2f:I[703058,["/_next/static/immutable/chunks/3bhq_5eh1d760.js","/_next/static/immutable/chunks/1855-ogyi8eh4.js","/_next/static/immutable/chunks/2pfsjpn6k6zlg.js","/_next/static/immutable/chunks/26c7v3_3uwnwu.js","/_next/static/immutable/chunks/25a3ozcrvi1ff.js","/_next/static/immutable/chunks/2r4wx50nvjh_4.js","/_next/static/immutable/chunks/23fsw06hsi5b1.js","/_next/static/immutable/chunks/06_yt270h41rg.js","/_next/static/immutable/chunks/3ezsiz-7shkna.js","/_next/static/immutable/chunks/20x2y96ldu5wx.js","/_next/static/immutable/chunks/2lxntcm3pd1_h.js","/_next/static/immutable/chunks/00dgco8irq3jw.js","/_next/static/immutable/chunks/00nwe-kq0rboc.js","/_next/static/immutable/chunks/2051ffx3xzio9.js"],"PaperListPageClient"] 30:Tab4,There is limited research investigating associations between extreme heat events (EHEs) and adverse outcomes for veterans and high-risk veteran subpopulations. To investigate the association between EHEs and mortality among California veterans with common cardiometabolic diseases and to examine how this risk differs by race and ethnicity, neighborhood health-related social risk factors, and history of homelessness. This case-crossover study used administrative data from the Veterans Health Administration (VA) Corporate Data Warehouse to identify veterans with a California address from October 1, 2015, to September 30, 2021, and a diagnosis of a cardiometabolic condition of hypertension, diabetes, ischemic heart disease, congestive heart failure, chronic kidney disease, stroke, or peripheral arterial disease. Data were analyzed from October 2023 to December 2024. Date- and address-specific EHEs as defined by daily maximum temperatures greater than the 90th, 95th, and 97.5th percentiles of 2006 to 2020 historical normal temperatures derived from National Center for Environmental Information meteorological data. Patient residential addresses were linked with the nearest weather station. The associations of same-day to 4-day EHEs with mortality were assessed. The main outcome was individual all-cause mortality during warm months (April-October). A time-stratified case-crossover design with conditional logistic regression was used to investigate associations between EHEs and mortality. Planned exploratory subgroup analyses were performed by race and ethnicity, Area Deprivation Index (ADI) for patient address, and homeless status. A total of 13 556 veterans (median age, 78 years [IQR, 71-87 years]; 13 265 men [97.9%]) experienced mortality during the study period. Extreme heat events were significantly associated with mortality at all percentile thresholds (eg, at 95th percentile odds ratio [OR] range, 1.10 [95% CI, 1.04-1.17] to 1.14 [95% CI, 1.08-1.20]; P = .005). Effect estimates were greater for veterans in high vs lower ADI neighborhoods (eg, 3-day EHEs at 95th percentile: OR, 1.44 [95% CI, 1.15-1.80] vs OR, 1.12 [95% CI, 1.06-1.19]), and veterans who experienced homelessness compared with those who did not (eg, 3-day EHEs at 95th percentile: OR, 1.25 [95% CI, 1.09-1.45] vs OR, 1.12 [95% CI, 1.05-1.19]) across EHE definitions, although there was no statistically significant effect modification. In this study of California veterans with common cardiometabolic diseases, EHEs were significantly associated with mortality. These results suggest that the VA should develop EHE risk-mitigating interventions to protect veterans, particularly those at greatest risk, from heat-related mortality.31:T1009, Importance There is limited research investigating associations between extreme heat events (EHEs) and adverse outcomes for veterans and high-risk veteran subpopulations. Objectives To investigate the association between EHEs and mortality among California veterans with common cardiometabolic diseases and to examine how this risk differs by race and ethnicity, neighborhood health-related social risk factors, and history of homelessness. Design, Setting, and Participants This case-crossover study used administrative data from the Veterans Health Administration (VA) Corporate Data Warehouse to identify veterans with a California address from October 1, 2015, to September 30, 2021, and a diagnosis of a cardiometabolic condition of hypertension, diabetes, ischemic heart disease, congestive heart failure, chronic kidney disease, stroke, or peripheral arterial disease. Data were analyzed from October 2023 to December 2024. Exposure Date- and address-specific