Information Systems and Business Analytics Department, Loyola Marymount University, Los Angeles, CA, United States.
Center for Information Systems and Technology, Claremont Graduate University, Claremont, CA, United States.
JMIR Public Health Surveill. 2023 Mar 29;9:e44070. doi: 10.2196/44070.
With the increased availability of data, a growing number of studies have been conducted to address the impact of social determinants of health (SDOH) factors on population health outcomes. However, such an impact is either examined at the county level or the state level in the United States. The results of analysis at lower administrative levels would be useful for local policy makers to make informed health policy decisions.
This study aimed to investigate the ecological association between SDOH factors and population health outcomes at the census tract level and the city level. The findings of this study can be applied to support local policy makers in efforts to improve population health, enhance the quality of care, and reduce health inequity.
This ecological analysis was conducted based on 29,126 census tracts in 499 cities across all 50 states in the United States. These cities were grouped into 5 categories based on their population density and political affiliation. Feature selection was applied to reduce the number of SDOH variables from 148 to 9. A linear mixed-effects model was then applied to account for the fixed effect and random effects of SDOH variables at both the census tract level and the city level.
The finding reveals that all 9 selected SDOH variables had a statistically significant impact on population health outcomes for ≥2 city groups classified by population density and political affiliation; however, the magnitude of the impact varied among the different groups. The results also show that 4 SDOH risk factors, namely, asthma, kidney disease, smoking, and food stamps, significantly affect population health outcomes in all groups (P<.01 or P<.001). The group differences in health outcomes for the 4 factors were further assessed using a predictive margin analysis.
The analysis reveals that population density and political affiliation are effective delineations for separating how the SDOH affects health outcomes. In addition, different SDOH risk factors have varied effects on health outcomes among different city groups but similar effects within city groups. Our study has 2 policy implications. First, cities in different groups should prioritize different resources for SDOH risk mitigation to maximize health outcomes. Second, cities in the same group can share knowledge and enable more effective SDOH-enabled policy transfers for population health.
随着数据的日益丰富，越来越多的研究致力于探讨健康的社会决定因素（SDOH）对人口健康结果的影响。然而，在美国，这种影响要么在县一级，要么在州一级进行研究。较低行政级别的分析结果将有助于地方决策者做出明智的卫生政策决策。
本研究旨在探讨 SDOH 因素与人口健康结果在普查区和城市层面的生态关联。本研究的结果可用于支持地方决策者努力改善人口健康、提高护理质量和减少健康不公平。
本生态分析基于美国 50 个州的 499 个城市的 29126 个普查区进行。这些城市根据人口密度和政治隶属关系分为 5 类。采用特征选择法将 148 个 SDOH 变量减少到 9 个。然后应用线性混合效应模型，以考虑普查区和城市层面 SDOH 变量的固定效应和随机效应。
研究结果表明，所有 9 个选定的 SDOH 变量对按人口密度和政治隶属关系分类的≥2 个城市群体的人口健康结果均有统计学意义的影响；然而，影响的大小在不同群体之间有所不同。结果还表明，4 个 SDOH 风险因素，即哮喘、肾病、吸烟和食品券，在所有组中均显著影响人口健康结果（P<.01 或 P<.001）。使用预测边际分析进一步评估了 4 个因素对健康结果的群体差异。
分析结果表明，人口密度和政治隶属关系是有效区分 SDOH 如何影响健康结果的因素。此外，不同的 SDOH 风险因素对不同城市群体的健康结果有不同的影响，但在城市群体内部有相似的影响。本研究有 2 项政策意义。首先，不同群体的城市应优先考虑针对不同的 SDOH 风险缓解资源，以最大限度地提高健康结果。其次，同一群体的城市可以共享知识，并为人口健康实施更有效的基于 SDOH 的政策转移。
