{"doi":"10.1021/ehp.6c00564","title":"Advancing Extreme Heat Risk Assessments to Better Capture Individually Experienced Temperatures: A New Approach to Describe Individual and Subgroup Vulnerabilities","abstract":"BACKGROUND: Extreme heat risk assessments often rely on epidemiologic studies that used the nearest available outdoor airport temperatures (OATs) rather than individually-experienced temperatures (IETs) and frequently lack key individual-level determinants of exposure, including occupation, housing, and air conditioning. This hampers efforts to characterize heat burden inequities and guide interventions for vulnerable populations. OBJECTIVES: We developed an approach to estimate individual and subgroup-specific health impacts from modeled IETs before and during extreme heat events for three U.S. cities: Atlanta, Georgia (hot-humid), Detroit, Michigan (temperate), and Phoenix, Arizona (hot-dry). METHODS: IET profiles were estimated using modeled parcel-linked population microdata, housing-specific indoor temperatures from building energy models, ambient temperatures from urban-scale climate models, and time activity patterns from surveys. We linked each IET profile to daily OATs, then fit mixed-effects regressions to predict \"equivalent\" OATs (eOATs), based on IET, housing, and demographics. We assigned risk ratios (RRs) from existing literature on all-cause mortality, all-cause emergency department (ED) visits, and preterm births to each person-day's eOAT and estimated 5-day-extreme-heat absolute risks (ARs) by age-race-income-occupation subgroup. RESULTS: The eOATs, RRs, and ARs differed between people due to variability in IETs and baseline health outcome incidence rates. All-cause mortality RRs ranges were 1.00-1.16 (Atlanta), 1.01-7.08 (Detroit), and 1.00-6.38 (Phoenix). All-cause-mortality ARs ranged 0.01-32 (Atlanta), 0.01-1,100 (Detroit), and 0.01-950 (Phoenix) per 100,000 persons. ED visit ARs ranges were 0.2-270 (Atlanta) and 0.04-6,200 (Phoenix) per 100,000 persons. Heat mortality ARs were higher among older adults and, only in Detroit, in young, Black, outdoor workers (median = 6.6 per 100,000) compared to young, non-Black, higher-income, indoor workers (median = 0.3 per 100,000). DISCUSSION: When IETs can be estimated or directly measured, person-specific eOATs can be used to estimate the subgroup-specific heat-health burdens that would be experienced without adaptive behaviors. This approach could be adapted for other contexts to inform climate preparedness and justice policies. https://doi.org/10.1289/EHP15223.","journal":"Environmental Health Perspectives","year":2025,"id":567948,"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":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9526,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":937766,"name":"David M. Hondula","orcid":"0000-0003-2465-2671","position":1,"is_corresponding":false},{"id":260373,"name":"Evan Mallen","orcid":"0000-0001-6151-752X","position":2,"is_corresponding":false},{"id":260377,"name":"Marie S. O’Neill","orcid":"0000-0002-8301-7742","position":3,"is_corresponding":false},{"id":1472106,"name":"Mayuri Rajput","orcid":"0000-0001-8830-5914","position":4,"is_corresponding":false},{"id":1472107,"name":"E. Scott Krayenhoff","orcid":"0000-0002-4776-4353","position":5,"is_corresponding":false},{"id":1472108,"name":"Ashley M. Broadbent","orcid":"0000-0003-1906-8112","position":6,"is_corresponding":false},{"id":1472109,"name":"Santiago Grijalva","orcid":"0000-0001-8601-4662","position":7,"is_corresponding":false},{"id":260376,"name":"Larissa Larsen","orcid":"0000-0002-3732-7934","position":8,"is_corresponding":false},{"id":937765,"name":"Sharon L. Harlan","orcid":"0000-0001-9945-2792","position":9,"is_corresponding":false},{"id":937767,"name":"Brian Stone","orcid":"0000-0002-2418-0486","position":10,"is_corresponding":false},{"id":260374,"name":"Carina J. Gronlund","orcid":"0000-0002-0533-745X","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T02:56:48.183032Z","pmid":"40497792","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":[]}