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      Lung Cancer Mortality in Tuscany from 1971 to 2010 and Its Connections with Silicosis: A Space-Cohort Analysis Based on Shared Models

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      Computational and Mathematical Methods in Medicine
      Hindawi

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          Abstract

          Lung cancer mortality in Tuscany (Italy) for males, from 1971 and 2010, is investigated. A hierarchical Bayesian model for space-time disease mapping is introduced. Such a model belongs to the class of shared random effect models and exploits the birth-cohort as the relevant time dimension. It allows for highlighting common and specific patterns of risk for each birth-cohort. The results show that different birth-cohorts exhibit quite different spatial patterns, even if the socioeconomic status is taken into account. In fact, there were different occupational exposures before and after the Second World War. The birth-cohort 1930–35 exhibits high relative risks related to particular areas. This fact could be connected with occupational exposure to risk factors for silicosis, perhaps a prognostic status for lung cancer.

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          Most cited references47

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          Inference from Iterative Simulation Using Multiple Sequences

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            The International Epidemiology of Lung Cancer: geographical distribution and secular trends.

            This review presents the latest available international data for lung cancer incidence, mortality and survival, emphasizing the established causal relationship between smoking and lung cancer. In 2002, it was estimated that 1.35 million people throughout the world were diagnosed with lung cancer, and 1.18 million died of lung cancer-more than for any other type of cancer. There are some key differences in the epidemiology of lung cancer between more developed and less developed countries. In more developed countries, incidence and mortality rates are generally declining among males and are starting to plateau for females, reflecting previous trends in smoking prevalence. In contrast, there are some populations in less developed countries where increasing lung cancer rates are predicted to continue, due to endemic use of tobacco. A higher proportion of lung cancer cases are attributable to nonsmoking causes within less developed countries, particularly among women. Worldwide, the majority of lung cancer patients are diagnosed after the disease has progressed to a more advanced stage. Despite advances in chemotherapy, prognosis for lung cancer patients remains poor, with 5-year relative survival less than 14% among males and less than 18% among females in most countries. Given the increasing incidence of lung cancer in less developed countries and the current lack of effective treatment for advanced lung cancers, these results highlight the need for ongoing global tobacco reform to reduce the international burden of lung cancer.
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              Models for temporal variation in cancer rates. I: Age-period and age-cohort models.

              A main concern of descriptive epidemiologists is the presentation and interpretation of temporal variations in cancer rates. In its simplest form, this problem is that of the analysis of a set of rates arranged in a two-way table by age group and calendar period. We review the modern approach to the analysis of such data which justifies traditional methods of age standardization in terms of the multiplicative risk model. We discuss the use of this model when the temporal variations are due to purely secular (period) influences and when they are attributable to generational (cohort) influences. Finally we demonstrate the serious difficulties which attend the interpretation of regular trends. The methods described are illustrated by examples for incidence rates of bladder cancer in Birmingham, U.K., mortality from bladder cancer in Italy, and mortality from lung cancer in Belgium.
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                Author and article information

                Contributors
                Journal
                Comput Math Methods Med
                Comput Math Methods Med
                CMMM
                Computational and Mathematical Methods in Medicine
                Hindawi
                1748-670X
                1748-6718
                2018
                28 January 2018
                : 2018
                : 4964569
                Affiliations
                Dipartimento di Statistica, Informatica, Applicazioni “G. Parenti”, Università di Firenze, Viale Morgagni 59, 50134 Firenze, Italy
                Author notes

                Academic Editor: Konstantin G. Arbeev

                Author information
                http://orcid.org/0000-0002-4863-1800
                Article
                10.1155/2018/4964569
                5896287
                87916b8a-50f1-48f8-99eb-92839e304a8e
                Copyright © 2018 Emanuela Dreassi.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 22 August 2017
                : 13 December 2017
                : 28 December 2017
                Categories
                Research Article

                Applied mathematics
                Applied mathematics

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