{"doi":"10.1101/2021.10.29.21265628","title":"Contributions of occupation characteristics and educational attainment to racial/ethnic inequities in COVID-19 mortality","abstract":"Abstract Background Racial/ethnic inequities in COVID-19 mortality are hypothesized to be driven by education and occupation, but limited empirical evidence has assessed these mechanisms. Objective To quantify the extent to which educational attainment and occupation explain racial/ethnic inequities in COVID-19 mortality. Design Observational cohort. Setting California. Participants Californians aged 18-65 years. Measurements We linked all COVID-19-confirmed deaths in California through February 12, 2021 (N=14,783), to population estimates within strata defined by race/ethnicity, sex, age, USA nativity, region of residence, education, and occupation. We characterized occupations using measures related to COVID-19 exposure including essential sector, telework-ability, and wages. Using sex-stratified regressions, we predicted COVID-19 mortality by race/ethnicity if all races/ethnicities had the same education and occupation distribution as White people and if all people held the safest educational/occupational positions. Results COVID-19 mortality per 100,000 ranged from 15 for White and Asian females to 139 for Latinx males. Accounting for differences in age, nativity, and region, if all races/ethnicities had the education and occupation distribution of Whites, COVID-19 mortality would be reduced for Latinx males (−22%) and females (−23%), and Black males (−1%) and females (−8%), but increased for Asian males (+22%) and females (+23%). Additionally, if all individuals had the COVID-19 mortality associated with the safest educational and occupational position (Bachelor’s degree, non-essential, telework, highest wage quintile), there would have been 57% fewer COVID-19 deaths. Conclusion Educational and occupational disadvantage are important risk factors for COVID-19 mortality across all racial/ethnic groups, especially Latinx individuals. Eliminating avoidable excess risk associated with low-education, essential, on-site, and low-wage jobs may reduce COVID-19 mortality and inequities, but is unlikely to be sufficient to achieve equity.","journal":"medRxiv","year":2021,"id":227302,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":0.0,"corpus_rank":10062,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.5582,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":242504,"name":"Kate A. Duchowny","orcid":"0000-0001-9771-8454","position":1,"is_corresponding":false},{"id":297925,"name":"Alicia R. Riley","orcid":"0000-0002-3341-6892","position":2,"is_corresponding":false},{"id":583576,"name":"Marilyn D. Thomas","orcid":"0000-0003-3245-6363","position":3,"is_corresponding":false},{"id":404146,"name":"Yea‐Hung Chen","orcid":"0000-0002-7949-0692","position":4,"is_corresponding":false},{"id":74900,"name":"Kirsten Bibbins‐Domingo","orcid":"0000-0002-8962-0622","position":5,"is_corresponding":false},{"id":106142,"name":"M. Maria Glymour","orcid":"0000-0001-9644-3081","position":6,"is_corresponding":false},{"id":638484,"name":"Ellicott C. Matthay","orcid":"0000-0003-4535-8252","position":0,"is_corresponding":true}],"reference_count":38,"raw_metadata":null,"created_at":"2026-07-18T23:54:42.179886Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}