Bigger Isn’t Better: Why Small Area Geographies Are Best for Actionable Index Development

Abstract

Area-level measures of the social exposome provide powerful tools to understand how context contributes to health disparities. Due to the geographic phenomenon of the modifiable aerial unit problem, the geographic level at which the index is constructed can threaten it utility. Previous work indicates that using smaller geographic levels lead to increased measurement precision which may result in closer alignment to policies that directly address health disparities. To provide an illustrative example of this phenomenon, we use Medicare 100% Fee-for Service hospitalization claims data to evaluate the association between area-level disadvantage and 30-day readmissions when the Area Deprivation Index (ADI) is constructed at different geographic levels. When area-level disadvantage is summarized at the “neighborhood” census block group—the study’s smallest geographic level—there was a 20% higher odds of readmissions for those living in the top 20% most disadvantaged neighborhoods compared to those living in the lowest 80% neighborhoods nationwide. Yet, evidence for an association with readmissions was not found when neighborhood disadvantaged was summarized at larger geographic levels. Smaller geographic levels appear most optimal to capture these effects. In order to provide publicly available data that is truly publicly useable, greater attention in providing small area health data is needed.