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What explains the socioeconomic status-health gradient? Evidence from workplace COVID-19 infections.

ABSTRACT: This paper studies the contribution of the workplace to the SES-health gradient. Our analysis is based on a unique dataset that tracks various health outcomes and workplace risks among healthcare workers during the first four months of the coronavirus 2019 (COVID-19) pandemic. The setting provides an exceptional opportunity to test for work-related disparities in health, while controlling for confounding determinants of the SES-health gradient. We find that low-SES nurses were systematically more likely to contract COVID-19 as a result of workplace exposure. These differentials existed in all healthcare institutions, but were particularly large in non-hospital settings. In contrast, we find no relationship between SES and nonwork-related infection rates. The differences in workplace infection rates are substantially larger than those implied by standard 'task-based' indices of transmission risk, and cannot be attributable to easily identifiable metrics of workplace risk. Together, our results show how subtle differences in work conditions or job duties can substantially contribute to the SES-health gradient.

SUBMITTER: Godefroy R

PROVIDER: S-EPMC9150908 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ImportanceHousing insecurity induced by evictions may increase the risk of contracting COVID-19.ObjectiveTo estimate the association of lifting state-level eviction moratoria, which increased housing insecurity during the COVID-19 pandemic, with the risk of being diagnosed with COVID-19.Design, setting, and participantsThis retrospective cohort study included individuals with commercial insurance or Medicare Advantage who lived in a state that issued an eviction moratorium and were diagnosed with COVID-19 as well as a control group comprising an equal number of randomly selected individuals in these states who were not diagnosed with COVID-19. Data were collected from OptumLabs Data Warehouse, a database of deidentified administrative claims. The study used a difference-in-differences analysis among states that implemented an eviction moratorium between March 13, 2020, and September 4, 2020.ExposuresTime since state-level eviction moratoria were lifted.Main outcomes and measuresThe primary outcome measure was a binary variable indicating whether an individual was diagnosed with COVID-19 for the first time in a given week with International Statistical Classification of Diseases and Related Health Problems, Tenth Revision code U07.1. The study analyzed changes in COVID-19 diagnosis before vs after a state lifted its moratorium compared with changes in states that did not lift it. For sensitivity analyses, models were reestimated on a 2% random sample of all individuals in the claims database during this period in these states.ResultsThe cohort consisted of 509 694 individuals (254 847 [50.0%] diagnosed with COVID-19; mean [SD] age, 47.0 [23.6] years; 239 056 [53.3%] men). During the study period, 43 states and the District of Columbia implemented an eviction moratorium and 7 did not. Among the states that implemented a moratorium, 26 (59.1%) lifted their moratorium before the US Centers for Disease Control and Prevention issued their national moratorium, while 18 (40.1%) maintained theirs. In a Cox difference-in-differences regression model, individuals living in a state that lifted its eviction moratorium experienced higher hazards of a COVID-19 diagnosis beginning 5 weeks after the moratorium was lifted (hazard ratio [HR], 1.39; 95% CI, 1.11-1.76; P = .004), reaching an HR of 1.83 (95% CI, 1.36-2.46; P < .001) 12 weeks after. Hazards increased in magnitude among individuals with preexisting comorbidities and those living in nonaffluent and rent-burdened areas. Individuals with a Charlson Comorbidity Index score of 3 or greater had an HR of 2.37 (95% CI, 1.67-3.36; P < .001) at the end of the study period. Those living in nonaffluent areas had an HR of 2.14 (95% CI, 1.51-3.05; P < .001), while those living in areas with a high rent burden had an HR of 2.31 (95% CI, 1.64-3.26; P < .001).Conclusions and relevanceThe findings of this difference-in-differences analysis suggest that eviction-led housing insecurity may have exacerbated the COVID-19 pandemic.

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What explains the socioeconomic status-health gradient? Evidence from workplace COVID-19 infections.

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