Machine learning classification of digital exclusion in ageing populations across five international cohorts

The science

Original title

Machine learning classification of digital exclusion in ageing populations across five international cohorts.

Authors

Wan S, Fu C, Lu C et al.

Journal

NPJ digital medicine

Published

Oct 23, 2026

Study type

Study

PMID

42919895 ↗

DOI

10.1038/s41746-026-03268-z ↗

Abstract

Digital exclusion may limit access to increasingly digital health and public services, yet its cross-national structure and model transportability remain uncertain. We analysed harmonised cross-sectional data from 71,689 middle-aged and older adults in CHARLS, ELSA, HRS, MHAS and SHARE. Digital exclusion was defined as no reported internet or email use, a proxy for basic digital participation. Gradient Boosting discriminated concurrent exclusion with AUCs of 0.734-0.850, but logistic regression performed similarly (0.721-0.846; differences 0.001-0.013). Within SHARE, descriptive country-level exclusion ranged from 20.1% in Denmark to 79.2% in Poland. Leave-one-cohort-out validation showed partial transportability, with mean AUC attenuation of 0.026. Cross-fitted classification scores were associated with concurrent depression (pooled adjusted OR 2.23, 95% CI 1.83-2.71). These findings support population-level characterization of digital exclusion but not prospective or individual-level risk prediction without external validation.

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