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Distribution of malaria exposure in endemic countries in Africa considering country levels of effective treatment

BACKGROUND: Malaria prevalence, clinical incidence, treatment, and transmission rates are dynamically interrelated. Prevalence is often considered a measure of malaria transmission, but treatment of clinical malaria reduces prevalence, and consequently also infectiousness to the mosquito vector and...

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Autores principales: Penny, Melissa A., Maire, Nicolas, Bever, Caitlin A., Pemberton-Ross, Peter, Briët, Olivier J. T., Smith, David L., Gething, Peter W., Smith, Thomas A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4595196/
https://www.ncbi.nlm.nih.gov/pubmed/26437798
http://dx.doi.org/10.1186/s12936-015-0864-3
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author Penny, Melissa A.
Maire, Nicolas
Bever, Caitlin A.
Pemberton-Ross, Peter
Briët, Olivier J. T.
Smith, David L.
Gething, Peter W.
Smith, Thomas A.
author_facet Penny, Melissa A.
Maire, Nicolas
Bever, Caitlin A.
Pemberton-Ross, Peter
Briët, Olivier J. T.
Smith, David L.
Gething, Peter W.
Smith, Thomas A.
author_sort Penny, Melissa A.
collection PubMed
description BACKGROUND: Malaria prevalence, clinical incidence, treatment, and transmission rates are dynamically interrelated. Prevalence is often considered a measure of malaria transmission, but treatment of clinical malaria reduces prevalence, and consequently also infectiousness to the mosquito vector and onward transmission. The impact of the frequency of treatment on prevalence in a population is generally not considered. This can lead to potential underestimation of malaria exposure in settings with good health systems. Furthermore, these dynamical relationships between prevalence, treatment, and transmission have not generally been taken into account in estimates of burden. METHODS: Using prevalence as an input, estimates of disease incidence and transmission [as the distribution of the entomological inoculation rate (EIR)] for Plasmodium falciparum have now been made for 43 countries in Africa using both empirical relationships (that do not allow for treatment) and OpenMalaria dynamic micro-simulation models (that explicitly include the effects of treatment). For each estimate, prevalence inputs were taken from geo-statistical models fitted for the year 2010 by the Malaria Atlas Project to all available observed prevalence data. National level estimates of the effectiveness of case management in treating clinical attacks were used as inputs to the estimation of both EIR and disease incidence by the dynamic models. RESULTS AND CONCLUSIONS: When coverage of effective treatment is taken into account, higher country level estimates of average EIR and thus higher disease burden, are obtained for a given prevalence level, especially where access to treatment is high, and prevalence relatively low. These methods provide a unified framework for comparison of both the immediate and longer-term impacts of case management and of preventive interventions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12936-015-0864-3) contains supplementary material, which is available to authorized users.
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spelling pubmed-45951962015-10-07 Distribution of malaria exposure in endemic countries in Africa considering country levels of effective treatment Penny, Melissa A. Maire, Nicolas Bever, Caitlin A. Pemberton-Ross, Peter Briët, Olivier J. T. Smith, David L. Gething, Peter W. Smith, Thomas A. Malar J Research BACKGROUND: Malaria prevalence, clinical incidence, treatment, and transmission rates are dynamically interrelated. Prevalence is often considered a measure of malaria transmission, but treatment of clinical malaria reduces prevalence, and consequently also infectiousness to the mosquito vector and onward transmission. The impact of the frequency of treatment on prevalence in a population is generally not considered. This can lead to potential underestimation of malaria exposure in settings with good health systems. Furthermore, these dynamical relationships between prevalence, treatment, and transmission have not generally been taken into account in estimates of burden. METHODS: Using prevalence as an input, estimates of disease incidence and transmission [as the distribution of the entomological inoculation rate (EIR)] for Plasmodium falciparum have now been made for 43 countries in Africa using both empirical relationships (that do not allow for treatment) and OpenMalaria dynamic micro-simulation models (that explicitly include the effects of treatment). For each estimate, prevalence inputs were taken from geo-statistical models fitted for the year 2010 by the Malaria Atlas Project to all available observed prevalence data. National level estimates of the effectiveness of case management in treating clinical attacks were used as inputs to the estimation of both EIR and disease incidence by the dynamic models. RESULTS AND CONCLUSIONS: When coverage of effective treatment is taken into account, higher country level estimates of average EIR and thus higher disease burden, are obtained for a given prevalence level, especially where access to treatment is high, and prevalence relatively low. These methods provide a unified framework for comparison of both the immediate and longer-term impacts of case management and of preventive interventions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12936-015-0864-3) contains supplementary material, which is available to authorized users. BioMed Central 2015-10-05 /pmc/articles/PMC4595196/ /pubmed/26437798 http://dx.doi.org/10.1186/s12936-015-0864-3 Text en © Penny et al. 2015 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
Penny, Melissa A.
Maire, Nicolas
Bever, Caitlin A.
Pemberton-Ross, Peter
Briët, Olivier J. T.
Smith, David L.
Gething, Peter W.
Smith, Thomas A.
Distribution of malaria exposure in endemic countries in Africa considering country levels of effective treatment
title Distribution of malaria exposure in endemic countries in Africa considering country levels of effective treatment
title_full Distribution of malaria exposure in endemic countries in Africa considering country levels of effective treatment
title_fullStr Distribution of malaria exposure in endemic countries in Africa considering country levels of effective treatment
title_full_unstemmed Distribution of malaria exposure in endemic countries in Africa considering country levels of effective treatment
title_short Distribution of malaria exposure in endemic countries in Africa considering country levels of effective treatment
title_sort distribution of malaria exposure in endemic countries in africa considering country levels of effective treatment
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4595196/
https://www.ncbi.nlm.nih.gov/pubmed/26437798
http://dx.doi.org/10.1186/s12936-015-0864-3
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