Mitchell Jessica H, Runkle Jennifer D, Andersen Lauren M, Shay Elizabeth, Sugg Margaret M
Department of Geography and Planning, Appalachian State University, Boone, North Carolina (Mss Mitchell and Andersen and Drs Shay and Sugg); and North Carolina Institute for Climate Studies, North Carolina State University, Asheville, North Carolina (Dr Runkle).
Fam Community Health. 2022;45(2):77-90. doi: 10.1097/FCH.0000000000000318.
Health inequalities are characterized by spatial patterns of social, economic, and political factors. Life expectancy (LE) is a commonly used indicator of overall population health and health inequalities that allows for comparison across different spatial and temporal regions. The objective of this study was to examine geographic inequalities in LE across North Carolina census tracts by comparing the performance of 2 popular geospatial health indices: Social Determinants of Health (SDoH) and the Index of Concentration at Extremes (ICE). A principal components analysis (PCA) was used to address multicollinearity among variables and aggregate data into components to examine SDoH, while the ICE was constructed using the simple subtraction of geospatial variables. Spatial regression models were employed to compare both indices in relation to LE to evaluate their predictability for population health. For individual SDoH and ICE components, poverty and income had the strongest positive correlation with LE. However, the common spatial techniques of adding PCA components together for a final SDoH aggregate measure resulted in a poor relationship with LE. Results indicated that both metrics can be used to determine spatial patterns of inequities in LE and that the ICE metric has similar success to the more computationally complex SDoH metric. Public health practitioners may find the ICE metric's high predictability matched with lower data requirements to be more feasible to implement in population health monitoring.
健康不平等具有社会、经济和政治因素的空间模式特征。预期寿命（LE）是衡量总体人群健康和健康不平等的常用指标，可用于不同空间和时间区域的比较。本研究的目的是通过比较两种常用的地理空间健康指数：健康的社会决定因素（SDoH）和极端集中度指数（ICE）的表现，来研究北卡罗来纳州人口普查区LE的地理不平等。主成分分析（PCA）用于解决变量间的多重共线性问题，并将数据汇总为成分以检验SDoH，而ICE则通过地理空间变量的简单相减构建。采用空间回归模型比较这两种指数与LE的关系，以评估它们对人群健康的预测能力。对于单个SDoH和ICE成分，贫困和收入与LE的正相关性最强。然而，将PCA成分相加得出最终SDoH综合指标的常用空间技术与LE的关系不佳。结果表明，这两种指标均可用于确定LE不平等的空间模式，且ICE指标与计算更复杂的SDoH指标具有相似的成效。公共卫生从业者可能会发现，ICE指标的高预测性与较低的数据要求使其在人群健康监测中更易于实施。
