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Comparison of covariate adjustment methods using space-time scan statistics for food animal syndromic surveillance

BACKGROUND: Abattoir condemnation data show promise as a rich source of data for syndromic surveillance of both animal and zoonotic diseases. However, inherent characteristics of abattoir condemnation data can bias results from space-time cluster detection methods for disease surveillance, and may n...

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Autores principales: Alton, Gillian D, Pearl, David L, Bateman, Ken G, McNab, Bruce, Berke, Olaf
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3842647/
https://www.ncbi.nlm.nih.gov/pubmed/24246040
http://dx.doi.org/10.1186/1746-6148-9-231
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author Alton, Gillian D
Pearl, David L
Bateman, Ken G
McNab, Bruce
Berke, Olaf
author_facet Alton, Gillian D
Pearl, David L
Bateman, Ken G
McNab, Bruce
Berke, Olaf
author_sort Alton, Gillian D
collection PubMed
description BACKGROUND: Abattoir condemnation data show promise as a rich source of data for syndromic surveillance of both animal and zoonotic diseases. However, inherent characteristics of abattoir condemnation data can bias results from space-time cluster detection methods for disease surveillance, and may need to be accounted for using various adjustment methods. The objective of this study was to compare the space-time scan statistics with different abilities to control for covariates and to assess their suitability for food animal syndromic surveillance. Four space-time scan statistic models were used including: animal class adjusted Poisson, space-time permutation, multi-level model adjusted Poisson, and a weighted normal scan statistic using model residuals. The scan statistics were applied to monthly bovine pneumonic lung and “parasitic liver” condemnation data from Ontario provincial abattoirs from 2001–2007. RESULTS: The number and space-time characteristics of identified clusters often varied between space-time scan tests for both “parasitic liver” and pneumonic lung condemnation data. While there were some similarities between isolated clusters in space, time and/or space-time, overall the results from space-time scan statistics differed substantially depending on the covariate adjustment approach used. CONCLUSIONS: Variability in results among methods suggests that caution should be used in selecting space-time scan methods for abattoir surveillance. Furthermore, validation of different approaches with simulated or real outbreaks is required before conclusive decisions can be made concerning the best approach for conducting surveillance with these data.
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spelling pubmed-38426472013-11-29 Comparison of covariate adjustment methods using space-time scan statistics for food animal syndromic surveillance Alton, Gillian D Pearl, David L Bateman, Ken G McNab, Bruce Berke, Olaf BMC Vet Res Research Article BACKGROUND: Abattoir condemnation data show promise as a rich source of data for syndromic surveillance of both animal and zoonotic diseases. However, inherent characteristics of abattoir condemnation data can bias results from space-time cluster detection methods for disease surveillance, and may need to be accounted for using various adjustment methods. The objective of this study was to compare the space-time scan statistics with different abilities to control for covariates and to assess their suitability for food animal syndromic surveillance. Four space-time scan statistic models were used including: animal class adjusted Poisson, space-time permutation, multi-level model adjusted Poisson, and a weighted normal scan statistic using model residuals. The scan statistics were applied to monthly bovine pneumonic lung and “parasitic liver” condemnation data from Ontario provincial abattoirs from 2001–2007. RESULTS: The number and space-time characteristics of identified clusters often varied between space-time scan tests for both “parasitic liver” and pneumonic lung condemnation data. While there were some similarities between isolated clusters in space, time and/or space-time, overall the results from space-time scan statistics differed substantially depending on the covariate adjustment approach used. CONCLUSIONS: Variability in results among methods suggests that caution should be used in selecting space-time scan methods for abattoir surveillance. Furthermore, validation of different approaches with simulated or real outbreaks is required before conclusive decisions can be made concerning the best approach for conducting surveillance with these data. BioMed Central 2013-11-18 /pmc/articles/PMC3842647/ /pubmed/24246040 http://dx.doi.org/10.1186/1746-6148-9-231 Text en Copyright © 2013 Alton et al.; 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 Research Article
Alton, Gillian D
Pearl, David L
Bateman, Ken G
McNab, Bruce
Berke, Olaf
Comparison of covariate adjustment methods using space-time scan statistics for food animal syndromic surveillance
title Comparison of covariate adjustment methods using space-time scan statistics for food animal syndromic surveillance
title_full Comparison of covariate adjustment methods using space-time scan statistics for food animal syndromic surveillance
title_fullStr Comparison of covariate adjustment methods using space-time scan statistics for food animal syndromic surveillance
title_full_unstemmed Comparison of covariate adjustment methods using space-time scan statistics for food animal syndromic surveillance
title_short Comparison of covariate adjustment methods using space-time scan statistics for food animal syndromic surveillance
title_sort comparison of covariate adjustment methods using space-time scan statistics for food animal syndromic surveillance
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3842647/
https://www.ncbi.nlm.nih.gov/pubmed/24246040
http://dx.doi.org/10.1186/1746-6148-9-231
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