key: cord-0703659-pjh2bat5 authors: van der Plaat, D.; Madan, I.; Coggon, D.; van Tongeren, M.; Edge, R.; Muiry, R.; Parsons, V.; Cullinan, P. title: Occupational risks of COVID-19 in NHS workers in England date: 2021-04-14 journal: nan DOI: 10.1101/2021.04.08.21255099 sha: 8f21d40e137469f96b61a9efc5daf0feccaa207f doc_id: 703659 cord_uid: pjh2bat5 Abstract Objective To quantify occupational risks of Covid-19 among healthcare staff during the first wave of the pandemic in England Methods Using pseudonymised data on 902,813 individuals continuously employed by 191 National Health Service trusts during 1.1.19 to 31.7.20, we explored demographic and occupational risk factors for sickness absence ascribed to Covid-19 during 9.3.20 to 31.7.20 (n = 92,880). We estimated odds ratios (ORs) by multivariate logistic regression. Results With adjustment for employing trust, demographic characteristics, and previous frequency of sickness absence, risk relative to administrative/clerical occupations was highest in additional clinical services (a group that included care assistants) (OR 2.31), registered nursing and midwifery professionals (OR 2.28) and allied health professionals (OR 1.94), and intermediate in doctors and dentists (OR 1.55). Differences in risk were higher after the employing trust had started to care for documented Covid-19 patients, and were reduced, but not eliminated, following additional adjustment for exposure to infected patients or materials, assessed by a job-exposure matrix. For prolonged Covid-19 sickness absence (episodes lasting >14 days), the variation in risk by staff group was somewhat greater. Conclusions After allowance for possible bias and confounding by non-occupational exposures, we estimated that relative risks for Covid-19 among most patient-facing occupations were between 1.5 and 2.5. The highest risks were in those working in additional clinical services, nursing and midwifery and in allied health professions. Better protective measures for these staff groups should be a priority. Covid-19 may meet criteria for compensation as an occupational disease in some healthcare occupations. Covid-19, like many communicable diseases, poses an occupational hazard to workers caring for infected patients. When the first wave of the pandemic hit the UK early in March 2020, precautions were implemented to reduce transmission to healthcare staff, including identification and segregation of infected patients, use of personal protective equipment (PPE), and enhanced personal hygiene. In the early weeks, however, these measures were far from ideal. PPE often was in short supply and testing of patients who might be carrying SARS-CoV-2 was not possible on the desired scale. Cases of occupationally-acquired disease were therefore to be expected. However, the level of risk has been uncertain, and also the extent to which it varied between different occupations within healthcare. Better understanding would help in prioritisation of preventive strategies during further waves of the pandemic, and in the management of other similar infectious diseases. It is also needed to inform decisions on possible compensation for Covid-19 as an occupational disease in healthcare workers. Evidence to date has indicated that male healthcare workers (taken as a group), nurses, nursing assistants and auxiliaries of both sexes have had higher age-adjusted mortality from Covid-19 in England and Wales compared with the general population (1). Several studies have found that patient-facing healthcare workers were infected with COVID-19 at substantially higher rates than non-healthcare workers during the first wave of the pandemic (March -July 2020), although studies differ in their findings as to which occupational groups had been at greatest risk (2) (3) (4) . Mortality, however, depends not only on risk of contracting Covid-19, but also on personal vulnerability when infection occurs, which may vary importantly between occupations. Furthermore, differences in the incidence of infection by occupation may be driven not only by exposures in the workplace (which in healthcare workers could be through proximity to infected colleagues as well as contact with patients and infected materials), but also away from work. For example, rates of infection have been higher among people living in large and crowded households (5) To get further insight regarding occupational risks of Covid-19 in healthcare workers, we analysed national data on sickness absence among employees of National Health Service (NHS) trusts in England, before and after they started to care for patients known to have the disease. