Covid-19 Risk by work-related factors: Pooled analysis of individual linked data from 14 cohorts (preprint)

BackgroundSARS-CoV-2 infection rates vary by occupation, but the association with work-related characteristics (such as home working, key-worker, or furlough) are not fully understood and may depend on ascertainment approach. We assessed infection risks across work-related characteristics and compared findings using different ascertainment approaches. MethodsParticipants of 14 UK-based longitudinal cohort studies completed surveys before and during the COVID-19 pandemic about their health, work, and behaviour. These data were linked to NHS digital health records, including COVID-19 diagnostic testing, within the UK Longitudinal Linkage Collaboration (UK-LLC) research environment. Poisson regression modelled self-reported infection and diagnostic test confirmed infection within each cohort for work-related characteristics. Risk Ratios (RR) were then combined using random effects meta-analysis. ResultsBetween March 2020 and March 2021, 72,290 individuals completed 167,302 surveys. Overall, 11% of 138,924 responses self-reported an infection, whereas 1.9% of 159,820 responses had a linked positive test. Self-reported infection risk was greater in key-workers vs not (RR=1.24(95%C.I.=1.17,1.31), among non-home working (1.08(0.98,1.19)) or some home working (1.08(0.97,1.17)) vs all home working. Part-time workers vs full-time (0.94(0.89,0.99)), and furlough vs not (0.97(0.88,1.01)) had reduced risk. Results for the linked positive test outcome were comparable in direction but greater in magnitude e.g. an 1.85(1.56,2.20) in key-workers. ConclusionThe UK-LLC provides new opportunities for researchers to investigate risk factors, including occupational factors, for ill-health events in multiple largescale UK cohorts. Risk of SARS-CoV-2 infection and COVID-19 illness appeared to be associated with work-related characteristics. Associations using linked diagnostic test data appeared stronger than self-reported infection status. What is already known on this topic- Infection of SARS-CoV2 during the pandemic was shown to vary by occupation, with occupations such as healthcare, and education at higher risk during some or all of the pandemic. - What is not clear, is how are work-related characteristics such employment status, part-time working from home, and schemes such as furlough and key worker status associated with the risk of infection. What this study adds- This is the one of the first studies to examine work-related characteristics including work related government policies, in terms of their infection risk within the working population. - This is also one of the first studies to analyse data from the UK Longitudinal Linkage Collaboration (UK-LLC), in which multiple UK national longitudinal cohorts were linked to national health data including diagnostic testing for SARS-CoV2. - We further compared definitions infection via either a self-reported case of COVID-19 or a linked diagnostic SARS-CoV2 infection. How this study might affect Research Practice or Policy- The findings contribute to our understanding of work-related characteristics and related schemes were associated with infection risk under two definitions. This is pertinent given new and emerging variants are continuing to drive an ever-changing SARS-CoV-2 infection risk within the population, along with the need to adequately prepare for future pandemics that may occur.

Synthesis and new evidence from the PROTECT UK National Core Study: Determining occupational risks of SARS-CoV-2 infection and COVID-19 mortality (preprint)

Introduction: The PROTECT National Core Study was funded by the UK Health and Safety Executive (HSE) to investigate routes of transmission for SARS-CoV-2 and variation between settings. Methods: A workshop was organised in Oct 2022.We brought together evidence from five published epidemiological studies that compared risks of SARS-CoV-2 infection or COVID-19 mortality by occupation or sector funded by PROTECT relating to three non-overlapping data sets, plus additional unpublished analyses relating to the Omicron period. We extracted descriptive study level data and model results. We investigated risk across four pandemic waves using forest plots for key occupational groups by time-period. Results: Results were largely consistent across different studies with different expected biases. Healthcare and social care sectors saw elevated risks of SARS-CoV-2 infection and COVID-19 mortality early in the pandemic, but thereafter this declined and varied by specific occupational subgroup. The education sector saw sustained elevated risks of infection after the initial lockdown period with little evidence of elevated mortality. Conclusions: Increased in risk of infection and mortality were consistently observed for occupations in high risk sectors particularly during the early stage of the pandemic. The education sector showed a different pattern compared to the other high risk sectors, as relative risk of infections remained high in the later phased of the pandemic, although no increased in COVID-19 mortality (compared to low-risk occupations) was observed in this sector in any point during the pandemic.

