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American Heart Association, Stroke, 12(30), p. 2574-2579, 1999

DOI: 10.1161/01.str.30.12.2574

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Stroke Treatment Economic Model (STEM) : Predicting Long-Term Costs From Functional Status

Journal article published in 1999 by J. Jaime Caro ORCID, Krista F. Huybrechts
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

Background and Purpose —Stroke is a debilitating disease with long-term social and economic consequences. As new therapies for acute ischemic stroke are forthcoming, there is an increasing need to understand their long-term economic implications. To address this need, a stroke economic model was created. Methods —The model consists of 3 modules. A short-term module incorporates short-term clinical trial data. A long-term module composed of several Markov submodels predicts patient transitions among various locations over time. The modules are connected via a bridge component that groups the survivors at the end of the short-term module according to their functional status and location. Examples of analyses that can be conducted with this model are provided with the use of data from 2 international trials. For illustration, UK unit costs were estimated. Results —With the trial data in the short-term module, the short-term management cost is estimated to be £8326 (US $13 649 [USD]). Hospital stay was the major cost driver. By the end of the trials, there was a pronounced difference in the distribution of patient locations between functional groups. It is predicted in the long-term module that the subsequent cost amounts to £75 985 (124 564 USD) for a major and £27 995 (45 893 USD) for a minor stroke. Conclusions —Linking functional recovery at the end of short-term treatment with patients’ treatment and residential locations allows this model to estimate the long-term economic impact of stroke interventions. Using patient location instead of the more common natural history as the model foundation allows quantification of the long-term impact to become data driven and hence increases confidence in the results.