Mortality Risks by Occupation During the Early Wave of the COVID‐19 Pandemic in New York City

ABSTRACT Background Social determinants of health play a crucial role as part of interlinking factors that shape health. Occupation is interrelated with socioeconomic status, a known predictor of mortality, that deserves a closer look in the context of the early period of the pandemic in New York City (NYC). To determine the highest mortality burden of COVID‐19 by occupation in NYC during the early pandemic time period, we analyzed NYC Department of Health and Mental Hygiene (DOHMH) death certificate data. Our study considers the role of occupation in relation to deaths among working age individuals. Methods We analyzed confirmed and probable COVID‐19 deaths among individuals 18–64 years using data from NYCDOHMH, the National Institute for Occupational Safety and Health (NIOSH), and the American Community Survey (ACS). Confirmed and probable COVID‐19 deaths occurring from March 11, 2020 to December 31, 2021 were identified among decedents 18–64 years old. Proportional distributions and mortality rates were calculated by occupation. Occupational mortality rates were examined by age, sex, and race and ethnicity. Comparisons by nativity were examined in the context of occupational mortality risk to help identify additional vulnerable populations at risk. Results The highest overall mortality rate by occupation for the study population was found among food preparation and serving workers (182.1 per 100,000 population). Stratified mortality rates indicated that older adults, males, and minority racial and ethnic groups were disproportionally affected. The highest overall mortality rate by occupation for individuals aged 45–64 were found among protective service workers (419.9 per 100,000 population). More than half of decedents aged 18–64 were foreign‐born ( n = 3591, 59.2%). Conclusions Our analysis highlights the inequities of COVID‐19 deaths across occupations in NYC. It informs future studies and tailored interventions that target high‐risk populations. This knowledge will ideally lead to more equitable, protective responses to future pandemics.