METHOD: We synthesised the evidence from our three previous systematic reviews (covering all literature from inception to 2023 from PubMed, Embase, and PsychInfo) on dementia risk prediction modelling. The aim was to identify models that have been specifically developed and tested specifically in LMICs. There were no language or time restrictions applied.

RESULT: To date, over 50 different dementia risk prediction models have been developed and tested with only 7 models reported from two LMICs including five studies from China and two studies from Mexico. The models incorporated variables typically linked to dementia including demographics (e.g., age, sex, education), health (e.g., diabetes, hypertension, heart disease) and lifestyle (e.g., smoking and alcohol) variables. The 7 models also have varying degrees of predictive accuracy (c-statistic range 0.65 [95%CI: 0.64-0.67] to 0.92 [95%CI: 0.88-0.95]) and none has undergone external validation. These models have been developed using traditional statistical approaches including Cox and Logistic Regression. Further, model development has not considered factors such as socioeconomic status, literacy, access to healthcare, diet, stress, pollution, and workplace hazards that may be crucial in predicting dementia risk in LMICs.

CONCLUSION: There is an urgent need to create context-specific dementia prediction models to inform the development of risk reduction and preventative interventions in LMICs where dementia case numbers are greatest. Dementia risk model development and testing need to be extended to LMICs across different regions (e.g., Asia, Middle East, Global South, Africa) and income levels (e.g., low, lower-middle, and upper-middle income).

METHODS: Data were from the 10/66 Study. Individuals aged 65 years or older and without dementia at baseline were selected from China, Cuba, the Dominican Republic, Mexico, Peru, Puerto Rico, and Venezuela. Dementia incidence was assessed over 3-5 years, with diagnosis according to the 10/66 Study diagnostic algorithm. Discrimination and calibration were tested for five models: the Cardiovascular Risk Factors, Aging and Dementia risk score (CAIDE); the Study on Aging, Cognition and Dementia (AgeCoDe) model; the Australian National University Alzheimer's Disease Risk Index (ANU-ADRI); the Brief Dementia Screening Indicator (BDSI); and the Rotterdam Study Basic Dementia Risk Model (BDRM). Models were tested with use of Cox regression. The discriminative accuracy of each model was assessed using Harrell's concordance (c)-statistic, with a value of 0·70 or higher considered to indicate acceptable discriminative ability. Calibration (model fit) was assessed statistically using the Grønnesby and Borgan test.

FINDINGS: 11 143 individuals without baseline dementia and with available follow-up data were included in the analysis. During follow-up (mean 3·8 years [SD 1·3]), 1069 people progressed to dementia across all sites (incidence rate 24·9 cases per 1000 person-years). Performance of the models varied. Across countries, the discriminative ability of the CAIDE (0·52≤c≤0·63) and AgeCoDe (0·57≤c≤0·74) models was poor. By contrast, the ANU-ADRI (0·66≤c≤0·78), BDSI (0·62≤c≤0·78), and BDRM (0·66≤c≤0·78) models showed similar levels of discriminative ability to those of the development cohorts. All models showed good calibration, especially at low and intermediate levels of predicted risk. The models validated best in Peru and poorest in the Dominican Republic and China.

INTERPRETATION: Not all dementia prediction models developed in HICs can be simply extrapolated to LMICs. Further work defining what number and which combination of risk variables works best for predicting risk of dementia in LMICs is needed. However, models that transport well could be used immediately for dementia prevention research and targeted risk reduction in LMICs.