An Application of Social Vulnerability Index to Infant Mortality Rates in Ohio Using Geospatial Analysis- A...

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.

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.

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.

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.

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.