Cargando…

Multistate analysis of prospective Legionnaires’ disease cluster detection using SaTScan, 2011–2015

Detection of clusters of Legionnaires’ disease, a leading waterborne cause of pneumonia, is challenging. Clusters vary in size and scope, are associated with a diverse range of aerosol-producing devices, including exposures such as whirlpool spas and hotel water systems typically associated with tra...

Descripción completa

Detalles Bibliográficos
Autores principales: Edens, Chris, Alden, Nisha B., Danila, Richard N., Fill, Mary-Margaret A., Gacek, Paul, Muse, Alison, Parker, Erin, Poissant, Tasha, Ryan, Patricia A., Smelser, Chad, Tobin-D’Angelo, Melissa, Schrag, Stephanie J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6542510/
https://www.ncbi.nlm.nih.gov/pubmed/31145765
http://dx.doi.org/10.1371/journal.pone.0217632
_version_ 1783422947545841664
author Edens, Chris
Alden, Nisha B.
Danila, Richard N.
Fill, Mary-Margaret A.
Gacek, Paul
Muse, Alison
Parker, Erin
Poissant, Tasha
Ryan, Patricia A.
Smelser, Chad
Tobin-D’Angelo, Melissa
Schrag, Stephanie J.
author_facet Edens, Chris
Alden, Nisha B.
Danila, Richard N.
Fill, Mary-Margaret A.
Gacek, Paul
Muse, Alison
Parker, Erin
Poissant, Tasha
Ryan, Patricia A.
Smelser, Chad
Tobin-D’Angelo, Melissa
Schrag, Stephanie J.
author_sort Edens, Chris
collection PubMed
description Detection of clusters of Legionnaires’ disease, a leading waterborne cause of pneumonia, is challenging. Clusters vary in size and scope, are associated with a diverse range of aerosol-producing devices, including exposures such as whirlpool spas and hotel water systems typically associated with travel, and can occur without an easily identified exposure source. Recently, jurisdictions have begun to use SaTScan spatio-temporal analysis software prospectively as part of routine cluster surveillance. We used data collected by the Active Bacterial Core surveillance platform to assess the ability of SaTScan to detect Legionnaires’ disease clusters. We found that SaTScan analysis using traditional surveillance data and geocoded residential addresses was unable to detect many common Legionnaires’ disease cluster types, such as those associated with travel or a prolonged time between cases. Additionally, signals from an analysis designed to simulate a real-time search for clusters did not align with clusters identified by traditional surveillance methods or a retrospective SaTScan analysis. A geospatial analysis platform better tailored to the unique characteristics of Legionnaires’ disease epidemiology would improve cluster detection and decrease time to public health action.
format Online
Article
Text
id pubmed-6542510
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-65425102019-06-17 Multistate analysis of prospective Legionnaires’ disease cluster detection using SaTScan, 2011–2015 Edens, Chris Alden, Nisha B. Danila, Richard N. Fill, Mary-Margaret A. Gacek, Paul Muse, Alison Parker, Erin Poissant, Tasha Ryan, Patricia A. Smelser, Chad Tobin-D’Angelo, Melissa Schrag, Stephanie J. PLoS One Research Article Detection of clusters of Legionnaires’ disease, a leading waterborne cause of pneumonia, is challenging. Clusters vary in size and scope, are associated with a diverse range of aerosol-producing devices, including exposures such as whirlpool spas and hotel water systems typically associated with travel, and can occur without an easily identified exposure source. Recently, jurisdictions have begun to use SaTScan spatio-temporal analysis software prospectively as part of routine cluster surveillance. We used data collected by the Active Bacterial Core surveillance platform to assess the ability of SaTScan to detect Legionnaires’ disease clusters. We found that SaTScan analysis using traditional surveillance data and geocoded residential addresses was unable to detect many common Legionnaires’ disease cluster types, such as those associated with travel or a prolonged time between cases. Additionally, signals from an analysis designed to simulate a real-time search for clusters did not align with clusters identified by traditional surveillance methods or a retrospective SaTScan analysis. A geospatial analysis platform better tailored to the unique characteristics of Legionnaires’ disease epidemiology would improve cluster detection and decrease time to public health action. Public Library of Science 2019-05-30 /pmc/articles/PMC6542510/ /pubmed/31145765 http://dx.doi.org/10.1371/journal.pone.0217632 Text en https://creativecommons.org/publicdomain/zero/1.0/ This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication.
spellingShingle Research Article
Edens, Chris
Alden, Nisha B.
Danila, Richard N.
Fill, Mary-Margaret A.
Gacek, Paul
Muse, Alison
Parker, Erin
Poissant, Tasha
Ryan, Patricia A.
Smelser, Chad
Tobin-D’Angelo, Melissa
Schrag, Stephanie J.
Multistate analysis of prospective Legionnaires’ disease cluster detection using SaTScan, 2011–2015
title Multistate analysis of prospective Legionnaires’ disease cluster detection using SaTScan, 2011–2015
title_full Multistate analysis of prospective Legionnaires’ disease cluster detection using SaTScan, 2011–2015
title_fullStr Multistate analysis of prospective Legionnaires’ disease cluster detection using SaTScan, 2011–2015
title_full_unstemmed Multistate analysis of prospective Legionnaires’ disease cluster detection using SaTScan, 2011–2015
title_short Multistate analysis of prospective Legionnaires’ disease cluster detection using SaTScan, 2011–2015
title_sort multistate analysis of prospective legionnaires’ disease cluster detection using satscan, 2011–2015
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6542510/
https://www.ncbi.nlm.nih.gov/pubmed/31145765
http://dx.doi.org/10.1371/journal.pone.0217632
work_keys_str_mv AT edenschris multistateanalysisofprospectivelegionnairesdiseaseclusterdetectionusingsatscan20112015
AT aldennishab multistateanalysisofprospectivelegionnairesdiseaseclusterdetectionusingsatscan20112015
AT danilarichardn multistateanalysisofprospectivelegionnairesdiseaseclusterdetectionusingsatscan20112015
AT fillmarymargareta multistateanalysisofprospectivelegionnairesdiseaseclusterdetectionusingsatscan20112015
AT gacekpaul multistateanalysisofprospectivelegionnairesdiseaseclusterdetectionusingsatscan20112015
AT musealison multistateanalysisofprospectivelegionnairesdiseaseclusterdetectionusingsatscan20112015
AT parkererin multistateanalysisofprospectivelegionnairesdiseaseclusterdetectionusingsatscan20112015
AT poissanttasha multistateanalysisofprospectivelegionnairesdiseaseclusterdetectionusingsatscan20112015
AT ryanpatriciaa multistateanalysisofprospectivelegionnairesdiseaseclusterdetectionusingsatscan20112015
AT smelserchad multistateanalysisofprospectivelegionnairesdiseaseclusterdetectionusingsatscan20112015
AT tobindangelomelissa multistateanalysisofprospectivelegionnairesdiseaseclusterdetectionusingsatscan20112015
AT schragstephaniej multistateanalysisofprospectivelegionnairesdiseaseclusterdetectionusingsatscan20112015