Evidence map›Paper›PMID 38699413›Full record

ArticleFrontiers in public health2024

Temporal change in multimorbidity prevalence, clustering patterns, and the association with mortality: findings from the China Kadoorie Biobank study in Jiangsu Province.

Hao Yu, Ran Tao, Jinyi Zhou, Jian Su, Yan Lu, Yujie Hua, Jianrong Jin, Pei Pei, Canqing Yu, Dianjianyi Sun and 3 more

Abstract read
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Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

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9citing papers in PubMed, 1 pooled it
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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

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3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

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5 · Who and what money

Authors and funding

13 authors.

Hao YuDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.
Ran TaoDepartment of Noncommunicable Chronic Disease and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.
Jinyi ZhouDepartment of Noncommunicable Chronic Disease and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.
Jian SuDepartment of Noncommunicable Chronic Disease and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.
Yan LuDepartment of Noncommunicable Chronic Disease Control and Prevention, Suzhou City Center for Disease Control and Prevention, Suzhou, China.
Yujie HuaDepartment of Noncommunicable Chronic Disease Control and Prevention, Suzhou City Center for Disease Control and Prevention, Suzhou, China.
Jianrong JinDepartment of Noncommunicable Chronic Disease Control and Prevention, Wuzhong District Center for Disease Control and Prevention, Suzhou, China.
Pei PeiPeking University Center for Public Health, Epidemic Preparedness and Response, Beijing, China.
Canqing YuDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.
Dianjianyi SunDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.
Zhengming ChenClinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.
Liming LiDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.
Jun LvDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.

Funding

British Heart Foundation CH/1996001/9454Cancer Research UK 29186Medical Research Council UK MC_U137686851Medical Research Council UK MC_UU_00017/1Medical Research Council UK MC_UU_12026/2Wellcome TrustWellcome Trust 088158/Z/09/ZWellcome Trust 104085/Z/14/ZWellcome Trust 202922/Z/16/ZWellcome Trust 212946/Z/18/Z
6 · The paper itself

Abstract

Objectives: The characteristics of multimorbidity in the Chinese population are currently unclear. We aimed to determine the temporal change in multimorbidity prevalence, clustering patterns, and the association of multimorbidity with mortality from all causes and four major chronic diseases. Methods: This study analyzed data from the China Kadoorie Biobank study performed in Wuzhong District, Jiangsu Province. A total of 53,269 participants aged 30-79 years were recruited between 2004 and 2008. New diagnoses of 15 chronic diseases and death events were collected during the mean follow-up of 10.9 years. Yule's Q cluster analysis method was used to determine the clustering patterns of multimorbidity. A Cox proportional hazards model was used to estimate the associations of multimorbidity with mortalities. Results: The overall multimorbidity prevalence rate was 21.1% at baseline and 27.7% at the end of follow-up. Multimorbidity increased more rapidly during the follow-up in individuals who had a higher risk at baseline. Three main multimorbidity patterns were identified: (i) cardiometabolic multimorbidity (diabetes, coronary heart disease, stroke, and hypertension), (ii) respiratory multimorbidity (tuberculosis, asthma, and chronic obstructive pulmonary disease), and (iii) mental, kidney and arthritis multimorbidity (neurasthenia, psychiatric disorders, chronic kidney disease, and rheumatoid arthritis). There were 3,433 deaths during the follow-up. The mortality risk increased by 24% with each additional disease [hazard ratio (HR) = 1.24, 95% confidence interval (CI) = 1.20-1.29]. Compared with those without multimorbidity at baseline, both cardiometabolic multimorbidity and respiratory multimorbidity were associated with increased mortality from all causes and four major chronic diseases. Cardiometabolic multimorbidity was additionally associated with mortality from cardiovascular diseases and diabetes, with HRs of 2.64 (95% CI = 2.19-3.19) and 28.19 (95% CI = 14.85-53.51), respectively. Respiratory multimorbidity was associated with respiratory disease mortality, with an HR of 9.76 (95% CI = 6.22-15.31). Conclusion: The prevalence of multimorbidity has increased substantially over the past decade. This study has revealed that cardiometabolic multimorbidity and respiratory multimorbidity have significantly increased mortality rates. These findings indicate the need to consider high-risk populations and to provide local evidence for intervention strategies and health management in economically developed regions.

Indexed as

MultimorbidityAdultAgedBiological Specimen BanksChinaChronic DiseaseCluster AnalysisFemaleHumansMaleMiddle AgedMortalityPrevalenceProportional Hazards ModelsRisk Factorscluster analysiscohort studymortalitymultimorbidityprevalence

Identifiers

PMID38699413
PMCPMC11064014

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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.