TY - JOUR
T1 - Prediction of dementia risk in low-income and middle-income countries (the 10/66 Study)
T2 - an independent external validation of existing models
AU - Stephan, Blossom C.M.
AU - Pakpahan, Eduwin
AU - Siervo, Mario
AU - Licher, Silvan
AU - Muniz-Terrera, Graciela
AU - Mohan, Devi
AU - Acosta, Daisy
AU - Rodriguez Pichardo, Guillermina
AU - Sosa, Ana Luisa
AU - Acosta, Isaac
AU - Llibre-Rodriguez, Juan J.
AU - Prince, Martin
AU - Robinson, Louise
AU - Prina, Matthew
N1 - Funding Information:
This is a secondary analysis of data collected by the 10/66 Dementia Research Group, which is led by Martin Prince (Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK). The other principal investigators, data custodians, and individuals responsible for research governance in each site are Juan Llibre Rodriguez (Cuba), Daisy Acosta (Dominican Republic), Mariella Guerra (Peru), Aquiles Salas (Venezuela), Ana Luisa Sosa (Mexico), KS Jacob (Vellore, India), Joseph D Williams (Chennai, India), Ivonne Jimenez (Puerto Rico) and Yueqin Huang (China). The 10/66 Dementia Research Group's research has been funded by the Wellcome Trust Health Consequences of Population Change Programme (GR066133 [prevalence phase in Cuba and Brazil] and GR080002 [incidence phase in Peru, Mexico, Argentina, Cuba, Dominican Republic, Venezuela, and China]), WHO (India, Dominican Republic, and China), the US Alzheimer's Association (IIRG–04–1286 [Peru, Mexico, and Argentina]), the Puerto Rico State Legislature (Puerto Rico), and FONACIT/CDCH/UCV (Venezuela). The study also has funding from the European Research Council (ERC-2013-ADG 340755 LIFE2YEARS1066). This analysis was funded by the National Institute for Health Research (NIHR; project reference 16/137/62) using aid from the UK Government to support global health research. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR or the UK Department of Health and Social Care.
Funding Information:
This is a secondary analysis of data collected by the 10/66 Dementia Research Group, which is led by Martin Prince (Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK). The other principal investigators, data custodians, and individuals responsible for research governance in each site are Juan Llibre Rodriguez (Cuba), Daisy Acosta (Dominican Republic), Mariella Guerra (Peru), Aquiles Salas (Venezuela), Ana Luisa Sosa (Mexico), KS Jacob (Vellore, India), Joseph D Williams (Chennai, India), Ivonne Jimenez (Puerto Rico) and Yueqin Huang (China). The 10/66 Dementia Research Group's research has been funded by the Wellcome Trust Health Consequences of Population Change Programme (GR066133 [prevalence phase in Cuba and Brazil] and GR080002 [incidence phase in Peru, Mexico, Argentina, Cuba, Dominican Republic, Venezuela, and China]), WHO (India, Dominican Republic, and China), the US Alzheimer's Association (IIRG?04?1286 [Peru, Mexico, and Argentina]), the Puerto Rico State Legislature (Puerto Rico), and FONACIT/CDCH/UCV (Venezuela). The study also has funding from the European Research Council (ERC-2013-ADG 340755 LIFE2YEARS1066). This analysis was funded by the National Institute for Health Research (NIHR; project reference 16/137/62) using aid from the UK Government to support global health research. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR or the UK Department of Health and Social Care.
Publisher Copyright:
© 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/4/1
Y1 - 2020/4/1
N2 - Background: To 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. 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.
AB - Background: To 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. 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.
UR - http://www.scopus.com/inward/record.url?scp=85081680566&partnerID=8YFLogxK
U2 - 10.1016/S2214-109X(20)30062-0
DO - 10.1016/S2214-109X(20)30062-0
M3 - Article
C2 - 32199121
AN - SCOPUS:85081680566
SN - 2214-109X
VL - 8
SP - e524-e535
JO - The Lancet Global Health
JF - The Lancet Global Health
IS - 4
ER -