Occupational differences in risk of COVID-19: findings and methodological issues

IntroductionIt is well-established that certain occupations that have frequent contact with patients (e.g. health care workers) or the public (e.g. bus drivers), may have increased risks of Covid-19. However, estimating these risks involves a number of important methodological problems. Not everyone who has a SARS-CoV-2 infection gets symptoms;not everyone with symptoms gets tested;not all tests yield valid results.MethodsTherefore, a variety of approaches are necessary for obtaoning valid estimates of the (relative) risks of Covid-19 in various occupational groups. These include: (i) linkage with census data to adjust for non-workplace factors;(ii) test-negative design studies of Covid-19 infection;(iii) excess mortality analyses;and (iv) proportional mortality analyses.ResultsSome occupations such as health care and social care workers have consistently increased risks of Covid-19 infection and mortality. Other occupations have markedly different results depending on the method of analysis used.ConclusionsThese methodological issues are not insurmountable, but require careful study design and data analysis. In particular: (i) when not everyone is being tested, then comparisons between and within occupations can be biased, but this bias can be minimised using the test-negative design;and (ii) comparisons between occupations require careful and rigorous adjustment for work-related ‘living conditions'.

Synthesis of evidence from the PROTECT UK national core study: exploring occupational risks of SARS-CoV-2 infection and COVID-19 mortality

IntroductionThe PROTECT National Core Study was funded by the UK Health and Safety Executive (HSE) to investigate how SARS-CoV-2 is transmitted from person to person, and how this varies in different settings.One area of research aimed to compare relative differences between occupational groups and sectors in SARS-CoV-2 infection and COVID-19 mortality over time and explore the likely reasons.MethodsWe brought together evidence from nine published epidemiological studies supported by PROTECT relating to four data sets, plus new analyses relating to the Omicron period. We organised these studies into the following categories: those that specifically compared risks of infection mortality;and those that looked at risk factors for SARS-CoV-2 infection and/or COVID-19 mortality. We extracted descriptive study level data and results. We investigated risk across four pandemic waves using forest plots for key occupational groups by time. A workshop was organised in Oct 2022 with authors from each study to discuss and document key strengths and expected biases.ResultsHealthcare and social care sectors saw elevated risks of SARS-CoV-2 infection and COVID-19 mortality early in the pandemic but thereafter these declined and varied by specific occupational subgroup. The education sector saw sustained elevated risks of infection after the initial lockdown period with little evidence of elevated mortality. Results were largely consistent across different studies with differing expected biases, although unmeasured confounding cannot be ruled out.ConclusionDifferences between occupations and sectors in the UK in terms of COVID-19 risks that were observed in the early stages of the pandemic largely dissipated over time. Studies investigating risk factors suggest that reasons could include vaccination roll out, introduction of risk mitigation within high risk sectors, changes in patterns of home-working and lifting of restriction on social mixing (thereby reducing the relative effect of work).

The impact of occupational exposure on the infections rates with Sars-Cov-2 in the Netherlands

IntroductionThis study aimed to investigate the role of occupational exposure in the risk to be infected with Sars-Cov-2, and whether this differ across waves, while correcting for non-work related factors.MethodsData from 207 034 workers from the Netherlands with test data on Sars-Cov-2 from June 2020 until August 2021 were available. Personal characteristics and living conditions were derived from Statistics Netherlands. Occupational exposure was estimated by using the COVID-19 JEM. A test-negative design was applied in which the risk for a positive test was analyzed in a conditional logit model for the entire study period and three separate waves.ResultsNine percent of tests were positive during the entire study period, and being exposed at work was related to a higher risk for a positive test. The multiple regression models showed that contaminated work spaces, (lack of) face covering and income insecurity are the most important risk factors for a positive test result. Differences were found across waves. Some occupations were at higher risk to be infected at the worksite than others, and this differed across waves.ConclusionsEven after correcting for non-work related factors, exposure to Sars-Cov-2 at work is still related with higher risk for a positive test. The type of jobs differed across the waves and depends largely on the measures taken by the government.

