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Achieving health equity necessitates high-quality data to address disparities that have remained stagnant or even worsened over time despite public health interventions. Data disaggregation, the breakdown of data into detailed subcategories, is crucial in health equity research. It reveals and contextualizes hidden trends and patterns about marginalized populations and guides resource allocation and program development for specific needs in these populations. Data disaggregation underpins data equity, which uses community engagement to democratize data and develop better solutions for communities. Years of research on disaggregation show that researchers must collaborate closely with communities for adequate representation. However, despite generally positive support for this approach in health disparities research, data disaggregation faces methodological and political challenges. This review offers a framework for understanding data disaggregation in the context of data equity and highlights critical aspects of implementation, including challenges, opportunities, and recent policy and community-based efforts to address hurdles.
Ponce et al. (Thu,) studied this question.