Developing a projection model for diabetic end stage renal disease in Saskatchewan using an agent based model

Abstract

Our epidemiology research found that the incident and prevalent rates for Diabetes mellitus (DM) and Diabetic End Stage Renal Disease (DM-ESRD) were at rise in Saskatchewan between year 1980 and 2005. Combining concerns regarding the rising trends reported by research studies with the concerns of the significant health and financial burden imposed by DM-ESRD on individuals and societies, we sought to project the number of DM-ESRD patients in Saskatchewan up to year 2025 with the cost required for caring for those patients. An agent-based model (ABM) is developed to simulate DM to ESRD progression, treatments for DM-ESRD patients, and the assessments and waiting list processes preparing patients for kidney transplants. The model parameters were estimated from a wide variety of data sources. The agent based modeling approach is chosen for projections regarding the DM-ESRD situation in Saskatchewan because of its advantage in capturing heterogeneities of individual patients, ability to retain biographical information on patients, capacity to capture time-varying competing risks, better presentations features and easy integration with existing models built in either agent based or System Dynamic methods. The approach was also attractive due to its flexibility for future expansion to represent social networks. The model projects the incident and prevalent case count, cost, and person years lived for the DM-ESRD population in Saskatchewan between year 1980 and 2025. The projections captured the great challenges brought by the fast growing number of DM-ESRD patients and substantial cost associated with managing the disease. In addition to producing projection results, the research presented here demonstrates how the model can be used by policy makers to experiment and evaluate different policy/interventions in a safe context. By capturing both the individual level records and population level statistics, the model provide a wealth of data for detailed analysis, which can help health policy makers gain insights in the current and future diabetic-ESRD situation in the province, aiding in resources planning for managing the fast-growing ESRD population and the growing need for dialysis services.