Article Text

Post COVID-19 syndrome among 5248 healthcare workers in England: longitudinal findings from NHS CHECK
  1. Brendan Dempsey1,
  2. Helen A Blake1,
  3. Ira Madan2,
  4. Sharon A M Stevelink3,
  5. Neil Greenberg3,
  6. Rosalind Raine1,
  7. Anne-Marie Rafferty4,
  8. Rupa Bhundia3,
  9. Simon Wessely3,
  10. Danielle Lamb1
  1. 1 Department of Primary Care and Population Health, University College London, London, UK
  2. 2 Guys and St Thomas NHS Foundation Trust, London, UK
  3. 3 Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
  4. 4 Florence Nightingale Faculty of Nursing, Midwifery and Palliative Care, King's College London, London, UK
  1. Correspondence to Dr Brendan Dempsey; b.dempsey{at}ucl.ac.uk

Abstract

Objectives The objectives of this study were to examine post COVID-19 syndrome (PCS) among healthcare workers (HCWs) in England and explore risk factors for the condition.

Methods Data were collected by National Health Service (NHS) CHECK, a longitudinal study exploring HCWs’ mental and physical well-being during and after the COVID-19 pandemic. NHS CHECK collected data at four timepoints: the baseline survey between April 2020 and January 2021, and then three follow-up surveys at approximately 6, 12 and 32 months post baseline. PCS data were collected at 12 and 32 months, while risk factor data were from baseline. HCWs were asked what COVID-19 symptoms they experienced and for how long and were classified as having PCS if they had any symptom for ≥12 weeks. Multilevel regressions were used to examine risk factors for PCS.

Results This study included 5248 HCWs. While 33.6% (n=1730) reported prolonged COVID-19 symptoms consistent with PCS, only 7.4% (n=385) reported a formal diagnosis of PCS. Fatigue, difficult concentrating, insomnia and anxiety or depression were the most common PCS symptoms. Baseline risk factors for reporting PCS included screening for common mental disorders, direct contact with COVID-19 patients, pre-existing respiratory illnesses, female sex and older age.

Conclusions While a third of HCWs reported prolonged COVID-19 symptoms consistent with PCS, a smaller percentage reported a formal diagnosis of the condition. We replicate findings that direct contact with COVID-19 patients, older age, female sex, pre-existing respiratory illness and symptoms of common mental disorders are associated with increased risk of PCS.

  • COVID-19
  • Epidemiology
  • Health Personnel
  • Longitudinal studies

Data availability statement

Data are available upon reasonable request. Due to Research Ethics Committee restrictions, the data set is not publicly available. Requests to access the deidentified data set can be made to the NHS CHECK team at nhscheck@kcl.ac.uk.

https://creativecommons.org/licenses/by/4.0/

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WHAT IS ALREADY KNOWN ON THIS TOPIC

  • The pathogenesis of post COVID-19 syndrome (PCS) remains unknown, though suggested risk factors may include female sex, older age, pre-existing respiratory illnesses and preinfection mental disorders.

WHAT THIS STUDY ADDS

  • While one-third of people in this sample with a previous COVID-19 infection reported symptoms lasting for ≥12 weeks, indicating PCS, only 7.4% reported having been formally diagnosed with PCS.

  • Screening positive for mental disorders was the strongest observed risk factor for reporting PCS at least 12 months later. Direct contact with COVID-19 patients, having a pre-existing respiratory illness, female sex and older age were also risk factors.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

  • Though PCS symptoms were prevalent, more work is needed to understand their prevalence in the general population in order to understand the specific impact of COVID-19 on their reporting.

  • Failure to assess mental disorders as a risk factor for PCS or Long COVID, along with other important factors, will weaken our understanding of the condition.

