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.
METHODOLOGY: The data for this study, consisting of 2926 community-dwelling older adults, were obtained from the National survey entitled "Mental Health and Quality of Life of Older Malaysians." Dementia was diagnosed using the Geriatric Mental State-Automated Geriatric Examination for Computer-Assisted Taxonomy.
RESULTS: Prevalence of dementia was considerably higher among older adults with gastritis (29.5%) compared to those without gastritis (13.2%). After adjusting for age, gender, marital status, educational attainment, hypertension, stroke, and diabetes, gastritis was significantly associated with more than twice odds of dementia (adjusted odds ratio = 2.42, P < .001, 95% confidence interval = 1.68-3.49).
CONCLUSIONS: The findings from this population-based observational study showing evidence that gastritis may increase the risk of dementia provide avenue for further inquiries into dementia.
METHODS: Data from TUA cohort study involving 1366 older adults (aged 60 years and above) categorized as low-income were analysed, for risk of MCR syndrome based on defined criteria. Chi-square analysis and independent t test were employed to examine differences in socioeconomic, demographic, chronic diseases and lifestyle factors between MCR and non-MCR groups. Risk factors of MCR syndrome were determined using hierarchical logistic regression.
RESULTS: A total of 3.4% of participants fulfilled the criteria of MCR syndrome. Majority of them were female (74.5%, p = 0.001), single/widow/widower/divorced (55.3%, p = 0.002), living in rural area (72.3%, p = 0.011), older age (72.74 ± 7.08 year old, p dementia, especially among individuals with low socioeconomic status.
METHODOLOGY: Data were derived from a group of 1210 Malaysian elderly individuals who were noninstitutionalized and demented. The multiple logistic regression model was applied to estimate the risk of falls in respondents.
RESULTS: Approximately the prevalence of falls was 17% among the individuals. The results of multiple logistic regression analysis revealed that age (odds ratio [OR] = 1.03), ethnicity (OR = 1.76), sleep quality (OR = 1.46), and environmental quality (OR = 0.62) significantly affected the risk of falls in individuals (P < .05). Furthermore, sex differences, marital status, educational level, and physical activity were not significant predictors of falls in samples (P > .05).
CONCLUSION: It was found that age, ethnic non-Malay, and sleep disruption increased the risk of falls in respondents, but high environmental quality reduced the risk of falls.