Impact of health information on medical, dental, and long-term care costs for patients with type 2 diabetes utilizing care insurance.

Abstract

Objective: With the growing burden of type 2 diabetes and its associated healthcare costs, the factors influencing future expenditures, particularly among long-term care insurance (LTCI) users, must be identified. Few studies have addressed the prediction of multiple cost domains, including medical, LTC, and dental expenditures. This study predicted medical, dental, and LTC costs in the following year for patients with type 2 diabetes and identified key predictors based on health information from the previous year. Methods: We applied three machine learning models-random forest, boosted trees, and neural networks-to LTCI users' data in Japan and incorporated prior-year healthcare costs, service usage patterns, and diabetes status. Results: In the 2019 medical cost model, boosted trees showed the best performance for those aged 74 or younger (R² = 0.46, RMSE = 151,804 JPY). LTC costs were influenced by prior LTC spending (∼40%) and facility service use (30-50%), while dental costs were predicted by prior dental expenditures. Conclusions: Prior-year medical costs strongly influenced later medical expenditures, while LTC costs reflected prior LTC spending and facility use. These quantified relationships provide insights for healthcare cost optimization and support policymakers in designing preventive strategies and care systems for aging populations with chronic diseases.