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Analysis of partial and complete protection in malaria cohort studies
BACKGROUND: Malaria transmission is highly heterogeneous and analysis of incidence data must account for this for correct statistical inference. Less widely appreciated is the occurrence of a large number of zero counts (children without a malaria episode) in malaria cohort studies. Zero-inflated re...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3850882/ https://www.ncbi.nlm.nih.gov/pubmed/24093726 http://dx.doi.org/10.1186/1475-2875-12-355 |
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author | Cairns, Matthew E Asante, Kwaku Poku Owusu-Agyei, Seth Chandramohan, Daniel Greenwood, Brian M Milligan, Paul J |
author_facet | Cairns, Matthew E Asante, Kwaku Poku Owusu-Agyei, Seth Chandramohan, Daniel Greenwood, Brian M Milligan, Paul J |
author_sort | Cairns, Matthew E |
collection | PubMed |
description | BACKGROUND: Malaria transmission is highly heterogeneous and analysis of incidence data must account for this for correct statistical inference. Less widely appreciated is the occurrence of a large number of zero counts (children without a malaria episode) in malaria cohort studies. Zero-inflated regression methods provide one means of addressing this issue, and also allow risk factors providing complete and partial protection to be disentangled. METHODS: Poisson, negative binomial (NB), zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB) regression models were fitted to data from two cohort studies of malaria in children in Ghana. Multivariate models were used to understand risk factors for elevated incidence of malaria and for remaining malaria-free, and to estimate the fraction of the population not at risk of malaria. RESULTS: ZINB models, which account for both heterogeneity in individual risk and an unexposed sub-group within the population, provided the best fit to data in both cohorts. These approaches gave additional insight into the mechanism of factors influencing the incidence of malaria compared to simpler approaches, such as NB regression. For example, compared to urban areas, rural residence was found to both increase the incidence rate of malaria among exposed children, and increase the probability of being exposed. In Navrongo, 34% of urban residents were estimated to be at no risk, compared to 3% of rural residents. In Kintampo, 47% of urban residents and 13% of rural residents were estimated to be at no risk. CONCLUSION: These results illustrate the utility of zero-inflated regression methods for analysis of malaria cohort data that include a large number of zero counts. Specifically, these results suggest that interventions that reach mainly urban residents will have limited overall impact, since some urban residents are essentially at no risk, even in areas of high endemicity, such as in Ghana. |
format | Online Article Text |
id | pubmed-3850882 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-38508822013-12-05 Analysis of partial and complete protection in malaria cohort studies Cairns, Matthew E Asante, Kwaku Poku Owusu-Agyei, Seth Chandramohan, Daniel Greenwood, Brian M Milligan, Paul J Malar J Research BACKGROUND: Malaria transmission is highly heterogeneous and analysis of incidence data must account for this for correct statistical inference. Less widely appreciated is the occurrence of a large number of zero counts (children without a malaria episode) in malaria cohort studies. Zero-inflated regression methods provide one means of addressing this issue, and also allow risk factors providing complete and partial protection to be disentangled. METHODS: Poisson, negative binomial (NB), zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB) regression models were fitted to data from two cohort studies of malaria in children in Ghana. Multivariate models were used to understand risk factors for elevated incidence of malaria and for remaining malaria-free, and to estimate the fraction of the population not at risk of malaria. RESULTS: ZINB models, which account for both heterogeneity in individual risk and an unexposed sub-group within the population, provided the best fit to data in both cohorts. These approaches gave additional insight into the mechanism of factors influencing the incidence of malaria compared to simpler approaches, such as NB regression. For example, compared to urban areas, rural residence was found to both increase the incidence rate of malaria among exposed children, and increase the probability of being exposed. In Navrongo, 34% of urban residents were estimated to be at no risk, compared to 3% of rural residents. In Kintampo, 47% of urban residents and 13% of rural residents were estimated to be at no risk. CONCLUSION: These results illustrate the utility of zero-inflated regression methods for analysis of malaria cohort data that include a large number of zero counts. Specifically, these results suggest that interventions that reach mainly urban residents will have limited overall impact, since some urban residents are essentially at no risk, even in areas of high endemicity, such as in Ghana. BioMed Central 2013-10-05 /pmc/articles/PMC3850882/ /pubmed/24093726 http://dx.doi.org/10.1186/1475-2875-12-355 Text en Copyright © 2013 Cairns et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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 Cairns, Matthew E Asante, Kwaku Poku Owusu-Agyei, Seth Chandramohan, Daniel Greenwood, Brian M Milligan, Paul J Analysis of partial and complete protection in malaria cohort studies |
title | Analysis of partial and complete protection in malaria cohort studies |
title_full | Analysis of partial and complete protection in malaria cohort studies |
title_fullStr | Analysis of partial and complete protection in malaria cohort studies |
title_full_unstemmed | Analysis of partial and complete protection in malaria cohort studies |
title_short | Analysis of partial and complete protection in malaria cohort studies |
title_sort | analysis of partial and complete protection in malaria cohort studies |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3850882/ https://www.ncbi.nlm.nih.gov/pubmed/24093726 http://dx.doi.org/10.1186/1475-2875-12-355 |
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