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Cholera hotspots and surveillance constraints contributing to recurrent epidemics in Tanzania
OBJECTIVE: We described the dynamics of cholera in Tanzania between 2007 and 2017 and assessed the weaknesses of the current surveillance system in providing necessary data in achieving the global roadmap to 2030 for cholera control. RESULTS: The Poisson-based spatial scan identified cholera hotspot...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6805412/ https://www.ncbi.nlm.nih.gov/pubmed/31639037 http://dx.doi.org/10.1186/s13104-019-4731-0 |
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author | Hounmanou, Yaovi M. G. Mølbak, Kåre Kähler, Jonas Mdegela, Robinson H. Olsen, John E. Dalsgaard, Anders |
author_facet | Hounmanou, Yaovi M. G. Mølbak, Kåre Kähler, Jonas Mdegela, Robinson H. Olsen, John E. Dalsgaard, Anders |
author_sort | Hounmanou, Yaovi M. G. |
collection | PubMed |
description | OBJECTIVE: We described the dynamics of cholera in Tanzania between 2007 and 2017 and assessed the weaknesses of the current surveillance system in providing necessary data in achieving the global roadmap to 2030 for cholera control. RESULTS: The Poisson-based spatial scan identified cholera hotspots in mainland Tanzania. A zero-inflated Poisson regression investigated the relationship between the incidence of cholera and available demographic, socio-economic and climatic exposure variables. Four cholera hotspots were detected covering 17 regions, home to 28 million people, including the central regions and those surrounding the Lakes Victoria, Tanganyika and Nyaza. The risk of experiencing cholera in these regions was up to 2.9 times higher than elsewhere in the country. Regression analyses revealed that every 100 km of water perimeter in a region increased the cholera incidence by 1.5%. Due to the compilation of surveillance data at regional level rather than at district, we were unable to reliably identify any other significant risk factors and specific hotspots. Cholera high-risk populations in Tanzania include those living near lakes and central regions. Successful surveillance require disaggregated data available weekly and at district levels in order to serve as data for action to support the roadmap for cholera control. |
format | Online Article Text |
id | pubmed-6805412 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-68054122019-10-24 Cholera hotspots and surveillance constraints contributing to recurrent epidemics in Tanzania Hounmanou, Yaovi M. G. Mølbak, Kåre Kähler, Jonas Mdegela, Robinson H. Olsen, John E. Dalsgaard, Anders BMC Res Notes Research Note OBJECTIVE: We described the dynamics of cholera in Tanzania between 2007 and 2017 and assessed the weaknesses of the current surveillance system in providing necessary data in achieving the global roadmap to 2030 for cholera control. RESULTS: The Poisson-based spatial scan identified cholera hotspots in mainland Tanzania. A zero-inflated Poisson regression investigated the relationship between the incidence of cholera and available demographic, socio-economic and climatic exposure variables. Four cholera hotspots were detected covering 17 regions, home to 28 million people, including the central regions and those surrounding the Lakes Victoria, Tanganyika and Nyaza. The risk of experiencing cholera in these regions was up to 2.9 times higher than elsewhere in the country. Regression analyses revealed that every 100 km of water perimeter in a region increased the cholera incidence by 1.5%. Due to the compilation of surveillance data at regional level rather than at district, we were unable to reliably identify any other significant risk factors and specific hotspots. Cholera high-risk populations in Tanzania include those living near lakes and central regions. Successful surveillance require disaggregated data available weekly and at district levels in order to serve as data for action to support the roadmap for cholera control. BioMed Central 2019-10-21 /pmc/articles/PMC6805412/ /pubmed/31639037 http://dx.doi.org/10.1186/s13104-019-4731-0 Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Note Hounmanou, Yaovi M. G. Mølbak, Kåre Kähler, Jonas Mdegela, Robinson H. Olsen, John E. Dalsgaard, Anders Cholera hotspots and surveillance constraints contributing to recurrent epidemics in Tanzania |
title | Cholera hotspots and surveillance constraints contributing to recurrent epidemics in Tanzania |
title_full | Cholera hotspots and surveillance constraints contributing to recurrent epidemics in Tanzania |
title_fullStr | Cholera hotspots and surveillance constraints contributing to recurrent epidemics in Tanzania |
title_full_unstemmed | Cholera hotspots and surveillance constraints contributing to recurrent epidemics in Tanzania |
title_short | Cholera hotspots and surveillance constraints contributing to recurrent epidemics in Tanzania |
title_sort | cholera hotspots and surveillance constraints contributing to recurrent epidemics in tanzania |
topic | Research Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6805412/ https://www.ncbi.nlm.nih.gov/pubmed/31639037 http://dx.doi.org/10.1186/s13104-019-4731-0 |
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