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.

Trade unions and mental health during an employment crisis. Evidence from the UK before and during the COVID-19 pandemic. (preprint)

BackgroundTrade union presence within a workplace could potentially affect employees working conditions and in turn health. We assessed the relationship between union (presence and membership) at the individual level and mental health in the context of COVID-19 employment disruptions. MethodsWe analysed panel data from Understanding Society collected before and during the COVID-19 pandemic (49,915 observations across 5,988 respondents) to assess the relationship between union presence and membership and a validated epidemiological measure of common mental disorders (CMD), the 12-Item General Health Questionnaire with a score of >/4 indicating probable anxiety/depression, referred to as caseness. A mixed-effect log-linear model assessed effect heterogeneity across time and industries, with average marginal effects (AME) indicating effect differences between groups. FindingsIn our sample, 49.1% worked in a unionised workplace, among which 53.8% were union members. Caseness prevalence was higher during the pandemic (25.4%) compared to pre-pandemic (18.4%). Working in a workplace with a trade union was associated with modest protection against CMD risk; (AMEpre-pandemic:0.010, 95%CI:-0.007; 0.027), (AME- pandemic:-0.002, 95%CI:-0.019; 0.016)]. Similarly, for union membership [(AMEpre- pandemic:0.016, 95%CI:-0.007; 0.039), (AMEpandemic:-0.010, 95%CI:-0.023; 0.020)]. Industry level heterogeneity exists in the relationship between union presence and membership and mental health. InterpretationTrade union presence may have a protective effect on workers mental health in periods of crisis, such as during a pandemic. Within unionised workplaces, trade union membership further mitigated the negative effects of the pandemic on mental health. Collective negotiation within workplaces may be protective in periods of uncertainty, benefiting all workers and not only those unionised. FundingMedical Research Council, Chief Scientist Office, European Research Council, Belgian National Scientific Fund (FNRS).

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.

Long COVID and financial outcomes: Evidence from four longitudinal population surveys (preprint)

Background: Long-term sequelae of COVID-19 (long COVID) include muscle weakness, fatigue, breathing difficulties and sleep disturbance over weeks or months. Using UK longitudinal data, we assessed the relationship between long COVID and financial disruption. Methods: We estimated associations between long COVID (derived using self-reported length of COVID-19 symptoms) and measures of financial disruption (subjective financial well-being, new benefit claims, changes in household income) by analysing data from four longitudinal population studies, gathered during the first year of the pandemic. We employed modified Poisson regression in a pooled analysis of the four cohorts adjusting for a range of potential confounders, including pre-pandemic (pre-long COVID) factors. Results: Among 20,112 observations across four population surveys, 13% reported having COVID-19 with symptoms that impeded their ability to function normally - 10.7% had such symptoms for <4 weeks (acute COVID-19), 1.2% had such symptoms for 4-12 weeks (ongoing symptomatic COVID-19) and 0.6% had such symptoms for >12 weeks (post-COVID-19 syndrome). We found that post-COVID-19 syndrome was associated with worse subjective financial well-being (adjusted relative risk ratios (aRRR)=1.57, 95% confidence interval (CI)=1.25, 1.96) and new benefit claims (aRRR=1.79, CI=1.27, 2.53). Associations were broadly similar across sexes and education levels. These results were not meaningfully altered when scaled to represent the population by age. Conclusions: Long COVID was associated with financial disruption in the UK. If our findings reflect causal effects, extending employment protection and financial support to people with long COVID may be warranted.

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.

