ObjectivesTo investigate severe COVID-19 risk by occupational group.MethodsBaseline UK Biobank data (2006–10) for England were linked to SARS-CoV-2 test results from Public Health England (16 March to 26 July 2020). Included participants were employed or self-employed at baseline, alive and aged <65 years in 2020. Poisson regression models were adjusted sequentially for baseline demographic, socioeconomic, work-related, health, and lifestyle-related risk factors to assess risk ratios (RRs) for testing positive in hospital or death due to COVID-19 by three occupational classification schemes (including Standard Occupation Classification (SOC) 2000).ResultsOf 120 075 participants, 271 had severe COVID-19. Relative to non-essential workers, healthcare workers (RR 7.43, 95% CI 5.52 to 10.00), social and education workers (RR 1.84, 95% CI 1.21 to 2.82) and other essential workers (RR 1.60, 95% CI 1.05 to 2.45) had a higher risk of severe COVID-19. Using more detailed groupings, medical support staff (RR 8.70, 95% CI 4.87 to 15.55), social care (RR 2.46, 95% CI 1.47 to 4.14) and transport workers (RR 2.20, 95% CI 1.21 to 4.00) had the highest risk within the broader groups. Compared with white non-essential workers, non-white non-essential workers had a higher risk (RR 3.27, 95% CI 1.90 to 5.62) and non-white essential workers had the highest risk (RR 8.34, 95% CI 5.17 to 13.47). Using SOC 2000 major groups, associate professional and technical occupations, personal service occupations and plant and machine operatives had a higher risk, compared with managers and senior officials.ConclusionsEssential workers have a higher risk of severe COVID-19. These findings underscore the need for national and organisational policies and practices that protect and support workers with an elevated risk of severe COVID-19.

Background: Understanding of the role of ethnicity and socioeconomic position in the risk of developing SARS-CoV-2 infection is limited. We investigated this in the UK Biobank study. Methods: The UK Biobank study recruited 40-70-year-olds in 2006-2010 from the general population, collecting information about self-defined ethnicity and socioeconomic variables (including area-level socioeconomic deprivation and educational attainment). SARS-CoV-2 test results from Public Health England were linked to baseline UK Biobank data. Poisson regression with robust standard errors was used to assess risk ratios (RRs) between the exposures and dichotomous variables for being tested, having a positive test and testing positive in hospital. We also investigated whether ethnicity and socioeconomic position were associated with having a positive test amongst those tested. We adjusted for covariates including age, sex, social variables (including healthcare work and household size), behavioural risk factors and baseline health. Results: Amongst 392,116 participants in England, 2658 had been tested for SARS-CoV-2 and 948 tested positive (726 in hospital) between 16 March and 3 May 2020. Black and south Asian groups were more likely to test positive (RR 3.35 (95% CI 2.48-4.53) and RR 2.42 (95% CI 1.75-3.36) respectively), with Pakistani ethnicity at highest risk within the south Asian group (RR 3.24 (95% CI 1.73-6.07)). These ethnic groups were more likely to be hospital cases compared to the white British. Adjustment for baseline health and behavioural risk factors led to little change, with only modest attenuation when accounting for socioeconomic variables. Socioeconomic deprivation and having no qualifications were consistently associated with a higher risk of confirmed infection (RR 2.19 for most deprived quartile vs least (95% CI 1.80-2.66) and RR 2.00 for no qualifications vs degree (95% CI 1.66-2.42)). Conclusions: Some minority ethnic groups have a higher risk of confirmed SARS-CoV-2 infection in the UK Biobank study, which was not accounted for by differences in socioeconomic conditions, baseline self-reported health or behavioural risk factors. An urgent response to addressing these elevated risks is required.

