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.

Gender inequalities in the disruption of long-term life satisfaction trajectories during the COVID-19 pandemic and the role of time use: evidence from the 1970 British birth cohort (preprint)

BackgroundThe COVID-19 pandemic has disproportionately impacted womens mental health, although most evidence has focused on mental illbeing outcomes. Previous research suggests that gendered differences in time-use may explain this disparity, as women generally spend more time doing psychologically taxing activities than men. We investigated gender differences in the long-term trajectories of life satisfaction, how these were impacted during the pandemic, and the role of time-use differences in explaining gender inequalities. MethodsWe used data from 6766 (56.2% women) members of the 1970 British Cohort Study (BCS70), a nationally representative birth cohort of people born in Great Britain in 1970, who were alive and residing in the UK between May 2020 and March 2021. Life satisfaction was prospectively assessed between the ages of 26 (1996) and 51 (2021) using a single question with responses ranging from 0 (lowest) to 10 (highest). We analysed life satisfaction trajectories using piecewise latent growth curve models and investigated whether gender differences in the change in the life satisfaction trajectories with the pandemic were explained by self-reported time spent doing different paid and unpaid activities. FindingsWomen had consistently higher life satisfaction than men prior to the pandemic ({Delta}intercept,unadjusted=0.213 [95% CI: 0.087, 0.340], p=.001) and experienced a more accelerated decline with the pandemic onset ({Delta}quad2,unadjusted=-0.018 [-0.026, -0.011], p<.001). Time-use differences did not account for the more accelerated decrease in womens life satisfaction levels with the pandemic ({Delta}quad2,adjusted=-0.016 [-0.031, -0.001], p=.035). InterpretationOur study shows pronounced gender inequalities in the impact of the pandemic on the long-term life satisfaction trajectories of adults in their 50s, with women losing their historical advantage over men. Self-reported time-use differences did not account for these inequalities, suggesting that they could be linked to other factors including the mental load (invisible, unrecognised labour disproportionately undertook by women) or the menopausal transition.

Associations between SARS-CoV-2 infection and subsequent economic inactivity and employment status: pooled analyses of five linked longitudinal surveys (preprint)

Background Following the acute phase of the COVID-19 pandemic, record numbers of people became economically inactive (i.e., neither working nor looking for work, e.g., retired), or non-employed (including unemployed job seekers and economically inactive people). A possible explanation is people leaving the workforce after contracting COVID-19. We aim to investigate whether testing positive for SARS-CoV-2 is related to subsequent economic inactivity and non-employment, among people who were in employment prior to the pandemic. Methods The primary source of data are UK longitudinal population studies linked to English NHS digital data, held by the UK Longitudinal Linkage Collaboration (UK LLC). We pooled data from five studies (1970 British Cohort Study, English Longitudinal Study of Ageing, 1958 National Child Development Study, Next Steps, and Understanding Society), established long before the pandemic with between two and eight follow up surveys during the pandemic. The study population comprised people aged 25-65 years during the study period (March 2020 to March 2021) who were employed pre-pandemic. Outcomes were economic inactivity and non-employment status measured at the time of the last follow-up survey (November 2020 to March 2021, depending on study). For participants who could be linked to NHS England data (n=8,174), COVID-19 infection was indicated by a positive SARS-CoV-2 test. For sensitivity analyses, we used a self-reported measure of COVID-19 infection from participants (n=13,881) in the public use files of the five studies. Potential confounders included sociodemographic variables, pre-pandemic self-rated health and occupational class. Logistic regression models estimated odds ratios (ORs) with 95% confidence intervals (95%CIs). Results In adjusted analyses, testing positive for SARS-CoV-2 was very weakly associated with economic inactivity (OR 1.08 95%CI 0.68-1.73) and non-employment status (OR 1.09. 95%CI 0.77-1.55). In sensitivity analyses, self-reported test-confirmed COVID-19 was not associated with either economic inactivity (OR 1.01: 95%CI 0.70 to 1.44) or non-employment status (OR1.03 95%CI 0.79-1.35). Conclusions Among people employed pre-pandemic, testing positive for SARS-CoV-2 was either weakly or not associated with increased economic inactivity or exiting employment. Wide confidence intervals limit the ability to make definitive conclusions, but it appears unlikely that COVID-19 disease explains the increase in economic inactivity among working-age people.

