. In addition, weakness in external rotation (MRC 3-) and abduction (MRC 4-) of the left shoulder was noted. A musculoskeletal exam of the cervical spine revealed no tenderness to palpation of the vertebral bodies and paraspinal muscles. Range of motion of the cervical spine was intact. A neuromuscular exam of the upper extremities showed normal strength in all other muscles distally. X-ray of the left shoulder was normal (Fig. 1 ) A MR arthrogram was then performed to rule out a labrum tear with a paralabral cyst compressing the suprascapular nerve as that was the physician’s initial differential diagnosis. MR arthrogram showed a small superior labrum tear but no evidence of a paralabral cyst (Fig. 2 ). EMG of the left shoulder was performed and showed increased insertional activity, widespread spontaneous activity, reduced recruitment and mildly reduced interference pattern in the supraspinatus and infraspinatus muscles. EMG studies also demonstrated increased insertional activity of the serratus anterior muscle (Fig 3 ). Clinical history, physical examination, magnetic resonance imaging, and EMG were consistent with PTS and suggested the etiology to be related to a recent COVID-19 infection. Treatment consisted of initiating physical therapy and corticosteroids to improve motor weakness and reduce inflammation. Four months after diagnosis, he reports feeling 70 percent improved, but still notes weakness mostly in external rotation and occasional aching pain.Figure 1 X-Ray of the left shoulder showed a normal acromioclavicular joint and no acute osseous abnormalities, lesions or arthritic changes. (A) Anterior-Posterior view (B) Axial view. Figure 2 MR arthrogram of the left shoulder shows a superior labrum tear and no evidence of a paralabral cyst compressing the suprascapular nerve. (A) Axial view (B) Coronal view. Figure 3 EMG of the left shoulder showed increased insertional activity, widespread spontaneous activity, reduced recruitment, and mildly reduced interference pattern in the supraspinatus and infraspinatus muscles. Insertional activity (Ins Act), Fibrillation potentials (Fibs), Peak sharp waves (PSW), Amplitude (Amp), Duration (Dur), polyphasic potentials (Poly), Reduced recruitment (Recrt), Interference pattern (Int Pat) Case 2 A 58-year-old male with a past medical history of work-related chronic left shoulder pain and cerebrovascular accident with no residual deficits presented to clinic with acute burning anterolateral left shoulder pain and severely decreased motor function in his left upper extremity. These symptoms were first noted following extubation after several weeks of intensive care treatment for COVID-19 pneumonia in January 2022. He denied weakness in his left upper extremity prior to contracting COVID-19. The pain radiated below the elbow and was worse at night, often disrupting the patient's sleep. The patient also reported numbness and tingling in his left upper extremity but denied any trauma or injury. Physical exam revealed significant atrophy of the deltoid, supraspinatus, and infraspinatus in addition to decreased active range of motion and painful passive range of motion. Active forward flexion was limited to 10 degrees, abduction to 10 degrees, and external rotation to 40 degrees. Passive range of motion was also significantly decreased. Strength testing revealed significant weakness in abduction (MRC 2), external rotation (MRC 3-), and internal rotation (MRC 3), and elbow flexion (MRC 4). X-rays of the left shoulder were unremarkable for fracture, dislocation, or arthritis (Fig 4 ). MRI of the shoulder was also normal. EMG demonstrated marked denervation of the deltoid, biceps, brachioradialis, infraspinatus, and supraspinatus while sparing the paraspinal, triceps, and forearm muscles (Fig 5 ). Combined with clinical history and physical exam findings, plain radiographs, MRI, and EMG results confirmed the diagnosis of PTS. He unfortunately had also developed a secondary adhesive capsulitis. The patient was counseled on the importance of physical therapy to improve his range of motion in addition to follow-up with neurology and his primary care provider for multidisciplinary management of his PTS. Nine months after having COVID-19, the patient reported his pain and range of motion were starting to improve.Figure 4 X-ray of left shoulder (A) Anterior-Posterior view (B) Axial view. Figure 5 EMG of the left shoulder shows denervation of the deltoid, biceps, brachioradialis, infraspinatus, and supraspinatus while sparing the paraspinal, triceps, and forearm muscles. Insertional activity (Ins Act), Fibrillation potentials (Fibs), Peak sharp waves (PSW), Amplitude (Amp), Duration (Dur), Polyphasic potentials (Poly), Reduced recruitment (Recruit). Case 3 A 76-year-old male with past medical history of left anatomic total shoulder arthroplasty presented for evaluation of several weeks of acute onset, dull, diffuse left shoulder pain radiating to his neck. The patient noted the onset of his symptoms after testing positive for COVID-19. A few weeks following onset of pain he began experiencing weakness, decreased range of motion, and paresthesias throughout his left upper extremity. Prior to testing positive for COVID-19 the patient reported his left shoulder to be fully functional and painless. Initial physical exam revealed significant atrophy of the deltoid, supraspinatus, and infraspinatus with full active shoulder range of motion. However, evaluation 2 months following initial presentation the patient demonstrated decreased shoulder active range of motion, with forward flexion to 150 degrees, abduction to 90 degrees, and external rotation to 50 degrees. Passive range of motion was preserved. Weakness in external rotation (MRC 4-) and abduction (MRC 3-) of the left shoulder was noted, while internal rotation remained intact (MRC 5-). Physical exam of the cervical spine revealed no tenderness to palpation of the vertebral bodies and paraspinal muscles and full active range of motion. X-rays of the left shoulder demonstrated a stable prosthesis and no fracture or dislocation (Fig 6 ). EMG studies revealed marked denervation of the left deltoid and biceps muscles, while the first dorsal interosseous, triceps, brachioradialis, and pronator teres showed no signs of electrical instability (Fig 7 ). Clinical presentation, physical exam findings, and EMG results confirmed the diagnosis of PTS and suggested the etiology to be related to a recent COVID-19 infection. The patient was counseled on starting physical therapy to improve his range of motion.Figure 6 X-ray of the left shoulder shows a stable total shoulder arthroplasty Figure 7 EMG showed increased insertional activity, slightly increased spontaneous activity, increased motor unit duration and moderately decreased interference pattern of the left deltoid muscle. The left biceps muscle showed increased insertional activity, moderately increased spontaneous activity, decreased motor unit amplitude and decreased motor unit duration. Insertional activity (Ins Act), Fibrillation potentials (Fibs), Peak sharp waves (PSW), Amplitude (Amp), Duration (Dur), polyphasic potentials (Poly), Interference pattern (Int Pat). Discussion and Conclusion PTS is an uncommon and complex condition with various potential etiologies. Historically, PTS onset has been chiefly reported after viral infections, with the most common virus being hepatitis E and recently post-COVID-19 infection1 , 4, 5, 6, 7 , 17 , 18. The diagnosis of PTS is typically a diagnosis of exclusion, established once other possible entities causing upper extremity dysfunction have been ruled out. Although the presentation of PTS can be variable, the characteristic pattern of severe aching or throbbing pain that may or may not radiate followed by upper extremity weakness are generally indications of the diagnosis12. However, MRI and EMG studies should be included when considering the possibility of a PTS diagnosis. The most common muscles affected are the deltoid, supraspinatus, infraspinatus, serratus anterior, biceps, and triceps12 , 15. Here, we present three patients with acute unilateral shoulder pain and weakness without trauma. All cases demonstrated atrophy of the shoulder girdle muscles and weakness of the upper extremity. Denervation of the supraspinatus and infraspinatus muscles was observed in the first two cases, while the third case presented with denervation of the deltoid, and biceps muscles. No sensory deficiencies of the upper extremities were seen in all patients. Treatment of PTS usually involves conservative measurements with analgesics and physical therapy focusing on range of motion and strengthening exercises. The disease course can vary among patients therefore it is also important to consider a multidisciplinary treatment approach. Some patients spontaneously recover within a month with conservative treatment only, while others may require surgical intervention such as nerve transfers and nerve grafts15. Overall, patients with PTS recover 80-90% of their previous health, however more than 70% are left with residual complications15. The primary peripheral nervous system manifestations of COVID-19 infection have been previously reported as Guillain-Barre syndrome, nerve pain, and myalgia. The cases we present here highlight the importance of including PTS as part of a differential diagnosis in a patient presenting with atraumatic shoulder pain followed by upper extremity muscle weakness after COVID-19 infection2. A limited number of previous case reports linking PTS with COVID-19 infection or vaccination have reported similar physical exam findings of atrophy of the shoulder girdle muscles, decreased force in anterior elevation, abduction, external rotation, and internal rotation, as well as denervation and fibrillation potentials findings in EMG studies of the shoulder muscles3 , 9, 10, 11, 12, 13, 14, 15 , 18. Similarly, if the patient presents with characteristic pain followed by muscle weakness and has had the COVID-19 vaccine, PTS should be included when considering a diagnosis10. However, it is important to keep in mind that this presentation has been reported to occur within days of vaccination rather than weeks as we have seen when it is associated with COVID-19 infection10 , 11. As the COVID-19 pandemic continues to unfold, it is essential that physicians include PTS in their differential diagnosis and are thorough in their physical examination. Although the diagnosis of PTS is often a challenging one, a detailed physical exam and high index of suspicion is essential in ensuring this diagnosis is properly identified and treated. Patient Consent All patients included in this article have provided verbal consent to the senior author, authorizing their clinical information and imaging studies be utilized in this report. They understand that all data will be portrayed in de-identified fashion. Institutional review board approval was not required for this case report. Disclaimers: Funding: No funding was disclosed by the authors. Conflicts of interest: Dr. Fedorka is a paid consultant of Stryker Corporation. No Stryker implants were used in this work and it is not related to the subject of this study. The other authors, their immediate families, and any research foundation with which they are affiliated have not received any financial payments or other benefits from any commercial entity related to the subject of this article. Consent: Obtained ==== Refs References 1 Alvarado M. Lin-Miao Y. Carrillo-Arolas M. Parsonage-Turner syndrome post-infection by SARS-CoV-2: a case report Neurologia (Engl Ed) 36 7 2021 568 571 10.1016/j.nrleng.2021.04.003 34315680 2 Andalib S. Biller J. Di Napoli M. Moghimi N. McCullough L. Rubinos C. Peripheral Nervous System Manifestations Associated with COVID-19 Curr Neurol Neurosci Rep 21 3 2021 9 10.1007/s11910-021-01102-5 33586020 3 Ansari B. Eishi Oskouei A. Moeinzadeh F. Parsonage-Turner Syndrome following COVID-19 Infection: A Rare and Unique Case Adv Biomed Res 11 2022 7 10.4103/abr.abr_167_21 35284354 4 Cabrera Pivaral C.E. Rincón Sánchez A.R. Dávalos Rodríguez N.O. Ramirez Garcia S.A. [Parsonage Turner syndrome associated with COVID-19: About two family cases] [published online ahead of print, 2022 Feb 3] Neurologia 2022 10.1016/j.nrl.2022.01.006 5 Cacciavillani M. Salvalaggio A. Briani C. Pure sensory neuralgic amyotrophy in COVID-19 infection Muscle Nerve 63 1 2021 E7 E8 10.1002/mus.27083 33001471 6 Coll C. Tessier M. Vandendries C. Seror P. Neuralgic amyotrophy and COVID-19 infection: 2 cases of spinal accessory nerve palsy Joint Bone Spine 88 5 2021 105196 10.1016/j.jbspin.2021.105196 7 Díaz C, Contreras JJ, Muñvoz M, Osorio M, Quiroz M, Pizarro R. Parsonage-Turner syndrome association with SARS-CoV-2 infection. JSES Rev Rep Tech. 2021;1(3):252-256. doi: 10.1016/j.xrrt.2021.04.002 8 Feinberg J.H. Radecki J. Parsonage turner syndrome HSS J 6 2 2010 199 205 10.1007/s11420-010-9176-x 21886536 9 Fibuch E.E. Mertz J. Geller B. Postoperative onset of idiopathic brachial neuritis Anesthesiology 84 2 1996 455 458 8602681 10 Flikkema K. Brossy K. Parsonage-Turner Syndrome After COVID-19 Vaccination: A Case Report JBJS Case Connect 11 4 2021 10.2106/JBJS.CC.21.00577 10.2106/JBJS.CC.21.00577 11 Mahajan S. Zhang F. Mahajan A. Zimnowodzki S. Parsonage Turner syndrome after COVID-19 vaccination Muscle & nerve 64 1 2021 E3 E4 10.1002/mus.27255 33890680 12 McCarty E.C. Tsairis P. Warren R.F. Brachial neuritis Clin Orthop Relat Res 368 1999 Nov 37 43 13 Miller J.D. Pruitt S. McDonald T.J. Acute brachial plexus neuritis: an uncommon cause of shoulder pain Am Fam Physician 62 9 2000 Nov 1 2067 2072 11087188 14 Mitry M.A. Collins L.K. Kazam J.J. Kaicker S. Kovanlikaya A. Parsonage-turner syndrome associated with SARS-CoV2 (COVID-19) infection Clin Imaging 72 2021 8 10 10.1016/j.clinimag.2020.11.017 33190028 15 Smith C.C. Bevelaqua A.C. Challenging pain syndromes: Parsonage-Turner syndrome Phys Med Rehabil Clin N Am 25 2 2014 265 277 10.1016/j.pmr.2014.01.001 24787332 16 Tjoumakaris F.P. Anakwenze O.A. Kancherla V. Pulos N. Neuralgic amyotrophy (Parsonage-Turner syndrome) J Am Acad Orthop Surg 20 7 2012 443 449 10.5435/JAAOS-20-07-443 22751163 17 Viatgé T. Noel-Savina E. Prévot G. Faviez G. Plat G. De Boissezon X. [Parsonage-Turner syndrome following severe SARS-CoV-2infection] RevMalRespir 38 8 2021 853 858 10.1016/j.rmr.2021.06.004 18 Voss T.G. Stewart C.M. Case report: Parsonage-Turner syndrome after COVID-19 infection [published online ahead of print, 2022 Jan 13] JSES Rev Rep Tech 2022 10.1016/j.xrrt.2021.12.004	36506342	PMC9721159	NO-CC CODE	2022-12-06 23:26:40	no	JSES Rev Rep Tech. 2022 Dec 5; doi: 10.1016/j.xrrt.2022.11.001	utf-8	JSES Rev Rep Tech	2,022	10.1016/j.xrrt.2022.11.001	oa_other
==== Front iScience iScience iScience 2589-0042 Elsevier S2589-0042(22)01999-X 10.1016/j.isci.2022.105726 105726 Article Maturation of SARS-CoV-2 Spike-specific memory B cells drives resilience to viral escape Marzi Roberta 118 Bassi Jessica 118 Fregni Chiara Silacci 118 Bartha Istvan 1 Muoio Francesco 1 Culap Katja 1 Sprugasci Nicole 1 Lombardo Gloria 1 Saliba Christian 1 Cameroni Elisabetta 1 Cassotta Antonino 2 Low Jun Siong 2 Walls Alexandra C. 3 McCallum Matthew 3 Tortorici M. Alejandra 3 Bowen John E. 3 Dellota Exequiel A. Jr. 4 Dillen Josh R. 4 Czudnochowski Nadine 4 Pertusini Laura 5 Terrot Tatiana 6 Lepori Valentino 7 Tarkowski Maciej 8 Riva Agostino 8 Biggiogero Maira 9 Pellanda Alessandra Franzetti 9 Garzoni Christian 9 Ferrari Paolo 51011 Ceschi Alessandro 6101213 Giannini Olivier 1014 Havenar-Daughton Colin 4 Telenti Amalio 4 Arvin Ann 4 Virgin Herbert W. 41516 Sallusto Federica 217 Veesler David 3 Lanzavecchia Antonio 1 Corti Davide 1 Piccoli Luca 119∗ 1 Humabs BioMed SA, a subsidiary of Vir Biotechnology, Bellinzona, Switzerland 2 Institute for Research in Biomedicine, Università della Svizzera italiana, Bellinzona, Switzerland 3 Department of Biochemistry, University of Washington, Seattle, WA, United States of America 4 Vir Biotechnology, San Francisco, CA, United States of America 5 Division of Nephrology, Ente Ospedaliero Cantonale, Lugano, Switzerland 6 Clinical Trial Unit, Ente Ospedaliero Cantonale, Lugano, Switzerland 7 Independent physician, Bellinzona, Switzerland 8 III Division of Infectious Diseases, ASST Fatebenefratelli Sacco, Luigi Sacco Hospital, Milan, Italy 9 Clinic of Internal Medicine and Infectious Diseases, Clinica Luganese Moncucco, Lugano, Switzerland 10 Faculty of Biomedical Sciences, Università della Svizzera italiana, Lugano, Switzerland 11 Clinical School, University of New South Wales, Sydney, Australia 12 Division of Clinical Pharmacology and Toxicology, Institute of Pharmacological Science of Southern Switzerland, Ente Ospedaliero Cantonale, Lugano, Switzerland 13 Department of Clinical Pharmacology and Toxicology, University Hospital Zurich, Zurich, Switzerland 14 Department of Medicine, Ente Ospedaliero Cantonale, Bellinzona, Switzerland 15 Department of Pathology and Immunology, Washington University School of Medicine, St Louis, MO, United States of America 16 Department of Internal Medicine, UT Southwestern Medical Center, Dallas, TX, United States of America 17 Institute of Microbiology, ETH Zurich, Zurich, Switzerland ∗ Corresponding author (L.P.) 18 These authors contributed equally 19 Lead contact 5 12 2022 5 12 2022 10572627 9 2022 21 10 2022 1 12 2022 © 2022. 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. Memory B cells (MBCs) generate rapid antibody responses upon secondary encounter with a pathogen. Here, we investigated the kinetics, avidity and cross-reactivity of serum antibodies and MBCs in 155 SARS-CoV-2 infected and vaccinated individuals over a 16-month timeframe. SARS-CoV-2-specific MBCs and serum antibodies reached steady-state titers with comparable kinetics in infected and vaccinated individuals. Whereas MBCs of infected individuals targeted both pre- and postfusion Spike (S), most vaccine-elicited MBCs were specific for prefusion S, consistent with the use of prefusion-stabilized S in mRNA vaccines. Furthermore, a large fraction of MBCs recognizing postfusion S cross-reacted with human betacoronaviruses. The avidity of MBC-derived and serum antibodies increased over time resulting in enhanced resilience to viral escape by SARS-CoV-2 variants, including Omicron BA.1 and BA.2 sub-lineages, albeit only partially for BA.4 and BA.5 sublineages. Overall, the maturation of high-affinity and broadly-reactive MBCs provides the basis for effective recall responses to future SARS-CoV-2 variants. Graphical abstract Published: ▪▪ ▪▪, ▪▪ ==== Body pmc	36507220	PMC9721160	NO-CC CODE	2022-12-06 23:26:27	no	iScience. 2022 Dec 5;:105726	utf-8	iScience	2,022	10.1016/j.isci.2022.105726	oa_other
==== Front J Theor Biol J Theor Biol Journal of Theoretical Biology 0022-5193 1095-8541 Elsevier Ltd. S0022-5193(22)00367-8 10.1016/j.jtbi.2022.111376 111376 Article Structural patterns of SARS-CoV-2 variants of concern (alpha, beta, gamma, delta) spike protein are influenced by variant-specific amino acid mutations: A computational study with implications on viral evolution Cueno Marni E. abc⁎ Wada Kanta b Tsuji Arisa c Ishikawa Kouta c Imai Kenichi a a Department of Microbiology, Nihon University School of Dentistry, Tokyo 101-8310 Japan b Immersion Physics, Department of Science, Tokyo Gakugei University International Secondary School, Tokyo 178-0063 Japan c Immersion Biology Class, Department of Science, Tokyo Gakugei University International Secondary School, Tokyo 178-0063 Japan ⁎ Corresponding author. 5 12 2022 5 12 2022 1113763 6 2022 28 11 2022 29 11 2022 © 2022 Elsevier Ltd. All rights reserved. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. SARS-CoV-2 (SARS2) regularly mutates resulting to variants of concern (VOC) which have higher virulence and transmissibility rates while concurrently evading available therapeutic strategies. This highlights the importance of amino acid mutations occurring in the SARS2 spike protein structure since it may affect virus biology. However, this was never fully elucidated. Here, network analysis was performed based on the COVID-19 genomic epidemiology network between December, 2019-July, 2021. Representative SARS2 VOC spike protein models were generated and quality checked, protein model superimposition was done, and common contact based on contact mapping was established. Throughout this study, we found that: (1) certain individual variant-specific amino acid mutations can affect the spike protein structural pattern; (2) certain individual variant-specific amino acid mutations had no affect on the spike protein structural pattern; and (3) certain combination of variant-specific amino acids are putatively epistatic mutations that can potentially influence the VOC spike protein structural pattern. This manuscript was submitted as part of a theme issue on “Modelling COVID-19 and Preparedness for Future Pandemics”. Keywords SARS-CoV-2 (SARS2) spike protein structural pattern variants of concern variant-specific amino acid ==== Body pmc1 Introduction Coronaviruses (CoV) belong to family Coronaviridae, order Nidovirales, and subfamily Othocoronavirinae (King et al., 2018) and, among the seven known human-infecting CoVs, only SARS-CoV-2 (SARS2) caused a pandemic (Tay et al., 2020). Moreover, since the detection of the original SARS2 strain, various SARS2 variants were likewise found and, in particular, variants of concern (VOC) were considered alarming since these particular SARS2 variants have higher virulence and transmissibility rates while simultaneously having the capacity to evade available therapeutic strategies (Harvey et al., 2021, Koyama et al., 2020, Sanyaolu et al., 2021). It was hypothesized by earlier works belonging to other groups that VOC arose attributable to increased selective pressure and changing host environment capable of affecting particular viral proteins, thus, VOC are composed of convergent mutations proposed to be ascribable to either individuals previously infected by SARS2 or chronic SARS2 infections (Avanzato et al., 2020, Choi et al., 2020, Kemp et al., 2021). It was reported that the SARS2 genome has a >7.23 mutations per sample (Day et al., 2020, Mercatelli and Giorgi, 2020) and approximately two mutations per month is acquired by the virus in the spike protein (Duchene et al., 2020). Considering the SARS2 spike protein binding is responsible for fusion of the virus and cell membranes and host cell-surface receptor attachment (Letko et al., 2020), this makes it the primary target of neutralizing antibodies (Piccoli et al., 2020). However, since VOC have structural alterations within the spike protein attributable to amino acid mutations occurring in the receptor-binding domain (RBD) and N-terminal domain (NTD) (Greaney et al., 2021, McCarthy et al., 2021), this could result in an immune escape and, likewise, possibly contribute to the occurrence of reinfections or breakthrough infections (Geers et al., 2021, Graham et al., 2021, Kemp et al., 2021). This would emphasize the importance of these amino acid mutations in affecting the SARS2 spike protein structure which in-turn may affect virus biology. However, this was never fully elucidated. A better understanding of how certain individual and combination of amino acid residues could affect SARS2 VOC spike protein structural patterns may shed light on how SARS2 spike protein evolves while maintaining host tropism and, similarly, could lead to novel therapeutic strategies. 2 Materials and Methods 2.1 Network analyses of the chronological order of mutating residues occurring among SARS2 VOC spike proteins between December, 2019-July, 2021 For this study, we used Cytoscape for both network design and analyses (Shannon et al., 2003). Network design following the coronavirus disease 2019 (COVID-19) genomic epidemiology based on the Nextstrain website (nextstrain.org) between December, 2019- July, 2021 was analyzed with a particular focus on the chronological occurrence of mutating residues among the SARS2 VOC (alpha, beta, gamma, delta) spike proteins. Subsequently, network analysis was performed to have a holistic insight (Gilman and Arkin, 2002) on the chronological order of mutating residues occurring among the SARS2 VOC spike proteins. In the designed network, nodes (black circles) represent either a mutating residue or branch point while the edges represent the chronological occurrence of the mutating residues. Similarly, network analyses was based on the following centrality measurements: (1) edge betweenness centrality which would identify significant chronological order transitions in amino acid mutations found within the spike protein; (2) betweenness centrality which would identify amino acid mutations within the spike protein that are vital for the spike protein; (3) closeness centrality which would identify amino acid mutations within the spike protein that may have affected virus biology; (4) eccentricity centrality which would identify amino acid mutations that can easily influence the spike protein structure; and (5) stress centrality which would identify amino acid mutations within the spike protein that are significant and can alter the spike protein structure (Koschutzki and Schreiber, 2008). Briefly, in order to establish whether a node or edge is significant, computed centrality measurements should be above the threshold for each centrality being considered. Moreover, nodes that were linked to either edges or nodes above the threshold based on all 5 centrality measurements used were determined and utilized for further downstream analyses. 2.2 Spike protein model quality assessment Both model:crystal superimposition and contact mapping were performed to confirm correctness of the generated SARS2 spike protein models. CMView applet (Vehlow et al., 2011) was used to establish the protein contact map and establish the contact map overlap (CMO) analyses. Generally, higher common contact (> 90%) would mean the generated SARS2 VOC spike protein models have more structural similarities (Holm and Sander, 1996). This would imply that the generated protein model is suitable for further downstream analyses. Moreover, both a representative SARS2 spike crystal structure (PDB ID: 6ZGE) and, using Phyre2, a generated monomeric 6ZGE model (crystal model) were used for for the model quality assessment. In this study and using TM-align (Zhang and Skolnick, 2005), SARS2 VOC spike models with Template Modeling scores (TM) > 0.90 were acceptable for further analyses. 2.3 SARS2 VOC spike protein modeling and structural analyses A minimum of ten SARS2 spike amino acid sequences (n= 10) per VOC were collected between December, 2019- July, 2021 and all sequences were obtained from the National Center for Biological Information (NCBI) website. All sequence models based on each SARS2 VOC spike amino acid sequence was generated using the Phyre2 web server (Kelley and Sternberg, 2009). Subsequently, SARS2 VOC spike models that have similar Template Modeling scores (TM) were established using TM-align (Zhang and Skolnick, 2005). Representative monomeric SARS2 VOC spike models (Alpha: QVI56682; Beta: QSS48553; Gamma: QUV65120; Delta: QVL68252) were utilized for further downstream structural analyses. Protein visualization was performed using the Jmol applet (Herraez, 2006). Additionally, CMView applet (Contact type: Cα; Distance cut-off: 8.0; Needleman-Wunsch alignment) was used for CMO analysis between superimposed structures. Briefly, superimposed structures with high common contact (> 90%) would imply high structural similarities (Holm and Sander, 1996). 2.4 Generating SARS2 VOC mutants for structural analyses Among the representative monomeric SARS2 VOC spike models used for this study, mutant SARS2 VOC were generated by either (1) individually reverting to the original amino acid residue identified in the original SARS2 strain; or (2) combining certain reverted amino acid residues identified in the original SARS2 strain. Moreover, SARS2 VOC spike proteins with deleted amino acid residues (Alpha and Beta variants) were likewise reverted to the original SARS2 strain by adding the missing amino acid residues. Both sets of mutants were based on a database (https://covdb.stanford.edu/page/mutation-viewer/) (Tzou et al., 2020). Phyre2 was used to generate mutated SARS2 VOC spike models. 3 Results 3.1 Certain amino acid residues that appeared in a chronological order are putatively significant among SARS2 VOC spike proteins based on network analyses SARS2 genome constantly undergoes mutation with high-effect mutations contributing to viral adaptation and fitness, whereas, low-effect mutations tend to have a neutral amino acid change (Frost et al., 2018). To identify possible significant chronologically mutating amino acid residues occurring among SARS2 VOC spike proteins, network design and analyses were performed using the COVID-19 genomic epidemiology network established between December, 2019- July, 2021 with a particular focus on the spike protein. Network analytics (help provide a holistic analyses of the overall network) were based on both nodal (Suppl. Fig. 1 ) and edge (Suppl. Fig. 2 ) analyses. We found that, among the SARS2 genomic clades identified, clades 20A, 20B, and 20C are considered significant based on network analyses (Fig. 1A, boxed panel). Moreover, further network analyses of amino acid residues that appeared in a chronological order within the spike protein that are only found in clades 20A, 20B, and 20C (Fig. 1A, unboxed panel) showed amino acid residues that are possibly significant to the spike protein (Fig. 1B). Mutations occurring in the SARS2 genome tend to be neutral or mildly deleterious, thus, only a small number of mutations may impact virus phenotype that may consequently affect virulence and transmissibility (Frost et al., 2018, MacLean et al., 2020). In this regard, this would suggest that among the amino acid residues identified to be significant for the varying VOC spike proteins (Fig. 1B), only certain amino acid residues (or possibly a combination of residues) may putatively affect the virus phenotype. Considering that SARS2 VOC dominate clades 20A, 20B, and 20C (Sanyaolu et al., 2021), we focused our protein structure analyses among the SARS2 VOC spike proteins.Figure 1 Network analyses of chronologically mutating amino acid residues among SARS-CoV-2 variant of concern spike proteins. (A) COVID-19 genomic epidemiology network based on the December, 2019-July,2021 phylogeny. (Upper panel) Simplified network with the genomic clades and significant edges (lines) are highlighted in red. (Lower panel) Actual network with the significant nodes (red) and edges (red) as determined by centrality analyses are shown. Nodes (dots) and edges (lines) are indicated. Chronologically mutating amino acid residues based on significant clades are encircled and labeled. (B) List of significant nodes representing mutating amino acid residues found in each significant clade are presented. Figure 2 Certain individual variant-specific amino acid residue mutation potentially affect SARS-CoV-2 variant of concern spike protein structural patterns. Superimposition of mutated variants (individual variant-specific amino acid mutations reverted to the original amino acid residue) and original variant. (A) Alpha, (B) Beta, (C) Gamma, and (D) Delta variants are indicated. Variant-specific amino acid residue mutations are listed. TM scores (normalized to the original variant) of the superimposed protein structures are shown. 3.2 Generated SARS2 VOC spike models are suitable for downstream structural analyses To determine whether the generated SARS2 VOC models are suitable for further downstream structural analyses, both protein structural superimpositions and contact mapping were done (Suppl. Fig. 3 ). TM score measurements mainly focuses on structural similarities between proteins without regard to protein size (Kufareva and Abagyan, 2012, Zhang and Skolnick, 2005), whereas, CMO analyses (through common contact measurements) give relevant information with regard to the pairwise spatial and functional relationship of residues within a protein (Bittrich et al., 2019, Wang and Xu, 2013).Figure 3 Certain combination of variant-specific amino acid residue mutations possibly influence SARS-CoV-2 variant of concern spike protein structural patterns. Alpha mutant with the combination of Alpha variant-specific amino acid residue mutations are listed. Alpha mutant superimposition (left panel) and contact mapping (right panel) with (A) Alpha original, (B) Beta original, (C) Gamma original, and (D) Delta original are shown. Beta mutant with the combination of Beta variant-specific amino acid residue mutations are listed. Beta mutant superimposition (left panel) and contact mapping (right panel) with (E) Alpha original, (F) Beta original, (G) Gamma original, and (H) Delta original are shown. Gamma mutant with the combination of Gamma variant-specific amino acid residue mutations are listed. Gamma mutant superimposition (left panel) and contact mapping (right panel) with (I) Alpha original, (J) Beta original, (K) Gamma original, and (L) Delta original are shown. Delta mutant with the Delta variant-specific amino acid residue mutation is listed. Delta mutant superimposition (left panel) and contact mapping (right panel) with (M) Alpha original, (N) Beta original, (O) Gamma original, and (P) Delta original are shown. TM scores (normalized to the original variant) of the superimposed protein structures are shown. Common contact of the protein structures being compared are labeled below each contact map. Spike models for mutated variants (red), Alpha original (cyan), Beta original (gold), Gamma original (green), and Delta original (pink) are shown. 3.3 Certain individual variant-specific amino acid residue mutations putatively affect spike protein structural patterns among SARS2 VOC spike models Spike protein mutations vary among the SARS2 VOC based on an online database (Tzou et al., 2020). To establish which amino acid residues and combination of residues affect the spike protein structural pattern among the SARS2 VOC, we reverted all variant-specific amino acid mutations in each VOC spike protein and, subsequently, superimposed the mutated SARS2 VOC spike protein with the original SARS2 VOC spike model. We opted to revert back to the original amino acid residues from the amino acid residue mutations found among the four representative monomeric SARS2 VOC spike models (Alpha, Beta, Gamma, Delta) in order to maintain the variant-unique spike protein structural backbone. Interestingly, we found that not all variant-specific amino acid residue changes affect the spike protein structural patterns of the Alpha (Fig. 2A), Beta (Fig. 2B), Gamma (Fig. 2C), and Delta (Fig. 2D) variants. This is consistent with low-effect mutations occurring within the SARS2 VOC spike protein (Frost et al., 2018), whereby, individually reverted amino acid residues showing TM=1.0 when superimposed with the original SARS2 VOC spike model can putatively imply that the spike protein structural pattern was unaffected. Similarly, reverted variant-specific amino acid residues showing TM<1.0 when superimposed with the original SARS2 VOC spike model can possibly mean that these residues cause high-effect mutations that may affect virus biology (Frost et al., 2018). Additionally, it is worth mentioning that some of the variant-specific amino acid residues (TM<1.0) found in each VOC spike model (Fig. 2A-D) that is within the RBD and close to the S1/S2 furin-like cleavage site are consistent with the mutations identified in network analyses (Fig. 1B). This possibly highlights the significance of each variant-specific individual amino acid residue mutation found in each VOC spike model that could affect the spike protein structural pattern. Furthermore, although it is clear that some of the individual amino acid residues can affect (TM<1.0) and not affect (TM=1.0) each VOC spike protein structural pattern, it is not clear how a combination of these variant-specific individual amino acid residues would affect each VOC spike protein structural pattern. 3.4 Certain combination of variant-specific amino acid residue mutations potentially influence the spike protein structural patterns among SARS2 VOC spike models Amino acid residues involved in protein-protein interaction that are considered structurally and functionally important are conserved during biological evolution and, equally important, these conserved amino acid residues could occur as a cluster (combination of amino acid residues) in order to maintain a degree of structural co-operativity among spatially distributed neighboring residues (Guharoy and Chakrabarti, 2010). To determine whether a certain combination of variant-specific individual amino acid residues could potentially influence the four VOC spike protein models, we generated VOC mutants that combined variant-specific individual amino acid residues (TM=1.0) that were observed to not affect the VOC spike protein structural patterns and, subsequently, compared these generated VOC mutants to each VOC original spike protein. Considering the Alpha mutant spike protein generated, we found that certain combination of Alpha variant-specific individual amino acid residues with TM=1.0 (N501+D614+T716+S982) did not significantly alter the spike protein structural pattern of Alpha mutant:Alpha original (Fig. 3A, left panel) and Alpha mutant:Beta original (Fig. 3B, left panel) spike protein comparisons, however, both Alpha mutant:Gamma original (Fig. 3C, left panel) and Alpha mutant:Delta original (Fig. 3D, left panel) spike protein comparisons had few structural pattern differences (TM=0.99860). Additionally, common contact slightly varied with a 99.8% common contact observed in the Alpha mutant:Alpha original comparison (Fig. 3A, right panel) and a 100% common contact observed in the Alpha mutant:Beta original (Fig. 3B, right panel) comparison, whereas, Alpha mutant:Gamma original (Fig. 3C, right panel) and Alpha mutant:Delta original (Fig. 3D, right panel) comparisons showed the same common contact (99.5%). Considering the Beta mutant spike protein generated, we similarly found that certain combination of Beta variant-specific individual amino acid residues with TM=1.0 (D80+E484+N501+D614+T701) had no effect on the spike protein structural patterns (TM=1.0) among Beta mutant:Alpha original (Fig. 3E, left panel) and Beta mutant:Beta original (Fig. 3F, left panel) spike protein comparisons while both Beta mutant:Gamma original (Fig. 3G, left panel) and Beta mutant:Delta original (Fig. 3H, left panel) spike protein comparisons had few structural pattern differences (TM=0.99860). Moreover, common contact likewise slightly varied with a 99.8% and 100% common contact observed in the Beta mutant:Alpha original comparison (Fig. 3E, right panel) and Beta mutant:Beta original (Fig. 3F, right panel) comparisons while Beta mutant:Gamma original (Fig. 3G, right panel) and Beta mutant:Delta original (Fig. 3H, right panel) comparisons showing the same 99.5% common contact. Considering the Gamma mutant spike protein generated, we established that certain Gamma variant-specific individual amino acid residue combinations with TM=1.0 (T20/S268+Y138+Q484) had the same difference (TM=0.99860) in the spike protein structural pattern among Gamma mutant:Alpha original (Fig. 3I, left panel) and Gamma mutant:Beta original (Fig. 3J, left panel) spike protein comparisons. In contrast, no structural pattern difference in spike protein structural pattern (TM=1.0) was found in both Gamma mutant:Gamma original (Fig. 3K, left panel) and Gamma mutant:Delta original (Fig. 3L, left panel) spike protein comparisons. Similarly, 99.5% common contact was detected in both Gamma mutant:Alpha original comparison (Fig. 3I, right panel) and Gamma mutant:Beta original (Fig. 3J, right panel) comparisons, whereas, a 100% common contact was observed in both Gamma mutant:Gamma original (Fig. 3K, right panel) and Gamma mutant:Delta original (Fig. 3L, right panel) comparisons. Considering the Delta mutant spike protein generated, we similarly established that a single Delta variant-specific individual amino acid residue mutation (P681) had the same difference (TM=0.99854) in the spike protein structural pattern among Delta mutant:Alpha original (Fig. 3M, left panel) and Delta mutant:Beta original (Fig. 3N, left panel) spike protein comparisons while similar spike protein structural pattern (TM=0.99994) was found in both Delta mutant:Gamma original (Fig. 3O, left panel) and Delta mutant:Delta original (Fig. 3P, left panel) spike protein comparisons. Furthermore, 99.5% common contact was also detected in both Delta mutant:Alpha original comparison (Fig. 3M, right panel) and Delta mutant:Beta original (Fig. 3N, right panel) comparisons while a 100% common contact was observed in both Delta mutant:Gamma original (Fig. 3O, right panel) and Delta mutant:Delta original (Fig. 3P, right panel) comparisons. Taken together, these results would insinuate that: (1) certain combination of variant-specific amino acid residues has no effect (TM=0.99994-1.0) on some VOC original spike protein structural patterns (Alpha and Beta spike protein pair; Gamma and Delta spike protein pair); and (2) certain combination of variant-specific amino acid residues does not alter the functional relationship of amino acid residues on both Alpha and Beta spike protein pair and both Gamma and Delta spike protein pair). Considering most of the variant-specific amino acid residues identified are particularly found within the spike RBD and close to the furin-like cleavage site, we hypothesize that this is ascribable to SARS2 maintaining viral pathogenesis and host tropism (Andersen et al., 2020, Coutard et al., 2020, Nao et al., 2017, Rochman et al., 2022, Sarkar and Guha, 2020). SARS2 spike protein has significantly evolved since it was detected in December, 2019 and changes in the spike protein have been ascribable to amino acid mutations, proteolytic cleavage, or linoleic acid binding (Berger and Schaffitzel, 2020). In terms of spike protein mutations, multiple amino acid mutations do occur within the spike protein (Duchene et al., 2020) and, more importantly, only few mutations are necessary to significantly impact spike protein structure (Henderson et al., 2020). In this regard, our results putatively establishes the following: (1) certain variant-specific individual amino acid residue mutations (TM=1.0) putatively does not affect the spike protein structural pattern; (2) certain variant-specific individual amino acid residue mutations (TM<1.0) can potentially affect the spike protein structural pattern; and (3) combinations of certain variant-specific individual amino acid residues (TM=1.0) possibly does not affect the spike protein structural pattern. 4 Discussion SARS2 genome regularly undergoes mutations which can either be highly deleterious and be rapidly purged or mildly deleterious and have a neutral effect (Harvey et al., 2021). Additionally, these observed SARS2 mutational changes have been correlated with: host-dependent RNA editing associated with the APOBEC mechanism (Di Giorgio et al., 2020), generally large genome of all coronaviruses (Woo et al., 2010), and high recombination frequency of the SARS2 genome (Banerjee et al., 2020). Throughout this study, we attempted to show whether certain individual or combination of variant-specific amino acid mutations within each VOC (alpha, beta, gamma, delta) spike protein can influence the structural pattern. It is important to study the function of mutations occurring within the viral genome since the effect of individual amino acid mutations on a viral protein may not truly reflect the impact of these mutations on viral features especially if these mutations appear in combination with other mutations (Castonguay et al., 2021). Epistasis in proteins refer to the combined effect of multiple (two or more) protein mutations, wherein, the resulting protein structure, function, and evolution deviates from the predicted individual effects of single protein mutations (Starr and Thornton, 2016). SARS2 spike protein has undergone several structural changes driven by selective pressures related to: protein-protein interaction with the host, viral fitness associated with pathogenesis, immune evasion, and competition among other related variants (Cai et al., 2021, Lopez-Cortes et al., 2021). In this regard, we hypothesize that certain combination of variant-specific amino acid mutations within each VOC spike protein are putatively epistatic mutations possibly involved in altering virus biology. This is consistent with each of the VOC having varying transmissibility, pathogenicity, and neutralizing antibody resistance (Castonguay et al., 2021, Rochman et al., 2022). Interestingly, the original Wuhan SARS2 spike protein was reported to have no epistatic mutations occurring (Zeng et al., 2020), whereas, the VOC spike proteins were found to have multiple epistatic mutations (Castonguay et al., 2021). Epistasis has played an essential role in driving protein evolution (Breen et al., 2012). In this regard, we postulate that the putative variant-specific epistatic mutations (possibly ascribable to selective pressure) occurring within the SARS2 spike protein drives viral evolution. In summary, we propose the following: (1) certain individual variant-specific amino acid mutations can affect the VOC spike protein structural pattern; (2) certain individual variant-specific amino acid mutations had no affect on the VOC spike protein structural pattern; and (3) certain combination of variant-specific amino acids are putative epistatic mutations that can potentially influence the VOC spike protein structural pattern. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements This work was supported by JSPS KAKENHI Grant Numbers 19K10078, 19K10097, and 22K09932; Uemura Fund, Dental Research Center, Nihon University School of Dentistry. ==== Refs References Andersen K.G. Rambaut A. Lipkin W.I. Holmes E.C. Garry R.F. The proximal origin of SARS-CoV-2 Nat Med 26 2020 450 452 32284615 Avanzato V.A. Matson M.J. Seifert S.N. Pryce R. Williamson B.N. Anzick S.L. Barbian K. Judson S.D. Fischer E.R. Martens C. Bowden T.A. de Wit E. Riedo F.X. Munster V.J. Case Study: Prolonged Infectious SARS-CoV-2 Shedding from an Asymptomatic Immunocompromised Individual with Cancer Cell 183 1901–1912 2020 e9 Banerjee A. Doxey A.C. Tremblay B.J. Mansfield M.J. Subudhi S. Hirota J.A. Miller M.S. McArthur A.G. Mubareka S. Mossman K. 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==== Front J Hosp Infect J Hosp Infect The Journal of Hospital Infection 0195-6701 1532-2939 The Authors. Published by Elsevier Ltd on behalf of The Healthcare Infection Society. S0195-6701(22)00372-3 10.1016/j.jhin.2022.11.018 Article Viral Cultures, Cycle Threshold Values and Viral Load Estimation for Assessing SARS-CoV-2 Infectiousness in Haematopoietic Stem Cell and Solid Organ Transplant Patients: A Systematic Review Jefferson Tom 1 Spencer Elizabeth A. 2 Conly John M. 7 Rosca Elena C. 3 Maltoni Susanna 4 Brassey Jon 5 Onakpoya Igho J. 1 Evans David H. 6 Heneghan Carl J. 2 Plüddemann Annette 2∗ 1 Department for Continuing Education, University of Oxford, UK 2 Centre for Evidence-Based Medicine, Nuffield Department of Primary Care Health Sciences, University of Oxford, UK 3 Victor Babes University of Medicine and Pharmacy, Timisoara, Romania 4 Division of Research and Innovation, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy 5 Trip Database Ltd, Newport, UK 6 Li Ka Shing Institute of Virology and Dept. of Medical Microbiology & Immunology, University of Alberta, Canada 7 Departments of Medicine, Microbiology, Immunology & Infectious Diseases, and Pathology & Laboratory Medicine, Synder Institute for Chronic Diseases and O’Brien Institute for Public Health, Cumming School of Medicine, University of Calgary and Alberta Health Services, Calgary, Canada ∗ Corresponding author. 5 12 2022 5 12 2022 7 9 2022 24 11 2022 24 11 2022 © 2022 The Authors. Published by Elsevier Ltd on behalf of The Healthcare Infection Society. 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 Solid organ and haematopoietic stem cell transplant recipients are at increased vulnerability to SARS-CoV-2 due to immunosuppression and may pose a continued transmission risk especially within hospital settings. Detailed case reports including symptoms, viral load and infectiousness, defined by the presence of replication-competent viruses in culture, provide an opportunity to examine the relationship between clinical course, burden and contagiousness, and provide guidance on release from isolation. Objectives We performed a systematic review to investigate the relationship in transplant recipients between serial SARS-CoV-2 RT-PCR cycle threshold (Ct) value or cycle of quantification value (Cq), or other measures of viral burden and the likelihood and duration of the presence of infectious virus based on viral culture including the influence of age, sex, underlying pathologies, degree of immunosuppression, and/or vaccination on this relationship. Methods We searched LitCovid, medRxiv, Google Scholar and WHO Covid-19 databases, from 1 November 2019 until 26 October 2022. We included studies reporting relevant data for transplantees with SARS-CoV-2 infection: results from serial RT-PCR testing and viral culture data from the same respiratory samples. We assessed methodological quality using five criteria, and synthesised the data narratively and graphically. Results We included 13 case reports and case series reporting on 41 transplantees including 22 renal, 5 cardiac, 1 bone marrow, 2 liver, 1 bilateral lung, and 10 blood stem cell transplants. We observed a relationship between proxies of viral burden and likelihood of shedding replication-competent SARS-CoV-2. Three individuals shed replication-competent viruses for over 100 days after infection onset. Lack of standardisation of testing and reporting platforms precludes establishing a definitive viral burden cut-off. However, the majority of transplantees stopped shedding replication-competent viruses when the RT-PCR cycle threshold was above 30 despite differences across platforms. Conclusions Viral burden is a reasonable proxy for infectivity when considered within the context of the clinical status of each patient. Standardised study design and reporting are essential to standardise guidance based on an increasing evidence base. Keywords COVID-19 SARS-CoV-2 transmission organ transplants viral culture polymerase chain reaction viral load cycle threshold calibration infectivity ==== Body pmcIntroduction Haematopoietic stem cell transplant (HSCT) and solid organ transplant (SOT) recipients have significant immunosuppression, affecting both cellular and humoral immunity, and less favourable outcomes with Severe Acute Respiratory Virus Syndrome 2 (SARS-CoV-2) infection, due to the immunosuppression and/or to pre-existing comorbidities. [1] Immunosuppression associated with transplantation places patients at risk for prolonged carriage and shedding of several respiratory viruses. [2] However, identification of respiratory viral shedding, by reverse transcriptase polymerase chain reaction (RT-PCR), depending on the testing platform does not necessarily correlate with the presence of replication-competent virus. [3] Accordingly, we sought to perform a systematic review of RT-PCR testing and viral culture of SARS-CoV-2, focussing on people receiving solid organ or haematopoietic stem cell transplants, following our published protocol. [4]. Our research questions were:1. What is the correlation between serial SARS-CoV-2 RT-PCR cycle threshold (Ct) value or cycle of quantification value (Cq), or other measures of viral burden and the likelihood of producing replication-competent virus among transplantees? 2. What is the likelihood and duration of the presence of infectious virus based on viral culture, among transplant recipients with SARS-CoV-2 infection? 3. What is the influence of age, sex, underlying pathologies and degree of immunosuppression on infectiousness of SARS-CoV-2? 4. What is the relationship of vaccination status on infectiousness with SARS-CoV-2? We included studies reporting serial Cts from sequential RT-PCR testing or other measures of viral burden such as RNA gene copies of respiratory samples (from nasopharyngeal or throat specimens) along with viral culture data on the same samples, from patients about to receive a transplant or who were post-transplant, with SARS-CoV-2 infection. Methods Search Strategy We searched the following electronic databases: LitCovid, medRxiv, Google Scholar and the WHO COVID-19 database from November 2019 until 26th October 2022. No language restrictions were applied. The literature search terms were: (coronavirus OR covid-19 OR SARS-CoV-2) AND (immunosuppressed OR immunocompromised OR transplant OR immunosuppression OR "immune deficient" OR HIV) AND (CPE OR “cytopathic effect” OR “Viral culture” OR “virus culture” OR vero OR "virus replication" OR "viral replication" OR "cell culture" or "viral load" OR "viral threshold" OR "log copies" OR "cycle threshold"). Screening Four reviewers independently screened titles and abstracts to identify studies for consideration of full text (JB, SM, ER, ES), performing full text screening in duplicate and resolving disagreements with a fifth reviewer (TJ). Inclusion criteria We included studies reporting serial Cts from sequential RT-PCR testing, or RNA gene copies of respiratory samples (nasopharyngeal, throat, sputum, bronchoalveolar lavage, endotracheal tube secretions) AND viral culture data from the same samples from patients about to receive a transplant or who were post-transplant with SARS-CoV-2 infection. We included primary studies provided they reported sufficient information to extract quantitative data on the PCR testing and the viral culture for each included individual. Studies that included transplant and non-transplant patients were included if we could ascertain the results separately. Studies reported only in poster or abstract form were excluded. Reviews were excluded but the reference lists screened for potential relevant primary studies, and the bibliographies of included primary studies were hand-searched for possible studies to include. Exclusion criteria We excluded studies using post-mortem samples only and non-respiratory samples only. We did not include studies of non-transplant patients or those not attempting viral cultures. Data extraction One reviewer (ER) extracted data, which was independently checked by a second reviewer (ES). Disagreements were arbitrated by a third reviewer (TJ). Data were extracted on study type and study characteristics, including population, setting, sampling and laboratory methods, clinical information, prescribed treatments, vaccination status, laboratory findings, and clinical outcomes. Where data were only available in figures or charts, these were estimated by two reviewers (ES and SM) and cross-checked by another reviewer (CH). For three studies we sought clarification from the corresponding authors. Quality assessment We based our assessment of bias on the updated QUADAS-2 criteria for assessing diagnostic accuracy studies. [5] Based on patient identification and reporting, timing, and index and reference tests, we developed the following five criteria:1. Were the criteria for diagnosing a case clearly reported and appropriate? 2. Was the reporting of patient/population characteristics including clinical symptoms, treatments with degree of immunosuppression and outcomes adequate? 3. Was the study period, including follow-up, sufficient to adequately assess any potential relationship between viral burden measures and likelihood of producing replication-competent virus and the rise in neutralising antibodies? We defined sufficient as more than one observation. 4. Were the methods used to obtain RT-PCR results replicable, generalisable and appropriate? We considered that each study should establish the relationship between their Ct values and the target gene copy number, using internal standards. 5. Were the methods used to obtain viral culture results replicable and appropriate? We considered the methods used should, at a minimum, include a description of specimen sampling and management, preparation, media and cell line used, exclusion of contamination or co-infection (use of controls and appropriate antibacterials and antimycotics in the cell culture and use of gene sequencing if available), and results of inspection of culture. One reviewer (ES) assessed the quality of reporting and these were independently verified by a second reviewer (ER). Disagreements were resolved through discussion. If an agreement could not be reached, a third reviewer (TJ) arbitrated. Data reporting and pooling We reported study flow according to PRISMA reporting standards. [6] We reported study characteristics including age, sex, clinical symptoms, treatments and events in the participants in tabular form. We presented data on disease burden measures and on viral culture in tabular form. For studies reporting more than one patient participant, data were extracted related to each participant if available. We plotted median, interquartile ranges (IQRs) and outliers for viral culture results in relation to the duration of symptoms, and individual study plots to day 120 of viral culture results and cycle thresholds. We were unable to meta-analyse the data on PCR cycle counts/RNA log copies and viral culture, due to a lack of detailed information on laboratory practices, assays and because of the absence of internal controls in some studies, and heterogeneous sampling. We therefore reviewed the studies narratively, and where possible presented the results graphically within the limitations noted. We analysed the relationship between cycle threshold, days of onset of symptoms and likelihood of shedding replication-competent virus by presenting the data on a scatter plot. Results The literature search identified 12,989 titles to be screened for inclusion (Figure 1 ). Of these 67 underwent full-text review. A total of 54 studies were excluded after full-text analysis: reasons are reported in the list of excluded studies (see Supplementary File: List of excluded studies).Figure 1 PRISMA flow chart of study screening for inclusion. Figure 1 The characteristics of the 13 included studies are shown in Supplementary Table 1. In total, they reported data for 41 transplant patients: 8 females and 25 males; for 8 patients in one study, sexes were not reported. [7] Studies were in nine countries: Austria, [8] Brazil, [9] Canada, [10] Denmark, [11] France, [12, 13] Germany, [[14], [15], [16]] Saudi Arabia, [17] and the USA [7] [18] and the UK [19]; age ranged from 26 to 77 years. Thirty-nine patients were infected with SARS-CoV-2 post-transplant: 22 patients in four studies had had kidney transplant, [12] [16] [17] [19] five patients in four studies had had a cardiac transplant (Alshukairi 2021, Decker 2020, Rajakumar 2022, Tarhini 2021), [10], [13]. [14]. [17] one previous bone marrow transplant for multiple myeloma, [11] one liver transplant [11] and ten haematopoietic cell transplants. [7], [18], [19] Two patients were infected with SARS-CoV-2 and subsequently underwent transplant: one received a liver transplant; [15] one patient had bilateral lung transplantation after a SARS-CoV-2 infection that severely affected the lungs (Lang 2020). [8]. Typically, patients received a mixture of antivirals, antibiotics, convalescent plasma and immune suppressants, as reported in Supplementary Table 1. Anti-SARS-CoV2 vaccination status was not reported for any of the included patients. The clinical course of COVID-19 varied widely amongst the included patients, from mild COVID-19 related symptoms to severe pneumonia and lung failure; no deaths were specifically reported for this group, although in one study, deaths were reported for four aggregate patients within 30 days of diagnosis. [7] Prescribed treatments reflected the variation in severity. Quality Assessment Table 1 reports study quality based on five criteria. Four studies met all five criteria. [9], [10]. [13], [16] Follow-up was judged adequate in all but one study which was a diagnostic test comparison; [19] in nine studies the reporting of patient characteristics was sufficiently comprehensive (Aydillo 2020, Benotmane 2021, Decker 2020, Lang 2020, Niess 2021, Niyonkuru 2021, Rajakumar 2022, Weigang 2021), [7], [8], [[10], [11], [12]], [[14], [15], [16]], and clinical information was not available for two studies. [17], [19] Case definition was missing or unclear in four studies, [7], [8], [14], [17] and methods for RT- PCR testing were unclear for three studies. [7], [8], [14] The methods used for viral culture were unclear in four studies, [8], [14], [15], [17] and one study reported using a cell line that has not typically been used to demonstrate SARS-CoV-2 growth - Buffalo green monkey kidney (BGMK) cell line. [12].Table 1 Quality of included studies. Table 1Study ID Were the criteria for diagnosing a case clearly reported and appropriate? Was the reporting of patient/population characteristics adequate? Was the study period, including follow-up, sufficient? Were the methods used to obtain RT-PCR results replicable and appropriate? Were the methods used to obtain viral culture results replicable and appropriate? Alshukairi et al [17] Uncleara Nob Yes Yes Unclear Aydillo et al [7] Unclear Yes Yes Unclear Yes Benotmane et al [12] Yes Yes Yes Yes Noc Decker et al [14] No Yes Yes Unclear Unclear Han et al [18] Yes Yes Yes Yes Unclear Lang et al [8] Yes Yes Yes Unclear Unclear Mendes-Correa et al [9] Yes Yes Yes Yes Yes Niess et al [15] Yes Yes Yes Yes Unclear Niyonkuru et al [11] Noa Yes Yes Yes Yes Pickering et al [19] Yes Unclear Unclear Yes Yes Rajakumar et al [10] Yes Yes Yes Yes Yes Tarhini et al [13] Yes Yes Yes Yes Yes Weigang et al [16] Yes Yes Yes Yes Yes a case definition unclear, article reports positive RT-PCR, but Ct cut-off not reported. b data on clinical symptoms lacking. c The cell line used was not one that is demonstrated to support SARS-CoV-2 growth. Results of the studies Details of the patient characteristics, clinical course, treatments and PCR and viral culture for transplant recipients in each individual study are reported in Supplementary Tables 1 and 2. Study results are also presented in Figures 2 (SARS-CoV-2 culture results in transplant patients from days of symptom onset), 3 (duration of infectivity as indicated by viral culture and corresponding PCR cycle counts/log copies among transplant recipients) and 4 (relationship between cycle threshold and symptom onset).Figure 2 SARS-CoV-2 culture results in transplant patients from days of symptom onset. Figure 2 The clinical course of infection was highly variable. The time between transplant receipt to COVID-19 infection varied from days to years. [7], [10] Sampling schedules varied between studies, with no regular timetable of testing taking place, so results for PCR and viral culture are available for different time points in a patient’s clinical course and with different gaps in time between samples being taken. Figure 2 shows that the median time from onset of symptoms for a positive viral culture to be found was 18 days (IQR 8 to 29; range 1-169 days, n = 68 cultures performed). The median for a negative culture was 40 days, mean 40.2 days (IQR 21 to 60; range 1-218 days, based on 116 cultures performed). The Alshukairi et al study (Figure 3 a) reported no positive viral cultures among samples collected from cardiac/renal transplantees between 9 and 26 days since COVID-19 symptom onset. [17] Aydillo et al (Figure 3b) reported repeated viral culture tests for eight stem cell transplant recipients, with fluctuations in and out of shedding replication-competent virus. [7] Decker et al (Figure 3d) reported a heart transplant recipient’s samples as giving a positive viral culture on days 18 and 21; [14] Han and colleagues (Figure 3b) reported on a young male adult recent transplantee, in whom COVID-19 symptoms fluctuated over several weeks, with a viral culture positive swab at 54 days post-symptom onset. [18].Figure 3 Duration of infectivity as indicated by viral culture and corresponding PCR cycle counts/log copies among transplant recipients. Figure 3 Mendes-Correa et al (Figure 3c) described the case of a patient whose symptoms led to three hospital admissions over several months, with NP swabs positive by RT-PCR over 163 days, and viral cultures of these swabs were positive in 9/12 tests, with the latest positive being found in the sample from day 169 of COVID-19. [9] Positive viral cultures were found in NP swabs from two transplantees reported by Niyonkuru et al (Figure 3a), indicating a duration of infectiousness of around two weeks. [11] Pickering and colleagues (Figure 3a) reported data for a renal transplant recipient with severe COVID-19 whose respiratory samples tested positive by RT-PCR for 12 days, and with a positive viral culture for the day 3 sample but no later samples. [19]. Rajakumar et al (Figure 3a) described two cardiac transplant patients: viral culture found replication-competent virus in samples from one patient on day 16 and in samples from the other patient on day 4 and repeatedly up to day 27, after which all viral cultures were negative. [10]. For each patient, viral culture was negative in samples with PCR cycle counts of over 25. Within the samples giving positive viral cultures, the PCR results showed that the cycle threshold for the N gene was lower than for the E gene by an average of 5.4 Ct values. A cardiac transplant patient described by Tarhini and colleagues tested culture-positive with a Ct of 23 on day 103 (Figure 3b); all other viral cultures were negative from samples with PCR Cts of 18 to over 40. [13]. Weigang et al described a kidney transplant patient who experienced three hospital admissions. [16] During the first one (day zero to day 72), 19 RT-qPCR tests were performed, and alongside that viral culture was performed, showing 8/19 positive cultures (Ct values ranging from 15 to 25) and 11/19 negative (Ct values from 25 to 30). The patient was culture positive again on day 105 (Ct of 23). After re-admission at day 140 the patient was still RT-qPCR positive, but with viral culture negative; he was treated for 10 days (days 141-149) with remdesivir. Subsequently, negative RT-qPCR tests until day 189 and negative cultures suggested that the infection had resolved (Figure 3b). The Benotmane study data appeared to be an outlier (Figure 3a), in that five positive viral results were reported from samples with a Ct of 30 or above. [12] However, the cell line used in this study is not demonstrated to readily support SARS-CoV-2 growth. In all other studies, despite a minimum of 10 different PCR platforms being used and different culture techniques, viral cultures were unsuccessful in samples with PCR cycle thresholds above 30. Figures 3a to 3d, reporting all the viral culture data points available from the included studies, show the wide range of duration of COVID-19 disease course across these 41 transplant patients, and suggest a correlation between viral burden (measured as log copies or Cq/Ct) and probable infectiousness as indicated by observing a positive viral culture. Data are too few and heterogeneous to allow combining to make a summary assessment, but a general trend is observable in the Figures, showing that most samples with low Ct values below 30 do generate a positive viral culture, whilst many samples with high Ct values of over 30 are unlikely to generate a positive viral culture. The viral load estimates appear to be related to the administration of courses of antiviral treatment including remdesivir. See Figures 3a (Cts/Cqs) and 3b (log copies). Prolonged shedding of replication-competent virus was associated with alternating increases and decreases of viral burden over time, which in some cases may be up to around 100 days. [13], [16] Figure 4 shows a scatter plot of cycle threshold versus days since symptom onset, and indicates whether positive or negative viral cultures were obtained using these samples; and this clearly displays a trend that higher Ct value samples were less likely to produce a positive viral culture, and lower Ct value samples were more likely to produce a positive viral culture.Figure 4 Relationship between cycle threshold and symptom onset (in days). Figure 4 The magnitude and robustness of the correlation is difficult to assess because laboratory methods differ; it was not possible to pool the data to produce a summary cut-off value for infectiousness, due to these variations in testing platforms and due to varying time windows for sampling from patients (see Figure 2 and Supplementary Table 2). Discussion This review included 13 reports of studies using viral culture and RT-qPCR testing among 41 transplant patients with immunosuppressive treatment who experienced COVID-19 infection. In response to our first research question, the evidence indicates a relationship between indicators of viral burden (Ct, Cq or RNA log copies) and probable infectiousness as indicated by the presence of replication-competent virus. The presence of replication-competent virus reflects the highest grade of evidence supporting the capability for forward transmission of SARS-CoV-2, [20], [21] and suggests here that some transplant patients remain potentially infectious over a period of months. Gaps in the data remain, with variable methods and reporting, and establishing summary estimates of the relationship has not been possible. The data show a long-term rise and fall of viral burden associated with the likelihood of infectiousness that in some transplant patients appears to be a sequential pattern of a vacillating state of infectiousness. Replication-competent virus was most commonly observed in samples with PCR Ct values under 25; one study was an exception to this by reporting viable virus at Ct>30, but the use of a cell line not typically used for SARS-CoV-2 isolation makes interpretation unclear. [12] The duration of viral RNA shedding was variable, with the longest duration reported at 169 days. [9]. Our findings suggest a Ct of 30 or greater indicates a low likelihood of the presence of replication-competent virus, consistent with findings from our recent review on fomite transmission of SARS-CoV-2. [22] This replicable observation suggests that a Ct of 30, regardless of the PCR testing platform used, may be a useful and reasonable proxy to rule out infectious SARS-CoV-2 as there is a consistent correlation between a rising Ct value and likelihood of isolating replication-competent virus. Such a value would be useful to guide clinicians managing these difficult patients, particularly if there were repeated values in this range. Below a Ct of 30, clinicians may choose to repeat NP or throat swabs to assess the direction of the Ct values to allow a more dynamic assessment which, taken in conjunction with the clinical status, may facilitate decision making for isolation or antiviral treatment considerations. We were unable to address the influence of age, sex, underlying pathologies and degree of immunosuppression on infectiousness (our third research question): at present the heterogeneity and limited amount of the available data preclude answering this question. We were unable to answer our fourth research question on the relationship of vaccination status on infectiousness because no study reported on vaccination status for these transplant patients. Immunisations, and differing virus variants, may mean that early studies are less applicable to current practice; however, we have no evidence to evaluate this. Variability in the clinical course of SARS-CoV-2 infection among transplant recipients has previously been reported, including observed prolonged viral shedding. [23] Antiviral drugs may impact on these observations, especially symptoms and viral burden. [24]. One well-designed study on immunosuppressed patients, which we were unable to include because disaggregated data solely for transplant patients were not fully available, supports our conclusions. [25] While this review is limited to transplant patients, evidence suggests similar prolonged viral cultures are found in immunosuppressed cancer patients. We plan to perform a further review in this group analysing the type of cancer and the impact of immunotherapies on viral culture findings. The transplant patient population is of particular importance: clinicians need guidance as to when to release the patient from quarantine or isolation, given the heavy burden of immunosuppression. We have tried to narrow the uncertainty and offer some general guidance as to when patients are unlikely to be shedding replication-competent virus, but clinical assessment of each patient must inform that decision because each patient and setting is different. The strengths of this review are that we followed our published protocol, entailing exhaustive literature searches, double checked data extraction and quality assessment, and a high level of clinical and epidemiological expertise input to deliberate the findings. We were also able to include data from an additional 8 transplant recipients after correspondence with the study authors. [7] Limitations include the small number of studies with viral culture and serial viral load estimates among transplant patients, high variability in study design and reporting and impossibility to pool results due to the well-known variability in sensitivity across assays. [26] Some of the data were extracted directly from figures in published papers, and these estimates may have been inaccurate. Case series are conventionally considered low in the evidence hierarchy, as they may entail inherent bias in the selection of study participants and therefore have limited generalisability; however, here they are essential in providing the detailed reports needed for this unusual patient group. The case reports included here comprise some of the most detailed longitudinal reports of this patient group for whom data are needed. The evidence base is limited, however, by heterogeneous design and reporting within the studies with different observation windows for reporting of viral burden and culturability or clinical characteristics of patients. Vaccinations and differing virus variants may mean that early studies are less applicable to current practice; however, we have no evidence to evaluate this. In addition to providing appropriate care for the individual patient, ongoing transmission of SARS-CoV-2 is a concern, and immunosuppressed individuals may pose a challenge by experiencing prolonged carriage of the virus that could lead to forward transmission. Based on our findings we can offer the following general guidance to clinicians. Physicians who are experienced with these immunosuppressed patient populations should work with public health and infection prevention and control to direct their isolation and quarantine requirements in the community and the healthcare setting, respectively. Infectious patients with immunosuppressive treatment following solid organ or stem cell transplantation should be isolated until at least two consecutive respiratory specimens collected ≥24 hours apart demonstrate a rising RT-PCR Ct (i.e. indicating diminishing viral burden) in conjunction with assessment of their clinical status. After discharge, they should be closely followed up for chronic or vacillating SARS-CoV-2 infection for several weeks to months, depending on the individual clinical scenario. For obtaining data, standardisation of methods is needed: each laboratory should use consistently applied platforms with suitable internal standards to calibrate the relationship between Ct and genome copy in these patient populations. Publication of results of case series or other longitudinal study should be reported in a standardised format to avoid loss of data. We suggest observation windows should be within a short range of 3 to 7 days during the acute periods post-transplantation and during periods of rejection when higher doses of immunosuppressants are employed, depending on clinical circumstances. Each observation window should include a summary of symptoms and interventions, the reporting of PCR cycle threshold and, for samples with Ct below 30, attempts at viral culture if available. Description of patients should include past medical histories and details of treatments received. Observed drug interactions should be highlighted. Reasons for admission, discharge and changes in isolation should be clearly reported. To investigate the duration of viral shedding, studies should report the time between the first positive and the first negative viral cultures. With additional data gathering and standardisation of methods, it will be possible for transplant physicians and infection prevention and control personnel to develop evidence-based approaches to dealing with these patients for the benefit of the patients and their families and the community at large. Finally, the differences in viral persistence and replication between transplant recipients and the general population demonstrates once again the protective value of an intact and fully functional immune system. Funding This work is supported by the National Institute for Health Research School for Primary Care Research [Project 569] and by the University of Calgary. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. Author contributions TJ, CH and JC designed the study. JB performed the literature searches. JB, TJ, SM, ER and ES, screened the studies for eligibility and performed data extraction. Additional expertise on clinical and laboratory issues was given by DE, JC, SM and ER. CH generated the data figures. All authors contributed to interpreting and writing up the results and conclusions. Ethics declarations Ethics approval and consent to participate: This is a systematic review and meta-analysis. Therefore, ethical approval is not applicable. Consent for publication: Not applicable. Availability of data and materials All data generated or analysed during this study are included in this published article [and its supplementary information files]. Declaration of Competing Interest TJ’s competing interests are accessible at: https://restoringtrials.org/competing-interests-tom-jefferson. CJH holds grant funding from the NIHR, the NIHR School of Primary Care Research, the NIHR BRC Oxford and the World Health Organization for a series of Living rapid review on the modes of transmission of SARs-CoV-2 reference WHO registration No2020/1077093. He has received financial remuneration from an asbestos case and given legal advice on mesh and hormone pregnancy tests cases. He has received expenses and fees for his media work including occasional payments from BBC Radio 4 Inside Health and The Spectator. He receives expenses for teaching EBM and is also paid for his GP work in NHS out of hours (contract Oxford Health NHS Foundation Trust). He has also received income from the publication of a series of toolkit books and for appraising treatment recommendations in non-NHS settings. He is Director of CEBM and is an NIHR Senior Investigator. DE holds grant funding from the Canadian Institutes for Health Research and Li Ka Shing Institute of Virology relating to the development of COVID-19 vaccines as well as the Canadian Natural Science and Engineering Research Council concerning COVID-19 aerosol transmission. He is a recipient of World Health Organization and Province of Alberta funding which supports the provision of BSL3-based SARS-CoV-2 culture services to regional investigators. He also holds public and private sector contract funding relating to the development of poxvirus-based COVID-19 vaccines, SARS-CoV-2-inactivation technologies, and serum neutralization testing. JMC holds grants from the Canadian Institutes for Health Research on acute and primary care preparedness for COVID-19 in Alberta, Canada and was the primary local Investigator for a Staphylococcus aureus vaccine study funded by Pfizer for which all funding was provided only to the University of Calgary. He is co-investigator on a WHO funded study using integrated human factors and ethnography approaches to identify and scale innovative IPC guidance implementation supports in primary care with a focus on low-resource settings and using drone aerial systems to deliver medical supplies and PPE to remote First Nations communities during the COVID-19 pandemic. He also received support from the Centers for Disease Control and Prevention (CDC) to attend an Infection Control Think Tank Meeting. He is a member and Chair of the WHO Infection Prevention and Control Research and Development Expert Group for COVID-19 and a member of the WHO Health Emergencies Programme (WHE) Ad-hoc COVID-19 IPC Guidance Development Group, both of which provide multidisciplinary advice to the WHO and for which no funding is received and from which no funding recommendations are made for any WHO contracts or grants. He is also a member of the Cochrane Acute Respiratory Infections Working Group. JB is a major shareholder in the Trip Database search engine (www.tripdatabase.com) as well as being an employee. In relation to this work Trip has worked with a large number of organisations over the years, none have any links with this work. The main current projects are with AXA and SARS-CoV-2 (WHO Registration 2020/1077093-0) and is part of the review group carrying out rapid reviews for Collateral Global. He worked on Living rapid literature review on the modes of transmission of SARS-CoV-2 and a scoping review of systematic reviews and meta-analyses of interventions designed to improve vaccination uptake (WHO Registration 2021/1138353-0). ECR was a member of the European Federation of Neurological Societies (EFNS)/European Academy of Neurology (EAN) Scientist Panel, Subcommittee of Infectious Diseases (2013 to 2017). Since 2021, she is a member of the International Parkinson and Movement Disorder Society (MDS) Multiple System Atrophy Study Group, the Mild Cognitive Impairment in Parkinson Disease Study Group, and the Infection Related Movement Disorders Study Group. She was an External Expert and sometimes Rapporteur for COST proposals (2013, 2016, 2017, 2018, 2019) for Neurology projects. She is a Scientific Officer for the Romanian National Council for Scientific Research. AP holds grants from the NIHR School for Primary Care Research. IJO and EAS have no interests to disclose. SM is a pharmacist working for the Italian National Health System since 2002 and a member of one of the three Institutional Review Boards of Emilia-Romagna Region (Comitato Etico Area Vasta Emilia Centro) since 2018. Appendix A Supplementary data The following are the Supplementary data to this article:Supplementary file List of excluded studies, with reasons. Supplementary file Supplementary file Literature search strategy. Supplementary file Supplementary Table 1 Characteristics of transplant patients in included studies. Supplementary Table 1 Supplementary Table 2 PCR cycle counts/log copies and viral culture results of included studies. Supplementary Table 2 Supplementary file PRISMA checklist for systematic review Supplementary file Acknowledgements We gratefully acknowledge the contributions of Drs Mini Kamboj and Jeroen van Kampen who provided additional data from their studies and helped us to progress this work. Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.jhin.2022.11.018. ==== Refs References 1 Jering K.S. McGrath M.M. McCausland F.R. Claggett B. Cunningham J.W. Solomon S.D. 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==== Front Pediatr Neurol Pediatr Neurol Pediatric Neurology 0887-8994 1873-5150 The Authors. Published by Elsevier Inc. S0887-8994(22)00257-0 10.1016/j.pediatrneurol.2022.11.018 Research Paper Safety and tolerability of COVID-19 vaccine in children with epilepsy: a prospective, multicenter study Wang Zhihao MM a∗ Fang Xiqin MM a∗ Han Tao PhD b Lv Shishen MM c Li Chunxiang PhD d Ma Aihua PhD e Jiang Zhaolun PhD c Li Wenke MM c Sun Wenxiu PhD e Sun Wenying PhD f Gao Yuxing PhD e Gao Zaifen PhD g Liu Yong PhD g Li Qiubo PhD h Wang Suli PhD i Li Baomin PhD j Liu Xinjie PhD j Liu Xuewu PhD ak∗∗ a Department of Neurology, Qilu Hospital of Shandong University, Jinan, China b Department of Neurology, Shandong Provincial Hospital, Jinan, China c Department of Pediatrics, Tengzhou Central People’s Hospital, Tengzhou, China d Department of Pediatrics, Yantai Yuhuangding Hospital, Yantai, China e Department of Pediatrics, Shandong Provincial Hospital, Jinan, China f Department of Pediatrics, Liaocheng People’s Hospital, Liaocheng, China g Department of Pediatrics, Qilu Children’s Hospital of Shandong University, Jinan, China h Department of Neurology, Affiliated Hospital of Jining Medical, Jining, China i Department of Pediatrics, Weifang Maternal and Child Health Care Hospital, Weifang, China j Department of Pediatrics, Qilu Hospital of Shandong University, Jinan, China k Institute of Epilepsy, Shandong University, Jinan, China ∗∗ Address correspondence to: Xuewu Liu, Department of Neurology, Qilu Hospital of Shandong University, 107 Xilu Wenhua, Jinan, China, 25063, [],185-6008-5520. ∗ Contributed equally as co-first authors 5 12 2022 5 12 2022 26 7 2022 28 11 2022 29 11 2022 © 2022 The Authors. Published by Elsevier Inc. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Introduction We designed this study to investigate the effects of the coronavirus disease 2019 (COVID-19) vaccine on epileptic seizures, as well as its adverse effects, in children with epilepsy (< 18 years). Methods This anonymous questionnaire study involved a multicenter prospective survey of outpatients and inpatients with epilepsy (＜18 years) registered in epilepsy clinics in 8 hospitals in six cities of Shandong Province. Results A total of 224 children with epilepsy were included in the study. Fifty of them experienced general adverse events after vaccination. The most common local adverse events were pain or tenderness at the injection site. The most common systemic adverse effects were muscle soreness and headache. No severe adverse events were reported. There were no significant differences in the number of anti-seizure medications (ASMs) (P =0.459), gender (P =0.336), etiology (P =0.449), age (P =0.499), duration of disease (P =0.546) or seizure type (P =0.475) between the patients with and without general adverse events. We found that the risk of seizure after vaccination was decreased in children who were seizure-free for more than 6 months before vaccination. There was no significant difference in the number of seizures during the first month before vaccination, the first month after the first dose and the first month after the second dose (P = 0.091). Conclusion The benefits of vaccination against COVID-19 outweighed the risks of seizures/relapses and severe adverse events after vaccination for children with epilepsy. Key Words COVID-19 vaccine epilepsy children Abbreviations ASMs, Anti-Seizure Medications ILAE, International Alliance against Epilepsy ==== Body pmcIntroduction According to the data of the World Health Organization (WHO), there have been approximately 505 million confirmed cases of infection and 6.21 million deaths from the first discovery of the severe acute respiratory syndrome coronavirus type 2 (SARS-COV-2) in December [1]. To effectively control the epidemic, the COVID-19 vaccine has been promoted globally; 11.3 billion doses have been inoculated globally [1]. Epilepsy, as one of the most common neurological diseases, has approximately 50 million epilepsy patients globally [2]. There has been no consensus on the increase in the susceptibility of the epileptic population to COVID-19 in existing studies [3, 4, 5]. However, a meta-analysis has shown that patients with epilepsy have a higher risk of severe disease and mortality than people without epilepsy after COVID-19 [6]. Another study has suggested that epilepsy may be an independent risk factor for the death of hospitalized patients with COVID-19 [5]. Children infected with SARS-CoV-2 are mainly mild or asymptomatic compared with adults, but a relatively small number of children and adolescents might be at risk for severe COVID-19, especially those with underlying health comorbidities [7, 8, 9, 10]. Studies have also found that the SARS-CoV-2 infection can lead to a serious complication called multisystem inflammatory syndrome in children, which includes myocardial dysfunction, shock, and respiratory failure requiring intensive care. Furthermore, children and adolescents can be important transmitters of SARS-CoV-2 in communities [8,11,12]. A systematic review found that 10% of children critically ill due to COVID-19 had epilepsy [13]. Given the threat of COVID-19 to children with epilepsy, vaccination against COVID-19 is critical for patients with epilepsy. Currently, 409 COVID-19 candidate vaccines have been documented: 276 vaccines are in development, 109 are now in clinical testing, 24 are in use [14], including protein subunit, inactivated, mRNA, and vector based. In China, BBIBP-CorV (Beijing Institute of Biological Products, Beijing China), WIBP-CorV (Wuhan Institute of Biological Products, Wuhan China), CoronaVac (Sinovac Life Sciences, Beijing China), Ad5-nCoV (CansinoBIO, Tianjin China) and ZF2001 (Chongqing Zhifei Biological Products, Chongqing China) have been widely used. BBIBP-CorV, WIBP-CorV and CoronaVac have been allowed for children aged 3-17 years old in 2021. Small scale clinical trials have shown that the seroconversion rates of neutralizing antibodies in children after two-dose vaccination is more than 96% [15, 16, 17]. However, there is limited information on the use of the COVID-19 vaccine for patients with chronic diseases such as epilepsy, and its safety and tolerability are of concern among patients with epilepsy [18]. Studies of other vaccines found that vaccination, except for diphtheria, tetanus, and pertussis (DTP) and measles, mumps and rubella (MMR), did not increase the risk of seizures [19]. In addition, clinical trials of COVID-19 vaccines have shown no severe neurological adverse events [20, 21, 22]. Based on available data, the International Alliance against Epilepsy (ILAE) has reported that there is no clear evidence that patients with epilepsy have a higher risk of side effects following COVID-19 vaccination. The ILAE specially recommends that patients with epilepsy receive vaccination for COVID-19 [23]. In contrast, early national guidelines by China listed severe neurological diseases including "uncontrolled epilepsy" as contraindications [24]. At present, there is no clear definition of "uncontrolled epilepsy". The limited large-scale studies on the safety and tolerability of COVID-19 vaccines for children with epilepsy motivated this study. We aimed to investigate the effects of COVID-19 vaccines on seizures in children (< 18 years old) with epilepsy, as well as their adverse effects. Methods Study Design This prospective multicenter study was conducted from November 2021 to March 2022 and involved 8 hospitals (Qilu Hospital of Shandong University, Shandong Provincial Hospital, Qilu Children's Hospital of Shandong University, Yantai Yuhuangding Hospital, Affiliated Hospital of Jining Medical, Weifang Maternal and Child Health Care Hospital, Tengzhou Central People's Hospital, Liaocheng People's Hospital). Shandong Province is located in eastern China and is divided into 16 cities classified into urban and suburban districts according to population density and local economic levels. As of the 2019 census, Shandong had a population of nearly 100 million [25]. Hospitals in China are categorized into three levels, depending on the level of sophistication, equipment available, and staff or bed numbers; level three is the highest. Only level two and three hospitals were included in this study [26]. Eligibility Criteria We included patients who met the following criteria: having been diagnosed with epilepsy according to the ILAE guidelines, age below 18 years of age, having had COVID-19 vaccination intentions, and having received at least two doses of vaccines during the follow-up period and by the time of follow-up, the second dose of vaccine had been vaccinated for one month [27]. We excluded any conditions listed as contraindications in national vaccination guidelines [24]. The 0-2-year-old infants were also not included in this study because the guidelines for vaccination against COVID-19 issued by China did not include them. Data Extraction All participants were provided a written description of the aims of the present study, and written consent was obtained from them and/or their legal guardians. Questionnaires were administered through face-to-face interviews by trained investigators. During the interviews, participants were asked to complete the questionnaire themselves or with the help of interviewers if they had difficulty reading or writing. Considering that some young participants (< 5 years old) could not understand the meaning of the questions, we allowed their parents to answer the questions for them. The survey was conducted using a self-administered, anonymous questionnaire that contained questions about the demographic and clinical characteristics of the patients and information about vaccination and its general adverse events. Seizures were classified according to ILAE Epilepsy Classification 2017 guidelines [27]. The following information about vaccination and vaccine-related general adverse events was recorded: vaccination date, type of vaccine, and vaccine-related general adverse events after vaccination (tenderness or pain at the injection site, pruritus, mass or induration at the injection site, fever, fatigue, headache, muscle soreness, nausea, vomiting, abdominal pain, and diarrhea). The severe adverse events (such as anaphylactic shock) were also counted. Post-vaccine fever was defined as body temperature of ＞38.0°C within 7 days after vaccination. Severe adverse events are defined by the World Health Organization as those resulting in death, hospitalization, or compelling or persistent disability [28]. Patients were asked about their seizures on one month, three months, six months, one year, and two years before the vaccination and the type and dosage of anti-seizure medications (ASMs) they were taking. The questionnaire included questions about vaccination-related general adverse events, the number of seizures within one month after the first dose, the number of seizures within one month after the second dose, and the types and dosage of ASMs after vaccination. Ethics Statement This study was approved by the Ethics Committee of Qilu Hospital, Shandong University (2021035) and conducted according to the principles of the Declaration of Helsinki. Written informed consent was obtained from all study participants and/or their legal guardians. Statistical Analysis The data were analyzed using SPSS version 25. Numeric variables, with a normal distribution, were summarized as means and standard deviations, and those without a normal distribution were summarized as median values. The Shapiro–Wilk test was used to test for the normality of distribution. The student’s t-test was used to compare the data with a normal distribution and the Mann–Whitney U test was used for data without a normal distribution. The χ2 and Fisher’s exact tests were used to determine the differences between groups. Logistic regression analysis was used to investigate the influencing factors of binary dependent variables. The Friedman test was used to evaluate statistically significant differences between the distributions of three or more paired groups. P-values of ≤0.05 were considered statistically significant. Results Demographic and clinical characteristics We conducted 247 interviewers from December, 2021 to March, 2022. In the later follow-up, 23 patients were excluded due to loss of follow-up or non-compliance with the criteria, and 224 patients were finally included in the study. All the patients were injected inactivated vaccine. Among them, 125 (55.80%) were male, 98 (43.75%) were female, and one patient (0.45%) did not want to reveal her/his gender. The median age of children with epilepsy was 8.50 (7.00,11.00) years. The majority of children included in this study were 3-12 years old (193 cases, 86.16%); only 13.84% (31 cases) were 13-17 years old. The median duration of epilepsy among the children was 3.0 (2.0, 4.875) years. Only one patient had a disease duration of more than 10 years. According to etiology, the patients were divided into genetic (17 cases, 7.59%), structural (10 cases, 4.46%), immune (2 cases, 0.89%), infectious (4 cases, 1.79%) and unknown (191 cases, 85.27%) groups. In this study, 140 (62.50%) children suffered from focal epilepsy, 59 (26.34%) suffered from generalized epilepsy and 25 (11.16%) suffered from epilepsy of unknown origin. The statistics of ASM prescriptions before vaccination in children with epilepsy shown that only 8 (3.57%) patients did not use any ASMs, 175 (78.13%) patients received monotherapy, 36 (16.07%) patients were taking two ASMs simultaneously, and 5 (2.23%) patients were taking three at the same time. The types of ASM prescribed did not change after vaccination. The clinical and demographic characteristics of the patients are shown in Table 1 .Table1 Demographic and clinical characteristics of the study population Children With Epilepsy (n, %) Median Age (years, (Q1, Q3)) 8.50 (7.00, 11.00) Gender Male 125 (55.80%) Female 98 (43.75%) Unknown 1 (0.45%) Median duration (years, (Q1, Q3)) 3.0 (2.0, 4.875) Etiology Genetic 17 (7.59%) Structural 10 (4.46%) Metabolic 0 (0.00%) Immune 2 (0.89%) Infectious 4 (1.79%) Unknown 191 (85.27%) Seizure type Generalized Epilepsy 59 (25.34%) Focal Epilepsy 140 (62.50%) Unknown 25 (11.16%) Numbers of ASMs 0 8 (3.57%) 1 175 (78.13%) 2 36 (16.07%) 3 5 (2.23%) Vaccine type Inactivated vaccine 224(100%) General Adverse events of children with epilepsy after vaccination with the COVID-19 vaccine A total of 50 patients experienced varying degrees of general adverse events after full vaccination with the COVID-19 vaccine; these were mainly local adverse events (61.19%). 16.07% of all of the patients (36 cases) had pain or tenderness at the injection site; 2.23% (5 cases) had pruritus, induration or a mass at the injection site. The number of patients with fever, fatigue, headache, muscle soreness, nausea and vomiting, abdominal pain and diarrhea were 1 (0.45%), 4 (1.79%), 5 (2.23%), 10 (4.46%), 5 (2.23%), and 1 (0.45%), respectively. There were no serious adverse events such as anaphylactic shock after COVID-19 vaccination. Thirteen children had two or more different general adverse events. For detailed information on general adverse events following COVID-19 vaccination, see Table 2 .Table 2 Status of general adverse events from the first dose of COVID-19 vaccine to one month after the second dose Frequency of general adverse event (n, %) Pain or tenderness at injection site 36 (16.07%) Pruritus, induration or mass at injection site 5 (2.23%) Fever 1 (0.45%) Fatigue 4 (1.79%) Headache 5(2.23%) Abdominal pain and diarrhea 1 (0.45%) Serious adverse event 0 (0.00%) To determine the relevant factors underlying general adverse events after the vaccination against COVID-19, we analyzed the clinical and demographic characteristics of children with and without general adverse events (Table 3 ). The median age of patients with general adverse events was 9.0 (7.0, 11.0) years, and the median age of patients without general adverse events was 8.0 (6.50,11.0) years. There was no significant difference between the two groups (P=0.499). The median duration of epilepsy was 3.0 (1.50, 5.00) years for the patients with general adverse events and 3.0 (2.00, 4.63) years for those without general adverse events; there was no significant difference between the two groups (P=0.546). There were no significant differences in the number of antiepileptic drugs (P=0.459), gender (P=0.336), etiology (P=0.449) and seizure type (P=0.504) between the two groups.Table 3 Clinical and demographic characteristics of children with and without adverse events Children with adverse events (n=50) Children without adverse events (n=174) p Median age (years, (Q1, Q3)) 9.00 (7.00, 11.00) 8.00 (6.50, 11.00) 0.499 Median duration (years, (Q1, Q3)) 3.00 (1.50, 5.00) 3.00 (2.00, 4.63) 0.546 Number of ASMs 0.459 0 3 5 1 36 139 2 10 26 3 1 4 Gender 0.336 Male 28 97 Female 21 77 Unknown 1 0 Etiology 0.449 Genetic 2 15 Structural 3 7 Immune 1 1 Infectious 0 4 Unknown 44 147 Seizure Type 0.504 Generalized 12 47 Focal 30 110 Unknown 8 17 Seizures in children with epilepsy before and after COVID-19 vaccination During the first month after the first and second doses of the COVID-19 vaccine, 24 (10.71%) of the 224 followed-up patients had seizures, and the frequencies increased by varying degrees in 19 (8.48%). Two had a seizure relapse after being seizure-free for more than 2 years. Only one of the 19 people had the adjustment of the ASM dosage. One person did not take medications before and after vaccination, and no one changed the type of ASM. 15 patients’ duration were more than two years, and 15 had seizures within a year before vaccination. After vaccination, the seizure frequency of 9 people decreased. The seizure frequencies of the 224 patients were counted a month before vaccination and the first month after the first and second doses. The frequency of seizures was 0.183 during the first month after the first dose of the COVID-19 vaccine, 0.125 during the first month after the second dose, and 0.183 within the month before vaccination. There were no significant differences among the three (P=0.091). To explore the effect of the seizure-free duration before the COVID-19 vaccination on seizures after vaccination, we analyzed the seizure data of the patients before vaccination. Using the OR results from Table 4 , we can assume that the risk of seizure after vaccination was decreased in children with seizure-free time duration before vaccination of more than 6 months (1-2 months, OR=2.917, p=0.287; 2-3 months, OR=0.875, p=0.888; 3-6 months, OR=0.117, p=0.070; 6-12 months, OR=0.188, p=0.023; 1-2 years, OR=0.032, p=0.003; ＞2 years, OR=0.032, p＜0.001).Table 4 Logistic regression analysis of seizure free time before vaccination and seizure results after vaccination Variable OR 95% CI P Seizure free time before vaccination ＜0.001 ＜1 month Reference Reference 1-2 months 2.917 0.407-20.899 0.287 2-3 months 0.875 0.137-5.576 0.888 3-6 months 0.117 0.011-1.195 0.070 6-12 months 0.188 0.044-0.796 0.023 1-2 years 0.032 0.003-0.313 0.003 ＞2 years 0.032 0.007-0.155 ＜0.001 Discussion The study used data from several hospitals in Shandong province, China. In our patients, the average number of seizures 1 month before vaccination was 0.183, one tenth the value of the same category in an Italian study [29]. This may be attributed to the enlistment of "uncontrolled epilepsy" as a contraindication by early Chinese vaccination guidelines. "Uncontrolled epilepsy" is not clearly defined, and this may cause some clinicians to be more cautious when vaccinating patients with epilepsy. In our study, the COVID-19 vaccine did not affect the number of seizures per month. An Italian study arrived at the same conclusion [29]. 8.48% reported an increase in the number of seizures, and 4.02% reported a decrease in the number of seizures after vaccination. We found that the patients who reported an increased frequency of seizures after vaccination either had epilepsy for more than 2 years or still had seizures in the year before vaccination. Several other studies have also reported that the proportion of patients with increased seizures after vaccination against COVID-19 was no more than 10% [29,30]. In a study on adult patients with epilepsy, the common points of patients with increased seizure frequency after vaccination: 1. Epilepsy duration more than 10 years; 2. Treatment with multiple ASMs; 3. Developed a fever after vaccination [29]. In the study by Massoud et al., patients who had more seizures than usual after being administered the COVID vaccine received polytherapy and had had epilepsy for more than 2 years [18]. This may suggest that children with poorly controlled epilepsy are more likely to experience an increase in seizure frequency after vaccination. We found that the risk of seizure after vaccination was decreased in children who were seizure-free for more than 6 months before vaccination. Among our patients, 50 (22.3%) had general adverse events of varying degrees from the first dose of the COVID-19 vaccine to a month after the second dose. The local adverse events were mainly pain and tenderness at the vaccination site (36 cases, 16.07%) and pruritus, mass, or induration at the vaccination site (5 cases, 2.23%). Some clinical trials have shown that headache and fatigue are the most common systemic adverse events [20,21]. A real-life study from Czechia also found fatigue and headache as the most common adverse events [30]. Headache and fatigue were also common systemic general adverse events in our study. Similar to our results, the incidence of adverse events in three small-scale clinical trials was 17%, 25.79% and 27% respectively [15, 16, 17]. Clinical trials have shown that severe adverse advents are infrequent with BNT162b2 and CoronaVac [20,21]. The incidence of severe adverse events was 0.1% for both vaccines. No serious adverse events were reported in small-scale clinical trials for children [15, 16, 17]. No patient reported severe adverse events in our study. In conclusion, on the basis of the good safety and protective efficacy exhibited by the vaccine, children with epilepsy, as a potential at-risk population, vaccination can better protect them from the threat of COVID-19. We explored the relationships between patient clinical and demographic characteristics and general adverse events after vaccination. No demographic and clinical characteristics, including the type of epilepsy, cause of epilepsy, patient age, duration of epilepsy, and the number of patients using ASM, were associated with vaccine-related general adverse events in our study. Von Wrede et al. indicated that having an earlier onset of epilepsy and using fewer ASMs can predict a vaccine adverse event with an accuracy of 83.3% [32]. Massoud et al. found that receiving multiple therapies was associated with a higher incidence of vaccine adverse events, while no association was found with the duration of epilepsy [7]. In our study, there were 191 cases of epileptic classification with unknown etiology, which may have influenced our conclusions. In addition, the few patients with immune and infectious epilepsy may also have affected our results. The differences between the findings of our study and the other studies may be attributed to the different populations. Our study has limitations. First, the self-reported questionnaires used in this study may have led to information bias. Some patients or their families may have ignored or exaggerated some situations when answering the questions. Second, most of the child patients we recruited were ≤12 years old, and our results may not accurately reflect the situation of adolescent epilepsy patients. Third, we did not analyze the manufactures of different inactivated vaccine in our study, which may cause us to ignore the differences between different inactivated vaccines. Fourth, the participants of this study were mainly from east China and are not representative of the country as a whole. Fifth, we partially cited in the article studies focused on adult populations, and these conclusions may have some differences from the actual situation in children. Larger cohort studies are needed in the future to improve the understanding of the safety of vaccination in children with epilepsy. Conclusion Data from this study suggest that vaccination against COVID-19 is safe and well-tolerated by patients with epilepsy. The vaccine did not affect the number of seizures patients had per month. The incidence of general adverse events after vaccination was 22.32%; they were mainly local adverse reactions. No severe adverse events occurred. A few patients (8.48%) experienced an increase in seizures after vaccination, this may be due to poor epilepsy control. We found that the risk of seizure after vaccination was decreased in children who were seizure-free for more than 6 months before vaccination. The existing research on adults and children shows that the COVID-19 vaccine has good protective effect and low adverse reactions. Overall, We believe that the benefits of vaccination against COVID-19 outweigh the risks of seizures and relapses and severe adverse events after vaccination in children with epilepsy. Uncited reference 31.. Acknowledgments The authors thank the patients and their family members for their cooperation. Conflict of Interest Disclosures: Authors have no example conflicts of interest to disclose. Funding: This work was supported by the National Natural Science Foundation (No. 81873786). The funder did not participate in the work. Contributors’ Statement Page Zhihao Wang and Xiqin Fang conceptualized and designed the study, collected and organized the data, drafted the initial manuscript, and reviewed and revised the manuscript. Tao Han, Chunxiang Li, Aihua Ma, Zhaolun Jiang, Shishen Lv, Wenke Li, Wenxiu Sun, Wenying Sun, Yuxing Gao, Zaifen Gao, Yong Liu, Qiubo Li, Suli Wang, Baomin Li, Xinjie Liu assisted in collecting data and provided important suggestions for the design of research scheme and the writing of manuscript. Xuewu Liu conceptualized and designed the study, coordinated and supervised data collection, and critically reviewed the manuscript for important intellectual content. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work. Conflict of Interest All authors disclosed no relevent relationships. ==== Refs References 1 World Health Organization. WHO Coronavirus (COVID-19) Dashboard [online]. Accessed at: https://covid19.who.int. 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==== Front Trends Analyt Chem Trends Analyt Chem Trends in Analytical Chemistry 0165-9936 1879-3142 Elsevier B.V. S0165-9936(22)00354-5 10.1016/j.trac.2022.116871 116871 Article Recent advancements in nucleic acid detection with microfluidic chip for molecular diagnostics Li Zheng Xu Xiaojian Wang Dou ∗∗ Jiang Xingyu ∗ Shenzhen Key Laboratory of Smart Healthcare Engineering, Guangdong Provincial Key Laboratory of Advanced Biomaterials, Department of Biomedical Engineering, Southern University of Science and Technology, No. 1088, Xueyuan Rd., Xili, Nanshan District, Shenzhen, Guangdong, 518055, PR China ∗ Corresponding author. ∗∗ Corresponding author. 5 12 2022 1 2023 5 12 2022 158 116871116871 31 8 2022 30 11 2022 2 12 2022 © 2022 Elsevier B.V. All rights reserved. 2022 Elsevier B.V. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. The coronavirus disease 2019 (COVID-19) has extensively promoted the application of nucleic acid testing technology in the field of clinical testing. The most widely used polymerase chain reaction (PCR)-based nucleic acid testing technology has problems such as complex operation, high requirements of personnel and laboratories, and contamination. The highly miniaturized microfluidic chip provides an essential tool for integrating the complex nucleic acid detection process. Various microfluidic chips have been developed for the rapid detection of nucleic acid, such as amplification-free microfluidics in combination with clustered regularly interspaced short palindromic repeats (CRISPR). In this review, we first summarized the routine process of nucleic acid testing, including sample processing and nucleic acid detection. Then the typical microfluidic chip technologies and new research advances are summarized. We also discuss the main problems of nucleic acid detection and the future developing trend of the microfluidic chip. Keywords Microfluidic chip Nucleic acid Amplification Digital CRISPR ==== Body pmc1 Introduction Nucleic acid sequence carries rich genetic information and is the most specific marker for biological information identification. The research and development of nucleic acid detection methods for biochemical analysis are significant. Currently, the most mature nucleic acid detection technology is amplification based on PCR technology, which has been developed and applied in clinical practice for decades [1]. Since the outbreak of COVID-19 in 2019, it has been widely used and is regarded as the gold standard for COVID-19 detection [2,3]. Nucleic acid testing is systematic engineering, including sample processing, nucleic acid extraction and detection steps [4,5]. Conventional PCR-based nucleic acid detection technologies usually have critical drawbacks such as complex operation, high requirements for expensive equipment and long reaction time [6,7]. Therefore, developing rapid, accurate and straight-forward nucleic acid detection testing techniques has crucial practical application value. Many new nucleic acid detection technologies have been developed in recent years, such as isothermal amplification and amplification-free nucleic acid detection technology [[8], [9], [10], [11], [12]]. The highly miniaturized microfluidic technology can integrate complex nucleic acid detection processes on one chip [13,14], which lessens the complexity of the operation and helps build an automatic and efficient diagnosis system [[15], [16], [17], [18]]. Various microfluidic chips have been developed to detect nucleic acid [[19], [20], [21], [22]]. A series of new nucleic acid detection technologies have been developed in the past two years with the vast demand for the detection of COVID-19 [[23], [24], [25], [26]]. Therefore, we write this review to summarize the recent progress of nucleic acid testing based on a microfluidic chip. We first summarized the routine process of nucleic acid testing, and then the advanced microfluidic chip technologies are summarized. Finally, we put forward the existing problems and development direction of the microfluidic chip for nucleic acid detection (Fig. 1 ).Fig. 1 Outline of microfluidics technology for the nucleic acid test. Fig. 1 2 Microfluidic chip and on-chip nucleic acid detection 2.1 Sample processing and nucleic acid extraction The first step in the testing process is extracting nucleic acid from clinical samples [27]. The extraction and purification of nucleic acids will directly affect the assay results. The process usually requires three steps: sample lysis, nucleic acid extraction, and purification. Nucleic acid extraction first requires lysis of the cells to release the intracellular nucleic acid. Ideal lysis agents should not interfere with downstream assays and can be adapted to miniaturized devices such as microfluidic chips. 2.1.1 Lysis method The lysis method falls into two major categories, chemical and mechanical lysis [28]. Chemical solutions, such as alkaline solutions, enzyme solutions, and decontaminants, can lyse cell membranes to release intracellular components [29]. The advantage of the chemical lysis method is that the process is simple and easy to handle, but the lysis reagent may affect the downstream nucleic acid amplification. Mechanical lysis is performed by physical means such as freeze-thawing, collision method, grinding, acoustic and electrolysis. For example, the grinding method uses shear stress to disrupt cell membranes from the outside, and the automated grinding microfluidic chip developed recently [30] can replace manual grinding with filtration and other steps after lysis, followed by elution using PCR buffer. The collision method is based on integrating nanostructures into the microfluidic chip to induce mutual collisions between cells to achieve lysis. In addition, introducing tiny beads (100 μm) into the microfluidic device can also induce cell fragmentation lysis. Other physical lysis methods also basically use physical means to disrupt the cell membrane to allow the escape of intracellular components, acoustic methods use mechanical vibrations to disrupt the cell membrane, and electrolysis uses high voltage to perforate the cell membrane and to perform its effect [31]. The advantages of the physical lysis method are that it does not require chemical reagents and is suitable for highly automated microfluidic devices. The disadvantage is that the high shear stress and heat generated in the process may damage the DNA. 2.1.2 Extraction methods There are two main extraction methods: liquid-liquid extraction and solid-phase extraction. Liquid-liquid extraction uses two immiscible solvents to extract and concentrate the analytes in one phase. The traditional phenol-chloroform system, for example, uses a large amount of volatile organic solvents and requires many sample processing steps. Nucleic acid extraction has been miniaturized to require only microliter or milliliter volumes to reduce the use of toxic organic solvents [32,33], but it is still difficult to be compatible with microfluidic devices for reasons such as corrosiveness. Instead, solid-phase nucleic acid extraction methods, which allow for the separation of substances with limited amounts of reagents, are more frequently used as assays paired with microfluidic chips [[34], [35], [36]]. Therefore, we focus on the latest advances in the solid phase extraction field. 2.1.2.1 Silicon-based material extraction method Silica is a biocompatible substrate material that is stable, easy to modify, and compatible with nucleic acids. Nucleic acids can be adsorbed or eluted from silica by changing pH or salt concentration conditions [37]. Various silica-based materials, such as silica beads and membranes, have been widely used for nucleic acid extraction in microfluidics. Depending on the material properties, silica-based materials can be used in various ways on microchips [38]. For example, silica beads or nano-filters can simply be placed into the wells or microchannels of a microfluidic chip to extract nucleic acids from the sample. Researchers [39] utilized the microfluidic chip embedded with chitosan-modified silicon dioxide capillaries and a smartphone-based detection unit to construct a system for rapidly extracting and detecting ZIKV RNA. Using nanofiltration membranes made from low-stress silicon nitride, researchers [40] proposed a method to collect negatively charged nucleic acids directly from biological samples by adding an electric field between the sample and the collection buffer separated by the nanofiltration membrane. 2.1.2.2 Magnetic-based strategies Magnetic nanoparticles generally contain magnetic nuclei and outer shell coated with silica or other derivatives with nucleic acid trapping ability. The excellent surface biocompatibility and large specific surface area of magnetic beads ensure their ability to adsorb nucleic acids effectively, and the superparamagnetic properties ensure that the beads can be uniformly dispersed throughout the medium in the absence of an external magnetic field [41]. Magnetic beads move and aggregate rapidly when placed in an external magnetic field for separation. Solid-phase extraction based on magnetic materials is the primary choice for high-throughput and automated nucleic acid extraction because it does not rely on centrifuges or other complicated equipment, significantly reduces separation times, and the implementation of automation eliminates the errors that tend to occur in manual operations. Commonly used magnetic particles include silica-coated, amino-coated, carboxyl-coated Fe3O4 or γ-Fe2O3 magnetic particles [42]. Compared to silica-based solid phase extraction techniques, magnetic particles are easier to manipulate and control with external magnets. The principle of extraction for nucleic acids is simple, nucleic acids are adsorbed on the surface of silica-coated magnetic particles at high salt and low pH conditions, while the molecules can be eluted again at low salt and high pH conditions [43]. The silica-coated magnetic beads allow the extraction of DNA from large volumes of samples with the help of magnetic field-guided enrichment. To fully integrate nucleic acid extraction, amplification and detection on a microfluidic system and to enable the detection of five high-risk HPV indicators, researchers [44] used chitosan-modified magnetic microspheres for pH-induced nucleic acid extraction and integrated this approach into a centrifugal microfluidic chip (Fig. 2A). The microfluidic system includes cell lysis, nucleic acid capture and release, isothermal amplification, and real-time fluorescence detection, all the processes are controlled by centrifugal force and magnetic control. The detection system exhibits good specificity, stability, and high detection speed. The microfluidic system that introduced chitosan-modified magnetic microspheres as a solid-phase extraction material can successfully achieve pH-induced nucleic acid extraction and avoid the adverse effects of organic solvents on subsequent procedures.Fig. 2 Diagram of nucleic acid extraction method. (A) Chitosan-modified magnetic microspheres for pH-induced nucleic acid extraction and integrated into a centrifugal microfluidic chip. Reproduced with permission from Ref. [44]. (B) Rapid plant DNA extraction method using disposable polymeric microneedle patches. Reproduced with permission from Ref. [46]. (C) Using ME chip with thermal gel electrophoresis to directly separate and quantify multiple miRNAs. Reproduced with permission from Ref. [51]. (D) A micro-pipette tip-based nucleic acid test (MTNT) for high-throughput sample-to-answer detection of both DNA and RNA from crude samples including cells, bacteria, and solid plants. Reproduced with permission from Ref. [53]. Fig. 2 For the detection of ultra-low-abundance exosomal nucleic acids, the researchers proposed a simple, efficient and "lab-in-tube" exosome nucleic acid detection system that fully integrates exosome enrichment using immunomagnetic beads (IMB) (10 min), rapid exosome-based lysis (5 min) and sensitive loop-mediated isothermal amplification (LAMP) in a tube [45]. The platform has demonstrated good performance in the direct analysis of exosomal HOTTIP RNA in human serum samples and has the potential to detect low abundance exosomal nucleic acid biomarkers in cancer. As an effective nucleic acid extraction tool, magnetic microspheres have dominated the point of care testing (POCT) field with many advantages such as low sample consumption, high automation and excellent stability. 2.1.2.3 Microneedle-based nucleic acid extraction Microneedle-based nucleic acid extraction methods, which allow rapid in situ extraction of nucleic acid molecules, have been applied in fields such as plant and food pathogen detection., etc. Current conventional protocols for extracting DNA from plant tissues and performing in situ molecular diagnoses are cumbersome and time-consuming. Researchers [46] have developed a rapid plant DNA extraction method using disposable polymeric microneedle (MN) patches (Fig. 2B). The MN-extracted DNA is used for direct PCR without purification. This simple, cell lysis-free and purification-free DNA extraction method may be a transformative approach to facilitate rapid sample preparation for molecular diagnosis of various plant diseases directly in situ. In addition, nucleic acid-based assays are very promising for risk assessment in the food sector. However, cumbersome protocols are often required to isolate nucleic acid components due to the complexity of food matrices. To rapidly track allergens in food, researchers [47] developed an instant and multiplex DNA extraction method based on polyvinyl alcohol microneedle patches. By performing a simple press-and-peel operation in less than 1 min, samples suitable for DNA-based analysis can be collected, and by further combining this with a recombinase polymerase amplification (RPA) assay, rapid screening of complex samples such as shrimp balls and cheesecake for allergy risk can be achieved in less than 30 min. 2.1.2.4 Separation based on electrophoresis Since electrophoresis-based separation methods do not require additional sample processing and nucleic acid extraction and can be directly combined with microchips for the separation and detection of nucleic acids, we summarized the latest developments in this field. Electrophoresis-based separation utilizes an electric field that allows the migration of negatively charged NA toward a positively charged cathode [48]. The principle of separation is that analytes have different migratory mobility when located in high applied electric fields (up to 800 V/cm) according to the size and charge-to-mass ratio. The use of electrophoresis has unique advantages over traditional DNA extraction, such as the isolation and enrichment of small nucleic acid fragments can be completed in a few minutes. Cleaving large DNA sequences into fragments using restriction endonucleases also allows for high-speed separation and detection of large nucleic acid fragments. Among the materials that can be used as matrices for capillary electrophoresis sieving, gels are indispensable materials similar to plate electrophoresis gels. Polyacrylamide and agarose are generally used as the matrices for conventional plate gel electrophoresis, which are prone to generate Joule heat resulting in poor reproducibility. As permanent polymers, they can only be used once [49]. Researchers [50] developed a DNA-modified polyacrylamide hydrogel that captures and releases 20–1000 bp ssDNA and dsDNA with high specificity and sensitivity. In this work, ssDNA and dsDNA from serum are electrophoresed into a modified hydrogel, and then thermos cycled to capture DNA by hybridization. After multiple electrophoreses, DNA is concentrated in the gel and retrieved, which can significantly improve the detection efficiency of free DNA in blood samples. In another example, researchers [51] developed a simple, rapid method to directly quantify multiple miRNAs using microfluidic thermal gel electrophoresis (TGE) (Fig. 2C). TGE is a thermally responsive polymer that changes viscosity based on temperature. The sample is poured directly into a liquid phase thermal gel that can be quickly loaded into the microfluidic channel. The gel is then cured by heating the device to lock the sample and electrolyte in the appropriate position and provide a sieving matrix. The method achieved a high resolution between four double-stranded miRNA-probe hybrids and four excess single-stranded probes. Wei et al. [52] developed a multiplex fluorescence signal amplification method based on an electrophoresis platform to separate and detect microRNAs. The method used two kinds of fluorescein-labelled DNA signal probes to hybridize with its target microRNAs and utilized T7 exonuclease to realize the fluorescence signal amplification. Two kinds of fluorescein-labelled DNA segments with different sizes were separated and detected on the Microelectrophoresis (ME) laser-induced fluorescence detection platform. The method produces an excellent linear relationship between the fluorescence intensity and the amount of microRNA. The detection limit for microRNA can be as low as 15 pM. 2.1.3 Extraction-free and one-step nucleic acid detection In some areas where resources are scarce extraction-free or one-step analysis methods for rapid detection are required. In addition to solid phase extraction techniques, techniques such as FTA rapid nucleic acid extraction cards are also widely used. Our group have developed a micro-pipette tip-based nucleic acid test (MTNT) for high-throughput sample-to-answer detection of both DNA and RNA from crude samples without the need for sample pretreatment and complex operation [53]. MTNT consists of micro-pipette tips and embedded solid phase nucleic acid extraction membranes and fully integrates the functions of nucleic acid extraction from crude samples, LAMP of nucleic acids, and visual read-out of assays (Fig. 2D). Some researchers developed a digitally enhanced one-pot method for nucleic acid detection using digital microwell arrays [54]. This method uses the combination of RT-RPA and CRISPR/Cas12. The target RNA is reverse-transcribed and amplified into DNA by RT-RPA, and the crRNA-Cas12a complex is activated to cut the single-stranded DNA fluorescent reporter gene to generate fluorescence. By confining this reaction to the digitized microwell, the local reaction concentration is increased, the signal is enhanced, and the sensitivity is improved. The method enables qualitative detection in <15 min and quantitative detection in 30 min with a high signal-to-background ratio, wide dynamic range, and high sensitivity. Other researchers have used CRISPR/Cas13 to detect COVID-19. Hsu et al. developed an integrated and rapid automated microfluidic detection system for nucleic acid detection [55]. The system consists of a single-use gravity-driven microfluidic cartridge and a compact instrument that automates the detection process from sample to result within 60 min, with a sensitivity of 40% for SARS-CoV-2 copies/μl. The combination with microfluidics makes one-step nucleic acid testing more feasible, and the simplicity and rapidity of these assays allows for on-site deployment, bedside diagnostics, and routine monitoring. The detailed comparison of the sample processing method is shown in Table 1 .Table 1 Comparation of the sample processing methods based on microfluidics. Table 1Method Material Type of Extraction microfluidic system Analyte Time Advantages Reference Silicon-based material Silica beads SPE Capillary chip ZIKV RNA 25 min Biocompatible material, [39] Silicon nitride nanoporous membrane SPE Microelectrophoresis chip miRNA 30 min easy to perform, reproducible [40] Magnetic-based material Magnetic Microspheres SPE Centrifugal chip DNA 18 min Rapid, automation, no need centrifugation [44] magnetic beads SPE N/A RNA 15 min [45] Microneedle Polyvinyl alcohol SPE N/A DNA 1 min In situ extraction, simple, cell-lysis-free, purification-free [46] electrophoresis-based separation methods Polyacrylamide hydrogel N/A Microelectrophoresis chip DNA 12 min Simple, fast, low sample consumption, low cost [50] Thermal gel N/A Microelectrophoresis chip microRNA 5 min [51] Agarose gel N/A Microelectrophoresis chip microRNA 2 min [52] FTA card Cellulose matrix SPE MTNT DNA and RNA 30 min Rapid, without pretreatment, easy operation [53] 2.2 Nucleic acid amplification techniques For the detection of a low concentration of nucleic acid in samples, an amplification reaction is essential. Microfluidics combined with nucleic acid amplification can lead to a more sensitive test result and make it a powerful tool for clinical microbiology testing. In recent years, various microfluidic devices based on nucleic acid amplification have emerged for the rapid and accurate detection of DNA and RNA [[56], [57], [58]]. In general, nucleic acid amplification-based techniques are classified as PCR and isothermal nucleic acid amplification. This section will summarize those nucleic acid detection techniques based on microfluidic systems. 