Estimating individual exposure to malaria using local prevalence of malaria infection in the field.
BACKGROUND: Heterogeneity in malaria exposure complicates survival analyses of vaccine efficacy trials and confounds the association between immune correlates of protection and malaria infection in longitudinal studies. Analysis may be facilitated by taking into account the variability in individual...
| Main Authors: | , , , , , , , |
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| Format: | Journal article |
| Language: | English |
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Public Library of Science
2012
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| _version_ | 1826289703556481024 |
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| author | Olotu, A Fegan, G Wambua, J Nyangweso, G Ogada, E Drakeley, C Marsh, K Bejon, P |
| author_facet | Olotu, A Fegan, G Wambua, J Nyangweso, G Ogada, E Drakeley, C Marsh, K Bejon, P |
| author_sort | Olotu, A |
| collection | OXFORD |
| description | BACKGROUND: Heterogeneity in malaria exposure complicates survival analyses of vaccine efficacy trials and confounds the association between immune correlates of protection and malaria infection in longitudinal studies. Analysis may be facilitated by taking into account the variability in individual exposure levels, but it is unclear how exposure can be estimated at an individual level. METHOD AND FINDINGS: We studied three cohorts (Chonyi, Junju and Ngerenya) in Kilifi District, Kenya to assess measures of malaria exposure. Prospective data were available on malaria episodes, geospatial coordinates, proximity to infected and uninfected individuals and residence in predefined malaria hotspots for 2,425 individuals. Antibody levels to the malaria antigens AMA1 and MSP1(142) were available for 291 children from Junju. We calculated distance-weighted local prevalence of malaria infection within 1 km radius as a marker of individual's malaria exposure. We used multivariable modified Poisson regression model to assess the discriminatory power of these markers for malaria infection (i.e. asymptomatic parasitaemia or clinical malaria). The area under the receiver operating characteristic (ROC) curve was used to assess the discriminatory power of the models. Local malaria prevalence within 1 km radius and AMA1 and MSP1(142) antibodies levels were independently associated with malaria infection. Weighted local malaria prevalence had an area under ROC curve of 0.72 (95%CI: 0.66-0.73), 0.71 (95%CI: 0.69-0.73) and 0.82 (95%CI: 0.80-0.83) among cohorts in Chonyi, Junju and Ngerenya respectively. In a small subset of children from Junju, a model incorporating weighted local malaria prevalence with AMA1 and MSP1(142) antibody levels provided an AUC of 0.83 (95%CI: 0.79-0.88). CONCLUSION: We have proposed an approach to estimating the intensity of an individual's malaria exposure in the field. The weighted local malaria prevalence can be used as individual marker of malaria exposure in malaria vaccine trials and longitudinal studies of natural immunity to malaria. |
| first_indexed | 2024-03-07T02:32:56Z |
| format | Journal article |
| id | oxford-uuid:a7daf97b-d761-48a3-98c5-6b8d403b25f4 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T02:32:56Z |
| publishDate | 2012 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | oxford-uuid:a7daf97b-d761-48a3-98c5-6b8d403b25f42022-03-27T02:57:20ZEstimating individual exposure to malaria using local prevalence of malaria infection in the field.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:a7daf97b-d761-48a3-98c5-6b8d403b25f4EnglishSymplectic Elements at OxfordPublic Library of Science2012Olotu, AFegan, GWambua, JNyangweso, GOgada, EDrakeley, CMarsh, KBejon, PBACKGROUND: Heterogeneity in malaria exposure complicates survival analyses of vaccine efficacy trials and confounds the association between immune correlates of protection and malaria infection in longitudinal studies. Analysis may be facilitated by taking into account the variability in individual exposure levels, but it is unclear how exposure can be estimated at an individual level. METHOD AND FINDINGS: We studied three cohorts (Chonyi, Junju and Ngerenya) in Kilifi District, Kenya to assess measures of malaria exposure. Prospective data were available on malaria episodes, geospatial coordinates, proximity to infected and uninfected individuals and residence in predefined malaria hotspots for 2,425 individuals. Antibody levels to the malaria antigens AMA1 and MSP1(142) were available for 291 children from Junju. We calculated distance-weighted local prevalence of malaria infection within 1 km radius as a marker of individual's malaria exposure. We used multivariable modified Poisson regression model to assess the discriminatory power of these markers for malaria infection (i.e. asymptomatic parasitaemia or clinical malaria). The area under the receiver operating characteristic (ROC) curve was used to assess the discriminatory power of the models. Local malaria prevalence within 1 km radius and AMA1 and MSP1(142) antibodies levels were independently associated with malaria infection. Weighted local malaria prevalence had an area under ROC curve of 0.72 (95%CI: 0.66-0.73), 0.71 (95%CI: 0.69-0.73) and 0.82 (95%CI: 0.80-0.83) among cohorts in Chonyi, Junju and Ngerenya respectively. In a small subset of children from Junju, a model incorporating weighted local malaria prevalence with AMA1 and MSP1(142) antibody levels provided an AUC of 0.83 (95%CI: 0.79-0.88). CONCLUSION: We have proposed an approach to estimating the intensity of an individual's malaria exposure in the field. The weighted local malaria prevalence can be used as individual marker of malaria exposure in malaria vaccine trials and longitudinal studies of natural immunity to malaria. |
| spellingShingle | Olotu, A Fegan, G Wambua, J Nyangweso, G Ogada, E Drakeley, C Marsh, K Bejon, P Estimating individual exposure to malaria using local prevalence of malaria infection in the field. |
| title | Estimating individual exposure to malaria using local prevalence of malaria infection in the field. |
| title_full | Estimating individual exposure to malaria using local prevalence of malaria infection in the field. |
| title_fullStr | Estimating individual exposure to malaria using local prevalence of malaria infection in the field. |
| title_full_unstemmed | Estimating individual exposure to malaria using local prevalence of malaria infection in the field. |
| title_short | Estimating individual exposure to malaria using local prevalence of malaria infection in the field. |
| title_sort | estimating individual exposure to malaria using local prevalence of malaria infection in the field |
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