Screening for preclinical Alzheimer’s disease: Deriving optimal policies using a partially observable Markov model

Zehra Önen Dumlu (), Serpil Sayın and İbrahim Hakan Gürvit
Additional contact information
Zehra Önen Dumlu: Koç University
Serpil Sayın: Koç University
İbrahim Hakan Gürvit: Istanbul University

Health Care Management Science, 2023, vol. 26, issue 1, No 1, 20 pages

Abstract: Abstract Alzheimer’s Disease (AD) is believed to be the most common type of dementia. Even though screening for AD has been discussed widely, there is no screening program implemented as part of a policy in any country. Current medical research motivates focusing on the preclinical stages of the disease in a modeling initiative. We develop a partially observable Markov decision process model to determine optimal screening programs. The model contains disease free and preclinical AD partially observable states and the screening decision is taken while an individual is in one of those states. An observable diagnosed preclinical AD state is integrated along with observable mild cognitive impairment, AD and death states. Transition probabilities among states are estimated using data from Knight Alzheimer’s Disease Research Center (KADRC) and relevant literature. With an objective of maximizing expected total quality-adjusted life years (QALYs), the output of the model is an optimal screening program that specifies at what points in time an individual over 50 years of age with a given risk of AD will be directed to undergo screening. The screening test used to diagnose preclinical AD has a positive disutility, is imperfect and its sensitivity and specificity are estimated using the KADRC data set. We study the impact of a potential intervention with a parameterized effectiveness and disutility on model outcomes for three different risk profiles (low, medium and high). When intervention effectiveness and disutility are at their best, the optimal screening policy is to screen every year between ages 50 and 95, with an overall QALY gain of 0.94, 1.9 and 2.9 for low, medium and high risk profiles, respectively. As intervention effectiveness diminishes and/or its disutility increases, the optimal policy changes to sporadic screening and then to never screening. Under several scenarios, some screening within the time horizon is optimal from a QALY perspective. Moreover, an in-depth analysis of costs reveals that implementing these policies are either cost-saving or cost-effective.

Keywords: Alzheimer’s disease; Screening; Partially observable Markov decision process; Optimal policy; Operations research (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10729-022-09608-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:hcarem:v:26:y:2023:i:1:d:10.1007_s10729-022-09608-1

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10729

DOI: 10.1007/s10729-022-09608-1

Access Statistics for this article

Health Care Management Science is currently edited by Yasar Ozcan

More articles in Health Care Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().