Epidemiological patterns of asbestos exposure and spatial clusters of incident cases of malignant mesothelioma from the Italian national registry

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Background

Previous ecological spatial studies of malignant mesothelioma cases, mostly based on mortality data, lack reliable data on individual exposure to asbestos, thus failing to assess the contribution of different occupational and environmental sources in the determination of risk excess in specific areas. This study aims to identify territorial clusters of malignant mesothelioma through a Bayesian spatial analysis and to characterize them by the integrated use of asbestos exposure information retrieved from the Italian national mesothelioma registry (ReNaM).

Methods

In the period 1993 to 2008, 15,322 incident cases of all-site malignant mesothelioma were recorded and 11,852 occupational, residential and familial histories were obtained by individual interviews. Observed cases were assigned to the municipality of residence at the time of diagnosis and compared to those expected based on the age-specific rates of the respective geographical area. A spatial cluster analysis was performed for each area applying a Bayesian hierarchical model. Information about modalities and economic sectors of asbestos exposure was analyzed for each cluster.

Results

Thirty-two clusters of malignant mesothelioma were identified and characterized using the exposure data. Asbestos cement manufacturing industries and shipbuilding and repair facilities represented the main sources of asbestos exposure, but a major contribution to asbestos exposure was also provided by sectors with no direct use of asbestos, such as non-asbestos textile industries, metal engineering and construction. A high proportion of cases with environmental exposure was found in clusters where asbestos cement plants were located or a natural source of asbestos (or asbestos-like) fibers was identifiable. Differences in type and sources of exposure can also explain the varying percentage of cases occurring in women among clusters.

Conclusions

Our study demonstrates shared exposure patterns in territorial clusters of malignant mesothelioma due to single or multiple industrial sources, with major implications for public health policies, health surveillance, compensation procedures and site remediation programs.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-015-1301-2) contains supplementary material, which is available to authorized users.

Related collections

Most cited references 49

Record: found
Abstract: found
Article: not found

Interpreting Posterior Relative Risk Estimates in Disease-Mapping Studies

Sylvia Richardson, Andrew Thomson, Nicky Best … (2004)

There is currently much interest in conducting spatial analyses of health outcomes at the small-area scale. This requires sophisticated statistical techniques, usually involving Bayesian models, to smooth the underlying risk estimates because the data are typically sparse. However, questions have been raised about the performance of these models for recovering the “true” risk surface, about the influence of the prior structure specified, and about the amount of smoothing of the risks that is actually performed. We describe a comprehensive simulation study designed to address these questions. Our results show that Bayesian disease-mapping models are essentially conservative, with high specificity even in situations with very sparse data but low sensitivity if the raised-risk areas have only a moderate ( 50 per area). Semiparametric spatial mixture models typically produce less smoothing than their conditional autoregressive counterpart when there is sufficient information in the data (moderate-size expected count and/or high true excess risk). Sensitivity may be improved by exploiting the whole posterior distribution to try to detect true raised-risk areas rather than just reporting and mapping the mean posterior relative risk. For the widely used conditional autoregressive model, we show that a decision rule based on computing the probability that the relative risk is above 1 with a cutoff between 70 and 80% gives a specific rule with reasonable sensitivity for a range of scenarios having moderate expected counts (~ 20) and excess risks (~1.5- to 2-fold). Larger (3-fold) excess risks are detected almost certainly using this rule, even when based on small expected counts, although the mean of the posterior distribution is typically smoothed to about half the true value.

0 comments Cited 173 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Occupational, domestic and environmental mesothelioma risks in the British population: a case–control study

C Rake, C Gilham, Graham J Hatch … (2009)

We obtained lifetime occupational and residential histories by telephone interview with 622 mesothelioma patients (512 men, 110 women) and 1420 population controls. Odds ratios (ORs) were converted to lifetime risk (LR) estimates for Britons born in the 1940s. Male ORs (95% confidence interval (CI)) relative to low-risk occupations for >10 years of exposure before the age of 30 years were 50.0 (25.8–96.8) for carpenters (LR 1 in 17), 17.1 (10.3–28.3) for plumbers, electricians and painters, 7.0 (3.2–15.2) for other construction workers, 15.3 (9.0–26.2) for other recognised high-risk occupations and 5.2 (3.1–8.5) in other industries where asbestos may be encountered. The LR was similar in apparently unexposed men and women (∼1 in 1000), and this was approximately doubled in exposed workers' relatives (OR 2.0, 95% CI 1.3–3.2). No other environmental hazards were identified. In all, 14% of male and 62% of female cases were not attributable to occupational or domestic asbestos exposure. Approximately half of the male cases were construction workers, and only four had worked for more than 5 years in asbestos product manufacture.

0 comments Cited 67 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Occupational and non-occupational attributable risk of asbestos exposure for malignant pleural mesothelioma.

P. A. Lacourt, C Gramond, Jannick Rolland … (2014)

To estimate the proportion of pleural mesothelioma cases that can be attributed to asbestos exposure in France including non-occupational exposure.