Characterising patterns of COVID-19 and long COVID symptoms: Evidence from nine UK longitudinal studies (preprint)

Multiple studies across global populations have established the primary symptoms characterising COVID-19 (Coronavirus Disease 2019) and long COVID. However, as symptoms may also occur in the absence of COVID-19, a lack of appropriate controls has often meant that specificity of symptoms to acute COVID-19 or long COVID, and the extent and length of time for which they are elevated after COVID-19, could not be examined. We analysed individual symptom prevalences and characterised patterns of COVID-19 and long COVID symptoms across nine UK longitudinal studies, totalling over 42,000 participants. Conducting latent class analyses separately in three groups ('no COVID-19', 'COVID-19 in last 12 weeks', 'COVID-19 > 12 weeks ago'), the data did not support the presence of more than two distinct symptom patterns, representing high and low symptom burden, in each group. Comparing the high symptom burden classes between the 'COVID-19 in last 12 week,' and 'no COVID-19' groups we identified symptoms characteristic of acute COVID-19, including loss of taste and smell, fatigue, cough, shortness of breath and muscle pains or aches. Comparing the high symptom burden classes between the 'COVID-19 > 12 weeks ago' and 'no COVID-19' groups we identified symptoms characteristic of long COVID, including fatigue, shortness of breath, muscle pain or aches, difficulty concentrating and chest tightness. The identified symptom patterns among individuals with COVID-19 > 12 weeks ago were strongly associated with self-reported length of time unable to function as normal due to COVID-19 symptoms, suggesting that the symptom pattern identified corresponds to long COVID. Building the evidence base regarding typical long COVID symptoms will improve diagnosis of this condition and the ability to elicit underlying biological mechanisms, leading to better patient access to treatment and services.

Antibody levels following vaccination against SARS-CoV-2: associations with post-vaccination infection and risk factors (preprint)

SARS-CoV-2 antibody levels can be used to assess humoral immune responses following SARS-CoV-2 infection or vaccination, and may predict risk of future infection. From cross-sectional antibody testing of 9,361 individuals from TwinsUK and ALSPAC UK population-based longitudinal studies (jointly in April-May 2021, and TwinsUK only in November 2021-January 2022), we tested associations between antibody levels following vaccination and: (1) SARS-CoV-2 infection following vaccination(s); (2) health, socio-demographic, SARS-CoV-2 infection and SARS-CoV-2 vaccination variables. Within TwinsUK, single-vaccinated individuals with the lowest 20% of anti-Spike antibody levels at initial testing had 3-fold greater odds of SARS-CoV-2 infection over the next six to nine months, compared to the top 20%. In TwinsUK and ALSPAC, individuals identified as at increased risk of COVID-19 complication through the UK "Shielded Patient List" had consistently greater odds (2 to 4-fold) of having antibody levels in the lowest 10%. Third vaccination increased absolute antibody levels for almost all individuals, and reduced relative disparities compared with earlier vaccinations. These findings quantify the association between antibody level and risk of subsequent infection, and support a policy of triple vaccination for the generation of protective antibodies.

Inequalities in mental and social wellbeing during the COVID-19 pandemic: prospective longitudinal observational study of five UK cohorts (preprint)

Background. Research suggests that there have been inequalities in the impact of the COVID-19 pandemic and related non-pharmaceutical interventions on population mental health. We explored these inequalities during the first year of the pandemic using nationally representative cohorts from the UK. Methods. We analysed data from 26,772 participants from five longitudinal cohorts representing generations born between 1946 and 2000, collected in May 2020, September-October 2020, and February-March 2021 across all five cohorts. We used a multilevel growth curve modelling approach to explore sociodemographic and socioeconomic differences in levels of anxiety and depressive symptomatology, loneliness, and life satisfaction over time. Results. Younger generations had worse levels of mental and social wellbeing throughout the first year of the pandemic. Whereas these generational inequalities narrowed between the first and last observation periods for life satisfaction (-0.33 [95% CI: -0.51, -0.15]), they became larger for anxiety (0.22 [0.10, 0.33]). Pre-existing generational inequalities in depression and loneliness did not change, but initial depression levels of the youngest cohort were worse than expected if the generational inequalities had not accelerated. Women and those experiencing financial difficulties had worse initial mental and social wellbeing levels than men and those financially living comfortably, respectively, and these gaps did not substantially differ between the first and last observation periods. Inequalities by additional factors are reported. Conclusions. By March 2021, mental and social wellbeing inequalities persisted in the UK adult population. Pre-existing generational inequalities may have been exacerbated with the pandemic onset. Policies aimed at protecting vulnerable groups are needed.

