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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...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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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 |
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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 |
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