Introduction

Long COVID (LC) occurs when COVID-19 symptoms persist or continue to develop after the acute infection.1 2 The National Institute for health and Care Excellence (NICE) differentiates between ongoing symptomatic COVID-19 (symptoms between 4 and 12 weeks) and post-COVID-19 syndrome (PCS; symptoms ≥12 weeks).1 LC was poorly understood early in the pandemic,3 4 and consensus on pathogenesis or prognosis remains elusive.5 6 Inflammation may be responsible, as a recent study found that among people hospitalised during the acute COVID-19 infection, prolonged symptoms were associated with inflammation of myeloid cells and activation of the complement system.7 The UK Office for National Statistics (ONS) estimates that the most common symptoms include fatigue (reported by 72% of those with PCS), difficulty concentrating (51%), muscle ache (49%) and shortness of breath (48%).8 Further, 59% of people with PCS reported that their symptoms had impacted their day-to-day activities ‘a little’, while 20% said they had been ‘limited a lot’.8

PCS prevalences vary. A meta-analysis found that prevalence of symptoms lasting ≥12 weeks among people with confirmed or probable COVID-19 infections was lower in studies using healthcare records to diagnose caseness (13.6%; 95% CI 1.2% to 68.0%) compared with studies using self-reported symptoms (43.9%; 95% CI 8.2% to 87.2%).9 The ONS estimated that approximately 1.7 million people in the UK had self-reported PCS in February 2023 (~3% of the population and ~9% of all PCR-confirmed COVID-19 infections).8 While the mechanisms of PCS remain unknown, a meta-analysis identified risk factors, including female sex, older age, having a pre-existing respiratory illness, being unvaccinated against COVID-19 and being hospitalised during the acute COVID-19 infection.10 History of common mental disorders (CMDs) prior to COVID-19 infection has also been explored, though this remains under-researched and poorly understood.10 11

Healthcare workers (HCWs) were at high risk to COVID-19 infection.12 In the UK, research using data from the ONS COVID-19 Infection Survey found that HCWs were at higher risk of contracting COVID-19 compared with non-essential workers, though this effect reversed by June 2021.13 Another study found that HCWs were among the occupational groups to self-report higher prevalences of COVID-19 symptoms lasting 4 or more weeks.14 ONS mortality data revealed that those working in the healthcare sector were more likely to die of COVID-19 in 2020 compared with other industries, with this effect diminishing thereafter.15 Given the increased risk of COVID-19 infection among HCWs, particularly early in the pandemic, they are likely to be at high risk of PCS. This may also be the case for other essential workers, with educators, bus and coach drivers, and police and protective services staff also at increased risk.12 13

Due to the associated symptoms and lack of a known mechanism, PCS has drawn comparisons with other conditions, such as myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS).5 ME/CFS is a long-term condition defined by postexertional malaise, which may have a delayed onset, physical and mental fatigue and fatigability, issues with sleeping, memory or concentration, and functional impairment, which may be substantial.16 Diagnoses are made if the defining symptoms last for 6 months or longer and cannot be attributed to an alternative cause.16 Risk factors include female sex, older age, having asthma and a history of CMDs.17 18

We examined the prevalence, symptoms and risk factors for PCS among a large sample of HCWs in England. Given the paucity of longitudinal evidence in this area, we explored whether factors identified in previous longitudinal post infectious fatigue syndrome work,17 18 such as female sex, older age and symptoms of CMDs, would be significant risk factors for reporting PCS.

Methods

NHS CHECK and data collection

We analysed data from NHS CHECK, a longitudinal survey distributed to all HCWs (clinical and non-clinical), students and volunteers in 18 NHS Trusts across England at four data collection periods from April 2020 to May 2023 (baseline, then 6, 12 and 32 months post baseline). Trusts were invited to distribute an online survey (paper copies available on request) via direct emails to senior leadership teams and were purposively selected to offer diversity in geographical location, urban and rural settings, and acute and mental health Trusts. Trusts promoted the survey to all eligible staff via existing group emails. We also promoted the survey via staff support teams/leads, chief nursing officers, medical directors, occupational health departments, trade union representatives and well-being hub users. NHS CHECK was discussed during team briefings, advertised via screen savers on Trust computers and included in Trust newsletters, news items on Trust intranet websites and closed social media groups. At baseline, participants completed a short survey and were given the option to complete an additional longer survey. To account for attrition, a replenishment sample of HCWs who did not complete the baseline or 6-month surveys was recruited at 12 months. The NHS CHECK cohort consists of 24 137 participants. For further information, see the cohort protocol19 and this study’s preregistered protocol.20 We followed the Strengthening the Reporting of Observational Studies in Epidemiology guideline for cohort studies for this report.21

