Abstract
Objectives
Severe Maternal Morbidity (SMM) is a group of pregnancy complications in which a woman nearly dies. Despite its increasing prevalence, little research has evaluated geographic patterns of SMM and the underlying social determinants that influence excess risk. This study examined the spatial clustering of SMM across South Carolina, US, and its associations with place-based social and environmental factors.
Methods
Hospitalized deliveries from 2012 to 2017 were analyzed using Kulldorff's spatial scan statistic to locate areas with abnormally high rates of SMM. SMM patients inside and outside risk clusters were compared using Generalized Estimating Equations (GEE) to determine underlying individual and community-level risk factors.
Results
GEE models revealed that the odds of living in a high-risk SMM21 (SMM including blood transfusions) cluster was 2.49 times higher among Black patients (p < .001) compared to those outside of a high-risk cluster. Women residing in a high-risk SMM20 (SMM excluding blood transfusions) cluster were 1.38 times more likely to experience the most number of extremely hot days and 1.70 times more likely to present with obesity than women in a low-risk SMM cluster (p < .001).
Conclusions
This study is the first to characterize the geographic clustering of SMM risk in the US. Our geospatial approach contributes a novel understanding to factors which influence SMM beyond patient-level characteristics and identifies the impact of hot ambient temperature on maternal morbidity. Findings address an important literature gap surrounding place-based risk factors by explaining the contextual social and built environmental factors that drive SMM risk.
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Data Availability
Data used in this manuscript were de-identified hospitalized delivery discharges authorized by the South Carolina Department of Health and Environmental Control and allowed for publication.
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This project was developed with support from Appalachian State University’s University Research Council (URC).
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SH-conception or design of the work, data analysis and interpretation, drafting the article; JR-conception or design of the work, securing data, data analysis and interpretation, drafting the article; and MS -conception or design of the work, securing data, data analysis and interpretation, drafting the article.
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Harden, S.R., Runkle, J.D. & Sugg, M.M. An Exploratory Spatiotemporal Analysis of Socio-Environmental Patterns in Severe Maternal Morbidity. Matern Child Health J 26, 1077–1086 (2022). https://doi.org/10.1007/s10995-021-03330-0
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DOI: https://doi.org/10.1007/s10995-021-03330-0