OBJECTIVE: Given this information, this study systematically explores what risk factors may be associated with ADRD in Indigenous populations.
METHODS: A search of all published literature was conducted in October 2016, March 2018, and July 2019 using Medline, Embase, and PsychINFO. Subject headings explored were inclusive of all terms related to Indigenous persons, dementia, and risk. All relevant words, phrases, and combinations were used. To be included in this systematic review, articles had to display an association of a risk factor and ADRD. Only studies that reported a quantifiable measure of risk, involved human subjects, and were published in English were included.
RESULTS: Of 237 articles originally identified through database searches, 45 were duplicates and 179 did not meet a priori inclusion criteria, resulting in 13 studies eligible for inclusion in this systematic review.
CONCLUSION: The large number of potentially modifiable risk factors reported relative to non-modifiable risk factors illustrates the importance of socioeconomic context in the pathogenesis of ADRD in Indigenous populations. The tendency to prioritize genetic over social explanations when encountering disproportionately high disease rates in Indigenous populations can distract from modifiable proximal, intermediate, and distal determinants of health.
Methodology: WESIHAT 2.0 was devised in a senior-friendly style, which includes touch screen, greater font size, larger icons, and employed multimedia components of text, images, and videos. The components employed in WESIHAT 2.0 were a screening tool called TUA-WELLNESS, 10 guides for memory improvement, health diary, and guide for a healthy menu. This application assessed a group of 73 candidates consisting of elderly people, health professionals, caregivers, and information technology (IT) professionals for 1 month.
Results: All the elderly people, caregivers, and 75% of IT and health professionals were satisfied with the subject matter of WESIHAT 2.0. About more than half of the elderly people, caregivers, and IT and health professionals had given a consensus on the comprehensive ease of the terminologies, sentences, images, table, and advice related to diet included in the web application. Proposals for improvements of the web portal included suggestions such as using smaller sentences, using greater font size, adding more images, and avoiding the use of unfamiliar terminologies.
Conclusion: WESIHAT 2.0 is a suitable tool for educating older people about the lifestyle modification strategies to slower progression to cognitive impairment, with regard to the significance of expert advice.
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.