Abstract / Summary
Background: Several professional medical societies have removed race and ethnicity from widely used clinical algorithms with implications for millions. Public perspectives on these reforms, particularly outside the United States, have not been systematically explored. Methods: We conducted a multinational cross-sectional online survey with closed and open-ended questions among adults recruited through Prolific in January 2026, with quota sampling for equal participation by sex and across 10 categories of self-identified race and ethnicity. Participants reviewed four guideline-referenced algorithms representing different reform approaches (kidney function, spirometry, cardiovascular risk, and fracture risk), and rated representation, harms, benefits, and preferences between race-specific and race-free versions. Net comfort was the percentage comfortable minus the percentage uncomfortable. Quantitative analyses were descriptive; open-ended responses underwent content analysis. Results: Of 1,050 responses, 994 (94.7%) from 43 countries were eligible. Net comfort with race or ethnicity as a predictor was +62.4% (95% CI, +57.8 to +66.9), compared with +14.5% (95% CI, +9.1 to +20.0) for postal or ZIP code. Overall, 87.9% (95% CI, 85.8 to 89.9) were comfortable with the use of race or ethnicity if a clinician explained its use, and only 12.8% agreed race and ethnicity should never be used clinically. Across three reformed algorithms, 40.0% to 47.6% preferred race-specific versions, whereas 16.7% to 28.2% preferred race-free alternatives. Between 22.0% (fracture risk) and 43.0% (cardiovascular risk) felt unrepresented by algorithm categories. Findings were largely consistent across countries, self-identified race and ethnicity, and among participants reporting prior experiences of racism in healthcare. Conclusions: In this diverse but non-representative multinational sample, respondents were comfortable with the use of race and ethnicity in clinical algorithms, although many did not feel represented by existing categories and were less comfortable with alternatives based on postal or ZIP codes.