Geographical variation in dementia prevalence across China: a geospatial analysis

Publication
BMC Primary Care

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).

Keywords: China; Dementia; Geographical analysis; Prevalence.

Yixuan Liu
Yixuan Liu
Master of Science in Epidemiology and Biostatistics, 2021-2024

geospatial analysis of dementia prevalence among older adults

Yongjin Zhang
Yongjin Zhang
Master of Science in Epidemiology and Biostatistics, 2022-2025

Alzheimer’s disease prediction model

Minrui Zeng
Minrui Zeng
Master of Science in Epidemiology and Biostatistics, 2021-2024

scenario analysis in dementia care

Yanjuan Wu
Yanjuan Wu
Master of Science in Epidemiology and Biostatistics, 2020-2023

Alzheimer’s socioeconomic costs prediction

Jing Liao
Jing Liao
Associate professor, Department of Medical Statistics & Epidemiology| SYSU Global Health Institute (SGHI), Sun Yat-sen University, China

Healthy ageing dynamics, examining social networks × health behaviors × multidimensional functioning (physical/cognitive/social). Uses longitudinal cohort modeling (global datasets) to pinpoint socio-determinants, with RCT-validated interventions.