Evidence map›Paper›PMID 35127761›Full record

ArticleFrontiers in medicine2021

Spatiotemporal Analysis of the Prevalence and Pattern of Multimorbidity in Older Chinese Adults.

Shimin Chen, Shengshu Wang, Wangping Jia, Ke Han, Yang Song, Shaohua Liu, Xuehang Li, Miao Liu, Yao He

Open access · goldAbstract read
In one paragraph

Article in Frontiers in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
2.5field-weighted citation impact, top 11% 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

13 citing papers in PubMed, 16 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

9 authors at 3 institutions in 1 country.

Shimin ChenInstitute of Geriatrics, Beijing Key Laboratory of Aging and Geriatrics, National Clinical Research Center for Geriatric Disease, Chinese People's Liberation Army Medical School, Second Medical Center of Chinese People's Liberation Army General Hospital, Beijing, China.
Shengshu WangInstitute of Geriatrics, Beijing Key Laboratory of Aging and Geriatrics, National Clinical Research Center for Geriatric Disease, Chinese People's Liberation Army Medical School, Second Medical Center of Chinese People's Liberation Army General Hospital, Beijing, China.
Wangping JiaSchool of Non-commissioned Officer, Army Medical University, Hebei, China.
Ke HanDepartment of Gastroenterology, Chinese People's Liberation Army Medical School, First Medical Center of Chinese People's Liberation Army General Hospital, Beijing, China.
Yang SongInstitute of Geriatrics, Beijing Key Laboratory of Aging and Geriatrics, National Clinical Research Center for Geriatric Disease, Chinese People's Liberation Army Medical School, Second Medical Center of Chinese People's Liberation Army General Hospital, Beijing, China.
Shaohua LiuInstitute of Geriatrics, Beijing Key Laboratory of Aging and Geriatrics, National Clinical Research Center for Geriatric Disease, Chinese People's Liberation Army Medical School, Second Medical Center of Chinese People's Liberation Army General Hospital, Beijing, China.
Xuehang LiInstitute of Geriatrics, Beijing Key Laboratory of Aging and Geriatrics, National Clinical Research Center for Geriatric Disease, Chinese People's Liberation Army Medical School, Second Medical Center of Chinese People's Liberation Army General Hospital, Beijing, China.
Miao LiuDepartment of Statistics and Epidemiology, Graduate School, Chinese People's Liberation Army Medical School, Chinese People's Liberation Army General Hospital, Beijing, China.
Yao HeInstitute of Geriatrics, Beijing Key Laboratory of Aging and Geriatrics, National Clinical Research Center for Geriatric Disease, Chinese People's Liberation Army Medical School, Second Medical Center of Chinese People's Liberation Army General Hospital, Beijing, China.
Chinese People's Liberation Army · CNArmy Medical University · CNPeople's Liberation Army No. 150 Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMultimorbidity presents an enormous problem to societal and healthcare utilization under the context of aging population in low- and middle-income countries (LMICs). Currently, systematic studies on the profile of multimorbidity and its characteristics among Chinese elderly are lacking. We described the temporal and spatial trends in the prevalence of multimorbidity and explored chronological changes of comorbidity patterns in a large elderly population survey.

methodsData were extracted from the Chinese Longitudinal Healthy Longevity Study (CLHLS) conducted between 1998 and 2018 in a random selection of half of the counties and city districts. All the elderly aged 65 and older were included in the survey of eight waves. We used 13 investigated chronic diseases to measure the prevalence of multimorbidity by means of geography, subpopulation, and chronological changes. The patterns of multimorbidity were assessed by computing the value of relative risk (RR indicates the likelihood of certain diseases to be associated with multimorbidity) and the observed-to-expected ratio (O/E indicates the likelihood of the coexistence of a multimorbidity combination).

resultsFrom 1998 to 2018, the prevalence of multimorbidity went from 15.60 to 30.76%, increasing in the fluctuation across the survey of eight waves (

conclusionsThe prevalence of multimorbidity has been increasing nationwide, and more attention should be paid to a rapid growth in the southern part of China. It demands the effective diagnosis and treatment adopted to the highly prevalent comorbidities, and strategies and measures were adjusted to strongly relevant clusters.

Indexed as

LMICsmultimorbidityolder adultspatternprevalence

Identifiers

PMID35127761
PMCPMC8811186
OpenAlexW4206541552

What OpenQuestion holds

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LicenceCC BY
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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.