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Waterborne Disease Outbreak Detection: A Simulation-Based Study

Waterborne disease outbreaks (WBDOs) remain a public health issue in developed countries, but to date the surveillance of WBDOs in France, mainly based on the voluntary reporting of clusters of acute gastrointestinal infections (AGIs) by general practitioners to health authorities, is characterized...

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Autores principales: Mouly, Damien, Goria, Sarah, Mounié, Michael, Beaudeau, Pascal, Galey, Catherine, Gallay, Anne, Ducrot, Christian, Le Strat, Yann
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6068474/
https://www.ncbi.nlm.nih.gov/pubmed/30018195
http://dx.doi.org/10.3390/ijerph15071505
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author Mouly, Damien
Goria, Sarah
Mounié, Michael
Beaudeau, Pascal
Galey, Catherine
Gallay, Anne
Ducrot, Christian
Le Strat, Yann
author_facet Mouly, Damien
Goria, Sarah
Mounié, Michael
Beaudeau, Pascal
Galey, Catherine
Gallay, Anne
Ducrot, Christian
Le Strat, Yann
author_sort Mouly, Damien
collection PubMed
description Waterborne disease outbreaks (WBDOs) remain a public health issue in developed countries, but to date the surveillance of WBDOs in France, mainly based on the voluntary reporting of clusters of acute gastrointestinal infections (AGIs) by general practitioners to health authorities, is characterized by low sensitivity. In this context, a detection algorithm using health insurance data and based on a space–time method was developed to improve WBDO detection. The objective of the present simulation-based study was to evaluate the performance of this algorithm for WBDO detection using health insurance data. The daily baseline counts of acute gastrointestinal infections were simulated. Two thousand simulated WBDO signals were then superimposed on the baseline data. Sensitivity (Se) and positive predictive value (PPV) were both used to evaluate the detection algorithm. Multivariate regression was also performed to identify the factors associated with WBDO detection. Almost three-quarters of the simulated WBDOs were detected (Se = 73.0%). More than 9 out of 10 detected signals corresponded to a WBDO (PPV = 90.5%). The probability of detecting a WBDO increased with the outbreak size. These results underline the value of using the detection algorithm for the implementation of a national surveillance system for WBDOs in France.
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spelling pubmed-60684742018-08-07 Waterborne Disease Outbreak Detection: A Simulation-Based Study Mouly, Damien Goria, Sarah Mounié, Michael Beaudeau, Pascal Galey, Catherine Gallay, Anne Ducrot, Christian Le Strat, Yann Int J Environ Res Public Health Article Waterborne disease outbreaks (WBDOs) remain a public health issue in developed countries, but to date the surveillance of WBDOs in France, mainly based on the voluntary reporting of clusters of acute gastrointestinal infections (AGIs) by general practitioners to health authorities, is characterized by low sensitivity. In this context, a detection algorithm using health insurance data and based on a space–time method was developed to improve WBDO detection. The objective of the present simulation-based study was to evaluate the performance of this algorithm for WBDO detection using health insurance data. The daily baseline counts of acute gastrointestinal infections were simulated. Two thousand simulated WBDO signals were then superimposed on the baseline data. Sensitivity (Se) and positive predictive value (PPV) were both used to evaluate the detection algorithm. Multivariate regression was also performed to identify the factors associated with WBDO detection. Almost three-quarters of the simulated WBDOs were detected (Se = 73.0%). More than 9 out of 10 detected signals corresponded to a WBDO (PPV = 90.5%). The probability of detecting a WBDO increased with the outbreak size. These results underline the value of using the detection algorithm for the implementation of a national surveillance system for WBDOs in France. MDPI 2018-07-17 2018-07 /pmc/articles/PMC6068474/ /pubmed/30018195 http://dx.doi.org/10.3390/ijerph15071505 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Mouly, Damien
Goria, Sarah
Mounié, Michael
Beaudeau, Pascal
Galey, Catherine
Gallay, Anne
Ducrot, Christian
Le Strat, Yann
Waterborne Disease Outbreak Detection: A Simulation-Based Study
title Waterborne Disease Outbreak Detection: A Simulation-Based Study
title_full Waterborne Disease Outbreak Detection: A Simulation-Based Study
title_fullStr Waterborne Disease Outbreak Detection: A Simulation-Based Study
title_full_unstemmed Waterborne Disease Outbreak Detection: A Simulation-Based Study
title_short Waterborne Disease Outbreak Detection: A Simulation-Based Study
title_sort waterborne disease outbreak detection: a simulation-based study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6068474/
https://www.ncbi.nlm.nih.gov/pubmed/30018195
http://dx.doi.org/10.3390/ijerph15071505
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