Evidence map›Paper›PMID 35615751›Full record

ArticleJournal of multimorbidity and comorbidity2022

Frequency and types of clusters of major chronic diseases in 0.5 million adults in urban and rural China.

Parisa Hariri, Robert Clarke, Fiona Bragg, Yiping Chen, Yu Guo, Ling Yang, Jun Lv, Canqing Yu, Liming Li, Zhengming Chen and 2 more

Open access · diamondAbstract read
In one paragraph

Article in Journal of multimorbidity and comorbidity, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.9field-weighted citation impact, top 26% 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

4 citing papers in PubMed, 6 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 4 institutions in 3 countries.

Parisa HaririClinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Robert ClarkeClinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Fiona BraggClinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Yiping ChenClinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Yu GuoNational Centre for Cardiovascular Diseases, Fuwai Hospital Chinese Academy of Medical Sciences, Beijing, China.
Ling YangClinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Jun LvDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China.
Canqing YuDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China.
Liming LiDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China.
Zhengming ChenClinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Derrick A BennettClinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.ORCID https://orcid.org/0000-0002-9170-8447
China Kadoorie Biobank Collaborative Group
University of Oxford · GBPeking University · CNChinese Academy of Medical Sciences & Peking Union Medical College · CNUniversity of Turku · FI

Funding

British Heart Foundation CH/1996001/9454Cancer Research UK 29186Medical Research Council MC_UU_00017/1Medical Research Council MC_UU_12026/2Wellcome Trust 088158Wellcome Trust 104085Wellcome Trust 212946Wellcome Trust 212946/Z/18/Z
6 · The paper itself

Abstract

Background: Little is known about the frequency and types of disease clusters involving major chronic diseases that contribute to multimorbidity in China. We examined the frequency of disease clusters involving major chronic diseases and their relationship with age and socioeconomic status in 0.5 million Chinese adults. Methods: Multimorbidity was defined as the presence of at least two or more of five major chronic diseases: stroke, ischaemic heart disease (IHD), diabetes, chronic obstructive pulmonary disease (COPD) and cancer. Multimorbid disease clusters were estimated using both self-reported doctor-diagnosed diseases at enrolment and incident cases during 10-year follow-up. Frequency of multimorbidity was assessed overall and by age, sex, region, education and income. Association rule mining (ARM) and latent class analysis (LCA) were used to assess clusters of the five major diseases. Results: Overall, 11% of Chinese adults had two or more major chronic diseases, and the frequency increased with age (11%, 24% and 33% at age 50-59, 60-69 and 70-79 years, respectively). Multimorbidity was more common in men than women (12% vs 11%) and in those living in urban than in rural areas (12% vs 10%), and was inversely related to levels of education. Stroke and IHD were the most frequent combinations, followed by diabetes and stroke. The patterns of self-reported disease clusters at baseline were similar to those that were recorded during the first 10 years of follow-up. Conclusions: Cardiometabolic and cardiorespiratory diseases were most common disease clusters. Understanding the nature of such clusters could have implications for future prevention strategies.

Indexed as

ChinaChronic diseasefrequencymultimorbiditynon-communicable disease

Identifiers

PMID35615751
PMCPMC9125108
OpenAlexW4285160326

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 OpenQuestion graph.