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Understanding racial disparities in severe maternal morbidity using Bayesian network analysis

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  • Mandana Rezaeiahari
  • Clare C Brown
  • Mir M Ali
  • Jyotishka Datta
  • J Mick Tilford

Abstract

Previous studies have evaluated the marginal effect of various factors on the risk of severe maternal morbidity (SMM) using regression approaches. We add to this literature by utilizing a Bayesian network (BN) approach to understand the joint effects of clinical, demographic, and area-level factors. We conducted a retrospective observational study using linked birth certificate and insurance claims data from the Arkansas All-Payer Claims Database (APCD), for the years 2013 through 2017. We used various learning algorithms and measures of arc strength to choose the most robust network structure. We then performed various conditional probabilistic queries using Monte Carlo simulation to understand disparities in SMM. We found that anemia and hypertensive disorder of pregnancy may be important clinical comorbidities to target in order to reduce SMM overall as well as racial disparities in SMM.

Suggested Citation

  • Mandana Rezaeiahari & Clare C Brown & Mir M Ali & Jyotishka Datta & J Mick Tilford, 2021. "Understanding racial disparities in severe maternal morbidity using Bayesian network analysis," PLOS ONE, Public Library of Science, vol. 16(10), pages 1-18, October.
    • Handle: RePEc:plo:pone00:0259258
    DOI: 10.1371/journal.pone.0259258
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    References listed on IDEAS

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    1. Syed Hasib Akhter Faruqui & Adel Alaeddini & Carlos A Jaramillo & Jennifer S Potter & Mary Jo Pugh, 2018. "Mining patterns of comorbidity evolution in patients with multiple chronic conditions using unsupervised multi-level temporal Bayesian network," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-22, July.
    2. Crone, Sven F. & Finlay, Steven, 2012. "Instance sampling in credit scoring: An empirical study of sample size and balancing," International Journal of Forecasting, Elsevier, vol. 28(1), pages 224-238.
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