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

Authors

Paul Sebo, Amrollah Shamsi, Ting Wang

Published in

Annals of epidemiology. Pages 110260. Aug 07, 2026. Epub Aug 07, 2026.

Abstract

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 137 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.

PMID:
42567457
Bibliographic data and abstract were imported from PubMed on 08 Aug 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.