Outcome

Data on PCS were collected at 12 and 32 months. At 12 months, staff were asked if they had ever been diagnosed with COVID-19. Those who had were given a list of 24 symptoms and asked to report whether they had developed each symptom due to COVID-19 and for how long they experienced it using a 4-point scale (1 day–2 weeks, 2–4 weeks, 4–12 weeks, ≥12 weeks). These symptoms were chosen based on NICE guidance at the time of data collection.1 At 32 months, we asked staff the same questions but only regarding COVID-19 infections within the preceding year, ensuring two unique measurements. We also asked staff to self-report if they had received a formal diagnosis of LC or PCS from a medical professional. Following NICE guidance, we defined staff as having PCS if they experienced any symptom for ≥12 weeks, regardless of whether they had recovered by the time of the study or had been formally diagnosed with LC/PCS.1 We created a binary outcome variable to represent caseness (0=did not report PCS, 1=reported PCS).

Risk factors

Variables were included if they were risk factors in the LC10 or the ME/CFS literature,17 18 as well as additional variables that were available in the NHS CHECK cohort.

Mental disorder variables

Our primary mental disorder variable was CMDs (measured by the General Health Questionnaire (GHQ-1222)). The robustness of the GHQ-12 measure was established even where response options differ slightly from the original, as was the case in this study where there was a small typographical error in one response option of one GHQ-12 item.23 24 We included five other mental disorder variables, but due to multicollinearity and higher levels of missing data, these were considered secondary variables and were examined in sensitivity analysis only: depression (Patient Health Questionnaire (PHQ-925)), generalised anxiety disorder (GAD-726), burnout (Burnout Assessment Tool (BAT-1227)), post-traumatic stress disorder (PTSD; Post-traumatic stress disorder Check List (PCL-628)) and alcohol use disorder (Alcohol Use Disorder Identification Test for Consumption (AUDIT-C29)). Using the recommended cut-off scores, we screened for probable mental disorders: GHQ-12 scores ≥4; PHQ-9 scores≥10, GAD-7 scores ≥10, BAT-12 scores ≥2.96, PCL-6 scores ≥14 and AUDIT-C scores ≥8. Alcohol-use disorder did not violate multicollinearity and was included in all models.

Covariates

Covariates included participants’ age, sex, ethnicity, job role, relationship status, self-reported pre-existing respiratory illnesses (ie, asthma or chronic obstructive pulmonary disease (COPD)), contact with COVID-19 patients, income, perceived access to personal protective equipment (PPE) and confidence in workplace infection control policies. All covariates were measured in the short baseline survey.

Analyses

We preregistered an analysis protocol on the Open Science Framework,20 and this paper addresses research questions 1, 2 and 3. Our sample was adequately powered. The cohort was weighted using a raking algorithm based on the age, sex, ethnicity and job role profile of the workforce at each Trust to maximise representativeness.19 To complete the weighting, missing data were imputed using the fifth nearest neighbour algorithm. These imputed data were only used to complete weighting. Weighting was conducted using R V.4.0.2. All percentages in the results were weighted.

As per our protocol, analysis began with descriptive statistics on the baseline risk factors, outcome variable and each COVID-19 symptom and its duration. To account for missing risk factors data and maintain power, we conducted Multiple Imputation using Chained Equations (MICE). See section A of the online supplemental materials for a detailed description of the MICE procedure (including online supplemental tables 1a,b and 2 and 3a,b and online supplemental figures 1 and 2).

