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Development and Comparison of Dengue Vulnerability Indices Using GIS-Based Multi-Criteria Decision Analysis in Lao PDR and Thailand

Dengue is a continuous health burden in Laos and Thailand. We assessed and mapped dengue vulnerability in selected provinces of Laos and Thailand using multi-criteria decision approaches. An ecohealth framework was used to develop dengue vulnerability indices (DVIs) that explain links between popula...

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Autores principales: Zafar, Sumaira, Shipin, Oleg, Paul, Richard E., Rocklöv, Joacim, Haque, Ubydul, Rahman, Md. Siddikur, Mayxay, Mayfong, Pientong, Chamsai, Aromseree, Sirinart, Poolphol, Petchaboon, Pongvongsa, Tiengkham, Vannavong, Nanthasane, Overgaard, Hans J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8430616/
https://www.ncbi.nlm.nih.gov/pubmed/34502007
http://dx.doi.org/10.3390/ijerph18179421
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author Zafar, Sumaira
Shipin, Oleg
Paul, Richard E.
Rocklöv, Joacim
Haque, Ubydul
Rahman, Md. Siddikur
Mayxay, Mayfong
Pientong, Chamsai
Aromseree, Sirinart
Poolphol, Petchaboon
Pongvongsa, Tiengkham
Vannavong, Nanthasane
Overgaard, Hans J.
author_facet Zafar, Sumaira
Shipin, Oleg
Paul, Richard E.
Rocklöv, Joacim
Haque, Ubydul
Rahman, Md. Siddikur
Mayxay, Mayfong
Pientong, Chamsai
Aromseree, Sirinart
Poolphol, Petchaboon
Pongvongsa, Tiengkham
Vannavong, Nanthasane
Overgaard, Hans J.
author_sort Zafar, Sumaira
collection PubMed
description Dengue is a continuous health burden in Laos and Thailand. We assessed and mapped dengue vulnerability in selected provinces of Laos and Thailand using multi-criteria decision approaches. An ecohealth framework was used to develop dengue vulnerability indices (DVIs) that explain links between population, social and physical environments, and health to identify exposure, susceptibility, and adaptive capacity indicators. Three DVIs were constructed using two objective approaches, Shannon’s Entropy (SE) and the Water-Associated Disease Index (WADI), and one subjective approach, the Best-Worst Method (BWM). Each DVI was validated by correlating the index score with dengue incidence for each spatial unit (district and subdistrict) over time. A Pearson’s correlation coefficient (r) larger than 0.5 and a p-value less than 0.05 implied a good spatial and temporal performance. Spatially, DVI(WADI) was significantly correlated on average in 19% (4–40%) of districts in Laos (mean r = 0.5) and 27% (15–53%) of subdistricts in Thailand (mean r = 0.85). The DVI(SE) was validated in 22% (12–40%) of districts in Laos and in 13% (3–38%) of subdistricts in Thailand. The DVI(BWM) was only developed for Laos because of lack of data in Thailand and was significantly associated with dengue incidence on average in 14% (0–28%) of Lao districts. The DVI(WADI) indicated high vulnerability in urban centers and in areas with plantations and forests. In 2019, high DVI(WADI) values were observed in sparsely populated areas due to elevated exposure, possibly from changes in climate and land cover, including urbanization, plantations, and dam construction. Of the three indices, DVI(WADI) was the most suitable vulnerability index for the study area. The DVI(WADI) can also be applied to other water-associated diseases, such as Zika and chikungunya, to highlight priority areas for further investigation and as a tool for prevention and interventions.
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spelling pubmed-84306162021-09-11 Development and Comparison of Dengue Vulnerability Indices Using GIS-Based Multi-Criteria Decision Analysis in Lao PDR and Thailand Zafar, Sumaira Shipin, Oleg Paul, Richard E. Rocklöv, Joacim Haque, Ubydul Rahman, Md. Siddikur Mayxay, Mayfong Pientong, Chamsai Aromseree, Sirinart Poolphol, Petchaboon Pongvongsa, Tiengkham Vannavong, Nanthasane Overgaard, Hans J. Int J Environ Res Public Health Article Dengue is a continuous health burden in Laos and Thailand. We assessed and mapped dengue vulnerability in selected provinces of Laos and Thailand using multi-criteria decision approaches. An ecohealth framework was used to develop dengue vulnerability indices (DVIs) that explain links between population, social and physical environments, and health to identify exposure, susceptibility, and adaptive capacity indicators. Three DVIs were constructed using two objective approaches, Shannon’s Entropy (SE) and the Water-Associated Disease Index (WADI), and one subjective approach, the Best-Worst Method (BWM). Each DVI was validated by correlating the index score with dengue incidence for each spatial unit (district and subdistrict) over time. A Pearson’s correlation coefficient (r) larger than 0.5 and a p-value less than 0.05 implied a good spatial and temporal performance. Spatially, DVI(WADI) was significantly correlated on average in 19% (4–40%) of districts in Laos (mean r = 0.5) and 27% (15–53%) of subdistricts in Thailand (mean r = 0.85). The DVI(SE) was validated in 22% (12–40%) of districts in Laos and in 13% (3–38%) of subdistricts in Thailand. The DVI(BWM) was only developed for Laos because of lack of data in Thailand and was significantly associated with dengue incidence on average in 14% (0–28%) of Lao districts. The DVI(WADI) indicated high vulnerability in urban centers and in areas with plantations and forests. In 2019, high DVI(WADI) values were observed in sparsely populated areas due to elevated exposure, possibly from changes in climate and land cover, including urbanization, plantations, and dam construction. Of the three indices, DVI(WADI) was the most suitable vulnerability index for the study area. The DVI(WADI) can also be applied to other water-associated diseases, such as Zika and chikungunya, to highlight priority areas for further investigation and as a tool for prevention and interventions. MDPI 2021-09-06 /pmc/articles/PMC8430616/ /pubmed/34502007 http://dx.doi.org/10.3390/ijerph18179421 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zafar, Sumaira
Shipin, Oleg
Paul, Richard E.
Rocklöv, Joacim
Haque, Ubydul
Rahman, Md. Siddikur
Mayxay, Mayfong
Pientong, Chamsai
Aromseree, Sirinart
Poolphol, Petchaboon
Pongvongsa, Tiengkham
Vannavong, Nanthasane
Overgaard, Hans J.
Development and Comparison of Dengue Vulnerability Indices Using GIS-Based Multi-Criteria Decision Analysis in Lao PDR and Thailand
title Development and Comparison of Dengue Vulnerability Indices Using GIS-Based Multi-Criteria Decision Analysis in Lao PDR and Thailand
title_full Development and Comparison of Dengue Vulnerability Indices Using GIS-Based Multi-Criteria Decision Analysis in Lao PDR and Thailand
title_fullStr Development and Comparison of Dengue Vulnerability Indices Using GIS-Based Multi-Criteria Decision Analysis in Lao PDR and Thailand
title_full_unstemmed Development and Comparison of Dengue Vulnerability Indices Using GIS-Based Multi-Criteria Decision Analysis in Lao PDR and Thailand
title_short Development and Comparison of Dengue Vulnerability Indices Using GIS-Based Multi-Criteria Decision Analysis in Lao PDR and Thailand
title_sort development and comparison of dengue vulnerability indices using gis-based multi-criteria decision analysis in lao pdr and thailand
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8430616/
https://www.ncbi.nlm.nih.gov/pubmed/34502007
http://dx.doi.org/10.3390/ijerph18179421
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