2.2.1 Polymerase chain reaction Since being invented in the 1980s, PCR has become a powerful tool for detecting virtually all clinical microorganisms. PCR is an in vitro enzymatic DNA amplification performed at three discrete temperatures. PCR consists of a thermal cycle that includes denaturation of double-stranded DNA at high temperature, annealing of primers to genomic DNA complementary sequences, and extension with the aid of polymerase. The polymerase used in the reaction is Taq polymerase, which can tolerate high temperatures [59]. PCR can amplify a small amount of target DNA/RNA at an exponential rate, and the amplification process generates up to one billion copies of the original target gene, making PCR a powerful tool for rapidly detecting nucleic acids [60]. Due to advances in microfluidics, PCR has been widely integrated into microfluidic platforms used for diagnosis. In recent decades, many portable microfluidic devices equipped with thermal cycling systems to perform PCR have been developed to meet the need for immediate diagnosis. Microfluidic PCR chips can be divided into two types of structures: chamber PCR and continuous flow PCR(CF-PCR) [2]. The chip is heated and cooled in a chamber PCR chip at a specific thermal cycling temperature after injecting the sample into the wells. CF-PCR chips transfer samples through fixed temperature zones for thermal cycling. CF-PCR chips can be further subdivided into serpentine, spiral, and straight-through channels [61,62]. These designs allow microfluidic PCR can be used in various molecular biology applications, such as gene detection and sequencing. CF-PCR solves the problem of a discontinuous and time-consuming analysis process caused by traditional PCR's temperature rise and fall. CF-PCR can achieve rapid amplification by actively circulating the reagents into three preheating zones with a pump connected to the chip, and the design of long serpentine channels or short straight-through channels successfully avoids the transition phase among different reaction temperatures, thus significantly reducing the analysis time. Researchers [63] designed a CF-PCR microfluidic chip and fabricated a portable system for multiplex amplification of periodontal pathogens. They analyzed the thermal distribution of the heater used for PCR and then studied typical primers for Porphyromonas gingivalis (Pg), Treponema denticola (T.d), and Tannerella forsythia (T.f). The CF-PCR showed an overwhelming advantage in reaction speed compared to conventional thermal cycler. Digital PCR (dPCR), based on microdroplets and microfluidic chips, has gained widespread attention and applications. dPCR is a third-generation PCR technique that achieves accurate absolute quantification of nucleic acids for quantitative analysis with high sensitivity [64]. In the dPCR chip, the reaction mixture was divided into a large number of independent reaction units according to finite dilution and Poisson statistics [65]. Compared to conventional PCR, dPCR achieves absolute quantification of DNA templates, is not dependent on standard curves, and is more resistant to PCR inhibitors. The dilution of the sample and the amount and homogeneity of the dispersion can significantly affect the accuracy of the quantitative results. Researchers [66] reported a chamber-based dPCR microarray structure for high-throughput, high-sensitivity quantitative measurements of SARS-CoV-2 viral genes and mutant lung cancer genes (Fig. 3 A). Samples will be assigned to each microchamber for independent reactions based on Poisson distribution. The prepared microarray chips successfully quantify a solution mixture containing both genes with a detection limit of 10 copies/μL at a throughput of 46200 microchambers. These chips are inexpensive and easy to industrialize. Researchers [67] proposed a simple method for rapid and low-cost dPCR assays. By subdividing a bulk sample volume into a large number of equal volume compartments using a self-digitizing chip, the method allows for low waste and large volume sample discretization in minutes.Fig. 3 Diagram of integrated microfluidic chips with nucleic acid amplification. (A) dPCR chip for high-throughput, high-sensitivity quantitative measurements of SARS-CoV-2 viral genes. Reproduced with permission from Ref. [66]. (B) Microcapillary LAMP for the detection of nucleic acids. Reproduced with permission from Ref. [73]. (C) Dual-mode LAMP incorporating magnetic bead separation to determine the methylated Septin9 gene in colorectal cancer. Reproduced with permission from Ref. [75]. (D) A CRISPR/Cas12a-based SNP detection genotyping method based on the centrifugal microfluidic device. Reproduced with permission from Ref. [16]. Fig. 3 2.2.2 Isothermal amplification Compared to PCR, which requires a complex thermal cycler, isothermal nucleic acid amplification is an effective solution to replace conventional PCR because of the mild conditions and constant temperature requirements. Isothermal amplification methods have been widely used in microfluidic detection devices where nucleic acids are amplified at a constant temperature, thus facilitating low-cost and portable POCT devices for molecular diagnostics [68,69]. Here we focus on two primary isothermal nucleic acid amplification techniques: LAMP and RPA. LAMP uses a set of four to six primers to identify the target gene sequence, including two outer primers (F3 and B3), a forward inner primer and a reverse inner primer. LAMP provides a highly sensitive molecular diagnostic tool with a reaction time of less than 1 h and can amplify billions of DNA copies [[70], [71], [72]]. Our group previously demonstrated a simple, robust, multiplexed and instant microcapillary LAMP (cLAMP) for the detection of nucleic acids [73]. The assay integrates capillaries (glass or plastic) for introducing and holding samples/reagents, a water droplet segment for preventing contamination, a pocket heater for providing heat, and a handheld flashlight for visual read-out of the fluorescent signal. cLAMP system allows simultaneous detection of two RNA targets of the human immunodeficiency virus (HIV) from multiple plasma samples and achieves two copies of the standard plasmid with high sensitivity. Our cLAMP holds great promise for immediate care applications in resource-poor settings (Fig. 3B). In a recent study, researchers [74] used a LAMP method to amplify and detect specific nucleic acid (DNA/RNA) targets by introducing a microfluidic device that can sequentially distribute samples into multiple reaction microchambers in a single operation. This method provides a fast and straight-forward platform for multiplex gene diagnosis of multiple viral infectious diseases. The determination of DNA methylation is still a challenge. Researchers have recently proposed a dual-mode LAMP incorporating magnetic bead separation to determine the methylated Septin9 gene in colorectal cancer [75]. A one-pot real-time fluorescent and colorimetric LAMP was used to detect the methylated Septin9 gene (60 min). The method was shown to detect methylated DNA in HCT 116 cells in the range of 2 to 0.02 ng/μL with a detection limit of 0.02 ± 0.002 ng/μL (RSD: 9.75%). The method also distinguished methylated Septin9 in 0.1% of HCT 116 cells (RSD: 6.60%), indicating its high specificity and sensitivity (Fig. 3C). To address the global threat of cryptococcal meningitis (CM), researchers [76] developed a multifunctional microfluidic module that integrates pathogen enrichment and on-chip nucleic acid extraction based on a portable one-pot, temperature-free LAMP lyophilization reagent bead. This module does not require additional instrumentation and is expected to develop a simple, rapid, and efficient method for "sample-in, result-out" testing of actual cerebrospinal fluid samples. To diagnose epidemic-transmitted infections caused by high-risk human papillomavirus (HPV), researchers [77] developed a microfluidic detection system consisting of a microfluidic chip and a corresponding detection device. The proposed method integrates nucleic acid purification, isothermal amplification and real-time fluorescence detection into a single device. Furthermore, it shows high specificity (100%) and excellent stability (coefficient of variation <6%) in five HPV genotypes. Compared to conventional qPCR, LAMP is more rapid. The integrated microfluidic assay system provides automated and rapid diagnosis in less than 40 min. However, the reaction temperature of LAMP reaches approximately 70 °C and inevitably generates air bubbles, which can lead to false positive results. The primer design of LAMP is more complicated than PCR. RPA can partially reduce LAMP's high false positive rate, as the relatively low reaction temperature (∼37 °C) leads to relatively minor evaporation problems. In the RPA system, two opposite primers initiate DNA synthesis by binding to recombinase and can complete amplification in less than 10 min. Thus, the whole process of RPA is much faster than PCR or LAMP [78]. In recent years, microfluidic technology has been shown to increase the speed and accuracy of RPA. In response to the global outbreak caused by SARS-CoV-2, researchers [79] developed a CRISPR/CRISPR-associated (Cas) 13a-based biosensor combined with RPA to detect the S and Orf1ab genes of SARS-CoV-2 within 30 min, with detection limits as low as 0.68 fM and 4.16 fM for the two genes, respectively. In addition, they used lateral flow strips to visualize SARS-CoV-2 detection, which became a promising tool in the field of SARS-CoV-2 detection. In another example, researchers [80] presented an integrated multiplex-digital recombinase polymerization amplification microfluidic chip. The chip combined DNA extraction, multiplex digital RPA, and fluorescence detection into a "sample-multiplex-digital-answer-output" system and was successfully demonstrated to detect three pathogenic bacteria simultaneously and give digital quantitative results in 45 min. However, the cost of individual RPA reaction is high. In addition, RPA may lead to non-specific amplification products under less rigorous reaction conditions. These limitations may affect the application of RPA in microfluidic systems and deserve further optimization. Well-designed primers and probes for different targets are also needed to improve the feasibility of RPA-based microfluidic strategies in POCT. 2.2.3 CRISPR-based nucleic acid detection methods Beyond the widespread application as genome-editing and regulatory tools, CRISPR-Cas systems also play a critical role in nucleic acid detection due to their high sensitivity and specificity [81]. The recently developed Cas family have opened the door to developing new strategies for detecting different types of nucleic acids for various purposes [82]. Precise and efficient nucleic acid detection using CRISPR-Cas systems has the potential to advance the application and development of POCT. Cas9, Cas13, and Cas12a have been combined with microfluidic technologies and are widely developed as diagnostic tools. They are activated upon binding to target DNA or RNA with the help of guide RNAs targeting pathogen-specific nucleic acids, respectively. Once activated, the Cas proteins begin cleaving, cutting off quenched fluorescent probes and releasing fluorescence [83]. We have developed a series of CRISPR-based nucleic acid assays in recent years and combined them with centrifugal microfluidic chips to develop a powerful tool suitable for POCT. To accurately and efficiently detect single nucleotide polymorphisms (SNPs) associated with multiple human diseases, we proposed a universal and high-fidelity genotyping method based on the microfluidic device of the CRISPR system [16]. Briefly, the universality of CRISPR/Cas12a-based SNP detection is improved by the systematic insertion of prototype spacer adjacent motif (PAM) sequences; sensitivity and specificity are improved by removing complementary ssDNA and introducing additional nucleotide mismatches. Preloading CRISPR/Cas12a reagents into the bedside biochip allows for process automation, improved stability and long-term storage. The biochip enables fast and easy genotype detection in less than 20 min (Fig. 3D). For rapid, accurate and early detection of SARS-CoV-2, we combined CRISPR with recombinant enzyme-assisted amplification (RAA) to develop a dual CRISPR/Cas12a-assisted RT-RAA assay and "sample-to-answer" centrifugation microfluidic platform that can automatically detect 1 copy/μL of SARS-CoV-2 in 30 min [15]. The chip separates amplification (RAA) from detection (CRISPR), maximizing sensitivity and reducing time consumption by up to three times. For 26 positive and 8 negative clinical SARS-CoV-2 samples, this automated centrifugation microfluidic achieves 100% accuracy compared to the gold standard RT-PCR technique. 2.3 Signal read-out method Signal acquisition is the final step in nucleic acid detection. Fluorescence-based, electrochemical, and colorimetric signal read-out strategies have been widely used. In addition, many signal amplification methods have been developed [84]. In this section, we will describe each method's principle, combination with microfluidics, and applications in molecular diagnostics. 2.3.1 Fluorescent read-out method Fluorescence-based assays are widely used in molecular detection and other fields because of their high sensitivity, low cost, and immediate analysis. The principle is to use labelled fluorophores (e.g., fluorescent dyes and nanomaterials) to generate a detectable signal (fluorescence enhancement or quenching). Two standard methods are used to quantify DNA samples by fluorescence detection. The first is the use of fluorescent dyes embedded in dsDNA. Dyes are added to the PCR mix to bind the amplified dsDNA, and the complex can emit fluorescence. As the amount of DNA product increases with each thermal cycle, the measured fluorescence intensity also increases, thus allowing simultaneous quantification of DNA concentration. An alternative approach is using a modified DNA oligonucleotide probe that fluoresces when target DNA exist. This real-time fluorescence detection method is highly sensitive compared to conventional gel electrophoresis detection of PCR products and has attracted much attention. Researchers [85] described a highly sensitive and versatile fluorescent biosensor for rapid and sensitive detection of pathogenic nucleic acids. The platform successfully detected four human-associated pathogens in clinical samples. The results were consistent with qPCR. The assay system can be designed to target different pathogen nucleic acids by simply changing the protector for different targets and the portion of the catalytic hairpin that binds to the protector. Researchers [26] demonstrated an integrated device for in situ fluorescence detection following reverse transcription, rapid thermal cycling (plasma heating via magnetic plasma nanoparticles) and magnetic removal of nanoparticles. SARS-CoV-2 RNA could be detected in 17 min using this portable device, which correctly classified all nasopharyngeal, oropharyngeal, and sputum samples from 75 COVID-19 patients and 75 healthy controls with fluorescence intensity in good agreement with standard RT-qPCR (Fig. 4 A).Fig. 4 Diagram of signal read-out method. (A) An integrated device for in situ fluorescence detection for SARS-CoV-2 RNA detecting. Reproduced with permission from Ref. [26].(B) Fluorescence-based digital warm-start CRISPR assay for sensitive, quantitative detection of SARS-CoV-2 Reproduced with permission from Ref. [88]. (C) A 3D-printed lab-on-a-chip that simultaneously detects SARS-CoV-2 RNA in saliva and anti-SARS-CoV-2 immunoglobulin within 2 h via multiplexed electrochemical output. Reproduced with permission from Ref. [22]. (D) A face mask with a lyophilized CRISPR sensor and a colorimetric sensing platform for wearable, noninvasive detection of SARS-CoV-2. Reproduced with permission from Ref. [98]. Fig. 4 Fluorescence-based CRISPR technologies, such as SHERLOCK, DETECTER, and HOLMES, have also been proposed with high sensitivity and specificity for very convenient and rapid nucleic acid detection [86,87]. Researchers [88] reported a digital warm-start CRISPR (dWS-CRISPR) assay for sensitive, quantitative detection of SARS-CoV-2 in clinical samples (Fig. 4B). The dWS-CRISPR assay is initiated above 50 °C, overcoming premature target amplification at room temperature, allowing accurate and reliable digital quantification of SARS-CoV-2. By targeting the nucleoprotein gene of SARS-CoV-2, the dWS-CRISPR assay is able to detect SARS-CoV-2 RNA as low as 5 copies/μl in the chip, making it a sensitive and reliable CRISPR assay that facilitates accurate detection of SARS-CoV-2 for digital quantification. 2.3.2 Electrochemical read-out method Electrochemical detection is a method to detect the measured components by converting the chemical signal generated by the analyte in solution into an electrical signal. Electrochemical detection is a powerful analytical method for POCT applications because it is fast, simple to manufacture, low cost, portable, and easy to control. The specific electrical signal can be generated by functionalized modifications of the electrode, such as immobilized probes, enzymes, and aptamers, which bind specifically to the amplification product [[89], [90], [91]]. Electrochemical detection is well suited to miniaturization and integration with microfluidics. To rapidly and accurately detect SARS-CoV-2 RNA to determine the immune status of individuals infected by the virus or vaccinated against the disease. The researchers [22] describe the development and application of a 3D-printed lab-on-a-chip that simultaneously detects SARS-CoV-2 RNA in saliva and anti-SARS-CoV-2 immunoglobulin in saliva with plasma added to the electrode as a function of SARS-CoV-2 Spike S1, nucleocapsid, and receptor-binding domain antigens within 2 h via multiplexed electrochemical output. The inexpensive microfluidic electrochemical sensor can aid in multiple diagnostics at the point of care (Fig. 4C). Researchers [92] have developed a new electrochemical method for sensitive and reliable detection of nucleic acids in biological fluids. The advantages of the lipid membrane, especially its excellent antifouling ability, are employed to enhance the method's applicability in a complex environment, while the significant solid-state Ag/AgCl response of AgNPs is used to ensure the detection sensitivity of the method. The core of this method's workflow is the target-induced Y-shape structure formation, which recruits AgNPs to the electrode surface, producing considerable electrochemical responses. Taking a liver cancer-related long non-coding RNA as a model target, the method exhibits high sensitivity, specificity, and reproducibility with a detection limit of 0.42 fM. In another example, researchers [93] developed a versatile and highly sensitive electrochemical biosensing strategy for analyzing dengue virus (DENV) nucleic acids using a triple nanostructure-mediated dendritic hybridization chain reaction (HCR). The dendritic products formed by a series of hybridizations are combined with avidin-labelled horseradish peroxidase (avidin-HRP) to obtain an amperometric signal for ultrasensitive electrochemical detection of DENV. The method has a detection range of 1.6–1000 pM, a detection limit of 188 fM, and the ability to distinguish single-base mutations. By changing the recognition sequence of the initiator, the detection of different DENV nucleic acid fragments can be achieved with the same performance. Therefore, the method is well scalable to other nucleic acids and provides a good candidate for nucleic acid detection in early clinical diagnosis. 2.3.3 Colorimetric assays method Colorimetric sensors are platforms that indicate the presence of a target through color change. Gold nanoparticle-based (AuNPs) colorimetric detection exhibits unique distance-dependent optical properties through gold nanoparticles' aggregation and dispersion behavior and can be identified with the naked eye [[94], [95], [96]]. Colorimetric assays have been used in POCT applications to benefit from portability, low cost, ease of preparation, and naked eye readings. Colorimetric assays can use the oxidation of peroxidase or peroxidase-like nanomaterials, aggregation of nanomaterials and addition of dye indicators to translate information about the presence of target nucleic acids into visible color changes [97]. Notably, gold nanoparticles are widely used in the establishment of colorimetric strategies. Due to the ability to induce rapid and significant color changes, there is a growing interest in developing colorimetric POCT platforms for on-site infectious disease diagnosis. As an example, researchers [98] demonstrate the development of a face mask with a lyophilized CRISPR sensor for wearable, noninvasive detection of SARS-CoV-2 at room temperature within 90 min. They embedded colorimetric genetic circuits into cellulose substrates surrounded by a fluid wicking and containment assembly made of flexible elastomers. The devices are flexible, elastic and can rapidly wick in splashed fluids through capillary action, using a lacZ β-galactosidase operon as the circuit output to hydrolyze chlorophenol red-β-D-galactopyranoside (CPRG), a yellow-to-purple color change develops upon exposure to a target (Fig. 4D). Researchers [99] presented a novel design of a colorimetric gene sensing platform based on the CRISPR/Cas system. In this strategy, programmable recognition of DNA by Cas12a/crRNA and programmable recognition of RNA with complementary targets by Cas13a/crRNA activate trans-ssDNA or -ssRNA cleavage. Target-induced trans-ssDNA or -ssRNA cleavage triggers changes in the aggregation behaviour of the designed AuNPs-DNA probe pairs, enabling naked-eye gene detection in less than 1 h. In another example, researchers [100] reported a colorimetric virus detection method based on the CRISPR/Cas9 system. In this method, RNA in virus lysates is directly recognized by the CRISPR/Cas9 system, and then streptavidin-horseradish peroxidase binds to biotin-PAMmer to induce colour changes by oxidation of 3,3′,5,5′-tetramethylbenzidine. SARS-CoV-2, pH1N1 and pH1N1/H275Y viruses could be successfully identified by visual inspection using this method. Despite the outstanding performance of the above detection methods, disadvantages still exist. A comparison of these methods is presented (Table 2 ), including detailed information on some applications (including advantages and disadvantages).Table 2 Comparation of the detecting methods based on microfluidics. Table 2Detecting methods Amplification methods Microfluidic systems Analyte Performance Advantages/disadvantages Reference Fluorescence PCR N/A HPV DNA 18 fM 1 h Superior sensitivity, low cost, easy to operate, rapid analysis [85] RT-PCR NanoPCR device SARS-CoV-2 RNA 3.2 copies/μl 17 min [26] CRISPR Digital droplet chip SARS-CoV-2 RNA 5 copies/μl High background noise [88] Electrochemistry LAMP-CRISPR/Cas12a Multiplexed EC sensors within an LOC microfluidic chip SARS-CoV-2 RNA 0.8 copies/μl 2 h Rapid detection, easy to fabricate, low cost, portable and self-controlled [22] N/A N/A RNA 0.42 fM [92] HCR N/A DENV DNA 188 fM Unstable and susceptible [93] Colorimetry RT-RPA-CRISPR/Cas12a wFDCF devices SARS-CoV-2 RNA 500 copies 90 min Portable, low cost, easy to prepare, naked eye readout [98] CRISPR/Cas13a N/A Bacteria RNA 200 copies 1 h [99] CRISPR/Cas9 N/A SARS-CoV-2 RNA 140 pM 90 min Unable to quantitatively detect, limited sensitivity [100] 3 Chip classification and structure 3.1 Centrifugal chip The centrifugal microfluidic chip uses centrifugal force to drive liquid to flow to different areas for different chemical analysis reactions. The centrifugal microfluidic chip usually consists of a miniaturized flow pipeline, valve, reaction chamber, detector, and other functional components. In recent years, centrifugal chips have emerged endlessly and have been widely used in basic research in the field of nucleic acid detection. Our group has been deeply cultivated in the field of the centrifugal microfluidic chip. We designed a pull-up centrifugal disc chip to drive the liquid flow with the power generated by the card pumping [20]. The chip has eight identical reaction units, and each unit has 4 sets of microchambers, which can complete the entire process from nucleic acid purification to detection. The chip implements a non-electrical driver detection, which can detect 6 kinds of pathogenic bacteria at the same time. The sensitivity is 200 bacteria/μL in cracking solution, which is of positive significance for clinical diagnosis. We also designed a centrifugal chip with 32 reaction chambers that can detect 16 targets at the same time, introduce CRISPR reactions into it [16,18], and develop The Cas12a-assisted straight-forward microfluidic equipment for analysis of nucleic acid (CASMEAN). We first applied it to the detection of Pseudomonas aeruginosa, which can be finished within 1.5 h, and has a 1000 CFU/mL detection limit and excellent specificity. After that, we also applied it to the DNA single nucleotide polymorphism detection. In 20 min, we completed the testing of three genotypes of the homozygous wild type, the homozygous mutant type, and the heterozygous mutant type with good preservation and accuracy. In addition, our group has recently developed an automated, integrated centrifugal chip that can complete the process from sample pre-processing to detection [17] (Fig. 5 A). The chip is composed of 5 layers of PMMA material: substrate, microstructures, ball valve, and injection holes. We use this chip to perform RT-Raa-T7-CRISPR/CAS13A reaction to achieve high-specific accuracy and fast detection and typing of HBV in human blood samples.Fig. 5 Diagram of centrifugal chip and valves chip. (A) An automated, integrated centrifugal chip that can complete the process from sample pre-processing to detection. Reproduced with permission from Ref. [17]. (B) A centrifugal chip used for RT-LAMP amplification detection has 20 independent reaction chambers, which can identify 6 influenza virus subtypes at the same time. Reproduced with permission from Ref. [102]. (C) A multifunctional microfluidic device based on a monolayer membrane valve consisting of three parts, including a CTCs capture region, a monolayer membrane flap region, and a microchamber nucleic acid-based dPCR detection and analysis region. Reproduced with permission from Ref. [109]. (D) A rotary valve-assisted fluidic chip coupled with CRISPR/Cas12a for fully integrated nucleic acid detection. Reproduced with permission from Ref. [110]. Fig. 5 In the detection of antibiotic resistance genes of Golden Staphylococcus aureus, researchers designed a centrifugal chip for the first time [101]. The chip has 30 independent reaction chambers that can restore frozen dry reagents, and it can conduct 5 groups of parallel testing simultaneously. In actual testing, the chip with RPA amplification can achieve a detection limit of 10 copies in less than 20 min, which has excellent performance. Researchers have designed a chip that can identify 6 influenza virus subtypes at the same time [102] (Fig. 5B). This centrifugal chip used for RT-LAMP amplification detection has 20 independent reaction chambers, which can be divided into seven layers, including two bottom caps and two pattern layers, two adhesive layers for adhesion, and a specially designed membrane valve layers that can be used for stabilizing pressure. For the detection of the five subtypes of influenza A and influenza B, the detection sensitivity of 100 copies can be achieved. It has excellent performance and is particularly hoped to monitor diseases in resource-scarce areas. In addition, for the test of SARS-COV-2, there are many applications of integrated disc chips. For example, the centrifuge chip that can be read out from the smartphone can complete the whole process of detection within 1 h [103], and the centrifugal disk chip can simultaneously detect seven human respiratory coronaviruses [104]. 3.2 Chips with valves as the primary function A key component in microfluidics is the microfluidic valve. Microfluidic valves can be used not only to direct flow at intersections but also to allow real-time adjustment of mixtures. Various valves give excellent expandability and functionality to microfluidic chips, such as membrane valves, rotary valves, ball valves, quake valves, so forth [105]. Microfluidic chips with good structure and performance will bring new vitality and infinite possibilities for nucleic acid detection functions [106,107]. Researchers [108]. developed a new microfluidic chip for nucleic acid detection using stretching acts as the driving force. The sample entered the chip by applying capillary force. The strain valve was opened under the action of tensile force, and the spring pump generated the power to drive the fluid to flow toward the detection chamber in a specific direction. The detection of Sars-CoV-2 was realized on the chip. In another example, researchers [109] developed a multifunctional microfluidic device based on a monolayer membrane valve consisting of three parts, including a circulating tumor cells (CTCs) capture region, a monolayer membrane flap region, and a microchamber nucleic acid-based dPCR detection and analysis region (Fig. 5C). The chip allows CTC capture, lysis, and genetic characterization on a single chip. CTCs are first captured in the CTC capture region and then cleaved using Proteinase K to release the nucleic acids. The CTCs lysates are then transferred to a nucleic acid detection region consisting of 12,800 microchambers for nucleic acid detection. Researchers [110]. established a simple rotary valve-assisted fluidic chip coupled with CRISPR/Cas12a for fully integrated nucleic acid detection (Fig. 5D). All detection reagents are pre-stored on the fluidic chip. With the help of a rotary valve and syringe, fluid flow and agitation can be precisely controlled. Nucleic acid extraction, LAMP reactions and CRISPR assays can be completed in less than 80 min. Using Vibrio parahaemolyticus as the target, the chip can reach a detection sensitivity of 31 copies of target DNA per reaction. 3.3 Digital droplet chip The microfluidic droplet chip is developed based on the single-phase microfluidic chip. Since Professor Rustem F. Ismagilov first designed the T-shaped microfluidic droplet chip, the microfluidic droplet chip has received extensive attention and research. Due to a series of potential advantages such as low sample consumption, fast mixing speed, simple operation, easy manipulation, and good repeatability, microfluidic droplet chip technology has been widely used in the field of high-throughput detection. With the development of droplet technology, the researchers divided the reaction solution into thousands of droplets and divided the reaction into many separate droplets, which can greatly improve the reaction sensitivity and realize a digital droplet-based nucleic acid assay. 3.3.1 Digital PCR chip In recent years, researchers have developed a variety of digital droplet PCR (ddPCR) assays by introducing PCR into droplet microfluidic chips. Researchers developed a ddPCR using droplet chip technology to achieve multiplex screening of genes in transgenic maize lines [111] (Fig. 6 A). This protocol adopted a single universal primer strategy to develop a single universal primer multiplex ddPCR (SUP-M-ddPCR). In the genetically modified screening assay for maize, a detection limit of 0.1% and a quantification limit of 0.01% are achieved with high specificity and a relative deviation of less than 25%, which has a good application prospect in the detection of GM food.Fig. 6 Diagram of digital droplet chip. (A) A ddPCR chip can achieve multiplex screening of genes in transgenic maize lines. Reproduced with permission from Ref. [111]. (B) A droplet chip with a water phase entrance and two oil phase entrances, the negative pressure formed by the piston syringe at the end exit to promote liquid flowing to form droplets. Reproduced with permission from Ref. [116]. (C) A droplet array chip platform CARMEN can detect 4000–5000 targets simultaneously. Reproduced with permission from Ref. [23]. (D) A "cross" junction droplet chip with hybrid detection technology to realize attomolar sensitivity of HPV virus without amplification. Reproduced with permission from Ref. [19]. Fig. 6 In the fight against SARS-CoV-2, ddPCR technology also played an important role. Researchers developed a ddPCR gene chip equipped with an imaging system [112]. The chip is designed as a three-layer structure, the upper and lower layers are made of transparent glass slides, and the middle is the PDMS layer with channels and microchambers. The entire chip has 20,000 independent chambers. The volume of each chamber is 0.81 nL, which can accommodate a total of about 16 μL of samples. In the detection of SARS-CoV-2 samples, the system achieved 99% accuracy, and the imaging system also reduced the imaging time by as much as 165 s, which has the possibility of further development of applications. 3.3.2 Digital isothermal amplification chip The PCR process involves changes in different temperature ranges and has certain challenges for chip design. The isothermal amplification with constant temperature can reduce the difficulty of instrument and chip design. In recent years, more and more researchers have taken the isothermal amplification technologies into the microfluidic droplet platform to develop digital isothermal amplification technologies. Researchers designed a droplet chip with a "cross" flow focusing junction design [113]. They combined this droplet chip with LAMP to detect Salmonella typhimurium and used the final fluorescence of the droplet to evaluate the result. The results show that this digital droplet LAMP method can detect the genes in the diluted 103–105 times in the pure Salmonella typhimurium with good sensitivity and specificity. RPA technology is also a temperature amplification technology that has attracted much attention. Researchers did some research in this field [114,115]. They designed a "cross" junction droplet microfluidic chip with a microfluidic pico-injector. When the RPA reagent (excluding magnesium ions) forms a droplet, the magnesium ion is added from the pico-injector so that the RPA amplification reaction is performed in the droplet. For the detection of actual samples, this method shows a high signal-to-noise ratio and 100% accuracy. They also combines CRISPR/Cas13a with RPA and introduces them in the droplet chip, which also shows excellent performance. In the detection of HPV viruses, excellent sensitivity and specificity are displayed. At the same time, the detection time is greatly shortened, and the relative test results can be obtained in only 10 min. 3.3.3 Digital CRISPR and other chip CRISPR technology has attracted much attention and has been extensively studied for nucleic acid detection. The target and crRNA can bind to the Cas protein to trigger specific cis-cutting and relevant non-specific trans-cutting to generate a lot of fluorescence signals. However, CRISPR-based assay mainly relies on reverse transcription and amplification to improve detection sensitivity. CRISPR-based assays are usually carried out in a reactor with a large volume of uL-level. The generated fluorescence is severely diluted and can be detected only when many targets are in the system. Digital CRISPR based on microdroplets was developed to detect RNA and DNA. Researchers designed a droplet chip to complete accurate digital CRIPR testing without amplification [10,116] (Fig. 6B). The chip structure is different from what we mentioned before. It uses a water phase entrance and two oil phase entrances. At the same time, the negative pressure formed by the piston syringe at the end exit promotes the liquid flow to form a droplet without a complex syringe pump. They have introduced the Cas12 and Cas13 systems into their designed chips and have achieved single molecular detection without any amplification. Compared with bulk CRISPR detection, the sensitivity of digital CRISPR has been increased by 50 times and 10000 times. At the same time, it only takes a few microliter reagents to consume, which is likely to be widely used in clinical situations. In addition to single index detection, the droplet chip also shows its multiple detection capabilities. Researchers have developed a droplet array chip platform CARMEN, which can be used for multiple pathogen detection [23] (Fig. 6C). After introducing the Cas13 system, more than 4500 targets can be detected on a single chip. At the same time, due to the strong multiple detection capacity, the detection cost has also dropped by about 300 times. The characteristics of multiple detection capabilities, low cost, and miniaturization of this method make it suitable for clinical diagnosis in the future. In addition to CRISPR, the droplet chip is also combined with other technologies to achieve high-sensitivity nucleic acid detection. Our group has designed a simple "cross" junction droplet chip and introduced hybrid detection technology into it [19]. The attomolar sensitivity to the HPV virus is achieved without amplification (Fig. 6D). The DNA molecular circuit is also combined with the droplet chip and achieves the femtomolar sensitivity for miRNA detection [117]. 3.4 Paper-based chip Paper is a promising material for constructing microfluidics for diagnosis. Cellulose paper is a ubiquitous, lightweight, biodegradable and inexpensive material that can be a natural platform for microfluidics [118] (Fig. 7 A). Cellulose paper-based microfluidics can handle 0.1–100 μL of liquid through millimeter-scale fluidic channels [119]. As one of the popular applications of paper-based materials, lateral flow assay (LFA) is a user-friendly diagnostic tool [[120], [121], [122], [123], [124]].Fig. 7 Diagram of paper-based chip and ME chip. (A) The paper-based barcode assay system. Reproduced with permission from Ref. [118]. (B) Multiplexed barcode paper-based inspection that is compatible with mobile devices. Reproduced with permission from Ref. [125]. (C) CRISPR-Cas12a-mediated SERS to improve the sensitivity and specificity of LFA-based nucleic acid detection. Reproduced with permission from Ref. [127]. (D) A portable all-in-one microfluidic device for rapid diagnosis of pathogens based on an integrated CF-PCR and electrophoresis biochip. Reproduced with permission from Ref. [135]. (E) A ME device combining microfluidics, on-chip electric field control, and CRISPR to rapidly detect SARS-CoV-2 RNA in clinical samples. Reproduced with permission from Ref. [136]. Fig. 7 Our group previously reported on a simple, fast, low-cost, robust and multiplexed barcode paper-based inspection (BPA) that is compatible with mobile devices [125]. The use of an inkjet printer and XYZ dispensing platform enabled the mass production of a highly accurate and efficient barcode paper-based analysis device. We designed a new set of barcodes and developed an application to read the new codes. The BPA system can be used to detect blood-borne infections, drug residues in milk, and multiplex nucleic acids. The entire testing process and read-out of results can be completed in less than 10 min (Fig. 7B). In addition, researchers [126] developed a novel flowmetry strip-based assay to improve the sensitivity and specificity of LbCas12a-mediated nucleic acid detection. The modified crRNA is incorporated into a paper-based LFA that can detect targets with ultra-high sensitivity within 30 min. Pang et al. [127] developed CRISPR-Cas12a-mediated surface-enhanced Raman scattering (SERS) LFA to improve the sensitivity and specificity of LFA-based nucleic acid detection (Fig. 7C). By combining the ultra-sensitive SERS tag with the target-specific signal amplification capability of CRISPR-Cas12a, HIV-1 dsDNA can be quantified directly with a LOD of 0.3 fM without any pre-amplification step, which is nearly 4 orders of magnitude lower than CRISPR-Cas12a. Compared with the traditional colorimetric LFA method. The whole detection process can be completed in less than 1 h. Simple and inexpensive paper strips have great potential for immediate detection of nucleic acid targets. The development of paper-based electroanalytical strips as powerful diagnostic tools has attracted a lot of attention in the sensor community. In particular, its application to nucleic acid detection in complex matrices has been evaluated by researchers [128] in combination with paper electrodes for two major nucleic acid detection methods based on target/probe hybridization, namely, a signal on and a signal off. The method uses single-stranded DNA associated with the H1047R (A3140G) missense mutation in exon 20 of breast cancer as a model target. In another example, researchers [129] presented a 3D microfluidic paper-based electrochemical device for POCT applications in nucleic acid amplification testing. The devices use gold plasma coated wires to integrate electroanalytical readings using in situ self-assembled layers on the wire before assembly to the paper-based device. They also include a sandwich hybridization assay area where sample incubation, rinsing, and detection steps are integrated using removable laminated filter paper for time-sequence-based reactions. These devices use glass fibre matrices to store RPA reagents and perform isothermal amplification. 3.5 Microelectrophoresis chip ME chip is a well-established separation technique at the microscale. It provided a robust tool for POCT. It has some advantages, such as lower sample and reagent requirements, lower risk of contamination, faster analysis time, and is suitable for high-throughput analysis. The portability of micro devices makes them ideal for forensic fieldwork or patient POCT, as mechanical and electronic structures can be integrated into the device, thus automating operations that usually require manual labour. These microfluidic devices allow ME chips to be miniaturized and portable, with fast analysis and low cost. Researchers from different fields have applied ME to various clinical, biomedical, and forensic assays [[130], [131], [132]]. The most significant potential of ME continues to be the multi-step integration capability of its analytical procedures and the portability of POC testing [133]. The effective POC platform is designed to miniaturize and automate sample handling, enabling minimally trained personnel to perform the tests required for diagnosis in a wide range of operating environments with minimal risk of contamination. Researchers [134] introduced a method coupling PCR with ME for detecting high-risk HPV16 and HPV18. The device has an LED and a fluorescence detector that can perform a complete analysis in less than 3 min and detect multiple samples simultaneously. The ME method described in this paper has been successfully applied to HPV detection, and the sequencing results have shown good reliability. The ME allows HPV detection on a small chip, enabling automated and high-throughput analysis. Current CF-PCR usually requires external precision syringe pumps and complex operations, researchers [135] developed a portable all-in-one microfluidic device for rapid diagnosis of pathogens based on an integrated CF-PCR and electrophoresis biochip (Fig. 7D). The device contained a polycarbonate microchannel, two parallel heating blocks for amplification, and a CCD camera for imaging. The new method achieved automatic sample injection into the chip, with no need for an external precision syringe pump. The all-in-one device can successfully detect three periodontal pathogens in a few minutes. In another example, researchers [136] combine microfluidics, on-chip electric field control, and CRISPR to rapidly detect SARS-CoV-2 RNA in clinical samples (Fig. 7E). In this design, isotachophoresis (ITP) was employed to extract and purify RNA from nasopharyngeal samples, followed by reverse transcriptase-LAMP. The authors then used electric fields to control and accelerate their CRISPR-Cas 12 enzymatic assay. The method takes about 35 min from sample to result, a significant improvement over existing nucleic acid-based diagnostic methods for COVID-19. In addition, researchers [137] integrated ME with isothermal strand-displacement polymerase reaction (ISDPR) to detect methicillin-resistant strains rapidly and sensitively. The amplified products were separated rapidly from other DNAs by ME, and the detection limit was as low as 12.3 pM. It is a label-free, ultrasensitive, and rapid method. We have compared the microfluidic platforms mentioned in this paper for nucleic acid detection (Table 3 ), including advantages and disadvantages, as well as details of some applications.Table 3 Microfluidics platform comparation for nucleic acid analysis. Table 3Microfluidic platform Analyte Amplification technique LOD Time Signal readout Advantages/disadvantages Sample-process integration Reference Centrifugal chip Bacteria DNA RPA 10 copies 20 min Fluorescence Automation, multiple detection, multiplexing, simple fluid control, no active valves or pumps, high integration; One-time use, high cost of single test, difficult fabrication No [101] Influenza virus RNA RT-LAMP 50-100 copies 45 min Colorimetry Yes [102] SARS-CoV-2 RNA RT-LAMP 100 copies 1 h Fluorescence No [103] Coronaviruses RNA RT-LAMP 10 copies/μL 40 min Fluorescence No [104] Bacteria RNA LAMP 200 CFU/μL 1 h Fluorescence Yes [20] HBV DNA RPA-CRISPR/Cas13a 1 aM 1 h Fluorescence Yes [17] Bacteria DNA RAA-CRISPR/Cas12a 103 CFU/mL 1.5 h Fluorescence No [18] Valves chip SARS-CoV-2 RNA RT-LAMP N/A N/A Colorimetry Complex fluid control, multi-functional integration, wide choice of materials, simple manufacturing and low cost; No [108] DNA ddPCR 100 copies/μL N/A Fluorescence Yes [109] Bacteria DNA LAMP-CRISPR/Cas12a 30 copies 80 min Fluorescence Requires pumps and other equipment to drive the fluid, complex structure, limited automation Yes [110] Digital droplet chip DNA ddPCR 8 copies N/A Fluorescence Ultra-high sensitivity, single molecule detection, absolute quantification, background-free fluorescence detection, high throughput, rapid generation of large numbers of microdroplets, digital information; Multiple processing steps, low sampling efficiency, requires complex image and data processing, time consuming No [111] SARS-CoV-2 DNA ddPCR 10 copies/μL N/A Fluorescence No [112] Bacteria DNA ddLAMP 25 copies N/A Fluorescence No [113] HPV DNA ddRPA-CRISPR/Cas13a 10 copies/μL 30 min Fluorescence No [114] Virus DNA CRISPR/Cas12a 17.5 copies/μL N/A Fluorescence No [116] SARS-CoV-2 RNA CRISPR/Cas13a 6 copies/μL N/A Fluorescence No [10] Virus DNA CRISPR/Cas13a 1 copy/μL N/A Fluorescence No [23] HPV DNA N/A 2 × 103 copies/mL N/A Fluorescence No [19] microRNA isothermal amplification 1 fM 3 h Fluorescence No [117] Paper-based chip ASFV DNA CRISPR/Cas9 150 copies 1 h Naked eyes Naked-eye readout, fast, no amplification, convenient, inexpensive, easy to manufacture, field-deployable, no need for external instruments; Poor quantification ability, low sensitivity, low accuracy, low integration No [120] SARS-CoV-2 RNA RT-LAMP-CRISPR/Cas12a 3-300 copies 30 min Fluorescence Naked eyes No [126] HIV DNA CRISPR-cas12a 0.3 fM 1 h SERS Naked eyes No [127] DNA N/A 6 nM N/A Electrochemistry No [128] DNA RPA 0.06 pM 95 min Electrochemistry No [129] Microelectrophoresis chip Bacteria DNA CF-PCR 125 CFU/μL 10 min Fluorescence Microsample detection, low reagent consumption, high integration, sample concentration/purification, fast; Requires external electric field, high detection cost No [135] SARS-CoV-2 RNA RT-LAMP- CRISPR-cas12a 10 copies/μL 35 min Fluorescence Yes [136] Bacteria DNA ISDPR 12.3 pM 1 h Fluorescence No [137] 4 Conclusion Various novel microfluidic detection techniques have been developed for nucleic acid detection and have a broad application prospect in the biomedical area. Many complicated chips are still in the laboratory research stage and are difficult to be developed as commercial kits. There still needs a lot of optimization in the process from laboratory to commercialization of microfluidic chips. Commercial highly integrated nucleic acid testing chip and supporting instrument is still expensive and deployed in extensive medical facilities. Rapid nucleic acid testing products for home use are also quite expensive when compared to antigen tests or widely used glucometers, so most of them are distributed as generous packages to employees by prosperous enterprises. To extend their application range to the broad grassroots and household market, further reducing the cost of chips is necessary. The development of rapid nucleic acid extraction reagents and amplification reagents with excellent adaptability to interference should be prioritized. These researches will significantly simplify the detection process, chip design, and manufacturing processes and thus have important significance for the reduction of the detection cost. Besides the cost, an important direction in the future is the development of amplification-free nucleic acid detection technology, especially the new strategies based on CRISPR, which can release a large number of signaling molecules without nucleic acid amplification. It can greatly reduce the requirement for complicated heating and cooling systems and complex chip design and expand the application scenarios of nucleic acid detection. Ultrasensitive digital detection is another research orientation. Established digital PCR and digital CRISPR show the extreme limit of detection and absolute quantification. However, they require extensive off-chip sample preparation and multiple instrumentations to realize monodispersing and digital detection. The highly miniaturized microfluidic chip provides an ideal tool for the integration of complex digital detection processes. Reducing manual manipulation can greatly improve the accuracy of absolute quantification. In conclusion, nucleic acid detection has many application scenarios, such as rapid home use tests and digital laboratory analysis. Nucleic acid testing is systematic engineering, including sample processing, nucleic acid extraction and detection steps. The realization of these applications in the future is inseparable from the development of microfluidic chip technology. With the progress in the study of chip material, chip driving force and control valve design, matching reagents, and supporting instrument construction, microfluidic chip technology will become an essential platform for fast, sensitive, and automated diagnosis in health care. The emergence of various flexible materials allows wearable devices to be more portable, highly integrated, and have excellent biocompatibility [[138], [139], [140]]. These excellent properties will be well suited for miniaturization and popularization of nucleic acid detection. Miniaturized devices combined with nucleic acid testing have increasingly become the trend and the mainstay of pandemic disease response. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability No data was used for the research described in the article. Acknowledgements We thank the 10.13039/501100012166 National Key R&D Program of China (2020YFA0908900, 2021YFF1200100), 10.13039/501100001809 National Natural Science Foundation of China (21761142006, 21535001, 22204068, and 81730051), Shenzhen Science and Technology Program (JCYJ20210324105006017, KQTD20190929172743294, KCXFZ20211020163544002), Shenzhen Key Laboratory of Smart Healthcare Engineering (ZDSYS20200811144003009), Guangdong Provincial Key Laboratory of Advanced Biomaterials (2022B1212010003), Guangdong Basic and Applied Basic Research Foundation (2019A1515110292), the 10.13039/501100002367 Chinese Academy of Sciences (Q YZDJ-SSW-SLH039), Shenzhen Bay Laboratory (SZBL2019062801004), 10.13039/100015803 Tencent Foundation through the XPLORER PRIZE for financial support. ==== Refs References 1 Chen S. Sun Y. Fan F. Chen S. Zhang Y. Zhang Y. Meng X. Lin J.M. Present status of microfluidic PCR chip in nucleic acid detection and future perspective TrAC Trends Anal. Chem. 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==== Front J Hosp Infect J Hosp Infect The Journal of Hospital Infection 0195-6701 1532-2939 The Healthcare Infection Society. Published by Elsevier Ltd. S0195-6701(22)00370-X 10.1016/j.jhin.2022.11.016 Article Timing of last COVID-19 vaccine dose and SARS-CoV-2 breakthrough infections in fully (boosted) vaccinated healthcare personnel Maltezou Helena C. a∗ Gamaletsou Maria N. b Giannouchos Theodoros V. c Koukou Dimitra-Maria d Karapanou Amalia e Sourri Flora f Syrimi Natalia fg Lemonakis Nikolaos h Peskelidou Emmanuela i Papanastasiou Konstantina j Souliotis Kyriakos kl Lourida Athanasia m Panagopoulos Periklis n Hatzigeorgiou Dimitrios o Sipsas Nikolaos V. b a Directorate of Research, Studies and Documentation, National Public Health Organization, Athens, Greece b Pathophysiology Department, Medical School, National and Kapodistrian University of Athens, Greece c Department of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, United States d First Department of Pediatrics, University of Athens, Aghia Sophia Children’s Hospital, Athens, Greece e Infection Control Committee, Laiko General Hospital, Athens, Greece f Department of Infection Control, 251 Hellenic Air Force General Hospital, Athens, Greece g Paediatric Department, 251 Air Force General Hospital, Athens, Greece h Infection Control Committee, University Hospital of Alexandroupolis, Alexandroupolis, Greece i COVID-19 Intensive Care Unit, 424 General Military Hospital of Thessaloniki, Thessaloniki, Greece j Operating Rooms, 424 General Military Hospital of Thessaloniki, Thessaloniki, Greece k Faculty of Social and Political Sciences, University of Peloponnese, Corinth, Greece l Health Policy Institute, Athens, Greece m Infection Control Committee, Aghia Sofia Children's Hospital, Athens, Greece n Second Department of Internal Medicine, Medical School, Democritus University of Thrace, Alexandroupolis, Greece o Medical Directorate, Hellenic National Defence General Staff, Athens, Greece ∗ Corresponding author. ate of Research, Studies and Documentation, National Public Health Organization, 3-5 Agrafon Street, Athens, 15123 Greece; Tel.: +30 210-5212-175; 5 12 2022 5 12 2022 5 10 2022 26 11 2022 26 11 2022 © 2022 The Healthcare Infection Society. Published by Elsevier Ltd. All rights reserved. 2022 The Healthcare Infection Society 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 estimate the incidence, timing, and severity of SARS-CoV-2 breakthrough infections in fully vaccinated healthcare personnel (HCP). Methods We prospectively studied 6496 fully vaccinated HCP from November 15, 2021 through April 17, 2022. Full COVID-19 vaccination was defined as a complete primary vaccination series followed by a booster dose at least six months later. Results A total of 1845 SARS-CoV-2 breakthrough infections occurred (28.4 episodes per 100 HCP), of which 1493 (80.9%) were COVID-19 cases and 352 (19.1%) were asymptomatic infections. Of the 1493 HCP with COVID-19, 4 were hospitalized for 3-6 days (hospitalization rate among HCP with COVID-19: 0.3%). No intubation or death occurred. SARS-CoV-2 breakthrough infections occurred at a mean of 16.2 weeks after the last vaccine dose. Multivariable regression analyses showed that among the 1845 HCP with a breakthrough infection, the administration of a COVID-19 vaccine dose >16.2 weeks before the infection was associated with an increased likelihood in developing COVID-19 rather than asymptomatic SARS-CoV-2 infection (OR: 1.58; 95% CI: 1.01-2.46; p-value=0.045) compared to administering a vaccine dose later. The likelihood of developing COVID-19 versus asymptomatic infection increased by 7% weekly after the last COVID-19 vaccine dose (OR: 1.07; 95% CI: 1.03-1.11; p-value=0.001). Conclusion SARS-CoV-2 breakthrough infections are common among fully (boosted) vaccinated HCP. However, full COVID-19 vaccination offered considerable protection against hospitalization. Our findings may contribute to defining the optimal timing for booster vaccinations. More efficient COVID-19 vaccines that will also confer protection against SARS-CoV-2 infection are urgently needed. Keywords COVID-19 SARS-CoV-2 breakthrough infection vaccination booster dose healthcare personnel ==== Body pmcIntroduction Amidst the first coronavirus disease 2019 (COVID-19) vaccination campaigns targeting healthcare personnel (HCP), real-life studies captured that complete vaccination conferred considerable protection against COVID-19, including severe COVID-19-associated outcomes, as well as contained staff absenteeism during periods of high demand for healthcare services [[1], [2], [3]]. However, soon it became evident that vaccinated individuals could develop severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) breakthrough infections, while they could also transmit the virus [4]. This was attributed to the waning vaccine-induced immunity over time, as well as to the emergence of highly transmissible SARS-CoV-2 variants with immune escape capacity [[5], [6], [7]]. Consequently, booster doses were recommended for HCP to enhance protection. To the best of our knowledge, there are scarce published data on SARS-CoV-2 breakthrough infections in HCP who had received a complete primary vaccination series followed by a booster dose [8]. The aim of the current study was to assess SARS-CoV-2 breakthrough infections in fully vaccinated HCP and in particular to investigate the association between timing of last vaccine dose and risk of onset of COVID-19. Methods Study setting The study was conducted in five tertiary-care hospitals in Greece with mandatory COVID-19 vaccination policies. A total of 7592 HCP were prospectively followed for SARS-CoV-2 infections from November 15, 2021 through April 17, 2022. Of those, 6496 (85.6%) HCP had been fully vaccinated against COVID-19 and comprised the study population of interest. Regarding SARS-CoV-2 activity, the SARS-CoV-2 variant B.1.617.2 (Delta) was the dominant variant until week 50/2021, co-circulated with B.1.1.529 (Omicron) in weeks 51/2021 and 52/2021, while from week 01/2022 Omicron prevailed [9]. Wearing a surgical mask was mandatory for HCP, patients, and visitors during the entire study period. Testing for SARS-CoV-2 Employees of the five hospitals were tested for SARS-CoV-2 infection by real-time polymerase chain reaction (PCR) and/or rapid antigen detection test (RADT) if they developed symptoms, following exposure to a COVID-19 case, in the context of investigations of a healthcare-associated cluster, and upon return to work after annual leave. Routine testing for SARS-CoV-2 infection was also ordered every 7 to 14 days for HCP working in high-risk departments/units, according to the guidelines of each hospital. Data collection The following data were prospectively collected per episode of SARS-CoV-2 infection: demographic, professional, and clinical characteristics of HCP, history of COVID-19 vaccination (number of vaccine doses and date of last vaccine dose), week of detection of SARS-CoV-2 infection, hospitalization, intubation, and outcome of HCP. Definitions HCP were defined as persons employed in healthcare facilities, regardless of their occupation. Full COVID-19 vaccination was defined as a complete primary vaccination series with 2 doses of BNT162b2 (Comirnaty), mRNA-1273 (Spikevax), or ChAdOx1-S (Vaxzevria), or 1 dose of AD26.COV2.S (Janssen), followed by a booster shot at least six months after the primary vaccination series. In our cohort, the BNT162b2 vaccine was used in 94.7% of vaccinations, while the Janssen, mRNA-1273, and ChAdOx1-S vaccines accounted for 3.1%, 2.0%, and 0.2% of vaccinations, respectively. SARS-CoV-2 infection was defined as a laboratory-confirmed SARS-CoV-2 infection by real-time RT-PCR and/or RADT, regardless of symptoms. COVID-19 was defined as a symptomatic case and a positive SARS-CoV-2 RT-PCR and/or RADT. Breakthrough infection was defined as a SARS-CoV-2 infection (asymptomatic or COVID-19) that occurred at least 14 days after the last COVID-19 vaccine shot in a fully vaccinated individual. Only the first positive SARS-CoV-2 result was considered per case of SARS-CoV-2 infection. Influenza-like illness (ILI) was defined as the sudden onset of symptoms and fever, malaise, myalgia or headache, and cough, sore throat or shortness of breath. Acute respiratory illness (ARI) was defined as the onset of at least one respiratory symptom (e.g. cough, sore throat, dyspnea). Febrile episode was defined as fever only. Statistical analysis We initially conducted a descriptive analysis of all HCP with a SARS-CoV-2 breakthrough infection. Absolute numbers and percentages were used for categorical variables. Means and standard deviation (SD) were used for continuous variables. We then stratified infected HCP by time-distance from the last COVID-19 vaccine (below or above average) and compared them using the chi-square test for categorical variables and the two-tailed t-test or the Mann-Whitney U test for continuous variables, depending on their distribution. Given that BNT162b2 mRNA vaccines were used in approximately 95% of vaccinations, vaccine brands were not considered in the data analysis. We similarly compared the characteristics between HCP based on the type of breakthrough infection (COVID-19 versus asymptomatic infection). Finally, we used multivariable logistic regressions to estimate the associations between the type of SARS-CoV-2 infection (asymptomatic or COVID-19) and time elapsed since the last COVID-19 vaccine dose, controlling for all HCP characteristics. Time elapsed from last vaccine in weeks was defined both as dichotomous (above or below average), and as continuous and two separate regressions were conducted. The odds ratio (OR) and 95% confidence interval (CI) were estimated. P-values of ≤0.05 were considered statistically significant. All regression models also included hospital fixed-effects, while robust standard errors were used, clustered at the hospital level. Statistical analyses were conducted using Stata version 17.0, StataCorp, College Station, TX, USA. Ethical issues The study was approved by The Ethics Committees of the participating hospitals (approval numbers: 30/8-1-2021, 2457/4-2-2021, 1/5-2-2021, ΥΣ 36/21-12-2020/251, 19814/11-2021). Results From November 15, 2021 through April 17, 2022, a total of 1845 SARS-CoV-2 breakthrough infections were detected among the 6496 fully vaccinated HCP, which corresponds to 28.4 episodes of infection per 100 HCP. Table 1 shows the characteristics of HCP with SARS-CoV-2 breakthrough infection.Table 1 Characteristics of fully vaccinated HCP with SARS-CoV-2 breakthrough infections Table 1Characteristic N = 1845 (%) Mean age, years (SD) 41.5 (10.5) Age groups, years 20-29 314 (17.0) 30-39 475 (25.8) 40-49 579 (31.4) >50 477 (25.8) Gender Male 640 (34.7) Female 1205 (65.3) Profession Physician 510 (27.6) nursing personnel 713 (38.6) paramedical personnel 188 (10.2) supportive personnel 275 (14.9) administrative personnel 159 (8.6) Number of COVID-19 vaccine doses 1-2 452 (24.5) 3-4 1393 (75.5) HCP: healthcare personnel; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2; SD: standard deviation; COVID-19: coronavirus disease 2019. Of the 1845 SARS-CoV-2 breakthrough infections, 1493 (80.9%) were symptomatic (COVID-19) and 352 (19.1%) were asymptomatic SARS-CoV-2 infections. ILI and ARI were the prevalent clinical manifestations of HCP with COVID-19, followed by febrile episode (46.2%, 44.1%, and 8.9%, respectively). Table 2 summarizes the characteristics of HCP with breakthrough infections by type of infection. HCP with COVID-19 were older than those with asymptomatic infection [mean age (SD): 41.9 (10.5) years versus 40.1 (10.4) years; p-value=0.004]. The two groups did not differ in terms of demographic and professional characteristics, and number of past COVID-19 vaccine doses. Four HCP with COVID-19 were hospitalized for a mean of 4.75 (range: 3 to 6) days. No intubation or death occurred. The hospitalization rate was estimated at 0.06% among all 6496 fully vaccinated HCP and 0.3% among 1493 HCP with COVID-19.Table 2 Characteristics of 1845 HCP with SARS-CoV-2 breakthrough infections by type of infection Table 2 Characteristic COVID-19 asymptomatica p-value N = 1493 (%) N = 352 (%) Mean age, years (SD) 41.9 (10.5) 40.1 (10.4) 0.004 Age groups, years 20-29 240 (16.1) 74 (21.0) 0.027 30-39 375 (25.1) 100 (28.4) 40-49 477 (32.0) 102 (29.0) >50 401 (26.8) 76 (21.6) Gender Male 524 (35.1) 116 (33.0) 0.447 Female 969 (64.9) 236 (67.0) Profession Physician 400 (26.8) 110 (31.2) 0.31 nursing personnel 585 (39.2) 128 (36.4) paramedical personnel 155 (10.4) 33 (9.4) supportive personnel 229 (15.3) 46 (13.1) administrative personnel 124 (8.3) 35 (9.9) Number of COVID-19 vaccine doses 1-2 371 (24.9) 81 (23.0) 0.471 3-4 1122 (75.1) 271 (77.0) HCP: healthcare personnel; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2; COVID-19: coronavirus disease 2019; SD: standard deviation. a asymptomatic SARS-CoV-2 infection. In our cohort of fully vaccinated and boosted HCP, a mean of 16.2 weeks elapsed between last COVID-19 vaccine dose and SARS-CoV-2 breakthrough infection. Table 3 summarizes the characteristics of HCP with breakthrough infections stratified by time elapsed since the last vaccine dose. Compared to HCP with breakthrough SARS-CoV-2 infection who had received the last COVID-19 vaccine dose >16.2 weeks before, HCP vaccinated <16.2 weeks before were less likely to develop COVID-19 (77.7% versus 84.6%; p-value<0.001), while the opposite was observed for asymptomatic SARS-CoV-2 infection (22.3% versus 15.4%; p-value<0.001).Table 3 Characteristics of 1845 HCP with SARS-CoV-2 breakthrough infections by time since last COVID-19 vaccine dose Table 3 Characteristic <16.2 weeksa >16.2 weeksa p-value N = 983 (%) N = 862 (%) Mean age, years (SD) 40.8 (10.3) 42.3 (41.7) 0.002 Age groups, years 20-29 177 (18.0) 137 (15.9) 0.004 30-39 262 (26.6) 213 (24.7) 40-49 324 (33.0) 255 (29.6) >50 220 (22.4) 257 (29.8) Gender Male 339 (34.5) 301 (34.9) 0.846 Female 644 (65.5) 561 (65.1) Profession Physician 254 (25.8) 256 (29.7) 0.254 nursing personnel 387 (39.4) 326 (37.8) paramedical personnel 111 (11.3) 77 (8.9) supportive personnel 145 (14.8) 130 (15.1) administrative personnel 86 (8.7) 73 (8.5) Number of COVID-19 vaccine doses 1-2 106 (10.8) 346 (40.1) <0.001 3-4 877 (89.2) 516 (59.9) HCP: healthcare personnel; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2; COVID-19: coronavirus disease 2019; SD: standard deviation. a time since last COVID-19 vaccine dose. Figure 1 illustrates the cumulative episodes of SARS-CoV-2 breakthrough infections (COVID-19 or asymptomatic) by time elapsed since the last COVID-19 vaccine dose, and particularly the rather sharp increase of COVID-19 cases >16.2 weeks after the last vaccine dose. Multivariate regression analysis showed that among the 1845 HCP with a SARS-CoV-2 breakthrough infection, compared with a COVID-19 vaccine dose <16.2 weeks before the infection, the administration of a COVID-19 vaccine dose >16.2 weeks before the infection was associated with increased likelihood in developing COVID-19 rather than asymptomatic SARS-CoV-2 infection (OR: 1.58; 95% CI: 1.01-2.46; p-value=0.045). Supplemental analyses using time elapsed from last vaccine as continuous, indicated that the likelihood of onset of COVID-19 versus asymptomatic infection increased by 7% per weekly increase in distance from last COVID-19 vaccine dose (OR:1.07; 95% CI:1.03-1.11; p-value=0.001).Figure 1 Cumulative episodes of SARS-CoV-2 breakthrough infections among 6496 fully vaccinated HCP by time elapsed since last COVID-19 vaccine dose. Figure 1 Discussion To the best of our knowledge, there are scarce published data on SARS-CoV-2 breakthrough infections among fully vaccinated (boosted) HCP [8,10]. The current prospective study of a cohort of 6496 fully vaccinated HCP in Greece estimated a total of 28.4 episodes of breakthrough infections per 100 HCP over a 22-week study period. An Israeli single-center study of a cohort of 1928 HCP followed for a median of 39 days found an incidence rate of 12.8 per 100,000 persons-days in boosted HCP versus 116 per 100,000 person-days in non-boosted HCP [10]. Similarly to our findings, in the latter study more than two of three SARS-CoV-2 breakthrough infections were symptomatic [10]. Our findings indicate that HCP remain at increased risk for SARS-CoV-2 infections, despite the wide availability of COVID-19 vaccines. The increased incidence of SARS-CoV-2 infections in our cohort is mostly explained by the post-vaccination waning immunity, the highly transmissible Omicron variants that prevailed for the most part of the study period and was highly prevalent in the community [9], as well as the wide routine use of RADT for SARS-CoV-2 testing compared to PCR only in the first waves of the COVID-19 pandemic [[5], [6], [7],11,12]. Nevertheless, in our study the hospitalization rate was extremely low, while no intubation or death occurred. In contrast, nationwide data recorded in Greece before the availability of COVID-19 vaccines, showed that up to 24.2% of HCP with COVID-19 developed pneumonia, while 19.7% of them required hospitalization [13]. Although protection against SARS-CoV-2 infection wanes over time, as reported by others [12,14,15], our findings confirm that full primary COVID-19 vaccination plus a booster dose confers exceptional protection against severe COVID-19-associated outcomes. Moreover, there is evidence that SARS-CoV-2 viral loads are significantly lower in upper respiratory tract in boosted patients with Omicron breakthrough infections compared to unvaccinated infected patients [16]. This finding may explain the reduced risk of fully vaccinated HCP to transmit SARS-CoV-2 to their household contacts [17]. Another finding of our study is that the risk for onset of COVID-19 increases over time since the last vaccine dose relative to asymptomatic SARS-CoV-2 infection. In practice, the likelihood of onset of COVID-19 versus asymptomatic infection increases by 7% per week since the last COVID-19 vaccine dose. Similar to our findings, breakthrough infections emerged 2-4 months post-vaccination among 2415 fully vaccinated HCP during a Delta-dominated surge in Japan [18]. In addition, the incidence of SARS-CoV-2 infections among fully vaccinated HCP in a French hospital increased with time, with no difference between vaccination regimens [19]. Beyond conferring protection at the host level, vaccination timing has practical implications for defining the optimal timing for booster doses for health systems as well. A recently published SARS-CoV-2-transmission model found that to reduce COVID-19-associated hospitalizations over the next two years, boosters should be provided to all eligible individuals annually 3-4 months ahead of peak winter season, whether or not new variants of concern emerge and dominate [20]. Lastly, ILI and ARI were the prevalent manifestations of COVID-19 among fully vaccinated HCP. This finding has diagnostic and infection control implications, which could be jeopardized given the puzzling influenza activity in the upcoming season [21]. Influenza vaccination of HCP should be regarded as a critical component of preparedness and response plans for healthcare facilities in the post-COVID-19 pandemic era [22]. Nevertheless, our findings also underline the need to strictly adhere to infection control measures. This study has several strengths. First, we actively followed a large cohort of fully (boosted) vaccinated HCP. Second, the study extended over a 22-week study period, when two highly transmissible SARS-CoV-2 variants, namely Delta and Omicron, subsequently dominated. Third, data on SARS-CoV-2 breakthrough infections were collected prospectively. The multi-site design of the study is an additional strength. A limitation of the study is its observational design. Additionally, the fact that SARS-CoV-2 transmission dynamics may vary across hospitals could insert systemic bias in our results. Towards this direction, we attempted to manage this limitation by adjusting for hospital heterogeneity and clustering in multivariable models. Lastly, we did not restrict our analysis to the 95% of HCP who have been vaccinated with the BNT 162b2 mRNA vaccine only. However, large population studies support the use of heterologous vaccine schedules [23,24]. In conclusion, our study provides real-life evidence on SARS-CoV-2 breakthrough infections in fully vaccinated HCP. In our cohort, more than one every four fully (boosted) vaccinated HCP developed a SARS-CoV-2 breakthrough infection over a 22-week study period. Most of these infections were symptomatic, while the risk for symptomatic infection increased over time since last COVID-19 vaccine dose. Nevertheless, full vaccination followed by a booster dose conferred exceptional protection against hospitalization and other severe outcomes. Our findings have implications for defining the optimal timing for booster vaccination for HCP to protect them. More efficient COVID-19 vaccines that will offer protection not only against severe COVID-19-associated outcomes but also against SARS-CoV-2 infection are urgently needed. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Declaration of Competing Interest The authors have no conflict of interest to declare. Acknowledgements We thank the Infection Control Committees of the participating hospitals. We also thank Mrs Tentoma for technical assistance. The opinions presented in this article are those of the authors and do not necessarily represent those of their institutions. ==== Refs References 1 Pilishvili T. Fleming-Dutra K.E. Farrar J.L. Gierke R. 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Theodoridou K. Poland G. Influenza immunization and COVID-19 Vaccine 38 2020 6078 6079 32773245 23 Starrfelt J. Danielsen A.S. Buanes E.A. Juvet L.K. Lyngstad T.M. Isaksson G.Ø. Age and product dependent vaccine effectiveness against SARS-CoV-2 infection and hospitalisation among adults in Norway: a national cohort study, July–November 2021 BMC Med 20 2022 278 36050718 24 Deng J. Ma Y. Liu Q. Du M. Liu M. Liu J. Comparison of the effectiveness and safety of heterologous booster doses with homologous booster doses for SARS-CoV-2 vaccines: a systematic review and meta-analysis Int J Environ Res Public Health 19 2022 10752	36473554	PMC9721165	NO-CC CODE	2022-12-06 23:26:27	no	J Hosp Infect. 2022 Dec 5; doi: 10.1016/j.jhin.2022.11.016	utf-8	J Hosp Infect	2,022	10.1016/j.jhin.2022.11.016	oa_other
==== Front J Hosp Infect J Hosp Infect The Journal of Hospital Infection 0195-6701 1532-2939 Published by Elsevier Ltd on behalf of The Healthcare Infection Society. S0195-6701(22)00373-5 10.1016/j.jhin.2022.11.019 Article Measurement of SARS-CoV-2 in air and on surfaces in Scottish hospitals Loh Miranda 1∗ Yaxley Nicola 2 Moore Ginny 2 Holmes David 1 Todd Susanne 1 Smith Alice 1 Macdonald Ewan 3 Semple Sean 4 Cherrie Mark 1 Patel Manish 5 Hamill Raymond 5 Leckie Alastair 7 Dancer Stephanie J. 56 Cherrie John W. 18 1 Institute of Occupational Medicine, Edinburgh, United Kingdom 2 UK Health Security Agency, Porton Down, UK 3 University of Glasgow, Glasgow, UK 4 Institute for Social Marketing & Health, University of Stirling, Stirling, UK 5 NHS Lanarkshire 6 Edinburgh Napier University, UK 7 NHS Lothian, UK 8 Heriot Watt University, Edinburgh, UK ∗ Corresponding author. Institute of Occupational Medicine, Research Avenue North, Riccarton, Edinburgh, EH14 4AP United Kingdom, 5 12 2022 5 12 2022 4 9 2022 27 11 2022 27 11 2022 © 2022 Published by Elsevier Ltd on behalf of The Healthcare Infection Society. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Background There are still uncertainties in our knowledge of the amount of SARS-CoV-2 virus present in the environment; where it can be found, and potential exposure determinants, limiting our ability to effectively model and compare interventions for risk management. Aim This study measured SARS-CoV-2 in three hospitals in Scotland on surfaces and air, alongside ventilation and patient care activities. Methods Air sampling at 200 L/min for 20 minutes and surface sampling were performed in two wards designated to treat COVID-19 -positive patients and two non-COVID-19 wards across three hospitals in November and December 2020. Findings Detectable samples of SARS-CoV-2 were found in COVID-19 treatment wards but not in non-COVID-19 wards. Most samples were below assay detection limits, but maximum concentrations reached 1.7x103 genomic copies/m3 in air and 1.9x104 copies per surface swab (3.2x102 copies/cm2 for surface loading). The estimated geometric mean air concentration (geometric standard deviation) across all hospitals was 0.41 (71) genomic copies/m3 and the corresponding values for surface contamination were 2.9 (29) copies/swab. SARS-CoV-2 RNA was found in non-patient areas (patient/visitor waiting rooms and personal protective equipment (PPE) changing areas) associated with COVID-19 treatment wards. Conclusions Non-patient areas of the hospital may pose risks for infection transmission and further attention should be paid to these areas. Standardization of sampling methods will improve understanding of levels of environmental contamination. The pandemic has demonstrated a need to review and act upon the challenges of older hospital buildings meeting current ventilation guidance. Keywords SARS-CoV-2 COVID-19 environmental sampling ==== Body pmcIntroduction Workers in healthcare settings have been at high risk of contracting COVID-19, particularly in the first phase of the pandemic[1], [2], [3]. While risks to health professionals (including doctors and nurses) decreased, health associate professionals’ (including technicians, personal care support staff) risks remained high even later on in the pandemic. Clinical and non-clinical staff may be exposed to the SARS-CoV-2 virus in the workplace through contact with infected patients, visitors and staff via airborne virus, or possibly droplets and/or contact with contaminated surfaces. In a systematic review of sampling studies, primarily in health care settings, Cherrie et al (2021) reported a median detection rate of SARS-CoV-2 RNA of 6% for surface samples (range zero to 74%) and 7% for air samples (zero to 100%) from studies carried out in 2020 and 2021. However, sampling methodologies and exposure metrics varied greatly in these studies and contextual information that may influence exposure was often not reported. Measurement methods generally lack the sensitivity to accurately detect the low concentrations of SARS-CoV-2 RNA typically present in work environments. Thus, there are still many uncertainties in quantifying surface contamination and/or airborne concentration of SARS-CoV-2. There is a need to better understand the concentrations of SARS-CoV-2 virus (or other pathogens) in workplace settings, along with relevant exposure determinants, by using comparable methods to evaluate human occupational exposure and infection risk. This information would allow consideration of risk management measures on workplace and community contamination. As part of a study to explore worker exposure to SARS-CoV-2 in healthcare settings, we conducted a screening campaign in three hospitals in Scotland. The aims of this work were to evaluate the presence of SARS-CoV-2 in COVID-19 positive patient wards and wards where patients were awaiting results of COVID-19 tests; and to examine contextual factors in these wards such as ventilation and worker activities. Methods Study design: Three hospitals in Scotland, United Kingdom were enrolled in our study. These were NHS district general hospitals, of varying ages, each with over 500 beds. We refer to a COVID-19-positive (COVID-19 TREATMENT) ward as one used to exclusively treat COVID-19 patients (as determined by PCR test), and non-COVID-19 wards as wards not specifically set aside for COVID-19 patients. At one hospital (A), we conducted the study in a respiratory ward which was repurposed to treat COVID-19-positive patients. At hospital (B), we investigated one COVID-19 treatment ward (B1) and another where patients awaited results of a COVID-19 test (B2) before being moved elsewhere. At Hospital C, we conducted the study in a similar ward as B2. The patient rooms were either general mixed-gender wards with multiple beds or single rooms (Table A.1), with ensuite bathrooms (one per room). Nurse stations were generally in the main thoroughfare of the ward and the waiting rooms did not include toilets. Site A was visited twice 12 days apart, while the other sites were visited once. More information on the areas visited is available in Table A.1. Hospitals were visited between 12 November 2020 and 16 December 2020, when the alpha SARS-CoV-2 strain was dominant. Site assessments: Measurements of room volume and ventilation, temperature and humidity, number of patients, and number staff present during sampling were recorded. Airflow measurements were taken at air inlet diffuser grilles within each ward/utility room using a TSI/Airflow PH731 electronic balometer (TSI, MN, USA). Pressure differentials between rooms were measured using a micromanometer (DPM RS323 Micromanometer, UK) with the inlet tube placed under the door between two rooms. The air change rate was then estimated based on the measured air flow volume (L/s) divided by the room volume (m3) multiplied by a conversion unit to obtain the air changes per hour. Observational study: Staff were observed to determine how much time they spent with patients and the surfaces they touched during care activities. One staff was observed at a time, for specific patient care activities. The field worker would allow for 5-10 minutes of first standing in the same area as if they were observing the worker before recording information to allow the worker to get used to the field worker’s presence. Surface touches were recorded for: bed, door, bed handrail, healthcare worker’s notes, patient, sink, window. Observations were based on staff contact per patient. Ethical approval was obtained from ACCORD and the Research and Development Offices of each NHS Board we worked with, under Rec No. 20/NRS/0020. Sample collection and analysis Surface swab samples were taken using sterile nylon flocked swabs which were placed in 2 ml liquid Amies medium (Sterilin, Ltd, UK) after collection. Surface area swabbed varied according to the object and ranged from 10 to 225 cm2. Locations swabbed included light switches to the toilet, sink taps, door handles, cot sides, patient bed tables, personal protective equipment (PPE) donning and doffing areas (biowaste disposal container and sink taps), waiting rooms (table and cupboard tops), and nurse workstations (keyboards, worktops). Sample blanks were taken into the field for each hospital surveyed. The surface sampling protocol was based on a study carried out in hospitals in England by Moore et al.4 Air samples were collected using the Coriolis micro sampler (Bertin, Montigny-le-Bretonneux, France) with an extended sampling attachment allowing fluid in the collection vessel to be replenished during sampling. Most samples were taken at 200 L/min for 20 minutes into 15 ml of RNAse free phosphate buffered saline (PBS). A pair of samples per visit were placed side-by-side to test if sampling time (20 vs. 60 minutes) might affect capture and recovery of virus RNA. Air samples were taken in the same rooms and bed areas as swab samples. For each hospital sample blanks using the same sample cones and media were taken into the field but no air was drawn through the media. Samples were sent from Edinburgh to the UK Health Security Agency (UK HSA) laboratory at Porton Down for analysis according to similar methods as Moore et al.4 140 μL aliquots of each Coriolis air sample media and each surface swab sample were extracted using the QIAamp viral RNA Mini Kit (Qiagen Ltd, Manchester, UK) according to manufacturer’s instruction. The remaining air sample volume was concentrated using a Vivaspin™ 20 centrifugal concentrator to ≤ 1 mL and 140 μL aliquots extracted using the same method. RNA extracts were eluted in 60 μL AVE buffer and stored at -80 °C until RT-PCR analysis. Each extraction event included two negative extractions containing 140 μL absolute ethanol which was analysed in the same RT-PCR run. Environmental RNA extracts were tested for the presence of SARS-CoV-2 using the CE Viasure SARS-CoV-2 Real Time PCR Detection Kit (CerTest Biotec, Zaragoza) on an Applied Biosystems™ QuantStudio™ 5 Real-Time PCR System. The Viasure RT-PCR assay targets the Nucleoplasmid (N) and the ORF1ab genes, RNA extracts were tested in duplicate and recorded as ‘positive’ if at least one target amplified in both replicates. Samples were recorded as ‘weakly positive’ if one replicate amplified (in one or both targets). All weakly positive samples were re-analysed with the potential to be re-classified as ‘positive’ or remain ‘weakly positive’. This was done to ensure the results were not due to experimental error, given that the level of RNA present was low. Quantification was carried out using the N gene. Each RT-PCR experiment contained an N gene standard curve (50,000 to 5 copies in 10-fold dilutions of in vitro transcribed RNA) and two negative (no-template) and two positive control wells. The limit of detection (LoD) of the Viasure assay reported by the manufacturer is 10 genomic copies per reaction, with the highest cycle threshold (Ct) value as ≥ 39. Cycle threshold refers to the number of amplification cycles needed to detect genetic material. The theoretical detection limit, therefore, can be calculated by multiplying the assay LoD first by 12 (60/5) as extractions are eluted in 60 uL and 5 uL of sample was added to the reaction. This gives copies/extraction, which is then multiplied by the sample volume/140 (uL) as 140 uL of the sample is extracted. This results in a theoretical LoD of 3200 copies/m3 for air and 1700 copies/swab for surfaces. It should be noted, however, that the laboratory was able to reliably detect below the reported detection limit (to 5 genomic copies per reaction), resulting in an LoD of 1600 copies/m3 and 860 copies per swab for air and surfaces, respectively. Estimated distribution of concentrations and surface loadings The R software package EnvStats was used to carry out the statistical analysis of the virus RNA air concentrations and surface loadings. Due to the large number of non-detected samples, the elnorm function was used to extrapolate the geometric mean and standard deviation for a lognormal function. For air samples, the lowest reported concentration in copies/m3 was used as the limit of detection (LOD), while the lowest reported copies/swab was used as the LOD for surface samples. The sample size was not extensive enough for a detailed statistical analysis, due to limited availability of staff and access to hospital wards during the pandemic, and we qualitatively explored the relationship between ventilation rates, occupancy, and concentration of viral RNA contamination. Results This study occurred at a time when it was estimated that around 1% of the population in Scotland would have tested positive for COVID-19.5 It was during the second peak of COVID-19 in the UK, just after the B1.1.7 strain was first detected in the UK (see Figure 1 ).Figure 1 Covid-19 Hospital admissions in Scotland in 2020. Vertical lines indicate sampling dates. Figure 1 In total, 186 samples (127 surface swabs and 59 Coriolis air samples), including blank and control samples were sent for analysis. The number of samples and their locations are shown in Table A.1. In Hospital A (COVID-19 TREATMENT ward), 59 surface swabs and 25 air samples were taken, Hospital B1 (Hospital B, COVID-19 TREATMENT ward), 23 surface swabs and 9 air samples, Hospital B2 (Hospital B, non-COVID ward), 13 surface swabs and 8 air samples and in Hospital C, 27 surface swabs and 11 air samples were taken. Hospital A was visited on two occasions and more bedrooms were sampled compared with the other hospitals’ wards. Additionally, Hospital A’s ward had more beds per room than the other two, and only rooms with 1 bed were sampled in Hospital C (non-COVID-19) (Table A.1). Table A.2 shows the Ct values and air concentrations or surface loadings for positive and weakly positive samples, all of which were found in the COVID-19 treatment wards, but not in any of the assessment wards. In Hospital A 19% of air samples and 23% of surface samples had detectable levels of SARS-CoV-2 RNA. Of the positive samples, Ct values ranged from 32 to 39, with the lowest measurable air sample concentration at 30 copies/m3 and the highest 4.2x102 copies/m3. The surface loadings ranged from 3 to 3.8x102 copies/cm2. In particular, on both visits to one of the ward rooms in which continuous positive airway pressure (CPAP) was being delivered showed the highest proportion of positive surface samples although there were no detectable positive (or weakly positive) air samples. Hospital Ward B1 had 44% detectable positive air samples (or weakly positive) and 17% detectable positive surface (or weakly positive) samples. Ct values had the same range as Ward A, and measurable air concentrations ranged from 35 to 1.7x103 copies/m3 while surface loadings ranged from 3 to 3.2x102 copies/cm2. Table 2 shows the estimated geometric mean and standard deviation for each COVID-19 hospital ward, assuming a lognormal distribution of the measured concentrations. We assume that the amount of virus material present is a continuum and extrapolated values below the estimated detection limits based on methods used for developing distributions of environmental contaminants.Table 2 Geometric mean, standard deviation, and confidence intervals of the mean estimate assuming the distributions are lognormal. Below detection limit values have been probabilistically estimated based on the assumption of lognormality. Table 2Parameter Number of samples Geometric Mean Geometric Standard Deviation Lower 95% confidence interval Upper 95% confidence interval Hospital A Air (copies/m3) 25 0.89 51 0.10 69 Surface (copies/swab) 59 10 24 1.9 34 Surface (copies/cm2)a 0.12 0.28 0.02 0.41 Hospital B1 Air (copies/m3) 9 16 24 1.0 560 Surface (copies/swab) 23 12 9.7 1.0 160 Surface (copies/cm2)a 0.15 0.12 0.01 1.9 a Surface loading distributions were estimated by dividing the copies/swab parameters by the mean of surface areas swabbed in the location reported. Two of the co-located air samples, testing the effect of sample time (and hence volume of air sampled) in Hospital A were not detectable for the shorter samples (20 minutes) but weakly positive for the longer samples (60 minutes). Ward volumes ranged from 113 to 161 m3 for multiple bed rooms and 43 to 48 m3 for single or treatment rooms (Table A.3). Most of the bedrooms did not have mechanical supply and extract ventilation, save one treatment room for a single patient in hospital A. The ensuite toilets and shower rooms (WC) were mechanically ventilated and the extract airflows were measured. In Hospital A, additional extract fans beside the bedroom windows were included, which were estimated to add an additional 68 L/s airflow. This would result in approximately two additional air changes per hour above the ventilation rate without the fans. Extract air changes per hour (ach) for toilets ranged from 3 to 10 ach. Both supply and extract could only be measured in one room in Hospital A (a treatment room) and Bedroom 05 in Hospital B2, as those were the only ones with mechanical supply and extract (i.e. provided through an air handling unit as part of the original design). In the COVID-19-positive wards, 20 staff were observed and in the COVID-19-assessment wards, 13 staff were observed. These included 25 nurses, three doctors, and two domestic or portering staff. Staff contact with the patients during care was very varied and ranged from under one minute to 45 minutes of contact, with longer time spent with patients who were elderly or had co-morbidities which required assistance (tasks observed are detailed in the Appendix).The most frequently touched surfaces are shown in Table 1 . These generally also correspond with the surface touch results finding that the beds were the most likely to have detectable levels of SARS-CoV-2, as well as the sink taps. The distribution of the touches to surfaces were positively skewed. We estimated that total contact with surfaces within 1 m of a patient per hour was: 0 (minimum), 1.38 (mode) and 240 (maximum); contact > 1 m from the patient was: 0 (minimum), 23.1 (mode) and 600 (maximum).Table 1 Surfaces recorded for observation study, based on 33 observed workers Table 1Surface touched Frequency Patient 57 Notes 36 Bed 20 Door 20 Sink 18 Window 4 Handrail 3 Discussion We measured SARS-CoV-2 RNA in air and on surfaces in patient rooms and nurse work areas in wards with and without COVID-19 patients across three Scottish hospitals during a period of high levels of community prevalence in 2020. Whilst SARS-CoV-2 RNA was not detected in the non-COVID-19 wards, viral RNA was detected in both COVID-19 treatment wards. This suggests that the hospitals were successful in identifying and isolating COVID-19 patients. SARS-CoV-2 detection rates varied from room-to-room but were about 20%, which is higher than the 6% median detection rate found in an earlier review for healthcare settings.6 The same review estimated the geometric mean for air concentrations of SARS-CoV-2 as 0.014 (95% CI 0.0034-0.047) RNA copies/m3, as imputed from the pooled study data. For comparison, in our study, Hospital A’s geometric mean was 0.89 (95% CI 0.10-69) copies/m3, and for Hospital B1 the geometric mean was 16 (95% CI 1.0-560). These are higher than the estimated geometric means from Cherrie et al., and despite the very wide confidence intervals in this estimate, the lower range of the interval implies a statistical difference between the review and our study.6 One should interpret these results with caution, however, as we are estimating distributions with a large number of non-detects (>50%) and the sample values span a wide range. Sampling methods across studies also varied. Our study sampled larger volumes of air than, e.g. two other studies using a similar sampler in the UK.4 , 7 Additionally, we cannot make any conclusions about the representativeness of our data for the hospitals over time, particularly as the patient population and predominant SARS-CoV-2 strains change. Our results also show similarities with other studies (Table 3 ) that focused on characterizing SARS-CoV-2 in hospital environments in the UK with one involving eight hospitals, including 12 non-ICU cohort bays with COVID-19 patients, and the other focused on seven clinical areas and one public area in a hospital in London.4 , 7 Although these other studies were done during the first peak of COVID-19 in the UK in contrast to ours, our study found similar air and surface concentrations in non-ICU wards. It appears that ICUs do not necessarily have higher levels of SARS-CoV-2, despite being areas with aerosol generating procedures. This may be because in critical care, patients are generally not in the acute phase of their infection where they are much more likely to be shedding virus, and even if the patients were contagious, patients were commonly usually ventilated with an enclosed system preventing environmental shedding. Our findings also suggest that increased amounts of virus may settle onto surfaces around patients who undergo CPAP, which occurred in general COVID-19 wards. This is also reflected in Zhou et al., which found that a temporary CPAP ward also had statistically significantly higher surface concentrations than the ICU, although no CPAP occurred during sampling. CPAP is not a closed system so virus-loaded aerosol could leak from masks which generally do not form a close seal with the patient’s face.Table 3 Comparison of studies on environmental SARS-CoV-2 done in UK hospitals Table 3Study Location Air concentration (copies/m3) Surface levels This study Three hospitals in Scotland <30 to 1,717 copies/m3 15% positive <160 to 19168 copies/swab <1.49 to 319 copies/cm2 16% positive Moore et al. Eight hospitals in England <10 to 460 copies/m3 7% positive 59 to 2.2 x 105 copies/swab 9% positive Zhou et al. One hospital in London, England 31 to 7,048 copies/m3 11% positive ∼101 to 104 copies/swab ∼0.4 to 400a copies/cm2 52% positive a surface loading estimated by dividing copies/swab by 25 cm2, which was the approximate area of surfaces swabbed reported by authors. At the time of the study, the relevant guidance for recommended air exchange rates and pressure differentials for various types of hospital areas receiving mechanical ventilation, was Scottish Health Technical Memorandum (SHTM) 03-01 Part A9 (the SHTM was updated in 2022). Due to the age of the buildings studied (all built prior to 2014), ventilation guidance at time of build would have been Health Technical Memorandum 2025 (HTM 2025, 1994), which did not advise ward bedrooms to have mechanical ventilation. We observed some interventions in place to improve the ventilation, such as window extract fans, which we estimated to provide up to 2 ACH when in use in these otherwise naturally ventilated rooms. However, due to the noise, these fans were not always in use. During the study period, we were told the fans were generally used when CPAP was in process. Other interventions, such as air cleaning units or opening of windows, were also used in Hospitals B and C if COVID-19 outbreaks were found to occur.8 Regular opening of windows was instituted as an intervention in January 2021, given that most of the ward bedrooms relied on natural ventilation, but compliance was not formally audited.10 Air extract for most rooms occurred through the ensuite toilet rather than within the room itself, which was often the only area where there was mechanical ventilation. The corridors have supplied filtered air resulting in positive pressure with respect to the wards. Pressures across the rooms were slightly negative, although opening the windows tends to then cause pressure cascades to become variable and occasionally positive with respect to the corridor. Although the SHTM does not prescribe pressure requirements for general wards it does for single rooms, they should not be positive with respect to the corridor. There was a high number of samples where the measured concentration was below the detection limit of the Viasure assay, even in COVID-19-positive wards. While Cherrie et al. (2021) found a positive correlation between number of positive surface samples and air samples, this was not clearly seen in our study.6 Measurements show both air and surface concentrations are low, and levels in other studies of SARS-CoV-2 in healthcare environments also find similar levels of viral material. It is possible that existing sampling methods are not able to successfully capture viable virus, or adequate amounts of virus material – two samples in one of the COVID-19 treatment wards of our study found that a longer sample time and larger sample volume could be more effective at capturing virus material, but this finding is not enough to draw any conclusions. Swabbed surface areas are also relatively small. It should also be noted that we were not able to account for any losses which may occur in the sampling or transport process. Our results show that SARS-CoV-2 RNA can be present on surfaces and in air around infected patients, and that touching the patient and nearby surfaces could potentially transmit virus elsewhere. Healthcare worker’s notes and the sink are frequently touched during patient care, and could lead to transmission of the virus to these areas. We did not take samples of the worker’s notes, but the sink taps in the rooms with COVID-19 patients had detectable levels of virus, although we cannot say whether this is due to touches by the healthcare workers or the patients. The levels found in this and other studies of environmental SARS-CoV-2 have frequently not been able to be cultured, therefore it is not possible to say anything about the infectiousness of the RNA detected. Results here may not be reflective of later, potentially more transmissible variants of SARS-CoV-2. SARS-CoV-2 is chiefly transmitted by airborne exposure although viral shedding varies hugely between individuals. It is difficult to rely on the randomised clinical trial to evaluate the real-world effectiveness of FFP vs. surgical masks for protecting HCW from infection, as it is impossible to control all confounding factors. Some evidence indicates potential benefits for HCW and patients with use of FFP’s11 , 12, but successful infection control will include a bundle of measures, not just RPE. Current guidance on use of respiratory protection for healthcare workers suggests use of FFP3 respirators for working with patients with known or suspected infectious diseases transmitted ‘wholly by the airborne route’ or when carrying out AGPs on patients with an infectious disease spread at least partially by droplet or airborne routes, or as a precautionary measure if the hazard is unknown.13 Much literature on COVID-19 and the SARS-CoV-2 virus in health care settings focus on patient wards or ICU’s. Other areas, such as staff break rooms, changing rooms, or public areas have not been well studied, but may be areas for transmission, especially as RPE use may be relaxed in these settings. We also found positive air and surface samples in the PPE changing area of one of the hospitals, which was a repurposed bathroom area. Changing areas may therefore be an area of high exposure risk for staff, given that staff may be removing their PPE and therefore vulnerable to virus exposure.14 The repurposed toilet was supposedly not used as a toilet, but we do not know if it might have been during sampling days. Additionally, we detected SARS-CoV-2 RNA in air and surface samples taken from waiting and meeting rooms in the wards. This is an indication that ventilation in those rooms may be insufficient to remove airborne virus particles, which may have been generated by an earlier occupant and spread throughout the room. Furthermore, cleaning in these areas is unlikely to be as rigorous or frequent as in patient areas so any surface contamination may not be as well controlled. There is a need to further evaluate controls for these non-clinical areas, given that people may feel more relaxed and be less likely to take precautions in these places. Studies on behaviours in various parts of hospitals, particularly non-patient areas, related to PPE use and surface touches compared to patient areas, could help further define predictors of risk in different areas of the hospitals. These, along with environmental surface contamination levels from sampling studies such as ours, can also be used to inform quantitative microbial risk assessment models. These models could help decision-makers in these hospitals evaluate risks and compare the efficacy of different control measures. Since this study population levels of infection have been low and although infectivity and health impacts have been reduced by immunisation, further more infective variants are possible. Lessons from the COVID-19 pandemic need to be translated to improve resilience of health and social care for the future. Conclusions Implications of our study for Policy makers:• Regular environmental sampling for SARS-CoV-2 RNA should be used to inform the effectiveness of control measures where there is evidence of occupationally acquired transmission in health care settings and elsewhere. • The protocol for such surveys should be standardised to facilitate comparability and inform improvement. • The widespread non-compliance with health care ventilation standards in older buildings justifies a national review and action plan. Funding This work was supported by the Chief Scientist’s Office (CSO) of Scotland, reference number COV/IOM/Portfolio. Declaration of Competing Interest The authors state that they do not have any conflicts of interest. Appendix A Supplementary data The following is the Supplementary data to this article: Acknowledgements We would like to thank all the hospital staff who have helped facilitate or participated in our study. 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Airborne protection for staff is associated with reduced hospital-acquired COVID-19 in English NHS trusts J Hosp Infect 120 2022 81 84 10.1016/j.jhin.2021.11.018 34861313 13 National Infection Prevention and Control Manual: Chapter 2-Transmission Based Precautions (TBPs). Accessed June 27, 2022. https://www.nipcm.hps.scot.nhs.uk/chapter-2-transmission-based-precautions-tbps/#a1091. 14 Birgand G. Peiffer-Smadja N. Fournier S. Kerneis S. Lescure F.X. Lucet J.C. Assessment of Air Contamination by SARS-CoV-2 in Hospital Settings JAMA Netw Open 3 12 2020 e2033232 10.1001/jamanetworkopen.2020.33232 33355679	36473553	PMC9721166	NO-CC CODE	2022-12-06 23:26:27	no	J Hosp Infect. 2022 Dec 5; doi: 10.1016/j.jhin.2022.11.019	utf-8	J Hosp Infect	2,022	10.1016/j.jhin.2022.11.019	oa_other
==== Front Heliyon Heliyon Heliyon 2405-8440 Published by Elsevier Ltd. S2405-8440(22)03093-6 10.1016/j.heliyon.2022.e11805 e11805 Research Article People with passive sleep delay have more severe depression and sleep problems than those with active sleep delays-a cross-sectional study after the COVID-19 pandemic Wan Zhen-Yu Xiao Ling Wang Gao-Hua ∗ Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, Hubei, China ∗ Corresponding author. 5 12 2022 5 12 2022 e118058 7 2022 31 10 2022 15 11 2022 © 2022 Published by Elsevier Ltd. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Objective This study was designed to investigate the effect of different types of sleep delay in depression and sleep characteristics after the pandemic. Meanwhile, risk factors for depression were also explored. Methods The survey was conducted in Wuhan from March 1 to May 30, 2021, and participants were recruited through a snowball process. A total of 1,583 people with sleep delays responded to the invitation, of which 1,296 were enrolled. Participants filled out a questionnaire including social demographics, sleep characteristics, Social Support Rating Scale (SSRS), Pittsburgh Sleep Quality Index (PSQI) and Patient Health Questionnaire-9 (PHQ-9). Results There were no significant differences in sex, social support and level of education between the two types of sleep delay (p = 0.961, p = 0.110, p = 0.090), but the average age of the passive sleep delay group was higher (p = 0.015). And most people with active sleep delay were caused by the use of electronic devices (73.6%), while most people with passive sleep delay were caused by work or study tasks (73.2%), with a significant difference between the two groups (p < 0.001). People who actively delayed sleep had more regular sleep (p < 0.001), better sleep quality and longer sleep duration (p < 0.001, p < 0.001). In addition, although they delayed sleep more frequently (p < 0.001), they had significantly lower depression degree than people who passively delayed sleep (p < 0.001). Conclusions Passive sleep delays, usually caused by work or study, has higher levels of depression and more adverse sleep behaviors than active sleep delay. The findings help further understand the effects of delayed sleep and provide insight for people with delayed sleep to evaluate their own condition. Future studies are required to standardize and accurately classify sleep delay and further explore it. Depression; Sleep delay; Sleep duration; Sleep quality. Keywords Depression Sleep delay Sleep duration Sleep quality ==== Body pmc1 Introduction Depression is one of the major contributors to the global burden of disease, seriously affecting human health and reducing quality of life. The sufferer may feel sad, lose interest or happiness, and severe depression can even lead to suicide, and more than half of suicides are linked to depression. The World Health Organization ranked major depression as the third largest burden of disease globally in 2008 and predicted it would become number one by 2030 1,2. The onset of depression may be related to inflammation, dysfunction of the hypothalamic–pituitary–adrenal axis (HPA) function and stress, but the specific mechanism has not been agreed 3, 4, 5, 6. The treatment of depression mainly includes drug therapy, physical therapy and psychological therapy 7, 8, but it cannot produce effective relief for most patients, so it is particularly important to prevent the occurrence and development of depression. Sleep is the most common biological phenomenon in nature, people spend one third of their life in sleep. Studies have shown that adequate and quality sleep improves memory and cognition [9]. And sleep disorders can lead to inflammation and endocrine disruption [10], and sleep has been linked to high blood pressure, diabetes and even a person's risk of death 11, 12, 13. Recently, more and more attention has been paid to the relationship between sleep and depression. Sleep disorder is not only a typical symptom of depression, but also a likely predictor of the occurrence and development of depression [14]. Sleep duration and quality are closely related to the occurrence and development of depression, and sleep delay has also been shown to be a risk factor for depression 15, 16. In recent years, many people delaying bedtime as a result of artificial light and social media 17, 18. What's worse, during the COVID-19 pandemic, people's mental health suffered major challenges due to the dual stress of the virus and isolation [19], sleep quality and circadian rhythms gradually worsen. Many people experienced delayed sleep and other sleep problems 20, 21. After the remission of the epidemic, people's sleep status did not completely recover from the effects of the epidemic [22], which seriously affected people's health. As more and more people develop sleep delay, more easily and efficiently identifying those at high risk could help prevent adverse outcomes in time. However, in daily life, it is difficult for people with sleep delay to judge their condition through standardized indicators because of the large individual variation in sleep. The reason for sleep phase delay that we observe in the clinic and in life is likely to differ due to specific circumstances. Some people must go to bed later due to work or school tasks [23], others stay up later because of social media and electronic equipment 24, 25, and some have a habit of staying up later [26]. Delayed sleep for different reasons and purposes may indicate different levels of stress and mood, which may have different effects on sleep behavior and mental health [27]. But this has barely attracted attention of researchers, so we decided to categorize sleep delay and explore the differences. Initially, we tried to further categorize sleep delays based on their causes but found that different people might have different experiences of the same event. For example, staying up late at work may not be pleasant for someone who enjoy working, but it doesn't produce strong negative emotions, while staying up late at work may be very painful for someone who hates work. So, we tried to divide sleep delay into active sleep delay, which means people voluntarily choose to go to bed later, and passive sleep delay, which means people may be forced to stay up later for some reason. In this way, there is no need to consider individual differences, people just need to choose their own perception of staying up late (active or passive) according to the actual situation. We hypothesized that people with different types of sleep delay have different sleep characteristics and depression levels. 2 Materials and methods 2.1 Participants The study was conducted from March 1 to May 30, 2021. After preliminary preparation, we started to conduct offline and online recruitment (via Wechat platform) by snowball sampling in Wuhan (the first city in China to be severely hit by the epidemic, which has been brought under control during the investigation) from the middle of April. Participants were first asked whether there was any sleep delay. If there was no sleep delay, the questionnaire ended automatically. If they answered yes, they went on to complete the follow-up, which included depression levels, sleep characteristics and basic demographic information. And the respondents can win a lottery. Inclusion criteria were as follows: (1) Individuals who could understand the assessment and provide written informed consent, (2) aged 18 years or above. Exclusion criteria were as follows:(1) Prior diagnosis or family history of mental illness. (2) Had taken psychotropic drugs in the past 3 months. (3) Chronic smoking or alcohol abuse. (4) Had severe childhood trauma or recently suffered a major trauma or major stressful event. (5) Spent < 5min in the online survey. (6) Get the “trap question” wrong or quit halfway in the online survey. A total of 1,583 people with sleep delays completed the questionnaire. In the end, 81.8% (n = 1,296) were included in the current study after inclusion and exclusion criteria were applied. All respondents participated voluntarily under the premise of written informed consent and could quit at any time. Ethical approval was obtained from Renmin Hospital of Wuhan University. 2.2 Variables 2.2.1 Basic sociodemographic information Basic sociodemographic information included sex, age, level of education, exercise time >150 min/week (yes or no). Social support was assessed by the SSRS (Social Support Rating Scale) [28], the questionnaire consists of three aspects with a total score of 66, which is classified into three categories: low (≤22), moderate (23–44), and high (≥45) levels of support, SSRS have been proven to be reliable and effective in assessing social support in Chinese populations [29]. 2.2.2 Sleep information and depression level Sleep-related problems included frequency of delayed sleep. The recommended sleep duration for young people and adults is 7–9h per day [30]. Since the start time of work and study is 8am, people should fall asleep before 1am in order to ensure the minimum recommended sleep time (≥7h), which is consistent with the time in DSM5 diagnostic criteria for Delayed Sleep Phase Disorder (falling asleep after 01:00 am at least 3 days per week and having a preferred wake up after 09:45 am, if possible). Strictly speaking, a bedtime of more than 1am is not a diagnostic criterion, but it can be used as a cut-off point for delaying sleep. Considering that the participants were younger and had higher sleep duration needs [31], this study defined bedtime beyond 12:30 as delayed sleep 32, 33. Since both short and long sleep are associated with increased risk of depression [34], we classified sleep duration into 4 categories: < 5h (Ultra short), 5–7h (short), 7–9h (Moderate), and >9h (long). Sleep regularity was defined as no more than a 30-minute difference in bedtime per night (yes or no). The reasons for delayed sleep include using electronic devices, studying and working or others. Sleep quality was assessed by the Pittsburgh Sleep Quality Index (PSQI) [35], with a score >7 considered “poor sleep quality”. Participants self-rated whether they delayed sleep actively or passively based on their actual sleep situation. Depression degree was assessed using the Patient Health Questionnaire (PHQ-9) [36], the PHQ-9 consists of nine items scored from 0 (none at all) to 3 (almost daily), with higher scores indicating more severe depression. 2.3 Statistical analyses Analysis was performed using SPSS Statistics 23.0. Descriptive analysis was used to describe basic social information and sleep behaviors of the participants. . The data are expressed by frequency (N) and percentage (%). A PSQI cutoff value was used to split all participants into a group of good sleepers and a group of poor sleepers (>7)。The level of social support was divided into three groups according to the score: low (≤22), moderate (23–44), and high (≥45) levels of support. Depression levels in different groups were summarized by the median and interquartile range (IQR). Chi-square test and Kruskal-Wallis H test were used to examine the differences in social information, sleep characteristics and depression among different types of sleep delay. Finally, we divided the population into non-depressed (PHQ9<5) and mildly depressed or above (PHQ9≥5) according to the PHQ9 score. Logistic regression was used to analyze the relationship between the type of sleep delay (active or passive) and the level of depression after adjusting for age, sex, education level, sleep duration, frequency of delayed sleep, causes of delayed sleep, sleep regularity, sleep quality, and social support, which may affect the degree of depression. Through the Hosmer-Lemeshow method to test, and derived the odds ratios and 95% confidence limits. A p < 0.05 indicated a significant difference. Missing or incomplete data will be deleted. 3 Results 3.1 Basic information and the degree of depression in different subgroups The average age of the participants was 24. The percentages of males and females were 32.3% and 67.7%. About half of the participants had sleep delays more than half the time each week. The main reason is use of electronic devices (60.6%). Only 44.1% of participants reported that they sleep more than 7 h a day. The proportions of individuals with active and passive sleep delay was 73.5% and 26.5%, respectively. And the results also showed that women had higher depression levels than men (p < 0.001). Higher education and social support are protective factors for depression (p < 0.001, p < 0.001). There were differences in depression levels based on the frequency of staying up late (p < 0.001). People who sleep regularly and sleep better have lower levels of depression (p < 0.001, p < 0.001), and sleep duration is also a protective factor for depression (p < 0.001). People who passively delayed sleep had higher levels of depression (p < 0.001). More detailed information is shown in Table 1 .Table 1 Baseline variables and depression levels in different groups (N = 1296). Table 1Variables N (%) Depression levels p Sex <0.001 Female 879 (67.7%) 5 (2–8) Male 417 (32.3%) 3 (0–6) Level of education <0.001 Master's degree or more 680 (52.5%) 3 (0–5) College degree 544 (42.0%) 6 (3–8) High school degree or less 72 (5.5%) 7 (5–10) Physical exercise (>150 min/week) 0.521 Yes 629 (48.5%) 4 (1–7) No 667 (51.5%) 5 (2–8) Social support <0.001 A high level 712 (54.9%) 3 (0–5) Medium level 390 (30.1%) 4 (2–7) The low level 194 (15.0%) 7 (4–10) Frequency of delayed sleep <0.001 <3/month 155 (12.0%) 1 (0–5) 1-3/week 501 (38.7%) 4 (1–7) 4-5/week 326 (25.1%) 5 (2–8) 6-7/week 314 (24.2%) 5 (2–8) Sleep duration <0.001 <5 h 24 (1.9%) 8 (6–10.5) 5–7 h 555 (54.0%) 5 (2–8) 7–9 h 700 (42.8%) 3 (1–6) >9 h 17 (1.3%) 0 (0–4) Regularity <0.001 Yes 840 (64.8%) 3 (1–6) No 456 (35.2%) 6 (3–9.75) Reasons for delayed sleep 0.046 Use of electronic equipment 786 (60.6%) 4 (2–7) Work or study tasks 494 (38.1%) 4 (1–7) others 16 (1.3%) 1.5 (0–4) Sleep quality <0.001 Good sleep quality 1167 (90.0%) 4 (1–7) Poor sleep quality 129 (10.0%) 10 (7–13) Active or passive delayed sleep <0.001 Active 953 (73.5%) 4 (1–7) Passive 343 (26.5%) 6 (2–9) IQR: Inter Quartile Range. 3.2 Differences in sociodemographic information, sleep behavior, and depression degree between the two types of sleep delay The results show that there were no significant differences in sex, social support and level of education among delayed sleep types (p = 0.961, p = 0.110, p = 0.090), but people who passively delayed sleep had a higher average age (p = 0.015). Most of active sleep delays were due to the use of electronic devices (73.6%), while the majority of passive sleep delays were due to work or study tasks, with a significant difference between the two groups (p < 0.001). In terms of sleep characteristics, the results showed that people with active sleep delay had lower depression degree (p < 0.001) despite more frequent delayed sleep (p < 0.001). At the same time, people who actively delayed sleep tended to have better sleep quality and more regular sleep (p < 0.001, p < 0.001), they also had longer sleep duration (p < 0.001). More detailed information is shown in Table 2 and Table 3 .Table 2 Differences in different types of sleep delay. Table 2Variables Active (N = 953) Passive (N = 343) Z p Average age (Median, IQR) 22 (22–26) 24 (22–26) -2.423 0.015 Depression levels (Median, IQR) 4 (1–7) 6 (2–9) -6.071 <0.001 Frequency of delayed sleep (N/%) -3.235 <0.001 <3/month N = 89 (9.3%) N = 66 (19.2%) 1-3/week N = 372 (39%) N = 129 (37.6%) 4-5/week N = 257 (27%) N = 69 (20.1%) 6-7/week N = 235 (24.7%) N = 79 (23.0%) Sleep duration -4.165 <0.001 <5 h N = 10 (1.0%) N = 14 (4.1%) 5–7 h N = 493 (51.7%) N = 207 (60.3%) 7–9 h N = 436 (45.8) N = 119 (34.7) >9 h N = 14 (1.5%) N = 3 (0.9%) Level of education -1.695 0.090 Master's degree or more N = 485 (50.9%) N = 195 (56.9%) College degree N = 416 (43.7%) N = 128 (37.3%) High school degree or less N = 52 (52%) N = 20 (5.8%) Social support -1.601 0.110 A high level N = 539 (56.6) N = 173 (50.4%) Medium level N = 89 (9.3%) N = 44 (12.8%) The low level N = 325 (34.1%) N = 126 (36.7%) Table 3 Differences in different types of sleep delay. Table 3Variables Active (N = 953) Passive (N = 343) x [2] p Sex 4.620 0.961 Female N = 307 (32.2%) N = 110 (32.1%) Male N = 646 (67.8%) N = 233 (67.9%) Physical exercise 17.783 <0.001 Yes N = 496 (52.0%) N = 133 (38.8%) NO N = 457 (48.0%) N = 210 (61.2%) Sleep regularity 29.675 <0.001 Yes N = 659 (69.2%) N = 181 (52.8%) No N = 294 (30.8%) N = 162 (47.2%) Sleep quality 21.136 <0.001 Good N = 880 (92.3%) N = 287 (83.7%) Poor N = 73 (7.7%) N = 56 (16.3%) Reasons for delayed sleep 251.822 <0.001 Use of electronic equipment N = 701 (73.6%) N = 85 (24.8%) Work or study tasks N = 243 (25.5%) N = 251 (73.2%) others N = 9 (0.9%) N = 16 (1.2%) Finally, we divided the population into non-depressed (PHQ9 < 5) and mildly depressed or above (PHQ9≥5) according to the PHQ9 score. Logistic regression analysis showed that depression level was significantly correlated with the types of delayed sleep, and people who passively delayed sleep had higher depression level (p < 0.001). More detailed information is shown in Figure 1 .Figure 1 OR: odd ratio, HR: hazard ratio. Figure 1 4 Discussion The development of the Internet and lighting has brought more choices for people's night life 18, 37. Coupled with the impact of the epidemic, more and more people are experiencing sleep delay, which has seriously affected people's mental health. In order to further understand the behavior of sleep delay and more accurately identify people at high risk. We conducted a cross-sectional study in Wuhan (the first city in China to be severely hit by the epidemic) after the epidemic was brought under control, classifying sleep delay into active and passive types to understand the differences between populations with different types of sleep delay. The results showed that there were no significant differences in basic social information, such as sex, education level and social support, among different delayed sleep types. This indicates that basic social information is not closely correlated with delayed sleep types, and it also means that even people with higher social support or education are still at risk for passive sleep delay. And older people are more prone to passive sleep delays, most likely because they tend to face more learning tasks and social responsibilities and have to delay bedtime. In addition, there were also significant differences between the two groups in the reasons for sleep delay. Most people who actively delay sleep use electronic devices (73.6%), and they experienced sleep delays more frequently. Frequent use of electronic devices at night is a manifestation of internet addiction, which often leads to decreased sleep duration and quality, which leads to a higher risk of depression 38, 39, 40. Delayed sleep is also a risk factor for depression. However, those who delayed sleep passively were mostly due to work or school tasks (73.2%), which is a way to improve themselves and gain social acceptance. From this perspective, the risk of adverse outcomes appears to be higher in people with active sleep delay. However, the results showed that people with active sleep delays had longer sleep duration, better sleep quality and lower levels of depression than those with passive sleep delays. This may be because people who actively delay sleep have greater autonomy to choose their bedtime based on their sleep needs and social arrangements the next day, thus obtaining longer sleep duration and higher sleep quality, which are protective factors that effectively reduce the adverse effects of sleep delay 41, 42. In addition, most of them use electronic devices for social entertainment. Although the frequent use of electronic devices at night is not a healthy lifestyle, we have to admit that it does bring relaxation and satisfaction [43]. While most people with passive sleep delay are working or studying, which is more positive than using electronic devices, it also means they may face more stress and bad feelings from being forced to work or study. Many studies have demonstrated that sleep delay is a risk factor for depression, and this study further shows that different sleep delay types have significant differences in sleep behavior and depression levels. People with passive sleep delay have worse sleep behavior and higher levels of depression. With the development of social media and the impact of the epidemic, more and more people are experiencing sleep delays. This study provides ideas for identifying the high-risk population, but this does not mean that we agree with the behavior of active sleep delay. Due to the recurrence of the epidemic and the existence of sporadic cases, more and more people began to work and study at home. The Internet has become an important channel for communication with the outside world, which also provides convenience for Internet addiction. Many people actively delay sleep and use their devices more frequently in the evening, a behavior that can carry the risk of addiction, which in turn can lead to poor sleep behavior. It's a vicious cycle that can lead to bad outcomes. Under the action of various factors, the phenomenon of sleep delay is difficult to reverse. Due to individual differences, it is difficult for people with sleep delay to judge their own condition through standardized indicators. However, people can easily and efficiently judge their sleep delay type according to their own feelings, and then take further measures. The Internet has become the primary means of communication during the pandemic, and Web-based remote services such as digital cognitive behavioral therapy can provide essential medical care while overcoming spatial barriers and reducing human mobility during the pandemic, which have been shown to be effective in improving sleep and depression 44, 45. Therefore, when people are aware that they are experiencing sleep delays, especially passive sleep delays, the adoption of digital cognitive behavioral therapy may be effective and convenient in improving sleep behavior and depression. 4.0.1 Advantages and limitations The main advantage of this study is that it is the first to explore the relationship between different types of sleep delay and depression, and to show that passive sleep delay is associated with higher levels of depression and sleep problems. As the number of people with delayed sleep continues to increase, this could help more accurately identify high-risk groups and timely intervene. The limitation of our study mainly lies in the definition of active or passive delayed sleep. Although the expected conclusion is still reached, some middle groups may be neglected if the prompts are set as dichotomies. It would be more accurate if we could design relevant scales to quantitatively evaluate people's attitude towards sleep delay. Passive sleep delay may involve stress-related factors, but this study only excluded participants with major stress, and did not quantify the stress of the subjects. In addition, due to the epidemic, this study was partly conducted through questionnaire survey. Although we strictly screened samples, the accuracy may still be slightly lower than that achieved with face-to-face interviews. 5 Conclusions Passive sleep delays, usually often caused by work or study, has higher levels of depression and more adverse sleep behaviors than active sleep delay. The findings help further understand the effects of delayed sleep and provide insight for people with delayed sleep to evaluate their own condition. Future studies are required to standardize and accurately classify sleep delay and further explore it. Declarations Author contribution statement Zhen-yu wan: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Wrote the paper. Ling xiao: Conceived and designed the experiments; Contributed reagents, materials, analysis tools or data; Wrote the paper. Gao-hua wang: Conceived and designed the experiments. Funding statement Gao-hua wang was supported by Medical Science Advancement Program of Wuhan University [NO. TFLC2018001], National Natural Science Foundation of China [No. 81871072 & NO. 82071523], Key research and development program of Hubei Province [2020BCA064]. Data availability statement Data will be made available on request. Declaration of interest's statement The authors declare no conflict of interest. Additional information No additional information is available for this paper. ==== Refs References 1 Malhi G.S. Mann J.J. Depression Lancet 392 10161 2018 2299 2312 Epub 2018 Nov 2. PMID: 30396512 30396512 2 Chand SP, Arif H. Depression. 2021 Jul 26. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2022 Jan–. PMID: 28613597. 3 Alvaro P.K. Roberts R.M. Harris J.K. The independent relationships between insomnia, depression, subtypes of anxiety, and chronotype during adolescence Sleep Med. 15 2014 934 941 24958244 4 Beurel E. Toups M. Nemeroff C.B. 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==== Front Epidemics Epidemics Epidemics 1755-4365 1878-0067 The Authors. Published by Elsevier B.V. S1755-4365(22)00098-6 10.1016/j.epidem.2022.100658 100658 Article Testing a Simple and Frugal Model of Health Protective Behaviour in Epidemic Times Martin-Lapoirie Dylan ab⁎ d’Onofrio Alberto cd McColl Kathleen ab Raude Jocelyn ab a École des Hautes Études en Santé Publique (EHESP), French School of Public Health, 35043 Rennes, France b UMR ARENES - Equipe de Recherche sur les Services et le Management en Santé (UniR, CNRS 6051, INSERM 1309), 35043 Rennes, France c Institut Camille Jordan, Université Claude Bernard - Lyon 1, 21 Av. Claude Bernard, 69100 Villeurbanne, France d Consiglio Nazionale delle Ricerche, Istituto di Analisi dei Sistemi e di Informatica Antonio Ruberti, Via dei Taurini 19, 00185 Roma, Italy ⁎ Correspondence to: École des Hautes Études en Santé Publique, 15 Avenue du Professeur Léon Bernard, CS 74312, 35043 Rennes cedex, France. E-mail: 5 12 2022 5 12 2022 10065829 1 2022 9 7 2022 1 9 2022 © 2022 The Authors. Published by Elsevier B.V. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. The COVID-19 epidemic highlighted the necessity to integrate dynamic human behaviour change into infectious disease transmission models. The adoption of health protective behaviour, such as handwashing or staying at home, depends on both epidemiological and personal variables. However, only a few models have been proposed in the recent literature to account for behavioural change in response to the health threat over time. This study aims to estimate the relevance of TELL ME, a simple and frugal agent-based model developed following the 2009 H1N1 outbreak to explain individual engagement in health protective behaviours in epidemic times and how communication can influence this. Basically, TELL ME includes a behavioural rule to simulate individual decisions to adopt health protective behaviours. To test this rule, we used behavioural data from a series of 12 cross-sectional surveys in France over a 6-month period (May to November 2020). Samples were representative of the French population (N=24,003). We found the TELL ME behavioural rule to be associated with a moderate to high error rate in representing the adoption of behaviours, indicating that parameter values are not constant over time and that other key variables influence individual decisions. These results highlight the crucial need for longitudinal behavioural data to better calibrate epidemiological models accounting for public responses to infectious disease threats. Keywords COVID-19 Health protective behaviour Agent-based model Calibration Pattern-oriented modelling ==== Body pmc1 Introduction The COVID-19 pandemic has shed light on the need for public health authorities to have reliable simulation tools in order to prevent or control the spread of infectious diseases (Keeling et al., 2021, Mari et al., 2021, Gatto et al., 2020). Typically, epidemiologists have long applied a compartmental approach to predict the dynamic nature of an epidemic, such as SIR models. However, these epidemiological models, often based on ordinary differential equations used to describe how the disease is spreading, rarely incorporate a dynamic behavioural component of decision making under risk to explain how people deal with the evolving parameters of an epidemic (Funk et al., 2015, Ferguson, 2007, Diekmann and Heesterbeek, 2000). Therefore, individuals’ decisions to adopt protective behaviours to prevent infection are often either exogenous or simply ignored. Despite the critical role that human behaviour, through person-to-person contacts, plays in increasing or reducing respiratory infectious disease transmission, risk of infection has to date mostly been examined through the lens of population density and mobility, which overlooks the dynamics of social and health behaviours. In the current literature, epidemiologists are increasingly interested in the modelling of human behaviour and its influence on the spread of an epidemic (Verelst et al., 2016, Manfredi and d’Onofrio, 2013, d’Onofrio and Manfredi, 2009). One possible way of modelling is to represent health protective behaviours in ordinary differential equations based models (Buonomo and Della Marca, 2020). However, this approach is not satisfactory because it does not adequately represent sophisticated behavioural decision rules. Whereas this type of modelling considering human behaviour as exogenous can lead to an overall good prediction, it often results in an inaccurate prediction at the individual level. It is only suitable if the modeller aims to predict the spread of the epidemic. If he wants to study the effect of non-pharmaceutical interventions on this one, modelling the dynamics of human behaviour is unavoidable (Bedson et al., 2021, Funk et al., 2010). In particular, he needs to take account of the interactions between individuals and those between people and their environment. In this respect, agent-based modelling is a promising method. This computational approach is increasingly used to describe infectious disease transmission (Weston et al., 2018, Verelst et al., 2016) because it is powerful to study the effects of changes in health protective behaviours on the spread of an epidemic. A few agent-based models have been developed to take account of the dynamics of human behaviour in disease transmission (Mniszewski and Del Valle (2013)), some of which a smaller number focused on COVID-19 (Lorig et al., 2021, Lu et al., 2021). In light of the COVID-19 pandemic and of the potential risk of even worse future pandemics, it seems crucial to estimate the relevance and interest of existing agent-based models of health protective behaviours, for which the decision to adopt protective behaviours depends on a small number of social cognitive variables. This is the objective of this article. Specifically, we focus on what, in our opinion, may represent one of the most promising agent-based models, the TELL ME model introduced by Badham and Gilbert (2015), as it offers a relatively simple and frugal tool to represent change in health protective behaviours over time. 1.1 The agent-based modelling of human behaviours Only a small number of disease transmission models provide an explicit modelling of individual behaviour during an epidemic. In their systematic review of behavioural change models applied to infectious disease transmission, Verelst et al. (2016) observe five types of modelling to represent the individual decision-making process. The behavioural decision can be exogenous, based on an information threshold, depend on information modelled as a dynamic parameter, or be based on an economic objective function with or without social learning or imitation. The TELL ME model belongs to this last category, rendering the decision to adopt protective behaviours dependent upon an objective function based on social cognitive factors, which includes subjective norms. A challenge with which infectious disease modellers are faced is that their models are useful only insofar as they are able to identify how the agents react in an epidemic context and how they respond to prevention and control measures. This corresponds to the purpose of agent-based models of health protective behaviours (Funk et al., 2015). This modelling approach enables the simulation of individuals making decisions according to programmable rules (Badham et al., 2018). Contrary to traditional modelling methods, agent-based modelling focuses on individual interactions rather than individuals. Programmed rules define the interactions between agents and the interactions of agents with their environment. The model is run during a simulated time, in which agents make their decisions and adapt them. This method is thus useful to generate heterogeneity across population characteristics and to observe large-scale patterns (Bedson et al., 2021). In their systematic review, Lorig et al. (2021) draw our attention to the rather striking heterogeneity among agent-based models in terms of purpose, transmission dynamics, geographical region and the number of simulated individuals. Only 14% of the 126 agent-based social simulations included in the review implement agent behaviour by a fixed behavioural pattern based on an empirical schedule derived from personal characteristics. Further, 5% of the simulations use dynamic or adaptive behavioural patterns, i.e. behaviour is based on needs or utility. Regarding simulation, only 43.7% use real-world census data to take into account sociodemographic features and to generate a population similar to the population in the simulated region or country. Furthermore, the simulation methods in agent-based modelling are based on the confrontation of data to multiple criteria to evaluate the best parameter set. Nonetheless, in the existing models, the choice of the best parameter set is made by using an arbitrary acceptance threshold, as categorical calibration, or arbitrarily by prioritising one criterion over the others. A rigorous selection method is rarely used. 1.2 The TELL ME model of health protective behaviours In this paper, we investigate the relevance of an adaptive behavioural pattern. This pattern is based on the three most cited psychological theories of health behaviour in disease transmission models and emergency response studies (Weston et al., 2020): i) the health belief model (Rosenstock, 1974); ii) the theory of planned behaviour (Ajzen, 1991); and iii) the protection motivation theory (Maddux and Rogers, 1983). The TELL ME model is one of the rare agent-based models to integrate the main components of these three main psychological theories into a model of behavioural change. More precisely, this agent-based model, constructed by Badham and Gilbert (2015), follows two pillars: i) an epidemiological model which simulates the spread of an epidemic, and ii) a behavioural rule which represents individual decision-making about protective behaviour.1 This model, which was developed in the framework of the TELL ME European Project on transparent communication during epidemics, aims to simulate the effect of different communication strategies on the individual protective decisions in an epidemic context (Barbrook-Johnson et al., 2017).2 The TELL ME behavioural rule bases the decision to adopt protective behaviours on attitude, subjective norms and perceived threat associated with the epidemic. The relevance of this design has been confirmed by empirical data. Reviewing studies of epidemics from 2002 to 2010, Bish and Michie (2010) found strong evidence that perceived susceptibility, perceived disease severity, and perceived efficacy of behaviour are significantly associated with engaging in protection against the disease. There is also a limited amount of evidence in favour of social norms as a predictor of protective behaviours. In a recent integrated narrative review based on the period from 2000 to 2020, Seale et al. (2020) found that isolation was influenced by perceived susceptibility and perceived efficacy. During the COVID-19 epidemic, some studies examined the association of sociodemographic, cognitive and psychological variables with the adoption of health protective behaviours. Once again, engaging in health protective behaviours, such as mask wearing and social distancing, is found to be strongly associated with perceived efficacy (Scholz and Freund, 2021, Clark et al., 2020, Zickfeld et al., 2020). Moreover, perceived infection risk is also revealed to be a significant predictor of behaviours (Qin et al., 2021, Schneider et al., 2021, Bruine de Bruin and Bennett, 2020, Ning et al., 2020, Storopoli et al., 2020, Vally, 2020). In France, two studies investigated these relationships during the first lockdown. Raude et al. (2020) highlighted the role of perceived efficacy, perceived severity and subjective norms in the adoption of protective behaviours. For their part, Guillon and Kergall (2020) found that perceived threat and perceived benefits influence attitudes and opinions regarding quarantine. Based on psychological theories, TELL ME is one of the rare agent-based models to simulate the spread of an epidemic by incorporating personal and epidemiologic variables. The objective of our study is to investigate the validity of the TELL ME behavioural rule to explain engagement in protective behaviours during the COVID-19 pandemic. In particular, we look at to what extent this rule is able to represent the overall behaviour of the population and individual behaviours. Further, we study whether the variables included in this rule are the most relevant and which variables should be endogenised, as well as what minimal level of detail is required to capture individual differences in protective behaviour. 