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted April 14, 2021. continuously employed by NHS trusts in England from 1 January 2019 to 31 July 2020, and on all their absences from work during that period, other than for annual leave. The latter included the reason for absence, and the start and end date of each episode. Supplementary File A describes in detail the methods by which we used the two databases to create a file for statistical analysis. We first checked for missing and inconsistent data, and corrected clear anomalies in a small minority of records by imputation according to a standard set of rules. We also reclassified some variables into aggregated categories that would facilitate more meaningful analysis. We then generated a single file with one record for each individual, which included the variables listed in Table 1 , and also the start and end dates of all absences during 1 January 2019 to 31July 2020, with the reason for absence. a c t o r s i n s t u d y s a m p l e a n d c u m u l a t i v e p r e v a l e n c e o f n e w C o v i d -1 9 s i c k n e s s a b s e n c e d u r i n g 9 M a r c h t o 3 1 J u l y 2 is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 14, 2021. ; https://doi.org/10.1101/2021.04.08.21255099 doi: medRxiv preprint a prevalence % in study sample (total N = 902,813) b Prevalence % among those with risk factor Staff group was assigned to 10 categories, according to a classification that had been used in the ESR records (Supplementary Table S1 ). The ESR system also held more detailed occupational data, but to protect privacy, that level of information could not be released to the research team. Instead, four members of the team (an occupational hygienist and three occupational physicians with experience in the NHS) compiled a job-exposure matrix (JEM), which the ESR Management Team then used to reclassify detailed occupational categories (n = 659) to the eight exposure categories that are listed in Table 1 . Using the information on absence episodes, we defined a variable which for each individual represented the number of new episodes of sickness absence (for any cause) that had started during 2019 (classified as 0, 1, 2-3 and >3). This was intended as a marker for longterm propensity to take sickness absence, which can vary importantly between individuals independently of morbidity (6) . In addition, we distinguished episodes of Covid-19 sickness absence, which we defined as being for any of five categories of sickness (cough/flu, chest/respiratory, infectious diseases, other or unknown) with Covid-19 recorded as a Data on the date by which each trust was known to have admitted at least three Covid-19 cases were obtained from an NHS COVID-19 daily situation report published on 12 November 2020 (7). We took 9 March 2020 as the date from which Covid-19 sickness absence could reasonably be assumed to reflect coronavirus infection. That was at least 10 days before most hospitals started to admit documented Covid-19 cases (see Supplementary File B for further justification). As a check on the validity of Covid-19 sickness absence as a marker for coronavirus infection, two collaborating trusts provided data on antibody tests that had been carried out on staff members before 7 August 2020. Individuals were identified by an encrypted code number that had been assigned by the ESR Management Team, allowing anonymised linkage with the other records to which we had access. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted April 14, 2021. ; https://doi.org/10.1101/2021.04.08.21255099 doi: medRxiv preprint Statistical analysis was carried out with R (version 4.0.4) software. We first generated descriptive statistics summarising the distributions of the main variables analysed. We then fitted two multivariate logistic regression models to estimate odds ratios (ORs) with 95% confidence intervals (95%CIs) for the start of any episode of Covid-19 sickness absence from 9 March to 31 July 2020. Next, the analysis was repeated, distinguishing between onset of the Covid-19 sickness absence before and after the employing trust had first cared for at least three documented Covid-19 cases. Our aim was to distinguish periods when acquisition of Covid-19 through transmission from patients was less and more likely, and we therefore incorporated a lag of four days to allow for an interval between exposure to infection and development of symptoms. Further logistic regression models were used to explore risk factors for prolonged Covid-19 sickness absence starting during 9 March to 16 July 2020 (because records were complete only up to 31 July 2020, we could not be confident of accurately distinguishing prolonged episodes of absence that started after 16 July 2020). Finally, to check on the reliability of Covid-19 sickness absence as a marker for the disease, we used data from two collaborating trusts to compare the prevalence of positive antibody tests in employees who underwent testing before 7 August 2020, according to their history of . