SSRI Open House - Faculty Flash Talks: Health and Health Disparities

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Presenter:  Dr. Xue Zhang

Title:  Spatiotemporal Trends in the Industrial Composition of Counties and Working-age Mortality

Abstract:  The labor market conditions – especially those affecting the industrial composition of places – are a key factor underlying recent trends in U.S. working-aged mortality. Yet, the limited research examining labor market conditions and rising working-age mortality in recent decades tends to focus on specific labor market attributes (e.g., trade liberalization) affecting specific industries (e.g., manufacturing), nor does it examine key sources of spatial or temporal variation in these associations. Drawing on a broader framework of precarious labor, we address this gap by looking at associations between the industrial composition of counties and both all-cause and drug poisoning mortality across distinct periods from 2000 to 2019, further accounting for the spatial proximity of these associations. Preliminary results show greater representation of agriculture and manufacturing industries consistently show protective effects on lower all-cause and drug overdose mortality, especially among males; however, higher employment in mining and services is related to increased mortality, particularly drug overdose deaths. Surprisingly, greater representation of professional work is associated with higher drug overdose deaths at different times. We discuss how these mixed results provide evidence of both direct pathways labor markets affect mortality, such as employment in specific industries, as well as indirect pathways through which industrial composition has spillover effects for surrounding communities’ health and wellbeing.

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Presenter:  Dr. Cleothia Frazier

Title: Working Around the Clock: The Association between Shift Work, Sleep Health, and Depressive Symptoms among Midlife Adults

Abstract: Shift work is an integral part of living in a 24-hour society. However, shift work can disrupt circadian rhythms, negatively impacting health. Guided by the Stress Process Model (SPM), this study examines the association between shift work and depressive symptoms and investigates whether sleep health (duration, quality, and latency) mediates this relationship among midlife adults. Utilizing data from the National Longitudinal Survey of Youth 1979 cohort (N = 6,372), findings show that working evening, night, and irregular shifts is associated with increased depressive symptoms. The results also show that part of the association between shift work and depressive symptoms among night and irregular shift workers, is indirect, operating through short sleep during the week and on the weekend. Although shift work can negatively affect mental health, getting more restorative sleep may mitigate part of the harmful mental health consequences of non-standard work schedules

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Presenter:  Dr. Nelson Roque

Title: Breathe Well, Age Well: A Comprehensive Look at Air Quality for Older Adults

Abstract:  As populations age, understanding the environmental factors that impact cognitive health becomes crucial. This presentation explores the relationship between air quality—specifically fine particulate matter (PM1, PM2.5)—and cognitive decline, including mild cognitive impairment (MCI) and Alzheimer's Disease Related Dementias (ADRD). While much research focuses on outdoor pollution, this study uniquely considers both indoor and outdoor air quality exposures, incorporating person-specific data from older adults. Leveraging portable air quality monitors and cognitive assessments, the research aims to link short-term exposure to pollutants with cognitive performance and long-term neurodegeneration biomarkers. The findings will inform how environmental exposures contribute to cognitive health in aging, highlighting the need for precise, geospatially accurate assessments of air quality.

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Presenter:  Dr. Tracee Saunders

Title: Administrative Burden and the Politics of Medicaid Take-up

Abstract: Only some people eligible for social safety net policies enroll. Policy design and administrative burden are ways the government controls who can access government benefits. The combined effect of policy design and administrative burden is particularly important because state legislators and bureaucrats make strategic decisions. A policy may have generous eligibility requirements in one state and offer extensive benefits. Still, if the application process is confusing and bureaucrats are unhelpful, benefits may not be accessible to many people who the policy could help. Legislators may design a strict policy in another state, but bureaucrats work hard to help everyone eligible overcome the administrative burden and enroll. In this paper, I use multiple regression, imputation, and post-stratification (MRP) on administrative and Census data to estimate Medicaid take-up. I then use these take-up estimates to identify systematic differences in who is eligible but not enrolled in Medicaid between and within all U.S. states. I use regression analysis to assess how these differences can be explained by administrative burdens and other policy design decisions states make for Medicaid programs. To measure Medicaid policy design and administrative burden, I have compiled measures from a variety of sources, such as state and federal websites, the Kaiser Family Foundation, and Code for America, and I have used surveys to construct several of my own, such as how long it takes individuals to find a state’s Medicaid application from a web search.

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Dr. Mohanraj Krishnan

Title:  Adiposity in Indigenous Populations: Pathways to Disease

Abstract:  Obesity [Body Mass Index (BMI) ≥ 30 kg/m2] is a driver of many cardiometabolic diseases and disproportionately impacts marginalized populations. Given the rapidly growing prevalence of obesity, deepening the genomic translational pipeline, and identifying the regulatory mechanisms of obesity is imperative. The heterogeneous nature of obesity and the underrepresentation of marginalized populations in multi-omic research have limited our understanding of the underpinnings of the pathogenesis and underlying mechanistic pathways of obesity. As a result, studies attempting to fine map genome wide association study (GWAS) loci and estimate quantitative trait loci (QTL) have been limited, constraining biologic insights and actionable target mechanisms. These fundamental gaps in data necessitate integrative multi-omic studies to propel mechanistic understanding of severe obesity susceptibility in multi-ancestry populations. My research focuses on multi-omic platforms and leverage machine learning (ML) approaches to identify clusters, networks, and individual biomarkers of obesity and its associated cardiometabolic diseases. Utilizing ML- based predictive algorithms we plan to integrate various omics data to discover new biomarkers to help in accurate disease prediction, patient stratification and delivery of precision medicine. The insights gained from this research will help prevent cardiometabolic complications and promote wellness across the lifespan in all populations.