2 Materials and methods 2.1 Variables of the TELL ME model As indicated previously, the TELL ME model focuses on the behavioural rule which is based on three leading theories in health psychology: i) the theory of planned behaviour; ii) the health belief model; and iii) the protection motivation theory. For an agent, it is assumed that the decision to adopt protective behaviour depends on three key variables drawn from these theories: attitude toward the behaviour, subjective norms and perceived threat. In the following, we summarise the main concepts regarding these constructs and how we implemented them in the present study: Attitude: This construct is generally defined as beliefs about the behaviour and its consequences, which underlie the willingness to adopt protective behaviour. Initially, Badham and Gilbert applied their model to the self-protective behaviours occurring during the 2009 H1N1 epidemic, for which they used an empirically-based distribution extracted from the responses to questions about hand hygiene throughout the epidemic, as reported in Cowling et al. (2010). In line with the psychological theories of health behaviours (Brewer and Rimer, 2008, Weinstein, 1993), in our analysis we disaggregated attitude into two associated variables: perceived efficacy and perceived barriers regarding protective behaviours. The first variable captures the expected benefit of the adoption of the protective behaviour by the agent, whereas the second assesses the expected cost of that behaviour. In our study, perceived efficacy and perceived barriers of protective behaviours were based on multi-item scale variables. These scores, between 0 and 1, represent averages of the responses to the following questions: i) for perceived efficacy (items from 0 to 10), “How effective do you think the improved hygiene measures are to prevent the COVID-19 infection?”; and ii) “How effective do you think the social distancing measures are to prevent the COVID-19 infection?”; and for perceived barriers (items from 0 to 10, in the reverse order for the analysis), “How difficult do you think it is to adopt improved hygiene measures to prevent COVID-19 infection?”, and “How difficult do you think it is to adopt social distancing measures to prevent COVID-19 infection?”. Subjective norms: This construct refers in the literature to the “beliefs about the normative expectations of others” which lead to perceived social pressure (Stroebe, 2011). They are captured in the model through the proportion of agents in the same region that have adopted the behaviour. The underlying assumption is that agents make their decisions according to the behaviour expected by their family, friends and other people who are important to them, and how they perceive this behaviour as a benchmark. In our study, subjective norms were measured through the proportion of agents’ in the same administrative region having adopted the behaviour, i.e. the proportion of “high compliance” response in each region. Perceived threat: This construct is often defined as the product of two components (Brewer et al., 2007): the perceived severity and frequency of the disease. Based on Durham and Casman’s method (2012), Badham and Gilbert (2015) represent the frequency of the disease as a cumulative incidence time series, i.e. the sum of the current incidence level and the discounted past incidence levels. In our study, we made two important modifications to this model. Firstly, we used the death incidence, defined by the number of new deaths per day, instead of the number of infected persons as the latter variable is not reliable due to underdetection of symptomatic COVID-19 cases (Pullano et al., 2021, Shaman, 2021). Secondly, we computed incidence at the national rather than the regional level. Indeed, during the epidemic, people were massively informed about the daily number of deaths in the country through intensive media coverage, whereas knowing the number of deaths in their region required an additional effort in the form of an information search. In our study, incidence was measured by the publicised number of deaths expressed in thousands. For each period, cumulative incidence time series comprises the current death incidence level and the death incidence in the last three weeks. The severity component refers to the perceived consequences of becoming infected. We performed an ANOVA to explore the difference in perceived severity over time. The difference was associated with a small effect (η²=.01). That is why we assumed in our analysis that the perceived severity of infection is stable over time and we set the severity multiplier to 1, as Badham and Gilbert did for this factor in their initial study. Health protective behaviours: In our analysis, we sought to explain the change in the adoption of a range of protective behaviours recommended by the authorities to tackle the COVID-19 epidemic. More precisely, we analysed six protective behaviours, including: 1) “Avoid close contacts with other people”; 2) “Avoid public transport”; 3) “Do not shake hands”; 4) “Stay at least 1 m away from other people”; 5) “Stay home as much as possible”; and 6) “Wash hands often”. In each of our surveys, participants were asked whether they engaged in each of these behaviours to reduce their risk of infection from COVID-19. They had to answer “Yes, systematically”, “Yes, often”, “Yes, sometimes”, or “No, never”. As we observed a ceiling effect in the responses in favour of the upper limit of the scale, we dichotomized each behaviour variable with the “high compliance” response (“Yes, systematically”) coded as 1, and the other options merged into a “lower compliance” category coded as 0. Percentages of people who reported engaging in protective behaviours over time are displayed in Fig. 1.Fig. 1 Percentages of participants engaging in protective behaviours over time in 2020. For each survey, each curve indicates the percentage of respondents who reported engaging in the particular protective behaviour. Fig. 1 For each protective behaviour, Table 1 indicates the difference in means of each component of the behaviour score between the agents who adopted the protective behaviour and the agents who did not. We see that all differences are significant, except for subjective norms, when the prescribed protective behaviour is “Wash hands often”, which can be explained by a small variance in this behaviour over time. Overall, our data show that all variables are significantly associated with the decision to adopt behaviour. Therefore, their inclusion in the TELL ME agent-based model is appropriate.Table 1 Mean differences according to the adoption of the protective behaviour (y=1 vs. y=0). Table 1 Protective behaviour Variables Avoid close contacts with other people Avoid public transport Do not shake hands Stay at least 1 m away from other people Stay home as much as possible Wash hands often Efficacy .0491223* .0309255* .0799952* .0610779* .0310983* .0570135* Barriers .0217058* .0328359* .0417856* .0357394* .0222339* .0394996* Norms .0757643* .0538871* .0248763* .0146448* .1057802* -.0006659 Incidence (day) .0645354* .0316636* .0430373* .0194769* .0743378* .00808* Incidence (a week ago) .0657756* .0401469* .0460148* .0242512* .0765453* .0096581* Incidence (two weeks ago) .0724176* .0509014* .0526307* .0303889* .0846327* .0113325* Incidence (three weeks ago) .0845054* .0683834* .0649798* .040627* .099991* .0139123* Note: Comparison of means by t-test. * p<0.01, ** p<0.05, * p<0.1 2.2 Behavioural decision rule The TELL ME behavioural decision rule can be described as follows. An agent i that at time t is in the region r adopts the protective behaviour if his behaviour score (Bi) at the time t is greater than or equal to a threshold score (T). In the converse case, the behaviour is dropped. The behaviour score (Bi) is a weighted average of perceived efficacy (Ei), perceived barriers (Ci), subjective norms (Nr) and incidence (INC):Bi(t)=αEi(t)+βCi(t)+γNr(t)+1−α−β−γW∑j=0tδjINC(t−j) where α,β,γ are weights, δ is a discount rate and W=1. The scores of perceived efficacy and perceived barriers are personal characteristics. Subjective norms are identical to all agents in the region r and, of course, cumulative incidence time series expressed in thousands is the same for all agents 2.3 Samples and Data Our data was collected through 12 online, cross-sectional surveys conducted from May to November 2020 among large representative samples of adults residing in France3 . Therefore, the period studied does not cover the strict French lockdown which was implemented from 17 March to 10 May. Only the last two surveys took place during a less strict lockdown, in which schools were open and face-to-face work was possible. A stratified sampling method was adopted to recruit participants so as to represent the distribution of the French population, based on sex, age, occupation, community size and region recorded during the 2016 national general census conducted by the National Institute of Statistics and Economic Studies (INSEE). Samples consisted of N=24,003 responses. Missing data were replaced by a multivariate imputation procedure (van Buuren and Groothuis-Oudshoorn, 2011). More than half of these participants were women (56.87%), and 14.18% had a high socioeconomic status, 34.81% had a low socioeconomic status, and another 51.01% were inactive (retired, students and persons engaged in activities in the household). Ages were between 18 and 99 years, with a proportion of participants aged 65 years or older of 27.71%. Ethical approval was granted by the University Hospital Institute “Mediterranee Infection” Ethics Committee Marseille, France and the EHESP School of Public Health Office for Personal Data Protection. 2.4 Calibration process We are not interested in this paper in the epidemiological component of the TELL ME model. Indeed, our purpose is not simulating the spread of the COVID-19 epidemic. Rather, we aim to determine to what extent the TELL ME behavioural rule is powerful in explaining the individual decisions to adopt protective behaviours during the epidemic. Five parameters are involved in the behavioural rule: the weights for perceived efficacy, perceived barriers and subjective norms, the discount rate of past incidence levels, and the threshold score (α,β,γ,δ,T). Our calibration process was based on the method used by Badham et al. (2017) in their own estimation of the TELL ME model. Their originality is to estimate their model against multiple macro validation criteria, that is pattern-oriented modelling (Railsback and Grimm, 2012, Wiegand et al., 2004). This simulation method is particularly useful to obtain both an overall good fit and an individual good fit. Indeed, with a sufficient number of parameters, an accurate assessment of percentages of individuals engaging in protective behaviours can be easily achieved, but such a fit may be associated with a problem of structural invalidity or other problems. For instance, the model can report the correct percentage of people adopting the behaviour in the targeted population, but it wrongly predicts that a careful agent does not adopt behaviours, or that a careless agent adopts behaviours. Having multiple selection criteria raises the question of how to choose the best fit parameter set. This may be chosen on the basis of an overall objective function in which each criterion would be weighted, the reasonableness of the model’s behaviour or an acceptance threshold for each criterion. The problem is that the choice of priority criterion or acceptance threshold is arbitrary and sometimes inefficient. That is why Badham et al. use a dominance analysis, which is already used in operations research for multi-criteria decision-making or optimisation (Müssel et al., 2012). Although this approach is scarce in social simulation, it is powerful in improving the fit with the empirical behaviour adoption curve. Dominance analysis involves determining all best fit candidates on the Pareto efficient frontier. On this frontier, an improvement in one criterion inevitably requires a reduction in another. Although it required some adjustments, in particular due to the absence of an epidemiological component in our analysis, the simulation method of Badham et al. was easy to implement with our data and consisted of four steps. The first challenge was to generate enough heterogeneity in the agents’ behaviour. To do this, as indicated in Table 2, we excluded parameter combinations for which the behaviour score was not computed from perceived efficacy, perceived barriers, subjective norms and perceived threat (α+β+γ≤0.95). Moreover, the range of the discount rate was restricted (δ≤0.44) to prevent cumulative incidence time series to exceed 1. We sampled the parameter space by the Latin Hypercube method and selected 3587 combinations, i.e. 1% of all possible combinations.Table 2 Parameter values tested in the calibration process. Table 2Parameter Range Efficacy weight (α) 0.05 by 0.05 to 0.85 Barriers weight (β) 0.05 by 0.05 to 0.85 Norms weight (γ) 0.05 by 0.05 to 0.85 Incidence discount (δ) 0.02 by 0.02 to 0.44 Behaviour threshold (T) 0.05 by 0.05 to 0.95 Second, we assessed the behavioural adoption curve associated with each combination against empirical data on two criteria: 1) the mean squared error between predicted and actual behaviour (MSE); and 2) the maximum difference in absolute terms between the predicted adoption proportion and actual adoption proportion per period (ΔMax). Indeed, MSE is an insufficient criterion to capture the shape of the behavioural adoption curve. Integrating the criterion ΔMax in the analysis permits us to take the shape into account. Contrary to Badham et al., we did not use a third criterion that would be the number of days between the timing of the maximum predicted adoption proportion and the maximum actual adoption proportion. This criterion is applicable only in the case where the agent-based model is launched. Our interest here is only to estimate the validity of the behavioural rule without looking at the epidemiological model. Third, dominance analysis was used to identify the best fit candidates. The principle is to assign each parameter set to a dominant front. Front 0 corresponds to the Pareto efficient frontier. Front 1 would be the Pareto efficient frontier if we remove front 0 parameter sets. We proceeded in the same way for higher front values until all parameter sets were assigned. Finally, to determine the best fit parameter set, we needed to distinguish between Pareto efficient sets. To this end, we selected the combination that minimised the average of MSE and ΔMax, i.e. individual and total estimation errors. Thus, the selected parameter set places the estimated behaviour curve close to the empirical one, while ensuring a small individual estimation error. Simulation and dominance analyses were performed with Matlab. 3 Results 3.1 Selection of the best fit parameter set The parameter sets on the Pareto efficient frontier are not dominated. On this frontier (front 0), it is not possible to distinguish between parameter sets on the basis of the two criteria MSE and ΔMax. An improvement in one criterion implies a reduction in the other one. For each protective behaviour, Fig. 2 displays the fit for all parameter sets. The best fit candidates on the Pareto efficient frontier are in bold and black. We see that the number of candidates depends on protective behaviour. For example, “Wash hands often” is associated with 61 best fit candidates, whereas there are only 11 for “Stay at least 1 m away from other people”. Regarding both selection criteria, on the one hand, the MSE associated with these parameter sets is never lower than 0.2247 (achieved for “Do not shake hands”). MSE indicates the mean squared difference between the observed value and the estimated one. In our analysis, as the dependent variable is binary, MSE also represents the percentage of errors in individual predictions of engagement in protective behaviours. In other words, MSE is a measure of individual accuracy of the behavioural rule. Thus, we can argue that this is not possible to reduce the percentage of error in estimating the agent’s protective behaviour below 22.47%. On the other hand, the Pareto efficient frontier exhibits parameter sets for which ΔMax is small (e.g. 0.0485 for “Avoid close contacts with other people”). ΔMax can be considered as a measure of overall accuracy. On this point, our simulation reveals that the TELL ME behavioural rule estimates quite accurately the total percentage of individuals engaging in protective behaviours. Our two accuracy measures lead us to conclude that while individual estimation error remains moderate to high, total estimation error can be very low. This means that although some decisions of engagement in protective behaviours are mispredicted, the predicted adoption proportion by period is not far from the observed one. Overall, the prediction of the percentages of individuals engaging in protective behaviours at a population level is good but the individual prediction is inaccurate.Fig. 2 Selection criteria for each of 3587 parameter sets by protective behaviour. Each graph corresponds to a different protective behaviour. In each graph, the values of both selection criteria MSE and ΔMax associated with each parameter set are represented. Parameter sets on the Pareto efficient frontier are in bold and black. Fig. 2 The Pareto approach highlights a trade-off between individual estimation error and the total one. To select the best fit parameter set among candidates on the Pareto efficient frontier, we minimised the average of both error types. This method led us to choose the parameter sets reported in Table 3. For each protective behaviour, the total estimation error is small, while individual estimation error remains moderate at best, i.e. the percentage of individuals engaging in protective behaviour is consistent but there are many individual estimation errors. Individual estimation error is the lowest for the behaviour “Do not shake hands” (28.52%) and the highest for the behaviour “Avoid close contacts with other people” (41.89%).Table 3 Best fit parameter set by protective behaviour and their assessment. Table 3 Parameter values Criteria Protective behaviour α β γ 1−α−β−γ δ T MSE ΔMax Avoid close contacts with other people 0.5 0.25 0.2 0.05 0.28 0.6 0.4189 0.0485 Avoid public transport 0.5 0.15 0.3 0.05 0.08 0.6 0.3649 0.0874 Do not shake hands 0.55 0.1 0.25 0.1 0.2 0.6 0.2852 0.063 Stay at least 1 m away from other people 0.75 0.05 0.1 0.1 0.24 0.65 0.4038 0.095 Stay home as much as possible 0.5 0.25 0.2 0.05 0.28 0.6 0.4088 0.0695 Wash hands often 0.5 0.15 0.3 0.05 0.08 0.6 0.3563 0.1045 For a better visualisation of fit quality, actual and estimated percentages of individuals engaging in protective behaviours by survey are represented in Fig. 3 and percentages of errors in individual predictions by survey are displayed in Fig. 4. As expected, we see in Fig. 3 that our method implies close behaviour curves. Moreover, except for some periods, the slopes of behaviour curves are of the same sign. Fig. 4 shows that individual estimation error varies over time for all behaviours and, in particular, for both “Avoid public transport” and “Do not shake hands”. Notably, it is at least 18.25% for the behaviour “Do not shake hands” on 13 May and at most 47.55% for the behaviour “Avoid public transport” on 21 September.Fig. 3 Actual and estimated percentages of individuals engaging in protective behaviours over time. Each graph corresponds to a different protective behaviour. In each graph, for each survey, the blue continuous curve indicates the percentage of respondents engaging in protective behaviour. The red dash curve indicates the percentage of individuals engaging in protective behaviour as estimated by the TELL ME behavioural rule. Fig. 3 Fig. 4 Percentages of errors in individual predictions over time. For each survey, each curve indicates the percentage of errors in individual predictions of engagement in the particular protective behaviour. Fig. 4 3.2 Analysis of the parameter set Although MSE is never small, the analysis of parameter values is interesting to understand how people make their decisions to adopt protective behaviours and how to provide them with good incentives. The six behaviours are characterised only by four different best fit parameter sets. Indeed, on the one hand “Avoid close contacts with other people” and “Stay home as much as possible”, and on the other hand, “Avoid public transport” and “Wash hands often” share the same parameter sets. This indicates that people use the same decision rule for these protective behaviours. We can analyse the importance of each parameter in the individual behaviour decisions. If each parameter behaves in the same way in the behaviour score, it should be equal to 0.25. If a parameter is more important, its weight should be greater than 0.25. In the opposite case, its weight should be lower than 0.25. With this method, we observe that all protective behaviours are associated with a high weight of perceived efficacy, which represents half of the behaviour score in each case (α≥0.5). On the contrary, perceived barriers (β≤0.25) and perceived threat (1−α−β−γ≤0.25) are never determining factors in the behaviour score. Subjective norms are important only for “Avoid public transport” and “Wash hands often” (γ=0.3). These distinctions suggest that the adoption of protective behaviours is mainly based on perceived efficacy, i.e. how improved hygiene measures and social distancing are perceived as effective in preventing COVID-19 infection. With discount rate as a proxy for time preference, we find that past incidence levels are practically ignored for two behaviours: “Avoid public transport” and “Wash hands often” (δ=0.08). For these last two protective behaviours, the perceived threat taken into account in the decision rule is almost reduced to the current incidence level. 4 Discussion As part of the TELL ME European project, the TELL ME agent-based model was initially designed by Badham and Gilbert (2015) to model, based on leading health psychology theories, the effect of communication plans on protective behaviours during an epidemic. In their primary model, only three variables were included to explain the adoption of protective behaviours over time: attitude toward the behaviour, subjective norms, and perceived threat associated with the risk of infection. In line with the psychological theories underlying the TELL ME model, the construct of attitude was replaced in our analysis by two variables underlying behavioural change: perceived efficacy, and perceived barriers related to the protective behaviours. After the calibration process of the behavioural decision rule, we found the best fit parameter values associated with these variables. Simulation led to a good prediction of each percentage of individuals engaging in protective behaviour at a population level but the individual prediction is unsatisfactory. Indeed, for each protective behaviour, the percentage of error in estimation remains relatively high. This high error could be due to a failure in the calibration process. In this paper, after considering all possible parameter values, we sampled the parameter space by the Latin Hypercube method to keep only heterogeneous parameter sets. Then, we selected the best fit parameter set on the basis of two criteria, the mean squared error between predicted and actual behaviour, and the maximum difference in absolute terms of the predicted adoption proportion and actual adoption proportion per period. A trade-off between these two criteria consisted of selecting the parameter set for which the average between the two error criteria was the smallest. This method is consistent with finding an estimated behaviour curve close to the observed one. However, this curve is associated with a high MSE. As shown in Fig. 2, even if we selected the parameter set with the minimum MSE, MSE would nonetheless remain high. Thus, it is clear that failure in calibration does not explain this error. In contrast, the nature of our data can explain the error in individual predictions. Our analysis is based on 12 cross-sectional surveys. Compared to longitudinal data, cross-sectional investigations prevent us from studying how the behaviour of the same agent evolves over time. However, as revealed in Fig. 1, protective behaviours, and specifically social distancing measures, are not stable over time. Indeed, in our analysis, we succeeded in capturing the preferences of many agents in different time periods. To better control an epidemic, it is important for an estimation to reveal how the preferences of agents change depending on the evolution of the parameters of the epidemic. Unfortunately, for many reasons, only a few published studies of the COVID-19 epidemic use longitudinal data (Qin et al., 2021, Machida et al., 2020, Wise et al., 2020). Moreover, these studies generally cover a short period corresponding to the first wave of the epidemic. Therefore, they capture change in individual preferences during a timeline which does not represent all epidemic stages. To estimate the effect of public policies on individual protective behaviours, surveys should ideally be repeated with the same agents from the beginning of the epidemic to its end, or at least over several waves of the epidemic. The high percentage of individual errors could also be the result of the design of the TELL ME behavioural rule. Predicting how individuals engage in protective behaviours in social simulations is mostly associated with high error. Indeed, point prediction with a simple rule in complex social systems cannot be inherently accurate (Polhill et al., 2021, Hofman et al., 2017). In our case, predictive power is limited by the individual heterogeneity that our frugal rule does not capture. The TELL ME behavioural rule including epidemiological and personal variables results in a MSE higher than 0.4 for three of the six protective behaviours, whereas a random decision rule would predict on average the right individual behaviour with a 50/50 chance, i.e. a MSE equal to 0.5. That is why the TELL ME behavioural rule cannot be used to predict individual protective behaviours and should be refined. Its simulation provides insights to understand better how to model the decision of engagement in protective behaviours. We can identify two sources to improve the modelling of individual decisions to be protected. Firstly, in the computation of the behaviour score, the weights of perceived efficacy, perceived barriers, perceived threat, and the discount rate are common for all agents over time. Moreover, all agents in the same region have the same weight of subjective norms, regardless of the period. Nevertheless, the lack of individualisation among parameters does not allow us to capture the heterogeneity between agents, leading to an error in prediction. In practice, the effect of variables can also decline over time. For instance, it is more likely that agents adopt mimetic behaviours due to higher perceived social pressure at the beginning of an outbreak, i.e. the effect of subjective norms would be higher in the first periods than in the later ones. Besides, the number of daily deaths is probably not taken into account in the same way over time due to the phenomenon of behavioural fatigue (Petherick et al., 2021, World Health Organization, 2020) or a change in the perception of severity of the disease. Secondly, our analysis tested a simple and frugal model, which includes only four epidemiological and personal variables. As referenced in the systematic review report of the TELL ME European project from studies on SARS and H1N1 epidemics (TELL ME, 2012), other variables might be involved in the decision to adopt protective behaviours. Among sociodemographic factors, recent empirical studies of the COVID-19 epidemic highlighted that being a woman, elderly or having a high level of formal education is associated with a higher probability of engaging in the various protective behaviours (Papageorge et al., 2021, Smith et al., 2021, Wright and Fancourt, 2021, Lüdecke and von dem Knesebeck, 2020). Similarly, other potential cognitive and cultural variables seem to determine the adoption of such behaviours. It is highly plausible that anxiety and emotional distress, perceived self-efficacy, trust in science and institutions, or ideological world views may be predictors of engagement in protective behaviours (Schneider et al., 2021, Scholz and Freund, 2021, Qin et al., 2021, Clark et al., 2020, Ning et al., 2020, Storopoli et al., 2020, Ye and Lyu, 2020). Obviously, neglecting these determinants of behavioural decision-making generates a large gap between empirical data and predicted values. Finally, by showing that a simple behavioural rule cannot result in an accurate prediction of the individual decision to be protected, our study also allows us to discuss the external validity of the results of epidemiological models. In particular, caution is advised towards extrapolating the results of compartmental models. As these models exclude personal variables, it is likely that their predictive power is even poorer than the TELL ME behavioural rule. Compartmental models may be efficient to predict the spread of an epidemic, but it is doubtful that they are suitable to predict individual behaviours. Instead, in order to make individual predictions, epidemiologists should rely on models in which the decision to engage in protective behaviours is represented by a rule based on recent psychological findings and on modelling sufficient individual heterogeneity. 5 Conclusion The objective of this paper was to estimate the relevance of the behavioural decision rule proposed by the TELL ME agent-based model in the COVID-19 epidemic context. According to this rule, the decision to adopt protective behaviours depends on attitude, subjective norms and perceived threat associated with the COVID-19 epidemic. Overall, our simulation of this decision rule, using 12 cross-sectional surveys conducted in France from May to November 2020, highlights a relatively high error in prediction of individual behaviours. It appears therefore that the relevance of this rule to predict the decision to adopt protective behaviours cannot be taken for granted. Notwithstanding this persistent error in prediction, our analysis provides some insights to bridge the gap between theory and empirical data. In particular, there is a need for individualising the effects of epidemiological and personal variables, including other variables which are cognitive, psychological and sociodemographic, as well as for the collection of longitudinal data during a sufficiently long period. These issues should be integrated in future epidemiological simulations to enable public authorities ultimately to better control epidemic disease spread. Funding This work was supported by the European Union’s Horizon 2020 research and innovation program “PERISCOPE: Pan European Response to the ImpactS of COvid-19 and future Pandemics and Epidemics”, under the grant agreement No. 101016233, H2020-SC1-PHE_CORONAVIRUS-2020-2-RTD. CRediT authorship contribution statement Dylan Martin-Lapoirie: Conceptualization, Data curation, Formal analysis, Methodology, Writing - original draft, Writing - review & editing, Kathleen McColl: Conceptualization, Writing - original draft, Writing - review & editing, Alberto d’Onofrio: Conceptualization, Formal analysis, Methodology, Writing - original draft, Writing - review & editing, Jocelyn Raude: Conceptualization, Data curation, Investigation, Writing - original draft, Writing - review & editing, Project administration, Supervision, Funding acquisition Uncited reference (Durham and Casman (2012)) Declaration of Competing Interest The authors declare no competing interests. Acknowledgements The authors are grateful to Dr Pierre Arwidson and the COVIPREV group from the Department of Health Promotion and Prevention (Santé Publique France) for their valuable support. 1 See TELL ME (2015) for details on prototype software. 2 A description of the TELL ME European project is available online: https://www.tellmeproject.eu/ Accessed July 8, 2022. 3 Surveys were conducted by the BVA research institute (https://www.bvagroup.com/en/about-us/). ==== Refs References Ajzen I. The theory of planned behavior Organizational Behavior and Human Decision Processes 50 2 1991 179 211 10.1016/0749-5978(91)90020-T Badham J. Chattoe-Brown E. Gilbert N. Chalabi Z. Kee F. Hunter R.F. Developing agent-based models of complex health behaviour Health and Place. 54 2018 170 177 10.1016/j.healthplace.2018.08.022 30290315 Badham J. Gilbert N. TELL ME design: protective behaviour during an epidemic CRESS Working Paper. 2015 2015 2 Badham J. Jansen C. Shardlow N. French T. 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==== Front Partial Differ Equ Appl Math Partial Differ Equ Appl Math Partial Differential Equations in Applied Mathematics 2666-8181 The Author(s). Published by Elsevier B.V. S2666-8181(22)00118-8 10.1016/j.padiff.2022.100470 100470 Article Studying of COVID-19 fractional model: Stability analysis Khalaf Sanaa L. ⁎ Kadhim Mohammed S. Khudair Ayad R. Department of Mathematics, College of Science, University of Basrah, Basrah, Iraq ⁎ Corresponding author. 5 12 2022 5 12 2022 10047024 6 2022 23 11 2022 2 12 2022 © 2022 The Author(s) 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. This article focuses on the recent epidemic caused by COVID-19 and takes into account several measures that have been taken by governments, including complete closure, media coverage, and attention to public hygiene. It is well known that mathematical models in epidemiology have helped determine the best strategies for disease control. This motivates us to construct a fractional mathematical model that includes quarantine categories as well as government sanctions. In this article, we prove the existence and uniqueness of positive bounded solutions for the suggested model. Also, we investigate the stability of the disease-free and endemic equilibriums by using the basic reproduction number (BRN). Moreover, we investigate the stability of the considering model in the sense of Ulam-Hyers criteria. To underpin and demonstrate this study, we provide a numerical simulation, whose results are consistent with the analysis presented in this article. Keywords COVID-19 Mathematical models Caputo fractional derivative Sensitivity analyses Stability analysis Ulam-Hyers stability Fractional Euler method ==== Body pmc1 Introduction To combat the spread of COVID-19, all governments around the world have made significant efforts and taken preventive measures.[1], [2], [3] In Wuhan, the capital of Hubei province, China, COVID-19 was first detected which is a new strain of SARS-CoV-2.4 , 5 In the months following its discovery, the number of patients grew at an exponential rate. According to the World Health Organization’s situation report, there were 5 304 772 total cases and 342 029 deaths worldwide as of May 25, 2020. The use of mathematical models in epidemics is very important for understanding the nature of these epidemics as well as for designing effective strategies for controlling them.[6], [7], [8] As a contribution from some mathematicians to reduce the COVID-19 pandemic, many researchers have adopted the development of models for this emerging epidemic. Where some researchers took from developing some models of the spread of epidemics such as SEIR and SIR to design a model that simulates the spread of Corona disease.[9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19] The COVID-19 severity was calculated by Wu et al. using the dynamics of transition in Ref. 20. There have been investigations into random transition models in Refs. 21, 22. The general multi-group SEIRA model for modeling COVID-19 diffusion in a heterogeneous population was represented and numerically tested in Ref. 23. Differential equations in their various forms (ordinary, randomly detected, partial, fractional, or with delay) are an essential mathematical tool for modeling many epidemics.8 Many research attempts have been made to prevent epidemic outbreaks via optimum control.[24], [25], [26] Mathematical studies of epidemic illnesses have become more relevant.[27], [28], [29] Several studies have been introduced to control HIV,30 dengue fever,31 TB,32 SIR,33 , 34 and SIRS.35 Fractional differential equations (FDEs) provided an accurate description of the dynamics of epidemiological models,36 taking into account information about a population’s memory and learning mechanisms, which influence disease spread. In this paper, we used a fractional mathematical model that includes the quarantine category and methods taken by the government to prevent the spread of disease and demonstrated the existence of non-negative and bounded solutions. Many authors prefer to use FDEs to describe epidemic models since they carry more memory information and provide a learning mechanism for the spread of disease in the population compared to the ordinary differential equation, which is incapable of serving this purpose. Also, the region of stability for the FDEs is larger than that for the ordinary differential equations. Furthermore, the fractional derivative is a non-local operator, whereas the classical derivative is a local operator. In other words, the description of epidemic models by using fractional differential equations takes into account all historical and current states, which makes them more realistic and more general in nature.37 , 38 This prompted us to develop the Caputo fractional mathematical model for COVID-19 and introduce details about the existence of a unique positive solution and its behaviour. In spite of the fact that there are many definitions of a fractional derivative, many scholars prefer to use the Caputo derivative to describe mathematical models by means of FDEs. In fact, due to the initial conditions of FDEs with Caputo derivatives containing integer order derivatives with physical meanings like distance, speed, and acceleration, FDEs with Caputo derivatives are widely used in real-world applications.39 2 Fundamentals of fractional calculus In this part, we will present some related material about FDE, including the Riemann–Liouville fractional (R-LF) integral, Caputo fractional derivative definition, the existence, and uniqueness of FDE solutions, as well as some key properties and theorems in the field of stability analysis. Definition 2.1 Ref. 40 The left and right R-LF integral of order α>0 of f are given by (2.1) aJtαf(t)=1Γ(α)∫at(t−ξ)α−1f(ξ)dξ, (2.2) tJbαf(t)=1Γ(α)∫tb(ξ−t)α−1f(ξ)dξ, respectively. Definition 2.2 Ref. 40 The right (left) Caputo fractional derivative of order m−1<α<m,m∈Z+ are defined as follows: (2.3) tCDbαy(t)=(−1)mΓ(m−α)∫tb(ξ−t)m−α−1y(m)(ξ)dξ, (2.4) aCDtαy(t)=1Γ(m−α)∫at(t−ξ)m−α−1y(m)(ξ)dξ. Lemma 2.1 Ref. 41 If g(t,y∗)=0 , then the Caputo FDE (2.5) t0CDtαy(t)=g(t,y(t)) y(t0)=y0 with 0<α≤1 has equilibrium point at y∗ . Theorem 2.1 Ref. 42 Consider y∗ be an equilibrium point of the Caputo FDE (2.5) , if \|arg(λ)\|>απ2 holds for all λ , where λ is the Jacobian matrix of g(t,y(t))=0 at y∗ , then y∗ is locally asymptotically stable. Definition 2.3 If F(s) be Laplace transform of F(t), then (2.6) L{aCDtαF(t),s}=sαF(s)−∑i=0m−1sα−i−1F(i)(0),α∈(m−1,m),m∈Z+ Theorem 2.2 Ref. 40 Let g(t,y(t)):R×Rn→R be a continuous function with respect y and Lebesgue measurable with respect to t . If there are two positive constants γ and μ such that ‖g(t,y(t))‖≤μ+γ‖y(t)‖ satisfy ∀(t,y)∈R×Rn , then there is a solution to the Caputo FDE (2.5) started from the point (t0,y0) . Lemma 2.2 Ref. 40 Suppose that all conditions in Theorem 2.2 hold and ∂g(t,y(t))∂y(t) is continuous with respect y , then the solution of the Caputo FDE (2.5) is unique. Definition 2.4 The function Er,n(t) for t∈R is defined by (2.7) Er,n(t)=∑i=0∞tiΓ(ri+n),r,n>0 where Er,n(t) is called the generalized Mittag-Leffler function and satisfies Er,n(t)=1Γ(n)+tEr,r+n(t),n,r>0 L{tn−1Er,n(±ρtr)}=sr−nsr∓ρ where L is the Laplace transform. Lemma 2.3 Ref. 43 If 0<α≤1 , y(t)∈Ca,b , and aCDtαyt∈Ca,b , then y(t)=ya+1αaCDtαyζt−aα, where ζ∈[a,t],∀t∈(a,b]. Corollary 2.1 Let 0<α≤1 , y(t)∈Ca,b , and aCDtαyt∈Ca,b . Than y(t) is non-increasing ∀t∈[a,b] , if aCDtαy(t)≤0,∀t∈(a,b) . While, y(t) is non-decreasing ∀t∈[a,b] , if aCDtαy(t)≥0,∀a<t<b . 3 Model formulation This section presents a fractional model for COVID-19. The total population is divided into five disease variables in the COVID-19 epidemic model, and the relationship between the variables is shown in Fig. 1, with the details of the variables is shown in Table 1. Assume a model composed of five fractional differential equations as follows: 0CDtαS=A−ρ1−ϑ21−ϑ1SE+b1Q−dS−pSu 0CDtαE=ρ1−ϑ21−ϑ1SE−b2E−wE−σE−dE (3.1) 0CDtαQ=b2E−b1Q−cQ−dQ 0CDtαI=wE+cQ−ξ+d+βI 0CDtαR=ξI+σE−dR+pSu where S(0)≥0,E(0)≥0,Q(0)≥0,I(0)≥0 and R(0)≥0 are the initial conditions. Also, Table 2 shows the parameters in the COVID-19 fractional model (3.1).Fig. 1 COVID-19 infection model diagram. Table 1 Represent variables for COVID-19 fractional model. Model symbols Symptoms of a disease S Susceptible population fraction. E Exposed population fraction. I Hospitalized infected population fraction. Q The population that is quarantined. R Recovered or Removed population fraction. Theorem 3.1 Existence and Uniqueness The COVID-19 fractional model (3.1) has unique solution for all t≥0 . Table 2 Represent parameters for COVID-19 fractional model. Parameters Description A Recruitment in its entirety ϑ1 The contact area of S with E. ϑ2 The contact area of E with S. ρ The disease transmission rate that is bi-linear. d The natural mortality rate. b1 The rate at which Q transforms into S. b2 The rate at which E turns into a quarantine. w The rate at which E transforms into I. σ The natural rate at which E transforms into R. ξ The natural rate at which I transforms into R. c The rate at which Q transforms into I. β The mortality rate for I. u The government policy parameter. p Some S becomes E due to media. Proof For the model (3.1), let y(t)=(S(t),E(t),Q(t),I(t),R(t))T and g(t,y(t))=g1(t,y(t))g2(t,y(t))g3(t,y(t))g4(t,y(t))g5(t,y(t))=A−ρ1−ϑ21−ϑ1SE+b1Q−dS−pSuρ1−ϑ21−ϑ1SE−b2E−wE−σE−dEb2E−b1Q−cQ−dQwE+cQ−ξ+d+βIξI+σE−dR+pSu. It is clear that g(t,y(t)) is quadratic function so it is continuous with respect y on R5, and Lebesgue-measurable for all t∈R. Now, we compute ‖g(t,y(t))‖1=∑j=15\|gj(t,y(t))\| ‖g(t,y(t))‖1=\|A−ρ1−ϑ21−ϑ1SE+b1Q−dS−pSu\|+\|ρ1−ϑ21−ϑ1SE−b2E−wE−σE−dE\|+\|b2E−b1Q−cQ−dQ\|+\|ξI+σE−dR+pSu\|+\|wE+cQ−ξ+d+βI\| Appling the triangular inequality, one can have; ‖g(t,y(t))‖1≤A+2ρ1−ϑ21−ϑ1\|S\|\|E\|+b1\|Q\|+d\|S\|+2pu\|S\|+(2b2+2w+2σ+d)\|E\|+(b1+2d+c)\|Q\|+d\|R\|+2ξ+d+β\|I\| Since, N=S+Q+E+I+R we have S≤N. This imply ‖g(t,y(t))‖1≤A+(2ρ1−ϑ21−ϑ1N+(2b2+2w+2σ+d))\|E\|+(d+2pu)\|S\|+(2b1+2d+c)\|Q\|+d\|R\|+2ξ+d+β\|I\| Now, add the following positive terms to the RHS of the above inequality (2ρ1−ϑ21−ϑ1N+2b2+2w+2σ+4d+2b1+2c+2ξ+β)\|S\|,(2ρ1−ϑ21−ϑ1N+2b2+2w+2σ+4d+2pu+2ξ+β)\|Q\|,(2ρ1−ϑ21−ϑ1N+2b2+2w+2σ+4d+2pu+2b1+2c)\|I\|,(2ρ1−ϑ21−ϑ1N+2b2+2w+2σ+4d+2pu+2b1+2c+2ξ+β)\|R\|,(4d+2pu+2b1+2c+2ξ+β)\|E\|, So, we have (3.2) ‖g(t,y(t))‖1≤μ+γ‖y(t)‖,∀t∈R,y(t)∈R5, where the positive constants, μ=A and γ=(2ρ1−ϑ21−ϑ1N+(2b2+2w+2σ+d))(d+2pu)(2b1+2d+c)(2ξ+d+βd)d. By applying Theorem 2.2, we deduce that The COVID-19 fractional model (3.1) has a solution for all R. Since, g(t,y(t)) is quadratic function so ∂g(t,y(t))∂y(t) is continuous with respect y on R5. By using Lemma 2.2, we deduce that that The COVID-19 fractional model (3.1) has a unique solution for all R. Theorem 3.2 Consider y¯(t)=(S¯(t),E¯(t),Q¯(t),I¯(t),R¯(t))T , then ‖(g(ξ,y¯(ξ))−g(ξ,y(ξ)))‖1≤ħ‖y¯(ξ)−y(ξ)‖1 for some positive constant ħ . The proof of Theorem 3.2 is similar to proof Theorem 3.1, so we omitted it. Now, ff the initial values are non-negative, it is simple to show that the solutions of model (3.1) are always non-negative, as we will prove in Theorem 3.3. Theorem 3.3 The admissible region Ψ={(S,E,Q,I,R)∈R+5:S≥0,E≥0,Q≥0,I≥0,R≥0} for model (3.1) is a positive invariant set. Proof The uniqueness and existence of the solution of model (3.1) has been proved in Theorem 3.1. Say the solution is y(t)=(S(t),E(t),Q(t),I(t),R(t))T. Also, 0CDtαy continuous function, since g(t,y(t)) is quadratic function. However, let us try to compute 0CDtαS\|S=0, 0CDtαE\|E=0, 0CDtαQ\|Q=0, 0CDtαI\|I=0, and 0CDtαR\|R=0 by substitute S=0 in the first equation of model (3.1), E=0 in the second equation of model (3.1), Q=0 in the third equation of model (3.1), I=0 in the fourth equation of model (3.1), and R=0 in the fifth equation of model (3.1). Since all parameter in model (3.1) is positive constants, we have 0CDtαS\|S=0=A+b1Q≥0 0CDtαE\|E=0=0 (3.3) 0CDtαQ\|Q=0=b2E≥0 0CDtαI\|I=0=wE+cQ≥0 0CDtαR\|R=0=ξI+σE+pSu≥0 for all t≥0. Now, from the second equation in Eq. (3.3), Now, let initial condition of model (3.1) lie in Ψ. According to Eq. (3.3) and Corollary 3.1 we find that (S(t),E(t),Q(t),I(t),R(t))∈Ψ. This result means that Ψ is a positive invariant set of solution for model (3.1). Theorem 3.4 For the COVID-19 fractional model (3.1) , the admissible region Ψ is uniformly bounded. Proof From (3.1) the total population satisfies 0CDtαNt=A−dNt−ɛIt. where N(t)=St+Et+Qt+It+Rt. 0CDtαNt≤A−dNt Now by using the Laplace transform for Eq. (3.3) we can get the following equation sαLNt−sα−1N0≤As−dLNt (sα+d)LNt≤As+sα−1N0 LNt≤s−1sα+dA+sα−1sα+dN0 Then from Eq. (5) and Eq. (6) we get Nt≤AtαEα,α+1−dtα+Eα,1−dtαN0 ≤AddtαEα,α+1−dtα+Eα,1−dtα ≤AddtαEα,α+1−dtα−dtαEα,α+1−dtα+11 (3.4) ≤Ad Solutions in the model (3.1) are restricted as follows Ψ={(S,E,Q,I,R)∈R+5:0≤N(t)≤Ad} 3.1 Infection equilibria points There are two equilibria in the Caputo fractional model (3.1). The disease-free equilibria Ξ0, in which infection is eradicated from the body is given as follows: Ξ0=(S0,E0,Q0,I0,R0)=Ad+pu,0,0,0,puAdd+pu. IF Aρ(1−ϑ1)(1−ϑ2)≥(d+pu)(b2+w+σ+d), then there is other type of equilibrium which is called endemic equilibria Ξ∗=(S∗,E∗,Q∗,I∗,R∗), which occurs when the infection is always present in the model, where S∗=b2+w+σ+d(1−ϑ1)(1−ϑ2)ρ, E∗=Aρ(1−ϑ1)(1−ϑ2)(b1+d+c)−(d+pu)(b2+w+σ+d)(b1+d+c)(1−ϑ1)(1−ϑ2)ρ[b2(d+c)+(w+σ+d)(b1+d+c)], Q∗=b2Aρ(1−ϑ1)(1−ϑ2)−b2(d+pu)(b2+w+σ+d)(1−ϑ1)(1−ϑ2)ρ[b2(d+c)+(w+σ+d)(b1+d+c)], I∗=[w(b1+d+c)+b2c][Aρ(1−ϑ1)(1−ϑ2)−(d+pu)(b2+w+σ+d)](1−ϑ1)(1−ϑ2)ρ(ξ+d+β)[b2(d+c)+(w+σ+d)(b1+d+c)], R∗=puS∗+σE∗+ξI∗d. 3.2 Calculating R0. The BRN, R0, is the most important epidemiological component for classifying the type of infection. In fact, R0 is defined as the total secondary number of people infected as a result of one infected person over the course of the entire time interval. Consequently, R0 is a non-dimensional quantity that measures the probability of a disease spreading. However, R0 is defined as the “number of secondarily infected individuals caused by a single infected individual over the entire time interval”.44 As a result, R0 is a dimensionless quantity that denotes the likelihood of the disease spreading. There are several techniques available for determining R0 for epidemic spread. The next-generation matrix approach45 , 46 is used in our current research article. Only E,Q and I are currently directly involved in the spread of disease. As a result of system (3.1), we have 0CDtαE=ρ1−ϑ11−ϑ2SE−b2E−wE−σE−dE (3.5) 0CDtαQ=b2E−b1Q−cQ−dQ 0CDtαI=wE+cQ−η+d+βI According in the procedure in Ref. 44, we rewrite the system (3.5) as 0CDtαx=P(x)−Θx, where x=EQI,P(x)=ρ1−ϑ11−ϑ2SE00,and Θx=(b2+w+σ+d)Eb1+c+dQ−b2Eη+d+βI−wE−cQ We note from the system (3.5) that E0=Ad+pu,0,0,0,puAd(d+pu) is a disease-free equilibrium. The Jacobean matrix of ρ(x) at point E0, is given by: (3.6) N=ℑP\|E0=ρ1−ϑ11−ϑ2Ad+pu00000000 Also, the Jacobean matrix of Θ at point E0, is given by: (3.7) M=ℑΘ\|E0=b2+w+σ+d00−b2b1+c+d0−w−cη+d+β The inverse of M is given by; (3.8) M−1=1b2+w+σ+d00b2(b2+w+σ+d)(b1+c+d)1b1+c+d0w(b1+c+d)+b2c(b2+w+σ+d)(b1+c+d)(η+d+β)c(b1+c+d)(η+d+β)1η+d+β The next generation matrix NM−1 is given by; (3.9) G=NM−1=Aρ1−ϑ11−ϑ2d+pub2+w+σ+d00000000 The eigenvalues from the matrix G are λ1=0, λ2=0, and λ3=Aρ1−ϑ11−ϑ2d+pub2+w+σ+d. As a result, the reproductive number R0 (which is the largest eigenvalue) is calculated as follows: (3.10) R0=Aρ1−ϑ11−ϑ2d+pub2+w+σ+d 3.3 Sensitivity analysis Due to the fact that R0 is an extremely biologically significant quantity that plays a chief role in the spread of any pandemic, investigating the sensitivity of R0 is very interesting and crucial to the elimination and effective control of the disease. R0 sensitivity to changes in parameter Υ is represented by this index. So, we can calculate the changes in all parameters in the formula of R0 by using the partial derivatives as follows: Now, we give the following relationship that describes the R0 forward sensitivity index with respect to the parameter Υ: (3.11) ΞΥR0=∂R0∂ΥΥR0 where Υ is a parameter to describe the basic reproductive number R0. It is well known that a negative (positive) index means that any increase in the parameter Υ leads to a decrease (increase) in R0.47 The sensitivity indices with respect to the parameters can be given A, ϑ1, ϑ2, ρ, d, b1, w, u, p, and σ respectively, by the basic reproductive number mentioned in the Eq. (3.10), as in Table 3. Table 3 Sensitivity indices. Parameters The sensitivity index of R0 Indexes of sensitivity with respect to the parameters A 1 +ve ρ 1 +ve ϑ1 −ϑ11−ϑ1 −ve ϑ2 −ϑ21−ϑ2 −ve d −dpu+2d+ σ +w+b2b2+w+ σ +dpu+d −ve p −uppu+d −ve b2 −b2b2+w+ σ +d −ve w −wb2+w+ σ +d −ve u −uppu+d −ve σ −σ b2+w+ σ +d −ve 3.4 Stability analysis of COVID-19 model In this section, we look for stability that is local to the equilibrium Ξ0 and the equilibrium Ξ∗. Theorem 3.5 The model (3.1) is locally asymptotic stable when R0<1 at Ξ0 and unstable when R0>1 . Proof The Jacobian matrix of the model (3.1) at the equilibrium Ξ0 is given as follows: ℑ is given in Box I. The eigenvalue equation of the ℑ at the equilibrium Ξ0 is (λ+d)(λ+ξ+d+β)(λ+b1+d+c)(λ+d+pu)×(λ+(b2+w+σ+d)(1−R0))=0 λ1=−d λ2=−ξ−d−β λ3=−b1−c−d λ4=−up−d λ5=−(b2+w+σ+d)(1−R0) Box I ℑ=−d+pu−ρ1−ϑ11−ϑ2Ad+pub1000ρ1−ϑ11−ϑ2S0−b2+w+σ+d0000b2−b1+d+c000wc−ξ+d+β0puσ0ξ−d We can note λ1,λ2,λ3,λ4 have a real negative part and λ5<0 has a real negative part when R0<1. As a result, the model (3.1) is locally asymptotically stable when R0<1, whereas it is unstable when R0>1 and so the theorem was proven. Theorem 3.6 If the endemic equilibrium, Ξ∗ , exists, then it is locally asymptotically stable. Proof From the model (3.1) the Jacobean matrix at equilibrium Ξ∗ we can get as follows: ℑ is given in Box II. The eigenvalue polynomial of the model (3.1) at the equilibrium Ξ∗, is given as follows: (3.12) (λ+d)(λ+ξ+d+β)(λ3+A1λ2+A2λ+A3)=0 where A1=(1−ϑ1)(1−ϑ2)ρ+2d+b1+c+pu, A2=(d+pu+ρ(1−ϑ1)(1−ϑ2)E∗)(b1+d+c)+(b2+w+σ+d)ρ(1−ϑ1)(1−ϑ2), and A3=ρ(1−ϑ1)(1−ϑ2)E∗[(b2+w+σ+d)(b1+d+c)−b1b2] Box II ℑ=−ρ(1−ϑ1)(1−ϑ2)E∗−(d+pu)−ρ(1−ϑ1)(1−ϑ2)S∗b100ρ(1−ϑ1)(1−ϑ2)E∗ρ(1−ϑ1)(1−ϑ2)S∗−(b2+w+σ+d)0000b2−(b1+d+c)000wc−(ξ+d+β)0puσ0ξ−d Indeed, one can notice that Eq. (3.12) have two negative roots and the other roots are the roots of the cubic equation λ3+A1λ2+A2λ+A3=0. If these roots lie inside the complex plane which satisfies \|arg(λ)\|>απ2 and by using Theorem 2.1, We can conclude that the model (3.1) is asymptotically stable locally close to its endemic equilibrium Ξ∗ because A1,A2,A3, and A1A2−A3 are always positive regardless of the parametric value. 3.5 Hyers-Ulam stability To study the global stability of the considered fractional model (3.1), we use the Ulam-Hyers sense. For this purpose, we define the following inequality: (3.13) \|0CDtαy(t)−g(t,y(t))\|≤ɛ,∀t∈[0,T]. Now, y¯∈Σ is solution of (10) if and only if there is h∈Σ such that: i \|h(t)\|≤ɛ. ii (3.14) 0CDtαy¯(t)=g(t,y¯(t))+h(t),∀t∈[0,T]. Now, apply the fractional R-LF integral to both sides of (3.14), one can have y¯(t)=y¯(0)+1Γ(α)∫0t(t−ξ)α−1g(ξ,y¯(ξ))dξ+1Γ(α)∫0t(t−ξ)α−1h(ξ)dξ,∀t∈[0,T]. By taken condition (i), we get \|y¯(t)−y¯(0)−1Γ(α)∫0t(t−ξ)α−1g(ξ,y¯(ξ))dξ\|≤ɛΓ(α)∫0t(t−ξ)α−1dξ,∀t∈[0,T]. So, we have (3.15) \|y¯(t)−y¯(0)−1Γ(α)∫0t(t−ξ)α−1g(ξ,y¯(ξ))dξ\|≤ɛTαΓ(α+1),∀t∈[0,T]. Definition 3.1 The COVID-19 fractional model (3.1) is Hyers-Ulam stability on [0,T] if there exists a constant Υg>0 such that for any ɛ>0, and any y¯(t) satisfying (3.13), then the COVID-19 fractional model (3.1) possess a solution y(t) satisfying ‖y¯(t)−y(t)‖1≤ɛΥg,∀t∈[0,T]. Theorem 3.7 Assume the assumptions (i) and (ii) holds. Then the COVID-19 fractional model (3.1) is Hyers-Ulam stability on [0,T] , if Γ(α+1)>Tαħ hold. Proof Recall Theorem 3.1, we let y(t) be a unique solution of the COVID-19 fractional model (3.1), let y¯(t) satisfy (3.13). By applying the fractional R-LF integral to both sides of (3.1), we get (3.16) y(t)=y(0)+1Γ(α)∫0t(t−ξ)α−1g(ξ,y(ξ))dξ,∀t∈[0,T]. Now, using (3.15), (3.16), we compute ‖y¯(t)−y(t)‖1 as follows: ‖y¯(t)−y(t)‖1=‖y¯(t)−y(0)−1Γ(α)∫0t(t−ξ)α−1g(ξ,y(ξ))dξ‖1 By adding and subtracting the term 1Γ(α)∫0t(t−ξ)α−1g(ξ,y¯(ξ))dξ and applying the triangle inequality, we have ‖y¯(t)−y(t)‖1≤‖y¯(t)−y(0)−1Γ(α)∫0t(t−ξ)α−1g(ξ,y¯(ξ))dξ‖1+‖1Γ(α)∫0t(t−ξ)α−1(g(ξ,y¯(ξ))−g(ξ,y(ξ)))dξ‖1. Using (3.15) and the norm properties, one can get ‖y¯(t)−y(t)‖1≤ɛTαΓ(α+1)+1Γ(α)∫0t(t−ξ)α−1‖(g(ξ,y¯(ξ))−g(ξ,y(ξ)))‖1dξ Applying Theorem 3.2, yields ‖y¯(t)−y(t)‖1≤ɛTαΓ(α+1)+TαħΓ(α+1)‖y¯(t)−y(t)‖1. So, we have ‖y¯(t)−y(t)‖1≤Υgɛ, where Υg=TαΓ(α+1)−Tαħ. Using Definition 3.1, we deduce that the COVID-19 fractional model (3.1) is Hyers-Ulam stability on [0,T]. 4 Numerical simulations It is common knowledge that there is no analytical method currently available to solve FDEs. Consequently, to find approximations of solutions to FDEs, accurate and efficient numerical methods are needed, such as the generalized fractional differential transform,48 the fractional difference method,49 the power series method,50 the fractional variational iteration method,51 the Haar wavelet collocation,52 the restricted fractional differential transform,53 the wavelet method,54 the fractional Adams method,55 the spectral method,56 and also numerous additional outstanding works in the following papers (see e.g. Refs. [57], [58], [59], [60], [61], [62], [63], [64]). However, to give a complete picture of the stability analysis in the previous section, we will use the fractional Euler method (FEM)37 to solve the model numerically. To underpin the analysis in this work, we will investigate the effect of fractional order on stability behaviour by taking α=0.95,0.9,0.8,0.7. In order to use the FEM for solving the fractional model (3.1), we rewrite the interval [0,T] into n subinterval [(k−1)h,kh],∀k=1,2,…,n with h=Tn. As a result, we have the discretized equations shown below: S(tk)=S(0)+hαΓ(α+1)∑i=0kχk,i[A−ρ1−ϑ21−ϑ1S(ti)E(ti)+b1Q(ti)−dS(ti)−pS(ti)u] E(tk)=E(0)+hαΓ(α+1)∑i=0kχk,i[ρ1−ϑ21−ϑ1S(ti)E(ti)−b2E(ti)−wE(ti)−σE(ti)−dE(ti)] Q(tk)=Q(0)+hαΓ(α+1)∑i=0kχk,i[b2E(ti)−b1Q(ti)−cQ(ti)−dQ(ti)] I(tk)=I(0)+hαΓ(α+1)∑i=0kχk,i[wE(ti)+cQ(ti)−ξ+d+βI(ti)] R(tk)=R(0)+hαΓ(α+1)∑i=0kχk,i[ξI(ti)+σE(ti)−dR(ti)+pS(ti)u] where χk,i=(k−i)α−(k−1−i)α,∀i=0,1,…,k, and ∀k=1,2,…,n. Using FEM, precise numerical solutions can be obtained over an extended period of time. As a starting point, we used the following initial conditions: (S(0),E(0),Q(0),I(0),R(0))=(500,10,5,0,0) with the following cases. Case 1: In this case, we use the following values of parameters; A=50,d=0.2,b1=0.25,b2=0.8,c=0.12,ρ=1.5,σ=0.2,ϑ1=0.78,ϑ2=0.92, w=0.0714,p=0.78,ξ=0.025,u=0.8 and β=0.25.65 Now, recall Eq. (28) and compute reproductive number, we find R0=1.259982498. So, the endemic equilibria Ξ∗=(48.15909091,11.20766266, 15.73005285, 5.658596752, 162.1713509) is locally asymptotically stable according Theorem 3.6 for various value of α. In fact, this is clearly through Fig. 2, Fig. 3, Fig. 4, Fig. 5, Fig. 6. Case 2: In this case, we use the following values of parameters; A=50,d=0.2,b1=0.25,b2=0.8,c=0.12,ρ=1.5,σ=0.2,ϑ1=0.78,ϑ2=0.92, w=0.0714,p=5,ξ=0.025,u=0.8 and β=0.25.65 Fig. 2 The susceptible population, S(t), for various value of α. Fig. 3 The exposed population, E(t), for various value of α. Fig. 4 The population that is quarantined, Q(t), for various value of α. Fig. 5 The hospitalized infected population, I(t), for various value of α. Fig. 6 The recovered or Removed population, R(t), for various value of α. Fig. 7 The susceptible population, S(t), for various value of α. Fig. 8 The exposed population, E(t), for various value of α. Fig. 9 The population that is quarantined, Q(t), for various value of α. Fig. 10 The hospitalized infected population, I(t), for various value of α. Fig. 11 The recovered or Removed population, R(t), for various value of α. Now, recall Eq. (28) and compute reproductive number, we find R0=0.2471965662. So, the disease-free equilibria Ξ0=(60.67961165,0, 0, 0, 189.3203883) is locally asymptotically stable according Theorem 3.5 In fact, this is clearly through Fig. 7, Fig. 8, Fig. 9, Fig. 10, Fig. 11. 5 Conclusions This study focused on the investigation of a five-dimensional fractional-order COVID-19 mathematical model. We proved prove some theorems related to the existence, uniqueness, and positively invariant of this model’s solution. Also, the basic reproductive number, R0, has been calculated in detail by using the next-generation matrix technique. Then the local asymptotic stability of the disease-free and endemic equilibriums have been studied. Additionally, we investigate the global stability of the proposed model in terms of the Ulam-Hyers criteria. Finally, we demonstrated the validity of our analysis by presenting an explanatory numerical simulation of the behaviour of this model for various fractional order values. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability No data was used for the research described in the article. ==== Refs References 1 Guan Wei-jie Ni Zheng-yi Hu Yu hua Liang Wen Ou Chun-quan He Jian-xing Liu Lei Shan Hong Lei Chun-liang Hui David S.C. 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==== Front Sci Total Environ Sci Total Environ The Science of the Total Environment 0048-9697 1879-1026 Published by Elsevier B.V. S0048-9697(22)07788-9 10.1016/j.scitotenv.2022.160685 160685 Article Norovirus, Hepatitis A and SARS-CoV-2 surveillance within Chilean rural wastewater treatment plants based on different biological treatment typologies Plaza-Garrido Angela a Ampuero Manuel b Gaggero Aldo b⁎⁎ Villamar-Ayala Cristina Alejandra a⁎ a Departamento de Ingeniería en Obras Civiles, Facultad de Ingeniería, Universidad Santiago de Chile (USACH), Av. Victor Jara 3659, Estación Central, Santiago, Chile b Programa de Virología, Instituto de Ciencias Biomédicas, Facultad de Medicina, Universidad de Chile, Santiago 8380453, Chile ⁎ Correspondence to: A. Gaggero, Brillouet, Laboratorio de Virología Ambiental, Programa de Virología, ICBM, Facultad de Medicina, Universidad de Chile, Av. Independencia 1027, P.O. Box: 8380453, Santiago, Chile. ⁎⁎ Correspondence to: C.A. Villamar-Ayala, Departamento de Ingeniería en Obras Civiles, Facultad de Ingeniería, Universidad Santiago de Chile, Av. Victor Jara 3659, Estación Central, P.O. Box: 9170124, Santiago, Chile. 5 12 2022 10 3 2023 5 12 2022 863 160685160685 18 8 2022 28 11 2022 30 11 2022 © 2022 Published by Elsevier B.V. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. During the COVID-19 pandemic, wastewater from WWTPs became an interesting source of epidemiological surveillance. However, there is uncertainty about the influence of treatment type on virus removal. The aim of this study was to assess viral surveillance within wastewater treatment plants (WWTPs) based on different biological treatments. Seasonal monitoring (autumn-winter and spring-summer) was conducted in 10 Chilean rural WWTPs, which were based on activated sludge, aerated lagoons, bio-discs, constructed wetlands, vermifilters and mixed systems. Viruses were measured (influent/effluent) by the RT-qPCR technique, using a commercial kit for SARS-CoV-2, NoV GI, NoV GII, and HAV. The detection of SARS-CoV-2 viral variants by genotyping was performed using SARS-CoV-2 Mutation Assays (ThermoFisher Scientific, USA). JC polyomavirus detection (control), as well as a qPCR technique. Results showed that SARS-CoV-2, NoV GI and GII were detected in influents at values between <5 and 462, 0 to 28, and 0 to 75 GC/mL, respectively. HAV was not detected among the studied WWTPs. The monitored WWTPs removed these viruses at percentages between 0 and 100 %. WWTPs based on activated sludge with bio-discs demonstrated to be the most efficient at removing SARS-CoV-2 (up to 98 %) and NoV GI and GII (100 %). Meanwhile, bio-discs technologies were the least efficient for viral removal, due to biofilm detachment, which could also adsorb viral aggregates. A correlation analysis established that solids, pH, and temperature are the most influential parameters in viral removal. Wastewater-based surveillance at WWTP allowed for the detection of Omicron before the Chilean health authorities notified its presence in the population. In addition, surveillance of viruses and other microorganisms could help assess the potential public health risk of wastewater recycling. Graphical abstract Unlabelled Image Keywords SARS-CoV-2 Enteric viruses Wastewater rural domestic Biological treatment Physicochemical parameters Editor: Warish Ahmed ==== Body pmc1 Introduction Domestic/municipal wastewaters are a sink for a wide variety of viruses (Ali et al., 2021; Tiwari et al., 2023). A bibliographic compilation by Plaza-Garrido et al. (2022) revealed at least 10 different virus types, including SARS-CoV-2, norovirus (NoV), and Hepatitis A (HAV). The severe acute respiratory syndrome associated with novel coronavirus (SARS-CoV-2) (December 2019, Wuhan-China) causing the current COVID-19 pandemic (>6 million people deceased) has been found in feces of sick patients (Ahmed et al., 2021; Jakariya et al., 2021; WHO, 2022; Islam et al., 2022a). In fact, the first clinical studies indicated that between 17.6 and 48.1 % of patients with COVID-19 had SARS-CoV-2 present in feces, even if they did not show gastrointestinal symptoms (Wang et al., 2020; Xiao et al., 2020). However, infected patients can excrete the virus in their feces even 12 days after oral transmission (up to40 days after) (Foladori et al., 2020; Wang et al., 2020). Therefore, municipal wastewater organizations worldwide have traced the presence of SARS-CoV-2 at variable ranges but reaching up to 7 log10 units GC/L in some places (Ampuero et al., 2020; Sherchan et al., 2020; Hata et al., 2021; D'Aoust et al., 2021; Kumar et al., 2021). Furthermore, this virus that belongs to the family of β-coronaviruses (single-stranded, enveloped, positive polarity, and non-segmented ribonucleic acid or RNA) (Foladori et al., 2020; Aguilar et al., 2020) has developed high mutagenicity. At least 11 variants of interest (Alpha, Beta, Gamma, Epsilon, Eta, Mu, Zeta, Iota, Kappa, Delta, and Omicron) and almost 22 linages have been found thus far (Ahmed et al., 2021; Jakariya et al., 2021; CDC, 2022). Norovirus is of great interest because it is the main reported cause of gastrointestinal diseases worldwide (Ekundayo et al., 2021). NoV belong to the Caliciviridae family (icosahedral virus capsid, single positive strand RNA genome), and can be classified into at least 10 genogroups (GI–GX), of which GI and GII are the most common in water (Chhabra et al., 2019; Ekundayo et al., 2021; Ennuschat et al., 2021). Worldwide, this virus and its genotypes (GI and GII) from wastewater have been detected at concentrations up to 9 log10 units GC/L (Eftim et al., 2017; Ekundayo et al., 2021). The Hepatitis A virus causes one of the most common liver infectious diseases in the world (Lin et al., 2017). Its high environmental resistance could explain its high transmissibility from water and gradual infection. HAV belongs to the Picornaviridae family Hepatovirus genus (non-enveloped RNA virus) and has been reported in wastewater in Brazil at concentrations up to 3 log10 units GC/L (Prado et al., 2012). Based on these precedents, viral surveillance from wastewater could be an effective tool for early warnings of viral circulation (e.g., HAV), public health risk (e.g., NoV) or identification of strategic quarantine zones (e.g., SARS-CoV-2) (Islam et al., 2022, Islam et al., 2022). Globally, sanitation gaps exist between developed (up to 85 %) and developing (up to 25 %) countries, being more critical between urban and rural areas (Malik et al., 2015). An example of this phenomenon is Chilean sanitation, in infrastructure coverage is high (90 % population, >99 % coverage) in urban areas but poor in rural areas (10 % population, <20 % coverage) (Villamar et al., 2018; Vera-Puerto et al., 2022). Rural domestic/municipal wastewater treatment plants (WWTPs) are mainly based on biological technologies that act as barriers for virus discharge into the environment, yet disinfection is usually occasional or careless in rural zones. Indeed, when comparing virus log removal values between non-specific (secondary, LRV: 2.27), non-specific-specific (secondary + tertiary, LRV: 2.22), and specific (tertiary, LRV: 2.81) technologies, differences are non-significant (p > 0.05) (Plaza-Garrido et al., 2022). Additionally, if disinfection exists, it is selective and not intended for viral removal. Thus, different chlorine concentrations (up to 1.0 mg/L) and contact time (up to 12.72 min) have been reported as appropriate inactivation mechanisms depending on virus type (CDC, 2022). These conditions do not necessarily target microorganisms (pathogenic bacteria) within WWTPs, where chlorine concentrations could be up to 2.5 mg/L and with contact times up to 30 min (Li et al., 2013). Therefore, biological treatment processes could have a greater influence on the viral removal. Activated sludge, the most used secondary treatment technology has been reported to remove NoV/genotypes of about 3.11 (GI) and 2.34 (GII) log10 units (Campos et al., 2016). Meanwhile, this same technology has been used to completely remove SARS-CoV-2 and HAV, although evidence is not conclusive (McCall et al., 2020; Sherchan et al., 2020). Other non-conventional technologies under full-scale (constructed wetlands) have reported NoV removal values of about 3.9 (NoV GII) log units (Gonzales-Gustavson et al., 2019). However, its performance on the removal of HAV and SARS-CoV-2 has not been analyzed. Not only could the secondary technology influence viral removal but also the operational and seasonal conditions in WWTPs. Thus, NoV genotypes have been reported to vary depending on seasonality. The highest concentrations of NoV GII and NoV GI have been recorded during winter and summer, respectively, and flow rate has been indicated as a parameter that could affect these concentrations (Nordgren et al., 2009). Literature reviews have determined that some performance/control parameters (COD, TSS, pH) could influence viral removal, particularly treatment type (Plaza-Garrido et al., 2022). In this sense, how each type of biological treatment (secondary phase) affects viral removal under different seasonal and operational conditions is still unclear. This becomes even more relevant considering the health barrier that a WWTPs represents for potential infections in the direct recycling of treated wastewater for irrigation or indirect use and its discharge into rivers. 2 Material and methods 2.1 Wastewater sample collection and processing The wastewater sampling sites selected for this study are rural areas of three Chilean regions (Metropolitan, Valparaíso, and O'Higgins). Samples were collected from six wastewater treatment plants (WWTPs) in the Metropolitan Region, two in the Valparaiso Region, and two in the O'Higgins Region (Fig. 1 ). Wastewater samples were gathered in two periods of the year. The first sampling was conducted during the autumn-winter period (April–September 2021), while the second one took place during the spring-summer period (November–December 2021). For semi-composite sampling, about 1000 mL of raw (influent) and treated (effluent) wastewater were collected every 15 min for 90 min. Wastewater samples were stored in propylene bottles and transported to the lab at cold temperature (~4 °C) under dark conditions. Table 1 summarizes the physical characteristics of the ten WWTPs studied. In addition, Fig. 2 describes their geographical location.Fig. 1 Geographic locations of the WWTPs monitored. AS: Activated sludge, AL: Aerated Lagoon, BD: Bio-disc, CW: Constructed Wetland, VF: Vermifilter, AS-BD: Activated sludge/Bio-disc. Fig. 1 Table 1 General information from rural WWTPs. Table 1WWTPs Name WWTPs Location Date Population WWTPs Treatment First sample Second sample Pretreatment Primary Secondary Tertiary AS_1 Patagua Cerro 34°17′52.8″S 71°23′37.6″W 04/29/2021 12/02/2021 3000 Screening Sedimentation Activated sludge Ca (OCl)2 AS_2 Patagua Orilla 34°17′43.9″S 71°21′31.0″W 05/04/2021 12/02/2021 2500 Screening – Activated sludge Ca (OCl)2 AS_3 Huelquén 33°49′59.6″S 70°38′49.6″W 06/04/2021 11/11/2021 279 Screening + sand trap – Activated sludge NaClO AL_1 Los Loros 32°50′44.4″S 70°56′23.5″W 09/24/2021 11/18/2021 4850 Degreaser – Aerated lagoon Ca (OCl)2 AL_2 Las Vegas 32°50′08.8″S 70°58′59.8″W 09/24/2021 11/18/2021 1800 Screening + homogenizer tank – Aerated lagoon NaClO BD_1 María Pinto 33°30′35.8″S 71°07′12.9″W 07/22/2021 12/09/2021 8000 Screening Rotary sieve Bio-disc NaClO BD_2 San Enrique 33°28′29.6″S 71°06′36.7″W 07/22/2021 12/09/2021 3000 Screening Rotary sieve Bio-disc NaClO CW_1 Polpaico 33°10′05.8″S 70°53′12.3″W 07/29/2021 11/04/2021 2,67 Grinder pump Sedimentation Constructed wetland Ca (OCl)2 VF_1 Rungue 33°00′29.7″S 70°53′22.7″W 07/29/2021 11/04/2021 372 Grinder pump Sedimentation Vermifilter NaClO AS-BD_1 Bicentenario 33°44′45.0″S 70°52′18.0″W 06/04/2021 11/11/2021 4340 Screening + sand tap + homogenizer tank – Activated sludge/Bio-disc NaClO Fig. 2 Quantification and detection of SARS-CoV-2 and its variants from rural domestic wastewater influent. A) Autumn-winter. B) Spring-summer. Active cases: corresponds to the population number infected with SARS-CoV-2 within each location, where WWTPs are located (MINSAL). % Population: corresponds to the percentage of the supplied population from studied WWTPs with respect to the total location population. Lockdown: period imposed by the health authorities, where the movement of inhabitants was restricted to essential activities. AS: Activated sludge, AL: Aerated Lagoon, BD: Bio-disc, CW: Constructed Wetland, VF: Vermifilter, AS-BD: Activated sludge/Bio-disc. Fig. 2 2.2 Analytical methods: wastewater physicochemical characterization The wastewater samples (influent/effluent) from ten WWTPs were characterized by total/volatile suspended solids (TSS/VSS, code method: 2540), chemical oxygen demand (COD, code method: 5220) and biochemical oxygen demand (BDO5, code method: 5210) according to; Standard Methods for the Examination of Water and Wastewater 23rd Edition by APHA/AWWA/WEF (2017). Additionally, nutrients such as ammonium (NH4 +-N, salicylate method: 10031), nitrate (NO3 −-N, cadmium reduction method: 8192), nitrite (NO2 −, ferrous sulfate method: 8153), and phosphate (PO4 3−, molybdovanadate with acid persulfate digestion method: 10127) were assessed though HACH methods. Finally, in-situ parameters, such as pH (code methods: 4500-H), temperature (code methods 2550) (LAQUA PC110, Hach), dissolved oxygen or DO (HQ30d Flexi, Hach; DO measuring range: 0.01–20 mg/L, code method 4500-OG membrane-electrode) were monitored based on Standard Methods for the Examination of Water and Wastewater 23rd Edition by APHA/AWWA/WEF (2017). 2.3 Analytical methods: wastewater viral detection Viral detection was conducted by concentrating wastewater samples, specifically 42 mL of each sample, using ultracentrifugation. The final pellet was re-suspended in 200 μL of sterile phosphate buffered saline (PBS), pH 7.4, and stored at −80 °C (Fumian et al., 2010). This concentrate was used to extract genetic material for the detection of Hepatitis A, Norovirus GI, Norovirus GII, SARS-CoV-2, and JC polyomavirus, which was employed as an internal control for the virus concentration and detection process (Levican et al., 2019). Total viral nucleic acid was extracted from concentrated wastewater using QIAamp® Viral RNA Mini kit (QIAGEN, CA, USA) as described by the manufacturer. All samples were analyzed directly and diluted 1:10 to discard the eventual effects of inhibitors. For SARS-CoV-2 RNA detection, we used a TaqMan 2019-nCoV Assay Kit v1 (ThermoFisher Scientific, USA). This kit contains a set of TaqMan RT-PCR assays for the detection of SARS-CoV-2 RNA and includes three assays targeting the SARS-CoV-2 genes (ORF1ab, S, and N), and one control assay for the human RNase P gene. In turn, the kit has a positive control that allows for the creation of a calibration curve and for the calculation of the genome detection limit. The detection of SARS-CoV-2 viral variants was performed using SARS-CoV-2 Mutation Assays (ThermoFisher Scientific, USA), L452R (Delta), L452Q (Lambda), R346K (Mu), K417T (Gamma), and Q493R (Omicron) TaqMan™ kits according to the manufacturer's instructions. In the case of Norovirus and Hepatitis A virus, the following kits were used: Norovirus Genogroups 1 and 2 (Primerdesign, UK) and Hepatitis A Virus (Primerdesign, UK). The kits were used with a one-step RT-qPCR master mix (Primerdesign, UK), following the manufacturer's instructions. Bacteriophage PP7 was used as internal control (IC) for the recovery of the concentration method used. An IC should be employed to avoid false negative results, which could be associated with failures during the concentration, extraction and/or PCR procedures (Rajal et al., 2007). Validation of hollow fiber ultrafiltration and real-time PCR were conducted using bacteriophage PP7 as a surrogate for virus quantification from water samples (Rajal et al., 2007). All wastewater samples analyzed by qPCR were positive for PP7. Since the presence of PCR inhibitors could lead to underestimation of virus concentration and the frequency of positive samples, to obtain more information on the level of inhibition in PCR techniques, JC polyomavirus was used as an internal control (Levican et al., 2019). No inhibitory effect was observed when comparing the Ct value of the internal control of the wastewater samples with the negative control (data not shown). 2.4 Statistical analysis A Principal Component Analysis (PCA) was performed to identify a correlation between physicochemical parameters and viruses (SARS-CoV-2, NoV GI, and NoV GII) from influent/effluent wastewaters. PCA allows the original data to be represented in a lower-dimensional space than the original space, limiting information loss as much as possible. The variables analyzed were NH4 + -N, NO3-N, NO2 −, PO4 3−, COD, BOD5, TSS, VSS, pH, temperature, and TDS. Thus, the twenty data pairs composed of the physicochemical parameters above mentioned and virus quantification (SARS-CoV-2, NoV GI, and NoV GII) were obtained from the two samplings periods. A PCA analysis transformed the data into principal components containing between 54.4 % and 60 % of the total variance. This exploratory method was chosen as it does not require a specific probability distribution for its analysis. Thus, positive correlations (Ɵ ≈ 0°), non-correlations (Ɵ ≈ 90°), and negative correlations (Ɵ ≈ 180°) between parameters (physicochemical parameters and viruses) were determined. The Prism version 9.1.1, GraphPad Software was used for statistical analysis. 3 Results and discussion 3.1 SARS-CoV-2 detection and its concentration in rural WWTPs Fig. 2 summarizes the quantification and detection of SARS-CoV-2 from wastewater treatment technologies. In this study, an analysis of SARS-CoV-2 from wastewater were performed within treatment plants using activated sludge (AS_1; AS_2; and AS_3), aerated lagoon (AL_1 and AL_2), bio-discs (BD_1 and BD_2), constructed wetland (CW_1), vermifilter (VF_1) and mixed technologies based on activated sludge and bio-discs (AS-BD_1) as secondary treatment. In addition, SARS-CoV-2 concentration from influent, active cases by location, active cases reported by MINSAL vs. population receiving a water supply from WWTPs were assessed. During the autumn-winter period (Fig. 2A), SARS-CoV-2 was detected in 8 of the 10 samples analyzed. Thus, SARS-CoV-2 influent concentrations ranged from 5 to 27 GC/mL. The two WWTPs that presented the highest level of SARS-CoV-2 virus concentration in the influent were AS_1 with 27 GC/mL and AS_BD_1 with 18 GC/mL, located in sectors with lockdown phase, due to the high number of SARS-CoV-2 active cases. Additionally, Mu, Lambda, and Gamma were detected during the autumn-winter period. There was one case that could not be identified with the available probes. Conversely, two WWTPs did not present SARS-CoV-2 (0 GC/mL) in influent and effluent; this was the case in one plant based on biodisc technology (BD_2) and another plant based on vermifilter (VF_1). Two reasons may be behind this phenomenon: first, no virus concentration was found during the sampling period, and second, the communities where the WWTPs are located did not have people infected with SARS-CoV-2 at the time of sampling. In the spring-summer period (Fig. 2B), SARS-CoV-2 was detected in 5 of the 10 WWTPs, with virus influent concentrations oscillating between 5 and 462 GC/mL. Specifically, AS-BD_1 reported a virus concentration 25 times higher than in the autumn-winter period (population in lockdown). Another interesting result is that AS_1 reported 10 GC/mL, but no active cases of SARS-CoV-2 were reported during the sampling period according to the health authorities. In contrast Delta, Lambda, Gamma, and Omicron were detected during this period. In general, the autumn-winter period reported the presence of more active cases, which was reflected in the influent of almost all the plants. It was not possible to observe a pattern of viral concentration directly attributable to seasonality but conditioned to the pandemic evolution. However, this could be conditioned by the patterns of contagion during the pandemic and the level of vaccination of the population. In specific, the AS-BD_1 WWTP showed great differences between the two periods. In the autumn-winter period, it reported 216 active cases but a virus concentration of about 18 GC/mL, whereas only 43 active cases were reported, but virus concentration increased to 462 GC/mL, in the spring-summer period. This may be due to active cases not being reported by the health authorities and the existence of asymptomatic cases that may be better reported by wastewater. Furthermore, it is noteworthy that during the autumn-winter period, the SARS-CoV-2 variants that were circulating and predominated in Chile were detected from wastewater. The Gamma variant was detected in both periods because it is a variant from neighboring countries (Brazil), being also predominant in the Chilean population until July (MINSAL, 2021). This sanitary condition was well represented in the monitoring conducted among WWTPs. In turn, the Omicron variant was detected from wastewater in mid-November (spring-summer period). In Chile, the first case of the Omicron variant was reported on December 3 (MINSAL, 2021) and it was a passenger arriving in the country at the end of November. According to our results, the Omicron variant could have entered the country before the first case reported by health authorities. These results further reinforce the need to perform epidemiology based on wastewater, especially in search of variants already circulating in the population. Wastewater-based epidemiology allows for rapid and low-cost monitoring, compared to detection of the virus in the community, both in symptomatic and asymptomatic people (Islam et al., 2022, Islam et al., 2022). This methodology can be used as a predictive marker of virus circulation in a population and as an early warning system. Additionally, it is possible to observe viral concentration from one wastewater sample to determine if lockdown is necessary for that area. Furthermore, this type of analysis permits to detect if variant of interest is already circulating in the country or population (Sherchan et al., 2020; Rimoldi et al., 2020; Arora et al., 2020; Hasan et al., 2020; Haramoto et al., 2020; Balboa et al., 2021). Table 2 summarizes the detection and quantification of SARS-CoV-2 among 10 monitored WWTPs (influent/effluent). In total, 40 wastewater samples (influent/effluent) were analyzed during both monitoring periods, detecting SARS-CoV-2 in 65 % of the influents and 50 % of effluent samples. The concentration of viruses in influents ranged from 5 to 462 GC/mL, while in effluent concentrations varied between 0 and 32 GC/mL. The SARS-CoV-2 concentration (influent/effluent) oscillated between 5 and 27 GC/mL for the autumn-winter period. Meanwhile, these values during the spring-summer period ranged between 5 and 462 GC/mL. The High values registered are related to the greater mobility of inhabitants inside and outside their location. In specific, AS-BD_1 reached the highest SARS-CoV-2 concentrations from influent because this location had 216 active cases, the largest number of active cases registered in the monitored WWTPs. However, during the spring-summer period, when fewer active cases were reported, the highest SARS-CoV-2 concentration was detected, which could be related to SARS-CoV-2 cases not being reported by the Ministry of Health from Chile or to asymptomatic cases.Table 2 Detection and quantification of SARS-CoV-2 and its variants from rural wastewater (influent and effluent). Table 2WWTPs Location Total, population (inhab) WWTP population (inhab) Population supplied (%) Seasonality Date Location stagea SARS-CoV-2 (GC/mL) Active cases Influent Effluent LVR Removal efficiency (%) AS_1 Pichidegua 20,743 3000 14.46 A-W 04/29/21 Lockdown 38 5 8 nr nr S-S 12/02/21 Opening 0 10 7 0.15 30 AS_2 Pichidegua 20,743 2500 12.05 A-W 04/05/21 Lockdown 26 27 0 – 100 S-S 12/02/21 Opening 0 0 0 0 0 AS_3 Paine 82,766 279 0.34 A-W 04/06/21 Lockdown 289 12 <5 ~0.38 ~58 S-S 11/11/21 Preparation 66 12 0 – 100 AL_1 Llay-Llay 26,533 4850 18.28 A-W 09/24/21 Opening 5 5 0 – 100 S-S 11/18/21 Opening 8 0 0 0 0 AL_2 Llay-Llay 26,533 1800 6.78 A-W 09/24/21 Opening 5 8 6 0.12 25 S-S 11/18/21 Opening 8 27 0 – 100 BD_1 Maria Pinto 14,926 8000 53.60 A-W 07/22/21 Opening 17 12 8 0.18 33 S-S 12/09/21 Opening 7 0 5 nr nr BD_2 Maria Pinto 14,926 3000 20.10 A-W 07/21/21 Opening 17 0 32 nr nr S-S 12/09/21 Opening 7 5 7 nr nr CW_1 Til Til 21,477 267 1.24 A-W 07/29/21 Preparation 5 15 0 – 100 S-S 11/04/21 Preparation 8 0 0 0 0 VF_1 Til Til 21,477 372 1.73 A-W 07/29/21 Preparation 5 0 0 0 0 S-S 11/04/21 Preparation 8 0 0 0 0 AS-BD_1 Isla de Maipo 40,171 4340 10.80 A-W 06/04/21 Lockdown 216 18 <5 ~0.56 ~72 S-S 11/11/21 Preparation 43 462 5 1.97 98 nr: not is removed, a higher or equal to concentration is detected in effluent. A-W: Autumn-winter, S-S: Spring-summer. AS: Activated Sludge. AL: Aerated Lagoon. BD: Biodisc. CW: Constructed wetland. VF: Vermifilter. AS-BD: Activated sludge/Bio-disc. a Location stage according to step-by-step plan implemented by Chilean Ministry of Health. Concerning SARS-CoV-2 removal efficiency (Table 2), the technologies that showed the best performance in both periods were activated sludge with bio-disc (AS-BD_1), with values from 72 to 98 %. It should be noted that AS-BD_1 was also the plant that reported the highest virus concentration in both periods, a condition that could be related to its mixed biomass (biofilm: attached solids + sludge: suspended solids). However, WWTPs using only activated sludge in this study reported wide differences in SARS-CoV-2 removal, ranging from 0 to 100 %. The removal rate (100 %) is attributed to the initial concentration. For example, in the case of AS_2, the influent detected between 26 and 27 GC/mL, while the effluent did not detect SARS-CoV-2 (0 GC/mL). This result would indicate that removal reaches 100 %, comparing the influent entering the WWTPs and the concentration of virus found in their effluent. Similarly, aerated lagoons (AL_1 and AL_2) registered SARS-CoV-2 removal efficiencies between 25 and 100 %. Thus, operational factors (residence time, sludge age, and dissolved oxygen, among others) in activated sludge WWTPs or aerated lagoons could explain these differences better than influent virus concentration and the viral patterns associated with the seasons of the year. Viral removal performance has been variable within activated sludge around the world, with WWTPs that have completely removed SARS-CoV-2 and others that have not (up 5 log10 units from effluent) (Sherchan et al., 2020; Rimoldi et al., 2020; Arora et al., 2020; Hasan et al., 2020; Haramoto et al., 2020; Balboa et al., 2021). In turn, WWTPs using bio-discs (BD_1 and BD_2) were the least efficient at removing viruses, as they failed to remove SARS-CoV-2 levels and even accumulated virus concentration in the effluent. Thus, viruses could potentially be adsorbed by the biofilm, considering that exopolysaccharides or EPS are electrically charged polymeric structures, which enable them to be electrostatic and hydrophobically with them (Armanious et al., 2016). Finally, natural based solutions (NBS), such as constructed wetlands (CW_1) reported to remove 100 % of SARS-CoV-2 only during the autumn-winter period, which is the most adverse scenario for these technologies due to vegetal senescence. However, this comprises other processes (filtration/adsorption), which have demonstrated to be successful in the removal of microorganisms (Wu et al., 2016). Therefore, the variations between the different treatments can have a direct impact on the levels of SARS-CoV-2 in the effluent, especially if the plant was in proper operation and operating conditions at the time of the visits. In WWTPs, virus removal may occur at different stages during biological treatment, which can make a difference in terms of efficiency. Activated sludge has a removal capacity of about 2.5 × 104 GC/L for SARS-CoV-2 after sludge treatment (Randazzo et al., 2020). In our case, activated sludge technologies presented a similar behavior for some cases, and lower virus removal efficiencies in others. The difference between activated sludge technologies could be related with differences in operational parameters, such as hydraulic retention time, and sludge age. Thus, the longer the retention time, the more effective the removal of SARS-CoV-2, being expected to increase with temperature (Saawarn and Hait, 2021). Furthermore, the extracellular enzymatic activity of hydrolases and proteases in activated sludge processes increases the sludge age, which could inactivate SARS-CoV-2 and other viruses (Ye et al., 2016). 3.2 Norovirus GI and GII detection in rural WWTPs Table 3 summarizes the quantification and detection of norovirus GI and GII from wastewater treatment technologies. NoV GI and NoV GII concentrations were measured in WWTPs using activated sludge (AS_1; AS_2; and AS_3), aerated lagoon (AL_1 and AL_2), bio-discs (BD_1 and BD_2), constructed wetland (CW_1), vermifilter (VF_1) and mixed technologies based on activated sludge and bio-discs (AS-BD_1) as secondary treatment.Table 3 Detection and quantification of norovirus GI and GII from rural wastewater (influent and effluent). Table 3WWTPS Sample Noroviruses GI Noroviruses GII GC/mL LVR Removal efficiency % GC/mL LRV Removal efficiency % Autumn Winter Spring Summer Autumn Winter Spring Summer Autumn Winter Spring Summer Autumn Winter Spring Summer Autumn Winter Spring Summer Autumn Winter Spring Summer AS_1 Influent 0 10 – nr – nr 0 19 – nr – nr Effluent 0 10 0 75 AS_2 Influent 0 0 – – – – 275 10 – – 100 nr Effluent 0 0 0 10 AS_3 Influent 0 0 – – – – 0 0 – – nr – Effluent 0 0 10 0 AL_1 Influent 0 0 – – – – 10 12 nr – nr 100 Effluent 0 0 85 0 AL_2 Influent 0 10 – – – 100 13 11 – – 100 100 Effluent 0 0 0 0 BD_1 Influent 0 0 – – – – 13 23 nr – nr 100 Effluent 10 0 275 0 BD_2 Influent 0 0 – – – nr 13 11 nr nr nr nr Effluent 0 10 39 47 CW_1 Influent 20 0 – – 100 – 1875 10 2.194 – 99.36 100 Effluent 0 0 12 0 VF_1 Influent 0 0 – – – – 18 28 – 0.447 100 64.29 Effluent 0 0 0 10 AS-BD_1 Influent 0 28 – – – 100 15 25 – – 100 100 Effluent 0 0 0 0 nr: Not removed, a higher or equal to concentration is detected in effluent. AS: Activated Sludge. AL: Aerated Lagoon. BD: Bio-disc. CW: Constructed wetland. VF: Vermifilter. AS-BD: Activated sludge/Bio-disc. During the autumn-winter period, NoV GI was detected in 2 of the 20 wastewater samples analyzed (10 %), with BD_1 reaching an effluent value of about 10 GC/mL and CW_1 an influent value of 20 GC/mL. In turn, during the spring-summer period, NoV GI was found in 3 of the 10 WWTPs analyzed (25 %). Thus, NoV GI influent concentration ranged from 10 to 28 GC/mL, with effluent concentrations being close to 10 GC/mL. NoV GI was efficiently removed by natural-based solutions or NBS-like, constructed wetlands (CW_1), reporting 100 % of viral removal during the autumn-winter period. In addition, NBS comprises other processes (filtration/adsorption), which have demonstrated to be successful in the removal of microorganisms (Wu et al., 2016). Conversely, as BD_1 did not remove NoV GI, this accumulated (effluent: 10 GC/mL, influent: 0 GC/mL). The technologies that showed the best performance during the spring-summer period were AL_2 and AS-BD_1, which completely removed NoV GI. However, AS_1 and BD_2 did not remove it, discharging a concentration equal to or higher than that reported in the influent. During the autumn-winter period, NoV GII was detected in 9 of the 10 WWTPs analyzed (65 %), with AS_1 alone not reporting this genotype. Virus influent concentrations varied between 10 and 1875 GC/mL, while virus effluent concentrations ranged from 10 to 275GC/mL. The two WWTPs that presented the highest influent level of NoV GII were AS_2 (275 GC/mL) and CW_1 (1875 GC/mL). Additionally, the highest effluent concentration was reported by BD_1 with values of 275 GC/mL. In the spring-summer period, NoV GII was detected in 9 of the 10 WWTPs analyzed (65 %), except for AS_3, which did not report this genotype. Thus, virus influent concentrations ranged between 10 and 28 GC/mL, while virus effluent concentrations varied from 10 to 75 GC/mL. It is noteworthy that the two WWTPs that presented the highest influent levels of NoV GII were VF_1 (28 GC/mL) and AS-BD_1 (25 GC/mL). Additionally, the highest effluent concentrations were reported by AS_1 (75 GC/mL) and BD_2 (47 GC/mL). Only during the autumn-winter period (cold period), the highest concentrations of NoV GI and NoV GII, were recorded for some WWTPs. This is since the NoV prevalence is seasonal and associated with cold months. In fact, other studies have reported that 78.9 % of norovirus cases occur during the cold months (Ahmed et al., 2013). The results obtained in the present study can be explained by the fact that viral circulation (other viruses) decreased during the quarantine and isolation measures. Therefore, this study could not verify whether NoV and its genotypes exhibit seasonal behavior. Concerning the removal efficiency of NoV GII, the technologies that showed the best performance in both periods were AS-BD_1 and AL_2, with values close to 100 %. Operational factors, such as residence time, sludge age, and dissolved oxygen, more than seasonal or influent virus concentration patterns. In fact, virus removal from WWTPs occurs in different stages during biological treatment, which can affect the efficiency of a process. Activated sludge has the capacity to remove 91 % of enteric viruses (Lizasoain et al., 2017). Additionally, CW_1 reported viral removal between 99.4 and 100 % during both periods, while VF_1 reported removal percentages between 64.3 and 100 % during both periods. Conversely, WWTPs using bio-discs (BD_1 and BD_2) were the least efficient for viral removal, as they failed to eliminate NoV GII traces and even accumulated viral concentration in the effluent. Only BD_1 reported to remove viruses at percentages of 100 % during spring-summer periods. In global terms, our results indicate that one of the least efficient systems for virus removal is bio-disc processes (BD_1 and BD_2). These systems would even lead to the accumulation of viral concentration, which is discharged into the effluent. This seems to be a generalized problem of bio-disc systems, as other countries have reported that they not only fail to remove viruses (e.g., astroviruses) but also increase their concentration in effluents (Ibrahim et al., 2017). Low or zero removal may occur due to the formation of aggregates from viruses, which may protect viruses against inactivating agents from different WWTPs. Another process that occurs between suspended solids from wastewater and viruses is the adsorption process, which has been shown to affect virus survival (Ibrahim et al., 2017). Viruses can be adsorbed into minerals, dissolved and particulate organic matter. In fact, adsorption is considered to occur through electrostatic interactions between viruses and oppositely charged surfaces. Viruses derive their charge from different functional groups present on their phospholipid envelope or capsid proteins (Pradhan et al., 2022). In addition to electrostatic interactions, Van der Waals interactions can occur, especially when they are attached to minerals. Viral aggregation is affected by several physicochemical parameters from the environment, including pH, temperature, and ionic composition. Therefore, the persistence of viruses from wastewater may depend on factors specific to a virus, pH, temperature, and organic matter, which may favor its permeation or removal from wastewater. 3.3 Detection of Hepatitis A virus in rural WWTPS The Hepatitis A virus (HAV) is another enteric virus (family: Picornaviridae; genus: Hepatovirus) that can be transmitted through food, contaminated surfaces, and water (Nasiri et al., 2021). HAV is a non-enveloped RNA virus (27–32 nm) that can survive environmental conditions as opposed to enveloped viruses. HAV has been shown to survive in the environment for long periods and its survival is influenced by environmental factors, such as temperature, and pH, among others. Therefore, to assess HAV in smaller populations to detect outbreaks from wastewater is of public interest. HAV virus was negative in the 40 samples analyzed from influent/effluent and during both periods (autumn-winter and spring-summer). The non-detection of HAV is not surprising since the population of the different WWTPs is quite layered, which could indicate the absence of an HAV outbreak. The detection of this virus type has also been used in environmental epidemiological surveillance. For instance, an Italian study reported that only 24.5 % of wastewater samples were HAV positive, due to a low number of infected individuals (La Rosa et al., 2014). In turn, Tunisia, which did not have a specific HAV surveillance system, detected Hepatitis A virus in influent (66.9 %, 6.0 × 103 GC/mL) and effluent samples (40.7 %, 2.7 × 103 GC/mL) (Ouardani et al., 2016). In addition, Rio de Janeiro detected HAV in only 58 % of influents, while no HAV was detected in effluent samples, indicating that good wastewater treatment contributes to reducing the risk of HAV transmission by contaminated water (Prado et al., 2012). This type of study contributes to epidemiological surveillance, especially when surveillance programs are more limited (Prado et al., 2012). In Chile, one of the most important outbreaks was in the Biobío region (2014–2015), which was related to the existence of environmental conditions that favored the transmission cycle, as well as the consumption of raw seafood (Sanhueza and Cachicas, 2020). Failures on wastewater collection and treatment systems, sewage outcrops, coastal areas without basic sanitation, and submarine outfalls are considered an important source of fecal contamination (Manríquez, 2016). In fact, 48.6 % of Magellan mussels (Aulacomya atra), chorus mussel (Mercenaria mercenaria), and choro malton (Choromytilus chorus) infected with HAV were detected in four of seven coastal communities from the Biobío region (Sanhueza and Cachicas, 2020). This finding raises health and management issues and requires systematic surveillance of outbreaks. This analysis complements the one carried out by González-Saldía et al. (2017), who described a positive correlation between fecal contamination in Concepción/Arauco Bays, and the HAV cases described in the region. The absence of HAV from wastewater in the locations monitored in this study could indicate that there is no current outbreak of this disease in the area. Additionally, the screening of HAV can contribute to the epidemiological surveillance of enteric viruses in the population. HAV detection from wastewater can promote containment measures or regulations on the discharge and recycling/reuse of contaminated wastewater. The objective of HAV detection is to prevent hepatitis A viruses from reaching species that are bio accumulators (shellfish), fruit, and vegetables and then enter the consumption chain, increasing the infection risk of the population. In general, viral permanence depends on type, state (dispersed, aggregated, associated with cells, absorbed to solids), medium in which viruses are present (water, solid), and environmental conditions (temperature, pH, organic matter, particles, salts, ions, proteolytic enzymes, microbiota activity, and light). All these factors influence virus survival and permanence (Ibrahim et al., 2017). Therefore, the assessment of the operational parameters of different WWTPs, and of the wastewater physicochemical parameters is crucial. 3.4 Influence of physicochemical parameters on the presence of SARS-CoV-2 in rural domestic wastewater Fig. 3 summarizes the influence of operation parameters on SARS-CoV-2 from influents (Fig. 3A) and effluents (Fig. 3B) from of studied WWTPs. Thus, in the case of SARS-CoV-2 influent, components are related to dimension 1 (PC1) and dimension 2 (PC2), which account for 42.46 % and 14.84 % of the database, respectively. In other words, the variance explained by the PCA was 57.33 %. Each variable is represented by a vector and its coordinates allow us to generate a biplot. Based on this analysis type, positive correlations (Ɵ < 90°) can be assessed. Thus, when the correlation is close to 1, and the closer to 1 (Ɵ = 0°), the correlation between the variables is stronger.Fig. 3 Variate plot based on the principal component analysis of SARS-CoV-2, NoV GI and NoG GII, with respect to physicochemical parameters from rural domestic wastewater (n = 40). A) SARS-CoV-2 influent; B) SARS-CoV-2 effluent; C) NoV GI influent; D) NoV GI effluent; E) NoV GII influent; F) NoV GII effluent. Ammonium (NH4+-N); Nitrate (NO3−-N); Nitrite (NO2−); Phosphate (PO43−); Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD5), Total Suspended Solids (TSS), Volatile Suspended Solids (VSS), pH, temperature (°C), and Total Dissolved Solids (TDS). Fig. 3 As for SARS-CoV-2 from influents (Fig. 3A), it had a positive relationship with pH, temperature, TDS, TSS, TVS, NO2 −, NH4 +-N, PO4 3−, CODt, and BOD5. The variables that presented a lower angle were pH (Ɵ = 68.51°) and CODt (Ɵ = 72.77°). Therefore, the more these variables increase, the more they favor the presence of SARS-CoV-2 in domestic wastewater. The presence of SARS-CoV-2 corresponds to the presence of viral RNA in wastewater, indicating a possible indirect infection pathway (Zhang et al., 2022). Therefore, an increase in pH and CODt promotes the presence of SARS-CoV-2 viral RNA in wastewater. None of the variables positively correlated with the presence of SARS-CoV-2 were significant (p > 0.05). In turn, NO3 −-N presents a negative correlation (Ɵ = 95.44) with SARS-CoV-2, which indicates that it influences its presence in domestic wastewater; yet, in this case, if one variable increases, the other decreases. SARS-CoV-2 from domestic wastewater effluents (Fig. 3B) showed that the main components of dimension 1 (PC1) and dimension 2 (PC2) are explained by 39.32 % and 16.05 % of the original database, respectively. In total, the explained variance of PCA is 55.37 %. Additionally, the variables that correlated positively with the permanence of SARS-CoV-2 in effluents (Fig. 3B) were pH, TDS, TSS, TVS, NO2 −, NH4 +-N, PO4 3−, and CODt. The variables that present a smaller angle with SARS-CoV-2 are pH (Ɵ = 71.04°) and TSS (Ɵ = 63.71°), although this was not a significant correlation (p > 0.05). Meanwhile, variables that present a negative correlation were temperature (Ɵ = 105.06°), NO3–N (Ɵ = 94.58°) and BOD5 (Ɵ = 99.69°), with neither strong nor significant correlations (p > 0.05). In summary, SARS-CoV-2 from influents and effluents, has variables that influence its presence in domestic wastewater, namely pH, TDS, TSS, TVS, NO2 −, NH4 +-N, PO4 3−, and CODt. The physicochemical parameters that correlate positively are not surprising, since the presence of SARS-CoV-2 has been reported in wastewater with pHs between 7.1 and 7.4. In addition, studies have indicated that a stable pH for coronaviruses is between 5 and 7.4 (Balboa et al., 2021). This may indicate that pH is a factor influencing the presence of SARS-CoV-2 in wastewater. Furthermore, solids (TDS, TSS, TVS) from wastewater have a close relationship with viral presence, as they bind to the virus. Therefore, if more solids are present, the virus concentration increases (Balboa et al., 2021; Amoah et al., 2020). Additionally, PCA indicates that temperature has a negative correlation with the presence of SARS-CoV-2. The presence of SARS-CoV-2 is due to the detection of SARS-CoV-2 specific ORF1ab, S, and N genes in the wastewater samples. During the autumn-winter period, the SARS-CoV-2 genome persists longer (Hart and Halden, 2020), which may correlate with our results. Another factor to consider is temperature range (23 to 25 °C), which favor decreases in viral concentration (La Rosa et al., 2020). Indeed, we have observed the same behavior with lower SARS-CoV-2 detection during the summer period (19.3–28.1 °C). Although some studies have been carried out to determine the parameters that influence the stability of SARS-CoV-2, some more studies are needed to confirm wastewater parameters and virus stability. Although the influence of SARS-CoV-2 has been demonstrated in both influent/effluent, effort has been made to characterize the behavior of the virus to enhance decision-making during the COVID-19 pandemic. There is not much information about the effects of environmental and physicochemical variables from wastewater. However, there isa need to consider them to ensure accurate estimations of SARS-CoV-2 that orient the preventive decision-making at the local level. 3.5 Influence of physicochemical parameters on the presence of NoV GI and GII from rural domestic wastewater NoV GI from domestic wastewater influents (Fig. 3C) is explained by the principal components of dimension 1 (PC1) and dimension 2 (PC2), which account for 42.95 % and 14.86 % of the original database, respectively. In total, the explained variance of the PCA is 57.81 %. Regarding variable correlations, a significant positive correlation was observed with pH (Ɵ = 59.64°) and NO3 −-N (Ɵ = 62.54°), which could indicate that if these variables increase NoV presence. However, temperature, TDS, TSS, TVS, NO2 −, NH4 +-N, PO4 3−, CODt, and BOD5 are also positively correlated with the presence of NoV GI, but less strongly. To assess the influence of the variables on the presence of NoV in effluents (Fig. 3D), a principal components analysis was performed. Dimension 1 (PC1) and dimension 2 (PC2) represent 38.38 % and 16.37 % of the original database, respectively. Indeed, the explained variance of PCA is 54.75 %. Furthermore, NoV GI was positively correlated with variables such as pH, temperature, TDS, TSS, TVS, NH4 +-N, PO4 3−, CODt. The variables that negatively correlate with Nov GI from effluents were NO2 −, NO3 −, and BOD5. Meanwhile, some correlations presented a lower pH (Ɵ = 74.69°) and temperature (Ɵ = 76.80°). In turn, variables such as NO2 −, NO3 −-N and BOD5 have exhibited negative correlations, being NO3 −-N (Ɵ = 102.30°) the one that presented the highest angle. None of the variables from the effluent presented a significantly strong correlation. Some variables that are involved in the presence of NoV in influents and effluents are pH, TDS, TSS, TVS, NH4 +-N, PO4 3−, and CODt. Another virus analyzed in rural domestic wastewater corresponds to NoV GII. In Fig. 3E, the principal component analysis is composed of dimension 1 (PC1) and dimension 2 (PC2), which represent 41.67 % and 18.34 % of the original data, respectively, i.e., the explained variance of the PCA is 60.01 %. The variables that positively correlate with the presence of NoV GII are pH, BOD5, and NO3 −-N, of which NO3 −-N (Ɵ = 25.59°) is the one that strongly correlates with NoV GII. The negative correlations are temperature, TDS, TVS, NO2 −, NH4 +-N, PO4 3−, and CODt. Of the variables that present a negative correlation, the strongest correspond to NH4 +-N (Ɵ = 103.67°) and TDS (Ɵ = 103.56°). The variables that show no correlation with NoV GII are TSS (Ɵ = 91.42°). The presence of NoV GII in effluents (Fig. 3F) and domestic wastewater has principal components of dimension 1 (PC1) and dimension 2 (PC2), which account for 38.40 and 16.01 % of the original database, respectively. In total, the explained variance of the PCA is 54.41 %. The variables that positively correlate with NoV presence in effluents are pH, TDS, TSS, TVS, NH4 +-N, PO4 3−, and CODt. In turn, the variables that presented a stronger correlation or smaller angle were NH4 +-N (Ɵ = 72.82°) and PO4 3− (Ɵ = 79.57°). The variables negatively correlated with the presence of NoV are temperature, NO3 −-N, NO2 −, and BOD5, with NO2 − presenting the highest angle (Ɵ = 100.32°). The only variable that positively correlates with the presence of NoV in influents and effluents is pH since the rest of them presents a negative correlation. In the case of noroviruses, they have been reported to resist pH-values from 3 and 4, and they can remain infective for 3 h after exposure to pH-values <3. Noroviruses are sensitive to basic pH (>9), even when viruses are inactivated at pH 9 (ESR, 2010; Tao et al., 2016). The pH-values of wastewater did not exceed pH 7 and 8, which would not be a factor that could radically influence the presence of norovirus. Perhaps, if pH levels were close to 9, a lower presence norovirus from wastewater would be observed, since at that pH, its capsid could be destabilized, causing its destruction (Pogan et al., 2018). Thus, the presence of SARS-CoV-2, NoV GI, and NoV GII in effluents could be related to aggregation and adsorption processes that can occur during the different processes involved in WWTPs. Aggregation and adsorption processes are favored by pH. For example, an acid-pH promotes viral aggregation, particularly in enteroviruses (Tao et al., 2016); however, this does not imply that this is the case with other viruses like the ones analyzed in this study. Another parameter that also plays a role in viral aggregation is temperature. Some researchers propose that the higher the temperature, the greater the particle aggregation. Virus aggregation occurs when they can remain and persist within WWTPs, favoring the permanence of these viruses in effluents (Pradhan et al., 2022). In our case, effluent temperature was positively correlated with virus concentration (SARS-CoV-2, NoV GI, and NoV GII). Furthermore, an increase in temperature could favor virus aggregation, which would partly justify the viral presence in effluents, especially in those treatments that have a lower removal efficiency. Conversely, low temperature could also favor the virus permanence in wastewater as it favors virus adsorption to solids (Tao et al., 2016). This may contribute to the virus' permanence in the effluent during cold periods such as autumn-winter. These processes can be favored depending on environmental conditions, added to the different processes in WWTPs. Another physicochemical parameter that correlates positively is suspended solids (TSS). This is probably due to interactions viruses may have with the solids from wastewater. It is important to mention that adsorption to suspended solids from wastewater depends on pH and temperature. Thus, an acid medium, with low temperatures and high solid concentrations facilitates the absorption of the virus into solids. It has even been suggested that there are differences in the presence of the different serotypes of enteroviruses. In fact, serotype E-3 has adsorption to the supernatant, versus E.6 which binds to the solid part (Tao et al., 2016). This suggests that the interaction of these viruses with different environmental conditions needs to be studied to understand what phenomena and processes favor their permanence in WWTPs. 4 Conclusions The different technologies used for wastewater treatment have the capacity to reduce the virus concentration of SARS-CoV-2, NoV GI, and NoV GII. However, the least efficient system in terms of virus removal is the bio-discs technology, which apparently accumulates the virus concentration that is removed by the effluent. This may be due to the technology used, formation of virus aggregates and virus adsorption. In turn, the detection of viruses, such as SARS-CoV-2, allowed us to confirm that wastewater-based surveillance is an effective strategy for health surveillance. The Omicron variant was detected in these rural WWTPs one month before the Chilean health authorities reported it. However, it is noteworthy that the detection of the viral genome does not necessarily represent the infectious capacity of the detected viruses. Therefore, studies on the infectious capacity of viruses in wastewater need to be conducted. Virus detection in wastewater can contribute to epidemiological surveillance in rural areas and become a model to find possible threats to human health. This can also reveal potential risks of outbreaks caused by this type of enteric virus for the population, especially considering the possibilities of recycling treated wastewater. CRediT authorship contribution statement Angela Plaza-Garrido: Investigation, Formal analysis, Writing – original draft. Manuel Ampuero: Investigation, Formal analysis. Aldo Gaggero: Resources, Supervision, Writing – review & editing, Project administration, Funding acquisition. Cristina Alejandra Villamar-Ayala: Conceptualization, Resources, Supervision, Writing – review & editing, 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. Acknowledgments This work was supported under project USA1956_Dycit (project grant 092118VA_POSTDOC) “Study of the permanence, interaction, and treatment of emerging pollutants and pathogenic microorganisms from domestic wastewater” of the 10.13039/100007194 Universidad de Santiago de Chile . Cristina A. Villamar-Ayala and Angela Plaza-Garrido acknowledge the financial support from 10.13039/501100002850 FONDECYT (project grant 11190352), and Aldo Gaggero acknowledge the financial support from 10.13039/501100002850 FONDECYT (project grant 1181656). Authors thank the availability of operators and administrators of the studied WWTPs, as well as the Campus office of Universidad de Santiago de Chile for their support in transportation, even in times of quarantine. This work was carried out at the Laboratorio de Investigación Interdisciplinaria en Ciencias y Tecnología del Agua Ko-Yaku (Universidad de Santiago de Chile) and Institute of Biomedical Sciences (Universidad de Chile). We also thank our Ko-Yaku staff (Yilbert Tovar) for his support in the development of this study. ==== Refs References Aguilar N. Hernández A. Ibanes C. SARS-CoV-2; virus characteristics and transmission Revista Latinoamericana de Infectología Pediátrica 33 3 2020 143 148 10.35366/95651 Ahmed S.M. Lopman B.A. Levy K. A systematic review and meta-analysis of the global seasonality of norovirus Plos One 8 2013 e75922 10.1371/journal.pone.0075922 Ahmed F. Islam A. Kumar M. Hossain M. Bhattacharya P. Islam T. Hossen F. Hossain S. Islam S. Uddin M. Islam N. 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==== Front Dialogues in Health 2772-6533 2772-6533 S2772-6533(22)00090-9 10.1016/j.dialog.2022.100090 100090 Article Prevalence and factors associated with depression, anxiety, and stress symptoms among home isolated COVID-19 patients in Western Nepal Adhikari Bikram a⁎ Poudel Lisasha a Thapa Tek Bahadur a Neupane Deekshya a Maharjan Pranita a Hagaman Ashley bc Bhandari Niroj ad Katuwal Nishan a Shrestha Bhawana a Maharjan Rashmi af Shrestha Sudip e Shrestha Akina g Tamrakar Dipesh ag Rajbhandari Bibek h Shahi Brish Bahadur i Shrestha Rajeev aj Karmacharya Biraj Man ag Shrestha Archana cdgk a Research and Development Division, Dhulikhel Hospital, Kathmandu University Hospital, Dhulikhel, Nepal b Social and Behavioral Sciences Department, Yale School of Public Health, New Haven, USA c Center for Methods in Implementation and Prevention Science, Yale School of Public Health, New Haven, CT, USA d Institute for Implementation Science, Nepal e Department of Community Programs, Dhulikhel Hospital-Kathmandu University Hospital, Dhulikhel, Nepal f Department of Nursing and Midwifery, Kathmandu University School of Medical Sciences, Dhulikhel, Nepal g Department of Public Health, Kathmandu University School of Medical Sciences, Dhulikhel, Nepal h Department of Emergency Medicine and General Practice, Nepal police Hospital, Kathmandu, Nepal i Ministry of Social Development, Karnali Province, Nepal j Department of Pharmacology, Kathmandu University School of Medical Sciences, Dhulikhel, Nepal k Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, USA ⁎ Corresponding author. 5 12 2022 5 12 2022 1000903 11 2022 2 12 2022 2 12 2022 . 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 Globally, COVID-19 pandemic has a significant impact on mental health. In Nepal, COVID-19 positive cases have to self-isolate at home in multi-generational and multi-family households. This could be strongly associated with depression, anxiety, and stress-related health outcomes. Additionally, COVID-19 related stigma and fear of transmission may intensify depression, anxiety, and stress symptoms. This study determined the prevalence of depression, anxiety, and stress symptoms and their association with presence of COVID-19 symptoms and comorbid conditions among home isolated COVID-19 positives in the Karnali province, Nepal. Methods We conducted a cross-sectional study to assess depression, anxiety, and stress symptoms among 402 home isolated COVID-19 patients of Karnali province from January to May 2021 using “Depression, Anxiety and Stress Scale-21 (DASS-21)”. We interviewed patients to collect socio-demographic, DASS-21, COVID-19 symptoms, comorbid conditions, and self-treatment. We conducted a telephonic interview using a standardized questionnaire using Kobotoolbox. We calculated the prevalence of depression, anxiety, and stress symptoms. We utilized univariate and multivariate logistic regression to determine their association with the presence of COVID-19 symptoms and comorbid conditions. In multivariate logistic regression, we adjusted sociodemographic factors (age, gender, ethnicity, marital status, monthly family income, education level), smoking status and history of self-treatment. We reported adjusted odds ratios (aOR) with 95% confidence intervals. All analyses were conducted in R (version: 4.0.3). Results The prevalence of depression, anxiety and stress symptoms among home isolated COVID-19 patients were 8.0% (95% CI: 5.5 to 11.1), 11.2% (95% CI:8.3 to 14.7), and 4.0% (95% CI:2.3 to 6.4) respectively. Higher odds of depressive symptoms (aOR:2.79; 95% CI:1.08–7.20, p = 0.03), anxiety symptoms (aOR:3.65; 95%CI:1.57 to 8.52; p = 0.003) and stress (aOR:7.41; 95% CI:1.37 to 39.94; p = 0.02) were associated significantly with presence of COVID-19 symptoms in past week. Higher odds of anxiety symptoms were associated with the presence of comorbid conditions (aOR = 2.79; 95%CI:1.05 to 7.44; p = 0.04). Conclusion Depression, anxiety, and stress symptoms were present in a significant proportion of home isolated COVID-19 patients in western Nepal and positively associated with the presence of COVID-19 symptoms. In this global COVID-19 pandemic, it is important to provide timely counseling to high-risk groups like those with comorbidities and COVID-19 symptoms to maintain a high level of mental health among home isolated COVID-19 patients. Keywords COVID-19 Depressive symptoms Anxiety symptoms Stress Home isolation Nepal ==== Body pmc1 Introduction There is growing evidence that the current COVID-19 pandemic is causing severe mental health effects on people globally [[1], [2], [3], [4]]. Studies conducted globally found a positive association between mental health with the presence of COVID-19 symptoms [5] and comorbid status [6]. The COVID-19 pandemic spread throughout the globe, it instilled significant fear, worry, and concern in the general public, as well as in specific groups such as older individuals, caregivers, and those with underlying health issues [7]. The COVID-19 pandemic is likely to exacerbate social isolation and loneliness which are strongly associated with anxiety, depression, self-harm, and suicide attempts across the lifespan [[8], [9], [10]]. The misinformation from the media, lack of knowledge on COVID-19, lack of effective treatments, and significant economic losses may have resulted in higher levels of depression, anxiety, and stress during the COVID-19 pandemic [11,12]. Anxiety and depression might also be triggered by COVID-19 symptoms such as fever and shortness of breath [13]. Psychological trauma could result in a mixture of emotional surges like nightmares, self-blame, and experiencing recurrent thoughts of trauma [14]. COVID-19 has the potential to cause mental health crises with long-term consequences, particularly in Nepal [15]. The Government of Nepal has reported over 710 thousand COVID-19 cases as of 29th August 2021. About 91.8% of the COVID-19 cases, 36,866 active cases, were isolated at home in Nepal [16]. The impact of the COVID-19 pandemic is huge, and it is already taking a serious toll on the health and economy of the country. In western Nepal, the impact was much higher compared to overall Nepal [17]. The research related to the mental health effects of COVID-19 is limited in Nepal, including the patient's physical well-being. Taking precedence over psychological assessment; this is particularly true in countries like Nepal [18], where infrastructure and psychological screening protocols are severely lacking. To our knowledge, limited studies have determined the depressive symptoms, anxiety symptoms, and stress among home isolated patients infected with COVID-19 in Nepal. Understanding the association between depressive, anxiety, and stress-related symptoms with the presence of COVID-19 symptoms and comorbid status among home isolated COVID-19 patients provides a concrete basis for tailoring and implementing relevant mental health intervention policies [19] to reduce their burden in Nepal. Therefore, we conducted this study to assess mental health status (depressive, anxiety, and stress-related symptoms) and their association with COVID-19 among COVID-19 patients under home isolation in western Nepal. 2 Materials and methods 2.1 Study design and setting We conducted a cross-sectional study among COVID-19 patients under home isolation in the Karnali province of Nepal. Karnali Province is one of the seven provinces of Nepal located in the western part of Nepal. A total of 1,769,788 people are residing in ten districts (Humla, Jumla, Kalikot, Mugu, Surkhet, Dailekh, Salyan, Dolpa, Rukum west and Jajarkot) of Karnali province of Nepal. Karnali Province has a lower human development index (0.469 vs 0.574) compared to overall Nepal [20]. 2.2 Participants recruitment The people who tested positive for COVID-19 in the laboratories of Karnali are reported to the Ministry of Social Development (MoSD), Karnali Province. We coordinated with MoSD of Karnali province to identify the COVID-19 positive patients under home isolation. The MoSD provided a list of identified home isolated every day. We recruited 402 eligible home isolated COVID-19 patients in Karnali province from January to May 2021 until the required sample size was met. The sample size was calculated using Cochran's formula [21] assuming a prevalence of depressive symptoms at 50% (because it was unknown in this setting), 5.0% absolute error, 5% level of significance, and 5.0% non-response rate. The inclusion criteria applied were: a) PCR positive COVID-19 patients who were isolated at home; b) respondents 18 years or older; c) respondents having a working telephone number and d) respondents who were able to talk on the telephone despite the weakness due to COVID-19. We excluded the respondents who did not answer our call. 2.3 Ethical clearance We obtained ethical approval from the Ethical Review Board (ERB) of Nepal Health Research Council (Ref. No: 1597). We obtained written approval to conduct the study in Karnali province from the Ministry of Social Development (MoSD), Karnali province, Nepal. We also obtained the telephone number of COVID-19 patients from the MoSD in Karnali province and provided detailed information about research to each participant via telephone. We used the telephone number just for research purposes and details of the telephone number were discarded after the completion of the research works. We provide detailed information about this study to all home isolated patients. The participants received ample time to think and ask questions (if needed), and we satisfactorily answered their all queries. We obtained verbal consent from each home isolated COVID-19 patient before enrolling them in this study. Due to COVID-19 travel restrictions and isolation policy with COVID-19 active cases, the ERB approved obtaining verbal consent for this study. We maintained confidentiality and anonymity by keeping all data on password-protected computers. 2.4 Data collection We conducted telephone-based interviews with home isolated COVID-19 patients using pretested structured questionnaires and we entered data directly entered into an online form, created in both Nepali and English languages, in the Kobotoolbox platform [22] during telephone-based interviews (Supplementary file 1). In the case of the elderly participants, we interviewed caretakers or family members but for the assessment of depression, anxiety, and stress symptoms we interviewed patients. 2.5 Assessment of depressive symptoms, anxiety symptoms, and stress The Nepali version of validated “Depression Anxiety and Stress Scale-21 (DASS-21)” [23] was used to assess depressive symptoms, anxiety symptoms, and stress. The internal consistency of the DASS-21 Nepali version was 0.77 for DASS-depression; 0.80 for DASS-anxiety; and 0.82 for DASS-stress, which indicates Cronbach's alpha values. The tool has already been tested in Nepal, its psychometric properties validated, and it was found to be simple, easy to administer, and simple to score. It has been used extensively in previous studies globally as well as in Nepal [24]. The DASS-21 scale contains 21 items (7 items each for depression, anxiety, and stress). Each participant is asked to score every item on a scale from 0 to 3, where 0 indicates “did not apply to me at all” and 3 indicates “apply to me at all”. Total scores for depression, anxiety, and stress are calculated by summing the scores for each scale, multiplied by factor two [23]. We classified patients into mild, moderate, severe, and extremely severe conditions of depression, anxiety, and stress based on the total score within each subdomain as follows [23].(a) Depression scores were classified as: normal (0–9), mild (10–13), moderate (14–20), severe (21–27), and extremely severe (27+). (b) Anxiety score was classified as: normal (0–7), mild (8–9), moderate (10–14), severe (15–19), and extremely severe (20+) (c) Stress score was classified as: normal (0–14), mild (15–18), moderate (19–25), severe (26–33), and extremely severe (34+). Depression, anxiety, and stress symptoms were further classified as binary (present/absent) outcomes. They were classified as present if at least mild symptoms were present. 2.6 Assessment of COVID-19 symptoms and comorbid condition 2.6.1 COVID-19 symptoms Symptomatic patients were those who reported at least one of the following COVID-19 symptoms in the past one week: fever, cough, sore throat, runny nose, headache, difficulty in breathing, loss of taste, loss of smell, vomiting, diarrhea, muscular pain, abdominal pain, joint pain, chest pain, and irritability/confusion [25]. 2.6.2 Comorbid condition We collected self-reported presence of the following conditions: hypertension, diabetes, heart diseases, thyroid dysfunction, chronic lung disease, chronic renal disease, chronic liver disease, malignancy, pregnancy, post-partum, and immuno-compromised conditions. Those with at least one of the above conditions were categorized as having comorbidity [26]. 2.6.3 Assessment of confounding variables Confounding variables as listed below were selected based on the literature review [27,28]. Socio-demographic characteristics included age (in years), sex (male/female), ethnicity (brahmin and chhetri/adhibasi and janajati/tamang, sherpa, rai, limbu, giri and puri), marital status (married/not married), number of formal years of education (in years), family type (joint/ nuclear), monthly family income in Nepalese Rupees (NRs). Behavior characteristics included smoking (never smoker/past smoker/current smoker) and alcohol use in the past month (yes/no) and self-treatment(yes/no). Participants were considered to have self-treatment if they used either traditional methods (like turmeric water, “gurjo” Tinospora cordifolia, steam water) or allopathic drugs (like paracetamol) to relieve the symptoms [29]. 2.7 Statistical analysis The participants' characteristics were presented in frequency and proportion for categorical variables; mean and standard deviation for normally distributed numerical variables; and median (interquartile range) for non-normally distributed numerical variables. The Clopper-Pearson [30] and Goodman method [31] were used to determine confidence intervals (CI) around binomial and multinomial variables respectively. We utilized a univariate and multivariate logistic regression analysis to determine association of depressive, anxiety and stress related symptoms with COVID-19 symptoms. The comorbid conditions were also considered. In the multivariate model, the association of depressive, anxiety and stress related symptoms with COVID-19 symptoms and comorbid condition was determined after adjusting for sociodemographic variables (age, gender, ethnicity, marital status, poverty level, education level), smoking status and home remedy. Crude odds ratio (cOR) and adjusted odds ratio (aOR) with 95% CI were presented. We analyzed data using R-programming software (R-version:4.0.3) [32]. 