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BACKGROUND AND PURPOSE: With decision-analytic models becoming more popular to assess the cost-effectiveness of health care interventions, the need for robust estimates on the costs of cerebrovascular disease is paramount. This study reports the results from a literature review of the costs of cerebrovascular diseases, and assesses the quality of the published evidence against a set of defined criteria. METHODS: A broad literature search was conducted. Those studies reporting mean/median costs of cerebrovascular diseases derived from patient-level data in a developed country setting were included. Data were abstracted using standardized reporting forms and assessed against 4 predefined criteria: use of adequate methodologies, use of a population-based study, inclusion of premorbid resource use, and reporting of costs by different patient subgroups. RESULTS: A total of 120 cost studies were identified. The cost estimates of stroke were compared by taking into account the effects of inflation and price differentials between countries. Average costs of stroke ranged from $468 to $146 149. Differences in costs were also found within country, with estimates in the USA varying 20-fold. Although the costing methodologies used were generally appropriate, only 5 studies were based on population-based studies, which are the gold standard study design when comparing incidence, outcome, and costs. CONCLUSIONS: This review showed large variations in the costs of stroke, mainly attributable to differences in the populations studied, methods, and cost categories included. The wide range of cost estimates could lead to selection bias in secondary health economic analyses, with authors including those costs that are more likely to produce the desired results.

Original publication

DOI

10.1161/STROKEAHA.108.529776

Type

Journal article

Journal

Stroke

Publication Date

02/2009

Volume

40

Pages

e18 - e23

Keywords

Brain Ischemia, Cerebrovascular Disorders, Costs and Cost Analysis, Decision Trees, Efficiency, Health Care Costs, Health Resources, Humans, Models, Econometric, Population, Stroke