Estimate of environmental and occupational components in the spatial distribution of malignant mesothelioma incidence in Lombardy (Italy)

Our plain-English summary of Estimate of environmental and occupational components in the spatial distribution of malignant mesothelioma incidence in Lombardy (Italy), by D. Catelan, D. Consonni, A. Biggeri, et al (Environmental Research, 2020).

The study

• Authors: D. Catelan, D. Consonni, A. Biggeri, B. Dallari, A.C. Pesatori, L. Riboldi, C. Mensi
• Journal: Environmental Research
• Published: 2020;188:109691
• Cited by: 11 articles, as counted by NIH iCite
• Read it: PubMed · Publisher

Abstract

Introduction: Measuring and mapping the occurrence of malignant mesothelioma (MM) is a useful means to monitor the impact of past asbestos exposure and possibly identify previously unknown sources of asbestos exposure.

Objective: Our goal is to decompose the observed spatial pattern of incidence of MM in the Lombardy region (Italy) in gender-specific components linked to occupational exposure and a shared component linked to environmental exposure.

Materials And Methods: We selected from the Lombardy Region Mesothelioma Registry (RML) all incident cases of MM (pleura, peritoneum, pericardium, and tunica vaginalis testis) with first diagnosis in the period 2000-2016. We mapped at municipality level crude incidence rates and smoothed rates using the Besag York and Mollié model separately for men and women. We then decomposed the spatial pattern of MM in gender-specific occupational components and a shared environmental component using a multivariate hierarchical Bayesian model.

Results: We globally analyzed 6226 MM cases, 4048 (2897 classified as occupational asbestos exposure at interview) in men and 2178 (780 classified as occupational asbestos exposure at interview) in women. The geographical analysis showed a strong spatial pattern in the distribution of incidence rates in both genders. The multivariate hierarchical Bayesian model decomposed the spatial pattern in occupational and environmental components and consistently identified some known occupational and environmental hot spots. Other areas at high risk for MM occurrence were highlighted, contributing to better characterize environmental exposures from industrial sources and suggesting a role of natural sources in the Alpine region.

Conclusion: The spatial pattern highlights areas at higher risk which are characterized by the presence of industrial sources - asbestos-cement, metallurgic, engineering, textile industries - and of natural sources in the Alpine region. The multivariate hierarchical Bayesian model was able to disentangle the geographical distribution of MM cases in two components interpreted as occupational and environmental.

Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.

