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The INR-control has a significant impact on the warfarin treatment costs. The choice of model influences the estimated mean costs. In addition, different models identify statistically significant effects between different background variables and costs.

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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.