Prediction of dementia risk in low-income and middle-income countries (the 10/66 Study): an independent external validation of existing models

BackgroundTo date, dementia prediction models have been exclusively developed and tested in high-income countries (HICs). However, most people with dementia live in low-income and middle-income countries (LMICs), where dementia risk prediction research is almost non-existent and the ability of current models to predict dementia is unknown. This study investigated whether dementia prediction models developed in HICs are applicable to LMICs.MethodsData 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.Findings11 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.InterpretationNot 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

"The contribution of chronic diseases to the prevalence of dependence among older people in Latin America, China and India: a 10/66 Dementia Research Group population-based survey"

<p>Abstract</p> <p>Background</p> <p>The number of older people is set to increase dramatically worldwide. Demographic changes are likely to result in the rise of age-related chronic diseases which largely contribute to years lived with a disability and future dependence. However dependence is much less studied although intrinsically linked to disability. We investigated the prevalence and correlates of dependence among older people from middle income countries.</p> <p>Methods</p> <p>A one-phase cross-sectional survey was carried out at 11 sites in seven countries (urban sites in Cuba, Venezuela, and Dominican Republic, urban and rural sites in Peru, Mexico, China and India). All those aged 65 years and over living in geographically defined catchment areas were eligible. In all, 15,022 interviews were completed with an informant interview for each participant. The full 10/66 Dementia Research Group survey protocol was applied, including ascertainment of depression, dementia, physical impairments and self-reported diagnoses. Dependence was interviewer-rated based on a key informant's responses to a set of open-ended questions on the participant's needs for care. We estimated the prevalence of dependence and the independent contribution of underlying health conditions. Site-specific prevalence ratios were meta-analysed, and population attributable prevalence fractions (PAPF) calculated.</p> <p>Results</p> <p>The prevalence of dependence increased with age at all sites, with a tendency for the prevalence to be lower in men than in women. Age-standardised prevalence was lower in all sites than in the USA. Other than in rural China, dementia made the largest independent contribution to dependence, with a median PAPF of 34% (range 23%-59%). Other substantial contributors were limb impairment (9%, 1%-46%), stroke (8%, 2%-17%), and depression (8%, 1%-27%).</p> <p>Conclusion</p> <p>The demographic and health transitions will lead to large and rapid increases in the numbers of dependent older people particularly in middle income countries (MIC). The prevention and control of chronic neurological and neuropsychiatric diseases and the development of long-term care policies and plans should be urgent priorities.</p

Measuring disability across cultures - the psychometric properties of the WHODAS II in older people from seven low- and middle-income countries. The 10/66 Dementia Research Group population-based survey

We evaluated the psychometric properties of the 12-item interviewer-administered screener version of the World Health Organization Disability Assessment Schedule - version II (WHODAS II) among older people living in seven low- and middle-income countries. Principal component analysis (PCA), confirmatory factor analysis (CFA) and Mokken analyses were carried out to test for unidimensionality, hierarchical structure, and measurement invariance across 10/66 Dementia Research Group sites. PCA generated a one-factor solution in most sites. In CFA, the two-factor solution generated in Dominican Republic fitted better for all sites other than rural China. The two factors were not easily interpretable, and may have been an artefact of differing item difficulties. Strong internal consistency and high factor loadings for the one-factor solution supported unidimensionality. Furthermore, the WHODAS II was found to be a &apos;strong&apos; Mokken scale. Measurement invariance was supported by the similarity of factor loadings across sites, and by the high between-site correlations in item difficulties. The Mokken results strongly support that the WHODAS II 12-item screener is a unidimensional and hierarchical scale confirming to item response theory (IRT) principles, at least at the monotone homogeneity model level. More work is needed to assess the generalizability of our findings to different populations. Copyright (C) 2010 John Wiley &amp; Sons, Ltd.PsychiatrySCI(E)SSCI33ARTICLE11-171

Contribution of chronic diseases to disability in elderly people in countries with low and middle incomes: a 10/66 Dementia Research Group population-based survey

Background Disability in elderly people in countries with low and middle incomes is little studied; according to Global Burden of Disease estimates, visual impairment is the leading contributor to years lived with disability in this population. We aimed to assess the contribution of physical, mental, and cognitive chronic diseases to disability, and the extent to which sociodemographic and health characteristics account for geographical variation in disability. Methods We undertook cross-sectional surveys of residents aged older than 65 years (n=15 022) in 11 sites in seven countries with low and middle incomes (China, India, Cuba, Dominican Republic, Venezuela, Mexico, and Peru). Disability was assessed with the 12-item WHO disability assessment schedule 2.0. Dementia, depression, hypertension, and chronic obstructive pulmonary disease were ascertained by clinical assessment; diabetes, stroke, and heart disease by self-reported diagnosis; and sensory, gastrointestinal, skin, limb, and arthritic disorders by self-reported impairment. Independent contributions to disability scores were assessed by zero-inflated negative binomial regression and Poisson regression to generate population-attributable prevalence fractions (PAPF). Findings In regions other than rural India and Venezuela, dementia made the largest contribution to disability (median PAPF 25.1% [IQR 19.2-43.6]). Other substantial contributors were stroke (11.4% [1.8-21.4]), limb impairment (10.5% [5.7-33.8]), arthritis (9.9% [3.2-34.8]), depression (8.3% [0.5-23.0]), eyesight problems (6.8% [1.7-17.6]), and gastrointestinal impairments (6.5% [0.3-23.1]). Associations with chronic diseases accounted for around two-thirds of prevalent disability. When zero inflation was taken into account, between-site differences in disability scores were largely attributable to compositional differences in health and sociodemographic characteristics. Interpretation On the basis of empirical research, dementia, not blindness, is overwhelmingly the most important independent contributor to disability for elderly people in countries with low and middle incomes. Chronic diseases of the brain and mind deserve increased prioritisation. Besides disability, they lead to dependency and present stressful, complex, long-term challenges to carers. Societal costs are enormous.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000272370200027&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Medicine, General &amp; InternalSCI(E)SSCI106ARTICLE97041821-183037