Multimorbidity patterns in the working age population with the top 10% medical cost from exhaustive insurance claims data of Japan Health Insurance Association

• Published in: PloS one Vol. 18; no. 9; p. e0291554
• Main Authors: Nishida, Yuki, Anzai, Tatsuhiko, Takahashi, Kunihiko, Kozuma, Takahide, Kanda, Eiichiro, Yamauchi, Keita, Katsukawa, Fuminori
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 28-09-2023; Public Library of Science (PLoS)
• Subjects: Age; Biology and Life Sciences; Breast cancer; Cardiovascular diseases; Care and treatment; Chronic illnesses; Codes; Colorectal cancer; Comorbidity; Costs; Diabetes mellitus (non-insulin dependent); Dyslipidemia; Economic aspects; Evaluation; Eye diseases; Gastric cancer; Generalized linear models; Health care expenditures; Health care policy; Health insurance; Health insurance industry; Hypertension; Insurance; Insurance claims; Kidney diseases; Malignancy; Medical care, Cost of; Medicine and Health Sciences; Metabolic disorders; Metabolic syndrome; Mortality; Multimorbidity; Older people; Oral diseases; Patients; People and Places; Population; Probability; Rankings; Social Sciences; Statistical models; Subgroups; Japan; Lithuania; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0291554

Although the economic burden of multimorbidity is a growing global challenge, the contribution of multimorbidity in patients with high medical expenses remains unclear. We aimed to clarify multimorbidity patterns that have a large impact on medical costs in the Japanese population. We conducted a cross-sectional study using health insurance claims data provided by the Japan Health Insurance Association. Latent class analysis (LCA) was used to identify multimorbidity patterns in 1,698,902 patients who had the top 10% of total medical costs in 2015. The present parameters of the LCA model included 68 disease labels that were frequent among this population. Moreover, subgroup analysis was performed using a generalized linear model (GLM) to assess the factors influencing annual medical cost and 5-year mortality. As a result of obtaining 30 latent classes, the kidney disease class required the most expensive cost per capita, while the highest portion (28.6%) of the total medical cost was spent on metabolic syndrome (MetS) classes, which were characterized by hypertension, dyslipidemia, and type 2 diabetes. GLM applied to patients with MetS classes showed that cardiovascular diseases or complex conditions, including malignancies, were powerful determinants of medical cost and mortality. MetS was classified into 7 classes based on real-world data and accounts for a large portion of the total medical costs. MetS classes with cardiovascular diseases or complex conditions, including malignancies, have a significant impact on medical costs and mortality.