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted April 14, 2021. ; https://doi.org/10.1101/2021.04.08.21255099 doi: medRxiv preprint Table 2 shows associations of Covid-19 sickness absence at any time during the study period with the main risk factors of interest. After adjustment for other covariates, risk was similar in men and women, and in age groups below 55 years, but lower at older ages (OR for age >60 relative to <30 years in fully adjusted model: 0.76). Risk was generally higher for non-white relative to white ethnicity, and particularly for those of Asian origin (ORs 1.43 and 1.73 in fully adjusted model). Frequency of sickness absence during 2019 was a further risk factor, with an OR of 2.41 for >3 relative to 0 episodes in the fully adjusted model. Risk estimates were derived from two logistic regression models that included all of the variables for which results are presented, together with trust (191 categories). . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted April 14, 2021 . T a b l e 3 A s s o c i a t i o n s o f s t a f f g r o u p w i t h a f i r s t e p i s o d e o f C o v i d -1 9 s i c k n e s s a b s e n c e d u r i n g 9 M a r c h t o 3 1 J u l y 2 0 2 0 , a c c o r d i n g t o w h e t h e r t h e e m p l o y i n g t r u s t h a d y e t c a r e d f o r a t l e a s t t h r e e d o c u m e n t e d C o v i d -1 9 p a t i e n t s Risk estimates were derived from two logistic regression models, each of which included all of the variables from Model 1 in Table 2 . and lower risk estimates for medical and dental staff (ORs 1.10 and 0.77 before and after adjustment for exposure category). . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted April 14, 2021. T a b l e 4 . A s s o c i a t i o n s o f r i s k f a c t o r s a t b a s e l i n e w i t h s t a r t o f a n y e p i s o d e o f p r o l o n g e d C o v i d -1 9 s i c k n e s s a b s e n c e d u r i n g 9 M a r c h t o 1 6 J u l y 2 0 2 0 Risk estimates were derived from two logistic regression models that included all of the variables for which results are presented, together with trust (191 categories). An episode of Covid-19 sickness absence was classed as prolonged if it lasted >14 days. Individuals who had only short-term Covid-19 sickness absence were excluded from these analyses (see text). is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted April 14, 2021. At the two collaborating trusts, results from antibody tests performed by 7 August 2020 were available for 11,050 staff members. The overall prevalence of positive results among those who had taken Covid-19 sickness absence (37.0%) was 3.3 times that in those who had not (11.1%). There were no differences in this ratio by staff group that could not easily be attributable to random sampling variation ( . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted April 14, 2021. After allowance for employing trust, demographic characteristics, and previous frequency of sickness absence, we found more than twofold variation in the risk of Covid-19 sickness absence across major NHS staff groups in England. Differences were reduced, but not eliminated, following adjustment also for potential exposure to infected patients or materials, assessed by a job-exposure matrix. For prolonged Covid-19 sickness absence (episodes lasting >14 days), the variation in risk was greater. The main uncertainty in interpretation of these observations is the extent to which they reflect differences in occupational exposure to SARS-CoV-2. The analysis benefitted from a large sample size, giving high statistical power, and from its use of data that had been collected prospectively in a standardised format. Information about employing trust, sex, age, staff group and frequency of earlier sickness absence should all have been highly reliable, and we would not expect serious misclassification between the categories of ethnicity that were specified. A limitation was that staff group distinguished only broad categories of work. Ideally, analysis would have discriminated between occupations in finer detail, but access to that level of information was precluded by data protection rules. Instead, therefore, we constructed a JEM to group the 659 occupations in the ESR database to eight exposure categories. As an indicator of occupational exposure to infection from patients, the JEM should have been superior to staff group. For example, within medical and dental personnel, it distinguished specialists in intensive care, who could be expected to have high exposure to patients with Covid-19, from orthopaedic surgeons, whose patients would be expected to have lower prevalence of the disease. However, it was far from perfect. Even in the detailed occupational classification to which the JEM was applied, some job categories were heterogeneous (e.g. nurses in medical wards could not be distinguished from those working in orthopaedics or gynaecology). Moreover, it did not allow for changes in duties during the epidemic, or for use of PPE and its effectiveness. We would expect any misclassification by the JEM to be non-differential with respect to Covid-19 outcomes, and therefore to bias risk estimates for exposure categories towards the null. Thus, the observed associations with the two highest exposure categories, even after adjustment for staff group, support its validity. However, the varying specificity of occupational categories in the JEM complicates interpretation of numerical estimates of risk for exposure levels. Also, the heterogeneous mix of occupations in individual exposure categories, makes it harder to assess the potential for confounding by non-occupational . