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Spatial variation in lymphatic filariasis risk factors of hotspot zones in Ghana

BACKGROUND: Lymphatic Filariasis (LF), a parasitic nematode infection, poses a huge economic burden to affected countries. LF endemicity is localized and its prevalence is spatially heterogeneous. In Ghana, there exists differences in LF prevalence and multiplicity of symptoms in the country’s north...

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Autores principales: Kwarteng, Efiba Vidda Senkyire, Andam-Akorful, Samuel Ato, Kwarteng, Alexander, Asare, Da-Costa Boakye, Quaye-Ballard, Jonathan Arthur, Osei, Frank Badu, Duker, Alfred Allan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7841995/
https://www.ncbi.nlm.nih.gov/pubmed/33509140
http://dx.doi.org/10.1186/s12889-021-10234-9
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author Kwarteng, Efiba Vidda Senkyire
Andam-Akorful, Samuel Ato
Kwarteng, Alexander
Asare, Da-Costa Boakye
Quaye-Ballard, Jonathan Arthur
Osei, Frank Badu
Duker, Alfred Allan
author_facet Kwarteng, Efiba Vidda Senkyire
Andam-Akorful, Samuel Ato
Kwarteng, Alexander
Asare, Da-Costa Boakye
Quaye-Ballard, Jonathan Arthur
Osei, Frank Badu
Duker, Alfred Allan
author_sort Kwarteng, Efiba Vidda Senkyire
collection PubMed
description BACKGROUND: Lymphatic Filariasis (LF), a parasitic nematode infection, poses a huge economic burden to affected countries. LF endemicity is localized and its prevalence is spatially heterogeneous. In Ghana, there exists differences in LF prevalence and multiplicity of symptoms in the country’s northern and southern parts. Species distribution models (SDMs) have been utilized to explore the suite of risk factors that influence the transmission of LF in these geographically distinct regions. METHODS: Presence-absence records of microfilaria (mf) cases were stratified into northern and southern zones and used to run SDMs, while climate, socioeconomic, and land cover variables provided explanatory information. Generalized Linear Model (GLM), Generalized Boosted Model (GBM), Artificial Neural Network (ANN), Surface Range Envelope (SRE), Multivariate Adaptive Regression Splines (MARS), and Random Forests (RF) algorithms were run for both study zones and also for the entire country for comparison. RESULTS: Best model quality was obtained with RF and GBM algorithms with the highest Area under the Curve (AUC) of 0.98 and 0.95, respectively. The models predicted high suitable environments for LF transmission in the short grass savanna (northern) and coastal (southern) areas of Ghana. Mainly, land cover and socioeconomic variables such as proximity to inland water bodies and population density uniquely influenced LF transmission in the south. At the same time, poor housing was a distinctive risk factor in the north. Precipitation, temperature, slope, and poverty were common risk factors but with subtle variations in response values, which were confirmed by the countrywide model. CONCLUSIONS: This study has demonstrated that different variable combinations influence the occurrence of lymphatic filariasis in northern and southern Ghana. Thus, an understanding of the geographic distinctness in risk factors is required to inform on the development of area-specific transmission control systems towards LF elimination in Ghana and internationally.
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spelling pubmed-78419952021-01-28 Spatial variation in lymphatic filariasis risk factors of hotspot zones in Ghana Kwarteng, Efiba Vidda Senkyire Andam-Akorful, Samuel Ato Kwarteng, Alexander Asare, Da-Costa Boakye Quaye-Ballard, Jonathan Arthur Osei, Frank Badu Duker, Alfred Allan BMC Public Health Research Article BACKGROUND: Lymphatic Filariasis (LF), a parasitic nematode infection, poses a huge economic burden to affected countries. LF endemicity is localized and its prevalence is spatially heterogeneous. In Ghana, there exists differences in LF prevalence and multiplicity of symptoms in the country’s northern and southern parts. Species distribution models (SDMs) have been utilized to explore the suite of risk factors that influence the transmission of LF in these geographically distinct regions. METHODS: Presence-absence records of microfilaria (mf) cases were stratified into northern and southern zones and used to run SDMs, while climate, socioeconomic, and land cover variables provided explanatory information. Generalized Linear Model (GLM), Generalized Boosted Model (GBM), Artificial Neural Network (ANN), Surface Range Envelope (SRE), Multivariate Adaptive Regression Splines (MARS), and Random Forests (RF) algorithms were run for both study zones and also for the entire country for comparison. RESULTS: Best model quality was obtained with RF and GBM algorithms with the highest Area under the Curve (AUC) of 0.98 and 0.95, respectively. The models predicted high suitable environments for LF transmission in the short grass savanna (northern) and coastal (southern) areas of Ghana. Mainly, land cover and socioeconomic variables such as proximity to inland water bodies and population density uniquely influenced LF transmission in the south. At the same time, poor housing was a distinctive risk factor in the north. Precipitation, temperature, slope, and poverty were common risk factors but with subtle variations in response values, which were confirmed by the countrywide model. CONCLUSIONS: This study has demonstrated that different variable combinations influence the occurrence of lymphatic filariasis in northern and southern Ghana. Thus, an understanding of the geographic distinctness in risk factors is required to inform on the development of area-specific transmission control systems towards LF elimination in Ghana and internationally. BioMed Central 2021-01-28 /pmc/articles/PMC7841995/ /pubmed/33509140 http://dx.doi.org/10.1186/s12889-021-10234-9 Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.
spellingShingle Research Article
Kwarteng, Efiba Vidda Senkyire
Andam-Akorful, Samuel Ato
Kwarteng, Alexander
Asare, Da-Costa Boakye
Quaye-Ballard, Jonathan Arthur
Osei, Frank Badu
Duker, Alfred Allan
Spatial variation in lymphatic filariasis risk factors of hotspot zones in Ghana
title Spatial variation in lymphatic filariasis risk factors of hotspot zones in Ghana
title_full Spatial variation in lymphatic filariasis risk factors of hotspot zones in Ghana
title_fullStr Spatial variation in lymphatic filariasis risk factors of hotspot zones in Ghana
title_full_unstemmed Spatial variation in lymphatic filariasis risk factors of hotspot zones in Ghana
title_short Spatial variation in lymphatic filariasis risk factors of hotspot zones in Ghana
title_sort spatial variation in lymphatic filariasis risk factors of hotspot zones in ghana
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7841995/
https://www.ncbi.nlm.nih.gov/pubmed/33509140
http://dx.doi.org/10.1186/s12889-021-10234-9
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