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Increase in Reported Cholera Cases in Haiti Following Hurricane Matthew: An Interrupted Time Series Model

Matthew, a category 4 hurricane, struck Haiti on October 4, 2016, causing widespread flooding and damage to buildings and crops, and resulted in many deaths. The damage caused by Matthew raised concerns of increased cholera transmission particularly in Sud and Grand’Anse departments, regions which w...

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Autores principales: Hulland, Erin, Subaiya, Saleena, Pierre, Katilla, Barthelemy, Nickolson, Pierre, Jean Samuel, Dismer, Amber, Juin, Stanley, Fitter, David, Brunkard, Joan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The American Society of Tropical Medicine and Hygiene 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6367609/
https://www.ncbi.nlm.nih.gov/pubmed/30594260
http://dx.doi.org/10.4269/ajtmh.17-0964
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author Hulland, Erin
Subaiya, Saleena
Pierre, Katilla
Barthelemy, Nickolson
Pierre, Jean Samuel
Dismer, Amber
Juin, Stanley
Fitter, David
Brunkard, Joan
author_facet Hulland, Erin
Subaiya, Saleena
Pierre, Katilla
Barthelemy, Nickolson
Pierre, Jean Samuel
Dismer, Amber
Juin, Stanley
Fitter, David
Brunkard, Joan
author_sort Hulland, Erin
collection PubMed
description Matthew, a category 4 hurricane, struck Haiti on October 4, 2016, causing widespread flooding and damage to buildings and crops, and resulted in many deaths. The damage caused by Matthew raised concerns of increased cholera transmission particularly in Sud and Grand’Anse departments, regions which were hit most heavily by the storm. To evaluate the change in reported cholera cases following Hurricane Matthew on reported cholera cases, we used interrupted time series regression models of daily reported cholera cases, controlling for the impact of both rainfall, following a 4-week lag, and seasonality, from 2013 through 2016. Our results indicate a significant increase in reported cholera cases after Matthew, suggesting that the storm resulted in an immediate surge in suspect cases, and a decline in reported cholera cases in the 46-day post-storm period, after controlling for rainfall and seasonality. Regression models stratified by the department indicate that the impact of the hurricane was regional, with larger surges in the two most highly storm-affected departments: Sud and Grand’Anse. These models were able to provide input to the Ministry of Health in Haiti on the national and regional impact of Hurricane Matthew and, with further development, could provide the flexibility of use in other emergency situations. This article highlights the need for continued cholera prevention and control efforts, particularly in the wake of natural disasters such as hurricanes, and the continued need for intensive cholera surveillance nationally.
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spelling pubmed-63676092019-02-13 Increase in Reported Cholera Cases in Haiti Following Hurricane Matthew: An Interrupted Time Series Model Hulland, Erin Subaiya, Saleena Pierre, Katilla Barthelemy, Nickolson Pierre, Jean Samuel Dismer, Amber Juin, Stanley Fitter, David Brunkard, Joan Am J Trop Med Hyg Articles Matthew, a category 4 hurricane, struck Haiti on October 4, 2016, causing widespread flooding and damage to buildings and crops, and resulted in many deaths. The damage caused by Matthew raised concerns of increased cholera transmission particularly in Sud and Grand’Anse departments, regions which were hit most heavily by the storm. To evaluate the change in reported cholera cases following Hurricane Matthew on reported cholera cases, we used interrupted time series regression models of daily reported cholera cases, controlling for the impact of both rainfall, following a 4-week lag, and seasonality, from 2013 through 2016. Our results indicate a significant increase in reported cholera cases after Matthew, suggesting that the storm resulted in an immediate surge in suspect cases, and a decline in reported cholera cases in the 46-day post-storm period, after controlling for rainfall and seasonality. Regression models stratified by the department indicate that the impact of the hurricane was regional, with larger surges in the two most highly storm-affected departments: Sud and Grand’Anse. These models were able to provide input to the Ministry of Health in Haiti on the national and regional impact of Hurricane Matthew and, with further development, could provide the flexibility of use in other emergency situations. This article highlights the need for continued cholera prevention and control efforts, particularly in the wake of natural disasters such as hurricanes, and the continued need for intensive cholera surveillance nationally. The American Society of Tropical Medicine and Hygiene 2019-02 2018-12-26 /pmc/articles/PMC6367609/ /pubmed/30594260 http://dx.doi.org/10.4269/ajtmh.17-0964 Text en © The American Society of Tropical Medicine and Hygiene This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Articles
Hulland, Erin
Subaiya, Saleena
Pierre, Katilla
Barthelemy, Nickolson
Pierre, Jean Samuel
Dismer, Amber
Juin, Stanley
Fitter, David
Brunkard, Joan
Increase in Reported Cholera Cases in Haiti Following Hurricane Matthew: An Interrupted Time Series Model
title Increase in Reported Cholera Cases in Haiti Following Hurricane Matthew: An Interrupted Time Series Model
title_full Increase in Reported Cholera Cases in Haiti Following Hurricane Matthew: An Interrupted Time Series Model
title_fullStr Increase in Reported Cholera Cases in Haiti Following Hurricane Matthew: An Interrupted Time Series Model
title_full_unstemmed Increase in Reported Cholera Cases in Haiti Following Hurricane Matthew: An Interrupted Time Series Model
title_short Increase in Reported Cholera Cases in Haiti Following Hurricane Matthew: An Interrupted Time Series Model
title_sort increase in reported cholera cases in haiti following hurricane matthew: an interrupted time series model
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6367609/
https://www.ncbi.nlm.nih.gov/pubmed/30594260
http://dx.doi.org/10.4269/ajtmh.17-0964
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