Evidence map›Paper›PMID 40993623›Full record

ArticleBMC public health2025

Sex and age-specific multimorbidity profiles among working-age inpatients in China: a comparative network analysis.

Yining Bao, Yang Sun, Mengjie Wang, Christopher K Fairley, Pengyi Lu, Terence J O'Brien, Shu Su, Xin Liu, Lin Wang, Hanting Liu and 14 more

Abstract readComparative Study
In one paragraph

Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
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

24 authors.

Yining BaoChina-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, 710061, China.
Yang SunDepartment of Transfusion Medicine, Shaanxi Provincial People's Hospital, 256 Youyi West Road, Xi'an, 710068, China.
Mengjie WangChina-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, 710061, China.
Christopher K FairleyArtificial Intelligence and Modelling in Epidemiology Program, Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia.
Pengyi LuChina-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, 710061, China.
Terence J O'BrienDepartment of Neuroscience, Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia.
Shu SuClinical Research Center, The Second Affiliated Hospital of Chong Qing Medical University, Chong Qing, China.
Xin LiuSchool of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi, 710061, China.
Lin WangDepartment of Ophthalmology, EYE & ENT Hospital of Fudan University, Shanghai, China.
Hanting LiuSchool of Public Health, Sun Yat-Sen University, Guangzhou, China.
Xueli ZhangInstitute of Scientific Instrumentation, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, PR China.
Xianwen ShangRoyal Melbourne Hospital, University of Melbourne, Melbourne, VIC, Australia.
Zhuoting ZhuCentre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, VIC, Australia.
Qianhui LuDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, 450001, PR China.
Zengbin LiChina-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, 710061, China.
Hao LaiChina-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, 710061, China.
Jing WangChina-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, 710061, China.
Ting MaDepartment of Transfusion Medicine, Shaanxi Provincial People's Hospital, 256 Youyi West Road, Xi'an, 710068, China.
Liqin WangDepartment of Transfusion Medicine, Shaanxi Provincial People's Hospital, 256 Youyi West Road, Xi'an, 710068, China.
Xinxin XieDepartment of Transfusion Medicine, Shaanxi Provincial People's Hospital, 256 Youyi West Road, Xi'an, 710068, China.
Wenhua WangDepartment of Transfusion Medicine, Shaanxi Provincial People's Hospital, 256 Youyi West Road, Xi'an, 710068, China.
Wenjie WuDepartment of Information Technological, Shaanxi Health Information Center, Xi'an, China.
Jiangcun YangDepartment of Transfusion Medicine, Shaanxi Provincial People's Hospital, 256 Youyi West Road, Xi'an, 710068, China. yjc65@sina.com.
Lei ZhangChina-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, 710061, China. lei.zhang1@monash.edu.

Funding

Ministry of Science and Technology of the People's Republic of China 2022YFC2304900National Key Research and Development Program of China 2022YFC2505100
6 · The paper itself

Abstract

backgroundMultimorbidity substantially increases health and economic burdens, and reduces productivity in working-age individuals. We conducted a study using data from 393,170 working-age inpatients (18-59 years) in Shaanxi, China, collected from 1998 to 2018. We aimed to identify multimorbidity profiles by sex and age and explore multimorbidity patterns using network analysis.

methodsThe sampling technique in our study involved forming two cohorts-blood donor and non-blood donor groups-from Shaanxi Province, China, based on age, sex, and region using a 1:1 matching method, resulting in 393,170 inpatient hospitalisation records for the working-age population. A total of 223,524 logistic regressions were conducted to explore statistically significant multimorbidity patterns, combined with network analysis, to create sex-specific and age-sex-stratified multimorbidity networks, identifying hub diseases with the most distinct multimorbidity patterns.

resultsOur study found that 46.61% of working-age inpatients had multimorbidity in the baseline hospitalisation dataset, with a higher prevalence in males (51.40%) than in females (41.46%). Males exhibited more complex multimorbidity networks with 1,233 unique multimorbidity patterns compared to 881 in females. Unemployment was associated with a higher multimorbidity risk (OR = 1.09, 95%CI: 1.02-1.15) in males, but had the opposite effect in females (OR = 0.93, 95%CI: 0.89-0.98). Hub diseases common to both sexes included liver diseases, dyslipidemia, fluid/electrolyte/acid-base balance disorders, type-2 diabetes mellitus, hypertension, heart failure, atherosclerosis, and gastritis/duodenitis. Hub diseases' associated patterns accounted for 66.44% of patterns in males and 58.63% in females. With age, both sexes experienced an increase in multimorbidity proportion and network complexity. Males shifted from respiratory, infectious/parasitic and genitourinary disease-associated patterns to endocrine/nutritional/metabolic and circulatory disease-associated patterns. Females experienced a similar shift, with a notable increase in musculoskeletal/connective tissue disease-associated patterns. Digestive disease-associated patterns remained prevalent across all ages and sexes.

conclusionsMultimorbidity networks in working-age inpatients exhibited greater complexity in males than females, growing with age. Hub diseases' associated multimorbidity patterns dominated the network and multimorbidity patterns shifted toward endocrine/nutritional/metabolic and circulatory disease-associated patterns with age. Our study could contribute to the development of clinical interventions targeting working-age inpatient multimorbidity.

Indexed as

InpatientsMultimorbidityAdolescentAdultAge FactorsChinaFemaleHospitalizationHumansMaleMiddle AgedSex FactorsYoung AdultChinaMultimorbidityNetwork analysisSex-specific medicineWorking-age

Identifiers

PMID40993623
PMCPMC12462151

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