Association of COVID-19 employment disruption with mental and social wellbeing: evidence from nine UK longitudinal studies (preprint)

Background: The COVID-19 pandemic has led to major economic disruptions. In March 2020, the UK implemented the Coronavirus Job Retention Scheme, known as furlough, to minimize the impact of job losses. We investigate associations between change in employment status and mental and social wellbeing during the early stages of the pandemic. Methods: Data from 25,670 respondents, aged 16 to 66, from nine UK longitudinal studies were analysed. Changes in employment (including being furloughed) were defined by comparing employment status pre-pandemic and during the first lockdown. Mental and social wellbeing outcomes included psychological distress, life satisfaction, self-rated health, social contact, and loneliness. Study-specific modified Poisson regression estimates, adjusting for socio-demographic characteristics and pre-pandemic outcome measures, were pooled using meta-analysis. Results: Compared to those who remained working, furloughed workers were at greater risk of psychological distress (adjusted risk ratio, ARR=1.12; 95% CI: 0.97, 1.29), low life satisfaction (ARR=1.14; 95% CI: 1.07, 1.22), loneliness (ARR=1.12; 95% CI: 1.01, 1.23), and fair/poor self-rated health (ARR=1.26; 95% CI: 1.05, 1.50), but risk ratios appear less pronounced compared to those no longer employed (e.g., psychological distress, ARR=1.39; 95% CI: 1.21, 1.59) or stable unemployed (e.g., psychological distress, ARR=1.33; 95% CI: 1.09, 1.62). Conclusions: During the early stages of the pandemic those furloughed had increased risk for poor mental and social wellbeing. However, their excess risk was lower in magnitude than those who became or remained unemployed, suggesting that furlough partly mitigated poorer outcomes.

The UK COVID-19 furlough scheme and associations with smoking, alcohol consumption and vaping: evidence from 8 UK longitudinal population surveys. (preprint)

BackgroundDisruptions to employment status can impact smoking and alcohol consumption. During the COVID-19 pandemic, the UK implemented a furlough scheme to prevent job loss. We examine how furlough was associated with participants smoking, vaping and alcohol consumption behaviours in the early stages of the pandemic. MethodsData were from 27,841 participants in eight UK adult longitudinal surveys. Participants self-reported employment status and current smoking, current vaping and drinking alcohol (>4 days/week or 5+ drinks per typical occasion) both before and during the pandemic (April-July 2020). Risk ratios were estimated within each study using modified Poisson regression, adjusting for a range of potential confounders, including pre-pandemic behaviour. Findings were synthesised using random effects meta-analysis. Sub-group analyses were used to identify whether associations differed by gender, age or education. ResultsCompared to stable employment, neither furlough, no longer being employed, nor stable unemployment were associated with smoking, vaping or drinking, following adjustment for pre-pandemic characteristics. However, some sex differences in these associations were observed, with stable unemployment associated with smoking for women (ARR=1.35; 95% CI: 1.00-1.82; I2: 47%) but not men (0.84; 95% CI: 0.67-1.05; I2: 0%). No longer being employed was associated with vaping among women (ARR=2.74; 95% CI: 1.59-4.72; I2: 0%) but not men (ARR=1.25; 95% CI: 0.83-1.87; I2: 0%). There was little indication of associations with drinking differing by age, gender or education. ConclusionsWe found no clear evidence of furlough or unemployment having adverse impacts on smoking, vaping or drinking behaviours during the early stages of the COVID-19 pandemic in the UK, with differences in risk compared to those who remained employed largely explained by pre-pandemic characteristics.

