Development and validation of paired MEDLINE and Embase search filters for cost-utility studies

Search filters are standardised sets of search terms, with validated performance, that are designed to retrieve studies with specific characteristics. A cost-utility analysis (CUA) is the preferred type of economic evaluation to underpin decision-making at the National Institute for Health and Care Excellence (NICE). Until now, when searching for economic evidence for NICE guidelines, we have used a broad set of health economic-related search terms, even when the reviewer's interest is confined to CUAs alone.

We developed search filters to retrieve CUAs from MEDLINE and Embase. Our aim was to achieve recall of 90% or better across both databases while reducing the overall yield compared with our existing broad economic filter. We used the relative recall method along with topic expert input to derive and validate 3 pairs of filters, assessed by their ability to identify a gold-standard set of CUAs that had been used in published NICE guidelines. We developed and validated MEDLINE and Embase filters in pairs (testing whether, when used together, they find target studies in at least 1 database), as this is how they are used in practice. We examined the proxy-precision of our new filters by comparing their overall yield with our previous approach using publications indexed in a randomly selected year (2010).

All 3 filter-pairs exceeded our target recall and led to substantial improvements in search proxy-precision. Our paired 'sensitive' filters achieved 100% recall (95% CI 99.0 to 100%) in the validation set. Our paired 'precise' filters also had very good recall (97.6% [95%CI: 95.4 to 98.9%]). We estimate that, compared with our previous search strategy, using the paired 'sensitive' filters would reduce reviewer screening burden by a factor of 5 and the 'precise' versions would do so by a factor of more than 20.

Each of the 3 paired cost-utility filters enable the identification of almost all CUAs from MEDLINE and Embase from the validation set, with substantial savings in screening workload compared to our previous search practice. We would encourage other researchers who regularly use multiple databases to consider validating search filters in combination as this will better reflect how they use databases in their everyday work.