Estimation of differential occupational risk of COVID‐19 by comparing risk factors with case data by occupational group

Zhang, Michael

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.