The authors tested a self-created Ovid Embase cost-effectiveness analysis (CEA) search filter, compared to six other published filters designed to identify economic evaluations in Embase. In this filter validation study, sensitivity, precision and specificity were measured against a gold standard set of economic evaluations derived from the National Health Service Economic Evaluation Database (NHS EED). The set was populated with NHS EED citations published in 2008-2013 that were also available in Ovid Embase (n = 2,198). Testing of the CEA filter resulted in a sensitivity of 0.899 and precision of 0.029. No filter achieved the stated objective of achieving a sensitivity of at least 0.90 with a precision of 0.10, and specificity of at least 0.95, although one tested filter was close to achieving that objective. The authors conclude that it is challenging to develop economic search filters that balance sensitivity and precision. Choice of filter should be based on the researcher’s acceptable levels of performance.

The review investigates the characteristics and methods, including literature searches, used in 202 systematic reviews of health economic evaluations. A median of 4 electronic databases were searched (range: 1 - 16). The systematic reviewers searched general medical databases (MEDLINE and Embase), 56% of the reviews searched specific economic databases and 33% searched specific systematic review and health technology assessment databases. In terms of the search strategies, the majority of authors did not use published filters and at least half of SRs of economic evaluations applied a limit to the literature search, mostly time limits. 

This paper investigates health economists’ use of routine data (routine data being defined as “electronically documented information which is generated in the process of administration, provision of services or reimbursement”) by reviewing practice reported in 35 economic evaluations.  In the studies routine data were typically obtained from health insurance funds or other reimbursement data sources rather than from  bibliographic databases. In future, economists may increasingly need to include data from non-traditional bibliographic sources.

The review investigates the literature searches used in 83 systematic reviews of economic evaluations. A median of 4 electronic databases were searched (range: 1 - 14). The systematic reviewers searched general medical databases (MEDLINE and Embase) and 60% of the reviews also searched specific economic evaluation and health technology assessment databases. In terms of the search strategies Luhnen et al report that economic terms were included in 55% of the literature searches and only 7% reported using a search filter.  48% of reports developed their own Boolean search string with economic evaluations. The authors also state that ‘in most reports’ a limitation was applied such as English language. Additional searches were also reported for online searches (49%), although this was not described further, and reference lists (63%).

NHS EED and HEED, two key databases for retrieving health economics information, have closed. Based on this changed landscape, this article assesses which databases are now the best sources of information for retrieving economic evaluations to inform systematic reviews. The authors built a quasi-gold standard database of 351 records compiled from 46 systematic reviews of economic evaluations. Nine databases were searched for each record. Embase had the highest yield (89%), followed by Scopus (84%) and MEDLINE and PubMed (both 81%). The HTA database identified the highest number of unique citations (13/351). Embase also uniquely identified two conference abstracts, an important consideration if this type of material is eligible for inclusion in a review. All nine database combined retrieved 337/351 (96%) records. Searching a combination of Embase, the HTA database and either PubMed or MEDLINE identified 95% of the quasi-gold standard records (333/351). The authors concluded that searching additional database outside the core group may be inefficient because of limited incremental yield. Searchers should not rely on PubMed or MEDLINE alone. Searching a multi-disciplinary database may also be useful, especially for non-clinical or public health topics. The authors conclude that searchers should focus on developing suitable search strategies in these key databases to ensure high sensitivity and adequate precision. Supplementary search techniques such as grey literature searching may be more efficient than searching a larger number of databases, as 14/351 (4%) citations were not identified in any of the databases.

MEDLINE search strategies reported in source systematic reviews were also assessed. 10/29 (34.5%) of re-run search strategies missed at least one of the included records found in MEDLINE (with 25 citations missed in total). Weaknesses in the population or intervention concepts, rather than the economics concept, were identified as negatively impacting search retrieval.

This report detail the search methods for identifying HSUs in the literature. It describes using iterative searching for identifying HSUs following the process of completing a scoping search. The paper states that the search required for addressing the full range of evidence for a cost-effectiveness model will differ from standard systematic review searches. The paper lists factors to consider when running these iterative searches. Advice is also included for the extent of the searching required and useful search tools.

