Elijah Watson, MA

This study uses the 1983 Philippine political–economic crisis as a natural experiment to test whether prenatal exposure to worsening conditions shapes life-course physiology. Leveraging variation in gestational timing from the Cebu Longitudinal Health and Nutrition Survey, we compare individuals who entered the same adverse postnatal environment with versus without fetal exposure to crisis onset. Prenatal exposure was associated with smaller birth size, earlier menarche among women, lower midlife overweight risk, and slower epigenetic aging by age 40—patterns consistent with the idea that informative prenatal signals can calibrate later-life metabolic and aging trajectories. At the same time, exposure to more severe stress earlier in gestation carried biological costs, highlighting tradeoffs between developmental preparedness and scarring.

This project examines how climate disasters translate into mental and biological distress. In the wake of Typhoon Odette’s 2021 destruction, this study documents how typhoon housing damage was associated with water insecurity experiences, psychological distress, and epigenetic aging measured one year later among the CLHNS birth cohort. Housing damage and water insecurity were correlated with worse mental health for all participants, but accelerated epigenetic aging appeared primarily among women—suggesting gendered disparities in the embodied toll of climate shocks.

This study considers institutional reform as a protective shock that redistributes the social resources buffering women from violence. Leveraging the 1988 Free Public Secondary Education Act, which eliminated tuition fees for public secondary schools, I use instrumental variables methods with national DHS data to assess whether expanded schooling reduced women’s lifetime risk of intimate partner violence. Among rural women, each additional year of schooling lowered IPV risk by roughly four percentage points, in part by delaying union formation and enabling more-educated partner selection.

This study used Bayesian multilevel regression with poststratification to analyze data from a 2020 community antibody survey and estimate neighborhood-level infection risk prior to vaccine rollout. Population estimates from the American Community Survey were used for poststratification, and racialized economic segregation was measured using an Index of Concentration at the Extremes (ICE) for race, ethnicity, and income, capturing the spatial polarization of advantage and disadvantage across the city. While the raw seroprevalence data erroneously suggested that exposure burdens were similar across Chicago neighborhoods, adjusted estimates revealed pronounced disparities, with higher infection risk in poorer, predominantly Black neighborhoods and lower risk in wealthier, predominantly white areas. The findings demonstrate how serological data and careful modeling to mitigate sampling bias can uncover inequities in infection exposure that routine surveillance based on case counts and hospitalizations cannot reveal.