Living alone and mental health: parallel analyses in longitudinal population surveys and electronic health records prior to and during the COVID-19 pandemic (preprint)

Objectives: To describe the mental health gap between those who live alone and those who live with others, and to examine whether the COVID-19 pandemic had an impact on this gap. Design: Ten population based prospective cohort studies, and a retrospective descriptive cohort study based on electronic health records (EHRs). Setting: UK Longitudinal population-based surveys (LPS), and primary and secondary care records within the OpenSAFELY-TPP database. Participants: Participants from the LPS were included if they had information on living status in early 2020, valid data on mental ill-health at the closest pre-pandemic assessment and at least once during the pandemic, and valid data on a key minimum set of covariates. The EHR dataset included 16 million adults registered with primary care practices in England using TPP SystmOne software on 1st February 2020, with at least three months of registration, valid address data, and living in households of <16 people. Main outcome measures: In the LPS, self-reported survey measures of psychological distress and life satisfaction were assessed in the nearest pre-pandemic sweep and three periods during the pandemic: April-June 2020, July-October 2020, and November 2020-March 2021. In the EHR analyses, outcomes were morbidity codes recorded in primary or secondary care between March 2018 and January 2022 reflecting the diagnoses of depression, self-harm, anxiety, obsessive compulsive disorder, eating disorders, and severe mental illnesses. Results: The LPS consisted of 37,544 participants (15.2% living alone) and we found greater psychological distress (SMD: 0.09 (95% CI: 0.04, 0.14) and lower life satisfaction (SMD: -0.22 (95% CI: -0.30, -0.15) in those living alone pre-pandemic, and the gap between the two groups stayed similar after the onset of the pandemic. In the EHR analysis of almost 16 million records (21.4% living alone), codes indicating mental health conditions were more common in those who lived alone compared to those who lived with others (e.g., depression 26 and severe mental illness 58 cases more per 100,000). Recording of mental health conditions fell during the pandemic for common mental health disorders and the gap between the two groups narrowed. Conclusions: Multiple sources of data indicate that those who live alone experience greater levels of common and severe mental illnesses, and lower life satisfaction. During the pandemic this gap in need remained, however, there was a narrowing of the gap in service use, suggesting greater barriers to healthcare access for those who live alone.

Associations between reported healthcare disruption due to COVID-19 and avoidable hospitalisation: Evidence from seven linked longitudinal studies for England (preprint)

Background: Health services across the UK struggled to cope during the COVID-19 pandemic. Many treatments were postponed or cancelled, although the impact was mitigated by new models of delivery. While the scale of disruption has been studied, much less is known about if this disruption impacted health outcomes. The aim of our paper is to examine whether there is an association between individuals experiencing disrupted access to healthcare during the pandemic and risk of an avoidable hospitalisation. Methods: We used individual-level data for England from seven longitudinal cohort studies linked to electronic health records from NHS Digital (n = 29 276) within the UK Longitudinal Linkage Collaboration trusted research environment. Avoidable hospitalisations were defined as emergency hospital admissions for ambulatory care sensitive and emergency urgent care sensitive conditions (1st March 2020 to 25th August 2022). Self-reported measures of whether people had experienced disruption during the pandemic to appointments (e.g., visiting their GP or an outpatient department), procedures (e.g., surgery, cancer treatment) or medications were used as our exposures. Logistic regression models examined associations. Results: 35% of people experienced some form of disrupted access to healthcare. Those whose access was disrupted were at increased risk of any (Odds Ratio (OR) = 1.80, 95% Confidence Intervals (CIs) = 1.34-2.41), acute (OR = 1.68, CIs = 1.13-2.53) and chronic (OR = 1.93, CIs = 1.40-2.64) ambulatory care sensitive hospital admissions. There were positive associations between disrupted access to appointments and procedures to measures of avoidable hospitalisations as well. Conclusions: Our study presents novel evidence from linked individual-level data showing that people whose access to healthcare was disrupted were more likely to have an avoidable or potentially preventable hospitalisation. Our findings highlight the need to increase healthcare investment to tackle the short- and long-term implications of the pandemic beyond directly dealing with SARS-CoV-2 infections.

