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Spatiotemporal characterisation and risk factor analysis of malaria outbreak in Cabo Verde in 2017

BACKGROUND: Cabo Verde is a country that has been in the pre-elimination stage of malaria since the year 2000. The country is still reporting cases, particularly in the capital of Praia, where more than 50% of the national population live. This study aims to examine the spatial and temporal epidemio...

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Autores principales: DePina, Adilson José, Andrade, Alex Jailson Barbosa, Dia, Abdoulaye Kane, Moreira, António Lima, Furtado, Ullardina Domingos, Baptista, Helga, Faye, Ousmane, Seck, Ibrahima, Niang, El Hadji Amadou
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6323763/
https://www.ncbi.nlm.nih.gov/pubmed/30636920
http://dx.doi.org/10.1186/s41182-018-0127-4
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author DePina, Adilson José
Andrade, Alex Jailson Barbosa
Dia, Abdoulaye Kane
Moreira, António Lima
Furtado, Ullardina Domingos
Baptista, Helga
Faye, Ousmane
Seck, Ibrahima
Niang, El Hadji Amadou
author_facet DePina, Adilson José
Andrade, Alex Jailson Barbosa
Dia, Abdoulaye Kane
Moreira, António Lima
Furtado, Ullardina Domingos
Baptista, Helga
Faye, Ousmane
Seck, Ibrahima
Niang, El Hadji Amadou
author_sort DePina, Adilson José
collection PubMed
description BACKGROUND: Cabo Verde is a country that has been in the pre-elimination stage of malaria since the year 2000. The country is still reporting cases, particularly in the capital of Praia, where more than 50% of the national population live. This study aims to examine the spatial and temporal epidemiological profile of malaria across the country during the 2017 outbreak and to analyse the risk factors, which may have influenced the trend in malaria cases. METHODS: Longitudinal data collected from all malaria cases in Cabo Verde for the year 2017 were used in this study. The epidemiological characteristics of the cases were analysed. Local and spatial clusters of malaria from Praia were detected by applying the Cluster and Outlier Analysis (Anselin Local Moran’s I) to determine the spatial clustering pattern. We then used the Pearson correlation coefficient to analyse the relationship between malaria cases and meteorological variables to identify underlying drivers. RESULTS: In 2017, 446 cases of malaria were reported in Cabo Verde with the peak of cases in October. These cases were primarily Plasmodium falciparum infections. Of these cases, 423 were indigenous infections recorded in Praia, while 23 were imported malaria cases from different African countries. One case of P. vivax infection was imported from Brazil. Spatial autocorrelation analysis revealed a cluster of high-high malaria cases in the centre of the city. Malaria case occurrence has a very weak correlation (r = 0.16) with breeding site location. Most of the cases (69.9%, R(2) = 0.699) were explained by the local environmental condition, with temperature being the primary risk factor followed by relative humidity. A moderately positive relationship was noted with the total pluviometry, while wind speed had a strong negative influence on malaria infections. CONCLUSIONS: In Cabo Verde, malaria remains a serious public health issue, especially in Praia. The high number of cases recorded in 2017 demonstrates the fragility of the situation and the challenges to eliminating indigenous malaria cases and preventing imported cases. Mosquito breeding sites have been the main risk factor, while temperature and precipitation were positively associated with malaria infection. In light of this study, there is an urgent need to reinforce control strategies to achieve the elimination goal in the country.
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spelling pubmed-63237632019-01-11 Spatiotemporal characterisation and risk factor analysis of malaria outbreak in Cabo Verde in 2017 DePina, Adilson José Andrade, Alex Jailson Barbosa Dia, Abdoulaye Kane Moreira, António Lima Furtado, Ullardina Domingos Baptista, Helga Faye, Ousmane Seck, Ibrahima Niang, El Hadji Amadou Trop Med Health Research BACKGROUND: Cabo Verde is a country that has been in the pre-elimination stage of malaria since the year 2000. The country is still reporting cases, particularly in the capital of Praia, where more than 50% of the national population live. This study aims to examine the spatial and temporal epidemiological profile of malaria across the country during the 2017 outbreak and to analyse the risk factors, which may have influenced the trend in malaria cases. METHODS: Longitudinal data collected from all malaria cases in Cabo Verde for the year 2017 were used in this study. The epidemiological characteristics of the cases were analysed. Local and spatial clusters of malaria from Praia were detected by applying the Cluster and Outlier Analysis (Anselin Local Moran’s I) to determine the spatial clustering pattern. We then used the Pearson correlation coefficient to analyse the relationship between malaria cases and meteorological variables to identify underlying drivers. RESULTS: In 2017, 446 cases of malaria were reported in Cabo Verde with the peak of cases in October. These cases were primarily Plasmodium falciparum infections. Of these cases, 423 were indigenous infections recorded in Praia, while 23 were imported malaria cases from different African countries. One case of P. vivax infection was imported from Brazil. Spatial autocorrelation analysis revealed a cluster of high-high malaria cases in the centre of the city. Malaria case occurrence has a very weak correlation (r = 0.16) with breeding site location. Most of the cases (69.9%, R(2) = 0.699) were explained by the local environmental condition, with temperature being the primary risk factor followed by relative humidity. A moderately positive relationship was noted with the total pluviometry, while wind speed had a strong negative influence on malaria infections. CONCLUSIONS: In Cabo Verde, malaria remains a serious public health issue, especially in Praia. The high number of cases recorded in 2017 demonstrates the fragility of the situation and the challenges to eliminating indigenous malaria cases and preventing imported cases. Mosquito breeding sites have been the main risk factor, while temperature and precipitation were positively associated with malaria infection. In light of this study, there is an urgent need to reinforce control strategies to achieve the elimination goal in the country. BioMed Central 2019-01-07 /pmc/articles/PMC6323763/ /pubmed/30636920 http://dx.doi.org/10.1186/s41182-018-0127-4 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
DePina, Adilson José
Andrade, Alex Jailson Barbosa
Dia, Abdoulaye Kane
Moreira, António Lima
Furtado, Ullardina Domingos
Baptista, Helga
Faye, Ousmane
Seck, Ibrahima
Niang, El Hadji Amadou
Spatiotemporal characterisation and risk factor analysis of malaria outbreak in Cabo Verde in 2017
title Spatiotemporal characterisation and risk factor analysis of malaria outbreak in Cabo Verde in 2017
title_full Spatiotemporal characterisation and risk factor analysis of malaria outbreak in Cabo Verde in 2017
title_fullStr Spatiotemporal characterisation and risk factor analysis of malaria outbreak in Cabo Verde in 2017
title_full_unstemmed Spatiotemporal characterisation and risk factor analysis of malaria outbreak in Cabo Verde in 2017
title_short Spatiotemporal characterisation and risk factor analysis of malaria outbreak in Cabo Verde in 2017
title_sort spatiotemporal characterisation and risk factor analysis of malaria outbreak in cabo verde in 2017
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6323763/
https://www.ncbi.nlm.nih.gov/pubmed/30636920
http://dx.doi.org/10.1186/s41182-018-0127-4
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