DYNAMIC SIMULATION MODELING OF THE HEALTH SYSTEM FOR CHRONIC KIDNEY DISEASE IN JAPAN
Author(s)
Ataru Igarashi, PhD1, tomonori okamura, MD, PhD2, Hirokazu Okada, MD, PhD3, Sota Kato, PhD4, Katsuhito Ihara, MD, MPH4, Koki Idehara, PhD5, Fumiko Ono, MPH, MSc4.
1Faculty of Pharmacy, Tokyo University, Tokyo, Japan, 2Preventive Medicine and Public Health, Keio University School of Medicine., Tokyo, Japan, 3Department of Nephrology, Faculty of Medicine, Saitama Medical University, Tokyo, Japan, 4Nippon Boehringer Ingelheim Co., Ltd., Tokyo, Japan, 5IQVIA Solutions Japan G. K, Tokyo, Japan.
1Faculty of Pharmacy, Tokyo University, Tokyo, Japan, 2Preventive Medicine and Public Health, Keio University School of Medicine., Tokyo, Japan, 3Department of Nephrology, Faculty of Medicine, Saitama Medical University, Tokyo, Japan, 4Nippon Boehringer Ingelheim Co., Ltd., Tokyo, Japan, 5IQVIA Solutions Japan G. K, Tokyo, Japan.
OBJECTIVES: This study aimed to assess the effectiveness and efficiency of the health system for chronic kidney disease (CKD) management in Japan, where dialysis is routinely performed, using a system dynamics (SD) simulation model.
METHODS: An SD simulation model targeting the Japanese population aged ≥40 years was developed to simulate transition across CKD health states and associated costs from 2008 to 2070, based on findings of public statistics and epidemiologic studies. The model incorporated CKD severity, physician consultation status, dialysis initiation, and mortality. Three health interventions from 2025 were evaluated: A) addition of serum creatinine in annual health checkups; B) enhanced participation in specific health checkups, health guidance, and physician consultation; and C) strengthened disease management through care coordination. Outcomes included the number of patients receiving dialysis, healthcare and societal costs. The cost neutral was estimated as the time when the total costs get equivalent between the epidemiological model and the health intervention models.
RESULTS: The simulated number of dialysis patients from 2008 to 2023 was comparable to observed data, and the number of dialysis patients in 2040 was projected to be 298,945. All three health interventions led to reductions in the number of dialysis patients. Intervention A) yielded the largest reduction of 8,842 dialysis patients in 2040, followed by Intervention B) of 5,500 and Intervention C) of 596. Intervention C) achieved cost savings earliest (from 2042), followed by Intervention A) (from 2051), whereas Intervention B) did not reach cost neutrality by 2070 due to high costs and limited impact on CKD outcomes under existing metabolic syndrome programs.
CONCLUSIONS: The SD simulations suggest that all health interventions examined are expected to provide social benefits by reducing the number of patients requiring dialysis. Universal screening at an early CKD stage and strengthened care coordination seem efficient health interventions achieving long‑term cost savings.
METHODS: An SD simulation model targeting the Japanese population aged ≥40 years was developed to simulate transition across CKD health states and associated costs from 2008 to 2070, based on findings of public statistics and epidemiologic studies. The model incorporated CKD severity, physician consultation status, dialysis initiation, and mortality. Three health interventions from 2025 were evaluated: A) addition of serum creatinine in annual health checkups; B) enhanced participation in specific health checkups, health guidance, and physician consultation; and C) strengthened disease management through care coordination. Outcomes included the number of patients receiving dialysis, healthcare and societal costs. The cost neutral was estimated as the time when the total costs get equivalent between the epidemiological model and the health intervention models.
RESULTS: The simulated number of dialysis patients from 2008 to 2023 was comparable to observed data, and the number of dialysis patients in 2040 was projected to be 298,945. All three health interventions led to reductions in the number of dialysis patients. Intervention A) yielded the largest reduction of 8,842 dialysis patients in 2040, followed by Intervention B) of 5,500 and Intervention C) of 596. Intervention C) achieved cost savings earliest (from 2042), followed by Intervention A) (from 2051), whereas Intervention B) did not reach cost neutrality by 2070 due to high costs and limited impact on CKD outcomes under existing metabolic syndrome programs.
CONCLUSIONS: The SD simulations suggest that all health interventions examined are expected to provide social benefits by reducing the number of patients requiring dialysis. Universal screening at an early CKD stage and strengthened care coordination seem efficient health interventions achieving long‑term cost savings.
Conference/Value in Health Info
2026-11, ISPOR Europe 2026, Vienna, Austria
Value in Health, Volume 29, Issue 12S
Code
EPH202
Topic
Epidemiology & Public Health, Health Service Delivery & Process of Care, Study Approaches
Topic Subcategory
Public Health
Disease
Diabetes/Endocrine/Metabolic Disorders (including obesity), Urinary/Kidney Disorders