Evidence mapPaperPMID 36337863Full record

ArticleFrontiers in cardiovascular medicine2022

Self-reported prevalence and potential factors influencing cardio-cerebral vascular disease among the Chinese elderly: A national cross-sectional study.

Lingbing Meng, Jiapei Xu, Jianyi Li, Jiabin Hu, Hongxuan Xu, Dishan Wu, Xing Hu, Xuezhai Zeng, Qiuxia Zhang, Juan Li and 2 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in cardiovascular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
2.3field-weighted citation impact, top 10% of its field
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

9 citing papers in PubMed, 14 citations in OpenAlex.

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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

12 authors at 2 institutions in 1 country.

Lingbing MengDepartment of Cardiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
Jiapei XuDepartment of Cardiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
Jianyi LiDepartment of Cardiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
Jiabin HuHealth Service Department of the Guard Bureau of the Joint Staff Department, Beijing, China.
Hongxuan XuDepartment of Cardiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
Dishan WuDepartment of Cardiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
Xing HuHealth Service Department of the Guard Bureau of the Joint Staff Department, Beijing, China.
Xuezhai ZengDepartment of Cardiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
Qiuxia ZhangChina Research Center on Aging, Beijing, China.
Juan LiCenter on Aging Psychology, Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
Tao GongDepartment of Neurology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
Deping LiuDepartment of Cardiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
Chinese Academy of Medical Sciences & Peking Union Medical College · CNInstitute of Psychology, Chinese Academy of Sciences · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Aging is an essential national condition throughout China in the 21st century. Cardio-cerebral vascular disease (CCVD) is a common chronic vascular disease in the elderly. Despite aging becoming an increasingly pressing issue, there has been no comprehensive national investigation into the risk factors, prevalence, and management of CCVD among the elderly population in China. Materials and methods: Through the 4th Survey of the Aged Population in Urban and Rural China (SSAPUR), a nationally representative sample of 224,142 adults aged more than 60 years was surveyed using a multistage, stratified sampling method. The 4th SSAPUR was used to investigate CCVD in the elderly. Univariate and multivariate logistic proportional regression analyses explored the risk factors. These risk factors were then entered into a multivariate linear regression model to identify independent predictive factors for CCVD. Disease management was assessed from the self-reported history of physician diagnosis, treatments, and hospital visits among individuals with CCVD. Results: After excluding samples with missing information, 215,041 individuals were included in the analysis. The overall prevalence of CCVD was 26%. Living in a rural area, being older, being female, having low literacy, smoking, getting little sleep, losing a spouse, being single, not getting enough exercise, having a bad financial situation, and not taking part in public welfare programs were the main risk factors for CCVD among the elderly in China ( Conclusion: Major risk factors for prevalent CCVD among the elderly in China include the following: rural residence, female, low literacy level, poor sleep quality, bereavement, non-marriage, living alone, lack of exercise, poor financial situation, and non-participation in public welfare activities. Chinese national policies for preventing, controlling, and managing risk factors for CCVD in the elderly must be urgently developed.

Indexed as

CCVDelderlymultivariate linear regression modelrisk factorsSSAPUR

Identifiers

PMID36337863
PMCPMC9630358
OpenAlexW4306946957

What Socratic holds

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LicenceCC BY
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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 Socratic graph.