Supplemental material

Supplemental material

Supplemental material

The imputed datasets were then pooled using Rubin’s rules and included within a multilevel logistic regression to examine risk factors. The model was multilevel as we grouped participants by Trust to account for clustering. In addition, we included an exposure variable. We identified ‘burden periods’, that is, periods of time at baseline when the reported number COVID-19 cases and deaths were higher or lower, which likely influenced pressure on HCWs and may have influenced the risk factors, particularly the mental disorder variables.19 Reported ORs are adjusted for all variables in the model. For post-hoc sensitivity analyses, we reran the regression model with a number of changes: using only formal diagnosis of LC/PCS as the outcome; using the broad NICE definition of LC (≥4 weeks of symptoms) as the outcome1; using only data collected at 12 or 32 months; and with the secondary mental disorder variables. Data analysis was completed using Stata V.18.

Patient and public involvement and engagement

A patient and public involvement and engagement (PPIE) group was set up as part of this study. The group comprised 16 HCWs with experience of PCS from a range of demographic and professional backgrounds. The group met several times to discuss the project aims, analysis plans and the findings.

Results

Sample characteristics

5248 HCWs reported a previous COVID-19 infection (22.5% of the full NHC CHECK cohort and 45.9% of the 11 776 HCWs who responded to this question at 12 or 32 months). As this study uses data from three waves of data collection, we examined participation across each (figure 1). Most HCWs (88.1%) participated at baseline, with 11.9% joining at 12 months. Of those who completed the baseline survey, most (n=3155; 68.9%) completed the longer survey. At 12 months, 2659 HCWs reported a previous COVID-19 infection, while 3070 reported a COVID-19 infection at 32 months; 481 HCWs reported a previous COVID-19 infection on both.

Figure 1

Participation in each National Health Service (NHS) CHECK survey wave for healthcare workers with a previous COVID-19 infection. 12mo=12 month follow-up survey; 32mo=32 month follow-up survey. *These 2093 participants (39.3% of all those who reported a previous COVID-19 infection) do not have complete scores for the secondary mental disorder variables as they were only included in the longer baseline survey.

Table 1 shows the demographic and occupational information for the HCWs. Most were female, white and in a relationship. Approximately 8% of those who reported a previous COVID-19 infection were doctors, and the remainder were roughly evenly split between nursing, other clinical and non-clinical staff. Most did not have asthma or COPD. The majority had direct contact with COVID-19 patients and perceived good access to PPE, but 35.1% of all HCWs with a previous COVID-19 infection reported inadeuqate workplace infection control policies. Most completed the baseline survey prior to testing positive for a COVID-19 infection. Table 2 shows information on probable mental disorders among the HCWs’. CMDs were the scale with the highest percentage of staff meeting the cut-off score (ie, above the threshold), followed by depression, PTSD, then GAD. See online supplemental table 4a–d for information about the sample at the 12-month and 32-month surveys.

Table 1

Baseline demographic and occupational information of the healthcare workers in NHS CHECK

Table 2

Baseline information on probable mental disorders among healthcare workers in NHS CHECK

Prevalence of PCS and most common symptoms

Across both timepoints, 1730 (33.6%) HCWs with a previous COVID-19 infection reported symptoms lasting ≥12 weeks, consistent with PCS (table 3). Of these, 385 also reported a formal diagnosis of LC or PCS (22.2% of those meeting criteria for PCS; 7.4% of all reporting a COVID-19 infection). The most common PCS symptom was fatigue, followed by difficulty concentrating, insomnia, anxiety/depression, shortness of breath and memory loss/confusion. See online supplemental table 5a,b for the prevalence of each symptom at 12 and 32 months, respectively. Additionally, 69.3% reported 2 or more PCS symptoms, and 7% reported a diabetes diagnosis following COVID-19 infection (online supplemental table 6).

Table 3

Prevalence of COVID-19 symptoms and their duration in the sample of HCWs who reported a previous COVID-19 infection in NHS CHECK (n=5248)

Risk factors for PCS

We found a very strong association between meeting the GHQ-12 cut-off score at baseline, indicating probable CMDs, and reporting PCS symptoms at follow-up (table 4). Having direct contact with COVID-19 patients also had a very strong association with reporting PCS symptoms, while having asthma/COPD or female sex had a strong association. Associations were also observed for age, as those aged between 51 and 60 were more likely to report PCS, and job role, where doctors were less likely to report PCS.