The potential contribution of vaccination uptake to occupational differences in risk of SARS-CoV-2: Analysis of the ONS COVID-19 Infection Survey (preprint)

Objectives To assess variation in vaccination uptake across occupational groups as a potential explanation for variation in risk of SARS-CoV-2 infection. Design We analysed data from the UK Office of National Statistics COVID-19 Infection Survey linked to vaccination data from the National Immunisation Management System in England from December 1st 2020 to 11th May 2022. We analysed vaccination uptake and SARS-CoV-2 infection risk by occupational group and assessed whether adjustment for vaccination reduced the variation in risk between occupational groups. Setting Results Estimated rates of triple-vaccination were high across all occupational groups (80% or above), but were lowest for food processing (80%), personal care (82%), hospitality (83%), manual occupations (84%), and retail (85%). High rates were observed for individuals working in health (95% for office-based, 92% for those in patient-facing roles) and education (91%) and office-based workers not included in other categories (90%). The impact of adjusting for vaccination when estimating relative risks of infection was generally modest (ratio of hazard ratios reduced from 1.38 to 1.32), but was consistent with the hypothesis that low vaccination rates contribute to elevated risk in some groups. Conversely, estimated relative risk for some occupational groups, such as people working in education, remained high despite high vaccine coverage. Conclusions Variation in vaccination coverage might account for a modest proportion of occupational differences in infection risk. Vaccination rates were uniformly very high in this cohort, which may suggest that the participants are not representative of the general population. Accordingly, these results should be considered tentative pending the accumulation of additional evidence.

Occupational differences in the prevalence and severity of long-COVID: Analysis of the ONS Coronavirus (COVID-19) Infection Survey (preprint)

Objectives: To establish whether prevalence and severity of long-COVID symptoms vary by industry and occupation. Methods: We utilised ONS Coronavirus Infection Survey (CIS) data (February 2021-April 2022) of working-age participants (16-65 years). Exposures were industrial sector, occupation and major Standard Occupational Classification (SOC) group. Outcomes were self-reported: (1) long-COVID symptoms; and (2) reduced function due to long-COVID. Binary (outcome 1) and ordered (outcome 2) logistic regression were used to estimate odds ratios (OR) and prevalence (marginal means) for all exposures. Results: Public facing industries, including teaching and education, social care, healthcare, civil service, retail and transport industries and occupations had highest odds ratios for long-COVID. By major SOC group, those in caring, leisure and other services (OR 1.44, CIs: 1.38-1.52) had substantially elevated odds than average. For almost all exposures, the pattern of odds ratios for long-COVID symptoms followed that for SARS-CoV-2 infections, except for professional occupations (OR<1 for infection; OR>1 for long-COVID). The probability of reporting long-COVID for industry ranged from 7.7% (financial services) to 11.6% (teaching and education); whereas the prevalence of reduced function by a lot ranged from 17.1% (arts, entertainment and recreation) to 22-23% (teaching and education and armed forces ) and to 27% (those not working). Conclusions: The risk and prevalence of long-COVID differs across industries and occupations. Generally, it appears that likelihood of developing long-COVID symptoms follows likelihood of SARS-CoV-2 infection, except for professional occupations. These findings highlight sectors and occupations where further research is needed to understand the occupational factors resulting in long-COVID.

Areas of enduring COVID-19 prevalence: drivers of prevalence and mitigating strategies. (preprint)

Background: UK local authorities that experience sustained high levels of COVID-19 are described as areas of enduring prevalence (1). This research was carried out to investigate reasons for sustained high prevalence, along with mitigation strategies employed by Directors of Public Health, who lead public health teams. Methods: Interviews were conducted with Directors of Public Health in 19 local authority areas across England, between July and November 2021. This included nine areas identified as areas of enduring prevalence and ten ‘comparison’ areas. Results: The research suggests that health inequalities influence the wider picture of prevalence rates of COVID-19. Structural factors including deprivation, employment, and housing, due to the disproportionate impact on specific groups, converged with demographic factors including ethnicity and age, and vaccination rates, and were identified as key drivers of enduring prevalence. There are key differences in these drivers both within local authorities, and to a lesser extent, between areas of enduring prevalence and their comparison areas. Conclusions: The research suggests that existing health inequalities influenced the wider picture of prevalence rates of COVID-19. Participants advised that more research is needed on the effectiveness of mitigation strategies, and to better understand the factors that drive prevalence. This would include an assessment of how these factors to combine to predict transmission, how this varies between different areas, and the relative importance of each factor.

