Which data should be included in equity-focused data collection to avoid biased conclusions?

• A. Socio-demographic data
• B. Only clinical data
• C. Socio-demographic and social determinants data
• D. No missing data allowed

Answer: (C)

To avoid biased conclusions about equity, data collection should include both who the patient is and the social context that shapes health. Socio-demographic information—age, race/ethnicity, gender, income, education, and where someone lives—shows where disparities exist and who is affected. But to understand and address those disparities, you also need data on social determinants of health—housing stability, food and financial security, employment conditions, education quality, transportation access, social support, neighborhood safety, discrimination, and the policies or system barriers people face. Together, these data let you see not just that differences exist, but why they occur and how to intervene upstream, rather than attributing outcomes to individuals alone. Relying only on clinical data misses these drivers and can bias conclusions, while collecting only socio-demographic data provides limited insight into mechanisms. And expecting zero missing data is impractical; appropriate strategies to handle missingness are essential to maintaining representative, fair analyses.