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Joint Modeling of Birth Outcomes Using a Copula Distributional Regression Approach
Giampiero Marra1, Rosalba Radice2
1Department of Statistical Science, University College London, London, UK.
Health Economics
|December 1, 2025
Summary
Low birth weight and preterm birth are critical neonatal health indicators. Joint modeling reveals shared maternal and geographic factors influencing these outcomes, improving public health strategies.
Area of Science:
- Neonatal Health
- Biostatistics
- Epidemiology
Background:
- Low birth weight (LBW) and preterm birth (PTB) are primary indicators of neonatal health.
- These conditions significantly impact immediate and long-term infant outcomes.
- Understanding their interdependence is crucial for identifying shared determinants.
Purpose of the Study:
- To jointly model LBW and PTB using a copula distributional regression framework.
- To identify shared factors influencing both LBW and PTB.
- To explore the impact of maternal characteristics and geographic effects on neonatal risk.
Main Methods:
- Copula distributional regression framework was employed.
- Joint modeling of LBW and PTB as flexible functions.
- Analysis of female birth data from North Carolina.
Main Results:
- Identified shared factors contributing to both LBW and PTB.
- Revealed how maternal health, socioeconomic status, and geographic disparities influence neonatal risk.
- Demonstrated the utility of joint modeling for understanding complex birth metrics.
Conclusions:
- Joint modeling offers a more nuanced understanding of LBW and PTB.
- Insights can inform targeted interventions and prenatal care.
- Findings support improved public health planning for neonatal health.
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