Dataset Information

Approximating dementia prevalence in population-based surveys of aging worldwide: An unsupervised machine learning approach.

ABSTRACT:

Introduction

Ability to determine dementia prevalence in low- and middle-income countries (LMIC) remains challenging because of frequent lack of data and large discrepancies in dementia case ascertainment.

Methods

High likelihood of dementia was determined with hierarchical clustering after principal component analysis applied in 10 population surveys of aging: HRS (USA, 2014), SHARE (Europe and Israel, 2015), MHAS (Mexico, 2015), ELSI (Brazil, 2016), CHARLS (China, 2015), IFLS (Indonesia, 2014-2015), LASI (India, 2016), SAGE-Ghana (2007), SAGE-South Africa (2007), SAGE-Russia (2007-2010). We approximated dementia prevalence using weighting methods.

Results

Estimated numbers of dementia cases were: China, 40.2 million; India, 18.0 million; Russia, 5.2 million; Europe and Israel, 5.0 million; United States, 4.4 million; Brazil, 2.2 million; Mexico, 1.6 million; Indonesia, 1.3 million; South Africa, 1.0 million; Ghana, 319,000.

Discussion

Our estimations were similar to prior ones in high-income countries but much higher in LMIC. Extrapolating these results globally, we suggest that almost 130 million people worldwide were living with dementia in 2015.

SUBMITTER: Cleret de Langavant L

PROVIDER: S-EPMC7453145 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Approximating dementia prevalence in population-based surveys of aging worldwide: An unsupervised machine learning approach.

Cleret de Langavant Laurent L Bayen Eléonore E Bachoud-Lévi Anne-Catherine AC Yaffe Kristine K

Alzheimer's & dementia (New York, N. Y.) 20200827 1

<h4>Introduction</h4>Ability to determine dementia prevalence in low- and middle-income countries (LMIC) remains challenging because of frequent lack of data and large discrepancies in dementia case ascertainment.<h4>Methods</h4>High likelihood of dementia was determined with hierarchical clustering after principal component analysis applied in 10 population surveys of aging: HRS (USA, 2014), SHARE (Europe and Israel, 2015), MHAS (Mexico, 2015), ELSI (Brazil, 2016), CHARLS (China, 2015), IFLS (I ...[more]

PMID: 32885026

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Dataset Information

Approximating dementia prevalence in population-based surveys of aging worldwide: An unsupervised machine learning approach.

Introduction

Methods

Results

Discussion

Publications

Approximating dementia prevalence in population-based surveys of aging worldwide: An unsupervised machine learning approach.

Similar Datasets

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