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100 Towards the implementation of a literature matrix to enhance the identification of occupational cancer in different working sectors
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100 Towards the implementation of a literature matrix to enhance the identification of occupational cancer in different working sectors
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Journal Article
100 Towards the implementation of a literature matrix to enhance the identification of occupational cancer in different working sectors
2025
Overview
ObjectiveThe national project BEST (Big data and deep learning in health surveillance for occupational cancer), financed by the Italian Institute for Safety at Work, aims to improve prevention practices in occupational medicine. Among all, the revamp and update of the literature matrix (LM), developed within the OCcupational CAncer Monitoring project, is presented. LM is a consolidated repository of scientific research, from national and international peer-reviewed journals, highlighting correlations between cancer sites and different occupational sectors. Rather than a literature review, LM enhances the understanding of occupational hazards and assist general practitioners and public health physicians in identifying potential cases of occupational neoplasms with robust scientific evidence, accessible and clear information. This work presents the methodology and state of progress of the LM update.Materials and MethodsThe collection of studies for LM was conducted through PubMed, employing a validated search string for the selection of relevant contents. Results from cohorts, cross-sectional and case-control studies, and meta-analyses published between 2010 and 2024 were considered. Moreover, only studies accounting for statistical estimates greater than 1.00 and with the lower value of the 95% CI greater than 1.00 were included.ResultsCurrently, 4 neoplastic sites were analyzed. A total of 1129 articles were examined, of which 145 were included in the matrix: 68 for the bladder, 50 for the hematopoietic and lymphoid system, 20 for the larynx and 7 for the nasopharynx. LM presents for each study the key information including the increased risk of each cancer site for any occupational sector (i.e. SMR, OR, RR).ConclusionsLM will be available on a website and will support the practitioners in recognizing occupational cancer. Stated the relevance of the tool and the effort for keeping it updated, as future development LM will be integrated with AI to support the source analysis.FundingNational Institute for Insurance against Accidents at Work (INAIL) within the BEST project (Grant: INAIL, ID 56/2022).
Publisher
BMJ Publishing Group LTD
Subject