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted April 14, 2021. ; https://doi.org/10.1101/2021.04.08.21255099 doi: medRxiv preprint exposures. For these reasons, we focused principally on risk by staff group (a wellestablished classification of jobs), and used exposure category to help understand the extent to which associations with staff group reflected patient-related exposures. . The other major limitation of our study design was the incomplete validity of sickness absence as a marker for Covid-19. Early in the epidemic, diagnostic tests were not widely available, and clinical diagnoses may not have been accurate. A few trusts never opted to record Covid-19 as a reason for absence and use of the code by other trusts may not have been consistent. Nevertheless, at the two collaborating trusts which provided data, antibody tests were more than three times as likely to be positive among individuals who had taken Covid-19 sickness absence. In assessing relative risks by staff group, we adjusted for demographic variables and for trust and frequency of sickness absence in 2019. The latter was intended as a marker of individual propensity to take sickness absence when ill and showed an expected association with Covid-19 sickness absence. Adjustment for trust was important because rates of infection were known to have varied geographically (8) . Moreover, there may have been systematic differences between trusts in the ascertainment and coding of reasons for absence. In all analyses, we took administrative and clerical workers as the reference against which risks in other staff groups were compared. Making up 21.5% of the total study sample, they encompassed a range of occupations, including senior managers as well as middle-grade administrative occupations, clerical workers and receptionists. Most will have been officebased, with little or no direct patient contact, and during the epidemic, some may have worked partially or totally from home. Their work may have entailed social contact with colleagues, but not at a level higher than in many occupations outside healthcare. Furthermore, their socio-economic circumstances will have been neither exceptionally good nor poor. Thus, within the demographic strata that we distinguished, their exposures to SARS-CoV-2 should have been fairly representative of the wider working population in their local area. An indication of differences in risk between staff groups for reasons other than patient-care comes from analysis that was restricted to the period before each trust began to care for documented Covid-19 cases ( Table 3 ). During that phase, much of the observed variation in risk might be expected to reflect exposure to infection away from work, or through proximity to infected colleagues. However, the highest ORs (between 1.6 and 1.9) were all in patient-. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted April 14, 2021. ; https://doi.org/10.1101/2021.04.08.21255099 doi: medRxiv preprint facing occupations, including medical and dental personnel, suggesting that there may also have been some unrecognised contact with infected patients. Once trusts were known to be caring for Covid-19 patients, the ORs for most of these occupations were higher, excess relative risks (estimated as OR -1) increasing by 0.6 to 0.9 (Table 3 ). An exception were doctors and dentists, in whom ORs were lower when trusts were known to be caring for Covid-19 patients. This may have been because in the early phase of the epidemic, some doctors contracted infection from undiagnosed patients, but that risk of such transmission was reduced once testing became more widely available. Another clue to the impact of patient-related exposures on differences in risk between staff groups is the effect of adjusting risk estimates for exposure category ( Table 2) . ORs reduced for all staff groups, which was to be expected given a partial correlation between staff group and exposure category. However, the reductions were greatest for patient-facing occupations. For example, the OR for additional clinical services (a group that included care assistants) fell from 2.31 to 1.63, and that for registered nurses and midwives from 2.28 to 1.57. Such changes point strongly to an important contribution from patient-related exposures, but because of the limitations of the JEM, may not have captured them fully. When allowance is made for the inaccuracy