Aims/hypothesis People with obesity and a normal metabolic profile are sometimes referred to as having ‘metabolically healthy obesity’ (MHO). However, whether this group of individuals are actually ‘healthy’ is uncertain. This study aims to examine the associations of MHO with a wide range of obesity-related outcomes. Methods This is a population-based prospective cohort study of 381,363 UK Biobank participants with a median follow-up of 11.2 years. MHO was defined as having a BMI ≥ 30 kg/m2 and at least four of the six metabolically healthy criteria. Outcomes included incident diabetes and incident and fatal atherosclerotic CVD (ASCVD), heart failure (HF) and respiratory diseases. Results Compared with people who were not obese at baseline, those with MHO had higher incident HF (HR 1.60; 95% CI 1.45, 1.75) and respiratory disease (HR 1.20; 95% CI 1.16, 1.25) rates, but not higher ASCVD. The associations of MHO were generally weaker for fatal outcomes and only significant for all-cause (HR 1.12; 95% CI 1.04, 1.21) and HF mortality rates (HR 1.44; 95% CI 1.09, 1.89). However, when compared with people who were metabolically healthy without obesity, participants with MHO had higher rates of incident diabetes (HR 4.32; 95% CI 3.83, 4.89), ASCVD (HR 1.18; 95% CI 1.10, 1.27), HF (HR 1.76; 95% CI 1.61, 1.92), respiratory diseases (HR 1.28; 95% CI 1.24, 1.33) and all-cause mortality (HR 1.22; 95% CI 1.14, 1.31). The results with a 5 year landmark analysis were similar. Conclusions/interpretation Weight management should be recommended to all people with obesity, irrespective of their metabolic status, to lower risk of diabetes, ASCVD, HF and respiratory diseases. The term ‘MHO’ should be avoided as it is misleading and different strategies for risk stratification should be explored. Graphical abstract

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Background It is now well recognised that the risk of severe COVID-19 increases with some long-term conditions (LTCs). However, prior research primarily focuses on individual LTCs and there is a lack of data on the influence of multimorbidity (≥2 LTCs) on the risk of COVID-19. Given the high prevalence of multimorbidity, more detailed understanding of the associations with multimorbidity and COVID-19 would improve risk stratification and help protect those most vulnerable to severe COVID-19. Here we examine the relationships between multimorbidity, polypharmacy (a proxy of multimorbidity), and COVID-19; and how these differ by sociodemographic, lifestyle, and physiological prognostic factors. Methods and findings We studied data from UK Biobank (428,199 participants; aged 37–73; recruited 2006–2010) on self-reported LTCs, medications, sociodemographic, lifestyle, and physiological measures which were linked to COVID-19 test data. Poisson regression models examined risk of COVID-19 by multimorbidity/polypharmacy and effect modification by COVID-19 prognostic factors (age/sex/ethnicity/socioeconomic status/smoking/physical activity/BMI/systolic blood pressure/renal function). 4,498 (1.05%) participants were tested; 1,324 (0.31%) tested positive for COVID-19. Compared with no LTCs, relative risk (RR) of COVID-19 in those with 1 LTC was no higher (RR 1.12 (CI 0.96–1.30)), whereas those with ≥2 LTCs had 48% higher risk; RR 1.48 (1.28–1.71). Compared with no cardiometabolic LTCs, having 1 and ≥2 cardiometabolic LTCs had a higher risk of COVID-19; RR 1.28 (1.12–1.46) and 1.77 (1.46–2.15), respectively. Polypharmacy was associated with a dose response higher risk of COVID-19. All prognostic factors were associated with a higher risk of COVID-19 infection in multimorbidity; being non-white, most socioeconomically deprived, BMI ≥40 kg/m2, and reduced renal function were associated with the highest risk of COVID-19 infection: RR 2.81 (2.09–3.78); 2.79 (2.00–3.90); 2.66 (1.88–3.76); 2.13 (1.46–3.12), respectively. No multiplicative interaction between multimorbidity and prognostic factors was identified. Important limitations include the low proportion of UK Biobank participants with COVID-19 test data (1.05%) and UK Biobank participants being more affluent, healthier and less ethnically diverse than the general population. Conclusions Increasing multimorbidity, especially cardiometabolic multimorbidity, and polypharmacy are associated with a higher risk of developing COVID-19. Those with multimorbidity and additional factors, such as non-white ethnicity, are at heightened risk of COVID-19.