Ethnic differences in the indirect impacts of the COVID-19 pandemic on clinical monitoring and hospitalisations for non-COVID conditions in England: An observational cohort study using OpenSAFELY (preprint)

Background: The COVID-19 pandemic disrupted healthcare and may have impacted ethnic inequalities in healthcare. We aimed to describe the impact of pandemic-related disruption on ethnic differences in clinical monitoring and hospital admissions for non-COVID conditions in England. Methods We conducted a cohort study using OpenSAFELY (2018-2022). We grouped ethnicity (exposure), into five categories: White, South Asian, Black, Other, Mixed. We used interrupted time-series regression to estimate ethnic differences in clinical monitoring frequency (e.g., blood pressure measurements) before and after 23rd March 2020. We used multivariable Cox regression to quantify ethnic differences in hospitalisations related to: diabetes, cardiovascular disease, respiratory disease, and mental health before and after 23rd March 2020. Findings Of 14,930,356 adults in 2020 with known ethnicity (92% of sample): 86.6% were White, 7.3% Asian, 2.6% Black, 1.4% Mixed ethnicity, and 2.2% Other ethnicities. Clinical monitoring did not return to pre-pandemic levels for any ethnic group. Ethnic differences were apparent pre-pandemic, except for diabetes monitoring, and remained unchanged, except for blood pressure monitoring in those with mental health conditions where differences narrowed during the pandemic. For those of Black ethnicity, there were seven additional admissions for diabetic ketoacidosis per month during the pandemic, and relative ethnic differences narrowed during the pandemic compared to White. There was increased admissions for heart failure during the pandemic for all ethnic groups, though highest in White ethnicity. Relatively, ethnic differences narrowed for heart failure admission in those of Asian and Black ethnicity compared to White. For other outcomes the pandemic had minimal impact on ethnic differences. Interpretation Our study suggests ethnic differences in clinical monitoring and hospitalisations remained largely unchanged during the pandemic for most conditions. Key exceptions were hospitalisations for diabetic ketoacidosis and heart failure, which warrant further investigation to understand the causes. Funding LSHTM COVID-19 Response Grant (DONAT15912).

Trends in inequalities in avoidable hospitalisations across the COVID-19 pandemic: A cohort study of 23.5 million people in England (preprint)

Background: The COVID-19 pandemic and associated national lockdowns created unprecedented disruption to healthcare, with reduced access to services and planned clinical encounters postponed or cancelled. It was widely anticipated that failure to obtain timely treatment would cause progression of illness and increased hospital admissions. Additional concerns were that social and spatial inequalities would widen given the disproportionate impacts of COVID-19 directly. The aim of our study is to determine whether this was observable in England. Methods: With the approval of NHS England we utilised individual-level electronic health records from OpenSAFELY, which covered ~40% of general practices in England (mean monthly population size 23.5 million people). We estimated crude and directly age-standardised rates for potentially preventable unplanned hospital admissions: ambulatory care sensitive conditions and urgent emergency sensitive conditions. We considered how trends in these outcomes varied by three measures of social and spatial inequality: neighbourhood socioeconomic deprivation, ethnicity, and geographical region. Findings: There were large declines in avoidable hospitalisations during the first national lockdown, which then reversed post-lockdown albeit never reaching pre-pandemic levels. While trends were consistent by each measure of inequality, absolute levels of inequalities narrowed throughout 2020 (especially during the first national lockdown) and remained lower than pre-pandemic trends. While the scale of inequalities remained similar into 2021 for deprivation and ethnicity, we found evidence of widening absolute and relative inequalities by geographic region in 2021 and 2022. Interpretation: The anticipation that healthcare disruption from the COVID-19 pandemic and lockdowns would result in more (avoidable) hospitalisations and widening social inequalities was wrong. However, the recent growing gap between geographic regions suggests that the effects of the pandemic has reinforced spatial inequalities.

