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Use of remote sensing to identify spatial risk factors for malaria in a region of declining transmission: a cross-sectional and longitudinal community survey

BACKGROUND: The burden of malaria has decreased dramatically within the past several years in parts of sub-Saharan Africa. Further malaria control will require targeted control strategies based on evidence of risk. The objective of this study was to identify environmental risk factors for malaria tr...

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Autores principales: Moss, William J, Hamapumbu, Harry, Kobayashi, Tamaki, Shields, Timothy, Kamanga, Aniset, Clennon, Julie, Mharakurwa, Sungano, Thuma, Philip E, Glass, Gregory
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3123248/
https://www.ncbi.nlm.nih.gov/pubmed/21663661
http://dx.doi.org/10.1186/1475-2875-10-163
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author Moss, William J
Hamapumbu, Harry
Kobayashi, Tamaki
Shields, Timothy
Kamanga, Aniset
Clennon, Julie
Mharakurwa, Sungano
Thuma, Philip E
Glass, Gregory
author_facet Moss, William J
Hamapumbu, Harry
Kobayashi, Tamaki
Shields, Timothy
Kamanga, Aniset
Clennon, Julie
Mharakurwa, Sungano
Thuma, Philip E
Glass, Gregory
author_sort Moss, William J
collection PubMed
description BACKGROUND: The burden of malaria has decreased dramatically within the past several years in parts of sub-Saharan Africa. Further malaria control will require targeted control strategies based on evidence of risk. The objective of this study was to identify environmental risk factors for malaria transmission using remote sensing technologies to guide malaria control interventions in a region of declining burden of malaria. METHODS: Satellite images were used to construct a sampling frame for the random selection of households enrolled in prospective longitudinal and cross-sectional surveys of malaria parasitaemia in Southern Province, Zambia. A digital elevation model (DEM) was derived from the Shuttle Radar Topography Mission version 3 DEM and used for landscape characterization, including landforms, elevation, aspect, slope, topographic wetness, topographic position index and hydrological models of stream networks. RESULTS: A total of 768 individuals from 128 randomly selected households were enrolled over 21 months, from the end of the rainy season in April 2007 through December 2008. Of the 768 individuals tested, 117 (15.2%) were positive by malaria rapid diagnostic test (RDT). Individuals residing within 3.75 km of a third order stream were at increased risk of malaria. Households at elevations above the baseline elevation for the region were at decreasing risk of having RDT-positive residents. Households where new infections occurred were overlaid on a risk map of RDT positive households and incident infections were more likely to be located in high-risk areas derived from prevalence data. Based on the spatial risk map, targeting households in the top 80(th )percentile of malaria risk would require malaria control interventions directed to only 24% of the households. CONCLUSIONS: Remote sensing technologies can be used to target malaria control interventions in a region of declining malaria transmission in southern Zambia, enabling a more efficient use of resources for malaria elimination.
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spelling pubmed-31232482011-06-25 Use of remote sensing to identify spatial risk factors for malaria in a region of declining transmission: a cross-sectional and longitudinal community survey Moss, William J Hamapumbu, Harry Kobayashi, Tamaki Shields, Timothy Kamanga, Aniset Clennon, Julie Mharakurwa, Sungano Thuma, Philip E Glass, Gregory Malar J Research BACKGROUND: The burden of malaria has decreased dramatically within the past several years in parts of sub-Saharan Africa. Further malaria control will require targeted control strategies based on evidence of risk. The objective of this study was to identify environmental risk factors for malaria transmission using remote sensing technologies to guide malaria control interventions in a region of declining burden of malaria. METHODS: Satellite images were used to construct a sampling frame for the random selection of households enrolled in prospective longitudinal and cross-sectional surveys of malaria parasitaemia in Southern Province, Zambia. A digital elevation model (DEM) was derived from the Shuttle Radar Topography Mission version 3 DEM and used for landscape characterization, including landforms, elevation, aspect, slope, topographic wetness, topographic position index and hydrological models of stream networks. RESULTS: A total of 768 individuals from 128 randomly selected households were enrolled over 21 months, from the end of the rainy season in April 2007 through December 2008. Of the 768 individuals tested, 117 (15.2%) were positive by malaria rapid diagnostic test (RDT). Individuals residing within 3.75 km of a third order stream were at increased risk of malaria. Households at elevations above the baseline elevation for the region were at decreasing risk of having RDT-positive residents. Households where new infections occurred were overlaid on a risk map of RDT positive households and incident infections were more likely to be located in high-risk areas derived from prevalence data. Based on the spatial risk map, targeting households in the top 80(th )percentile of malaria risk would require malaria control interventions directed to only 24% of the households. CONCLUSIONS: Remote sensing technologies can be used to target malaria control interventions in a region of declining malaria transmission in southern Zambia, enabling a more efficient use of resources for malaria elimination. BioMed Central 2011-06-10 /pmc/articles/PMC3123248/ /pubmed/21663661 http://dx.doi.org/10.1186/1475-2875-10-163 Text en Copyright ©2011 Moss 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.
spellingShingle Research
Moss, William J
Hamapumbu, Harry
Kobayashi, Tamaki
Shields, Timothy
Kamanga, Aniset
Clennon, Julie
Mharakurwa, Sungano
Thuma, Philip E
Glass, Gregory
Use of remote sensing to identify spatial risk factors for malaria in a region of declining transmission: a cross-sectional and longitudinal community survey
title Use of remote sensing to identify spatial risk factors for malaria in a region of declining transmission: a cross-sectional and longitudinal community survey
title_full Use of remote sensing to identify spatial risk factors for malaria in a region of declining transmission: a cross-sectional and longitudinal community survey
title_fullStr Use of remote sensing to identify spatial risk factors for malaria in a region of declining transmission: a cross-sectional and longitudinal community survey
title_full_unstemmed Use of remote sensing to identify spatial risk factors for malaria in a region of declining transmission: a cross-sectional and longitudinal community survey
title_short Use of remote sensing to identify spatial risk factors for malaria in a region of declining transmission: a cross-sectional and longitudinal community survey
title_sort use of remote sensing to identify spatial risk factors for malaria in a region of declining transmission: a cross-sectional and longitudinal community survey
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3123248/
https://www.ncbi.nlm.nih.gov/pubmed/21663661
http://dx.doi.org/10.1186/1475-2875-10-163
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