Published in:
Open Access 01-12-2018 | Research article
A comparison of metrics and performance characteristics of different search strategies for article retrieval for a systematic review of the global epidemiology of kidney and urinary diseases
Published in: BMC Medical Research Methodology | Issue 1/2018
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Background
Conducting a systematic review requires a comprehensive bibliographic search. Comparing different search strategies is essential for choosing those that cover all useful data sources. Our aim was to develop search strategies for article retrieval for a systematic review of the global epidemiology of kidney and urinary diseases, and evaluate their metrics and performance characteristics that could be useful for other systematic epidemiologic reviews.
Methods
We described the methodological framework and analysed approaches applied in the previously conducted systematic review intended to obtain published data for global estimates of the kidney and urinary disease burden. We used several search strategies in PubMed and EMBASE, and compared several metrics: number needed to retrieve (NNR), number of extracted data rows, number of covered countries, and when appropriate, sensitivity, specificity, precision, and accuracy.
Results
The initial search obtained 29,460 records from PubMed, and 4247 from EMBASE. After the revision, the full text of 381 and 14 articles respectively was obtained for data extraction (the percentage of useful records is 1.3% for PubMed, 0.3% for EMBASE). For PubMed we developed two search strategies and compared them with a ‘gold standard’ formed by merging their results: free word search strategy (FreeWoSS) was based on the search for keywords in all fields, and subject headings based search strategy (SuHeSS) used only MeSH-mapped conditions and countries names. SuHeSS excluded almost 15% of useful articles and data rows extracted from them, but had a lower NNR of 40 and higher specificity. FreeWoSS had better sensitivity and was able to cover the vast majority of articles and extracted data rows, but had a higher NNR of 65.
Conclusions
The sensitive FreeWoSS strategy provides more data for modelling, while the more specific SuHeSS strategy could be used when resources are limited. EMBASE has limited value for our systematic review.