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.
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: 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 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.
MATERIALS AND METHODS: Data were used from the Well-being of the Singapore Elderly (WiSE) study, a nationally representative, cross-sectional survey among Singapore residents aged 60 years and above. Caregiver dependence was ascertained by asking the informant (the person who knows the older person best) a series of open-ended questions about the older person's care needs.
RESULTS: The older adult sample comprised 57.1% females and the majority were aged 60 to 74 years (74.8%), while 19.5% were 75 to 84 years, and 5.7% were 85 years and above. The prevalence of caregiver dependence was 17.2% among older adults. Significant sociodemographic risk factors of caregiver dependence included older age (75 to 84 years, and 85 years and above, P <0.001), Malay and Indian ethnicity (P <0.001), those who have never been married (P = 0.048) or have no education (P = 0.035), as well as being homemakers or retired (P <0.001). After adjusting for sociodemographic variables and all health conditions in multiple logistic regression analyses, dementia (P <0.001), depression (P = 0.011), stroke (P = 0.002), eyesight problems (P = 0.003), persistent cough (P = 0.016), paralysis (P <0.001), asthma (P = 0.016) and cancer (P = 0.026) were significantly associated with caregiver dependence.
CONCLUSION: Various sociodemographic and health-related conditions were significantly associated with caregiver dependence. Dependent older adults will put greater demands on health and social services, resulting in greater healthcare expenditures. Hence, effective planning, services and support are crucial to meet the needs of dependent older adults and their caregivers.