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Detecting local risk factors for residual malaria in northern Ghana using Bayesian model averaging

BACKGROUND: There is a need for comprehensive evaluations of the underlying local factors that contribute to residual malaria in sub-Saharan Africa. However, it is difficult to compare the wide array of demographic, socio-economic, and environmental variables associated with malaria transmission usi...

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Autores principales: Millar, Justin, Psychas, Paul, Abuaku, Benjamin, Ahorlu, Collins, Amratia, Punam, Koram, Kwadwo, Oppong, Samuel, Valle, Denis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6162921/
https://www.ncbi.nlm.nih.gov/pubmed/30268127
http://dx.doi.org/10.1186/s12936-018-2491-2
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author Millar, Justin
Psychas, Paul
Abuaku, Benjamin
Ahorlu, Collins
Amratia, Punam
Koram, Kwadwo
Oppong, Samuel
Valle, Denis
author_facet Millar, Justin
Psychas, Paul
Abuaku, Benjamin
Ahorlu, Collins
Amratia, Punam
Koram, Kwadwo
Oppong, Samuel
Valle, Denis
author_sort Millar, Justin
collection PubMed
description BACKGROUND: There is a need for comprehensive evaluations of the underlying local factors that contribute to residual malaria in sub-Saharan Africa. However, it is difficult to compare the wide array of demographic, socio-economic, and environmental variables associated with malaria transmission using standard statistical approaches while accounting for seasonal differences and nonlinear relationships. This article uses a Bayesian model averaging (BMA) approach for identifying and comparing potential risk and protective factors associated with residual malaria. RESULTS: The relative influence of a comprehensive set of demographic, socio-economic, environmental, and malaria intervention variables on malaria prevalence were modelled using BMA for variable selection. Data were collected in Bunkpurugu-Yunyoo, a rural district in northeast Ghana that experiences holoendemic seasonal malaria transmission, over six biannual surveys from 2010 to 2013. A total of 10,022 children between the ages 6 to 59 months were used in the analysis. Multiple models were developed to identify important risk and protective factors, accounting for seasonal patterns and nonlinear relationships. These models revealed pronounced nonlinear associations between malaria risk and distance from the nearest urban centre and health facility. Furthermore, the association between malaria risk and age and some ethnic groups was significantly different in the rainy and dry seasons. BMA outperformed other commonly used regression approaches in out-of-sample predictive ability using a season-to-season validation approach. CONCLUSIONS: This modelling framework offers an alternative approach to disease risk factor analysis that generates interpretable models, can reveal complex, nonlinear relationships, incorporates uncertainty in model selection, and produces accurate predictions. Certain modelling applications, such as designing targeted local interventions, require more sophisticated statistical methods which are capable of handling a wide range of relevant data while maintaining interpretability and predictive performance, and directly characterize uncertainty. To this end, BMA represents a valuable tool for constructing more informative models for understanding risk factors for malaria, as well as other vector-borne and environmentally mediated diseases. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12936-018-2491-2) contains supplementary material, which is available to authorized users.
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spelling pubmed-61629212018-10-04 Detecting local risk factors for residual malaria in northern Ghana using Bayesian model averaging Millar, Justin Psychas, Paul Abuaku, Benjamin Ahorlu, Collins Amratia, Punam Koram, Kwadwo Oppong, Samuel Valle, Denis Malar J Research BACKGROUND: There is a need for comprehensive evaluations of the underlying local factors that contribute to residual malaria in sub-Saharan Africa. However, it is difficult to compare the wide array of demographic, socio-economic, and environmental variables associated with malaria transmission using standard statistical approaches while accounting for seasonal differences and nonlinear relationships. This article uses a Bayesian model averaging (BMA) approach for identifying and comparing potential risk and protective factors associated with residual malaria. RESULTS: The relative influence of a comprehensive set of demographic, socio-economic, environmental, and malaria intervention variables on malaria prevalence were modelled using BMA for variable selection. Data were collected in Bunkpurugu-Yunyoo, a rural district in northeast Ghana that experiences holoendemic seasonal malaria transmission, over six biannual surveys from 2010 to 2013. A total of 10,022 children between the ages 6 to 59 months were used in the analysis. Multiple models were developed to identify important risk and protective factors, accounting for seasonal patterns and nonlinear relationships. These models revealed pronounced nonlinear associations between malaria risk and distance from the nearest urban centre and health facility. Furthermore, the association between malaria risk and age and some ethnic groups was significantly different in the rainy and dry seasons. BMA outperformed other commonly used regression approaches in out-of-sample predictive ability using a season-to-season validation approach. CONCLUSIONS: This modelling framework offers an alternative approach to disease risk factor analysis that generates interpretable models, can reveal complex, nonlinear relationships, incorporates uncertainty in model selection, and produces accurate predictions. Certain modelling applications, such as designing targeted local interventions, require more sophisticated statistical methods which are capable of handling a wide range of relevant data while maintaining interpretability and predictive performance, and directly characterize uncertainty. To this end, BMA represents a valuable tool for constructing more informative models for understanding risk factors for malaria, as well as other vector-borne and environmentally mediated diseases. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12936-018-2491-2) contains supplementary material, which is available to authorized users. BioMed Central 2018-09-29 /pmc/articles/PMC6162921/ /pubmed/30268127 http://dx.doi.org/10.1186/s12936-018-2491-2 Text en © The Author(s) 2018 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
Millar, Justin
Psychas, Paul
Abuaku, Benjamin
Ahorlu, Collins
Amratia, Punam
Koram, Kwadwo
Oppong, Samuel
Valle, Denis
Detecting local risk factors for residual malaria in northern Ghana using Bayesian model averaging
title Detecting local risk factors for residual malaria in northern Ghana using Bayesian model averaging
title_full Detecting local risk factors for residual malaria in northern Ghana using Bayesian model averaging
title_fullStr Detecting local risk factors for residual malaria in northern Ghana using Bayesian model averaging
title_full_unstemmed Detecting local risk factors for residual malaria in northern Ghana using Bayesian model averaging
title_short Detecting local risk factors for residual malaria in northern Ghana using Bayesian model averaging
title_sort detecting local risk factors for residual malaria in northern ghana using bayesian model averaging
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6162921/
https://www.ncbi.nlm.nih.gov/pubmed/30268127
http://dx.doi.org/10.1186/s12936-018-2491-2
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