Önen Dumlu Zehra, Sayın Serpil, Gürvit İbrahim Hakan
College of Administrative Sciences and Economics, Koç University, Istanbul, Turkey.
School of Management, University of Bath, Bath, United Kingdom.
Health Care Manag Sci. 2023 Mar;26(1):1-20. doi: 10.1007/s10729-022-09608-1. Epub 2022 Aug 31.
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.
阿尔茨海默病（AD）被认为是最常见的痴呆类型。尽管对AD的筛查已被广泛讨论，但尚无任何国家将其作为一项政策实施筛查计划。当前的医学研究促使在建模计划中关注该疾病的临床前期阶段。我们开发了一个部分可观测马尔可夫决策过程模型来确定最优筛查计划。该模型包含无病和临床前期AD的部分可观测状态，且筛查决策是在个体处于这些状态之一时做出的。一个可观测的已诊断临床前期AD状态与可观测的轻度认知障碍、AD和死亡状态相结合。状态之间的转移概率使用来自奈特阿尔茨海默病研究中心（KADRC）的数据和相关文献进行估计。以最大化预期总质量调整生命年（QALY）为目标，该模型的输出是一个最优筛查计划，该计划规定了具有给定AD风险的50岁以上个体将在什么时间点接受筛查。用于诊断临床前期AD的筛查测试具有负效用，并不完美，其敏感性和特异性使用KADRC数据集进行估计。我们研究了具有参数化有效性和负效用的潜在干预对三种不同风险概况（低、中、高）的模型结果的影响。当干预有效性和负效用处于最佳状态时，最优筛查策略是在50至95岁之间每年进行筛查，低、中、高风险概况的总体QALY增益分别为0.94、1.9和2.9。随着干预有效性降低和/或其负效用增加，最优策略变为零星筛查，然后变为从不筛查。在几种情况下，从QALY角度来看，在时间范围内进行一些筛查是最优的。此外，对成本的深入分析表明，实施这些政策要么节省成本，要么具有成本效益。
