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Applying the compound Poisson process model to the reporting of injury-related mortality rates

Injury-related mortality rate estimates are often analyzed under the assumption that case counts follow a Poisson distribution. Certain types of injury incidents occasionally involve multiple fatalities, however, resulting in dependencies between cases that are not reflected in the simple Poisson mo...

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Detalles Bibliográficos
Autor principal: Kegler, Scott R
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2007
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1828152/
https://www.ncbi.nlm.nih.gov/pubmed/17306020
http://dx.doi.org/10.1186/1742-5573-4-1
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author Kegler, Scott R
author_facet Kegler, Scott R
author_sort Kegler, Scott R
collection PubMed
description Injury-related mortality rate estimates are often analyzed under the assumption that case counts follow a Poisson distribution. Certain types of injury incidents occasionally involve multiple fatalities, however, resulting in dependencies between cases that are not reflected in the simple Poisson model and which can affect even basic statistical analyses. This paper explores the compound Poisson process model as an alternative, emphasizing adjustments to some commonly used interval estimators for population-based rates and rate ratios. The adjusted estimators involve relatively simple closed-form computations, which in the absence of multiple-case incidents reduce to familiar estimators based on the simpler Poisson model. Summary data from the National Violent Death Reporting System are referenced in several examples demonstrating application of the proposed methodology.
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spelling pubmed-18281522007-03-19 Applying the compound Poisson process model to the reporting of injury-related mortality rates Kegler, Scott R Epidemiol Perspect Innov Methodology Injury-related mortality rate estimates are often analyzed under the assumption that case counts follow a Poisson distribution. Certain types of injury incidents occasionally involve multiple fatalities, however, resulting in dependencies between cases that are not reflected in the simple Poisson model and which can affect even basic statistical analyses. This paper explores the compound Poisson process model as an alternative, emphasizing adjustments to some commonly used interval estimators for population-based rates and rate ratios. The adjusted estimators involve relatively simple closed-form computations, which in the absence of multiple-case incidents reduce to familiar estimators based on the simpler Poisson model. Summary data from the National Violent Death Reporting System are referenced in several examples demonstrating application of the proposed methodology. BioMed Central 2007-02-16 /pmc/articles/PMC1828152/ /pubmed/17306020 http://dx.doi.org/10.1186/1742-5573-4-1 Text en Copyright © 2007 Kegler; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methodology
Kegler, Scott R
Applying the compound Poisson process model to the reporting of injury-related mortality rates
title Applying the compound Poisson process model to the reporting of injury-related mortality rates
title_full Applying the compound Poisson process model to the reporting of injury-related mortality rates
title_fullStr Applying the compound Poisson process model to the reporting of injury-related mortality rates
title_full_unstemmed Applying the compound Poisson process model to the reporting of injury-related mortality rates
title_short Applying the compound Poisson process model to the reporting of injury-related mortality rates
title_sort applying the compound poisson process model to the reporting of injury-related mortality rates
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1828152/
https://www.ncbi.nlm.nih.gov/pubmed/17306020
http://dx.doi.org/10.1186/1742-5573-4-1
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