Psychological Distress Before and During the COVID-19 Pandemic: Sociodemographic Inequalities in 11 UK Longitudinal Studies (preprint)

Background: How population mental health has evolved across the COVID-19 pandemic under varied lockdown measures is poorly understood, with impacts on health inequalities unclear. We investigated changes in mental health and sociodemographic inequalities from before and across the first year of the pandemic in 11 longitudinal studies. Methods: Data from 11 UK longitudinal population-based studies with pre-pandemic measures of psychological distress were analysed and estimates pooled. Trends in the prevalence of poor mental health were assessed across the pandemic at three time periods: initial lockdown (TP1, Mar-June 20); easing of restrictions (TP2, July-Oct 20); and a subsequent lockdown (TP3, Nov 20-Mar 21). Multi-level regression was used to examine changes in psychological distress compared to pre-pandemic; with stratified analyses by sex, ethnicity, education, age, and UK country. Results: Across the 11 studies (n=54,609), mental health had deteriorated from pre-pandemic scores across all three pandemic time periods (TP1 Standardised Mean Difference (SMD): 0.13 (95% CI: 0.03, 0.23); TP2 SMD: 0.18 (0.09, 0.27); TP3 SMD: 0.20 (0.09, 0.31)). Changes in psychological distress across the pandemic were higher in females (TP3 SMD: 0.23 (0.11, 0.35)) than males (TP3 SMD: 0.16 (0.06, 0.26)), and slightly lower in below-degree level educated persons at some time periods (TP3 SMD: 0.18 (0.06, 0.30)) compared to those who held degrees (TP3 SMD: 0.26 (0.14, 0.38)). Increased distress was most prominent amongst adults aged 35-44 years (TP3 SMD: 0.49 (0.15, 0.84)). We did not find evidence of changes in distress differing by ethnicity or UK country. Conclusions: The substantial deterioration in mental health seen in the UK during the first lockdown did not reverse when lockdown lifted, and a sustained worsening is observed across the pandemic. Mental health declines have not been equal across the population, with females, those with higher degrees, and younger adults more affected.

Risk factors for long COVID: analyses of 10 longitudinal studies and electronic health records in the UK (preprint)

The impact of long COVID is increasingly recognised, but risk factors are poorly characterised. We analysed questionnaire data on symptom duration from 10 longitudinal study (LS) samples and electronic healthcare records (EHR) to investigate sociodemographic and health risk factors associated with long COVID, as part of the UK National Core Study for Longitudinal Health and Wellbeing. Methods Analysis was conducted on 6,899 adults self-reporting COVID-19 from 45,096 participants of the UK LS, and on 3,327 cases assigned a long COVID code in primary care EHR out of 1,199,812 adults diagnosed with acute COVID-19. In LS, we derived two outcomes: symptoms lasting 4+ weeks and symptoms lasting 12+ weeks. Associations of potential risk factors (age, sex, ethnicity, socioeconomic factors, smoking, general and mental health, overweight/obesity, diabetes, hypertension, hypercholesterolaemia, and asthma) with these two outcomes were assessed, using logistic regression, with meta-analyses of findings presented alongside equivalent results from EHR analyses. Results Functionally limiting long COVID for 12+ weeks affected between 1.2% (age 20), and 4.8% (age 63) of people reporting COVID-19 in LS. The proportion reporting symptoms overall for 12+ weeks ranged from 7.8 (mean age 28) to 17% (mean age 58) and for 4+ weeks 4.2% (age 20) to 33.1% (age 56). Age was associated with a linear increase in long COVID between age 20-70. Being female (LS: OR=1.49; 95%CI:1.24-1.79; EHR: OR=1.51 [1.41-1.61]), poor pre-pandemic mental health (LS: OR=1.46 [1.17-1.83]; EHR: OR=1.57 [1.47-1.68]) and poor general health (LS: OR=1.62 [1.25-2.09]; EHR: OR=1.26; [1.18-1.35]) were associated with higher risk of long COVID. Individuals with asthma also had higher risk (LS: OR=1.32 [1.07-1.62]; EHR: OR=1.56 [1.46-1.67]), as did those categorised as overweight or obese (LS: OR=1.25 [1.01-1.55]; EHR: OR=1.31 [1.21-1.42]) though associations for symptoms lasting 12+ weeks were less pronounced. Non-white ethnic minority groups had lower 4+ week symptom risk (LS: OR=0.32 [0.22-0.47]), a finding consistent in EHR. Associations were not observed for other risk factors. Few participants in the studies had been admitted to hospital (0.8-5.2%). Conclusions Long COVID is clearly distributed differentially according to several sociodemographic and pre-existing health factors. Establishing which of these risk factors are causal and predisposing is necessary to further inform strategies for preventing and treating long COVID.

