Estimation of differential occupational risk of COVID-19 by comparing risk factors with case data by occupational group
Estimation of differential occupational risk of COVID-19 by comparing risk factors with case data by occupational group
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DOI：
10.1002/ajim.23199
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发表时间：
2020-11-18
影响因子：
3.5
通讯作者：
Zhang, Michael
中科院分区：
文献类型：
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作者：
Zhang, Michael
背景：2019年冠状病毒病(新冠肺炎)的疾病负担在不同职业之间并不一致。尽管众所周知，医护人员面临的风险增加，但其他职业的数据却很匮乏。取而代之的是，模型已经被用来使用各种预测因子来预测职业风险，但到目前为止还没有模型使用来自实际病例数量的数据。本研究利用职业信息网(O*NET)数据库中的预测因子评估不同职业患新冠肺炎的风险，并将其与华盛顿州卫生部公布的病例计数相关联，以确定个别职业中感染新冠肺炎风险最高的工人。方法对O*NET数据库进行筛选，以寻找按职业区分新冠肺炎风险的潜在预测因素。按职业组别划分的病例计数从公共来源获得。结果疾病暴露(r=0.66；p=0.001)和身体接触(r=0.64；p=0.002)两个变量与病例患病率相关，多元线性回归分析预测患病率变异的47.5%(p=0.003)。风险最高的职业是医疗保健，特别是牙科，但许多非医疗保健职业也是脆弱的。结论模型可以用来识别易受新冠肺炎影响的工人，但预测受到方法上的限制。必须收集许多州的全面数据，以充分指导在对抗新冠肺炎的战斗中实施针对特定职业的干预措施。
Background The disease burden of coronavirus disease 2019 (COVID-19) is not uniform across occupations. Although healthcare workers are well-known to be at increased risk, data for other occupations are lacking. In lieu of this, models have been used to forecast occupational risk using various predictors, but no model heretofore has used data from actual case numbers. This study assesses the differential risk of COVID-19 by occupation using predictors from the Occupational Information Network (O*NET) database and correlating them with case counts published by the Washington State Department of Health to identify workers in individual occupations at highest risk of COVID-19 infection.Methods The O*NET database was screened for potential predictors of differential COVID-19 risk by occupation. Case counts delineated by occupational group were obtained from public sources. Prevalence by occupation was estimated and correlated with O*NET data to build a regression model to predict individual occupations at greatest risk.Results Two variables correlate with case prevalence: disease exposure (r = 0.66; p = 0.001) and physical proximity (r = 0.64; p = 0.002), and predict 47.5% of prevalence variance (p = 0.003) on multiple linear regression analysis. The highest risk occupations are in healthcare, particularly dental, but many nonhealthcare occupations are also vulnerable.Conclusions Models can be used to identify workers vulnerable to COVID-19, but predictions are tempered by methodological limitations. Comprehensive data across many states must be collected to adequately guide implementation of occupation-specific interventions in the battle against COVID-19.