Evidence-based search strings for the study of farmers' occupational diseases

ObjectivesThe aim of the study was to identify efficient PubMed search strategies to retrieve articles regarding putative occupational determinants of farmers9 diseases.MethodsBased on Medical Subject Heading (MeSH) definitions and expert knowledge, we selected the MeSH term agricultural workers9 disease and, as candidate search terms, five MeSH terms describing farm work (pesticides, agriculture, rural population, rural health, agrochemicals NOT pesticides) alongside 25 other promising terms. Using random samples of abstracts retrieved by each term, we estimated proportions of articles containing potentially pertinent information regarding occupational aetiology in order to formulate two search strategies (one more “specific”, one more “sensitive”). We applied these strategies to retrieve information on possible occupational aetiology among farmers of knee osteoarthritis, multiple sclerosis and kidney cancer. We evaluated the number of abstracts needed to read (NNR) to identify one potentially pertinent article in the context of these pathologies.ResultsThe more “specific” search string was based on the combination of terms that yielded the highest proportion (40%) of potentially pertinent abstracts. The more “sensitive” string was based on use of broader search fields and additional coverage provided by other search terms under study. Using the specific string, the NNR to find one potentially pertinent article were: 1.3 for knee osteoarthritis; 1.3 for multiple sclerosis; 1.1 for kidney cancer. Using the sensitive strategy, the NNR were 1.8, 2.4 and 1.4, respectively.ConclusionsThe proposed strings could help healthcare professionals explore putative occupational aetiology for farmers9 diseases (even if not generally thought to be work-related).