Comorbidity Disease Pattern in Japanese Elderly Population: A Network-Based Analysis

Abstract

Objective

To clarify the disease pattern for elderly individuals in the general population in Japan with multimorbidity networks based on an analysis of common chronic conditions.

Patients and Methods

This study is a population study of disease network analysis in elderly in Japan. We obtained medical records for 4,797,907 elderly patients in Japan (from January 1, 2020 to December 31, 2020) from administrative claim databases. The administrative claim records were transformed for temporal disease occurrence pattern analysis and used to find frequently occurring disease sequences to predict the onset of disease progression.

Results

In those aged 75 years and older, circulatory and respiratory diseases gained predominance, while the burden of mental disorders remained substantial across all age groups. In men aged below 75 years, disseminated intravascular coagulation co-occurred frequently with acute myocardial infarction and respiratory failure. In women aged below 75 years, the pairing of vestibular dysfunction and hearing loss exhibited the highest association. For women aged 75 years and older, renal disease pairings, psychiatric comorbidities, and fall-related injuries were dominant. Network analyses reported increasing multimorbidity complexity with age. Respiratory, circulatory, and digestive disease clusters were central across age groups, with psychiatric and fracture-related conditions becoming more prominent in older adults, particularly women. Disease heatmaps further highlighted systemic comorbidity hubs involving pneumonia, iron deficiency anemia, kidney disease, and cardiovascular conditions.

Conclusion

In this study, we identified major comorbidity patterns and reported that atherosclerosis, angina pectoris, gastritis, and duodenitis were the most important comorbid diseases in the elderly population in Japan. The identification of distinctive comorbidity profiles from routine clinical data provides insights that may be leveraged to improve diagnosis and identify treatment targets for patients.