Alzheimer’s disease (AD) constitutes a serious societal healthcare issue as the proportion of the aging population increases. There are ongoing discussions about the necessity of screening the population for AD. We investigate optimal population screening policies for AD using Markov Decision Processes (MDPs). The objective function combines quality-adjusted life years and costs. The disease states are identified according to Clinical Dementia Rating (CDR) scores. The screening test in the model is the Mini Mental State Examination (MMSE), a cognitive test that is widely used in clinical practice. A numerical implementation of the MDP model is presented based on data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and existing literature. In the baseline case, the optimal outcome is not to employ a population-wide screening program. We conduct extensive sensitivity analyses on several model parameters. Our study reveals that the optimal policy may be sensitive to changes in transition probability estimates. When we focus on transitions that are related to treatment effectiveness, we find that implementing a population screening policy becomes socially optimal when plans that lead to cognitive ability stabilization or improvement become available.
|Number of pages||12|
|Journal||IISE Transactions on Healthcare Systems Engineering|
|Publication status||Published - 26 Feb 2019|
- Alzheimer’s disease
- Markov Decision Process
- Optimal Policy
- Population Screening