Spatial variation of mortality for common and rare cancers in Piedmont, Italy, from 1980 to 2000: a Bayesian approach

Our plain-English summary of Spatial variation of mortality for common and rare cancers in Piedmont, Italy, from 1980 to 2000: a Bayesian approach, by M. Maule, F. Merletti, D. Mirabelli, et al (European Journal of Cancer Prevention, 2006).

The study

• Authors: M. Maule, F. Merletti, D. Mirabelli, C. La Vecchia
• Journal: European Journal of Cancer Prevention
• Published: 2006;15(2):108-16
• Cited by: 3 articles, as counted by NIH iCite
• Read it: PubMed · Publisher

Abstract

A Bayesian hierarchical model was used to study the spatial variation in mortality risk from lung and pleural cancer in both sexes, and breast and soft tissue sarcoma (STS) in women in Piedmont (north-west Italy, average population 4 349 411) from 1980 to 2000. Of these four neoplasms, two are common (lung and breast) and two rare (pleura and STS); two have well recognized risk factors (lung and pleura) while the other two (breast and STS) have no single strong risk factor. Data were analysed at a small-area level (1206 municipalities, population 39 to 989 663), using both standardized mortality ratios and Bayesian-estimated mortality risks. The Bayesian model allowed for both heterogeneity (through spatially independent random effects) and clustering (through spatially correlated random effects) and, by borrowing information from neighbouring areas, provided stable estimates for areas with sparse data. The aim was to reduce the noise in the disease maps to highlight the true underlying mortality distribution. Lung cancer in men showed strong spatial structure with a marked east-west gradient, but no appreciable urban-rural differences. In contrast, high mortality areas for female lung cancer were observed around conurbations. Female breast cancer and STS appeared to be spread uniformly across the region. Pleural cancer mortality clusters were evident around areas with major asbestos manufacturers, or natural asbestiform fibre pollution. Maps of Bayesian-estimated mortality risk provided appreciably clearer pictures of risk distribution than did maps of the standardized mortality ratio.

In plain English

This study mapped deaths from four cancers across the 1206 municipalities of Piedmont, north-west Italy, from 1980 to 2000 (average annual population 4 349 411). Alongside standardized mortality ratios, the authors used a Bayesian statistical model that borrows information from neighbouring areas to give steadier estimates for small towns. The cancers were lung cancer, pleural cancer and soft tissue sarcoma in both sexes, and breast cancer in women. There were 1503 pleural cancer deaths in men and 988 in women. The authors compared the pleural cancer maps with known local asbestos sources. Pleural cancer formed five clusters in men and six in women, including areas around Turin, Biella and Casale Monferrato, where an asbestos cement factory operated from 1907 to 1985. The highest estimated relative risk among women, 23.37, was in Casale Monferrato, based on 162 deaths. Some other clusters rested on very few deaths. Breast cancer and soft tissue sarcoma appeared evenly spread. The authors concluded that the Bayesian maps made pleural cancer clusters around asbestos-polluted areas more clearly identifiable. They noted limited statistical power and possibly inaccurate death certificates.

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: Geographical (small-area) mortality mapping study using standardized mortality ratios and a Bayesian hierarchical spatial model
• Who was studied: Residents of the 1206 municipalities of Piedmont, north-west Italy, 1980 to 2000. The average annual population was 4 349 411, and municipalities ranged from 39 to 989 663 inhabitants.
• Exposure: No measured exposures. The authors compared the maps with known asbestos sources: naturally surfacing asbestos-containing rocks in western alpine valleys, the Balangero chrysotile mine, asbestos manufacturing around Turin, an asbestos cement factory in Casale Monferrato (1907 to 1985), and tremolite in soil in the Susa and Chisone Valleys. Smoking and herbicide use were also discussed for the other cancers.
• Main results:
  • Pleural cancer deaths over the study period: 1503 in men and 988 in women
  • Pleural cancer in men formed five spatial clusters: a western mountain area, around Turin, around Biella, around Casale Monferrato, and north of Cuneo
  • Pleural cancer in women showed a similar but not identical pattern of clusters, including one around Verbania not seen in men
  • The highest Bayesian-estimated mortality risk for pleural cancer in women was 23.37 in Casale Monferrato, based on 162 deaths
  • The western clusters, which the authors linked to tremolite in soil, were based on six deaths among men and two among women. A southern cluster in women with no obvious asbestos exposure was based on 13 deaths, with a maximum estimate of 1.93.
  • Spatial clustering, measured as the weight of heterogeneity versus clustering, was 1 : 3 for lung cancer in men, 1 : 11 for pleural cancer in men and 1 : 14 for pleural cancer in women
  • Lung cancer in men showed a north-east to south-west gradient, and the Turin metropolitan area was not a high-risk area
  • Lung cancer in women showed high-risk patterns around urban areas
  • Breast cancer in women and soft tissue sarcoma appeared uniformly distributed. No statistically significant estimates were obtained for soft tissue sarcoma.
• The authors' conclusion: The authors concluded that Bayesian methods had a clear advantage over standard SMRs and made the clusters of pleural cancer mortality around asbestos-polluted areas more clearly identifiable
• Limitations the authors state:
  • Statistical power was limited, especially for rare diseases such as soft tissue sarcoma
  • The accuracy of death certificates may have varied across places and over time
  • Population makeup changed over time, including migration, at different times in different areas
  • Results depend on the choice of prior distribution in the Bayesian model
  • Estimates are less precise near the region's boundaries
  • No municipality-level data on smoking or reproductive factors were available

Mines named here

Asbestos mines this study is about. Each mine's page says who ran it, when it worked, and what it produced.

• Balangero mine, Balangero, about 20 km north of Turin, Piedmont

All asbestos mines

Topics

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

Medical terms in this study, explained in our Lexicon: Sarcoma, Neoplasm, Pleura, standardized mortality ratio (SMR), Lung Cancer, Asbestiform.

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