Analysis of spatial distribution characteristics and influencing factors of depression among elderly people in China

Objective Using spatial epidemiological methods to explore the spatial distribution characteristics and influencing factors of depression among elderly people in China at the provincial and municipal scales.Methods Based on the data from the fifth round of China Health and Retirement Longitudinal Study in 2020, ArcGIS Pro was used to analyze the spatial distribution characteristics of depression incidence in the elderly. The differences between the depression group and the non depression group were compared, and multiple Logistic regression analysis was used to explore the influencing factors of depression in the elderly.Results A total of 8,351 elderly people were included. The detection rate of depression symptoms among elderly people in China was 40.97%, with 31.84% in urban areas and 47.19% in rural areas. The detection rate of depression among elderly people in Qinghai, Gansu, Chongqing, and Hubei were relatively high, while the detection rate were lower in Beijing, Tianjin, Shanghai, and Zhejiang. Spatial statistical analysis showed that the distribution of depression detection rates among the elderly exhibited significant spatial clustering. The "high-high" clustering areas of depression detection rates were mainly concentrated in the northwest, southwest, and central China regions, while the "low-low" clustering areas were concentrated around urban agglomerations in coastal areas such as Beijing-Tianjin-Hebei, Yangtze River Delta, and Pearl River Delta. Multivariate Logistic regression analysis revealed that factors such as gender, current marital status, educational level, self-rated health status, life satisfaction, outpatient visits to medical institutions in the past month, physical pain, physical activity, activity of daily living (ADL), and stroke, emotional, neurological or mental problems, memory related diseases, and the number of chronic diseases were influencing factors for depression in the elderly (P

Publication Details

Journal
SHILAP Revista de lepidopterología
Published
2025-03-01
DOI
https://doi.org/10.12173/j.issn.1004-5511.202408073
Primary Topic
Mycorrhizal Fungi and Plant Interactions
Type
article
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Analysis of spatial distribution characteristics and influencing factors of depression among elderly people in China

SHILAP Revista de lepidopterología
Mycorrhizal Fungi and Plant Interactions
article

Analysis of spatial distribution characteristics and influencing factors of depression among elderly people in China

article en

Abstract

Objective Using spatial epidemiological methods to explore the spatial distribution characteristics and influencing factors of depression among elderly people in China at the provincial and municipal scales.Methods Based on the data from the fifth round of China Health and Retirement Longitudinal Study in 2020, ArcGIS Pro was used to analyze the spatial distribution characteristics of depression incidence in the elderly. The differences between the depression group and the non depression group were compared, and multiple Logistic regression analysis was used to explore the influencing factors of depression in the elderly.Results A total of 8,351 elderly people were included. The detection rate of depression symptoms among elderly people in China was 40.97%, with 31.84% in urban areas and 47.19% in rural areas. The detection rate of depression among elderly people in Qinghai, Gansu, Chongqing, and Hubei were relatively high, while the detection rate were lower in Beijing, Tianjin, Shanghai, and Zhejiang. Spatial statistical analysis showed that the distribution of depression detection rates among the elderly exhibited significant spatial clustering. The "high-high" clustering areas of depression detection rates were mainly concentrated in the northwest, southwest, and central China regions, while the "low-low" clustering areas were concentrated around urban agglomerations in coastal areas such as Beijing-Tianjin-Hebei, Yangtze River Delta, and Pearl River Delta. Multivariate Logistic regression analysis revealed that factors such as gender, current marital status, educational level, self-rated health status, life satisfaction, outpatient visits to medical institutions in the past month, physical pain, physical activity, activity of daily living (ADL), and stroke, emotional, neurological or mental problems, memory related diseases, and the number of chronic diseases were influencing factors for depression in the elderly (P

SHILAP Revista de lepidopterología
Good health and well-being
Openalex Percentile: Top 52%
Mycorrhizal Fungi and Plant Interactions
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