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

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          Abstract

          Objectives

          To estimate occupational differences in COVID-19 mortality and test whether these are confounded by factors such as regional differences, ethnicity and education or due to non-workplace factors, such as deprivation or prepandemic health.

          Methods

          Using a cohort study of over 14 million people aged 40–64 years living in England, we analysed occupational differences in death involving COVID-19, assessed between 24 January 2020 and 28 December 2020.

          We estimated age-standardised mortality rates (ASMRs) per 100 000 person-years at risk stratified by sex and occupation. We estimated the effect of occupation on COVID-19 mortality using Cox proportional hazard models adjusted for confounding factors. We further adjusted for non-workplace factors and interpreted the residual effects of occupation as being due to workplace exposures to SARS-CoV-2.

          Results

          In men, the ASMRs were highest among those working as taxi and cab drivers or chauffeurs at 119.7 deaths per 100 000 (95% CI 98.0 to 141.4), followed by other elementary occupations at 106.5 (84.5 to 132.4) and care workers and home carers at 99.2 (74.5 to 129.4). Adjusting for confounding factors strongly attenuated the HRs for many occupations, but many remained at elevated risk. Adjusting for living conditions reduced further the HRs, and many occupations were no longer at excess risk. For most occupations, confounding factors and mediators other than workplace exposure to SARS-CoV-2 explained 70%–80% of the excess age-adjusted occupational differences.

          Conclusions

          Working conditions play a role in COVID-19 mortality, particularly in occupations involving contact with patients or the public. However, there is also a substantial contribution from non-workplace factors.

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          Most cited references24

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          Is Open Access

          OpenSAFELY: factors associated with COVID-19 death in 17 million patients

          COVID-19 has rapidly impacted on mortality worldwide. 1 There is unprecedented urgency to understand who is most at risk of severe outcomes, requiring new approaches for timely analysis of large datasets. Working on behalf of NHS England we created OpenSAFELY: a secure health analytics platform covering 40% of all patients in England, holding patient data within the existing data centre of a major primary care electronic health records vendor. Primary care records of 17,278,392 adults were pseudonymously linked to 10,926 COVID-19 related deaths. COVID-19 related death was associated with: being male (hazard ratio 1.59, 95%CI 1.53-1.65); older age and deprivation (both with a strong gradient); diabetes; severe asthma; and various other medical conditions. Compared to people with white ethnicity, black and South Asian people were at higher risk even after adjustment for other factors (HR 1.48, 1.29-1.69 and 1.45, 1.32-1.58 respectively). We have quantified a range of clinical risk factors for COVID-19 related death in the largest cohort study conducted by any country to date. OpenSAFELY is rapidly adding further patients’ records; we will update and extend results regularly.
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            Is Open Access

            Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study

            Abstract Objective To derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults. Design Population based cohort study. Setting and participants QResearch database, comprising 1205 general practices in England with linkage to covid-19 test results, Hospital Episode Statistics, and death registry data. 6.08 million adults aged 19-100 years were included in the derivation dataset and 2.17 million in the validation dataset. The derivation and first validation cohort period was 24 January 2020 to 30 April 2020. The second temporal validation cohort covered the period 1 May 2020 to 30 June 2020. Main outcome measures The primary outcome was time to death from covid-19, defined as death due to confirmed or suspected covid-19 as per the death certification or death occurring in a person with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the period 24 January to 30 April 2020. The secondary outcome was time to hospital admission with confirmed SARS-CoV-2 infection. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance, including measures of discrimination and calibration, was evaluated in each validation time period. Results 4384 deaths from covid-19 occurred in the derivation cohort during follow-up and 1722 in the first validation cohort period and 621 in the second validation cohort period. The final risk algorithms included age, ethnicity, deprivation, body mass index, and a range of comorbidities. The algorithm had good calibration in the first validation cohort. For deaths from covid-19 in men, it explained 73.1% (95% confidence interval 71.9% to 74.3%) of the variation in time to death (R2); the D statistic was 3.37 (95% confidence interval 3.27 to 3.47), and Harrell’s C was 0.928 (0.919 to 0.938). Similar results were obtained for women, for both outcomes, and in both time periods. In the top 5% of patients with the highest predicted risks of death, the sensitivity for identifying deaths within 97 days was 75.7%. People in the top 20% of predicted risk of death accounted for 94% of all deaths from covid-19. Conclusion The QCOVID population based risk algorithm performed well, showing very high levels of discrimination for deaths and hospital admissions due to covid-19. The absolute risks presented, however, will change over time in line with the prevailing SARS-C0V-2 infection rate and the extent of social distancing measures in place, so they should be interpreted with caution. The model can be recalibrated for different time periods, however, and has the potential to be dynamically updated as the pandemic evolves.
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              Occupation and risk of severe COVID-19: prospective cohort study of 120 075 UK Biobank participants

              To investigate severe COVID-19 risk by occupational group. Baseline 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). Of 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. Essential 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.
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                Author and article information

                Journal
                Occup Environ Med
                Occup Environ Med
                oemed
                oem
                Occupational and Environmental Medicine
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                1351-0711
                1470-7926
                December 2021
                27 December 2021
                27 December 2021
                : oemed-2021-107818
                Affiliations
                [1 ] departmentHealth Analysis Division , Office for National Statistics , Newport, UK
                [2 ] departmentFaculty of Public Health and Policy , London School of Hygiene and Tropical Medicine , London, London, UK
                [3 ] departmentMethodology Division , Office for National Statistics , Newport, Newport, UK
                [4 ] departmentCentre for Biostatistics, School of Health Sciences, Faculty of Biology, Medicine and Health, School of Health Sciences , The University of Manchester , Manchester, Manchester, UK
                [5 ] departmentDepartment of Medical Statistics, Faculty of Epidemiology and Population Health , London School of Hygiene and Tropical Medicine , London, UK
                [6 ] departmentCancer Survival Group, Department of Non-Communicable Disease Epidemiology , London School of Hygiene and Tropical Medicine , London, UK
                [7 ] departmentCentre for Occupational and Environmental Health, School of Health Sciences, Faculty of Biology, Medicine and Health , University of Manchester , Manchester, Greater Manchester, UK
                [8 ] departmentEpidemiology and Population Health , London School of Hygiene , London, UK
                Author notes
                [Correspondence to ] Dr Vahe Nafilyan, Office for National Statistics, Newport, Newport, UK; vahe.nafilyan@ 123456ons.gov.uk
                Author information
                http://orcid.org/0000-0003-0160-217X
                http://orcid.org/0000-0001-6352-0394
                http://orcid.org/0000-0002-5837-801X
                http://orcid.org/0000-0002-1205-1898
                http://orcid.org/0000-0002-9938-7852
                Article
                oemed-2021-107818
                10.1136/oemed-2021-107818
                8718934
                34965981
                7a32cfc9-ddc1-4a6a-b687-c9b2b3950c10
                © Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.

                This article is made freely available for use in accordance with BMJ’s website terms and conditions for the duration of the covid-19 pandemic or until otherwise determined by BMJ. You may use, download and print the article for any lawful, non-commercial purpose (including text and data mining) provided that all copyright notices and trade marks are retained.

                History
                : 05 July 2021
                : 09 December 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000856, Colt Foundation;
                Award ID: CF/05/20
                Categories
                Workplace
                Original research
                Custom metadata
                free

                Occupational & Environmental medicine
                covid-19,occupational health
                Occupational & Environmental medicine
                covid-19, occupational health

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