Abstract
BACKGROUND: Ohio ranks 43rd in the nation in infant mortality rates (IMR); with IMR among non-Hispanic black infants is three times higher than white infants. OBJECTIVE: To identify the social factors determining the vulnerability of Ohio counties to IMR and visualize the spatial association between relative social vulnerability and IMR at county and census tract levels. METHODS: The social vulnerability index (SVI(CDC)) is a measure of the relative social vulnerability of a geographic unit. Five out of 15 social variables in the SVI(CDC) were utilized to create a customized index for IMR (SVI(IMR)) in Ohio. The bivariate descriptive maps and spatial lag model were applied to visualize the quantitative relationship between SVI(IMR) and IMR, accounting for the spatial autocorrelation in the data. RESULTS: Southeastern counties in Ohio displayed highest IMRs and highest overall SVI(IMR); specifically, highest vulnerability to poverty, no high school diploma, and mobile housing. In contrast, extreme northwestern counties exhibited high IMRs but lower overall SVI(IMR). Spatial regression showed five clusters where vulnerability to low per capita income in one county significantly impacted IMR (p = 0.001) in the neighboring counties within each cluster. At the census tract-level within Lucas county, the Toledo city area (compared to the remaining county) had higher overlap between high IMR and SVI(IMR). CONCLUSION: The application of SVI using geospatial techniques could identify priority areas, where social factors are increasing the vulnerability to infant mortality rates, for potential interventions that could reduce disparities through strategic and equitable policies.