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Peer-reviewedCC BY-NC-NDNPJ digital medicine

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

  • Shenwei Wan1
  • Cong Fu2
  • Can Lu3
  • Zhiwen Ding4
  • Shiyuan Wang1
  • Lei Zhou1
  • Xiaoguang Lei5
5 affiliations
  1. 1School of Agricultural Economics and Rural Development, Renmin University of China, Beijing, China.
  2. 2Department of Gastrointestinal Colorectal Surgery, The First Hospital of Jilin University, Changchun, China.
  3. 3School of Medicine and Health Management, Huazhong University of Science and Technology, Wuhan, China.
  4. 4Institute of Agricultural Information, Chinese Academy of Agricultural Sciences, Beijing, China.
  5. 5First Department of Neurology, First Affiliated Hospital of Kunming Medical University, Kunming, China. [email protected].
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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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Cite

BibTeXRIS
BibTeX
@article{wan2026machine,
  title = {{Machine learning classification of digital exclusion in ageing populations across five international cohorts.}},
  author = {Shenwei Wan and Cong Fu and Can Lu and Zhiwen Ding and Shiyuan Wang and Lei Zhou and Xiaoguang Lei},
  journal = {NPJ digital medicine},
  year = {2026},
  month = oct,
  doi = {10.1038/s41746-026-03268-z},
  url = {https://europepmc.org/article/MED/42919895},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - Machine learning classification of digital exclusion in ageing populations across five international cohorts.
AU  - Shenwei Wan
AU  - Cong Fu
AU  - Can Lu
AU  - Zhiwen Ding
AU  - Shiyuan Wang
AU  - Lei Zhou
AU  - Xiaoguang Lei
PY  - 2026
DA  - 2026/10/06
JO  - NPJ digital medicine
DO  - 10.1038/s41746-026-03268-z
UR  - https://europepmc.org/article/MED/42919895
AB  - 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.
SN  - 2398-6352
ER  -