3 Results We approached 521 home isolated COVID-19 patients via telephone, of which 409 (78.5%) responded to our survey and 402 (98.3%) were eligible in the study. Table 1 presents the characteristics of surveyed participants. The age of the participants ranged from 18 to 87 years with a mean age of 36.7 (SD) where about two-thirds were male. The majority were Brahmin and Chhetri (77.4%), followed by Adibasi/Janajati (8.5%). The mean monthly income of the participants was NRs. 44,311.0 ± 34,983.1. The majority of the home isolated patients (91.0%) were non-smokers and 0.7% consumed alcohol in the past one week. About 42.0% experienced COVID-19 related symptoms in the past week and 309 (76.9%) participants were using homemade remedies.Table 1 Characteristics of the participants (n = 402). Table 1Characteristics (n = 402) n (%) Sex Male 271 (67.4) Female 131 (32.6) Age(in years), Mean ± SD 36.7 ± 12.8 min: 18, max: 87 Education (in years) 12.2 ± 3.7 min: 0, max: 19 Ethnicity Brahmin and Chhetri 311 (77.4) Adhibasi/Janjati 34 (8.5) Other (Tamang, Rai, Gurung) 57 (14.2) Marital status Married 296 (74.8) Not married 106 (26.4) Type of Family Nuclear 335 (83.3) Joint 67 (16.7) Monthly family income (NRs), Mean ± SD 44,311.0 ± 34,983.1 minimum: 2000.0 maximum: 300000.0 Presence of Comorbidities 42 (10.4) Presence of COVID-19 symptoms 167 (41.5) Self-treatment 309 (76.9) Smoking Never smoker 366 (91.0) Current smoker 20 (5.0) Past smoker 16 (4.0) Alcohol use within past one month 3 (0.7) n: frequency, min: minimum; max: maximum, SD: standard deviation; NRs: Nepalese Rupees. Comorbidities: hypertension, diabetes, heart diseases, thyroid dysfunction, chronic lung disease, chronic renal disease, chronic liver disease, malignancy, pregnancy, post-partum, and immuno-compromised conditions. COVID-19 symptoms: fever, cough, sore throat, runny nose, headache, difficulty in breathing, loss of taste, loss of smell, vomiting, diarrhoea, muscular pain, abdominal pain, joint pain, chest pain, and irritability/confusion. Fig. 1 shows the prevalence of different levels of the severity of depression, anxiety, and stress symptoms among home isolated COVID-19 patients of Karnali province. Of the total participants, 8.0% (95% CI: 5.5 to 11.1) had depressive symptoms, 11.2% (95% CI: 8.3 to 14.7) had anxiety symptoms and 4.0% (95% CI: 2.3 to 6.4) had stress related symptoms.Fig. 1 Prevalence of depressive symptoms, anxiety symptoms, and stress(n = 402). Fig. 1 Table 2 shows the univariate and multivariate logistic regression to determine association of presence of comorbidity and COVID-19 symptoms with depressive symptoms among home isolated COVID-19 patients in Karnali province. Univariate analysis showed significant positive association of depressive symptoms with both presence of comorbid condition (cOR = 8.05 (95% CI: 3.61 to 17.92); p < 0.01) and COVID-19 symptoms (cOR = 4.01 (95% CI: 1.80 to 8.91); p < 0.01). Symptomatic patients had twice higher odds of having depressive symptoms compared to those who were asymptomatic (aOR:2.86; 95% CI:1.10 to 7.44, p-value:0.03) after adjusting age, gender, monthly family income, marital status, type of family, ethnicity, education level, presence of the comorbid condition, smoking status, and home remedy.Table 2 Factors associated with depressive symptoms among home isolated COVID-19 patients Karnali province (n = 402). Table 2Variables Depressive symptoms n(%) cOR (95% CI) p-value aOR (95%CI)⁎ p-value Presence of Comorbidities Absent 19(5.3) 1 1 Present 13(31.0) 8.05 (3.61–17.92) <0.01 2.96 (0.99–8.85) 0.05 Presence of COVID-19 symptoms No 9(3.8) 1 1 Yes 23(13.8) 4.01((1.80–8.91) <0.01 2.86 (1.10–7.44) 0.03 n: frequency; cOR: crude Odds Ratio; aOR: adjusted Odds Ratio; CI: Confidence interval. Bold indicates statistically significant at 95%CI. ⁎ Adjusting variables: age, gender, monthly income, marital status, family type, ethnicity, education level, smoking status, and home remedy. Table 3 shows the univariate and multivariate logistic regression to determine association of presence of comorbidity and symptoms with anxiety symptoms among home isolated COVID-19 patients in Karnali province. In univariate analysis and multivariate analysis, anxiety symptoms were positively associated with presence of comorbidity and COVID-19 symptoms. The participants with at least one COVID-19 symptoms in the past week were four times more likely (aOR:3.81; 95%CI: 1.62–8.93; p-value:<0.01) to have anxiety symptoms compared to those without symptoms and participants having comorbid conditions were three times more likely (aOR:2.92; 95%CI: 1.09–7.80; p-value: 0.03) to have anxiety symptoms after adjusting for age, gender, monthly family income, marital status, ethnicity, education level, smoking status and use of the home remedy.Table 3 Factors associated with anxiety symptoms among home isolated COVID-19 patients Karnali province(n = 402). Table 3Variables Anxiety symptoms n (%) cOR (95% CI) p-value aOR(95%CI) ⁎ p-value Comorbidities Absent 27(7.5) 1 1 Present 18(42.9) 9.25(4.47–19.12) <0.01 2.92(1.09–7.80) 0.03 COVID-19 symptoms No 13(5.5) 1 1 Yes 32(19.2) 4.05(2.05–7.98) <0.01 3.81(1.62–8.93) <0.01 n: frequency; cOR: crude Odds Ratio; aOR: adjusted Odds Ratio; CI: Confidence interval. Bold indicates statistically significant at 95%CI. ⁎ Adjusting variables: age, gender, monthly family income, marital status, type of family, ethnicity, education level, smoking status and self-treatment. Table 4 shows the univariate and multivariate logistic regression to determine association of presence of comorbidity and symptoms with stress among home isolated COVID-19 patients in Karnali province. In univariate analysis, stress was positively associated with presence of comorbidity and symptoms. The participants with at least one COVID-19 symptoms in the past week were 7.78 times more likely (aOR:7.78; 95%CI: 1.43–42.28; p-value:<0.02) to have stress compared to those without symptoms after adjusting for age, gender, monthly family income, marital status, ethnicity, education level, smoking status, and use of the home remedy.Table 4 Factors associated with stress among home isolated COVID-19 patients in Karnali province (n = 402). Table 4Variables Stress n(%) cOR (95% CI) p-value aOR(95%CI) ⁎ p-value Comorbidities Absent 10(2.8) 1 1 Present 6(14.3) 5.83(2.00–16.98) 0.01 1.22(0.24–6.26) 0.81 COVID-19 symptoms No 2(5.5) 1 1 Yes 14(19.2) 10.66(2.39–47.56) <0.01 7.78 (1.43–42.28) 0.02 n: frequency; cOR: crude Odds Ratio; aOR: adjusted Odds Ratio; CI: Confidence interval. Bold indicates statistically significant at 95%CI. ⁎ Adjusting variables: age, gender, monthly family income, marital status, type of family, ethnicity, education level, smoking status, and self-treatment. 4 Discussion Our results showed that depressive, anxiety, and stress related symptoms were prevalent among the home isolated patients of Karnali province. The most common was anxiety symptoms affecting 11.0% of the study population, followed by depression affecting 8.0% and stress affecting 4% of the study population. Depressive, anxiety, and stress were positively associated with the presence of COVID-19 symptoms. In addition, anxiety related symptoms were positively associated with the presence of comorbid conditions. The prevalence of depressive and anxiety symptoms were higher in home isolated COVID-19 patients in Nepal, compared to the general population. The prevalence of depression and anxiety is estimated to be 3.2% and 3.6%, respectively [33]. A study conducted among fever clinic attendants in Nepal reported a similar prevalence of depression anxiety and stress - 7.0%, 14.0%, and 5.0%, respectively [15]. This higher prevalence might be attributable to experience due to COVID-19 infection [34]. The SARS-CoV-2 (COVID-19) pandemic has resulted in increased levels of anxiety, depression, and stress around the globe because of different factors like lockdown, grief, survivor guilt, unemployment, insecure employment and economic loss [34]. The prevalence of depressive symptoms, anxiety symptoms, and stress were lower in our study compared to the COVID-19 patients in other countries. A study conducted among hospitalized and home isolated COVID-19 patients in Sharkia Governorate, Egypt reported the prevalence of depression and anxiety reported to be 69.7% and 32.6% respectively among home isolated COVID-19 patients [35]. Similarly, a study conducted in Iran reported the prevalence of “extremely severe” depression, anxiety and stress symptoms to be 54.3% and 97.3%, 46.6% among positive patients [36]. According to a meta-analysis, the pooled prevalence of depressive and anxiety symptoms among COVID-19 patients were 45% (95% CI: 37–54%), and 47% (95% CI: 37–57%) respectively [37] which was higher compared to the findings of our study among home isolated COVID-19 patients [37]. This vast variance in prevalence might be explained by differences in study participants, circumstances, or the use of different definitions and tools in the studies. In our study, the presence of COVID-19 symptoms was associated with the increased odds of depressive symptoms, anxiety symptoms, and stress. A cohort study in a Brazilian city showed a positive association of COVID-19 symptoms with depression, anxiety, and post-traumatic stress [38]. A study showed that anxiety and depression might be triggered by COVID-19 related symptoms such as fever and shortness of breath [13]. In our study, anxiety symptoms and stress were found to be associated with having comorbidity, which may be because of awareness among the participants that comorbid patients have a higher risk of mortality due to COVID-19 [39,40] which contributed to their fear of morbidity and mortality thus resulting in stress, anxiety symptoms, and depressive symptoms [34]. Our study has several strengths. This is one of the first studies reporting the prevalence and factors associated with depressive symptoms, anxiety symptoms, and stress among home isolated COVID-19 patients in Nepal. We used a pre-tested validated standard Nepali-translated DASS-21 scale to estimate depressive symptoms, anxiety symptoms and stress levels among participants of the study. The collected data were cross-checked for cleanliness and errors at the end of every week. However, there were some limitations. First, we could not establish the directionality of the risk associations because of the cross-sectional nature of the study. Second, generalizability of the study is limited to western Nepal rather than overall Nepal. Third, comorbidity status of the patients was self-reported which may have been underestimated since some may be undiagnosed comorbid conditions. Self-reported symptomatic status of home isolated patients may have been underestimated because of risk of recall bias. Fourth, pre-existing mental health status like depression, anxiety, and stress was not assessed. Finally, we could not measure and adjust childhood adversity, social support, perceived stress, perceived stigma and occupation-related factors like job insecurity and family work balance that can potentially affect mental health resulting in depressive symptoms, anxiety symptoms, and stress [41]. 5 Conclusion The prevalence of depressive symptoms, anxiety symptoms, and stress among home isolated COVID-19 patients of Karnali province were common. They were positively associated with the presence of COVID-19 symptoms whereas anxiety symptoms with comorbid status. One key implication of this study is that, in this global COVID-19 pandemic, Nepal government should provide timely counseling and psychological support to those who are already affected with depressive symptoms, anxiety symptoms and stress; and those under high-risk like those with comorbidities and COVID-19 symptoms to maintain a high level of mental health among home isolated COVID-19 patients. Funding This study was conducted from a project entitled “COVID-19: Strengthening Provincial Level COVID Response in Nepal” which was funded by the Bill Gates and Melinda Gates foundation (INV-021518). Data availability statement All data are fully available without restriction. Declaration of Competing Interest We/ Authors declare no conflict of interests. Appendix A Supplementary data Supplementary material Image 1 Acknowledgment We would like to acknowledge the Ministry of Social Development of Karnali Province, all the participants of the study, and all those who directly or indirectly helped us for the successful completion of this study. Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.dialog.2022.100090. ==== Refs References 1 Torales J. O'Higgins M. Castaldelli-Maia J.M. Ventriglio A. The outbreak of COVID-19 coronavirus and its impact on global mental health Int J Soc Psychiatry 2020 317 320 10.1177/0020764020915212 32233719 2 Zandifar A. Badrfam R. Yazdani S. Arzaghi S.M. Rahimi F. Ghasemi S. Prevalence and severity of depression, anxiety, stress and perceived stress in hospitalized patients with COVID-19 J Diabetes Metab Disord 2020 1 8 10.1007/s40200-020-00667-1 3 Huang Y. Zhao N. 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==== Front Int J Infect Dis Int J Infect Dis International Journal of Infectious Diseases 1201-9712 1878-3511 The Author(s). Published by Elsevier Ltd on behalf of International Society for Infectious Diseases. S1201-9712(22)00633-6 10.1016/j.ijid.2022.11.041 Article Serum immunoglobulin G and mucosal immunoglobulin A antibodies from prepandemic samples collected in Kilifi, Kenya, neutralize SARS-CoV-2 in vitro Nyagwange James 1⁎ Kutima Bernadette 1 Mwai Kennedy 12 Karanja Henry K. 1 Gitonga John N. 1 Mugo Daisy 1 Sein Yiakon 1 Wright Daniel 3 Omuoyo Donwilliams O. 1 Nyiro Joyce U. 1 Tuju James 1 Nokes D. James 145 Agweyu Ambrose 1 Bejon Philip 13 Ochola-Oyier Lynette I. 1 Scott J． Anthony G. 136 Lambe Teresa 3 Nduati Eunice 1 Agoti Charles 1 Warimwe George M. 13 1 KEMRI-Wellcome Trust Research Programme,PO Box 230, Kilifi, Kenya 2 School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, 27 St Andrews Road, Parktown 2193, Johannesburg, South Africa 3 Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, United Kingdom 4 The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry, CV4 7AL, United Kingdom 5 School of Life Sciences, University of Warwick, Coventry CV4 7AL, United Kingdom 6 Department of Infectious Diseases Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street WC1E 7HT, London, United Kingdom ⁎ Corresponding author. 5 12 2022 2 2023 5 12 2022 127 1116 6 9 2022 17 11 2022 30 11 2022 © 2022 The Author(s) 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Objectives Many regions of Africa have experienced lower COVID-19 morbidity and mortality than Europe. Pre-existing humoral responses to endemic human coronaviruses (HCoV) may cross-protect against SARS-CoV-2. We investigated the neutralizing capacity of SARS-CoV-2 spike reactive and nonreactive immunoglobulin (Ig)G and IgA antibodies in prepandemic samples. Methods To investigate the presence of pre-existing immunity, we performed enzyme-linked immunosorbent assay using spike antigens from reference SARS-CoV-2, HCoV HKU1, OC43, NL63, and 229E using prepandemic samples from Kilifi in coastal Kenya. In addition, we performed neutralization assays using pseudotyped reference SARS-CoV-2 to determine the functionality of the identified reactive antibodies. Results We demonstrate the presence of HCoV serum IgG and mucosal IgA antibodies, which cross-react with the SARS-CoV-2 spike. We show pseudotyped reference SARS-CoV-2 neutralization by prepandemic serum, with a mean infective dose 50 of 1: 251, which is 10-fold less than that of the pooled convalescent sera from patients with COVID-19 but still within predicted protection levels. The prepandemic naso-oropharyngeal fluid neutralized pseudo-SARS-CoV-2 at a mean infective dose 50 of 1: 5.9 in the neutralization assay. Conclusion Our data provide evidence for pre-existing functional humoral responses to SARS-CoV-2 in Kilifi, coastal Kenya and adds to data showing pre-existing immunity for COVID-19 from other regions. Keywords SARS-CoV-2 Human coronaviruses Pre-existing antibodies Spike proteins ==== Body pmcIntroduction SARS-CoV-2 emerged in 2019 and has caused morbidity, mortality, and disruptions in the global economy [1]. SARS-CoV-2 is a single-stranded RNA betacoronavirus in the Coronaviridae family that includes four human endemic coronaviruses (HCoVs): two betacoronaviruses, HCoV-OC43 and HCoV-HKU1, and two alphacoronaviruses, HCoV-NL63 and HCoV-229E, which are all associated with mild forms of respiratory infections; although, they can lead to severe disease in individuals with compromised immunity [2,3]. HCoVs are endemic in the human population and may be responsible for prepandemic SARS-CoV-2 cross-reactive T cell immunity and humoral immunity [4], [5], [6], [7], [8]. Pre-existing HCoV antibodies cross-reactive to SARS-CoV-2 are of great importance to COVID-19 progression and they have been reported in most settings as providing protection against COVID-19 [7,9,10] and in a few settings, as increasing COVID-19 pathogenesis possibly through the original antigenic sin phenomenon [11]. We recently reported about 10% spike reactive prepandemic serum at 1: 800 dilution in blood donors [12], and in the current study, we aimed to investigate the spike reactive prepandemic serum at lower dilutions in detail. We tested prepandemic antibodies in serum and naso-oropharyngeal (NP/OP) fluid collected in Kilifi, Kenya for HCoV binding and SARS-CoV-2 neutralization. Methods Study samples The prepandemic serum samples (adults, n = 195 and children aged ≤15 years, n = 431) were from biobanked KEMRI-CGMRC annual cross-sectional surveys for malaria surveillance in coastal Kenya in 2018. The positive control was a pool of serum from 50 Kenyan adults with COVID-19 symptoms and SARS-CoV-2 reverse transcriptase-polymerase chain reaction (RT-PCR)-confirmed NP/OP samples. The prepandemic NP/OP samples (n = 786) were obtained from biobanked human NP/OP samples collected from study participants between 2015 and 2017 in the Kilifi Health and Demographic Surveillance System. The pandemic NP/OP samples set (n = 1115) were SARS-CoV-2 RT-PCR test samples performed at the KEMRI-CGMRC between March and July 2020. Samples from both populations were collected using similar flocked nasopharyngeal swabs. Recombinant antigens production We have recently described the production and purification of full-length SARS-CoV-2 spike protein in the mammalian expression system [12]. Recombinant spike antigens to HCoV HKU1, OC43, NL63, and 229E were stabilized as described previously [13], codon-optimized for mammalian expression, and His-tagged for purification. The constructs for the antigens were ordered from GeneArt, and the plasmids were made and transfected in mammalian cells using Expifectamine (ThermoFisher, A14525) according to the manufacturer's protocol. Enzyme-linked immunosorbent assay (ELISA) The immunoglobulin (Ig)G assays were performed as described previously [12]. For the IgA, Maxisorp NUNC-immuno flat-bottomed 96-well plates (Thermo Scientific) were coated with 2 µg/ml of spike antigens of the four endemic coronaviruses and SARS-CoV-2 at 37°C for 1 hour, then washed three times in 0.1% Tween 20 (Sigma) and once in phosphate-buffered saline (Sigma), placed in wash buffer, and blocked with Blocker™ Casein (Thermo Fisher) for 1 hour. Human NP/OP in viral transport media were heat-inactivated for 1 hour at 56°C, and samples were diluted 1: 1 in Blocker™ Casein, added to both receptor binding domain and spike-coated plates, and incubated for 2 hours at room temperature. After washing four times with wash buffer, a 1: 1000 dilution of horseradish peroxidase-conjugated goat antihuman IgA antibody (Sigma) in wash buffer was added to plates, incubated for 1 hour at room temperature, washed, and added with o-phenylenediamine dihydrochloride substrate (Sigma) for color development for 10 minutes. Plates were read on an Infinite® 200 PRO microplate reader (TECAN) at 492 nm, and optical density (OD) values for each sample acquired for analysis. For SARS-CoV-2, IgG seropositivity was defined as a sample OD greater than two times the negative OD. For the HCoV assays, negative OD was defined as three times the blank OD. The cut-offs were defined following a validation exercise during the development of the ELISA, with 174 SARS-CoV-2 PCR-positive Kenyan adults and a panel of sera from the UK National Institute of Biological Standards and Control (NIBSC) and 910 serum samples from Kilifi drawn in 2018, prepandemic [12]. In the World Health Organization-sponsored multilaboratory study of SARS-CoV-2 antibody assays, our results were consistent with the majority of the test laboratories [14]. Pseudo-neutralization assay We adapted a lentivirus-based SARS-CoV-2 pseudovirus assay, developed by the Craig laboratory, with minor modifications [15]. Under biosafety level 2 laboratory (BSL2) conditions, the three plasmids, coding the murine leukemia virus (MLV), MLV-gag/pol backbone, luciferase, and full-length spike protein were co-transfected into HEK293T cells using polyethylenimine (PEI) (Polysciences, 24765-1) to produce single round of infection competent pseudoviruses. The medium was changed 24 hours after transfection, and the supernatant containing MLV-pseudotyped viral particles was collected 72 hours after transfection, aliquoted, and frozen at -80°C for the neutralization assay. Virus infectivity was determined by titration on HeLa angiotensin-converting enzyme (ACE2) stable cells as described before [16], and the dilution of pseudoviruses giving >20,000 relative light units (RLU) was selected for assaying. To test for neutralization of the cross-reactive antibodies, we selected the 30 highest responders and 15 of the lowest responders of SARS-CoV-2 spike protein in both serum and NP/OP ELISA. All serum and NP/OP samples were heat-inactivated at 56°C for 1 hour. In sterile 96-well plates (Corning, 353077), 50 μl of the virus was immediately mixed with 50 μl of serially diluted (2 ×) serum or NP/OP, starting at 1: 50 and 1: 1 dilution, respectively, and incubated for 1 hour at 37°C to allow antibody neutralization of the pseudotyped virus. In all, 10,000 HeLa-ACE2 cells/well (in 100 μl of media containing 20 μg/ml dextran) were directly added to the antibody-virus mixture. Plates were incubated at 37°C for 72 hours. After the infection, HeLa-ACE2 cells were lysed using lysis buffer (25 mM glycylglycine pH 7.8, 15 mM MgSO4, 4 mM EGTA, 1% Triton X-100, Promega, E2661). Luciferase intensity was then read on a luminometer with luciferase substrate according to the manufacturer's instructions (Promega, E2650). The percentage of neutralization was calculated using the following equation: 100 × (1 – [RLU of sample – average RLU of background/average of RLU of probe alone – average RLU of background]), where background was the cell only control and probe was the virus and cells without serum or NP/OP. As a positive assay control for seroneutralization, a pool of convalescent serum from 50 individuals with confirmed COVID-19 was included. As part of validating the pseudovirus assay, 21 SARS-CoV-2 PCR-positive Kenyan adults and a panel of sera from the UK NIBSC, and 30 serum samples from Kilifi drawn in 2018 and nonreactive to SARS-CoV-2 spike were analyzed with expected results (Supplementary Figure S1). Statistical analysis Data analysis was conducted using R v4.1.0. ELISA responses were compared using Student's t-test and Wilcoxon signed rank test. Data were considered statistically significant at P <0.05, P <0.01, P <0.001, **P 0.0001, and not significant. To estimate the inhibitory dilution 50(ID50), the dilution curves were fit to each sample and the mean of each group, with the neutralization percentage modeled using a five-parameter log-logistic function of the dilution factor based on the Reed-Muench method [17]. This yielded an ID50 value for each sample and group, where the curves were fit using drc package v3.0-1 in R v4.1.0 [18]. Samples that did not show a dilution response because of no neutralization were not assigned an ID50 value. Results SARS-CoV-2 spike reactive IgG antibodies were found in 93/220 (42.3%) Kenyan prepandemic serum samples at 1: 100 of dilution, but these levels reduced with increasing dilutions, and at 1: 800 dilution, only 5/220 (2.5%) were above our positivity cut-off (Figure 1 a). Furthermore, there were pre-existing IgA antibodies reactive to SARS-CoV-2 spike in prepandemic NP/OP samples at similar levels to those in NP/OP samples collected from patients with positive PCR results for SARS-CoV-2 on diagnostic testing (Figure 2 a).Figure 1 Reactivity of prepandemic and COVID-19 serum to coronaviruses spike and to SARS-CoV-2 pseudo-type. (a) Enzyme-linked immunosorbent assay to SARS-CoV-2 spike antigen with prepandemic human serum (n = 220) showing high cross-reactivity which decreases with increasing fold dilutions. Dotted line shows cut-off for positivity. (b) Enzyme-linked immunosorbent assay to HCoV spike antigens with the prepandemic human serum showing responses among adults (n = 195) and children (n = 431). There were significantly higher responses in adults than children with all HCoV spike antigens except HCoV-NL63 spike. (c) Pseudotyped SARS-CoV-2 neutralization using the selected SARS-CoV-2 spike (S2) reactive IgG (n = 30) and nonreactive IgG (n = 15) samples are shown. There was neutralization with the S2-reactive samples, mean ID50 of 1:251 compared with mean ID50 of 1: 2461 of COVID-19 pooled (C19 pool) convalescent serum used as assay-positive control but no neutralization with S2-nonreactive IgG samples. (d) There were significantly higher IgG responses for HCoV-HKU1 and OC43 for S2-reactive than S2-nonreactive sera but no significant difference for NL63 and 229E. HCoV, human coronaviruses; Ig, immunoglobulin; ns, not significant; OD,optical density. Figure 1 Figure 2 Reactivity of prepandemic and COVID-19 naso-oropharyngeal swabs to coronaviruses spike and to SARS-CoV-2 pseudo-type. (a) Enzyme-linked immunosorbent assay to SARS-CoV-2 spike antigen with prepandemic and pandemic nasopharyngeal swabs with SARS-CoV-2-positive reverse transcriptase-polymerase chain reaction result showing high cross-reactivity with no significant difference between the two sample sets. (b) Pseudotyped SARS-CoV-2 neutralization using the selected SARS-CoV-2 spike (S2) reactive IgA (n = 30) and nonreactive IgA (n = 15) samples are shown. There was neutralization with the S2-reactive samples, mean ID50 of 1:5.9 but no neutralization with nonreactive IgA (n = 15) samples. (c) There were also significantly higher responses of S2-reactive IgA samples than the S2-nonreactive IgA samples among the four endemic HCoV. HCoV, human coronaviruses; Ig, immunoglobulin; ns, not significant; OD, optical density. Figure 2 We hypothesized that the apparent prepandemic immunity was driven by the presence of HCoV antibodies in these samples, and that the responses would be stronger toward the closely related betacoronaviruses (HKU1 and OC43) than the alphacoronaviruses (NL63 and 229E). To investigate this, we designed constructs for full-length trimeric spike proteins for HCoV HKU1, OC43, 229E, and NL63 as described previously [13] and expressed the proteins in mammalian cells and confirmed expression of the specific recombinant proteins on SDS-PAGE and Western blot (Supplementary Figure S2a). We developed ELISAs using the recombinant antigens and validated the responses using convalescent serum from seven individuals with specific RT-PCR-confirmed HCoV infections (Supplementary Figure S2b). IgG antibodies to the infecting HCoV were detected with strong OD responses by ELISA (log of the area under the curve >10); although, there were additional responses at lower levels (log of area under the curve <10) directed at other HCoV not detected by PCR (Supplementary Figure S2b). Next, we investigated the presence of the HCoV in 626 prepandemic serum samples and found IgG antibodies to all the four HCoV, and three of the four HCoV had significantly higher responses in adults than in children (P <0.001; Figure 1b). To investigate the neutralization functions of the pre-existing cross-reactive IgG and IgA antibodies, we determined the median response and designated samples above the median as “SARS-CoV-2-S-reactive” and below the median as “SARS-CoV-2-S-nonreactive”. We then randomly selected 30 SARS-CoV-2-S-reactive IgG samples and 30 SARS-CoV-2-S-reactive IgA samples. As controls, we randomly selected 15 SARS-CoV-2-S-nonreactive IgG samples and 15 SARS-CoV-2-S-nonreactive IgA samples and performed a neutralization assay using the wild-type pseudo-SARS-CoV-2. Of the 30 SARS-CoV-2-S-reactive IgG samples, 29 exhibited neutralizing activity against pseudo-SARS-CoV-2, with a mean ID50 of 1: 251, whereas all the 15 SARS-CoV-2-S-nonreactive IgG samples showed no neutralizing activity (Figure 1c). Compared with a pool of 50 convalescent serum collected from individuals with confirmed COVID-19 (ID50 of 1: 2461), their neutralizing titers were about 10-fold less (Figure 1c). Khoury et al. [19] have estimated the 50% protective neutralization titer of most of the SARS-CoV-2 convalescent serum to be between 1: 10 and 1: 1200 in in vitro neutralization experiments, suggesting that the titers observed in our study could be protective. To further establish whether these antibodies could be protective, we determined the levels of SARS-CoV-2 binding IgG antibodies by normalizing both the reactive and nonreactive SARS-CoV-2 IgG binding antibodies using the World Health Organization standard, NIBSC 20/136. We found that 5/30 (16.7%) of the SARS-CoV-2-S-reactive IgG samples had greater than the 60-154 binding antibody units/ml suggested to be protective for IgG binding antibodies [20], whereas 23/30 (76.7%) had levels considered SARS-CoV-2-seropositive according to the positivity (>32 binding antibody units/ml) threshold suggested by Chibwana et al. [21] (Supplementary Figure S3). One of the 15 SARS-CoV-2-S-nonreactive IgG samples was seropositive, but none reached the protective threshold. Interestingly, when we compared the ELISA responses between the 30 SARS-CoV-2-S-reactive IgG and 15 SARS-CoV-2-S-nonreactive IgG samples, only HKU1 (P <0.001) and OC43 (P <0.01) had significantly different responses, implying that the neutralization of SARS-CoV-2 was mainly associated with these two HCoVs in serum, which are both betacoronaviruses as SARS-CoV-2 (Figure 1d). In contrast, there were significant differences between 30 SARS-CoV-2-S-reactive IgA samples and 15 SARS-CoV-2-S-nonreactive IgA samples among the four HCoVs, implying a nonbetacoronaviruses-specific neutralization effect (Figure 2c). However, 28/30 (93.3%) of SARS-CoV-2-S-reactive IgA samples exhibited neutralizing activity to the reference pseudo-SARS-CoV-2 virus, with mean ID50 of 1: 5.9, whereas the SARS-CoV-2-S-nonreactive IgA samples did not neutralize (Figure 2b). Discussion COVID-19 morbidity and mortality has been surprisingly low in sub-Saharan Africa (SSA) compared with the rest of the world, despite the burden of infectious diseases, malnutrition, and insufficient health care [22]. The low burden has been variously hypothesized to be due to Africa's favorable weather; timely mitigation measures; younger population structure; high exposure to infectious diseases, such as malaria, resulting in immune priming and production of protective cross-reactive T cells and antibodies from bacteria and endemic HCoVs, such as HKU1, OC43, NL63, and 229E [22,23]. We have previously reported SARS-CoV-2 spike reactive antibodies in prepandemic serum in a section of our in-house ELISA validation panel [12]; here, we report the presence of SARS-CoV-2 neutralizing serum IgG (mean ID50 of 1: 251) and mucosal IgA (mean ID50 of 1: 5.9) antibodies reactive to HCoV spike proteins in prepandemic samples. Consistent with our IgG data, Ng et al. [7] reported HCoV-induced IgG antibodies capable of neutralizing SARS-CoV-2 in the prepandemic samples from the UK, with neutralizing titers ranging from 1: 100 to 1: 3000 dilution. In contrast to our study, Ng et al. [7] observed higher cross-reactive antibodies in children (21/48 [44%]) than in adults (16/302 [5.3%]) than the children (19/95 [20%]) and adults (74/125 [59.2%]) in our study. Some studies have reported similar findings as our study and attributed the results to continued boosting after reinfection and provided an explanation to better protection in children as not the high levels of mature class-switched IgG and IgA antibodies but higher levels of immature HCoV IgM, which are more adaptable in antigen recognition and fragment crystallizable (Fc) responses [24,25]. However, there are several studies with contrasting data on HCoV antibody levels in adults versus children; age could therefore be a confounder [24,25]. Nevertheless, neutralization by prepandemic sera was attributed to antibodies against antigenic epitopes conserved within the spike S2 subunit of SARS-CoV-2 and HCoV, especially HKU1 and OC43 [7]. SARS-CoV-2 neutralizing antibodies (ID50 ranging from 1: 10 to 1: 100) in the prepandemic sera, targeting both S1 and receptor binding domain have also been reported in children and adults in the United Kingdom and illustrates these as additional targets for cross-neutralization [26]. These studies suggest protective role of pre-existing HCoV immunity to the clinical course of COVID-19 after SARS-CoV-2 infection, and this might be the case in our population; although, the 42.3% prevalence of cross-reactive antibodies does not fully account for the 92.4% asymptomatic individuals observed in our population [27], implying that other factors contribute [22]. Notably, Tso et al. [28] have reported a higher prevalence of HCoV in SSA than in the United States and associated the lower mortality and morbidity observed in SSA with prepandemic HCoV serological cross-reactivity. Apart from humoral immunity, SARS-CoV-2-specific T cells from prepandemic individuals have been reported to cross-react with sequences from endemic coronaviruses, plasmodium, and commensal bacteria, implying that the latter may also contribute to the protective properties of the prepandemic samples [23,29]. In contrast, a recent study involving hospitalized patients with COVID-19 associated pre-existing HCoV antibodies with severe and fatal outcomes of COVID-19 and attributed the effect to the original antigenic sin phenomenon [11]. However, the study included only hospitalized patients sampled at a single time point, making it impossible to determine the level of the previous HCoV immunity [11]. In fact, a 7-month longitudinal cohort involving asymptomatic and participants with mild/moderate symptoms showed that a previous HCoV exposure had a protective effect against SARS-CoV-2 infection and disease [30]. Nevertheless, knowing the duration of HCoV protective immunity to SARS-CoV-2 infection and COVID-19 will be the key to the understanding of the role of HCoV on COVID-19 epidemiology and pathology at the population level. Prepandemic breast milk IgA antibodies binding to both SARS-CoV-2 and HCoV spike proteins have been reported in mothers in Uganda and the United States [31], but to the best of our knowledge, our study is the first report of neutralizing mucosal IgA antibodies to SARS-CoV-2 in prepandemic NP/OP samples. However, neutralizing mucosal IgA antibodies after SARS-CoV-2 infection have been reported elsewhere, with better neutralizing capacities than monomeric IgA and IgG in the circulation and providing heterologous protection [32,33]. Apart from acting at the primary SARS-CoV-2 invasion sites, mucosal IgA exists in a dimeric form, which has a better antigen binding capacities and can perform both nonspecific (immune exclusion) and specific neutralization and Fc-mediated immune functions [33,34]. We have reported results of mucosal IgA and serum IgG in prepandemic samples from two distinct populations. It would have been better to compare the two antibody classes in the corresponding samples. Therefore, in the absence of corresponding samples in these retrospective samples, our study is limited in drawing inferences from the two populations about the likely behavior of the two classes of antibodies. Furthermore, failure to measure antibodies to SARS and Middle East respiratory syndrome (MERS), presents another limitation because the antibodies are also cross-reactive to SARS-CoV-2 and may contribute a proportion of the responses we have observed. However, SARS and MERS are rare in our setting and therefore widespread responses are unexpected [35,36]. Nevertheless, teasing out the virus-specific responses from a mixture of antibodies would require adsorption of the antibodies with purified spike antigens from the specific coronaviruses, which we have not performed in the current study due to the limited quantities of the retrospective samples. Overall, our data provide evidence of functional cross-reactive antibodies in prepandemic samples from an African population and suggests an additional explanation for why members of this population appear to be less susceptible to severe COVID-19 disease. A full understanding would need a direct comparison to samples from other geographic locations and longitudinal studies measuring HCoV antibodies before SARS-CoV-2 infection and follow-up of the individuals through the pandemic to estimate the percentage of those who were infected with SARS-CoV-2, percentage of those who were sick and admitted to hospital, and percentage of those who died. Declaration of Competing Interest The authors have no competing interests to declare. Appendix Supplementary materials Image, application 1 Funding This research was funded in whole or in part by the Wellcome Trust (grants 220991/Z/20/Z and 203077/Z/16/Z, SRF214320). For the purpose of Open Access, the author has applied a CC-BY public copyright license to any author accepted manuscript version arising from this submission. Ethical approval This study was approved by the Scientific and Ethics Review Unit of the Kenya Medical Research Institute (Protocol SSC 3426). Written informed consent was obtained from the participants for the collection, storage, and further use for the sample sets in the research (Scientific and Ethics Review Unit numbers: 1433, 3103, 4077, 3149, 3426). Acknowledgments The authors thank all the sample donors for their contribution to the research. The authors also thank Dr Craig Thompson for generously donating the plasmids used to produce the pseudoviruses and Dr Elise Landais, Deli Huang, and David Nemazee Scripps of the Research Institute for generously donating the HeLa-ACE2 cell lines. Author contributions Conceptualization and methodology: JN, TL, GMW. Investigation: BK, JNG, DM, HKK, JT, JN, JUN, LIO-O, YS, EN, DJN, DOO, DW. Formal analysis: JN, KM, AA, BK, GMW. Resources and funding acquisition: TL, CA, GMW. Writing, original draft preparation: JN, PB, JAGS. Writing, review, and editing: all authors. 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==== Front Virus Res Virus Res Virus Research 0168-1702 1872-7492 Elsevier Science S0168-1702(22)00345-8 10.1016/j.virusres.2022.199016 199016 Article SARS‐CoV‐2 Non-structural protein 1(NSP1) mutation virulence and natural selection: Evolutionary trends in the six continents Ghaleh Samira Salami a1 Rahimian Karim b1 Mahmanzar Mohammadamin c1 Mahdavi Bahar d Tokhanbigli Samaneh e Sisakht Mahsa Mollapour f Farhadi Amin g Bakhtiari Mahsa Mousakhan h Kuehu Donna Lee c Deng Youping c⁎ a Department of Biology, Faculty of Science, University of Guilan, Rasht, Iran b Bioinformatics and Computational Omics Lab (BioCOOL), Department of Biophysics. Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran c Department of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawaii at Manoa, Honolulu, HI 96813, USA d Department of Molecular Biotechnology, Cell Science Research Center, Royan Institute for Biotechnology, ACECR, Isfahan, Iran e Department of Molecular and Cellular Sciences, Faculty of Advanced Sciences and Technology, pharmaceutical Sciences Branch, Islamic Azad University, Tehran, Iran (IAUPS) f Department of Biochemistry, Erasmus University Medical Center, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands g Department of Biology, Payame Noor University, Tehran, Iran h Pediatric Cell Therapy Research Center, Children's Medical Center, Tehran University of Medical Sciences, Tehran, Iran ⁎ Corresponding author. 1 These authors contributed equally 5 12 2022 2 1 2023 5 12 2022 323 199016199016 7 10 2022 27 11 2022 28 11 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Objective Rapid transmission and reproduction of RNA viruses prepare conducive conditions to have a high rate of mutations in their genetic sequence. The viral mutations make adapt the severe acute respiratory syndrome coronavirus 2 in the host environment and help the evolution of the virus then also caused a high mortality rate by the virus that threatens worldwide health. Mutations and adaptation help the virus to escape confrontations that are done against it. Methods In the present study, we analyzed 6,510,947 sequences of non-structural protein 1 as one of the conserved regions of the virus to find out frequent mutations and substitute amino acids in comparison with the wild type. NSP1 mutations rate divided into continents were different. Results Based on this continental categorization, E87D in global vision and also in Europe notably increased. The E87D mutation has signed up to January 2022 as the first frequent mutation observed. The remarkable mutations, H110Y and R24C have the second and third frequencies, respectively. Conclusion According to the important role of non-structural protein 1 on the host mRNA translation, developing drug design against the protein could be so hopeful to find more effective ways the control and then treatment of the global pandemic coronavirus disease 2019. Keywords COVID-19 SARS-CoV-2 NSP1 Mutations Translational inhibition E87D ==== Body pmcAbbreviations Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Open reading frames (ORF), Spike proteins (S), Envelope protein (E), Membrane protein (M), Nucleocapsid protein (N) Non-structural protein 1 (NSP1), Coronavirus disease 2019 (COVID-19), Amino acid sequence (AAS), Amino acid (AA), Interferon (IFN). 1 Introduction Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an unsegmented positive-sense single-stranded RNA virus that belongs to the β-coronavirus genus and Coronaviridae family, first reported in Wuhan (Wang et al., 2020; Ye et al., 2020). Phylogenetic analysis revealed that SARS-CoV-2 has a 79% sequence similarity with SARS-CoV that caused an outbreak in 2002 (Xu et al., 2020). Therefore, it is of vital importance to understand the differences and the relationships between these two types of viruses (Li et al., 2021). The SARS-CoV-2 genome is ∼ 30 kb in length and composed of 13–15 (12 functional) open reading frames (ORFs) which are located 5′ to 3′ and known as ORF1a and ORF1b that encode 16 non-structural proteins (Fooladinezhad et al., 2022; Mazhari et al., 2021) (Fig. 1 B). In the following, several structural proteins such as spike proteins (S), envelope protein (E), membrane protein (M), and nucleocapsid protein (N) encode via the structural region of the genome (Benedetti et al., 2020; Zhao et al., 2021) that have a significant role in virus pathogenicity and infectivity (Emam et al., 2021). Among these structural regions, there are accessory factors such as ORF3a, ORF6, ORF7a, ORF7b, ORF8, and ORF10. This segmentation is displayed in Fig. 1A. These gene products play important roles in viral entry and RNA synthesis, fusion, and survival in host cells, and also have the capability of gaining rapid mutations as the virus spreads (Banerjee et al., 2021; Laha et al., 2020; Naqvi et al., 2020).Fig 1 The schematic diagram illustrates an overview of the SARS-CoV-2 genome more detailed NSP1. (A) SARS-CoV-2 genome organization includes the ORF1ab sequence, the structural regions (S, E, M, and N) and accessory factors (ORF3a, ORF6, ORF7a, ORF7b, ORF8, and ORF10). (B) The ORF1ab sequence is self-cleaved into 16 non-structural proteins, and (C) NSP1 domains, 3D structure, and its amino acid sequence. Fig 1 A major virulence factor of SARS-CoVs is the non-structural protein 1 (NSP1), which has a significant role in suppressing host gene expression by ribosome association. Binding NSP1 from SARS-CoV-2 to the 40S ribosomal subunit of the human cell as a host results in shutting down of mRNA translation both in vitro and in vivo. Cryo-electron microscopy analysis helps to clarify how they are placed together. When the NSP1 C-terminus (Fig. 1C) binds to the 40S subunit of the ribosome (Zhao et al., 2021), the formed complex prevents binding the mRNA to the ribosomal entry tunnel. NSP1 blocks gene I-dependent innate immune responses by retinoic acid-inducible that finally leads to eradication of the infection. The structural characterization of the inhibitory mechanism of NSP1 against SARS-CoV-2 can be useful for further significant insights into the drug design (Thoms et al., 2020). NSP1 acts as a ribosome gatekeeper and binds the small ribosomal subunit and stalls canonical mRNA translation at various stages during initiation (Kamitani et al., 2009; Lokugamage et al., 2012). In addition, degradation of host mRNA occurs because of binding to the ribosome which leads to endonucleolytic cleavage (Huang et al., 2011). Thus, NSP1 is a major viral virulence factor and because of this sensitive role of NSP1 in Coronavirus disease 2019 (COVID-19) tracking any structural mutation is vitally important (Nakagawa and Makino, 2021). Over time, the increase in a number of patients suffering from COVID-19 and mortality rate differed in geographical regions. This difference appears because of multiple reasons that can affect these studied mutations (Zhou et al., 2021). Due to the modifications on NSP1 protein amino acids (AAs), the incidence of different variations on the SARS-CoV-2 was observed. These mutations can affect binding to the human 40S subunit in ribosomal complexes and then, change flexibility and stabilization in the virus. Translation inhibition takes place as a result of this phenomenon that occurs in both in vitro translation systems and in vivo (Schubert et al., 2020). Despite global vaccination efforts, various mutations continue to be observed that can represent virus escape from vaccination and all other administered drugs (eg. Remdesivir, hydroxychloroquine, chloroquine, ribavirin, ritonavir, lopinavir, favipiravir, interferons, bevacizumab, azithromycin, etc.) (Dos Santos, 2021). Location-dependent species in each continent demonstrate vaccination has not been able to have the expected effect of controlling the disease (Rubin, 2021). Thus, having clear information from amino acid sequences (AASs) and the virus structure is useful and necessary to detect more details about the mutation dynamics of the SARS-CoV-2 genome (Albahli and Albattah, 2021). In this regard, we aimed to investigate the amino acid mutation patterns and their specificities divided into geographical areas from the beginning of the pandemic to January 2022. In addition, we represented the link between mutations and incidence of them on different continents that can be helpful in the prediction of dynamic transmission in the future. 2 Materials and methods 2.1 Sequence and source This study focuses on evaluating a big database of NSP1 AASs of the SARS-CoV-2 genome. NSP1 is located in the N-terminal region of the ORF1ab sequence that is susceptible to many mutations. The reference sequence is the Wuhan-2019 virus with access number ‘EPI_ISL_402124′ also known as wild type. All of the AAS samples were compared with the reference sequence. 180 AAs of the NSP1 region were extracted from GISAID (www.gisaid.org) (Elbe and Buckland‐Merrett, 2017; Khare et al.; Shu and McCauley, 2017) database belong period January 2020 to January 2022. Erasmus Medical Center grunted the accession to this database. 2.2 Sequence analyses and processing NSP1 was extracted from other genes and after analyzing mutations and sequence alignment, FASTA files were processed by Python 3.8.0 software. ‘Numpy’ and ‘Pandas’ libraries optimized the whole process. Mutations were defined as each difference between sample and reference, then the location and replaced AA were reported. Less or more than 180 AAs belong to non-human samples such as pangolin or bat, and samples containing non-specified AAs (reported as X) were eliminated, and finally, 6510,947 refined samples were examined in the study. The identifying algorithm for detecting mutants is as follows: In this algorithm, since all sequences have equal lengths, respectively ‘Refseq,’ and ‘seq’ refer to reference sequence and sample sequence. for refitem, seqitem in zip (refseq, seq) if (refitem! = seqitem) Report a new mutant Each sample's continent name and geographical coordinates were labeled on them. Pycountry-convert 0.5.8 software and ‘Titlecase’ library in Python were used to report global prevalence maps of mutations. The procedure of data refining was presented in Fig. 2 .Fig 2 Data processing workflow steps are used to represent and validate NSP1 AAs mutations. Fig 2 2.3 Secondary protein structure and dynamic prediction Analyzing the mutational structure and molecular flexibility of NSP1 protein modeling on the E87D mutation as the most frequent was performed by the DynaMut web server (http://biosig.unimelb.edu.au/dynamut/). The PDB ID of protein (7K3N) was taken from the Protein Data Bank (https://www.rcsb.org/) and used for prediction on the DynaMut web server. 2.4 Statistical analysis R 4.0.3 and Microsoft Power BI software were used to conduct data normalization and comparison charts outlining. GraphPad Prism 8.0.2 software were used for data visualization. To improve and compare the data's results, the normalized frequency of each region was reported. Then the number of mutations was divided by the number of sequences on that region comparable in equal proportions. 3 Results 3.1 Numbers and incidence of mutations in NSP1 AAS based on geographical areas Relevant statistical analyses were performed to identify the potential mutations in the NSP1 protein structure. At the present, the number of AAS mutations in the whole of 6510,947 sequences was examined. Of the total number of NSP1 FASTA files, 277,242 data belong to not-matched length samples, 3434 data relate to non-human sequences, and 264,251 data belong to sequences that contained X. Finally, 544,927 data were not included in the analysis, and 5966,020 NSP1 data remained for examination in the study. The statistical results showed that 6.3106% of AASs had one mutation, 0.2024% of AASs included two mutations, 0.0039% of AASs comprised three mutations, and 0.0010% of AASs contained more than four mutations. In contrast, 93.4819% of sequences had no mutation in their AASs. In addition, the frequency percentage of mutations was investigated in six geographical regions; North America, South America, Europe, Asia, Oceania, and Africa (Fig. 3 A and Supplementary file1).Fig 3 The heat map of the number and approximate region of mutations in NSP1 of SARS-CoV-2. (A) Heat map chart of the mutations’ number in NSP1 of SARS-CoV-2 until January 2022 in each continent. (B) The NSP1 protein was divided into ten segments. The rate of each mutation per segmented 18 amino acids was indicated in the heat map of the protein. The highest frequency rate occurred in the 72 to 126 amino acid sequence of the NSP1 protein that we can consider as a hot spot region in NSP1. The percentage of data were used to draw heat maps. Supplementary file1 and Supplementary file 2. Fig 3 In continental-related analysis regarding the frequency of mutations number, Oceania has the least frequency of samples that have one or more than one mutations in their AASs. Also, the top rank frequency of no mutant samples is dedicated to this area. In this way, the data regarding Oceania with 27,270 AASs comprising of no mutations in 96.2923% of sequences, one mutation in 3.6379%, and two mutations in 0.0696% of AASs, but in the examined AASs from this continent, more than two mutations were not detected. Europe was labeled the top rank frequency of one and two mutations in samples compared to other regions. Despite that, Europe has the least frequency of no mutant samples. Data related to Europe indicates 2931,894 AASs represent that no mutation was detected in 92.5615% of sequences, although one mutation in 7.1954% and two mutations in 0.2374% of their AASs were detected. In Africa, we observed a high frequency of three or more than three mutations in samples. This continent with 31,907 AASs data revealed three mutations in 0.0094% of AASs, and more than four mutations in 0.0094% of AASs represented. The details about these frequencies are represented in Fig. 3A. According to the high percentage of no mutant regions on NSP1 AASs in all geographical areas, the data demonstrate NSP1 as a conserved region that was preserved against a high replication and high mutation rate allowing RNA viruses to escape the immune system during the evolution (Hadj Hassine et al., 2022). The mutation frequency was determined based on the number of mutations in each segment relative to the total mass. The worldwide results depicted that the highest mutation frequency occurred in the region of 72 to 90 AA (0.0990%) and then in the districts of 108 to 126 AA (0.0549%). More details about the frequency of mutations in each geographical area and the location where they belong separately were mentioned in Fig. 3B and Supplementary file2. 3.2 Mutation's specificities according to geographical areas More detail to survey the NSP1 AASs mutations, particularly the location of mutations in the protein structure and their frequency were investigated between January 2020 to January 2022. The first five frequent mutations regardless of geographical distribution were represented in Table 1 .Table 1 NSP1 top five frequent mutations from January 2020 to January 2022. Table 1Rank Residue Frequency (%) Top 1 E(87)D 1.1754 Top 2 H(110)Y 0.3100 Top 3 R(24)C 0.2937 Top 4 S(100)N 0.1148 Top 5 E(37)D 0.1104 The statistical incidence of these five mutations based on the continents is listed in Table 2 . The significant point is all of these mutations didn't appear among continents as the top mutations.Table 2 The incidence of the global NSP1 top five mutations is based on the continents. Table 2Residue Variant frequency (%) North America South America Europe Asia Oceania Africa E(87)D 0.3475 0.1021 2.0835 0.1243 0.0806 0.1441 H(110)Y 0.3051 0.3862 0.3222 0.1946 1.5219 0.5077 R(24)C 0.3421 0.2229 0.2628 0.2001 0.1796 0.1723 S(100)N 0 0 0.1982 0 0 0 E(37)D 0.0728 0.1344 0.1480 0 0.0147 0.0752 Due to the results of our analysis, E87D in NSP1 was labeled the most frequent mutation up until January 2022 with a frequency rate of E87D at 1.1754%. Among these mutations, the E87D mutation was present on all continents except South America, as one of the top mutations. Therefore, Europe by 2.0835% of frequency is the highest, and Oceania by 0.0806% of frequency is the least ranked for E87D mutation. H110Y mutation has been observed as one of the top mutations with the highest frequency in Oceania and the least frequency in Asia by 1.5219% and 0.1946% of frequency respectively. R24C mutation has been remarked in North America as the highest prevalent mutation and in Africa as the least frequent mutation among the top mutations with 0.3421% and 0.1723% of frequency respectively. S100N mutation was detected only in Europe by 0.1982% frequency among the top mutations. E37D mutation has been shown in North America, South America, and Europe by 0.0728%, 0.1344%, and 0.1480% of frequency respectively. In North America, R24C, E87D, and H110Y are the top three mutations that have more frequency than others, respectively. Also, there is more variety in the type of amino acid to which it is converted in the last ranks of mutation. South America has a different pattern in the frequency of the high-rank mutations. In South America, H110Y was labeled the first frequent mutation. Regarding the Europe data, E87D is the first rank among mutations and there is a significant difference between the frequency of E87D and other mutations that have less frequency. This pattern has the most matches in the global ranking. In Asia, E148G is the most frequent mutation by 0.8849% of frequency that has a considerable discrepancy with other mutations. Indeed in the 148th position of AAs, glutamic acid (E) replaces Glycine (G). Oceania especially has a remarkable role in enhancing the H110Y mutation global rate by 1.5219% of frequency that should be considered. In Africa, E102Q is the first frequent mutation. In this mutation, glutamic acid converts to glutamine with 0.9120% of frequency, and also lysine replaces glutamic acid with 0.5391% of frequency causing structural modification in AAs. The global distribution in mutation rates represents that the NSP1 region is one of the conserved regions in the virus genome during evolution. In diverse geographical distributions, different percentages of mutations were observed that may lead to significant impacts on mortality rate, drug resistance or vaccine escape, and severity of the disease (McLean et al., 2022; Mohammed, 2021). The top five mutations of each geographical area and which amino acids they have been replaced are shown in Fig. 4 based on the logarithm 10 of data frequency in the percentage of substituted AAs. The details about other happened mutations are available in the Supplementary file3.Fig 4 NSP1 top ten mutations with the highest frequency globally and geographic areas including North America, South America, Europe, Asia, Oceania, and Africa. The position of changed amino acids and substituted ones is shown differently based on the logarithm 10 of data frequency in the percentage of substituted AAs. The mutation frequency was evaluated for each of them by normalizing the number of genomes carrying a given mutation in an intended geographic area. Fig 4 3.3 Evolutionary assessment of happened mutations in the position of AAs of top five mutations according to time and geographical regions To detect the incidence of each mutation, AASs of each geographic area were analyzed over time by classifying them according to the month of sample collection from January 2020 until January 2022, as represented in the GISAID database (Elbe and Buckland‐Merrett, 2017; Khare et al.; Shu and McCauley, 2017). The continuation of mutations was investigated and collected monthly. In the following, fluctuations with a prevalence rate higher than 0.01 per AASs were reported. In the global timeline, two fluctuations of mutations that were incident in the R24 and E87 positions are more significant than the prevalence of other mutations. R24 had increased at the beginning of the pandemic which had a higher rate than other mutations and also had a peak in May 2020 to almost July 2020. On the other hand, the peak of E87 mutation started in May 2021. Three high slope peaks of D75 (February 2020 by the highest rate 0.03), and R24 (May 2020, by the highest rate 0.02) have occurred in North America. D144 mutation peak incidence in this area is different from others and it was prevalent with less slope than the start in March 2020 and raised in September 2020 to 0.02 frequency. In South America, I114 has three continuous peaks between July 2020 and February 2021. V86 peaked in April 2021 up to 0.01 rates. In the last month of study in this area, we detected raising the frequency of V84. In Europe, particularly mutations that belong to the E87 position were increased and raised to a 0.03 frequency rate in July 2021. The frequency was overtaking during four months and got close to 0.04 rate in November 2021, the last month of the study. The timeline of Asia illustrated a peak of E148 in October 2021 with 0.09 frequency. Oceania has a peak of H110 mutation in May 2021 with the highest rate of 0.2. E102, the top mutation in Africa, reached a rate of 0.18 in November 2020. Detailed distribution of the five ranks in high mutation rates of NSP1 variants globally and on each continent is provided by the month of sample collection and displayed in Fig. 5 . The details about other peaks of mutations that happened during the study timeline are also available in Supplementary file4.Fig 5 Plots time evolution pathways of top five high-rate mutations of NSP1 globally and in different geographic areas including North America, South America, Europe, Asia, Oceania, and Africa. The data was computed as the number of AASs having a given mutation over the total number of AASs according to the month of sample collection. Fig 5 3.4 Variant of concern by the E102K mutation The E102K mutation in the N-terminal of NSP1 results in substitutes glutamic acid (E) for lysine (K), a replacement from negative to positive charge. This positive charge may increase the affinity of NSP1 for negatively charged RNA. The noticed change can affect the replication rate and preferential expression of viral RNA in the infected cell and increase them. The E102K mutation that reported as the top mutation in Africa, was founded in the Tanzanian variant (Hadj Hassine et al., 2022). 3.5 Assessment of E87D mutation on dynamicity and flexibility of NSP1 To unveil the effect of E87D mutation on the structure of NSP1 protein, we used the DynaMut website for protein modeling. The variation in vibrational entropy energy (∆∆SvibENCoM) between wild and mutant types was calculated. The obtained data represent that the mutation at E87D increases the molecule flexibility on the NSP1 protein structure by a value 0.569 kcal.mol−1.K − 1. On the other hand, binding affinity change caused by the alteration between glutamate and aspartate in position 87 can result in destabilizing the structure of NSP1 protein by a value −0.624 kcal/mol. Investigation on the effects of this alteration in amino acids intramolecular interactions can give us a vision of the reason for destabilization in the protein structure after mutation. Although both glutamate and aspartate are brønsted base and have a negative net charge in mutant type, by deleting a CH2 in the structure of residue, the three hydrogen bonds that made the protein more stable in wild-type were eliminated (Fig. 6 ).Fig 6 Impact of E87D mutation on structural dynamics of the NSP1 protein. Amino acids are colored according to the vibrational entropy change upon mutation. The red color represents a gain in flexibility of NSP1 protein. Wild-type and mutant residues are colored in light-green and are also represented as sticks alongside the surrounding residues which are involved in any type of interaction. Fig 6 4 Discussion SARS-CoV-2, as RNA viruses are more susceptible to mutations than DNA viruses(Pachetti et al., 2020; Rahimian et al., 2022). NSP1 is known as a critical virulent factor with significant effects on the virus-host interaction interface, such as inhibiting host mRNA translation (Lokugamage et al., 2015; Narayanan et al., 2015), antagonizing interferon (IFN) signaling (Wathelet et al., 2007), and inducing inflammatory cytokines and chemokines (Law et al., 2007). Furthermore, some of these effects on NSP1 are conserved in other β-CoVs and even α-CoVs. Given the very likely structural similarity between SARS-CoV-2 NSP1 and SARS-CoV NSP1, the information of SARS-CoV NSP1 functions could be highly helpful for understanding the biological and pathological roles of NSP1 in SARS-CoV-2 (Min et al., 2020). This study was designed to analyze all mutations that occurred between January 2020 to January 2022, and regional patterns of mutations on NSP1 detected in the following six regions; North and South America, Europe, Asia, Oceania, and Africa. A recent investigation demonstrated the effects of tirilazad, phthalocyanine, and zk-806,450, which showed lower energy scores compared to alisporivir and cyclosporine, two compounds with in vitro activity against NSP1 that may have higher inhibition effectiveness (de Lima Menezes and da Silva, 2021). Hence, it is expected inhibiting NSP1 activity in the virus by these compounds leads to resuming host translation normally. Esculin is a glucoside and naturally occurs in barley, and horse chestnut. It is given to improve capillary permeability and fragility and has been reported to inhibit collagenase and hyaluronidase enzymes. The molecule has been shown to have antioxidant and anti-inflammatory activity (Wishart et al., 2018). Esculin interacts mainly with R62, S63, A68, H72, and M74 (major interacting residues in the docking pose) through H-bond interactions with esculin. According to the preformed analysis, these interactions are located in the hotspot region of NSP1 that cause the influence of esculin on NSP1 protein function. This suggests the ability of esculin to not only inhibit NSP1 activity but also play a role against secondary symptoms such as inflammation. Hence, designing a drug against NSP1 in addition to other methods for treatment of COVID-19 likely gives a hopeful insight into possible control and finally eradicating the disease (Sharma et al., 2020). As for previous studies, E87D mutation is located on the globular domain of in vitro protein that can affect the stability and flexibility of the virus. Therefore, this mutation can help in gaining flexibility and destabilization in the protein structure. Based on a recent study, a decrease in molecular flexibility was detected in H110Y NSP1 as the second frequent mutation and D75E which is one of the less frequent mutants. When the R24C, as the third frequent mutation, is happening in NSP1, flexibility was changed in the region containing AA residues E65, L64, Q63, P62, C24, D16, and V14. Similarly, in some less frequent mutants like D48G NSP1, an increase in molecular flexibility was detected at AA residues S40, R43, Q44, K47, and G48. These alerts in AAS might have effects on its natural function in terms of blocking host mRNA translation and evading the immune system (Mou et al., 2021). Different reasons involve natural selection in determining the fate of mutations in each continent (Sanjuán and Domingo-Calap, 2021). Depending on conditions and every factor that affects the viral genome, different mutations with different symptoms in patients are possible. Mutations that have enough potential of resistance against the host immune system, can adapt to the conditions and then, increase proliferation, and facilitate virus transmission. Based on our results, there are some less frequent mutations such as S100N, E37D, V28I, K120N, and P62S in NSP1. But due to evolutionary factors, based on our results, there are some less frequent mutations such as S100N, E37D, V28I, K120N, and P62S in NSP1. However, there is no significant increase in the frequency of these mutations, while the frequency of obvious mutation E87D, notably has increased. The top five mutations position on AASs of NSP1 protein were shown in Fig. 7 .Fig 7 3D structure of NSP1 protein. The positions of top five frequent mutation on the protein structure are displayed in different colors (E87, R24, H110, S100, E37). Fig 7 The tissue and cell expression patterns of known SARS-CoV-2 interacting human proteins, based on transcriptomics and antibody-based proteomics in the Human Protein Atlas database (https://www.proteinatlas.org/humanproteome/sars-cov-2) helped us to find the human genes that are related to NSP1 of SARS-CoV-2. COLGALT1, PKP2, POLA1, POLA2, PRIM1, and PRIM2 are the genes that encode collagen beta(1-O)galactosyltransferase 1, plakophilin 2, the catalytic subunit of DNA polymerase alpha 1, the accessory subunit of DNA polymerase alpha 2, DNA primase subunit 1, and 2 respectively. The products of these genes have low immune cell specificity and commonly medium tissue expression. Generally, these genes play a role in DNA replication except COLGALT1 and PKP2. Collagen beta(1-O)galactosyltransferase 1 by acting on collagen glycosylation facilitates the formation of collagen triple helix and plakophilin 2, which may play a role in junctional plaques (Chung et al., 2012; Hennet, 2019; Kim et al., 2020; Novelli et al., 2018; Parry et al., 2020; Toukoki and Gryllos, 2013). There are two limitations in the present study that should be noted. One of the limitations is focusing only on AASs without considering the nucleotide sequences. Another limitation in this process is that samples reported from Europe had more rates than the data which were reported from other regions, and reciprocally Oceania is the one area that has the least sample rate. Thus, there may be other significant mutations present without extensive sequencing of samples that have not been available in the data. 5 Conclusion Our findings suggest mutations of SARS-CoV-2 are expanding and help the virus adapt inside of hosts, providing conditions for virus survival and making other mutations in new geographical areas. E87D, H110Y, and R24C mutations increased in the timeline of the study as the first, second, and third frequent mutant sequences, respectively. The effects of these mutations on the flexibility and stabilizing of NSP1 and subsequently on survival features of the virus can have different results in patients that should be considered. To figure out the precise impact of the mutations on the disease, further studies should be done in the future. Funding This work was partially supported by the 10.13039/100000002 NIH grants 5P30GM114737, 5P20GM103466, 5U54MD007601, 1P20GM139753, 5P30CA071789, 2U54CA14372. Authors’ contributions K.R., M.M.S., M.M.B., and A.F. contributed to data collection. Y.D., M.M., and K.R. contribute to study design, K.R., and M.M. design workflow and code, and data analysis. B.M, K.R., and M.M., and A.F. contributed to data visualization. S.S.G. wrote the manuscript. S.T., M.M., D.L.K. and B.M. corrected the manuscript and provided useful comments. M.M., K.R., and B.M. monitored the accuracy of Additional data. B.M. designed graphical contents. Y.D. have final edited and supervised the work. Data availability The raw data supporting the conclusions of this article is available in supplementary file(s). Declaration of competing interest The authors declare that they have no conflicts of interest that might be relevant to the contents of this manuscript and the research was carried out regardless of commercial or financial relationships that may cause any conflict of interests. Appendix Supplementary materials Image, application 1 Image, application 2 Image, application 3 Image, application 4 Acknowledgments The authors thank all of the researchers who have shared genome data openly via the Global Initiative on Sharing All Influenza Data (GISAID). Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.virusres.2022.199016. ==== Refs References Albahli S. Albattah W. Deep transfer learning for COVID-19 prediction: case study for limited data problems Curr. Med. Imag. 17 8 2021 973 Banerjee S. Seal S. Dey R. Mondal K.K. Bhattacharjee P. 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Virol. 93 5 2021 2694 2704 33368358	36473671	PMC9721189	NO-CC CODE	2022-12-06 23:26:39	no	Virus Res. 2023 Jan 2; 323:199016	utf-8	Virus Res	2,022	10.1016/j.virusres.2022.199016	oa_other
==== Front J Econ Dyn Control J Econ Dyn Control Journal of Economic Dynamics & Control 0165-1889 1879-1743 Elsevier B.V. S0165-1889(22)00284-6 10.1016/j.jedc.2022.104581 104581 Article The long-term impact of the COVID-19 unemployment shock on life expectancy and mortality rates☆ Bianchi Francesco ⁎a Bianchi Giada b Song Dongho c a Department of Economics, Duke, 213 Social Sciences building, Box 90097, JHU, CEPR, and NBER, Durham, NC 27708, United States b Department of Medicine, Division of Hematology, Brigham and Women’s Hospital, Harvard Medical School, United States c Carey Business School, John Hopkins University, JHU Carey, 100 International Drive, Baltimore, MD 21202, United States ⁎ Corresponding author. 5 12 2022 1 2023 5 12 2022 146 104581104581 9 8 2022 18 11 2022 1 12 2022 © 2022 Elsevier B.V. All rights reserved. 2022 Elsevier B.V. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. We adopt a time series approach to investigate the historical relation between unemployment, life expectancy, and mortality rates. We fit Vector-autoregressions for the overall US population and for groups identified based on gender and race. We use our results to assess the long-run effects of the COVID-19 economic recession on mortality and life expectancy. We estimate the size of the COVID-19-related unemployment shock to be between 2 and 5 times larger than the typical unemployment shock, depending on race and gender, resulting in a significant increase in mortality rates and drop in life expectancy. We also predict that the shock will disproportionately affect African-Americans and women, over a short horizon, while the effects for white men will unfold over longer horizons. These figures translate in more than 0.8 million additional deaths over the next 15 years. Keywords COVID-19 Life expectancy Mortality Unemployment rate Bayesian methods VAR ==== Body pmc1 Introduction Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the pathogenic agent of coronavirus disease 2019 (COVID-19), see Gorbalenya et al. (2020). Initially reported as an outbreak in the province of Wuhan, China at the end of 2019, COVID-19 was recognized as a pandemic by the World Health Organization (WHO) on March 11, 2020, e.g., Guan et al. (2020). Approximately 74 million cases and 1.6 million deaths have been reported worldwide, with over 17 million people infected and approximately 0.3 million deceased in the USA alone (updated December 16, 2020; see WHO, 2020 and CDC, 2020). SARS-CoV-2 is shed by asymptomatic individuals and persists in the environment for days, implying that public health measures to halt virus spreading could be effective at reducing transmission and mortality, see Li et al. (2020) and Pan et al. (2020). Universal masking, social distancing, contact tracing, and quarantine were later identified as effective tools to contain SARS-CoV-2 spreading, see Leung et al. (2020) and Moghadas et al. (2020). Mathematical modeling predicted a catastrophic exhaustion of health care personnel and resources, particularly ventilators, unless strict containment measures to limit SARS-CoV-2 spreading were established, see Anderson et al. (2020) and Davies et al. (2020). Between late March-early April, most U.S. states imposed stay-at-home orders and lockdowns, resulting in widespread shut down of business. Unemployment rate rose from 3.8% in February 2020 to 14.7% in April 2020 with 23.1 million unemployed Americans. Despite a decline to 6.7% in December 2020, the average unemployment rate over the year is comparable with the 10% unemployment rate at the peak of the 2007–2009 Great Recession and it is near the post-World War II historical maximum reached in the early 1980s (10.8%). Importantly, COVID-19 related job losses disproportionately affect women, particularly of Hispanic heritage; African Americans; foreign born individuals; less educated adults and individuals age 16–24. In fact, the unemployment rate underestimates the extent of the economic contraction as many potential workers have abandoned the workforce (especially women). The impact of the loss of income on psychological and physical health has been well documented in white males, see McKee-Ryan et al. (2005), Brenner (2005), and Wilson and Walker (1993). In epidemiological studies, unemployment at the individual level associates with decreased health and higher mortality, regardless of aggregate unemployment rate, see Roelfs et al. (2015). A surge in suicide rates has been clearly observed in unemployed individuals, particularly men. Cardiovascular diseases peak in face of financial stress and preventive ontological care declines, thus contributing to excess mortality. While the short-run trade-off between containing the COVID-19 pandemic and preserving economic activity has been extensively analyzed, there is currently no analysis regarding the long-term impact of the COVID-19-related economic recession on public health. What is more, most of the papers interested in the relation between the COVID-19 pandemic and economic activity argue, correctly, that lockdowns can save lives at the cost of reducing economic activity,1 but they do not consider the possibility that severe economic distress might also have important consequences on human well-being (Gordon and Sommers, 2016 and Ruhm, 2015). This shortcoming is arguably explained by the fact that current macroeconomic models do not allow for the possibility that economic activity might affect mortality rates of the agents in the economy. In this paper, we aim at making progress on this gap in the literature by taking a time-series approach. We use annual data on average life expectancy and age-adjusted mortality rates collected from the Centers for Disease Control and Prevention (CDC) website, which are paired with the unemployment rates from the St. Louis Fred website. We construct the data set not just for the overall US population, but also for subgroups of the population categorized by race (African-American or White) and gender (women or men). We use a vector autoregression (VAR, Sims, 1980) to model the joint dynamics of the growth rates of life expectancy and mortality rates together with the unemployment rates. We acknowledge that life expectancy and mortality rates are likely to present observation errors as the US population is not continuously and perfectly observed. Furthermore, mortality rates in the short run might be affected by factors not directly related to economic conditions or progress in health care. To address these issues, we allow for observation errors that essentially wash out the non-persistent idiosyncratic components in life expectancy and mortality rates. We rely on Bayesian methods to construct posterior estimates of model unknowns including the VAR coefficients and the smoothed growth rates of life expectancy and mortality rates. Equipped with posterior estimates for the VAR parameters, we use an impulse response analysis based on a Cholesky decomposition method to assess the effects of an increase in unemployment on the life expectancy and mortality rates. The main message arising from our exercise is that the typical unemployment shock results in a significant decline in life expectancy and increase in mortality rates for the overall population. In normal times, the size of unemployment shock is around 0.79% on an annual basis and it is quite persistent. The effect of the unemployment shock on the growth rate of life expectancy and the death rate reaches its peak in the fourth year and remains elevated for a long time. We repeat the exercise with data for different population groups identified based on race and gender to highlight substantial heterogeneity. We find that the size of the typical unemployment shock is much larger for African-Americans, with a standard deviation of around 1.05%, than for the White population (standard deviation around 0.74%). Specifically, African-American men typically experience the largest unemployment shocks, with a standard deviation of around 1.31%, approximately 60% larger than the typical shock experienced by White men. We find that the typical unemployment shock for White women is the smallest, around 0.63%, which is about 75% of the unemployment shock experienced by White men. Similarly, the size of a one-standard deviation unemployment shock for African-American women (0.92%) is about 70% of the typical shock experienced by African-American men, but its absolute magnitude is larger than that experienced by White men. In light of this evidence, it is perhaps not surprising that the effects of the typical unemployment shock on life expectancy and the death rate are more severe for the African-American population. However, we emphasize that this is not entirely the consequence of larger shocks, as the pattern largely persists when controlling for the size of the shock, especially for the case of life expectancy. When controlling for the size of the shock, we find that women present a relatively larger increase in death rates. This pattern is especially visible for White women, indicating that even if they generally suffer smaller shocks, they are disproportionately more affected by them. To understand the channels behind our results, we extend the analysis to study the response of the death rates for the leading causes of death to an unemployment shock. Because of data availability, we focus on the overall population. We find that an unemployment shock leads to increases in death rates due to heart disease (the leading cause of death in the United States), stroke, influenza and pneumonia, and accidents, while we do not find a significant response of the death rate due to cancer. These results suggest that multiple channels might be at work. On the one hand, the effects on heart disease and stroke death rates indicate that access to preventive care and lifestyle might play an important role in explaining why unemployment is followed by an increase in mortality rates. The increase in pneumonia/influenza-related mortality may be also explained based on the lack of access to preventive care and the decline in a healthy life style. On the other hand, the large effect on accidents also indicates that other channels are likely to be at work. Possible explanations are that people engage in more dangerous activities, spend more time driving in a less careful way, and are more subject to domestic accidents during periods of economic distress. We then move to use our estimates to examine the long-run effect of the COVID-19 unemployment shock on life expectancy and the age adjusted death rates across difference races and gender. Our data for life-expectancy and death rates stop in 2017. However, we have unemployment data until 2020. We thus use observations for the unemployment rate to construct an estimate of the COVID-19 unemployment shock, while treating the corresponding ones for the life expectancy and mortality rates as missing observations. As before, we adopt an identification strategy based on a Cholesky decomposition. By comparing the magnitude of the COVID-19 unemployment shock to those in the normal (non-critical) times, we can infer the severity of the COVID-19 pandemic unemployment shock. According to our estimates, the COVID-19 unemployment shock is about 3.64 standard deviations larger than the typical shock to the unemployment rate for the overall population (about 2.90% in magnitude). We estimate that this unprecedented unemployment shock will result in a 2.43% increase in mortality rates and a 0.83% drop in life expectancy over the next 15 years. Compared to the typical unemployment shock, we find that women (both African-American and White) are disproportionately affected relative to men. Particularly for White women, the COVID-19 unemployment shock is about 4.91 standard deviation larger (about 3.10% in magnitude) than their typical shock to the unemployment rate, by far the largest in relative magnitude with respect to the typical shock. However, African-Americans still suffer the largest shocks in absolute terms (approximately 3.4% for both African-American men and African-American women). As a result, the impact of the COVID-19 unemployment shock on the death rate is large for all groups, but visibly larger for African-Americans and White women. As explained above, this is in part the result of a larger shock, but also of a larger response conditional on the size of the shock. The long-term effects of the COVID-19 related unemployment surge on the US aggregate mortality rate have not been characterized in the literature. Thus, as a last step, we compute an estimate of the excess deaths associated with the COVID-19 unemployment shock. This corresponds to the difference between the number of deaths predicted by the model with and without the unemployment shock observed in 2020. For the overall population, the increase in the death rate following the COVID-19 pandemic implies staggering 0.84 and 1.22 million excess deaths over the next 15 and 20 years, respectively. These numbers correspond to 0.23% and 0.33% of the projected US population at the 15- and 20-year horizons, respectively. For African-Americans, we estimate 200 thousand and 290 thousand excess deaths over the next 15 and 20 years, respectively. These numbers correspond to 0.38% and 0.52% of the projected African-American population at the 15- and 20-year horizons, respectively. For Whites, we estimate 0.76 and 1.09 million excess deaths over the next 15 and 20 years, respectively. These numbers correspond to 0.28% and 0.40% of the projected White population at the 15- and 20-year horizons, respectively. Overall, our results indicate that, based on the historical evidence, the COVID-19 pandemic might have long-lasting consequences on human health through its impact on economic activity. We interpret these results as a strong indication that policymakers should take into consideration the severe, long-run implications of such a large economic recession on people’s lives when deliberating on COVID-19 recovery and containment measures. Without any doubt, lockdowns save lives, but they also contribute to the decline in real activity that can have severe consequences on health. Policy-makers should therefore consider combining lockdowns with policy interventions meant to reduce economic distress, guarantee access to health care, and facilitate effective economic reopening under health care policies to limit SARS-CoV-19 spread. The idea that economic activity might affect human well-being has been studied before. Contrary to what might be expected, there is no widespread agreement on the effect of economic activity on mortality rates. Ruhm, 2000, Ruhm, 2003, Ruhm, 2005, Ruhm, 2012 and Mulas-Granados (2005) argue for a procyclical relation between macroeconomic activity and mortality, with death rates increasing during periods of high employment. However, in a more recent contribution, Ruhm (2015) finds that since 1990 the relationship has become weak or non-existent. This seems to be due to a change in the composition of the causes of deaths. Specifically, fatalities due to cardiovascular disease and, to a smaller degree, transport accidents are procyclical, whereas cancer and some external sources of death (particularly accidental poisonings) have emerged as strongly countercyclical. Gordon and Sommers (2016) use county-level data to study the effects of unemployment, poverty rates, and median incomes on mortality rates over the period 1993–2012. They find that higher unemployment has modest negative impacts on mortality, in contrast with previous studies and in line with our findings. Furthermore, they emphasize that county-level poverty rates and lower median incomes are better predictors of mortality rates. In our study, we use unemployment because it is available over a prolonged period of time. In this respect, we should interpret unemployment as a proxy for the overall state of the economy, correlated with other variables of interest such as poverty rates and household income. The effects of unemployment can be heterogeneous across different age groups. For example, Coile et al. (2014) argue that individuals who are approaching retirement when a recession hits may be particularly likely to suffer long-lasting negative consequences, such as reduced longevity. With respect to these studies, our methodological approach is quite different, given that we take a time series approach, as opposed to panel regressions. This allows for a dynamic relation between the variables of interest and for a discussion of the effects of the national business cycle that in these studies is absorbed by the time fixed effect (see Ruhm, 2015 for an excellent discussion). We see these two approaches as complementary (Ruhm, 2000, Ruhm, 2003, Ruhm, 2005, Ruhm, 2012). The evidence for a casual link from job loss to poor health is mixed in the literature. Browning et al. (2006) find that there is no impact of job displacement on hospitalization for stress-related diseases for men. Bckerman and Ilmakunnas (2009) find that the cross-sectional negative relationship between unemployment and self-assessed health is not found longitudinally. On the other hand, Sullivan and von Wachter (2009) provide evidence that displaced workers experience higher rates of mortality. Noelke and Avendano (2015) find that job loss in the years before retirement is associated with a higher risk of cardiovascular disease and death. Schwandt and von Wachter (2020) show that cohorts coming of age during a deep recession suffer increases in mortality later in their middle age. von Wachter (2020) studies the potential long-run effects of large-scale unemployment during the COVID-19 crisis focusing on vulnerable job losers and labor market entrants. He finds that these losses could be substantially larger than losses in potential life years from deaths directly due to COVID-19. Our results are in line with his findings, despite the different methodological approach taken in the paper. With respect to these contributions, our approach based on aggregate data allows for the possibility that unemployment, as a proxy for the overall performance of the economy, might affect mortality rates and health through indirect channels (e.g., income, crime rates, drug abuse,...). Our benchmark results are based on an identifying assumption that relies on unemployment not having any contemporaneous effect on mortality rates and life-expectancy. Our results on the long-term effects of unemployment on mortality and life-expectancy are qualitatively unchanged when using a different identification strategy in which shocks to unemployment are allowed to have a contemporaneous effect on the other two variables. When using this alternative identification assumption, an interesting result emerges. For some groups, an increase in unemployment leads to a contemporaneous decline in mortality rates and to a contemporaneous increase in life-expectancy. However, error bands for this initial effect tend to be large and the response reverts in two-three years. In the long run, mortality increases and life expectancy declines. The long-term effect dominates and our key results on the long-term cumulative effects of unemployment remain unchanged. At the same time, these results could help reconcile the mixed evidence in the existing literature discussed above. On impact, unemployment can lead to a reduction in mortality as deaths due to work-related causes or motor vehicle accidents decline, but over time economic distress takes a toll on human well-being. We consider this an interesting direction for future research. Our results add to the body of literature that analyzes the macroeconomic consequences of COVID-19. This literature is growing exponentially and we apologize to our colleagues for being unable to cite all relevant contributions here. A few papers that rely on historical pandemic episodes to provide plausible estimates for outcomes due to COVID-19 include Barro et al. (2020), Ludvigson et al. (2020), and Jorda et al. (2020). Other papers study the interaction between economic decisions (e.g., optimal policy) and epidemics, e.g., Kaplan et al. (2020), Eichenbaum et al. (2020a), Eichenbaum et al. (2020b), Acemoglu et al. (2020), Alvarez et al. (2020), Jones et al. (2020), Krueger et al. (2020), and Glover et al. (2020). Among the existing papers, our work is more closely related to those that examine the medium- to long-term effects of pandemics such as Jorda et al. (2020), who argue that significant macroeconomic after-effects of pandemics can persist for a long time. However, they focus on the long-term economic consequences, while we focus on the long-term health consequences (the two are likely to be related). Overall, the economic literature has extensively analyzed the short-run trade-off between economic activity and the containment of the pandemic. We emphasize that an equally important long-run trade-off exists. It is worth clarifying that with this claim, we do not want to suggest that policymakers should refrain from ordering lockdowns, as necessary lifesaving measures, but rather that, if they decide to do so, they should provide alongside enhanced health and economic support for the most vulnerable portions of the population. Finally, it is worth noticing that in our analysis we focus on unemployment shocks without considering whether movements in unemployment are caused by demand or supply shocks. In future research, it could be interesting to develop the analysis to distinguish between the two cases, as adverse supply shocks might also cause an increase in inflation that could exacerbate the effects of a recession. The rest of the paper is organized as follows. Section 2 presents the data and the methodological approach. Section 3 presents the historical relation between shocks to unemployment and life expectancy and mortality rates. Section 4 studies the implications of the historical results for the COVID-19 unemployment shock. Section 5 concludes. 2 Empirical strategy In this section, we first describe the data set used in our analysis and then introduce the statistical model employed to study the dynamic relation between real activity, mortality rates, and life expectancy. The statistical model is a VAR in which we allow for the possibility that mortality rates and life expectancy are observed with an error. 2.1 Data We collect data on average life expectancy and age-adjusted mortality rates from the Centers for Disease Control and Prevention (CDC) website. CDC data are available to the public only until 2017. All data are available at annual frequency. The CDC website also provides age-adjusted life expectancy and mortality rates based on gender and limited race breakdown (African-American versus White). Race (African-American, White, Asian, etc) and Hispanic origin (yes/no) are classified independently by the CDC. Thus, Hispanic can be of any race and within each race group there can be descendants of Hispanic origin. No extended data series are available for Hispanic/Latinos heritage, Asians, American Indian/Alaska Natives, Native Hawaiian/Other Pacific Islanders or mixed races. CDC data for the African-American population span a shorter period of time (1972–2017). The series for White population are also shorter (1954–2017) than the series for the overall population (1950–2017). This is important when comparing results across races and with respect to the overall population, because the sample used for the estimation is not homogeneous. We obtain the Bureau of Labor Statistics unemployment rates for the overall population and for each race and gender groups from FRED, the website of the St. Louis Fed. We pair the CDC series with the corresponding unemployment series to obtain seven groups: Overall population, African-American population, African-American men (20 year old and over), African-American women (20 year old and over), White population, White men (20 year old and over), White women (20 year old and over). Note that the unemployment rates based on gender only include workers 20 year and older. This explains why the unemployment rate based only on race can be larger than both the unemployment rates based on gender and race. This feature is particularly evident for the African-American population, characterized by very high unemployment rates for workers between 16 and 19. Gordon and Sommers (2016), using county level data and panel regressions, find that median income and poverty rates are better predictors of mortality rates than unemployment rates. In our analysis, we use unemployment rates because these series are available over a prolonged period of time. In interpreting our results, unemployment should be regarded as a proxy for the overall state of the economy, correlated with other variables of interest such as poverty rates and household income. In line with this interpretation, an increase in unemployment captures a downturn in real activity that can affect health through multiple channels, direct and indirect, such as income, crime rates, drug abuse, and the life style of the peer group. Furthermore, the panel regression approach used in previous studies assumes that the effects of the national business cycle is absorbed by the time fixed effect (Ruhm, 2015). Thus, our approach is inherently different and complementary to the previous studies interested in the relation between real activity and human health. Figure 1 provides a first look at the raw data series for the overall population and for the six groups identified based on race and gender. We highlight a series of features in the data that are noteworthy. First, for all groups considered, the average life expectancy has been increasing over time. Second, and consistent with the increase in life-expectancy, the age-adjusted death rates have been falling over time. Third, the African-American population (both men and women) has historically experienced shorter average life expectancy, higher age-adjusted death rates, and higher unemployment rates relative to the White population. Fourth, men have shorter life expectancy and higher death rates, regardless of race. Finally, these differences have been declining over time and the conditions for African-American men have improved the most as they currently experience much longer life expectancy and lower (age adjusted) death rates relative to the 1970s. However, the differences remain large and visible.Fig. 1 Raw data. Notes: This figure presents the average life expectancy (first chart), the age-adjusted death rate (second chart), and the unemployment rate (third chart) for the overall US population and for the US population classified according to race and gender. The data span from 1950 to 2017 for the overall population; 1954 to 2017 for White population; and 1972–2017 for African-American population. Fig. 1 To further elaborate on these points, we compute percentage changes of life expectancy and death rates by taking their log differences and multiplying them by 100. Table 1 provides the summary statistics (i.e., sample average, autocorrelation at first lag, and standard deviation) of these two transformed variables and the unemployment rate. The summary statistics are computed for the overall population and for the six groups identified based on race and gender. In the left part of the table, we report the summary statistics for each group based on the longest sample available for that group. In the right part of the table we report the same statistics over the common subsample (1972–2017) for which we have data for all groups. This approach facilitates the comparison across groups.Table 1 VAR data summary statistics. Table 1 Available sample Common sample Mean Autocorr Stdev Mean Autocorr Stdev (A) Percentage change in life expectancy Overall population 0.21 -0.08 0.31 0.22 0.09 0.27 African-American 0.34 0.32 0.46 0.34 0.32 0.46 African-American (M) 0.39 0.50 0.52 0.39 0.50 0.52 African-American (W) 0.28 0.25 0.39 0.28 0.25 0.39 White 0.18 -0.05 0.26 0.20 0.06 0.26 White (M) 0.20 0.07 0.28 0.25 0.28 0.24 White (W) 0.15 -0.08 0.25 0.15 0.16 0.24 (B) Percentage change in the age-adjusted death rate Overall population -1.02 -0.20 1.77 -1.13 -0.13 1.58 African-American -1.23 0.04 2.07 -1.23 0.04 2.07 African-American (M) -1.29 0.12 2.09 -1.29 0.12 2.09 African-American (W) -1.14 -0.01 2.21 -1.14 -0.01 2.21 White -0.89 -0.16 1.68 -1.06 -0.14 1.57 White (M) -0.89 -0.09 1.67 -1.26 -0.19 1.42 White (W) -0.87 -0.17 1.80 -0.90 -0.12 1.77 (C) Unemployment rate Overall population 5.82 0.77 1.62 6.36 0.75 1.56 African-American 12.08 0.81 2.91 12.08 0.81 2.91 African-American (M) 10.94 0.76 3.06 10.94 0.76 3.06 African-American (W) 10.23 0.83 2.41 10.23 0.83 2.41 White 5.26 0.76 1.40 5.59 0.74 1.43 White (M) 4.51 0.77 1.56 4.94 0.71 1.53 White (W) 4.79 0.77 1.14 4.96 0.79 1.23 Notes: We provide the sample moments (mean, autocorrelation at first lag, and standard deviation) of the three series used in the estimation: the growth rate of the average life expectancy (panel A), the growth rate of the age-adjusted death rate (panel B), and the unemployment rate (panel C) for the overall US population and for distinct groups identified based on race and gender. The available data span from 1950 to 2017 for the overall population; from 1954 to 2017 for white workers; and from 1972 to 2017 for African-American workers. The common sample is from 1972 to 2017. In line with what we outlined above, all groups have experienced an improvement in life-expectancy and a decline in death rates. However, the table reveals substantial heterogeneity across gender and race. Specifically, we find that proportionally the African-American population has experienced a more significant improvement in these measures of human well-being. Given that the White population was starting with lower mortality rates and higher life-expectancy, this difference in growth rates translates into a gap that has been narrowing over time. At the same time, the variables corresponding to the African-American population are much more volatile than those of the White or overall populations. Furthermore, the unemployment rates for the African-American population are also larger and more volatile when compared to those of the White population. This feature of the data already suggests that the impact of the business cycle on human well-being could be more relevant for the African-American population. The data also reveal high frequency movements in the growth rates of life-expectancy and mortality rates. These can be due to measurement error, as the US population is not continuously observable, or other idiosyncratic factors unrelated to the business cycle or the historical improvement in the quality of life. The negative sample autocorrelation of the growth rates speaks to this evidence. Thus, in our empirical analysis we allow for the possibility that life-expectancy and death-rates are measured with error. We discuss the details in the next subsection. 2.2 The model We specify a VAR to describe the joint dynamics of the growth rates of life expectancy, mortality rates, and the level of unemployment rate for each group i that we are interested in (overall population and the six groups organized based on race and gender):(1) xi,t=μi+Φixi,t−1+ηi,t,ηi,t∼N(0,Σi). where xi,t is a three-dimensional vector containing three series for group i: the growth rate of life-expectancy, the growth rate of the age-adjusted death rate, and the unemployment rate; μi is a vector of constants, the matrix Φi contains the autoregressive coefficients, and ηi,t is a vector of Gaussian innovations. The VAR above is specified with one lag. This is the number of lags that we use in our empirical analysis and it is chosen based on the Akaike Information criterion. However, the methodology that we describe below easily allows for more lags. In estimating (1), we want to allow for the possibility that life expectancy and death rates are observed with an error or have high frequency swings around a central trend. Thus, we cannot directly run the VAR. To tackle this issue, we specify a state-space model in which the measurement equation allows for measurement errors in the levels of the observables:(2) yi,t=zi,t+ϵi,t,ϵi,t∼N(0,Ωi), where yi,t includes the log of life expectancy, the log of death rates, and the unemployment rate for group i. Here, zi,t contains the true log-level series for life expectancy and age-adjusted death rates, free from measurement errors. We model the measurement errors as i.i.d. random variables with a diagonal covariance matrix Ωi. We do not allow for measurement errors in the unemployment rate, ϵi,3,t=0, as the measurement issue for the unemployment rate is much less of a concern at annual frequency. As the measurement equation is expressed in log-levels, we express xi,t as linear transformation of the log-level series for consistency:(3) xi,t=zi,t−Mzi,t−1,M=[100010000]. Combining (1) with (3), the state transition equation (1) can be re-expressed in terms of the true log-level series(4) zi,t=μi+(M+Φi)zi,t−1−ΦMzi,t−2+ηi,t,ηi,t∼N(0,Σi). In sum, the measurement Eq. (2) and the state transition Eq. (4) constitute our state-space representation of the system. Thus, our state-space model enables the estimation of the VAR parameters, Φi and Σi, which are crucial for the analysis presented below, while at the same time allowing for measurement errors. The fact that observation errors apply to the log-levels of the age-adjusted death rates and life expectancy as opposed to their growth rates implies that the filtered series for life-expectancy and death rates cannot persistently deviate from the observed ones. Data exploration. We estimate a total of seven VARs: Overall population (our benchmark case), African-American, African-American men, African-American women, White, White men, and White women. All data are available at annual frequency. Lag order selection. We set the lag order of the VAR in the state transition equation to one. The choice of a one lag in (1) is guided by the Akaike information criterion (AIC) when we directly estimate (1) with bandpass filtered series as a first test. Furthermore, it seems appropriate given the small sample that we have available and the use of data at annual frequency. Bayesian inference. We conduct Bayesian inference using a Gibbs sampling algorithm. In essence, we draw for the VAR coefficients conditional on the states, and we draw for the state conditional on the VAR coefficients. The priors for the VAR parameters are diffuse and symmetric with respect to the relation between unemployment and the growth rates of life-expectancy and the age-adjusted death rates. In other words, the priors imply that a negative relation is as likely as a positive relation. Furthermore, we use the same priors for the different groups identified based on race and gender. Thus, all the differences between groups documented below are driven by the information contained in the data. The Appendix contains more details about the priors and the Gibbs sampling algorithm. Cholesky decomposition. To quantify the effects of an increase in unemployment on life expectancy and mortality rates, we need to isolate structural shocks to unemployment, i.e., shocks that are exogenous with respect to the mortality rate and life expectancy. To do this, we orthogonalized the covariance matrix of the VAR residuals using a Cholesky decomposition with unemployment placed last. This assumption implies that the unemployment shock can affect the other two series only with a lag of one year. For robustness, we also consider a specification in which the ordering is reverted and unemployment is allowed to affect the other two variables contemporaneously. 3 Results From the estimation of our state-space model, we obtain posterior estimates of model unknowns including the VAR parameters in (1) and the smoothed estimates of life expectancy and death (mortality) rates free from measurement errors. We first discuss the properties of the smoothed growth rates of life expectancy and mortality rates, and subsequently, examine how an increase in unemployment affects the growth rates of life expectancy and mortality rates conditional on the posterior estimates of the VAR coefficients. 3.1 Model-implied growth rates of life expectancy and mortality Figure 2 presents the growth rates of life expectancy and the death rate for the overall population implied by the smoothed estimates and compares them with the corresponding data series. The smoothed and raw series for the sub-groups are reported in Fig. 3 . We find that the smoothed and raw growth rates move very closely. However, a non-negligible portion of the fluctuations in the raw growth rates is attributable to measurement errors or high frequency changes unrelated to the state of the economy or progress in health care, most notably so for the growth rate of death rates. One way to clearly see this pattern is by analyzing the behavior of the autocorrelation and standard deviations of the raw series and of the smoothed series as reported in the right part of Table 2 . For ease of comparison, the left part of Table 1 is reproduced in Table 2. We find that compared to the data moments, the model-implied autocorrelation value is higher and the standard deviation is smaller, as the noise in the original series is removed.Fig. 2 VAR data. Notes: This figure presents the growth rate of the average life expectancy (first chart), the growth rate of the age-adjusted death rate (second chart), and the unemployment rate (third chart) for the overall US population. The original series are presented in gray circled lines. For the first two series, we compare with the smoothed estimates from our model (green lines). The data span from 1950 to 2017. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) Fig. 2 Fig. 3 VAR data by race and gender. Notes: This figure presents the growth rate of the average life expectancy (first column), the growth rate of the age-adjusted death rate (second column), and the unemployment rate (third column) for distinct groups identified based on race and gender. The original series are presented in gray circled lines. For the first two series, we compare with the smoothed estimates from our model (green lines). The data span from 1954 to 2017 for the White population, and from 1972 to 2017 for the African-American population. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) Fig. 3 Table 2 Model summary statistics. Table 2 Data Model Mean Autocorr Stdev Mean Autocorr Stdev (A) Percentage change in life expectancy Overall population 0.21 -0.08 0.31 0.21[0.19,0.22] 0.66[0.51,0.76] 0.19[0.16,0.23] African-American 0.34 0.32 0.46 0.33[0.31,0.36] 0.78[0.66,0.85] 0.31[0.25,0.38] African-American (M) 0.39 0.50 0.52 0.38[0.36,0.41] 0.80[0.71,0.86] 0.38[0.32,0.46] African-American (W) 0.28 0.25 0.39 0.27[0.25,0.30] 0.76[0.64,0.83] 0.27[0.21,0.33] White 0.18 -0.05 0.26 0.18[0.16,0.19] 0.65[0.51,0.76] 0.19[0.15,0.23] White (M) 0.20 0.07 0.28 0.20[0.18,0.21] 0.70[0.57,0.79] 0.20[0.17,0.24] White (W) 0.15 -0.08 0.25 0.15[0.14,0.17] 0.63[0.48,0.75] 0.18[0.15,0.21] (B) Percentage change in the age-adjusted death rate Overall population -1.02 -0.20 1.77 −1.00[−1.03,−0.96] 0.59[0.39,0.73] 0.87[0.75,1.03] African-American -1.23 0.04 2.07 −1.23[−1.29,−1.16] 0.70[0.54,0.79] 1.27[1.09,1.48] African-American (M) -1.29 0.12 2.09 −1.29[−1.36,−1.23] 0.76[0.64,0.83] 1.36[1.19,1.55] African-American (W) -1.14 -0.01 2.21 −1.12[−1.19,−1.06] 0.72[0.58,0.81] 1.25[1.07,1.46] White -0.89 -0.16 1.68 −0.92[−0.96,−0.88] 0.65[0.49,0.76] 0.89[0.78,1.03] White (M) -0.89 -0.09 1.67 −0.92[−0.96,−0.88] 0.71[0.57,0.80] 0.95[0.84,1.07] White (W) -0.87 -0.17 1.80 −0.89[−0.93,−0.85] 0.69[0.54,0.79] 0.94[0.83,1.08] (C) Unemployment rate Overall population 5.82 0.77 1.62 5.82 0.77 1.62 African-American 12.08 0.81 2.91 12.08 0.81 2.91 African-American (M) 10.94 0.76 3.06 10.94 0.76 3.06 African-American (W) 10.23 0.83 2.41 10.23 0.83 2.41 White 5.26 0.76 1.40 5.26 0.76 1.40 White (M) 4.51 0.77 1.56 4.51 0.77 1.56 White (W) 4.79 0.77 1.14 4.79 0.77 1.14 Notes: We provide the sample moments (mean, autocorrelation at first lag, and standard deviation) of the three series used in the estimation: the growth rate of the average life expectancy (panel A), the growth rate of the age-adjusted death rate (panel B), and the unemployment rate (panel C) for the overall US population and for distinct groups identified based on race and gender. We compare with those computed from the smoothed estimates (without measurement errors) of our model. The data span from 1950 to 2017 for the overall population; from 1954 to 2017 for white workers; and from 1972 to 2017 for African-American workers. At the same time, observation errors only account for the high frequency movements of the original variables. The Appendix shows that the log level of the two (actual and smoothed) series move very closely, implying that the smoothing algorithm does not alter the core dynamics of the original variables. In sum, our state-space model is able to smooth out fluctuations in the raw growth rates while preserving the core dynamics of the underlying variables. As a result, at lower frequencies (i.e., cycles longer than one year), the smoothed growth rates comove with the raw growth rates. These are arguably the frequencies that we are interested in when studying the relation between unemployment and human health and they are less likely to be contaminated by significant observation errors. 3.2 Impulse responses to an unemployment shock To understand the effects of an increase in unemployment on the mortality rate and life expectancy, we use an impulse response analysis based on a Cholesky decomposition. We order unemployment last, implying that all contemporaneous co-movements between the variables of interest are attributed to structural shocks to the other two variables. In other words, under this identification assumption, shocks to unemployment can affect mortality rates and life-expectancy only with a lag. Below, we also discuss results for the case in which unemployment is ordered first. Figure 4 reports the responses of life expectancy and the age-adjusted death rate to a one-standard deviation shock to the unemployment rate for the overall population. The figure reports the median response and 68% and 90% credible sets. The shock to unemployment is quite persistent and is followed by sizable changes in the growth rates of life expectancy and the death rate. These effects are also quite long lasting. Focusing on the median response, we can see that the effect of the unemployment shock on the growth rates of life expectancy and the death rate reaches its peak in the fourth year. The median values for these peaks are around -0.04% and 0.15%, respectively.Fig. 4 Impulse responses to a one-standard-deviation shock to unemployment. Notes: We provide impulse responses to a one-standard-deviation shock to unemployment for the overall US population. The solid-lines represent the median values and the dark and light-shaded areas indicate 68% and 90% bands, respectively. Fig. 4 The impulse responses based on race and gender are presented in Fig. 5 . To facilitate the comparison across groups, the median impulse responses for each group are reported in Fig. 6 . The first row of Fig. 6 contains the median impulse responses to a one-standard deviation unemployment shock (as in Figs. 4 and 5). Given that the size of the typical shock varies across groups, the second row of Fig. 6 reports the impulse responses to a normalized (1%) unemployment shock to keep the size of the shock identical across groups.Fig. 5 Impulse responses to a one-standard-deviation shock to unemployment. Notes: We provide impulse responses to a one-standard-deviation shock to unemployment for the US population classified according to race and gender. The solid-lines represent the median values and the dark and light-shaded areas indicate 68% and 90% bands, respectively. Fig. 5 Fig. 6 Impulse response comparison. Notes: We compare median impulse responses to a one-standard-deviation (first row panels) or a unit shock (second row panels) to unemployment, which is identified via a Cholesky decomposition, across the overall US population and for the US population classified according to race and gender. Fig. 6 Several important observations can be made. The responses of the growth rates of the age-adjusted death rates are sizable for all groups. For the growth rate of life expectancy, 90% credible sets exclude zero for three out of six groups, while 68% credible sets exclude zero for all groups. Thus, we find strong statistical evidence of a dynamic relation between unemployment shocks and human well-being for all groups. African-Americans experience larger unemployment shocks and the effects of these shocks on unemployment are more persistent. Conditional on the same race, the shocks for women are smaller. The effects on life expectancy and death rates are more severe for African-Americans, overall. This cannot be entirely explained as a consequence of larger shocks, as the pattern largely persists when controlling for the size of the shock. However, it could be the result of the larger persistence of unemployment following the shocks. Conditional on the size of the shock, we find that women (both African-American and White) present a relatively larger increase in death rates. Finally, we notice that the effects for White men unfold over a longer period of time. They experience a mild reaction of death rates in the short-run, but they seem to have the larger response of age-adjusted death rates in the long run (past 10-year horizon). In comparing these impulse responses, it is important to keep in mind that the sample is not homogeneous across groups. The sample of the African-American population is significantly shorter (1972–2017) than that of the White population (1954–2017). However, most of the results described below are robust to using the common sample 1972–2017 (see Fig. E.3 in the appendix). The only pattern that changes when using the common sample is the persistent response of white men death rates at long horizons. When using the shorter sample, this result does not appear. Thus, we infer that it is mostly driven by the dynamics of the three variables at the beginning of the sample. The Appendix also reports results for an identification strategy based on ordering unemployment first in the Cholesky decomposition. In this case, unemployment shocks are allowed to have contemporaneous effects on the other variables in the system. As explained in the introduction, there are several reasons explaining why unemployment could have contemporaneous effects on mortality rates (not commuting to work, no risk of work related deaths, etc.). The finding that an unemployment shock is eventually followed by an increase in mortality and a decline in life-expectancy is robust to using this different identification strategy. However, when using this alternative identification assumption, an interesting result emerges with respect to the short-run dynamics. For some groups, we find evidence that an increase in unemployment can lead to a contemporaneous decline in mortality rates and to a contemporaneous increase in life-expectancy. However, the error bands for this initial response are large and the effect quickly reverts in two-three years. In the long-run, mortality increases and life expectancy declines, in line with our benchmark results. In other words, the long-term effect dominates the short-term dynamics. Thus, the results about the impact of the COVID-19 unemployment shock that we will describe below are not qualitatively affected by the specific identification strategy. At the same time, this different identification strategy could be helpful in understanding why previous studies have often found conflicting results on the effects of unemployment on human health. On impact, unemployment can lead to a decline in mortality as deaths due to motor vehicle or work-related accidents diminish, but over time economic distress might take a toll on human well-being. 3.3 Historical importance of unemployment shocks The previous subsection highlights that unemployment shocks are followed by statistically significant changes in the growth rates of the age adjusted death rate and life expectancy. In this subsection, we are interested in assessing the relative importance of unemployment shocks for these two variables. To assess the historical importance of unemployment shocks, we compute a variance decomposition by comparing the unconditional variance, as implied by the VAR model, when only the orthogonalized unemployment shock is active, to the overall variance. Specifically, we compute the fraction of forecast error variance of the percentage change in life expectancy and the age-adjusted mortality rate explained by the unemployment shock at 5-, 10-, 15-, and 20-year horizons. Table 3 summarizes our findings. The first row in each panel of Table 3 reports the results for the overall population. At 15 years, the fractions of forecast error variance explained by unemployment shocks are 13.31% and 10.89% for life expectancy and the death rate, respectively. Thus, the contribution of unemployment shocks to these measures of human health is by no means negligible.Table 3 Fraction of forecast error variance explained by unemployment shock (%). Table 3 (1) Percentage change in life expectancy 5 years 10 years 15 years 20 years Overall population 8.33[0.27,28.24] 12.99[0.64,38.81] 13.31[0.72,38.75] 13.36[0.73,39.04] African-American 17.62[2.79,39.16] 23.98[5.57,46.57] 23.95[5.79,45.98] 24.02[5.79,46.03] African-American (M) 34.43[7.89,58.38] 39.00[14.05,62.21] 38.63[14.14,61.39] 38.49[14.25,61.48] African-American (W) 12.51[0.91,34.20] 20.70[2.49,46.04] 20.84[2.96,44.81] 21.12[3.03,45.23] White 7.04[0.19,30.31] 11.04[0.44,40.22] 11.37[0.49,40.24] 11.44[0.50,40.69] White (M) 8.15[0.17,31.83] 14.03[0.43,45.19] 14.89[0.52,46.27] 15.02[0.53,46.58] White (W) 6.65[0.17,28.76] 10.85[0.42,39.07] 11.19[0.47,39.71] 11.22[0.49,40.03] (2) Percentage change in the age-adjusted death rate 5 years 10 years 15 years 20 years Overall population 8.80[0.56,24.69] 10.73[0.97,28.75] 10.89[1.07,28.97] 10.93[1.09,29.08] African-American 25.88[10.86,44.00] 26.53[12.76,44.59] 26.75[12.84,44.36] 26.78[12.85,44.46] African-American (M) 24.42[5.16,46.45] 28.37[8.05,49.90] 28.65[8.43,49.82] 28.82[8.47,50.15] African-American (W) 22.42[6.11,42.08] 26.09[10.22,45.68] 26.18[10.52,44.46] 26.33[10.67,44.99] White 12.76[1.06,31.58] 15.31[1.76,35.92] 15.50[1.86,36.06] 15.55[1.86,36.22] White (M) 8.90[0.28,27.55] 13.34[0.70,36.54] 13.83[0.82,37.71] 13.92[0.86,37.98] White (W) 12.72[0.93,32.70] 16.20[1.52,38.86] 16.47[1.66,39.02] 16.50[1.69,39.30] Notes: The table provides the fraction of forecast error variance of the percentage change in life expectancy and the age-adjusted mortality rate explained by unemployment shock at 5, 10, 15 and 20 years. We present the median values and provide the values that correspond to the 90% bands in brackets. In line with our previous findings, Table 3 reveals substantial heterogeneity across gender and race. We find that unemployment shocks account for larger fractions of the variations in life expectancy in the case of men relative to women. For example, the fraction of variance for the growth rate of life expectancy is twice as large (38.63% vs 20.84%) for African-American men with respect to African-American women, while the difference between White men and White women is smaller (14.89% vs 11.19%). A more similar decomposition emerges for the contribution to the age-adjusted death rate: The fraction of variance is similar (28.65% vs 26.18%) for African-American men with respect to African-American women and lower for White men with respect to White women (13.83% vs 16.47% at the 15-year horizon). The gap across races is in this case the most noticeable feature of the results: The contribution of the unemployment shocks to the variance of life expectancy and death rates is around twice as large for the African-American population. 3.4 Causes of death In this section, we explore the effects of economic distress, as captured by the unemployment rate, on the main causes of death. This analysis is helpful in understanding the channels through which economic distress affects the overall mortality rate. Due to data availability, we focus on the overall population. According to the National Center for Health Statistics (NCHS), National Vital Statistics System, historically, the five leading causes of age-adjusted deaths for the overall population (per 100,000 U.S. standard population) are (1) Heart disease; (2) Cancer; (3) Accidents; (4) Stroke; and (5) Influenza and pneumonia (ordered based on their relative importance). Figure 7 reports the age-adjusted death rates for the five major causes of deaths over the period 1954–2018. These five causes of deaths appear to be consistently important over the years. Since 1950, heart disease and cancer have been identified as the top two leading causes of deaths in the U.S. However, while the death rate due to cancer appears relatively flat over the years, the death rate for heart disease shows a significant decline that contributes significantly to the reduction in the overall death rate documented in Fig. 1. Interestingly, the two leading causes of death were about to swap their relative importance, but the decline in the death rate due to hearth diseases visibly slowed down around 2010, in the aftermath of the Great Recession. We notice that the late years of the sample also present an increase in the deaths due to accidents, that become the third most important cause of death at the expenses of stroke.Fig. 7 Leading causes of deaths. Notes: According to the National Center for Health Statistics (NCHS), National Vital Statistics System, historically, the five leading causes of age-adjusted deaths for the overall population (per 100,000 U.S. standard population) are (1) Heart disease; (2) Cancer; (3) Accidents; (4) Stroke; and (5) Influenza and pneumonia (ordered based on their relative importance). Fig. 7 We use our state-space model to relate changes in age-adjusted death rates across the different causes to unemployment shocks. We estimate five separate models, one for each of the five leading causes of death. Due to data availability, we consider a bivariate system by dropping the log change of life expectancy. We then compute the impulse responses of the growth rate of age-adjusted death rates for each of the leading causes of deaths to a unit shock to unemployment rate and report the results in Fig. 8 . With respect to the exercise reported in Fig. 5, we normalize the size of the initial shock to unemployment to 1% in order to facilitate the comparison across the different impulse responses.Fig. 8 Impulse responses of the growth rate of age-adjusted death rates for the leading causes of deaths to a unit shock to unemployment rate. Notes: According to National Center for Health Statistics (NCHS), National Vital Statistics System, the five leading causes of age-adjusted deaths in the U.S. are (1) Heart disease; (2) Cancer; (3) Accidents; (4) Stroke; and (5) Influenza and pneumonia (ordered based on their relative importance).The NCHS Data Visualization Gallery provides the time-series of (1)–(5) as well as the age-adjusted overall death rates from 1900 to 2017. Fig. 8 Consistent with our previous findings, an increase in the unemployment rate is associated with significant increases in the growth rates of the leading causes of deaths, with the exception of cancer. The increase is particularly pronounced for accidents and influenza and pneumonia, but at longer horizon it becomes large also for stroke. Of particular interest is the effect on heart disease, the first cause of death. In this case, the increase in the growth rate is smaller, but given the importance of this cause of death, the estimated effect on the growth rate can still translate into an important effect on the overall mortality rate. Table 4 provides the cumulative effect of the unemployment shock on the age-adjusted death rates for the leading causes of deaths at a 5-, 10-, 15-, and 20-year horizon. Panel (A) of Table 4 summarizes Fig. 8. We translate the percentage changes in age-adjusted death rates of the leading causes of deaths into changes in death rates by multiplying the predicted change at 5, 10, 15, and 20 years provided in Panel (1) of Table 4 with the most recent age-adjusted death rates of the leading causes of deaths in 2018. The age-adjusted death rates (deaths per 100,000 U.S. standard population) for the five leading causes of death in 2018 were: (1) Heart disease, 163.6; (2) Cancer, 149.1; (3) Accidents, 48.0; (4) Stroke, 37.1; and (5) Influenza and pneumonia 14.9. The results are reported in Panel (2) of Table 4. Focusing on the 15 and 20 years horizon, we find that a large increase in the overall death rate can be accounted for by accidents and heart disease, while stroke also plays an important role.Table 4 Cumulative changes of age-adjusted death rates for the leading causes of deaths over different horizons following a 1% unemployment shock. Table 4 (1) Percentage change 5 years 10 years 15 years 20 years Accidents 3.10[1.96,4.36] 6.24[3.75,10.19] 5.93[3.26,12.24] 5.06[3.12,11.81] Cancer −0.10[−0.72,0.51] −0.25[−1.74,1.29] −0.33[−2.46,1.95] −0.37[−2.92,2.51] Heart disease 0.62[−0.07,1.36] 1.38[−0.17,3.21] 1.66[−0.23,4.30] 1.71[−0.27,4.72] Influenza and pneumonia 1.84[0.52,3.18] 4.50[1.36,8.19] 5.65[1.87,11.55] 5.79[2.13,13.36] Stroke 1.56[0.51,2.63] 3.56[1.20,6.86] 4.28[1.52,9.48] 4.36[1.63,10.99] (2) Change 5 years 10 years 15 years 20 years Accidents 1.49[0.94,2.09] 3.00[1.80,4.89] 2.85[1.57,5.87] 2.43[1.50,5.67] Cancer −0.15[−1.07,0.76] −0.37[−2.60,1.93] −0.49[−3.66,2.91] −0.55[−4.36,3.74] Heart disease 1.01[−0.12,2.23] 2.25[−0.28,5.25] 2.72[−0.38,7.03] 2.80[−0.44,7.73] Influenza and pneumonia 0.27[0.08,0.47] 0.67[0.20,1.22] 0.84[0.28,1.72] 0.86[0.32,1.99] Stroke 0.58[0.19,0.98] 1.32[0.44,2.55] 1.59[0.56,3.52] 1.62[0.60,4.08] Notes: We multiply the predicted change at 5, 10, 15, and 20 years provided in Panel (1) of Table 4 with the most recent age-adjusted death rates of the leading causes of deaths in 2018. The age-adjusted death rates (deaths per 100,000 U.S. standard population) for the 10 leading causes of death in the most recent year 2018 are: (1) Heart disease, 163.6; (2) Cancer, 149.1; (3) Accidents, 48.0; (4) Chronic lower respiratory disease, 39.7; (5) Stroke, 37.1; (6) Alzheimer disease, 30.5; (7) Diabetes, 21.4; (8) Kidney disease, 12.9; (9) Influenza and pneumonia 14.9; and (10) Suicide, 14.2. The results presented in this section provide some interesting insights about the channels through which economic activity can affect mortality rates. On the one hand, the effects on mortality due to heart diseases, the leading cause of death in the USA (659,041 deaths in 2019, CDC data), suggest that access to preventive care and lifestyle might play an important role in explaining why unemployment is followed by an increase in mortality rates. Consistently, a similar pattern is noted for stroke-related mortality that is largely affected by similar risk and life style factors as heart diseases. A body of literature shows that the use of health services and preventive care are reduced during recessions (Ruhm, 2000). Interestingly, our estimates also indicate an increase in pneumonia/influenza-related mortality. These results may be also explained based on the lack of access to preventive care and the decline in a healthy life style. Vaccination strategies are highly effective in preventing the flu and certain types of pneumonia, but unemployed individuals and their relatives are unlikely to timely seek vaccinations and attend regular doctor visits. Also, an unhealthy lifestyle is likely to increase risks of contracting these infections by weakening the immune system. Importantly, cancer is the second leading cause of death in the USA (599,601 deaths in 2019, CDC data) and it is well established that cancer prevention screening is crucial for early identification of the disease, leading to early staging and better outcomes. To our surprise cancer mortality appears to remain stable over time following an increase in unemployment. This result is likely the consequence of the steady progress in cancer care that might avoid the worst outcomes related to a delayed diagnosis. On the other hand, the large effect of an unemployment shock on accidents indicates that other channels are likely to be at work. Possible explanations are that people engage in more dangerous activities, spend more time driving in a non-careful way, and are more subject to domestic accidents during periods of economic distress. Notice that unemployment is for us an indicator of the state of the economy. Thus, the less safe behavior and its fatal consequences do not exclusively pertain to unemployed workers, but they might arise as a result of a general equilibrium effect. Finally, an extensive literature provides evidence of an increase in suicides during times of economic distress and data are surfacing regarding worsening opioid crisis during the COVID-19 pandemic. 4 Impact of the COVID-19 unemployment shock The COVID-19 pandemic is having immediate, substantial consequences on the death rate in the United States. At the same time, it has led to a severe macroeconomic contraction. Part of this contraction can be explained in light of the shock itself, as several people autonomously decided to scale down their consumption, especially for services such as restaurant and entertainment. On top of this, lockdowns have also contributed to further reduce economic activity. These measures have arguably saved lives, reducing the contagion rate and mitigating the risk of exhaustion of health care personnel and resources. However, the severe economic contraction due to the pandemic itself and the measures used to contain it might have long-term consequences on life-expectancy and death rates. In this section, we are interested in using our VAR to assess the potential impact of the economic contraction in light of the historical relation between unemployment and human health. 