Document Type
Article
Department
Brain and Mind Institute
Abstract
As populations age, identifying individuals at high risk for dementia has become a cornerstone of prevention-oriented public health policy. Accurate and scalable risk stratification could enable early intervention and resource targeting. However, existing models face methodological limitations, including constraints in available data and follow-up duration, challenges in dementia ascertainment, heterogeneity in modelling approaches, and limited external validation. This commentary discusses these challenges and identifies methodological priorities for developing dementia risk algorithms that are robust and suitable for real-world implementation.
Publication (Name of Journal)
International Journal of Public Health
DOI
https://doi.org/10.3389/ijph.2026.1609520
Recommended Citation
Nistor, P.,
Espin-Garcia, O.,
Merali, Z.,
Ali, S.
(2026). Predicting dementia risk: Progress, pitfalls, and priorities. International Journal of Public Health, 71, 1-3.
Available at:
https://ecommons.aku.edu/bmi/504
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.