Evidence map›Paper›PMID 38449590›Full record

ArticleHeliyon2024

Exploring relevant factors of cognitive impairment in the elderly Chinese population using Lasso regression and Bayesian networks.

Qiao Chen, Tianyi Zhou, Cong Zhang, Xiaoni Zhong

Open access · goldAbstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.5field-weighted citation impact, top 18% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
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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

4 authors at 2 institutions in 1 country.

Qiao ChenCollege of Public Health, Chongqing Medical University, Chongqing, 400016, China.
Tianyi ZhouCollege of Public Health, Chongqing Medical University, Chongqing, 400016, China.
Cong ZhangCollege of Public Health, Chongqing Medical University, Chongqing, 400016, China.
Xiaoni ZhongCollege of Public Health, Chongqing Medical University, Chongqing, 400016, China.
Chongqing Medical University · CNChongqing Public Health Medical Center · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Older adults are highly susceptible to developing cognitive impairment(CI). Various factors contribute to the prevalence of CI, but the potential relationships among these factors remain unclear. This study aims to explore the relevant factors associated with CI in Chinese older adults and analyze the potential relationships between CI and these factors.We analyzed the data on 6886 older adults aged≥60 from the China Health and Retirement Longitudinal Study (CHARLS) 2018. Lasso regression was initially used to screening variables. Bayesian Networks(BNs) were used to identify the correlates of CI and potential associations between factors. After screening with Lasso regression, 11 variables were finally included in the BNs. The BNs, by establishing a complex network relationship, revealed that age, education, and indoor air pollution were the direct correlates affecting the occurrence of CI in older adults. It also indicated that marital status indirectly influenced CI through age, and residence indirectly linked to CI through two pathways: indoor air pollution and education.Our findings underscore the effectiveness of BNs in unveiling the intricate network linkages among CI and its associated factors, holding promising applications. It can serve as a reference for public health departments to address the prevention of CI in the elderly.

Indexed as

Bayesian networksCHALRSCognitive impairmentLassoOlder adults

Identifiers

PMID38449590
PMCPMC10915566
OpenAlexW4392133203

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.