Hostname: page-component-65f69f4695-kztdx Total loading time: 0 Render date: 2025-06-29T15:30:32.497Z Has data issue: false hasContentIssue false

PD85 Testing Search Filters To Retrieve Economic Evaluations In Embase

Published online by Cambridge University Press:  03 January 2019

Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
Introduction:

Health technology assessments (HTAs) are increasingly used by Norwegian health authorities as the evidence base when prioritizing which health care services to offer. HTAs typically consist of a systematic review of the effects and safety of two or more health care interventions, and an economic evaluation of the interventions, based on systematic literature searches in bibliographic databases. Objective: To identify the best performing of seven search filters to retrieve health economic evaluations used to inform HTAs, by comparing the cost-effectiveness analysis (CEA) filter to six published filters in Ovid Embase, and achieve a sensitivity of at least 0.90 with a precision of 0.10, and specificity of at least 0.95.

Methods:

In this filter validation study, the included filters’ performances were compared against a gold standard of economic evaluations published in 2008–2013 (n = 2,248) from the National Health Service Economic Evaluation Database (NHS EED), and the corresponding records (n = 2,198) in the current version of Ovid Embase.

Results:

The CEA filter had a sensitivity of 0.899 and precision of 0.029. One filter had a sensitivity of 0.880 and a precision of 0.075, which was closest to the objective. The filter with lowest sensitivity (0.702) had a precision of 0.141.

Conclusions:

Developing search filters for identifying health economic evaluations, with a good balance between sensitivity and precision, is possible but challenging. Researchers should agree on acceptable levels of performance before concluding on which search filter to use.