Pearce Neil, Rhodes Sarah, Stocking Katie, Pembrey Lucy, van Veldhoven Karin, Brickley Elizabeth B, Robertson Steve, Davoren Donna, Nafilyan Vahe, Windsor-Shellard Ben, Fletcher Tony, van Tongeren Martie
Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK.
University of Manchester, Manchester, M13 9PL, UK.
Wellcome Open Res. 2023 Jan 13;6:102. doi: 10.12688/wellcomeopenres.16729.2. eCollection 2021.
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.
根据职业不同，感染新冠病毒以及死亡的风险存在显著差异。医护人员的感染情况受到了最多关注，照顾新冠肺炎患者的重症监护病房工作人员面临的风险显然更高。然而，其他一些职业的风险可能也会增加，尤其是那些涉及社会护理或与公众接触的职业。有大量数据集可用于评估新冠病毒感染、严重程度或死亡率的职业风险。作为职业、交通、环境新冠病毒传播研究合作计划（PROTECT）倡议的一部分，我们正在审查这些数据集，该倡议是国家新冠病毒核心研究的一部分。在本报告中，我们审查了现有的数据集（包括职业和潜在混杂因素的关键变量），以研究新冠病毒感染、新冠发病率、严重程度和死亡率方面的职业差异。我们还讨论了对这些数据集可能进行的分析类型以及（职业）暴露和结果的定义。我们得出的结论是，这些数据集中没有一个是理想的，它们都有各自的优缺点。例如，死亡率数据存在新冠死亡编码问题，而且（在英格兰和威尔士）提交给验尸官的死亡数据无法获取。另一方面，检测数据在某些时期（尤其是第一波疫情期间）存在严重偏差，因为某些职业（如医护人员）比普通人群接受检测的频率更高。原则上，随机人口调查非常适合估计人群患病率和发病率，但也会受到无应答的影响。因此，对特定职业或部门（如交通行业）的风险进行任何分析，都需要对各种可用数据集的结果进行仔细分析和综合判断。
