O32-4 Spatial analysis of mesothelioma incidence and tracing of past asbestos exposure in italy. (1st September 2016)

• Record Type: Journal Article
• Authors: Marinaccio, Alessandro; Corfiati, Marisa; Binazzi, Alessandra; Verardo, Marina; Mirabelli, Dario; Gennaro, Valerio; Mensi, Carolina; Schallemberg, Gert; Merler, Enzo; Negro, Corrado; Romanelli, Antonio; Chellini, Elisabetta; Silvestri, Stefano; Cocchioni, Mario; Pascucci, Cristiana; Stracci, Fabrizio; Romeo, Elisa; Trafficante, Luana; Angelillo, Italo; Musti, Marina; Cavone, Domenica; Cauzillo, Gabriella; Tallarigo, Federico; Tumino, Rosario; Melis, Massimo; Iavicoli, Sergio
• Is Part Of: Occupational and environmental medicine. Volume 73(2016)Supplement 1
• Page Start: A59
• Page End: A60
• Publication Date: 2016-09-01
• Subjects: Medicine, Industrial -- Periodicals; Environmental health -- Periodicals; 616.980305
• DOI: 10.1136/oemed-2016-103951.161
• Languages: English
• ISSNs: 1351-0711

Abstract

Abstract : Introduction: Due to the strong causal association with asbestos fibres, the distribution of incident malignant mesothelioma (MM) cases over time and space likely reflect different kinds of asbestos exposures occurring in the past, as previously demonstrated in several studies. Starting from the data of a disease-specific national registry, namely the Italian mesothelioma registry (ReNaM), a geographic cluster analysis was integrated with information about exposure, either occupational or non occupational, in order to trace patterns of asbestos exposure in Italy. Methods: A total of 15, 322 incident cases of all-site MM, recorded by ReNaM in the period 1993 to 2008 were analysed, corresponding to 11, 852 occupational, residential and familial histories reconstructed by interview. Observed cases were mapped based on the municipality of residence at diagnosis and age-specific rates of the area where municipality was located was used to calculate expected cases. A Bayesian hierarchical model was applied to identify municipality clusters and each cluster was characterised by type and source of asbestos exposure of cases. Results: Thirty-two clusters of MM were mapped. Asbestos manufacturing industries and shipbuilding and repair facilities were the more common sources of asbestos exposure, but other activity sectors, such as non-asbestos textile, metal engineering and construction had a relevant contribution in some clusters. Environmental asbestos exposure was detected more frequently in clusters with an asbestos cement industry or a natural source of asbestos-like fibres. Differences by gender among clusters were attributable to environmental fraction but also to high female employment in selected activity sectors, such as textile industry. Discussion: This study while indirectly confirming the role of specific natural or industrial sources of asbestos exposure for mesothelioma risk, highlights the great public health implications of spatial tracing of asbestos exposure for risk prevention, health surveillance and welfare policies.