Peer-Reviewed Publication
Wellbeing Space Soc2026;11December 1, 2026Journal Article
LocaliType: Development of locality-based social determinants of health archetypes using census tract data from the UCSF Health Atlas.
Hyelee Kim1,2,3,4,5, Nancy F Cheng2,6, Shakiba Ghasemi Assl1,2,6, Yulin Hswen1,4,7,8,9, Catherine E Oldenburg1,10, Kim F Rhoads1,2, Edwin D Charlebois2,3, William Brown1,2,3,4,7, Stuart A Gansky2,6,7
1Department of Epidemiology and Biostatistics, University of California, San Francisco (UCSF).
2Research Coordinating Center to Reduce Disparities in Multiple Chronic Diseases, UCSF.
3Division of Preventive Science, Department of Medicine, UCSF.
4Computational Precision Health, University of California, Berkeley, and UCSF.
5Social and Behavioral Sciences Branch, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development.
6Division of Oral Epidemiology & Dental Public Health, UCSF.
7Bakar Computational Health Sciences Institute, UCSF.
8Artificial Intelligence Interdisciplinary Institute at Maryland, College of Computer, Mathematical, and Natural Sciences, University of Maryland.
9Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland.
10Francis I. Proctor Foundation, UCSF.
Abstract
Locality archetypes (denoted as LocaliTypes) - neighborhood, district, or nearby region patterns shaped by multiple social determinants of health (SDOH) - offer a novel approach to understanding place-based inequalities while preserving data confidentiality in the era of big data. Using 2020 U.S. Census shaped census tract-level data from the UCSF Health Atlas, we developed LocaliTypes and conduct…
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