EHEs as defined by daily maximum temperatures greater than the 90th, 95th, and 97.5th percentiles of 2006 to 2020 historical normal temperatures derived from National Center for Environmental Information meteorological data. Patient residential addresses were linked with the nearest weather station. The associations of same-day to 4-day EHEs with mortality were assessed. Main Outcomes and Measures The main outcome was individual all-cause mortality during warm months (April-October). A time-stratified case-crossover design with conditional logistic regression was used to investigate associations between EHEs and mortality. Planned exploratory subgroup analyses were performed by race and ethnicity, Area Deprivation Index (ADI) for patient address, and homeless status. Results A total of 13 556 veterans (median age, 78 years [IQR, 71-87 years]; 13 265 men [97.9%]) experienced mortality during the study period. Extreme heat events were significantly associated with mortality at all percentile thresholds (eg, at 95th percentile odds ratio [OR] range, 1.10 [95% CI, 1.04-1.17] to 1.14 [95% CI, 1.08-1.20]; P = .005). Effect estimates were greater for veterans in high vs lower ADI neighborhoods (eg, 3-day EHEs at 95th percentile: OR, 1.44 [95% CI, 1.15-1.80] vs OR, 1.12 [95% CI, 1.06-1.19]), and veterans who experienced homelessness compared with those who did not (eg, 3-day EHEs at 95th percentile: OR, 1.25 [95% CI, 1.09-1.45] vs OR, 1.12 [95% CI, 1.05-1.19]) across EHE definitions, although there was no statistically significant effect modification. Conclusions and Relevance In this study of California veterans with common cardiometabolic diseases, EHEs were significantly associated with mortality. These results suggest that the VA should develop EHE risk-mitigating interventions to protect veterans, particularly those at greatest risk, from heat-related mortality. 25:[[["$","script","0",{"type":"application/ld+json","dangerouslySetInnerHTML":{"__html":"{\"@context\":\"https://schema.org\",\"@type\":\"CollectionPage\",\"name\":\"Case-Crossover: study design Research & Clinical Takeaways\",\"description\":\"Explore peer-reviewed papers on Case-Crossover (study design). Clinician-written summaries and actionable practice insights. 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Effect estimates were greater for veterans in high vs lower ADI neighborhoods (eg, 3-day EHEs at 95th percentile: OR, 1.44 [95% CI, 1.15-1.80] vs OR, 1.12 [95% CI, 1.06-1.19]), and veterans who experienced homelessness compared with those who did not (eg, 3-day EHEs at 95th percentile: OR, 1.25 [95% CI, 1.09-1.45] vs OR, 1.12 [95% CI, 1.05-1.19]) across EHE definitions, although there was no statistically significant effect 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Residential addresses were linked to the nearest weather station.","key_result":"Among 13,556 decedents (median age 78; 97.9% men), extreme heat events were associated with higher mortality across thresholds (at the 95th percentile, OR 1.10 [95% CI, 1.04–1.17] to 1.14 [95% CI, 1.08–1.20]; P=.005). Point estimates were larger for veterans in high vs lower ADI neighborhoods (3-day EHE, 95th percentile: OR 1.44 [95% CI, 1.15–1.80] vs 1.12 [95% CI, 1.06–1.19]) and for those with a history of homelessness vs not (OR 1.25 [95% CI, 1.09–1.45] vs 1.12 [95% CI, 1.05–1.19]), without statistically significant effect modification.","limitations":"Exploratory subgroup differences (ADI, homelessness) were not statistically significant despite larger point estimates. Analyses were limited to warm months and to percentile-based heat definitions.","practice_takeaway":"During hot spells, veterans with cardiometabolic disease face higher short-term mortality risk; prioritize heat-mitigation outreach and resources, especially for those in high-ADI neighborhoods and with histories of homelessness, while noting subgroup differences were not statistically significant."}}],"initialOffset":1,"fetchConfig":{"kind":"tags","tags":[{"tag_type":"study_design","value":"Case-Crossover"}]},"breadcrumbItems":[{"name":"Home","href":"/"},{"name":"Research Hub","href":"/hub"},{"name":"Study Designs","href":"/hub/tag/study_design"},{"name":"Case-Crossover"}],"hrefConfig":{"basePath":"/hub/papers","webViewQueryParams":{"hub_ctx":"tag","hub_tag_type":"study_design","hub_tag_value":"Case-Crossover"}}}]]

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