Profiles of stressful life experiences in pregnancy: a latent class approach linking early childhood adversity, intimate partner coercive control, and material hardship to adverse birth outcomes

Dr. Gia Elise Barboza-Salerno, Taylor Harrington
August 2026
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Abstract

Acute antenatal stressful life events (SLEs) during pregnancy are well-established risk factors for adverse birth outcomes. However, little is known about how distinct constellations of antenatal SLEs, shaped by early adversity, material hardship, and intimate partner coercive control, combine to influence perinatal health. Data were drawn from 5,280 participants in the Kansas Pregnancy Risk Assessment Monitoring System. Thirteen antenatal SLEs were analyzed using latent class analysis to identify distinct stress profiles. Five distinct SLE profiles were identified: (1) Family Member Illness and Death (16.3%); (2) Multiple Overlapping Stressors (5.2%); (3) Financial Insecurity (11.2%); (4) Residential Instability and Family Conflict (8.7%); and (5) Low Stress (58.6%). The Multiple Overlapping Stressors class showed the greatest risk of low birth weight, small for gestational age, and preterm birth. Substantial heterogeneity in antenatal SLEs highlight distinct pathways through which early adversity, material hardship, and relational traumas shape perinatal health.

Stressful Life Experiences Latent Class Analysis Maternal Health Neonatal Health Adverse Childhood Experiences Intimate Partner Violence Preterm Birth Low Birth Weight
Dr. Gia Elise Barboza-Salerno
Dr. Gia Elise Barboza-Salerno
Director

I am an interdisciplinary scholar whose research integrates law, public health, social work, and advanced statistics to study childhood adversity, violence-related harm, and neighborhood-level inequities. I lead the ISSUES Lab at Ohio State, translating spatial and legal analysis into policy and practice.

Taylor Harrington
Taylor Harrington
PhD Candidate, Methodologist

Taylor Harrington, MS, is a PhD candidate in Public Health at The Ohio State University. To investigate how multi-level factors shape health behaviors and outcomes, she integrates advanced computational and geospatial methods, including machine learning (e.g., computer vision, random forests, structured topic modeling, text mining) and geospatial modeling (e.g., Bayesian spatial modeling, remote sensing). Through this work, she aims to generate actionable evidence to inform equitable health policy and community-level interventions.