OBJECTIVE: This systematic review aimed to review epidemiological reports to determine the prevalence of MCI and its associated risk factors in LMICs.
METHODS: Medline, Embase, and PsycINFO were searched from inception until November 2019. Eligible articles reported on MCI in population or community-based studies from LMICs and were included as long as MCI was clearly defined.
RESULTS: 5,568 articles were screened, and 78 retained. In total, n = 23 different LMICs were represented; mostly from China (n = 55 studies). Few studies were from countries defined as lower-middle income (n = 14), low income (n = 4), or from population representative samples (n = 4). There was large heterogeneity in how MCI was diagnosed; with Petersen criteria the most commonly applied (n = 26). Prevalence of amnesic MCI (aMCI) (Petersen criteria) ranged from 0.6%to 22.3%. Similar variability existed across studies using the International Working Group Criteria for aMCI (range 4.5%to 18.3%) and all-MCI (range 6.1%to 30.4%). Risk of MCI was associated with demographic (e.g., age), health (e.g., cardio-metabolic disease), and lifestyle (e.g., social isolation, smoking, diet and physical activity) factors.
CONCLUSION: Outside of China, few MCI studies have been conducted in LMIC settings. There is an urgent need for population representative epidemiological studies to determine MCI prevalence in LMICs. MCI diagnostic methodology also needs to be standardized. This will allow for cross-study comparison and future resource planning.
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.