4.1 Effects on life expectancy and death rates What is the cumulative effect of the COVID-19 unemployment shock on life expectancy and the age-adjusted death rate at a 5-, 10-, 15-, and 20-year horizon? To answer this question, we rely on our state-space model to produce a measure of the unemployment shock experienced by the US economy. Despite the estimation sample ending in 2017, unemployment data for 2018, 2019, and 2020 are available for the overall population and for each group. Conditional on the posterior coefficient estimates and the 2018–2020 values of the unemployment rates, we can filter out the reduced-form shocks ηi,t in (1) based on the state-space model. We can do so by treating the 2018–2020 values of the life expectancy and the death rate as missing observations. We then apply a transformation based on the Cholesky decomposition to the inferred reduced-form shocks to back out the structural unemployment shocks. The implied COVID-19 unemployment shock distributions are provided in Table 5 . This empirical approach aims at distinguishing and isolating the consequences for human health of the economic distress related to the COVID-19 pandemic as compared to the COVID-19 pandemic itself. Obviously, the original shock that led to the increase in unemployment is a health shock, but our goal here is to only consider the consequences for human health of the COVID-19-related increase in unemployment. In this context, unemployment acts as a readily available proxy for the overall economic distress.Table 5 COVID-19 unemployment shocks. Table 5 Standard deviation Magnitude (%) Overall population 3.64[2.37,4.73] 2.90[1.79,3.80] African-American 2.83[1.48,4.02] 2.97[1.48,4.37] African-American (M) 2.59[1.38,3.58] 3.39[1.71,4.79] African-American (W) 3.71[2.39,4.90] 3.40[2.13,4.52] White 3.61[2.42,4.62] 2.68[1.70,3.46] White (M) 3.34[2.23,4.21] 2.79[1.81,3.52] White (W) 4.91[3.62,6.09] 3.10[2.23,3.82] Notes: The table provides the COVID-19 unemployment shock implied by our state-space model for overall US population and for US population classified according to race and gender. To facilitate the interpretation, we provide the scale of standard deviation as well as the actual magnitude (%) of the shock. Based on our approach, the COVID-19 unemployment shock is about 3.64 standard deviations larger (about 2.90% in magnitude) than the typical shock to the unemployment rate. We applied the same method to obtain a measure of the unemployment shock for the groups sorted by gender and race. The scale of these shocks in terms of standard deviations (or percentage points) are: 2.83 (or 2.97%) for the African-American population, 2.59 (or 3.39%) for African-American men, 3.71 (or 3.40%) for African-American women, 3.61 (or 2.68%) for the White population, 3.34 (or 2.79%) for White men, and 4.91 (or 3.10%) for White women. Thus, we find that, with respect to the typical unemployment shock, women (both African-American and White) have been disproportionately affected by the COVID-19 unemployment shock. This effect is particularly visible for White women, who experienced the largest shock with respect to the typical innovation, emphasizing the atypical nature of the current recession. However, when measured in terms of actual changes in the unemployment rate, African-Americans still show the largest increase in unemployment. Table 6 reports the cumulative effect of the COVID-19 unemployment shock on life expectancy and death rates as predicted by our model at different horizons. The first row in each panel of Table 6 reports the results for the overall population. At the 15-year horizon, the death rate is 2.43% higher and life expectancy is 0.83% lower. These numbers represent the marginal effect of the shock: they indicate the expected change in life expectancy and death rates following the COVID-19 unemployment shock keeping fixed other factors that affect these measures of well-being, like the progress in health care.Table 6 Cumulative changes of life expectancy and age-adjusted death rates over different horizons following the COVID-19 unemployment shock. Table 6 (1) Percentage change in life expectancy 5 years 10 years 15 years 20 years Overall population −0.42[−0.95,0.01] −0.80[−1.97,0.00] −0.83[−2.27,0.00] −0.83[−2.29,0.00] African-American −0.58[−1.13,−0.16] −1.20[−2.64,−0.32] −1.16[−3.16,−0.25] −1.09[−3.17,−0.28] African-American (M) −0.84[−1.46,−0.33] −1.57[−3.06,−0.58] −1.53[−3.64,−0.52] −1.47[−3.70,−0.54] African-American (W) −0.62[−1.21,−0.14] −1.34[−2.97,−0.27] −1.32[−3.65,−0.16] −1.21[−3.66,−0.15] White −0.37[−0.94,0.10] −0.72[−2.01,0.15] −0.75[−2.34,0.16] −0.76[−2.41,0.17] White (M) −0.40[−0.93,0.07] −0.85[−2.14,0.09] −0.94[−2.66,0.11] −0.94[−2.85,0.12] White (W) −0.52[−1.28,0.16] −0.99[−2.74,0.28] −1.01[−3.16,0.34] −1.00[−3.15,0.35] (2) Percentage change in the age-adjusted death rate 5 years 10 years 15 years 20 years Overall population 1.83[0.41,3.55] 2.56[0.61,5.67] 2.43[0.57,5.70] 2.42[0.56,5.52] African-American 2.90[1.34,4.81] 4.38[1.81,8.88] 3.70[1.34,9.22] 3.56[1.41,8.76] African-American (M) 2.72[1.00,4.83] 4.33[1.44,9.15] 3.92[1.03,9.92] 3.84[1.00,9.95] African-American (W) 3.46[1.67,5.70] 5.95[2.68,11.86] 5.21[1.98,12.55] 4.71[1.86,11.71] White 2.16[0.62,4.14] 3.09[0.89,6.97] 2.91[0.87,7.02] 2.93[0.87,6.82] White (M) 1.71[0.14,3.54] 3.04[0.34,7.20] 3.16[0.37,8.47] 3.14[0.33,8.72] White (W) 3.19[0.80,5.98] 4.78[1.30,10.73] 4.41[1.25,11.04] 4.40[1.27,10.41] Notes: The table shows the predicted cumulative percentage change in life expectancy and age adjusted mortality rate at 5, 10, 15 and 20 years. We present the median values and provide the values that correspond to the 90% bands in brackets. Results are presented for the overall US population and subdivided based on race and gender. Table 6 also reveals substantial heterogeneity across genders and races. The impact on the death rate is large for all groups, but visibly larger for African-Americans. As explained above, this is in part the result of a larger shock, but also of a larger response conditional on the size of the shock. At a 15-year horizon, we expect a decline in life expectancy of 1.16% for African-American citizens, 1.53% for African-American men, 1.32% for African-American women, 0.75% for White citizens, 0.94% for White men, and 1.01% for White women. At a 15-year horizon, the increases in death rates are 3.70% for African-Americans, 3.92% for African-American men, 5.21% for African-American women, 2.91% for Whites, 3.16% for White men, and 4.41% for White women. To translate these percentage changes in actual changes of age-adjusted lost lives and years of life expectancy, we multiply the predicted percentage change in life expectancy and the age-adjusted mortality rate at 5, 10, 15 and 20 years provided in Table 6 with the most recent life expectancy and age-adjusted death rate data provided in Table 7 . Table 8 shows the predicted cumulative changes in life expectancy and age-adjusted death rate. Based on these data, a 0.83% decline in life expectancy translates into a decline of 0.65 life years while a 2.43% rise in death rate translates in 17.77 age-adjusted excess deaths every 100,000 citizens at a 15-year horizon for the overall population. The implied declines in years of life expectancy are 0.87 for African-Americans, 1.10 for African-American men, 1.04 for African-American women, 0.59 for Whites, 0.72 for White men, and 0.82 for White women. The implied increases in age-adjusted deaths every 100,000 citizens are 31.59 for African-Americans, 41.12 for African-American men, 36.77 for African-American women, 21.41 for White, 27.31 for White men, 27.47 for White women.Table 7 Life expectancy and deaths in 2017. Table 7 Life Deaths Implied expectancy Age-adj. rates Crude rates Crude numbers population (per million) (per million) (A) (B) (C) (D) (E) Overall population 78.6 73.2 86.4 2,813,503 325,710,000 African-American 75.3 85.4 74.2 340,644 45,883,808 African-American (M) 71.9 105.0 80.5 177,332 22,025,436 African-American (W) 78.5 70.6 68.5 163,312 23,858,372 White 78.8 73.5 93.7 2,378,385 253,935,650 White (M) 76.4 86.3 96.3 1,212,488 125,870,081 White (W) 81.2 62.4 91.0 1,165,897 128,065,569 Notes: The table summarizes the life expectancy and deaths for the overall US population and for different groups based on race (African-American vs White) and gender (men vs women) in 2017. Table 8 Changes of life expectancy and age-adjusted deaths over different horizons following the COVID-19 unemployment shock. Table 8 (1) Change in life expectancy 5 years 10 years 15 years 20 years Overall population −0.33[−0.75,0.01] −0.63[−1.55,0.00] −0.65[−1.78,0.00] −0.66[−1.80,0.00] African-American −0.44[−0.85,−0.12] −0.90[−1.98,−0.24] −0.87[−2.38,−0.19] −0.82[−2.39,−0.21] African-American (M) −0.60[−1.05,−0.24] −1.13[−2.20,−0.42] −1.10[−2.62,−0.38] −1.06[−2.66,−0.39] African-American (W) −0.48[−0.95,−0.11] −1.05[−2.33,−0.21] −1.04[−2.86,−0.13] −0.95[−2.87,−0.12] White −0.29[−0.74,0.08] −0.57[−1.59,0.12] −0.59[−1.85,0.13] −0.60[−1.90,0.13] White (M) −0.30[−0.71,0.06] −0.65[−1.63,0.07] −0.72[−2.03,0.09] −0.72[−2.18,0.09] White (W) −0.42[−1.04,0.13] −0.81[−2.23,0.23] −0.82[−2.56,0.27] −0.82[−2.56,0.28] (2) Change in the age-adjusted deaths 5 years 10 years 15 years 20 years Overall population 13.39[2.97,25.95] 18.73[4.45,41.48] 17.77[4.16,41.70] 17.71[4.10,40.42] African-American 24.78[11.47,41.11] 37.39[15.49,75.84] 31.59[11.44,78.77] 30.41[12.06,74.85] African-American (M) 28.49[10.53,50.64] 45.44[15.14,95.98] 41.12[10.83,104.15] 40.25[10.45,104.39] African-American (W) 24.43[11.78,40.24] 42.04[18.94,83.75] 36.77[13.97,88.64] 33.26[13.13,82.69] White 15.86[4.53,30.43] 22.68[6.56,51.22] 21.41[6.38,51.55] 21.52[6.41,50.08] White (M) 14.75[1.22,30.57] 26.23[2.90,62.18] 27.31[3.17,73.10] 27.09[2.89,75.25] White (W) 19.87[5.02,37.27] 29.82[8.13,66.90] 27.47[7.80,68.85] 27.44[7.91,64.93] Notes: The table shows the predicted cumulative change in life expectancy and age adjusted deaths at 5, 10, 15 and 20 years. We multiply the predicted percentage change in life expectancy and age adjusted mortality rate at 5, 10, 15 and 20 years provided in Table 6 with the most recent life expectancy and age-adjusted deaths data provided in Table 7. It is interesting to notice that the response of death rates for White men unfolds over a longer period of time. In the short run, White women experience a visibly larger increase in age-adjusted death rates compared to White men. However, at the 15- and 20- year horizon the change is very similar. This is the result of two factors. First, in line with the impulse responses presented above, the percentage change in death rates for white men is initially lower, but it eventually becomes larger past the 10-year horizon. Second, White men start from a less favorable death rate (86.3 for White men vs 62.4 for White women). Thus, White men eventually experience a similar change in death rates at the 15- and 20- year horizon. Overall, these results suggest that the COVID-19 economic distress will be followed by significant changes in mortality rates and life expectancy. Our evidence shows that excess deaths will disproportionately affect African Americans, consistent with previously published work on the impact of race on recovery post disasters and all-cause mortality, see Fothergill et al. (1999), FitzGerald and Hurst (2017), and Schroeder (2020). These figures might be a conservative projection once we recognize that the pandemic has led many workers, especially women, to exit the labor force, with the result that the measured unemployment might underestimate the real dimension of the shock to the labor market. 4.2 Excess deaths The long-term effects of the COVID-19 related unemployment surge on the aggregate US mortality rate have not been characterized in the literature. Thus, as a last step, we compute an estimate of the excess deaths associated with the COVID-19 unemployment shock. This corresponds to the difference between the number of deaths predicted by the model with and without the unemployment shock observed in 2020 and provides a measure of the death toll due to the economic distress associated with the COVID19 pandemic. Here, we briefly explain how we compute the excess deaths and refer the reader to Appendix D for details. In essence, our state-space model provides an estimate for the change in the age-adjusted death rate following the COVID-19 unemployment shock. Using the latest death rate as an initial value, we can construct projections for the age adjusted death rates with, Rtu, and without, Rt, the unemployment shock observed in 2020. We can do this for any horizon t>2020. Here, we omit the group identifier i for ease of exposition. Note that we use a superscript u to indicate that the variable is computed taking into account the effect of the COVID-19 unemployment shock. We then need to convert Rtu and Rt into a prediction for the crude death rates with and without the unemployment shock, Rc,tu and Rc,t, respectively. For this step, we model the historical relations between the crude and age adjusted death rates and use this historical relation to convert projections of the age-adjusted death rate into projections for the crude death rate. Specifically, we run an OLS regression of the crude death rate on an intercept and the age-adjusted death rates. Through linear transformation via the OLS coefficients, we obtain Rc,tu and Rc,t, respectively. Our results are robust to alternative approaches to convert age-djusted death rates into crude death rates. Finally, we require long-run projection of the US population. We treat the Census Bureau projection as the projection consistent with the model absent the unemployment shock. We then reconstruct the alternative projection based on the change in the death rate as predicted by the model. In sum, we compute the excess deaths EDtu as:(5) EDtu=Rc,tuPtu−Rc,tPt, where Rc,tu is the predicted crude death rate with the unemployment shock, Rc,t is the predicted crude death rate without the unemployment shock, Ptu is the population projection with the unemployment shock, Pt is the population projection without the unemployment shock (available from the Census Bureau). Table 9 provides the respective numbers for the overall population and the different groups identified based on race and gender. For the overall population, the increase in the death rate following the COVID-19 pandemic implies a staggering 0.84 and 1.22 million excess deaths over the next 15 and 20 years, respectively. For African-Americans, we estimate 200 thousand and 290 thousand excess deaths over the next 15 and 20 years, respectively. For White, we estimate 0.76 and 1.09 million excess deaths over the next 15 and 20 years, respectively. These numbers are roughly equally split (40% to 60%) between men and women. These numbers indicate that the consequences of the pandemic might go well beyond the deaths directly caused by the disease. In fact, the order of magnitudes are such that the long-term effects of economic distress might end up being larger than the direct impact of the pandemic.Table 9 Excess deaths associated with the COVID-19 unemployment shock (per million). Table 9 5 years 10 years 15 years 20 years Overall population 0.13[0.03,0.24] 0.47[0.11,0.98] 0.84[0.19,1.83] 1.22[0.28,2.69] African-American 0.03[0.01,0.05] 0.12[0.05,0.21] 0.20[0.09,0.42] 0.29[0.12,0.62] African-American (M) 0.01[0.00,0.02] 0.06[0.02,0.11] 0.10[0.03,0.22] 0.15[0.05,0.34] African-American (W) 0.02[0.01,0.03] 0.07[0.03,0.13] 0.13[0.06,0.27] 0.19[0.08,0.41] White 0.11[0.03,0.21] 0.43[0.12,0.88] 0.76[0.22,1.67] 1.09[0.32,2.44] White (M) 0.04[0.00,0.09] 0.19[0.02,0.42] 0.37[0.04,0.88] 0.56[0.06,1.38] White (W) 0.08[0.02,0.15] 0.32[0.08,0.64] 0.56[0.15,1.25] 0.81[0.22,1.83] Notes: The table provides an estimate of the excess deaths associated with the COVID-19 unemployment shock based on the CENSUS projections of the US population adjusted to account for the additional deaths resulting from the unemployment shock. As explained above, African-Americans tend to experience larger unemployment shocks and a larger response for a given size of the shocks. The COVID-19 recession has been atypical so far to the extent that it affected white women more than the typical shock to unemployment. However, in absolute terms African-Americans still experienced a larger shock. This implies that in relative terms, they might suffer larger consequences as a result of economic distress. To see this, it is useful to rescale the expected excess deaths based on the projected population of the different groups. The numbers of excess deaths presented above correspond to 0.38% and 0.52% of the projected African-American population at the 15- and 20-year horizons, and 0.28% and 0.40% of the projected White population at the 15- and 20-year horizons, respectively. Due to data limitations, we cannot further refine our analysis to distinguish between Hispanics and non-Hispanics (a distinction based on heritage instead of race). While the extent of the long-term excess mortality related to coronavirus crisis is staggering, several considerations are in order. First, the additional number of lives that would have been lost secondary to COVID-19 acute illness and health care resources exhaustion if lockdowns had not been implemented is estimated to be over 100,000 in the US alone, Fothergill et al. (1999), Stone (2020), and Emanuel et al. (2020). Second, our analysis makes a number of implicit assumptions based on historical data regarding the time and severity of the recession. It is important to keep in mind the significant amount of uncertainty around this variable. It is possible that the economy will recover faster than in the past. Furthermore, a shift in economic and social policies can affect the duration and severity of the recession, and consequently modify our excess mortality estimates, Bianchi and Melosi (2017). Third, based on emerging data, it is likely that the limited access to health care during the lockdown, temporary discontinuation of preventive care interventions, massive loss of employer-provided health insurance coverage, and the lingering concern of the population about seeking medical care out to fear of contracting COVID-19 will impact mortality rates and life expectancy even more severely, Garfield et al. (2020) and Sharpless (2020). Fourth, this is the first recession with the Affordable Care Act (ACA) in place, a critical resource to mitigate the effects of unemployment on citizens well-being, see Gruber and Sommers (2020). Thus, there are factors that could make the projections presented here better, but there are also factors that could make them worse. Despite these caveats, it is important to emphasize that our results are in line with studies that take a completely different methodological approach and focus on specific cohorts or individuals. For example, Schwandt and von Wachter (2020) argue that cohorts coming of age during a deep recession suffer increases in mortality later in their middle age. von Wachter (2020) focuses on the effects of the COVID-19 recession for mortality rates of vulnerable job losers and labor market entrants and also finds that the losses in potential life years due to unemployment could be substantially larger than those from deaths directly due to COVID-19. With respect to these studies, our time-series approach allows for the possibility that the state of the economy, as captured by the unemployment rate, might affect the general population through indirect channels such as income, poverty rates, crime rates. It is nevertheless reinsuring that the two different methodological approaches lead to similar conclusions about the long-term impact of the COVID-19 recession. Our analysis focuses on the consequences of economic distress on mortality rates. It is entirely possible that the COVID-19 pandemic will become endemic and that those who contracted the disease might suffer from long-term illness due to the collateral effects of COVID-19. It is much harder to capture this second health channel due to the lack of data. Focusing on the consequences of the recession is relatively easier because we can learn from previous recessions. In this respect, it is worth emphasizing that unlike the Spanish flu of 1918, the younger portion of the population has been less affected by an increase in mortality rates as a result of the COVID-19 pandemic. Thus, we should expect that a large portion of the population that suffered the economic consequences of the recession might also encounter the long-term health consequences of the recession going forward. This consideration can be phrased in terms of the harvesting vs scarring debate: The young population has not been harvested during the COVID-19 pandemic, but it might carry the scars associated with the COVID-19 recession. Our results have three important policy implications for this and future pandemics. First, it would be desirable to study and implement health policy measures to guarantee activities remain open with minimal risks to workers and public, whenever possible. For instance, implementation of universal masking and social distancing policies at a large health care system in the Northeastern US resulted in a decline of SARS-CoV-2 transmission across health care workers, despite the high-risk setting (see Wang et al., 2020). Second, it is of utmost importance to facilitate routine preventive care and health care access for the whole US population, including the over 20 million Americans who lost employer-provided health care coverage, Garfield et al. (2020) and Gruber and Sommers (2020). Third, policy interventions meant to reduce the economic impact of the recession are likely to also contribute to save lives. 5 Conclusion We examine the historical relation between life-expectancy, death-rates, and unemployment for the overall US population and groups organized based on race and gender. We use a VAR that allows for observation errors and we find that increases in unemployment are followed by statistically significant increases in death rates and declines in life-expectancy. A sizable fraction of the variation of these two variables can be accounted by unemployment shocks. We then use this historical relation to form predictions about the potential impact of the recession caused by the COVID-19 pandemic on human health. Our results suggest that the toll of lives claimed by the SARS-CoV-2 virus far exceeds those immediately related to the acute COVID-19 critical illness and that the recession caused by the pandemic can jeopardize population health for the next two decades. Based on our findings, African American citizens and women will be suffering more profoundly from the coronavirus-driven recession, compounding their disproportionate adverse outcome in the setting of acute SARS-CoV-2 infection, Garg et al. (2020). In light of our analysis, large, sustained, and swift government maneuvers to support the currently unemployed labor force and to abate unemployment itself will be as important as the massive efforts focused on limiting and eventually eradicating transmission of SARS-CoV-2 with effective vaccination strategies. Appendix A The long-term impact of the COVID-19 unemployment shock on life expectancy and mortality rates State-Space Representation We denote the “log average life expectancy,” “log age-adjusted mortality rate,” and the “unemployment rate” as y1,t,y2,t,y3,t, respectively. We allow for (potentially serially correlated but mutually uncorrelated) measurement errors ϵi,t in the level series. The joint dynamics of the growth rates of z1,t and z2,t and the level of z3,t follow a VAR(1). Put together,[y1,ty2,ty3,t]=[z1,tz2,tz3,t]+[ϵ1,tϵ2,tϵ3,t],ϵi,t=ρiϵi,t−1+ui,t,ui,t∼N(0,σi2)[Δz1,tΔz2,tz3,t]=[μ1μ2μ3]+[ϕ11ϕ12ϕ13ϕ21ϕ22ϕ23ϕ31ϕ32ϕ33][Δz1,t−1Δz2,t−1z3,t−1]+[η1,tη2,tη3,t],ηt∼N(0,Σ). Note that we can re-express (A-1) by(A.1) yt=zt+ϵtzt=μ+(M+Φ)zt−1−ΦMzt−2+ηt where(A.2) [Δz1,t−1Δz2,t−1z3,t−1]=zt−Mzt−1,Mzt=[100010000][z1,tz2,tz3,t]=[z1,tz2,t0]. The state space representation is(A.3) yt=Γstst=C+Λst−1+εt,εt∼N(0,Ω) where(A.4) Γ=[II0],St=[ϵtztzt−1],C=[0μ0],Λ=[ρ000M+Φ−ΦM0I0],M=[100010000],εt=[utηt0],Ω=[σ2000Σ0000],σ2=[σ12000σ22000σ32]. For ease of exposition, we collect parameters in(A.5) Θϵ={ρ,σ},Θz={μ,Φ,Σ}. Appendix B Gibbs sampler We use the Gibbs sampler to estimate the model unknowns. We rely on the state-space representation of (A.3). For the jth iteration,• Run Kalman smoother to generate Z0:Tj and ϵ1:Tj conditional on Θϵj,Θzj: This is explained in Section B.1. • Obtain posterior estimates of Θϵj+1,Θzj+1 from the MNIW conditional on Z0:Tj and ϵ1:Tj: This is explained in Section B.2. B1 Kalman smoother We rely on the state-space representation (A.3). Conditional on the jth draw of Θϵj,Θzj, we apply the standard Kalman filter as described in Durbin and Koopman (2001). Suppose that the distribution ofst−1\|{y1:t−1,Θϵj,Θzj}∼N(st−1\|t−1,Pt−1\|t−1). Then, the Kalman filter forecasting and updating equations take the formst\|t−1=C+Λst−1\|t−1Pt\|t−1=ΛPt−1\|t−1Λ′+Ωst\|t=st\|t−1+(ΓPt\|t−1)′(ΓPt\|t−1Γ′)−1(yt−Γst\|t−1)Pt\|t=Pt\|t−1−(ΓPt\|t−1)′(ΓPt\|t−1Γ′)−1(ΓPt\|t−1). In turn,st\|{y1:t,Θϵj,Θzj}∼N(st\|t,Pt\|t). Next, the backward smoothing algorithm developed by Carter and Kohn (1994) is applied to recursively generate draws from the distributions st\|(St+1:T,Y1:T,Θϵj,Θzj) for t=T−1,T−2,…,1. The last element of the Kalman filter recursion provides the initialization for the simulation smoother:(B.1) st\|t+1=st\|t+Pt\|tΛ′Pt+1\|t−1(st+1−C−Λst\|t)Pt\|t+1=Pt\|t−Pt\|tΛ′Pt+1\|t−1ΛPt\|tstj∼N(st\|t+1,Pt\|t+1),t=T−1,T−2,…,1. In sum, we obtain smoothed estimates of Z0:Tj and ϵ1:Tj. B2 Posterior draw We treat the smoothed estimates of Z0:Tj and ϵ1:Tj as data points. The objective is to draw Θϵj+1 and Θzj+1. B2.1 VAR coefficients For ease of exposition, we omit the superscript j below. Conditional on Z0:T, we transform zt into xt via(B.2) xt=zt−Mzt−1 for t∈{1,…,T}. We re-express the VAR as(B.3) xt′=[1xt−1′]︸wt′[μΦ′]︸β+ηt′,ηt∼N(0,Σ). Define X=[xp,…,xT]′, W=[wp,…,wT]′, and η=[ηp,…,ηT]′ conditional on the initial p=1 observations. If the prior distributions for β and Σ are(B.4) β\|Σ∼MN(β_,Σ⊗(V_βξ)),Σ∼IW(Ψ_,d_), then because of the conjugacy the posterior distributions can be expressed as(B.5) β\|Σ∼MN(β¯,Σ⊗V¯β),Σ∼IW(Ψ¯,d¯) where(B.6) β¯=(W′W+(V_βξ)−1)−1(W′X+(V_βξ)−1β_),V¯β=(W′W+(V_βξ)−1)−1,Ψ¯=(X−Wβ¯)′(X−Wβ¯)+(β¯−β_)′(V_βξ)−1(β¯−β_)+Ψ_,d¯=T−p+d_. We follow the exposition in Giannone et al. (2015) in which ξ is a scalar parameter controlling the tightness of the prior information in (B.4). For instance, prior becomes more informative when ξ→0. In contrast, when ξ=∞, then it is easy to see that β¯=β^, i.e., an OLS estimate. In sum, we draw βj+1 and Σj+1 from (B.5). Hence, we obtain Θzj+1={μj+1,Φj+1,Σj+1}. B2.2 Measurement error variances Conditional on ϵ1:Tj={ϵ1,ϵ2,ϵ3}j, the objective is to draw Θϵj+1={ρij+1,σi2,j+1}. For ease of exposition, we omit the superscript j below. Note that(B.7) ϵi,t=ρiϵi,t−1+ui,t,ui,t∼N(0,σi2). In this analysis, we set ρi=0 for i∈{1,2,3} and σ32=0. Thus, for i∈{1,2}, we can draw(B.8) σi2∼IG(T¯i,ν¯i) where(B.9) T¯i=T_i+T2,ν¯i=ν_i+ϵi′ϵi2. In sum, we obtain Θϵj+1 from (B.8). Appendix C Prior predictive check C1 Prior distributions We set the priors for the VAR coefficients (B.4) as(C.1) β_=[0000.90000.90000.9],V_β=[1000010000100001],ξ=5000Ψ_=(1e-4)×[0.50001000010],d_=25. The priors for the measurement error variances are(C.2) T_1=30,ν_1=0.002T_2=30,ν_2=0.010. C2 Impulse response We simulate {βj,Σj}j=1N based on (B.4) and (C.1) and compute impulse responses to a one-standard-deviation shock to unemployment via Cholesky decomposition. The results are provided in Fig. C.1 .Fig. C.1 Impulse responses to a one-standard-deviation shock to unemployment. Notes: We provide impulse responses to a one-standard-deviation shock to unemployment. The solid-lines represent the median values and the dark and light-shaded areas indicate 68% and 90% bands, respectively. Fig. C.1 C3 Measurement error variance We simulate {βj,Σj}j=1N based on (B.4) and (C.1) and {σ1j,σ2j}j=1N based on (B.8) and (C.1). We then compute the unconditional variance of (B.3) and of yt−Myt−1 (which includes the measurement errors and the VAR). Table C.1 reports the quantiles of the fraction of variance explained by the VAR.Table C.1 The role of measurement error variances. Table C.1 50% [5%, 95%] Δ avg. life expectancy 0.19 [0.05, 0.85] Δ age-adj. death rate 0.19 [0.06, 0.78] Notes: We simulate {βj,Σj}j=1N based on (B.4) and (C.1) and {σ1j,σ2j}j=1N based on (B.8) and (C.1). We then compute the unconditional variance of (B.3) and of yt−Myt−1 (which includes the measurement errors and the VAR). The table reports the quantiles of the fraction of variance explained by the VAR. Appendix D Computing excess deaths We omit the group identifier i to ease exposition. The law of motion of the state vector is(D.1) Xt=μ+ΦXt−1+ηt. We define an indicator vector eΔr that selects the mortality growth rate Δrt=eΔr′Xt from the state vector. Denote the cumulative sum as(D.2) X0,t≡∑j=0tXj=∑j=1t+1(I−Φj)(I−Φ)−1μ︸E(Xt)+(I−Φt+1)(I−Φ)−1η0,=(I(t+1)−Φ(I−Φt+1)(I−Φ)−1)E(Xt)+(I−Φt+1)(I−Φ)−1η0. D1 Mortality rate Let η0u be the vector containing the contemporaneous impact of the COVID-19 unemployment shock. Then, the cumulative effect of the shock is then driving the difference between the case with the COVID-19 unemployment shock and the counterfactual scenario(D.3) Δr0,tu=eΔr′(I(t+1)−Φ(I−Φt+1)(I−Φ)−1)E(Xt)︸Δr0,t+eΔr′t+1)(I−Φ)−1η0u︸cirtu. Conditional on R−1, we can construct the level of deaths(D.4) Rtu=exp(Δr0,tu)R−1Rt=exp(Δr0,t)R−1 based on the cumulative growth rates of Δr0,tu and Δr0,t in (D.3), respectively. Note that (D.4) are the age adjusted death rates implied from the VAR estimates. D2 Converting from the age adjusted- to crude death rates Currently, Rtu and Rt are defined as the age adjusted death rates. We want to convert to the crude death rates based on Fig. D.1 , which is to regress the crude death rates on the age adjusted death rates and construct the fitted crude death rates based on the OLS coefficients. Let β^0 be the constant OLS coefficient and β^1 be the slope coefficient. Then, we construct(D.5) Rc,t=β^0+β^1RtRc,tu=β^0+β^1Rtu. For each race and gender group, we use β^0,β^1 (based on the overall population) for conversion as we do not have the access to historical crude death rates for different race and gender groups. In sum, the excess death rate caused by the COVID-19 unemployment shock is defined as(D.6) ERtu=Rc,tu−Rc,t. Fig. D.1 Age adjusted- and crude death rates for the overall population. Notes: Data source: The World Bank. We regress the crude death rates on the age adjusted death rates and obtain the following OLS coefficients: the constant coefficient is β^0=651.9 and the slope coefficient is β^1=0.2255. We plot the fitted crude death rates based on the age adjusted death rates in red line. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) Fig. D.1 D3 Population projection Denote the Census population projections by(D.7) Pt=Gt−1CPt−1=∏j=0t−1GjCP0. For measures of (D.7), we take the population projections for the United States provided by the United States Census Bureau. As we find that the population numbers provided by the CDC, i.e., (E) of Table 7, and Census Bureau are somewhat different, we adjust the 2017 population numbers in the Census Bureau projections to be consistent with (E) of Table 7. Because the COVID-19 unemployment shock causes a change in the expected path for the death rate, we adjust the projection as(D.8) Ptu=(Gt−1−ERt−1u)Pt−1u=∏j=0t−1(GjC−ERju)P0u. Note that we initialize by P0=P0u. D4 Excess deaths The excess deaths caused by the COVID-19 unemployment shock is defined as(D.9) EDtu=Rc,tuPtu−Rc,tPt, which can be computed from (D.4), (D.5), (D.6), (D.7), and (D.8). Appendix E Additional figures and tables Figures E.1 and E.2 .Fig. E.1 Data. Notes: This figure presents the log average life expectancy (first column), the log age-adjusted death rate (second column), and the unemployment rate (third column) for the overall US population. The original series are presented in black solid lines. For the first two series, we compare with the smoothed estimates from our model (green lines). The data span from 1950 to 2017. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) Fig. E.1 Fig. E.2 Data by race and gender. Notes: This figure presents the log average life expectancy (first column), the log age-adjusted death rate (second column), and the unemployment rate (third column) for the overall US population. The original series are presented in black solid lines. For the first two series, we compare with the smoothed estimates from our model (green lines). The data span from 1954 to 2017 for the White population, and from 1972 to 2017 for the African-American population. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) Fig. E.2 Fig. E.3 Impulse response comparison: Common sample. Notes: We compare median impulse responses to a one-standard-deviation (first panel) or a unit shock (second panel) to unemployment, which is identified via a Cholesky decomposition, across the overall US population and for the US population classified according to race and gender. Results are based on an identical estimation sample which spans from 1972 to 2017 for all workers. Fig. E.3 Appendix F Results from the alternative identification strategy The alternative identification strategy allows for contemporaneous impact of unemployment shock on life expectancy and death rates. With this identification assumption, we replicate the tables and figures in the main text below (Tables F.1 , F.2 , F.3 , F.4 , F.5 , F.6 and Figures F.1 , F.2 , F.3 , F.4 ).Table F.1 Fraction of forecast error variance explained by unemployment shock (%). Table F.1 (1) Percentage change in life expectancy 5 years 10 years 15 years 20 years Overall population 10.46[1.30,31.45] 15.68[2.10,40.74] 16.11[2.22,40.99] 16.15[2.21,41.23] African-American 19.78[4.33,43.64] 32.28[8.68,58.40] 32.74[9.01,58.75] 32.97[9.07,59.01] African-American (M) 33.60[7.95,59.80] 43.65[14.83,68.26] 43.58[15.02,68.17] 43.64[14.85,68.40] African-American (W) 14.50[2.33,37.04] 25.78[4.66,52.43] 26.49[5.33,52.80] 26.73[5.43,53.53] White 8.32[0.86,29.70] 12.72[1.46,40.13] 13.21[1.53,40.85] 13.33[1.56,41.30] White (M) 9.60[0.86,32.18] 15.09[1.39,45.40] 16.02[1.46,46.15] 16.18[1.47,46.51] White (W) 7.72[0.68,28.96] 12.13[1.15,38.92] 12.53[1.29,39.66] 12.57[1.31,39.96] (2) Percentage change in the age-adjusted death rate 5 years 10 years 15 years 20 years Overall population 11.67[1.85,29.03] 14.00[2.55,32.24] 14.15[2.67,32.88] 14.20[2.70,33.08] African-American 34.39[14.19,55.69] 40.11[19.48,61.00] 40.38[19.52,61.39] 40.67[19.66,61.68] African-American (M) 26.25[6.55,49.69] 33.31[10.04,56.71] 33.56[10.74,57.25] 33.75[10.74,57.54] African-American (W) 25.62[7.43,46.07] 32.70[13.18,54.43] 32.80[13.42,53.98] 32.91[13.64,54.47] White 13.93[2.39,32.72] 17.41[3.23,37.47] 17.58[3.38,37.91] 17.65[3.43,38.13] White (M) 10.57[1.37,29.99] 15.25[2.03,38.28] 15.73[2.21,39.15] 15.85[2.23,39.40] White (W) 13.02[1.90,32.24] 17.19[2.80,38.75] 17.52[2.87,39.07] 17.61[2.91,39.55] Notes: The table provides the fraction of forecast error variance of the percentage change in life expectancy and the age-adjusted mortality rate explained by unemployment shock at 5, 10, 15 and 20 years. We present the median values and provide the values that correspond to the 90% bands in brackets. Table F.2 Cumulative changes of age-adjusted death rates for the leading causes of deaths over different horizons following a 1% unemployment shock. Table F.2 (1) Percentage change 5 years 10 years 15 years 20 years Accidents 1.82[0.26,3.48] 4.86[2.35,8.52] 4.85[2.40,10.42] 4.07[2.17,9.88] Cancer −0.18[−0.99,0.65] −0.34[−1.93,1.29] −0.42[−2.55,1.82] −0.47[−2.98,2.23] Heart disease 0.77[−0.16,1.77] 1.56[−0.02,3.53] 1.85[0.03,4.50] 1.89[0.04,4.98] Influenza and pneumonia 3.05[1.52,4.73] 6.06[3.23,9.97] 7.25[3.89,13.60] 7.30[3.90,15.66] Stroke 1.22[−0.17,2.64] 3.12[0.53,6.40] 3.87[0.93,8.71] 3.94[1.07,10.05] The residual causes 0.61[−0.32,1.56] 1.71[0.04,3.55] 2.16[0.26,4.81] 2.27[0.35,5.44] Overall 0.56[−0.10,1.25] 0.81[0.08,1.64] 0.77[0.10,1.50] 0.77[0.10,1.45] (2) Change 5 years 10 years 15 years 20 years Accidents 0.88[0.12,1.67] 2.33[1.13,4.09] 2.33[1.15,5.00] 1.95[1.04,4.74] Cancer −0.27[−1.48,0.97] −0.50[−2.88,1.92] −0.63[−3.80,2.72] −0.71[−4.45,3.32] Heart disease 1.26[−0.26,2.89] 2.55[−0.04,5.78] 3.03[0.04,7.36] 3.09[0.06,8.14] Influenza and pneumonia 0.46[0.23,0.71] 0.90[0.48,1.48] 1.08[0.58,2.03] 1.09[0.58,2.33] Stroke 0.45[−0.06,0.98] 1.16[0.20,2.37] 1.44[0.34,3.23] 1.46[0.40,3.73] The residual causes 1.90[−1.01,4.84] 5.31[0.13,11.05] 6.73[0.81,14.95] 7.07[1.10,16.92] Overall 4.05[−0.76,9.01] 5.85[0.61,11.83] 5.55[0.71,10.83] 5.58[0.73,10.48] Notes: We multiply the predicted change at 5, 10, 15, and 20 years provided in Panel (1) of Table 4 with the most recent age-adjusted death rates of the leading causes of deaths in 2019. The age-adjusted death rates (deaths per 100,000 U.S. standard population) for the 10 leading causes of death in the most recent year 2019 are: (1) Heart disease, 163.6; (2) Cancer, 149.1; (3) Accidents, 48.0; (4) Chronic lower respiratory disease, 39.7; (5) Stroke, 37.1; (6) Alzheimer disease, 30.5; (7) Diabetes, 21.4; (8) Kidney disease, 12.9; (9) Influenza and pneumonia 14.9; and (10) Suicide, 14.2. The overall death rate was 723.6. Table F.3 COVID-19 unemployment shocks. Table F.3 Standard deviation Magnitude (%) Overall population 3.82[2.61,4.80] 3.20[2.06,4.02] African-American 3.15[2.07,4.16] 3.77[2.37,4.88] African-American (M) 2.85[1.92,3.67] 4.14[2.74,5.18] African-American (W) 3.98[2.81,5.04] 3.92[2.71,4.92] White 3.75[2.61,4.67] 2.90[1.93,3.61] White (M) 3.46[2.50,4.25] 3.04[2.12,3.62] White (W) 5.09[3.86,6.19] 3.32[2.47,3.98] Notes: The table provides the COVID-19 unemployment shock implied by our state-space model for overall US population and for US population classified according to race and gender. To facilitate the interpretation, we provide the scale of standard deviation as well as the actual magnitude (%) of the shock. Table F.4 Cumulative changes of life expectancy and age-adjusted death rates over different horizons following the COVID-19 unemployment shock. Table F.4 (1) Percentage change in lifeexpectancy 5 years 10 years 15 years 20 years Overall population −0.43[−1.09,0.18] −0.86[−2.16,0.06] −0.91[−2.50,0.04] −0.91[−2.57,0.04] African-American −0.37[−1.16,0.34] −1.26[−2.96,−0.17] −1.29[−3.68,−0.08] −1.18[−3.67,−0.09] African-American (M) −0.63[−1.34,0.05] −1.56[−3.12,−0.48] −1.53[−3.75,−0.45] −1.45[−3.81,−0.44] African-American (W) −0.49[−1.37,0.33] −1.40[−3.29,−0.09] −1.44[−4.18,−0.02] −1.30[−4.27,0.04] White −0.36[−1.05,0.26] −0.75[−2.14,0.26] −0.79[−2.52,0.28] −0.79[−2.56,0.29] White (M) −0.37[−1.01,0.23] −0.83[−2.17,0.21] −0.94[−2.71,0.20] −0.94[−2.92,0.20] White (W) −0.47[−1.39,0.40] −0.97[−2.87,0.48] −1.00[−3.33,0.53] −0.99[−3.38,0.55] (2) Percentage change in the age-adjusted death rate 5 years 10 years 15 years 20 years Overall population 1.51[−0.53,3.68] 2.39[0.02,5.74] 2.29[0.03,5.73] 2.29[0.00,5.61] African-American 2.32[0.29,4.34] 4.93[2.07,9.87] 4.11[1.38,10.67] 3.84[1.35,10.19] African-American (M) 2.18[−0.37,4.56] 4.36[0.87,9.44] 3.94[0.34,10.42] 3.81[0.16,10.21] African-American (W) 2.85[0.11,5.52] 6.14[2.56,12.49] 5.45[1.85,13.97] 4.79[1.54,13.08] White 1.95[−0.24,4.21] 2.99[0.36,7.03] 2.82[0.38,7.14] 2.84[0.39,7.00] White (M) 1.72[−0.52,4.06] 3.17[0.03,7.51] 3.27[0.16,8.78] 3.28[0.11,9.13] White (W) 2.67[−0.68,6.03] 4.49[0.21,10.88] 4.13[0.21,11.20] 4.11[0.18,10.45] Notes: The table shows the predicted cumulative percentage change in life expectancy and age adjusted mortality rate at 5, 10, 15 and 20 years. We present the median values and provide the values that correspond to the 90% bands in brackets. Results are presented for the overall US population and subdivided based on race and gender. Table F.5 Changes of life expectancy and age-adjusted deaths over different horizons following the COVID-19 unemployment shock . Table F.5 (1) Change in life expectancy 5 years 10 years 15 years 20 years Overall population −0.34[−0.86,0.14] −0.68[−1.69,0.05] −0.72[−1.96,0.03] −0.72[−2.02,0.03] African-American −0.28[−0.87,0.26] −0.95[−2.23,−0.12] −0.97[−2.77,−0.06] −0.89[−2.76,−0.07] African-American (M) −0.45[−0.96,0.03] −1.12[−2.24,−0.35] −1.10[−2.70,−0.32] −1.04[−2.74,−0.32] African-American (W) −0.38[−1.07,0.26] −1.10[−2.58,−0.07] −1.13[−3.29,−0.02] −1.02[−3.35,0.03] White −0.29[−0.83,0.21] −0.59[−1.68,0.21] −0.62[−1.99,0.22] −0.62[−2.01,0.23] White (M) −0.28[−0.77,0.17] −0.64[−1.66,0.16] −0.72[−2.07,0.15] −0.71[−2.23,0.15] White (W) −0.38[−1.13,0.32] −0.79[−2.33,0.39] −0.81[−2.70,0.43] −0.80[−2.74,0.45] (2) Change in the age-adjusted deaths 5 years 10 years 15 years 20 years Overall population 11.06[−3.87,26.90] 17.46[0.15,42.00] 16.78[0.22,41.90] 16.76[0.01,41.08] African-American 19.83[2.44,37.10] 42.08[17.66,84.25] 35.07[11.80,91.16] 32.80[11.55,86.98] African-American (M) 22.89[−3.89,47.80] 45.71[9.11,99.10] 41.37[3.59,109.38] 40.02[1.67,107.13] African-American (W) 20.11[0.78,38.94] 43.34[18.06,88.19] 38.46[13.06,98.62] 33.83[10.86,92.34] White 14.31[−1.75,30.93] 21.95[2.66,51.65] 20.69[2.79,52.45] 20.83[2.84,51.40] White (M) 14.83[−4.45,35.02] 27.35[0.23,64.80] 28.26[1.35,75.81] 28.32[0.98,78.85] White (W) 16.63[−4.22,37.60] 27.97[1.31,67.83] 25.74[1.30,69.83] 25.64[1.11,65.13] Notes: The table shows the predicted cumulative change in life expectancy and age adjusted deaths at 5, 10, 15 and 20 years. We multiply the predicted percentage change in life expectancy and age adjusted mortality rate at 5, 10, 15 and 20 years provided in Table 6 with the most recent life expectancy and age-adjusted deaths data provided in Table 7. Table F.6 Excess deaths associated with the COVID-19 unemployment shock (per million). Table F.6 5 years 10 years 15 years 20 years Overall population 0.08[−0.14,0.29] 0.40[−0.16,1.03] 0.74[−0.15,1.89] 1.09[−0.15,2.75] African-American 0.01[−0.02,0.04] 0.10[0.02,0.21] 0.20[0.05,0.45] 0.29[0.08,0.69] African-American (M) 0.01[−0.01,0.02] 0.05[−0.00,0.11] 0.10[0.00,0.22] 0.14[0.00,0.34] African-American (W) 0.01[−0.01,0.03] 0.06[0.01,0.13] 0.13[0.03,0.28] 0.19[0.05,0.44] White 0.09[−0.08,0.24] 0.39[−0.05,0.91] 0.71[−0.01,1.71] 1.03[0.03,2.51] White (M) 0.04[−0.04,0.12] 0.20[−0.05,0.48] 0.38[−0.04,0.95] 0.57[−0.03,1.48] White (W) 0.05[−0.06,0.16] 0.27[−0.06,0.66] 0.50[−0.05,1.27] 0.73[−0.04,1.85] Notes: The table provides an estimate of the excess deaths associated with the COVID-19 unemployment shock based on the CENSUS projections of the US population adjusted to account for the additional deaths resulting from the unemployment shock. Fig. F.1 Impulse responses to a one-standard-deviation shock to unemployment. Notes: We provide impulse responses to a one-standard-deviation shock to unemployment for the overall US population. The solid-lines represent the median values and the dark and light-shaded areas indicate 68% and 90% bands, respectively. Fig. F.1 Fig. F.2 Impulse responses to a one-standard-deviation shock to unemployment. Notes: We provide impulse responses to a one-standard-deviation shock to unemployment for the US population classified according to race and gender. The solid-lines represent the median values and the dark and light-shaded areas indicate 68% and 90% bands, respectively. Fig. F.2 Fig. F.3 Impulse response comparison. Notes: We compare median impulse responses to a one-standard-deviation (first row panels) or a unit shock (second row panels) to unemployment, which is identified via a Cholesky decomposition, across the overall US population and for the US population classified according to race and gender. Fig. F.3 Fig. F.4 Impulse responses of the growth rate of age-adjusted death rates for the leading causes of deaths to a unit shock to unemployment rate based on the alternative Choleski ordering. Notes: According to National Center for Health Statistics (NCHS), National Vital Statistics System, the five leading causes of age-adjusted deaths in the U.S. are (1) Accidents; (2) Cancer; (3) Heart disease; (4) Influenza and pneumonia; and (5) Stroke in alphabetical order. The NCHS Data Visualization Gallery provides the time-series of (1)-(5) as well as the age-adjusted overall death rates from 1900 to 2017. We construct (6) The residual causes by subtracting the sum of (1)-(5) from the overall death rates. Fig. F.4 ☆ We thank Michael Boutros for great research assistance. We thank Nancy Berliner, Janet Currie, David Cutler, Cosmin Ilut, Jim Poterba, Emilia Simeonova, Jon Skinner, Ben Sommers, and all seminar participants at the NBER COVID-19 and health outcomes spring 2021 meeting, Duke University, and the U.S. Government Accountability Office for useful comments and suggestions. We thank Arialdi Minimo for helping us to navigate the CDC datasets. 1 This literature is constantly growing. A non-exhaustive list includes Eichenbaum et al. (2020a), Coibion et al. (2020), Eichenbaum et al. (2020b), Jones et al. (2020), Hall et al. (2020), among others. ==== Refs References Acemoglu D. Chernozhukov V. Werning I. Whinston M.D. Optimal Targeted Lockdowns in a Multi-Group SIR Model NBER Working Paper 27102 2020 Alvarez F.E. Argente D. Lippi F. A Simple Planning Problem for COVID-19 Lockdown NBER Working Paper 26981 2020 Anderson R.M. Heesterbeek H. Klinkenberg D. Hollingsworth T.D. 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==== Front Am J Otolaryngol Am J Otolaryngol American Journal of Otolaryngology 0196-0709 1532-818X Elsevier Inc. S0196-0709(22)00352-0 10.1016/j.amjoto.2022.103725 103725 Article Characterization of otologic symptoms appearing after COVID-19 vaccination Leong Stephen a Teh Bing M. bc Kim Ana H. b⁎ a Columbia University Vagelos College of Physicians & Surgeons, New York, NY, United States of America b Department of Otolaryngology/Head and Neck Surgery, NewYork-Presbyterian/Columbia University Irving Medical Center, New York, NY, United States of America c Department of Otolaryngology—Head & Neck Surgery, Monash Health, Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia ⁎ Corresponding author at: Otology/Neurotology & Skull Base Surgery, Department of Otolaryngology—Head & Neck Surgery, New York-Presbyterian/Columbia University Irving Medical Center, 180 Fort Washington Ave, HP8, New York, NY 10032, United States of America. 5 12 2022 March-April 2023 5 12 2022 44 2 103725103725 27 9 2022 © 2022 Elsevier Inc. All rights reserved. 2022 Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Objectives Anecdotal reports of sudden sensorineural hearing loss (SSNHL) following COVID-19 vaccination have emerged in the otolaryngology community. Studies have demonstrated no association between COVID-19 vaccination and SSNHL. We aim to characterize the spectrum of otologic symptoms following COVID-19 vaccination. Methods A cross-sectional study of patients seen in the otology clinic at an academic center was performed. Patients completed a questionnaire on the development of new otologic symptoms within 4 weeks of COVID-19 vaccination. Diagnostic and audiometric data was collected retrospectively for patients reporting otologic symptoms. Results Between May and July 2021, 500 patients were screened. Median age was 56.6 years old, with 59.4 % female and 40.2 % male. 420 patients (84.0 %) were vaccinated, with 58.4 % receiving Pfizer, 29.1 % receiving Moderna, and 3.8 % receiving Johnson & Johnson. 61 patients (14.5 %) reported one or more otologic symptoms within 4 weeks of vaccination, including 21 (5.0 %) with hearing loss, 26 (6.2 %) with tinnitus, 33 (7.9 %) with dizziness, and 19 (4.5 %) with vertigo. Of the 16 patients (3.2 %) reporting tinnitus with no associated hearing loss, 8 were diagnosed with subjective tinnitus and 4 were diagnosed with temporomandibular joint syndrome. Of the 18 patients reporting hearing loss, 11 had exacerbations of underlying pathologies (e.g. Meniere's disease, presbycusis) and 7 were newly diagnosed with SSNHL (1.4 %). Conclusions Patients reporting otologic symptoms following COVID-19 vaccination received various diagnoses of uncertain etiology. The incidence of SSNHL in these patients is comparable to the general otology patient population. Additional studies are required to determine the incidence of specific diagnoses following vaccination. Keywords COVID-19 COVID-19 vaccination Hearing loss Idiopathic sudden sensorineural hearing loss Tinnitus ==== Body pmc1 Introduction The coronavirus disease 2019 (COVID-19) pandemic has widely affected the global population, with over 565 million worldwide cases and 6.4 million deaths as of July 2022, according to the World Health Organization [1]. In December 2020, the US Food and Drug Administration (FDA) issued emergency use authorizations (EUA) for two vaccinations against COVID-19, the BNT162b2 mRNA Pfizer-BioNTech (Pfizer) vaccine and the mRNA-1273 SARS-CoV2 Moderna (Moderna) vaccine, both of which are two-dose vaccination series spaced 3 and 4 weeks apart, respectively [2], [3]. In February 2021, the one-dose Ad26.COV2.S Johnson & Johnson/Janssen (J&J) vaccine received EUA, thus increasing the number of approved vaccines to three [4]. In November 2021, due to evidence of waning immunity over time, the FDA approved additional booster doses of Pfizer, Moderna, and J&J vaccines at least 6 months following Pfizer or Moderna vaccination or at least 2 months following J&J vaccination. All three vaccines have been largely well-tolerated, with common side effects including injection site pain, fever, fatigue, myalgia, and headache. However, anecdotal reports of increased incidence of idiopathic sudden sensorineural hearing loss (ISSNHL) following COVID-19 vaccination have circulated among the otolaryngology community. A number of case reports have characterized symptoms of ISSNHL following vaccination [5], [6], and a preliminary analysis using the Vaccine Adverse Events Reporting System (VAERS) found no association between COVID-19 vaccination and ISSNHL [7]. Additionally, a number of studies have attempted to quantify the incidence of otologic or otolaryngologic symptoms following COVID-19 vaccination using retrospective chart review [8] or questionnaire [9]. In one study, retrospective chart review revealed a 2.2 % incidence of otologic symptoms following vaccination, with mean onset of 10.18 days following vaccination [8]. In that study, the predominant symptoms were hearing loss (83.3 % of affected) and tinnitus (50 % of affected). Use of a survey questionnaire to evaluate the incidence of otolaryngologic symptoms following COVID-19 vaccination has thus far been limited to healthcare workers with confirmed vaccination [9]. In this study, we use survey questionnaires to evaluate new otologic symptoms appearing after COVID-19 vaccination in the otology patient population. For vaccinated patients reporting otologic symptoms, we perform a retrospective chart review to further characterize their symptomatology and final diagnoses. 2 Materials and methods Patients presenting to the otology clinic at a tertiary care, urban medical center between May and July 2021 were screened with a short questionnaire for development of otologic symptoms following COVID-19 vaccination. Patients were inquired about previous COVID-19 infection, method and date of diagnosis if previously infected, COVID-19 vaccination status, and vaccine brand and dates if vaccinated. Screened symptoms included hearing loss, tinnitus, ear drainage, dizziness, vertigo, imbalance, facial nerve palsy, smell change, and taste change; patients were specifically asked if such symptoms appeared during active COVID-19 infection or within 4 weeks of any dose of COVID-19 vaccination. Additionally, demographic data, including age and gender, were obtained. All above uses of patient data were approved by the university Institutional Review Board (IRB). For patients reporting any otologic symptoms, a retrospective chart review was performed to collect audiometric data and final diagnoses. Audiometric data from the initial presentation were gathered and notes written by the primary otologist were reviewed for relevant medical history, diagnoses, and treatments. Pearson's chi-square test was used to evaluate for significant differences in vaccination proportions. 3 Results In total, 500 patients were screened for otologic symptoms, with mean age of 56.6 ± 19.0 (range 16–101), 59.4 % female, 40.2 % male, and 0.4 % non-binary (Table 1 ). Overall, 420 patients (84.0 %) were vaccinated, with 244 receiving Pfizer (58.4 % of vaccinated group), 123 receiving Moderna (29.1 %), 16 receiving J&J (3.8 %), and 37 who were unsure of their vaccine brand (8.7 %) (Table 1). Out of 500 patients, 87 were previously diagnosed with COVID-19 (17.3 %); 66 of 420 vaccinated patients were previously diagnosed with COVID-19 (15.7 %).Table 1 Demographic characteristics of the patient population. Vaccination status and previous COVID-19 infection are also displayed. Table 1Demographic N (%) Total patients 500 (100 %) Mean age (std, range) 56.6 (19.0, 16–101) Female 297 (59.4 %) Male 201 (40.2 %) Non-binary 2 (0.4 %) Vaccinated 420 (84.0 %) Pfizer 244 (58.1 % of vaccinated) Moderna 123 (29.3 % of vaccinated) J&J 16 (3.8 % of vaccinated) Unsure 37 (8.8 % of vaccinated) Previous COVID-19 87 (17.3 %) Out of 420 vaccinated patients, 61 (14.5 %) reported one or more otologic symptoms appearing within 4 weeks of COVID-19 vaccination (Fig. 1 ). The most frequently reported symptom was dizziness (33 or 7.9 %), followed by tinnitus (26 or 6.2 %) and hearing loss (21 or 5.0 %). Vertigo was reported in 19 patients (4.5 %), imbalance in 18 patients (4.3 %), ear drainage in 5 patients (1.2 %), and facial nerve palsy in 1 patient (0.2 %). Out of 87 patients diagnosed with COVID-19, 24 (27.6 %) reported one or more otologic symptoms appearing during active infection (Fig. 1). Of these, 14 reported dizziness (16.1 %), 10 reported hearing loss (11.5 %), 10 reported tinnitus (11.5 %), 10 reported imbalance (11.5 %), 4 reported vertigo (4.6 %), 1 reported ear drainage (1.1 %) and 1 reported facial nerve palsy (1.1 %).Fig. 1 Otologic symptoms appearing 4 weeks after COVID-19 vaccination (left, purple) and COVID-19 infection (right, blue). Symptoms including dizziness, hearing loss, tinnitus, imbalance, vertigo, ear drainage, and facial palsy were assessed. In total, 420 patients received COVID-19 vaccination and 87 had previous COVID-19 infection. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) Fig. 1 Of the 21 (5.0 %) patients reporting hearing loss following COVID-19 vaccination, 5 had exacerbations of underlying ear pathologies, including cochlear hydrops, conductive hearing loss of unknown origin, and presbycusis (Table 2 ). The most common new diagnosis that patients with hearing loss received was ISSNHL, with a total of 7 diagnoses (1.7 %) (Table 2). 3 patients were newly diagnosed with presbycusis (0.7 %). Out of 7 patients diagnosed with ISSNHL, 4 had received the Pfizer vaccine and 3 had received Moderna; 5 developed symptoms after their second dose, and 2 developed symptoms after the first dose (Table 2). As treatment, 4 received oral (PO) prednisone, 2 received intratympanic (IT) dexamethasone, and 1 received conservative management; 4 patients were eventually referred for cochlear implantation. Of the three patients who did not receive cochlear implantation referral, one patient had improved hearing after PO steroid treatment (Patient 17) while the others had relatively non-impairing deficits (Patients 4 and 18). Audiometric data at presentation for patients diagnosed with ISSNHL is summarized in Fig. 2 .Table 2 Description of all patients reporting hearing loss following COVID-19 vaccination, including vaccine brand, other otologic symptoms, primary diagnoses, and treatment. Of note, patients may have had other, secondary, otolaryngologic diagnoses that are not displayed here. Table 2Patient # Vaccine brand Other otologic symptoms Primary diagnosis Treatment 1 Moderna n/a Cochlear hydropsa Acetazolamide, prednisone 2 Pfizer n/a Unspecified conductive hearing lossa Observation 3 Moderna Dizziness, vertigo, imbalance Meniere's diseasea IT dexamethasone 4 Moderna n/a ISSNHL Observation 5 Unsure Dizziness, vertigo Presbycusis Hearing aids, vestibular therapy 6 Pfizer Tinnitus ISSNHL PO prednisone, cochlear implantation 7 Pfizer Tinnitus, dizziness ISSNHL PO prednisone, cochlear implantation 8 Pfizer Tinnitus ISSNHL IT dexamethasone, cochlear implantation 9 Moderna Tinnitus, dizziness, vertigo, imbalance Presbycusis Hearing aids, Epley maneuver 10 Pfizer Tinnitus TMJ syndrome Lifestyle modification 11 Moderna Tinnitus ISSNHL IT dexamethasone, cochlear implantation 12 J&J n/a Presbycusisa Observation 13 Pfizer n/a Cerumen impaction Disimpaction 14 Pfizer Dizziness, vertigo Meniere's disease Acetazolamide 15 Moderna Dizziness, vertigo, imbalance Presbycusis Meclizine 16 Pfizer Ear drainage, dizziness, vertigo, imbalance CSF leak Repair (MCF) 17 Moderna Tinnitus ISSNHL PO prednisone 18 Pfizer Tinnitus ISSNHL PO prednisone 19 Pfizer n/a Presbycusis Observation 20 Moderna Tinnitus Vestibular schwannoma Resection (retrosigmoid) 21 Moderna Tinnitus Acute otitis media Myringotomy a Italics indicate exacerbation of a previously diagnosed condition. Fig. 2 Audiometric results for patients diagnosed with idiopathic sudden sensorineural hearing loss (ISSNHL) following COVID-19 vaccination. Results from the symptomatic ear are displayed. The average hearing threshold across all patients diagnosed with ISSNHL is displayed in black. Fig. 2 Of the 16 (3.8 %) patients reporting tinnitus without hearing loss following COVID-19 vaccination, 6 (1.4 %) had new diagnoses of subjective tinnitus and 2 had exacerbations of previously diagnosed subjective tinnitus, for a total of 8 patients (1.9 %) with subjective tinnitus as the primary diagnosis (Table 3). Among the 19 patients reporting dizziness (4.5 %) without hearing loss or tinnitus, no diagnosis was notably more or less common than others (Table 4). The patients reporting ear drainage or vertigo as their chief concern are summarized in Supplementary Table 1. In total, the most common primary diagnoses, including exacerbations, were subjective tinnitus (n = 9, 2.1 %), temporomandibular joint (TMJ) syndrome (n = 7, 1.7 %), ISSNHL (n = 7, 1.7 %), and presbycusis (n = 6, 1.2 %). Among patients experiencing otologic symptoms following vaccination, 32 (52.5 %) received the Pfizer vaccine, 21 (34.4 %) received Moderna, 5 (8.2 %) received J&J, and 3 (4.9 %) were unsure of the vaccine brand (Table 5 ). These proportions were not significantly different for patients who did not report otologic symptoms (p = 0.12). Among patients experiencing otologic symptoms following vaccination, 14 (23.0 %) were previously diagnosed with COVID-19 (Table 5). This proportion was not significantly different for patients who did not report otologic symptoms (p = 0.09).Table 3 Description of all patients reporting tinnitus, but no significant hearing loss, following COVID-19 vaccination, including vaccine brand, other otologic symptoms, primary diagnoses, and treatment. Of note, patients may have had other, secondary, otolaryngologic diagnoses that are not displayed here. Table 3Patient # Vaccine brand Other otologic symptoms Primary diagnosis Treatment 22 J&J n/a TMJ syndrome Lifestyle modification 23 Moderna n/a Subjective tinnitus vs. TMJ syndrome Prednisone 24 Moderna n/a Subjective tinnitus Lifestyle modification 25 Pfizer Dizziness TMJ syndrome Lifestyle modification 26 Pfizer Dizziness Subjective tinnitus Lifestyle modification 27 Pfizer n/a Subjective tinnitusa Lifestyle modification 28 Moderna Dizziness, vertigo Subjective tinnitus Lifestyle modification 29 Pfizer n/a Unspecified conductive hearing lossa Observation 30 Unsure Dizziness Acute serous otitis media Observation 31 Pfizer n/a Subjective tinnitus Lifestyle modification 32 Pfizer n/a Cerumen impaction Disimpaction 33 Pfizer Dizziness, imbalance Endolymphatic hydrops vs. TMJ syndrome Low salt diet 34 Pfizer Dizziness, imbalance Subjective tinnitusa Lifestyle modification 35 Pfizer n/a Subjective tinnitus Lifestyle modification 36 Pfizer n/a Endolymphatic sac tumor Resection (PCF) 37 Pfizer n/a Cerumen impaction Disimpaction a Italics indicate exacerbation of a previously diagnosed condition. Table 4 Description of all patients reporting dizziness following COVID-19 vaccination, including vaccine brand, other otologic symptoms, primary diagnoses, and treatment. Of note, patients may have had other, secondary, otolaryngologic diagnoses that are not displayed here. Table 4Patient # Vaccine brand Other otologic symptoms Primary diagnosis Treatment 38 Pfizer n/a Otosclerosis Observation 39 J&J n/a Cerumen impaction Disimpaction 40 Moderna Imbalance Cerumen impaction Disimpaction 41 Unsure Imbalance, facial nerve palsy Bell's palsya Prednisone, valacyclovir 42 Moderna Imbalance s/p mastoidectomya Mastoid bowl debridement 43 Pfizer Vertigo BPPV Epley maneuver 44 Moderna Vertigo, imbalance Subjective tinnitus vs. central auditory pathology Prednisone 45 Moderna Vertigo, imbalance Unspecified conductive hearing lossa Observation 46 J&J Vertigo, imbalance BPPV Epley maneuver 47 Moderna n/a Presbycusisa Hearing aid evaluation 48 Moderna Imbalance Cerumen impaction Disimpaction 49 Moderna n/a Ear itch Triamcinolone 50 Pfizer Vertigo TMJ syndrome Lifestyle modification 51 Moderna Vertigo, imbalance Vestibular schwannoma Observation 52 J&J Vertigo, imbalance Unspecified sensorineural hearing lossa Hearing aid evaluation 53 Pfizer Vertigo, imbalance Unspecified sensorineural hearing lossa Hearing aid evaluation 54 Pfizer Vertigo, imbalance TMJ syndrome Lifestyle modification 55 Pfizer Vertigo Vestibular migraine Lifestyle modification 56 Pfizer n/a s/p mastoidectomya Mastoid bowl debridement a Italics indicate exacerbation of a previously diagnosed condition. Table 5 Percentage breakdown of vaccine brand and previous COVID-19 infection among patients not reporting and reporting otologic symptoms following COVID-19 vaccination. P-values were generating using Chi-square tests. Table 5No otologic symptoms Otologic symptoms Vaccine brand N (%) Vaccine brand N (%) Total vaccinated 359 (100 %) Total vaccinated 61 (100 %) Pfizer 212 (59.1 %) Pfizer 32 (52.5 %) Moderna 102 (28.4 %) Moderna 21 (34.4 %) J&J 11 (3.1 %) J&J 5 (8.2 %) Unsure 34 (9.5 %) Unsure 3 (4.9 %) p = 0.12 No otologic symptoms Otologic symptoms COVID-19 status N (%) COVID-19 status N (%) Total vaccinated 359 (100 %) Total vaccinated 61 (100 %) Previous COVID 52 (14.5 %) Previous COVID 14 (23.0 %) No previous COVID 307 (85.5 %) No previous COVID 47 (77.0 %) p = 0.09 4 Discussion In recent months, COVID-19 vaccination has been shown to be efficacious with minimal side effects for most individuals, proving to be the first highly successful intervention in the COVID-19 pandemic. Universal COVID-19 vaccination has decreased rates of infection and minimized symptoms for individuals with breakthrough infections. In particular, the severe symptoms of COVID-19, including respiratory distress and multiorgan failure, are rare in fully vaccinated individuals [2], [3], [4]; in contrast, the most notable otolaryngologic symptoms of COVID-19 infection, smell and taste dysfunction [10], [11], [12], occur at similar frequencies in breakthrough COVID-19 infections, albeit with lesser severity compared to infections in non-vaccinated individuals [13]. Notably, a small number of patients with smell and taste dysfunction following COVID-19 vaccination have been reported, but these side effect are exceedingly rare [14]. In this study, we examined the prevalence of otologic symptoms following COVID-19 vaccination, with a particular focus on sensorineural hearing loss. Sensorineural hearing loss as a symptom of COVID-19 infection has been described in a number of reports [15], [16], [17], [18], [19], [20], [21], but the association between COVID-19 vaccination and sensorineural hearing loss is not well documented. We find that the incidence of ISSNHL following COVID-19 vaccination in otology patients is 1.7 %, which is comparable to the overall otology patient population [8], [22], [23]. Our data lend further evidence to the fact that the benefits of COVID-19 vaccination greatly outweigh the risks. Nearly all patients (6/7) diagnosed with ISSNHL following COVID-19 vaccination were treated with steroid therapy, in the form of intratympanic dexamethasone or oral prednisone. Of these patients, 4 were eventually referred for cochlear implantation due to the degree of hearing loss and lack of improvement. In these patients and in the larger ISSNHL group, there were no notable trends in vaccine brand or previous COVID-19 infection; however, 5/7 patients with a diagnosis of ISSNHL experienced symptoms after the second dose rather than the first. Incidence of ISSNHL may thus have been related to increased systemic inflammation produced by a primed immune response, though the incidence of ISSNHL following COVID-19 vaccination was comparable to the overall otology population. A recent study by the House Ear Clinic reported increased otologic symptoms following the first vaccine dose as opposed to the second, which authors suggested was correlated with the initial appearance of immunoglobulin G (IgG) within 10–14 days of the first dose [8]. The fact that our results conflicted with these findings suggests that the mechanism linking vaccination and appearance of ISSNHL or otologic symptoms is unclear and cannot be substantiated based on observational studies. In this study, we found that dizziness (7.9 %), tinnitus (6.2 %), and hearing loss (5.0 %) are the most commonly reported otologic symptoms following COVID-19 vaccination, comparable to findings by the House Ear Clinic, although exact percentages differed [8]. Given that no specific patterns in diagnosis were observed for dizziness, this symptom can likely be categorized as an expected result of increased immune activation following vaccination, as are the other common symptoms of COVID-19 vaccination such as headache, fever, myalgia. In contrast, tinnitus and hearing loss following vaccination were associated with specific diagnoses, the most common of which were subjective tinnitus (2.1 %), TMJ syndrome (1.7 %), ISSNHL (1.7 %), and presbycusis (1.2 %). By definition, new diagnoses or exacerbations of presbycusis are unlikely to be related to COVID-19 vaccination and will not be discussed here. Subjective tinnitus following COVID-19 vaccination has been reported in a number of case reports [24], [25], but no studies have focused on post-vaccination tinnitus on a larger scale. In 3 of 4 patients described in these case reports, tinnitus was transient and potentially related to acute inflammation; autoimmune or vasculitic processes have been suggested as potential culprits [24]. In our patient population, new onset tinnitus appearing after COVID-19 vaccination appears to persist for a longer timeframe than reported in the above cases, which suggests a potential mechanistic difference. Importantly, an increased incidence of persistent subjective tinnitus has been reported in the general population since the start of the COVID-19 pandemic, which has been attributed to the increased stress and depression of isolation and lockdown [26], [27], [28], [29]. Increased physical and emotional stress, potentially from COVID-19 vaccination itself, may thus have been responsible for subjective tinnitus appearing after vaccination. The fact that TMJ syndrome was one of the more frequent diagnoses following vaccination lends further evidence for this notion, as TMJ syndrome is commonly associated with increased stress [30]. Vaccine brands received by patients reporting otologic symptoms were comparable to those received by vaccinated patients who did not report otologic symptoms (p = 0.1183), suggesting that otologic symptoms are unlikely to be related to the particular molecular or chemical makeup of a vaccine brand. Previous studies of otologic symptoms following vaccination have mainly focused on Pfizer and Moderna vaccines, and have not reached any conclusions about the association between vaccine brand and resultant symptoms [8], [24]. Patients reporting otologic symptoms following vaccination did not have significantly greater odds of past COVID-19 infection compared to patients without otologic symptoms (p = 0.09331), although our study was likely underpowered to detect this difference. A recent study by Avci et al. found that otolaryngologic symptoms like taste dysfunction, sinonasal dysfunction, and hearing loss were more frequent following vaccination in patients with past COVID-19 infection [9]. An additional study with a larger population may thus be more informative about otologic symptoms following vaccination and past COVID-19 infection. Our study had several limitations. Firstly, because we presented questionnaires to patients reporting to an otology clinic, incidences of otologic symptoms following COVID-19 vaccination are likely over-represented and thus cannot be generalized to the overall population. Secondly, diagnostic and therapeutic data was obtained via retrospective chart review and may not have been reflective of the full workup that patients received. In fact, several diagnoses reported in Table 2, Table 3, Table 4 were ambiguous and under active evaluation—longitudinal follow-up may have provided more information about the ultimate diagnosis. Lastly, our study population of 500 otology patients represents a relatively small cohort; a larger population may have had sufficient power to detect differences in a number of our subgroup analyses. 5 Conclusion In this study, we reported the incidence of otologic symptoms appearing 4 weeks after COVID-19 vaccination. In total, 14.5 % of patients presenting to the otology clinic reported otologic symptoms appearing or worsening following COVID-19 vaccination. The most frequently reported symptoms were dizziness (7.9 %), tinnitus (6.2 %), and hearing loss (5.0 %), and the most common diagnoses were subjective tinnitus (2.1 %), temporomandibular joint syndrome (1.7 %), and idiopathic sudden sensorineural hearing loss (1.7 %). Notably, we do not find an increased incidence of ISSNHL following COVID-19 vaccination. We attribute tinnitus and TMJ syndrome following vaccination to increased physical and emotional stress, although acute inflammatory processes producing otologic symptoms cannot be ruled out. Otologic symptoms following COVID-19 vaccination do not appear to have mechanistic associations with these specific vaccines; thus, our study further affirms that the benefits of COVID-19 vaccination significantly outweigh the risks. The following is the supplementary data related to this article.Supplementary Table 1 Description of all patients reporting otologic symptoms other than hearing loss, tinnitus, and dizziness following COVID-19 vaccination, including vaccine brand, other otologic symptoms, primary diagnoses, and treatment. Of note, patients may have had other, secondary, otolaryngologic diagnoses that are not displayed here. Supplementary Table 1 Meeting information The Triological Society 2022 Combined Sections Meeting, Coronado, CA, United States, January 20–22, 2022 (poster presentation). Declaration of competing interest Stephen Leong, BA: none; Bing M. Teh, MBBS, PhD, FRACS: none; Ana H. Kim, MD: none. Acknowledgements N/a. ==== Refs References 1 WHO Coronavirus (COVID-19) dashboard https://covid19.who.int 2 Polack F.P. Thomas S.J. Kitchin N. Safety and efficacy of the BNT162b2 mRNA Covid-19 vaccine N Engl J Med 383 27 2020 2603 2615 10.1056/NEJMoa2034577 33301246 3 Baden L.R. El Sahly H.M. 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Tinnitus in COVID-19 pandemic Ear Nose Throat J 100 3_suppl 2021 10.1177/0145561320988364 197S–198S 28 Beukes E.W. Baguley D.M. Jacquemin L. Changes in tinnitus experiences during the COVID-19 pandemic Front Public Health 8 2020 592878 10.3389/fpubh.2020.592878 29 Beukes E. Ulep A.J. Eubank T. Manchaiah V. The impact of COVID-19 and the pandemic on tinnitus: a systematic review J Clin Med 10 13 2021 2763 10.3390/jcm10132763 34201831 30 Emodi-Perlman A. Eli I. Smardz J. Temporomandibular disorders and bruxism outbreak as a possible factor of orofacial pain worsening during the COVID-19 Pandemic—Concomitant research in two countries J Clin Med 9 10 2020 3250 10.3390/jcm9103250 33053640	0	PMC9721195	NO-CC CODE	2022-12-14 23:52:22	no	Am J Otolaryngol. 2023 Dec 5 March-April; 44(2):103725	utf-8	Am J Otolaryngol	2,022	10.1016/j.amjoto.2022.103725	oa_other
==== Front Ann Diagn Pathol Ann Diagn Pathol Annals of Diagnostic Pathology 1092-9134 1532-8198 Elsevier Inc. S1092-9134(22)00178-2 10.1016/j.anndiagpath.2022.152076 152076 Original Contribution The effects of preconception and early gestation SARS-CoV-2 infection on pregnancy outcomes and placental pathology Hernandez Patricia V. a Chen Ling b Zhang Ray a Jackups Ronald a Nelson D. Michael c He Mai a⁎ a Department of Pathology & Immunology, Washington University in St. Louis School of Medicine, St. Louis, MO 63110, USA b Division of Statistics, Washington University in St. Louis School of Medicine, St. Louis, MO 63110, USA c Department of Obstetrics & Gynecology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA ⁎ Corresponding author. 5 12 2022 2 2023 5 12 2022 62 152076152076 © 2022 Elsevier Inc. All rights reserved. 2022 Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Objective To evaluate if peri-pregnancy timing of a PCR+ test for SARS-CoV-2 RNA affects pregnancy outcomes and placental pathology. Methods This is a retrospective cohort study conducted in a tertiary center. Pregnancy outcomes and placental pathology were compiled for women who tested positive for SARS-CoV-2 RNA from a nasopharyngeal swab assessed by RT-PCR. The population comprised four groups that were PCR+ preconception (T0) or in the 1st (T1), 2nd (T2), or 3rd (T3) trimester of pregnancy. A fifth, control group (TC) tested PCR- for SARS-CoV-2 before delivery. Results Seventy-one pregnancies were studied. The T0 group exhibited lower gestational ages at delivery, had infants with the lowest birth weights, the highest rate of pregnancy loss before 20 weeks. Features of maternal vascular malperfusion and accelerated villous maturation were prominent findings in the histopathology of placentas from women PCR+ for SARS-CoV-2 RNA, especially in the T0 and the T1 groups. Conclusion Women at highest risk for pregnancy complications are those who test PCR+ for viral RNA preconception or during first trimester of pregnancy. Keywords SARS-CoV-2 Timing COVID-19 Placental pathology Pregnancy outcome Neonatal outcome ==== Body pmc1 Introduction The data support pregnancy as a risk factor for severe disease associated with COVID-19 [1], [2], [3]. SARS-CoV-2 infection during pregnancy associates with numerous adverse pregnancy outcomes, including preeclampsia, preterm birth, and stillbirth, especially among pregnant women with clinically severe COVID-19 disease [2]. As an organ unique to pregnancy, the placenta is pivotal to pregnancy outcomes. A simplified view of the human chorioallantoic placental structure reveals two autonomous circulations interfaced by trophoblast on the connective tissue surface of villi. The two circulations are discreet and normally do not mix. The maternal circulation flows through arteriole blood vessels in the uterine bed, traverses the placental intervillous space, and exits via endometrial veins. The fetal circulation includes ramifying villous blood vessels that merge into vessels on the chorionic plate to connect to the umbilical cord and to provide circulation to the fetal body. Histopathological abnormalities in blood vessels, intervillous space, or villi can lead to characteristic microscopic features. For example, maternal hypertensive disorders commonly yield small placentas and microscopic features related to abnormal remodeling of decidual arterioles in the basal plate and superficial implantation, termed maternal vascular malperfusion (MVM). This histopathology may be localized or global within the placental disc, and may include infarct, accelerated villous maturation (AVM), distal villous hypoplasia and decidual arteriopathy. Umbilical cord disorders can also produce abnormalities in the fetal circulation (fetal vascular malperfusion, FVM), such as fetal vessel thrombosis, avascular villi, and villous stromal karyorrhexis [4]. The pathology may evolve with multiple lesions, interacting with each other to affect downstream organ function. A review of 56 studies reporting on placental pathology of pregnant women with SARS-CoV-2 infection showed that the percentage of placental examinations with histopathologic findings of MVM (30.7 %), FVM (27.1 %), and acute (22.7 %) and chronic inflammation (25.7 %), was higher than expected [5]. However, our prior study of COVID-19 related placental pathology included a control population and demonstrated that placentas from women who were PCR+ for SARS-CoV-2 during the 3rd trimester did not show differences in histopathology compared with gestational age matched PCR- controls [6]. Whether the timing of maternal SARS-CoV-2 infection relative to conception influences pregnancy outcome or placental histopathology is debated [7].The above discrepancy among results raised the specific question of whether or not the timing in gestation a patient becomes SARS-CoV-2 PCR+ influences the pathology of the placenta at delivery [8]. Indeed, Glynn et al. reported that infection by SARS-CoV-2 < 14 days from delivery, designated acute onset infection, exhibited FVM lesions prominently. We tested the hypothesis that the time peri-pregnancy when a woman tests SARS-CoV-2 PCR+ influences pregnancy outcomes and placental pathology. We specifically question whether or not a positive PCR for SARS-CoV-2 during the 1st trimester leads to worse pregnancy outcomes, more severe placental pathology, or both compared to infections later in gestation. 2 Methods 2.1 Institution review board approval This study was approved under IRB ID# 201902092 by the institutional Office of Institution Review Board. 2.2 Study design We conducted a retrospective cohort study between April 2020 and September 2021. Maternal SARS-CoV-2 PCR results were identified from the laboratory information system. We retrieved the corresponding obstetrical and newborn data and the written pathology reports, and original specimens from placenta or products of conception, for further analysis. 2.3 Study population Patients were universally tested on admission to labor and delivery and were tested for cause (for example, if they had respiratory symptoms) at other times. In general patients were tested once before labor and delivery. Patients' chart was also reviewed for SARS-CoV-2 PCR testing. Patients were assigned to a trimester for onset of infection based on the time of their 1st positive PCR test. Patients testing PCR+ for SARS-CoV-2 were classified into four groups: pre-conception during the study period (T0), in the 1st (T1), 2nd (T2), or 3rd (T3) trimester of pregnancy. A fifth group (TC) served as control pregnancies, who tested negative by PCR and delivered a singleton newborn in the 3rd trimester and had a placenta pathology evaluation due to comorbidities or for any other reason. For the T0 group, based on gestational age obtained by obstetric ultrasonography, we estimated the difference between the COVID-19 testing date and the conception date. The current study was an expansion of a prior study, by adding more patients with PCR+ for SARS-CoV-2 before and during 1st or 2nd trimesters of pregnancy [7]. 2.4 Submission criteria and methodology for placental examination Placentas were submitted to pathology following established guidelines [7] for evaluation of maternal or fetal conditions or gross abnormalities of the placenta [8]. Notably, a maternal positive SARS-CoV-2 test was included as an indication for placental submission. All placentas from the pregnant woman who tested positive for SARS-CoV-2 were expected to be submitted for pathological examination. Placental reports were composed by pathologists who were board certified in Anatomical and Gynecological Pathology fellowship training, or board certified in both Anatomical and Pediatric Pathology. Slides stained with hematoxylin and eosin (H&E) in selected cases, where the pathology report was unclear, were centrally reviewed, and a final disposition made by a board-certified pediatric pathologist (MH) blinded to clinical history, as previously described [6]. Gross and microscopic features were extracted from the pathology reports, including the following: placental trimmed weight, placental weight percentile, cord insertion, features of MVM including infarcts, retroplacental hemorrhage, distal villous hypoplasia, AVM and decidual arteriopathy, features of FVM, including thrombosis, avascular villi, intramural fibrin deposition, villous stromal-vascular karyorrhexis, chorangiosis, delayed villous maturation, villous edema, perivillous fibrin type fibrinoid deposition, acute and chronic inflammation, as recommended5. Placental weight percentile for gestational age was determined based on a published chart [9]. For acute inflammation, we sub-categorized into maternal inflammatory response, represented by acute chorioamnionitis, and fetal inflammatory response, represented by acute vasculitis of umbilical cord, chorionic plate blood vessels, and acute funisitis of the umbilical cord. For chronic inflammation, we sub-categorized into villitis of unknown etiology, chronic chorioamnionitis and chronic (lymphoplasmacytic) deciduitis. We specifically searched for features of SARS-CoV-2 placentitis [10], including the triad of histiocytic intervillositis, increased perivillous fibrin, and villous trophoblastic necrosis. 2.5 Clinical information Maternal age, obstetric history, respiratory symptoms, maternal comorbidities, including history of hypertension and diabetes before or during gestation, cardiomyopathy, uterine malformations, gestational age (GA) of SARS-CoV-2 PCR testing, GA at delivery, birth weight, one- and five-minute APGAR scores, and COVID-19 vaccination history were extracted from the electronic medical records. Severity of COVID-19 was classified based on clinical spectrum of SARS-CoV-2 from National Institute of Health (NIH) as asymptomatic, if the individual tested positive without any symptom consistent with COVID-19; mild, if the individual presented with fever, cough, sore throat, malaise, headache, muscle pain, nausea, vomiting, diarrhea, loss of taste or smell without shortness of breath or need for hospital admission; moderate, if the individual showed lower respiratory tract disease with dyspnea or need for oxygen supplementation by nasal cannula; and severe, if the individual needed mechanical ventilation or was admitted to the intensive care unit due to COVID-19 [11]. Adverse pregnancy and neonatal outcomes included preterm birth if before 37 weeks' GA, preeclampsia, intrauterine fetal demise (IUFD), neonatal death, and neonatal intensive care unit (NICU) admission. 2.6 SARS-CoV-2 testing Testing for pregnant women via nasopharyngeal swabs was performed at the Molecular Infectious Disease Laboratory of Barnes-Jewish Hospital. The 2019-Novel Coronavirus Assay (COVID-19) real-time polymerase chain reaction (RT-PCR) assay was used to detect the presence of SARS-CoV-2 RNA. 2.7 Statistical analysis The Shapiro–Wilks test was used to check normality of the distribution of continuous outcome variables. For normal distributed data, the mean and standard deviation and comparisons among groups were carried out using one-way ANOVA. For non-parametric values, median and interquartile range was obtained, and groups were compared by Kruskal-Wallis. If significant, post-hoc pairwise comparison was conducted using Tukey's Studentized Range test or Dunn's Test if appropriate. Chi-square or Fisher's exact test was used for categorical variables. Significance was set as P < 0.05. All statistical tests were performed with SAS 9.4 (SAS Inc., Cary, NC) and R programming version 4.1.2 (Vienna, Austria). 3 Results 3.1 Demographic features Seventy-one pregnancies were studied, including 19 that were PCR- in the 3rd trimester (TC) and 52 that were maternal SARS-CoV-2 PCR+. Maternal age, comorbidities, and mode of delivery did not differ significantly among the groups (Table 1 ). Among patients testing positive, 11 were in the T0 group, 8 were in the T1 group, 16 were in the T2, and 17 were in the T3. T0 group ranged from 12 to 399 days, with a median of 97 days and an inter-quartile ratio of IQR 32–222 days.Table 1 Maternal demographics, pregnancy, and neonatal outcomes (T0 = COVID-19 before pregnancy; T1 = COVID-19 during 1st of gestation; T2 = 2nd of gestation; T3 = 3rd of gestation; TC = no COVID). For comorbidities, preeclampsia, cesarean delivery, preterm birth, stillborn, NICU admission, APGAR and birth weight analysis, we exclude patients who had a pregnancy loss <20 weeks. Table 1Features T0 (N = 11) T1 (N = 8) T2 (N = 16) T3 (N = 17) TC (N = 19) P-value Mean maternal age in years (range) 30.8 ± 4.7 (25–39) 27.1 ± 5.1 (19–34) 30.8 ± 5.9 (20–39) 26.1 ± 7.2 (15–37) 26.9 ± 5.9 (18–37) 0.11 Comorbidities 4/7 (26.3 %) 3/8 (37.5 %) 5/16 (31.3 %) 1/17 (5.9 %) 5/14 (26.3 %) 0.28 Preeclampsia 2/7 (28.6 %) 4/8 (50 %) 4/16 (25 %) 0/17 (0.0 %) 4/19 (21.1 %) 0.06 Pregnancy loss before 20 weeks 4/11 (36.4 %) 0/8 (0.0 %) 0/16 (0.0 %) 0/17 (0.0 %) 0/19 (0.0 %) <0.01 Cesarean deliverya 2/7 (28.6 %) 3/8 (37.5 %) 10/16 (65.5 %) 7/17 (41.18 %) 4/15 (21.1 %) 0.10 Preterm birtha 5/7 (71.3 %) 4/8 (50 %) 4/16 (25 %) 7/17 (23.5 %) 3/19 (15.8 %) 0.05 Stillborna 0/7 0/8 0/16 0/17 0/19 N/A Neonatal deatha 2/7 (28.6 %) 0/8 (0 %) 0/16 (0 %) 1/17 (5.9 %) 0/19 (0 %) 0.02 NICU admissiona 2/7 (28.6 %) 2/8 (25.0 %) 3/16 (18.8 %) 4/17 (23.5 %) 7/18 (38.9 %) 0.11 Median APGAR 1 min (range)a 8.0 (6.0–8.0) 8.0 (6.0–8.0) 8.0 (3.0–9.0) 9.0 (1.0–8.0) 8.0 (3.0–8.0) 0.46 Median APGAR 5 min (range)a 8.0 (5.0–9.0) 9.0 (8.0–9.0) 9.0 (8.0–9.0) 9.0 (2.0–9.0) 9.0 (7.0–9.0) 0.19 Mean birth weight in g (range)a 2011.4 ± 979.9 (660.0–3150.0) 2595.0 ± 291.3 (2160.0–3040.0) 3012.0 ± 744.1 (1990.0–4706.0) 2904.7 ± 472.1 (2180–3710) 2839.44 ± 728.9 (1370.0–3980.0) 0.02 a Excluding cases with pregnancy loss <20 weeks. All patients in the T1 and 91 % in the T0 group presented the mild form of COVID-19 infection, with none presenting the moderate or severe form. Conversely, 62 % and 35 % of patients in T2 and T3 groups, respectively, presented the mild form of COVID-19 (Table 2 ).Table 2 Distribution and comparison of COVID-19 infection severity among the participants. Table 2Severity T0 (N = 11) T1 (N = 8) T2 (N = 16) T3 (N = 17) TC (N = 19) P-value Asymptomatic 1/11 (9.09 %) 0/8 (0 %) 2/16 (12.50 %) 8/17 (47.06 %) N/A 0.03⁎ Mild 10/11 (90.91 %) 8/8 (100 %) 10/16 (62.50 %) 6/17 (35.29 %) N/A Moderate 0/11 (0 %) 8/8 (0 %) 3/16 (18.75 %) 2/17 (11.76 %) N/A Severe 0/11 (0 %) 0/8 (0 %) 0/16 (0 %) 1/17 (5.88 %) N/A Missing data 0/11 (0 %) 0/8 (0 %) 1/16 (6.25 %) 0/17 (0 %) 19/19 (100 %) ⁎ P-value was calculated excluding Tc. 3.2 Pregnancy outcomes Pregnancy loss before 20 weeks was seen only in the T0 group who tested PCR+ before conception. Preterm labor was more often seen in the T0 group, but the trend was at the borderline for statistical significance (P = 0.05; Table 1). Importantly, GA at delivery in T0 patients was remarkably lower when compared to all other groups (Fig. 1A). Among all PCR+ groups progressing to delivery, there was a significant difference (P = 0.01) in preterm birth rates. Infection in the T1 group associated with a non-significant (P = 0.06) higher incidence of preeclampsia, and preeclampsia frequencies were similar among all other groups (Table 1). Furthermore, no statistical differences were seen regarding cesarean delivery rate among the participants. None of the pregnancies resulted in stillbirth.Fig. 1 A: Distribution and comparison of gestational ages at delivery among groups – T0, T1, T2, T3, and TC (P = 0.01). Significance difference seen due to comparison of T0 with remaining groups. B: Differences in birth weight among groups, P < 0.01 (T0 = COVID-19 before pregnancy; T1 = COVID-19 during 1st of gestation; T2 = 2nd of gestation; T3 = 3rd of gestation; TC = no COVID). Significance difference seen due to comparison of T0 with remaining groups. Fig. 1 3.3 Neonatal outcomes Birth weight at delivery was lowest in T0 patients (2100 g mean birthweight) who were infected preconception, compared to TC controls (2900 g mean birthweight), followed by T1 patients infected during the first trimester of gestation (P < 0.01; Figs. 1B). Two of seven (28.6 %) T0 patients who were infected by SARS-CoV-2 preconception had a neonatal demise, but only one of 17 patients (5.9 %) who developed a SARS-CoV-2 PCR+ test during the third trimester of gestation (T3) had babies who died in the neonatal period (P = 0.02). No neonatal demises occurred in the T1, T2, or TC patient groups. APGAR scores at one- and five- minutes and neonatal intensive care admissions were no different among the groups (Table 1). 3.4 Placental histology No significant differences were observed for placental weights, birth weights or fetoplacental weight ratios, percentage of small-for-gestational age placentas defined as placental weight < 10th percentile),or large-for-gestational age defined as placental weight > 90th percentile. Among all patients with PCR+ at any time in the peri-pregnancy period, 63.0 % of placentas demonstrated features of MVM and 44.7 % showed AVM. Moreover, >70 % of placentas in the T0, T1, and T2 groups had features of MVM, significantly higher than the T3 and TC groups. The T0 and T1 groups also had the highest frequency of AVM in their placentas, while TC patients who were PCR- for SARS-CoV-2 presented the lowest incidence (P < 0.01) of this histopathology (Table 3 , Fig. 2 ).Table 3 Histopathological features of placentas (T0 = COVID-19 before pregnancy; T1 = COVID-19 during 1st of gestation; T2 = 2nd of gestation; T3 = 3rd of gestation; TC = no COVID). Table 3Histologic feature All groups infected T0 (N = 7) T1 (N = 8) T2 (N = 16) T3 (N = 17) TC (N = 19) P-value Mean placental weight in g (range) 330.0 ± 152.4 (149.0–550.0) 404.1 ± 85.9 (264.0–537.0) 467.6 ± 123.6 (246.0–699.0) 451.5 ± 75.4 (337.5–577.0) 434.5 ± 106.6 (255.4–632.8) 0.09 Small-for-gestational age placenta 3 6 10 11 11 0.74 Large-for-gestational age placenta 0 0 1 0 0 0.45 Mean birth weight: Placental weight ratio (range) 5.8 ± 1.5 3.8–8.5 6.8 ± 2.1 4.0–10.6 6.6 ± 1.3 3.1–9.4 6.5 ± 0.9 5.2–8.3 6.6 ± 1.1 4.2–8.6 0.70 Maternal vascular mal perfusion 29/46 (63.0 %)a 5/7 (71.4 %) 5/7 (71.4 %) 11/15 (73.3 %)b 8/17 (47.1 %) 6/19 (31.6 %) 0.03 Accelerated villous maturation 21/47 (44.7 %)a 5/7 (71.4 %)b 5/7 (71.4 %)b 6/15 (37.5 %) 3/17 (17.6 %) 2/19 (10.5 %) <0.01 Fetal vascular mal perfusion 8/45 (17.8 %) 1/7 (14.3 %) 1/6 (16.6 %) 1/15 (6.7 %) 6/17 (35.3 %) 7/19 (36.8 %) 0.12 Maternal inflammatory response 4/45 (8.9 %) 1/7 (14.3 %) 1/7 (14.3 %) 0/15 (0.0 %) 2/17 (11.7 %) 3/19 (15.8 %) 0.11 Fetal inflammatory response 4/45 (8.9 %) 2/7 (11.7 %) 0/7 (0.0 %) 0/15 (0.0 %) 2/17 (11.7 %) 4/19 (21.1 %) 0.18 Chronic inflammation 4/45 (8.9 %) 1/7 (14.3 %) 2/7 (11.8 %) 1/15 (6.7 %) 0/17 (0 %) 2/19 (10.5 %) 0.26 a Comparison between T+ vs Tc. b Comparison between T3 vs Tc. Fig. 2 Typical histopathology of placentas from patients who tested PCR+ for SARS-CoV-2 RNA. A: Villous morphology of a placenta at 32 weeks GA, appearing unusually similar to villi of a term placenta, with numerous small diameter or hyper mature villi. H&E, 40×. B: Avascular villi from a placenta delivered at 39 weeks' GA from a patient PCR+ for SARS-CoV-2 RNA. There are normal villi with fetal villous blood vessels apparent in the upper field, but the central portion shows chorionic villi with loss of fetal villous capillaries and deposits of hyaline fibrosis. H&E, 200×. C: Acute chorioamnionitis with numerous neutrophils distributed from the sub chorionic intervillous space (bottom) to the amniocyte covering the chorionic plate (top). H&E, 100×. D: High grade villitis of unknown etiology, with inflammatory cells colonizing more than ten adjacent villi. H&E, 200×. E: SARS-CoV-2 placentitis showing the triad of histiocytic inter-villositis, intervillous fibrin deposition and trophoblastic necrosis. H&E, 100×. F: Image of SARS-CoV-2 placentitis after specimen immunostaining for CD68 as a histiocyte, macrophage marker. The field shows the triad listed in E, with intervillous space cells immune-stained for CD68. 100×. Fig. 2 Features of FVM were not significantly different among the PCR+ and PCR- groups. Importantly, there were no differences in the histological features of inflammation among the placentas, including maternal acute inflammatory response, fetal acute inflammatory response, and chronic inflammation (Table 3). None of the PCR+ groups nor the control group exhibited chronic histiocytic intervillositis (Fig. 2E and F). There were no other group differences in the histopathology. 4 Discussion To our best knowledge, this is the first article that found different pregnancy and pathology outcomes according to the timing of SARS-CoV-2 infection. The data support our hypothesis that the time peri-pregnancy when a woman tests SARS-CoV-2 PCR+ influences both pregnancy outcomes and placental pathology. We found that T0 patients who were infected by SARS-CoV-2 before the pregnancy delivered at the lowest GA, had infants with the lowest birth weight, had the most pregnancy losses before 20 weeks, and experienced some neonatal deaths, compared to other groups. Moreover, T1 patients with COVID-19 during the 1st trimester of pregnancy had a higher incidence of preterm labor, when compared to the other groups. There was a high frequency of MVM and AVM in the placental pathology of PCR+ patients, especially in T0 and T1 patients, respectively, compared to the other groups. Pregnancy generally worsens COVID-19 outcomes [2], but how timing of infection during pregnancy influences outcomes is poorly understood [1], [2], [3], [12]. Indeed, while there are >50 studies of placental pathology in delivered patients, only four cases of miscarriage and one neonatal death were reported among 324 pregnancies compiled from 24 of the studies [12]. Pathological examination in gonadal and uterine tissues of COVID-19 infected patients is limited [13] and the ability to relate timing of infection and pregnancy GA has not previously been done. Collectively, the data indicate that patients testing PCR+ for SARS CoV-2 RNA in preconception and in early pregnancy are at high risk for sub-optimal pregnancy outcome and should receive antenatal surveillance tuned to the high risk for adverse obstetrical outcomes. 4.1 COVID-19 and pregnancy outcome, and potential mechanisms The mechanisms involved in poor pregnancy outcomes in pregnancies infected early with SARS-CoV-2 virus remain speculative at this point in the pandemic. They include different expression of ACE2 at different time of pregnancy including preconception, virus related chronic inflammation, chronic presence of virus in uterine bed or abnormal angiogenesis of local uterine/placental microenvironment. The “long COVID” is a condition attracting more and more attention, with a broad spectrum of subacute and/or chronic symptoms and signs that follow the acute phase of SARS-CoV-2 infection [14]. Pathophysiology, prevalence, or during of “long COVID” are all not known. We speculate that it would be interesting to see if the more adverse outcome associated with preconception SARS-CoV-2 infection part of this “long COVID”, which seems to be independent of the severity of the acute SARS-CoV-2 infection. Unfortunately, we do not have enough data to analyze “long COVID” in our study population. Interestingly, none of patients who had COVID-19 before pregnancy and at early pregnancy presented moderate or severe symptoms, in contrast to those who had COVID-19 during 2nd and 3rd trimester, despite our findings of worse gestational outcomes seen in the T0 and T1 groups. Angiotensin converting enzyme 2 (ACE2), which is the entry point receptor of SARS-CoV-2 into human cells, is expressed in a wide array of cells in human placenta and there is an increased expression of ACE2 from the early to mid-secretory phases to early pregnancy [15], [16], [17]. This pattern of expression implies an increase in ACE2 during the implantation [18], suggesting abundant receptors are available peri-implantation for SARS-CoV-2 to bind, establish infection, and influence the development of the decidua, trophectoderm, and chorioallantoic placenta at the beginning of pregnancy. This observation suggests one potential mechanism by which the poorest gestational outcomes are seen in women testing PCR+ for SARS-CoV-2 viral RNA preconception and in the 1st trimester of pregnancy. The effects on development could continue well beyond the time viral shedding ceases. TMPRSS2 is a serine protease that increases protein priming to increase infectivity for SARS-CoV-2 infection [13], [19], [20]. The presence of SARS-CoV-2 is highest in maternal decidua, increases angiotensin II receptor type 1 (AT1R), and increases soluble fms-like tyrosine kinase-1 (sFlt-1; also known as VEGF-1 receptor). Thus, we consider that preconception or early gestational infections by SARS-CoV-2 may result in an increased burden of virus in local utero-placental vasculature, predisposing to endothelial injury, microthrombi, and local ischemia. 4.2 SARS-CoV-2 infection, maternal vascular malperfusion and accelerated villous maturation We discovered a high incidence of the histopathology for MVM and AVM in placentas of PCR+ women [20]. MVM reflects the structural changes induced by an impaired maternal blood supply to the feto-placental unit. MVM is characteristic of hypertensive disorders in pregnancy, which are well known to derive from superficial implantation, failed remodeling of maternal uterine spiral arterioles, and increased maternal levels of the antiangiogenic mediator sFlt-1. MVM associates with higher-than-expected SGA newborns in pregnancies with hypertensive disorders, and a higher incidence of MVM in the pregnancies testing PCR+ may partly explain the disproportionately low birthweights delivered among the group testing positive for SARS-CoV-2. Notably, preeclampsia was not more common in PCR+ vs. PCR- pregnancies in our study. We speculate there was an adequate balance of angiogenic/antiangiogenic mediators, or lack of maternal susceptibility to any imbalances, in women infected with SARS-CoV-2. 4.3 Strengths and limitations of our study The strengths of our study are the correlation of SARS-CoV-2 PCR+ test results preconception, and at each trimester, with maternal and neonatal outcomes. All groups were similar for maternal age, comorbidities, and rate of Cesarean delivery. Moreover, the detailed analyses of the placentas were conducted by board certified pathologists. A final strength of the study was collection of vaccination status. We believe the fact that only five patients were vaccinated, spread among the groups, effectively eliminates vaccination status as a confounding independent variable in the results. Limitations of our study include the retrospective design, single center analysis of patients and small sample size. Lack of placental tissue testing by RT-PCR or immunohistochemistry was another limitation but this could be a direction for future studies. The fact that the control group was tested close to the delivery date is a limitation of the study. We limited bias as best we could by conducting blinded analyses of clinical data extracted, pathology reports, and specimen slides reviewed. We do not have an extensive list of all possible comorbidities. Additionally, some groups had a small sample size, making a beta statistical error more likely in our results. Finally, the PCR-control group without infection by SARS-CoV-2 was tested close to their delivery date and not throughout gestation. This approach was pragmatic yet has the potential to miss an asymptomatic patient with a PCR+ result earlier in gestation. At this point, no serologic assays have been proven to efficiently identify previous exposure by SARS-CoV-2, since they do not capture cellular-mediated immunity and the duration of humoral response is unclear [21]. In conclusion, our single institution experience confirms our hypothesis that the time peri-pregnancy when a woman tests PCR+ for SARS-CoV-2 RNA influences clinical outcomes and placental pathology. Importantly, mothers testing PCR+ for SARS-CoV-2 RNA preconception, or during early gestation, are at most risk for adverse clinical outcomes and placental pathology. This finding is important to consider in public health approaches to limit adverse effects of COVID for patients desiring pregnancy, not only during the pandemic but also, for those who may delay childbearing and yet already be at higher risk from having a previous SARS-CoV-2 infection. Declaration of competing interest All co-authors declare no conflict of interest. This study was supported by faculty developmental fund to Dr. Mai He by the Department of Pathology & Immunology, Washington University in St. Louis School of Medicine. ==== Refs References 1 Zambrano L.D. Ellinton S. Strid P. Update: characteristics of symptomatic women of reproductive age with laboratory-confirmed SARS-CoV-2 infection by pregnancy status - United States, January 22-October 3, 2020 MMWR Morb Mortal Wkly Rep 69 2020 1641 1647 33151921 2 Jamieson D.J. Rasmussen S.A. An update on COVID-19 and pregnancy Am J Obstet Gynecol 226 2022 177 186 34534497 3 Lokken E.M. Huebner E.M. Taylor G.G. Disease severity, pregnancy outcomes, and maternal deaths among pregnant patients with severe acute respiratory syndrome coronavirus 2 infection in Washington State Am J Obstet Gynecol 225 77 2021 e1 77.e14 4 Khong T.Y. Mooney E.E. Ariel I. Sampling and definitions of placental lesions: Amsterdam placental workshop group consensus statement Arch Pathol Lab Med 140 2016 698 713 27223167 5 Di Girolamo R. Khalil A. Alameddine S. Placental histopathology after SARS-CoV-2 infection in pregnancy: a systematic review and meta-analysis Am J Obstet Gynecol MFM 3 2021 100468 6 He M. Skaria P. Kreutz K. Histopathology of third trimester placenta from SARS-CoV-2-positive women Fetal Pediatr Pathol 1–10 2020 10.1080/15513815.2020.1828517 7 Glynn S.M. Yang Y.J. Thomas C. SARS-CoV-2 and placental pathology: malperfusion patterns are dependent on timing of infection during pregnancy Am J Surg Pathol 46 2022 51 57 34310367 8 Langston C. Kaplan C. Macpherson T. Practice guideline for examination of the placenta: developed by the Placental Pathology Practice Guideline Development Task Force of the College of American Pathologists Arch Pathol Lab Med 121 1997 449 476 9167599 9 Almog B. Shehata F. Aljabri S. Levin I. Shalom-Paz E. Shrim A. Placenta weight percentile curves for singleton and twins deliveries Placenta 32 1 2011 Jan 58 62 10.1016/j.placenta.2010.10.008 Epub 2010 Oct 30 PMID: 21036395 21036395 10 Watkins J.C. Torous V.F. Roberts D.J. Defining severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) placentitis Arch Pathol Lab Med 145 2021 1341 1349 34338723 11 National Institutes of Health (NIH) COVID-19 treatment guidelines https://www.covid19treatmentguidelines.nih.gov/overview/clinical-spectrum/ 12 Juan J. Gil M.M. Rong Z. Effect of coronavirus disease 2019 (COVID-19) on maternal, perinatal and neonatal outcome: systematic review Ultrasound Obstet Gynecol 56 2020 15 27 32430957 13 Madjunkov M. Dviri M. Librach C. A comprehensive review of the impact of COVID-19 on human reproductive biology, assisted reproduction care and pregnancy: a Canadian perspective J Ovarian Res 13 2020 140 33246480 14 Staffolani S. Iencinella V. Cimatti M. Tavio M. Long COVID-19 syndrome as a fourth phase of SARS-CoV-2 infection Infez Med 30 2022 22 29 35350258 15 Ou X. Liu Y. Lei X. Characterization of spike glycoprotein of SARS-CoV-2 on virus entry and its immune cross-reactivity with SARS-CoV Nat Commun 11 2020 1620 32221306 16 . G Valdés LA Neves L Anton et al Distribution of angiotensin-(1-7) and ACE2 in human placentas of normal and pathological pregnancies. Placenta 27, 200–7. 17 Jing Y. Run-Qian L. Hao-Ran W. Potential influence of COVID-19/ACE2 on the female reproductive system Mol Hum Reprod 26 2020 367 373 32365180 18 Henarejos-Castillo I. Sebastian-Leon P. Devesa-Peiro A. Pellicer A. Diaz-Gimeno P. SARS-CoV-2 infection risk assessment in the endometrium: viral infection-related gene expression across the menstrual cycle Fertil Steril 114 2020 223 232 32641214 19 Hoffmann M. Kleine-Weber H. Schroeder S. SARS-CoV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor Cell 181 2020 271 280.e8 32142651 20 Redline R.W. Ravishankar S. Bagby C.M. Saab S.T. Zarei S. Four major patterns of placental injury: a stepwise guide for understanding and implementing the 2016 Amsterdam consensus Mod Pathol 34 2021 1074 1092 33558658 21 IDSA - Infectious Disease Society of America https://www.idsociety.org/practice-guideline/covid-19-guideline-serology/	36495735	PMC9721196	NO-CC CODE	2022-12-07 23:16:14	no	Ann Diagn Pathol. 2023 Feb 5; 62:152076	utf-8	Ann Diagn Pathol	2,022	10.1016/j.anndiagpath.2022.152076	oa_other
==== Front Vaccine X Vaccine X Vaccine: X 2590-1362 Published by Elsevier Ltd. S2590-1362(22)00106-1 10.1016/j.jvacx.2022.100246 100246 Article Humoral and T-cell mediated response after administration of mRNA vaccine BNT162b2 in frail populations Campagna Roberta a⁎ Mazzuti Laura a Guerrizio Giuliana a Nonne Chiara a Migliara Giuseppe b De Vito Corrado b Mezzaroma Ivano c Chiaretti Sabina c Fimiani Caterina d Pistolesi Valentina e Morabito Santo e Turriziani Ombretta a a Department of Molecular Medicine Sapienza University of Rome, Viale dell’Università, 33, 000185, Rome, Italy b Department of Public Health and Infectious Diseases, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185, Rome, Italy c Department of Translational and Precision Medicine, Sapienza University of Rome, Viale dell'Università, 37, 00185, Rome, Italy d Department of Internal Medicine, Endocrine-Metabolic Sciences and Infectious Disease, Policlinico Umberto I, 155, 00161 e Department of Internal Medicine and Medical Specialties Sapienza University of Rome, Policlinico, 155, 00161, Rome, Italy ⁎ Corresponding author. 5 12 2022 5 12 2022 10024615 6 2022 1 12 2022 2 12 2022 © 2022 Published by Elsevier Ltd. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Patients with frailty are considered to be at greater risk to get severe infection from SARS-CoV-2. One of the most effective strategies is vaccination. In our study we evaluated both the humoral immune response elicited by the vaccination at different time points, and the T-cell response in terms of interferon (IFN)-γ production in frail patients and healthy donors. Fifty-seven patients (31 patients undergoing hemodialysis and 26 HIV positive subjects) and 39 healthcare workers were enrolled. All participants received two doses of the mRNA vaccine BNT162b2. Healthcare workers showed a significantly higher antibody titer than patients twenty-one days after the first dose (p<0.001). From the same time point we observed for both groups a decay of the antibody levels with a steeper slope of decline in the patients group. Regarding T-cell response the only significant difference between non-reactive and reactive subjects was found in median antibody levels, higher in the responders group than in non-responders. The healthcare workers seem to better respond to the vaccination in terms of antibodies production; the lack of T-cell response in about 50% of the participants seems to suggest that in our study population both humoral and cell-mediated response decline over time remarking the importance of the booster doses, particularly for frail patients. Keywords Vaccine SARS-CoV-2 HIV Hemodialysis Humoral response T-cell response ==== Body pmcIntroduction At the end of 2019, a novel coronavirus, later named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in the city of Wuhan, in Hubei province in China, causing the coronavirus disease 2019 (COVID-19) responsible for the pandemic state [1]. Coronaviruses can infect several avian and mammalian hosts [2]. Most coronaviruses that are pathogenic to humans only cause mild illnesses [3] exception made for severe acute respiratory syndrome coronavirus (SARS-CoV) [4] and Middle East respiratory syndrome coronavirus (MERS-CoV) [5], [6] responsible for two major epidemic outbreaks of the 21st century. People infected with SARS-CoV-2 show a wide range of symptoms among which general malaise, cough and fever [7]. In addition to that, the most severe cases can be characterized by acute respiratory distress syndrome (ARDS) and acute lung injury which leads to inflammation, damage of the alveolar lumen and pneumonia with possible fatal outcome[8], [9]. Patients with frailty are at a greater risk to get severe infection which can require hospitalization and lead to poor outcome [10], [11]. In particular patients with kidney disease may experience an immune system dysregulation that makes them more susceptible to infections [12]. Not only patients receiving in-centre dialysis can be more exposed to SARS-CoV-2 infection [13] but also chronic kidney disease (CKD) has been found to be a risk factor for severe COVID-19 and mortality[14], [15], [16], [17]. Different works also seem to demonstrate that people living with HIV (PLWH) having a dysregulated immune response could be at greater risk to develop severe COVID-19, and that’s especially true for those who experienced previous pulmonary events [18], [19]. Despite the contrasting results from different groups [20], WHO considers HIV a significant risk factor for developing critical illness [21]. Even though the use of antiviral drugs such as remdesivir [22] and treatment with monoclonal antibodies have been approved [23] the most effective strategy to limit the viral spread and to protect most vulnerable patients remains, at present, vaccination which enables the activation of all the components of the adaptative immune system. The first authorized vaccine to treat SARS-CoV-2 infection has been the BNT162b2 (Pfizer–BioNTech), an mRNA-based vaccine encoding for the spike protein, the expression of which elicits the activation of the immune response [24]. On December 2020 Italy started the vaccination campaign first involving healthcare workers, followed by some categories of patients with frailty. The aim of our study was to evaluate the kinetic of the antibody response and the T-cell mediated response, in terms of interferon (IFN)-γ production, after the administration of two doses of the Pfizer–BioNTech mRNA vaccine in frail patients and healthy donors. Methods Study population For this study 58 patients and 69 healthcare workers (HCWs) were enrolled. Twenty-six HIV-1 infected subjects, followed in an out-patient basis at the Department of Internal Medicine and Infectious diseases, were included. The following information were extracted from the medical records: demographics (age, gender), time from HIV-1 diagnosis (years), time of ARV exposure (years), CDC classification stage, current ARV, hepatitis C virus (HCV) co-infection, HIV-RNA level, CD4+ T cells nadir, current CD4+ T cell % and absolute count. Moreover, the presence of co-morbidities (including diabetes, hypertension, cardiovascular diseases (CVD), dyslipidemia) was recorded (Table 1 ).Table 2. Table 3. Table 4. Table 1 Demographic and clinical features of HIV patients HIV patients, n 26 Male gender, n, % 19 (73%) Age, years, median, IQR 65 (58-73) HIV+, years, median, IQR 28 (20-32) TARV, years, median, IQR 25 (20-28) CD4+ NADIR, median, IQR 87 (42-249) CD4+%, median, IQR 28 (20-38) CD4+ absolute, median, IQR 639 (451-786) HCV, n, % 5 (21%) Co-morbidities, n, % 15 (58%) Diabetes, n, % 3 (20%) Dyslipidemia, n, % 8 (53%) CVD, n, % 4 (27%) Hypertension, n, % 7 (47%) Table 2 Demographic and clinical features of hemodialysis patients Maintenance hemodialysis patients, n 32 Age, years, median, IQR 64 (55-78) Male gender, n, % 20 (62.5%) Body Mass Index, Kg/m2, median, IQR 22.4 (20.4-25.1) Hemodialysis vintage, months, median, IQR 47 (23-98) Previous kidney transplant, n, % 6 (18.7%) Vascular Access Arteriovenous fistula, n, % 26 (81.3%) Tunnelled central venous catheter, n, % 6 (18.7%) Dialysis frequency Thrice weekly, n, % 26 (81.3%) Twice weekly, n, % 6 (18.7%) Dialysis modality Bicarbonate dialysis, n, % 20 (62.5%) On-line hemodiafiltration, n, % 12 (37.5%) Table 3 Characteristics of participants enrolled in the study and antibody levels measured at the different time points. HIV/Hemodialysis Healthcare Workers p-value Participants, n, % 39 (40.63%) 57 (59.4%) Gender (male), n, % 27 (69%) 14 (25%) <0.001 Age, years, median, IQR 69 (58, 76) 46 (34, 59) <0.001 T21 I Dose, BAU/ml, median, IQR 28.6 (8.814, 70.2) (n=39) 386.1 (210.6, 651.3) (n=57) <0.001 T7 II Dose, BAU/ml, median, IQR 261.3 (56.94, 774.8) (n=39) 5083 (3172, 7150) (n=57) <0.001 T14 II Dose, BAU/ml, median, IQR 525.2 (248.3, 1404) (n=35) 4849 (3172, 6344) (n=49) <0.001 T21 II Dose, BAU/ml, median, IQR 418.6 (163.93, 781.95) (n=36) 3926 (2262, 5694) (n=49) <0.001 T90 II Dose, BAU/ml, median, IQR 88.92 (39.78, 228.02) (n=25) 405.6 (191.88, 1021.8) (n=33) <0.001 T270 II Dose, BAU/ml, median, IQR 56.4 (31.8, 152) (n=21) 128.5 (84.11, 203) (n=18) 0.052 HIV: Human Immunodeficiency Virus; BAU: Binding Antibody Unity; ml: Milliliters. n: Numbers of observation Table 4 Multivariable generalized estimating equation population-averaged regression model for IgG antibodies levels. β (95% CI) p-value Group Healthcare Workers Ref. - HIV/Hemodialysis Patients -3351.78 (-4202.87, -2500.69) <0.001 Gender Female Ref. - Male 27.8 (-538.44, 594.00) 0.923 Age (years) -15.3 (-35.95, 5.31) 0.146 T after II dose, days T7 Ref. - T14 -1530.42 (-676.46, 369.63) 0.565 T21 -997.21 (-1520.53, -473.89) <0.001 T90 -4596.11 (-5194.82, -3997.41) <0.001 T270 -5079.03 (-5833.97, -4324.09) <0.001 IgG at T21 after first dose, BAU/ml 2.34 (1.55, 3.13) <0.001 TGroup 7HIV/Hemodialysis Patients Ref. - 14* HIV/Hemodialysis Patients 676.07 (-136.99, 1489.14) 0.103 21* HIV/Hemodialysis Patients 1317.91 (509.66, 2126.16) 0.001 90HIV/Hemodialysis Patients 4373.07 (3454.34, 5291.81) <0.001 270HIV/Hemodialysis Patients 4798.57 (3740.87, 5856.27) <0.001 β: Beta Coefficient; CI: Confidence Interval; BAU: Binding Antibody Unity; Ref.: Reference; HIV: Human Immunodeficiency Virus. Thirty-two patients undergoing maintenance hemodialysis (MHD) for end stage chronic kidney disease (ESKD) were included. Demographic and clinical features of hemodialysis patients are reported in table 2. All participants received two doses of the mRNA vaccine BNT162b2 produced by Pfizer-BioNTech. To analyze the kinetic of the antibody response, sera samples were collected 7 and 21 days after receiving the first dose, and 7, 14, 21, 90 and 270 days after the second dose. Samples were centrifuged for serum separation and stored at -20° C until analysis. Sera were tested using the LIAISON® SARS-CoV-2 TrimericS IgG kit (DiaSorin S.p.A., Saluggia, Italy) an indirect chemiluminescence immunoassay (CLIA) technology for the detection of serum IgG antibodies to SARS-CoV-2 trimeric spike protein. IgG titers were expressed in Binding Antibody Units/ml (BAU/ml), the assay quantification range is 4.81 to 2080 BAU/ml and the cut-off value is 33.8 BAU/ml. For a sub-group/-set of this population we were also able to study the cell-mediated response. Specifically, 29 patients undergoing hemodialysis and 23 HCWs were included. For each participant a plasma sample was collected 270 days after receiving second dose; the samples were analysed to evaluate the production of IFN-γ by T-cells after stimulation with different peptides using the IFN-gamma release assay Covi-FERON test by SD biosensor. The study was granted ethical approval by the local ethical committee, protocol number 0486/2021. Statistical analysis Descriptive statistics were reported using median and interquartile ranges for continuous variables and using absolute and relative frequencies for dichotomous variables. Univariable analysis was performed using the Wilcoxon rank-sum test to compare continuous variables between patients and HCWs, whereas Pearson’s chi-squared test or Fisher’s exact test was used for dichotomous and categorical variables, as appropriate. A multivariable generalized estimating equation population-averaged regression model with an identity link, a gaussian error structure and exchangeable correlation structure was built to estimate beta coefficients (β) and associated confidence intervals (CI) of factors influencing IgG levels over time after the second dose of vaccine, considering the clustering within participant due to repeated measures [25]. Variables were included in the model based on expert opinion. The final model included the following variables: sex (0= woman; 1= man); age (continuous); HIV/Hemodialysis (0= No; 1= Yes); antibody levels 21 day after first dose of vaccine (continuous); time from second dose of vaccine (7 days (t7, ref.), 14 days (t14), 21 days (t21), 90 days (t90), 270 days (t270)); interaction term between HIV/Hemodialysis and time from second dose. For the participants undergone Covi-FERON test a univariable analysis was performed to compare continuous and dichotomous variables between reactive and non-reactive subjects. Due to the small sample size, no multivariable analysis was performed. All analyses were performed using STATA 17.0 (StataCorp LLC, 4905 Lakeway Drive, College Station, 322 Texas, USA) and SPSS version 27.0. A two-sided p-value <0.05 was considered statistically significant. Results A total of 57 HIV/Hemodialysis patients and 39 HCWs were enrolled in the study. Demographic characteristics of the two groups and antibody titration at sample collection time points are reported in table 1, with the statistical characteristics of the appropriate univariate test. The median age for the HIV group was 65 years (IQR 58-73). In addition, 19/26 were males with a median of 28 years (IQR 20–32) from HIV-1 diagnosis. All the patients were under antiretroviral treatment from a median of 25 years (IQR 20-28). Thirteen subjects had a previous history of AIDS diagnosis, according with CDC classification. HIV-RNA plasma level at baseline was under the threshold of 37 copies/ml in all subjects. The immunological profile was represented by a median CD4+ T cell nadir of 87 (IQR 42–249) cells/μL, a median current CD4+ T cell count of 639 (IQR 451–786) cells/μL and a mean CD4+ T cell percentage of 28 (IQR 20-38). HCV co-infection was present in 5 enrolled participants. At least one non-communicable disease was presented by 15 PLWH: the most prevalent was dyslipidemia followed by hypertension, type 2 diabetes and cardiovascular diseases. Overall, the vast majority of PLWH enrolled in the current study were receiving an INSTI-based regimen, followed by a DRVc-based therapy. In the hemodialysis group 20 patients (62.5%) were male; the median age was 64 (IQR 55-78). Median dialysis vintage was 47 months (IQR 23-98). Hemodialysis vascular access was arteriovenous fistula in 26 patients; the remaining 6 patients underwent dialysis treatment trough a tunnelled central venous catheter. In 26 patients (81.3%) a thrice-weekly MHD was prescribed, while 6 patients received twice-weekly treatment. Dialysis modalities adopted were on-line hemodiafiltration (37.5%) and bicarbonate dialysis (62.5%). Anticoagulation of extracorporeal circuit was performed by using low molecular weight heparin (LMWH) in all patients. Six patients had a previous kidney transplant, but in any case, they started MHD at least 18 months prior to the study. Grouping the patients together we found that subjects in the HIV/Hemodialysis group were older (69 vs. 46 years, p<0.001) and more frequently males (69% vs. 25%, p<0.001) than HCWs. Twenty-one days after the first dose antibody levels were higher in the HCWs than in HIV/Hemodialysis patients (386.1 BAU/ml, IQR 210.6 – 651.3 vs. 28.6, IQR 8.8 – 70.2, p<0.001), see Fig 1 . This difference held true at each T after second dose, except for T270 (56, IQR 31.8-152 vs. 128.5, IQR 84.1-203 BAU/ml, p=0.052), see Fig. 2 . The average number of IgG antibodies measurements per participants was 3.8. In table 3 are reported the antibody levels (median, IQR) measured at all time points.Figure 1 Box plot of IgG levels 21 days after first dose in healthcare workers and HIV/Dialysis Patients, showing medians, interquartile ranges and outliers. Figure 2 Trend of median IgG levels at 7, 14, 21, 90 and 270 day after the second vaccine dose for healthcare workers (blue line) and HIV/Hemodialysis patients (orange line). The regression model showed that both male gender (β 27.8, 95% CI -538.44, 594.00, p=0.923) and age (β -15.3, 95% CI -35.95, 5.31, p=0.146) didn’t influence IgG levels over time. At the multivariable analysis, HIV/Hemodialysis patients had significant lower levels of IgG levels (β -3351.8, 95%CI -4202.9 - -2500.7), while antibody levels 21 days after the first dose were slightly correlated with post-second dose levels (β 2.3, 95%CI 1.5 – 3.1). Over time, the model showed a significant decrease of IgG with respect to the reference time (7 days after second dose) at t21 (β 997.2, 95%CI 1520.5 – 473.9), t90 (β -4596.1, 95%CI -5194.8 – -3997.4) and t270 (β -5079.0, 95%CI -5834.0 – -4324.1), although for HIV/Hemodialysis patients it is greatly reduced (T14: β 1317.9, 95%CI 509.7 – 2126.1;T90: β 4373.1, 95%CI 3454.3 – 5291.8; T270: β 4798.6, 95%CI 3740.9 – 5856.3). In table 4 are reported the estimated beta coefficients (β) and associated confidence intervals (CI) of factors influencing IgG levels over time after the second dose of vaccine. Covi-FERON response Fifty-one participated in the Covi-FERON analysis. Considering the two groups together we observed that 46% of the subject responded to the test. Individuals in which no effector T-cell mediated response was detected were considered non-reactive, while those with a detectable effector T-cell mediated response were considered reactive. Patients and HCWs responded similarly. The only significant difference between non-reactive and reactive subjects was found in median antibody levels, higher in the responders group than in non-responders (177.5, IQR 81.7 – 1160, vs. 61.4, IQR 23.4 – 189 BAU/ml, respectively), see Table 5 and Fig. 3 .Table 5 Characteristics of participants subset for the Covi-FERON analysis. Non-Reactive Reactive p-value Participants, n, % 27 (52.9%) 24 (47.1%) Group (Hemodialysis Patients), n, % 14 (51.9%) 15 (62.5%) 0.44 Gender (Male), n, % 9 (33.4%) 13 (61.9%) 0.13 Age, years, median, IQR 57 (50, 72) 55.5 (38, 67) 0.47 IgG, BAU/ml, median, IQR 61.4 (23.4, 189) 177.5 (81.7, 1160) 0.009 HIV: Human Immunodeficiency Virus; BAU: Binding Antibody Unity; ml: Milliliters. Figure 3 Box plot of IgG levels before the third vaccine dose in Non-Reactive (subjects in which no effector T-cell-mediated response was detected) and Reactive (subjects in which an effector T-cell-mediated response was detected) subjects, showing medians, interquartile ranges and outliers. Discussion In our study we focused our attention on the ability of the BNT162b2 mRNA vaccine of inducing an immune response both in patients and healthy donors. We observed that both groups were able to mount an antibody response after receiving two doses of vaccine. Twenty-one days after receiving the first dose we had the highest number of measurements and at that time point the seroconversion rate was different between patients and HCWs (50% and 92% respectively). The seroconversion rate reached its peak 14 days after the second dose for the patients group (91%) while at the same time point 100% of the HCWs showed seroconversion. Using the antibody levels measured 7 days after the first dose as our baseline, we found that they were significantly higher in the HCWs group at all time points except for 270 days after the second dose. The individuals in the patient group were older than HCWs and more frequently males. To rule out the possibility that our results could have been confounded by those parameters a multivariable analysis was performed including gender and age as independent variables. Moreover, the model showed that neither age nor gender had an independent influence on the humoral response. Despite being effective in both populations, our results seem to suggest that the individuals presenting an underlying medical condition are less capable to develop and maintain a strong antibody response after vaccination. Several studies show that most PLWH successfully build an efficient humoral response after the delivery of the BNT162b2 mRNA vaccine even though it is weaker if compared with immunocompetent individuals [26], [27]. Antinori et al. also found that in HIV-1 patients the antibody production is strongly related to the CD4+ T cell count at the time of vaccination, suggesting that the measurement of this parameter could be used to better adjust the vaccination strategy in this specific population. In contrast, in our study we didn’t observe any correlation between the antibody levels and the CD4+ T cell count at any time point (data not shown), probably due to the small number of patients. The decreased immunogenicity of the vaccine has also been demonstrated for ESKD patients that show a lower magnitude of the humoral response when compared to the general population with seroconversion rates varying from 17.4 to 96% [28], [29]. Consistently with other data in literature, we observed for both groups a decline of the antibody levels some weeks after the administration of the second dose of vaccine [30], [31]. In our study population the decay started extremely early, 21 days after receiving the second vaccine dose, even though for the HIV/Hemodialysis patients the decay was greater. In a study from Anand et al. conducted on hemodialysis patients, they found that 20% lost detectable IgG response within 6 months following vaccination. In the same study 56 participants had a breakthrough COVID-19 infection and among these, patients had lower peak and pre-breakthrough RBD IgG index values compared with controls [32]. A reduction of antibody levels following vaccination has been documented as well for HIV patients who received ChAdOx1 viral vector vaccine. However in this study population the antibody drop does not seem to be related to the HIV status but rather to the older age and the number of chronic conditions [33]. For what concerns the T-cell response, the only noteworthy aspect was the higher level of antibodies in those who showed an IFN- γ production. This observation seems to suggest that the preservation of the T-cell response could be linked to a stronger production of antibodies following vaccination. Unfortunately, in our study we were only able to measure T-cell response at t270, therefore it is not possible to establish with certainty if the cell mediated response wanes together with the antibody level. This result seems to be in contrast with what observed in people who recovered from COVID-19 where no correlation between antibody levels and T-cells was found [34], [35]. The low percentage of individuals able to produce a T-cell mediated response could be linked with the kind of test used since we were not able to define the different cell population or the different cytokines produced in response to vaccination, but only to evaluate the IFN- γ production. The clinical trial of BNT162b2 vaccine and along with other studies proved that protection against the symptomatic disease exerted by T cells starts about 10 days after the administration of the first dose, while high levels of neutralizing antibodies are only detectable 21 days after the first dose, emphasizing that the humoral response is not the only being necessary for protection against viral infection [36], [37]. Furthermore, despite the waning of the antibody levels and the ability of SARS-CoV-2 variants to partially escape the humoral response, most people who receive two vaccine doses and are later infected with viral variants generally develop only mild symptoms. This could be explained by the presence of heterogeneous Spike-specific T-cells able to recognize different regions of the Spike protein [38], [39]. Unlike humoral response it is more difficult to quantify the magnitude of the cell-mediated immunity. Significant variations of the T-cell response were observed not only in particular categories, like people over 80 [40] and patients with immune system deficiencies [41] but also in individuals of similar age and without medical conditions [42]. In view of these considerations, it seems clear that further studies are necessary to fully understand the correlation between all the immune system components. The decay of the antibody levels and the weak T-cell response that we observed support the necessity of booster doses for both patients and healthy donors. The main limitations that should be acknowledged in our study are its monocentric design, the small number of participants that were recruited, especially for the analysis of the T-cell response and the lack of plasma samples at different time points to ascertain if the production of IFN- γ follows a similar trend as the one observed for the humoral response. Despite the limitations of our study, it seems undeniable the importance of vaccination to maintain a protection against SARS-CoV-2 infection especially in patients with frailty. Given these considerations we can affirm that monitoring the extent and the duration of the immune response in people considered at a higher risk of severe infection could help improving the immunization plan for those categories of patients. Author contributions All authors contributed to the study conception and design. Material preparation and analysis were performed by Roberta Campagna, Laura Mazzuti, Giuliana Guerrizio and Chiara Nonne. Statistical analysis was performed by Giuseppe Migliara and Corrado De Vito. Clinical data and samples were collected by Ivano Mezzaroma, Caterina Fimiani, Sabina Chiaretti, Santo Morabito and Valentina Pistolesi. The first draft of the manuscript was written by Roberta Campagna, and Ombretta Turriziani, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Funding This work was supported by “Progetto di Ricerca di Ateneo” 2019. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability Data will be made available on request. ==== Refs References 1 Hu B. Guo H. Zhou P. Shi Z.-L. Characteristics of SARS-CoV-2 and COVID-19 Nat Rev Microbiol 2020 1 14 10.1038/s41579-020-00459-7 31740776 2 Peck K.M. Burch C.L. Heise M.T. Baric R.S. 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==== Front Soc Sci Med Soc Sci Med Social Science & Medicine (1982) 0277-9536 1873-5347 Elsevier Ltd. S0277-9536(22)00906-6 10.1016/j.socscimed.2022.115600 115600 Article Reconfiguring the social organization of work in the intensive care unit: Changed relationships and new roles during COVID-19 Rodriquez Jason Department of Sociology, 100 Morrissey Blvd, Boston, MA, 02115, USA 5 12 2022 5 12 2022 1156009 8 2022 28 10 2022 2 12 2022 © 2022 Elsevier Ltd. All rights reserved. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. The COVID-19 pandemic caused hospitals to make changes to workflow that exacerbated emotional exhaustion and burnout among health care workers. This article examines one of those changes, restricted visitation, showing how it changed the social organization of work by upending established interactional patterns and relationships between health care workers, patients, and patients’ families. Based on 40 interviews with intensive care unit (ICU) workers in units that were full of COVID-19 patients and had fully restricted visitation, study findings show that staff took on emotional support roles with patients that had typically been done by families at the bedside. They also faced increased anger, distrust, and misunderstandings from families who were not allowed to see their dying loved one. With each other, staff bonded together with dark humor and candid talk about the scale of deaths, constructing a shared understanding and solidarity amidst the tragedy of the pandemic. Keywords COVID-19 Emotions Family visitation Intensive care unit Medical sociology Work and occupations Qualitative ==== Body pmc1 Introduction The COVID-19 pandemic necessitated an enormous number of changes for health care workers, upending routine, established patterns of medical care across the globe. The shortages of personal protective equipment, increased workload, and the substantial fear, risk, and uncertainty about getting the virus at work has led to high rates of psychological distress among health care workers, exacerbating the already substantial problems of burnout and emotional exhaustion that existed before the pandemic, particularly among intensive care workers (Azoulay et al., 2020). The widespread implementation of policies that restricted visitation is one of the significant changes to medical care work that has received plenty of attention with respect to its impact on patients and their loved ones, but much less so from the perspective of health care workers (Jaswaney et al., 2022; Andrist et al., 2020). These restrictions generated unanticipated, and unintended, consequences for the social organization of health care work (Robert et al., 2020). This article shows how, when visitation ceased, health care workers in intensive care units (ICUs) took on new roles with patients and redefined relationships with patients’ families, deepening the emotional demands on them and likely contributing to emotional exhaustion and burnout, even as the restrictions also generated a shared understanding among staff that only those who were there could understand what they had gone through. For some time there has been an ongoing shift in hospitals towards allowing patients’ significant others greater access to the bedside, especially in ICUs where patients benefit from the social support, and their significant others do too (Burchardi, 2002; Berwick and Meera, 2004). Guidance for ICUs at the start of the pandemic recommended continuing bedside visitation as consistent with “patient-and-family centered care,” which is a medical ethic grounded in the belief that health care ought to be a collaborative endeavor with patients and their loved ones to ensure that the values that matter to patients are respected and their preferences are honored (Aziz et al., 2020; Kaslow et al., 2021; Millenson et al., 2016). Nevertheless, out of a concern for the risk of spreading the virus, the vast majority of inpatient health care facilities restricted visitation, often without exceptions (Moss et al., 2021). To understand how the restriction of visitors changed the social organization of work in the ICU, this article utilizes a symbolic interactionist perspective, which stresses the significance of how the social structure people are situated in shapes what they think, say, and do (Blumer, 1969). In particular, Goffman's concept of “performance teams” (1959) suggests that individuals, in this case the ICU staff, work together in the “frontstage” to convey a particular impression about who they are and what they do. It is in the “backstage,” away from outsiders such as patients' visitors, that staff can be more open and honest with each other, especially about what happens in the frontstage, without having to worry about what outsiders will think. As hospitals increasingly integrated non-medically trained persons such as patients' family members as participants at the bedside, the frontstage emotions called for in clinical interactions has shifted away from a stance of “detached concern” (Fox, 1988) and towards one of “clinical empathy” (Vinson and Underman 2020). Yet when it comes to hospital care during a disaster or unexpected medical crisis, this empathetic shift does not seem to neatly capture health care workers’ emotional repertoires. For example, in the aftermath of a deadly typhoon in the Philippines, researchers found that health care workers took on a dual role as helpers and victims, concluding that disaster medicine is “a multi-faceted, powerful, and ambiguous experience” (Hugelius et al., 2017: 117). This dual role is evident in research on other natural disasters, as health care workers in Sierra Leone during the 2014 Ebola epidemic described stigmatization due to an erosion of trust between providers, patients, and the community more generally while at the same time putting themselves at personal risk of catching the virus while caring for sick patients (McMahon et al., 2016). Hospital workers in South Korea during the 2015 MERS outbreak faced similar conditions, putting themselves at risk in caring for infectious patients while also experiencing negative emotions and high stress due to working in an unsterile environment and miscommunications about safety from hospital officials (Son et al., 2019). Taken together, health care work during disasters is characterized by heightened risk and fears that may generate significant shift in work roles and relationships. While the new roles health care workers are thrown into during natural disasters or epidemics indicate the unique challenges of caregiving under duress, the ongoing, global COVID-19 pandemic has upped the ante, putting extraordinary demands on workers that have resulted in significant increases in psychological distress and resultant physical symptoms compared to pre-COVID estimates (Azoulay et al., 2020; Gordon et al. 2021). For nurses in intensive care units, the impact of caring for what seemed like a nonstop stream of suffering patients with poor prognoses and likely death put them at high risk for severe emotional stress, compassion fatigue, and burnout (Alharbi et al., 2020). Indeed, researchers have found that the multiple surges caused by new variants have exacerbated compassion fatigue and burnout leading to depersonalization, and disillusionment, and secondary traumatic stress among ICU nurses (Gee et al. 2022). COVID-19 offers unprecedented challenges, but it is also an opportunity to understand how disasters impact the social organization of medical work, and the lives of healthcare workers, particularly in intensive care units where the most critically ill patients go to be cared for by people who are among the most technically skilled in medicine. During the initial waves of the pandemic, almost every ICU restricted visitation to reduce the likelihood of transmission of COVID-19 (Moss et al., 2021). Emerging research has shown these policies may have exacerbated the psychological distress experienced by healthcare workers (Andrist et al., 2020; Hugelius et al., 2021; Dos Santos and Soares, 2022; Dragoi et al., 2022; Guttormson et al., 2022). For example, a recent survey of ICU nurses who worked on units that restricted visitors created new dilemmas and distress due to compromising the quality of care for patients' families (Jensen et al., 2022). Another study showed that over 60% of surveyed hospital workers reported moral distress due to the exclusion of patients’ family members going against their values (Smallwood et al., 2021). Azoulay et al. (2020) found that regret over restricted visitation was a preventable cause of COVID-19 related moral distress among intensive care workers. The increased workload has further contributed to the challenges of medical caregiving during COVID-19. Several studies have shown a significant increase in the number of patients per nurse during COVID-19 compared to pre-COVID and more nursing activities performed for COVID-19 patients compared to non-COVID patients (Bruyneel et al., 2021; Hoogendoorn et al., 2021). Research on ICU nurses similarly showed that unanticipated changes to care practices brought on by the pandemic increased work intensity and introduced new roles and ethical challenges for which they were unprepared (Stenlund and Strandberg 2021). A survey on the issue found that nurses reported more time spent on patient care while physicians reported increased time spent on phone calls to surrogate decision-makers (Wendlandt et al., 2022). Although there is scant direct evidence specific to COVID-19, emotion-focused coping strategies may buffer against the harmful effects of moral distress and exhaustion brought about by the pandemic (Huang et al., 2020). Humor is an emotion-focused coping mechanism among intensive care workers that allows them to maintain a stance of detached concern in emotionally charged moments (Coombs and Goldman, 1973). A recent analysis suggested humor could be taught as a tool to reduce burnout in intensive care, noting that it relieves stress, creates community, and provides an innocuous way to express fear (Oczkowski, 2015). In related research, Cain (2012) found that hospice workers’ “backstage” use of dark humor about death was a coping technique that allowed them to connect with each other in ways outsiders would find morbid, while sustaining a caring and compassionate presentation of self in the “frontstage”. Such humor could enhance social support among colleagues and mitigate the risk of emotional exhaustion and burnout (Cadge et al., 2021). While research has extensively documented the increase in psychological distress, emotional exhaustion, and potential for burnout among health care workers during the pandemic, there has been less research on the precise social processes that have led to these outcomes. In the pages below, I detail the role of restricted visitation, showing how the frontstage interactional patterns expanded as staff took on emotional support roles that families typically provided, while also dealing with increased anger, distrust, and misunderstandings from patients’ families. At the same time, the absence of visitors allowed talk typically held in the “backstage” to spill over into the frontstage, as staff spoke freely with each other, using dark humor and having candid discussions about the scale of deaths they were witnessing, developing a sense of solidarity and collective understanding that, they said, only those who were there could possibly understand. 