Abstract:
This thesis explores the multifaceted dimensions of preterm birth, a leading cause of neonatal morbidity and mortality worldwide, through a series of interconnected studies that span from identifying determinants and risk factors to assessing the impact of socio-economic and healthcare crises on maternal and child health.
Utilizing a life course perspective, the first section constructs a multilevel life course conceptual framework which integrates biological, individual, psychological, family, community, and national level factors to map the complex interplay influencing the occurrence of preterm birth.
Subsequently, the thesis presents a prospective cohort study in Lebanon, a country facing economic collapse and healthcare challenges, to examine the association between social determinants, preterm birth, and developmental outcomes. Despite recruitment and follow-up challenges, findings indicate that lower quality of life, higher stress levels, and reduced social support correlate with preterm birth, while supportive social environments contribute to better developmental outcomes.
Finally, the thesis applies machine learning techniques to a large dataset to detect risk factors associated with preterm birth, aiming to develop a predictive model. Despite the complexity of predicting preterm births, the study highlights the potential of machine learning to enhance understanding and develop preventive strategies, demonstrating significant associations between various variables and preterm birth outcomes.
Together, these sections contribute to our understanding of the determinants and effects of preterm birth, advocating for comprehensive approaches that incorporate socio-economic and emotional well-being in prenatal care and policymaking.
