Irish W D, Burch A E, Landry A, Honaker M D, Wong J
Department of Surgery, Brody School of Medicine at East Carolina University, Greenville, NC, USA; Department of Public Health, Brody School of Medicine at East Carolina University, Greenville, NC, USA.
Department of Health Services & Information Management, College of Allied Health Sciences, East Carolina University, Greenville, NC, USA.
Public Health. 2025 Mar;240:56-62. doi: 10.1016/j.puhe.2025.01.002. Epub 2025 Jan 27.
To develop and validate a county deprivation index (CDI) that assesses socio-economic disparities and their impact on health outcomes at the county level.
A retrospective, cross-sectional study using publicly available county-level data.
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.
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.
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.
制定并验证一个县级贫困指数(CDI),该指数用于评估县级层面的社会经济差异及其对健康结果的影响。
一项使用公开可得县级数据的回顾性横断面研究。
采用分层聚类分析将18个县级社会经济指标分为三类:经济福祉与技术连通性、社会经济劣势与脆弱性、住房可负担性与生活质量。CDI由模型系数得出,并通过将其在预测特定疾病死亡率方面的表现与既定的县级贫困衡量指标(包括社会剥夺指数(SDI)和多维剥夺指数(MDI))进行比较来验证。我们还评估了CDI解释随机选择的八个州中罗伯特·伍德·约翰逊基金会(RWJF)县级健康得分变异性的能力。
分析纳入了美国本土的3107个县。CDI解释了年龄调整后的可避免心脏病和中风死亡率方差的45%、癌症死亡率方差的20%、肺癌死亡率方差的19%以及全因死亡率方差的52%,优于SDI和MDI。它还解释了所选州中RWJF健康结果和因子得分方差的63%至91%。
与现有指数相比,CDI显示出更高的预测准确性,使其成为识别健康差异和指导有针对性的公共卫生干预措施的宝贵工具。为了保持其在反映不断变化的社会经济状况方面的相关性和有效性,有必要定期更新CDI。