2 Data & methods The data for this article comes from 40 phone interviews with health care workers who staffed the ICUs of two hospitals located in Massachusetts. Both ICUs had long stretches of time in 2020 in which they were completely full of COVID positive patients. They were general pulmonary units specialized in treating very ill patients that needed ventilators, dialysis, and other sophisticated treatments that required constant monitoring. Consistent with the worldwide trend, for much of 2020 the two hospitals where the study participants worked had implemented policies that completely restricted visitors throughout the hospital. Between the scarcity of personal protective equipment, initial low availability and slow turnaround time of tests, and the unknown risk of transmission, restricting visitors seemed prudent. A physician assistant who was a unit supervisor explained, “It was just, from a public health perspective no one could come in because of the exposure to themselves and to the community.” The restrictions were loosened a bit late in 2020 as both hospitals allowed one visitor for patients who were dying and receiving comfort measures only. These interviews were conducted between January and May 2021. Vaccines became available for ICU staff in December of 2020, so all the workers who wanted vaccines had gotten them by the time of the interviews, but vaccines remained scarce for the general population. Staff reported that the ICUs were still seeing a high number of COVID-19 patients at the time of the interviews, virtually none of whom were vaccinated, as the slow rollout of the vaccine to the general public through spring 2021 took place. Among the interviewees are 15 nurses, six physicians, six nurse practitioners, six physician assistants, six respiratory therapists, and one unit coordinator. All were full-time ICU workers prior to and during the pandemic. Eight had supervisory responsibilities. I purposively sampled participants to include the key occupational groups in ICUs and mirrored the proportions of total workers on the unit. In these ICUs, nurse practitioners and physician assistants had the same role, working under physicians authority but during evening, nights, and weekends they were often the highest-level worker present on the unit. I initially found people to interview through personal and professional contacts, and branched out with a referral sampling method to locate additional potential interviewees within the two units. Staff were supportive of this project and eager to discuss their experiences, generously forwarding my contact information to potential interviewees or checking with others to see if they would be open to an interview. I framed the project for interviewees as a study of the experiences of ICU workers during COVID-19. Almost everyone I was in touch with about participating in an interview did so. I believe my position as someone they knew only as a weak tie may have made them more open to speaking with me than if I had cold-called them or knew them well, since plenty of people I interviewed said at the end of it that they had never talked so much about their experiences in the ICU during COVID or that they told me things they had not told to anyone outside work, even to their close friends and family members (Small, 2017). Doing these interviews over the phone provided a kind of quasi-anonymity that allowed for openness and, on a more practical note, allowed me to reach them at a time of their convenience, such as when they were driving to or from work. The interviews were semi-structured, there was a set of common themes I asked everyone, but specific questions varied depending on the particularities of the interview. I focused on four themes: 1) treating COVID patients, including end-of-life care and decision-making, 2) interactions with patients’ families, 3) the social experience of treating COVID patients, including risk management, fear, and emotions, and 4) workplace policies and practices regarding COVID. I followed a responsive interview technique (Holstein and Gubrium, 1995; Rubin and Rubin 2011; Weiss, 1995), in which interviews were like extended conversations, allowing me to probe with follow-up questions and eliciting stories to provide details about the experience of ICU care during COVID. The interviews lasted on average a little under an hour with a range between 40 and 90 min. All the interviews were audio recorded and transcribed. The transcriptions were analyzed thematically using Atlas. ti. An initial coding guide was created based on the themes covered in the interviews, such as “COVID-19 Policies” and “Interactions with Families” and then branched out into more limited and specific codes such as “Family Visitation Policy” and “Anger from Families” Since coding data is an iterative, analytic process, the coding guide was adjusted to capture newly emerging themes, delete codes that were not being used, and in some cases certain codes were modified or combined as I analyzed the data and refined interpretive judgments (Charmaz, 2014). 3 Results The results below detail the new roles staff took on as a result of restricted visitation and the changed relationships they navigated with patients, patients’ families, and each other. 3.1 Taking on family roles to patients ICU staff members, especially nurses, took on new roles for patients that were typically performed by patients' families, such as being the primary social-emotional supports for patients and bearing witness as they died. One physician assistant supervisor noted, for example, “I think the nurses have had to step up in a huge way to be the bedside support that families normally give them.” Similarly, a physician said, “Back in the spring [2020] when we were having four or five deaths a day, the nurses really took it upon themselves to make sure that the patients weren't dying alone.” The nurses spent the most time in patients' rooms, often gathering everything needed to complete their patient assessments and treatments before entering the room and staying in there for hours to reduce how often they had to don and doff personal protective equipment. Furthermore, many COVID-19 positive patients were admitted to the ICU alert, oriented, and able to talk. Patients could get lonely without family there at the bedside. A nurse supervisor explained, “Some of the patients that weren't intubated yet, and were on high-flow oxygen and they're in the room with the door closed for days. You just tried to offer support, encouragement. Probably what a family member would be doing if they were there with the patient.” Another nurse who was a supervisor said, “I've held many [patients'] hands because there's nobody there for them,” adding, “It breaks my heart, it really does. Because again, it's not having the family nearby.” Not having family on the units created an opening that was filled by ICU staff, especially the nurses who spent most time inside patient rooms. Although nurses took on this new role the most, staff throughout the ICU were impacted. A physician assistant said, “A lot of people's relationships with patients changed,” and noted that, “I felt like we were trying really hard to have a human connection with people to help with the fact that their families weren't able to be there,” but noted the difficulty of providing support “while you're in a space suit” of personal protective equipment. It is worth recalling that taking on emotional-support responsibilities previously done by families entailed increased risk of contracting COVID-19, this is why families were restricted from visiting in the first place. Given the absence of patients' loved ones, staff throughout the occupational hierarchy explained it was important to offer increased support for dying patients. Indeed, many staff members insisted with total conviction that they never let a patient die alone. A respiratory therapist explained, for example, that upon seeing a patient alone while on comfort measures, he thought, “This is no way for someone to pass … so I've gone into rooms where we've taken the tubes out and just talked to patients, like, ‘It's okay. It's okay to go. You're not alone. I'm here.’ It's on multiple occasions. It's not just one person. It's a lot.” A nurse reflected, “These people were dying by, well, not by themselves because we were there. And thankfully I work with such a great team, that we made sure someone was always holding someone's hand when they were dying.” A nurse supervisor told me, “If I have the opportunity, I'm going to be right there [for dying patients].” Another nurse told me a story of a COVID-19 patient who died, “Four of us stayed in the room with him while he passed. And I feel like that's not a normal thing. Usually, the families would be in there with five, 10, 15 people, and you just try to stay out of their way. Definitely a different role for us to play.” One nurse said, “we're hoping that [families] see that we're allowing these patients to go with dignity and that they're not alone.” Whether or not a patient ever did die alone is beside the point. The point is that ICU staff took on this role due to the absence of visitors, intensifying the emotional demands on staff beyond their pre-COVID work routines. Before COVID-19, staff members usually did not personally witness last goodbyes between patients and loved ones. They stepped out of the room. But when visitation ceased, ICU staff organized virtual visits using iPads to bring patients and their loved ones into closer contact especially when patients were dying. These visits allowed families a glimpse of what patients were going through, but they did more than that. They also put staff in a new role that heightened the emotional intensity of work because now they were present for last goodbyes, holding the iPad. A respiratory therapist said, “They bring in an iPad so the family can FaceTime with them. And patients can't respond. They're unresponsive on a ventilator, and that's that family's last vision of their loved one. And that, I think was just awful.” A physician assistant echoed that sentiment, explaining, “The terribly unfortunate patients and families who have had to withstand this whole crisis, being able to watch their suffering parent on an iPad, that's heart breaking.” A nurse pointed out, “The hard part of the COVID patients is if you do bring in the iPad and you have the family on the iPad and they're talking to their loved one, you're listening to their heartbreak. Whereas when they're there in person, you can step out of the room and separate from it.” Although these virtual visits provided families of COVID-19 patients a sense of closure (Rose et al., 2021), they also contributed to staff moral distress and secondary traumatic stress. At one of the two hospitals, the ICU was on the first floor and patients' rooms had windows that looked out to the parking lot. Patients' significant others were encouraged to sit outside and look through the windows. At the other hospital, the policy changed in late 2020 to allow one visitor to sit outside the patients’ room and look in through the closed glass door. A unit coordinator explained how it worked:We arranged the beds, and the family could see them and say goodbye to them, at least, through the window. So we could do that. But we could not allow them to come in and see them. It was the policy. So we had to say no, and it was very sad. It was very sad. It was only through the window. A nurse supervisor described the emotional pain of observing this. “It kills me,” she said, “watching family members when I literally have to go move the chairs up to the door,” noting that she sat with them, “because it's heartbreaking.” She added that nurses had talked about it and that, “It feels so wrong and inhumane that's how families have to say goodbye, through a glass door.” ICU staff took on more emotional support not only to patients, but also to the family members. A nurse reflected, “It's sad when there's only that one family member” and explained with a story, “I remember this one woman, she was crying, ‘Am I doing the right thing?’ And in that circumstance, I ended up stepping in. And I'm Catholic, so I'm like, ‘Do you want to pray? How can I make this better for you?’” Another nurse explained that in one instance she had “watched this young wife who had not seen her husband” since being admitted to the hospital, and “it was beyond heartbreaking watching her sit on the other side of the door as he died.” She added, “I sat there with her because I didn't want her to be alone. And the nurse was in the room holding the patient's hand for the wife. And we're all tears streaming down our eyes because he was a young guy.” She could not help but wonder afterwards, “Am I going to get an email in the morning that I should not have let her come in?” 3.2 Managing patients’ families In addition to taking on additional roles for patients often performed by patients' families, ICU workers also said having families separated from patients – and from staff members – changed relationships in ways that heightened the demands on staff workload and emotional resilience. The restrictions significantly increased the volume of phone calls with patients’ families, particularly by physicians, nurse practitioners and physician assistants who gave frequent updates on clinical status. These updates have always been part of ICU care work, but giving bad news from a distance increased the anger of families, distrust towards staff, and misunderstandings that made it difficult for families to understand how sick the patients were without the benefit of visuals. At first, it seemed to some staff members that the absence of patients' families would improve workflow. For example, one nurse said, “So at first everybody was like, ‘Oh my God, this is going to make it easier because we're not going to have the family component,’ which obviously can be super challenging sometimes.” What can be challenging about it, she continued, is that visitors “are very anxious when they're in the room with you” and “you're always explaining things.” Others said restricted visitation gave more time for patient care. For example, a physician assistant explained, “We were all so busy that not having visitors made us more efficient. We could dedicate 100% to patient care, as opposed to 60%.” A nurse added, “A lot of times family members would actually hinder the patient's care, without the family even knowing that … so we would always kind of complain to each other about that kind of stuff.” Another nurse said the absence of families, “made life a lot easier for us, because families when their families are that sick are very needy.” She continued, “Families can be difficult. They're very demanding … some will handle it well; others start putting blame on you. And they pitch one staff member against the other or one shift against the other.” Not everyone saw things the same way. Some believed restricting families from the unit was the wrong policy. For example, one nurse supervisor said, “And what I think is the worst is, even now with not having family members allowed to come in is extreme. I mean, especially for these COVID patients it's terrible to watch somebody die with no family around.” A respiratory therapist explained, “The thing that was the worst about it was every single one of these people was barred from seeing their family. No one was allowed in. Everyone died alone. No one was allowed to say goodbye. That, I think, is the worst part.” A physician reflected, “I think early on when there was no visitation it was horrible.” Although those who opposed the restriction on visitation did so on moral grounds, what became clear in the interviews was the policy created unanticipated, new disruptions to established workflows. One doctor said that, “One of the first changes to my practice was starting to call patients' families every day.” A physician assistant explained he communicated with families much more than before the pandemic. “Before COVID,” he said, “it wouldn't be like a reflex to call every single patient family member every single day. If the patient wasn't intubated, you might not. But now, I talk to family every single day on every single patient.” A nurse practitioner reflected, “A lot of families have been very dissatisfied. But it's hard because I don't think people understand that if we have 16 patients in our ICU, 16 phone calls, you're on the phone at least 15 min per family. And it's a huge time suck.” In addition to fielding a lot of calls, staff also faced increased anger from family members. One nurse noted, “Multiple people will call. It's like we only have one person that you give information to, so some of them do get very angry.” Another nurse explained, “By not being able to come in, I think it automatically put us as enemies in their mind a little bit … It's just a lot of control issues from not being able to have a presence, I think that families were definitely a lot more upset with us [than prior to the pandemic].” Another nurse reflected:They were angry that they weren't being let in. That's tough to deal with. Because all you want to tell them is, ‘I'm sorry, we're trying to protect you, protect the staff, protect the community,’ but they don't understand that because all they want is to be with the family member and you can't begrudge them for that.” Since family members could not see what was happening to patients, it was more difficult for staff to explain how sick their loved one was. Some family members seemed to think, according to a physician assistant and unit supervisor, that perhaps staff were not doing everything possible to save their loved ones' life. He noticed, “Families are just very skeptical” and explained that he recently had a patients' mother who believed the staff were “disregarding” her son because he had COVID-19. He said, “I really found myself having to convince her that we actually were caring well for her son, because she can't come in and be at his bedside 12-h a day.” He added, “I think that's kind of a side effect of not having families intimately involved daily.” In many cases, patients came to the hospital walking and talking, and then the next time their loved ones saw them was on an iPad, unconscious and intubated. It was hard for them to grasp what was happening, according to staff. A nurse practitioner said, “I think it's hard because the families can't see the patients … they haven't seen their loved one in a week, two weeks, three weeks, four weeks, and then, the only time they're seeing them is when we're getting ready to terminate care.” A nurse supervisor reflected that families “feel completely out of the loop and not knowing what's going on. I can't even imagine having a family member that I can't go see at all. Especially that sick.” A physician assistant supervisor said, “The most difficult thing by far has been lack of family access to see their family members … they just don't get the visual representation of how sick they are.” She went on to explain that before COVID-19, families would be with a patient in the ICU and see “their kidneys fail, they look sick, they gain tons of fluid weight, families see that and they say yes, this looks awful, they have bed sores, they're not going to survive this.” A nurse described similarly, “The biggest struggle today is because you can't see it you don't really appreciate it … there's all these lines going into your mom or your dad or your brother, your sister. There's no way to really convey that over the phone. It's an experience you have to see yourself.” The only people who were able to see it for themselves were the ICU staff, something that, as shown below, had a significant impact in terms of generating a shared sense of understanding that they said nobody else could have unless you were there. 3.3 Bonding with coworkers Different departments in the hospital faced different issues related to treating patients with COVID-19. In the Emergency Department, for example, patients were alert and talking while their diagnosis was pending, which brought its own challenges. On the floors, patients had been admitted and either got better and went home or got worse and went to the ICU. It was in the ICU where COVID-19 patients went to die. One nurse reflected:I don't think anybody saw death like ICU staff saw death. Because, especially in the beginning, once a patient came to ICU, I mean, I don't know, 90, 95% died. So, I mean, we were just going all out, everything we can do to try to keep the patients alive that we couldn't keep alive. And it was just death after death after death after death … unless you worked in ICU, I don't think you saw how much death it really was. That's where all the death was happening was in ICUs.” This extraordinarily tragic context was unique even by the standards of intensive care, as the death rate for COVID-19 patients at the beginning of the pandemic seemed to them far greater than the death rate for ICU patients with typical conditions like influenza, COPD, or alcohol withdrawal. The seemingly sky-high death rate of COVID-19 ICU patients formed the backdrop to which staff found themselves mask-to-mask with each other. They expressed a heightened sense of solidarity, in that they could speak freely amongst themselves, commiserating with dark humor and a firm belief that nobody else could understand what it was like to be there. While restricted visitation heightened the emotional demands on ICU staff, it also allowed staff to drop the formalities of the frontstage. One doctor explained, “We were just talking freely because there was no families, no visitors at all, very few patients were awake, so we were so free to talk in there … we were very noisy and loud, didn't have to worry about it.” A physician assistant added that, “Families not in the hospitals has made an impact on staff, simply from the standpoint that you can let your hair down, so to speak.” Another physician assistant said that now she could be, “really blunt or crass, for me anyways sometimes as I'm doing things matter of fact like, ‘Yeah, that patient's going to die.’” Without family on the unit, dark humor emerged from the breakroom onto the unit floor, since staff were no longer concerned that someone may misunderstand such humor to mean they were not taking patient care seriously. For example, a nurse mentioned, “The families definitely, without being there, have made it easier … to get kind of carried away with off-color conversation, if you know what I mean? So when you're reintroducing families to coming back, it's a problem, because now you've got to be cautious of who's around you.” Similarly, another nurse reflected,I think that if the family members were there as like a fly-on-the-wall and they heard us joking, they would think we're terrible people. I mean, they would be like, ‘Oh my God, I can't believe you're taking my loved ones' life so lightly.’ And we're really not. We're just trying to find some sort of levity in a really terrible situation. I think humor is a huge factor in us being able to keep putting on the scrubs and going back into work. A physician assistant supervisor said, “If an unknowing person heard some of our conversations, they'd probably be horrified of what we say to each other, but that's just the way we cope.” A nurse practitioner said the humor does get dark, as they may joke about a patient being “dead in the bed” for example, but added that “you do what you have to do to keep going. I'll tell you, there have been times where we've said, ‘Oh my God, if people just heard what we were saying, then we'd all be fired right now.’” He added that “You use humor to vent how you feel and to try to make sense of what you're experiencing, and you know the only people that are going to find that funny are your colleagues who are experiencing the same thing that you are.” In other words, the dark humor was an expression of solidarity borne of an experience they believed nobody else could understand who was not there at the time. ICU staff told me over and over that they felt like nobody could understand what they were going through in treating critically ill COVID-19 patients. A physician assistant said, for example, “If it's not someone that's close to the situation, there's just no way for them to really comprehend what a bad day means.” A nurse practitioner reflected, “If you haven't done it, if you haven't lived it, it's difficult to really understand how you feel, because you can explain the situation that you're in but not the feeling of it.” A nurse supervisor explained, “I feel like I couldn't even explain it to the extent of what was going on. I felt like no one would really understand who wasn't there. You know what I mean?” A respiratory therapist said, “I don't think that anyone could really understand what it is unless you're in the room with these patients.” A nurse remarked that the pandemic, “did sort of bond us as a team, because in essence, we're the only ones that really understood what that was like.” Along these lines, staff said they felt more connected to each other since the pandemic. A nurse for example said, “When the rest of the world couldn't see their friends, I felt fortunate that I could still go to work and see my friends.” She added that, “I think that we were lucky to have each other to vent because I think only once you're in it can you appreciate it and really understand the magnitude of what we felt.” A nurse supervisor explained, “I definitely feel like it's an us-against-the-world type of feeling, because nobody else really fully gets it. The people that you're working beside, I feel like you can fully vent and let it all out with those people because they get it.” A physician assistant noted, “The fact that my whole social life is at work, thank God. As serious as it is, I get to still be around my coworkers and my friends at work.” Another nurse reflected, “Having nurse friends as support was everything. We spent a lot of time together during COVID … And that was definitely the best support system.” A nurse practitioner similarly stated, “I think it's kind of a centripetal force in that people kind of lock in tighter. Everybody's tired and people are snappy, but the work is understood, the requirements are understood.” ICU staff contrasted their shared understanding with each other against the feeling that others, especially hospital officials and even their family members, just could not understand what it was like. For example, a respiratory therapist said that hospital officials do not understand what they had gone through: “They're not there when these tubes are going in, or the patient is asking you, ‘Am I going to die?’ And you don't know what to tell them.” Regarding family, staff members suggested that for various reasons they often did not talk to their family members much about what they were seeing on the unit. A nurse practitioner said, for example:You know, it's a little bit of a Debbie Downer if you're like, ‘So, honey, how was your day?’ at dinner, and they tell you about their day, and then you're like, ‘Well, I intubated four people, and of the four people I intubated, three of them died. And two of the families screamed at me, and one family threw themselves on the floor.’ I think in that respect it's been stressful because it's easier just to kind of hold it in than it is to share it. 4 Discussion Restricted visitation intensified the emotional demands on ICU staff at a time when they were called on to utilize all their technical-medical expertise to try and save the lives of people with new, deadly, and highly contagious disease about which little was known. They took on emotional support roles for patients that had been done by family members and other loved ones at the bedside. At the same time, ICU staff faced more anger and misunderstandings from family members about the grave nature of their loved ones’ illness, further exacerbating the emotional charge of the labor process. At the same time, staff had more space to speak candidly and make jokes without concern about what others might think, generating a kind of solidarity based on a shared sense of understanding. The findings in this paper extend our understanding of hospital medicine during disasters to a long-term pandemic. While previous work has highlighted health care workers' dual roles such as helper and victim after a natural disaster (Hugelius et al., 2017), in this case we see a different kind of duality – of taking on emotional-support roles typically done by families in addition to their roles as ICU workers, which typically focus not primarily on emotions so much as on technical-medical expertise (Brilli et al., 2001). Furthermore, the reconfigured relationships with patients' families were marked by increased conflict, distrust, and misunderstandings, leaving staff having to manage new interactional patterns and expectations. The absence of family members also reconfigured relationships among coworkers, who found themselves isolated on the unit, forging a shared understanding of the situation that they believed nobody could understand unless they were there. Future research should continue to explore how the presence or absence of outsiders such as patients’ family members, particularly in the aftermath f a disaster or an ongoing pandemic, shapes the roles and relationships embedded in the labor process of medical work. While studies have shown that the pandemic has increased rates of psychological distress, emotional exhaustion, and burnout among hospital workers (Mehta et al., 2021), particularly in ICUs, there has been less specification of how the labor process itself is implicated in those increases. The findings of this study show that the response to the pandemic by hospital administrators to restrict visitation reconfigured roles and relationships in ways that intensified the emotional demands on workers, on top of the challenge of saving lives from a novel, deadly virus about which little was known. The increased solidarity forged among staff from the fact that there were no visitors may have buffered against the negative impact of staff's expanded work roles and relationships. Indeed, some research has shown that social support from coworkers and managers may have diminished the extent of emotional wear-and-tear of the pandemic on ICU nurses (Cadge et al., 2021). But on the other hand, being there for each other as a shoulder to lean on in the darkest hours of the pandemic still raised the demands on staff to heights previously unseen. Furthermore, this study shows the importance of family members at the bedside not for patients, but for ICU health care workers. While some ICU staff perceive family members involvement to be a burden on their time, family members also provide social-emotional support to patients that frees up staff to focus on other things and may reduce the likelihood of emotional exhaustion and burnout. Indeed, the findings of this study add to the emerging evidence to suggest that fully restricting family members was a mistake from the perspective of “patient-and-family centered care” as well as from the perspective of the supporting health care workers (Hugelius et al., 2021; Dos Santos and Soares, 2022). Now, perhaps we can look towards the next wave of COVID-19 or the next pandemic or natural disaster and be better prepared to ensure that the emotional burden of taking on new roles and expanding relationships does not fall solely on health care workers who are busy trying to save lives. Ironically, it is this same rationale – more time for medical care – that led ICU staff to say they preferred when families were not present at the bedside. These findings clarify and extend our understanding of Goffman's dramaturgical perspective of interaction. In Presentation of Self in Everyday Life, Goffman (1959) argues that the “frontstage” and “backstage” are constantly shifting and each new boundary creates a new location that marks the backstage and the frontstage. One might expect that the removal of visitors would reduce or perhaps even eliminate the frontstage, since in Goffman's notion of staging hinges on the presence of an audience, such as patients' family members, that guides the staff's impression management. Yet the findings show that the absence of visitors expanded the frontstage, which is exactly the opposite of what a straightforward reading of Goffman's (1959) work would suggest. Staff talk typically relegated to the backstage spilled over into the frontstage, expanding it even further. It seems more like the absence of visitors collapsed the backstage, as frontstage formalities were dropped, new roles and relationships were constituted. Finally, the findings of this study are limited by the methodological choices made along the way. For instance, the absence of observational data gathered from fieldwork, which would have provided rich, empirical evidence would allow for the comparison of what people said and what they did (Jerolmack and Khan, 2014). Consequently, the data in this study captured ICU staff's recollections, interpretations and beliefs about their work during a critical period in the pandemic, which may have been different from the observable behaviors on the floors of the units. However, the intent of this paper is to understand the how health care workers made sense of their experiences and in turn, how those experiences are linked to broader social forces. On another note, the data was collected in early 2021, right after vaccines became available for health care workers but before they were available to the public. If these interviews had been done at a different stage of the pandemic, the study participants may have had different views. Nevertheless, medical sociologists should continue to examine how disasters including the pandemic shape the social organization of health care work, paying particular attention to changes in the labor process and their effects on the people who do the work. Credit author statement Jason Rodriquez: Conceptualization; Data curation; Formal analysis; Funding acquisition; Investigation; Methodology; Project administration; Resources; Software; Supervision; Validation; Visualization; Roles/Writing - original draft; Writing - review & editing. Uncited references Davidson et al., 2017; England Paula, 2005; Hammonds and Cadge, 2014; Henneman and Suzette, 2002; Shapiro, 2019, Underman and Hirshfield, 2016. Data availability The data that has been used is confidential. ==== Refs References Alharbi Jalal Jackson Debra Kim Usher The potential for COVID‐19 to contribute to compassion fatigue in critical care nurses J. Clin. Nurs. 29 15–16 2020 2762 2764 32344460 Andrist Erica Clarke Rachel G. Harding Meghan Paved with good intentions: hospital visitation restrictions in the age of coronavirus disease 2019 Pediatr. Crit. Care Med. 21 10 2020 e924 10.1097/pcc.0000000000002506 32541371 Aziz S. Arabi Y.M. Alhazzani W. 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==== Front Chem Eng J Chem Eng J Chemical Engineering Journal 1385-8947 1385-8947 Elsevier B.V. S1385-8947(22)06233-7 10.1016/j.cej.2022.140753 140753 Article Capsule-based Colorimetric Temperature Monitoring System for Customizable Cold Chain Management Chu Jin-Ok a Jeong Hye-Seon a Park Jong-Pil c Park Kyeongsoon d Kim Sun-Ki d Yi Hyunmin b⁎ Choi Chang-Hyung a⁎ a Division of Cosmetic Science and Technology, Daegu Haany University, 1 Haanydaero, Gyeongsan, Gyeongbuk 38610, Republic of Korea b Department of Chemical and Biological Engineering, Tufts University, Medford, Massachusetts, 02155, USA c Basic Research Laboratory, Department of Food Science and Technology, Chung-Ang University, 4726, Seodongdaero, Daedeok, Anseong, Gyeonggi, 17546 Republic of Korea d Department of Systems Biotechnology, Chung-Ang University, 4726, Seodongdaero, Daedeok, Anseong, Gyeonggi, 17546 Republic of Korea ⁎ Corresponding authors. 5 12 2022 5 12 2022 14075323 8 2022 8 11 2022 30 11 2022 © 2022 Elsevier B.V. All rights reserved. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. The COVID-19 pandemic and the resulting supply chain disruption have rekindled crucial needs for safe storage and transportation of essential items. Despite recent advances, existing temperature monitoring technologies for cold chain management fall short in reliability, cost, and flexibility toward customized cold chain management for various products with different required temperature. In this work, we report a novel capsule-based colorimetric temperature monitoring system with precise and readily tunable temperature ranges. Triple emulsion drop-based microfluidic technique enables rapid production of monodisperse microcapsules with an interstitial phase-change oil (PCO) layer with precise control over its dimension and composition. Liquid-solid phase transition of the PCO layer below its freezing point triggers the release of the encapsulated payload yielding drastic change in color, allowing user-friendly visual monitoring in a highly sensitive manner. Simple tuning of the PCO layer’s compositions can further broaden the temperature range in a precisely controlled manner. The proposed simple scheme can readily be formulated to detect both temperature rise in the frozen environment and freeze detection as well as multiple temperature monitoring. Combined, these results support a significant step forward for the development of customizable colorimetric monitoring of a broad range of temperatures with precision. Keywords colorimetric temperature monitoring cold chain management microcapsules microfluidics temperature-responsive release ==== Body pmc1 Introduction The COVID-19 pandemic and the subsequent supply chain disruption have renewed needs and attention for safe storage and transportation of crucial items, particularly vaccines and food products that require controlled low temperature. Temperature-controlled supply routes, also known as cold chain systems, consist of an uninterrupted series of refrigerated manufacture, storage, and distribution activities, along with associated equipment and logistics, maintaining quality within a desired low-temperature range,[1], [2] as shown in Figure 1 . Disruption of the cold chain system may cause the growth of harmful microorganisms for food products,[1], [3] and an irreversible decrease in medical efficacy for vaccines even with a short temperature breach,[4], [5], [6] which are critical to human health. Despite such significance and recent advances, existing temperature monitoring technologies for cold chain management are limited and face multiple challenges.Figure 1 Capsule-based temperature monitoring system for cold chain management. Traditional temperature monitoring routes such as manual recording and automated systems are inadequate since they require human labor, are high cost, and/or prohibit broad deployment.[1], [7], [8], [9] It is crucial that temperature monitoring for cold chain management via a simple, cost-efficient, and user-friendly approach attains the ideal characteristics desired for the applications above. Colorimetric visual indicators have gained substantial attention for cold chain temperature monitoring due to the simplicity and reliability arising from disposable approach. These indicators can commonly provide a clear, visual guide to the vaccine's efficacy throughout the delivery or transport to the point of administration.[10], [11] In addition, the indicator can also warn staff whether the vaccine's stability has been affected at any deployment stage before reaching the end-users. This can avoid unnecessary waste and facilitate vaccination programs in remote areas where sophisticated monitoring technologies are not practical. There currently exist two representative indicators that are commercially available: time/temperature indicator (TTI)[12], [13], [14] and freeze indicator. TTIs have been studied in the past decades due to the advantage of real-time monitoring and consumer-friendliness, which has been developed mainly based on chemical,[15], [16], [17], [18], [19], [20] biological,[21], [22], [23] enzymatic,[24], [25] and electrochemical reaction[26] or mechanical deformation.[27], [28] TTIs are potent means to monitor the time of exposure to a higher temperature, but it is difficult to monitor the exposure to a lower temperature, limiting application toward cold chain monitoring. While temperature indicators (i.e., freeze indicators) enable cold chain temperature monitoring, they can only monitor one chosen limited temperature setting (e.g., 0, -1 and -6°C from temptime Corporation). However, even if the temperature required for each product is different,[29] it is not only difficult to monitor the temperature ranges outside of those pre-set values but also cannot monitor multiple temperature settings at once.[1] Thus, precision multiple temperature monitoring for cold chain management in a single platform via a simple, cost-efficient, and user-friendly routes would represent a significant step forward in safe transportation that require exquisite temperature control due to their direct impact on human health and potentially catastrophic outcomes in case of failure. In addition, there is an unmet need to develop readily customizable monitoring systems that allow for precisely tunable temperature detection with broad ranges depending on diverse product requirements. However, this potential has never been explored. In this report, we present a simple capsule-based colorimetric temperature monitoring system for cold chain management with precise and readily tunable temperature ranges (Figure 1). The microcapsules with the phase change oil (PCO) layer are rapidly produced by a triple emulsion drops-based microfluidic device in a consistent and reliable manner. Upon phase transition of the PCO layer below its freezing point, the encapsulated payload releases and triggers drastic change in color of indicator reagents, enabling simple visual monitoring in a highly sensitive manner. High flexibility to simply tune the PCO layer enables colorimetric detection of broad temperature ranges with precision. The monitoring sensor containing this microcapsule suspension allows freeze detection by the temperature decrease as well as temperature rise detection in the frozen environment. By simply formulating several sets of microcapsules with varying PCO layers and sequential release of the payload, we can further achieve multiple temperature monitoring in a simple and readily customizable manner. Combined, these results support a significant step forward for customized colorimetric temperature monitoring based on microcapsules with the PCO layer formed by microfluidic approach. 2 Results and Discussion 2.1 Rapid capillary microfluidic production of hydrogel microcapsules with a thin phase-change oil (PCO) layer We first demonstrate rapid and potent microfluidic production of hydrogel microcapsules with a thin phase-change oil (PCO) layer using triple emulsion drops as templates, as shown in the schematic diagram of Figure 2 A. For this, we employed a glass capillary microfluidic device consisting of three circular capillaries with varying orifice sizes assembled inside a square capillary. First, two circular capillaries for injection and collection respectively are treated to render hydrophobicity to surfaces with trichloro(octadecyl) silane; this leads to stable fluid flow due to preferential wetting. We then inserted and coaxially aligned the injection and the collection capillaries within the square capillary. In addition, a thinner tapered circular capillary was inserted into the injection capillary. Finally, the collection capillary was connected to polyethylene (PE) microtubing, where photopolymerization occurs before the resulting microcapsules are collected in water.Figure 2 Microfluidic production of hydrogel microcapsules with a thin phase-change oil (PCO) layer. (A) Schematic diagram showing the glass capillary microfluidic device used to generate triple emulsion drops. These emulsion drops are transformed into hydrogel microcapsules upon photopolymerization. The resulting microcapsules are collected in water to separate them from the outer oil phase. (B) Optical micrograph demonstrating consistent production of uniform microcapsules. (C) Size distribution of the overall diameter of the microcapsules (C.V.=1.3%). (D) Optical and optical-fluorescence composite micrographs showing each compartment of the microcapsules; FITC-dextran (FITC-DEX) for the hydrogel shell and Nile red for the PCO layer. (E) Plot showing the controllable overall diameter of the microcapsules by varying flow rate of the outer oil phase (Qouter). (F) Plot showing tunable shell thickness of the microcapsules by varying flow rate of the hydrogel prepolymer phase (Qmiddle). To produce triple emulsion drops, an aqueous solution is supplied through the tapered thin capillary to form the innermost drop. An oil phase (n-hexadecane with 2 wt% Span80 as a surfactant) is supplied through the injection capillary to form a thin PCO layer within the emulsion drop. The coaxial biphasic flows in the injection capillary lead to a periodic stream of plug-like aqueous drops surrounded in the PCO due to the high surface free energy of the PCO phase to the hydrophobic injection capillary surface. This flow behaviour allows a lubrication oil stream between the innermost aqueous drop and the hydrophobic wall of the injection capillary; this stream transforms into a thin PCO layer within the emulsion drop upon emulsification. Next, an aqueous prepolymer solution (10 vol% polyethylene glycol diacrylate (PEGDA) with photoinitiator) is supplied through the interstice between the injection capillary and the square capillary. The resulting triphasic streams (from the left side on Figure 2A) are emulsified by an outer oil phase (mineral oil with 2 wt% Span80 as a surfactant) at the exit of the injection capillary, forming a consistent stream of uniform triple emulsion drops with the thin PCO layer, as shown in Movie S1 (Supplementary material). These triple emulsion drops are then irradiated with UV light (estimated exposure time: 3s) while flowing through the PE tubing to allow in-situ photopolymerization of the aqueous prepolymer phase into a hydrogel, resulting in monodisperse microcapsules with a thin PCO layer. The bright-field micrograph in Figure 2 B shows the consistent production of uniform microcapsules containing a blue dye as a model payload in a rapid manner (i.e., approximately 15,000 capsules/min). Figure 2 C shows narrow size distribution on the diameter of the microcapsules, indicating their monodispersity. The size distribution of each batch from total 5 batches examined is 1.3% C.V. for 100 particles out of the entire population per each batch, demonstrating minimal batch-to-batch variation, reproducibility and robust nature of our microfluidic approach. The optical micrograph of a microcapsule in Figure 2 D shows a uniform hydrogel shell with high fidelity, enabling reliable encapsulation of the small blue dye (Mw=792Da) as a model payload without leakage due to presence of the thin PCO layer as a diffusion barrier. Next to confirm each domain, we incorporated fluorescein isothiocyanate-dextran (FITC-DEX, Mw=2 MDa) and Nile red as hydrophilic and hydrophobic tracers within the hydrogel shell and the PCO layer respectively; the hydrodynamic diameter (Dh) of FITC-DEX is approximately 60 nm, allowing successful incorporation within the hydrogel shell network having the mesh size of approximately 2 nm.[30], [31] These are evidenced by the bright-field and fluorescence micrographs of Figure 2D. Specifically, the green hydrophilic FITC-DEX is located exclusively within the outermost hydrogel shell layer, while the hydrophobic Nile red is located solely within the PCO layer. This result illustrates that we can impart functionalities in each compartment in an independently controlled manner. For example, we can potentially create functional microcapsules for controlled release of the encapsulated payload by tuning the PCO layer or physical properties of the hydrogel shell (e.g., porosity or stiffness).[30], [32] The size and respective dimensions of the microcapsules are highly tunable in our microfluidic approach. For example, Figure 2 E shows varying overall microcapsule sizes ranging from 150 to 300 μm, by simply controlling the flow rate of the outer oil phase (Qouter, 2,000-10,000 μL/h). The shell thickness of the microcapsules (ranging from 3 to 30 μm) can also be readily tuned by controlling the flow rate of the hydrogel prepolymer phase (Qmiddle, 100-900 μL/h), as shown in Figure 2 F. Specifically, the open dots in Figure 2F show that a wide range of shell thicknesses (i.e., ultra-thin or thick hydrogel shell) of the microcapsules is achieved in a simple and reproducible manner, as indicated by the consistently small error bars obtained for all the conditions examined. In short summary, the results in Figure 2 show consistent and rapid production of the microcapsules with a thin PCO layer and precise control over the microcapsule’s dimensions (i.e., size and shell thickness) enabled by our simple capillary microfluidic approach. 2.2 Temperature-responsive release of the encapsulated payload Next, we demonstrate temperature-responsive release of the encapsulated payload by utilizing liquid-solid transition of the PCO layer, as shown in Figure 3 . For this, we prepared hydrogel microcapsules with the aqueous core compartment containing blue dyes surrounded by a thin PCO layer consisting of n-hexadecane with a freezing point (Tf) of 18°C. The release behaviour of the microcapsules dispersed in an aqueous media with varying temperatures was observed via bright-field microscopy equipped with a temperature-controlled stage.Figure 3 Temperature-responsive release of the blue dyes encapsulated within the microcapsules. (A) Sequential schematic diagrams and corresponding micrographs showing a detailed mechanism of temperature-responsive release using a single microcapsule. When storage temperature reaches the PCO layer's freezing point, the encapsulated dyes are released through the interconnected cracks that result from the solidification of the PCO layer. (B) Micrographs showing potential for simple visual monitoring of the storage temperature (Ts) of a cuvette containing microcapsules. The color change is readily observed at the same temperature with a naked eye and correlated with an UV-vis absorbance (λmax=629 nm) of the dyes released from the microcapsules. Sequential schematic diagrams and the corresponding micrographs in Figure 3 A show how the encapsulated blue dyes can be released upon temperature-dependent liquid-solid transition. The as-prepared microcapsules are highly stable in an aqueous media at room temperature (25°C) for a long period, demonstrating robustness of the microcapsules, as shown in Figure 3 A-i and Figure S1 (Supplementary material). When the storage temperature (Ts) of the dispersed aqueous media becomes lower than the freezing point of the PCO (Tf, PCO), the PCO layer solidifies forming cracks during freezing, as shown in Figure 3 A-ii; the changing surface roughness is optically observed within the interior of the hydrogel shell, supporting the crystal formation of hexadecane upon the PCO’s phase change.[33], [34], [35], [36] The cracks are interconnected and make diffusion paths for the blue dye between the innermost aqueous phase and the hydrogel shell, leading to the release of the blue dyes while maintaining the shell’s integrity, as shown in Figure 3 A-iii. In addition, we carried out two sets of additional experiments to further examine the stability in practical scenarios during distribution and transportation such as exposure to high temperature or external stress. Exposure of the microcapsules at substantially higher temperatures up to 70°C did not yield any noticeable change as expected, as shown in Figure S2. In the meantime, neither continuous shaking at 1,500 rpm nor high-speed centrifugation 15,000 rpm for 30 min yielded any noticeable disruption of our microcapsules (Figure S3). These stability tests in complex and harsh conditions demonstrate the robustness and stability of our proposed microcapsule, which is suitable for practical application scenarios. The simple visual monitoring of temperature-responsive release is illustrated in Figure 3 B, where a cuvette containing ∼35,000 microcapsules is exposed to decreasing temperature from 15 to 10°C. When the temperature reaches 12°C (Tf, PCO), most microcapsules (98 %) abruptly release the blue dyes, making the aqueous media turn blue. Color change upon the release consistently occurs at same temperature (total 10 batches), showing the reliability of the proposed microcapsules, as indicated by the small error bars of absorbance (λmax=629 nm) of the dye obtained for all the conditions. The release occurs at 12°C which is different from Tf of the n-hexadecane, 18°C; we attribute this decrease in release temperature to the presence of the surfactant (Span80) in the PCO layer, which interferes with the ice crystal formation. In sum, the results in Figure 3 illustrate the temperature-responsive release of the encapsulated payload by utilizing liquid-solid transition of the PCO layer, enabling simple visual monitoring of Ts. 2.3 Precisely tunable release temperatures by varying compositions of the PCO layer in microcapsules We next demonstrate ready and precise tunability of the release temperature (Tr) simply by varying the composition of the PCO layer, as shown in Figure 4 . For this, we prepared four sets of microcapsules with the PCO layer consisting of hydrocarbon oils with varying chain lengths and freezing points; decane (C10, Tf = -30°C), undecane (C11, Tf = -26°C), dodecane (C12, Tf = -10°C), and hexadecane (C16, Tf = 18°C), as shown in Figure 4 A. The plot of the Tr vs. hydrocarbon oils in Figure 4 B shows that Tr can be readily controlled across a broad range from 12.3 to -36.6°C by simply choosing different hydrocarbon oil for the PCO, while there exist slight gaps (approximately ≈ 6°C) between Tr and Tf due to the presence of surfactants in the PCO layer, consistent with the result in Figure 3.Figure 4 Controllable release temperatures by varying compositions of the PCO layer in microcapsules. (A) A schematic diagram showing microcapsules with the PCO layer consisting of various hydrocarbon oils that have different freezing points. (B) A plot showing varying release temperatures depending on hydrocarbon oils for the PCO layer (C) A plot showing that release temperatures are precisely tunable by using a mixture of dodecane (C12) and hexadecane (C16) for the PCO layer. The release temperatures can be further fine-tuned by utilizing mixtures of two hydrocarbon oils with different freezing points for the PCO layer. As shown in the Tr vs. oil composition plot of Figure 4 C, Tr can be tuned from -16°C at 0 vol% to 5°C at 75 vol% of hexadecane (C16) in dodecane (C12). While typical TTI studies accompany calculations of required heat, our systems focus on precise tuning responsive temperature in broad ranges (i.e., -36°C - 12°C) by utilizing phase-change oils with varying freezing points. This result supports that our proposed microcapsule system can be readily tailored to enable simple visual monitoring of a wide range of temperatures with precision by simply tuning the PCO layer composition. 2.4 Capsule-based cold chain monitoring systems for freeze detection by the temperature decrease This capability to fine-tune Tr at precise values can be readily applied to simple visual monitoring systems for cold chain management of essential items including frozen food and vaccines as shown in Figure 5 . As in the schematic diagram of Figure 5 A, our simple colorimetric sensors consist of microcapsules containing hydrochloric acid (HCl, 10mN) within the core compartment dispersed in an aqueous media (pH 7) of small pH indicator dyes methyl red (MR, 0.02%) and bromothymol blue (BTB, 0.04%).Figure 5 Capsule-based cold chain monitoring systems for freezing detection by the temperature decrease (A) Schematic diagrams showing microcapsules containing hydrochloric acid (10mN), which are dispersed in an aqueous media of the pH indicator consisting of methyl red (MR) and bromothymol blue (BTB). (B) Cold chain monitoring for frozen food indicators. Photographs and corresponding schematic diagram showing the prototype sensor (including one set of microcapsules) attached to meat packaging and the mechanism of colorimetric temperature change detection. (C) Cold chain monitoring for safe vaccine transportation. Photographs and corresponding schematic diagrams showing the prototype sensor (including two sets of microcapsules) attached to vaccine packaging and the mechanism of colorimetric detections of stepwise temperature change. MR is yellow in pH over 6.3, red in pH under 4.2, and orange in between, with its pKa of 5.1, while BTB is blue in pH over 7.6, yellow in pH under 6.0, and green in between, with the pKa of 7.0. These pH indicator dyes change color depending on the HCl (H+) content released from the microcapsules at precise target temperatures tuned by the PCO layer composition, enabling smart detection of multiple temperature ranges in a highly sensitive manner. As shown in Figure 4 above, we used microcapsules with the PCO layer consisting of mixed ratios between hexadecane (C16) and dodecane (C12) to tune the Tr. First, to demonstrate that the microcapsules can be applied as a frozen food indicator for recommended storage temperatures from -2 to 4°C, we used microcapsules with the PCO layer consisting of C16 and C12 at 49:51 ratio to achieve Tr=-2°C through the liquid-solid transition. The prototype sensor contains an aqueous suspension of the microcapsules (approximately 12,000 capsules/cm3) in a plastic container attached to a wrapped packaging of meat, as shown in photographs at the top of Figure 5 B. The schematic diagram at the bottom of Figure 5B shows the detailed mechanism of the colorimetric cold chain monitoring sensor upon the HCl release below the recommended storage temperature; at temperature from -2 to 4°C the sensor appears green (i.e., no color change) indicating proper meat storage condition without freezing. When Ts decreases below -2°C, the sensor turns red due to the release of HCl and the resulting pH change (pH 2), indicating a breach from the recommended storage temperature in the cold chain system and a warning sign for frozen meat. The simple visual monitoring system for cold chain management can also be used for safe vaccine transportation that require exquisite temperature control due to their direct impact on human health and potentially catastrophic outcomes in case of failure; recommended storage temperature range is from 2 to 8°C. To achieve smart and precision detection, we establish dual-temperature monitoring systems with two separate temperature ranges simply by utilizing a mixture of two different microcapsules; caution (0°C <Ts<2°C) and warning (Ts<0°C). For this, we used two sets of the microcapsules with different sizes and varying the PCO compositions; the small one having C16:C12=63:37 (Tf, PCO=2°C) and the large one with C16:C12=57:43 (Tf, PCO=0°C) as the PCO layer formulations at two different Tf’s, allowing the sequential release of HCl at 2°C and 0°C, respectively. As shown in photographs of at the top of Figure 5 C, the prototype sensor containing the microcapsule suspension was attached to the vaccine packaging, and varying colors were monitored under different temperature conditions. At Ts from 2 to 8°C, the sensor’s color is green because all the microcapsules remain stable, indicating safe storage at recommended temperature ranges in the cold chain system, as shown in Figure 5 C-i. When Ts decreases, the small capsules allow HCl release while the large ones stay intact, resulting in pH change (pH 5) and turning yellow rising from the color change of the BTB; this indicates a caution sign, as shown in Figure 5 C-ii. As Ts further decreases below 0°C, the large capsules release HCl, resulting in pH change (pH 2) that turns the indicator to red due to the color change of the MR; this indicates a warning sign, as shown in Figure 5 C-iii. In short summary, the results in Figure 5 support multiple temperature monitoring for products that require precise control of temperature monitoring by formulating several sets of microcapsules, each varying the PCO’s composition. While many chemicals are involved in the preparation of microcapsules, quite a minute amount is needed to exert visible responses due to the nature of microcapsule formulation, and most chemicals (e.g., biopolymer, food dye) is generally regarded as safe;[37] thus, we believe that the potential environmental impact of our system is minimal. 2.5 Capsule-based cold chain monitoring systems for temperature rise detection in the frozen environment We can also achieve temperature rise detection in the frozen environment simply by adjusting the composition of aqueous media. For this, we prepared microcapsules containing blue dyes within the core compartment dispersed in aqueous media with varying ethylene glycol (EG, 0-40 vol%) contents for tuning of Tf, ranging from -24°C to 0°C as shown in the table of Figure 6 A-I. We use n-decane as the PCO layer, whose Tf, PCO is around -36.6°C, allowing it to freeze at lower temperature than the aqueous EG media.Figure 6 Capsule-based cold chain monitoring systems for temperature rise detection in the frozen environment (A) Schematic diagrams showing microcapsules containing blue dye, which are suspended in aqueous media of ethylene glycol (10-40 vol%) whose concentration determines freezing point of the aqueous media. For Ts < Tf, PCO, the microcapsules are highly stable while the aqueous media and the PCO layer becomes frozen state, which does not allow release of the blue dye while the PCO layer ruptures. If Ts is higher than Tf, media, the blue dye releases as the aqueous media thaws. (B) Photographs and corresponding schematic diagrams showing the prototype sensor that enables colorimetric detection of temperature change ranges recommended for mRNA vaccine transportation. At room temperature, the microcapsules are highly stable in the aqueous media without releasing the dye, evidenced by the isolation of blue dye at the bottom of the tube shown in Figure 6A-I and i. For Ts < Tf, PCO, the aqueous EG media and the PCO layer are frozen sequentially, as shown in the schematic diagram of Figure 6A-II and the corresponding photograph of Figure 6A-ii. The blue dye remains within the microcapsules during the PCO layer’s rupture because the dye diffusion does not occur through the frozen state of aqueous media (Figure 6A-III and iii). When Ts >Tf, Media, the aqueous media thaws and becomes a liquid state, leading to the release of the blue dye (Figure 6A-IV and iv). If the aqueous media only becomes frozen except for the PCO layer, capsules are intact and the dye release does not occur despite repeated freezing and thawing (Figure S4). This result indicates that the PCO layer should be frozen for rupturing, leading to dye release at Ts >Tf, Media. Meanwhile, the time-course study results in Figure S5 indicate a modest time delay of 25-30 minutes for sufficient release of the blue dye, suggesting the ready applicability of our system for cold chain management where consideration of the small amount of time in high-temperature exposure is acceptable and should be accounted for. Finally, we show that this approach enables simple visual monitoring systems for cold chain management of mRNA vaccine; for example, Moderna’s COVID-19 vaccine has recommended temperatures that ranges from -50°C to -15°C. As shown in photographs at the top of Figure 6 B, the colorimetric sensor consists of the microcapsules containing HCl (10mN) suspended in 30 vol% aqueous media of EG (Tf, Media=-15°C) with same pH indicator as in Figure 5, and the color change according to the temperature transition around -15°C was monitored. For Ts<-15°C, HCl is confined (isolated) within the microcapsule due to the aqueous media's frozen state in the sensor, retaining the original color (green) of the pH indicator. When Ts is over -15°C, the released HCl reacts with the pH indicator in the aqueous media, making the sensor turn red and allowing simple visual detection. In summary, the results in Figure 5 and Figures 6 successfully demonstrate that fine-tuned temperature-responsive payload release enables simple yet smart and highly sensitive colorimetric visual monitoring of temperature change for cold chain management. Furthermore, this capsule-based cold chain monitoring system allows temperature rise detection in the frozen environment and freezing detection by a temperature drop. 3 Conclusion In this work, we presented a capsule-based colorimetric temperature monitoring system for customizable cold chain management. Triple emulsion-based microfluidic techniques enabled consistent and rapid production of the hydrogel microcapsules with a thin PCO layer and precise control over their dimensions (i.e., size and shell thickness). Temperature-responsive liquid-solid phase transition of the PCO layer in microcapsules led to the release of the encapsulated payload depending on their freezing points, allowing ready visual monitoring of temperature change in a highly sensitive manner. Simple tuning of the PCO layer’s compositions in microcapsules allowed fine-tuned temperature-responsive payload release, enabling ready visual monitoring of a wide range of temperatures with precision. The monitoring sensor containing the microcapsule suspension enabled freezing detection by the temperature decrease, which is highly desirable for items requiring proper storage to maintain quality or the safe transportation (e.g., food or general vaccine). Also, we achieved temperature rise detection in the frozen environment by varying the composition of aqueous media, which is applicable for safe transportation for mRNA COVID-19 vaccines. Furthermore, multiple temperature monitoring was achieved by utilizing two sets of microcapsule suspension each with varying PCO layer composition and temperature-dependent sequential release, indicating that our proposed system is user-friendly yet intelligent colorimetric monitoring capable of temperature change for cold chain management. While the results demonstrated throughout our report focused on the ability to precisely tune the target temperature in a wide range via simple PCO layer formulation, our approach can be readily extended to impart further flexibility in response time as well similar to existing TTI studies;[12], [13], [14] such efforts are currently underway. We anticipate that further development in controlling compositions in each compartment within the hydrogel microcapsules will lead to a new pathway in designing more complex and programmable temperature or temperature/time monitoring systems. 4 Experimental Section 4.1 Materials Mineral oil, n-hexadecane, n-dodecane, n-undecane, n-decane, polyethylene glycol diacrylate (PEGDA, Mn 700), poly(ethylene glycol)-block-poly(propylene glycol)-block-poly(ethylene glycol) (F108, non-ionic surfactant, Mw 14,600), poly(vinyl alcohol) (PVA, Mw 13,000-23,000, 87-89% hydrolyzed), 2,2-dimethoxy-2-phenylacetopheonon (photoinitiator), Span® 80 (Sorbitan monooleate, viscosity 1000-2000 mPa.s (20°C)), erioglaucine disodium salt, Fluorescein isothiocyanate-dextran (Mw 2,000,000), Nile Red, hydrochloric acid solution (1.0 N, BioReagent, suitable for cell culture), methyl red sodium salt (water-soluble ACS), bromothymol blue sodium salt (indicator water-soluble ACS), ethylene glycol and trichloro(octadecyl) silane were purchased from Sigma-Aldrich. 2-[methoxy(polyethyleneoxy)propyl]trimethoxyl silane was purchased from Gelest. Deionized (DI) water (EXL® 18.2 MΩ∙cm at 28°C) was used for all aqueous solutions. Square glass capillaries with an inner diameter of 1.05 mm were purchased from Atlantic International Technology (AIT) and cylindrical glass capillaries with inner diameter of 0.58 mm and outer diameter of 1.00 mm were purchased from World Precision Instruments Inc. (WPI). 5 min epoxy (Devcon) was used for assembling the glass capillary microfluidic devices. Microscope slide (3×1 inch, DURAN) and cover glass (Deckglaser) was used to fabricate custom-made chamber for micropipette aspiration. 4.2 Methods 4.2.1 Preparation of glass capillary microfluidic device and its operation We prepare an injection capillary by tapering a 580 µm inner diameter circular glass capillary to 100 µm inner diameter; to render the inner wall hydrophobic, we put them into trichloro(octadecyl) silane solution for 5 minute and subsequently wash it with isopropyl alcohol. We insert the injection capillary into a square capillary whose inner width (1.05 mm) is slightly larger than that of the outer diameter of the injection capillary (1 mm). Next, we prepare a small tapered glass capillary (20 µm outer diameter) by pulling a cylindrical capillary. Next, a collection capillary tapered (Inner diameter of orifice: 350 μm) is inserted into the square capillary from the other end; we also render this collection capillary to trichloro(octadecyl) silane to make the capillary wall hydrophobic. During microfluidic emulsification process, the volumetric flow rate is precisely tuned by syringe pumps (Legato100, KD Scientific) and the manufacturing of triple emulsion droplets is monitored using an inverted fluorescence microscope (Eclipse Ti2, Nikon) equipped with a high-speed camera (MINI UX 50). 4.2.2 Characterization of hydrogel microcapsules An inverted fluorescence microscope (Eclipse Ti2, Nikon) equipped with a CCD camera (sCMOS Zyla, Andor) was used to observe the resulting hydrogel microcapsules, and image analysis of the particles was performed using the ImageJ (National Institute of Health) and NIS-Elements (Nikon) software programs. The detailed internal structure of the hydrogel microcapsules and temperature-responsive release patterns were observed and characterized by a confocal microscope (SP‐5, Leica). 4.2.3 Performance evaluation of temperature monitoring sensor The temperature monitoring sensor, including microcapsule suspension attached to the product package, was kept in a freezer to evaluate sensing performance. Color change of the monitoring sensor was observed at varying storage temperatures. 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. Acknowledgment We gratefully acknowledge financial support by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1F1A1056481) and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1A4A1022206). The work was also supported in part by the OTTOGI HAM TAIHO Foundation. ==== Refs References 1 Mercier S. Villeneuve S. Mondor M. Uysal I. Time-Temperature Management Along the Food Cold Chain: A Review of Recent Developments Compr. Rev. Food Sci. Food Saf. 16 4 2017 647 667 10.1111/1541-4337.12269 33371570 2 Montanari R. Cold chain tracking: a managerial perspective Trends in Food Science & Technology 19 8 2008 425 431 10.1016/j.tifs.2008.03.009 3 Li Y. Man S. Ye S. Liu G. Ma L. CRISPR‐Cas‐based detection for food safety problems: Current status, challenges, and opportunities Comp Rev Food Sci Food Safe 21 4 2022 3770 3798 4 Cornish N.E. Anderson N.L. Arambula D.G. Arduino M.J. Bryan A. 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Application of polysaccharide-based biopolymers as supports in photocatalytic treatment of water and wastewater: a review Environmental Chemistry Letters 20 6 2022 3789 3809	36506703	PMC9721202	NO-CC CODE	2022-12-10 23:15:23	no	Chem Eng J. 2023 Mar 1; 455:140753	utf-8	Chem Eng J	2,022	10.1016/j.cej.2022.140753	oa_other
==== Front Therapie Therapie Therapie 0040-5957 1958-5578 Société française de pharmacologie et de thérapeutique. Published by Elsevier Masson SAS. S0040-5957(22)00276-1 10.1016/j.therap.2022.11.011 Article Les essais plateformes Plateform trialsRoustit Matthieu a⁎ Demarcq Olivier b Laporte Silvy c Barthélemy Philippe d Chassany Olivier e Cucherat Michel f1 Demotes Jacques g1 Diebolt Vincent h1 Espérou Hélène i1 Fouret Cécile j1 Galaup Ariane k1 Gambotti Laetitia l1 Gourio Charlotte m1 Guérin Aurélie n1 Labruyère Carine c1 Paoletti Xavier o1 Porcher Raphael p1 Simon Tabassome q1 Varoqueaux Nathalie r1 a Univ. Grenoble Alpes, Inserm CIC1406, CHU de Grenoble, 38000 Grenoble, France b Pfizer, direction des affaires médicales, 75668 Paris, France c Univ Jean Monnet, Mines Saint-Étienne, INSERM, U 1059 Sainbiose, CHU de Saint-Etienne, unité de recherche clinique, innovation, pharmacologie, 42023 Saint-Etienne, France d AstraZeneca, direction recherche clinique, 92400 Courbevoie, France e Unité de recherche clinique en economie de la santé (URC-ECO), hôpital Hôtel-Dieu, AP-HP, 75004 Paris, France f MetaEvidence.org, service hospitalo-universitaire de pharmacologie et toxicologie, Hospices civils de Lyon, 69000 Lyon, France g ECRIN, 75013 Paris, France h F-CRIN, UMS 015, Pavillon Leriche, hôpital Purpan/CHU de Toulouse, 31059 Toulouse, France i Pôle de recherche clinique, Institut de santé publique, Inserm, 75013 Paris, France j Medtronic, direction des affaires scientifiques, 75014 Paris, France k Leem, 75017 Paris, France l Département recherche clinique, Institut national du cancer, 92100 Boulogne-Billancourt, France m CHU de Caen, 14033 Caen, France n Pfizer, recherche clinique, 75668 Paris, France o Institut Curie ; Université de Versailles St Quentin / Paris-Saclay, INSERM U900, équipe de statistique pour la médecine de précision (STAMPM), 92210 St Cloud, France p Université Paris Cité, METHODS Team, CRESS, INSERM, INRA; Centre d’épidémiologie clinique, Assistance Publique-Hôpitaux de Paris, Hôtel-Dieu, 75004 Paris, France q Assistance Publique-Hôpitaux de Paris ; Sorbonne Université, service de pharmacologie, plateforme de recherche clinique de l’est parisien, 75012 Paris, France r Amgen, direction des affaires médicales, 92100 Boulogne Billancourt, France ⁎ Auteur correspondant: Centre d’investigation clinique – Inserm CIC1406, CHU Grenoble Alpes, 38043 Grenoble cedex 09, France 1 Les participants à la table ronde « Recherche clinique et évaluation des produits de santé” des Ateliers de Giens 2022. 5 12 2022 5 12 2022 13 10 2022 14 11 2022 © 2022 Société française de pharmacologie et de thérapeutique. Published by Elsevier Masson SAS. All rights reserved. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Résumé Les essais plateformes connaissent depuis quelques années un essor important, amplifié récemment par la pandémie de coronavirus disease 2019 (COVID-19). La mise en œuvre d’un essai plateforme s’avère particulièrement utile dans certaines pathologies, notamment lorsqu’il y a un nombre important de candidats médicaments à évaluer, une évolution rapide du traitement de référence ou dans les situations de besoin urgent d’évaluation, au cours desquelles la mutualisation des protocoles et des infrastructures permet d’optimiser le nombre de patients à inclure, les coûts et les délais de réalisation de l’investigation. Toutefois, la spécificité des essais plateformes soulève des problématiques méthodologiques, éthiques et règlementaires, qui ont fait l’objet de la table ronde et qui sont exposées dans cet article. La table ronde a également été l’occasion d’aborder la complexité de la promotion et de la gestion des données liée à la multiplicité des partenaires, le financement et la gouvernance de ces essais, et le niveau d’acceptabilité de leurs résultats par les autorités compétentes. For the past few years, platform trials have experienced a significant increase, recently amplified by the COVID-19 pandemic. The implementation of a platform trial is particularly useful in certain pathologies, particularly when there is a significant number of drug candidates to be assessed, a rapid evolution of the standard of care or in situations of urgent need for evaluation, during which the pooling of protocols and infrastructure optimizes the number of patients to be enrolled, the costs, and the deadlines for carrying out the investigation. However, the specificity of platform trials raises methodological, ethical, and regulatory issues, which have been the subject of the round table and which are presented in this article. The round table was also an opportunity to discuss the complexity of sponsorship and data management related to the multiplicity of partners, funding, and governance of these trials, and the level of acceptability of their findings by the competent authorities. Keywords Platform trial Adaptive trial Randomized controlled trial ==== Body pmcLes articles, analyses et propositions issus des Ateliers de Giens sont ceux des auteurs et ne préjugent pas des propositions de leur organisation	0	PMC9721267	NO-CC CODE	2022-12-07 23:20:04	no	Therapie. 2022 Dec 5; doi: 10.1016/j.therap.2022.11.011	utf-8	Therapie	2,022	10.1016/j.therap.2022.11.011	oa_other
==== Front Eval Program Plann Eval Program Plann Evaluation and Program Planning 0149-7189 1873-7870 Elsevier Ltd. S0149-7189(22)00154-9 10.1016/j.evalprogplan.2022.102200 102200 Article Parental decision-making on summer program enrollment: A mixed methods Covid-19 impact study Dugger Roddrick a Reesor-Oyer Layton. a Beets Michael W. a Wilson Dawn K. b Weaver Robert Glenn a⁎ a University of South Carolina, Department of Exercise Science, Arnold School of Public Health, USA b University of South Carolina, Department of Psychology, College of Art and Sciences, USA ⁎ Correspondence to: Arnold School of Public Health, University of South Carolina, 921 Assembly Street, Room 130, Columbia, SC 29205, USA. 5 12 2022 4 2023 5 12 2022 97 102200102200 18 10 2021 3 5 2022 2 12 2022 © 2022 Elsevier Ltd. All rights reserved. 2022 Elsevier Ltd Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Background The closure of childcare organizations (e.g. schools, childcare centers, afterschool programs, summer camps) during the Covid-19 pandemic impacted the health and wellbeing of families. Despite their reopening, parents may be reluctant to enroll their children in summer programming. Knowledge of the beliefs that underlie parental concerns will inform best practices for organizations that serve children. Methods Parents (n = 17) participated in qualitative interviews (October 2020) to discuss Covid-19 risk perceptions and summer program enrollment intentions. Based on interview responses to perceived Covid-19 risk, two groups emerged for analysis- “Elevated Risk (ER)” and “Conditional Risk (CR)”. Themes were identified utilizing independent coding and constant-comparison analysis. Follow-up interviews (n = 12) in the Spring of 2021 evaluated the impact of vaccine availability on parent risk perceptions. Additionally, parents (n = 17) completed the Covid-19 Impact survey to assess perceived exposure (Range: 0–25) and household impact (Range: 2–60) of the pandemic. Scores were summed and averaged for the sample and by risk classification group. Results Parents overwhelmingly supported the operation of summer programming during the pandemic due to perceived child benefits. Parent willingness to enroll their children in summer programming evolved with time and was contingent upon the successful implementation of safety precautions (e.g. outdoor activities, increased handwashing/sanitizing of surfaces). Interestingly, parents indicated low exposure (ER: Avg. 6.3 ± 3.1 Range [2–12], CR: Avg. 7.5 ± 3.6 Range [1–14]) and moderate family impact (ER: Avg. 27.1 ± 6.9 Range [20–36], CR: Avg. 33.7 ± 11.4 Range [9–48]) on the impact survey. Conclusion Childcare organizations should mandate and evaluate the implementation of desired Covid-19 safety precautions for their patrons. Abbreviations CEFIS, Covid 19 Exposure and Family Impact Survey CR, Conditional Risk ER, Elevated Risk Keywords Parental Summer Program Covid-19 ==== Body pmc1 Introduction The implementation of Covid-19 safety precautions led to the temporary closure of childcare organizations (e.g. schools, childcare centers, after school programs, and summer camps) across the United States (Park et al., 2020, Van Lancker and Parolin, 2020). The benefits of the programming offered at these organizations extend beyond routine childcare to encompass cognitive, social emotional, and health benefits for children (Durlak & Weissberg, 2007). For example, children from low-income households may experience reduced access to free or reduced-price lunches and safe places to play during the summer months. Summer programming meals and activities provide an invaluable opportunity to mitigate food insecurity and physical activity (PA) declines among children from low-income households(Hesketh, Lakshman, & van Sluijs, 2017; Moore et al., 2010) (McCombs et al., 2019). The social benefits of summer programming is particularly salient to children and adolescents (Richmond, Sibthorp, & Wilson, 2019). Unfortunately, pandemic-closure of these organizations occurred at a time when their social benefits were most needed (Kuhfeld et al., 2020, Lee, 2020, Van Lancker and Parolin, 2020). The reduction of safe spaces for social interaction placed an undue mental health burden on children as they experienced high rates of anxiety and depression (de Miranda, da Silva Athanasio, de Sena Oliveira, & Silva, 2020). Thus, a unique tension exists between Covid-19 risk reduction strategies (e.g. childcare organization closures) and the benefits of program attendance. As childcare organizations re-open, parent perspectives about Covid-19 risk and desired mitigation strategies should be considered to facilitate the optimal operation of programming. Given the widespread impact of Covid-19, it is likely that individual perceptions of risk may influence adherence to pandemic guidelines (e.g. facial masks, social distancing). Risk perception theories have been used in behavior change research to address several public health problems including smoking cessation, HIV prevention, environmental hazards, among others (Corneli et al., 2014, Gibbons et al., 1991, Slovic et al., 1985). Several key constructs in the theory (e.g. perception of risk, risk-adjustment, risk regulation, immediacy of effect, knowledge and control of risk, and severity of consequences) may provide a valuable framework to describe how individuals conceptualize the risk of Covid-19 infection. Previous research on risk perception and protective health behaviors indicates that individuals with a greater perceived risk were more likely to engage in protective behaviors (e.g. handwashing, travel avoidance, vaccination) than individuals with a lower perceived risk (Brug et al., 2004, de Bruin and Bennett, 2020, Gidengil et al., 2012). Similarly, parents’ perceived risk of Covid-19 may influence their perspective on Covid-19 risk mitigation strategies.Due to the politicization of Covid-19 pandemic precautions, parents may have diverse perspectives regarding the value of protective behaviors (Perry et al., 2020, Whitehead and Perry, 2020). Further, rapidly evolving scientific knowledge and the emergence of several Covid-19 vaccines has the potential to impact risk perception and precaution adherence. Consequently, the collective influence of Covid-19 risk perceptions and precaution adherence on parents’ summer program enrollment decision-making for their children should be evaluated. The purpose of this study is to describe parent perceptions of Covid-19 risk and to explore how changes in risk perceptions may impact parental decision-making on summer program enrollment for their children. To address this, the following research questions have been developed for the study. 1. What are the thoughts and expectations of parents regarding the operation of summer programming next year (Summer of 2021)? 2. How do parents perceive the risk and severity of the Covid-19 virus? 3. How has the COVID-19 pandemic impacted the well-being (e.g. mental, social, financial) of parents? 4. What strategies do parents use to cope with the challenges COVID has caused in their life? Given the persistence of the Covid-19 virus, we anticipate that findings from this study will inform future out-of-school time programming research and implementation. 2 Methods 2.1 Study design/procedure All procedures were approved by the lead author’s university institutional review board prior to recruitment of the first participant. This study used a convergent mixed method design to quantitatively and qualitatively assess family lived experiences during the Covid-19 pandemic, perceived Covid-19 risk, and summer program enrollment decision-making (Wu, Deatrick, McQuaid, & Thompson, 2019). Parents who completed the quantitative survey were contacted within two days to request a qualitative interview. Results from the quantitative survey were compared with explanatory information from qualitative interviews. To assess change in perceived Covid-19 risk, follow-up interviews were conducted with parents 6 months after the initial interview. 2.2 Setting, recruitment, participants This study recruited a subset of parents (n = 60) participating in a larger study investigating the impact of summer programming vouchers on children’s obesogenic behaviors in one southeastern U.S. state. In the September of 2020, parents were invited to complete an online survey (i.e. Covid-19 impact survey) evaluating their lived experiences and children’s summer activities during the Covid-19 pandemic. The survey link was sent through SMS text-message and at the end of the survey parents indicated their willingness to participate in a qualitative interview. A total of 55 of 60 parents completed the survey, and 17 parents responded to request for interview. A subset of parents (n = 12 of 17 parents completed follow-up interviews in the Spring of 2021. All parents were compensated for completing the survey ($10) and qualitative interviews ($15). 2.3 Quantitative measures Parents completed a Covid-19 impact survey using a Qualtrics survey link sent via SMS message in September of 2020. Survey questions were drawn from the Covid-19 Exposure and Family Impact Scales (CEFIS), a trauma-based framework examining the socioeconomic and family effects of the Covid-19 pandemic (Abuse, 2017, Kazak et al., 2021b). CEFIS questions were divided and scored into two separate sections evaluating 1) Covid-19 exposure (25 questions), 2) Covid-19 family impact (12 questions). In the Covid-19 exposure section, respondents indicated agreement (i.e. yes or no) with several pandemic-related experiences (e.g. stay at home order, missed important family event, family income decreased, Covid-19 diagnosis). In the family impact section, respondents utilized a 5-point Likert scale to indicate the overall impact Covid-19 had on several family-life areas including parenting, childcare, and physical/emotional well-being. Higher scores for each section indicate greater exposure to and perceived impact of Covid-19. 2.4 Qualitative methods 2.4.1 Preliminary interviews Preliminary qualitative interviews were conducted to refine the interview guide by the lead author with 3 parents, not included in the study from 2 separate households. Preliminary interviews lasted approximately twenty minutes and were conducted socially distanced in-person. Specifically, interview questions about pandemic experiences and the perceived risk of Covid-19 were narrowed to elicit clear responses from participants. Other questions were eliminated to reduce redundancy (e.g. “How comfortable should parents feel about enrolling their child in summer programming during Covid?” was eliminated from the final semi-structured interview guide due to overlap with, “What advice would you give a parent considering sending their child to a summer camp?”). Edits to the final interview guide from the preliminary interviews strengthened the clarity of the interview questions and increased the likelihood that study research questions would be addressed. 2.4.2 Primary interviews Phone interviews were conducted in October of 2020 by the lead author with parents (n = 17) who indicated interest in a qualitative interview on the Covid-19 Impact survey. Participants were contacted a total of 3 times to request an interview. Phone interviews were ∼20 min in duration and were conducted using a semi-structured interview guide (Appendix B). The interview questions were grounded in constructs from the health belief model, health behavior change theory and risk perception literature (Kahr et al., 2015, Schwarzer, 2016, Slovic et al., 1985). Questions addressed several theoretical constructs including perceived knowledge about risk, perceived risk, perceived severity and dread, control, and relevance. 2.4.3 Follow-up interviews The emergence of several Covid-19 vaccines during the Winter of 2020 had the potential to impact perceptions of Covid-19 risk. To evaluate changes in risk perceptions, follow-up interviews were conducted via phone with parents (n = 12) in the March of 2021. All previously interviewed parents were contacted via SMS message/phone call a maximum of 3 times to request a second interview. Interviews were ∼10 min in duration and were conducted using a semi-structured interview guide (Appendix C). Interview questions were designed to assess the change in perceived risk of Covid-19, the impact of vaccine availability on perceived risk, desired Covid-19 precautions for summer programming, and parent intentions to enroll their child in summer programming. All parents were compensated $10 for completing the survey and $15 for completing each interview (e.g. primary and follow-up). 2.5 Data analysis 2.5.1 Qualitative analysis Interviews were transcribed and recorded using an online transcription software, Otter.ai and imported into NVIVO 12 software. Online access to transcripts and recordings were password-secured. Data analysis was conducted by authors (RD, LRO) trained in qualitative methodology. To generate themes, inductive analysis was applied using an immersion crystallization approach and constant comparison methodology (Boeije, 2002, Crabtree et al., 1999, Strauss and Corbin, 1998). 2.5.1.1 Analytical approach A two-step approach was employed to conduct data analysis. First, in keeping with the overarching purpose of the study, the sample was stratified into two groups based on parent-voiced perceptions of Covid-19 risk. Consistent with the constant comparison analytical approach, sample stratification occurred prior to analysis(Boeije, 2002). Parents were categorized based on their response to interview questions evaluating the risk Covid-19 presents to their family. Responses to these questions were independently coded and discussed until a consensus on group labels was reached. Parents who indicated that Covid-19 presented a significant risk to their household were classified as “Elevated Risk (ER)”. Parents who held a more nuanced view of the risk, given the widespread implementation of Covid-19 precautions, were classified as “Conditional Risk (CR)”. Second, after parents were classified into groups, separate thematic coding analyses were conducted within unique NVIVO files for each respective group (n = 2). Follow-up interviews were not stratified into risk perception groups to account for potential bias due to attrition. 2.5.1.2 Three steps of thematic coding analysis Coders utilized a three-step latent coding technique for analysis (Bernard, Wutich, & Ryan, 2016) of all interviews (e.g. primary and follow-up interviews). First, coders independently read and generated codes for a single transcript by grouping recurring words, phrases, and themes. Second, coders and a third reviewer (RGW) met in order to review codes, integrate/add codes to a running list of codes generated from each transcript (i.e. coding guide), and to arbitrate any disagreements between coders to 100% agreement. Third, transcripts were revisited by the coders to determine if additional codes were needed and if the coding guide had reached saturation (Strauss & Corbin, 1998). Saturation was determined utilizing a code meaning saturation approach. Saturation was determined through collaborative discussion when coders agreed that a full understanding of codes had been reached and no new data, themes, or codes had emerged within the data (ER: ∼Interview 5–6, CR: ∼Interview 11, Follow-up: ∼Interview 8) (Fusch & Ness, 2015; Hennink & Kaiser, 2020). This iterative process was repeated until all transcripts were read and a comprehensive coding guide was created. This comprehensive coding guide was subsequently used to review and code all interview transcripts. Afterwards, in a final meeting coders met to discuss and reconcile all coded interviews. Themes were identified using inductive analysis through a constructionist epistemology. Consistent with constant comparison approach, themes were identified as patterns of similarities and differences between groups for each interview guide question. The prevalence of a theme was considered in terms of the number of different speakers who articulated a similar idea. Although, no specific threshold was established, coders evaluated the relative importance or ‘keyness’ of a theme to answer the research questions. The themes presented herein are semantic in nature and adhere to a simple description and interpretation of participant responses. Additionally, primary interview themes related to parent perceptions of Covid-19 risk were nested within constructs from risk perception theory (Slovic et al., 1985). Risk perception theory attempts to understand how individuals conceptualize risk and describe the association between risk perception and protective behaviors (Borrelli, Hayes, Dunsiger, & Fava, 2010). Relevant theoretical constructs and their definitions can be found in Appendix D. Similarly, the social ecological model was utilized to frame coping strategies parents employed to manage pandemic-related stress. Intrapersonal resources were defined as attitudes and personal practices (e.g. diet or exercise) that parents engaged in to reduce stress. Interpersonal resources were support systems that operate between individuals, typically at the family and friend locus (e.g family activities, childcare support, emotional support). Community resources were defined as external support existing at institutions beyond the home setting (e.g. schools, churches, recreation centers). Policy support included federal, state, and local policies parents referenced as supportive during the pandemic. 2.6 Trustworthiness of findings Several steps were taken to ensure trustworthiness of the study findings. First, the lead author of the study engaged in a reflective process to examine personal biases and assumptions that may be associated with this research. The lead author explored his personal value system and subsequently identified potential areas for role conflict during interviews. This process culminated in a written positionality statement acknowledging his subjectivities which was shared with the corresponding author (Appendix A). To establish clarity of the research findings, peer scrutiny of the project was conducted with two research colleagues in the health psychology field who were not involved in transcript coding or theme generation. Feedback was incorporated to modify theme development. During the interview, several tactics to help ensure honesty in informants were employed including, encouraging participants to be frank (i.e. there are no right or wrong answers) at the outset of the interview, interviewer attempts to establish rapport with respondents, and participants were reminded that they are not required to disclose information. Iterative questioning was used to uncover contradictions in statements and elicit detailed information and greater transparency. Negative case analysis was utilized to revisit the data and confirm that the established themes account for all instances of Covid-19 risk perception. Frequent debriefing sessions occurred during the analysis process with coders (LRO, RD) and the third reviewer (RGW) to resolve disagreements and clarify interpretation of participant responses. Lastly, quantitative survey data were triangulated with qualitative interview findings to create a holistic understanding of parent perceptions of Covid-19 risk (Patton, 1999). 2.7 Quantitative analysis Demographics for the sample are reported by risk classification (e.g. ER & CR). Using the scoring rubric for the CEFIS scale, parent responses were scored for sections one (i.e. exposure) and two (i.e. family impact) using establish scoring criteria (Kazak et al., 2021a). Section one included twenty-five dichotomous (Yes/No responses) question items. Responses were scored using a summary count of yes responses ranging from 0 to 25. Scores greater than 16 were considered as high exposure. Section two consisted of twelve questions. Ten of twelve questions were summed and scored using a four-point Likert scale, and the remaining two questions were summed using at 10 point (i.e. 1–10) distress scale. Combined scores for this section ranged from 2 to 60 with higher scores (>40) denoting greater negative impact and higher distress (Kazak et al., 2021b). Average scores, standard deviations, and range were calculated for each section (i.e. exposure & impact) and are reported by risk classification (e.g. ER & CR). Additionally, frequency counts for all question items and the distribution of the exposure and impact scores are reported in tertiles (1−3) by risk classification. 3 Results 3.1 Qualitative findings Parents in the CR group perceived Covid-19 risk as contingent upon a variety of factors including personal behavior (e.g. taking precautions) and population characteristics (e.g. age, health complications). Whereas parents in the ER group considered Covid-19 risk to be elevated and considered precautions necessary. Utilizing the constant comparison approach, the findings herein are presented as similarities and/or differences between these groups (see below and Table 3).Table 1 Demographics of Participants by Risk Classification. Table 1Program All Participants Conditional Risk Elevated Risk Number of Participants 17 10 7 Mean Parent Age in Years 38 ( ± 7) 39 ( ± 7) 37 ( ± 5.7) Mean Child Age in Years 8 ( ± 0.7) 9 ( ± 0.5) 8 ( ± 0.9) Female (n) 16 10 6 Male (n) 1 0 1 Participants by Race (n) Non-Hispanic Black 5 1 4 Non-Hispanic White 11 9 2 Race not specified 1 0 1 Children in home (n) 0 24 16 1 2 1 1 2 7 4 3 3 6 3 3 5 1 1 0 Not reported 1 1 0 Income (n) < $30,000 2 2 0 $30,000-$50,000 5 4 1 $50,000-$70,000 2 1 1 > $70,000 8 3 5 Table 2 Covid-19 Exposure and Family Impact survey. Table 2Part 1. Exposure Root: Please tell us about your family’s experiences during the novel Coronavirus (COVID-19) pandemic. In answering these questions, please think about what has happened from March 2020 to the present, due to COVID-19. By family we mean people who live in your household, extended family, and close friends who you consider “like family.” Please answer Yes or No for each of the following statements. Elevated Risk (ER) (n = 7) Conditional Risk (CR) (n = 10) Stem Yes No Yes No 1. We had a “stay at home” order 3 4 7 3 2. Our schools / childcare centers were closed 6 1 9 1 3. Our child/ren’s education was disrupted 5 2 8 2 4. We were unable to visit or care for a family member 6 1 4 6 5. Our family lived separately for health, safety or job demands 0 7 1 9 6. Someone moved into (or back into) our home 0 7 0 10 7. We had to move out of our home 0 7 0 10 8. Someone in the family kept working outside the home (essential personnel) 5 2 7 3 9. Someone in the family is a healthcare provider/first responder providing direct care 3 4 3 7 10. We had difficulty getting food 0 7 2 8 11. We had difficulty getting medicine 0 7 2 8 12. We had difficulty getting health care when we needed it 0 7 2 8 13. We had difficulty getting other essentials 0 7 4 6 14. We self-quarantined due to travel or possible exposure 0 7 1 9 15. Our family income decreased 3 4 7 3 16. A member of the family had to cut back hours at work 1 6 3 7 17. A member of the family was required to stop working (expect to be called back) 2 5 2 8 18. A member of the family lost their job permanently 0 7 1 9 19. We lost health insurance/benefits 0 7 0 10 20. We missed an important family event or it was canceled (e.g., wedding, graduation, birth, funeral, travel [including vacation], other) 5 2 5 5 21. Someone in the family was exposed to someone with COVID-19 1 6 4 6 22. Someone in the family had symptoms or was diagnosed with COVID-19 1 6 3 7 23. Someone in the family was hospitalized for COVID-19 2 5 0 10 24. Someone in the family was in the Intensive Care Unit (ICU) for COVID-19 2 5 0 10 25. Someone in the family died from COVID-19 1 6 0 10 Part 2. Family Impact Root: COVID-19 may have many impacts on you and your family life. In general, how has the COVID-19 pandemic affected each of the following? Elevated Risk Stem Made it a lot better Made it a little better Made it a little worse Made it a lot worse Not applicable 26. Parenting 2 0 3 0 2 27. How family members get along with each other 3 1 3 0 0 28. Ability to care for your child with [add illness/condition] 1 2 2 0 2 29. Ability to care for other children in your family 2 0 1 0 4 30. Ability to care for older adults or people with disabilities in your family 0 0 0 0 7 31. Your physical wellbeing – exercise 1 2 3 0 1 32. Your physical wellbeing - eating 1 1 3 1 1 33. Your physical wellbeing – sleeping 2 1 1 0 3 34. Your emotional wellbeing – anxiety 0 0 4 1 2 35. Your emotional wellbeing – mood 1 0 5 1 0 Conditional Risk Stem 26. Parenting 4 1 3 0 1 27. How family members get along with each other 4 1 3 1 1 28. Ability to care for your child with [add illness/condition] 4 0 5 0 1 29. Ability to care for other children in your family 4 0 0 0 6 30. Ability to care for older adults or people with disabilities in your family 0 0 2 0 8 31. Your physical wellbeing – exercise 3 0 3 4 0 32. Your physical wellbeing - eating 2 1 3 4 0 33. Your physical wellbeing – sleeping 2 0 5 3 0 34. Your emotional wellbeing – anxiety 2 0 1 7 0 35. Your emotional wellbeing – mood 1 0 4 5 0 Stem Group 1 – No Distress 2 3 4 5 6 7 8 9 10- Extreme Distress 36. Overall, how much distress have you experienced related to COVID-19? ER 0 0 0 1 2 2 1 1 0 0 36. Overall, how much distress have you experienced related to COVID-19? CR 0 2 0 1 0 1 2 1 2 1 37. In general, across all your children, how much distress have your children experienced related to COVID-19? ER 1 0 1 1 0 3 0 0 1 0 37. In general, across all your children, how much distress have your children experienced related to COVID-19? CR 1 0 1 1 0 2 3 1 1 0 Summary Group Frequency First Tercile (<33%) Second Tercile (34-66%) Third Tercile (>67%) Total Exposure (ER) 5 1 1 7 Exposure (CR) 3 3 4 10 Impact (ER) 4 3 0 7 Impact (CR) 2 3 5 10 Total Sample Scores Mean Std. Dev Minimum Max Exposure 7 3.39 1 14 Impact 31 10.11 9 48 Note: Exposure scores range from (0-25) and Impact Score range from (2-60). Higher scores denote more negative impact/higher distress. Table 3 Primary Interview Themes with Representative quotes. Table 3Section Similarities (S) or Differences (D) Theme Risk Perception Construct Quote 1 Quote 2 Covid-19 Impact Similarity Shared Difficulty – “When it [Covid-19] first happened last spring, it was terrible. None of us parents knew what we were doing. The teachers didn’t know what they were doing. None of the work was really aimed at what the children were learning, it was just stuff thrown [at them]. ‘Here. Do this.’” –CR Parent #14 “I have another toddler here, so it’s hard for me to bounce back and forth with her school work and taking care of him [toddler], the tantrums etc. It’s hard to really focus and give her the attention that she needs when she has questions or needs some assistance.” -ER Parent #4 “As everything was opening back up people were trying to go back to work. The job my wife had wasn’t going back, it wasn’t a call back type of job. [Unfortunately] they were one of the ones [businesses] that weren’t making it after Covid.” -ER Parent #1 Coping Strategies Similarity Support at higher ecological levels- Emotional Social Support – “I have a sister that lives in Missouri. And I mean, she's not physically related to me, but she's always there when I need somebody to talk to.” -CR Parent #13 “Yeah, mainly my sister, my older sister, I talk to her every day. I mean, I feel like we both kind of support each other during this process.” -ER Parent #2 Interpersonal-Childcare Support – “We have friends who will watch the kids for us if we need them to.” -ER Parent #3 “My mom came down and visited for a while. It wasn’t really like financial support, and it was more of a ‘let me help you with the kids for a couple weeks.’” -CR Parent #12 Interpersonal-Healthy Habits – “We got back into yoga because I had more time at home. My daughter’s dance studio offered classes that we were able to attend. [We] are just trying to stay active and keep a positive spin on it.” -CR Parent #9 “We also exercise and we will go bike riding as a family in the afternoon to release that stress. It really helps us…to come in contact with nature and just feel better.” -ER Parent #2 Community- School meal assistance – “The school system did a free program with food. So that [really] did assist and help…with being able to have a little bit more food in the home.” -ER Parent #5 “We received these EBT cards [from the school] for each one of the kids because they had been enrolled in the reduce meal program. Each one had a certain amount of money on it, so it was helpful to pay for groceries. because we weren’t used to having them home all the time.” -CR Parent #11 Coping Strategies Differences Variable Intrapersonal Support – “Honestly, I have taken to overeating. I am eating my feelings.” -CR Parent #8 “I am one of those [people] who internalize everything. I am not the best on mental health.” -CR Parent #10 “I’m actually focusing on positive things; I do a lot of reading that deals with being mindful and learning gratification. I try to not look at the worst situation, just keep a positive [outlook] knowing that each day is a gift.” -ER Parent #2 Family Risk of Covid-19 Similarity Variable Risk Control over Risk “It is a little bit of an increased risk because I’m a nurse, I’m exposed to it every day. But as long as I’m doing what I’m supposed to do [following precautions], I’ve made sure that my risk of bringing it home to my loved ones is very low.” -CR Parent #9 “Oh my goodness, without the precautionary measures that we take, I will say high because you just don’t know who is asymptomatic.” -ER Parent #5 Common Dread “Well, I went back and forth. When it [Covid-19] started we were scared, we went on lock down. We kept our kids home, we didn’t go to church and did grocery store pick-up. But you start going in waves [feeling fatigued with precautions], you can stress out with all that. But today, we still take a lot of precautions.” -ER Parent #1 “We were scared at first because we didn’t know what was going on. But month after month, you saw the news, you look at the numbers, and your friends telling you that somebody died from Covid. It kind of became less frustrating. It reached the point of this is everyday life now. This is something that we just have to deal with until something happens.” -CR Parent #17 Variable Risk- Mental Health Risk “It is a little bit of an increased risk because I’m a nurse, I’m exposed to it every day. But as long as I’m doing what I’m supposed to do, I’ve made sure that my risk of bringing it home to my loved ones is very low.” -CR Parent #9 “He didn’t want to go out and be around other people because of his asthma. We don’t know a lot about Covid, but we do know that it does attack the respiratory [system].” -ER Parent #5 Family Risk of Covid-19 Differences Uncertainty- Discomfort Knowledge about Risk “I really don’t know. It’s so hard to really navigate all of the stuff you read on the news and in the media. What’s true and what’s not true. What’s really a risk? I don’t know.” -ER Parent #3 “You never know if someone is coming to work sick or if other children are coming to daycare sick. You just don’t know. Since he [toddler] can’t communicate with me, I just don’t feel comfortable right now.” -ER Parent #4 Summer Programming Risk of Covid-19 Differences Potency of Precautions Control over Risk “Well I mean if the precautions are taken and they have all the safety things in place, I think the risk is really low.” -CR Parent #13 Differences Continuum of Risk (Low Risk) Common Risk “I would still send my child [to a summer camp] because it’s the same risk as going to the store, church, or school.” -ER Parent #6 Differences Continuum of Risk (High Risk) Common Risk “I really do believe it's high until they come out with a vaccine that's an FDA approved and that’s safe to administer to the kids.” -ER Parent #2 Differences Continuum of Risk (Contingent risk) Common Risk “Finances are a challenge to sending my child to camp because we’re on a very tight budget…So it would be tough, but we would still try to put our children in [camp], if we possibly can because I feel that it’s [important] for children to communicate with other children their age and do different activities.” -ER Parent #4 Differences Continuum of Risk (Uncertainty) Common Risk “It’s [risk of attending summer programming] hard to say because the information we’re getting from the media contradicts itself at times. So honestly…I just don’t know. I don’t know what the risks are.” -ER Parent #4 Safe Summer Camp Similarities Safe Precautions are necessary – “Probably limit the children maybe. I noticed that when she was in summer camp last year there was a lot of kids. So maybe limit [the number of] kids if they’re going to be with counselors.” -CR Parent #17 “If you can stop the spread using social distancing, then why would you have to wear the added mask? Would I send him [son] to a camp where he had to wear a mask all the time? No, because he is not comfortable wearing a mask eight hours a day.” -CR Parent #15 “I feel like a summer camp [should] properly ensure that workspaces and things that are touched are sanitized. And that the kids are frequently washing and sanitizing their hands.” -ER Parent #3 Safe Summer Camp Differences Decision to enroll- Depends on multiple factors – “It [summer program enrollment] more than likely will be an option, but it all depends on if I’m still working from home.” -CR Parent #17 “It [summer program enrollment] would depend on finances because I actually did take a pay cut switching over to a different school district.” -CR Parent #10 3.2 Similarity-Covid-19 impact – “shared difficulty” Across groups, parents experienced significant difficulty with the Covid-19 pandemic and resulting precautions. Parents described financial hardship due to loss of income, reduced work hours, and increased costs as a negative impact of Covid-19 precautions. Additionally, other negative effects of implemented safety precautions included challenges with virtual school, disruptions in daily routines, boredom, and decreased physical activity. Several parents discussed a personal experience with Covid-19 (e.g. Covid-19 diagnosis, family member death). Parents in both groups noted several positive effects of the pandemic, namely increased discretionary time to engage in preferred activities, new beneficial family routines (e.g. family bike rides), and virtual school kept families safe and together. 3.3 Similarity- coping strategies- “support at higher levels” Parents employed a variety of practices and resources to cope with the stress the Covid-19 pandemic precipitated. These resources aligned with a social ecological framework, and notably across groups parents referenced greater support at higher ecological levels (e.g. interpersonal and community). At the interpersonal level, emotional social support (e.g. phone calls, communication) received from family and friends was a consistent coping strategy utilized by parents. Similarly, to balance virtual school and work outside the home requirements, parents relied upon friends and family to provide childcare support. Families engaged in a variety of activities together to keep busy and some adopted healthy habits including time spent outdoors, family bike rides, and cooking together. At the community level, schools continued to provide meals for students during the pandemic. Parents described this service as beneficial due to household income losses. Table 4. 3.4 Differences- coping strategies- “variable intrapersonal support” Clear differences in intrapersonal coping strategies emerged between groups. Parents in the CR group displayed an awareness that they engaged in maladaptive health behaviors including overeating and internalizing/ignoring their stress. Conversely, parents in the ER group adopted a more positive mental framework to cope with their stress. 3.5 Similarities- family risk of Covid-19- “variable risk” Several risk perception theory constructs align with parent descriptions of the risk Covid-19 presents to their family. Similarities in perceived control over risk and reflections of common-dread were observed between groups. Parents perceived their control over Covid-19 risk as dependent upon individual factors including individual behavior (e.g. following Covid-19 precautions), personal health conditions, and age. Adherence to Covid-19 precautions was described as a strategy to control/reduce disease risk. Parents also described an evolving understanding and ultimate acceptance of Covid-19 risk and pandemic precautions. This evolution reveals that parents had minimal dread and learned to live with Covid-19 risk. In addition to physical health risks, parents recognized that the pandemic presented significant risks to their mental health by increasing feelings of social isolation, fear, and anxiety. 3.6 Differences- family risk of Covid-19- “uncertainty-discomfort” Differences in risk perception constructs (e.g. knowledge about risk, newness of risk) were observed between groups. Parents in the ER group acknowledged that the novelty of the disease and limited knowledge contributed to feelings of uncertainty and discomfort with Covid-19 risk. Specifically, contradicting media information, mistrust of the public’s precautionary behavior, and a lack of clarity on Covid-19 risk classification all contributed to perceptions of uncertainty. Notably, perceptions of uncertainty and discomfort with Covid-19 risk were not discussed by parents in the CR group. 3.7 Differences- summer programming risk of Covid-19- “potency of precautions” Although parents widely agreed upon the risk that Covid-19 presents to their family, key differences in summer programming risk were observed. Parents in the CR group were more likely to suggest that Covid-19 precautions could mitigate the risk of infection at a summer program. This finding aligns with the risk perception construct of control over risk. The implementation of precautions at summer program sites afforded parents a perceived degree of control over Covid-19 risk. 3.8 Differences- summer programming risk of Covid-19- “continuum of risk” Parents in the ER group displayed diverse perspectives about the risk Covid-19 presents to children who attend summer programming. Some parents perceived the risk as comparable to the risk of going to public places. This risk comparison aligns with risk perception theory and suggests that parents in the ER group are able to reasonably evaluate the risk of Covid-19 infection (i.e. Common Risk perception). Others considered the risk to be high due to the lack of a vaccine and limited scientific understanding of the disease. Between the polar ends of the spectrum (i.e. high and low risk), some parents held a more nuanced view and described a tension between child benefits of summer program attendance (e.g social interaction, learning, activities) and the risk of infection or financial risk (e.g. cost of attendance). Parents also expressed difficulty in evaluating the risk due to conflicting information presented in the media. The limited knowledge about risk contributed to parent perceptions of uncertainty. 3.9 Similarities – safe summer camp- “safety precautions are necessary” Across groups, parents described a need for Covid-19 safety precautions to be implemented to keep children safe. Parents desired a variety of safety precautions, these included increased handwashing, masks, sanitizing of surfaces, small-contained groups, limited enrollment, and outdoor activities. Despite widespread enthusiasm for precautions, some safety precautions were considered unnecessary by parents. Masks for children and masks worn outdoors were perceived as restrictive. Social distancing and temperature checks were also considered unnecessary by some parents. 3.10 Differences-decision to enroll- “depends on multiple factors” Most parents in the CR group described how a variety of factors influenced their decision to enroll their child in summer programming. These factors include the number of Covid-19 cases in their county, family need for childcare (e.g. programming unnecessary if parent works from home), cost of attendance, and if precautions are implemented. Parents in the CR group were also more likely to enroll their child in summer programming than parents in the ER group. 3.11 Follow-up parent interviews A total of 12 of 17 (CR=9, ER=3) parents participated in follow-up interviews. Due to loss to follow-up among parents in the ER group, themes were not stratified by risk group. Notably, among participating parents, perceptions of Covid-19 risk seemed to align irrespective of group status. The themes presented herein represent an explanation of the contributing factors for this alignment.Table 4 Follow-Up Interview Themes with Representative quotes. Table 4Theme Sub-theme Quote 1 Quote 2 Precautions mitigate risk Precautions mitigate risk Precautions mitigate risk. “I’m more comfortable with it [Covid-19 risk]. Interviewer: ‘What contributed to that change?’ ‘The fact most everybody knows what to do, how to stay safe. Like the whole hand washing, distancing, masks.” -Parent #10 “I know the precautions that are going to be taken, the protocols that will be put in place. So I don’t feel there’s as great of a risk.” -Parent #9 Parents interested in summer programming precautions “Before we sign them up for the program, we would like to know what their plan is. And then actually see it implemented.” -Parent #6 “When they present the summer programs it would be nice to have a flyer that lays out all the precautions that they’re taking, and what’s required.” -Parent #13 Parents willing to enroll child in summer programming “But I mean [it’s] likely for him [to go to camp] if possible to get out and do some type of camp so he can have a nice summer.” -Parent #15 Personal experience with the pandemic Decreased cases-severity “We’re not seeing as great a spikes anymore. So that definitely helps.” -Parent #9 “What helped motivate you to allow your children to go to school? ‘Not having so many phone calls from the school saying that a child has been exposed to Covid-19.’” -Parent #14 In-person schooling “It’s [Covid-19 risk] still a little nerve racking. In school, they have been safe, and they’ve been going to school just fine. So I guess that’s a little more calming.” -Parent #12 “I wouldn’t have any concern sending my kids to summer school. It would be the same as them going to regular school. And they have been.” -Parent #3 Safety Precautions “The kids are going to hopefully follow whatever precaution they’re being told. The basics are just wash your hands…” -Parent #10 “What would a safe summer camp look like this year? ‘Something that stays outside all day long!’” -Parent #8 Increased knowledge about risk “I have kind of believed in kids can be closer together than adults, I have read into that.” -Parent #14 Covid-19 diagnosis “I actually tested positive for Coronavirus [since the first interview]. I had no symptoms and didn’t feel sick, my kids never had symptoms…. So I am not [worried] We still wear masks when they’re mandated. But we just don’t really have the fear of getting it.” -Parent # 3 Vaccine Variable Impact Family risk of Covid-19- No impact “I am not one to trust a vaccine that was mass produced in such a short time.” -Parent #13 “[The vaccine] hasn’t [made an impact] because it’s a preventative measure, you can still get the disease if you’ve been vaccinated.” -Parent #15 Family risk of Covid-19- Decreased concern “Over the last few months, I got my first dose and my husband will get his, so I felt a little bit safer.” -Parent #12 “I was leery of the vaccine at first, but after someone associated with the CDC came and talked with us at work it made my thoughts about the vaccine a littler better. I thought ‘Okay this will really help.’” -Parent #17 Summer programming risk of Covid-19- Decreased concern “It has made it much better knowing that there’s a vaccine that can help protect people from getting sick.” -Parent #17 “I do feel like we’re getting close to that herd immunity so it has lessened the fear of another big outbreak.” -Parent #9 Summer programming risk of Covid-19- Increased with vaccines “I think the risk for kids is going up because parents are going to get a bit more relaxed.” -Parent #8 Summer programming risk of Covid-19- No impact “It [vaccines] doesn’t make me feel either way about sending my kid to camp. Even if there wasn’t a vaccination, I probably would still send my kid to summer camp.” -Parent #3 “I like data…and want to make sure there’s plenty of research. I want to hold out as long as possible to see what the possible long term side effects are.” -Parent #9 Risk vs. Benefits Live life “I really wish they would open everything back up, so a lot of the kids can do things.” -Parent #15 “I think we’re going to be exposed no matter what we do. People just need to go back to living life. That’s just how I feel.” -Parent #16 Kids need social interaction “They [kids] do need the interaction with other kids…they need normal life.” -Parent #14 3.12 Theme- precautions mitigate risk (n = 11) Overall, parents perceived that the implementation of Covid-19 precautions mitigated Covid-19 risk. Consequently, parents desired to be informed of the precautions summer programs would implement. With implemented precautions, parents were largely willing to enroll their children in summer programming. The perceived importance of implementing precautions aligns with the risk perception theory construct of control over risk. Parents considered precaution implementation as a means to reduce their child’s Covid-19 risk. 3.13 Theme- personal experience with the pandemic (n = 12) Parents referenced their pandemic experiences as influential to their perception of Covid-19 risk. Parents attributed a decrease in cases and the successful operation of in-person schooling to the decreased perception of risk. The experience with in-person schooling assuaged risk concerns for some parents, suggesting a shift towards a common, rather than dreaded understanding of risk. Additionally, increased knowledge/experiences with Covid-19 diagnosis provided greater clarity about the risk. Given the widespread implementation of Covid-19 precautions, parents seem to value the implementation of these precautions at summer programs. 3.14 Theme- vaccine variable impact (n = 12) Parent perceptions of the impact of vaccines on Covid-19 risk was variable. Some credited vaccinations with decreasing concern about risk, while others described a level of mistrust/uncertainty about the vaccine, therefore limiting its impact. One parent considered the vaccination campaign as an increase to Covid-19 risk. 3.15 Theme- risk v. benefits (n = 3) Parents recognize that other benefits of SDC attendance may outweigh the risk of Covid-19 infection. This influenced their willingness to enroll their children in summer programming. 3.16 Quantitative findings Demographics for the sample are reported by risk classification in Table 1. Notably, risk classification groups were largely dissimilar by race. Among parents classified as ER, 57.1% were Non-Hispanic Black and 28.6% were Non-Hispanic White, whereas 90.0% of parents classified as CR were Non-Hispanic White and 10.0% were Non-Hispanic Black. Question item responses and summary scores for the family impact survey are reported in tertiles and can be found in Table 2. Parents’ (n = 17), average score for the exposure section was relatively low (<33% tertile avg= 4.3 [2.1], 33%−66% tertile avg=7.8 [0.5], >67% tertile avg=10.8 [2.2]). Similarly, parents (n = 17) reported a low average score on the family impact section (<33% tertile avg= 5.7 [2.7], 33%−66% tertile avg=7.2 [4.9], >67% tertile avg=8.4 [1.1]). Notably, no parents classified as ER had family impact scores in the upper end (i.e. third tercile) of the distribution. Conversely, parents (n = 5) classified as CR had family impact scores in the upper end (i.e. third tercile) of the distribution. Parents in the CR group reported a disproportionately negative impact on their physical (i.e. exercise, eating, sleeping) and emotional well-being (e.g. anxiety, mood), while ER parents did not. The perceived decline in well-being may drive observed differences in family impact score between groups. 4 Discussion The Covid-19 pandemic significantly disrupted the lives of parents interviewed in this study. Irrespective of risk perception classification (i.e. ER or CR), parents perceived both positive and negative disruptions to their daily routine. Interestingly, parents reported relatively low exposure and low perceived family impact on the Covid-19 impact survey. These results can be contrasted with parent perceptions of Covid-19′s family impact discussed in the qualitative interviews. Across groups, parents described having difficulty with finances (i.e. loss of income), virtual learning, following pandemic precautions, and maintaining mental wellness during the pandemic. Parents also noted several positive aspects of pandemic-precipitated changes including increased discretionary time, family activities, and improved health habits. These results suggest that the Covid-19 impact survey did not have appropriate sensitivity to capture the nuances of Covid-19′s impact on households. Moreover, perceptions of Covid-19 family impact (survey and interviews) did not differ between groups. This indicates that parents’ lived experiences during the pandemic do not completely explain differences in Covid-19 risk perception. Parents employed a variety of coping strategies that were categorized at multiple levels of the social ecological model. Although strategies were identified at the intrapersonal level, these strategies were disparate, and commonalities were not found between groups. Similarities in coping strategies were found at the interpersonal and community level. The similarities in interpersonal and community support align with resilience literature about the pivotal role social support and social capital resources play in mitigating the harmful effects of chronic stress (Labrague, 2021, Ozbay et al., 2007, Palacio G et al., 2020). Social support has both A.) structural (i.e. network size, frequency of contact) and B.) functional (emotional- receiving love/empathy & instrumental- practical help) dimensions. Research has also demonstrated that the quality of socially supportive relationships is a stronger predictor of resilience than quantity. Parents in the present study referenced functional social support (emotional [e.g. talk with friends] & instrumental [e.g child-care assistance]) received from relatively few sources. It appears that the quality of these relationships buffered some of the harmful effects of the pandemic. Parent perceptions of the risk Covid-19 presented to their family aligned with several constructs in risk perception theory. Parents in both groups described the importance of following pandemic precautions to reduce the spread of Covid-19. This aligns with the risk perception principle of control over risk. Parents believed that the risk of Covid-19 could be controlled by personal behavior (i.e. following precautions) (Slovic et al., 1985). Previous research in environmental hazards has found that non-experts rate risks more highly when the hazard is uncontrollable and involuntary (Boholm, 1998, Slovic, 1987, Sullivan-Wiley and Gianotti, 2017). Further, non-experts are more likely to rate risks as controllable, if the risks are voluntary (Slovic, 1987). For parents in the present study, the implementation of Covid-19 safety precautions may have inspired a sense of control over risk and transformed the risk from involuntary to voluntary. The increased feeling of control may have promoted greater comfort with participation in public activities (e.g. school, in-store grocery shopping, summer programming). Despite their similarities, parents differed in their perception of Covid-19 risk. Eighty percent of Non-Hispanic Black parents perceived the risk of Covid-19 to their family to be severe and were classified as elevated risk. This sober evaluation of risk may be due in part to the disproportionate burden of Covid-19 hospitalizations and deaths experienced by Non-Hispanic Black Americans (Millett et al., 2020, Muñoz-Price et al., 2020). Due to the persistent effects of structural racism, Non-Hispanic Black Americans are more likely to be employed in the service, transportation, and healthcare industry, placing them at greater risk for Covid-19 infection (Statistics, 2016). Perhaps this disproportionate disease burden influenced risk perceptions among ethnically-minoritized participants. Additional explanations for the observed racial differences in Covid-19 risk perception are multi-faceted and intersect with political & religious ideology and socioeconomic hardship (Vargas, Mora, & Gleeson, 2021). Consequently, parents in the ER group maintained that the risk of Covid-19 was high and expressed uncertainty and discomfort with this risk. This aligns with the concept of dread risk which states that the higher the perceived risk, the more likely people will want to see it reduced through strict regulation (Slovic, 1987). Familiarity and level of knowledge about the threat also affect perceptions of risk (Slovic, 1987). The lack of clear information about the virus may have contributed to a higher perceived dread that was unique to parents in the ER group. In hazard risk studies, public education program are founded on the assumption that providing information about hazardous activities may motivate people to adopt protective behaviors (Smith, 2013). However, several studies indicate that increased information does not necessarily translate to precautionary behavior (Ballantyne, 2000, Paton et al., 2008). Thus, increased knowledge about the threat may reduce perceptions of dread, but it may not result in taking safety precautions. Overall, we observed that no single risk perception construct completely explains differences in parents’ perception of Covid-19 risk. Rather, risk perception is multi-faceted and what constitutes an acceptable risk varies depending upon the individual. Taken together, several factors emerged from our qualitative data analysis that may explain parent summer program enrollment decision-making. First, a dual-effect of vaccine availability and successful precaution implementation increased parents’ perceived control over the risk of Covid-19. During follow-up interviews, overwhelmingly, parents expressed comfort with enrolling their children in summer programming, provided that precautions were implemented. The availability of the vaccine seemed to reduce concern about the risk of Covid-19 spread for the majority of parents. Second, the passage of time produced an evolved perception of Covid-19 risk for most parents. Over time, parents gained a greater understanding of the novel Covid-19 virus and acclimated to following precautions. This finding is consistent with “normalization bias”, in which people believe that their ability to cope with previous experiences with the risk provides them the capability to address future risks (Paton et al., 2008). This was evidenced by parents who referenced the successful operation of in-person school (e.g. low Covid-19 cases) as a rationale for the safety of summer programming for children. Third, parents displayed decisional balance to determine if they would enroll their children in summer programming. Parents weighed the benefits and risks of summer program attendance. As seen in risk perception literature, non-experts’ evaluation of risk is sensitive to a variety of factors (e.g catastrophic potential, impact of future generations, voluntariness, knowledge, control etc.). Parents in the present study based their decision-making on factors beyond disease risk, including financial, mental wellness, and child benefit. Parents willingness to enroll their children into summer programming seemed to be largely based on precaution implementation and the perceived benefit of program attendance. Although the findings of this study contribute to our understanding of how individuals conceptualize Covid-19 risk, several limitations exist. The findings are not generalizable to the broader population due to the absence of risk perceptions of other marginalized populations (e.g. Asian-Americans, Hispanic-Americans). Despite this, the inclusion of perspectives from a historically marginalized group (e.g. Non-Hispanic Black parents) is a strength of the study. Additionally, the study findings could have been strengthened by a pre-pandemic assessment of parents’ summer program enrollment intentions and perceived benefits of program attendance. Parent intentions were assessed at two timepoints during the pandemic which allowed the authors to capture changes in risk perception and summer program enrollment intentions. However, a loss to follow-up of parents in the ER group limits the generalizability of findings. The authors of this study also acknowledge that their subjectivities (e.g. professionals in public health research) may have influenced the interpretation of parent responses and subsequent creation of risk classification groups. Several steps were taken to establish trustworthiness (e.g. reflexivity memo, positionality statement, peer debriefing) and mitigate any undue influence. 