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The seasonality of cholera in sub-Saharan Africa: a statistical modelling study

BACKGROUND: Cholera remains a major threat in sub-Saharan Africa (SSA), where some of the highest case-fatality rates are reported. Knowing in what months and where cholera tends to occur across the continent could aid in improving efforts to eliminate cholera as a public health concern. However, la...

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Autores principales: Perez-Saez, Javier, Lessler, Justin, Lee, Elizabeth C, Luquero, Francisco J, Malembaka, Espoir Bwenge, Finger, Flavio, Langa, José Paulo, Yennan, Sebastian, Zaitchik, Benjamin, Azman, Andrew S
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9090905/
https://www.ncbi.nlm.nih.gov/pubmed/35461521
http://dx.doi.org/10.1016/S2214-109X(22)00007-9
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author Perez-Saez, Javier
Lessler, Justin
Lee, Elizabeth C
Luquero, Francisco J
Malembaka, Espoir Bwenge
Finger, Flavio
Langa, José Paulo
Yennan, Sebastian
Zaitchik, Benjamin
Azman, Andrew S
author_facet Perez-Saez, Javier
Lessler, Justin
Lee, Elizabeth C
Luquero, Francisco J
Malembaka, Espoir Bwenge
Finger, Flavio
Langa, José Paulo
Yennan, Sebastian
Zaitchik, Benjamin
Azman, Andrew S
author_sort Perez-Saez, Javier
collection PubMed
description BACKGROUND: Cholera remains a major threat in sub-Saharan Africa (SSA), where some of the highest case-fatality rates are reported. Knowing in what months and where cholera tends to occur across the continent could aid in improving efforts to eliminate cholera as a public health concern. However, largely due to the absence of unified large-scale datasets, no continent-wide estimates exist. In this study, we aimed to estimate cholera seasonality across SSA and explore the correlation between hydroclimatic variables and cholera seasonality. METHODS: Using the global cholera database of the Global Task Force on Cholera Control, we developed statistical models to synthesise data across spatial and temporal scales to infer the seasonality of excess (defined as incidence higher than the 2010–16 mean incidence rate) suspected cholera occurrence in SSA. We developed a Bayesian statistical model to infer the monthly risk of excess cholera at the first and second administrative levels. Seasonality patterns were then grouped into spatial clusters. Finally, we studied the association between seasonality estimates and hydroclimatic variables (mean monthly fraction of area flooded, mean monthly air temperature, and cumulative monthly precipitation). FINDINGS: 24 (71%) of the 34 countries studied had seasonal patterns of excess cholera risk, corresponding to approximately 86% of the SSA population. 12 (50%) of these 24 countries also had subnational differences in seasonality patterns, with strong differences in seasonality strength between regions. Seasonality patterns clustered into two macroregions (west Africa and the Sahel vs eastern and southern Africa), which were composed of subregional clusters with varying degrees of seasonality. Exploratory association analysis found most consistent and positive correlations between cholera seasonality and precipitation and, to a lesser extent, between cholera seasonality and temperature and flooding. INTERPRETATION: Widespread cholera seasonality in SSA offers opportunities for intervention planning. Further studies are needed to study the association between cholera and climate. FUNDING: US National Aeronautics and Space Administration Applied Sciences Program and the Bill & Melinda Gates Foundation.
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spelling pubmed-90909052022-06-14 The seasonality of cholera in sub-Saharan Africa: a statistical modelling study Perez-Saez, Javier Lessler, Justin Lee, Elizabeth C Luquero, Francisco J Malembaka, Espoir Bwenge Finger, Flavio Langa, José Paulo Yennan, Sebastian Zaitchik, Benjamin Azman, Andrew S Lancet Glob Health Articles BACKGROUND: Cholera remains a major threat in sub-Saharan Africa (SSA), where some of the highest case-fatality rates are reported. Knowing in what months and where cholera tends to occur across the continent could aid in improving efforts to eliminate cholera as a public health concern. However, largely due to the absence of unified large-scale datasets, no continent-wide estimates exist. In this study, we aimed to estimate cholera seasonality across SSA and explore the correlation between hydroclimatic variables and cholera seasonality. METHODS: Using the global cholera database of the Global Task Force on Cholera Control, we developed statistical models to synthesise data across spatial and temporal scales to infer the seasonality of excess (defined as incidence higher than the 2010–16 mean incidence rate) suspected cholera occurrence in SSA. We developed a Bayesian statistical model to infer the monthly risk of excess cholera at the first and second administrative levels. Seasonality patterns were then grouped into spatial clusters. Finally, we studied the association between seasonality estimates and hydroclimatic variables (mean monthly fraction of area flooded, mean monthly air temperature, and cumulative monthly precipitation). FINDINGS: 24 (71%) of the 34 countries studied had seasonal patterns of excess cholera risk, corresponding to approximately 86% of the SSA population. 12 (50%) of these 24 countries also had subnational differences in seasonality patterns, with strong differences in seasonality strength between regions. Seasonality patterns clustered into two macroregions (west Africa and the Sahel vs eastern and southern Africa), which were composed of subregional clusters with varying degrees of seasonality. Exploratory association analysis found most consistent and positive correlations between cholera seasonality and precipitation and, to a lesser extent, between cholera seasonality and temperature and flooding. INTERPRETATION: Widespread cholera seasonality in SSA offers opportunities for intervention planning. Further studies are needed to study the association between cholera and climate. FUNDING: US National Aeronautics and Space Administration Applied Sciences Program and the Bill & Melinda Gates Foundation. Elsevier Ltd 2022-04-21 /pmc/articles/PMC9090905/ /pubmed/35461521 http://dx.doi.org/10.1016/S2214-109X(22)00007-9 Text en © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Articles
Perez-Saez, Javier
Lessler, Justin
Lee, Elizabeth C
Luquero, Francisco J
Malembaka, Espoir Bwenge
Finger, Flavio
Langa, José Paulo
Yennan, Sebastian
Zaitchik, Benjamin
Azman, Andrew S
The seasonality of cholera in sub-Saharan Africa: a statistical modelling study
title The seasonality of cholera in sub-Saharan Africa: a statistical modelling study
title_full The seasonality of cholera in sub-Saharan Africa: a statistical modelling study
title_fullStr The seasonality of cholera in sub-Saharan Africa: a statistical modelling study
title_full_unstemmed The seasonality of cholera in sub-Saharan Africa: a statistical modelling study
title_short The seasonality of cholera in sub-Saharan Africa: a statistical modelling study
title_sort seasonality of cholera in sub-saharan africa: a statistical modelling study
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9090905/
https://www.ncbi.nlm.nih.gov/pubmed/35461521
http://dx.doi.org/10.1016/S2214-109X(22)00007-9
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