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Quantifying cross-border movements and migrations for guiding the strategic planning of malaria control and elimination

BACKGROUND: Identifying human and malaria parasite movements is important for control planning across all transmission intensities. Imported infections can reintroduce infections into areas previously free of infection, maintain ‘hotspots’ of transmission and import drug resistant strains, challengi...

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Autores principales: Pindolia, Deepa K, Garcia, Andres J, Huang, Zhuojie, Fik, Timothy, Smith, David L, Tatem, Andrew J
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4057586/
https://www.ncbi.nlm.nih.gov/pubmed/24886389
http://dx.doi.org/10.1186/1475-2875-13-169
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author Pindolia, Deepa K
Garcia, Andres J
Huang, Zhuojie
Fik, Timothy
Smith, David L
Tatem, Andrew J
author_facet Pindolia, Deepa K
Garcia, Andres J
Huang, Zhuojie
Fik, Timothy
Smith, David L
Tatem, Andrew J
author_sort Pindolia, Deepa K
collection PubMed
description BACKGROUND: Identifying human and malaria parasite movements is important for control planning across all transmission intensities. Imported infections can reintroduce infections into areas previously free of infection, maintain ‘hotspots’ of transmission and import drug resistant strains, challenging national control programmes at a variety of temporal and spatial scales. Recent analyses based on mobile phone usage data have provided valuable insights into population and likely parasite movements within countries, but these data are restricted to sub-national analyses, leaving important cross-border movements neglected. METHODS: National census data were used to analyse and model cross-border migration and movement, using East Africa as an example. ‘Hotspots’ of origin-specific immigrants from neighbouring countries were identified for Kenya, Tanzania and Uganda. Populations of origin-specific migrants were compared to distance from origin country borders and population size at destination, and regression models were developed to quantify and compare differences in migration patterns. Migration data were then combined with existing spatially-referenced malaria data to compare the relative propensity for cross-border malaria movement in the region. RESULTS: The spatial patterns and processes for immigration were different between each origin and destination country pair. Hotspots of immigration, for example, were concentrated close to origin country borders for most immigrants to Tanzania, but for Kenya, a similar pattern was only seen for Tanzanian and Ugandan immigrants. Regression model fits also differed between specific migrant groups, with some migration patterns more dependent on population size at destination and distance travelled than others. With these differences between immigration patterns and processes, and heterogeneous transmission risk in East Africa and the surrounding region, propensities to import malaria infections also likely show substantial variations. CONCLUSION: This was a first attempt to quantify and model cross-border movements relevant to malaria transmission and control. With national census available worldwide, this approach can be translated to construct a cross-border human and malaria movement evidence base for other malaria endemic countries. The outcomes of this study will feed into wider efforts to quantify and model human and malaria movements in endemic regions to facilitate improved intervention planning, resource allocation and collaborative policy decisions.
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spelling pubmed-40575862014-06-23 Quantifying cross-border movements and migrations for guiding the strategic planning of malaria control and elimination Pindolia, Deepa K Garcia, Andres J Huang, Zhuojie Fik, Timothy Smith, David L Tatem, Andrew J Malar J Research BACKGROUND: Identifying human and malaria parasite movements is important for control planning across all transmission intensities. Imported infections can reintroduce infections into areas previously free of infection, maintain ‘hotspots’ of transmission and import drug resistant strains, challenging national control programmes at a variety of temporal and spatial scales. Recent analyses based on mobile phone usage data have provided valuable insights into population and likely parasite movements within countries, but these data are restricted to sub-national analyses, leaving important cross-border movements neglected. METHODS: National census data were used to analyse and model cross-border migration and movement, using East Africa as an example. ‘Hotspots’ of origin-specific immigrants from neighbouring countries were identified for Kenya, Tanzania and Uganda. Populations of origin-specific migrants were compared to distance from origin country borders and population size at destination, and regression models were developed to quantify and compare differences in migration patterns. Migration data were then combined with existing spatially-referenced malaria data to compare the relative propensity for cross-border malaria movement in the region. RESULTS: The spatial patterns and processes for immigration were different between each origin and destination country pair. Hotspots of immigration, for example, were concentrated close to origin country borders for most immigrants to Tanzania, but for Kenya, a similar pattern was only seen for Tanzanian and Ugandan immigrants. Regression model fits also differed between specific migrant groups, with some migration patterns more dependent on population size at destination and distance travelled than others. With these differences between immigration patterns and processes, and heterogeneous transmission risk in East Africa and the surrounding region, propensities to import malaria infections also likely show substantial variations. CONCLUSION: This was a first attempt to quantify and model cross-border movements relevant to malaria transmission and control. With national census available worldwide, this approach can be translated to construct a cross-border human and malaria movement evidence base for other malaria endemic countries. The outcomes of this study will feed into wider efforts to quantify and model human and malaria movements in endemic regions to facilitate improved intervention planning, resource allocation and collaborative policy decisions. BioMed Central 2014-05-03 /pmc/articles/PMC4057586/ /pubmed/24886389 http://dx.doi.org/10.1186/1475-2875-13-169 Text en Copyright © 2014 Pindolia 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 credited. 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
Pindolia, Deepa K
Garcia, Andres J
Huang, Zhuojie
Fik, Timothy
Smith, David L
Tatem, Andrew J
Quantifying cross-border movements and migrations for guiding the strategic planning of malaria control and elimination
title Quantifying cross-border movements and migrations for guiding the strategic planning of malaria control and elimination
title_full Quantifying cross-border movements and migrations for guiding the strategic planning of malaria control and elimination
title_fullStr Quantifying cross-border movements and migrations for guiding the strategic planning of malaria control and elimination
title_full_unstemmed Quantifying cross-border movements and migrations for guiding the strategic planning of malaria control and elimination
title_short Quantifying cross-border movements and migrations for guiding the strategic planning of malaria control and elimination
title_sort quantifying cross-border movements and migrations for guiding the strategic planning of malaria control and elimination
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4057586/
https://www.ncbi.nlm.nih.gov/pubmed/24886389
http://dx.doi.org/10.1186/1475-2875-13-169
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