Differences in COVID-19 vaccination coverage by occupation in England: a national linked data study

BackgroundMonitoring differences in COVID-19 vaccination uptake in different groups is crucial to help inform the policy response to the pandemic. A key data gap is the absence of data on uptake by occupation. This study investigates differences in vaccination rates by occupation in England, using nationwide population-level data.MethodsWe calculated the proportion of people who had received three COVID-19 vaccinations (assessed on 28 February 2022) by detailed occupational categories in adults aged 18–64 and estimated adjusted ORs to examine whether these differences were driven by occupation or other factors, such as education. We also examined whether vaccination rates differed by ability to work from home.ResultsOur study population included 15 456 651 adults aged 18–64 years. Vaccination rates differed markedly by occupation, being higher in health professionals (84.7%) and teaching and other educational professionals (83.6%) and lowest in people working in elementary trades and related occupations (57.6%). We found substantial differences in vaccination rates looking at finer occupational groups. Adjusting for other factors likely to be linked to occupation and vaccination, such as education, did not substantially alter the results. Vaccination rates were associated with ability to work from home, the rate being higher in occupations which can be done from home. Many occupations with low vaccination rates also involved contact with the public or with vulnerable peopleConclusionsIncreasing vaccination coverage in occupations with low vaccination rates is crucial to help protecting the public and control infection. Efforts should be made to increase vaccination rates in occupations that cannot be done from home and involve contact with the public.

Areas of Covid-19 prevalence: drivers of prevalence and mitigating strategies

BackgroundUK local authorities that experience high sustained levels of COVID-19 prevalence are termed areas of enduring prevalence (AEP) according to SAGE (2021). A study was carried out as part of the PROTECT National Core Study on Transmission and Environment, to investigate reasons for sustained high prevalence, along with mitigation strategies employed by Directors of Public Health (DsPH).MethodsInterviews were conducted with DsPH in 19 local authority areas across England, between July and October 2021. This included nine areas identified as AEP and ten comparison areas (CA), including statistical neighbours of the AEP (PHE, 2019) that had not experienced enduring prevalence. DsPH were asked about factors contributing to enduring prevalence, along with mitigating strategies employed during the pandemic.ResultsParticipants identified various factors associated with enduring prevalence, including high deprivation levels, overcrowded housing, and low vaccination rates. Deprivation and employment were often jointly discussed as creating major barriers for people to be able to sef-isolate or work remotely. Not receiving sick pay, working on zero-hours contracts or in insecure employment were associated with inability to self-isolate. There were strong similarities in the drivers of enduring prevalence described by DsPH in the AEP and CA. All participants asserted that there were differences in these drivers between wards or geographical areas within their local authority area, and between different groups of people, including people from different and age groups and ethnic backgrounds.Participants in the AEP were more likely to discuss the impact of structural factors such as deprivation or work insecurity. All DsPH implemented a variety of effective mitigation strategies over the course of the pandemic, including local contact tracing, local testing and vaccination efforts, isolation support, communication campaigns, engagement with business and education, and community engagement.ConclusionThe research shows that deprivation, employment and housing, along with vaccination rates, are key drivers of COVID-19 prevalence. Work related factors such as insecurity and absence of sick pay may influence behaviours and increase the risk of virus transmission. DsPH advised that more research is needed on the factors that drive prevalence, on the effectiveness of mitigation strategies, and on the long-term impacts of the pandemic, including the impact on health inequalities and on the wider system for recovery.

Exploring the relationship between job characteristics and infection: Application of a COVID-19 Job Exposure Matrix to SARS-CoV-2 infection data in the United Kingdom (preprint)

ObjectivesTo assess whether workplace exposures as estimated via a COVID-19 Job Exposure Matrix (JEM) are associated with SARS-CoV-2. MethodsData on 244,470 participants were available from the ONS Coronavirus Infection Survey (CIS) and 16,801 participants from the Virus Watch Cohort, restricted to workers aged 20 to 64. Analysis used logistic regression models with SARS-CoV-2 as the dependent variable for eight individual JEM domains (number of workers, nature of contacts, contact via surfaces, indoor or outdoor location, ability to social distance, use of face covering, job insecurity, migrant workers) with adjustment for age, sex, ethnicity, Index of Multiple Deprivation (IMD), region, household size, urban vs rural area, and health conditions. Analyses were repeated for three time periods (i) February 2020 (Virus Watch)/April 2020 (CIS) to May 2021), (ii)June 2021 to November 2021, (iii) December 2021 to January 2022. ResultsOverall, higher risk classifications for the first six domains tended to be associated with an increased risk of infection, with little evidence of a relationship for domains relating to proportion of workers with job insecurity or migrant workers. By time there was a clear exposure-response relationship for these domains in the first period only. Results were largely consistent across the two cohorts. ConclusionsAn exposure-response relationship exists in the early phase of the COVID-19 pandemic for number of contacts, nature of contacts, contacts via surfaces, indoor or outdoor location, ability to social distance and use of face coverings. These associations appear to have diminished over time.

