Evidence map›Paper›PMID 42902291›Full record

ArticleBMC public health2026

Comorbidity patterns and inpatient expenditure among patients with coronary heart disease in China.

Xiali Chen, Zhixin Liu, Pan Han, Rui Dong, Hongjie Zhang, Xing Lin Feng

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Article in BMC public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Xiali Chen *Yuncheng Central Hospital affiliated to Shanxi Medical University, Yuncheng, shanxi, 044000, China.
Zhixin Liu *Department of Health Policy and Management, School of Public Health, Peking University, Beijing, 100191, China.
Pan HanYuncheng Central Hospital affiliated to Shanxi Medical University, Yuncheng, shanxi, 044000, China.
Rui DongYuncheng Central Hospital affiliated to Shanxi Medical University, Yuncheng, shanxi, 044000, China.
Hongjie ZhangYuncheng Central Hospital affiliated to Shanxi Medical University, Yuncheng, shanxi, 044000, China. wjwzhj686@163.com.
Xing Lin FengDepartment of Health Policy and Management, School of Public Health, Peking University, Beijing, 100191, China. fxl@bjmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCoronary heart disease (CHD) often co-occurs with other chronic conditions. This increases care complexity and hospital costs. Most previous studies have relied on single diseases or simple comorbidity counts. Less is known about whether different multimorbidity patterns are associated with different levels and structures of inpatient expenditure. We aimed to identify comorbidity patterns among middle-aged and older inpatients with CHD and to examine their associations with hospital costs.

methodsWe conducted a retrospective study using electronic health record data from a tertiary hospital in Shanxi Province, China, from 2020 to 2024. Patients aged 45 years or older with a principal discharge diagnosis of CHD (ICD-10: I20-I25) were included. The outcome was total inpatient expenditure per admission. Latent class analysis was applied to 14 comorbidities with prevalence of at least 1%. We then used generalised linear models with a gamma family and log link to examine the association between latent classes and inpatient expenditure, adjusting for sociodemographic and clinical covariates.

resultsAmong 33,044 patients used for pattern identification, 85.16% (28,140) had at least one included comorbidity. Five classes were identified: low comorbidity (39.89%), vascular disease (5.20%), hypertension-dominant (42.27%), diabetes-renal (3.16%), and cardiac dysfunction (9.48%). Compared with the low-comorbidity class, all other classes were associated with higher inpatient expenditure, with the largest increases observed in the cardiac dysfunction class (ER = 1.33, 95% CI: 1.30-1.36) and the diabetes-renal class (ER = 1.27, 95% CI: 1.23-1.32). Predicted mean expenditure was highest in the cardiac dysfunction class ($2,615.77; 95% CI: 2,562.30-2,669.24) and the diabetes-renal class ($2,502.99; 95% CI: 2,421.49-2,584.49), compared with $1,964.85 (95% CI: 1,943.54-1,986.16) in the low-comorbidity class. Medical consumables accounted for the largest share of expenditure in all groups, while the cardiac dysfunction class had the highest proportions of diagnostic and medication costs.

conclusionsMultimorbidity was highly prevalent among middle-aged and older inpatients with CHD. Distinct comorbidity patterns were associated with different levels and structures of inpatient expenditure. These findings may help inform more tailored care planning and more refined payment adjustment in tertiary inpatient settings.

Indexed as

ComorbidityCoronary DiseaseHealth ExpendituresHospitalizationInpatientsAgedChinaFemaleHumansMaleMiddle AgedRetrospective StudiesChinaCoronary heart diseaseInpatient expenditureLatent class analysisMultimorbidity patterns

Identifiers

PMID42902291
PMCPMC13685335

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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.