0 comments Cited 46 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

Marisa Corfiati: m.corfiati@inail.it

Alberto Scarselli: a.scarselli@inail.it

Alessandra Binazzi: a.binazzi@inail.it

Davide Di Marzio: d.dimarzio@inail.it

Marina Verardo: mverardo@ausl.vda.it

Dario Mirabelli: dario.mirabelli@cpo.it

Valerio Gennaro: valerio.gennaro@istge.it

Carolina Mensi: carolina.mensi@unimi.it

Gert Schallemberg: gert.schallenberg@apss.tn.it

Enzo Merler: enzo.merler@sanita.padova.it

Corrado Negro: negro@units.it

Antonio Romanelli: romanellia@ausl.re.it

Elisabetta Chellini: e.chellini@ispo.toscana.it

Stefano Silvestri: s.silvestri@ispo.toscana.it

Mario Cocchioni: mario.cocchioni@unicam.it

Cristiana Pascucci: cristiana.pascucci@unicam.it

Fabrizio Stracci: fabs@unipg.it

Elisa Romeo: e.romeo@deplazio.it

Luana Trafficante: medlav.tocco@virgilio.it

Italo Angelillo: italof.angelillo@unina2.it

Simona Menegozzo: simonamenegozzo@alice.it

Marina Musti: m.musti@medlav.uniba.it

Domenica Cavone: d.cavone@medlav.uniba.it

Gabriella Cauzillo: gabriella.cauzillo@regione.basilicata.it

Federico Tallarigo: federicotallarigo@libero.it

Rosario Tumino: rtumino@tin.it

Massimo Melis: massimelis@gmail.com

Sergio Iavicoli: s.iavicoli@inail.it

Alessandro Marinaccio: a.marinaccio@inail.it

Journal

Journal ID (nlm-ta): BMC Cancer

Journal ID (iso-abbrev): BMC Cancer

Title: BMC Cancer

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2407

Publication date (Electronic): 15 April 2015

Publication date PMC-release: 15 April 2015

Publication date Collection: 2015

Volume: 15

Electronic Location Identifier: 286

Affiliations

[ ]Epidemiology Unit, Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, Italian Workers’ Compensation Authority (INAIL), Rome, Italy

[ ]Regional Operating Center of Valle d’Aosta (COR Valle d’Aosta), Valle d’Aosta Health Local Unit, Aosta, Italy

[ ]COR Piedmont, Unit of Cancer Prevention, University of Turin and CPO-Piemonte, Torino, Italy

[ ]COR Liguria, Epidemiology and Prevention Department, National Cancer Research Institute (IST), Genova, Italy

[ ]COR Lombardy, Department of Preventive Medicine, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico and University of Milan, Milano, Italy

[ ]COR Province of Trento, Provincial Unit of Health, Hygiene and Occupational Medicine, Trento, Italy

[ ]COR Veneto, Occupational Health Unit, Department of Prevention, Padua, Italy

[ ]COR Friuli-Venezia Giulia, University of Trieste -Trieste General Hospitals, Clinical Unit of Occupational Medicine, Trieste, Italy

[ ]COR Emilia-Romagna, Health Local Unit, Public Health Department, Reggio Emilia, Italy

[ ]COR Tuscany, Cancer Prevention and Research Institute, Unit of Environmental and Occupational Epidemiology, Firenze, Italy

[ ]COR Marche, Environmental and Health Sciences Department, University of Camerino, Hygienistic, Camerino, Italy

[ ]COR Umbria, University of Perugia, Department of Hygiene and public health, Perugia, Italy

[ ]COR Lazio, Department of Experimental Medicine, University La Sapienza, Roma, Italy

[ ]COR Abruzzo, Health Local Unit, Occupational Medicine Unit, Pescara, Italy

[ ]COR Campania, Department of Experimental Medicine, Second University of Naples, Napoli, Italy

[ ]COR Puglia, Department of Internal Medicine and Public Medicine, University of Bari, Section of Occupational Medicine “B. Ramazzini”, Bari, Italy

[ ]COR Basilicata, Epidemiologic Regional Center, Potenza, Italy

[ ]COR Calabria, Public Health Unit, Crotone, Italy

[ ]COR Sicily, “Civile - M.P. Arezzo” Hospital, Ragusa Cancer Register Unit, Ragusa, Italy

[ ]COR Sardegna, Regional Epidemiological Center, Cagliari, Italy

Article

Publisher ID: 1301

DOI: 10.1186/s12885-015-1301-2

PMC ID: 4404011

PubMed ID: 25885893

SO-VID: f70a1c95-7203-4be6-a7f3-1724179da688

License:

This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 4 June 2014

Date accepted : 31 March 2015

Custom metadata

ScienceOpen disciplines: Oncology & Radiotherapy

Keywords: mesothelioma,asbestos,national registry,clusters,italy

Data availability:

ScienceOpen disciplines: Oncology & Radiotherapy

Keywords: mesothelioma, asbestos, national registry, clusters, italy

Comments

Comment on this article

scite_

Cited by 19

See all cited by

Most referenced authors 623

See all reference authors

Epidemiological patterns of asbestos exposure and spatial clusters of incident cases of malignant mesothelioma from the Italian national registry

Read this article at

Abstract

Background

Methods

Results

Conclusions

Electronic supplementary material

Related collections

Karger: Oncology

Most cited references 49

Interpreting Posterior Relative Risk Estimates in Disease-Mapping Studies

Occupational, domestic and environmental mesothelioma risks in the British population: a case–control study

Occupational and non-occupational attributable risk of asbestos exposure for malignant pleural mesothelioma.

Author and article information

Contributors

Journal

Affiliations

Article

History

Categories

Custom metadata

Comments

Comment on this article

Similar content 369

Cited by 19

Most referenced authors 623