Joint Modeling of Birth Outcomes Using a Copula Distributional Regression Approach
采用Copula分布回归框架，联合建模低出生体重和早产两个指标，分析北卡罗来纳州女性出生数据，揭示母亲特征、社会经济状况和地理差异对新生儿风险的共同影响，为产前护理和公共卫生规划提供参考。
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.