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A Bayesian zero-inflated spatially varying coefficients model for overdispersed binomial data.

Chun-Che Wen1, Rajib Paul2, Kelly J Hunt1

  • 1Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA.

Journal of the Royal Statistical Society. Series A, (Statistics in Society)
|November 20, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical model to analyze cardiometabolic risk factors (CRFs) in pregnant women during the COVID-19 pandemic. Findings highlight specific South Carolina counties with racial health disparities, suggesting targeted community interventions.

Keywords:
Gaussian Markov random fieldPólya-Gamma data augmentationcardiometabolic riskhealth disparityspatiotemporal modelzero-inflated beta-binomial distribution

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Maternal Health

Background:

  • Cardiometabolic risk factors (CRFs) in pregnancy predict future maternal diseases like stroke and type 2 diabetes.
  • CRF counts often show overdispersion and zero inflation due to correlated risk factors.
  • Racial disparities in CRFs among pregnant women in South Carolina are a significant public health concern.

Purpose of the Study:

  • To develop and apply a spatiotemporal statistical model for analyzing CRFs in pregnant women.
  • To investigate geographic and temporal trends in CRFs, focusing on racial disparities during the COVID-19 pandemic.
  • To identify areas for targeted interventions to reduce health inequities.

Main Methods:

  • Developed a zero-inflated beta-binomial model within a spatiotemporal framework to handle overdispersion and zero inflation in CRF counts.
  • Incorporated a spatially varying coefficient model to examine racial disparities (non-Hispanic White vs. non-Hispanic Black women) across geographic areas and time.
  • Utilized an efficient hybrid Markov Chain Monte Carlo algorithm for posterior inference.

Main Results:

  • The model effectively captured spatiotemporal patterns and zero inflation in CRFs among pregnant women in South Carolina.
  • Analysis revealed significant racial disparities in CRFs that varied by county and over time.
  • Identified specific counties (e.g., Chesterfield, Clarendon) exhibiting smaller gaps in racial health disparities.

Conclusions:

  • The developed statistical model provides a robust framework for analyzing complex count data with overdispersion and zero inflation in epidemiological studies.
  • Findings underscore the need for geographically tailored interventions to address racial inequities in maternal cardiometabolic health.
  • Certain counties present opportunities for focused community-level efforts to reduce health disparities.