Date of Award

Fall 2025

Degree Type

Thesis

Department

Statistics

Director of Thesis

Ray Bai

Second Reader

Emily Mann

Abstract

Maternal death serves as a public health indicator due to fact that it is considered preventable with the availability of modern biomedicine, however, it persists broadly throughout the United States. Current literature outlines national trends in maternal mortality with complicating, preexisting conditions, and structural upstream factors often cited as being the largest contributors to increased risk. This study utilizes publicly available, county-level data for maternal death in addition to demographic and descriptive data in order to estimate maternal mortality rates in each of South Carolina’s 46 counties from 2018 to 2023. In order to address sparsity in the outcome variable and interval censoring for counts 1-9 within the CDC’s publicly available data, an interval-censored, spatiotemporal Bayesian hierarchical model is deployed with Markov Chain Monte Carlo methods utilized in estimating posterior distributions. The model found no significant regression coefficients; however, a few significant spatiotemporal random effects indicate that maternal death rates vary across time and county due to the influence of space and time. Estimated rates showed an increased risk of maternal mortality during the year 2020 and around the lower Midlands, lower PeeDee, and upper Lowcountry regions. K-means clustering separated counties over time into low, medium, and high-risk categories. These clusters had significantly different means for the percentage of individuals on government assistance within the cluster, proportion of Black residents and proportion of Hispanic residents, Gini coefficients, and the percentage with health insurance found through ANOVA testing. This supports prior literature, demonstrating the impact that structural inequities, specifically across race, have on the rate of maternal mortality in South Carolina.

First Page

1

Last Page

38

Rights

© 2025, Leah Wood , Ray Bai, and Emily Mann

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