Customer sovereignty, algorithmic management, economic precarity and networked support: A Job Demands-Resources analysis of stress and response among Chinese couriers (preprint)

The COVID-19 pandemic has given the global e-commerce market a strong boost, of which China has the largest share and is growing rapidly. Concerns have been raised about intensified work stress and its consequences on health and safety among Chinese couriers. Sociological research of work and occupations has offered important insights into the labour process and politics of the gig economy, although how exactly the workers perceive and respond to technology-driven structural changes remains less clear. We conducted 14 semi-structured interviews with frontline couriers in May-June 2021 in China and interpreted the emerged themes following the Job Demands-Resources (JD-R) model. Four major work-stressor themes were identified: customer sovereignty, algorithmic management, economic precarity and networked support. These work conditions rarely worked alone. Technological, managerial and customer controlling mechanisms reinforced each other and increased work stress. In the absence of adequate organizational support, workers found support and resources through personal networks.

Occupational differences in SARS-CoV-2 infection: Analysis of the UK ONS Coronavirus (COVID-19) Infection Survey (preprint)

Background Considerable concern remains about how occupational SARS-CoV-2 risk has evolved during the COVID-19 pandemic. We aimed to ascertain which occupations had the greatest risk of SARS-CoV-2 infection and explore how relative differences varied over the pandemic. Methods Analysis of cohort data from the UK Office of National Statistics Coronavirus (COVID-19) Infection Survey from April 2020 to November 2021. This survey is designed to be representative of the UK population and uses regular PCR testing. Cox and multilevel logistic regression to compare SARS-CoV-2 infection between occupational/sector groups, overall and by four time periods with interactions, adjusted for age, sex, ethnicity, deprivation, region, household size, urban/rural neighbourhood and current health conditions. Results Based on 3,910,311 observations from 312,304 working age adults, elevated risks of infection can be seen overall for social care (HR 1.14; 95% CI 1.04 to 1.24), education (HR 1.31; 95% CI 1.23 to 1.39), bus and coach drivers (1.43; 95% CI 1.03 to 1.97) and police and protective services (HR 1.45; 95% CI 1.29 to 1.62) when compared to non-essential workers. By time period, relative differences were more pronounced early in the pandemic. For healthcare elevated odds in the early waves switched to a reduction in the later stages. Education saw raises after the initial lockdown and this has persisted. Adjustment for covariates made very little difference to effect estimates. Conclusions Elevated risks among healthcare workers have diminished over time but education workers have had persistently higher risks. Long-term mitigation measures in certain workplaces may be warranted.

Modelling the impact of non-pharmaceutical interventions on workplace transmission of SARS-CoV-2 in the home-delivery sector (preprint)

Objective: We aimed to use mathematical models of SARS-COV-2 to assess the potential efficacy of non-pharmaceutical interventions on transmission in the parcel delivery and logistics sector. Methods: We developed a network-based model of workplace contacts based on data and consultations from companies in the parcel delivery and logistics sectors. We used these in stochastic simulations of disease transmission to predict the probability of workplace outbreaks in this settings. Individuals in the model have different viral load trajectories based on SARS-CoV-2 in-host dynamics, which couple to their infectiousness and test positive probability over time, in order to determine the impact of testing and isolation measures. Results: The baseline model (without any interventions) showed different workplace infection rates for staff in different job roles. Based on our assumptions of contact patterns in the parcel delivery work setting we found that when a delivery driver was the index case, on average they infect only 0.18 other employees , while for warehouse and office workers this went up to 0.93 and 2.58 respectively. In the large-item delivery setting this was predicted to be 0.83, 0.94, and 1.61 respectively. Nonetheless, the vast majority of simulations resulted in $0$ secondary cases among customers (even without contact-free delivery). Our results showed that a combination of social distancing, office staff working from home, and fixed driver pairings (all interventions carried out by the companies we consulted) reduce the risk of workplace outbreaks by 3-4 times. Conclusion: This work suggests that, without interventions, significant transmission could occur in these workplaces, but that these pose minimal risk to customers. We found that identifying and isolating regular close-contacts of infectious individuals (i.e. house-share, carpools, or delivery pairs) is an efficient measure for stopping workplace outbreaks. Regular testing can make these isolation measures even more effective but also increases the number of staff isolating at one time. It is therefore more efficient to combine these measures with social distancing and contact reduction interventions, as these reduce both transmission and the number of people needing to isolate.