Understanding Inequalities in Mental Health by Family Structure during COVID-19 Lockdowns: Evidence from the UK Household Longitudinal Study (preprint)

Background: The COVID-19 pandemic increased psychiatric distress and impacts differed by family structure. We aimed to identify mechanisms contributing to these inequalities. Methods: Survey data were from the UK Household Longitudinal Study. Psychiatric distress (GHQ-12) was measured in April 2020 (first UK lockdown; n=10,516), and January 2021 (lockdown re-introduced following eased restrictions; n=6,893). Pre-lockdown family structure comprised partner status and presence of children (<16 years). Mediating mechanisms included: active employment, financial strain, childcare/home-schooling, caring, and loneliness. Monte Carlo g-computation simulations were used to adjust for confounding and estimate total effects and decompositions into: controlled direct effects (effects if the mediator was absent), and portions eliminated (PE; representing differential exposure and vulnerability to the mediator). Results: In January 2021, after adjustment, we estimated increased risk of distress among couples with children compared to couples with no children (RR: 1.48; 95% CI: 1.15-1.82), largely because of childcare/home-schooling (PE RR: 1.32; 95% CI: 1.00-1.64). Single respondents without children also had increased risk of distress compared to couples with no children (RR: 1.55; 95% CI: 1.27-1.83), and the largest PE was for loneliness (RR: 1.16; 95% CI: 1.05-1.27), though financial strain contributed (RR: 1.05; 95% CI: 0.99-1.12). Single parents demonstrated the highest levels of distress, but confounder adjustment suggested uncertain effects with wide confidence intervals. Findings were similar in April 2020 and when stratified by sex. Conclusions: Access to childcare/schooling, financial security and social connection are important mechanisms that need addressing to avoid widening mental health inequalities during public health crises.

Home working and its association with social and mental wellbeing at different stages of the COVID-19 pandemic: Evidence from seven UK longitudinal population surveys. (preprint)

Background: Home working rates have increased since the COVID-19 pandemic's onset, but the health implications of this transformation are unclear. We assessed the association between home working and social and mental wellbeing through harmonised analyses of seven UK longitudinal studies. Methods: We estimated associations between home working and measures of psychological distress, low life satisfaction, poor self-rated health, low social contact, and loneliness across three different stages of the COVID-19 pandemic (T1= Apr-Jun 2020 - first lockdown, T2=Jul-Oct 2020 - eased restrictions, T3=Nov 2020-Mar 2021 - second lockdown), in seven population-based cohort studies using modified Poisson regression and meta-analyses to pool results across studies. Findings: Among 34,131 observations spread over three time points, we found higher rates of home working at T1 and T3 compared with T2, reflecting lockdown periods. Home working was not associated with psychological distress at T1 (RR=0.92, 95%CI=0.79-1.08) or T2 (RR=0.99, 95%CI=0.88-1.11), but a detrimental association was found with psychological distress at T3 (RR=1.17, 95%CI=1.05-1.30). Poorer psychological distress associated with home working was observed for those educated to below degree level at T2 and T3. Men working from home reported poorer self-reported health at T2. Interpretation: No clear evidence of an association between home working and mental wellbeing was found, apart from greater risk of psychological distress associated with home working during the second lockdown, but differences across sub-groups may exist. Longer term shifts to home working might not have adverse impacts on population wellbeing in the absence of pandemic restrictions but further monitoring of health inequalities is required.

Exploring the health and sociodemographic characteristics of people seeking advice with claiming Universal Credit: A longitudinal analysis of UK Citizens Advice data, 2017-2021 (preprint)

