Adverse pregnancy outcomes (APOs), such as preeclampsia, gestational diabetes, and placental abruption, affect nearly one in three pregnancies in high-income countries and have lasting health effects for both mothers and children. While clinical risk factors are well established, growing evidence points to the critical roles of psychological, social, and environmental conditions in shaping maternal health. Sabrina Chiodo’s doctoral research aims to bridge these domains by developing a population-based risk prediction model, the Adverse Pregnancy Outcomes Population Risk Tool (PregPoRT), that integrates clinical, social, and environmental determinants to inform equitable public-health interventions.
Using a newly linked national cohort combining data from the Canadian Community Health Survey and the Discharge Abstract Database, along with contextual datasets on neighbourhood deprivation, air quality, and active-living environments, Sabrina’s study captures an unprecedented view of the multidimensional influences on pregnancy health. Early analyses of over 13,000 individuals revealed that the risk of APOs is not only driven by clinical conditions such as obesity, hypertension, and diabetes, but also by broader social and environmental inequities. Women with lower income, immigrant backgrounds, or residence in materially deprived or ethnoculturally diverse neighbourhoods, characterized by higher proportions of recent immigrants, visible minorities, and those with limited English or French proficiency, experienced higher risk of adverse pregnancy outcomes.
PregPoRT is being developed using advanced statistical learning techniques to quantify these intersecting influences and predict which populations are most at risk. In collaboration with public health partners, the model will ultimately be adapted for real-world application, helping local health units forecast the burden of APOs and target prevention strategies, such as enhanced prenatal supports or social-policy interventions, to populations who need them most.
By embedding equity, environment, and social context into predictive modeling, Sabrina’s research moves beyond individualized clinical care toward precision public health. This work directly aligns with the Leong Centre’s mission to promote the flourishing of every child and family by addressing upstream determinants that shape maternal and child well-being across the life course.
