{"doi":"10.2105/ajph.2023.307227","title":"Dead Labor: Mortality Inequities by Class, Gender, and Race/Ethnicity in the United States, 1986–2019","abstract":"Objectives. To estimate social class inequities in US mortality using a relational measure based on power over productive property and workers’ labor. Methods. We used nationally representative 1986–2018 National Health Interview Survey data with mortality follow-up through December 31, 2019 (n = 911 850). First, using business-ownership, occupational, and employment-status data, we classified respondents as incorporated business owners (IBOs), unincorporated business owners (UBOs), managers, workers, or not in the labor force (NLFs). Next, using inverse-probability-weighted survival curves, we estimated class mortality inequities overall, after subdividing workers by employment status and occupation, and by period, gender, race/ethnicity, and education. Results. UBOs, workers, and NLFs had, respectively, 6.3 (95% confidence interval [CI] = −8.1, −4.6), 6.6 (95% CI = −8.1, −5.0), and 19.4 (95% CI = −21.0, −17.7) per 100 lower 34-year survival rates than IBOs. Mortality risk was especially high for unemployed, blue-collar, and service workers. Inequities increased over time and were greater among male, racially minoritized, and less-educated respondents. Conclusions. We estimated considerable mortality inequities by class, gender, and race/ethnicity. We also estimated that class mortality inequities are increasing, threatening population health. Public Health Implications. Addressing class inequities likely requires structural, worker-empowering interventions. (Am J Public Health. 2023;113(6):637–646. https://doi.org/10.2105/AJPH.2023.307227 )","journal":"American Journal of Public Health","year":2023,"id":358294,"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":null,"corpus_rank":null,"citation_count":11,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.7538,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":983115,"name":"Megan C. Finsaas","orcid":"0000-0001-8290-7500","position":1,"is_corresponding":false},{"id":695161,"name":"Seth J. Prins","orcid":"0000-0003-2622-614X","position":2,"is_corresponding":false},{"id":666466,"name":"Jerzy Eisenberg‐Guyot","orcid":"0000-0003-3851-267X","position":0,"is_corresponding":true}],"reference_count":24,"raw_metadata":null,"created_at":"2026-07-19T01:13:43.629667Z","pmid":"36926964","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":[]}