Data Science Training to Advance Behavioral and Social Science Expertise for Health Disparities Research (DaTABASE)

Behavioral and social science researchers are essential for advancing research on reducing health disparities. The next generation of such researchers must be equipped with the most powerful, contemporary analytic tools. Newly available data and methods have the potential to transform the questions asked, the research designs used, and the statistical approaches applied to deepen our understanding of fundamental drivers of health and increase our capacity to improve population health and reduce disparities. To take advantage of these opportunities, we need researchers with interdisciplinary training that allows them to connect traditional behavioral and social science expertise with the rapidly evolving technical repertoire of computational health scientists.

The UCSF Data Science Training to Advance Behavioral and Social Science Expertise for Health Research (DaTABASE), which falls within the PhD program in Epidemiology and Translational Science, provides training in advanced data analytics to predoctoral researchers studying behavioral and social determinants of health. The program is a collaboration between the UCSF Department of Epidemiology and Biostatistics, Bakar Computational Health Sciences Institute, and the Center for Health and Community.

Unique strengths of the program include:

  1. Grounding in a rigorous, interdisciplinary quantitative training program for a foundation in study designs and statistical analyses to deliver actionable evidence on health;
  2. Integration of multiple data streams, including: ’omics, clinical (e.g., UC-wide, geolocated and longitudinal medical records, clickstream data from UCSF’s unique clinical informatics infrastructure), digital (device-based data flows, social media data, technology enabled cohorts), population (e.g., San Francisco Department of Public Health data linking health and social services provided to homeless individuals or other vulnerable populations) and policy data sets (e.g., local policies regulating cannabis retail outlets in California communities)
  3. Training and research activities organized around the elimination of health disparities.

DaTABASE trainees complete the rigorous methodological training for a PhD in Epidemiology and Translational Science. Additional quantitative methods training are delivered via the Bakar Computational Health Sciences Institute (BCHSI), which emphasizes machine learning tools to mine data sources for novel insights and discovery, including those in the arenas of precision medicine and population health. Additional content training, overseen by faculty in the UCSF Center for Health and Community, provides theoretical frameworks and disciplinary background for social and behavioral sciences.

This training initiative prepares graduates to apply advanced analytic tools to novel data sets for research on behavioral and social processes underlying health disparities. The DaTABASE program provides intensive mentoring from both content and methods experts. DaTABASE alumni will be prepared to lead research addressing current and emerging public health challenges with innovative computational and data science analytic approaches.

DaTABASE is funded by the National Institute on Minority Health and Health Disparities of the National Institutes of Health under Award Number T32MD015070. The Principal Investigators of this training program are Will Brown, Maria Glymour and Aric Prather.

Eligibility and How to Apply

Scientists eligible for this program must demonstrate a strong interest in using advanced data analytics for health disparities research. Trainees must be citizens or non-citizen nationals of the U.S. or have been admitted for permanent residency and meet the admissions criteria for the PhD in Epidemiology and Translational Science program.

To apply, indicate your interest in DaTABASE on the Research Interest page of your application to the PhD in Epidemiology and Translational Science. The deadline to apply is December 1.

For more information, contact Eva Wong-Moy.