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Joint Modeling of Birth Outcomes Using a Copula Distributional Regression Approach

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  • Giampiero Marra
  • Rosalba Radice

Abstract

Low birth weight and preterm birth are key indicators of neonatal health, influencing both immediate and long‐term infant outcomes. While low birth weight may reflect fetal growth restrictions, preterm birth captures disruptions in gestational development. Ignoring the potential interdependence between these variables may lead to an incomplete understanding of their shared determinants and underlying dynamics. To address this, a copula distributional regression framework is adopted to jointly model both indicators as flexible functions of maternal characteristics and geographic effects. Applied to female birth data from North Carolina, the methodology identifies shared factors of low birth weight and preterm birth, and reveals how maternal health, socioeconomic conditions and geographic disparities shape neonatal risk. The joint modeling approach provides a more nuanced understanding of these birth metrics, offering insights that can inform targeted interventions, prenatal care strategies and public health planning.

Suggested Citation

  • Giampiero Marra & Rosalba Radice, 2026. "Joint Modeling of Birth Outcomes Using a Copula Distributional Regression Approach," Health Economics, John Wiley & Sons, Ltd., vol. 35(3), pages 399-408, March.
    • Handle: RePEc:wly:hlthec:v:35:y:2026:i:3:p:399-408
    DOI: 10.1002/hec.70067
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    References listed on IDEAS

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    1. Cindy Xin Feng, 2015. "Bayesian joint modeling of correlated counts data with application to adverse birth outcomes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(6), pages 1206-1222, June.
    2. Lu Yang & Edward W. Frees & Zhengjun Zhang, 2020. "Nonparametric Estimation of Copula Regression Models With Discrete Outcomes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(530), pages 707-720, April.
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