Search

Background: The National Institute for Health and Care Excellence's (NICE) United Kingdom (UK) geographic search filters for MEDLINE and Embase (Ovid) retrieve evidence in literature searches for UK-focused research topics with high recall. Their precision and number-needed-to-read (NNR) was examined previously in case studies using a single review. This paper details a larger post-development study that was conducted to test the NICE UK filters' precision and NNR more extensively. Methods: The filters' recall of included UK references from 100 reviews was calculated. As reproducible search strategies were not available for every review, the MEDLINE filter's precision and NNR were calculated using strategies from 25 reviews. Strategies from nine reviews were used for the Embase filter. Results: The MEDLINE filter achieved an average of 96.4% recall for the included UK references from the 100 reviews and the Embase filter achieved an average of 97.4% recall. Compared to not using a filter, the MEDLINE filter achieved an average of 98.9% recall for the 25 reviews. Precision was increased by an average of 7.8 times, reducing the NNR from 357 to 46. The Embase filter achieved an average of 97.1% recall for the nine reviews. Precision was increased by an average of 5.1 times, reducing the NNR from 746 to 146. Conclusion: There is more evidence to demonstrate that the NICE UK filters retrieve the majority of UK evidence from MEDLINE and Embase while increasing precision and reducing NNR. The filters can save time spent on selecting evidence for UK-focused research topics.

Objectives: Health apps are software programs that are designed to prevent, diagnose, monitor, or manage conditions. Inconsistent terminology for apps is used in research literature and bibliographic database subject headings. It can therefore be challenging to retrieve evidence about them in literature searches. Information specialists at the United Kingdom's National Institute for Health and Care Excellence (NICE) have developed novel validated search filters to retrieve evidence about apps from MEDLINE and Embase (Ovid). Methods: A selection of medical informatics journals was hand searched to identify a "gold standard" (GS) set of references about apps. The GS set was divided into a development and validation set. The filters' search terms were derived from and tested against the development set. An external development set containing app references from published NICE products was also used to inform the development of the filters. The filters were then validated using the validation set. Target recall was >90 percent. The filters' overall recall, specificity, and precision were calculated using all the references identified from the hand search. Results: Both filters achieved 98.6 percent recall against their validation sets. Overall, the MEDLINE filter had 98.8 percent recall, 71.3 percent specificity, and 22.6 percent precision. The Embase filter had 98.6 percent recall, 74.9 percent specificity, and 24.5 percent precision. Conclusions: The NICE health apps search filters retrieve evidence about apps from MEDLINE and Embase with high recall. They can be applied to literature searches to retrieve evidence about the interventions by information professionals, researchers, and clinicians. [ABSTRACT FROM AUTHOR]

This article presents information on Facilitated Online Learning as an Interactive Opportunity (Folio), a continuing professional development program of 12 online courses in Great Britain. Folio forms part of the National Library for Health Librarian Development Programme, established in 2000 to provide support for librarians moving into new roles. The programme focuses on development of skills in health informatics, information communication technology skills, knowledge management, managerial skills, learning and teaching skills, evidence-based librarianship, epidemiological skills and processes of clinical decision making. Although all Folio courses are contextualized to the health sector, course topics are of general relevance to all information sectors. Folio courses are delivered primarily by electronic mail, utilizing two Jiscmail distribution lists.

Chronic obstructive pulmonary disease (COPD) is a common lung disease that negatively affects health-related quality of life (QoL). Utility values, which measure QoL by weighting health states with societal preferences, are required for the cost-utility models that drive economic evaluations and policy decisions. Moayeri et al. published a systematic review and meta-analysis of utilities (EQ-5D) in COPD in June 2016. The current study investigated changes in mean utilities in more recent studies thereafter, exploring heterogeneity in utilities across diverse clinical and study characteristics. Systematic searches of databases, such as MEDLINE and Embase were undertaken from 1 July 2015 until 20 May 2024. A random-effects meta-analysis of utilities (EQ-5D) was performed which addressed inter-study heterogeneity and subgroup analyses. The pooled general mean (95% CI) utility value was 0.761 (0.726-0.795) from 43 studies, whereas Moayeri et al. reported 0.673 (0.653-0.693) from 32 studies. This improvement in mean utilities could be due to increased awareness, early detection, and better medical interventions over the past decade, but demonstrates that a general utility value should be approached with caution given significant heterogeneity. Four meta-regressions were performed on each subgroup: region, method of elicitation, reported comorbidities, and disease stage; of which, method of elicitation, disease stage, and region were found to be significant moderators of utilities. It is, therefore, important to use meta-analysed utilities for cost-utility analyses that reflect the context and patient population of the model. Moreover, these results provide additional evidence for the precision and sensitivity of EQ-5D-5L over EQ-5D-3L.