Advancing health equity through rigorous data collection and disaggregation practices

Health disparities are frequently masked when data lack disaggregation by race, ethnicity, language, sexual orientation, gender identity, disability, rurality, or other factors. Data Equity, Collection, and Disaggregation for Advancing Health Equity synthesizes evolving research, practices, and policies around health data equity. It offers a structured approach to closing data gaps in the measurement of physical, behavioral, and social-emotional health.

The book provides recommendations and applied practices for conducting quantitative, qualitative, and mixed methods evaluations through a data equity-informed lens. It addresses methodological challenges, including small sample sizes, missing data, data privacy, and more, across the health data lifecycle.

Readers will also find:

• Strategies for strengthening data governance, infrastructure, and systems that support disaggregated data collection across diverse health settings and populations
• Guidance on applying data equity principles to health research, policy, or practice, including culturally responsive categories, measurement, analysis, and dissemination
• Frameworks for navigating ethical considerations, privacy protections, and use of artificial intelligence in the collection of sensitive data from populations with limited access to services or resources
• Case examples of data equity applications across community, clinical, virtual, and global health settings
• Practical approaches for using disaggregated data to identify unmet needs, inform decisions, and improve population health

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

Dr. Tran T. Doan, PhD, MPH, is an Assistant Professor of Health Systems, Management, and Policy in the Colorado School of Public Health at the University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States. As a decision scientist and applied health economist, she evaluates how the integration of primary, specialty, and social care affects long-term health and economic outcomes for children and families. She completed a PhD in Health Services Organization and Policy from the University of Michigan and a postdoctoral fellowship in General Academic Pediatrics from the University of Pittsburgh. Her research has been published in leading journals, including JAMA Health Forum, JAMA Network Open, and The New England Journal of Medicine.

Dr. Bradley E. Iott, PhD, MPH, MS, is an Assistant Professor in the Division of General Medicine, Department of Internal Medicine at the University of Michigan Medical School, Ann Arbor, Michigan, United States. As a health informatician and health services researcher, he evaluates social needs screening and referral programs, including how clinicians capture and use information about patients' social needs. He completed a PhD in Health Services Organization and Policy from the University of Michigan and a postdoctoral fellowship in Social Informatics at the University of California San Francisco (UCSF). His research has been published in leading informatics, health policy, and clinical journals, including the Journal of the American Medical Informatics Association, The Milbank Quarterly, and the Journal of General Internal Medicine.