On Monday, Feb. 1 at 1 pm, the Virginia Tech Chapter of the Association for Women in Mathematics (AWM-VT), the Speakers and Undergradute Research Engagement Program (SURE), and the Supporting Women In Mathematics through Mentoring Program (SWIMM) will jointly host a research seminar with Dr. Loni Philip Tabb  on "Exploring the Geography of Health in the US." The seminar will be followed by a less formal fireside chat with students from 2 - 2:30 pm.

You can join the seminar on February 1st at 1 pm through this Zoom link.
If you have any questions, contact the host of the seminar Quiyana Murphy.

Dr. Loni Philip Tabb received her MS (2005) and BS (2003) in Mathematics from Drexel University, and her PhD (2010) and AM (2007) degrees in Biostatistics from Harvard University. Since joining the faculty at Drexel, her research focuses primarily on spatial statistics and epidemiology with applications in health and social disparities, violence, and toxicity studies. Much of Dr. Tabb’s work involves using Bayesian statistical methods in the presence of complex data structures. Earlier research focused on the intersection of alcohol and violence in urban settings; with a more recent focus on the additional impact of marijuana access and availability - given the changing landscape of legalization of marijuana in the US. More recently, Dr. Tabb has concentrated her research efforts on the intersection of health and place, specifically as it applies to cardiovascular health. In particular, she uses novel spatial and spatio-temporal statistical methods to look at the local and national geographic patterns of black-white inequities in this country. An abstract of her talk follows below.

Abstract: Improving population health requires a firm understanding of geographic influences of modifiable health factors, and, to do so, requires measuring and mapping the relationship between health outcomes, factors, as well as demographics. Using recent County Health Ranking data for 3,108 US counties, we investigated the spatial patterning in these relationships using spatial regression methods. Although we found that spatial patterning in health outcomes was substantially explained by spatial differences in levels of health factors, substantial residual spatial patterning remained. Findings suggested that both the outcomes and the health factors of neighboring counties have an impact on the outcomes for a given county. Finally, using geographically weighted regression models, we found that the associations of health factors with outcomes showed substantial spatial patterning and varied significantly across the US. Greater understanding of the spatial heterogeneity we observed is important to identifying the most effective interventions and evidence-based policies to improve population health.