Spatial analysis of the risk of multiple cancers in relation to a petrochemical plant
In Environmental Epidemiology studies, the effects of the presence of a source of pollution on the population health can be evaluated by models that consider the distance from the source as a possible risk factor. We introduce a hierarchical Bayesian model in order to investigate the association bet...
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| Published in | Environmetrics (London, Ont.) Vol. 23; no. 2; pp. 175 - 182 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Chichester, UK
John Wiley & Sons, Ltd
01.03.2012
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| Abstract | In Environmental Epidemiology studies, the effects of the presence of a source of pollution on the population health can be evaluated by models that consider the distance from the source as a possible risk factor. We introduce a hierarchical Bayesian model in order to investigate the association between the risk of multiple pathologies and the presence of a single pollution source. Our approach provides the possibility to incorporate spatial effects and other confounding factors within a logistic regression model. Spatial effects are decomposed into the sum of a disease‐specific parametric component accounting for the distance from the point source and a common semi‐parametric component that can be interpreted as a residual spatial variation. The model is applied to data from a spatial case–control study to evaluate the association of the incidence of different cancers with the residential location in the neighborhood of a petrochemical plant in the Brindisi area (Italy). Copyright © 2011 John Wiley & Sons, Ltd. |
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| AbstractList | In Environmental Epidemiology studies, the effects of the presence of a source of pollution on the population health can be evaluated by models that consider the distance from the source as a possible risk factor. We introduce a hierarchical Bayesian model in order to investigate the association between the risk of multiple pathologies and the presence of a single pollution source. Our approach provides the possibility to incorporate spatial effects and other confounding factors within a logistic regression model. Spatial effects are decomposed into the sum of a disease‐specific parametric component accounting for the distance from the point source and a common semi‐parametric component that can be interpreted as a residual spatial variation. The model is applied to data from a spatial case–control study to evaluate the association of the incidence of different cancers with the residential location in the neighborhood of a petrochemical plant in the Brindisi area (Italy). Copyright © 2011 John Wiley & Sons, Ltd. |
| Author | Pollice, Alessio Serinelli, Maria Calculli, Crescenza |
| Author_xml | – sequence: 1 givenname: Crescenza surname: Calculli fullname: Calculli, Crescenza email: e.calculli@dss.uniba.it, e.calculli@dss.uniba.it organization: Dipartimento di Scienze Statistiche "Carlo Cecchi", Università degli Studi di Bari "Aldo Moro", Bari, Italy – sequence: 2 givenname: Alessio surname: Pollice fullname: Pollice, Alessio organization: Dipartimento di Scienze Statistiche "Carlo Cecchi", Università degli Studi di Bari "Aldo Moro", Bari, Italy – sequence: 3 givenname: Maria surname: Serinelli fullname: Serinelli, Maria organization: ARPA Puglia, Bari, Italy |
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| Title | Spatial analysis of the risk of multiple cancers in relation to a petrochemical plant |
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