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      Modeling Mesothelioma Risk Associated with Environmental Asbestos Exposure


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          Environmental asbestos pollution can cause malignant mesothelioma, but few studies have involved dose–response analyses with detailed information on occupational, domestic, and environmental exposures.


          In the present study, we examined the spatial variation of mesothelioma risk in an area with high levels of asbestos pollution from an industrial plant, adjusting for occupational and domestic exposures.


          This population-based case–control study included 103 incident cases of mesothelioma and 272 controls in 1987–1993 in the area around Casale Monferrato, Italy, where an important asbestos cement plant had been active for decades. Information collected included lifelong occupational and residential histories. Mesothelioma risk was estimated through logistic regression and a mixed additive–multiplicative model in which an additive scale was assumed for the risk associated with both residential distance from the plant and occupational exposures. The adjusted excess risk gradient by residential distance was modeled as an exponential decay with a threshold.


          Residents at the location of the asbestos cement factory had a relative risk for mesothelioma of 10.5 [95% confidence interval (CI), 3.8–50.1), adjusted for occupational and domestic exposures. Risk decreased rapidly with increasing distance from the factory, but at 10-km the risk was still 60% of its value at the source. The relative risk for occupational exposure was 6.0 (95% CI, 2.9–13.0), but this increased to 27.5 (95% CI, 7.8–153.4) when adjusted for residential distance.


          This study provides strong evidence that asbestos pollution from an industrial source greatly increases mesothelioma risk. Furthermore, relative risks from occupational exposure were underestimated and were markedly increased when adjusted for residential distance.

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          Most cited references44

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          The Second-Order Analysis of Stationary Point Processes

          B. Ripley (1976)
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            The quantitative risks of mesothelioma and lung cancer in relation to asbestos exposure.

            Mortality reports on asbestos exposed cohorts which gave information on exposure levels from which (as a minimum) a cohort average cumulative exposure could be estimated were reviewed. At exposure levels seen in occupational cohorts it is concluded that the exposure specific risk of mesothelioma from the three principal commercial asbestos types is broadly in the ratio 1:100:500 for chrysotile, amosite and crocidolite respectively. For lung cancer the conclusions are less clear cut. Cohorts exposed only to crocidolite or amosite record similar exposure specific risk levels (around 5% excess lung cancer per f/ml.yr); but chrysotile exposed cohorts show a less consistent picture, with a clear discrepancy between the mortality experience of a cohort of xhrysotile textile workers in Carolina and the Quebec miners cohort. Taking account of the excess risk recorded by cohorts with mixed fibre exposures (generally<1%), the Carolina experience looks uptypically high. It is suggested that a best estimate lung cancer risk for chrysotile alone would be 0.1%, with a highest reasonable estimate of 0.5%. The risk differential between chrysotile and the two amphibole fibres for lunc cancer is thus between 1:10 and 1:50. Examination of the inter-study dose response relationship for the amphibole fibres suggests a non-linear relationship for all three cancer endpoints (pleural and peritoneal mesotheliomas, and lung cancer). The peritoneal mesothelioma risk is proportional to the square of cumulative exposure, lung cancer risk lies between a linear and square relationship and pleural mesothelioma seems to rise less than linearly with cumulative dose. Although these non-linear relationships provide a best fit ot the data, statistical and other uncertainties mean that a linear relationship remains arguable for pleural and lung tumours (but not or peritoneal tumours). Based on these considerations, and a discussion fo the associated uncertainties, a series of quantified risk summary statements for different elvels of cumulative exposure are presented.
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              A new look at statistical-model identification


                Author and article information

                Environ Health Perspect
                Environmental Health Perspectives
                National Institute of Environmental Health Sciences
                July 2007
                22 March 2007
                : 115
                : 7
                : 1066-1071
                [1 ] Cancer Epidemiology Unit, CeRMS and CPO Piemonte, University of Turin, Turin, Italy
                [2 ] Unit of Medical Statistics and Epidemiology, Department of Medical Sciences, University of Eastern Piedmont and CPO Piemonte, Novara, Italy
                [3 ] Interdepartmental Center ‘G. Scansetti’ for the Study of Asbestos and other Toxic Particulates, University of Turin, Turin, Italy
                [4 ] Medical Statistics Unit, Department of Public Health and Microbiology, University of Turin, Turin, Italy
                [5 ] Department of Statistics ‘G. Parenti’, University of Florence, Florence, Italy; Department of Statistics, Biostatistics Unit, Institute for Cancer Prevention (CSPO), Florence, Italy
                Author notes
                Address correspondence to M.M. Maule, Cancer Epidemiology Unit, University of Turin, Via Santena 7, 10126, Turin, Italy. Telephone: 39 0116334628. Fax: 39 0116334664. E-mail: milena.maule@ 123456unito.it

                The authors declare they have no competing financial interests.

                This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original DOI
                : 14 November 2006
                : 22 March 2007

                Public health
                asbestos,mesothelioma,spatial models
                Public health
                asbestos, mesothelioma, spatial models


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