Table 4

Results from the multilevel logistic regression exploring baseline risk factors for reporting post COVID-19 syndrome at least 12 months later among the HCWs included in NHS CHECK (n=5248)

Scoring above the GHQ-12 cut-off score maintained a very strong association in sensitivity analyses using formal diagnosis of LC/PCS as the outcome (online supplemental table 7), the broad definition of LC as the outcome (symptoms for 4+weeks1 ; online supplemental table 8) and data collected at 12 and 32 months separately (online supplemental table 9a–c). Hospitalisation during the acute COVID-19 infection had a very strong association with reporting PCS symptoms at 12 months (online supplemental table 9b). Meeting the PHQ-9, GAD-7, BAT-12 and PCL-6 cut-off scores at baseline all had a very strong association with reporting PCS symptoms at follow-up (online supplemental table 10a–d). Differences were observed in the other risk factors. Having contact with COVID-19 patients was not a risk factor when including hospitalisation during the acute infection, asthma/COPD was not a risk factor using only 12-month data, and female sex was not a risk factor using formal LC/PCS diagnosis as the outcome or only 32-month data. Age and job role were risk factors for reporting PCS symptoms in most models, but the observed associations between levels of these variables varied. Due to this variability, we do not focus on these in our discussion. See the online supplemental materials for commentary on the MICE procedure.

Discussion

Our finding that one-third of HCWs reported prolonged COVID-19 symptoms consistent with PCS is comparable to the results of the REal-time Assessment of Community Transmission (REACT) survey, where 37.7% of their representative UK community sample reported at least one COVID-19 symptom lasting ≥12 weeks.30 Despite this, only 7.4% of all HCWs in our study reported a formal diagnosis of LC/PCS. This is similar to the results of a meta-analysis which found that measuring PCS by self-reported symptoms, as in this study, estimated significantly higher prevalences than healthcare records,9 while another study found that only ~9% of HCWs who reported COVID-19 symptoms for ≥12 weeks also reported a formal diagnosis.31 We theroise it may be that self-reported measures overestimate prevalence and may not alone be specific enough to correctly identify those impacted by PCS. For example, given that fatigue was our most prevalent symptom and HCWs often work shifts, it could be unsurprising that a high proportion met the definition of PCS. We think that it could also be that those experiencing symptoms do not attempt to obtain a diagnosis, believing that nothing can be done to help them, their symptoms are mild and do not require care, or they will recover in time. Alternatively, it may be that those who do request a diagnosis are not given one.

Our findings highlight the complexity of PCS symptoms. The most common PCS symptom was fatigue, and most experienced cognitive symptoms (66.2%), such as difficulty concentrating and memory loss. The majority reported multiple PCS symptoms (69.3%), and some attributed the development of new conditions to their COVID-19 infection. In particular, 7% reported a diagnosis of type 1 or 2 diabetes following COVID-19, fitting with previous work which found a link between COVID-19 and increased risk of diabetes.32

Our analysis also found that screening positive for mental disorders, direct contact with COVID-19 patients, having a pre-existing respiratory condition, female sex and older age at baseline were risk factors for reporting PCS at follow-up. Additionally, sensitivity analyses of the 12-month data indicated that hospitalisation during the acute infection was a risk factor. Without a known pathogenesis of PCS, it is difficult to speculate why these were risk factors, though studies with similar results have proposed explanations.11 33–37 These have speculated that symptoms of CMDs may be a risk factor due to potential physical manifestation of psychological distress,11 36 while female sex and older age may be associated with PCS due to overlap between PCS and menopausal symptoms.37 Contact with COVID-19 patients may be a risk factor as these HCWs may have experienced multiple COVID-19 infections, a known risk factor.10 It may also be that HCWs who worked with COVID-19 patients had greater knowledge of the condition than those who did not and were better able to identify their symptoms. While we do not identify any individual in this study as having ME/CFS, we note the similarity between not just symptoms, as many have pointed out, but also at least five risk factors for PCS and ME/CFS (infection, asthma, a history of CMDs, female sex and older age) which should not be ignored and similarities between the conditions may require further exploration.5