Background: The UK Department for Work and Pensions (DWP) administers Universal Credit (UC) – the main UK benefit for people in- and out-of-work. UC is being rolled out nationally from 2013 to 2024. Citizens Advice (CA) is an independent charity that provides advice and support to people making a claim for UC. The aim of this study is to understand who is seeking advice from CA when making a UC claim and how the types of people seeking advice are changing as the rollout of UC continues. Methods Co-developed with Citizens Advice Newcastle and Citizens Advice Northumberland we performed longitudinal analysis of national data from Citizens Advice for England and Wales on the health (mental health and limiting long term conditions) and socio-demographic of 1,003,411 observations for people seeking advice with claiming UC over four financial years (2017/18 to 2020/21). We summarised population characteristics and estimated the differences between the four financial years using population-weighted t-tests. Findings were discussed with three people with lived experience of seeking advice to claim UC to help frame our interpretation and policy recommendations. Results When comparing 2017/18 to 2018/19, there was a significantly higher proportion of people with limiting long term conditions seeking advice with claiming UC than those without (+ 2.40%, 95%CI: 1.31%-3.50%). However as the rollout continued between 2018/29 and 2019/20 (-6.75%, 95%CI: -9.62%--3.88%) and between 2019/20 and 2020/21 (-2.09%, 95%CI: -2.54%--1.64%), there were significantly higher proportions of those without a limiting long term condition seeking advice than with. When comparing 2018/19 to 2019/20 and 2019/20 to 2020/21, there was a significant increase in the proportion of self-employed compared to unemployed people seeking advice with claiming UC (5.64%, 95%CI: 3.79%-7.49%) and (2.26%, 95%CI: 1.29%-3.23%) respectively. Conclusion As the rollout for UC continues, it is important to understand how changes in eligibility for UC may impact on those who need help with applying for UC. Ensuring that the advice process and application process is responsive to a range of people with different needs can help to reduce the likelihood that the process of claiming UC will exacerbate health inequalities.

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.

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.

Health care use attributable to COVID-19: A propensity matched national electronic health records cohort study of 249,390 people in Wales, UK. (preprint)

BackgroundTo determine the extent and nature of changes in infected patients healthcare utilization, we studied healthcare contact in the 1-4 weeks and 5-24 weeks following a COVID-19 diagnosis compared to propensity matched controls. MethodsSurvival analysis was used for time to death and first clinical outcomes including clinical terminology concepts for post-viral illness, fatigue, embolism, respiratory conditions, mental and developmental conditions, fit note, or hospital attendance. Increased instantaneous risk for the occurrence of an outcome for positive individuals was quantified using hazard ratios (HR) from Cox Regression and absolute risk was quantified using relative risk (RR) from life table analysis. ResultsCompared to matched individuals testing negative, surviving positive community-tested patients had a higher risk of post-viral illness (HR: 4.57, 95%CI: 1.77-11.80, p=0.002), fatigue (HR: 1.47, 95%CI: 1.24-1.75, p<0.001) and embolism (HR: 1.51, 95%CI: 1.13-2.02, p=0.005) at 5-24 weeks post-diagnosis. In the four weeks after COVID-19 higher rates of sick notes were being issued for community-tested (HR: 3.04, 95%CI: 0.88 to 10.50, p<0.079); the risk was reduced after four weeks, compared to controls. Overall healthcare attendance for anxiety, depression was less likely in those with COVID-19 in the first four weeks (HR: 0.83, 95%CI: 0.73-1.06, p=0.007). After four weeks, anxiety, depression is less likely to occur for the positive community-tested individuals (HR: 0.87, 95%CI: 0.77-1.00, p=0.048), but more likely for positive hospital-tested individuals (HR: 1.16, 95%CI: 1.00-1.45, p=0.053). Although statistical associations between positive infection and post-infection healthcare use are clear, the absolute use of healthcare is very. ConclusionsCommunity COVID-19 disease is associated with increased risks of post-viral illness, fatigue, embolism, depression, anxiety and respiratory conditions. Despite these elevated risks, the absolute healthcare burden is low. Either very small proportions of people experience adverse outcomes following COVID-19 or they are not presenting to healthcare. Trial registrationData held in SAIL databank are anonymised and therefore, no ethical approval is required. All data in SAIL has the permission from the relevant Caldicott Guardian or Data Protection Officer and SAIL-related projects are required to obtain Information Governance Review Panel (IGRP) approval. The IGRP approval number for this study is 1259.