Prediction of dementia risk in low-income and middle-income countries (the 10/66 Study): an independent external validation of existing models

Abstract

Background To date, dementia prediction models have been exclusively developed and tested in high-income countries (HICs). However, most people with dementia live in low-income and middle-income countries (LMICs), where dementia risk prediction research is almost non-existent and the ability of current models to predict dementia is unknown. This study investigated whether dementia prediction models developed in HICs are applicable to LMICs. MethodsData 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.

A Novel BrainHealth Index Prototype Improved by Telehealth-Delivered Training During COVID-19

“…The cognitive capital of a country is also crucial for its economic prosperity because it enables populations to pivot more adeptly and adaptively in the face of economic shocks, rapid technological change, and environmental challenges ( 4 , 5 ). An absence or reduction of such a flexible mindset leads to economic and social deterioration that will worsen mental and physical well-being ( 6 ).…”

Development and Validation of a Novel Dementia Risk Score in the UK Biobank Cohort

“…These authors [14] offer an explanation of their results that may also extend to our findings. [20,42,13,43]; ANU-ADRI: 0.49 to 0.78 [44,42,13], DRS: 0.56 to 0.84 [18,13]). Among the risk scores assessed, the ANU-ADRI had the poorest discrimination accuracy (AUC 0.45 in WHII) relative to the UKB-DRS.…”

“…Further, these cohorts are predominately Caucasian, based in the UK, and are less likely to live in socioeconomically deprived areas than the general population [48,16]. Given that a recent study showed that not all dementia risk models developed in high-income countries are applicable to low and middle-income countries [42], we emphasise the need to evaluate the UKB-DRS across a diverse range of cohorts before translating it for use in routine care. Second, with respect to other existing risk models, this study (and several others) has shown that while dementia risk scores may have good-tomoderate performance in their development cohorts, they typically have reduced predictive accuracy when applied under different settings.…”

Challenges in dementia risk prediction in low-income and middle-income countries

“…1 However, the greatest increase in the number of people with dementia will be in developing countries, 2 for which research on dementia is often limited or non-existent. Blossom Stephan and colleagues' study 3 in The Lancet Global Health fills this research gap. The authors selected five dementia risk prediction models developed in HICs and assessed their validity in low-income and middle-income countries (LMICs), using data from the 10/66 Study.…”