Searching PubMed during a Pandemic

BACKGROUND: The 2009 influenza A(H1N1) pandemic has generated thousands of articles and news items. However, finding relevant scientific articles in such rapidly developing health crises is a major challenge which, in turn, can affect decision-makers' ability to utilise up-to-date findings and ultimately shape public health interventions. This study set out to show the impact that the inconsistent naming of the pandemic can have on retrieving relevant scientific articles in PubMed/MEDLINE. METHODOLOGY: We first formulated a PubMed search algorithm covering different names of the influenza pandemic and simulated the results that it would have retrieved from weekly searches for relevant new records during the first 10 weeks of the pandemic. To assess the impact of failing to include every term in this search, we then conducted the same searches but omitted in turn “h1n1,” “swine,” “influenza” and “flu” from the search string, and compared the results to those for the full string. PRINCIPAL FINDINGS: On average, our core search string identified 44.3 potentially relevant new records at the end of each week. Of these, we determined that an average of 27.8 records were relevant. When we excluded one term from the string, the percentage of records missed out of the total number of relevant records averaged 18.7% for omitting “h1n1,” 13.6% for “swine,” 17.5% for “influenza,” and 20.6% for “flu.” CONCLUSIONS: Due to inconsistent naming, while searching for scientific material about rapidly evolving situations such as the influenza A(H1N1) pandemic, there is a risk that one will miss relevant articles. To address this problem, the international scientific community should agree on nomenclature and the specific name to be used earlier, and the National Library of Medicine in the US could index potentially relevant materials faster and allow publishers to add alert tags to such materials.