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: 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.
Authors: Bryan D James; Sue E Leurgans; Liesi E Hebert; Paul A Scherr; Kristine Yaffe; David A Bennett Journal: Neurology Date: 2014-03-05 Impact factor: 9.910
Authors: Emily P Hyle; Julia H A Foote; Fatma M Shebl; Yiqi Qian; Krishna P Reddy; Shibani S Mukerji; Nattanicha Wattananimitgul; Anand Viswanathan; Lee H Schwamm; Ankur Pandya; Kenneth A Freedberg Journal: BMJ Open Date: 2022-07-06 Impact factor: 3.006