Smart literature search strategies for putative occupational diseases

Introduction:  Some diseases can have rather obscure occupational determinants. Well defined search strategies can be helpful in answering evidence-based questions in the occupational health field.    

Objective:  To identify efficient PubMed search strategies to retrieve articles regarding putative occupational determinants of conditions not generally considered to be work-related.    

Methods:  Based on MeSH definitions and expert knowledge, we selected as candidate search terms the four MeSH terms describing ‘occupational disease’, ‘occupational exposure’, ‘occupational health’ and ‘occupational medicine’ (DEHM) alongside 22 other promising terms. We first explored overlaps between the candidate terms in PubMed. Using random samples of abstracts retrieved by each term, we estimated the 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 explore the possible occupational aetiology of meningioma, pancreatitis and atrial fibrillation.    

Results:  Only 20.3% of abstracts were retrieved by more than one DEHM term. The ‘specific’ search string was based on the combination of terms that yielded the highest proportion (40%) of potentially pertinent abstracts. The ‘sensitive’ string was based on the use of broader search fields and additional coverage provided by other search terms under study. Using the ‘specific’ string, the Numbers of abstracts Needed to Read (NNR) to find one potentially pertinent article were 1.2 for meningioma, 1.9 for pancreatitis and 1.8 for atrial fibrillation. Using the ‘sensitive’ strategy, the NNR were 4.4 for meningioma, 8.9 for pancreatitis and 10.5 for atrial fibrillation.    

Conclusion:  The proposed strings could help health care professionals explore putative occupational aetiology for diseases not generally thought to be work-related. The methodology used for this study could be replicated to formulate further PubMed search strategies; for instance, we developed two strings to identify potentially pertinent articles regarding the aetiology of farmers’ diseases.