Estimating nationality from personal names in a multinational cohort: Performance of NamSor across country, regional,...

Nationality, ethnicity, and geographic background are frequently required in medical research but are often unavailable in administrative or registry-based datasets. Name-based inference tools have demonstrated good performance for predicting country of origin, yet their ability to approximate legal nationality remains unclear.

To evaluate the performance of NamSor in predicting nationality from personal names in a large multinational cohort and to assess whether aggregation into broader geographic or onomastic regions improves classification accuracy.

This cross-sectional study included 11,989 marathon participants representing 135 nationalities. Self-reported nationality, as recorded in the official race results, served as the reference standard. NamSor predictions were evaluated at the country level, fine/coarse United Nations (UN) regional levels, and predefined onomastic macro-regions. Performance was assessed using classification accuracy (proportion of correct predictions among classified observations) across probability thresholds.

Country-level accuracy was 60.2%. Aggregation improved performance to 69.7% for fine UN regions and 75.2% for coarse UN regions. Coarse onomastic macro-regions achieved the highest accuracy (88.3%). Increasing probability thresholds improved accuracy among classified observations (e.g., 92.5% at ≥0.9 at the country level) but substantially reduced the proportion of observations retained for analysis, with similar trade-offs observed for regional and onomastic classifications.

Name-based inference aligns more closely with linguistic-cultural groupings than with exact legal nationality. While country-level prediction showed substantial misclassification in a highly multinational setting, aggregation into broader regional or onomastic categories markedly improved performance. Broader regional or onomastic classifications may therefore represent a pragmatic alternative when direct nationality data are unavailable.