Multimorbidity patterns and associated hospitalization costs among different age groups of patients in a single medical center.
10.3724/zdxbyxb-2025-0054
- Author:
Tao LI
1
;
Xiaolin XU
2
;
Yangyang CHENG
3
;
Kai LIN
4
Author Information
1. School of Public Health, Zhejiang University, Hangzhou 310058, China. 22118940@zju.edu.cn.
2. School of Public Health, Zhejiang University, Hangzhou 310058, China. xiaolin.xu@zju.edu.cn.
3. School of Public Health, Zhejiang University, Hangzhou 310058, China.
4. Geriatric Medicine Department, Ningbo Medical Center Lihuili Hospital, Ningbo 315000, Zhejiang Province, China.
- Publication Type:Journal Article
- Keywords:
Association rules;
Complex network analysis;
Generalized estimating equations;
Hospitalization costs;
Inpatients;
Multimorbidity;
Single-center study
- MeSH:
Humans;
Multimorbidity;
Male;
Hospitalization/economics*;
Female;
Aged;
Middle Aged;
Adult;
Age Factors;
Young Adult;
Adolescent;
Diabetes Mellitus/epidemiology*;
Electronic Health Records;
Aged, 80 and over;
Hospital Costs;
China/epidemiology*;
Hypertension/economics*;
Liver Diseases/epidemiology*
- From:
Journal of Zhejiang University. Medical sciences
2025;54(4):423-433
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVES:To analyze the multimorbidity patterns and core diseases among hospitalized patients in different age groups and to explore the impacts of multimorbidity patterns on hospitalization costs.
METHODS:Electronic medical records of adult inpatients (aged ≥18 years) from Ningbo Medical Center Lihuili Hospital between January 1, 2018, and June 30, 2023 were collected. The multimorbidity status involving 53 specific diseases was analyzed across different age groups. Association rule mining was used to identify common multimorbidity patterns. Complex network analysis was used to identify core diseases within the multimorbidity networks. Generalized estimating equations (GEE) were used to analyze the impact of different multimorbidity patterns on hospitalization costs.
RESULTS:The prevalence of multimorbidity among the 359 402 adult inpatients was 38.51%, with higher rates observed in males (43.60%) and elderly patients (58.29%). Association rule mining identified 15 common multimorbidity patterns, which exhibited differences across age groups. The most prevalent multimorbidity pattern overall was "diabetes→hypertension" (support=7.04%, confidence=62.17%, lift=2.17). In the young adult group, the most prevalent pattern was "dyslipidemia→chronic liver disease" (support=1.19%, confidence=53.17%, lift=6.04). In the middle-aged group, it was "diabetes→hypertension" (support=4.84%, confidence=50.28%, lift=2.15). In the elderly group, it was "coronary heart disease, diabetes→hypertension" (support=2.38%, confidence=77.43%, lift=1.63). Complex network analysis revealed that the core diseases within multimorbidity networks differed across age groups. The core disease identified in the young adult group was chronic liver disease (degree centrality=50, betweenness centrality=0.055, closeness centrality=0.963). Core diseases in the middle-aged group included hypertension, chronic liver disease, and diabetes (all with degree centrality=52, betweenness centrality=0.022, closeness centrality=1.000). Core diseases in the elderly group comprised hypertension, diabetes, malignant tumors, chronic liver disease, thyroid disease, anemia, and arrhythmia (all with degree centrality=52, betweenness centrality=0.009, closeness centrality=1.000). Generalized estimating equations analysis indicated that, most multimorbidity patterns were significantly associated with increased hospitalization costs. However, the magnitude of cost increase varied across different multimorbidity patterns. Specifically, hospitalization costs for patients with patterns such as "heart failure→hypertension", "stroke→hypertension", "malignant tumor, diabetes→hypertension", "stroke, diabetes→hypertension", and "diabetes, heart failure→hypertension" were more than double those of patients without any target diseases.
CONCLUSIONS:Multimorbidity patterns and core diseases among hospitalized patients differ significantly across age groups, and different patterns exert varying impacts on hospitalization costs. These findings underscore the necessity for age-stratified and multimorbidity pattern specific management strategies.
