Methods Used in Economic Evaluations of Testing and Diagnosis for Ovarian Cancer: A Systematic Review

OBJECTIVE: There are multiple tests available that can help diagnose ovarian cancer, and the cost-effective analysis of these diagnostic interventions is essential for making well-informed decisions regarding resource allocation. There are multiple factors that can impact on the conclusions drawn from economic evaluations including test accuracy, the impact of the testing pathway on patient costs and outcomes, and delays along the ovarian cancer test-treat pathway. The objective of this study was to evaluate how test accuracy, the choice of perspective, and delays along the testing and diagnostic pathway have been incorporated in economic evaluations of testing for ovarian cancer.

METHODS: A systematic review of published literature was undertaken to identify economic evaluations (eg, cost-effectiveness, cost-utility analysis) focused on testing and diagnosis for ovarian cancer.

RESULTS: Seven studies met the inclusion criteria. Six studies incorporated test accuracy and its impact on patients to some extent. Four studies adopted a societal perspective, but only one considered the costs incurred by patients on the testing and diagnosis pathway. Where delays on the testing pathway were incorporated into the analysis, these were frequently due to false-negative test results leading to delays in patients accessing treatment. Any anxiety that patients might experience as a result of a positive test was not considered in these studies.

CONCLUSIONS: The impact on patients of receiving a positive test in terms of anxiety and the costs incurred by patients having to attend for testing and diagnosis are rarely considered. Delays along the testing and diagnosis pathway can have a major effect on patient outcomes, and it is important that these are acknowledged in economic evaluations focused on testing. Future economic analysis should incorporate these key determinants in order that diagnostic tests for ovarian cancer can be robustly evaluated.