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Start Over Author "Kirby, Russell" Journal annals of epidemiology
17 results on '"Kirby, Russell"'

16. Disease mapping of zero-excessive mesothelioma data in Flanders.

Author

Neyens, Thomas, Lawson, Andrew B, Kirby, Russell S, Nuyts, Valerie, Watjou, Kevin, Aregay, Mehreteab, Carroll, Rachel, Nawrot, Tim S, and Faes, Christel

Subjects
*DEMOGRAPHY, *GEOGRAPHIC information systems, *LUNG tumors, *MESOTHELIOMA, *PERICARDIUM, *POISSON distribution, *PROBABILITY theory, *RESEARCH funding, *RISK assessment, *SURVIVAL analysis (Biometry), *PERITONEUM tumors, *PLEURAL tumors, *DISEASE incidence, *ACQUISITION of data, *DIAGNOSIS
Abstract

Purpose: To investigate the distribution of mesothelioma in Flanders using Bayesian disease mapping models that account for both an excess of zeros and overdispersion.Methods: The numbers of newly diagnosed mesothelioma cases within all Flemish municipalities between 1999 and 2008 were obtained from the Belgian Cancer Registry. To deal with overdispersion, zero inflation, and geographical association, the hurdle combined model was proposed, which has three components: a Bernoulli zero-inflation mixture component to account for excess zeros, a gamma random effect to adjust for overdispersion, and a normal conditional autoregressive random effect to attribute spatial association. This model was compared with other existing methods in literature.Results: The results indicate that hurdle models with a random effects term accounting for extra variance in the Bernoulli zero-inflation component fit the data better than hurdle models that do not take overdispersion in the occurrence of zeros into account. Furthermore, traditional models that do not take into account excessive zeros but contain at least one random effects term that models extra variance in the counts have better fits compared to their hurdle counterparts. In other words, the extra variability, due to an excess of zeros, can be accommodated by spatially structured and/or unstructured random effects in a Poisson model such that the hurdle mixture model is not necessary.Conclusions: Models taking into account zero inflation do not always provide better fits to data with excessive zeros than less complex models. In this study, a simple conditional autoregressive model identified a cluster in mesothelioma cases near a former asbestos processing plant (Kapelle-op-den-Bos). This observation is likely linked with historical local asbestos exposures. Future research will clarify this. [ABSTRACT FROM AUTHOR]

Published
2016
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17. Disease mapping of zero-excessive mesothelioma data in Flanders

Author

Mehreteab Aregay, Andrew B. Lawson, Christel Faes, Rachel Carroll, Thomas Neyens, Russell S. Kirby, Kevin Watjou, Tim S. Nawrot, Valerie Nuyts, NEYENS, Thomas, LAWSON, Andrew, Kirby, Russell S., Nuyts, Valerie, WATJOU, Kevin, AREGAY, Mehreteab, Carroll, Rachel, NAWROT, Tim, and FAES, Christel

Subjects
Adult, Male, Mesothelioma, excess zeros, mesothelioma, disease mapping, conditional autoregressive convolution model, Bayesian analysis, Lung Neoplasms, Epidemiology, Pleural Neoplasms, Bayesian probability, Geographic Mapping, 01 natural sciences, Risk Assessment, Article, 010104 statistics & probability, 03 medical and health sciences, symbols.namesake, 0302 clinical medicine, Age Distribution, Overdispersion, Belgium, Statistics, Econometrics, Humans, 030212 general & internal medicine, Poisson regression, Poisson Distribution, Registries, 0101 mathematics, Sex Distribution, Peritoneal Neoplasms, Mathematics, Aged, Incidence, Mesothelioma, Malignant, Bayes Theorem, Variance (accounting), Middle Aged, Mixture model, Random effects model, Survival Analysis, Term (time), Deviance information criterion, symbols, Female, Pericardium
Abstract

Purpose: To investigate the distribution of mesothelioma in Flanders using Bayesian disease mapping models that account for both an excess of zeros and overdispersion. Methods: The numbers of newly diagnosed mesothelioma cases within all Flemish municipalities between 1999 and 2008 were obtained from the Belgian Cancer Registry. To deal with overdispersion, zero inflation, and geographical association, the hurdle combined model was proposed, which has three components: a Bernoulli zero-inflation mixture component to account for excess zeros, a gamma random effect to adjust for overdispersion, and a normal conditional autoregressive random effect to attribute spatial association. This model was compared with other existing methods in literature. Results: The results indicate that hurdle models with a random effects term accounting for extra variance in the Bernoulli zero -inflation component fit the data better than hurdle models that do not take overdispersion in the occurrence of zeros into account. Furthermore, traditional models that do not take into account excessive zeros but contain at least one random effects term that models extra variance in the counts have better fits compared to their hurdle counterparts. In other words, the extra variability, due to an excess of zeros, can be accommodated by spatially structured and/or unstructured random effects in a Poisson model such that the hurdle mixture model is not necessary. Conclusions: Models taking into account zero inflation do not always provide better fits to data with excessive zeros than less complex models. In this study, a simple conditional autoregressive model identified a cluster in mesothelioma cases near a former asbestos processing plant (Kapelle-op-den-Bos). This observation is likely linked with historical local asbestos exposures. Future research will clarify this. (C) 2016 Elsevier Inc. All rights reserved. Support from the National Institutes of Health is acknowledged (award number R01CA172805). Support from the IAP Research Network, P7/06, of the Belgian State (Belgian Science Policy) is gratefully acknowledged.

Published
2016

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