Dean’s 2024 Pilot Innovation Award Winners Announced

Joshua Jeong

Faculty Bio: Joshua Jeong, ScD, assistant professor of global health,  is a developmental scientist who uses mixed methods approaches to understand how parents and family caregiving environments shape early child development with a focus on fathers and current implementation research projects in East Africa.

Project Title: Spillover Effects of a Parenting Intervention in Rural Kenya

Project Summary: Globally, parenting programs are effective for improving early child development. Most parenting programs only involve mothers and are evaluated in terms of intervention effects on maternal caregiving and the developmental outcomes of a single child in each household. This study will explore whether there are intervention spillover effects on sibling children and fathers who were not explicitly targeted in a parenting intervention but co-reside with the targeted mother and child. The project will involve an ongoing cluster randomized control trial evaluation of a parenting intervention in rural Kenya. The aim of this trial is to evaluate program effects on maternal and child outcomes. This project will measure the developmental outcomes of siblings to the targeted children and the caregiving practices of the male partners to enrolled mothers. The results of this study will provide a more comprehensive understanding of the family-wide impacts of parenting interventions.

Emily Peterson

Faculty Bio: Emily Peterson, PhD, research assistant professor of biostatistics and bioinformatics, researches the intersection of spatial epidemiological mapping, Bayesian hierarchical methods, and population dynamics to assess population level determinants of health and global health indicators. Her current work focuses on the estimation and assessment of maternal health outcomes and opioid mortality.

Project Title: Development of Data Science Pipeline to Identify Culturally Relevant Risk Factors for Gestational Diabetes in Tribal Communities

Project Summary: Gestational Diabetes Mellitus (GDM) is a common and serious complication during pregnancy and delivery and can result in cesarean delivery, preterm birth, and stillbirth. American Indian women are 1.5 times more likely to suffer from GDM than white non-Hispanic mothers, resulting in higher rates of negative maternal and infant outcomes. To reduce the severe risk and harm suffered by American Indian mothers, it is critical to identify community-level risk factors within a tribal culturally relevant context. Understanding the impact of tribal and culturally relevant factors related to tribal health care coverage, health care services, tribal resources and programs, and geography is essential to reducing GDM risk among tribal mothers. This study aims to bridge the current information gap by incorporating tribal determinants of health within a methodological framework to identify what important tribal community-level risk factors are for GDM among American Indian mothers and how to identify geographic clusters of high-risk tribal populations and areas of unmet need. In collaboration with Cherokee Nation Public Health, this study will develop a methodological framework that will both evaluate the impact of the county-level characteristics on county-level risk of GDM for Cherokee mothers including tribal characteristics and produce county-level GDM risk estimates to identify geographic areas of high risk and high unmet need for tribal mothers. The study will also develop a suite of tools aimed at effectively communicating findings including visualization and documentation. The study aims to create a culturally relevant surveillance system that rigorously assesses the impact of social determinants of health on GDM risk among tribal mothers. This pilot study will determine the feasibility of a larger research initiative focusing on both community- and individual-level factors to improve care and interventions within tribal communities.

Kaitlyn Stanhope

Faculty Bio: Kaitlyn Stanhope, PhD, assistant professor of epidemiology, focuses on how structural and life course stressors impact maternal and infant health during pregnancy and postpartum and interventions to address and ameliorate those pathways.

Project Title: Birth Trauma, Stress Reactivity, and Cardiometabolic Health Among Postpartum People

Project Summary: Nearly 1 in 5 people experience giving birth as traumatic. They feel intense fear while giving birth and the sense that their life, or that of their baby, is at risk. Traumatic birth is associated with post-traumatic stress symptoms in the months following. In other populations, post-traumatic stress symptoms result in greater risk of cardiovascular disease. However, it is unknown if and how birth-related post-traumatic stress results in similar changes to cardiovascular health for postpartum people. This study will work to understand if and how people’s experiences while giving birth impact their physical health. It will measure birth trauma and its effects in a sample of 72 people who have given birth in the past four months and how birth experiences impact health in four ways: self-reported post-traumatic stress symptoms, levels of inflammation, nervous system response to sharing their birth experience, and cardiometabolic health. These data will show potential pathways through which birth trauma may change people’s health, possibly leading to the development of early interventions to prevent negative consequences following birth trauma.

Julia Wrobel

Faculty Bio: Julia Wrobel, PhD, assistant professor of biostatistics and bioinformatics, focuses on the development and application of statistical methodologies to facilitate scientific discoveries in biomedicine and public health, with emphasis on functional data analysis, spatial proteomics, and the analysis of data from continuously streaming sensor devices.

Project Title: Statistical and Machine Learning Methods for Characterizing Driving Impairment due to Recent Cannabis Use

Project Summary: The legalization of cannabis in 38 states and the District of Columbia has led to widespread consumption, with an estimated 61.9 million Americans aged 12 or older using cannabis in 2022. As cannabis use increases, so do concerns about its impact on public safety, particularly regarding occupational and driving-related impairments. Delta-9-tetrahydrocannabinol, the primary psychoactive compound in cannabis, impairs cognitive and motor functions, increasing the risk of accidents and injuries. Current detection methods are invasive, subjective, and do not accurately indicate recent use or impairment. Furthermore, common metrics for assessing alcohol-related driving impairment are not well-validated for detecting cannabis-related driving impairment, underscoring the need for cannabis-specific driving impairment metrics. This project aims to develop advanced statistical and machine learning tools to identify and predict acute cannabis use and driving-related impairment, utilizing data from seven completed NIH-funded studies.