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 the 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) program braids training strands previously operating independently within UCSF. Particular strengths of DaTABASE 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 will complete the rigorous methodological training for a PhD in Epidemiology and Translational Science. Additional content training, overseen by faculty in the UCSF Center for Health and Community, will provide theoretical frameworks and disciplinary background for social and behavioral sciences. Additional quantitative methods training will be delivered via the Bakar Computational Health Sciences Institute (BCHSI). BCHSI emphasizes machine learning tools to mine data sources for novel insights and discovery, including precision medicine and population health. This training initiative will prepare graduates to apply those analytic tools to novel data sets for research on behavioral and social processes underlying health disparities. The DaTABASE program will provide 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.

Public Health Relevance

This UCSF training grant (DaTABASE) will provide 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) a focus on health disparities; 2) exceptional clinical and population data sources; and 3) integrating advanced machine learning methods into causal frameworks for actionable results.

National Institute of Health (NIH)
National Institute on Minority Health and Health Disparities (NIMHD)
Institutional National Research Service Award (T32)
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Special Emphasis Panel (ZRG1)
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Castille, Dorothy M
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University of California San Francisco
Public Health & Prev Medicine
Schools of Medicine
San Francisco
United States
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