Cargando…
Prioritizing interventions for cholera control in Kenya, 2015–2020
Kenya has experienced cholera outbreaks since 1971, with the most recent wave beginning in late 2014. Between 2015–2020, 32 of 47 counties reported 30,431 suspected cholera cases. The Global Task Force for Cholera Control (GTFCC) developed a Global Roadmap for Ending Cholera by 2030, which emphasize...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10228803/ https://www.ncbi.nlm.nih.gov/pubmed/37196011 http://dx.doi.org/10.1371/journal.pntd.0010928 |
_version_ | 1785051055319416832 |
---|---|
author | Boru, Waqo Xiao, Shaoming Amoth, Patrick Kareko, David Langat, Daniel Were, Ian Ali, Mohammad Sack, David A. Lee, Elizabeth C. Debes, Amanda K. |
author_facet | Boru, Waqo Xiao, Shaoming Amoth, Patrick Kareko, David Langat, Daniel Were, Ian Ali, Mohammad Sack, David A. Lee, Elizabeth C. Debes, Amanda K. |
author_sort | Boru, Waqo |
collection | PubMed |
description | Kenya has experienced cholera outbreaks since 1971, with the most recent wave beginning in late 2014. Between 2015–2020, 32 of 47 counties reported 30,431 suspected cholera cases. The Global Task Force for Cholera Control (GTFCC) developed a Global Roadmap for Ending Cholera by 2030, which emphasizes the need to target multi-sectoral interventions in priority cholera burden hotspots. This study utilizes the GTFCC’s hotspot method to identify hotspots in Kenya at the county and sub-county administrative levels from 2015 through 2020. 32 of 47 (68.1%) counties reported cholera cases during this time while only 149 of 301 (49.5%) sub-counties reported cholera cases. The analysis identifies hotspots based on the mean annual incidence (MAI) over the past five-year period and cholera’s persistence in the area. Applying a MAI threshold of 90(th) percentile and the median persistence at both the county and sub-county levels, we identified 13 high risk sub-counties from 8 counties, including the 3 high risk counties of Garissa, Tana River and Wajir. This demonstrates that several sub-counties are high level hotspots while their counties are not. In addition, when cases reported by county versus sub-county hotspot risk are compared, 1.4 million people overlapped in the areas identified as both high-risk county and high-risk sub-county. However, assuming that finer scale data is more accurate, 1.6 million high risk sub-county people would have been misclassified as medium risk with a county-level analysis. Furthermore, an additional 1.6 million people would have been classified as living in high-risk in a county-level analysis when at the sub-county level, they were medium, low or no-risk sub-counties. This results in 3.2 million people being misclassified when county level analysis is utilized rather than a more-focused sub-county level analysis. This analysis highlights the need for more localized risk analyses to target cholera intervention and prevention efforts towards the populations most vulnerable. |
format | Online Article Text |
id | pubmed-10228803 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-102288032023-05-31 Prioritizing interventions for cholera control in Kenya, 2015–2020 Boru, Waqo Xiao, Shaoming Amoth, Patrick Kareko, David Langat, Daniel Were, Ian Ali, Mohammad Sack, David A. Lee, Elizabeth C. Debes, Amanda K. PLoS Negl Trop Dis Research Article Kenya has experienced cholera outbreaks since 1971, with the most recent wave beginning in late 2014. Between 2015–2020, 32 of 47 counties reported 30,431 suspected cholera cases. The Global Task Force for Cholera Control (GTFCC) developed a Global Roadmap for Ending Cholera by 2030, which emphasizes the need to target multi-sectoral interventions in priority cholera burden hotspots. This study utilizes the GTFCC’s hotspot method to identify hotspots in Kenya at the county and sub-county administrative levels from 2015 through 2020. 32 of 47 (68.1%) counties reported cholera cases during this time while only 149 of 301 (49.5%) sub-counties reported cholera cases. The analysis identifies hotspots based on the mean annual incidence (MAI) over the past five-year period and cholera’s persistence in the area. Applying a MAI threshold of 90(th) percentile and the median persistence at both the county and sub-county levels, we identified 13 high risk sub-counties from 8 counties, including the 3 high risk counties of Garissa, Tana River and Wajir. This demonstrates that several sub-counties are high level hotspots while their counties are not. In addition, when cases reported by county versus sub-county hotspot risk are compared, 1.4 million people overlapped in the areas identified as both high-risk county and high-risk sub-county. However, assuming that finer scale data is more accurate, 1.6 million high risk sub-county people would have been misclassified as medium risk with a county-level analysis. Furthermore, an additional 1.6 million people would have been classified as living in high-risk in a county-level analysis when at the sub-county level, they were medium, low or no-risk sub-counties. This results in 3.2 million people being misclassified when county level analysis is utilized rather than a more-focused sub-county level analysis. This analysis highlights the need for more localized risk analyses to target cholera intervention and prevention efforts towards the populations most vulnerable. Public Library of Science 2023-05-17 /pmc/articles/PMC10228803/ /pubmed/37196011 http://dx.doi.org/10.1371/journal.pntd.0010928 Text en © 2023 Boru et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://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 | Research Article Boru, Waqo Xiao, Shaoming Amoth, Patrick Kareko, David Langat, Daniel Were, Ian Ali, Mohammad Sack, David A. Lee, Elizabeth C. Debes, Amanda K. Prioritizing interventions for cholera control in Kenya, 2015–2020 |
title | Prioritizing interventions for cholera control in Kenya, 2015–2020 |
title_full | Prioritizing interventions for cholera control in Kenya, 2015–2020 |
title_fullStr | Prioritizing interventions for cholera control in Kenya, 2015–2020 |
title_full_unstemmed | Prioritizing interventions for cholera control in Kenya, 2015–2020 |
title_short | Prioritizing interventions for cholera control in Kenya, 2015–2020 |
title_sort | prioritizing interventions for cholera control in kenya, 2015–2020 |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10228803/ https://www.ncbi.nlm.nih.gov/pubmed/37196011 http://dx.doi.org/10.1371/journal.pntd.0010928 |
work_keys_str_mv | AT boruwaqo prioritizinginterventionsforcholeracontrolinkenya20152020 AT xiaoshaoming prioritizinginterventionsforcholeracontrolinkenya20152020 AT amothpatrick prioritizinginterventionsforcholeracontrolinkenya20152020 AT karekodavid prioritizinginterventionsforcholeracontrolinkenya20152020 AT langatdaniel prioritizinginterventionsforcholeracontrolinkenya20152020 AT wereian prioritizinginterventionsforcholeracontrolinkenya20152020 AT alimohammad prioritizinginterventionsforcholeracontrolinkenya20152020 AT sackdavida prioritizinginterventionsforcholeracontrolinkenya20152020 AT leeelizabethc prioritizinginterventionsforcholeracontrolinkenya20152020 AT debesamandak prioritizinginterventionsforcholeracontrolinkenya20152020 |