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.
FUNDING: National Institute for Health Research, Wellcome Trust, WHO, US Alzheimer's Association, and European Research Council.
METHOD: Data were obtained from 36 members of COSMIC, representing 28 countries across 6 continents (HICs: Australia, Canada, Faroe Islands, France, Germany, Greece, Italy, Japan, Netherlands, South Korea, Spain, Sweden, & USA; LMICs: Brazil, China, Cuba, Dominican Republic, Ecuador, Indonesia, Malaysia, Mexico, Nigeria, Peru, Philippines, Republic of Congo, & Tanzania). For each member study, we calculated incidence rates for all-cause dementia. Findings from 14 studies, with a consensus diagnosis are presented in the results. Using an Item Response Theory approach, we are currently calculating a comparable incidence rate for those studies without a consensus diagnosis.
RESULT: Consistent with previous trends, incidence rates (per 100 person-years) increased with age, from 65-70 years-old to 85-90 years-old, for both males (i.e., Republic of Congo, 4.41 to 19.57; France, 0.46 to 3.89; USA, 0.17 to 3.22; Spain, 0.31 to 4.22; 65-70 & 85-90 cohorts respectively) and females (i.e., Republic of Congo, 3.57 to 15.31; France, 0.45 to 3.72; USA, 0.22 to 4.25; Spain, 0.36 to 4.96; 65-70 & 85-90 cohorts respectively). There were no sex differences in incidence rates in younger age groups (60-65). Among older age groups, however, women tended to have higher incidence rates than men, in some countries (Faroe Islands, Germany, Sweden, and USA).
CONCLUSION: Geographical differences in dementia incidence rates likely represent inherent variation among countries, beyond methodological considerations. We are working to expand the range of studies and regions for which we calculate dementia incidence rates. This involves the development of approaches to classify and harmonise incident dementia in studies lacking consensus diagnoses. Doing so will bolster LMIC representation.