The strongest observed statistical association among risk factors for PCS was reporting symptoms of CMDs at baseline, which also included specific measures for depression, GAD, PTSD and burnout. The majority of HCWs in our sample had not tested for COVID-19 or had received a negative test result prior to reporting baseline data, indicating that for most, symptoms of CMDs predated COVID-19. Despite emerging evidence that CMDs are risk factors for PCS, research examining this remains sparse.10 11 33 36 This may raise potentially controversial questions regarding the implications of significant findings. For example, Burton et al admitted to excluding measures of mood or cognitive features when looking for risk factors for PCS as ‘patient input into the design of the study saw this as potentially implying psychological causation of symptoms’.38 While acknowledging this challenge, we argue that it is vital to examine the relationship between CMDs and PCS, and failure to confirm or refute this will weaken our understanding of the condition.

The primary strength of this study was its longitudinal nature, which allowed us to examine risk factors for PCS that were measured at least 1 year before staff reported COVID-19 data. Additionally, using data provided by each Trust, our sample was weighted by sex, age, ethnicity and job role to better represent the population from which they were drawn. Finally, our PPIE group helped to ensure that this study addressed issues relevant to HCWs with experience of PCS.

Our results must be considered within some limitations. First, the symptoms used to identify PCS are not condition specific and may be prevalent regardless of previous COVID-19 infections. This may have contributed to the difference in the number of HCWs who reported symptoms consistent with PCS (33.6%) and who reported a formal diagnosis of LC or PCS (7.4%). As we only asked those who reported a previous COVID-19 infection at 12 and 32 months to describe what symptoms they experienced, we cannot approximate the general prevalence of each symptom in our sample. Two solutions may have been to ask the symptom questions to all participants either at baseline or 12-month and 32-month follow-up. Future NHS CHECK data collection waves will ask symptom questions to all participants. There may also be recall bias in symptom prevalence, as participants were asked to self-report how long they experienced each symptom for, perhaps after they had fully recovered.

Relatedly, the lack of a consensus definition of LC is a problem within this field.39 We included symptoms based on guidance from NICE at the time of data collection,1 and note some symptoms in other studies were not measured here. Several other definitions exist, such as post COVID-19 condition from the WHO.2 Differing definitions, symptoms and timeframes used to denote LC make it difficult to compare results across studies, and differences in observed risk factors may exist based on how the condition is defined. We reflect on this in greater detail in a recent review.40 We also did not examine PCS-symptom severity, and it may be that those most impacted did not participate. Additionally, we intended to examine if hospitalisation during the acute infection was a risk factor for reporting PCS; however, this was erroneously omitted from the 32-month survey. Sensitivity analysis indicated that hospitalisation was a risk factor at 12 months. Finally, certain variables which have been found to be risk factors for PCS were not collected by NHS CHECK, such as number of acute COVID-19 infections, vaccination against COVID-19, body mass index and comorbidities, such as diabetes.10 These omissions may have contributed to residual confounding in our analyses.

Conclusions

The difference between the number of HCWs reporting symptoms consistent with PCS and those reporting a formal diagnosis of LC or PCS is striking. We also confirm previous reports that baseline symptoms of CMDs increase the risk of PCS, while bearing in mind that the majority of studies exploring PCS lack this risk factor. The cause, or more likely causes, of PCS remain unclear and may do so for some time. Until further research leads to more specific treatments, the need to support and provide rehabilitation for those with PCS remains.

Data availability statement

Data are available upon reasonable request. Due to Research Ethics Committee restrictions, the data set is not publicly available. Requests to access the deidentified data set can be made to the NHS CHECK team at nhscheck@kcl.ac.uk.