of sickness absence as a marker for disease, and the possibility of a small occupational risk in the reference group of administrative and clerical workers, the results in Tables 2 and 3 suggest that occupational exposures increased the risk of contracting Covid-19 in additional clinical services, registered nurses and midwives, and allied health professionals by a factor of between 1.5 and 2.5. The average relative risk in doctors and dentists appears to have been somewhat lower, but still elevated. Few studies have explored infection rates of Covid-19 in healthcare staff by occupational group during the first wave of infection in England. Zheng, in a study of 1045 staff at a London hospital tested in March or April 2020, found a higher than expected rate of Covid-19 positivity and correspondingly high Covid-19 sickness absence episodes in medical and dental, nursing, midwifery and additional clinical services staff (2) . In a study of 11,500 staff at Oxford University Hospitals NHS, tested between March and early June, porters and cleaners had the highest rates of Covid-19 positivity (3). In our study, it is notable that risk among laboratory scientists was little higher than in administrative and clerical occupations. This suggests that even early in the epidemic, precautions against transmission of SARS-CoV-2 through the handling of clinical samples were fairly effective. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 14, 2021. ; https://doi.org/10.1101/2021.04.08.21255099 doi: medRxiv preprint While our main outcome measure was cumulative prevalence of any Covid-19 sickness absence, we also explored risk factors for episodes lasting >14 days. We expected that prolonged absence might have higher specificity as a marker for Covid-19. Moreover, it would tend to reflect more disabling disease of the type that was most likely to be considered for compensation. A complication is that it will have depended not only the risk of contracting infection, but also on personal vulnerability once infection occurred. Thus, while risk of any Covid-19 sickness absence was lowest in the oldest age group, that of prolonged absence increased with age (age being a major determinant of vulnerability to Covid-19 (9)). Similarly, the higher risk of prolonged Covid-19 sickness absence among non-white ethnic groups may have been a consequence of higher vulnerability (9) . This will be explored further in a separate report. For most staff groups, ORs were higher for prolonged than for any Covid-19 sickness absence (Table 4 ), reinforcing the case for a relative risk in the order of two from occupational exposures. The occupational hazard in medical and dental personnel may have been obscured by relatively low vulnerability to severe disease. Our analysis suggests that during the first wave of the Covid-19 pandemic in England, occupationally-attributable relative risks for Covid-19 among most patient-facing occupations in healthcare workers employed by NHS trusts were in the order of 1.5 to 2.5. For medical and dental personnel, relative risks were a little lower, but still elevated. Better protective measures for these groups should be a priority in the future. Whether relative risks are sufficient to warrant compensation for Covid-19 as an occupational disease in healthcare workers will depend on the regulatory framework, and the required confidence of occupational attribution. COVID-19) related deaths by occupation Characteristics and transmission dynamics of COVID-19 in healthcare workers at a London teaching hospital Differential occupational risks to healthcare workers from SARS-CoV-2 observed during a prospective observational study Effective control of SARS-CoV-2 transmission between healthcare workers during a period of diminished community prevalence of COVID-19 Better housing is crucial for our health and the COVID-19 recovery. The Health Foundation A literature review on sick leave determinants (1984-2004) COVID-19 NHS Situation Report. UK: NHS England Infection rates from Covid-19 in Great Britain by geographical units: A model-based estimation from mortality data Inequality and COVID-19 We are very grateful to the following, without whom the study would not have been possible: Sam Wright, Workforce Information Advisor, NHS Electronic Staff Record, and Mike Vickerman, Workforce Information and Analysis, DHSC. Dr Gavin Debrera (Public Health England) and Dr Kit Harling gave invaluable help in planning the study.We are grateful too, to Lee Isidore, Manal Sadik and Victoria Thorpe for their helpful input into the interpretation of our findings.We would also like to thank Cambridge University Hospitals NHS Foundation Trust (Dr Mark Ferris), Guys and St Thomas's NHS Foundation Trust (Dr Ali Hashtroudi) and Bolton NHS Foundation Trust (Dr Martin Seed) for providing results from their staff antibody testing programmes. This study was funded by a grant from the COLT Foundation