Data Equity, Collection, and Disaggregation for Advancing Health Equity

Name: Data Equity, Collection, and Disaggregation for Advancing Health Equity
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Bol 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. 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.

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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. 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.

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