Which statement best describes equity-sensitive data disaggregation?

• A. Aggregating data across all populations to a single average.
• B. Collecting data only on clinical measures.
• C. Breaking down data by demographics (e.g., ethnicity, income) to identify disparities.
• D. Focusing data collection solely on geographic regions.

Answer: (C)

Equity-sensitive data disaggregation means analyzing outcomes by social categories such as ethnicity, income, gender, and age so that differences between groups are visible. By breaking data down into these demographic groups, disparities emerge rather than being hidden under an overall average. This is why the description of breaking data down by demographics to identify disparities is the best fit: it directly supports detecting inequities and guiding targeted action. Aggregating data to a single average masks who is affected and by how much. Focusing only on clinical measures misses the social and demographic context needed for equity analysis. Limiting attention to geographic regions can reveal place-based differences but doesn’t capture disparities across other dimensions like ethnicity or income, which are essential for equity work.