W.D. Irish , A.E. Burch , A. Landry , M.D. Honaker , J. Wong
{"title":"制定和验证用于评估美国社会经济差异的县剥夺指数:对公共卫生结果和缓解战略的影响。","authors":"W.D. Irish , A.E. Burch , A. Landry , M.D. Honaker , J. Wong","doi":"10.1016/j.puhe.2025.01.002","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><div>To develop and validate a county deprivation index (CDI) that assesses socio-economic disparities and their impact on health outcomes at the county level.</div></div><div><h3>Study design</h3><div>A retrospective, cross-sectional study using publicly available county-level data.</div></div><div><h3>Methods</h3><div>Hierarchical cluster analysis was used to group 18 county-level socio-economic indicators into three clusters: economic well-being and technical connectivity, socio-economic disadvantage and vulnerability, and housing affordability and quality of life. The CDI was derived from model coefficients and validated by comparing its performance to established county deprivation measures, including the Social Deprivation Index (SDI) and the Multidimensional Deprivation Index (MDI), in predicting disease-specific mortality rates. We also assessed the CDI's ability to explain variability in Robert Wood Johnson Foundation (RWJF) county health scores across eight randomly selected states.</div></div><div><h3>Results</h3><div>The analysis included 3107 counties from the contiguous US. The CDI explained 45 % of the variance in age-adjusted avoidable heart disease and stroke death rates, 20 % in cancer mortality rates, 19 % in lung cancer mortality rates, and 52 % in all-cause mortality rates, outperforming the SDI and MDI. It also accounted for 63–91 % of the variance in RWJF health outcome and factor scores across selected states.</div></div><div><h3>Conclusions</h3><div>The CDI demonstrates superior predictive accuracy compared to existing indices, making it a valuable tool for identifying health disparities and guiding targeted public health interventions. Regular updates of the CDI will be necessary to maintain its relevance and effectiveness in capturing evolving socio-economic conditions.</div></div>","PeriodicalId":49651,"journal":{"name":"Public Health","volume":"240 ","pages":"Pages 56-62"},"PeriodicalIF":3.5000,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development and validation of a county deprivation index for assessing socio-economic disparities in the United States: Implications for public health outcomes and mitigation strategies\",\"authors\":\"W.D. Irish , A.E. Burch , A. Landry , M.D. Honaker , J. Wong\",\"doi\":\"10.1016/j.puhe.2025.01.002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objective</h3><div>To develop and validate a county deprivation index (CDI) that assesses socio-economic disparities and their impact on health outcomes at the county level.</div></div><div><h3>Study design</h3><div>A retrospective, cross-sectional study using publicly available county-level data.</div></div><div><h3>Methods</h3><div>Hierarchical cluster analysis was used to group 18 county-level socio-economic indicators into three clusters: economic well-being and technical connectivity, socio-economic disadvantage and vulnerability, and housing affordability and quality of life. The CDI was derived from model coefficients and validated by comparing its performance to established county deprivation measures, including the Social Deprivation Index (SDI) and the Multidimensional Deprivation Index (MDI), in predicting disease-specific mortality rates. We also assessed the CDI's ability to explain variability in Robert Wood Johnson Foundation (RWJF) county health scores across eight randomly selected states.</div></div><div><h3>Results</h3><div>The analysis included 3107 counties from the contiguous US. The CDI explained 45 % of the variance in age-adjusted avoidable heart disease and stroke death rates, 20 % in cancer mortality rates, 19 % in lung cancer mortality rates, and 52 % in all-cause mortality rates, outperforming the SDI and MDI. It also accounted for 63–91 % of the variance in RWJF health outcome and factor scores across selected states.</div></div><div><h3>Conclusions</h3><div>The CDI demonstrates superior predictive accuracy compared to existing indices, making it a valuable tool for identifying health disparities and guiding targeted public health interventions. Regular updates of the CDI will be necessary to maintain its relevance and effectiveness in capturing evolving socio-economic conditions.</div></div>\",\"PeriodicalId\":49651,\"journal\":{\"name\":\"Public Health\",\"volume\":\"240 \",\"pages\":\"Pages 56-62\"},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2025-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Public Health\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0033350625000149\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/27 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Public Health","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0033350625000149","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/27 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
Development and validation of a county deprivation index for assessing socio-economic disparities in the United States: Implications for public health outcomes and mitigation strategies
Objective
To develop and validate a county deprivation index (CDI) that assesses socio-economic disparities and their impact on health outcomes at the county level.
Study design
A retrospective, cross-sectional study using publicly available county-level data.
Methods
Hierarchical cluster analysis was used to group 18 county-level socio-economic indicators into three clusters: economic well-being and technical connectivity, socio-economic disadvantage and vulnerability, and housing affordability and quality of life. The CDI was derived from model coefficients and validated by comparing its performance to established county deprivation measures, including the Social Deprivation Index (SDI) and the Multidimensional Deprivation Index (MDI), in predicting disease-specific mortality rates. We also assessed the CDI's ability to explain variability in Robert Wood Johnson Foundation (RWJF) county health scores across eight randomly selected states.
Results
The analysis included 3107 counties from the contiguous US. The CDI explained 45 % of the variance in age-adjusted avoidable heart disease and stroke death rates, 20 % in cancer mortality rates, 19 % in lung cancer mortality rates, and 52 % in all-cause mortality rates, outperforming the SDI and MDI. It also accounted for 63–91 % of the variance in RWJF health outcome and factor scores across selected states.
Conclusions
The CDI demonstrates superior predictive accuracy compared to existing indices, making it a valuable tool for identifying health disparities and guiding targeted public health interventions. Regular updates of the CDI will be necessary to maintain its relevance and effectiveness in capturing evolving socio-economic conditions.
期刊介绍:
Public Health is an international, multidisciplinary peer-reviewed journal. It publishes original papers, reviews and short reports on all aspects of the science, philosophy, and practice of public health.