Stable and unstable malaria hotspots in longitudinal cohort studies in Kenya.
<h4<Background</h4<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 effectiv...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Philip Bejon [verfasserIn] Thomas N Williams [verfasserIn] Anne Liljander [verfasserIn] Abdisalan M Noor [verfasserIn] Juliana Wambua [verfasserIn] Edna Ogada [verfasserIn] Ally Olotu [verfasserIn] Faith H A Osier [verfasserIn] Simon I Hay [verfasserIn] Anna Färnert [verfasserIn] Kevin Marsh [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2010 |
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| Übergeordnetes Werk: |
In: PLoS Medicine - Public Library of Science (PLoS), 2004, 7(2010), 7, p e1000304 |
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| Übergeordnetes Werk: |
volume:7 ; year:2010 ; number:7, p e1000304 |
| Links: |
Link aufrufen |
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| DOI / URN: |
10.1371/journal.pmed.1000304 |
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| Katalog-ID: |
DOAJ051460165 |
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| 520 | |a <h4<Background</h4<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.<h4<Methods and findings</h4<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.<h4<Conclusions</h4<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. | ||
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| 700 | 0 | |a Juliana Wambua |e verfasserin |4 aut | |
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| 700 | 0 | |a Anna Färnert |e verfasserin |4 aut | |
| 700 | 0 | |a Kevin Marsh |e verfasserin |4 aut | |
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10.1371/journal.pmed.1000304 doi (DE-627)DOAJ051460165 (DE-599)DOAJ6784a10e6a1e4030a6dc307c291beb26 DE-627 ger DE-627 rakwb eng Philip Bejon verfasserin aut Stable and unstable malaria hotspots in longitudinal cohort studies in Kenya. 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <h4<Background</h4<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.<h4<Methods and findings</h4<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.<h4<Conclusions</h4<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. Medicine R Thomas N Williams verfasserin aut Anne Liljander verfasserin aut Abdisalan M Noor verfasserin aut Juliana Wambua verfasserin aut Edna Ogada verfasserin aut Ally Olotu verfasserin aut Faith H A Osier verfasserin aut Simon I Hay verfasserin aut Anna Färnert verfasserin aut Kevin Marsh verfasserin aut In PLoS Medicine Public Library of Science (PLoS), 2004 7(2010), 7, p e1000304 (DE-627)470151471 (DE-600)2164823-2 15491676 nnns volume:7 year:2010 number:7, p e1000304 https://doi.org/10.1371/journal.pmed.1000304 kostenfrei https://doaj.org/article/6784a10e6a1e4030a6dc307c291beb26 kostenfrei https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20625549/?tool=EBI kostenfrei https://doaj.org/toc/1549-1277 Journal toc kostenfrei https://doaj.org/toc/1549-1676 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2010 7, p e1000304 |
| spelling |
10.1371/journal.pmed.1000304 doi (DE-627)DOAJ051460165 (DE-599)DOAJ6784a10e6a1e4030a6dc307c291beb26 DE-627 ger DE-627 rakwb eng Philip Bejon verfasserin aut Stable and unstable malaria hotspots in longitudinal cohort studies in Kenya. 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <h4<Background</h4<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.<h4<Methods and findings</h4<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.<h4<Conclusions</h4<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. Medicine R Thomas N Williams verfasserin aut Anne Liljander verfasserin aut Abdisalan M Noor verfasserin aut Juliana Wambua verfasserin aut Edna Ogada verfasserin aut Ally Olotu verfasserin aut Faith H A Osier verfasserin aut Simon I Hay verfasserin aut Anna Färnert verfasserin aut Kevin Marsh verfasserin aut In PLoS Medicine Public Library of Science (PLoS), 2004 7(2010), 7, p e1000304 (DE-627)470151471 (DE-600)2164823-2 15491676 nnns volume:7 year:2010 number:7, p e1000304 https://doi.org/10.1371/journal.pmed.1000304 kostenfrei https://doaj.org/article/6784a10e6a1e4030a6dc307c291beb26 kostenfrei https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20625549/?tool=EBI kostenfrei https://doaj.org/toc/1549-1277 Journal toc kostenfrei https://doaj.org/toc/1549-1676 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2010 7, p e1000304 |
| allfields_unstemmed |
