Occupational differences in COVID-19 incidence, severity, and mortality in the United Kingdom: Available data and framework for analyses.
Occupational differences in COVID-19 incidence, severity, and mortality in the United Kingdom: Available data and framework for analyses.
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DOI：
10.12688/wellcomeopenres.16729.1
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发表时间：
2021-01-01
影响因子：
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通讯作者：
van Tongeren, Martie
中科院分区：
文献类型：
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作者：
Pearce, Neil;Rhodes, Sarah;van Tongeren, Martie
SARS-CoV-2感染和死亡的风险因职业而异。医护人员中的感染受到的关注最多，照顾COVID-19患者的重症监护病房工作人员的风险显然增加了。然而，一些其他职业也可能面临更高的风险，特别是那些涉及社会护理或与公众接触的职业。现有大量数据集可用于评估COVID-19发病率、严重程度或死亡率的职业风险。作为COVID-19职业、交通和环境传播研究伙伴关系（PROTECT）倡议的一部分，我们正在审查这些数据集，该倡议是国家COVID-19核心研究的一部分。在本报告中，我们回顾了现有的数据集（包括职业和潜在混杂因素的关键变量），用于研究SARS-CoV-2感染和COVID-19发病率、严重程度和死亡率的职业差异。我们还讨论了这些数据集的可能分析类型以及（职业）暴露和结果的定义。我们得出的结论是，这些数据集都不是理想的，它们都有不同的优缺点。例如，死亡率数据受到COVID-19死亡编码问题的影响，并且无法获得（英格兰和威尔士）提交给验尸官的死亡人数。另一方面，在某些时期（特别是第一波），检测数据存在严重偏差，因为某些职业（如卫生保健工作者）的检测频率高于一般人群。随机人口调查原则上是估计人口流行率和发病率的理想方法，但也会受到无反应的影响。因此，对特定职业或部门（例如运输）的任何风险分析都需要对各种可用数据集的调查结果进行仔细分析和三角测量。
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.