The MITRE Corporation, McLean, VA, USA.
Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
J Urban Health. 2024 Apr;101(2):392-401. doi: 10.1007/s11524-024-00841-3.
Neighborhood characteristics including housing status can profoundly influence health. Recently, increasing attention has been paid to present-day impacts of "redlining," or historic area classifications that indicated less desirable (redlined) areas subject to decreased investment. Scholarship of redlining and health is emerging; limited guidance exists regarding optimal approaches to measuring historic redlining in studies of present-day health outcomes. We evaluated how different redlining approaches (map alignment methods) influence associations between redlining and health outcomes. We first identified 11 existing redlining map alignment methods and their 37 logical extensions, then merged these 48 map alignment methods with census tract life expectancy data to construct 9696 linear models of each method and life expectancy for all 202 redlined cities. We evaluated each model's statistical significance and R values and compared changes between historical and contemporary geographies and populations using Root Mean Squared Error (RMSE). RMSE peaked with a normal distribution at 0.175, indicating persistent difference between historical and contemporary geographies and populations. Continuous methods with low thresholds provided higher neighborhood coverage. Weighting methods had more significant associations, while high threshold methods had higher R values. In light of these findings, we recommend continuous methods that consider contemporary population distributions and mapping overlap for studies of redlining and health. We developed an R application {holcmapr} to enable map alignment method comparison and easier method selection.
社区特征，包括住房状况，可以深刻地影响健康。最近，人们越来越关注“红线”（redlining）的当前影响，即历史区域分类，这些分类表明了不太理想的（redlined）区域，投资减少。关于红线和健康的学术研究正在兴起；关于在研究当前健康结果时衡量历史红线的最佳方法，目前还没有多少指导。我们评估了不同的红线方法（地图对齐方法）如何影响红线与健康结果之间的关联。我们首先确定了 11 种现有的红线地图对齐方法及其 37 种逻辑扩展，然后将这些 48 种地图对齐方法与人口普查区预期寿命数据合并，为每个方法和所有 202 个被红线划分的城市构建了 9696 个线性模型。我们评估了每个模型的统计显著性和 R 值，并使用均方根误差（RMSE）比较了历史和当代地理和人口之间的变化。RMSE 在正态分布中达到峰值 0.175，表明历史和当代地理和人口之间存在持续差异。具有低阈值的连续方法提供了更高的邻里覆盖率。加权方法具有更显著的关联，而高阈值方法具有更高的 R 值。鉴于这些发现，我们建议在研究红线和健康时使用考虑当代人口分布和映射重叠的连续方法。我们开发了一个 R 应用程序{holcmapr}，以实现地图对齐方法的比较和更简单的方法选择。