10.1371/journal.pmed.1000304 doi (DE-627)DOAJ051460165 (DE-599)DOAJ6784a10e6a1e4030a6dc307c291beb26 DE-627 ger DE-627 rakwb eng Philip Bejon verfasserin aut Stable and unstable malaria hotspots in longitudinal cohort studies in Kenya. 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <h4<Background</h4<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.<h4<Methods and findings</h4<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.<h4<Conclusions</h4<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. Medicine R Thomas N Williams verfasserin aut Anne Liljander verfasserin aut Abdisalan M Noor verfasserin aut Juliana Wambua verfasserin aut Edna Ogada verfasserin aut Ally Olotu verfasserin aut Faith H A Osier verfasserin aut Simon I Hay verfasserin aut Anna Färnert verfasserin aut Kevin Marsh verfasserin aut In PLoS Medicine Public Library of Science (PLoS), 2004 7(2010), 7, p e1000304 (DE-627)470151471 (DE-600)2164823-2 15491676 nnns volume:7 year:2010 number:7, p e1000304 https://doi.org/10.1371/journal.pmed.1000304 kostenfrei https://doaj.org/article/6784a10e6a1e4030a6dc307c291beb26 kostenfrei https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20625549/?tool=EBI kostenfrei https://doaj.org/toc/1549-1277 Journal toc kostenfrei https://doaj.org/toc/1549-1676 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2010 7, p e1000304 |
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10.1371/journal.pmed.1000304 doi (DE-627)DOAJ051460165 (DE-599)DOAJ6784a10e6a1e4030a6dc307c291beb26 DE-627 ger DE-627 rakwb eng Philip Bejon verfasserin aut Stable and unstable malaria hotspots in longitudinal cohort studies in Kenya. 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <h4<Background</h4<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.<h4<Methods and findings</h4<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.<h4<Conclusions</h4<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. Medicine R Thomas N Williams verfasserin aut Anne Liljander verfasserin aut Abdisalan M Noor verfasserin aut Juliana Wambua verfasserin aut Edna Ogada verfasserin aut Ally Olotu verfasserin aut Faith H A Osier verfasserin aut Simon I Hay verfasserin aut Anna Färnert verfasserin aut Kevin Marsh verfasserin aut In PLoS Medicine Public Library of Science (PLoS), 2004 7(2010), 7, p e1000304 (DE-627)470151471 (DE-600)2164823-2 15491676 nnns volume:7 year:2010 number:7, p e1000304 https://doi.org/10.1371/journal.pmed.1000304 kostenfrei https://doaj.org/article/6784a10e6a1e4030a6dc307c291beb26 kostenfrei https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20625549/?tool=EBI kostenfrei https://doaj.org/toc/1549-1277 Journal toc kostenfrei https://doaj.org/toc/1549-1676 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2010 7, p e1000304 |
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10.1371/journal.pmed.1000304 doi (DE-627)DOAJ051460165 (DE-599)DOAJ6784a10e6a1e4030a6dc307c291beb26 DE-627 ger DE-627 rakwb eng Philip Bejon verfasserin aut Stable and unstable malaria hotspots in longitudinal cohort studies in Kenya. 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <h4<Background</h4<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.<h4<Methods and findings</h4<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.<h4<Conclusions</h4<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. Medicine R Thomas N Williams verfasserin aut Anne Liljander verfasserin aut Abdisalan M Noor verfasserin aut Juliana Wambua verfasserin aut Edna Ogada verfasserin aut Ally Olotu verfasserin aut Faith H A Osier verfasserin aut Simon I Hay verfasserin aut Anna Färnert verfasserin aut Kevin Marsh verfasserin aut In PLoS Medicine Public Library of Science (PLoS), 2004 7(2010), 7, p e1000304 (DE-627)470151471 (DE-600)2164823-2 15491676 nnns volume:7 year:2010 number:7, p e1000304 https://doi.org/10.1371/journal.pmed.1000304 kostenfrei https://doaj.org/article/6784a10e6a1e4030a6dc307c291beb26 kostenfrei https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20625549/?tool=EBI kostenfrei https://doaj.org/toc/1549-1277 Journal toc kostenfrei https://doaj.org/toc/1549-1676 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2010 7, p e1000304 |
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Stable and unstable malaria hotspots in longitudinal cohort studies in Kenya |
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stable and unstable malaria hotspots in longitudinal cohort studies in kenya |
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Stable and unstable malaria hotspots in longitudinal cohort studies in Kenya. |
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<h4<Background</h4<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.<h4<Methods and findings</h4<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.<h4<Conclusions</h4<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. |
| abstractGer |
<h4<Background</h4<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.<h4<Methods and findings</h4<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.<h4<Conclusions</h4<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. |
| abstract_unstemmed |
<h4<Background</h4<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.<h4<Methods and findings</h4<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.<h4<Conclusions</h4<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. |
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