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Background: Dementia is a common disease that has an impact on both the affected individual and family members who provide caregiving. Simulation models can assist in setting policy that anticipates public health needs by predicting the demand for and availability of care., Objective: We developed a relatively simple method for simulating the onset of dementia that can be used in combination with an existing microsimulation model., Methods: We started with Socsim, a demographic microsimulation model that simulates a population with family kinship networks. We simulated dementia in the Socsim population by simulating the number of individuals diagnosed with dementia in their lifetime and the ages of onset and death from dementia for each of these dementia cases. We then matched dementia cases to the simulated population based on age at death, so for each individual, we simulate whether they develop dementia and, if so, their age at onset. This approach simulates dementia onset but does not alter the demographic model's simulated age of death., Results: We selected model dementia parameters so that the combined Socsim-Dementia model reproduces published dementia prevalence rates and survival times after diagnosis., Conclusions: Adding simulation of dementia to a kinship network model enables prediction of the availability of family caregivers for people with dementia under a range of different assumptions about future fertility, mortality, and dementia risk. We demonstrated how to add simulation of dementia onset and death to an existing microsimulation model to obtain a method for predicting dementia prevalence in the context of another more detailed model. The approach we developed can be generalized to simulate other progressive health conditions that affect mortality.

Background: Microsimulation models (MSMs) for health outcomes simulate individual event histories associated with key components of a disease process; these simulated life histories can be aggregated to estimate population-level effects of treatment on disease outcomes and the comparative effectiveness of treatments. Although MSMs are used to address a wide range of research questions, methodological improvements in MSM approaches have been slowed by the lack of communication among modelers. In addition, there are few resources to guide individuals who may wish to use MSM projections to inform decisions., Methods: . This article presents an overview of microsimulation modeling, focusing on the development and application of MSMs for health policy questions. The authors discuss MSM goals, overall components of MSMs, methods for selecting MSM parameters to reproduce observed or expected results (calibration), methods for MSM checking (validation), and issues related to reporting and interpreting MSM findings(sensitivity analyses, reporting of variability, and model transparency)., Conclusions: . MSMs are increasingly being used to provide information to guide health policy decisions. This increased use brings with it the need for both better understanding of MSMs by policy researchers, and continued improvement in methods for developing and applying MSMs.