Multicentric study on malignant pleural mesothelioma and non-occupational exposure to asbestos

Magnani, Corrado; Agudo, Antonio; González, Carlos; Andrion, A; Calleja, A; Chellini, Elisabetta; Dalmasso, Paola; Escolar, Antonio; Hernández, S.; Ivaldi, Cristiana; Mirabelli, Dario; Ramírez, José; Turuguet, D; Usel, M.; Terracini, Benedetto

doi:10.1054/bjoc.2000.1161

Cited by 148 publications

(108 citation statements)

References 16 publications

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“…In general, these studies were not able to quantify the magnitude of environmental asbestos exposure, but used proxy measures such as distance to source. This study showed that the distance of the plant to the midpoint of a postal code area was associated with number of polluted sites and exposed households, but correlation coefficients between 0.36 and 0.52 indicate that spatial risk models based on proximity measures (Magnani et al, 2000;Magnani et al, 2001;Maule et al, 2007;Kurumatani and Kumagai, 2008;Musti et al, 2009) may give rise to considerable misclassification.…”

Section: External Validitymentioning

confidence: 80%

“…In contrast to other studies we could not take into account the asbestos emission from the factory during its asbestos producing period (Magnani et al, 2000(Magnani et al, , 2001Maule et al, 2007;Kurumatani and Kumagai, 2008;Musti et al, 2009). Although some information was available on the total amount of asbestos emitted by the pipe plant permitted by legal authorities in certain years, the information was too sparse.…”

Section: Restrictions In Available Informationmentioning

confidence: 95%

“…Typically, these studies have relied on proxy estimates of exposure (Bourdes et al, 2000). Studies in the cities of Casale Monferrato and Bari in Italy and Amagasaki City in Japan showed that the distance of place of residence to an asbestos plant was associated with an increased occurrence of MM (Magnani et al, 2000;Maule et al, 2007;Kurumatani and Kumagai, 2008;Musti et al, 2009). The Japanese study modelled a relative asbestos exposure index, based on distance to plant and meteorological conditions, and could show a linear exposure-response relationship between this index and the MM risk (Kurumatani and Kumagai, 2008).…”

Section: Introductionmentioning

confidence: 99%

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Assessment of cancer risks due to environmental exposure to asbestos

Driece

Siesling

Swuste

et al. 2009

J Expo Sci Environ Epidemiol

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In a rural area widespread pollution of friable and non-friable waste products was present, used to harden dirt tracks, yards, and driveways during . Exposure to environmental asbestos was assessed by a site approach, based on number of polluted sites within postal code areas, and by a household approach, based on number of households in the close vicinity to polluted sites within postal code areas. Based on asbestos soil investigations, 293 sites were identified with asbestos waste material at the surface, of which 77% contained crocidolite fibres as well as chrysotile fibres. The 293 sitesat-risk varied from 5 m 2 to 2722 m 2 and were surrounded by 347 households within 100 m of these sites. Distance to the plant was associated with the number of sites (r ¼ 0.36), and with the number of households (r ¼ 0.52). However, categorization of postal code areas into low, intermediate or high likelihood of exposure to asbestos showed a modest agreement between the site and household approach. In the site approach a total of 2.3 million person-years at risk were estimated with an average exposure of 1674 fibres/m 3 and an expected 1.8 cases of malignant mesothelioma each year. The household approach resulted in estimates of 1.2 million person-years at risk, and 0.9 cases of malignant mesothelioma per year, respectively. This study illustrates that asbestos waste on the surface of roads and yards in an area with over 130,000 inhabitants may result in long-term exposure to asbestos that will cause several cases of malignant mesothelioma each year. Although distance to plant, number of polluted sites and number of exposed household were associated, the modest agreement among these measures of exposure indicate that the exposure assessment strategy chosen in a particular study may result in considerable misclassification. Without detailed information on individual behaviour within the polluted area, it is difficult to show that a more individually oriented approach will perform better than an ecological approach.

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Section: External Validitymentioning

confidence: 80%

Section: Restrictions In Available Informationmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

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Assessment of cancer risks due to environmental exposure to asbestos

Driece

Siesling

Swuste

et al. 2009

J Expo Sci Environ Epidemiol

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“…Other work had shown an increased risk of mesothelioma among residents of the LHA (Magnani et al 1995). Their method was based on one used for epidemic diseases and predicted a total number of deaths and a date for the end of the 'epidemic'.…”

Section: Predictions Of Mesothelioma From Non-occupational Exposurementioning

confidence: 99%

Review of the effectiveness of predictive models for mesothelioma to identify lessons for asbestos-related policy

Reid¹

2016

Evidence Base

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Predictions of future cases of asbestos-related disease have been undertaken at a national level to inform government policy and planning for future health needs. In general, we can separate the methods used to predict future cases of mesothelioma into models that use a) direct or b) indirect estimates of asbestos exposure. Direct estimates are those that have been derived mostly for occupationally exposed cohorts, where airborne fibre levels were measured over time. Indirect estimates tend to be information about total or fibre-specific asbestos imports or use from a range of time points. Most predictions undertaken at the national level have predicted future cases for males only and assume that indirect estimates of asbestos consumption reflect occupational asbestos exposure. These models tend to fit the observed data reasonably well but have undergone several refinements in order to improve their fit. Fewer attempts have been made to predict cases of mesothelioma resulting from non-occupational asbestos exposure, and most have not subsequently revisited their prediction to ascertain its accuracy so the robustness of these methods is unclear. Because of the change in asbestos use in recent decades, more attention should be paid to understanding the risks and burden of future cases arising from non-occupational exposure. A range of current data exist that should be sufficient to incorporate into models to predict future cases of mesothelioma arising from non-occupational asbestos exposure. Models could be tested for their accuracy by comparing them against the most recent 10 years of observed cases or against cases in women, whose most common source of exposure is non-occupational.Predictions of the future burden of disease are undertaken for a variety of policy and planning reasons. Knowledge about the future number of cases of a particular disease can assist health planners to allocate resources for primary prevention, screening and diagnosis, treatment, and palliative care. Estimates of the future burden of disease can also be used to evaluate prevention programs. For example, the number of observed cases after the introduction of a prevention program could be evaluated against the number of expected cases, assuming that the disease trends before the program was introduced continued into the future. In the same vein, a prediction that estimated a heavy future burden of a particular disease could alert public health specialists to instigate a prevention program in order to avoid that prediction scenario (Bray and Moller 2006).The simplest type of cancer prediction extrapolates past trends of cancer incidence or mortality to some date in to the future, but more information is required in order to estimate more complex scenarios. Ideally, information about the effect of a risk factor on the cancer of interest would be known by sex and age group for the past, present and future. In addition, the prevalence of exposure to that risk factor in the population would also be known. This information would then ...

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“…1 There is strong evidence of increased risk following occupational exposure and, to some extent, environmental exposure, usually from industrial point sources or household contact 2 and asbestos-contaminated soil. 3 The dimension of excess risk at levels of exposure encountered in urban areas is almost unknown.…”

Section: Dear Sirmentioning

confidence: 99%