Evidence map›Paper›PMID 39185089›Full record

ArticleThe Lancet regional health. Western Pacific2024

Projection for dementia burden in China to 2050: a macro-simulation study by scenarios of dementia incidence trends.

Yuyang Liu, Yanjuan Wu, Yuntao Chen, Sophia Lobanov-Rostovsky, Yixuan Liu, Minrui Zeng, Piotr Bandosz, Dong Roman Xu, Xueqin Wang, Yuanli Liu and 4 more

Abstract read
In one paragraph

Article in The Lancet regional health. Western Pacific, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

29 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  8. Projects to support people with dementia, Shanghai, China.Bulletin of the World Health Organization · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Yuyang LiuDepartment of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, 510000, China.
Yanjuan WuDepartment of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, 510000, China.
Yuntao ChenDepartment of Epidemiology & Public Health, University College London, London, WC1E 7HB, UK.
Sophia Lobanov-RostovskyDepartment of Epidemiology & Public Health, University College London, London, WC1E 7HB, UK.
Yixuan LiuDepartment of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, 510000, China.
Minrui ZengDepartment of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, 510000, China.
Piotr BandoszDepartment of Prevention and Medical Education, Medical University of Gdansk, Gdansk, 80-210, Poland.
Dong Roman XuAcacia Lab for Implementation Science, SMU Institute for Global Health (SIGHT) and Center for World Health Organization Studies, School of Health Management and Dermatology Hospital of Southern Medical University (SMU), Guangzhou, China.
Xueqin WangDepartment of Statistics and Finance/International Institute of Finance, School of Management, University of Science and Technology of China, Hefei, China.
Yuanli LiuSchool of Health Policy and Management, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Yuantao HaoPeking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100871, China.
Eric FrenchFaculty of Economics, University of Cambridge, Cambridge, CB2 3AX, UK.
Eric J BrunnerDepartment of Epidemiology & Public Health, University College London, London, WC1E 7HB, UK.
Jing LiaoDepartment of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, 510000, China.

Funding

World Health Organization 001
6 · The paper itself

Abstract

Background: It is unclear how temporal trends in dementia incidence, alongside fast-changing demography, will influence China's future dementia burden. We developed a Markov model that combines population trends in dementia, mortality, and dementia-related comorbidities, to forecast and decompose the burden of dementia in China to 2050. Methods: Population-based Chinese ageing cohorts provided input data for a 10-health-state Markov macrosimulation model, IMPACT-China Ageing Model (CAM), to predict sex- and age-specific dementia prevalence among people aged 50+ by year to 2050. We assumed three potential future scenarios representing the range of likely dementia incidence trends: upward (+2.9%), flat (0%) or downward (-1.0%). Sensitivity analyses were conducted to examine uncertainty associated with trends in mortality rates and CVD incidence. The projected dementia burden was decomposed into population growth, population ageing, and changing dementia prevalence corresponding to the three incidence trend scenarios. Findings: Under the upward trend scenario, the estimated number of people living with dementia is projected to rise to 66.3 million (95% uncertainty interval (UI) 64.7-68.0 million), accounting for 10.4% of the Chinese population aged 50+ by 2050. This large burden will be lower, 43.9 (95% UI 42.9-45.0) million and 37.5 (95% UI 36.5-38.4) million, if dementia incidence remains constant or decreases. Robustness of the projection is confirmed by sensitivity analyses. Decomposition of the change in projected dementia cases indicates dominate effects of increasing dementia prevalence and population ageing, and a relatively minor contribution from negative population growth. Interpretation: Our findings highlight an impending surge in dementia cases in China in the forthcoming decades if the upward trend in dementia incidence continues. Public health interventions geared towards dementia prevention could play a pivotal role in alleviating this burgeoning disease issue. Funding: National Science Foundation of China/UK Economic and Social Research Council.

Indexed as

ChinaDecompose analysisDementia incidence trendDementia prevalence projectionModelling study

Identifiers

PMID39185089
PMCPMC11342197

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.