Data Equity, Collection, and Disaggregation for Ac hieving Health Equity

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.