ArticleBMC public health2026
Comorbidity patterns and inpatient expenditure among patients with coronary heart disease in China.
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.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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
Identifiers
What OpenQuestion holds
Registered trials
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.