In plain English

This ecological study analyzed all 6226 malignant mesothelioma cases first diagnosed in 2000–2016 among residents of Lombardy, Italy, using the regional registry. The authors mapped incidence across 1546 municipalities and modeled separate occupational components for men and women plus a shared environmental component. At interview, occupational asbestos exposure was identified in 2897 of 4048 men (71.6%) and 780 of 2178 women (35.8%). Non-occupational rates in men and women, excluding not classified cases, were correlated (r 0.67, 95% CI 0.64 to 0.70); including them, r was 0.41 (95% CI 0.37 to 0.45). Both kinds of component highlighted Broni, site of an asbestos-cement factory. The environmental component also highlighted the Alpine Valmalenco area, where serpentinite is extracted, and municipalities near Casale Monferrato. The model estimated that 59% of women's cases (1287 of 2178) were not attributable to occupational exposure; the article reports the credible interval inconsistently (39% to 94%, and 39% to 73%). The authors concluded that the two components could be disentangled and that the environmental component was larger than previously found. They noted that exposure may have occurred away from the residence at diagnosis.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: Registry-based geographical (ecological) study of incidence at municipality level, using spatial Bayesian models (BYM smoothing and a shared multivariate hierarchical Bayesian model)
• Who was studied: All 6226 malignant mesothelioma cases (4048 men, 2178 women) of the pleura, peritoneum, pericardium and tunica vaginalis testis, first diagnosed from 2000 to 2016 among residents of the Lombardy region, Italy (1546 municipalities), from the Lombardy Region Mesothelioma Registry
• Exposure: Asbestos exposure was classified at interview as occupational. If there was no occupational exposure, it was classified as familial (para-occupational), home-related, or indoor/outdoor environmental. Cases were grouped as occupational or non-occupational, and the non-occupational group included cases with no identified exposure and not classified cases. Fiber types were not analyzed, apart from a cited mention of chrysotile and antigorite in Malenco serpentinite.
• Main results:
  • Occupational asbestos exposure was identified in 2897 men (71.6%) and 780 women (35.8%). No identified asbestos exposure was recorded in 40.8% of women and 19.1% of men. Not classified cases were 10.1% of women and 5.8% of men.
  • Across municipalities, non-occupational mesothelioma rates in men and women, with not classified cases excluded, were correlated: Pearson's r 0.67 (95% CI 0.64; 0.70). The correlation between occupational rates in men and women was 0.28 (95% CI 0.23; 0.33).
  • When not classified cases were included among the non-occupational cases, the correlation between men and women was r = 0.41 (95% CI 0.37; 0.45).
  • In Broni (Province of Pavia), the observed rate of occupationally classified mesothelioma in men was 98.27 per 100,000 person-years, against an average regional rate of 3.48.
  • The occupational component for women showed two hot-spot municipalities where asbestos textile industries had been present: Calcio (Province of Bergamo) and Romanengo (Province of Cremona).
  • The environmental component showed a cluster around Broni with a radius of up to 30 km. The occupational component there was restricted to less than 10 km.
  • The environmental component also identified a large area in the northern mountain territories around the Valmalenco valley (Province of Sondrio), where Malenco serpentinites are extracted and processed.
  • A further environmental-component cluster appeared in municipalities on the south-western border near Casale Monferrato (Alessandria, Piedmont).
  • The model estimated that 59% of cases in women were not attributable to occupational exposure (1287/2178). The Results give a 90% credible interval of 39% to 94%, while the Discussion and Conclusion give a 95% credible interval of 39% to 73%.
  • The model estimated that 111 of the 1398 cases classified at interview as non-occupational had been misclassified.
  • The scaling parameter for the shared environmental component was 0.91 (90% CrI 0.85; 0.98). The authors interpreted this as meaning the environmental component was more important among women than among men.
• The authors' conclusion: The authors concluded that their method could separate occupational and environmental components in the spatial distribution of mesothelioma, and that the environmental component's impact was larger than previously found. They linked known hot spots to asbestos-cement, metallurgic, engineering and textile industries, and said the results suggested a role of natural sources in the Alpine region. They stated that geographical studies like theirs can be useful to quantify the extent of environmental asbestos exposure on mesothelioma incidence.
• Limitations the authors state:
  • The relevant exposure may have occurred at a location other than the residence at diagnosis, which these data cannot resolve.
  • Residual confounding from using aggregate data, a common fallacy in ecological studies, cannot be excluded.
  • Age or cohort effects cannot be excluded in this cross-sectional analysis.
  • Using correlation coefficients on spatially structured data has limitations.
  • Registry interview classification overestimates non-occupational cases because some are misclassified. Environmental exposure is also difficult to investigate by interview.

Topics

The National Library of Medicine indexes this study under subjects our site covers: Mesothelioma.

Medical terms in this study, explained in our Lexicon: Malignant Mesothelioma, Asbestos Exposure, Occupational Exposure, Environmental Exposure, Mesothelioma, Pleura, Peritoneum, Pericardium.

Related studies

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• Incidence of mesothelioma in Lombardy, Italy: exposure to asbestos, time patterns and future projections — C. Mensi, S. De Matteis, et al. (2016)
• Gender differences in pleural mesothelioma occurrence in Lombardy and Piedmont, Italy — D. Consonni, E. Migliore, et al. (2019)
• Peritoneal mesothelioma and asbestos exposure: a population-based case-control study in Lombardy, Italy — D. Consonni, C. Calvi, et al. (2019)
• Differences between peritoneal and pleural mesothelioma in Lombardy, Italy — C. Mensi, M. Mendola, et al. (2017)
• Mesothelioma in Agriculture in Lombardy, Italy: An Unrecognized Risk — C. Mensi, B. Dallari, et al. (2021)

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