Evidence map›Paper›PMID 39830139›Full record

ArticleThe Lancet regional health. Western Pacific2025

Using primary and routinely collected data to determine prevalence and patterns of multimorbidity in rural China: a representative cross-sectional study of 6474 Chinese adults.

Xinyi Zhang, Tingzhuo Liu, Zhifang Li, Jiajuan Yang, Huinan Hou, Tianyou Hao, Pei Zhang, Chi Hu, Mingjia Bao, Pengpeng Ye and 18 more

Abstract read
In one paragraph

Article in The Lancet regional health. Western Pacific, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

5 citing papers in PubMed.

  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

28 authors.

Xinyi ZhangSchool of Public Health, Harbin Medical University, Harbin, China.
Tingzhuo LiuSchool of Public Health, Harbin Medical University, Harbin, China.
Zhifang LiSchool of Public Health, Changzhi Medical College, Changzhi, China.
Jiajuan YangYichang Centre for Disease Control and Prevention, Yichang, China.
Huinan HouJiamusi Centre for Disease Control and Prevention, Jiamusi, China.
Tianyou HaoHeping Hospital Affiliated to Changzhi Medical College, Changzhi, China.
Pei ZhangYichang Centre for Disease Control and Prevention, Yichang, China.
Chi HuYichang Centre for Disease Control and Prevention, Yichang, China.
Mingjia BaoHeilongjiang Provincial Centre for Disease Control and Prevention, Harbin, China.
Pengpeng YeNational Centre for Non-communicable Disease Control and Prevention, Chinese Centre for Diseases Control and Prevention, Beijing, China.
Shangzhi XiongThe George Institute for Global Health, Faculty of Medicine and Health, University of New South Wales, Sydney, Australia.
Wei TianSchool of Public Health, Harbin Medical University, Harbin, China.
Guangcan YanSchool of Public Health, Harbin Medical University, Harbin, China.
Jing ZhangSchool of Public Health, Harbin Medical University, Harbin, China.
Yue WangSchool of Public Health, Harbin Medical University, Harbin, China.
Wei JiangNational Centre for Non-communicable Disease Control and Prevention, Chinese Centre for Diseases Control and Prevention, Beijing, China.
Anqi GeSchool of Public Health, Harbin Medical University, Harbin, China.
Yonghui PanDivision of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, China.
Devarsetty PraveenThe George Institute for Global Health, Faculty of Medicine and Health, University of New South Wales, Sydney, Australia.
David PeirisThe George Institute for Global Health, Faculty of Medicine and Health, University of New South Wales, Sydney, Australia.
Xiaoqi FengSchool of Population Health, Faculty of Medicine and Health, University of New South Wales, Sydney, Australia.
Ding DingSydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia.
Lijing L YanGlobal Health Research Centre, Duke Kunshan University, Kunshan, China.
Xiaolin XuSchool of Public Health, Zhejiang University, Hangzhou, China.
Hanbin ZhangEuropean Centre for Environment and Human Health, University of Exeter, United Kingdom.
Yongchen WangDivision of General Practice, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Wenjing TianSchool of Public Health, Harbin Medical University, Harbin, China.
Maoyi TianSchool of Public Health, Harbin Medical University, Harbin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: In China, rising chronic diseases has coincided with the increasing burden of multimorbidity, particularly for vulnerable populations. Limited primary data are available to understand the prevalence and patterns of multimorbidity, especially in resource-limited rural areas. This study aims to conduct robust evaluations of the prevalence and patterns of multimorbidity among rural adults in China, and to compare the differences in prevalence and patterns when using primary data alone versus in combination with routinely collected data. Methods: This cross-sectional study was conducted in three provinces in China, with two counties per province and 40 villages per county, resulted in a total of 240 villages. Participants were randomly selected and stratified by sex and age in each village. Multimorbidity, defined as the coexistence of two or more diseases in same individual, was assessed through data collection involving primary data (face-to-face questionnaire, physical examination and fasting blood sample collection) and routinely collected data (health insurance claims, hospital electronic records and infectious disease surveillance system). Multimorbidity prevalence and patterns were compared based on 1) primary data alone and 2) primary data complemented by routinely collected data. Findings: A total of 6474 individuals participated in this study (50.9% women, mean age 57.1). Combining routinely collected data with the primary data increased the prevalence of all single disease conditions. Multimorbidity prevalence rose from 35.7% with primary data alone to 44.4% with the addition of routinely collected data. The top three dyad multimorbidity patterns (hypertension with heart disease, stroke, or chronic digestive diseases) remained consistent between the two ascertainment methods, while triad pattern rankings had a substantial shift. According to blood pressure measurements, over 40% of participants had elevated blood pressure and may have undiagnosed hypertension. Over 20% may have undiagnosed mental health disorders base on the questionnaires, and nearly 10% with undiagnosed chronic kidney disease as indicated by blood testing. Interpretation: The utilisation of primary data combined with routinely collected data provided a robust estimation of multimorbidity burden in three rural regions in China. Yet, the prevalence may still have been underestimated due to inaccuracies in self-reported data and underdiagnosis of diseases. Future research should incorporate routinely collected data for more robust epidemiological evidence of multimorbidity. Funding: Harbin Medical University Leading Talent Grant (31021220002) and National Natural Science Foundation of China (72074065 and 72474063).

Indexed as

ChinaMultimorbidityRoutinely collected data

Identifiers

PMID39830139
PMCPMC11741085

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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.