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A Novel Method for Involving Women of Color at High Risk for Preterm Birth in Research Priority Setting
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Understanding racial disparities in severe maternal morbidity using Bayesian network analysis.

Mandana Rezaeiahari1, Clare C Brown1, Mir M Ali2

  • 1Department of Health Policy and Management, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America.

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Summary

This study used a Bayesian network to analyze severe maternal morbidity (SMM) risk factors. Targeting anemia and hypertensive disorders of pregnancy could reduce SMM and racial disparities.

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Area of Science:

  • Reproductive Health
  • Maternal Morbidity Research
  • Health Disparities

Background:

  • Previous research often examined risk factors for severe maternal morbidity (SMM) individually.
  • A comprehensive understanding of the interplay between clinical, demographic, and area-level factors is needed.
  • Existing regression methods may not fully capture the complex, joint effects influencing SMM.

Purpose of the Study:

  • To employ a Bayesian network (BN) approach to investigate the joint effects of multiple factors on SMM risk.
  • To identify key clinical, demographic, and area-level predictors contributing to severe maternal morbidity.
  • To explore and understand disparities in SMM through a robust network analysis.

Main Methods:

  • Retrospective observational study utilizing linked birth certificate and insurance claims data (Arkansas APCD, 2013-2017).
  • Application of Bayesian network (BN) modeling with various learning algorithms and arc strength measures for network structure selection.
  • Conditional probabilistic queries executed via Monte Carlo simulation to analyze SMM disparities.

Main Results:

  • The Bayesian network identified significant joint effects among clinical, demographic, and area-level factors influencing SMM.
  • Anemia and hypertensive disorder of pregnancy emerged as critical clinical comorbidities associated with SMM.
  • Findings suggest these comorbidities play a role in both overall SMM rates and racial disparities.

Conclusions:

  • A Bayesian network approach provides a powerful tool for understanding complex SMM determinants.
  • Targeting anemia and hypertensive disorders of pregnancy is a potential strategy to mitigate severe maternal morbidity.
  • Interventions focused on these conditions may help reduce existing racial disparities in SMM outcomes.