Ethics statements

Patient consent for publication

Ethics approval

Ethical approval for the NHS CHECK study was granted by the Health Research Authority (reference: 20/HRA/2107, IRAS: 282686) and local Trust Research and Development approval. Participants gave informed consent to participate in the study before taking part.

Acknowledgments

We are especially grateful to all the participants who took part in the study. We wish to acknowledge the National Institute of Health Research (NIHR) Applied Research Collaboration (ARC) National NHS and Social Care Workforce Group, with the following ARCs: East Midlands, East of England, South West Peninsula, South London, West, North West Coast, Yorkshire and Humber, and North East and North Cumbria. They enabled the set-up of the national network of participating hospital sites and aided the research team to recruit effectively during the COVID-19 pandemic. The NHS CHECK consortium includes the following site leads: Siobhan Coleman, Sean Cross, Amy Dewar, Chris Dickens, Frances Farnworth, Adam Gordon, Charles Goss, Jessica Harvey, Nusrat Husain, Peter Jones, Damien Longson, Paul Moran, Jesus Perez, Mark Pietroni, Ian Smith, Tayyeb Tahir, Peter Trigwell, Jeremy Turner, Julian Walker, Scott Weich, and Ashley Wilkie. The NHS CHECK consortium includes the following coinvestigators and collaborators: Peter Aitken, Ewan Carr, Anthony David, Mary Jane Doherty, Sarah Dorrington, Rosie Duncan, Sam Gnanapragasam, Cerisse Gunasinghe, Stephani Hatch, Matthew Hotopf, Daniel Leightley, Richard Morriss, Isabel McMullen, Dominic Murphy, Martin Parsons, Catherine Polling, Alexandra Pollitt, Rebecca Rhead, Danai Serfioti, Chloe Simela, and Charlotte Wilson Jones.

References

Supplementary materials

Footnotes

  • X @Brendan_Dempsey, @helen_a_blake

  • Contributors DL, IM, RR, NG, SAMS, A-MR and SW conceptualised the NHS CHECK study and secured funding. RB completed project administration. DL curated the data. DL, SAMS and IM initiated the current substudy and secured funding. All authors contributed to the design of the study. BD performed the analyses, with support from HAB and DL. BD drafted the manuscript. All authors contributed to the editing of the manuscript and all authors read and approved the final manuscript. DL is responsible for the overall content as guarantor.

  • Funding Funding for the current study was received from the Colt Foundation (CF/01/22). Funding for the main NHS CHECK cohort study from which the data used in these analyses were drawn (co-chief investigators SW, SAMS, RR, MH, NG) was received from the following sources: Medical Research Council (MR/V034405/1); UCL/Wellcome (ISSF3/ H17RCO/C3); Rosetrees (M952); Economic and Social Research Council (ES/V009931/1); NHS England and NHS Improvement; as well as seed funding from National Institute for Health Research Maudsley Biomedical Research Centre, King’s College London, National Institute for Health Research Health Protection Research Unit in Emergency Preparedness and Response at King’s College London. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

  • Competing interests This report is independent research supported by the National Institute for Health Research (NIHR) Applied Research Collaboration (ARC) North Thames and the National Institute for Health and Care Research Health Protection Research Unit (NIHR HPRU) in Emergency Preparedness and Response, a partnership between the UK Health Security Agency, King’s College London and the University of East Anglia. The views expressed are those of the author(s) and not necessarily those of the NIHR, UKHSA or the Department of Health and Social Care. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) license to any Author Accepted Manuscript version arising from this submission. SAMS is supported by the National Institute for Health and Care Research (NIHR) Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and funded by the National Institute for Health and Care Research, NIHR Advanced Fellowship, Dr SAMS, NIHR300592. SAMS is also an independent scientific member of the Industrial Injuries Advisory Council. The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the NIHR, the Department of Health and Social Care, or the Industrial Injuries Advisory Council. SW is a member of the NHS England board.

  • Provenance and peer review Not commissioned; internally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.