Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 4 de 4
Filter
1.
Wellcome Open Res ; 2023.
Article in English | EuropePMC | ID: covidwho-2203714

ABSTRACT

There are important differences in the risk of SARS-CoV-2 infection and death depending on occupation. Infections in healthcare workers have received the most attention, and there are clearly increased risks for intensive care unit workers who are caring for COVID-19 patients. However, a number of other occupations may also be at an increased risk, particularly those which involve social care or contact with the public. A large number of data sets are available with the potential to assess occupational risks of COVID-19 incidence, severity, or mortality. We are reviewing these data sets as part of the Partnership for Research in Occupational, Transport, Environmental COVID Transmission (PROTECT) initiative, which is part of the National COVID-19 Core Studies. In this report, we review the data sets available (including the key variables on occupation and potential confounders) for examining occupational differences in SARS-CoV-2 infection and COVID-19 incidence, severity and mortality. We also discuss the possible types of analyses of these data sets and the definitions of (occupational) exposure and outcomes. We conclude that none of these data sets are ideal, and all have various strengths and weaknesses. For example, mortality data suffer from problems of coding of COVID-19 deaths, and the deaths (in England and Wales) that have been referred to the coroner are unavailable. On the other hand, testing data is heavily biased in some periods (particularly the first wave) because some occupations (e.g. healthcare workers) were tested more often than the general population. Random population surveys are, in principle, ideal for estimating population prevalence and incidence, but are also affected by non-response. Thus, any analysis of the risks in a particular occupation or sector (e.g. transport), will require a careful analysis and triangulation of findings across the various available data sets.

2.
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
3.
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.

4.
Wellcome Open Res ; 6: 102, 2021.
Article in English | MEDLINE | ID: covidwho-1278725

ABSTRACT

There are important differences in the risk of SARS-CoV-2 infection and death depending on occupation. Infections in healthcare workers have received the most attention, and there are clearly increased risks for intensive care unit workers who are caring for COVID-19 patients. However, a number of other occupations may also be at an increased risk, particularly those which involve social care or contact with the public. A large number of data sets are available with the potential to assess occupational risks of COVID-19 incidence, severity, or mortality. We are reviewing these data sets as part of the Partnership for Research in Occupational, Transport, Environmental COVID Transmission (PROTECT) initiative, which is part of the National COVID-19 Core Studies. In this report, we review the data sets available (including the key variables on occupation and potential confounders) for examining occupational differences in SARS-CoV-2 infection and COVID-19 incidence, severity and mortality. We also discuss the possible types of analyses of these data sets and the definitions of (occupational) exposure and outcomes. We conclude that none of these data sets are ideal, and all have various strengths and weaknesses. For example, mortality data suffer from problems of coding of COVID-19 deaths, and the deaths (in England and Wales) that have been referred to the coroner are unavailable. On the other hand, testing data is heavily biased in some periods (particularly the first wave) because some occupations (e.g. healthcare workers) were tested more often than the general population. Random population surveys are, in principle, ideal for estimating population prevalence and incidence, but are also affected by non-response. Thus, any analysis of the risks in a particular occupation or sector (e.g. transport), will require a careful analysis and triangulation of findings across the various available data sets.

SELECTION OF CITATIONS
SEARCH DETAIL