Advancing health equity through rigorous data collection and disaggregation practices

Health disparities are frequently masked when data lack disaggregation by race, ethnicity, gender identity, sexual orientation, disability, language, and rurality. Data Equity, Collection, and Disaggregation for Advancing Health Equity synthesizes evolving research, practices, and policies around equitable data collection. It offers a structured approach to closing data gaps that obscure inequities in physical, mental, and emotional health.

The book provides recommendations and applied practices for conducting quantitative, qualitative, and mixed methods research through a data equity-informed lens. It addresses methodological challenges including small sample sizes, missing data, data privacy, and the role of artificial intelligence in medical research and practice.

Readers will also find:

• Strategies for strengthening data governance, infrastructure, and systems that support disaggregated collection across diverse health care settings and populations
• Guidance on applying data equity principles to health research, including culturally responsive categories, measurement, and analytic approaches
• Frameworks for navigating ethical considerations and privacy protections when collecting sensitive demographic information from marginalized communities
• Examples of data equity implementation across community, clinical, virtual, and global health settings
• Practical approaches for policymakers and funders using disaggregated data to inform decisions and allocate resources to reduce population-level disparities

Written for health researchers, clinicians, psychologists, public health professionals, social workers, policymakers, and funders, this book equips those who interpret, collect, or use health data with the frameworks and methodological tools needed to improve health equity and health outcomes for all.

Tran T. Doan, PhD, MPH, is an Assistant Professor of Health Systems, Management, and Policy at the Colorado School of Public Health, University of Colorado Anschutz Medical Campus. A decision scientist and applied health economist, she specializes in incorporating data equity considerations into health data collection and decision analysis, including cost-effectiveness analysis and stated preference surveys. Her work has appeared in JAMA Health Forum, JAMA Open Network, and The New England Journal of Medicine.

Bradley E. Iott, PhD, MPH, MS, is an Assistant Professor of Internal Medicine at the University of Michigan Medical School. A health informatician and health services researcher, he specializes in evaluating interventions to address patients’ social needs and efforts to leverage health information technology to reduce health disparities. His research has been published in the Journal of the American Medical Informatics Association, the Journal of General Internal Medicine, and the Milbank Quarterly.