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

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 (SVICDC) is a measure of the relative social vulnerability of a geographic unit. Five out of 15 social variables in the SVICDC were utilized to create a customized index for IMR (SVIIMR) in Ohio. The bivariate descriptive maps and spatial lag model were applied to visualize the quantitative relationship between SVIIMR and IMR, accounting for the spatial autocorrelation in the data. RESULTS: Southeastern counties in Ohio displayed highest IMRs and highest overall SVIIMR; specifically, highest vulnerability to poverty, no high school diploma, and mobile housing. In contrast, extreme northwestern counties exhibited high IMRs but lower overall SVIIMR. 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 SVIIMR. 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.

Maternal diabetes-related malformations in Utah: A population study of birth prevalence 2001-2016.

Maternal pregestational diabetes mellitus is associated with an increased risk for congenital malformations of about 2-4 times the background risk. Notably, the types and patterns of congenital malformations associated with maternal diabetes are nonrandom, with a well-established increased risk for specific classes of malformations, especially of the heart, central nervous system, and skeleton. While the increased risk in clinical and epidemiological studies is well documented in the literature, a precise estimate of overall birth prevalence of these specific congenital malformations among women with maternal pregestational diabetes, is lacking. The purpose of this study was to determine total prevalence of structural malformations associated with maternal pregestational diabetes mellitus in a population-based study. We identified infants with specific birth defects whose mother had pregestational diabetes mellitus in the Utah Birth Defect Network (UBDN), an active birth defects surveillance program that registers the occurrence of selected structural defects in the state of Utah. We defined specific maternal diabetes-related malformations based on epidemiologic and clinical studies in the literature. Of the 825,138 recorded Utah births between 2001 and 2016, a total of 91 cases were identified as likely having diabetic embryopathy within UBDN data. The prevalence of diabetes-related congenital malformation cases was calculated per year; the overall prevalence of diabetes-related malformations 2001-2016 was 1.1 per 10,000 births in Utah (95% CI, 0.9-1.3). Knowledge of the overall prevalence of diabetes-related malformations is important in predicting the number of cases that are potentially prevented with the implementation of programs to foster preconceptional management of maternal pregestational diabetes.