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

• Published in: Maternal and child health journal Vol. 28; no. 6; pp. 999 - 1009
• Main Authors: Polavarapu, Mounika, Barasa, Topista N., Singh, Shipra, Orbain, Matthew M., Ibrahim, Safa
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-06-2024; Springer Nature B.V
• Subjects: Babies; Census; Censuses; Cross-Sectional Studies; Female; Gynecology; Humans; Infant; Infant mortality; Infant Mortality - trends; Infant, Newborn; Infants; Male; Maternal and Child Health; Medicine; Medicine & Public Health; Ohio - epidemiology; Pediatrics; Population Economics; Poverty; Poverty - statistics & numerical data; Public Health; Social Vulnerability; Socioeconomic Factors; Sociology; Spatial Analysis; Spatial distribution; Vulnerable Populations - statistics & numerical data; Ohio; United States > US; Social Vulnerability Index; Infant Mortality rate; Relative Social Vulnerability
• ISSN: 1092-7875; 1573-6628
• DOI: 10.1007/s10995-024-03925-3

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.
Significance
Prior studies have recognized the impact of social determinants on infant mortality and explored the spatial distribution in the state of Ohio. This study introduces a novel integration of the Social Vulnerability Index with geospatial analysis to pinpoint where social vulnerabilities overlap with high infant mortality rates. By mapping these intersections at the county and census tract levels, our research identifies specific areas in Ohio that are priority targets for intervention. This contribution not only advances the understanding of spatial patterns but also strategizes a prioritized response to addressing social determinants to reduce disparities in infant mortality.