Data Disaggregation in Health Equity Studies: Where do we go from here?

Thomas M. Menino Convention & Exhibition Center 

There have been numerous calls in the medical, public health and health policy fields for "data disaggregation" (i.e., breaking out data by more granular key characteristics) when studying minority populations, including Latinos, to better understand health and healthcare inequity. The broad racial and ethnic categories whose capture is required by the Office of Management and Budget (OMB) can mask significant variation within race and ethnicity categories, limiting the ability to target resources where they are needed most. At the same time, the landscape for race and ethnicity data collection is shifting. The 2024 OMB standards call for more detailed categories (including a combined race/ethnicity question and a new MENA category), yet implementation remains uncertain. Recent political developments also threaten the consistency and completeness of demographic data collection, creating new obstacles for health equity research. This presentation will discuss opportunities and challenges for data disaggregation of race and ethnicity data with a case study in Latino populations using electronic health records. The case study will highlight the real-world potential, possible risk, and practical approaches for using this data.