This paper presents an updated systematic review of studies reporting mapping algorithms from clinical or patient reported outcome measures to EQ-5D-3L or EQ-5D-5L, as well as an assessment of how far mapping studies conform to the MAPS reporting statement.

The review identified 144 mapping studies reporting 190 algorithms mapping from 110 different instruments onto EQ-5D. The 15/17 studies published in 2016 had low adherence to the MAPS checklist.

The mapping studies can be accessed in the Health Economics Research Centre (HERC) Database of Mapping Studies, available at https://www.herc.ox.ac.uk/downloads/herc-database-of-mapping-studies. This database was created in 2013 and is focused on studies mapping to EQ-5D.

NHS EED and HEED, two key databases for retrieving health economics information, have closed. Based on this changed landscape, this abstract assesses which database are now the best sources of information for retrieving economic evaluations for models and systematic reviews. The authors built a quasi-gold standard database of 351 records compiled from 46 systematic reviews of economic evaluations. Nine databases were searched for each record. Embase had the highest yield (0.89), followed by Scopus (0.84) and MEDLINE and PubMed (both 0.81). The HTA database identified the highest number of unique citations (13/351). All nine database combined retrieved 337/351 (0.96) records. The authors conclude that for most systematic reviews, Embase, the HTA database and either PubMed or MEDLINE are likely sufficient to identify economic evaluations found in bibliographic databases. Searching a multi-disciplinary database may also be useful, especially in non-clinical topics. Supplementary search techniques may be more efficient than searching a larger number of databases.

MEDLINE search strategies reported in source systematic reviews were also assessed. 10/29 (34.5%) of re-run search strategies missed at least one of the included records found in MEDLINE (with 25 citations missed in total). Weaknesses in the population or intervention concepts, rather than the economics concept, were identified as negatively impacting search retrieval.

The retrieval of studies that report health state utility values (HSUVs) is an important aspect of information retrieval for HTA and economic model production.  This study first assessed three MEDLINE search filters designed by the York Health Economic Consortium (YHEC) to identify studies reporting HSUVs. The relative recall method was used to test the sensitivity of each filter. Three quasi gold standard (QSG) sets of relevant studies were compiled from reviews of studies reporting HSUVs. The first QSG (consisting of 294 records) was used to assess the performance of the three initial filters. The best performing of the three filters was further developed using the second QSG (139 records). Ultimately, three final search filters were validated using the third QSG (139 records). The first final search filter is sensitivity maximizing, with 95% sensitivity and a number needed to read (NNR) of 842. The second filter balances sensitivity and precision, and has a 92% sensitivity with an NNR of 502. The third filter is precision maximizing, with 88% sensitivity and an NNR of 383. Real world volume of retrieved records was also tested to illustrate the impact of using the three filters for three example health conditions. Having a range of sensitivity and precision options allows researchers to choose filters based on their search requirements. The authors believe that these are the first validated filters for retrieving HSURs. Search strategies for all three final filters are presented in the article.

This paper is a guidance document providing an overview of how health state utility values can be identified, reviewed and synthesised when conducting systematic reviews. The paper includes a case study of a review in osteoporosis-related conditions.

In terms of study identification, the authors note that a range of study designs could be relevant and that a number of instruments could be required including condition-specific preference based measures or generic preference based measures. They recommend that a variety of resources and methods should be used to identify studies. As well as electronic databases, searchers should also look at reference lists, conduct key author and citation searches and contact experts.

The authors recommend caution in using filters too early in the search process, to avoid missing potentially relevant studies. The authors also note the absence of dedicated subject headings within MeSH and EMTREE, and that although general subject headings such as ‘Quality of life’ will yield relevant studies, they are likely to demonstrate poor precision. Free text terms should be included in searches and are categorised as general terms (such as QALY), instrument specific terms (such as EQ-5D) and terms describing methods of utility elicitation such as standard gamble.