4.1 Lessons learned 4.1.1 Parents maintained interest in summer program enrollment for children Despite the risk of Covid-19 infection, overwhelmingly parents supported the operation of summer programming, and most were interested in enrolling their child in a summer program. Most parents perceived that the benefits of program attendance outweighed the risks of Covid-19 infection. Such benefits included social-peer interaction for children, outdoor activities, child learning, and physical health benefits (e.g. exercise). This finding suggests that summer programming is important to parents and the reduction of summer programming offerings presents its own unique risks. 4.1.2 Precaution implementation is important Parents valued the implementation of Covid-19 safety precautions at summer program sites. While there was a lack of consensus on certain precautions (e.g. masks, social distancing), parents broadly supported greater incorporation of outdoor activities and increased sanitizing/cleaning of surfaces at program sites. This reveals that parents were comfortable enrolling their children in summer programming if precautions are implemented. Consequently, child-care organizations should continue to mandate and evaluate the implementation of desired Covid-19 safety precautions for their patrons. Funding source This work was supported by the 10.13039/100000002 National Institutes of Health - NIGMS 1P20GM130420-01A1. CRediT authorship contribution statement Roddrick Dugger: Conceptualization, Data curation, Formal analysis, Methodology, Writing. Layton Reesor-Oyer: Formal analysis, Writing. Dawn K. Wilson: Conceptualization, Methodology, Funding acquisition, Writing. Michael Beets: Conceptualization, Project administration. Robert Glenn Weaver: Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Writing. Funding NIGMS #5P20GM130420-02 7370. Declaration of Competing Interest None to disclose. Dawn K. Wilson, Ph.D. \|Dr. Wilson is a Professor of Psychology at the University of South Carolina (USC). Her nationally funded program of research has focused on developing innovative, theoretically based interventions for health promotion in minority adolescents and their families. Her theoretical approach integrates bio-ecological models, family systems, and motivational approaches for understanding social and environmental influences of long-term health behavior change. Her current trials are evaluating the efficacy and cost-effectiveness of social marketing interventions on increasing safety and access for walking in high crime, under-served communities. Glenn Weaver, Ph.D. \| Dr. Weaver’s work focuses on helping professionals that teach and care for school age children to create safe and healthy environments. He is currently conducting research in schools and out of school time programs to address unhealthy weight gain in youth. He has expertise in physical education, promotion of youth healthy eating and physical activity, and measurement of healthy eating and physical activity. Michael Beets, Ph.D.\| Dr. Beets is a leader in the field of public health interventions targeting children’s health. His research employs a public health, community-based participatory approach with a focus on identifying real-world and scalable strategies to prevent or revers obesity and improve children’s overall health. Layton Reesor-Oyer, Ph.D. Dr. Reesor-Oyer’s research investigates obesity-related health disparities among underserved populations, particularly those with low-income and racial/ethnic minorities. Currently, she is focused on prevention of childhood obesity by targeting out-of-school time (e.g. summer vacation), and the influence of parenting practices on children’s obesogenic behaviors and weight trajectories. Roddrick Dugger, MPH \| Mr. Dugger’s research focuses on childhood obesity prevention among children from low-income, ethnically minoritized backgrounds. His work focuses on understanding and amplifying the resilience strengths of families who live in under-resourced communities. Mr. Dugger utilizes qualitative methodology and a person-centered approach to identify innovative solutions to improve children’s health. Acknowledgements We would like to acknowledge parents, children, and staff at the Boys and Girls Club of South Carolina. Data statement This data is unavailable for public access. ==== Refs References Abuse, S. (2017). Mental Health Services Administration. (2014). SAMHSA’s concept of trauma and guidance for a trauma-informed approach. HHS Publication. 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==== Front Arts Psychother Arts Psychother The Arts in Psychotherapy 0197-4556 1873-5878 The Authors. Published by Elsevier Ltd. S0197-4556(22)00111-3 10.1016/j.aip.2022.101990 101990 Article A mixed methods exploration of a pilot photo-reflection intervention for enhancing coping and well-being during COVID-19 Burton A.E. ⁎1 Elliott J.M. 2 Centre for Psychological Research, Staffordshire University, UK ⁎ Correspondence to: Staffordshire University, Leek Road, Stoke on Trent, UK. 1 ORCID: 000-0002-3698-0712. 2 ORCID: 0000-0003-2011-6476. 5 12 2022 2 2023 5 12 2022 82 101990101990 8 4 2022 29 11 2022 1 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. Restrictions enforced during the COVID-19 pandemic are associated with negative impacts on mental health and well-being. There is a need to support individuals to cope with these challenging circumstances. An embedded design mixed methods approach was employed to explore challenges experienced during the pandemic, the effectiveness of a photo-reflection intervention for enhancing coping, wellbeing, and resilience, and how this intervention functioned to impact on these outcomes. 108 participants were randomised to one of three photo-taking conditions; challenges experienced, coping strategies, or experiences and were assessed with measures of wellbeing, coping and resilience. In addition, open-ended survey questions were used to assess perceptions of experiences and of the effects of the intervention. There were no significant differences across the groups, however subjective psychological well-being, and emotional and functional well-being improved post-intervention regardless of intervention type. There was also an increase in planning and self-distraction coping for those whom the intervention elicited reflection. Qualitative data highlighted a range of challenges experienced and examples of both adaptive and maladaptive coping approaches. Photo-reflection intervention approaches may improve well-being and enhance coping during these challenging circumstances through providing opportunity to review and reflect on life experiences. Keywords COVID-19 Coping Wellbeing Intervention Mixed-methods Photo-taking ==== Body pmcStress and coping during the COVID-19 pandemic On 11th March 2020 the World Health Organization declared the Coronavirus Disease 2019 (COVID-19) a global pandemic (World Health Organization, 2020). On the 23rd March 2020 lockdown restrictions began in the United Kingdom (UK) resulting in severe curtailment of day-to-day life with individuals only permitted to leave their homes for: shopping for basic necessities, one form of exercise per day, medical needs, or essential travel for work (Prime Minister's Office, 2020). According to the transactional model of stress and coping, stress is a function of the interaction between individual characteristics and context and impacts on well-being via appraisal (Lazarus & Folkman, 1984). The COVID-19 pandemic is a traumatic stressor capable of eliciting PTSD-like symptoms (Bridgland et al., 2021). Restrictions placed during the COVID-19 pandemic have had detrimental impacts on health and well-being worldwide. Depression and anxiety levels increased (Rettie and Daniels, 2020, White and Van Der Boor, 2020) with levels of stress, anxiety and depression rising as lockdowns progressed (Ozamiz-Etxebarria et al., 2020). Encounters appraised as stressful, such as the COVID-19 pandemic, activate a coping response (Lazarus & Folkman, 1984) and given the impact of these complex and challenging circumstances on well-being it is important to understand how individuals cope with these restrictions. Coping strategies represent attempts to minimise distress in response to stressors (Carver, 1997). Survey evidence from Spain highlighted coping behaviours, including following a balanced diet, keeping to routine, restricting engagement with news about the pandemic, taking time to pursue hobbies, and staying outdoors or looking outside, as being linked to lower levels of anxiety during lockdowns (Fullana et al., 2020). In the UK, clinically high levels of distress were observed in those with avoidant coping approaches, while psychological flexibility was associated with greater well-being (Dawson & Golijani-Moghaddam, 2020). During lockdown, many of the coping strategies that individuals employ to access support and ease stress and anxiety were curtailed (Elmer et al., 2020). This reduction in coping opportunities means that it is important to understand whether other options may be effective in reducing stress and facilitating well-being. Therapeutic photography for stress and coping Photography has long been recognised as a valuable tool for assessment and therapeutic intervention within counselling and psychotherapy, with potential to uncover both verbal and non-verbal representations of a clients’ world (Amerikaner et al., 1980). Embedding ‘Photo-Therapy Techniques’ within therapeutic practice provides visual metaphors through which clients can remember, confront, imagine, and explore complex elements of their lives (Weiser, 2004). Such techniques have value as they can be used for a range of client groups regardless of age, culture, or setting (Weiser, 2004), and by any kind of trained mental health professional to improve their practice (Weiser, 2014). Photographs, when used in therapy settings can help uncover information which would not be discovered through direct questioning (Weiser, 2004) and can be a valued therapeutic activity (Cosden & Reynolds, 1982). Reflective focus on a photograph, rather than direct experience, can provide an emotionally safe and comfortable space for self-disclosure (Kleckner, 2004) facilitating self-understanding and conflict resolution (Hunsberger, 1984). This facilitation of ‘objectification’ (Wadeson, 1980) can help a client to view themselves as separate from their thoughts and emotions enabling analysis of these without feeling threatened. The process and use of photography therefore provides benefits when used in one-to-one therapeutic practice (Weiser, 1990, 2004, 2014). In addition, research has shown that self-guided photography interventions can have positive impacts. An ethnographic study by Brewster and Cox (2019) used observation and interviews to explore the experiences of individuals committed to taking a ‘photo-a-day’ and sharing this on social media. The interaction with online communities enhanced well-being for some, however the process of photography itself, through creativity and being mindful of daily events was experienced as a form of self-care with the potential to enhance well-being. Chen et al. (2016) also explored the value of photography for promoting positive affect. Students were allocated to one of three daily photography conditions: (1) taking a smiling selfie, (2) taking a photo of something that makes them happy, (3) taking a photo that would make another person happy and sending it to them. Happiness increased across all conditions and interview data indicated that those taking photographs for their own affect reported becoming more reflective, while those taking photos to send to others found the connection with family and friends helped to relieve stress. The mechanisms of therapeutic photography There is evidence that photo-taking modulates attentional processes and memory accuracy (Henkel, 2014), increases experiential engagement (Diehl et al., 2016) and potentially provides a protective coping effect to mental health in traumatic environments (Feinstein et al., 2020, Ramirez et al., 2019). Different forms of photo-taking therefore appear to have different impacts on experience (Chen et al., 2016, Diehl et al., 2016, Henkel, 2014). Documenting coping through photography may activate mental rehearsal of successful performance and focus on past success, both valuable behaviour change techniques (Michie et al., 2013) which may help individuals cope with challenges. Furthermore, a focus on achievements through coping may facilitate a mindset shift from a negative view of lockdown restrictions towards an awareness of potential benefits, with positive implications for well-being (Crum et al., 2013). In addition, purposeful reflection on difficult past events through journaling can help to reduce rumination with potential for both biological and psychological benefits (Pennebaker, 1995) and this may be facilitated through reflection on photography. A directional focus on challenges experienced may therefore facilitate in moment reflection, offering the opportunity for objective consideration and mindfulness around the challenge at hand (Diehl et al., 2016, Nardini et al., 2019, Ramirez et al., 2019). There is a lack of research into the most effective approaches to photo-taking interventions, and how these might function to influence well-being. Therefore, an exploration of different types of photo-taking and their impacts on psychological well-being is needed to inform the direction of future photo-reflection interventions. The present study This project aims to explore the effectiveness of a pilot photo-taking reflection intervention for enhancing well-being and coping during the COVID-19 pandemic to identify whether a simple intervention of taking and reflecting on photographs during lockdown can influence coping and well-being. The comparison of different forms of photo-taking will establish whether coping and well-being impacts are dependent on the photograph taken. In addition, qualitative reflections on the photographs taken and the intervention experience will be used to explore the challenges and coping strategies used during COVID-19 lockdown and how the creative process of photography may impact on coping and well-being. This exploration will help to better understand the value of photography as a therapeutic tool during times of unprecedented stress. Method Design This research employed an embedded mixed methods design (Creswell et al., 2003). Quantitative data were collected as part of a between-subjects experiment to pilot and evaluate the effectiveness of a photo-taking intervention. Qualitative data in the form of participant photographs, reflections and responses to open-ended questions were gathered using online surveys as part of the intervention process. Ethical approval for this research was granted by Staffordshire University Health Science and Wellbeing Ethics Committee. Conditions Participants were allocated to one of three conditions. In condition one participants were instructed to capture images of coping strategies used. In condition two participants were instructed to capture images of the challenges experienced. In condition three participants were instructed to capture general daily experiences. Condition three acted as an active control condition which did not explicitly encourage conscious consideration of whether an event being captured was a challenge to them or a method that they were employing as coping mechanism. Procedure Study advertisements were circulated using a range of social media platforms including Facebook, Instagram, and Twitter. Participants had to be 18 years old or over, able to read and write in English, live in the UK, and have access to a photography device. Interested participants followed a direct link to an information sheet and consent form via Qualtrics. All were entered into a prize draw to win shopping vouchers. Following completion of consent and baseline surveys, instructions for each condition were randomised and automatically displayed via Qualtrics and sent via email. Qualtrics automatically alternated the conditions for each participant. Participants were instructed to spend seven days collecting photographs and after one week they selected between four and seven of these images to send to the research team. Instruction wording differed by group: “Over the next 7 days, we would like you to be mindful of things that you are (doing to help you to cope at the moment/finding challenging at the moment/experiencing at the moment). We would like you to take photographs of anything that you are doing/experiencing (that is helping you to cope/that is challenging/in your current day-to-day life). Ideally, try to take at least one picture a day”. Reminders were sent to send the photographs after seven days in addition to a second Qualtrics link to complete the follow up survey. As part of this survey participants were asked to reflect on the images chosen. This photo-elicitation approach (Collier, 1957) is an established method of exploring lived experience, with the potential to empower individuals to lead discussions about their own perceptions and experiences (Burton et al., 2017, Mansfield and Burton, 2020). For each photograph participants responded to open-ended questions asking them to: describe the photograph, explain what was happening, explain why the image was chosen, explain what it represents in terms of their coping/challenges/experiences, explain how they felt when taking the photograph and now looking back, and explain why it is important to help us understand their experience. Open-ended questions were also used to assess participants reflections on the intervention process. Upon completion debrief information was provided via Qualtrics. Recruitment took place from 6th May 2020–17 th June 2020 with data collection for the 1 week follow up continuing until 27th June 2020. When data collection began full lockdown was in place and there were 3816 COVID-19 cases diagnosed per day (UK Government, 2021). At the end of data collection there were 602 cases diagnosed per day and lifting of lockdown restrictions was due to commence (UK Government, 2021). Due to the lifting of restrictions on 4th July potentially impacting coping and well-being we did not recruit further participants after this date. Measures WHO-5 Well-being: The World Health Organisation well-being index (WHO-5) is a 5-item measure of general subjective psychological well-being scored on a 6-point Likert scale (World Health Organization, 1998). The measure has a unidimensional structure and has good construct validity in both younger and older populations (Topp et al., 2015). For this sample Cronbach’s alpha was.74 at baseline and.87 at follow up. The Warwick-Edinburgh Mental Well-being Scale (WEMWBS): The WEMWBS is a 14-item measure of emotional and functional well-being scored on a five-point Likert scale. The scale is psychometrically robust with good test-retest reliability (Tennant et al., 2007) and is responsive to change (Maheswaran et al., 2012). For this sample Cronbach’s alpha was.88 at baseline and.92 at follow up. Brief COPE: The Brief COPE (Carver, 1997) is a 28-item measure assessing 14 specific problem based coping strategies. Each item is rated on a 4-point scale from ‘I haven’t been doing this at all” to “I have been doing this a lot”. The Brief COPE has been shown to demonstrate internal consistency across a range of samples (Cooper et al., 2008, Eisenberg et al., 2012). For this sample Cronbach’s alpha was.82 at baseline and.80 at follow up. Brief Resilience Scale (BRS): The brief resilience scale is a 6-item measure assessing the ability to cope with and recover from stress, scored on a five-point Likert scale (Smith et al., 2008). The scale has been shown to have a single factor structure (Smith et al., 2008) and good internal consistency in a range of samples (Smith et al., 2008, Windle et al., 2011). For this sample Cronbach’s alpha was.90 at baseline and.90 at follow up. Reflection Elicitation: The final survey questions explored participants’ perceptions of taking part in the research and engaging with the photo-taking activity: “What impact did taking the photographs have on how you responded/felt to any particular circumstances?”, “Did being asked to take pictures change anything about your day to day experiences, if so how?”, “How did you find taking part in the research process?”. Responses were used to group participants into those who engaged in reflection (N = 43) and those who did not (N = 30) comprised of those who reported; “no impact” (N = 25), “other” (N = 4) or a “negative impact” (N = 1). Sample justification Sample size was determined a priori based on the only two quantitative academic papers in this area (Diehl et al., 2016, Nardini et al., 2019) both completed studies using undergraduate student cohorts ranging from 160 to 230 participants. The effect size in these studies range from 0.38 to 0.5. GPower calculations support that a sample size of 175 would result in a power of 0.8 for p < .05. This justification is appropriate for the qualitative element which is recommended to have 50 + participants for a large qualitative survey (Braun & Clarke, 2013). Qualitative analysis Each photograph and associated survey response were reviewed by AB and coded using inductive thematic analysis (Joffe & Yardley, 2004). The content depicted was reviewed alongside the descriptive accounts, and codes were created to capture the experience being represented. A semantic data-driven approach was taken assuming that the images and accounts were representative of the participants explicit meanings. For photographs where the image was representative of an experience, rather than capturing the experience itself the narrative of the participant was prioritised to understand the experience being portrayed. For some examples the narrative was suggestive of the image representing several types of experience, for these AB made a judgement on which appeared the most prominent issue in the account. AB then reviewed initial codes and clustered these into broader categories. Once completed for each condition categories were reviewed across the conditions and a final coding frame was created by comparing, contrasting, and merging similar category titles and producing a short definition for each category. JE was blinded to the coding conducted by AB and employed the coding frame using a deductive approach. Coding agreement was assessed using Cohen’s Kappa and percentage agreement (Challenges:.74, 76 %, Coping:.72, 75 %, Experiences:.60, 64 %) and achieved good to excellent agreement across all conditions (Robson, 1993). Where disagreement was identified both authors reviewed the image and account collaboratively to agree the final code allocation. For a small number of cases (Challenges: N = 5, Coping N = 8, Experiences N = 13) it was agreed that the image and account equally represented two categories within the coding frame and coded under both. Statistical analysis Quantitative data were analysed using IBM SPSS version 27 and an alpha level of.05 was considered significant. Baseline differences between the three groups were examined using one-way ANOVA. Where baseline differences were reported, change from baseline scores were analysed. Due to changes in COVID-19 restrictions over the time of the study the timepoint of participant enrolment (day 1 – day 42) was included as a covariate. To investigate the effect of group on each of the well-being measures, mixed 2 (timepoint: pre and post task) × 3 (photo task: coping/challenges/experience) × 2 (Reflection elicited: photo task elicited reflection/no reflection elicited) ANCOVA were employed. A mixed 3 (photo task: coping/challenges/experience) × 14 (Coping Subscales) × 2 (Reflection elicited: photo task elicited reflection/no reflection elicited) MANCOVA on change from baseline data was used to analyse Brief COPE subscale data. Results A total of 108 participants were recruited at baseline, 35 were allocated to the challenges group, 36 to the coping group, 37 to the experiences group. At follow up 11 (31 %) participants were lost from the challenges group, 13 (36 %) from the coping group, and 11 (30 %) from the experiences group (total attrition 32 %) depicted in the CONSORT flow diagram ( Fig. 1). Most participants were recruited in the first 2 weeks of data collection (week 1 n = 26, Week 2 n = 22, week 3 n = 8, week 4 n = 10, week 5 n = 3, week 6 n = 4). Demographic characteristics at baseline for each of the groups can be seen in Table 1. There were no significant between groups differences in any of the sample characteristics when analysed via one-way ANOVA and Chi Squared as appropriate. No significant baseline differences for any of the well-being and resilience measures were found in completing participants for the timepoint at which participants enrolled into the study.Fig. 1 Consort flow diagram. Fig. 1 Table 1 Participant characteristics (M, SD/ Frequency, %). Table 1 Challenges (n = 24) Coping (n = 23) Experience (n = 26) Age (years) 38.25 (15.81) 37.70 (17.01) 39.81 (14.69) Gender Male 2 (2.7 %) 7 (9.6 %) 6 (8.2 %) Female 20 (27.4 %) 16 (21.9 %) 20 (27.4 %) Trans 1 (1.4 %) 0 (0 %) 0 (0 %) Non-binary 1 (1.4 %) 0 (0 %) 0 (0 %) Ethnicity White: British 21 (28.77 %) 17 (23.29 %) 22 (30.14 %) White: Other 1 (1.37 %) 2 (2.74 %) 1 (1.37 %) Mixed/Multiple ethnic groups: White and Black Caribbean 1 (1.37 %) 0 (0 %) 1 (1.37 %) Mixed/Multiple ethnic groups: White and Black African 0 (0.00 %) 1 (1.37 %) 0 (0.00 %) Mixed/Multiple ethnic groups: White and Asian 0 (0.00 %) 1 (1.37 %) 0 (0.00 %) Asian/Asian British: Pakistani 1 (1.37 %) 1 (1.37 %) 0 (0.00 %) Any other ethnic group 0 (0.00 %) 0 (0 %) 2 (2.74 %) Preferred not to say 0 (0.00 %) 1 (1.37 %) 0 (0.00 %) Highest Level of Education Completed School Leaver before 16 years 1 (1.37 %) 1 (1.37 %) 0 (0.00 %) School leaver at 16 years (GCSE or equiv) 0 (0.00 %) 2 (2.74 %) 1 (1.37 %) Further Education (A level or equivalent) 8 (10.96 %) 11 (15.07 %) 11 (15.07 %) Higher Education (Degree or equivalent) 10 (13.70 %) 5 (6.85 %) 8 (10.96 %) Post Graduate Education (MSc, PhD etc) 5 (6.85 %) 3 (4.12 %) 6 (8.22 %) Preferred not to say 0 (0.00 %) 1 (1.37 %) 0 (0.00 %) Living Arrangements Adults in household 2.42 (1.21) 2.61 (2.04) 1.92 (0.89) Children in household 0.54 (0.78) 0.65 (0.88) 0.96 (1.00) Photo-taking Behaviour No Change in photo-taking behaviour 9 (37.5 %) 10 (43.5 %) 16 (61.5 %) Increased photo-taking 6 (25 %) 4 (17.4 %) 5 (19.2 %) Change in type / purpose of photo-taking 8 (33.3 %) 6 (26.1 %) 3 (11.5 %) Decreased photo-taking 0 (0.00 %) 0 (0.00 %) 0 (0.00 %) Other/unclear 1 (4.2 %) 3 (13 %) 2 (7.7 %) Impact of Photo-taking Engaged in Reflection 15 (62.5 %) 14 (60.9 %) 14 (53.8 %) No Impact 8 (33.3 %) 6 (26.1 %) 11 (42.3 %) Negative Impact 0 (0.00 %) 1 (4.3 %) 0 (0.00 %) Other/unclear 1 (4.2 %) 2 (8.7 %) 1 (3.8 %) Attrition analysis Due to the high attrition rate, an attrition analysis was conducted. There was no difference in attrition rates across the intervention groups, age, gender, years in education or living situation. However, participants who did not complete the study had significantly lower well-being (WHO 5; (F(1,102)= 13.910, p < .001, ηp2 = .120) and mental well-being (WEMBS; F(1,)= 5.615, p = .02, ηp2= .052) at enrolment. There was no differential attrition between intervention conditions which would have posed a major threat to validity (Crutzen et al., 2015). See Table 2.Table 2 Participant Baseline well-being and mental well-being characteristics (M, SD/ Frequency, %) and attrition rates of all participants that completed the baseline measures and were randomised to an intervention type. Table 2 Challenges (n = 35) Coping (n = 36) Experience (n = 37) Overall (n = 108) Attrition Rate 11/35 (31.43 %) 13/36 (36.11 %) 11/37 (29.73 %) 35/108 (32.41 %) Mean (SD) WHO-5a Non Completer 49.50 (15.73) 52.52 (16.15) 54.62 (17.56) 52.27 (16.44) Completer 36.36 (16.53) 40.92 (18.55) 38.55 (24.35) 38.74 (19.49) WEMBSb Non Completer 37.73 (11.43) 51.00 (19.38) 42.00 (13.94) 44.00 (16.15) Completer 48.75 (20.34) 50.30 (17.40) 57.46 (17.51) 52.27 (18.61) BRS Non Completer 3.32 (0.87) 3.44 (0.71) 3.24 (0.98) 3.34 (0.83) Completer 3.08 (0.68) 3.20 (0.80) 3.31 (0.82) 3.20 (0.77) BRIEF COPE OVERALL Non Completer 35.27 (12.54) 61.31 (7.32) 65.27 (8.11) 63.80 (9.40) Completer 63.04 (10.65) 59.26 (10.46) 59.23 (9.47) 60.49 (10.20) a p < .001. b p < .05. Perceptions of the intervention For reference, data regarding changes in photo-taking behaviour are included in Table 1. No meaningful quantitative analysis could be conducted on this data due to violation of assumptions. Generally, participants reflected on the photograph taking exercise positively. Across the entire sample 43 participants (14, 61 % coping, 15, 63 % challenges, 14, 54 % experiences) expressed a statement that indicated the photo-taking exercise had encouraged them to reflect on their experiences. For example: “It has made me think about all the people and things that are important to me and to dwell on these and appreciate them more. It also made me consider activities that make me happy.”, (Coping); “I think taking a photo of otherwise normal things/activities made me take a step back and consider them more subjectively. (What am I doing? Why am I doing it? Why do I enjoy it? Why is it challenging at the moment?)” (Challenges); “Just gave me a reason to reflect more on my feelings, instead of ignoring them” (Experiences). Only one participant, who was in the coping condition, highlighted that the exercise had a potentially negative impact as they felt reflecting on their photographs had “emphasised my separation from my family”. Experiences captured by participant photographs A total of 416 photographs were submitted (Coping: 128, Challenges: 130, Experiences: 158). Examples are referred to in text with a theme title acronym and identifier number in brackets, these can be cross referenced to the data extracts in Table 3.Table 4 Mean and standard deviation for Well-being, Resilience and Coping scores pre- and post-photo intervention. Table 4 Challenges (n = 24) Coping (n = 23) Experience (WHO 5, WEMBS & BRS n = 26 BRIEF COPE n = 24) Pre Post Pre Post Pre Post WHO 5 49.50 (15.73) 52.00 (21.10) 52.52 (16.15) 58.61 (17.04) 54.27 (16.44) 63.85 (17.03) WEMBSa 44.38 (7.41) 45.71 (9.72) 45.83 (8.90) 49.57 (9.11) 46.73 (7.66) 50.19 (7.89) BRS 3.11 (.90) 3.15 ((0.65 3.20 ((0.72 3.29 ((0.79 3.02 ((0.85 3.41 ((0.84 BRIEF COPE Self-Distractionb 6.38 (1.31) 6.21 (1.50) 6.26 (1.48) 6.43 (1.20) 5.83 (1.63) 5.75 (1.92) Active Coping 5.08 (1.44) 5.42 (1.84) 5.09 (1.98) 5.43 (1.78) 5.25 (1.07) 5.71 (1.73) Denial 2.67 (1.34) 2.63 (1.41) 2.65 (1.23) 2.17 (0.49) 2.67 (1.24) 2.79 (0.98) Substance Use 3.50 (1.72) 3.38 (1.84) 3.13 (1.46) 2.91 (1.56) 3.29 (1.65) 3.13 (1.45) Emotional Support 5.75 (1.89) 5.08 (1.72) 5.13 (1.77) 5.22 (1.78) 4.83 (1.74) 4.92 (1.84) Instrumental Support 3.83 (1.61) 3.75 (1.48) 3.43 (1.62) 3.91 (1.70) 4.00 (1.47) 4.54 (1.77) Behavioural Disengagement 3.50 (1.74) 3.08 (1.44) 2.65 (1.19) 2.57 (0.90) 2.46 (0.98) 2.67 (1.01) Venting 4.17 (1.66) 4.00 (1.35) 3.83 (1.30) 3.48 (1.20) 3.96 (1.40) 4.21 (1.77) Positive Reframing 4.83 (1.61) 5.13 (1.70) 4.83 (1.92) 5.13 (1.84) 5.13 (1.45) 5.17 (1.71) Planningb 4.79 (1.79) 4.67 (1.76) 4.96 (1.58) 5.04 (1.82) 4.88 (1.33) 5.17 (1.61) Humour 4.58 (2.22) 4.46 (2.11) 4.43 (2.04) 4.26 (2.16) 4.83 (1.69) 4.92 (1.86) Acceptance 6.83 (1.55) 6.79 (1.32) 6.43 (1.44) 6.91 (1.16) 6.25 (1.48) 6.79 (1.22) Religion 3.33 (1.88) 3.13 (1.36) 3.09 (1.90) 3.04 (1.87) 2.54 (1.38) 2.54 (1.32) Self Blame 3.79 (1.74) 3.58 (1.77) 3.35 (1.50) 3.09 (1.31) 2.08 (1.06) 3.04 (1.33) a Significant main effect of time. b Significant coping strategy × reflection triggered interaction. Coping strategies Not all images and reflections submitted in the coping condition represented coping strategies, some captured other elements of the experience (e.g., an image of a workplace “To show how my work life has changed”). However, the majority represented five categories of coping: relaxation and self-care (33 %), hobbies and keeping busy (29 %), opportunities for quality family time (14 %), pets and companion animals (14 %), and maintaining social connections (10 %). Relaxation and self-care: Relaxation and self-care were the most frequently discussed strategies within the coping group. Examples included exercise, spending time in nature and ‘treats’ such as ordering take away food (RSC1). This category was also found in the challenges and experiences groups. For the challenges group relaxation and self-care was often portrayed as a response to the challenging situation, and the majority depicted going for walks or exercising outside in nature (RSC2). In the experiences condition examples highlighted how relaxation and self-care was used to cope, for example one reflecting on an image of a woodland walk as allowing happiness in difficult circumstances (RSC3). Hobbies and keeping busy: Participants in the coping condition enjoyed the opportunity provided by extra time at home during lockdown to engage in hobbies, some continued with old hobbies while others found new hobbies or skills to occupy their time. These activities helped participants to feel ‘happy’ and ‘relaxed’ and many expressed being ‘proud’ of their achievements. For example, reflecting on a photograph of her sewing machine and making masks for others one commented: “I feel proud that I was able to do this for the people I care about and proud that I was able to remember how to sew!” (HKB1). These activities provided a feeling of purpose during a time when other activities were curtailed. As with relaxation and self-care, participants in the challenges condition captured images representing hobbies and keeping busy as examples of how they overcame the challenges experienced (HKB2). Keeping busy also included house maintenance and voluntary work, for example one family had taken up litter picking in the local community (HKB3). Maintaining social connections: For those in the coping group finding alternative ways to maintain social connections, such as through video chat services and, as lockdown eased, socially distanced visits were viewed as essential for coping with lockdown isolation. One participant explained how Zoom had enabled him to spend time with his friends in a way that was closer to ‘real life’ than text messaging (MSC1). Rather than illustrating social connection as a coping strategy the challenges group reflected on the difficulties of being separated from others and being forced to communicate virtually or at a distance. For example, one participant reflected on seeing a family member at a distance when meeting up in a park (MSC2). The experiences group captured similar images, several referring to virtual family quiz nights, one reflected on how this represented an increase in family contact and bonding (MSC3). Opportunities for quality family time: The coping group highlighted how lockdown created extra time to spend with those in their household. This included quality time with children and partners within the household, opportunities viewed as a positive outcome of a challenging situation. For example, one participant reflected on the opportunity to spend more time walking with her partner (QFT1). The challenges group highlighted ways in which time with family still needed to go on ‘as normal’ despite the pandemic, in particular examples tended to illustrate the ability to spend more time with children (QFT2). Similarly, the experiences group captured the ‘new normal’ and the value of this for enhancing relationships within the household due to freedom to spend time together (QFT3). Pets and companion animals: Pets or companion animals were mentioned often as a source of comfort, joy, distraction, and company during times of isolation. For the coping group animals represented coping through companionship in the absence of human contact (PCA1). The challenges group used images of animals to represent opportunities to cope, illustrations of the lack of human contact, and the potential impact of lockdown on the well-being of their pets (PCA2). The experiences group also highlighted the value of pets for comfort, coping and maintaining a sense of routine and normality (PCA3). Challenges of the pandemic Threats to mental health and well-being: The most frequently exemplified challenges recorded by the challenges group were illustrations of threats to mental health and well-being (37 images from 14 participants). Negative emotions discussed included anxiety, stress, sadness, frustration, loneliness, and anger. Events and situations that threatened mental health and well-being included anxiety about shopping, lack of sleep, disrupted routines, media reports about COVID-19, inability to engage in usual activities or hobbies, and separation from friends and family members (TMW1). For some participants these threats alluded to the development of risky or unhealthy coping strategies including excessive alcohol intake raised in both the challenges (TMW2) and coping groups (TMW3). Work and study: While some participants highlighted the challenges of being at home and furloughed, others illustrated the difficulties engaging with work during the pandemic. Some were frontline healthcare professionals or key workers dealing with COVID-19 daily (WAS1). Others struggled to manage academic study and adapt to new ways of learning or keep up with the pace of work while abiding by restrictions (WAS2/3). Childcare and home-schooling: Childcare and home-schooling was recognised as a challenge for many of the parents within the sample. This was evidenced in both the challenges (CHS1) and experience conditions (CHS2). No participants in the coping group captured this experience, perhaps because individuals did not perceive these as activities to cope or struggled to identify coping strategies to help with these challenges. The impact of the intervention types on well-being, resilience, and coping Well-being and resilience: Emotional and functional well-being as measured by the WEMBS increased significantly from baseline to post intervention (main effect of time: F(1,66) = 7.687) = p = [ 0.007 ηp2= .104). There were no significant effects on resilience as measured by the BRS or general subjective well-being as measured by the WHO-5 ( Table 4).Table 3 Theme titles with example images and participant quotes. Table 3Theme Quote (identifier, group) Example Image Relaxation and self-care (RSC) It shows self-love and how treating yourself is deserved (RSC1, cop) Image 1 I chose this photograph as I have attempted to exercise more often than I usually do during the lockdown (RSC2, cha) Enjoying our evening walks to bring a little happiness in difficult circumstances. (RSC3, exp) Hobbies and keeping busy (HKB) Making masks is one way I feel I can help. With this virus I feel very out of control and unhelpful, but this is one way that I’ve been able to help keep my family and friends safe (HKB1, Cop) Image 2 It shows that I have been attempting to make the use of my time being unemployed and "stuck" at home and turn it into something for my own benefit. I can cook a little bit but I'm not very confident in the kitchen and I usually stick to making the same things. I have been using this time to improve my skills and it has helped me to feel better about myself. (HKB2, cha) Me, (name) and (name) were provided with equipment to litter pick via a friend who got them from someone in the community. We cleared a whole area of litter behind the park…It was something that we would have not likely done under normal circumstances. It shows people finding purpose and being happy and useful (HKB3, Exp) Maintaining social connections (MSC) The opportunity to properly catch up while seeing facial expressions, reactions and not just text was refreshing (MSC1, Cop) Image 3 The two sides of the pandemic. The part of you that desperately wants to follow the rules for the safety of yourself and your family. Then the part of you who just wants to run up to relatives and hug them. It has been very hard for me to be around people and be unable to offer them hospitality or even sit down with them. I come from a close family and the terror of thinking I would never see my mother again was very hard (MSC2, Cha) We are coming closer together as a family. This is my partner's side of the family- who really, we only see at Christmas and weddings, however we are having weekly quizzes with bith(sic) my own and his family and we are really enjoying it. (MSC3, Exp) Opportunities for quality family time (QFT) This represents the bonding time my husband and I have had since being locked own[sic] together. We take the dogs for a long walk everyday together. We have become closer because of this time together. We have also started working on our fitness together…Before lockdown, we would only take the dogs for walks on weekends or just one of us would walk them. Now we have the time to go as a family and just appreciate nature. (QFT1, Cop) Image 4 It shows that life does carry on regardless of COVID-19. Also that it’s still important to celebrate milestones, when we in a pandemic or not. Celebrating his birthday, masked what is still going on in the real world. (QFT2, Cha) It represents a new normal, more ways to pass the time, reverting back to traditional methods, instead of parents being at work all the time, now that they work at home, they have enough time to play games like this with myself and my siblings. (QFT3, Exp) Pets and companion animals (PCA) Since the start of lockdown I have seen this lovely cat most days…The cat has given me something to play with during this time…Living on my own it it just nice to feel loved even if it is by a cat whose name you don't even know (PCA1, Cop) Image 5 It's both a challenge and a comfort. Without my cats I would have found it much harder to be confined most of the day to the house and garden. The cats represent a very important part of my coping strategy (PCA2, Cha) It represents a bit of routine in the day as I have to get up for them and take care of them. And routine helps me cope throughout the day. If I feel sad I can go to the animals and it makes me relax (PCA3, Exp) Threats to mental health and well-being (TMW) that has been the way I have had communicate over the last 8 weeks! I am a people person. I like to communicate face to face. I am not technologically minded and so struggle with the options that technology offers. Nothing beats talking to someone in the flesh, particularly at times of stress (TMW1, Cha) Image 6 I have come to realise that I have been drinking too much during lock down […] It got to a point during the first couple of weeks where I was drinking almost daily, and my partner intervened told me that I need to take it easy. Life is not normal. Drinking daily is not (or was not) normal for me, but it started to become an unhealthy part of my 'new normal'. [Taking this photo I felt] Shocked, and a bit disgusted with myself. (TMW2, Cha) It represents the fact that I have been enjoying a glass of alcohol probably a little too much these last few weeks. It has been a topic of conversation and dark humour between my friends that we are all indulging a little too much […] I suppose it's another coping mechanism. (TMW3, Cop) Work and study (WAS) I am in PPE during a shift at the doctors I work at. I chose this photograph because it shows the struggle of having to work in such an environment during these times. it depicts me doing my part to help save lives (WAS1, Cha) It represents the challenge of online learning (WAS2, Cha) Shows the challenge of picking up work after so long and meeting whilst abiding by government advice (WAS3, Cha) Image 7 Childcare and home schooling (CHS) Shows the challenge of picking up work after so long and meeting whilst abiding by government advice (WAS3, Cha) How much I appreciate school and teachers as I would never home school. It's really hard to get the children to do schoolwork in a home environment (CHS1, Cha) It shows that although we're in lockdown we can still keep on top of school work while learning new skills (CHS2, Exp) Image 8 * Bold identifiers signify quotes linked to presented images. Coping: The MANCOVA revealed a significant coping strategy × reflection triggered interaction (F(13,54 = 2.060, p = .033, ηp2 = .332), see Fig. 2. Those who engaged in reflection as a result of photo-taking reported increased self-distraction coping (which is comprised of the statements; “I've been turning to work or other activities to take my mind off things” and “I've been doing something to think about it less, such as going to movies, watching TV, reading, daydreaming, sleeping, or shopping.”) at follow up than at baseline (p = .005). Those who engaged in reflection also reported increased planning coping (which is comprised of the statements; “I've been trying to come up with a strategy about what to do” and “I've been thinking hard about what steps to take”), at follow up than at baseline (p = .007).Fig. 2 Coping strategy × engagement in reflection interaction. Fig. 2 Discussion This paper evaluated the effectiveness of a pilot photo-taking and reflection intervention for enhancing well-being and coping during the COVID-19 pandemic, and whether photography focussing on coping, challenges, or experiences, would have an impact on these effects. There were no significant differences across the photo-taking conditions suggesting that the type of photograph taken did not have an impact. There were improvements across the sample in relation to emotional and functional well-being which improved over time regardless of intervention type. Participants engaged for one week only, with most enrolled into the study in the first 3 weeks of recruitment. Whilst we cannot state definitively that the well-being improvements were due to the intervention, there were no baseline differences in the measures dependent on when participants took part. In addition, the timepoint at which participants enrolled into the study was included in the analysis as a covariate, supporting the case for this effect being due to the intervention rather than changes in COVID-19 restrictions and case numbers over time. More conclusively, there was an increase in planning and self-distraction coping strategies over time in those participants for whom taking part triggered reflection. The lack of significant changes in resilience may reflect the measure employed or the more stable nature of resilience being unlikely to shift dramatically over a short period. The BRS has also been criticised for an exclusive focus on personal agency (Windle et al., 2011). The process of taking images may have helped participants become more aware of external influences on the ability to cope such as social relationships and opportunities to engage in enjoyable activities, as highlighted in the qualitative data, rather than factors central to personal agency. The improvements in functional and emotional well-being and coping over time may reflect the sample adjusting to the restrictions posed by the pandemic as time progressed. However, research has suggested that depression and anxiety levels increased (Rettie and Daniels, 2020, White and Van Der Boor, 2020), and mental health impacts are likely to continue to increase during the pandemic (Kumar and Nayar, 2020, Yao et al., 2020). Furthermore, these negative impacts on well-being have been shown to rise as lockdown progresses (Ozamiz-Etxebarria et al., 2020). Therefore, the intervention may have counteracted these negative impacts on well-being over time, regardless of the photograph type. This argument is supported by the qualitative data highlighting how engaging with the photo-reflection process had encouraged some participants to reflect on their experiences. Hence, the presence of changes in coping in those who consciously reported engaging in reflection when compared to those who did not suggests an intervention effect. These findings support previous research arguing for photo-taking resulting in well-being improvements (Brewster and Cox, 2019, Chen et al., 2016). Photography is proposed to have therapeutic benefits (Cosden & Reynolds, 1982; Weiser, 1990, 2004, 2014) and confirmation of this proposed effect could be achieved by repeating the study with the inclusion of a non-active control group. For participants who reported that the intervention consciously elicited reflection there was a significant increase in the use of self-distraction and planning techniques over time. The specifics of how these techniques were employed was illustrated in the qualitative data with participants reporting engagement in distraction activities including relaxation and self-care, keeping busy and engaging in hobbies, finding new ways to be socially engaged with others, and the value of pets and companion animals. In some circumstances self-distraction may be viewed as maladaptive due to being perceived as an avoidant strategy, however when used in response to situations where individuals have limited personal control this approach can enhance well-being as an individual can gain control over the situation through their own actions (Hofmann & Hay, 2018). In the case of the COVID-19 pandemic this approach may reduce anxiety by distracting from the threat of ill health. Planning can include goal setting, action planning and prompts and cues all of which are important components of behaviour change interventions (Michie et al., 2013). It may be that the need to document activities through photographs acted as a prompt for some participants to plan enjoyable activities and carry these out. Large numbers of the images related to engagement in physical activity, particularly out in natural environments. Government lockdown rules restricted the ability to leave the home unless for specific reasons, one of which was exercise (Prime Minister's Office, 2020) and some evidence has suggested a fall in physical activity during the pandemic (Rhodes et al., 2020). It is therefore reassuring that many participants were engaging in these activities. Though this data is unable to indicate whether the participants had increased or decreased physical activity when compared to prior to the pandemic. Time spent in natural outdoor environments is associated with, increased levels of physical activity, increased social contact with neighbours and enhanced well-being (Kruize et al., 2020). Furthermore, spending time in green spaces has been illustrated to be beneficial for mental health and stress reduction (Triguero-Mas et al., 2017). Participants also captured images reflecting their engagement with pets and companion animals. Engagement with animals can be beneficial for well-being, for example, pet ownership has been shown to act as a buffer against the negative psychological impacts of social losses such as bereavement or divorce (Carr et al., 2020), and dog acquisition (Powell et al., 2019) has been shown to reduce feelings of loneliness. Strategies to overcome isolation were also reported in relation to the use of internet mediated communication. These approaches helped participants to remain connected with wider social networks during the pandemic, some even commented that they were now more actively engaged with friends and family than they had been in the past. Perceived social support and number of social connections can be protective against the link between COVID-19 stressors and mental health and well-being (Nitschke et al., 2020, Szkody et al., 2020), and were reported as beneficial in this study. In addition to positive coping, there were also coping strategies that raised cause for concern. Several participants reported the use of alcohol to cope which links to research indicating an increase in alcohol purchasing and consumption during the COVID-19 pandemic, particularly in individuals reporting high levels of stress (Callinan et al., 2021). In the UK, this increase in consumption is associated with depressive symptoms, poorer mental health, and reduced well-being (Jacob et al., 2021). Though research is largely conducted with adolescent and student samples, there is a known association between stress and alcohol consumption, particularly in individuals who have strong motivations to drink alcohol to cope with negative emotions (Corbin et al., 2013). The qualitative data illustrated concern about alcohol use, suggesting the opportunity to document through photography and reflect on this coping approach may enhance the ability of individuals to identify unhelpful coping strategies and therefore seek to change these behaviours. In a similar way to the effectiveness of self-monitoring of diet for weight loss (e.g. Wang et al., 2012), self-monitoring of experience and coping may also result in behaviour change and could be facilitated by photography. This is an area that warrants further investigation. The photographs and qualitative data have also highlighted the types of challenges experienced during the COVID-19 pandemic. The most common included threats to health and well-being, engaging with work, and for parents, engaging in childcare and home-schooling. These are all areas marked as research priorities for psychological science during the pandemic (Connor et al., 2020). The concerns of parents in our study echo research findings from around the world. For example, similar ‘circuit-breaker’ restrictions in Singapore, resulting in parents needing to juggle both work and childcare, have been shown to increase parental stress, in turn impacting on the child-parent bond and increases in the use of harsh parenting strategies (Chung et al., 2020). Similarly, in the US during early COVID-19 researchers found a link between caregiver burden and mental health and perceptions of child mental health with associated impacts on child-parent closeness and levels of conflict (Russell et al., 2020). However, our research has also highlighted approaches to coping with these stressors, including seeing value in the opportunities created for quality family time within the household. The cognitive appraisal theory of stress and coping (Lazarus & Folkman, 1984) highlights how stress responses are a dynamic interaction between an individual and their environment. Reflection on in the moment experiences, such as through photography, may have helped some participants appraise the situation positively through acknowledgement of benefits. This represents an adaptive response to the universal stressor of living through a pandemic with potential for enhancing well-being. This reflection and recognition of opportunities may be a valuable starting point to protect both parent and child well-being during times of intense stress and could be a valuable tool in therapeutic practice. Limitations While it seemed the intervention was effective, it is not possible to determine this conclusively due to the absence of a non-active control condition in this pilot study. In addition, sample size was relatively small and failed to meet our target of 175 participants due to lifting of lockdown restrictions forcing cessation of data collection at the end of June. Therefore, despite the evidence of reflection reported in the qualitative data and the use of time point enrolled in the study as a covariate, claims regarding the taking of photographs as influences on the outcome measures should be taken with caution. Further research including a non-active control are needed to verify these findings. There are several factors which may underpin the high levels of attrition in this study. Online studies are more prone to higher levels of absolute attrition rates, as are technology assisted behaviour change research (Rosser et al., 2009). Health behaviour change (HBC) interventions also often elicit higher attrition rates, with the intervention demands being relatively onerous, in comparison to the perceived benefit (Crutzen et al., 2015). The higher attrition rate in those with lower well-being and mental well-being, suggests that this type of intervention may not be as beneficial to this population, with an online technology assisted self-guided intervention being potentially too onerous for their current state. It should also be noted that all participants took part voluntarily knowing that photography would be required, it may be that the intervention effects are a result of the participants pre-existing interest in photography and desire to find ways to cope with the pandemic. Qualitative analysis of the photographs taken illustrated similar images captured across the conditions, this lack of distinction between groups may be a function of the guidance given to participants. Further research instructing participants to take images of more specific types of experience may be of interest. Conclusions This research has highlighted the variety of challenges faced by individuals in the UK during the COVID-19 pandemic in relation to homelife, work, childcare and threats to health and well-being. The photographs collected highlighted several coping strategies including adaptive approaches such as keeping busy, relaxation and self-care, in addition to more risky activities including increased alcohol consumption. Furthermore, this work has illustrated the potential for simple photo-reflection intervention approaches to improve well-being and enhance coping during these challenging circumstances through providing opportunity to review and reflect on life experiences. These findings are of value to therapists, illustrating how photo-taking can be an accessible and simple strategy for facilitating client well-being. CRediT authorship contribution statement Amy Burton: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Visualization, Writing – original draft, Writing – review & editing. Jade Elliott: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Visualization, Writing – original draft, Writing – review & editing. Conflict of interest The authors declare no conflicts of interest. Data Availability Data will be made available on request. Acknowledgements The authors would like to thank the Staffordshire University Life Sciences and Education support fund for funding this research. ==== Refs References Amerikaner M. Schauble P. Ziller R. Images: The Use of Photographs in Personal Counseling The Personnel and Guidance Journal 59 2 1980 68 73 10.1002/j.2164-4918.1980.tb00500.x Braun V. Clarke V. 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==== Front Biosens Bioelectron Biosens Bioelectron Biosensors & Bioelectronics 0956-5663 1873-4235 Elsevier B.V. S0956-5663(22)01027-2 10.1016/j.bios.2022.114987 114987 Article Rapid point-of-care detection of SARS-CoV-2 RNA with smartphone-based upconversion luminescence diagnostics Song Menglin a1 Wong Man-Chung a1 Li Lihua a Guo Feng a Liu Yuan a Ma Yingjing a Lao Xinyue a Wang Pui b Chen Honglin b Yang Mo c Hao Jianhua a∗ a Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon, 999077, Hong Kong, China b State Key Laboratory for Emerging Infectious Diseases, Department of Microbiology, LKS Faculty of Medicine, The University of Hong Kong, Pokfulam, 999077, Hong Kong, China c Department of Biomedical Engineering, The Hong Kong Polytechnic University, Kowloon, 999077, Hong Kong, China ∗ Corresponding author. 1 Contributed equally to this work. 5 12 2022 15 2 2023 5 12 2022 222 114987114987 26 9 2022 2 12 2022 3 12 2022 © 2022 Elsevier B.V. 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Accurate COVID-19 screening via molecular technologies is still hampered by bulky instrumentation, complicated procedure, high cost, lengthy testing time, and the need for specialized personnel. Herein, we develop point-of-care upconversion luminescence diagnostics (PULD), and a streamlined smartphone-based portable platform facilitated by a ready-to-use assay for rapid SARS-CoV-2 nucleocapsid (N) gene testing. With the complementary oligo-modified upconversion nanoprobes and gold nanoprobes specifically hybridized with the target N gene, the luminescence resonance energy transfer effect leads to a quenching of fluorescence intensity that can be detected by the easy-to-use diagnostic system. A remarkable detection limit of 11.46 fM is achieved in this diagnostic platform without the need of target amplification, demonstrating high sensitivity and signal-to-noise ratio of the assay. The capability of the developed PULD is further assessed by probing 9 RT-qPCR-validated SARS-CoV-2 variant clinical samples (B.1.1.529/Omicron) within 20 min, producing reliable diagnostic results consistent with those obtained from a standard fluorescence spectrometer. Importantly, PULD is capable of identifying the positive COVID-19 samples with superior sensitivity and specificity, making it a promising front-line tool for rapid, high-throughput screening and infection control of COVID-19 or other infectious diseases. Keywords Upconversion nanoparticles Luminescence resonance energy transfer (LRET) Point-of-care diagnostics Viral RNA detection COVID-19 SARS-CoV-2 ==== Body pmc1 Introduction The ongoing coronavirus disease 2019 (COVID-19) pandemic has already infected over 540 million people and claimed more than 6 million lives worldwide as of this submission date. Since the coronavirus outbreak has begun, the battle of developing methodology to contain the virus has raged. (Chinazzi et al., 2020; Singhal, 2020; Weissleder et al., 2020). Current detection methods, however, are limited by their ability to provide precise, rapid on-site diagnosis and epidemiological surveillance. (Bai et al., 2020; Rothe et al., 2020). For instance, COVID-19 screening relying on conventional lateral flow antigen test suffers from inherently low specificity, which prohibits a relativity reliable outcome despite enabling on-site detection with fast read-out time. (De La Rica and Stevens, 2012; Sheridan, 2020). Quantitative reverse transcription polymerase chain reaction (RT-PCR) is the gold standard method for viral nucleic acid detection. (Nolan et al., 2006; Santiago et al., 2018; Tahamtan and Ardebili, 2020). Nevertheless, its accessibility is significantly constrained by the requirement of high-end instrumentation, multi-step reactions, special reagents, skilled personnel operating in a centralized laboratory and lengthy sample-to-answer time (∼2 h). Therefore, developing a rapid, reliable, ultrasensitive, yet simple-to-use point-of-care virus screening platform has been critical in the midst of the pandemic. Although some on-site molecular tests were developed in an attempt to achieve a rapid diagnosis of COVID-19, these methods generally require expensive reagents and target amplification. In addition, dye-labelled fluorescent probes used in these techniques have poor photostability, which leads to unreliable results. (Broughton et al., 2020; Ganguli et al., 2020; Bi et al., 2017). Fluorescent nanoprobes-based point-of-care devices have the advantages of miniaturization, ease of operation, and fast turnaround time, which facilizes them to be suitable for clinical diagnosis and offer an alternative to current nucleic detection technologies. (Kong et al., 2017). With recent advances in nanotechnology, a myriad of luminescent nanoparticles had already been employed in various types of clinical or biological assays (Song et al., 2021; Tsang et al. 2015, 2019; Ye et al., 2014; Zhang et al., 2022). Among them, lanthanide-doped upconversion nanoparticles (UCNPs) are promising nanoprobes for biodetection not only because of their superb biocompatibility (Li et al., 2018; Tsang et al., 2016; Yi et al., 2020). Their fluorescence mechanism permits them to be well-suited for reliable and rapid nucleic acid detection at the point-of-care (Huang et al., 2019). Particularly, luminescence resonance energy transfer (LRET) effect can be applied to facilitate ultrasensitive detection when modified UCNPs are combined with gold nanoparticles (Au NPs). UCNPs possess low autofluorescence and exhibit a large anti-Stokes shift and minimal photodamage to genetic molecules upon near-infrared (NIR) excitation, which bestowed them with prodigious advantages over other conventional luminescent materials for point-of-care nucleic acid testing (Gong et al., 2019). More importantly, portable optical diagnostic devices typically have a simple device structure consisting of an excitation source and an optical sensor (He et al., 2018). With the advancement of cheap and compact NIR light source and other microelectronic modules, a portable nucleic acid detection device based on LRET can be devised to realize rapid and on-site testing. Further integrating the device with a smartphone not only promotes a facile read-out but also aids in uploading and sharing the diagnosis results. Considering these merits, herein, we propose and demonstrate a point-of-care upconversion luminescence diagnostics (PULD) platform, and a smartphone-controlled portable device enables a rapid, ultrasensitive and on-site detection of SARS-CoV-2 with an upconversion LRET-based assay. The platform utilizes a distinctive signal detection and processing method based on current-to-frequency conversion that can significantly increase the sensor's sensitivity. Additionally, two ready-to-use probes, oligo-modified lanthanide-doped core-shell UCNPs (csUCNPs) and Au NPs, are used to capture the N gene of SARS-CoV-2 without other refrained reagents and target amplification in the single assay, demonstrating simplicity, high selectivity and sensitivity of the system. Essentially, fluorescence quenching of upconversion luminescence sandwich assay is performed, which can achieve a limit of detection (LOD) of 11.46 × 10−15 M. These exemplary performances are further optimized by structural modification of csUCNPs and Au NPs oligo-nanoprobes. The excellent quenching efficiency and detection capability of the assay are determined using the standard fluorescence spectrometer. Moreover, the capability of the developed PULD is further assessed using SARS-CoV-2 variant (B.1.1.529/Omicron) clinical samples and cross-validated with RT-PCR. The positive and negative results of the viral gene testing are obtained over the short turnaround time of 20 min, which are fully agreed with the PCR testing. Thus, our smartphone-controlled diagnostic platform can fulfill on-site, rapid and ultrasensitive detection of SARS-CoV-2 through a simple detection workflow, making the PULD a promising portable tool for rapid and direct screening of various infectious diseases. 2 Material and methods The reagents, apparatus and experimental methods are shown in Supplementary Material. 3 Results and discussion 3.1 Design of PULD for SARS-CoV-2 detection The designed rapid and point-of-care COVID-19 diagnostic system should be capable of providing a simple workflow, therefore we developed an easy-to-operate diagnostic platform for virus screening by adopting a one-pot detection protocol with an effective upconversion luminescence sandwich assay and a smartphone-controlled portable device. This diagnostic system is termed as PULD. As shown in Fig. 1 A, the ready-to-use assay consisting of complementary oligo-modified csUCNPs and Au NPs probes for rapid sensing N gene of the extracted RNA from the nasal/oropharyngeal swabs. The resultant upconversion quenching due to LRET effect is recorded by the portable device and the results are presented to the user through a smartphone via Bluetooth, thus enabling fast and accurate SARS-CoV-2 detection. More specifically, Au NPs were conjugated with thiol-modified oligonucleotides, whereas polyacrylic acid-modified csUCNPs were conjugated with amine-modified oligonucleotides. In the presence of the SARS-CoV-2 N gene, oligonucleotide hybridization between complementary pairs occurs, bringing csUCNPs and Au NPs into proximity. Under the excitation of 980 nm, the fluorescence of csUCNPs can be absorbed by Au NPs due to LRET effect. Thereby, the N gene of SARS-CoV-2 is quantified and screened by measuring the variation of the fluorescence intensity. Benefiting from the superior sensitivity and specificity of the LRET-based sandwich assay in direct and rapid detection of the SARS-CoV-2 Omicron variant, we designed and customized a low-cost smartphone-controlled device to demonstrate its practicality and accessibility. Signals were recorded by the microcontroller (Arduino Nano) in the device that also coordinated viral RNA detection and transmission of detection data to the user's smartphone via Bluetooth connection. The screening results were subsequently displayed in the smartphone when the recorded values had been processed by the microcontroller. The detection setup of the portable device allows for ultrasensitive fluorescence detection. Firstly, a 500 μL sample cell containing clinic sample was introduced into the chamber. The screening test was initiated by the user via Bluetooth connection between mobile phone and the microcontroller. When the NIR excitation beam was turned on, fluorescent signal was then recorded by a photodiode. A current to frequency converter composed of an operational amplifier integrator was incorporated into the photodiode to enhance the detection sensitivity. The detected signals were recorded by the microcontroller to determine the outcome of the screening. Finally, the test results were presented to the user's mobile phone. For the selective detection of SARS-CoV-2 isolate 2019-nCoV/USA-WA1-A12/2020, we selected N gene as a well-conserved target since it was able to demonstrate organism-specificity with a sufficient distinction from related species (Fig. S1). A set of antisense oligonucleotides were predicted by following the methodology described in Materials and Methods. According to their binding disruption energies and binding energies with the target sequence, one of the predicted antisense oligonucleotide sequences was selected (Table S1). Moreover, to achieve effective probe conjugation to nanoparticles and hybridization, the oligo probes were attached to poly-A bases (Zhang et al., 2012). Design of the oligo probes for the target sequences is based on Sfold software analysis. The oligonucleotides used in this work are listed in Table S2.Fig. 1 Schematic representation of streamlined design: a point-of care mobile phone-controlled COVID-19 diagnostic platform facilitated by a ready-to-use, target amplification-free, one-pot upconversion luminescence sandwich assay. Fig. 1 3.2 Structural and optical properties of csUCNPs modified with oligo probes The core NaGdF4:Yb/Er nanoparticles with an average diameter of 16 nm were synthesized through co-precipitation strategy in a binary solution of oleic acid and 1-octadecene (Wang et al., 2014) (Fig. 2 a and Fig. S2). To enhance the fluorescent intensity for a more sensitive detection, we further modified the UCNPs to form a core-shell structure. Specifically, similar to the preparation of the NaGdF4, NaGdF4:Yb3+/Er3+ @ NaGdF4 core-shell structure can be obtained by adding core nanoparticles to the precursor solution as seed crystals prior to adding the precipitator. The corresponding transmission electron microscope (TEM) images of Er3+-doped core-shell UCNPs are illustrated in Fig. 2b, revealing a uniform morphology with an average size of 18 nm (Fig. 2c). A high-resolution TEM image depicted the single crystalline nature of the core-shell nanocrystals, as exhibited by clear lattice fringes spaced at 0.52 nm (Fig. 2d), which agrees with the lattice spacing in planes (100) of hexagonal-phase NaGdF4. As shown in Fig. 2e, the selected area electron diffraction (SAED) pattern represents the polycrystalline diffraction rings associated with the specific planes of the hexagonal NaGdF4 lattice (Dong et al., 2015). It is a fact that the size of the acceptor can determine the LRET distance as well as the spatial conditions of initial contact, zipping, and stability during hybridization (Fig. S3). The emission spectrum of the csUCNPs under the excitation of 980 nm is depicted in Fig. 2f. Two UC bands were observed at 520/540 nm and 654 nm, corresponding to the 2H11/2/4S3/2 → 4I15/2 and 4F9/2 → 4I15/2 transitions of Er3+, respectively. The visible UC emissions from NaGdF4:Yb3+/Er3+ in hexagonal phase were enhanced by 12.8 times because of the growth of a thin layer of NaGdF4 (∼2 nm). The core-shell structure can effectively suppress surface-related deactivations and spatially isolate the core nanoparticle from other deactivators (ligands, solvents, etc.) (Chen et al., 2014). Importantly, in our design, the oligo-modification is a vital step not only for capturing N gene of SARS-CoV-2 during the detection but also for facilitating the LRET effect to produce fluorescence intensity variation. Precisely, csUCNPs modified with polyacrylic acid (PAA) were prepared for bioconjugation of the oligo probe via coordination interaction between carboxylic groups and lanthanides ions. Afterwards, the oligo probe was conjugated to the carboxylic acid terminals using covalent crosslinking of carbodiimide and n-hydroxysuccinimide (EDC/NHS) (Fig. 2g). It is worth noting that the oligonucleotides have a characteristic peak of 260 nm in the UV–vis absorption spectrum (Ge et al., 2020). Herein, we made use of this feature to confirm whether cDNAs were successfully modified on csUCNPs. As shown in Fig. 2h, a sharp peak at around 265 nm after oligo-conjugation under UV–Vis absorption investigation, which indicates functionalization of P1 on csUCNPs. Furthermore, Fourier-transform infrared spectroscopy (FTIR) spectra of the oleate, PAA and oligo capped UCNPs are shown in Fig. 2i. It is evident that the CH2 groups have been successfully removed from the layer of oleate by considering the disappearance of the major peaks at 2939 and 2857 cm−1. Besides, the carbonyl stretch of a carboxylic acid C <svg xmlns="http://www.w3.org/2000/svg" version="1.0" width="20.666667pt" height="16.000000pt" viewBox="0 0 20.666667 16.000000" preserveAspectRatio="xMidYMid meet"><metadata> Created by potrace 1.16, written by Peter Selinger 2001-2019 </metadata><g transform="translate(1.000000,15.000000) scale(0.019444,-0.019444)" fill="currentColor" stroke="none"><path d="M0 440 l0 -40 480 0 480 0 0 40 0 40 -480 0 -480 0 0 -40z M0 280 l0 -40 480 0 480 0 0 40 0 40 -480 0 -480 0 0 -40z"/></g></svg> O appears as a very intense band at 1720 cm−1 while the carboxylic acid O–H stretch appears as a very broad band at 2932 cm−1 after PAA modification. In particular, the wavenumber at 1223 cm−1 relevant to the oligo represents the vibration of symmetric phosphate (PO2), which confirms the conjugation of oligo onto the csUCNPs.Fig. 2 TEM images of (a) oleic acid capped core UCNPs and (b) csUCNPs. (c) Particle size distribution histogram of the prepared csUCNPs. (d) high-resolution TEM image and (e) selected area electron diffraction of csUCNPs. (f) Upconversion emission spectra of core UCNPs and core-shell UCNPs excited by 980 nm laser diode. (g) The P1 functionalizing process on csUCNPs. (h) UV–vis absorption of P1, csUCNPs, csUCNPs-P1. (i) FTIR spectra of oleate-csUCNPs, polyacrylic acid-csUCNPs, P1-csUCNPs. Fig. 2 3.3 Structural properties of Au NPs and modification with oligo probes To ensure accurate detection of COVID-19 using our sandwich assay, methodical control of Au NPs size and DNA adsorption efficiency on Au NPs are important. Based on previous studies, strong luminescence quenching due to resonance energy transfer is preferentially achieved for small gold nanoparticles (Mendez-Gonzalez et al., 2019). Thus, we employed a step-by-step protocol for synthesizing small Au NPs via sequential injections of HAuCl4 precursors (Piella et al., 2016). TEM image of Au NPs in Fig. 3 a exhibits their uniform size with an average diameter of 4.5 nm (Fig. S6), which benefits the quenching efficiency in a donor-to-acceptor LRET-based biosensor. The conjugation of probes plays an important role in hybridization efficiency. Thereby, using a pH-assisted and surfactant-free route (Zhang et al., 2012), instantaneous functionalization of Au NPs with thiolated DNA can be achieved (Fig. 3b). UV–vis spectroscopy was performed to study the surface modification of oligo probe 2 (thiol functionalized oligos and the detailed nucleotides were shown in Table S1). Fig. 3c shows the UV–vis absorption of P2, P2–Au NPs and Au NPs. The intensity at 260 nm was selected as an identification signal to quantify the successful conjugation of probes onto Au NPs. Apart from the surface modification, the surface plasmonic absorption of nanoprobes is crucial since spectral overlapping between donor and acceptor is required for achieving LRET effect. Fig. 3d shows the UV–vis spectrum of Au NPs and upconversion emission of csUCNPs, which overlaps well and allows for high LRET efficiency. The melting characteristics of the assay are vital for determining the temperature in the DNA hybridization. The melting curve in Fig. 3e is plotted from 35 to 60 °C using 20 × 10−9 M of N-targets while monitoring the absorption at the wavelength of 260 nm. The melting temperature (T m) was determined based on half of the percentage drop in the absorption at 260 nm (A260), which was conducted at about 45 °C. After oligo-modified probes hybridization with target N-gene, the csUCNPs probe is surrounded by the Au NPs probe to form a network structure which is conducive to triggering the LRET effect (Fig. 3f and Fig. S8), demonstrating the excellent feasibility of the assay. Upon the completion of the synthetization, modification and feasibility validation, the assay is already primed to specifically target the N gene of COVID-19 and commence viral RNA detection instantly, thereby showing the simplicity of our one-pot viral RNA detection method without the need for refrained types of reagents and target amplification.Fig. 3 (a) TEM image of citrate capped Au NPs. (b) Modification of Au NPs with thiolated DNA via a pH-assisted and surfactant-free method. (c) UV–vis absorption of P2, citrate-stabilized Au NPs, P2. (d) Upconversion emission spectra of core-shell UCNPs excited by 980 nm laser and UV–vis absorption spectra of Au NPs. (e) Melting curve of the sandwich assay with the N-target concentration of 20 nM. (f) TEM image of hybridized duplex structure of Au NPs-Probe 1 and UCNPs-Probe 2. Fig. 3 3.4 Detection of N-target by upconversion luminescence sandwich assay As one of the merits of our sandwich assay, the superb sensitivity of this rapid and simple virus detection without the burden of target amplification herein was characterized and demonstrated by the analysis of photoluminescence (PL) spectra. Taking advantage of the LRET and strong quenching capability of Au NPs, the PL emission was quenched in accordance with the concentration of N-target increase from 200 fM to 10 nM. As a result of hybridization between N gene targets and the complimentary oligonucleotide probes (UCNPs-P1 and Au NPs-P2), luminescence quenching was realized (Fig. 4 a). In addition, the quenching efficiency for each N-target concentration was calculated by the E 1 and the enhancement of the efficiency is directly proportional to the amount of N-target (Fig. 4b). Fig. 4c shows a linear response from 10 × 10−9 M to 200 × 10−15 M as y = 0.2959x+1.525. As a result, the control signal plus three times the background signal was used to determine the limit of detection (LOD), which was calculated to be 11.46 fM. The detection limit is much lower than that of the previously published work (Tsang et al., 2019). Since the luminescence intensity of UCNPs excited at 980 nm is much higher than that excited at 808 nm, so the quenching can be effectively detected. Moreover, the design and fabrication of the UCNP–Au LRET system (e.g. the size of the nanomaterials and dispersion, surface modification, and the UCNP–Au fluorescence resonance distance) are further optimized in this work, and the LOD correspondingly improved. As compared other detection techniques, COVID-19 screening using lateral flow antigen tests suffers from low specificity and the accessibility of RT-PCR is severely constrained by the need for high-end instruments, multi-step reactions, special reagents, highly skilled personnel in a centralized laboratory, and a long turnaround time. Therefore, the upconversion luminescence diagnostic method is a good alternative as a compromise assay that achieves fast, reliable, ultrasensitive, and easy-to-use point-of-care detection during the pandemic (Table S3). It is worth noting that the plasmonic effect of Au NPs can affect the luminescence intensity of UCNPs by decreasing the luminescence lifetime. In order to further evaluate this effect, we investigated the decay of the green emission at 540 nm. According to Fig. 4d, the lifetime of the green emission was reduced from 128.04 μs to 118.35 μs when 200 pM N-target was added. The lifetime reduction is caused by the plasmonic effect upon the existence of the Au NPs and the associated energy transfer. Considering the 520/540 nm emission, energy transfer from the UCNP to the Au NP could provide a non-radiative pathway that competes with the radiative one, resulting in decreasing emission intensity and shortening the lifetime (Liu et al., 2013). Since SARS-CoV-2 is experiencing different variations, specificity is paramount for the effectiveness of the assay. Therefore, the specificity of the assay (Fig. 4e and f) was investigated with samples containing 2 × 10−9 M of N-target and base mismatch (BM) gene fragments (1 BM and 3 BM). In comparison to the base mismatch group, the N-target group showed a higher quenching efficiency of 55.01%. The initial contact process was not efficient due to one and three base (1 BM and 3 BM) mismatches, resulting in the quenching efficiency by 30.58% and 25.92%, respectively. However, the sensing signals of 1 BM or 3 BM were significantly different from those of the non-targets (DEPC treated water), thus indicating that our assay is capable of detecting SARS-CoV-2 variants. These characteristics implicate the excellent selectivity of our sandwich assay.Fig. 4 (a) UC emission spectra of NaGdF4:Yb/Er @ NaGdF4-probes UCNPs with various concentrations of SARS-CoV-2 oligo target in the homogeneous assay. (b) Quenching efficiency with different concentrations of SARS-CoV-2 oligo target in the assay. (c) Quenching efficiency of target concentrations from 200 × 10−15 M to 10 × 10−9 M. The relative standard deviation (RSD) was 10.38%. (d) Lifetime measurement of the control sample and 200 × 10−12 M N-target. (e) Specificity test of the sandwich assay for SARS-CoV-2 virus oligo detection based on one-base (1 BM) and three-base mismatch (3 BM) gene and a complementary target at 2 nM and (f) The calculated quenching efficiency according to (e). Fig. 4 3.5 Detection of SARS-CoV-2 Omicron (B.1.1.529) variant in viral samples To demonstrate the readiness of our sandwich assay for rapid COVID-19 detection, we applied the sandwich assay to conduct a clinical COVID-19 test. An isolate of B.1.1.529/Omicron (GeneBank: OM212472) was obtained from laboratory-confirmed COVID-19 patients in Hong Kong. Specimens (nasopharyngeal or oropharyngeal swabs) were then cultured in Vero-E6-TMPRSS2 cells and the corresponding viral RNA was extracted from the cells (Fig. 5 a). RT-PCR was used to detect the N gene in the viral samples with defined quantities as a positive control. The lysis buffer and kit buffer without virus were used as a negative control. Accordingly, the cycle threshold (Ct) value of lysis buffer and kit buffer by RT-PCR PCR (ABI, QS5) was 38.109 ± 1.651 and 37.609 ± 0.56, while the Ct values of viral sample with quantities of 0.5 μL, 0.8 μL, and 1 μL were 25.857 ± 0.315, 25.507 ± 0.207, 23.190 ± 2.321, respectively (Fig. 5b). According to Fig. S9, the concentrations of 1 μL, 0.8 μL, and 0.5 μL virus samples were 95.1 fM, 16.98 fM, and 13.083 fM, respectively. The Ct values of positive RT-PCR results have been determined to be lower than 35 according to clinical reports of diagnosing COVID-19 patients with N-genes. Fig. 5c presents the PL emission spectra of control sample and 1 μL stock solution of a viral sample, where the spectra were recorded from a standard photomultiplier tube in a commercial PL measurement system (FLS900, Edinburgh Instruments Ltd). From this figure, the QE was determined to be 46.11% and 50.99% for the control and viral sample groups that were incubated for 20 min, respectively (Fig. S10). Additionally, we further investigated their fluorescence quenching by incubating them for 40 min. As can be seen from Fig. S11, their fluorescence quenching is further enhanced, 45.64% and 62.98% respectively. Furthermore, we then extended the incubation time. The significantly enhanced QE of the viral sample groups compared with the control group is attributed to the presence of the target viral RNA inducing the “on-off” mechanism of our assay (Fig. 5d). Furthermore, as the concentration of the virus increases, the quenching of the fluorescence increases accordingly (Fig. 5e and Fig. S12). All these results demonstrate that our sandwich assay enables the screening of positive and negative samples through PL spectrum with high sensitivity. More importantly, the diagnosis results observed by the standard PL analyzer are in good agreement with the RT-PCR results, indicating that our upconversion luminescence sandwich assay is capable of reliably screening and detecting the SARS-CoV-2 Omicron variants (B.1.1.529) with a significantly shorter turnaround time of 20 min. Therefore, this rapid and reliable viral detection method provides not only a viable alternative to the commercial RT-PCR. It shows promise in numerous point-of-care scenarios owing to its speedy sample-to-answer time, simplicity of workflow, and high sensitivity.Fig. 5 (a) The representative illustration of Omicron (B.1.1.529) isolated from specimens of patients with laboratory-confirmed COVID-19 in Hong Kong. Samples were cultured in Vero-E6-TMPRSS2 cells and viral RNA was extracted from the cells. (b) RT-PCR results of N gene in viral samples. N gene at various amounts from the viral sample serves as positive control groups, while kit buffer and lysis buffer without virus serves as a negative control. (c) Upconversion emission spectra of the viral sample (1 μL of SARS-CoV-2 Omicron/B.1.1.529 stock solution extracted from cells, sample to answer time: 20 min). (d) Upconversion emission spectra of negative samples and positive samples with different viral amounts verified by RT-PCR (sample to answer time: 20 min). (e) Peak intensities of the upconversion luminescence sandwich assay after detecting viral samples, corresponding to (d). Fig. 5 3.6 PULD screening SARS-CoV-2 Omicron (B.1.1.529) variant As aforementioned, SARS-CoV-2 RNA was extracted from the specimens and added to the ready-to-use assay, where hybridization was occurred between the oligonucleotide-modified csUCNPs and Au NPs probes and therefore, Au NPs absorbed the fluorescent emission from the UCNPs. This scheme based on the upconversion luminescence quenching is able to produce sensitive and reliable results as compared to other fluorescent dye-based nucleic acid detection techniques. Moreover, due to the simple detection workflow, we thereby conceptualize and demonstrate a PULD system that enables rapid detection of viral RNA and on-board data processing with mobile displaying notification capability (Fig. 6 a). This portable platform has a small footprint (15 × 6 × 7 cm3) and weighs only a few hundred grams (Fig. S13). Despite its compact size and lightweight, the device integrates all the components needed for the detection of SARS-CoV-2. In particular, the device includes a light source for irradiating UCNP, two bandpass filters, and a highly sensitive light sensor to detect fluorescence-quenching signals. (Fig. 6b). The microcontroller records the collected signals in its memory while simultaneously transmitting them to the user's smartphone via Bluetooth. An APP accompany the PULD was developed to realize a high throughput, facile, simple diagnosis with instantaneous wireless data transmission to a mobile user interface. This APP adopted a simple graphic user interface (GUI) and notifies the user with the diagnostic results by an instinctive graphical representation. The underlying finite state machine of the APP was designed such that it transmits qualitative diagnostic results, or quantitative result according to the user instruction (Fig. S14). It is worth mentioning that the sensitivity of the light sensor is significantly higher than that of conventional photodiode. Briefly, our light sensor is comprised of a photodiode and an operational amplifier integrator. During measurement, the emission intensity from the assay is determined based on the current changes instead of directly recording the magnitude of the photocurrent. Hence, the sensitivity of our sensor is several magnitudes higher than that of other commercial light sensors. Combining the short processing time of the device with the high sensitivity and reliability of the sandwich assay, PULD provides an ideal point-of-care screening platform with a short sample-to-answer time. Thus, we evaluated the capabilities of PULD by testing clinical samples of SARS-CoV-2 Omicron variant. In total, five PCR-validated SARS-CoV-2 positive samples and four PCR-validated negative samples were employed in our study. These samples were prepared by mixing DEPC-treated water and lysis buffer with 0.5 μL, 0.8 μL, 1 μL of Omicron viral RNA, respectively, while lysis buffer and DEPC-treated water were used as controls. Fig. 6c shows the waterfall plot of the read-out values from PULD, with a cutoff of 2400. As shown in Fig. S15, the specific read-out values of viral sample with different concentrations were recorded on a smartphone via Bluetooth. This set point clearly distinguishes the positive samples from the negative COVID-19 ones, giving a 100% concordance result with RT-PCR (Table S4). A standard PL analyzer was also utilized to compare the diagnosis results of the PULD system. The results from PL analyzer and portable PULD device are well correlated, as shown in Fig. 6d.Fig. 6 (a) The detection workflow of SARS-CoV-2 via PULD. (b) Schematic of the optical and electronic components of the portable device. (c) Waterfall plot of PULD read-out results for this portable platform (cutoff: 2400). (d) Comparison of COVID-19 diagnostic results between the PULD and standard photoluminescence analyzer (Edinburgh Instruments FLS900). Fig. 6 4 Conclusions In summary, we demonstrate a novel, simple, target amplification-free, smartphone-based diagnostic platform, termed PULD, for on-site detection of SARS-CoV-2 by monitoring the changes in the upconversion luminescence in a one-pot assay when the oligo-modified Au NPs and UCNPs are hybridized with the N gene of SARS-CoV-2. With the short processing time of our portable device, rapid and ultrasensitive detection can be achieved using a ready-to-use assay and a sensitive photosensor. The sandwich assay exhibits high selectivity and specificity against the N gene of SARS-CoV-2 after comparison of fragments of the bases mismatch gene. Notably, a remarkably low LOD (11.46 fM) is realized with no amplification of target. Furthermore, viral detection of the SARS-CoV-2 variant (B.1.1.529/Omicron) by the portable device is conducted with short turnaround time of 20 min. The reliability and accuracy of PULD are confirmed by comparing the detection results obtained from commercial Edinburgh FLS900 and RT-PCR diagnosis with 100% agreement. By offering a simple sample-in and answer-out route, PULD enables more reliable point-of-care COVID-19 screening and promises to be applied for rapid diagnosis of other infectious diseases such as Ebola, HIV, and Zika. In the future, PULD will be further improved to facilitate its application in the field. Isothermal amplification will be included that may lower the detection limit of the test. By incorporating RNA extraction into the platform, a true “sample-in and answer-out” test would be possible. Additionally, the assay throughput needs to be increased, particularly for COVID-19 diagnostics. PULD can be readily parallelized due to their simple structure. In order to obtain rigorous assay statistics, it is desirable to test a larger cohort of clinic samples using the PULD which is expected to employ as a front-line diagnostic tool to guide clinical diagnosis and infection control. CRediT authorship contribution statement Menglin Song: Conceptualization, Methodology, Investigation, Writing – original draft, Writing – review & editing. Man-Chung Wong: Investigation, Writing – original draft, Writing – review & editing, Software. Lihua Li: Investigation, Writing – review & editing. Feng Guo: Writing – review & editing. Yuan Liu: Investigation, Software. Yingjing Ma: Investigation. Xinyue Lao: Visualization, Investigation. Pui Wang: Resources. Honglin Chen: Resources. Mo Yang: Resources, Supervision. Jianhua Hao: Conceptualization, Supervision, Writing – review & editing. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix A Supplementary data The following is the Supplementary data to this article:Multimedia component 1 Multimedia component 1 Data availability Data will be made available on request. Acknowledgements The research was supported by the grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. CRF No. PolyU C5110-20G) and 10.13039/501100004377 PolyU Internal Research Fund (1-CD4S, 1-W21G). Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.bios.2022.114987. ==== Refs References Bai Y. Yao L. Wei T. Tian F. Jin D.-Y. Chen L. Wang M. JAMA 323 2020 1406 1407 32083643 Bi S. Yue S. Zhang S. Chem. Soc. Rev. 46 2017 4281 4298 28573275 Broughton J.P. Deng X. Yu G. Fasching C.L. Servellita V. 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Soc. 134 2012 7266 7269 22506486	36495722	PMC9721270	NO-CC CODE	2022-12-07 23:22:08	no	Biosens Bioelectron. 2023 Feb 15; 222:114987	utf-8	Biosens Bioelectron	2,022	10.1016/j.bios.2022.114987	oa_other
==== Front Biochim Biophys Acta Mol Basis Dis Biochim Biophys Acta Mol Basis Dis Biochimica et Biophysica Acta. Molecular Basis of Disease 0925-4439 1879-260X Elsevier B.V. S0925-4439(22)00283-6 10.1016/j.bbadis.2022.166612 166612 Article Host microRNAs exhibit differential propensity to interact with SARS-CoV-2 and variants of concern Capistrano Kristelle J. a Richner Justin b Schwartz Joel c Mukherjee Sunil K. d Shukla Deepak be Naqvi Afsar R. a⁎ a Mucosal Immunology Lab, College of Dentistry, University of Illinois Chicago, Chicago 60612, IL, USA b Department of Microbiology and Immunology, College of Medicine, University of Illinois Chicago, Chicago 60612, IL, USA c Molecular Pathology Lab, College of Dentistry, University of Illinois at Chicago, Chicago, IL, USA d Division of Plant Pathology, Indian Agricultural Research Institute, New Delhi, India e Department of Ophthalmology and Visual Sciences, Ocular Virology Laboratory, University of Illinois Chicago, Chicago 60612, IL, USA ⁎ Corresponding author at: University of Illinois at Chicago, College of Dentistry, Department of Periodontics, 561B Dent MC 859, 801 South Paulina, Chicago, IL 60612, USA. 5 12 2022 5 12 2022 16661215 5 2022 19 10 2022 18 11 2022 © 2022 Elsevier B.V. All rights reserved. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. A significant number of SARS-CoV-2-infected individuals naturally overcome viral infection, suggesting the existence of a potent endogenous antiviral mechanism. As an innate defense mechanism, microRNA (miRNA) pathways in mammals have evolved to restrict viruses, besides regulating endogenous mRNAs. In this study, we systematically examined the complete repertoire of human miRNAs for potential binding sites on SARS-CoV-2 Wuhan-Hu-1, Beta, Delta, and Omicron. Human miRNA and viral genome interaction were analyzed using RNAhybrid 2.2 with stringent parameters to identify highly bonafide miRNA targets. Using publicly available data, we filtered for miRNAs expressed in lung epithelial cells/tissue and oral keratinocytes, concentrating on the miRNAs that target SARS-CoV-2 S protein mRNAs. Our results show a significant loss of human miRNA and SARS-CoV-2 interactions in Omicron (130 miRNAs) compared to Wuhan-Hu-1 (271 miRNAs), Beta (279 miRNAs), and Delta (275 miRNAs). In particular, hsa-miR-3150b-3p and hsa-miR-4784 show binding affinity for S protein of Wuhan strain but not Beta, Delta, and Omicron. Loss of miRNA binding sites on N protein was also observed for Omicron. Through Ingenuity Pathway Analysis (IPA), we examined the experimentally validated and highly predicted functional role of these miRNAs. We found that hsa-miR-3150b-3p and hsa-miR-4784 have several experimentally validated or highly predicted target genes in the Toll-like receptor, IL-17, Th1, Th2, interferon, and coronavirus pathogenesis pathways. Focusing on the coronavirus pathogenesis pathway, we found that hsa-miR-3150b-3p and hsa-miR-4784 are highly predicted to target MAPK13. Exploring miRNAs to manipulate viral genome/gene expression can provide a promising strategy with successful outcomes by targeting specific VOCs. Keywords SARS-CoV-2 Variants of concern MicroRNAs Post-transcriptional regulation Antiviral immunity ==== Body pmc1 Introduction The causative virus of Covid-19, SARS-CoV-2, has undergone more than 10,000 recorded single mutations since the emergence of the original strain (Wuhan-Hu-1) in December 2019 [1]. While most of these mutations are inconsequential, a fraction has significantly affected viral structure and function, resulting in numerous genetic variants. As of February 2022, the WHO has classified five SARS-CoV-2 strains as variants of concern (VOCs): (1) Alpha (B.1.1.7), (2) Beta (B.1.351), (3) Gamma (P.1), (4) Delta (B.1.617.2), and (5) Omicron (BA.1) [2]. These VOCs are characterized by a set of key mutations, which multiple studies confirm to impact viral characteristics, including increased transmissibility, disease severity, and/or escape of neutralization by antibodies [3], [4], [5]. The viral genome is a positive-sense, single strand, ribonucleic acid (RNA) approximately 30 kilobases long [6]. An extensive repertoire of structural, virulence, and accessory factors facilitate rapid virus replication that can overwhelm the immune system and may cause overt cytokine storm, leading to chronic clinical manifestations of Covid-19. Nonetheless, a predominant fraction of infected subjects naturally overcome viral infection, suggesting a potent endogenous antiviral mechanism that operates effectively in clearing viruses [7]. Identifying such active endogenous molecules/pathways can accelerate the discovery of novel therapeutic targets to mitigate SARS-CoV-2-associated mortality and morbidity. RNA interference (RNAi) is a naturally occurring pathway that involves various non-coding RNAs classes, including microRNAs (miRNA). In higher vertebrates, miRNAs have evolved as an innate antiviral mechanism [8]. miRNAs are a group of small, single-stranded non-coding RNAs, ranging from 19 to 24 base pairs in length, that are endogenous post-transcriptional regulators of gene expression [9], [10]. The canonical mechanism of miRNA-mediated gene regulation occurs as follows: the seed sequence of miRNA, nucleotides 2–8 of the miRNA 5′-UTR (untranslated region), bind directly to complementary sequences at the 3′-UTR of their target mRNAs to induce translational repression or promote mRNA cleavage [11]. As post-transcriptional regulators, host miRNAs play diverse roles in biological processes, including development, apoptosis, differentiation, and immune response [10], [12], [13]. Published in-silico and few in-vitro studies have shown putative binding sites for human miRNAs in the SARS-CoV-2 genome [11], [14], [15], [16], [17], [18]. Barreda-Manso et al. have bioinformatically predicted and experimentally validated the capacity of human miRNAs (hsa-miR), hsa-miR-3941 and hsa-miR-138-5p, to target the SARS-CoV-2 3′-UTR [11]. Among the computational analyses, the study of Fulzele et al. identified 558 human miRNAs that target both SARS-CoV and SARS-CoV-2; out of these miRNAs, 315 human miRNAs uniquely interact with SARS-CoV-2 [14]. Focusing on SARS-CoV-2, another research group used the miRanda program to predict 160 miRNAs with binding sites throughout the viral genome [15]. The SARS-CoV-2 genome consists of 14 open reading frames (ORFs). These ORFs encode four structural proteins—S (spike glycoprotein), E (envelope protein), N (nucleocapsid protein), and M (membrane protein)—and more than twenty processed nonstructural proteins [19], [20]. Although mutations have been detected across the genome, mutations altering S-protein function especially affect host immune response and SARS-CoV-2 infectivity [21], [22]. During the early stages of infection, SARS-CoV-2 spike binds to the angiotensin-converting enzyme 2 (ACE2) host receptor through its receptor-binding domain (RBD). Subsequent proteolytic cleavage of the S-protein by human proteases (e.g. furin, TMPRSS2, cathepsin proteases) at the S1/S2 and/or S2′ sites allows for the initiation of viral-host membrane fusion [23]. Relevant to Covid-19, host miRNAs are known modulators of the antiviral response against herpes-viruses, HIV, influenza A, etc. [24], [25]. Studies propose three main pathways through which host miRNAs counteract SARS-CoV-2 infection: inhibiting viral replication, blocking viral attachment and entry, and interfering with the function of viral proteins [26], [27]. However, in some cases, host miRNAs can function as a pro-viral factor and aid in host evasion. For example, host miRNAs induced during SARS-CoV-2 infection can downregulate antiviral response pathways, such as Toll-like receptors (TLRs), uPA-UPAR signaling, TRAF6 signaling, S1P1 signaling, and estrogen receptor signaling [16], [28], [29], [30], [31], [32]. Given that miRNA binding is through sequence-specific base pairing, it is crucial to examine the implications of SARS-CoV-2 mutations on the interplay between host miRNAs and SARS-CoV-2. We thereby performed computational analyses to investigate how sequence alterations in miRNA-binding sites on the SARS-CoV-2 genome alter host miRNAs—SARS-CoV-2 interactions. Because the S-protein is critical for viral attachment, fusion, and entry into the host cell, we narrowed down our search to host miRNAs with predicted binding sites on the “S-gene” encoding spike protein. Based on phylogenetic analysis, we focused on comparing miRNA interactions on Wuhan-Hu-1, Beta (B.1.351), Delta (B.1.617.2), and Omicron (BA.1) SARS-CoV-2 strains. We further analyzed differentially expressed miRNAs through Ingenuity Pathway Analysis (QIAGEN Inc.) to discover putative predicted mRNA targets (highly predicted and experimentally validated). 2 Materials and methods 2.1 SARS-CoV-2 and microRNA sequences We obtained SARS-CoV-2 sequence information from the NIH's GenBank and GISAID: Wuhan-Hu-1 (NC_045512.2), Alpha (B.1.1.7) (GenBank accession: MZ344997.1), Beta (B.1.351) (GenBank accession: MW981442.1), Gamma (P.1.14) (GenBank accession: MZ169911.1), Delta (B.1.617.2) (GenBank accession: MZ359841.1), and Omicron (B.1.1.529) (GISAID accession: EPI_ISL_6640916). Sequence information of all mature human miRNAs (2636 hsa-miRs) were attained from miRbase database version 22.1. 2.2 Phylogenetic analysis We uploaded each whole sequence information to Nextclade v1.14.0 © 2020–2022 Nextstrain developers [33]. 2.3 Multiple sequence alignment analysis For multiple alignment sequence analysis, uploaded the whole FASTA sequence information of Wuhan-Hu-1 (NC_045512.2), Beta (B.1.351) (GenBank accession: MW981442.1), Delta (B.1.617.2) (GenBank accession: MZ359841.1), and Omicron (B.1.1.529) (GISAID accession: EPI_ISL_6640916) to MUltiple Sequence Comparison by Log-Expectation (MUSCLE) of EMBL-EBI [34]. 2.4 Predicting SARS-CoV-2 target sequences by host miRNAs RNAhybrid 2.2 was used to predict potential human mature miRNA binding sites on the whole genome SARS-CoV-2 sequences mentioned in Section 2.3. Binding parameters were set as the following: 4 hits per target, −25 kcal/mol energy threshold, helix constraint = 2–8, and max bulge loop length = 2. We further narrowed this list of putative human miRNAs by filtering for those that target the S-protein and are expressed in oral epithelial cells, and lung epithelial cells, and lung tissue. A list of human miRNAs expressed in normal lung epithelial cells and lung tissue and was created by combining host miRNAs expressed in healthy lung tissue (DIANA-mITED 2021 [35]; Health status = healthy), Calu3 cell line [36], and normal alveolar epithelial primary cells (FANTOM 5) [35]. We obtained a list of miRNAs expressed in oral keratinocyte cells from FANTOM5 databases [37]. 2.5 Comparison of host miRNA–SARS-CoV-2 spike interactions We uploaded our list of host miRNAs that target the SARS-CoV-2 spike for Wuhan-Hu-1, Beta, Delta, and Omicron and expressed in lung epithelial cells, lung tissue, and oral keratinocytes to the Multiple List Comparator of molbiotools (2022) to find data intersection. Using this tool, we generated the Venn diagrams to identify common and unique miRNAs in each cell type. 2.6 Ingenuity Pathway Analysis QIAGEN Ingenuity Pathway Analysis Software (© QIAGEN 2013–2022) was used for subsequent bioinformatics analysis, including miRNA target filter and canonical pathway analysis. This web-based software compiles information from publicly available databases built from published relationships, mechanisms, biological functions, canonical pathways, and networks. IPA's predicted miRNA regulation of target mRNAs are based on information from miRBase, TargetScan, and the QIAGEN Knowledge Base. The search pipeline in our analysis is detailed in Supplementary Fig. 1. The IPA Path Designer Graphical Representation was used to generate Fig. 4. Biological relationships between each molecule is represented as an edge (line). Details regarding functional class of gene products and nature of relationships between each molecule are provided in Fig. 4 legend. 3 Results and discussion 3.1 Significant loss of human microRNA binding sites on omicron spike SARS-CoV-2 variants of concern (VOCs) feature fitness-enhancing mutations, altering viral pathogenicity and virulence. Despite these genetic changes, there is high sequence similarity between all VOCs. We performed phylogenetic analysis using Nextclade (v1.14.1) to determine which variants to compare in our study. Our investigation resulted in three distinct clades: Clade 1 (Beta), Clade 2 (Alpha, Gamma, and Omicron), and Clade 3 (Delta). We included one variant from each clade for further investigation, focusing on Wuhan-Hu-1, Beta, Delta, and Omicron (Supplementary Fig. 2). To obtain an overall view of how SARS-CoV-2 mutations affect miRNA binding, we first aligned all hsa-miRs to the reference and variant whole genomes using RNAHybrid 2.2. Since the human genome encodes an estimated 2636 mature miRNAs (miRbase v22.1), we set stringent binding parameters via RNAhybrid 2.2: 4 hits per target, −25 kcal/mol energy threshold, helix constraint from positions 2 to 8, and a max bulge loop length of 2. We also chose strict binding parameters to reduce the false discovery prediction rate of miRNA target sites. This approach identified a comparable number of hsa-miRs that could target the Wuhan-Hu-1 (1229 hsa-miRs), Beta (1227 hsa-miRs), and Delta (1232 hsa-miRs) genomic sequences (Fig. 1a and Supplementary Table 1). Interestingly, the predicted number of host miRNAs targeting Omicron was significantly lower at 598 hsa-miRs (Fig. 1a; Supplementary Table 1). Because our main interest is to investigate whether SARS-CoV-2 mutations alter the binding of host miRNAs to the virus, we narrowed our investigation to the genomic region with the highest concentration of clinically influential mutations, the S protein [4]. Although mutations have occurred throughout the entire SARS-CoV-2 genome, defining spike mutations are featured in all VOCs and critically affect viral transmissibility, pathogenicity, and antibody neutralization escape [4]. Hence, we focused on the differential miRNA targeting in Wuhan-Hu-1, Beta, Delta, and Omicron concerning host miRNA–SARS-CoV-2 spike interaction. In addition, we further filtered our query miRNAs by selecting those that are expressed in oral epithelial cells, lung epithelial cells, and lung tissue, which are the major trophic sites for SARS-CoV-2. Previous studies revealed that the airway and alveolar epithelia are the main targets of SARS-CoV-2 [38], [39]. Expression of ACE2 and furin in oral epithelial cells indicate potential tropism of SARS-CoV-2 for the oral mucosa [40], [41]. Further examination revealed that the majority of our identified spike-targeting host miRNAs were expressed in either lung and/or oral epithelial cells, implying that our predicted hsa-miRs play potential roles in SARS-CoV-2 infection. Comparable to our whole genome target prediction analysis, RNAhybrid 2.2 identified the following number of potential host miRNAs that target the spike of reference/variant strains and are expressed in lung and/or oral epithelial cells: Wuhan-Hu-1 (271 hsa-miRs), Beta (279 hsa-miRs), Delta (275 hsa-miRs), and Omicron (130 hsa-miRs) (Fig. 1a).Fig. 1 In silico prediction of host miRNA and SARS-CoV-2 interactions. (a) Computational pipeline used to predict human miRNAs that uniquely target SARS-CoV-2 S gene of Wuhan-Hu-1, Beta (B.1.351), Delta (B.1.617.2), and Omicron (BA.1). All 2636 mature human miRNAs (miRbase v. 22.1) were aligned against the whole genomes of each SARS-CoV-2 strain (Wuhan-Hu-1, GenBank: NC_045512.2; Beta, GenBank: MW981442.1); Delta, GenBank: MZ359841.1; Omicron, GISAID: EPI_ISL_6640916) via RNAhybrid 2.2. Target sequences identified by RNAhybrid 2.2 were filtered to select miRNAs (1) specifically target SARS-CoV-2 S gene. p-Value = nothing (helix constraint and p-values cannot be used simultaneously), (2) expressed in human oral keratinocytes and (3) expressed in Calu3 human lung epithelial cell lines, alveolar epithelial primary cells, and normal lung tissue. (b) Schematic of representative Spike protein mutations in Beta (B.1.351), Delta (B.1.617.2), and Omicron (BA.1) SARS-CoV-2 variants. Key mutations are noted in red. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) Fig. 1 Given that Omicron is the most heavily mutated VOC, it is not surprising that the variant has emerged as the clear outlier in our in-silico analysis. Specifically, there are significantly fewer host miRNA–SARS-CoV-2 interactions in Omicron than Wuhan-Hu-1, Beta, and Delta throughout the whole genome, including S-protein and N-protein (Supplementary Table 1 and Supplementary Table 2). Relative to Wuhan-Hu-1, Omicron possesses more than 50 amino acid substitutions throughout its whole genome, 32 of which are in the spike [42]. By comparison, Beta and Delta feature nine and ten spike amino acid substitutions, respectively (Fig. 1b). The SARS-CoV-2 S glycoprotein consists of 1273 amino acids (AA) separated into three regions: a signal peptide at the N-terminus (AA 1–13), the S1 subunit (AA 14–685), and the S2 subunit (AA 686–1273). The key mutations are concentrated in the S1 subunit, a crucial region for determining host range and tissue tropism. Within the S1 subunit are two principal targets for neutralizing antibodies, the N-terminal domain (NTD) (AA 14–305) and receptor binding domain (RBD) (AA 319–541). SARS-CoV-2 can bind to its host-receptor, ACE2, via its RBD. While the function of NTD in SARS-CoV-2 is not well characterized, in other coronaviruses, the NTD is said to aid viral-host membrane fusion [43], [44], [45], [46]. Importantly, reduced antibody neutralization across the variants is attributed to mutations within the NTD. Concentrations of mutations within Omicron's spike are found within the RBD (fifteen amino acid substitutions) and NTD (three deletions, one insertion, and four substitutions). The location of these mutations suggests that they increase viral fitness by reducing the binding of natural or vaccine-induced antibodies and increasing viral binding affinity to ACE2 [47]. In the context of ACE2-mediated infection, reports show that Omicron results in infection rates quadrupling that of the ancestral Wuhan-Hu-1 strain [48]. Moreover, Planas et al. [49] showed Omicron's significant resistance to neutralization by potent monoclonal antibodies, sera from convalescent individuals at 6- and 12-months post-infection, and to an extent, sera from vaccinated individuals (5 months post-vaccination; 2 doses of Pfizer or AstraZeneca vaccine) [49]. Additionally, our data suggest that these mutations significantly dampen host-miRNA response against Omicron (Fig. 1a). Indeed, a comparison of potential miRNA targets in human coronaviruses by Bartoszewski et al. [50] suggest that SARS-CoV-2 acts as a microRNA “sponge” to deplete specific host miRNAs, amplifying viral replication. Altogether, our results as well as those of other groups reinforce why understanding how genomic alterations in the spike region affects host miRNA–SARS-CoV-2 interactions is of importance and could explain the varying fitness and pathogenicity displayed by the variants of concern. 3.2 Select mutations alter the binding sites of human microRNAs to SARS-CoV-2 spike and N proteins Comparative analysis of spike-targeting host miRNAs revealed that while most host miRNAs commonly target at least two SARS-CoV-2 strains, there is a small subset of miRNAs (26 miRNAs) that interacts exclusively with either Wuhan-Hu-1, Beta, Delta, or Omicron (Fig. 2a and b; Table 1). Therefore, we interrogated the binding sites of these host miRNAs unique to each SARS-CoV-2 variant to investigate the source of this differential targeting. Our multiple sequence alignment analysis of spike revealed 56 nucleobases that have undergone substitution [28], deletion [19], or addition [9] in Omicron. The same analysis identified eight substitutions, nine deletions, and no insertions for Beta; for Delta, there were five substitutions, six deletions, and no insertions (Supplementary Fig. 3). We show that these mutations result in two possible scenarios relative to Wuhan-Hu-1: addition of a novel target-binding site (Fig. 3a; II, IV–VI) or alteration of an existing target-binding site (Fig. 3a; I, III, VII–VIII). The S1/S2 cleavage site consists of a polybasic motif (RRAR) at position 682–685 of the SARS-CoV-2 spike and is the suspected binding site of the protease furin. Multiple studies demonstrate a critical role of this furin cleavage site in promoting efficient syncytia formation and facilitating viral entry. Previous publications have detected notable mutations in and adjacent to the S1/S2 cleavage site [47], [48], [49], [50], [51]. For example, in this region, the substitution of the wildtype cytosine (C) with guanine (G) results in the Delta spike mutation P681R (Fig. 3b). By introducing an additional basic arginine residue (RRRAR), P681R reportedly facilitates spike protein cleavage and increases viral fusogenicity [49]. Aside from amino acid substitutions, our results demonstrate the potential of nucleotide substitutions within this region to alter host miRNA–SARS-CoV-2 interactions (Fig. 3a; VII). These mutations result in the loss of the target sequence (UCUCCUCG) for hsa-miR-3150b-3p and hsa-miR-4784, which are exclusively specific to Wuhan-Hu-1 (Fig. 3a; VII). Instead, alternative host miRNAs can bind to Delta (hsa-miR-4502) and Beta (hsa-miR-7113-5p and hsa-miR-6760-5p) (Fig. 3b; VII). Due to these mutations, no host miRNAs bind to this region in Omicron (Fig. 3b; VII).Fig. 2 Host miRNAs targeting SARS-CoV-2 Wuhan-Hu-1, Beta (B.1.351), Delta (B.1.617.2), and Omicron (BA.1) spike and expressed in oral epithelial cells, lung epithelial cells, and lung tissue. (a) 28 representative host miRNAs with lowest minimum free energy (mfe) values common to at least two SARS-CoV-2 strains (274 in total; * not shown here; refer to Supplementary Fig. 2). (b) Venn diagram showing commonly and differentially targeting host miRNAs. Host miRNAs targeting SARS-CoV-2 Wuhan-Hu-1, Beta (B.1.351), Delta (B.1.617.2), and Omicron (BA.1) spike and expressed in oral epithelial cells, lung epithelial cells, and lung tissue. (a) 28 representative host miRNAs with lowest minimum free energy (mfe) values common to at least two SARS-CoV-2 strains (274 in total; * not shown here; refer to Supplementary Fig. 2). (b) Venn diagram showing commonly and differentially targeting host miRNAs. Fig. 2 Fig. 3 Representative mutations resulting in differential targeting of SARS-CoV-2 spike by host miRNAs. (a) Regions of mutations resulting in the binding of uniquely specific host miRNAs (noted in red) to Beta, Delta, and Omicron. Host miRNAs lost in the variants (but bind to Wuhan-Hu-1) due to these mutations are noted in black. x = no miRNA interaction in the region of mutation. (b) Representative multiple sequence alignment analysis of Wuhan-Hu-1 (GenBank: NC_045512.2), Beta (GenBank: MW981442.1), Delta (GenBank: MZ359841.1), and Omicron (GISAID: EPI_ISL_6640916; hCov-19/Botswana) spike. Nucleobase substitutions and deletions are in red. Relative to Wuhan-Hu-1, specific mutations resulting in the addition of novel target binding sites or alter an existing target binding sites are numbered from I to VIII. (c) Omicron N-protein mutations resulting in the loss of miRNA binding sites previously present in Wuhan-Hu-1. x = no miRNA interaction in the region of mutation. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) Fig. 3 Table 1 Twenty-six host miRNAs uniquely specific to each SARS-CoV-2 strain and their target sequence position. Sequence alignment of host miRNA–target mRNA interactions are shown and seed regions are noted in blue. Table 1 Beta, Delta, and Omicron have accumulated key mutations throughout the spike NTD and RBD (Fig. 1b). Our results link mutations in these regions to the creation of novel target binding sites by host miRNAs (Fig. 3b). Of note, a stretch of nucleotide insertion (GAGCCAGAA; position 22122 to 22131) in Omicron's Spike NTD provides a binding site for hsa-miR-6799-5p, hsa-miR-4525, hsa-miR-4257, hsa-miR-3928-3p, hsa-miR-329-5p, and hsa-miR-1275 (Fig. 3a; IV). Similarly, a single substitution of the wildtype adenine (A) to G (position 22160) in Beta's Spike NTD allows for the binding of six host miRNAs: hsa-miR-6885-5p, hsa-miR-6827-5p, hsa-miR-6746-5p, hsa-miR-4656, hsa-miR-1291, and hsa-miR-1233-3p (Fig. 3a; IV). Binding sites for hsa-miR-6771-5p (Fig. 3a; V) and hsa-miR-6499-3p (Fig. 3a; VI), which are exclusive to Omicron, are created due to two separate instances of nucleotide substitution within the RBD. Our earlier results showed a reduction of overall target binding sites for host miRNA in Omicron (Fig. 1a). Here, we present the opposite scenario in which mutations create new binding sites for a select few microRNAs (Fig. 3a; II, IV–VI). While these two contrasting phenomena appear confounding, they serve as perfect examples of how host miRNAs can act as a double-edged sword. Differential targeting by host miRNAs due to accumulated mutations is an example of how SARS-CoV-2 can manipulate the host immune response. Other studies have demonstrated the ability of host miRNAs to bind directly to RNA virus genomes, which can inhibit translation and thereby halt viral replication. However, in some cases, a slower replication rate may contribute to virus survival. Accumulation of target-binding sites for host miRNAs due to mutations attenuate SARS-CoV-2 replication, it can also enhance pathogenic stealth and thereby increase viral fitness [51]. Indeed, numerous studies have reported enhanced immune escape potential in Beta, Delta, and Omicron [42], [52], [53], [54], [55]. Because our earlier results showed a dramatic loss of human miRNA and SARS-CoV-2 interactions in the variant (Fig. 1a), we investigated whether the reduction of miRNA binding site occurs in the rest of the genome of Omicron. Unsurprisingly, miRNA binding site evasion in Omicron was also observed for non-spike proteins (Supplementary Fig. 4). For instance, we found that accumulated mutations in Omicron's N-proteins, which is critical for viral replication and features unique mutations that are absent in other VOCs [56], results in the loss of binding sites of twelve miRNAs previously bound to Wuhan-Hu-1 (Fig. 3c and Supplementary Fig. 4). Our multiple sequence alignment analysis of the N-protein of Wuhan-Hu-1 and Omicron revealed that two major sites of mutation are responsible for this differential binding (Fig. 3c and Supplementary Fig. 4). First, a stretch of nucleotide deletions (position 28,362 to 28,370) in Omicron results in the loss of binding sites for five host miRNAs previously bound to Wuhan-Hu-1: (hsa-miR-324-3p, hsa-miR-3173-5p, hsa-miR-6837-3p, hsa-miR-6774-3p, and hsa-miR-6781-5p). Second, a substitution of the wildtype GGG to AAC at positions 28,881 to 28,884 results in the loss of binding sites of six host miRNAs: hsa-miR-3059-5p, hsa-miR-623, hsa-miR-449c-3p, hsa-miR-4781-5p, hsa-miR-4665-3p, and hsa-miR-1914-3p (Fig. 3c and Supplementary Fig. 4). Because Omicron is heavily mutated [42], it is likely that this observation of overall miRNA binding reduction in Omicron could be true for other mutation prone viral ORFs. Our results demonstrated how SARS-CoV-2 mutations alter host miRNA target sites relative to Wuhan-Hu-1. We further showed that these mutations result in the binding of host miRNAs uniquely specific to Wuhan-Hu-1, Beta, Delta, or Omicron (Table 1). In some cases, the binding of miRNAs uniquely specific to each variant results in substituting miRNAs (e.g., hsa-miR-6796-5p, hsa-miR-326, hsa-miR-3150b-3p, hsa-miR-4784, and hsa-miR-380-5p) with previous binding affinity to Wuhan-Hu-1 spike (Fig. 3a; I, III, VII–VIII). On the basis of our findings, we hypothesize that the loss of these miRNAs contributes to the increased virulence and pathogenicity of Beta, Delta, and Omicron. Focusing on hsa-miR-6796-5p, hsa-miR-326, hsa-miR-3150b-3p, hsa-miR-4784, and hsa-miR-380-5p, in the next section, we performed an ingenuity pathway analysis (IPA) to investigate their potential involvement in SARS-CoV-2 infection. 3.3 Host miRNA substitutions result in loss of key miRNA mediated coronavirus pathogenesis signaling pathways Following SARS-CoV-2 entry into the target cell, host immune recognition of pathogen-associated molecular patterns (PAMPs) by pattern-recognition receptors (PRRs) elicits the innate immune response. Upon activation, these PRRs enhance the production of type I interferon (IFN) antiviral response and subsequent pro-inflammatory cytokine response through NFκB-dependent pathways. In addition, type 1 IFN signaling is crucial for proper antigen presentation and activation of the adaptive immune response [57], [58]. Dysregulation of T- and B-cell responses is implicated in poor Covid-19 outcomes. Patients with severe Covid-19 disease demonstrate elevated levels of pro-inflammatory cytokines in bronchoalveolar lavage fluid (BALF) and peripheral blood mononuclear cells (PBMC) [59]. Other indicators of poor prognosis include Th1/Th2 imbalance and an increase in leukocyte counts and neutrophil-lymphocyte ratio (NLR) [57]. All of these suggest that hyperinflammation, impaired or suboptimal IFN activation, and excessive T cell activation exacerbate SARS-CoV-2 infection. In addition, virus-mediated impairment of host antiviral pathways allows viral replication and persistence in host. The gene targets of the coronavirus pathogenesis pathway are involved in apoptosis, antiviral interferon response, cytokine storms, and inflammasomes (Fig. 4 ). Briefly, other studies implicate Covid-19-induced apoptosis (via p38 MAPK, JNK, ERK, and ER stress-response pathways) in the liberation of extracellular metabolites and virions from host cells for enhanced viral reproduction and propagation, respectively [60], [61]. Another hallmark of coronavirus pathogenesis includes suppressing the antiviral interferon response system, particularly through the inhibition of NFκB induction of interferon production [60]. Finally, Covid-19-induced cytokine storms activate multiple mechanisms, including inflammasomes, which contribute to cell destruction [62], [63].Fig. 4 Coronavirus pathogenesis pathway target genes of hsa-miR-326, hsa-miR-6796-5p, hsa-miR-380-5p, hsa-miR-3150b-3p, and hsa-miR-4784. These host miRNAs with predicted binding affinity to Wuhan-Hu-1 are substituted by miRNAs uniquely specific to Beta, Delta, and Omicron. We used IPA's miRNA Target Filter Analysis to analyze whether these substitutions result in the loss of key miRNA-mediated regulation of coronavirus pathogenesis pathways. To narrow down the potential mRNA targets, we focused on experimentally validated and highly predicted target genes of hsa-miR-326, hsa-miR-6796-5p, hsa-miR-380-5p, hsa-miR-3150b-3p, and hsa-miR-4784 in the coronavirus pathogenesis pathway. Experimentally validated and highly predicted target genes are highlighted in orange. Moderately predicted target genes are highlighted in blue. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) Fig. 4 Because miRNAs regulate the global gene expression, precise target prediction is vital in understanding their function. Through the miRNA target filter of IPA (QIAGEN Inc.), we identified the target genes of hsa-miR-6796-5p, hsa-miR-326, hsa-miR-3150b-3p, hsa-miR-4784, and hsa-miR-380-5p. Of note, because hsa-miR-3150b-3p and hsa-miR-4784 share the same seed sequence (GAGGAGA), IPA considers these two miRNAs as duplicates. We focused on highly confident predictions or experimentally validated miRNA target interactions. Considering the above-mentioned, we searched for target genes involved in cellular immune response, humoral immune response, cytokine signaling, disease-specific pathways, and pathogen-influenced signaling. We further focused on specific signaling pathways and obtained the following results: IL17 (4 miRs targeting 9 mRNAs), NFκB (4 miRs targeting 10 mRNAs), Th1 (3 miRs targeting 9 mRNAs), Th2 (3 miRs targeting 6 mRNAs), Toll-like receptor (4 miRs targeting 5 mRNAs), IFN (1 miR targeting 1 mRNA), coronavirus replication (2 miRs targeting 1 mRNA), and coronavirus pathogenesis (4 miRNAs targeting 7 mRNAs). First, we investigated hsa-miR-3150b-3p and hsa-miR-4784 because they are uniquely specific to Wuhan-Hu-1 Spike. Both miRs are replaced by hsa-miR-7113-5p in Beta (Fig. 3b; VII). Our pathway analysis shows that these substitutions result in the loss of miRNA-mediated regulation of select genes in multiple SARS-CoV-2 related pathways (i.e., IL-17, NFκB, Th1, Th2, TLR, IFN, coronavirus pathogenesis, and coronavirus replication pathways). Among these pathways, we focused on target genes involved in coronavirus pathogenesis and coronavirus replication pathways to investigate whether these host miRNA substitutions confer increased infectivity and virulence to SARS-CoV-2 VOCs. By doing so, we found that hsa-miR-3150b-3p and hsa-miR-4784 are highly predicted to target MAPK13 (Fig. 4). Moreover, Yu et al. show that hsa-miR-3150b-3p directly targets TNFRSF11a (TNF Receptor Superfamily Member 11a) to inactivate p38 mitogen-activated protein kinases (p38 MAPK) p38 MAPK signaling in vitro [64]. The dependence of SARS-CoV-2 on p38 MAPK signaling is established by previous publications. Activation of p38 MAPK by SARS-CoV-2 increases the production of pro-inflammatory cytokines, including IL6 and TNFα, resulting in airway epithelial cell and alveolar tissue damage. In respiratory viruses SARS-CoV-1 and H5N1, p38 MAPK signaling reportedly induces receptor-mediated endocytosis for viral entry and endocytosis of ACE2 [65], [66], [67], [68], [69], [70], [71], [72]. Indeed, Bouhaddou et al. show that small interfering RNA (siRNA)-mediated knockdown of MAPK13 significantly decreases SARS-CoV-2 replication in A549-ACE2 cells [71], [73], [74]. Altogether, these results indicate that the loss of coronavirus pathogenesis regulation by hsa-miR-3150b-3p and hsa-miR-4784 in Beta, Delta, and Omicron contributes to the higher replication rates and pathogenesis of these VOCs compared to Wuhan-Hu-1. Next, we examined the function of other host miRNAs replaced due to the alteration of an existing target binding site in Wuhan-Hu-1: hsa-miR-6796-5p (binds to Wuhan-Hu-1 and Omicron Spike), hsa-miR-326 (binds to Wuhan-Hu-1 and Beta Spike), and hsa-miR-380-5p (binds to Wuhan-Hu-1, Beta, and Delta Spike). Through IPA, we found that hsa-miR-6796-5p and hsa-miR-326 are highly predicted to target H2BW2 of the coronavirus replication pathway. Using the same analysis, we found that hsa-miR-326, hsa-miR-6796-5p, and hsa-miR-380-5p have highly predicted or experimentally validated target genes involved in coronavirus pathogenesis signaling pathways, notably histone deacetylase 3 (Hdac3), ras-related protein Rab-7a (Rab7a), and androgen receptor (Ar) (Fig. 4). Multiple studies report HDAC inhibitors as promising therapeutics against SARS-CoV-2 infection by limiting or preventing ACE2-virus interactions [75], [76], [77], [78]. Similarly, loss of Rab7a is shown to reduce ACE2 expression substantially by sequestering ACE2 receptors inside cells [79], [80], [81]. Finally, Qiao et al. report that inhibitors of Ar are effective in reducing ACE2 and TMPRSS2 in-vitro [82]. Our pathway analysis show that host miRNAs also regulate critical cellular pathways relevant to viral tropism and replication, in addition to direct interaction with viral genome suggesting a multifunctional role of cellular miRNAs in shaping SARS-CoV-2 and host interaction. 4 Conclusion Despite the extensive bioinformatic analyses performed by other groups, there is a lack of studies that address how genomic differences between SARS-CoV-2 variants, notably Omicron (BA.1), can alter human miRNA binding to the virus relative to Wuhan-Hu-1. Even less investigated are the implications of mutation-induced differential targeting of SARS-CoV-2 by host miRNAs on the differences in replication rates and pathogenicity among the variants. Importantly, the miRNA signatures of oral mucosal epithelial cells during Covid-19 remains unexplored but must be examined due to multiple studies demonstrating the importance of the oral cavity as a portal of entry for SARS-CoV-2 [41], [83], [84], [85]. Specifically, ACE2 and furin is highly expressed in oral mucosal epithelial cells, indicating that these cells are highly susceptible to SARS-CoV-2 infection [41]. The present study addresses these critical gaps in knowledge by providing evidence of differential host miRNA targeting to Wuhan-Hu-1, Beta, Delta, and Omicron SARS-CoV-2 genomes in oral keratinocytes, lung epithelial cells, and lung tissue. Specifically, there was significant depletion of host miRNA target sites in Omicron, suggesting dampened antiviral miRNA-mediated response. Global pathway analysis reveals that the loss of hsa-miR-3150b-3p and hsa-miR-4784, which bind to Wuhan-Hu-1 Spike but not Beta, Delta, and Omicron Spike, results in the loss of miRNA-mediated regulation of MAPK13, which other studies have shown to indirectly promote inflammation-associated alveolar tissue damage and is integral for SARS-CoV-2 replication. Furthermore, we found that other miRNAs (viz., hsa-miR-6796-5p, hsa-miR-6816-5p, hsa-miR-326, and hsa-miR-380-5p) with binding affinity to Wuhan-Hu-1 regulate coronavirus pathogenesis pathways, notably Hdac3, Rab7a, and Ar. We suggest that mutations leading to the loss of target binding sites for these miRNAs confer increased SARS-CoV-2 replication and pathogenicity. Our results clearly show that most heavily mutated viral proteins required for entry (S protein) and replication (N protein) exhibit loss of host miRNA binding sites suggesting an antiviral function of multiple host miRNAs. For instance, a loss of hsa-miR-3150b-3p and hsa-miR-4784 binding sites on S protein in Beta, Delta, and Omicron may contribute to the higher replication rates and pathogenesis of these VOCs compared to Wuhan-Hu-1. However, there are limitations in this current study. Due to the computational nature of our methods, future studies warrant the experimental validation of our predicted host miRNA and SARS-CoV-2 interactions. Another major limitation is the lack of data regarding the expression of these identified miRNAs in Covid-19 patients. Out of our five host miRNAs of interest (viz., hsa-miR-3150b-3p and hsa-miR-4784, hsa-miR-6796-5p, hsa-miR-326, and hsa-miR-380-5p), only hsa-miR-3150b-3p has been reported by other groups to be differentially expressed between Covid-19 patients and healthy controls. Plasma miRNome profiling by Fernández-Pato et al. showed the upregulation of hsa-miR-3150b-3p in severe and moderate SARS-CoV-2 patients [86]. This data supports the idea that hsa-miR-3150b-3p plays an important and clinically relevant role in Covid-19. Nonetheless, the biological role of host miRNAs in SARS-CoV-2, specifically the impact of miRNA expression in the replication rates and pathogenicity of the virus, is unclear. Further studies on miRNA profiles of oral keratinocytes infected with SARS-CoV-2 or oral biospecimens (saliva, gingiva, gingival crevicular fluid, or brush biopsies) may yield novel information on the direct impact of virus on global miRNA expression. Functional assays that assess whether altered miRNA expression could lead to a differential viral load will further support the role of miRNAs in viral pathogenesis. Despite the limitations of our study, our findings remain useful; here, we show differential host miRNA-viral mRNA regulatory networks in Wuhan-Hu-1, Beta, Delta, and Omicron, providing a limited list of miRNAs that can serve as potential therapeutic targets against future SARS-CoV-2 variants. The following are the supplementary data related to this article.Supplementary Fig. 1 Search pipeline used during miRna target filter analysis (IPA, QIAGEN) of miRnas with binding to Wuhan-Hu-1 but displaced due to VOC mutations. Supplementary Fig. 1 Supplementary Fig. 2 Whole genome phylogenetic analysis of SARS-CoV-2 VOCs. Clade 1 (20C): Beta. Clade 2 (20B): Alpha, Omicron, and Gamma. Clade 3 (21A) Delta. Supplementary Fig. 2 Supplementary Fig. 3 Supplementary Fig. 3 Supplementary Fig. 4 Supplementary Fig. 4 Supplementary Table 1 Host miRNAs with binding affinity to the entire genome of Wuhan-Hu-1 (GenBank: NC_045512.2), Beta (GenBank: MW981442.1), Delta (GenBank: MZ359841.1), and Omicron (GISAID: EPI_ISL_6640916; hCov-19/Botswana). Host miRNAs that bind specifically to SARS-CoV-2 and are expressed in lung cells/tissue and oral keratinocytes (cell and tissue profiles based on public databases described in the Materials and methods section) are in blue. Host miRNAs with binding sites to Wuhan-Hu-1 but are lost due to accumulated Omicron mutations in the N-protein are in green. Binding interactions were predicted via RNAhybrid using binding parameters described in the Materials and methods section. Supplementary Table 1 Supplementary Table 2 Prediction of host miRNA binding sites to SARS-CoV-2 via RNAhybrid 2.2. Alignments for Wuhan-Hu-1 whole genome (pages 1–843); Beta whole genome (pages 843–1683); Delta whole genome (pages 1683–2519); and Omicron whole genome (pages 2519–2974); Wuhan-Hu-1 N-protein (pages 2975–3127); Omicron N-protein (pages 3128–3285). Binding parameters were set according to the Materials and methods section. Supplementary Table 2 Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 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Virol. 84 2010 11359 11373 20702616 71 Pillaiyar T. Laufer S. Kinases as potential therapeutic targets for anti-coronaviral therapy J. Med. Chem. 65 2022 955 982 34081439 72 Xiao L. Haack K.K.V. Zucker I.H. Angiotensin II regulates ACE and ACE2 in neurons through p38 mitogen-activated protein kinase and extracellular signal-regulated kinase 1/2 signaling Am. J. Physiol.-Cell Physiol. 304 2013 C1073 C1079 23535237 73 Bouhaddou M. The global phosphorylation landscape of SARS-CoV-2 infection Cell 182 2020 685 712 e19 32645325 74 Goel S. SARS-CoV-2 switches ‘on’ MAPK and NFκB signaling via the reduction of nuclear DUSP1 and DUSP5 expression Front. Pharmacol. 12 2021 631879 75 Liu K. Clinical HDAC inhibitors are effective drugs to prevent the entry of SARS-CoV2 ACS Pharmacol. Transl. Sci. 3 2020 1361 1370 34778724 76 Sixto-López Y. Correa-Basurto J. HDAC inhibition as neuroprotection in COVID-19 infection Curr. Top. Med. Chem. 22 2022 77 Takahashi Y. Histone deacetylase inhibitors suppress ACE2 and ABO simultaneously, suggesting a preventive potential against COVID-19 Sci. Rep. 11 2021 3379 33564039 78 Teodori L. MicroRNAs bioinformatics analyses identifying HDAC pathway as a putative target for existing anti-COVID-19 therapeutics Front. Pharmacol. 11 2020 582003 79 Baggen J. Vanstreels E. Jansen S. Daelemans D. Cellular host factors for SARS-CoV-2 infection Nat. Microbiol. 6 2021 1219 1232 34471255 80 Baumann K. Cellular basis for SARS-CoV-2 infection Nat. Rev. Mol. Cell Biol. 22 2021 2-2 81 Daniloski Z. Identification of required host factors for SARS-CoV-2 infection in human cells Cell 184 2021 92 105 e16 33147445 82 Qiao Y. Targeting transcriptional regulation of SARS-CoV-2 entry factors ACE2 and TMPRSS2 Proc. Natl. Acad. Sci. 118 2021 e2021450118 83 Altaie A.M. Hamdy R. Venkatachalam T. Hamoudi R. Soliman S.S.M. Estimating the viral loads of SARS-CoV-2 in the oral cavity when complicated with periapical lesions BMC Oral Health 21 2021 567 34749700 84 Drozdzik A. Drozdzik M. Oral pathology in COVID-19 and SARS-CoV-2 infection-molecular aspects Int. J. Mol. Sci. 23 2022 1431 35163355 85 NIH COVID-19 Autopsy Consortium et al. SARS-CoV-2 infection of the oral cavity and saliva Nat. Med. 27 2021 892 903 33767405 86 Fernández-Pato A. Plasma miRNA profile at COVID-19 onset predicts severity status and mortality Emerg. Microbes Infect. 11 2022 676 688 35130828	36481486	PMC9721271	NO-CC CODE	2022-12-13 23:17:36	no	Biochim Biophys Acta Mol Basis Dis. 2023 Feb 5; 1869(2):166612	utf-8	Biochim Biophys Acta Mol Basis Dis	2,022	10.1016/j.bbadis.2022.166612	oa_other
