Administration Division, Southern Nevada Health District, Las Vegas, Nevada, USA.
Department of Epidemiology and Biostatistics, School of Public Health, University of Nevada, Las Vegas, Nevada, USA.
Am J Ind Med. 2021 Jan;64(1):39-47. doi: 10.1002/ajim.23199. Epub 2020 Nov 18.
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.
The ONET 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 ONET data to build a regression model to predict individual occupations at greatest risk.
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.
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.
2019 年冠状病毒病（COVID-19）的疾病负担在职业间并不均匀。虽然众所周知医护人员的风险增加，但缺乏其他职业的数据。在缺乏这些数据的情况下，使用各种预测因素的模型已被用于预测职业风险，但迄今为止没有模型使用实际病例数的数据。本研究使用职业信息网络（O*NET）数据库中的预测因素评估 COVID-19 按职业的差异风险，并将其与华盛顿州卫生部公布的病例数相关联，以确定个别职业中 COVID-19 感染风险最高的工人。
从 ONET 数据库中筛选出可能导致 COVID-19 风险差异的职业预测因素。从公共来源获得按职业划分的病例数。估计职业患病率，并将其与 ONET 数据相关联，以构建回归模型预测感染 COVID-19 风险最高的个别职业。
两个变量与病例发生率相关：疾病暴露（r=0.66；p=0.001）和身体接近度（r=0.64；p=0.002），并通过多元线性回归分析预测 47.5%的患病率方差（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很脆弱。
可以使用模型来识别易感染 COVID-19 的工人，但预测受到方法学限制的影响。必须在多个州收集全面的数据，以充分指导针对 COVID-19 的特定职业干预措施的实施。
