Detecting local risk factors for residual malaria in northern Ghana using Bayesian model averaging
Abstract 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 transmis...
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ftdoajarticles:oai:doaj.org/article:9f9997c6875a4ee4bbd55eabe9b528d9 2023-05-15T15:16:49+02:00 Detecting local risk factors for residual malaria in northern Ghana using Bayesian model averaging Justin Millar Paul Psychas Benjamin Abuaku Collins Ahorlu Punam Amratia Kwadwo Koram Samuel Oppong Denis Valle 2018-09-01T00:00:00Z https://doi.org/10.1186/s12936-018-2491-2 https://doaj.org/article/9f9997c6875a4ee4bbd55eabe9b528d9 EN eng BMC http://link.springer.com/article/10.1186/s12936-018-2491-2 https://doaj.org/toc/1475-2875 doi:10.1186/s12936-018-2491-2 1475-2875 https://doaj.org/article/9f9997c6875a4ee4bbd55eabe9b528d9 Malaria Journal, Vol 17, Iss 1, Pp 1-14 (2018) Risk factors Bayesian model averaging Nonlinear patterns Statistical methods Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 article 2018 ftdoajarticles https://doi.org/10.1186/s12936-018-2491-2 2022-12-31T03:04:40Z Abstract 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 ... Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic Malaria Journal 17 1 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Risk factors Bayesian model averaging Nonlinear patterns Statistical methods Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 |
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Risk factors Bayesian model averaging Nonlinear patterns Statistical methods Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 Justin Millar Paul Psychas Benjamin Abuaku Collins Ahorlu Punam Amratia Kwadwo Koram Samuel Oppong Denis Valle Detecting local risk factors for residual malaria in northern Ghana using Bayesian model averaging |
topic_facet |
Risk factors Bayesian model averaging Nonlinear patterns Statistical methods Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 |
description |
Abstract 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 ... |
format |
Article in Journal/Newspaper |
author |
Justin Millar Paul Psychas Benjamin Abuaku Collins Ahorlu Punam Amratia Kwadwo Koram Samuel Oppong Denis Valle |
author_facet |
Justin Millar Paul Psychas Benjamin Abuaku Collins Ahorlu Punam Amratia Kwadwo Koram Samuel Oppong Denis Valle |
author_sort |
Justin Millar |
title |
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_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_sort |
detecting local risk factors for residual malaria in northern ghana using bayesian model averaging |
publisher |
BMC |
publishDate |
2018 |
url |
https://doi.org/10.1186/s12936-018-2491-2 https://doaj.org/article/9f9997c6875a4ee4bbd55eabe9b528d9 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Malaria Journal, Vol 17, Iss 1, Pp 1-14 (2018) |
op_relation |
http://link.springer.com/article/10.1186/s12936-018-2491-2 https://doaj.org/toc/1475-2875 doi:10.1186/s12936-018-2491-2 1475-2875 https://doaj.org/article/9f9997c6875a4ee4bbd55eabe9b528d9 |
op_doi |
https://doi.org/10.1186/s12936-018-2491-2 |
container_title |
Malaria Journal |
container_volume |
17 |
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1 |
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1766347112111407104 |