BACKGROUND: Most people with dementia live in LMICs, underscoring the need for LMIC-specific identification of high-risk individuals. This study aimed to develop and validate a simple dementia risk prediction model for these settings. METHODS: Data from seven 10/66 Study sites were analyzed. Over 100 candidate predictors were screened based on existing models and the 2024 Lancet Commission, including LMIC-specific variables (eg, food insecurity and household assets). Predictors were selected using LASSO and modelled with the Fine-Gray method to generate a risk score. Predictive accuracy was pooled via meta-analysis. RESULTS: 11143 participants were included, among whom 1069 (9.6%) developed dementia during follow-up. A five-factor risk score comprising age, social engagement, physical activity, hypertension, and difficulty in handling money was developed. The pooled c-statistic was 0.75 (95% CI: 0.72-0.78), with good calibration across sites. Decision curve analysis showed a modest net benefit, with variation across countries. CONCLUSION: It is possible to predict incident dementia with reasonable accuracy using a simple model across different LMICs. Our findings support the use of context-specific risk assessment tools to identify individuals at elevated dementia risk in LMIC settings, which may inform resource allocation for dementia care services and public health planning.
Pakpahan et al. (Thu,) studied this question.