Evidence map›Paper›PMID 38974661›Full record

ArticleThe Lancet regional health. Western Pacific2024

Geographical variation in dementia prevalence across China: a geospatial analysis.

Yixuan Liu, Xinyuan Gao, Yongjin Zhang, Minrui Zeng, Yuyang Liu, Yanjuan Wu, Weihua Hu, Yingsi Lai, Jing Liao

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 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
–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

27 citing papers in PubMed.

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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

9 authors.

Yixuan LiuDepartment of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong 510000, China.
Xinyuan GaoDepartment of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong 510000, China.
Yongjin ZhangDepartment 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.
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.
Weihua HuPeking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100871, China.
Yingsi LaiDepartment of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong 510000, China.
Jing LiaoDepartment of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong 510000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Dementia poses great health and social challenges in China. Dementia prevalence may vary across geographic areas, while comparable estimations on provincial level is lacking. This study aims to estimate dementia prevalence by provinces across China, taking into account risk factors of individual level and potential spatial correlation of provinces. Methods: In this study, 17,176 adults aged 50 years or older were included from the fourth wave of the China Health and Retirement Longitudinal Study (CHARLS 2018), covering 28 provinces, autonomous regions and municipalities. To improve provincial representativeness, we constructed provincial survey weights based on China 7th census (2020). The prevalence of dementia and 95% Bayesian credible intervals (BCIs) were estimated using a Bayesian conditional autoregressive (CAR) model with spatially varying coefficients of covariates. Findings: The weighted prevalence of dementia at provincial level in China in 2018 ranged from 2.62% (95%BCI: 1.70%, 3.91%) to 13.53% (95%BCI: 8.82%, 20.93%). High dementia prevalence was concentrated in North China, with a prominent high-high cluster, while provinces of low prevalence were concentrated on East and South China, characterized by a low-low cluster. Ordered by the median estimation of prevalence, the top 10% of provinces, include Xinjiang, Jilin, and Beijing. Meanwhile, Fujian, Zhejiang, and Guangdong rank among the last. The association between dementia prevalence and drinking, smoking, social isolation, physical inactivity, hearing impairment, hypertension, and diabetes exhibits provincial variation. Interpretation: Our study identifies a geospatial disparity in dementia prevalence and risk factor effects across China's provinces, with high-high and low-low clusters in some northern and southern provinces, respectively. The findings emphasize the need for targeted strategies, such as addressing hypertension and hearing impairment, in specific regions for more effective dementia prevention and treatment. Funding: National Science Foundation of China/the Economic and Social Research Council, UK Research and Innovation joint call: Understanding and Addressing Health and Social Challenges for Ageing in the UK and China. UK-China Health And Social Challenges Ageing Project (UKCHASCAP): present and future burden of dementia, and policy responses (grant number 72061137003, ES/T014377/1).

Indexed as

ChinaDementiaGeographical analysisPrevalence

Identifiers

PMID38974661
PMCPMC11225804

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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.