Differences in COVID-19 vaccination coverage by occupation in England: a national linked data study (preprint)

Background: Monitoring differences in COVID-19 vaccination uptake in different groups is crucial to help inform the policy response to the pandemic. A key gap is the absence of data on uptake by occupation. Methods: Using nationwide population-level data, we calculated the proportion of people who had received two doses of a COVID-19 vaccine (assessed on 31 August 2021) by detailed occupational categories in adults aged 40-64 and estimated adjusted odds ratios to examine whether these differences were driven by occupation or other factors, such as education. We also examined whether vaccination rates differed by ability to work from home. Results: Our study population included 14,298,147 adults 40-64. Vaccination rates differed markedly by occupation, being higher in administrative and secretarial occupations (90.8%); professional occupations (90.7%); and managers, directors and senior officials (90.6%); and lowest (83.1%) in people working in elementary occupations. We found substantial differences in vaccination rates looking at finer occupational groups even after adjusting for confounding factors, such as education. Vaccination rates were higher in occupations which can be done from home and lower in those which cannot. Many occupations with low vaccination rates also involved contact with the public or with vulnerable people. Conclusions: Increasing vaccination coverage in occupations with low vaccination rates is crucial to help protecting the public and control infection, especially in occupations that cannot be done from home and involve contacts with the public. Policies such as 'work from home if you can' may only have limited future impact on hospitalisations and deaths

Prevalence of and risk factors for burnout and occupational stress among couriers: A systematic review (preprint)

Objective To estimate the prevalence of burnout and occupational stress (OS) among couriers and to identify the main risk factors. Method We followed PRISMA guidelines to search studies published in English and Chinese databases before February 2022. Results The search yielded 15 (7 English and 8 Chinese) papers, 12 of which studied Chinese couriers, and three studied French, Israeli and Malaysian couriers. Twelve studies reported OS using various validated measurement tools, and six reported burnout using adapted versions of Maslach Burnout Inventory. Only four included studies suggested cut-off points to define the condition, hence we used the midpoint of the scales to estimate the prevalence (the proportion of the cases that were above the midpoint). The estimated prevalence of burnout (or a dimension of burnout) among couriers ranged from 20% to 73% (median=33%). The prevalence of OS (or a dimension of OS) ranged from 7% to 90% (median=40%). Twelve studies reported risk factors for burnout or OS; the main ones were physical demands, customer behaviour and a range of working and employment conditions, such as employment precarity and financial insecurity. Job resources (i.e. social support and decision latitude) and organizational support had mitigating effects. Conclusions Burnout and OS are relatively high among couriers. Interventions to prevent or reduce burnout in this occupation are currently limited. The use of platform technology to shift risks, intensify work and tighten managerial controls could be a potential work-related stressor for app-based couriers but remains a knowledge gap.

Occupation and COVID-19 mortality in England: a national linked data study of 14.3 million adults

IntroductionThe coronavirus pandemic has been particularly severe in the UK, with high infection and death rates, including among working age population.ObjectiveTo estimate occupational differences in COVID-19 mortality, taking into account confounding factors, such as regional differences, ethnicity, education, deprivation and pre-pandemic health.MethodsWe used data on 14,295,900 individuals who completed the UK Census in 2011, who were alive on 24 January 2020, were employed and aged 31–55 years in 2011. Data were linked to death and other health records. We examined differences between occupational groups in the risk of COVID-19 death from 24 January to 28 December 2020. We estimated age-standardised mortality rates per 100,000 person-years at risk stratified by sex and occupations. To estimate the effect of occupation due to work-related exposures, we used Cox proportional hazard models to adjust for confounding factors.ResultsThere is wide variation between occupations in COVID-19 mortality. Several occupations, particularly those involving contact with patients or the public, show three- or four-fold risks. These elevated risks were greatly attenuated after adjustment for confounding and mediating factors. For example, the hazard ratio (HR) for men working as taxi and cab drivers or chauffeurs changed from 4.60 [95%CI 3.62–5.84] to 1.47 [1.14–1.89] after adjustment. The overall HR for men working in essential occupations compared with men in non-essential occupations changed from 1.45 [1.34 - 1.56] to 1.22 [1.13 - 1.32] after adjustment. For most occupations, confounding and other mediating factors explained about 70% to 80% of the age-adjusted hazard ratios.ConclusionsWorking conditions are likely to play a role in COVID-19 mortality, particularly in occupations involving contact with COVID-19 patients or the public. However, there is also a substantial contribution from non-workplace factors, including regional factors, socio-demographic factors, and pre-pandemic health.

