Validation of A Markov Model for Economic Evaluation of Screening and Preventive Interventions In Alzheimer’s Disease In Denmark

Abstract

A695Objectives: Alzheimer's disease (AD) afflicts up to 9% of people aged 65 and over worldwide, with prevalence projected to increase. AD is associated with reduced quality of life and high treatment and management costs. A number of recently developed screening and preventative interventions offer reduction in resource use and improvement in quality of life for AD patients. The majority of existing models for economic evaluation of AD interventions focus on pharmaceuticals and due to their limited scope and time-horizon are unsuitable for evaluation of screening and preventative strategies. It is proposed to develop a decision model to ascertain the most cost-effective 'mix' of preventative and screening methods for Denmark. The objective of this study is to develop and validate such a model for economic evaluation of non-pharmaceutical interventions for AD MethOds: A Markov model was developed, representing transitions of a hypothetical cohort of 65 year olds from 'no AD' to different stages of AD (Very Mild through to Severe). AD could either be 'identified' or 'not identified' to reflect the difference in costs associated with treatment and management. Due to absence of Danish data, the model utilised transition probabilities based on US data; AD-associated costs and utilities were obtained from Danish and Swedish data, respectively. The model was externally validated against an epidemiological study of AD in Denmark to predict prevalence and stage of AD by age. Results: The model accurately predicted Danish age-specific prevalence of AD, although the prevalence for the 75-79 age group was overestimated by 3%. The model also produced accurate predictions of the distribution of AD severity. cOnclusiOns: The model provides a simple and robust framework for economic evaluation of screening and other non-pharmaceutical interventions for AD. The lack of up to date epidemiological data on AD is a challenge for model validation and introduces uncertainty.

Cost-effectiveness analysis of comprehensive intervention programs to control blood glucose in overweight and obese type 2 diabetes mellitus patients based on a real-world setting: Markov modeling

“…A Markov chain model, which is the simplest Markov model, was used to estimate the cost-effectiveness of this comprehensive intervention program. The Markov model is a process that models random events that occur over time and has been increasingly applied to areas in health economic research in recent years, such as screening for disease, decision analysis of clinical interventions, pharmacoeconomic evaluation, and so on (18)(19)(20)(21). The principle is to divide the natural course of the disease into several different health states (Markov state) and to simulate the development of the disease according to the transition probability of each state in a certain period of time (Markov cycle).…”