Inequalities in healthcare disruptions during the Covid-19 pandemic: Evidence from 12 UK population-based longitudinal studies (preprint)

Background: Health systems worldwide have faced major disruptions due to COVID-19 which could exacerbate health inequalities. The UK National Health Service (NHS) provides free healthcare and prioritises equity of delivery, but the pandemic may be hindering the achievement of these goals. We investigated associations between multiple social characteristics (sex, age, occupational social class, education and ethnicity) and self-reported healthcare disruptions in over 65,000 participants across twelve UK longitudinal studies. Methods: Participants reported disruptions from March 2020 up to late January 2021. Associations between social characteristics and three types of self-reported healthcare disruption (medication access, procedures, appointments) and a composite of any of these were assessed in logistic regression models, adjusting for age, sex and ethnicity where relevant. Random-effects meta-analysis was conducted to obtain pooled estimates. Results: Prevalence of disruption varied across studies; between 6.4% (TwinsUK) and 31.8 % (Understanding Society) of study participants reported any disruption. Females (Odd Ratio (OR): 1.27 [95%CI: 1.15,1.40]; I2=53%), older persons (e.g. OR: 1.39 [1.13,1.72]; I2=77% for 65-75y vs 45-54y), and Ethnic minorities (excluding White minorities) (OR: 1.19 [1.05,1.35]; I2=0% vs White) were more likely to report healthcare disruptions. Those in a more disadvantaged social class (e.g. OR: 1.17 [1.08, 1.27]; I2=0% for manual/routine vs managerial/professional) were also more likely to report healthcare disruptions, but no clear differences were observed by education levels. Conclusion: The COVID-19 pandemic has led to unequal healthcare disruptions, which, if unaddressed, could contribute to the maintenance or widening of existing health inequalities.

The UK Coronavirus Job Retention Scheme and changes in diet, physical activity and sleep during the COVID-19 pandemic: Evidence from eight longitudinal studies (preprint)

BackgroundIn March 2020 the UK implemented the Coronavirus Job Retention Scheme (furlough) to minimize job losses. Our aim was to investigate associations between furlough and diet, physical activity, and sleep during the early stages of the COVID-19 pandemic. MethodsWe analysed data from 25,092 participants aged 16 to 66 years from eight UK longitudinal studies. Changes in employment (including being furloughed) were defined by comparing employment status pre- and during the first lockdown. Health behaviours included fruit and vegetable consumption, physical activity, and sleeping patterns. Study-specific estimates obtained using modified Poisson regression, adjusting for socio-demographic characteristics and pre-pandemic health and health behaviours, were statistically pooled using random effects meta-analysis. Associations were also stratified by sex, age, and education. ResultsAcross studies, between 8 and 25% of participants were furloughed. Compared to those who remained working, furloughed workers were slightly less likely to be physically inactive (RR:0.85, [0.75-0.97], I2=59%) and did not differ in diet and sleep behaviours, although findings for sleep were heterogenous (I2=85%). In stratified analyses, furlough was associated with low fruit and vegetable consumption among males (RR=1.11; 95%CI: 1.01-1.22; I2: 0%) but not females (RR=0.84; 95%CI: 0.68-1.04; I2: 65%). Considering change in these health behaviours, furloughed workers were more likely than those who remained working to report increased fruit and vegetable consumption, exercise, and hours of sleep. ConclusionsThose furloughed exhibited broadly similar levels of health behaviours to those who remained in employment during the initial stages of the pandemic. There was little evidence to suggest that such social protection policies if used in the post-pandemic recovery period and during future economic crises would have adverse impacts on population health behaviours.