Risk of exposure to a SARS-CoV-2 infection at work: development of a Job Exposure Matrix (COVID-19-JEM) for Denmark, the Netherlands and the United Kingdom

IntroductionAs workplaces may be one of the key settings in the spread of SARS-COV-2 infections, among both essential and non-essential workers, it is important to assess the occupations at increased risk of exposure to SARS-COV-2 in large study populations.ObjectivesThis study aimed to construct a job-exposure matrix (JEM) for the risk of becoming infected with SARS-COV-2 in an occupational setting.MethodsExperts in occupational epidemiology from three European countries (Denmark, the Netherlands and United Kingdom) defined relevant exposure and workplace characteristics with regard to possible exposure to SARS-COV-2. Within an iterative qualitative process, experts rated the different dimensions of the COVID-19-JEM for each job title within the International Standard Classification of Occupations 2008 (ISCO-08). The agreement scores including confidence intervals, weighted kappas, and explained variances were estimated.ResultsThe COVID-19-JEM contains four determinants of transmission risk (number of people, type of contacts, indirect contact and location), two mitigation measures (social distancing and face covering), and two social factors (income insecurity and migrant workers). Agreement scores ranged from 0.58 (95%CI 0.55;0.61) for ‘number of people’ to 0.76 (95%CI 0.74;0.78) for ‘type of contacts’. Agreement scores ranged from 0.58 (95%CI 0.55;0.61) for ‘number of people’ to 0.76 (95%CI 0.74;0.78) for ‘type of contacts. Weighted kappas ranged from 0.60 for ‘face covering’ to 0.80 for ‘indirect contact’. Due to some between-country differences, COVID-19-JEMs are separately presented for Denmark, the Netherlands and the United Kingdom.ConclusionsThe COVID-19-JEM provides risk estimates at population level of the eight dimensions related to a SARS-COV-2 infection at the workplace, and is a valuable tool for epidemiological studies. Additionally, the eight dimensions of the COVID-19-JEM could also be used for other communicable diseases at worksites.

Contributors to stress and burnout in junior doctors during the COVID-19 pandemic

IntroductionJunior doctors have previously reported high levels of burnout;and additional stressors have likely emerged from the COVID-19 pandemic may further accelerate burnout. There is a need to identify which particular stressors are most likely to lead to burnout in junior doctors in order to develop appropriate interventions. Objectives1) To develop a comprehensive list of stressors that are relevant to junior doctors, which includes general work and non-work related stressors as well as stressors that have specifically emerged with the pandemic. 2) To assess which of these stressors are most strongly associated with burnout.MethodsAn anonymous online questionnaire was sent to 1000 randomly selected junior doctors in the North West of England between 10/07/20 to 04/08/20. It included 37 questions on general and pandemic-specific stressors, and the Maslach Burnout Inventory (MBI) Health Services Survey. Stepwise regression analyses were undertaken to assess associations between stressors and burnout.ResultsIn total, 326 responses were collected (response rate=33%). Of the top 10 stressors rated by junior doctors, 60% were related to the pandemic. Multiple stressors were found to be associated with the burnout dimensions. Fatigue (β=0.43), pandemic-related workload increase (β=0.33) and feeling isolated (β=0.24) had the strongest associations with Emotional Exhaustion, whereas fatigue (β=0.24), uncertainty around COVID-19 information (β=0.22) and doing unproductive tasks (β =0.22) had the strongest associations with Depersonalisation.ConclusionJunior doctors experience a combination of general stressors and additional stressors emerging from the pandemic which significantly to impact on burnout. Monitoring these stressors and targeting them as part of interventions could help mitigating burnout in junior doctors.