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1.
Occupational and Environmental Medicine ; 2022.
Article in English | ProQuest Central | ID: covidwho-2020248

ABSTRACT

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.

2.
J Infect Dis ; 2022 Jul 01.
Article in English | MEDLINE | ID: covidwho-1978237

ABSTRACT

BACKGROUND: Although most adults infected with SARS-CoV-2 fully recover, a proportion have ongoing symptoms, or post-COVID conditions (PCC), after infection. The objective of this analysis was to estimate the number of US adults with activity-limiting PCC on November 1, 2021. METHODS: We modeled the prevalence of PCC using reported infections occurring from February 1, 2020 - September 30, 2021, and population-based, household survey data on new activity-limiting symptoms ≥1 month following SARS-CoV-2 infection. From these data sources, we estimated the number and proportion of US adults with activity-limiting PCC on November 1, 2021, as 95% uncertainty intervals, stratified by sex and age. Sensitivity analyses adjusted for under-ascertainment of infections and uncertainty about symptom duration. RESULTS: On November 1, 2021, at least 3.0-5.0 million US adults were estimated to have activity-limiting PCC of ≥1 month duration, or 1.2%-1.9% of US adults. Population prevalence was higher in females (1.4%-2.2%) than males. The estimated prevalence after adjusting for under-ascertainment of infections was 1.7%-3.8%. CONCLUSION: Millions of US adults were estimated to have activity-limiting PCC. These estimates can support future efforts to address the impact of PCC on the U.S. population.

3.
Occup Environ Med ; 79(7): 433-441, 2022 07.
Article in English | MEDLINE | ID: covidwho-1596312

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.


Subject(s)
COVID-19 , Adult , Cohort Studies , Humans , Male , Occupations , SARS-CoV-2 , Semantic Web
4.
Occupational and Environmental Medicine ; 78(Suppl 1):A151, 2021.
Article in English | ProQuest Central | ID: covidwho-1480284

ABSTRACT

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.

5.
Lancet Digit Health ; 3(7): e425-e433, 2021 07.
Article in English | MEDLINE | ID: covidwho-1246269

ABSTRACT

BACKGROUND: Public policy measures and clinical risk assessments relevant to COVID-19 need to be aided by risk prediction models that are rigorously developed and validated. We aimed to externally validate a risk prediction algorithm (QCovid) to estimate mortality outcomes from COVID-19 in adults in England. METHODS: We did a population-based cohort study using the UK Office for National Statistics Public Health Linked Data Asset, a cohort of individuals aged 19-100 years, based on the 2011 census and linked to Hospital Episode Statistics, the General Practice Extraction Service data for pandemic planning and research, and radiotherapy and systemic chemotherapy records. The primary outcome was time to COVID-19 death, defined as confirmed or suspected COVID-19 death as per death certification. Two periods were used: (1) Jan 24 to April 30, 2020, and (2) May 1 to July 28, 2020. We assessed the performance of the QCovid algorithms using measures of discrimination and calibration. Using predicted 90-day risk of COVID-19 death, we calculated r2 values, Brier scores, and measures of discrimination and calibration with corresponding 95% CIs over the two time periods. FINDINGS: We included 34 897 648 adults aged 19-100 years resident in England. 26 985 (0·08%) COVID-19 deaths occurred during the first period and 13 177 (0·04%) during the second. The algorithms had good discrimination and calibration in both periods. In the first period, they explained 77·1% (95% CI 76·9-77·4) of the variation in time to death in men and 76·3% (76·0-76·6) in women. The D statistic was 3·761 (3·732-3·789) for men and 3·671 (3·640-3·702) for women and Harrell's C was 0·935 (0·933-0·937) for men and 0·945 (0·943-0·947) for women. Similar results were obtained for the second time period. In the top 5% of patients with the highest predicted risks of death, the sensitivity for identifying deaths in the first period was 65·94% for men and 71·67% for women. INTERPRETATION: The QCovid population-based risk algorithm performed well, showing high levels of discrimination for COVID-19 deaths in men and women for both time periods. QCovid has the potential to be dynamically updated as the pandemic evolves and, therefore, has potential use in guiding national policy. FUNDING: UK National Institute for Health Research.


Subject(s)
Algorithms , COVID-19/mortality , Risk Assessment/statistics & numerical data , Adult , Aged , Aged, 80 and over , Cohort Studies , Databases, Factual , England/epidemiology , Female , Humans , Male , Middle Aged , Pandemics , SARS-CoV-2 , Young Adult
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