Clare Meernik, Quan Chen, Lauren E Wilson, Ashwini Joshi, Fariha Rahman, Maria Pisu, Margaret Liang, Kevin C Ward, Margaret Gates Kuliszewski, Thomas Tucker, Andrew Berchuck, Bin Huang, Tomi Akinyemiju
{"title":"作为卵巢癌种族生存差异中介因素的医疗服务领域和治疗:SEER-Medicare 的结构方程模型分析。","authors":"Clare Meernik, Quan Chen, Lauren E Wilson, Ashwini Joshi, Fariha Rahman, Maria Pisu, Margaret Liang, Kevin C Ward, Margaret Gates Kuliszewski, Thomas Tucker, Andrew Berchuck, Bin Huang, Tomi Akinyemiju","doi":"10.1093/aje/kwae404","DOIUrl":null,"url":null,"abstract":"<p><p>Racial differences in health care access (HCA) may contribute to disparities in ovarian cancer (OC) survival. We used structural equation models (SEMs) to examine associations between race and HCA domains (affordability, availability, accessibility) in relation to overall and OC-specific mortality. Non-Hispanic (NH)-Black and non-Black (Hispanic, NH-White) women diagnosed with OC in 2008-2015 were identified from Surveillance, Epidemiology, and End Results-Medicare. Cox proportional hazards regression was used to conduct mediation analysis for associations between race and HCA domains with overall and OC-specific mortality. SEM models adjusting for demographic and clinical covariates were used to estimate hazard ratios (HRs) and 95% CIs. A total of 4629 eligible patients with OC were identified, including 255 (5.5%) patients who were NH-Black. In SEM adjusting for demographic, clinical, and HCA latent variables, there was a total effect of NH-Black race on overall (HR, 1.11, 95% CI, 1.03-1.19) and OC-specific mortality (HR, 1.16; 95% CI, 1.08, 1.24), which was primarily driven by a direct effect. There was a modest indirect association between NH-Black race and mortality through decreased treatment receipt, though not through HCA. There is a need for studies investigating additional social and biological mechanisms that contribute to worse cancer survival among NH-Black patients. This article is part of a Special Collection on Gynecological Cancer.</p>","PeriodicalId":7472,"journal":{"name":"American journal of epidemiology","volume":" ","pages":"2230-2240"},"PeriodicalIF":4.8000,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Health care access domains and treatment as mediators of ovarian cancer racial disparities in survival: a structural equation modeling analysis in SEER-Medicare.\",\"authors\":\"Clare Meernik, Quan Chen, Lauren E Wilson, Ashwini Joshi, Fariha Rahman, Maria Pisu, Margaret Liang, Kevin C Ward, Margaret Gates Kuliszewski, Thomas Tucker, Andrew Berchuck, Bin Huang, Tomi Akinyemiju\",\"doi\":\"10.1093/aje/kwae404\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Racial differences in health care access (HCA) may contribute to disparities in ovarian cancer (OC) survival. We used structural equation models (SEMs) to examine associations between race and HCA domains (affordability, availability, accessibility) in relation to overall and OC-specific mortality. Non-Hispanic (NH)-Black and non-Black (Hispanic, NH-White) women diagnosed with OC in 2008-2015 were identified from Surveillance, Epidemiology, and End Results-Medicare. Cox proportional hazards regression was used to conduct mediation analysis for associations between race and HCA domains with overall and OC-specific mortality. SEM models adjusting for demographic and clinical covariates were used to estimate hazard ratios (HRs) and 95% CIs. A total of 4629 eligible patients with OC were identified, including 255 (5.5%) patients who were NH-Black. In SEM adjusting for demographic, clinical, and HCA latent variables, there was a total effect of NH-Black race on overall (HR, 1.11, 95% CI, 1.03-1.19) and OC-specific mortality (HR, 1.16; 95% CI, 1.08, 1.24), which was primarily driven by a direct effect. There was a modest indirect association between NH-Black race and mortality through decreased treatment receipt, though not through HCA. There is a need for studies investigating additional social and biological mechanisms that contribute to worse cancer survival among NH-Black patients. 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Health care access domains and treatment as mediators of ovarian cancer racial disparities in survival: a structural equation modeling analysis in SEER-Medicare.
Racial differences in health care access (HCA) may contribute to disparities in ovarian cancer (OC) survival. We used structural equation models (SEMs) to examine associations between race and HCA domains (affordability, availability, accessibility) in relation to overall and OC-specific mortality. Non-Hispanic (NH)-Black and non-Black (Hispanic, NH-White) women diagnosed with OC in 2008-2015 were identified from Surveillance, Epidemiology, and End Results-Medicare. Cox proportional hazards regression was used to conduct mediation analysis for associations between race and HCA domains with overall and OC-specific mortality. SEM models adjusting for demographic and clinical covariates were used to estimate hazard ratios (HRs) and 95% CIs. A total of 4629 eligible patients with OC were identified, including 255 (5.5%) patients who were NH-Black. In SEM adjusting for demographic, clinical, and HCA latent variables, there was a total effect of NH-Black race on overall (HR, 1.11, 95% CI, 1.03-1.19) and OC-specific mortality (HR, 1.16; 95% CI, 1.08, 1.24), which was primarily driven by a direct effect. There was a modest indirect association between NH-Black race and mortality through decreased treatment receipt, though not through HCA. There is a need for studies investigating additional social and biological mechanisms that contribute to worse cancer survival among NH-Black patients. This article is part of a Special Collection on Gynecological Cancer.
期刊介绍:
The American Journal of Epidemiology is the oldest and one of the premier epidemiologic journals devoted to the publication of empirical research findings, opinion pieces, and methodological developments in the field of epidemiologic research.
It is a peer-reviewed journal aimed at both fellow epidemiologists and those who use epidemiologic data, including public health workers and clinicians.