==== Front Arch Med Res Arch Med Res Archives of Medical Research 0188-4409 1873-5487 Instituto Mexicano del Seguro Social (IMSS). Published by Elsevier Inc. S0188-4409(22)00164-3 10.1016/j.arcmed.2022.11.017 Opinion Post-COVID-19 Agenda. Who Controls the Narrative? Rodríguez-Álvarez Mauricio ab⁎ Ponce-de-León-Rosales Samuel ab a Facultad de Medicina, Universidad Nacional Autónoma de México, Ciudad de México, México b Programa Universitario de Investigación en Salud, Universidad Nacional Autónoma de México, Ciudad de México, México ⁎ Corresponding author: Mauricio Rodríguez-Álvarez, Departamento de Microbiología y Parasitología, Facultad de Medicina, Universidad Nacional Autónoma de México, Av. Universidad 3000, Ciudad de México 04510, México; Tell: (+52) (55) 56225220 5 12 2022 5 12 2022 5 10 2022 29 11 2022 © 2022 Instituto Mexicano del Seguro Social (IMSS). Published by Elsevier Inc. All rights reserved. 2022 Instituto Mexicano del Seguro Social (IMSS) 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 pmcClear and precise definitions are essential in medical practice for ensuring accurate diagnoses and safe and effective therapeutic interventions. Conversely, recognizing the uncertainties and limitations inherent to any classification system gives the physician room to fine-tune patient care. The importance of the written word, both as a communication tool and as knowledge heritage is essential for the advancement and legacy of medicine, as it has been since the first codex of antiquity, passing through the Hippocratic Corpus, manuscripts, medical treatises and current clinical practice guidelines. The infection caused by SARS-CoV-2, including all its variants of concern, triggers complex mechanisms where the final outcomes are not always clear but certainly take a toll on both personal and collective health (1,2). Maybe more than ever before, we seem to envision an unlimited number of sequelae attributable to a single disease, and the apparent complexity of the post-COVID-19 condition, if left unchecked, could run amok. This could result in associations being established where none exist, leaving patients confused about the causes of their illness and, worse still, complicating their medical care. Starting in October 2021, the World Health Organization (WHO) proposed a consensus definition for the post-COVID-19 condition, which “occurs in individuals with a history of probable or confirmed SARS-CoV-2 infection, usually 3 months from the onset of COVID-19 with symptoms that last for at least 2 months and cannot be explained by an alternative diagnosis. Common symptoms include fatigue, shortness of breath, cognitive dysfunction but also others and generally have an impact on everyday functioning. Symptoms may be new onset following initial recovery from an acute COVID-19 episode or persist from the initial illness. Symptoms may also fluctuate or relapse over time” (3). The definition has seldom been used, or even mentioned, by many of the scientific works that deal with the issue. This is one of the many reasons why a large swath of symptoms and even clinical signs have been associated, at different times after the acute infection in patients with a history of COVID-19, with the notion of long COVID, acute post-COVID, or COVID sequelae (4., 5., 6.). While most manifestations of the post-COVID condition appear to be proper physical manifestations and are thus amenable to objective analysis, some may include a strong psychosomatic component, something not usually discussed. There is plenty of scientific evidence to clearly uphold the notion that COVID-19 is not just an infection of the upper or lower airways that sometimes presents with a systemic component (a notion quite prevalent at the time of the first circulating variants, or when first infections present on naïve or unvaccinated individuals);(1) we now recognize that there might be complications arising from repeated infections with different variants, or due to the effects of previous vaccinations on the immune response of the individual (7,8). It also becomes clearer each day that both biological and psychological repercussions come, not only form the infection, but also from the pandemic in general. Hence, most health providers, not just the medical specialists will have to deal with these consequences (9). Within the current state of confusion, where a wide and complex array of signs and symptoms have yet to be fully analyzed and assigned to the true post-COVID-19 condition, we find that, as is to be expected given human nature, many groups of scientists from different disciplines work tirelessly to discover new post-COVID-19 conditions, and thus, the body of signs and symptoms continues to grow, apparently unabated. The whole scenario reminds us, if not quite so pessimistically, of the tortuous paths taken by the knowledge of chronic fatigue syndrome (10,11), or the impact brought on by new diagnostic criteria for autistic spectrum disorders (12). If we were to continue down this road, we risk a narrative where the guiding light is no longer the welfare of the patient but economic and political interests. Patients, both those with real post-COVID-19 conditions and those with doubts and questions regarding their health after an episode of COVID-19 will get lost in a sea of misinformation. In the absence of rational guidelines for the study, diagnosis, and treatment of the post-COVID-19 condition, the void will soon be filled byunproven, and sometimes risky, alternative treatments or, even worse, miraculous ones. Even if medical deontology, ethics codes, and regulations across the globe guide day to day medical practice, not everybody will stop at the chance to take advantage of the sick and desperate. The magnitude of the problem is not yet fully known, and there is no doubt that the post-COVID-19 condition will be an additional burden on both the individual and public health levels in the months and years to come. Recent surveys in the Netherlands and the United States found a prevalence of post-COVID-19 symptom persistence in adults of 12.7 and 7.5% (13,14), respectively, and a recent modeling estimates that in Europe alone there were 17 million people who had experienced a post-COVID condition in the first two years of the pandemic (15). To properly implement operational measures, definitions that are too lax or porous as well as very rigid ones should be avoided, and reliable and validated instruments must be used. On the one hand, loose diagnostic criteria and definitions would contribute to the pathologization of everyday life and could have a profound economic impact, overloading primary health care services, making it difficult to prioritize services, and severely disrupting working life due to absenteeism, presenteeism, or disability benefits. On the other hand, extremely complex or strict criteria would result in underdiagnosing the condition, leaving many unattended, some of whom would be in urgent need of care. Those left to fend for themselves could incur significant expenses to treat themselves and prevent further deterioration of their health. To move forward in the care of patients with post-COVID-19 condition we need an interest-free academic discussion based on clear technical and scientific knowledge, which is also empathic to the suffering imposed by COVID-19 on patients and their families, yet retains a clear, public health perspective and remains capable of adjusting as new knowledge becomes available. We need objective and, ideally, simple criteria to identify through clinical and laboratory studies including biomarkers, and then correctly treat, those whose health has been compromised beyond the acute phase of the infection, and also those whose problems might derive not as direct sequelae form the disease, but form the pandemic in all its dimensions. Furthermore, these starting points should not only lead to clinical guidelines, but also to the adoption of institutional administrative procedures, and to the creation of materials aimed at educating the population at large, taking into account both international guidelines as well as local regulations. It is time to implement a science-based agenda with a clear sense of social justice that strives to bring biological and psychological wellbeing to those affected by the post-COVID-19 condition and their families. Appendix Supplementary materials Image, application 1 Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.arcmed.2022.11.017. ==== Refs References 1. Couzin-Frankel J. Clues to long COVID Science 376 2022 1261 1265 10.1126/science.add4297 35709281 2. McSpedon C. Long COVID What We Know Now Am J Nurs 122 2022 20 22 10.1097/01.NAJ.0000842232.15530.f1 3. World Health Organization. A clinical case definition of post COVID-19 condition by a Delphi consensus. WHO reference number: WHO/2019-nCoV/Post_COVID-19_condition/Clinical_case_definition/2021.1. Available from: https://www.who.int/publications/i/item/WHO-2019-nCoV-Post_COVID-19_condition-Clinical_case_definition-2021.1. (Accessed October 6, 2021). 4. Proal AD VanElzakker MB. Long COVID or Post-acute Sequelae of COVID-19 (PASC): An Overview of Biological Factors That May Contribute to Persistent Symptoms Front Microbiol 12 2021 698169 10.3389/fmicb.2021.698169 5. Lopez-Leon S Wegman-Ostrosky T Perelman C More than 50 long-term effects of COVID-19: a systematic review and meta-analysis Sci Rep 11 2021 16144 10.1038/s41598-021-95565-8 34373540 6. Lopez-Leon S Wegman-Ostrosky T Ayuzo Del Valle NC Long-COVID in children and adolescents: a systematic review and meta-analyses Sci Rep 12 2022 9950 10.1038/s41598-022-13495-5 35739136 7. Sciscent BY Eisele CD Ho L COVID-19 reinfection: the role of natural immunity, vaccines, and variants J Community Hosp Intern Med Perspect 11 2021 733 739 10.1080/20009666.2021.1974665 34804382 8. Ayoubkhani D Bermingham C Pouwels KB Trajectory of long covid symptoms after covid-19 vaccination: community based cohort study BMJ 377 2022 e069676 10.1136/bmj-2021-069676 9. Munblit D Nicholson TR Needham DM Studying the post-COVID-19 condition: research challenges, strategies, and importance of Core Outcome Set development BMC Med 20 2022 50 10.1186/s12916-021-02222-y 35114994 10. Maxmen A. A reboot for chronic fatigue syndrome research Nature 553 2018 14 17 10.1038/d41586-017-08965-0 11. Noor N Urits I Degueure A A Comprehensive Update of the Current Understanding of Chronic Fatigue Syndrome Anesth Pain Med 11 2021 e113629 10.5812/aapm.113629 12. Williams ME Wheeler BY Linder L Evolving Definitions of Autism and Impact on Eligibility for Developmental Disability Services: California Case Example Intellect Dev Disabil 55 2017 192 209 10.1352/1934-9556-55.3.192 28608770 13. Ballering AV van Zon SKR Olde Hartman TC Persistence of somatic symptoms after COVID-19 in the Netherlands: an observational cohort study Lancet 400 2022 452 461 10.1016/S0140-6736(22)01214-4 35934007 14. Centers for Disease Control and Prevention (CDC). Nearly One in Five American Adults Who Have Had COVID-19 Still Have “Long COVID”. Available from: https://www.cdc.gov/nchs/pressroom/nchs_press_releases/2022/20220622.htm. (Accessed September 10, 2022). 15. Institute for Health Metrics and Evaluation (IHME). WHO: At least 17 million people in the WHO European Region experienced long COVID in the first two years of the pandemic; millions may have to live with it for years to come. Available from: https://www.healthdata.org/news-release/who-least-17-million-people-who-european-region-experienced-long-covid-first-two-years. (Accessed September 13, 2022).	0	PMC9721274	NO-CC CODE	2022-12-16 23:18:14	no	Arch Med Res. 2022 Dec 5; doi: 10.1016/j.arcmed.2022.11.017	utf-8	Arch Med Res	2,022	10.1016/j.arcmed.2022.11.017	oa_other
==== Front Gastroenterol Clin North Am Gastroenterol Clin North Am Gastroenterology Clinics of North America 0889-8553 1558-1942 Elsevier Inc. S0889-8553(22)00091-7 10.1016/j.gtc.2022.12.001 Article The pathogenesis of gastrointestinal, hepatic and pancreatic injury in acute and long COVID-19 infection Meringer Hadar MD 12 Wang Andrew 12 Mehandru Saurabh MD 12∗ 1 Henry D. Janowitz Division of Gastroenterology, Department of Medicine, 2 Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029 ∗ Please address correspondence to Saurabh Mehandru () 5 12 2022 5 12 2022 © 2022 Elsevier Inc. All rights reserved. 2022 Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Synopsis The gastrointestinal tract (GI) is targeted by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). The present review examines GI involvement in patients with long COVID and discusses the underlying pathophysiological mechanisms that include viral persistence, mucosal and systemic immune dysregulation, microbial dysbiosis, insulin resistance and metabolic abnormalities. Due to the complex and potentially multifactorial nature of this syndrome, rigorous clinical definitions and pathophysiology-based therapeutic approaches are warranted. Key words SARS-CoV-2 Long COVID Gastrointestinal MAFLD Insulin resistance ==== Body [email protected] 1425 Madison Avenue, Icahn Building 11-02, New York, NY 10029 Disclosures SM reports receiving research grants from Genentech and Takeda; receiving payment for lectures from Takeda, Genentech, Morphic; and receiving consulting fees from Takeda, Morphic, Ferring and Arena Pharmaceuticals. HM, AW, and SM do not declare any conflicts of interest relating to this work	0	PMC9721275	NO-CC CODE	2022-12-07 23:19:07	no	Gastroenterol Clin North Am. 2022 Dec 5; doi: 10.1016/j.gtc.2022.12.001	utf-8	Gastroenterol Clin North Am	2,022	10.1016/j.gtc.2022.12.001	oa_other
==== Front Med Intensiva Med Intensiva Medicina Intensiva 0210-5691 1578-6749 Published by Elsevier España, S.L.U. S0210-5691(22)00339-4 10.1016/j.medin.2022.11.002 Article “Valoración multidisciplinar de las secuelas al mes del alta hospitalaria por neumonía grave COVID 19, ¿existen diferencias en función de la terapia respiratoria empleada durante su ingreso en Cuidados Intensivos?” "Multidisciplinary Approach of the sequelae one month after hospital discharge in patients with severe bilateral COVID-19 pneumonia, are there differences depending on the respiratory therapy used during admission to Intensive Care?”Sánchez-García Ana M. 12 Martínez-López Pilar 12 Gómez-González Adela M. 13 Rodriguez-Capitán Jorge 145 Jiménez-lópez Rafael J. 16 García Almeida José M. 17 Avanesi-Molina Elma 18 Zamboschi Nicolás 12 Rueda-Molina Carolina 12 Doncel-Abad Victoria 145 Molina-ramos Ana I. 145 Cabrera-César Eva 19 Ben-Abdellatif Imad 12 Gordillo-Resina Marina 12 Pérez-Mesa Esteban 12 Nieto-González María 12 Nuevo-Ortega Pilar 12 Reina-Artacho Carmen 12 Fernández Pedro L. Sánchez 410 Jiménez-Navarro Manuel F. 145⁎ Estecha-Foncea María A. 12 1 Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina (IBIMA Plataforma BIONAND), Málaga, España 2 Servicio de Medicina Intensiva, Hospital Clínico Universitario Virgen de la Victoria, Málaga, España 3 Servicio de Medicina Física y Rehabilitación, Hospital Clínico Universitario Virgen de la Victoria, Málaga, España 4 Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Instituto de Salud Carlos III, Madrid, España 5 Servicio de Cardiología, Hospital Clínico Universitario Virgen de la Victoria, Málaga, España 6 Servicio de Medicina Familiar y Comunitaria, Hospital Clínico Universitario Virgen de la Victoria, Málaga, España 7 Servicio de Endocrinología y Nutrición, Hospital Clínico Universitario Virgen de la Victoria, Málaga, España 8 Servicio de Salud Mental, Hospital Clínico Universitario Virgen de la Victoria, Málaga, España 9 Servicio de Neumología, Hospital Clínico Universitario Virgen de la Victoria, Málaga, España 10 Servicio de Cardiología. Hospital Universitario de Salamanca-IBSAL. Universidad de Salamanca, España ⁎ Autor de Correspondencia: 5 12 2022 5 12 2022 7 9 2022 22 11 2022 © 2022 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. Objetivo: Describir las secuelas al mes del alta hospitalaria en pacientes que precisaron ingreso en Cuidados Intensivos por neumonía grave COVID 19 y analizar las diferencias entre los que recibieron terapia exclusivamente con oxigenoterapia con alto flujo con respecto a los que precisaron ventilación mecánica invasiva. Diseño: Estudio de cohorte, prospectivo y observacional Ámbito: Consulta multidisciplinar post cuidados intensivos Pacientes o participantes: Pacientes que superaron el ingreso en la unidad de cuidados intensivos (UCI) por neumonía grave COVID 19 desde abril 2020 hasta octubre 2021 Intervenciones: Inclusión en el programa multidisciplinar post UCI Variables de interés principales: Secuelas motoras, sensitivas, psicológicas/psiquiátricas, respiratorias y nutricionales tras el ingreso hospitalario. Resultados: Se incluyeron 104 pacientes. 48 pacientes recibieron oxigenoterapia nasal de alto flujo (ONAF) y 56 ventilación mecánica invasiva (VMI). Las principales secuelas encontradas fueron la neuropatía distal (33,9% VMI vs 10,4% ONAF); plexopatía braquial (10,7% VMI vs 0% ONAF); disminución de fuerza de agarre: mano derecha 20,67kg (+/- 8,27) en VMI vs 31,8 kg (+/- 11,59) en ONAF y mano izquierda 19,39kg (+/- 8,45) en VMI vs 30,26kg (+/- 12,74) en ONAF; y balance muscular limitado en miembros inferiores (28,6% VMI vs 8,6% ONAF). Las diferencias observadas entre ambos grupos no alcanzaron significación estadística en el estudio multivariable. Conclusiones: Los resultados obtenidos tras el estudio multivariable sugieren no existir diferencias en cuanto a las secuelas físicas percibidas al mes del alta hospitalaria en función de la terapia respiratoria empleada, ya fuera oxigenoterapia nasal de alto flujo o ventilación mecánica prolongada, si bien son precisos más estudios para poder obtener conclusiones al respecto. Objective: To describe the sequelae one month after hospital discharge in patients who required admission to Intensive Care for severe COVID 19 pneumonia and to analyze the differences between those who received therapy exclusively with high-flow oxygen therapy compared to those who required invasive mechanical ventilation. Design: Cohort, prospective and observational study. Setting: Post-intensive care multidisciplinary program Patients or participants: Patients who survived admission to the intensive care unit (ICU) for severe COVID 19 pneumonia from April 2020 to October 2021 Interventions: Inclusion in the post-ICU multidisciplinary program Main variables of interest: Motor, sensory, psychological/psychiatric, respiratory and nutritional sequelae after hospital admission Results. 104 patients were included. 48 patients received high-flow nasal oxygen therapy (ONAF) and 56 invasive mechanical ventilation (IMV). The main sequelae found were distal neuropathy (33.9% IMV vs 10.4% ONAF); brachial plexopathy (10.7% IMV vs 0% ONAF); decrease in grip strength: right hand 20.67kg (+/- 8.27) in VMI vs 31.8kg (+/- 11.59) in ONAF and left hand 19.39kg (+/- 8.45) in VMI vs 30.26kg (+/- 12.74) in ONAF; and limited muscle balance in the lower limbs (28.6% VMI vs 8.6% ONAF). The differences observed between both groups did not reach statistical significance in the multivariable study. Conclusions. The results obtained after the multivariate study suggest that there are no differences in the perceived physical sequelae one month after hospital discharge depending on the respiratory therapy used, whether it was high-flow nasal oxygen therapy or prolonged mechanical ventilation, although more studies are needed to be able to draw conclusions. Palabras clave COVID-19 Insuficiencia Respiratoria Ventilación Mecánica ONAF Secuela Keywords COVID-19 Respiratory failure Mechanical ventilation ONAF sequelae ==== Body pmc	36506823	PMC9721276	NO-CC CODE	2022-12-12 23:20:22	no	Med Intensiva. 2022 Dec 5; doi: 10.1016/j.medin.2022.11.002	utf-8	Med Intensiva	2,022	10.1016/j.medin.2022.11.002	oa_other
==== Front Ethique Sante Ethique Sante Ethique & Sante 1765-4629 1769-695X Elsevier Masson SAS. S1765-4629(22)00108-8 10.1016/j.etiqe.2022.11.003 Article Gestion de la pandémie à Sars-Cov2 en France – Balance bénéfice-risque à l’échelle collective versus à l’échelle individuelle chez les enfants Claudet Isabelle Pr 12⿎ Bréhin Camille 13 1 Service des Urgences pédiatriques, Hôpital des Enfants, CHU Toulouse, France 2 Inserm, UMR 1295, Cerpop, Université Paul Sabatier Toulouse III, France 3 Service de Pédiatrie et Infectiologie générale médicochirurgicale, Hôpital des Enfants, CHU Toulouse, France ⿎ Auteur correspondant: Urgences pédiatriques, Hôpital des Enfants, 330, avenue de Grande-Bretagne, TSA 70039, 31059 Toulouse cedex 9, France 5 12 2022 5 12 2022 © 2022 Elsevier Masson SAS. All rights reserved. 2022 Elsevier Masson SAS 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. Résumé Selon le principe de précaution et face à l’incertitude initiale de la gravité potentielle de la Covid-19, la France a adopté des mesures collectives comprises comme acceptables malgré la privation de liberté et les risques connus d’un confinement long sur la santé mentale des individus. De telles mesures devraient être appliquées de façon proportionnelle et causer le moins de tort possible. Parmi celles-ci, la fermeture des écoles a été décidée par déclinaison de celles figurant dans des plans de réponse à des pandémies virales où les enfants jouent un rôle majeur dans la transmission de la maladie (ex. grippe). De façon inédite, des mesures et contraintes ont été prises à l’encontre de l’intérêt des enfants et pour protéger un autre groupe vulnérable que les enfants eux-mêmes. Du point de vue de la santé des enfants, le rapport entre gains de santé liés à ces mesures et conséquences négatives a été déséquilibré. La diminution du temps d’instruction a réduit le rendement scolaire général et a eu des conséquences défavorables sur la socialisation et le développement des enfants. Le confinement a généré une accidentologie domestique plus grave, une majoration des violences intrafamiliales et des effets collatéraux marqués en termes de santé mentale des adolescents. Très tôt, les différentes publications ont montré que les enfants n’étaient pas moteur de cette pandémie – Si l’application initiale de mesures collectives était légitime, l’adaptation des mesures à l’échelle individuelle a été en décalage avec les répercussions déjà connues suivies de celles constatées sur la santé de l’enfant. According to the precautionary principle and facing the initial uncertainty of the potential seriousness of Covid-19, France has adopted collective measures understood as acceptable despite the deprivation of liberty and the known risks of long confinement on mental health. Such measures should be applied proportionately and cause the least possible harm. Among these, the closure of schools was decided by declination of those appearing in response plans to viral pandemics where children play a major role in the transmission of the disease (e.g. flu). In an unprecedented way, measures and constraints have been taken against the interests of children and to protect a vulnerable group other than the children themselves. From the perspective of children's health, the relationship between health gains from these measures and negative consequences has been unbalanced. The reduction in instruction time has reduced overall academic performance and has had adverse consequences for the socialization and development of children. Confinement has generated more serious domestic accidents, an increase in intra-family violence and marked collateral effects in terms of the mental health of adolescents. Very early on, the various Covid19-related publications showed that children were not the driving force behind this pandemic – If the initial application of collective measures was legitimate, the adaptation of measures at the individual level was out of step with the already known repercussions followed by those observed on the health of the child. Mots clés Pandémie Sars-Cov2 enfant autonomie principe de precaution proportionnalité Key words Sars-Cov2 pandemic children autonomy proportionality principle of cautionary ==== Body pmc	36506714	PMC9721277	NO-CC CODE	2022-12-07 23:19:07	no	Ethique Sante. 2022 Dec 5; doi: 10.1016/j.etiqe.2022.11.003	utf-8	Ethique Sante	2,022	10.1016/j.etiqe.2022.11.003	oa_other
==== Front Eur J Intern Med Eur J Intern Med European Journal of Internal Medicine 0953-6205 1879-0828 European Federation of Internal Medicine. Published by Elsevier B.V. S0953-6205(22)00430-7 10.1016/j.ejim.2022.12.001 Letter to the Editor Trend in the proportion of subjects with SARS-CoV-2 infection without COVID-19 specific symptoms among patients admitted to a COVID-19 referral hospital Giacomelli Andrea a⁎1 Ridolfo Anna Lisa a1 Oreni Letizia a Rizzardini Giuliano b Antinori Spinello ac on behalf of COVID-Sacco Study group# a III Division of Infectious Diseases, ASST Fatebenefratelli Sacco, Luigi Sacco Hospital, Milan, Italy b I Division of Infectious Diseases, ASST Fatebenefratelli Sacco, Luigi Sacco Hospital, Milan, Italy c Department of Biomedical and Clinical Sciences, University of Milan, Italy ⁎ Corresponding author. 1 These authors contributed equally to the paper # COVID-Sacco Study group: Giacomo Casalini, Fabio Borgonovo, Andrea Poloni, Giorgia Carrozzo, Stefania Caronni, Federico Sabaini, Alessandra Helen Behring, Aurora Civati, Serena Reato. 5 12 2022 5 12 2022 25 11 2022 30 11 2022 2 12 2022 © 2022 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved. 2022 European Federation of Internal Medicine 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 Incidental COVID-19 Respiratory support Elderly Vaccine Asymptomatic patients ==== Body pmcDear editor The clinical and epidemiological characteristics of COVID-19 gradually changed during different waves [1]. The highly transmissible omicron variant that has globally dominated during 2022 has led to an unprecedented surge in the number of infections although producing lower hospital admission rates and less severe disease amongst the patients who are admitted [2]. Moreover, a recent study in the United States has estimated that almost 14% of the SARS-CoV-2 positive patients admitted to hospitals during the omicron BA1 wave had a reason for admission other than COVID-19 (cases of incidental COVID-19) [3], and study conducted in The Netherlands during the omicron BA.1/BA.2 wave found that 31% of the patients admitted with COVID-19 could be classified as incidental COVID-19 cases [4]. Continuing to monitor the clinical characteristics of hospitalised subjects with SARS-CoV-2 infection is therefore a crucial means of ensuring an appropriate response to the evolving picture of the disease. The aim of this study was to compare the characteristics of the patients hospitalised at a COVID-19 referral centre in Milan, Italy, during the last three consecutive waves of the COVID-19 epidemic. This was a repeated cross-sectional study carried out at the Department of Infectious Diseases and Intensive Care Unit of Luigi Sacco hospital, which has acted as a COVID-19 referral centre for the city of Milan since the start of the epidemic in Italy. We extracted from our prospective COVID-19 hospital registry (the characteristics of which have been extensively described elsewhere [1,[5], [6], [7]]) the patients who were hospitalised on a random day of the week of peak COVID-19 hospitalisations during the third, fourth and fifth waves (W3-W5) of the Italian epidemic: the third week of March 2021, the third week of January 2022, and the fourth week of July 2022. National surveillance data indicate that W3 was a pre-delta wave characterised by the predominance of the alpha variant, W4 was characterised by the first omicron surge in Italy, and W5 was characterised by a mix of omicron sub-variants (0.4% BA1.1, 29.7% BA.2, 13% BA.4, and 56.7% BA.5) [8]. The demographic and clinical characteristics of the selected patients were used to categorise them on the basis of the current COVID-19 treatment guidelines as cases of mild, moderate, severe or critical COVID-19, or cases of SARS-CoV-2 infection (confirmed by a positive nasopharyngeal swab test) without any COVID-19-specific symptoms. The reasons for the hospitalisation of the asymptomatic patients were also examined. The descriptive statistics use proportions for categorical variables, and median values and IQRs for continuous variables. The demographic and clinico-epidemiological characteristics of the patients by period of hospital stay were compared using the χ 2 test or, when necessary, Fisher's exact test in the case of categorical variables or Wilcoxon's rank-sum test in the case of continuous variables. On the randomly selected days during W3, W4 and W5, there were respectively 153, 119 and 62 hospitalised patients with SARS-CoV-2 infection: Table 1 shows their demographic and clinical characteristics. The median age of the patients progressively increased from 68 (IQR 57–76) years in W3, to 75 (IQR 65–84) years in W4, and 84 (IQR 79–90) years in W5 (p<0.001). The median time from symptom onset to admission progressively decreased from nine (IQR 7–10) days in W3 to seven (IQR 4–10) days in W4 and 3 (IQR 2–5) days in W5 (p<0.001). The only patient to have received early SARS-CoV-2 treatment before hospitalisation was one hospitalised during W5. The severity of the disease significantly decreased, with 53 (34.6%) of the patients hospitalised during W3 having critical disease as against 14 (11.8%) hospitalised during W4 and two (3.2%) hospitalised during W5 (p<0.001). The proportion of patients hospitalised with SARS-CoV-2 infection but without COVID-19-specific symptoms significantly increased from 0.7% in W3 to 18.5% in W4 and 40.3% in W5 (p<0.001). The main reasons for hospitalisation of subjects without COVID-19 specific symptoms during W4 and W5 were similar: surgical procedures (5/22 and 4/25), acute cardiovascular disease (2/22 and 4/25), acute neurological disorders (3/2 and 2/25), psychiatric disorders (3/22 and 0/25), and complicated urinary tract infections (3/22 and 3/25).Table 1 Characteristics of the study population. Table 1 W3 n = 153 W4 n = 119 W5 n = 62 p-value Biological sex, n (%) Female 52 (34.0) 59 (49.6) 29 (46.8) 0.024 Male 101 (66.0) 60 (50.4) 33 (53.2) Median age, years (IQR) 68 [57, 76] 75 [65, 84] 84 [79, 90] <0.001 Age strata, n (%) <46 11 (7.2) 7 (5.9) 1 (1.6) <0.001 46–60 36 (23.5) 17 (14.3) 1 (1.6) 61–75 69 (45.1) 40 (33.6) 9 (14.5) >75 37 (24.2) 55 (46.2) 51 (82.3) Median CCI, median [IQR] 3 [2, 5] 4 [3, 6] 5 [5, 7] <0.001 Co-morbidities Obesity, n (%) 46 (30.1) 37 (31.1) 6 (9.7) 0.004 Diabetes, n (%) 32 (20.9) 18 (15.1) 6 (9.7) 0.114 Lung disease, n (%) 18 (11.8) 28 (23.5) 17 (27.4) 0.008 Heart disease, n (%) 97 (63.4) 70 (58.8) 53 (85.5) 0.001 Renal disease, n (%) 10 (6.5) 19 (16.0) 11 (17.7) 0.018 Oncological disease, n (%) 13 (8.5) 25 (21.0) 14 (22.6) 0.004 Immune system disorder, n (%) 9 (5.9) 10 (8.4) 5 (8.1) 0.696 Liver disease, n (%) 7 (4.6) 5 (4.2) 0 (0) 0.239 X-ray documenting pneumonia upon admission, n (%) 149 (97.4) 85 (71.4) 25 (40.3) <0.001 Median number of days from admission to data collection (IQR) 9 [5, 14] 8.00 [3, 16] 7[2, 14] 0.245 O2 therapy support at the time of data collection, n (%) No O2 43 (28.1) 60 (50.4) 29 (46.8) <0.001 Nasal cannula 26 (17.0) 22 (18.5) 18 (29.0) Venturi 27 (17.6) 20 (16.8) 10 (16.1) Reservoir 4 (2.6) 3 (2.5) 2 (3.2) C-PAP 26 (17.0) 7 (5.9) 3 (4.8) MV 27 (17.6) 7 (5.9) 0 (0) Disease severity at the time of data collection, n (%) No COVID-19-related symptoms 1 (0.7) 22 (18.5) 25 (40.3) <0.001 Mild 2 (1.3) 8 (6.7) 10 (16.1) Moderate 66 (43.1) 52 (43.7) 15 (24.2) Severe 31 (20.3) 23 (19.3) 10 (16.1) Critical 53 (34.6) 14 (11.8) 2 (3.2) SARS-CoV-2 vaccine, n (%) 1 (0.7) 72 (60.5) 58 (93.5) <0.001 Doses of SARS-CoV-2 vaccine, n (%) 0 152 (99.3) 47 (39.5) 4 (6.7) <0.001 1 1 (0.7) 6 (5.0) 1 (1.7) 2 0 (0) 45 (37.8) 6 (10.0) 3 0 (0) 21 (17.6) 49 (81.7) List of abbreviations: n, number; IQR, Inter Quartile Range; CCI, Charlson comorbidity index; C-PAP, Continuous Positive Airway Pressure; MV, Mechanical Ventilation. Our data show a trend towards a reduction of the number of patients hospitalised at our COVID-19 referral centre with SARS-CoV-2 infection and COVID-19-specific symptoms during the current wave of infections due to the omicron variant that is line with national estimates [9]. The proportion of asymptomatic SARS-CoV-2 infections reached 40% during the latest peak of COVID-19 hospitalisations at the end of July 2022, when infections due to the omicron sub-variants BA.2, BA.4 and BA.5 were spreading throughout Italy [8]. This finding is consistent with what was initially observed after the emergence of the omicron variant in the US [3] and other European countries [4]. A number of factors may have contributed to the changes observed by us. First of all, the successful development and roll-out of effective vaccines dramatically reduced the odds of SARS-CoV-2 infected subjects developing severe/critical disease [2], and the availability of early antiviral and monoclonal treatments further reduced the likelihood of hospitalisation amongst subjects at increased risk of severe COVID-19 [10]. Secondly, omicron variant is more transmissible but less virulent than its predecessors [8] and, although it has increased the total number of SARS-CoV-2 infections detected in the general population, this has not been accompanied by a proportional increase in hospitalisations due to COVID-19 [8]. Furthermore, the increased spread of omicron variants in the general population may also explain the increasing number of people who need to be hospitalised for reasons other than COVID who incidentally test positive for SARS-CoV-2. Our observation of a predominance of elderly, fully vaccinated subjects with a high co-morbidity burden during W5 should not be considered surprising because: 1) it is known that the vaccines are most effective in younger subjects,; 2) SARS-CoV-2 infection can worsen the general condition of frailer subjects; and 3) although the time from symptom onset to hospital admission was within the window of opportunity for effective early treatment during W5, only one patient actually received an anti-viral agent before being hospitalised. Many Italian hospitals still continue the existence of COVID-19-dedicated wards that were originally intended to provide adequate isolation and respiratory support to COVID-19 patients with respiratory symptoms. This policy may now be questionable and consideration should be given to creation of isolation areas (‘bubbles’) in every specialist ward in order to provide optimal care to SARS-CoV-2 positive patients requiring hospitalisation for reasons other than COVID-19. The main limitations of our study are the use of a convenience sample of subjects in order to provide a picture of hospitalised subjects during the peak periods of different epidemic waves and that our observations are limited to setting in which dedicated COVID-19 wards admit patients solely on the basis of a positive naspharyngeal swab. In conclusion, the characteristics of hospitalised subjects with SARS-CoV-2 infection evolved during the course of the pandemic, and 40% of those admitted to our COVID-19 referral centre during the last epidemic period were admitted for reasons other than COVID-19. The persistent predominance of elderly patients and patients with multiple morbidities amongst COVID-19 cases suggests the need to strengthen preventive interventions that counteract the current under-use of early antiviral and monoclonal treatments in outpatients and encourage the uptake of booster doses of COVID-19 vaccines. Authors’ contributions AG and ALR designed the study; AG and LO were responsible for the statistical analysis. All of the authors contributed to patient enrolment, and the collection and interpretation of the data. GR, ALR and SA supervised the project. AG prepared a preliminary draft of the manuscript, which was critically reviewed by ALR and SA. All of the authors have read and approved the final manuscript. Data availability statement The complete dataset will be provided in txt format upon reasonable request to the corresponding author. Funding statement The study was not funded. Declaration of Competing Interest The authors have no conflict of interest relating to this study. AG has received consultancy fees from Mylan and Jansen, and non-financial educational support and a research grant from Gilead sciences and ViiV Healthcare. GR has received grants and fees for speaker bureaux, advisory boards and CME activities from BMS, ViiV, MSD, AbbVie, Gilead, Janssen and Roche. SA has received support for research activities from Pfizer and Merck Sharp & Dome. The other authors have nothing to declare. Acknowledgement the authors would like to thank all the subjects involved in the present study and all the health care workers involved in COVID-19 care. COVID-Sacco Study group: Giacomo Casalini, Fabio Borgonovo, Andrea Poloni, Giorgia Carrozzo, Stefania Caronni, Federico Sabaini, Alessandra Helen Behring, Aurora Civati, Serena Reato. Ethics approval statement This study was approved by our Ethics Committee (Comitato Etico Interaziendale Area 1, Milan, Italy: Protocol No. 16088). Patient consent statement Informed consent was obtained directly from the patients capable of making informed decisions about their medical care and participation in the study; otherwise, informed consent was obtained from his/her legal guardian or representative. ==== Refs References 1 Giacomelli A. Ridolfo A.L. Pezzati L. Oreni L. Carrozzo G. Beltrami M. Mortality rates among COVID-19 patients hospitalised during the first three waves of the epidemic in Milan, Italy: a prospective observational study PLoS ONE 17 4 2022 e0263548 2 Andrews N. Stowe J. Kirsebom F. Covid-19 vaccine effectiveness against the omicron (B.1.1.529) Variant N Engl J Med 386 16 2022 1532 1546 35249272 3 Harris J.E. Population-Based estimation of the fraction of incidental COVID-19 hospitalizations during the omicron BA.1 wave in the United States. medRxiv 2022. 01.22.22269700; doi: 10.1101/2022.01.22.22269700. 4 Voor In 't Holt A.F. Haanappel C.P. Rahamat-Langendoen J. Admissions to a large tertiary care hospital and Omicron BA.1 and BA.2 SARS-CoV-2 polymerase chain reaction positivity: primary, contributing, or incidental COVID-19 Int J Infect Dis 122 2022 665 668 35842214 5 Giacomelli A. Ridolfo A.L. Milazzo L. Oreni L. Bernacchia D. Siano M. 30-day mortality in patients hospitalized with COVID-19 during the first wave of the Italian epidemic: a prospective cohort study Pharmacol Res 158 2020 104931 6 Giacomelli A. Pagani G. Ridolfo A.L. Oreni L. Conti F. Pezzati L. Early administration of lopinavir/ritonavir plus hydroxychloroquine does not alter the clinical course of SARS-CoV-2 infection: a retrospective cohort study J Med Virol 93 3 2021 1421 1427 32776534 7 Giacomelli A. Ridolfo A.L. Bonazzetti C. Oreni L. Conti F. Pezzati L. Mortality among Italians and immigrants with COVID-19 hospitalised in Milan, Italy: data from the Luigi Sacco Hospital registry BMC Infect Dis 22 1 2022 63 35045808 8 https://www.epicentro.iss.it/coronavirus/pdf/sars-cov-2-monitoraggio-varianti-rapporti-periodici-28-luglio-2022.pdf Accessed 30 October 2022. 9 Marziano V. Guzzetta G. Menegale F. The decline of COVID-19 severity and lethality over two years of pandemic medRxiv 2022 10.1101/2022.07.01.22277137 07.01.22277137; doi 10 Wen W. Chen C. Tang J. Efficacy and safety of three new oral antiviral treatment (molnupiravir, fluvoxamine and Paxlovid) for COVID-19:a meta-analysis Ann Med 54 1 2022 516 523 35118917	36481096	PMC9721278	NO-CC CODE	2022-12-07 23:19:10	no	Eur J Intern Med. 2022 Dec 5; doi: 10.1016/j.ejim.2022.12.001	utf-8	Eur J Intern Med	2,022	10.1016/j.ejim.2022.12.001	oa_other
==== Front Transp Res Interdiscip Perspect Transp Res Interdiscip Perspect Transportation Research Interdisciplinary Perspectives 2590-1982 The Author(s). Published by Elsevier Ltd. S2590-1982(22)00197-X 10.1016/j.trip.2022.100737 100737 Article The impact of COVID-19 and related containment measures on Bangkok’s public transport ridership Siewwuttanagul Somsiri a Jittrapirom Peraphan b⁎ a The Cluster of Logistics and Rail Engineering, Faculty of Engineering, Mahidol University, Thailand b Research Methodology Group, Business Administration Nijmegen School of Management, Radboud University, the Netherlands ⁎ Corresponding author. 5 12 2022 1 2023 5 12 2022 17 100737100737 26 8 2022 22 11 2022 28 11 2022 © 2022 The Author(s) 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. The COVID-19 pandemic and related measures used to contain its spread affected public transport ridership in cities around the world. In Thailand, the government issued 41 Royal Decrees between April 2020 and December 2021 to mitigate the spread of the pandemic. In this study, we investigate how Bangkok's public transport services (bus, metro, and boat) have been affected during this period by analyzing the daily ridership data, confirmed COVID-19 cases, and aggregated travel trends by trip destinations using from Google mobility reports. The results show that public transport ridership decreased as daily COVID cases increased and the levels of restraining measures became higher. However, other factors, such as relative strictness compared to earlier measures and sequencing of the measures seems to have had an impact on the ridership. Moreover, the impact on ridership trends is unique for each of the three modes. Bus and metro ridership appear to be more sensitive to the changes in restrictions than the boats. Bus and metro ridership also shows similar changes in the travel trends concerning the place of visit. The findings reported here provide first insights into how Bangkok's public transport systems were affected and suggest the rationale of why different public transport modes were affected differently. These results can be useful for researchers and for decision-makers who plan and design policies and measures for public transport services. Keywords Public transport Ridership COVID-19 Level of restriction Travel behavior ==== Body pmcIntroduction The widespread COVID-19 pandemic has significantly affected how urban public transport services are used and organized. In several cities, the use of these services is considered a high-risk activity, as limited space on public transport vehicles can make it difficult to practice the recommended physical distancing, increasing the chances of being infected. In their responses to these risks, travelers have altered their travel patterns or avoided commuting altogether by working from home. Transport providers and governments have adjusted the availability of public transport services in some cities (Shen, 2021). These significant changes in travel patterns on public transport and service availability have imposed new and important challenges for public transport policymaking and planning (Budd and Ison, 2020). Several studies have provided insights into how the pandemic and public mitigation measures associated with the pandemic have impacted urban public transport systems. For example, Parker et al., 2021, Liu et al., 2020 highlighted the impact in the form of reduced frequency services in the United States. Several studies have reported cases from Europe (e.g. London (Prez, 2020); (TfL, 2021), Naples, Rome, and Valencia (Schulte-Fischedick et al., 2021)). However, there is still limited research set in developing countries. For example, studies such as Mogaji et al., 2022, Abdullah et al., 2021 were set in Nigeria and Pakistan. Previous studies have highlighted how public transport systems in developing countries are unique in their characteristics and the challenges faced, including the lack of public funding, availability of infrastructure, and affordability by users (Iles, 2005). It is therefore important to gather insights into how public transport services were affected during the pandemic in these contexts. This study aims to contribute to the field by providing information and analysis to illustrate how the public transport services of Bangkok, Thailand, were affected during the outbreak of the pandemic. We analyzed Bangkok's public transport ridership data (metro, bus, and boat) and analyzed its correlations with daily COVID-19 cases and government measures during the period. We also examined correlations between ridership and aggregated data from Google’s COVID-19 Community Mobility Reports which illustrate changing trends in trip destinations. The period covered by this study is between January 2020 and December 2021 (24 months), which covers three months before the pandemic declaration in Thailand (Jan - Mar 2020), the first wave of infection (April - June 2020), and the second wave of infection (April - Nov 2021). Impact of COVID-19 on public transport services Several scientific studies have examined how public transport usage has been affected by the COVID-19 pandemic. The literature included here can be broadly classified by the focus into two categories: a) studies that examined the change in public transport usage; and b) studies focusing on the changes in the travel behavior of public transport users.a) Changes in public transport usage Typically, public transport ridership is adversely affected by a pandemic. This is related to the frame of mind of its users toward any disruptive event that can impact their health and safety in populated spaces. Wang (2014) reported on how the public perceived the risk of contracting the SARS virus when traveling on the metro system. The study has relevance to the current COVID-19 pandemic, which is even more contagious than SARS. This could a reason for a significant reduction in public transportation trips observed in cities around the world (Haas et al., 2020, Jenelius and Cebecauer, 2020). In some studies, negative correlations between transit ridership numbers and daily confirmed COVID-19 cases were observed; the number of transit trips decreased at the moment when COVID-19 cases in these cities started to rise (Arellana et al., 2020, Wang and Noland, 2021). In comparison with other modes, the travel patterns of public transport users were found to be more significant than other modes of transport, in accordance with studies by Parker et al. (2021) conducted in New York, USA and Aloi et al. (2020) in Santander, Spain. However, the difference is not universal as reported by Arellana et al. (2020). They found that personal car trips decreased at a relatively higher proportion than public transport trips in Colombia. Possible explanations are the socio-economic factors that influenced the ability to work from home and the proportion of mode-share transport in these cities. The restraint measures during a lockdown may reduce the frequency of public transport and cause suspension of services, which can reduce demand in certain cases. Some public transport services may also change their service frequency, but it is not a dominant factor that reduces ridership (Jenelius and Cebecauer, 2020). The reduction in transit user numbers resulted mainly from the changes in travel behavior by those trying to avoid the risks of infection while commuting and traveling (Zhang et al., 2021). It was found that the reduction in transit use occurred not only during the weekdays but also during the weekends: particularly journeys to and from stations with large-scale social activities nearby. A study by Liu et al. (2020) revealed that the pattern of transit demand during each day can also be affected during a pandemic. The study reported a change in temporal dynamics of subway users in New York City: the highest period of subway usage shifted from the morning period to the afternoon of weekdays. However, this change in dynamics can also be different as revealed by Aloi et al. (2020). In their study, the bus ridership during the morning and midday peaks decreased less than during the afternoon peaks. Also, the study of Mützel and Scheiner (2021) shows that although there was a significant drop in ridership there was not much change in the trend of temporal metro usage in Taipei during the pandemic. A physical lockdown is considered by several authorities to be an effective preventive measure to halt the spread of a virus. Government policies and restraining measures during pandemics can have a diverse impact on public transport ridership, depending on how the restraining measures were implemented and the levels of strictness imposed. Typically, government restraining measures include orders to stay at or work from home, as well as order that limit movement and travel between locations, which can significantly decrease demand for public transport (Arellana et al., 2020). At the peak of the pandemic, public transport ridership across the world, as observed by Gkiotsalitis and Cats (2021), fell below its norm by 50 % to 90 %. Lockdown measures can keep mobility behavior at a low level for an extended period, which appears to have been an effective measure to contain and reduce the spread of COVID-19 in several cases as observed by Rasca et al. (2021). The recovery rate of each transport mode after the full lockdown was widely discussed. Governmental restraining measures, such as staying at home, affects transit ridership numbers, but after relaxing the measures, numbers did not improve much, as reported in Wang and Noland (2021) with subway trips in New York city still showing negative effects even after government measures had been relaxed for two phases. The study of public transport demand by Arellana et al. (2020) also found a similar trend: transit demand did not recover much after the government ended the mandatory quarantine and announced reopening measures. There are also studies on easing restraining measures and returning to (new) norms. Beck and Hensher (2020) observed key events, including governmental restraining measures and the change in travel behavior between the first and second waves of COVID-19 in Australia. Results show that mobility started to recover after the government announced nationwide guidelines for easing restrictions. The results from the survey show that working from home continues to be an important strategy in reducing travel and pressure on constrained transport networks. The change in public transport travel behavior also caused financial instability for transit operations as reported by Chang et al. (2021). Tiikkaja and Viri (2021) investigated the changes in public transport ridership, service frequencies, and average fill rates during the epidemic. The results suggested that the city center area was most affected by the pandemic as the ridership dropped more than anywhere else.b) Changes in the travel behavior of public transport users Two studies (Zhang et al., 2021, Chang et al., 2021) found that travelers with distinct demographics adjusted their travel behavior differently in the face of pandemics. Even though there is a great reduction in travel, especially on weekends, there was no significant change in patterns of movement for children (ages 3–11 years) before or during the pandemic. A similar finding was also revealed in a study by Ahangari et al. (2020) which compared the impact on rail and bus ridership in the United States. They found that demographic and socioeconomic factors (racial background and unemployment) have correlations with rail ridership reduction. For bus ridership, Parker et al., 2021, Fatmi et al., 2021 revealed that higher foreign-born background, public transport ridership, and unemployment appear to be associated with a higher reduction in bus ridership during the pandemic. Additionally, lower-income transit users were found to have a significantly smaller reduction in the number of trips and distance traveled than higher-income transit riders. A perceived risk of becoming infected in crowded and enclosed spaces can also drive travelers to avoid public transport and shift to private vehicles. The study by Labonté-LeMoyne et al. (2020) suggested commuters shifted to using their private cars instead of mass transit (including subway, bus, and commuter train) because of the perceived impacts on physical and psychological risk. The study suggests cleaning practices, mask-wearing, hand sanitizing, and physical distancing are major measures to be considered by public transport operators. Several studies focused on assessing the impacts of the pandemic on different modes of transport. They compared ridership across different public modes, such as transit and bike-sharing against private cars. Single-occupant modes, such as private cars, bicycles, and walking, seem to be preferred (Kolarova et al., 2021). An increase in bicycle trips was observed by Huang et al. (2020), during a period when the number of COVID-19 cases rose and public transport trips decreased. Another study by Wang and Noland (2021) also shows bicycle trips in New York city rose significantly during the period of the pandemic. The daily number of bicycle trips was higher than in the pre-COVID-19 period. However, as public transport trips started to recover, the number of bicycle trips decreased. Xin et al. (2021) studied the effect of COVID-19 on the daily ridership of urban rail transit and found that some Chinese cities had already experienced a more severe ridership reduction but a lower infection rate than others. The study found that the ridership reduction was not strongly associated with the infection rate. The urban rail transit ridership reductions are associated with the severity and duration of governmental restrictions and lockdowns: More stringent and longer lockdowns can lead to a greater ridership reduction under the assumption of similar health risks perceived by citizens. The study by Rasca et al. (2021) observed how transit use in Vienna, Innsbruck, Oslo, and Agder was affected by different levels of government restraining measures, such as gradual restriction, sudden imposed restrictions, and relaxed measures. This study revealed a stronger decrease in public transport ridership during the early phase of the pandemic than in other periods, even though the subsequent daily COVID-19 cases increased dramatically from October to December. The finding indicates how emerging disruption can have a stronger impact on the use of public transport due to uncertainty or what was described as “fresh fear”. It appears that different modes of transport may have different patterns in how their ridership bounced back. A study by Orro et al. (2020) shows that Coruña’s bus ridership recovered to its pre-COVID-19 level at a slower pace than the city’s shared bicycles and private cars. Even as bus service operations and frequencies returned to normal, the ridership was at only 50–60 % compared with the pre-COVID-19 period. Bike-sharing also seemed to be more resilient than public transportation during the pandemic as its use showed a quick recovery to the same level as the pre-pandemic time in 2019, soon after the first relaxation of government mitigation measures (Wang and Noland, 2021). The brief scan of the literature above highlights that while several aspects of COVID-19 affecting the use of public transport have been investigated, there is a lack of empirical evidence on the dynamics of public transport usage related to governmental restraining measures during the global crisis that affects transport restrictions. This study attempts to fill the gap by outlining the relationship between the change in public transport use during each level of restriction and the COVID-19 situation in Bangkok to provide insights into public transport adaptation in the future. Methodology The methodology applied in this study is guided by the litureature (e.g. Brakewood et al., 2015, Liu et al., 2020, Quéré et al., 2020) and applied here to describe the impact of COVID-19 on public transport use under the sequence of governmental restraining measures. A descriptive analysis is include in Section 4.1 to examine the general patterns of public transport ridership in Bangkok city during the period. We then examined the correlations between the ridership data with the daily confirmed COVID-19 cases in Bangkok retrieved from OTP (2020) in Section 4.2 using Pearson’s correlation coefficient analysis (Kurumida et al., 2020). Finally, we analyzed the correlation between the ridership data with aggregated trip destination data obtained from Google COVID-19 Community Mobility Reports (Google, 2021) to ascertain public transport use trends according to traveler destination types (work and leisure) during the period (Section 4.3). These analyses are combined to provide insights into how the pandemic and associated public restraining measures affected public transport travel behavior. Data The daily public transport ridership of Bangkok city was obtained from the Ministry of Transport (MOT, 2021) and the number of daily COVID-19 cases was obtained from the Department of Disease Control of Thailand (DDC, 2021). Both sets of data were publicly accessible. Additionally, we collected place visit data from Google’s COVID-19 Community Mobility Reports (Google, 2021). The Bangkok public transport ridership data consists of the ridership of metro, bus, and boat services for which the modal split accounted for 46 %, 52 %, and 2 % respectively. The Bangkok Metropolitan Administration (BMA) has five metro lines (Blue Line, Purple Line, Light-Green line, Dark-Green line, and the Airport Rail Link). This study excludes the Gold Line which started operations during the pandemic due to incomplete ridership data for comparison with the other public transport ridership data used in this study. The bus services included the official public buses operated by the Bangkok Mass Transit Authority (BMTA) and affiliated bus operators. The use of the informal minibus and vans is not included in this study due to unavailable data. The public boat services included are express boats with routes on the Chao Phraya river and canal boats. These public transport trips faced approximately a 90 % drop in ridership during the first lockdown to mitigate COVID-19 in April 2020. The Google mobility data set illustrates the aggregated trends in the travel behavior of Android phone users during the period, which accounted for approximately 75 % of all smartphone users in Thailand (around 54 millions or 78 % of the population). The data is classified by trip purpose into two categories: a) leisure activity visits, which include retail and park visits, and b) workplace visits only. The classification was made to highlight the differences in leisure and work-based trips during the pandemic. The Google data illustrates the relative changes in trip purposes and destinations with a reference to the baseline value (average value of the 5-week pre-COVID-19 period from 3rd January to 6th February 2020). The details of the restraining measures announced by the Thai government to mitigate the pandemic were obtained from the government website. Physical restraining measures The Thai government issued a total of 41 Royal Decrees to enforce physical limitations on its citizens within the study period (between 1st January 2020 and 31st December 2021). Decrees are proposed by the government and endorsed by the head of the state, the King, to provide a legalized order to enforce the physical movement limitation of the population. Each decree is unique in detail as they were crafted in accordance with the severity of the pandemic at the time. For this study, we clustered the decrees into ten periods (called a decree period) and classified them by their levels of restriction (levels 1 to 4). A level 4 decree is the most restrictive imposing a full prohibition of any social activities and gatherings. Schools and universities were suspended and limited to online classes. Access to parks and other public venues also ceased and a street curfew was set from 8 pm to 4 am. Access to restaurants was only allowed for take-out meals. There was limited coverage and frequency of public transport services. Registration with the police would be required to travel across different administration areas or when crossing a high-risk area. A level 3 decree imposed similar restrictions to level 4 on social activities but it would allow access (with a controlled number of people) to public and communal spaces, such as schools, universities, restaurants, and sports complexes. Street curfews were still enforced but with an extended time that coincided with the operations hours of public transport systems (approximately between 4 am and 11 pm). A level 2 decree provided restrictions on entertainment activities and large social gatherings, such as large concerts and pubs, but without a night curfew nor shortened hours of operation for public transportation. Finally, a level 1 decree resulted in an official warning to ensure personal hygiene practices, such as wearing masks and washing hands, being enforced in closed spaces and public buildings. The decree periods, including duration and restriction level, are explained in Table 1 .Table 1 Details of level restriction and decree period to enforce physical limitations of its citizens and average COVID-19 cases within the study period. Decree period Date Duration Av. daily COVID-19 case (person) Restriction level 1 12 Jan 2020 – 1 April 2020 92 days 11.34 level 1 2 2 April 2020 – 14 May 2020 43 days 11.67 level 4 3 15 May 2020 – 11 June 2020 28 days 1.39 level 3 4 12 June 2020 – 2 Jan 2021 205 days 4 level 1 5 3 Jan 2021 – 28 Jan 2021 26 days 28 level 2 6 29 Jan 2021 – 15 April 2021 77 days 55 level 1 7 16 April 2021 – 9 July 2021 85 days 1,283 level 3 8 10 July 2021 – 9 Sept 2021 62 days 3,044 level 4 9 15 Oct 2021 – 29 Nov 2021 46 days 820 level 3 10 30 Nov 2021 – 31 Dec 2021 32 days 590 level 2 Note: Decree period 1 is the period before WHO declared a pandemic which no royal decree contained. The relative strictness of the containment measures during the previous period is indicated by stating Step-up (+) to indicate the change to stricter measures and Step-down (−) to indicate the change to lesser measures. This aims to explain the situation of physical restraining measures that changed regarding governmental enforcement. Results Descriptive analysis The average daily ridership of the three public transport modes (metro, bus, and boat) are presented as a bar chart superimposed with the average daily number of reported COVID-19 cases in Fig. 1 . The data is clustered by the decree period (see Table 1). Ridership appears to have a negative correlation with the restriction level of the decree, which is assumed to be determined by the number of daily COVID-19 cases. The overall net changed trends suggest that transit ridership decreased most during a period with a higher level of restriction. Considered from the baseline (Decree period 1), level 1 caused a public transport ridership reduction of between 18 and 53 %, level 2 - between 37 and 77 %, level 3 - between 48 and 85 %, and level 4 - between 64 and 91 %. There also seems to be a variation in how ridership was affected across different modes of transport. For example, during Decree period 8 the average number of boat trips decreased the most (91 %) from the baseline. In the same period, the observed ridership of metro systems decreased by 74 % and of the bus by 72 %.Fig. 1 Public transport ridership divided into decree periods. The relative aggregated changes by trip destination of all modes obtained from Google are presented similarly (See Fig. 2 ). We include three main types of trip: retail, park, and workplace visits. The overall number of trips, and the leisure trips (retail and park visits) declined significantly more than for the workplace. The percentage of trip changes was also found to be correlated with the public transit data according to the level of restriction imposed. This also seems to be a variation in how ridership is affected across different trip purposes. For example, during Decree period 2 - level 4, the leisure trip disruption is the most significant. Park visit trips decreased by 45 %, retail trips declined by 45 %, and workplace trips decreased by 29 %.Fig. 2 Relative change (%) of aggregated trip destinations. Correlation analysis The correlation between ridership and daily COVID-19 cases is examined according to the level of containment strictness. As mentioned in Section 3.2, the decree is a means for the government to mitigate the spreading of the COVID-19 pandemic by restraining the physical movement of its citizens. It is assumed here that the strictness of the decrees is determined by decision-makers who observed and analyzed the trends in COVID-19 infections. Thus, the strictness level of the decree is a direct reflection of the relative change between the number of COVID-19 daily cases and the use of public transportation. This can be illustrated by the number of average daily COVID-19 cases. The results are presented in Table 2 .Table 2 Correlation between the number of confirmed COVID-19 cases and public transportation ridership in Bangkok divided by the COVID-19 control measure. Decree period (Relative strictness to the previous Period) level Pearson correlation between ridership and daily Covid-19 case Bus Boat Metro Period 1 level 1 -0.84 -0.64 -0.75** Period 2 (+) level 4 -0.38* -0.08 -0.32* Period 3 (−) level 3 0.258 0.22 0.27 Period 4 (−) level 1 -0.28** 0.07 -0.15* Period 5 (+) level 2 0.11 0.35 0.19 Period 6 (−) level 1 -0.52 -0.30 -0.33 Period 7 (+) level 3 -0.05 -0.05 0.02 Period 8 (+) level 4 -0.56 -0.09 -0.63 Period 9 (−) level 3 -0.38 -0.14 -0.38* Period 10 (−) level 2 -0.14 -0.12 -0.12 Note: (+) the relative strictness to the containment measures during the previous period. p < 0.05. p < 0.01. The results indicate that there is generally a negative correlation between the daily COVID-19 cases and public transport ridership (i.e. a relatively stricter physical constraint will result in a drop in ridership and vice versa). It appears that strong negative correlations between daily COVID-19 cases and public transport ridership can be observed in periods with the highest level of strictness (level 4) and the lowest level of strictness (level 1). Besides the above periods, no significant correlation can be observed. This may be due to several reasons, including the partial restriction, strong encouragement to work from home, time leads effects of policies (Bian et al., 2021). Another interesting observation is a rise in strictness disrupts the use of public transport. For instance, the mild restriction during periods 4 to 6 when citizens were allowed to travel freely, there was a short increase in COVID-19 infections which made the government implement partial travel-control measures (period 5). We can conclude that the surveillance periods are shown in the periods before and after curfew measures were used (from 2 April 2020 to 12 June 2020 - period 2 to 4) as the ridership of all modes indicates no relationship to the daily COVID-19 cases. However, there are exceptions. A step down of restriction in Period 9 (for bus and metro ridership) indicates a strong relationship to the change in COVID-19 daily cases. This could be explained by the time lag effects of policies studied by (Bian et al., 2021). Aggregated trip purpose analysis In this section, we examine the correlations between public transport ridership and the relative changes of different trip purposes: workplace trips and leisure activities (retail visitor and park visits) trips. We focus on these two trip types because they constitute 99 % of the total trips. The data is presented for each decree period in Table 3 .Table 3 Correlation between public transportation ridership and aggregated trips by the purpose for each decree period. Decree period of COVID-19 control measure (Relative strictness to the previous measure) level Correlation between activities visit rates and public transport ridership (Pearson correlation) BUS BOAT METRO retail park work retail park work retail park work Period 1 level 1 (0) 0.90* 0.94 0.78 0.74 0.81 0.52 0.86 0.90 0.62 Period 2 level 4 (+) 0.79 0.80 0.00 0.58 0.63 -0.06 0.76 0.79 -0.08 Period 3 level 3 (−) 0.51 0.69 -0.25 0.34 0.63** -0.40* 0.55 0.73 -0.31 Period 4 level 1 (−) 0.04 -0.35 0.44 -0.07 -0.30 0.22 0.00 -0.32 0.35 Period 5 level 2 (+) 0.49* 0.15 -0.52** 0.22 0.41* -0.75 0.38 0.30 -0.65 Period 6 level 1 (−) 0.50 -0.38 0.68** 0.24* -0.36 0.39 0.52 -0.20 0.50 Period 7 level 3 (+) 0.29 0.41 -0.20 0.10 0.30 -0.40 0.40 0.54 -0.19 Period 8 level 4 (+) 0.71 0.63 -0.01 0.13 0.27** -0.35* 0.76 0.70 -0.05 Period 9 level 3 (−) 0.56** 0.27 -0.19 0.32* 0.14 -0.44 0.52 0.33* -0.21 Period 10 level 2 (−) -0.28 -0.57** 0.29 −0.12 -0.28 -0.05 -0.37* -0.57** 0.64 Note: (+) the relative strictness to the containment measures during the previous period. p < 0.05. *p < 0.01. Decree period 1 considered data from 15th January 2020 to 1st April 2020. Leisure activities visits We analyzed the two types of leisure trips: retail visit trips and park visit trips. Public transport ridership and leisure activities show strong correlations during the early stages of the pandemic (Decree periods 2 and 3). The use of public transport and venue visits changed relatively. This presents the impact of travel constraining measures that discouraged people to travel and the compulsory closing of public venues. This relationship became uncorrelated in a period of mild restriction and the travel situation returned to almost normal (levels 1 and 2). This shows the irregular pattern of leisure trip travel behavior during the period of uncertainty. The results demonstrated that the relationships between leisure activity visits to bus and metro ridership are more closely linked compared to boat ridership, especially in retail visit trips. For instance, in the second wave (periods 7 to 10) the boat ridership shows a correlation only in period 9 while the bus and metro ridership shows correlations throughout periods 7 to 9. Park visits and the use of public transport were also found to be strongly related throughout the observation of this study. It shows a positive correlation in times with high restrictions and a negative correlation in times with fewer restrictions. After the government lifted the first curfew in Decree period 4, all modes of public transport reported negative correlations. Although this study classified the retail and park visits as the same period of activity, boat use shows a correlation with retail visits in contrast to park visits. Workplace visits For workplace visits (Table 3), the results suggest that bus and metro ridership have a similar correlation, while boat ridership is different. Bus and metro have strong positive correlations in the mildest restriction period (level 1) including Decree periods 1, 4, and 6. Decree period 5 is the only time that bus and metro both have negative correlations. In this period (level 2), a sudden rise in daily COVID-19 cases after a long period with mild restrictions can be observed. The change in public transport ridership during period 5 shows the ridership had a drop with regard to the stricter measures issued by the government to mitigate the spread of COVID-19. The correlation between all modes of public transport observed in this study and the workplace visits was reported as negative. The results also show that travelers avoided going to work using public transport during this time. Discussion and conclusion In this empirical study, we explore how the spread of COVID-19 and the physical containment measures enforced by the Thai government affected the ridership of public transport services (metro, bus, and boat services) in Bangkok city. The study includes descriptive and correlation analyses of the ridership data of these modes, Google Mobility aggregated travel trends, and the daily confirmed COVID-19 cases (January 2020–December 2021). This study is unique in its approach to clustering the containment measures by strictness and how it integrates the data sets mentioned. We highlight three main findings of the study here. First, there is a negative correlation between the number of daily COVID-19 cases and public transport ridership which is associated with the level of restraining measures imposed as well. Transit ridership appears to decline more significantly in periods with a higher level of restriction, and increases in periods with a lower level of restriction. For instance, in the first wave of the spread in 2020, the ridership dropped significantly when the government issued the first lockdown measures which included a curfew and strict venue closures. In the following period, the ridership recovered as the daily COVID-19 cases were under control and the government lifted restricting measures. The significant drop in the first period could be due to ‘fresh fear’ or reaction in the face of uncertainty as observed by (Rasca et al., 2021) that illustrates the sensitivity of transit users toward an unknown threat, which in this case was the pandemic. The sensitivity is apparent when comparing the fresh fear period (Decree period 2) with the wider spreading period (Decree period 8). In the latter period, the relative changes in transit ridership and the levels of restriction between the two periods are similar even though the number of daily infections in the latter period is significantly higher. On the other hand, the most obvious ridership recovery was seen in Period 6 (level 1) when the number of daily COVID-19 cases was under control and the government relaxed measures and re-opened venues. The transit ridership recovered significantly and its correlation to the rate of place visits was almost identical to the pre-announcement period. This shows that people were traveling in normally at this stage. Second, there are differences in how the service ridership was affected. The bus and metro ridership showed similar trends throughout the study period, both in the periods with strict restrictions (decreases) and in the periods with fewer restrictions (increases) when the mitigation measures were lifted. For instance, the ridership of the two modes shows significant and strong correlations during level 1 of restriction throughout the observation time of this study. Also, bus and metro were both strongly significant compared to the number of COVID-19 cases during the level 4 restriction while boat ridership was not found to have any significant difference in this period. This happened in both waves of the spread of COVID-19 disease in 2020 and 2021. A similar pattern of bus-metro similarity was also found in work-based trips. It shows the significance of workplace visits in the mild restriction periods, especially between the first and second waves of the spread. However, the boat ridership data illustrates different patterns with both daily COVID-19 cases and workplace visit rates compared with other modes. Boat ridership was significantly disrupted during the period. Its ridership declined the most and recovered at the slowest rate. The relationship between boat ridership and daily COVID-19 cases is only related to the long period with mild restrictions in Decree period 6 as displayed in Table 2. In contrast, the relationship between boat ridership and workplace visit rates is mostly related throughout the pandemic by having a strong positive correlation in times of relaxed control with partial lockdown as shown in Table 3. It had a lower correlation in full lockdown except during the first lockdown which is considered a fresh fear that does not correlate. The different pattern of impacts observed suggests that the ridership of boat services may be different from the other two modes, which leads to different sensitivity and recovery patterns. We discuss this point concerning the change in work-based trips observed in the next paragraphs. Finally, differences between service ridership observed and the aggregated mobility trends suggest how these services may be utilized by different user groups, each of which was affected by the restraining measures differently. During the strong restriction period when curfew and working-from-home measures were applied, people were discouraged from unnecessary travel. The use of public transport declined significantly and it showed no pattern of work-based trips as there was no relationship between workplace visit rates and the use of bus and metro. This means there are numbers of workers who commute by bus and metro still traveling to the workplace during the high risk of COVID-19 infection by shifting to other modes of transport such as private vehicles. While workers who need to be at the workplace during lockdown were still using the bus and metro, those numbers may be large enough to affect the results of our analysis. The discussion on mode shift also associated with the modal shift survey during the COVID-19 pandemic conducted by Das et al. (2021) stated that during the pandemic public transport users tend to shift to cars and other forms of transport, such as motorized two-wheel vehicles. External factors, such as the percentage of the public who have been vaccinated or the perceived effectiveness of the vaccine by the public, may also influence the appearance of the uncorrelated work-based trips. There were initial delays in the public vaccination program and the confidence level of vaccination effectiveness was low. These factors may affect how individuals made their travel plan during the time. Contrary to the other two services, boat ridership was not highly affected except in the first wave of the pandemic (fresh fear). The ridership also correlates with the level of restriction: it dropped in high restriction periods (negative correlation) and recovered in mild restriction periods (positive correlation). This infers that workers who use boats as their main mode of transport may be able to practice working from home but have limitations in shifting to other modes of public transport or private vehicles to access the workplace during a time that has a high risk of COVID-19 infection. Additionally, those that utilize boat services may not enjoy the flexibility in working arrangements like bus and metro users. There is an apparent difference between the leisure and workplace trips made by the bus and metro users in the second wave of the spread (Decree periods 7–9). We discuss the point that some groups of workers have become used to working from home or teleworking. This does not need to be at the workplace and there are many of these types of workers. Therefore, there was no correlation between bus and metro ridership to workplace visits in this period because some of them do not need to be at the workplace but they are still using bus and metro to access leisure activities (retail and park). It can be assumed from the correlation between boats and buses that reported strongly positive relative to leisure activities as shown in data displayed in Table 3. Note that this positive correlation may be caused by the travel behavior of students as well. This study illustrates the impact of transit ridership that changed according to the government restraining measures to mitigate the spread of COVID-19 in Bangkok. It provides evidence of how public transit ridership would change in each circumstance depending on the level of restriction as well as its relationship to COVID-19 cases and place visits. In addition, the effectiveness and consequences of travel restrictions on boat users differed from bus and metro. These findings suggest that the mitigation measures used in public transportation need to consider the characteristics of the mode of each user as well. The one-size-fits-all approach implemented in Bangkok may have been effective in preventing the spread of the pandemic in the initial stages. However, as we demonstrate here, different transport services are affected by the measures differently. The findings suggest a need to find appropriate measures for different public transportation services during a pandemic. The approach may require wider participation from different stakeholders in policymaking, but a balance should be made to ensure a timely response to an emerging event, such as Covid-19. Moreover, there is a need to have a balance between preventing the spread of disease and facilitating daily activities with the least disruption to the city’s economy possible. This has been one of the biggest challenges for transport policy during the pandemic. A limitation of this study is that we analyze data from secondary sources to explain the impact of COVID-19 and related government restraining measures on Bangkok’s public transport demand. Therefore, it cannot identify the impacts caused by socioeconomic factors. Neither can this study identify any travel behavioral changes from gaining experience that influenced self and organization adaptation to deal with the government restraining measures. In addition, data is not available for analysis of other modes of paratransit that accounted for several work-based trips in Bangkok city. We recommend that further studies include the paratransit data, such as minibus transit, taxi, and bike-taxi, which play a big role in the transit desert areas in Bangkok to provide a comprehensive explanation of the impact of governmental restraining measures on public transport demand. 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. CRediT authorship contribution statement Somsiri Siewwuttanagul: Conceptualization, Methodology, Data curation, Investigation, Visualization, Writing – original draft, Writing – review & editing. Peraphan Jittrapirom: Conceptualization, Methodology, Writing – original draft, Writing – review & editing. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability Data will be made available on request. ==== Refs References Abdullah M. Ali N. Hussain S. Aslam A. Javid M. Measuring changes in travel behavior pattern due to COVID-19 in a developing country: A case study of Pakistan Transp. Policy 108 2021 21 33 Ahangari, S., Chavis, C., Jeihani, M., 2020. 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==== Front Early Child Res Q Early Child Res Q Early Childhood Research Quarterly 0885-2006 0885-2006 Elsevier Inc. S0885-2006(22)00130-2 10.1016/j.ecresq.2022.12.001 Article “A Win-Win for All of Us": COVID-19 Sheds Light on the Essentialness of Child Care as Key Infrastructure Yamoah Owusua ab†⁎ Balser Sarah c† Ogland-Hand Callie ab Doernberg Ellen d Lewis-Miller Carlos ab Freedman Darcy A. ab a Mary Ann Swetland Center for Environmental Health, School of Medicine, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106 b Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106 c Jack, Joseph and Morton Mandel School of Applied Social Sciences, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106 d Department of Psychological Sciences, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106 ⁎ Corresponding Author. Owusua Yamoah, PhD, MA, Case Western Reserve University, BioEnterprise Building, Room 423, 11000 Cedar Avenue, Cleveland, Ohio 44106 † First author 5 12 2022 5 12 2022 27 1 2022 10 11 2022 1 12 2022 © 2022 Elsevier Inc. All rights reserved. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Child care centers in the United States allow many parents and caregivers to work in and outside of the home and support the growth and development of children. Child care closures and COVID-19 mitigation measures at the onset of the pandemic heightened the need for and awareness of the role of child care as core infrastructure. The purpose of our study was to examine the perceived role and benefits of child care based on the lived experiences of parents/caregivers and staff navigating child care during the pandemic. We conducted in-depth qualitative interviews with parents/caregivers (n=20) of children who attended child care and staff (n=12) who were working at child care programs in Ohio from September to November 2020. Qualitative data were coded and analyzed through the lens of four frameworks (i.e., capabilities, developmental, economics, and mutualism) related to child well-being. Our results highlight the perceived value of child care (a) for fostering capabilities and developmental growth in children; (b) for providing economic benefits for children, parents, and staff of child care programs; and (c) as an essential infrastructure that mutually benefits children, parents, families, staff, and the community. Findings support existing evidence regarding the broader impacts of child care and further investigation into the role of child care. We highlight the potential need for further investments in policies, resources, and supports for child care that reflects its essentialness and generative role. Keywords child care COVID-19 child development qualitative research ==== Body pmcIntroduction Child care centers support the growth and development of children while providing a service that allows parents and caregivers to work in and outside of the home. The need for nonparental child care prior to kindergarten is highlighted in a 2016 poll of socioeconomically diverse children aged one to five years, which found that 60% of children required a nonparental care arrangement (National Center for Education Statistics, 2016). This is largely due to significant increases in primary caregivers’ involvement in the labor force, necessitating additional care outside of the home (Hotz & Wiswall, 2019). As child care usage has become more prevalent, the cost of child care has increased (Herbst, 2018). In March 2020, COVID-19 was declared a national emergency in the United States, and millions of families and children across the country were left without child care or experienced limitations with child care (Koltai et al., 2021). Like many states, most Ohio child care programs were shut down in March 2020 due to COVID-19. Some child care programs in Ohio were permitted to operate under a pandemic license, which allowed them to stay open under new guidelines, including reduced student-to-teacher ratios (Acton, 2020). Throughout the United States, pandemic licenses allowed some child care programs to continue their care of essential workers' children, affecting an estimated 28.8% of healthcare workers nationally who required access to child care (Bayham & Fenichel, 2020). With these pandemic licenses, child care programs assumed the responsibility of providing safe spaces for children of parents and caregivers deemed most essential and most vulnerable from being at the forefront of responding to the pandemic (i.e., healthcare and delivery workers, etc.). That said, child care remained an essential service for all parents and caregivers, including those that were not considered essential workers and were without child care during this time. For the remainder of this paper, we use the term “parents” to describe both biological parents and other primary caregivers, “staff” to describe those who are employed by child care centers, and “community” to denote the community at large. The purpose of our study was to examine the perceived role and benefits of child care based on the lived experiences of parents and staff in Ohio navigating child care during the pandemic after all child care centers in the state were re-opened on May 31, 2020. We sought to reveal the essentialness of child care for children, parents, staff, and their community through the eyes of parents and staff that were impacted by child care shutdowns during the earlier period of the COVID-19 pandemic. Theoretical frameworks In this research, we explored the contributions of child care for children, parents, staff, and the community revealed through parent and staff experiences navigating child care during the COVID-19 pandemic. While there are limited frameworks describing the overall impact of child care, most are situated within an ecological model and mutualism framework (Avan & Kirkwood, 2010; Egan & Pope, 2022; Lumpkin & Pallais, 2018; Shumba et al., 2020; Twintoh et al., 2021), which illustrate that investment in early childhood development has benefits beyond children. The Nurturing Care Framework describes how the multifactorial impact of nurturing care for early childhood includes “caregivers’ capabilities, empowered communities, supportive services, and enabling policies” (Shumba et al., 2020, p. 2). Similarly, the bioecological systems model has been utilized to understand the role COVID-19 has played in child development, positing that layered systems interact and influence child development (Egan & Pope, 2022). Four established frameworks related to child well-being describe the synergist impacts and symbiotic benefits of investments in childhood development, like child care, within society: capabilities, developmental, economics, and mutualism (Cohen et al., 2019). We explored the contributions and essentialness of child care during the early period of the COVID-19 pandemic in Ohio by using these four child well-being frameworks (Cohen et al., 2019). Capabilities Framework The capabilities framework encapsulates a set of 10 standard human capabilities: life, bodily health, bodily integrity, senses/imagination/thought, emotions, practical reason, affiliation, access to other species, play, and control over one's environment (Cohen et al., 2019; Nussbaum, 2006). These capabilities address the present experience of children and extend across the lifespan, representing one's right to reach “the best of their potential” (Cohen et al., 2019, p. 453). The capabilities framework allows for analysis of the benefits of child care related to the 10 standard human capabilities across different members of the community. Developmental Framework The developmental framework emphasizes children's developmental processes and depicts how early life experiences influence trajectories of health and wellness across the lifespan (Cohen et al., 2019; Halfon & Hochstein, 2002; Halfon et al., 2014). Additionally, this framework posits that the vulnerabilities children face are based on their developmental stage, such that children require special protection and care to ensure their well-being (Cohen et al., 2019). While this framework focuses on how children can be negatively impacted by adverse experiences, it also sheds light on how negative exposures and/or experiences can be mitigated by positive protective factors (i.e., child care). The developmental framework allows for the examination of benefits based on children's developmental milestones and meeting expectations based on social norms (Committee on the Prevention of Mental Disorders and Substance Abuse Among Children, Youth, and Young Adults, 2009). Economics Framework The economics framework focuses on supporting the economy of the community and the investments in critical “sensitive” periods of early childhood (Cohen et al., 2019). This aspect of the conceptual framework is focused on the importance of viewing an investment in children as a “return on investment” to society at large (Cohen et al., 2019, p. 453; Heckman, 2017, pg.1). Numerous studies have shown investing in early childhood programs can produce economic returns (Cannon et al., 2017; Cohen et al., 2019; Heckman, 2017; Nurse-Family Partnership, 2018). The economics framework allows for the consideration of economic benefits to the community and the short and long-term economic benefits of investing in children through center-based nonparental child care. Mutualism Framework The mutualism framework captures the symbiotic benefit that both a child and parent reap from the improvement and maintenance of their own well-being (Cohen et al., 2019; Murray, 1996). From this perspective, child care may nurture and help to facilitate the development of children, who then have improved behavior and emotional skills that allow family members to benefit in subsequent interactions with these children. This “reciprocity of care” also highlights that when children are treated as community members, “everyone's sense of worth increases” (Cohen et al., 2019, p. 454; Melton, 2008, p. 918). Therefore, the mutualism framework allows for the analysis of generative benefits of child care across different members of the community, allowing for the multilevel benefits of child care to be better understood (see Figure 1 ).Figure 1 The perceived multilevel beneficiaries of child care. Figure 1 Material and Methods This qualitative analysis is an ancillary study within a statewide study, XXXX, focused on understanding factors associated with infection and spread of COVID-19 among child care programs throughout Ohio. Child care re-opened in Ohio after pandemic closures on May 31, 2020. Study enrollment opened on August 15, 2020, and the study concluded in December 2020. The methodology of the larger study can be referred to in Burkhart et al. (2021). Although the initial focus of the larger study was to understand the spread of COVID-19 in child care settings, during data collection the importance of the role of child care for children, parents, staff, and the community emerged from the data. The present paper represents this emergent analysis derived from interviews completed with parents and staff. The study and all procedures were approved by the XXXX Institutional Review Board (IRB). Sampling Procedure Sampling for interviews was designed to achieve a diversity of perspectives based on the type of child care program and the county context. Eligible participants either worked at or had children attending one of the child care programs (N=46) across 10 counties in Ohio (Franklin, Cuyahoga, Hamilton, Summit, Montgomery, Lucas, Butler, Stark, Licking, and Ashtabula) that participated in the larger study. The goal was to enroll 20 parents and 12 staff from both family child care home providers and child care centers licensed by the state. In total, 34 parents were invited, 23 consented, and 20 completed an interview; 20 staff were invited, 17 consented, and 12 completed an interview (see Table 1 for a breakdown of demographics). Interviewed staff represented 11 programs and interviewed parents represented 17 programs. Accounting for overlap, interviewees were from a total of 25 child care programs in Ohio.Table 1 Demographic Characteristics, Staff and Parents. Table 1 Staff Parents Total Participants, N 12 20 Female, n (%) 12 (100.0) 17 (85.0) Age, mean (range) 42.8 (22-69) 35.5 (26-60) Racea, n (%) White 10 (83.3) 17 (85.0) Black 2 (16.7) 2 (10.0) Asian 0 (0.0) 1 (5.0) Chose not to respond 0 (0.0) 1 (5.0) Hispanic, Latino or Spanish Origin, n (%) 1 (8.3) 0 (0.0) Highest Level of Education, n (%) High School Graduate or Less 0 (0.0) 1 (5.0) Some College 4 (33.3) 1 (5.0) College Graduate 8 (66.7) 10 (50.0) Graduate Degree or Higher 0 (0.0) 8 (40.0) Employed, n (%) 12 (100.0) 17 (85.0) Business Office 0 (0.0) 6 (35.3) Childcare 12 (100) 1 (5.9) Delivery Driver 0 (0.0) 1 (5.9) Education 0 (0.0) 5 (29.4) Government 0 (0.0) 2 (11.8) Healthcare 0 (0.0) 1 (5.9) Non-profit 0 (0.0) 1 (5.9) Health Insurance, n (%) Private Health Insurance 8 (66.7) 17 (85.0) Medicaid 2 (16.7) 3 (15.0) Military Healthcare 1 (8.3) 0 (0.0) No Health Insurance 1 (8.3) 0 (0.0) a Participants could identify more than one race. Data Collection Interview data were collected from September 10 to November 6, 2020. We collected data virtually to comply with safety guidelines during the COVID-19 pandemic. All interview participants completed an informed consent prior to participating in the interview. We conducted 32 interviews with parents (n=20) and staff (n=12) over a secure Zoom account that lasted approximately 60 minutes each. There was no prior relationship between the interviewers and participants. Participants had the option to use video or audio only. Each interview was conducted by two trained research staff (one interviewer and one observer). Interviews included three open-ended question sets (interview guide is available upon request). The first question set asked about the participant's background information (e.g., child care center details). The second question set asked questions informed by the health belief model, including questions about the perceived benefits of reopening child care (Glanz et al., 2008). In this section, there were questions about the benefits of reopening child care for children, parents, staff, and the community. Responses to this second set of questions provided rich data for the present qualitative analysis. The third question set was general questions related to child care during the COVID-19 pandemic. Interview participants were compensated with a $50 e-gift card for their participation. Data Analysis We used a multi-step, team-based approach to qualitative analysis. After each interview, the interviewer and observer completed a debrief session to capture high-level information about themes related to the main research questions of the primary study. In this process, emergent concepts, such as the essentialness of child care, were recorded. Next, each interview was transcribed verbatim using NVivo transcription software (QSR, 2020) for a first-pass transcription followed by line-by-line reconciliation of each transcript with the audio file by a member of the research team. Before starting the data analysis, we reviewed the four frameworks (capabilities, developmental, economics, and mutualism) to guide our team's understanding of the different benefits of investing in children (Cohen et al., 2019). We then used a team-based approach for iterative-inductive analysis involving detailed open coding of each transcript to generate rich thematic analysis and increase confidence in the dependability and trustworthiness of the findings (Cascio et al., 2019). Since the interview protocol was not initially designed to capture details about the importance of child care, we coded transcripts in their entirety to ensure we captured any reference to this emergent finding. We met frequently over a period of five months (February to July 2021) to analyze the qualitative data and prepare a codebook for this analysis. First, to generate a common understanding of the coding process and begin to develop the codebook, five team members (SB, OY, CLM, COH, ED) open-coded the same transcript using an inductive, “in vivo” coding approach (i.e., the codes were named using the exact words and phrases from the text) in the QSR NVivo 12 Software (2018). Second, all authors met and discussed a priori and emergent themes relevant to the essentialness of child care and established a preliminary axial coding structure to begin to group the relevant open codes. Third, the first two steps in the process were repeated by dyads within our team as we coded three more transcripts. Fourth, once the team established a common understanding of the inductive, open coding approach, we then divided and analyzed 20 transcripts individually, meeting regularly to discuss emerging axial codes and further develop the codebook. Fifth, with five transcripts left, the team reached saturation in the codebook with no new or relevant axial codes. The final version of the codebook was applied by two members (SB and COH) to code all 32 transcripts. Finally, we reviewed the axial codes and organized them based on benefits to children, parents/families, staff, and the community. Results Most of the sample self-identified as female (96.7%), 81.2% as White, 9% as Black/African American, and 3% as Asian (see Table 1). About 25% of staff participants had Medicaid or no insurance, and 15% of parent participants had Medicaid. We identified axial codes that illustrate the perceived benefits of child care for children, parents/families, staff, and the community at large. Participants were assigned an identification number (P1-P20 for parents; S1-S12 for staff) and the source of the quote was identified and labeled with its associated identification number. Children Parents and staff described several ways child care benefited children. These benefits were categorized into the following axial codes: ‘safe space,’ ‘development of abilities,’ ‘educational growth,’ ‘routine and structure,’ and ‘expertise in child care.’ Parents and staff discussed how child care provides a safe space for children, which referred to child care as an environment that minimizes children's exposure to harm. This axial code ranged from child care serving as a haven for children, to staff keeping children safe from COVID-19 through the adopted mitigation procedures. One parent (P15) explained, “I just felt safe and, you know, thinking about all the things they told me they would do [to mitigate COVID-19], and they had no outbreaks [of COVID-19].” A staff member (S12) added: I know for some kids, the daycare or the school is like a saving grace…. Because parents, as people, not everybody is equipped and have the ability to kind of like process things and, you know, sometimes take it out on the child. Parents also shared that child care also provided a safe space for supplemental programming for children. This included swim lessons, summer camps, and before-and-after school care. Parents and staff described child care as supporting the development of abilities in children. This axial code included developmental growth and socioemotional growth. Developmental growth captured the different ways that child care programming was perceived to support children's motor skills development, speech development, crying and fussiness behaviors, ability to manage separation from a parent, bedtime routine, eating behaviors, napping, and aggressive behaviors (Burchinal et al., 2008). Many parents commented on the developmental gains made by their children in child care settings, including gains in behavioral, speech, and motoric development. One parent (P8) stated: She (child) has made huge strides, I'd say. So, she is more vocal and is, you know, crawling on things and climbing up stairs and doing a lot of things she wasn't doing.... I think that being around older kids in her class helped her achieve those things quicker. Socioemotional growth referred to child care providing children opportunities for socializing, building friendships, and learning and practicing emotional regulation skills. For example, staff (S12) provided opportunities to teach children emotional regulation skills, stating, “We try to teach our children when they're having a hard time or something to breathe…. We're playing meditation music in the classroom just to calm the situation down.” One parent (P16) explained: Learning how to function with children who don't have the same rules at home or don't have the same likes and interests or don't play the same way. I've already seen him (child) grow in that. Parents and staff additionally emphasized the role child care plays in the educational growth of children. Educational growth referred to the ways the staff aid children's academic learning. One parent (P9) noted that academic support helps “kids to be ready to go into kindergarten.” Another parent (P16) highlighted: I still don't have a classroom worth of resources at my fingertips, and I don't have a curriculum in the same way. And so, him being able to go back [to child care] and even my daughter being able to go as a 3-year-old ... they're engaging in growth and playing and learning in ways that I can't necessarily provide at my house. Some parents described how the COVID-19 pandemic and lack of child care led to “relapse” in their children's development of abilities and educational growth. Many participants highlighted child care's role in catching children up on any loss or stagnation of development while at home during the pandemic. A staff member (S8) shared, “These are children that we had worked with previously. You did see a little bit of a decline there, but over time, you know, they catch on really quickly…. And I feel as though they've really gotten back to the level that we would want them to be at this time of the year.” Another staff member (S6) noted child care allowed children to get “back to normal” after child care programs were shut down, meeting the expectations of the structured child care setting. After experiencing limited child care access during COVID-19 closures, parents and staff emphasized the benefits of child care's routine and structure for children. Routine and structure referred to the impact of the patterns in the day that child care provided. Several parents shared that their children “thrive on the routine” (P3). A staff member noted: And after getting back into a routine, you could just see them (children), you know, physically relax and they were just happy to have that structure back. Many parents spoke to the improvement they saw in their children's emotional and behavioral functioning from having a routine in place at child care. A parent (P2) shared: He listens better. He goes to sleep better. He's eating better because he has a routine. He's doing the same thing every day. And it makes my life better because he's eating and sleeping and listening. Fundamental to the previous axial codes, expertise in child care speaks to the specific and unique skills and training possessed by staff to foster safe spaces for children, support child development, and provide age-appropriate structure to a child's day. For parents, the concept of expertise was spoken about as an important reason why they chose to send their children to child care, and why they chose to send them back in the wake of the pandemic. In particular, the expertise of child care was made apparent by the emphasis one parent (P18) shared, stating: And at least I have, like I said, experts there who I can ask, ‘Is that normal? Is it OK? Should I be concerned?’ ... And so even just to get him (child) there and get his poor academic skills like that and just have an expert set of eyes to catch any concern. Parents (P4) also described staff as “advocates for children.” One staff member (S3) shared, “We as a staff are here or eager to do whatever we need to do to protect the children and make sure that they have, you know, a good time here.” Parents and families Parents and staff described several ways child care benefited parents and families. These benefits were categorized through the following axial codes: ‘ability to make a living,’ ‘improves family dynamics,’ ‘social needs of adults,’ and ‘pathway to government-issued services.’ Parents expressed that the ability to make a living, or work, is highly dependent on having reliable and trusted child care for their children, with one parent (P2) stating, “I trust the facility my child was in. I trusted them before the pandemic. I will trust them after the pandemic.” The need for reliable and trusted child care became more apparent at the beginning of the COVID-19 pandemic when child care programs and schools were closed. While some parents reported having family to mitigate some of the burden, most reported they had “no choice” whether or not to use child care due to the fact that they had “no family that would be able to watch” their children (P4). For some households, the ability for both parents to work while their children are in child care was needed to financially sustain their families. For others, being able to work was described as critical for parents’ mental stability. For parents that had the flexibility to work from home, the need for child care did not decline as they needed time away from their children “to work without interruption” (P18). One parent (P1) reported: My daughter was babysat the entire pandemic by one of the child care workers to help us out so that my husband could still work from home, because if we had her (daughter) at home, he wouldn't be able to get much work done. Parents shared that child care improves family dynamics, which referred to ways child care allows families to operate, such as decreasing parental burdens and providing relief to parents. A parent (P8) remarked child care is “a win-win for all of us,” such that child care improves the family's entire functioning. Parents described child care as supporting their own mental health, such as allowing time for personal care. Parents explained that personal time allows them to have needed separation from their parental roles. One parent (P2) shared that they chose to use child care out of “wanting to have a sense of myself again, as opposed to just always being Mom.” One parent (P16) commented: [Having child care] meant that I don't have to be mommy and teacher … I'm not trying to balance more than one hat with him (child). And so it (child care) protects our relationship in a way that is very valuable to me because he's my little guy. Parents and staff also described benefits related to the social needs of adults, which referred to ways in which child care provided opportunities for connection and improved parents’ social well-being. A staff member (S2) observed: [Parents are] just glad to be around other adults to talk to other than having just their children to talk to all the time. But I think that that's like one of the– just the social part of it is huge. Just interacting with others. In this way, parents shared that having child care and interacting with other parents allowed them to “have sort of more energy at the end of the day to see people” (P20) and improved their social well-being. Due to restrictions from COVID-19, parents and staff indicated missing the typical interactions they would have during special events or dropping off and picking up children from child care. A staff member (S1) shared, “We're used to doing, you know, parent involvement. We might have, you know, a little cookout or, you know, a little invite the parents in for snack time and, you know, eat together. Whereas kind of like, we can't really do that so much right now [because of COVID-19 mitigation restrictions].” Child care was also described as a pathway to government-issued services for families in need. Through child care vouchers issued by the state, child care offered an opportunity for households with low income to have access to quality early childhood education. Child care was also described as a pathway to connect to other services (e.g., food assistance) if families were in need. A staff member (S5) noted, “We have some families who are on county assistance with tuition vouchers and stuff. But I think that whatever, if the families who are struggling financially to provide food for their children … we would be able to help with making sure that the child was at least fed.” A parent (P1) also recognized that child care vouchers provided access to families “on fixed incomes and they're trying to make it in the world.” Staff Staff described several ways child care benefited staff members. These benefits were categorized through the following axial codes: ‘employment,’ ‘social connection,’ and ‘routine.’ Staff members reported employment as the main reason why staff members returned to their child care roles during the pandemic. This included the ability of staff to make a living for their own families while generating revenue for their child care center. One staff member (S8) explained: It was more just a financial decision for me to go back to work [after the shutdown] because being off work, even with unemployment, even with the extra unemployment assistance they were sending out, I still was losing money and using my savings. Staff also described child care as a source of meaningful employment beyond the economic benefit. For many staff, child care was described as their passion or calling. One staff member (S7) exclaimed, “I love my job.” Another staff member (S2) explained, “Children are my passion. This (COVID-19 pandemic) would have never changed my mind on working with children. I'm here to protect them as long as they're in my care.” A staff member (S9) also shared: I love what I do. The kids are like the number one reason why I do what I do. I think it's very important that they have someone who's looking out for them, someone who has their best interests at hand and that's why I continue to do what I do. Staff members highlighted that social connection was another benefit of child care, which allowed them to be around each other and children. A staff member (S8) reported, “When I was given the opportunity to go back to work, I was very excited to go back just because I hadn't really been around any other people.” Staff (S5) also shared the benefits of “camaraderie,” stating “Just to have another adult, you know, that share different issues, same views or whatever the case may be.” Another staff member (S6) explained, “Everybody gets along well, if you're having a bad day, you can always go in the office and talk to them.” A staff member (S8) also pointed out, “And just being around the kids always makes you feel really good as well.” Staff described routine as a benefit to themselves as well. For staff, routine was related to having structure and purpose in one's day. A staff member (S10) exclaimed, “I was eager to get back into a routine.” Another staff member (S11) added, “We're excited to return to work. We were tired of being in our homes [due to COVID-19 mitigation guidelines].” One staff member (S2) shared, “I've got to keep going. I mean the children need me here. It's a stability kind of thing, routine thing every day.” Routine was also connected to other benefits, including mental health as one staff member (S12) explained, “The employees (staff) can get their jobs back and have a sense of normalcy. You know, maybe think of like, getting their life back and maybe the mental health also helps with that.” Community Parents and staff described ways child care benefited the community as a whole. These benefits were categorized through two axial codes: ‘core infrastructure’ and ‘improves the community.’ Parents and staff viewed child care as core infrastructure, which related to sentiments that child care allowed everything else to operate. A parent (P11) stated, “It's (child care) basically a first step kind of in reopening the community. It's giving the kids and the community a sense of normalcy and kind of getting back to daily routines.” A staff member (S1) identified child care as that which “keeps the wheels rolling for the community and the world.” Another staff member (S9) reported child care was necessary “to keep the economy afloat.” A parent (P8) also focused on the role of child care in the economy, stating child care needs to be open “in order to open the rest of the economy and get people back to work.” For many families and staff, opening child care programs was a sign of hope and a necessary step of returning to “pre-COVID life” (P5). Without child care, a parent (P19) argued that any efforts to reopen businesses were futile if parents “don't have a place to stick (take) [their] kids.” A parent (P3) added: Well, for the community, keeping places like this, you know, open and keeping jobs. It's been really sad here in our community and just close to our home. You know, businesses that have had to close. So, keeping the child care center open … trying to build their enrollment back. I think that's been really important for them (child care) to keep these businesses open and keep them going, keep them supported. Parents and staff shared that child care improves the community, which included examples like assistance in meeting the needs of families to the general benefits their space provided to their local community. One staff member (S10) shared that their agency “does provide boxes of food and stuff to try to take the stress off the groceries and those types of things to make sure our children are fed.” A parent (P7) noted, “You're used to certain things and you know, a lot of daycares do plant trees and plant gardens and things of that nature and try to fix up area.” As far as general overall benefits child care space provided, one parent (P16) described: There's a more positive feeling. I just felt like all summer (in 2020 at the height of the COVID-19 pandemic) there was just this constant stress and fear and frustration over- we just couldn't do it all. Like you just couldn't do a good job at anything because you couldn't do everything. There just wasn't time and energy. And so I think that has widespread ripple effects in communities… seeing that balance partially being restored (opening child care and supplemental programming they provide to the community) has had a positive effect in our neighborhood for sure.” A parent (P2) added, “It (child care) just reopens a lot of opportunity…. So, you know, having some of this stuff back open allows them (children) to have access to other activities and resources that they didn't have for a while either.” A staff member (S10) remarked, “And our parents, they're happy and everything and that gets around also, which gives the rest of our [community]- because we have a rec center next to us. That gives them (rec center) more confidence, too, that they can do it, that they can also maintain a safe environment, even through this COVID. I think we're (child care) a pretty good example for them also.” Staff and parents noted collectively that child care benefitted not only those directly participating in their services but also the community at large by inspiring other businesses. Discussion Our study provided a rare opportunity to examine the value of child care infrastructure after access to this service was temporarily disrupted due to the COVID-19 pandemic. Through the unique challenges faced during the early stages of the COVID-19 pandemic, the findings of our qualitative study revealed the perceived essentialness and mutual benefits of child care for children, parents/families, staff, and communities represented in our sample. Our findings are situated within the four child well-being frameworks discussed earlier: capabilities, developmental, economics, and mutualism. In line with the capabilities and developmental frameworks, our results show that parents and staff perceived child care as safe environments providing children with opportunities to acquire new skills, develop, and grow their capabilities that will be necessary over their entire lifespan. Midgley and Sherraden (2000) suggest that growth in development and capabilities acquired during the early stages of a child's life ultimately results in the accumulation of human and social capital that is necessary for building relationships and stronger families. Additionally, child care programs’ investments in critically sensitive periods of early childhood ultimately lead to compounded gains in children's health potential and health reserve (Cohen et al., 2019; Heckman, 2017). Our findings corroborate other research demonstrating that child care provides staff and parents, including low-income households receiving government tuition vouchers, the opportunity to financially support their households while contributing in other ways to the local economy (Conley, 2010; Scarr & Eisenberg, 1993). For working parents, we found that safe, reliable, and trusted child care programs allowed them to productively work and participate in their community. This was especially true for essential workers during the pandemic, which Bayham and Fenichel (2020) estimated that longer child care and school closures could have resulted in a 15% decline in healthcare workers, which would have potentially caused a significant increase in COVID-19 related deaths. Additionally, existing evidence suggests that quality early childhood education, which parents and staff alluded to as benefits for children in child care, can increase a child's future adult earnings by over 25 percent (Bartik, 2014). Findings provide additional evidence, based on the lived experiences of our sample, that child care is an essential infrastructure necessary for community and economic development. Ultimately, our findings from this qualitative study of parent and staff perspectives during the early periods of the COVID-19 pandemic in Ohio illustrate that child care programs are a “win-win for all”, offering mutual benefit for children, parents/families, staff, and the community. Our study highlights the synergistic role of child care, as reflected in the mutualism framework. The mutual benefits of child care for children, parents/families, staff, and the community in fostering capabilities, developmental growth, and supporting the economy are testaments to this industry's essentialness. Although we do not capture the generative benefits of child care in this study, findings of the Perry Preschool Study among African American children in households with low income found a return on investment for high-quality child care to be 7-10%, accounting for the social benefits (Heckman et. al., 2009). Another study found that government investment of $9,519 for 1.55 years of preschool per child in 2014 dollars resulted in $27,455 per child in government savings through reductions in special education services, child abuse and neglect costs, criminal justice system costs, and welfare costs as well as increased taxes from higher earnings (Temple & Reynolds, 2015). Our findings provide further support to the growing body of evidence that investments in child care generate multiplied effects beyond the direct benefits received by children in child care programs. Despite the increasing evidence of child care's contributions to the overall functioning of communities, government funding for child care remains significantly low. Recent estimates show that the United States government is an outlier among other developed countries with how little it allocates in public funding on early childhood education, only $500 per child annually (Miller, 2021). This lower public funding for early childhood education in the United States has contributed to wide disparities in access to quality care, income inequalities especially for women, and a generally negative impact on family well-being (Bivens et al., 2016; Collins et al. 2020; Miller, 2021). Future research on the long-term implications of the disruption in child care during the COVID-19 pandemic for children, parents, the community, and the economy could provide stronger evidence for increased government investments in child care. Research in this area may be furthered through additional studies that quantify the cost-benefits of closing child care similar to those done on school closures (Lempel et al., 2009; Psacharopoulos et al., 2021; Sadique et al., 2008). Limitations There were several limitations to our study. It was conducted during the early phase of the COVID-19 pandemic, requiring a rapid approach to study design, implementation, and completion to facilitate data-driven decision-making of statewide strategies for COVID-19 mitigation. The interviews analyzed in the present study allowed for the identification of emergent findings such as the perceived essentialness of child care, which is the focus of this analysis. Our interview questions were not designed to purposefully assess the perceived essentialness of child care or the four conceptual models related to child wellbeing. More targeted approaches for examining these topics may have generated additional findings. Future studies are warranted to further explore the perceived benefits and value of child care. The cross-sectional interview data reflect perspectives at a point in time (Fall 2020) when the recency of child care closures and the novelty of the COVID-19 pandemic were intensified. Views about child care may have evolved as families were returning to work and/or facing different waves of the pandemic. Participants were parents and staff who chose to return to child care programs during the COVID-19 pandemic and may not reflect the views of those who did not use child care or did not return to work. Additionally, the cross-sectional data limited our ability to consider the long-term economic benefits of child care for children. The parent sample was primarily White, female, and well-educated (college degree or higher), which limits generalizability. The sample aligns with national trends that most children in child care are non-Hispanic White (Paschall et al., 2020). Additionally, Koltai et. al (2021) found that low-income, Black and Hispanic households were more likely to experience loss of child care leading to increased unemployment among these populations during the pandemic. Other studies have recorded the underrepresentation of racial minorities and low socio-economic groups due to the disparities in COVID-19 cases and deaths and the challenges associated with virtual recruitment and data collection processes that were adopted during the pandemic (Borno et al., 2020: Gilmore-Bykovskyi et al., 2021; Lathen & Laestadius, 2021). Future research is warranted to explore the value of child care among more diverse populations. Finally, while the spread of our sample over several child care programs provided the opportunity to assess the essentialness of child care across many different settings, it also limited the number of participants from each child care program that participated in the study. The results presented are based on the participants we interviewed and may not represent the views of all staff and parents in participating child care programs. Conclusion The closure of child care programs at the onset of the COVID-19 pandemic brought to light the critical and essential role of child care in our society. Findings from our study reveal child care is perceived to support the capabilities and developmental growth of children, provide safe and reliable care of children for working parents, and provide employment for child care workers. Furthermore, child care is perceived to be an essential infrastructure that mutually benefits children, parents, families, staff, and the community. Our findings support existing evidence regarding the broader impacts of child care beyond children and the vital role child care plays in society (Bayham & Fenichel, 2020; Conley, 2010; Scarr & Eisenberg, 1993; Temple & Reynolds, 2015). Further investigations into the value of child care for diverse populations will be an essential addition to the growing evidence that supports significant investments in the child care industry. Uncited References QSR International Pty Ltd, 2018, QSR International Pty Ltd et al., 2020 Declaration of Competing Interest No potential conflict of interest was reported by the authors. Data Availability The authors are unable or have chosen not to specify which data has been used. Funding This work was supported by the Ohio Bureau of Workers’ Compensation in partnership with the Ohio Department of Job and Family Services [No grant number] CRediT author statement Owusua Yamoah: Conceptualization, Methodology, Validation, Formal analysis, Data curation, Writing - Original Draft, Writing - Review & Editing, Visualization; Sarah Balser: Conceptualization, Methodology, Validation, Formal analysis, Resources, Data curation, Writing - Original Draft, Writing - Review & Editing, Visualization, Project administration; Callie Ogland-Hand: Conceptualization, Formal analysis, Writing - Original Draft, Writing - Review & Editing; Ellen Doernberg: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Writing - Original Draft, Writing - Review & Editing; Carlos Lewis-Miller: Conceptualization, Methodology, Formal analysis, Investigation, Writing - Original Draft, Writing - Review & Editing; Darcy Freedman: Conceptualization, Methodology, Validation, Resources, Writing - Review & Editing, Visualization, Supervision, Funding acquisition Research data for this article Due to the sensitive nature of the questions asked in this study, survey respondents were assured raw data would remain confidential and would not be shared. 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