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Stable and Unstable Malaria Hotspots in Longitudinal Cohort Studies in Kenya

BACKGROUND: Infectious diseases often demonstrate heterogeneity of transmission among host populations. This heterogeneity reduces the efficacy of control strategies, but also implies that focusing control strategies on “hotspots” of transmission could be highly effective. METHODS AND FINDINGS: In o...

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Autores principales: Bejon, Philip, Williams, Thomas N., Liljander, Anne, Noor, Abdisalan M., Wambua, Juliana, Ogada, Edna, Olotu, Ally, Osier, Faith H. A., Hay, Simon I., Färnert, Anna, Marsh, Kevin
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2897769/
https://www.ncbi.nlm.nih.gov/pubmed/20625549
http://dx.doi.org/10.1371/journal.pmed.1000304
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author Bejon, Philip
Williams, Thomas N.
Liljander, Anne
Noor, Abdisalan M.
Wambua, Juliana
Ogada, Edna
Olotu, Ally
Osier, Faith H. A.
Hay, Simon I.
Färnert, Anna
Marsh, Kevin
author_facet Bejon, Philip
Williams, Thomas N.
Liljander, Anne
Noor, Abdisalan M.
Wambua, Juliana
Ogada, Edna
Olotu, Ally
Osier, Faith H. A.
Hay, Simon I.
Färnert, Anna
Marsh, Kevin
author_sort Bejon, Philip
collection PubMed
description BACKGROUND: Infectious diseases often demonstrate heterogeneity of transmission among host populations. This heterogeneity reduces the efficacy of control strategies, but also implies that focusing control strategies on “hotspots” of transmission could be highly effective. METHODS AND FINDINGS: In order to identify hotspots of malaria transmission, we analysed longitudinal data on febrile malaria episodes, asymptomatic parasitaemia, and antibody titres over 12 y from 256 homesteads in three study areas in Kilifi District on the Kenyan coast. We examined heterogeneity by homestead, and identified groups of homesteads that formed hotspots using a spatial scan statistic. Two types of statistically significant hotspots were detected; stable hotspots of asymptomatic parasitaemia and unstable hotspots of febrile malaria. The stable hotspots were associated with higher average AMA-1 antibody titres than the unstable clusters (optical density [OD] = 1.24, 95% confidence interval [CI] 1.02–1.47 versus OD = 1.1, 95% CI 0.88–1.33) and lower mean ages of febrile malaria episodes (5.8 y, 95% CI 5.6–6.0 versus 5.91 y, 95% CI 5.7–6.1). A falling gradient of febrile malaria incidence was identified in the penumbrae of both hotspots. Hotspots were associated with AMA-1 titres, but not seroconversion rates. In order to target control measures, homesteads at risk of febrile malaria could be predicted by identifying the 20% of homesteads that experienced an episode of febrile malaria during one month in the dry season. That 20% subsequently experienced 65% of all febrile malaria episodes during the following year. A definition based on remote sensing data was 81% sensitive and 63% specific for the stable hotspots of asymptomatic malaria. CONCLUSIONS: Hotspots of asymptomatic parasitaemia are stable over time, but hotspots of febrile malaria are unstable. This finding may be because immunity offsets the high rate of febrile malaria that might otherwise result in stable hotspots, whereas unstable hotspots necessarily affect a population with less prior exposure to malaria. Please see later in the article for the Editors' Summary
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spelling pubmed-28977692010-07-12 Stable and Unstable Malaria Hotspots in Longitudinal Cohort Studies in Kenya Bejon, Philip Williams, Thomas N. Liljander, Anne Noor, Abdisalan M. Wambua, Juliana Ogada, Edna Olotu, Ally Osier, Faith H. A. Hay, Simon I. Färnert, Anna Marsh, Kevin PLoS Med Research Article BACKGROUND: Infectious diseases often demonstrate heterogeneity of transmission among host populations. This heterogeneity reduces the efficacy of control strategies, but also implies that focusing control strategies on “hotspots” of transmission could be highly effective. METHODS AND FINDINGS: In order to identify hotspots of malaria transmission, we analysed longitudinal data on febrile malaria episodes, asymptomatic parasitaemia, and antibody titres over 12 y from 256 homesteads in three study areas in Kilifi District on the Kenyan coast. We examined heterogeneity by homestead, and identified groups of homesteads that formed hotspots using a spatial scan statistic. Two types of statistically significant hotspots were detected; stable hotspots of asymptomatic parasitaemia and unstable hotspots of febrile malaria. The stable hotspots were associated with higher average AMA-1 antibody titres than the unstable clusters (optical density [OD] = 1.24, 95% confidence interval [CI] 1.02–1.47 versus OD = 1.1, 95% CI 0.88–1.33) and lower mean ages of febrile malaria episodes (5.8 y, 95% CI 5.6–6.0 versus 5.91 y, 95% CI 5.7–6.1). A falling gradient of febrile malaria incidence was identified in the penumbrae of both hotspots. Hotspots were associated with AMA-1 titres, but not seroconversion rates. In order to target control measures, homesteads at risk of febrile malaria could be predicted by identifying the 20% of homesteads that experienced an episode of febrile malaria during one month in the dry season. That 20% subsequently experienced 65% of all febrile malaria episodes during the following year. A definition based on remote sensing data was 81% sensitive and 63% specific for the stable hotspots of asymptomatic malaria. CONCLUSIONS: Hotspots of asymptomatic parasitaemia are stable over time, but hotspots of febrile malaria are unstable. This finding may be because immunity offsets the high rate of febrile malaria that might otherwise result in stable hotspots, whereas unstable hotspots necessarily affect a population with less prior exposure to malaria. Please see later in the article for the Editors' Summary Public Library of Science 2010-07-06 /pmc/articles/PMC2897769/ /pubmed/20625549 http://dx.doi.org/10.1371/journal.pmed.1000304 Text en Bejon et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Bejon, Philip
Williams, Thomas N.
Liljander, Anne
Noor, Abdisalan M.
Wambua, Juliana
Ogada, Edna
Olotu, Ally
Osier, Faith H. A.
Hay, Simon I.
Färnert, Anna
Marsh, Kevin
Stable and Unstable Malaria Hotspots in Longitudinal Cohort Studies in Kenya
title Stable and Unstable Malaria Hotspots in Longitudinal Cohort Studies in Kenya
title_full Stable and Unstable Malaria Hotspots in Longitudinal Cohort Studies in Kenya
title_fullStr Stable and Unstable Malaria Hotspots in Longitudinal Cohort Studies in Kenya
title_full_unstemmed Stable and Unstable Malaria Hotspots in Longitudinal Cohort Studies in Kenya
title_short Stable and Unstable Malaria Hotspots in Longitudinal Cohort Studies in Kenya
title_sort stable and unstable malaria hotspots in longitudinal cohort studies in kenya
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2897769/
https://www.ncbi.nlm.nih.gov/pubmed/20625549
http://dx.doi.org/10.1371/journal.pmed.1000304
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