Disaggregating data by race/ethnicity and socioeconomic status helps identify disparities. True or false?

Disaggregating data by race/ethnicity and socioeconomic status helps identify disparities. True or false?

• A. True — disaggregating data by race/ethnicity and SES helps identify disparities.
• B. False — disaggregation hides disparities.
• C. It is only useful for marketing.
• D. It is optional and rarely affects program design.

Answer: (A)

Disaggregating data by race/ethnicity and socioeconomic status reveals disparities that pooled data can hide. When you lump everyone together, differences among groups can cancel out or appear as a single average, masking which communities are underserved or experiencing higher disease burdens. By breaking data into subgroups, you can see where gaps exist, prioritize resources, and design interventions that target those specific communities. This is why the statement is true: disaggregation illuminates inequities rather than hiding them and informs equity-focused program design. The other ideas don’t fit because they suggest the opposite—that disparities aren’t revealed by subgroup data or that disaggregation isn’t useful beyond marketing or optional practice.

Disaggregating data by race/ethnicity and socioeconomic status reveals disparities that pooled data can hide. When you lump everyone together, differences among groups can cancel out or appear as a single average, masking which communities are underserved or experiencing higher disease burdens. By breaking data into subgroups, you can see where gaps exist, prioritize resources, and design interventions that target those specific communities. This is why the statement is true: disaggregation illuminates inequities rather than hiding them and informs equity-focused program design. The other ideas don’t fit because they suggest the opposite—that disparities aren’t revealed by subgroup data or that disaggregation isn’t useful beyond marketing or optional practice.