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Quantifying Poverty as a Driver of Ebola Transmission
BACKGROUND: Poverty has been implicated as a challenge in the control of the current Ebola outbreak in West Africa. Although disparities between affected countries have been appreciated, disparities within West African countries have not been investigated as drivers of Ebola transmission. To quantif...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4697799/ https://www.ncbi.nlm.nih.gov/pubmed/26720278 http://dx.doi.org/10.1371/journal.pntd.0004260 |
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author | Fallah, Mosoka P. Skrip, Laura A. Gertler, Shai Yamin, Dan Galvani, Alison P. |
author_facet | Fallah, Mosoka P. Skrip, Laura A. Gertler, Shai Yamin, Dan Galvani, Alison P. |
author_sort | Fallah, Mosoka P. |
collection | PubMed |
description | BACKGROUND: Poverty has been implicated as a challenge in the control of the current Ebola outbreak in West Africa. Although disparities between affected countries have been appreciated, disparities within West African countries have not been investigated as drivers of Ebola transmission. To quantify the role that poverty plays in the transmission of Ebola, we analyzed heterogeneity of Ebola incidence and transmission factors among over 300 communities, categorized by socioeconomic status (SES), within Montserrado County, Liberia. METHODOLOGY/PRINCIPAL FINDINGS: We evaluated 4,437 Ebola cases reported between February 28, 2014 and December 1, 2014 for Montserrado County to determine SES-stratified temporal trends and drivers of Ebola transmission. A dataset including dates of symptom onset, hospitalization, and death, and specified community of residence was used to stratify cases into high, middle and low SES. Additionally, information about 9,129 contacts was provided for a subset of 1,585 traced individuals. To evaluate transmission within and across socioeconomic subpopulations, as well as over the trajectory of the outbreak, we analyzed these data with a time-dependent stochastic model. Cases in the most impoverished communities reported three more contacts on average than cases in high SES communities (p<0.001). Our transmission model shows that infected individuals from middle and low SES communities were associated with 1.5 (95% CI: 1.4–1.6) and 3.5 (95% CI: 3.1–3.9) times as many secondary cases as those from high SES communities, respectively. Furthermore, most of the spread of Ebola across Montserrado County originated from areas of lower SES. CONCLUSIONS/SIGNIFICANCE: Individuals from areas of poverty were associated with high rates of transmission and spread of Ebola to other regions. Thus, Ebola could most effectively be prevented or contained if disease interventions were targeted to areas of extreme poverty and funding was dedicated to development projects that meet basic needs. |
format | Online Article Text |
id | pubmed-4697799 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-46977992016-01-13 Quantifying Poverty as a Driver of Ebola Transmission Fallah, Mosoka P. Skrip, Laura A. Gertler, Shai Yamin, Dan Galvani, Alison P. PLoS Negl Trop Dis Research Article BACKGROUND: Poverty has been implicated as a challenge in the control of the current Ebola outbreak in West Africa. Although disparities between affected countries have been appreciated, disparities within West African countries have not been investigated as drivers of Ebola transmission. To quantify the role that poverty plays in the transmission of Ebola, we analyzed heterogeneity of Ebola incidence and transmission factors among over 300 communities, categorized by socioeconomic status (SES), within Montserrado County, Liberia. METHODOLOGY/PRINCIPAL FINDINGS: We evaluated 4,437 Ebola cases reported between February 28, 2014 and December 1, 2014 for Montserrado County to determine SES-stratified temporal trends and drivers of Ebola transmission. A dataset including dates of symptom onset, hospitalization, and death, and specified community of residence was used to stratify cases into high, middle and low SES. Additionally, information about 9,129 contacts was provided for a subset of 1,585 traced individuals. To evaluate transmission within and across socioeconomic subpopulations, as well as over the trajectory of the outbreak, we analyzed these data with a time-dependent stochastic model. Cases in the most impoverished communities reported three more contacts on average than cases in high SES communities (p<0.001). Our transmission model shows that infected individuals from middle and low SES communities were associated with 1.5 (95% CI: 1.4–1.6) and 3.5 (95% CI: 3.1–3.9) times as many secondary cases as those from high SES communities, respectively. Furthermore, most of the spread of Ebola across Montserrado County originated from areas of lower SES. CONCLUSIONS/SIGNIFICANCE: Individuals from areas of poverty were associated with high rates of transmission and spread of Ebola to other regions. Thus, Ebola could most effectively be prevented or contained if disease interventions were targeted to areas of extreme poverty and funding was dedicated to development projects that meet basic needs. Public Library of Science 2015-12-31 /pmc/articles/PMC4697799/ /pubmed/26720278 http://dx.doi.org/10.1371/journal.pntd.0004260 Text en https://creativecommons.org/publicdomain/zero/1.0/ This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration, which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. |
spellingShingle | Research Article Fallah, Mosoka P. Skrip, Laura A. Gertler, Shai Yamin, Dan Galvani, Alison P. Quantifying Poverty as a Driver of Ebola Transmission |
title | Quantifying Poverty as a Driver of Ebola Transmission |
title_full | Quantifying Poverty as a Driver of Ebola Transmission |
title_fullStr | Quantifying Poverty as a Driver of Ebola Transmission |
title_full_unstemmed | Quantifying Poverty as a Driver of Ebola Transmission |
title_short | Quantifying Poverty as a Driver of Ebola Transmission |
title_sort | quantifying poverty as a driver of ebola transmission |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4697799/ https://www.ncbi.nlm.nih.gov/pubmed/26720278 http://dx.doi.org/10.1371/journal.pntd.0004260 |
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