Impact of disasters on adverse pregnancy outcomes: insights from the Environmental Influences on Child Health Outcomes (ECHO) Program

Abstract

As disasters grow in frequency and severity across the United States, there is increasing concern about their impact on maternal and child health. Pregnant individuals represent a uniquely vulnerable population, but few epidemiologic studies have rigorously examined how disaster exposure influences adverse pregnancy outcomes. Limitations in prior research include small, non-diverse samples; inconsistent measurement of disaster exposure; and inadequate attention to the role of timing, frequency, severity, and social determinants of health. This dissertation leverages harmonized, individual-level data from the NIH-funded Environmental Influences on Child Health Outcomes (ECHO) Program to evaluate disaster-related pregnancy risks in a large, diverse cohort. ☐ This dissertation aimed to: (1) characterize sociodemographic and health differences among disaster-exposed and unexposed pregnant individuals; (2) estimate the association between disaster exposure and five key adverse pregnancy outcomes—gestational hypertension (GHTN), gestational diabetes mellitus (GDM), preterm birth (PTB), small for gestational age (SGA), and large for gestational age (LGA); (3) examine whether associations differ by race and ethnicity, education, and income; and (4) explore the influence of disaster timing, frequency, and severity on pregnancy risks. ☐ A retrospective cohort study was conducted using data from 3,142 participants across 14 cohorts within the ECHO Release 2 dataset. Disaster exposure was self-reported using the Life Stressor Checklist-Revised and operationalized in binary, frequency, severity, and timing formats. Modified Poisson regression with generalized estimating equations (GEE) was used to estimate relative risks (RR) for each outcome, accounting for cohort-level clustering. Multiple imputation addressed missing covariate data. Stratified models and interaction terms tested effect modification. Sensitivity analyses included complete case models, leave-one-out cohort exclusion, and non-parametric Kaplan–Meier–like survival curves using maternal age as the time scale. ☐ Disaster exposure was reported by 48% of participants. Exposed individuals were more likely to have lower income and education levels, identify as Black or Hispanic, and report higher rates of asthma, depression, and interpersonal violence. These findings underscore the intersection of disaster vulnerability and pre-existing social inequities. ☐ While disaster exposure showed modest associations with reduced risk of GHTN (RR = 0.59; 95% CI: 0.29–1.07) and PTB (RR = 0.84; 95% CI: 0.70–1.00), the magnitude and direction of associations varied across subgroups. For example, protective associations for GHTN were more pronounced among low-income individuals and those with lower educational attainment. No strong associations were found for GDM, SGA, or LGA, and effect modification by race or income was generally weak or inconsistent for these outcomes. ☐ Recent disaster exposure (within five years of conception) was associated with reduced risk of GHTN and PTB, while disaster severity was positively associated with GHTN. Exposure frequency did not show a clear dose-response relationship. Kaplan–Meier–like curves illustrated outcome distributions by disaster exposure, but curve crossings suggested violations of the proportional hazards assumption, limiting interpretation of traditional Cox survival models. ☐ This dissertation provides new evidence on the nuanced relationship between disaster exposure and pregnancy outcomes. While some findings suggest a protective association between disaster exposure and select outcomes such as GHTN and PTB, these patterns may reflect underlying resilience, access to care, or selection mechanisms. Importantly, disaster exposure intersects with broader social determinants of health, highlighting the need for equity-centered disaster response and maternal health planning. Heterogeneity in associations across timing and severity of exposure supports the need for more refined disaster characterization in perinatal epidemiology. Future research should incorporate longitudinal designs and mechanistic pathways to better understand how disasters shape reproductive health across different population contexts.