Adverse pregnancy outcomes (APOs), such as preeclampsia and preterm birth, are common and devastating. The human and economic costs of APOs are tremendous, and the United States has among the highest APO rates among developed nations. APOs are especially common in non-White and low-income communities. For example, in the United States Black women are 50% more likely to deliver preterm compared to White women. Research has shown that the increased risk of adverse outcomes in overburdened populations is not fully explained by socioeconomic status or other socio-demographic factors. In addition to having elevated risk for adverse outcomes, non-White women in the United States may be less likely to receive certain interventions, such as treatment for postpartum depression, but are more likely to receive others, such as cesarean section, suggesting that there may be unwarranted and discriminatory variation in pregnancy care.

Our lab is interested in understanding both the factors that mediate elevated risk for APOs in over-burdened populations, and whether there exists discrimination in how treatments are offered and utilized. We are looking for a student to apply state of the art methods from causal inference to a rich dataset of clinical and sociodemographic data from over 10,000 pregnant women. This project aims to identify adverse outcomes where there is evidence of racial health disparities as well as the causal mediators of the increased risk in the over-burdened class. We additionally aim to identify treatments and interventions where there exists disparities in utilization among high-risk populations and determine whether disparities may be causally explained by discrimination. Unraveling the causal chain both with respect to increased risk for adverse pregnancy outcomes and with respect to discrimination in treatment will yield actionable insights and guide strategies for reducing maternal and neonatal morbidity and mortality while also combating health inequities.

Selected candidate(s) can receive a stipend directly from the faculty advisor. This is not a guarantee of payment, and the total amount is subject to available funding.

Faculty Advisor

  • Professor: Tal Korem
  • Center/Lab: Systems Biology
  • Location: PH18-200
  • We develop data analysis methods for multi-omic microbiome data. We focus on integrating clinical, microbiome, lifestyle and environmental data in a way that advances from statistical associations to actionable insights that can be used in clinical practice.

Project Timeline

  • Earliest starting date: 10/15/2022
  • End date:
  • Number of hours per week of research expected during Fall 2022: ~12

Candidate requirements

  • Skill sets: MSc students, seniors and exceptional juniors preferred. Students should have familiarity with the Unix environment and Python. Students should have a strong foundation in the core skills and concepts of causal inference, such as counterfactual analysis, confounding, directed acyclic graphs, regression based mediation analysis, experimental design, matching, and probability weighting.
  • Student eligibility: freshman, sophomore, junior, senior, master’s
  • International students on F1 or J1 visa: eligible
  • Academic Credit Possible: Yes