Developing search strategies in Medline on the occupational origin of diseases.
BACKGROUND
In order to support occupational health practitioners, researchers, and experts (clinical librarians, guideline developers) who search the scientific literature in PubMed (Medline), we developed and evaluated an easy-to-use and effective search strategy.


METHODS
We tested different combinations of search terms, in answering four case vignettes calculating sensitivity and precision in retrieving literature from a gold standard. The use of the Clinical Queries filter from PubMed was evaluated. Based on the outcomes, we developed guidelines for a professional search strategy, using a flow chart.


RESULTS
For the occupational health practitioner, we found a sensitivity of at least 65% with a precision of 20%, when terms for occupation and type of industry were combined with terms for disease. For the occupational health expert, a high sensitivity of more than 90% was found adding terms for risk factors to the strategy. Combining the search terms with the sensitive Clinical Queries etiology filter in PubMed, enhanced the precision. Using the specific Clinical Queries etiology filter in PubMed results in a substantial loss of relevant articles.


CONCLUSIONS
To search for studies on the occupational origin of diseases in Medline, we propose to start with the proper name for the occupational disease. If this does not exist, use a specific term for the occupation and type of industry in combination with terms for disease. To improve the sensitivity of the search, a search term for the specific risk factor should be added. If there is no relevant occupational title available, it is worth trying the general search terms occupational diseases or occupational risk.
Schaafsma
Frederieke
F
Coronel Institute for Work, Environment and Health, Academic Medical Center, University of Amsterdam, PO Box 22700, 1100 DE Amsterdam, The Netherlands. f.g.schaafsma@amc.uva.nl
Hulshof
Carel
C
Verbeek
Jos
J
Bos
Jurriaan
J
Dyserinck
Heleen
H
van Dijk
Frank
F
D000328:Adult; D019317:Evidence-Based Medicine; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D007221:Industry / Q000145:classification; D016247:Information Storage and Retrieval; D016239:MEDLINE; D008297:Male; D008875:Middle Aged; D009784:Occupational Diseases / Q000209:etiology*; D009787:Occupational Medicine; D018570:Risk Assessment; D012307:Risk Factors; D012985:Software Design