Objectives: To investigate COVID-19 risk by occupational group. Design: Prospective study of linked population-based and administrative data. Setting: UK Biobank data linked to SARS-CoV-2 test results from Public Health England from 16 March to 3 May 2020. Participants: 120,621 UK Biobank participants who were employed or self-employed at baseline (2006-2010) and were 65 years or younger in March 2020. Overall, 29% (n=37,890) were employed in essential occupational groups, which included healthcare workers, social and education workers, and other essential workers comprising of police and protective service, food, and transport workers. Poisson regression models, adjusted for baseline sociodemographic, work-related, health, and lifestyle-related risk factors were used to assess risk ratios (RRs) of testing positive in hospital by occupational group as reported at baseline relative to non-essential workers. Main outcome measures: Positive SARS-CoV-2 test within a hospital setting (i.e. as an inpatient or in an Emergency Department). Results: 817 participants were tested for SARS-CoV-2 and of these, 206 (0.2%) individuals had a positive test in a hospital setting. Relative to non-essential workers, healthcare workers (RR 7.59, 95% CI: 5.43 to 10.62) and social and education workers (RR 2.17, 95% CI: 1.37 to 3.46) had a higher risk of testing positive for SARS-CoV-2 in hospital. Using more detailed groupings, medical support staff (RR 8.57, 95% CI: 4.35 to 16.87) and social care workers (RR 2.99, 95% CI: 1.71 to 5.24) had highest risk within the healthcare worker and social and education worker categories, respectively. In general, adjustment for covariates did not substantially change the pattern of occupational differences in risk. Conclusions: Essential workers in health and social care have a higher risk of severe SARS-CoV-2 infection. These findings underscore the need for national and organisational policies and practices that protect and support workers with elevated risk of SARS-CoV-2 infection.

Background Dementia is associated with a high burden of dependency and disability. Physical frailty (hereafter referred to as frailty) is a multisystem dysregulation that has been identified as a risk factor for dementia. The aim of this study was to examine the association of frailty and its individual components with all-cause dementia incidence in a cohort of UK adults.Methods Participants in UK Biobank with data available for dementia incidence and without any form of dementia at baseline were included in this prospective study. Frailty was defined using a modified version of the frailty phenotype based on five individual components (weight loss, tiredness, physical activity, gait speed, and grip strength), with participants classified as pre-frail if they fulfilled one or two criteria or frail if they fulfilled three or more. Associations between frailty and dementia incidence were investigated using Cox proportional hazard models adjusted for sociodemographic factors, lifestyle factors, and morbidity count. The population attributable fraction was also estimated. FindingsOf 502 535 participants in UK Biobank, 143 215 met the inclusion criteria and were included in our analyses. 68 500 (47•8%) of the participants were pre-frail and 5565 (3•9%) were frail. During a median follow-up period of 5•4 years, 726 individuals developed dementia. Compared with non-frail individuals, the risk of dementia incidence was increased for individuals with pre-frailty (hazard ratio 1•21 [95% CI 1•04-1•42]) and frailty (1•98 [1•47-2•67]) in the fully adjusted model. Of the five components used to define frailty, weight loss (1•31 [1•09-1•58]), tiredness (1•48 [1•18-1•86]), low grip strength (1•38 [1•17-1•63]), and slow gait speed (1•55 [1•22-1•96]) were independently associated with incident dementia. Based on population attributable fraction analyses, in the study sample, pre-frailty and frailty accounted for 9•9% and 8•6% of dementia cases, respectively.Interpretation Individuals with pre-frailty and frailty were at a higher risk of dementia incidence even after adjusting for a wide range of confounding factors. Early detection and interventions for frailty could translate into prevention or delayed onset of dementia.

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